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llama.cpp/convert-hf-to-gguf.py

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scripts: Generalize convert scripts (#3838) * Replace convert-*-hf-to-gguf.py files with convert-hf-to-gguf.py
2023-11-09 10:09:29 +00:00
#!/usr/bin/env python3
from __future__ import annotations
convert.py : add python logging instead of print() (#6511) * convert.py: add python logging instead of print() * convert.py: verbose flag takes priority over dump flag log suppression * convert.py: named instance logging * convert.py: use explicit logger id string * convert.py: convert extra print() to named logger * convert.py: sys.stderr.write --> logger.error * *.py: Convert all python scripts to use logging module * requirements.txt: remove extra line * flake8: update flake8 ignore and exclude to match ci settings * gh-actions: add flake8-no-print to flake8 lint step * pre-commit: add flake8-no-print to flake8 and also update pre-commit version * convert-hf-to-gguf.py: print() to logger conversion * *.py: logging basiconfig refactor to use conditional expression * *.py: removed commented out logging * fixup! *.py: logging basiconfig refactor to use conditional expression * constant.py: logger.error then exit should be a raise exception instead * *.py: Convert logger error and sys.exit() into a raise exception (for atypical error) * gguf-convert-endian.py: refactor convert_byteorder() to use tqdm progressbar * verify-checksum-model.py: This is the result of the program, it should be printed to stdout. * compare-llama-bench.py: add blank line for readability during missing repo response * reader.py: read_gguf_file() use print() over logging * convert.py: warning goes to stderr and won't hurt the dump output * gguf-dump.py: dump_metadata() should print to stdout * convert-hf-to-gguf.py: print --> logger.debug or ValueError() * verify-checksum-models.py: use print() for printing table * *.py: refactor logging.basicConfig() * gguf-py/gguf/*.py: use __name__ as logger name Since they will be imported and not run directly. * python-lint.yml: use .flake8 file instead * constants.py: logger no longer required * convert-hf-to-gguf.py: add additional logging * convert-hf-to-gguf.py: print() --> logger * *.py: fix flake8 warnings * revert changes to convert-hf-to-gguf.py for get_name() * convert-hf-to-gguf-update.py: use triple quoted f-string instead * *.py: accidentally corrected the wrong line * *.py: add compilade warning suggestions and style fixes
2024-05-03 19:36:41 +00:00
import logging
scripts: Generalize convert scripts (#3838) * Replace convert-*-hf-to-gguf.py files with convert-hf-to-gguf.py
2023-11-09 10:09:29 +00:00
import argparse
import contextlib
import json
import os
import re
import sys
from enum import IntEnum
from pathlib import Path
llama : fix BPE pre-tokenization (#6920) * merged the changes from deepseeker models to main branch * Moved regex patterns to unicode.cpp and updated unicode.h * Moved header files * Resolved issues * added and refactored unicode_regex_split and related functions * Updated/merged the deepseek coder pr * Refactored code * Adding unicode regex mappings * Adding unicode regex function * Added needed functionality, testing remains * Fixed issues * Fixed issue with gpt2 regex custom preprocessor * unicode : fix? unicode_wstring_to_utf8 * lint : fix whitespaces * tests : add tokenizer tests for numbers * unicode : remove redundant headers * tests : remove and rename tokenizer test scripts * tests : add sample usage * gguf-py : reader prints warnings on duplicate keys * llama : towards llama3 tokenization support (wip) * unicode : shot in the dark to fix tests on Windows * unicode : first try custom implementations * convert : add "tokenizer.ggml.pre" GGUF KV (wip) * llama : use new pre-tokenizer type * convert : fix pre-tokenizer type writing * lint : fix * make : add test-tokenizer-0-llama-v3 * wip * models : add llama v3 vocab file * llama : adapt punctuation regex + add llama 3 regex * minor * unicode : set bomb * unicode : set bomb * unicode : always use std::wregex * unicode : support \p{N}, \p{L} and \p{P} natively * unicode : try fix windows * unicode : category support via std::regex * unicode : clean-up * unicode : simplify * convert : add convert-hf-to-gguf-update.py ggml-ci * lint : update * convert : add falcon ggml-ci * unicode : normalize signatures * lint : fix * lint : fix * convert : remove unused functions * convert : add comments * convert : exercise contractions ggml-ci * lint : fix * cmake : refactor test targets * tests : refactor vocab tests ggml-ci * tests : add more vocabs and tests ggml-ci * unicode : cleanup * scripts : ignore new update script in check-requirements.sh * models : add phi-3, mpt, gpt-2, starcoder * tests : disable obsolete ggml-ci * tests : use faster bpe test ggml-ci * llama : more prominent warning for old BPE models * tests : disable test-tokenizer-1-bpe due to slowness ggml-ci --------- Co-authored-by: Jaggzh <jaggz.h@gmail.com> Co-authored-by: Kazim Abrar Mahi <kazimabrarmahi135@gmail.com>
2024-04-29 13:58:41 +00:00
from hashlib import sha256
convert-hf : support bfloat16 conversion (#7158) * convert-hf : support bfloat16 conversion * gguf-py : flake8 fixes * convert-hf : add missing space after comma * convert-hf : get bit-exact same output as ./quantize The quantization version was missing. * convert-hf : don't round bf16 NANs * convert-hf : save some memory with np.int16 intermediate bf16 weights * convert-hf : more closely match llama.cpp with which weights to keep in f32 * convert-hf : add --outtype auto-f16 A reason for this to exist is for model quantizers who want an initial GGUF with the most fidelity to the original model while still using a 16-bit float type instead of 32-bit floats. * convert-hf : remove a semicolon because flake8 doesn't like it It's a reflex from when programming in C/C++, I guess. * convert-hf : support outtype templating in outfile name * convert-hf : rename --outtype auto-f16 to --outtype auto
2024-05-11 15:06:26 +00:00
from typing import TYPE_CHECKING, Any, Callable, ContextManager, Iterable, Iterator, Sequence, TypeVar, cast
scripts: Generalize convert scripts (#3838) * Replace convert-*-hf-to-gguf.py files with convert-hf-to-gguf.py
2023-11-09 10:09:29 +00:00
import numpy as np
import torch
if TYPE_CHECKING:
from torch import Tensor
if 'NO_LOCAL_GGUF' not in os.environ:
sys.path.insert(1, str(Path(__file__).parent / 'gguf-py'))
import gguf
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
from convert import LlamaHfVocab
llama : add MiniCPM support (#5346) * support minicpm arch. * fix tab/space typo. * convert minicpm model via convert-hf-gguf.py * try to make tokenizer work * fix bug for quantize minicpm * fix for flake8 lint * remove convert-minicpm.py * fix for editorconfig * correct minicpm model type (size) * constants expanded for minicpm * Minor change of the constant names for minicpm
2024-02-07 06:15:56 +00:00
convert.py : add python logging instead of print() (#6511) * convert.py: add python logging instead of print() * convert.py: verbose flag takes priority over dump flag log suppression * convert.py: named instance logging * convert.py: use explicit logger id string * convert.py: convert extra print() to named logger * convert.py: sys.stderr.write --> logger.error * *.py: Convert all python scripts to use logging module * requirements.txt: remove extra line * flake8: update flake8 ignore and exclude to match ci settings * gh-actions: add flake8-no-print to flake8 lint step * pre-commit: add flake8-no-print to flake8 and also update pre-commit version * convert-hf-to-gguf.py: print() to logger conversion * *.py: logging basiconfig refactor to use conditional expression * *.py: removed commented out logging * fixup! *.py: logging basiconfig refactor to use conditional expression * constant.py: logger.error then exit should be a raise exception instead * *.py: Convert logger error and sys.exit() into a raise exception (for atypical error) * gguf-convert-endian.py: refactor convert_byteorder() to use tqdm progressbar * verify-checksum-model.py: This is the result of the program, it should be printed to stdout. * compare-llama-bench.py: add blank line for readability during missing repo response * reader.py: read_gguf_file() use print() over logging * convert.py: warning goes to stderr and won't hurt the dump output * gguf-dump.py: dump_metadata() should print to stdout * convert-hf-to-gguf.py: print --> logger.debug or ValueError() * verify-checksum-models.py: use print() for printing table * *.py: refactor logging.basicConfig() * gguf-py/gguf/*.py: use __name__ as logger name Since they will be imported and not run directly. * python-lint.yml: use .flake8 file instead * constants.py: logger no longer required * convert-hf-to-gguf.py: add additional logging * convert-hf-to-gguf.py: print() --> logger * *.py: fix flake8 warnings * revert changes to convert-hf-to-gguf.py for get_name() * convert-hf-to-gguf-update.py: use triple quoted f-string instead * *.py: accidentally corrected the wrong line * *.py: add compilade warning suggestions and style fixes
2024-05-03 19:36:41 +00:00
logger = logging.getLogger("hf-to-gguf")
scripts: Generalize convert scripts (#3838) * Replace convert-*-hf-to-gguf.py files with convert-hf-to-gguf.py
2023-11-09 10:09:29 +00:00
###### MODEL DEFINITIONS ######
class SentencePieceTokenTypes(IntEnum):
NORMAL = 1
UNKNOWN = 2
CONTROL = 3
USER_DEFINED = 4
UNUSED = 5
BYTE = 6
flake : fix
2024-03-04 19:50:50 +00:00
convert-hf : make model class definitions self-contained (#5825)
2024-03-02 17:21:47 +00:00
AnyModel = TypeVar("AnyModel", bound="type[Model]")
flake : fix
2024-03-04 19:50:50 +00:00
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
class Model:
convert-hf : make model class definitions self-contained (#5825)
2024-03-02 17:21:47 +00:00
_model_classes: dict[str, type[Model]] = {}
scripts: Generalize convert scripts (#3838) * Replace convert-*-hf-to-gguf.py files with convert-hf-to-gguf.py
2023-11-09 10:09:29 +00:00
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
dir_model: Path
ftype: int
is_big_endian: bool
endianess: gguf.GGUFEndian
use_temp_file: bool
lazy: bool
part_names: list[str]
is_safetensors: bool
hparams: dict[str, Any]
block_count: int
tensor_map: gguf.TensorNameMap
tensor_names: set[str] | None
convert-hf : support bfloat16 conversion (#7158) * convert-hf : support bfloat16 conversion * gguf-py : flake8 fixes * convert-hf : add missing space after comma * convert-hf : get bit-exact same output as ./quantize The quantization version was missing. * convert-hf : don't round bf16 NANs * convert-hf : save some memory with np.int16 intermediate bf16 weights * convert-hf : more closely match llama.cpp with which weights to keep in f32 * convert-hf : add --outtype auto-f16 A reason for this to exist is for model quantizers who want an initial GGUF with the most fidelity to the original model while still using a 16-bit float type instead of 32-bit floats. * convert-hf : remove a semicolon because flake8 doesn't like it It's a reflex from when programming in C/C++, I guess. * convert-hf : support outtype templating in outfile name * convert-hf : rename --outtype auto-f16 to --outtype auto
2024-05-11 15:06:26 +00:00
fname_out: Path
gguf_writer: gguf.GGUFWriter
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
# subclasses should define this!
model_arch: gguf.MODEL_ARCH
convert-hf : support bfloat16 conversion (#7158) * convert-hf : support bfloat16 conversion * gguf-py : flake8 fixes * convert-hf : add missing space after comma * convert-hf : get bit-exact same output as ./quantize The quantization version was missing. * convert-hf : don't round bf16 NANs * convert-hf : save some memory with np.int16 intermediate bf16 weights * convert-hf : more closely match llama.cpp with which weights to keep in f32 * convert-hf : add --outtype auto-f16 A reason for this to exist is for model quantizers who want an initial GGUF with the most fidelity to the original model while still using a 16-bit float type instead of 32-bit floats. * convert-hf : remove a semicolon because flake8 doesn't like it It's a reflex from when programming in C/C++, I guess. * convert-hf : support outtype templating in outfile name * convert-hf : rename --outtype auto-f16 to --outtype auto
2024-05-11 15:06:26 +00:00
def __init__(self, dir_model: Path, ftype: gguf.LlamaFileType, fname_out: Path, is_big_endian: bool, use_temp_file: bool, eager: bool):
if type(self) is Model:
raise TypeError(f"{type(self).__name__!r} should not be directly instantiated")
scripts: Generalize convert scripts (#3838) * Replace convert-*-hf-to-gguf.py files with convert-hf-to-gguf.py
2023-11-09 10:09:29 +00:00
self.dir_model = dir_model
self.ftype = ftype
self.is_big_endian = is_big_endian
self.endianess = gguf.GGUFEndian.BIG if is_big_endian else gguf.GGUFEndian.LITTLE
convert : enable the `--use-temp-file` cli flag (#6645)
2024-04-14 08:40:18 +00:00
self.use_temp_file = use_temp_file
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
self.lazy = not eager
self.part_names = Model.get_model_part_names(self.dir_model, ".safetensors")
self.is_safetensors = len(self.part_names) > 0
if not self.is_safetensors:
self.part_names = Model.get_model_part_names(self.dir_model, ".bin")
scripts: Generalize convert scripts (#3838) * Replace convert-*-hf-to-gguf.py files with convert-hf-to-gguf.py
2023-11-09 10:09:29 +00:00
self.hparams = Model.load_hparams(self.dir_model)
llama : add support for Nomic Embed (#5468)
2024-02-13 17:03:53 +00:00
self.block_count = self.find_hparam(["n_layers", "num_hidden_layers", "n_layer"])
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
self.tensor_map = gguf.get_tensor_name_map(self.model_arch, self.block_count)
self.tensor_names = None
convert-hf : support bfloat16 conversion (#7158) * convert-hf : support bfloat16 conversion * gguf-py : flake8 fixes * convert-hf : add missing space after comma * convert-hf : get bit-exact same output as ./quantize The quantization version was missing. * convert-hf : don't round bf16 NANs * convert-hf : save some memory with np.int16 intermediate bf16 weights * convert-hf : more closely match llama.cpp with which weights to keep in f32 * convert-hf : add --outtype auto-f16 A reason for this to exist is for model quantizers who want an initial GGUF with the most fidelity to the original model while still using a 16-bit float type instead of 32-bit floats. * convert-hf : remove a semicolon because flake8 doesn't like it It's a reflex from when programming in C/C++, I guess. * convert-hf : support outtype templating in outfile name * convert-hf : rename --outtype auto-f16 to --outtype auto
2024-05-11 15:06:26 +00:00
if self.ftype == gguf.LlamaFileType.GUESSED:
# NOTE: can't use field "torch_dtype" in config.json, because some finetunes lie.
_, first_tensor = next(self.get_tensors())
if first_tensor.dtype == torch.float16:
logger.info(f"choosing --outtype f16 from first tensor type ({first_tensor.dtype})")
self.ftype = gguf.LlamaFileType.MOSTLY_F16
else:
logger.info(f"choosing --outtype bf16 from first tensor type ({first_tensor.dtype})")
self.ftype = gguf.LlamaFileType.MOSTLY_BF16
ftype_up: str = self.ftype.name.partition("_")[2].upper()
ftype_lw: str = ftype_up.lower()
# allow templating the file name with the output ftype, useful with the "auto" ftype
self.fname_out = fname_out.parent / fname_out.name.format(ftype_lw, outtype=ftype_lw, ftype=ftype_lw, OUTTYPE=ftype_up, FTYPE=ftype_up)
self.gguf_writer = gguf.GGUFWriter(self.fname_out, gguf.MODEL_ARCH_NAMES[self.model_arch], endianess=self.endianess, use_temp_file=self.use_temp_file)
llama : add support for Nomic Embed (#5468)
2024-02-13 17:03:53 +00:00
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
@classmethod
def __init_subclass__(cls):
# can't use an abstract property, because overriding it without type errors
# would require using decorated functions instead of simply defining the property
if "model_arch" not in cls.__dict__:
raise TypeError(f"Missing property 'model_arch' for {cls.__name__!r}")
convert-hf : make model class definitions self-contained (#5825)
2024-03-02 17:21:47 +00:00
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
def find_hparam(self, keys: Iterable[str], optional: bool = False) -> Any:
llama : add support for Nomic Embed (#5468)
2024-02-13 17:03:53 +00:00
key = next((k for k in keys if k in self.hparams), None)
if key is not None:
return self.hparams[key]
if optional:
return None
raise KeyError(f"could not find any of: {keys}")
scripts: Generalize convert scripts (#3838) * Replace convert-*-hf-to-gguf.py files with convert-hf-to-gguf.py
2023-11-09 10:09:29 +00:00
def set_vocab(self):
self._set_vocab_gpt2()
def get_tensors(self) -> Iterator[tuple[str, Tensor]]:
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
tensor_names_from_parts: set[str] = set()
if len(self.part_names) > 1:
self.tensor_names = set()
index_name = "model.safetensors" if self.is_safetensors else "pytorch_model.bin"
index_name += ".index.json"
logger.info(f"gguf: loading model weight map from '{index_name}'")
with open(self.dir_model / index_name, "r", encoding="utf-8") as f:
index: dict[str, Any] = json.load(f)
weight_map = index.get("weight_map")
if weight_map is None or not isinstance(weight_map, dict):
raise ValueError(f"Can't load 'weight_map' from {index_name!r}")
self.tensor_names.update(weight_map.keys())
else:
self.tensor_names = tensor_names_from_parts
scripts: Generalize convert scripts (#3838) * Replace convert-*-hf-to-gguf.py files with convert-hf-to-gguf.py
2023-11-09 10:09:29 +00:00
for part_name in self.part_names:
convert.py : add python logging instead of print() (#6511) * convert.py: add python logging instead of print() * convert.py: verbose flag takes priority over dump flag log suppression * convert.py: named instance logging * convert.py: use explicit logger id string * convert.py: convert extra print() to named logger * convert.py: sys.stderr.write --> logger.error * *.py: Convert all python scripts to use logging module * requirements.txt: remove extra line * flake8: update flake8 ignore and exclude to match ci settings * gh-actions: add flake8-no-print to flake8 lint step * pre-commit: add flake8-no-print to flake8 and also update pre-commit version * convert-hf-to-gguf.py: print() to logger conversion * *.py: logging basiconfig refactor to use conditional expression * *.py: removed commented out logging * fixup! *.py: logging basiconfig refactor to use conditional expression * constant.py: logger.error then exit should be a raise exception instead * *.py: Convert logger error and sys.exit() into a raise exception (for atypical error) * gguf-convert-endian.py: refactor convert_byteorder() to use tqdm progressbar * verify-checksum-model.py: This is the result of the program, it should be printed to stdout. * compare-llama-bench.py: add blank line for readability during missing repo response * reader.py: read_gguf_file() use print() over logging * convert.py: warning goes to stderr and won't hurt the dump output * gguf-dump.py: dump_metadata() should print to stdout * convert-hf-to-gguf.py: print --> logger.debug or ValueError() * verify-checksum-models.py: use print() for printing table * *.py: refactor logging.basicConfig() * gguf-py/gguf/*.py: use __name__ as logger name Since they will be imported and not run directly. * python-lint.yml: use .flake8 file instead * constants.py: logger no longer required * convert-hf-to-gguf.py: add additional logging * convert-hf-to-gguf.py: print() --> logger * *.py: fix flake8 warnings * revert changes to convert-hf-to-gguf.py for get_name() * convert-hf-to-gguf-update.py: use triple quoted f-string instead * *.py: accidentally corrected the wrong line * *.py: add compilade warning suggestions and style fixes
2024-05-03 19:36:41 +00:00
logger.info(f"gguf: loading model part '{part_name}'")
scripts: Generalize convert scripts (#3838) * Replace convert-*-hf-to-gguf.py files with convert-hf-to-gguf.py
2023-11-09 10:09:29 +00:00
ctx: ContextManager[Any]
if self.is_safetensors:
from safetensors import safe_open
ctx = cast(ContextManager[Any], safe_open(self.dir_model / part_name, framework="pt", device="cpu"))
else:
py : re-enable mmap in convert hf (#4732) * update: awq support llama-7b model * update: change order * update: benchmark results for llama2-7b * update: mistral 7b v1 benchmark * update: support 4 models * fix: Readme * update: ready for PR * update: readme * fix: readme * update: change order import * black * format code * update: work for bot mpt and awqmpt * update: readme * Rename to llm_build_ffn_mpt_awq * Formatted other files * Fixed params count * fix: remove code * update: more detail for mpt * fix: readme * fix: readme * update: change folder architecture * fix: common.cpp * fix: readme * fix: remove ggml_repeat * update: cicd * update: cicd * uppdate: remove use_awq arg * update: readme * llama : adapt plamo to new ffn ggml-ci * fix: update torch version --------- Co-authored-by: Trần Đức Nam <v.namtd12@vinai.io> Co-authored-by: Le Hoang Anh <v.anhlh33@vinai.io> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-01-02 09:23:38 +00:00
ctx = contextlib.nullcontext(torch.load(str(self.dir_model / part_name), map_location="cpu", mmap=True, weights_only=True))
scripts: Generalize convert scripts (#3838) * Replace convert-*-hf-to-gguf.py files with convert-hf-to-gguf.py
2023-11-09 10:09:29 +00:00
with ctx as model_part:
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
tensor_names_from_parts.update(model_part.keys())
scripts: Generalize convert scripts (#3838) * Replace convert-*-hf-to-gguf.py files with convert-hf-to-gguf.py
2023-11-09 10:09:29 +00:00
for name in model_part.keys():
data = model_part.get_tensor(name) if self.is_safetensors else model_part[name]
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
if self.lazy:
data = LazyTorchTensor.from_eager(data)
scripts: Generalize convert scripts (#3838) * Replace convert-*-hf-to-gguf.py files with convert-hf-to-gguf.py
2023-11-09 10:09:29 +00:00
yield name, data
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
# only verify tensor name presence; it doesn't matter if they are not in the right files
if len(sym_diff := tensor_names_from_parts.symmetric_difference(self.tensor_names)) > 0:
raise ValueError(f"Mismatch between weight map and model parts for tensor names: {sym_diff}")
def format_tensor_name(self, key: gguf.MODEL_TENSOR, bid: int | None = None, suffix: str = ".weight") -> str:
if key not in gguf.MODEL_TENSORS[self.model_arch]:
raise ValueError(f"Missing {key!r} for MODEL_TENSORS of {self.model_arch!r}")
convert-hf : support bfloat16 conversion (#7158) * convert-hf : support bfloat16 conversion * gguf-py : flake8 fixes * convert-hf : add missing space after comma * convert-hf : get bit-exact same output as ./quantize The quantization version was missing. * convert-hf : don't round bf16 NANs * convert-hf : save some memory with np.int16 intermediate bf16 weights * convert-hf : more closely match llama.cpp with which weights to keep in f32 * convert-hf : add --outtype auto-f16 A reason for this to exist is for model quantizers who want an initial GGUF with the most fidelity to the original model while still using a 16-bit float type instead of 32-bit floats. * convert-hf : remove a semicolon because flake8 doesn't like it It's a reflex from when programming in C/C++, I guess. * convert-hf : support outtype templating in outfile name * convert-hf : rename --outtype auto-f16 to --outtype auto
2024-05-11 15:06:26 +00:00
name: str = gguf.TENSOR_NAMES[key]
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
if "{bid}" in name:
assert bid is not None
name = name.format(bid=bid)
return name + suffix
convert-hf : support bfloat16 conversion (#7158) * convert-hf : support bfloat16 conversion * gguf-py : flake8 fixes * convert-hf : add missing space after comma * convert-hf : get bit-exact same output as ./quantize The quantization version was missing. * convert-hf : don't round bf16 NANs * convert-hf : save some memory with np.int16 intermediate bf16 weights * convert-hf : more closely match llama.cpp with which weights to keep in f32 * convert-hf : add --outtype auto-f16 A reason for this to exist is for model quantizers who want an initial GGUF with the most fidelity to the original model while still using a 16-bit float type instead of 32-bit floats. * convert-hf : remove a semicolon because flake8 doesn't like it It's a reflex from when programming in C/C++, I guess. * convert-hf : support outtype templating in outfile name * convert-hf : rename --outtype auto-f16 to --outtype auto
2024-05-11 15:06:26 +00:00
def match_model_tensor_name(self, name: str, key: gguf.MODEL_TENSOR, bid: int | None, suffix: str = ".weight") -> bool:
if key not in gguf.MODEL_TENSORS[self.model_arch]:
return False
key_name: str = gguf.TENSOR_NAMES[key]
if "{bid}" in key_name:
if bid is None:
return False
key_name = key_name.format(bid=bid)
else:
if bid is not None:
return False
return name == (key_name + suffix)
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
def map_tensor_name(self, name: str, try_suffixes: Sequence[str] = (".weight", ".bias")) -> str:
new_name = self.tensor_map.get_name(key=name, try_suffixes=try_suffixes)
if new_name is None:
raise ValueError(f"Can not map tensor {name!r}")
return new_name
scripts: Generalize convert scripts (#3838) * Replace convert-*-hf-to-gguf.py files with convert-hf-to-gguf.py
2023-11-09 10:09:29 +00:00
def set_gguf_parameters(self):
self.gguf_writer.add_name(self.dir_model.name)
llama : add support for Nomic Embed (#5468)
2024-02-13 17:03:53 +00:00
self.gguf_writer.add_block_count(self.block_count)
if (n_ctx := self.find_hparam(["max_position_embeddings", "n_ctx"], optional=True)) is not None:
scripts: Generalize convert scripts (#3838) * Replace convert-*-hf-to-gguf.py files with convert-hf-to-gguf.py
2023-11-09 10:09:29 +00:00
self.gguf_writer.add_context_length(n_ctx)
convert.py : add python logging instead of print() (#6511) * convert.py: add python logging instead of print() * convert.py: verbose flag takes priority over dump flag log suppression * convert.py: named instance logging * convert.py: use explicit logger id string * convert.py: convert extra print() to named logger * convert.py: sys.stderr.write --> logger.error * *.py: Convert all python scripts to use logging module * requirements.txt: remove extra line * flake8: update flake8 ignore and exclude to match ci settings * gh-actions: add flake8-no-print to flake8 lint step * pre-commit: add flake8-no-print to flake8 and also update pre-commit version * convert-hf-to-gguf.py: print() to logger conversion * *.py: logging basiconfig refactor to use conditional expression * *.py: removed commented out logging * fixup! *.py: logging basiconfig refactor to use conditional expression * constant.py: logger.error then exit should be a raise exception instead * *.py: Convert logger error and sys.exit() into a raise exception (for atypical error) * gguf-convert-endian.py: refactor convert_byteorder() to use tqdm progressbar * verify-checksum-model.py: This is the result of the program, it should be printed to stdout. * compare-llama-bench.py: add blank line for readability during missing repo response * reader.py: read_gguf_file() use print() over logging * convert.py: warning goes to stderr and won't hurt the dump output * gguf-dump.py: dump_metadata() should print to stdout * convert-hf-to-gguf.py: print --> logger.debug or ValueError() * verify-checksum-models.py: use print() for printing table * *.py: refactor logging.basicConfig() * gguf-py/gguf/*.py: use __name__ as logger name Since they will be imported and not run directly. * python-lint.yml: use .flake8 file instead * constants.py: logger no longer required * convert-hf-to-gguf.py: add additional logging * convert-hf-to-gguf.py: print() --> logger * *.py: fix flake8 warnings * revert changes to convert-hf-to-gguf.py for get_name() * convert-hf-to-gguf-update.py: use triple quoted f-string instead * *.py: accidentally corrected the wrong line * *.py: add compilade warning suggestions and style fixes
2024-05-03 19:36:41 +00:00
logger.info(f"gguf: context length = {n_ctx}")
llama : add support for Nomic Embed (#5468)
2024-02-13 17:03:53 +00:00
n_embd = self.find_hparam(["hidden_size", "n_embd"])
self.gguf_writer.add_embedding_length(n_embd)
convert.py : add python logging instead of print() (#6511) * convert.py: add python logging instead of print() * convert.py: verbose flag takes priority over dump flag log suppression * convert.py: named instance logging * convert.py: use explicit logger id string * convert.py: convert extra print() to named logger * convert.py: sys.stderr.write --> logger.error * *.py: Convert all python scripts to use logging module * requirements.txt: remove extra line * flake8: update flake8 ignore and exclude to match ci settings * gh-actions: add flake8-no-print to flake8 lint step * pre-commit: add flake8-no-print to flake8 and also update pre-commit version * convert-hf-to-gguf.py: print() to logger conversion * *.py: logging basiconfig refactor to use conditional expression * *.py: removed commented out logging * fixup! *.py: logging basiconfig refactor to use conditional expression * constant.py: logger.error then exit should be a raise exception instead * *.py: Convert logger error and sys.exit() into a raise exception (for atypical error) * gguf-convert-endian.py: refactor convert_byteorder() to use tqdm progressbar * verify-checksum-model.py: This is the result of the program, it should be printed to stdout. * compare-llama-bench.py: add blank line for readability during missing repo response * reader.py: read_gguf_file() use print() over logging * convert.py: warning goes to stderr and won't hurt the dump output * gguf-dump.py: dump_metadata() should print to stdout * convert-hf-to-gguf.py: print --> logger.debug or ValueError() * verify-checksum-models.py: use print() for printing table * *.py: refactor logging.basicConfig() * gguf-py/gguf/*.py: use __name__ as logger name Since they will be imported and not run directly. * python-lint.yml: use .flake8 file instead * constants.py: logger no longer required * convert-hf-to-gguf.py: add additional logging * convert-hf-to-gguf.py: print() --> logger * *.py: fix flake8 warnings * revert changes to convert-hf-to-gguf.py for get_name() * convert-hf-to-gguf-update.py: use triple quoted f-string instead * *.py: accidentally corrected the wrong line * *.py: add compilade warning suggestions and style fixes
2024-05-03 19:36:41 +00:00
logger.info(f"gguf: embedding length = {n_embd}")
llama : add support for Nomic Embed (#5468)
2024-02-13 17:03:53 +00:00
if (n_ff := self.find_hparam(["intermediate_size", "n_inner"], optional=True)) is not None:
scripts: Generalize convert scripts (#3838) * Replace convert-*-hf-to-gguf.py files with convert-hf-to-gguf.py
2023-11-09 10:09:29 +00:00
self.gguf_writer.add_feed_forward_length(n_ff)
convert.py : add python logging instead of print() (#6511) * convert.py: add python logging instead of print() * convert.py: verbose flag takes priority over dump flag log suppression * convert.py: named instance logging * convert.py: use explicit logger id string * convert.py: convert extra print() to named logger * convert.py: sys.stderr.write --> logger.error * *.py: Convert all python scripts to use logging module * requirements.txt: remove extra line * flake8: update flake8 ignore and exclude to match ci settings * gh-actions: add flake8-no-print to flake8 lint step * pre-commit: add flake8-no-print to flake8 and also update pre-commit version * convert-hf-to-gguf.py: print() to logger conversion * *.py: logging basiconfig refactor to use conditional expression * *.py: removed commented out logging * fixup! *.py: logging basiconfig refactor to use conditional expression * constant.py: logger.error then exit should be a raise exception instead * *.py: Convert logger error and sys.exit() into a raise exception (for atypical error) * gguf-convert-endian.py: refactor convert_byteorder() to use tqdm progressbar * verify-checksum-model.py: This is the result of the program, it should be printed to stdout. * compare-llama-bench.py: add blank line for readability during missing repo response * reader.py: read_gguf_file() use print() over logging * convert.py: warning goes to stderr and won't hurt the dump output * gguf-dump.py: dump_metadata() should print to stdout * convert-hf-to-gguf.py: print --> logger.debug or ValueError() * verify-checksum-models.py: use print() for printing table * *.py: refactor logging.basicConfig() * gguf-py/gguf/*.py: use __name__ as logger name Since they will be imported and not run directly. * python-lint.yml: use .flake8 file instead * constants.py: logger no longer required * convert-hf-to-gguf.py: add additional logging * convert-hf-to-gguf.py: print() --> logger * *.py: fix flake8 warnings * revert changes to convert-hf-to-gguf.py for get_name() * convert-hf-to-gguf-update.py: use triple quoted f-string instead * *.py: accidentally corrected the wrong line * *.py: add compilade warning suggestions and style fixes
2024-05-03 19:36:41 +00:00
logger.info(f"gguf: feed forward length = {n_ff}")
llama : add support for Nomic Embed (#5468)
2024-02-13 17:03:53 +00:00
n_head = self.find_hparam(["num_attention_heads", "n_head"])
self.gguf_writer.add_head_count(n_head)
convert.py : add python logging instead of print() (#6511) * convert.py: add python logging instead of print() * convert.py: verbose flag takes priority over dump flag log suppression * convert.py: named instance logging * convert.py: use explicit logger id string * convert.py: convert extra print() to named logger * convert.py: sys.stderr.write --> logger.error * *.py: Convert all python scripts to use logging module * requirements.txt: remove extra line * flake8: update flake8 ignore and exclude to match ci settings * gh-actions: add flake8-no-print to flake8 lint step * pre-commit: add flake8-no-print to flake8 and also update pre-commit version * convert-hf-to-gguf.py: print() to logger conversion * *.py: logging basiconfig refactor to use conditional expression * *.py: removed commented out logging * fixup! *.py: logging basiconfig refactor to use conditional expression * constant.py: logger.error then exit should be a raise exception instead * *.py: Convert logger error and sys.exit() into a raise exception (for atypical error) * gguf-convert-endian.py: refactor convert_byteorder() to use tqdm progressbar * verify-checksum-model.py: This is the result of the program, it should be printed to stdout. * compare-llama-bench.py: add blank line for readability during missing repo response * reader.py: read_gguf_file() use print() over logging * convert.py: warning goes to stderr and won't hurt the dump output * gguf-dump.py: dump_metadata() should print to stdout * convert-hf-to-gguf.py: print --> logger.debug or ValueError() * verify-checksum-models.py: use print() for printing table * *.py: refactor logging.basicConfig() * gguf-py/gguf/*.py: use __name__ as logger name Since they will be imported and not run directly. * python-lint.yml: use .flake8 file instead * constants.py: logger no longer required * convert-hf-to-gguf.py: add additional logging * convert-hf-to-gguf.py: print() --> logger * *.py: fix flake8 warnings * revert changes to convert-hf-to-gguf.py for get_name() * convert-hf-to-gguf-update.py: use triple quoted f-string instead * *.py: accidentally corrected the wrong line * *.py: add compilade warning suggestions and style fixes
2024-05-03 19:36:41 +00:00
logger.info(f"gguf: head count = {n_head}")
llama : add support for Nomic Embed (#5468)
2024-02-13 17:03:53 +00:00
llama : add Mixtral support (#4406) * convert : support Mixtral as LLAMA arch * convert : fix n_ff typo * llama : model loading * ggml : sync latest ggml_mul_mat_id * llama : update graph to support MoE * llama : fix cur -> cur_expert * llama : first working version * llama : fix expert weighting in the FFN * ggml : ggml_get_rows support 2D indexing [n_tokens, n_experts] (cpu only) * ggml : add n_as argument to ggml_mul_mat_id * ggml : fix ggml_get_rows to take into account ne02 / ne11 * metal : add more general support for ggml_get_rows + tests * llama : add basic support for offloading moe with CUDA * metal : add/mul/div use general kernel when src1 not cont * metal : reduce the kernel launches for ggml_mul_mat_id * ggml : get_rows : support non-contiguos tensors with gaps, generalize up to 3D * ggml : update get_rows f16 and q * cuda : support non-contiguous src1 in get_rows * llama : offload missing ffn_moe_silu * metal : fix ggml_get_rows to work with non-cont src1 * metal : add indirect mat-vec kernels for all quantization types * llama : do not quantize expert gating tensors * llama : add n_expert and n_expert_used to hparams + change quants * test-backend-ops : add moe test * cuda : fix get_rows when ncols is odd * convert : determine n_ctx correctly * metal : fix ggml_mul_mat_id for F32 * test-backend-ops : make experts more evenly probable (test_moe) * test-backend-ops : cleanup, add moe test for batches * test-backend-ops : add cpy from f32 -> all types test * test-backend-ops : fix dequantize block offset * llama : fix hard-coded number of experts * test-backend-ops : simplify and disable slow tests to avoid CI timeout * test-backend-ops : disable MOE test with thread sanitizer * cuda : fix mul_mat_id with multi gpu * convert : use 1e6 rope_freq_base for mixtral * convert : fix style * convert : support safetensors format * gguf-py : bump version * metal : add cpy f16 -> f32 kernel * metal : fix binary ops for ne10 % 4 != 0 * test-backend-ops : add one more sum_rows test * ggml : do not use BLAS with ggml_mul_mat_id * convert-hf : support for mixtral-instruct (#4428) * convert : typo fix, add additional hyperparameters, use LLaMA arch for Mixtral-instruct * convert : use sentencepiece tokenizer for Mixtral-instruct * convert : make flake8 happy * metal : fix soft_max kernels ref: https://github.com/ggerganov/ggml/pull/621/commits/1914017863d2f9ab8ecc0281cc2a56d683668b92 * metal : limit kernels to not use more than the allowed threads --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> Co-authored-by: Radek Pilar <github@mrkva.eu>
2023-12-13 12:04:25 +00:00
if (n_head_kv := self.hparams.get("num_key_value_heads")) is not None:
self.gguf_writer.add_head_count_kv(n_head_kv)
convert.py : add python logging instead of print() (#6511) * convert.py: add python logging instead of print() * convert.py: verbose flag takes priority over dump flag log suppression * convert.py: named instance logging * convert.py: use explicit logger id string * convert.py: convert extra print() to named logger * convert.py: sys.stderr.write --> logger.error * *.py: Convert all python scripts to use logging module * requirements.txt: remove extra line * flake8: update flake8 ignore and exclude to match ci settings * gh-actions: add flake8-no-print to flake8 lint step * pre-commit: add flake8-no-print to flake8 and also update pre-commit version * convert-hf-to-gguf.py: print() to logger conversion * *.py: logging basiconfig refactor to use conditional expression * *.py: removed commented out logging * fixup! *.py: logging basiconfig refactor to use conditional expression * constant.py: logger.error then exit should be a raise exception instead * *.py: Convert logger error and sys.exit() into a raise exception (for atypical error) * gguf-convert-endian.py: refactor convert_byteorder() to use tqdm progressbar * verify-checksum-model.py: This is the result of the program, it should be printed to stdout. * compare-llama-bench.py: add blank line for readability during missing repo response * reader.py: read_gguf_file() use print() over logging * convert.py: warning goes to stderr and won't hurt the dump output * gguf-dump.py: dump_metadata() should print to stdout * convert-hf-to-gguf.py: print --> logger.debug or ValueError() * verify-checksum-models.py: use print() for printing table * *.py: refactor logging.basicConfig() * gguf-py/gguf/*.py: use __name__ as logger name Since they will be imported and not run directly. * python-lint.yml: use .flake8 file instead * constants.py: logger no longer required * convert-hf-to-gguf.py: add additional logging * convert-hf-to-gguf.py: print() --> logger * *.py: fix flake8 warnings * revert changes to convert-hf-to-gguf.py for get_name() * convert-hf-to-gguf-update.py: use triple quoted f-string instead * *.py: accidentally corrected the wrong line * *.py: add compilade warning suggestions and style fixes
2024-05-03 19:36:41 +00:00
logger.info(f"gguf: key-value head count = {n_head_kv}")
llama : add Mixtral support (#4406) * convert : support Mixtral as LLAMA arch * convert : fix n_ff typo * llama : model loading * ggml : sync latest ggml_mul_mat_id * llama : update graph to support MoE * llama : fix cur -> cur_expert * llama : first working version * llama : fix expert weighting in the FFN * ggml : ggml_get_rows support 2D indexing [n_tokens, n_experts] (cpu only) * ggml : add n_as argument to ggml_mul_mat_id * ggml : fix ggml_get_rows to take into account ne02 / ne11 * metal : add more general support for ggml_get_rows + tests * llama : add basic support for offloading moe with CUDA * metal : add/mul/div use general kernel when src1 not cont * metal : reduce the kernel launches for ggml_mul_mat_id * ggml : get_rows : support non-contiguos tensors with gaps, generalize up to 3D * ggml : update get_rows f16 and q * cuda : support non-contiguous src1 in get_rows * llama : offload missing ffn_moe_silu * metal : fix ggml_get_rows to work with non-cont src1 * metal : add indirect mat-vec kernels for all quantization types * llama : do not quantize expert gating tensors * llama : add n_expert and n_expert_used to hparams + change quants * test-backend-ops : add moe test * cuda : fix get_rows when ncols is odd * convert : determine n_ctx correctly * metal : fix ggml_mul_mat_id for F32 * test-backend-ops : make experts more evenly probable (test_moe) * test-backend-ops : cleanup, add moe test for batches * test-backend-ops : add cpy from f32 -> all types test * test-backend-ops : fix dequantize block offset * llama : fix hard-coded number of experts * test-backend-ops : simplify and disable slow tests to avoid CI timeout * test-backend-ops : disable MOE test with thread sanitizer * cuda : fix mul_mat_id with multi gpu * convert : use 1e6 rope_freq_base for mixtral * convert : fix style * convert : support safetensors format * gguf-py : bump version * metal : add cpy f16 -> f32 kernel * metal : fix binary ops for ne10 % 4 != 0 * test-backend-ops : add one more sum_rows test * ggml : do not use BLAS with ggml_mul_mat_id * convert-hf : support for mixtral-instruct (#4428) * convert : typo fix, add additional hyperparameters, use LLaMA arch for Mixtral-instruct * convert : use sentencepiece tokenizer for Mixtral-instruct * convert : make flake8 happy * metal : fix soft_max kernels ref: https://github.com/ggerganov/ggml/pull/621/commits/1914017863d2f9ab8ecc0281cc2a56d683668b92 * metal : limit kernels to not use more than the allowed threads --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> Co-authored-by: Radek Pilar <github@mrkva.eu>
2023-12-13 12:04:25 +00:00
llama : add StarCoder2 support (#5795) * Add support for starcoder2 * handle rope type * skip rope freq and rotary embeddings from being serialized * resolve comments * Update llama.cpp * remove redundant changes * handle `rope-theta` * llama : change starcoder2 rope type * address comment --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-03-01 19:30:46 +00:00
if (rope_theta := self.hparams.get("rope_theta")) is not None:
self.gguf_writer.add_rope_freq_base(rope_theta)
convert.py : add python logging instead of print() (#6511) * convert.py: add python logging instead of print() * convert.py: verbose flag takes priority over dump flag log suppression * convert.py: named instance logging * convert.py: use explicit logger id string * convert.py: convert extra print() to named logger * convert.py: sys.stderr.write --> logger.error * *.py: Convert all python scripts to use logging module * requirements.txt: remove extra line * flake8: update flake8 ignore and exclude to match ci settings * gh-actions: add flake8-no-print to flake8 lint step * pre-commit: add flake8-no-print to flake8 and also update pre-commit version * convert-hf-to-gguf.py: print() to logger conversion * *.py: logging basiconfig refactor to use conditional expression * *.py: removed commented out logging * fixup! *.py: logging basiconfig refactor to use conditional expression * constant.py: logger.error then exit should be a raise exception instead * *.py: Convert logger error and sys.exit() into a raise exception (for atypical error) * gguf-convert-endian.py: refactor convert_byteorder() to use tqdm progressbar * verify-checksum-model.py: This is the result of the program, it should be printed to stdout. * compare-llama-bench.py: add blank line for readability during missing repo response * reader.py: read_gguf_file() use print() over logging * convert.py: warning goes to stderr and won't hurt the dump output * gguf-dump.py: dump_metadata() should print to stdout * convert-hf-to-gguf.py: print --> logger.debug or ValueError() * verify-checksum-models.py: use print() for printing table * *.py: refactor logging.basicConfig() * gguf-py/gguf/*.py: use __name__ as logger name Since they will be imported and not run directly. * python-lint.yml: use .flake8 file instead * constants.py: logger no longer required * convert-hf-to-gguf.py: add additional logging * convert-hf-to-gguf.py: print() --> logger * *.py: fix flake8 warnings * revert changes to convert-hf-to-gguf.py for get_name() * convert-hf-to-gguf-update.py: use triple quoted f-string instead * *.py: accidentally corrected the wrong line * *.py: add compilade warning suggestions and style fixes
2024-05-03 19:36:41 +00:00
logger.info(f"gguf: rope theta = {rope_theta}")
llama : add support for Nomic Embed (#5468)
2024-02-13 17:03:53 +00:00
if (f_rms_eps := self.hparams.get("rms_norm_eps")) is not None:
self.gguf_writer.add_layer_norm_rms_eps(f_rms_eps)
convert.py : add python logging instead of print() (#6511) * convert.py: add python logging instead of print() * convert.py: verbose flag takes priority over dump flag log suppression * convert.py: named instance logging * convert.py: use explicit logger id string * convert.py: convert extra print() to named logger * convert.py: sys.stderr.write --> logger.error * *.py: Convert all python scripts to use logging module * requirements.txt: remove extra line * flake8: update flake8 ignore and exclude to match ci settings * gh-actions: add flake8-no-print to flake8 lint step * pre-commit: add flake8-no-print to flake8 and also update pre-commit version * convert-hf-to-gguf.py: print() to logger conversion * *.py: logging basiconfig refactor to use conditional expression * *.py: removed commented out logging * fixup! *.py: logging basiconfig refactor to use conditional expression * constant.py: logger.error then exit should be a raise exception instead * *.py: Convert logger error and sys.exit() into a raise exception (for atypical error) * gguf-convert-endian.py: refactor convert_byteorder() to use tqdm progressbar * verify-checksum-model.py: This is the result of the program, it should be printed to stdout. * compare-llama-bench.py: add blank line for readability during missing repo response * reader.py: read_gguf_file() use print() over logging * convert.py: warning goes to stderr and won't hurt the dump output * gguf-dump.py: dump_metadata() should print to stdout * convert-hf-to-gguf.py: print --> logger.debug or ValueError() * verify-checksum-models.py: use print() for printing table * *.py: refactor logging.basicConfig() * gguf-py/gguf/*.py: use __name__ as logger name Since they will be imported and not run directly. * python-lint.yml: use .flake8 file instead * constants.py: logger no longer required * convert-hf-to-gguf.py: add additional logging * convert-hf-to-gguf.py: print() --> logger * *.py: fix flake8 warnings * revert changes to convert-hf-to-gguf.py for get_name() * convert-hf-to-gguf-update.py: use triple quoted f-string instead * *.py: accidentally corrected the wrong line * *.py: add compilade warning suggestions and style fixes
2024-05-03 19:36:41 +00:00
logger.info(f"gguf: rms norm epsilon = {f_rms_eps}")
llama : add StarCoder2 support (#5795) * Add support for starcoder2 * handle rope type * skip rope freq and rotary embeddings from being serialized * resolve comments * Update llama.cpp * remove redundant changes * handle `rope-theta` * llama : change starcoder2 rope type * address comment --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-03-01 19:30:46 +00:00
if (f_norm_eps := self.find_hparam(["layer_norm_eps", "layer_norm_epsilon", "norm_epsilon"], optional=True)) is not None:
llama : add support for Nomic Embed (#5468)
2024-02-13 17:03:53 +00:00
self.gguf_writer.add_layer_norm_eps(f_norm_eps)
convert.py : add python logging instead of print() (#6511) * convert.py: add python logging instead of print() * convert.py: verbose flag takes priority over dump flag log suppression * convert.py: named instance logging * convert.py: use explicit logger id string * convert.py: convert extra print() to named logger * convert.py: sys.stderr.write --> logger.error * *.py: Convert all python scripts to use logging module * requirements.txt: remove extra line * flake8: update flake8 ignore and exclude to match ci settings * gh-actions: add flake8-no-print to flake8 lint step * pre-commit: add flake8-no-print to flake8 and also update pre-commit version * convert-hf-to-gguf.py: print() to logger conversion * *.py: logging basiconfig refactor to use conditional expression * *.py: removed commented out logging * fixup! *.py: logging basiconfig refactor to use conditional expression * constant.py: logger.error then exit should be a raise exception instead * *.py: Convert logger error and sys.exit() into a raise exception (for atypical error) * gguf-convert-endian.py: refactor convert_byteorder() to use tqdm progressbar * verify-checksum-model.py: This is the result of the program, it should be printed to stdout. * compare-llama-bench.py: add blank line for readability during missing repo response * reader.py: read_gguf_file() use print() over logging * convert.py: warning goes to stderr and won't hurt the dump output * gguf-dump.py: dump_metadata() should print to stdout * convert-hf-to-gguf.py: print --> logger.debug or ValueError() * verify-checksum-models.py: use print() for printing table * *.py: refactor logging.basicConfig() * gguf-py/gguf/*.py: use __name__ as logger name Since they will be imported and not run directly. * python-lint.yml: use .flake8 file instead * constants.py: logger no longer required * convert-hf-to-gguf.py: add additional logging * convert-hf-to-gguf.py: print() --> logger * *.py: fix flake8 warnings * revert changes to convert-hf-to-gguf.py for get_name() * convert-hf-to-gguf-update.py: use triple quoted f-string instead * *.py: accidentally corrected the wrong line * *.py: add compilade warning suggestions and style fixes
2024-05-03 19:36:41 +00:00
logger.info(f"gguf: layer norm epsilon = {f_norm_eps}")
llama : add Mixtral support (#4406) * convert : support Mixtral as LLAMA arch * convert : fix n_ff typo * llama : model loading * ggml : sync latest ggml_mul_mat_id * llama : update graph to support MoE * llama : fix cur -> cur_expert * llama : first working version * llama : fix expert weighting in the FFN * ggml : ggml_get_rows support 2D indexing [n_tokens, n_experts] (cpu only) * ggml : add n_as argument to ggml_mul_mat_id * ggml : fix ggml_get_rows to take into account ne02 / ne11 * metal : add more general support for ggml_get_rows + tests * llama : add basic support for offloading moe with CUDA * metal : add/mul/div use general kernel when src1 not cont * metal : reduce the kernel launches for ggml_mul_mat_id * ggml : get_rows : support non-contiguos tensors with gaps, generalize up to 3D * ggml : update get_rows f16 and q * cuda : support non-contiguous src1 in get_rows * llama : offload missing ffn_moe_silu * metal : fix ggml_get_rows to work with non-cont src1 * metal : add indirect mat-vec kernels for all quantization types * llama : do not quantize expert gating tensors * llama : add n_expert and n_expert_used to hparams + change quants * test-backend-ops : add moe test * cuda : fix get_rows when ncols is odd * convert : determine n_ctx correctly * metal : fix ggml_mul_mat_id for F32 * test-backend-ops : make experts more evenly probable (test_moe) * test-backend-ops : cleanup, add moe test for batches * test-backend-ops : add cpy from f32 -> all types test * test-backend-ops : fix dequantize block offset * llama : fix hard-coded number of experts * test-backend-ops : simplify and disable slow tests to avoid CI timeout * test-backend-ops : disable MOE test with thread sanitizer * cuda : fix mul_mat_id with multi gpu * convert : use 1e6 rope_freq_base for mixtral * convert : fix style * convert : support safetensors format * gguf-py : bump version * metal : add cpy f16 -> f32 kernel * metal : fix binary ops for ne10 % 4 != 0 * test-backend-ops : add one more sum_rows test * ggml : do not use BLAS with ggml_mul_mat_id * convert-hf : support for mixtral-instruct (#4428) * convert : typo fix, add additional hyperparameters, use LLaMA arch for Mixtral-instruct * convert : use sentencepiece tokenizer for Mixtral-instruct * convert : make flake8 happy * metal : fix soft_max kernels ref: https://github.com/ggerganov/ggml/pull/621/commits/1914017863d2f9ab8ecc0281cc2a56d683668b92 * metal : limit kernels to not use more than the allowed threads --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> Co-authored-by: Radek Pilar <github@mrkva.eu>
2023-12-13 12:04:25 +00:00
if (n_experts := self.hparams.get("num_local_experts")) is not None:
self.gguf_writer.add_expert_count(n_experts)
convert.py : add python logging instead of print() (#6511) * convert.py: add python logging instead of print() * convert.py: verbose flag takes priority over dump flag log suppression * convert.py: named instance logging * convert.py: use explicit logger id string * convert.py: convert extra print() to named logger * convert.py: sys.stderr.write --> logger.error * *.py: Convert all python scripts to use logging module * requirements.txt: remove extra line * flake8: update flake8 ignore and exclude to match ci settings * gh-actions: add flake8-no-print to flake8 lint step * pre-commit: add flake8-no-print to flake8 and also update pre-commit version * convert-hf-to-gguf.py: print() to logger conversion * *.py: logging basiconfig refactor to use conditional expression * *.py: removed commented out logging * fixup! *.py: logging basiconfig refactor to use conditional expression * constant.py: logger.error then exit should be a raise exception instead * *.py: Convert logger error and sys.exit() into a raise exception (for atypical error) * gguf-convert-endian.py: refactor convert_byteorder() to use tqdm progressbar * verify-checksum-model.py: This is the result of the program, it should be printed to stdout. * compare-llama-bench.py: add blank line for readability during missing repo response * reader.py: read_gguf_file() use print() over logging * convert.py: warning goes to stderr and won't hurt the dump output * gguf-dump.py: dump_metadata() should print to stdout * convert-hf-to-gguf.py: print --> logger.debug or ValueError() * verify-checksum-models.py: use print() for printing table * *.py: refactor logging.basicConfig() * gguf-py/gguf/*.py: use __name__ as logger name Since they will be imported and not run directly. * python-lint.yml: use .flake8 file instead * constants.py: logger no longer required * convert-hf-to-gguf.py: add additional logging * convert-hf-to-gguf.py: print() --> logger * *.py: fix flake8 warnings * revert changes to convert-hf-to-gguf.py for get_name() * convert-hf-to-gguf-update.py: use triple quoted f-string instead * *.py: accidentally corrected the wrong line * *.py: add compilade warning suggestions and style fixes
2024-05-03 19:36:41 +00:00
logger.info(f"gguf: expert count = {n_experts}")
llama : add Mixtral support (#4406) * convert : support Mixtral as LLAMA arch * convert : fix n_ff typo * llama : model loading * ggml : sync latest ggml_mul_mat_id * llama : update graph to support MoE * llama : fix cur -> cur_expert * llama : first working version * llama : fix expert weighting in the FFN * ggml : ggml_get_rows support 2D indexing [n_tokens, n_experts] (cpu only) * ggml : add n_as argument to ggml_mul_mat_id * ggml : fix ggml_get_rows to take into account ne02 / ne11 * metal : add more general support for ggml_get_rows + tests * llama : add basic support for offloading moe with CUDA * metal : add/mul/div use general kernel when src1 not cont * metal : reduce the kernel launches for ggml_mul_mat_id * ggml : get_rows : support non-contiguos tensors with gaps, generalize up to 3D * ggml : update get_rows f16 and q * cuda : support non-contiguous src1 in get_rows * llama : offload missing ffn_moe_silu * metal : fix ggml_get_rows to work with non-cont src1 * metal : add indirect mat-vec kernels for all quantization types * llama : do not quantize expert gating tensors * llama : add n_expert and n_expert_used to hparams + change quants * test-backend-ops : add moe test * cuda : fix get_rows when ncols is odd * convert : determine n_ctx correctly * metal : fix ggml_mul_mat_id for F32 * test-backend-ops : make experts more evenly probable (test_moe) * test-backend-ops : cleanup, add moe test for batches * test-backend-ops : add cpy from f32 -> all types test * test-backend-ops : fix dequantize block offset * llama : fix hard-coded number of experts * test-backend-ops : simplify and disable slow tests to avoid CI timeout * test-backend-ops : disable MOE test with thread sanitizer * cuda : fix mul_mat_id with multi gpu * convert : use 1e6 rope_freq_base for mixtral * convert : fix style * convert : support safetensors format * gguf-py : bump version * metal : add cpy f16 -> f32 kernel * metal : fix binary ops for ne10 % 4 != 0 * test-backend-ops : add one more sum_rows test * ggml : do not use BLAS with ggml_mul_mat_id * convert-hf : support for mixtral-instruct (#4428) * convert : typo fix, add additional hyperparameters, use LLaMA arch for Mixtral-instruct * convert : use sentencepiece tokenizer for Mixtral-instruct * convert : make flake8 happy * metal : fix soft_max kernels ref: https://github.com/ggerganov/ggml/pull/621/commits/1914017863d2f9ab8ecc0281cc2a56d683668b92 * metal : limit kernels to not use more than the allowed threads --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> Co-authored-by: Radek Pilar <github@mrkva.eu>
2023-12-13 12:04:25 +00:00
if (n_experts_used := self.hparams.get("num_experts_per_tok")) is not None:
self.gguf_writer.add_expert_used_count(n_experts_used)
convert.py : add python logging instead of print() (#6511) * convert.py: add python logging instead of print() * convert.py: verbose flag takes priority over dump flag log suppression * convert.py: named instance logging * convert.py: use explicit logger id string * convert.py: convert extra print() to named logger * convert.py: sys.stderr.write --> logger.error * *.py: Convert all python scripts to use logging module * requirements.txt: remove extra line * flake8: update flake8 ignore and exclude to match ci settings * gh-actions: add flake8-no-print to flake8 lint step * pre-commit: add flake8-no-print to flake8 and also update pre-commit version * convert-hf-to-gguf.py: print() to logger conversion * *.py: logging basiconfig refactor to use conditional expression * *.py: removed commented out logging * fixup! *.py: logging basiconfig refactor to use conditional expression * constant.py: logger.error then exit should be a raise exception instead * *.py: Convert logger error and sys.exit() into a raise exception (for atypical error) * gguf-convert-endian.py: refactor convert_byteorder() to use tqdm progressbar * verify-checksum-model.py: This is the result of the program, it should be printed to stdout. * compare-llama-bench.py: add blank line for readability during missing repo response * reader.py: read_gguf_file() use print() over logging * convert.py: warning goes to stderr and won't hurt the dump output * gguf-dump.py: dump_metadata() should print to stdout * convert-hf-to-gguf.py: print --> logger.debug or ValueError() * verify-checksum-models.py: use print() for printing table * *.py: refactor logging.basicConfig() * gguf-py/gguf/*.py: use __name__ as logger name Since they will be imported and not run directly. * python-lint.yml: use .flake8 file instead * constants.py: logger no longer required * convert-hf-to-gguf.py: add additional logging * convert-hf-to-gguf.py: print() --> logger * *.py: fix flake8 warnings * revert changes to convert-hf-to-gguf.py for get_name() * convert-hf-to-gguf-update.py: use triple quoted f-string instead * *.py: accidentally corrected the wrong line * *.py: add compilade warning suggestions and style fixes
2024-05-03 19:36:41 +00:00
logger.info(f"gguf: experts used count = {n_experts_used}")
llama : add Mixtral support (#4406) * convert : support Mixtral as LLAMA arch * convert : fix n_ff typo * llama : model loading * ggml : sync latest ggml_mul_mat_id * llama : update graph to support MoE * llama : fix cur -> cur_expert * llama : first working version * llama : fix expert weighting in the FFN * ggml : ggml_get_rows support 2D indexing [n_tokens, n_experts] (cpu only) * ggml : add n_as argument to ggml_mul_mat_id * ggml : fix ggml_get_rows to take into account ne02 / ne11 * metal : add more general support for ggml_get_rows + tests * llama : add basic support for offloading moe with CUDA * metal : add/mul/div use general kernel when src1 not cont * metal : reduce the kernel launches for ggml_mul_mat_id * ggml : get_rows : support non-contiguos tensors with gaps, generalize up to 3D * ggml : update get_rows f16 and q * cuda : support non-contiguous src1 in get_rows * llama : offload missing ffn_moe_silu * metal : fix ggml_get_rows to work with non-cont src1 * metal : add indirect mat-vec kernels for all quantization types * llama : do not quantize expert gating tensors * llama : add n_expert and n_expert_used to hparams + change quants * test-backend-ops : add moe test * cuda : fix get_rows when ncols is odd * convert : determine n_ctx correctly * metal : fix ggml_mul_mat_id for F32 * test-backend-ops : make experts more evenly probable (test_moe) * test-backend-ops : cleanup, add moe test for batches * test-backend-ops : add cpy from f32 -> all types test * test-backend-ops : fix dequantize block offset * llama : fix hard-coded number of experts * test-backend-ops : simplify and disable slow tests to avoid CI timeout * test-backend-ops : disable MOE test with thread sanitizer * cuda : fix mul_mat_id with multi gpu * convert : use 1e6 rope_freq_base for mixtral * convert : fix style * convert : support safetensors format * gguf-py : bump version * metal : add cpy f16 -> f32 kernel * metal : fix binary ops for ne10 % 4 != 0 * test-backend-ops : add one more sum_rows test * ggml : do not use BLAS with ggml_mul_mat_id * convert-hf : support for mixtral-instruct (#4428) * convert : typo fix, add additional hyperparameters, use LLaMA arch for Mixtral-instruct * convert : use sentencepiece tokenizer for Mixtral-instruct * convert : make flake8 happy * metal : fix soft_max kernels ref: https://github.com/ggerganov/ggml/pull/621/commits/1914017863d2f9ab8ecc0281cc2a56d683668b92 * metal : limit kernels to not use more than the allowed threads --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> Co-authored-by: Radek Pilar <github@mrkva.eu>
2023-12-13 12:04:25 +00:00
llama : add support for Nomic Embed (#5468)
2024-02-13 17:03:53 +00:00
self.gguf_writer.add_file_type(self.ftype)
convert.py : add python logging instead of print() (#6511) * convert.py: add python logging instead of print() * convert.py: verbose flag takes priority over dump flag log suppression * convert.py: named instance logging * convert.py: use explicit logger id string * convert.py: convert extra print() to named logger * convert.py: sys.stderr.write --> logger.error * *.py: Convert all python scripts to use logging module * requirements.txt: remove extra line * flake8: update flake8 ignore and exclude to match ci settings * gh-actions: add flake8-no-print to flake8 lint step * pre-commit: add flake8-no-print to flake8 and also update pre-commit version * convert-hf-to-gguf.py: print() to logger conversion * *.py: logging basiconfig refactor to use conditional expression * *.py: removed commented out logging * fixup! *.py: logging basiconfig refactor to use conditional expression * constant.py: logger.error then exit should be a raise exception instead * *.py: Convert logger error and sys.exit() into a raise exception (for atypical error) * gguf-convert-endian.py: refactor convert_byteorder() to use tqdm progressbar * verify-checksum-model.py: This is the result of the program, it should be printed to stdout. * compare-llama-bench.py: add blank line for readability during missing repo response * reader.py: read_gguf_file() use print() over logging * convert.py: warning goes to stderr and won't hurt the dump output * gguf-dump.py: dump_metadata() should print to stdout * convert-hf-to-gguf.py: print --> logger.debug or ValueError() * verify-checksum-models.py: use print() for printing table * *.py: refactor logging.basicConfig() * gguf-py/gguf/*.py: use __name__ as logger name Since they will be imported and not run directly. * python-lint.yml: use .flake8 file instead * constants.py: logger no longer required * convert-hf-to-gguf.py: add additional logging * convert-hf-to-gguf.py: print() --> logger * *.py: fix flake8 warnings * revert changes to convert-hf-to-gguf.py for get_name() * convert-hf-to-gguf-update.py: use triple quoted f-string instead * *.py: accidentally corrected the wrong line * *.py: add compilade warning suggestions and style fixes
2024-05-03 19:36:41 +00:00
logger.info(f"gguf: file type = {self.ftype}")
scripts: Generalize convert scripts (#3838) * Replace convert-*-hf-to-gguf.py files with convert-hf-to-gguf.py
2023-11-09 10:09:29 +00:00
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
del bid # unused
return [(self.map_tensor_name(name), data_torch)]
def extra_f32_tensors(self, name: str, new_name: str, bid: int | None, n_dims: int) -> bool:
del name, new_name, bid, n_dims # unused
return False
def extra_f16_tensors(self, name: str, new_name: str, bid: int | None, n_dims: int) -> bool:
del name, new_name, bid, n_dims # unused
return False
scripts: Generalize convert scripts (#3838) * Replace convert-*-hf-to-gguf.py files with convert-hf-to-gguf.py
2023-11-09 10:09:29 +00:00
def write_tensors(self):
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
max_name_len = max(len(s) for _, s in self.tensor_map.mapping.values()) + len(".weight,")
scripts: Generalize convert scripts (#3838) * Replace convert-*-hf-to-gguf.py files with convert-hf-to-gguf.py
2023-11-09 10:09:29 +00:00
for name, data_torch in self.get_tensors():
# we don't need these
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
if name.endswith((".attention.masked_bias", ".attention.bias", ".rotary_emb.inv_freq")):
scripts: Generalize convert scripts (#3838) * Replace convert-*-hf-to-gguf.py files with convert-hf-to-gguf.py
2023-11-09 10:09:29 +00:00
continue
old_dtype = data_torch.dtype
# convert any unsupported data types to float32
if data_torch.dtype not in (torch.float16, torch.float32):
data_torch = data_torch.to(torch.float32)
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
# use the first number-like part of the tensor name as the block id
bid = None
for part in name.split("."):
if part.isdecimal():
bid = int(part)
break
for new_name, data in ((n, d.squeeze().numpy()) for n, d in self.modify_tensors(data_torch, name, bid)):
data: np.ndarray = data # type hint
n_dims = len(data.shape)
data_dtype = data.dtype
convert-hf : support bfloat16 conversion (#7158) * convert-hf : support bfloat16 conversion * gguf-py : flake8 fixes * convert-hf : add missing space after comma * convert-hf : get bit-exact same output as ./quantize The quantization version was missing. * convert-hf : don't round bf16 NANs * convert-hf : save some memory with np.int16 intermediate bf16 weights * convert-hf : more closely match llama.cpp with which weights to keep in f32 * convert-hf : add --outtype auto-f16 A reason for this to exist is for model quantizers who want an initial GGUF with the most fidelity to the original model while still using a 16-bit float type instead of 32-bit floats. * convert-hf : remove a semicolon because flake8 doesn't like it It's a reflex from when programming in C/C++, I guess. * convert-hf : support outtype templating in outfile name * convert-hf : rename --outtype auto-f16 to --outtype auto
2024-05-11 15:06:26 +00:00
data_qtype: gguf.GGMLQuantizationType | None = None
scripts: Generalize convert scripts (#3838) * Replace convert-*-hf-to-gguf.py files with convert-hf-to-gguf.py
2023-11-09 10:09:29 +00:00
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
# when both are True, f32 should win
extra_f32 = self.extra_f32_tensors(name, new_name, bid, n_dims)
extra_f16 = self.extra_f16_tensors(name, new_name, bid, n_dims)
# Most of the codebase that takes in 1D tensors or norms only handles F32 tensors
convert-hf : support bfloat16 conversion (#7158) * convert-hf : support bfloat16 conversion * gguf-py : flake8 fixes * convert-hf : add missing space after comma * convert-hf : get bit-exact same output as ./quantize The quantization version was missing. * convert-hf : don't round bf16 NANs * convert-hf : save some memory with np.int16 intermediate bf16 weights * convert-hf : more closely match llama.cpp with which weights to keep in f32 * convert-hf : add --outtype auto-f16 A reason for this to exist is for model quantizers who want an initial GGUF with the most fidelity to the original model while still using a 16-bit float type instead of 32-bit floats. * convert-hf : remove a semicolon because flake8 doesn't like it It's a reflex from when programming in C/C++, I guess. * convert-hf : support outtype templating in outfile name * convert-hf : rename --outtype auto-f16 to --outtype auto
2024-05-11 15:06:26 +00:00
# Conditions should closely match those in llama_model_quantize_internal in llama.cpp
extra_f32 = any(cond for cond in (
extra_f32,
n_dims == 1,
new_name.endswith("_norm.weight"),
))
# Some tensor types are always in float32
extra_f32 = extra_f32 or any(self.match_model_tensor_name(new_name, key, bid) for key in (
gguf.MODEL_TENSOR.FFN_GATE_INP,
gguf.MODEL_TENSOR.POS_EMBD,
gguf.MODEL_TENSOR.TOKEN_TYPES,
))
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
# if f16 desired, convert any float32 2-dim weight tensors to float16
convert-hf : support bfloat16 conversion (#7158) * convert-hf : support bfloat16 conversion * gguf-py : flake8 fixes * convert-hf : add missing space after comma * convert-hf : get bit-exact same output as ./quantize The quantization version was missing. * convert-hf : don't round bf16 NANs * convert-hf : save some memory with np.int16 intermediate bf16 weights * convert-hf : more closely match llama.cpp with which weights to keep in f32 * convert-hf : add --outtype auto-f16 A reason for this to exist is for model quantizers who want an initial GGUF with the most fidelity to the original model while still using a 16-bit float type instead of 32-bit floats. * convert-hf : remove a semicolon because flake8 doesn't like it It's a reflex from when programming in C/C++, I guess. * convert-hf : support outtype templating in outfile name * convert-hf : rename --outtype auto-f16 to --outtype auto
2024-05-11 15:06:26 +00:00
extra_f16 = any(cond for cond in (
extra_f16,
(name.endswith(".weight") and n_dims >= 2),
))
if self.ftype != gguf.LlamaFileType.ALL_F32 and extra_f16 and not extra_f32:
convert-hf : support direct Q8_0 conversion (#7234) * convert-hf : support q8_0 conversion * convert-hf : add missing ftype This was messing with the checksums otherwise. * convert-hf : add missing ftype to Baichuan and Xverse I didn't notice these on my first pass.
2024-05-13 18:10:51 +00:00
if self.ftype == gguf.LlamaFileType.MOSTLY_BF16:
data = gguf.quantize_bf16(data)
assert data.dtype == np.int16
data_qtype = gguf.GGMLQuantizationType.BF16
elif self.ftype == gguf.LlamaFileType.MOSTLY_Q8_0 and gguf.can_quantize_to_q8_0(data):
data = gguf.quantize_q8_0(data)
assert data.dtype == np.uint8
data_qtype = gguf.GGMLQuantizationType.Q8_0
else: # default to float16 for quantized tensors
convert-hf : support bfloat16 conversion (#7158) * convert-hf : support bfloat16 conversion * gguf-py : flake8 fixes * convert-hf : add missing space after comma * convert-hf : get bit-exact same output as ./quantize The quantization version was missing. * convert-hf : don't round bf16 NANs * convert-hf : save some memory with np.int16 intermediate bf16 weights * convert-hf : more closely match llama.cpp with which weights to keep in f32 * convert-hf : add --outtype auto-f16 A reason for this to exist is for model quantizers who want an initial GGUF with the most fidelity to the original model while still using a 16-bit float type instead of 32-bit floats. * convert-hf : remove a semicolon because flake8 doesn't like it It's a reflex from when programming in C/C++, I guess. * convert-hf : support outtype templating in outfile name * convert-hf : rename --outtype auto-f16 to --outtype auto
2024-05-11 15:06:26 +00:00
if data_dtype != np.float16:
data = data.astype(np.float16)
data_qtype = gguf.GGMLQuantizationType.F16
convert-hf : support direct Q8_0 conversion (#7234) * convert-hf : support q8_0 conversion * convert-hf : add missing ftype This was messing with the checksums otherwise. * convert-hf : add missing ftype to Baichuan and Xverse I didn't notice these on my first pass.
2024-05-13 18:10:51 +00:00
if data_qtype is None: # by default, convert to float32
convert-hf : support bfloat16 conversion (#7158) * convert-hf : support bfloat16 conversion * gguf-py : flake8 fixes * convert-hf : add missing space after comma * convert-hf : get bit-exact same output as ./quantize The quantization version was missing. * convert-hf : don't round bf16 NANs * convert-hf : save some memory with np.int16 intermediate bf16 weights * convert-hf : more closely match llama.cpp with which weights to keep in f32 * convert-hf : add --outtype auto-f16 A reason for this to exist is for model quantizers who want an initial GGUF with the most fidelity to the original model while still using a 16-bit float type instead of 32-bit floats. * convert-hf : remove a semicolon because flake8 doesn't like it It's a reflex from when programming in C/C++, I guess. * convert-hf : support outtype templating in outfile name * convert-hf : rename --outtype auto-f16 to --outtype auto
2024-05-11 15:06:26 +00:00
if data_dtype != np.float32:
data = data.astype(np.float32)
data_qtype = gguf.GGMLQuantizationType.F32
convert-hf : support direct Q8_0 conversion (#7234) * convert-hf : support q8_0 conversion * convert-hf : add missing ftype This was messing with the checksums otherwise. * convert-hf : add missing ftype to Baichuan and Xverse I didn't notice these on my first pass.
2024-05-13 18:10:51 +00:00
block_size, type_size = gguf.GGML_QUANT_SIZES[data_qtype]
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
# reverse shape to make it similar to the internal ggml dimension order
convert-hf : support direct Q8_0 conversion (#7234) * convert-hf : support q8_0 conversion * convert-hf : add missing ftype This was messing with the checksums otherwise. * convert-hf : add missing ftype to Baichuan and Xverse I didn't notice these on my first pass.
2024-05-13 18:10:51 +00:00
shape_str = f"""{{{', '.join(str(n) for n in reversed(
(*data.shape[:-1], data.shape[-1] * data.dtype.itemsize // type_size * block_size))
)}}}"""
scripts: Generalize convert scripts (#3838) * Replace convert-*-hf-to-gguf.py files with convert-hf-to-gguf.py
2023-11-09 10:09:29 +00:00
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
# n_dims is implicit in the shape
convert-hf : support bfloat16 conversion (#7158) * convert-hf : support bfloat16 conversion * gguf-py : flake8 fixes * convert-hf : add missing space after comma * convert-hf : get bit-exact same output as ./quantize The quantization version was missing. * convert-hf : don't round bf16 NANs * convert-hf : save some memory with np.int16 intermediate bf16 weights * convert-hf : more closely match llama.cpp with which weights to keep in f32 * convert-hf : add --outtype auto-f16 A reason for this to exist is for model quantizers who want an initial GGUF with the most fidelity to the original model while still using a 16-bit float type instead of 32-bit floats. * convert-hf : remove a semicolon because flake8 doesn't like it It's a reflex from when programming in C/C++, I guess. * convert-hf : support outtype templating in outfile name * convert-hf : rename --outtype auto-f16 to --outtype auto
2024-05-11 15:06:26 +00:00
logger.info(f"{f'%-{max_name_len}s' % f'{new_name},'} {old_dtype} --> {data_qtype.name}, shape = {shape_str}")
scripts: Generalize convert scripts (#3838) * Replace convert-*-hf-to-gguf.py files with convert-hf-to-gguf.py
2023-11-09 10:09:29 +00:00
convert-hf : support bfloat16 conversion (#7158) * convert-hf : support bfloat16 conversion * gguf-py : flake8 fixes * convert-hf : add missing space after comma * convert-hf : get bit-exact same output as ./quantize The quantization version was missing. * convert-hf : don't round bf16 NANs * convert-hf : save some memory with np.int16 intermediate bf16 weights * convert-hf : more closely match llama.cpp with which weights to keep in f32 * convert-hf : add --outtype auto-f16 A reason for this to exist is for model quantizers who want an initial GGUF with the most fidelity to the original model while still using a 16-bit float type instead of 32-bit floats. * convert-hf : remove a semicolon because flake8 doesn't like it It's a reflex from when programming in C/C++, I guess. * convert-hf : support outtype templating in outfile name * convert-hf : rename --outtype auto-f16 to --outtype auto
2024-05-11 15:06:26 +00:00
self.gguf_writer.add_tensor(new_name, data, raw_dtype=data_qtype)
scripts: Generalize convert scripts (#3838) * Replace convert-*-hf-to-gguf.py files with convert-hf-to-gguf.py
2023-11-09 10:09:29 +00:00
def write(self):
self.write_tensors()
self.gguf_writer.write_header_to_file()
self.gguf_writer.write_kv_data_to_file()
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
self.gguf_writer.write_tensors_to_file(progress=True)
scripts: Generalize convert scripts (#3838) * Replace convert-*-hf-to-gguf.py files with convert-hf-to-gguf.py
2023-11-09 10:09:29 +00:00
self.gguf_writer.close()
def write_vocab(self):
self.gguf_writer.write_header_to_file()
self.gguf_writer.write_kv_data_to_file()
self.gguf_writer.close()
@staticmethod
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
def get_model_part_names(dir_model: Path, suffix: str) -> list[str]:
part_names: list[str] = []
scripts: Generalize convert scripts (#3838) * Replace convert-*-hf-to-gguf.py files with convert-hf-to-gguf.py
2023-11-09 10:09:29 +00:00
for filename in os.listdir(dir_model):
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
if filename.endswith(suffix):
part_names.append(filename)
scripts: Generalize convert scripts (#3838) * Replace convert-*-hf-to-gguf.py files with convert-hf-to-gguf.py
2023-11-09 10:09:29 +00:00
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
part_names.sort()
return part_names
scripts: Generalize convert scripts (#3838) * Replace convert-*-hf-to-gguf.py files with convert-hf-to-gguf.py
2023-11-09 10:09:29 +00:00
@staticmethod
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
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def load_hparams(dir_model: Path):
scripts: Generalize convert scripts (#3838) * Replace convert-*-hf-to-gguf.py files with convert-hf-to-gguf.py
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with open(dir_model / "config.json", "r", encoding="utf-8") as f:
return json.load(f)
convert-hf : make model class definitions self-contained (#5825)
2024-03-02 17:21:47 +00:00
@classmethod
def register(cls, *names: str) -> Callable[[AnyModel], AnyModel]:
assert names
flake : fix
2024-03-04 19:50:50 +00:00
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
def func(modelcls: AnyModel) -> AnyModel:
convert-hf : make model class definitions self-contained (#5825)
2024-03-02 17:21:47 +00:00
for name in names:
cls._model_classes[name] = modelcls
return modelcls
return func
@classmethod
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
def from_model_architecture(cls, arch: str) -> type[Model]:
convert-hf : make model class definitions self-contained (#5825)
2024-03-02 17:21:47 +00:00
try:
return cls._model_classes[arch]
except KeyError:
raise NotImplementedError(f'Architecture {arch!r} not supported!') from None
scripts: Generalize convert scripts (#3838) * Replace convert-*-hf-to-gguf.py files with convert-hf-to-gguf.py
2023-11-09 10:09:29 +00:00
BERT tokenizer fixes (#6498) Key changes: * BERT conversion: fix abuse of LlamaHfVocab, do not set BOS or EOS * Nomic Embed conversion: pad vocab instead of slicing embedding tensor * llama_tokenize: handle added special tokens like HF does
2024-04-09 17:44:08 +00:00
# used for GPT-2 BPE and WordPiece vocabs
llama : fix BPE pre-tokenization (#6920) * merged the changes from deepseeker models to main branch * Moved regex patterns to unicode.cpp and updated unicode.h * Moved header files * Resolved issues * added and refactored unicode_regex_split and related functions * Updated/merged the deepseek coder pr * Refactored code * Adding unicode regex mappings * Adding unicode regex function * Added needed functionality, testing remains * Fixed issues * Fixed issue with gpt2 regex custom preprocessor * unicode : fix? unicode_wstring_to_utf8 * lint : fix whitespaces * tests : add tokenizer tests for numbers * unicode : remove redundant headers * tests : remove and rename tokenizer test scripts * tests : add sample usage * gguf-py : reader prints warnings on duplicate keys * llama : towards llama3 tokenization support (wip) * unicode : shot in the dark to fix tests on Windows * unicode : first try custom implementations * convert : add "tokenizer.ggml.pre" GGUF KV (wip) * llama : use new pre-tokenizer type * convert : fix pre-tokenizer type writing * lint : fix * make : add test-tokenizer-0-llama-v3 * wip * models : add llama v3 vocab file * llama : adapt punctuation regex + add llama 3 regex * minor * unicode : set bomb * unicode : set bomb * unicode : always use std::wregex * unicode : support \p{N}, \p{L} and \p{P} natively * unicode : try fix windows * unicode : category support via std::regex * unicode : clean-up * unicode : simplify * convert : add convert-hf-to-gguf-update.py ggml-ci * lint : update * convert : add falcon ggml-ci * unicode : normalize signatures * lint : fix * lint : fix * convert : remove unused functions * convert : add comments * convert : exercise contractions ggml-ci * lint : fix * cmake : refactor test targets * tests : refactor vocab tests ggml-ci * tests : add more vocabs and tests ggml-ci * unicode : cleanup * scripts : ignore new update script in check-requirements.sh * models : add phi-3, mpt, gpt-2, starcoder * tests : disable obsolete ggml-ci * tests : use faster bpe test ggml-ci * llama : more prominent warning for old BPE models * tests : disable test-tokenizer-1-bpe due to slowness ggml-ci --------- Co-authored-by: Jaggzh <jaggz.h@gmail.com> Co-authored-by: Kazim Abrar Mahi <kazimabrarmahi135@gmail.com>
2024-04-29 13:58:41 +00:00
def get_vocab_base(self) -> tuple[list[str], list[int], str]:
convert : refactor vocab selection logic (#6355)
2024-03-28 15:44:36 +00:00
tokens: list[str] = []
scripts: Generalize convert scripts (#3838) * Replace convert-*-hf-to-gguf.py files with convert-hf-to-gguf.py
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toktypes: list[int] = []
python : add check-requirements.sh and GitHub workflow (#4585) * python: add check-requirements.sh and GitHub workflow This script and workflow forces package versions to remain compatible across all convert*.py scripts, while allowing secondary convert scripts to import dependencies not wanted in convert.py. * Move requirements into ./requirements * Fail on "==" being used for package requirements (but can be suppressed) * Enforce "compatible release" syntax instead of == * Update workflow * Add upper version bound for transformers and protobuf * improve check-requirements.sh * small syntax change * don't remove venvs if nocleanup is passed * See if this fixes docker workflow * Move check-requirements.sh into ./scripts/ --------- Co-authored-by: Jared Van Bortel <jared@nomic.ai>
2023-12-29 14:50:29 +00:00
from transformers import AutoTokenizer
BERT tokenizer fixes (#6498) Key changes: * BERT conversion: fix abuse of LlamaHfVocab, do not set BOS or EOS * Nomic Embed conversion: pad vocab instead of slicing embedding tensor * llama_tokenize: handle added special tokens like HF does
2024-04-09 17:44:08 +00:00
tokenizer = AutoTokenizer.from_pretrained(self.dir_model)
vocab_size = self.hparams.get("vocab_size", len(tokenizer.vocab))
scripts: Generalize convert scripts (#3838) * Replace convert-*-hf-to-gguf.py files with convert-hf-to-gguf.py
2023-11-09 10:09:29 +00:00
assert max(tokenizer.vocab.values()) < vocab_size
llama : fix BPE pre-tokenization (#6920) * merged the changes from deepseeker models to main branch * Moved regex patterns to unicode.cpp and updated unicode.h * Moved header files * Resolved issues * added and refactored unicode_regex_split and related functions * Updated/merged the deepseek coder pr * Refactored code * Adding unicode regex mappings * Adding unicode regex function * Added needed functionality, testing remains * Fixed issues * Fixed issue with gpt2 regex custom preprocessor * unicode : fix? unicode_wstring_to_utf8 * lint : fix whitespaces * tests : add tokenizer tests for numbers * unicode : remove redundant headers * tests : remove and rename tokenizer test scripts * tests : add sample usage * gguf-py : reader prints warnings on duplicate keys * llama : towards llama3 tokenization support (wip) * unicode : shot in the dark to fix tests on Windows * unicode : first try custom implementations * convert : add "tokenizer.ggml.pre" GGUF KV (wip) * llama : use new pre-tokenizer type * convert : fix pre-tokenizer type writing * lint : fix * make : add test-tokenizer-0-llama-v3 * wip * models : add llama v3 vocab file * llama : adapt punctuation regex + add llama 3 regex * minor * unicode : set bomb * unicode : set bomb * unicode : always use std::wregex * unicode : support \p{N}, \p{L} and \p{P} natively * unicode : try fix windows * unicode : category support via std::regex * unicode : clean-up * unicode : simplify * convert : add convert-hf-to-gguf-update.py ggml-ci * lint : update * convert : add falcon ggml-ci * unicode : normalize signatures * lint : fix * lint : fix * convert : remove unused functions * convert : add comments * convert : exercise contractions ggml-ci * lint : fix * cmake : refactor test targets * tests : refactor vocab tests ggml-ci * tests : add more vocabs and tests ggml-ci * unicode : cleanup * scripts : ignore new update script in check-requirements.sh * models : add phi-3, mpt, gpt-2, starcoder * tests : disable obsolete ggml-ci * tests : use faster bpe test ggml-ci * llama : more prominent warning for old BPE models * tests : disable test-tokenizer-1-bpe due to slowness ggml-ci --------- Co-authored-by: Jaggzh <jaggz.h@gmail.com> Co-authored-by: Kazim Abrar Mahi <kazimabrarmahi135@gmail.com>
2024-04-29 13:58:41 +00:00
tokpre = self.get_vocab_base_pre(tokenizer)
scripts: Generalize convert scripts (#3838) * Replace convert-*-hf-to-gguf.py files with convert-hf-to-gguf.py
2023-11-09 10:09:29 +00:00
reverse_vocab = {id_: encoded_tok for encoded_tok, id_ in tokenizer.vocab.items()}
added_vocab = tokenizer.get_added_vocab()
for i in range(vocab_size):
if i not in reverse_vocab:
convert : refactor vocab selection logic (#6355)
2024-03-28 15:44:36 +00:00
tokens.append(f"[PAD{i}]")
scripts: Generalize convert scripts (#3838) * Replace convert-*-hf-to-gguf.py files with convert-hf-to-gguf.py
2023-11-09 10:09:29 +00:00
toktypes.append(gguf.TokenType.USER_DEFINED)
elif reverse_vocab[i] in added_vocab:
tokens.append(reverse_vocab[i])
py : remove unnecessary hasattr (#4903)
2024-01-16 18:59:31 +00:00
if tokenizer.added_tokens_decoder[i].special:
toktypes.append(gguf.TokenType.CONTROL)
else:
toktypes.append(gguf.TokenType.USER_DEFINED)
scripts: Generalize convert scripts (#3838) * Replace convert-*-hf-to-gguf.py files with convert-hf-to-gguf.py
2023-11-09 10:09:29 +00:00
else:
tokens.append(reverse_vocab[i])
toktypes.append(gguf.TokenType.NORMAL)
llama : fix BPE pre-tokenization (#6920) * merged the changes from deepseeker models to main branch * Moved regex patterns to unicode.cpp and updated unicode.h * Moved header files * Resolved issues * added and refactored unicode_regex_split and related functions * Updated/merged the deepseek coder pr * Refactored code * Adding unicode regex mappings * Adding unicode regex function * Added needed functionality, testing remains * Fixed issues * Fixed issue with gpt2 regex custom preprocessor * unicode : fix? unicode_wstring_to_utf8 * lint : fix whitespaces * tests : add tokenizer tests for numbers * unicode : remove redundant headers * tests : remove and rename tokenizer test scripts * tests : add sample usage * gguf-py : reader prints warnings on duplicate keys * llama : towards llama3 tokenization support (wip) * unicode : shot in the dark to fix tests on Windows * unicode : first try custom implementations * convert : add "tokenizer.ggml.pre" GGUF KV (wip) * llama : use new pre-tokenizer type * convert : fix pre-tokenizer type writing * lint : fix * make : add test-tokenizer-0-llama-v3 * wip * models : add llama v3 vocab file * llama : adapt punctuation regex + add llama 3 regex * minor * unicode : set bomb * unicode : set bomb * unicode : always use std::wregex * unicode : support \p{N}, \p{L} and \p{P} natively * unicode : try fix windows * unicode : category support via std::regex * unicode : clean-up * unicode : simplify * convert : add convert-hf-to-gguf-update.py ggml-ci * lint : update * convert : add falcon ggml-ci * unicode : normalize signatures * lint : fix * lint : fix * convert : remove unused functions * convert : add comments * convert : exercise contractions ggml-ci * lint : fix * cmake : refactor test targets * tests : refactor vocab tests ggml-ci * tests : add more vocabs and tests ggml-ci * unicode : cleanup * scripts : ignore new update script in check-requirements.sh * models : add phi-3, mpt, gpt-2, starcoder * tests : disable obsolete ggml-ci * tests : use faster bpe test ggml-ci * llama : more prominent warning for old BPE models * tests : disable test-tokenizer-1-bpe due to slowness ggml-ci --------- Co-authored-by: Jaggzh <jaggz.h@gmail.com> Co-authored-by: Kazim Abrar Mahi <kazimabrarmahi135@gmail.com>
2024-04-29 13:58:41 +00:00
return tokens, toktypes, tokpre
# NOTE: this function is generated by convert-hf-to-gguf-update.py
# do not modify it manually!
# ref: https://github.com/ggerganov/llama.cpp/pull/6920
py : convert-hf-to-gguf-update improvements (#7340) * convert-hf-to-gguf-update: automate updating * convert-hf-to-gguf-update: improve download * share requests session for performance * create directories only when needed, don't skip downloads when empty directory encountered * be more graceful about errors
2024-05-17 12:11:45 +00:00
# Marker: Start get_vocab_base_pre
llama : fix BPE pre-tokenization (#6920) * merged the changes from deepseeker models to main branch * Moved regex patterns to unicode.cpp and updated unicode.h * Moved header files * Resolved issues * added and refactored unicode_regex_split and related functions * Updated/merged the deepseek coder pr * Refactored code * Adding unicode regex mappings * Adding unicode regex function * Added needed functionality, testing remains * Fixed issues * Fixed issue with gpt2 regex custom preprocessor * unicode : fix? unicode_wstring_to_utf8 * lint : fix whitespaces * tests : add tokenizer tests for numbers * unicode : remove redundant headers * tests : remove and rename tokenizer test scripts * tests : add sample usage * gguf-py : reader prints warnings on duplicate keys * llama : towards llama3 tokenization support (wip) * unicode : shot in the dark to fix tests on Windows * unicode : first try custom implementations * convert : add "tokenizer.ggml.pre" GGUF KV (wip) * llama : use new pre-tokenizer type * convert : fix pre-tokenizer type writing * lint : fix * make : add test-tokenizer-0-llama-v3 * wip * models : add llama v3 vocab file * llama : adapt punctuation regex + add llama 3 regex * minor * unicode : set bomb * unicode : set bomb * unicode : always use std::wregex * unicode : support \p{N}, \p{L} and \p{P} natively * unicode : try fix windows * unicode : category support via std::regex * unicode : clean-up * unicode : simplify * convert : add convert-hf-to-gguf-update.py ggml-ci * lint : update * convert : add falcon ggml-ci * unicode : normalize signatures * lint : fix * lint : fix * convert : remove unused functions * convert : add comments * convert : exercise contractions ggml-ci * lint : fix * cmake : refactor test targets * tests : refactor vocab tests ggml-ci * tests : add more vocabs and tests ggml-ci * unicode : cleanup * scripts : ignore new update script in check-requirements.sh * models : add phi-3, mpt, gpt-2, starcoder * tests : disable obsolete ggml-ci * tests : use faster bpe test ggml-ci * llama : more prominent warning for old BPE models * tests : disable test-tokenizer-1-bpe due to slowness ggml-ci --------- Co-authored-by: Jaggzh <jaggz.h@gmail.com> Co-authored-by: Kazim Abrar Mahi <kazimabrarmahi135@gmail.com>
2024-04-29 13:58:41 +00:00
def get_vocab_base_pre(self, tokenizer) -> str:
# encoding this string and hashing the resulting tokens would (hopefully) give us a unique identifier that
# is specific for the BPE pre-tokenizer used by the model
# we will use this unique identifier to write a "tokenizer.ggml.pre" entry in the GGUF file which we can
# use in llama.cpp to implement the same pre-tokenizer
chktxt = '\n \n\n \n\n\n \t \t\t \t\n \n \n \n \n🚀 (normal) 😶\u200d🌫️ (multiple emojis concatenated) ✅ 🦙🦙 3 33 333 3333 33333 333333 3333333 33333333 3.3 3..3 3...3 កាន់តែពិសេសអាច😁 ?我想在apple工作1314151天～ ------======= нещо на Български \'\'\'\'\'\'```````""""......!!!!!!?????? I\'ve been \'told he\'s there, \'RE you sure? \'M not sure I\'ll make it, \'D you like some tea? We\'Ve a\'lL'
chktok = tokenizer.encode(chktxt)
chkhsh = sha256(str(chktok).encode()).hexdigest()
convert.py : add python logging instead of print() (#6511) * convert.py: add python logging instead of print() * convert.py: verbose flag takes priority over dump flag log suppression * convert.py: named instance logging * convert.py: use explicit logger id string * convert.py: convert extra print() to named logger * convert.py: sys.stderr.write --> logger.error * *.py: Convert all python scripts to use logging module * requirements.txt: remove extra line * flake8: update flake8 ignore and exclude to match ci settings * gh-actions: add flake8-no-print to flake8 lint step * pre-commit: add flake8-no-print to flake8 and also update pre-commit version * convert-hf-to-gguf.py: print() to logger conversion * *.py: logging basiconfig refactor to use conditional expression * *.py: removed commented out logging * fixup! *.py: logging basiconfig refactor to use conditional expression * constant.py: logger.error then exit should be a raise exception instead * *.py: Convert logger error and sys.exit() into a raise exception (for atypical error) * gguf-convert-endian.py: refactor convert_byteorder() to use tqdm progressbar * verify-checksum-model.py: This is the result of the program, it should be printed to stdout. * compare-llama-bench.py: add blank line for readability during missing repo response * reader.py: read_gguf_file() use print() over logging * convert.py: warning goes to stderr and won't hurt the dump output * gguf-dump.py: dump_metadata() should print to stdout * convert-hf-to-gguf.py: print --> logger.debug or ValueError() * verify-checksum-models.py: use print() for printing table * *.py: refactor logging.basicConfig() * gguf-py/gguf/*.py: use __name__ as logger name Since they will be imported and not run directly. * python-lint.yml: use .flake8 file instead * constants.py: logger no longer required * convert-hf-to-gguf.py: add additional logging * convert-hf-to-gguf.py: print() --> logger * *.py: fix flake8 warnings * revert changes to convert-hf-to-gguf.py for get_name() * convert-hf-to-gguf-update.py: use triple quoted f-string instead * *.py: accidentally corrected the wrong line * *.py: add compilade warning suggestions and style fixes
2024-05-03 19:36:41 +00:00
logger.debug(f"chktok: {chktok}")
logger.debug(f"chkhsh: {chkhsh}")
llama : fix BPE pre-tokenization (#6920) * merged the changes from deepseeker models to main branch * Moved regex patterns to unicode.cpp and updated unicode.h * Moved header files * Resolved issues * added and refactored unicode_regex_split and related functions * Updated/merged the deepseek coder pr * Refactored code * Adding unicode regex mappings * Adding unicode regex function * Added needed functionality, testing remains * Fixed issues * Fixed issue with gpt2 regex custom preprocessor * unicode : fix? unicode_wstring_to_utf8 * lint : fix whitespaces * tests : add tokenizer tests for numbers * unicode : remove redundant headers * tests : remove and rename tokenizer test scripts * tests : add sample usage * gguf-py : reader prints warnings on duplicate keys * llama : towards llama3 tokenization support (wip) * unicode : shot in the dark to fix tests on Windows * unicode : first try custom implementations * convert : add "tokenizer.ggml.pre" GGUF KV (wip) * llama : use new pre-tokenizer type * convert : fix pre-tokenizer type writing * lint : fix * make : add test-tokenizer-0-llama-v3 * wip * models : add llama v3 vocab file * llama : adapt punctuation regex + add llama 3 regex * minor * unicode : set bomb * unicode : set bomb * unicode : always use std::wregex * unicode : support \p{N}, \p{L} and \p{P} natively * unicode : try fix windows * unicode : category support via std::regex * unicode : clean-up * unicode : simplify * convert : add convert-hf-to-gguf-update.py ggml-ci * lint : update * convert : add falcon ggml-ci * unicode : normalize signatures * lint : fix * lint : fix * convert : remove unused functions * convert : add comments * convert : exercise contractions ggml-ci * lint : fix * cmake : refactor test targets * tests : refactor vocab tests ggml-ci * tests : add more vocabs and tests ggml-ci * unicode : cleanup * scripts : ignore new update script in check-requirements.sh * models : add phi-3, mpt, gpt-2, starcoder * tests : disable obsolete ggml-ci * tests : use faster bpe test ggml-ci * llama : more prominent warning for old BPE models * tests : disable test-tokenizer-1-bpe due to slowness ggml-ci --------- Co-authored-by: Jaggzh <jaggz.h@gmail.com> Co-authored-by: Kazim Abrar Mahi <kazimabrarmahi135@gmail.com>
2024-04-29 13:58:41 +00:00
res = None
convert : use utf8 encoding (#7000) * convert : use utf8 encoding * convert : update instructions and warning message
2024-04-30 08:05:25 +00:00
# NOTE: if you get an error here, you need to update the convert-hf-to-gguf-update.py script
# or pull the latest version of the model from Huggingface
# don't edit the hashes manually!
llama : fix BPE pre-tokenization (#6920) * merged the changes from deepseeker models to main branch * Moved regex patterns to unicode.cpp and updated unicode.h * Moved header files * Resolved issues * added and refactored unicode_regex_split and related functions * Updated/merged the deepseek coder pr * Refactored code * Adding unicode regex mappings * Adding unicode regex function * Added needed functionality, testing remains * Fixed issues * Fixed issue with gpt2 regex custom preprocessor * unicode : fix? unicode_wstring_to_utf8 * lint : fix whitespaces * tests : add tokenizer tests for numbers * unicode : remove redundant headers * tests : remove and rename tokenizer test scripts * tests : add sample usage * gguf-py : reader prints warnings on duplicate keys * llama : towards llama3 tokenization support (wip) * unicode : shot in the dark to fix tests on Windows * unicode : first try custom implementations * convert : add "tokenizer.ggml.pre" GGUF KV (wip) * llama : use new pre-tokenizer type * convert : fix pre-tokenizer type writing * lint : fix * make : add test-tokenizer-0-llama-v3 * wip * models : add llama v3 vocab file * llama : adapt punctuation regex + add llama 3 regex * minor * unicode : set bomb * unicode : set bomb * unicode : always use std::wregex * unicode : support \p{N}, \p{L} and \p{P} natively * unicode : try fix windows * unicode : category support via std::regex * unicode : clean-up * unicode : simplify * convert : add convert-hf-to-gguf-update.py ggml-ci * lint : update * convert : add falcon ggml-ci * unicode : normalize signatures * lint : fix * lint : fix * convert : remove unused functions * convert : add comments * convert : exercise contractions ggml-ci * lint : fix * cmake : refactor test targets * tests : refactor vocab tests ggml-ci * tests : add more vocabs and tests ggml-ci * unicode : cleanup * scripts : ignore new update script in check-requirements.sh * models : add phi-3, mpt, gpt-2, starcoder * tests : disable obsolete ggml-ci * tests : use faster bpe test ggml-ci * llama : more prominent warning for old BPE models * tests : disable test-tokenizer-1-bpe due to slowness ggml-ci --------- Co-authored-by: Jaggzh <jaggz.h@gmail.com> Co-authored-by: Kazim Abrar Mahi <kazimabrarmahi135@gmail.com>
2024-04-29 13:58:41 +00:00
if chkhsh == "0ef9807a4087ebef797fc749390439009c3b9eda9ad1a097abbe738f486c01e5":
# ref: https://huggingface.co/meta-llama/Meta-Llama-3-8B
res = "llama-bpe"
if chkhsh == "049ecf7629871e3041641907f3de7c733e4dbfdc736f57d882ba0b0845599754":
# ref: https://huggingface.co/deepseek-ai/deepseek-llm-7b-base
res = "deepseek-llm"
if chkhsh == "347715f544604f9118bb75ed199f68779f423cabb20db6de6f31b908d04d7821":
# ref: https://huggingface.co/deepseek-ai/deepseek-coder-6.7b-base
res = "deepseek-coder"
if chkhsh == "8aeee3860c56296a157a1fe2fad249ec40aa59b1bb5709f4ade11c4e6fe652ed":
# ref: https://huggingface.co/tiiuae/falcon-7b
res = "falcon"
if chkhsh == "0876d13b50744004aa9aeae05e7b0647eac9d801b5ba4668afc01e709c15e19f":
# ref: https://huggingface.co/BAAI/bge-small-en-v1.5
res = "bert-bge"
if chkhsh == "b6dc8df998e1cfbdc4eac8243701a65afe638679230920b50d6f17d81c098166":
# ref: https://huggingface.co/mosaicml/mpt-7b
res = "mpt"
if chkhsh == "35d91631860c815f952d711435f48d356ebac988362536bed955d43bfa436e34":
# ref: https://huggingface.co/bigcode/starcoder2-3b
res = "starcoder"
if chkhsh == "3ce83efda5659b07b1ad37ca97ca5797ea4285d9b9ab0dc679e4a720c9da7454":
# ref: https://huggingface.co/openai-community/gpt2
res = "gpt-2"
Add StableLM2 pre-tokenizer (#7349) * Add StableLM pre-tokenizer * Fix space * Fix trailing whitespace
2024-05-19 12:46:46 +00:00
if chkhsh == "32d85c31273f8019248f2559fed492d929ea28b17e51d81d3bb36fff23ca72b3":
# ref: https://huggingface.co/stabilityai/stablelm-2-1_6b
res = "stablelm2"
tests : add test-tokenizer-0.sh + fix some tokenizers (#7036) * tests : add test-tokenizer-0.sh * unicode : add all unicode number ranges * starcoder : fix pre-tokenizer * tests : add test that fails with DeepSeek tokenizers * falcon : fix regex * unicode : regenerate unicode tables * refact : add tokenizer model * lint : fix * tests : disable failing tests ggml-ci * refact : add tests files ggml-ci * convert : print -> logging ggml-ci * lint : fix * unicode : digit -> number * phi-3 : update
2024-05-04 05:32:32 +00:00
if chkhsh == "6221ad2852e85ce96f791f476e0b390cf9b474c9e3d1362f53a24a06dc8220ff":
# ref: https://huggingface.co/smallcloudai/Refact-1_6-base
res = "refact"
command-r : add BPE pre-tokenization (#7063) * Add BPE pre-tokenization for Command-R/R+. * Bump transformers convert requirement. * command-r : add individual digits regex --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-05-05 05:19:30 +00:00
if chkhsh == "9c2227e4dd922002fb81bde4fc02b0483ca4f12911410dee2255e4987644e3f8":
# ref: https://huggingface.co/CohereForAI/c4ai-command-r-v01
res = "command-r"
llama : add BPE pre-tokenization for Qwen2 (#7114) * Add BPE pre-tokenization for Qwen2. * minor : fixes --------- Co-authored-by: Ren Xuancheng <17811943+jklj077@users.noreply.github.com> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-05-08 12:06:43 +00:00
if chkhsh == "e636dc30a262dcc0d8c323492e32ae2b70728f4df7dfe9737d9f920a282b8aea":
# ref: https://huggingface.co/Qwen/Qwen1.5-7B
res = "qwen2"
Fix OLMo HF to GGUF conversion (#6910)
2024-05-07 19:39:43 +00:00
if chkhsh == "b6dc8df998e1cfbdc4eac8243701a65afe638679230920b50d6f17d81c098166":
# ref: https://huggingface.co/allenai/OLMo-1.7-7B-hf
res = "olmo"
convert : add BPE pre-tokenization for DBRX (#7132) * Add BPE pre-tokenization for DBRX. * Add vocab GGUFs. * Remove test. * Remove GGUFs.
2024-05-08 10:43:23 +00:00
if chkhsh == "a8594e3edff7c29c003940395316294b2c623e09894deebbc65f33f1515df79e":
llama : add Jina Embeddings architecture (#6826) * feat: first things to do * feat: create tensors for Jina architecture * fix: use other tensors * feat: embedding gets results * fix: fix usage of ALIBI * fix: clean prints * fix: do some cleanup unused vars * fix: revert changes to Makefile and CMakeLists * fix: revert some changes * fix: fix small detail * fix: fix convert formatting * fix: fix linting and editor * feat: set proper vocab settings * fix: JinaBertForMaskedLM registration * feat: support q_normalization and k_normalization in Jina arch * feat: handle gpt2 tokenizer with Jina architecture * feat: example comments in embedding * feat: rename Jina Bert to Jina Bert V2 * fix: add some changes as per review * feat: proper KQ_pos for Jina embeddings * feat: add capacity to load models ES and DE for Spanish * llama : fix pre-tokenizers * ggml : full ALiBi support * ggml : update ggml_soft_max_ext() CUDA, SYCL * ggml : ggml_flash_attn_ext() support ALiBi (CPU) * ggml : ggml_flash_attn_ext() support ALiBi (Metal) * ggml : fix warning * ggml : ggml_flash_attn_ext() support ALiBi (CUDA) ggml-ci * minor : clean-up * embedding : add warning about missing SEP --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-05-11 07:46:09 +00:00
# ref: https://huggingface.co/databricks/dbrx-base
convert : add BPE pre-tokenization for DBRX (#7132) * Add BPE pre-tokenization for DBRX. * Add vocab GGUFs. * Remove test. * Remove GGUFs.
2024-05-08 10:43:23 +00:00
res = "dbrx"
llama : add Jina Embeddings architecture (#6826) * feat: first things to do * feat: create tensors for Jina architecture * fix: use other tensors * feat: embedding gets results * fix: fix usage of ALIBI * fix: clean prints * fix: do some cleanup unused vars * fix: revert changes to Makefile and CMakeLists * fix: revert some changes * fix: fix small detail * fix: fix convert formatting * fix: fix linting and editor * feat: set proper vocab settings * fix: JinaBertForMaskedLM registration * feat: support q_normalization and k_normalization in Jina arch * feat: handle gpt2 tokenizer with Jina architecture * feat: example comments in embedding * feat: rename Jina Bert to Jina Bert V2 * fix: add some changes as per review * feat: proper KQ_pos for Jina embeddings * feat: add capacity to load models ES and DE for Spanish * llama : fix pre-tokenizers * ggml : full ALiBi support * ggml : update ggml_soft_max_ext() CUDA, SYCL * ggml : ggml_flash_attn_ext() support ALiBi (CPU) * ggml : ggml_flash_attn_ext() support ALiBi (Metal) * ggml : fix warning * ggml : ggml_flash_attn_ext() support ALiBi (CUDA) ggml-ci * minor : clean-up * embedding : add warning about missing SEP --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-05-11 07:46:09 +00:00
if chkhsh == "0876d13b50744004aa9aeae05e7b0647eac9d801b5ba4668afc01e709c15e19f":
# ref: https://huggingface.co/jinaai/jina-embeddings-v2-base-en
llama : rename jina tokenizers to v2 (#7249) * refactor: rename jina tokenizers to v2 * refactor: keep refactoring non-breaking
2024-05-13 08:35:14 +00:00
res = "jina-v2-en"
llama : add Jina Embeddings architecture (#6826) * feat: first things to do * feat: create tensors for Jina architecture * fix: use other tensors * feat: embedding gets results * fix: fix usage of ALIBI * fix: clean prints * fix: do some cleanup unused vars * fix: revert changes to Makefile and CMakeLists * fix: revert some changes * fix: fix small detail * fix: fix convert formatting * fix: fix linting and editor * feat: set proper vocab settings * fix: JinaBertForMaskedLM registration * feat: support q_normalization and k_normalization in Jina arch * feat: handle gpt2 tokenizer with Jina architecture * feat: example comments in embedding * feat: rename Jina Bert to Jina Bert V2 * fix: add some changes as per review * feat: proper KQ_pos for Jina embeddings * feat: add capacity to load models ES and DE for Spanish * llama : fix pre-tokenizers * ggml : full ALiBi support * ggml : update ggml_soft_max_ext() CUDA, SYCL * ggml : ggml_flash_attn_ext() support ALiBi (CPU) * ggml : ggml_flash_attn_ext() support ALiBi (Metal) * ggml : fix warning * ggml : ggml_flash_attn_ext() support ALiBi (CUDA) ggml-ci * minor : clean-up * embedding : add warning about missing SEP --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-05-11 07:46:09 +00:00
if chkhsh == "171aeeedd6fb548d418a7461d053f11b6f1f1fc9b387bd66640d28a4b9f5c643":
# ref: https://huggingface.co/jinaai/jina-embeddings-v2-base-es
llama : rename jina tokenizers to v2 (#7249) * refactor: rename jina tokenizers to v2 * refactor: keep refactoring non-breaking
2024-05-13 08:35:14 +00:00
res = "jina-v2-es"
llama : add Jina Embeddings architecture (#6826) * feat: first things to do * feat: create tensors for Jina architecture * fix: use other tensors * feat: embedding gets results * fix: fix usage of ALIBI * fix: clean prints * fix: do some cleanup unused vars * fix: revert changes to Makefile and CMakeLists * fix: revert some changes * fix: fix small detail * fix: fix convert formatting * fix: fix linting and editor * feat: set proper vocab settings * fix: JinaBertForMaskedLM registration * feat: support q_normalization and k_normalization in Jina arch * feat: handle gpt2 tokenizer with Jina architecture * feat: example comments in embedding * feat: rename Jina Bert to Jina Bert V2 * fix: add some changes as per review * feat: proper KQ_pos for Jina embeddings * feat: add capacity to load models ES and DE for Spanish * llama : fix pre-tokenizers * ggml : full ALiBi support * ggml : update ggml_soft_max_ext() CUDA, SYCL * ggml : ggml_flash_attn_ext() support ALiBi (CPU) * ggml : ggml_flash_attn_ext() support ALiBi (Metal) * ggml : fix warning * ggml : ggml_flash_attn_ext() support ALiBi (CUDA) ggml-ci * minor : clean-up * embedding : add warning about missing SEP --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-05-11 07:46:09 +00:00
if chkhsh == "27949a2493fc4a9f53f5b9b029c82689cfbe5d3a1929bb25e043089e28466de6":
# ref: https://huggingface.co/jinaai/jina-embeddings-v2-base-de
llama : rename jina tokenizers to v2 (#7249) * refactor: rename jina tokenizers to v2 * refactor: keep refactoring non-breaking
2024-05-13 08:35:14 +00:00
res = "jina-v2-de"
llama : fix BPE pre-tokenization (#6920) * merged the changes from deepseeker models to main branch * Moved regex patterns to unicode.cpp and updated unicode.h * Moved header files * Resolved issues * added and refactored unicode_regex_split and related functions * Updated/merged the deepseek coder pr * Refactored code * Adding unicode regex mappings * Adding unicode regex function * Added needed functionality, testing remains * Fixed issues * Fixed issue with gpt2 regex custom preprocessor * unicode : fix? unicode_wstring_to_utf8 * lint : fix whitespaces * tests : add tokenizer tests for numbers * unicode : remove redundant headers * tests : remove and rename tokenizer test scripts * tests : add sample usage * gguf-py : reader prints warnings on duplicate keys * llama : towards llama3 tokenization support (wip) * unicode : shot in the dark to fix tests on Windows * unicode : first try custom implementations * convert : add "tokenizer.ggml.pre" GGUF KV (wip) * llama : use new pre-tokenizer type * convert : fix pre-tokenizer type writing * lint : fix * make : add test-tokenizer-0-llama-v3 * wip * models : add llama v3 vocab file * llama : adapt punctuation regex + add llama 3 regex * minor * unicode : set bomb * unicode : set bomb * unicode : always use std::wregex * unicode : support \p{N}, \p{L} and \p{P} natively * unicode : try fix windows * unicode : category support via std::regex * unicode : clean-up * unicode : simplify * convert : add convert-hf-to-gguf-update.py ggml-ci * lint : update * convert : add falcon ggml-ci * unicode : normalize signatures * lint : fix * lint : fix * convert : remove unused functions * convert : add comments * convert : exercise contractions ggml-ci * lint : fix * cmake : refactor test targets * tests : refactor vocab tests ggml-ci * tests : add more vocabs and tests ggml-ci * unicode : cleanup * scripts : ignore new update script in check-requirements.sh * models : add phi-3, mpt, gpt-2, starcoder * tests : disable obsolete ggml-ci * tests : use faster bpe test ggml-ci * llama : more prominent warning for old BPE models * tests : disable test-tokenizer-1-bpe due to slowness ggml-ci --------- Co-authored-by: Jaggzh <jaggz.h@gmail.com> Co-authored-by: Kazim Abrar Mahi <kazimabrarmahi135@gmail.com>
2024-04-29 13:58:41 +00:00
if res is None:
convert.py : add python logging instead of print() (#6511) * convert.py: add python logging instead of print() * convert.py: verbose flag takes priority over dump flag log suppression * convert.py: named instance logging * convert.py: use explicit logger id string * convert.py: convert extra print() to named logger * convert.py: sys.stderr.write --> logger.error * *.py: Convert all python scripts to use logging module * requirements.txt: remove extra line * flake8: update flake8 ignore and exclude to match ci settings * gh-actions: add flake8-no-print to flake8 lint step * pre-commit: add flake8-no-print to flake8 and also update pre-commit version * convert-hf-to-gguf.py: print() to logger conversion * *.py: logging basiconfig refactor to use conditional expression * *.py: removed commented out logging * fixup! *.py: logging basiconfig refactor to use conditional expression * constant.py: logger.error then exit should be a raise exception instead * *.py: Convert logger error and sys.exit() into a raise exception (for atypical error) * gguf-convert-endian.py: refactor convert_byteorder() to use tqdm progressbar * verify-checksum-model.py: This is the result of the program, it should be printed to stdout. * compare-llama-bench.py: add blank line for readability during missing repo response * reader.py: read_gguf_file() use print() over logging * convert.py: warning goes to stderr and won't hurt the dump output * gguf-dump.py: dump_metadata() should print to stdout * convert-hf-to-gguf.py: print --> logger.debug or ValueError() * verify-checksum-models.py: use print() for printing table * *.py: refactor logging.basicConfig() * gguf-py/gguf/*.py: use __name__ as logger name Since they will be imported and not run directly. * python-lint.yml: use .flake8 file instead * constants.py: logger no longer required * convert-hf-to-gguf.py: add additional logging * convert-hf-to-gguf.py: print() --> logger * *.py: fix flake8 warnings * revert changes to convert-hf-to-gguf.py for get_name() * convert-hf-to-gguf-update.py: use triple quoted f-string instead * *.py: accidentally corrected the wrong line * *.py: add compilade warning suggestions and style fixes
2024-05-03 19:36:41 +00:00
logger.warning("\n")
logger.warning("**************************************************************************************")
logger.warning("** WARNING: The BPE pre-tokenizer was not recognized!")
logger.warning("** There are 2 possible reasons for this:")
logger.warning("** - the model has not been added to convert-hf-to-gguf-update.py yet")
logger.warning("** - the pre-tokenization config has changed upstream")
logger.warning("** Check your model files and convert-hf-to-gguf-update.py and update them accordingly.")
logger.warning("** ref: https://github.com/ggerganov/llama.cpp/pull/6920")
logger.warning("**")
logger.warning(f"** chkhsh: {chkhsh}")
logger.warning("**************************************************************************************")
logger.warning("\n")
llama : fix BPE pre-tokenization (#6920) * merged the changes from deepseeker models to main branch * Moved regex patterns to unicode.cpp and updated unicode.h * Moved header files * Resolved issues * added and refactored unicode_regex_split and related functions * Updated/merged the deepseek coder pr * Refactored code * Adding unicode regex mappings * Adding unicode regex function * Added needed functionality, testing remains * Fixed issues * Fixed issue with gpt2 regex custom preprocessor * unicode : fix? unicode_wstring_to_utf8 * lint : fix whitespaces * tests : add tokenizer tests for numbers * unicode : remove redundant headers * tests : remove and rename tokenizer test scripts * tests : add sample usage * gguf-py : reader prints warnings on duplicate keys * llama : towards llama3 tokenization support (wip) * unicode : shot in the dark to fix tests on Windows * unicode : first try custom implementations * convert : add "tokenizer.ggml.pre" GGUF KV (wip) * llama : use new pre-tokenizer type * convert : fix pre-tokenizer type writing * lint : fix * make : add test-tokenizer-0-llama-v3 * wip * models : add llama v3 vocab file * llama : adapt punctuation regex + add llama 3 regex * minor * unicode : set bomb * unicode : set bomb * unicode : always use std::wregex * unicode : support \p{N}, \p{L} and \p{P} natively * unicode : try fix windows * unicode : category support via std::regex * unicode : clean-up * unicode : simplify * convert : add convert-hf-to-gguf-update.py ggml-ci * lint : update * convert : add falcon ggml-ci * unicode : normalize signatures * lint : fix * lint : fix * convert : remove unused functions * convert : add comments * convert : exercise contractions ggml-ci * lint : fix * cmake : refactor test targets * tests : refactor vocab tests ggml-ci * tests : add more vocabs and tests ggml-ci * unicode : cleanup * scripts : ignore new update script in check-requirements.sh * models : add phi-3, mpt, gpt-2, starcoder * tests : disable obsolete ggml-ci * tests : use faster bpe test ggml-ci * llama : more prominent warning for old BPE models * tests : disable test-tokenizer-1-bpe due to slowness ggml-ci --------- Co-authored-by: Jaggzh <jaggz.h@gmail.com> Co-authored-by: Kazim Abrar Mahi <kazimabrarmahi135@gmail.com>
2024-04-29 13:58:41 +00:00
raise NotImplementedError("BPE pre-tokenizer was not recognized - update get_vocab_base_pre()")
tests : add test-tokenizer-0.sh + fix some tokenizers (#7036) * tests : add test-tokenizer-0.sh * unicode : add all unicode number ranges * starcoder : fix pre-tokenizer * tests : add test that fails with DeepSeek tokenizers * falcon : fix regex * unicode : regenerate unicode tables * refact : add tokenizer model * lint : fix * tests : disable failing tests ggml-ci * refact : add tests files ggml-ci * convert : print -> logging ggml-ci * lint : fix * unicode : digit -> number * phi-3 : update
2024-05-04 05:32:32 +00:00
logger.debug(f"tokenizer.ggml.pre: {repr(res)}")
convert.py : add python logging instead of print() (#6511) * convert.py: add python logging instead of print() * convert.py: verbose flag takes priority over dump flag log suppression * convert.py: named instance logging * convert.py: use explicit logger id string * convert.py: convert extra print() to named logger * convert.py: sys.stderr.write --> logger.error * *.py: Convert all python scripts to use logging module * requirements.txt: remove extra line * flake8: update flake8 ignore and exclude to match ci settings * gh-actions: add flake8-no-print to flake8 lint step * pre-commit: add flake8-no-print to flake8 and also update pre-commit version * convert-hf-to-gguf.py: print() to logger conversion * *.py: logging basiconfig refactor to use conditional expression * *.py: removed commented out logging * fixup! *.py: logging basiconfig refactor to use conditional expression * constant.py: logger.error then exit should be a raise exception instead * *.py: Convert logger error and sys.exit() into a raise exception (for atypical error) * gguf-convert-endian.py: refactor convert_byteorder() to use tqdm progressbar * verify-checksum-model.py: This is the result of the program, it should be printed to stdout. * compare-llama-bench.py: add blank line for readability during missing repo response * reader.py: read_gguf_file() use print() over logging * convert.py: warning goes to stderr and won't hurt the dump output * gguf-dump.py: dump_metadata() should print to stdout * convert-hf-to-gguf.py: print --> logger.debug or ValueError() * verify-checksum-models.py: use print() for printing table * *.py: refactor logging.basicConfig() * gguf-py/gguf/*.py: use __name__ as logger name Since they will be imported and not run directly. * python-lint.yml: use .flake8 file instead * constants.py: logger no longer required * convert-hf-to-gguf.py: add additional logging * convert-hf-to-gguf.py: print() --> logger * *.py: fix flake8 warnings * revert changes to convert-hf-to-gguf.py for get_name() * convert-hf-to-gguf-update.py: use triple quoted f-string instead * *.py: accidentally corrected the wrong line * *.py: add compilade warning suggestions and style fixes
2024-05-03 19:36:41 +00:00
logger.debug(f"chkhsh: {chkhsh}")
llama : fix BPE pre-tokenization (#6920) * merged the changes from deepseeker models to main branch * Moved regex patterns to unicode.cpp and updated unicode.h * Moved header files * Resolved issues * added and refactored unicode_regex_split and related functions * Updated/merged the deepseek coder pr * Refactored code * Adding unicode regex mappings * Adding unicode regex function * Added needed functionality, testing remains * Fixed issues * Fixed issue with gpt2 regex custom preprocessor * unicode : fix? unicode_wstring_to_utf8 * lint : fix whitespaces * tests : add tokenizer tests for numbers * unicode : remove redundant headers * tests : remove and rename tokenizer test scripts * tests : add sample usage * gguf-py : reader prints warnings on duplicate keys * llama : towards llama3 tokenization support (wip) * unicode : shot in the dark to fix tests on Windows * unicode : first try custom implementations * convert : add "tokenizer.ggml.pre" GGUF KV (wip) * llama : use new pre-tokenizer type * convert : fix pre-tokenizer type writing * lint : fix * make : add test-tokenizer-0-llama-v3 * wip * models : add llama v3 vocab file * llama : adapt punctuation regex + add llama 3 regex * minor * unicode : set bomb * unicode : set bomb * unicode : always use std::wregex * unicode : support \p{N}, \p{L} and \p{P} natively * unicode : try fix windows * unicode : category support via std::regex * unicode : clean-up * unicode : simplify * convert : add convert-hf-to-gguf-update.py ggml-ci * lint : update * convert : add falcon ggml-ci * unicode : normalize signatures * lint : fix * lint : fix * convert : remove unused functions * convert : add comments * convert : exercise contractions ggml-ci * lint : fix * cmake : refactor test targets * tests : refactor vocab tests ggml-ci * tests : add more vocabs and tests ggml-ci * unicode : cleanup * scripts : ignore new update script in check-requirements.sh * models : add phi-3, mpt, gpt-2, starcoder * tests : disable obsolete ggml-ci * tests : use faster bpe test ggml-ci * llama : more prominent warning for old BPE models * tests : disable test-tokenizer-1-bpe due to slowness ggml-ci --------- Co-authored-by: Jaggzh <jaggz.h@gmail.com> Co-authored-by: Kazim Abrar Mahi <kazimabrarmahi135@gmail.com>
2024-04-29 13:58:41 +00:00
return res
py : convert-hf-to-gguf-update improvements (#7340) * convert-hf-to-gguf-update: automate updating * convert-hf-to-gguf-update: improve download * share requests session for performance * create directories only when needed, don't skip downloads when empty directory encountered * be more graceful about errors
2024-05-17 12:11:45 +00:00
# Marker: End get_vocab_base_pre
BERT tokenizer fixes (#6498) Key changes: * BERT conversion: fix abuse of LlamaHfVocab, do not set BOS or EOS * Nomic Embed conversion: pad vocab instead of slicing embedding tensor * llama_tokenize: handle added special tokens like HF does
2024-04-09 17:44:08 +00:00
def _set_vocab_gpt2(self) -> None:
llama : fix BPE pre-tokenization (#6920) * merged the changes from deepseeker models to main branch * Moved regex patterns to unicode.cpp and updated unicode.h * Moved header files * Resolved issues * added and refactored unicode_regex_split and related functions * Updated/merged the deepseek coder pr * Refactored code * Adding unicode regex mappings * Adding unicode regex function * Added needed functionality, testing remains * Fixed issues * Fixed issue with gpt2 regex custom preprocessor * unicode : fix? unicode_wstring_to_utf8 * lint : fix whitespaces * tests : add tokenizer tests for numbers * unicode : remove redundant headers * tests : remove and rename tokenizer test scripts * tests : add sample usage * gguf-py : reader prints warnings on duplicate keys * llama : towards llama3 tokenization support (wip) * unicode : shot in the dark to fix tests on Windows * unicode : first try custom implementations * convert : add "tokenizer.ggml.pre" GGUF KV (wip) * llama : use new pre-tokenizer type * convert : fix pre-tokenizer type writing * lint : fix * make : add test-tokenizer-0-llama-v3 * wip * models : add llama v3 vocab file * llama : adapt punctuation regex + add llama 3 regex * minor * unicode : set bomb * unicode : set bomb * unicode : always use std::wregex * unicode : support \p{N}, \p{L} and \p{P} natively * unicode : try fix windows * unicode : category support via std::regex * unicode : clean-up * unicode : simplify * convert : add convert-hf-to-gguf-update.py ggml-ci * lint : update * convert : add falcon ggml-ci * unicode : normalize signatures * lint : fix * lint : fix * convert : remove unused functions * convert : add comments * convert : exercise contractions ggml-ci * lint : fix * cmake : refactor test targets * tests : refactor vocab tests ggml-ci * tests : add more vocabs and tests ggml-ci * unicode : cleanup * scripts : ignore new update script in check-requirements.sh * models : add phi-3, mpt, gpt-2, starcoder * tests : disable obsolete ggml-ci * tests : use faster bpe test ggml-ci * llama : more prominent warning for old BPE models * tests : disable test-tokenizer-1-bpe due to slowness ggml-ci --------- Co-authored-by: Jaggzh <jaggz.h@gmail.com> Co-authored-by: Kazim Abrar Mahi <kazimabrarmahi135@gmail.com>
2024-04-29 13:58:41 +00:00
tokens, toktypes, tokpre = self.get_vocab_base()
scripts: Generalize convert scripts (#3838) * Replace convert-*-hf-to-gguf.py files with convert-hf-to-gguf.py
2023-11-09 10:09:29 +00:00
self.gguf_writer.add_tokenizer_model("gpt2")
llama : fix BPE pre-tokenization (#6920) * merged the changes from deepseeker models to main branch * Moved regex patterns to unicode.cpp and updated unicode.h * Moved header files * Resolved issues * added and refactored unicode_regex_split and related functions * Updated/merged the deepseek coder pr * Refactored code * Adding unicode regex mappings * Adding unicode regex function * Added needed functionality, testing remains * Fixed issues * Fixed issue with gpt2 regex custom preprocessor * unicode : fix? unicode_wstring_to_utf8 * lint : fix whitespaces * tests : add tokenizer tests for numbers * unicode : remove redundant headers * tests : remove and rename tokenizer test scripts * tests : add sample usage * gguf-py : reader prints warnings on duplicate keys * llama : towards llama3 tokenization support (wip) * unicode : shot in the dark to fix tests on Windows * unicode : first try custom implementations * convert : add "tokenizer.ggml.pre" GGUF KV (wip) * llama : use new pre-tokenizer type * convert : fix pre-tokenizer type writing * lint : fix * make : add test-tokenizer-0-llama-v3 * wip * models : add llama v3 vocab file * llama : adapt punctuation regex + add llama 3 regex * minor * unicode : set bomb * unicode : set bomb * unicode : always use std::wregex * unicode : support \p{N}, \p{L} and \p{P} natively * unicode : try fix windows * unicode : category support via std::regex * unicode : clean-up * unicode : simplify * convert : add convert-hf-to-gguf-update.py ggml-ci * lint : update * convert : add falcon ggml-ci * unicode : normalize signatures * lint : fix * lint : fix * convert : remove unused functions * convert : add comments * convert : exercise contractions ggml-ci * lint : fix * cmake : refactor test targets * tests : refactor vocab tests ggml-ci * tests : add more vocabs and tests ggml-ci * unicode : cleanup * scripts : ignore new update script in check-requirements.sh * models : add phi-3, mpt, gpt-2, starcoder * tests : disable obsolete ggml-ci * tests : use faster bpe test ggml-ci * llama : more prominent warning for old BPE models * tests : disable test-tokenizer-1-bpe due to slowness ggml-ci --------- Co-authored-by: Jaggzh <jaggz.h@gmail.com> Co-authored-by: Kazim Abrar Mahi <kazimabrarmahi135@gmail.com>
2024-04-29 13:58:41 +00:00
self.gguf_writer.add_tokenizer_pre(tokpre)
scripts: Generalize convert scripts (#3838) * Replace convert-*-hf-to-gguf.py files with convert-hf-to-gguf.py
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self.gguf_writer.add_token_list(tokens)
self.gguf_writer.add_token_types(toktypes)
BERT tokenizer fixes (#6498) Key changes: * BERT conversion: fix abuse of LlamaHfVocab, do not set BOS or EOS * Nomic Embed conversion: pad vocab instead of slicing embedding tensor * llama_tokenize: handle added special tokens like HF does
2024-04-09 17:44:08 +00:00
special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=True)
scripts: Generalize convert scripts (#3838) * Replace convert-*-hf-to-gguf.py files with convert-hf-to-gguf.py
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special_vocab.add_to_gguf(self.gguf_writer)
llama : support StableLM 2 1.6B (#5052) * llama : support StableLM 2 1.6B * convert : fix Qwen's set_vocab wrongly naming all special tokens [PAD{id}] * convert : refactor Qwen's set_vocab to use it for StableLM 2 too * nix : add tiktoken to llama-python-extra * convert : use presence of tokenizer.json to determine StableLM tokenizer loader It's a less arbitrary heuristic than the vocab size.
2024-01-22 11:21:52 +00:00
def _set_vocab_qwen(self):
dir_model = self.dir_model
hparams = self.hparams
convert : refactor vocab selection logic (#6355)
2024-03-28 15:44:36 +00:00
tokens: list[str] = []
llama : support StableLM 2 1.6B (#5052) * llama : support StableLM 2 1.6B * convert : fix Qwen's set_vocab wrongly naming all special tokens [PAD{id}] * convert : refactor Qwen's set_vocab to use it for StableLM 2 too * nix : add tiktoken to llama-python-extra * convert : use presence of tokenizer.json to determine StableLM tokenizer loader It's a less arbitrary heuristic than the vocab size.
2024-01-22 11:21:52 +00:00
toktypes: list[int] = []
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(dir_model, trust_remote_code=True)
vocab_size = hparams["vocab_size"]
assert max(tokenizer.get_vocab().values()) < vocab_size
llama : fix BPE pre-tokenization (#6920) * merged the changes from deepseeker models to main branch * Moved regex patterns to unicode.cpp and updated unicode.h * Moved header files * Resolved issues * added and refactored unicode_regex_split and related functions * Updated/merged the deepseek coder pr * Refactored code * Adding unicode regex mappings * Adding unicode regex function * Added needed functionality, testing remains * Fixed issues * Fixed issue with gpt2 regex custom preprocessor * unicode : fix? unicode_wstring_to_utf8 * lint : fix whitespaces * tests : add tokenizer tests for numbers * unicode : remove redundant headers * tests : remove and rename tokenizer test scripts * tests : add sample usage * gguf-py : reader prints warnings on duplicate keys * llama : towards llama3 tokenization support (wip) * unicode : shot in the dark to fix tests on Windows * unicode : first try custom implementations * convert : add "tokenizer.ggml.pre" GGUF KV (wip) * llama : use new pre-tokenizer type * convert : fix pre-tokenizer type writing * lint : fix * make : add test-tokenizer-0-llama-v3 * wip * models : add llama v3 vocab file * llama : adapt punctuation regex + add llama 3 regex * minor * unicode : set bomb * unicode : set bomb * unicode : always use std::wregex * unicode : support \p{N}, \p{L} and \p{P} natively * unicode : try fix windows * unicode : category support via std::regex * unicode : clean-up * unicode : simplify * convert : add convert-hf-to-gguf-update.py ggml-ci * lint : update * convert : add falcon ggml-ci * unicode : normalize signatures * lint : fix * lint : fix * convert : remove unused functions * convert : add comments * convert : exercise contractions ggml-ci * lint : fix * cmake : refactor test targets * tests : refactor vocab tests ggml-ci * tests : add more vocabs and tests ggml-ci * unicode : cleanup * scripts : ignore new update script in check-requirements.sh * models : add phi-3, mpt, gpt-2, starcoder * tests : disable obsolete ggml-ci * tests : use faster bpe test ggml-ci * llama : more prominent warning for old BPE models * tests : disable test-tokenizer-1-bpe due to slowness ggml-ci --------- Co-authored-by: Jaggzh <jaggz.h@gmail.com> Co-authored-by: Kazim Abrar Mahi <kazimabrarmahi135@gmail.com>
2024-04-29 13:58:41 +00:00
tokpre = self.get_vocab_base_pre(tokenizer)
llama : support StableLM 2 1.6B (#5052) * llama : support StableLM 2 1.6B * convert : fix Qwen's set_vocab wrongly naming all special tokens [PAD{id}] * convert : refactor Qwen's set_vocab to use it for StableLM 2 too * nix : add tiktoken to llama-python-extra * convert : use presence of tokenizer.json to determine StableLM tokenizer loader It's a less arbitrary heuristic than the vocab size.
2024-01-22 11:21:52 +00:00
merges = []
vocab = {}
mergeable_ranks = tokenizer.mergeable_ranks
for token, rank in mergeable_ranks.items():
vocab[QwenModel.token_bytes_to_string(token)] = rank
if len(token) == 1:
continue
merged = QwenModel.bpe(mergeable_ranks, token, max_rank=rank)
assert len(merged) == 2
merges.append(' '.join(map(QwenModel.token_bytes_to_string, merged)))
# for this kind of tokenizer, added_vocab is not a subset of vocab, so they need to be combined
added_vocab = tokenizer.special_tokens
convert : fix Qwen/Qwen-7b conversion (#7308)
2024-05-17 07:01:58 +00:00
reverse_vocab = {id_ : encoded_tok for encoded_tok, id_ in {**vocab, **added_vocab}.items()}
llama : support StableLM 2 1.6B (#5052) * llama : support StableLM 2 1.6B * convert : fix Qwen's set_vocab wrongly naming all special tokens [PAD{id}] * convert : refactor Qwen's set_vocab to use it for StableLM 2 too * nix : add tiktoken to llama-python-extra * convert : use presence of tokenizer.json to determine StableLM tokenizer loader It's a less arbitrary heuristic than the vocab size.
2024-01-22 11:21:52 +00:00
for i in range(vocab_size):
if i not in reverse_vocab:
convert : refactor vocab selection logic (#6355)
2024-03-28 15:44:36 +00:00
tokens.append(f"[PAD{i}]")
llama : support StableLM 2 1.6B (#5052) * llama : support StableLM 2 1.6B * convert : fix Qwen's set_vocab wrongly naming all special tokens [PAD{id}] * convert : refactor Qwen's set_vocab to use it for StableLM 2 too * nix : add tiktoken to llama-python-extra * convert : use presence of tokenizer.json to determine StableLM tokenizer loader It's a less arbitrary heuristic than the vocab size.
2024-01-22 11:21:52 +00:00
toktypes.append(gguf.TokenType.USER_DEFINED)
elif reverse_vocab[i] in added_vocab:
tokens.append(reverse_vocab[i])
toktypes.append(gguf.TokenType.CONTROL)
else:
tokens.append(reverse_vocab[i])
toktypes.append(gguf.TokenType.NORMAL)
self.gguf_writer.add_tokenizer_model("gpt2")
llama : fix BPE pre-tokenization (#6920) * merged the changes from deepseeker models to main branch * Moved regex patterns to unicode.cpp and updated unicode.h * Moved header files * Resolved issues * added and refactored unicode_regex_split and related functions * Updated/merged the deepseek coder pr * Refactored code * Adding unicode regex mappings * Adding unicode regex function * Added needed functionality, testing remains * Fixed issues * Fixed issue with gpt2 regex custom preprocessor * unicode : fix? unicode_wstring_to_utf8 * lint : fix whitespaces * tests : add tokenizer tests for numbers * unicode : remove redundant headers * tests : remove and rename tokenizer test scripts * tests : add sample usage * gguf-py : reader prints warnings on duplicate keys * llama : towards llama3 tokenization support (wip) * unicode : shot in the dark to fix tests on Windows * unicode : first try custom implementations * convert : add "tokenizer.ggml.pre" GGUF KV (wip) * llama : use new pre-tokenizer type * convert : fix pre-tokenizer type writing * lint : fix * make : add test-tokenizer-0-llama-v3 * wip * models : add llama v3 vocab file * llama : adapt punctuation regex + add llama 3 regex * minor * unicode : set bomb * unicode : set bomb * unicode : always use std::wregex * unicode : support \p{N}, \p{L} and \p{P} natively * unicode : try fix windows * unicode : category support via std::regex * unicode : clean-up * unicode : simplify * convert : add convert-hf-to-gguf-update.py ggml-ci * lint : update * convert : add falcon ggml-ci * unicode : normalize signatures * lint : fix * lint : fix * convert : remove unused functions * convert : add comments * convert : exercise contractions ggml-ci * lint : fix * cmake : refactor test targets * tests : refactor vocab tests ggml-ci * tests : add more vocabs and tests ggml-ci * unicode : cleanup * scripts : ignore new update script in check-requirements.sh * models : add phi-3, mpt, gpt-2, starcoder * tests : disable obsolete ggml-ci * tests : use faster bpe test ggml-ci * llama : more prominent warning for old BPE models * tests : disable test-tokenizer-1-bpe due to slowness ggml-ci --------- Co-authored-by: Jaggzh <jaggz.h@gmail.com> Co-authored-by: Kazim Abrar Mahi <kazimabrarmahi135@gmail.com>
2024-04-29 13:58:41 +00:00
self.gguf_writer.add_tokenizer_pre(tokpre)
llama : support StableLM 2 1.6B (#5052) * llama : support StableLM 2 1.6B * convert : fix Qwen's set_vocab wrongly naming all special tokens [PAD{id}] * convert : refactor Qwen's set_vocab to use it for StableLM 2 too * nix : add tiktoken to llama-python-extra * convert : use presence of tokenizer.json to determine StableLM tokenizer loader It's a less arbitrary heuristic than the vocab size.
2024-01-22 11:21:52 +00:00
self.gguf_writer.add_token_list(tokens)
self.gguf_writer.add_token_types(toktypes)
special_vocab = gguf.SpecialVocab(dir_model, load_merges=False)
special_vocab.merges = merges
# only add special tokens when they were not already loaded from config.json
if len(special_vocab.special_token_ids) == 0:
special_vocab._set_special_token("bos", tokenizer.special_tokens["<|endoftext|>"])
special_vocab._set_special_token("eos", tokenizer.special_tokens["<|endoftext|>"])
# this one is usually not in config.json anyway
special_vocab._set_special_token("unk", tokenizer.special_tokens["<|endoftext|>"])
special_vocab.add_to_gguf(self.gguf_writer)
scripts: Generalize convert scripts (#3838) * Replace convert-*-hf-to-gguf.py files with convert-hf-to-gguf.py
2023-11-09 10:09:29 +00:00
def _set_vocab_sentencepiece(self):
from sentencepiece import SentencePieceProcessor
tokenizer_path = self.dir_model / 'tokenizer.model'
tokens: list[bytes] = []
scores: list[float] = []
toktypes: list[int] = []
if not tokenizer_path.is_file():
Missing tokenizer.model error during gguf conversion (#6443) Co-authored-by: Jared Van Bortel <jared@nomic.ai>
2024-04-03 15:42:52 +00:00
raise FileNotFoundError(f"File not found: {tokenizer_path}")
scripts: Generalize convert scripts (#3838) * Replace convert-*-hf-to-gguf.py files with convert-hf-to-gguf.py
2023-11-09 10:09:29 +00:00
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
tokenizer = SentencePieceProcessor()
tokenizer.LoadFromFile(str(tokenizer_path))
scripts: Generalize convert scripts (#3838) * Replace convert-*-hf-to-gguf.py files with convert-hf-to-gguf.py
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vocab_size = self.hparams.get('vocab_size', tokenizer.vocab_size())
convert : fix set_vocab_sentencepiece (#6866) * convert : fix set_vocab_sentencepiece * Update convert-hf-to-gguf.py
2024-05-18 05:46:20 +00:00
tokens: list[bytes] = [f"[PAD{i}]".encode("utf-8") for i in range(vocab_size)]
scores: list[float] = [-10000.0] * vocab_size
toktypes: list[int] = [SentencePieceTokenTypes.UNKNOWN] * vocab_size
convert-hf : fix exception in sentencepiece with added tokens (#6320)
2024-03-26 12:32:19 +00:00
for token_id in range(tokenizer.vocab_size()):
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
piece = tokenizer.IdToPiece(token_id)
scripts: Generalize convert scripts (#3838) * Replace convert-*-hf-to-gguf.py files with convert-hf-to-gguf.py
2023-11-09 10:09:29 +00:00
text = piece.encode("utf-8")
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
score = tokenizer.GetScore(token_id)
scripts: Generalize convert scripts (#3838) * Replace convert-*-hf-to-gguf.py files with convert-hf-to-gguf.py
2023-11-09 10:09:29 +00:00
toktype = SentencePieceTokenTypes.NORMAL
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
if tokenizer.IsUnknown(token_id):
scripts: Generalize convert scripts (#3838) * Replace convert-*-hf-to-gguf.py files with convert-hf-to-gguf.py
2023-11-09 10:09:29 +00:00
toktype = SentencePieceTokenTypes.UNKNOWN
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
elif tokenizer.IsControl(token_id):
scripts: Generalize convert scripts (#3838) * Replace convert-*-hf-to-gguf.py files with convert-hf-to-gguf.py
2023-11-09 10:09:29 +00:00
toktype = SentencePieceTokenTypes.CONTROL
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
elif tokenizer.IsUnused(token_id):
scripts: Generalize convert scripts (#3838) * Replace convert-*-hf-to-gguf.py files with convert-hf-to-gguf.py
2023-11-09 10:09:29 +00:00
toktype = SentencePieceTokenTypes.UNUSED
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
elif tokenizer.IsByte(token_id):
scripts: Generalize convert scripts (#3838) * Replace convert-*-hf-to-gguf.py files with convert-hf-to-gguf.py
2023-11-09 10:09:29 +00:00
toktype = SentencePieceTokenTypes.BYTE
convert : fix set_vocab_sentencepiece (#6866) * convert : fix set_vocab_sentencepiece * Update convert-hf-to-gguf.py
2024-05-18 05:46:20 +00:00
tokens[token_id] = text
scores[token_id] = score
toktypes[token_id] = toktype
scripts: Generalize convert scripts (#3838) * Replace convert-*-hf-to-gguf.py files with convert-hf-to-gguf.py
2023-11-09 10:09:29 +00:00
added_tokens_file = self.dir_model / 'added_tokens.json'
if added_tokens_file.is_file():
with open(added_tokens_file, "r", encoding="utf-8") as f:
added_tokens_json = json.load(f)
for key in added_tokens_json:
convert : fix set_vocab_sentencepiece (#6866) * convert : fix set_vocab_sentencepiece * Update convert-hf-to-gguf.py
2024-05-18 05:46:20 +00:00
token_id = added_tokens_json[key]
if (token_id >= vocab_size):
logger.warning(f'ignore token {token_id}: id is out of range, max={vocab_size - 1}')
continue
tokens[token_id] = key.encode("utf-8")
scores[token_id] = -1000.0
toktypes[token_id] = SentencePieceTokenTypes.USER_DEFINED
convert-hf : fix exception in sentencepiece with added tokens (#6320)
2024-03-26 12:32:19 +00:00
convert : add support of codeqwen due to tokenizer (#6707) * add support of codeqwen due to tokenizer * override load_hparams * fix typo * fix load_params * convert : fix whitespace --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-04-24 07:16:21 +00:00
if vocab_size > len(tokens):
pad_count = vocab_size - len(tokens)
convert.py : add python logging instead of print() (#6511) * convert.py: add python logging instead of print() * convert.py: verbose flag takes priority over dump flag log suppression * convert.py: named instance logging * convert.py: use explicit logger id string * convert.py: convert extra print() to named logger * convert.py: sys.stderr.write --> logger.error * *.py: Convert all python scripts to use logging module * requirements.txt: remove extra line * flake8: update flake8 ignore and exclude to match ci settings * gh-actions: add flake8-no-print to flake8 lint step * pre-commit: add flake8-no-print to flake8 and also update pre-commit version * convert-hf-to-gguf.py: print() to logger conversion * *.py: logging basiconfig refactor to use conditional expression * *.py: removed commented out logging * fixup! *.py: logging basiconfig refactor to use conditional expression * constant.py: logger.error then exit should be a raise exception instead * *.py: Convert logger error and sys.exit() into a raise exception (for atypical error) * gguf-convert-endian.py: refactor convert_byteorder() to use tqdm progressbar * verify-checksum-model.py: This is the result of the program, it should be printed to stdout. * compare-llama-bench.py: add blank line for readability during missing repo response * reader.py: read_gguf_file() use print() over logging * convert.py: warning goes to stderr and won't hurt the dump output * gguf-dump.py: dump_metadata() should print to stdout * convert-hf-to-gguf.py: print --> logger.debug or ValueError() * verify-checksum-models.py: use print() for printing table * *.py: refactor logging.basicConfig() * gguf-py/gguf/*.py: use __name__ as logger name Since they will be imported and not run directly. * python-lint.yml: use .flake8 file instead * constants.py: logger no longer required * convert-hf-to-gguf.py: add additional logging * convert-hf-to-gguf.py: print() --> logger * *.py: fix flake8 warnings * revert changes to convert-hf-to-gguf.py for get_name() * convert-hf-to-gguf-update.py: use triple quoted f-string instead * *.py: accidentally corrected the wrong line * *.py: add compilade warning suggestions and style fixes
2024-05-03 19:36:41 +00:00
logger.debug(f"Padding vocab with {pad_count} token(s) - [PAD1] through [PAD{pad_count}]")
convert : add support of codeqwen due to tokenizer (#6707) * add support of codeqwen due to tokenizer * override load_hparams * fix typo * fix load_params * convert : fix whitespace --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-04-24 07:16:21 +00:00
for i in range(1, pad_count + 1):
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
tokens.append(bytes(f"[PAD{i}]", encoding="utf-8"))
convert : add support of codeqwen due to tokenizer (#6707) * add support of codeqwen due to tokenizer * override load_hparams * fix typo * fix load_params * convert : fix whitespace --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-04-24 07:16:21 +00:00
scores.append(-1000.0)
toktypes.append(SentencePieceTokenTypes.UNUSED)
scripts: Generalize convert scripts (#3838) * Replace convert-*-hf-to-gguf.py files with convert-hf-to-gguf.py
2023-11-09 10:09:29 +00:00
self.gguf_writer.add_tokenizer_model("llama")
llama : fix BPE pre-tokenization (#6920) * merged the changes from deepseeker models to main branch * Moved regex patterns to unicode.cpp and updated unicode.h * Moved header files * Resolved issues * added and refactored unicode_regex_split and related functions * Updated/merged the deepseek coder pr * Refactored code * Adding unicode regex mappings * Adding unicode regex function * Added needed functionality, testing remains * Fixed issues * Fixed issue with gpt2 regex custom preprocessor * unicode : fix? unicode_wstring_to_utf8 * lint : fix whitespaces * tests : add tokenizer tests for numbers * unicode : remove redundant headers * tests : remove and rename tokenizer test scripts * tests : add sample usage * gguf-py : reader prints warnings on duplicate keys * llama : towards llama3 tokenization support (wip) * unicode : shot in the dark to fix tests on Windows * unicode : first try custom implementations * convert : add "tokenizer.ggml.pre" GGUF KV (wip) * llama : use new pre-tokenizer type * convert : fix pre-tokenizer type writing * lint : fix * make : add test-tokenizer-0-llama-v3 * wip * models : add llama v3 vocab file * llama : adapt punctuation regex + add llama 3 regex * minor * unicode : set bomb * unicode : set bomb * unicode : always use std::wregex * unicode : support \p{N}, \p{L} and \p{P} natively * unicode : try fix windows * unicode : category support via std::regex * unicode : clean-up * unicode : simplify * convert : add convert-hf-to-gguf-update.py ggml-ci * lint : update * convert : add falcon ggml-ci * unicode : normalize signatures * lint : fix * lint : fix * convert : remove unused functions * convert : add comments * convert : exercise contractions ggml-ci * lint : fix * cmake : refactor test targets * tests : refactor vocab tests ggml-ci * tests : add more vocabs and tests ggml-ci * unicode : cleanup * scripts : ignore new update script in check-requirements.sh * models : add phi-3, mpt, gpt-2, starcoder * tests : disable obsolete ggml-ci * tests : use faster bpe test ggml-ci * llama : more prominent warning for old BPE models * tests : disable test-tokenizer-1-bpe due to slowness ggml-ci --------- Co-authored-by: Jaggzh <jaggz.h@gmail.com> Co-authored-by: Kazim Abrar Mahi <kazimabrarmahi135@gmail.com>
2024-04-29 13:58:41 +00:00
self.gguf_writer.add_tokenizer_pre("default")
scripts: Generalize convert scripts (#3838) * Replace convert-*-hf-to-gguf.py files with convert-hf-to-gguf.py
2023-11-09 10:09:29 +00:00
self.gguf_writer.add_token_list(tokens)
self.gguf_writer.add_token_scores(scores)
self.gguf_writer.add_token_types(toktypes)
special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens))
special_vocab.add_to_gguf(self.gguf_writer)
convert : refactor vocab selection logic (#6355)
2024-03-28 15:44:36 +00:00
def _set_vocab_llama_hf(self):
vocab = LlamaHfVocab(self.dir_model)
llama : add MiniCPM support (#5346) * support minicpm arch. * fix tab/space typo. * convert minicpm model via convert-hf-gguf.py * try to make tokenizer work * fix bug for quantize minicpm * fix for flake8 lint * remove convert-minicpm.py * fix for editorconfig * correct minicpm model type (size) * constants expanded for minicpm * Minor change of the constant names for minicpm
2024-02-07 06:15:56 +00:00
tokens = []
scores = []
toktypes = []
for text, score, toktype in vocab.all_tokens():
tokens.append(text)
scores.append(score)
toktypes.append(toktype)
assert len(tokens) == vocab.vocab_size
self.gguf_writer.add_tokenizer_model("llama")
llama : fix BPE pre-tokenization (#6920) * merged the changes from deepseeker models to main branch * Moved regex patterns to unicode.cpp and updated unicode.h * Moved header files * Resolved issues * added and refactored unicode_regex_split and related functions * Updated/merged the deepseek coder pr * Refactored code * Adding unicode regex mappings * Adding unicode regex function * Added needed functionality, testing remains * Fixed issues * Fixed issue with gpt2 regex custom preprocessor * unicode : fix? unicode_wstring_to_utf8 * lint : fix whitespaces * tests : add tokenizer tests for numbers * unicode : remove redundant headers * tests : remove and rename tokenizer test scripts * tests : add sample usage * gguf-py : reader prints warnings on duplicate keys * llama : towards llama3 tokenization support (wip) * unicode : shot in the dark to fix tests on Windows * unicode : first try custom implementations * convert : add "tokenizer.ggml.pre" GGUF KV (wip) * llama : use new pre-tokenizer type * convert : fix pre-tokenizer type writing * lint : fix * make : add test-tokenizer-0-llama-v3 * wip * models : add llama v3 vocab file * llama : adapt punctuation regex + add llama 3 regex * minor * unicode : set bomb * unicode : set bomb * unicode : always use std::wregex * unicode : support \p{N}, \p{L} and \p{P} natively * unicode : try fix windows * unicode : category support via std::regex * unicode : clean-up * unicode : simplify * convert : add convert-hf-to-gguf-update.py ggml-ci * lint : update * convert : add falcon ggml-ci * unicode : normalize signatures * lint : fix * lint : fix * convert : remove unused functions * convert : add comments * convert : exercise contractions ggml-ci * lint : fix * cmake : refactor test targets * tests : refactor vocab tests ggml-ci * tests : add more vocabs and tests ggml-ci * unicode : cleanup * scripts : ignore new update script in check-requirements.sh * models : add phi-3, mpt, gpt-2, starcoder * tests : disable obsolete ggml-ci * tests : use faster bpe test ggml-ci * llama : more prominent warning for old BPE models * tests : disable test-tokenizer-1-bpe due to slowness ggml-ci --------- Co-authored-by: Jaggzh <jaggz.h@gmail.com> Co-authored-by: Kazim Abrar Mahi <kazimabrarmahi135@gmail.com>
2024-04-29 13:58:41 +00:00
self.gguf_writer.add_tokenizer_pre("default")
llama : add MiniCPM support (#5346) * support minicpm arch. * fix tab/space typo. * convert minicpm model via convert-hf-gguf.py * try to make tokenizer work * fix bug for quantize minicpm * fix for flake8 lint * remove convert-minicpm.py * fix for editorconfig * correct minicpm model type (size) * constants expanded for minicpm * Minor change of the constant names for minicpm
2024-02-07 06:15:56 +00:00
self.gguf_writer.add_token_list(tokens)
self.gguf_writer.add_token_scores(scores)
self.gguf_writer.add_token_types(toktypes)
special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens))
special_vocab.add_to_gguf(self.gguf_writer)
scripts: Generalize convert scripts (#3838) * Replace convert-*-hf-to-gguf.py files with convert-hf-to-gguf.py
2023-11-09 10:09:29 +00:00
convert-hf : make model class definitions self-contained (#5825)
2024-03-02 17:21:47 +00:00
@Model.register("GPTNeoXForCausalLM")
scripts: Generalize convert scripts (#3838) * Replace convert-*-hf-to-gguf.py files with convert-hf-to-gguf.py
2023-11-09 10:09:29 +00:00
class GPTNeoXModel(Model):
convert-hf : make model class definitions self-contained (#5825)
2024-03-02 17:21:47 +00:00
model_arch = gguf.MODEL_ARCH.GPTNEOX
scripts: Generalize convert scripts (#3838) * Replace convert-*-hf-to-gguf.py files with convert-hf-to-gguf.py
2023-11-09 10:09:29 +00:00
def set_gguf_parameters(self):
block_count = self.hparams["num_hidden_layers"]
self.gguf_writer.add_name(self.dir_model.name)
self.gguf_writer.add_context_length(self.hparams["max_position_embeddings"])
self.gguf_writer.add_embedding_length(self.hparams["hidden_size"])
self.gguf_writer.add_block_count(block_count)
self.gguf_writer.add_feed_forward_length(self.hparams["intermediate_size"])
self.gguf_writer.add_rope_dimension_count(
int(self.hparams["rotary_pct"] * (self.hparams["hidden_size"] // self.hparams["num_attention_heads"])),
)
self.gguf_writer.add_head_count(self.hparams["num_attention_heads"])
self.gguf_writer.add_parallel_residual(self.hparams.get("use_parallel_residual", True))
self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_eps"])
convert-hf : make model class definitions self-contained (#5825)
2024-03-02 17:21:47 +00:00
@Model.register("BloomForCausalLM")
scripts: Generalize convert scripts (#3838) * Replace convert-*-hf-to-gguf.py files with convert-hf-to-gguf.py
2023-11-09 10:09:29 +00:00
class BloomModel(Model):
convert-hf : make model class definitions self-contained (#5825)
2024-03-02 17:21:47 +00:00
model_arch = gguf.MODEL_ARCH.BLOOM
scripts: Generalize convert scripts (#3838) * Replace convert-*-hf-to-gguf.py files with convert-hf-to-gguf.py
2023-11-09 10:09:29 +00:00
def set_gguf_parameters(self):
self.gguf_writer.add_name("Bloom")
n_embed = self.hparams.get("hidden_size", self.hparams.get("n_embed"))
n_head = self.hparams.get("n_head", self.hparams.get("num_attention_heads"))
self.gguf_writer.add_context_length(self.hparams.get("seq_length", n_embed))
self.gguf_writer.add_embedding_length(n_embed)
self.gguf_writer.add_feed_forward_length(4 * n_embed)
self.gguf_writer.add_block_count(self.hparams["n_layer"])
self.gguf_writer.add_head_count(n_head)
self.gguf_writer.add_head_count_kv(n_head)
self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_epsilon"])
self.gguf_writer.add_file_type(self.ftype)
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
del bid # unused
scripts: Generalize convert scripts (#3838) * Replace convert-*-hf-to-gguf.py files with convert-hf-to-gguf.py
2023-11-09 10:09:29 +00:00
n_head = self.hparams.get("n_head", self.hparams.get("num_attention_heads"))
n_embed = self.hparams.get("hidden_size", self.hparams.get("n_embed"))
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
name = re.sub(r'transformer\.', '', name)
tensors: list[tuple[str, Tensor]] = []
if re.match(r"h\.\d+\.self_attention\.query_key_value\.weight", name):
# Map bloom-style qkv_linear to gpt-style qkv_linear
# bloom: https://github.com/huggingface/transformers/blob/main/src/transformers/models/bloom/modeling_bloom.py#L238-L252 # noqa
# gpt-2: https://github.com/huggingface/transformers/blob/main/src/transformers/models/gpt2/modeling_gpt2.py#L312 # noqa
qkv_weights = data_torch.reshape((n_head, 3, n_embed // n_head, n_embed))
data_torch = torch.cat(
(
qkv_weights[:, 0, :, :].reshape((-1, n_embed)),
qkv_weights[:, 1, :, :].reshape((-1, n_embed)),
qkv_weights[:, 2, :, :].reshape((-1, n_embed)),
),
dim=0,
)
logger.info("re-format attention.linear_qkv.weight")
elif re.match(r"h\.\d+\.self_attention\.query_key_value\.bias", name):
qkv_bias = data_torch.reshape((n_head, 3, n_embed // n_head))
data_torch = torch.cat(
(
qkv_bias[:, 0, :].reshape((n_embed,)),
qkv_bias[:, 1, :].reshape((n_embed,)),
qkv_bias[:, 2, :].reshape((n_embed,)),
),
dim=0,
)
logger.info("re-format attention.linear_qkv.bias")
scripts: Generalize convert scripts (#3838) * Replace convert-*-hf-to-gguf.py files with convert-hf-to-gguf.py
2023-11-09 10:09:29 +00:00
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
tensors.append((self.map_tensor_name(name), data_torch))
scripts: Generalize convert scripts (#3838) * Replace convert-*-hf-to-gguf.py files with convert-hf-to-gguf.py
2023-11-09 10:09:29 +00:00
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
if name == "word_embeddings.weight":
assert self.tensor_names is not None
scripts: Generalize convert scripts (#3838) * Replace convert-*-hf-to-gguf.py files with convert-hf-to-gguf.py
2023-11-09 10:09:29 +00:00
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
# TODO: tie them at runtime, don't duplicate in the model file
if all(s not in self.tensor_names for s in ("lm_head.weight", "output.weight")):
tensors.append((self.format_tensor_name(gguf.MODEL_TENSOR.OUTPUT), data_torch))
scripts: Generalize convert scripts (#3838) * Replace convert-*-hf-to-gguf.py files with convert-hf-to-gguf.py
2023-11-09 10:09:29 +00:00
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
return tensors
scripts: Generalize convert scripts (#3838) * Replace convert-*-hf-to-gguf.py files with convert-hf-to-gguf.py
2023-11-09 10:09:29 +00:00
convert-hf : make model class definitions self-contained (#5825)
2024-03-02 17:21:47 +00:00
@Model.register("MPTForCausalLM")
scripts: Generalize convert scripts (#3838) * Replace convert-*-hf-to-gguf.py files with convert-hf-to-gguf.py
2023-11-09 10:09:29 +00:00
class MPTModel(Model):
convert-hf : make model class definitions self-contained (#5825)
2024-03-02 17:21:47 +00:00
model_arch = gguf.MODEL_ARCH.MPT
llama : add SEA-LION support (#6448) * initial commit for sealion support * add sealion support * minor fix * q/k ln and pos_embd only if required * Apply suggestions from code review Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * minor : clear whitespaces --------- Co-authored-by: bryan <bryansiow@aisingapore.org> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-04-03 18:05:10 +00:00
def set_vocab(self):
try:
self._set_vocab_gpt2()
convert : fix for lint error complaining of bare except (#6470)
2024-04-04 06:32:53 +00:00
except Exception:
# Fallback for SEA-LION model
llama : add SEA-LION support (#6448) * initial commit for sealion support * add sealion support * minor fix * q/k ln and pos_embd only if required * Apply suggestions from code review Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * minor : clear whitespaces --------- Co-authored-by: bryan <bryansiow@aisingapore.org> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-04-03 18:05:10 +00:00
self._set_vocab_sentencepiece()
self.gguf_writer.add_add_bos_token(False)
self.gguf_writer.add_pad_token_id(3)
self.gguf_writer.add_eos_token_id(1)
self.gguf_writer.add_unk_token_id(0)
scripts: Generalize convert scripts (#3838) * Replace convert-*-hf-to-gguf.py files with convert-hf-to-gguf.py
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def set_gguf_parameters(self):
block_count = self.hparams["n_layers"]
self.gguf_writer.add_name(self.dir_model.name)
self.gguf_writer.add_context_length(self.hparams["max_seq_len"])
self.gguf_writer.add_embedding_length(self.hparams["d_model"])
self.gguf_writer.add_block_count(block_count)
self.gguf_writer.add_feed_forward_length(4 * self.hparams["d_model"])
self.gguf_writer.add_head_count(self.hparams["n_heads"])
if kv_n_heads := self.hparams["attn_config"].get("kv_n_heads"):
self.gguf_writer.add_head_count_kv(kv_n_heads)
self.gguf_writer.add_layer_norm_eps(1e-5)
if self.hparams["attn_config"]["clip_qkv"] is not None:
self.gguf_writer.add_clamp_kqv(self.hparams["attn_config"]["clip_qkv"])
llama : add SEA-LION support (#6448) * initial commit for sealion support * add sealion support * minor fix * q/k ln and pos_embd only if required * Apply suggestions from code review Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * minor : clear whitespaces --------- Co-authored-by: bryan <bryansiow@aisingapore.org> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-04-03 18:05:10 +00:00
if self.hparams["attn_config"]["alibi"]:
self.gguf_writer.add_max_alibi_bias(self.hparams["attn_config"]["alibi_bias_max"])
else:
self.gguf_writer.add_max_alibi_bias(0.0)
scripts: Generalize convert scripts (#3838) * Replace convert-*-hf-to-gguf.py files with convert-hf-to-gguf.py
2023-11-09 10:09:29 +00:00
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
del bid # unused
scripts: Generalize convert scripts (#3838) * Replace convert-*-hf-to-gguf.py files with convert-hf-to-gguf.py
2023-11-09 10:09:29 +00:00
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
if "scales" in name:
new_name = self.map_tensor_name(name, try_suffixes=(".weight", ".bias", ".scales"))
new_name = new_name.replace("scales", "act.scales")
else:
new_name = self.map_tensor_name(name, try_suffixes=(".weight", ".bias"))
scripts: Generalize convert scripts (#3838) * Replace convert-*-hf-to-gguf.py files with convert-hf-to-gguf.py
2023-11-09 10:09:29 +00:00
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
return [(new_name, data_torch)]
scripts: Generalize convert scripts (#3838) * Replace convert-*-hf-to-gguf.py files with convert-hf-to-gguf.py
2023-11-09 10:09:29 +00:00
convert-hf : make model class definitions self-contained (#5825)
2024-03-02 17:21:47 +00:00
@Model.register("OrionForCausalLM")
llama : add support for Orion-14B (#5118) * add support for Orion-14B(https://huggingface.co/OrionStarAI/Orion-14B-Chat) * flake8 support * Update llama.cpp Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * Update llama.cpp Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * Update llama.cpp Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * Update llama.cpp Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * Update llama.cpp Co-authored-by: slaren <slarengh@gmail.com> * Update llama.cpp * Update llama.cpp --------- Co-authored-by: lixiaopu <lixiaopu@cmcm.com> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> Co-authored-by: slaren <slarengh@gmail.com>
2024-01-28 08:00:30 +00:00
class OrionModel(Model):
convert-hf : make model class definitions self-contained (#5825)
2024-03-02 17:21:47 +00:00
model_arch = gguf.MODEL_ARCH.ORION
llama : add support for Orion-14B (#5118) * add support for Orion-14B(https://huggingface.co/OrionStarAI/Orion-14B-Chat) * flake8 support * Update llama.cpp Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * Update llama.cpp Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * Update llama.cpp Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * Update llama.cpp Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * Update llama.cpp Co-authored-by: slaren <slarengh@gmail.com> * Update llama.cpp * Update llama.cpp --------- Co-authored-by: lixiaopu <lixiaopu@cmcm.com> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> Co-authored-by: slaren <slarengh@gmail.com>
2024-01-28 08:00:30 +00:00
def set_vocab(self):
self._set_vocab_sentencepiece()
def set_gguf_parameters(self):
block_count = self.hparams["num_hidden_layers"]
head_count = self.hparams["num_attention_heads"]
head_count_kv = self.hparams.get("num_key_value_heads", head_count)
hf_repo = self.hparams.get("_name_or_path", "")
ctx_length = 0
if "max_sequence_length" in self.hparams:
ctx_length = self.hparams["max_sequence_length"]
elif "max_position_embeddings" in self.hparams:
ctx_length = self.hparams["max_position_embeddings"]
elif "model_max_length" in self.hparams:
ctx_length = self.hparams["model_max_length"]
else:
convert.py : add python logging instead of print() (#6511) * convert.py: add python logging instead of print() * convert.py: verbose flag takes priority over dump flag log suppression * convert.py: named instance logging * convert.py: use explicit logger id string * convert.py: convert extra print() to named logger * convert.py: sys.stderr.write --> logger.error * *.py: Convert all python scripts to use logging module * requirements.txt: remove extra line * flake8: update flake8 ignore and exclude to match ci settings * gh-actions: add flake8-no-print to flake8 lint step * pre-commit: add flake8-no-print to flake8 and also update pre-commit version * convert-hf-to-gguf.py: print() to logger conversion * *.py: logging basiconfig refactor to use conditional expression * *.py: removed commented out logging * fixup! *.py: logging basiconfig refactor to use conditional expression * constant.py: logger.error then exit should be a raise exception instead * *.py: Convert logger error and sys.exit() into a raise exception (for atypical error) * gguf-convert-endian.py: refactor convert_byteorder() to use tqdm progressbar * verify-checksum-model.py: This is the result of the program, it should be printed to stdout. * compare-llama-bench.py: add blank line for readability during missing repo response * reader.py: read_gguf_file() use print() over logging * convert.py: warning goes to stderr and won't hurt the dump output * gguf-dump.py: dump_metadata() should print to stdout * convert-hf-to-gguf.py: print --> logger.debug or ValueError() * verify-checksum-models.py: use print() for printing table * *.py: refactor logging.basicConfig() * gguf-py/gguf/*.py: use __name__ as logger name Since they will be imported and not run directly. * python-lint.yml: use .flake8 file instead * constants.py: logger no longer required * convert-hf-to-gguf.py: add additional logging * convert-hf-to-gguf.py: print() --> logger * *.py: fix flake8 warnings * revert changes to convert-hf-to-gguf.py for get_name() * convert-hf-to-gguf-update.py: use triple quoted f-string instead * *.py: accidentally corrected the wrong line * *.py: add compilade warning suggestions and style fixes
2024-05-03 19:36:41 +00:00
raise ValueError("gguf: can not find ctx length parameter.")
llama : add support for Orion-14B (#5118) * add support for Orion-14B(https://huggingface.co/OrionStarAI/Orion-14B-Chat) * flake8 support * Update llama.cpp Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * Update llama.cpp Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * Update llama.cpp Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * Update llama.cpp Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * Update llama.cpp Co-authored-by: slaren <slarengh@gmail.com> * Update llama.cpp * Update llama.cpp --------- Co-authored-by: lixiaopu <lixiaopu@cmcm.com> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> Co-authored-by: slaren <slarengh@gmail.com>
2024-01-28 08:00:30 +00:00
self.gguf_writer.add_file_type(self.ftype)
self.gguf_writer.add_name(self.dir_model.name)
self.gguf_writer.add_source_hf_repo(hf_repo)
self.gguf_writer.add_tensor_data_layout("Meta AI original pth")
self.gguf_writer.add_context_length(ctx_length)
self.gguf_writer.add_embedding_length(self.hparams["hidden_size"])
self.gguf_writer.add_block_count(block_count)
self.gguf_writer.add_feed_forward_length(self.hparams["intermediate_size"])
self.gguf_writer.add_head_count(head_count)
self.gguf_writer.add_head_count_kv(head_count_kv)
py : minor fixes (#5668)
2024-02-22 18:13:25 +00:00
# note: config provides rms norm but it is actually layer norm
# ref: https://huggingface.co/OrionStarAI/Orion-14B-Chat/blob/276a17221ce42beb45f66fac657a41540e71f4f5/modeling_orion.py#L570-L571
llama : add support for Orion-14B (#5118) * add support for Orion-14B(https://huggingface.co/OrionStarAI/Orion-14B-Chat) * flake8 support * Update llama.cpp Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * Update llama.cpp Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * Update llama.cpp Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * Update llama.cpp Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * Update llama.cpp Co-authored-by: slaren <slarengh@gmail.com> * Update llama.cpp * Update llama.cpp --------- Co-authored-by: lixiaopu <lixiaopu@cmcm.com> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> Co-authored-by: slaren <slarengh@gmail.com>
2024-01-28 08:00:30 +00:00
self.gguf_writer.add_layer_norm_eps(self.hparams["rms_norm_eps"])
convert-hf : make model class definitions self-contained (#5825)
2024-03-02 17:21:47 +00:00
@Model.register("BaichuanForCausalLM", "BaiChuanForCausalLM")
scripts: Generalize convert scripts (#3838) * Replace convert-*-hf-to-gguf.py files with convert-hf-to-gguf.py
2023-11-09 10:09:29 +00:00
class BaichuanModel(Model):
convert-hf : make model class definitions self-contained (#5825)
2024-03-02 17:21:47 +00:00
model_arch = gguf.MODEL_ARCH.BAICHUAN
scripts: Generalize convert scripts (#3838) * Replace convert-*-hf-to-gguf.py files with convert-hf-to-gguf.py
2023-11-09 10:09:29 +00:00
def set_vocab(self):
self._set_vocab_sentencepiece()
def set_gguf_parameters(self):
block_count = self.hparams["num_hidden_layers"]
head_count = self.hparams["num_attention_heads"]
head_count_kv = self.hparams.get("num_key_value_heads", head_count)
hf_repo = self.hparams.get("_name_or_path", "")
ctx_length = 0
if "max_sequence_length" in self.hparams:
ctx_length = self.hparams["max_sequence_length"]
elif "max_position_embeddings" in self.hparams:
ctx_length = self.hparams["max_position_embeddings"]
elif "model_max_length" in self.hparams:
ctx_length = self.hparams["model_max_length"]
else:
convert.py : add python logging instead of print() (#6511) * convert.py: add python logging instead of print() * convert.py: verbose flag takes priority over dump flag log suppression * convert.py: named instance logging * convert.py: use explicit logger id string * convert.py: convert extra print() to named logger * convert.py: sys.stderr.write --> logger.error * *.py: Convert all python scripts to use logging module * requirements.txt: remove extra line * flake8: update flake8 ignore and exclude to match ci settings * gh-actions: add flake8-no-print to flake8 lint step * pre-commit: add flake8-no-print to flake8 and also update pre-commit version * convert-hf-to-gguf.py: print() to logger conversion * *.py: logging basiconfig refactor to use conditional expression * *.py: removed commented out logging * fixup! *.py: logging basiconfig refactor to use conditional expression * constant.py: logger.error then exit should be a raise exception instead * *.py: Convert logger error and sys.exit() into a raise exception (for atypical error) * gguf-convert-endian.py: refactor convert_byteorder() to use tqdm progressbar * verify-checksum-model.py: This is the result of the program, it should be printed to stdout. * compare-llama-bench.py: add blank line for readability during missing repo response * reader.py: read_gguf_file() use print() over logging * convert.py: warning goes to stderr and won't hurt the dump output * gguf-dump.py: dump_metadata() should print to stdout * convert-hf-to-gguf.py: print --> logger.debug or ValueError() * verify-checksum-models.py: use print() for printing table * *.py: refactor logging.basicConfig() * gguf-py/gguf/*.py: use __name__ as logger name Since they will be imported and not run directly. * python-lint.yml: use .flake8 file instead * constants.py: logger no longer required * convert-hf-to-gguf.py: add additional logging * convert-hf-to-gguf.py: print() --> logger * *.py: fix flake8 warnings * revert changes to convert-hf-to-gguf.py for get_name() * convert-hf-to-gguf-update.py: use triple quoted f-string instead * *.py: accidentally corrected the wrong line * *.py: add compilade warning suggestions and style fixes
2024-05-03 19:36:41 +00:00
raise ValueError("gguf: can not find ctx length parameter.")
scripts: Generalize convert scripts (#3838) * Replace convert-*-hf-to-gguf.py files with convert-hf-to-gguf.py
2023-11-09 10:09:29 +00:00
self.gguf_writer.add_name(self.dir_model.name)
self.gguf_writer.add_source_hf_repo(hf_repo)
self.gguf_writer.add_tensor_data_layout("Meta AI original pth")
self.gguf_writer.add_context_length(ctx_length)
self.gguf_writer.add_embedding_length(self.hparams["hidden_size"])
self.gguf_writer.add_block_count(block_count)
self.gguf_writer.add_feed_forward_length(self.hparams["intermediate_size"])
self.gguf_writer.add_rope_dimension_count(self.hparams["hidden_size"] // self.hparams["num_attention_heads"])
self.gguf_writer.add_head_count(head_count)
self.gguf_writer.add_head_count_kv(head_count_kv)
self.gguf_writer.add_layer_norm_rms_eps(self.hparams["rms_norm_eps"])
convert-hf : support direct Q8_0 conversion (#7234) * convert-hf : support q8_0 conversion * convert-hf : add missing ftype This was messing with the checksums otherwise. * convert-hf : add missing ftype to Baichuan and Xverse I didn't notice these on my first pass.
2024-05-13 18:10:51 +00:00
self.gguf_writer.add_file_type(self.ftype)
scripts: Generalize convert scripts (#3838) * Replace convert-*-hf-to-gguf.py files with convert-hf-to-gguf.py
2023-11-09 10:09:29 +00:00
if self.hparams.get("rope_scaling") is not None and "factor" in self.hparams["rope_scaling"]:
if self.hparams["rope_scaling"].get("type") == "linear":
self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LINEAR)
self.gguf_writer.add_rope_scaling_factor(self.hparams["rope_scaling"]["factor"])
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
scripts: Generalize convert scripts (#3838) * Replace convert-*-hf-to-gguf.py files with convert-hf-to-gguf.py
2023-11-09 10:09:29 +00:00
head_count = self.hparams["num_attention_heads"]
head_count_kv = self.hparams.get("num_key_value_heads", head_count)
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
tensors: list[tuple[str, Tensor]] = []
if bid is not None and name == f"model.layers.{bid}.self_attn.W_pack.weight":
logger.info(f"Unpacking and permuting layer {bid}")
tensors = [
(self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_Q, bid),
self._reverse_hf_permute_part(data_torch, 0, head_count, head_count)),
(self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_K, bid),
self._reverse_hf_permute_part(data_torch, 1, head_count, head_count_kv)),
(self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_V, bid),
self._reverse_hf_part(data_torch, 2)),
]
else:
tensors = [(self.map_tensor_name(name), data_torch)]
scripts: Generalize convert scripts (#3838) * Replace convert-*-hf-to-gguf.py files with convert-hf-to-gguf.py
2023-11-09 10:09:29 +00:00
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
return tensors
scripts: Generalize convert scripts (#3838) * Replace convert-*-hf-to-gguf.py files with convert-hf-to-gguf.py
2023-11-09 10:09:29 +00:00
def _reverse_hf_permute(self, weights: Tensor, n_head: int, n_kv_head: int | None = None) -> Tensor:
if n_kv_head is not None and n_head != n_kv_head:
n_head //= n_kv_head
return (
weights.reshape(n_head, 2, weights.shape[0] // n_head // 2, *weights.shape[1:])
.swapaxes(1, 2)
.reshape(weights.shape)
)
def _reverse_hf_permute_part(
self, weights: Tensor, n_part: int, n_head: int, n_head_kv: int | None = None,
) -> Tensor:
r = weights.shape[0] // 3
return self._reverse_hf_permute(weights[r * n_part:r * n_part + r, ...], n_head, n_head_kv)
def _reverse_hf_part(self, weights: Tensor, n_part: int) -> Tensor:
r = weights.shape[0] // 3
return weights[r * n_part:r * n_part + r, ...]
[Model] Add support for xverse (#6301) * Support xverse model convert to gguf format. * 1. Convert xverse models to gguf; 2. Add LLM_ARCH_XVERSE inference in llama.cpp; 3. Add xverse item in Supported models in README.md; * * gguf-py: remove redundant logs * llama: remove the init_mapping_prefetch custom parameter * llama.cpp: Include the changes from #6122 to exclude the unused outputs of the last layers. * - Fix format issues - Remove duplicate set kqv_out to llm_build_kv * Update llama.cpp --------- Co-authored-by: willhe <willhe@xverse.cn> Co-authored-by: willhe <hexin@xverse.cn>
2024-03-29 13:37:03 +00:00
@Model.register("XverseForCausalLM")
class XverseModel(Model):
model_arch = gguf.MODEL_ARCH.XVERSE
def set_vocab(self):
assert (self.dir_model / "tokenizer.json").is_file()
dir_model = self.dir_model
hparams = self.hparams
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
tokens: list[bytes] = []
[Model] Add support for xverse (#6301) * Support xverse model convert to gguf format. * 1. Convert xverse models to gguf; 2. Add LLM_ARCH_XVERSE inference in llama.cpp; 3. Add xverse item in Supported models in README.md; * * gguf-py: remove redundant logs * llama: remove the init_mapping_prefetch custom parameter * llama.cpp: Include the changes from #6122 to exclude the unused outputs of the last layers. * - Fix format issues - Remove duplicate set kqv_out to llm_build_kv * Update llama.cpp --------- Co-authored-by: willhe <willhe@xverse.cn> Co-authored-by: willhe <hexin@xverse.cn>
2024-03-29 13:37:03 +00:00
toktypes: list[int] = []
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(dir_model)
vocab_size = hparams.get("vocab_size", len(tokenizer.vocab))
assert max(tokenizer.vocab.values()) < vocab_size
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
reverse_vocab: dict[int, str] = {id_: encoded_tok for encoded_tok, id_ in tokenizer.vocab.items()}
[Model] Add support for xverse (#6301) * Support xverse model convert to gguf format. * 1. Convert xverse models to gguf; 2. Add LLM_ARCH_XVERSE inference in llama.cpp; 3. Add xverse item in Supported models in README.md; * * gguf-py: remove redundant logs * llama: remove the init_mapping_prefetch custom parameter * llama.cpp: Include the changes from #6122 to exclude the unused outputs of the last layers. * - Fix format issues - Remove duplicate set kqv_out to llm_build_kv * Update llama.cpp --------- Co-authored-by: willhe <willhe@xverse.cn> Co-authored-by: willhe <hexin@xverse.cn>
2024-03-29 13:37:03 +00:00
added_vocab = tokenizer.get_added_vocab()
for token_id in range(vocab_size):
token_text = reverse_vocab[token_id].encode('utf-8')
# replace "\x00" to string with length > 0
if token_text == b"\x00":
toktype = gguf.TokenType.BYTE # special
token_text = f"<{token_text}>".encode('utf-8')
elif re.fullmatch(br"<0x[0-9A-Fa-f]{2}>", token_text):
toktype = gguf.TokenType.BYTE # special
elif reverse_vocab[token_id] in added_vocab:
if tokenizer.added_tokens_decoder[token_id].special:
toktype = gguf.TokenType.CONTROL
else:
toktype = gguf.TokenType.USER_DEFINED
else:
toktype = gguf.TokenType.NORMAL
tokens.append(token_text)
toktypes.append(toktype)
self.gguf_writer.add_tokenizer_model("llama")
llama : fix BPE pre-tokenization (#6920) * merged the changes from deepseeker models to main branch * Moved regex patterns to unicode.cpp and updated unicode.h * Moved header files * Resolved issues * added and refactored unicode_regex_split and related functions * Updated/merged the deepseek coder pr * Refactored code * Adding unicode regex mappings * Adding unicode regex function * Added needed functionality, testing remains * Fixed issues * Fixed issue with gpt2 regex custom preprocessor * unicode : fix? unicode_wstring_to_utf8 * lint : fix whitespaces * tests : add tokenizer tests for numbers * unicode : remove redundant headers * tests : remove and rename tokenizer test scripts * tests : add sample usage * gguf-py : reader prints warnings on duplicate keys * llama : towards llama3 tokenization support (wip) * unicode : shot in the dark to fix tests on Windows * unicode : first try custom implementations * convert : add "tokenizer.ggml.pre" GGUF KV (wip) * llama : use new pre-tokenizer type * convert : fix pre-tokenizer type writing * lint : fix * make : add test-tokenizer-0-llama-v3 * wip * models : add llama v3 vocab file * llama : adapt punctuation regex + add llama 3 regex * minor * unicode : set bomb * unicode : set bomb * unicode : always use std::wregex * unicode : support \p{N}, \p{L} and \p{P} natively * unicode : try fix windows * unicode : category support via std::regex * unicode : clean-up * unicode : simplify * convert : add convert-hf-to-gguf-update.py ggml-ci * lint : update * convert : add falcon ggml-ci * unicode : normalize signatures * lint : fix * lint : fix * convert : remove unused functions * convert : add comments * convert : exercise contractions ggml-ci * lint : fix * cmake : refactor test targets * tests : refactor vocab tests ggml-ci * tests : add more vocabs and tests ggml-ci * unicode : cleanup * scripts : ignore new update script in check-requirements.sh * models : add phi-3, mpt, gpt-2, starcoder * tests : disable obsolete ggml-ci * tests : use faster bpe test ggml-ci * llama : more prominent warning for old BPE models * tests : disable test-tokenizer-1-bpe due to slowness ggml-ci --------- Co-authored-by: Jaggzh <jaggz.h@gmail.com> Co-authored-by: Kazim Abrar Mahi <kazimabrarmahi135@gmail.com>
2024-04-29 13:58:41 +00:00
self.gguf_writer.add_tokenizer_pre("default")
[Model] Add support for xverse (#6301) * Support xverse model convert to gguf format. * 1. Convert xverse models to gguf; 2. Add LLM_ARCH_XVERSE inference in llama.cpp; 3. Add xverse item in Supported models in README.md; * * gguf-py: remove redundant logs * llama: remove the init_mapping_prefetch custom parameter * llama.cpp: Include the changes from #6122 to exclude the unused outputs of the last layers. * - Fix format issues - Remove duplicate set kqv_out to llm_build_kv * Update llama.cpp --------- Co-authored-by: willhe <willhe@xverse.cn> Co-authored-by: willhe <hexin@xverse.cn>
2024-03-29 13:37:03 +00:00
self.gguf_writer.add_token_list(tokens)
self.gguf_writer.add_token_types(toktypes)
special_vocab = gguf.SpecialVocab(dir_model, n_vocab=len(tokens))
special_vocab.add_to_gguf(self.gguf_writer)
def set_gguf_parameters(self):
block_count = self.hparams["num_hidden_layers"]
head_count = self.hparams["num_attention_heads"]
head_count_kv = self.hparams.get("num_key_value_heads", head_count)
hf_repo = self.hparams.get("_name_or_path", "")
ctx_length = 0
if "max_sequence_length" in self.hparams:
ctx_length = self.hparams["max_sequence_length"]
elif "max_position_embeddings" in self.hparams:
ctx_length = self.hparams["max_position_embeddings"]
elif "model_max_length" in self.hparams:
ctx_length = self.hparams["model_max_length"]
else:
convert.py : add python logging instead of print() (#6511) * convert.py: add python logging instead of print() * convert.py: verbose flag takes priority over dump flag log suppression * convert.py: named instance logging * convert.py: use explicit logger id string * convert.py: convert extra print() to named logger * convert.py: sys.stderr.write --> logger.error * *.py: Convert all python scripts to use logging module * requirements.txt: remove extra line * flake8: update flake8 ignore and exclude to match ci settings * gh-actions: add flake8-no-print to flake8 lint step * pre-commit: add flake8-no-print to flake8 and also update pre-commit version * convert-hf-to-gguf.py: print() to logger conversion * *.py: logging basiconfig refactor to use conditional expression * *.py: removed commented out logging * fixup! *.py: logging basiconfig refactor to use conditional expression * constant.py: logger.error then exit should be a raise exception instead * *.py: Convert logger error and sys.exit() into a raise exception (for atypical error) * gguf-convert-endian.py: refactor convert_byteorder() to use tqdm progressbar * verify-checksum-model.py: This is the result of the program, it should be printed to stdout. * compare-llama-bench.py: add blank line for readability during missing repo response * reader.py: read_gguf_file() use print() over logging * convert.py: warning goes to stderr and won't hurt the dump output * gguf-dump.py: dump_metadata() should print to stdout * convert-hf-to-gguf.py: print --> logger.debug or ValueError() * verify-checksum-models.py: use print() for printing table * *.py: refactor logging.basicConfig() * gguf-py/gguf/*.py: use __name__ as logger name Since they will be imported and not run directly. * python-lint.yml: use .flake8 file instead * constants.py: logger no longer required * convert-hf-to-gguf.py: add additional logging * convert-hf-to-gguf.py: print() --> logger * *.py: fix flake8 warnings * revert changes to convert-hf-to-gguf.py for get_name() * convert-hf-to-gguf-update.py: use triple quoted f-string instead * *.py: accidentally corrected the wrong line * *.py: add compilade warning suggestions and style fixes
2024-05-03 19:36:41 +00:00
raise ValueError("gguf: can not find ctx length parameter.")
[Model] Add support for xverse (#6301) * Support xverse model convert to gguf format. * 1. Convert xverse models to gguf; 2. Add LLM_ARCH_XVERSE inference in llama.cpp; 3. Add xverse item in Supported models in README.md; * * gguf-py: remove redundant logs * llama: remove the init_mapping_prefetch custom parameter * llama.cpp: Include the changes from #6122 to exclude the unused outputs of the last layers. * - Fix format issues - Remove duplicate set kqv_out to llm_build_kv * Update llama.cpp --------- Co-authored-by: willhe <willhe@xverse.cn> Co-authored-by: willhe <hexin@xverse.cn>
2024-03-29 13:37:03 +00:00
self.gguf_writer.add_name(self.dir_model.name)
self.gguf_writer.add_source_hf_repo(hf_repo)
self.gguf_writer.add_tensor_data_layout("Meta AI original pth")
self.gguf_writer.add_context_length(ctx_length)
self.gguf_writer.add_embedding_length(self.hparams["hidden_size"])
self.gguf_writer.add_block_count(block_count)
self.gguf_writer.add_feed_forward_length(self.hparams["intermediate_size"])
self.gguf_writer.add_rope_dimension_count(self.hparams["hidden_size"] // self.hparams["num_attention_heads"])
self.gguf_writer.add_head_count(head_count)
self.gguf_writer.add_head_count_kv(head_count_kv)
self.gguf_writer.add_layer_norm_rms_eps(self.hparams["rms_norm_eps"])
convert-hf : support direct Q8_0 conversion (#7234) * convert-hf : support q8_0 conversion * convert-hf : add missing ftype This was messing with the checksums otherwise. * convert-hf : add missing ftype to Baichuan and Xverse I didn't notice these on my first pass.
2024-05-13 18:10:51 +00:00
self.gguf_writer.add_file_type(self.ftype)
[Model] Add support for xverse (#6301) * Support xverse model convert to gguf format. * 1. Convert xverse models to gguf; 2. Add LLM_ARCH_XVERSE inference in llama.cpp; 3. Add xverse item in Supported models in README.md; * * gguf-py: remove redundant logs * llama: remove the init_mapping_prefetch custom parameter * llama.cpp: Include the changes from #6122 to exclude the unused outputs of the last layers. * - Fix format issues - Remove duplicate set kqv_out to llm_build_kv * Update llama.cpp --------- Co-authored-by: willhe <willhe@xverse.cn> Co-authored-by: willhe <hexin@xverse.cn>
2024-03-29 13:37:03 +00:00
if self.hparams.get("rope_scaling") is not None and "factor" in self.hparams["rope_scaling"]:
if self.hparams["rope_scaling"].get("type") == "linear":
self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LINEAR)
self.gguf_writer.add_rope_scaling_factor(self.hparams["rope_scaling"]["factor"])
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
del bid # unused
[Model] Add support for xverse (#6301) * Support xverse model convert to gguf format. * 1. Convert xverse models to gguf; 2. Add LLM_ARCH_XVERSE inference in llama.cpp; 3. Add xverse item in Supported models in README.md; * * gguf-py: remove redundant logs * llama: remove the init_mapping_prefetch custom parameter * llama.cpp: Include the changes from #6122 to exclude the unused outputs of the last layers. * - Fix format issues - Remove duplicate set kqv_out to llm_build_kv * Update llama.cpp --------- Co-authored-by: willhe <willhe@xverse.cn> Co-authored-by: willhe <hexin@xverse.cn>
2024-03-29 13:37:03 +00:00
head_count = self.hparams["num_attention_heads"]
head_count_kv = self.hparams.get("num_key_value_heads", head_count)
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
# HF models permute some of the tensors, so we need to undo that
if name.endswith("q_proj.weight"):
data_torch = self._reverse_hf_permute(data_torch, head_count, head_count)
if name.endswith("k_proj.weight"):
data_torch = self._reverse_hf_permute(data_torch, head_count, head_count_kv)
[Model] Add support for xverse (#6301) * Support xverse model convert to gguf format. * 1. Convert xverse models to gguf; 2. Add LLM_ARCH_XVERSE inference in llama.cpp; 3. Add xverse item in Supported models in README.md; * * gguf-py: remove redundant logs * llama: remove the init_mapping_prefetch custom parameter * llama.cpp: Include the changes from #6122 to exclude the unused outputs of the last layers. * - Fix format issues - Remove duplicate set kqv_out to llm_build_kv * Update llama.cpp --------- Co-authored-by: willhe <willhe@xverse.cn> Co-authored-by: willhe <hexin@xverse.cn>
2024-03-29 13:37:03 +00:00
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
return [(self.map_tensor_name(name), data_torch)]
[Model] Add support for xverse (#6301) * Support xverse model convert to gguf format. * 1. Convert xverse models to gguf; 2. Add LLM_ARCH_XVERSE inference in llama.cpp; 3. Add xverse item in Supported models in README.md; * * gguf-py: remove redundant logs * llama: remove the init_mapping_prefetch custom parameter * llama.cpp: Include the changes from #6122 to exclude the unused outputs of the last layers. * - Fix format issues - Remove duplicate set kqv_out to llm_build_kv * Update llama.cpp --------- Co-authored-by: willhe <willhe@xverse.cn> Co-authored-by: willhe <hexin@xverse.cn>
2024-03-29 13:37:03 +00:00
def _reverse_hf_permute(self, weights: Tensor, n_head: int, n_kv_head: int | None = None) -> Tensor:
if n_kv_head is not None and n_head != n_kv_head:
n_head //= n_kv_head
return (
weights.reshape(n_head, 2, weights.shape[0] // n_head // 2, *weights.shape[1:])
.swapaxes(1, 2)
.reshape(weights.shape)
)
convert-hf : make model class definitions self-contained (#5825)
2024-03-02 17:21:47 +00:00
@Model.register("FalconForCausalLM", "RWForCausalLM")
scripts: Generalize convert scripts (#3838) * Replace convert-*-hf-to-gguf.py files with convert-hf-to-gguf.py
2023-11-09 10:09:29 +00:00
class FalconModel(Model):
convert-hf : make model class definitions self-contained (#5825)
2024-03-02 17:21:47 +00:00
model_arch = gguf.MODEL_ARCH.FALCON
scripts: Generalize convert scripts (#3838) * Replace convert-*-hf-to-gguf.py files with convert-hf-to-gguf.py
2023-11-09 10:09:29 +00:00
def set_gguf_parameters(self):
block_count = self.hparams.get("num_hidden_layers")
if block_count is None:
block_count = self.hparams["n_layer"] # old name
n_head = self.hparams.get("num_attention_heads")
if n_head is None:
n_head = self.hparams["n_head"] # old name
n_head_kv = self.hparams.get("num_kv_heads")
if n_head_kv is None:
n_head_kv = self.hparams.get("n_head_kv", 1) # old name
self.gguf_writer.add_name("Falcon")
self.gguf_writer.add_context_length(2048) # not in config.json
self.gguf_writer.add_tensor_data_layout("jploski") # qkv tensor transform
self.gguf_writer.add_embedding_length(self.hparams["hidden_size"])
self.gguf_writer.add_feed_forward_length(4 * self.hparams["hidden_size"])
self.gguf_writer.add_block_count(block_count)
self.gguf_writer.add_head_count(n_head)
self.gguf_writer.add_head_count_kv(n_head_kv)
self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_epsilon"])
self.gguf_writer.add_file_type(self.ftype)
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
del bid # unused
scripts: Generalize convert scripts (#3838) * Replace convert-*-hf-to-gguf.py files with convert-hf-to-gguf.py
2023-11-09 10:09:29 +00:00
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
# QKV tensor transform
# The original query_key_value tensor contains n_head_kv "kv groups",
# each consisting of n_head/n_head_kv query weights followed by one key
# and one value weight (shared by all query heads in the kv group).
# This layout makes it a big pain to work with in GGML.
# So we rearrange them here,, so that we have n_head query weights
# followed by n_head_kv key weights followed by n_head_kv value weights,
# in contiguous fashion.
# ref: https://github.com/jploski/ggml/blob/falcon40b/examples/falcon/convert-hf-to-ggml.py
scripts: Generalize convert scripts (#3838) * Replace convert-*-hf-to-gguf.py files with convert-hf-to-gguf.py
2023-11-09 10:09:29 +00:00
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
if "query_key_value" in name:
n_head = self.find_hparam(["num_attention_heads", "n_head"])
n_head_kv = self.find_hparam(["num_kv_heads", "n_head_kv"], optional=True) or 1
head_dim = self.hparams["hidden_size"] // n_head
scripts: Generalize convert scripts (#3838) * Replace convert-*-hf-to-gguf.py files with convert-hf-to-gguf.py
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convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
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qkv = data_torch.view(n_head_kv, n_head // n_head_kv + 2, head_dim, head_dim * n_head)
q = qkv[:, :-2].reshape(n_head * head_dim, head_dim * n_head)
k = qkv[:, [-2]].reshape(n_head_kv * head_dim, head_dim * n_head)
v = qkv[:, [-1]].reshape(n_head_kv * head_dim, head_dim * n_head)
data_torch = torch.cat((q, k, v)).reshape_as(data_torch)
scripts: Generalize convert scripts (#3838) * Replace convert-*-hf-to-gguf.py files with convert-hf-to-gguf.py
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convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
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return [(self.map_tensor_name(name), data_torch)]
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convert-hf : make model class definitions self-contained (#5825)
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@Model.register("GPTBigCodeForCausalLM")
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class StarCoderModel(Model):
convert-hf : make model class definitions self-contained (#5825)
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model_arch = gguf.MODEL_ARCH.STARCODER
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def set_gguf_parameters(self):
block_count = self.hparams["n_layer"]
self.gguf_writer.add_name("StarCoder")
self.gguf_writer.add_context_length(self.hparams["n_positions"])
self.gguf_writer.add_embedding_length(self.hparams["n_embd"])
self.gguf_writer.add_feed_forward_length(4 * self.hparams["n_embd"])
self.gguf_writer.add_block_count(block_count)
self.gguf_writer.add_head_count(self.hparams["n_head"])
self.gguf_writer.add_head_count_kv(1)
self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_epsilon"])
self.gguf_writer.add_file_type(self.ftype)
convert-hf : make model class definitions self-contained (#5825)
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@Model.register("GPTRefactForCausalLM")
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class RefactModel(Model):
convert-hf : make model class definitions self-contained (#5825)
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model_arch = gguf.MODEL_ARCH.REFACT
ggml : full ALiBi support (#7192) * ggml : full ALiBi support * ggml : update ggml_soft_max_ext() CUDA, SYCL * ggml : ggml_flash_attn_ext() support ALiBi (CPU) * ggml : ggml_flash_attn_ext() support ALiBi (Metal) * ggml : fix warning * ggml : ggml_flash_attn_ext() support ALiBi (CUDA) ggml-ci * ggml : fix assert message * vulkan : add dev notes * ggml : require mask when using ALiBi ggml-ci * convert : fix convert for refact models
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def set_vocab(self):
super().set_vocab()
# TODO: how to determine special FIM tokens automatically?
special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=False,
special_token_types = ['prefix', 'suffix', 'middle', 'fsep', 'eot'])
special_vocab._set_special_token("prefix", 1)
special_vocab._set_special_token("suffix", 3)
special_vocab._set_special_token("middle", 2)
special_vocab._set_special_token("fsep", 4) # is this correct?
special_vocab.add_to_gguf(self.gguf_writer)
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def set_gguf_parameters(self):
hidden_dim = self.hparams["n_embd"]
inner_dim = 4 * hidden_dim
hidden_dim = int(2 * inner_dim / 3)
multiple_of = 256
ff_dim = multiple_of * ((hidden_dim + multiple_of - 1) // multiple_of)
block_count = self.hparams["n_layer"]
self.gguf_writer.add_name("Refact")
# refact uses Alibi. So this is from config.json which might be used by training.
self.gguf_writer.add_context_length(self.hparams["n_positions"])
self.gguf_writer.add_embedding_length(self.hparams["n_embd"])
self.gguf_writer.add_feed_forward_length(ff_dim)
self.gguf_writer.add_block_count(block_count)
self.gguf_writer.add_head_count(self.hparams["n_head"])
self.gguf_writer.add_head_count_kv(1)
self.gguf_writer.add_layer_norm_rms_eps(self.hparams["layer_norm_epsilon"])
self.gguf_writer.add_file_type(self.ftype)
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
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def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
scripts: Generalize convert scripts (#3838) * Replace convert-*-hf-to-gguf.py files with convert-hf-to-gguf.py
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hidden_dim = self.hparams["n_embd"]
inner_dim = 4 * hidden_dim
hidden_dim = int(2 * inner_dim / 3)
multiple_of = 256
ff_dim = multiple_of * ((hidden_dim + multiple_of - 1) // multiple_of)
n_head = self.hparams["n_head"]
n_head_kv = 1
head_dim = self.hparams["n_embd"] // n_head
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
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tensors: list[tuple[str, Tensor]] = []
scripts: Generalize convert scripts (#3838) * Replace convert-*-hf-to-gguf.py files with convert-hf-to-gguf.py
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convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
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if bid is not None:
if name == f"transformer.h.{bid}.attn.kv.weight":
tensors.append((self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_K, bid), data_torch[:n_head_kv * head_dim]))
tensors.append((self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_V, bid), data_torch[n_head_kv * head_dim:]))
elif name == f"transformer.h.{bid}.attn.q.weight":
tensors.append((self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_Q, bid), data_torch))
elif name == f"transformer.h.{bid}.mlp.gate_up_proj.weight":
tensors.append((self.format_tensor_name(gguf.MODEL_TENSOR.FFN_GATE, bid), data_torch[:ff_dim]))
tensors.append((self.format_tensor_name(gguf.MODEL_TENSOR.FFN_UP, bid), data_torch[ff_dim:]))
scripts: Generalize convert scripts (#3838) * Replace convert-*-hf-to-gguf.py files with convert-hf-to-gguf.py
2023-11-09 10:09:29 +00:00
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
if len(tensors) == 0:
tensors.append((self.map_tensor_name(name), data_torch))
scripts: Generalize convert scripts (#3838) * Replace convert-*-hf-to-gguf.py files with convert-hf-to-gguf.py
2023-11-09 10:09:29 +00:00
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
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return tensors
scripts: Generalize convert scripts (#3838) * Replace convert-*-hf-to-gguf.py files with convert-hf-to-gguf.py
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convert-hf : make model class definitions self-contained (#5825)
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@Model.register("StableLmForCausalLM", "StableLMEpochForCausalLM", "LlavaStableLMEpochForCausalLM")
stablelm : StableLM support (#3586) * Add support for stablelm-3b-4e1t * Supports GPU offloading of (n-1) layers
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class StableLMModel(Model):
convert-hf : make model class definitions self-contained (#5825)
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model_arch = gguf.MODEL_ARCH.STABLELM
llama : support StableLM 2 1.6B (#5052) * llama : support StableLM 2 1.6B * convert : fix Qwen's set_vocab wrongly naming all special tokens [PAD{id}] * convert : refactor Qwen's set_vocab to use it for StableLM 2 too * nix : add tiktoken to llama-python-extra * convert : use presence of tokenizer.json to determine StableLM tokenizer loader It's a less arbitrary heuristic than the vocab size.
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def set_vocab(self):
if (self.dir_model / "tokenizer.json").is_file():
self._set_vocab_gpt2()
else:
# StableLM 2 1.6B uses a vocab in a similar format to Qwen's vocab
self._set_vocab_qwen()
stablelm : StableLM support (#3586) * Add support for stablelm-3b-4e1t * Supports GPU offloading of (n-1) layers
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def set_gguf_parameters(self):
hparams = self.hparams
block_count = hparams["num_hidden_layers"]
python : add check-requirements.sh and GitHub workflow (#4585) * python: add check-requirements.sh and GitHub workflow This script and workflow forces package versions to remain compatible across all convert*.py scripts, while allowing secondary convert scripts to import dependencies not wanted in convert.py. * Move requirements into ./requirements * Fail on "==" being used for package requirements (but can be suppressed) * Enforce "compatible release" syntax instead of == * Update workflow * Add upper version bound for transformers and protobuf * improve check-requirements.sh * small syntax change * don't remove venvs if nocleanup is passed * See if this fixes docker workflow * Move check-requirements.sh into ./scripts/ --------- Co-authored-by: Jared Van Bortel <jared@nomic.ai>
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self.gguf_writer.add_name(self.dir_model.name)
stablelm : StableLM support (#3586) * Add support for stablelm-3b-4e1t * Supports GPU offloading of (n-1) layers
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self.gguf_writer.add_context_length(hparams["max_position_embeddings"])
self.gguf_writer.add_embedding_length(hparams["hidden_size"])
self.gguf_writer.add_block_count(block_count)
self.gguf_writer.add_feed_forward_length(hparams["intermediate_size"])
py : fix StableLM conversion after config.json changes (#5703) * Fix issues during StableLM models conversion * Fix hard coded layer_norm_eps * Support layer_norm_eps for LlavaStableLM Co-authored-by: Jared Van Bortel <cebtenzzre@gmail.com> * Add missing parenthesis Co-authored-by: Jared Van Bortel <cebtenzzre@gmail.com> * Support rotary_factor for LlavaStableLM Co-authored-by: Jared Van Bortel <cebtenzzre@gmail.com> * fix typo * Add StableLMEpochForCausalLM for safety Co-authored-by: compilade <113953597+compilade@users.noreply.github.com> * Add StableLMEpochForCausalLM for safety 2 Co-authored-by: compilade <113953597+compilade@users.noreply.github.com> --------- Co-authored-by: Jared Van Bortel <cebtenzzre@gmail.com> Co-authored-by: Jared Van Bortel <jared@nomic.ai> Co-authored-by: compilade <113953597+compilade@users.noreply.github.com>
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rotary_factor = self.find_hparam(["partial_rotary_factor", "rope_pct"])
self.gguf_writer.add_rope_dimension_count(int(rotary_factor * (hparams["hidden_size"] // hparams["num_attention_heads"])))
stablelm : StableLM support (#3586) * Add support for stablelm-3b-4e1t * Supports GPU offloading of (n-1) layers
2023-11-14 10:17:12 +00:00
self.gguf_writer.add_head_count(hparams["num_attention_heads"])
llama : add StableLM2 12B (#6635) * StableLM2 12B support for huggingface -> GGUF * StableLM12 tensormapping and constants * StableLM-2-12b model support * fix * Added 12B support * Removed autoformatting; resolved bug where model_arch was not selecting StableLM2 * Formatting * Do QK norm stacking in model conversion step * Converge StableLM and StableLM2 code to simplify graph construction * Fix accidental removal * Removed warnings * Revert formatter * Move QK norm stack to private function so it's easier to read * refactor stablelm graph builder to support 1.6, 3b and 12b more efficiently * Proper check for None type for new_name to avoid crash; formatting; revert change to base class `write_tensors()` * Format * Formatting * format Co-authored-by: compilade <git@compilade.net> * Fix incorrect check for K norm * space after commas; Keep indentation multiple of 4 spaces * Flake8 format * Removed unnecessary conditional branches * Removed unused comment * Fixed incorrect tensor passing * Format --------- Co-authored-by: compilade <git@compilade.net>
2024-04-16 15:48:35 +00:00
self.gguf_writer.add_head_count_kv(hparams["num_key_value_heads"])
stablelm : StableLM support (#3586) * Add support for stablelm-3b-4e1t * Supports GPU offloading of (n-1) layers
2023-11-14 10:17:12 +00:00
self.gguf_writer.add_parallel_residual(hparams["use_parallel_residual"] if "use_parallel_residual" in hparams else True)
py : fix StableLM conversion after config.json changes (#5703) * Fix issues during StableLM models conversion * Fix hard coded layer_norm_eps * Support layer_norm_eps for LlavaStableLM Co-authored-by: Jared Van Bortel <cebtenzzre@gmail.com> * Add missing parenthesis Co-authored-by: Jared Van Bortel <cebtenzzre@gmail.com> * Support rotary_factor for LlavaStableLM Co-authored-by: Jared Van Bortel <cebtenzzre@gmail.com> * fix typo * Add StableLMEpochForCausalLM for safety Co-authored-by: compilade <113953597+compilade@users.noreply.github.com> * Add StableLMEpochForCausalLM for safety 2 Co-authored-by: compilade <113953597+compilade@users.noreply.github.com> --------- Co-authored-by: Jared Van Bortel <cebtenzzre@gmail.com> Co-authored-by: Jared Van Bortel <jared@nomic.ai> Co-authored-by: compilade <113953597+compilade@users.noreply.github.com>
2024-02-25 09:54:04 +00:00
self.gguf_writer.add_layer_norm_eps(self.find_hparam(["layer_norm_eps", "norm_eps"]))
convert-hf : support direct Q8_0 conversion (#7234) * convert-hf : support q8_0 conversion * convert-hf : add missing ftype This was messing with the checksums otherwise. * convert-hf : add missing ftype to Baichuan and Xverse I didn't notice these on my first pass.
2024-05-13 18:10:51 +00:00
self.gguf_writer.add_file_type(self.ftype)
stablelm : StableLM support (#3586) * Add support for stablelm-3b-4e1t * Supports GPU offloading of (n-1) layers
2023-11-14 10:17:12 +00:00
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
_q_norms: list[dict[str, Tensor]] | None = None
_k_norms: list[dict[str, Tensor]] | None = None
llama : add StableLM2 12B (#6635) * StableLM2 12B support for huggingface -> GGUF * StableLM12 tensormapping and constants * StableLM-2-12b model support * fix * Added 12B support * Removed autoformatting; resolved bug where model_arch was not selecting StableLM2 * Formatting * Do QK norm stacking in model conversion step * Converge StableLM and StableLM2 code to simplify graph construction * Fix accidental removal * Removed warnings * Revert formatter * Move QK norm stack to private function so it's easier to read * refactor stablelm graph builder to support 1.6, 3b and 12b more efficiently * Proper check for None type for new_name to avoid crash; formatting; revert change to base class `write_tensors()` * Format * Formatting * format Co-authored-by: compilade <git@compilade.net> * Fix incorrect check for K norm * space after commas; Keep indentation multiple of 4 spaces * Flake8 format * Removed unnecessary conditional branches * Removed unused comment * Fixed incorrect tensor passing * Format --------- Co-authored-by: compilade <git@compilade.net>
2024-04-16 15:48:35 +00:00
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
n_head = self.hparams["num_attention_heads"]
n_kv_head = self.hparams["num_key_value_heads"]
llama : add StableLM2 12B (#6635) * StableLM2 12B support for huggingface -> GGUF * StableLM12 tensormapping and constants * StableLM-2-12b model support * fix * Added 12B support * Removed autoformatting; resolved bug where model_arch was not selecting StableLM2 * Formatting * Do QK norm stacking in model conversion step * Converge StableLM and StableLM2 code to simplify graph construction * Fix accidental removal * Removed warnings * Revert formatter * Move QK norm stack to private function so it's easier to read * refactor stablelm graph builder to support 1.6, 3b and 12b more efficiently * Proper check for None type for new_name to avoid crash; formatting; revert change to base class `write_tensors()` * Format * Formatting * format Co-authored-by: compilade <git@compilade.net> * Fix incorrect check for K norm * space after commas; Keep indentation multiple of 4 spaces * Flake8 format * Removed unnecessary conditional branches * Removed unused comment * Fixed incorrect tensor passing * Format --------- Co-authored-by: compilade <git@compilade.net>
2024-04-16 15:48:35 +00:00
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
if name.find("q_layernorm.norms") != -1:
assert bid is not None
llama : add StableLM2 12B (#6635) * StableLM2 12B support for huggingface -> GGUF * StableLM12 tensormapping and constants * StableLM-2-12b model support * fix * Added 12B support * Removed autoformatting; resolved bug where model_arch was not selecting StableLM2 * Formatting * Do QK norm stacking in model conversion step * Converge StableLM and StableLM2 code to simplify graph construction * Fix accidental removal * Removed warnings * Revert formatter * Move QK norm stack to private function so it's easier to read * refactor stablelm graph builder to support 1.6, 3b and 12b more efficiently * Proper check for None type for new_name to avoid crash; formatting; revert change to base class `write_tensors()` * Format * Formatting * format Co-authored-by: compilade <git@compilade.net> * Fix incorrect check for K norm * space after commas; Keep indentation multiple of 4 spaces * Flake8 format * Removed unnecessary conditional branches * Removed unused comment * Fixed incorrect tensor passing * Format --------- Co-authored-by: compilade <git@compilade.net>
2024-04-16 15:48:35 +00:00
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
if self._q_norms is None:
self._q_norms = [{} for _ in range(self.block_count)]
llama : add StableLM2 12B (#6635) * StableLM2 12B support for huggingface -> GGUF * StableLM12 tensormapping and constants * StableLM-2-12b model support * fix * Added 12B support * Removed autoformatting; resolved bug where model_arch was not selecting StableLM2 * Formatting * Do QK norm stacking in model conversion step * Converge StableLM and StableLM2 code to simplify graph construction * Fix accidental removal * Removed warnings * Revert formatter * Move QK norm stack to private function so it's easier to read * refactor stablelm graph builder to support 1.6, 3b and 12b more efficiently * Proper check for None type for new_name to avoid crash; formatting; revert change to base class `write_tensors()` * Format * Formatting * format Co-authored-by: compilade <git@compilade.net> * Fix incorrect check for K norm * space after commas; Keep indentation multiple of 4 spaces * Flake8 format * Removed unnecessary conditional branches * Removed unused comment * Fixed incorrect tensor passing * Format --------- Co-authored-by: compilade <git@compilade.net>
2024-04-16 15:48:35 +00:00
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
self._q_norms[bid][name] = data_torch
llama : add StableLM2 12B (#6635) * StableLM2 12B support for huggingface -> GGUF * StableLM12 tensormapping and constants * StableLM-2-12b model support * fix * Added 12B support * Removed autoformatting; resolved bug where model_arch was not selecting StableLM2 * Formatting * Do QK norm stacking in model conversion step * Converge StableLM and StableLM2 code to simplify graph construction * Fix accidental removal * Removed warnings * Revert formatter * Move QK norm stack to private function so it's easier to read * refactor stablelm graph builder to support 1.6, 3b and 12b more efficiently * Proper check for None type for new_name to avoid crash; formatting; revert change to base class `write_tensors()` * Format * Formatting * format Co-authored-by: compilade <git@compilade.net> * Fix incorrect check for K norm * space after commas; Keep indentation multiple of 4 spaces * Flake8 format * Removed unnecessary conditional branches * Removed unused comment * Fixed incorrect tensor passing * Format --------- Co-authored-by: compilade <git@compilade.net>
2024-04-16 15:48:35 +00:00
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
if len(self._q_norms[bid]) >= n_head:
return self._stack_qk_norm(bid, n_head, self._q_norms[bid], "q_layernorm")
else:
return []
llama : add StableLM2 12B (#6635) * StableLM2 12B support for huggingface -> GGUF * StableLM12 tensormapping and constants * StableLM-2-12b model support * fix * Added 12B support * Removed autoformatting; resolved bug where model_arch was not selecting StableLM2 * Formatting * Do QK norm stacking in model conversion step * Converge StableLM and StableLM2 code to simplify graph construction * Fix accidental removal * Removed warnings * Revert formatter * Move QK norm stack to private function so it's easier to read * refactor stablelm graph builder to support 1.6, 3b and 12b more efficiently * Proper check for None type for new_name to avoid crash; formatting; revert change to base class `write_tensors()` * Format * Formatting * format Co-authored-by: compilade <git@compilade.net> * Fix incorrect check for K norm * space after commas; Keep indentation multiple of 4 spaces * Flake8 format * Removed unnecessary conditional branches * Removed unused comment * Fixed incorrect tensor passing * Format --------- Co-authored-by: compilade <git@compilade.net>
2024-04-16 15:48:35 +00:00
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
if name.find("k_layernorm.norms") != -1:
assert bid is not None
llama : add StableLM2 12B (#6635) * StableLM2 12B support for huggingface -> GGUF * StableLM12 tensormapping and constants * StableLM-2-12b model support * fix * Added 12B support * Removed autoformatting; resolved bug where model_arch was not selecting StableLM2 * Formatting * Do QK norm stacking in model conversion step * Converge StableLM and StableLM2 code to simplify graph construction * Fix accidental removal * Removed warnings * Revert formatter * Move QK norm stack to private function so it's easier to read * refactor stablelm graph builder to support 1.6, 3b and 12b more efficiently * Proper check for None type for new_name to avoid crash; formatting; revert change to base class `write_tensors()` * Format * Formatting * format Co-authored-by: compilade <git@compilade.net> * Fix incorrect check for K norm * space after commas; Keep indentation multiple of 4 spaces * Flake8 format * Removed unnecessary conditional branches * Removed unused comment * Fixed incorrect tensor passing * Format --------- Co-authored-by: compilade <git@compilade.net>
2024-04-16 15:48:35 +00:00
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
if self._k_norms is None:
self._k_norms = [{} for _ in range(self.block_count)]
llama : add StableLM2 12B (#6635) * StableLM2 12B support for huggingface -> GGUF * StableLM12 tensormapping and constants * StableLM-2-12b model support * fix * Added 12B support * Removed autoformatting; resolved bug where model_arch was not selecting StableLM2 * Formatting * Do QK norm stacking in model conversion step * Converge StableLM and StableLM2 code to simplify graph construction * Fix accidental removal * Removed warnings * Revert formatter * Move QK norm stack to private function so it's easier to read * refactor stablelm graph builder to support 1.6, 3b and 12b more efficiently * Proper check for None type for new_name to avoid crash; formatting; revert change to base class `write_tensors()` * Format * Formatting * format Co-authored-by: compilade <git@compilade.net> * Fix incorrect check for K norm * space after commas; Keep indentation multiple of 4 spaces * Flake8 format * Removed unnecessary conditional branches * Removed unused comment * Fixed incorrect tensor passing * Format --------- Co-authored-by: compilade <git@compilade.net>
2024-04-16 15:48:35 +00:00
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
self._k_norms[bid][name] = data_torch
llama : add StableLM2 12B (#6635) * StableLM2 12B support for huggingface -> GGUF * StableLM12 tensormapping and constants * StableLM-2-12b model support * fix * Added 12B support * Removed autoformatting; resolved bug where model_arch was not selecting StableLM2 * Formatting * Do QK norm stacking in model conversion step * Converge StableLM and StableLM2 code to simplify graph construction * Fix accidental removal * Removed warnings * Revert formatter * Move QK norm stack to private function so it's easier to read * refactor stablelm graph builder to support 1.6, 3b and 12b more efficiently * Proper check for None type for new_name to avoid crash; formatting; revert change to base class `write_tensors()` * Format * Formatting * format Co-authored-by: compilade <git@compilade.net> * Fix incorrect check for K norm * space after commas; Keep indentation multiple of 4 spaces * Flake8 format * Removed unnecessary conditional branches * Removed unused comment * Fixed incorrect tensor passing * Format --------- Co-authored-by: compilade <git@compilade.net>
2024-04-16 15:48:35 +00:00
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
if len(self._k_norms[bid]) >= n_kv_head:
return self._stack_qk_norm(bid, n_kv_head, self._k_norms[bid], "k_layernorm")
else:
return []
llama : add StableLM2 12B (#6635) * StableLM2 12B support for huggingface -> GGUF * StableLM12 tensormapping and constants * StableLM-2-12b model support * fix * Added 12B support * Removed autoformatting; resolved bug where model_arch was not selecting StableLM2 * Formatting * Do QK norm stacking in model conversion step * Converge StableLM and StableLM2 code to simplify graph construction * Fix accidental removal * Removed warnings * Revert formatter * Move QK norm stack to private function so it's easier to read * refactor stablelm graph builder to support 1.6, 3b and 12b more efficiently * Proper check for None type for new_name to avoid crash; formatting; revert change to base class `write_tensors()` * Format * Formatting * format Co-authored-by: compilade <git@compilade.net> * Fix incorrect check for K norm * space after commas; Keep indentation multiple of 4 spaces * Flake8 format * Removed unnecessary conditional branches * Removed unused comment * Fixed incorrect tensor passing * Format --------- Co-authored-by: compilade <git@compilade.net>
2024-04-16 15:48:35 +00:00
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
return [(self.map_tensor_name(name), data_torch)]
llama : add StableLM2 12B (#6635) * StableLM2 12B support for huggingface -> GGUF * StableLM12 tensormapping and constants * StableLM-2-12b model support * fix * Added 12B support * Removed autoformatting; resolved bug where model_arch was not selecting StableLM2 * Formatting * Do QK norm stacking in model conversion step * Converge StableLM and StableLM2 code to simplify graph construction * Fix accidental removal * Removed warnings * Revert formatter * Move QK norm stack to private function so it's easier to read * refactor stablelm graph builder to support 1.6, 3b and 12b more efficiently * Proper check for None type for new_name to avoid crash; formatting; revert change to base class `write_tensors()` * Format * Formatting * format Co-authored-by: compilade <git@compilade.net> * Fix incorrect check for K norm * space after commas; Keep indentation multiple of 4 spaces * Flake8 format * Removed unnecessary conditional branches * Removed unused comment * Fixed incorrect tensor passing * Format --------- Co-authored-by: compilade <git@compilade.net>
2024-04-16 15:48:35 +00:00
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
def _stack_qk_norm(self, bid: int, n_head: int, norms: dict[str, Tensor], layer_name: str = "q_layernorm"):
datas: list[Tensor] = []
# extract the norms in order
for xid in range(n_head):
ename = f"model.layers.{bid}.self_attn.{layer_name}.norms.{xid}.weight"
datas.append(norms[ename])
del norms[ename]
data_torch = torch.stack(datas, dim=0)
llama : add StableLM2 12B (#6635) * StableLM2 12B support for huggingface -> GGUF * StableLM12 tensormapping and constants * StableLM-2-12b model support * fix * Added 12B support * Removed autoformatting; resolved bug where model_arch was not selecting StableLM2 * Formatting * Do QK norm stacking in model conversion step * Converge StableLM and StableLM2 code to simplify graph construction * Fix accidental removal * Removed warnings * Revert formatter * Move QK norm stack to private function so it's easier to read * refactor stablelm graph builder to support 1.6, 3b and 12b more efficiently * Proper check for None type for new_name to avoid crash; formatting; revert change to base class `write_tensors()` * Format * Formatting * format Co-authored-by: compilade <git@compilade.net> * Fix incorrect check for K norm * space after commas; Keep indentation multiple of 4 spaces * Flake8 format * Removed unnecessary conditional branches * Removed unused comment * Fixed incorrect tensor passing * Format --------- Co-authored-by: compilade <git@compilade.net>
2024-04-16 15:48:35 +00:00
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
merged_name = f"model.layers.{bid}.self_attn.{layer_name}.weight"
new_name = self.map_tensor_name(merged_name)
llama : add StableLM2 12B (#6635) * StableLM2 12B support for huggingface -> GGUF * StableLM12 tensormapping and constants * StableLM-2-12b model support * fix * Added 12B support * Removed autoformatting; resolved bug where model_arch was not selecting StableLM2 * Formatting * Do QK norm stacking in model conversion step * Converge StableLM and StableLM2 code to simplify graph construction * Fix accidental removal * Removed warnings * Revert formatter * Move QK norm stack to private function so it's easier to read * refactor stablelm graph builder to support 1.6, 3b and 12b more efficiently * Proper check for None type for new_name to avoid crash; formatting; revert change to base class `write_tensors()` * Format * Formatting * format Co-authored-by: compilade <git@compilade.net> * Fix incorrect check for K norm * space after commas; Keep indentation multiple of 4 spaces * Flake8 format * Removed unnecessary conditional branches * Removed unused comment * Fixed incorrect tensor passing * Format --------- Co-authored-by: compilade <git@compilade.net>
2024-04-16 15:48:35 +00:00
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
return [(new_name, data_torch)]
llama : add StableLM2 12B (#6635) * StableLM2 12B support for huggingface -> GGUF * StableLM12 tensormapping and constants * StableLM-2-12b model support * fix * Added 12B support * Removed autoformatting; resolved bug where model_arch was not selecting StableLM2 * Formatting * Do QK norm stacking in model conversion step * Converge StableLM and StableLM2 code to simplify graph construction * Fix accidental removal * Removed warnings * Revert formatter * Move QK norm stack to private function so it's easier to read * refactor stablelm graph builder to support 1.6, 3b and 12b more efficiently * Proper check for None type for new_name to avoid crash; formatting; revert change to base class `write_tensors()` * Format * Formatting * format Co-authored-by: compilade <git@compilade.net> * Fix incorrect check for K norm * space after commas; Keep indentation multiple of 4 spaces * Flake8 format * Removed unnecessary conditional branches * Removed unused comment * Fixed incorrect tensor passing * Format --------- Co-authored-by: compilade <git@compilade.net>
2024-04-16 15:48:35 +00:00
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
def write_tensors(self):
super().write_tensors()
if self._q_norms is not None or self._k_norms is not None:
# flatten two `list[dict[str, Tensor]]` into a single `list[str]`
norms = (
[k for d in self._q_norms for k in d.keys()] if self._q_norms is not None else []
) + (
[k for d in self._k_norms for k in d.keys()] if self._k_norms is not None else []
)
if len(norms) > 0:
raise ValueError(f"Unprocessed norms: {norms}")
llama : add StableLM2 12B (#6635) * StableLM2 12B support for huggingface -> GGUF * StableLM12 tensormapping and constants * StableLM-2-12b model support * fix * Added 12B support * Removed autoformatting; resolved bug where model_arch was not selecting StableLM2 * Formatting * Do QK norm stacking in model conversion step * Converge StableLM and StableLM2 code to simplify graph construction * Fix accidental removal * Removed warnings * Revert formatter * Move QK norm stack to private function so it's easier to read * refactor stablelm graph builder to support 1.6, 3b and 12b more efficiently * Proper check for None type for new_name to avoid crash; formatting; revert change to base class `write_tensors()` * Format * Formatting * format Co-authored-by: compilade <git@compilade.net> * Fix incorrect check for K norm * space after commas; Keep indentation multiple of 4 spaces * Flake8 format * Removed unnecessary conditional branches * Removed unused comment * Fixed incorrect tensor passing * Format --------- Co-authored-by: compilade <git@compilade.net>
2024-04-16 15:48:35 +00:00
llama : add Qwen support (#4281) * enable qwen to llama.cpp * llama : do not GPU split bias tensors --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-12-01 18:16:31 +00:00
convert : allow conversion of Mistral HF models (#6144) * Allow conversion of Mistral HF models * Homogenize Llama, Mistral, Mixtral under the same entry. * Fix tokenizer, permute tensors * Use sentencepiece tokenizer, or fall back to hfft. * convert-hf : small fix for mypy * convert-hf : fix duplicated block_count * convert-hf : add vocab size to metadata --------- Co-authored-by: Jared Van Bortel <jared@nomic.ai>
2024-03-29 07:15:00 +00:00
@Model.register("LlamaForCausalLM", "MistralForCausalLM", "MixtralForCausalLM")
class LlamaModel(Model):
convert-hf : make model class definitions self-contained (#5825)
2024-03-02 17:21:47 +00:00
model_arch = gguf.MODEL_ARCH.LLAMA
llama : add Mixtral support (#4406) * convert : support Mixtral as LLAMA arch * convert : fix n_ff typo * llama : model loading * ggml : sync latest ggml_mul_mat_id * llama : update graph to support MoE * llama : fix cur -> cur_expert * llama : first working version * llama : fix expert weighting in the FFN * ggml : ggml_get_rows support 2D indexing [n_tokens, n_experts] (cpu only) * ggml : add n_as argument to ggml_mul_mat_id * ggml : fix ggml_get_rows to take into account ne02 / ne11 * metal : add more general support for ggml_get_rows + tests * llama : add basic support for offloading moe with CUDA * metal : add/mul/div use general kernel when src1 not cont * metal : reduce the kernel launches for ggml_mul_mat_id * ggml : get_rows : support non-contiguos tensors with gaps, generalize up to 3D * ggml : update get_rows f16 and q * cuda : support non-contiguous src1 in get_rows * llama : offload missing ffn_moe_silu * metal : fix ggml_get_rows to work with non-cont src1 * metal : add indirect mat-vec kernels for all quantization types * llama : do not quantize expert gating tensors * llama : add n_expert and n_expert_used to hparams + change quants * test-backend-ops : add moe test * cuda : fix get_rows when ncols is odd * convert : determine n_ctx correctly * metal : fix ggml_mul_mat_id for F32 * test-backend-ops : make experts more evenly probable (test_moe) * test-backend-ops : cleanup, add moe test for batches * test-backend-ops : add cpy from f32 -> all types test * test-backend-ops : fix dequantize block offset * llama : fix hard-coded number of experts * test-backend-ops : simplify and disable slow tests to avoid CI timeout * test-backend-ops : disable MOE test with thread sanitizer * cuda : fix mul_mat_id with multi gpu * convert : use 1e6 rope_freq_base for mixtral * convert : fix style * convert : support safetensors format * gguf-py : bump version * metal : add cpy f16 -> f32 kernel * metal : fix binary ops for ne10 % 4 != 0 * test-backend-ops : add one more sum_rows test * ggml : do not use BLAS with ggml_mul_mat_id * convert-hf : support for mixtral-instruct (#4428) * convert : typo fix, add additional hyperparameters, use LLaMA arch for Mixtral-instruct * convert : use sentencepiece tokenizer for Mixtral-instruct * convert : make flake8 happy * metal : fix soft_max kernels ref: https://github.com/ggerganov/ggml/pull/621/commits/1914017863d2f9ab8ecc0281cc2a56d683668b92 * metal : limit kernels to not use more than the allowed threads --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> Co-authored-by: Radek Pilar <github@mrkva.eu>
2023-12-13 12:04:25 +00:00
def set_vocab(self):
convert : allow conversion of Mistral HF models (#6144) * Allow conversion of Mistral HF models * Homogenize Llama, Mistral, Mixtral under the same entry. * Fix tokenizer, permute tensors * Use sentencepiece tokenizer, or fall back to hfft. * convert-hf : small fix for mypy * convert-hf : fix duplicated block_count * convert-hf : add vocab size to metadata --------- Co-authored-by: Jared Van Bortel <jared@nomic.ai>
2024-03-29 07:15:00 +00:00
try:
self. _set_vocab_sentencepiece()
except FileNotFoundError:
llama : support Llama 3 HF conversion (#6745) * Support Llama 3 conversion The tokenizer is BPE. * style * Accept suggestion Co-authored-by: Sourab Mangrulkar <13534540+pacman100@users.noreply.github.com> * llama : add llama_token_is_eog() ggml-ci * llama : auto-detect more EOT tokens when missing in KV data * convert : replacing EOS token is a hack * llama : fix codegemma EOT token + add TODOs * llama : fix model type string for 8B model --------- Co-authored-by: Sourab Mangrulkar <13534540+pacman100@users.noreply.github.com> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-04-21 11:50:41 +00:00
try:
self._set_vocab_llama_hf()
except (FileNotFoundError, TypeError):
# Llama 3
self._set_vocab_gpt2()
# Apply to CodeLlama only (and ignore for Llama 3 with a vocab size of 128256)
if self.hparams.get("vocab_size", 32000) == 32016:
special_vocab = gguf.SpecialVocab(
self.dir_model, load_merges=False,
special_token_types = ['prefix', 'suffix', 'middle', 'eot']
)
special_vocab._set_special_token("prefix", 32007)
special_vocab._set_special_token("suffix", 32008)
special_vocab._set_special_token("middle", 32009)
special_vocab._set_special_token("eot", 32010)
special_vocab.add_to_gguf(self.gguf_writer)
gguf : add special tokens metadata for FIM/Infill (#6689) This commit adds special token metadata for Fill-In-the-Middle (FIM)/Infill to the GGUF model. The motivation for this is that currently there is support for CodeLlama but other models exist now like CodeGemma, but the different models use different token ids for the special tokens and this commit allows for supporting multiple models. Signed-off-by: Daniel Bevenius <daniel.bevenius@gmail.com>
2024-04-16 06:13:13 +00:00
convert : allow conversion of Mistral HF models (#6144) * Allow conversion of Mistral HF models * Homogenize Llama, Mistral, Mixtral under the same entry. * Fix tokenizer, permute tensors * Use sentencepiece tokenizer, or fall back to hfft. * convert-hf : small fix for mypy * convert-hf : fix duplicated block_count * convert-hf : add vocab size to metadata --------- Co-authored-by: Jared Van Bortel <jared@nomic.ai>
2024-03-29 07:15:00 +00:00
def set_gguf_parameters(self):
super().set_gguf_parameters()
hparams = self.hparams
self.gguf_writer.add_vocab_size(hparams["vocab_size"])
self.gguf_writer.add_rope_dimension_count(hparams["hidden_size"] // hparams["num_attention_heads"])
llama : fix BPE pre-tokenization (#6920) * merged the changes from deepseeker models to main branch * Moved regex patterns to unicode.cpp and updated unicode.h * Moved header files * Resolved issues * added and refactored unicode_regex_split and related functions * Updated/merged the deepseek coder pr * Refactored code * Adding unicode regex mappings * Adding unicode regex function * Added needed functionality, testing remains * Fixed issues * Fixed issue with gpt2 regex custom preprocessor * unicode : fix? unicode_wstring_to_utf8 * lint : fix whitespaces * tests : add tokenizer tests for numbers * unicode : remove redundant headers * tests : remove and rename tokenizer test scripts * tests : add sample usage * gguf-py : reader prints warnings on duplicate keys * llama : towards llama3 tokenization support (wip) * unicode : shot in the dark to fix tests on Windows * unicode : first try custom implementations * convert : add "tokenizer.ggml.pre" GGUF KV (wip) * llama : use new pre-tokenizer type * convert : fix pre-tokenizer type writing * lint : fix * make : add test-tokenizer-0-llama-v3 * wip * models : add llama v3 vocab file * llama : adapt punctuation regex + add llama 3 regex * minor * unicode : set bomb * unicode : set bomb * unicode : always use std::wregex * unicode : support \p{N}, \p{L} and \p{P} natively * unicode : try fix windows * unicode : category support via std::regex * unicode : clean-up * unicode : simplify * convert : add convert-hf-to-gguf-update.py ggml-ci * lint : update * convert : add falcon ggml-ci * unicode : normalize signatures * lint : fix * lint : fix * convert : remove unused functions * convert : add comments * convert : exercise contractions ggml-ci * lint : fix * cmake : refactor test targets * tests : refactor vocab tests ggml-ci * tests : add more vocabs and tests ggml-ci * unicode : cleanup * scripts : ignore new update script in check-requirements.sh * models : add phi-3, mpt, gpt-2, starcoder * tests : disable obsolete ggml-ci * tests : use faster bpe test ggml-ci * llama : more prominent warning for old BPE models * tests : disable test-tokenizer-1-bpe due to slowness ggml-ci --------- Co-authored-by: Jaggzh <jaggz.h@gmail.com> Co-authored-by: Kazim Abrar Mahi <kazimabrarmahi135@gmail.com>
2024-04-29 13:58:41 +00:00
if self.hparams.get("rope_scaling") is not None and "factor" in self.hparams["rope_scaling"]:
if self.hparams["rope_scaling"].get("type") == "linear":
self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LINEAR)
self.gguf_writer.add_rope_scaling_factor(self.hparams["rope_scaling"]["factor"])
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
@staticmethod
def permute(weights: Tensor, n_head: int, n_head_kv: int | None):
if n_head_kv is not None and n_head != n_head_kv:
n_head = n_head_kv
return (weights.reshape(n_head, 2, weights.shape[0] // n_head // 2, *weights.shape[1:])
.swapaxes(1, 2)
.reshape(weights.shape))
convert : allow conversion of Mistral HF models (#6144) * Allow conversion of Mistral HF models * Homogenize Llama, Mistral, Mixtral under the same entry. * Fix tokenizer, permute tensors * Use sentencepiece tokenizer, or fall back to hfft. * convert-hf : small fix for mypy * convert-hf : fix duplicated block_count * convert-hf : add vocab size to metadata --------- Co-authored-by: Jared Van Bortel <jared@nomic.ai>
2024-03-29 07:15:00 +00:00
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
_experts: list[dict[str, Tensor]] | None = None
ggml : mul_mat_id use the same tensor for all the experts (#6387) * ggml : update mul_mat_id to use the same tensor for all the experts * update cuda * minor * update metal * update test-backend-ops * fix cuda * Update ggml-metal.m Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * update convert.py * update convert-hf-to-gguf.py * update convert.py for mixtral hf models * Update convert-hf-to-gguf.py Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * cuda : support non-pow-2 number of experts * allow quantize to work for split and merged experts models in the same way * cleanup + disable mmap automatically with split tensors models * update imatrix * test-backend-ops : test qwen argsort * update grok model loading * llama : add merged experts tensors to the grok tensor map * minor * gguf : bump version * fix quantizing of merged experts * convert-hf-to-gguf.py : update grok (untested) * make linter happy * cuda/argsort : use shared memory instead of pool memory * convert : fix grok tensor names * metal : add support for non-pow-2 argsort * llama : more loader cleanup, better error checking * cuda : fix warning * llama : still use mmap for loading old models, but copy the data to a host buffer * add review note * llama : remove ffn tensor counting + add sanity check ggml-ci * convert : fix handling of n_experts == None ggml-ci * imatrix : fix ncall counters * llama : produce error if imatrix size does not match * quantize : terminate on errors + trace logs ggml-ci * metal : pad shared memory to 16 bytes --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-04-03 13:07:05 +00:00
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
n_head = self.hparams["num_attention_heads"]
n_kv_head = self.hparams.get("num_key_value_heads")
ggml : mul_mat_id use the same tensor for all the experts (#6387) * ggml : update mul_mat_id to use the same tensor for all the experts * update cuda * minor * update metal * update test-backend-ops * fix cuda * Update ggml-metal.m Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * update convert.py * update convert-hf-to-gguf.py * update convert.py for mixtral hf models * Update convert-hf-to-gguf.py Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * cuda : support non-pow-2 number of experts * allow quantize to work for split and merged experts models in the same way * cleanup + disable mmap automatically with split tensors models * update imatrix * test-backend-ops : test qwen argsort * update grok model loading * llama : add merged experts tensors to the grok tensor map * minor * gguf : bump version * fix quantizing of merged experts * convert-hf-to-gguf.py : update grok (untested) * make linter happy * cuda/argsort : use shared memory instead of pool memory * convert : fix grok tensor names * metal : add support for non-pow-2 argsort * llama : more loader cleanup, better error checking * cuda : fix warning * llama : still use mmap for loading old models, but copy the data to a host buffer * add review note * llama : remove ffn tensor counting + add sanity check ggml-ci * convert : fix handling of n_experts == None ggml-ci * imatrix : fix ncall counters * llama : produce error if imatrix size does not match * quantize : terminate on errors + trace logs ggml-ci * metal : pad shared memory to 16 bytes --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-04-03 13:07:05 +00:00
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
if name.endswith("q_proj.weight"):
data_torch = LlamaModel.permute(data_torch, n_head, n_head)
if name.endswith("k_proj.weight"):
data_torch = LlamaModel.permute(data_torch, n_head, n_kv_head)
ggml : mul_mat_id use the same tensor for all the experts (#6387) * ggml : update mul_mat_id to use the same tensor for all the experts * update cuda * minor * update metal * update test-backend-ops * fix cuda * Update ggml-metal.m Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * update convert.py * update convert-hf-to-gguf.py * update convert.py for mixtral hf models * Update convert-hf-to-gguf.py Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * cuda : support non-pow-2 number of experts * allow quantize to work for split and merged experts models in the same way * cleanup + disable mmap automatically with split tensors models * update imatrix * test-backend-ops : test qwen argsort * update grok model loading * llama : add merged experts tensors to the grok tensor map * minor * gguf : bump version * fix quantizing of merged experts * convert-hf-to-gguf.py : update grok (untested) * make linter happy * cuda/argsort : use shared memory instead of pool memory * convert : fix grok tensor names * metal : add support for non-pow-2 argsort * llama : more loader cleanup, better error checking * cuda : fix warning * llama : still use mmap for loading old models, but copy the data to a host buffer * add review note * llama : remove ffn tensor counting + add sanity check ggml-ci * convert : fix handling of n_experts == None ggml-ci * imatrix : fix ncall counters * llama : produce error if imatrix size does not match * quantize : terminate on errors + trace logs ggml-ci * metal : pad shared memory to 16 bytes --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-04-03 13:07:05 +00:00
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
# process the experts separately
if name.find("block_sparse_moe.experts") != -1:
n_experts = self.hparams["num_local_experts"]
ggml : mul_mat_id use the same tensor for all the experts (#6387) * ggml : update mul_mat_id to use the same tensor for all the experts * update cuda * minor * update metal * update test-backend-ops * fix cuda * Update ggml-metal.m Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * update convert.py * update convert-hf-to-gguf.py * update convert.py for mixtral hf models * Update convert-hf-to-gguf.py Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * cuda : support non-pow-2 number of experts * allow quantize to work for split and merged experts models in the same way * cleanup + disable mmap automatically with split tensors models * update imatrix * test-backend-ops : test qwen argsort * update grok model loading * llama : add merged experts tensors to the grok tensor map * minor * gguf : bump version * fix quantizing of merged experts * convert-hf-to-gguf.py : update grok (untested) * make linter happy * cuda/argsort : use shared memory instead of pool memory * convert : fix grok tensor names * metal : add support for non-pow-2 argsort * llama : more loader cleanup, better error checking * cuda : fix warning * llama : still use mmap for loading old models, but copy the data to a host buffer * add review note * llama : remove ffn tensor counting + add sanity check ggml-ci * convert : fix handling of n_experts == None ggml-ci * imatrix : fix ncall counters * llama : produce error if imatrix size does not match * quantize : terminate on errors + trace logs ggml-ci * metal : pad shared memory to 16 bytes --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-04-03 13:07:05 +00:00
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
assert bid is not None
ggml : mul_mat_id use the same tensor for all the experts (#6387) * ggml : update mul_mat_id to use the same tensor for all the experts * update cuda * minor * update metal * update test-backend-ops * fix cuda * Update ggml-metal.m Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * update convert.py * update convert-hf-to-gguf.py * update convert.py for mixtral hf models * Update convert-hf-to-gguf.py Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * cuda : support non-pow-2 number of experts * allow quantize to work for split and merged experts models in the same way * cleanup + disable mmap automatically with split tensors models * update imatrix * test-backend-ops : test qwen argsort * update grok model loading * llama : add merged experts tensors to the grok tensor map * minor * gguf : bump version * fix quantizing of merged experts * convert-hf-to-gguf.py : update grok (untested) * make linter happy * cuda/argsort : use shared memory instead of pool memory * convert : fix grok tensor names * metal : add support for non-pow-2 argsort * llama : more loader cleanup, better error checking * cuda : fix warning * llama : still use mmap for loading old models, but copy the data to a host buffer * add review note * llama : remove ffn tensor counting + add sanity check ggml-ci * convert : fix handling of n_experts == None ggml-ci * imatrix : fix ncall counters * llama : produce error if imatrix size does not match * quantize : terminate on errors + trace logs ggml-ci * metal : pad shared memory to 16 bytes --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-04-03 13:07:05 +00:00
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
if self._experts is None:
self._experts = [{} for _ in range(self.block_count)]
ggml : mul_mat_id use the same tensor for all the experts (#6387) * ggml : update mul_mat_id to use the same tensor for all the experts * update cuda * minor * update metal * update test-backend-ops * fix cuda * Update ggml-metal.m Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * update convert.py * update convert-hf-to-gguf.py * update convert.py for mixtral hf models * Update convert-hf-to-gguf.py Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * cuda : support non-pow-2 number of experts * allow quantize to work for split and merged experts models in the same way * cleanup + disable mmap automatically with split tensors models * update imatrix * test-backend-ops : test qwen argsort * update grok model loading * llama : add merged experts tensors to the grok tensor map * minor * gguf : bump version * fix quantizing of merged experts * convert-hf-to-gguf.py : update grok (untested) * make linter happy * cuda/argsort : use shared memory instead of pool memory * convert : fix grok tensor names * metal : add support for non-pow-2 argsort * llama : more loader cleanup, better error checking * cuda : fix warning * llama : still use mmap for loading old models, but copy the data to a host buffer * add review note * llama : remove ffn tensor counting + add sanity check ggml-ci * convert : fix handling of n_experts == None ggml-ci * imatrix : fix ncall counters * llama : produce error if imatrix size does not match * quantize : terminate on errors + trace logs ggml-ci * metal : pad shared memory to 16 bytes --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-04-03 13:07:05 +00:00
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
self._experts[bid][name] = data_torch
ggml : mul_mat_id use the same tensor for all the experts (#6387) * ggml : update mul_mat_id to use the same tensor for all the experts * update cuda * minor * update metal * update test-backend-ops * fix cuda * Update ggml-metal.m Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * update convert.py * update convert-hf-to-gguf.py * update convert.py for mixtral hf models * Update convert-hf-to-gguf.py Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * cuda : support non-pow-2 number of experts * allow quantize to work for split and merged experts models in the same way * cleanup + disable mmap automatically with split tensors models * update imatrix * test-backend-ops : test qwen argsort * update grok model loading * llama : add merged experts tensors to the grok tensor map * minor * gguf : bump version * fix quantizing of merged experts * convert-hf-to-gguf.py : update grok (untested) * make linter happy * cuda/argsort : use shared memory instead of pool memory * convert : fix grok tensor names * metal : add support for non-pow-2 argsort * llama : more loader cleanup, better error checking * cuda : fix warning * llama : still use mmap for loading old models, but copy the data to a host buffer * add review note * llama : remove ffn tensor counting + add sanity check ggml-ci * convert : fix handling of n_experts == None ggml-ci * imatrix : fix ncall counters * llama : produce error if imatrix size does not match * quantize : terminate on errors + trace logs ggml-ci * metal : pad shared memory to 16 bytes --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-04-03 13:07:05 +00:00
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
if len(self._experts[bid]) >= n_experts * 3:
tensors: list[tuple[str, Tensor]] = []
ggml : mul_mat_id use the same tensor for all the experts (#6387) * ggml : update mul_mat_id to use the same tensor for all the experts * update cuda * minor * update metal * update test-backend-ops * fix cuda * Update ggml-metal.m Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * update convert.py * update convert-hf-to-gguf.py * update convert.py for mixtral hf models * Update convert-hf-to-gguf.py Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * cuda : support non-pow-2 number of experts * allow quantize to work for split and merged experts models in the same way * cleanup + disable mmap automatically with split tensors models * update imatrix * test-backend-ops : test qwen argsort * update grok model loading * llama : add merged experts tensors to the grok tensor map * minor * gguf : bump version * fix quantizing of merged experts * convert-hf-to-gguf.py : update grok (untested) * make linter happy * cuda/argsort : use shared memory instead of pool memory * convert : fix grok tensor names * metal : add support for non-pow-2 argsort * llama : more loader cleanup, better error checking * cuda : fix warning * llama : still use mmap for loading old models, but copy the data to a host buffer * add review note * llama : remove ffn tensor counting + add sanity check ggml-ci * convert : fix handling of n_experts == None ggml-ci * imatrix : fix ncall counters * llama : produce error if imatrix size does not match * quantize : terminate on errors + trace logs ggml-ci * metal : pad shared memory to 16 bytes --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-04-03 13:07:05 +00:00
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
# merge the experts into a single 3d tensor
for wid in ["w1", "w2", "w3"]:
datas: list[Tensor] = []
ggml : mul_mat_id use the same tensor for all the experts (#6387) * ggml : update mul_mat_id to use the same tensor for all the experts * update cuda * minor * update metal * update test-backend-ops * fix cuda * Update ggml-metal.m Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * update convert.py * update convert-hf-to-gguf.py * update convert.py for mixtral hf models * Update convert-hf-to-gguf.py Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * cuda : support non-pow-2 number of experts * allow quantize to work for split and merged experts models in the same way * cleanup + disable mmap automatically with split tensors models * update imatrix * test-backend-ops : test qwen argsort * update grok model loading * llama : add merged experts tensors to the grok tensor map * minor * gguf : bump version * fix quantizing of merged experts * convert-hf-to-gguf.py : update grok (untested) * make linter happy * cuda/argsort : use shared memory instead of pool memory * convert : fix grok tensor names * metal : add support for non-pow-2 argsort * llama : more loader cleanup, better error checking * cuda : fix warning * llama : still use mmap for loading old models, but copy the data to a host buffer * add review note * llama : remove ffn tensor counting + add sanity check ggml-ci * convert : fix handling of n_experts == None ggml-ci * imatrix : fix ncall counters * llama : produce error if imatrix size does not match * quantize : terminate on errors + trace logs ggml-ci * metal : pad shared memory to 16 bytes --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-04-03 13:07:05 +00:00
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
for xid in range(n_experts):
ename = f"model.layers.{bid}.block_sparse_moe.experts.{xid}.{wid}.weight"
datas.append(self._experts[bid][ename])
del self._experts[bid][ename]
convert : allow conversion of Mistral HF models (#6144) * Allow conversion of Mistral HF models * Homogenize Llama, Mistral, Mixtral under the same entry. * Fix tokenizer, permute tensors * Use sentencepiece tokenizer, or fall back to hfft. * convert-hf : small fix for mypy * convert-hf : fix duplicated block_count * convert-hf : add vocab size to metadata --------- Co-authored-by: Jared Van Bortel <jared@nomic.ai>
2024-03-29 07:15:00 +00:00
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
data_torch = torch.stack(datas, dim=0)
convert : allow conversion of Mistral HF models (#6144) * Allow conversion of Mistral HF models * Homogenize Llama, Mistral, Mixtral under the same entry. * Fix tokenizer, permute tensors * Use sentencepiece tokenizer, or fall back to hfft. * convert-hf : small fix for mypy * convert-hf : fix duplicated block_count * convert-hf : add vocab size to metadata --------- Co-authored-by: Jared Van Bortel <jared@nomic.ai>
2024-03-29 07:15:00 +00:00
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
merged_name = f"layers.{bid}.feed_forward.experts.{wid}.weight"
convert : allow conversion of Mistral HF models (#6144) * Allow conversion of Mistral HF models * Homogenize Llama, Mistral, Mixtral under the same entry. * Fix tokenizer, permute tensors * Use sentencepiece tokenizer, or fall back to hfft. * convert-hf : small fix for mypy * convert-hf : fix duplicated block_count * convert-hf : add vocab size to metadata --------- Co-authored-by: Jared Van Bortel <jared@nomic.ai>
2024-03-29 07:15:00 +00:00
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
new_name = self.map_tensor_name(merged_name)
convert : allow conversion of Mistral HF models (#6144) * Allow conversion of Mistral HF models * Homogenize Llama, Mistral, Mixtral under the same entry. * Fix tokenizer, permute tensors * Use sentencepiece tokenizer, or fall back to hfft. * convert-hf : small fix for mypy * convert-hf : fix duplicated block_count * convert-hf : add vocab size to metadata --------- Co-authored-by: Jared Van Bortel <jared@nomic.ai>
2024-03-29 07:15:00 +00:00
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
tensors.append((new_name, data_torch))
return tensors
else:
return []
convert : allow conversion of Mistral HF models (#6144) * Allow conversion of Mistral HF models * Homogenize Llama, Mistral, Mixtral under the same entry. * Fix tokenizer, permute tensors * Use sentencepiece tokenizer, or fall back to hfft. * convert-hf : small fix for mypy * convert-hf : fix duplicated block_count * convert-hf : add vocab size to metadata --------- Co-authored-by: Jared Van Bortel <jared@nomic.ai>
2024-03-29 07:15:00 +00:00
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
return [(self.map_tensor_name(name), data_torch)]
convert : allow conversion of Mistral HF models (#6144) * Allow conversion of Mistral HF models * Homogenize Llama, Mistral, Mixtral under the same entry. * Fix tokenizer, permute tensors * Use sentencepiece tokenizer, or fall back to hfft. * convert-hf : small fix for mypy * convert-hf : fix duplicated block_count * convert-hf : add vocab size to metadata --------- Co-authored-by: Jared Van Bortel <jared@nomic.ai>
2024-03-29 07:15:00 +00:00
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
def write_tensors(self):
super().write_tensors()
llama : add Mixtral support (#4406) * convert : support Mixtral as LLAMA arch * convert : fix n_ff typo * llama : model loading * ggml : sync latest ggml_mul_mat_id * llama : update graph to support MoE * llama : fix cur -> cur_expert * llama : first working version * llama : fix expert weighting in the FFN * ggml : ggml_get_rows support 2D indexing [n_tokens, n_experts] (cpu only) * ggml : add n_as argument to ggml_mul_mat_id * ggml : fix ggml_get_rows to take into account ne02 / ne11 * metal : add more general support for ggml_get_rows + tests * llama : add basic support for offloading moe with CUDA * metal : add/mul/div use general kernel when src1 not cont * metal : reduce the kernel launches for ggml_mul_mat_id * ggml : get_rows : support non-contiguos tensors with gaps, generalize up to 3D * ggml : update get_rows f16 and q * cuda : support non-contiguous src1 in get_rows * llama : offload missing ffn_moe_silu * metal : fix ggml_get_rows to work with non-cont src1 * metal : add indirect mat-vec kernels for all quantization types * llama : do not quantize expert gating tensors * llama : add n_expert and n_expert_used to hparams + change quants * test-backend-ops : add moe test * cuda : fix get_rows when ncols is odd * convert : determine n_ctx correctly * metal : fix ggml_mul_mat_id for F32 * test-backend-ops : make experts more evenly probable (test_moe) * test-backend-ops : cleanup, add moe test for batches * test-backend-ops : add cpy from f32 -> all types test * test-backend-ops : fix dequantize block offset * llama : fix hard-coded number of experts * test-backend-ops : simplify and disable slow tests to avoid CI timeout * test-backend-ops : disable MOE test with thread sanitizer * cuda : fix mul_mat_id with multi gpu * convert : use 1e6 rope_freq_base for mixtral * convert : fix style * convert : support safetensors format * gguf-py : bump version * metal : add cpy f16 -> f32 kernel * metal : fix binary ops for ne10 % 4 != 0 * test-backend-ops : add one more sum_rows test * ggml : do not use BLAS with ggml_mul_mat_id * convert-hf : support for mixtral-instruct (#4428) * convert : typo fix, add additional hyperparameters, use LLaMA arch for Mixtral-instruct * convert : use sentencepiece tokenizer for Mixtral-instruct * convert : make flake8 happy * metal : fix soft_max kernels ref: https://github.com/ggerganov/ggml/pull/621/commits/1914017863d2f9ab8ecc0281cc2a56d683668b92 * metal : limit kernels to not use more than the allowed threads --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> Co-authored-by: Radek Pilar <github@mrkva.eu>
2023-12-13 12:04:25 +00:00
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
if self._experts is not None:
# flatten `list[dict[str, Tensor]]` into `list[str]`
experts = [k for d in self._experts for k in d.keys()]
if len(experts) > 0:
raise ValueError(f"Unprocessed experts: {experts}")
ggml : mul_mat_id use the same tensor for all the experts (#6387) * ggml : update mul_mat_id to use the same tensor for all the experts * update cuda * minor * update metal * update test-backend-ops * fix cuda * Update ggml-metal.m Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * update convert.py * update convert-hf-to-gguf.py * update convert.py for mixtral hf models * Update convert-hf-to-gguf.py Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * cuda : support non-pow-2 number of experts * allow quantize to work for split and merged experts models in the same way * cleanup + disable mmap automatically with split tensors models * update imatrix * test-backend-ops : test qwen argsort * update grok model loading * llama : add merged experts tensors to the grok tensor map * minor * gguf : bump version * fix quantizing of merged experts * convert-hf-to-gguf.py : update grok (untested) * make linter happy * cuda/argsort : use shared memory instead of pool memory * convert : fix grok tensor names * metal : add support for non-pow-2 argsort * llama : more loader cleanup, better error checking * cuda : fix warning * llama : still use mmap for loading old models, but copy the data to a host buffer * add review note * llama : remove ffn tensor counting + add sanity check ggml-ci * convert : fix handling of n_experts == None ggml-ci * imatrix : fix ncall counters * llama : produce error if imatrix size does not match * quantize : terminate on errors + trace logs ggml-ci * metal : pad shared memory to 16 bytes --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-04-03 13:07:05 +00:00
llama : add Mixtral support (#4406) * convert : support Mixtral as LLAMA arch * convert : fix n_ff typo * llama : model loading * ggml : sync latest ggml_mul_mat_id * llama : update graph to support MoE * llama : fix cur -> cur_expert * llama : first working version * llama : fix expert weighting in the FFN * ggml : ggml_get_rows support 2D indexing [n_tokens, n_experts] (cpu only) * ggml : add n_as argument to ggml_mul_mat_id * ggml : fix ggml_get_rows to take into account ne02 / ne11 * metal : add more general support for ggml_get_rows + tests * llama : add basic support for offloading moe with CUDA * metal : add/mul/div use general kernel when src1 not cont * metal : reduce the kernel launches for ggml_mul_mat_id * ggml : get_rows : support non-contiguos tensors with gaps, generalize up to 3D * ggml : update get_rows f16 and q * cuda : support non-contiguous src1 in get_rows * llama : offload missing ffn_moe_silu * metal : fix ggml_get_rows to work with non-cont src1 * metal : add indirect mat-vec kernels for all quantization types * llama : do not quantize expert gating tensors * llama : add n_expert and n_expert_used to hparams + change quants * test-backend-ops : add moe test * cuda : fix get_rows when ncols is odd * convert : determine n_ctx correctly * metal : fix ggml_mul_mat_id for F32 * test-backend-ops : make experts more evenly probable (test_moe) * test-backend-ops : cleanup, add moe test for batches * test-backend-ops : add cpy from f32 -> all types test * test-backend-ops : fix dequantize block offset * llama : fix hard-coded number of experts * test-backend-ops : simplify and disable slow tests to avoid CI timeout * test-backend-ops : disable MOE test with thread sanitizer * cuda : fix mul_mat_id with multi gpu * convert : use 1e6 rope_freq_base for mixtral * convert : fix style * convert : support safetensors format * gguf-py : bump version * metal : add cpy f16 -> f32 kernel * metal : fix binary ops for ne10 % 4 != 0 * test-backend-ops : add one more sum_rows test * ggml : do not use BLAS with ggml_mul_mat_id * convert-hf : support for mixtral-instruct (#4428) * convert : typo fix, add additional hyperparameters, use LLaMA arch for Mixtral-instruct * convert : use sentencepiece tokenizer for Mixtral-instruct * convert : make flake8 happy * metal : fix soft_max kernels ref: https://github.com/ggerganov/ggml/pull/621/commits/1914017863d2f9ab8ecc0281cc2a56d683668b92 * metal : limit kernels to not use more than the allowed threads --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> Co-authored-by: Radek Pilar <github@mrkva.eu>
2023-12-13 12:04:25 +00:00
llama : add grok-1 support (#6204) * Add support for Grok model architecture * Revert convert-hf-to-gguf to default options * Fixed f_norm_rms_eps bug * Fix whitespaces * llama : fix grok rope type * llama : minor --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-03-23 16:41:53 +00:00
@Model.register("GrokForCausalLM")
class GrokModel(Model):
model_arch = gguf.MODEL_ARCH.GROK
def set_vocab(self):
self._set_vocab_sentencepiece()
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
def set_gguf_parameters(self):
super().set_gguf_parameters()
self.gguf_writer.add_name("Grok")
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
_experts: list[dict[str, Tensor]] | None = None
ggml : mul_mat_id use the same tensor for all the experts (#6387) * ggml : update mul_mat_id to use the same tensor for all the experts * update cuda * minor * update metal * update test-backend-ops * fix cuda * Update ggml-metal.m Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * update convert.py * update convert-hf-to-gguf.py * update convert.py for mixtral hf models * Update convert-hf-to-gguf.py Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * cuda : support non-pow-2 number of experts * allow quantize to work for split and merged experts models in the same way * cleanup + disable mmap automatically with split tensors models * update imatrix * test-backend-ops : test qwen argsort * update grok model loading * llama : add merged experts tensors to the grok tensor map * minor * gguf : bump version * fix quantizing of merged experts * convert-hf-to-gguf.py : update grok (untested) * make linter happy * cuda/argsort : use shared memory instead of pool memory * convert : fix grok tensor names * metal : add support for non-pow-2 argsort * llama : more loader cleanup, better error checking * cuda : fix warning * llama : still use mmap for loading old models, but copy the data to a host buffer * add review note * llama : remove ffn tensor counting + add sanity check ggml-ci * convert : fix handling of n_experts == None ggml-ci * imatrix : fix ncall counters * llama : produce error if imatrix size does not match * quantize : terminate on errors + trace logs ggml-ci * metal : pad shared memory to 16 bytes --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-04-03 13:07:05 +00:00
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
# process the experts separately
if name.find(".moe.") != -1:
n_experts = self.hparams["num_local_experts"]
ggml : mul_mat_id use the same tensor for all the experts (#6387) * ggml : update mul_mat_id to use the same tensor for all the experts * update cuda * minor * update metal * update test-backend-ops * fix cuda * Update ggml-metal.m Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * update convert.py * update convert-hf-to-gguf.py * update convert.py for mixtral hf models * Update convert-hf-to-gguf.py Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * cuda : support non-pow-2 number of experts * allow quantize to work for split and merged experts models in the same way * cleanup + disable mmap automatically with split tensors models * update imatrix * test-backend-ops : test qwen argsort * update grok model loading * llama : add merged experts tensors to the grok tensor map * minor * gguf : bump version * fix quantizing of merged experts * convert-hf-to-gguf.py : update grok (untested) * make linter happy * cuda/argsort : use shared memory instead of pool memory * convert : fix grok tensor names * metal : add support for non-pow-2 argsort * llama : more loader cleanup, better error checking * cuda : fix warning * llama : still use mmap for loading old models, but copy the data to a host buffer * add review note * llama : remove ffn tensor counting + add sanity check ggml-ci * convert : fix handling of n_experts == None ggml-ci * imatrix : fix ncall counters * llama : produce error if imatrix size does not match * quantize : terminate on errors + trace logs ggml-ci * metal : pad shared memory to 16 bytes --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-04-03 13:07:05 +00:00
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
assert bid is not None
ggml : mul_mat_id use the same tensor for all the experts (#6387) * ggml : update mul_mat_id to use the same tensor for all the experts * update cuda * minor * update metal * update test-backend-ops * fix cuda * Update ggml-metal.m Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * update convert.py * update convert-hf-to-gguf.py * update convert.py for mixtral hf models * Update convert-hf-to-gguf.py Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * cuda : support non-pow-2 number of experts * allow quantize to work for split and merged experts models in the same way * cleanup + disable mmap automatically with split tensors models * update imatrix * test-backend-ops : test qwen argsort * update grok model loading * llama : add merged experts tensors to the grok tensor map * minor * gguf : bump version * fix quantizing of merged experts * convert-hf-to-gguf.py : update grok (untested) * make linter happy * cuda/argsort : use shared memory instead of pool memory * convert : fix grok tensor names * metal : add support for non-pow-2 argsort * llama : more loader cleanup, better error checking * cuda : fix warning * llama : still use mmap for loading old models, but copy the data to a host buffer * add review note * llama : remove ffn tensor counting + add sanity check ggml-ci * convert : fix handling of n_experts == None ggml-ci * imatrix : fix ncall counters * llama : produce error if imatrix size does not match * quantize : terminate on errors + trace logs ggml-ci * metal : pad shared memory to 16 bytes --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-04-03 13:07:05 +00:00
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
if self._experts is None:
self._experts = [{} for _ in range(self.block_count)]
ggml : mul_mat_id use the same tensor for all the experts (#6387) * ggml : update mul_mat_id to use the same tensor for all the experts * update cuda * minor * update metal * update test-backend-ops * fix cuda * Update ggml-metal.m Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * update convert.py * update convert-hf-to-gguf.py * update convert.py for mixtral hf models * Update convert-hf-to-gguf.py Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * cuda : support non-pow-2 number of experts * allow quantize to work for split and merged experts models in the same way * cleanup + disable mmap automatically with split tensors models * update imatrix * test-backend-ops : test qwen argsort * update grok model loading * llama : add merged experts tensors to the grok tensor map * minor * gguf : bump version * fix quantizing of merged experts * convert-hf-to-gguf.py : update grok (untested) * make linter happy * cuda/argsort : use shared memory instead of pool memory * convert : fix grok tensor names * metal : add support for non-pow-2 argsort * llama : more loader cleanup, better error checking * cuda : fix warning * llama : still use mmap for loading old models, but copy the data to a host buffer * add review note * llama : remove ffn tensor counting + add sanity check ggml-ci * convert : fix handling of n_experts == None ggml-ci * imatrix : fix ncall counters * llama : produce error if imatrix size does not match * quantize : terminate on errors + trace logs ggml-ci * metal : pad shared memory to 16 bytes --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-04-03 13:07:05 +00:00
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
self._experts[bid][name] = data_torch
ggml : mul_mat_id use the same tensor for all the experts (#6387) * ggml : update mul_mat_id to use the same tensor for all the experts * update cuda * minor * update metal * update test-backend-ops * fix cuda * Update ggml-metal.m Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * update convert.py * update convert-hf-to-gguf.py * update convert.py for mixtral hf models * Update convert-hf-to-gguf.py Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * cuda : support non-pow-2 number of experts * allow quantize to work for split and merged experts models in the same way * cleanup + disable mmap automatically with split tensors models * update imatrix * test-backend-ops : test qwen argsort * update grok model loading * llama : add merged experts tensors to the grok tensor map * minor * gguf : bump version * fix quantizing of merged experts * convert-hf-to-gguf.py : update grok (untested) * make linter happy * cuda/argsort : use shared memory instead of pool memory * convert : fix grok tensor names * metal : add support for non-pow-2 argsort * llama : more loader cleanup, better error checking * cuda : fix warning * llama : still use mmap for loading old models, but copy the data to a host buffer * add review note * llama : remove ffn tensor counting + add sanity check ggml-ci * convert : fix handling of n_experts == None ggml-ci * imatrix : fix ncall counters * llama : produce error if imatrix size does not match * quantize : terminate on errors + trace logs ggml-ci * metal : pad shared memory to 16 bytes --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-04-03 13:07:05 +00:00
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
if len(self._experts[bid]) >= n_experts * 3:
tensors: list[tuple[str, Tensor]] = []
ggml : mul_mat_id use the same tensor for all the experts (#6387) * ggml : update mul_mat_id to use the same tensor for all the experts * update cuda * minor * update metal * update test-backend-ops * fix cuda * Update ggml-metal.m Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * update convert.py * update convert-hf-to-gguf.py * update convert.py for mixtral hf models * Update convert-hf-to-gguf.py Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * cuda : support non-pow-2 number of experts * allow quantize to work for split and merged experts models in the same way * cleanup + disable mmap automatically with split tensors models * update imatrix * test-backend-ops : test qwen argsort * update grok model loading * llama : add merged experts tensors to the grok tensor map * minor * gguf : bump version * fix quantizing of merged experts * convert-hf-to-gguf.py : update grok (untested) * make linter happy * cuda/argsort : use shared memory instead of pool memory * convert : fix grok tensor names * metal : add support for non-pow-2 argsort * llama : more loader cleanup, better error checking * cuda : fix warning * llama : still use mmap for loading old models, but copy the data to a host buffer * add review note * llama : remove ffn tensor counting + add sanity check ggml-ci * convert : fix handling of n_experts == None ggml-ci * imatrix : fix ncall counters * llama : produce error if imatrix size does not match * quantize : terminate on errors + trace logs ggml-ci * metal : pad shared memory to 16 bytes --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-04-03 13:07:05 +00:00
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
# merge the experts into a single 3d tensor
for wid in ["linear", "linear_1", "linear_v"]:
datas: list[Tensor] = []
ggml : mul_mat_id use the same tensor for all the experts (#6387) * ggml : update mul_mat_id to use the same tensor for all the experts * update cuda * minor * update metal * update test-backend-ops * fix cuda * Update ggml-metal.m Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * update convert.py * update convert-hf-to-gguf.py * update convert.py for mixtral hf models * Update convert-hf-to-gguf.py Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * cuda : support non-pow-2 number of experts * allow quantize to work for split and merged experts models in the same way * cleanup + disable mmap automatically with split tensors models * update imatrix * test-backend-ops : test qwen argsort * update grok model loading * llama : add merged experts tensors to the grok tensor map * minor * gguf : bump version * fix quantizing of merged experts * convert-hf-to-gguf.py : update grok (untested) * make linter happy * cuda/argsort : use shared memory instead of pool memory * convert : fix grok tensor names * metal : add support for non-pow-2 argsort * llama : more loader cleanup, better error checking * cuda : fix warning * llama : still use mmap for loading old models, but copy the data to a host buffer * add review note * llama : remove ffn tensor counting + add sanity check ggml-ci * convert : fix handling of n_experts == None ggml-ci * imatrix : fix ncall counters * llama : produce error if imatrix size does not match * quantize : terminate on errors + trace logs ggml-ci * metal : pad shared memory to 16 bytes --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-04-03 13:07:05 +00:00
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
for xid in range(n_experts):
ename = f"transformer.decoder_layer.{bid}.moe.{xid}.{wid}.weight"
datas.append(self._experts[bid][ename])
del self._experts[bid][ename]
ggml : mul_mat_id use the same tensor for all the experts (#6387) * ggml : update mul_mat_id to use the same tensor for all the experts * update cuda * minor * update metal * update test-backend-ops * fix cuda * Update ggml-metal.m Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * update convert.py * update convert-hf-to-gguf.py * update convert.py for mixtral hf models * Update convert-hf-to-gguf.py Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * cuda : support non-pow-2 number of experts * allow quantize to work for split and merged experts models in the same way * cleanup + disable mmap automatically with split tensors models * update imatrix * test-backend-ops : test qwen argsort * update grok model loading * llama : add merged experts tensors to the grok tensor map * minor * gguf : bump version * fix quantizing of merged experts * convert-hf-to-gguf.py : update grok (untested) * make linter happy * cuda/argsort : use shared memory instead of pool memory * convert : fix grok tensor names * metal : add support for non-pow-2 argsort * llama : more loader cleanup, better error checking * cuda : fix warning * llama : still use mmap for loading old models, but copy the data to a host buffer * add review note * llama : remove ffn tensor counting + add sanity check ggml-ci * convert : fix handling of n_experts == None ggml-ci * imatrix : fix ncall counters * llama : produce error if imatrix size does not match * quantize : terminate on errors + trace logs ggml-ci * metal : pad shared memory to 16 bytes --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-04-03 13:07:05 +00:00
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
data_torch = torch.stack(datas, dim=0)
ggml : mul_mat_id use the same tensor for all the experts (#6387) * ggml : update mul_mat_id to use the same tensor for all the experts * update cuda * minor * update metal * update test-backend-ops * fix cuda * Update ggml-metal.m Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * update convert.py * update convert-hf-to-gguf.py * update convert.py for mixtral hf models * Update convert-hf-to-gguf.py Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * cuda : support non-pow-2 number of experts * allow quantize to work for split and merged experts models in the same way * cleanup + disable mmap automatically with split tensors models * update imatrix * test-backend-ops : test qwen argsort * update grok model loading * llama : add merged experts tensors to the grok tensor map * minor * gguf : bump version * fix quantizing of merged experts * convert-hf-to-gguf.py : update grok (untested) * make linter happy * cuda/argsort : use shared memory instead of pool memory * convert : fix grok tensor names * metal : add support for non-pow-2 argsort * llama : more loader cleanup, better error checking * cuda : fix warning * llama : still use mmap for loading old models, but copy the data to a host buffer * add review note * llama : remove ffn tensor counting + add sanity check ggml-ci * convert : fix handling of n_experts == None ggml-ci * imatrix : fix ncall counters * llama : produce error if imatrix size does not match * quantize : terminate on errors + trace logs ggml-ci * metal : pad shared memory to 16 bytes --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-04-03 13:07:05 +00:00
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
merged_name = f"transformer.decoder_layer.{bid}.moe.{wid}.weight"
ggml : mul_mat_id use the same tensor for all the experts (#6387) * ggml : update mul_mat_id to use the same tensor for all the experts * update cuda * minor * update metal * update test-backend-ops * fix cuda * Update ggml-metal.m Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * update convert.py * update convert-hf-to-gguf.py * update convert.py for mixtral hf models * Update convert-hf-to-gguf.py Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * cuda : support non-pow-2 number of experts * allow quantize to work for split and merged experts models in the same way * cleanup + disable mmap automatically with split tensors models * update imatrix * test-backend-ops : test qwen argsort * update grok model loading * llama : add merged experts tensors to the grok tensor map * minor * gguf : bump version * fix quantizing of merged experts * convert-hf-to-gguf.py : update grok (untested) * make linter happy * cuda/argsort : use shared memory instead of pool memory * convert : fix grok tensor names * metal : add support for non-pow-2 argsort * llama : more loader cleanup, better error checking * cuda : fix warning * llama : still use mmap for loading old models, but copy the data to a host buffer * add review note * llama : remove ffn tensor counting + add sanity check ggml-ci * convert : fix handling of n_experts == None ggml-ci * imatrix : fix ncall counters * llama : produce error if imatrix size does not match * quantize : terminate on errors + trace logs ggml-ci * metal : pad shared memory to 16 bytes --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-04-03 13:07:05 +00:00
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
new_name = self.map_tensor_name(merged_name)
ggml : mul_mat_id use the same tensor for all the experts (#6387) * ggml : update mul_mat_id to use the same tensor for all the experts * update cuda * minor * update metal * update test-backend-ops * fix cuda * Update ggml-metal.m Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * update convert.py * update convert-hf-to-gguf.py * update convert.py for mixtral hf models * Update convert-hf-to-gguf.py Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * cuda : support non-pow-2 number of experts * allow quantize to work for split and merged experts models in the same way * cleanup + disable mmap automatically with split tensors models * update imatrix * test-backend-ops : test qwen argsort * update grok model loading * llama : add merged experts tensors to the grok tensor map * minor * gguf : bump version * fix quantizing of merged experts * convert-hf-to-gguf.py : update grok (untested) * make linter happy * cuda/argsort : use shared memory instead of pool memory * convert : fix grok tensor names * metal : add support for non-pow-2 argsort * llama : more loader cleanup, better error checking * cuda : fix warning * llama : still use mmap for loading old models, but copy the data to a host buffer * add review note * llama : remove ffn tensor counting + add sanity check ggml-ci * convert : fix handling of n_experts == None ggml-ci * imatrix : fix ncall counters * llama : produce error if imatrix size does not match * quantize : terminate on errors + trace logs ggml-ci * metal : pad shared memory to 16 bytes --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-04-03 13:07:05 +00:00
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
tensors.append((new_name, data_torch))
return tensors
else:
return []
ggml : mul_mat_id use the same tensor for all the experts (#6387) * ggml : update mul_mat_id to use the same tensor for all the experts * update cuda * minor * update metal * update test-backend-ops * fix cuda * Update ggml-metal.m Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * update convert.py * update convert-hf-to-gguf.py * update convert.py for mixtral hf models * Update convert-hf-to-gguf.py Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * cuda : support non-pow-2 number of experts * allow quantize to work for split and merged experts models in the same way * cleanup + disable mmap automatically with split tensors models * update imatrix * test-backend-ops : test qwen argsort * update grok model loading * llama : add merged experts tensors to the grok tensor map * minor * gguf : bump version * fix quantizing of merged experts * convert-hf-to-gguf.py : update grok (untested) * make linter happy * cuda/argsort : use shared memory instead of pool memory * convert : fix grok tensor names * metal : add support for non-pow-2 argsort * llama : more loader cleanup, better error checking * cuda : fix warning * llama : still use mmap for loading old models, but copy the data to a host buffer * add review note * llama : remove ffn tensor counting + add sanity check ggml-ci * convert : fix handling of n_experts == None ggml-ci * imatrix : fix ncall counters * llama : produce error if imatrix size does not match * quantize : terminate on errors + trace logs ggml-ci * metal : pad shared memory to 16 bytes --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-04-03 13:07:05 +00:00
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
return [(self.map_tensor_name(name), data_torch)]
ggml : mul_mat_id use the same tensor for all the experts (#6387) * ggml : update mul_mat_id to use the same tensor for all the experts * update cuda * minor * update metal * update test-backend-ops * fix cuda * Update ggml-metal.m Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * update convert.py * update convert-hf-to-gguf.py * update convert.py for mixtral hf models * Update convert-hf-to-gguf.py Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * cuda : support non-pow-2 number of experts * allow quantize to work for split and merged experts models in the same way * cleanup + disable mmap automatically with split tensors models * update imatrix * test-backend-ops : test qwen argsort * update grok model loading * llama : add merged experts tensors to the grok tensor map * minor * gguf : bump version * fix quantizing of merged experts * convert-hf-to-gguf.py : update grok (untested) * make linter happy * cuda/argsort : use shared memory instead of pool memory * convert : fix grok tensor names * metal : add support for non-pow-2 argsort * llama : more loader cleanup, better error checking * cuda : fix warning * llama : still use mmap for loading old models, but copy the data to a host buffer * add review note * llama : remove ffn tensor counting + add sanity check ggml-ci * convert : fix handling of n_experts == None ggml-ci * imatrix : fix ncall counters * llama : produce error if imatrix size does not match * quantize : terminate on errors + trace logs ggml-ci * metal : pad shared memory to 16 bytes --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-04-03 13:07:05 +00:00
llama : add grok-1 support (#6204) * Add support for Grok model architecture * Revert convert-hf-to-gguf to default options * Fixed f_norm_rms_eps bug * Fix whitespaces * llama : fix grok rope type * llama : minor --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-03-23 16:41:53 +00:00
model: support arch `DbrxForCausalLM` (#6515) * model: dbrx convert to gguf #6344 * llama: support dbrx #6344 * doc: dbrx: add the model as supported * scripts: get-wikitext-2 add unzip * llama: increase maximum experts allowed * llama: factorize moe graph implementation between grok, mixtral and dbrx --------- Co-authored-by: Megha Agarwal <16129366+megha95@users.noreply.github.com>
2024-04-13 09:33:52 +00:00
@Model.register("DbrxForCausalLM")
class DbrxModel(Model):
model_arch = gguf.MODEL_ARCH.DBRX
def set_gguf_parameters(self):
ffn_config = self.hparams["ffn_config"]
attn_config = self.hparams["attn_config"]
self.gguf_writer.add_name(self.hparams["model_type"])
self.gguf_writer.add_block_count(self.hparams["n_layers"])
self.gguf_writer.add_context_length(self.hparams["max_seq_len"])
self.gguf_writer.add_embedding_length(self.hparams["d_model"])
self.gguf_writer.add_feed_forward_length(ffn_config["ffn_hidden_size"])
self.gguf_writer.add_head_count(self.hparams["n_heads"])
self.gguf_writer.add_head_count_kv(attn_config["kv_n_heads"])
self.gguf_writer.add_rope_freq_base(attn_config["rope_theta"])
self.gguf_writer.add_clamp_kqv(attn_config["clip_qkv"])
self.gguf_writer.add_file_type(self.ftype)
self.gguf_writer.add_expert_count(ffn_config["moe_num_experts"])
self.gguf_writer.add_expert_used_count(ffn_config["moe_top_k"])
self.gguf_writer.add_layer_norm_eps(1e-5)
self.gguf_writer.add_file_type(self.ftype)
convert.py : add python logging instead of print() (#6511) * convert.py: add python logging instead of print() * convert.py: verbose flag takes priority over dump flag log suppression * convert.py: named instance logging * convert.py: use explicit logger id string * convert.py: convert extra print() to named logger * convert.py: sys.stderr.write --> logger.error * *.py: Convert all python scripts to use logging module * requirements.txt: remove extra line * flake8: update flake8 ignore and exclude to match ci settings * gh-actions: add flake8-no-print to flake8 lint step * pre-commit: add flake8-no-print to flake8 and also update pre-commit version * convert-hf-to-gguf.py: print() to logger conversion * *.py: logging basiconfig refactor to use conditional expression * *.py: removed commented out logging * fixup! *.py: logging basiconfig refactor to use conditional expression * constant.py: logger.error then exit should be a raise exception instead * *.py: Convert logger error and sys.exit() into a raise exception (for atypical error) * gguf-convert-endian.py: refactor convert_byteorder() to use tqdm progressbar * verify-checksum-model.py: This is the result of the program, it should be printed to stdout. * compare-llama-bench.py: add blank line for readability during missing repo response * reader.py: read_gguf_file() use print() over logging * convert.py: warning goes to stderr and won't hurt the dump output * gguf-dump.py: dump_metadata() should print to stdout * convert-hf-to-gguf.py: print --> logger.debug or ValueError() * verify-checksum-models.py: use print() for printing table * *.py: refactor logging.basicConfig() * gguf-py/gguf/*.py: use __name__ as logger name Since they will be imported and not run directly. * python-lint.yml: use .flake8 file instead * constants.py: logger no longer required * convert-hf-to-gguf.py: add additional logging * convert-hf-to-gguf.py: print() --> logger * *.py: fix flake8 warnings * revert changes to convert-hf-to-gguf.py for get_name() * convert-hf-to-gguf-update.py: use triple quoted f-string instead * *.py: accidentally corrected the wrong line * *.py: add compilade warning suggestions and style fixes
2024-05-03 19:36:41 +00:00
logger.info(f"gguf: file type = {self.ftype}")
model: support arch `DbrxForCausalLM` (#6515) * model: dbrx convert to gguf #6344 * llama: support dbrx #6344 * doc: dbrx: add the model as supported * scripts: get-wikitext-2 add unzip * llama: increase maximum experts allowed * llama: factorize moe graph implementation between grok, mixtral and dbrx --------- Co-authored-by: Megha Agarwal <16129366+megha95@users.noreply.github.com>
2024-04-13 09:33:52 +00:00
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
del bid # unused
n_expert = self.hparams["ffn_config"]["moe_num_experts"]
n_ff = self.hparams["ffn_config"]["ffn_hidden_size"]
n_embd = self.hparams["d_model"]
# Specific behavior for experts tensors: suffix .weight, view as 3D and transpose
# original implementation expects (n_expert, n_ff, n_embd) for all experts weights
# But llama.cpp moe graph works differently
# AND the dimensions in ggml are typically in the reverse order of the pytorch dimensions
# so (n_expert, n_ff, n_embd) in pytorch is {n_embd, n_ff, n_expert} in ggml_tensor
exp_tensor_names = {"ffn.experts.mlp.w1": None, # LLM_TENSOR_FFN_GATE_EXPS ggml_tensor->ne{n_embd, n_ff, n_expert}
"ffn.experts.mlp.w2": (0, 2, 1), # LLM_TENSOR_FFN_DOWN_EXPS ggml_tensor->ne{n_ff, n_embd, n_expert}
"ffn.experts.mlp.v1": None} # LLM_TENSOR_FFN_UP_EXPS ggml_tensor->ne{n_embd, n_ff, n_expert}
experts = False
for exp_tensor_name in exp_tensor_names.keys():
if name.find(exp_tensor_name) != -1 and name.find(".weight") == -1:
experts = True
data_torch = data_torch.view(n_expert, n_ff, n_embd)
if (permute_tensor := exp_tensor_names[exp_tensor_name]) is not None:
data_torch = data_torch.permute(*permute_tensor)
break
model: support arch `DbrxForCausalLM` (#6515) * model: dbrx convert to gguf #6344 * llama: support dbrx #6344 * doc: dbrx: add the model as supported * scripts: get-wikitext-2 add unzip * llama: increase maximum experts allowed * llama: factorize moe graph implementation between grok, mixtral and dbrx --------- Co-authored-by: Megha Agarwal <16129366+megha95@users.noreply.github.com>
2024-04-13 09:33:52 +00:00
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
# map tensor names
# In MoE models the ffn tensors are typically most of the model weights,
# and need to be quantizable. Quantize expects tensor names to be suffixed by .weight.
# Every other model has the weight names ending in .weight,
# let's assume that is the convention which is not the case for dbrx:
# https://huggingface.co/databricks/dbrx-instruct/blob/main/model.safetensors.index.json#L15
new_name = self.map_tensor_name(name if not experts else name + ".weight", try_suffixes=(".weight",))
model: support arch `DbrxForCausalLM` (#6515) * model: dbrx convert to gguf #6344 * llama: support dbrx #6344 * doc: dbrx: add the model as supported * scripts: get-wikitext-2 add unzip * llama: increase maximum experts allowed * llama: factorize moe graph implementation between grok, mixtral and dbrx --------- Co-authored-by: Megha Agarwal <16129366+megha95@users.noreply.github.com>
2024-04-13 09:33:52 +00:00
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
return [(new_name, data_torch)]
model: support arch `DbrxForCausalLM` (#6515) * model: dbrx convert to gguf #6344 * llama: support dbrx #6344 * doc: dbrx: add the model as supported * scripts: get-wikitext-2 add unzip * llama: increase maximum experts allowed * llama: factorize moe graph implementation between grok, mixtral and dbrx --------- Co-authored-by: Megha Agarwal <16129366+megha95@users.noreply.github.com>
2024-04-13 09:33:52 +00:00
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
def extra_f16_tensors(self, name: str, new_name: str, bid: int | None, n_dims: int) -> bool:
del name, new_name, bid # unused
model: support arch `DbrxForCausalLM` (#6515) * model: dbrx convert to gguf #6344 * llama: support dbrx #6344 * doc: dbrx: add the model as supported * scripts: get-wikitext-2 add unzip * llama: increase maximum experts allowed * llama: factorize moe graph implementation between grok, mixtral and dbrx --------- Co-authored-by: Megha Agarwal <16129366+megha95@users.noreply.github.com>
2024-04-13 09:33:52 +00:00
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
return n_dims > 1
model: support arch `DbrxForCausalLM` (#6515) * model: dbrx convert to gguf #6344 * llama: support dbrx #6344 * doc: dbrx: add the model as supported * scripts: get-wikitext-2 add unzip * llama: increase maximum experts allowed * llama: factorize moe graph implementation between grok, mixtral and dbrx --------- Co-authored-by: Megha Agarwal <16129366+megha95@users.noreply.github.com>
2024-04-13 09:33:52 +00:00
convert-hf : make model class definitions self-contained (#5825)
2024-03-02 17:21:47 +00:00
@Model.register("MiniCPMForCausalLM")
llama : add MiniCPM support (#5346) * support minicpm arch. * fix tab/space typo. * convert minicpm model via convert-hf-gguf.py * try to make tokenizer work * fix bug for quantize minicpm * fix for flake8 lint * remove convert-minicpm.py * fix for editorconfig * correct minicpm model type (size) * constants expanded for minicpm * Minor change of the constant names for minicpm
2024-02-07 06:15:56 +00:00
class MiniCPMModel(Model):
convert-hf : make model class definitions self-contained (#5825)
2024-03-02 17:21:47 +00:00
model_arch = gguf.MODEL_ARCH.MINICPM
llama : add MiniCPM support (#5346) * support minicpm arch. * fix tab/space typo. * convert minicpm model via convert-hf-gguf.py * try to make tokenizer work * fix bug for quantize minicpm * fix for flake8 lint * remove convert-minicpm.py * fix for editorconfig * correct minicpm model type (size) * constants expanded for minicpm * Minor change of the constant names for minicpm
2024-02-07 06:15:56 +00:00
def set_gguf_parameters(self):
block_count = self.hparams["num_hidden_layers"]
self.gguf_writer.add_name("MiniCPM")
self.gguf_writer.add_context_length(self.hparams["max_position_embeddings"])
self.gguf_writer.add_embedding_length(self.hparams["hidden_size"])
self.gguf_writer.add_block_count(block_count)
llama : fix MiniCPM (#5392) * fix bug for norm_rms_eps missing * to align with the same order as convert.py for model write * fix: undo HF models permute tensor * update for flake8 lint
2024-02-08 10:36:19 +00:00
self.gguf_writer.add_feed_forward_length(self.hparams["intermediate_size"])
self.gguf_writer.add_rope_dimension_count(self.hparams["hidden_size"] // self.hparams["num_attention_heads"])
llama : add MiniCPM support (#5346) * support minicpm arch. * fix tab/space typo. * convert minicpm model via convert-hf-gguf.py * try to make tokenizer work * fix bug for quantize minicpm * fix for flake8 lint * remove convert-minicpm.py * fix for editorconfig * correct minicpm model type (size) * constants expanded for minicpm * Minor change of the constant names for minicpm
2024-02-07 06:15:56 +00:00
self.gguf_writer.add_head_count(self.hparams["num_attention_heads"])
self.gguf_writer.add_head_count_kv(self.hparams["num_key_value_heads"])
self.gguf_writer.add_layer_norm_rms_eps(self.hparams["rms_norm_eps"])
self.gguf_writer.add_file_type(self.ftype)
def set_vocab(self):
convert : refactor vocab selection logic (#6355)
2024-03-28 15:44:36 +00:00
self._set_vocab_llama_hf()
llama : add MiniCPM support (#5346) * support minicpm arch. * fix tab/space typo. * convert minicpm model via convert-hf-gguf.py * try to make tokenizer work * fix bug for quantize minicpm * fix for flake8 lint * remove convert-minicpm.py * fix for editorconfig * correct minicpm model type (size) * constants expanded for minicpm * Minor change of the constant names for minicpm
2024-02-07 06:15:56 +00:00
llama : fix MiniCPM (#5392) * fix bug for norm_rms_eps missing * to align with the same order as convert.py for model write * fix: undo HF models permute tensor * update for flake8 lint
2024-02-08 10:36:19 +00:00
def _reverse_hf_permute(self, weights: Tensor, n_head: int, n_kv_head: int | None = None) -> Tensor:
if n_kv_head is not None and n_head != n_kv_head:
n_head //= n_kv_head
return (
weights.reshape(n_head, 2, weights.shape[0] // n_head // 2, *weights.shape[1:])
.swapaxes(1, 2)
.reshape(weights.shape)
)
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
del bid # unused
llama : fix MiniCPM (#5392) * fix bug for norm_rms_eps missing * to align with the same order as convert.py for model write * fix: undo HF models permute tensor * update for flake8 lint
2024-02-08 10:36:19 +00:00
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
n_head = self.hparams["num_attention_heads"]
n_kv_head = self.hparams.get("num_key_value_heads")
llama : fix MiniCPM (#5392) * fix bug for norm_rms_eps missing * to align with the same order as convert.py for model write * fix: undo HF models permute tensor * update for flake8 lint
2024-02-08 10:36:19 +00:00
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
# HF models permute some of the tensors, so we need to undo that
if name.endswith(("q_proj.weight")):
data_torch = self._reverse_hf_permute(data_torch, n_head, n_head)
if name.endswith(("k_proj.weight")):
data_torch = self._reverse_hf_permute(data_torch, n_head, n_kv_head)
llama : fix MiniCPM (#5392) * fix bug for norm_rms_eps missing * to align with the same order as convert.py for model write * fix: undo HF models permute tensor * update for flake8 lint
2024-02-08 10:36:19 +00:00
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
return [(self.map_tensor_name(name), data_torch)]
llama : fix MiniCPM (#5392) * fix bug for norm_rms_eps missing * to align with the same order as convert.py for model write * fix: undo HF models permute tensor * update for flake8 lint
2024-02-08 10:36:19 +00:00
llama : add MiniCPM support (#5346) * support minicpm arch. * fix tab/space typo. * convert minicpm model via convert-hf-gguf.py * try to make tokenizer work * fix bug for quantize minicpm * fix for flake8 lint * remove convert-minicpm.py * fix for editorconfig * correct minicpm model type (size) * constants expanded for minicpm * Minor change of the constant names for minicpm
2024-02-07 06:15:56 +00:00
convert-hf : make model class definitions self-contained (#5825)
2024-03-02 17:21:47 +00:00
@Model.register("QWenLMHeadModel")
llama : add Qwen support (#4281) * enable qwen to llama.cpp * llama : do not GPU split bias tensors --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-12-01 18:16:31 +00:00
class QwenModel(Model):
convert-hf : make model class definitions self-contained (#5825)
2024-03-02 17:21:47 +00:00
model_arch = gguf.MODEL_ARCH.QWEN
llama : add Qwen support (#4281) * enable qwen to llama.cpp * llama : do not GPU split bias tensors --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-12-01 18:16:31 +00:00
@staticmethod
def token_bytes_to_string(b):
from transformers.models.gpt2.tokenization_gpt2 import bytes_to_unicode
byte_encoder = bytes_to_unicode()
return ''.join([byte_encoder[ord(char)] for char in b.decode('latin-1')])
@staticmethod
convert : partially revert PR #4818 (#5041)
2024-01-20 23:14:18 +00:00
def bpe(mergeable_ranks: dict[bytes, int], token: bytes, max_rank: int | None = None) -> list[bytes]:
llama : add Qwen support (#4281) * enable qwen to llama.cpp * llama : do not GPU split bias tensors --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-12-01 18:16:31 +00:00
parts = [bytes([b]) for b in token]
while True:
min_idx = None
min_rank = None
for i, pair in enumerate(zip(parts[:-1], parts[1:])):
rank = mergeable_ranks.get(pair[0] + pair[1])
if rank is not None and (min_rank is None or rank < min_rank):
min_idx = i
min_rank = rank
if min_rank is None or (max_rank is not None and min_rank >= max_rank):
break
assert min_idx is not None
parts = parts[:min_idx] + [parts[min_idx] + parts[min_idx + 1]] + parts[min_idx + 2:]
return parts
def set_vocab(self):
llama : support StableLM 2 1.6B (#5052) * llama : support StableLM 2 1.6B * convert : fix Qwen's set_vocab wrongly naming all special tokens [PAD{id}] * convert : refactor Qwen's set_vocab to use it for StableLM 2 too * nix : add tiktoken to llama-python-extra * convert : use presence of tokenizer.json to determine StableLM tokenizer loader It's a less arbitrary heuristic than the vocab size.
2024-01-22 11:21:52 +00:00
self._set_vocab_qwen()
llama : add Qwen support (#4281) * enable qwen to llama.cpp * llama : do not GPU split bias tensors --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-12-01 18:16:31 +00:00
def set_gguf_parameters(self):
self.gguf_writer.add_name("Qwen")
self.gguf_writer.add_context_length(self.hparams["max_position_embeddings"])
self.gguf_writer.add_block_count(self.hparams["num_hidden_layers"])
self.gguf_writer.add_embedding_length(self.hparams["hidden_size"])
self.gguf_writer.add_feed_forward_length(self.hparams["intermediate_size"])
self.gguf_writer.add_rope_freq_base(self.hparams["rotary_emb_base"])
self.gguf_writer.add_rope_dimension_count(self.hparams["hidden_size"] // self.hparams["num_attention_heads"])
self.gguf_writer.add_head_count(self.hparams["num_attention_heads"])
self.gguf_writer.add_layer_norm_rms_eps(self.hparams["layer_norm_epsilon"])
convert-hf : support direct Q8_0 conversion (#7234) * convert-hf : support q8_0 conversion * convert-hf : add missing ftype This was messing with the checksums otherwise. * convert-hf : add missing ftype to Baichuan and Xverse I didn't notice these on my first pass.
2024-05-13 18:10:51 +00:00
self.gguf_writer.add_file_type(self.ftype)
llama : add Qwen support (#4281) * enable qwen to llama.cpp * llama : do not GPU split bias tensors --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-12-01 18:16:31 +00:00
llama : add phi-2 + fix NeoX rope + ggml_mul_mat_set_prec (#4490) * phi2 implementation * fix breaking change * phi-2 : various fixes * phi-2 : use layer norm eps * py : whitespaces * llama : fix meta KV override bug * convert : phi don't add BOS token * convert : revert "added_tokens_decoder" change * phi-2 : scale Q instead of KQ for better precision * ggml : fix NeoX rope to rotate just first n_dims * cuda : less diff in the rope_neox kernel * ggml : add ggml_mul_mat_set_prec ggml-ci * Update ggml-cuda.cu Co-authored-by: slaren <slarengh@gmail.com> * Update ggml-cuda.cu Co-authored-by: slaren <slarengh@gmail.com> * cuda : ggml_cuda_op_mul_mat_cublas support F32 precision * cuda : remove oboslete comment --------- Co-authored-by: Ebey Abraham <ebeyabraham@microsoft.com> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> Co-authored-by: slaren <slarengh@gmail.com>
2023-12-18 17:27:47 +00:00
convert-hf : make model class definitions self-contained (#5825)
2024-03-02 17:21:47 +00:00
@Model.register("Qwen2ForCausalLM")
class Qwen2Model(Model):
model_arch = gguf.MODEL_ARCH.QWEN2
convert : add support of codeqwen due to tokenizer (#6707) * add support of codeqwen due to tokenizer * override load_hparams * fix typo * fix load_params * convert : fix whitespace --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-04-24 07:16:21 +00:00
def set_vocab(self):
try:
self._set_vocab_sentencepiece()
except FileNotFoundError:
self._set_vocab_gpt2()
convert-hf : make model class definitions self-contained (#5825)
2024-03-02 17:21:47 +00:00
llama : add qwen2moe (#6074) * support qwen2moe * fix-review * metal : support unary ops for nelements % 4 != 0 * metal : require contiguousness for float4 unary kernels * metal : require contiguousness for float4 unary kernels (cont) * fix-review * names : for brevity "SHARED_EXP" -> "SHEXP" * llama : reuse build_moe_ffn() * llama : add model type name --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-04-16 15:40:48 +00:00
@Model.register("Qwen2MoeForCausalLM")
class Qwen2MoeModel(Model):
model_arch = gguf.MODEL_ARCH.QWEN2MOE
def set_gguf_parameters(self):
super().set_gguf_parameters()
if (n_experts := self.hparams.get("num_experts")) is not None:
self.gguf_writer.add_expert_count(n_experts)
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
_experts: list[dict[str, Tensor]] | None = None
llama : add qwen2moe (#6074) * support qwen2moe * fix-review * metal : support unary ops for nelements % 4 != 0 * metal : require contiguousness for float4 unary kernels * metal : require contiguousness for float4 unary kernels (cont) * fix-review * names : for brevity "SHARED_EXP" -> "SHEXP" * llama : reuse build_moe_ffn() * llama : add model type name --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-04-16 15:40:48 +00:00
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
# process the experts separately
if name.find("experts") != -1:
n_experts = self.hparams["num_experts"]
assert bid is not None
llama : add qwen2moe (#6074) * support qwen2moe * fix-review * metal : support unary ops for nelements % 4 != 0 * metal : require contiguousness for float4 unary kernels * metal : require contiguousness for float4 unary kernels (cont) * fix-review * names : for brevity "SHARED_EXP" -> "SHEXP" * llama : reuse build_moe_ffn() * llama : add model type name --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-04-16 15:40:48 +00:00
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
if self._experts is None:
self._experts = [{} for _ in range(self.block_count)]
llama : add qwen2moe (#6074) * support qwen2moe * fix-review * metal : support unary ops for nelements % 4 != 0 * metal : require contiguousness for float4 unary kernels * metal : require contiguousness for float4 unary kernels (cont) * fix-review * names : for brevity "SHARED_EXP" -> "SHEXP" * llama : reuse build_moe_ffn() * llama : add model type name --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-04-16 15:40:48 +00:00
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
self._experts[bid][name] = data_torch
llama : add qwen2moe (#6074) * support qwen2moe * fix-review * metal : support unary ops for nelements % 4 != 0 * metal : require contiguousness for float4 unary kernels * metal : require contiguousness for float4 unary kernels (cont) * fix-review * names : for brevity "SHARED_EXP" -> "SHEXP" * llama : reuse build_moe_ffn() * llama : add model type name --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-04-16 15:40:48 +00:00
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
if len(self._experts[bid]) >= n_experts * 3:
tensors: list[tuple[str, Tensor]] = []
llama : add qwen2moe (#6074) * support qwen2moe * fix-review * metal : support unary ops for nelements % 4 != 0 * metal : require contiguousness for float4 unary kernels * metal : require contiguousness for float4 unary kernels (cont) * fix-review * names : for brevity "SHARED_EXP" -> "SHEXP" * llama : reuse build_moe_ffn() * llama : add model type name --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-04-16 15:40:48 +00:00
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
# merge the experts into a single 3d tensor
for w_name in ["down_proj", "gate_proj", "up_proj"]:
datas: list[Tensor] = []
llama : add qwen2moe (#6074) * support qwen2moe * fix-review * metal : support unary ops for nelements % 4 != 0 * metal : require contiguousness for float4 unary kernels * metal : require contiguousness for float4 unary kernels (cont) * fix-review * names : for brevity "SHARED_EXP" -> "SHEXP" * llama : reuse build_moe_ffn() * llama : add model type name --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-04-16 15:40:48 +00:00
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
for xid in range(n_experts):
ename = f"model.layers.{bid}.mlp.experts.{xid}.{w_name}.weight"
datas.append(self._experts[bid][ename])
del self._experts[bid][ename]
llama : add qwen2moe (#6074) * support qwen2moe * fix-review * metal : support unary ops for nelements % 4 != 0 * metal : require contiguousness for float4 unary kernels * metal : require contiguousness for float4 unary kernels (cont) * fix-review * names : for brevity "SHARED_EXP" -> "SHEXP" * llama : reuse build_moe_ffn() * llama : add model type name --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-04-16 15:40:48 +00:00
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
data_torch = torch.stack(datas, dim=0)
llama : add qwen2moe (#6074) * support qwen2moe * fix-review * metal : support unary ops for nelements % 4 != 0 * metal : require contiguousness for float4 unary kernels * metal : require contiguousness for float4 unary kernels (cont) * fix-review * names : for brevity "SHARED_EXP" -> "SHEXP" * llama : reuse build_moe_ffn() * llama : add model type name --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-04-16 15:40:48 +00:00
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
merged_name = f"model.layers.{bid}.mlp.experts.{w_name}.weight"
llama : add qwen2moe (#6074) * support qwen2moe * fix-review * metal : support unary ops for nelements % 4 != 0 * metal : require contiguousness for float4 unary kernels * metal : require contiguousness for float4 unary kernels (cont) * fix-review * names : for brevity "SHARED_EXP" -> "SHEXP" * llama : reuse build_moe_ffn() * llama : add model type name --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-04-16 15:40:48 +00:00
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
new_name = self.map_tensor_name(merged_name)
llama : add qwen2moe (#6074) * support qwen2moe * fix-review * metal : support unary ops for nelements % 4 != 0 * metal : require contiguousness for float4 unary kernels * metal : require contiguousness for float4 unary kernels (cont) * fix-review * names : for brevity "SHARED_EXP" -> "SHEXP" * llama : reuse build_moe_ffn() * llama : add model type name --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-04-16 15:40:48 +00:00
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
tensors.append((new_name, data_torch))
return tensors
else:
return []
llama : add qwen2moe (#6074) * support qwen2moe * fix-review * metal : support unary ops for nelements % 4 != 0 * metal : require contiguousness for float4 unary kernels * metal : require contiguousness for float4 unary kernels (cont) * fix-review * names : for brevity "SHARED_EXP" -> "SHEXP" * llama : reuse build_moe_ffn() * llama : add model type name --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-04-16 15:40:48 +00:00
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
return [(self.map_tensor_name(name), data_torch)]
llama : add qwen2moe (#6074) * support qwen2moe * fix-review * metal : support unary ops for nelements % 4 != 0 * metal : require contiguousness for float4 unary kernels * metal : require contiguousness for float4 unary kernels (cont) * fix-review * names : for brevity "SHARED_EXP" -> "SHEXP" * llama : reuse build_moe_ffn() * llama : add model type name --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-04-16 15:40:48 +00:00
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
def write_tensors(self):
super().write_tensors()
llama : add qwen2moe (#6074) * support qwen2moe * fix-review * metal : support unary ops for nelements % 4 != 0 * metal : require contiguousness for float4 unary kernels * metal : require contiguousness for float4 unary kernels (cont) * fix-review * names : for brevity "SHARED_EXP" -> "SHEXP" * llama : reuse build_moe_ffn() * llama : add model type name --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-04-16 15:40:48 +00:00
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
if self._experts is not None:
# flatten `list[dict[str, Tensor]]` into `list[str]`
experts = [k for d in self._experts for k in d.keys()]
if len(experts) > 0:
raise ValueError(f"Unprocessed experts: {experts}")
llama : add qwen2moe (#6074) * support qwen2moe * fix-review * metal : support unary ops for nelements % 4 != 0 * metal : require contiguousness for float4 unary kernels * metal : require contiguousness for float4 unary kernels (cont) * fix-review * names : for brevity "SHARED_EXP" -> "SHEXP" * llama : reuse build_moe_ffn() * llama : add model type name --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-04-16 15:40:48 +00:00
convert-hf : make model class definitions self-contained (#5825)
2024-03-02 17:21:47 +00:00
@Model.register("GPT2LMHeadModel")
gpt2 : Add gpt2 architecture integration (#4555)
2023-12-28 14:03:57 +00:00
class GPT2Model(Model):
convert-hf : make model class definitions self-contained (#5825)
2024-03-02 17:21:47 +00:00
model_arch = gguf.MODEL_ARCH.GPT2
gpt2 : Add gpt2 architecture integration (#4555)
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def set_gguf_parameters(self):
self.gguf_writer.add_name(self.dir_model.name)
self.gguf_writer.add_block_count(self.hparams["n_layer"])
self.gguf_writer.add_context_length(self.hparams["n_ctx"])
self.gguf_writer.add_embedding_length(self.hparams["n_embd"])
self.gguf_writer.add_feed_forward_length(4 * self.hparams["n_embd"])
self.gguf_writer.add_head_count(self.hparams["n_head"])
self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_epsilon"])
self.gguf_writer.add_file_type(self.ftype)
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
del bid # unused
gpt2 : Add gpt2 architecture integration (#4555)
2023-12-28 14:03:57 +00:00
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
tensors: list[tuple[str, Tensor]] = []
gpt2 : Add gpt2 architecture integration (#4555)
2023-12-28 14:03:57 +00:00
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
# we don't need these
if name.endswith((".attn.bias", ".attn.masked_bias")):
return tensors
gpt2 : Add gpt2 architecture integration (#4555)
2023-12-28 14:03:57 +00:00
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
if name.endswith((".c_attn.weight", ".c_proj.weight", ".c_fc.weight", ".c_proj.weight")):
data_torch = data_torch.transpose(1, 0)
gpt2 : Add gpt2 architecture integration (#4555)
2023-12-28 14:03:57 +00:00
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
new_name = self.map_tensor_name(name)
gpt2 : Add gpt2 architecture integration (#4555)
2023-12-28 14:03:57 +00:00
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
tensors.append((new_name, data_torch))
gpt2 : Add gpt2 architecture integration (#4555)
2023-12-28 14:03:57 +00:00
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
# note: GPT2 output is tied to (same as) wte in original model
if new_name == self.format_tensor_name(gguf.MODEL_TENSOR.TOKEN_EMBD):
tensors.append((self.format_tensor_name(gguf.MODEL_TENSOR.OUTPUT), data_torch))
gpt2 : Add gpt2 architecture integration (#4555)
2023-12-28 14:03:57 +00:00
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
return tensors
gpt2 : Add gpt2 architecture integration (#4555)
2023-12-28 14:03:57 +00:00
convert-hf : make model class definitions self-contained (#5825)
2024-03-02 17:21:47 +00:00
@Model.register("PhiForCausalLM")
llama : add phi-2 + fix NeoX rope + ggml_mul_mat_set_prec (#4490) * phi2 implementation * fix breaking change * phi-2 : various fixes * phi-2 : use layer norm eps * py : whitespaces * llama : fix meta KV override bug * convert : phi don't add BOS token * convert : revert "added_tokens_decoder" change * phi-2 : scale Q instead of KQ for better precision * ggml : fix NeoX rope to rotate just first n_dims * cuda : less diff in the rope_neox kernel * ggml : add ggml_mul_mat_set_prec ggml-ci * Update ggml-cuda.cu Co-authored-by: slaren <slarengh@gmail.com> * Update ggml-cuda.cu Co-authored-by: slaren <slarengh@gmail.com> * cuda : ggml_cuda_op_mul_mat_cublas support F32 precision * cuda : remove oboslete comment --------- Co-authored-by: Ebey Abraham <ebeyabraham@microsoft.com> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> Co-authored-by: slaren <slarengh@gmail.com>
2023-12-18 17:27:47 +00:00
class Phi2Model(Model):
convert-hf : make model class definitions self-contained (#5825)
2024-03-02 17:21:47 +00:00
model_arch = gguf.MODEL_ARCH.PHI2
llama : add phi-2 + fix NeoX rope + ggml_mul_mat_set_prec (#4490) * phi2 implementation * fix breaking change * phi-2 : various fixes * phi-2 : use layer norm eps * py : whitespaces * llama : fix meta KV override bug * convert : phi don't add BOS token * convert : revert "added_tokens_decoder" change * phi-2 : scale Q instead of KQ for better precision * ggml : fix NeoX rope to rotate just first n_dims * cuda : less diff in the rope_neox kernel * ggml : add ggml_mul_mat_set_prec ggml-ci * Update ggml-cuda.cu Co-authored-by: slaren <slarengh@gmail.com> * Update ggml-cuda.cu Co-authored-by: slaren <slarengh@gmail.com> * cuda : ggml_cuda_op_mul_mat_cublas support F32 precision * cuda : remove oboslete comment --------- Co-authored-by: Ebey Abraham <ebeyabraham@microsoft.com> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> Co-authored-by: slaren <slarengh@gmail.com>
2023-12-18 17:27:47 +00:00
def set_gguf_parameters(self):
llama : add support for Nomic Embed (#5468)
2024-02-13 17:03:53 +00:00
block_count = self.find_hparam(["num_hidden_layers", "n_layer"])
convert : update phi-2 to latest HF repo (#4903) * convert : update phi-2 to latest HF repo ggml-ci * py : try to fix flake stuff
2024-01-13 11:44:37 +00:00
llama : add support for Nomic Embed (#5468)
2024-02-13 17:03:53 +00:00
rot_pct = self.find_hparam(["partial_rotary_factor"])
n_embd = self.find_hparam(["hidden_size", "n_embd"])
n_head = self.find_hparam(["num_attention_heads", "n_head"])
llama : add phi-2 + fix NeoX rope + ggml_mul_mat_set_prec (#4490) * phi2 implementation * fix breaking change * phi-2 : various fixes * phi-2 : use layer norm eps * py : whitespaces * llama : fix meta KV override bug * convert : phi don't add BOS token * convert : revert "added_tokens_decoder" change * phi-2 : scale Q instead of KQ for better precision * ggml : fix NeoX rope to rotate just first n_dims * cuda : less diff in the rope_neox kernel * ggml : add ggml_mul_mat_set_prec ggml-ci * Update ggml-cuda.cu Co-authored-by: slaren <slarengh@gmail.com> * Update ggml-cuda.cu Co-authored-by: slaren <slarengh@gmail.com> * cuda : ggml_cuda_op_mul_mat_cublas support F32 precision * cuda : remove oboslete comment --------- Co-authored-by: Ebey Abraham <ebeyabraham@microsoft.com> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> Co-authored-by: slaren <slarengh@gmail.com>
2023-12-18 17:27:47 +00:00
self.gguf_writer.add_name("Phi2")
llama : add support for Nomic Embed (#5468)
2024-02-13 17:03:53 +00:00
self.gguf_writer.add_context_length(self.find_hparam(["n_positions", "max_position_embeddings"]))
convert : update phi-2 to latest HF repo (#4903) * convert : update phi-2 to latest HF repo ggml-ci * py : try to fix flake stuff
2024-01-13 11:44:37 +00:00
self.gguf_writer.add_embedding_length(n_embd)
self.gguf_writer.add_feed_forward_length(4 * n_embd)
llama : add phi-2 + fix NeoX rope + ggml_mul_mat_set_prec (#4490) * phi2 implementation * fix breaking change * phi-2 : various fixes * phi-2 : use layer norm eps * py : whitespaces * llama : fix meta KV override bug * convert : phi don't add BOS token * convert : revert "added_tokens_decoder" change * phi-2 : scale Q instead of KQ for better precision * ggml : fix NeoX rope to rotate just first n_dims * cuda : less diff in the rope_neox kernel * ggml : add ggml_mul_mat_set_prec ggml-ci * Update ggml-cuda.cu Co-authored-by: slaren <slarengh@gmail.com> * Update ggml-cuda.cu Co-authored-by: slaren <slarengh@gmail.com> * cuda : ggml_cuda_op_mul_mat_cublas support F32 precision * cuda : remove oboslete comment --------- Co-authored-by: Ebey Abraham <ebeyabraham@microsoft.com> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> Co-authored-by: slaren <slarengh@gmail.com>
2023-12-18 17:27:47 +00:00
self.gguf_writer.add_block_count(block_count)
convert : update phi-2 to latest HF repo (#4903) * convert : update phi-2 to latest HF repo ggml-ci * py : try to fix flake stuff
2024-01-13 11:44:37 +00:00
self.gguf_writer.add_head_count(n_head)
self.gguf_writer.add_head_count_kv(n_head)
llama : add support for Nomic Embed (#5468)
2024-02-13 17:03:53 +00:00
self.gguf_writer.add_layer_norm_eps(self.find_hparam(["layer_norm_epsilon", "layer_norm_eps"]))
convert : update phi-2 to latest HF repo (#4903) * convert : update phi-2 to latest HF repo ggml-ci * py : try to fix flake stuff
2024-01-13 11:44:37 +00:00
self.gguf_writer.add_rope_dimension_count(int(rot_pct * n_embd) // n_head)
llama : add phi-2 + fix NeoX rope + ggml_mul_mat_set_prec (#4490) * phi2 implementation * fix breaking change * phi-2 : various fixes * phi-2 : use layer norm eps * py : whitespaces * llama : fix meta KV override bug * convert : phi don't add BOS token * convert : revert "added_tokens_decoder" change * phi-2 : scale Q instead of KQ for better precision * ggml : fix NeoX rope to rotate just first n_dims * cuda : less diff in the rope_neox kernel * ggml : add ggml_mul_mat_set_prec ggml-ci * Update ggml-cuda.cu Co-authored-by: slaren <slarengh@gmail.com> * Update ggml-cuda.cu Co-authored-by: slaren <slarengh@gmail.com> * cuda : ggml_cuda_op_mul_mat_cublas support F32 precision * cuda : remove oboslete comment --------- Co-authored-by: Ebey Abraham <ebeyabraham@microsoft.com> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> Co-authored-by: slaren <slarengh@gmail.com>
2023-12-18 17:27:47 +00:00
self.gguf_writer.add_file_type(self.ftype)
self.gguf_writer.add_add_bos_token(False)
llama : add phi3 support (#6852) * add explicit phi3 support * add explicit phi3 support * remove unused code * convert : add BOS token * llama : match EOT token <|end|> * llama : minor / style * llama : tabs -> spaces * convert : fix lint checks --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-04-24 07:00:37 +00:00
@Model.register("Phi3ForCausalLM")
class Phi3MiniModel(Model):
model_arch = gguf.MODEL_ARCH.PHI3
def set_vocab(self):
from sentencepiece import SentencePieceProcessor
tokenizer_path = self.dir_model / 'tokenizer.model'
if not tokenizer_path.is_file():
convert.py : add python logging instead of print() (#6511) * convert.py: add python logging instead of print() * convert.py: verbose flag takes priority over dump flag log suppression * convert.py: named instance logging * convert.py: use explicit logger id string * convert.py: convert extra print() to named logger * convert.py: sys.stderr.write --> logger.error * *.py: Convert all python scripts to use logging module * requirements.txt: remove extra line * flake8: update flake8 ignore and exclude to match ci settings * gh-actions: add flake8-no-print to flake8 lint step * pre-commit: add flake8-no-print to flake8 and also update pre-commit version * convert-hf-to-gguf.py: print() to logger conversion * *.py: logging basiconfig refactor to use conditional expression * *.py: removed commented out logging * fixup! *.py: logging basiconfig refactor to use conditional expression * constant.py: logger.error then exit should be a raise exception instead * *.py: Convert logger error and sys.exit() into a raise exception (for atypical error) * gguf-convert-endian.py: refactor convert_byteorder() to use tqdm progressbar * verify-checksum-model.py: This is the result of the program, it should be printed to stdout. * compare-llama-bench.py: add blank line for readability during missing repo response * reader.py: read_gguf_file() use print() over logging * convert.py: warning goes to stderr and won't hurt the dump output * gguf-dump.py: dump_metadata() should print to stdout * convert-hf-to-gguf.py: print --> logger.debug or ValueError() * verify-checksum-models.py: use print() for printing table * *.py: refactor logging.basicConfig() * gguf-py/gguf/*.py: use __name__ as logger name Since they will be imported and not run directly. * python-lint.yml: use .flake8 file instead * constants.py: logger no longer required * convert-hf-to-gguf.py: add additional logging * convert-hf-to-gguf.py: print() --> logger * *.py: fix flake8 warnings * revert changes to convert-hf-to-gguf.py for get_name() * convert-hf-to-gguf-update.py: use triple quoted f-string instead * *.py: accidentally corrected the wrong line * *.py: add compilade warning suggestions and style fixes
2024-05-03 19:36:41 +00:00
raise ValueError(f'Error: Missing {tokenizer_path}')
llama : add phi3 support (#6852) * add explicit phi3 support * add explicit phi3 support * remove unused code * convert : add BOS token * llama : match EOT token <|end|> * llama : minor / style * llama : tabs -> spaces * convert : fix lint checks --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-04-24 07:00:37 +00:00
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
tokenizer = SentencePieceProcessor()
tokenizer.LoadFromFile(str(tokenizer_path))
llama : add phi3 support (#6852) * add explicit phi3 support * add explicit phi3 support * remove unused code * convert : add BOS token * llama : match EOT token <|end|> * llama : minor / style * llama : tabs -> spaces * convert : fix lint checks --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-04-24 07:00:37 +00:00
vocab_size = self.hparams.get('vocab_size', tokenizer.vocab_size())
tokens: list[bytes] = [f"[PAD{i}]".encode("utf-8") for i in range(vocab_size)]
scores: list[float] = [-10000.0] * vocab_size
toktypes: list[int] = [SentencePieceTokenTypes.UNKNOWN] * vocab_size
for token_id in range(tokenizer.vocab_size()):
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
piece = tokenizer.IdToPiece(token_id)
llama : add phi3 support (#6852) * add explicit phi3 support * add explicit phi3 support * remove unused code * convert : add BOS token * llama : match EOT token <|end|> * llama : minor / style * llama : tabs -> spaces * convert : fix lint checks --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-04-24 07:00:37 +00:00
text = piece.encode("utf-8")
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
score = tokenizer.GetScore(token_id)
llama : add phi3 support (#6852) * add explicit phi3 support * add explicit phi3 support * remove unused code * convert : add BOS token * llama : match EOT token <|end|> * llama : minor / style * llama : tabs -> spaces * convert : fix lint checks --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-04-24 07:00:37 +00:00
toktype = SentencePieceTokenTypes.NORMAL
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
if tokenizer.IsUnknown(token_id):
llama : add phi3 support (#6852) * add explicit phi3 support * add explicit phi3 support * remove unused code * convert : add BOS token * llama : match EOT token <|end|> * llama : minor / style * llama : tabs -> spaces * convert : fix lint checks --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-04-24 07:00:37 +00:00
toktype = SentencePieceTokenTypes.UNKNOWN
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
elif tokenizer.IsControl(token_id):
llama : add phi3 support (#6852) * add explicit phi3 support * add explicit phi3 support * remove unused code * convert : add BOS token * llama : match EOT token <|end|> * llama : minor / style * llama : tabs -> spaces * convert : fix lint checks --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-04-24 07:00:37 +00:00
toktype = SentencePieceTokenTypes.CONTROL
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
elif tokenizer.IsUnused(token_id):
llama : add phi3 support (#6852) * add explicit phi3 support * add explicit phi3 support * remove unused code * convert : add BOS token * llama : match EOT token <|end|> * llama : minor / style * llama : tabs -> spaces * convert : fix lint checks --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-04-24 07:00:37 +00:00
toktype = SentencePieceTokenTypes.UNUSED
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
elif tokenizer.IsByte(token_id):
llama : add phi3 support (#6852) * add explicit phi3 support * add explicit phi3 support * remove unused code * convert : add BOS token * llama : match EOT token <|end|> * llama : minor / style * llama : tabs -> spaces * convert : fix lint checks --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-04-24 07:00:37 +00:00
toktype = SentencePieceTokenTypes.BYTE
tokens[token_id] = text
scores[token_id] = score
toktypes[token_id] = toktype
added_tokens_file = self.dir_model / 'added_tokens.json'
if added_tokens_file.is_file():
with open(added_tokens_file, "r", encoding="utf-8") as f:
added_tokens_json = json.load(f)
for key in added_tokens_json:
token_id = added_tokens_json[key]
if (token_id >= vocab_size):
convert.py : add python logging instead of print() (#6511) * convert.py: add python logging instead of print() * convert.py: verbose flag takes priority over dump flag log suppression * convert.py: named instance logging * convert.py: use explicit logger id string * convert.py: convert extra print() to named logger * convert.py: sys.stderr.write --> logger.error * *.py: Convert all python scripts to use logging module * requirements.txt: remove extra line * flake8: update flake8 ignore and exclude to match ci settings * gh-actions: add flake8-no-print to flake8 lint step * pre-commit: add flake8-no-print to flake8 and also update pre-commit version * convert-hf-to-gguf.py: print() to logger conversion * *.py: logging basiconfig refactor to use conditional expression * *.py: removed commented out logging * fixup! *.py: logging basiconfig refactor to use conditional expression * constant.py: logger.error then exit should be a raise exception instead * *.py: Convert logger error and sys.exit() into a raise exception (for atypical error) * gguf-convert-endian.py: refactor convert_byteorder() to use tqdm progressbar * verify-checksum-model.py: This is the result of the program, it should be printed to stdout. * compare-llama-bench.py: add blank line for readability during missing repo response * reader.py: read_gguf_file() use print() over logging * convert.py: warning goes to stderr and won't hurt the dump output * gguf-dump.py: dump_metadata() should print to stdout * convert-hf-to-gguf.py: print --> logger.debug or ValueError() * verify-checksum-models.py: use print() for printing table * *.py: refactor logging.basicConfig() * gguf-py/gguf/*.py: use __name__ as logger name Since they will be imported and not run directly. * python-lint.yml: use .flake8 file instead * constants.py: logger no longer required * convert-hf-to-gguf.py: add additional logging * convert-hf-to-gguf.py: print() --> logger * *.py: fix flake8 warnings * revert changes to convert-hf-to-gguf.py for get_name() * convert-hf-to-gguf-update.py: use triple quoted f-string instead * *.py: accidentally corrected the wrong line * *.py: add compilade warning suggestions and style fixes
2024-05-03 19:36:41 +00:00
logger.debug(f'ignore token {token_id}: id is out of range, max={vocab_size - 1}')
llama : add phi3 support (#6852) * add explicit phi3 support * add explicit phi3 support * remove unused code * convert : add BOS token * llama : match EOT token <|end|> * llama : minor / style * llama : tabs -> spaces * convert : fix lint checks --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-04-24 07:00:37 +00:00
continue
tokens[token_id] = key.encode("utf-8")
scores[token_id] = -1000.0
toktypes[token_id] = SentencePieceTokenTypes.USER_DEFINED
Tokenizer SPM fixes for phi-3 and llama-spm (#7375) * Update brute force test: special tokens * Fix added tokens - Try to read 'added_tokens.json'. - Try to read 'tokenizer_config.json'. - Try to read 'tokenizer.json'. * Fix special tokens rtrim Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * server : fix test regexes
2024-05-20 18:15:57 +00:00
tokenizer_config_file = self.dir_model / 'tokenizer_config.json'
if tokenizer_config_file.is_file():
with open(tokenizer_config_file, "r", encoding="utf-8") as f:
tokenizer_config_json = json.load(f)
added_tokens_decoder = tokenizer_config_json.get("added_tokens_decoder", {})
for token_id, foken_data in added_tokens_decoder.items():
token_id = int(token_id)
token = foken_data["content"].encode("utf-8")
if toktypes[token_id] != SentencePieceTokenTypes.UNKNOWN:
Tokenizer SPM fixes for phi-3 and llama-spm (bugfix) (#7425) * Update brute force test: add_special * Update brute force test: default values for add_bos_token and add_eos_token * Enable rtrim when pre-inserting BOS Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * Revert "server : fix test regexes"
2024-05-21 12:39:48 +00:00
assert tokens[token_id] == token
Tokenizer SPM fixes for phi-3 and llama-spm (#7375) * Update brute force test: special tokens * Fix added tokens - Try to read 'added_tokens.json'. - Try to read 'tokenizer_config.json'. - Try to read 'tokenizer.json'. * Fix special tokens rtrim Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * server : fix test regexes
2024-05-20 18:15:57 +00:00
tokens[token_id] = token
scores[token_id] = -1000.0
toktypes[token_id] = SentencePieceTokenTypes.USER_DEFINED
if foken_data.get("special"):
toktypes[token_id] = SentencePieceTokenTypes.CONTROL
tokenizer_file = self.dir_model / 'tokenizer.json'
if tokenizer_file.is_file():
with open(tokenizer_file, "r", encoding="utf-8") as f:
tokenizer_json = json.load(f)
added_tokens = tokenizer_json.get("added_tokens", [])
for foken_data in added_tokens:
token_id = int(foken_data["id"])
token = foken_data["content"].encode("utf-8")
if toktypes[token_id] != SentencePieceTokenTypes.UNKNOWN:
Tokenizer SPM fixes for phi-3 and llama-spm (bugfix) (#7425) * Update brute force test: add_special * Update brute force test: default values for add_bos_token and add_eos_token * Enable rtrim when pre-inserting BOS Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * Revert "server : fix test regexes"
2024-05-21 12:39:48 +00:00
assert tokens[token_id] == token
Tokenizer SPM fixes for phi-3 and llama-spm (#7375) * Update brute force test: special tokens * Fix added tokens - Try to read 'added_tokens.json'. - Try to read 'tokenizer_config.json'. - Try to read 'tokenizer.json'. * Fix special tokens rtrim Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * server : fix test regexes
2024-05-20 18:15:57 +00:00
tokens[token_id] = token
scores[token_id] = -1000.0
toktypes[token_id] = SentencePieceTokenTypes.USER_DEFINED
if foken_data.get("special"):
toktypes[token_id] = SentencePieceTokenTypes.CONTROL
llama : add phi3 support (#6852) * add explicit phi3 support * add explicit phi3 support * remove unused code * convert : add BOS token * llama : match EOT token <|end|> * llama : minor / style * llama : tabs -> spaces * convert : fix lint checks --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-04-24 07:00:37 +00:00
self.gguf_writer.add_tokenizer_model("llama")
llama : fix BPE pre-tokenization (#6920) * merged the changes from deepseeker models to main branch * Moved regex patterns to unicode.cpp and updated unicode.h * Moved header files * Resolved issues * added and refactored unicode_regex_split and related functions * Updated/merged the deepseek coder pr * Refactored code * Adding unicode regex mappings * Adding unicode regex function * Added needed functionality, testing remains * Fixed issues * Fixed issue with gpt2 regex custom preprocessor * unicode : fix? unicode_wstring_to_utf8 * lint : fix whitespaces * tests : add tokenizer tests for numbers * unicode : remove redundant headers * tests : remove and rename tokenizer test scripts * tests : add sample usage * gguf-py : reader prints warnings on duplicate keys * llama : towards llama3 tokenization support (wip) * unicode : shot in the dark to fix tests on Windows * unicode : first try custom implementations * convert : add "tokenizer.ggml.pre" GGUF KV (wip) * llama : use new pre-tokenizer type * convert : fix pre-tokenizer type writing * lint : fix * make : add test-tokenizer-0-llama-v3 * wip * models : add llama v3 vocab file * llama : adapt punctuation regex + add llama 3 regex * minor * unicode : set bomb * unicode : set bomb * unicode : always use std::wregex * unicode : support \p{N}, \p{L} and \p{P} natively * unicode : try fix windows * unicode : category support via std::regex * unicode : clean-up * unicode : simplify * convert : add convert-hf-to-gguf-update.py ggml-ci * lint : update * convert : add falcon ggml-ci * unicode : normalize signatures * lint : fix * lint : fix * convert : remove unused functions * convert : add comments * convert : exercise contractions ggml-ci * lint : fix * cmake : refactor test targets * tests : refactor vocab tests ggml-ci * tests : add more vocabs and tests ggml-ci * unicode : cleanup * scripts : ignore new update script in check-requirements.sh * models : add phi-3, mpt, gpt-2, starcoder * tests : disable obsolete ggml-ci * tests : use faster bpe test ggml-ci * llama : more prominent warning for old BPE models * tests : disable test-tokenizer-1-bpe due to slowness ggml-ci --------- Co-authored-by: Jaggzh <jaggz.h@gmail.com> Co-authored-by: Kazim Abrar Mahi <kazimabrarmahi135@gmail.com>
2024-04-29 13:58:41 +00:00
self.gguf_writer.add_tokenizer_pre("default")
llama : add phi3 support (#6852) * add explicit phi3 support * add explicit phi3 support * remove unused code * convert : add BOS token * llama : match EOT token <|end|> * llama : minor / style * llama : tabs -> spaces * convert : fix lint checks --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-04-24 07:00:37 +00:00
self.gguf_writer.add_token_list(tokens)
self.gguf_writer.add_token_scores(scores)
self.gguf_writer.add_token_types(toktypes)
special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens))
special_vocab.add_to_gguf(self.gguf_writer)
def set_gguf_parameters(self):
block_count = self.find_hparam(["num_hidden_layers", "n_layer"])
rot_pct = 1.0
n_embd = self.find_hparam(["hidden_size", "n_embd"])
n_head = self.find_hparam(["num_attention_heads", "n_head"])
rms_eps = self.find_hparam(["rms_norm_eps"])
self.gguf_writer.add_name("Phi3")
self.gguf_writer.add_context_length(self.find_hparam(["n_positions", "max_position_embeddings"]))
self.gguf_writer.add_embedding_length(n_embd)
self.gguf_writer.add_feed_forward_length(8192)
self.gguf_writer.add_block_count(block_count)
self.gguf_writer.add_head_count(n_head)
self.gguf_writer.add_head_count_kv(n_head)
self.gguf_writer.add_layer_norm_rms_eps(rms_eps)
self.gguf_writer.add_rope_dimension_count(int(rot_pct * n_embd) // n_head)
self.gguf_writer.add_file_type(self.ftype)
convert-hf : make model class definitions self-contained (#5825)
2024-03-02 17:21:47 +00:00
@Model.register("PlamoForCausalLM")
llama : add PLaMo model (#3557) * add plamo mock * add tensor loading * plamo convert * update norm * able to compile * fix norm_rms_eps hparam * runnable * use inp_pos * seems ok * update kqv code * remove develop code * update README * shuffle attn_q.weight and attn_output.weight for broadcasting * remove plamo_llm_build_kqv and use llm_build_kqv * fix style * update * llama : remove obsolete KQ_scale * plamo : fix tensor names for correct GPU offload --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-12-24 13:35:49 +00:00
class PlamoModel(Model):
convert-hf : make model class definitions self-contained (#5825)
2024-03-02 17:21:47 +00:00
model_arch = gguf.MODEL_ARCH.PLAMO
llama : add PLaMo model (#3557) * add plamo mock * add tensor loading * plamo convert * update norm * able to compile * fix norm_rms_eps hparam * runnable * use inp_pos * seems ok * update kqv code * remove develop code * update README * shuffle attn_q.weight and attn_output.weight for broadcasting * remove plamo_llm_build_kqv and use llm_build_kqv * fix style * update * llama : remove obsolete KQ_scale * plamo : fix tensor names for correct GPU offload --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-12-24 13:35:49 +00:00
def set_vocab(self):
self._set_vocab_sentencepiece()
def set_gguf_parameters(self):
hparams = self.hparams
block_count = hparams["num_hidden_layers"]
self.gguf_writer.add_name("PLaMo")
self.gguf_writer.add_context_length(4096) # not in config.json
self.gguf_writer.add_embedding_length(hparams["hidden_size"])
self.gguf_writer.add_feed_forward_length(hparams["intermediate_size"])
self.gguf_writer.add_block_count(block_count)
self.gguf_writer.add_head_count(hparams["num_attention_heads"])
self.gguf_writer.add_head_count_kv(5) # hparams["num_key_value_heads"]) is wrong
self.gguf_writer.add_layer_norm_rms_eps(hparams["rms_norm_eps"])
convert-hf : support direct Q8_0 conversion (#7234) * convert-hf : support q8_0 conversion * convert-hf : add missing ftype This was messing with the checksums otherwise. * convert-hf : add missing ftype to Baichuan and Xverse I didn't notice these on my first pass.
2024-05-13 18:10:51 +00:00
self.gguf_writer.add_file_type(self.ftype)
llama : add PLaMo model (#3557) * add plamo mock * add tensor loading * plamo convert * update norm * able to compile * fix norm_rms_eps hparam * runnable * use inp_pos * seems ok * update kqv code * remove develop code * update README * shuffle attn_q.weight and attn_output.weight for broadcasting * remove plamo_llm_build_kqv and use llm_build_kqv * fix style * update * llama : remove obsolete KQ_scale * plamo : fix tensor names for correct GPU offload --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-12-24 13:35:49 +00:00
def shuffle_attn_q_weight(self, data_torch):
assert data_torch.size() == (5120, 5120)
data_torch = data_torch.reshape(8, 5, 128, 5120)
data_torch = torch.permute(data_torch, (1, 0, 2, 3))
data_torch = torch.reshape(data_torch, (5120, 5120))
return data_torch
def shuffle_attn_output_weight(self, data_torch):
assert data_torch.size() == (5120, 5120)
data_torch = data_torch.reshape(5120, 8, 5, 128)
data_torch = torch.permute(data_torch, (0, 2, 1, 3))
data_torch = torch.reshape(data_torch, (5120, 5120))
return data_torch
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
del bid # unused
llama : add PLaMo model (#3557) * add plamo mock * add tensor loading * plamo convert * update norm * able to compile * fix norm_rms_eps hparam * runnable * use inp_pos * seems ok * update kqv code * remove develop code * update README * shuffle attn_q.weight and attn_output.weight for broadcasting * remove plamo_llm_build_kqv and use llm_build_kqv * fix style * update * llama : remove obsolete KQ_scale * plamo : fix tensor names for correct GPU offload --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-12-24 13:35:49 +00:00
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
new_name = self.map_tensor_name(name)
llama : add PLaMo model (#3557) * add plamo mock * add tensor loading * plamo convert * update norm * able to compile * fix norm_rms_eps hparam * runnable * use inp_pos * seems ok * update kqv code * remove develop code * update README * shuffle attn_q.weight and attn_output.weight for broadcasting * remove plamo_llm_build_kqv and use llm_build_kqv * fix style * update * llama : remove obsolete KQ_scale * plamo : fix tensor names for correct GPU offload --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-12-24 13:35:49 +00:00
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
# shuffle for broadcasting of gqa in ggml_mul_mat
if new_name.endswith("attn_q.weight"):
data_torch = self.shuffle_attn_q_weight(data_torch)
elif new_name.endswith("attn_output.weight"):
data_torch = self.shuffle_attn_output_weight(data_torch)
llama : add PLaMo model (#3557) * add plamo mock * add tensor loading * plamo convert * update norm * able to compile * fix norm_rms_eps hparam * runnable * use inp_pos * seems ok * update kqv code * remove develop code * update README * shuffle attn_q.weight and attn_output.weight for broadcasting * remove plamo_llm_build_kqv and use llm_build_kqv * fix style * update * llama : remove obsolete KQ_scale * plamo : fix tensor names for correct GPU offload --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-12-24 13:35:49 +00:00
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
return [(new_name, data_torch)]
llama : add PLaMo model (#3557) * add plamo mock * add tensor loading * plamo convert * update norm * able to compile * fix norm_rms_eps hparam * runnable * use inp_pos * seems ok * update kqv code * remove develop code * update README * shuffle attn_q.weight and attn_output.weight for broadcasting * remove plamo_llm_build_kqv and use llm_build_kqv * fix style * update * llama : remove obsolete KQ_scale * plamo : fix tensor names for correct GPU offload --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-12-24 13:35:49 +00:00
py : fix flake8 lint
2024-01-19 11:52:22 +00:00
convert-hf : make model class definitions self-contained (#5825)
2024-03-02 17:21:47 +00:00
@Model.register("CodeShellForCausalLM")
llama : add CodeShell support (#5016) * llama: add codeshell support * llama.cpp: fix codeshell with NeoX rope Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-01-19 09:07:27 +00:00
class CodeShellModel(Model):
convert-hf : make model class definitions self-contained (#5825)
2024-03-02 17:21:47 +00:00
model_arch = gguf.MODEL_ARCH.CODESHELL
llama : add CodeShell support (#5016) * llama: add codeshell support * llama.cpp: fix codeshell with NeoX rope Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-01-19 09:07:27 +00:00
def set_gguf_parameters(self):
block_count = self.hparams["n_layer"]
self.gguf_writer.add_name("CodeShell")
self.gguf_writer.add_context_length(self.hparams["n_positions"])
self.gguf_writer.add_embedding_length(self.hparams["n_embd"])
self.gguf_writer.add_feed_forward_length(4 * self.hparams["n_embd"])
self.gguf_writer.add_block_count(block_count)
self.gguf_writer.add_head_count(self.hparams["n_head"])
self.gguf_writer.add_head_count_kv(self.hparams["num_query_groups"])
self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_epsilon"])
self.gguf_writer.add_file_type(self.ftype)
self.gguf_writer.add_rope_freq_base(10000.0)
self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LINEAR)
self.gguf_writer.add_rope_scaling_factor(1.0)
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
del bid # unused
llama : add CodeShell support (#5016) * llama: add codeshell support * llama.cpp: fix codeshell with NeoX rope Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-01-19 09:07:27 +00:00
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
new_name = self.map_tensor_name(name)
llama : add CodeShell support (#5016) * llama: add codeshell support * llama.cpp: fix codeshell with NeoX rope Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-01-19 09:07:27 +00:00
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
tensors: list[tuple[str, Tensor]] = [(new_name, data_torch)]
llama : add CodeShell support (#5016) * llama: add codeshell support * llama.cpp: fix codeshell with NeoX rope Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-01-19 09:07:27 +00:00
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
if new_name == self.format_tensor_name(gguf.MODEL_TENSOR.TOKEN_EMBD):
assert self.tensor_names is not None
llama : add CodeShell support (#5016) * llama: add codeshell support * llama.cpp: fix codeshell with NeoX rope Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-01-19 09:07:27 +00:00
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
if all(s not in self.tensor_names for s in ("lm_head.weight", "output.weight")):
# copy tok_embd.weight to output.weight
tensors.append((self.format_tensor_name(gguf.MODEL_TENSOR.OUTPUT), data_torch))
llama : add CodeShell support (#5016) * llama: add codeshell support * llama.cpp: fix codeshell with NeoX rope Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-01-19 09:07:27 +00:00
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
return tensors
llama : add PLaMo model (#3557) * add plamo mock * add tensor loading * plamo convert * update norm * able to compile * fix norm_rms_eps hparam * runnable * use inp_pos * seems ok * update kqv code * remove develop code * update README * shuffle attn_q.weight and attn_output.weight for broadcasting * remove plamo_llm_build_kqv and use llm_build_kqv * fix style * update * llama : remove obsolete KQ_scale * plamo : fix tensor names for correct GPU offload --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-12-24 13:35:49 +00:00
llama : support InternLM2 (#5184) * support InternLM2 inference * add add_space_prefix KV pair
2024-02-01 09:19:51 +00:00
convert-hf : make model class definitions self-contained (#5825)
2024-03-02 17:21:47 +00:00
@Model.register("InternLM2ForCausalLM")
llama : support InternLM2 (#5184) * support InternLM2 inference * add add_space_prefix KV pair
2024-02-01 09:19:51 +00:00
class InternLM2Model(Model):
convert-hf : make model class definitions self-contained (#5825)
2024-03-02 17:21:47 +00:00
model_arch = gguf.MODEL_ARCH.INTERNLM2
llama : support InternLM2 (#5184) * support InternLM2 inference * add add_space_prefix KV pair
2024-02-01 09:19:51 +00:00
def set_vocab(self):
# (TODO): Is there a better way?
# Copy from _set_vocab_sentencepiece, The only difference is that we will treat the character
# \x00 specially and convert it into an emoji character to prevent it from being mistakenly
# recognized as an empty string in C++.
from sentencepiece import SentencePieceProcessor
from sentencepiece import sentencepiece_model_pb2 as model
tokenizer_path = self.dir_model / 'tokenizer.model'
tokens: list[bytes] = []
scores: list[float] = []
toktypes: list[int] = []
if not tokenizer_path.is_file():
convert.py : add python logging instead of print() (#6511) * convert.py: add python logging instead of print() * convert.py: verbose flag takes priority over dump flag log suppression * convert.py: named instance logging * convert.py: use explicit logger id string * convert.py: convert extra print() to named logger * convert.py: sys.stderr.write --> logger.error * *.py: Convert all python scripts to use logging module * requirements.txt: remove extra line * flake8: update flake8 ignore and exclude to match ci settings * gh-actions: add flake8-no-print to flake8 lint step * pre-commit: add flake8-no-print to flake8 and also update pre-commit version * convert-hf-to-gguf.py: print() to logger conversion * *.py: logging basiconfig refactor to use conditional expression * *.py: removed commented out logging * fixup! *.py: logging basiconfig refactor to use conditional expression * constant.py: logger.error then exit should be a raise exception instead * *.py: Convert logger error and sys.exit() into a raise exception (for atypical error) * gguf-convert-endian.py: refactor convert_byteorder() to use tqdm progressbar * verify-checksum-model.py: This is the result of the program, it should be printed to stdout. * compare-llama-bench.py: add blank line for readability during missing repo response * reader.py: read_gguf_file() use print() over logging * convert.py: warning goes to stderr and won't hurt the dump output * gguf-dump.py: dump_metadata() should print to stdout * convert-hf-to-gguf.py: print --> logger.debug or ValueError() * verify-checksum-models.py: use print() for printing table * *.py: refactor logging.basicConfig() * gguf-py/gguf/*.py: use __name__ as logger name Since they will be imported and not run directly. * python-lint.yml: use .flake8 file instead * constants.py: logger no longer required * convert-hf-to-gguf.py: add additional logging * convert-hf-to-gguf.py: print() --> logger * *.py: fix flake8 warnings * revert changes to convert-hf-to-gguf.py for get_name() * convert-hf-to-gguf-update.py: use triple quoted f-string instead * *.py: accidentally corrected the wrong line * *.py: add compilade warning suggestions and style fixes
2024-05-03 19:36:41 +00:00
logger.error(f'Error: Missing {tokenizer_path}')
llama : support InternLM2 (#5184) * support InternLM2 inference * add add_space_prefix KV pair
2024-02-01 09:19:51 +00:00
sys.exit(1)
sentencepiece_model = model.ModelProto()
sentencepiece_model.ParseFromString(open(tokenizer_path, "rb").read())
add_prefix = sentencepiece_model.normalizer_spec.add_dummy_prefix
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
tokenizer = SentencePieceProcessor()
tokenizer.LoadFromFile(str(tokenizer_path))
llama : support InternLM2 (#5184) * support InternLM2 inference * add add_space_prefix KV pair
2024-02-01 09:19:51 +00:00
vocab_size = self.hparams.get('vocab_size', tokenizer.vocab_size())
for token_id in range(vocab_size):
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
piece = tokenizer.IdToPiece(token_id)
llama : support InternLM2 (#5184) * support InternLM2 inference * add add_space_prefix KV pair
2024-02-01 09:19:51 +00:00
text = piece.encode("utf-8")
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
score = tokenizer.GetScore(token_id)
llama : support InternLM2 (#5184) * support InternLM2 inference * add add_space_prefix KV pair
2024-02-01 09:19:51 +00:00
if text == b"\x00":
# (TODO): fixme
# Hack here and replace the \x00 characters.
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
logger.warning(f"InternLM2 convert token '{text}' to '🐉'!")
text = "🐉".encode("utf-8")
llama : support InternLM2 (#5184) * support InternLM2 inference * add add_space_prefix KV pair
2024-02-01 09:19:51 +00:00
toktype = SentencePieceTokenTypes.NORMAL
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
if tokenizer.IsUnknown(token_id):
llama : support InternLM2 (#5184) * support InternLM2 inference * add add_space_prefix KV pair
2024-02-01 09:19:51 +00:00
toktype = SentencePieceTokenTypes.UNKNOWN
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
elif tokenizer.IsControl(token_id):
llama : support InternLM2 (#5184) * support InternLM2 inference * add add_space_prefix KV pair
2024-02-01 09:19:51 +00:00
toktype = SentencePieceTokenTypes.CONTROL
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
elif tokenizer.IsUnused(token_id):
llama : support InternLM2 (#5184) * support InternLM2 inference * add add_space_prefix KV pair
2024-02-01 09:19:51 +00:00
toktype = SentencePieceTokenTypes.UNUSED
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
elif tokenizer.IsByte(token_id):
llama : support InternLM2 (#5184) * support InternLM2 inference * add add_space_prefix KV pair
2024-02-01 09:19:51 +00:00
toktype = SentencePieceTokenTypes.BYTE
tokens.append(text)
scores.append(score)
toktypes.append(toktype)
added_tokens_file = self.dir_model / 'added_tokens.json'
if added_tokens_file.is_file():
with open(added_tokens_file, "r", encoding="utf-8") as f:
added_tokens_json = json.load(f)
for key in added_tokens_json:
tokens.append(key.encode("utf-8"))
scores.append(-1000.0)
toktypes.append(SentencePieceTokenTypes.USER_DEFINED)
self.gguf_writer.add_tokenizer_model("llama")
llama : fix BPE pre-tokenization (#6920) * merged the changes from deepseeker models to main branch * Moved regex patterns to unicode.cpp and updated unicode.h * Moved header files * Resolved issues * added and refactored unicode_regex_split and related functions * Updated/merged the deepseek coder pr * Refactored code * Adding unicode regex mappings * Adding unicode regex function * Added needed functionality, testing remains * Fixed issues * Fixed issue with gpt2 regex custom preprocessor * unicode : fix? unicode_wstring_to_utf8 * lint : fix whitespaces * tests : add tokenizer tests for numbers * unicode : remove redundant headers * tests : remove and rename tokenizer test scripts * tests : add sample usage * gguf-py : reader prints warnings on duplicate keys * llama : towards llama3 tokenization support (wip) * unicode : shot in the dark to fix tests on Windows * unicode : first try custom implementations * convert : add "tokenizer.ggml.pre" GGUF KV (wip) * llama : use new pre-tokenizer type * convert : fix pre-tokenizer type writing * lint : fix * make : add test-tokenizer-0-llama-v3 * wip * models : add llama v3 vocab file * llama : adapt punctuation regex + add llama 3 regex * minor * unicode : set bomb * unicode : set bomb * unicode : always use std::wregex * unicode : support \p{N}, \p{L} and \p{P} natively * unicode : try fix windows * unicode : category support via std::regex * unicode : clean-up * unicode : simplify * convert : add convert-hf-to-gguf-update.py ggml-ci * lint : update * convert : add falcon ggml-ci * unicode : normalize signatures * lint : fix * lint : fix * convert : remove unused functions * convert : add comments * convert : exercise contractions ggml-ci * lint : fix * cmake : refactor test targets * tests : refactor vocab tests ggml-ci * tests : add more vocabs and tests ggml-ci * unicode : cleanup * scripts : ignore new update script in check-requirements.sh * models : add phi-3, mpt, gpt-2, starcoder * tests : disable obsolete ggml-ci * tests : use faster bpe test ggml-ci * llama : more prominent warning for old BPE models * tests : disable test-tokenizer-1-bpe due to slowness ggml-ci --------- Co-authored-by: Jaggzh <jaggz.h@gmail.com> Co-authored-by: Kazim Abrar Mahi <kazimabrarmahi135@gmail.com>
2024-04-29 13:58:41 +00:00
self.gguf_writer.add_tokenizer_pre("default")
llama : support InternLM2 (#5184) * support InternLM2 inference * add add_space_prefix KV pair
2024-02-01 09:19:51 +00:00
self.gguf_writer.add_token_list(tokens)
self.gguf_writer.add_token_scores(scores)
self.gguf_writer.add_token_types(toktypes)
self.gguf_writer.add_add_space_prefix(add_prefix)
special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens))
py : fix internlm2-hf convert to gguf (#5305) * py : fix internlm2-hf convert to gguf * ggml-ci
2024-02-05 09:04:06 +00:00
old_eos = special_vocab.special_token_ids["eos"]
if "chat" in os.path.basename(self.dir_model.absolute()):
# For the chat model, we replace the eos with '<|im_end|>'.
llama : support Llama 3 HF conversion (#6745) * Support Llama 3 conversion The tokenizer is BPE. * style * Accept suggestion Co-authored-by: Sourab Mangrulkar <13534540+pacman100@users.noreply.github.com> * llama : add llama_token_is_eog() ggml-ci * llama : auto-detect more EOT tokens when missing in KV data * convert : replacing EOS token is a hack * llama : fix codegemma EOT token + add TODOs * llama : fix model type string for 8B model --------- Co-authored-by: Sourab Mangrulkar <13534540+pacman100@users.noreply.github.com> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-04-21 11:50:41 +00:00
# TODO: this is a hack, should be fixed
# https://github.com/ggerganov/llama.cpp/pull/6745#issuecomment-2067687048
py : fix internlm2-hf convert to gguf (#5305) * py : fix internlm2-hf convert to gguf * ggml-ci
2024-02-05 09:04:06 +00:00
special_vocab.special_token_ids["eos"] = self._try_get_sft_eos(tokenizer)
convert.py : add python logging instead of print() (#6511) * convert.py: add python logging instead of print() * convert.py: verbose flag takes priority over dump flag log suppression * convert.py: named instance logging * convert.py: use explicit logger id string * convert.py: convert extra print() to named logger * convert.py: sys.stderr.write --> logger.error * *.py: Convert all python scripts to use logging module * requirements.txt: remove extra line * flake8: update flake8 ignore and exclude to match ci settings * gh-actions: add flake8-no-print to flake8 lint step * pre-commit: add flake8-no-print to flake8 and also update pre-commit version * convert-hf-to-gguf.py: print() to logger conversion * *.py: logging basiconfig refactor to use conditional expression * *.py: removed commented out logging * fixup! *.py: logging basiconfig refactor to use conditional expression * constant.py: logger.error then exit should be a raise exception instead * *.py: Convert logger error and sys.exit() into a raise exception (for atypical error) * gguf-convert-endian.py: refactor convert_byteorder() to use tqdm progressbar * verify-checksum-model.py: This is the result of the program, it should be printed to stdout. * compare-llama-bench.py: add blank line for readability during missing repo response * reader.py: read_gguf_file() use print() over logging * convert.py: warning goes to stderr and won't hurt the dump output * gguf-dump.py: dump_metadata() should print to stdout * convert-hf-to-gguf.py: print --> logger.debug or ValueError() * verify-checksum-models.py: use print() for printing table * *.py: refactor logging.basicConfig() * gguf-py/gguf/*.py: use __name__ as logger name Since they will be imported and not run directly. * python-lint.yml: use .flake8 file instead * constants.py: logger no longer required * convert-hf-to-gguf.py: add additional logging * convert-hf-to-gguf.py: print() --> logger * *.py: fix flake8 warnings * revert changes to convert-hf-to-gguf.py for get_name() * convert-hf-to-gguf-update.py: use triple quoted f-string instead * *.py: accidentally corrected the wrong line * *.py: add compilade warning suggestions and style fixes
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logger.warning(f"Replace eos:{old_eos} with a special token:{special_vocab.special_token_ids['eos']} \
py : fix internlm2-hf convert to gguf (#5305) * py : fix internlm2-hf convert to gguf * ggml-ci
2024-02-05 09:04:06 +00:00
in chat mode so that the conversation can end normally.")
llama : support InternLM2 (#5184) * support InternLM2 inference * add add_space_prefix KV pair
2024-02-01 09:19:51 +00:00
special_vocab.add_to_gguf(self.gguf_writer)
py : fix internlm2-hf convert to gguf (#5305) * py : fix internlm2-hf convert to gguf * ggml-ci
2024-02-05 09:04:06 +00:00
def _try_get_sft_eos(self, tokenizer):
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
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unused_145_list = tokenizer.Encode('[UNUSED_TOKEN_145]')
im_end_list = tokenizer.Encode('<|im_end|>')
eos_token = None
py : fix internlm2-hf convert to gguf (#5305) * py : fix internlm2-hf convert to gguf * ggml-ci
2024-02-05 09:04:06 +00:00
assert (len(unused_145_list) == 1) ^ (len(im_end_list) == 1)
if len(unused_145_list) == 1:
eos_token = unused_145_list[0]
if len(im_end_list) == 1:
eos_token = im_end_list[0]
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
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assert eos_token
py : fix internlm2-hf convert to gguf (#5305) * py : fix internlm2-hf convert to gguf * ggml-ci
2024-02-05 09:04:06 +00:00
return eos_token
def _hf_permute_qk(self, weights, n_head: int, n_head_kv: int):
if n_head_kv is not None and n_head != n_head_kv:
n_head = n_head_kv
return (weights.reshape(n_head, 2, weights.shape[0] // n_head // 2, *weights.shape[1:])
.swapaxes(1, 2)
.reshape(weights.shape))
llama : support InternLM2 (#5184) * support InternLM2 inference * add add_space_prefix KV pair
2024-02-01 09:19:51 +00:00
def set_gguf_parameters(self):
self.gguf_writer.add_name("InternLM2")
self.gguf_writer.add_context_length(self.hparams["max_position_embeddings"])
self.gguf_writer.add_block_count(self.hparams["num_hidden_layers"])
self.gguf_writer.add_embedding_length(self.hparams["hidden_size"])
self.gguf_writer.add_feed_forward_length(self.hparams["intermediate_size"])
self.gguf_writer.add_rope_freq_base(self.hparams["rope_theta"])
self.gguf_writer.add_head_count(self.hparams["num_attention_heads"])
self.gguf_writer.add_layer_norm_rms_eps(self.hparams["rms_norm_eps"])
self.gguf_writer.add_head_count_kv(self.hparams["num_key_value_heads"])
convert-hf : support direct Q8_0 conversion (#7234) * convert-hf : support q8_0 conversion * convert-hf : add missing ftype This was messing with the checksums otherwise. * convert-hf : add missing ftype to Baichuan and Xverse I didn't notice these on my first pass.
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self.gguf_writer.add_file_type(self.ftype)
llama : support InternLM2 (#5184) * support InternLM2 inference * add add_space_prefix KV pair
2024-02-01 09:19:51 +00:00
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
num_heads = self.hparams["num_attention_heads"]
num_kv_heads = self.hparams["num_key_value_heads"]
hidden_size = self.hparams["hidden_size"]
llama : support InternLM2 (#5184) * support InternLM2 inference * add add_space_prefix KV pair
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q_per_kv = num_heads // num_kv_heads
head_dim = hidden_size // num_heads
num_groups = num_heads // q_per_kv
qkv_pattern = r"model\.layers\.(\d+)\.attention\.wqkv"
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
if re.match(qkv_pattern, name):
bid = re.findall(qkv_pattern, name)[0]
qkv = data_torch
# qkv = rearrange(qkv.T, " o (g n i) ->o g n i", g=num_groups, n=q_per_kv + 2, i=head_dim)
qkv = qkv.T.reshape((-1, num_groups, q_per_kv + 2, head_dim))
q, k, v = qkv[..., : q_per_kv, :], qkv[..., q_per_kv: q_per_kv + 1, :], qkv[..., q_per_kv + 1: q_per_kv + 2, :]
# The model weights of q and k equire additional reshape.
# q = self._hf_permute_qk(rearrange(q, " o g n i -> o (g n i)").T, num_heads, num_heads)
q = self._hf_permute_qk(q.reshape((q.shape[0], -1)).T, num_heads, num_heads)
# k = self._hf_permute_qk(rearrange(k, " o g n i -> o (g n i)").T, num_heads, num_kv_heads)
k = self._hf_permute_qk(k.reshape((k.shape[0], -1)).T, num_heads, num_kv_heads)
# v = rearrange(v, " o g n i -> o (g n i)").T
v = v.reshape((v.shape[0], -1)).T
return [
(self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_Q, bid), q),
(self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_K, bid), k),
(self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_V, bid), v),
]
else:
return [(self.map_tensor_name(name), data_torch)]
llama : support InternLM2 (#5184) * support InternLM2 inference * add add_space_prefix KV pair
2024-02-01 09:19:51 +00:00
convert : add support for CamembertModel architecture (#6119) Adding support for CamembertModel architecture used by : https://huggingface.co/dangvantuan/sentence-camembert-large
2024-03-18 08:17:00 +00:00
@Model.register("BertModel", "CamembertModel")
Add support for BERT embedding models (#5423) * BERT model graph construction (build_bert) * WordPiece tokenizer (llm_tokenize_wpm) * Add flag for non-causal attention models * Allow for models that only output embeddings * Support conversion of BERT models to GGUF * Based on prior work by @xyzhang626 and @skeskinen --------- Co-authored-by: Jared Van Bortel <jared@nomic.ai> Co-authored-by: Jared Van Bortel <cebtenzzre@gmail.com> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
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class BertModel(Model):
convert-hf : make model class definitions self-contained (#5825)
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model_arch = gguf.MODEL_ARCH.BERT
Add support for BERT embedding models (#5423) * BERT model graph construction (build_bert) * WordPiece tokenizer (llm_tokenize_wpm) * Add flag for non-causal attention models * Allow for models that only output embeddings * Support conversion of BERT models to GGUF * Based on prior work by @xyzhang626 and @skeskinen --------- Co-authored-by: Jared Van Bortel <jared@nomic.ai> Co-authored-by: Jared Van Bortel <cebtenzzre@gmail.com> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-02-11 16:21:38 +00:00
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
llama : add support for Nomic Embed (#5468)
2024-02-13 17:03:53 +00:00
self.vocab_size = None
Add support for BERT embedding models (#5423) * BERT model graph construction (build_bert) * WordPiece tokenizer (llm_tokenize_wpm) * Add flag for non-causal attention models * Allow for models that only output embeddings * Support conversion of BERT models to GGUF * Based on prior work by @xyzhang626 and @skeskinen --------- Co-authored-by: Jared Van Bortel <jared@nomic.ai> Co-authored-by: Jared Van Bortel <cebtenzzre@gmail.com> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
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def set_gguf_parameters(self):
llama : add support for Nomic Embed (#5468)
2024-02-13 17:03:53 +00:00
super().set_gguf_parameters()
Add support for BERT embedding models (#5423) * BERT model graph construction (build_bert) * WordPiece tokenizer (llm_tokenize_wpm) * Add flag for non-causal attention models * Allow for models that only output embeddings * Support conversion of BERT models to GGUF * Based on prior work by @xyzhang626 and @skeskinen --------- Co-authored-by: Jared Van Bortel <jared@nomic.ai> Co-authored-by: Jared Van Bortel <cebtenzzre@gmail.com> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-02-11 16:21:38 +00:00
self.gguf_writer.add_causal_attention(False)
Use correct type of pooling for embedding models (#5500) Use correct type of pooling for embedding models
2024-02-15 17:21:49 +00:00
# get pooling path
pooling_path = None
llama : allow for user specified embedding pooling type (#5849) * allow for user specified pooling type * llama : use enum types over int --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-03-03 10:40:27 +00:00
module_path = self.dir_model / "modules.json"
if module_path.is_file():
with open(module_path, encoding="utf-8") as f:
modules = json.load(f)
for mod in modules:
if mod["type"] == "sentence_transformers.models.Pooling":
pooling_path = mod["path"]
break
Use correct type of pooling for embedding models (#5500) Use correct type of pooling for embedding models
2024-02-15 17:21:49 +00:00
# get pooling type
if pooling_path is not None:
with open(self.dir_model / pooling_path / "config.json", encoding="utf-8") as f:
pooling = json.load(f)
if pooling["pooling_mode_mean_tokens"]:
pooling_type = gguf.PoolingType.MEAN
elif pooling["pooling_mode_cls_token"]:
pooling_type = gguf.PoolingType.CLS
else:
raise NotImplementedError("Only MEAN and CLS pooling types supported")
llama : allow for user specified embedding pooling type (#5849) * allow for user specified pooling type * llama : use enum types over int --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-03-03 10:40:27 +00:00
self.gguf_writer.add_pooling_type(pooling_type)
Add support for BERT embedding models (#5423) * BERT model graph construction (build_bert) * WordPiece tokenizer (llm_tokenize_wpm) * Add flag for non-causal attention models * Allow for models that only output embeddings * Support conversion of BERT models to GGUF * Based on prior work by @xyzhang626 and @skeskinen --------- Co-authored-by: Jared Van Bortel <jared@nomic.ai> Co-authored-by: Jared Van Bortel <cebtenzzre@gmail.com> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-02-11 16:21:38 +00:00
def set_vocab(self):
llama : fix BPE pre-tokenization (#6920) * merged the changes from deepseeker models to main branch * Moved regex patterns to unicode.cpp and updated unicode.h * Moved header files * Resolved issues * added and refactored unicode_regex_split and related functions * Updated/merged the deepseek coder pr * Refactored code * Adding unicode regex mappings * Adding unicode regex function * Added needed functionality, testing remains * Fixed issues * Fixed issue with gpt2 regex custom preprocessor * unicode : fix? unicode_wstring_to_utf8 * lint : fix whitespaces * tests : add tokenizer tests for numbers * unicode : remove redundant headers * tests : remove and rename tokenizer test scripts * tests : add sample usage * gguf-py : reader prints warnings on duplicate keys * llama : towards llama3 tokenization support (wip) * unicode : shot in the dark to fix tests on Windows * unicode : first try custom implementations * convert : add "tokenizer.ggml.pre" GGUF KV (wip) * llama : use new pre-tokenizer type * convert : fix pre-tokenizer type writing * lint : fix * make : add test-tokenizer-0-llama-v3 * wip * models : add llama v3 vocab file * llama : adapt punctuation regex + add llama 3 regex * minor * unicode : set bomb * unicode : set bomb * unicode : always use std::wregex * unicode : support \p{N}, \p{L} and \p{P} natively * unicode : try fix windows * unicode : category support via std::regex * unicode : clean-up * unicode : simplify * convert : add convert-hf-to-gguf-update.py ggml-ci * lint : update * convert : add falcon ggml-ci * unicode : normalize signatures * lint : fix * lint : fix * convert : remove unused functions * convert : add comments * convert : exercise contractions ggml-ci * lint : fix * cmake : refactor test targets * tests : refactor vocab tests ggml-ci * tests : add more vocabs and tests ggml-ci * unicode : cleanup * scripts : ignore new update script in check-requirements.sh * models : add phi-3, mpt, gpt-2, starcoder * tests : disable obsolete ggml-ci * tests : use faster bpe test ggml-ci * llama : more prominent warning for old BPE models * tests : disable test-tokenizer-1-bpe due to slowness ggml-ci --------- Co-authored-by: Jaggzh <jaggz.h@gmail.com> Co-authored-by: Kazim Abrar Mahi <kazimabrarmahi135@gmail.com>
2024-04-29 13:58:41 +00:00
tokens, toktypes, tokpre = self.get_vocab_base()
BERT tokenizer fixes (#6498) Key changes: * BERT conversion: fix abuse of LlamaHfVocab, do not set BOS or EOS * Nomic Embed conversion: pad vocab instead of slicing embedding tensor * llama_tokenize: handle added special tokens like HF does
2024-04-09 17:44:08 +00:00
self.vocab_size = len(tokens)
Add support for BERT embedding models (#5423) * BERT model graph construction (build_bert) * WordPiece tokenizer (llm_tokenize_wpm) * Add flag for non-causal attention models * Allow for models that only output embeddings * Support conversion of BERT models to GGUF * Based on prior work by @xyzhang626 and @skeskinen --------- Co-authored-by: Jared Van Bortel <jared@nomic.ai> Co-authored-by: Jared Van Bortel <cebtenzzre@gmail.com> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-02-11 16:21:38 +00:00
# we need this to validate the size of the token_type embeddings
# though currently we are passing all zeros to the token_type embeddings
BERT tokenizer fixes (#6498) Key changes: * BERT conversion: fix abuse of LlamaHfVocab, do not set BOS or EOS * Nomic Embed conversion: pad vocab instead of slicing embedding tensor * llama_tokenize: handle added special tokens like HF does
2024-04-09 17:44:08 +00:00
self.gguf_writer.add_token_type_count(2) # "Sequence A" or "Sequence B"
Add support for BERT embedding models (#5423) * BERT model graph construction (build_bert) * WordPiece tokenizer (llm_tokenize_wpm) * Add flag for non-causal attention models * Allow for models that only output embeddings * Support conversion of BERT models to GGUF * Based on prior work by @xyzhang626 and @skeskinen --------- Co-authored-by: Jared Van Bortel <jared@nomic.ai> Co-authored-by: Jared Van Bortel <cebtenzzre@gmail.com> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-02-11 16:21:38 +00:00
# convert to phantom space vocab
BERT tokenizer fixes (#6498) Key changes: * BERT conversion: fix abuse of LlamaHfVocab, do not set BOS or EOS * Nomic Embed conversion: pad vocab instead of slicing embedding tensor * llama_tokenize: handle added special tokens like HF does
2024-04-09 17:44:08 +00:00
def phantom(tok):
if tok.startswith("[") and tok.endswith("]"):
Add support for BERT embedding models (#5423) * BERT model graph construction (build_bert) * WordPiece tokenizer (llm_tokenize_wpm) * Add flag for non-causal attention models * Allow for models that only output embeddings * Support conversion of BERT models to GGUF * Based on prior work by @xyzhang626 and @skeskinen --------- Co-authored-by: Jared Van Bortel <jared@nomic.ai> Co-authored-by: Jared Van Bortel <cebtenzzre@gmail.com> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-02-11 16:21:38 +00:00
return tok
BERT tokenizer fixes (#6498) Key changes: * BERT conversion: fix abuse of LlamaHfVocab, do not set BOS or EOS * Nomic Embed conversion: pad vocab instead of slicing embedding tensor * llama_tokenize: handle added special tokens like HF does
2024-04-09 17:44:08 +00:00
if tok.startswith("##"):
Add support for BERT embedding models (#5423) * BERT model graph construction (build_bert) * WordPiece tokenizer (llm_tokenize_wpm) * Add flag for non-causal attention models * Allow for models that only output embeddings * Support conversion of BERT models to GGUF * Based on prior work by @xyzhang626 and @skeskinen --------- Co-authored-by: Jared Van Bortel <jared@nomic.ai> Co-authored-by: Jared Van Bortel <cebtenzzre@gmail.com> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-02-11 16:21:38 +00:00
return tok[2:]
BERT tokenizer fixes (#6498) Key changes: * BERT conversion: fix abuse of LlamaHfVocab, do not set BOS or EOS * Nomic Embed conversion: pad vocab instead of slicing embedding tensor * llama_tokenize: handle added special tokens like HF does
2024-04-09 17:44:08 +00:00
return "\u2581" + tok
tokens = list(map(phantom, tokens))
Add support for BERT embedding models (#5423) * BERT model graph construction (build_bert) * WordPiece tokenizer (llm_tokenize_wpm) * Add flag for non-causal attention models * Allow for models that only output embeddings * Support conversion of BERT models to GGUF * Based on prior work by @xyzhang626 and @skeskinen --------- Co-authored-by: Jared Van Bortel <jared@nomic.ai> Co-authored-by: Jared Van Bortel <cebtenzzre@gmail.com> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-02-11 16:21:38 +00:00
# add vocab to gguf
self.gguf_writer.add_tokenizer_model("bert")
llama : fix BPE pre-tokenization (#6920) * merged the changes from deepseeker models to main branch * Moved regex patterns to unicode.cpp and updated unicode.h * Moved header files * Resolved issues * added and refactored unicode_regex_split and related functions * Updated/merged the deepseek coder pr * Refactored code * Adding unicode regex mappings * Adding unicode regex function * Added needed functionality, testing remains * Fixed issues * Fixed issue with gpt2 regex custom preprocessor * unicode : fix? unicode_wstring_to_utf8 * lint : fix whitespaces * tests : add tokenizer tests for numbers * unicode : remove redundant headers * tests : remove and rename tokenizer test scripts * tests : add sample usage * gguf-py : reader prints warnings on duplicate keys * llama : towards llama3 tokenization support (wip) * unicode : shot in the dark to fix tests on Windows * unicode : first try custom implementations * convert : add "tokenizer.ggml.pre" GGUF KV (wip) * llama : use new pre-tokenizer type * convert : fix pre-tokenizer type writing * lint : fix * make : add test-tokenizer-0-llama-v3 * wip * models : add llama v3 vocab file * llama : adapt punctuation regex + add llama 3 regex * minor * unicode : set bomb * unicode : set bomb * unicode : always use std::wregex * unicode : support \p{N}, \p{L} and \p{P} natively * unicode : try fix windows * unicode : category support via std::regex * unicode : clean-up * unicode : simplify * convert : add convert-hf-to-gguf-update.py ggml-ci * lint : update * convert : add falcon ggml-ci * unicode : normalize signatures * lint : fix * lint : fix * convert : remove unused functions * convert : add comments * convert : exercise contractions ggml-ci * lint : fix * cmake : refactor test targets * tests : refactor vocab tests ggml-ci * tests : add more vocabs and tests ggml-ci * unicode : cleanup * scripts : ignore new update script in check-requirements.sh * models : add phi-3, mpt, gpt-2, starcoder * tests : disable obsolete ggml-ci * tests : use faster bpe test ggml-ci * llama : more prominent warning for old BPE models * tests : disable test-tokenizer-1-bpe due to slowness ggml-ci --------- Co-authored-by: Jaggzh <jaggz.h@gmail.com> Co-authored-by: Kazim Abrar Mahi <kazimabrarmahi135@gmail.com>
2024-04-29 13:58:41 +00:00
self.gguf_writer.add_tokenizer_pre(tokpre)
Add support for BERT embedding models (#5423) * BERT model graph construction (build_bert) * WordPiece tokenizer (llm_tokenize_wpm) * Add flag for non-causal attention models * Allow for models that only output embeddings * Support conversion of BERT models to GGUF * Based on prior work by @xyzhang626 and @skeskinen --------- Co-authored-by: Jared Van Bortel <jared@nomic.ai> Co-authored-by: Jared Van Bortel <cebtenzzre@gmail.com> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-02-11 16:21:38 +00:00
self.gguf_writer.add_token_list(tokens)
self.gguf_writer.add_token_types(toktypes)
# handle special tokens
special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens))
special_vocab.add_to_gguf(self.gguf_writer)
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
del bid # unused
Add support for BERT embedding models (#5423) * BERT model graph construction (build_bert) * WordPiece tokenizer (llm_tokenize_wpm) * Add flag for non-causal attention models * Allow for models that only output embeddings * Support conversion of BERT models to GGUF * Based on prior work by @xyzhang626 and @skeskinen --------- Co-authored-by: Jared Van Bortel <jared@nomic.ai> Co-authored-by: Jared Van Bortel <cebtenzzre@gmail.com> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-02-11 16:21:38 +00:00
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
# we are only using BERT for embeddings so we don't need the pooling layer
if name in ("embeddings.position_ids", "pooler.dense.weight", "pooler.dense.bias"):
return [] # we don't need these
convert : fix conversion of some BERT embedding models (#6937)
2024-04-29 13:34:41 +00:00
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
return [(self.map_tensor_name(name), data_torch)]
Add support for BERT embedding models (#5423) * BERT model graph construction (build_bert) * WordPiece tokenizer (llm_tokenize_wpm) * Add flag for non-causal attention models * Allow for models that only output embeddings * Support conversion of BERT models to GGUF * Based on prior work by @xyzhang626 and @skeskinen --------- Co-authored-by: Jared Van Bortel <jared@nomic.ai> Co-authored-by: Jared Van Bortel <cebtenzzre@gmail.com> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-02-11 16:21:38 +00:00
convert-hf : make model class definitions self-contained (#5825)
2024-03-02 17:21:47 +00:00
@Model.register("NomicBertModel")
llama : add support for Nomic Embed (#5468)
2024-02-13 17:03:53 +00:00
class NomicBertModel(BertModel):
convert-hf : make model class definitions self-contained (#5825)
2024-03-02 17:21:47 +00:00
model_arch = gguf.MODEL_ARCH.NOMIC_BERT
llama : add support for Nomic Embed (#5468)
2024-02-13 17:03:53 +00:00
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
# the HF config claims n_ctx=8192, but it uses RoPE scaling
self.hparams["n_ctx"] = 2048
# SwigLU activation
assert self.hparams["activation_function"] == "swiglu"
# this doesn't do anything in the HF version
assert self.hparams["causal"] is False
# no bias tensors
assert self.hparams["qkv_proj_bias"] is False
assert self.hparams["mlp_fc1_bias"] is False
assert self.hparams["mlp_fc2_bias"] is False
# norm at end of layer
assert self.hparams["prenorm"] is False
# standard RoPE
assert self.hparams["rotary_emb_fraction"] == 1.0
assert self.hparams["rotary_emb_interleaved"] is False
assert self.hparams["rotary_emb_scale_base"] is None
def set_gguf_parameters(self):
super().set_gguf_parameters()
self.gguf_writer.add_rope_freq_base(self.hparams["rotary_emb_base"])
convert-hf : make model class definitions self-contained (#5825)
2024-03-02 17:21:47 +00:00
@Model.register("GemmaForCausalLM")
py : add Gemma conversion from HF models (#5647) * py : add gemma conversion from HF models * Update convert-hf-to-gguf.py Co-authored-by: Aarni Koskela <akx@iki.fi> * Update convert-hf-to-gguf.py Co-authored-by: Aarni Koskela <akx@iki.fi> * Update convert-hf-to-gguf.py Co-authored-by: Jared Van Bortel <jared@nomic.ai> --------- Co-authored-by: Aarni Koskela <akx@iki.fi> Co-authored-by: Jared Van Bortel <jared@nomic.ai>
2024-02-22 21:22:48 +00:00
class GemmaModel(Model):
convert-hf : make model class definitions self-contained (#5825)
2024-03-02 17:21:47 +00:00
model_arch = gguf.MODEL_ARCH.GEMMA
py : add Gemma conversion from HF models (#5647) * py : add gemma conversion from HF models * Update convert-hf-to-gguf.py Co-authored-by: Aarni Koskela <akx@iki.fi> * Update convert-hf-to-gguf.py Co-authored-by: Aarni Koskela <akx@iki.fi> * Update convert-hf-to-gguf.py Co-authored-by: Jared Van Bortel <jared@nomic.ai> --------- Co-authored-by: Aarni Koskela <akx@iki.fi> Co-authored-by: Jared Van Bortel <jared@nomic.ai>
2024-02-22 21:22:48 +00:00
def set_vocab(self):
self._set_vocab_sentencepiece()
llama : support Llama 3 HF conversion (#6745) * Support Llama 3 conversion The tokenizer is BPE. * style * Accept suggestion Co-authored-by: Sourab Mangrulkar <13534540+pacman100@users.noreply.github.com> * llama : add llama_token_is_eog() ggml-ci * llama : auto-detect more EOT tokens when missing in KV data * convert : replacing EOS token is a hack * llama : fix codegemma EOT token + add TODOs * llama : fix model type string for 8B model --------- Co-authored-by: Sourab Mangrulkar <13534540+pacman100@users.noreply.github.com> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-04-21 11:50:41 +00:00
# TODO: these special tokens should be exported only for the CodeGemma family
gguf : add special tokens metadata for FIM/Infill (#6689) This commit adds special token metadata for Fill-In-the-Middle (FIM)/Infill to the GGUF model. The motivation for this is that currently there is support for CodeLlama but other models exist now like CodeGemma, but the different models use different token ids for the special tokens and this commit allows for supporting multiple models. Signed-off-by: Daniel Bevenius <daniel.bevenius@gmail.com>
2024-04-16 06:13:13 +00:00
special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=False,
llama : support Llama 3 HF conversion (#6745) * Support Llama 3 conversion The tokenizer is BPE. * style * Accept suggestion Co-authored-by: Sourab Mangrulkar <13534540+pacman100@users.noreply.github.com> * llama : add llama_token_is_eog() ggml-ci * llama : auto-detect more EOT tokens when missing in KV data * convert : replacing EOS token is a hack * llama : fix codegemma EOT token + add TODOs * llama : fix model type string for 8B model --------- Co-authored-by: Sourab Mangrulkar <13534540+pacman100@users.noreply.github.com> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-04-21 11:50:41 +00:00
special_token_types = ['prefix', 'suffix', 'middle', 'fsep', 'eot'])
gguf : add special tokens metadata for FIM/Infill (#6689) This commit adds special token metadata for Fill-In-the-Middle (FIM)/Infill to the GGUF model. The motivation for this is that currently there is support for CodeLlama but other models exist now like CodeGemma, but the different models use different token ids for the special tokens and this commit allows for supporting multiple models. Signed-off-by: Daniel Bevenius <daniel.bevenius@gmail.com>
2024-04-16 06:13:13 +00:00
special_vocab._set_special_token("prefix", 67)
special_vocab._set_special_token("suffix", 69)
special_vocab._set_special_token("middle", 68)
llama : support Llama 3 HF conversion (#6745) * Support Llama 3 conversion The tokenizer is BPE. * style * Accept suggestion Co-authored-by: Sourab Mangrulkar <13534540+pacman100@users.noreply.github.com> * llama : add llama_token_is_eog() ggml-ci * llama : auto-detect more EOT tokens when missing in KV data * convert : replacing EOS token is a hack * llama : fix codegemma EOT token + add TODOs * llama : fix model type string for 8B model --------- Co-authored-by: Sourab Mangrulkar <13534540+pacman100@users.noreply.github.com> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-04-21 11:50:41 +00:00
special_vocab._set_special_token("fsep", 70)
special_vocab._set_special_token("eot", 107)
gguf : add special tokens metadata for FIM/Infill (#6689) This commit adds special token metadata for Fill-In-the-Middle (FIM)/Infill to the GGUF model. The motivation for this is that currently there is support for CodeLlama but other models exist now like CodeGemma, but the different models use different token ids for the special tokens and this commit allows for supporting multiple models. Signed-off-by: Daniel Bevenius <daniel.bevenius@gmail.com>
2024-04-16 06:13:13 +00:00
special_vocab.add_to_gguf(self.gguf_writer)
py : add Gemma conversion from HF models (#5647) * py : add gemma conversion from HF models * Update convert-hf-to-gguf.py Co-authored-by: Aarni Koskela <akx@iki.fi> * Update convert-hf-to-gguf.py Co-authored-by: Aarni Koskela <akx@iki.fi> * Update convert-hf-to-gguf.py Co-authored-by: Jared Van Bortel <jared@nomic.ai> --------- Co-authored-by: Aarni Koskela <akx@iki.fi> Co-authored-by: Jared Van Bortel <jared@nomic.ai>
2024-02-22 21:22:48 +00:00
def set_gguf_parameters(self):
hparams = self.hparams
block_count = hparams["num_hidden_layers"]
self.gguf_writer.add_name(self.dir_model.name)
self.gguf_writer.add_context_length(hparams["max_position_embeddings"])
self.gguf_writer.add_embedding_length(hparams["hidden_size"])
self.gguf_writer.add_block_count(block_count)
self.gguf_writer.add_feed_forward_length(hparams["intermediate_size"])
self.gguf_writer.add_head_count(hparams["num_attention_heads"])
self.gguf_writer.add_head_count_kv(self.hparams["num_key_value_heads"] if "num_key_value_heads" in hparams else hparams["num_attention_heads"])
self.gguf_writer.add_layer_norm_rms_eps(self.hparams["rms_norm_eps"])
self.gguf_writer.add_key_length(hparams["head_dim"])
self.gguf_writer.add_value_length(hparams["head_dim"])
convert : fix missing ftype for gemma (#5690)
2024-02-23 18:39:14 +00:00
self.gguf_writer.add_file_type(self.ftype)
py : add Gemma conversion from HF models (#5647) * py : add gemma conversion from HF models * Update convert-hf-to-gguf.py Co-authored-by: Aarni Koskela <akx@iki.fi> * Update convert-hf-to-gguf.py Co-authored-by: Aarni Koskela <akx@iki.fi> * Update convert-hf-to-gguf.py Co-authored-by: Jared Van Bortel <jared@nomic.ai> --------- Co-authored-by: Aarni Koskela <akx@iki.fi> Co-authored-by: Jared Van Bortel <jared@nomic.ai>
2024-02-22 21:22:48 +00:00
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
del bid # unused
py : add Gemma conversion from HF models (#5647) * py : add gemma conversion from HF models * Update convert-hf-to-gguf.py Co-authored-by: Aarni Koskela <akx@iki.fi> * Update convert-hf-to-gguf.py Co-authored-by: Aarni Koskela <akx@iki.fi> * Update convert-hf-to-gguf.py Co-authored-by: Jared Van Bortel <jared@nomic.ai> --------- Co-authored-by: Aarni Koskela <akx@iki.fi> Co-authored-by: Jared Van Bortel <jared@nomic.ai>
2024-02-22 21:22:48 +00:00
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
# lm_head is not used in llama.cpp, while autoawq will include this tensor in model
# To prevent errors, skip loading lm_head.weight.
if name == "lm_head.weight":
logger.debug(f"Skipping get tensor {name!r} in safetensors so that convert can end normally.")
return []
py : add Gemma conversion from HF models (#5647) * py : add gemma conversion from HF models * Update convert-hf-to-gguf.py Co-authored-by: Aarni Koskela <akx@iki.fi> * Update convert-hf-to-gguf.py Co-authored-by: Aarni Koskela <akx@iki.fi> * Update convert-hf-to-gguf.py Co-authored-by: Jared Van Bortel <jared@nomic.ai> --------- Co-authored-by: Aarni Koskela <akx@iki.fi> Co-authored-by: Jared Van Bortel <jared@nomic.ai>
2024-02-22 21:22:48 +00:00
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
# ref: https://github.com/huggingface/transformers/blob/fc37f38915372c15992b540dfcbbe00a916d4fc6/src/transformers/models/gemma/modeling_gemma.py#L89
if name.endswith("norm.weight"):
data_torch = data_torch + 1
py : add Gemma conversion from HF models (#5647) * py : add gemma conversion from HF models * Update convert-hf-to-gguf.py Co-authored-by: Aarni Koskela <akx@iki.fi> * Update convert-hf-to-gguf.py Co-authored-by: Aarni Koskela <akx@iki.fi> * Update convert-hf-to-gguf.py Co-authored-by: Jared Van Bortel <jared@nomic.ai> --------- Co-authored-by: Aarni Koskela <akx@iki.fi> Co-authored-by: Jared Van Bortel <jared@nomic.ai>
2024-02-22 21:22:48 +00:00
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
return [(self.map_tensor_name(name), data_torch)]
py : add Gemma conversion from HF models (#5647) * py : add gemma conversion from HF models * Update convert-hf-to-gguf.py Co-authored-by: Aarni Koskela <akx@iki.fi> * Update convert-hf-to-gguf.py Co-authored-by: Aarni Koskela <akx@iki.fi> * Update convert-hf-to-gguf.py Co-authored-by: Jared Van Bortel <jared@nomic.ai> --------- Co-authored-by: Aarni Koskela <akx@iki.fi> Co-authored-by: Jared Van Bortel <jared@nomic.ai>
2024-02-22 21:22:48 +00:00
convert-hf : make model class definitions self-contained (#5825)
2024-03-02 17:21:47 +00:00
@Model.register("Starcoder2ForCausalLM")
class StarCoder2Model(Model):
model_arch = gguf.MODEL_ARCH.STARCODER2
llama : support Mamba Selective State Space Models (#5328) * mamba : begin working on support for Mamba SSM * mamba : begin figuring out how to (ab)use the kv cache for Mamba * mamba : recurrent inference almost works, but incoherent * mamba : recurrent inference WORKS!!! * convert : optionally use d_conv and d_state from config.json for Mamba * mamba : refactor recurrent conv, resulting in 20% perf increase It's still slower than I'd like, but I did not really optimize `ggml_exp` yet. I also refactored `ggml_exp` to work with tensors with more than 2 dimensions. * ggml : parallelize ggml_exp This results in 8% faster token generation for Mamba-130M. * mamba : simplify the conv step with a self-overlapping view Turns out the conv_state can be made smaller by one column. Note that this breaks existing GGUFs of Mamba, because the key_value_length field is tied to the conv_state size. Convolution with a self-overlapping view is cool! And it's much simpler than what I initially thought would be necessary to make the convolution step work with more than 1 token at a time. Next step is to make the SSM step work on batches of tokens too, and thus I need to figure out a way to make a parallel selective scan which will keep the ssm_state small and won't make it bigger by a factor of (n_layer * batch_size). * llama : fix Mamba KV self size wrongly displaying as f16 instead of f32 Relatedly, I also tried to see if other types than f32 worked for the states, but they don't, because of the operators used. It's probably better anyway to keep lots of precision there, since the states are small anyway. * mamba : fix self-overlapping view depth stride * mamba : handle batches of more than 1 token This means running Mamba no longer crashes when using the default settings! And probably also slightly faster prompt processing. Both batched and non-batched processing yield the same output. Previously, the state was not cleared when starting a sequence. Next step is to make the KV cache API work as expected for Mamba models. * ggml: add ggml_ssm_scan to help with parallel selective scan If the selective scan was implemented without a custom operator, there would be waaay too many nodes in the graph. For example, for Mamba-130M, with a batch size of 512 (the default), a naive selective scan could add at least 24*512=12288 nodes, which is more than LLAMA_MAX_NODES (8192), and that's only for the smallest Mamba model. So it's much cleaner with a custom operator. Not sure about the name, though. * ggml : in ggml_ssm_scan, merge multiple rows in the same vec operation This will help with performance on CPU if ggml_vec_mul_f32 and ggml_vec_add_f32 are ever optimized with SIMD. * mamba : very basic quantization support Mostly works, but there is currently no difference between the variants of a k-quant (e.g. Q4_K_S and Q4_K_M are the same). Most of the SSM-specific weights can be kept in f32 without affecting the size that much, since they are relatively small. (the linear projection weights are responsible for most of Mamba's size) Too much quantization seems to make the state degrade quite fast, and the model begins to output gibberish. It seems to affect bigger models to a lesser extent than small models, but I'm not sure by how much. Experimentation will be needed to figure out which weights are more important for the _M (and _L?) variants of k-quants for Mamba. * convert : fix wrong name for layer norm weight of offical Mamba models I was using Q-bert/Mamba-* models before, which have a slighlty different naming scheme for the weights. (they start with "model.layers" instead of "backbone.layers") * mamba : fuse more steps of the SSM scan in the ggml_ssm_scan operator This increases performance on CPU by around 30% for prompt processing, and by around 20% for text generation. However, it also makes the ggml_exp and ggml_soft_plus operators unused. Whether or not they should be kept will be decided later. * convert : for Mamba, also consider the "MambaLMHeadModel" arch name It's the name of the class of the official implementation, though they don't use it (yet) in the "architectures" field of config.json * mamba : fix vocab size problems with official models The perplexity was waaaay to high for models with a non-round vocab size. Not sure why, but it needed to be fixed in the metadata. Note that this breaks existing GGUF-converted Mamba models, but **only if** the vocab size was not already rounded. * ggml : remove ggml_exp and ggml_soft_plus They did not exist anyway outside of this branch, and since ggml_ssm_scan fused operations together, they are unused. It's always possible to bring them back if needed. * mamba : remove some useless comments No code change. * convert : fix flake8 linter errors * mamba : apply suggestions from code review * mamba : remove unecessary branch for row-wise ssm_state and C multiplication It was previously done to avoid permuting when only one token is processed at a time (like when generating text), but permuting is cheap, and dynamically changing the compute graph is not future-proof. * ggml : in ggml_ssm_scan, use more appropriate asserts * ggml : rename the destination pointer in ggml_compute_forward_ssm_scan_f32 * mamba : multiple sequences, but one at a time This is a step towards making this Mamba implementation usable with the server example (the way the system prompt is kept when clearing the client slots will need to be changed before this can work, though). The KV cache size for this kind of model is tied to the maximum number of sequences kept at any single time. For now, this number is obtained from n_parallel (plus one, to have an extra sequence to dedicate to the system prompt), but there might be a better way to do this which won't also make the main example use 2 cells even if only 1 is really used. (for this specific case, --parallel 0 helps) Simultaneous sequence processing will probably require changes to ggml_ssm_scan, and possibly a new operator for the conv step. * mamba : support llama_kv_cache_seq_cp This (mis)uses the logic around K shifts, because tokens in a state can't be shifted anyway, and because inp_K_shift has the right shape and type. Using ggml_get_rows is a nice way to do copies, but copy chains can't work. Fortunately, copy chains don't really seem to be used in the examples. Each KV cell is dedicated to the sequence ID corresponding to its own index. * mamba : use a state mask It's cleaner than the previous heuristic of checking for the pos of the first token in the batch. inp_KQ_mask could not be re-used for this, because it has the wrong shape and because it seems more suited to the next step of simultaneous sequence processing (helping with the problem of remembering which token belongs to which sequence(s)/state(s)). * llama : replace the usage of n_ctx with kv_self.size in many places * mamba : use n_tokens directly instead of n_tok * mamba : in comments, properly refer to KV cells instead of slots * mamba : reduce memory usage of ggml_ssm_scan From 290.37 MiB to 140.68 MiB of CPU compute buffer size with Mamba 3B with a batch size of 512. The result tensor of ggml_ssm_scan was previously a big part of the CPU compute buffer size. To make it smaller, it does not contain the intermediate ssm states anymore. Both y and the last ssm state are combined in the result tensor, because it seems only a single tensor can be returned by an operator with the way the graph is built. * mamba : simultaneous sequence processing A batch can now contain tokens from multiple sequences. This is necessary for at least the parallel example, the server example, and the HellaSwag test in the perplexity example. However, for this to be useful, uses of llama_kv_cache_seq_rm/cp will need to be changed to work on whole sequences. * ggml : add ggml_ssm_conv as a new operator for the conv step of Mamba This operator makes it possible to use and update the correct states for each token of the batch in the same way as ggml_ssm_scan. Other solutions which use existing operators would need loops which would add too many nodes to the graph (at least the ones I thought of). Using this operator further reduces the size of the CPU compute buffer from 140.68 MiB to 103.20 MiB with Mamba 3B with a batch size of 512. And (at least on CPU), it's a bit faster than before. Note that "ggml_ssm_conv" is probably not the most appropriate name, and it could be changed if a better one is found. * llama : add inp_s_seq as a new input tensor The most convenient implementation to select the correct state (for Mamba) for each token is to directly get the correct index from a tensor. This is why inp_s_seq is storing int32_t and not floats. The other, less convenient way to select the correct state would be to have inp_KQ_mask contain 1.0f for each state used by a token and 0.0f otherwise. This complicates quickly fetching the first used state of a token, and is also less efficient because a whole row of the mask would always need to be read for each token. Using indexes makes it easy to stop searching when there are no more sequences for a token, and the first sequence assigned is always very quickly available (it's the first element of each row). * mamba : support llama_kv_cache_seq_cp copy chains * mamba : support shifting and dividing the kv cache pos * mamba : make the server and parallel examples work with whole sequences A seq_id is dedicated to the system prompt in both cases. * llama : make llama_kv_cache_seq_rm return whether it succeeded or not * mamba : dedicate an input tensor for state copy indices This is cleaner and makes it easier to adapt when/if token positions (and by extension, inp_K_shift) are no longer integers. * mamba : adapt perplexity, batched, and batched-bench examples * perplexity : limit the max number of sequences This adapts to what the loaded model can provide. * llama : add llama_n_max_seq to get the upper limit for seq_ids Used by the perplexity example. * batched : pass n_parallel to the model's context params This should have been there already, but it wasn't. * batched-bench : reserve sequences to support Mamba * batched-bench : fix tokens being put in wrong sequences Generation quality isn't what's measured in there anyway, but at least using the correct sequences avoids using non-consecutive token positions. * mamba : stop abusing attention metadata This breaks existing converted-to-GGUF Mamba models, but will allow supporting mixed architectures like MambaFormer without needing to break Mamba models. This will also allow changing the size of Mamba's states without having to reconvert models in the future. (e.g. using something else than d_conv - 1 columns for the conv_states will not require breaking existing converted Mamba models again) * gguf-py : add new KV metadata key-value pairs for Mamba * llama : add new metadata key-value pairs for Mamba * llama : guard against divisions by zero when n_head is 0 * mamba : rename "unlimited" KV cache property to "recurrent" * mamba : more correctly update the "used" field of the KV cache * ggml : in ggml_ssm_scan, use a threshold for soft_plus This is how the official Mamba implementation does it, and it's also what torch.nn.Softplus does. * convert : for Mamba, fallback to internal NeoX tokenizer The resulting models are exactly the same as if the tokenizer.json and tokenizer_config.json of GPT-NeoX were there. * mamba : support state saving and restoring * ggml : implicitly pass src tensors through dst for Mamba-related ops * mamba : clarify some comments * server : fix cache_tokens not getting correctly resized Otherwise, when the "we have to evaluate at least 1 token" special case was triggered, an extra token was kept in cache_tokens even if it was removed from the KV cache. For Mamba, this caused useless prompt reprocessing when the previous request triggered the above case. * convert-hf : support new metadata keys for Mamba For the models available at https://huggingface.co/collections/state-spaces/transformers-compatible-mamba-65e7b40ab87e5297e45ae406 * mamba : rename metadata to be more similar to transformers library This breaks existing converted-to-GGUF models, but the metadata names are more "standard". * mamba : support mamba-*-hf models These models share their token_embd.weight with their output.weight * mamba : add missing spaces This is purely a formatting change. * convert-hf : omit output.weight when identical with token_embd.weight Only for Mamba for now, but it might be relevant for other models eventually. Most Mamba models actually share these two tensors, albeit implicitly. * readme : add Mamba to supported models, and add recent API changes * mamba : move state_seq and state_mask views outside layer loop A few tensors were also missing `struct` in front of `ggml_tensor`.
2024-03-08 22:31:00 +00:00
@Model.register("MambaForCausalLM", "MambaLMHeadModel")
class MambaModel(Model):
model_arch = gguf.MODEL_ARCH.MAMBA
def set_vocab(self):
vocab_size = self.hparams["vocab_size"]
# Round vocab size to next multiple of 8
pad_vocab = self.hparams.get("pad_vocab_size_multiple", 8)
# pad using ceiling division
# ref: https://stackoverflow.com/a/17511341/22827863
vocab_size = -(vocab_size // -pad_vocab) * pad_vocab
self.hparams["vocab_size"] = vocab_size
if (self.dir_model / "tokenizer.json").is_file():
self._set_vocab_gpt2()
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
elif (self.dir_model / "tokenizer.model").is_file():
self._set_vocab_sentencepiece()
llama : support Mamba Selective State Space Models (#5328) * mamba : begin working on support for Mamba SSM * mamba : begin figuring out how to (ab)use the kv cache for Mamba * mamba : recurrent inference almost works, but incoherent * mamba : recurrent inference WORKS!!! * convert : optionally use d_conv and d_state from config.json for Mamba * mamba : refactor recurrent conv, resulting in 20% perf increase It's still slower than I'd like, but I did not really optimize `ggml_exp` yet. I also refactored `ggml_exp` to work with tensors with more than 2 dimensions. * ggml : parallelize ggml_exp This results in 8% faster token generation for Mamba-130M. * mamba : simplify the conv step with a self-overlapping view Turns out the conv_state can be made smaller by one column. Note that this breaks existing GGUFs of Mamba, because the key_value_length field is tied to the conv_state size. Convolution with a self-overlapping view is cool! And it's much simpler than what I initially thought would be necessary to make the convolution step work with more than 1 token at a time. Next step is to make the SSM step work on batches of tokens too, and thus I need to figure out a way to make a parallel selective scan which will keep the ssm_state small and won't make it bigger by a factor of (n_layer * batch_size). * llama : fix Mamba KV self size wrongly displaying as f16 instead of f32 Relatedly, I also tried to see if other types than f32 worked for the states, but they don't, because of the operators used. It's probably better anyway to keep lots of precision there, since the states are small anyway. * mamba : fix self-overlapping view depth stride * mamba : handle batches of more than 1 token This means running Mamba no longer crashes when using the default settings! And probably also slightly faster prompt processing. Both batched and non-batched processing yield the same output. Previously, the state was not cleared when starting a sequence. Next step is to make the KV cache API work as expected for Mamba models. * ggml: add ggml_ssm_scan to help with parallel selective scan If the selective scan was implemented without a custom operator, there would be waaay too many nodes in the graph. For example, for Mamba-130M, with a batch size of 512 (the default), a naive selective scan could add at least 24*512=12288 nodes, which is more than LLAMA_MAX_NODES (8192), and that's only for the smallest Mamba model. So it's much cleaner with a custom operator. Not sure about the name, though. * ggml : in ggml_ssm_scan, merge multiple rows in the same vec operation This will help with performance on CPU if ggml_vec_mul_f32 and ggml_vec_add_f32 are ever optimized with SIMD. * mamba : very basic quantization support Mostly works, but there is currently no difference between the variants of a k-quant (e.g. Q4_K_S and Q4_K_M are the same). Most of the SSM-specific weights can be kept in f32 without affecting the size that much, since they are relatively small. (the linear projection weights are responsible for most of Mamba's size) Too much quantization seems to make the state degrade quite fast, and the model begins to output gibberish. It seems to affect bigger models to a lesser extent than small models, but I'm not sure by how much. Experimentation will be needed to figure out which weights are more important for the _M (and _L?) variants of k-quants for Mamba. * convert : fix wrong name for layer norm weight of offical Mamba models I was using Q-bert/Mamba-* models before, which have a slighlty different naming scheme for the weights. (they start with "model.layers" instead of "backbone.layers") * mamba : fuse more steps of the SSM scan in the ggml_ssm_scan operator This increases performance on CPU by around 30% for prompt processing, and by around 20% for text generation. However, it also makes the ggml_exp and ggml_soft_plus operators unused. Whether or not they should be kept will be decided later. * convert : for Mamba, also consider the "MambaLMHeadModel" arch name It's the name of the class of the official implementation, though they don't use it (yet) in the "architectures" field of config.json * mamba : fix vocab size problems with official models The perplexity was waaaay to high for models with a non-round vocab size. Not sure why, but it needed to be fixed in the metadata. Note that this breaks existing GGUF-converted Mamba models, but **only if** the vocab size was not already rounded. * ggml : remove ggml_exp and ggml_soft_plus They did not exist anyway outside of this branch, and since ggml_ssm_scan fused operations together, they are unused. It's always possible to bring them back if needed. * mamba : remove some useless comments No code change. * convert : fix flake8 linter errors * mamba : apply suggestions from code review * mamba : remove unecessary branch for row-wise ssm_state and C multiplication It was previously done to avoid permuting when only one token is processed at a time (like when generating text), but permuting is cheap, and dynamically changing the compute graph is not future-proof. * ggml : in ggml_ssm_scan, use more appropriate asserts * ggml : rename the destination pointer in ggml_compute_forward_ssm_scan_f32 * mamba : multiple sequences, but one at a time This is a step towards making this Mamba implementation usable with the server example (the way the system prompt is kept when clearing the client slots will need to be changed before this can work, though). The KV cache size for this kind of model is tied to the maximum number of sequences kept at any single time. For now, this number is obtained from n_parallel (plus one, to have an extra sequence to dedicate to the system prompt), but there might be a better way to do this which won't also make the main example use 2 cells even if only 1 is really used. (for this specific case, --parallel 0 helps) Simultaneous sequence processing will probably require changes to ggml_ssm_scan, and possibly a new operator for the conv step. * mamba : support llama_kv_cache_seq_cp This (mis)uses the logic around K shifts, because tokens in a state can't be shifted anyway, and because inp_K_shift has the right shape and type. Using ggml_get_rows is a nice way to do copies, but copy chains can't work. Fortunately, copy chains don't really seem to be used in the examples. Each KV cell is dedicated to the sequence ID corresponding to its own index. * mamba : use a state mask It's cleaner than the previous heuristic of checking for the pos of the first token in the batch. inp_KQ_mask could not be re-used for this, because it has the wrong shape and because it seems more suited to the next step of simultaneous sequence processing (helping with the problem of remembering which token belongs to which sequence(s)/state(s)). * llama : replace the usage of n_ctx with kv_self.size in many places * mamba : use n_tokens directly instead of n_tok * mamba : in comments, properly refer to KV cells instead of slots * mamba : reduce memory usage of ggml_ssm_scan From 290.37 MiB to 140.68 MiB of CPU compute buffer size with Mamba 3B with a batch size of 512. The result tensor of ggml_ssm_scan was previously a big part of the CPU compute buffer size. To make it smaller, it does not contain the intermediate ssm states anymore. Both y and the last ssm state are combined in the result tensor, because it seems only a single tensor can be returned by an operator with the way the graph is built. * mamba : simultaneous sequence processing A batch can now contain tokens from multiple sequences. This is necessary for at least the parallel example, the server example, and the HellaSwag test in the perplexity example. However, for this to be useful, uses of llama_kv_cache_seq_rm/cp will need to be changed to work on whole sequences. * ggml : add ggml_ssm_conv as a new operator for the conv step of Mamba This operator makes it possible to use and update the correct states for each token of the batch in the same way as ggml_ssm_scan. Other solutions which use existing operators would need loops which would add too many nodes to the graph (at least the ones I thought of). Using this operator further reduces the size of the CPU compute buffer from 140.68 MiB to 103.20 MiB with Mamba 3B with a batch size of 512. And (at least on CPU), it's a bit faster than before. Note that "ggml_ssm_conv" is probably not the most appropriate name, and it could be changed if a better one is found. * llama : add inp_s_seq as a new input tensor The most convenient implementation to select the correct state (for Mamba) for each token is to directly get the correct index from a tensor. This is why inp_s_seq is storing int32_t and not floats. The other, less convenient way to select the correct state would be to have inp_KQ_mask contain 1.0f for each state used by a token and 0.0f otherwise. This complicates quickly fetching the first used state of a token, and is also less efficient because a whole row of the mask would always need to be read for each token. Using indexes makes it easy to stop searching when there are no more sequences for a token, and the first sequence assigned is always very quickly available (it's the first element of each row). * mamba : support llama_kv_cache_seq_cp copy chains * mamba : support shifting and dividing the kv cache pos * mamba : make the server and parallel examples work with whole sequences A seq_id is dedicated to the system prompt in both cases. * llama : make llama_kv_cache_seq_rm return whether it succeeded or not * mamba : dedicate an input tensor for state copy indices This is cleaner and makes it easier to adapt when/if token positions (and by extension, inp_K_shift) are no longer integers. * mamba : adapt perplexity, batched, and batched-bench examples * perplexity : limit the max number of sequences This adapts to what the loaded model can provide. * llama : add llama_n_max_seq to get the upper limit for seq_ids Used by the perplexity example. * batched : pass n_parallel to the model's context params This should have been there already, but it wasn't. * batched-bench : reserve sequences to support Mamba * batched-bench : fix tokens being put in wrong sequences Generation quality isn't what's measured in there anyway, but at least using the correct sequences avoids using non-consecutive token positions. * mamba : stop abusing attention metadata This breaks existing converted-to-GGUF Mamba models, but will allow supporting mixed architectures like MambaFormer without needing to break Mamba models. This will also allow changing the size of Mamba's states without having to reconvert models in the future. (e.g. using something else than d_conv - 1 columns for the conv_states will not require breaking existing converted Mamba models again) * gguf-py : add new KV metadata key-value pairs for Mamba * llama : add new metadata key-value pairs for Mamba * llama : guard against divisions by zero when n_head is 0 * mamba : rename "unlimited" KV cache property to "recurrent" * mamba : more correctly update the "used" field of the KV cache * ggml : in ggml_ssm_scan, use a threshold for soft_plus This is how the official Mamba implementation does it, and it's also what torch.nn.Softplus does. * convert : for Mamba, fallback to internal NeoX tokenizer The resulting models are exactly the same as if the tokenizer.json and tokenizer_config.json of GPT-NeoX were there. * mamba : support state saving and restoring * ggml : implicitly pass src tensors through dst for Mamba-related ops * mamba : clarify some comments * server : fix cache_tokens not getting correctly resized Otherwise, when the "we have to evaluate at least 1 token" special case was triggered, an extra token was kept in cache_tokens even if it was removed from the KV cache. For Mamba, this caused useless prompt reprocessing when the previous request triggered the above case. * convert-hf : support new metadata keys for Mamba For the models available at https://huggingface.co/collections/state-spaces/transformers-compatible-mamba-65e7b40ab87e5297e45ae406 * mamba : rename metadata to be more similar to transformers library This breaks existing converted-to-GGUF models, but the metadata names are more "standard". * mamba : support mamba-*-hf models These models share their token_embd.weight with their output.weight * mamba : add missing spaces This is purely a formatting change. * convert-hf : omit output.weight when identical with token_embd.weight Only for Mamba for now, but it might be relevant for other models eventually. Most Mamba models actually share these two tensors, albeit implicitly. * readme : add Mamba to supported models, and add recent API changes * mamba : move state_seq and state_mask views outside layer loop A few tensors were also missing `struct` in front of `ggml_tensor`.
2024-03-08 22:31:00 +00:00
else:
# Use the GPT-NeoX tokenizer when no tokenizer files are present
tokenizer_path = Path(sys.path[0]) / "models" / "ggml-vocab-gpt-neox.gguf"
convert.py : add python logging instead of print() (#6511) * convert.py: add python logging instead of print() * convert.py: verbose flag takes priority over dump flag log suppression * convert.py: named instance logging * convert.py: use explicit logger id string * convert.py: convert extra print() to named logger * convert.py: sys.stderr.write --> logger.error * *.py: Convert all python scripts to use logging module * requirements.txt: remove extra line * flake8: update flake8 ignore and exclude to match ci settings * gh-actions: add flake8-no-print to flake8 lint step * pre-commit: add flake8-no-print to flake8 and also update pre-commit version * convert-hf-to-gguf.py: print() to logger conversion * *.py: logging basiconfig refactor to use conditional expression * *.py: removed commented out logging * fixup! *.py: logging basiconfig refactor to use conditional expression * constant.py: logger.error then exit should be a raise exception instead * *.py: Convert logger error and sys.exit() into a raise exception (for atypical error) * gguf-convert-endian.py: refactor convert_byteorder() to use tqdm progressbar * verify-checksum-model.py: This is the result of the program, it should be printed to stdout. * compare-llama-bench.py: add blank line for readability during missing repo response * reader.py: read_gguf_file() use print() over logging * convert.py: warning goes to stderr and won't hurt the dump output * gguf-dump.py: dump_metadata() should print to stdout * convert-hf-to-gguf.py: print --> logger.debug or ValueError() * verify-checksum-models.py: use print() for printing table * *.py: refactor logging.basicConfig() * gguf-py/gguf/*.py: use __name__ as logger name Since they will be imported and not run directly. * python-lint.yml: use .flake8 file instead * constants.py: logger no longer required * convert-hf-to-gguf.py: add additional logging * convert-hf-to-gguf.py: print() --> logger * *.py: fix flake8 warnings * revert changes to convert-hf-to-gguf.py for get_name() * convert-hf-to-gguf-update.py: use triple quoted f-string instead * *.py: accidentally corrected the wrong line * *.py: add compilade warning suggestions and style fixes
2024-05-03 19:36:41 +00:00
logger.warning(f"Using tokenizer from '{os.path.relpath(tokenizer_path, os.getcwd())}'")
llama : support Mamba Selective State Space Models (#5328) * mamba : begin working on support for Mamba SSM * mamba : begin figuring out how to (ab)use the kv cache for Mamba * mamba : recurrent inference almost works, but incoherent * mamba : recurrent inference WORKS!!! * convert : optionally use d_conv and d_state from config.json for Mamba * mamba : refactor recurrent conv, resulting in 20% perf increase It's still slower than I'd like, but I did not really optimize `ggml_exp` yet. I also refactored `ggml_exp` to work with tensors with more than 2 dimensions. * ggml : parallelize ggml_exp This results in 8% faster token generation for Mamba-130M. * mamba : simplify the conv step with a self-overlapping view Turns out the conv_state can be made smaller by one column. Note that this breaks existing GGUFs of Mamba, because the key_value_length field is tied to the conv_state size. Convolution with a self-overlapping view is cool! And it's much simpler than what I initially thought would be necessary to make the convolution step work with more than 1 token at a time. Next step is to make the SSM step work on batches of tokens too, and thus I need to figure out a way to make a parallel selective scan which will keep the ssm_state small and won't make it bigger by a factor of (n_layer * batch_size). * llama : fix Mamba KV self size wrongly displaying as f16 instead of f32 Relatedly, I also tried to see if other types than f32 worked for the states, but they don't, because of the operators used. It's probably better anyway to keep lots of precision there, since the states are small anyway. * mamba : fix self-overlapping view depth stride * mamba : handle batches of more than 1 token This means running Mamba no longer crashes when using the default settings! And probably also slightly faster prompt processing. Both batched and non-batched processing yield the same output. Previously, the state was not cleared when starting a sequence. Next step is to make the KV cache API work as expected for Mamba models. * ggml: add ggml_ssm_scan to help with parallel selective scan If the selective scan was implemented without a custom operator, there would be waaay too many nodes in the graph. For example, for Mamba-130M, with a batch size of 512 (the default), a naive selective scan could add at least 24*512=12288 nodes, which is more than LLAMA_MAX_NODES (8192), and that's only for the smallest Mamba model. So it's much cleaner with a custom operator. Not sure about the name, though. * ggml : in ggml_ssm_scan, merge multiple rows in the same vec operation This will help with performance on CPU if ggml_vec_mul_f32 and ggml_vec_add_f32 are ever optimized with SIMD. * mamba : very basic quantization support Mostly works, but there is currently no difference between the variants of a k-quant (e.g. Q4_K_S and Q4_K_M are the same). Most of the SSM-specific weights can be kept in f32 without affecting the size that much, since they are relatively small. (the linear projection weights are responsible for most of Mamba's size) Too much quantization seems to make the state degrade quite fast, and the model begins to output gibberish. It seems to affect bigger models to a lesser extent than small models, but I'm not sure by how much. Experimentation will be needed to figure out which weights are more important for the _M (and _L?) variants of k-quants for Mamba. * convert : fix wrong name for layer norm weight of offical Mamba models I was using Q-bert/Mamba-* models before, which have a slighlty different naming scheme for the weights. (they start with "model.layers" instead of "backbone.layers") * mamba : fuse more steps of the SSM scan in the ggml_ssm_scan operator This increases performance on CPU by around 30% for prompt processing, and by around 20% for text generation. However, it also makes the ggml_exp and ggml_soft_plus operators unused. Whether or not they should be kept will be decided later. * convert : for Mamba, also consider the "MambaLMHeadModel" arch name It's the name of the class of the official implementation, though they don't use it (yet) in the "architectures" field of config.json * mamba : fix vocab size problems with official models The perplexity was waaaay to high for models with a non-round vocab size. Not sure why, but it needed to be fixed in the metadata. Note that this breaks existing GGUF-converted Mamba models, but **only if** the vocab size was not already rounded. * ggml : remove ggml_exp and ggml_soft_plus They did not exist anyway outside of this branch, and since ggml_ssm_scan fused operations together, they are unused. It's always possible to bring them back if needed. * mamba : remove some useless comments No code change. * convert : fix flake8 linter errors * mamba : apply suggestions from code review * mamba : remove unecessary branch for row-wise ssm_state and C multiplication It was previously done to avoid permuting when only one token is processed at a time (like when generating text), but permuting is cheap, and dynamically changing the compute graph is not future-proof. * ggml : in ggml_ssm_scan, use more appropriate asserts * ggml : rename the destination pointer in ggml_compute_forward_ssm_scan_f32 * mamba : multiple sequences, but one at a time This is a step towards making this Mamba implementation usable with the server example (the way the system prompt is kept when clearing the client slots will need to be changed before this can work, though). The KV cache size for this kind of model is tied to the maximum number of sequences kept at any single time. For now, this number is obtained from n_parallel (plus one, to have an extra sequence to dedicate to the system prompt), but there might be a better way to do this which won't also make the main example use 2 cells even if only 1 is really used. (for this specific case, --parallel 0 helps) Simultaneous sequence processing will probably require changes to ggml_ssm_scan, and possibly a new operator for the conv step. * mamba : support llama_kv_cache_seq_cp This (mis)uses the logic around K shifts, because tokens in a state can't be shifted anyway, and because inp_K_shift has the right shape and type. Using ggml_get_rows is a nice way to do copies, but copy chains can't work. Fortunately, copy chains don't really seem to be used in the examples. Each KV cell is dedicated to the sequence ID corresponding to its own index. * mamba : use a state mask It's cleaner than the previous heuristic of checking for the pos of the first token in the batch. inp_KQ_mask could not be re-used for this, because it has the wrong shape and because it seems more suited to the next step of simultaneous sequence processing (helping with the problem of remembering which token belongs to which sequence(s)/state(s)). * llama : replace the usage of n_ctx with kv_self.size in many places * mamba : use n_tokens directly instead of n_tok * mamba : in comments, properly refer to KV cells instead of slots * mamba : reduce memory usage of ggml_ssm_scan From 290.37 MiB to 140.68 MiB of CPU compute buffer size with Mamba 3B with a batch size of 512. The result tensor of ggml_ssm_scan was previously a big part of the CPU compute buffer size. To make it smaller, it does not contain the intermediate ssm states anymore. Both y and the last ssm state are combined in the result tensor, because it seems only a single tensor can be returned by an operator with the way the graph is built. * mamba : simultaneous sequence processing A batch can now contain tokens from multiple sequences. This is necessary for at least the parallel example, the server example, and the HellaSwag test in the perplexity example. However, for this to be useful, uses of llama_kv_cache_seq_rm/cp will need to be changed to work on whole sequences. * ggml : add ggml_ssm_conv as a new operator for the conv step of Mamba This operator makes it possible to use and update the correct states for each token of the batch in the same way as ggml_ssm_scan. Other solutions which use existing operators would need loops which would add too many nodes to the graph (at least the ones I thought of). Using this operator further reduces the size of the CPU compute buffer from 140.68 MiB to 103.20 MiB with Mamba 3B with a batch size of 512. And (at least on CPU), it's a bit faster than before. Note that "ggml_ssm_conv" is probably not the most appropriate name, and it could be changed if a better one is found. * llama : add inp_s_seq as a new input tensor The most convenient implementation to select the correct state (for Mamba) for each token is to directly get the correct index from a tensor. This is why inp_s_seq is storing int32_t and not floats. The other, less convenient way to select the correct state would be to have inp_KQ_mask contain 1.0f for each state used by a token and 0.0f otherwise. This complicates quickly fetching the first used state of a token, and is also less efficient because a whole row of the mask would always need to be read for each token. Using indexes makes it easy to stop searching when there are no more sequences for a token, and the first sequence assigned is always very quickly available (it's the first element of each row). * mamba : support llama_kv_cache_seq_cp copy chains * mamba : support shifting and dividing the kv cache pos * mamba : make the server and parallel examples work with whole sequences A seq_id is dedicated to the system prompt in both cases. * llama : make llama_kv_cache_seq_rm return whether it succeeded or not * mamba : dedicate an input tensor for state copy indices This is cleaner and makes it easier to adapt when/if token positions (and by extension, inp_K_shift) are no longer integers. * mamba : adapt perplexity, batched, and batched-bench examples * perplexity : limit the max number of sequences This adapts to what the loaded model can provide. * llama : add llama_n_max_seq to get the upper limit for seq_ids Used by the perplexity example. * batched : pass n_parallel to the model's context params This should have been there already, but it wasn't. * batched-bench : reserve sequences to support Mamba * batched-bench : fix tokens being put in wrong sequences Generation quality isn't what's measured in there anyway, but at least using the correct sequences avoids using non-consecutive token positions. * mamba : stop abusing attention metadata This breaks existing converted-to-GGUF Mamba models, but will allow supporting mixed architectures like MambaFormer without needing to break Mamba models. This will also allow changing the size of Mamba's states without having to reconvert models in the future. (e.g. using something else than d_conv - 1 columns for the conv_states will not require breaking existing converted Mamba models again) * gguf-py : add new KV metadata key-value pairs for Mamba * llama : add new metadata key-value pairs for Mamba * llama : guard against divisions by zero when n_head is 0 * mamba : rename "unlimited" KV cache property to "recurrent" * mamba : more correctly update the "used" field of the KV cache * ggml : in ggml_ssm_scan, use a threshold for soft_plus This is how the official Mamba implementation does it, and it's also what torch.nn.Softplus does. * convert : for Mamba, fallback to internal NeoX tokenizer The resulting models are exactly the same as if the tokenizer.json and tokenizer_config.json of GPT-NeoX were there. * mamba : support state saving and restoring * ggml : implicitly pass src tensors through dst for Mamba-related ops * mamba : clarify some comments * server : fix cache_tokens not getting correctly resized Otherwise, when the "we have to evaluate at least 1 token" special case was triggered, an extra token was kept in cache_tokens even if it was removed from the KV cache. For Mamba, this caused useless prompt reprocessing when the previous request triggered the above case. * convert-hf : support new metadata keys for Mamba For the models available at https://huggingface.co/collections/state-spaces/transformers-compatible-mamba-65e7b40ab87e5297e45ae406 * mamba : rename metadata to be more similar to transformers library This breaks existing converted-to-GGUF models, but the metadata names are more "standard". * mamba : support mamba-*-hf models These models share their token_embd.weight with their output.weight * mamba : add missing spaces This is purely a formatting change. * convert-hf : omit output.weight when identical with token_embd.weight Only for Mamba for now, but it might be relevant for other models eventually. Most Mamba models actually share these two tensors, albeit implicitly. * readme : add Mamba to supported models, and add recent API changes * mamba : move state_seq and state_mask views outside layer loop A few tensors were also missing `struct` in front of `ggml_tensor`.
2024-03-08 22:31:00 +00:00
neox_reader = gguf.GGUFReader(tokenizer_path, "r")
field = neox_reader.get_field(gguf.Keys.Tokenizer.MODEL)
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
self.gguf_writer.add_tokenizer_model(bytes(field.parts[-1]).decode("utf-8") if field else "gpt2")
llama : support Llama 3 HF conversion (#6745) * Support Llama 3 conversion The tokenizer is BPE. * style * Accept suggestion Co-authored-by: Sourab Mangrulkar <13534540+pacman100@users.noreply.github.com> * llama : add llama_token_is_eog() ggml-ci * llama : auto-detect more EOT tokens when missing in KV data * convert : replacing EOS token is a hack * llama : fix codegemma EOT token + add TODOs * llama : fix model type string for 8B model --------- Co-authored-by: Sourab Mangrulkar <13534540+pacman100@users.noreply.github.com> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-04-21 11:50:41 +00:00
llama : fix BPE pre-tokenization (#6920) * merged the changes from deepseeker models to main branch * Moved regex patterns to unicode.cpp and updated unicode.h * Moved header files * Resolved issues * added and refactored unicode_regex_split and related functions * Updated/merged the deepseek coder pr * Refactored code * Adding unicode regex mappings * Adding unicode regex function * Added needed functionality, testing remains * Fixed issues * Fixed issue with gpt2 regex custom preprocessor * unicode : fix? unicode_wstring_to_utf8 * lint : fix whitespaces * tests : add tokenizer tests for numbers * unicode : remove redundant headers * tests : remove and rename tokenizer test scripts * tests : add sample usage * gguf-py : reader prints warnings on duplicate keys * llama : towards llama3 tokenization support (wip) * unicode : shot in the dark to fix tests on Windows * unicode : first try custom implementations * convert : add "tokenizer.ggml.pre" GGUF KV (wip) * llama : use new pre-tokenizer type * convert : fix pre-tokenizer type writing * lint : fix * make : add test-tokenizer-0-llama-v3 * wip * models : add llama v3 vocab file * llama : adapt punctuation regex + add llama 3 regex * minor * unicode : set bomb * unicode : set bomb * unicode : always use std::wregex * unicode : support \p{N}, \p{L} and \p{P} natively * unicode : try fix windows * unicode : category support via std::regex * unicode : clean-up * unicode : simplify * convert : add convert-hf-to-gguf-update.py ggml-ci * lint : update * convert : add falcon ggml-ci * unicode : normalize signatures * lint : fix * lint : fix * convert : remove unused functions * convert : add comments * convert : exercise contractions ggml-ci * lint : fix * cmake : refactor test targets * tests : refactor vocab tests ggml-ci * tests : add more vocabs and tests ggml-ci * unicode : cleanup * scripts : ignore new update script in check-requirements.sh * models : add phi-3, mpt, gpt-2, starcoder * tests : disable obsolete ggml-ci * tests : use faster bpe test ggml-ci * llama : more prominent warning for old BPE models * tests : disable test-tokenizer-1-bpe due to slowness ggml-ci --------- Co-authored-by: Jaggzh <jaggz.h@gmail.com> Co-authored-by: Kazim Abrar Mahi <kazimabrarmahi135@gmail.com>
2024-04-29 13:58:41 +00:00
field = neox_reader.get_field(gguf.Keys.Tokenizer.PRE)
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
self.gguf_writer.add_tokenizer_pre(bytes(field.parts[-1]).decode("utf-8") if field else "mpt")
llama : fix BPE pre-tokenization (#6920) * merged the changes from deepseeker models to main branch * Moved regex patterns to unicode.cpp and updated unicode.h * Moved header files * Resolved issues * added and refactored unicode_regex_split and related functions * Updated/merged the deepseek coder pr * Refactored code * Adding unicode regex mappings * Adding unicode regex function * Added needed functionality, testing remains * Fixed issues * Fixed issue with gpt2 regex custom preprocessor * unicode : fix? unicode_wstring_to_utf8 * lint : fix whitespaces * tests : add tokenizer tests for numbers * unicode : remove redundant headers * tests : remove and rename tokenizer test scripts * tests : add sample usage * gguf-py : reader prints warnings on duplicate keys * llama : towards llama3 tokenization support (wip) * unicode : shot in the dark to fix tests on Windows * unicode : first try custom implementations * convert : add "tokenizer.ggml.pre" GGUF KV (wip) * llama : use new pre-tokenizer type * convert : fix pre-tokenizer type writing * lint : fix * make : add test-tokenizer-0-llama-v3 * wip * models : add llama v3 vocab file * llama : adapt punctuation regex + add llama 3 regex * minor * unicode : set bomb * unicode : set bomb * unicode : always use std::wregex * unicode : support \p{N}, \p{L} and \p{P} natively * unicode : try fix windows * unicode : category support via std::regex * unicode : clean-up * unicode : simplify * convert : add convert-hf-to-gguf-update.py ggml-ci * lint : update * convert : add falcon ggml-ci * unicode : normalize signatures * lint : fix * lint : fix * convert : remove unused functions * convert : add comments * convert : exercise contractions ggml-ci * lint : fix * cmake : refactor test targets * tests : refactor vocab tests ggml-ci * tests : add more vocabs and tests ggml-ci * unicode : cleanup * scripts : ignore new update script in check-requirements.sh * models : add phi-3, mpt, gpt-2, starcoder * tests : disable obsolete ggml-ci * tests : use faster bpe test ggml-ci * llama : more prominent warning for old BPE models * tests : disable test-tokenizer-1-bpe due to slowness ggml-ci --------- Co-authored-by: Jaggzh <jaggz.h@gmail.com> Co-authored-by: Kazim Abrar Mahi <kazimabrarmahi135@gmail.com>
2024-04-29 13:58:41 +00:00
llama : support Mamba Selective State Space Models (#5328) * mamba : begin working on support for Mamba SSM * mamba : begin figuring out how to (ab)use the kv cache for Mamba * mamba : recurrent inference almost works, but incoherent * mamba : recurrent inference WORKS!!! * convert : optionally use d_conv and d_state from config.json for Mamba * mamba : refactor recurrent conv, resulting in 20% perf increase It's still slower than I'd like, but I did not really optimize `ggml_exp` yet. I also refactored `ggml_exp` to work with tensors with more than 2 dimensions. * ggml : parallelize ggml_exp This results in 8% faster token generation for Mamba-130M. * mamba : simplify the conv step with a self-overlapping view Turns out the conv_state can be made smaller by one column. Note that this breaks existing GGUFs of Mamba, because the key_value_length field is tied to the conv_state size. Convolution with a self-overlapping view is cool! And it's much simpler than what I initially thought would be necessary to make the convolution step work with more than 1 token at a time. Next step is to make the SSM step work on batches of tokens too, and thus I need to figure out a way to make a parallel selective scan which will keep the ssm_state small and won't make it bigger by a factor of (n_layer * batch_size). * llama : fix Mamba KV self size wrongly displaying as f16 instead of f32 Relatedly, I also tried to see if other types than f32 worked for the states, but they don't, because of the operators used. It's probably better anyway to keep lots of precision there, since the states are small anyway. * mamba : fix self-overlapping view depth stride * mamba : handle batches of more than 1 token This means running Mamba no longer crashes when using the default settings! And probably also slightly faster prompt processing. Both batched and non-batched processing yield the same output. Previously, the state was not cleared when starting a sequence. Next step is to make the KV cache API work as expected for Mamba models. * ggml: add ggml_ssm_scan to help with parallel selective scan If the selective scan was implemented without a custom operator, there would be waaay too many nodes in the graph. For example, for Mamba-130M, with a batch size of 512 (the default), a naive selective scan could add at least 24*512=12288 nodes, which is more than LLAMA_MAX_NODES (8192), and that's only for the smallest Mamba model. So it's much cleaner with a custom operator. Not sure about the name, though. * ggml : in ggml_ssm_scan, merge multiple rows in the same vec operation This will help with performance on CPU if ggml_vec_mul_f32 and ggml_vec_add_f32 are ever optimized with SIMD. * mamba : very basic quantization support Mostly works, but there is currently no difference between the variants of a k-quant (e.g. Q4_K_S and Q4_K_M are the same). Most of the SSM-specific weights can be kept in f32 without affecting the size that much, since they are relatively small. (the linear projection weights are responsible for most of Mamba's size) Too much quantization seems to make the state degrade quite fast, and the model begins to output gibberish. It seems to affect bigger models to a lesser extent than small models, but I'm not sure by how much. Experimentation will be needed to figure out which weights are more important for the _M (and _L?) variants of k-quants for Mamba. * convert : fix wrong name for layer norm weight of offical Mamba models I was using Q-bert/Mamba-* models before, which have a slighlty different naming scheme for the weights. (they start with "model.layers" instead of "backbone.layers") * mamba : fuse more steps of the SSM scan in the ggml_ssm_scan operator This increases performance on CPU by around 30% for prompt processing, and by around 20% for text generation. However, it also makes the ggml_exp and ggml_soft_plus operators unused. Whether or not they should be kept will be decided later. * convert : for Mamba, also consider the "MambaLMHeadModel" arch name It's the name of the class of the official implementation, though they don't use it (yet) in the "architectures" field of config.json * mamba : fix vocab size problems with official models The perplexity was waaaay to high for models with a non-round vocab size. Not sure why, but it needed to be fixed in the metadata. Note that this breaks existing GGUF-converted Mamba models, but **only if** the vocab size was not already rounded. * ggml : remove ggml_exp and ggml_soft_plus They did not exist anyway outside of this branch, and since ggml_ssm_scan fused operations together, they are unused. It's always possible to bring them back if needed. * mamba : remove some useless comments No code change. * convert : fix flake8 linter errors * mamba : apply suggestions from code review * mamba : remove unecessary branch for row-wise ssm_state and C multiplication It was previously done to avoid permuting when only one token is processed at a time (like when generating text), but permuting is cheap, and dynamically changing the compute graph is not future-proof. * ggml : in ggml_ssm_scan, use more appropriate asserts * ggml : rename the destination pointer in ggml_compute_forward_ssm_scan_f32 * mamba : multiple sequences, but one at a time This is a step towards making this Mamba implementation usable with the server example (the way the system prompt is kept when clearing the client slots will need to be changed before this can work, though). The KV cache size for this kind of model is tied to the maximum number of sequences kept at any single time. For now, this number is obtained from n_parallel (plus one, to have an extra sequence to dedicate to the system prompt), but there might be a better way to do this which won't also make the main example use 2 cells even if only 1 is really used. (for this specific case, --parallel 0 helps) Simultaneous sequence processing will probably require changes to ggml_ssm_scan, and possibly a new operator for the conv step. * mamba : support llama_kv_cache_seq_cp This (mis)uses the logic around K shifts, because tokens in a state can't be shifted anyway, and because inp_K_shift has the right shape and type. Using ggml_get_rows is a nice way to do copies, but copy chains can't work. Fortunately, copy chains don't really seem to be used in the examples. Each KV cell is dedicated to the sequence ID corresponding to its own index. * mamba : use a state mask It's cleaner than the previous heuristic of checking for the pos of the first token in the batch. inp_KQ_mask could not be re-used for this, because it has the wrong shape and because it seems more suited to the next step of simultaneous sequence processing (helping with the problem of remembering which token belongs to which sequence(s)/state(s)). * llama : replace the usage of n_ctx with kv_self.size in many places * mamba : use n_tokens directly instead of n_tok * mamba : in comments, properly refer to KV cells instead of slots * mamba : reduce memory usage of ggml_ssm_scan From 290.37 MiB to 140.68 MiB of CPU compute buffer size with Mamba 3B with a batch size of 512. The result tensor of ggml_ssm_scan was previously a big part of the CPU compute buffer size. To make it smaller, it does not contain the intermediate ssm states anymore. Both y and the last ssm state are combined in the result tensor, because it seems only a single tensor can be returned by an operator with the way the graph is built. * mamba : simultaneous sequence processing A batch can now contain tokens from multiple sequences. This is necessary for at least the parallel example, the server example, and the HellaSwag test in the perplexity example. However, for this to be useful, uses of llama_kv_cache_seq_rm/cp will need to be changed to work on whole sequences. * ggml : add ggml_ssm_conv as a new operator for the conv step of Mamba This operator makes it possible to use and update the correct states for each token of the batch in the same way as ggml_ssm_scan. Other solutions which use existing operators would need loops which would add too many nodes to the graph (at least the ones I thought of). Using this operator further reduces the size of the CPU compute buffer from 140.68 MiB to 103.20 MiB with Mamba 3B with a batch size of 512. And (at least on CPU), it's a bit faster than before. Note that "ggml_ssm_conv" is probably not the most appropriate name, and it could be changed if a better one is found. * llama : add inp_s_seq as a new input tensor The most convenient implementation to select the correct state (for Mamba) for each token is to directly get the correct index from a tensor. This is why inp_s_seq is storing int32_t and not floats. The other, less convenient way to select the correct state would be to have inp_KQ_mask contain 1.0f for each state used by a token and 0.0f otherwise. This complicates quickly fetching the first used state of a token, and is also less efficient because a whole row of the mask would always need to be read for each token. Using indexes makes it easy to stop searching when there are no more sequences for a token, and the first sequence assigned is always very quickly available (it's the first element of each row). * mamba : support llama_kv_cache_seq_cp copy chains * mamba : support shifting and dividing the kv cache pos * mamba : make the server and parallel examples work with whole sequences A seq_id is dedicated to the system prompt in both cases. * llama : make llama_kv_cache_seq_rm return whether it succeeded or not * mamba : dedicate an input tensor for state copy indices This is cleaner and makes it easier to adapt when/if token positions (and by extension, inp_K_shift) are no longer integers. * mamba : adapt perplexity, batched, and batched-bench examples * perplexity : limit the max number of sequences This adapts to what the loaded model can provide. * llama : add llama_n_max_seq to get the upper limit for seq_ids Used by the perplexity example. * batched : pass n_parallel to the model's context params This should have been there already, but it wasn't. * batched-bench : reserve sequences to support Mamba * batched-bench : fix tokens being put in wrong sequences Generation quality isn't what's measured in there anyway, but at least using the correct sequences avoids using non-consecutive token positions. * mamba : stop abusing attention metadata This breaks existing converted-to-GGUF Mamba models, but will allow supporting mixed architectures like MambaFormer without needing to break Mamba models. This will also allow changing the size of Mamba's states without having to reconvert models in the future. (e.g. using something else than d_conv - 1 columns for the conv_states will not require breaking existing converted Mamba models again) * gguf-py : add new KV metadata key-value pairs for Mamba * llama : add new metadata key-value pairs for Mamba * llama : guard against divisions by zero when n_head is 0 * mamba : rename "unlimited" KV cache property to "recurrent" * mamba : more correctly update the "used" field of the KV cache * ggml : in ggml_ssm_scan, use a threshold for soft_plus This is how the official Mamba implementation does it, and it's also what torch.nn.Softplus does. * convert : for Mamba, fallback to internal NeoX tokenizer The resulting models are exactly the same as if the tokenizer.json and tokenizer_config.json of GPT-NeoX were there. * mamba : support state saving and restoring * ggml : implicitly pass src tensors through dst for Mamba-related ops * mamba : clarify some comments * server : fix cache_tokens not getting correctly resized Otherwise, when the "we have to evaluate at least 1 token" special case was triggered, an extra token was kept in cache_tokens even if it was removed from the KV cache. For Mamba, this caused useless prompt reprocessing when the previous request triggered the above case. * convert-hf : support new metadata keys for Mamba For the models available at https://huggingface.co/collections/state-spaces/transformers-compatible-mamba-65e7b40ab87e5297e45ae406 * mamba : rename metadata to be more similar to transformers library This breaks existing converted-to-GGUF models, but the metadata names are more "standard". * mamba : support mamba-*-hf models These models share their token_embd.weight with their output.weight * mamba : add missing spaces This is purely a formatting change. * convert-hf : omit output.weight when identical with token_embd.weight Only for Mamba for now, but it might be relevant for other models eventually. Most Mamba models actually share these two tensors, albeit implicitly. * readme : add Mamba to supported models, and add recent API changes * mamba : move state_seq and state_mask views outside layer loop A few tensors were also missing `struct` in front of `ggml_tensor`.
2024-03-08 22:31:00 +00:00
field = neox_reader.get_field(gguf.Keys.Tokenizer.LIST)
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
assert field
llama : support Mamba Selective State Space Models (#5328) * mamba : begin working on support for Mamba SSM * mamba : begin figuring out how to (ab)use the kv cache for Mamba * mamba : recurrent inference almost works, but incoherent * mamba : recurrent inference WORKS!!! * convert : optionally use d_conv and d_state from config.json for Mamba * mamba : refactor recurrent conv, resulting in 20% perf increase It's still slower than I'd like, but I did not really optimize `ggml_exp` yet. I also refactored `ggml_exp` to work with tensors with more than 2 dimensions. * ggml : parallelize ggml_exp This results in 8% faster token generation for Mamba-130M. * mamba : simplify the conv step with a self-overlapping view Turns out the conv_state can be made smaller by one column. Note that this breaks existing GGUFs of Mamba, because the key_value_length field is tied to the conv_state size. Convolution with a self-overlapping view is cool! And it's much simpler than what I initially thought would be necessary to make the convolution step work with more than 1 token at a time. Next step is to make the SSM step work on batches of tokens too, and thus I need to figure out a way to make a parallel selective scan which will keep the ssm_state small and won't make it bigger by a factor of (n_layer * batch_size). * llama : fix Mamba KV self size wrongly displaying as f16 instead of f32 Relatedly, I also tried to see if other types than f32 worked for the states, but they don't, because of the operators used. It's probably better anyway to keep lots of precision there, since the states are small anyway. * mamba : fix self-overlapping view depth stride * mamba : handle batches of more than 1 token This means running Mamba no longer crashes when using the default settings! And probably also slightly faster prompt processing. Both batched and non-batched processing yield the same output. Previously, the state was not cleared when starting a sequence. Next step is to make the KV cache API work as expected for Mamba models. * ggml: add ggml_ssm_scan to help with parallel selective scan If the selective scan was implemented without a custom operator, there would be waaay too many nodes in the graph. For example, for Mamba-130M, with a batch size of 512 (the default), a naive selective scan could add at least 24*512=12288 nodes, which is more than LLAMA_MAX_NODES (8192), and that's only for the smallest Mamba model. So it's much cleaner with a custom operator. Not sure about the name, though. * ggml : in ggml_ssm_scan, merge multiple rows in the same vec operation This will help with performance on CPU if ggml_vec_mul_f32 and ggml_vec_add_f32 are ever optimized with SIMD. * mamba : very basic quantization support Mostly works, but there is currently no difference between the variants of a k-quant (e.g. Q4_K_S and Q4_K_M are the same). Most of the SSM-specific weights can be kept in f32 without affecting the size that much, since they are relatively small. (the linear projection weights are responsible for most of Mamba's size) Too much quantization seems to make the state degrade quite fast, and the model begins to output gibberish. It seems to affect bigger models to a lesser extent than small models, but I'm not sure by how much. Experimentation will be needed to figure out which weights are more important for the _M (and _L?) variants of k-quants for Mamba. * convert : fix wrong name for layer norm weight of offical Mamba models I was using Q-bert/Mamba-* models before, which have a slighlty different naming scheme for the weights. (they start with "model.layers" instead of "backbone.layers") * mamba : fuse more steps of the SSM scan in the ggml_ssm_scan operator This increases performance on CPU by around 30% for prompt processing, and by around 20% for text generation. However, it also makes the ggml_exp and ggml_soft_plus operators unused. Whether or not they should be kept will be decided later. * convert : for Mamba, also consider the "MambaLMHeadModel" arch name It's the name of the class of the official implementation, though they don't use it (yet) in the "architectures" field of config.json * mamba : fix vocab size problems with official models The perplexity was waaaay to high for models with a non-round vocab size. Not sure why, but it needed to be fixed in the metadata. Note that this breaks existing GGUF-converted Mamba models, but **only if** the vocab size was not already rounded. * ggml : remove ggml_exp and ggml_soft_plus They did not exist anyway outside of this branch, and since ggml_ssm_scan fused operations together, they are unused. It's always possible to bring them back if needed. * mamba : remove some useless comments No code change. * convert : fix flake8 linter errors * mamba : apply suggestions from code review * mamba : remove unecessary branch for row-wise ssm_state and C multiplication It was previously done to avoid permuting when only one token is processed at a time (like when generating text), but permuting is cheap, and dynamically changing the compute graph is not future-proof. * ggml : in ggml_ssm_scan, use more appropriate asserts * ggml : rename the destination pointer in ggml_compute_forward_ssm_scan_f32 * mamba : multiple sequences, but one at a time This is a step towards making this Mamba implementation usable with the server example (the way the system prompt is kept when clearing the client slots will need to be changed before this can work, though). The KV cache size for this kind of model is tied to the maximum number of sequences kept at any single time. For now, this number is obtained from n_parallel (plus one, to have an extra sequence to dedicate to the system prompt), but there might be a better way to do this which won't also make the main example use 2 cells even if only 1 is really used. (for this specific case, --parallel 0 helps) Simultaneous sequence processing will probably require changes to ggml_ssm_scan, and possibly a new operator for the conv step. * mamba : support llama_kv_cache_seq_cp This (mis)uses the logic around K shifts, because tokens in a state can't be shifted anyway, and because inp_K_shift has the right shape and type. Using ggml_get_rows is a nice way to do copies, but copy chains can't work. Fortunately, copy chains don't really seem to be used in the examples. Each KV cell is dedicated to the sequence ID corresponding to its own index. * mamba : use a state mask It's cleaner than the previous heuristic of checking for the pos of the first token in the batch. inp_KQ_mask could not be re-used for this, because it has the wrong shape and because it seems more suited to the next step of simultaneous sequence processing (helping with the problem of remembering which token belongs to which sequence(s)/state(s)). * llama : replace the usage of n_ctx with kv_self.size in many places * mamba : use n_tokens directly instead of n_tok * mamba : in comments, properly refer to KV cells instead of slots * mamba : reduce memory usage of ggml_ssm_scan From 290.37 MiB to 140.68 MiB of CPU compute buffer size with Mamba 3B with a batch size of 512. The result tensor of ggml_ssm_scan was previously a big part of the CPU compute buffer size. To make it smaller, it does not contain the intermediate ssm states anymore. Both y and the last ssm state are combined in the result tensor, because it seems only a single tensor can be returned by an operator with the way the graph is built. * mamba : simultaneous sequence processing A batch can now contain tokens from multiple sequences. This is necessary for at least the parallel example, the server example, and the HellaSwag test in the perplexity example. However, for this to be useful, uses of llama_kv_cache_seq_rm/cp will need to be changed to work on whole sequences. * ggml : add ggml_ssm_conv as a new operator for the conv step of Mamba This operator makes it possible to use and update the correct states for each token of the batch in the same way as ggml_ssm_scan. Other solutions which use existing operators would need loops which would add too many nodes to the graph (at least the ones I thought of). Using this operator further reduces the size of the CPU compute buffer from 140.68 MiB to 103.20 MiB with Mamba 3B with a batch size of 512. And (at least on CPU), it's a bit faster than before. Note that "ggml_ssm_conv" is probably not the most appropriate name, and it could be changed if a better one is found. * llama : add inp_s_seq as a new input tensor The most convenient implementation to select the correct state (for Mamba) for each token is to directly get the correct index from a tensor. This is why inp_s_seq is storing int32_t and not floats. The other, less convenient way to select the correct state would be to have inp_KQ_mask contain 1.0f for each state used by a token and 0.0f otherwise. This complicates quickly fetching the first used state of a token, and is also less efficient because a whole row of the mask would always need to be read for each token. Using indexes makes it easy to stop searching when there are no more sequences for a token, and the first sequence assigned is always very quickly available (it's the first element of each row). * mamba : support llama_kv_cache_seq_cp copy chains * mamba : support shifting and dividing the kv cache pos * mamba : make the server and parallel examples work with whole sequences A seq_id is dedicated to the system prompt in both cases. * llama : make llama_kv_cache_seq_rm return whether it succeeded or not * mamba : dedicate an input tensor for state copy indices This is cleaner and makes it easier to adapt when/if token positions (and by extension, inp_K_shift) are no longer integers. * mamba : adapt perplexity, batched, and batched-bench examples * perplexity : limit the max number of sequences This adapts to what the loaded model can provide. * llama : add llama_n_max_seq to get the upper limit for seq_ids Used by the perplexity example. * batched : pass n_parallel to the model's context params This should have been there already, but it wasn't. * batched-bench : reserve sequences to support Mamba * batched-bench : fix tokens being put in wrong sequences Generation quality isn't what's measured in there anyway, but at least using the correct sequences avoids using non-consecutive token positions. * mamba : stop abusing attention metadata This breaks existing converted-to-GGUF Mamba models, but will allow supporting mixed architectures like MambaFormer without needing to break Mamba models. This will also allow changing the size of Mamba's states without having to reconvert models in the future. (e.g. using something else than d_conv - 1 columns for the conv_states will not require breaking existing converted Mamba models again) * gguf-py : add new KV metadata key-value pairs for Mamba * llama : add new metadata key-value pairs for Mamba * llama : guard against divisions by zero when n_head is 0 * mamba : rename "unlimited" KV cache property to "recurrent" * mamba : more correctly update the "used" field of the KV cache * ggml : in ggml_ssm_scan, use a threshold for soft_plus This is how the official Mamba implementation does it, and it's also what torch.nn.Softplus does. * convert : for Mamba, fallback to internal NeoX tokenizer The resulting models are exactly the same as if the tokenizer.json and tokenizer_config.json of GPT-NeoX were there. * mamba : support state saving and restoring * ggml : implicitly pass src tensors through dst for Mamba-related ops * mamba : clarify some comments * server : fix cache_tokens not getting correctly resized Otherwise, when the "we have to evaluate at least 1 token" special case was triggered, an extra token was kept in cache_tokens even if it was removed from the KV cache. For Mamba, this caused useless prompt reprocessing when the previous request triggered the above case. * convert-hf : support new metadata keys for Mamba For the models available at https://huggingface.co/collections/state-spaces/transformers-compatible-mamba-65e7b40ab87e5297e45ae406 * mamba : rename metadata to be more similar to transformers library This breaks existing converted-to-GGUF models, but the metadata names are more "standard". * mamba : support mamba-*-hf models These models share their token_embd.weight with their output.weight * mamba : add missing spaces This is purely a formatting change. * convert-hf : omit output.weight when identical with token_embd.weight Only for Mamba for now, but it might be relevant for other models eventually. Most Mamba models actually share these two tensors, albeit implicitly. * readme : add Mamba to supported models, and add recent API changes * mamba : move state_seq and state_mask views outside layer loop A few tensors were also missing `struct` in front of `ggml_tensor`.
2024-03-08 22:31:00 +00:00
self.gguf_writer.add_token_list([bytes(field.parts[i]) for i in field.data][:vocab_size])
llama : support Llama 3 HF conversion (#6745) * Support Llama 3 conversion The tokenizer is BPE. * style * Accept suggestion Co-authored-by: Sourab Mangrulkar <13534540+pacman100@users.noreply.github.com> * llama : add llama_token_is_eog() ggml-ci * llama : auto-detect more EOT tokens when missing in KV data * convert : replacing EOS token is a hack * llama : fix codegemma EOT token + add TODOs * llama : fix model type string for 8B model --------- Co-authored-by: Sourab Mangrulkar <13534540+pacman100@users.noreply.github.com> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-04-21 11:50:41 +00:00
llama : support Mamba Selective State Space Models (#5328) * mamba : begin working on support for Mamba SSM * mamba : begin figuring out how to (ab)use the kv cache for Mamba * mamba : recurrent inference almost works, but incoherent * mamba : recurrent inference WORKS!!! * convert : optionally use d_conv and d_state from config.json for Mamba * mamba : refactor recurrent conv, resulting in 20% perf increase It's still slower than I'd like, but I did not really optimize `ggml_exp` yet. I also refactored `ggml_exp` to work with tensors with more than 2 dimensions. * ggml : parallelize ggml_exp This results in 8% faster token generation for Mamba-130M. * mamba : simplify the conv step with a self-overlapping view Turns out the conv_state can be made smaller by one column. Note that this breaks existing GGUFs of Mamba, because the key_value_length field is tied to the conv_state size. Convolution with a self-overlapping view is cool! And it's much simpler than what I initially thought would be necessary to make the convolution step work with more than 1 token at a time. Next step is to make the SSM step work on batches of tokens too, and thus I need to figure out a way to make a parallel selective scan which will keep the ssm_state small and won't make it bigger by a factor of (n_layer * batch_size). * llama : fix Mamba KV self size wrongly displaying as f16 instead of f32 Relatedly, I also tried to see if other types than f32 worked for the states, but they don't, because of the operators used. It's probably better anyway to keep lots of precision there, since the states are small anyway. * mamba : fix self-overlapping view depth stride * mamba : handle batches of more than 1 token This means running Mamba no longer crashes when using the default settings! And probably also slightly faster prompt processing. Both batched and non-batched processing yield the same output. Previously, the state was not cleared when starting a sequence. Next step is to make the KV cache API work as expected for Mamba models. * ggml: add ggml_ssm_scan to help with parallel selective scan If the selective scan was implemented without a custom operator, there would be waaay too many nodes in the graph. For example, for Mamba-130M, with a batch size of 512 (the default), a naive selective scan could add at least 24*512=12288 nodes, which is more than LLAMA_MAX_NODES (8192), and that's only for the smallest Mamba model. So it's much cleaner with a custom operator. Not sure about the name, though. * ggml : in ggml_ssm_scan, merge multiple rows in the same vec operation This will help with performance on CPU if ggml_vec_mul_f32 and ggml_vec_add_f32 are ever optimized with SIMD. * mamba : very basic quantization support Mostly works, but there is currently no difference between the variants of a k-quant (e.g. Q4_K_S and Q4_K_M are the same). Most of the SSM-specific weights can be kept in f32 without affecting the size that much, since they are relatively small. (the linear projection weights are responsible for most of Mamba's size) Too much quantization seems to make the state degrade quite fast, and the model begins to output gibberish. It seems to affect bigger models to a lesser extent than small models, but I'm not sure by how much. Experimentation will be needed to figure out which weights are more important for the _M (and _L?) variants of k-quants for Mamba. * convert : fix wrong name for layer norm weight of offical Mamba models I was using Q-bert/Mamba-* models before, which have a slighlty different naming scheme for the weights. (they start with "model.layers" instead of "backbone.layers") * mamba : fuse more steps of the SSM scan in the ggml_ssm_scan operator This increases performance on CPU by around 30% for prompt processing, and by around 20% for text generation. However, it also makes the ggml_exp and ggml_soft_plus operators unused. Whether or not they should be kept will be decided later. * convert : for Mamba, also consider the "MambaLMHeadModel" arch name It's the name of the class of the official implementation, though they don't use it (yet) in the "architectures" field of config.json * mamba : fix vocab size problems with official models The perplexity was waaaay to high for models with a non-round vocab size. Not sure why, but it needed to be fixed in the metadata. Note that this breaks existing GGUF-converted Mamba models, but **only if** the vocab size was not already rounded. * ggml : remove ggml_exp and ggml_soft_plus They did not exist anyway outside of this branch, and since ggml_ssm_scan fused operations together, they are unused. It's always possible to bring them back if needed. * mamba : remove some useless comments No code change. * convert : fix flake8 linter errors * mamba : apply suggestions from code review * mamba : remove unecessary branch for row-wise ssm_state and C multiplication It was previously done to avoid permuting when only one token is processed at a time (like when generating text), but permuting is cheap, and dynamically changing the compute graph is not future-proof. * ggml : in ggml_ssm_scan, use more appropriate asserts * ggml : rename the destination pointer in ggml_compute_forward_ssm_scan_f32 * mamba : multiple sequences, but one at a time This is a step towards making this Mamba implementation usable with the server example (the way the system prompt is kept when clearing the client slots will need to be changed before this can work, though). The KV cache size for this kind of model is tied to the maximum number of sequences kept at any single time. For now, this number is obtained from n_parallel (plus one, to have an extra sequence to dedicate to the system prompt), but there might be a better way to do this which won't also make the main example use 2 cells even if only 1 is really used. (for this specific case, --parallel 0 helps) Simultaneous sequence processing will probably require changes to ggml_ssm_scan, and possibly a new operator for the conv step. * mamba : support llama_kv_cache_seq_cp This (mis)uses the logic around K shifts, because tokens in a state can't be shifted anyway, and because inp_K_shift has the right shape and type. Using ggml_get_rows is a nice way to do copies, but copy chains can't work. Fortunately, copy chains don't really seem to be used in the examples. Each KV cell is dedicated to the sequence ID corresponding to its own index. * mamba : use a state mask It's cleaner than the previous heuristic of checking for the pos of the first token in the batch. inp_KQ_mask could not be re-used for this, because it has the wrong shape and because it seems more suited to the next step of simultaneous sequence processing (helping with the problem of remembering which token belongs to which sequence(s)/state(s)). * llama : replace the usage of n_ctx with kv_self.size in many places * mamba : use n_tokens directly instead of n_tok * mamba : in comments, properly refer to KV cells instead of slots * mamba : reduce memory usage of ggml_ssm_scan From 290.37 MiB to 140.68 MiB of CPU compute buffer size with Mamba 3B with a batch size of 512. The result tensor of ggml_ssm_scan was previously a big part of the CPU compute buffer size. To make it smaller, it does not contain the intermediate ssm states anymore. Both y and the last ssm state are combined in the result tensor, because it seems only a single tensor can be returned by an operator with the way the graph is built. * mamba : simultaneous sequence processing A batch can now contain tokens from multiple sequences. This is necessary for at least the parallel example, the server example, and the HellaSwag test in the perplexity example. However, for this to be useful, uses of llama_kv_cache_seq_rm/cp will need to be changed to work on whole sequences. * ggml : add ggml_ssm_conv as a new operator for the conv step of Mamba This operator makes it possible to use and update the correct states for each token of the batch in the same way as ggml_ssm_scan. Other solutions which use existing operators would need loops which would add too many nodes to the graph (at least the ones I thought of). Using this operator further reduces the size of the CPU compute buffer from 140.68 MiB to 103.20 MiB with Mamba 3B with a batch size of 512. And (at least on CPU), it's a bit faster than before. Note that "ggml_ssm_conv" is probably not the most appropriate name, and it could be changed if a better one is found. * llama : add inp_s_seq as a new input tensor The most convenient implementation to select the correct state (for Mamba) for each token is to directly get the correct index from a tensor. This is why inp_s_seq is storing int32_t and not floats. The other, less convenient way to select the correct state would be to have inp_KQ_mask contain 1.0f for each state used by a token and 0.0f otherwise. This complicates quickly fetching the first used state of a token, and is also less efficient because a whole row of the mask would always need to be read for each token. Using indexes makes it easy to stop searching when there are no more sequences for a token, and the first sequence assigned is always very quickly available (it's the first element of each row). * mamba : support llama_kv_cache_seq_cp copy chains * mamba : support shifting and dividing the kv cache pos * mamba : make the server and parallel examples work with whole sequences A seq_id is dedicated to the system prompt in both cases. * llama : make llama_kv_cache_seq_rm return whether it succeeded or not * mamba : dedicate an input tensor for state copy indices This is cleaner and makes it easier to adapt when/if token positions (and by extension, inp_K_shift) are no longer integers. * mamba : adapt perplexity, batched, and batched-bench examples * perplexity : limit the max number of sequences This adapts to what the loaded model can provide. * llama : add llama_n_max_seq to get the upper limit for seq_ids Used by the perplexity example. * batched : pass n_parallel to the model's context params This should have been there already, but it wasn't. * batched-bench : reserve sequences to support Mamba * batched-bench : fix tokens being put in wrong sequences Generation quality isn't what's measured in there anyway, but at least using the correct sequences avoids using non-consecutive token positions. * mamba : stop abusing attention metadata This breaks existing converted-to-GGUF Mamba models, but will allow supporting mixed architectures like MambaFormer without needing to break Mamba models. This will also allow changing the size of Mamba's states without having to reconvert models in the future. (e.g. using something else than d_conv - 1 columns for the conv_states will not require breaking existing converted Mamba models again) * gguf-py : add new KV metadata key-value pairs for Mamba * llama : add new metadata key-value pairs for Mamba * llama : guard against divisions by zero when n_head is 0 * mamba : rename "unlimited" KV cache property to "recurrent" * mamba : more correctly update the "used" field of the KV cache * ggml : in ggml_ssm_scan, use a threshold for soft_plus This is how the official Mamba implementation does it, and it's also what torch.nn.Softplus does. * convert : for Mamba, fallback to internal NeoX tokenizer The resulting models are exactly the same as if the tokenizer.json and tokenizer_config.json of GPT-NeoX were there. * mamba : support state saving and restoring * ggml : implicitly pass src tensors through dst for Mamba-related ops * mamba : clarify some comments * server : fix cache_tokens not getting correctly resized Otherwise, when the "we have to evaluate at least 1 token" special case was triggered, an extra token was kept in cache_tokens even if it was removed from the KV cache. For Mamba, this caused useless prompt reprocessing when the previous request triggered the above case. * convert-hf : support new metadata keys for Mamba For the models available at https://huggingface.co/collections/state-spaces/transformers-compatible-mamba-65e7b40ab87e5297e45ae406 * mamba : rename metadata to be more similar to transformers library This breaks existing converted-to-GGUF models, but the metadata names are more "standard". * mamba : support mamba-*-hf models These models share their token_embd.weight with their output.weight * mamba : add missing spaces This is purely a formatting change. * convert-hf : omit output.weight when identical with token_embd.weight Only for Mamba for now, but it might be relevant for other models eventually. Most Mamba models actually share these two tensors, albeit implicitly. * readme : add Mamba to supported models, and add recent API changes * mamba : move state_seq and state_mask views outside layer loop A few tensors were also missing `struct` in front of `ggml_tensor`.
2024-03-08 22:31:00 +00:00
field = neox_reader.get_field(gguf.Keys.Tokenizer.TOKEN_TYPE)
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
assert field
llama : support Mamba Selective State Space Models (#5328) * mamba : begin working on support for Mamba SSM * mamba : begin figuring out how to (ab)use the kv cache for Mamba * mamba : recurrent inference almost works, but incoherent * mamba : recurrent inference WORKS!!! * convert : optionally use d_conv and d_state from config.json for Mamba * mamba : refactor recurrent conv, resulting in 20% perf increase It's still slower than I'd like, but I did not really optimize `ggml_exp` yet. I also refactored `ggml_exp` to work with tensors with more than 2 dimensions. * ggml : parallelize ggml_exp This results in 8% faster token generation for Mamba-130M. * mamba : simplify the conv step with a self-overlapping view Turns out the conv_state can be made smaller by one column. Note that this breaks existing GGUFs of Mamba, because the key_value_length field is tied to the conv_state size. Convolution with a self-overlapping view is cool! And it's much simpler than what I initially thought would be necessary to make the convolution step work with more than 1 token at a time. Next step is to make the SSM step work on batches of tokens too, and thus I need to figure out a way to make a parallel selective scan which will keep the ssm_state small and won't make it bigger by a factor of (n_layer * batch_size). * llama : fix Mamba KV self size wrongly displaying as f16 instead of f32 Relatedly, I also tried to see if other types than f32 worked for the states, but they don't, because of the operators used. It's probably better anyway to keep lots of precision there, since the states are small anyway. * mamba : fix self-overlapping view depth stride * mamba : handle batches of more than 1 token This means running Mamba no longer crashes when using the default settings! And probably also slightly faster prompt processing. Both batched and non-batched processing yield the same output. Previously, the state was not cleared when starting a sequence. Next step is to make the KV cache API work as expected for Mamba models. * ggml: add ggml_ssm_scan to help with parallel selective scan If the selective scan was implemented without a custom operator, there would be waaay too many nodes in the graph. For example, for Mamba-130M, with a batch size of 512 (the default), a naive selective scan could add at least 24*512=12288 nodes, which is more than LLAMA_MAX_NODES (8192), and that's only for the smallest Mamba model. So it's much cleaner with a custom operator. Not sure about the name, though. * ggml : in ggml_ssm_scan, merge multiple rows in the same vec operation This will help with performance on CPU if ggml_vec_mul_f32 and ggml_vec_add_f32 are ever optimized with SIMD. * mamba : very basic quantization support Mostly works, but there is currently no difference between the variants of a k-quant (e.g. Q4_K_S and Q4_K_M are the same). Most of the SSM-specific weights can be kept in f32 without affecting the size that much, since they are relatively small. (the linear projection weights are responsible for most of Mamba's size) Too much quantization seems to make the state degrade quite fast, and the model begins to output gibberish. It seems to affect bigger models to a lesser extent than small models, but I'm not sure by how much. Experimentation will be needed to figure out which weights are more important for the _M (and _L?) variants of k-quants for Mamba. * convert : fix wrong name for layer norm weight of offical Mamba models I was using Q-bert/Mamba-* models before, which have a slighlty different naming scheme for the weights. (they start with "model.layers" instead of "backbone.layers") * mamba : fuse more steps of the SSM scan in the ggml_ssm_scan operator This increases performance on CPU by around 30% for prompt processing, and by around 20% for text generation. However, it also makes the ggml_exp and ggml_soft_plus operators unused. Whether or not they should be kept will be decided later. * convert : for Mamba, also consider the "MambaLMHeadModel" arch name It's the name of the class of the official implementation, though they don't use it (yet) in the "architectures" field of config.json * mamba : fix vocab size problems with official models The perplexity was waaaay to high for models with a non-round vocab size. Not sure why, but it needed to be fixed in the metadata. Note that this breaks existing GGUF-converted Mamba models, but **only if** the vocab size was not already rounded. * ggml : remove ggml_exp and ggml_soft_plus They did not exist anyway outside of this branch, and since ggml_ssm_scan fused operations together, they are unused. It's always possible to bring them back if needed. * mamba : remove some useless comments No code change. * convert : fix flake8 linter errors * mamba : apply suggestions from code review * mamba : remove unecessary branch for row-wise ssm_state and C multiplication It was previously done to avoid permuting when only one token is processed at a time (like when generating text), but permuting is cheap, and dynamically changing the compute graph is not future-proof. * ggml : in ggml_ssm_scan, use more appropriate asserts * ggml : rename the destination pointer in ggml_compute_forward_ssm_scan_f32 * mamba : multiple sequences, but one at a time This is a step towards making this Mamba implementation usable with the server example (the way the system prompt is kept when clearing the client slots will need to be changed before this can work, though). The KV cache size for this kind of model is tied to the maximum number of sequences kept at any single time. For now, this number is obtained from n_parallel (plus one, to have an extra sequence to dedicate to the system prompt), but there might be a better way to do this which won't also make the main example use 2 cells even if only 1 is really used. (for this specific case, --parallel 0 helps) Simultaneous sequence processing will probably require changes to ggml_ssm_scan, and possibly a new operator for the conv step. * mamba : support llama_kv_cache_seq_cp This (mis)uses the logic around K shifts, because tokens in a state can't be shifted anyway, and because inp_K_shift has the right shape and type. Using ggml_get_rows is a nice way to do copies, but copy chains can't work. Fortunately, copy chains don't really seem to be used in the examples. Each KV cell is dedicated to the sequence ID corresponding to its own index. * mamba : use a state mask It's cleaner than the previous heuristic of checking for the pos of the first token in the batch. inp_KQ_mask could not be re-used for this, because it has the wrong shape and because it seems more suited to the next step of simultaneous sequence processing (helping with the problem of remembering which token belongs to which sequence(s)/state(s)). * llama : replace the usage of n_ctx with kv_self.size in many places * mamba : use n_tokens directly instead of n_tok * mamba : in comments, properly refer to KV cells instead of slots * mamba : reduce memory usage of ggml_ssm_scan From 290.37 MiB to 140.68 MiB of CPU compute buffer size with Mamba 3B with a batch size of 512. The result tensor of ggml_ssm_scan was previously a big part of the CPU compute buffer size. To make it smaller, it does not contain the intermediate ssm states anymore. Both y and the last ssm state are combined in the result tensor, because it seems only a single tensor can be returned by an operator with the way the graph is built. * mamba : simultaneous sequence processing A batch can now contain tokens from multiple sequences. This is necessary for at least the parallel example, the server example, and the HellaSwag test in the perplexity example. However, for this to be useful, uses of llama_kv_cache_seq_rm/cp will need to be changed to work on whole sequences. * ggml : add ggml_ssm_conv as a new operator for the conv step of Mamba This operator makes it possible to use and update the correct states for each token of the batch in the same way as ggml_ssm_scan. Other solutions which use existing operators would need loops which would add too many nodes to the graph (at least the ones I thought of). Using this operator further reduces the size of the CPU compute buffer from 140.68 MiB to 103.20 MiB with Mamba 3B with a batch size of 512. And (at least on CPU), it's a bit faster than before. Note that "ggml_ssm_conv" is probably not the most appropriate name, and it could be changed if a better one is found. * llama : add inp_s_seq as a new input tensor The most convenient implementation to select the correct state (for Mamba) for each token is to directly get the correct index from a tensor. This is why inp_s_seq is storing int32_t and not floats. The other, less convenient way to select the correct state would be to have inp_KQ_mask contain 1.0f for each state used by a token and 0.0f otherwise. This complicates quickly fetching the first used state of a token, and is also less efficient because a whole row of the mask would always need to be read for each token. Using indexes makes it easy to stop searching when there are no more sequences for a token, and the first sequence assigned is always very quickly available (it's the first element of each row). * mamba : support llama_kv_cache_seq_cp copy chains * mamba : support shifting and dividing the kv cache pos * mamba : make the server and parallel examples work with whole sequences A seq_id is dedicated to the system prompt in both cases. * llama : make llama_kv_cache_seq_rm return whether it succeeded or not * mamba : dedicate an input tensor for state copy indices This is cleaner and makes it easier to adapt when/if token positions (and by extension, inp_K_shift) are no longer integers. * mamba : adapt perplexity, batched, and batched-bench examples * perplexity : limit the max number of sequences This adapts to what the loaded model can provide. * llama : add llama_n_max_seq to get the upper limit for seq_ids Used by the perplexity example. * batched : pass n_parallel to the model's context params This should have been there already, but it wasn't. * batched-bench : reserve sequences to support Mamba * batched-bench : fix tokens being put in wrong sequences Generation quality isn't what's measured in there anyway, but at least using the correct sequences avoids using non-consecutive token positions. * mamba : stop abusing attention metadata This breaks existing converted-to-GGUF Mamba models, but will allow supporting mixed architectures like MambaFormer without needing to break Mamba models. This will also allow changing the size of Mamba's states without having to reconvert models in the future. (e.g. using something else than d_conv - 1 columns for the conv_states will not require breaking existing converted Mamba models again) * gguf-py : add new KV metadata key-value pairs for Mamba * llama : add new metadata key-value pairs for Mamba * llama : guard against divisions by zero when n_head is 0 * mamba : rename "unlimited" KV cache property to "recurrent" * mamba : more correctly update the "used" field of the KV cache * ggml : in ggml_ssm_scan, use a threshold for soft_plus This is how the official Mamba implementation does it, and it's also what torch.nn.Softplus does. * convert : for Mamba, fallback to internal NeoX tokenizer The resulting models are exactly the same as if the tokenizer.json and tokenizer_config.json of GPT-NeoX were there. * mamba : support state saving and restoring * ggml : implicitly pass src tensors through dst for Mamba-related ops * mamba : clarify some comments * server : fix cache_tokens not getting correctly resized Otherwise, when the "we have to evaluate at least 1 token" special case was triggered, an extra token was kept in cache_tokens even if it was removed from the KV cache. For Mamba, this caused useless prompt reprocessing when the previous request triggered the above case. * convert-hf : support new metadata keys for Mamba For the models available at https://huggingface.co/collections/state-spaces/transformers-compatible-mamba-65e7b40ab87e5297e45ae406 * mamba : rename metadata to be more similar to transformers library This breaks existing converted-to-GGUF models, but the metadata names are more "standard". * mamba : support mamba-*-hf models These models share their token_embd.weight with their output.weight * mamba : add missing spaces This is purely a formatting change. * convert-hf : omit output.weight when identical with token_embd.weight Only for Mamba for now, but it might be relevant for other models eventually. Most Mamba models actually share these two tensors, albeit implicitly. * readme : add Mamba to supported models, and add recent API changes * mamba : move state_seq and state_mask views outside layer loop A few tensors were also missing `struct` in front of `ggml_tensor`.
2024-03-08 22:31:00 +00:00
self.gguf_writer.add_token_types([field.parts[i].tolist()[0] for i in field.data][:vocab_size])
llama : support Llama 3 HF conversion (#6745) * Support Llama 3 conversion The tokenizer is BPE. * style * Accept suggestion Co-authored-by: Sourab Mangrulkar <13534540+pacman100@users.noreply.github.com> * llama : add llama_token_is_eog() ggml-ci * llama : auto-detect more EOT tokens when missing in KV data * convert : replacing EOS token is a hack * llama : fix codegemma EOT token + add TODOs * llama : fix model type string for 8B model --------- Co-authored-by: Sourab Mangrulkar <13534540+pacman100@users.noreply.github.com> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-04-21 11:50:41 +00:00
llama : support Mamba Selective State Space Models (#5328) * mamba : begin working on support for Mamba SSM * mamba : begin figuring out how to (ab)use the kv cache for Mamba * mamba : recurrent inference almost works, but incoherent * mamba : recurrent inference WORKS!!! * convert : optionally use d_conv and d_state from config.json for Mamba * mamba : refactor recurrent conv, resulting in 20% perf increase It's still slower than I'd like, but I did not really optimize `ggml_exp` yet. I also refactored `ggml_exp` to work with tensors with more than 2 dimensions. * ggml : parallelize ggml_exp This results in 8% faster token generation for Mamba-130M. * mamba : simplify the conv step with a self-overlapping view Turns out the conv_state can be made smaller by one column. Note that this breaks existing GGUFs of Mamba, because the key_value_length field is tied to the conv_state size. Convolution with a self-overlapping view is cool! And it's much simpler than what I initially thought would be necessary to make the convolution step work with more than 1 token at a time. Next step is to make the SSM step work on batches of tokens too, and thus I need to figure out a way to make a parallel selective scan which will keep the ssm_state small and won't make it bigger by a factor of (n_layer * batch_size). * llama : fix Mamba KV self size wrongly displaying as f16 instead of f32 Relatedly, I also tried to see if other types than f32 worked for the states, but they don't, because of the operators used. It's probably better anyway to keep lots of precision there, since the states are small anyway. * mamba : fix self-overlapping view depth stride * mamba : handle batches of more than 1 token This means running Mamba no longer crashes when using the default settings! And probably also slightly faster prompt processing. Both batched and non-batched processing yield the same output. Previously, the state was not cleared when starting a sequence. Next step is to make the KV cache API work as expected for Mamba models. * ggml: add ggml_ssm_scan to help with parallel selective scan If the selective scan was implemented without a custom operator, there would be waaay too many nodes in the graph. For example, for Mamba-130M, with a batch size of 512 (the default), a naive selective scan could add at least 24*512=12288 nodes, which is more than LLAMA_MAX_NODES (8192), and that's only for the smallest Mamba model. So it's much cleaner with a custom operator. Not sure about the name, though. * ggml : in ggml_ssm_scan, merge multiple rows in the same vec operation This will help with performance on CPU if ggml_vec_mul_f32 and ggml_vec_add_f32 are ever optimized with SIMD. * mamba : very basic quantization support Mostly works, but there is currently no difference between the variants of a k-quant (e.g. Q4_K_S and Q4_K_M are the same). Most of the SSM-specific weights can be kept in f32 without affecting the size that much, since they are relatively small. (the linear projection weights are responsible for most of Mamba's size) Too much quantization seems to make the state degrade quite fast, and the model begins to output gibberish. It seems to affect bigger models to a lesser extent than small models, but I'm not sure by how much. Experimentation will be needed to figure out which weights are more important for the _M (and _L?) variants of k-quants for Mamba. * convert : fix wrong name for layer norm weight of offical Mamba models I was using Q-bert/Mamba-* models before, which have a slighlty different naming scheme for the weights. (they start with "model.layers" instead of "backbone.layers") * mamba : fuse more steps of the SSM scan in the ggml_ssm_scan operator This increases performance on CPU by around 30% for prompt processing, and by around 20% for text generation. However, it also makes the ggml_exp and ggml_soft_plus operators unused. Whether or not they should be kept will be decided later. * convert : for Mamba, also consider the "MambaLMHeadModel" arch name It's the name of the class of the official implementation, though they don't use it (yet) in the "architectures" field of config.json * mamba : fix vocab size problems with official models The perplexity was waaaay to high for models with a non-round vocab size. Not sure why, but it needed to be fixed in the metadata. Note that this breaks existing GGUF-converted Mamba models, but **only if** the vocab size was not already rounded. * ggml : remove ggml_exp and ggml_soft_plus They did not exist anyway outside of this branch, and since ggml_ssm_scan fused operations together, they are unused. It's always possible to bring them back if needed. * mamba : remove some useless comments No code change. * convert : fix flake8 linter errors * mamba : apply suggestions from code review * mamba : remove unecessary branch for row-wise ssm_state and C multiplication It was previously done to avoid permuting when only one token is processed at a time (like when generating text), but permuting is cheap, and dynamically changing the compute graph is not future-proof. * ggml : in ggml_ssm_scan, use more appropriate asserts * ggml : rename the destination pointer in ggml_compute_forward_ssm_scan_f32 * mamba : multiple sequences, but one at a time This is a step towards making this Mamba implementation usable with the server example (the way the system prompt is kept when clearing the client slots will need to be changed before this can work, though). The KV cache size for this kind of model is tied to the maximum number of sequences kept at any single time. For now, this number is obtained from n_parallel (plus one, to have an extra sequence to dedicate to the system prompt), but there might be a better way to do this which won't also make the main example use 2 cells even if only 1 is really used. (for this specific case, --parallel 0 helps) Simultaneous sequence processing will probably require changes to ggml_ssm_scan, and possibly a new operator for the conv step. * mamba : support llama_kv_cache_seq_cp This (mis)uses the logic around K shifts, because tokens in a state can't be shifted anyway, and because inp_K_shift has the right shape and type. Using ggml_get_rows is a nice way to do copies, but copy chains can't work. Fortunately, copy chains don't really seem to be used in the examples. Each KV cell is dedicated to the sequence ID corresponding to its own index. * mamba : use a state mask It's cleaner than the previous heuristic of checking for the pos of the first token in the batch. inp_KQ_mask could not be re-used for this, because it has the wrong shape and because it seems more suited to the next step of simultaneous sequence processing (helping with the problem of remembering which token belongs to which sequence(s)/state(s)). * llama : replace the usage of n_ctx with kv_self.size in many places * mamba : use n_tokens directly instead of n_tok * mamba : in comments, properly refer to KV cells instead of slots * mamba : reduce memory usage of ggml_ssm_scan From 290.37 MiB to 140.68 MiB of CPU compute buffer size with Mamba 3B with a batch size of 512. The result tensor of ggml_ssm_scan was previously a big part of the CPU compute buffer size. To make it smaller, it does not contain the intermediate ssm states anymore. Both y and the last ssm state are combined in the result tensor, because it seems only a single tensor can be returned by an operator with the way the graph is built. * mamba : simultaneous sequence processing A batch can now contain tokens from multiple sequences. This is necessary for at least the parallel example, the server example, and the HellaSwag test in the perplexity example. However, for this to be useful, uses of llama_kv_cache_seq_rm/cp will need to be changed to work on whole sequences. * ggml : add ggml_ssm_conv as a new operator for the conv step of Mamba This operator makes it possible to use and update the correct states for each token of the batch in the same way as ggml_ssm_scan. Other solutions which use existing operators would need loops which would add too many nodes to the graph (at least the ones I thought of). Using this operator further reduces the size of the CPU compute buffer from 140.68 MiB to 103.20 MiB with Mamba 3B with a batch size of 512. And (at least on CPU), it's a bit faster than before. Note that "ggml_ssm_conv" is probably not the most appropriate name, and it could be changed if a better one is found. * llama : add inp_s_seq as a new input tensor The most convenient implementation to select the correct state (for Mamba) for each token is to directly get the correct index from a tensor. This is why inp_s_seq is storing int32_t and not floats. The other, less convenient way to select the correct state would be to have inp_KQ_mask contain 1.0f for each state used by a token and 0.0f otherwise. This complicates quickly fetching the first used state of a token, and is also less efficient because a whole row of the mask would always need to be read for each token. Using indexes makes it easy to stop searching when there are no more sequences for a token, and the first sequence assigned is always very quickly available (it's the first element of each row). * mamba : support llama_kv_cache_seq_cp copy chains * mamba : support shifting and dividing the kv cache pos * mamba : make the server and parallel examples work with whole sequences A seq_id is dedicated to the system prompt in both cases. * llama : make llama_kv_cache_seq_rm return whether it succeeded or not * mamba : dedicate an input tensor for state copy indices This is cleaner and makes it easier to adapt when/if token positions (and by extension, inp_K_shift) are no longer integers. * mamba : adapt perplexity, batched, and batched-bench examples * perplexity : limit the max number of sequences This adapts to what the loaded model can provide. * llama : add llama_n_max_seq to get the upper limit for seq_ids Used by the perplexity example. * batched : pass n_parallel to the model's context params This should have been there already, but it wasn't. * batched-bench : reserve sequences to support Mamba * batched-bench : fix tokens being put in wrong sequences Generation quality isn't what's measured in there anyway, but at least using the correct sequences avoids using non-consecutive token positions. * mamba : stop abusing attention metadata This breaks existing converted-to-GGUF Mamba models, but will allow supporting mixed architectures like MambaFormer without needing to break Mamba models. This will also allow changing the size of Mamba's states without having to reconvert models in the future. (e.g. using something else than d_conv - 1 columns for the conv_states will not require breaking existing converted Mamba models again) * gguf-py : add new KV metadata key-value pairs for Mamba * llama : add new metadata key-value pairs for Mamba * llama : guard against divisions by zero when n_head is 0 * mamba : rename "unlimited" KV cache property to "recurrent" * mamba : more correctly update the "used" field of the KV cache * ggml : in ggml_ssm_scan, use a threshold for soft_plus This is how the official Mamba implementation does it, and it's also what torch.nn.Softplus does. * convert : for Mamba, fallback to internal NeoX tokenizer The resulting models are exactly the same as if the tokenizer.json and tokenizer_config.json of GPT-NeoX were there. * mamba : support state saving and restoring * ggml : implicitly pass src tensors through dst for Mamba-related ops * mamba : clarify some comments * server : fix cache_tokens not getting correctly resized Otherwise, when the "we have to evaluate at least 1 token" special case was triggered, an extra token was kept in cache_tokens even if it was removed from the KV cache. For Mamba, this caused useless prompt reprocessing when the previous request triggered the above case. * convert-hf : support new metadata keys for Mamba For the models available at https://huggingface.co/collections/state-spaces/transformers-compatible-mamba-65e7b40ab87e5297e45ae406 * mamba : rename metadata to be more similar to transformers library This breaks existing converted-to-GGUF models, but the metadata names are more "standard". * mamba : support mamba-*-hf models These models share their token_embd.weight with their output.weight * mamba : add missing spaces This is purely a formatting change. * convert-hf : omit output.weight when identical with token_embd.weight Only for Mamba for now, but it might be relevant for other models eventually. Most Mamba models actually share these two tensors, albeit implicitly. * readme : add Mamba to supported models, and add recent API changes * mamba : move state_seq and state_mask views outside layer loop A few tensors were also missing `struct` in front of `ggml_tensor`.
2024-03-08 22:31:00 +00:00
field = neox_reader.get_field(gguf.Keys.Tokenizer.MERGES)
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
assert field
llama : support Mamba Selective State Space Models (#5328) * mamba : begin working on support for Mamba SSM * mamba : begin figuring out how to (ab)use the kv cache for Mamba * mamba : recurrent inference almost works, but incoherent * mamba : recurrent inference WORKS!!! * convert : optionally use d_conv and d_state from config.json for Mamba * mamba : refactor recurrent conv, resulting in 20% perf increase It's still slower than I'd like, but I did not really optimize `ggml_exp` yet. I also refactored `ggml_exp` to work with tensors with more than 2 dimensions. * ggml : parallelize ggml_exp This results in 8% faster token generation for Mamba-130M. * mamba : simplify the conv step with a self-overlapping view Turns out the conv_state can be made smaller by one column. Note that this breaks existing GGUFs of Mamba, because the key_value_length field is tied to the conv_state size. Convolution with a self-overlapping view is cool! And it's much simpler than what I initially thought would be necessary to make the convolution step work with more than 1 token at a time. Next step is to make the SSM step work on batches of tokens too, and thus I need to figure out a way to make a parallel selective scan which will keep the ssm_state small and won't make it bigger by a factor of (n_layer * batch_size). * llama : fix Mamba KV self size wrongly displaying as f16 instead of f32 Relatedly, I also tried to see if other types than f32 worked for the states, but they don't, because of the operators used. It's probably better anyway to keep lots of precision there, since the states are small anyway. * mamba : fix self-overlapping view depth stride * mamba : handle batches of more than 1 token This means running Mamba no longer crashes when using the default settings! And probably also slightly faster prompt processing. Both batched and non-batched processing yield the same output. Previously, the state was not cleared when starting a sequence. Next step is to make the KV cache API work as expected for Mamba models. * ggml: add ggml_ssm_scan to help with parallel selective scan If the selective scan was implemented without a custom operator, there would be waaay too many nodes in the graph. For example, for Mamba-130M, with a batch size of 512 (the default), a naive selective scan could add at least 24*512=12288 nodes, which is more than LLAMA_MAX_NODES (8192), and that's only for the smallest Mamba model. So it's much cleaner with a custom operator. Not sure about the name, though. * ggml : in ggml_ssm_scan, merge multiple rows in the same vec operation This will help with performance on CPU if ggml_vec_mul_f32 and ggml_vec_add_f32 are ever optimized with SIMD. * mamba : very basic quantization support Mostly works, but there is currently no difference between the variants of a k-quant (e.g. Q4_K_S and Q4_K_M are the same). Most of the SSM-specific weights can be kept in f32 without affecting the size that much, since they are relatively small. (the linear projection weights are responsible for most of Mamba's size) Too much quantization seems to make the state degrade quite fast, and the model begins to output gibberish. It seems to affect bigger models to a lesser extent than small models, but I'm not sure by how much. Experimentation will be needed to figure out which weights are more important for the _M (and _L?) variants of k-quants for Mamba. * convert : fix wrong name for layer norm weight of offical Mamba models I was using Q-bert/Mamba-* models before, which have a slighlty different naming scheme for the weights. (they start with "model.layers" instead of "backbone.layers") * mamba : fuse more steps of the SSM scan in the ggml_ssm_scan operator This increases performance on CPU by around 30% for prompt processing, and by around 20% for text generation. However, it also makes the ggml_exp and ggml_soft_plus operators unused. Whether or not they should be kept will be decided later. * convert : for Mamba, also consider the "MambaLMHeadModel" arch name It's the name of the class of the official implementation, though they don't use it (yet) in the "architectures" field of config.json * mamba : fix vocab size problems with official models The perplexity was waaaay to high for models with a non-round vocab size. Not sure why, but it needed to be fixed in the metadata. Note that this breaks existing GGUF-converted Mamba models, but **only if** the vocab size was not already rounded. * ggml : remove ggml_exp and ggml_soft_plus They did not exist anyway outside of this branch, and since ggml_ssm_scan fused operations together, they are unused. It's always possible to bring them back if needed. * mamba : remove some useless comments No code change. * convert : fix flake8 linter errors * mamba : apply suggestions from code review * mamba : remove unecessary branch for row-wise ssm_state and C multiplication It was previously done to avoid permuting when only one token is processed at a time (like when generating text), but permuting is cheap, and dynamically changing the compute graph is not future-proof. * ggml : in ggml_ssm_scan, use more appropriate asserts * ggml : rename the destination pointer in ggml_compute_forward_ssm_scan_f32 * mamba : multiple sequences, but one at a time This is a step towards making this Mamba implementation usable with the server example (the way the system prompt is kept when clearing the client slots will need to be changed before this can work, though). The KV cache size for this kind of model is tied to the maximum number of sequences kept at any single time. For now, this number is obtained from n_parallel (plus one, to have an extra sequence to dedicate to the system prompt), but there might be a better way to do this which won't also make the main example use 2 cells even if only 1 is really used. (for this specific case, --parallel 0 helps) Simultaneous sequence processing will probably require changes to ggml_ssm_scan, and possibly a new operator for the conv step. * mamba : support llama_kv_cache_seq_cp This (mis)uses the logic around K shifts, because tokens in a state can't be shifted anyway, and because inp_K_shift has the right shape and type. Using ggml_get_rows is a nice way to do copies, but copy chains can't work. Fortunately, copy chains don't really seem to be used in the examples. Each KV cell is dedicated to the sequence ID corresponding to its own index. * mamba : use a state mask It's cleaner than the previous heuristic of checking for the pos of the first token in the batch. inp_KQ_mask could not be re-used for this, because it has the wrong shape and because it seems more suited to the next step of simultaneous sequence processing (helping with the problem of remembering which token belongs to which sequence(s)/state(s)). * llama : replace the usage of n_ctx with kv_self.size in many places * mamba : use n_tokens directly instead of n_tok * mamba : in comments, properly refer to KV cells instead of slots * mamba : reduce memory usage of ggml_ssm_scan From 290.37 MiB to 140.68 MiB of CPU compute buffer size with Mamba 3B with a batch size of 512. The result tensor of ggml_ssm_scan was previously a big part of the CPU compute buffer size. To make it smaller, it does not contain the intermediate ssm states anymore. Both y and the last ssm state are combined in the result tensor, because it seems only a single tensor can be returned by an operator with the way the graph is built. * mamba : simultaneous sequence processing A batch can now contain tokens from multiple sequences. This is necessary for at least the parallel example, the server example, and the HellaSwag test in the perplexity example. However, for this to be useful, uses of llama_kv_cache_seq_rm/cp will need to be changed to work on whole sequences. * ggml : add ggml_ssm_conv as a new operator for the conv step of Mamba This operator makes it possible to use and update the correct states for each token of the batch in the same way as ggml_ssm_scan. Other solutions which use existing operators would need loops which would add too many nodes to the graph (at least the ones I thought of). Using this operator further reduces the size of the CPU compute buffer from 140.68 MiB to 103.20 MiB with Mamba 3B with a batch size of 512. And (at least on CPU), it's a bit faster than before. Note that "ggml_ssm_conv" is probably not the most appropriate name, and it could be changed if a better one is found. * llama : add inp_s_seq as a new input tensor The most convenient implementation to select the correct state (for Mamba) for each token is to directly get the correct index from a tensor. This is why inp_s_seq is storing int32_t and not floats. The other, less convenient way to select the correct state would be to have inp_KQ_mask contain 1.0f for each state used by a token and 0.0f otherwise. This complicates quickly fetching the first used state of a token, and is also less efficient because a whole row of the mask would always need to be read for each token. Using indexes makes it easy to stop searching when there are no more sequences for a token, and the first sequence assigned is always very quickly available (it's the first element of each row). * mamba : support llama_kv_cache_seq_cp copy chains * mamba : support shifting and dividing the kv cache pos * mamba : make the server and parallel examples work with whole sequences A seq_id is dedicated to the system prompt in both cases. * llama : make llama_kv_cache_seq_rm return whether it succeeded or not * mamba : dedicate an input tensor for state copy indices This is cleaner and makes it easier to adapt when/if token positions (and by extension, inp_K_shift) are no longer integers. * mamba : adapt perplexity, batched, and batched-bench examples * perplexity : limit the max number of sequences This adapts to what the loaded model can provide. * llama : add llama_n_max_seq to get the upper limit for seq_ids Used by the perplexity example. * batched : pass n_parallel to the model's context params This should have been there already, but it wasn't. * batched-bench : reserve sequences to support Mamba * batched-bench : fix tokens being put in wrong sequences Generation quality isn't what's measured in there anyway, but at least using the correct sequences avoids using non-consecutive token positions. * mamba : stop abusing attention metadata This breaks existing converted-to-GGUF Mamba models, but will allow supporting mixed architectures like MambaFormer without needing to break Mamba models. This will also allow changing the size of Mamba's states without having to reconvert models in the future. (e.g. using something else than d_conv - 1 columns for the conv_states will not require breaking existing converted Mamba models again) * gguf-py : add new KV metadata key-value pairs for Mamba * llama : add new metadata key-value pairs for Mamba * llama : guard against divisions by zero when n_head is 0 * mamba : rename "unlimited" KV cache property to "recurrent" * mamba : more correctly update the "used" field of the KV cache * ggml : in ggml_ssm_scan, use a threshold for soft_plus This is how the official Mamba implementation does it, and it's also what torch.nn.Softplus does. * convert : for Mamba, fallback to internal NeoX tokenizer The resulting models are exactly the same as if the tokenizer.json and tokenizer_config.json of GPT-NeoX were there. * mamba : support state saving and restoring * ggml : implicitly pass src tensors through dst for Mamba-related ops * mamba : clarify some comments * server : fix cache_tokens not getting correctly resized Otherwise, when the "we have to evaluate at least 1 token" special case was triggered, an extra token was kept in cache_tokens even if it was removed from the KV cache. For Mamba, this caused useless prompt reprocessing when the previous request triggered the above case. * convert-hf : support new metadata keys for Mamba For the models available at https://huggingface.co/collections/state-spaces/transformers-compatible-mamba-65e7b40ab87e5297e45ae406 * mamba : rename metadata to be more similar to transformers library This breaks existing converted-to-GGUF models, but the metadata names are more "standard". * mamba : support mamba-*-hf models These models share their token_embd.weight with their output.weight * mamba : add missing spaces This is purely a formatting change. * convert-hf : omit output.weight when identical with token_embd.weight Only for Mamba for now, but it might be relevant for other models eventually. Most Mamba models actually share these two tensors, albeit implicitly. * readme : add Mamba to supported models, and add recent API changes * mamba : move state_seq and state_mask views outside layer loop A few tensors were also missing `struct` in front of `ggml_tensor`.
2024-03-08 22:31:00 +00:00
self.gguf_writer.add_token_merges([bytes(field.parts[i]) for i in field.data])
llama : support Llama 3 HF conversion (#6745) * Support Llama 3 conversion The tokenizer is BPE. * style * Accept suggestion Co-authored-by: Sourab Mangrulkar <13534540+pacman100@users.noreply.github.com> * llama : add llama_token_is_eog() ggml-ci * llama : auto-detect more EOT tokens when missing in KV data * convert : replacing EOS token is a hack * llama : fix codegemma EOT token + add TODOs * llama : fix model type string for 8B model --------- Co-authored-by: Sourab Mangrulkar <13534540+pacman100@users.noreply.github.com> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-04-21 11:50:41 +00:00
llama : support Mamba Selective State Space Models (#5328) * mamba : begin working on support for Mamba SSM * mamba : begin figuring out how to (ab)use the kv cache for Mamba * mamba : recurrent inference almost works, but incoherent * mamba : recurrent inference WORKS!!! * convert : optionally use d_conv and d_state from config.json for Mamba * mamba : refactor recurrent conv, resulting in 20% perf increase It's still slower than I'd like, but I did not really optimize `ggml_exp` yet. I also refactored `ggml_exp` to work with tensors with more than 2 dimensions. * ggml : parallelize ggml_exp This results in 8% faster token generation for Mamba-130M. * mamba : simplify the conv step with a self-overlapping view Turns out the conv_state can be made smaller by one column. Note that this breaks existing GGUFs of Mamba, because the key_value_length field is tied to the conv_state size. Convolution with a self-overlapping view is cool! And it's much simpler than what I initially thought would be necessary to make the convolution step work with more than 1 token at a time. Next step is to make the SSM step work on batches of tokens too, and thus I need to figure out a way to make a parallel selective scan which will keep the ssm_state small and won't make it bigger by a factor of (n_layer * batch_size). * llama : fix Mamba KV self size wrongly displaying as f16 instead of f32 Relatedly, I also tried to see if other types than f32 worked for the states, but they don't, because of the operators used. It's probably better anyway to keep lots of precision there, since the states are small anyway. * mamba : fix self-overlapping view depth stride * mamba : handle batches of more than 1 token This means running Mamba no longer crashes when using the default settings! And probably also slightly faster prompt processing. Both batched and non-batched processing yield the same output. Previously, the state was not cleared when starting a sequence. Next step is to make the KV cache API work as expected for Mamba models. * ggml: add ggml_ssm_scan to help with parallel selective scan If the selective scan was implemented without a custom operator, there would be waaay too many nodes in the graph. For example, for Mamba-130M, with a batch size of 512 (the default), a naive selective scan could add at least 24*512=12288 nodes, which is more than LLAMA_MAX_NODES (8192), and that's only for the smallest Mamba model. So it's much cleaner with a custom operator. Not sure about the name, though. * ggml : in ggml_ssm_scan, merge multiple rows in the same vec operation This will help with performance on CPU if ggml_vec_mul_f32 and ggml_vec_add_f32 are ever optimized with SIMD. * mamba : very basic quantization support Mostly works, but there is currently no difference between the variants of a k-quant (e.g. Q4_K_S and Q4_K_M are the same). Most of the SSM-specific weights can be kept in f32 without affecting the size that much, since they are relatively small. (the linear projection weights are responsible for most of Mamba's size) Too much quantization seems to make the state degrade quite fast, and the model begins to output gibberish. It seems to affect bigger models to a lesser extent than small models, but I'm not sure by how much. Experimentation will be needed to figure out which weights are more important for the _M (and _L?) variants of k-quants for Mamba. * convert : fix wrong name for layer norm weight of offical Mamba models I was using Q-bert/Mamba-* models before, which have a slighlty different naming scheme for the weights. (they start with "model.layers" instead of "backbone.layers") * mamba : fuse more steps of the SSM scan in the ggml_ssm_scan operator This increases performance on CPU by around 30% for prompt processing, and by around 20% for text generation. However, it also makes the ggml_exp and ggml_soft_plus operators unused. Whether or not they should be kept will be decided later. * convert : for Mamba, also consider the "MambaLMHeadModel" arch name It's the name of the class of the official implementation, though they don't use it (yet) in the "architectures" field of config.json * mamba : fix vocab size problems with official models The perplexity was waaaay to high for models with a non-round vocab size. Not sure why, but it needed to be fixed in the metadata. Note that this breaks existing GGUF-converted Mamba models, but **only if** the vocab size was not already rounded. * ggml : remove ggml_exp and ggml_soft_plus They did not exist anyway outside of this branch, and since ggml_ssm_scan fused operations together, they are unused. It's always possible to bring them back if needed. * mamba : remove some useless comments No code change. * convert : fix flake8 linter errors * mamba : apply suggestions from code review * mamba : remove unecessary branch for row-wise ssm_state and C multiplication It was previously done to avoid permuting when only one token is processed at a time (like when generating text), but permuting is cheap, and dynamically changing the compute graph is not future-proof. * ggml : in ggml_ssm_scan, use more appropriate asserts * ggml : rename the destination pointer in ggml_compute_forward_ssm_scan_f32 * mamba : multiple sequences, but one at a time This is a step towards making this Mamba implementation usable with the server example (the way the system prompt is kept when clearing the client slots will need to be changed before this can work, though). The KV cache size for this kind of model is tied to the maximum number of sequences kept at any single time. For now, this number is obtained from n_parallel (plus one, to have an extra sequence to dedicate to the system prompt), but there might be a better way to do this which won't also make the main example use 2 cells even if only 1 is really used. (for this specific case, --parallel 0 helps) Simultaneous sequence processing will probably require changes to ggml_ssm_scan, and possibly a new operator for the conv step. * mamba : support llama_kv_cache_seq_cp This (mis)uses the logic around K shifts, because tokens in a state can't be shifted anyway, and because inp_K_shift has the right shape and type. Using ggml_get_rows is a nice way to do copies, but copy chains can't work. Fortunately, copy chains don't really seem to be used in the examples. Each KV cell is dedicated to the sequence ID corresponding to its own index. * mamba : use a state mask It's cleaner than the previous heuristic of checking for the pos of the first token in the batch. inp_KQ_mask could not be re-used for this, because it has the wrong shape and because it seems more suited to the next step of simultaneous sequence processing (helping with the problem of remembering which token belongs to which sequence(s)/state(s)). * llama : replace the usage of n_ctx with kv_self.size in many places * mamba : use n_tokens directly instead of n_tok * mamba : in comments, properly refer to KV cells instead of slots * mamba : reduce memory usage of ggml_ssm_scan From 290.37 MiB to 140.68 MiB of CPU compute buffer size with Mamba 3B with a batch size of 512. The result tensor of ggml_ssm_scan was previously a big part of the CPU compute buffer size. To make it smaller, it does not contain the intermediate ssm states anymore. Both y and the last ssm state are combined in the result tensor, because it seems only a single tensor can be returned by an operator with the way the graph is built. * mamba : simultaneous sequence processing A batch can now contain tokens from multiple sequences. This is necessary for at least the parallel example, the server example, and the HellaSwag test in the perplexity example. However, for this to be useful, uses of llama_kv_cache_seq_rm/cp will need to be changed to work on whole sequences. * ggml : add ggml_ssm_conv as a new operator for the conv step of Mamba This operator makes it possible to use and update the correct states for each token of the batch in the same way as ggml_ssm_scan. Other solutions which use existing operators would need loops which would add too many nodes to the graph (at least the ones I thought of). Using this operator further reduces the size of the CPU compute buffer from 140.68 MiB to 103.20 MiB with Mamba 3B with a batch size of 512. And (at least on CPU), it's a bit faster than before. Note that "ggml_ssm_conv" is probably not the most appropriate name, and it could be changed if a better one is found. * llama : add inp_s_seq as a new input tensor The most convenient implementation to select the correct state (for Mamba) for each token is to directly get the correct index from a tensor. This is why inp_s_seq is storing int32_t and not floats. The other, less convenient way to select the correct state would be to have inp_KQ_mask contain 1.0f for each state used by a token and 0.0f otherwise. This complicates quickly fetching the first used state of a token, and is also less efficient because a whole row of the mask would always need to be read for each token. Using indexes makes it easy to stop searching when there are no more sequences for a token, and the first sequence assigned is always very quickly available (it's the first element of each row). * mamba : support llama_kv_cache_seq_cp copy chains * mamba : support shifting and dividing the kv cache pos * mamba : make the server and parallel examples work with whole sequences A seq_id is dedicated to the system prompt in both cases. * llama : make llama_kv_cache_seq_rm return whether it succeeded or not * mamba : dedicate an input tensor for state copy indices This is cleaner and makes it easier to adapt when/if token positions (and by extension, inp_K_shift) are no longer integers. * mamba : adapt perplexity, batched, and batched-bench examples * perplexity : limit the max number of sequences This adapts to what the loaded model can provide. * llama : add llama_n_max_seq to get the upper limit for seq_ids Used by the perplexity example. * batched : pass n_parallel to the model's context params This should have been there already, but it wasn't. * batched-bench : reserve sequences to support Mamba * batched-bench : fix tokens being put in wrong sequences Generation quality isn't what's measured in there anyway, but at least using the correct sequences avoids using non-consecutive token positions. * mamba : stop abusing attention metadata This breaks existing converted-to-GGUF Mamba models, but will allow supporting mixed architectures like MambaFormer without needing to break Mamba models. This will also allow changing the size of Mamba's states without having to reconvert models in the future. (e.g. using something else than d_conv - 1 columns for the conv_states will not require breaking existing converted Mamba models again) * gguf-py : add new KV metadata key-value pairs for Mamba * llama : add new metadata key-value pairs for Mamba * llama : guard against divisions by zero when n_head is 0 * mamba : rename "unlimited" KV cache property to "recurrent" * mamba : more correctly update the "used" field of the KV cache * ggml : in ggml_ssm_scan, use a threshold for soft_plus This is how the official Mamba implementation does it, and it's also what torch.nn.Softplus does. * convert : for Mamba, fallback to internal NeoX tokenizer The resulting models are exactly the same as if the tokenizer.json and tokenizer_config.json of GPT-NeoX were there. * mamba : support state saving and restoring * ggml : implicitly pass src tensors through dst for Mamba-related ops * mamba : clarify some comments * server : fix cache_tokens not getting correctly resized Otherwise, when the "we have to evaluate at least 1 token" special case was triggered, an extra token was kept in cache_tokens even if it was removed from the KV cache. For Mamba, this caused useless prompt reprocessing when the previous request triggered the above case. * convert-hf : support new metadata keys for Mamba For the models available at https://huggingface.co/collections/state-spaces/transformers-compatible-mamba-65e7b40ab87e5297e45ae406 * mamba : rename metadata to be more similar to transformers library This breaks existing converted-to-GGUF models, but the metadata names are more "standard". * mamba : support mamba-*-hf models These models share their token_embd.weight with their output.weight * mamba : add missing spaces This is purely a formatting change. * convert-hf : omit output.weight when identical with token_embd.weight Only for Mamba for now, but it might be relevant for other models eventually. Most Mamba models actually share these two tensors, albeit implicitly. * readme : add Mamba to supported models, and add recent API changes * mamba : move state_seq and state_mask views outside layer loop A few tensors were also missing `struct` in front of `ggml_tensor`.
2024-03-08 22:31:00 +00:00
field = neox_reader.get_field(gguf.Keys.Tokenizer.BOS_ID)
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
self.gguf_writer.add_bos_token_id(field.parts[-1].tolist()[0] if field else 1)
llama : support Llama 3 HF conversion (#6745) * Support Llama 3 conversion The tokenizer is BPE. * style * Accept suggestion Co-authored-by: Sourab Mangrulkar <13534540+pacman100@users.noreply.github.com> * llama : add llama_token_is_eog() ggml-ci * llama : auto-detect more EOT tokens when missing in KV data * convert : replacing EOS token is a hack * llama : fix codegemma EOT token + add TODOs * llama : fix model type string for 8B model --------- Co-authored-by: Sourab Mangrulkar <13534540+pacman100@users.noreply.github.com> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-04-21 11:50:41 +00:00
llama : support Mamba Selective State Space Models (#5328) * mamba : begin working on support for Mamba SSM * mamba : begin figuring out how to (ab)use the kv cache for Mamba * mamba : recurrent inference almost works, but incoherent * mamba : recurrent inference WORKS!!! * convert : optionally use d_conv and d_state from config.json for Mamba * mamba : refactor recurrent conv, resulting in 20% perf increase It's still slower than I'd like, but I did not really optimize `ggml_exp` yet. I also refactored `ggml_exp` to work with tensors with more than 2 dimensions. * ggml : parallelize ggml_exp This results in 8% faster token generation for Mamba-130M. * mamba : simplify the conv step with a self-overlapping view Turns out the conv_state can be made smaller by one column. Note that this breaks existing GGUFs of Mamba, because the key_value_length field is tied to the conv_state size. Convolution with a self-overlapping view is cool! And it's much simpler than what I initially thought would be necessary to make the convolution step work with more than 1 token at a time. Next step is to make the SSM step work on batches of tokens too, and thus I need to figure out a way to make a parallel selective scan which will keep the ssm_state small and won't make it bigger by a factor of (n_layer * batch_size). * llama : fix Mamba KV self size wrongly displaying as f16 instead of f32 Relatedly, I also tried to see if other types than f32 worked for the states, but they don't, because of the operators used. It's probably better anyway to keep lots of precision there, since the states are small anyway. * mamba : fix self-overlapping view depth stride * mamba : handle batches of more than 1 token This means running Mamba no longer crashes when using the default settings! And probably also slightly faster prompt processing. Both batched and non-batched processing yield the same output. Previously, the state was not cleared when starting a sequence. Next step is to make the KV cache API work as expected for Mamba models. * ggml: add ggml_ssm_scan to help with parallel selective scan If the selective scan was implemented without a custom operator, there would be waaay too many nodes in the graph. For example, for Mamba-130M, with a batch size of 512 (the default), a naive selective scan could add at least 24*512=12288 nodes, which is more than LLAMA_MAX_NODES (8192), and that's only for the smallest Mamba model. So it's much cleaner with a custom operator. Not sure about the name, though. * ggml : in ggml_ssm_scan, merge multiple rows in the same vec operation This will help with performance on CPU if ggml_vec_mul_f32 and ggml_vec_add_f32 are ever optimized with SIMD. * mamba : very basic quantization support Mostly works, but there is currently no difference between the variants of a k-quant (e.g. Q4_K_S and Q4_K_M are the same). Most of the SSM-specific weights can be kept in f32 without affecting the size that much, since they are relatively small. (the linear projection weights are responsible for most of Mamba's size) Too much quantization seems to make the state degrade quite fast, and the model begins to output gibberish. It seems to affect bigger models to a lesser extent than small models, but I'm not sure by how much. Experimentation will be needed to figure out which weights are more important for the _M (and _L?) variants of k-quants for Mamba. * convert : fix wrong name for layer norm weight of offical Mamba models I was using Q-bert/Mamba-* models before, which have a slighlty different naming scheme for the weights. (they start with "model.layers" instead of "backbone.layers") * mamba : fuse more steps of the SSM scan in the ggml_ssm_scan operator This increases performance on CPU by around 30% for prompt processing, and by around 20% for text generation. However, it also makes the ggml_exp and ggml_soft_plus operators unused. Whether or not they should be kept will be decided later. * convert : for Mamba, also consider the "MambaLMHeadModel" arch name It's the name of the class of the official implementation, though they don't use it (yet) in the "architectures" field of config.json * mamba : fix vocab size problems with official models The perplexity was waaaay to high for models with a non-round vocab size. Not sure why, but it needed to be fixed in the metadata. Note that this breaks existing GGUF-converted Mamba models, but **only if** the vocab size was not already rounded. * ggml : remove ggml_exp and ggml_soft_plus They did not exist anyway outside of this branch, and since ggml_ssm_scan fused operations together, they are unused. It's always possible to bring them back if needed. * mamba : remove some useless comments No code change. * convert : fix flake8 linter errors * mamba : apply suggestions from code review * mamba : remove unecessary branch for row-wise ssm_state and C multiplication It was previously done to avoid permuting when only one token is processed at a time (like when generating text), but permuting is cheap, and dynamically changing the compute graph is not future-proof. * ggml : in ggml_ssm_scan, use more appropriate asserts * ggml : rename the destination pointer in ggml_compute_forward_ssm_scan_f32 * mamba : multiple sequences, but one at a time This is a step towards making this Mamba implementation usable with the server example (the way the system prompt is kept when clearing the client slots will need to be changed before this can work, though). The KV cache size for this kind of model is tied to the maximum number of sequences kept at any single time. For now, this number is obtained from n_parallel (plus one, to have an extra sequence to dedicate to the system prompt), but there might be a better way to do this which won't also make the main example use 2 cells even if only 1 is really used. (for this specific case, --parallel 0 helps) Simultaneous sequence processing will probably require changes to ggml_ssm_scan, and possibly a new operator for the conv step. * mamba : support llama_kv_cache_seq_cp This (mis)uses the logic around K shifts, because tokens in a state can't be shifted anyway, and because inp_K_shift has the right shape and type. Using ggml_get_rows is a nice way to do copies, but copy chains can't work. Fortunately, copy chains don't really seem to be used in the examples. Each KV cell is dedicated to the sequence ID corresponding to its own index. * mamba : use a state mask It's cleaner than the previous heuristic of checking for the pos of the first token in the batch. inp_KQ_mask could not be re-used for this, because it has the wrong shape and because it seems more suited to the next step of simultaneous sequence processing (helping with the problem of remembering which token belongs to which sequence(s)/state(s)). * llama : replace the usage of n_ctx with kv_self.size in many places * mamba : use n_tokens directly instead of n_tok * mamba : in comments, properly refer to KV cells instead of slots * mamba : reduce memory usage of ggml_ssm_scan From 290.37 MiB to 140.68 MiB of CPU compute buffer size with Mamba 3B with a batch size of 512. The result tensor of ggml_ssm_scan was previously a big part of the CPU compute buffer size. To make it smaller, it does not contain the intermediate ssm states anymore. Both y and the last ssm state are combined in the result tensor, because it seems only a single tensor can be returned by an operator with the way the graph is built. * mamba : simultaneous sequence processing A batch can now contain tokens from multiple sequences. This is necessary for at least the parallel example, the server example, and the HellaSwag test in the perplexity example. However, for this to be useful, uses of llama_kv_cache_seq_rm/cp will need to be changed to work on whole sequences. * ggml : add ggml_ssm_conv as a new operator for the conv step of Mamba This operator makes it possible to use and update the correct states for each token of the batch in the same way as ggml_ssm_scan. Other solutions which use existing operators would need loops which would add too many nodes to the graph (at least the ones I thought of). Using this operator further reduces the size of the CPU compute buffer from 140.68 MiB to 103.20 MiB with Mamba 3B with a batch size of 512. And (at least on CPU), it's a bit faster than before. Note that "ggml_ssm_conv" is probably not the most appropriate name, and it could be changed if a better one is found. * llama : add inp_s_seq as a new input tensor The most convenient implementation to select the correct state (for Mamba) for each token is to directly get the correct index from a tensor. This is why inp_s_seq is storing int32_t and not floats. The other, less convenient way to select the correct state would be to have inp_KQ_mask contain 1.0f for each state used by a token and 0.0f otherwise. This complicates quickly fetching the first used state of a token, and is also less efficient because a whole row of the mask would always need to be read for each token. Using indexes makes it easy to stop searching when there are no more sequences for a token, and the first sequence assigned is always very quickly available (it's the first element of each row). * mamba : support llama_kv_cache_seq_cp copy chains * mamba : support shifting and dividing the kv cache pos * mamba : make the server and parallel examples work with whole sequences A seq_id is dedicated to the system prompt in both cases. * llama : make llama_kv_cache_seq_rm return whether it succeeded or not * mamba : dedicate an input tensor for state copy indices This is cleaner and makes it easier to adapt when/if token positions (and by extension, inp_K_shift) are no longer integers. * mamba : adapt perplexity, batched, and batched-bench examples * perplexity : limit the max number of sequences This adapts to what the loaded model can provide. * llama : add llama_n_max_seq to get the upper limit for seq_ids Used by the perplexity example. * batched : pass n_parallel to the model's context params This should have been there already, but it wasn't. * batched-bench : reserve sequences to support Mamba * batched-bench : fix tokens being put in wrong sequences Generation quality isn't what's measured in there anyway, but at least using the correct sequences avoids using non-consecutive token positions. * mamba : stop abusing attention metadata This breaks existing converted-to-GGUF Mamba models, but will allow supporting mixed architectures like MambaFormer without needing to break Mamba models. This will also allow changing the size of Mamba's states without having to reconvert models in the future. (e.g. using something else than d_conv - 1 columns for the conv_states will not require breaking existing converted Mamba models again) * gguf-py : add new KV metadata key-value pairs for Mamba * llama : add new metadata key-value pairs for Mamba * llama : guard against divisions by zero when n_head is 0 * mamba : rename "unlimited" KV cache property to "recurrent" * mamba : more correctly update the "used" field of the KV cache * ggml : in ggml_ssm_scan, use a threshold for soft_plus This is how the official Mamba implementation does it, and it's also what torch.nn.Softplus does. * convert : for Mamba, fallback to internal NeoX tokenizer The resulting models are exactly the same as if the tokenizer.json and tokenizer_config.json of GPT-NeoX were there. * mamba : support state saving and restoring * ggml : implicitly pass src tensors through dst for Mamba-related ops * mamba : clarify some comments * server : fix cache_tokens not getting correctly resized Otherwise, when the "we have to evaluate at least 1 token" special case was triggered, an extra token was kept in cache_tokens even if it was removed from the KV cache. For Mamba, this caused useless prompt reprocessing when the previous request triggered the above case. * convert-hf : support new metadata keys for Mamba For the models available at https://huggingface.co/collections/state-spaces/transformers-compatible-mamba-65e7b40ab87e5297e45ae406 * mamba : rename metadata to be more similar to transformers library This breaks existing converted-to-GGUF models, but the metadata names are more "standard". * mamba : support mamba-*-hf models These models share their token_embd.weight with their output.weight * mamba : add missing spaces This is purely a formatting change. * convert-hf : omit output.weight when identical with token_embd.weight Only for Mamba for now, but it might be relevant for other models eventually. Most Mamba models actually share these two tensors, albeit implicitly. * readme : add Mamba to supported models, and add recent API changes * mamba : move state_seq and state_mask views outside layer loop A few tensors were also missing `struct` in front of `ggml_tensor`.
2024-03-08 22:31:00 +00:00
field = neox_reader.get_field(gguf.Keys.Tokenizer.EOS_ID)
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
self.gguf_writer.add_eos_token_id(field.parts[-1].tolist()[0] if field else 0)
llama : support Llama 3 HF conversion (#6745) * Support Llama 3 conversion The tokenizer is BPE. * style * Accept suggestion Co-authored-by: Sourab Mangrulkar <13534540+pacman100@users.noreply.github.com> * llama : add llama_token_is_eog() ggml-ci * llama : auto-detect more EOT tokens when missing in KV data * convert : replacing EOS token is a hack * llama : fix codegemma EOT token + add TODOs * llama : fix model type string for 8B model --------- Co-authored-by: Sourab Mangrulkar <13534540+pacman100@users.noreply.github.com> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-04-21 11:50:41 +00:00
llama : support Mamba Selective State Space Models (#5328) * mamba : begin working on support for Mamba SSM * mamba : begin figuring out how to (ab)use the kv cache for Mamba * mamba : recurrent inference almost works, but incoherent * mamba : recurrent inference WORKS!!! * convert : optionally use d_conv and d_state from config.json for Mamba * mamba : refactor recurrent conv, resulting in 20% perf increase It's still slower than I'd like, but I did not really optimize `ggml_exp` yet. I also refactored `ggml_exp` to work with tensors with more than 2 dimensions. * ggml : parallelize ggml_exp This results in 8% faster token generation for Mamba-130M. * mamba : simplify the conv step with a self-overlapping view Turns out the conv_state can be made smaller by one column. Note that this breaks existing GGUFs of Mamba, because the key_value_length field is tied to the conv_state size. Convolution with a self-overlapping view is cool! And it's much simpler than what I initially thought would be necessary to make the convolution step work with more than 1 token at a time. Next step is to make the SSM step work on batches of tokens too, and thus I need to figure out a way to make a parallel selective scan which will keep the ssm_state small and won't make it bigger by a factor of (n_layer * batch_size). * llama : fix Mamba KV self size wrongly displaying as f16 instead of f32 Relatedly, I also tried to see if other types than f32 worked for the states, but they don't, because of the operators used. It's probably better anyway to keep lots of precision there, since the states are small anyway. * mamba : fix self-overlapping view depth stride * mamba : handle batches of more than 1 token This means running Mamba no longer crashes when using the default settings! And probably also slightly faster prompt processing. Both batched and non-batched processing yield the same output. Previously, the state was not cleared when starting a sequence. Next step is to make the KV cache API work as expected for Mamba models. * ggml: add ggml_ssm_scan to help with parallel selective scan If the selective scan was implemented without a custom operator, there would be waaay too many nodes in the graph. For example, for Mamba-130M, with a batch size of 512 (the default), a naive selective scan could add at least 24*512=12288 nodes, which is more than LLAMA_MAX_NODES (8192), and that's only for the smallest Mamba model. So it's much cleaner with a custom operator. Not sure about the name, though. * ggml : in ggml_ssm_scan, merge multiple rows in the same vec operation This will help with performance on CPU if ggml_vec_mul_f32 and ggml_vec_add_f32 are ever optimized with SIMD. * mamba : very basic quantization support Mostly works, but there is currently no difference between the variants of a k-quant (e.g. Q4_K_S and Q4_K_M are the same). Most of the SSM-specific weights can be kept in f32 without affecting the size that much, since they are relatively small. (the linear projection weights are responsible for most of Mamba's size) Too much quantization seems to make the state degrade quite fast, and the model begins to output gibberish. It seems to affect bigger models to a lesser extent than small models, but I'm not sure by how much. Experimentation will be needed to figure out which weights are more important for the _M (and _L?) variants of k-quants for Mamba. * convert : fix wrong name for layer norm weight of offical Mamba models I was using Q-bert/Mamba-* models before, which have a slighlty different naming scheme for the weights. (they start with "model.layers" instead of "backbone.layers") * mamba : fuse more steps of the SSM scan in the ggml_ssm_scan operator This increases performance on CPU by around 30% for prompt processing, and by around 20% for text generation. However, it also makes the ggml_exp and ggml_soft_plus operators unused. Whether or not they should be kept will be decided later. * convert : for Mamba, also consider the "MambaLMHeadModel" arch name It's the name of the class of the official implementation, though they don't use it (yet) in the "architectures" field of config.json * mamba : fix vocab size problems with official models The perplexity was waaaay to high for models with a non-round vocab size. Not sure why, but it needed to be fixed in the metadata. Note that this breaks existing GGUF-converted Mamba models, but **only if** the vocab size was not already rounded. * ggml : remove ggml_exp and ggml_soft_plus They did not exist anyway outside of this branch, and since ggml_ssm_scan fused operations together, they are unused. It's always possible to bring them back if needed. * mamba : remove some useless comments No code change. * convert : fix flake8 linter errors * mamba : apply suggestions from code review * mamba : remove unecessary branch for row-wise ssm_state and C multiplication It was previously done to avoid permuting when only one token is processed at a time (like when generating text), but permuting is cheap, and dynamically changing the compute graph is not future-proof. * ggml : in ggml_ssm_scan, use more appropriate asserts * ggml : rename the destination pointer in ggml_compute_forward_ssm_scan_f32 * mamba : multiple sequences, but one at a time This is a step towards making this Mamba implementation usable with the server example (the way the system prompt is kept when clearing the client slots will need to be changed before this can work, though). The KV cache size for this kind of model is tied to the maximum number of sequences kept at any single time. For now, this number is obtained from n_parallel (plus one, to have an extra sequence to dedicate to the system prompt), but there might be a better way to do this which won't also make the main example use 2 cells even if only 1 is really used. (for this specific case, --parallel 0 helps) Simultaneous sequence processing will probably require changes to ggml_ssm_scan, and possibly a new operator for the conv step. * mamba : support llama_kv_cache_seq_cp This (mis)uses the logic around K shifts, because tokens in a state can't be shifted anyway, and because inp_K_shift has the right shape and type. Using ggml_get_rows is a nice way to do copies, but copy chains can't work. Fortunately, copy chains don't really seem to be used in the examples. Each KV cell is dedicated to the sequence ID corresponding to its own index. * mamba : use a state mask It's cleaner than the previous heuristic of checking for the pos of the first token in the batch. inp_KQ_mask could not be re-used for this, because it has the wrong shape and because it seems more suited to the next step of simultaneous sequence processing (helping with the problem of remembering which token belongs to which sequence(s)/state(s)). * llama : replace the usage of n_ctx with kv_self.size in many places * mamba : use n_tokens directly instead of n_tok * mamba : in comments, properly refer to KV cells instead of slots * mamba : reduce memory usage of ggml_ssm_scan From 290.37 MiB to 140.68 MiB of CPU compute buffer size with Mamba 3B with a batch size of 512. The result tensor of ggml_ssm_scan was previously a big part of the CPU compute buffer size. To make it smaller, it does not contain the intermediate ssm states anymore. Both y and the last ssm state are combined in the result tensor, because it seems only a single tensor can be returned by an operator with the way the graph is built. * mamba : simultaneous sequence processing A batch can now contain tokens from multiple sequences. This is necessary for at least the parallel example, the server example, and the HellaSwag test in the perplexity example. However, for this to be useful, uses of llama_kv_cache_seq_rm/cp will need to be changed to work on whole sequences. * ggml : add ggml_ssm_conv as a new operator for the conv step of Mamba This operator makes it possible to use and update the correct states for each token of the batch in the same way as ggml_ssm_scan. Other solutions which use existing operators would need loops which would add too many nodes to the graph (at least the ones I thought of). Using this operator further reduces the size of the CPU compute buffer from 140.68 MiB to 103.20 MiB with Mamba 3B with a batch size of 512. And (at least on CPU), it's a bit faster than before. Note that "ggml_ssm_conv" is probably not the most appropriate name, and it could be changed if a better one is found. * llama : add inp_s_seq as a new input tensor The most convenient implementation to select the correct state (for Mamba) for each token is to directly get the correct index from a tensor. This is why inp_s_seq is storing int32_t and not floats. The other, less convenient way to select the correct state would be to have inp_KQ_mask contain 1.0f for each state used by a token and 0.0f otherwise. This complicates quickly fetching the first used state of a token, and is also less efficient because a whole row of the mask would always need to be read for each token. Using indexes makes it easy to stop searching when there are no more sequences for a token, and the first sequence assigned is always very quickly available (it's the first element of each row). * mamba : support llama_kv_cache_seq_cp copy chains * mamba : support shifting and dividing the kv cache pos * mamba : make the server and parallel examples work with whole sequences A seq_id is dedicated to the system prompt in both cases. * llama : make llama_kv_cache_seq_rm return whether it succeeded or not * mamba : dedicate an input tensor for state copy indices This is cleaner and makes it easier to adapt when/if token positions (and by extension, inp_K_shift) are no longer integers. * mamba : adapt perplexity, batched, and batched-bench examples * perplexity : limit the max number of sequences This adapts to what the loaded model can provide. * llama : add llama_n_max_seq to get the upper limit for seq_ids Used by the perplexity example. * batched : pass n_parallel to the model's context params This should have been there already, but it wasn't. * batched-bench : reserve sequences to support Mamba * batched-bench : fix tokens being put in wrong sequences Generation quality isn't what's measured in there anyway, but at least using the correct sequences avoids using non-consecutive token positions. * mamba : stop abusing attention metadata This breaks existing converted-to-GGUF Mamba models, but will allow supporting mixed architectures like MambaFormer without needing to break Mamba models. This will also allow changing the size of Mamba's states without having to reconvert models in the future. (e.g. using something else than d_conv - 1 columns for the conv_states will not require breaking existing converted Mamba models again) * gguf-py : add new KV metadata key-value pairs for Mamba * llama : add new metadata key-value pairs for Mamba * llama : guard against divisions by zero when n_head is 0 * mamba : rename "unlimited" KV cache property to "recurrent" * mamba : more correctly update the "used" field of the KV cache * ggml : in ggml_ssm_scan, use a threshold for soft_plus This is how the official Mamba implementation does it, and it's also what torch.nn.Softplus does. * convert : for Mamba, fallback to internal NeoX tokenizer The resulting models are exactly the same as if the tokenizer.json and tokenizer_config.json of GPT-NeoX were there. * mamba : support state saving and restoring * ggml : implicitly pass src tensors through dst for Mamba-related ops * mamba : clarify some comments * server : fix cache_tokens not getting correctly resized Otherwise, when the "we have to evaluate at least 1 token" special case was triggered, an extra token was kept in cache_tokens even if it was removed from the KV cache. For Mamba, this caused useless prompt reprocessing when the previous request triggered the above case. * convert-hf : support new metadata keys for Mamba For the models available at https://huggingface.co/collections/state-spaces/transformers-compatible-mamba-65e7b40ab87e5297e45ae406 * mamba : rename metadata to be more similar to transformers library This breaks existing converted-to-GGUF models, but the metadata names are more "standard". * mamba : support mamba-*-hf models These models share their token_embd.weight with their output.weight * mamba : add missing spaces This is purely a formatting change. * convert-hf : omit output.weight when identical with token_embd.weight Only for Mamba for now, but it might be relevant for other models eventually. Most Mamba models actually share these two tensors, albeit implicitly. * readme : add Mamba to supported models, and add recent API changes * mamba : move state_seq and state_mask views outside layer loop A few tensors were also missing `struct` in front of `ggml_tensor`.
2024-03-08 22:31:00 +00:00
field = neox_reader.get_field(gguf.Keys.Tokenizer.UNK_ID)
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
self.gguf_writer.add_unk_token_id(field.parts[-1].tolist()[0] if field else 0)
field = neox_reader.get_field(gguf.Keys.Tokenizer.PAD_ID)
self.gguf_writer.add_pad_token_id(field.parts[-1].tolist()[0] if field else 0)
llama : support Mamba Selective State Space Models (#5328) * mamba : begin working on support for Mamba SSM * mamba : begin figuring out how to (ab)use the kv cache for Mamba * mamba : recurrent inference almost works, but incoherent * mamba : recurrent inference WORKS!!! * convert : optionally use d_conv and d_state from config.json for Mamba * mamba : refactor recurrent conv, resulting in 20% perf increase It's still slower than I'd like, but I did not really optimize `ggml_exp` yet. I also refactored `ggml_exp` to work with tensors with more than 2 dimensions. * ggml : parallelize ggml_exp This results in 8% faster token generation for Mamba-130M. * mamba : simplify the conv step with a self-overlapping view Turns out the conv_state can be made smaller by one column. Note that this breaks existing GGUFs of Mamba, because the key_value_length field is tied to the conv_state size. Convolution with a self-overlapping view is cool! And it's much simpler than what I initially thought would be necessary to make the convolution step work with more than 1 token at a time. Next step is to make the SSM step work on batches of tokens too, and thus I need to figure out a way to make a parallel selective scan which will keep the ssm_state small and won't make it bigger by a factor of (n_layer * batch_size). * llama : fix Mamba KV self size wrongly displaying as f16 instead of f32 Relatedly, I also tried to see if other types than f32 worked for the states, but they don't, because of the operators used. It's probably better anyway to keep lots of precision there, since the states are small anyway. * mamba : fix self-overlapping view depth stride * mamba : handle batches of more than 1 token This means running Mamba no longer crashes when using the default settings! And probably also slightly faster prompt processing. Both batched and non-batched processing yield the same output. Previously, the state was not cleared when starting a sequence. Next step is to make the KV cache API work as expected for Mamba models. * ggml: add ggml_ssm_scan to help with parallel selective scan If the selective scan was implemented without a custom operator, there would be waaay too many nodes in the graph. For example, for Mamba-130M, with a batch size of 512 (the default), a naive selective scan could add at least 24*512=12288 nodes, which is more than LLAMA_MAX_NODES (8192), and that's only for the smallest Mamba model. So it's much cleaner with a custom operator. Not sure about the name, though. * ggml : in ggml_ssm_scan, merge multiple rows in the same vec operation This will help with performance on CPU if ggml_vec_mul_f32 and ggml_vec_add_f32 are ever optimized with SIMD. * mamba : very basic quantization support Mostly works, but there is currently no difference between the variants of a k-quant (e.g. Q4_K_S and Q4_K_M are the same). Most of the SSM-specific weights can be kept in f32 without affecting the size that much, since they are relatively small. (the linear projection weights are responsible for most of Mamba's size) Too much quantization seems to make the state degrade quite fast, and the model begins to output gibberish. It seems to affect bigger models to a lesser extent than small models, but I'm not sure by how much. Experimentation will be needed to figure out which weights are more important for the _M (and _L?) variants of k-quants for Mamba. * convert : fix wrong name for layer norm weight of offical Mamba models I was using Q-bert/Mamba-* models before, which have a slighlty different naming scheme for the weights. (they start with "model.layers" instead of "backbone.layers") * mamba : fuse more steps of the SSM scan in the ggml_ssm_scan operator This increases performance on CPU by around 30% for prompt processing, and by around 20% for text generation. However, it also makes the ggml_exp and ggml_soft_plus operators unused. Whether or not they should be kept will be decided later. * convert : for Mamba, also consider the "MambaLMHeadModel" arch name It's the name of the class of the official implementation, though they don't use it (yet) in the "architectures" field of config.json * mamba : fix vocab size problems with official models The perplexity was waaaay to high for models with a non-round vocab size. Not sure why, but it needed to be fixed in the metadata. Note that this breaks existing GGUF-converted Mamba models, but **only if** the vocab size was not already rounded. * ggml : remove ggml_exp and ggml_soft_plus They did not exist anyway outside of this branch, and since ggml_ssm_scan fused operations together, they are unused. It's always possible to bring them back if needed. * mamba : remove some useless comments No code change. * convert : fix flake8 linter errors * mamba : apply suggestions from code review * mamba : remove unecessary branch for row-wise ssm_state and C multiplication It was previously done to avoid permuting when only one token is processed at a time (like when generating text), but permuting is cheap, and dynamically changing the compute graph is not future-proof. * ggml : in ggml_ssm_scan, use more appropriate asserts * ggml : rename the destination pointer in ggml_compute_forward_ssm_scan_f32 * mamba : multiple sequences, but one at a time This is a step towards making this Mamba implementation usable with the server example (the way the system prompt is kept when clearing the client slots will need to be changed before this can work, though). The KV cache size for this kind of model is tied to the maximum number of sequences kept at any single time. For now, this number is obtained from n_parallel (plus one, to have an extra sequence to dedicate to the system prompt), but there might be a better way to do this which won't also make the main example use 2 cells even if only 1 is really used. (for this specific case, --parallel 0 helps) Simultaneous sequence processing will probably require changes to ggml_ssm_scan, and possibly a new operator for the conv step. * mamba : support llama_kv_cache_seq_cp This (mis)uses the logic around K shifts, because tokens in a state can't be shifted anyway, and because inp_K_shift has the right shape and type. Using ggml_get_rows is a nice way to do copies, but copy chains can't work. Fortunately, copy chains don't really seem to be used in the examples. Each KV cell is dedicated to the sequence ID corresponding to its own index. * mamba : use a state mask It's cleaner than the previous heuristic of checking for the pos of the first token in the batch. inp_KQ_mask could not be re-used for this, because it has the wrong shape and because it seems more suited to the next step of simultaneous sequence processing (helping with the problem of remembering which token belongs to which sequence(s)/state(s)). * llama : replace the usage of n_ctx with kv_self.size in many places * mamba : use n_tokens directly instead of n_tok * mamba : in comments, properly refer to KV cells instead of slots * mamba : reduce memory usage of ggml_ssm_scan From 290.37 MiB to 140.68 MiB of CPU compute buffer size with Mamba 3B with a batch size of 512. The result tensor of ggml_ssm_scan was previously a big part of the CPU compute buffer size. To make it smaller, it does not contain the intermediate ssm states anymore. Both y and the last ssm state are combined in the result tensor, because it seems only a single tensor can be returned by an operator with the way the graph is built. * mamba : simultaneous sequence processing A batch can now contain tokens from multiple sequences. This is necessary for at least the parallel example, the server example, and the HellaSwag test in the perplexity example. However, for this to be useful, uses of llama_kv_cache_seq_rm/cp will need to be changed to work on whole sequences. * ggml : add ggml_ssm_conv as a new operator for the conv step of Mamba This operator makes it possible to use and update the correct states for each token of the batch in the same way as ggml_ssm_scan. Other solutions which use existing operators would need loops which would add too many nodes to the graph (at least the ones I thought of). Using this operator further reduces the size of the CPU compute buffer from 140.68 MiB to 103.20 MiB with Mamba 3B with a batch size of 512. And (at least on CPU), it's a bit faster than before. Note that "ggml_ssm_conv" is probably not the most appropriate name, and it could be changed if a better one is found. * llama : add inp_s_seq as a new input tensor The most convenient implementation to select the correct state (for Mamba) for each token is to directly get the correct index from a tensor. This is why inp_s_seq is storing int32_t and not floats. The other, less convenient way to select the correct state would be to have inp_KQ_mask contain 1.0f for each state used by a token and 0.0f otherwise. This complicates quickly fetching the first used state of a token, and is also less efficient because a whole row of the mask would always need to be read for each token. Using indexes makes it easy to stop searching when there are no more sequences for a token, and the first sequence assigned is always very quickly available (it's the first element of each row). * mamba : support llama_kv_cache_seq_cp copy chains * mamba : support shifting and dividing the kv cache pos * mamba : make the server and parallel examples work with whole sequences A seq_id is dedicated to the system prompt in both cases. * llama : make llama_kv_cache_seq_rm return whether it succeeded or not * mamba : dedicate an input tensor for state copy indices This is cleaner and makes it easier to adapt when/if token positions (and by extension, inp_K_shift) are no longer integers. * mamba : adapt perplexity, batched, and batched-bench examples * perplexity : limit the max number of sequences This adapts to what the loaded model can provide. * llama : add llama_n_max_seq to get the upper limit for seq_ids Used by the perplexity example. * batched : pass n_parallel to the model's context params This should have been there already, but it wasn't. * batched-bench : reserve sequences to support Mamba * batched-bench : fix tokens being put in wrong sequences Generation quality isn't what's measured in there anyway, but at least using the correct sequences avoids using non-consecutive token positions. * mamba : stop abusing attention metadata This breaks existing converted-to-GGUF Mamba models, but will allow supporting mixed architectures like MambaFormer without needing to break Mamba models. This will also allow changing the size of Mamba's states without having to reconvert models in the future. (e.g. using something else than d_conv - 1 columns for the conv_states will not require breaking existing converted Mamba models again) * gguf-py : add new KV metadata key-value pairs for Mamba * llama : add new metadata key-value pairs for Mamba * llama : guard against divisions by zero when n_head is 0 * mamba : rename "unlimited" KV cache property to "recurrent" * mamba : more correctly update the "used" field of the KV cache * ggml : in ggml_ssm_scan, use a threshold for soft_plus This is how the official Mamba implementation does it, and it's also what torch.nn.Softplus does. * convert : for Mamba, fallback to internal NeoX tokenizer The resulting models are exactly the same as if the tokenizer.json and tokenizer_config.json of GPT-NeoX were there. * mamba : support state saving and restoring * ggml : implicitly pass src tensors through dst for Mamba-related ops * mamba : clarify some comments * server : fix cache_tokens not getting correctly resized Otherwise, when the "we have to evaluate at least 1 token" special case was triggered, an extra token was kept in cache_tokens even if it was removed from the KV cache. For Mamba, this caused useless prompt reprocessing when the previous request triggered the above case. * convert-hf : support new metadata keys for Mamba For the models available at https://huggingface.co/collections/state-spaces/transformers-compatible-mamba-65e7b40ab87e5297e45ae406 * mamba : rename metadata to be more similar to transformers library This breaks existing converted-to-GGUF models, but the metadata names are more "standard". * mamba : support mamba-*-hf models These models share their token_embd.weight with their output.weight * mamba : add missing spaces This is purely a formatting change. * convert-hf : omit output.weight when identical with token_embd.weight Only for Mamba for now, but it might be relevant for other models eventually. Most Mamba models actually share these two tensors, albeit implicitly. * readme : add Mamba to supported models, and add recent API changes * mamba : move state_seq and state_mask views outside layer loop A few tensors were also missing `struct` in front of `ggml_tensor`.
2024-03-08 22:31:00 +00:00
def set_gguf_parameters(self):
llama : support Llama 3 HF conversion (#6745) * Support Llama 3 conversion The tokenizer is BPE. * style * Accept suggestion Co-authored-by: Sourab Mangrulkar <13534540+pacman100@users.noreply.github.com> * llama : add llama_token_is_eog() ggml-ci * llama : auto-detect more EOT tokens when missing in KV data * convert : replacing EOS token is a hack * llama : fix codegemma EOT token + add TODOs * llama : fix model type string for 8B model --------- Co-authored-by: Sourab Mangrulkar <13534540+pacman100@users.noreply.github.com> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-04-21 11:50:41 +00:00
d_model = self.find_hparam(["hidden_size", "d_model"])
d_conv = self.find_hparam(["conv_kernel", "d_conv"], optional=True) or 4
llama : support Mamba Selective State Space Models (#5328) * mamba : begin working on support for Mamba SSM * mamba : begin figuring out how to (ab)use the kv cache for Mamba * mamba : recurrent inference almost works, but incoherent * mamba : recurrent inference WORKS!!! * convert : optionally use d_conv and d_state from config.json for Mamba * mamba : refactor recurrent conv, resulting in 20% perf increase It's still slower than I'd like, but I did not really optimize `ggml_exp` yet. I also refactored `ggml_exp` to work with tensors with more than 2 dimensions. * ggml : parallelize ggml_exp This results in 8% faster token generation for Mamba-130M. * mamba : simplify the conv step with a self-overlapping view Turns out the conv_state can be made smaller by one column. Note that this breaks existing GGUFs of Mamba, because the key_value_length field is tied to the conv_state size. Convolution with a self-overlapping view is cool! And it's much simpler than what I initially thought would be necessary to make the convolution step work with more than 1 token at a time. Next step is to make the SSM step work on batches of tokens too, and thus I need to figure out a way to make a parallel selective scan which will keep the ssm_state small and won't make it bigger by a factor of (n_layer * batch_size). * llama : fix Mamba KV self size wrongly displaying as f16 instead of f32 Relatedly, I also tried to see if other types than f32 worked for the states, but they don't, because of the operators used. It's probably better anyway to keep lots of precision there, since the states are small anyway. * mamba : fix self-overlapping view depth stride * mamba : handle batches of more than 1 token This means running Mamba no longer crashes when using the default settings! And probably also slightly faster prompt processing. Both batched and non-batched processing yield the same output. Previously, the state was not cleared when starting a sequence. Next step is to make the KV cache API work as expected for Mamba models. * ggml: add ggml_ssm_scan to help with parallel selective scan If the selective scan was implemented without a custom operator, there would be waaay too many nodes in the graph. For example, for Mamba-130M, with a batch size of 512 (the default), a naive selective scan could add at least 24*512=12288 nodes, which is more than LLAMA_MAX_NODES (8192), and that's only for the smallest Mamba model. So it's much cleaner with a custom operator. Not sure about the name, though. * ggml : in ggml_ssm_scan, merge multiple rows in the same vec operation This will help with performance on CPU if ggml_vec_mul_f32 and ggml_vec_add_f32 are ever optimized with SIMD. * mamba : very basic quantization support Mostly works, but there is currently no difference between the variants of a k-quant (e.g. Q4_K_S and Q4_K_M are the same). Most of the SSM-specific weights can be kept in f32 without affecting the size that much, since they are relatively small. (the linear projection weights are responsible for most of Mamba's size) Too much quantization seems to make the state degrade quite fast, and the model begins to output gibberish. It seems to affect bigger models to a lesser extent than small models, but I'm not sure by how much. Experimentation will be needed to figure out which weights are more important for the _M (and _L?) variants of k-quants for Mamba. * convert : fix wrong name for layer norm weight of offical Mamba models I was using Q-bert/Mamba-* models before, which have a slighlty different naming scheme for the weights. (they start with "model.layers" instead of "backbone.layers") * mamba : fuse more steps of the SSM scan in the ggml_ssm_scan operator This increases performance on CPU by around 30% for prompt processing, and by around 20% for text generation. However, it also makes the ggml_exp and ggml_soft_plus operators unused. Whether or not they should be kept will be decided later. * convert : for Mamba, also consider the "MambaLMHeadModel" arch name It's the name of the class of the official implementation, though they don't use it (yet) in the "architectures" field of config.json * mamba : fix vocab size problems with official models The perplexity was waaaay to high for models with a non-round vocab size. Not sure why, but it needed to be fixed in the metadata. Note that this breaks existing GGUF-converted Mamba models, but **only if** the vocab size was not already rounded. * ggml : remove ggml_exp and ggml_soft_plus They did not exist anyway outside of this branch, and since ggml_ssm_scan fused operations together, they are unused. It's always possible to bring them back if needed. * mamba : remove some useless comments No code change. * convert : fix flake8 linter errors * mamba : apply suggestions from code review * mamba : remove unecessary branch for row-wise ssm_state and C multiplication It was previously done to avoid permuting when only one token is processed at a time (like when generating text), but permuting is cheap, and dynamically changing the compute graph is not future-proof. * ggml : in ggml_ssm_scan, use more appropriate asserts * ggml : rename the destination pointer in ggml_compute_forward_ssm_scan_f32 * mamba : multiple sequences, but one at a time This is a step towards making this Mamba implementation usable with the server example (the way the system prompt is kept when clearing the client slots will need to be changed before this can work, though). The KV cache size for this kind of model is tied to the maximum number of sequences kept at any single time. For now, this number is obtained from n_parallel (plus one, to have an extra sequence to dedicate to the system prompt), but there might be a better way to do this which won't also make the main example use 2 cells even if only 1 is really used. (for this specific case, --parallel 0 helps) Simultaneous sequence processing will probably require changes to ggml_ssm_scan, and possibly a new operator for the conv step. * mamba : support llama_kv_cache_seq_cp This (mis)uses the logic around K shifts, because tokens in a state can't be shifted anyway, and because inp_K_shift has the right shape and type. Using ggml_get_rows is a nice way to do copies, but copy chains can't work. Fortunately, copy chains don't really seem to be used in the examples. Each KV cell is dedicated to the sequence ID corresponding to its own index. * mamba : use a state mask It's cleaner than the previous heuristic of checking for the pos of the first token in the batch. inp_KQ_mask could not be re-used for this, because it has the wrong shape and because it seems more suited to the next step of simultaneous sequence processing (helping with the problem of remembering which token belongs to which sequence(s)/state(s)). * llama : replace the usage of n_ctx with kv_self.size in many places * mamba : use n_tokens directly instead of n_tok * mamba : in comments, properly refer to KV cells instead of slots * mamba : reduce memory usage of ggml_ssm_scan From 290.37 MiB to 140.68 MiB of CPU compute buffer size with Mamba 3B with a batch size of 512. The result tensor of ggml_ssm_scan was previously a big part of the CPU compute buffer size. To make it smaller, it does not contain the intermediate ssm states anymore. Both y and the last ssm state are combined in the result tensor, because it seems only a single tensor can be returned by an operator with the way the graph is built. * mamba : simultaneous sequence processing A batch can now contain tokens from multiple sequences. This is necessary for at least the parallel example, the server example, and the HellaSwag test in the perplexity example. However, for this to be useful, uses of llama_kv_cache_seq_rm/cp will need to be changed to work on whole sequences. * ggml : add ggml_ssm_conv as a new operator for the conv step of Mamba This operator makes it possible to use and update the correct states for each token of the batch in the same way as ggml_ssm_scan. Other solutions which use existing operators would need loops which would add too many nodes to the graph (at least the ones I thought of). Using this operator further reduces the size of the CPU compute buffer from 140.68 MiB to 103.20 MiB with Mamba 3B with a batch size of 512. And (at least on CPU), it's a bit faster than before. Note that "ggml_ssm_conv" is probably not the most appropriate name, and it could be changed if a better one is found. * llama : add inp_s_seq as a new input tensor The most convenient implementation to select the correct state (for Mamba) for each token is to directly get the correct index from a tensor. This is why inp_s_seq is storing int32_t and not floats. The other, less convenient way to select the correct state would be to have inp_KQ_mask contain 1.0f for each state used by a token and 0.0f otherwise. This complicates quickly fetching the first used state of a token, and is also less efficient because a whole row of the mask would always need to be read for each token. Using indexes makes it easy to stop searching when there are no more sequences for a token, and the first sequence assigned is always very quickly available (it's the first element of each row). * mamba : support llama_kv_cache_seq_cp copy chains * mamba : support shifting and dividing the kv cache pos * mamba : make the server and parallel examples work with whole sequences A seq_id is dedicated to the system prompt in both cases. * llama : make llama_kv_cache_seq_rm return whether it succeeded or not * mamba : dedicate an input tensor for state copy indices This is cleaner and makes it easier to adapt when/if token positions (and by extension, inp_K_shift) are no longer integers. * mamba : adapt perplexity, batched, and batched-bench examples * perplexity : limit the max number of sequences This adapts to what the loaded model can provide. * llama : add llama_n_max_seq to get the upper limit for seq_ids Used by the perplexity example. * batched : pass n_parallel to the model's context params This should have been there already, but it wasn't. * batched-bench : reserve sequences to support Mamba * batched-bench : fix tokens being put in wrong sequences Generation quality isn't what's measured in there anyway, but at least using the correct sequences avoids using non-consecutive token positions. * mamba : stop abusing attention metadata This breaks existing converted-to-GGUF Mamba models, but will allow supporting mixed architectures like MambaFormer without needing to break Mamba models. This will also allow changing the size of Mamba's states without having to reconvert models in the future. (e.g. using something else than d_conv - 1 columns for the conv_states will not require breaking existing converted Mamba models again) * gguf-py : add new KV metadata key-value pairs for Mamba * llama : add new metadata key-value pairs for Mamba * llama : guard against divisions by zero when n_head is 0 * mamba : rename "unlimited" KV cache property to "recurrent" * mamba : more correctly update the "used" field of the KV cache * ggml : in ggml_ssm_scan, use a threshold for soft_plus This is how the official Mamba implementation does it, and it's also what torch.nn.Softplus does. * convert : for Mamba, fallback to internal NeoX tokenizer The resulting models are exactly the same as if the tokenizer.json and tokenizer_config.json of GPT-NeoX were there. * mamba : support state saving and restoring * ggml : implicitly pass src tensors through dst for Mamba-related ops * mamba : clarify some comments * server : fix cache_tokens not getting correctly resized Otherwise, when the "we have to evaluate at least 1 token" special case was triggered, an extra token was kept in cache_tokens even if it was removed from the KV cache. For Mamba, this caused useless prompt reprocessing when the previous request triggered the above case. * convert-hf : support new metadata keys for Mamba For the models available at https://huggingface.co/collections/state-spaces/transformers-compatible-mamba-65e7b40ab87e5297e45ae406 * mamba : rename metadata to be more similar to transformers library This breaks existing converted-to-GGUF models, but the metadata names are more "standard". * mamba : support mamba-*-hf models These models share their token_embd.weight with their output.weight * mamba : add missing spaces This is purely a formatting change. * convert-hf : omit output.weight when identical with token_embd.weight Only for Mamba for now, but it might be relevant for other models eventually. Most Mamba models actually share these two tensors, albeit implicitly. * readme : add Mamba to supported models, and add recent API changes * mamba : move state_seq and state_mask views outside layer loop A few tensors were also missing `struct` in front of `ggml_tensor`.
2024-03-08 22:31:00 +00:00
d_inner = self.find_hparam(["intermediate_size", "d_inner"], optional=True) or 2 * d_model
llama : support Llama 3 HF conversion (#6745) * Support Llama 3 conversion The tokenizer is BPE. * style * Accept suggestion Co-authored-by: Sourab Mangrulkar <13534540+pacman100@users.noreply.github.com> * llama : add llama_token_is_eog() ggml-ci * llama : auto-detect more EOT tokens when missing in KV data * convert : replacing EOS token is a hack * llama : fix codegemma EOT token + add TODOs * llama : fix model type string for 8B model --------- Co-authored-by: Sourab Mangrulkar <13534540+pacman100@users.noreply.github.com> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-04-21 11:50:41 +00:00
d_state = self.find_hparam(["state_size", "d_state"], optional=True) or 16
llama : support Mamba Selective State Space Models (#5328) * mamba : begin working on support for Mamba SSM * mamba : begin figuring out how to (ab)use the kv cache for Mamba * mamba : recurrent inference almost works, but incoherent * mamba : recurrent inference WORKS!!! * convert : optionally use d_conv and d_state from config.json for Mamba * mamba : refactor recurrent conv, resulting in 20% perf increase It's still slower than I'd like, but I did not really optimize `ggml_exp` yet. I also refactored `ggml_exp` to work with tensors with more than 2 dimensions. * ggml : parallelize ggml_exp This results in 8% faster token generation for Mamba-130M. * mamba : simplify the conv step with a self-overlapping view Turns out the conv_state can be made smaller by one column. Note that this breaks existing GGUFs of Mamba, because the key_value_length field is tied to the conv_state size. Convolution with a self-overlapping view is cool! And it's much simpler than what I initially thought would be necessary to make the convolution step work with more than 1 token at a time. Next step is to make the SSM step work on batches of tokens too, and thus I need to figure out a way to make a parallel selective scan which will keep the ssm_state small and won't make it bigger by a factor of (n_layer * batch_size). * llama : fix Mamba KV self size wrongly displaying as f16 instead of f32 Relatedly, I also tried to see if other types than f32 worked for the states, but they don't, because of the operators used. It's probably better anyway to keep lots of precision there, since the states are small anyway. * mamba : fix self-overlapping view depth stride * mamba : handle batches of more than 1 token This means running Mamba no longer crashes when using the default settings! And probably also slightly faster prompt processing. Both batched and non-batched processing yield the same output. Previously, the state was not cleared when starting a sequence. Next step is to make the KV cache API work as expected for Mamba models. * ggml: add ggml_ssm_scan to help with parallel selective scan If the selective scan was implemented without a custom operator, there would be waaay too many nodes in the graph. For example, for Mamba-130M, with a batch size of 512 (the default), a naive selective scan could add at least 24*512=12288 nodes, which is more than LLAMA_MAX_NODES (8192), and that's only for the smallest Mamba model. So it's much cleaner with a custom operator. Not sure about the name, though. * ggml : in ggml_ssm_scan, merge multiple rows in the same vec operation This will help with performance on CPU if ggml_vec_mul_f32 and ggml_vec_add_f32 are ever optimized with SIMD. * mamba : very basic quantization support Mostly works, but there is currently no difference between the variants of a k-quant (e.g. Q4_K_S and Q4_K_M are the same). Most of the SSM-specific weights can be kept in f32 without affecting the size that much, since they are relatively small. (the linear projection weights are responsible for most of Mamba's size) Too much quantization seems to make the state degrade quite fast, and the model begins to output gibberish. It seems to affect bigger models to a lesser extent than small models, but I'm not sure by how much. Experimentation will be needed to figure out which weights are more important for the _M (and _L?) variants of k-quants for Mamba. * convert : fix wrong name for layer norm weight of offical Mamba models I was using Q-bert/Mamba-* models before, which have a slighlty different naming scheme for the weights. (they start with "model.layers" instead of "backbone.layers") * mamba : fuse more steps of the SSM scan in the ggml_ssm_scan operator This increases performance on CPU by around 30% for prompt processing, and by around 20% for text generation. However, it also makes the ggml_exp and ggml_soft_plus operators unused. Whether or not they should be kept will be decided later. * convert : for Mamba, also consider the "MambaLMHeadModel" arch name It's the name of the class of the official implementation, though they don't use it (yet) in the "architectures" field of config.json * mamba : fix vocab size problems with official models The perplexity was waaaay to high for models with a non-round vocab size. Not sure why, but it needed to be fixed in the metadata. Note that this breaks existing GGUF-converted Mamba models, but **only if** the vocab size was not already rounded. * ggml : remove ggml_exp and ggml_soft_plus They did not exist anyway outside of this branch, and since ggml_ssm_scan fused operations together, they are unused. It's always possible to bring them back if needed. * mamba : remove some useless comments No code change. * convert : fix flake8 linter errors * mamba : apply suggestions from code review * mamba : remove unecessary branch for row-wise ssm_state and C multiplication It was previously done to avoid permuting when only one token is processed at a time (like when generating text), but permuting is cheap, and dynamically changing the compute graph is not future-proof. * ggml : in ggml_ssm_scan, use more appropriate asserts * ggml : rename the destination pointer in ggml_compute_forward_ssm_scan_f32 * mamba : multiple sequences, but one at a time This is a step towards making this Mamba implementation usable with the server example (the way the system prompt is kept when clearing the client slots will need to be changed before this can work, though). The KV cache size for this kind of model is tied to the maximum number of sequences kept at any single time. For now, this number is obtained from n_parallel (plus one, to have an extra sequence to dedicate to the system prompt), but there might be a better way to do this which won't also make the main example use 2 cells even if only 1 is really used. (for this specific case, --parallel 0 helps) Simultaneous sequence processing will probably require changes to ggml_ssm_scan, and possibly a new operator for the conv step. * mamba : support llama_kv_cache_seq_cp This (mis)uses the logic around K shifts, because tokens in a state can't be shifted anyway, and because inp_K_shift has the right shape and type. Using ggml_get_rows is a nice way to do copies, but copy chains can't work. Fortunately, copy chains don't really seem to be used in the examples. Each KV cell is dedicated to the sequence ID corresponding to its own index. * mamba : use a state mask It's cleaner than the previous heuristic of checking for the pos of the first token in the batch. inp_KQ_mask could not be re-used for this, because it has the wrong shape and because it seems more suited to the next step of simultaneous sequence processing (helping with the problem of remembering which token belongs to which sequence(s)/state(s)). * llama : replace the usage of n_ctx with kv_self.size in many places * mamba : use n_tokens directly instead of n_tok * mamba : in comments, properly refer to KV cells instead of slots * mamba : reduce memory usage of ggml_ssm_scan From 290.37 MiB to 140.68 MiB of CPU compute buffer size with Mamba 3B with a batch size of 512. The result tensor of ggml_ssm_scan was previously a big part of the CPU compute buffer size. To make it smaller, it does not contain the intermediate ssm states anymore. Both y and the last ssm state are combined in the result tensor, because it seems only a single tensor can be returned by an operator with the way the graph is built. * mamba : simultaneous sequence processing A batch can now contain tokens from multiple sequences. This is necessary for at least the parallel example, the server example, and the HellaSwag test in the perplexity example. However, for this to be useful, uses of llama_kv_cache_seq_rm/cp will need to be changed to work on whole sequences. * ggml : add ggml_ssm_conv as a new operator for the conv step of Mamba This operator makes it possible to use and update the correct states for each token of the batch in the same way as ggml_ssm_scan. Other solutions which use existing operators would need loops which would add too many nodes to the graph (at least the ones I thought of). Using this operator further reduces the size of the CPU compute buffer from 140.68 MiB to 103.20 MiB with Mamba 3B with a batch size of 512. And (at least on CPU), it's a bit faster than before. Note that "ggml_ssm_conv" is probably not the most appropriate name, and it could be changed if a better one is found. * llama : add inp_s_seq as a new input tensor The most convenient implementation to select the correct state (for Mamba) for each token is to directly get the correct index from a tensor. This is why inp_s_seq is storing int32_t and not floats. The other, less convenient way to select the correct state would be to have inp_KQ_mask contain 1.0f for each state used by a token and 0.0f otherwise. This complicates quickly fetching the first used state of a token, and is also less efficient because a whole row of the mask would always need to be read for each token. Using indexes makes it easy to stop searching when there are no more sequences for a token, and the first sequence assigned is always very quickly available (it's the first element of each row). * mamba : support llama_kv_cache_seq_cp copy chains * mamba : support shifting and dividing the kv cache pos * mamba : make the server and parallel examples work with whole sequences A seq_id is dedicated to the system prompt in both cases. * llama : make llama_kv_cache_seq_rm return whether it succeeded or not * mamba : dedicate an input tensor for state copy indices This is cleaner and makes it easier to adapt when/if token positions (and by extension, inp_K_shift) are no longer integers. * mamba : adapt perplexity, batched, and batched-bench examples * perplexity : limit the max number of sequences This adapts to what the loaded model can provide. * llama : add llama_n_max_seq to get the upper limit for seq_ids Used by the perplexity example. * batched : pass n_parallel to the model's context params This should have been there already, but it wasn't. * batched-bench : reserve sequences to support Mamba * batched-bench : fix tokens being put in wrong sequences Generation quality isn't what's measured in there anyway, but at least using the correct sequences avoids using non-consecutive token positions. * mamba : stop abusing attention metadata This breaks existing converted-to-GGUF Mamba models, but will allow supporting mixed architectures like MambaFormer without needing to break Mamba models. This will also allow changing the size of Mamba's states without having to reconvert models in the future. (e.g. using something else than d_conv - 1 columns for the conv_states will not require breaking existing converted Mamba models again) * gguf-py : add new KV metadata key-value pairs for Mamba * llama : add new metadata key-value pairs for Mamba * llama : guard against divisions by zero when n_head is 0 * mamba : rename "unlimited" KV cache property to "recurrent" * mamba : more correctly update the "used" field of the KV cache * ggml : in ggml_ssm_scan, use a threshold for soft_plus This is how the official Mamba implementation does it, and it's also what torch.nn.Softplus does. * convert : for Mamba, fallback to internal NeoX tokenizer The resulting models are exactly the same as if the tokenizer.json and tokenizer_config.json of GPT-NeoX were there. * mamba : support state saving and restoring * ggml : implicitly pass src tensors through dst for Mamba-related ops * mamba : clarify some comments * server : fix cache_tokens not getting correctly resized Otherwise, when the "we have to evaluate at least 1 token" special case was triggered, an extra token was kept in cache_tokens even if it was removed from the KV cache. For Mamba, this caused useless prompt reprocessing when the previous request triggered the above case. * convert-hf : support new metadata keys for Mamba For the models available at https://huggingface.co/collections/state-spaces/transformers-compatible-mamba-65e7b40ab87e5297e45ae406 * mamba : rename metadata to be more similar to transformers library This breaks existing converted-to-GGUF models, but the metadata names are more "standard". * mamba : support mamba-*-hf models These models share their token_embd.weight with their output.weight * mamba : add missing spaces This is purely a formatting change. * convert-hf : omit output.weight when identical with token_embd.weight Only for Mamba for now, but it might be relevant for other models eventually. Most Mamba models actually share these two tensors, albeit implicitly. * readme : add Mamba to supported models, and add recent API changes * mamba : move state_seq and state_mask views outside layer loop A few tensors were also missing `struct` in front of `ggml_tensor`.
2024-03-08 22:31:00 +00:00
# ceiling division
# ref: https://stackoverflow.com/a/17511341/22827863
# ref: https://github.com/state-spaces/mamba/blob/ce59daea3a090d011d6476c6e5b97f6d58ddad8b/mamba_ssm/modules/mamba_simple.py#L58
llama : support Llama 3 HF conversion (#6745) * Support Llama 3 conversion The tokenizer is BPE. * style * Accept suggestion Co-authored-by: Sourab Mangrulkar <13534540+pacman100@users.noreply.github.com> * llama : add llama_token_is_eog() ggml-ci * llama : auto-detect more EOT tokens when missing in KV data * convert : replacing EOS token is a hack * llama : fix codegemma EOT token + add TODOs * llama : fix model type string for 8B model --------- Co-authored-by: Sourab Mangrulkar <13534540+pacman100@users.noreply.github.com> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-04-21 11:50:41 +00:00
dt_rank = self.find_hparam(["time_step_rank", "dt_rank"], optional=True) or -(d_model // -16)
llama : support Mamba Selective State Space Models (#5328) * mamba : begin working on support for Mamba SSM * mamba : begin figuring out how to (ab)use the kv cache for Mamba * mamba : recurrent inference almost works, but incoherent * mamba : recurrent inference WORKS!!! * convert : optionally use d_conv and d_state from config.json for Mamba * mamba : refactor recurrent conv, resulting in 20% perf increase It's still slower than I'd like, but I did not really optimize `ggml_exp` yet. I also refactored `ggml_exp` to work with tensors with more than 2 dimensions. * ggml : parallelize ggml_exp This results in 8% faster token generation for Mamba-130M. * mamba : simplify the conv step with a self-overlapping view Turns out the conv_state can be made smaller by one column. Note that this breaks existing GGUFs of Mamba, because the key_value_length field is tied to the conv_state size. Convolution with a self-overlapping view is cool! And it's much simpler than what I initially thought would be necessary to make the convolution step work with more than 1 token at a time. Next step is to make the SSM step work on batches of tokens too, and thus I need to figure out a way to make a parallel selective scan which will keep the ssm_state small and won't make it bigger by a factor of (n_layer * batch_size). * llama : fix Mamba KV self size wrongly displaying as f16 instead of f32 Relatedly, I also tried to see if other types than f32 worked for the states, but they don't, because of the operators used. It's probably better anyway to keep lots of precision there, since the states are small anyway. * mamba : fix self-overlapping view depth stride * mamba : handle batches of more than 1 token This means running Mamba no longer crashes when using the default settings! And probably also slightly faster prompt processing. Both batched and non-batched processing yield the same output. Previously, the state was not cleared when starting a sequence. Next step is to make the KV cache API work as expected for Mamba models. * ggml: add ggml_ssm_scan to help with parallel selective scan If the selective scan was implemented without a custom operator, there would be waaay too many nodes in the graph. For example, for Mamba-130M, with a batch size of 512 (the default), a naive selective scan could add at least 24*512=12288 nodes, which is more than LLAMA_MAX_NODES (8192), and that's only for the smallest Mamba model. So it's much cleaner with a custom operator. Not sure about the name, though. * ggml : in ggml_ssm_scan, merge multiple rows in the same vec operation This will help with performance on CPU if ggml_vec_mul_f32 and ggml_vec_add_f32 are ever optimized with SIMD. * mamba : very basic quantization support Mostly works, but there is currently no difference between the variants of a k-quant (e.g. Q4_K_S and Q4_K_M are the same). Most of the SSM-specific weights can be kept in f32 without affecting the size that much, since they are relatively small. (the linear projection weights are responsible for most of Mamba's size) Too much quantization seems to make the state degrade quite fast, and the model begins to output gibberish. It seems to affect bigger models to a lesser extent than small models, but I'm not sure by how much. Experimentation will be needed to figure out which weights are more important for the _M (and _L?) variants of k-quants for Mamba. * convert : fix wrong name for layer norm weight of offical Mamba models I was using Q-bert/Mamba-* models before, which have a slighlty different naming scheme for the weights. (they start with "model.layers" instead of "backbone.layers") * mamba : fuse more steps of the SSM scan in the ggml_ssm_scan operator This increases performance on CPU by around 30% for prompt processing, and by around 20% for text generation. However, it also makes the ggml_exp and ggml_soft_plus operators unused. Whether or not they should be kept will be decided later. * convert : for Mamba, also consider the "MambaLMHeadModel" arch name It's the name of the class of the official implementation, though they don't use it (yet) in the "architectures" field of config.json * mamba : fix vocab size problems with official models The perplexity was waaaay to high for models with a non-round vocab size. Not sure why, but it needed to be fixed in the metadata. Note that this breaks existing GGUF-converted Mamba models, but **only if** the vocab size was not already rounded. * ggml : remove ggml_exp and ggml_soft_plus They did not exist anyway outside of this branch, and since ggml_ssm_scan fused operations together, they are unused. It's always possible to bring them back if needed. * mamba : remove some useless comments No code change. * convert : fix flake8 linter errors * mamba : apply suggestions from code review * mamba : remove unecessary branch for row-wise ssm_state and C multiplication It was previously done to avoid permuting when only one token is processed at a time (like when generating text), but permuting is cheap, and dynamically changing the compute graph is not future-proof. * ggml : in ggml_ssm_scan, use more appropriate asserts * ggml : rename the destination pointer in ggml_compute_forward_ssm_scan_f32 * mamba : multiple sequences, but one at a time This is a step towards making this Mamba implementation usable with the server example (the way the system prompt is kept when clearing the client slots will need to be changed before this can work, though). The KV cache size for this kind of model is tied to the maximum number of sequences kept at any single time. For now, this number is obtained from n_parallel (plus one, to have an extra sequence to dedicate to the system prompt), but there might be a better way to do this which won't also make the main example use 2 cells even if only 1 is really used. (for this specific case, --parallel 0 helps) Simultaneous sequence processing will probably require changes to ggml_ssm_scan, and possibly a new operator for the conv step. * mamba : support llama_kv_cache_seq_cp This (mis)uses the logic around K shifts, because tokens in a state can't be shifted anyway, and because inp_K_shift has the right shape and type. Using ggml_get_rows is a nice way to do copies, but copy chains can't work. Fortunately, copy chains don't really seem to be used in the examples. Each KV cell is dedicated to the sequence ID corresponding to its own index. * mamba : use a state mask It's cleaner than the previous heuristic of checking for the pos of the first token in the batch. inp_KQ_mask could not be re-used for this, because it has the wrong shape and because it seems more suited to the next step of simultaneous sequence processing (helping with the problem of remembering which token belongs to which sequence(s)/state(s)). * llama : replace the usage of n_ctx with kv_self.size in many places * mamba : use n_tokens directly instead of n_tok * mamba : in comments, properly refer to KV cells instead of slots * mamba : reduce memory usage of ggml_ssm_scan From 290.37 MiB to 140.68 MiB of CPU compute buffer size with Mamba 3B with a batch size of 512. The result tensor of ggml_ssm_scan was previously a big part of the CPU compute buffer size. To make it smaller, it does not contain the intermediate ssm states anymore. Both y and the last ssm state are combined in the result tensor, because it seems only a single tensor can be returned by an operator with the way the graph is built. * mamba : simultaneous sequence processing A batch can now contain tokens from multiple sequences. This is necessary for at least the parallel example, the server example, and the HellaSwag test in the perplexity example. However, for this to be useful, uses of llama_kv_cache_seq_rm/cp will need to be changed to work on whole sequences. * ggml : add ggml_ssm_conv as a new operator for the conv step of Mamba This operator makes it possible to use and update the correct states for each token of the batch in the same way as ggml_ssm_scan. Other solutions which use existing operators would need loops which would add too many nodes to the graph (at least the ones I thought of). Using this operator further reduces the size of the CPU compute buffer from 140.68 MiB to 103.20 MiB with Mamba 3B with a batch size of 512. And (at least on CPU), it's a bit faster than before. Note that "ggml_ssm_conv" is probably not the most appropriate name, and it could be changed if a better one is found. * llama : add inp_s_seq as a new input tensor The most convenient implementation to select the correct state (for Mamba) for each token is to directly get the correct index from a tensor. This is why inp_s_seq is storing int32_t and not floats. The other, less convenient way to select the correct state would be to have inp_KQ_mask contain 1.0f for each state used by a token and 0.0f otherwise. This complicates quickly fetching the first used state of a token, and is also less efficient because a whole row of the mask would always need to be read for each token. Using indexes makes it easy to stop searching when there are no more sequences for a token, and the first sequence assigned is always very quickly available (it's the first element of each row). * mamba : support llama_kv_cache_seq_cp copy chains * mamba : support shifting and dividing the kv cache pos * mamba : make the server and parallel examples work with whole sequences A seq_id is dedicated to the system prompt in both cases. * llama : make llama_kv_cache_seq_rm return whether it succeeded or not * mamba : dedicate an input tensor for state copy indices This is cleaner and makes it easier to adapt when/if token positions (and by extension, inp_K_shift) are no longer integers. * mamba : adapt perplexity, batched, and batched-bench examples * perplexity : limit the max number of sequences This adapts to what the loaded model can provide. * llama : add llama_n_max_seq to get the upper limit for seq_ids Used by the perplexity example. * batched : pass n_parallel to the model's context params This should have been there already, but it wasn't. * batched-bench : reserve sequences to support Mamba * batched-bench : fix tokens being put in wrong sequences Generation quality isn't what's measured in there anyway, but at least using the correct sequences avoids using non-consecutive token positions. * mamba : stop abusing attention metadata This breaks existing converted-to-GGUF Mamba models, but will allow supporting mixed architectures like MambaFormer without needing to break Mamba models. This will also allow changing the size of Mamba's states without having to reconvert models in the future. (e.g. using something else than d_conv - 1 columns for the conv_states will not require breaking existing converted Mamba models again) * gguf-py : add new KV metadata key-value pairs for Mamba * llama : add new metadata key-value pairs for Mamba * llama : guard against divisions by zero when n_head is 0 * mamba : rename "unlimited" KV cache property to "recurrent" * mamba : more correctly update the "used" field of the KV cache * ggml : in ggml_ssm_scan, use a threshold for soft_plus This is how the official Mamba implementation does it, and it's also what torch.nn.Softplus does. * convert : for Mamba, fallback to internal NeoX tokenizer The resulting models are exactly the same as if the tokenizer.json and tokenizer_config.json of GPT-NeoX were there. * mamba : support state saving and restoring * ggml : implicitly pass src tensors through dst for Mamba-related ops * mamba : clarify some comments * server : fix cache_tokens not getting correctly resized Otherwise, when the "we have to evaluate at least 1 token" special case was triggered, an extra token was kept in cache_tokens even if it was removed from the KV cache. For Mamba, this caused useless prompt reprocessing when the previous request triggered the above case. * convert-hf : support new metadata keys for Mamba For the models available at https://huggingface.co/collections/state-spaces/transformers-compatible-mamba-65e7b40ab87e5297e45ae406 * mamba : rename metadata to be more similar to transformers library This breaks existing converted-to-GGUF models, but the metadata names are more "standard". * mamba : support mamba-*-hf models These models share their token_embd.weight with their output.weight * mamba : add missing spaces This is purely a formatting change. * convert-hf : omit output.weight when identical with token_embd.weight Only for Mamba for now, but it might be relevant for other models eventually. Most Mamba models actually share these two tensors, albeit implicitly. * readme : add Mamba to supported models, and add recent API changes * mamba : move state_seq and state_mask views outside layer loop A few tensors were also missing `struct` in front of `ggml_tensor`.
2024-03-08 22:31:00 +00:00
rms_norm_eps = self.find_hparam(["layer_norm_epsilon", "rms_norm_eps"], optional=True) or 1e-5
# Fail early for models which don't have a block expansion factor of 2
assert d_inner == 2 * d_model
self.gguf_writer.add_name(self.dir_model.name)
self.gguf_writer.add_context_length(2**20) # arbitrary value; for those who use the default
self.gguf_writer.add_embedding_length(d_model)
self.gguf_writer.add_feed_forward_length(0) # unused, but seemingly required when loading
self.gguf_writer.add_head_count(0) # unused, but seemingly required when loading
self.gguf_writer.add_block_count(self.hparams["n_layer"])
self.gguf_writer.add_ssm_conv_kernel(d_conv)
self.gguf_writer.add_ssm_inner_size(d_inner)
self.gguf_writer.add_ssm_state_size(d_state)
self.gguf_writer.add_ssm_time_step_rank(dt_rank)
self.gguf_writer.add_layer_norm_rms_eps(rms_norm_eps)
self.gguf_writer.add_file_type(self.ftype)
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
_tok_embd = None
llama : support Mamba Selective State Space Models (#5328) * mamba : begin working on support for Mamba SSM * mamba : begin figuring out how to (ab)use the kv cache for Mamba * mamba : recurrent inference almost works, but incoherent * mamba : recurrent inference WORKS!!! * convert : optionally use d_conv and d_state from config.json for Mamba * mamba : refactor recurrent conv, resulting in 20% perf increase It's still slower than I'd like, but I did not really optimize `ggml_exp` yet. I also refactored `ggml_exp` to work with tensors with more than 2 dimensions. * ggml : parallelize ggml_exp This results in 8% faster token generation for Mamba-130M. * mamba : simplify the conv step with a self-overlapping view Turns out the conv_state can be made smaller by one column. Note that this breaks existing GGUFs of Mamba, because the key_value_length field is tied to the conv_state size. Convolution with a self-overlapping view is cool! And it's much simpler than what I initially thought would be necessary to make the convolution step work with more than 1 token at a time. Next step is to make the SSM step work on batches of tokens too, and thus I need to figure out a way to make a parallel selective scan which will keep the ssm_state small and won't make it bigger by a factor of (n_layer * batch_size). * llama : fix Mamba KV self size wrongly displaying as f16 instead of f32 Relatedly, I also tried to see if other types than f32 worked for the states, but they don't, because of the operators used. It's probably better anyway to keep lots of precision there, since the states are small anyway. * mamba : fix self-overlapping view depth stride * mamba : handle batches of more than 1 token This means running Mamba no longer crashes when using the default settings! And probably also slightly faster prompt processing. Both batched and non-batched processing yield the same output. Previously, the state was not cleared when starting a sequence. Next step is to make the KV cache API work as expected for Mamba models. * ggml: add ggml_ssm_scan to help with parallel selective scan If the selective scan was implemented without a custom operator, there would be waaay too many nodes in the graph. For example, for Mamba-130M, with a batch size of 512 (the default), a naive selective scan could add at least 24*512=12288 nodes, which is more than LLAMA_MAX_NODES (8192), and that's only for the smallest Mamba model. So it's much cleaner with a custom operator. Not sure about the name, though. * ggml : in ggml_ssm_scan, merge multiple rows in the same vec operation This will help with performance on CPU if ggml_vec_mul_f32 and ggml_vec_add_f32 are ever optimized with SIMD. * mamba : very basic quantization support Mostly works, but there is currently no difference between the variants of a k-quant (e.g. Q4_K_S and Q4_K_M are the same). Most of the SSM-specific weights can be kept in f32 without affecting the size that much, since they are relatively small. (the linear projection weights are responsible for most of Mamba's size) Too much quantization seems to make the state degrade quite fast, and the model begins to output gibberish. It seems to affect bigger models to a lesser extent than small models, but I'm not sure by how much. Experimentation will be needed to figure out which weights are more important for the _M (and _L?) variants of k-quants for Mamba. * convert : fix wrong name for layer norm weight of offical Mamba models I was using Q-bert/Mamba-* models before, which have a slighlty different naming scheme for the weights. (they start with "model.layers" instead of "backbone.layers") * mamba : fuse more steps of the SSM scan in the ggml_ssm_scan operator This increases performance on CPU by around 30% for prompt processing, and by around 20% for text generation. However, it also makes the ggml_exp and ggml_soft_plus operators unused. Whether or not they should be kept will be decided later. * convert : for Mamba, also consider the "MambaLMHeadModel" arch name It's the name of the class of the official implementation, though they don't use it (yet) in the "architectures" field of config.json * mamba : fix vocab size problems with official models The perplexity was waaaay to high for models with a non-round vocab size. Not sure why, but it needed to be fixed in the metadata. Note that this breaks existing GGUF-converted Mamba models, but **only if** the vocab size was not already rounded. * ggml : remove ggml_exp and ggml_soft_plus They did not exist anyway outside of this branch, and since ggml_ssm_scan fused operations together, they are unused. It's always possible to bring them back if needed. * mamba : remove some useless comments No code change. * convert : fix flake8 linter errors * mamba : apply suggestions from code review * mamba : remove unecessary branch for row-wise ssm_state and C multiplication It was previously done to avoid permuting when only one token is processed at a time (like when generating text), but permuting is cheap, and dynamically changing the compute graph is not future-proof. * ggml : in ggml_ssm_scan, use more appropriate asserts * ggml : rename the destination pointer in ggml_compute_forward_ssm_scan_f32 * mamba : multiple sequences, but one at a time This is a step towards making this Mamba implementation usable with the server example (the way the system prompt is kept when clearing the client slots will need to be changed before this can work, though). The KV cache size for this kind of model is tied to the maximum number of sequences kept at any single time. For now, this number is obtained from n_parallel (plus one, to have an extra sequence to dedicate to the system prompt), but there might be a better way to do this which won't also make the main example use 2 cells even if only 1 is really used. (for this specific case, --parallel 0 helps) Simultaneous sequence processing will probably require changes to ggml_ssm_scan, and possibly a new operator for the conv step. * mamba : support llama_kv_cache_seq_cp This (mis)uses the logic around K shifts, because tokens in a state can't be shifted anyway, and because inp_K_shift has the right shape and type. Using ggml_get_rows is a nice way to do copies, but copy chains can't work. Fortunately, copy chains don't really seem to be used in the examples. Each KV cell is dedicated to the sequence ID corresponding to its own index. * mamba : use a state mask It's cleaner than the previous heuristic of checking for the pos of the first token in the batch. inp_KQ_mask could not be re-used for this, because it has the wrong shape and because it seems more suited to the next step of simultaneous sequence processing (helping with the problem of remembering which token belongs to which sequence(s)/state(s)). * llama : replace the usage of n_ctx with kv_self.size in many places * mamba : use n_tokens directly instead of n_tok * mamba : in comments, properly refer to KV cells instead of slots * mamba : reduce memory usage of ggml_ssm_scan From 290.37 MiB to 140.68 MiB of CPU compute buffer size with Mamba 3B with a batch size of 512. The result tensor of ggml_ssm_scan was previously a big part of the CPU compute buffer size. To make it smaller, it does not contain the intermediate ssm states anymore. Both y and the last ssm state are combined in the result tensor, because it seems only a single tensor can be returned by an operator with the way the graph is built. * mamba : simultaneous sequence processing A batch can now contain tokens from multiple sequences. This is necessary for at least the parallel example, the server example, and the HellaSwag test in the perplexity example. However, for this to be useful, uses of llama_kv_cache_seq_rm/cp will need to be changed to work on whole sequences. * ggml : add ggml_ssm_conv as a new operator for the conv step of Mamba This operator makes it possible to use and update the correct states for each token of the batch in the same way as ggml_ssm_scan. Other solutions which use existing operators would need loops which would add too many nodes to the graph (at least the ones I thought of). Using this operator further reduces the size of the CPU compute buffer from 140.68 MiB to 103.20 MiB with Mamba 3B with a batch size of 512. And (at least on CPU), it's a bit faster than before. Note that "ggml_ssm_conv" is probably not the most appropriate name, and it could be changed if a better one is found. * llama : add inp_s_seq as a new input tensor The most convenient implementation to select the correct state (for Mamba) for each token is to directly get the correct index from a tensor. This is why inp_s_seq is storing int32_t and not floats. The other, less convenient way to select the correct state would be to have inp_KQ_mask contain 1.0f for each state used by a token and 0.0f otherwise. This complicates quickly fetching the first used state of a token, and is also less efficient because a whole row of the mask would always need to be read for each token. Using indexes makes it easy to stop searching when there are no more sequences for a token, and the first sequence assigned is always very quickly available (it's the first element of each row). * mamba : support llama_kv_cache_seq_cp copy chains * mamba : support shifting and dividing the kv cache pos * mamba : make the server and parallel examples work with whole sequences A seq_id is dedicated to the system prompt in both cases. * llama : make llama_kv_cache_seq_rm return whether it succeeded or not * mamba : dedicate an input tensor for state copy indices This is cleaner and makes it easier to adapt when/if token positions (and by extension, inp_K_shift) are no longer integers. * mamba : adapt perplexity, batched, and batched-bench examples * perplexity : limit the max number of sequences This adapts to what the loaded model can provide. * llama : add llama_n_max_seq to get the upper limit for seq_ids Used by the perplexity example. * batched : pass n_parallel to the model's context params This should have been there already, but it wasn't. * batched-bench : reserve sequences to support Mamba * batched-bench : fix tokens being put in wrong sequences Generation quality isn't what's measured in there anyway, but at least using the correct sequences avoids using non-consecutive token positions. * mamba : stop abusing attention metadata This breaks existing converted-to-GGUF Mamba models, but will allow supporting mixed architectures like MambaFormer without needing to break Mamba models. This will also allow changing the size of Mamba's states without having to reconvert models in the future. (e.g. using something else than d_conv - 1 columns for the conv_states will not require breaking existing converted Mamba models again) * gguf-py : add new KV metadata key-value pairs for Mamba * llama : add new metadata key-value pairs for Mamba * llama : guard against divisions by zero when n_head is 0 * mamba : rename "unlimited" KV cache property to "recurrent" * mamba : more correctly update the "used" field of the KV cache * ggml : in ggml_ssm_scan, use a threshold for soft_plus This is how the official Mamba implementation does it, and it's also what torch.nn.Softplus does. * convert : for Mamba, fallback to internal NeoX tokenizer The resulting models are exactly the same as if the tokenizer.json and tokenizer_config.json of GPT-NeoX were there. * mamba : support state saving and restoring * ggml : implicitly pass src tensors through dst for Mamba-related ops * mamba : clarify some comments * server : fix cache_tokens not getting correctly resized Otherwise, when the "we have to evaluate at least 1 token" special case was triggered, an extra token was kept in cache_tokens even if it was removed from the KV cache. For Mamba, this caused useless prompt reprocessing when the previous request triggered the above case. * convert-hf : support new metadata keys for Mamba For the models available at https://huggingface.co/collections/state-spaces/transformers-compatible-mamba-65e7b40ab87e5297e45ae406 * mamba : rename metadata to be more similar to transformers library This breaks existing converted-to-GGUF models, but the metadata names are more "standard". * mamba : support mamba-*-hf models These models share their token_embd.weight with their output.weight * mamba : add missing spaces This is purely a formatting change. * convert-hf : omit output.weight when identical with token_embd.weight Only for Mamba for now, but it might be relevant for other models eventually. Most Mamba models actually share these two tensors, albeit implicitly. * readme : add Mamba to supported models, and add recent API changes * mamba : move state_seq and state_mask views outside layer loop A few tensors were also missing `struct` in front of `ggml_tensor`.
2024-03-08 22:31:00 +00:00
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
del bid # unused
llama : support Mamba Selective State Space Models (#5328) * mamba : begin working on support for Mamba SSM * mamba : begin figuring out how to (ab)use the kv cache for Mamba * mamba : recurrent inference almost works, but incoherent * mamba : recurrent inference WORKS!!! * convert : optionally use d_conv and d_state from config.json for Mamba * mamba : refactor recurrent conv, resulting in 20% perf increase It's still slower than I'd like, but I did not really optimize `ggml_exp` yet. I also refactored `ggml_exp` to work with tensors with more than 2 dimensions. * ggml : parallelize ggml_exp This results in 8% faster token generation for Mamba-130M. * mamba : simplify the conv step with a self-overlapping view Turns out the conv_state can be made smaller by one column. Note that this breaks existing GGUFs of Mamba, because the key_value_length field is tied to the conv_state size. Convolution with a self-overlapping view is cool! And it's much simpler than what I initially thought would be necessary to make the convolution step work with more than 1 token at a time. Next step is to make the SSM step work on batches of tokens too, and thus I need to figure out a way to make a parallel selective scan which will keep the ssm_state small and won't make it bigger by a factor of (n_layer * batch_size). * llama : fix Mamba KV self size wrongly displaying as f16 instead of f32 Relatedly, I also tried to see if other types than f32 worked for the states, but they don't, because of the operators used. It's probably better anyway to keep lots of precision there, since the states are small anyway. * mamba : fix self-overlapping view depth stride * mamba : handle batches of more than 1 token This means running Mamba no longer crashes when using the default settings! And probably also slightly faster prompt processing. Both batched and non-batched processing yield the same output. Previously, the state was not cleared when starting a sequence. Next step is to make the KV cache API work as expected for Mamba models. * ggml: add ggml_ssm_scan to help with parallel selective scan If the selective scan was implemented without a custom operator, there would be waaay too many nodes in the graph. For example, for Mamba-130M, with a batch size of 512 (the default), a naive selective scan could add at least 24*512=12288 nodes, which is more than LLAMA_MAX_NODES (8192), and that's only for the smallest Mamba model. So it's much cleaner with a custom operator. Not sure about the name, though. * ggml : in ggml_ssm_scan, merge multiple rows in the same vec operation This will help with performance on CPU if ggml_vec_mul_f32 and ggml_vec_add_f32 are ever optimized with SIMD. * mamba : very basic quantization support Mostly works, but there is currently no difference between the variants of a k-quant (e.g. Q4_K_S and Q4_K_M are the same). Most of the SSM-specific weights can be kept in f32 without affecting the size that much, since they are relatively small. (the linear projection weights are responsible for most of Mamba's size) Too much quantization seems to make the state degrade quite fast, and the model begins to output gibberish. It seems to affect bigger models to a lesser extent than small models, but I'm not sure by how much. Experimentation will be needed to figure out which weights are more important for the _M (and _L?) variants of k-quants for Mamba. * convert : fix wrong name for layer norm weight of offical Mamba models I was using Q-bert/Mamba-* models before, which have a slighlty different naming scheme for the weights. (they start with "model.layers" instead of "backbone.layers") * mamba : fuse more steps of the SSM scan in the ggml_ssm_scan operator This increases performance on CPU by around 30% for prompt processing, and by around 20% for text generation. However, it also makes the ggml_exp and ggml_soft_plus operators unused. Whether or not they should be kept will be decided later. * convert : for Mamba, also consider the "MambaLMHeadModel" arch name It's the name of the class of the official implementation, though they don't use it (yet) in the "architectures" field of config.json * mamba : fix vocab size problems with official models The perplexity was waaaay to high for models with a non-round vocab size. Not sure why, but it needed to be fixed in the metadata. Note that this breaks existing GGUF-converted Mamba models, but **only if** the vocab size was not already rounded. * ggml : remove ggml_exp and ggml_soft_plus They did not exist anyway outside of this branch, and since ggml_ssm_scan fused operations together, they are unused. It's always possible to bring them back if needed. * mamba : remove some useless comments No code change. * convert : fix flake8 linter errors * mamba : apply suggestions from code review * mamba : remove unecessary branch for row-wise ssm_state and C multiplication It was previously done to avoid permuting when only one token is processed at a time (like when generating text), but permuting is cheap, and dynamically changing the compute graph is not future-proof. * ggml : in ggml_ssm_scan, use more appropriate asserts * ggml : rename the destination pointer in ggml_compute_forward_ssm_scan_f32 * mamba : multiple sequences, but one at a time This is a step towards making this Mamba implementation usable with the server example (the way the system prompt is kept when clearing the client slots will need to be changed before this can work, though). The KV cache size for this kind of model is tied to the maximum number of sequences kept at any single time. For now, this number is obtained from n_parallel (plus one, to have an extra sequence to dedicate to the system prompt), but there might be a better way to do this which won't also make the main example use 2 cells even if only 1 is really used. (for this specific case, --parallel 0 helps) Simultaneous sequence processing will probably require changes to ggml_ssm_scan, and possibly a new operator for the conv step. * mamba : support llama_kv_cache_seq_cp This (mis)uses the logic around K shifts, because tokens in a state can't be shifted anyway, and because inp_K_shift has the right shape and type. Using ggml_get_rows is a nice way to do copies, but copy chains can't work. Fortunately, copy chains don't really seem to be used in the examples. Each KV cell is dedicated to the sequence ID corresponding to its own index. * mamba : use a state mask It's cleaner than the previous heuristic of checking for the pos of the first token in the batch. inp_KQ_mask could not be re-used for this, because it has the wrong shape and because it seems more suited to the next step of simultaneous sequence processing (helping with the problem of remembering which token belongs to which sequence(s)/state(s)). * llama : replace the usage of n_ctx with kv_self.size in many places * mamba : use n_tokens directly instead of n_tok * mamba : in comments, properly refer to KV cells instead of slots * mamba : reduce memory usage of ggml_ssm_scan From 290.37 MiB to 140.68 MiB of CPU compute buffer size with Mamba 3B with a batch size of 512. The result tensor of ggml_ssm_scan was previously a big part of the CPU compute buffer size. To make it smaller, it does not contain the intermediate ssm states anymore. Both y and the last ssm state are combined in the result tensor, because it seems only a single tensor can be returned by an operator with the way the graph is built. * mamba : simultaneous sequence processing A batch can now contain tokens from multiple sequences. This is necessary for at least the parallel example, the server example, and the HellaSwag test in the perplexity example. However, for this to be useful, uses of llama_kv_cache_seq_rm/cp will need to be changed to work on whole sequences. * ggml : add ggml_ssm_conv as a new operator for the conv step of Mamba This operator makes it possible to use and update the correct states for each token of the batch in the same way as ggml_ssm_scan. Other solutions which use existing operators would need loops which would add too many nodes to the graph (at least the ones I thought of). Using this operator further reduces the size of the CPU compute buffer from 140.68 MiB to 103.20 MiB with Mamba 3B with a batch size of 512. And (at least on CPU), it's a bit faster than before. Note that "ggml_ssm_conv" is probably not the most appropriate name, and it could be changed if a better one is found. * llama : add inp_s_seq as a new input tensor The most convenient implementation to select the correct state (for Mamba) for each token is to directly get the correct index from a tensor. This is why inp_s_seq is storing int32_t and not floats. The other, less convenient way to select the correct state would be to have inp_KQ_mask contain 1.0f for each state used by a token and 0.0f otherwise. This complicates quickly fetching the first used state of a token, and is also less efficient because a whole row of the mask would always need to be read for each token. Using indexes makes it easy to stop searching when there are no more sequences for a token, and the first sequence assigned is always very quickly available (it's the first element of each row). * mamba : support llama_kv_cache_seq_cp copy chains * mamba : support shifting and dividing the kv cache pos * mamba : make the server and parallel examples work with whole sequences A seq_id is dedicated to the system prompt in both cases. * llama : make llama_kv_cache_seq_rm return whether it succeeded or not * mamba : dedicate an input tensor for state copy indices This is cleaner and makes it easier to adapt when/if token positions (and by extension, inp_K_shift) are no longer integers. * mamba : adapt perplexity, batched, and batched-bench examples * perplexity : limit the max number of sequences This adapts to what the loaded model can provide. * llama : add llama_n_max_seq to get the upper limit for seq_ids Used by the perplexity example. * batched : pass n_parallel to the model's context params This should have been there already, but it wasn't. * batched-bench : reserve sequences to support Mamba * batched-bench : fix tokens being put in wrong sequences Generation quality isn't what's measured in there anyway, but at least using the correct sequences avoids using non-consecutive token positions. * mamba : stop abusing attention metadata This breaks existing converted-to-GGUF Mamba models, but will allow supporting mixed architectures like MambaFormer without needing to break Mamba models. This will also allow changing the size of Mamba's states without having to reconvert models in the future. (e.g. using something else than d_conv - 1 columns for the conv_states will not require breaking existing converted Mamba models again) * gguf-py : add new KV metadata key-value pairs for Mamba * llama : add new metadata key-value pairs for Mamba * llama : guard against divisions by zero when n_head is 0 * mamba : rename "unlimited" KV cache property to "recurrent" * mamba : more correctly update the "used" field of the KV cache * ggml : in ggml_ssm_scan, use a threshold for soft_plus This is how the official Mamba implementation does it, and it's also what torch.nn.Softplus does. * convert : for Mamba, fallback to internal NeoX tokenizer The resulting models are exactly the same as if the tokenizer.json and tokenizer_config.json of GPT-NeoX were there. * mamba : support state saving and restoring * ggml : implicitly pass src tensors through dst for Mamba-related ops * mamba : clarify some comments * server : fix cache_tokens not getting correctly resized Otherwise, when the "we have to evaluate at least 1 token" special case was triggered, an extra token was kept in cache_tokens even if it was removed from the KV cache. For Mamba, this caused useless prompt reprocessing when the previous request triggered the above case. * convert-hf : support new metadata keys for Mamba For the models available at https://huggingface.co/collections/state-spaces/transformers-compatible-mamba-65e7b40ab87e5297e45ae406 * mamba : rename metadata to be more similar to transformers library This breaks existing converted-to-GGUF models, but the metadata names are more "standard". * mamba : support mamba-*-hf models These models share their token_embd.weight with their output.weight * mamba : add missing spaces This is purely a formatting change. * convert-hf : omit output.weight when identical with token_embd.weight Only for Mamba for now, but it might be relevant for other models eventually. Most Mamba models actually share these two tensors, albeit implicitly. * readme : add Mamba to supported models, and add recent API changes * mamba : move state_seq and state_mask views outside layer loop A few tensors were also missing `struct` in front of `ggml_tensor`.
2024-03-08 22:31:00 +00:00
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
output_name = self.format_tensor_name(gguf.MODEL_TENSOR.OUTPUT)
tok_embd_name = self.format_tensor_name(gguf.MODEL_TENSOR.TOKEN_EMBD)
llama : support Mamba Selective State Space Models (#5328) * mamba : begin working on support for Mamba SSM * mamba : begin figuring out how to (ab)use the kv cache for Mamba * mamba : recurrent inference almost works, but incoherent * mamba : recurrent inference WORKS!!! * convert : optionally use d_conv and d_state from config.json for Mamba * mamba : refactor recurrent conv, resulting in 20% perf increase It's still slower than I'd like, but I did not really optimize `ggml_exp` yet. I also refactored `ggml_exp` to work with tensors with more than 2 dimensions. * ggml : parallelize ggml_exp This results in 8% faster token generation for Mamba-130M. * mamba : simplify the conv step with a self-overlapping view Turns out the conv_state can be made smaller by one column. Note that this breaks existing GGUFs of Mamba, because the key_value_length field is tied to the conv_state size. Convolution with a self-overlapping view is cool! And it's much simpler than what I initially thought would be necessary to make the convolution step work with more than 1 token at a time. Next step is to make the SSM step work on batches of tokens too, and thus I need to figure out a way to make a parallel selective scan which will keep the ssm_state small and won't make it bigger by a factor of (n_layer * batch_size). * llama : fix Mamba KV self size wrongly displaying as f16 instead of f32 Relatedly, I also tried to see if other types than f32 worked for the states, but they don't, because of the operators used. It's probably better anyway to keep lots of precision there, since the states are small anyway. * mamba : fix self-overlapping view depth stride * mamba : handle batches of more than 1 token This means running Mamba no longer crashes when using the default settings! And probably also slightly faster prompt processing. Both batched and non-batched processing yield the same output. Previously, the state was not cleared when starting a sequence. Next step is to make the KV cache API work as expected for Mamba models. * ggml: add ggml_ssm_scan to help with parallel selective scan If the selective scan was implemented without a custom operator, there would be waaay too many nodes in the graph. For example, for Mamba-130M, with a batch size of 512 (the default), a naive selective scan could add at least 24*512=12288 nodes, which is more than LLAMA_MAX_NODES (8192), and that's only for the smallest Mamba model. So it's much cleaner with a custom operator. Not sure about the name, though. * ggml : in ggml_ssm_scan, merge multiple rows in the same vec operation This will help with performance on CPU if ggml_vec_mul_f32 and ggml_vec_add_f32 are ever optimized with SIMD. * mamba : very basic quantization support Mostly works, but there is currently no difference between the variants of a k-quant (e.g. Q4_K_S and Q4_K_M are the same). Most of the SSM-specific weights can be kept in f32 without affecting the size that much, since they are relatively small. (the linear projection weights are responsible for most of Mamba's size) Too much quantization seems to make the state degrade quite fast, and the model begins to output gibberish. It seems to affect bigger models to a lesser extent than small models, but I'm not sure by how much. Experimentation will be needed to figure out which weights are more important for the _M (and _L?) variants of k-quants for Mamba. * convert : fix wrong name for layer norm weight of offical Mamba models I was using Q-bert/Mamba-* models before, which have a slighlty different naming scheme for the weights. (they start with "model.layers" instead of "backbone.layers") * mamba : fuse more steps of the SSM scan in the ggml_ssm_scan operator This increases performance on CPU by around 30% for prompt processing, and by around 20% for text generation. However, it also makes the ggml_exp and ggml_soft_plus operators unused. Whether or not they should be kept will be decided later. * convert : for Mamba, also consider the "MambaLMHeadModel" arch name It's the name of the class of the official implementation, though they don't use it (yet) in the "architectures" field of config.json * mamba : fix vocab size problems with official models The perplexity was waaaay to high for models with a non-round vocab size. Not sure why, but it needed to be fixed in the metadata. Note that this breaks existing GGUF-converted Mamba models, but **only if** the vocab size was not already rounded. * ggml : remove ggml_exp and ggml_soft_plus They did not exist anyway outside of this branch, and since ggml_ssm_scan fused operations together, they are unused. It's always possible to bring them back if needed. * mamba : remove some useless comments No code change. * convert : fix flake8 linter errors * mamba : apply suggestions from code review * mamba : remove unecessary branch for row-wise ssm_state and C multiplication It was previously done to avoid permuting when only one token is processed at a time (like when generating text), but permuting is cheap, and dynamically changing the compute graph is not future-proof. * ggml : in ggml_ssm_scan, use more appropriate asserts * ggml : rename the destination pointer in ggml_compute_forward_ssm_scan_f32 * mamba : multiple sequences, but one at a time This is a step towards making this Mamba implementation usable with the server example (the way the system prompt is kept when clearing the client slots will need to be changed before this can work, though). The KV cache size for this kind of model is tied to the maximum number of sequences kept at any single time. For now, this number is obtained from n_parallel (plus one, to have an extra sequence to dedicate to the system prompt), but there might be a better way to do this which won't also make the main example use 2 cells even if only 1 is really used. (for this specific case, --parallel 0 helps) Simultaneous sequence processing will probably require changes to ggml_ssm_scan, and possibly a new operator for the conv step. * mamba : support llama_kv_cache_seq_cp This (mis)uses the logic around K shifts, because tokens in a state can't be shifted anyway, and because inp_K_shift has the right shape and type. Using ggml_get_rows is a nice way to do copies, but copy chains can't work. Fortunately, copy chains don't really seem to be used in the examples. Each KV cell is dedicated to the sequence ID corresponding to its own index. * mamba : use a state mask It's cleaner than the previous heuristic of checking for the pos of the first token in the batch. inp_KQ_mask could not be re-used for this, because it has the wrong shape and because it seems more suited to the next step of simultaneous sequence processing (helping with the problem of remembering which token belongs to which sequence(s)/state(s)). * llama : replace the usage of n_ctx with kv_self.size in many places * mamba : use n_tokens directly instead of n_tok * mamba : in comments, properly refer to KV cells instead of slots * mamba : reduce memory usage of ggml_ssm_scan From 290.37 MiB to 140.68 MiB of CPU compute buffer size with Mamba 3B with a batch size of 512. The result tensor of ggml_ssm_scan was previously a big part of the CPU compute buffer size. To make it smaller, it does not contain the intermediate ssm states anymore. Both y and the last ssm state are combined in the result tensor, because it seems only a single tensor can be returned by an operator with the way the graph is built. * mamba : simultaneous sequence processing A batch can now contain tokens from multiple sequences. This is necessary for at least the parallel example, the server example, and the HellaSwag test in the perplexity example. However, for this to be useful, uses of llama_kv_cache_seq_rm/cp will need to be changed to work on whole sequences. * ggml : add ggml_ssm_conv as a new operator for the conv step of Mamba This operator makes it possible to use and update the correct states for each token of the batch in the same way as ggml_ssm_scan. Other solutions which use existing operators would need loops which would add too many nodes to the graph (at least the ones I thought of). Using this operator further reduces the size of the CPU compute buffer from 140.68 MiB to 103.20 MiB with Mamba 3B with a batch size of 512. And (at least on CPU), it's a bit faster than before. Note that "ggml_ssm_conv" is probably not the most appropriate name, and it could be changed if a better one is found. * llama : add inp_s_seq as a new input tensor The most convenient implementation to select the correct state (for Mamba) for each token is to directly get the correct index from a tensor. This is why inp_s_seq is storing int32_t and not floats. The other, less convenient way to select the correct state would be to have inp_KQ_mask contain 1.0f for each state used by a token and 0.0f otherwise. This complicates quickly fetching the first used state of a token, and is also less efficient because a whole row of the mask would always need to be read for each token. Using indexes makes it easy to stop searching when there are no more sequences for a token, and the first sequence assigned is always very quickly available (it's the first element of each row). * mamba : support llama_kv_cache_seq_cp copy chains * mamba : support shifting and dividing the kv cache pos * mamba : make the server and parallel examples work with whole sequences A seq_id is dedicated to the system prompt in both cases. * llama : make llama_kv_cache_seq_rm return whether it succeeded or not * mamba : dedicate an input tensor for state copy indices This is cleaner and makes it easier to adapt when/if token positions (and by extension, inp_K_shift) are no longer integers. * mamba : adapt perplexity, batched, and batched-bench examples * perplexity : limit the max number of sequences This adapts to what the loaded model can provide. * llama : add llama_n_max_seq to get the upper limit for seq_ids Used by the perplexity example. * batched : pass n_parallel to the model's context params This should have been there already, but it wasn't. * batched-bench : reserve sequences to support Mamba * batched-bench : fix tokens being put in wrong sequences Generation quality isn't what's measured in there anyway, but at least using the correct sequences avoids using non-consecutive token positions. * mamba : stop abusing attention metadata This breaks existing converted-to-GGUF Mamba models, but will allow supporting mixed architectures like MambaFormer without needing to break Mamba models. This will also allow changing the size of Mamba's states without having to reconvert models in the future. (e.g. using something else than d_conv - 1 columns for the conv_states will not require breaking existing converted Mamba models again) * gguf-py : add new KV metadata key-value pairs for Mamba * llama : add new metadata key-value pairs for Mamba * llama : guard against divisions by zero when n_head is 0 * mamba : rename "unlimited" KV cache property to "recurrent" * mamba : more correctly update the "used" field of the KV cache * ggml : in ggml_ssm_scan, use a threshold for soft_plus This is how the official Mamba implementation does it, and it's also what torch.nn.Softplus does. * convert : for Mamba, fallback to internal NeoX tokenizer The resulting models are exactly the same as if the tokenizer.json and tokenizer_config.json of GPT-NeoX were there. * mamba : support state saving and restoring * ggml : implicitly pass src tensors through dst for Mamba-related ops * mamba : clarify some comments * server : fix cache_tokens not getting correctly resized Otherwise, when the "we have to evaluate at least 1 token" special case was triggered, an extra token was kept in cache_tokens even if it was removed from the KV cache. For Mamba, this caused useless prompt reprocessing when the previous request triggered the above case. * convert-hf : support new metadata keys for Mamba For the models available at https://huggingface.co/collections/state-spaces/transformers-compatible-mamba-65e7b40ab87e5297e45ae406 * mamba : rename metadata to be more similar to transformers library This breaks existing converted-to-GGUF models, but the metadata names are more "standard". * mamba : support mamba-*-hf models These models share their token_embd.weight with their output.weight * mamba : add missing spaces This is purely a formatting change. * convert-hf : omit output.weight when identical with token_embd.weight Only for Mamba for now, but it might be relevant for other models eventually. Most Mamba models actually share these two tensors, albeit implicitly. * readme : add Mamba to supported models, and add recent API changes * mamba : move state_seq and state_mask views outside layer loop A few tensors were also missing `struct` in front of `ggml_tensor`.
2024-03-08 22:31:00 +00:00
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
new_name = self.map_tensor_name(name)
llama : support Mamba Selective State Space Models (#5328) * mamba : begin working on support for Mamba SSM * mamba : begin figuring out how to (ab)use the kv cache for Mamba * mamba : recurrent inference almost works, but incoherent * mamba : recurrent inference WORKS!!! * convert : optionally use d_conv and d_state from config.json for Mamba * mamba : refactor recurrent conv, resulting in 20% perf increase It's still slower than I'd like, but I did not really optimize `ggml_exp` yet. I also refactored `ggml_exp` to work with tensors with more than 2 dimensions. * ggml : parallelize ggml_exp This results in 8% faster token generation for Mamba-130M. * mamba : simplify the conv step with a self-overlapping view Turns out the conv_state can be made smaller by one column. Note that this breaks existing GGUFs of Mamba, because the key_value_length field is tied to the conv_state size. Convolution with a self-overlapping view is cool! And it's much simpler than what I initially thought would be necessary to make the convolution step work with more than 1 token at a time. Next step is to make the SSM step work on batches of tokens too, and thus I need to figure out a way to make a parallel selective scan which will keep the ssm_state small and won't make it bigger by a factor of (n_layer * batch_size). * llama : fix Mamba KV self size wrongly displaying as f16 instead of f32 Relatedly, I also tried to see if other types than f32 worked for the states, but they don't, because of the operators used. It's probably better anyway to keep lots of precision there, since the states are small anyway. * mamba : fix self-overlapping view depth stride * mamba : handle batches of more than 1 token This means running Mamba no longer crashes when using the default settings! And probably also slightly faster prompt processing. Both batched and non-batched processing yield the same output. Previously, the state was not cleared when starting a sequence. Next step is to make the KV cache API work as expected for Mamba models. * ggml: add ggml_ssm_scan to help with parallel selective scan If the selective scan was implemented without a custom operator, there would be waaay too many nodes in the graph. For example, for Mamba-130M, with a batch size of 512 (the default), a naive selective scan could add at least 24*512=12288 nodes, which is more than LLAMA_MAX_NODES (8192), and that's only for the smallest Mamba model. So it's much cleaner with a custom operator. Not sure about the name, though. * ggml : in ggml_ssm_scan, merge multiple rows in the same vec operation This will help with performance on CPU if ggml_vec_mul_f32 and ggml_vec_add_f32 are ever optimized with SIMD. * mamba : very basic quantization support Mostly works, but there is currently no difference between the variants of a k-quant (e.g. Q4_K_S and Q4_K_M are the same). Most of the SSM-specific weights can be kept in f32 without affecting the size that much, since they are relatively small. (the linear projection weights are responsible for most of Mamba's size) Too much quantization seems to make the state degrade quite fast, and the model begins to output gibberish. It seems to affect bigger models to a lesser extent than small models, but I'm not sure by how much. Experimentation will be needed to figure out which weights are more important for the _M (and _L?) variants of k-quants for Mamba. * convert : fix wrong name for layer norm weight of offical Mamba models I was using Q-bert/Mamba-* models before, which have a slighlty different naming scheme for the weights. (they start with "model.layers" instead of "backbone.layers") * mamba : fuse more steps of the SSM scan in the ggml_ssm_scan operator This increases performance on CPU by around 30% for prompt processing, and by around 20% for text generation. However, it also makes the ggml_exp and ggml_soft_plus operators unused. Whether or not they should be kept will be decided later. * convert : for Mamba, also consider the "MambaLMHeadModel" arch name It's the name of the class of the official implementation, though they don't use it (yet) in the "architectures" field of config.json * mamba : fix vocab size problems with official models The perplexity was waaaay to high for models with a non-round vocab size. Not sure why, but it needed to be fixed in the metadata. Note that this breaks existing GGUF-converted Mamba models, but **only if** the vocab size was not already rounded. * ggml : remove ggml_exp and ggml_soft_plus They did not exist anyway outside of this branch, and since ggml_ssm_scan fused operations together, they are unused. It's always possible to bring them back if needed. * mamba : remove some useless comments No code change. * convert : fix flake8 linter errors * mamba : apply suggestions from code review * mamba : remove unecessary branch for row-wise ssm_state and C multiplication It was previously done to avoid permuting when only one token is processed at a time (like when generating text), but permuting is cheap, and dynamically changing the compute graph is not future-proof. * ggml : in ggml_ssm_scan, use more appropriate asserts * ggml : rename the destination pointer in ggml_compute_forward_ssm_scan_f32 * mamba : multiple sequences, but one at a time This is a step towards making this Mamba implementation usable with the server example (the way the system prompt is kept when clearing the client slots will need to be changed before this can work, though). The KV cache size for this kind of model is tied to the maximum number of sequences kept at any single time. For now, this number is obtained from n_parallel (plus one, to have an extra sequence to dedicate to the system prompt), but there might be a better way to do this which won't also make the main example use 2 cells even if only 1 is really used. (for this specific case, --parallel 0 helps) Simultaneous sequence processing will probably require changes to ggml_ssm_scan, and possibly a new operator for the conv step. * mamba : support llama_kv_cache_seq_cp This (mis)uses the logic around K shifts, because tokens in a state can't be shifted anyway, and because inp_K_shift has the right shape and type. Using ggml_get_rows is a nice way to do copies, but copy chains can't work. Fortunately, copy chains don't really seem to be used in the examples. Each KV cell is dedicated to the sequence ID corresponding to its own index. * mamba : use a state mask It's cleaner than the previous heuristic of checking for the pos of the first token in the batch. inp_KQ_mask could not be re-used for this, because it has the wrong shape and because it seems more suited to the next step of simultaneous sequence processing (helping with the problem of remembering which token belongs to which sequence(s)/state(s)). * llama : replace the usage of n_ctx with kv_self.size in many places * mamba : use n_tokens directly instead of n_tok * mamba : in comments, properly refer to KV cells instead of slots * mamba : reduce memory usage of ggml_ssm_scan From 290.37 MiB to 140.68 MiB of CPU compute buffer size with Mamba 3B with a batch size of 512. The result tensor of ggml_ssm_scan was previously a big part of the CPU compute buffer size. To make it smaller, it does not contain the intermediate ssm states anymore. Both y and the last ssm state are combined in the result tensor, because it seems only a single tensor can be returned by an operator with the way the graph is built. * mamba : simultaneous sequence processing A batch can now contain tokens from multiple sequences. This is necessary for at least the parallel example, the server example, and the HellaSwag test in the perplexity example. However, for this to be useful, uses of llama_kv_cache_seq_rm/cp will need to be changed to work on whole sequences. * ggml : add ggml_ssm_conv as a new operator for the conv step of Mamba This operator makes it possible to use and update the correct states for each token of the batch in the same way as ggml_ssm_scan. Other solutions which use existing operators would need loops which would add too many nodes to the graph (at least the ones I thought of). Using this operator further reduces the size of the CPU compute buffer from 140.68 MiB to 103.20 MiB with Mamba 3B with a batch size of 512. And (at least on CPU), it's a bit faster than before. Note that "ggml_ssm_conv" is probably not the most appropriate name, and it could be changed if a better one is found. * llama : add inp_s_seq as a new input tensor The most convenient implementation to select the correct state (for Mamba) for each token is to directly get the correct index from a tensor. This is why inp_s_seq is storing int32_t and not floats. The other, less convenient way to select the correct state would be to have inp_KQ_mask contain 1.0f for each state used by a token and 0.0f otherwise. This complicates quickly fetching the first used state of a token, and is also less efficient because a whole row of the mask would always need to be read for each token. Using indexes makes it easy to stop searching when there are no more sequences for a token, and the first sequence assigned is always very quickly available (it's the first element of each row). * mamba : support llama_kv_cache_seq_cp copy chains * mamba : support shifting and dividing the kv cache pos * mamba : make the server and parallel examples work with whole sequences A seq_id is dedicated to the system prompt in both cases. * llama : make llama_kv_cache_seq_rm return whether it succeeded or not * mamba : dedicate an input tensor for state copy indices This is cleaner and makes it easier to adapt when/if token positions (and by extension, inp_K_shift) are no longer integers. * mamba : adapt perplexity, batched, and batched-bench examples * perplexity : limit the max number of sequences This adapts to what the loaded model can provide. * llama : add llama_n_max_seq to get the upper limit for seq_ids Used by the perplexity example. * batched : pass n_parallel to the model's context params This should have been there already, but it wasn't. * batched-bench : reserve sequences to support Mamba * batched-bench : fix tokens being put in wrong sequences Generation quality isn't what's measured in there anyway, but at least using the correct sequences avoids using non-consecutive token positions. * mamba : stop abusing attention metadata This breaks existing converted-to-GGUF Mamba models, but will allow supporting mixed architectures like MambaFormer without needing to break Mamba models. This will also allow changing the size of Mamba's states without having to reconvert models in the future. (e.g. using something else than d_conv - 1 columns for the conv_states will not require breaking existing converted Mamba models again) * gguf-py : add new KV metadata key-value pairs for Mamba * llama : add new metadata key-value pairs for Mamba * llama : guard against divisions by zero when n_head is 0 * mamba : rename "unlimited" KV cache property to "recurrent" * mamba : more correctly update the "used" field of the KV cache * ggml : in ggml_ssm_scan, use a threshold for soft_plus This is how the official Mamba implementation does it, and it's also what torch.nn.Softplus does. * convert : for Mamba, fallback to internal NeoX tokenizer The resulting models are exactly the same as if the tokenizer.json and tokenizer_config.json of GPT-NeoX were there. * mamba : support state saving and restoring * ggml : implicitly pass src tensors through dst for Mamba-related ops * mamba : clarify some comments * server : fix cache_tokens not getting correctly resized Otherwise, when the "we have to evaluate at least 1 token" special case was triggered, an extra token was kept in cache_tokens even if it was removed from the KV cache. For Mamba, this caused useless prompt reprocessing when the previous request triggered the above case. * convert-hf : support new metadata keys for Mamba For the models available at https://huggingface.co/collections/state-spaces/transformers-compatible-mamba-65e7b40ab87e5297e45ae406 * mamba : rename metadata to be more similar to transformers library This breaks existing converted-to-GGUF models, but the metadata names are more "standard". * mamba : support mamba-*-hf models These models share their token_embd.weight with their output.weight * mamba : add missing spaces This is purely a formatting change. * convert-hf : omit output.weight when identical with token_embd.weight Only for Mamba for now, but it might be relevant for other models eventually. Most Mamba models actually share these two tensors, albeit implicitly. * readme : add Mamba to supported models, and add recent API changes * mamba : move state_seq and state_mask views outside layer loop A few tensors were also missing `struct` in front of `ggml_tensor`.
2024-03-08 22:31:00 +00:00
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
if name.endswith(".A_log"):
logger.debug("A_log --> A ==> " + new_name)
data_torch = -torch.exp(data_torch)
llama : support Mamba Selective State Space Models (#5328) * mamba : begin working on support for Mamba SSM * mamba : begin figuring out how to (ab)use the kv cache for Mamba * mamba : recurrent inference almost works, but incoherent * mamba : recurrent inference WORKS!!! * convert : optionally use d_conv and d_state from config.json for Mamba * mamba : refactor recurrent conv, resulting in 20% perf increase It's still slower than I'd like, but I did not really optimize `ggml_exp` yet. I also refactored `ggml_exp` to work with tensors with more than 2 dimensions. * ggml : parallelize ggml_exp This results in 8% faster token generation for Mamba-130M. * mamba : simplify the conv step with a self-overlapping view Turns out the conv_state can be made smaller by one column. Note that this breaks existing GGUFs of Mamba, because the key_value_length field is tied to the conv_state size. Convolution with a self-overlapping view is cool! And it's much simpler than what I initially thought would be necessary to make the convolution step work with more than 1 token at a time. Next step is to make the SSM step work on batches of tokens too, and thus I need to figure out a way to make a parallel selective scan which will keep the ssm_state small and won't make it bigger by a factor of (n_layer * batch_size). * llama : fix Mamba KV self size wrongly displaying as f16 instead of f32 Relatedly, I also tried to see if other types than f32 worked for the states, but they don't, because of the operators used. It's probably better anyway to keep lots of precision there, since the states are small anyway. * mamba : fix self-overlapping view depth stride * mamba : handle batches of more than 1 token This means running Mamba no longer crashes when using the default settings! And probably also slightly faster prompt processing. Both batched and non-batched processing yield the same output. Previously, the state was not cleared when starting a sequence. Next step is to make the KV cache API work as expected for Mamba models. * ggml: add ggml_ssm_scan to help with parallel selective scan If the selective scan was implemented without a custom operator, there would be waaay too many nodes in the graph. For example, for Mamba-130M, with a batch size of 512 (the default), a naive selective scan could add at least 24*512=12288 nodes, which is more than LLAMA_MAX_NODES (8192), and that's only for the smallest Mamba model. So it's much cleaner with a custom operator. Not sure about the name, though. * ggml : in ggml_ssm_scan, merge multiple rows in the same vec operation This will help with performance on CPU if ggml_vec_mul_f32 and ggml_vec_add_f32 are ever optimized with SIMD. * mamba : very basic quantization support Mostly works, but there is currently no difference between the variants of a k-quant (e.g. Q4_K_S and Q4_K_M are the same). Most of the SSM-specific weights can be kept in f32 without affecting the size that much, since they are relatively small. (the linear projection weights are responsible for most of Mamba's size) Too much quantization seems to make the state degrade quite fast, and the model begins to output gibberish. It seems to affect bigger models to a lesser extent than small models, but I'm not sure by how much. Experimentation will be needed to figure out which weights are more important for the _M (and _L?) variants of k-quants for Mamba. * convert : fix wrong name for layer norm weight of offical Mamba models I was using Q-bert/Mamba-* models before, which have a slighlty different naming scheme for the weights. (they start with "model.layers" instead of "backbone.layers") * mamba : fuse more steps of the SSM scan in the ggml_ssm_scan operator This increases performance on CPU by around 30% for prompt processing, and by around 20% for text generation. However, it also makes the ggml_exp and ggml_soft_plus operators unused. Whether or not they should be kept will be decided later. * convert : for Mamba, also consider the "MambaLMHeadModel" arch name It's the name of the class of the official implementation, though they don't use it (yet) in the "architectures" field of config.json * mamba : fix vocab size problems with official models The perplexity was waaaay to high for models with a non-round vocab size. Not sure why, but it needed to be fixed in the metadata. Note that this breaks existing GGUF-converted Mamba models, but **only if** the vocab size was not already rounded. * ggml : remove ggml_exp and ggml_soft_plus They did not exist anyway outside of this branch, and since ggml_ssm_scan fused operations together, they are unused. It's always possible to bring them back if needed. * mamba : remove some useless comments No code change. * convert : fix flake8 linter errors * mamba : apply suggestions from code review * mamba : remove unecessary branch for row-wise ssm_state and C multiplication It was previously done to avoid permuting when only one token is processed at a time (like when generating text), but permuting is cheap, and dynamically changing the compute graph is not future-proof. * ggml : in ggml_ssm_scan, use more appropriate asserts * ggml : rename the destination pointer in ggml_compute_forward_ssm_scan_f32 * mamba : multiple sequences, but one at a time This is a step towards making this Mamba implementation usable with the server example (the way the system prompt is kept when clearing the client slots will need to be changed before this can work, though). The KV cache size for this kind of model is tied to the maximum number of sequences kept at any single time. For now, this number is obtained from n_parallel (plus one, to have an extra sequence to dedicate to the system prompt), but there might be a better way to do this which won't also make the main example use 2 cells even if only 1 is really used. (for this specific case, --parallel 0 helps) Simultaneous sequence processing will probably require changes to ggml_ssm_scan, and possibly a new operator for the conv step. * mamba : support llama_kv_cache_seq_cp This (mis)uses the logic around K shifts, because tokens in a state can't be shifted anyway, and because inp_K_shift has the right shape and type. Using ggml_get_rows is a nice way to do copies, but copy chains can't work. Fortunately, copy chains don't really seem to be used in the examples. Each KV cell is dedicated to the sequence ID corresponding to its own index. * mamba : use a state mask It's cleaner than the previous heuristic of checking for the pos of the first token in the batch. inp_KQ_mask could not be re-used for this, because it has the wrong shape and because it seems more suited to the next step of simultaneous sequence processing (helping with the problem of remembering which token belongs to which sequence(s)/state(s)). * llama : replace the usage of n_ctx with kv_self.size in many places * mamba : use n_tokens directly instead of n_tok * mamba : in comments, properly refer to KV cells instead of slots * mamba : reduce memory usage of ggml_ssm_scan From 290.37 MiB to 140.68 MiB of CPU compute buffer size with Mamba 3B with a batch size of 512. The result tensor of ggml_ssm_scan was previously a big part of the CPU compute buffer size. To make it smaller, it does not contain the intermediate ssm states anymore. Both y and the last ssm state are combined in the result tensor, because it seems only a single tensor can be returned by an operator with the way the graph is built. * mamba : simultaneous sequence processing A batch can now contain tokens from multiple sequences. This is necessary for at least the parallel example, the server example, and the HellaSwag test in the perplexity example. However, for this to be useful, uses of llama_kv_cache_seq_rm/cp will need to be changed to work on whole sequences. * ggml : add ggml_ssm_conv as a new operator for the conv step of Mamba This operator makes it possible to use and update the correct states for each token of the batch in the same way as ggml_ssm_scan. Other solutions which use existing operators would need loops which would add too many nodes to the graph (at least the ones I thought of). Using this operator further reduces the size of the CPU compute buffer from 140.68 MiB to 103.20 MiB with Mamba 3B with a batch size of 512. And (at least on CPU), it's a bit faster than before. Note that "ggml_ssm_conv" is probably not the most appropriate name, and it could be changed if a better one is found. * llama : add inp_s_seq as a new input tensor The most convenient implementation to select the correct state (for Mamba) for each token is to directly get the correct index from a tensor. This is why inp_s_seq is storing int32_t and not floats. The other, less convenient way to select the correct state would be to have inp_KQ_mask contain 1.0f for each state used by a token and 0.0f otherwise. This complicates quickly fetching the first used state of a token, and is also less efficient because a whole row of the mask would always need to be read for each token. Using indexes makes it easy to stop searching when there are no more sequences for a token, and the first sequence assigned is always very quickly available (it's the first element of each row). * mamba : support llama_kv_cache_seq_cp copy chains * mamba : support shifting and dividing the kv cache pos * mamba : make the server and parallel examples work with whole sequences A seq_id is dedicated to the system prompt in both cases. * llama : make llama_kv_cache_seq_rm return whether it succeeded or not * mamba : dedicate an input tensor for state copy indices This is cleaner and makes it easier to adapt when/if token positions (and by extension, inp_K_shift) are no longer integers. * mamba : adapt perplexity, batched, and batched-bench examples * perplexity : limit the max number of sequences This adapts to what the loaded model can provide. * llama : add llama_n_max_seq to get the upper limit for seq_ids Used by the perplexity example. * batched : pass n_parallel to the model's context params This should have been there already, but it wasn't. * batched-bench : reserve sequences to support Mamba * batched-bench : fix tokens being put in wrong sequences Generation quality isn't what's measured in there anyway, but at least using the correct sequences avoids using non-consecutive token positions. * mamba : stop abusing attention metadata This breaks existing converted-to-GGUF Mamba models, but will allow supporting mixed architectures like MambaFormer without needing to break Mamba models. This will also allow changing the size of Mamba's states without having to reconvert models in the future. (e.g. using something else than d_conv - 1 columns for the conv_states will not require breaking existing converted Mamba models again) * gguf-py : add new KV metadata key-value pairs for Mamba * llama : add new metadata key-value pairs for Mamba * llama : guard against divisions by zero when n_head is 0 * mamba : rename "unlimited" KV cache property to "recurrent" * mamba : more correctly update the "used" field of the KV cache * ggml : in ggml_ssm_scan, use a threshold for soft_plus This is how the official Mamba implementation does it, and it's also what torch.nn.Softplus does. * convert : for Mamba, fallback to internal NeoX tokenizer The resulting models are exactly the same as if the tokenizer.json and tokenizer_config.json of GPT-NeoX were there. * mamba : support state saving and restoring * ggml : implicitly pass src tensors through dst for Mamba-related ops * mamba : clarify some comments * server : fix cache_tokens not getting correctly resized Otherwise, when the "we have to evaluate at least 1 token" special case was triggered, an extra token was kept in cache_tokens even if it was removed from the KV cache. For Mamba, this caused useless prompt reprocessing when the previous request triggered the above case. * convert-hf : support new metadata keys for Mamba For the models available at https://huggingface.co/collections/state-spaces/transformers-compatible-mamba-65e7b40ab87e5297e45ae406 * mamba : rename metadata to be more similar to transformers library This breaks existing converted-to-GGUF models, but the metadata names are more "standard". * mamba : support mamba-*-hf models These models share their token_embd.weight with their output.weight * mamba : add missing spaces This is purely a formatting change. * convert-hf : omit output.weight when identical with token_embd.weight Only for Mamba for now, but it might be relevant for other models eventually. Most Mamba models actually share these two tensors, albeit implicitly. * readme : add Mamba to supported models, and add recent API changes * mamba : move state_seq and state_mask views outside layer loop A few tensors were also missing `struct` in front of `ggml_tensor`.
2024-03-08 22:31:00 +00:00
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
# assuming token_embd.weight is seen before output.weight
if self._tok_embd is not None and new_name == output_name:
if torch.equal(self._tok_embd, data_torch):
logger.debug(f"{output_name} is equivalent to {tok_embd_name}, omitting")
return []
elif new_name == tok_embd_name:
self._tok_embd = data_torch
llama : support Mamba Selective State Space Models (#5328) * mamba : begin working on support for Mamba SSM * mamba : begin figuring out how to (ab)use the kv cache for Mamba * mamba : recurrent inference almost works, but incoherent * mamba : recurrent inference WORKS!!! * convert : optionally use d_conv and d_state from config.json for Mamba * mamba : refactor recurrent conv, resulting in 20% perf increase It's still slower than I'd like, but I did not really optimize `ggml_exp` yet. I also refactored `ggml_exp` to work with tensors with more than 2 dimensions. * ggml : parallelize ggml_exp This results in 8% faster token generation for Mamba-130M. * mamba : simplify the conv step with a self-overlapping view Turns out the conv_state can be made smaller by one column. Note that this breaks existing GGUFs of Mamba, because the key_value_length field is tied to the conv_state size. Convolution with a self-overlapping view is cool! And it's much simpler than what I initially thought would be necessary to make the convolution step work with more than 1 token at a time. Next step is to make the SSM step work on batches of tokens too, and thus I need to figure out a way to make a parallel selective scan which will keep the ssm_state small and won't make it bigger by a factor of (n_layer * batch_size). * llama : fix Mamba KV self size wrongly displaying as f16 instead of f32 Relatedly, I also tried to see if other types than f32 worked for the states, but they don't, because of the operators used. It's probably better anyway to keep lots of precision there, since the states are small anyway. * mamba : fix self-overlapping view depth stride * mamba : handle batches of more than 1 token This means running Mamba no longer crashes when using the default settings! And probably also slightly faster prompt processing. Both batched and non-batched processing yield the same output. Previously, the state was not cleared when starting a sequence. Next step is to make the KV cache API work as expected for Mamba models. * ggml: add ggml_ssm_scan to help with parallel selective scan If the selective scan was implemented without a custom operator, there would be waaay too many nodes in the graph. For example, for Mamba-130M, with a batch size of 512 (the default), a naive selective scan could add at least 24*512=12288 nodes, which is more than LLAMA_MAX_NODES (8192), and that's only for the smallest Mamba model. So it's much cleaner with a custom operator. Not sure about the name, though. * ggml : in ggml_ssm_scan, merge multiple rows in the same vec operation This will help with performance on CPU if ggml_vec_mul_f32 and ggml_vec_add_f32 are ever optimized with SIMD. * mamba : very basic quantization support Mostly works, but there is currently no difference between the variants of a k-quant (e.g. Q4_K_S and Q4_K_M are the same). Most of the SSM-specific weights can be kept in f32 without affecting the size that much, since they are relatively small. (the linear projection weights are responsible for most of Mamba's size) Too much quantization seems to make the state degrade quite fast, and the model begins to output gibberish. It seems to affect bigger models to a lesser extent than small models, but I'm not sure by how much. Experimentation will be needed to figure out which weights are more important for the _M (and _L?) variants of k-quants for Mamba. * convert : fix wrong name for layer norm weight of offical Mamba models I was using Q-bert/Mamba-* models before, which have a slighlty different naming scheme for the weights. (they start with "model.layers" instead of "backbone.layers") * mamba : fuse more steps of the SSM scan in the ggml_ssm_scan operator This increases performance on CPU by around 30% for prompt processing, and by around 20% for text generation. However, it also makes the ggml_exp and ggml_soft_plus operators unused. Whether or not they should be kept will be decided later. * convert : for Mamba, also consider the "MambaLMHeadModel" arch name It's the name of the class of the official implementation, though they don't use it (yet) in the "architectures" field of config.json * mamba : fix vocab size problems with official models The perplexity was waaaay to high for models with a non-round vocab size. Not sure why, but it needed to be fixed in the metadata. Note that this breaks existing GGUF-converted Mamba models, but **only if** the vocab size was not already rounded. * ggml : remove ggml_exp and ggml_soft_plus They did not exist anyway outside of this branch, and since ggml_ssm_scan fused operations together, they are unused. It's always possible to bring them back if needed. * mamba : remove some useless comments No code change. * convert : fix flake8 linter errors * mamba : apply suggestions from code review * mamba : remove unecessary branch for row-wise ssm_state and C multiplication It was previously done to avoid permuting when only one token is processed at a time (like when generating text), but permuting is cheap, and dynamically changing the compute graph is not future-proof. * ggml : in ggml_ssm_scan, use more appropriate asserts * ggml : rename the destination pointer in ggml_compute_forward_ssm_scan_f32 * mamba : multiple sequences, but one at a time This is a step towards making this Mamba implementation usable with the server example (the way the system prompt is kept when clearing the client slots will need to be changed before this can work, though). The KV cache size for this kind of model is tied to the maximum number of sequences kept at any single time. For now, this number is obtained from n_parallel (plus one, to have an extra sequence to dedicate to the system prompt), but there might be a better way to do this which won't also make the main example use 2 cells even if only 1 is really used. (for this specific case, --parallel 0 helps) Simultaneous sequence processing will probably require changes to ggml_ssm_scan, and possibly a new operator for the conv step. * mamba : support llama_kv_cache_seq_cp This (mis)uses the logic around K shifts, because tokens in a state can't be shifted anyway, and because inp_K_shift has the right shape and type. Using ggml_get_rows is a nice way to do copies, but copy chains can't work. Fortunately, copy chains don't really seem to be used in the examples. Each KV cell is dedicated to the sequence ID corresponding to its own index. * mamba : use a state mask It's cleaner than the previous heuristic of checking for the pos of the first token in the batch. inp_KQ_mask could not be re-used for this, because it has the wrong shape and because it seems more suited to the next step of simultaneous sequence processing (helping with the problem of remembering which token belongs to which sequence(s)/state(s)). * llama : replace the usage of n_ctx with kv_self.size in many places * mamba : use n_tokens directly instead of n_tok * mamba : in comments, properly refer to KV cells instead of slots * mamba : reduce memory usage of ggml_ssm_scan From 290.37 MiB to 140.68 MiB of CPU compute buffer size with Mamba 3B with a batch size of 512. The result tensor of ggml_ssm_scan was previously a big part of the CPU compute buffer size. To make it smaller, it does not contain the intermediate ssm states anymore. Both y and the last ssm state are combined in the result tensor, because it seems only a single tensor can be returned by an operator with the way the graph is built. * mamba : simultaneous sequence processing A batch can now contain tokens from multiple sequences. This is necessary for at least the parallel example, the server example, and the HellaSwag test in the perplexity example. However, for this to be useful, uses of llama_kv_cache_seq_rm/cp will need to be changed to work on whole sequences. * ggml : add ggml_ssm_conv as a new operator for the conv step of Mamba This operator makes it possible to use and update the correct states for each token of the batch in the same way as ggml_ssm_scan. Other solutions which use existing operators would need loops which would add too many nodes to the graph (at least the ones I thought of). Using this operator further reduces the size of the CPU compute buffer from 140.68 MiB to 103.20 MiB with Mamba 3B with a batch size of 512. And (at least on CPU), it's a bit faster than before. Note that "ggml_ssm_conv" is probably not the most appropriate name, and it could be changed if a better one is found. * llama : add inp_s_seq as a new input tensor The most convenient implementation to select the correct state (for Mamba) for each token is to directly get the correct index from a tensor. This is why inp_s_seq is storing int32_t and not floats. The other, less convenient way to select the correct state would be to have inp_KQ_mask contain 1.0f for each state used by a token and 0.0f otherwise. This complicates quickly fetching the first used state of a token, and is also less efficient because a whole row of the mask would always need to be read for each token. Using indexes makes it easy to stop searching when there are no more sequences for a token, and the first sequence assigned is always very quickly available (it's the first element of each row). * mamba : support llama_kv_cache_seq_cp copy chains * mamba : support shifting and dividing the kv cache pos * mamba : make the server and parallel examples work with whole sequences A seq_id is dedicated to the system prompt in both cases. * llama : make llama_kv_cache_seq_rm return whether it succeeded or not * mamba : dedicate an input tensor for state copy indices This is cleaner and makes it easier to adapt when/if token positions (and by extension, inp_K_shift) are no longer integers. * mamba : adapt perplexity, batched, and batched-bench examples * perplexity : limit the max number of sequences This adapts to what the loaded model can provide. * llama : add llama_n_max_seq to get the upper limit for seq_ids Used by the perplexity example. * batched : pass n_parallel to the model's context params This should have been there already, but it wasn't. * batched-bench : reserve sequences to support Mamba * batched-bench : fix tokens being put in wrong sequences Generation quality isn't what's measured in there anyway, but at least using the correct sequences avoids using non-consecutive token positions. * mamba : stop abusing attention metadata This breaks existing converted-to-GGUF Mamba models, but will allow supporting mixed architectures like MambaFormer without needing to break Mamba models. This will also allow changing the size of Mamba's states without having to reconvert models in the future. (e.g. using something else than d_conv - 1 columns for the conv_states will not require breaking existing converted Mamba models again) * gguf-py : add new KV metadata key-value pairs for Mamba * llama : add new metadata key-value pairs for Mamba * llama : guard against divisions by zero when n_head is 0 * mamba : rename "unlimited" KV cache property to "recurrent" * mamba : more correctly update the "used" field of the KV cache * ggml : in ggml_ssm_scan, use a threshold for soft_plus This is how the official Mamba implementation does it, and it's also what torch.nn.Softplus does. * convert : for Mamba, fallback to internal NeoX tokenizer The resulting models are exactly the same as if the tokenizer.json and tokenizer_config.json of GPT-NeoX were there. * mamba : support state saving and restoring * ggml : implicitly pass src tensors through dst for Mamba-related ops * mamba : clarify some comments * server : fix cache_tokens not getting correctly resized Otherwise, when the "we have to evaluate at least 1 token" special case was triggered, an extra token was kept in cache_tokens even if it was removed from the KV cache. For Mamba, this caused useless prompt reprocessing when the previous request triggered the above case. * convert-hf : support new metadata keys for Mamba For the models available at https://huggingface.co/collections/state-spaces/transformers-compatible-mamba-65e7b40ab87e5297e45ae406 * mamba : rename metadata to be more similar to transformers library This breaks existing converted-to-GGUF models, but the metadata names are more "standard". * mamba : support mamba-*-hf models These models share their token_embd.weight with their output.weight * mamba : add missing spaces This is purely a formatting change. * convert-hf : omit output.weight when identical with token_embd.weight Only for Mamba for now, but it might be relevant for other models eventually. Most Mamba models actually share these two tensors, albeit implicitly. * readme : add Mamba to supported models, and add recent API changes * mamba : move state_seq and state_mask views outside layer loop A few tensors were also missing `struct` in front of `ggml_tensor`.
2024-03-08 22:31:00 +00:00
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
return [(new_name, data_torch)]
llama : support Mamba Selective State Space Models (#5328) * mamba : begin working on support for Mamba SSM * mamba : begin figuring out how to (ab)use the kv cache for Mamba * mamba : recurrent inference almost works, but incoherent * mamba : recurrent inference WORKS!!! * convert : optionally use d_conv and d_state from config.json for Mamba * mamba : refactor recurrent conv, resulting in 20% perf increase It's still slower than I'd like, but I did not really optimize `ggml_exp` yet. I also refactored `ggml_exp` to work with tensors with more than 2 dimensions. * ggml : parallelize ggml_exp This results in 8% faster token generation for Mamba-130M. * mamba : simplify the conv step with a self-overlapping view Turns out the conv_state can be made smaller by one column. Note that this breaks existing GGUFs of Mamba, because the key_value_length field is tied to the conv_state size. Convolution with a self-overlapping view is cool! And it's much simpler than what I initially thought would be necessary to make the convolution step work with more than 1 token at a time. Next step is to make the SSM step work on batches of tokens too, and thus I need to figure out a way to make a parallel selective scan which will keep the ssm_state small and won't make it bigger by a factor of (n_layer * batch_size). * llama : fix Mamba KV self size wrongly displaying as f16 instead of f32 Relatedly, I also tried to see if other types than f32 worked for the states, but they don't, because of the operators used. It's probably better anyway to keep lots of precision there, since the states are small anyway. * mamba : fix self-overlapping view depth stride * mamba : handle batches of more than 1 token This means running Mamba no longer crashes when using the default settings! And probably also slightly faster prompt processing. Both batched and non-batched processing yield the same output. Previously, the state was not cleared when starting a sequence. Next step is to make the KV cache API work as expected for Mamba models. * ggml: add ggml_ssm_scan to help with parallel selective scan If the selective scan was implemented without a custom operator, there would be waaay too many nodes in the graph. For example, for Mamba-130M, with a batch size of 512 (the default), a naive selective scan could add at least 24*512=12288 nodes, which is more than LLAMA_MAX_NODES (8192), and that's only for the smallest Mamba model. So it's much cleaner with a custom operator. Not sure about the name, though. * ggml : in ggml_ssm_scan, merge multiple rows in the same vec operation This will help with performance on CPU if ggml_vec_mul_f32 and ggml_vec_add_f32 are ever optimized with SIMD. * mamba : very basic quantization support Mostly works, but there is currently no difference between the variants of a k-quant (e.g. Q4_K_S and Q4_K_M are the same). Most of the SSM-specific weights can be kept in f32 without affecting the size that much, since they are relatively small. (the linear projection weights are responsible for most of Mamba's size) Too much quantization seems to make the state degrade quite fast, and the model begins to output gibberish. It seems to affect bigger models to a lesser extent than small models, but I'm not sure by how much. Experimentation will be needed to figure out which weights are more important for the _M (and _L?) variants of k-quants for Mamba. * convert : fix wrong name for layer norm weight of offical Mamba models I was using Q-bert/Mamba-* models before, which have a slighlty different naming scheme for the weights. (they start with "model.layers" instead of "backbone.layers") * mamba : fuse more steps of the SSM scan in the ggml_ssm_scan operator This increases performance on CPU by around 30% for prompt processing, and by around 20% for text generation. However, it also makes the ggml_exp and ggml_soft_plus operators unused. Whether or not they should be kept will be decided later. * convert : for Mamba, also consider the "MambaLMHeadModel" arch name It's the name of the class of the official implementation, though they don't use it (yet) in the "architectures" field of config.json * mamba : fix vocab size problems with official models The perplexity was waaaay to high for models with a non-round vocab size. Not sure why, but it needed to be fixed in the metadata. Note that this breaks existing GGUF-converted Mamba models, but **only if** the vocab size was not already rounded. * ggml : remove ggml_exp and ggml_soft_plus They did not exist anyway outside of this branch, and since ggml_ssm_scan fused operations together, they are unused. It's always possible to bring them back if needed. * mamba : remove some useless comments No code change. * convert : fix flake8 linter errors * mamba : apply suggestions from code review * mamba : remove unecessary branch for row-wise ssm_state and C multiplication It was previously done to avoid permuting when only one token is processed at a time (like when generating text), but permuting is cheap, and dynamically changing the compute graph is not future-proof. * ggml : in ggml_ssm_scan, use more appropriate asserts * ggml : rename the destination pointer in ggml_compute_forward_ssm_scan_f32 * mamba : multiple sequences, but one at a time This is a step towards making this Mamba implementation usable with the server example (the way the system prompt is kept when clearing the client slots will need to be changed before this can work, though). The KV cache size for this kind of model is tied to the maximum number of sequences kept at any single time. For now, this number is obtained from n_parallel (plus one, to have an extra sequence to dedicate to the system prompt), but there might be a better way to do this which won't also make the main example use 2 cells even if only 1 is really used. (for this specific case, --parallel 0 helps) Simultaneous sequence processing will probably require changes to ggml_ssm_scan, and possibly a new operator for the conv step. * mamba : support llama_kv_cache_seq_cp This (mis)uses the logic around K shifts, because tokens in a state can't be shifted anyway, and because inp_K_shift has the right shape and type. Using ggml_get_rows is a nice way to do copies, but copy chains can't work. Fortunately, copy chains don't really seem to be used in the examples. Each KV cell is dedicated to the sequence ID corresponding to its own index. * mamba : use a state mask It's cleaner than the previous heuristic of checking for the pos of the first token in the batch. inp_KQ_mask could not be re-used for this, because it has the wrong shape and because it seems more suited to the next step of simultaneous sequence processing (helping with the problem of remembering which token belongs to which sequence(s)/state(s)). * llama : replace the usage of n_ctx with kv_self.size in many places * mamba : use n_tokens directly instead of n_tok * mamba : in comments, properly refer to KV cells instead of slots * mamba : reduce memory usage of ggml_ssm_scan From 290.37 MiB to 140.68 MiB of CPU compute buffer size with Mamba 3B with a batch size of 512. The result tensor of ggml_ssm_scan was previously a big part of the CPU compute buffer size. To make it smaller, it does not contain the intermediate ssm states anymore. Both y and the last ssm state are combined in the result tensor, because it seems only a single tensor can be returned by an operator with the way the graph is built. * mamba : simultaneous sequence processing A batch can now contain tokens from multiple sequences. This is necessary for at least the parallel example, the server example, and the HellaSwag test in the perplexity example. However, for this to be useful, uses of llama_kv_cache_seq_rm/cp will need to be changed to work on whole sequences. * ggml : add ggml_ssm_conv as a new operator for the conv step of Mamba This operator makes it possible to use and update the correct states for each token of the batch in the same way as ggml_ssm_scan. Other solutions which use existing operators would need loops which would add too many nodes to the graph (at least the ones I thought of). Using this operator further reduces the size of the CPU compute buffer from 140.68 MiB to 103.20 MiB with Mamba 3B with a batch size of 512. And (at least on CPU), it's a bit faster than before. Note that "ggml_ssm_conv" is probably not the most appropriate name, and it could be changed if a better one is found. * llama : add inp_s_seq as a new input tensor The most convenient implementation to select the correct state (for Mamba) for each token is to directly get the correct index from a tensor. This is why inp_s_seq is storing int32_t and not floats. The other, less convenient way to select the correct state would be to have inp_KQ_mask contain 1.0f for each state used by a token and 0.0f otherwise. This complicates quickly fetching the first used state of a token, and is also less efficient because a whole row of the mask would always need to be read for each token. Using indexes makes it easy to stop searching when there are no more sequences for a token, and the first sequence assigned is always very quickly available (it's the first element of each row). * mamba : support llama_kv_cache_seq_cp copy chains * mamba : support shifting and dividing the kv cache pos * mamba : make the server and parallel examples work with whole sequences A seq_id is dedicated to the system prompt in both cases. * llama : make llama_kv_cache_seq_rm return whether it succeeded or not * mamba : dedicate an input tensor for state copy indices This is cleaner and makes it easier to adapt when/if token positions (and by extension, inp_K_shift) are no longer integers. * mamba : adapt perplexity, batched, and batched-bench examples * perplexity : limit the max number of sequences This adapts to what the loaded model can provide. * llama : add llama_n_max_seq to get the upper limit for seq_ids Used by the perplexity example. * batched : pass n_parallel to the model's context params This should have been there already, but it wasn't. * batched-bench : reserve sequences to support Mamba * batched-bench : fix tokens being put in wrong sequences Generation quality isn't what's measured in there anyway, but at least using the correct sequences avoids using non-consecutive token positions. * mamba : stop abusing attention metadata This breaks existing converted-to-GGUF Mamba models, but will allow supporting mixed architectures like MambaFormer without needing to break Mamba models. This will also allow changing the size of Mamba's states without having to reconvert models in the future. (e.g. using something else than d_conv - 1 columns for the conv_states will not require breaking existing converted Mamba models again) * gguf-py : add new KV metadata key-value pairs for Mamba * llama : add new metadata key-value pairs for Mamba * llama : guard against divisions by zero when n_head is 0 * mamba : rename "unlimited" KV cache property to "recurrent" * mamba : more correctly update the "used" field of the KV cache * ggml : in ggml_ssm_scan, use a threshold for soft_plus This is how the official Mamba implementation does it, and it's also what torch.nn.Softplus does. * convert : for Mamba, fallback to internal NeoX tokenizer The resulting models are exactly the same as if the tokenizer.json and tokenizer_config.json of GPT-NeoX were there. * mamba : support state saving and restoring * ggml : implicitly pass src tensors through dst for Mamba-related ops * mamba : clarify some comments * server : fix cache_tokens not getting correctly resized Otherwise, when the "we have to evaluate at least 1 token" special case was triggered, an extra token was kept in cache_tokens even if it was removed from the KV cache. For Mamba, this caused useless prompt reprocessing when the previous request triggered the above case. * convert-hf : support new metadata keys for Mamba For the models available at https://huggingface.co/collections/state-spaces/transformers-compatible-mamba-65e7b40ab87e5297e45ae406 * mamba : rename metadata to be more similar to transformers library This breaks existing converted-to-GGUF models, but the metadata names are more "standard". * mamba : support mamba-*-hf models These models share their token_embd.weight with their output.weight * mamba : add missing spaces This is purely a formatting change. * convert-hf : omit output.weight when identical with token_embd.weight Only for Mamba for now, but it might be relevant for other models eventually. Most Mamba models actually share these two tensors, albeit implicitly. * readme : add Mamba to supported models, and add recent API changes * mamba : move state_seq and state_mask views outside layer loop A few tensors were also missing `struct` in front of `ggml_tensor`.
2024-03-08 22:31:00 +00:00
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
def extra_f32_tensors(self, name: str, new_name: str, bid: int | None, n_dims: int) -> bool:
del n_dims # unused
llama : support Mamba Selective State Space Models (#5328) * mamba : begin working on support for Mamba SSM * mamba : begin figuring out how to (ab)use the kv cache for Mamba * mamba : recurrent inference almost works, but incoherent * mamba : recurrent inference WORKS!!! * convert : optionally use d_conv and d_state from config.json for Mamba * mamba : refactor recurrent conv, resulting in 20% perf increase It's still slower than I'd like, but I did not really optimize `ggml_exp` yet. I also refactored `ggml_exp` to work with tensors with more than 2 dimensions. * ggml : parallelize ggml_exp This results in 8% faster token generation for Mamba-130M. * mamba : simplify the conv step with a self-overlapping view Turns out the conv_state can be made smaller by one column. Note that this breaks existing GGUFs of Mamba, because the key_value_length field is tied to the conv_state size. Convolution with a self-overlapping view is cool! And it's much simpler than what I initially thought would be necessary to make the convolution step work with more than 1 token at a time. Next step is to make the SSM step work on batches of tokens too, and thus I need to figure out a way to make a parallel selective scan which will keep the ssm_state small and won't make it bigger by a factor of (n_layer * batch_size). * llama : fix Mamba KV self size wrongly displaying as f16 instead of f32 Relatedly, I also tried to see if other types than f32 worked for the states, but they don't, because of the operators used. It's probably better anyway to keep lots of precision there, since the states are small anyway. * mamba : fix self-overlapping view depth stride * mamba : handle batches of more than 1 token This means running Mamba no longer crashes when using the default settings! And probably also slightly faster prompt processing. Both batched and non-batched processing yield the same output. Previously, the state was not cleared when starting a sequence. Next step is to make the KV cache API work as expected for Mamba models. * ggml: add ggml_ssm_scan to help with parallel selective scan If the selective scan was implemented without a custom operator, there would be waaay too many nodes in the graph. For example, for Mamba-130M, with a batch size of 512 (the default), a naive selective scan could add at least 24*512=12288 nodes, which is more than LLAMA_MAX_NODES (8192), and that's only for the smallest Mamba model. So it's much cleaner with a custom operator. Not sure about the name, though. * ggml : in ggml_ssm_scan, merge multiple rows in the same vec operation This will help with performance on CPU if ggml_vec_mul_f32 and ggml_vec_add_f32 are ever optimized with SIMD. * mamba : very basic quantization support Mostly works, but there is currently no difference between the variants of a k-quant (e.g. Q4_K_S and Q4_K_M are the same). Most of the SSM-specific weights can be kept in f32 without affecting the size that much, since they are relatively small. (the linear projection weights are responsible for most of Mamba's size) Too much quantization seems to make the state degrade quite fast, and the model begins to output gibberish. It seems to affect bigger models to a lesser extent than small models, but I'm not sure by how much. Experimentation will be needed to figure out which weights are more important for the _M (and _L?) variants of k-quants for Mamba. * convert : fix wrong name for layer norm weight of offical Mamba models I was using Q-bert/Mamba-* models before, which have a slighlty different naming scheme for the weights. (they start with "model.layers" instead of "backbone.layers") * mamba : fuse more steps of the SSM scan in the ggml_ssm_scan operator This increases performance on CPU by around 30% for prompt processing, and by around 20% for text generation. However, it also makes the ggml_exp and ggml_soft_plus operators unused. Whether or not they should be kept will be decided later. * convert : for Mamba, also consider the "MambaLMHeadModel" arch name It's the name of the class of the official implementation, though they don't use it (yet) in the "architectures" field of config.json * mamba : fix vocab size problems with official models The perplexity was waaaay to high for models with a non-round vocab size. Not sure why, but it needed to be fixed in the metadata. Note that this breaks existing GGUF-converted Mamba models, but **only if** the vocab size was not already rounded. * ggml : remove ggml_exp and ggml_soft_plus They did not exist anyway outside of this branch, and since ggml_ssm_scan fused operations together, they are unused. It's always possible to bring them back if needed. * mamba : remove some useless comments No code change. * convert : fix flake8 linter errors * mamba : apply suggestions from code review * mamba : remove unecessary branch for row-wise ssm_state and C multiplication It was previously done to avoid permuting when only one token is processed at a time (like when generating text), but permuting is cheap, and dynamically changing the compute graph is not future-proof. * ggml : in ggml_ssm_scan, use more appropriate asserts * ggml : rename the destination pointer in ggml_compute_forward_ssm_scan_f32 * mamba : multiple sequences, but one at a time This is a step towards making this Mamba implementation usable with the server example (the way the system prompt is kept when clearing the client slots will need to be changed before this can work, though). The KV cache size for this kind of model is tied to the maximum number of sequences kept at any single time. For now, this number is obtained from n_parallel (plus one, to have an extra sequence to dedicate to the system prompt), but there might be a better way to do this which won't also make the main example use 2 cells even if only 1 is really used. (for this specific case, --parallel 0 helps) Simultaneous sequence processing will probably require changes to ggml_ssm_scan, and possibly a new operator for the conv step. * mamba : support llama_kv_cache_seq_cp This (mis)uses the logic around K shifts, because tokens in a state can't be shifted anyway, and because inp_K_shift has the right shape and type. Using ggml_get_rows is a nice way to do copies, but copy chains can't work. Fortunately, copy chains don't really seem to be used in the examples. Each KV cell is dedicated to the sequence ID corresponding to its own index. * mamba : use a state mask It's cleaner than the previous heuristic of checking for the pos of the first token in the batch. inp_KQ_mask could not be re-used for this, because it has the wrong shape and because it seems more suited to the next step of simultaneous sequence processing (helping with the problem of remembering which token belongs to which sequence(s)/state(s)). * llama : replace the usage of n_ctx with kv_self.size in many places * mamba : use n_tokens directly instead of n_tok * mamba : in comments, properly refer to KV cells instead of slots * mamba : reduce memory usage of ggml_ssm_scan From 290.37 MiB to 140.68 MiB of CPU compute buffer size with Mamba 3B with a batch size of 512. The result tensor of ggml_ssm_scan was previously a big part of the CPU compute buffer size. To make it smaller, it does not contain the intermediate ssm states anymore. Both y and the last ssm state are combined in the result tensor, because it seems only a single tensor can be returned by an operator with the way the graph is built. * mamba : simultaneous sequence processing A batch can now contain tokens from multiple sequences. This is necessary for at least the parallel example, the server example, and the HellaSwag test in the perplexity example. However, for this to be useful, uses of llama_kv_cache_seq_rm/cp will need to be changed to work on whole sequences. * ggml : add ggml_ssm_conv as a new operator for the conv step of Mamba This operator makes it possible to use and update the correct states for each token of the batch in the same way as ggml_ssm_scan. Other solutions which use existing operators would need loops which would add too many nodes to the graph (at least the ones I thought of). Using this operator further reduces the size of the CPU compute buffer from 140.68 MiB to 103.20 MiB with Mamba 3B with a batch size of 512. And (at least on CPU), it's a bit faster than before. Note that "ggml_ssm_conv" is probably not the most appropriate name, and it could be changed if a better one is found. * llama : add inp_s_seq as a new input tensor The most convenient implementation to select the correct state (for Mamba) for each token is to directly get the correct index from a tensor. This is why inp_s_seq is storing int32_t and not floats. The other, less convenient way to select the correct state would be to have inp_KQ_mask contain 1.0f for each state used by a token and 0.0f otherwise. This complicates quickly fetching the first used state of a token, and is also less efficient because a whole row of the mask would always need to be read for each token. Using indexes makes it easy to stop searching when there are no more sequences for a token, and the first sequence assigned is always very quickly available (it's the first element of each row). * mamba : support llama_kv_cache_seq_cp copy chains * mamba : support shifting and dividing the kv cache pos * mamba : make the server and parallel examples work with whole sequences A seq_id is dedicated to the system prompt in both cases. * llama : make llama_kv_cache_seq_rm return whether it succeeded or not * mamba : dedicate an input tensor for state copy indices This is cleaner and makes it easier to adapt when/if token positions (and by extension, inp_K_shift) are no longer integers. * mamba : adapt perplexity, batched, and batched-bench examples * perplexity : limit the max number of sequences This adapts to what the loaded model can provide. * llama : add llama_n_max_seq to get the upper limit for seq_ids Used by the perplexity example. * batched : pass n_parallel to the model's context params This should have been there already, but it wasn't. * batched-bench : reserve sequences to support Mamba * batched-bench : fix tokens being put in wrong sequences Generation quality isn't what's measured in there anyway, but at least using the correct sequences avoids using non-consecutive token positions. * mamba : stop abusing attention metadata This breaks existing converted-to-GGUF Mamba models, but will allow supporting mixed architectures like MambaFormer without needing to break Mamba models. This will also allow changing the size of Mamba's states without having to reconvert models in the future. (e.g. using something else than d_conv - 1 columns for the conv_states will not require breaking existing converted Mamba models again) * gguf-py : add new KV metadata key-value pairs for Mamba * llama : add new metadata key-value pairs for Mamba * llama : guard against divisions by zero when n_head is 0 * mamba : rename "unlimited" KV cache property to "recurrent" * mamba : more correctly update the "used" field of the KV cache * ggml : in ggml_ssm_scan, use a threshold for soft_plus This is how the official Mamba implementation does it, and it's also what torch.nn.Softplus does. * convert : for Mamba, fallback to internal NeoX tokenizer The resulting models are exactly the same as if the tokenizer.json and tokenizer_config.json of GPT-NeoX were there. * mamba : support state saving and restoring * ggml : implicitly pass src tensors through dst for Mamba-related ops * mamba : clarify some comments * server : fix cache_tokens not getting correctly resized Otherwise, when the "we have to evaluate at least 1 token" special case was triggered, an extra token was kept in cache_tokens even if it was removed from the KV cache. For Mamba, this caused useless prompt reprocessing when the previous request triggered the above case. * convert-hf : support new metadata keys for Mamba For the models available at https://huggingface.co/collections/state-spaces/transformers-compatible-mamba-65e7b40ab87e5297e45ae406 * mamba : rename metadata to be more similar to transformers library This breaks existing converted-to-GGUF models, but the metadata names are more "standard". * mamba : support mamba-*-hf models These models share their token_embd.weight with their output.weight * mamba : add missing spaces This is purely a formatting change. * convert-hf : omit output.weight when identical with token_embd.weight Only for Mamba for now, but it might be relevant for other models eventually. Most Mamba models actually share these two tensors, albeit implicitly. * readme : add Mamba to supported models, and add recent API changes * mamba : move state_seq and state_mask views outside layer loop A few tensors were also missing `struct` in front of `ggml_tensor`.
2024-03-08 22:31:00 +00:00
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
return bid is not None and new_name in (
self.format_tensor_name(n, bid, ".weight" if name.endswith(".weight") else "") for n in [
gguf.MODEL_TENSOR.SSM_CONV1D,
gguf.MODEL_TENSOR.SSM_X,
gguf.MODEL_TENSOR.SSM_DT,
gguf.MODEL_TENSOR.SSM_A,
gguf.MODEL_TENSOR.SSM_D,
]
)
llama : support Mamba Selective State Space Models (#5328) * mamba : begin working on support for Mamba SSM * mamba : begin figuring out how to (ab)use the kv cache for Mamba * mamba : recurrent inference almost works, but incoherent * mamba : recurrent inference WORKS!!! * convert : optionally use d_conv and d_state from config.json for Mamba * mamba : refactor recurrent conv, resulting in 20% perf increase It's still slower than I'd like, but I did not really optimize `ggml_exp` yet. I also refactored `ggml_exp` to work with tensors with more than 2 dimensions. * ggml : parallelize ggml_exp This results in 8% faster token generation for Mamba-130M. * mamba : simplify the conv step with a self-overlapping view Turns out the conv_state can be made smaller by one column. Note that this breaks existing GGUFs of Mamba, because the key_value_length field is tied to the conv_state size. Convolution with a self-overlapping view is cool! And it's much simpler than what I initially thought would be necessary to make the convolution step work with more than 1 token at a time. Next step is to make the SSM step work on batches of tokens too, and thus I need to figure out a way to make a parallel selective scan which will keep the ssm_state small and won't make it bigger by a factor of (n_layer * batch_size). * llama : fix Mamba KV self size wrongly displaying as f16 instead of f32 Relatedly, I also tried to see if other types than f32 worked for the states, but they don't, because of the operators used. It's probably better anyway to keep lots of precision there, since the states are small anyway. * mamba : fix self-overlapping view depth stride * mamba : handle batches of more than 1 token This means running Mamba no longer crashes when using the default settings! And probably also slightly faster prompt processing. Both batched and non-batched processing yield the same output. Previously, the state was not cleared when starting a sequence. Next step is to make the KV cache API work as expected for Mamba models. * ggml: add ggml_ssm_scan to help with parallel selective scan If the selective scan was implemented without a custom operator, there would be waaay too many nodes in the graph. For example, for Mamba-130M, with a batch size of 512 (the default), a naive selective scan could add at least 24*512=12288 nodes, which is more than LLAMA_MAX_NODES (8192), and that's only for the smallest Mamba model. So it's much cleaner with a custom operator. Not sure about the name, though. * ggml : in ggml_ssm_scan, merge multiple rows in the same vec operation This will help with performance on CPU if ggml_vec_mul_f32 and ggml_vec_add_f32 are ever optimized with SIMD. * mamba : very basic quantization support Mostly works, but there is currently no difference between the variants of a k-quant (e.g. Q4_K_S and Q4_K_M are the same). Most of the SSM-specific weights can be kept in f32 without affecting the size that much, since they are relatively small. (the linear projection weights are responsible for most of Mamba's size) Too much quantization seems to make the state degrade quite fast, and the model begins to output gibberish. It seems to affect bigger models to a lesser extent than small models, but I'm not sure by how much. Experimentation will be needed to figure out which weights are more important for the _M (and _L?) variants of k-quants for Mamba. * convert : fix wrong name for layer norm weight of offical Mamba models I was using Q-bert/Mamba-* models before, which have a slighlty different naming scheme for the weights. (they start with "model.layers" instead of "backbone.layers") * mamba : fuse more steps of the SSM scan in the ggml_ssm_scan operator This increases performance on CPU by around 30% for prompt processing, and by around 20% for text generation. However, it also makes the ggml_exp and ggml_soft_plus operators unused. Whether or not they should be kept will be decided later. * convert : for Mamba, also consider the "MambaLMHeadModel" arch name It's the name of the class of the official implementation, though they don't use it (yet) in the "architectures" field of config.json * mamba : fix vocab size problems with official models The perplexity was waaaay to high for models with a non-round vocab size. Not sure why, but it needed to be fixed in the metadata. Note that this breaks existing GGUF-converted Mamba models, but **only if** the vocab size was not already rounded. * ggml : remove ggml_exp and ggml_soft_plus They did not exist anyway outside of this branch, and since ggml_ssm_scan fused operations together, they are unused. It's always possible to bring them back if needed. * mamba : remove some useless comments No code change. * convert : fix flake8 linter errors * mamba : apply suggestions from code review * mamba : remove unecessary branch for row-wise ssm_state and C multiplication It was previously done to avoid permuting when only one token is processed at a time (like when generating text), but permuting is cheap, and dynamically changing the compute graph is not future-proof. * ggml : in ggml_ssm_scan, use more appropriate asserts * ggml : rename the destination pointer in ggml_compute_forward_ssm_scan_f32 * mamba : multiple sequences, but one at a time This is a step towards making this Mamba implementation usable with the server example (the way the system prompt is kept when clearing the client slots will need to be changed before this can work, though). The KV cache size for this kind of model is tied to the maximum number of sequences kept at any single time. For now, this number is obtained from n_parallel (plus one, to have an extra sequence to dedicate to the system prompt), but there might be a better way to do this which won't also make the main example use 2 cells even if only 1 is really used. (for this specific case, --parallel 0 helps) Simultaneous sequence processing will probably require changes to ggml_ssm_scan, and possibly a new operator for the conv step. * mamba : support llama_kv_cache_seq_cp This (mis)uses the logic around K shifts, because tokens in a state can't be shifted anyway, and because inp_K_shift has the right shape and type. Using ggml_get_rows is a nice way to do copies, but copy chains can't work. Fortunately, copy chains don't really seem to be used in the examples. Each KV cell is dedicated to the sequence ID corresponding to its own index. * mamba : use a state mask It's cleaner than the previous heuristic of checking for the pos of the first token in the batch. inp_KQ_mask could not be re-used for this, because it has the wrong shape and because it seems more suited to the next step of simultaneous sequence processing (helping with the problem of remembering which token belongs to which sequence(s)/state(s)). * llama : replace the usage of n_ctx with kv_self.size in many places * mamba : use n_tokens directly instead of n_tok * mamba : in comments, properly refer to KV cells instead of slots * mamba : reduce memory usage of ggml_ssm_scan From 290.37 MiB to 140.68 MiB of CPU compute buffer size with Mamba 3B with a batch size of 512. The result tensor of ggml_ssm_scan was previously a big part of the CPU compute buffer size. To make it smaller, it does not contain the intermediate ssm states anymore. Both y and the last ssm state are combined in the result tensor, because it seems only a single tensor can be returned by an operator with the way the graph is built. * mamba : simultaneous sequence processing A batch can now contain tokens from multiple sequences. This is necessary for at least the parallel example, the server example, and the HellaSwag test in the perplexity example. However, for this to be useful, uses of llama_kv_cache_seq_rm/cp will need to be changed to work on whole sequences. * ggml : add ggml_ssm_conv as a new operator for the conv step of Mamba This operator makes it possible to use and update the correct states for each token of the batch in the same way as ggml_ssm_scan. Other solutions which use existing operators would need loops which would add too many nodes to the graph (at least the ones I thought of). Using this operator further reduces the size of the CPU compute buffer from 140.68 MiB to 103.20 MiB with Mamba 3B with a batch size of 512. And (at least on CPU), it's a bit faster than before. Note that "ggml_ssm_conv" is probably not the most appropriate name, and it could be changed if a better one is found. * llama : add inp_s_seq as a new input tensor The most convenient implementation to select the correct state (for Mamba) for each token is to directly get the correct index from a tensor. This is why inp_s_seq is storing int32_t and not floats. The other, less convenient way to select the correct state would be to have inp_KQ_mask contain 1.0f for each state used by a token and 0.0f otherwise. This complicates quickly fetching the first used state of a token, and is also less efficient because a whole row of the mask would always need to be read for each token. Using indexes makes it easy to stop searching when there are no more sequences for a token, and the first sequence assigned is always very quickly available (it's the first element of each row). * mamba : support llama_kv_cache_seq_cp copy chains * mamba : support shifting and dividing the kv cache pos * mamba : make the server and parallel examples work with whole sequences A seq_id is dedicated to the system prompt in both cases. * llama : make llama_kv_cache_seq_rm return whether it succeeded or not * mamba : dedicate an input tensor for state copy indices This is cleaner and makes it easier to adapt when/if token positions (and by extension, inp_K_shift) are no longer integers. * mamba : adapt perplexity, batched, and batched-bench examples * perplexity : limit the max number of sequences This adapts to what the loaded model can provide. * llama : add llama_n_max_seq to get the upper limit for seq_ids Used by the perplexity example. * batched : pass n_parallel to the model's context params This should have been there already, but it wasn't. * batched-bench : reserve sequences to support Mamba * batched-bench : fix tokens being put in wrong sequences Generation quality isn't what's measured in there anyway, but at least using the correct sequences avoids using non-consecutive token positions. * mamba : stop abusing attention metadata This breaks existing converted-to-GGUF Mamba models, but will allow supporting mixed architectures like MambaFormer without needing to break Mamba models. This will also allow changing the size of Mamba's states without having to reconvert models in the future. (e.g. using something else than d_conv - 1 columns for the conv_states will not require breaking existing converted Mamba models again) * gguf-py : add new KV metadata key-value pairs for Mamba * llama : add new metadata key-value pairs for Mamba * llama : guard against divisions by zero when n_head is 0 * mamba : rename "unlimited" KV cache property to "recurrent" * mamba : more correctly update the "used" field of the KV cache * ggml : in ggml_ssm_scan, use a threshold for soft_plus This is how the official Mamba implementation does it, and it's also what torch.nn.Softplus does. * convert : for Mamba, fallback to internal NeoX tokenizer The resulting models are exactly the same as if the tokenizer.json and tokenizer_config.json of GPT-NeoX were there. * mamba : support state saving and restoring * ggml : implicitly pass src tensors through dst for Mamba-related ops * mamba : clarify some comments * server : fix cache_tokens not getting correctly resized Otherwise, when the "we have to evaluate at least 1 token" special case was triggered, an extra token was kept in cache_tokens even if it was removed from the KV cache. For Mamba, this caused useless prompt reprocessing when the previous request triggered the above case. * convert-hf : support new metadata keys for Mamba For the models available at https://huggingface.co/collections/state-spaces/transformers-compatible-mamba-65e7b40ab87e5297e45ae406 * mamba : rename metadata to be more similar to transformers library This breaks existing converted-to-GGUF models, but the metadata names are more "standard". * mamba : support mamba-*-hf models These models share their token_embd.weight with their output.weight * mamba : add missing spaces This is purely a formatting change. * convert-hf : omit output.weight when identical with token_embd.weight Only for Mamba for now, but it might be relevant for other models eventually. Most Mamba models actually share these two tensors, albeit implicitly. * readme : add Mamba to supported models, and add recent API changes * mamba : move state_seq and state_mask views outside layer loop A few tensors were also missing `struct` in front of `ggml_tensor`.
2024-03-08 22:31:00 +00:00
llama : add Command-R support (#6033) Information about the Command-R 35B model (128k context) can be found at: https://huggingface.co/CohereForAI/c4ai-command-r-v01 Based on the llama2 model with a few changes: 1) New hyper parameter to scale output logits (logit_scale) 2) Uses LayerNorm instead of RMSNorm 3) Transfomer layers have a single shared LayerNorm that feeds into both the self-attention and FFN layers in parallel. There is no post-attention LayerNorm. 4) No support for Rotary Position Embeddings (RoPE) scaling 5) No biases used Find GGUF files here: https://huggingface.co/andrewcanis/c4ai-command-r-v01-GGUF To convert model to GGUF format yourself: 1) Download Command-R Hugging Face safetensors: git lfs install git clone https://huggingface.co/CohereForAI/c4ai-command-r-v01 2) Run: python3 convert-hf-to-gguf.py --outtype f16 ./c4ai-command-r-v01
2024-03-15 20:41:22 +00:00
@Model.register("CohereForCausalLM")
class CommandR2Model(Model):
model_arch = gguf.MODEL_ARCH.COMMAND_R
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
# max_position_embeddings = 8192 in config.json but model was actually
# trained on 128k context length
self.hparams["max_position_embeddings"] = self.hparams["model_max_length"]
def set_gguf_parameters(self):
super().set_gguf_parameters()
self.gguf_writer.add_logit_scale(self.hparams["logit_scale"])
self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.NONE)
Implement the OLMo architecture (#6741) * implement olmo architecture * remove unused variable * remove unused moe branch * remove check for weight * remove superfluous moe, bias and rope tensors * clarified comment * fix clamp_kqv setting * remove obsolete parameter name filter
2024-04-19 09:35:54 +00:00
@Model.register("OlmoForCausalLM")
@Model.register("OLMoForCausalLM")
class OlmoModel(Model):
model_arch = gguf.MODEL_ARCH.OLMO
def set_gguf_parameters(self):
super().set_gguf_parameters()
self.gguf_writer.add_layer_norm_eps(1e-5)
Fix OLMo HF to GGUF conversion (#6910)
2024-05-07 19:39:43 +00:00
clip_qkv = self.hparams.get("clip_qkv")
if clip_qkv is not None:
self.gguf_writer.add_clamp_kqv(clip_qkv)
Implement the OLMo architecture (#6741) * implement olmo architecture * remove unused variable * remove unused moe branch * remove check for weight * remove superfluous moe, bias and rope tensors * clarified comment * fix clamp_kqv setting * remove obsolete parameter name filter
2024-04-19 09:35:54 +00:00
# Same as super class, but permuting q_proj, k_proj
# Copied from: LlamaModel
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
del bid # unused
n_head = self.hparams["num_attention_heads"]
Implement the OLMo architecture (#6741) * implement olmo architecture * remove unused variable * remove unused moe branch * remove check for weight * remove superfluous moe, bias and rope tensors * clarified comment * fix clamp_kqv setting * remove obsolete parameter name filter
2024-04-19 09:35:54 +00:00
n_kv_head = self.hparams.get("num_key_value_heads")
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
if name.endswith("q_proj.weight"):
data_torch = LlamaModel.permute(data_torch, n_head, n_head)
if name.endswith("k_proj.weight"):
data_torch = LlamaModel.permute(data_torch, n_head, n_kv_head)
Implement the OLMo architecture (#6741) * implement olmo architecture * remove unused variable * remove unused moe branch * remove check for weight * remove superfluous moe, bias and rope tensors * clarified comment * fix clamp_kqv setting * remove obsolete parameter name filter
2024-04-19 09:35:54 +00:00
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
return [(self.map_tensor_name(name), data_torch)]
Implement the OLMo architecture (#6741) * implement olmo architecture * remove unused variable * remove unused moe branch * remove check for weight * remove superfluous moe, bias and rope tensors * clarified comment * fix clamp_kqv setting * remove obsolete parameter name filter
2024-04-19 09:35:54 +00:00
llama : add Jina Embeddings architecture (#6826) * feat: first things to do * feat: create tensors for Jina architecture * fix: use other tensors * feat: embedding gets results * fix: fix usage of ALIBI * fix: clean prints * fix: do some cleanup unused vars * fix: revert changes to Makefile and CMakeLists * fix: revert some changes * fix: fix small detail * fix: fix convert formatting * fix: fix linting and editor * feat: set proper vocab settings * fix: JinaBertForMaskedLM registration * feat: support q_normalization and k_normalization in Jina arch * feat: handle gpt2 tokenizer with Jina architecture * feat: example comments in embedding * feat: rename Jina Bert to Jina Bert V2 * fix: add some changes as per review * feat: proper KQ_pos for Jina embeddings * feat: add capacity to load models ES and DE for Spanish * llama : fix pre-tokenizers * ggml : full ALiBi support * ggml : update ggml_soft_max_ext() CUDA, SYCL * ggml : ggml_flash_attn_ext() support ALiBi (CPU) * ggml : ggml_flash_attn_ext() support ALiBi (Metal) * ggml : fix warning * ggml : ggml_flash_attn_ext() support ALiBi (CUDA) ggml-ci * minor : clean-up * embedding : add warning about missing SEP --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-05-11 07:46:09 +00:00
@Model.register("JinaBertModel", "JinaBertForMaskedLM")
class JinaBertV2Model(BertModel):
model_arch = gguf.MODEL_ARCH.JINA_BERT_V2
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.intermediate_size = self.hparams["intermediate_size"]
def get_tensors(self):
for name, data in super().get_tensors():
if 'gated_layers' in name:
d1 = data[:self.intermediate_size, :]
name1 = name.replace('gated_layers', 'gated_layers_w')
d2 = data[self.intermediate_size:, :]
name2 = name.replace('gated_layers', 'gated_layers_v')
yield name1, d1
yield name2, d2
continue
yield name, data
def set_vocab(self, *args, **kwargs):
tokenizer_class = 'BertTokenizer'
with open(self.dir_model / "tokenizer_config.json", "r", encoding="utf-8") as f:
tokenizer_class = json.load(f)['tokenizer_class']
if tokenizer_class == 'BertTokenizer':
super().set_vocab()
elif tokenizer_class == 'RobertaTokenizer':
self._set_vocab_gpt2()
self.gguf_writer.add_token_type_count(2)
else:
raise NotImplementedError(f'Tokenizer {tokenizer_class} is not supported for JinaBertModel')
self.gguf_writer.add_add_bos_token(True)
self.gguf_writer.add_add_eos_token(True)
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
###### CONVERSION LOGIC ######
Implement the OLMo architecture (#6741) * implement olmo architecture * remove unused variable * remove unused moe branch * remove check for weight * remove superfluous moe, bias and rope tensors * clarified comment * fix clamp_kqv setting * remove obsolete parameter name filter
2024-04-19 09:35:54 +00:00
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
# tree of lazy tensors
convert-hf : support bfloat16 conversion (#7158) * convert-hf : support bfloat16 conversion * gguf-py : flake8 fixes * convert-hf : add missing space after comma * convert-hf : get bit-exact same output as ./quantize The quantization version was missing. * convert-hf : don't round bf16 NANs * convert-hf : save some memory with np.int16 intermediate bf16 weights * convert-hf : more closely match llama.cpp with which weights to keep in f32 * convert-hf : add --outtype auto-f16 A reason for this to exist is for model quantizers who want an initial GGUF with the most fidelity to the original model while still using a 16-bit float type instead of 32-bit floats. * convert-hf : remove a semicolon because flake8 doesn't like it It's a reflex from when programming in C/C++, I guess. * convert-hf : support outtype templating in outfile name * convert-hf : rename --outtype auto-f16 to --outtype auto
2024-05-11 15:06:26 +00:00
class LazyTorchTensor(gguf.LazyBase):
_tensor_type = torch.Tensor
# to keep the type-checker happy
dtype: torch.dtype
shape: torch.Size
Implement the OLMo architecture (#6741) * implement olmo architecture * remove unused variable * remove unused moe branch * remove check for weight * remove superfluous moe, bias and rope tensors * clarified comment * fix clamp_kqv setting * remove obsolete parameter name filter
2024-04-19 09:35:54 +00:00
convert-hf : support bfloat16 conversion (#7158) * convert-hf : support bfloat16 conversion * gguf-py : flake8 fixes * convert-hf : add missing space after comma * convert-hf : get bit-exact same output as ./quantize The quantization version was missing. * convert-hf : don't round bf16 NANs * convert-hf : save some memory with np.int16 intermediate bf16 weights * convert-hf : more closely match llama.cpp with which weights to keep in f32 * convert-hf : add --outtype auto-f16 A reason for this to exist is for model quantizers who want an initial GGUF with the most fidelity to the original model while still using a 16-bit float type instead of 32-bit floats. * convert-hf : remove a semicolon because flake8 doesn't like it It's a reflex from when programming in C/C++, I guess. * convert-hf : support outtype templating in outfile name * convert-hf : rename --outtype auto-f16 to --outtype auto
2024-05-11 15:06:26 +00:00
# only used when converting a torch.Tensor to a np.ndarray
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
_dtype_map: dict[torch.dtype, type] = {
torch.float16: np.float16,
torch.float32: np.float32,
}
Implement the OLMo architecture (#6741) * implement olmo architecture * remove unused variable * remove unused moe branch * remove check for weight * remove superfluous moe, bias and rope tensors * clarified comment * fix clamp_kqv setting * remove obsolete parameter name filter
2024-04-19 09:35:54 +00:00
convert-hf : support bfloat16 conversion (#7158) * convert-hf : support bfloat16 conversion * gguf-py : flake8 fixes * convert-hf : add missing space after comma * convert-hf : get bit-exact same output as ./quantize The quantization version was missing. * convert-hf : don't round bf16 NANs * convert-hf : save some memory with np.int16 intermediate bf16 weights * convert-hf : more closely match llama.cpp with which weights to keep in f32 * convert-hf : add --outtype auto-f16 A reason for this to exist is for model quantizers who want an initial GGUF with the most fidelity to the original model while still using a 16-bit float type instead of 32-bit floats. * convert-hf : remove a semicolon because flake8 doesn't like it It's a reflex from when programming in C/C++, I guess. * convert-hf : support outtype templating in outfile name * convert-hf : rename --outtype auto-f16 to --outtype auto
2024-05-11 15:06:26 +00:00
def numpy(self) -> gguf.LazyNumpyTensor:
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
dtype = self._dtype_map[self.dtype]
convert-hf : support bfloat16 conversion (#7158) * convert-hf : support bfloat16 conversion * gguf-py : flake8 fixes * convert-hf : add missing space after comma * convert-hf : get bit-exact same output as ./quantize The quantization version was missing. * convert-hf : don't round bf16 NANs * convert-hf : save some memory with np.int16 intermediate bf16 weights * convert-hf : more closely match llama.cpp with which weights to keep in f32 * convert-hf : add --outtype auto-f16 A reason for this to exist is for model quantizers who want an initial GGUF with the most fidelity to the original model while still using a 16-bit float type instead of 32-bit floats. * convert-hf : remove a semicolon because flake8 doesn't like it It's a reflex from when programming in C/C++, I guess. * convert-hf : support outtype templating in outfile name * convert-hf : rename --outtype auto-f16 to --outtype auto
2024-05-11 15:06:26 +00:00
return gguf.LazyNumpyTensor(
convert-hf : support direct Q8_0 conversion (#7234) * convert-hf : support q8_0 conversion * convert-hf : add missing ftype This was messing with the checksums otherwise. * convert-hf : add missing ftype to Baichuan and Xverse I didn't notice these on my first pass.
2024-05-13 18:10:51 +00:00
meta=gguf.LazyNumpyTensor.meta_with_dtype_and_shape(dtype, self.shape),
convert-hf : support bfloat16 conversion (#7158) * convert-hf : support bfloat16 conversion * gguf-py : flake8 fixes * convert-hf : add missing space after comma * convert-hf : get bit-exact same output as ./quantize The quantization version was missing. * convert-hf : don't round bf16 NANs * convert-hf : save some memory with np.int16 intermediate bf16 weights * convert-hf : more closely match llama.cpp with which weights to keep in f32 * convert-hf : add --outtype auto-f16 A reason for this to exist is for model quantizers who want an initial GGUF with the most fidelity to the original model while still using a 16-bit float type instead of 32-bit floats. * convert-hf : remove a semicolon because flake8 doesn't like it It's a reflex from when programming in C/C++, I guess. * convert-hf : support outtype templating in outfile name * convert-hf : rename --outtype auto-f16 to --outtype auto
2024-05-11 15:06:26 +00:00
lazy=self._lazy,
args=(self,),
func=(lambda s: s[0].numpy())
)
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
convert-hf : support bfloat16 conversion (#7158) * convert-hf : support bfloat16 conversion * gguf-py : flake8 fixes * convert-hf : add missing space after comma * convert-hf : get bit-exact same output as ./quantize The quantization version was missing. * convert-hf : don't round bf16 NANs * convert-hf : save some memory with np.int16 intermediate bf16 weights * convert-hf : more closely match llama.cpp with which weights to keep in f32 * convert-hf : add --outtype auto-f16 A reason for this to exist is for model quantizers who want an initial GGUF with the most fidelity to the original model while still using a 16-bit float type instead of 32-bit floats. * convert-hf : remove a semicolon because flake8 doesn't like it It's a reflex from when programming in C/C++, I guess. * convert-hf : support outtype templating in outfile name * convert-hf : rename --outtype auto-f16 to --outtype auto
2024-05-11 15:06:26 +00:00
@classmethod
convert-hf : support direct Q8_0 conversion (#7234) * convert-hf : support q8_0 conversion * convert-hf : add missing ftype This was messing with the checksums otherwise. * convert-hf : add missing ftype to Baichuan and Xverse I didn't notice these on my first pass.
2024-05-13 18:10:51 +00:00
def meta_with_dtype_and_shape(cls, dtype: torch.dtype, shape: torch.Size) -> Tensor:
return torch.empty(size=shape, dtype=dtype, device="meta")
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
@classmethod
def __torch_function__(cls, func, types, args=(), kwargs=None):
del types # unused
if kwargs is None:
kwargs = {}
if func is torch.Tensor.numpy:
return args[0].numpy()
convert-hf : support bfloat16 conversion (#7158) * convert-hf : support bfloat16 conversion * gguf-py : flake8 fixes * convert-hf : add missing space after comma * convert-hf : get bit-exact same output as ./quantize The quantization version was missing. * convert-hf : don't round bf16 NANs * convert-hf : save some memory with np.int16 intermediate bf16 weights * convert-hf : more closely match llama.cpp with which weights to keep in f32 * convert-hf : add --outtype auto-f16 A reason for this to exist is for model quantizers who want an initial GGUF with the most fidelity to the original model while still using a 16-bit float type instead of 32-bit floats. * convert-hf : remove a semicolon because flake8 doesn't like it It's a reflex from when programming in C/C++, I guess. * convert-hf : support outtype templating in outfile name * convert-hf : rename --outtype auto-f16 to --outtype auto
2024-05-11 15:06:26 +00:00
return LazyTorchTensor._wrap_fn(func)(*args, **kwargs)
scripts: Generalize convert scripts (#3838) * Replace convert-*-hf-to-gguf.py files with convert-hf-to-gguf.py
2023-11-09 10:09:29 +00:00
ci : add flake8 to github actions (python linting) (#4129) Disabled rules: * E203 Whitespace before ':' - disabled because we often use 'C' Style where values are aligned * E211 Whitespace before '(' (E211) - disabled because we often use 'C' Style where values are aligned * E221 Multiple spaces before operator - disabled because we often use 'C' Style where values are aligned * E225 Missing whitespace around operator - disabled because it's broken so often it seems like a standard * E231 Missing whitespace after ',', ';', or ':' - disabled because we often use 'C' Style where values are aligned * E241 Multiple spaces after ',' - disabled because we often use 'C' Style where values are aligned * E251 Unexpected spaces around keyword / parameter equals - disabled because it's broken so often it seems like a standard * E261 At least two spaces before inline comment - disabled because it's broken so often it seems like a standard * E266 Too many leading '#' for block comment - sometimes used as "section" separator * E501 Line too long - disabled because it's broken so often it seems like a standard * E701 Multiple statements on one line (colon) - broken only in convert.py when defining abstract methods (we can use# noqa instead) * E704 Multiple statements on one line - broken only in convert.py when defining abstract methods (we can use# noqa instead)
2023-11-20 10:35:47 +00:00
scripts: Generalize convert scripts (#3838) * Replace convert-*-hf-to-gguf.py files with convert-hf-to-gguf.py
2023-11-09 10:09:29 +00:00
def parse_args() -> argparse.Namespace:
llama : add PLaMo model (#3557) * add plamo mock * add tensor loading * plamo convert * update norm * able to compile * fix norm_rms_eps hparam * runnable * use inp_pos * seems ok * update kqv code * remove develop code * update README * shuffle attn_q.weight and attn_output.weight for broadcasting * remove plamo_llm_build_kqv and use llm_build_kqv * fix style * update * llama : remove obsolete KQ_scale * plamo : fix tensor names for correct GPU offload --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-12-24 13:35:49 +00:00
parser = argparse.ArgumentParser(
description="Convert a huggingface model to a GGML compatible file")
scripts: Generalize convert scripts (#3838) * Replace convert-*-hf-to-gguf.py files with convert-hf-to-gguf.py
2023-11-09 10:09:29 +00:00
parser.add_argument(
"--vocab-only", action="store_true",
help="extract only the vocab",
)
llama : add AWQ for llama, llama2, mpt, and mistral models (#4593) * update: awq support llama-7b model * update: change order * update: benchmark results for llama2-7b * update: mistral 7b v1 benchmark * update: support 4 models * fix: Readme * update: ready for PR * update: readme * fix: readme * update: change order import * black * format code * update: work for bot mpt and awqmpt * update: readme * Rename to llm_build_ffn_mpt_awq * Formatted other files * Fixed params count * fix: remove code * update: more detail for mpt * fix: readme * fix: readme * update: change folder architecture * fix: common.cpp * fix: readme * fix: remove ggml_repeat * update: cicd * update: cicd * uppdate: remove use_awq arg * update: readme * llama : adapt plamo to new ffn ggml-ci --------- Co-authored-by: Trần Đức Nam <v.namtd12@vinai.io> Co-authored-by: Le Hoang Anh <v.anhlh33@vinai.io> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-12-27 15:39:45 +00:00
parser.add_argument(
"--awq-path", type=Path, default=None,
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
help="Path to scale awq cache file",
)
scripts: Generalize convert scripts (#3838) * Replace convert-*-hf-to-gguf.py files with convert-hf-to-gguf.py
2023-11-09 10:09:29 +00:00
parser.add_argument(
"--outfile", type=Path,
convert-hf : support bfloat16 conversion (#7158) * convert-hf : support bfloat16 conversion * gguf-py : flake8 fixes * convert-hf : add missing space after comma * convert-hf : get bit-exact same output as ./quantize The quantization version was missing. * convert-hf : don't round bf16 NANs * convert-hf : save some memory with np.int16 intermediate bf16 weights * convert-hf : more closely match llama.cpp with which weights to keep in f32 * convert-hf : add --outtype auto-f16 A reason for this to exist is for model quantizers who want an initial GGUF with the most fidelity to the original model while still using a 16-bit float type instead of 32-bit floats. * convert-hf : remove a semicolon because flake8 doesn't like it It's a reflex from when programming in C/C++, I guess. * convert-hf : support outtype templating in outfile name * convert-hf : rename --outtype auto-f16 to --outtype auto
2024-05-11 15:06:26 +00:00
help="path to write to; default: based on input. {ftype} will be replaced by the outtype.",
scripts: Generalize convert scripts (#3838) * Replace convert-*-hf-to-gguf.py files with convert-hf-to-gguf.py
2023-11-09 10:09:29 +00:00
)
parser.add_argument(
convert-hf : support direct Q8_0 conversion (#7234) * convert-hf : support q8_0 conversion * convert-hf : add missing ftype This was messing with the checksums otherwise. * convert-hf : add missing ftype to Baichuan and Xverse I didn't notice these on my first pass.
2024-05-13 18:10:51 +00:00
"--outtype", type=str, choices=["f32", "f16", "bf16", "q8_0", "auto"], default="f16",
help="output format - use f32 for float32, f16 for float16, bf16 for bfloat16, q8_0 for Q8_0, auto for the highest-fidelity 16-bit float type depending on the first loaded tensor type",
scripts: Generalize convert scripts (#3838) * Replace convert-*-hf-to-gguf.py files with convert-hf-to-gguf.py
2023-11-09 10:09:29 +00:00
)
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
parser.add_argument(
"--bigendian", action="store_true",
help="model is executed on big endian machine",
)
scripts: Generalize convert scripts (#3838) * Replace convert-*-hf-to-gguf.py files with convert-hf-to-gguf.py
2023-11-09 10:09:29 +00:00
parser.add_argument(
"model", type=Path,
help="directory containing model file",
)
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
parser.add_argument(
"--use-temp-file", action="store_true",
help="use the tempfile library while processing (helpful when running out of memory, process killed)",
)
parser.add_argument(
"--no-lazy", action="store_true",
help="use more RAM by computing all outputs before writing (use in case lazy evaluation is broken)",
)
parser.add_argument(
"--model-name", type=str, default=None,
help="name of the model",
)
parser.add_argument(
"--verbose", action="store_true",
help="increase output verbosity",
)
scripts: Generalize convert scripts (#3838) * Replace convert-*-hf-to-gguf.py files with convert-hf-to-gguf.py
2023-11-09 10:09:29 +00:00
return parser.parse_args()
python : add check-requirements.sh and GitHub workflow (#4585) * python: add check-requirements.sh and GitHub workflow This script and workflow forces package versions to remain compatible across all convert*.py scripts, while allowing secondary convert scripts to import dependencies not wanted in convert.py. * Move requirements into ./requirements * Fail on "==" being used for package requirements (but can be suppressed) * Enforce "compatible release" syntax instead of == * Update workflow * Add upper version bound for transformers and protobuf * improve check-requirements.sh * small syntax change * don't remove venvs if nocleanup is passed * See if this fixes docker workflow * Move check-requirements.sh into ./scripts/ --------- Co-authored-by: Jared Van Bortel <jared@nomic.ai>
2023-12-29 14:50:29 +00:00
def main() -> None:
args = parse_args()
scripts: Generalize convert scripts (#3838) * Replace convert-*-hf-to-gguf.py files with convert-hf-to-gguf.py
2023-11-09 10:09:29 +00:00
convert.py : add python logging instead of print() (#6511) * convert.py: add python logging instead of print() * convert.py: verbose flag takes priority over dump flag log suppression * convert.py: named instance logging * convert.py: use explicit logger id string * convert.py: convert extra print() to named logger * convert.py: sys.stderr.write --> logger.error * *.py: Convert all python scripts to use logging module * requirements.txt: remove extra line * flake8: update flake8 ignore and exclude to match ci settings * gh-actions: add flake8-no-print to flake8 lint step * pre-commit: add flake8-no-print to flake8 and also update pre-commit version * convert-hf-to-gguf.py: print() to logger conversion * *.py: logging basiconfig refactor to use conditional expression * *.py: removed commented out logging * fixup! *.py: logging basiconfig refactor to use conditional expression * constant.py: logger.error then exit should be a raise exception instead * *.py: Convert logger error and sys.exit() into a raise exception (for atypical error) * gguf-convert-endian.py: refactor convert_byteorder() to use tqdm progressbar * verify-checksum-model.py: This is the result of the program, it should be printed to stdout. * compare-llama-bench.py: add blank line for readability during missing repo response * reader.py: read_gguf_file() use print() over logging * convert.py: warning goes to stderr and won't hurt the dump output * gguf-dump.py: dump_metadata() should print to stdout * convert-hf-to-gguf.py: print --> logger.debug or ValueError() * verify-checksum-models.py: use print() for printing table * *.py: refactor logging.basicConfig() * gguf-py/gguf/*.py: use __name__ as logger name Since they will be imported and not run directly. * python-lint.yml: use .flake8 file instead * constants.py: logger no longer required * convert-hf-to-gguf.py: add additional logging * convert-hf-to-gguf.py: print() --> logger * *.py: fix flake8 warnings * revert changes to convert-hf-to-gguf.py for get_name() * convert-hf-to-gguf-update.py: use triple quoted f-string instead * *.py: accidentally corrected the wrong line * *.py: add compilade warning suggestions and style fixes
2024-05-03 19:36:41 +00:00
logging.basicConfig(level=logging.DEBUG if args.verbose else logging.INFO)
python : add check-requirements.sh and GitHub workflow (#4585) * python: add check-requirements.sh and GitHub workflow This script and workflow forces package versions to remain compatible across all convert*.py scripts, while allowing secondary convert scripts to import dependencies not wanted in convert.py. * Move requirements into ./requirements * Fail on "==" being used for package requirements (but can be suppressed) * Enforce "compatible release" syntax instead of == * Update workflow * Add upper version bound for transformers and protobuf * improve check-requirements.sh * small syntax change * don't remove venvs if nocleanup is passed * See if this fixes docker workflow * Move check-requirements.sh into ./scripts/ --------- Co-authored-by: Jared Van Bortel <jared@nomic.ai>
2023-12-29 14:50:29 +00:00
dir_model = args.model
llama : add AWQ for llama, llama2, mpt, and mistral models (#4593) * update: awq support llama-7b model * update: change order * update: benchmark results for llama2-7b * update: mistral 7b v1 benchmark * update: support 4 models * fix: Readme * update: ready for PR * update: readme * fix: readme * update: change order import * black * format code * update: work for bot mpt and awqmpt * update: readme * Rename to llm_build_ffn_mpt_awq * Formatted other files * Fixed params count * fix: remove code * update: more detail for mpt * fix: readme * fix: readme * update: change folder architecture * fix: common.cpp * fix: readme * fix: remove ggml_repeat * update: cicd * update: cicd * uppdate: remove use_awq arg * update: readme * llama : adapt plamo to new ffn ggml-ci --------- Co-authored-by: Trần Đức Nam <v.namtd12@vinai.io> Co-authored-by: Le Hoang Anh <v.anhlh33@vinai.io> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-12-27 15:39:45 +00:00
python : add check-requirements.sh and GitHub workflow (#4585) * python: add check-requirements.sh and GitHub workflow This script and workflow forces package versions to remain compatible across all convert*.py scripts, while allowing secondary convert scripts to import dependencies not wanted in convert.py. * Move requirements into ./requirements * Fail on "==" being used for package requirements (but can be suppressed) * Enforce "compatible release" syntax instead of == * Update workflow * Add upper version bound for transformers and protobuf * improve check-requirements.sh * small syntax change * don't remove venvs if nocleanup is passed * See if this fixes docker workflow * Move check-requirements.sh into ./scripts/ --------- Co-authored-by: Jared Van Bortel <jared@nomic.ai>
2023-12-29 14:50:29 +00:00
if args.awq_path:
sys.path.insert(1, str(Path(__file__).parent / 'awq-py'))
convert : partially revert PR #4818 (#5041)
2024-01-20 23:14:18 +00:00
from awq.apply_awq import add_scale_weights # type: ignore[import-not-found]
python : add check-requirements.sh and GitHub workflow (#4585) * python: add check-requirements.sh and GitHub workflow This script and workflow forces package versions to remain compatible across all convert*.py scripts, while allowing secondary convert scripts to import dependencies not wanted in convert.py. * Move requirements into ./requirements * Fail on "==" being used for package requirements (but can be suppressed) * Enforce "compatible release" syntax instead of == * Update workflow * Add upper version bound for transformers and protobuf * improve check-requirements.sh * small syntax change * don't remove venvs if nocleanup is passed * See if this fixes docker workflow * Move check-requirements.sh into ./scripts/ --------- Co-authored-by: Jared Van Bortel <jared@nomic.ai>
2023-12-29 14:50:29 +00:00
tmp_model_path = args.model / "weighted_model"
dir_model = tmp_model_path
if tmp_model_path.is_dir():
convert.py : add python logging instead of print() (#6511) * convert.py: add python logging instead of print() * convert.py: verbose flag takes priority over dump flag log suppression * convert.py: named instance logging * convert.py: use explicit logger id string * convert.py: convert extra print() to named logger * convert.py: sys.stderr.write --> logger.error * *.py: Convert all python scripts to use logging module * requirements.txt: remove extra line * flake8: update flake8 ignore and exclude to match ci settings * gh-actions: add flake8-no-print to flake8 lint step * pre-commit: add flake8-no-print to flake8 and also update pre-commit version * convert-hf-to-gguf.py: print() to logger conversion * *.py: logging basiconfig refactor to use conditional expression * *.py: removed commented out logging * fixup! *.py: logging basiconfig refactor to use conditional expression * constant.py: logger.error then exit should be a raise exception instead * *.py: Convert logger error and sys.exit() into a raise exception (for atypical error) * gguf-convert-endian.py: refactor convert_byteorder() to use tqdm progressbar * verify-checksum-model.py: This is the result of the program, it should be printed to stdout. * compare-llama-bench.py: add blank line for readability during missing repo response * reader.py: read_gguf_file() use print() over logging * convert.py: warning goes to stderr and won't hurt the dump output * gguf-dump.py: dump_metadata() should print to stdout * convert-hf-to-gguf.py: print --> logger.debug or ValueError() * verify-checksum-models.py: use print() for printing table * *.py: refactor logging.basicConfig() * gguf-py/gguf/*.py: use __name__ as logger name Since they will be imported and not run directly. * python-lint.yml: use .flake8 file instead * constants.py: logger no longer required * convert-hf-to-gguf.py: add additional logging * convert-hf-to-gguf.py: print() --> logger * *.py: fix flake8 warnings * revert changes to convert-hf-to-gguf.py for get_name() * convert-hf-to-gguf-update.py: use triple quoted f-string instead * *.py: accidentally corrected the wrong line * *.py: add compilade warning suggestions and style fixes
2024-05-03 19:36:41 +00:00
logger.info(f"{tmp_model_path} exists as a weighted model.")
python : add check-requirements.sh and GitHub workflow (#4585) * python: add check-requirements.sh and GitHub workflow This script and workflow forces package versions to remain compatible across all convert*.py scripts, while allowing secondary convert scripts to import dependencies not wanted in convert.py. * Move requirements into ./requirements * Fail on "==" being used for package requirements (but can be suppressed) * Enforce "compatible release" syntax instead of == * Update workflow * Add upper version bound for transformers and protobuf * improve check-requirements.sh * small syntax change * don't remove venvs if nocleanup is passed * See if this fixes docker workflow * Move check-requirements.sh into ./scripts/ --------- Co-authored-by: Jared Van Bortel <jared@nomic.ai>
2023-12-29 14:50:29 +00:00
else:
tmp_model_path.mkdir(parents=True, exist_ok=True)
convert.py : add python logging instead of print() (#6511) * convert.py: add python logging instead of print() * convert.py: verbose flag takes priority over dump flag log suppression * convert.py: named instance logging * convert.py: use explicit logger id string * convert.py: convert extra print() to named logger * convert.py: sys.stderr.write --> logger.error * *.py: Convert all python scripts to use logging module * requirements.txt: remove extra line * flake8: update flake8 ignore and exclude to match ci settings * gh-actions: add flake8-no-print to flake8 lint step * pre-commit: add flake8-no-print to flake8 and also update pre-commit version * convert-hf-to-gguf.py: print() to logger conversion * *.py: logging basiconfig refactor to use conditional expression * *.py: removed commented out logging * fixup! *.py: logging basiconfig refactor to use conditional expression * constant.py: logger.error then exit should be a raise exception instead * *.py: Convert logger error and sys.exit() into a raise exception (for atypical error) * gguf-convert-endian.py: refactor convert_byteorder() to use tqdm progressbar * verify-checksum-model.py: This is the result of the program, it should be printed to stdout. * compare-llama-bench.py: add blank line for readability during missing repo response * reader.py: read_gguf_file() use print() over logging * convert.py: warning goes to stderr and won't hurt the dump output * gguf-dump.py: dump_metadata() should print to stdout * convert-hf-to-gguf.py: print --> logger.debug or ValueError() * verify-checksum-models.py: use print() for printing table * *.py: refactor logging.basicConfig() * gguf-py/gguf/*.py: use __name__ as logger name Since they will be imported and not run directly. * python-lint.yml: use .flake8 file instead * constants.py: logger no longer required * convert-hf-to-gguf.py: add additional logging * convert-hf-to-gguf.py: print() --> logger * *.py: fix flake8 warnings * revert changes to convert-hf-to-gguf.py for get_name() * convert-hf-to-gguf-update.py: use triple quoted f-string instead * *.py: accidentally corrected the wrong line * *.py: add compilade warning suggestions and style fixes
2024-05-03 19:36:41 +00:00
logger.info("Saving new weighted model ...")
python : add check-requirements.sh and GitHub workflow (#4585) * python: add check-requirements.sh and GitHub workflow This script and workflow forces package versions to remain compatible across all convert*.py scripts, while allowing secondary convert scripts to import dependencies not wanted in convert.py. * Move requirements into ./requirements * Fail on "==" being used for package requirements (but can be suppressed) * Enforce "compatible release" syntax instead of == * Update workflow * Add upper version bound for transformers and protobuf * improve check-requirements.sh * small syntax change * don't remove venvs if nocleanup is passed * See if this fixes docker workflow * Move check-requirements.sh into ./scripts/ --------- Co-authored-by: Jared Van Bortel <jared@nomic.ai>
2023-12-29 14:50:29 +00:00
add_scale_weights(str(args.model), str(args.awq_path), str(tmp_model_path))
convert.py : add python logging instead of print() (#6511) * convert.py: add python logging instead of print() * convert.py: verbose flag takes priority over dump flag log suppression * convert.py: named instance logging * convert.py: use explicit logger id string * convert.py: convert extra print() to named logger * convert.py: sys.stderr.write --> logger.error * *.py: Convert all python scripts to use logging module * requirements.txt: remove extra line * flake8: update flake8 ignore and exclude to match ci settings * gh-actions: add flake8-no-print to flake8 lint step * pre-commit: add flake8-no-print to flake8 and also update pre-commit version * convert-hf-to-gguf.py: print() to logger conversion * *.py: logging basiconfig refactor to use conditional expression * *.py: removed commented out logging * fixup! *.py: logging basiconfig refactor to use conditional expression * constant.py: logger.error then exit should be a raise exception instead * *.py: Convert logger error and sys.exit() into a raise exception (for atypical error) * gguf-convert-endian.py: refactor convert_byteorder() to use tqdm progressbar * verify-checksum-model.py: This is the result of the program, it should be printed to stdout. * compare-llama-bench.py: add blank line for readability during missing repo response * reader.py: read_gguf_file() use print() over logging * convert.py: warning goes to stderr and won't hurt the dump output * gguf-dump.py: dump_metadata() should print to stdout * convert-hf-to-gguf.py: print --> logger.debug or ValueError() * verify-checksum-models.py: use print() for printing table * *.py: refactor logging.basicConfig() * gguf-py/gguf/*.py: use __name__ as logger name Since they will be imported and not run directly. * python-lint.yml: use .flake8 file instead * constants.py: logger no longer required * convert-hf-to-gguf.py: add additional logging * convert-hf-to-gguf.py: print() --> logger * *.py: fix flake8 warnings * revert changes to convert-hf-to-gguf.py for get_name() * convert-hf-to-gguf-update.py: use triple quoted f-string instead * *.py: accidentally corrected the wrong line * *.py: add compilade warning suggestions and style fixes
2024-05-03 19:36:41 +00:00
logger.info(f"Saved weighted model at {tmp_model_path}.")
python : add check-requirements.sh and GitHub workflow (#4585) * python: add check-requirements.sh and GitHub workflow This script and workflow forces package versions to remain compatible across all convert*.py scripts, while allowing secondary convert scripts to import dependencies not wanted in convert.py. * Move requirements into ./requirements * Fail on "==" being used for package requirements (but can be suppressed) * Enforce "compatible release" syntax instead of == * Update workflow * Add upper version bound for transformers and protobuf * improve check-requirements.sh * small syntax change * don't remove venvs if nocleanup is passed * See if this fixes docker workflow * Move check-requirements.sh into ./scripts/ --------- Co-authored-by: Jared Van Bortel <jared@nomic.ai>
2023-12-29 14:50:29 +00:00
if not dir_model.is_dir():
convert.py : add python logging instead of print() (#6511) * convert.py: add python logging instead of print() * convert.py: verbose flag takes priority over dump flag log suppression * convert.py: named instance logging * convert.py: use explicit logger id string * convert.py: convert extra print() to named logger * convert.py: sys.stderr.write --> logger.error * *.py: Convert all python scripts to use logging module * requirements.txt: remove extra line * flake8: update flake8 ignore and exclude to match ci settings * gh-actions: add flake8-no-print to flake8 lint step * pre-commit: add flake8-no-print to flake8 and also update pre-commit version * convert-hf-to-gguf.py: print() to logger conversion * *.py: logging basiconfig refactor to use conditional expression * *.py: removed commented out logging * fixup! *.py: logging basiconfig refactor to use conditional expression * constant.py: logger.error then exit should be a raise exception instead * *.py: Convert logger error and sys.exit() into a raise exception (for atypical error) * gguf-convert-endian.py: refactor convert_byteorder() to use tqdm progressbar * verify-checksum-model.py: This is the result of the program, it should be printed to stdout. * compare-llama-bench.py: add blank line for readability during missing repo response * reader.py: read_gguf_file() use print() over logging * convert.py: warning goes to stderr and won't hurt the dump output * gguf-dump.py: dump_metadata() should print to stdout * convert-hf-to-gguf.py: print --> logger.debug or ValueError() * verify-checksum-models.py: use print() for printing table * *.py: refactor logging.basicConfig() * gguf-py/gguf/*.py: use __name__ as logger name Since they will be imported and not run directly. * python-lint.yml: use .flake8 file instead * constants.py: logger no longer required * convert-hf-to-gguf.py: add additional logging * convert-hf-to-gguf.py: print() --> logger * *.py: fix flake8 warnings * revert changes to convert-hf-to-gguf.py for get_name() * convert-hf-to-gguf-update.py: use triple quoted f-string instead * *.py: accidentally corrected the wrong line * *.py: add compilade warning suggestions and style fixes
2024-05-03 19:36:41 +00:00
logger.error(f'Error: {args.model} is not a directory')
python : add check-requirements.sh and GitHub workflow (#4585) * python: add check-requirements.sh and GitHub workflow This script and workflow forces package versions to remain compatible across all convert*.py scripts, while allowing secondary convert scripts to import dependencies not wanted in convert.py. * Move requirements into ./requirements * Fail on "==" being used for package requirements (but can be suppressed) * Enforce "compatible release" syntax instead of == * Update workflow * Add upper version bound for transformers and protobuf * improve check-requirements.sh * small syntax change * don't remove venvs if nocleanup is passed * See if this fixes docker workflow * Move check-requirements.sh into ./scripts/ --------- Co-authored-by: Jared Van Bortel <jared@nomic.ai>
2023-12-29 14:50:29 +00:00
sys.exit(1)
convert-hf : support bfloat16 conversion (#7158) * convert-hf : support bfloat16 conversion * gguf-py : flake8 fixes * convert-hf : add missing space after comma * convert-hf : get bit-exact same output as ./quantize The quantization version was missing. * convert-hf : don't round bf16 NANs * convert-hf : save some memory with np.int16 intermediate bf16 weights * convert-hf : more closely match llama.cpp with which weights to keep in f32 * convert-hf : add --outtype auto-f16 A reason for this to exist is for model quantizers who want an initial GGUF with the most fidelity to the original model while still using a 16-bit float type instead of 32-bit floats. * convert-hf : remove a semicolon because flake8 doesn't like it It's a reflex from when programming in C/C++, I guess. * convert-hf : support outtype templating in outfile name * convert-hf : rename --outtype auto-f16 to --outtype auto
2024-05-11 15:06:26 +00:00
ftype_map: dict[str, gguf.LlamaFileType] = {
"f32": gguf.LlamaFileType.ALL_F32,
"f16": gguf.LlamaFileType.MOSTLY_F16,
"bf16": gguf.LlamaFileType.MOSTLY_BF16,
convert-hf : support direct Q8_0 conversion (#7234) * convert-hf : support q8_0 conversion * convert-hf : add missing ftype This was messing with the checksums otherwise. * convert-hf : add missing ftype to Baichuan and Xverse I didn't notice these on my first pass.
2024-05-13 18:10:51 +00:00
"q8_0": gguf.LlamaFileType.MOSTLY_Q8_0,
convert-hf : support bfloat16 conversion (#7158) * convert-hf : support bfloat16 conversion * gguf-py : flake8 fixes * convert-hf : add missing space after comma * convert-hf : get bit-exact same output as ./quantize The quantization version was missing. * convert-hf : don't round bf16 NANs * convert-hf : save some memory with np.int16 intermediate bf16 weights * convert-hf : more closely match llama.cpp with which weights to keep in f32 * convert-hf : add --outtype auto-f16 A reason for this to exist is for model quantizers who want an initial GGUF with the most fidelity to the original model while still using a 16-bit float type instead of 32-bit floats. * convert-hf : remove a semicolon because flake8 doesn't like it It's a reflex from when programming in C/C++, I guess. * convert-hf : support outtype templating in outfile name * convert-hf : rename --outtype auto-f16 to --outtype auto
2024-05-11 15:06:26 +00:00
"auto": gguf.LlamaFileType.GUESSED,
python : add check-requirements.sh and GitHub workflow (#4585) * python: add check-requirements.sh and GitHub workflow This script and workflow forces package versions to remain compatible across all convert*.py scripts, while allowing secondary convert scripts to import dependencies not wanted in convert.py. * Move requirements into ./requirements * Fail on "==" being used for package requirements (but can be suppressed) * Enforce "compatible release" syntax instead of == * Update workflow * Add upper version bound for transformers and protobuf * improve check-requirements.sh * small syntax change * don't remove venvs if nocleanup is passed * See if this fixes docker workflow * Move check-requirements.sh into ./scripts/ --------- Co-authored-by: Jared Van Bortel <jared@nomic.ai>
2023-12-29 14:50:29 +00:00
}
if args.outfile is not None:
fname_out = args.outfile
llama : add AWQ for llama, llama2, mpt, and mistral models (#4593) * update: awq support llama-7b model * update: change order * update: benchmark results for llama2-7b * update: mistral 7b v1 benchmark * update: support 4 models * fix: Readme * update: ready for PR * update: readme * fix: readme * update: change order import * black * format code * update: work for bot mpt and awqmpt * update: readme * Rename to llm_build_ffn_mpt_awq * Formatted other files * Fixed params count * fix: remove code * update: more detail for mpt * fix: readme * fix: readme * update: change folder architecture * fix: common.cpp * fix: readme * fix: remove ggml_repeat * update: cicd * update: cicd * uppdate: remove use_awq arg * update: readme * llama : adapt plamo to new ffn ggml-ci --------- Co-authored-by: Trần Đức Nam <v.namtd12@vinai.io> Co-authored-by: Le Hoang Anh <v.anhlh33@vinai.io> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-12-27 15:39:45 +00:00
else:
python : add check-requirements.sh and GitHub workflow (#4585) * python: add check-requirements.sh and GitHub workflow This script and workflow forces package versions to remain compatible across all convert*.py scripts, while allowing secondary convert scripts to import dependencies not wanted in convert.py. * Move requirements into ./requirements * Fail on "==" being used for package requirements (but can be suppressed) * Enforce "compatible release" syntax instead of == * Update workflow * Add upper version bound for transformers and protobuf * improve check-requirements.sh * small syntax change * don't remove venvs if nocleanup is passed * See if this fixes docker workflow * Move check-requirements.sh into ./scripts/ --------- Co-authored-by: Jared Van Bortel <jared@nomic.ai>
2023-12-29 14:50:29 +00:00
# output in the same directory as the model by default
convert-hf : support bfloat16 conversion (#7158) * convert-hf : support bfloat16 conversion * gguf-py : flake8 fixes * convert-hf : add missing space after comma * convert-hf : get bit-exact same output as ./quantize The quantization version was missing. * convert-hf : don't round bf16 NANs * convert-hf : save some memory with np.int16 intermediate bf16 weights * convert-hf : more closely match llama.cpp with which weights to keep in f32 * convert-hf : add --outtype auto-f16 A reason for this to exist is for model quantizers who want an initial GGUF with the most fidelity to the original model while still using a 16-bit float type instead of 32-bit floats. * convert-hf : remove a semicolon because flake8 doesn't like it It's a reflex from when programming in C/C++, I guess. * convert-hf : support outtype templating in outfile name * convert-hf : rename --outtype auto-f16 to --outtype auto
2024-05-11 15:06:26 +00:00
fname_out = dir_model / 'ggml-model-{ftype}.gguf'
scripts: Generalize convert scripts (#3838) * Replace convert-*-hf-to-gguf.py files with convert-hf-to-gguf.py
2023-11-09 10:09:29 +00:00
convert.py : add python logging instead of print() (#6511) * convert.py: add python logging instead of print() * convert.py: verbose flag takes priority over dump flag log suppression * convert.py: named instance logging * convert.py: use explicit logger id string * convert.py: convert extra print() to named logger * convert.py: sys.stderr.write --> logger.error * *.py: Convert all python scripts to use logging module * requirements.txt: remove extra line * flake8: update flake8 ignore and exclude to match ci settings * gh-actions: add flake8-no-print to flake8 lint step * pre-commit: add flake8-no-print to flake8 and also update pre-commit version * convert-hf-to-gguf.py: print() to logger conversion * *.py: logging basiconfig refactor to use conditional expression * *.py: removed commented out logging * fixup! *.py: logging basiconfig refactor to use conditional expression * constant.py: logger.error then exit should be a raise exception instead * *.py: Convert logger error and sys.exit() into a raise exception (for atypical error) * gguf-convert-endian.py: refactor convert_byteorder() to use tqdm progressbar * verify-checksum-model.py: This is the result of the program, it should be printed to stdout. * compare-llama-bench.py: add blank line for readability during missing repo response * reader.py: read_gguf_file() use print() over logging * convert.py: warning goes to stderr and won't hurt the dump output * gguf-dump.py: dump_metadata() should print to stdout * convert-hf-to-gguf.py: print --> logger.debug or ValueError() * verify-checksum-models.py: use print() for printing table * *.py: refactor logging.basicConfig() * gguf-py/gguf/*.py: use __name__ as logger name Since they will be imported and not run directly. * python-lint.yml: use .flake8 file instead * constants.py: logger no longer required * convert-hf-to-gguf.py: add additional logging * convert-hf-to-gguf.py: print() --> logger * *.py: fix flake8 warnings * revert changes to convert-hf-to-gguf.py for get_name() * convert-hf-to-gguf-update.py: use triple quoted f-string instead * *.py: accidentally corrected the wrong line * *.py: add compilade warning suggestions and style fixes
2024-05-03 19:36:41 +00:00
logger.info(f"Loading model: {dir_model.name}")
scripts: Generalize convert scripts (#3838) * Replace convert-*-hf-to-gguf.py files with convert-hf-to-gguf.py
2023-11-09 10:09:29 +00:00
python : add check-requirements.sh and GitHub workflow (#4585) * python: add check-requirements.sh and GitHub workflow This script and workflow forces package versions to remain compatible across all convert*.py scripts, while allowing secondary convert scripts to import dependencies not wanted in convert.py. * Move requirements into ./requirements * Fail on "==" being used for package requirements (but can be suppressed) * Enforce "compatible release" syntax instead of == * Update workflow * Add upper version bound for transformers and protobuf * improve check-requirements.sh * small syntax change * don't remove venvs if nocleanup is passed * See if this fixes docker workflow * Move check-requirements.sh into ./scripts/ --------- Co-authored-by: Jared Van Bortel <jared@nomic.ai>
2023-12-29 14:50:29 +00:00
hparams = Model.load_hparams(dir_model)
scripts: Generalize convert scripts (#3838) * Replace convert-*-hf-to-gguf.py files with convert-hf-to-gguf.py
2023-11-09 10:09:29 +00:00
python : add check-requirements.sh and GitHub workflow (#4585) * python: add check-requirements.sh and GitHub workflow This script and workflow forces package versions to remain compatible across all convert*.py scripts, while allowing secondary convert scripts to import dependencies not wanted in convert.py. * Move requirements into ./requirements * Fail on "==" being used for package requirements (but can be suppressed) * Enforce "compatible release" syntax instead of == * Update workflow * Add upper version bound for transformers and protobuf * improve check-requirements.sh * small syntax change * don't remove venvs if nocleanup is passed * See if this fixes docker workflow * Move check-requirements.sh into ./scripts/ --------- Co-authored-by: Jared Van Bortel <jared@nomic.ai>
2023-12-29 14:50:29 +00:00
with torch.inference_mode():
model_class = Model.from_model_architecture(hparams["architectures"][0])
convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file
2024-05-08 22:16:38 +00:00
model_instance = model_class(dir_model, ftype_map[args.outtype], fname_out, args.bigendian, args.use_temp_file, args.no_lazy)
scripts: Generalize convert scripts (#3838) * Replace convert-*-hf-to-gguf.py files with convert-hf-to-gguf.py
2023-11-09 10:09:29 +00:00
convert.py : add python logging instead of print() (#6511) * convert.py: add python logging instead of print() * convert.py: verbose flag takes priority over dump flag log suppression * convert.py: named instance logging * convert.py: use explicit logger id string * convert.py: convert extra print() to named logger * convert.py: sys.stderr.write --> logger.error * *.py: Convert all python scripts to use logging module * requirements.txt: remove extra line * flake8: update flake8 ignore and exclude to match ci settings * gh-actions: add flake8-no-print to flake8 lint step * pre-commit: add flake8-no-print to flake8 and also update pre-commit version * convert-hf-to-gguf.py: print() to logger conversion * *.py: logging basiconfig refactor to use conditional expression * *.py: removed commented out logging * fixup! *.py: logging basiconfig refactor to use conditional expression * constant.py: logger.error then exit should be a raise exception instead * *.py: Convert logger error and sys.exit() into a raise exception (for atypical error) * gguf-convert-endian.py: refactor convert_byteorder() to use tqdm progressbar * verify-checksum-model.py: This is the result of the program, it should be printed to stdout. * compare-llama-bench.py: add blank line for readability during missing repo response * reader.py: read_gguf_file() use print() over logging * convert.py: warning goes to stderr and won't hurt the dump output * gguf-dump.py: dump_metadata() should print to stdout * convert-hf-to-gguf.py: print --> logger.debug or ValueError() * verify-checksum-models.py: use print() for printing table * *.py: refactor logging.basicConfig() * gguf-py/gguf/*.py: use __name__ as logger name Since they will be imported and not run directly. * python-lint.yml: use .flake8 file instead * constants.py: logger no longer required * convert-hf-to-gguf.py: add additional logging * convert-hf-to-gguf.py: print() --> logger * *.py: fix flake8 warnings * revert changes to convert-hf-to-gguf.py for get_name() * convert-hf-to-gguf-update.py: use triple quoted f-string instead * *.py: accidentally corrected the wrong line * *.py: add compilade warning suggestions and style fixes
2024-05-03 19:36:41 +00:00
logger.info("Set model parameters")
python : add check-requirements.sh and GitHub workflow (#4585) * python: add check-requirements.sh and GitHub workflow This script and workflow forces package versions to remain compatible across all convert*.py scripts, while allowing secondary convert scripts to import dependencies not wanted in convert.py. * Move requirements into ./requirements * Fail on "==" being used for package requirements (but can be suppressed) * Enforce "compatible release" syntax instead of == * Update workflow * Add upper version bound for transformers and protobuf * improve check-requirements.sh * small syntax change * don't remove venvs if nocleanup is passed * See if this fixes docker workflow * Move check-requirements.sh into ./scripts/ --------- Co-authored-by: Jared Van Bortel <jared@nomic.ai>
2023-12-29 14:50:29 +00:00
model_instance.set_gguf_parameters()
scripts: Generalize convert scripts (#3838) * Replace convert-*-hf-to-gguf.py files with convert-hf-to-gguf.py
2023-11-09 10:09:29 +00:00
convert.py : add python logging instead of print() (#6511) * convert.py: add python logging instead of print() * convert.py: verbose flag takes priority over dump flag log suppression * convert.py: named instance logging * convert.py: use explicit logger id string * convert.py: convert extra print() to named logger * convert.py: sys.stderr.write --> logger.error * *.py: Convert all python scripts to use logging module * requirements.txt: remove extra line * flake8: update flake8 ignore and exclude to match ci settings * gh-actions: add flake8-no-print to flake8 lint step * pre-commit: add flake8-no-print to flake8 and also update pre-commit version * convert-hf-to-gguf.py: print() to logger conversion * *.py: logging basiconfig refactor to use conditional expression * *.py: removed commented out logging * fixup! *.py: logging basiconfig refactor to use conditional expression * constant.py: logger.error then exit should be a raise exception instead * *.py: Convert logger error and sys.exit() into a raise exception (for atypical error) * gguf-convert-endian.py: refactor convert_byteorder() to use tqdm progressbar * verify-checksum-model.py: This is the result of the program, it should be printed to stdout. * compare-llama-bench.py: add blank line for readability during missing repo response * reader.py: read_gguf_file() use print() over logging * convert.py: warning goes to stderr and won't hurt the dump output * gguf-dump.py: dump_metadata() should print to stdout * convert-hf-to-gguf.py: print --> logger.debug or ValueError() * verify-checksum-models.py: use print() for printing table * *.py: refactor logging.basicConfig() * gguf-py/gguf/*.py: use __name__ as logger name Since they will be imported and not run directly. * python-lint.yml: use .flake8 file instead * constants.py: logger no longer required * convert-hf-to-gguf.py: add additional logging * convert-hf-to-gguf.py: print() --> logger * *.py: fix flake8 warnings * revert changes to convert-hf-to-gguf.py for get_name() * convert-hf-to-gguf-update.py: use triple quoted f-string instead * *.py: accidentally corrected the wrong line * *.py: add compilade warning suggestions and style fixes
2024-05-03 19:36:41 +00:00
logger.info("Set model tokenizer")
python : add check-requirements.sh and GitHub workflow (#4585) * python: add check-requirements.sh and GitHub workflow This script and workflow forces package versions to remain compatible across all convert*.py scripts, while allowing secondary convert scripts to import dependencies not wanted in convert.py. * Move requirements into ./requirements * Fail on "==" being used for package requirements (but can be suppressed) * Enforce "compatible release" syntax instead of == * Update workflow * Add upper version bound for transformers and protobuf * improve check-requirements.sh * small syntax change * don't remove venvs if nocleanup is passed * See if this fixes docker workflow * Move check-requirements.sh into ./scripts/ --------- Co-authored-by: Jared Van Bortel <jared@nomic.ai>
2023-12-29 14:50:29 +00:00
model_instance.set_vocab()
scripts: Generalize convert scripts (#3838) * Replace convert-*-hf-to-gguf.py files with convert-hf-to-gguf.py
2023-11-09 10:09:29 +00:00
convert-hf : support bfloat16 conversion (#7158) * convert-hf : support bfloat16 conversion * gguf-py : flake8 fixes * convert-hf : add missing space after comma * convert-hf : get bit-exact same output as ./quantize The quantization version was missing. * convert-hf : don't round bf16 NANs * convert-hf : save some memory with np.int16 intermediate bf16 weights * convert-hf : more closely match llama.cpp with which weights to keep in f32 * convert-hf : add --outtype auto-f16 A reason for this to exist is for model quantizers who want an initial GGUF with the most fidelity to the original model while still using a 16-bit float type instead of 32-bit floats. * convert-hf : remove a semicolon because flake8 doesn't like it It's a reflex from when programming in C/C++, I guess. * convert-hf : support outtype templating in outfile name * convert-hf : rename --outtype auto-f16 to --outtype auto
2024-05-11 15:06:26 +00:00
model_instance.gguf_writer.add_quantization_version(gguf.GGML_QUANT_VERSION)
python : add check-requirements.sh and GitHub workflow (#4585) * python: add check-requirements.sh and GitHub workflow This script and workflow forces package versions to remain compatible across all convert*.py scripts, while allowing secondary convert scripts to import dependencies not wanted in convert.py. * Move requirements into ./requirements * Fail on "==" being used for package requirements (but can be suppressed) * Enforce "compatible release" syntax instead of == * Update workflow * Add upper version bound for transformers and protobuf * improve check-requirements.sh * small syntax change * don't remove venvs if nocleanup is passed * See if this fixes docker workflow * Move check-requirements.sh into ./scripts/ --------- Co-authored-by: Jared Van Bortel <jared@nomic.ai>
2023-12-29 14:50:29 +00:00
if args.vocab_only:
convert-hf : support bfloat16 conversion (#7158) * convert-hf : support bfloat16 conversion * gguf-py : flake8 fixes * convert-hf : add missing space after comma * convert-hf : get bit-exact same output as ./quantize The quantization version was missing. * convert-hf : don't round bf16 NANs * convert-hf : save some memory with np.int16 intermediate bf16 weights * convert-hf : more closely match llama.cpp with which weights to keep in f32 * convert-hf : add --outtype auto-f16 A reason for this to exist is for model quantizers who want an initial GGUF with the most fidelity to the original model while still using a 16-bit float type instead of 32-bit floats. * convert-hf : remove a semicolon because flake8 doesn't like it It's a reflex from when programming in C/C++, I guess. * convert-hf : support outtype templating in outfile name * convert-hf : rename --outtype auto-f16 to --outtype auto
2024-05-11 15:06:26 +00:00
logger.info(f"Exporting model vocab to '{model_instance.fname_out}'")
python : add check-requirements.sh and GitHub workflow (#4585) * python: add check-requirements.sh and GitHub workflow This script and workflow forces package versions to remain compatible across all convert*.py scripts, while allowing secondary convert scripts to import dependencies not wanted in convert.py. * Move requirements into ./requirements * Fail on "==" being used for package requirements (but can be suppressed) * Enforce "compatible release" syntax instead of == * Update workflow * Add upper version bound for transformers and protobuf * improve check-requirements.sh * small syntax change * don't remove venvs if nocleanup is passed * See if this fixes docker workflow * Move check-requirements.sh into ./scripts/ --------- Co-authored-by: Jared Van Bortel <jared@nomic.ai>
2023-12-29 14:50:29 +00:00
model_instance.write_vocab()
else:
convert-hf : support bfloat16 conversion (#7158) * convert-hf : support bfloat16 conversion * gguf-py : flake8 fixes * convert-hf : add missing space after comma * convert-hf : get bit-exact same output as ./quantize The quantization version was missing. * convert-hf : don't round bf16 NANs * convert-hf : save some memory with np.int16 intermediate bf16 weights * convert-hf : more closely match llama.cpp with which weights to keep in f32 * convert-hf : add --outtype auto-f16 A reason for this to exist is for model quantizers who want an initial GGUF with the most fidelity to the original model while still using a 16-bit float type instead of 32-bit floats. * convert-hf : remove a semicolon because flake8 doesn't like it It's a reflex from when programming in C/C++, I guess. * convert-hf : support outtype templating in outfile name * convert-hf : rename --outtype auto-f16 to --outtype auto
2024-05-11 15:06:26 +00:00
logger.info(f"Exporting model to '{model_instance.fname_out}'")
python : add check-requirements.sh and GitHub workflow (#4585) * python: add check-requirements.sh and GitHub workflow This script and workflow forces package versions to remain compatible across all convert*.py scripts, while allowing secondary convert scripts to import dependencies not wanted in convert.py. * Move requirements into ./requirements * Fail on "==" being used for package requirements (but can be suppressed) * Enforce "compatible release" syntax instead of == * Update workflow * Add upper version bound for transformers and protobuf * improve check-requirements.sh * small syntax change * don't remove venvs if nocleanup is passed * See if this fixes docker workflow * Move check-requirements.sh into ./scripts/ --------- Co-authored-by: Jared Van Bortel <jared@nomic.ai>
2023-12-29 14:50:29 +00:00
model_instance.write()
scripts: Generalize convert scripts (#3838) * Replace convert-*-hf-to-gguf.py files with convert-hf-to-gguf.py
2023-11-09 10:09:29 +00:00
convert-hf : support bfloat16 conversion (#7158) * convert-hf : support bfloat16 conversion * gguf-py : flake8 fixes * convert-hf : add missing space after comma * convert-hf : get bit-exact same output as ./quantize The quantization version was missing. * convert-hf : don't round bf16 NANs * convert-hf : save some memory with np.int16 intermediate bf16 weights * convert-hf : more closely match llama.cpp with which weights to keep in f32 * convert-hf : add --outtype auto-f16 A reason for this to exist is for model quantizers who want an initial GGUF with the most fidelity to the original model while still using a 16-bit float type instead of 32-bit floats. * convert-hf : remove a semicolon because flake8 doesn't like it It's a reflex from when programming in C/C++, I guess. * convert-hf : support outtype templating in outfile name * convert-hf : rename --outtype auto-f16 to --outtype auto
2024-05-11 15:06:26 +00:00
logger.info(f"Model successfully exported to '{model_instance.fname_out}'")
scripts: Generalize convert scripts (#3838) * Replace convert-*-hf-to-gguf.py files with convert-hf-to-gguf.py
2023-11-09 10:09:29 +00:00
python : add check-requirements.sh and GitHub workflow (#4585) * python: add check-requirements.sh and GitHub workflow This script and workflow forces package versions to remain compatible across all convert*.py scripts, while allowing secondary convert scripts to import dependencies not wanted in convert.py. * Move requirements into ./requirements * Fail on "==" being used for package requirements (but can be suppressed) * Enforce "compatible release" syntax instead of == * Update workflow * Add upper version bound for transformers and protobuf * improve check-requirements.sh * small syntax change * don't remove venvs if nocleanup is passed * See if this fixes docker workflow * Move check-requirements.sh into ./scripts/ --------- Co-authored-by: Jared Van Bortel <jared@nomic.ai>
2023-12-29 14:50:29 +00:00
if __name__ == '__main__':
main()
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