mirror of
https://github.com/ggerganov/llama.cpp.git
synced 2024-11-11 21:39:52 +00:00
7acfd4e8d5
* convert_hf : faster lazy safetensors This makes '--dry-run' much, much faster. * convert_hf : fix memory leak in lazy MoE conversion The '_lazy' queue was sometimes self-referential, which caused reference cycles of objects old enough to avoid garbage collection until potential memory exhaustion.
3632 lines
160 KiB
Python
Executable File
3632 lines
160 KiB
Python
Executable File
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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from __future__ import annotations
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import logging
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import argparse
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import contextlib
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import json
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import os
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import re
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import sys
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from enum import IntEnum
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from pathlib import Path
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from hashlib import sha256
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from typing import TYPE_CHECKING, Any, Callable, ContextManager, Iterable, Iterator, Literal, Sequence, TypeVar, cast
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import math
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import numpy as np
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import torch
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if TYPE_CHECKING:
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from torch import Tensor
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if 'NO_LOCAL_GGUF' not in os.environ:
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sys.path.insert(1, str(Path(__file__).parent / 'gguf-py'))
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import gguf
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logger = logging.getLogger("hf-to-gguf")
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###### MODEL DEFINITIONS ######
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class SentencePieceTokenTypes(IntEnum):
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NORMAL = 1
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UNKNOWN = 2
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CONTROL = 3
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USER_DEFINED = 4
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UNUSED = 5
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BYTE = 6
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AnyModel = TypeVar("AnyModel", bound="type[Model]")
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class Model:
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_model_classes: dict[str, type[Model]] = {}
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dir_model: Path
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ftype: gguf.LlamaFileType
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is_big_endian: bool
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endianess: gguf.GGUFEndian
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use_temp_file: bool
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lazy: bool
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model_name: str | None
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part_names: list[str]
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is_safetensors: bool
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hparams: dict[str, Any]
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block_count: int
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tensor_map: gguf.TensorNameMap
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tensor_names: set[str] | None
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fname_out: Path
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gguf_writer: gguf.GGUFWriter
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# subclasses should define this!
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model_arch: gguf.MODEL_ARCH
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def __init__(self, dir_model: Path, ftype: gguf.LlamaFileType, fname_out: Path, is_big_endian: bool, use_temp_file: bool, eager: bool,
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model_name: str | None, split_max_tensors: int = 0, split_max_size: int = 0, dry_run: bool = False, small_first_shard: bool = False):
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if type(self) is Model:
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raise TypeError(f"{type(self).__name__!r} should not be directly instantiated")
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self.dir_model = dir_model
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self.ftype = ftype
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self.is_big_endian = is_big_endian
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self.endianess = gguf.GGUFEndian.BIG if is_big_endian else gguf.GGUFEndian.LITTLE
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self.use_temp_file = use_temp_file
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self.lazy = not eager
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self.model_name = model_name
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self.part_names = Model.get_model_part_names(self.dir_model, "model", ".safetensors")
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self.is_safetensors = len(self.part_names) > 0
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if not self.is_safetensors:
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self.part_names = Model.get_model_part_names(self.dir_model, "pytorch_model", ".bin")
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self.hparams = Model.load_hparams(self.dir_model)
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self.block_count = self.find_hparam(["n_layers", "num_hidden_layers", "n_layer", "num_layers"])
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self.tensor_map = gguf.get_tensor_name_map(self.model_arch, self.block_count)
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self.tensor_names = None
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if self.ftype == gguf.LlamaFileType.GUESSED:
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# NOTE: can't use field "torch_dtype" in config.json, because some finetunes lie.
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_, first_tensor = next(self.get_tensors())
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if first_tensor.dtype == torch.float16:
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logger.info(f"choosing --outtype f16 from first tensor type ({first_tensor.dtype})")
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self.ftype = gguf.LlamaFileType.MOSTLY_F16
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else:
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logger.info(f"choosing --outtype bf16 from first tensor type ({first_tensor.dtype})")
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self.ftype = gguf.LlamaFileType.MOSTLY_BF16
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ftype_up: str = self.ftype.name.partition("_")[2].upper()
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ftype_lw: str = ftype_up.lower()
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# allow templating the file name with the output ftype, useful with the "auto" ftype
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self.fname_out = fname_out.parent / fname_out.name.format(ftype_lw, outtype=ftype_lw, ftype=ftype_lw, OUTTYPE=ftype_up, FTYPE=ftype_up)
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self.gguf_writer = gguf.GGUFWriter(path=None, arch=gguf.MODEL_ARCH_NAMES[self.model_arch], endianess=self.endianess, use_temp_file=self.use_temp_file,
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split_max_tensors=split_max_tensors, split_max_size=split_max_size, dry_run=dry_run, small_first_shard=small_first_shard)
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@classmethod
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def __init_subclass__(cls):
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# can't use an abstract property, because overriding it without type errors
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# would require using decorated functions instead of simply defining the property
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if "model_arch" not in cls.__dict__:
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raise TypeError(f"Missing property 'model_arch' for {cls.__name__!r}")
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def find_hparam(self, keys: Iterable[str], optional: bool = False) -> Any:
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key = next((k for k in keys if k in self.hparams), None)
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if key is not None:
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return self.hparams[key]
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if optional:
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return None
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raise KeyError(f"could not find any of: {keys}")
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def set_vocab(self):
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self._set_vocab_gpt2()
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def get_tensors(self) -> Iterator[tuple[str, Tensor]]:
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tensor_names_from_parts: set[str] = set()
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if len(self.part_names) > 1:
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self.tensor_names = set()
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index_name = "model.safetensors" if self.is_safetensors else "pytorch_model.bin"
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index_name += ".index.json"
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logger.info(f"gguf: loading model weight map from '{index_name}'")
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with open(self.dir_model / index_name, "r", encoding="utf-8") as f:
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index: dict[str, Any] = json.load(f)
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weight_map = index.get("weight_map")
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if weight_map is None or not isinstance(weight_map, dict):
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raise ValueError(f"Can't load 'weight_map' from {index_name!r}")
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self.tensor_names.update(weight_map.keys())
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else:
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self.tensor_names = tensor_names_from_parts
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for part_name in self.part_names:
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logger.info(f"gguf: loading model part '{part_name}'")
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ctx: ContextManager[Any]
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if self.is_safetensors:
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from safetensors import safe_open
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ctx = cast(ContextManager[Any], safe_open(self.dir_model / part_name, framework="pt", device="cpu"))
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else:
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ctx = contextlib.nullcontext(torch.load(str(self.dir_model / part_name), map_location="cpu", mmap=True, weights_only=True))
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with ctx as model_part:
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tensor_names_from_parts.update(model_part.keys())
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for name in model_part.keys():
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if self.is_safetensors:
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if self.lazy:
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data = model_part.get_slice(name)
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data = LazyTorchTensor.from_safetensors_slice(data)
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else:
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data = model_part.get_tensor(name)
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else:
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data = model_part[name]
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if self.lazy:
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data = LazyTorchTensor.from_eager(data)
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yield name, data
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# only verify tensor name presence; it doesn't matter if they are not in the right files
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if len(sym_diff := tensor_names_from_parts.symmetric_difference(self.tensor_names)) > 0:
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raise ValueError(f"Mismatch between weight map and model parts for tensor names: {sym_diff}")
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def format_tensor_name(self, key: gguf.MODEL_TENSOR, bid: int | None = None, suffix: str = ".weight") -> str:
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if key not in gguf.MODEL_TENSORS[self.model_arch]:
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raise ValueError(f"Missing {key!r} for MODEL_TENSORS of {self.model_arch!r}")
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name: str = gguf.TENSOR_NAMES[key]
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if "{bid}" in name:
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assert bid is not None
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name = name.format(bid=bid)
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return name + suffix
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def match_model_tensor_name(self, name: str, key: gguf.MODEL_TENSOR, bid: int | None, suffix: str = ".weight") -> bool:
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if key not in gguf.MODEL_TENSORS[self.model_arch]:
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return False
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key_name: str = gguf.TENSOR_NAMES[key]
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if "{bid}" in key_name:
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if bid is None:
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return False
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key_name = key_name.format(bid=bid)
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else:
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if bid is not None:
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return False
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return name == (key_name + suffix)
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def map_tensor_name(self, name: str, try_suffixes: Sequence[str] = (".weight", ".bias")) -> str:
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new_name = self.tensor_map.get_name(key=name, try_suffixes=try_suffixes)
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if new_name is None:
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raise ValueError(f"Can not map tensor {name!r}")
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return new_name
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def set_gguf_parameters(self):
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self.gguf_writer.add_name(self.dir_model.name if self.model_name is None else self.model_name)
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self.gguf_writer.add_block_count(self.block_count)
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if (n_ctx := self.find_hparam(["max_position_embeddings", "n_ctx"], optional=True)) is not None:
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self.gguf_writer.add_context_length(n_ctx)
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logger.info(f"gguf: context length = {n_ctx}")
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n_embd = self.find_hparam(["hidden_size", "n_embd"])
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self.gguf_writer.add_embedding_length(n_embd)
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logger.info(f"gguf: embedding length = {n_embd}")
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if (n_ff := self.find_hparam(["intermediate_size", "n_inner"], optional=True)) is not None:
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self.gguf_writer.add_feed_forward_length(n_ff)
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logger.info(f"gguf: feed forward length = {n_ff}")
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n_head = self.find_hparam(["num_attention_heads", "n_head"])
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self.gguf_writer.add_head_count(n_head)
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logger.info(f"gguf: head count = {n_head}")
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if (n_head_kv := self.hparams.get("num_key_value_heads")) is not None:
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self.gguf_writer.add_head_count_kv(n_head_kv)
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logger.info(f"gguf: key-value head count = {n_head_kv}")
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if (rope_theta := self.hparams.get("rope_theta")) is not None:
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self.gguf_writer.add_rope_freq_base(rope_theta)
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logger.info(f"gguf: rope theta = {rope_theta}")
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if (f_rms_eps := self.hparams.get("rms_norm_eps")) is not None:
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self.gguf_writer.add_layer_norm_rms_eps(f_rms_eps)
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logger.info(f"gguf: rms norm epsilon = {f_rms_eps}")
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if (f_norm_eps := self.find_hparam(["layer_norm_eps", "layer_norm_epsilon", "norm_epsilon"], optional=True)) is not None:
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self.gguf_writer.add_layer_norm_eps(f_norm_eps)
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logger.info(f"gguf: layer norm epsilon = {f_norm_eps}")
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if (n_experts := self.hparams.get("num_local_experts")) is not None:
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self.gguf_writer.add_expert_count(n_experts)
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logger.info(f"gguf: expert count = {n_experts}")
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if (n_experts_used := self.hparams.get("num_experts_per_tok")) is not None:
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self.gguf_writer.add_expert_used_count(n_experts_used)
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logger.info(f"gguf: experts used count = {n_experts_used}")
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self.gguf_writer.add_file_type(self.ftype)
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logger.info(f"gguf: file type = {self.ftype}")
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def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
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del bid # unused
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return [(self.map_tensor_name(name), data_torch)]
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def extra_f32_tensors(self, name: str, new_name: str, bid: int | None, n_dims: int) -> bool:
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del name, new_name, bid, n_dims # unused
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return False
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def extra_f16_tensors(self, name: str, new_name: str, bid: int | None, n_dims: int) -> bool:
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del name, new_name, bid, n_dims # unused
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return False
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def write_tensors(self):
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max_name_len = max(len(s) for _, s in self.tensor_map.mapping.values()) + len(".weight,")
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for name, data_torch in self.get_tensors():
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# we don't need these
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if name.endswith((".attention.masked_bias", ".attention.bias", ".rotary_emb.inv_freq")):
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continue
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old_dtype = data_torch.dtype
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# convert any unsupported data types to float32
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if data_torch.dtype not in (torch.float16, torch.float32):
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data_torch = data_torch.to(torch.float32)
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# use the first number-like part of the tensor name as the block id
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bid = None
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for part in name.split("."):
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if part.isdecimal():
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bid = int(part)
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break
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for new_name, data in ((n, d.squeeze().numpy()) for n, d in self.modify_tensors(data_torch, name, bid)):
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data: np.ndarray # type hint
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n_dims = len(data.shape)
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data_dtype = data.dtype
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data_qtype: gguf.GGMLQuantizationType | None = None
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# when both are True, f32 should win
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extra_f32 = self.extra_f32_tensors(name, new_name, bid, n_dims)
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extra_f16 = self.extra_f16_tensors(name, new_name, bid, n_dims)
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# Most of the codebase that takes in 1D tensors or norms only handles F32 tensors
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# Conditions should closely match those in llama_model_quantize_internal in llama.cpp
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extra_f32 = any(cond for cond in (
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extra_f32,
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n_dims == 1,
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new_name.endswith("_norm.weight"),
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))
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# Some tensor types are always in float32
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extra_f32 = extra_f32 or any(self.match_model_tensor_name(new_name, key, bid) for key in (
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gguf.MODEL_TENSOR.FFN_GATE_INP,
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gguf.MODEL_TENSOR.POS_EMBD,
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gguf.MODEL_TENSOR.TOKEN_TYPES,
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))
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# if f16 desired, convert any float32 2-dim weight tensors to float16
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extra_f16 = any(cond for cond in (
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extra_f16,
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(name.endswith(".weight") and n_dims >= 2),
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))
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if self.ftype != gguf.LlamaFileType.ALL_F32 and extra_f16 and not extra_f32:
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if self.ftype == gguf.LlamaFileType.MOSTLY_BF16:
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data = gguf.quantize_bf16(data)
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assert data.dtype == np.int16
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data_qtype = gguf.GGMLQuantizationType.BF16
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elif self.ftype == gguf.LlamaFileType.MOSTLY_Q8_0 and gguf.can_quantize_to_q8_0(data):
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data = gguf.quantize_q8_0(data)
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assert data.dtype == np.uint8
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data_qtype = gguf.GGMLQuantizationType.Q8_0
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else: # default to float16 for quantized tensors
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if data_dtype != np.float16:
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data = data.astype(np.float16)
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data_qtype = gguf.GGMLQuantizationType.F16
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if data_qtype is None: # by default, convert to float32
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if data_dtype != np.float32:
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data = data.astype(np.float32)
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data_qtype = gguf.GGMLQuantizationType.F32
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shape = gguf.quant_shape_from_byte_shape(data.shape, data_qtype) if data.dtype == np.uint8 else data.shape
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# reverse shape to make it similar to the internal ggml dimension order
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shape_str = f"{{{', '.join(str(n) for n in reversed(shape))}}}"
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# n_dims is implicit in the shape
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logger.info(f"{f'%-{max_name_len}s' % f'{new_name},'} {old_dtype} --> {data_qtype.name}, shape = {shape_str}")
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self.gguf_writer.add_tensor(new_name, data, raw_dtype=data_qtype)
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def write(self):
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self.write_tensors()
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self.gguf_writer.write_header_to_file(self.fname_out)
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self.gguf_writer.write_kv_data_to_file()
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self.gguf_writer.write_tensors_to_file(progress=True)
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self.gguf_writer.close()
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def write_vocab(self):
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if len(self.gguf_writer.tensors) != 1:
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raise ValueError('Splitting the vocabulary is not supported')
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self.gguf_writer.write_header_to_file(self.fname_out)
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self.gguf_writer.write_kv_data_to_file()
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self.gguf_writer.close()
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@staticmethod
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def get_model_part_names(dir_model: Path, prefix: str, suffix: str) -> list[str]:
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part_names: list[str] = []
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for filename in os.listdir(dir_model):
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if filename.startswith(prefix) and filename.endswith(suffix):
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part_names.append(filename)
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part_names.sort()
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return part_names
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@staticmethod
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def load_hparams(dir_model: Path):
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with open(dir_model / "config.json", "r", encoding="utf-8") as f:
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return json.load(f)
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@classmethod
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def register(cls, *names: str) -> Callable[[AnyModel], AnyModel]:
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assert names
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def func(modelcls: AnyModel) -> AnyModel:
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for name in names:
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cls._model_classes[name] = modelcls
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return modelcls
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return func
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@classmethod
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def from_model_architecture(cls, arch: str) -> type[Model]:
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try:
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return cls._model_classes[arch]
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except KeyError:
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raise NotImplementedError(f'Architecture {arch!r} not supported!') from None
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|
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def does_token_look_special(self, token: str | bytes) -> bool:
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if isinstance(token, (bytes, bytearray)):
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token_text = token.decode(encoding="utf-8")
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elif isinstance(token, memoryview):
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token_text = token.tobytes().decode(encoding="utf-8")
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else:
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token_text = token
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# Some models mark some added tokens which ought to be control tokens as not special.
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# (e.g. command-r, command-r-plus, deepseek-coder, gemma{,-2})
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seems_special = token_text in (
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"<pad>", # deepseek-coder
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"<mask>", "<2mass>", "[@BOS@]", # gemma{,-2}
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)
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seems_special = seems_special or (token_text.startswith("<|") and token_text.endswith("|>"))
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seems_special = seems_special or (token_text.startswith("<｜") and token_text.endswith("｜>")) # deepseek-coder
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# TODO: should these be marked as UNUSED instead? (maybe not)
|
||
seems_special = seems_special or (token_text.startswith("<unused") and token_text.endswith(">")) # gemma{,-2}
|
||
|
||
return seems_special
|
||
|
||
# used for GPT-2 BPE and WordPiece vocabs
|
||
def get_vocab_base(self) -> tuple[list[str], list[int], str]:
|
||
tokens: list[str] = []
|
||
toktypes: list[int] = []
|
||
|
||
from transformers import AutoTokenizer
|
||
tokenizer = AutoTokenizer.from_pretrained(self.dir_model)
|
||
vocab_size = self.hparams.get("vocab_size", len(tokenizer.vocab))
|
||
assert max(tokenizer.vocab.values()) < vocab_size
|
||
|
||
tokpre = self.get_vocab_base_pre(tokenizer)
|
||
|
||
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:
|
||
tokens.append(f"[PAD{i}]")
|
||
toktypes.append(gguf.TokenType.UNUSED)
|
||
else:
|
||
token: str = reverse_vocab[i]
|
||
if token in added_vocab:
|
||
if tokenizer.added_tokens_decoder[i].special or self.does_token_look_special(token):
|
||
toktypes.append(gguf.TokenType.CONTROL)
|
||
else:
|
||
token = token.replace(b"\xe2\x96\x81".decode("utf-8"), " ") # pre-normalize user-defined spaces
|
||
toktypes.append(gguf.TokenType.USER_DEFINED)
|
||
else:
|
||
toktypes.append(gguf.TokenType.NORMAL)
|
||
tokens.append(token)
|
||
|
||
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
|
||
# Marker: Start get_vocab_base_pre
|
||
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()
|
||
|
||
logger.debug(f"chktok: {chktok}")
|
||
logger.debug(f"chkhsh: {chkhsh}")
|
||
|
||
res = None
|
||
|
||
# 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!
|
||
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"
|
||
if chkhsh == "32d85c31273f8019248f2559fed492d929ea28b17e51d81d3bb36fff23ca72b3":
|
||
# ref: https://huggingface.co/stabilityai/stablelm-2-zephyr-1_6b
|
||
res = "stablelm2"
|
||
if chkhsh == "6221ad2852e85ce96f791f476e0b390cf9b474c9e3d1362f53a24a06dc8220ff":
|
||
# ref: https://huggingface.co/smallcloudai/Refact-1_6-base
|
||
res = "refact"
|
||
if chkhsh == "9c2227e4dd922002fb81bde4fc02b0483ca4f12911410dee2255e4987644e3f8":
|
||
# ref: https://huggingface.co/CohereForAI/c4ai-command-r-v01
|
||
res = "command-r"
|
||
if chkhsh == "e636dc30a262dcc0d8c323492e32ae2b70728f4df7dfe9737d9f920a282b8aea":
|
||
# ref: https://huggingface.co/Qwen/Qwen1.5-7B
|
||
res = "qwen2"
|
||
if chkhsh == "b6dc8df998e1cfbdc4eac8243701a65afe638679230920b50d6f17d81c098166":
|
||
# ref: https://huggingface.co/allenai/OLMo-1.7-7B-hf
|
||
res = "olmo"
|
||
if chkhsh == "a8594e3edff7c29c003940395316294b2c623e09894deebbc65f33f1515df79e":
|
||
# ref: https://huggingface.co/databricks/dbrx-base
|
||
res = "dbrx"
|
||
if chkhsh == "0876d13b50744004aa9aeae05e7b0647eac9d801b5ba4668afc01e709c15e19f":
|
||
# ref: https://huggingface.co/jinaai/jina-embeddings-v2-base-en
|
||
res = "jina-v2-en"
|
||
if chkhsh == "171aeeedd6fb548d418a7461d053f11b6f1f1fc9b387bd66640d28a4b9f5c643":
|
||
# ref: https://huggingface.co/jinaai/jina-embeddings-v2-base-es
|
||
res = "jina-v2-es"
|
||
if chkhsh == "27949a2493fc4a9f53f5b9b029c82689cfbe5d3a1929bb25e043089e28466de6":
|
||
# ref: https://huggingface.co/jinaai/jina-embeddings-v2-base-de
|
||
res = "jina-v2-de"
|
||
if chkhsh == "c136ed14d01c2745d4f60a9596ae66800e2b61fa45643e72436041855ad4089d":
|
||
# ref: https://huggingface.co/abacusai/Smaug-Llama-3-70B-Instruct
|
||
res = "smaug-bpe"
|
||
if chkhsh == "c7ea5862a53e4272c035c8238367063e2b270d51faa48c0f09e9d5b54746c360":
|
||
# ref: https://huggingface.co/LumiOpen/Poro-34B-chat
|
||
res = "poro-chat"
|
||
if chkhsh == "7967bfa498ade6b757b064f31e964dddbb80f8f9a4d68d4ba7998fcf281c531a":
|
||
# ref: https://huggingface.co/jinaai/jina-embeddings-v2-base-code
|
||
res = "jina-v2-code"
|
||
if chkhsh == "b6e8e1518dc4305be2fe39c313ed643381c4da5db34a98f6a04c093f8afbe99b":
|
||
# ref: https://huggingface.co/THUDM/glm-4-9b-chat
|
||
res = "chatglm-bpe"
|
||
if chkhsh == "7fc505bd3104ca1083b150b17d088b59534ede9bde81f0dd2090967d7fe52cee":
|
||
# ref: https://huggingface.co/LumiOpen/Viking-7B
|
||
res = "viking"
|
||
if chkhsh == "b53802fb28e26d645c3a310b34bfe07da813026ec7c7716883404d5e0f8b1901":
|
||
# ref: https://huggingface.co/core42/jais-13b
|
||
res = "jais"
|
||
|
||
if res is None:
|
||
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")
|
||
raise NotImplementedError("BPE pre-tokenizer was not recognized - update get_vocab_base_pre()")
|
||
|
||
logger.debug(f"tokenizer.ggml.pre: {repr(res)}")
|
||
logger.debug(f"chkhsh: {chkhsh}")
|
||
|
||
return res
|
||
# Marker: End get_vocab_base_pre
|
||
|
||
def _set_vocab_gpt2(self) -> None:
|
||
tokens, toktypes, tokpre = self.get_vocab_base()
|
||
self.gguf_writer.add_tokenizer_model("gpt2")
|
||
self.gguf_writer.add_tokenizer_pre(tokpre)
|
||
self.gguf_writer.add_token_list(tokens)
|
||
self.gguf_writer.add_token_types(toktypes)
|
||
|
||
special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=True)
|
||
special_vocab.add_to_gguf(self.gguf_writer)
|
||
|
||
def _set_vocab_qwen(self):
|
||
dir_model = self.dir_model
|
||
hparams = self.hparams
|
||
tokens: list[str] = []
|
||
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
|
||
|
||
tokpre = self.get_vocab_base_pre(tokenizer)
|
||
|
||
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
|
||
reverse_vocab = {id_ : encoded_tok for encoded_tok, id_ in {**vocab, **added_vocab}.items()}
|
||
|
||
for i in range(vocab_size):
|
||
if i not in reverse_vocab:
|
||
tokens.append(f"[PAD{i}]")
|
||
toktypes.append(gguf.TokenType.UNUSED)
|
||
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")
|
||
self.gguf_writer.add_tokenizer_pre(tokpre)
|
||
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)
|
||
|
||
def _set_vocab_sentencepiece(self, add_to_gguf=True):
|
||
tokens, scores, toktypes = self._create_vocab_sentencepiece()
|
||
|
||
self.gguf_writer.add_tokenizer_model("llama")
|
||
self.gguf_writer.add_tokenizer_pre("default")
|
||
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 _create_vocab_sentencepiece(self):
|
||
from sentencepiece import SentencePieceProcessor
|
||
|
||
tokenizer_path = self.dir_model / 'tokenizer.model'
|
||
|
||
if not tokenizer_path.is_file():
|
||
raise FileNotFoundError(f"File not found: {tokenizer_path}")
|
||
|
||
tokenizer = SentencePieceProcessor()
|
||
tokenizer.LoadFromFile(str(tokenizer_path))
|
||
|
||
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.UNUSED] * vocab_size
|
||
|
||
for token_id in range(tokenizer.vocab_size()):
|
||
piece = tokenizer.IdToPiece(token_id)
|
||
text = piece.encode("utf-8")
|
||
score = tokenizer.GetScore(token_id)
|
||
|
||
toktype = SentencePieceTokenTypes.NORMAL
|
||
if tokenizer.IsUnknown(token_id):
|
||
toktype = SentencePieceTokenTypes.UNKNOWN
|
||
elif tokenizer.IsControl(token_id):
|
||
toktype = SentencePieceTokenTypes.CONTROL
|
||
elif tokenizer.IsUnused(token_id):
|
||
toktype = SentencePieceTokenTypes.UNUSED
|
||
elif tokenizer.IsByte(token_id):
|
||
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):
|
||
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
|
||
|
||
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, token_data in added_tokens_decoder.items():
|
||
token_id = int(token_id)
|
||
token: str = token_data["content"]
|
||
if toktypes[token_id] != SentencePieceTokenTypes.UNUSED:
|
||
assert tokens[token_id] == token.encode("utf-8")
|
||
if token_data.get("special") or self.does_token_look_special(token):
|
||
toktypes[token_id] = SentencePieceTokenTypes.CONTROL
|
||
else:
|
||
token = token.replace(b"\xe2\x96\x81".decode("utf-8"), " ") # pre-normalize user-defined spaces
|
||
toktypes[token_id] = SentencePieceTokenTypes.USER_DEFINED
|
||
|
||
scores[token_id] = -1000.0
|
||
tokens[token_id] = token.encode("utf-8")
|
||
|
||
if vocab_size > len(tokens):
|
||
pad_count = vocab_size - len(tokens)
|
||
logger.debug(f"Padding vocab with {pad_count} token(s) - [PAD1] through [PAD{pad_count}]")
|
||
for i in range(1, pad_count + 1):
|
||
tokens.append(bytes(f"[PAD{i}]", encoding="utf-8"))
|
||
scores.append(-1000.0)
|
||
toktypes.append(SentencePieceTokenTypes.UNUSED)
|
||
|
||
return tokens, scores, toktypes
|
||
|
||
def _set_vocab_llama_hf(self):
|
||
vocab = gguf.LlamaHfVocab(self.dir_model)
|
||
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")
|
||
self.gguf_writer.add_tokenizer_pre("default")
|
||
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_vocab_builtin(self, model_name: Literal["gpt-neox", "llama-spm"], vocab_size: int):
|
||
tokenizer_path = Path(sys.path[0]) / "models" / f"ggml-vocab-{model_name}.gguf"
|
||
logger.warning(f"Using tokenizer from '{os.path.relpath(tokenizer_path, os.getcwd())}'")
|
||
vocab_reader = gguf.GGUFReader(tokenizer_path, "r")
|
||
|
||
default_pre = "mpt" if model_name == "gpt-neox" else "default"
|
||
|
||
field = vocab_reader.get_field(gguf.Keys.Tokenizer.MODEL)
|
||
assert field # tokenizer model
|
||
self.gguf_writer.add_tokenizer_model(bytes(field.parts[-1]).decode("utf-8"))
|
||
|
||
field = vocab_reader.get_field(gguf.Keys.Tokenizer.PRE)
|
||
self.gguf_writer.add_tokenizer_pre(bytes(field.parts[-1]).decode("utf-8") if field else default_pre)
|
||
|
||
field = vocab_reader.get_field(gguf.Keys.Tokenizer.LIST)
|
||
assert field # token list
|
||
self.gguf_writer.add_token_list([bytes(field.parts[i]) for i in field.data][:vocab_size])
|
||
|
||
if model_name == "llama-spm":
|
||
field = vocab_reader.get_field(gguf.Keys.Tokenizer.SCORES)
|
||
assert field # token scores
|
||
self.gguf_writer.add_token_scores([field.parts[i].tolist()[0] for i in field.data][:vocab_size])
|
||
|
||
field = vocab_reader.get_field(gguf.Keys.Tokenizer.TOKEN_TYPE)
|
||
assert field # token types
|
||
self.gguf_writer.add_token_types([field.parts[i].tolist()[0] for i in field.data][:vocab_size])
|
||
|
||
if model_name != "llama-spm":
|
||
field = vocab_reader.get_field(gguf.Keys.Tokenizer.MERGES)
|
||
assert field # token merges
|
||
self.gguf_writer.add_token_merges([bytes(field.parts[i]) for i in field.data])
|
||
|
||
if (field := vocab_reader.get_field(gguf.Keys.Tokenizer.BOS_ID)) is not None:
|
||
self.gguf_writer.add_bos_token_id(field.parts[-1].tolist()[0])
|
||
if (field := vocab_reader.get_field(gguf.Keys.Tokenizer.EOS_ID)) is not None:
|
||
self.gguf_writer.add_eos_token_id(field.parts[-1].tolist()[0])
|
||
if (field := vocab_reader.get_field(gguf.Keys.Tokenizer.UNK_ID)) is not None:
|
||
self.gguf_writer.add_unk_token_id(field.parts[-1].tolist()[0])
|
||
if (field := vocab_reader.get_field(gguf.Keys.Tokenizer.PAD_ID)) is not None:
|
||
self.gguf_writer.add_pad_token_id(field.parts[-1].tolist()[0])
|
||
if (field := vocab_reader.get_field(gguf.Keys.Tokenizer.ADD_BOS)) is not None:
|
||
self.gguf_writer.add_add_bos_token(field.parts[-1].tolist()[0])
|
||
if (field := vocab_reader.get_field(gguf.Keys.Tokenizer.ADD_EOS)) is not None:
|
||
self.gguf_writer.add_add_eos_token(field.parts[-1].tolist()[0])
|
||
|
||
|
||
@Model.register("GPTNeoXForCausalLM")
|
||
class GPTNeoXModel(Model):
|
||
model_arch = gguf.MODEL_ARCH.GPTNEOX
|
||
|
||
def set_gguf_parameters(self):
|
||
block_count = self.hparams["num_hidden_layers"]
|
||
|
||
self.gguf_writer.add_name(self.dir_model.name if self.model_name is None else self.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"])
|
||
|
||
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
|
||
del bid # unused
|
||
|
||
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"))
|
||
|
||
tensors: list[tuple[str, Tensor]] = []
|
||
|
||
if re.match(r"gpt_neox\.layers\.\d+\.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"gpt_neox\.layers\.\d+\.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")
|
||
|
||
tensors.append((self.map_tensor_name(name), data_torch))
|
||
|
||
return tensors
|
||
|
||
|
||
@Model.register("BloomForCausalLM")
|
||
class BloomModel(Model):
|
||
model_arch = gguf.MODEL_ARCH.BLOOM
|
||
|
||
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)
|
||
|
||
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
|
||
del bid # unused
|
||
|
||
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"))
|
||
|
||
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")
|
||
|
||
tensors.append((self.map_tensor_name(name), data_torch))
|
||
|
||
if name == "word_embeddings.weight":
|
||
assert self.tensor_names is not None
|
||
|
||
# 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))
|
||
|
||
return tensors
|
||
|
||
|
||
@Model.register("MPTForCausalLM")
|
||
class MPTModel(Model):
|
||
model_arch = gguf.MODEL_ARCH.MPT
|
||
|
||
def set_vocab(self):
|
||
try:
|
||
self._set_vocab_gpt2()
|
||
except Exception:
|
||
# Fallback for SEA-LION model
|
||
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)
|
||
|
||
def set_gguf_parameters(self):
|
||
block_count = self.hparams["n_layers"]
|
||
self.gguf_writer.add_name(self.dir_model.name if self.model_name is None else self.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"])
|
||
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)
|
||
|
||
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
|
||
del bid # unused
|
||
|
||
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"))
|
||
|
||
return [(new_name, data_torch)]
|
||
|
||
|
||
@Model.register("OrionForCausalLM")
|
||
class OrionModel(Model):
|
||
model_arch = gguf.MODEL_ARCH.ORION
|
||
|
||
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:
|
||
raise ValueError("gguf: can not find ctx length parameter.")
|
||
|
||
self.gguf_writer.add_file_type(self.ftype)
|
||
self.gguf_writer.add_name(self.dir_model.name if self.model_name is None else self.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)
|
||
# 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
|
||
self.gguf_writer.add_layer_norm_eps(self.hparams["rms_norm_eps"])
|
||
|
||
|
||
@Model.register("BaichuanForCausalLM", "BaiChuanForCausalLM")
|
||
class BaichuanModel(Model):
|
||
model_arch = gguf.MODEL_ARCH.BAICHUAN
|
||
|
||
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:
|
||
raise ValueError("gguf: can not find ctx length parameter.")
|
||
|
||
self.gguf_writer.add_name(self.dir_model.name if self.model_name is None else self.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"])
|
||
self.gguf_writer.add_file_type(self.ftype)
|
||
|
||
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"])
|
||
|
||
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
|
||
head_count = self.hparams["num_attention_heads"]
|
||
head_count_kv = self.hparams.get("num_key_value_heads", head_count)
|
||
|
||
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)]
|
||
|
||
return tensors
|
||
|
||
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.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
|
||
|
||
tokens: list[bytes] = []
|
||
toktypes: list[int] = []
|
||
|
||
from transformers import AutoTokenizer
|
||
tokenizer = AutoTokenizer.from_pretrained(dir_model)
|
||
vocab_size = hparams.get("vocab_size", len(tokenizer.vocab))
|
||
# Since we are checking the maximum index, we need to ensure it's strictly less than vocab_size,
|
||
# because vocab_size is the count of items, and indexes start at 0.
|
||
max_vocab_index = max(tokenizer.get_vocab().values())
|
||
if max_vocab_index >= vocab_size:
|
||
raise ValueError("Vocabulary size exceeds expected maximum size.")
|
||
|
||
reverse_vocab: dict[int, str] = {id_: encoded_tok for encoded_tok, id_ in tokenizer.vocab.items()}
|
||
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")
|
||
self.gguf_writer.add_tokenizer_pre("default")
|
||
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:
|
||
raise ValueError("gguf: can not find ctx length parameter.")
|
||
|
||
self.gguf_writer.add_name(self.dir_model.name if self.model_name is None else self.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"])
|
||
self.gguf_writer.add_file_type(self.ftype)
|
||
|
||
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"])
|
||
|
||
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
|
||
del bid # unused
|
||
|
||
head_count = self.hparams["num_attention_heads"]
|
||
head_count_kv = self.hparams.get("num_key_value_heads", head_count)
|
||
|
||
# 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)
|
||
|
||
return [(self.map_tensor_name(name), data_torch)]
|
||
|
||
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)
|
||
)
|
||
|
||
|
||
@Model.register("FalconForCausalLM", "RWForCausalLM")
|
||
class FalconModel(Model):
|
||
model_arch = gguf.MODEL_ARCH.FALCON
|
||
|
||
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)
|
||
|
||
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
|
||
del bid # unused
|
||
|
||
# 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
|
||
|
||
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
|
||
|
||
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)
|
||
|
||
return [(self.map_tensor_name(name), data_torch)]
|
||
|
||
|
||
@Model.register("GPTBigCodeForCausalLM")
|
||
class StarCoderModel(Model):
|
||
model_arch = gguf.MODEL_ARCH.STARCODER
|
||
|
||
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)
|
||
|
||
|
||
@Model.register("GPTRefactForCausalLM")
|
||
class RefactModel(Model):
|
||
model_arch = gguf.MODEL_ARCH.REFACT
|
||
|
||
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', 'eot'])
|
||
special_vocab._set_special_token("prefix", 1)
|
||
special_vocab._set_special_token("suffix", 3)
|
||
special_vocab._set_special_token("middle", 2)
|
||
special_vocab.add_to_gguf(self.gguf_writer)
|
||
|
||
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)
|
||
|
||
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
|
||
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
|
||
|
||
tensors: list[tuple[str, Tensor]] = []
|
||
|
||
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:]))
|
||
|
||
if len(tensors) == 0:
|
||
tensors.append((self.map_tensor_name(name), data_torch))
|
||
|
||
return tensors
|
||
|
||
|
||
@Model.register("StableLmForCausalLM", "StableLMEpochForCausalLM", "LlavaStableLMEpochForCausalLM")
|
||
class StableLMModel(Model):
|
||
model_arch = gguf.MODEL_ARCH.STABLELM
|
||
|
||
def set_vocab(self):
|
||
if (self.dir_model / "tokenizer.json").is_file():
|
||
self._set_vocab_gpt2()
|
||
else:
|
||
# StableLM 2 1.6B used to have a vocab in a similar format to Qwen's vocab
|
||
self._set_vocab_qwen()
|
||
|
||
def set_gguf_parameters(self):
|
||
hparams = self.hparams
|
||
block_count = hparams["num_hidden_layers"]
|
||
|
||
self.gguf_writer.add_name(self.dir_model.name if self.model_name is None else self.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"])
|
||
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"])))
|
||
self.gguf_writer.add_head_count(hparams["num_attention_heads"])
|
||
self.gguf_writer.add_head_count_kv(hparams["num_key_value_heads"])
|
||
self.gguf_writer.add_parallel_residual(hparams["use_parallel_residual"] if "use_parallel_residual" in hparams else True)
|
||
self.gguf_writer.add_layer_norm_eps(self.find_hparam(["layer_norm_eps", "norm_eps"]))
|
||
self.gguf_writer.add_file_type(self.ftype)
|
||
|
||
_q_norms: list[dict[str, Tensor]] | None = None
|
||
_k_norms: list[dict[str, Tensor]] | None = None
|
||
|
||
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"]
|
||
|
||
if name.find("q_layernorm.norms") != -1:
|
||
assert bid is not None
|
||
|
||
if self._q_norms is None:
|
||
self._q_norms = [{} for _ in range(self.block_count)]
|
||
|
||
self._q_norms[bid][name] = data_torch
|
||
|
||
if len(self._q_norms[bid]) >= n_head:
|
||
return self._stack_qk_norm(bid, n_head, self._q_norms[bid], "q_layernorm")
|
||
else:
|
||
return []
|
||
|
||
if name.find("k_layernorm.norms") != -1:
|
||
assert bid is not None
|
||
|
||
if self._k_norms is None:
|
||
self._k_norms = [{} for _ in range(self.block_count)]
|
||
|
||
self._k_norms[bid][name] = data_torch
|
||
|
||
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 []
|
||
|
||
return [(self.map_tensor_name(name), data_torch)]
|
||
|
||
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)
|
||
|
||
merged_name = f"model.layers.{bid}.self_attn.{layer_name}.weight"
|
||
new_name = self.map_tensor_name(merged_name)
|
||
|
||
return [(new_name, data_torch)]
|
||
|
||
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}")
|
||
|
||
|
||
@Model.register("LlamaForCausalLM", "MistralForCausalLM", "MixtralForCausalLM")
|
||
class LlamaModel(Model):
|
||
model_arch = gguf.MODEL_ARCH.LLAMA
|
||
|
||
def set_vocab(self):
|
||
try:
|
||
self._set_vocab_sentencepiece()
|
||
except FileNotFoundError:
|
||
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)
|
||
|
||
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"])
|
||
|
||
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"])
|
||
|
||
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)
|
||
if "add_prefix_space" in tokenizer_config_json:
|
||
self.gguf_writer.add_add_space_prefix(tokenizer_config_json["add_prefix_space"])
|
||
|
||
# Apply to granite small models only
|
||
if self.hparams.get("vocab_size", 32000) == 49152:
|
||
self.gguf_writer.add_add_bos_token(False)
|
||
|
||
@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))
|
||
|
||
_experts: list[dict[str, Tensor]] | None = None
|
||
|
||
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")
|
||
|
||
if name.endswith(("q_proj.weight", "q_proj.bias")):
|
||
data_torch = LlamaModel.permute(data_torch, n_head, n_head)
|
||
if name.endswith(("k_proj.weight", "k_proj.bias")):
|
||
data_torch = LlamaModel.permute(data_torch, n_head, n_kv_head)
|
||
|
||
# process the experts separately
|
||
if name.find("block_sparse_moe.experts") != -1:
|
||
n_experts = self.hparams["num_local_experts"]
|
||
|
||
assert bid is not None
|
||
|
||
if self._experts is None:
|
||
self._experts = [{} for _ in range(self.block_count)]
|
||
|
||
self._experts[bid][name] = data_torch
|
||
|
||
if len(self._experts[bid]) >= n_experts * 3:
|
||
tensors: list[tuple[str, Tensor]] = []
|
||
|
||
# merge the experts into a single 3d tensor
|
||
for wid in ["w1", "w2", "w3"]:
|
||
datas: list[Tensor] = []
|
||
|
||
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]
|
||
|
||
data_torch = torch.stack(datas, dim=0)
|
||
|
||
merged_name = f"layers.{bid}.feed_forward.experts.{wid}.weight"
|
||
|
||
new_name = self.map_tensor_name(merged_name)
|
||
|
||
tensors.append((new_name, data_torch))
|
||
return tensors
|
||
else:
|
||
return []
|
||
|
||
return [(self.map_tensor_name(name), data_torch)]
|
||
|
||
def write_tensors(self):
|
||
super().write_tensors()
|
||
|
||
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}")
|
||
|
||
|
||
@Model.register("BitnetForCausalLM")
|
||
class BitnetModel(Model):
|
||
model_arch = gguf.MODEL_ARCH.BITNET
|
||
|
||
def set_vocab(self):
|
||
self._set_vocab_sentencepiece()
|
||
|
||
def set_gguf_parameters(self):
|
||
super().set_gguf_parameters()
|
||
self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LINEAR)
|
||
self.gguf_writer.add_rope_scaling_factor(1.0)
|
||
|
||
def weight_quant(self, weight):
|
||
dtype = weight.dtype
|
||
weight = weight.float()
|
||
s = 1 / weight.abs().mean().clamp(min=1e-5)
|
||
weight = (weight * s).round().clamp(-1, 1) / s
|
||
scale = weight.abs().max().unsqueeze(0)
|
||
weight = torch.where(weight.abs().less(1e-6), 0, weight).type(dtype)
|
||
weight = torch.sign(weight).type(dtype)
|
||
return weight.type(dtype), scale.type(torch.float32)
|
||
|
||
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
|
||
new_name = self.map_tensor_name(name)
|
||
|
||
if any(self.match_model_tensor_name(new_name, key, bid) for key in [
|
||
gguf.MODEL_TENSOR.ATTN_Q,
|
||
gguf.MODEL_TENSOR.ATTN_K,
|
||
gguf.MODEL_TENSOR.ATTN_V,
|
||
gguf.MODEL_TENSOR.ATTN_OUT,
|
||
gguf.MODEL_TENSOR.FFN_UP,
|
||
gguf.MODEL_TENSOR.FFN_DOWN,
|
||
gguf.MODEL_TENSOR.FFN_GATE,
|
||
]):
|
||
# transform weight into 1/0/-1 (in fp32)
|
||
weight_torch, scale_torch = self.weight_quant(data_torch)
|
||
yield (new_name, weight_torch)
|
||
yield (new_name.removesuffix(".weight") + ".scale", scale_torch)
|
||
else:
|
||
yield (new_name, data_torch)
|
||
|
||
|
||
@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")
|
||
|
||
_experts: list[dict[str, Tensor]] | None = None
|
||
|
||
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"]
|
||
|
||
assert bid is not None
|
||
|
||
if self._experts is None:
|
||
self._experts = [{} for _ in range(self.block_count)]
|
||
|
||
self._experts[bid][name] = data_torch
|
||
|
||
if len(self._experts[bid]) >= n_experts * 3:
|
||
tensors: list[tuple[str, Tensor]] = []
|
||
|
||
# merge the experts into a single 3d tensor
|
||
for wid in ["linear", "linear_1", "linear_v"]:
|
||
datas: list[Tensor] = []
|
||
|
||
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]
|
||
|
||
data_torch = torch.stack(datas, dim=0)
|
||
|
||
merged_name = f"transformer.decoder_layer.{bid}.moe.{wid}.weight"
|
||
|
||
new_name = self.map_tensor_name(merged_name)
|
||
|
||
tensors.append((new_name, data_torch))
|
||
return tensors
|
||
else:
|
||
return []
|
||
|
||
return [(self.map_tensor_name(name), data_torch)]
|
||
|
||
|
||
@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_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)
|
||
logger.info(f"gguf: file type = {self.ftype}")
|
||
|
||
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
|
||
|
||
# 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",))
|
||
|
||
return [(new_name, data_torch)]
|
||
|
||
def extra_f16_tensors(self, name: str, new_name: str, bid: int | None, n_dims: int) -> bool:
|
||
del name, new_name, bid # unused
|
||
|
||
return n_dims > 1
|
||
|
||
|
||
@Model.register("MiniCPMForCausalLM")
|
||
class MiniCPMModel(Model):
|
||
model_arch = gguf.MODEL_ARCH.MINICPM
|
||
|
||
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)
|
||
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(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):
|
||
self._set_vocab_llama_hf()
|
||
|
||
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 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"]
|
||
n_kv_head = self.hparams.get("num_key_value_heads")
|
||
|
||
# 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)
|
||
|
||
return [(self.map_tensor_name(name), data_torch)]
|
||
|
||
|
||
@Model.register("QWenLMHeadModel")
|
||
class QwenModel(Model):
|
||
model_arch = gguf.MODEL_ARCH.QWEN
|
||
|
||
@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
|
||
def bpe(mergeable_ranks: dict[bytes, int], token: bytes, max_rank: int | None = None) -> list[bytes]:
|
||
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):
|
||
self._set_vocab_qwen()
|
||
|
||
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"])
|
||
self.gguf_writer.add_file_type(self.ftype)
|
||
|
||
|
||
@Model.register("Qwen2ForCausalLM")
|
||
class Qwen2Model(Model):
|
||
model_arch = gguf.MODEL_ARCH.QWEN2
|
||
|
||
def set_vocab(self):
|
||
try:
|
||
self._set_vocab_sentencepiece()
|
||
except FileNotFoundError:
|
||
self._set_vocab_gpt2()
|
||
|
||
|
||
@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)
|
||
if (moe_intermediate_size := self.hparams.get("moe_intermediate_size")) is not None:
|
||
self.gguf_writer.add_expert_feed_forward_length(moe_intermediate_size)
|
||
logger.info(f"gguf: expert feed forward length = {moe_intermediate_size}")
|
||
if (shared_expert_intermediate_size := self.hparams.get('shared_expert_intermediate_size')) is not None:
|
||
self.gguf_writer.add_expert_shared_feed_forward_length(shared_expert_intermediate_size)
|
||
logger.info(f"gguf: expert shared feed forward length = {shared_expert_intermediate_size}")
|
||
|
||
_experts: list[dict[str, Tensor]] | None = None
|
||
|
||
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
|
||
|
||
if self._experts is None:
|
||
self._experts = [{} for _ in range(self.block_count)]
|
||
|
||
self._experts[bid][name] = data_torch
|
||
|
||
if len(self._experts[bid]) >= n_experts * 3:
|
||
tensors: list[tuple[str, Tensor]] = []
|
||
|
||
# merge the experts into a single 3d tensor
|
||
for w_name in ["down_proj", "gate_proj", "up_proj"]:
|
||
datas: list[Tensor] = []
|
||
|
||
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]
|
||
|
||
data_torch = torch.stack(datas, dim=0)
|
||
|
||
merged_name = f"model.layers.{bid}.mlp.experts.{w_name}.weight"
|
||
|
||
new_name = self.map_tensor_name(merged_name)
|
||
|
||
tensors.append((new_name, data_torch))
|
||
return tensors
|
||
else:
|
||
return []
|
||
|
||
return [(self.map_tensor_name(name), data_torch)]
|
||
|
||
def write_tensors(self):
|
||
super().write_tensors()
|
||
|
||
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}")
|
||
|
||
|
||
@Model.register("GPT2LMHeadModel")
|
||
class GPT2Model(Model):
|
||
model_arch = gguf.MODEL_ARCH.GPT2
|
||
|
||
def set_gguf_parameters(self):
|
||
self.gguf_writer.add_name(self.dir_model.name if self.model_name is None else self.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)
|
||
|
||
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
|
||
del bid # unused
|
||
|
||
tensors: list[tuple[str, Tensor]] = []
|
||
|
||
# we don't need these
|
||
if name.endswith((".attn.bias", ".attn.masked_bias")):
|
||
return tensors
|
||
|
||
if name.endswith((".c_attn.weight", ".c_proj.weight", ".c_fc.weight", ".c_proj.weight")):
|
||
data_torch = data_torch.transpose(1, 0)
|
||
|
||
new_name = self.map_tensor_name(name)
|
||
|
||
tensors.append((new_name, data_torch))
|
||
|
||
# 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))
|
||
|
||
return tensors
|
||
|
||
|
||
@Model.register("PhiForCausalLM")
|
||
class Phi2Model(Model):
|
||
model_arch = gguf.MODEL_ARCH.PHI2
|
||
|
||
def set_gguf_parameters(self):
|
||
block_count = self.find_hparam(["num_hidden_layers", "n_layer"])
|
||
|
||
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"])
|
||
|
||
self.gguf_writer.add_name("Phi2")
|
||
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(4 * n_embd)
|
||
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_eps(self.find_hparam(["layer_norm_epsilon", "layer_norm_eps"]))
|
||
self.gguf_writer.add_rope_dimension_count(int(rot_pct * n_embd) // n_head)
|
||
self.gguf_writer.add_file_type(self.ftype)
|
||
self.gguf_writer.add_add_bos_token(False)
|
||
|
||
|
||
@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():
|
||
raise ValueError(f'Error: Missing {tokenizer_path}')
|
||
|
||
tokenizer = SentencePieceProcessor()
|
||
tokenizer.LoadFromFile(str(tokenizer_path))
|
||
|
||
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.UNUSED] * vocab_size
|
||
|
||
for token_id in range(tokenizer.vocab_size()):
|
||
|
||
piece = tokenizer.IdToPiece(token_id)
|
||
text = piece.encode("utf-8")
|
||
score = tokenizer.GetScore(token_id)
|
||
|
||
toktype = SentencePieceTokenTypes.NORMAL
|
||
if tokenizer.IsUnknown(token_id):
|
||
toktype = SentencePieceTokenTypes.UNKNOWN
|
||
elif tokenizer.IsControl(token_id):
|
||
toktype = SentencePieceTokenTypes.CONTROL
|
||
elif tokenizer.IsUnused(token_id):
|
||
toktype = SentencePieceTokenTypes.UNUSED
|
||
elif tokenizer.IsByte(token_id):
|
||
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):
|
||
logger.debug(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
|
||
|
||
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.UNUSED:
|
||
assert tokens[token_id] == token
|
||
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.UNUSED:
|
||
assert tokens[token_id] == token
|
||
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
|
||
|
||
self.gguf_writer.add_tokenizer_model("llama")
|
||
self.gguf_writer.add_tokenizer_pre("default")
|
||
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"])
|
||
|
||
n_embd = self.find_hparam(["hidden_size", "n_embd"])
|
||
n_head = self.find_hparam(["num_attention_heads", "n_head"])
|
||
n_head_kv = self.find_hparam(["num_key_value_heads", "n_head_kv"])
|
||
rms_eps = self.find_hparam(["rms_norm_eps"])
|
||
max_pos_embds = self.find_hparam(["n_positions", "max_position_embeddings"])
|
||
orig_max_pos_embds = self.find_hparam(["original_max_position_embeddings"])
|
||
rope_dims = n_embd // n_head
|
||
|
||
self.gguf_writer.add_name("Phi3")
|
||
self.gguf_writer.add_context_length(max_pos_embds)
|
||
self.gguf_writer.add_rope_scaling_orig_ctx_len(orig_max_pos_embds)
|
||
self.gguf_writer.add_embedding_length(n_embd)
|
||
self.gguf_writer.add_feed_forward_length(self.find_hparam(["intermediate_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_rms_eps(rms_eps)
|
||
self.gguf_writer.add_rope_dimension_count(rope_dims)
|
||
self.gguf_writer.add_rope_freq_base(self.find_hparam(["rope_theta"]))
|
||
self.gguf_writer.add_file_type(self.ftype)
|
||
|
||
# write rope scaling for long context (128k) model
|
||
rope_scaling = self.find_hparam(['rope_scaling'], True)
|
||
if (rope_scaling is None):
|
||
return
|
||
|
||
scale = max_pos_embds / orig_max_pos_embds
|
||
|
||
rope_scaling_type = rope_scaling.get('type', '').lower()
|
||
if len(rope_scaling_type) == 0:
|
||
raise KeyError('Missing the required key rope_scaling.type')
|
||
|
||
if rope_scaling_type == 'su' or rope_scaling_type == 'longrope':
|
||
attn_factor = math.sqrt(1 + math.log(scale) / math.log(orig_max_pos_embds)) if scale > 1.0 else 1.0
|
||
elif rope_scaling_type == 'yarn':
|
||
attn_factor = 0.1 * math.log(scale) + 1.0 if scale > 1.0 else 1.0
|
||
else:
|
||
raise NotImplementedError(f'The rope scaling type {rope_scaling_type} is not supported yet')
|
||
|
||
self.gguf_writer.add_rope_scaling_attn_factors(attn_factor)
|
||
|
||
long_factors = rope_scaling.get('long_factor', None)
|
||
short_factors = rope_scaling.get('short_factor', None)
|
||
|
||
if long_factors is None or short_factors is None:
|
||
raise KeyError('Missing the required key rope_scaling.long_factor or rope_scaling_short_factor')
|
||
|
||
if len(long_factors) != len(short_factors) or len(long_factors) != rope_dims / 2:
|
||
raise ValueError(f'The length of rope long and short factors must be {rope_dims / 2}')
|
||
|
||
self.gguf_writer.add_tensor(gguf.TENSOR_NAMES[gguf.MODEL_TENSOR.ROPE_FACTORS_LONG] + ".weight", np.array(long_factors, dtype=np.float32))
|
||
self.gguf_writer.add_tensor(gguf.TENSOR_NAMES[gguf.MODEL_TENSOR.ROPE_FACTORS_SHORT] + ".weight", np.array(short_factors, dtype=np.float32))
|
||
|
||
|
||
@Model.register("PlamoForCausalLM")
|
||
class PlamoModel(Model):
|
||
model_arch = gguf.MODEL_ARCH.PLAMO
|
||
|
||
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"])
|
||
self.gguf_writer.add_file_type(self.ftype)
|
||
|
||
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
|
||
|
||
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
|
||
del bid # unused
|
||
|
||
new_name = self.map_tensor_name(name)
|
||
|
||
# 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)
|
||
|
||
return [(new_name, data_torch)]
|
||
|
||
|
||
@Model.register("CodeShellForCausalLM")
|
||
class CodeShellModel(Model):
|
||
model_arch = gguf.MODEL_ARCH.CODESHELL
|
||
|
||
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)
|
||
|
||
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
|
||
del bid # unused
|
||
|
||
new_name = self.map_tensor_name(name)
|
||
|
||
tensors: list[tuple[str, Tensor]] = [(new_name, data_torch)]
|
||
|
||
if new_name == self.format_tensor_name(gguf.MODEL_TENSOR.TOKEN_EMBD):
|
||
assert self.tensor_names is not None
|
||
|
||
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))
|
||
|
||
return tensors
|
||
|
||
|
||
@Model.register("InternLM2ForCausalLM")
|
||
class InternLM2Model(Model):
|
||
model_arch = gguf.MODEL_ARCH.INTERNLM2
|
||
|
||
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():
|
||
logger.error(f'Error: Missing {tokenizer_path}')
|
||
sys.exit(1)
|
||
|
||
sentencepiece_model = model.ModelProto() # pyright: ignore[reportAttributeAccessIssue]
|
||
sentencepiece_model.ParseFromString(open(tokenizer_path, "rb").read())
|
||
add_prefix = sentencepiece_model.normalizer_spec.add_dummy_prefix
|
||
|
||
tokenizer = SentencePieceProcessor()
|
||
tokenizer.LoadFromFile(str(tokenizer_path))
|
||
|
||
vocab_size = self.hparams.get('vocab_size', tokenizer.vocab_size())
|
||
|
||
for token_id in range(vocab_size):
|
||
piece = tokenizer.IdToPiece(token_id)
|
||
text = piece.encode("utf-8")
|
||
score = tokenizer.GetScore(token_id)
|
||
if text == b"\x00":
|
||
# (TODO): fixme
|
||
# Hack here and replace the \x00 characters.
|
||
logger.warning(f"InternLM2 convert token '{text}' to '🐉'!")
|
||
text = "🐉".encode("utf-8")
|
||
|
||
toktype = SentencePieceTokenTypes.NORMAL
|
||
if tokenizer.IsUnknown(token_id):
|
||
toktype = SentencePieceTokenTypes.UNKNOWN
|
||
elif tokenizer.IsControl(token_id):
|
||
toktype = SentencePieceTokenTypes.CONTROL
|
||
elif tokenizer.IsUnused(token_id):
|
||
toktype = SentencePieceTokenTypes.UNUSED
|
||
elif tokenizer.IsByte(token_id):
|
||
toktype = SentencePieceTokenTypes.BYTE
|
||
# take care of ununsed raw token
|
||
if piece.startswith('[UNUSED'):
|
||
toktype = SentencePieceTokenTypes.UNUSED
|
||
|
||
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)
|
||
|
||
chat_eos_token = '<|im_end|>'
|
||
chat_eos_token_id = None
|
||
|
||
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"]
|
||
if token == chat_eos_token:
|
||
chat_eos_token_id = token_id
|
||
token = token.encode("utf-8")
|
||
if toktypes[token_id] != SentencePieceTokenTypes.UNUSED:
|
||
assert(tokens[token_id] == token)
|
||
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"]
|
||
if token == chat_eos_token:
|
||
chat_eos_token_id = token_id
|
||
token = token.encode("utf-8")
|
||
if toktypes[token_id] != SentencePieceTokenTypes.UNUSED:
|
||
assert(tokens[token_id] == token)
|
||
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
|
||
|
||
self.gguf_writer.add_tokenizer_model("llama")
|
||
self.gguf_writer.add_tokenizer_pre("default")
|
||
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))
|
||
old_eos = special_vocab.special_token_ids["eos"]
|
||
if chat_eos_token_id is not None:
|
||
# For the chat model, we replace the eos with '<|im_end|>'.
|
||
# TODO: this is a hack, should be fixed
|
||
# https://github.com/ggerganov/llama.cpp/pull/6745#issuecomment-2067687048
|
||
special_vocab.special_token_ids["eos"] = chat_eos_token_id
|
||
logger.warning(f"Replace eos:{old_eos} with a special token:{chat_eos_token_id}"
|
||
" in chat mode so that the conversation can end normally.")
|
||
|
||
special_vocab.add_to_gguf(self.gguf_writer)
|
||
|
||
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"])
|
||
self.gguf_writer.add_file_type(self.ftype)
|
||
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"])
|
||
|
||
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"]
|
||
n_embd = self.hparams["hidden_size"]
|
||
q_per_kv = num_heads // num_kv_heads
|
||
head_dim = n_embd // num_heads
|
||
num_groups = num_heads // q_per_kv
|
||
|
||
if bid is not None and f"model.layers.{bid}.attention.wqkv" in name:
|
||
qkv = data_torch
|
||
|
||
qkv = qkv.reshape((num_groups, q_per_kv + 2, head_dim, n_embd))
|
||
q, k, v = qkv[:, : q_per_kv], qkv[:, -2], qkv[:, -1]
|
||
|
||
# The model weights of q and k equire additional reshape.
|
||
q = LlamaModel.permute(q.reshape((-1, q.shape[-1])), num_heads, num_heads)
|
||
k = LlamaModel.permute(k.reshape((-1, k.shape[-1])), num_heads, num_kv_heads)
|
||
v = v.reshape((-1, v.shape[-1]))
|
||
|
||
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)]
|
||
|
||
|
||
@Model.register("BertModel", "CamembertModel")
|
||
class BertModel(Model):
|
||
model_arch = gguf.MODEL_ARCH.BERT
|
||
|
||
def __init__(self, *args, **kwargs):
|
||
super().__init__(*args, **kwargs)
|
||
self.vocab_size = None
|
||
|
||
def set_gguf_parameters(self):
|
||
super().set_gguf_parameters()
|
||
self.gguf_writer.add_causal_attention(False)
|
||
|
||
# get pooling path
|
||
pooling_path = None
|
||
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
|
||
|
||
# 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")
|
||
self.gguf_writer.add_pooling_type(pooling_type)
|
||
|
||
def set_vocab(self):
|
||
tokens, toktypes, tokpre = self.get_vocab_base()
|
||
self.vocab_size = len(tokens)
|
||
|
||
# we need this to validate the size of the token_type embeddings
|
||
# though currently we are passing all zeros to the token_type embeddings
|
||
self.gguf_writer.add_token_type_count(2) # "Sequence A" or "Sequence B"
|
||
|
||
# convert to phantom space vocab
|
||
def phantom(tok):
|
||
if tok.startswith("[") and tok.endswith("]"):
|
||
return tok
|
||
if tok.startswith("##"):
|
||
return tok[2:]
|
||
return "\u2581" + tok
|
||
tokens = list(map(phantom, tokens))
|
||
|
||
# add vocab to gguf
|
||
self.gguf_writer.add_tokenizer_model("bert")
|
||
self.gguf_writer.add_tokenizer_pre(tokpre)
|
||
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)
|
||
|
||
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
|
||
del bid # unused
|
||
|
||
# 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
|
||
|
||
return [(self.map_tensor_name(name), data_torch)]
|
||
|
||
|
||
@Model.register("NomicBertModel")
|
||
class NomicBertModel(BertModel):
|
||
model_arch = gguf.MODEL_ARCH.NOMIC_BERT
|
||
|
||
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"])
|
||
|
||
|
||
@Model.register("GemmaForCausalLM")
|
||
class GemmaModel(Model):
|
||
model_arch = gguf.MODEL_ARCH.GEMMA
|
||
|
||
def set_vocab(self):
|
||
self._set_vocab_sentencepiece()
|
||
|
||
# TODO: these special tokens should be exported only for the CodeGemma family
|
||
special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=False,
|
||
special_token_types = ['prefix', 'suffix', 'middle', 'fsep', 'eot'])
|
||
special_vocab._set_special_token("prefix", 67)
|
||
special_vocab._set_special_token("suffix", 69)
|
||
special_vocab._set_special_token("middle", 68)
|
||
special_vocab._set_special_token("fsep", 70)
|
||
special_vocab._set_special_token("eot", 107)
|
||
special_vocab.add_to_gguf(self.gguf_writer)
|
||
|
||
self.gguf_writer.add_add_space_prefix(False)
|
||
|
||
def set_gguf_parameters(self):
|
||
hparams = self.hparams
|
||
block_count = hparams["num_hidden_layers"]
|
||
|
||
self.gguf_writer.add_name(self.dir_model.name if self.model_name is None else self.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"])
|
||
self.gguf_writer.add_file_type(self.ftype)
|
||
|
||
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
|
||
del bid # unused
|
||
|
||
# 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 []
|
||
|
||
# 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
|
||
|
||
return [(self.map_tensor_name(name), data_torch)]
|
||
|
||
|
||
@Model.register("Gemma2ForCausalLM")
|
||
class Gemma2Model(Model):
|
||
model_arch = gguf.MODEL_ARCH.GEMMA2
|
||
|
||
def set_vocab(self):
|
||
self._set_vocab_sentencepiece()
|
||
|
||
self.gguf_writer.add_add_space_prefix(False)
|
||
|
||
def set_gguf_parameters(self):
|
||
hparams = self.hparams
|
||
block_count = hparams["num_hidden_layers"]
|
||
|
||
self.gguf_writer.add_name(self.dir_model.name if self.model_name is None else self.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"])
|
||
self.gguf_writer.add_file_type(self.ftype)
|
||
self.gguf_writer.add_attn_logit_softcapping(
|
||
self.hparams["attn_logit_softcapping"]
|
||
)
|
||
self.gguf_writer.add_final_logit_softcapping(
|
||
self.hparams["final_logit_softcapping"]
|
||
)
|
||
self.gguf_writer.add_sliding_window(self.hparams["sliding_window"])
|
||
|
||
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
|
||
del bid # unused
|
||
|
||
# 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 []
|
||
|
||
# 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
|
||
|
||
return [(self.map_tensor_name(name), data_torch)]
|
||
|
||
|
||
@Model.register("Starcoder2ForCausalLM")
|
||
class StarCoder2Model(Model):
|
||
model_arch = gguf.MODEL_ARCH.STARCODER2
|
||
|
||
|
||
@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()
|
||
elif (self.dir_model / "tokenizer.model").is_file():
|
||
self._set_vocab_sentencepiece()
|
||
else:
|
||
# Use the GPT-NeoX tokenizer when no tokenizer files are present
|
||
self._set_vocab_builtin("gpt-neox", vocab_size)
|
||
|
||
def set_gguf_parameters(self):
|
||
d_model = self.find_hparam(["hidden_size", "d_model"])
|
||
d_conv = self.find_hparam(["conv_kernel", "d_conv"], optional=True) or 4
|
||
d_inner = self.find_hparam(["intermediate_size", "d_inner"], optional=True) or 2 * d_model
|
||
d_state = self.find_hparam(["state_size", "d_state"], optional=True) or 16
|
||
# 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
|
||
dt_rank = self.find_hparam(["time_step_rank", "dt_rank"], optional=True) or -(d_model // -16)
|
||
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 if self.model_name is None else self.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)
|
||
|
||
_tok_embd = None
|
||
|
||
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
|
||
del bid # unused
|
||
|
||
output_name = self.format_tensor_name(gguf.MODEL_TENSOR.OUTPUT)
|
||
tok_embd_name = self.format_tensor_name(gguf.MODEL_TENSOR.TOKEN_EMBD)
|
||
|
||
new_name = self.map_tensor_name(name)
|
||
|
||
if name.endswith(".A_log"):
|
||
logger.debug("A_log --> A ==> " + new_name)
|
||
data_torch = -torch.exp(data_torch)
|
||
|
||
# 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
|
||
|
||
return [(new_name, data_torch)]
|
||
|
||
def extra_f32_tensors(self, name: str, new_name: str, bid: int | None, n_dims: int) -> bool:
|
||
del n_dims # unused
|
||
|
||
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,
|
||
]
|
||
)
|
||
|
||
|
||
@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
|
||
# aya-23 models don't have model_max_length specified
|
||
self.hparams["max_position_embeddings"] = self.find_hparam(["model_max_length", "max_position_embeddings"])
|
||
|
||
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)
|
||
|
||
|
||
@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)
|
||
clip_qkv = self.hparams.get("clip_qkv")
|
||
if clip_qkv is not None:
|
||
self.gguf_writer.add_clamp_kqv(clip_qkv)
|
||
|
||
# Same as super class, but permuting q_proj, k_proj
|
||
# Copied from: LlamaModel
|
||
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"]
|
||
n_kv_head = self.hparams.get("num_key_value_heads")
|
||
|
||
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)
|
||
|
||
return [(self.map_tensor_name(name), data_torch)]
|
||
|
||
|
||
@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_layer' in name:
|
||
d1 = data[:self.intermediate_size, :]
|
||
name1 = name.replace('gated_layers', 'gated_layers_w')
|
||
name1 = name1.replace('up_gated_layer', 'gated_layers_v')
|
||
d2 = data[self.intermediate_size:, :]
|
||
name2 = name.replace('gated_layers', 'gated_layers_v')
|
||
name2 = name2.replace('up_gated_layer', 'gated_layers_w')
|
||
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)
|
||
|
||
|
||
@Model.register("OpenELMForCausalLM")
|
||
class OpenELMModel(Model):
|
||
model_arch = gguf.MODEL_ARCH.OPENELM
|
||
|
||
@staticmethod
|
||
def _make_divisible(v: float | int, divisor: int) -> int:
|
||
# ref: https://huggingface.co/apple/OpenELM-270M-Instruct/blob/eb111ff2e6724348e5b905984063d4064d4bc579/configuration_openelm.py#L34-L38
|
||
new_v = max(divisor, int(v + divisor / 2) // divisor * divisor)
|
||
# Make sure that round down does not go down by more than 10%.
|
||
if new_v < 0.9 * v:
|
||
new_v += divisor
|
||
return new_v
|
||
|
||
def __init__(self, *args, **kwargs):
|
||
super().__init__(*args, **kwargs)
|
||
|
||
ffn_multipliers: list[float] = self.hparams["ffn_multipliers"]
|
||
ffn_dim_divisor: int = self.hparams["ffn_dim_divisor"]
|
||
self._n_embd: int = self.hparams["model_dim"]
|
||
self._num_kv_heads: list[int] = self.hparams["num_kv_heads"]
|
||
self._num_query_heads: list[int] = self.hparams["num_query_heads"]
|
||
self._ffn_dims: list[int] = [
|
||
OpenELMModel._make_divisible(multiplier * self._n_embd, ffn_dim_divisor)
|
||
for multiplier in ffn_multipliers
|
||
]
|
||
assert isinstance(self._num_kv_heads, list) and isinstance(self._num_kv_heads[0], int)
|
||
assert isinstance(self._num_query_heads, list) and isinstance(self._num_query_heads[0], int)
|
||
|
||
# Uses the tokenizer from meta-llama/Llama-2-7b-hf
|
||
def set_vocab(self):
|
||
try:
|
||
self._set_vocab_sentencepiece()
|
||
except FileNotFoundError:
|
||
self._set_vocab_builtin("llama-spm", self.hparams["vocab_size"])
|
||
|
||
def set_gguf_parameters(self):
|
||
n_embd = self._n_embd
|
||
head_dim = self.hparams["head_dim"]
|
||
rot_pct = 1.0
|
||
assert self.block_count == len(self._num_kv_heads)
|
||
assert self.block_count == len(self._num_query_heads)
|
||
assert self.block_count == len(self._ffn_dims)
|
||
|
||
self.gguf_writer.add_name(self.dir_model.name if self.model_name is None else self.model_name)
|
||
self.gguf_writer.add_block_count(self.block_count)
|
||
self.gguf_writer.add_context_length(self.hparams["max_context_length"])
|
||
self.gguf_writer.add_embedding_length(n_embd)
|
||
self.gguf_writer.add_feed_forward_length(self._ffn_dims)
|
||
self.gguf_writer.add_head_count(self._num_query_heads)
|
||
self.gguf_writer.add_head_count_kv(self._num_kv_heads)
|
||
self.gguf_writer.add_rope_freq_base(self.hparams["rope_freq_constant"])
|
||
# https://huggingface.co/apple/OpenELM-270M-Instruct/blob/c401df2/modeling_openelm.py#L30
|
||
self.gguf_writer.add_layer_norm_rms_eps(1e-6)
|
||
self.gguf_writer.add_rope_dimension_count(int(rot_pct * head_dim))
|
||
self.gguf_writer.add_key_length(head_dim)
|
||
self.gguf_writer.add_value_length(head_dim)
|
||
self.gguf_writer.add_file_type(self.ftype)
|
||
|
||
def find_hparam(self, keys: Iterable[str], optional: bool = False) -> Any:
|
||
if "n_layers" in keys:
|
||
return self.hparams["num_transformer_layers"]
|
||
|
||
return super().find_hparam(keys, optional)
|
||
|
||
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
|
||
|
||
# split ff
|
||
if bid is not None and name == f"transformer.layers.{bid}.ffn.proj_1.weight":
|
||
ff_dim = self._ffn_dims[bid]
|
||
yield (self.format_tensor_name(gguf.MODEL_TENSOR.FFN_GATE, bid), data_torch[:ff_dim])
|
||
yield (self.format_tensor_name(gguf.MODEL_TENSOR.FFN_UP, bid), data_torch[ff_dim:])
|
||
return
|
||
|
||
yield (self.map_tensor_name(name), data_torch)
|
||
|
||
|
||
@Model.register("ArcticForCausalLM")
|
||
class ArcticModel(Model):
|
||
model_arch = gguf.MODEL_ARCH.ARCTIC
|
||
|
||
def set_vocab(self):
|
||
# The reason for using a custom implementation here is that the
|
||
# snowflake-arctic-instruct model redefined tokens 31998 and 31999 from
|
||
# tokenizer.model and used them as BOS and EOS instead of adding new tokens.
|
||
from sentencepiece import SentencePieceProcessor
|
||
|
||
tokenizer_path = self.dir_model / 'tokenizer.model'
|
||
|
||
if not tokenizer_path.is_file():
|
||
logger.error(f'Error: Missing {tokenizer_path}')
|
||
sys.exit(1)
|
||
|
||
# Read the whole vocabulary from the tokenizer.model file
|
||
tokenizer = SentencePieceProcessor()
|
||
tokenizer.LoadFromFile(str(tokenizer_path))
|
||
|
||
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.UNUSED] * vocab_size
|
||
|
||
for token_id in range(tokenizer.vocab_size()):
|
||
|
||
piece = tokenizer.IdToPiece(token_id)
|
||
text = piece.encode("utf-8")
|
||
score = tokenizer.GetScore(token_id)
|
||
|
||
toktype = SentencePieceTokenTypes.NORMAL
|
||
if tokenizer.IsUnknown(token_id):
|
||
toktype = SentencePieceTokenTypes.UNKNOWN
|
||
elif tokenizer.IsControl(token_id):
|
||
toktype = SentencePieceTokenTypes.CONTROL
|
||
elif tokenizer.IsUnused(token_id):
|
||
toktype = SentencePieceTokenTypes.UNUSED
|
||
elif tokenizer.IsByte(token_id):
|
||
toktype = SentencePieceTokenTypes.BYTE
|
||
|
||
tokens[token_id] = text
|
||
scores[token_id] = score
|
||
toktypes[token_id] = toktype
|
||
|
||
# Use the added_tokens_decoder field from tokeniser_config.json as the source
|
||
# of information about added/redefined tokens and modify them accordingly.
|
||
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)
|
||
|
||
if "added_tokens_decoder" in tokenizer_config_json:
|
||
added_tokens_decoder = tokenizer_config_json["added_tokens_decoder"]
|
||
for token_id, token_json in added_tokens_decoder.items():
|
||
token_id = int(token_id)
|
||
if (token_id >= vocab_size):
|
||
logger.debug(f'ignore token {token_id}: id is out of range, max={vocab_size - 1}')
|
||
continue
|
||
|
||
token_content = token_json["content"]
|
||
token_type = SentencePieceTokenTypes.USER_DEFINED
|
||
token_score = -10000.0
|
||
|
||
# Map unk_token to UNKNOWN, other special tokens to CONTROL
|
||
# Set the score to 0.0 as in the original tokenizer.model
|
||
if ("special" in token_json) and token_json["special"]:
|
||
if token_content == tokenizer_config_json["unk_token"]:
|
||
token_type = SentencePieceTokenTypes.UNKNOWN
|
||
else:
|
||
token_type = SentencePieceTokenTypes.CONTROL
|
||
token_score = 0.0
|
||
|
||
logger.info(f"Setting added token {token_id} to '{token_content}' (type: {token_type}, score: {token_score:.2f})")
|
||
tokens[token_id] = token_content.encode("utf-8")
|
||
toktypes[token_id] = token_type
|
||
scores[token_id] = token_score
|
||
|
||
self.gguf_writer.add_tokenizer_model("llama")
|
||
self.gguf_writer.add_tokenizer_pre("default")
|
||
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):
|
||
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"])
|
||
|
||
_experts: list[dict[str, Tensor]] | None = None
|
||
|
||
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")
|
||
|
||
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)
|
||
|
||
# process the experts separately
|
||
if name.find("block_sparse_moe.experts") != -1:
|
||
n_experts = self.hparams["num_local_experts"]
|
||
|
||
assert bid is not None
|
||
|
||
if self._experts is None:
|
||
self._experts = [{} for _ in range(self.block_count)]
|
||
|
||
self._experts[bid][name] = data_torch
|
||
|
||
if len(self._experts[bid]) >= n_experts * 3:
|
||
tensors: list[tuple[str, Tensor]] = []
|
||
|
||
# merge the experts into a single 3d tensor
|
||
for wid in ["w1", "w2", "w3"]:
|
||
datas: list[Tensor] = []
|
||
|
||
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]
|
||
|
||
data_torch = torch.stack(datas, dim=0)
|
||
|
||
merged_name = f"layers.{bid}.feed_forward.experts.{wid}.weight"
|
||
|
||
new_name = self.map_tensor_name(merged_name)
|
||
|
||
tensors.append((new_name, data_torch))
|
||
return tensors
|
||
else:
|
||
return []
|
||
|
||
return [(self.map_tensor_name(name), data_torch)]
|
||
|
||
def write_tensors(self):
|
||
super().write_tensors()
|
||
|
||
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}")
|
||
|
||
|
||
@Model.register("DeepseekV2ForCausalLM")
|
||
class DeepseekV2Model(Model):
|
||
model_arch = gguf.MODEL_ARCH.DEEPSEEK2
|
||
|
||
def set_vocab(self):
|
||
self._set_vocab_gpt2()
|
||
|
||
def set_gguf_parameters(self):
|
||
super().set_gguf_parameters()
|
||
hparams = self.hparams
|
||
|
||
self.gguf_writer.add_leading_dense_block_count(hparams["first_k_dense_replace"])
|
||
self.gguf_writer.add_vocab_size(hparams["vocab_size"])
|
||
if "q_lora_rank" in hparams and hparams["q_lora_rank"] is not None:
|
||
self.gguf_writer.add_q_lora_rank(hparams["q_lora_rank"])
|
||
self.gguf_writer.add_kv_lora_rank(hparams["kv_lora_rank"])
|
||
self.gguf_writer.add_key_length(hparams["qk_nope_head_dim"] + hparams["qk_rope_head_dim"])
|
||
self.gguf_writer.add_value_length(hparams["v_head_dim"])
|
||
self.gguf_writer.add_expert_feed_forward_length(hparams["moe_intermediate_size"])
|
||
self.gguf_writer.add_expert_count(hparams["n_routed_experts"])
|
||
self.gguf_writer.add_expert_shared_count(hparams["n_shared_experts"])
|
||
self.gguf_writer.add_expert_weights_scale(hparams["routed_scaling_factor"])
|
||
self.gguf_writer.add_rope_dimension_count(hparams["qk_rope_head_dim"])
|
||
|
||
if self.hparams.get("rope_scaling") is not None and "factor" in self.hparams["rope_scaling"]:
|
||
if self.hparams["rope_scaling"].get("type") == "yarn":
|
||
self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.YARN)
|
||
self.gguf_writer.add_rope_scaling_factor(self.hparams["rope_scaling"]["factor"])
|
||
self.gguf_writer.add_rope_scaling_orig_ctx_len(self.hparams["rope_scaling"]["original_max_position_embeddings"])
|
||
self.gguf_writer.add_rope_scaling_yarn_log_mul(0.1 * hparams["rope_scaling"]["mscale_all_dim"])
|
||
|
||
_experts: list[dict[str, Tensor]] | None = None
|
||
|
||
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
|
||
# process the experts separately
|
||
if name.find("mlp.experts") != -1:
|
||
n_experts = self.hparams["n_routed_experts"]
|
||
assert bid is not None
|
||
|
||
if self._experts is None:
|
||
self._experts = [{} for _ in range(self.block_count)]
|
||
|
||
self._experts[bid][name] = data_torch
|
||
|
||
if len(self._experts[bid]) >= n_experts * 3:
|
||
tensors: list[tuple[str, Tensor]] = []
|
||
|
||
# merge the experts into a single 3d tensor
|
||
for w_name in ["down_proj", "gate_proj", "up_proj"]:
|
||
datas: list[Tensor] = []
|
||
|
||
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]
|
||
|
||
data_torch = torch.stack(datas, dim=0)
|
||
|
||
merged_name = f"model.layers.{bid}.mlp.experts.{w_name}.weight"
|
||
|
||
new_name = self.map_tensor_name(merged_name)
|
||
|
||
tensors.append((new_name, data_torch))
|
||
return tensors
|
||
else:
|
||
return []
|
||
|
||
return [(self.map_tensor_name(name), data_torch)]
|
||
|
||
def write_tensors(self):
|
||
super().write_tensors()
|
||
|
||
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}")
|
||
|
||
|
||
@Model.register("T5WithLMHeadModel")
|
||
@Model.register("T5ForConditionalGeneration")
|
||
@Model.register("MT5ForConditionalGeneration")
|
||
@Model.register("UMT5ForConditionalGeneration")
|
||
class T5Model(Model):
|
||
model_arch = gguf.MODEL_ARCH.T5
|
||
|
||
def __init__(self, *args, **kwargs):
|
||
super().__init__(*args, **kwargs)
|
||
self.shared_token_embeddings_found = False
|
||
|
||
def set_vocab(self):
|
||
# to avoid TypeError: Descriptors cannot be created directly
|
||
# exception when importing sentencepiece_model_pb2
|
||
os.environ["PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION"] = "python"
|
||
from sentencepiece import SentencePieceProcessor
|
||
from sentencepiece import sentencepiece_model_pb2 as model
|
||
|
||
tokenizer_path = self.dir_model / 'tokenizer.model'
|
||
|
||
# many older models use spiece.model tokenizer model filename
|
||
if not tokenizer_path.is_file():
|
||
tokenizer_path = self.dir_model / 'spiece.model'
|
||
|
||
if not tokenizer_path.is_file():
|
||
raise FileNotFoundError(f"File not found: {tokenizer_path}")
|
||
|
||
sentencepiece_model = model.ModelProto() # pyright: ignore[reportAttributeAccessIssue]
|
||
sentencepiece_model.ParseFromString(open(tokenizer_path, "rb").read())
|
||
|
||
# some models like Pile-T5 family use BPE tokenizer instead of Unigram
|
||
if sentencepiece_model.trainer_spec.model_type == 2: # BPE
|
||
# assure the tokenizer model file name is correct
|
||
assert tokenizer_path.name == 'tokenizer.model'
|
||
return self._set_vocab_sentencepiece()
|
||
else:
|
||
assert sentencepiece_model.trainer_spec.model_type == 1 # UNIGRAM
|
||
|
||
add_prefix = sentencepiece_model.normalizer_spec.add_dummy_prefix
|
||
remove_whitespaces = sentencepiece_model.normalizer_spec.remove_extra_whitespaces
|
||
precompiled_charsmap = sentencepiece_model.normalizer_spec.precompiled_charsmap
|
||
|
||
tokenizer = SentencePieceProcessor()
|
||
tokenizer.LoadFromFile(str(tokenizer_path))
|
||
|
||
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.UNUSED] * vocab_size
|
||
|
||
for token_id in range(tokenizer.vocab_size()):
|
||
piece = tokenizer.IdToPiece(token_id)
|
||
text = piece.encode("utf-8")
|
||
score = tokenizer.GetScore(token_id)
|
||
|
||
toktype = SentencePieceTokenTypes.NORMAL
|
||
if tokenizer.IsUnknown(token_id):
|
||
toktype = SentencePieceTokenTypes.UNKNOWN
|
||
elif tokenizer.IsControl(token_id):
|
||
toktype = SentencePieceTokenTypes.CONTROL
|
||
elif tokenizer.IsUnused(token_id):
|
||
toktype = SentencePieceTokenTypes.UNUSED
|
||
elif tokenizer.IsByte(token_id):
|
||
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):
|
||
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
|
||
|
||
if vocab_size > len(tokens):
|
||
pad_count = vocab_size - len(tokens)
|
||
logger.debug(f"Padding vocab with {pad_count} token(s) - [PAD1] through [PAD{pad_count}]")
|
||
for i in range(1, pad_count + 1):
|
||
tokens.append(bytes(f"[PAD{i}]", encoding="utf-8"))
|
||
scores.append(-1000.0)
|
||
toktypes.append(SentencePieceTokenTypes.UNUSED)
|
||
|
||
self.gguf_writer.add_tokenizer_model("t5")
|
||
self.gguf_writer.add_tokenizer_pre("default")
|
||
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)
|
||
self.gguf_writer.add_remove_extra_whitespaces(remove_whitespaces)
|
||
if precompiled_charsmap:
|
||
self.gguf_writer.add_precompiled_charsmap(precompiled_charsmap)
|
||
|
||
special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens))
|
||
special_vocab.add_to_gguf(self.gguf_writer)
|
||
|
||
self.gguf_writer.add_add_bos_token(False)
|
||
self.gguf_writer.add_add_eos_token(True)
|
||
|
||
def set_gguf_parameters(self):
|
||
self.gguf_writer.add_name("T5")
|
||
if (n_ctx := self.find_hparam(["n_positions"], optional=True)) is None:
|
||
logger.warning("Couldn't find context length in config.json, assuming default value of 512")
|
||
n_ctx = 512
|
||
self.gguf_writer.add_context_length(n_ctx)
|
||
self.gguf_writer.add_embedding_length(self.hparams["d_model"])
|
||
self.gguf_writer.add_feed_forward_length(self.hparams["d_ff"])
|
||
self.gguf_writer.add_block_count(self.hparams["num_layers"])
|
||
self.gguf_writer.add_head_count(self.hparams["num_heads"])
|
||
self.gguf_writer.add_key_length(self.hparams["d_kv"])
|
||
self.gguf_writer.add_value_length(self.hparams["d_kv"])
|
||
self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_epsilon"])
|
||
self.gguf_writer.add_relative_attn_buckets_count(self.hparams["relative_attention_num_buckets"])
|
||
self.gguf_writer.add_layer_norm_rms_eps(self.hparams["layer_norm_epsilon"])
|
||
self.gguf_writer.add_decoder_start_token_id(self.hparams["decoder_start_token_id"])
|
||
self.gguf_writer.add_file_type(self.ftype)
|
||
|
||
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
|
||
del bid # unused
|
||
|
||
# T5 based models contain shared token embeddings tensors saved randomly as either "encoder.embed_tokens.weight",
|
||
# "decoder.embed_tokens.weight" or "shared.weight" tensor. In some models there are even multiple of them stored
|
||
# in the safetensors files. We use the first tensor from these three as the token embeddings for both encoder
|
||
# and decoder and ignore the remaining ones.
|
||
if name in ["decoder.embed_tokens.weight", "encoder.embed_tokens.weight", "shared.weight"]:
|
||
if not self.shared_token_embeddings_found:
|
||
name = "shared.weight"
|
||
self.shared_token_embeddings_found = True
|
||
else:
|
||
logger.debug(f"Skipping shared tensor {name!r} in safetensors so that convert can end normally.")
|
||
return []
|
||
|
||
return [(self.map_tensor_name(name), data_torch)]
|
||
|
||
|
||
@Model.register("JAISLMHeadModel")
|
||
class JaisModel(Model):
|
||
model_arch = gguf.MODEL_ARCH.JAIS
|
||
|
||
def __init__(self, *args, **kwargs):
|
||
super().__init__(*args, **kwargs)
|
||
|
||
# SwigLU activation
|
||
assert self.hparams["activation_function"] == "swiglu"
|
||
# ALiBi position embedding
|
||
assert self.hparams["position_embedding_type"] == "alibi"
|
||
|
||
# Embeddings scale
|
||
self.embeddings_scale = 1.0
|
||
# note: For some JAIS flavors, output is tied to (same as) wte in original model
|
||
self.output_is_wte = False
|
||
if 'mup_embeddings_scale' in self.hparams:
|
||
self.output_is_wte = True # Hack (?)
|
||
self.embeddings_scale = self.hparams['mup_embeddings_scale']
|
||
elif 'embeddings_scale' in self.hparams:
|
||
self.embeddings_scale = self.hparams['embeddings_scale']
|
||
else:
|
||
assert False
|
||
|
||
self.width_scale = 1.0
|
||
if 'mup_output_alpha' in self.hparams:
|
||
assert 'mup_width_scale' in self.hparams
|
||
self.width_scale = self.hparams['mup_output_alpha'] * self.hparams['mup_width_scale']
|
||
elif 'width_scale' in self.hparams:
|
||
self.width_scale = self.hparams['width_scale']
|
||
else:
|
||
assert False
|
||
|
||
self.max_alibi_bias = 8.0
|
||
|
||
def set_vocab(self):
|
||
self._set_vocab_gpt2()
|
||
|
||
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_positions"])
|
||
self.gguf_writer.add_embedding_length(self.hparams["n_embd"])
|
||
self.gguf_writer.add_feed_forward_length(self.hparams["n_inner"])
|
||
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)
|
||
|
||
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
|
||
del bid # unused
|
||
|
||
tensors: list[tuple[str, Tensor]] = []
|
||
|
||
# we don't need these
|
||
if name.endswith((".attn.bias")):
|
||
return tensors
|
||
|
||
if name.endswith(("relative_pe.slopes")):
|
||
# Calculate max ALiBi bias (this is the inverse of the ALiBi calculation)
|
||
# Some other models has max_alibi_bias spelled out explicitly in the hyperparams,
|
||
# but Jais's PyTorch model simply precalculates the slope values and places them
|
||
# in relative_pes.slopes
|
||
n_head_closest_log2 = 2 ** math.floor(math.log2(self.hparams["n_head"]))
|
||
first_val = float(data_torch[0].item())
|
||
self.max_alibi_bias = -round(math.log2(first_val) * n_head_closest_log2)
|
||
|
||
return tensors
|
||
|
||
if name.endswith((".c_attn.weight", ".c_proj.weight", ".c_fc.weight", ".c_fc2.weight")):
|
||
data_torch = data_torch.transpose(1, 0)
|
||
|
||
new_name = self.map_tensor_name(name)
|
||
|
||
if new_name == self.format_tensor_name(gguf.MODEL_TENSOR.TOKEN_EMBD):
|
||
tensors.append((new_name, data_torch * self.embeddings_scale))
|
||
if self.output_is_wte:
|
||
tensors.append((self.format_tensor_name(gguf.MODEL_TENSOR.OUTPUT), data_torch * self.width_scale))
|
||
elif new_name == self.format_tensor_name(gguf.MODEL_TENSOR.OUTPUT):
|
||
assert not self.output_is_wte
|
||
tensors.append((new_name, data_torch * self.width_scale))
|
||
else:
|
||
tensors.append((new_name, data_torch))
|
||
|
||
return tensors
|
||
|
||
def write_tensors(self):
|
||
super().write_tensors()
|
||
self.gguf_writer.add_max_alibi_bias(self.max_alibi_bias)
|
||
|
||
|
||
@Model.register("ChatGLMModel", "ChatGLMForConditionalGeneration")
|
||
class ChatGLMModel(Model):
|
||
model_arch = gguf.MODEL_ARCH.CHATGLM
|
||
|
||
def set_vocab_chatglm3(self):
|
||
dir_model = self.dir_model
|
||
hparams = self.hparams
|
||
tokens: list[bytes] = []
|
||
toktypes: list[int] = []
|
||
scores: list[float] = []
|
||
|
||
from transformers import AutoTokenizer
|
||
tokenizer = AutoTokenizer.from_pretrained(dir_model, trust_remote_code=True)
|
||
vocab_size = hparams.get("padded_vocab_size", len(tokenizer.get_vocab()))
|
||
assert max(tokenizer.get_vocab().values()) < vocab_size
|
||
role_special_tokens = ["<|system|>", "<|user|>", "<|assistant|>", "<|observation|>"]
|
||
special_tokens = ["[MASK]", "[gMASK]", "[sMASK]", "sop", "eop"] + role_special_tokens
|
||
for token_id in range(vocab_size):
|
||
piece = tokenizer._convert_id_to_token(token_id)
|
||
if token_id == 0:
|
||
piece = "<unk>"
|
||
elif token_id == 1:
|
||
piece = "<bos>"
|
||
elif token_id == 2:
|
||
piece = "<eos>"
|
||
|
||
text = piece.encode("utf-8")
|
||
score = 0.0
|
||
# Referencing the tokenizer Python implementation(https://huggingface.co/THUDM/chatglm3-6b/blob/main/tokenization_chatglm.py),
|
||
# it is only valid if it is less than tokenizer.tokenizer.sp_model.vocab_size()
|
||
if len(piece) != 0 and token_id < tokenizer.tokenizer.sp_model.vocab_size():
|
||
score = tokenizer.tokenizer.sp_model.get_score(token_id)
|
||
|
||
if token_id >= tokenizer.tokenizer.sp_model.vocab_size():
|
||
if piece in special_tokens:
|
||
toktype = SentencePieceTokenTypes.CONTROL
|
||
elif len(piece) == 0:
|
||
text = f"[PAD{token_id}]".encode("utf-8")
|
||
toktype = SentencePieceTokenTypes.UNUSED
|
||
else:
|
||
toktype = SentencePieceTokenTypes.USER_DEFINED
|
||
tokens.append(text)
|
||
scores.append(score)
|
||
toktypes.append(toktype)
|
||
continue
|
||
|
||
toktype = SentencePieceTokenTypes.NORMAL
|
||
if tokenizer.tokenizer.sp_model.is_unknown(token_id):
|
||
toktype = SentencePieceTokenTypes.UNKNOWN
|
||
elif tokenizer.tokenizer.sp_model.is_control(token_id):
|
||
toktype = SentencePieceTokenTypes.CONTROL
|
||
elif tokenizer.tokenizer.sp_model.is_unused(token_id):
|
||
toktype = SentencePieceTokenTypes.UNUSED
|
||
elif tokenizer.tokenizer.sp_model.is_byte(token_id):
|
||
toktype = SentencePieceTokenTypes.BYTE
|
||
|
||
tokens.append(text)
|
||
scores.append(score)
|
||
toktypes.append(toktype)
|
||
|
||
self.gguf_writer.add_tokenizer_model("llama")
|
||
# glm3 needs prefix and suffix formatted as:
|
||
# prompt = "[gMASK]sop<|user|>\n" + prompt + "<|assistant|>"
|
||
self.gguf_writer.add_tokenizer_pre("chatglm-spm")
|
||
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)
|
||
|
||
@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
|
||
def bpe(mergeable_ranks: dict[bytes, int], token: bytes, max_rank: int | None = None) -> list[bytes]:
|
||
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):
|
||
if "THUDM/chatglm3-6b" in self.hparams.get("_name_or_path", ""):
|
||
self.set_vocab_chatglm3()
|
||
return
|
||
|
||
dir_model = self.dir_model
|
||
hparams = self.hparams
|
||
tokens: list[str] = []
|
||
toktypes: list[int] = []
|
||
|
||
from transformers import AutoTokenizer
|
||
tokenizer = AutoTokenizer.from_pretrained(dir_model, trust_remote_code=True)
|
||
vocab_size = hparams["padded_vocab_size"]
|
||
assert max(tokenizer.get_vocab().values()) < vocab_size
|
||
|
||
tokpre = self.get_vocab_base_pre(tokenizer)
|
||
|
||
merges = []
|
||
vocab = {}
|
||
mergeable_ranks = tokenizer.mergeable_ranks
|
||
for token, rank in mergeable_ranks.items():
|
||
vocab[ChatGLMModel.token_bytes_to_string(token)] = rank
|
||
if len(token) == 1:
|
||
continue
|
||
merged = ChatGLMModel.bpe(mergeable_ranks, token, max_rank=rank)
|
||
assert len(merged) >= 2 and len(merged) <= 7
|
||
merges.append(' '.join(map(ChatGLMModel.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.get_added_vocab()
|
||
reverse_vocab = {id_ : encoded_tok for encoded_tok, id_ in {**vocab, **added_vocab}.items()}
|
||
|
||
for i in range(vocab_size):
|
||
if i not in reverse_vocab:
|
||
tokens.append(f"[PAD{i}]")
|
||
toktypes.append(gguf.TokenType.UNUSED)
|
||
elif reverse_vocab[i] in added_vocab:
|
||
tokens.append(reverse_vocab[i])
|
||
if tokenizer.added_tokens_decoder[i].special:
|
||
toktypes.append(gguf.TokenType.CONTROL)
|
||
else:
|
||
toktypes.append(gguf.TokenType.USER_DEFINED)
|
||
else:
|
||
tokens.append(reverse_vocab[i])
|
||
toktypes.append(gguf.TokenType.NORMAL)
|
||
|
||
self.gguf_writer.add_tokenizer_model("gpt2")
|
||
self.gguf_writer.add_tokenizer_pre(tokpre)
|
||
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
|
||
special_vocab._set_special_token("eos", tokenizer.get_added_vocab()["<|endoftext|>"])
|
||
special_vocab._set_special_token("eot", tokenizer.get_added_vocab()["<|user|>"])
|
||
# this one is usually not in config.json anyway
|
||
special_vocab._set_special_token("unk", tokenizer.get_added_vocab()["<|endoftext|>"])
|
||
special_vocab.add_to_gguf(self.gguf_writer)
|
||
|
||
def set_gguf_parameters(self):
|
||
self.gguf_writer.add_name(self.hparams["_name_or_path"].split("/")[1]) # THUDM/glm4-9b-chat or THUDM/chatglm3-6b
|
||
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"))
|
||
n_head_kv = self.hparams.get("multi_query_group_num", n_head)
|
||
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(self.hparams.get("ffn_hidden_size", 4 * n_embed))
|
||
self.gguf_writer.add_block_count(self.hparams["num_layers"])
|
||
self.gguf_writer.add_head_count(n_head)
|
||
self.gguf_writer.add_head_count_kv(n_head_kv)
|
||
self.gguf_writer.add_layer_norm_rms_eps(self.hparams["layernorm_epsilon"])
|
||
self.gguf_writer.add_file_type(self.ftype)
|
||
self.gguf_writer.add_rope_dimension_count(64)
|
||
self.gguf_writer.add_add_bos_token(False)
|
||
rope_freq = 10000
|
||
if "rope_ratio" in self.hparams:
|
||
rope_freq = rope_freq * self.hparams["rope_ratio"]
|
||
self.gguf_writer.add_rope_freq_base(rope_freq)
|
||
|
||
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
|
||
del bid # unused
|
||
|
||
if name.endswith(".rotary_pos_emb.inv_freq"):
|
||
return []
|
||
|
||
name = name.removeprefix("transformer.")
|
||
return [(self.map_tensor_name(name), data_torch)]
|
||
|
||
###### CONVERSION LOGIC ######
|
||
|
||
|
||
# tree of lazy tensors
|
||
class LazyTorchTensor(gguf.LazyBase):
|
||
_tensor_type = torch.Tensor
|
||
# to keep the type-checker happy
|
||
dtype: torch.dtype
|
||
shape: torch.Size
|
||
|
||
# only used when converting a torch.Tensor to a np.ndarray
|
||
_dtype_map: dict[torch.dtype, type] = {
|
||
torch.float16: np.float16,
|
||
torch.float32: np.float32,
|
||
}
|
||
|
||
# used for safetensors slices
|
||
# ref: https://github.com/huggingface/safetensors/blob/079781fd0dc455ba0fe851e2b4507c33d0c0d407/bindings/python/src/lib.rs#L1046
|
||
# TODO: uncomment U64, U32, and U16, ref: https://github.com/pytorch/pytorch/issues/58734
|
||
_dtype_str_map: dict[str, torch.dtype] = {
|
||
"F64": torch.float64,
|
||
"F32": torch.float32,
|
||
"BF16": torch.bfloat16,
|
||
"F16": torch.float16,
|
||
# "U64": torch.uint64,
|
||
"I64": torch.int64,
|
||
# "U32": torch.uint32,
|
||
"I32": torch.int32,
|
||
# "U16": torch.uint16,
|
||
"I16": torch.int16,
|
||
"U8": torch.uint8,
|
||
"I8": torch.int8,
|
||
"BOOL": torch.bool,
|
||
"F8_E4M3": torch.float8_e4m3fn,
|
||
"F8_E5M2": torch.float8_e5m2,
|
||
}
|
||
|
||
def numpy(self) -> gguf.LazyNumpyTensor:
|
||
dtype = self._dtype_map[self.dtype]
|
||
return gguf.LazyNumpyTensor(
|
||
meta=gguf.LazyNumpyTensor.meta_with_dtype_and_shape(dtype, self.shape),
|
||
args=(self,),
|
||
func=(lambda s: s.numpy())
|
||
)
|
||
|
||
@classmethod
|
||
def meta_with_dtype_and_shape(cls, dtype: torch.dtype, shape: tuple[int, ...]) -> Tensor:
|
||
return torch.empty(size=shape, dtype=dtype, device="meta")
|
||
|
||
@classmethod
|
||
def from_safetensors_slice(cls, st_slice: Any) -> Tensor:
|
||
dtype = cls._dtype_str_map[st_slice.get_dtype()]
|
||
shape: tuple[int, ...] = tuple(st_slice.get_shape())
|
||
lazy = cls(meta=cls.meta_with_dtype_and_shape(dtype, shape), args=(st_slice,), func=lambda s: s[:])
|
||
return cast(torch.Tensor, lazy)
|
||
|
||
@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()
|
||
|
||
return cls._wrap_fn(func)(*args, **kwargs)
|
||
|
||
|
||
def parse_args() -> argparse.Namespace:
|
||
parser = argparse.ArgumentParser(
|
||
description="Convert a huggingface model to a GGML compatible file")
|
||
parser.add_argument(
|
||
"--vocab-only", action="store_true",
|
||
help="extract only the vocab",
|
||
)
|
||
parser.add_argument(
|
||
"--outfile", type=Path,
|
||
help="path to write to; default: based on input. {ftype} will be replaced by the outtype.",
|
||
)
|
||
parser.add_argument(
|
||
"--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",
|
||
)
|
||
parser.add_argument(
|
||
"--bigendian", action="store_true",
|
||
help="model is executed on big endian machine",
|
||
)
|
||
parser.add_argument(
|
||
"model", type=Path,
|
||
help="directory containing model file",
|
||
)
|
||
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",
|
||
)
|
||
parser.add_argument(
|
||
"--split-max-tensors", type=int, default=0,
|
||
help="max tensors in each split",
|
||
)
|
||
parser.add_argument(
|
||
"--split-max-size", type=str, default="0",
|
||
help="max size per split N(M|G)",
|
||
)
|
||
parser.add_argument(
|
||
"--dry-run", action="store_true",
|
||
help="only print out a split plan and exit, without writing any new files",
|
||
)
|
||
parser.add_argument(
|
||
"--no-tensor-first-split", action="store_true",
|
||
help="do not add tensors to the first split (disabled by default)"
|
||
)
|
||
|
||
return parser.parse_args()
|
||
|
||
|
||
def split_str_to_n_bytes(split_str: str) -> int:
|
||
if split_str.endswith("K"):
|
||
n = int(split_str[:-1]) * 1000
|
||
elif split_str.endswith("M"):
|
||
n = int(split_str[:-1]) * 1000 * 1000
|
||
elif split_str.endswith("G"):
|
||
n = int(split_str[:-1]) * 1000 * 1000 * 1000
|
||
elif split_str.isnumeric():
|
||
n = int(split_str)
|
||
else:
|
||
raise ValueError(f"Invalid split size: {split_str}, must be a number, optionally followed by K, M, or G")
|
||
|
||
if n < 0:
|
||
raise ValueError(f"Invalid split size: {split_str}, must be positive")
|
||
|
||
return n
|
||
|
||
|
||
def main() -> None:
|
||
args = parse_args()
|
||
|
||
logging.basicConfig(level=logging.DEBUG if args.verbose else logging.INFO)
|
||
|
||
dir_model = args.model
|
||
|
||
if not dir_model.is_dir():
|
||
logger.error(f'Error: {args.model} is not a directory')
|
||
sys.exit(1)
|
||
|
||
ftype_map: dict[str, gguf.LlamaFileType] = {
|
||
"f32": gguf.LlamaFileType.ALL_F32,
|
||
"f16": gguf.LlamaFileType.MOSTLY_F16,
|
||
"bf16": gguf.LlamaFileType.MOSTLY_BF16,
|
||
"q8_0": gguf.LlamaFileType.MOSTLY_Q8_0,
|
||
"auto": gguf.LlamaFileType.GUESSED,
|
||
}
|
||
|
||
is_split = args.split_max_tensors > 0 or args.split_max_size != "0"
|
||
if args.use_temp_file and is_split:
|
||
logger.error("Error: Cannot use temp file when splitting")
|
||
sys.exit(1)
|
||
|
||
if args.outfile is not None:
|
||
fname_out = args.outfile
|
||
else:
|
||
# output in the same directory as the model by default
|
||
fname_out = dir_model / 'ggml-model-{ftype}.gguf'
|
||
|
||
logger.info(f"Loading model: {dir_model.name}")
|
||
|
||
hparams = Model.load_hparams(dir_model)
|
||
|
||
with torch.inference_mode():
|
||
try:
|
||
model_class = Model.from_model_architecture(hparams["architectures"][0])
|
||
except NotImplementedError:
|
||
logger.error(f"Model {hparams['architectures'][0]} is not supported")
|
||
sys.exit(1)
|
||
|
||
model_instance = model_class(dir_model, ftype_map[args.outtype], fname_out, args.bigendian, args.use_temp_file,
|
||
args.no_lazy, args.model_name, split_max_tensors=args.split_max_tensors,
|
||
split_max_size=split_str_to_n_bytes(args.split_max_size), dry_run=args.dry_run,
|
||
small_first_shard=args.no_tensor_first_split)
|
||
|
||
logger.info("Set model parameters")
|
||
model_instance.gguf_writer.add_type(gguf.GGUFType.MODEL)
|
||
model_instance.set_gguf_parameters()
|
||
|
||
logger.info("Set model tokenizer")
|
||
model_instance.set_vocab()
|
||
|
||
model_instance.gguf_writer.add_quantization_version(gguf.GGML_QUANT_VERSION)
|
||
|
||
if args.vocab_only:
|
||
logger.info("Exporting model vocab...")
|
||
model_instance.write_vocab()
|
||
logger.info(f"Model vocab successfully exported to {model_instance.fname_out}")
|
||
else:
|
||
logger.info("Exporting model...")
|
||
model_instance.write()
|
||
out_path = f"{model_instance.fname_out.parent}{os.sep}" if is_split else model_instance.fname_out
|
||
logger.info(f"Model successfully exported to {out_path}")
|
||
|
||
|
||
if __name__ == '__main__':
|
||
main()
|