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#!/usr/bin/env python3
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from __future__ import annotations
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import logging
py : new conversion script (#545)
Current status: Working, except for the latest GPTQ-for-LLaMa format
that includes `g_idx`. This turns out to require changes to GGML, so
for now it only works if you use the `--outtype` option to dequantize it
back to f16 (which is pointless except for debugging).
I also included some cleanup for the C++ code.
This script is meant to replace all the existing conversion scripts
(including the ones that convert from older GGML formats), while also
adding support for some new formats. Specifically, I've tested with:
- [x] `LLaMA` (original)
- [x] `llama-65b-4bit`
- [x] `alpaca-native`
- [x] `alpaca-native-4bit`
- [x] LLaMA converted to 'transformers' format using
`convert_llama_weights_to_hf.py`
- [x] `alpaca-native` quantized with `--true-sequential --act-order
--groupsize 128` (dequantized only)
- [x] same as above plus `--save_safetensors`
- [x] GPT4All
- [x] stock unversioned ggml
- [x] ggmh
There's enough overlap in the logic needed to handle these different
cases that it seemed best to move to a single script.
I haven't tried this with Alpaca-LoRA because I don't know where to find
it.
Useful features:
- Uses multiple threads for a speedup in some cases (though the Python
GIL limits the gain, and sometimes it's disk-bound anyway).
- Combines split models into a single file (both the intra-tensor split
of the original and the inter-tensor split of 'transformers' format
files). Single files are more convenient to work with and more
friendly to future changes to use memory mapping on the C++ side. To
accomplish this without increasing memory requirements, it has some
custom loading code which avoids loading whole input files into memory
at once.
- Because of the custom loading code, it no longer depends in PyTorch,
which might make installing dependencies slightly easier or faster...
although it still depends on NumPy and sentencepiece, so I don't know
if there's any meaningful difference. In any case, I also added a
requirements.txt file to lock the dependency versions in case of any
future breaking changes.
- Type annotations checked with mypy.
- Some attempts to be extra user-friendly:
- The script tries to be forgiving with arguments, e.g. you can
specify either the model file itself or the directory containing
it.
- The script doesn't depend on config.json / params.json, just in
case the user downloaded files individually and doesn't have those
handy. But you still need tokenizer.model and, for Alpaca,
added_tokens.json.
- The script tries to give a helpful error message if
added_tokens.json is missing.
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import argparse
import concurrent . futures
import enum
import faulthandler
import functools
import itertools
import json
import math
import mmap
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import os
py : new conversion script (#545)
Current status: Working, except for the latest GPTQ-for-LLaMa format
that includes `g_idx`. This turns out to require changes to GGML, so
for now it only works if you use the `--outtype` option to dequantize it
back to f16 (which is pointless except for debugging).
I also included some cleanup for the C++ code.
This script is meant to replace all the existing conversion scripts
(including the ones that convert from older GGML formats), while also
adding support for some new formats. Specifically, I've tested with:
- [x] `LLaMA` (original)
- [x] `llama-65b-4bit`
- [x] `alpaca-native`
- [x] `alpaca-native-4bit`
- [x] LLaMA converted to 'transformers' format using
`convert_llama_weights_to_hf.py`
- [x] `alpaca-native` quantized with `--true-sequential --act-order
--groupsize 128` (dequantized only)
- [x] same as above plus `--save_safetensors`
- [x] GPT4All
- [x] stock unversioned ggml
- [x] ggmh
There's enough overlap in the logic needed to handle these different
cases that it seemed best to move to a single script.
I haven't tried this with Alpaca-LoRA because I don't know where to find
it.
Useful features:
- Uses multiple threads for a speedup in some cases (though the Python
GIL limits the gain, and sometimes it's disk-bound anyway).
- Combines split models into a single file (both the intra-tensor split
of the original and the inter-tensor split of 'transformers' format
files). Single files are more convenient to work with and more
friendly to future changes to use memory mapping on the C++ side. To
accomplish this without increasing memory requirements, it has some
custom loading code which avoids loading whole input files into memory
at once.
- Because of the custom loading code, it no longer depends in PyTorch,
which might make installing dependencies slightly easier or faster...
although it still depends on NumPy and sentencepiece, so I don't know
if there's any meaningful difference. In any case, I also added a
requirements.txt file to lock the dependency versions in case of any
future breaking changes.
- Type annotations checked with mypy.
- Some attempts to be extra user-friendly:
- The script tries to be forgiving with arguments, e.g. you can
specify either the model file itself or the directory containing
it.
- The script doesn't depend on config.json / params.json, just in
case the user downloaded files individually and doesn't have those
handy. But you still need tokenizer.model and, for Alpaca,
added_tokens.json.
- The script tries to give a helpful error message if
added_tokens.json is missing.
2023-04-14 07:03:03 +00:00
import pickle
import re
import signal
import struct
import sys
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import textwrap
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import time
py : new conversion script (#545)
Current status: Working, except for the latest GPTQ-for-LLaMa format
that includes `g_idx`. This turns out to require changes to GGML, so
for now it only works if you use the `--outtype` option to dequantize it
back to f16 (which is pointless except for debugging).
I also included some cleanup for the C++ code.
This script is meant to replace all the existing conversion scripts
(including the ones that convert from older GGML formats), while also
adding support for some new formats. Specifically, I've tested with:
- [x] `LLaMA` (original)
- [x] `llama-65b-4bit`
- [x] `alpaca-native`
- [x] `alpaca-native-4bit`
- [x] LLaMA converted to 'transformers' format using
`convert_llama_weights_to_hf.py`
- [x] `alpaca-native` quantized with `--true-sequential --act-order
--groupsize 128` (dequantized only)
- [x] same as above plus `--save_safetensors`
- [x] GPT4All
- [x] stock unversioned ggml
- [x] ggmh
There's enough overlap in the logic needed to handle these different
cases that it seemed best to move to a single script.
I haven't tried this with Alpaca-LoRA because I don't know where to find
it.
Useful features:
- Uses multiple threads for a speedup in some cases (though the Python
GIL limits the gain, and sometimes it's disk-bound anyway).
- Combines split models into a single file (both the intra-tensor split
of the original and the inter-tensor split of 'transformers' format
files). Single files are more convenient to work with and more
friendly to future changes to use memory mapping on the C++ side. To
accomplish this without increasing memory requirements, it has some
custom loading code which avoids loading whole input files into memory
at once.
- Because of the custom loading code, it no longer depends in PyTorch,
which might make installing dependencies slightly easier or faster...
although it still depends on NumPy and sentencepiece, so I don't know
if there's any meaningful difference. In any case, I also added a
requirements.txt file to lock the dependency versions in case of any
future breaking changes.
- Type annotations checked with mypy.
- Some attempts to be extra user-friendly:
- The script tries to be forgiving with arguments, e.g. you can
specify either the model file itself or the directory containing
it.
- The script doesn't depend on config.json / params.json, just in
case the user downloaded files individually and doesn't have those
handy. But you still need tokenizer.model and, for Alpaca,
added_tokens.json.
- The script tries to give a helpful error message if
added_tokens.json is missing.
2023-04-14 07:03:03 +00:00
import zipfile
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from abc import ABC , abstractmethod
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from concurrent . futures import ProcessPoolExecutor , ThreadPoolExecutor
py : new conversion script (#545)
Current status: Working, except for the latest GPTQ-for-LLaMa format
that includes `g_idx`. This turns out to require changes to GGML, so
for now it only works if you use the `--outtype` option to dequantize it
back to f16 (which is pointless except for debugging).
I also included some cleanup for the C++ code.
This script is meant to replace all the existing conversion scripts
(including the ones that convert from older GGML formats), while also
adding support for some new formats. Specifically, I've tested with:
- [x] `LLaMA` (original)
- [x] `llama-65b-4bit`
- [x] `alpaca-native`
- [x] `alpaca-native-4bit`
- [x] LLaMA converted to 'transformers' format using
`convert_llama_weights_to_hf.py`
- [x] `alpaca-native` quantized with `--true-sequential --act-order
--groupsize 128` (dequantized only)
- [x] same as above plus `--save_safetensors`
- [x] GPT4All
- [x] stock unversioned ggml
- [x] ggmh
There's enough overlap in the logic needed to handle these different
cases that it seemed best to move to a single script.
I haven't tried this with Alpaca-LoRA because I don't know where to find
it.
Useful features:
- Uses multiple threads for a speedup in some cases (though the Python
GIL limits the gain, and sometimes it's disk-bound anyway).
- Combines split models into a single file (both the intra-tensor split
of the original and the inter-tensor split of 'transformers' format
files). Single files are more convenient to work with and more
friendly to future changes to use memory mapping on the C++ side. To
accomplish this without increasing memory requirements, it has some
custom loading code which avoids loading whole input files into memory
at once.
- Because of the custom loading code, it no longer depends in PyTorch,
which might make installing dependencies slightly easier or faster...
although it still depends on NumPy and sentencepiece, so I don't know
if there's any meaningful difference. In any case, I also added a
requirements.txt file to lock the dependency versions in case of any
future breaking changes.
- Type annotations checked with mypy.
- Some attempts to be extra user-friendly:
- The script tries to be forgiving with arguments, e.g. you can
specify either the model file itself or the directory containing
it.
- The script doesn't depend on config.json / params.json, just in
case the user downloaded files individually and doesn't have those
handy. But you still need tokenizer.model and, for Alpaca,
added_tokens.json.
- The script tries to give a helpful error message if
added_tokens.json is missing.
2023-04-14 07:03:03 +00:00
from dataclasses import dataclass
from pathlib import Path
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from typing import TYPE_CHECKING , Any , Callable , ClassVar , IO , Iterable , Literal , Protocol , TypeVar , runtime_checkable
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import numpy as np
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from sentencepiece import SentencePieceProcessor
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if ' NO_LOCAL_GGUF ' not in os . environ :
sys . path . insert ( 1 , str ( Path ( __file__ ) . parent / ' gguf-py ' ) )
import gguf
if TYPE_CHECKING :
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from typing_extensions import Self , TypeAlias
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logger = logging . getLogger ( " convert " )
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if hasattr ( faulthandler , ' register ' ) and hasattr ( signal , ' SIGUSR1 ' ) :
py : new conversion script (#545)
Current status: Working, except for the latest GPTQ-for-LLaMa format
that includes `g_idx`. This turns out to require changes to GGML, so
for now it only works if you use the `--outtype` option to dequantize it
back to f16 (which is pointless except for debugging).
I also included some cleanup for the C++ code.
This script is meant to replace all the existing conversion scripts
(including the ones that convert from older GGML formats), while also
adding support for some new formats. Specifically, I've tested with:
- [x] `LLaMA` (original)
- [x] `llama-65b-4bit`
- [x] `alpaca-native`
- [x] `alpaca-native-4bit`
- [x] LLaMA converted to 'transformers' format using
`convert_llama_weights_to_hf.py`
- [x] `alpaca-native` quantized with `--true-sequential --act-order
--groupsize 128` (dequantized only)
- [x] same as above plus `--save_safetensors`
- [x] GPT4All
- [x] stock unversioned ggml
- [x] ggmh
There's enough overlap in the logic needed to handle these different
cases that it seemed best to move to a single script.
I haven't tried this with Alpaca-LoRA because I don't know where to find
it.
Useful features:
- Uses multiple threads for a speedup in some cases (though the Python
GIL limits the gain, and sometimes it's disk-bound anyway).
- Combines split models into a single file (both the intra-tensor split
of the original and the inter-tensor split of 'transformers' format
files). Single files are more convenient to work with and more
friendly to future changes to use memory mapping on the C++ side. To
accomplish this without increasing memory requirements, it has some
custom loading code which avoids loading whole input files into memory
at once.
- Because of the custom loading code, it no longer depends in PyTorch,
which might make installing dependencies slightly easier or faster...
although it still depends on NumPy and sentencepiece, so I don't know
if there's any meaningful difference. In any case, I also added a
requirements.txt file to lock the dependency versions in case of any
future breaking changes.
- Type annotations checked with mypy.
- Some attempts to be extra user-friendly:
- The script tries to be forgiving with arguments, e.g. you can
specify either the model file itself or the directory containing
it.
- The script doesn't depend on config.json / params.json, just in
case the user downloaded files individually and doesn't have those
handy. But you still need tokenizer.model and, for Alpaca,
added_tokens.json.
- The script tries to give a helpful error message if
added_tokens.json is missing.
2023-04-14 07:03:03 +00:00
faulthandler . register ( signal . SIGUSR1 )
2024-01-20 23:14:18 +00:00
NDArray : TypeAlias = ' np.ndarray[Any, Any] '
py : new conversion script (#545)
Current status: Working, except for the latest GPTQ-for-LLaMa format
that includes `g_idx`. This turns out to require changes to GGML, so
for now it only works if you use the `--outtype` option to dequantize it
back to f16 (which is pointless except for debugging).
I also included some cleanup for the C++ code.
This script is meant to replace all the existing conversion scripts
(including the ones that convert from older GGML formats), while also
adding support for some new formats. Specifically, I've tested with:
- [x] `LLaMA` (original)
- [x] `llama-65b-4bit`
- [x] `alpaca-native`
- [x] `alpaca-native-4bit`
- [x] LLaMA converted to 'transformers' format using
`convert_llama_weights_to_hf.py`
- [x] `alpaca-native` quantized with `--true-sequential --act-order
--groupsize 128` (dequantized only)
- [x] same as above plus `--save_safetensors`
- [x] GPT4All
- [x] stock unversioned ggml
- [x] ggmh
There's enough overlap in the logic needed to handle these different
cases that it seemed best to move to a single script.
I haven't tried this with Alpaca-LoRA because I don't know where to find
it.
Useful features:
- Uses multiple threads for a speedup in some cases (though the Python
GIL limits the gain, and sometimes it's disk-bound anyway).
- Combines split models into a single file (both the intra-tensor split
of the original and the inter-tensor split of 'transformers' format
files). Single files are more convenient to work with and more
friendly to future changes to use memory mapping on the C++ side. To
accomplish this without increasing memory requirements, it has some
custom loading code which avoids loading whole input files into memory
at once.
- Because of the custom loading code, it no longer depends in PyTorch,
which might make installing dependencies slightly easier or faster...
although it still depends on NumPy and sentencepiece, so I don't know
if there's any meaningful difference. In any case, I also added a
requirements.txt file to lock the dependency versions in case of any
future breaking changes.
- Type annotations checked with mypy.
- Some attempts to be extra user-friendly:
- The script tries to be forgiving with arguments, e.g. you can
specify either the model file itself or the directory containing
it.
- The script doesn't depend on config.json / params.json, just in
case the user downloaded files individually and doesn't have those
handy. But you still need tokenizer.model and, for Alpaca,
added_tokens.json.
- The script tries to give a helpful error message if
added_tokens.json is missing.
2023-04-14 07:03:03 +00:00
2023-10-02 18:58:46 +00:00
ARCH = gguf . MODEL_ARCH . LLAMA
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DEFAULT_CONCURRENCY = 8
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ADDED_TOKENS_FILE = ' added_tokens.json '
FAST_TOKENIZER_FILE = ' tokenizer.json '
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#
# data types
#
py : new conversion script (#545)
Current status: Working, except for the latest GPTQ-for-LLaMa format
that includes `g_idx`. This turns out to require changes to GGML, so
for now it only works if you use the `--outtype` option to dequantize it
back to f16 (which is pointless except for debugging).
I also included some cleanup for the C++ code.
This script is meant to replace all the existing conversion scripts
(including the ones that convert from older GGML formats), while also
adding support for some new formats. Specifically, I've tested with:
- [x] `LLaMA` (original)
- [x] `llama-65b-4bit`
- [x] `alpaca-native`
- [x] `alpaca-native-4bit`
- [x] LLaMA converted to 'transformers' format using
`convert_llama_weights_to_hf.py`
- [x] `alpaca-native` quantized with `--true-sequential --act-order
--groupsize 128` (dequantized only)
- [x] same as above plus `--save_safetensors`
- [x] GPT4All
- [x] stock unversioned ggml
- [x] ggmh
There's enough overlap in the logic needed to handle these different
cases that it seemed best to move to a single script.
I haven't tried this with Alpaca-LoRA because I don't know where to find
it.
Useful features:
- Uses multiple threads for a speedup in some cases (though the Python
GIL limits the gain, and sometimes it's disk-bound anyway).
- Combines split models into a single file (both the intra-tensor split
of the original and the inter-tensor split of 'transformers' format
files). Single files are more convenient to work with and more
friendly to future changes to use memory mapping on the C++ side. To
accomplish this without increasing memory requirements, it has some
custom loading code which avoids loading whole input files into memory
at once.
- Because of the custom loading code, it no longer depends in PyTorch,
which might make installing dependencies slightly easier or faster...
although it still depends on NumPy and sentencepiece, so I don't know
if there's any meaningful difference. In any case, I also added a
requirements.txt file to lock the dependency versions in case of any
future breaking changes.
- Type annotations checked with mypy.
- Some attempts to be extra user-friendly:
- The script tries to be forgiving with arguments, e.g. you can
specify either the model file itself or the directory containing
it.
- The script doesn't depend on config.json / params.json, just in
case the user downloaded files individually and doesn't have those
handy. But you still need tokenizer.model and, for Alpaca,
added_tokens.json.
- The script tries to give a helpful error message if
added_tokens.json is missing.
2023-04-14 07:03:03 +00:00
2023-11-20 10:35:47 +00:00
py : new conversion script (#545)
Current status: Working, except for the latest GPTQ-for-LLaMa format
that includes `g_idx`. This turns out to require changes to GGML, so
for now it only works if you use the `--outtype` option to dequantize it
back to f16 (which is pointless except for debugging).
I also included some cleanup for the C++ code.
This script is meant to replace all the existing conversion scripts
(including the ones that convert from older GGML formats), while also
adding support for some new formats. Specifically, I've tested with:
- [x] `LLaMA` (original)
- [x] `llama-65b-4bit`
- [x] `alpaca-native`
- [x] `alpaca-native-4bit`
- [x] LLaMA converted to 'transformers' format using
`convert_llama_weights_to_hf.py`
- [x] `alpaca-native` quantized with `--true-sequential --act-order
--groupsize 128` (dequantized only)
- [x] same as above plus `--save_safetensors`
- [x] GPT4All
- [x] stock unversioned ggml
- [x] ggmh
There's enough overlap in the logic needed to handle these different
cases that it seemed best to move to a single script.
I haven't tried this with Alpaca-LoRA because I don't know where to find
it.
Useful features:
- Uses multiple threads for a speedup in some cases (though the Python
GIL limits the gain, and sometimes it's disk-bound anyway).
- Combines split models into a single file (both the intra-tensor split
of the original and the inter-tensor split of 'transformers' format
files). Single files are more convenient to work with and more
friendly to future changes to use memory mapping on the C++ side. To
accomplish this without increasing memory requirements, it has some
custom loading code which avoids loading whole input files into memory
at once.
- Because of the custom loading code, it no longer depends in PyTorch,
which might make installing dependencies slightly easier or faster...
although it still depends on NumPy and sentencepiece, so I don't know
if there's any meaningful difference. In any case, I also added a
requirements.txt file to lock the dependency versions in case of any
future breaking changes.
- Type annotations checked with mypy.
- Some attempts to be extra user-friendly:
- The script tries to be forgiving with arguments, e.g. you can
specify either the model file itself or the directory containing
it.
- The script doesn't depend on config.json / params.json, just in
case the user downloaded files individually and doesn't have those
handy. But you still need tokenizer.model and, for Alpaca,
added_tokens.json.
- The script tries to give a helpful error message if
added_tokens.json is missing.
2023-04-14 07:03:03 +00:00
@dataclass ( frozen = True )
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class DataType :
py : new conversion script (#545)
Current status: Working, except for the latest GPTQ-for-LLaMa format
that includes `g_idx`. This turns out to require changes to GGML, so
for now it only works if you use the `--outtype` option to dequantize it
back to f16 (which is pointless except for debugging).
I also included some cleanup for the C++ code.
This script is meant to replace all the existing conversion scripts
(including the ones that convert from older GGML formats), while also
adding support for some new formats. Specifically, I've tested with:
- [x] `LLaMA` (original)
- [x] `llama-65b-4bit`
- [x] `alpaca-native`
- [x] `alpaca-native-4bit`
- [x] LLaMA converted to 'transformers' format using
`convert_llama_weights_to_hf.py`
- [x] `alpaca-native` quantized with `--true-sequential --act-order
--groupsize 128` (dequantized only)
- [x] same as above plus `--save_safetensors`
- [x] GPT4All
- [x] stock unversioned ggml
- [x] ggmh
There's enough overlap in the logic needed to handle these different
cases that it seemed best to move to a single script.
I haven't tried this with Alpaca-LoRA because I don't know where to find
it.
Useful features:
- Uses multiple threads for a speedup in some cases (though the Python
GIL limits the gain, and sometimes it's disk-bound anyway).
- Combines split models into a single file (both the intra-tensor split
of the original and the inter-tensor split of 'transformers' format
files). Single files are more convenient to work with and more
friendly to future changes to use memory mapping on the C++ side. To
accomplish this without increasing memory requirements, it has some
custom loading code which avoids loading whole input files into memory
at once.
- Because of the custom loading code, it no longer depends in PyTorch,
which might make installing dependencies slightly easier or faster...
although it still depends on NumPy and sentencepiece, so I don't know
if there's any meaningful difference. In any case, I also added a
requirements.txt file to lock the dependency versions in case of any
future breaking changes.
- Type annotations checked with mypy.
- Some attempts to be extra user-friendly:
- The script tries to be forgiving with arguments, e.g. you can
specify either the model file itself or the directory containing
it.
- The script doesn't depend on config.json / params.json, just in
case the user downloaded files individually and doesn't have those
handy. But you still need tokenizer.model and, for Alpaca,
added_tokens.json.
- The script tries to give a helpful error message if
added_tokens.json is missing.
2023-04-14 07:03:03 +00:00
name : str
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dtype : np . dtype [ Any ]
valid_conversions : list [ str ]
py : new conversion script (#545)
Current status: Working, except for the latest GPTQ-for-LLaMa format
that includes `g_idx`. This turns out to require changes to GGML, so
for now it only works if you use the `--outtype` option to dequantize it
back to f16 (which is pointless except for debugging).
I also included some cleanup for the C++ code.
This script is meant to replace all the existing conversion scripts
(including the ones that convert from older GGML formats), while also
adding support for some new formats. Specifically, I've tested with:
- [x] `LLaMA` (original)
- [x] `llama-65b-4bit`
- [x] `alpaca-native`
- [x] `alpaca-native-4bit`
- [x] LLaMA converted to 'transformers' format using
`convert_llama_weights_to_hf.py`
- [x] `alpaca-native` quantized with `--true-sequential --act-order
--groupsize 128` (dequantized only)
- [x] same as above plus `--save_safetensors`
- [x] GPT4All
- [x] stock unversioned ggml
- [x] ggmh
There's enough overlap in the logic needed to handle these different
cases that it seemed best to move to a single script.
I haven't tried this with Alpaca-LoRA because I don't know where to find
it.
Useful features:
- Uses multiple threads for a speedup in some cases (though the Python
GIL limits the gain, and sometimes it's disk-bound anyway).
- Combines split models into a single file (both the intra-tensor split
of the original and the inter-tensor split of 'transformers' format
files). Single files are more convenient to work with and more
friendly to future changes to use memory mapping on the C++ side. To
accomplish this without increasing memory requirements, it has some
custom loading code which avoids loading whole input files into memory
at once.
- Because of the custom loading code, it no longer depends in PyTorch,
which might make installing dependencies slightly easier or faster...
although it still depends on NumPy and sentencepiece, so I don't know
if there's any meaningful difference. In any case, I also added a
requirements.txt file to lock the dependency versions in case of any
future breaking changes.
- Type annotations checked with mypy.
- Some attempts to be extra user-friendly:
- The script tries to be forgiving with arguments, e.g. you can
specify either the model file itself or the directory containing
it.
- The script doesn't depend on config.json / params.json, just in
case the user downloaded files individually and doesn't have those
handy. But you still need tokenizer.model and, for Alpaca,
added_tokens.json.
- The script tries to give a helpful error message if
added_tokens.json is missing.
2023-04-14 07:03:03 +00:00
2023-08-26 20:13:36 +00:00
def elements_to_bytes ( self , n_elements : int ) - > int :
return n_elements * self . dtype . itemsize
py : new conversion script (#545)
Current status: Working, except for the latest GPTQ-for-LLaMa format
that includes `g_idx`. This turns out to require changes to GGML, so
for now it only works if you use the `--outtype` option to dequantize it
back to f16 (which is pointless except for debugging).
I also included some cleanup for the C++ code.
This script is meant to replace all the existing conversion scripts
(including the ones that convert from older GGML formats), while also
adding support for some new formats. Specifically, I've tested with:
- [x] `LLaMA` (original)
- [x] `llama-65b-4bit`
- [x] `alpaca-native`
- [x] `alpaca-native-4bit`
- [x] LLaMA converted to 'transformers' format using
`convert_llama_weights_to_hf.py`
- [x] `alpaca-native` quantized with `--true-sequential --act-order
--groupsize 128` (dequantized only)
- [x] same as above plus `--save_safetensors`
- [x] GPT4All
- [x] stock unversioned ggml
- [x] ggmh
There's enough overlap in the logic needed to handle these different
cases that it seemed best to move to a single script.
I haven't tried this with Alpaca-LoRA because I don't know where to find
it.
Useful features:
- Uses multiple threads for a speedup in some cases (though the Python
GIL limits the gain, and sometimes it's disk-bound anyway).
- Combines split models into a single file (both the intra-tensor split
of the original and the inter-tensor split of 'transformers' format
files). Single files are more convenient to work with and more
friendly to future changes to use memory mapping on the C++ side. To
accomplish this without increasing memory requirements, it has some
custom loading code which avoids loading whole input files into memory
at once.
- Because of the custom loading code, it no longer depends in PyTorch,
which might make installing dependencies slightly easier or faster...
although it still depends on NumPy and sentencepiece, so I don't know
if there's any meaningful difference. In any case, I also added a
requirements.txt file to lock the dependency versions in case of any
future breaking changes.
- Type annotations checked with mypy.
- Some attempts to be extra user-friendly:
- The script tries to be forgiving with arguments, e.g. you can
specify either the model file itself or the directory containing
it.
- The script doesn't depend on config.json / params.json, just in
case the user downloaded files individually and doesn't have those
handy. But you still need tokenizer.model and, for Alpaca,
added_tokens.json.
- The script tries to give a helpful error message if
added_tokens.json is missing.
2023-04-14 07:03:03 +00:00
2023-11-20 10:35:47 +00:00
2023-08-26 20:13:36 +00:00
@dataclass ( frozen = True )
class UnquantizedDataType ( DataType ) :
pass
py : new conversion script (#545)
Current status: Working, except for the latest GPTQ-for-LLaMa format
that includes `g_idx`. This turns out to require changes to GGML, so
for now it only works if you use the `--outtype` option to dequantize it
back to f16 (which is pointless except for debugging).
I also included some cleanup for the C++ code.
This script is meant to replace all the existing conversion scripts
(including the ones that convert from older GGML formats), while also
adding support for some new formats. Specifically, I've tested with:
- [x] `LLaMA` (original)
- [x] `llama-65b-4bit`
- [x] `alpaca-native`
- [x] `alpaca-native-4bit`
- [x] LLaMA converted to 'transformers' format using
`convert_llama_weights_to_hf.py`
- [x] `alpaca-native` quantized with `--true-sequential --act-order
--groupsize 128` (dequantized only)
- [x] same as above plus `--save_safetensors`
- [x] GPT4All
- [x] stock unversioned ggml
- [x] ggmh
There's enough overlap in the logic needed to handle these different
cases that it seemed best to move to a single script.
I haven't tried this with Alpaca-LoRA because I don't know where to find
it.
Useful features:
- Uses multiple threads for a speedup in some cases (though the Python
GIL limits the gain, and sometimes it's disk-bound anyway).
- Combines split models into a single file (both the intra-tensor split
of the original and the inter-tensor split of 'transformers' format
files). Single files are more convenient to work with and more
friendly to future changes to use memory mapping on the C++ side. To
accomplish this without increasing memory requirements, it has some
custom loading code which avoids loading whole input files into memory
at once.
- Because of the custom loading code, it no longer depends in PyTorch,
which might make installing dependencies slightly easier or faster...
although it still depends on NumPy and sentencepiece, so I don't know
if there's any meaningful difference. In any case, I also added a
requirements.txt file to lock the dependency versions in case of any
future breaking changes.
- Type annotations checked with mypy.
- Some attempts to be extra user-friendly:
- The script tries to be forgiving with arguments, e.g. you can
specify either the model file itself or the directory containing
it.
- The script doesn't depend on config.json / params.json, just in
case the user downloaded files individually and doesn't have those
handy. But you still need tokenizer.model and, for Alpaca,
added_tokens.json.
- The script tries to give a helpful error message if
added_tokens.json is missing.
2023-04-14 07:03:03 +00:00
2023-11-20 10:35:47 +00:00
2023-12-13 12:04:25 +00:00
DT_F16 = UnquantizedDataType ( ' F16 ' , dtype = np . dtype ( np . float16 ) , valid_conversions = [ ' F32 ' , ' Q8_0 ' ] )
DT_F32 = UnquantizedDataType ( ' F32 ' , dtype = np . dtype ( np . float32 ) , valid_conversions = [ ' F16 ' , ' Q8_0 ' ] )
DT_I32 = UnquantizedDataType ( ' I32 ' , dtype = np . dtype ( np . int16 ) , valid_conversions = [ ] )
DT_BF16 = UnquantizedDataType ( ' BF16 ' , dtype = np . dtype ( np . uint16 ) , valid_conversions = [ ' F32 ' , ' F16 ' , ' Q8_0 ' ] )
2023-08-26 20:13:36 +00:00
2023-11-20 10:35:47 +00:00
2023-08-26 20:13:36 +00:00
@dataclass ( frozen = True )
class QuantizedDataType ( DataType ) :
block_size : int
2023-08-31 05:02:23 +00:00
quantized_dtype : np . dtype [ Any ]
2023-08-26 20:13:36 +00:00
ggml_type : gguf . GGMLQuantizationType
py : new conversion script (#545)
Current status: Working, except for the latest GPTQ-for-LLaMa format
that includes `g_idx`. This turns out to require changes to GGML, so
for now it only works if you use the `--outtype` option to dequantize it
back to f16 (which is pointless except for debugging).
I also included some cleanup for the C++ code.
This script is meant to replace all the existing conversion scripts
(including the ones that convert from older GGML formats), while also
adding support for some new formats. Specifically, I've tested with:
- [x] `LLaMA` (original)
- [x] `llama-65b-4bit`
- [x] `alpaca-native`
- [x] `alpaca-native-4bit`
- [x] LLaMA converted to 'transformers' format using
`convert_llama_weights_to_hf.py`
- [x] `alpaca-native` quantized with `--true-sequential --act-order
--groupsize 128` (dequantized only)
- [x] same as above plus `--save_safetensors`
- [x] GPT4All
- [x] stock unversioned ggml
- [x] ggmh
There's enough overlap in the logic needed to handle these different
cases that it seemed best to move to a single script.
I haven't tried this with Alpaca-LoRA because I don't know where to find
it.
Useful features:
- Uses multiple threads for a speedup in some cases (though the Python
GIL limits the gain, and sometimes it's disk-bound anyway).
- Combines split models into a single file (both the intra-tensor split
of the original and the inter-tensor split of 'transformers' format
files). Single files are more convenient to work with and more
friendly to future changes to use memory mapping on the C++ side. To
accomplish this without increasing memory requirements, it has some
custom loading code which avoids loading whole input files into memory
at once.
- Because of the custom loading code, it no longer depends in PyTorch,
which might make installing dependencies slightly easier or faster...
although it still depends on NumPy and sentencepiece, so I don't know
if there's any meaningful difference. In any case, I also added a
requirements.txt file to lock the dependency versions in case of any
future breaking changes.
- Type annotations checked with mypy.
- Some attempts to be extra user-friendly:
- The script tries to be forgiving with arguments, e.g. you can
specify either the model file itself or the directory containing
it.
- The script doesn't depend on config.json / params.json, just in
case the user downloaded files individually and doesn't have those
handy. But you still need tokenizer.model and, for Alpaca,
added_tokens.json.
- The script tries to give a helpful error message if
added_tokens.json is missing.
2023-04-14 07:03:03 +00:00
2023-08-26 20:13:36 +00:00
def quantize ( self , arr : NDArray ) - > NDArray :
raise NotImplementedError ( f ' Quantization for { self . name } not implemented ' )
def elements_to_bytes ( self , n_elements : int ) - > int :
assert n_elements % self . block_size == 0 , f ' Invalid number of elements { n_elements } for { self . name } with block size { self . block_size } '
return self . quantized_dtype . itemsize * ( n_elements / / self . block_size )
2023-11-20 10:35:47 +00:00
2023-08-26 20:13:36 +00:00
@dataclass ( frozen = True )
class Q8_0QuantizedDataType ( QuantizedDataType ) :
# Mini Q8_0 quantization in Python!
def quantize ( self , arr : NDArray ) - > NDArray :
assert arr . size % self . block_size == 0 and arr . size != 0 , f ' Bad array size { arr . size } '
assert arr . dtype == np . float32 , f ' Bad array type { arr . dtype } '
n_blocks = arr . size / / self . block_size
blocks = arr . reshape ( ( n_blocks , self . block_size ) )
# Much faster implementation of block quantization contributed by @Cebtenzzre
2023-11-20 10:35:47 +00:00
2023-08-31 05:02:23 +00:00
def quantize_blocks_q8_0 ( blocks : NDArray ) - > Iterable [ tuple [ Any , Any ] ] :
2023-08-26 20:13:36 +00:00
d = abs ( blocks ) . max ( axis = 1 ) / np . float32 ( 127 )
with np . errstate ( divide = ' ignore ' ) :
qs = ( blocks / d [ : , None ] ) . round ( )
qs [ d == 0 ] = 0
yield from zip ( d , qs )
return np . fromiter ( quantize_blocks_q8_0 ( blocks ) , count = n_blocks , dtype = self . quantized_dtype )
2023-11-20 10:35:47 +00:00
2023-08-26 20:13:36 +00:00
DT_Q8_0 = Q8_0QuantizedDataType ( ' Q8_0 ' ,
2023-11-20 10:35:47 +00:00
dtype = np . dtype ( np . float32 ) , valid_conversions = [ ] ,
ggml_type = gguf . GGMLQuantizationType . Q8_0 , block_size = 32 ,
quantized_dtype = np . dtype ( [ ( ' d ' , ' <f2 ' ) , ( ' qs ' , ' i1 ' , ( 32 , ) ) ] ) )
2023-08-26 20:13:36 +00:00
# Quantized types skipped here because they may also map to np.float32
2023-08-31 05:02:23 +00:00
NUMPY_TYPE_TO_DATA_TYPE : dict [ np . dtype [ Any ] , DataType ] = { }
2023-08-26 20:13:36 +00:00
for dt in ( DT_BF16 , DT_F16 , DT_F32 , DT_I32 ) :
if dt . dtype in NUMPY_TYPE_TO_DATA_TYPE :
raise ValueError ( f ' Invalid duplicate data type { dt } ' )
NUMPY_TYPE_TO_DATA_TYPE [ dt . dtype ] = dt
py : new conversion script (#545)
Current status: Working, except for the latest GPTQ-for-LLaMa format
that includes `g_idx`. This turns out to require changes to GGML, so
for now it only works if you use the `--outtype` option to dequantize it
back to f16 (which is pointless except for debugging).
I also included some cleanup for the C++ code.
This script is meant to replace all the existing conversion scripts
(including the ones that convert from older GGML formats), while also
adding support for some new formats. Specifically, I've tested with:
- [x] `LLaMA` (original)
- [x] `llama-65b-4bit`
- [x] `alpaca-native`
- [x] `alpaca-native-4bit`
- [x] LLaMA converted to 'transformers' format using
`convert_llama_weights_to_hf.py`
- [x] `alpaca-native` quantized with `--true-sequential --act-order
--groupsize 128` (dequantized only)
- [x] same as above plus `--save_safetensors`
- [x] GPT4All
- [x] stock unversioned ggml
- [x] ggmh
There's enough overlap in the logic needed to handle these different
cases that it seemed best to move to a single script.
I haven't tried this with Alpaca-LoRA because I don't know where to find
it.
Useful features:
- Uses multiple threads for a speedup in some cases (though the Python
GIL limits the gain, and sometimes it's disk-bound anyway).
- Combines split models into a single file (both the intra-tensor split
of the original and the inter-tensor split of 'transformers' format
files). Single files are more convenient to work with and more
friendly to future changes to use memory mapping on the C++ side. To
accomplish this without increasing memory requirements, it has some
custom loading code which avoids loading whole input files into memory
at once.
- Because of the custom loading code, it no longer depends in PyTorch,
which might make installing dependencies slightly easier or faster...
although it still depends on NumPy and sentencepiece, so I don't know
if there's any meaningful difference. In any case, I also added a
requirements.txt file to lock the dependency versions in case of any
future breaking changes.
- Type annotations checked with mypy.
- Some attempts to be extra user-friendly:
- The script tries to be forgiving with arguments, e.g. you can
specify either the model file itself or the directory containing
it.
- The script doesn't depend on config.json / params.json, just in
case the user downloaded files individually and doesn't have those
handy. But you still need tokenizer.model and, for Alpaca,
added_tokens.json.
- The script tries to give a helpful error message if
added_tokens.json is missing.
2023-04-14 07:03:03 +00:00
2023-08-31 05:02:23 +00:00
SAFETENSORS_DATA_TYPES : dict [ str , DataType ] = {
2023-08-21 20:07:43 +00:00
' BF16 ' : DT_BF16 ,
' F16 ' : DT_F16 ,
' F32 ' : DT_F32 ,
' I32 ' : DT_I32 ,
}
py : new conversion script (#545)
Current status: Working, except for the latest GPTQ-for-LLaMa format
that includes `g_idx`. This turns out to require changes to GGML, so
for now it only works if you use the `--outtype` option to dequantize it
back to f16 (which is pointless except for debugging).
I also included some cleanup for the C++ code.
This script is meant to replace all the existing conversion scripts
(including the ones that convert from older GGML formats), while also
adding support for some new formats. Specifically, I've tested with:
- [x] `LLaMA` (original)
- [x] `llama-65b-4bit`
- [x] `alpaca-native`
- [x] `alpaca-native-4bit`
- [x] LLaMA converted to 'transformers' format using
`convert_llama_weights_to_hf.py`
- [x] `alpaca-native` quantized with `--true-sequential --act-order
--groupsize 128` (dequantized only)
- [x] same as above plus `--save_safetensors`
- [x] GPT4All
- [x] stock unversioned ggml
- [x] ggmh
There's enough overlap in the logic needed to handle these different
cases that it seemed best to move to a single script.
I haven't tried this with Alpaca-LoRA because I don't know where to find
it.
Useful features:
- Uses multiple threads for a speedup in some cases (though the Python
GIL limits the gain, and sometimes it's disk-bound anyway).
- Combines split models into a single file (both the intra-tensor split
of the original and the inter-tensor split of 'transformers' format
files). Single files are more convenient to work with and more
friendly to future changes to use memory mapping on the C++ side. To
accomplish this without increasing memory requirements, it has some
custom loading code which avoids loading whole input files into memory
at once.
- Because of the custom loading code, it no longer depends in PyTorch,
which might make installing dependencies slightly easier or faster...
although it still depends on NumPy and sentencepiece, so I don't know
if there's any meaningful difference. In any case, I also added a
requirements.txt file to lock the dependency versions in case of any
future breaking changes.
- Type annotations checked with mypy.
- Some attempts to be extra user-friendly:
- The script tries to be forgiving with arguments, e.g. you can
specify either the model file itself or the directory containing
it.
- The script doesn't depend on config.json / params.json, just in
case the user downloaded files individually and doesn't have those
handy. But you still need tokenizer.model and, for Alpaca,
added_tokens.json.
- The script tries to give a helpful error message if
added_tokens.json is missing.
2023-04-14 07:03:03 +00:00
2023-08-22 17:05:59 +00:00
# TODO: match this with `llama_ftype`
# TODO: rename to LLAMAFileType
# TODO: move to `gguf.py`
2023-11-20 10:35:47 +00:00
2023-08-22 17:05:59 +00:00
class GGMLFileType ( enum . IntEnum ) :
2023-08-26 20:13:36 +00:00
AllF32 = 0
MostlyF16 = 1 # except 1d tensors
MostlyQ8_0 = 7 # except 1d tensors
py : new conversion script (#545)
Current status: Working, except for the latest GPTQ-for-LLaMa format
that includes `g_idx`. This turns out to require changes to GGML, so
for now it only works if you use the `--outtype` option to dequantize it
back to f16 (which is pointless except for debugging).
I also included some cleanup for the C++ code.
This script is meant to replace all the existing conversion scripts
(including the ones that convert from older GGML formats), while also
adding support for some new formats. Specifically, I've tested with:
- [x] `LLaMA` (original)
- [x] `llama-65b-4bit`
- [x] `alpaca-native`
- [x] `alpaca-native-4bit`
- [x] LLaMA converted to 'transformers' format using
`convert_llama_weights_to_hf.py`
- [x] `alpaca-native` quantized with `--true-sequential --act-order
--groupsize 128` (dequantized only)
- [x] same as above plus `--save_safetensors`
- [x] GPT4All
- [x] stock unversioned ggml
- [x] ggmh
There's enough overlap in the logic needed to handle these different
cases that it seemed best to move to a single script.
I haven't tried this with Alpaca-LoRA because I don't know where to find
it.
Useful features:
- Uses multiple threads for a speedup in some cases (though the Python
GIL limits the gain, and sometimes it's disk-bound anyway).
- Combines split models into a single file (both the intra-tensor split
of the original and the inter-tensor split of 'transformers' format
files). Single files are more convenient to work with and more
friendly to future changes to use memory mapping on the C++ side. To
accomplish this without increasing memory requirements, it has some
custom loading code which avoids loading whole input files into memory
at once.
- Because of the custom loading code, it no longer depends in PyTorch,
which might make installing dependencies slightly easier or faster...
although it still depends on NumPy and sentencepiece, so I don't know
if there's any meaningful difference. In any case, I also added a
requirements.txt file to lock the dependency versions in case of any
future breaking changes.
- Type annotations checked with mypy.
- Some attempts to be extra user-friendly:
- The script tries to be forgiving with arguments, e.g. you can
specify either the model file itself or the directory containing
it.
- The script doesn't depend on config.json / params.json, just in
case the user downloaded files individually and doesn't have those
handy. But you still need tokenizer.model and, for Alpaca,
added_tokens.json.
- The script tries to give a helpful error message if
added_tokens.json is missing.
2023-04-14 07:03:03 +00:00
2023-08-31 05:02:23 +00:00
def type_for_tensor ( self , name : str , tensor : LazyTensor ) - > DataType :
2023-08-26 20:13:36 +00:00
dt = GGML_FILE_TYPE_TO_DATA_TYPE . get ( self )
if dt is None :
py : new conversion script (#545)
Current status: Working, except for the latest GPTQ-for-LLaMa format
that includes `g_idx`. This turns out to require changes to GGML, so
for now it only works if you use the `--outtype` option to dequantize it
back to f16 (which is pointless except for debugging).
I also included some cleanup for the C++ code.
This script is meant to replace all the existing conversion scripts
(including the ones that convert from older GGML formats), while also
adding support for some new formats. Specifically, I've tested with:
- [x] `LLaMA` (original)
- [x] `llama-65b-4bit`
- [x] `alpaca-native`
- [x] `alpaca-native-4bit`
- [x] LLaMA converted to 'transformers' format using
`convert_llama_weights_to_hf.py`
- [x] `alpaca-native` quantized with `--true-sequential --act-order
--groupsize 128` (dequantized only)
- [x] same as above plus `--save_safetensors`
- [x] GPT4All
- [x] stock unversioned ggml
- [x] ggmh
There's enough overlap in the logic needed to handle these different
cases that it seemed best to move to a single script.
I haven't tried this with Alpaca-LoRA because I don't know where to find
it.
Useful features:
- Uses multiple threads for a speedup in some cases (though the Python
GIL limits the gain, and sometimes it's disk-bound anyway).
- Combines split models into a single file (both the intra-tensor split
of the original and the inter-tensor split of 'transformers' format
files). Single files are more convenient to work with and more
friendly to future changes to use memory mapping on the C++ side. To
accomplish this without increasing memory requirements, it has some
custom loading code which avoids loading whole input files into memory
at once.
- Because of the custom loading code, it no longer depends in PyTorch,
which might make installing dependencies slightly easier or faster...
although it still depends on NumPy and sentencepiece, so I don't know
if there's any meaningful difference. In any case, I also added a
requirements.txt file to lock the dependency versions in case of any
future breaking changes.
- Type annotations checked with mypy.
- Some attempts to be extra user-friendly:
- The script tries to be forgiving with arguments, e.g. you can
specify either the model file itself or the directory containing
it.
- The script doesn't depend on config.json / params.json, just in
case the user downloaded files individually and doesn't have those
handy. But you still need tokenizer.model and, for Alpaca,
added_tokens.json.
- The script tries to give a helpful error message if
added_tokens.json is missing.
2023-04-14 07:03:03 +00:00
raise ValueError ( self )
2024-04-08 15:44:19 +00:00
# Convert all 1D tensors to F32. Most of the codebase that takes in 1D tensors only handles F32 tensors, and most of the outputs tensors are F32.
# Also The 1d tensors aren't much of a performance/size issue. So instead of having to have separate F32 and F16 implementations of both, just convert everything to F32 for now.
2023-08-26 20:13:36 +00:00
return dt if len ( tensor . shape ) > 1 else DT_F32
py : new conversion script (#545)
Current status: Working, except for the latest GPTQ-for-LLaMa format
that includes `g_idx`. This turns out to require changes to GGML, so
for now it only works if you use the `--outtype` option to dequantize it
back to f16 (which is pointless except for debugging).
I also included some cleanup for the C++ code.
This script is meant to replace all the existing conversion scripts
(including the ones that convert from older GGML formats), while also
adding support for some new formats. Specifically, I've tested with:
- [x] `LLaMA` (original)
- [x] `llama-65b-4bit`
- [x] `alpaca-native`
- [x] `alpaca-native-4bit`
- [x] LLaMA converted to 'transformers' format using
`convert_llama_weights_to_hf.py`
- [x] `alpaca-native` quantized with `--true-sequential --act-order
--groupsize 128` (dequantized only)
- [x] same as above plus `--save_safetensors`
- [x] GPT4All
- [x] stock unversioned ggml
- [x] ggmh
There's enough overlap in the logic needed to handle these different
cases that it seemed best to move to a single script.
I haven't tried this with Alpaca-LoRA because I don't know where to find
it.
Useful features:
- Uses multiple threads for a speedup in some cases (though the Python
GIL limits the gain, and sometimes it's disk-bound anyway).
- Combines split models into a single file (both the intra-tensor split
of the original and the inter-tensor split of 'transformers' format
files). Single files are more convenient to work with and more
friendly to future changes to use memory mapping on the C++ side. To
accomplish this without increasing memory requirements, it has some
custom loading code which avoids loading whole input files into memory
at once.
- Because of the custom loading code, it no longer depends in PyTorch,
which might make installing dependencies slightly easier or faster...
although it still depends on NumPy and sentencepiece, so I don't know
if there's any meaningful difference. In any case, I also added a
requirements.txt file to lock the dependency versions in case of any
future breaking changes.
- Type annotations checked with mypy.
- Some attempts to be extra user-friendly:
- The script tries to be forgiving with arguments, e.g. you can
specify either the model file itself or the directory containing
it.
- The script doesn't depend on config.json / params.json, just in
case the user downloaded files individually and doesn't have those
handy. But you still need tokenizer.model and, for Alpaca,
added_tokens.json.
- The script tries to give a helpful error message if
added_tokens.json is missing.
2023-04-14 07:03:03 +00:00
2023-11-20 10:35:47 +00:00
2023-08-31 05:02:23 +00:00
GGML_FILE_TYPE_TO_DATA_TYPE : dict [ GGMLFileType , DataType ] = {
2023-08-26 20:13:36 +00:00
GGMLFileType . AllF32 : DT_F32 ,
GGMLFileType . MostlyF16 : DT_F16 ,
GGMLFileType . MostlyQ8_0 : DT_Q8_0 ,
}
py : new conversion script (#545)
Current status: Working, except for the latest GPTQ-for-LLaMa format
that includes `g_idx`. This turns out to require changes to GGML, so
for now it only works if you use the `--outtype` option to dequantize it
back to f16 (which is pointless except for debugging).
I also included some cleanup for the C++ code.
This script is meant to replace all the existing conversion scripts
(including the ones that convert from older GGML formats), while also
adding support for some new formats. Specifically, I've tested with:
- [x] `LLaMA` (original)
- [x] `llama-65b-4bit`
- [x] `alpaca-native`
- [x] `alpaca-native-4bit`
- [x] LLaMA converted to 'transformers' format using
`convert_llama_weights_to_hf.py`
- [x] `alpaca-native` quantized with `--true-sequential --act-order
--groupsize 128` (dequantized only)
- [x] same as above plus `--save_safetensors`
- [x] GPT4All
- [x] stock unversioned ggml
- [x] ggmh
There's enough overlap in the logic needed to handle these different
cases that it seemed best to move to a single script.
I haven't tried this with Alpaca-LoRA because I don't know where to find
it.
Useful features:
- Uses multiple threads for a speedup in some cases (though the Python
GIL limits the gain, and sometimes it's disk-bound anyway).
- Combines split models into a single file (both the intra-tensor split
of the original and the inter-tensor split of 'transformers' format
files). Single files are more convenient to work with and more
friendly to future changes to use memory mapping on the C++ side. To
accomplish this without increasing memory requirements, it has some
custom loading code which avoids loading whole input files into memory
at once.
- Because of the custom loading code, it no longer depends in PyTorch,
which might make installing dependencies slightly easier or faster...
although it still depends on NumPy and sentencepiece, so I don't know
if there's any meaningful difference. In any case, I also added a
requirements.txt file to lock the dependency versions in case of any
future breaking changes.
- Type annotations checked with mypy.
- Some attempts to be extra user-friendly:
- The script tries to be forgiving with arguments, e.g. you can
specify either the model file itself or the directory containing
it.
- The script doesn't depend on config.json / params.json, just in
case the user downloaded files individually and doesn't have those
handy. But you still need tokenizer.model and, for Alpaca,
added_tokens.json.
- The script tries to give a helpful error message if
added_tokens.json is missing.
2023-04-14 07:03:03 +00:00
2023-08-21 20:07:43 +00:00
#
# hparams loading
#
2023-06-22 12:20:47 +00:00
2023-11-20 10:35:47 +00:00
py : new conversion script (#545)
Current status: Working, except for the latest GPTQ-for-LLaMa format
that includes `g_idx`. This turns out to require changes to GGML, so
for now it only works if you use the `--outtype` option to dequantize it
back to f16 (which is pointless except for debugging).
I also included some cleanup for the C++ code.
This script is meant to replace all the existing conversion scripts
(including the ones that convert from older GGML formats), while also
adding support for some new formats. Specifically, I've tested with:
- [x] `LLaMA` (original)
- [x] `llama-65b-4bit`
- [x] `alpaca-native`
- [x] `alpaca-native-4bit`
- [x] LLaMA converted to 'transformers' format using
`convert_llama_weights_to_hf.py`
- [x] `alpaca-native` quantized with `--true-sequential --act-order
--groupsize 128` (dequantized only)
- [x] same as above plus `--save_safetensors`
- [x] GPT4All
- [x] stock unversioned ggml
- [x] ggmh
There's enough overlap in the logic needed to handle these different
cases that it seemed best to move to a single script.
I haven't tried this with Alpaca-LoRA because I don't know where to find
it.
Useful features:
- Uses multiple threads for a speedup in some cases (though the Python
GIL limits the gain, and sometimes it's disk-bound anyway).
- Combines split models into a single file (both the intra-tensor split
of the original and the inter-tensor split of 'transformers' format
files). Single files are more convenient to work with and more
friendly to future changes to use memory mapping on the C++ side. To
accomplish this without increasing memory requirements, it has some
custom loading code which avoids loading whole input files into memory
at once.
- Because of the custom loading code, it no longer depends in PyTorch,
which might make installing dependencies slightly easier or faster...
although it still depends on NumPy and sentencepiece, so I don't know
if there's any meaningful difference. In any case, I also added a
requirements.txt file to lock the dependency versions in case of any
future breaking changes.
- Type annotations checked with mypy.
- Some attempts to be extra user-friendly:
- The script tries to be forgiving with arguments, e.g. you can
specify either the model file itself or the directory containing
it.
- The script doesn't depend on config.json / params.json, just in
case the user downloaded files individually and doesn't have those
handy. But you still need tokenizer.model and, for Alpaca,
added_tokens.json.
- The script tries to give a helpful error message if
added_tokens.json is missing.
2023-04-14 07:03:03 +00:00
@dataclass
class Params :
2024-01-20 23:14:18 +00:00
n_vocab : int
n_embd : int
n_layer : int
n_ctx : int
n_ff : int
n_head : int
n_head_kv : int
n_experts : int | None = None
n_experts_used : int | None = None
f_norm_eps : float | None = None
rope_scaling_type : gguf . RopeScalingType | None = None
f_rope_freq_base : float | None = None
f_rope_scale : float | None = None
n_orig_ctx : int | None = None
rope_finetuned : bool | None = None
ftype : GGMLFileType | None = None
2023-08-22 17:05:59 +00:00
2023-08-24 16:26:19 +00:00
# path to the directory containing the model files
2024-01-20 23:14:18 +00:00
path_model : Path | None = None
2023-08-24 16:26:19 +00:00
py : new conversion script (#545)
Current status: Working, except for the latest GPTQ-for-LLaMa format
that includes `g_idx`. This turns out to require changes to GGML, so
for now it only works if you use the `--outtype` option to dequantize it
back to f16 (which is pointless except for debugging).
I also included some cleanup for the C++ code.
This script is meant to replace all the existing conversion scripts
(including the ones that convert from older GGML formats), while also
adding support for some new formats. Specifically, I've tested with:
- [x] `LLaMA` (original)
- [x] `llama-65b-4bit`
- [x] `alpaca-native`
- [x] `alpaca-native-4bit`
- [x] LLaMA converted to 'transformers' format using
`convert_llama_weights_to_hf.py`
- [x] `alpaca-native` quantized with `--true-sequential --act-order
--groupsize 128` (dequantized only)
- [x] same as above plus `--save_safetensors`
- [x] GPT4All
- [x] stock unversioned ggml
- [x] ggmh
There's enough overlap in the logic needed to handle these different
cases that it seemed best to move to a single script.
I haven't tried this with Alpaca-LoRA because I don't know where to find
it.
Useful features:
- Uses multiple threads for a speedup in some cases (though the Python
GIL limits the gain, and sometimes it's disk-bound anyway).
- Combines split models into a single file (both the intra-tensor split
of the original and the inter-tensor split of 'transformers' format
files). Single files are more convenient to work with and more
friendly to future changes to use memory mapping on the C++ side. To
accomplish this without increasing memory requirements, it has some
custom loading code which avoids loading whole input files into memory
at once.
- Because of the custom loading code, it no longer depends in PyTorch,
which might make installing dependencies slightly easier or faster...
although it still depends on NumPy and sentencepiece, so I don't know
if there's any meaningful difference. In any case, I also added a
requirements.txt file to lock the dependency versions in case of any
future breaking changes.
- Type annotations checked with mypy.
- Some attempts to be extra user-friendly:
- The script tries to be forgiving with arguments, e.g. you can
specify either the model file itself or the directory containing
it.
- The script doesn't depend on config.json / params.json, just in
case the user downloaded files individually and doesn't have those
handy. But you still need tokenizer.model and, for Alpaca,
added_tokens.json.
- The script tries to give a helpful error message if
added_tokens.json is missing.
2023-04-14 07:03:03 +00:00
@staticmethod
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def guessed ( model : LazyModel ) - > Params :
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# try transformer naming first
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n_vocab , n_embd = model [ " model.embed_tokens.weight " ] . shape if " model.embed_tokens.weight " in model else model [ " tok_embeddings.weight " ] . shape
2023-06-22 12:20:47 +00:00
# try transformer naming first
if " model.layers.0.self_attn.q_proj.weight " in model :
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n_layer = next ( i for i in itertools . count ( ) if f " model.layers. { i } .self_attn.q_proj.weight " not in model )
elif " model.layers.0.self_attn.W_pack.weight " in model : # next: try baichuan naming
n_layer = next ( i for i in itertools . count ( ) if f " model.layers. { i } .self_attn.W_pack.weight " not in model )
2023-06-22 12:20:47 +00:00
else :
2024-01-20 23:14:18 +00:00
n_layer = next ( i for i in itertools . count ( ) if f " layers. { i } .attention.wq.weight " not in model )
2023-06-22 12:20:47 +00:00
2023-07-06 16:23:49 +00:00
if n_layer < 1 :
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msg = """ \
failed to guess ' n_layer ' . This model is unknown or unsupported .
Suggestion : provide ' config.json ' of the model in the same directory containing model files . """
raise KeyError ( textwrap . dedent ( msg ) )
2023-07-06 16:23:49 +00:00
2024-01-20 23:14:18 +00:00
n_head = n_embd / / 128 # guessed
n_mult = 256 # guessed
2023-08-21 20:07:43 +00:00
# TODO: verify this
n_ff = int ( 2 * ( 4 * n_embd ) / 3 )
n_ff = n_mult * ( ( n_ff + n_mult - 1 ) / / n_mult )
py : new conversion script (#545)
Current status: Working, except for the latest GPTQ-for-LLaMa format
that includes `g_idx`. This turns out to require changes to GGML, so
for now it only works if you use the `--outtype` option to dequantize it
back to f16 (which is pointless except for debugging).
I also included some cleanup for the C++ code.
This script is meant to replace all the existing conversion scripts
(including the ones that convert from older GGML formats), while also
adding support for some new formats. Specifically, I've tested with:
- [x] `LLaMA` (original)
- [x] `llama-65b-4bit`
- [x] `alpaca-native`
- [x] `alpaca-native-4bit`
- [x] LLaMA converted to 'transformers' format using
`convert_llama_weights_to_hf.py`
- [x] `alpaca-native` quantized with `--true-sequential --act-order
--groupsize 128` (dequantized only)
- [x] same as above plus `--save_safetensors`
- [x] GPT4All
- [x] stock unversioned ggml
- [x] ggmh
There's enough overlap in the logic needed to handle these different
cases that it seemed best to move to a single script.
I haven't tried this with Alpaca-LoRA because I don't know where to find
it.
Useful features:
- Uses multiple threads for a speedup in some cases (though the Python
GIL limits the gain, and sometimes it's disk-bound anyway).
- Combines split models into a single file (both the intra-tensor split
of the original and the inter-tensor split of 'transformers' format
files). Single files are more convenient to work with and more
friendly to future changes to use memory mapping on the C++ side. To
accomplish this without increasing memory requirements, it has some
custom loading code which avoids loading whole input files into memory
at once.
- Because of the custom loading code, it no longer depends in PyTorch,
which might make installing dependencies slightly easier or faster...
although it still depends on NumPy and sentencepiece, so I don't know
if there's any meaningful difference. In any case, I also added a
requirements.txt file to lock the dependency versions in case of any
future breaking changes.
- Type annotations checked with mypy.
- Some attempts to be extra user-friendly:
- The script tries to be forgiving with arguments, e.g. you can
specify either the model file itself or the directory containing
it.
- The script doesn't depend on config.json / params.json, just in
case the user downloaded files individually and doesn't have those
handy. But you still need tokenizer.model and, for Alpaca,
added_tokens.json.
- The script tries to give a helpful error message if
added_tokens.json is missing.
2023-04-14 07:03:03 +00:00
return Params (
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n_vocab = n_vocab ,
n_embd = n_embd ,
n_layer = n_layer ,
n_ctx = - 1 ,
n_ff = n_ff ,
n_head = n_head ,
n_head_kv = n_head ,
f_norm_eps = 1e-5 ,
py : new conversion script (#545)
Current status: Working, except for the latest GPTQ-for-LLaMa format
that includes `g_idx`. This turns out to require changes to GGML, so
for now it only works if you use the `--outtype` option to dequantize it
back to f16 (which is pointless except for debugging).
I also included some cleanup for the C++ code.
This script is meant to replace all the existing conversion scripts
(including the ones that convert from older GGML formats), while also
adding support for some new formats. Specifically, I've tested with:
- [x] `LLaMA` (original)
- [x] `llama-65b-4bit`
- [x] `alpaca-native`
- [x] `alpaca-native-4bit`
- [x] LLaMA converted to 'transformers' format using
`convert_llama_weights_to_hf.py`
- [x] `alpaca-native` quantized with `--true-sequential --act-order
--groupsize 128` (dequantized only)
- [x] same as above plus `--save_safetensors`
- [x] GPT4All
- [x] stock unversioned ggml
- [x] ggmh
There's enough overlap in the logic needed to handle these different
cases that it seemed best to move to a single script.
I haven't tried this with Alpaca-LoRA because I don't know where to find
it.
Useful features:
- Uses multiple threads for a speedup in some cases (though the Python
GIL limits the gain, and sometimes it's disk-bound anyway).
- Combines split models into a single file (both the intra-tensor split
of the original and the inter-tensor split of 'transformers' format
files). Single files are more convenient to work with and more
friendly to future changes to use memory mapping on the C++ side. To
accomplish this without increasing memory requirements, it has some
custom loading code which avoids loading whole input files into memory
at once.
- Because of the custom loading code, it no longer depends in PyTorch,
which might make installing dependencies slightly easier or faster...
although it still depends on NumPy and sentencepiece, so I don't know
if there's any meaningful difference. In any case, I also added a
requirements.txt file to lock the dependency versions in case of any
future breaking changes.
- Type annotations checked with mypy.
- Some attempts to be extra user-friendly:
- The script tries to be forgiving with arguments, e.g. you can
specify either the model file itself or the directory containing
it.
- The script doesn't depend on config.json / params.json, just in
case the user downloaded files individually and doesn't have those
handy. But you still need tokenizer.model and, for Alpaca,
added_tokens.json.
- The script tries to give a helpful error message if
added_tokens.json is missing.
2023-04-14 07:03:03 +00:00
)
2023-06-22 12:20:47 +00:00
@staticmethod
2024-01-20 23:14:18 +00:00
def loadHFTransformerJson ( model : LazyModel , config_path : Path ) - > Params :
2024-03-28 15:44:36 +00:00
with open ( config_path ) as f :
config = json . load ( f )
2023-06-22 12:20:47 +00:00
2023-11-01 22:04:33 +00:00
rope_scaling_type = f_rope_scale = n_orig_ctx = rope_finetuned = None
2023-08-26 12:11:17 +00:00
rope_scaling = config . get ( " rope_scaling " )
2023-11-01 22:04:33 +00:00
if rope_scaling is not None and ( typ := rope_scaling . get ( " type " ) ) :
rope_factor = rope_scaling . get ( " factor " )
f_rope_scale = rope_factor
if typ == " linear " :
rope_scaling_type = gguf . RopeScalingType . LINEAR
elif typ == " yarn " :
rope_scaling_type = gguf . RopeScalingType . YARN
2024-01-20 23:14:18 +00:00
n_orig_ctx = rope_scaling [ ' original_max_position_embeddings ' ]
rope_finetuned = rope_scaling [ ' finetuned ' ]
2023-11-01 22:04:33 +00:00
else :
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raise NotImplementedError ( f ' Unknown rope scaling type: { typ } ' )
2023-08-25 14:41:52 +00:00
2023-08-21 20:07:43 +00:00
if " max_sequence_length " in config :
n_ctx = config [ " max_sequence_length " ]
elif " max_position_embeddings " in config :
n_ctx = config [ " max_position_embeddings " ]
else :
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msg = """ \
failed to guess ' n_ctx ' . This model is unknown or unsupported .
Suggestion : provide ' config.json ' of the model in the same directory containing model files . """
raise KeyError ( textwrap . dedent ( msg ) )
2023-06-22 12:20:47 +00:00
2024-01-20 23:14:18 +00:00
n_experts = None
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n_experts_used = None
if " num_local_experts " in config :
n_experts = config [ " num_local_experts " ]
n_experts_used = config [ " num_experts_per_tok " ]
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return Params (
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n_vocab = config [ " vocab_size " ] ,
n_embd = config [ " hidden_size " ] ,
n_layer = config [ " num_hidden_layers " ] ,
n_ctx = n_ctx ,
n_ff = config [ " intermediate_size " ] ,
n_head = ( n_head := config [ " num_attention_heads " ] ) ,
n_head_kv = config . get ( " num_key_value_heads " , n_head ) ,
n_experts = n_experts ,
n_experts_used = n_experts_used ,
f_norm_eps = config [ " rms_norm_eps " ] ,
f_rope_freq_base = config . get ( " rope_theta " ) ,
rope_scaling_type = rope_scaling_type ,
f_rope_scale = f_rope_scale ,
n_orig_ctx = n_orig_ctx ,
rope_finetuned = rope_finetuned ,
2023-07-23 12:09:47 +00:00
)
# LLaMA v2 70B params.json
2023-09-10 15:06:53 +00:00
# {"dim": 8192, "multiple_of": 4096, "ffn_dim_multiplier": 1.3, "n_heads": 64, "n_kv_heads": 8, "n_layers": 80, "norm_eps": 1e-05, "vocab_size": -1}
2023-07-23 12:09:47 +00:00
@staticmethod
2024-01-20 23:14:18 +00:00
def loadOriginalParamsJson ( model : LazyModel , config_path : Path ) - > Params :
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with open ( config_path ) as f :
config = json . load ( f )
2023-07-23 12:09:47 +00:00
2024-01-20 23:14:18 +00:00
n_experts = None
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n_experts_used = None
f_rope_freq_base = None
2023-08-24 19:10:39 +00:00
# hack to determine LLaMA v1 vs v2 vs CodeLlama
2023-12-13 12:04:25 +00:00
if config . get ( " moe " ) :
# Mixtral
n_ctx = 32768
elif config . get ( " rope_theta " ) == 1000000 :
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# CodeLlama
n_ctx = 16384
elif config [ " norm_eps " ] == 1e-05 :
# LLaMA v2
n_ctx = 4096
else :
# LLaMA v1
n_ctx = 2048
2023-12-13 12:04:25 +00:00
if " layers.0.feed_forward.w1.weight " in model :
n_ff = model [ " layers.0.feed_forward.w1.weight " ] . shape [ 0 ]
if config . get ( " moe " ) :
n_ff = model [ " layers.0.feed_forward.experts.0.w1.weight " ] . shape [ 0 ]
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n_experts = config [ " moe " ] [ " num_experts " ]
2023-12-13 12:04:25 +00:00
n_experts_used = config [ " moe " ] [ " num_experts_per_tok " ]
f_rope_freq_base = 1e6
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return Params (
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n_vocab = model [ " tok_embeddings.weight " ] . shape [ 0 ] ,
n_embd = config [ " dim " ] ,
n_layer = config [ " n_layers " ] ,
n_ctx = n_ctx ,
n_ff = n_ff ,
n_head = ( n_head := config [ " n_heads " ] ) ,
n_head_kv = config . get ( " n_kv_heads " , n_head ) ,
n_experts = n_experts ,
n_experts_used = n_experts_used ,
f_norm_eps = config [ " norm_eps " ] ,
f_rope_freq_base = config . get ( " rope_theta " , f_rope_freq_base ) ,
2023-06-22 12:20:47 +00:00
)
@staticmethod
2024-01-20 23:14:18 +00:00
def load ( model_plus : ModelPlus ) - > Params :
hf_config_path = model_plus . paths [ 0 ] . parent / " config.json "
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orig_config_path = model_plus . paths [ 0 ] . parent / " params.json "
2023-07-23 12:09:47 +00:00
if hf_config_path . exists ( ) :
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params = Params . loadHFTransformerJson ( model_plus . model , hf_config_path )
2023-07-23 12:09:47 +00:00
elif orig_config_path . exists ( ) :
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params = Params . loadOriginalParamsJson ( model_plus . model , orig_config_path )
elif model_plus . format != ' none ' :
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params = Params . guessed ( model_plus . model )
2023-08-30 08:25:50 +00:00
else :
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raise ValueError ( ' Cannot guess params when model format is none ' )
2023-06-22 12:20:47 +00:00
2023-08-24 16:26:19 +00:00
params . path_model = model_plus . paths [ 0 ] . parent
2023-06-22 12:20:47 +00:00
return params
py : new conversion script (#545)
Current status: Working, except for the latest GPTQ-for-LLaMa format
that includes `g_idx`. This turns out to require changes to GGML, so
for now it only works if you use the `--outtype` option to dequantize it
back to f16 (which is pointless except for debugging).
I also included some cleanup for the C++ code.
This script is meant to replace all the existing conversion scripts
(including the ones that convert from older GGML formats), while also
adding support for some new formats. Specifically, I've tested with:
- [x] `LLaMA` (original)
- [x] `llama-65b-4bit`
- [x] `alpaca-native`
- [x] `alpaca-native-4bit`
- [x] LLaMA converted to 'transformers' format using
`convert_llama_weights_to_hf.py`
- [x] `alpaca-native` quantized with `--true-sequential --act-order
--groupsize 128` (dequantized only)
- [x] same as above plus `--save_safetensors`
- [x] GPT4All
- [x] stock unversioned ggml
- [x] ggmh
There's enough overlap in the logic needed to handle these different
cases that it seemed best to move to a single script.
I haven't tried this with Alpaca-LoRA because I don't know where to find
it.
Useful features:
- Uses multiple threads for a speedup in some cases (though the Python
GIL limits the gain, and sometimes it's disk-bound anyway).
- Combines split models into a single file (both the intra-tensor split
of the original and the inter-tensor split of 'transformers' format
files). Single files are more convenient to work with and more
friendly to future changes to use memory mapping on the C++ side. To
accomplish this without increasing memory requirements, it has some
custom loading code which avoids loading whole input files into memory
at once.
- Because of the custom loading code, it no longer depends in PyTorch,
which might make installing dependencies slightly easier or faster...
although it still depends on NumPy and sentencepiece, so I don't know
if there's any meaningful difference. In any case, I also added a
requirements.txt file to lock the dependency versions in case of any
future breaking changes.
- Type annotations checked with mypy.
- Some attempts to be extra user-friendly:
- The script tries to be forgiving with arguments, e.g. you can
specify either the model file itself or the directory containing
it.
- The script doesn't depend on config.json / params.json, just in
case the user downloaded files individually and doesn't have those
handy. But you still need tokenizer.model and, for Alpaca,
added_tokens.json.
- The script tries to give a helpful error message if
added_tokens.json is missing.
2023-04-14 07:03:03 +00:00
2024-01-20 23:14:18 +00:00
#
# vocab
#
2024-03-28 15:44:36 +00:00
@runtime_checkable
class BaseVocab ( Protocol ) :
tokenizer_model : ClassVar [ str ]
name : ClassVar [ str ]
class NoVocab ( BaseVocab ) :
tokenizer_model = " no_vocab "
name = " no_vocab "
def __repr__ ( self ) - > str :
return " <NoVocab for a model without integrated vocabulary> "
@runtime_checkable
class Vocab ( BaseVocab , Protocol ) :
vocab_size : int
added_tokens_dict : dict [ str , int ]
added_tokens_list : list [ str ]
fname_tokenizer : Path
def __init__ ( self , base_path : Path ) : . . .
def all_tokens ( self ) - > Iterable [ tuple [ bytes , float , gguf . TokenType ] ] : . . .
class BpeVocab ( Vocab ) :
2024-03-14 16:21:56 +00:00
tokenizer_model = " gpt2 "
name = " bpe "
2024-03-28 15:44:36 +00:00
def __init__ ( self , base_path : Path ) :
added_tokens : dict [ str , int ] = { }
if ( fname_tokenizer := base_path / ' vocab.json ' ) . exists ( ) :
# "slow" tokenizer
with open ( fname_tokenizer , encoding = " utf-8 " ) as f :
self . vocab = json . load ( f )
try :
# FIXME: Verify that added tokens here _cannot_ overlap with the main vocab.
with open ( base_path / ADDED_TOKENS_FILE , encoding = " utf-8 " ) as f :
added_tokens = json . load ( f )
except FileNotFoundError :
pass
convert.py : fix vanilla LLaMA model conversion (#4818)
* Update Imports and Add Notes for Future Reference
- Updated import statements in `convert.py`.
- Added import for `AutoTokenizer` from `transformers` module.
- Added conditional import for `gguf` from the local directory.
- Added comments and notes for future reference.
Additional Notes:
- Noted removal of a redundant `TypeAlias` import.
- Noted the removal of a `gguf` debug statement.
- Commented on the presence of `ARCH` and `NDArray` definitions.
- Commented on cleaning up and refactoring data type definitions.
* Refine Model Hyperparameters and Params Class
- Updated type annotations to use `Optional` for clarity.
- Improved method names and attribute consistency.
- Removed unnecessary variables for better code readability.
Additional Notes:
- Highlighted the use of `Optional` for clearer intent.
- Ensured backward and forward compatibility.
* Restore BpeVocab and SentencePieceVocab classes
- Restored the BpeVocab class for handling BPE tokenization.
- Restored the SentencePieceVocab class for SentencePiece tokenization.
These classes are essential for maintaining the original behavior of the codebase.
* refactor: Standardize vocabulary handling with HfVocab
- Replaced VocabLoader with HfVocab, aligning vocabulary handling across classes.
- Updated initialization of HfVocab with local_files_only=True for AutoTokenizer.
- Introduced optional parameter fname_added_tokens for flexible added token management.
- Streamlined added token handling for clarity and conciseness.
- Maintained special tokens and IDs, enhancing token management.
- Simplified token processing methods for improved readability.
- Added a placeholder for score computation with a default value of -1000.0.
- Optimized newline token check for efficiency.
- Updated __repr__ function for clarity in representation.
- Adjusted type alias Vocab to include BpeVocab, SentencePieceVocab, and HfVocab.
- Removed redundant code related to special token handling, reverse vocabulary mapping, and vocabulary file detection.
This refactoring promotes a standardized and modular approach to vocabulary management, facilitating future integration with a VocabFactory and improving code maintainability and scalability.
* refactor: Enhance readability, functionality, and code quality
- Improved code formatting and readability for better maintainability.
- Refactored LazyUnpickler's CLASSES dictionary for clarity.
- Added print statements and warnings in check_vocab_size for user feedback.
- Removed find_vocab_file_path, as it's superseded by VocabFactory.
- Preparatory changes for upcoming classes: OutputFile and VocabFactory.
- Overall focus on code quality, error handling, and consistency.
These changes reflect a continuous effort to refine the codebase, ensuring it meets best practices and prepares for future enhancements, such as the VocabFactory.
* refactor: Update OutputFile class for enhanced model vocabulary management
- Restructured the constructor for improved readability.
- Updated `add_meta_arch` method for flexible model name determination.
- Introduced `handle_tokenizer_model` for mapping vocab types to supported tokenizer models.
- Streamlined vocabulary extraction with `extract_vocabulary_from_model`.
- Simplified vocabulary metadata addition using `add_meta_vocab`.
- Refactored `add_tensor_info` for clarity and consistency.
- Improved error handling for better user feedback.
These changes signify the development of a versatile and comprehensive `OutputFile` class, enabling efficient management of model conversion output, metadata, vocabulary, and tensor information.
* feat: Introduce VocabFactory for flexible vocabulary management in model conversion
- The VocabFactory class is added to facilitate modular vocabulary handling.
- The constructor initializes a directory path and detects vocabulary-related files.
- The _select_file method provides file paths based on vocabulary type (e.g., BPE, SentencePiece).
- _create_special_vocab generates special vocabularies, accommodating different types.
- The load_vocab method loads vocabularies, handling BPE, SentencePiece, and Hugging Face Fast Tokenizer.
- Error handling and logging enhance debugging and user feedback.
- The modular and flexible design simplifies vocabulary management and supports future extensions.
The VocabFactory class enhances code modularity and maintainability, allowing versatile vocabulary handling in the model conversion process.
* refactor: Improve code organization, argument parsing, and user interface
- Renamed 'default_outfile' to 'default_output_file' for clarity.
- Refactored argument parser setup into 'get_argument_parser' function.
- Introduced descriptive comments for each argument in the parser.
- Added '--vocab-type' argument with choices ["spm", "bpe", "hfft"] for vocabulary processing.
- Improved flag naming consistency: '--outfile' to '--out-file' and '--bigendian' to '--big-endian'.
- Enhanced error handling to prevent overwriting input data in 'default_output_file'.
- Made 'argv' in 'main' an optional parameter for flexibility.
- Introduced dynamic import for 'awq.apply_awq' based on 'args.awq_path' for conditional dependency.
These changes enhance code clarity, organization, and the user interface of the script, aligning it with Python best practices and improving maintainability.
* refactor: Further refine functionality, improve user interaction, and streamline vocabulary handling
- Renamed command-line arguments for clarity and consistency.
- Improved path resolution and import adjustments for robustness.
- Thoughtfully handled 'awq-path' and conditional logic for the weighted model.
- Enhanced model and vocabulary loading with the 'VocabFactory' class for structured and adaptable loading.
- Strengthened error handling and user feedback for a more user-friendly experience.
- Structured output file handling with clear conditions and defaults.
- Streamlined and organized the 'main' function for better logic flow.
- Passed 'sys.argv[1:]' to 'main' for adaptability and testability.
These changes solidify the script's functionality, making it more robust, user-friendly, and adaptable. The use of the 'VocabFactory' class is a notable enhancement in efficient vocabulary handling, reflecting a thoughtful and iterative approach to script development.
* chore: Apply ruff formatting to convert.py
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
* Revert to commit 0614c33
* chore: Apply flake8 formatting rules
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
* refactor: Revise `check_vocab_size` for Enhanced Clarity and Correctness
- Resolved an unreachable branch issue by reorganizing the conditional structure.
- Moved the special case check for `params.n_vocab == -1` to the top for immediate assertion.
- Flattened the conditional logic for improved clarity and predictability of the function's behavior.
These changes enhance the readability and functional correctness of the `check_vocab_size` function without altering its intended functionality.
* py : fix outfile and outtype
* py : suggest hint for missing vocab size
---------
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-01-09 18:46:46 +00:00
else :
2024-03-28 15:44:36 +00:00
# "fast" tokenizer
fname_tokenizer = base_path / FAST_TOKENIZER_FILE
# if this fails, FileNotFoundError propagates to caller
with open ( fname_tokenizer , encoding = " utf-8 " ) as f :
tokenizer_json = json . load ( f )
tokenizer_model : dict [ str , Any ] = tokenizer_json [ ' model ' ]
if (
tokenizer_model [ ' type ' ] != ' BPE ' or tokenizer_model . get ( ' byte_fallback ' , False )
or tokenizer_json [ ' decoder ' ] [ ' type ' ] != ' ByteLevel '
) :
raise FileNotFoundError ( ' Cannot find GPT-2 BPE tokenizer ' )
self . vocab = tokenizer_model [ " vocab " ]
if ( added := tokenizer_json . get ( ' added_tokens ' ) ) is not None :
# Added tokens here can be duplicates of the main vocabulary.
added_tokens = { item [ ' content ' ] : item [ ' id ' ]
for item in added
if item [ ' content ' ] not in self . vocab }
vocab_size = len ( self . vocab )
expected_ids = list ( range ( vocab_size , vocab_size + len ( added_tokens ) ) )
actual_ids = sorted ( added_tokens . values ( ) )
convert.py : fix vanilla LLaMA model conversion (#4818)
* Update Imports and Add Notes for Future Reference
- Updated import statements in `convert.py`.
- Added import for `AutoTokenizer` from `transformers` module.
- Added conditional import for `gguf` from the local directory.
- Added comments and notes for future reference.
Additional Notes:
- Noted removal of a redundant `TypeAlias` import.
- Noted the removal of a `gguf` debug statement.
- Commented on the presence of `ARCH` and `NDArray` definitions.
- Commented on cleaning up and refactoring data type definitions.
* Refine Model Hyperparameters and Params Class
- Updated type annotations to use `Optional` for clarity.
- Improved method names and attribute consistency.
- Removed unnecessary variables for better code readability.
Additional Notes:
- Highlighted the use of `Optional` for clearer intent.
- Ensured backward and forward compatibility.
* Restore BpeVocab and SentencePieceVocab classes
- Restored the BpeVocab class for handling BPE tokenization.
- Restored the SentencePieceVocab class for SentencePiece tokenization.
These classes are essential for maintaining the original behavior of the codebase.
* refactor: Standardize vocabulary handling with HfVocab
- Replaced VocabLoader with HfVocab, aligning vocabulary handling across classes.
- Updated initialization of HfVocab with local_files_only=True for AutoTokenizer.
- Introduced optional parameter fname_added_tokens for flexible added token management.
- Streamlined added token handling for clarity and conciseness.
- Maintained special tokens and IDs, enhancing token management.
- Simplified token processing methods for improved readability.
- Added a placeholder for score computation with a default value of -1000.0.
- Optimized newline token check for efficiency.
- Updated __repr__ function for clarity in representation.
- Adjusted type alias Vocab to include BpeVocab, SentencePieceVocab, and HfVocab.
- Removed redundant code related to special token handling, reverse vocabulary mapping, and vocabulary file detection.
This refactoring promotes a standardized and modular approach to vocabulary management, facilitating future integration with a VocabFactory and improving code maintainability and scalability.
* refactor: Enhance readability, functionality, and code quality
- Improved code formatting and readability for better maintainability.
- Refactored LazyUnpickler's CLASSES dictionary for clarity.
- Added print statements and warnings in check_vocab_size for user feedback.
- Removed find_vocab_file_path, as it's superseded by VocabFactory.
- Preparatory changes for upcoming classes: OutputFile and VocabFactory.
- Overall focus on code quality, error handling, and consistency.
These changes reflect a continuous effort to refine the codebase, ensuring it meets best practices and prepares for future enhancements, such as the VocabFactory.
* refactor: Update OutputFile class for enhanced model vocabulary management
- Restructured the constructor for improved readability.
- Updated `add_meta_arch` method for flexible model name determination.
- Introduced `handle_tokenizer_model` for mapping vocab types to supported tokenizer models.
- Streamlined vocabulary extraction with `extract_vocabulary_from_model`.
- Simplified vocabulary metadata addition using `add_meta_vocab`.
- Refactored `add_tensor_info` for clarity and consistency.
- Improved error handling for better user feedback.
These changes signify the development of a versatile and comprehensive `OutputFile` class, enabling efficient management of model conversion output, metadata, vocabulary, and tensor information.
* feat: Introduce VocabFactory for flexible vocabulary management in model conversion
- The VocabFactory class is added to facilitate modular vocabulary handling.
- The constructor initializes a directory path and detects vocabulary-related files.
- The _select_file method provides file paths based on vocabulary type (e.g., BPE, SentencePiece).
- _create_special_vocab generates special vocabularies, accommodating different types.
- The load_vocab method loads vocabularies, handling BPE, SentencePiece, and Hugging Face Fast Tokenizer.
- Error handling and logging enhance debugging and user feedback.
- The modular and flexible design simplifies vocabulary management and supports future extensions.
The VocabFactory class enhances code modularity and maintainability, allowing versatile vocabulary handling in the model conversion process.
* refactor: Improve code organization, argument parsing, and user interface
- Renamed 'default_outfile' to 'default_output_file' for clarity.
- Refactored argument parser setup into 'get_argument_parser' function.
- Introduced descriptive comments for each argument in the parser.
- Added '--vocab-type' argument with choices ["spm", "bpe", "hfft"] for vocabulary processing.
- Improved flag naming consistency: '--outfile' to '--out-file' and '--bigendian' to '--big-endian'.
- Enhanced error handling to prevent overwriting input data in 'default_output_file'.
- Made 'argv' in 'main' an optional parameter for flexibility.
- Introduced dynamic import for 'awq.apply_awq' based on 'args.awq_path' for conditional dependency.
These changes enhance code clarity, organization, and the user interface of the script, aligning it with Python best practices and improving maintainability.
* refactor: Further refine functionality, improve user interaction, and streamline vocabulary handling
- Renamed command-line arguments for clarity and consistency.
- Improved path resolution and import adjustments for robustness.
- Thoughtfully handled 'awq-path' and conditional logic for the weighted model.
- Enhanced model and vocabulary loading with the 'VocabFactory' class for structured and adaptable loading.
- Strengthened error handling and user feedback for a more user-friendly experience.
- Structured output file handling with clear conditions and defaults.
- Streamlined and organized the 'main' function for better logic flow.
- Passed 'sys.argv[1:]' to 'main' for adaptability and testability.
These changes solidify the script's functionality, making it more robust, user-friendly, and adaptable. The use of the 'VocabFactory' class is a notable enhancement in efficient vocabulary handling, reflecting a thoughtful and iterative approach to script development.
* chore: Apply ruff formatting to convert.py
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
* Revert to commit 0614c33
* chore: Apply flake8 formatting rules
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
* refactor: Revise `check_vocab_size` for Enhanced Clarity and Correctness
- Resolved an unreachable branch issue by reorganizing the conditional structure.
- Moved the special case check for `params.n_vocab == -1` to the top for immediate assertion.
- Flattened the conditional logic for improved clarity and predictability of the function's behavior.
These changes enhance the readability and functional correctness of the `check_vocab_size` function without altering its intended functionality.
* py : fix outfile and outtype
* py : suggest hint for missing vocab size
---------
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-01-09 18:46:46 +00:00
if expected_ids != actual_ids :
expected_end_id = vocab_size + len ( actual_ids ) - 1
2024-03-28 15:44:36 +00:00
raise ValueError ( f " Expected the { len ( actual_ids ) } added token ID(s) to be sequential in the range "
f " { vocab_size } - { expected_end_id } ; got { actual_ids } " )
convert.py : fix vanilla LLaMA model conversion (#4818)
* Update Imports and Add Notes for Future Reference
- Updated import statements in `convert.py`.
- Added import for `AutoTokenizer` from `transformers` module.
- Added conditional import for `gguf` from the local directory.
- Added comments and notes for future reference.
Additional Notes:
- Noted removal of a redundant `TypeAlias` import.
- Noted the removal of a `gguf` debug statement.
- Commented on the presence of `ARCH` and `NDArray` definitions.
- Commented on cleaning up and refactoring data type definitions.
* Refine Model Hyperparameters and Params Class
- Updated type annotations to use `Optional` for clarity.
- Improved method names and attribute consistency.
- Removed unnecessary variables for better code readability.
Additional Notes:
- Highlighted the use of `Optional` for clearer intent.
- Ensured backward and forward compatibility.
* Restore BpeVocab and SentencePieceVocab classes
- Restored the BpeVocab class for handling BPE tokenization.
- Restored the SentencePieceVocab class for SentencePiece tokenization.
These classes are essential for maintaining the original behavior of the codebase.
* refactor: Standardize vocabulary handling with HfVocab
- Replaced VocabLoader with HfVocab, aligning vocabulary handling across classes.
- Updated initialization of HfVocab with local_files_only=True for AutoTokenizer.
- Introduced optional parameter fname_added_tokens for flexible added token management.
- Streamlined added token handling for clarity and conciseness.
- Maintained special tokens and IDs, enhancing token management.
- Simplified token processing methods for improved readability.
- Added a placeholder for score computation with a default value of -1000.0.
- Optimized newline token check for efficiency.
- Updated __repr__ function for clarity in representation.
- Adjusted type alias Vocab to include BpeVocab, SentencePieceVocab, and HfVocab.
- Removed redundant code related to special token handling, reverse vocabulary mapping, and vocabulary file detection.
This refactoring promotes a standardized and modular approach to vocabulary management, facilitating future integration with a VocabFactory and improving code maintainability and scalability.
* refactor: Enhance readability, functionality, and code quality
- Improved code formatting and readability for better maintainability.
- Refactored LazyUnpickler's CLASSES dictionary for clarity.
- Added print statements and warnings in check_vocab_size for user feedback.
- Removed find_vocab_file_path, as it's superseded by VocabFactory.
- Preparatory changes for upcoming classes: OutputFile and VocabFactory.
- Overall focus on code quality, error handling, and consistency.
These changes reflect a continuous effort to refine the codebase, ensuring it meets best practices and prepares for future enhancements, such as the VocabFactory.
* refactor: Update OutputFile class for enhanced model vocabulary management
- Restructured the constructor for improved readability.
- Updated `add_meta_arch` method for flexible model name determination.
- Introduced `handle_tokenizer_model` for mapping vocab types to supported tokenizer models.
- Streamlined vocabulary extraction with `extract_vocabulary_from_model`.
- Simplified vocabulary metadata addition using `add_meta_vocab`.
- Refactored `add_tensor_info` for clarity and consistency.
- Improved error handling for better user feedback.
These changes signify the development of a versatile and comprehensive `OutputFile` class, enabling efficient management of model conversion output, metadata, vocabulary, and tensor information.
* feat: Introduce VocabFactory for flexible vocabulary management in model conversion
- The VocabFactory class is added to facilitate modular vocabulary handling.
- The constructor initializes a directory path and detects vocabulary-related files.
- The _select_file method provides file paths based on vocabulary type (e.g., BPE, SentencePiece).
- _create_special_vocab generates special vocabularies, accommodating different types.
- The load_vocab method loads vocabularies, handling BPE, SentencePiece, and Hugging Face Fast Tokenizer.
- Error handling and logging enhance debugging and user feedback.
- The modular and flexible design simplifies vocabulary management and supports future extensions.
The VocabFactory class enhances code modularity and maintainability, allowing versatile vocabulary handling in the model conversion process.
* refactor: Improve code organization, argument parsing, and user interface
- Renamed 'default_outfile' to 'default_output_file' for clarity.
- Refactored argument parser setup into 'get_argument_parser' function.
- Introduced descriptive comments for each argument in the parser.
- Added '--vocab-type' argument with choices ["spm", "bpe", "hfft"] for vocabulary processing.
- Improved flag naming consistency: '--outfile' to '--out-file' and '--bigendian' to '--big-endian'.
- Enhanced error handling to prevent overwriting input data in 'default_output_file'.
- Made 'argv' in 'main' an optional parameter for flexibility.
- Introduced dynamic import for 'awq.apply_awq' based on 'args.awq_path' for conditional dependency.
These changes enhance code clarity, organization, and the user interface of the script, aligning it with Python best practices and improving maintainability.
* refactor: Further refine functionality, improve user interaction, and streamline vocabulary handling
- Renamed command-line arguments for clarity and consistency.
- Improved path resolution and import adjustments for robustness.
- Thoughtfully handled 'awq-path' and conditional logic for the weighted model.
- Enhanced model and vocabulary loading with the 'VocabFactory' class for structured and adaptable loading.
- Strengthened error handling and user feedback for a more user-friendly experience.
- Structured output file handling with clear conditions and defaults.
- Streamlined and organized the 'main' function for better logic flow.
- Passed 'sys.argv[1:]' to 'main' for adaptability and testability.
These changes solidify the script's functionality, making it more robust, user-friendly, and adaptable. The use of the 'VocabFactory' class is a notable enhancement in efficient vocabulary handling, reflecting a thoughtful and iterative approach to script development.
* chore: Apply ruff formatting to convert.py
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
* Revert to commit 0614c33
* chore: Apply flake8 formatting rules
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
* refactor: Revise `check_vocab_size` for Enhanced Clarity and Correctness
- Resolved an unreachable branch issue by reorganizing the conditional structure.
- Moved the special case check for `params.n_vocab == -1` to the top for immediate assertion.
- Flattened the conditional logic for improved clarity and predictability of the function's behavior.
These changes enhance the readability and functional correctness of the `check_vocab_size` function without altering its intended functionality.
* py : fix outfile and outtype
* py : suggest hint for missing vocab size
---------
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-01-09 18:46:46 +00:00
items = sorted ( added_tokens . items ( ) , key = lambda text_idx : text_idx [ 1 ] )
2024-01-20 23:14:18 +00:00
self . added_tokens_dict = added_tokens
self . added_tokens_list = [ text for ( text , idx ) in items ]
2024-03-28 15:44:36 +00:00
self . vocab_size_base = vocab_size
self . vocab_size = self . vocab_size_base + len ( self . added_tokens_list )
2024-01-20 23:14:18 +00:00
self . fname_tokenizer = fname_tokenizer
convert.py : fix vanilla LLaMA model conversion (#4818)
* Update Imports and Add Notes for Future Reference
- Updated import statements in `convert.py`.
- Added import for `AutoTokenizer` from `transformers` module.
- Added conditional import for `gguf` from the local directory.
- Added comments and notes for future reference.
Additional Notes:
- Noted removal of a redundant `TypeAlias` import.
- Noted the removal of a `gguf` debug statement.
- Commented on the presence of `ARCH` and `NDArray` definitions.
- Commented on cleaning up and refactoring data type definitions.
* Refine Model Hyperparameters and Params Class
- Updated type annotations to use `Optional` for clarity.
- Improved method names and attribute consistency.
- Removed unnecessary variables for better code readability.
Additional Notes:
- Highlighted the use of `Optional` for clearer intent.
- Ensured backward and forward compatibility.
* Restore BpeVocab and SentencePieceVocab classes
- Restored the BpeVocab class for handling BPE tokenization.
- Restored the SentencePieceVocab class for SentencePiece tokenization.
These classes are essential for maintaining the original behavior of the codebase.
* refactor: Standardize vocabulary handling with HfVocab
- Replaced VocabLoader with HfVocab, aligning vocabulary handling across classes.
- Updated initialization of HfVocab with local_files_only=True for AutoTokenizer.
- Introduced optional parameter fname_added_tokens for flexible added token management.
- Streamlined added token handling for clarity and conciseness.
- Maintained special tokens and IDs, enhancing token management.
- Simplified token processing methods for improved readability.
- Added a placeholder for score computation with a default value of -1000.0.
- Optimized newline token check for efficiency.
- Updated __repr__ function for clarity in representation.
- Adjusted type alias Vocab to include BpeVocab, SentencePieceVocab, and HfVocab.
- Removed redundant code related to special token handling, reverse vocabulary mapping, and vocabulary file detection.
This refactoring promotes a standardized and modular approach to vocabulary management, facilitating future integration with a VocabFactory and improving code maintainability and scalability.
* refactor: Enhance readability, functionality, and code quality
- Improved code formatting and readability for better maintainability.
- Refactored LazyUnpickler's CLASSES dictionary for clarity.
- Added print statements and warnings in check_vocab_size for user feedback.
- Removed find_vocab_file_path, as it's superseded by VocabFactory.
- Preparatory changes for upcoming classes: OutputFile and VocabFactory.
- Overall focus on code quality, error handling, and consistency.
These changes reflect a continuous effort to refine the codebase, ensuring it meets best practices and prepares for future enhancements, such as the VocabFactory.
* refactor: Update OutputFile class for enhanced model vocabulary management
- Restructured the constructor for improved readability.
- Updated `add_meta_arch` method for flexible model name determination.
- Introduced `handle_tokenizer_model` for mapping vocab types to supported tokenizer models.
- Streamlined vocabulary extraction with `extract_vocabulary_from_model`.
- Simplified vocabulary metadata addition using `add_meta_vocab`.
- Refactored `add_tensor_info` for clarity and consistency.
- Improved error handling for better user feedback.
These changes signify the development of a versatile and comprehensive `OutputFile` class, enabling efficient management of model conversion output, metadata, vocabulary, and tensor information.
* feat: Introduce VocabFactory for flexible vocabulary management in model conversion
- The VocabFactory class is added to facilitate modular vocabulary handling.
- The constructor initializes a directory path and detects vocabulary-related files.
- The _select_file method provides file paths based on vocabulary type (e.g., BPE, SentencePiece).
- _create_special_vocab generates special vocabularies, accommodating different types.
- The load_vocab method loads vocabularies, handling BPE, SentencePiece, and Hugging Face Fast Tokenizer.
- Error handling and logging enhance debugging and user feedback.
- The modular and flexible design simplifies vocabulary management and supports future extensions.
The VocabFactory class enhances code modularity and maintainability, allowing versatile vocabulary handling in the model conversion process.
* refactor: Improve code organization, argument parsing, and user interface
- Renamed 'default_outfile' to 'default_output_file' for clarity.
- Refactored argument parser setup into 'get_argument_parser' function.
- Introduced descriptive comments for each argument in the parser.
- Added '--vocab-type' argument with choices ["spm", "bpe", "hfft"] for vocabulary processing.
- Improved flag naming consistency: '--outfile' to '--out-file' and '--bigendian' to '--big-endian'.
- Enhanced error handling to prevent overwriting input data in 'default_output_file'.
- Made 'argv' in 'main' an optional parameter for flexibility.
- Introduced dynamic import for 'awq.apply_awq' based on 'args.awq_path' for conditional dependency.
These changes enhance code clarity, organization, and the user interface of the script, aligning it with Python best practices and improving maintainability.
* refactor: Further refine functionality, improve user interaction, and streamline vocabulary handling
- Renamed command-line arguments for clarity and consistency.
- Improved path resolution and import adjustments for robustness.
- Thoughtfully handled 'awq-path' and conditional logic for the weighted model.
- Enhanced model and vocabulary loading with the 'VocabFactory' class for structured and adaptable loading.
- Strengthened error handling and user feedback for a more user-friendly experience.
- Structured output file handling with clear conditions and defaults.
- Streamlined and organized the 'main' function for better logic flow.
- Passed 'sys.argv[1:]' to 'main' for adaptability and testability.
These changes solidify the script's functionality, making it more robust, user-friendly, and adaptable. The use of the 'VocabFactory' class is a notable enhancement in efficient vocabulary handling, reflecting a thoughtful and iterative approach to script development.
* chore: Apply ruff formatting to convert.py
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
* Revert to commit 0614c33
* chore: Apply flake8 formatting rules
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
* refactor: Revise `check_vocab_size` for Enhanced Clarity and Correctness
- Resolved an unreachable branch issue by reorganizing the conditional structure.
- Moved the special case check for `params.n_vocab == -1` to the top for immediate assertion.
- Flattened the conditional logic for improved clarity and predictability of the function's behavior.
These changes enhance the readability and functional correctness of the `check_vocab_size` function without altering its intended functionality.
* py : fix outfile and outtype
* py : suggest hint for missing vocab size
---------
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-01-09 18:46:46 +00:00
def bpe_tokens ( self ) - > Iterable [ tuple [ bytes , float , gguf . TokenType ] ] :
2024-01-17 13:45:03 +00:00
reverse_vocab = { id : encoded_tok for encoded_tok , id in self . vocab . items ( ) }
convert.py : fix vanilla LLaMA model conversion (#4818)
* Update Imports and Add Notes for Future Reference
- Updated import statements in `convert.py`.
- Added import for `AutoTokenizer` from `transformers` module.
- Added conditional import for `gguf` from the local directory.
- Added comments and notes for future reference.
Additional Notes:
- Noted removal of a redundant `TypeAlias` import.
- Noted the removal of a `gguf` debug statement.
- Commented on the presence of `ARCH` and `NDArray` definitions.
- Commented on cleaning up and refactoring data type definitions.
* Refine Model Hyperparameters and Params Class
- Updated type annotations to use `Optional` for clarity.
- Improved method names and attribute consistency.
- Removed unnecessary variables for better code readability.
Additional Notes:
- Highlighted the use of `Optional` for clearer intent.
- Ensured backward and forward compatibility.
* Restore BpeVocab and SentencePieceVocab classes
- Restored the BpeVocab class for handling BPE tokenization.
- Restored the SentencePieceVocab class for SentencePiece tokenization.
These classes are essential for maintaining the original behavior of the codebase.
* refactor: Standardize vocabulary handling with HfVocab
- Replaced VocabLoader with HfVocab, aligning vocabulary handling across classes.
- Updated initialization of HfVocab with local_files_only=True for AutoTokenizer.
- Introduced optional parameter fname_added_tokens for flexible added token management.
- Streamlined added token handling for clarity and conciseness.
- Maintained special tokens and IDs, enhancing token management.
- Simplified token processing methods for improved readability.
- Added a placeholder for score computation with a default value of -1000.0.
- Optimized newline token check for efficiency.
- Updated __repr__ function for clarity in representation.
- Adjusted type alias Vocab to include BpeVocab, SentencePieceVocab, and HfVocab.
- Removed redundant code related to special token handling, reverse vocabulary mapping, and vocabulary file detection.
This refactoring promotes a standardized and modular approach to vocabulary management, facilitating future integration with a VocabFactory and improving code maintainability and scalability.
* refactor: Enhance readability, functionality, and code quality
- Improved code formatting and readability for better maintainability.
- Refactored LazyUnpickler's CLASSES dictionary for clarity.
- Added print statements and warnings in check_vocab_size for user feedback.
- Removed find_vocab_file_path, as it's superseded by VocabFactory.
- Preparatory changes for upcoming classes: OutputFile and VocabFactory.
- Overall focus on code quality, error handling, and consistency.
These changes reflect a continuous effort to refine the codebase, ensuring it meets best practices and prepares for future enhancements, such as the VocabFactory.
* refactor: Update OutputFile class for enhanced model vocabulary management
- Restructured the constructor for improved readability.
- Updated `add_meta_arch` method for flexible model name determination.
- Introduced `handle_tokenizer_model` for mapping vocab types to supported tokenizer models.
- Streamlined vocabulary extraction with `extract_vocabulary_from_model`.
- Simplified vocabulary metadata addition using `add_meta_vocab`.
- Refactored `add_tensor_info` for clarity and consistency.
- Improved error handling for better user feedback.
These changes signify the development of a versatile and comprehensive `OutputFile` class, enabling efficient management of model conversion output, metadata, vocabulary, and tensor information.
* feat: Introduce VocabFactory for flexible vocabulary management in model conversion
- The VocabFactory class is added to facilitate modular vocabulary handling.
- The constructor initializes a directory path and detects vocabulary-related files.
- The _select_file method provides file paths based on vocabulary type (e.g., BPE, SentencePiece).
- _create_special_vocab generates special vocabularies, accommodating different types.
- The load_vocab method loads vocabularies, handling BPE, SentencePiece, and Hugging Face Fast Tokenizer.
- Error handling and logging enhance debugging and user feedback.
- The modular and flexible design simplifies vocabulary management and supports future extensions.
The VocabFactory class enhances code modularity and maintainability, allowing versatile vocabulary handling in the model conversion process.
* refactor: Improve code organization, argument parsing, and user interface
- Renamed 'default_outfile' to 'default_output_file' for clarity.
- Refactored argument parser setup into 'get_argument_parser' function.
- Introduced descriptive comments for each argument in the parser.
- Added '--vocab-type' argument with choices ["spm", "bpe", "hfft"] for vocabulary processing.
- Improved flag naming consistency: '--outfile' to '--out-file' and '--bigendian' to '--big-endian'.
- Enhanced error handling to prevent overwriting input data in 'default_output_file'.
- Made 'argv' in 'main' an optional parameter for flexibility.
- Introduced dynamic import for 'awq.apply_awq' based on 'args.awq_path' for conditional dependency.
These changes enhance code clarity, organization, and the user interface of the script, aligning it with Python best practices and improving maintainability.
* refactor: Further refine functionality, improve user interaction, and streamline vocabulary handling
- Renamed command-line arguments for clarity and consistency.
- Improved path resolution and import adjustments for robustness.
- Thoughtfully handled 'awq-path' and conditional logic for the weighted model.
- Enhanced model and vocabulary loading with the 'VocabFactory' class for structured and adaptable loading.
- Strengthened error handling and user feedback for a more user-friendly experience.
- Structured output file handling with clear conditions and defaults.
- Streamlined and organized the 'main' function for better logic flow.
- Passed 'sys.argv[1:]' to 'main' for adaptability and testability.
These changes solidify the script's functionality, making it more robust, user-friendly, and adaptable. The use of the 'VocabFactory' class is a notable enhancement in efficient vocabulary handling, reflecting a thoughtful and iterative approach to script development.
* chore: Apply ruff formatting to convert.py
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
* Revert to commit 0614c33
* chore: Apply flake8 formatting rules
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
* refactor: Revise `check_vocab_size` for Enhanced Clarity and Correctness
- Resolved an unreachable branch issue by reorganizing the conditional structure.
- Moved the special case check for `params.n_vocab == -1` to the top for immediate assertion.
- Flattened the conditional logic for improved clarity and predictability of the function's behavior.
These changes enhance the readability and functional correctness of the `check_vocab_size` function without altering its intended functionality.
* py : fix outfile and outtype
* py : suggest hint for missing vocab size
---------
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-01-09 18:46:46 +00:00
2024-01-17 13:45:03 +00:00
for i , _ in enumerate ( self . vocab ) :
convert.py : fix vanilla LLaMA model conversion (#4818)
* Update Imports and Add Notes for Future Reference
- Updated import statements in `convert.py`.
- Added import for `AutoTokenizer` from `transformers` module.
- Added conditional import for `gguf` from the local directory.
- Added comments and notes for future reference.
Additional Notes:
- Noted removal of a redundant `TypeAlias` import.
- Noted the removal of a `gguf` debug statement.
- Commented on the presence of `ARCH` and `NDArray` definitions.
- Commented on cleaning up and refactoring data type definitions.
* Refine Model Hyperparameters and Params Class
- Updated type annotations to use `Optional` for clarity.
- Improved method names and attribute consistency.
- Removed unnecessary variables for better code readability.
Additional Notes:
- Highlighted the use of `Optional` for clearer intent.
- Ensured backward and forward compatibility.
* Restore BpeVocab and SentencePieceVocab classes
- Restored the BpeVocab class for handling BPE tokenization.
- Restored the SentencePieceVocab class for SentencePiece tokenization.
These classes are essential for maintaining the original behavior of the codebase.
* refactor: Standardize vocabulary handling with HfVocab
- Replaced VocabLoader with HfVocab, aligning vocabulary handling across classes.
- Updated initialization of HfVocab with local_files_only=True for AutoTokenizer.
- Introduced optional parameter fname_added_tokens for flexible added token management.
- Streamlined added token handling for clarity and conciseness.
- Maintained special tokens and IDs, enhancing token management.
- Simplified token processing methods for improved readability.
- Added a placeholder for score computation with a default value of -1000.0.
- Optimized newline token check for efficiency.
- Updated __repr__ function for clarity in representation.
- Adjusted type alias Vocab to include BpeVocab, SentencePieceVocab, and HfVocab.
- Removed redundant code related to special token handling, reverse vocabulary mapping, and vocabulary file detection.
This refactoring promotes a standardized and modular approach to vocabulary management, facilitating future integration with a VocabFactory and improving code maintainability and scalability.
* refactor: Enhance readability, functionality, and code quality
- Improved code formatting and readability for better maintainability.
- Refactored LazyUnpickler's CLASSES dictionary for clarity.
- Added print statements and warnings in check_vocab_size for user feedback.
- Removed find_vocab_file_path, as it's superseded by VocabFactory.
- Preparatory changes for upcoming classes: OutputFile and VocabFactory.
- Overall focus on code quality, error handling, and consistency.
These changes reflect a continuous effort to refine the codebase, ensuring it meets best practices and prepares for future enhancements, such as the VocabFactory.
* refactor: Update OutputFile class for enhanced model vocabulary management
- Restructured the constructor for improved readability.
- Updated `add_meta_arch` method for flexible model name determination.
- Introduced `handle_tokenizer_model` for mapping vocab types to supported tokenizer models.
- Streamlined vocabulary extraction with `extract_vocabulary_from_model`.
- Simplified vocabulary metadata addition using `add_meta_vocab`.
- Refactored `add_tensor_info` for clarity and consistency.
- Improved error handling for better user feedback.
These changes signify the development of a versatile and comprehensive `OutputFile` class, enabling efficient management of model conversion output, metadata, vocabulary, and tensor information.
* feat: Introduce VocabFactory for flexible vocabulary management in model conversion
- The VocabFactory class is added to facilitate modular vocabulary handling.
- The constructor initializes a directory path and detects vocabulary-related files.
- The _select_file method provides file paths based on vocabulary type (e.g., BPE, SentencePiece).
- _create_special_vocab generates special vocabularies, accommodating different types.
- The load_vocab method loads vocabularies, handling BPE, SentencePiece, and Hugging Face Fast Tokenizer.
- Error handling and logging enhance debugging and user feedback.
- The modular and flexible design simplifies vocabulary management and supports future extensions.
The VocabFactory class enhances code modularity and maintainability, allowing versatile vocabulary handling in the model conversion process.
* refactor: Improve code organization, argument parsing, and user interface
- Renamed 'default_outfile' to 'default_output_file' for clarity.
- Refactored argument parser setup into 'get_argument_parser' function.
- Introduced descriptive comments for each argument in the parser.
- Added '--vocab-type' argument with choices ["spm", "bpe", "hfft"] for vocabulary processing.
- Improved flag naming consistency: '--outfile' to '--out-file' and '--bigendian' to '--big-endian'.
- Enhanced error handling to prevent overwriting input data in 'default_output_file'.
- Made 'argv' in 'main' an optional parameter for flexibility.
- Introduced dynamic import for 'awq.apply_awq' based on 'args.awq_path' for conditional dependency.
These changes enhance code clarity, organization, and the user interface of the script, aligning it with Python best practices and improving maintainability.
* refactor: Further refine functionality, improve user interaction, and streamline vocabulary handling
- Renamed command-line arguments for clarity and consistency.
- Improved path resolution and import adjustments for robustness.
- Thoughtfully handled 'awq-path' and conditional logic for the weighted model.
- Enhanced model and vocabulary loading with the 'VocabFactory' class for structured and adaptable loading.
- Strengthened error handling and user feedback for a more user-friendly experience.
- Structured output file handling with clear conditions and defaults.
- Streamlined and organized the 'main' function for better logic flow.
- Passed 'sys.argv[1:]' to 'main' for adaptability and testability.
These changes solidify the script's functionality, making it more robust, user-friendly, and adaptable. The use of the 'VocabFactory' class is a notable enhancement in efficient vocabulary handling, reflecting a thoughtful and iterative approach to script development.
* chore: Apply ruff formatting to convert.py
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
* Revert to commit 0614c33
* chore: Apply flake8 formatting rules
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
* refactor: Revise `check_vocab_size` for Enhanced Clarity and Correctness
- Resolved an unreachable branch issue by reorganizing the conditional structure.
- Moved the special case check for `params.n_vocab == -1` to the top for immediate assertion.
- Flattened the conditional logic for improved clarity and predictability of the function's behavior.
These changes enhance the readability and functional correctness of the `check_vocab_size` function without altering its intended functionality.
* py : fix outfile and outtype
* py : suggest hint for missing vocab size
---------
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-01-09 18:46:46 +00:00
yield reverse_vocab [ i ] , 0.0 , gguf . TokenType . NORMAL
2023-08-21 20:07:43 +00:00
convert.py : fix vanilla LLaMA model conversion (#4818)
* Update Imports and Add Notes for Future Reference
- Updated import statements in `convert.py`.
- Added import for `AutoTokenizer` from `transformers` module.
- Added conditional import for `gguf` from the local directory.
- Added comments and notes for future reference.
Additional Notes:
- Noted removal of a redundant `TypeAlias` import.
- Noted the removal of a `gguf` debug statement.
- Commented on the presence of `ARCH` and `NDArray` definitions.
- Commented on cleaning up and refactoring data type definitions.
* Refine Model Hyperparameters and Params Class
- Updated type annotations to use `Optional` for clarity.
- Improved method names and attribute consistency.
- Removed unnecessary variables for better code readability.
Additional Notes:
- Highlighted the use of `Optional` for clearer intent.
- Ensured backward and forward compatibility.
* Restore BpeVocab and SentencePieceVocab classes
- Restored the BpeVocab class for handling BPE tokenization.
- Restored the SentencePieceVocab class for SentencePiece tokenization.
These classes are essential for maintaining the original behavior of the codebase.
* refactor: Standardize vocabulary handling with HfVocab
- Replaced VocabLoader with HfVocab, aligning vocabulary handling across classes.
- Updated initialization of HfVocab with local_files_only=True for AutoTokenizer.
- Introduced optional parameter fname_added_tokens for flexible added token management.
- Streamlined added token handling for clarity and conciseness.
- Maintained special tokens and IDs, enhancing token management.
- Simplified token processing methods for improved readability.
- Added a placeholder for score computation with a default value of -1000.0.
- Optimized newline token check for efficiency.
- Updated __repr__ function for clarity in representation.
- Adjusted type alias Vocab to include BpeVocab, SentencePieceVocab, and HfVocab.
- Removed redundant code related to special token handling, reverse vocabulary mapping, and vocabulary file detection.
This refactoring promotes a standardized and modular approach to vocabulary management, facilitating future integration with a VocabFactory and improving code maintainability and scalability.
* refactor: Enhance readability, functionality, and code quality
- Improved code formatting and readability for better maintainability.
- Refactored LazyUnpickler's CLASSES dictionary for clarity.
- Added print statements and warnings in check_vocab_size for user feedback.
- Removed find_vocab_file_path, as it's superseded by VocabFactory.
- Preparatory changes for upcoming classes: OutputFile and VocabFactory.
- Overall focus on code quality, error handling, and consistency.
These changes reflect a continuous effort to refine the codebase, ensuring it meets best practices and prepares for future enhancements, such as the VocabFactory.
* refactor: Update OutputFile class for enhanced model vocabulary management
- Restructured the constructor for improved readability.
- Updated `add_meta_arch` method for flexible model name determination.
- Introduced `handle_tokenizer_model` for mapping vocab types to supported tokenizer models.
- Streamlined vocabulary extraction with `extract_vocabulary_from_model`.
- Simplified vocabulary metadata addition using `add_meta_vocab`.
- Refactored `add_tensor_info` for clarity and consistency.
- Improved error handling for better user feedback.
These changes signify the development of a versatile and comprehensive `OutputFile` class, enabling efficient management of model conversion output, metadata, vocabulary, and tensor information.
* feat: Introduce VocabFactory for flexible vocabulary management in model conversion
- The VocabFactory class is added to facilitate modular vocabulary handling.
- The constructor initializes a directory path and detects vocabulary-related files.
- The _select_file method provides file paths based on vocabulary type (e.g., BPE, SentencePiece).
- _create_special_vocab generates special vocabularies, accommodating different types.
- The load_vocab method loads vocabularies, handling BPE, SentencePiece, and Hugging Face Fast Tokenizer.
- Error handling and logging enhance debugging and user feedback.
- The modular and flexible design simplifies vocabulary management and supports future extensions.
The VocabFactory class enhances code modularity and maintainability, allowing versatile vocabulary handling in the model conversion process.
* refactor: Improve code organization, argument parsing, and user interface
- Renamed 'default_outfile' to 'default_output_file' for clarity.
- Refactored argument parser setup into 'get_argument_parser' function.
- Introduced descriptive comments for each argument in the parser.
- Added '--vocab-type' argument with choices ["spm", "bpe", "hfft"] for vocabulary processing.
- Improved flag naming consistency: '--outfile' to '--out-file' and '--bigendian' to '--big-endian'.
- Enhanced error handling to prevent overwriting input data in 'default_output_file'.
- Made 'argv' in 'main' an optional parameter for flexibility.
- Introduced dynamic import for 'awq.apply_awq' based on 'args.awq_path' for conditional dependency.
These changes enhance code clarity, organization, and the user interface of the script, aligning it with Python best practices and improving maintainability.
* refactor: Further refine functionality, improve user interaction, and streamline vocabulary handling
- Renamed command-line arguments for clarity and consistency.
- Improved path resolution and import adjustments for robustness.
- Thoughtfully handled 'awq-path' and conditional logic for the weighted model.
- Enhanced model and vocabulary loading with the 'VocabFactory' class for structured and adaptable loading.
- Strengthened error handling and user feedback for a more user-friendly experience.
- Structured output file handling with clear conditions and defaults.
- Streamlined and organized the 'main' function for better logic flow.
- Passed 'sys.argv[1:]' to 'main' for adaptability and testability.
These changes solidify the script's functionality, making it more robust, user-friendly, and adaptable. The use of the 'VocabFactory' class is a notable enhancement in efficient vocabulary handling, reflecting a thoughtful and iterative approach to script development.
* chore: Apply ruff formatting to convert.py
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
* Revert to commit 0614c33
* chore: Apply flake8 formatting rules
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
* refactor: Revise `check_vocab_size` for Enhanced Clarity and Correctness
- Resolved an unreachable branch issue by reorganizing the conditional structure.
- Moved the special case check for `params.n_vocab == -1` to the top for immediate assertion.
- Flattened the conditional logic for improved clarity and predictability of the function's behavior.
These changes enhance the readability and functional correctness of the `check_vocab_size` function without altering its intended functionality.
* py : fix outfile and outtype
* py : suggest hint for missing vocab size
---------
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-01-09 18:46:46 +00:00
def added_tokens ( self ) - > Iterable [ tuple [ bytes , float , gguf . TokenType ] ] :
for text in self . added_tokens_list :
score = - 1000.0
yield text . encode ( " utf-8 " ) , score , gguf . TokenType . CONTROL
def all_tokens ( self ) - > Iterable [ tuple [ bytes , float , gguf . TokenType ] ] :
yield from self . bpe_tokens ( )
yield from self . added_tokens ( )
2023-08-21 20:07:43 +00:00
convert.py : fix vanilla LLaMA model conversion (#4818)
* Update Imports and Add Notes for Future Reference
- Updated import statements in `convert.py`.
- Added import for `AutoTokenizer` from `transformers` module.
- Added conditional import for `gguf` from the local directory.
- Added comments and notes for future reference.
Additional Notes:
- Noted removal of a redundant `TypeAlias` import.
- Noted the removal of a `gguf` debug statement.
- Commented on the presence of `ARCH` and `NDArray` definitions.
- Commented on cleaning up and refactoring data type definitions.
* Refine Model Hyperparameters and Params Class
- Updated type annotations to use `Optional` for clarity.
- Improved method names and attribute consistency.
- Removed unnecessary variables for better code readability.
Additional Notes:
- Highlighted the use of `Optional` for clearer intent.
- Ensured backward and forward compatibility.
* Restore BpeVocab and SentencePieceVocab classes
- Restored the BpeVocab class for handling BPE tokenization.
- Restored the SentencePieceVocab class for SentencePiece tokenization.
These classes are essential for maintaining the original behavior of the codebase.
* refactor: Standardize vocabulary handling with HfVocab
- Replaced VocabLoader with HfVocab, aligning vocabulary handling across classes.
- Updated initialization of HfVocab with local_files_only=True for AutoTokenizer.
- Introduced optional parameter fname_added_tokens for flexible added token management.
- Streamlined added token handling for clarity and conciseness.
- Maintained special tokens and IDs, enhancing token management.
- Simplified token processing methods for improved readability.
- Added a placeholder for score computation with a default value of -1000.0.
- Optimized newline token check for efficiency.
- Updated __repr__ function for clarity in representation.
- Adjusted type alias Vocab to include BpeVocab, SentencePieceVocab, and HfVocab.
- Removed redundant code related to special token handling, reverse vocabulary mapping, and vocabulary file detection.
This refactoring promotes a standardized and modular approach to vocabulary management, facilitating future integration with a VocabFactory and improving code maintainability and scalability.
* refactor: Enhance readability, functionality, and code quality
- Improved code formatting and readability for better maintainability.
- Refactored LazyUnpickler's CLASSES dictionary for clarity.
- Added print statements and warnings in check_vocab_size for user feedback.
- Removed find_vocab_file_path, as it's superseded by VocabFactory.
- Preparatory changes for upcoming classes: OutputFile and VocabFactory.
- Overall focus on code quality, error handling, and consistency.
These changes reflect a continuous effort to refine the codebase, ensuring it meets best practices and prepares for future enhancements, such as the VocabFactory.
* refactor: Update OutputFile class for enhanced model vocabulary management
- Restructured the constructor for improved readability.
- Updated `add_meta_arch` method for flexible model name determination.
- Introduced `handle_tokenizer_model` for mapping vocab types to supported tokenizer models.
- Streamlined vocabulary extraction with `extract_vocabulary_from_model`.
- Simplified vocabulary metadata addition using `add_meta_vocab`.
- Refactored `add_tensor_info` for clarity and consistency.
- Improved error handling for better user feedback.
These changes signify the development of a versatile and comprehensive `OutputFile` class, enabling efficient management of model conversion output, metadata, vocabulary, and tensor information.
* feat: Introduce VocabFactory for flexible vocabulary management in model conversion
- The VocabFactory class is added to facilitate modular vocabulary handling.
- The constructor initializes a directory path and detects vocabulary-related files.
- The _select_file method provides file paths based on vocabulary type (e.g., BPE, SentencePiece).
- _create_special_vocab generates special vocabularies, accommodating different types.
- The load_vocab method loads vocabularies, handling BPE, SentencePiece, and Hugging Face Fast Tokenizer.
- Error handling and logging enhance debugging and user feedback.
- The modular and flexible design simplifies vocabulary management and supports future extensions.
The VocabFactory class enhances code modularity and maintainability, allowing versatile vocabulary handling in the model conversion process.
* refactor: Improve code organization, argument parsing, and user interface
- Renamed 'default_outfile' to 'default_output_file' for clarity.
- Refactored argument parser setup into 'get_argument_parser' function.
- Introduced descriptive comments for each argument in the parser.
- Added '--vocab-type' argument with choices ["spm", "bpe", "hfft"] for vocabulary processing.
- Improved flag naming consistency: '--outfile' to '--out-file' and '--bigendian' to '--big-endian'.
- Enhanced error handling to prevent overwriting input data in 'default_output_file'.
- Made 'argv' in 'main' an optional parameter for flexibility.
- Introduced dynamic import for 'awq.apply_awq' based on 'args.awq_path' for conditional dependency.
These changes enhance code clarity, organization, and the user interface of the script, aligning it with Python best practices and improving maintainability.
* refactor: Further refine functionality, improve user interaction, and streamline vocabulary handling
- Renamed command-line arguments for clarity and consistency.
- Improved path resolution and import adjustments for robustness.
- Thoughtfully handled 'awq-path' and conditional logic for the weighted model.
- Enhanced model and vocabulary loading with the 'VocabFactory' class for structured and adaptable loading.
- Strengthened error handling and user feedback for a more user-friendly experience.
- Structured output file handling with clear conditions and defaults.
- Streamlined and organized the 'main' function for better logic flow.
- Passed 'sys.argv[1:]' to 'main' for adaptability and testability.
These changes solidify the script's functionality, making it more robust, user-friendly, and adaptable. The use of the 'VocabFactory' class is a notable enhancement in efficient vocabulary handling, reflecting a thoughtful and iterative approach to script development.
* chore: Apply ruff formatting to convert.py
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
* Revert to commit 0614c33
* chore: Apply flake8 formatting rules
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
* refactor: Revise `check_vocab_size` for Enhanced Clarity and Correctness
- Resolved an unreachable branch issue by reorganizing the conditional structure.
- Moved the special case check for `params.n_vocab == -1` to the top for immediate assertion.
- Flattened the conditional logic for improved clarity and predictability of the function's behavior.
These changes enhance the readability and functional correctness of the `check_vocab_size` function without altering its intended functionality.
* py : fix outfile and outtype
* py : suggest hint for missing vocab size
---------
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-01-09 18:46:46 +00:00
def __repr__ ( self ) - > str :
return f " <BpeVocab with { self . vocab_size_base } base tokens and { len ( self . added_tokens_list ) } added tokens> "
2023-08-21 20:07:43 +00:00
2024-03-28 15:44:36 +00:00
class SentencePieceVocab ( Vocab ) :
2024-03-14 16:21:56 +00:00
tokenizer_model = " llama "
name = " spm "
2024-03-28 15:44:36 +00:00
def __init__ ( self , base_path : Path ) :
added_tokens : dict [ str , int ] = { }
if ( fname_tokenizer := base_path / ' tokenizer.model ' ) . exists ( ) :
# normal location
try :
with open ( base_path / ADDED_TOKENS_FILE , encoding = " utf-8 " ) as f :
added_tokens = json . load ( f )
except FileNotFoundError :
pass
elif not ( fname_tokenizer := base_path . parent / ' tokenizer.model ' ) . exists ( ) :
# not found in alternate location either
raise FileNotFoundError ( ' Cannot find tokenizer.model ' )
convert.py : fix vanilla LLaMA model conversion (#4818)
* Update Imports and Add Notes for Future Reference
- Updated import statements in `convert.py`.
- Added import for `AutoTokenizer` from `transformers` module.
- Added conditional import for `gguf` from the local directory.
- Added comments and notes for future reference.
Additional Notes:
- Noted removal of a redundant `TypeAlias` import.
- Noted the removal of a `gguf` debug statement.
- Commented on the presence of `ARCH` and `NDArray` definitions.
- Commented on cleaning up and refactoring data type definitions.
* Refine Model Hyperparameters and Params Class
- Updated type annotations to use `Optional` for clarity.
- Improved method names and attribute consistency.
- Removed unnecessary variables for better code readability.
Additional Notes:
- Highlighted the use of `Optional` for clearer intent.
- Ensured backward and forward compatibility.
* Restore BpeVocab and SentencePieceVocab classes
- Restored the BpeVocab class for handling BPE tokenization.
- Restored the SentencePieceVocab class for SentencePiece tokenization.
These classes are essential for maintaining the original behavior of the codebase.
* refactor: Standardize vocabulary handling with HfVocab
- Replaced VocabLoader with HfVocab, aligning vocabulary handling across classes.
- Updated initialization of HfVocab with local_files_only=True for AutoTokenizer.
- Introduced optional parameter fname_added_tokens for flexible added token management.
- Streamlined added token handling for clarity and conciseness.
- Maintained special tokens and IDs, enhancing token management.
- Simplified token processing methods for improved readability.
- Added a placeholder for score computation with a default value of -1000.0.
- Optimized newline token check for efficiency.
- Updated __repr__ function for clarity in representation.
- Adjusted type alias Vocab to include BpeVocab, SentencePieceVocab, and HfVocab.
- Removed redundant code related to special token handling, reverse vocabulary mapping, and vocabulary file detection.
This refactoring promotes a standardized and modular approach to vocabulary management, facilitating future integration with a VocabFactory and improving code maintainability and scalability.
* refactor: Enhance readability, functionality, and code quality
- Improved code formatting and readability for better maintainability.
- Refactored LazyUnpickler's CLASSES dictionary for clarity.
- Added print statements and warnings in check_vocab_size for user feedback.
- Removed find_vocab_file_path, as it's superseded by VocabFactory.
- Preparatory changes for upcoming classes: OutputFile and VocabFactory.
- Overall focus on code quality, error handling, and consistency.
These changes reflect a continuous effort to refine the codebase, ensuring it meets best practices and prepares for future enhancements, such as the VocabFactory.
* refactor: Update OutputFile class for enhanced model vocabulary management
- Restructured the constructor for improved readability.
- Updated `add_meta_arch` method for flexible model name determination.
- Introduced `handle_tokenizer_model` for mapping vocab types to supported tokenizer models.
- Streamlined vocabulary extraction with `extract_vocabulary_from_model`.
- Simplified vocabulary metadata addition using `add_meta_vocab`.
- Refactored `add_tensor_info` for clarity and consistency.
- Improved error handling for better user feedback.
These changes signify the development of a versatile and comprehensive `OutputFile` class, enabling efficient management of model conversion output, metadata, vocabulary, and tensor information.
* feat: Introduce VocabFactory for flexible vocabulary management in model conversion
- The VocabFactory class is added to facilitate modular vocabulary handling.
- The constructor initializes a directory path and detects vocabulary-related files.
- The _select_file method provides file paths based on vocabulary type (e.g., BPE, SentencePiece).
- _create_special_vocab generates special vocabularies, accommodating different types.
- The load_vocab method loads vocabularies, handling BPE, SentencePiece, and Hugging Face Fast Tokenizer.
- Error handling and logging enhance debugging and user feedback.
- The modular and flexible design simplifies vocabulary management and supports future extensions.
The VocabFactory class enhances code modularity and maintainability, allowing versatile vocabulary handling in the model conversion process.
* refactor: Improve code organization, argument parsing, and user interface
- Renamed 'default_outfile' to 'default_output_file' for clarity.
- Refactored argument parser setup into 'get_argument_parser' function.
- Introduced descriptive comments for each argument in the parser.
- Added '--vocab-type' argument with choices ["spm", "bpe", "hfft"] for vocabulary processing.
- Improved flag naming consistency: '--outfile' to '--out-file' and '--bigendian' to '--big-endian'.
- Enhanced error handling to prevent overwriting input data in 'default_output_file'.
- Made 'argv' in 'main' an optional parameter for flexibility.
- Introduced dynamic import for 'awq.apply_awq' based on 'args.awq_path' for conditional dependency.
These changes enhance code clarity, organization, and the user interface of the script, aligning it with Python best practices and improving maintainability.
* refactor: Further refine functionality, improve user interaction, and streamline vocabulary handling
- Renamed command-line arguments for clarity and consistency.
- Improved path resolution and import adjustments for robustness.
- Thoughtfully handled 'awq-path' and conditional logic for the weighted model.
- Enhanced model and vocabulary loading with the 'VocabFactory' class for structured and adaptable loading.
- Strengthened error handling and user feedback for a more user-friendly experience.
- Structured output file handling with clear conditions and defaults.
- Streamlined and organized the 'main' function for better logic flow.
- Passed 'sys.argv[1:]' to 'main' for adaptability and testability.
These changes solidify the script's functionality, making it more robust, user-friendly, and adaptable. The use of the 'VocabFactory' class is a notable enhancement in efficient vocabulary handling, reflecting a thoughtful and iterative approach to script development.
* chore: Apply ruff formatting to convert.py
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
* Revert to commit 0614c33
* chore: Apply flake8 formatting rules
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
* refactor: Revise `check_vocab_size` for Enhanced Clarity and Correctness
- Resolved an unreachable branch issue by reorganizing the conditional structure.
- Moved the special case check for `params.n_vocab == -1` to the top for immediate assertion.
- Flattened the conditional logic for improved clarity and predictability of the function's behavior.
These changes enhance the readability and functional correctness of the `check_vocab_size` function without altering its intended functionality.
* py : fix outfile and outtype
* py : suggest hint for missing vocab size
---------
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-01-09 18:46:46 +00:00
2024-03-28 15:44:36 +00:00
self . sentencepiece_tokenizer = SentencePieceProcessor ( str ( fname_tokenizer ) )
vocab_size = self . sentencepiece_tokenizer . vocab_size ( )
convert.py : fix vanilla LLaMA model conversion (#4818)
* Update Imports and Add Notes for Future Reference
- Updated import statements in `convert.py`.
- Added import for `AutoTokenizer` from `transformers` module.
- Added conditional import for `gguf` from the local directory.
- Added comments and notes for future reference.
Additional Notes:
- Noted removal of a redundant `TypeAlias` import.
- Noted the removal of a `gguf` debug statement.
- Commented on the presence of `ARCH` and `NDArray` definitions.
- Commented on cleaning up and refactoring data type definitions.
* Refine Model Hyperparameters and Params Class
- Updated type annotations to use `Optional` for clarity.
- Improved method names and attribute consistency.
- Removed unnecessary variables for better code readability.
Additional Notes:
- Highlighted the use of `Optional` for clearer intent.
- Ensured backward and forward compatibility.
* Restore BpeVocab and SentencePieceVocab classes
- Restored the BpeVocab class for handling BPE tokenization.
- Restored the SentencePieceVocab class for SentencePiece tokenization.
These classes are essential for maintaining the original behavior of the codebase.
* refactor: Standardize vocabulary handling with HfVocab
- Replaced VocabLoader with HfVocab, aligning vocabulary handling across classes.
- Updated initialization of HfVocab with local_files_only=True for AutoTokenizer.
- Introduced optional parameter fname_added_tokens for flexible added token management.
- Streamlined added token handling for clarity and conciseness.
- Maintained special tokens and IDs, enhancing token management.
- Simplified token processing methods for improved readability.
- Added a placeholder for score computation with a default value of -1000.0.
- Optimized newline token check for efficiency.
- Updated __repr__ function for clarity in representation.
- Adjusted type alias Vocab to include BpeVocab, SentencePieceVocab, and HfVocab.
- Removed redundant code related to special token handling, reverse vocabulary mapping, and vocabulary file detection.
This refactoring promotes a standardized and modular approach to vocabulary management, facilitating future integration with a VocabFactory and improving code maintainability and scalability.
* refactor: Enhance readability, functionality, and code quality
- Improved code formatting and readability for better maintainability.
- Refactored LazyUnpickler's CLASSES dictionary for clarity.
- Added print statements and warnings in check_vocab_size for user feedback.
- Removed find_vocab_file_path, as it's superseded by VocabFactory.
- Preparatory changes for upcoming classes: OutputFile and VocabFactory.
- Overall focus on code quality, error handling, and consistency.
These changes reflect a continuous effort to refine the codebase, ensuring it meets best practices and prepares for future enhancements, such as the VocabFactory.
* refactor: Update OutputFile class for enhanced model vocabulary management
- Restructured the constructor for improved readability.
- Updated `add_meta_arch` method for flexible model name determination.
- Introduced `handle_tokenizer_model` for mapping vocab types to supported tokenizer models.
- Streamlined vocabulary extraction with `extract_vocabulary_from_model`.
- Simplified vocabulary metadata addition using `add_meta_vocab`.
- Refactored `add_tensor_info` for clarity and consistency.
- Improved error handling for better user feedback.
These changes signify the development of a versatile and comprehensive `OutputFile` class, enabling efficient management of model conversion output, metadata, vocabulary, and tensor information.
* feat: Introduce VocabFactory for flexible vocabulary management in model conversion
- The VocabFactory class is added to facilitate modular vocabulary handling.
- The constructor initializes a directory path and detects vocabulary-related files.
- The _select_file method provides file paths based on vocabulary type (e.g., BPE, SentencePiece).
- _create_special_vocab generates special vocabularies, accommodating different types.
- The load_vocab method loads vocabularies, handling BPE, SentencePiece, and Hugging Face Fast Tokenizer.
- Error handling and logging enhance debugging and user feedback.
- The modular and flexible design simplifies vocabulary management and supports future extensions.
The VocabFactory class enhances code modularity and maintainability, allowing versatile vocabulary handling in the model conversion process.
* refactor: Improve code organization, argument parsing, and user interface
- Renamed 'default_outfile' to 'default_output_file' for clarity.
- Refactored argument parser setup into 'get_argument_parser' function.
- Introduced descriptive comments for each argument in the parser.
- Added '--vocab-type' argument with choices ["spm", "bpe", "hfft"] for vocabulary processing.
- Improved flag naming consistency: '--outfile' to '--out-file' and '--bigendian' to '--big-endian'.
- Enhanced error handling to prevent overwriting input data in 'default_output_file'.
- Made 'argv' in 'main' an optional parameter for flexibility.
- Introduced dynamic import for 'awq.apply_awq' based on 'args.awq_path' for conditional dependency.
These changes enhance code clarity, organization, and the user interface of the script, aligning it with Python best practices and improving maintainability.
* refactor: Further refine functionality, improve user interaction, and streamline vocabulary handling
- Renamed command-line arguments for clarity and consistency.
- Improved path resolution and import adjustments for robustness.
- Thoughtfully handled 'awq-path' and conditional logic for the weighted model.
- Enhanced model and vocabulary loading with the 'VocabFactory' class for structured and adaptable loading.
- Strengthened error handling and user feedback for a more user-friendly experience.
- Structured output file handling with clear conditions and defaults.
- Streamlined and organized the 'main' function for better logic flow.
- Passed 'sys.argv[1:]' to 'main' for adaptability and testability.
These changes solidify the script's functionality, making it more robust, user-friendly, and adaptable. The use of the 'VocabFactory' class is a notable enhancement in efficient vocabulary handling, reflecting a thoughtful and iterative approach to script development.
* chore: Apply ruff formatting to convert.py
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
* Revert to commit 0614c33
* chore: Apply flake8 formatting rules
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
* refactor: Revise `check_vocab_size` for Enhanced Clarity and Correctness
- Resolved an unreachable branch issue by reorganizing the conditional structure.
- Moved the special case check for `params.n_vocab == -1` to the top for immediate assertion.
- Flattened the conditional logic for improved clarity and predictability of the function's behavior.
These changes enhance the readability and functional correctness of the `check_vocab_size` function without altering its intended functionality.
* py : fix outfile and outtype
* py : suggest hint for missing vocab size
---------
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-01-09 18:46:46 +00:00
2024-01-20 23:14:18 +00:00
new_tokens = { id : piece for piece , id in added_tokens . items ( ) if id > = vocab_size }
convert.py : fix vanilla LLaMA model conversion (#4818)
* Update Imports and Add Notes for Future Reference
- Updated import statements in `convert.py`.
- Added import for `AutoTokenizer` from `transformers` module.
- Added conditional import for `gguf` from the local directory.
- Added comments and notes for future reference.
Additional Notes:
- Noted removal of a redundant `TypeAlias` import.
- Noted the removal of a `gguf` debug statement.
- Commented on the presence of `ARCH` and `NDArray` definitions.
- Commented on cleaning up and refactoring data type definitions.
* Refine Model Hyperparameters and Params Class
- Updated type annotations to use `Optional` for clarity.
- Improved method names and attribute consistency.
- Removed unnecessary variables for better code readability.
Additional Notes:
- Highlighted the use of `Optional` for clearer intent.
- Ensured backward and forward compatibility.
* Restore BpeVocab and SentencePieceVocab classes
- Restored the BpeVocab class for handling BPE tokenization.
- Restored the SentencePieceVocab class for SentencePiece tokenization.
These classes are essential for maintaining the original behavior of the codebase.
* refactor: Standardize vocabulary handling with HfVocab
- Replaced VocabLoader with HfVocab, aligning vocabulary handling across classes.
- Updated initialization of HfVocab with local_files_only=True for AutoTokenizer.
- Introduced optional parameter fname_added_tokens for flexible added token management.
- Streamlined added token handling for clarity and conciseness.
- Maintained special tokens and IDs, enhancing token management.
- Simplified token processing methods for improved readability.
- Added a placeholder for score computation with a default value of -1000.0.
- Optimized newline token check for efficiency.
- Updated __repr__ function for clarity in representation.
- Adjusted type alias Vocab to include BpeVocab, SentencePieceVocab, and HfVocab.
- Removed redundant code related to special token handling, reverse vocabulary mapping, and vocabulary file detection.
This refactoring promotes a standardized and modular approach to vocabulary management, facilitating future integration with a VocabFactory and improving code maintainability and scalability.
* refactor: Enhance readability, functionality, and code quality
- Improved code formatting and readability for better maintainability.
- Refactored LazyUnpickler's CLASSES dictionary for clarity.
- Added print statements and warnings in check_vocab_size for user feedback.
- Removed find_vocab_file_path, as it's superseded by VocabFactory.
- Preparatory changes for upcoming classes: OutputFile and VocabFactory.
- Overall focus on code quality, error handling, and consistency.
These changes reflect a continuous effort to refine the codebase, ensuring it meets best practices and prepares for future enhancements, such as the VocabFactory.
* refactor: Update OutputFile class for enhanced model vocabulary management
- Restructured the constructor for improved readability.
- Updated `add_meta_arch` method for flexible model name determination.
- Introduced `handle_tokenizer_model` for mapping vocab types to supported tokenizer models.
- Streamlined vocabulary extraction with `extract_vocabulary_from_model`.
- Simplified vocabulary metadata addition using `add_meta_vocab`.
- Refactored `add_tensor_info` for clarity and consistency.
- Improved error handling for better user feedback.
These changes signify the development of a versatile and comprehensive `OutputFile` class, enabling efficient management of model conversion output, metadata, vocabulary, and tensor information.
* feat: Introduce VocabFactory for flexible vocabulary management in model conversion
- The VocabFactory class is added to facilitate modular vocabulary handling.
- The constructor initializes a directory path and detects vocabulary-related files.
- The _select_file method provides file paths based on vocabulary type (e.g., BPE, SentencePiece).
- _create_special_vocab generates special vocabularies, accommodating different types.
- The load_vocab method loads vocabularies, handling BPE, SentencePiece, and Hugging Face Fast Tokenizer.
- Error handling and logging enhance debugging and user feedback.
- The modular and flexible design simplifies vocabulary management and supports future extensions.
The VocabFactory class enhances code modularity and maintainability, allowing versatile vocabulary handling in the model conversion process.
* refactor: Improve code organization, argument parsing, and user interface
- Renamed 'default_outfile' to 'default_output_file' for clarity.
- Refactored argument parser setup into 'get_argument_parser' function.
- Introduced descriptive comments for each argument in the parser.
- Added '--vocab-type' argument with choices ["spm", "bpe", "hfft"] for vocabulary processing.
- Improved flag naming consistency: '--outfile' to '--out-file' and '--bigendian' to '--big-endian'.
- Enhanced error handling to prevent overwriting input data in 'default_output_file'.
- Made 'argv' in 'main' an optional parameter for flexibility.
- Introduced dynamic import for 'awq.apply_awq' based on 'args.awq_path' for conditional dependency.
These changes enhance code clarity, organization, and the user interface of the script, aligning it with Python best practices and improving maintainability.
* refactor: Further refine functionality, improve user interaction, and streamline vocabulary handling
- Renamed command-line arguments for clarity and consistency.
- Improved path resolution and import adjustments for robustness.
- Thoughtfully handled 'awq-path' and conditional logic for the weighted model.
- Enhanced model and vocabulary loading with the 'VocabFactory' class for structured and adaptable loading.
- Strengthened error handling and user feedback for a more user-friendly experience.
- Structured output file handling with clear conditions and defaults.
- Streamlined and organized the 'main' function for better logic flow.
- Passed 'sys.argv[1:]' to 'main' for adaptability and testability.
These changes solidify the script's functionality, making it more robust, user-friendly, and adaptable. The use of the 'VocabFactory' class is a notable enhancement in efficient vocabulary handling, reflecting a thoughtful and iterative approach to script development.
* chore: Apply ruff formatting to convert.py
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
* Revert to commit 0614c33
* chore: Apply flake8 formatting rules
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
* refactor: Revise `check_vocab_size` for Enhanced Clarity and Correctness
- Resolved an unreachable branch issue by reorganizing the conditional structure.
- Moved the special case check for `params.n_vocab == -1` to the top for immediate assertion.
- Flattened the conditional logic for improved clarity and predictability of the function's behavior.
These changes enhance the readability and functional correctness of the `check_vocab_size` function without altering its intended functionality.
* py : fix outfile and outtype
* py : suggest hint for missing vocab size
---------
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-01-09 18:46:46 +00:00
expected_new_ids = list ( range ( vocab_size , vocab_size + len ( new_tokens ) ) )
2024-01-20 23:14:18 +00:00
actual_new_ids = sorted ( new_tokens . keys ( ) )
convert.py : fix vanilla LLaMA model conversion (#4818)
* Update Imports and Add Notes for Future Reference
- Updated import statements in `convert.py`.
- Added import for `AutoTokenizer` from `transformers` module.
- Added conditional import for `gguf` from the local directory.
- Added comments and notes for future reference.
Additional Notes:
- Noted removal of a redundant `TypeAlias` import.
- Noted the removal of a `gguf` debug statement.
- Commented on the presence of `ARCH` and `NDArray` definitions.
- Commented on cleaning up and refactoring data type definitions.
* Refine Model Hyperparameters and Params Class
- Updated type annotations to use `Optional` for clarity.
- Improved method names and attribute consistency.
- Removed unnecessary variables for better code readability.
Additional Notes:
- Highlighted the use of `Optional` for clearer intent.
- Ensured backward and forward compatibility.
* Restore BpeVocab and SentencePieceVocab classes
- Restored the BpeVocab class for handling BPE tokenization.
- Restored the SentencePieceVocab class for SentencePiece tokenization.
These classes are essential for maintaining the original behavior of the codebase.
* refactor: Standardize vocabulary handling with HfVocab
- Replaced VocabLoader with HfVocab, aligning vocabulary handling across classes.
- Updated initialization of HfVocab with local_files_only=True for AutoTokenizer.
- Introduced optional parameter fname_added_tokens for flexible added token management.
- Streamlined added token handling for clarity and conciseness.
- Maintained special tokens and IDs, enhancing token management.
- Simplified token processing methods for improved readability.
- Added a placeholder for score computation with a default value of -1000.0.
- Optimized newline token check for efficiency.
- Updated __repr__ function for clarity in representation.
- Adjusted type alias Vocab to include BpeVocab, SentencePieceVocab, and HfVocab.
- Removed redundant code related to special token handling, reverse vocabulary mapping, and vocabulary file detection.
This refactoring promotes a standardized and modular approach to vocabulary management, facilitating future integration with a VocabFactory and improving code maintainability and scalability.
* refactor: Enhance readability, functionality, and code quality
- Improved code formatting and readability for better maintainability.
- Refactored LazyUnpickler's CLASSES dictionary for clarity.
- Added print statements and warnings in check_vocab_size for user feedback.
- Removed find_vocab_file_path, as it's superseded by VocabFactory.
- Preparatory changes for upcoming classes: OutputFile and VocabFactory.
- Overall focus on code quality, error handling, and consistency.
These changes reflect a continuous effort to refine the codebase, ensuring it meets best practices and prepares for future enhancements, such as the VocabFactory.
* refactor: Update OutputFile class for enhanced model vocabulary management
- Restructured the constructor for improved readability.
- Updated `add_meta_arch` method for flexible model name determination.
- Introduced `handle_tokenizer_model` for mapping vocab types to supported tokenizer models.
- Streamlined vocabulary extraction with `extract_vocabulary_from_model`.
- Simplified vocabulary metadata addition using `add_meta_vocab`.
- Refactored `add_tensor_info` for clarity and consistency.
- Improved error handling for better user feedback.
These changes signify the development of a versatile and comprehensive `OutputFile` class, enabling efficient management of model conversion output, metadata, vocabulary, and tensor information.
* feat: Introduce VocabFactory for flexible vocabulary management in model conversion
- The VocabFactory class is added to facilitate modular vocabulary handling.
- The constructor initializes a directory path and detects vocabulary-related files.
- The _select_file method provides file paths based on vocabulary type (e.g., BPE, SentencePiece).
- _create_special_vocab generates special vocabularies, accommodating different types.
- The load_vocab method loads vocabularies, handling BPE, SentencePiece, and Hugging Face Fast Tokenizer.
- Error handling and logging enhance debugging and user feedback.
- The modular and flexible design simplifies vocabulary management and supports future extensions.
The VocabFactory class enhances code modularity and maintainability, allowing versatile vocabulary handling in the model conversion process.
* refactor: Improve code organization, argument parsing, and user interface
- Renamed 'default_outfile' to 'default_output_file' for clarity.
- Refactored argument parser setup into 'get_argument_parser' function.
- Introduced descriptive comments for each argument in the parser.
- Added '--vocab-type' argument with choices ["spm", "bpe", "hfft"] for vocabulary processing.
- Improved flag naming consistency: '--outfile' to '--out-file' and '--bigendian' to '--big-endian'.
- Enhanced error handling to prevent overwriting input data in 'default_output_file'.
- Made 'argv' in 'main' an optional parameter for flexibility.
- Introduced dynamic import for 'awq.apply_awq' based on 'args.awq_path' for conditional dependency.
These changes enhance code clarity, organization, and the user interface of the script, aligning it with Python best practices and improving maintainability.
* refactor: Further refine functionality, improve user interaction, and streamline vocabulary handling
- Renamed command-line arguments for clarity and consistency.
- Improved path resolution and import adjustments for robustness.
- Thoughtfully handled 'awq-path' and conditional logic for the weighted model.
- Enhanced model and vocabulary loading with the 'VocabFactory' class for structured and adaptable loading.
- Strengthened error handling and user feedback for a more user-friendly experience.
- Structured output file handling with clear conditions and defaults.
- Streamlined and organized the 'main' function for better logic flow.
- Passed 'sys.argv[1:]' to 'main' for adaptability and testability.
These changes solidify the script's functionality, making it more robust, user-friendly, and adaptable. The use of the 'VocabFactory' class is a notable enhancement in efficient vocabulary handling, reflecting a thoughtful and iterative approach to script development.
* chore: Apply ruff formatting to convert.py
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
* Revert to commit 0614c33
* chore: Apply flake8 formatting rules
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
* refactor: Revise `check_vocab_size` for Enhanced Clarity and Correctness
- Resolved an unreachable branch issue by reorganizing the conditional structure.
- Moved the special case check for `params.n_vocab == -1` to the top for immediate assertion.
- Flattened the conditional logic for improved clarity and predictability of the function's behavior.
These changes enhance the readability and functional correctness of the `check_vocab_size` function without altering its intended functionality.
* py : fix outfile and outtype
* py : suggest hint for missing vocab size
---------
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-01-09 18:46:46 +00:00
if expected_new_ids != actual_new_ids :
2024-01-20 23:14:18 +00:00
raise ValueError ( f " Expected new token IDs { expected_new_ids } to be sequential; got { actual_new_ids } " )
convert.py : fix vanilla LLaMA model conversion (#4818)
* Update Imports and Add Notes for Future Reference
- Updated import statements in `convert.py`.
- Added import for `AutoTokenizer` from `transformers` module.
- Added conditional import for `gguf` from the local directory.
- Added comments and notes for future reference.
Additional Notes:
- Noted removal of a redundant `TypeAlias` import.
- Noted the removal of a `gguf` debug statement.
- Commented on the presence of `ARCH` and `NDArray` definitions.
- Commented on cleaning up and refactoring data type definitions.
* Refine Model Hyperparameters and Params Class
- Updated type annotations to use `Optional` for clarity.
- Improved method names and attribute consistency.
- Removed unnecessary variables for better code readability.
Additional Notes:
- Highlighted the use of `Optional` for clearer intent.
- Ensured backward and forward compatibility.
* Restore BpeVocab and SentencePieceVocab classes
- Restored the BpeVocab class for handling BPE tokenization.
- Restored the SentencePieceVocab class for SentencePiece tokenization.
These classes are essential for maintaining the original behavior of the codebase.
* refactor: Standardize vocabulary handling with HfVocab
- Replaced VocabLoader with HfVocab, aligning vocabulary handling across classes.
- Updated initialization of HfVocab with local_files_only=True for AutoTokenizer.
- Introduced optional parameter fname_added_tokens for flexible added token management.
- Streamlined added token handling for clarity and conciseness.
- Maintained special tokens and IDs, enhancing token management.
- Simplified token processing methods for improved readability.
- Added a placeholder for score computation with a default value of -1000.0.
- Optimized newline token check for efficiency.
- Updated __repr__ function for clarity in representation.
- Adjusted type alias Vocab to include BpeVocab, SentencePieceVocab, and HfVocab.
- Removed redundant code related to special token handling, reverse vocabulary mapping, and vocabulary file detection.
This refactoring promotes a standardized and modular approach to vocabulary management, facilitating future integration with a VocabFactory and improving code maintainability and scalability.
* refactor: Enhance readability, functionality, and code quality
- Improved code formatting and readability for better maintainability.
- Refactored LazyUnpickler's CLASSES dictionary for clarity.
- Added print statements and warnings in check_vocab_size for user feedback.
- Removed find_vocab_file_path, as it's superseded by VocabFactory.
- Preparatory changes for upcoming classes: OutputFile and VocabFactory.
- Overall focus on code quality, error handling, and consistency.
These changes reflect a continuous effort to refine the codebase, ensuring it meets best practices and prepares for future enhancements, such as the VocabFactory.
* refactor: Update OutputFile class for enhanced model vocabulary management
- Restructured the constructor for improved readability.
- Updated `add_meta_arch` method for flexible model name determination.
- Introduced `handle_tokenizer_model` for mapping vocab types to supported tokenizer models.
- Streamlined vocabulary extraction with `extract_vocabulary_from_model`.
- Simplified vocabulary metadata addition using `add_meta_vocab`.
- Refactored `add_tensor_info` for clarity and consistency.
- Improved error handling for better user feedback.
These changes signify the development of a versatile and comprehensive `OutputFile` class, enabling efficient management of model conversion output, metadata, vocabulary, and tensor information.
* feat: Introduce VocabFactory for flexible vocabulary management in model conversion
- The VocabFactory class is added to facilitate modular vocabulary handling.
- The constructor initializes a directory path and detects vocabulary-related files.
- The _select_file method provides file paths based on vocabulary type (e.g., BPE, SentencePiece).
- _create_special_vocab generates special vocabularies, accommodating different types.
- The load_vocab method loads vocabularies, handling BPE, SentencePiece, and Hugging Face Fast Tokenizer.
- Error handling and logging enhance debugging and user feedback.
- The modular and flexible design simplifies vocabulary management and supports future extensions.
The VocabFactory class enhances code modularity and maintainability, allowing versatile vocabulary handling in the model conversion process.
* refactor: Improve code organization, argument parsing, and user interface
- Renamed 'default_outfile' to 'default_output_file' for clarity.
- Refactored argument parser setup into 'get_argument_parser' function.
- Introduced descriptive comments for each argument in the parser.
- Added '--vocab-type' argument with choices ["spm", "bpe", "hfft"] for vocabulary processing.
- Improved flag naming consistency: '--outfile' to '--out-file' and '--bigendian' to '--big-endian'.
- Enhanced error handling to prevent overwriting input data in 'default_output_file'.
- Made 'argv' in 'main' an optional parameter for flexibility.
- Introduced dynamic import for 'awq.apply_awq' based on 'args.awq_path' for conditional dependency.
These changes enhance code clarity, organization, and the user interface of the script, aligning it with Python best practices and improving maintainability.
* refactor: Further refine functionality, improve user interaction, and streamline vocabulary handling
- Renamed command-line arguments for clarity and consistency.
- Improved path resolution and import adjustments for robustness.
- Thoughtfully handled 'awq-path' and conditional logic for the weighted model.
- Enhanced model and vocabulary loading with the 'VocabFactory' class for structured and adaptable loading.
- Strengthened error handling and user feedback for a more user-friendly experience.
- Structured output file handling with clear conditions and defaults.
- Streamlined and organized the 'main' function for better logic flow.
- Passed 'sys.argv[1:]' to 'main' for adaptability and testability.
These changes solidify the script's functionality, making it more robust, user-friendly, and adaptable. The use of the 'VocabFactory' class is a notable enhancement in efficient vocabulary handling, reflecting a thoughtful and iterative approach to script development.
* chore: Apply ruff formatting to convert.py
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
* Revert to commit 0614c33
* chore: Apply flake8 formatting rules
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
* refactor: Revise `check_vocab_size` for Enhanced Clarity and Correctness
- Resolved an unreachable branch issue by reorganizing the conditional structure.
- Moved the special case check for `params.n_vocab == -1` to the top for immediate assertion.
- Flattened the conditional logic for improved clarity and predictability of the function's behavior.
These changes enhance the readability and functional correctness of the `check_vocab_size` function without altering its intended functionality.
* py : fix outfile and outtype
* py : suggest hint for missing vocab size
---------
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-01-09 18:46:46 +00:00
# Token pieces that were added to the base vocabulary.
2024-03-28 15:44:36 +00:00
self . added_tokens_dict = added_tokens
2024-01-20 23:14:18 +00:00
self . added_tokens_list = [ new_tokens [ id ] for id in actual_new_ids ]
self . vocab_size_base = vocab_size
self . vocab_size = self . vocab_size_base + len ( self . added_tokens_list )
self . fname_tokenizer = fname_tokenizer
convert.py : fix vanilla LLaMA model conversion (#4818)
* Update Imports and Add Notes for Future Reference
- Updated import statements in `convert.py`.
- Added import for `AutoTokenizer` from `transformers` module.
- Added conditional import for `gguf` from the local directory.
- Added comments and notes for future reference.
Additional Notes:
- Noted removal of a redundant `TypeAlias` import.
- Noted the removal of a `gguf` debug statement.
- Commented on the presence of `ARCH` and `NDArray` definitions.
- Commented on cleaning up and refactoring data type definitions.
* Refine Model Hyperparameters and Params Class
- Updated type annotations to use `Optional` for clarity.
- Improved method names and attribute consistency.
- Removed unnecessary variables for better code readability.
Additional Notes:
- Highlighted the use of `Optional` for clearer intent.
- Ensured backward and forward compatibility.
* Restore BpeVocab and SentencePieceVocab classes
- Restored the BpeVocab class for handling BPE tokenization.
- Restored the SentencePieceVocab class for SentencePiece tokenization.
These classes are essential for maintaining the original behavior of the codebase.
* refactor: Standardize vocabulary handling with HfVocab
- Replaced VocabLoader with HfVocab, aligning vocabulary handling across classes.
- Updated initialization of HfVocab with local_files_only=True for AutoTokenizer.
- Introduced optional parameter fname_added_tokens for flexible added token management.
- Streamlined added token handling for clarity and conciseness.
- Maintained special tokens and IDs, enhancing token management.
- Simplified token processing methods for improved readability.
- Added a placeholder for score computation with a default value of -1000.0.
- Optimized newline token check for efficiency.
- Updated __repr__ function for clarity in representation.
- Adjusted type alias Vocab to include BpeVocab, SentencePieceVocab, and HfVocab.
- Removed redundant code related to special token handling, reverse vocabulary mapping, and vocabulary file detection.
This refactoring promotes a standardized and modular approach to vocabulary management, facilitating future integration with a VocabFactory and improving code maintainability and scalability.
* refactor: Enhance readability, functionality, and code quality
- Improved code formatting and readability for better maintainability.
- Refactored LazyUnpickler's CLASSES dictionary for clarity.
- Added print statements and warnings in check_vocab_size for user feedback.
- Removed find_vocab_file_path, as it's superseded by VocabFactory.
- Preparatory changes for upcoming classes: OutputFile and VocabFactory.
- Overall focus on code quality, error handling, and consistency.
These changes reflect a continuous effort to refine the codebase, ensuring it meets best practices and prepares for future enhancements, such as the VocabFactory.
* refactor: Update OutputFile class for enhanced model vocabulary management
- Restructured the constructor for improved readability.
- Updated `add_meta_arch` method for flexible model name determination.
- Introduced `handle_tokenizer_model` for mapping vocab types to supported tokenizer models.
- Streamlined vocabulary extraction with `extract_vocabulary_from_model`.
- Simplified vocabulary metadata addition using `add_meta_vocab`.
- Refactored `add_tensor_info` for clarity and consistency.
- Improved error handling for better user feedback.
These changes signify the development of a versatile and comprehensive `OutputFile` class, enabling efficient management of model conversion output, metadata, vocabulary, and tensor information.
* feat: Introduce VocabFactory for flexible vocabulary management in model conversion
- The VocabFactory class is added to facilitate modular vocabulary handling.
- The constructor initializes a directory path and detects vocabulary-related files.
- The _select_file method provides file paths based on vocabulary type (e.g., BPE, SentencePiece).
- _create_special_vocab generates special vocabularies, accommodating different types.
- The load_vocab method loads vocabularies, handling BPE, SentencePiece, and Hugging Face Fast Tokenizer.
- Error handling and logging enhance debugging and user feedback.
- The modular and flexible design simplifies vocabulary management and supports future extensions.
The VocabFactory class enhances code modularity and maintainability, allowing versatile vocabulary handling in the model conversion process.
* refactor: Improve code organization, argument parsing, and user interface
- Renamed 'default_outfile' to 'default_output_file' for clarity.
- Refactored argument parser setup into 'get_argument_parser' function.
- Introduced descriptive comments for each argument in the parser.
- Added '--vocab-type' argument with choices ["spm", "bpe", "hfft"] for vocabulary processing.
- Improved flag naming consistency: '--outfile' to '--out-file' and '--bigendian' to '--big-endian'.
- Enhanced error handling to prevent overwriting input data in 'default_output_file'.
- Made 'argv' in 'main' an optional parameter for flexibility.
- Introduced dynamic import for 'awq.apply_awq' based on 'args.awq_path' for conditional dependency.
These changes enhance code clarity, organization, and the user interface of the script, aligning it with Python best practices and improving maintainability.
* refactor: Further refine functionality, improve user interaction, and streamline vocabulary handling
- Renamed command-line arguments for clarity and consistency.
- Improved path resolution and import adjustments for robustness.
- Thoughtfully handled 'awq-path' and conditional logic for the weighted model.
- Enhanced model and vocabulary loading with the 'VocabFactory' class for structured and adaptable loading.
- Strengthened error handling and user feedback for a more user-friendly experience.
- Structured output file handling with clear conditions and defaults.
- Streamlined and organized the 'main' function for better logic flow.
- Passed 'sys.argv[1:]' to 'main' for adaptability and testability.
These changes solidify the script's functionality, making it more robust, user-friendly, and adaptable. The use of the 'VocabFactory' class is a notable enhancement in efficient vocabulary handling, reflecting a thoughtful and iterative approach to script development.
* chore: Apply ruff formatting to convert.py
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
* Revert to commit 0614c33
* chore: Apply flake8 formatting rules
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
* refactor: Revise `check_vocab_size` for Enhanced Clarity and Correctness
- Resolved an unreachable branch issue by reorganizing the conditional structure.
- Moved the special case check for `params.n_vocab == -1` to the top for immediate assertion.
- Flattened the conditional logic for improved clarity and predictability of the function's behavior.
These changes enhance the readability and functional correctness of the `check_vocab_size` function without altering its intended functionality.
* py : fix outfile and outtype
* py : suggest hint for missing vocab size
---------
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-01-09 18:46:46 +00:00
def sentencepiece_tokens ( self ) - > Iterable [ tuple [ bytes , float , gguf . TokenType ] ] :
tokenizer = self . sentencepiece_tokenizer
for i in range ( tokenizer . vocab_size ( ) ) :
piece = tokenizer . id_to_piece ( i )
2024-03-28 15:44:36 +00:00
text = piece . encode ( " utf-8 " )
convert.py : fix vanilla LLaMA model conversion (#4818)
* Update Imports and Add Notes for Future Reference
- Updated import statements in `convert.py`.
- Added import for `AutoTokenizer` from `transformers` module.
- Added conditional import for `gguf` from the local directory.
- Added comments and notes for future reference.
Additional Notes:
- Noted removal of a redundant `TypeAlias` import.
- Noted the removal of a `gguf` debug statement.
- Commented on the presence of `ARCH` and `NDArray` definitions.
- Commented on cleaning up and refactoring data type definitions.
* Refine Model Hyperparameters and Params Class
- Updated type annotations to use `Optional` for clarity.
- Improved method names and attribute consistency.
- Removed unnecessary variables for better code readability.
Additional Notes:
- Highlighted the use of `Optional` for clearer intent.
- Ensured backward and forward compatibility.
* Restore BpeVocab and SentencePieceVocab classes
- Restored the BpeVocab class for handling BPE tokenization.
- Restored the SentencePieceVocab class for SentencePiece tokenization.
These classes are essential for maintaining the original behavior of the codebase.
* refactor: Standardize vocabulary handling with HfVocab
- Replaced VocabLoader with HfVocab, aligning vocabulary handling across classes.
- Updated initialization of HfVocab with local_files_only=True for AutoTokenizer.
- Introduced optional parameter fname_added_tokens for flexible added token management.
- Streamlined added token handling for clarity and conciseness.
- Maintained special tokens and IDs, enhancing token management.
- Simplified token processing methods for improved readability.
- Added a placeholder for score computation with a default value of -1000.0.
- Optimized newline token check for efficiency.
- Updated __repr__ function for clarity in representation.
- Adjusted type alias Vocab to include BpeVocab, SentencePieceVocab, and HfVocab.
- Removed redundant code related to special token handling, reverse vocabulary mapping, and vocabulary file detection.
This refactoring promotes a standardized and modular approach to vocabulary management, facilitating future integration with a VocabFactory and improving code maintainability and scalability.
* refactor: Enhance readability, functionality, and code quality
- Improved code formatting and readability for better maintainability.
- Refactored LazyUnpickler's CLASSES dictionary for clarity.
- Added print statements and warnings in check_vocab_size for user feedback.
- Removed find_vocab_file_path, as it's superseded by VocabFactory.
- Preparatory changes for upcoming classes: OutputFile and VocabFactory.
- Overall focus on code quality, error handling, and consistency.
These changes reflect a continuous effort to refine the codebase, ensuring it meets best practices and prepares for future enhancements, such as the VocabFactory.
* refactor: Update OutputFile class for enhanced model vocabulary management
- Restructured the constructor for improved readability.
- Updated `add_meta_arch` method for flexible model name determination.
- Introduced `handle_tokenizer_model` for mapping vocab types to supported tokenizer models.
- Streamlined vocabulary extraction with `extract_vocabulary_from_model`.
- Simplified vocabulary metadata addition using `add_meta_vocab`.
- Refactored `add_tensor_info` for clarity and consistency.
- Improved error handling for better user feedback.
These changes signify the development of a versatile and comprehensive `OutputFile` class, enabling efficient management of model conversion output, metadata, vocabulary, and tensor information.
* feat: Introduce VocabFactory for flexible vocabulary management in model conversion
- The VocabFactory class is added to facilitate modular vocabulary handling.
- The constructor initializes a directory path and detects vocabulary-related files.
- The _select_file method provides file paths based on vocabulary type (e.g., BPE, SentencePiece).
- _create_special_vocab generates special vocabularies, accommodating different types.
- The load_vocab method loads vocabularies, handling BPE, SentencePiece, and Hugging Face Fast Tokenizer.
- Error handling and logging enhance debugging and user feedback.
- The modular and flexible design simplifies vocabulary management and supports future extensions.
The VocabFactory class enhances code modularity and maintainability, allowing versatile vocabulary handling in the model conversion process.
* refactor: Improve code organization, argument parsing, and user interface
- Renamed 'default_outfile' to 'default_output_file' for clarity.
- Refactored argument parser setup into 'get_argument_parser' function.
- Introduced descriptive comments for each argument in the parser.
- Added '--vocab-type' argument with choices ["spm", "bpe", "hfft"] for vocabulary processing.
- Improved flag naming consistency: '--outfile' to '--out-file' and '--bigendian' to '--big-endian'.
- Enhanced error handling to prevent overwriting input data in 'default_output_file'.
- Made 'argv' in 'main' an optional parameter for flexibility.
- Introduced dynamic import for 'awq.apply_awq' based on 'args.awq_path' for conditional dependency.
These changes enhance code clarity, organization, and the user interface of the script, aligning it with Python best practices and improving maintainability.
* refactor: Further refine functionality, improve user interaction, and streamline vocabulary handling
- Renamed command-line arguments for clarity and consistency.
- Improved path resolution and import adjustments for robustness.
- Thoughtfully handled 'awq-path' and conditional logic for the weighted model.
- Enhanced model and vocabulary loading with the 'VocabFactory' class for structured and adaptable loading.
- Strengthened error handling and user feedback for a more user-friendly experience.
- Structured output file handling with clear conditions and defaults.
- Streamlined and organized the 'main' function for better logic flow.
- Passed 'sys.argv[1:]' to 'main' for adaptability and testability.
These changes solidify the script's functionality, making it more robust, user-friendly, and adaptable. The use of the 'VocabFactory' class is a notable enhancement in efficient vocabulary handling, reflecting a thoughtful and iterative approach to script development.
* chore: Apply ruff formatting to convert.py
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
* Revert to commit 0614c33
* chore: Apply flake8 formatting rules
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
* refactor: Revise `check_vocab_size` for Enhanced Clarity and Correctness
- Resolved an unreachable branch issue by reorganizing the conditional structure.
- Moved the special case check for `params.n_vocab == -1` to the top for immediate assertion.
- Flattened the conditional logic for improved clarity and predictability of the function's behavior.
These changes enhance the readability and functional correctness of the `check_vocab_size` function without altering its intended functionality.
* py : fix outfile and outtype
* py : suggest hint for missing vocab size
---------
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-01-09 18:46:46 +00:00
score : float = tokenizer . get_score ( i )
toktype = gguf . TokenType . NORMAL
if tokenizer . is_unknown ( i ) :
toktype = gguf . TokenType . UNKNOWN
if tokenizer . is_control ( i ) :
toktype = gguf . TokenType . CONTROL
# NOTE: I think added_tokens are user defined.
# ref: https://github.com/google/sentencepiece/blob/master/src/sentencepiece_model.proto
# if tokenizer.is_user_defined(i): toktype = gguf.TokenType.USER_DEFINED
if tokenizer . is_unused ( i ) :
toktype = gguf . TokenType . UNUSED
if tokenizer . is_byte ( i ) :
toktype = gguf . TokenType . BYTE
yield text , score , toktype
2023-08-21 20:07:43 +00:00
convert.py : fix vanilla LLaMA model conversion (#4818)
* Update Imports and Add Notes for Future Reference
- Updated import statements in `convert.py`.
- Added import for `AutoTokenizer` from `transformers` module.
- Added conditional import for `gguf` from the local directory.
- Added comments and notes for future reference.
Additional Notes:
- Noted removal of a redundant `TypeAlias` import.
- Noted the removal of a `gguf` debug statement.
- Commented on the presence of `ARCH` and `NDArray` definitions.
- Commented on cleaning up and refactoring data type definitions.
* Refine Model Hyperparameters and Params Class
- Updated type annotations to use `Optional` for clarity.
- Improved method names and attribute consistency.
- Removed unnecessary variables for better code readability.
Additional Notes:
- Highlighted the use of `Optional` for clearer intent.
- Ensured backward and forward compatibility.
* Restore BpeVocab and SentencePieceVocab classes
- Restored the BpeVocab class for handling BPE tokenization.
- Restored the SentencePieceVocab class for SentencePiece tokenization.
These classes are essential for maintaining the original behavior of the codebase.
* refactor: Standardize vocabulary handling with HfVocab
- Replaced VocabLoader with HfVocab, aligning vocabulary handling across classes.
- Updated initialization of HfVocab with local_files_only=True for AutoTokenizer.
- Introduced optional parameter fname_added_tokens for flexible added token management.
- Streamlined added token handling for clarity and conciseness.
- Maintained special tokens and IDs, enhancing token management.
- Simplified token processing methods for improved readability.
- Added a placeholder for score computation with a default value of -1000.0.
- Optimized newline token check for efficiency.
- Updated __repr__ function for clarity in representation.
- Adjusted type alias Vocab to include BpeVocab, SentencePieceVocab, and HfVocab.
- Removed redundant code related to special token handling, reverse vocabulary mapping, and vocabulary file detection.
This refactoring promotes a standardized and modular approach to vocabulary management, facilitating future integration with a VocabFactory and improving code maintainability and scalability.
* refactor: Enhance readability, functionality, and code quality
- Improved code formatting and readability for better maintainability.
- Refactored LazyUnpickler's CLASSES dictionary for clarity.
- Added print statements and warnings in check_vocab_size for user feedback.
- Removed find_vocab_file_path, as it's superseded by VocabFactory.
- Preparatory changes for upcoming classes: OutputFile and VocabFactory.
- Overall focus on code quality, error handling, and consistency.
These changes reflect a continuous effort to refine the codebase, ensuring it meets best practices and prepares for future enhancements, such as the VocabFactory.
* refactor: Update OutputFile class for enhanced model vocabulary management
- Restructured the constructor for improved readability.
- Updated `add_meta_arch` method for flexible model name determination.
- Introduced `handle_tokenizer_model` for mapping vocab types to supported tokenizer models.
- Streamlined vocabulary extraction with `extract_vocabulary_from_model`.
- Simplified vocabulary metadata addition using `add_meta_vocab`.
- Refactored `add_tensor_info` for clarity and consistency.
- Improved error handling for better user feedback.
These changes signify the development of a versatile and comprehensive `OutputFile` class, enabling efficient management of model conversion output, metadata, vocabulary, and tensor information.
* feat: Introduce VocabFactory for flexible vocabulary management in model conversion
- The VocabFactory class is added to facilitate modular vocabulary handling.
- The constructor initializes a directory path and detects vocabulary-related files.
- The _select_file method provides file paths based on vocabulary type (e.g., BPE, SentencePiece).
- _create_special_vocab generates special vocabularies, accommodating different types.
- The load_vocab method loads vocabularies, handling BPE, SentencePiece, and Hugging Face Fast Tokenizer.
- Error handling and logging enhance debugging and user feedback.
- The modular and flexible design simplifies vocabulary management and supports future extensions.
The VocabFactory class enhances code modularity and maintainability, allowing versatile vocabulary handling in the model conversion process.
* refactor: Improve code organization, argument parsing, and user interface
- Renamed 'default_outfile' to 'default_output_file' for clarity.
- Refactored argument parser setup into 'get_argument_parser' function.
- Introduced descriptive comments for each argument in the parser.
- Added '--vocab-type' argument with choices ["spm", "bpe", "hfft"] for vocabulary processing.
- Improved flag naming consistency: '--outfile' to '--out-file' and '--bigendian' to '--big-endian'.
- Enhanced error handling to prevent overwriting input data in 'default_output_file'.
- Made 'argv' in 'main' an optional parameter for flexibility.
- Introduced dynamic import for 'awq.apply_awq' based on 'args.awq_path' for conditional dependency.
These changes enhance code clarity, organization, and the user interface of the script, aligning it with Python best practices and improving maintainability.
* refactor: Further refine functionality, improve user interaction, and streamline vocabulary handling
- Renamed command-line arguments for clarity and consistency.
- Improved path resolution and import adjustments for robustness.
- Thoughtfully handled 'awq-path' and conditional logic for the weighted model.
- Enhanced model and vocabulary loading with the 'VocabFactory' class for structured and adaptable loading.
- Strengthened error handling and user feedback for a more user-friendly experience.
- Structured output file handling with clear conditions and defaults.
- Streamlined and organized the 'main' function for better logic flow.
- Passed 'sys.argv[1:]' to 'main' for adaptability and testability.
These changes solidify the script's functionality, making it more robust, user-friendly, and adaptable. The use of the 'VocabFactory' class is a notable enhancement in efficient vocabulary handling, reflecting a thoughtful and iterative approach to script development.
* chore: Apply ruff formatting to convert.py
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
* Revert to commit 0614c33
* chore: Apply flake8 formatting rules
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
* refactor: Revise `check_vocab_size` for Enhanced Clarity and Correctness
- Resolved an unreachable branch issue by reorganizing the conditional structure.
- Moved the special case check for `params.n_vocab == -1` to the top for immediate assertion.
- Flattened the conditional logic for improved clarity and predictability of the function's behavior.
These changes enhance the readability and functional correctness of the `check_vocab_size` function without altering its intended functionality.
* py : fix outfile and outtype
* py : suggest hint for missing vocab size
---------
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-01-09 18:46:46 +00:00
def added_tokens ( self ) - > Iterable [ tuple [ bytes , float , gguf . TokenType ] ] :
for text in self . added_tokens_list :
score = - 1000.0
yield text . encode ( " utf-8 " ) , score , gguf . TokenType . USER_DEFINED
def all_tokens ( self ) - > Iterable [ tuple [ bytes , float , gguf . TokenType ] ] :
yield from self . sentencepiece_tokens ( )
yield from self . added_tokens ( )
def __repr__ ( self ) - > str :
return f " <SentencePieceVocab with { self . vocab_size_base } base tokens and { len ( self . added_tokens_list ) } added tokens> "
2024-03-28 15:44:36 +00:00
class LlamaHfVocab ( Vocab ) :
2024-03-14 16:21:56 +00:00
tokenizer_model = " llama "
name = " hfft "
2024-04-09 17:44:08 +00:00
def __init__ ( self , base_path : Path ) :
2024-03-28 15:44:36 +00:00
fname_tokenizer = base_path / FAST_TOKENIZER_FILE
# if this fails, FileNotFoundError propagates to caller
with open ( fname_tokenizer , encoding = ' utf-8 ' ) as f :
tokenizer_json = json . load ( f )
# pre-check so we know if we need transformers
tokenizer_model : dict [ str , Any ] = tokenizer_json [ ' model ' ]
2024-04-21 11:50:41 +00:00
is_llama3 = (
tokenizer_model [ ' type ' ] == ' BPE ' and tokenizer_model . get ( ' ignore_merges ' , False )
and not tokenizer_model . get ( ' byte_fallback ' , True )
)
if is_llama3 :
raise TypeError ( ' Llama 3 must be converted with BpeVocab ' )
if not is_llama3 and (
2024-03-28 15:44:36 +00:00
tokenizer_model [ ' type ' ] != ' BPE ' or not tokenizer_model . get ( ' byte_fallback ' , False )
or tokenizer_json [ ' decoder ' ] [ ' type ' ] != ' Sequence '
) :
raise FileNotFoundError ( ' Cannot find Llama BPE tokenizer ' )
2024-01-20 23:14:18 +00:00
try :
from transformers import AutoTokenizer
except ImportError as e :
raise ImportError (
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" To use LlamaHfVocab, please install the `transformers` package. "
2024-01-20 23:14:18 +00:00
" You can install it with `pip install transformers`. "
) from e
convert.py : fix vanilla LLaMA model conversion (#4818)
* Update Imports and Add Notes for Future Reference
- Updated import statements in `convert.py`.
- Added import for `AutoTokenizer` from `transformers` module.
- Added conditional import for `gguf` from the local directory.
- Added comments and notes for future reference.
Additional Notes:
- Noted removal of a redundant `TypeAlias` import.
- Noted the removal of a `gguf` debug statement.
- Commented on the presence of `ARCH` and `NDArray` definitions.
- Commented on cleaning up and refactoring data type definitions.
* Refine Model Hyperparameters and Params Class
- Updated type annotations to use `Optional` for clarity.
- Improved method names and attribute consistency.
- Removed unnecessary variables for better code readability.
Additional Notes:
- Highlighted the use of `Optional` for clearer intent.
- Ensured backward and forward compatibility.
* Restore BpeVocab and SentencePieceVocab classes
- Restored the BpeVocab class for handling BPE tokenization.
- Restored the SentencePieceVocab class for SentencePiece tokenization.
These classes are essential for maintaining the original behavior of the codebase.
* refactor: Standardize vocabulary handling with HfVocab
- Replaced VocabLoader with HfVocab, aligning vocabulary handling across classes.
- Updated initialization of HfVocab with local_files_only=True for AutoTokenizer.
- Introduced optional parameter fname_added_tokens for flexible added token management.
- Streamlined added token handling for clarity and conciseness.
- Maintained special tokens and IDs, enhancing token management.
- Simplified token processing methods for improved readability.
- Added a placeholder for score computation with a default value of -1000.0.
- Optimized newline token check for efficiency.
- Updated __repr__ function for clarity in representation.
- Adjusted type alias Vocab to include BpeVocab, SentencePieceVocab, and HfVocab.
- Removed redundant code related to special token handling, reverse vocabulary mapping, and vocabulary file detection.
This refactoring promotes a standardized and modular approach to vocabulary management, facilitating future integration with a VocabFactory and improving code maintainability and scalability.
* refactor: Enhance readability, functionality, and code quality
- Improved code formatting and readability for better maintainability.
- Refactored LazyUnpickler's CLASSES dictionary for clarity.
- Added print statements and warnings in check_vocab_size for user feedback.
- Removed find_vocab_file_path, as it's superseded by VocabFactory.
- Preparatory changes for upcoming classes: OutputFile and VocabFactory.
- Overall focus on code quality, error handling, and consistency.
These changes reflect a continuous effort to refine the codebase, ensuring it meets best practices and prepares for future enhancements, such as the VocabFactory.
* refactor: Update OutputFile class for enhanced model vocabulary management
- Restructured the constructor for improved readability.
- Updated `add_meta_arch` method for flexible model name determination.
- Introduced `handle_tokenizer_model` for mapping vocab types to supported tokenizer models.
- Streamlined vocabulary extraction with `extract_vocabulary_from_model`.
- Simplified vocabulary metadata addition using `add_meta_vocab`.
- Refactored `add_tensor_info` for clarity and consistency.
- Improved error handling for better user feedback.
These changes signify the development of a versatile and comprehensive `OutputFile` class, enabling efficient management of model conversion output, metadata, vocabulary, and tensor information.
* feat: Introduce VocabFactory for flexible vocabulary management in model conversion
- The VocabFactory class is added to facilitate modular vocabulary handling.
- The constructor initializes a directory path and detects vocabulary-related files.
- The _select_file method provides file paths based on vocabulary type (e.g., BPE, SentencePiece).
- _create_special_vocab generates special vocabularies, accommodating different types.
- The load_vocab method loads vocabularies, handling BPE, SentencePiece, and Hugging Face Fast Tokenizer.
- Error handling and logging enhance debugging and user feedback.
- The modular and flexible design simplifies vocabulary management and supports future extensions.
The VocabFactory class enhances code modularity and maintainability, allowing versatile vocabulary handling in the model conversion process.
* refactor: Improve code organization, argument parsing, and user interface
- Renamed 'default_outfile' to 'default_output_file' for clarity.
- Refactored argument parser setup into 'get_argument_parser' function.
- Introduced descriptive comments for each argument in the parser.
- Added '--vocab-type' argument with choices ["spm", "bpe", "hfft"] for vocabulary processing.
- Improved flag naming consistency: '--outfile' to '--out-file' and '--bigendian' to '--big-endian'.
- Enhanced error handling to prevent overwriting input data in 'default_output_file'.
- Made 'argv' in 'main' an optional parameter for flexibility.
- Introduced dynamic import for 'awq.apply_awq' based on 'args.awq_path' for conditional dependency.
These changes enhance code clarity, organization, and the user interface of the script, aligning it with Python best practices and improving maintainability.
* refactor: Further refine functionality, improve user interaction, and streamline vocabulary handling
- Renamed command-line arguments for clarity and consistency.
- Improved path resolution and import adjustments for robustness.
- Thoughtfully handled 'awq-path' and conditional logic for the weighted model.
- Enhanced model and vocabulary loading with the 'VocabFactory' class for structured and adaptable loading.
- Strengthened error handling and user feedback for a more user-friendly experience.
- Structured output file handling with clear conditions and defaults.
- Streamlined and organized the 'main' function for better logic flow.
- Passed 'sys.argv[1:]' to 'main' for adaptability and testability.
These changes solidify the script's functionality, making it more robust, user-friendly, and adaptable. The use of the 'VocabFactory' class is a notable enhancement in efficient vocabulary handling, reflecting a thoughtful and iterative approach to script development.
* chore: Apply ruff formatting to convert.py
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
* Revert to commit 0614c33
* chore: Apply flake8 formatting rules
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
* refactor: Revise `check_vocab_size` for Enhanced Clarity and Correctness
- Resolved an unreachable branch issue by reorganizing the conditional structure.
- Moved the special case check for `params.n_vocab == -1` to the top for immediate assertion.
- Flattened the conditional logic for improved clarity and predictability of the function's behavior.
These changes enhance the readability and functional correctness of the `check_vocab_size` function without altering its intended functionality.
* py : fix outfile and outtype
* py : suggest hint for missing vocab size
---------
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-01-09 18:46:46 +00:00
# Allow the tokenizer to default to slow or fast versions.
# Explicitly set tokenizer to use local paths.
self . tokenizer = AutoTokenizer . from_pretrained (
2024-03-28 15:44:36 +00:00
base_path ,
cache_dir = base_path ,
convert.py : fix vanilla LLaMA model conversion (#4818)
* Update Imports and Add Notes for Future Reference
- Updated import statements in `convert.py`.
- Added import for `AutoTokenizer` from `transformers` module.
- Added conditional import for `gguf` from the local directory.
- Added comments and notes for future reference.
Additional Notes:
- Noted removal of a redundant `TypeAlias` import.
- Noted the removal of a `gguf` debug statement.
- Commented on the presence of `ARCH` and `NDArray` definitions.
- Commented on cleaning up and refactoring data type definitions.
* Refine Model Hyperparameters and Params Class
- Updated type annotations to use `Optional` for clarity.
- Improved method names and attribute consistency.
- Removed unnecessary variables for better code readability.
Additional Notes:
- Highlighted the use of `Optional` for clearer intent.
- Ensured backward and forward compatibility.
* Restore BpeVocab and SentencePieceVocab classes
- Restored the BpeVocab class for handling BPE tokenization.
- Restored the SentencePieceVocab class for SentencePiece tokenization.
These classes are essential for maintaining the original behavior of the codebase.
* refactor: Standardize vocabulary handling with HfVocab
- Replaced VocabLoader with HfVocab, aligning vocabulary handling across classes.
- Updated initialization of HfVocab with local_files_only=True for AutoTokenizer.
- Introduced optional parameter fname_added_tokens for flexible added token management.
- Streamlined added token handling for clarity and conciseness.
- Maintained special tokens and IDs, enhancing token management.
- Simplified token processing methods for improved readability.
- Added a placeholder for score computation with a default value of -1000.0.
- Optimized newline token check for efficiency.
- Updated __repr__ function for clarity in representation.
- Adjusted type alias Vocab to include BpeVocab, SentencePieceVocab, and HfVocab.
- Removed redundant code related to special token handling, reverse vocabulary mapping, and vocabulary file detection.
This refactoring promotes a standardized and modular approach to vocabulary management, facilitating future integration with a VocabFactory and improving code maintainability and scalability.
* refactor: Enhance readability, functionality, and code quality
- Improved code formatting and readability for better maintainability.
- Refactored LazyUnpickler's CLASSES dictionary for clarity.
- Added print statements and warnings in check_vocab_size for user feedback.
- Removed find_vocab_file_path, as it's superseded by VocabFactory.
- Preparatory changes for upcoming classes: OutputFile and VocabFactory.
- Overall focus on code quality, error handling, and consistency.
These changes reflect a continuous effort to refine the codebase, ensuring it meets best practices and prepares for future enhancements, such as the VocabFactory.
* refactor: Update OutputFile class for enhanced model vocabulary management
- Restructured the constructor for improved readability.
- Updated `add_meta_arch` method for flexible model name determination.
- Introduced `handle_tokenizer_model` for mapping vocab types to supported tokenizer models.
- Streamlined vocabulary extraction with `extract_vocabulary_from_model`.
- Simplified vocabulary metadata addition using `add_meta_vocab`.
- Refactored `add_tensor_info` for clarity and consistency.
- Improved error handling for better user feedback.
These changes signify the development of a versatile and comprehensive `OutputFile` class, enabling efficient management of model conversion output, metadata, vocabulary, and tensor information.
* feat: Introduce VocabFactory for flexible vocabulary management in model conversion
- The VocabFactory class is added to facilitate modular vocabulary handling.
- The constructor initializes a directory path and detects vocabulary-related files.
- The _select_file method provides file paths based on vocabulary type (e.g., BPE, SentencePiece).
- _create_special_vocab generates special vocabularies, accommodating different types.
- The load_vocab method loads vocabularies, handling BPE, SentencePiece, and Hugging Face Fast Tokenizer.
- Error handling and logging enhance debugging and user feedback.
- The modular and flexible design simplifies vocabulary management and supports future extensions.
The VocabFactory class enhances code modularity and maintainability, allowing versatile vocabulary handling in the model conversion process.
* refactor: Improve code organization, argument parsing, and user interface
- Renamed 'default_outfile' to 'default_output_file' for clarity.
- Refactored argument parser setup into 'get_argument_parser' function.
- Introduced descriptive comments for each argument in the parser.
- Added '--vocab-type' argument with choices ["spm", "bpe", "hfft"] for vocabulary processing.
- Improved flag naming consistency: '--outfile' to '--out-file' and '--bigendian' to '--big-endian'.
- Enhanced error handling to prevent overwriting input data in 'default_output_file'.
- Made 'argv' in 'main' an optional parameter for flexibility.
- Introduced dynamic import for 'awq.apply_awq' based on 'args.awq_path' for conditional dependency.
These changes enhance code clarity, organization, and the user interface of the script, aligning it with Python best practices and improving maintainability.
* refactor: Further refine functionality, improve user interaction, and streamline vocabulary handling
- Renamed command-line arguments for clarity and consistency.
- Improved path resolution and import adjustments for robustness.
- Thoughtfully handled 'awq-path' and conditional logic for the weighted model.
- Enhanced model and vocabulary loading with the 'VocabFactory' class for structured and adaptable loading.
- Strengthened error handling and user feedback for a more user-friendly experience.
- Structured output file handling with clear conditions and defaults.
- Streamlined and organized the 'main' function for better logic flow.
- Passed 'sys.argv[1:]' to 'main' for adaptability and testability.
These changes solidify the script's functionality, making it more robust, user-friendly, and adaptable. The use of the 'VocabFactory' class is a notable enhancement in efficient vocabulary handling, reflecting a thoughtful and iterative approach to script development.
* chore: Apply ruff formatting to convert.py
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
* Revert to commit 0614c33
* chore: Apply flake8 formatting rules
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
* refactor: Revise `check_vocab_size` for Enhanced Clarity and Correctness
- Resolved an unreachable branch issue by reorganizing the conditional structure.
- Moved the special case check for `params.n_vocab == -1` to the top for immediate assertion.
- Flattened the conditional logic for improved clarity and predictability of the function's behavior.
These changes enhance the readability and functional correctness of the `check_vocab_size` function without altering its intended functionality.
* py : fix outfile and outtype
* py : suggest hint for missing vocab size
---------
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-01-09 18:46:46 +00:00
local_files_only = True ,
)
2024-03-28 15:44:36 +00:00
assert self . tokenizer . is_fast # assume tokenizer.json is used
convert.py : fix vanilla LLaMA model conversion (#4818)
* Update Imports and Add Notes for Future Reference
- Updated import statements in `convert.py`.
- Added import for `AutoTokenizer` from `transformers` module.
- Added conditional import for `gguf` from the local directory.
- Added comments and notes for future reference.
Additional Notes:
- Noted removal of a redundant `TypeAlias` import.
- Noted the removal of a `gguf` debug statement.
- Commented on the presence of `ARCH` and `NDArray` definitions.
- Commented on cleaning up and refactoring data type definitions.
* Refine Model Hyperparameters and Params Class
- Updated type annotations to use `Optional` for clarity.
- Improved method names and attribute consistency.
- Removed unnecessary variables for better code readability.
Additional Notes:
- Highlighted the use of `Optional` for clearer intent.
- Ensured backward and forward compatibility.
* Restore BpeVocab and SentencePieceVocab classes
- Restored the BpeVocab class for handling BPE tokenization.
- Restored the SentencePieceVocab class for SentencePiece tokenization.
These classes are essential for maintaining the original behavior of the codebase.
* refactor: Standardize vocabulary handling with HfVocab
- Replaced VocabLoader with HfVocab, aligning vocabulary handling across classes.
- Updated initialization of HfVocab with local_files_only=True for AutoTokenizer.
- Introduced optional parameter fname_added_tokens for flexible added token management.
- Streamlined added token handling for clarity and conciseness.
- Maintained special tokens and IDs, enhancing token management.
- Simplified token processing methods for improved readability.
- Added a placeholder for score computation with a default value of -1000.0.
- Optimized newline token check for efficiency.
- Updated __repr__ function for clarity in representation.
- Adjusted type alias Vocab to include BpeVocab, SentencePieceVocab, and HfVocab.
- Removed redundant code related to special token handling, reverse vocabulary mapping, and vocabulary file detection.
This refactoring promotes a standardized and modular approach to vocabulary management, facilitating future integration with a VocabFactory and improving code maintainability and scalability.
* refactor: Enhance readability, functionality, and code quality
- Improved code formatting and readability for better maintainability.
- Refactored LazyUnpickler's CLASSES dictionary for clarity.
- Added print statements and warnings in check_vocab_size for user feedback.
- Removed find_vocab_file_path, as it's superseded by VocabFactory.
- Preparatory changes for upcoming classes: OutputFile and VocabFactory.
- Overall focus on code quality, error handling, and consistency.
These changes reflect a continuous effort to refine the codebase, ensuring it meets best practices and prepares for future enhancements, such as the VocabFactory.
* refactor: Update OutputFile class for enhanced model vocabulary management
- Restructured the constructor for improved readability.
- Updated `add_meta_arch` method for flexible model name determination.
- Introduced `handle_tokenizer_model` for mapping vocab types to supported tokenizer models.
- Streamlined vocabulary extraction with `extract_vocabulary_from_model`.
- Simplified vocabulary metadata addition using `add_meta_vocab`.
- Refactored `add_tensor_info` for clarity and consistency.
- Improved error handling for better user feedback.
These changes signify the development of a versatile and comprehensive `OutputFile` class, enabling efficient management of model conversion output, metadata, vocabulary, and tensor information.
* feat: Introduce VocabFactory for flexible vocabulary management in model conversion
- The VocabFactory class is added to facilitate modular vocabulary handling.
- The constructor initializes a directory path and detects vocabulary-related files.
- The _select_file method provides file paths based on vocabulary type (e.g., BPE, SentencePiece).
- _create_special_vocab generates special vocabularies, accommodating different types.
- The load_vocab method loads vocabularies, handling BPE, SentencePiece, and Hugging Face Fast Tokenizer.
- Error handling and logging enhance debugging and user feedback.
- The modular and flexible design simplifies vocabulary management and supports future extensions.
The VocabFactory class enhances code modularity and maintainability, allowing versatile vocabulary handling in the model conversion process.
* refactor: Improve code organization, argument parsing, and user interface
- Renamed 'default_outfile' to 'default_output_file' for clarity.
- Refactored argument parser setup into 'get_argument_parser' function.
- Introduced descriptive comments for each argument in the parser.
- Added '--vocab-type' argument with choices ["spm", "bpe", "hfft"] for vocabulary processing.
- Improved flag naming consistency: '--outfile' to '--out-file' and '--bigendian' to '--big-endian'.
- Enhanced error handling to prevent overwriting input data in 'default_output_file'.
- Made 'argv' in 'main' an optional parameter for flexibility.
- Introduced dynamic import for 'awq.apply_awq' based on 'args.awq_path' for conditional dependency.
These changes enhance code clarity, organization, and the user interface of the script, aligning it with Python best practices and improving maintainability.
* refactor: Further refine functionality, improve user interaction, and streamline vocabulary handling
- Renamed command-line arguments for clarity and consistency.
- Improved path resolution and import adjustments for robustness.
- Thoughtfully handled 'awq-path' and conditional logic for the weighted model.
- Enhanced model and vocabulary loading with the 'VocabFactory' class for structured and adaptable loading.
- Strengthened error handling and user feedback for a more user-friendly experience.
- Structured output file handling with clear conditions and defaults.
- Streamlined and organized the 'main' function for better logic flow.
- Passed 'sys.argv[1:]' to 'main' for adaptability and testability.
These changes solidify the script's functionality, making it more robust, user-friendly, and adaptable. The use of the 'VocabFactory' class is a notable enhancement in efficient vocabulary handling, reflecting a thoughtful and iterative approach to script development.
* chore: Apply ruff formatting to convert.py
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
* Revert to commit 0614c33
* chore: Apply flake8 formatting rules
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
* refactor: Revise `check_vocab_size` for Enhanced Clarity and Correctness
- Resolved an unreachable branch issue by reorganizing the conditional structure.
- Moved the special case check for `params.n_vocab == -1` to the top for immediate assertion.
- Flattened the conditional logic for improved clarity and predictability of the function's behavior.
These changes enhance the readability and functional correctness of the `check_vocab_size` function without altering its intended functionality.
* py : fix outfile and outtype
* py : suggest hint for missing vocab size
---------
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-01-09 18:46:46 +00:00
# Initialize lists and dictionaries for added tokens
self . added_tokens_list = [ ]
self . added_tokens_dict = dict ( )
2024-01-20 23:14:18 +00:00
self . added_tokens_ids = set ( )
convert.py : fix vanilla LLaMA model conversion (#4818)
* Update Imports and Add Notes for Future Reference
- Updated import statements in `convert.py`.
- Added import for `AutoTokenizer` from `transformers` module.
- Added conditional import for `gguf` from the local directory.
- Added comments and notes for future reference.
Additional Notes:
- Noted removal of a redundant `TypeAlias` import.
- Noted the removal of a `gguf` debug statement.
- Commented on the presence of `ARCH` and `NDArray` definitions.
- Commented on cleaning up and refactoring data type definitions.
* Refine Model Hyperparameters and Params Class
- Updated type annotations to use `Optional` for clarity.
- Improved method names and attribute consistency.
- Removed unnecessary variables for better code readability.
Additional Notes:
- Highlighted the use of `Optional` for clearer intent.
- Ensured backward and forward compatibility.
* Restore BpeVocab and SentencePieceVocab classes
- Restored the BpeVocab class for handling BPE tokenization.
- Restored the SentencePieceVocab class for SentencePiece tokenization.
These classes are essential for maintaining the original behavior of the codebase.
* refactor: Standardize vocabulary handling with HfVocab
- Replaced VocabLoader with HfVocab, aligning vocabulary handling across classes.
- Updated initialization of HfVocab with local_files_only=True for AutoTokenizer.
- Introduced optional parameter fname_added_tokens for flexible added token management.
- Streamlined added token handling for clarity and conciseness.
- Maintained special tokens and IDs, enhancing token management.
- Simplified token processing methods for improved readability.
- Added a placeholder for score computation with a default value of -1000.0.
- Optimized newline token check for efficiency.
- Updated __repr__ function for clarity in representation.
- Adjusted type alias Vocab to include BpeVocab, SentencePieceVocab, and HfVocab.
- Removed redundant code related to special token handling, reverse vocabulary mapping, and vocabulary file detection.
This refactoring promotes a standardized and modular approach to vocabulary management, facilitating future integration with a VocabFactory and improving code maintainability and scalability.
* refactor: Enhance readability, functionality, and code quality
- Improved code formatting and readability for better maintainability.
- Refactored LazyUnpickler's CLASSES dictionary for clarity.
- Added print statements and warnings in check_vocab_size for user feedback.
- Removed find_vocab_file_path, as it's superseded by VocabFactory.
- Preparatory changes for upcoming classes: OutputFile and VocabFactory.
- Overall focus on code quality, error handling, and consistency.
These changes reflect a continuous effort to refine the codebase, ensuring it meets best practices and prepares for future enhancements, such as the VocabFactory.
* refactor: Update OutputFile class for enhanced model vocabulary management
- Restructured the constructor for improved readability.
- Updated `add_meta_arch` method for flexible model name determination.
- Introduced `handle_tokenizer_model` for mapping vocab types to supported tokenizer models.
- Streamlined vocabulary extraction with `extract_vocabulary_from_model`.
- Simplified vocabulary metadata addition using `add_meta_vocab`.
- Refactored `add_tensor_info` for clarity and consistency.
- Improved error handling for better user feedback.
These changes signify the development of a versatile and comprehensive `OutputFile` class, enabling efficient management of model conversion output, metadata, vocabulary, and tensor information.
* feat: Introduce VocabFactory for flexible vocabulary management in model conversion
- The VocabFactory class is added to facilitate modular vocabulary handling.
- The constructor initializes a directory path and detects vocabulary-related files.
- The _select_file method provides file paths based on vocabulary type (e.g., BPE, SentencePiece).
- _create_special_vocab generates special vocabularies, accommodating different types.
- The load_vocab method loads vocabularies, handling BPE, SentencePiece, and Hugging Face Fast Tokenizer.
- Error handling and logging enhance debugging and user feedback.
- The modular and flexible design simplifies vocabulary management and supports future extensions.
The VocabFactory class enhances code modularity and maintainability, allowing versatile vocabulary handling in the model conversion process.
* refactor: Improve code organization, argument parsing, and user interface
- Renamed 'default_outfile' to 'default_output_file' for clarity.
- Refactored argument parser setup into 'get_argument_parser' function.
- Introduced descriptive comments for each argument in the parser.
- Added '--vocab-type' argument with choices ["spm", "bpe", "hfft"] for vocabulary processing.
- Improved flag naming consistency: '--outfile' to '--out-file' and '--bigendian' to '--big-endian'.
- Enhanced error handling to prevent overwriting input data in 'default_output_file'.
- Made 'argv' in 'main' an optional parameter for flexibility.
- Introduced dynamic import for 'awq.apply_awq' based on 'args.awq_path' for conditional dependency.
These changes enhance code clarity, organization, and the user interface of the script, aligning it with Python best practices and improving maintainability.
* refactor: Further refine functionality, improve user interaction, and streamline vocabulary handling
- Renamed command-line arguments for clarity and consistency.
- Improved path resolution and import adjustments for robustness.
- Thoughtfully handled 'awq-path' and conditional logic for the weighted model.
- Enhanced model and vocabulary loading with the 'VocabFactory' class for structured and adaptable loading.
- Strengthened error handling and user feedback for a more user-friendly experience.
- Structured output file handling with clear conditions and defaults.
- Streamlined and organized the 'main' function for better logic flow.
- Passed 'sys.argv[1:]' to 'main' for adaptability and testability.
These changes solidify the script's functionality, making it more robust, user-friendly, and adaptable. The use of the 'VocabFactory' class is a notable enhancement in efficient vocabulary handling, reflecting a thoughtful and iterative approach to script development.
* chore: Apply ruff formatting to convert.py
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
* Revert to commit 0614c33
* chore: Apply flake8 formatting rules
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
* refactor: Revise `check_vocab_size` for Enhanced Clarity and Correctness
- Resolved an unreachable branch issue by reorganizing the conditional structure.
- Moved the special case check for `params.n_vocab == -1` to the top for immediate assertion.
- Flattened the conditional logic for improved clarity and predictability of the function's behavior.
These changes enhance the readability and functional correctness of the `check_vocab_size` function without altering its intended functionality.
* py : fix outfile and outtype
* py : suggest hint for missing vocab size
---------
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-01-09 18:46:46 +00:00
# Process added tokens
for tok , tokidx in sorted (
self . tokenizer . get_added_vocab ( ) . items ( ) , key = lambda x : x [ 1 ]
) :
# Only consider added tokens that are not in the base vocabulary
if tokidx > = self . tokenizer . vocab_size :
self . added_tokens_list . append ( tok )
self . added_tokens_dict [ tok ] = tokidx
self . added_tokens_ids . add ( tokidx )
# Store special tokens and their IDs
self . specials = {
2023-12-14 08:09:34 +00:00
tok : self . tokenizer . get_vocab ( ) [ tok ]
for tok in self . tokenizer . all_special_tokens
}
convert.py : fix vanilla LLaMA model conversion (#4818)
* Update Imports and Add Notes for Future Reference
- Updated import statements in `convert.py`.
- Added import for `AutoTokenizer` from `transformers` module.
- Added conditional import for `gguf` from the local directory.
- Added comments and notes for future reference.
Additional Notes:
- Noted removal of a redundant `TypeAlias` import.
- Noted the removal of a `gguf` debug statement.
- Commented on the presence of `ARCH` and `NDArray` definitions.
- Commented on cleaning up and refactoring data type definitions.
* Refine Model Hyperparameters and Params Class
- Updated type annotations to use `Optional` for clarity.
- Improved method names and attribute consistency.
- Removed unnecessary variables for better code readability.
Additional Notes:
- Highlighted the use of `Optional` for clearer intent.
- Ensured backward and forward compatibility.
* Restore BpeVocab and SentencePieceVocab classes
- Restored the BpeVocab class for handling BPE tokenization.
- Restored the SentencePieceVocab class for SentencePiece tokenization.
These classes are essential for maintaining the original behavior of the codebase.
* refactor: Standardize vocabulary handling with HfVocab
- Replaced VocabLoader with HfVocab, aligning vocabulary handling across classes.
- Updated initialization of HfVocab with local_files_only=True for AutoTokenizer.
- Introduced optional parameter fname_added_tokens for flexible added token management.
- Streamlined added token handling for clarity and conciseness.
- Maintained special tokens and IDs, enhancing token management.
- Simplified token processing methods for improved readability.
- Added a placeholder for score computation with a default value of -1000.0.
- Optimized newline token check for efficiency.
- Updated __repr__ function for clarity in representation.
- Adjusted type alias Vocab to include BpeVocab, SentencePieceVocab, and HfVocab.
- Removed redundant code related to special token handling, reverse vocabulary mapping, and vocabulary file detection.
This refactoring promotes a standardized and modular approach to vocabulary management, facilitating future integration with a VocabFactory and improving code maintainability and scalability.
* refactor: Enhance readability, functionality, and code quality
- Improved code formatting and readability for better maintainability.
- Refactored LazyUnpickler's CLASSES dictionary for clarity.
- Added print statements and warnings in check_vocab_size for user feedback.
- Removed find_vocab_file_path, as it's superseded by VocabFactory.
- Preparatory changes for upcoming classes: OutputFile and VocabFactory.
- Overall focus on code quality, error handling, and consistency.
These changes reflect a continuous effort to refine the codebase, ensuring it meets best practices and prepares for future enhancements, such as the VocabFactory.
* refactor: Update OutputFile class for enhanced model vocabulary management
- Restructured the constructor for improved readability.
- Updated `add_meta_arch` method for flexible model name determination.
- Introduced `handle_tokenizer_model` for mapping vocab types to supported tokenizer models.
- Streamlined vocabulary extraction with `extract_vocabulary_from_model`.
- Simplified vocabulary metadata addition using `add_meta_vocab`.
- Refactored `add_tensor_info` for clarity and consistency.
- Improved error handling for better user feedback.
These changes signify the development of a versatile and comprehensive `OutputFile` class, enabling efficient management of model conversion output, metadata, vocabulary, and tensor information.
* feat: Introduce VocabFactory for flexible vocabulary management in model conversion
- The VocabFactory class is added to facilitate modular vocabulary handling.
- The constructor initializes a directory path and detects vocabulary-related files.
- The _select_file method provides file paths based on vocabulary type (e.g., BPE, SentencePiece).
- _create_special_vocab generates special vocabularies, accommodating different types.
- The load_vocab method loads vocabularies, handling BPE, SentencePiece, and Hugging Face Fast Tokenizer.
- Error handling and logging enhance debugging and user feedback.
- The modular and flexible design simplifies vocabulary management and supports future extensions.
The VocabFactory class enhances code modularity and maintainability, allowing versatile vocabulary handling in the model conversion process.
* refactor: Improve code organization, argument parsing, and user interface
- Renamed 'default_outfile' to 'default_output_file' for clarity.
- Refactored argument parser setup into 'get_argument_parser' function.
- Introduced descriptive comments for each argument in the parser.
- Added '--vocab-type' argument with choices ["spm", "bpe", "hfft"] for vocabulary processing.
- Improved flag naming consistency: '--outfile' to '--out-file' and '--bigendian' to '--big-endian'.
- Enhanced error handling to prevent overwriting input data in 'default_output_file'.
- Made 'argv' in 'main' an optional parameter for flexibility.
- Introduced dynamic import for 'awq.apply_awq' based on 'args.awq_path' for conditional dependency.
These changes enhance code clarity, organization, and the user interface of the script, aligning it with Python best practices and improving maintainability.
* refactor: Further refine functionality, improve user interaction, and streamline vocabulary handling
- Renamed command-line arguments for clarity and consistency.
- Improved path resolution and import adjustments for robustness.
- Thoughtfully handled 'awq-path' and conditional logic for the weighted model.
- Enhanced model and vocabulary loading with the 'VocabFactory' class for structured and adaptable loading.
- Strengthened error handling and user feedback for a more user-friendly experience.
- Structured output file handling with clear conditions and defaults.
- Streamlined and organized the 'main' function for better logic flow.
- Passed 'sys.argv[1:]' to 'main' for adaptability and testability.
These changes solidify the script's functionality, making it more robust, user-friendly, and adaptable. The use of the 'VocabFactory' class is a notable enhancement in efficient vocabulary handling, reflecting a thoughtful and iterative approach to script development.
* chore: Apply ruff formatting to convert.py
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
* Revert to commit 0614c33
* chore: Apply flake8 formatting rules
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
* refactor: Revise `check_vocab_size` for Enhanced Clarity and Correctness
- Resolved an unreachable branch issue by reorganizing the conditional structure.
- Moved the special case check for `params.n_vocab == -1` to the top for immediate assertion.
- Flattened the conditional logic for improved clarity and predictability of the function's behavior.
These changes enhance the readability and functional correctness of the `check_vocab_size` function without altering its intended functionality.
* py : fix outfile and outtype
* py : suggest hint for missing vocab size
---------
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-01-09 18:46:46 +00:00
self . special_ids = set ( self . tokenizer . all_special_ids )
2023-08-21 20:07:43 +00:00
convert.py : fix vanilla LLaMA model conversion (#4818)
* Update Imports and Add Notes for Future Reference
- Updated import statements in `convert.py`.
- Added import for `AutoTokenizer` from `transformers` module.
- Added conditional import for `gguf` from the local directory.
- Added comments and notes for future reference.
Additional Notes:
- Noted removal of a redundant `TypeAlias` import.
- Noted the removal of a `gguf` debug statement.
- Commented on the presence of `ARCH` and `NDArray` definitions.
- Commented on cleaning up and refactoring data type definitions.
* Refine Model Hyperparameters and Params Class
- Updated type annotations to use `Optional` for clarity.
- Improved method names and attribute consistency.
- Removed unnecessary variables for better code readability.
Additional Notes:
- Highlighted the use of `Optional` for clearer intent.
- Ensured backward and forward compatibility.
* Restore BpeVocab and SentencePieceVocab classes
- Restored the BpeVocab class for handling BPE tokenization.
- Restored the SentencePieceVocab class for SentencePiece tokenization.
These classes are essential for maintaining the original behavior of the codebase.
* refactor: Standardize vocabulary handling with HfVocab
- Replaced VocabLoader with HfVocab, aligning vocabulary handling across classes.
- Updated initialization of HfVocab with local_files_only=True for AutoTokenizer.
- Introduced optional parameter fname_added_tokens for flexible added token management.
- Streamlined added token handling for clarity and conciseness.
- Maintained special tokens and IDs, enhancing token management.
- Simplified token processing methods for improved readability.
- Added a placeholder for score computation with a default value of -1000.0.
- Optimized newline token check for efficiency.
- Updated __repr__ function for clarity in representation.
- Adjusted type alias Vocab to include BpeVocab, SentencePieceVocab, and HfVocab.
- Removed redundant code related to special token handling, reverse vocabulary mapping, and vocabulary file detection.
This refactoring promotes a standardized and modular approach to vocabulary management, facilitating future integration with a VocabFactory and improving code maintainability and scalability.
* refactor: Enhance readability, functionality, and code quality
- Improved code formatting and readability for better maintainability.
- Refactored LazyUnpickler's CLASSES dictionary for clarity.
- Added print statements and warnings in check_vocab_size for user feedback.
- Removed find_vocab_file_path, as it's superseded by VocabFactory.
- Preparatory changes for upcoming classes: OutputFile and VocabFactory.
- Overall focus on code quality, error handling, and consistency.
These changes reflect a continuous effort to refine the codebase, ensuring it meets best practices and prepares for future enhancements, such as the VocabFactory.
* refactor: Update OutputFile class for enhanced model vocabulary management
- Restructured the constructor for improved readability.
- Updated `add_meta_arch` method for flexible model name determination.
- Introduced `handle_tokenizer_model` for mapping vocab types to supported tokenizer models.
- Streamlined vocabulary extraction with `extract_vocabulary_from_model`.
- Simplified vocabulary metadata addition using `add_meta_vocab`.
- Refactored `add_tensor_info` for clarity and consistency.
- Improved error handling for better user feedback.
These changes signify the development of a versatile and comprehensive `OutputFile` class, enabling efficient management of model conversion output, metadata, vocabulary, and tensor information.
* feat: Introduce VocabFactory for flexible vocabulary management in model conversion
- The VocabFactory class is added to facilitate modular vocabulary handling.
- The constructor initializes a directory path and detects vocabulary-related files.
- The _select_file method provides file paths based on vocabulary type (e.g., BPE, SentencePiece).
- _create_special_vocab generates special vocabularies, accommodating different types.
- The load_vocab method loads vocabularies, handling BPE, SentencePiece, and Hugging Face Fast Tokenizer.
- Error handling and logging enhance debugging and user feedback.
- The modular and flexible design simplifies vocabulary management and supports future extensions.
The VocabFactory class enhances code modularity and maintainability, allowing versatile vocabulary handling in the model conversion process.
* refactor: Improve code organization, argument parsing, and user interface
- Renamed 'default_outfile' to 'default_output_file' for clarity.
- Refactored argument parser setup into 'get_argument_parser' function.
- Introduced descriptive comments for each argument in the parser.
- Added '--vocab-type' argument with choices ["spm", "bpe", "hfft"] for vocabulary processing.
- Improved flag naming consistency: '--outfile' to '--out-file' and '--bigendian' to '--big-endian'.
- Enhanced error handling to prevent overwriting input data in 'default_output_file'.
- Made 'argv' in 'main' an optional parameter for flexibility.
- Introduced dynamic import for 'awq.apply_awq' based on 'args.awq_path' for conditional dependency.
These changes enhance code clarity, organization, and the user interface of the script, aligning it with Python best practices and improving maintainability.
* refactor: Further refine functionality, improve user interaction, and streamline vocabulary handling
- Renamed command-line arguments for clarity and consistency.
- Improved path resolution and import adjustments for robustness.
- Thoughtfully handled 'awq-path' and conditional logic for the weighted model.
- Enhanced model and vocabulary loading with the 'VocabFactory' class for structured and adaptable loading.
- Strengthened error handling and user feedback for a more user-friendly experience.
- Structured output file handling with clear conditions and defaults.
- Streamlined and organized the 'main' function for better logic flow.
- Passed 'sys.argv[1:]' to 'main' for adaptability and testability.
These changes solidify the script's functionality, making it more robust, user-friendly, and adaptable. The use of the 'VocabFactory' class is a notable enhancement in efficient vocabulary handling, reflecting a thoughtful and iterative approach to script development.
* chore: Apply ruff formatting to convert.py
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
* Revert to commit 0614c33
* chore: Apply flake8 formatting rules
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
* refactor: Revise `check_vocab_size` for Enhanced Clarity and Correctness
- Resolved an unreachable branch issue by reorganizing the conditional structure.
- Moved the special case check for `params.n_vocab == -1` to the top for immediate assertion.
- Flattened the conditional logic for improved clarity and predictability of the function's behavior.
These changes enhance the readability and functional correctness of the `check_vocab_size` function without altering its intended functionality.
* py : fix outfile and outtype
* py : suggest hint for missing vocab size
---------
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-01-09 18:46:46 +00:00
# Set vocabulary sizes
self . vocab_size_base = self . tokenizer . vocab_size
2024-01-20 23:14:18 +00:00
self . vocab_size = self . vocab_size_base + len ( self . added_tokens_list )
2023-10-28 12:25:15 +00:00
2024-03-28 15:44:36 +00:00
self . fname_tokenizer = fname_tokenizer
2023-10-28 12:25:15 +00:00
2024-01-20 23:14:18 +00:00
def hf_tokens ( self ) - > Iterable [ tuple [ bytes , float , gguf . TokenType ] ] :
convert.py : fix vanilla LLaMA model conversion (#4818)
* Update Imports and Add Notes for Future Reference
- Updated import statements in `convert.py`.
- Added import for `AutoTokenizer` from `transformers` module.
- Added conditional import for `gguf` from the local directory.
- Added comments and notes for future reference.
Additional Notes:
- Noted removal of a redundant `TypeAlias` import.
- Noted the removal of a `gguf` debug statement.
- Commented on the presence of `ARCH` and `NDArray` definitions.
- Commented on cleaning up and refactoring data type definitions.
* Refine Model Hyperparameters and Params Class
- Updated type annotations to use `Optional` for clarity.
- Improved method names and attribute consistency.
- Removed unnecessary variables for better code readability.
Additional Notes:
- Highlighted the use of `Optional` for clearer intent.
- Ensured backward and forward compatibility.
* Restore BpeVocab and SentencePieceVocab classes
- Restored the BpeVocab class for handling BPE tokenization.
- Restored the SentencePieceVocab class for SentencePiece tokenization.
These classes are essential for maintaining the original behavior of the codebase.
* refactor: Standardize vocabulary handling with HfVocab
- Replaced VocabLoader with HfVocab, aligning vocabulary handling across classes.
- Updated initialization of HfVocab with local_files_only=True for AutoTokenizer.
- Introduced optional parameter fname_added_tokens for flexible added token management.
- Streamlined added token handling for clarity and conciseness.
- Maintained special tokens and IDs, enhancing token management.
- Simplified token processing methods for improved readability.
- Added a placeholder for score computation with a default value of -1000.0.
- Optimized newline token check for efficiency.
- Updated __repr__ function for clarity in representation.
- Adjusted type alias Vocab to include BpeVocab, SentencePieceVocab, and HfVocab.
- Removed redundant code related to special token handling, reverse vocabulary mapping, and vocabulary file detection.
This refactoring promotes a standardized and modular approach to vocabulary management, facilitating future integration with a VocabFactory and improving code maintainability and scalability.
* refactor: Enhance readability, functionality, and code quality
- Improved code formatting and readability for better maintainability.
- Refactored LazyUnpickler's CLASSES dictionary for clarity.
- Added print statements and warnings in check_vocab_size for user feedback.
- Removed find_vocab_file_path, as it's superseded by VocabFactory.
- Preparatory changes for upcoming classes: OutputFile and VocabFactory.
- Overall focus on code quality, error handling, and consistency.
These changes reflect a continuous effort to refine the codebase, ensuring it meets best practices and prepares for future enhancements, such as the VocabFactory.
* refactor: Update OutputFile class for enhanced model vocabulary management
- Restructured the constructor for improved readability.
- Updated `add_meta_arch` method for flexible model name determination.
- Introduced `handle_tokenizer_model` for mapping vocab types to supported tokenizer models.
- Streamlined vocabulary extraction with `extract_vocabulary_from_model`.
- Simplified vocabulary metadata addition using `add_meta_vocab`.
- Refactored `add_tensor_info` for clarity and consistency.
- Improved error handling for better user feedback.
These changes signify the development of a versatile and comprehensive `OutputFile` class, enabling efficient management of model conversion output, metadata, vocabulary, and tensor information.
* feat: Introduce VocabFactory for flexible vocabulary management in model conversion
- The VocabFactory class is added to facilitate modular vocabulary handling.
- The constructor initializes a directory path and detects vocabulary-related files.
- The _select_file method provides file paths based on vocabulary type (e.g., BPE, SentencePiece).
- _create_special_vocab generates special vocabularies, accommodating different types.
- The load_vocab method loads vocabularies, handling BPE, SentencePiece, and Hugging Face Fast Tokenizer.
- Error handling and logging enhance debugging and user feedback.
- The modular and flexible design simplifies vocabulary management and supports future extensions.
The VocabFactory class enhances code modularity and maintainability, allowing versatile vocabulary handling in the model conversion process.
* refactor: Improve code organization, argument parsing, and user interface
- Renamed 'default_outfile' to 'default_output_file' for clarity.
- Refactored argument parser setup into 'get_argument_parser' function.
- Introduced descriptive comments for each argument in the parser.
- Added '--vocab-type' argument with choices ["spm", "bpe", "hfft"] for vocabulary processing.
- Improved flag naming consistency: '--outfile' to '--out-file' and '--bigendian' to '--big-endian'.
- Enhanced error handling to prevent overwriting input data in 'default_output_file'.
- Made 'argv' in 'main' an optional parameter for flexibility.
- Introduced dynamic import for 'awq.apply_awq' based on 'args.awq_path' for conditional dependency.
These changes enhance code clarity, organization, and the user interface of the script, aligning it with Python best practices and improving maintainability.
* refactor: Further refine functionality, improve user interaction, and streamline vocabulary handling
- Renamed command-line arguments for clarity and consistency.
- Improved path resolution and import adjustments for robustness.
- Thoughtfully handled 'awq-path' and conditional logic for the weighted model.
- Enhanced model and vocabulary loading with the 'VocabFactory' class for structured and adaptable loading.
- Strengthened error handling and user feedback for a more user-friendly experience.
- Structured output file handling with clear conditions and defaults.
- Streamlined and organized the 'main' function for better logic flow.
- Passed 'sys.argv[1:]' to 'main' for adaptability and testability.
These changes solidify the script's functionality, making it more robust, user-friendly, and adaptable. The use of the 'VocabFactory' class is a notable enhancement in efficient vocabulary handling, reflecting a thoughtful and iterative approach to script development.
* chore: Apply ruff formatting to convert.py
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
* Revert to commit 0614c33
* chore: Apply flake8 formatting rules
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
* refactor: Revise `check_vocab_size` for Enhanced Clarity and Correctness
- Resolved an unreachable branch issue by reorganizing the conditional structure.
- Moved the special case check for `params.n_vocab == -1` to the top for immediate assertion.
- Flattened the conditional logic for improved clarity and predictability of the function's behavior.
These changes enhance the readability and functional correctness of the `check_vocab_size` function without altering its intended functionality.
* py : fix outfile and outtype
* py : suggest hint for missing vocab size
---------
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-01-09 18:46:46 +00:00
reverse_vocab = {
id : encoded_tok for encoded_tok , id in self . tokenizer . get_vocab ( ) . items ( )
}
py : new conversion script (#545)
Current status: Working, except for the latest GPTQ-for-LLaMa format
that includes `g_idx`. This turns out to require changes to GGML, so
for now it only works if you use the `--outtype` option to dequantize it
back to f16 (which is pointless except for debugging).
I also included some cleanup for the C++ code.
This script is meant to replace all the existing conversion scripts
(including the ones that convert from older GGML formats), while also
adding support for some new formats. Specifically, I've tested with:
- [x] `LLaMA` (original)
- [x] `llama-65b-4bit`
- [x] `alpaca-native`
- [x] `alpaca-native-4bit`
- [x] LLaMA converted to 'transformers' format using
`convert_llama_weights_to_hf.py`
- [x] `alpaca-native` quantized with `--true-sequential --act-order
--groupsize 128` (dequantized only)
- [x] same as above plus `--save_safetensors`
- [x] GPT4All
- [x] stock unversioned ggml
- [x] ggmh
There's enough overlap in the logic needed to handle these different
cases that it seemed best to move to a single script.
I haven't tried this with Alpaca-LoRA because I don't know where to find
it.
Useful features:
- Uses multiple threads for a speedup in some cases (though the Python
GIL limits the gain, and sometimes it's disk-bound anyway).
- Combines split models into a single file (both the intra-tensor split
of the original and the inter-tensor split of 'transformers' format
files). Single files are more convenient to work with and more
friendly to future changes to use memory mapping on the C++ side. To
accomplish this without increasing memory requirements, it has some
custom loading code which avoids loading whole input files into memory
at once.
- Because of the custom loading code, it no longer depends in PyTorch,
which might make installing dependencies slightly easier or faster...
although it still depends on NumPy and sentencepiece, so I don't know
if there's any meaningful difference. In any case, I also added a
requirements.txt file to lock the dependency versions in case of any
future breaking changes.
- Type annotations checked with mypy.
- Some attempts to be extra user-friendly:
- The script tries to be forgiving with arguments, e.g. you can
specify either the model file itself or the directory containing
it.
- The script doesn't depend on config.json / params.json, just in
case the user downloaded files individually and doesn't have those
handy. But you still need tokenizer.model and, for Alpaca,
added_tokens.json.
- The script tries to give a helpful error message if
added_tokens.json is missing.
2023-04-14 07:03:03 +00:00
convert.py : fix vanilla LLaMA model conversion (#4818)
* Update Imports and Add Notes for Future Reference
- Updated import statements in `convert.py`.
- Added import for `AutoTokenizer` from `transformers` module.
- Added conditional import for `gguf` from the local directory.
- Added comments and notes for future reference.
Additional Notes:
- Noted removal of a redundant `TypeAlias` import.
- Noted the removal of a `gguf` debug statement.
- Commented on the presence of `ARCH` and `NDArray` definitions.
- Commented on cleaning up and refactoring data type definitions.
* Refine Model Hyperparameters and Params Class
- Updated type annotations to use `Optional` for clarity.
- Improved method names and attribute consistency.
- Removed unnecessary variables for better code readability.
Additional Notes:
- Highlighted the use of `Optional` for clearer intent.
- Ensured backward and forward compatibility.
* Restore BpeVocab and SentencePieceVocab classes
- Restored the BpeVocab class for handling BPE tokenization.
- Restored the SentencePieceVocab class for SentencePiece tokenization.
These classes are essential for maintaining the original behavior of the codebase.
* refactor: Standardize vocabulary handling with HfVocab
- Replaced VocabLoader with HfVocab, aligning vocabulary handling across classes.
- Updated initialization of HfVocab with local_files_only=True for AutoTokenizer.
- Introduced optional parameter fname_added_tokens for flexible added token management.
- Streamlined added token handling for clarity and conciseness.
- Maintained special tokens and IDs, enhancing token management.
- Simplified token processing methods for improved readability.
- Added a placeholder for score computation with a default value of -1000.0.
- Optimized newline token check for efficiency.
- Updated __repr__ function for clarity in representation.
- Adjusted type alias Vocab to include BpeVocab, SentencePieceVocab, and HfVocab.
- Removed redundant code related to special token handling, reverse vocabulary mapping, and vocabulary file detection.
This refactoring promotes a standardized and modular approach to vocabulary management, facilitating future integration with a VocabFactory and improving code maintainability and scalability.
* refactor: Enhance readability, functionality, and code quality
- Improved code formatting and readability for better maintainability.
- Refactored LazyUnpickler's CLASSES dictionary for clarity.
- Added print statements and warnings in check_vocab_size for user feedback.
- Removed find_vocab_file_path, as it's superseded by VocabFactory.
- Preparatory changes for upcoming classes: OutputFile and VocabFactory.
- Overall focus on code quality, error handling, and consistency.
These changes reflect a continuous effort to refine the codebase, ensuring it meets best practices and prepares for future enhancements, such as the VocabFactory.
* refactor: Update OutputFile class for enhanced model vocabulary management
- Restructured the constructor for improved readability.
- Updated `add_meta_arch` method for flexible model name determination.
- Introduced `handle_tokenizer_model` for mapping vocab types to supported tokenizer models.
- Streamlined vocabulary extraction with `extract_vocabulary_from_model`.
- Simplified vocabulary metadata addition using `add_meta_vocab`.
- Refactored `add_tensor_info` for clarity and consistency.
- Improved error handling for better user feedback.
These changes signify the development of a versatile and comprehensive `OutputFile` class, enabling efficient management of model conversion output, metadata, vocabulary, and tensor information.
* feat: Introduce VocabFactory for flexible vocabulary management in model conversion
- The VocabFactory class is added to facilitate modular vocabulary handling.
- The constructor initializes a directory path and detects vocabulary-related files.
- The _select_file method provides file paths based on vocabulary type (e.g., BPE, SentencePiece).
- _create_special_vocab generates special vocabularies, accommodating different types.
- The load_vocab method loads vocabularies, handling BPE, SentencePiece, and Hugging Face Fast Tokenizer.
- Error handling and logging enhance debugging and user feedback.
- The modular and flexible design simplifies vocabulary management and supports future extensions.
The VocabFactory class enhances code modularity and maintainability, allowing versatile vocabulary handling in the model conversion process.
* refactor: Improve code organization, argument parsing, and user interface
- Renamed 'default_outfile' to 'default_output_file' for clarity.
- Refactored argument parser setup into 'get_argument_parser' function.
- Introduced descriptive comments for each argument in the parser.
- Added '--vocab-type' argument with choices ["spm", "bpe", "hfft"] for vocabulary processing.
- Improved flag naming consistency: '--outfile' to '--out-file' and '--bigendian' to '--big-endian'.
- Enhanced error handling to prevent overwriting input data in 'default_output_file'.
- Made 'argv' in 'main' an optional parameter for flexibility.
- Introduced dynamic import for 'awq.apply_awq' based on 'args.awq_path' for conditional dependency.
These changes enhance code clarity, organization, and the user interface of the script, aligning it with Python best practices and improving maintainability.
* refactor: Further refine functionality, improve user interaction, and streamline vocabulary handling
- Renamed command-line arguments for clarity and consistency.
- Improved path resolution and import adjustments for robustness.
- Thoughtfully handled 'awq-path' and conditional logic for the weighted model.
- Enhanced model and vocabulary loading with the 'VocabFactory' class for structured and adaptable loading.
- Strengthened error handling and user feedback for a more user-friendly experience.
- Structured output file handling with clear conditions and defaults.
- Streamlined and organized the 'main' function for better logic flow.
- Passed 'sys.argv[1:]' to 'main' for adaptability and testability.
These changes solidify the script's functionality, making it more robust, user-friendly, and adaptable. The use of the 'VocabFactory' class is a notable enhancement in efficient vocabulary handling, reflecting a thoughtful and iterative approach to script development.
* chore: Apply ruff formatting to convert.py
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
* Revert to commit 0614c33
* chore: Apply flake8 formatting rules
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
* refactor: Revise `check_vocab_size` for Enhanced Clarity and Correctness
- Resolved an unreachable branch issue by reorganizing the conditional structure.
- Moved the special case check for `params.n_vocab == -1` to the top for immediate assertion.
- Flattened the conditional logic for improved clarity and predictability of the function's behavior.
These changes enhance the readability and functional correctness of the `check_vocab_size` function without altering its intended functionality.
* py : fix outfile and outtype
* py : suggest hint for missing vocab size
---------
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-01-09 18:46:46 +00:00
for token_id in range ( self . vocab_size_base ) :
# Skip processing added tokens here
if token_id in self . added_tokens_ids :
continue
2023-08-21 20:07:43 +00:00
convert.py : fix vanilla LLaMA model conversion (#4818)
* Update Imports and Add Notes for Future Reference
- Updated import statements in `convert.py`.
- Added import for `AutoTokenizer` from `transformers` module.
- Added conditional import for `gguf` from the local directory.
- Added comments and notes for future reference.
Additional Notes:
- Noted removal of a redundant `TypeAlias` import.
- Noted the removal of a `gguf` debug statement.
- Commented on the presence of `ARCH` and `NDArray` definitions.
- Commented on cleaning up and refactoring data type definitions.
* Refine Model Hyperparameters and Params Class
- Updated type annotations to use `Optional` for clarity.
- Improved method names and attribute consistency.
- Removed unnecessary variables for better code readability.
Additional Notes:
- Highlighted the use of `Optional` for clearer intent.
- Ensured backward and forward compatibility.
* Restore BpeVocab and SentencePieceVocab classes
- Restored the BpeVocab class for handling BPE tokenization.
- Restored the SentencePieceVocab class for SentencePiece tokenization.
These classes are essential for maintaining the original behavior of the codebase.
* refactor: Standardize vocabulary handling with HfVocab
- Replaced VocabLoader with HfVocab, aligning vocabulary handling across classes.
- Updated initialization of HfVocab with local_files_only=True for AutoTokenizer.
- Introduced optional parameter fname_added_tokens for flexible added token management.
- Streamlined added token handling for clarity and conciseness.
- Maintained special tokens and IDs, enhancing token management.
- Simplified token processing methods for improved readability.
- Added a placeholder for score computation with a default value of -1000.0.
- Optimized newline token check for efficiency.
- Updated __repr__ function for clarity in representation.
- Adjusted type alias Vocab to include BpeVocab, SentencePieceVocab, and HfVocab.
- Removed redundant code related to special token handling, reverse vocabulary mapping, and vocabulary file detection.
This refactoring promotes a standardized and modular approach to vocabulary management, facilitating future integration with a VocabFactory and improving code maintainability and scalability.
* refactor: Enhance readability, functionality, and code quality
- Improved code formatting and readability for better maintainability.
- Refactored LazyUnpickler's CLASSES dictionary for clarity.
- Added print statements and warnings in check_vocab_size for user feedback.
- Removed find_vocab_file_path, as it's superseded by VocabFactory.
- Preparatory changes for upcoming classes: OutputFile and VocabFactory.
- Overall focus on code quality, error handling, and consistency.
These changes reflect a continuous effort to refine the codebase, ensuring it meets best practices and prepares for future enhancements, such as the VocabFactory.
* refactor: Update OutputFile class for enhanced model vocabulary management
- Restructured the constructor for improved readability.
- Updated `add_meta_arch` method for flexible model name determination.
- Introduced `handle_tokenizer_model` for mapping vocab types to supported tokenizer models.
- Streamlined vocabulary extraction with `extract_vocabulary_from_model`.
- Simplified vocabulary metadata addition using `add_meta_vocab`.
- Refactored `add_tensor_info` for clarity and consistency.
- Improved error handling for better user feedback.
These changes signify the development of a versatile and comprehensive `OutputFile` class, enabling efficient management of model conversion output, metadata, vocabulary, and tensor information.
* feat: Introduce VocabFactory for flexible vocabulary management in model conversion
- The VocabFactory class is added to facilitate modular vocabulary handling.
- The constructor initializes a directory path and detects vocabulary-related files.
- The _select_file method provides file paths based on vocabulary type (e.g., BPE, SentencePiece).
- _create_special_vocab generates special vocabularies, accommodating different types.
- The load_vocab method loads vocabularies, handling BPE, SentencePiece, and Hugging Face Fast Tokenizer.
- Error handling and logging enhance debugging and user feedback.
- The modular and flexible design simplifies vocabulary management and supports future extensions.
The VocabFactory class enhances code modularity and maintainability, allowing versatile vocabulary handling in the model conversion process.
* refactor: Improve code organization, argument parsing, and user interface
- Renamed 'default_outfile' to 'default_output_file' for clarity.
- Refactored argument parser setup into 'get_argument_parser' function.
- Introduced descriptive comments for each argument in the parser.
- Added '--vocab-type' argument with choices ["spm", "bpe", "hfft"] for vocabulary processing.
- Improved flag naming consistency: '--outfile' to '--out-file' and '--bigendian' to '--big-endian'.
- Enhanced error handling to prevent overwriting input data in 'default_output_file'.
- Made 'argv' in 'main' an optional parameter for flexibility.
- Introduced dynamic import for 'awq.apply_awq' based on 'args.awq_path' for conditional dependency.
These changes enhance code clarity, organization, and the user interface of the script, aligning it with Python best practices and improving maintainability.
* refactor: Further refine functionality, improve user interaction, and streamline vocabulary handling
- Renamed command-line arguments for clarity and consistency.
- Improved path resolution and import adjustments for robustness.
- Thoughtfully handled 'awq-path' and conditional logic for the weighted model.
- Enhanced model and vocabulary loading with the 'VocabFactory' class for structured and adaptable loading.
- Strengthened error handling and user feedback for a more user-friendly experience.
- Structured output file handling with clear conditions and defaults.
- Streamlined and organized the 'main' function for better logic flow.
- Passed 'sys.argv[1:]' to 'main' for adaptability and testability.
These changes solidify the script's functionality, making it more robust, user-friendly, and adaptable. The use of the 'VocabFactory' class is a notable enhancement in efficient vocabulary handling, reflecting a thoughtful and iterative approach to script development.
* chore: Apply ruff formatting to convert.py
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
* Revert to commit 0614c33
* chore: Apply flake8 formatting rules
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
* refactor: Revise `check_vocab_size` for Enhanced Clarity and Correctness
- Resolved an unreachable branch issue by reorganizing the conditional structure.
- Moved the special case check for `params.n_vocab == -1` to the top for immediate assertion.
- Flattened the conditional logic for improved clarity and predictability of the function's behavior.
These changes enhance the readability and functional correctness of the `check_vocab_size` function without altering its intended functionality.
* py : fix outfile and outtype
* py : suggest hint for missing vocab size
---------
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-01-09 18:46:46 +00:00
# Convert token text to bytes
token_text = reverse_vocab [ token_id ] . encode ( " utf-8 " )
# Yield token text, score, and type
yield token_text , self . get_token_score ( token_id ) , self . get_token_type (
2024-02-06 05:47:22 +00:00
token_id , token_text , self . special_ids # Reuse already stored special IDs
convert.py : fix vanilla LLaMA model conversion (#4818)
* Update Imports and Add Notes for Future Reference
- Updated import statements in `convert.py`.
- Added import for `AutoTokenizer` from `transformers` module.
- Added conditional import for `gguf` from the local directory.
- Added comments and notes for future reference.
Additional Notes:
- Noted removal of a redundant `TypeAlias` import.
- Noted the removal of a `gguf` debug statement.
- Commented on the presence of `ARCH` and `NDArray` definitions.
- Commented on cleaning up and refactoring data type definitions.
* Refine Model Hyperparameters and Params Class
- Updated type annotations to use `Optional` for clarity.
- Improved method names and attribute consistency.
- Removed unnecessary variables for better code readability.
Additional Notes:
- Highlighted the use of `Optional` for clearer intent.
- Ensured backward and forward compatibility.
* Restore BpeVocab and SentencePieceVocab classes
- Restored the BpeVocab class for handling BPE tokenization.
- Restored the SentencePieceVocab class for SentencePiece tokenization.
These classes are essential for maintaining the original behavior of the codebase.
* refactor: Standardize vocabulary handling with HfVocab
- Replaced VocabLoader with HfVocab, aligning vocabulary handling across classes.
- Updated initialization of HfVocab with local_files_only=True for AutoTokenizer.
- Introduced optional parameter fname_added_tokens for flexible added token management.
- Streamlined added token handling for clarity and conciseness.
- Maintained special tokens and IDs, enhancing token management.
- Simplified token processing methods for improved readability.
- Added a placeholder for score computation with a default value of -1000.0.
- Optimized newline token check for efficiency.
- Updated __repr__ function for clarity in representation.
- Adjusted type alias Vocab to include BpeVocab, SentencePieceVocab, and HfVocab.
- Removed redundant code related to special token handling, reverse vocabulary mapping, and vocabulary file detection.
This refactoring promotes a standardized and modular approach to vocabulary management, facilitating future integration with a VocabFactory and improving code maintainability and scalability.
* refactor: Enhance readability, functionality, and code quality
- Improved code formatting and readability for better maintainability.
- Refactored LazyUnpickler's CLASSES dictionary for clarity.
- Added print statements and warnings in check_vocab_size for user feedback.
- Removed find_vocab_file_path, as it's superseded by VocabFactory.
- Preparatory changes for upcoming classes: OutputFile and VocabFactory.
- Overall focus on code quality, error handling, and consistency.
These changes reflect a continuous effort to refine the codebase, ensuring it meets best practices and prepares for future enhancements, such as the VocabFactory.
* refactor: Update OutputFile class for enhanced model vocabulary management
- Restructured the constructor for improved readability.
- Updated `add_meta_arch` method for flexible model name determination.
- Introduced `handle_tokenizer_model` for mapping vocab types to supported tokenizer models.
- Streamlined vocabulary extraction with `extract_vocabulary_from_model`.
- Simplified vocabulary metadata addition using `add_meta_vocab`.
- Refactored `add_tensor_info` for clarity and consistency.
- Improved error handling for better user feedback.
These changes signify the development of a versatile and comprehensive `OutputFile` class, enabling efficient management of model conversion output, metadata, vocabulary, and tensor information.
* feat: Introduce VocabFactory for flexible vocabulary management in model conversion
- The VocabFactory class is added to facilitate modular vocabulary handling.
- The constructor initializes a directory path and detects vocabulary-related files.
- The _select_file method provides file paths based on vocabulary type (e.g., BPE, SentencePiece).
- _create_special_vocab generates special vocabularies, accommodating different types.
- The load_vocab method loads vocabularies, handling BPE, SentencePiece, and Hugging Face Fast Tokenizer.
- Error handling and logging enhance debugging and user feedback.
- The modular and flexible design simplifies vocabulary management and supports future extensions.
The VocabFactory class enhances code modularity and maintainability, allowing versatile vocabulary handling in the model conversion process.
* refactor: Improve code organization, argument parsing, and user interface
- Renamed 'default_outfile' to 'default_output_file' for clarity.
- Refactored argument parser setup into 'get_argument_parser' function.
- Introduced descriptive comments for each argument in the parser.
- Added '--vocab-type' argument with choices ["spm", "bpe", "hfft"] for vocabulary processing.
- Improved flag naming consistency: '--outfile' to '--out-file' and '--bigendian' to '--big-endian'.
- Enhanced error handling to prevent overwriting input data in 'default_output_file'.
- Made 'argv' in 'main' an optional parameter for flexibility.
- Introduced dynamic import for 'awq.apply_awq' based on 'args.awq_path' for conditional dependency.
These changes enhance code clarity, organization, and the user interface of the script, aligning it with Python best practices and improving maintainability.
* refactor: Further refine functionality, improve user interaction, and streamline vocabulary handling
- Renamed command-line arguments for clarity and consistency.
- Improved path resolution and import adjustments for robustness.
- Thoughtfully handled 'awq-path' and conditional logic for the weighted model.
- Enhanced model and vocabulary loading with the 'VocabFactory' class for structured and adaptable loading.
- Strengthened error handling and user feedback for a more user-friendly experience.
- Structured output file handling with clear conditions and defaults.
- Streamlined and organized the 'main' function for better logic flow.
- Passed 'sys.argv[1:]' to 'main' for adaptability and testability.
These changes solidify the script's functionality, making it more robust, user-friendly, and adaptable. The use of the 'VocabFactory' class is a notable enhancement in efficient vocabulary handling, reflecting a thoughtful and iterative approach to script development.
* chore: Apply ruff formatting to convert.py
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
* Revert to commit 0614c33
* chore: Apply flake8 formatting rules
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
* refactor: Revise `check_vocab_size` for Enhanced Clarity and Correctness
- Resolved an unreachable branch issue by reorganizing the conditional structure.
- Moved the special case check for `params.n_vocab == -1` to the top for immediate assertion.
- Flattened the conditional logic for improved clarity and predictability of the function's behavior.
These changes enhance the readability and functional correctness of the `check_vocab_size` function without altering its intended functionality.
* py : fix outfile and outtype
* py : suggest hint for missing vocab size
---------
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-01-09 18:46:46 +00:00
)
2023-08-21 20:07:43 +00:00
2024-02-06 05:47:22 +00:00
def get_token_type ( self , token_id : int , token_text : bytes , special_ids : set [ int ] ) - > gguf . TokenType :
# Special case for byte tokens
if re . fullmatch ( br " <0x[0-9A-Fa-f] {2} > " , token_text ) :
return gguf . TokenType . BYTE
convert.py : fix vanilla LLaMA model conversion (#4818)
* Update Imports and Add Notes for Future Reference
- Updated import statements in `convert.py`.
- Added import for `AutoTokenizer` from `transformers` module.
- Added conditional import for `gguf` from the local directory.
- Added comments and notes for future reference.
Additional Notes:
- Noted removal of a redundant `TypeAlias` import.
- Noted the removal of a `gguf` debug statement.
- Commented on the presence of `ARCH` and `NDArray` definitions.
- Commented on cleaning up and refactoring data type definitions.
* Refine Model Hyperparameters and Params Class
- Updated type annotations to use `Optional` for clarity.
- Improved method names and attribute consistency.
- Removed unnecessary variables for better code readability.
Additional Notes:
- Highlighted the use of `Optional` for clearer intent.
- Ensured backward and forward compatibility.
* Restore BpeVocab and SentencePieceVocab classes
- Restored the BpeVocab class for handling BPE tokenization.
- Restored the SentencePieceVocab class for SentencePiece tokenization.
These classes are essential for maintaining the original behavior of the codebase.
* refactor: Standardize vocabulary handling with HfVocab
- Replaced VocabLoader with HfVocab, aligning vocabulary handling across classes.
- Updated initialization of HfVocab with local_files_only=True for AutoTokenizer.
- Introduced optional parameter fname_added_tokens for flexible added token management.
- Streamlined added token handling for clarity and conciseness.
- Maintained special tokens and IDs, enhancing token management.
- Simplified token processing methods for improved readability.
- Added a placeholder for score computation with a default value of -1000.0.
- Optimized newline token check for efficiency.
- Updated __repr__ function for clarity in representation.
- Adjusted type alias Vocab to include BpeVocab, SentencePieceVocab, and HfVocab.
- Removed redundant code related to special token handling, reverse vocabulary mapping, and vocabulary file detection.
This refactoring promotes a standardized and modular approach to vocabulary management, facilitating future integration with a VocabFactory and improving code maintainability and scalability.
* refactor: Enhance readability, functionality, and code quality
- Improved code formatting and readability for better maintainability.
- Refactored LazyUnpickler's CLASSES dictionary for clarity.
- Added print statements and warnings in check_vocab_size for user feedback.
- Removed find_vocab_file_path, as it's superseded by VocabFactory.
- Preparatory changes for upcoming classes: OutputFile and VocabFactory.
- Overall focus on code quality, error handling, and consistency.
These changes reflect a continuous effort to refine the codebase, ensuring it meets best practices and prepares for future enhancements, such as the VocabFactory.
* refactor: Update OutputFile class for enhanced model vocabulary management
- Restructured the constructor for improved readability.
- Updated `add_meta_arch` method for flexible model name determination.
- Introduced `handle_tokenizer_model` for mapping vocab types to supported tokenizer models.
- Streamlined vocabulary extraction with `extract_vocabulary_from_model`.
- Simplified vocabulary metadata addition using `add_meta_vocab`.
- Refactored `add_tensor_info` for clarity and consistency.
- Improved error handling for better user feedback.
These changes signify the development of a versatile and comprehensive `OutputFile` class, enabling efficient management of model conversion output, metadata, vocabulary, and tensor information.
* feat: Introduce VocabFactory for flexible vocabulary management in model conversion
- The VocabFactory class is added to facilitate modular vocabulary handling.
- The constructor initializes a directory path and detects vocabulary-related files.
- The _select_file method provides file paths based on vocabulary type (e.g., BPE, SentencePiece).
- _create_special_vocab generates special vocabularies, accommodating different types.
- The load_vocab method loads vocabularies, handling BPE, SentencePiece, and Hugging Face Fast Tokenizer.
- Error handling and logging enhance debugging and user feedback.
- The modular and flexible design simplifies vocabulary management and supports future extensions.
The VocabFactory class enhances code modularity and maintainability, allowing versatile vocabulary handling in the model conversion process.
* refactor: Improve code organization, argument parsing, and user interface
- Renamed 'default_outfile' to 'default_output_file' for clarity.
- Refactored argument parser setup into 'get_argument_parser' function.
- Introduced descriptive comments for each argument in the parser.
- Added '--vocab-type' argument with choices ["spm", "bpe", "hfft"] for vocabulary processing.
- Improved flag naming consistency: '--outfile' to '--out-file' and '--bigendian' to '--big-endian'.
- Enhanced error handling to prevent overwriting input data in 'default_output_file'.
- Made 'argv' in 'main' an optional parameter for flexibility.
- Introduced dynamic import for 'awq.apply_awq' based on 'args.awq_path' for conditional dependency.
These changes enhance code clarity, organization, and the user interface of the script, aligning it with Python best practices and improving maintainability.
* refactor: Further refine functionality, improve user interaction, and streamline vocabulary handling
- Renamed command-line arguments for clarity and consistency.
- Improved path resolution and import adjustments for robustness.
- Thoughtfully handled 'awq-path' and conditional logic for the weighted model.
- Enhanced model and vocabulary loading with the 'VocabFactory' class for structured and adaptable loading.
- Strengthened error handling and user feedback for a more user-friendly experience.
- Structured output file handling with clear conditions and defaults.
- Streamlined and organized the 'main' function for better logic flow.
- Passed 'sys.argv[1:]' to 'main' for adaptability and testability.
These changes solidify the script's functionality, making it more robust, user-friendly, and adaptable. The use of the 'VocabFactory' class is a notable enhancement in efficient vocabulary handling, reflecting a thoughtful and iterative approach to script development.
* chore: Apply ruff formatting to convert.py
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
* Revert to commit 0614c33
* chore: Apply flake8 formatting rules
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
* refactor: Revise `check_vocab_size` for Enhanced Clarity and Correctness
- Resolved an unreachable branch issue by reorganizing the conditional structure.
- Moved the special case check for `params.n_vocab == -1` to the top for immediate assertion.
- Flattened the conditional logic for improved clarity and predictability of the function's behavior.
These changes enhance the readability and functional correctness of the `check_vocab_size` function without altering its intended functionality.
* py : fix outfile and outtype
* py : suggest hint for missing vocab size
---------
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-01-09 18:46:46 +00:00
# Determine token type based on whether it's a special token
2024-01-20 23:14:18 +00:00
return gguf . TokenType . CONTROL if token_id in special_ids else gguf . TokenType . NORMAL
2023-12-14 08:09:34 +00:00
def get_token_score ( self , token_id : int ) - > float :
convert.py : fix vanilla LLaMA model conversion (#4818)
* Update Imports and Add Notes for Future Reference
- Updated import statements in `convert.py`.
- Added import for `AutoTokenizer` from `transformers` module.
- Added conditional import for `gguf` from the local directory.
- Added comments and notes for future reference.
Additional Notes:
- Noted removal of a redundant `TypeAlias` import.
- Noted the removal of a `gguf` debug statement.
- Commented on the presence of `ARCH` and `NDArray` definitions.
- Commented on cleaning up and refactoring data type definitions.
* Refine Model Hyperparameters and Params Class
- Updated type annotations to use `Optional` for clarity.
- Improved method names and attribute consistency.
- Removed unnecessary variables for better code readability.
Additional Notes:
- Highlighted the use of `Optional` for clearer intent.
- Ensured backward and forward compatibility.
* Restore BpeVocab and SentencePieceVocab classes
- Restored the BpeVocab class for handling BPE tokenization.
- Restored the SentencePieceVocab class for SentencePiece tokenization.
These classes are essential for maintaining the original behavior of the codebase.
* refactor: Standardize vocabulary handling with HfVocab
- Replaced VocabLoader with HfVocab, aligning vocabulary handling across classes.
- Updated initialization of HfVocab with local_files_only=True for AutoTokenizer.
- Introduced optional parameter fname_added_tokens for flexible added token management.
- Streamlined added token handling for clarity and conciseness.
- Maintained special tokens and IDs, enhancing token management.
- Simplified token processing methods for improved readability.
- Added a placeholder for score computation with a default value of -1000.0.
- Optimized newline token check for efficiency.
- Updated __repr__ function for clarity in representation.
- Adjusted type alias Vocab to include BpeVocab, SentencePieceVocab, and HfVocab.
- Removed redundant code related to special token handling, reverse vocabulary mapping, and vocabulary file detection.
This refactoring promotes a standardized and modular approach to vocabulary management, facilitating future integration with a VocabFactory and improving code maintainability and scalability.
* refactor: Enhance readability, functionality, and code quality
- Improved code formatting and readability for better maintainability.
- Refactored LazyUnpickler's CLASSES dictionary for clarity.
- Added print statements and warnings in check_vocab_size for user feedback.
- Removed find_vocab_file_path, as it's superseded by VocabFactory.
- Preparatory changes for upcoming classes: OutputFile and VocabFactory.
- Overall focus on code quality, error handling, and consistency.
These changes reflect a continuous effort to refine the codebase, ensuring it meets best practices and prepares for future enhancements, such as the VocabFactory.
* refactor: Update OutputFile class for enhanced model vocabulary management
- Restructured the constructor for improved readability.
- Updated `add_meta_arch` method for flexible model name determination.
- Introduced `handle_tokenizer_model` for mapping vocab types to supported tokenizer models.
- Streamlined vocabulary extraction with `extract_vocabulary_from_model`.
- Simplified vocabulary metadata addition using `add_meta_vocab`.
- Refactored `add_tensor_info` for clarity and consistency.
- Improved error handling for better user feedback.
These changes signify the development of a versatile and comprehensive `OutputFile` class, enabling efficient management of model conversion output, metadata, vocabulary, and tensor information.
* feat: Introduce VocabFactory for flexible vocabulary management in model conversion
- The VocabFactory class is added to facilitate modular vocabulary handling.
- The constructor initializes a directory path and detects vocabulary-related files.
- The _select_file method provides file paths based on vocabulary type (e.g., BPE, SentencePiece).
- _create_special_vocab generates special vocabularies, accommodating different types.
- The load_vocab method loads vocabularies, handling BPE, SentencePiece, and Hugging Face Fast Tokenizer.
- Error handling and logging enhance debugging and user feedback.
- The modular and flexible design simplifies vocabulary management and supports future extensions.
The VocabFactory class enhances code modularity and maintainability, allowing versatile vocabulary handling in the model conversion process.
* refactor: Improve code organization, argument parsing, and user interface
- Renamed 'default_outfile' to 'default_output_file' for clarity.
- Refactored argument parser setup into 'get_argument_parser' function.
- Introduced descriptive comments for each argument in the parser.
- Added '--vocab-type' argument with choices ["spm", "bpe", "hfft"] for vocabulary processing.
- Improved flag naming consistency: '--outfile' to '--out-file' and '--bigendian' to '--big-endian'.
- Enhanced error handling to prevent overwriting input data in 'default_output_file'.
- Made 'argv' in 'main' an optional parameter for flexibility.
- Introduced dynamic import for 'awq.apply_awq' based on 'args.awq_path' for conditional dependency.
These changes enhance code clarity, organization, and the user interface of the script, aligning it with Python best practices and improving maintainability.
* refactor: Further refine functionality, improve user interaction, and streamline vocabulary handling
- Renamed command-line arguments for clarity and consistency.
- Improved path resolution and import adjustments for robustness.
- Thoughtfully handled 'awq-path' and conditional logic for the weighted model.
- Enhanced model and vocabulary loading with the 'VocabFactory' class for structured and adaptable loading.
- Strengthened error handling and user feedback for a more user-friendly experience.
- Structured output file handling with clear conditions and defaults.
- Streamlined and organized the 'main' function for better logic flow.
- Passed 'sys.argv[1:]' to 'main' for adaptability and testability.
These changes solidify the script's functionality, making it more robust, user-friendly, and adaptable. The use of the 'VocabFactory' class is a notable enhancement in efficient vocabulary handling, reflecting a thoughtful and iterative approach to script development.
* chore: Apply ruff formatting to convert.py
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
* Revert to commit 0614c33
* chore: Apply flake8 formatting rules
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
* refactor: Revise `check_vocab_size` for Enhanced Clarity and Correctness
- Resolved an unreachable branch issue by reorganizing the conditional structure.
- Moved the special case check for `params.n_vocab == -1` to the top for immediate assertion.
- Flattened the conditional logic for improved clarity and predictability of the function's behavior.
These changes enhance the readability and functional correctness of the `check_vocab_size` function without altering its intended functionality.
* py : fix outfile and outtype
* py : suggest hint for missing vocab size
---------
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-01-09 18:46:46 +00:00
# Placeholder for actual logic to determine the token's score
# This needs to be implemented based on specific requirements
return - 1000.0 # Default score
2023-08-21 20:07:43 +00:00
2023-08-31 05:02:23 +00:00
def added_tokens ( self ) - > Iterable [ tuple [ bytes , float , gguf . TokenType ] ] :
convert.py : fix vanilla LLaMA model conversion (#4818)
* Update Imports and Add Notes for Future Reference
- Updated import statements in `convert.py`.
- Added import for `AutoTokenizer` from `transformers` module.
- Added conditional import for `gguf` from the local directory.
- Added comments and notes for future reference.
Additional Notes:
- Noted removal of a redundant `TypeAlias` import.
- Noted the removal of a `gguf` debug statement.
- Commented on the presence of `ARCH` and `NDArray` definitions.
- Commented on cleaning up and refactoring data type definitions.
* Refine Model Hyperparameters and Params Class
- Updated type annotations to use `Optional` for clarity.
- Improved method names and attribute consistency.
- Removed unnecessary variables for better code readability.
Additional Notes:
- Highlighted the use of `Optional` for clearer intent.
- Ensured backward and forward compatibility.
* Restore BpeVocab and SentencePieceVocab classes
- Restored the BpeVocab class for handling BPE tokenization.
- Restored the SentencePieceVocab class for SentencePiece tokenization.
These classes are essential for maintaining the original behavior of the codebase.
* refactor: Standardize vocabulary handling with HfVocab
- Replaced VocabLoader with HfVocab, aligning vocabulary handling across classes.
- Updated initialization of HfVocab with local_files_only=True for AutoTokenizer.
- Introduced optional parameter fname_added_tokens for flexible added token management.
- Streamlined added token handling for clarity and conciseness.
- Maintained special tokens and IDs, enhancing token management.
- Simplified token processing methods for improved readability.
- Added a placeholder for score computation with a default value of -1000.0.
- Optimized newline token check for efficiency.
- Updated __repr__ function for clarity in representation.
- Adjusted type alias Vocab to include BpeVocab, SentencePieceVocab, and HfVocab.
- Removed redundant code related to special token handling, reverse vocabulary mapping, and vocabulary file detection.
This refactoring promotes a standardized and modular approach to vocabulary management, facilitating future integration with a VocabFactory and improving code maintainability and scalability.
* refactor: Enhance readability, functionality, and code quality
- Improved code formatting and readability for better maintainability.
- Refactored LazyUnpickler's CLASSES dictionary for clarity.
- Added print statements and warnings in check_vocab_size for user feedback.
- Removed find_vocab_file_path, as it's superseded by VocabFactory.
- Preparatory changes for upcoming classes: OutputFile and VocabFactory.
- Overall focus on code quality, error handling, and consistency.
These changes reflect a continuous effort to refine the codebase, ensuring it meets best practices and prepares for future enhancements, such as the VocabFactory.
* refactor: Update OutputFile class for enhanced model vocabulary management
- Restructured the constructor for improved readability.
- Updated `add_meta_arch` method for flexible model name determination.
- Introduced `handle_tokenizer_model` for mapping vocab types to supported tokenizer models.
- Streamlined vocabulary extraction with `extract_vocabulary_from_model`.
- Simplified vocabulary metadata addition using `add_meta_vocab`.
- Refactored `add_tensor_info` for clarity and consistency.
- Improved error handling for better user feedback.
These changes signify the development of a versatile and comprehensive `OutputFile` class, enabling efficient management of model conversion output, metadata, vocabulary, and tensor information.
* feat: Introduce VocabFactory for flexible vocabulary management in model conversion
- The VocabFactory class is added to facilitate modular vocabulary handling.
- The constructor initializes a directory path and detects vocabulary-related files.
- The _select_file method provides file paths based on vocabulary type (e.g., BPE, SentencePiece).
- _create_special_vocab generates special vocabularies, accommodating different types.
- The load_vocab method loads vocabularies, handling BPE, SentencePiece, and Hugging Face Fast Tokenizer.
- Error handling and logging enhance debugging and user feedback.
- The modular and flexible design simplifies vocabulary management and supports future extensions.
The VocabFactory class enhances code modularity and maintainability, allowing versatile vocabulary handling in the model conversion process.
* refactor: Improve code organization, argument parsing, and user interface
- Renamed 'default_outfile' to 'default_output_file' for clarity.
- Refactored argument parser setup into 'get_argument_parser' function.
- Introduced descriptive comments for each argument in the parser.
- Added '--vocab-type' argument with choices ["spm", "bpe", "hfft"] for vocabulary processing.
- Improved flag naming consistency: '--outfile' to '--out-file' and '--bigendian' to '--big-endian'.
- Enhanced error handling to prevent overwriting input data in 'default_output_file'.
- Made 'argv' in 'main' an optional parameter for flexibility.
- Introduced dynamic import for 'awq.apply_awq' based on 'args.awq_path' for conditional dependency.
These changes enhance code clarity, organization, and the user interface of the script, aligning it with Python best practices and improving maintainability.
* refactor: Further refine functionality, improve user interaction, and streamline vocabulary handling
- Renamed command-line arguments for clarity and consistency.
- Improved path resolution and import adjustments for robustness.
- Thoughtfully handled 'awq-path' and conditional logic for the weighted model.
- Enhanced model and vocabulary loading with the 'VocabFactory' class for structured and adaptable loading.
- Strengthened error handling and user feedback for a more user-friendly experience.
- Structured output file handling with clear conditions and defaults.
- Streamlined and organized the 'main' function for better logic flow.
- Passed 'sys.argv[1:]' to 'main' for adaptability and testability.
These changes solidify the script's functionality, making it more robust, user-friendly, and adaptable. The use of the 'VocabFactory' class is a notable enhancement in efficient vocabulary handling, reflecting a thoughtful and iterative approach to script development.
* chore: Apply ruff formatting to convert.py
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
* Revert to commit 0614c33
* chore: Apply flake8 formatting rules
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
* refactor: Revise `check_vocab_size` for Enhanced Clarity and Correctness
- Resolved an unreachable branch issue by reorganizing the conditional structure.
- Moved the special case check for `params.n_vocab == -1` to the top for immediate assertion.
- Flattened the conditional logic for improved clarity and predictability of the function's behavior.
These changes enhance the readability and functional correctness of the `check_vocab_size` function without altering its intended functionality.
* py : fix outfile and outtype
* py : suggest hint for missing vocab size
---------
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-01-09 18:46:46 +00:00
for text in self . added_tokens_list :
2023-12-14 08:09:34 +00:00
if text in self . specials :
2024-02-06 05:47:22 +00:00
toktype = self . get_token_type ( self . specials [ text ] , b ' ' , self . special_ids )
2023-12-14 08:09:34 +00:00
score = self . get_token_score ( self . specials [ text ] )
else :
toktype = gguf . TokenType . USER_DEFINED
score = - 1000.0
yield text . encode ( " utf-8 " ) , score , toktype
convert.py : fix vanilla LLaMA model conversion (#4818)
* Update Imports and Add Notes for Future Reference
- Updated import statements in `convert.py`.
- Added import for `AutoTokenizer` from `transformers` module.
- Added conditional import for `gguf` from the local directory.
- Added comments and notes for future reference.
Additional Notes:
- Noted removal of a redundant `TypeAlias` import.
- Noted the removal of a `gguf` debug statement.
- Commented on the presence of `ARCH` and `NDArray` definitions.
- Commented on cleaning up and refactoring data type definitions.
* Refine Model Hyperparameters and Params Class
- Updated type annotations to use `Optional` for clarity.
- Improved method names and attribute consistency.
- Removed unnecessary variables for better code readability.
Additional Notes:
- Highlighted the use of `Optional` for clearer intent.
- Ensured backward and forward compatibility.
* Restore BpeVocab and SentencePieceVocab classes
- Restored the BpeVocab class for handling BPE tokenization.
- Restored the SentencePieceVocab class for SentencePiece tokenization.
These classes are essential for maintaining the original behavior of the codebase.
* refactor: Standardize vocabulary handling with HfVocab
- Replaced VocabLoader with HfVocab, aligning vocabulary handling across classes.
- Updated initialization of HfVocab with local_files_only=True for AutoTokenizer.
- Introduced optional parameter fname_added_tokens for flexible added token management.
- Streamlined added token handling for clarity and conciseness.
- Maintained special tokens and IDs, enhancing token management.
- Simplified token processing methods for improved readability.
- Added a placeholder for score computation with a default value of -1000.0.
- Optimized newline token check for efficiency.
- Updated __repr__ function for clarity in representation.
- Adjusted type alias Vocab to include BpeVocab, SentencePieceVocab, and HfVocab.
- Removed redundant code related to special token handling, reverse vocabulary mapping, and vocabulary file detection.
This refactoring promotes a standardized and modular approach to vocabulary management, facilitating future integration with a VocabFactory and improving code maintainability and scalability.
* refactor: Enhance readability, functionality, and code quality
- Improved code formatting and readability for better maintainability.
- Refactored LazyUnpickler's CLASSES dictionary for clarity.
- Added print statements and warnings in check_vocab_size for user feedback.
- Removed find_vocab_file_path, as it's superseded by VocabFactory.
- Preparatory changes for upcoming classes: OutputFile and VocabFactory.
- Overall focus on code quality, error handling, and consistency.
These changes reflect a continuous effort to refine the codebase, ensuring it meets best practices and prepares for future enhancements, such as the VocabFactory.
* refactor: Update OutputFile class for enhanced model vocabulary management
- Restructured the constructor for improved readability.
- Updated `add_meta_arch` method for flexible model name determination.
- Introduced `handle_tokenizer_model` for mapping vocab types to supported tokenizer models.
- Streamlined vocabulary extraction with `extract_vocabulary_from_model`.
- Simplified vocabulary metadata addition using `add_meta_vocab`.
- Refactored `add_tensor_info` for clarity and consistency.
- Improved error handling for better user feedback.
These changes signify the development of a versatile and comprehensive `OutputFile` class, enabling efficient management of model conversion output, metadata, vocabulary, and tensor information.
* feat: Introduce VocabFactory for flexible vocabulary management in model conversion
- The VocabFactory class is added to facilitate modular vocabulary handling.
- The constructor initializes a directory path and detects vocabulary-related files.
- The _select_file method provides file paths based on vocabulary type (e.g., BPE, SentencePiece).
- _create_special_vocab generates special vocabularies, accommodating different types.
- The load_vocab method loads vocabularies, handling BPE, SentencePiece, and Hugging Face Fast Tokenizer.
- Error handling and logging enhance debugging and user feedback.
- The modular and flexible design simplifies vocabulary management and supports future extensions.
The VocabFactory class enhances code modularity and maintainability, allowing versatile vocabulary handling in the model conversion process.
* refactor: Improve code organization, argument parsing, and user interface
- Renamed 'default_outfile' to 'default_output_file' for clarity.
- Refactored argument parser setup into 'get_argument_parser' function.
- Introduced descriptive comments for each argument in the parser.
- Added '--vocab-type' argument with choices ["spm", "bpe", "hfft"] for vocabulary processing.
- Improved flag naming consistency: '--outfile' to '--out-file' and '--bigendian' to '--big-endian'.
- Enhanced error handling to prevent overwriting input data in 'default_output_file'.
- Made 'argv' in 'main' an optional parameter for flexibility.
- Introduced dynamic import for 'awq.apply_awq' based on 'args.awq_path' for conditional dependency.
These changes enhance code clarity, organization, and the user interface of the script, aligning it with Python best practices and improving maintainability.
* refactor: Further refine functionality, improve user interaction, and streamline vocabulary handling
- Renamed command-line arguments for clarity and consistency.
- Improved path resolution and import adjustments for robustness.
- Thoughtfully handled 'awq-path' and conditional logic for the weighted model.
- Enhanced model and vocabulary loading with the 'VocabFactory' class for structured and adaptable loading.
- Strengthened error handling and user feedback for a more user-friendly experience.
- Structured output file handling with clear conditions and defaults.
- Streamlined and organized the 'main' function for better logic flow.
- Passed 'sys.argv[1:]' to 'main' for adaptability and testability.
These changes solidify the script's functionality, making it more robust, user-friendly, and adaptable. The use of the 'VocabFactory' class is a notable enhancement in efficient vocabulary handling, reflecting a thoughtful and iterative approach to script development.
* chore: Apply ruff formatting to convert.py
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
* Revert to commit 0614c33
* chore: Apply flake8 formatting rules
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
* refactor: Revise `check_vocab_size` for Enhanced Clarity and Correctness
- Resolved an unreachable branch issue by reorganizing the conditional structure.
- Moved the special case check for `params.n_vocab == -1` to the top for immediate assertion.
- Flattened the conditional logic for improved clarity and predictability of the function's behavior.
These changes enhance the readability and functional correctness of the `check_vocab_size` function without altering its intended functionality.
* py : fix outfile and outtype
* py : suggest hint for missing vocab size
---------
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-01-09 18:46:46 +00:00
def has_newline_token ( self ) :
return " <0x0A> " in self . tokenizer . vocab or " \n " in self . tokenizer . vocab
py : new conversion script (#545)
Current status: Working, except for the latest GPTQ-for-LLaMa format
that includes `g_idx`. This turns out to require changes to GGML, so
for now it only works if you use the `--outtype` option to dequantize it
back to f16 (which is pointless except for debugging).
I also included some cleanup for the C++ code.
This script is meant to replace all the existing conversion scripts
(including the ones that convert from older GGML formats), while also
adding support for some new formats. Specifically, I've tested with:
- [x] `LLaMA` (original)
- [x] `llama-65b-4bit`
- [x] `alpaca-native`
- [x] `alpaca-native-4bit`
- [x] LLaMA converted to 'transformers' format using
`convert_llama_weights_to_hf.py`
- [x] `alpaca-native` quantized with `--true-sequential --act-order
--groupsize 128` (dequantized only)
- [x] same as above plus `--save_safetensors`
- [x] GPT4All
- [x] stock unversioned ggml
- [x] ggmh
There's enough overlap in the logic needed to handle these different
cases that it seemed best to move to a single script.
I haven't tried this with Alpaca-LoRA because I don't know where to find
it.
Useful features:
- Uses multiple threads for a speedup in some cases (though the Python
GIL limits the gain, and sometimes it's disk-bound anyway).
- Combines split models into a single file (both the intra-tensor split
of the original and the inter-tensor split of 'transformers' format
files). Single files are more convenient to work with and more
friendly to future changes to use memory mapping on the C++ side. To
accomplish this without increasing memory requirements, it has some
custom loading code which avoids loading whole input files into memory
at once.
- Because of the custom loading code, it no longer depends in PyTorch,
which might make installing dependencies slightly easier or faster...
although it still depends on NumPy and sentencepiece, so I don't know
if there's any meaningful difference. In any case, I also added a
requirements.txt file to lock the dependency versions in case of any
future breaking changes.
- Type annotations checked with mypy.
- Some attempts to be extra user-friendly:
- The script tries to be forgiving with arguments, e.g. you can
specify either the model file itself or the directory containing
it.
- The script doesn't depend on config.json / params.json, just in
case the user downloaded files individually and doesn't have those
handy. But you still need tokenizer.model and, for Alpaca,
added_tokens.json.
- The script tries to give a helpful error message if
added_tokens.json is missing.
2023-04-14 07:03:03 +00:00
2023-08-31 05:02:23 +00:00
def all_tokens ( self ) - > Iterable [ tuple [ bytes , float , gguf . TokenType ] ] :
2023-12-14 08:09:34 +00:00
yield from self . hf_tokens ( )
py : new conversion script (#545)
Current status: Working, except for the latest GPTQ-for-LLaMa format
that includes `g_idx`. This turns out to require changes to GGML, so
for now it only works if you use the `--outtype` option to dequantize it
back to f16 (which is pointless except for debugging).
I also included some cleanup for the C++ code.
This script is meant to replace all the existing conversion scripts
(including the ones that convert from older GGML formats), while also
adding support for some new formats. Specifically, I've tested with:
- [x] `LLaMA` (original)
- [x] `llama-65b-4bit`
- [x] `alpaca-native`
- [x] `alpaca-native-4bit`
- [x] LLaMA converted to 'transformers' format using
`convert_llama_weights_to_hf.py`
- [x] `alpaca-native` quantized with `--true-sequential --act-order
--groupsize 128` (dequantized only)
- [x] same as above plus `--save_safetensors`
- [x] GPT4All
- [x] stock unversioned ggml
- [x] ggmh
There's enough overlap in the logic needed to handle these different
cases that it seemed best to move to a single script.
I haven't tried this with Alpaca-LoRA because I don't know where to find
it.
Useful features:
- Uses multiple threads for a speedup in some cases (though the Python
GIL limits the gain, and sometimes it's disk-bound anyway).
- Combines split models into a single file (both the intra-tensor split
of the original and the inter-tensor split of 'transformers' format
files). Single files are more convenient to work with and more
friendly to future changes to use memory mapping on the C++ side. To
accomplish this without increasing memory requirements, it has some
custom loading code which avoids loading whole input files into memory
at once.
- Because of the custom loading code, it no longer depends in PyTorch,
which might make installing dependencies slightly easier or faster...
although it still depends on NumPy and sentencepiece, so I don't know
if there's any meaningful difference. In any case, I also added a
requirements.txt file to lock the dependency versions in case of any
future breaking changes.
- Type annotations checked with mypy.
- Some attempts to be extra user-friendly:
- The script tries to be forgiving with arguments, e.g. you can
specify either the model file itself or the directory containing
it.
- The script doesn't depend on config.json / params.json, just in
case the user downloaded files individually and doesn't have those
handy. But you still need tokenizer.model and, for Alpaca,
added_tokens.json.
- The script tries to give a helpful error message if
added_tokens.json is missing.
2023-04-14 07:03:03 +00:00
yield from self . added_tokens ( )
def __repr__ ( self ) - > str :
2024-03-28 15:44:36 +00:00
return f " <LlamaHfVocab with { self . vocab_size_base } base tokens and { len ( self . added_tokens_list ) } added tokens> "
2023-11-20 10:35:47 +00:00
py : new conversion script (#545)
Current status: Working, except for the latest GPTQ-for-LLaMa format
that includes `g_idx`. This turns out to require changes to GGML, so
for now it only works if you use the `--outtype` option to dequantize it
back to f16 (which is pointless except for debugging).
I also included some cleanup for the C++ code.
This script is meant to replace all the existing conversion scripts
(including the ones that convert from older GGML formats), while also
adding support for some new formats. Specifically, I've tested with:
- [x] `LLaMA` (original)
- [x] `llama-65b-4bit`
- [x] `alpaca-native`
- [x] `alpaca-native-4bit`
- [x] LLaMA converted to 'transformers' format using
`convert_llama_weights_to_hf.py`
- [x] `alpaca-native` quantized with `--true-sequential --act-order
--groupsize 128` (dequantized only)
- [x] same as above plus `--save_safetensors`
- [x] GPT4All
- [x] stock unversioned ggml
- [x] ggmh
There's enough overlap in the logic needed to handle these different
cases that it seemed best to move to a single script.
I haven't tried this with Alpaca-LoRA because I don't know where to find
it.
Useful features:
- Uses multiple threads for a speedup in some cases (though the Python
GIL limits the gain, and sometimes it's disk-bound anyway).
- Combines split models into a single file (both the intra-tensor split
of the original and the inter-tensor split of 'transformers' format
files). Single files are more convenient to work with and more
friendly to future changes to use memory mapping on the C++ side. To
accomplish this without increasing memory requirements, it has some
custom loading code which avoids loading whole input files into memory
at once.
- Because of the custom loading code, it no longer depends in PyTorch,
which might make installing dependencies slightly easier or faster...
although it still depends on NumPy and sentencepiece, so I don't know
if there's any meaningful difference. In any case, I also added a
requirements.txt file to lock the dependency versions in case of any
future breaking changes.
- Type annotations checked with mypy.
- Some attempts to be extra user-friendly:
- The script tries to be forgiving with arguments, e.g. you can
specify either the model file itself or the directory containing
it.
- The script doesn't depend on config.json / params.json, just in
case the user downloaded files individually and doesn't have those
handy. But you still need tokenizer.model and, for Alpaca,
added_tokens.json.
- The script tries to give a helpful error message if
added_tokens.json is missing.
2023-04-14 07:03:03 +00:00
2023-08-21 20:07:43 +00:00
#
# data loading
# TODO: reuse (probably move to gguf.py?)
#
py : new conversion script (#545)
Current status: Working, except for the latest GPTQ-for-LLaMa format
that includes `g_idx`. This turns out to require changes to GGML, so
for now it only works if you use the `--outtype` option to dequantize it
back to f16 (which is pointless except for debugging).
I also included some cleanup for the C++ code.
This script is meant to replace all the existing conversion scripts
(including the ones that convert from older GGML formats), while also
adding support for some new formats. Specifically, I've tested with:
- [x] `LLaMA` (original)
- [x] `llama-65b-4bit`
- [x] `alpaca-native`
- [x] `alpaca-native-4bit`
- [x] LLaMA converted to 'transformers' format using
`convert_llama_weights_to_hf.py`
- [x] `alpaca-native` quantized with `--true-sequential --act-order
--groupsize 128` (dequantized only)
- [x] same as above plus `--save_safetensors`
- [x] GPT4All
- [x] stock unversioned ggml
- [x] ggmh
There's enough overlap in the logic needed to handle these different
cases that it seemed best to move to a single script.
I haven't tried this with Alpaca-LoRA because I don't know where to find
it.
Useful features:
- Uses multiple threads for a speedup in some cases (though the Python
GIL limits the gain, and sometimes it's disk-bound anyway).
- Combines split models into a single file (both the intra-tensor split
of the original and the inter-tensor split of 'transformers' format
files). Single files are more convenient to work with and more
friendly to future changes to use memory mapping on the C++ side. To
accomplish this without increasing memory requirements, it has some
custom loading code which avoids loading whole input files into memory
at once.
- Because of the custom loading code, it no longer depends in PyTorch,
which might make installing dependencies slightly easier or faster...
although it still depends on NumPy and sentencepiece, so I don't know
if there's any meaningful difference. In any case, I also added a
requirements.txt file to lock the dependency versions in case of any
future breaking changes.
- Type annotations checked with mypy.
- Some attempts to be extra user-friendly:
- The script tries to be forgiving with arguments, e.g. you can
specify either the model file itself or the directory containing
it.
- The script doesn't depend on config.json / params.json, just in
case the user downloaded files individually and doesn't have those
handy. But you still need tokenizer.model and, for Alpaca,
added_tokens.json.
- The script tries to give a helpful error message if
added_tokens.json is missing.
2023-04-14 07:03:03 +00:00
2023-11-20 10:35:47 +00:00
2023-08-21 20:07:43 +00:00
def permute ( weights : NDArray , n_head : int , n_head_kv : int ) - > NDArray :
if n_head_kv is not None and n_head != n_head_kv :
2023-09-27 18:45:20 +00:00
n_head = n_head_kv
py : new conversion script (#545)
Current status: Working, except for the latest GPTQ-for-LLaMa format
that includes `g_idx`. This turns out to require changes to GGML, so
for now it only works if you use the `--outtype` option to dequantize it
back to f16 (which is pointless except for debugging).
I also included some cleanup for the C++ code.
This script is meant to replace all the existing conversion scripts
(including the ones that convert from older GGML formats), while also
adding support for some new formats. Specifically, I've tested with:
- [x] `LLaMA` (original)
- [x] `llama-65b-4bit`
- [x] `alpaca-native`
- [x] `alpaca-native-4bit`
- [x] LLaMA converted to 'transformers' format using
`convert_llama_weights_to_hf.py`
- [x] `alpaca-native` quantized with `--true-sequential --act-order
--groupsize 128` (dequantized only)
- [x] same as above plus `--save_safetensors`
- [x] GPT4All
- [x] stock unversioned ggml
- [x] ggmh
There's enough overlap in the logic needed to handle these different
cases that it seemed best to move to a single script.
I haven't tried this with Alpaca-LoRA because I don't know where to find
it.
Useful features:
- Uses multiple threads for a speedup in some cases (though the Python
GIL limits the gain, and sometimes it's disk-bound anyway).
- Combines split models into a single file (both the intra-tensor split
of the original and the inter-tensor split of 'transformers' format
files). Single files are more convenient to work with and more
friendly to future changes to use memory mapping on the C++ side. To
accomplish this without increasing memory requirements, it has some
custom loading code which avoids loading whole input files into memory
at once.
- Because of the custom loading code, it no longer depends in PyTorch,
which might make installing dependencies slightly easier or faster...
although it still depends on NumPy and sentencepiece, so I don't know
if there's any meaningful difference. In any case, I also added a
requirements.txt file to lock the dependency versions in case of any
future breaking changes.
- Type annotations checked with mypy.
- Some attempts to be extra user-friendly:
- The script tries to be forgiving with arguments, e.g. you can
specify either the model file itself or the directory containing
it.
- The script doesn't depend on config.json / params.json, just in
case the user downloaded files individually and doesn't have those
handy. But you still need tokenizer.model and, for Alpaca,
added_tokens.json.
- The script tries to give a helpful error message if
added_tokens.json is missing.
2023-04-14 07:03:03 +00:00
return ( weights . reshape ( n_head , 2 , weights . shape [ 0 ] / / n_head / / 2 , * weights . shape [ 1 : ] )
2023-11-20 10:35:47 +00:00
. swapaxes ( 1 , 2 )
. reshape ( weights . shape ) )
py : new conversion script (#545)
Current status: Working, except for the latest GPTQ-for-LLaMa format
that includes `g_idx`. This turns out to require changes to GGML, so
for now it only works if you use the `--outtype` option to dequantize it
back to f16 (which is pointless except for debugging).
I also included some cleanup for the C++ code.
This script is meant to replace all the existing conversion scripts
(including the ones that convert from older GGML formats), while also
adding support for some new formats. Specifically, I've tested with:
- [x] `LLaMA` (original)
- [x] `llama-65b-4bit`
- [x] `alpaca-native`
- [x] `alpaca-native-4bit`
- [x] LLaMA converted to 'transformers' format using
`convert_llama_weights_to_hf.py`
- [x] `alpaca-native` quantized with `--true-sequential --act-order
--groupsize 128` (dequantized only)
- [x] same as above plus `--save_safetensors`
- [x] GPT4All
- [x] stock unversioned ggml
- [x] ggmh
There's enough overlap in the logic needed to handle these different
cases that it seemed best to move to a single script.
I haven't tried this with Alpaca-LoRA because I don't know where to find
it.
Useful features:
- Uses multiple threads for a speedup in some cases (though the Python
GIL limits the gain, and sometimes it's disk-bound anyway).
- Combines split models into a single file (both the intra-tensor split
of the original and the inter-tensor split of 'transformers' format
files). Single files are more convenient to work with and more
friendly to future changes to use memory mapping on the C++ side. To
accomplish this without increasing memory requirements, it has some
custom loading code which avoids loading whole input files into memory
at once.
- Because of the custom loading code, it no longer depends in PyTorch,
which might make installing dependencies slightly easier or faster...
although it still depends on NumPy and sentencepiece, so I don't know
if there's any meaningful difference. In any case, I also added a
requirements.txt file to lock the dependency versions in case of any
future breaking changes.
- Type annotations checked with mypy.
- Some attempts to be extra user-friendly:
- The script tries to be forgiving with arguments, e.g. you can
specify either the model file itself or the directory containing
it.
- The script doesn't depend on config.json / params.json, just in
case the user downloaded files individually and doesn't have those
handy. But you still need tokenizer.model and, for Alpaca,
added_tokens.json.
- The script tries to give a helpful error message if
added_tokens.json is missing.
2023-04-14 07:03:03 +00:00
2024-03-28 15:44:36 +00:00
class Tensor ( ABC ) :
2024-04-09 17:44:08 +00:00
ndarray : NDArray
py : new conversion script (#545)
Current status: Working, except for the latest GPTQ-for-LLaMa format
that includes `g_idx`. This turns out to require changes to GGML, so
for now it only works if you use the `--outtype` option to dequantize it
back to f16 (which is pointless except for debugging).
I also included some cleanup for the C++ code.
This script is meant to replace all the existing conversion scripts
(including the ones that convert from older GGML formats), while also
adding support for some new formats. Specifically, I've tested with:
- [x] `LLaMA` (original)
- [x] `llama-65b-4bit`
- [x] `alpaca-native`
- [x] `alpaca-native-4bit`
- [x] LLaMA converted to 'transformers' format using
`convert_llama_weights_to_hf.py`
- [x] `alpaca-native` quantized with `--true-sequential --act-order
--groupsize 128` (dequantized only)
- [x] same as above plus `--save_safetensors`
- [x] GPT4All
- [x] stock unversioned ggml
- [x] ggmh
There's enough overlap in the logic needed to handle these different
cases that it seemed best to move to a single script.
I haven't tried this with Alpaca-LoRA because I don't know where to find
it.
Useful features:
- Uses multiple threads for a speedup in some cases (though the Python
GIL limits the gain, and sometimes it's disk-bound anyway).
- Combines split models into a single file (both the intra-tensor split
of the original and the inter-tensor split of 'transformers' format
files). Single files are more convenient to work with and more
friendly to future changes to use memory mapping on the C++ side. To
accomplish this without increasing memory requirements, it has some
custom loading code which avoids loading whole input files into memory
at once.
- Because of the custom loading code, it no longer depends in PyTorch,
which might make installing dependencies slightly easier or faster...
although it still depends on NumPy and sentencepiece, so I don't know
if there's any meaningful difference. In any case, I also added a
requirements.txt file to lock the dependency versions in case of any
future breaking changes.
- Type annotations checked with mypy.
- Some attempts to be extra user-friendly:
- The script tries to be forgiving with arguments, e.g. you can
specify either the model file itself or the directory containing
it.
- The script doesn't depend on config.json / params.json, just in
case the user downloaded files individually and doesn't have those
handy. But you still need tokenizer.model and, for Alpaca,
added_tokens.json.
- The script tries to give a helpful error message if
added_tokens.json is missing.
2023-04-14 07:03:03 +00:00
data_type : DataType
@abstractmethod
2024-04-09 17:44:08 +00:00
def astype ( self , data_type : DataType ) - > Self : . . .
py : new conversion script (#545)
Current status: Working, except for the latest GPTQ-for-LLaMa format
that includes `g_idx`. This turns out to require changes to GGML, so
for now it only works if you use the `--outtype` option to dequantize it
back to f16 (which is pointless except for debugging).
I also included some cleanup for the C++ code.
This script is meant to replace all the existing conversion scripts
(including the ones that convert from older GGML formats), while also
adding support for some new formats. Specifically, I've tested with:
- [x] `LLaMA` (original)
- [x] `llama-65b-4bit`
- [x] `alpaca-native`
- [x] `alpaca-native-4bit`
- [x] LLaMA converted to 'transformers' format using
`convert_llama_weights_to_hf.py`
- [x] `alpaca-native` quantized with `--true-sequential --act-order
--groupsize 128` (dequantized only)
- [x] same as above plus `--save_safetensors`
- [x] GPT4All
- [x] stock unversioned ggml
- [x] ggmh
There's enough overlap in the logic needed to handle these different
cases that it seemed best to move to a single script.
I haven't tried this with Alpaca-LoRA because I don't know where to find
it.
Useful features:
- Uses multiple threads for a speedup in some cases (though the Python
GIL limits the gain, and sometimes it's disk-bound anyway).
- Combines split models into a single file (both the intra-tensor split
of the original and the inter-tensor split of 'transformers' format
files). Single files are more convenient to work with and more
friendly to future changes to use memory mapping on the C++ side. To
accomplish this without increasing memory requirements, it has some
custom loading code which avoids loading whole input files into memory
at once.
- Because of the custom loading code, it no longer depends in PyTorch,
which might make installing dependencies slightly easier or faster...
although it still depends on NumPy and sentencepiece, so I don't know
if there's any meaningful difference. In any case, I also added a
requirements.txt file to lock the dependency versions in case of any
future breaking changes.
- Type annotations checked with mypy.
- Some attempts to be extra user-friendly:
- The script tries to be forgiving with arguments, e.g. you can
specify either the model file itself or the directory containing
it.
- The script doesn't depend on config.json / params.json, just in
case the user downloaded files individually and doesn't have those
handy. But you still need tokenizer.model and, for Alpaca,
added_tokens.json.
- The script tries to give a helpful error message if
added_tokens.json is missing.
2023-04-14 07:03:03 +00:00
@abstractmethod
2024-04-09 17:44:08 +00:00
def permute ( self , n_head : int , n_head_kv : int ) - > Self : . . .
py : new conversion script (#545)
Current status: Working, except for the latest GPTQ-for-LLaMa format
that includes `g_idx`. This turns out to require changes to GGML, so
for now it only works if you use the `--outtype` option to dequantize it
back to f16 (which is pointless except for debugging).
I also included some cleanup for the C++ code.
This script is meant to replace all the existing conversion scripts
(including the ones that convert from older GGML formats), while also
adding support for some new formats. Specifically, I've tested with:
- [x] `LLaMA` (original)
- [x] `llama-65b-4bit`
- [x] `alpaca-native`
- [x] `alpaca-native-4bit`
- [x] LLaMA converted to 'transformers' format using
`convert_llama_weights_to_hf.py`
- [x] `alpaca-native` quantized with `--true-sequential --act-order
--groupsize 128` (dequantized only)
- [x] same as above plus `--save_safetensors`
- [x] GPT4All
- [x] stock unversioned ggml
- [x] ggmh
There's enough overlap in the logic needed to handle these different
cases that it seemed best to move to a single script.
I haven't tried this with Alpaca-LoRA because I don't know where to find
it.
Useful features:
- Uses multiple threads for a speedup in some cases (though the Python
GIL limits the gain, and sometimes it's disk-bound anyway).
- Combines split models into a single file (both the intra-tensor split
of the original and the inter-tensor split of 'transformers' format
files). Single files are more convenient to work with and more
friendly to future changes to use memory mapping on the C++ side. To
accomplish this without increasing memory requirements, it has some
custom loading code which avoids loading whole input files into memory
at once.
- Because of the custom loading code, it no longer depends in PyTorch,
which might make installing dependencies slightly easier or faster...
although it still depends on NumPy and sentencepiece, so I don't know
if there's any meaningful difference. In any case, I also added a
requirements.txt file to lock the dependency versions in case of any
future breaking changes.
- Type annotations checked with mypy.
- Some attempts to be extra user-friendly:
- The script tries to be forgiving with arguments, e.g. you can
specify either the model file itself or the directory containing
it.
- The script doesn't depend on config.json / params.json, just in
case the user downloaded files individually and doesn't have those
handy. But you still need tokenizer.model and, for Alpaca,
added_tokens.json.
- The script tries to give a helpful error message if
added_tokens.json is missing.
2023-04-14 07:03:03 +00:00
@abstractmethod
2024-04-09 17:44:08 +00:00
def permute_part ( self , n_part : int , n_head : int , n_head_kv : int ) - > Self : . . .
2023-07-01 17:00:25 +00:00
@abstractmethod
2024-04-09 17:44:08 +00:00
def part ( self , n_part : int ) - > Self : . . .
2023-07-01 17:00:25 +00:00
@abstractmethod
2023-08-31 05:02:23 +00:00
def to_ggml ( self ) - > GGMLCompatibleTensor : . . .
py : new conversion script (#545)
Current status: Working, except for the latest GPTQ-for-LLaMa format
that includes `g_idx`. This turns out to require changes to GGML, so
for now it only works if you use the `--outtype` option to dequantize it
back to f16 (which is pointless except for debugging).
I also included some cleanup for the C++ code.
This script is meant to replace all the existing conversion scripts
(including the ones that convert from older GGML formats), while also
adding support for some new formats. Specifically, I've tested with:
- [x] `LLaMA` (original)
- [x] `llama-65b-4bit`
- [x] `alpaca-native`
- [x] `alpaca-native-4bit`
- [x] LLaMA converted to 'transformers' format using
`convert_llama_weights_to_hf.py`
- [x] `alpaca-native` quantized with `--true-sequential --act-order
--groupsize 128` (dequantized only)
- [x] same as above plus `--save_safetensors`
- [x] GPT4All
- [x] stock unversioned ggml
- [x] ggmh
There's enough overlap in the logic needed to handle these different
cases that it seemed best to move to a single script.
I haven't tried this with Alpaca-LoRA because I don't know where to find
it.
Useful features:
- Uses multiple threads for a speedup in some cases (though the Python
GIL limits the gain, and sometimes it's disk-bound anyway).
- Combines split models into a single file (both the intra-tensor split
of the original and the inter-tensor split of 'transformers' format
files). Single files are more convenient to work with and more
friendly to future changes to use memory mapping on the C++ side. To
accomplish this without increasing memory requirements, it has some
custom loading code which avoids loading whole input files into memory
at once.
- Because of the custom loading code, it no longer depends in PyTorch,
which might make installing dependencies slightly easier or faster...
although it still depends on NumPy and sentencepiece, so I don't know
if there's any meaningful difference. In any case, I also added a
requirements.txt file to lock the dependency versions in case of any
future breaking changes.
- Type annotations checked with mypy.
- Some attempts to be extra user-friendly:
- The script tries to be forgiving with arguments, e.g. you can
specify either the model file itself or the directory containing
it.
- The script doesn't depend on config.json / params.json, just in
case the user downloaded files individually and doesn't have those
handy. But you still need tokenizer.model and, for Alpaca,
added_tokens.json.
- The script tries to give a helpful error message if
added_tokens.json is missing.
2023-04-14 07:03:03 +00:00
2023-08-30 08:25:50 +00:00
def bf16_to_fp32 ( bf16_arr : np . ndarray [ Any , np . dtype [ np . uint16 ] ] ) - > NDArray :
2023-05-04 16:54:37 +00:00
assert bf16_arr . dtype == np . uint16 , f " Input array should be of dtype uint16, but got { bf16_arr . dtype } "
fp32_arr = bf16_arr . astype ( np . uint32 ) << 16
return fp32_arr . view ( np . float32 )
py : new conversion script (#545)
Current status: Working, except for the latest GPTQ-for-LLaMa format
that includes `g_idx`. This turns out to require changes to GGML, so
for now it only works if you use the `--outtype` option to dequantize it
back to f16 (which is pointless except for debugging).
I also included some cleanup for the C++ code.
This script is meant to replace all the existing conversion scripts
(including the ones that convert from older GGML formats), while also
adding support for some new formats. Specifically, I've tested with:
- [x] `LLaMA` (original)
- [x] `llama-65b-4bit`
- [x] `alpaca-native`
- [x] `alpaca-native-4bit`
- [x] LLaMA converted to 'transformers' format using
`convert_llama_weights_to_hf.py`
- [x] `alpaca-native` quantized with `--true-sequential --act-order
--groupsize 128` (dequantized only)
- [x] same as above plus `--save_safetensors`
- [x] GPT4All
- [x] stock unversioned ggml
- [x] ggmh
There's enough overlap in the logic needed to handle these different
cases that it seemed best to move to a single script.
I haven't tried this with Alpaca-LoRA because I don't know where to find
it.
Useful features:
- Uses multiple threads for a speedup in some cases (though the Python
GIL limits the gain, and sometimes it's disk-bound anyway).
- Combines split models into a single file (both the intra-tensor split
of the original and the inter-tensor split of 'transformers' format
files). Single files are more convenient to work with and more
friendly to future changes to use memory mapping on the C++ side. To
accomplish this without increasing memory requirements, it has some
custom loading code which avoids loading whole input files into memory
at once.
- Because of the custom loading code, it no longer depends in PyTorch,
which might make installing dependencies slightly easier or faster...
although it still depends on NumPy and sentencepiece, so I don't know
if there's any meaningful difference. In any case, I also added a
requirements.txt file to lock the dependency versions in case of any
future breaking changes.
- Type annotations checked with mypy.
- Some attempts to be extra user-friendly:
- The script tries to be forgiving with arguments, e.g. you can
specify either the model file itself or the directory containing
it.
- The script doesn't depend on config.json / params.json, just in
case the user downloaded files individually and doesn't have those
handy. But you still need tokenizer.model and, for Alpaca,
added_tokens.json.
- The script tries to give a helpful error message if
added_tokens.json is missing.
2023-04-14 07:03:03 +00:00
class UnquantizedTensor ( Tensor ) :
2024-03-28 15:44:36 +00:00
def __init__ ( self , ndarray : NDArray ) :
py : new conversion script (#545)
Current status: Working, except for the latest GPTQ-for-LLaMa format
that includes `g_idx`. This turns out to require changes to GGML, so
for now it only works if you use the `--outtype` option to dequantize it
back to f16 (which is pointless except for debugging).
I also included some cleanup for the C++ code.
This script is meant to replace all the existing conversion scripts
(including the ones that convert from older GGML formats), while also
adding support for some new formats. Specifically, I've tested with:
- [x] `LLaMA` (original)
- [x] `llama-65b-4bit`
- [x] `alpaca-native`
- [x] `alpaca-native-4bit`
- [x] LLaMA converted to 'transformers' format using
`convert_llama_weights_to_hf.py`
- [x] `alpaca-native` quantized with `--true-sequential --act-order
--groupsize 128` (dequantized only)
- [x] same as above plus `--save_safetensors`
- [x] GPT4All
- [x] stock unversioned ggml
- [x] ggmh
There's enough overlap in the logic needed to handle these different
cases that it seemed best to move to a single script.
I haven't tried this with Alpaca-LoRA because I don't know where to find
it.
Useful features:
- Uses multiple threads for a speedup in some cases (though the Python
GIL limits the gain, and sometimes it's disk-bound anyway).
- Combines split models into a single file (both the intra-tensor split
of the original and the inter-tensor split of 'transformers' format
files). Single files are more convenient to work with and more
friendly to future changes to use memory mapping on the C++ side. To
accomplish this without increasing memory requirements, it has some
custom loading code which avoids loading whole input files into memory
at once.
- Because of the custom loading code, it no longer depends in PyTorch,
which might make installing dependencies slightly easier or faster...
although it still depends on NumPy and sentencepiece, so I don't know
if there's any meaningful difference. In any case, I also added a
requirements.txt file to lock the dependency versions in case of any
future breaking changes.
- Type annotations checked with mypy.
- Some attempts to be extra user-friendly:
- The script tries to be forgiving with arguments, e.g. you can
specify either the model file itself or the directory containing
it.
- The script doesn't depend on config.json / params.json, just in
case the user downloaded files individually and doesn't have those
handy. But you still need tokenizer.model and, for Alpaca,
added_tokens.json.
- The script tries to give a helpful error message if
added_tokens.json is missing.
2023-04-14 07:03:03 +00:00
assert isinstance ( ndarray , np . ndarray )
self . ndarray = ndarray
self . data_type = NUMPY_TYPE_TO_DATA_TYPE [ ndarray . dtype ]
2024-04-09 17:44:08 +00:00
def astype ( self , data_type : DataType ) - > UnquantizedTensor :
2023-08-26 20:13:36 +00:00
dtype = data_type . dtype
2023-05-04 16:54:37 +00:00
if self . data_type == DT_BF16 :
self . ndarray = bf16_to_fp32 ( self . ndarray )
py : new conversion script (#545)
Current status: Working, except for the latest GPTQ-for-LLaMa format
that includes `g_idx`. This turns out to require changes to GGML, so
for now it only works if you use the `--outtype` option to dequantize it
back to f16 (which is pointless except for debugging).
I also included some cleanup for the C++ code.
This script is meant to replace all the existing conversion scripts
(including the ones that convert from older GGML formats), while also
adding support for some new formats. Specifically, I've tested with:
- [x] `LLaMA` (original)
- [x] `llama-65b-4bit`
- [x] `alpaca-native`
- [x] `alpaca-native-4bit`
- [x] LLaMA converted to 'transformers' format using
`convert_llama_weights_to_hf.py`
- [x] `alpaca-native` quantized with `--true-sequential --act-order
--groupsize 128` (dequantized only)
- [x] same as above plus `--save_safetensors`
- [x] GPT4All
- [x] stock unversioned ggml
- [x] ggmh
There's enough overlap in the logic needed to handle these different
cases that it seemed best to move to a single script.
I haven't tried this with Alpaca-LoRA because I don't know where to find
it.
Useful features:
- Uses multiple threads for a speedup in some cases (though the Python
GIL limits the gain, and sometimes it's disk-bound anyway).
- Combines split models into a single file (both the intra-tensor split
of the original and the inter-tensor split of 'transformers' format
files). Single files are more convenient to work with and more
friendly to future changes to use memory mapping on the C++ side. To
accomplish this without increasing memory requirements, it has some
custom loading code which avoids loading whole input files into memory
at once.
- Because of the custom loading code, it no longer depends in PyTorch,
which might make installing dependencies slightly easier or faster...
although it still depends on NumPy and sentencepiece, so I don't know
if there's any meaningful difference. In any case, I also added a
requirements.txt file to lock the dependency versions in case of any
future breaking changes.
- Type annotations checked with mypy.
- Some attempts to be extra user-friendly:
- The script tries to be forgiving with arguments, e.g. you can
specify either the model file itself or the directory containing
it.
- The script doesn't depend on config.json / params.json, just in
case the user downloaded files individually and doesn't have those
handy. But you still need tokenizer.model and, for Alpaca,
added_tokens.json.
- The script tries to give a helpful error message if
added_tokens.json is missing.
2023-04-14 07:03:03 +00:00
return UnquantizedTensor ( self . ndarray . astype ( dtype ) )
2024-04-09 17:44:08 +00:00
def to_ggml ( self ) - > Self :
py : new conversion script (#545)
Current status: Working, except for the latest GPTQ-for-LLaMa format
that includes `g_idx`. This turns out to require changes to GGML, so
for now it only works if you use the `--outtype` option to dequantize it
back to f16 (which is pointless except for debugging).
I also included some cleanup for the C++ code.
This script is meant to replace all the existing conversion scripts
(including the ones that convert from older GGML formats), while also
adding support for some new formats. Specifically, I've tested with:
- [x] `LLaMA` (original)
- [x] `llama-65b-4bit`
- [x] `alpaca-native`
- [x] `alpaca-native-4bit`
- [x] LLaMA converted to 'transformers' format using
`convert_llama_weights_to_hf.py`
- [x] `alpaca-native` quantized with `--true-sequential --act-order
--groupsize 128` (dequantized only)
- [x] same as above plus `--save_safetensors`
- [x] GPT4All
- [x] stock unversioned ggml
- [x] ggmh
There's enough overlap in the logic needed to handle these different
cases that it seemed best to move to a single script.
I haven't tried this with Alpaca-LoRA because I don't know where to find
it.
Useful features:
- Uses multiple threads for a speedup in some cases (though the Python
GIL limits the gain, and sometimes it's disk-bound anyway).
- Combines split models into a single file (both the intra-tensor split
of the original and the inter-tensor split of 'transformers' format
files). Single files are more convenient to work with and more
friendly to future changes to use memory mapping on the C++ side. To
accomplish this without increasing memory requirements, it has some
custom loading code which avoids loading whole input files into memory
at once.
- Because of the custom loading code, it no longer depends in PyTorch,
which might make installing dependencies slightly easier or faster...
although it still depends on NumPy and sentencepiece, so I don't know
if there's any meaningful difference. In any case, I also added a
requirements.txt file to lock the dependency versions in case of any
future breaking changes.
- Type annotations checked with mypy.
- Some attempts to be extra user-friendly:
- The script tries to be forgiving with arguments, e.g. you can
specify either the model file itself or the directory containing
it.
- The script doesn't depend on config.json / params.json, just in
case the user downloaded files individually and doesn't have those
handy. But you still need tokenizer.model and, for Alpaca,
added_tokens.json.
- The script tries to give a helpful error message if
added_tokens.json is missing.
2023-04-14 07:03:03 +00:00
return self
2023-08-31 05:02:23 +00:00
def permute_part ( self , n_part : int , n_head : int , n_head_kv : int ) - > UnquantizedTensor :
2023-07-01 17:00:25 +00:00
r = self . ndarray . shape [ 0 ] / / 3
2023-08-30 08:25:50 +00:00
return UnquantizedTensor ( permute ( self . ndarray [ r * n_part : r * n_part + r , . . . ] , n_head , n_head_kv ) )
2023-07-01 17:00:25 +00:00
2023-08-31 05:02:23 +00:00
def part ( self , n_part : int ) - > UnquantizedTensor :
2023-07-01 17:00:25 +00:00
r = self . ndarray . shape [ 0 ] / / 3
return UnquantizedTensor ( self . ndarray [ r * n_part : r * n_part + r , . . . ] )
2023-08-31 05:02:23 +00:00
def permute ( self , n_head : int , n_head_kv : int ) - > UnquantizedTensor :
2023-08-21 20:07:43 +00:00
return UnquantizedTensor ( permute ( self . ndarray , n_head , n_head_kv ) )
py : new conversion script (#545)
Current status: Working, except for the latest GPTQ-for-LLaMa format
that includes `g_idx`. This turns out to require changes to GGML, so
for now it only works if you use the `--outtype` option to dequantize it
back to f16 (which is pointless except for debugging).
I also included some cleanup for the C++ code.
This script is meant to replace all the existing conversion scripts
(including the ones that convert from older GGML formats), while also
adding support for some new formats. Specifically, I've tested with:
- [x] `LLaMA` (original)
- [x] `llama-65b-4bit`
- [x] `alpaca-native`
- [x] `alpaca-native-4bit`
- [x] LLaMA converted to 'transformers' format using
`convert_llama_weights_to_hf.py`
- [x] `alpaca-native` quantized with `--true-sequential --act-order
--groupsize 128` (dequantized only)
- [x] same as above plus `--save_safetensors`
- [x] GPT4All
- [x] stock unversioned ggml
- [x] ggmh
There's enough overlap in the logic needed to handle these different
cases that it seemed best to move to a single script.
I haven't tried this with Alpaca-LoRA because I don't know where to find
it.
Useful features:
- Uses multiple threads for a speedup in some cases (though the Python
GIL limits the gain, and sometimes it's disk-bound anyway).
- Combines split models into a single file (both the intra-tensor split
of the original and the inter-tensor split of 'transformers' format
files). Single files are more convenient to work with and more
friendly to future changes to use memory mapping on the C++ side. To
accomplish this without increasing memory requirements, it has some
custom loading code which avoids loading whole input files into memory
at once.
- Because of the custom loading code, it no longer depends in PyTorch,
which might make installing dependencies slightly easier or faster...
although it still depends on NumPy and sentencepiece, so I don't know
if there's any meaningful difference. In any case, I also added a
requirements.txt file to lock the dependency versions in case of any
future breaking changes.
- Type annotations checked with mypy.
- Some attempts to be extra user-friendly:
- The script tries to be forgiving with arguments, e.g. you can
specify either the model file itself or the directory containing
it.
- The script doesn't depend on config.json / params.json, just in
case the user downloaded files individually and doesn't have those
handy. But you still need tokenizer.model and, for Alpaca,
added_tokens.json.
- The script tries to give a helpful error message if
added_tokens.json is missing.
2023-04-14 07:03:03 +00:00
2023-08-31 05:02:23 +00:00
def load_unquantized ( lazy_tensor : LazyTensor , expected_dtype : Any = None , convert : bool = False ) - > NDArray :
py : new conversion script (#545)
Current status: Working, except for the latest GPTQ-for-LLaMa format
that includes `g_idx`. This turns out to require changes to GGML, so
for now it only works if you use the `--outtype` option to dequantize it
back to f16 (which is pointless except for debugging).
I also included some cleanup for the C++ code.
This script is meant to replace all the existing conversion scripts
(including the ones that convert from older GGML formats), while also
adding support for some new formats. Specifically, I've tested with:
- [x] `LLaMA` (original)
- [x] `llama-65b-4bit`
- [x] `alpaca-native`
- [x] `alpaca-native-4bit`
- [x] LLaMA converted to 'transformers' format using
`convert_llama_weights_to_hf.py`
- [x] `alpaca-native` quantized with `--true-sequential --act-order
--groupsize 128` (dequantized only)
- [x] same as above plus `--save_safetensors`
- [x] GPT4All
- [x] stock unversioned ggml
- [x] ggmh
There's enough overlap in the logic needed to handle these different
cases that it seemed best to move to a single script.
I haven't tried this with Alpaca-LoRA because I don't know where to find
it.
Useful features:
- Uses multiple threads for a speedup in some cases (though the Python
GIL limits the gain, and sometimes it's disk-bound anyway).
- Combines split models into a single file (both the intra-tensor split
of the original and the inter-tensor split of 'transformers' format
files). Single files are more convenient to work with and more
friendly to future changes to use memory mapping on the C++ side. To
accomplish this without increasing memory requirements, it has some
custom loading code which avoids loading whole input files into memory
at once.
- Because of the custom loading code, it no longer depends in PyTorch,
which might make installing dependencies slightly easier or faster...
although it still depends on NumPy and sentencepiece, so I don't know
if there's any meaningful difference. In any case, I also added a
requirements.txt file to lock the dependency versions in case of any
future breaking changes.
- Type annotations checked with mypy.
- Some attempts to be extra user-friendly:
- The script tries to be forgiving with arguments, e.g. you can
specify either the model file itself or the directory containing
it.
- The script doesn't depend on config.json / params.json, just in
case the user downloaded files individually and doesn't have those
handy. But you still need tokenizer.model and, for Alpaca,
added_tokens.json.
- The script tries to give a helpful error message if
added_tokens.json is missing.
2023-04-14 07:03:03 +00:00
tensor = lazy_tensor . load ( )
assert isinstance ( tensor , UnquantizedTensor )
# double-check:
actual_shape = list ( tensor . ndarray . shape )
assert actual_shape == lazy_tensor . shape , ( actual_shape , lazy_tensor . shape )
if expected_dtype is not None and expected_dtype != tensor . ndarray . dtype :
if convert :
tensor . ndarray = tensor . ndarray . astype ( expected_dtype )
else :
raise ValueError ( f ' expected this tensor to have dtype { expected_dtype } , got { tensor . ndarray . dtype } ' )
return tensor . ndarray
2023-08-31 05:02:23 +00:00
GGMLCompatibleTensor = UnquantizedTensor
py : new conversion script (#545)
Current status: Working, except for the latest GPTQ-for-LLaMa format
that includes `g_idx`. This turns out to require changes to GGML, so
for now it only works if you use the `--outtype` option to dequantize it
back to f16 (which is pointless except for debugging).
I also included some cleanup for the C++ code.
This script is meant to replace all the existing conversion scripts
(including the ones that convert from older GGML formats), while also
adding support for some new formats. Specifically, I've tested with:
- [x] `LLaMA` (original)
- [x] `llama-65b-4bit`
- [x] `alpaca-native`
- [x] `alpaca-native-4bit`
- [x] LLaMA converted to 'transformers' format using
`convert_llama_weights_to_hf.py`
- [x] `alpaca-native` quantized with `--true-sequential --act-order
--groupsize 128` (dequantized only)
- [x] same as above plus `--save_safetensors`
- [x] GPT4All
- [x] stock unversioned ggml
- [x] ggmh
There's enough overlap in the logic needed to handle these different
cases that it seemed best to move to a single script.
I haven't tried this with Alpaca-LoRA because I don't know where to find
it.
Useful features:
- Uses multiple threads for a speedup in some cases (though the Python
GIL limits the gain, and sometimes it's disk-bound anyway).
- Combines split models into a single file (both the intra-tensor split
of the original and the inter-tensor split of 'transformers' format
files). Single files are more convenient to work with and more
friendly to future changes to use memory mapping on the C++ side. To
accomplish this without increasing memory requirements, it has some
custom loading code which avoids loading whole input files into memory
at once.
- Because of the custom loading code, it no longer depends in PyTorch,
which might make installing dependencies slightly easier or faster...
although it still depends on NumPy and sentencepiece, so I don't know
if there's any meaningful difference. In any case, I also added a
requirements.txt file to lock the dependency versions in case of any
future breaking changes.
- Type annotations checked with mypy.
- Some attempts to be extra user-friendly:
- The script tries to be forgiving with arguments, e.g. you can
specify either the model file itself or the directory containing
it.
- The script doesn't depend on config.json / params.json, just in
case the user downloaded files individually and doesn't have those
handy. But you still need tokenizer.model and, for Alpaca,
added_tokens.json.
- The script tries to give a helpful error message if
added_tokens.json is missing.
2023-04-14 07:03:03 +00:00
@dataclass
class LazyTensor :
_load : Callable [ [ ] , Tensor ]
2023-08-31 05:02:23 +00:00
shape : list [ int ]
py : new conversion script (#545)
Current status: Working, except for the latest GPTQ-for-LLaMa format
that includes `g_idx`. This turns out to require changes to GGML, so
for now it only works if you use the `--outtype` option to dequantize it
back to f16 (which is pointless except for debugging).
I also included some cleanup for the C++ code.
This script is meant to replace all the existing conversion scripts
(including the ones that convert from older GGML formats), while also
adding support for some new formats. Specifically, I've tested with:
- [x] `LLaMA` (original)
- [x] `llama-65b-4bit`
- [x] `alpaca-native`
- [x] `alpaca-native-4bit`
- [x] LLaMA converted to 'transformers' format using
`convert_llama_weights_to_hf.py`
- [x] `alpaca-native` quantized with `--true-sequential --act-order
--groupsize 128` (dequantized only)
- [x] same as above plus `--save_safetensors`
- [x] GPT4All
- [x] stock unversioned ggml
- [x] ggmh
There's enough overlap in the logic needed to handle these different
cases that it seemed best to move to a single script.
I haven't tried this with Alpaca-LoRA because I don't know where to find
it.
Useful features:
- Uses multiple threads for a speedup in some cases (though the Python
GIL limits the gain, and sometimes it's disk-bound anyway).
- Combines split models into a single file (both the intra-tensor split
of the original and the inter-tensor split of 'transformers' format
files). Single files are more convenient to work with and more
friendly to future changes to use memory mapping on the C++ side. To
accomplish this without increasing memory requirements, it has some
custom loading code which avoids loading whole input files into memory
at once.
- Because of the custom loading code, it no longer depends in PyTorch,
which might make installing dependencies slightly easier or faster...
although it still depends on NumPy and sentencepiece, so I don't know
if there's any meaningful difference. In any case, I also added a
requirements.txt file to lock the dependency versions in case of any
future breaking changes.
- Type annotations checked with mypy.
- Some attempts to be extra user-friendly:
- The script tries to be forgiving with arguments, e.g. you can
specify either the model file itself or the directory containing
it.
- The script doesn't depend on config.json / params.json, just in
case the user downloaded files individually and doesn't have those
handy. But you still need tokenizer.model and, for Alpaca,
added_tokens.json.
- The script tries to give a helpful error message if
added_tokens.json is missing.
2023-04-14 07:03:03 +00:00
data_type : DataType
description : str
def load ( self ) - > Tensor :
ret = self . _load ( )
2023-08-26 20:13:36 +00:00
# Should be okay if it maps to the same numpy type?
assert ret . data_type == self . data_type or ( self . data_type . dtype == ret . data_type . dtype ) , \
2023-11-20 10:35:47 +00:00
( self . data_type , ret . data_type , self . description )
py : new conversion script (#545)
Current status: Working, except for the latest GPTQ-for-LLaMa format
that includes `g_idx`. This turns out to require changes to GGML, so
for now it only works if you use the `--outtype` option to dequantize it
back to f16 (which is pointless except for debugging).
I also included some cleanup for the C++ code.
This script is meant to replace all the existing conversion scripts
(including the ones that convert from older GGML formats), while also
adding support for some new formats. Specifically, I've tested with:
- [x] `LLaMA` (original)
- [x] `llama-65b-4bit`
- [x] `alpaca-native`
- [x] `alpaca-native-4bit`
- [x] LLaMA converted to 'transformers' format using
`convert_llama_weights_to_hf.py`
- [x] `alpaca-native` quantized with `--true-sequential --act-order
--groupsize 128` (dequantized only)
- [x] same as above plus `--save_safetensors`
- [x] GPT4All
- [x] stock unversioned ggml
- [x] ggmh
There's enough overlap in the logic needed to handle these different
cases that it seemed best to move to a single script.
I haven't tried this with Alpaca-LoRA because I don't know where to find
it.
Useful features:
- Uses multiple threads for a speedup in some cases (though the Python
GIL limits the gain, and sometimes it's disk-bound anyway).
- Combines split models into a single file (both the intra-tensor split
of the original and the inter-tensor split of 'transformers' format
files). Single files are more convenient to work with and more
friendly to future changes to use memory mapping on the C++ side. To
accomplish this without increasing memory requirements, it has some
custom loading code which avoids loading whole input files into memory
at once.
- Because of the custom loading code, it no longer depends in PyTorch,
which might make installing dependencies slightly easier or faster...
although it still depends on NumPy and sentencepiece, so I don't know
if there's any meaningful difference. In any case, I also added a
requirements.txt file to lock the dependency versions in case of any
future breaking changes.
- Type annotations checked with mypy.
- Some attempts to be extra user-friendly:
- The script tries to be forgiving with arguments, e.g. you can
specify either the model file itself or the directory containing
it.
- The script doesn't depend on config.json / params.json, just in
case the user downloaded files individually and doesn't have those
handy. But you still need tokenizer.model and, for Alpaca,
added_tokens.json.
- The script tries to give a helpful error message if
added_tokens.json is missing.
2023-04-14 07:03:03 +00:00
return ret
2023-08-31 05:02:23 +00:00
def astype ( self , data_type : DataType ) - > LazyTensor :
py : new conversion script (#545)
Current status: Working, except for the latest GPTQ-for-LLaMa format
that includes `g_idx`. This turns out to require changes to GGML, so
for now it only works if you use the `--outtype` option to dequantize it
back to f16 (which is pointless except for debugging).
I also included some cleanup for the C++ code.
This script is meant to replace all the existing conversion scripts
(including the ones that convert from older GGML formats), while also
adding support for some new formats. Specifically, I've tested with:
- [x] `LLaMA` (original)
- [x] `llama-65b-4bit`
- [x] `alpaca-native`
- [x] `alpaca-native-4bit`
- [x] LLaMA converted to 'transformers' format using
`convert_llama_weights_to_hf.py`
- [x] `alpaca-native` quantized with `--true-sequential --act-order
--groupsize 128` (dequantized only)
- [x] same as above plus `--save_safetensors`
- [x] GPT4All
- [x] stock unversioned ggml
- [x] ggmh
There's enough overlap in the logic needed to handle these different
cases that it seemed best to move to a single script.
I haven't tried this with Alpaca-LoRA because I don't know where to find
it.
Useful features:
- Uses multiple threads for a speedup in some cases (though the Python
GIL limits the gain, and sometimes it's disk-bound anyway).
- Combines split models into a single file (both the intra-tensor split
of the original and the inter-tensor split of 'transformers' format
files). Single files are more convenient to work with and more
friendly to future changes to use memory mapping on the C++ side. To
accomplish this without increasing memory requirements, it has some
custom loading code which avoids loading whole input files into memory
at once.
- Because of the custom loading code, it no longer depends in PyTorch,
which might make installing dependencies slightly easier or faster...
although it still depends on NumPy and sentencepiece, so I don't know
if there's any meaningful difference. In any case, I also added a
requirements.txt file to lock the dependency versions in case of any
future breaking changes.
- Type annotations checked with mypy.
- Some attempts to be extra user-friendly:
- The script tries to be forgiving with arguments, e.g. you can
specify either the model file itself or the directory containing
it.
- The script doesn't depend on config.json / params.json, just in
case the user downloaded files individually and doesn't have those
handy. But you still need tokenizer.model and, for Alpaca,
added_tokens.json.
- The script tries to give a helpful error message if
added_tokens.json is missing.
2023-04-14 07:03:03 +00:00
self . validate_conversion_to ( data_type )
def load ( ) - > Tensor :
return self . load ( ) . astype ( data_type )
return LazyTensor ( load , self . shape , data_type , f ' convert( { data_type } ) { self . description } ' )
def validate_conversion_to ( self , data_type : DataType ) - > None :
2023-08-26 20:13:36 +00:00
if data_type != self . data_type and data_type . name not in self . data_type . valid_conversions :
raise ValueError ( f ' Cannot validate conversion from { self . data_type } to { data_type } . ' )
py : new conversion script (#545)
Current status: Working, except for the latest GPTQ-for-LLaMa format
that includes `g_idx`. This turns out to require changes to GGML, so
for now it only works if you use the `--outtype` option to dequantize it
back to f16 (which is pointless except for debugging).
I also included some cleanup for the C++ code.
This script is meant to replace all the existing conversion scripts
(including the ones that convert from older GGML formats), while also
adding support for some new formats. Specifically, I've tested with:
- [x] `LLaMA` (original)
- [x] `llama-65b-4bit`
- [x] `alpaca-native`
- [x] `alpaca-native-4bit`
- [x] LLaMA converted to 'transformers' format using
`convert_llama_weights_to_hf.py`
- [x] `alpaca-native` quantized with `--true-sequential --act-order
--groupsize 128` (dequantized only)
- [x] same as above plus `--save_safetensors`
- [x] GPT4All
- [x] stock unversioned ggml
- [x] ggmh
There's enough overlap in the logic needed to handle these different
cases that it seemed best to move to a single script.
I haven't tried this with Alpaca-LoRA because I don't know where to find
it.
Useful features:
- Uses multiple threads for a speedup in some cases (though the Python
GIL limits the gain, and sometimes it's disk-bound anyway).
- Combines split models into a single file (both the intra-tensor split
of the original and the inter-tensor split of 'transformers' format
files). Single files are more convenient to work with and more
friendly to future changes to use memory mapping on the C++ side. To
accomplish this without increasing memory requirements, it has some
custom loading code which avoids loading whole input files into memory
at once.
- Because of the custom loading code, it no longer depends in PyTorch,
which might make installing dependencies slightly easier or faster...
although it still depends on NumPy and sentencepiece, so I don't know
if there's any meaningful difference. In any case, I also added a
requirements.txt file to lock the dependency versions in case of any
future breaking changes.
- Type annotations checked with mypy.
- Some attempts to be extra user-friendly:
- The script tries to be forgiving with arguments, e.g. you can
specify either the model file itself or the directory containing
it.
- The script doesn't depend on config.json / params.json, just in
case the user downloaded files individually and doesn't have those
handy. But you still need tokenizer.model and, for Alpaca,
added_tokens.json.
- The script tries to give a helpful error message if
added_tokens.json is missing.
2023-04-14 07:03:03 +00:00
2023-09-01 02:13:51 +00:00
LazyModel : TypeAlias = ' dict[str, LazyTensor] '
py : new conversion script (#545)
Current status: Working, except for the latest GPTQ-for-LLaMa format
that includes `g_idx`. This turns out to require changes to GGML, so
for now it only works if you use the `--outtype` option to dequantize it
back to f16 (which is pointless except for debugging).
I also included some cleanup for the C++ code.
This script is meant to replace all the existing conversion scripts
(including the ones that convert from older GGML formats), while also
adding support for some new formats. Specifically, I've tested with:
- [x] `LLaMA` (original)
- [x] `llama-65b-4bit`
- [x] `alpaca-native`
- [x] `alpaca-native-4bit`
- [x] LLaMA converted to 'transformers' format using
`convert_llama_weights_to_hf.py`
- [x] `alpaca-native` quantized with `--true-sequential --act-order
--groupsize 128` (dequantized only)
- [x] same as above plus `--save_safetensors`
- [x] GPT4All
- [x] stock unversioned ggml
- [x] ggmh
There's enough overlap in the logic needed to handle these different
cases that it seemed best to move to a single script.
I haven't tried this with Alpaca-LoRA because I don't know where to find
it.
Useful features:
- Uses multiple threads for a speedup in some cases (though the Python
GIL limits the gain, and sometimes it's disk-bound anyway).
- Combines split models into a single file (both the intra-tensor split
of the original and the inter-tensor split of 'transformers' format
files). Single files are more convenient to work with and more
friendly to future changes to use memory mapping on the C++ side. To
accomplish this without increasing memory requirements, it has some
custom loading code which avoids loading whole input files into memory
at once.
- Because of the custom loading code, it no longer depends in PyTorch,
which might make installing dependencies slightly easier or faster...
although it still depends on NumPy and sentencepiece, so I don't know
if there's any meaningful difference. In any case, I also added a
requirements.txt file to lock the dependency versions in case of any
future breaking changes.
- Type annotations checked with mypy.
- Some attempts to be extra user-friendly:
- The script tries to be forgiving with arguments, e.g. you can
specify either the model file itself or the directory containing
it.
- The script doesn't depend on config.json / params.json, just in
case the user downloaded files individually and doesn't have those
handy. But you still need tokenizer.model and, for Alpaca,
added_tokens.json.
- The script tries to give a helpful error message if
added_tokens.json is missing.
2023-04-14 07:03:03 +00:00
@dataclass
class ModelPlus :
model : LazyModel
2023-08-31 05:02:23 +00:00
paths : list [ Path ] # Where this was read from.
2023-08-30 08:25:50 +00:00
format : Literal [ ' ggml ' , ' torch ' , ' safetensors ' , ' none ' ]
2024-03-28 15:44:36 +00:00
vocab : BaseVocab | None # For GGML models (which have vocab built in), the vocab.
py : new conversion script (#545)
Current status: Working, except for the latest GPTQ-for-LLaMa format
that includes `g_idx`. This turns out to require changes to GGML, so
for now it only works if you use the `--outtype` option to dequantize it
back to f16 (which is pointless except for debugging).
I also included some cleanup for the C++ code.
This script is meant to replace all the existing conversion scripts
(including the ones that convert from older GGML formats), while also
adding support for some new formats. Specifically, I've tested with:
- [x] `LLaMA` (original)
- [x] `llama-65b-4bit`
- [x] `alpaca-native`
- [x] `alpaca-native-4bit`
- [x] LLaMA converted to 'transformers' format using
`convert_llama_weights_to_hf.py`
- [x] `alpaca-native` quantized with `--true-sequential --act-order
--groupsize 128` (dequantized only)
- [x] same as above plus `--save_safetensors`
- [x] GPT4All
- [x] stock unversioned ggml
- [x] ggmh
There's enough overlap in the logic needed to handle these different
cases that it seemed best to move to a single script.
I haven't tried this with Alpaca-LoRA because I don't know where to find
it.
Useful features:
- Uses multiple threads for a speedup in some cases (though the Python
GIL limits the gain, and sometimes it's disk-bound anyway).
- Combines split models into a single file (both the intra-tensor split
of the original and the inter-tensor split of 'transformers' format
files). Single files are more convenient to work with and more
friendly to future changes to use memory mapping on the C++ side. To
accomplish this without increasing memory requirements, it has some
custom loading code which avoids loading whole input files into memory
at once.
- Because of the custom loading code, it no longer depends in PyTorch,
which might make installing dependencies slightly easier or faster...
although it still depends on NumPy and sentencepiece, so I don't know
if there's any meaningful difference. In any case, I also added a
requirements.txt file to lock the dependency versions in case of any
future breaking changes.
- Type annotations checked with mypy.
- Some attempts to be extra user-friendly:
- The script tries to be forgiving with arguments, e.g. you can
specify either the model file itself or the directory containing
it.
- The script doesn't depend on config.json / params.json, just in
case the user downloaded files individually and doesn't have those
handy. But you still need tokenizer.model and, for Alpaca,
added_tokens.json.
- The script tries to give a helpful error message if
added_tokens.json is missing.
2023-04-14 07:03:03 +00:00
2023-08-31 05:02:23 +00:00
def merge_sharded ( models : list [ LazyModel ] ) - > LazyModel :
py : new conversion script (#545)
Current status: Working, except for the latest GPTQ-for-LLaMa format
that includes `g_idx`. This turns out to require changes to GGML, so
for now it only works if you use the `--outtype` option to dequantize it
back to f16 (which is pointless except for debugging).
I also included some cleanup for the C++ code.
This script is meant to replace all the existing conversion scripts
(including the ones that convert from older GGML formats), while also
adding support for some new formats. Specifically, I've tested with:
- [x] `LLaMA` (original)
- [x] `llama-65b-4bit`
- [x] `alpaca-native`
- [x] `alpaca-native-4bit`
- [x] LLaMA converted to 'transformers' format using
`convert_llama_weights_to_hf.py`
- [x] `alpaca-native` quantized with `--true-sequential --act-order
--groupsize 128` (dequantized only)
- [x] same as above plus `--save_safetensors`
- [x] GPT4All
- [x] stock unversioned ggml
- [x] ggmh
There's enough overlap in the logic needed to handle these different
cases that it seemed best to move to a single script.
I haven't tried this with Alpaca-LoRA because I don't know where to find
it.
Useful features:
- Uses multiple threads for a speedup in some cases (though the Python
GIL limits the gain, and sometimes it's disk-bound anyway).
- Combines split models into a single file (both the intra-tensor split
of the original and the inter-tensor split of 'transformers' format
files). Single files are more convenient to work with and more
friendly to future changes to use memory mapping on the C++ side. To
accomplish this without increasing memory requirements, it has some
custom loading code which avoids loading whole input files into memory
at once.
- Because of the custom loading code, it no longer depends in PyTorch,
which might make installing dependencies slightly easier or faster...
although it still depends on NumPy and sentencepiece, so I don't know
if there's any meaningful difference. In any case, I also added a
requirements.txt file to lock the dependency versions in case of any
future breaking changes.
- Type annotations checked with mypy.
- Some attempts to be extra user-friendly:
- The script tries to be forgiving with arguments, e.g. you can
specify either the model file itself or the directory containing
it.
- The script doesn't depend on config.json / params.json, just in
case the user downloaded files individually and doesn't have those
handy. But you still need tokenizer.model and, for Alpaca,
added_tokens.json.
- The script tries to give a helpful error message if
added_tokens.json is missing.
2023-04-14 07:03:03 +00:00
# Original LLaMA models have each file contain one part of each tensor.
# Use a dict instead of a set to preserve order.
names = { name : None for model in models for name in model }
def convert ( name : str ) - > LazyTensor :
2024-03-28 15:44:36 +00:00
lazy_tensors = [ model [ name ] for model in models ]
py : new conversion script (#545)
Current status: Working, except for the latest GPTQ-for-LLaMa format
that includes `g_idx`. This turns out to require changes to GGML, so
for now it only works if you use the `--outtype` option to dequantize it
back to f16 (which is pointless except for debugging).
I also included some cleanup for the C++ code.
This script is meant to replace all the existing conversion scripts
(including the ones that convert from older GGML formats), while also
adding support for some new formats. Specifically, I've tested with:
- [x] `LLaMA` (original)
- [x] `llama-65b-4bit`
- [x] `alpaca-native`
- [x] `alpaca-native-4bit`
- [x] LLaMA converted to 'transformers' format using
`convert_llama_weights_to_hf.py`
- [x] `alpaca-native` quantized with `--true-sequential --act-order
--groupsize 128` (dequantized only)
- [x] same as above plus `--save_safetensors`
- [x] GPT4All
- [x] stock unversioned ggml
- [x] ggmh
There's enough overlap in the logic needed to handle these different
cases that it seemed best to move to a single script.
I haven't tried this with Alpaca-LoRA because I don't know where to find
it.
Useful features:
- Uses multiple threads for a speedup in some cases (though the Python
GIL limits the gain, and sometimes it's disk-bound anyway).
- Combines split models into a single file (both the intra-tensor split
of the original and the inter-tensor split of 'transformers' format
files). Single files are more convenient to work with and more
friendly to future changes to use memory mapping on the C++ side. To
accomplish this without increasing memory requirements, it has some
custom loading code which avoids loading whole input files into memory
at once.
- Because of the custom loading code, it no longer depends in PyTorch,
which might make installing dependencies slightly easier or faster...
although it still depends on NumPy and sentencepiece, so I don't know
if there's any meaningful difference. In any case, I also added a
requirements.txt file to lock the dependency versions in case of any
future breaking changes.
- Type annotations checked with mypy.
- Some attempts to be extra user-friendly:
- The script tries to be forgiving with arguments, e.g. you can
specify either the model file itself or the directory containing
it.
- The script doesn't depend on config.json / params.json, just in
case the user downloaded files individually and doesn't have those
handy. But you still need tokenizer.model and, for Alpaca,
added_tokens.json.
- The script tries to give a helpful error message if
added_tokens.json is missing.
2023-04-14 07:03:03 +00:00
if len ( lazy_tensors ) == 1 :
# only one file; don't go through this procedure since there might
# be quantized tensors
return lazy_tensors [ 0 ]
if len ( lazy_tensors [ 0 ] . shape ) == 1 :
# the tensor is just duplicated in every file
return lazy_tensors [ 0 ]
if name . startswith ( ' tok_embeddings. ' ) or \
name . endswith ( ' .attention.wo.weight ' ) or \
name . endswith ( ' .feed_forward.w2.weight ' ) :
# split by columns
axis = 1
else :
# split by rows
axis = 0
concatenated_shape = list ( lazy_tensors [ 0 ] . shape )
concatenated_shape [ axis ] = sum ( tensor . shape [ axis ] for tensor in lazy_tensors )
def load ( ) - > UnquantizedTensor :
ndarrays = [ load_unquantized ( tensor ) for tensor in lazy_tensors ]
2024-03-28 15:44:36 +00:00
concatenated = np . concatenate ( ndarrays , axis = axis )
py : new conversion script (#545)
Current status: Working, except for the latest GPTQ-for-LLaMa format
that includes `g_idx`. This turns out to require changes to GGML, so
for now it only works if you use the `--outtype` option to dequantize it
back to f16 (which is pointless except for debugging).
I also included some cleanup for the C++ code.
This script is meant to replace all the existing conversion scripts
(including the ones that convert from older GGML formats), while also
adding support for some new formats. Specifically, I've tested with:
- [x] `LLaMA` (original)
- [x] `llama-65b-4bit`
- [x] `alpaca-native`
- [x] `alpaca-native-4bit`
- [x] LLaMA converted to 'transformers' format using
`convert_llama_weights_to_hf.py`
- [x] `alpaca-native` quantized with `--true-sequential --act-order
--groupsize 128` (dequantized only)
- [x] same as above plus `--save_safetensors`
- [x] GPT4All
- [x] stock unversioned ggml
- [x] ggmh
There's enough overlap in the logic needed to handle these different
cases that it seemed best to move to a single script.
I haven't tried this with Alpaca-LoRA because I don't know where to find
it.
Useful features:
- Uses multiple threads for a speedup in some cases (though the Python
GIL limits the gain, and sometimes it's disk-bound anyway).
- Combines split models into a single file (both the intra-tensor split
of the original and the inter-tensor split of 'transformers' format
files). Single files are more convenient to work with and more
friendly to future changes to use memory mapping on the C++ side. To
accomplish this without increasing memory requirements, it has some
custom loading code which avoids loading whole input files into memory
at once.
- Because of the custom loading code, it no longer depends in PyTorch,
which might make installing dependencies slightly easier or faster...
although it still depends on NumPy and sentencepiece, so I don't know
if there's any meaningful difference. In any case, I also added a
requirements.txt file to lock the dependency versions in case of any
future breaking changes.
- Type annotations checked with mypy.
- Some attempts to be extra user-friendly:
- The script tries to be forgiving with arguments, e.g. you can
specify either the model file itself or the directory containing
it.
- The script doesn't depend on config.json / params.json, just in
case the user downloaded files individually and doesn't have those
handy. But you still need tokenizer.model and, for Alpaca,
added_tokens.json.
- The script tries to give a helpful error message if
added_tokens.json is missing.
2023-04-14 07:03:03 +00:00
return UnquantizedTensor ( concatenated )
description = ' concatenated[[ ' + ' ] | [ ' . join ( lt . description for lt in lazy_tensors ) + ' ]] '
return LazyTensor ( load , concatenated_shape , lazy_tensors [ 0 ] . data_type , description )
return { name : convert ( name ) for name in names }
2023-08-31 05:02:23 +00:00
def merge_multifile_models ( models_plus : list [ ModelPlus ] ) - > ModelPlus :
py : new conversion script (#545)
Current status: Working, except for the latest GPTQ-for-LLaMa format
that includes `g_idx`. This turns out to require changes to GGML, so
for now it only works if you use the `--outtype` option to dequantize it
back to f16 (which is pointless except for debugging).
I also included some cleanup for the C++ code.
This script is meant to replace all the existing conversion scripts
(including the ones that convert from older GGML formats), while also
adding support for some new formats. Specifically, I've tested with:
- [x] `LLaMA` (original)
- [x] `llama-65b-4bit`
- [x] `alpaca-native`
- [x] `alpaca-native-4bit`
- [x] LLaMA converted to 'transformers' format using
`convert_llama_weights_to_hf.py`
- [x] `alpaca-native` quantized with `--true-sequential --act-order
--groupsize 128` (dequantized only)
- [x] same as above plus `--save_safetensors`
- [x] GPT4All
- [x] stock unversioned ggml
- [x] ggmh
There's enough overlap in the logic needed to handle these different
cases that it seemed best to move to a single script.
I haven't tried this with Alpaca-LoRA because I don't know where to find
it.
Useful features:
- Uses multiple threads for a speedup in some cases (though the Python
GIL limits the gain, and sometimes it's disk-bound anyway).
- Combines split models into a single file (both the intra-tensor split
of the original and the inter-tensor split of 'transformers' format
files). Single files are more convenient to work with and more
friendly to future changes to use memory mapping on the C++ side. To
accomplish this without increasing memory requirements, it has some
custom loading code which avoids loading whole input files into memory
at once.
- Because of the custom loading code, it no longer depends in PyTorch,
which might make installing dependencies slightly easier or faster...
although it still depends on NumPy and sentencepiece, so I don't know
if there's any meaningful difference. In any case, I also added a
requirements.txt file to lock the dependency versions in case of any
future breaking changes.
- Type annotations checked with mypy.
- Some attempts to be extra user-friendly:
- The script tries to be forgiving with arguments, e.g. you can
specify either the model file itself or the directory containing
it.
- The script doesn't depend on config.json / params.json, just in
case the user downloaded files individually and doesn't have those
handy. But you still need tokenizer.model and, for Alpaca,
added_tokens.json.
- The script tries to give a helpful error message if
added_tokens.json is missing.
2023-04-14 07:03:03 +00:00
formats = set ( mp . format for mp in models_plus )
assert len ( formats ) == 1 , " different formats? "
format = formats . pop ( )
paths = [ path for mp in models_plus for path in mp . paths ]
# Use the first non-None vocab, if any.
try :
vocab = next ( mp . vocab for mp in models_plus if mp . vocab is not None )
except StopIteration :
vocab = None
if any ( " model.embed_tokens.weight " in mp . model for mp in models_plus ) :
# Transformers models put different tensors in different files, but
2023-12-12 09:53:36 +00:00
# don't split individual tensors between files.
py : new conversion script (#545)
Current status: Working, except for the latest GPTQ-for-LLaMa format
that includes `g_idx`. This turns out to require changes to GGML, so
for now it only works if you use the `--outtype` option to dequantize it
back to f16 (which is pointless except for debugging).
I also included some cleanup for the C++ code.
This script is meant to replace all the existing conversion scripts
(including the ones that convert from older GGML formats), while also
adding support for some new formats. Specifically, I've tested with:
- [x] `LLaMA` (original)
- [x] `llama-65b-4bit`
- [x] `alpaca-native`
- [x] `alpaca-native-4bit`
- [x] LLaMA converted to 'transformers' format using
`convert_llama_weights_to_hf.py`
- [x] `alpaca-native` quantized with `--true-sequential --act-order
--groupsize 128` (dequantized only)
- [x] same as above plus `--save_safetensors`
- [x] GPT4All
- [x] stock unversioned ggml
- [x] ggmh
There's enough overlap in the logic needed to handle these different
cases that it seemed best to move to a single script.
I haven't tried this with Alpaca-LoRA because I don't know where to find
it.
Useful features:
- Uses multiple threads for a speedup in some cases (though the Python
GIL limits the gain, and sometimes it's disk-bound anyway).
- Combines split models into a single file (both the intra-tensor split
of the original and the inter-tensor split of 'transformers' format
files). Single files are more convenient to work with and more
friendly to future changes to use memory mapping on the C++ side. To
accomplish this without increasing memory requirements, it has some
custom loading code which avoids loading whole input files into memory
at once.
- Because of the custom loading code, it no longer depends in PyTorch,
which might make installing dependencies slightly easier or faster...
although it still depends on NumPy and sentencepiece, so I don't know
if there's any meaningful difference. In any case, I also added a
requirements.txt file to lock the dependency versions in case of any
future breaking changes.
- Type annotations checked with mypy.
- Some attempts to be extra user-friendly:
- The script tries to be forgiving with arguments, e.g. you can
specify either the model file itself or the directory containing
it.
- The script doesn't depend on config.json / params.json, just in
case the user downloaded files individually and doesn't have those
handy. But you still need tokenizer.model and, for Alpaca,
added_tokens.json.
- The script tries to give a helpful error message if
added_tokens.json is missing.
2023-04-14 07:03:03 +00:00
model : LazyModel = { }
for mp in models_plus :
model . update ( mp . model )
else :
model = merge_sharded ( [ mp . model for mp in models_plus ] )
2024-01-20 23:14:18 +00:00
return ModelPlus ( model , paths , format , vocab ) # pytype: disable=wrong-arg-types
py : new conversion script (#545)
Current status: Working, except for the latest GPTQ-for-LLaMa format
that includes `g_idx`. This turns out to require changes to GGML, so
for now it only works if you use the `--outtype` option to dequantize it
back to f16 (which is pointless except for debugging).
I also included some cleanup for the C++ code.
This script is meant to replace all the existing conversion scripts
(including the ones that convert from older GGML formats), while also
adding support for some new formats. Specifically, I've tested with:
- [x] `LLaMA` (original)
- [x] `llama-65b-4bit`
- [x] `alpaca-native`
- [x] `alpaca-native-4bit`
- [x] LLaMA converted to 'transformers' format using
`convert_llama_weights_to_hf.py`
- [x] `alpaca-native` quantized with `--true-sequential --act-order
--groupsize 128` (dequantized only)
- [x] same as above plus `--save_safetensors`
- [x] GPT4All
- [x] stock unversioned ggml
- [x] ggmh
There's enough overlap in the logic needed to handle these different
cases that it seemed best to move to a single script.
I haven't tried this with Alpaca-LoRA because I don't know where to find
it.
Useful features:
- Uses multiple threads for a speedup in some cases (though the Python
GIL limits the gain, and sometimes it's disk-bound anyway).
- Combines split models into a single file (both the intra-tensor split
of the original and the inter-tensor split of 'transformers' format
files). Single files are more convenient to work with and more
friendly to future changes to use memory mapping on the C++ side. To
accomplish this without increasing memory requirements, it has some
custom loading code which avoids loading whole input files into memory
at once.
- Because of the custom loading code, it no longer depends in PyTorch,
which might make installing dependencies slightly easier or faster...
although it still depends on NumPy and sentencepiece, so I don't know
if there's any meaningful difference. In any case, I also added a
requirements.txt file to lock the dependency versions in case of any
future breaking changes.
- Type annotations checked with mypy.
- Some attempts to be extra user-friendly:
- The script tries to be forgiving with arguments, e.g. you can
specify either the model file itself or the directory containing
it.
- The script doesn't depend on config.json / params.json, just in
case the user downloaded files individually and doesn't have those
handy. But you still need tokenizer.model and, for Alpaca,
added_tokens.json.
- The script tries to give a helpful error message if
added_tokens.json is missing.
2023-04-14 07:03:03 +00:00
2023-08-21 20:07:43 +00:00
def permute_lazy ( lazy_tensor : LazyTensor , n_head : int , n_head_kv : int ) - > LazyTensor :
py : new conversion script (#545)
Current status: Working, except for the latest GPTQ-for-LLaMa format
that includes `g_idx`. This turns out to require changes to GGML, so
for now it only works if you use the `--outtype` option to dequantize it
back to f16 (which is pointless except for debugging).
I also included some cleanup for the C++ code.
This script is meant to replace all the existing conversion scripts
(including the ones that convert from older GGML formats), while also
adding support for some new formats. Specifically, I've tested with:
- [x] `LLaMA` (original)
- [x] `llama-65b-4bit`
- [x] `alpaca-native`
- [x] `alpaca-native-4bit`
- [x] LLaMA converted to 'transformers' format using
`convert_llama_weights_to_hf.py`
- [x] `alpaca-native` quantized with `--true-sequential --act-order
--groupsize 128` (dequantized only)
- [x] same as above plus `--save_safetensors`
- [x] GPT4All
- [x] stock unversioned ggml
- [x] ggmh
There's enough overlap in the logic needed to handle these different
cases that it seemed best to move to a single script.
I haven't tried this with Alpaca-LoRA because I don't know where to find
it.
Useful features:
- Uses multiple threads for a speedup in some cases (though the Python
GIL limits the gain, and sometimes it's disk-bound anyway).
- Combines split models into a single file (both the intra-tensor split
of the original and the inter-tensor split of 'transformers' format
files). Single files are more convenient to work with and more
friendly to future changes to use memory mapping on the C++ side. To
accomplish this without increasing memory requirements, it has some
custom loading code which avoids loading whole input files into memory
at once.
- Because of the custom loading code, it no longer depends in PyTorch,
which might make installing dependencies slightly easier or faster...
although it still depends on NumPy and sentencepiece, so I don't know
if there's any meaningful difference. In any case, I also added a
requirements.txt file to lock the dependency versions in case of any
future breaking changes.
- Type annotations checked with mypy.
- Some attempts to be extra user-friendly:
- The script tries to be forgiving with arguments, e.g. you can
specify either the model file itself or the directory containing
it.
- The script doesn't depend on config.json / params.json, just in
case the user downloaded files individually and doesn't have those
handy. But you still need tokenizer.model and, for Alpaca,
added_tokens.json.
- The script tries to give a helpful error message if
added_tokens.json is missing.
2023-04-14 07:03:03 +00:00
def load ( ) - > Tensor :
2023-08-21 20:07:43 +00:00
return lazy_tensor . load ( ) . permute ( n_head , n_head_kv )
return LazyTensor ( load , lazy_tensor . shape , lazy_tensor . data_type , f ' permute( { n_head } , { n_head_kv } ) ' + lazy_tensor . description )
py : new conversion script (#545)
Current status: Working, except for the latest GPTQ-for-LLaMa format
that includes `g_idx`. This turns out to require changes to GGML, so
for now it only works if you use the `--outtype` option to dequantize it
back to f16 (which is pointless except for debugging).
I also included some cleanup for the C++ code.
This script is meant to replace all the existing conversion scripts
(including the ones that convert from older GGML formats), while also
adding support for some new formats. Specifically, I've tested with:
- [x] `LLaMA` (original)
- [x] `llama-65b-4bit`
- [x] `alpaca-native`
- [x] `alpaca-native-4bit`
- [x] LLaMA converted to 'transformers' format using
`convert_llama_weights_to_hf.py`
- [x] `alpaca-native` quantized with `--true-sequential --act-order
--groupsize 128` (dequantized only)
- [x] same as above plus `--save_safetensors`
- [x] GPT4All
- [x] stock unversioned ggml
- [x] ggmh
There's enough overlap in the logic needed to handle these different
cases that it seemed best to move to a single script.
I haven't tried this with Alpaca-LoRA because I don't know where to find
it.
Useful features:
- Uses multiple threads for a speedup in some cases (though the Python
GIL limits the gain, and sometimes it's disk-bound anyway).
- Combines split models into a single file (both the intra-tensor split
of the original and the inter-tensor split of 'transformers' format
files). Single files are more convenient to work with and more
friendly to future changes to use memory mapping on the C++ side. To
accomplish this without increasing memory requirements, it has some
custom loading code which avoids loading whole input files into memory
at once.
- Because of the custom loading code, it no longer depends in PyTorch,
which might make installing dependencies slightly easier or faster...
although it still depends on NumPy and sentencepiece, so I don't know
if there's any meaningful difference. In any case, I also added a
requirements.txt file to lock the dependency versions in case of any
future breaking changes.
- Type annotations checked with mypy.
- Some attempts to be extra user-friendly:
- The script tries to be forgiving with arguments, e.g. you can
specify either the model file itself or the directory containing
it.
- The script doesn't depend on config.json / params.json, just in
case the user downloaded files individually and doesn't have those
handy. But you still need tokenizer.model and, for Alpaca,
added_tokens.json.
- The script tries to give a helpful error message if
added_tokens.json is missing.
2023-04-14 07:03:03 +00:00
2023-11-20 10:35:47 +00:00
2023-08-30 08:25:50 +00:00
def permute_part_lazy ( lazy_tensor : LazyTensor , n_part : int , n_head : int , n_head_kv : int ) - > LazyTensor :
2023-07-01 17:00:25 +00:00
def load ( ) - > Tensor :
2023-08-30 08:25:50 +00:00
return lazy_tensor . load ( ) . permute_part ( n_part , n_head , n_head_kv )
2023-07-01 17:00:25 +00:00
s = lazy_tensor . shape . copy ( )
s [ 0 ] = s [ 0 ] / / 3
2023-08-30 08:25:50 +00:00
return LazyTensor ( load , s , lazy_tensor . data_type , f ' permute( { n_head } , { n_head_kv } ) ' + lazy_tensor . description )
2023-07-01 17:00:25 +00:00
2023-11-20 10:35:47 +00:00
2023-07-01 17:00:25 +00:00
def part_lazy ( lazy_tensor : LazyTensor , n_part : int ) - > LazyTensor :
def load ( ) - > Tensor :
return lazy_tensor . load ( ) . part ( n_part )
s = lazy_tensor . shape . copy ( )
s [ 0 ] = s [ 0 ] / / 3
return LazyTensor ( load , s , lazy_tensor . data_type , ' part ' + lazy_tensor . description )
py : new conversion script (#545)
Current status: Working, except for the latest GPTQ-for-LLaMa format
that includes `g_idx`. This turns out to require changes to GGML, so
for now it only works if you use the `--outtype` option to dequantize it
back to f16 (which is pointless except for debugging).
I also included some cleanup for the C++ code.
This script is meant to replace all the existing conversion scripts
(including the ones that convert from older GGML formats), while also
adding support for some new formats. Specifically, I've tested with:
- [x] `LLaMA` (original)
- [x] `llama-65b-4bit`
- [x] `alpaca-native`
- [x] `alpaca-native-4bit`
- [x] LLaMA converted to 'transformers' format using
`convert_llama_weights_to_hf.py`
- [x] `alpaca-native` quantized with `--true-sequential --act-order
--groupsize 128` (dequantized only)
- [x] same as above plus `--save_safetensors`
- [x] GPT4All
- [x] stock unversioned ggml
- [x] ggmh
There's enough overlap in the logic needed to handle these different
cases that it seemed best to move to a single script.
I haven't tried this with Alpaca-LoRA because I don't know where to find
it.
Useful features:
- Uses multiple threads for a speedup in some cases (though the Python
GIL limits the gain, and sometimes it's disk-bound anyway).
- Combines split models into a single file (both the intra-tensor split
of the original and the inter-tensor split of 'transformers' format
files). Single files are more convenient to work with and more
friendly to future changes to use memory mapping on the C++ side. To
accomplish this without increasing memory requirements, it has some
custom loading code which avoids loading whole input files into memory
at once.
- Because of the custom loading code, it no longer depends in PyTorch,
which might make installing dependencies slightly easier or faster...
although it still depends on NumPy and sentencepiece, so I don't know
if there's any meaningful difference. In any case, I also added a
requirements.txt file to lock the dependency versions in case of any
future breaking changes.
- Type annotations checked with mypy.
- Some attempts to be extra user-friendly:
- The script tries to be forgiving with arguments, e.g. you can
specify either the model file itself or the directory containing
it.
- The script doesn't depend on config.json / params.json, just in
case the user downloaded files individually and doesn't have those
handy. But you still need tokenizer.model and, for Alpaca,
added_tokens.json.
- The script tries to give a helpful error message if
added_tokens.json is missing.
2023-04-14 07:03:03 +00:00
2024-04-03 13:07:05 +00:00
def pack_experts_lazy ( lazy_tensors : list [ LazyTensor ] ) - > LazyTensor :
def load ( ) - > Tensor :
tensors = [ lazy_tensor . load ( ) for lazy_tensor in lazy_tensors ]
return UnquantizedTensor ( np . array ( [ tensor . ndarray for tensor in tensors ] ) )
s = lazy_tensors [ 0 ] . shape . copy ( )
s . insert ( 0 , len ( lazy_tensors ) )
return LazyTensor ( load , s , lazy_tensors [ 0 ] . data_type , ' pack_experts ' + ' | ' . join ( lt . description for lt in lazy_tensors ) )
py : new conversion script (#545)
Current status: Working, except for the latest GPTQ-for-LLaMa format
that includes `g_idx`. This turns out to require changes to GGML, so
for now it only works if you use the `--outtype` option to dequantize it
back to f16 (which is pointless except for debugging).
I also included some cleanup for the C++ code.
This script is meant to replace all the existing conversion scripts
(including the ones that convert from older GGML formats), while also
adding support for some new formats. Specifically, I've tested with:
- [x] `LLaMA` (original)
- [x] `llama-65b-4bit`
- [x] `alpaca-native`
- [x] `alpaca-native-4bit`
- [x] LLaMA converted to 'transformers' format using
`convert_llama_weights_to_hf.py`
- [x] `alpaca-native` quantized with `--true-sequential --act-order
--groupsize 128` (dequantized only)
- [x] same as above plus `--save_safetensors`
- [x] GPT4All
- [x] stock unversioned ggml
- [x] ggmh
There's enough overlap in the logic needed to handle these different
cases that it seemed best to move to a single script.
I haven't tried this with Alpaca-LoRA because I don't know where to find
it.
Useful features:
- Uses multiple threads for a speedup in some cases (though the Python
GIL limits the gain, and sometimes it's disk-bound anyway).
- Combines split models into a single file (both the intra-tensor split
of the original and the inter-tensor split of 'transformers' format
files). Single files are more convenient to work with and more
friendly to future changes to use memory mapping on the C++ side. To
accomplish this without increasing memory requirements, it has some
custom loading code which avoids loading whole input files into memory
at once.
- Because of the custom loading code, it no longer depends in PyTorch,
which might make installing dependencies slightly easier or faster...
although it still depends on NumPy and sentencepiece, so I don't know
if there's any meaningful difference. In any case, I also added a
requirements.txt file to lock the dependency versions in case of any
future breaking changes.
- Type annotations checked with mypy.
- Some attempts to be extra user-friendly:
- The script tries to be forgiving with arguments, e.g. you can
specify either the model file itself or the directory containing
it.
- The script doesn't depend on config.json / params.json, just in
case the user downloaded files individually and doesn't have those
handy. But you still need tokenizer.model and, for Alpaca,
added_tokens.json.
- The script tries to give a helpful error message if
added_tokens.json is missing.
2023-04-14 07:03:03 +00:00
# Functionality that simulates `torch.load` but where individual tensors are
# only loaded into memory on demand, not all at once.
# PyTorch can't do this natively as of time of writing:
# - https://github.com/pytorch/pytorch/issues/64327
# This allows us to de-shard without multiplying RAM usage, and also
# conveniently drops the PyTorch dependency (though we still need numpy).
@dataclass
class LazyStorageKind :
data_type : DataType
@dataclass
class LazyStorage :
load : Callable [ [ int , int ] , NDArray ]
kind : LazyStorageKind
description : str
class LazyUnpickler ( pickle . Unpickler ) :
def __init__ ( self , fp : IO [ bytes ] , data_base_path : str , zip_file : zipfile . ZipFile ) :
super ( ) . __init__ ( fp )
self . data_base_path = data_base_path
self . zip_file = zip_file
def persistent_load ( self , pid : Any ) - > Any :
assert pid [ 0 ] == ' storage '
assert isinstance ( pid [ 1 ] , LazyStorageKind )
data_type = pid [ 1 ] . data_type
filename_stem = pid [ 2 ]
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filename = f ' { self . data_base_path } / { filename_stem } '
py : new conversion script (#545)
Current status: Working, except for the latest GPTQ-for-LLaMa format
that includes `g_idx`. This turns out to require changes to GGML, so
for now it only works if you use the `--outtype` option to dequantize it
back to f16 (which is pointless except for debugging).
I also included some cleanup for the C++ code.
This script is meant to replace all the existing conversion scripts
(including the ones that convert from older GGML formats), while also
adding support for some new formats. Specifically, I've tested with:
- [x] `LLaMA` (original)
- [x] `llama-65b-4bit`
- [x] `alpaca-native`
- [x] `alpaca-native-4bit`
- [x] LLaMA converted to 'transformers' format using
`convert_llama_weights_to_hf.py`
- [x] `alpaca-native` quantized with `--true-sequential --act-order
--groupsize 128` (dequantized only)
- [x] same as above plus `--save_safetensors`
- [x] GPT4All
- [x] stock unversioned ggml
- [x] ggmh
There's enough overlap in the logic needed to handle these different
cases that it seemed best to move to a single script.
I haven't tried this with Alpaca-LoRA because I don't know where to find
it.
Useful features:
- Uses multiple threads for a speedup in some cases (though the Python
GIL limits the gain, and sometimes it's disk-bound anyway).
- Combines split models into a single file (both the intra-tensor split
of the original and the inter-tensor split of 'transformers' format
files). Single files are more convenient to work with and more
friendly to future changes to use memory mapping on the C++ side. To
accomplish this without increasing memory requirements, it has some
custom loading code which avoids loading whole input files into memory
at once.
- Because of the custom loading code, it no longer depends in PyTorch,
which might make installing dependencies slightly easier or faster...
although it still depends on NumPy and sentencepiece, so I don't know
if there's any meaningful difference. In any case, I also added a
requirements.txt file to lock the dependency versions in case of any
future breaking changes.
- Type annotations checked with mypy.
- Some attempts to be extra user-friendly:
- The script tries to be forgiving with arguments, e.g. you can
specify either the model file itself or the directory containing
it.
- The script doesn't depend on config.json / params.json, just in
case the user downloaded files individually and doesn't have those
handy. But you still need tokenizer.model and, for Alpaca,
added_tokens.json.
- The script tries to give a helpful error message if
added_tokens.json is missing.
2023-04-14 07:03:03 +00:00
info = self . zip_file . getinfo ( filename )
def load ( offset : int , elm_count : int ) - > NDArray :
2023-08-26 20:13:36 +00:00
dtype = data_type . dtype
2024-03-28 15:44:36 +00:00
with self . zip_file . open ( info ) as fp :
fp . seek ( offset * dtype . itemsize )
size = elm_count * dtype . itemsize
data = fp . read ( size )
py : new conversion script (#545)
Current status: Working, except for the latest GPTQ-for-LLaMa format
that includes `g_idx`. This turns out to require changes to GGML, so
for now it only works if you use the `--outtype` option to dequantize it
back to f16 (which is pointless except for debugging).
I also included some cleanup for the C++ code.
This script is meant to replace all the existing conversion scripts
(including the ones that convert from older GGML formats), while also
adding support for some new formats. Specifically, I've tested with:
- [x] `LLaMA` (original)
- [x] `llama-65b-4bit`
- [x] `alpaca-native`
- [x] `alpaca-native-4bit`
- [x] LLaMA converted to 'transformers' format using
`convert_llama_weights_to_hf.py`
- [x] `alpaca-native` quantized with `--true-sequential --act-order
--groupsize 128` (dequantized only)
- [x] same as above plus `--save_safetensors`
- [x] GPT4All
- [x] stock unversioned ggml
- [x] ggmh
There's enough overlap in the logic needed to handle these different
cases that it seemed best to move to a single script.
I haven't tried this with Alpaca-LoRA because I don't know where to find
it.
Useful features:
- Uses multiple threads for a speedup in some cases (though the Python
GIL limits the gain, and sometimes it's disk-bound anyway).
- Combines split models into a single file (both the intra-tensor split
of the original and the inter-tensor split of 'transformers' format
files). Single files are more convenient to work with and more
friendly to future changes to use memory mapping on the C++ side. To
accomplish this without increasing memory requirements, it has some
custom loading code which avoids loading whole input files into memory
at once.
- Because of the custom loading code, it no longer depends in PyTorch,
which might make installing dependencies slightly easier or faster...
although it still depends on NumPy and sentencepiece, so I don't know
if there's any meaningful difference. In any case, I also added a
requirements.txt file to lock the dependency versions in case of any
future breaking changes.
- Type annotations checked with mypy.
- Some attempts to be extra user-friendly:
- The script tries to be forgiving with arguments, e.g. you can
specify either the model file itself or the directory containing
it.
- The script doesn't depend on config.json / params.json, just in
case the user downloaded files individually and doesn't have those
handy. But you still need tokenizer.model and, for Alpaca,
added_tokens.json.
- The script tries to give a helpful error message if
added_tokens.json is missing.
2023-04-14 07:03:03 +00:00
assert len ( data ) == size
return np . frombuffer ( data , dtype )
description = f ' storage data_type= { data_type } path-in-zip= { filename } path= { self . zip_file . filename } '
return LazyStorage ( load = load , kind = pid [ 1 ] , description = description )
2023-08-30 08:25:50 +00:00
@staticmethod
2023-06-17 10:32:48 +00:00
def lazy_rebuild_tensor_v2 ( storage : Any , storage_offset : Any , size : Any , stride : Any ,
py : new conversion script (#545)
Current status: Working, except for the latest GPTQ-for-LLaMa format
that includes `g_idx`. This turns out to require changes to GGML, so
for now it only works if you use the `--outtype` option to dequantize it
back to f16 (which is pointless except for debugging).
I also included some cleanup for the C++ code.
This script is meant to replace all the existing conversion scripts
(including the ones that convert from older GGML formats), while also
adding support for some new formats. Specifically, I've tested with:
- [x] `LLaMA` (original)
- [x] `llama-65b-4bit`
- [x] `alpaca-native`
- [x] `alpaca-native-4bit`
- [x] LLaMA converted to 'transformers' format using
`convert_llama_weights_to_hf.py`
- [x] `alpaca-native` quantized with `--true-sequential --act-order
--groupsize 128` (dequantized only)
- [x] same as above plus `--save_safetensors`
- [x] GPT4All
- [x] stock unversioned ggml
- [x] ggmh
There's enough overlap in the logic needed to handle these different
cases that it seemed best to move to a single script.
I haven't tried this with Alpaca-LoRA because I don't know where to find
it.
Useful features:
- Uses multiple threads for a speedup in some cases (though the Python
GIL limits the gain, and sometimes it's disk-bound anyway).
- Combines split models into a single file (both the intra-tensor split
of the original and the inter-tensor split of 'transformers' format
files). Single files are more convenient to work with and more
friendly to future changes to use memory mapping on the C++ side. To
accomplish this without increasing memory requirements, it has some
custom loading code which avoids loading whole input files into memory
at once.
- Because of the custom loading code, it no longer depends in PyTorch,
which might make installing dependencies slightly easier or faster...
although it still depends on NumPy and sentencepiece, so I don't know
if there's any meaningful difference. In any case, I also added a
requirements.txt file to lock the dependency versions in case of any
future breaking changes.
- Type annotations checked with mypy.
- Some attempts to be extra user-friendly:
- The script tries to be forgiving with arguments, e.g. you can
specify either the model file itself or the directory containing
it.
- The script doesn't depend on config.json / params.json, just in
case the user downloaded files individually and doesn't have those
handy. But you still need tokenizer.model and, for Alpaca,
added_tokens.json.
- The script tries to give a helpful error message if
added_tokens.json is missing.
2023-04-14 07:03:03 +00:00
requires_grad : Any , backward_hooks : Any , metadata : Any = None ) - > LazyTensor :
assert isinstance ( storage , LazyStorage )
def load ( ) - > UnquantizedTensor :
elm_count = stride [ 0 ] * size [ 0 ]
return UnquantizedTensor ( storage . load ( storage_offset , elm_count ) . reshape ( size ) )
description = f ' pickled storage_offset= { storage_offset } in { storage . description } '
return LazyTensor ( load , list ( size ) , storage . kind . data_type , description )
2023-08-30 08:25:50 +00:00
@staticmethod
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def rebuild_from_type_v2 ( func , new_type , args , state ) :
return func ( * args )
2024-03-28 15:44:36 +00:00
CLASSES = {
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# getattr used here as a workaround for mypy not being smart enough to determine
2023-08-30 08:25:50 +00:00
# the staticmethods have a __func__ attribute.
2024-01-20 23:14:18 +00:00
( ' torch._tensor ' , ' _rebuild_from_type_v2 ' ) : getattr ( rebuild_from_type_v2 , ' __func__ ' ) ,
( ' torch._utils ' , ' _rebuild_tensor_v2 ' ) : getattr ( lazy_rebuild_tensor_v2 , ' __func__ ' ) ,
( ' torch ' , ' BFloat16Storage ' ) : LazyStorageKind ( DT_BF16 ) ,
( ' torch ' , ' HalfStorage ' ) : LazyStorageKind ( DT_F16 ) ,
( ' torch ' , ' FloatStorage ' ) : LazyStorageKind ( DT_F32 ) ,
( ' torch ' , ' IntStorage ' ) : LazyStorageKind ( DT_I32 ) ,
( ' torch ' , ' Tensor ' ) : LazyTensor ,
py : new conversion script (#545)
Current status: Working, except for the latest GPTQ-for-LLaMa format
that includes `g_idx`. This turns out to require changes to GGML, so
for now it only works if you use the `--outtype` option to dequantize it
back to f16 (which is pointless except for debugging).
I also included some cleanup for the C++ code.
This script is meant to replace all the existing conversion scripts
(including the ones that convert from older GGML formats), while also
adding support for some new formats. Specifically, I've tested with:
- [x] `LLaMA` (original)
- [x] `llama-65b-4bit`
- [x] `alpaca-native`
- [x] `alpaca-native-4bit`
- [x] LLaMA converted to 'transformers' format using
`convert_llama_weights_to_hf.py`
- [x] `alpaca-native` quantized with `--true-sequential --act-order
--groupsize 128` (dequantized only)
- [x] same as above plus `--save_safetensors`
- [x] GPT4All
- [x] stock unversioned ggml
- [x] ggmh
There's enough overlap in the logic needed to handle these different
cases that it seemed best to move to a single script.
I haven't tried this with Alpaca-LoRA because I don't know where to find
it.
Useful features:
- Uses multiple threads for a speedup in some cases (though the Python
GIL limits the gain, and sometimes it's disk-bound anyway).
- Combines split models into a single file (both the intra-tensor split
of the original and the inter-tensor split of 'transformers' format
files). Single files are more convenient to work with and more
friendly to future changes to use memory mapping on the C++ side. To
accomplish this without increasing memory requirements, it has some
custom loading code which avoids loading whole input files into memory
at once.
- Because of the custom loading code, it no longer depends in PyTorch,
which might make installing dependencies slightly easier or faster...
although it still depends on NumPy and sentencepiece, so I don't know
if there's any meaningful difference. In any case, I also added a
requirements.txt file to lock the dependency versions in case of any
future breaking changes.
- Type annotations checked with mypy.
- Some attempts to be extra user-friendly:
- The script tries to be forgiving with arguments, e.g. you can
specify either the model file itself or the directory containing
it.
- The script doesn't depend on config.json / params.json, just in
case the user downloaded files individually and doesn't have those
handy. But you still need tokenizer.model and, for Alpaca,
added_tokens.json.
- The script tries to give a helpful error message if
added_tokens.json is missing.
2023-04-14 07:03:03 +00:00
}
def find_class ( self , module : str , name : str ) - > Any :
if not module . startswith ( ' torch ' ) :
return super ( ) . find_class ( module , name )
return self . CLASSES [ ( module , name ) ]
def lazy_load_torch_file ( outer_fp : IO [ bytes ] , path : Path ) - > ModelPlus :
zf = zipfile . ZipFile ( outer_fp )
pickle_paths = [ name for name in zf . namelist ( ) if name . endswith ( ' .pkl ' ) ]
assert len ( pickle_paths ) == 1 , pickle_paths
pickle_fp = zf . open ( pickle_paths [ 0 ] , ' r ' )
unpickler = LazyUnpickler ( pickle_fp ,
data_base_path = pickle_paths [ 0 ] [ : - 4 ] ,
zip_file = zf )
model = unpickler . load ( )
2023-11-17 15:32:34 +00:00
if ' model ' in model : model = model [ ' model ' ]
py : new conversion script (#545)
Current status: Working, except for the latest GPTQ-for-LLaMa format
that includes `g_idx`. This turns out to require changes to GGML, so
for now it only works if you use the `--outtype` option to dequantize it
back to f16 (which is pointless except for debugging).
I also included some cleanup for the C++ code.
This script is meant to replace all the existing conversion scripts
(including the ones that convert from older GGML formats), while also
adding support for some new formats. Specifically, I've tested with:
- [x] `LLaMA` (original)
- [x] `llama-65b-4bit`
- [x] `alpaca-native`
- [x] `alpaca-native-4bit`
- [x] LLaMA converted to 'transformers' format using
`convert_llama_weights_to_hf.py`
- [x] `alpaca-native` quantized with `--true-sequential --act-order
--groupsize 128` (dequantized only)
- [x] same as above plus `--save_safetensors`
- [x] GPT4All
- [x] stock unversioned ggml
- [x] ggmh
There's enough overlap in the logic needed to handle these different
cases that it seemed best to move to a single script.
I haven't tried this with Alpaca-LoRA because I don't know where to find
it.
Useful features:
- Uses multiple threads for a speedup in some cases (though the Python
GIL limits the gain, and sometimes it's disk-bound anyway).
- Combines split models into a single file (both the intra-tensor split
of the original and the inter-tensor split of 'transformers' format
files). Single files are more convenient to work with and more
friendly to future changes to use memory mapping on the C++ side. To
accomplish this without increasing memory requirements, it has some
custom loading code which avoids loading whole input files into memory
at once.
- Because of the custom loading code, it no longer depends in PyTorch,
which might make installing dependencies slightly easier or faster...
although it still depends on NumPy and sentencepiece, so I don't know
if there's any meaningful difference. In any case, I also added a
requirements.txt file to lock the dependency versions in case of any
future breaking changes.
- Type annotations checked with mypy.
- Some attempts to be extra user-friendly:
- The script tries to be forgiving with arguments, e.g. you can
specify either the model file itself or the directory containing
it.
- The script doesn't depend on config.json / params.json, just in
case the user downloaded files individually and doesn't have those
handy. But you still need tokenizer.model and, for Alpaca,
added_tokens.json.
- The script tries to give a helpful error message if
added_tokens.json is missing.
2023-04-14 07:03:03 +00:00
as_dict = dict ( model . items ( ) )
return ModelPlus ( model = as_dict , paths = [ path ] , format = ' torch ' , vocab = None )
def lazy_load_safetensors_file ( fp : IO [ bytes ] , path : Path ) - > ModelPlus :
header_size , = struct . unpack ( ' <Q ' , fp . read ( 8 ) )
2023-08-31 05:02:23 +00:00
header : dict [ str , dict [ str , Any ] ] = json . loads ( fp . read ( header_size ) )
py : new conversion script (#545)
Current status: Working, except for the latest GPTQ-for-LLaMa format
that includes `g_idx`. This turns out to require changes to GGML, so
for now it only works if you use the `--outtype` option to dequantize it
back to f16 (which is pointless except for debugging).
I also included some cleanup for the C++ code.
This script is meant to replace all the existing conversion scripts
(including the ones that convert from older GGML formats), while also
adding support for some new formats. Specifically, I've tested with:
- [x] `LLaMA` (original)
- [x] `llama-65b-4bit`
- [x] `alpaca-native`
- [x] `alpaca-native-4bit`
- [x] LLaMA converted to 'transformers' format using
`convert_llama_weights_to_hf.py`
- [x] `alpaca-native` quantized with `--true-sequential --act-order
--groupsize 128` (dequantized only)
- [x] same as above plus `--save_safetensors`
- [x] GPT4All
- [x] stock unversioned ggml
- [x] ggmh
There's enough overlap in the logic needed to handle these different
cases that it seemed best to move to a single script.
I haven't tried this with Alpaca-LoRA because I don't know where to find
it.
Useful features:
- Uses multiple threads for a speedup in some cases (though the Python
GIL limits the gain, and sometimes it's disk-bound anyway).
- Combines split models into a single file (both the intra-tensor split
of the original and the inter-tensor split of 'transformers' format
files). Single files are more convenient to work with and more
friendly to future changes to use memory mapping on the C++ side. To
accomplish this without increasing memory requirements, it has some
custom loading code which avoids loading whole input files into memory
at once.
- Because of the custom loading code, it no longer depends in PyTorch,
which might make installing dependencies slightly easier or faster...
although it still depends on NumPy and sentencepiece, so I don't know
if there's any meaningful difference. In any case, I also added a
requirements.txt file to lock the dependency versions in case of any
future breaking changes.
- Type annotations checked with mypy.
- Some attempts to be extra user-friendly:
- The script tries to be forgiving with arguments, e.g. you can
specify either the model file itself or the directory containing
it.
- The script doesn't depend on config.json / params.json, just in
case the user downloaded files individually and doesn't have those
handy. But you still need tokenizer.model and, for Alpaca,
added_tokens.json.
- The script tries to give a helpful error message if
added_tokens.json is missing.
2023-04-14 07:03:03 +00:00
# Use mmap for the actual data to avoid race conditions with the file offset.
mapped = memoryview ( mmap . mmap ( fp . fileno ( ) , 0 , access = mmap . ACCESS_READ ) )
2023-04-15 21:53:21 +00:00
byte_buf = mapped [ 8 + header_size : ]
py : new conversion script (#545)
Current status: Working, except for the latest GPTQ-for-LLaMa format
that includes `g_idx`. This turns out to require changes to GGML, so
for now it only works if you use the `--outtype` option to dequantize it
back to f16 (which is pointless except for debugging).
I also included some cleanup for the C++ code.
This script is meant to replace all the existing conversion scripts
(including the ones that convert from older GGML formats), while also
adding support for some new formats. Specifically, I've tested with:
- [x] `LLaMA` (original)
- [x] `llama-65b-4bit`
- [x] `alpaca-native`
- [x] `alpaca-native-4bit`
- [x] LLaMA converted to 'transformers' format using
`convert_llama_weights_to_hf.py`
- [x] `alpaca-native` quantized with `--true-sequential --act-order
--groupsize 128` (dequantized only)
- [x] same as above plus `--save_safetensors`
- [x] GPT4All
- [x] stock unversioned ggml
- [x] ggmh
There's enough overlap in the logic needed to handle these different
cases that it seemed best to move to a single script.
I haven't tried this with Alpaca-LoRA because I don't know where to find
it.
Useful features:
- Uses multiple threads for a speedup in some cases (though the Python
GIL limits the gain, and sometimes it's disk-bound anyway).
- Combines split models into a single file (both the intra-tensor split
of the original and the inter-tensor split of 'transformers' format
files). Single files are more convenient to work with and more
friendly to future changes to use memory mapping on the C++ side. To
accomplish this without increasing memory requirements, it has some
custom loading code which avoids loading whole input files into memory
at once.
- Because of the custom loading code, it no longer depends in PyTorch,
which might make installing dependencies slightly easier or faster...
although it still depends on NumPy and sentencepiece, so I don't know
if there's any meaningful difference. In any case, I also added a
requirements.txt file to lock the dependency versions in case of any
future breaking changes.
- Type annotations checked with mypy.
- Some attempts to be extra user-friendly:
- The script tries to be forgiving with arguments, e.g. you can
specify either the model file itself or the directory containing
it.
- The script doesn't depend on config.json / params.json, just in
case the user downloaded files individually and doesn't have those
handy. But you still need tokenizer.model and, for Alpaca,
added_tokens.json.
- The script tries to give a helpful error message if
added_tokens.json is missing.
2023-04-14 07:03:03 +00:00
2023-08-31 05:02:23 +00:00
def convert ( info : dict [ str , Any ] ) - > LazyTensor :
py : new conversion script (#545)
Current status: Working, except for the latest GPTQ-for-LLaMa format
that includes `g_idx`. This turns out to require changes to GGML, so
for now it only works if you use the `--outtype` option to dequantize it
back to f16 (which is pointless except for debugging).
I also included some cleanup for the C++ code.
This script is meant to replace all the existing conversion scripts
(including the ones that convert from older GGML formats), while also
adding support for some new formats. Specifically, I've tested with:
- [x] `LLaMA` (original)
- [x] `llama-65b-4bit`
- [x] `alpaca-native`
- [x] `alpaca-native-4bit`
- [x] LLaMA converted to 'transformers' format using
`convert_llama_weights_to_hf.py`
- [x] `alpaca-native` quantized with `--true-sequential --act-order
--groupsize 128` (dequantized only)
- [x] same as above plus `--save_safetensors`
- [x] GPT4All
- [x] stock unversioned ggml
- [x] ggmh
There's enough overlap in the logic needed to handle these different
cases that it seemed best to move to a single script.
I haven't tried this with Alpaca-LoRA because I don't know where to find
it.
Useful features:
- Uses multiple threads for a speedup in some cases (though the Python
GIL limits the gain, and sometimes it's disk-bound anyway).
- Combines split models into a single file (both the intra-tensor split
of the original and the inter-tensor split of 'transformers' format
files). Single files are more convenient to work with and more
friendly to future changes to use memory mapping on the C++ side. To
accomplish this without increasing memory requirements, it has some
custom loading code which avoids loading whole input files into memory
at once.
- Because of the custom loading code, it no longer depends in PyTorch,
which might make installing dependencies slightly easier or faster...
although it still depends on NumPy and sentencepiece, so I don't know
if there's any meaningful difference. In any case, I also added a
requirements.txt file to lock the dependency versions in case of any
future breaking changes.
- Type annotations checked with mypy.
- Some attempts to be extra user-friendly:
- The script tries to be forgiving with arguments, e.g. you can
specify either the model file itself or the directory containing
it.
- The script doesn't depend on config.json / params.json, just in
case the user downloaded files individually and doesn't have those
handy. But you still need tokenizer.model and, for Alpaca,
added_tokens.json.
- The script tries to give a helpful error message if
added_tokens.json is missing.
2023-04-14 07:03:03 +00:00
data_type = SAFETENSORS_DATA_TYPES [ info [ ' dtype ' ] ]
2023-08-26 20:13:36 +00:00
numpy_dtype = data_type . dtype
2023-08-31 05:02:23 +00:00
shape : list [ int ] = info [ ' shape ' ]
py : new conversion script (#545)
Current status: Working, except for the latest GPTQ-for-LLaMa format
that includes `g_idx`. This turns out to require changes to GGML, so
for now it only works if you use the `--outtype` option to dequantize it
back to f16 (which is pointless except for debugging).
I also included some cleanup for the C++ code.
This script is meant to replace all the existing conversion scripts
(including the ones that convert from older GGML formats), while also
adding support for some new formats. Specifically, I've tested with:
- [x] `LLaMA` (original)
- [x] `llama-65b-4bit`
- [x] `alpaca-native`
- [x] `alpaca-native-4bit`
- [x] LLaMA converted to 'transformers' format using
`convert_llama_weights_to_hf.py`
- [x] `alpaca-native` quantized with `--true-sequential --act-order
--groupsize 128` (dequantized only)
- [x] same as above plus `--save_safetensors`
- [x] GPT4All
- [x] stock unversioned ggml
- [x] ggmh
There's enough overlap in the logic needed to handle these different
cases that it seemed best to move to a single script.
I haven't tried this with Alpaca-LoRA because I don't know where to find
it.
Useful features:
- Uses multiple threads for a speedup in some cases (though the Python
GIL limits the gain, and sometimes it's disk-bound anyway).
- Combines split models into a single file (both the intra-tensor split
of the original and the inter-tensor split of 'transformers' format
files). Single files are more convenient to work with and more
friendly to future changes to use memory mapping on the C++ side. To
accomplish this without increasing memory requirements, it has some
custom loading code which avoids loading whole input files into memory
at once.
- Because of the custom loading code, it no longer depends in PyTorch,
which might make installing dependencies slightly easier or faster...
although it still depends on NumPy and sentencepiece, so I don't know
if there's any meaningful difference. In any case, I also added a
requirements.txt file to lock the dependency versions in case of any
future breaking changes.
- Type annotations checked with mypy.
- Some attempts to be extra user-friendly:
- The script tries to be forgiving with arguments, e.g. you can
specify either the model file itself or the directory containing
it.
- The script doesn't depend on config.json / params.json, just in
case the user downloaded files individually and doesn't have those
handy. But you still need tokenizer.model and, for Alpaca,
added_tokens.json.
- The script tries to give a helpful error message if
added_tokens.json is missing.
2023-04-14 07:03:03 +00:00
begin , end = info [ ' data_offsets ' ]
assert 0 < = begin < = end < = len ( byte_buf )
assert end - begin == math . prod ( shape ) * numpy_dtype . itemsize
buf = byte_buf [ begin : end ]
def load ( ) - > UnquantizedTensor :
return UnquantizedTensor ( np . frombuffer ( buf , dtype = numpy_dtype ) . reshape ( shape ) )
description = f ' safetensors begin= { begin } end= { end } type= { data_type } path= { path } '
return LazyTensor ( load , shape , data_type , description )
2023-05-08 11:54:26 +00:00
model = { name : convert ( info ) for ( name , info ) in header . items ( ) if name != ' __metadata__ ' }
py : new conversion script (#545)
Current status: Working, except for the latest GPTQ-for-LLaMa format
that includes `g_idx`. This turns out to require changes to GGML, so
for now it only works if you use the `--outtype` option to dequantize it
back to f16 (which is pointless except for debugging).
I also included some cleanup for the C++ code.
This script is meant to replace all the existing conversion scripts
(including the ones that convert from older GGML formats), while also
adding support for some new formats. Specifically, I've tested with:
- [x] `LLaMA` (original)
- [x] `llama-65b-4bit`
- [x] `alpaca-native`
- [x] `alpaca-native-4bit`
- [x] LLaMA converted to 'transformers' format using
`convert_llama_weights_to_hf.py`
- [x] `alpaca-native` quantized with `--true-sequential --act-order
--groupsize 128` (dequantized only)
- [x] same as above plus `--save_safetensors`
- [x] GPT4All
- [x] stock unversioned ggml
- [x] ggmh
There's enough overlap in the logic needed to handle these different
cases that it seemed best to move to a single script.
I haven't tried this with Alpaca-LoRA because I don't know where to find
it.
Useful features:
- Uses multiple threads for a speedup in some cases (though the Python
GIL limits the gain, and sometimes it's disk-bound anyway).
- Combines split models into a single file (both the intra-tensor split
of the original and the inter-tensor split of 'transformers' format
files). Single files are more convenient to work with and more
friendly to future changes to use memory mapping on the C++ side. To
accomplish this without increasing memory requirements, it has some
custom loading code which avoids loading whole input files into memory
at once.
- Because of the custom loading code, it no longer depends in PyTorch,
which might make installing dependencies slightly easier or faster...
although it still depends on NumPy and sentencepiece, so I don't know
if there's any meaningful difference. In any case, I also added a
requirements.txt file to lock the dependency versions in case of any
future breaking changes.
- Type annotations checked with mypy.
- Some attempts to be extra user-friendly:
- The script tries to be forgiving with arguments, e.g. you can
specify either the model file itself or the directory containing
it.
- The script doesn't depend on config.json / params.json, just in
case the user downloaded files individually and doesn't have those
handy. But you still need tokenizer.model and, for Alpaca,
added_tokens.json.
- The script tries to give a helpful error message if
added_tokens.json is missing.
2023-04-14 07:03:03 +00:00
return ModelPlus ( model = model , paths = [ path ] , format = ' safetensors ' , vocab = None )
def must_read ( fp : IO [ bytes ] , length : int ) - > bytes :
ret = fp . read ( length )
if len ( ret ) < length :
2024-03-28 15:44:36 +00:00
raise EOFError ( " unexpectedly reached end of file " )
py : new conversion script (#545)
Current status: Working, except for the latest GPTQ-for-LLaMa format
that includes `g_idx`. This turns out to require changes to GGML, so
for now it only works if you use the `--outtype` option to dequantize it
back to f16 (which is pointless except for debugging).
I also included some cleanup for the C++ code.
This script is meant to replace all the existing conversion scripts
(including the ones that convert from older GGML formats), while also
adding support for some new formats. Specifically, I've tested with:
- [x] `LLaMA` (original)
- [x] `llama-65b-4bit`
- [x] `alpaca-native`
- [x] `alpaca-native-4bit`
- [x] LLaMA converted to 'transformers' format using
`convert_llama_weights_to_hf.py`
- [x] `alpaca-native` quantized with `--true-sequential --act-order
--groupsize 128` (dequantized only)
- [x] same as above plus `--save_safetensors`
- [x] GPT4All
- [x] stock unversioned ggml
- [x] ggmh
There's enough overlap in the logic needed to handle these different
cases that it seemed best to move to a single script.
I haven't tried this with Alpaca-LoRA because I don't know where to find
it.
Useful features:
- Uses multiple threads for a speedup in some cases (though the Python
GIL limits the gain, and sometimes it's disk-bound anyway).
- Combines split models into a single file (both the intra-tensor split
of the original and the inter-tensor split of 'transformers' format
files). Single files are more convenient to work with and more
friendly to future changes to use memory mapping on the C++ side. To
accomplish this without increasing memory requirements, it has some
custom loading code which avoids loading whole input files into memory
at once.
- Because of the custom loading code, it no longer depends in PyTorch,
which might make installing dependencies slightly easier or faster...
although it still depends on NumPy and sentencepiece, so I don't know
if there's any meaningful difference. In any case, I also added a
requirements.txt file to lock the dependency versions in case of any
future breaking changes.
- Type annotations checked with mypy.
- Some attempts to be extra user-friendly:
- The script tries to be forgiving with arguments, e.g. you can
specify either the model file itself or the directory containing
it.
- The script doesn't depend on config.json / params.json, just in
case the user downloaded files individually and doesn't have those
handy. But you still need tokenizer.model and, for Alpaca,
added_tokens.json.
- The script tries to give a helpful error message if
added_tokens.json is missing.
2023-04-14 07:03:03 +00:00
return ret
@functools.lru_cache ( maxsize = None )
def lazy_load_file ( path : Path ) - > ModelPlus :
fp = open ( path , ' rb ' )
first8 = fp . read ( 8 )
fp . seek ( 0 )
if first8 [ : 2 ] == b ' PK ' :
# A zip file, i.e. PyTorch format
return lazy_load_torch_file ( fp , path )
elif struct . unpack ( ' <Q ' , first8 ) [ 0 ] < 16 * 1024 * 1024 :
# Probably safetensors
return lazy_load_safetensors_file ( fp , path )
else :
raise ValueError ( f " unknown format: { path } " )
In = TypeVar ( ' In ' )
Out = TypeVar ( ' Out ' )
2023-11-20 10:35:47 +00:00
2023-08-31 05:02:23 +00:00
def bounded_parallel_map ( func : Callable [ [ In ] , Out ] , iterable : Iterable [ In ] , concurrency : int , max_workers : int | None = None , use_processpool_executor : bool = False ) - > Iterable [ Out ] :
py : new conversion script (#545)
Current status: Working, except for the latest GPTQ-for-LLaMa format
that includes `g_idx`. This turns out to require changes to GGML, so
for now it only works if you use the `--outtype` option to dequantize it
back to f16 (which is pointless except for debugging).
I also included some cleanup for the C++ code.
This script is meant to replace all the existing conversion scripts
(including the ones that convert from older GGML formats), while also
adding support for some new formats. Specifically, I've tested with:
- [x] `LLaMA` (original)
- [x] `llama-65b-4bit`
- [x] `alpaca-native`
- [x] `alpaca-native-4bit`
- [x] LLaMA converted to 'transformers' format using
`convert_llama_weights_to_hf.py`
- [x] `alpaca-native` quantized with `--true-sequential --act-order
--groupsize 128` (dequantized only)
- [x] same as above plus `--save_safetensors`
- [x] GPT4All
- [x] stock unversioned ggml
- [x] ggmh
There's enough overlap in the logic needed to handle these different
cases that it seemed best to move to a single script.
I haven't tried this with Alpaca-LoRA because I don't know where to find
it.
Useful features:
- Uses multiple threads for a speedup in some cases (though the Python
GIL limits the gain, and sometimes it's disk-bound anyway).
- Combines split models into a single file (both the intra-tensor split
of the original and the inter-tensor split of 'transformers' format
files). Single files are more convenient to work with and more
friendly to future changes to use memory mapping on the C++ side. To
accomplish this without increasing memory requirements, it has some
custom loading code which avoids loading whole input files into memory
at once.
- Because of the custom loading code, it no longer depends in PyTorch,
which might make installing dependencies slightly easier or faster...
although it still depends on NumPy and sentencepiece, so I don't know
if there's any meaningful difference. In any case, I also added a
requirements.txt file to lock the dependency versions in case of any
future breaking changes.
- Type annotations checked with mypy.
- Some attempts to be extra user-friendly:
- The script tries to be forgiving with arguments, e.g. you can
specify either the model file itself or the directory containing
it.
- The script doesn't depend on config.json / params.json, just in
case the user downloaded files individually and doesn't have those
handy. But you still need tokenizer.model and, for Alpaca,
added_tokens.json.
- The script tries to give a helpful error message if
added_tokens.json is missing.
2023-04-14 07:03:03 +00:00
''' Parallel map, but with backpressure. If the caller doesn ' t call `next`
fast enough , this will stop calling ` func ` at some point rather than
letting results pile up in memory . Specifically , there is a max of one
output value buffered per thread . '''
2023-08-26 20:13:36 +00:00
if concurrency < 2 :
yield from map ( func , iterable )
# Not reached.
iterable = iter ( iterable )
2023-08-31 05:02:23 +00:00
executor_class : type [ ThreadPoolExecutor ] | type [ ProcessPoolExecutor ]
2023-08-30 08:25:50 +00:00
if use_processpool_executor :
executor_class = ProcessPoolExecutor
else :
executor_class = ThreadPoolExecutor
2024-01-20 23:14:18 +00:00
with executor_class ( max_workers = max_workers ) as executor :
2023-08-31 05:02:23 +00:00
futures : list [ concurrent . futures . Future [ Out ] ] = [ ]
2023-08-26 20:13:36 +00:00
done = False
for _ in range ( concurrency ) :
try :
futures . append ( executor . submit ( func , next ( iterable ) ) )
except StopIteration :
done = True
break
py : new conversion script (#545)
Current status: Working, except for the latest GPTQ-for-LLaMa format
that includes `g_idx`. This turns out to require changes to GGML, so
for now it only works if you use the `--outtype` option to dequantize it
back to f16 (which is pointless except for debugging).
I also included some cleanup for the C++ code.
This script is meant to replace all the existing conversion scripts
(including the ones that convert from older GGML formats), while also
adding support for some new formats. Specifically, I've tested with:
- [x] `LLaMA` (original)
- [x] `llama-65b-4bit`
- [x] `alpaca-native`
- [x] `alpaca-native-4bit`
- [x] LLaMA converted to 'transformers' format using
`convert_llama_weights_to_hf.py`
- [x] `alpaca-native` quantized with `--true-sequential --act-order
--groupsize 128` (dequantized only)
- [x] same as above plus `--save_safetensors`
- [x] GPT4All
- [x] stock unversioned ggml
- [x] ggmh
There's enough overlap in the logic needed to handle these different
cases that it seemed best to move to a single script.
I haven't tried this with Alpaca-LoRA because I don't know where to find
it.
Useful features:
- Uses multiple threads for a speedup in some cases (though the Python
GIL limits the gain, and sometimes it's disk-bound anyway).
- Combines split models into a single file (both the intra-tensor split
of the original and the inter-tensor split of 'transformers' format
files). Single files are more convenient to work with and more
friendly to future changes to use memory mapping on the C++ side. To
accomplish this without increasing memory requirements, it has some
custom loading code which avoids loading whole input files into memory
at once.
- Because of the custom loading code, it no longer depends in PyTorch,
which might make installing dependencies slightly easier or faster...
although it still depends on NumPy and sentencepiece, so I don't know
if there's any meaningful difference. In any case, I also added a
requirements.txt file to lock the dependency versions in case of any
future breaking changes.
- Type annotations checked with mypy.
- Some attempts to be extra user-friendly:
- The script tries to be forgiving with arguments, e.g. you can
specify either the model file itself or the directory containing
it.
- The script doesn't depend on config.json / params.json, just in
case the user downloaded files individually and doesn't have those
handy. But you still need tokenizer.model and, for Alpaca,
added_tokens.json.
- The script tries to give a helpful error message if
added_tokens.json is missing.
2023-04-14 07:03:03 +00:00
while futures :
result = futures . pop ( 0 ) . result ( )
2023-08-26 20:13:36 +00:00
while not done and len ( futures ) < concurrency :
try :
futures . append ( executor . submit ( func , next ( iterable ) ) )
except StopIteration :
done = True
break
py : new conversion script (#545)
Current status: Working, except for the latest GPTQ-for-LLaMa format
that includes `g_idx`. This turns out to require changes to GGML, so
for now it only works if you use the `--outtype` option to dequantize it
back to f16 (which is pointless except for debugging).
I also included some cleanup for the C++ code.
This script is meant to replace all the existing conversion scripts
(including the ones that convert from older GGML formats), while also
adding support for some new formats. Specifically, I've tested with:
- [x] `LLaMA` (original)
- [x] `llama-65b-4bit`
- [x] `alpaca-native`
- [x] `alpaca-native-4bit`
- [x] LLaMA converted to 'transformers' format using
`convert_llama_weights_to_hf.py`
- [x] `alpaca-native` quantized with `--true-sequential --act-order
--groupsize 128` (dequantized only)
- [x] same as above plus `--save_safetensors`
- [x] GPT4All
- [x] stock unversioned ggml
- [x] ggmh
There's enough overlap in the logic needed to handle these different
cases that it seemed best to move to a single script.
I haven't tried this with Alpaca-LoRA because I don't know where to find
it.
Useful features:
- Uses multiple threads for a speedup in some cases (though the Python
GIL limits the gain, and sometimes it's disk-bound anyway).
- Combines split models into a single file (both the intra-tensor split
of the original and the inter-tensor split of 'transformers' format
files). Single files are more convenient to work with and more
friendly to future changes to use memory mapping on the C++ side. To
accomplish this without increasing memory requirements, it has some
custom loading code which avoids loading whole input files into memory
at once.
- Because of the custom loading code, it no longer depends in PyTorch,
which might make installing dependencies slightly easier or faster...
although it still depends on NumPy and sentencepiece, so I don't know
if there's any meaningful difference. In any case, I also added a
requirements.txt file to lock the dependency versions in case of any
future breaking changes.
- Type annotations checked with mypy.
- Some attempts to be extra user-friendly:
- The script tries to be forgiving with arguments, e.g. you can
specify either the model file itself or the directory containing
it.
- The script doesn't depend on config.json / params.json, just in
case the user downloaded files individually and doesn't have those
handy. But you still need tokenizer.model and, for Alpaca,
added_tokens.json.
- The script tries to give a helpful error message if
added_tokens.json is missing.
2023-04-14 07:03:03 +00:00
yield result
2023-11-20 10:35:47 +00:00
2024-03-28 15:44:36 +00:00
def check_vocab_size ( params : Params , vocab : BaseVocab , pad_vocab : bool = False ) - > None :
convert.py : fix vanilla LLaMA model conversion (#4818)
* Update Imports and Add Notes for Future Reference
- Updated import statements in `convert.py`.
- Added import for `AutoTokenizer` from `transformers` module.
- Added conditional import for `gguf` from the local directory.
- Added comments and notes for future reference.
Additional Notes:
- Noted removal of a redundant `TypeAlias` import.
- Noted the removal of a `gguf` debug statement.
- Commented on the presence of `ARCH` and `NDArray` definitions.
- Commented on cleaning up and refactoring data type definitions.
* Refine Model Hyperparameters and Params Class
- Updated type annotations to use `Optional` for clarity.
- Improved method names and attribute consistency.
- Removed unnecessary variables for better code readability.
Additional Notes:
- Highlighted the use of `Optional` for clearer intent.
- Ensured backward and forward compatibility.
* Restore BpeVocab and SentencePieceVocab classes
- Restored the BpeVocab class for handling BPE tokenization.
- Restored the SentencePieceVocab class for SentencePiece tokenization.
These classes are essential for maintaining the original behavior of the codebase.
* refactor: Standardize vocabulary handling with HfVocab
- Replaced VocabLoader with HfVocab, aligning vocabulary handling across classes.
- Updated initialization of HfVocab with local_files_only=True for AutoTokenizer.
- Introduced optional parameter fname_added_tokens for flexible added token management.
- Streamlined added token handling for clarity and conciseness.
- Maintained special tokens and IDs, enhancing token management.
- Simplified token processing methods for improved readability.
- Added a placeholder for score computation with a default value of -1000.0.
- Optimized newline token check for efficiency.
- Updated __repr__ function for clarity in representation.
- Adjusted type alias Vocab to include BpeVocab, SentencePieceVocab, and HfVocab.
- Removed redundant code related to special token handling, reverse vocabulary mapping, and vocabulary file detection.
This refactoring promotes a standardized and modular approach to vocabulary management, facilitating future integration with a VocabFactory and improving code maintainability and scalability.
* refactor: Enhance readability, functionality, and code quality
- Improved code formatting and readability for better maintainability.
- Refactored LazyUnpickler's CLASSES dictionary for clarity.
- Added print statements and warnings in check_vocab_size for user feedback.
- Removed find_vocab_file_path, as it's superseded by VocabFactory.
- Preparatory changes for upcoming classes: OutputFile and VocabFactory.
- Overall focus on code quality, error handling, and consistency.
These changes reflect a continuous effort to refine the codebase, ensuring it meets best practices and prepares for future enhancements, such as the VocabFactory.
* refactor: Update OutputFile class for enhanced model vocabulary management
- Restructured the constructor for improved readability.
- Updated `add_meta_arch` method for flexible model name determination.
- Introduced `handle_tokenizer_model` for mapping vocab types to supported tokenizer models.
- Streamlined vocabulary extraction with `extract_vocabulary_from_model`.
- Simplified vocabulary metadata addition using `add_meta_vocab`.
- Refactored `add_tensor_info` for clarity and consistency.
- Improved error handling for better user feedback.
These changes signify the development of a versatile and comprehensive `OutputFile` class, enabling efficient management of model conversion output, metadata, vocabulary, and tensor information.
* feat: Introduce VocabFactory for flexible vocabulary management in model conversion
- The VocabFactory class is added to facilitate modular vocabulary handling.
- The constructor initializes a directory path and detects vocabulary-related files.
- The _select_file method provides file paths based on vocabulary type (e.g., BPE, SentencePiece).
- _create_special_vocab generates special vocabularies, accommodating different types.
- The load_vocab method loads vocabularies, handling BPE, SentencePiece, and Hugging Face Fast Tokenizer.
- Error handling and logging enhance debugging and user feedback.
- The modular and flexible design simplifies vocabulary management and supports future extensions.
The VocabFactory class enhances code modularity and maintainability, allowing versatile vocabulary handling in the model conversion process.
* refactor: Improve code organization, argument parsing, and user interface
- Renamed 'default_outfile' to 'default_output_file' for clarity.
- Refactored argument parser setup into 'get_argument_parser' function.
- Introduced descriptive comments for each argument in the parser.
- Added '--vocab-type' argument with choices ["spm", "bpe", "hfft"] for vocabulary processing.
- Improved flag naming consistency: '--outfile' to '--out-file' and '--bigendian' to '--big-endian'.
- Enhanced error handling to prevent overwriting input data in 'default_output_file'.
- Made 'argv' in 'main' an optional parameter for flexibility.
- Introduced dynamic import for 'awq.apply_awq' based on 'args.awq_path' for conditional dependency.
These changes enhance code clarity, organization, and the user interface of the script, aligning it with Python best practices and improving maintainability.
* refactor: Further refine functionality, improve user interaction, and streamline vocabulary handling
- Renamed command-line arguments for clarity and consistency.
- Improved path resolution and import adjustments for robustness.
- Thoughtfully handled 'awq-path' and conditional logic for the weighted model.
- Enhanced model and vocabulary loading with the 'VocabFactory' class for structured and adaptable loading.
- Strengthened error handling and user feedback for a more user-friendly experience.
- Structured output file handling with clear conditions and defaults.
- Streamlined and organized the 'main' function for better logic flow.
- Passed 'sys.argv[1:]' to 'main' for adaptability and testability.
These changes solidify the script's functionality, making it more robust, user-friendly, and adaptable. The use of the 'VocabFactory' class is a notable enhancement in efficient vocabulary handling, reflecting a thoughtful and iterative approach to script development.
* chore: Apply ruff formatting to convert.py
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
* Revert to commit 0614c33
* chore: Apply flake8 formatting rules
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
* refactor: Revise `check_vocab_size` for Enhanced Clarity and Correctness
- Resolved an unreachable branch issue by reorganizing the conditional structure.
- Moved the special case check for `params.n_vocab == -1` to the top for immediate assertion.
- Flattened the conditional logic for improved clarity and predictability of the function's behavior.
These changes enhance the readability and functional correctness of the `check_vocab_size` function without altering its intended functionality.
* py : fix outfile and outtype
* py : suggest hint for missing vocab size
---------
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-01-09 18:46:46 +00:00
# Handle special case where the model's vocab size is not set
if params . n_vocab == - 1 :
raise ValueError (
2024-03-28 15:44:36 +00:00
" The model ' s vocab size is set to -1 in params.json. Please update it manually. "
+ ( f " Maybe { vocab . vocab_size } ? " if isinstance ( vocab , Vocab ) else " " ) ,
convert.py : fix vanilla LLaMA model conversion (#4818)
* Update Imports and Add Notes for Future Reference
- Updated import statements in `convert.py`.
- Added import for `AutoTokenizer` from `transformers` module.
- Added conditional import for `gguf` from the local directory.
- Added comments and notes for future reference.
Additional Notes:
- Noted removal of a redundant `TypeAlias` import.
- Noted the removal of a `gguf` debug statement.
- Commented on the presence of `ARCH` and `NDArray` definitions.
- Commented on cleaning up and refactoring data type definitions.
* Refine Model Hyperparameters and Params Class
- Updated type annotations to use `Optional` for clarity.
- Improved method names and attribute consistency.
- Removed unnecessary variables for better code readability.
Additional Notes:
- Highlighted the use of `Optional` for clearer intent.
- Ensured backward and forward compatibility.
* Restore BpeVocab and SentencePieceVocab classes
- Restored the BpeVocab class for handling BPE tokenization.
- Restored the SentencePieceVocab class for SentencePiece tokenization.
These classes are essential for maintaining the original behavior of the codebase.
* refactor: Standardize vocabulary handling with HfVocab
- Replaced VocabLoader with HfVocab, aligning vocabulary handling across classes.
- Updated initialization of HfVocab with local_files_only=True for AutoTokenizer.
- Introduced optional parameter fname_added_tokens for flexible added token management.
- Streamlined added token handling for clarity and conciseness.
- Maintained special tokens and IDs, enhancing token management.
- Simplified token processing methods for improved readability.
- Added a placeholder for score computation with a default value of -1000.0.
- Optimized newline token check for efficiency.
- Updated __repr__ function for clarity in representation.
- Adjusted type alias Vocab to include BpeVocab, SentencePieceVocab, and HfVocab.
- Removed redundant code related to special token handling, reverse vocabulary mapping, and vocabulary file detection.
This refactoring promotes a standardized and modular approach to vocabulary management, facilitating future integration with a VocabFactory and improving code maintainability and scalability.
* refactor: Enhance readability, functionality, and code quality
- Improved code formatting and readability for better maintainability.
- Refactored LazyUnpickler's CLASSES dictionary for clarity.
- Added print statements and warnings in check_vocab_size for user feedback.
- Removed find_vocab_file_path, as it's superseded by VocabFactory.
- Preparatory changes for upcoming classes: OutputFile and VocabFactory.
- Overall focus on code quality, error handling, and consistency.
These changes reflect a continuous effort to refine the codebase, ensuring it meets best practices and prepares for future enhancements, such as the VocabFactory.
* refactor: Update OutputFile class for enhanced model vocabulary management
- Restructured the constructor for improved readability.
- Updated `add_meta_arch` method for flexible model name determination.
- Introduced `handle_tokenizer_model` for mapping vocab types to supported tokenizer models.
- Streamlined vocabulary extraction with `extract_vocabulary_from_model`.
- Simplified vocabulary metadata addition using `add_meta_vocab`.
- Refactored `add_tensor_info` for clarity and consistency.
- Improved error handling for better user feedback.
These changes signify the development of a versatile and comprehensive `OutputFile` class, enabling efficient management of model conversion output, metadata, vocabulary, and tensor information.
* feat: Introduce VocabFactory for flexible vocabulary management in model conversion
- The VocabFactory class is added to facilitate modular vocabulary handling.
- The constructor initializes a directory path and detects vocabulary-related files.
- The _select_file method provides file paths based on vocabulary type (e.g., BPE, SentencePiece).
- _create_special_vocab generates special vocabularies, accommodating different types.
- The load_vocab method loads vocabularies, handling BPE, SentencePiece, and Hugging Face Fast Tokenizer.
- Error handling and logging enhance debugging and user feedback.
- The modular and flexible design simplifies vocabulary management and supports future extensions.
The VocabFactory class enhances code modularity and maintainability, allowing versatile vocabulary handling in the model conversion process.
* refactor: Improve code organization, argument parsing, and user interface
- Renamed 'default_outfile' to 'default_output_file' for clarity.
- Refactored argument parser setup into 'get_argument_parser' function.
- Introduced descriptive comments for each argument in the parser.
- Added '--vocab-type' argument with choices ["spm", "bpe", "hfft"] for vocabulary processing.
- Improved flag naming consistency: '--outfile' to '--out-file' and '--bigendian' to '--big-endian'.
- Enhanced error handling to prevent overwriting input data in 'default_output_file'.
- Made 'argv' in 'main' an optional parameter for flexibility.
- Introduced dynamic import for 'awq.apply_awq' based on 'args.awq_path' for conditional dependency.
These changes enhance code clarity, organization, and the user interface of the script, aligning it with Python best practices and improving maintainability.
* refactor: Further refine functionality, improve user interaction, and streamline vocabulary handling
- Renamed command-line arguments for clarity and consistency.
- Improved path resolution and import adjustments for robustness.
- Thoughtfully handled 'awq-path' and conditional logic for the weighted model.
- Enhanced model and vocabulary loading with the 'VocabFactory' class for structured and adaptable loading.
- Strengthened error handling and user feedback for a more user-friendly experience.
- Structured output file handling with clear conditions and defaults.
- Streamlined and organized the 'main' function for better logic flow.
- Passed 'sys.argv[1:]' to 'main' for adaptability and testability.
These changes solidify the script's functionality, making it more robust, user-friendly, and adaptable. The use of the 'VocabFactory' class is a notable enhancement in efficient vocabulary handling, reflecting a thoughtful and iterative approach to script development.
* chore: Apply ruff formatting to convert.py
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
* Revert to commit 0614c33
* chore: Apply flake8 formatting rules
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
* refactor: Revise `check_vocab_size` for Enhanced Clarity and Correctness
- Resolved an unreachable branch issue by reorganizing the conditional structure.
- Moved the special case check for `params.n_vocab == -1` to the top for immediate assertion.
- Flattened the conditional logic for improved clarity and predictability of the function's behavior.
These changes enhance the readability and functional correctness of the `check_vocab_size` function without altering its intended functionality.
* py : fix outfile and outtype
* py : suggest hint for missing vocab size
---------
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-01-09 18:46:46 +00:00
)
2024-03-28 15:44:36 +00:00
if not isinstance ( vocab , Vocab ) :
2024-03-14 16:21:56 +00:00
return # model has no vocab
convert.py : fix vanilla LLaMA model conversion (#4818)
* Update Imports and Add Notes for Future Reference
- Updated import statements in `convert.py`.
- Added import for `AutoTokenizer` from `transformers` module.
- Added conditional import for `gguf` from the local directory.
- Added comments and notes for future reference.
Additional Notes:
- Noted removal of a redundant `TypeAlias` import.
- Noted the removal of a `gguf` debug statement.
- Commented on the presence of `ARCH` and `NDArray` definitions.
- Commented on cleaning up and refactoring data type definitions.
* Refine Model Hyperparameters and Params Class
- Updated type annotations to use `Optional` for clarity.
- Improved method names and attribute consistency.
- Removed unnecessary variables for better code readability.
Additional Notes:
- Highlighted the use of `Optional` for clearer intent.
- Ensured backward and forward compatibility.
* Restore BpeVocab and SentencePieceVocab classes
- Restored the BpeVocab class for handling BPE tokenization.
- Restored the SentencePieceVocab class for SentencePiece tokenization.
These classes are essential for maintaining the original behavior of the codebase.
* refactor: Standardize vocabulary handling with HfVocab
- Replaced VocabLoader with HfVocab, aligning vocabulary handling across classes.
- Updated initialization of HfVocab with local_files_only=True for AutoTokenizer.
- Introduced optional parameter fname_added_tokens for flexible added token management.
- Streamlined added token handling for clarity and conciseness.
- Maintained special tokens and IDs, enhancing token management.
- Simplified token processing methods for improved readability.
- Added a placeholder for score computation with a default value of -1000.0.
- Optimized newline token check for efficiency.
- Updated __repr__ function for clarity in representation.
- Adjusted type alias Vocab to include BpeVocab, SentencePieceVocab, and HfVocab.
- Removed redundant code related to special token handling, reverse vocabulary mapping, and vocabulary file detection.
This refactoring promotes a standardized and modular approach to vocabulary management, facilitating future integration with a VocabFactory and improving code maintainability and scalability.
* refactor: Enhance readability, functionality, and code quality
- Improved code formatting and readability for better maintainability.
- Refactored LazyUnpickler's CLASSES dictionary for clarity.
- Added print statements and warnings in check_vocab_size for user feedback.
- Removed find_vocab_file_path, as it's superseded by VocabFactory.
- Preparatory changes for upcoming classes: OutputFile and VocabFactory.
- Overall focus on code quality, error handling, and consistency.
These changes reflect a continuous effort to refine the codebase, ensuring it meets best practices and prepares for future enhancements, such as the VocabFactory.
* refactor: Update OutputFile class for enhanced model vocabulary management
- Restructured the constructor for improved readability.
- Updated `add_meta_arch` method for flexible model name determination.
- Introduced `handle_tokenizer_model` for mapping vocab types to supported tokenizer models.
- Streamlined vocabulary extraction with `extract_vocabulary_from_model`.
- Simplified vocabulary metadata addition using `add_meta_vocab`.
- Refactored `add_tensor_info` for clarity and consistency.
- Improved error handling for better user feedback.
These changes signify the development of a versatile and comprehensive `OutputFile` class, enabling efficient management of model conversion output, metadata, vocabulary, and tensor information.
* feat: Introduce VocabFactory for flexible vocabulary management in model conversion
- The VocabFactory class is added to facilitate modular vocabulary handling.
- The constructor initializes a directory path and detects vocabulary-related files.
- The _select_file method provides file paths based on vocabulary type (e.g., BPE, SentencePiece).
- _create_special_vocab generates special vocabularies, accommodating different types.
- The load_vocab method loads vocabularies, handling BPE, SentencePiece, and Hugging Face Fast Tokenizer.
- Error handling and logging enhance debugging and user feedback.
- The modular and flexible design simplifies vocabulary management and supports future extensions.
The VocabFactory class enhances code modularity and maintainability, allowing versatile vocabulary handling in the model conversion process.
* refactor: Improve code organization, argument parsing, and user interface
- Renamed 'default_outfile' to 'default_output_file' for clarity.
- Refactored argument parser setup into 'get_argument_parser' function.
- Introduced descriptive comments for each argument in the parser.
- Added '--vocab-type' argument with choices ["spm", "bpe", "hfft"] for vocabulary processing.
- Improved flag naming consistency: '--outfile' to '--out-file' and '--bigendian' to '--big-endian'.
- Enhanced error handling to prevent overwriting input data in 'default_output_file'.
- Made 'argv' in 'main' an optional parameter for flexibility.
- Introduced dynamic import for 'awq.apply_awq' based on 'args.awq_path' for conditional dependency.
These changes enhance code clarity, organization, and the user interface of the script, aligning it with Python best practices and improving maintainability.
* refactor: Further refine functionality, improve user interaction, and streamline vocabulary handling
- Renamed command-line arguments for clarity and consistency.
- Improved path resolution and import adjustments for robustness.
- Thoughtfully handled 'awq-path' and conditional logic for the weighted model.
- Enhanced model and vocabulary loading with the 'VocabFactory' class for structured and adaptable loading.
- Strengthened error handling and user feedback for a more user-friendly experience.
- Structured output file handling with clear conditions and defaults.
- Streamlined and organized the 'main' function for better logic flow.
- Passed 'sys.argv[1:]' to 'main' for adaptability and testability.
These changes solidify the script's functionality, making it more robust, user-friendly, and adaptable. The use of the 'VocabFactory' class is a notable enhancement in efficient vocabulary handling, reflecting a thoughtful and iterative approach to script development.
* chore: Apply ruff formatting to convert.py
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
* Revert to commit 0614c33
* chore: Apply flake8 formatting rules
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
* refactor: Revise `check_vocab_size` for Enhanced Clarity and Correctness
- Resolved an unreachable branch issue by reorganizing the conditional structure.
- Moved the special case check for `params.n_vocab == -1` to the top for immediate assertion.
- Flattened the conditional logic for improved clarity and predictability of the function's behavior.
These changes enhance the readability and functional correctness of the `check_vocab_size` function without altering its intended functionality.
* py : fix outfile and outtype
* py : suggest hint for missing vocab size
---------
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-01-09 18:46:46 +00:00
# Check for a vocab size mismatch
if params . n_vocab == vocab . vocab_size :
2024-05-03 19:36:41 +00:00
logger . warning ( " Ignoring added_tokens.json since model matches vocab size without it. " )
convert.py : fix vanilla LLaMA model conversion (#4818)
* Update Imports and Add Notes for Future Reference
- Updated import statements in `convert.py`.
- Added import for `AutoTokenizer` from `transformers` module.
- Added conditional import for `gguf` from the local directory.
- Added comments and notes for future reference.
Additional Notes:
- Noted removal of a redundant `TypeAlias` import.
- Noted the removal of a `gguf` debug statement.
- Commented on the presence of `ARCH` and `NDArray` definitions.
- Commented on cleaning up and refactoring data type definitions.
* Refine Model Hyperparameters and Params Class
- Updated type annotations to use `Optional` for clarity.
- Improved method names and attribute consistency.
- Removed unnecessary variables for better code readability.
Additional Notes:
- Highlighted the use of `Optional` for clearer intent.
- Ensured backward and forward compatibility.
* Restore BpeVocab and SentencePieceVocab classes
- Restored the BpeVocab class for handling BPE tokenization.
- Restored the SentencePieceVocab class for SentencePiece tokenization.
These classes are essential for maintaining the original behavior of the codebase.
* refactor: Standardize vocabulary handling with HfVocab
- Replaced VocabLoader with HfVocab, aligning vocabulary handling across classes.
- Updated initialization of HfVocab with local_files_only=True for AutoTokenizer.
- Introduced optional parameter fname_added_tokens for flexible added token management.
- Streamlined added token handling for clarity and conciseness.
- Maintained special tokens and IDs, enhancing token management.
- Simplified token processing methods for improved readability.
- Added a placeholder for score computation with a default value of -1000.0.
- Optimized newline token check for efficiency.
- Updated __repr__ function for clarity in representation.
- Adjusted type alias Vocab to include BpeVocab, SentencePieceVocab, and HfVocab.
- Removed redundant code related to special token handling, reverse vocabulary mapping, and vocabulary file detection.
This refactoring promotes a standardized and modular approach to vocabulary management, facilitating future integration with a VocabFactory and improving code maintainability and scalability.
* refactor: Enhance readability, functionality, and code quality
- Improved code formatting and readability for better maintainability.
- Refactored LazyUnpickler's CLASSES dictionary for clarity.
- Added print statements and warnings in check_vocab_size for user feedback.
- Removed find_vocab_file_path, as it's superseded by VocabFactory.
- Preparatory changes for upcoming classes: OutputFile and VocabFactory.
- Overall focus on code quality, error handling, and consistency.
These changes reflect a continuous effort to refine the codebase, ensuring it meets best practices and prepares for future enhancements, such as the VocabFactory.
* refactor: Update OutputFile class for enhanced model vocabulary management
- Restructured the constructor for improved readability.
- Updated `add_meta_arch` method for flexible model name determination.
- Introduced `handle_tokenizer_model` for mapping vocab types to supported tokenizer models.
- Streamlined vocabulary extraction with `extract_vocabulary_from_model`.
- Simplified vocabulary metadata addition using `add_meta_vocab`.
- Refactored `add_tensor_info` for clarity and consistency.
- Improved error handling for better user feedback.
These changes signify the development of a versatile and comprehensive `OutputFile` class, enabling efficient management of model conversion output, metadata, vocabulary, and tensor information.
* feat: Introduce VocabFactory for flexible vocabulary management in model conversion
- The VocabFactory class is added to facilitate modular vocabulary handling.
- The constructor initializes a directory path and detects vocabulary-related files.
- The _select_file method provides file paths based on vocabulary type (e.g., BPE, SentencePiece).
- _create_special_vocab generates special vocabularies, accommodating different types.
- The load_vocab method loads vocabularies, handling BPE, SentencePiece, and Hugging Face Fast Tokenizer.
- Error handling and logging enhance debugging and user feedback.
- The modular and flexible design simplifies vocabulary management and supports future extensions.
The VocabFactory class enhances code modularity and maintainability, allowing versatile vocabulary handling in the model conversion process.
* refactor: Improve code organization, argument parsing, and user interface
- Renamed 'default_outfile' to 'default_output_file' for clarity.
- Refactored argument parser setup into 'get_argument_parser' function.
- Introduced descriptive comments for each argument in the parser.
- Added '--vocab-type' argument with choices ["spm", "bpe", "hfft"] for vocabulary processing.
- Improved flag naming consistency: '--outfile' to '--out-file' and '--bigendian' to '--big-endian'.
- Enhanced error handling to prevent overwriting input data in 'default_output_file'.
- Made 'argv' in 'main' an optional parameter for flexibility.
- Introduced dynamic import for 'awq.apply_awq' based on 'args.awq_path' for conditional dependency.
These changes enhance code clarity, organization, and the user interface of the script, aligning it with Python best practices and improving maintainability.
* refactor: Further refine functionality, improve user interaction, and streamline vocabulary handling
- Renamed command-line arguments for clarity and consistency.
- Improved path resolution and import adjustments for robustness.
- Thoughtfully handled 'awq-path' and conditional logic for the weighted model.
- Enhanced model and vocabulary loading with the 'VocabFactory' class for structured and adaptable loading.
- Strengthened error handling and user feedback for a more user-friendly experience.
- Structured output file handling with clear conditions and defaults.
- Streamlined and organized the 'main' function for better logic flow.
- Passed 'sys.argv[1:]' to 'main' for adaptability and testability.
These changes solidify the script's functionality, making it more robust, user-friendly, and adaptable. The use of the 'VocabFactory' class is a notable enhancement in efficient vocabulary handling, reflecting a thoughtful and iterative approach to script development.
* chore: Apply ruff formatting to convert.py
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
* Revert to commit 0614c33
* chore: Apply flake8 formatting rules
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
* refactor: Revise `check_vocab_size` for Enhanced Clarity and Correctness
- Resolved an unreachable branch issue by reorganizing the conditional structure.
- Moved the special case check for `params.n_vocab == -1` to the top for immediate assertion.
- Flattened the conditional logic for improved clarity and predictability of the function's behavior.
These changes enhance the readability and functional correctness of the `check_vocab_size` function without altering its intended functionality.
* py : fix outfile and outtype
* py : suggest hint for missing vocab size
---------
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-01-09 18:46:46 +00:00
return
if pad_vocab and params . n_vocab > vocab . vocab_size :
pad_count = params . n_vocab - vocab . vocab_size
2024-05-03 19:36:41 +00:00
logger . debug (
convert.py : fix vanilla LLaMA model conversion (#4818)
* Update Imports and Add Notes for Future Reference
- Updated import statements in `convert.py`.
- Added import for `AutoTokenizer` from `transformers` module.
- Added conditional import for `gguf` from the local directory.
- Added comments and notes for future reference.
Additional Notes:
- Noted removal of a redundant `TypeAlias` import.
- Noted the removal of a `gguf` debug statement.
- Commented on the presence of `ARCH` and `NDArray` definitions.
- Commented on cleaning up and refactoring data type definitions.
* Refine Model Hyperparameters and Params Class
- Updated type annotations to use `Optional` for clarity.
- Improved method names and attribute consistency.
- Removed unnecessary variables for better code readability.
Additional Notes:
- Highlighted the use of `Optional` for clearer intent.
- Ensured backward and forward compatibility.
* Restore BpeVocab and SentencePieceVocab classes
- Restored the BpeVocab class for handling BPE tokenization.
- Restored the SentencePieceVocab class for SentencePiece tokenization.
These classes are essential for maintaining the original behavior of the codebase.
* refactor: Standardize vocabulary handling with HfVocab
- Replaced VocabLoader with HfVocab, aligning vocabulary handling across classes.
- Updated initialization of HfVocab with local_files_only=True for AutoTokenizer.
- Introduced optional parameter fname_added_tokens for flexible added token management.
- Streamlined added token handling for clarity and conciseness.
- Maintained special tokens and IDs, enhancing token management.
- Simplified token processing methods for improved readability.
- Added a placeholder for score computation with a default value of -1000.0.
- Optimized newline token check for efficiency.
- Updated __repr__ function for clarity in representation.
- Adjusted type alias Vocab to include BpeVocab, SentencePieceVocab, and HfVocab.
- Removed redundant code related to special token handling, reverse vocabulary mapping, and vocabulary file detection.
This refactoring promotes a standardized and modular approach to vocabulary management, facilitating future integration with a VocabFactory and improving code maintainability and scalability.
* refactor: Enhance readability, functionality, and code quality
- Improved code formatting and readability for better maintainability.
- Refactored LazyUnpickler's CLASSES dictionary for clarity.
- Added print statements and warnings in check_vocab_size for user feedback.
- Removed find_vocab_file_path, as it's superseded by VocabFactory.
- Preparatory changes for upcoming classes: OutputFile and VocabFactory.
- Overall focus on code quality, error handling, and consistency.
These changes reflect a continuous effort to refine the codebase, ensuring it meets best practices and prepares for future enhancements, such as the VocabFactory.
* refactor: Update OutputFile class for enhanced model vocabulary management
- Restructured the constructor for improved readability.
- Updated `add_meta_arch` method for flexible model name determination.
- Introduced `handle_tokenizer_model` for mapping vocab types to supported tokenizer models.
- Streamlined vocabulary extraction with `extract_vocabulary_from_model`.
- Simplified vocabulary metadata addition using `add_meta_vocab`.
- Refactored `add_tensor_info` for clarity and consistency.
- Improved error handling for better user feedback.
These changes signify the development of a versatile and comprehensive `OutputFile` class, enabling efficient management of model conversion output, metadata, vocabulary, and tensor information.
* feat: Introduce VocabFactory for flexible vocabulary management in model conversion
- The VocabFactory class is added to facilitate modular vocabulary handling.
- The constructor initializes a directory path and detects vocabulary-related files.
- The _select_file method provides file paths based on vocabulary type (e.g., BPE, SentencePiece).
- _create_special_vocab generates special vocabularies, accommodating different types.
- The load_vocab method loads vocabularies, handling BPE, SentencePiece, and Hugging Face Fast Tokenizer.
- Error handling and logging enhance debugging and user feedback.
- The modular and flexible design simplifies vocabulary management and supports future extensions.
The VocabFactory class enhances code modularity and maintainability, allowing versatile vocabulary handling in the model conversion process.
* refactor: Improve code organization, argument parsing, and user interface
- Renamed 'default_outfile' to 'default_output_file' for clarity.
- Refactored argument parser setup into 'get_argument_parser' function.
- Introduced descriptive comments for each argument in the parser.
- Added '--vocab-type' argument with choices ["spm", "bpe", "hfft"] for vocabulary processing.
- Improved flag naming consistency: '--outfile' to '--out-file' and '--bigendian' to '--big-endian'.
- Enhanced error handling to prevent overwriting input data in 'default_output_file'.
- Made 'argv' in 'main' an optional parameter for flexibility.
- Introduced dynamic import for 'awq.apply_awq' based on 'args.awq_path' for conditional dependency.
These changes enhance code clarity, organization, and the user interface of the script, aligning it with Python best practices and improving maintainability.
* refactor: Further refine functionality, improve user interaction, and streamline vocabulary handling
- Renamed command-line arguments for clarity and consistency.
- Improved path resolution and import adjustments for robustness.
- Thoughtfully handled 'awq-path' and conditional logic for the weighted model.
- Enhanced model and vocabulary loading with the 'VocabFactory' class for structured and adaptable loading.
- Strengthened error handling and user feedback for a more user-friendly experience.
- Structured output file handling with clear conditions and defaults.
- Streamlined and organized the 'main' function for better logic flow.
- Passed 'sys.argv[1:]' to 'main' for adaptability and testability.
These changes solidify the script's functionality, making it more robust, user-friendly, and adaptable. The use of the 'VocabFactory' class is a notable enhancement in efficient vocabulary handling, reflecting a thoughtful and iterative approach to script development.
* chore: Apply ruff formatting to convert.py
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
* Revert to commit 0614c33
* chore: Apply flake8 formatting rules
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
* refactor: Revise `check_vocab_size` for Enhanced Clarity and Correctness
- Resolved an unreachable branch issue by reorganizing the conditional structure.
- Moved the special case check for `params.n_vocab == -1` to the top for immediate assertion.
- Flattened the conditional logic for improved clarity and predictability of the function's behavior.
These changes enhance the readability and functional correctness of the `check_vocab_size` function without altering its intended functionality.
* py : fix outfile and outtype
* py : suggest hint for missing vocab size
---------
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-01-09 18:46:46 +00:00
f " Padding vocab with { pad_count } token(s) - <dummy00001> through <dummy { pad_count : 05 } > "
)
for i in range ( 1 , pad_count + 1 ) :
vocab . added_tokens_dict [ f " <dummy { i : 05 } > " ] = - 1
2024-01-17 13:45:03 +00:00
vocab . added_tokens_list . append ( f " <dummy { i : 05 } > " )
convert.py : fix vanilla LLaMA model conversion (#4818)
* Update Imports and Add Notes for Future Reference
- Updated import statements in `convert.py`.
- Added import for `AutoTokenizer` from `transformers` module.
- Added conditional import for `gguf` from the local directory.
- Added comments and notes for future reference.
Additional Notes:
- Noted removal of a redundant `TypeAlias` import.
- Noted the removal of a `gguf` debug statement.
- Commented on the presence of `ARCH` and `NDArray` definitions.
- Commented on cleaning up and refactoring data type definitions.
* Refine Model Hyperparameters and Params Class
- Updated type annotations to use `Optional` for clarity.
- Improved method names and attribute consistency.
- Removed unnecessary variables for better code readability.
Additional Notes:
- Highlighted the use of `Optional` for clearer intent.
- Ensured backward and forward compatibility.
* Restore BpeVocab and SentencePieceVocab classes
- Restored the BpeVocab class for handling BPE tokenization.
- Restored the SentencePieceVocab class for SentencePiece tokenization.
These classes are essential for maintaining the original behavior of the codebase.
* refactor: Standardize vocabulary handling with HfVocab
- Replaced VocabLoader with HfVocab, aligning vocabulary handling across classes.
- Updated initialization of HfVocab with local_files_only=True for AutoTokenizer.
- Introduced optional parameter fname_added_tokens for flexible added token management.
- Streamlined added token handling for clarity and conciseness.
- Maintained special tokens and IDs, enhancing token management.
- Simplified token processing methods for improved readability.
- Added a placeholder for score computation with a default value of -1000.0.
- Optimized newline token check for efficiency.
- Updated __repr__ function for clarity in representation.
- Adjusted type alias Vocab to include BpeVocab, SentencePieceVocab, and HfVocab.
- Removed redundant code related to special token handling, reverse vocabulary mapping, and vocabulary file detection.
This refactoring promotes a standardized and modular approach to vocabulary management, facilitating future integration with a VocabFactory and improving code maintainability and scalability.
* refactor: Enhance readability, functionality, and code quality
- Improved code formatting and readability for better maintainability.
- Refactored LazyUnpickler's CLASSES dictionary for clarity.
- Added print statements and warnings in check_vocab_size for user feedback.
- Removed find_vocab_file_path, as it's superseded by VocabFactory.
- Preparatory changes for upcoming classes: OutputFile and VocabFactory.
- Overall focus on code quality, error handling, and consistency.
These changes reflect a continuous effort to refine the codebase, ensuring it meets best practices and prepares for future enhancements, such as the VocabFactory.
* refactor: Update OutputFile class for enhanced model vocabulary management
- Restructured the constructor for improved readability.
- Updated `add_meta_arch` method for flexible model name determination.
- Introduced `handle_tokenizer_model` for mapping vocab types to supported tokenizer models.
- Streamlined vocabulary extraction with `extract_vocabulary_from_model`.
- Simplified vocabulary metadata addition using `add_meta_vocab`.
- Refactored `add_tensor_info` for clarity and consistency.
- Improved error handling for better user feedback.
These changes signify the development of a versatile and comprehensive `OutputFile` class, enabling efficient management of model conversion output, metadata, vocabulary, and tensor information.
* feat: Introduce VocabFactory for flexible vocabulary management in model conversion
- The VocabFactory class is added to facilitate modular vocabulary handling.
- The constructor initializes a directory path and detects vocabulary-related files.
- The _select_file method provides file paths based on vocabulary type (e.g., BPE, SentencePiece).
- _create_special_vocab generates special vocabularies, accommodating different types.
- The load_vocab method loads vocabularies, handling BPE, SentencePiece, and Hugging Face Fast Tokenizer.
- Error handling and logging enhance debugging and user feedback.
- The modular and flexible design simplifies vocabulary management and supports future extensions.
The VocabFactory class enhances code modularity and maintainability, allowing versatile vocabulary handling in the model conversion process.
* refactor: Improve code organization, argument parsing, and user interface
- Renamed 'default_outfile' to 'default_output_file' for clarity.
- Refactored argument parser setup into 'get_argument_parser' function.
- Introduced descriptive comments for each argument in the parser.
- Added '--vocab-type' argument with choices ["spm", "bpe", "hfft"] for vocabulary processing.
- Improved flag naming consistency: '--outfile' to '--out-file' and '--bigendian' to '--big-endian'.
- Enhanced error handling to prevent overwriting input data in 'default_output_file'.
- Made 'argv' in 'main' an optional parameter for flexibility.
- Introduced dynamic import for 'awq.apply_awq' based on 'args.awq_path' for conditional dependency.
These changes enhance code clarity, organization, and the user interface of the script, aligning it with Python best practices and improving maintainability.
* refactor: Further refine functionality, improve user interaction, and streamline vocabulary handling
- Renamed command-line arguments for clarity and consistency.
- Improved path resolution and import adjustments for robustness.
- Thoughtfully handled 'awq-path' and conditional logic for the weighted model.
- Enhanced model and vocabulary loading with the 'VocabFactory' class for structured and adaptable loading.
- Strengthened error handling and user feedback for a more user-friendly experience.
- Structured output file handling with clear conditions and defaults.
- Streamlined and organized the 'main' function for better logic flow.
- Passed 'sys.argv[1:]' to 'main' for adaptability and testability.
These changes solidify the script's functionality, making it more robust, user-friendly, and adaptable. The use of the 'VocabFactory' class is a notable enhancement in efficient vocabulary handling, reflecting a thoughtful and iterative approach to script development.
* chore: Apply ruff formatting to convert.py
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
* Revert to commit 0614c33
* chore: Apply flake8 formatting rules
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
* refactor: Revise `check_vocab_size` for Enhanced Clarity and Correctness
- Resolved an unreachable branch issue by reorganizing the conditional structure.
- Moved the special case check for `params.n_vocab == -1` to the top for immediate assertion.
- Flattened the conditional logic for improved clarity and predictability of the function's behavior.
These changes enhance the readability and functional correctness of the `check_vocab_size` function without altering its intended functionality.
* py : fix outfile and outtype
* py : suggest hint for missing vocab size
---------
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-01-09 18:46:46 +00:00
vocab . vocab_size = params . n_vocab
return
msg = f " Vocab size mismatch (model has { params . n_vocab } , but { vocab . fname_tokenizer } has { vocab . vocab_size } ). "
if vocab . vocab_size < params . n_vocab < vocab . vocab_size + 20 :
msg + = f " Most likely you are missing added_tokens.json (should be in { vocab . fname_tokenizer . parent } ). "
if vocab . vocab_size < params . n_vocab :
msg + = " Add the --pad-vocab option and try again. "
2024-03-28 15:44:36 +00:00
raise ValueError ( msg )
py : new conversion script (#545)
Current status: Working, except for the latest GPTQ-for-LLaMa format
that includes `g_idx`. This turns out to require changes to GGML, so
for now it only works if you use the `--outtype` option to dequantize it
back to f16 (which is pointless except for debugging).
I also included some cleanup for the C++ code.
This script is meant to replace all the existing conversion scripts
(including the ones that convert from older GGML formats), while also
adding support for some new formats. Specifically, I've tested with:
- [x] `LLaMA` (original)
- [x] `llama-65b-4bit`
- [x] `alpaca-native`
- [x] `alpaca-native-4bit`
- [x] LLaMA converted to 'transformers' format using
`convert_llama_weights_to_hf.py`
- [x] `alpaca-native` quantized with `--true-sequential --act-order
--groupsize 128` (dequantized only)
- [x] same as above plus `--save_safetensors`
- [x] GPT4All
- [x] stock unversioned ggml
- [x] ggmh
There's enough overlap in the logic needed to handle these different
cases that it seemed best to move to a single script.
I haven't tried this with Alpaca-LoRA because I don't know where to find
it.
Useful features:
- Uses multiple threads for a speedup in some cases (though the Python
GIL limits the gain, and sometimes it's disk-bound anyway).
- Combines split models into a single file (both the intra-tensor split
of the original and the inter-tensor split of 'transformers' format
files). Single files are more convenient to work with and more
friendly to future changes to use memory mapping on the C++ side. To
accomplish this without increasing memory requirements, it has some
custom loading code which avoids loading whole input files into memory
at once.
- Because of the custom loading code, it no longer depends in PyTorch,
which might make installing dependencies slightly easier or faster...
although it still depends on NumPy and sentencepiece, so I don't know
if there's any meaningful difference. In any case, I also added a
requirements.txt file to lock the dependency versions in case of any
future breaking changes.
- Type annotations checked with mypy.
- Some attempts to be extra user-friendly:
- The script tries to be forgiving with arguments, e.g. you can
specify either the model file itself or the directory containing
it.
- The script doesn't depend on config.json / params.json, just in
case the user downloaded files individually and doesn't have those
handy. But you still need tokenizer.model and, for Alpaca,
added_tokens.json.
- The script tries to give a helpful error message if
added_tokens.json is missing.
2023-04-14 07:03:03 +00:00
class OutputFile :
2024-03-28 15:44:36 +00:00
def __init__ ( self , fname_out : Path , endianess : gguf . GGUFEndian = gguf . GGUFEndian . LITTLE ) :
2024-01-20 23:14:18 +00:00
self . gguf = gguf . GGUFWriter ( fname_out , gguf . MODEL_ARCH_NAMES [ ARCH ] , endianess = endianess )
2023-08-21 20:07:43 +00:00
def add_meta_arch ( self , params : Params ) - > None :
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name = " LLaMA "
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# TODO: better logic to determine model name
2023-09-07 18:27:42 +00:00
if params . n_ctx == 4096 :
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name = " LLaMA v2 "
2023-09-07 18:27:42 +00:00
elif params . path_model is not None :
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name = str ( params . path_model . parent ) . split ( ' / ' ) [ - 1 ]
2023-08-23 20:08:04 +00:00
2024-01-20 23:14:18 +00:00
self . gguf . add_name ( name )
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self . gguf . add_vocab_size ( params . n_vocab )
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self . gguf . add_context_length ( params . n_ctx )
self . gguf . add_embedding_length ( params . n_embd )
self . gguf . add_block_count ( params . n_layer )
self . gguf . add_feed_forward_length ( params . n_ff )
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self . gguf . add_rope_dimension_count ( params . n_embd / / params . n_head )
2024-01-20 23:14:18 +00:00
self . gguf . add_head_count ( params . n_head )
self . gguf . add_head_count_kv ( params . n_head_kv )
2023-12-13 12:04:25 +00:00
if params . n_experts :
self . gguf . add_expert_count ( params . n_experts )
if params . n_experts_used :
self . gguf . add_expert_used_count ( params . n_experts_used )
2024-01-20 23:14:18 +00:00
if params . f_norm_eps :
self . gguf . add_layer_norm_rms_eps ( params . f_norm_eps )
else :
raise ValueError ( ' f_norm_eps is None ' )
2023-09-07 18:27:42 +00:00
if params . f_rope_freq_base is not None :
2023-08-24 18:04:05 +00:00
self . gguf . add_rope_freq_base ( params . f_rope_freq_base )
2023-11-01 22:04:33 +00:00
if params . rope_scaling_type :
assert params . f_rope_scale is not None
self . gguf . add_rope_scaling_type ( params . rope_scaling_type )
self . gguf . add_rope_scaling_factor ( params . f_rope_scale )
if params . n_orig_ctx is not None :
self . gguf . add_rope_scaling_orig_ctx_len ( params . n_orig_ctx )
if params . rope_finetuned is not None :
self . gguf . add_rope_scaling_finetuned ( params . rope_finetuned )
2023-08-25 14:41:52 +00:00
2023-09-07 18:27:42 +00:00
if params . ftype is not None :
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self . gguf . add_file_type ( params . ftype )
2024-01-20 23:14:18 +00:00
def extract_vocabulary_from_model ( self , vocab : Vocab ) - > tuple [ list [ bytes ] , list [ float ] , list [ gguf . TokenType ] ] :
2023-08-21 20:07:43 +00:00
tokens = [ ]
scores = [ ]
toktypes = [ ]
convert.py : fix vanilla LLaMA model conversion (#4818)
* Update Imports and Add Notes for Future Reference
- Updated import statements in `convert.py`.
- Added import for `AutoTokenizer` from `transformers` module.
- Added conditional import for `gguf` from the local directory.
- Added comments and notes for future reference.
Additional Notes:
- Noted removal of a redundant `TypeAlias` import.
- Noted the removal of a `gguf` debug statement.
- Commented on the presence of `ARCH` and `NDArray` definitions.
- Commented on cleaning up and refactoring data type definitions.
* Refine Model Hyperparameters and Params Class
- Updated type annotations to use `Optional` for clarity.
- Improved method names and attribute consistency.
- Removed unnecessary variables for better code readability.
Additional Notes:
- Highlighted the use of `Optional` for clearer intent.
- Ensured backward and forward compatibility.
* Restore BpeVocab and SentencePieceVocab classes
- Restored the BpeVocab class for handling BPE tokenization.
- Restored the SentencePieceVocab class for SentencePiece tokenization.
These classes are essential for maintaining the original behavior of the codebase.
* refactor: Standardize vocabulary handling with HfVocab
- Replaced VocabLoader with HfVocab, aligning vocabulary handling across classes.
- Updated initialization of HfVocab with local_files_only=True for AutoTokenizer.
- Introduced optional parameter fname_added_tokens for flexible added token management.
- Streamlined added token handling for clarity and conciseness.
- Maintained special tokens and IDs, enhancing token management.
- Simplified token processing methods for improved readability.
- Added a placeholder for score computation with a default value of -1000.0.
- Optimized newline token check for efficiency.
- Updated __repr__ function for clarity in representation.
- Adjusted type alias Vocab to include BpeVocab, SentencePieceVocab, and HfVocab.
- Removed redundant code related to special token handling, reverse vocabulary mapping, and vocabulary file detection.
This refactoring promotes a standardized and modular approach to vocabulary management, facilitating future integration with a VocabFactory and improving code maintainability and scalability.
* refactor: Enhance readability, functionality, and code quality
- Improved code formatting and readability for better maintainability.
- Refactored LazyUnpickler's CLASSES dictionary for clarity.
- Added print statements and warnings in check_vocab_size for user feedback.
- Removed find_vocab_file_path, as it's superseded by VocabFactory.
- Preparatory changes for upcoming classes: OutputFile and VocabFactory.
- Overall focus on code quality, error handling, and consistency.
These changes reflect a continuous effort to refine the codebase, ensuring it meets best practices and prepares for future enhancements, such as the VocabFactory.
* refactor: Update OutputFile class for enhanced model vocabulary management
- Restructured the constructor for improved readability.
- Updated `add_meta_arch` method for flexible model name determination.
- Introduced `handle_tokenizer_model` for mapping vocab types to supported tokenizer models.
- Streamlined vocabulary extraction with `extract_vocabulary_from_model`.
- Simplified vocabulary metadata addition using `add_meta_vocab`.
- Refactored `add_tensor_info` for clarity and consistency.
- Improved error handling for better user feedback.
These changes signify the development of a versatile and comprehensive `OutputFile` class, enabling efficient management of model conversion output, metadata, vocabulary, and tensor information.
* feat: Introduce VocabFactory for flexible vocabulary management in model conversion
- The VocabFactory class is added to facilitate modular vocabulary handling.
- The constructor initializes a directory path and detects vocabulary-related files.
- The _select_file method provides file paths based on vocabulary type (e.g., BPE, SentencePiece).
- _create_special_vocab generates special vocabularies, accommodating different types.
- The load_vocab method loads vocabularies, handling BPE, SentencePiece, and Hugging Face Fast Tokenizer.
- Error handling and logging enhance debugging and user feedback.
- The modular and flexible design simplifies vocabulary management and supports future extensions.
The VocabFactory class enhances code modularity and maintainability, allowing versatile vocabulary handling in the model conversion process.
* refactor: Improve code organization, argument parsing, and user interface
- Renamed 'default_outfile' to 'default_output_file' for clarity.
- Refactored argument parser setup into 'get_argument_parser' function.
- Introduced descriptive comments for each argument in the parser.
- Added '--vocab-type' argument with choices ["spm", "bpe", "hfft"] for vocabulary processing.
- Improved flag naming consistency: '--outfile' to '--out-file' and '--bigendian' to '--big-endian'.
- Enhanced error handling to prevent overwriting input data in 'default_output_file'.
- Made 'argv' in 'main' an optional parameter for flexibility.
- Introduced dynamic import for 'awq.apply_awq' based on 'args.awq_path' for conditional dependency.
These changes enhance code clarity, organization, and the user interface of the script, aligning it with Python best practices and improving maintainability.
* refactor: Further refine functionality, improve user interaction, and streamline vocabulary handling
- Renamed command-line arguments for clarity and consistency.
- Improved path resolution and import adjustments for robustness.
- Thoughtfully handled 'awq-path' and conditional logic for the weighted model.
- Enhanced model and vocabulary loading with the 'VocabFactory' class for structured and adaptable loading.
- Strengthened error handling and user feedback for a more user-friendly experience.
- Structured output file handling with clear conditions and defaults.
- Streamlined and organized the 'main' function for better logic flow.
- Passed 'sys.argv[1:]' to 'main' for adaptability and testability.
These changes solidify the script's functionality, making it more robust, user-friendly, and adaptable. The use of the 'VocabFactory' class is a notable enhancement in efficient vocabulary handling, reflecting a thoughtful and iterative approach to script development.
* chore: Apply ruff formatting to convert.py
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
* Revert to commit 0614c33
* chore: Apply flake8 formatting rules
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
* refactor: Revise `check_vocab_size` for Enhanced Clarity and Correctness
- Resolved an unreachable branch issue by reorganizing the conditional structure.
- Moved the special case check for `params.n_vocab == -1` to the top for immediate assertion.
- Flattened the conditional logic for improved clarity and predictability of the function's behavior.
These changes enhance the readability and functional correctness of the `check_vocab_size` function without altering its intended functionality.
* py : fix outfile and outtype
* py : suggest hint for missing vocab size
---------
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-01-09 18:46:46 +00:00
2023-08-30 10:29:40 +00:00
# NOTE: `all_tokens` returns the base vocabulary and added tokens
2023-08-21 20:07:43 +00:00
for text , score , toktype in vocab . all_tokens ( ) :
tokens . append ( text )
scores . append ( score )
toktypes . append ( toktype )
2024-01-17 16:37:36 +00:00
assert len ( tokens ) == vocab . vocab_size
2024-01-17 13:45:03 +00:00
convert.py : fix vanilla LLaMA model conversion (#4818)
* Update Imports and Add Notes for Future Reference
- Updated import statements in `convert.py`.
- Added import for `AutoTokenizer` from `transformers` module.
- Added conditional import for `gguf` from the local directory.
- Added comments and notes for future reference.
Additional Notes:
- Noted removal of a redundant `TypeAlias` import.
- Noted the removal of a `gguf` debug statement.
- Commented on the presence of `ARCH` and `NDArray` definitions.
- Commented on cleaning up and refactoring data type definitions.
* Refine Model Hyperparameters and Params Class
- Updated type annotations to use `Optional` for clarity.
- Improved method names and attribute consistency.
- Removed unnecessary variables for better code readability.
Additional Notes:
- Highlighted the use of `Optional` for clearer intent.
- Ensured backward and forward compatibility.
* Restore BpeVocab and SentencePieceVocab classes
- Restored the BpeVocab class for handling BPE tokenization.
- Restored the SentencePieceVocab class for SentencePiece tokenization.
These classes are essential for maintaining the original behavior of the codebase.
* refactor: Standardize vocabulary handling with HfVocab
- Replaced VocabLoader with HfVocab, aligning vocabulary handling across classes.
- Updated initialization of HfVocab with local_files_only=True for AutoTokenizer.
- Introduced optional parameter fname_added_tokens for flexible added token management.
- Streamlined added token handling for clarity and conciseness.
- Maintained special tokens and IDs, enhancing token management.
- Simplified token processing methods for improved readability.
- Added a placeholder for score computation with a default value of -1000.0.
- Optimized newline token check for efficiency.
- Updated __repr__ function for clarity in representation.
- Adjusted type alias Vocab to include BpeVocab, SentencePieceVocab, and HfVocab.
- Removed redundant code related to special token handling, reverse vocabulary mapping, and vocabulary file detection.
This refactoring promotes a standardized and modular approach to vocabulary management, facilitating future integration with a VocabFactory and improving code maintainability and scalability.
* refactor: Enhance readability, functionality, and code quality
- Improved code formatting and readability for better maintainability.
- Refactored LazyUnpickler's CLASSES dictionary for clarity.
- Added print statements and warnings in check_vocab_size for user feedback.
- Removed find_vocab_file_path, as it's superseded by VocabFactory.
- Preparatory changes for upcoming classes: OutputFile and VocabFactory.
- Overall focus on code quality, error handling, and consistency.
These changes reflect a continuous effort to refine the codebase, ensuring it meets best practices and prepares for future enhancements, such as the VocabFactory.
* refactor: Update OutputFile class for enhanced model vocabulary management
- Restructured the constructor for improved readability.
- Updated `add_meta_arch` method for flexible model name determination.
- Introduced `handle_tokenizer_model` for mapping vocab types to supported tokenizer models.
- Streamlined vocabulary extraction with `extract_vocabulary_from_model`.
- Simplified vocabulary metadata addition using `add_meta_vocab`.
- Refactored `add_tensor_info` for clarity and consistency.
- Improved error handling for better user feedback.
These changes signify the development of a versatile and comprehensive `OutputFile` class, enabling efficient management of model conversion output, metadata, vocabulary, and tensor information.
* feat: Introduce VocabFactory for flexible vocabulary management in model conversion
- The VocabFactory class is added to facilitate modular vocabulary handling.
- The constructor initializes a directory path and detects vocabulary-related files.
- The _select_file method provides file paths based on vocabulary type (e.g., BPE, SentencePiece).
- _create_special_vocab generates special vocabularies, accommodating different types.
- The load_vocab method loads vocabularies, handling BPE, SentencePiece, and Hugging Face Fast Tokenizer.
- Error handling and logging enhance debugging and user feedback.
- The modular and flexible design simplifies vocabulary management and supports future extensions.
The VocabFactory class enhances code modularity and maintainability, allowing versatile vocabulary handling in the model conversion process.
* refactor: Improve code organization, argument parsing, and user interface
- Renamed 'default_outfile' to 'default_output_file' for clarity.
- Refactored argument parser setup into 'get_argument_parser' function.
- Introduced descriptive comments for each argument in the parser.
- Added '--vocab-type' argument with choices ["spm", "bpe", "hfft"] for vocabulary processing.
- Improved flag naming consistency: '--outfile' to '--out-file' and '--bigendian' to '--big-endian'.
- Enhanced error handling to prevent overwriting input data in 'default_output_file'.
- Made 'argv' in 'main' an optional parameter for flexibility.
- Introduced dynamic import for 'awq.apply_awq' based on 'args.awq_path' for conditional dependency.
These changes enhance code clarity, organization, and the user interface of the script, aligning it with Python best practices and improving maintainability.
* refactor: Further refine functionality, improve user interaction, and streamline vocabulary handling
- Renamed command-line arguments for clarity and consistency.
- Improved path resolution and import adjustments for robustness.
- Thoughtfully handled 'awq-path' and conditional logic for the weighted model.
- Enhanced model and vocabulary loading with the 'VocabFactory' class for structured and adaptable loading.
- Strengthened error handling and user feedback for a more user-friendly experience.
- Structured output file handling with clear conditions and defaults.
- Streamlined and organized the 'main' function for better logic flow.
- Passed 'sys.argv[1:]' to 'main' for adaptability and testability.
These changes solidify the script's functionality, making it more robust, user-friendly, and adaptable. The use of the 'VocabFactory' class is a notable enhancement in efficient vocabulary handling, reflecting a thoughtful and iterative approach to script development.
* chore: Apply ruff formatting to convert.py
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
* Revert to commit 0614c33
* chore: Apply flake8 formatting rules
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
* refactor: Revise `check_vocab_size` for Enhanced Clarity and Correctness
- Resolved an unreachable branch issue by reorganizing the conditional structure.
- Moved the special case check for `params.n_vocab == -1` to the top for immediate assertion.
- Flattened the conditional logic for improved clarity and predictability of the function's behavior.
These changes enhance the readability and functional correctness of the `check_vocab_size` function without altering its intended functionality.
* py : fix outfile and outtype
* py : suggest hint for missing vocab size
---------
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-01-09 18:46:46 +00:00
return tokens , scores , toktypes
def add_meta_vocab ( self , vocab : Vocab ) - > None :
# Ensure that tokenizer_model is added to the GGUF model
2024-03-14 16:21:56 +00:00
self . gguf . add_tokenizer_model ( vocab . tokenizer_model )
convert.py : fix vanilla LLaMA model conversion (#4818)
* Update Imports and Add Notes for Future Reference
- Updated import statements in `convert.py`.
- Added import for `AutoTokenizer` from `transformers` module.
- Added conditional import for `gguf` from the local directory.
- Added comments and notes for future reference.
Additional Notes:
- Noted removal of a redundant `TypeAlias` import.
- Noted the removal of a `gguf` debug statement.
- Commented on the presence of `ARCH` and `NDArray` definitions.
- Commented on cleaning up and refactoring data type definitions.
* Refine Model Hyperparameters and Params Class
- Updated type annotations to use `Optional` for clarity.
- Improved method names and attribute consistency.
- Removed unnecessary variables for better code readability.
Additional Notes:
- Highlighted the use of `Optional` for clearer intent.
- Ensured backward and forward compatibility.
* Restore BpeVocab and SentencePieceVocab classes
- Restored the BpeVocab class for handling BPE tokenization.
- Restored the SentencePieceVocab class for SentencePiece tokenization.
These classes are essential for maintaining the original behavior of the codebase.
* refactor: Standardize vocabulary handling with HfVocab
- Replaced VocabLoader with HfVocab, aligning vocabulary handling across classes.
- Updated initialization of HfVocab with local_files_only=True for AutoTokenizer.
- Introduced optional parameter fname_added_tokens for flexible added token management.
- Streamlined added token handling for clarity and conciseness.
- Maintained special tokens and IDs, enhancing token management.
- Simplified token processing methods for improved readability.
- Added a placeholder for score computation with a default value of -1000.0.
- Optimized newline token check for efficiency.
- Updated __repr__ function for clarity in representation.
- Adjusted type alias Vocab to include BpeVocab, SentencePieceVocab, and HfVocab.
- Removed redundant code related to special token handling, reverse vocabulary mapping, and vocabulary file detection.
This refactoring promotes a standardized and modular approach to vocabulary management, facilitating future integration with a VocabFactory and improving code maintainability and scalability.
* refactor: Enhance readability, functionality, and code quality
- Improved code formatting and readability for better maintainability.
- Refactored LazyUnpickler's CLASSES dictionary for clarity.
- Added print statements and warnings in check_vocab_size for user feedback.
- Removed find_vocab_file_path, as it's superseded by VocabFactory.
- Preparatory changes for upcoming classes: OutputFile and VocabFactory.
- Overall focus on code quality, error handling, and consistency.
These changes reflect a continuous effort to refine the codebase, ensuring it meets best practices and prepares for future enhancements, such as the VocabFactory.
* refactor: Update OutputFile class for enhanced model vocabulary management
- Restructured the constructor for improved readability.
- Updated `add_meta_arch` method for flexible model name determination.
- Introduced `handle_tokenizer_model` for mapping vocab types to supported tokenizer models.
- Streamlined vocabulary extraction with `extract_vocabulary_from_model`.
- Simplified vocabulary metadata addition using `add_meta_vocab`.
- Refactored `add_tensor_info` for clarity and consistency.
- Improved error handling for better user feedback.
These changes signify the development of a versatile and comprehensive `OutputFile` class, enabling efficient management of model conversion output, metadata, vocabulary, and tensor information.
* feat: Introduce VocabFactory for flexible vocabulary management in model conversion
- The VocabFactory class is added to facilitate modular vocabulary handling.
- The constructor initializes a directory path and detects vocabulary-related files.
- The _select_file method provides file paths based on vocabulary type (e.g., BPE, SentencePiece).
- _create_special_vocab generates special vocabularies, accommodating different types.
- The load_vocab method loads vocabularies, handling BPE, SentencePiece, and Hugging Face Fast Tokenizer.
- Error handling and logging enhance debugging and user feedback.
- The modular and flexible design simplifies vocabulary management and supports future extensions.
The VocabFactory class enhances code modularity and maintainability, allowing versatile vocabulary handling in the model conversion process.
* refactor: Improve code organization, argument parsing, and user interface
- Renamed 'default_outfile' to 'default_output_file' for clarity.
- Refactored argument parser setup into 'get_argument_parser' function.
- Introduced descriptive comments for each argument in the parser.
- Added '--vocab-type' argument with choices ["spm", "bpe", "hfft"] for vocabulary processing.
- Improved flag naming consistency: '--outfile' to '--out-file' and '--bigendian' to '--big-endian'.
- Enhanced error handling to prevent overwriting input data in 'default_output_file'.
- Made 'argv' in 'main' an optional parameter for flexibility.
- Introduced dynamic import for 'awq.apply_awq' based on 'args.awq_path' for conditional dependency.
These changes enhance code clarity, organization, and the user interface of the script, aligning it with Python best practices and improving maintainability.
* refactor: Further refine functionality, improve user interaction, and streamline vocabulary handling
- Renamed command-line arguments for clarity and consistency.
- Improved path resolution and import adjustments for robustness.
- Thoughtfully handled 'awq-path' and conditional logic for the weighted model.
- Enhanced model and vocabulary loading with the 'VocabFactory' class for structured and adaptable loading.
- Strengthened error handling and user feedback for a more user-friendly experience.
- Structured output file handling with clear conditions and defaults.
- Streamlined and organized the 'main' function for better logic flow.
- Passed 'sys.argv[1:]' to 'main' for adaptability and testability.
These changes solidify the script's functionality, making it more robust, user-friendly, and adaptable. The use of the 'VocabFactory' class is a notable enhancement in efficient vocabulary handling, reflecting a thoughtful and iterative approach to script development.
* chore: Apply ruff formatting to convert.py
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
* Revert to commit 0614c33
* chore: Apply flake8 formatting rules
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
* refactor: Revise `check_vocab_size` for Enhanced Clarity and Correctness
- Resolved an unreachable branch issue by reorganizing the conditional structure.
- Moved the special case check for `params.n_vocab == -1` to the top for immediate assertion.
- Flattened the conditional logic for improved clarity and predictability of the function's behavior.
These changes enhance the readability and functional correctness of the `check_vocab_size` function without altering its intended functionality.
* py : fix outfile and outtype
* py : suggest hint for missing vocab size
---------
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-01-09 18:46:46 +00:00
# Extract model vocabulary for model conversion
tokens , scores , toktypes = self . extract_vocabulary_from_model ( vocab )
# Add extracted token information for model conversion
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self . gguf . add_token_list ( tokens )
self . gguf . add_token_scores ( scores )
self . gguf . add_token_types ( toktypes )
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def add_meta_special_vocab ( self , svocab : gguf . SpecialVocab ) - > None :
svocab . add_to_gguf ( self . gguf )
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def add_tensor_info ( self , name : str , tensor : LazyTensor ) - > None :
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n_elements = int ( np . prod ( tensor . shape ) )
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raw_dtype = getattr ( tensor . data_type , ' ggml_type ' , None )
data_type = getattr ( tensor . data_type , ' quantized_type ' , None ) or tensor . data_type . dtype
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data_nbytes = tensor . data_type . elements_to_bytes ( n_elements )
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self . gguf . add_tensor_info ( name , tensor . shape , data_type , data_nbytes , raw_dtype = raw_dtype )
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def write_meta ( self ) - > None :
self . gguf . write_header_to_file ( )
self . gguf . write_kv_data_to_file ( )
def write_tensor_info ( self ) - > None :
self . gguf . write_ti_data_to_file ( )
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def write_tensor_data ( self , ftype : GGMLFileType , model : LazyModel , concurrency : int ) - > None :
ndarrays_inner = bounded_parallel_map ( OutputFile . do_item , model . items ( ) , concurrency = concurrency )
if ftype == GGMLFileType . MostlyQ8_0 :
ndarrays = bounded_parallel_map (
OutputFile . maybe_do_quantize , ndarrays_inner , concurrency = concurrency , max_workers = concurrency ,
use_processpool_executor = True ,
)
else :
ndarrays = map ( OutputFile . maybe_do_quantize , ndarrays_inner )
start = time . time ( )
for i , ( ( name , lazy_tensor ) , ndarray ) in enumerate ( zip ( model . items ( ) , ndarrays ) ) :
elapsed = time . time ( ) - start
size = ' x ' . join ( f " { dim : 6d } " for dim in lazy_tensor . shape )
padi = len ( str ( len ( model ) ) )
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logger . info (
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f " [ { i + 1 : { padi } d } / { len ( model ) } ] Writing tensor { name : 38s } | size { size : 16 } | type { lazy_tensor . data_type . name : 4 } | T+ { int ( elapsed ) : 4 } "
)
self . gguf . write_tensor_data ( ndarray )
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def close ( self ) - > None :
self . gguf . close ( )
py : new conversion script (#545)
Current status: Working, except for the latest GPTQ-for-LLaMa format
that includes `g_idx`. This turns out to require changes to GGML, so
for now it only works if you use the `--outtype` option to dequantize it
back to f16 (which is pointless except for debugging).
I also included some cleanup for the C++ code.
This script is meant to replace all the existing conversion scripts
(including the ones that convert from older GGML formats), while also
adding support for some new formats. Specifically, I've tested with:
- [x] `LLaMA` (original)
- [x] `llama-65b-4bit`
- [x] `alpaca-native`
- [x] `alpaca-native-4bit`
- [x] LLaMA converted to 'transformers' format using
`convert_llama_weights_to_hf.py`
- [x] `alpaca-native` quantized with `--true-sequential --act-order
--groupsize 128` (dequantized only)
- [x] same as above plus `--save_safetensors`
- [x] GPT4All
- [x] stock unversioned ggml
- [x] ggmh
There's enough overlap in the logic needed to handle these different
cases that it seemed best to move to a single script.
I haven't tried this with Alpaca-LoRA because I don't know where to find
it.
Useful features:
- Uses multiple threads for a speedup in some cases (though the Python
GIL limits the gain, and sometimes it's disk-bound anyway).
- Combines split models into a single file (both the intra-tensor split
of the original and the inter-tensor split of 'transformers' format
files). Single files are more convenient to work with and more
friendly to future changes to use memory mapping on the C++ side. To
accomplish this without increasing memory requirements, it has some
custom loading code which avoids loading whole input files into memory
at once.
- Because of the custom loading code, it no longer depends in PyTorch,
which might make installing dependencies slightly easier or faster...
although it still depends on NumPy and sentencepiece, so I don't know
if there's any meaningful difference. In any case, I also added a
requirements.txt file to lock the dependency versions in case of any
future breaking changes.
- Type annotations checked with mypy.
- Some attempts to be extra user-friendly:
- The script tries to be forgiving with arguments, e.g. you can
specify either the model file itself or the directory containing
it.
- The script doesn't depend on config.json / params.json, just in
case the user downloaded files individually and doesn't have those
handy. But you still need tokenizer.model and, for Alpaca,
added_tokens.json.
- The script tries to give a helpful error message if
added_tokens.json is missing.
2023-04-14 07:03:03 +00:00
@staticmethod
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def write_vocab_only (
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fname_out : Path , params : Params , vocab : Vocab , svocab : gguf . SpecialVocab ,
endianess : gguf . GGUFEndian = gguf . GGUFEndian . LITTLE , pad_vocab : bool = False ,
2023-12-14 08:09:34 +00:00
) - > None :
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check_vocab_size ( params , vocab , pad_vocab = pad_vocab )
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2023-10-20 11:19:40 +00:00
of = OutputFile ( fname_out , endianess = endianess )
2023-08-21 20:07:43 +00:00
# meta data
of . add_meta_arch ( params )
of . add_meta_vocab ( vocab )
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of . add_meta_special_vocab ( svocab )
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of . write_meta ( )
of . close ( )
py : new conversion script (#545)
Current status: Working, except for the latest GPTQ-for-LLaMa format
that includes `g_idx`. This turns out to require changes to GGML, so
for now it only works if you use the `--outtype` option to dequantize it
back to f16 (which is pointless except for debugging).
I also included some cleanup for the C++ code.
This script is meant to replace all the existing conversion scripts
(including the ones that convert from older GGML formats), while also
adding support for some new formats. Specifically, I've tested with:
- [x] `LLaMA` (original)
- [x] `llama-65b-4bit`
- [x] `alpaca-native`
- [x] `alpaca-native-4bit`
- [x] LLaMA converted to 'transformers' format using
`convert_llama_weights_to_hf.py`
- [x] `alpaca-native` quantized with `--true-sequential --act-order
--groupsize 128` (dequantized only)
- [x] same as above plus `--save_safetensors`
- [x] GPT4All
- [x] stock unversioned ggml
- [x] ggmh
There's enough overlap in the logic needed to handle these different
cases that it seemed best to move to a single script.
I haven't tried this with Alpaca-LoRA because I don't know where to find
it.
Useful features:
- Uses multiple threads for a speedup in some cases (though the Python
GIL limits the gain, and sometimes it's disk-bound anyway).
- Combines split models into a single file (both the intra-tensor split
of the original and the inter-tensor split of 'transformers' format
files). Single files are more convenient to work with and more
friendly to future changes to use memory mapping on the C++ side. To
accomplish this without increasing memory requirements, it has some
custom loading code which avoids loading whole input files into memory
at once.
- Because of the custom loading code, it no longer depends in PyTorch,
which might make installing dependencies slightly easier or faster...
although it still depends on NumPy and sentencepiece, so I don't know
if there's any meaningful difference. In any case, I also added a
requirements.txt file to lock the dependency versions in case of any
future breaking changes.
- Type annotations checked with mypy.
- Some attempts to be extra user-friendly:
- The script tries to be forgiving with arguments, e.g. you can
specify either the model file itself or the directory containing
it.
- The script doesn't depend on config.json / params.json, just in
case the user downloaded files individually and doesn't have those
handy. But you still need tokenizer.model and, for Alpaca,
added_tokens.json.
- The script tries to give a helpful error message if
added_tokens.json is missing.
2023-04-14 07:03:03 +00:00
@staticmethod
2023-08-31 05:02:23 +00:00
def do_item ( item : tuple [ str , LazyTensor ] ) - > tuple [ DataType , NDArray ] :
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name , lazy_tensor = item
tensor = lazy_tensor . load ( ) . to_ggml ( )
return ( lazy_tensor . data_type , tensor . ndarray )
@staticmethod
2023-08-31 05:02:23 +00:00
def maybe_do_quantize ( item : tuple [ DataType , NDArray ] ) - > NDArray :
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dt , arr = item
if not isinstance ( dt , QuantizedDataType ) :
return arr
return dt . quantize ( arr )
@staticmethod
2023-12-14 08:09:34 +00:00
def write_all (
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fname_out : Path , ftype : GGMLFileType , params : Params , model : LazyModel , vocab : BaseVocab , svocab : gguf . SpecialVocab ,
2024-01-20 23:14:18 +00:00
concurrency : int = DEFAULT_CONCURRENCY , endianess : gguf . GGUFEndian = gguf . GGUFEndian . LITTLE ,
2023-12-14 08:09:34 +00:00
pad_vocab : bool = False ,
) - > None :
convert.py : fix vanilla LLaMA model conversion (#4818)
* Update Imports and Add Notes for Future Reference
- Updated import statements in `convert.py`.
- Added import for `AutoTokenizer` from `transformers` module.
- Added conditional import for `gguf` from the local directory.
- Added comments and notes for future reference.
Additional Notes:
- Noted removal of a redundant `TypeAlias` import.
- Noted the removal of a `gguf` debug statement.
- Commented on the presence of `ARCH` and `NDArray` definitions.
- Commented on cleaning up and refactoring data type definitions.
* Refine Model Hyperparameters and Params Class
- Updated type annotations to use `Optional` for clarity.
- Improved method names and attribute consistency.
- Removed unnecessary variables for better code readability.
Additional Notes:
- Highlighted the use of `Optional` for clearer intent.
- Ensured backward and forward compatibility.
* Restore BpeVocab and SentencePieceVocab classes
- Restored the BpeVocab class for handling BPE tokenization.
- Restored the SentencePieceVocab class for SentencePiece tokenization.
These classes are essential for maintaining the original behavior of the codebase.
* refactor: Standardize vocabulary handling with HfVocab
- Replaced VocabLoader with HfVocab, aligning vocabulary handling across classes.
- Updated initialization of HfVocab with local_files_only=True for AutoTokenizer.
- Introduced optional parameter fname_added_tokens for flexible added token management.
- Streamlined added token handling for clarity and conciseness.
- Maintained special tokens and IDs, enhancing token management.
- Simplified token processing methods for improved readability.
- Added a placeholder for score computation with a default value of -1000.0.
- Optimized newline token check for efficiency.
- Updated __repr__ function for clarity in representation.
- Adjusted type alias Vocab to include BpeVocab, SentencePieceVocab, and HfVocab.
- Removed redundant code related to special token handling, reverse vocabulary mapping, and vocabulary file detection.
This refactoring promotes a standardized and modular approach to vocabulary management, facilitating future integration with a VocabFactory and improving code maintainability and scalability.
* refactor: Enhance readability, functionality, and code quality
- Improved code formatting and readability for better maintainability.
- Refactored LazyUnpickler's CLASSES dictionary for clarity.
- Added print statements and warnings in check_vocab_size for user feedback.
- Removed find_vocab_file_path, as it's superseded by VocabFactory.
- Preparatory changes for upcoming classes: OutputFile and VocabFactory.
- Overall focus on code quality, error handling, and consistency.
These changes reflect a continuous effort to refine the codebase, ensuring it meets best practices and prepares for future enhancements, such as the VocabFactory.
* refactor: Update OutputFile class for enhanced model vocabulary management
- Restructured the constructor for improved readability.
- Updated `add_meta_arch` method for flexible model name determination.
- Introduced `handle_tokenizer_model` for mapping vocab types to supported tokenizer models.
- Streamlined vocabulary extraction with `extract_vocabulary_from_model`.
- Simplified vocabulary metadata addition using `add_meta_vocab`.
- Refactored `add_tensor_info` for clarity and consistency.
- Improved error handling for better user feedback.
These changes signify the development of a versatile and comprehensive `OutputFile` class, enabling efficient management of model conversion output, metadata, vocabulary, and tensor information.
* feat: Introduce VocabFactory for flexible vocabulary management in model conversion
- The VocabFactory class is added to facilitate modular vocabulary handling.
- The constructor initializes a directory path and detects vocabulary-related files.
- The _select_file method provides file paths based on vocabulary type (e.g., BPE, SentencePiece).
- _create_special_vocab generates special vocabularies, accommodating different types.
- The load_vocab method loads vocabularies, handling BPE, SentencePiece, and Hugging Face Fast Tokenizer.
- Error handling and logging enhance debugging and user feedback.
- The modular and flexible design simplifies vocabulary management and supports future extensions.
The VocabFactory class enhances code modularity and maintainability, allowing versatile vocabulary handling in the model conversion process.
* refactor: Improve code organization, argument parsing, and user interface
- Renamed 'default_outfile' to 'default_output_file' for clarity.
- Refactored argument parser setup into 'get_argument_parser' function.
- Introduced descriptive comments for each argument in the parser.
- Added '--vocab-type' argument with choices ["spm", "bpe", "hfft"] for vocabulary processing.
- Improved flag naming consistency: '--outfile' to '--out-file' and '--bigendian' to '--big-endian'.
- Enhanced error handling to prevent overwriting input data in 'default_output_file'.
- Made 'argv' in 'main' an optional parameter for flexibility.
- Introduced dynamic import for 'awq.apply_awq' based on 'args.awq_path' for conditional dependency.
These changes enhance code clarity, organization, and the user interface of the script, aligning it with Python best practices and improving maintainability.
* refactor: Further refine functionality, improve user interaction, and streamline vocabulary handling
- Renamed command-line arguments for clarity and consistency.
- Improved path resolution and import adjustments for robustness.
- Thoughtfully handled 'awq-path' and conditional logic for the weighted model.
- Enhanced model and vocabulary loading with the 'VocabFactory' class for structured and adaptable loading.
- Strengthened error handling and user feedback for a more user-friendly experience.
- Structured output file handling with clear conditions and defaults.
- Streamlined and organized the 'main' function for better logic flow.
- Passed 'sys.argv[1:]' to 'main' for adaptability and testability.
These changes solidify the script's functionality, making it more robust, user-friendly, and adaptable. The use of the 'VocabFactory' class is a notable enhancement in efficient vocabulary handling, reflecting a thoughtful and iterative approach to script development.
* chore: Apply ruff formatting to convert.py
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
* Revert to commit 0614c33
* chore: Apply flake8 formatting rules
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
* refactor: Revise `check_vocab_size` for Enhanced Clarity and Correctness
- Resolved an unreachable branch issue by reorganizing the conditional structure.
- Moved the special case check for `params.n_vocab == -1` to the top for immediate assertion.
- Flattened the conditional logic for improved clarity and predictability of the function's behavior.
These changes enhance the readability and functional correctness of the `check_vocab_size` function without altering its intended functionality.
* py : fix outfile and outtype
* py : suggest hint for missing vocab size
---------
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-01-09 18:46:46 +00:00
check_vocab_size ( params , vocab , pad_vocab = pad_vocab )
2023-08-21 20:07:43 +00:00
2023-10-20 11:19:40 +00:00
of = OutputFile ( fname_out , endianess = endianess )
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# meta data
of . add_meta_arch ( params )
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if isinstance ( vocab , Vocab ) :
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of . add_meta_vocab ( vocab )
of . add_meta_special_vocab ( svocab )
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else : # NoVocab
of . gguf . add_tokenizer_model ( vocab . tokenizer_model )
2023-08-21 20:07:43 +00:00
# tensor info
for name , lazy_tensor in model . items ( ) :
of . add_tensor_info ( name , lazy_tensor )
of . write_meta ( )
of . write_tensor_info ( )
py : new conversion script (#545)
Current status: Working, except for the latest GPTQ-for-LLaMa format
that includes `g_idx`. This turns out to require changes to GGML, so
for now it only works if you use the `--outtype` option to dequantize it
back to f16 (which is pointless except for debugging).
I also included some cleanup for the C++ code.
This script is meant to replace all the existing conversion scripts
(including the ones that convert from older GGML formats), while also
adding support for some new formats. Specifically, I've tested with:
- [x] `LLaMA` (original)
- [x] `llama-65b-4bit`
- [x] `alpaca-native`
- [x] `alpaca-native-4bit`
- [x] LLaMA converted to 'transformers' format using
`convert_llama_weights_to_hf.py`
- [x] `alpaca-native` quantized with `--true-sequential --act-order
--groupsize 128` (dequantized only)
- [x] same as above plus `--save_safetensors`
- [x] GPT4All
- [x] stock unversioned ggml
- [x] ggmh
There's enough overlap in the logic needed to handle these different
cases that it seemed best to move to a single script.
I haven't tried this with Alpaca-LoRA because I don't know where to find
it.
Useful features:
- Uses multiple threads for a speedup in some cases (though the Python
GIL limits the gain, and sometimes it's disk-bound anyway).
- Combines split models into a single file (both the intra-tensor split
of the original and the inter-tensor split of 'transformers' format
files). Single files are more convenient to work with and more
friendly to future changes to use memory mapping on the C++ side. To
accomplish this without increasing memory requirements, it has some
custom loading code which avoids loading whole input files into memory
at once.
- Because of the custom loading code, it no longer depends in PyTorch,
which might make installing dependencies slightly easier or faster...
although it still depends on NumPy and sentencepiece, so I don't know
if there's any meaningful difference. In any case, I also added a
requirements.txt file to lock the dependency versions in case of any
future breaking changes.
- Type annotations checked with mypy.
- Some attempts to be extra user-friendly:
- The script tries to be forgiving with arguments, e.g. you can
specify either the model file itself or the directory containing
it.
- The script doesn't depend on config.json / params.json, just in
case the user downloaded files individually and doesn't have those
handy. But you still need tokenizer.model and, for Alpaca,
added_tokens.json.
- The script tries to give a helpful error message if
added_tokens.json is missing.
2023-04-14 07:03:03 +00:00
2023-08-21 20:07:43 +00:00
# tensor data
2024-03-14 16:21:56 +00:00
of . write_tensor_data ( ftype , model , concurrency )
py : new conversion script (#545)
Current status: Working, except for the latest GPTQ-for-LLaMa format
that includes `g_idx`. This turns out to require changes to GGML, so
for now it only works if you use the `--outtype` option to dequantize it
back to f16 (which is pointless except for debugging).
I also included some cleanup for the C++ code.
This script is meant to replace all the existing conversion scripts
(including the ones that convert from older GGML formats), while also
adding support for some new formats. Specifically, I've tested with:
- [x] `LLaMA` (original)
- [x] `llama-65b-4bit`
- [x] `alpaca-native`
- [x] `alpaca-native-4bit`
- [x] LLaMA converted to 'transformers' format using
`convert_llama_weights_to_hf.py`
- [x] `alpaca-native` quantized with `--true-sequential --act-order
--groupsize 128` (dequantized only)
- [x] same as above plus `--save_safetensors`
- [x] GPT4All
- [x] stock unversioned ggml
- [x] ggmh
There's enough overlap in the logic needed to handle these different
cases that it seemed best to move to a single script.
I haven't tried this with Alpaca-LoRA because I don't know where to find
it.
Useful features:
- Uses multiple threads for a speedup in some cases (though the Python
GIL limits the gain, and sometimes it's disk-bound anyway).
- Combines split models into a single file (both the intra-tensor split
of the original and the inter-tensor split of 'transformers' format
files). Single files are more convenient to work with and more
friendly to future changes to use memory mapping on the C++ side. To
accomplish this without increasing memory requirements, it has some
custom loading code which avoids loading whole input files into memory
at once.
- Because of the custom loading code, it no longer depends in PyTorch,
which might make installing dependencies slightly easier or faster...
although it still depends on NumPy and sentencepiece, so I don't know
if there's any meaningful difference. In any case, I also added a
requirements.txt file to lock the dependency versions in case of any
future breaking changes.
- Type annotations checked with mypy.
- Some attempts to be extra user-friendly:
- The script tries to be forgiving with arguments, e.g. you can
specify either the model file itself or the directory containing
it.
- The script doesn't depend on config.json / params.json, just in
case the user downloaded files individually and doesn't have those
handy. But you still need tokenizer.model and, for Alpaca,
added_tokens.json.
- The script tries to give a helpful error message if
added_tokens.json is missing.
2023-04-14 07:03:03 +00:00
2023-08-21 20:07:43 +00:00
of . close ( )
py : new conversion script (#545)
Current status: Working, except for the latest GPTQ-for-LLaMa format
that includes `g_idx`. This turns out to require changes to GGML, so
for now it only works if you use the `--outtype` option to dequantize it
back to f16 (which is pointless except for debugging).
I also included some cleanup for the C++ code.
This script is meant to replace all the existing conversion scripts
(including the ones that convert from older GGML formats), while also
adding support for some new formats. Specifically, I've tested with:
- [x] `LLaMA` (original)
- [x] `llama-65b-4bit`
- [x] `alpaca-native`
- [x] `alpaca-native-4bit`
- [x] LLaMA converted to 'transformers' format using
`convert_llama_weights_to_hf.py`
- [x] `alpaca-native` quantized with `--true-sequential --act-order
--groupsize 128` (dequantized only)
- [x] same as above plus `--save_safetensors`
- [x] GPT4All
- [x] stock unversioned ggml
- [x] ggmh
There's enough overlap in the logic needed to handle these different
cases that it seemed best to move to a single script.
I haven't tried this with Alpaca-LoRA because I don't know where to find
it.
Useful features:
- Uses multiple threads for a speedup in some cases (though the Python
GIL limits the gain, and sometimes it's disk-bound anyway).
- Combines split models into a single file (both the intra-tensor split
of the original and the inter-tensor split of 'transformers' format
files). Single files are more convenient to work with and more
friendly to future changes to use memory mapping on the C++ side. To
accomplish this without increasing memory requirements, it has some
custom loading code which avoids loading whole input files into memory
at once.
- Because of the custom loading code, it no longer depends in PyTorch,
which might make installing dependencies slightly easier or faster...
although it still depends on NumPy and sentencepiece, so I don't know
if there's any meaningful difference. In any case, I also added a
requirements.txt file to lock the dependency versions in case of any
future breaking changes.
- Type annotations checked with mypy.
- Some attempts to be extra user-friendly:
- The script tries to be forgiving with arguments, e.g. you can
specify either the model file itself or the directory containing
it.
- The script doesn't depend on config.json / params.json, just in
case the user downloaded files individually and doesn't have those
handy. But you still need tokenizer.model and, for Alpaca,
added_tokens.json.
- The script tries to give a helpful error message if
added_tokens.json is missing.
2023-04-14 07:03:03 +00:00
2023-11-20 10:35:47 +00:00
2023-08-31 05:02:23 +00:00
def pick_output_type ( model : LazyModel , output_type_str : str | None ) - > GGMLFileType :
2023-12-13 12:04:25 +00:00
wq_type = model [ gguf . TENSOR_NAMES [ gguf . MODEL_TENSOR . ATTN_Q ] . format ( bid = 0 ) + " .weight " ] . data_type
2023-08-21 20:07:43 +00:00
2024-03-18 08:04:41 +00:00
if output_type_str == " f32 " or ( output_type_str is None and wq_type in ( DT_F32 , DT_BF16 ) ) :
py : new conversion script (#545)
Current status: Working, except for the latest GPTQ-for-LLaMa format
that includes `g_idx`. This turns out to require changes to GGML, so
for now it only works if you use the `--outtype` option to dequantize it
back to f16 (which is pointless except for debugging).
I also included some cleanup for the C++ code.
This script is meant to replace all the existing conversion scripts
(including the ones that convert from older GGML formats), while also
adding support for some new formats. Specifically, I've tested with:
- [x] `LLaMA` (original)
- [x] `llama-65b-4bit`
- [x] `alpaca-native`
- [x] `alpaca-native-4bit`
- [x] LLaMA converted to 'transformers' format using
`convert_llama_weights_to_hf.py`
- [x] `alpaca-native` quantized with `--true-sequential --act-order
--groupsize 128` (dequantized only)
- [x] same as above plus `--save_safetensors`
- [x] GPT4All
- [x] stock unversioned ggml
- [x] ggmh
There's enough overlap in the logic needed to handle these different
cases that it seemed best to move to a single script.
I haven't tried this with Alpaca-LoRA because I don't know where to find
it.
Useful features:
- Uses multiple threads for a speedup in some cases (though the Python
GIL limits the gain, and sometimes it's disk-bound anyway).
- Combines split models into a single file (both the intra-tensor split
of the original and the inter-tensor split of 'transformers' format
files). Single files are more convenient to work with and more
friendly to future changes to use memory mapping on the C++ side. To
accomplish this without increasing memory requirements, it has some
custom loading code which avoids loading whole input files into memory
at once.
- Because of the custom loading code, it no longer depends in PyTorch,
which might make installing dependencies slightly easier or faster...
although it still depends on NumPy and sentencepiece, so I don't know
if there's any meaningful difference. In any case, I also added a
requirements.txt file to lock the dependency versions in case of any
future breaking changes.
- Type annotations checked with mypy.
- Some attempts to be extra user-friendly:
- The script tries to be forgiving with arguments, e.g. you can
specify either the model file itself or the directory containing
it.
- The script doesn't depend on config.json / params.json, just in
case the user downloaded files individually and doesn't have those
handy. But you still need tokenizer.model and, for Alpaca,
added_tokens.json.
- The script tries to give a helpful error message if
added_tokens.json is missing.
2023-04-14 07:03:03 +00:00
return GGMLFileType . AllF32
2024-03-18 08:04:41 +00:00
if output_type_str == " f16 " or ( output_type_str is None and wq_type == DT_F16 ) :
py : new conversion script (#545)
Current status: Working, except for the latest GPTQ-for-LLaMa format
that includes `g_idx`. This turns out to require changes to GGML, so
for now it only works if you use the `--outtype` option to dequantize it
back to f16 (which is pointless except for debugging).
I also included some cleanup for the C++ code.
This script is meant to replace all the existing conversion scripts
(including the ones that convert from older GGML formats), while also
adding support for some new formats. Specifically, I've tested with:
- [x] `LLaMA` (original)
- [x] `llama-65b-4bit`
- [x] `alpaca-native`
- [x] `alpaca-native-4bit`
- [x] LLaMA converted to 'transformers' format using
`convert_llama_weights_to_hf.py`
- [x] `alpaca-native` quantized with `--true-sequential --act-order
--groupsize 128` (dequantized only)
- [x] same as above plus `--save_safetensors`
- [x] GPT4All
- [x] stock unversioned ggml
- [x] ggmh
There's enough overlap in the logic needed to handle these different
cases that it seemed best to move to a single script.
I haven't tried this with Alpaca-LoRA because I don't know where to find
it.
Useful features:
- Uses multiple threads for a speedup in some cases (though the Python
GIL limits the gain, and sometimes it's disk-bound anyway).
- Combines split models into a single file (both the intra-tensor split
of the original and the inter-tensor split of 'transformers' format
files). Single files are more convenient to work with and more
friendly to future changes to use memory mapping on the C++ side. To
accomplish this without increasing memory requirements, it has some
custom loading code which avoids loading whole input files into memory
at once.
- Because of the custom loading code, it no longer depends in PyTorch,
which might make installing dependencies slightly easier or faster...
although it still depends on NumPy and sentencepiece, so I don't know
if there's any meaningful difference. In any case, I also added a
requirements.txt file to lock the dependency versions in case of any
future breaking changes.
- Type annotations checked with mypy.
- Some attempts to be extra user-friendly:
- The script tries to be forgiving with arguments, e.g. you can
specify either the model file itself or the directory containing
it.
- The script doesn't depend on config.json / params.json, just in
case the user downloaded files individually and doesn't have those
handy. But you still need tokenizer.model and, for Alpaca,
added_tokens.json.
- The script tries to give a helpful error message if
added_tokens.json is missing.
2023-04-14 07:03:03 +00:00
return GGMLFileType . MostlyF16
2023-08-26 20:13:36 +00:00
if output_type_str == " q8_0 " :
return GGMLFileType . MostlyQ8_0
2023-08-21 20:07:43 +00:00
py : new conversion script (#545)
Current status: Working, except for the latest GPTQ-for-LLaMa format
that includes `g_idx`. This turns out to require changes to GGML, so
for now it only works if you use the `--outtype` option to dequantize it
back to f16 (which is pointless except for debugging).
I also included some cleanup for the C++ code.
This script is meant to replace all the existing conversion scripts
(including the ones that convert from older GGML formats), while also
adding support for some new formats. Specifically, I've tested with:
- [x] `LLaMA` (original)
- [x] `llama-65b-4bit`
- [x] `alpaca-native`
- [x] `alpaca-native-4bit`
- [x] LLaMA converted to 'transformers' format using
`convert_llama_weights_to_hf.py`
- [x] `alpaca-native` quantized with `--true-sequential --act-order
--groupsize 128` (dequantized only)
- [x] same as above plus `--save_safetensors`
- [x] GPT4All
- [x] stock unversioned ggml
- [x] ggmh
There's enough overlap in the logic needed to handle these different
cases that it seemed best to move to a single script.
I haven't tried this with Alpaca-LoRA because I don't know where to find
it.
Useful features:
- Uses multiple threads for a speedup in some cases (though the Python
GIL limits the gain, and sometimes it's disk-bound anyway).
- Combines split models into a single file (both the intra-tensor split
of the original and the inter-tensor split of 'transformers' format
files). Single files are more convenient to work with and more
friendly to future changes to use memory mapping on the C++ side. To
accomplish this without increasing memory requirements, it has some
custom loading code which avoids loading whole input files into memory
at once.
- Because of the custom loading code, it no longer depends in PyTorch,
which might make installing dependencies slightly easier or faster...
although it still depends on NumPy and sentencepiece, so I don't know
if there's any meaningful difference. In any case, I also added a
requirements.txt file to lock the dependency versions in case of any
future breaking changes.
- Type annotations checked with mypy.
- Some attempts to be extra user-friendly:
- The script tries to be forgiving with arguments, e.g. you can
specify either the model file itself or the directory containing
it.
- The script doesn't depend on config.json / params.json, just in
case the user downloaded files individually and doesn't have those
handy. But you still need tokenizer.model and, for Alpaca,
added_tokens.json.
- The script tries to give a helpful error message if
added_tokens.json is missing.
2023-04-14 07:03:03 +00:00
name_to_type = { name : lazy_tensor . data_type for ( name , lazy_tensor ) in model . items ( ) }
2023-08-21 20:07:43 +00:00
2024-03-28 15:44:36 +00:00
raise ValueError ( f " Unexpected combination of types: { name_to_type } " )
py : new conversion script (#545)
Current status: Working, except for the latest GPTQ-for-LLaMa format
that includes `g_idx`. This turns out to require changes to GGML, so
for now it only works if you use the `--outtype` option to dequantize it
back to f16 (which is pointless except for debugging).
I also included some cleanup for the C++ code.
This script is meant to replace all the existing conversion scripts
(including the ones that convert from older GGML formats), while also
adding support for some new formats. Specifically, I've tested with:
- [x] `LLaMA` (original)
- [x] `llama-65b-4bit`
- [x] `alpaca-native`
- [x] `alpaca-native-4bit`
- [x] LLaMA converted to 'transformers' format using
`convert_llama_weights_to_hf.py`
- [x] `alpaca-native` quantized with `--true-sequential --act-order
--groupsize 128` (dequantized only)
- [x] same as above plus `--save_safetensors`
- [x] GPT4All
- [x] stock unversioned ggml
- [x] ggmh
There's enough overlap in the logic needed to handle these different
cases that it seemed best to move to a single script.
I haven't tried this with Alpaca-LoRA because I don't know where to find
it.
Useful features:
- Uses multiple threads for a speedup in some cases (though the Python
GIL limits the gain, and sometimes it's disk-bound anyway).
- Combines split models into a single file (both the intra-tensor split
of the original and the inter-tensor split of 'transformers' format
files). Single files are more convenient to work with and more
friendly to future changes to use memory mapping on the C++ side. To
accomplish this without increasing memory requirements, it has some
custom loading code which avoids loading whole input files into memory
at once.
- Because of the custom loading code, it no longer depends in PyTorch,
which might make installing dependencies slightly easier or faster...
although it still depends on NumPy and sentencepiece, so I don't know
if there's any meaningful difference. In any case, I also added a
requirements.txt file to lock the dependency versions in case of any
future breaking changes.
- Type annotations checked with mypy.
- Some attempts to be extra user-friendly:
- The script tries to be forgiving with arguments, e.g. you can
specify either the model file itself or the directory containing
it.
- The script doesn't depend on config.json / params.json, just in
case the user downloaded files individually and doesn't have those
handy. But you still need tokenizer.model and, for Alpaca,
added_tokens.json.
- The script tries to give a helpful error message if
added_tokens.json is missing.
2023-04-14 07:03:03 +00:00
2023-11-20 10:35:47 +00:00
2023-08-21 20:07:43 +00:00
def convert_to_output_type ( model : LazyModel , output_type : GGMLFileType ) - > LazyModel :
return { name : tensor . astype ( output_type . type_for_tensor ( name , tensor ) )
for ( name , tensor ) in model . items ( ) }
py : new conversion script (#545)
Current status: Working, except for the latest GPTQ-for-LLaMa format
that includes `g_idx`. This turns out to require changes to GGML, so
for now it only works if you use the `--outtype` option to dequantize it
back to f16 (which is pointless except for debugging).
I also included some cleanup for the C++ code.
This script is meant to replace all the existing conversion scripts
(including the ones that convert from older GGML formats), while also
adding support for some new formats. Specifically, I've tested with:
- [x] `LLaMA` (original)
- [x] `llama-65b-4bit`
- [x] `alpaca-native`
- [x] `alpaca-native-4bit`
- [x] LLaMA converted to 'transformers' format using
`convert_llama_weights_to_hf.py`
- [x] `alpaca-native` quantized with `--true-sequential --act-order
--groupsize 128` (dequantized only)
- [x] same as above plus `--save_safetensors`
- [x] GPT4All
- [x] stock unversioned ggml
- [x] ggmh
There's enough overlap in the logic needed to handle these different
cases that it seemed best to move to a single script.
I haven't tried this with Alpaca-LoRA because I don't know where to find
it.
Useful features:
- Uses multiple threads for a speedup in some cases (though the Python
GIL limits the gain, and sometimes it's disk-bound anyway).
- Combines split models into a single file (both the intra-tensor split
of the original and the inter-tensor split of 'transformers' format
files). Single files are more convenient to work with and more
friendly to future changes to use memory mapping on the C++ side. To
accomplish this without increasing memory requirements, it has some
custom loading code which avoids loading whole input files into memory
at once.
- Because of the custom loading code, it no longer depends in PyTorch,
which might make installing dependencies slightly easier or faster...
although it still depends on NumPy and sentencepiece, so I don't know
if there's any meaningful difference. In any case, I also added a
requirements.txt file to lock the dependency versions in case of any
future breaking changes.
- Type annotations checked with mypy.
- Some attempts to be extra user-friendly:
- The script tries to be forgiving with arguments, e.g. you can
specify either the model file itself or the directory containing
it.
- The script doesn't depend on config.json / params.json, just in
case the user downloaded files individually and doesn't have those
handy. But you still need tokenizer.model and, for Alpaca,
added_tokens.json.
- The script tries to give a helpful error message if
added_tokens.json is missing.
2023-04-14 07:03:03 +00:00
2023-11-20 10:35:47 +00:00
llava : support v1.6 (#5267)
* Create llava-survery-v2.py
* Update convert-image-encoder-to-gguf.py
* Update convert-image-encoder-to-gguf.py
* Rename llava-survery-v2.py to llava-surgery-v2.py
* Update convert-image-encoder-to-gguf.py
will now search for projector
* Update convert-image-encoder-to-gguf.py
whoops
* Update llava-surgery-v2.py
* Clip: Bugfix for normalization (it did not loat the 3 std and mean values)
Clip: bicubic resize function
Clip: added save-to-bmp/pil for debugging and conversion from/to 32/8 images
Clip: added normalization with FP16 precision simulation (image tensors match HF implementation, can be switched off, only used for llava-1.6)
Clip: added newline tensor, mergetype kv, image-grid kv, new resize-pad function with resolution from gridpoints
Clip: clip_image_preprocess now returns a float * vector instead of float, this way llava 1.5 and 1.6 is supported
llava: added ggml cpu graph for embedding patching, added spatial_unpad preliminary support, added a lot of comments that need to be cleaned when all is final
convert-image-encoder: fixed image-grid flattening
* whitespace corrections
* ws
* Tensors are now properly permuted.
Before the embeddings were inserted 1:1, now they are split into the 24x24 patches as in reference.
* ws
* added verbose_prompt support into cli
added stopwords for llava-1.6 into cli
* moved llava functions to llava.cpp, made clip.h C compatible API, replaced vector style functions with pointers, added a debug define to remove functions from compilation while not needed
* ws
* convert : skip unknown tensors (need for LLaVA)
* llava : update readme
* llava : fix compile warnings
* llava : style
* convert : add --skip-unknown CLI arg
* server : remove clip structs
* bugfix for non llava-1.6
It should now work with llava-1.5 as well
* clip : minor code rearrange
* llava : update readme a bit
---------
Co-authored-by: John <cmt-nct@users.noreply.github.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-02-14 07:38:35 +00:00
def convert_model_names ( model : LazyModel , params : Params , skip_unknown : bool ) - > LazyModel :
2023-08-30 08:25:50 +00:00
tmap = gguf . TensorNameMap ( ARCH , params . n_layer )
2024-03-28 15:44:36 +00:00
should_skip = set ( gguf . MODEL_TENSOR_SKIP . get ( ARCH , [ ] ) )
py : new conversion script (#545)
Current status: Working, except for the latest GPTQ-for-LLaMa format
that includes `g_idx`. This turns out to require changes to GGML, so
for now it only works if you use the `--outtype` option to dequantize it
back to f16 (which is pointless except for debugging).
I also included some cleanup for the C++ code.
This script is meant to replace all the existing conversion scripts
(including the ones that convert from older GGML formats), while also
adding support for some new formats. Specifically, I've tested with:
- [x] `LLaMA` (original)
- [x] `llama-65b-4bit`
- [x] `alpaca-native`
- [x] `alpaca-native-4bit`
- [x] LLaMA converted to 'transformers' format using
`convert_llama_weights_to_hf.py`
- [x] `alpaca-native` quantized with `--true-sequential --act-order
--groupsize 128` (dequantized only)
- [x] same as above plus `--save_safetensors`
- [x] GPT4All
- [x] stock unversioned ggml
- [x] ggmh
There's enough overlap in the logic needed to handle these different
cases that it seemed best to move to a single script.
I haven't tried this with Alpaca-LoRA because I don't know where to find
it.
Useful features:
- Uses multiple threads for a speedup in some cases (though the Python
GIL limits the gain, and sometimes it's disk-bound anyway).
- Combines split models into a single file (both the intra-tensor split
of the original and the inter-tensor split of 'transformers' format
files). Single files are more convenient to work with and more
friendly to future changes to use memory mapping on the C++ side. To
accomplish this without increasing memory requirements, it has some
custom loading code which avoids loading whole input files into memory
at once.
- Because of the custom loading code, it no longer depends in PyTorch,
which might make installing dependencies slightly easier or faster...
although it still depends on NumPy and sentencepiece, so I don't know
if there's any meaningful difference. In any case, I also added a
requirements.txt file to lock the dependency versions in case of any
future breaking changes.
- Type annotations checked with mypy.
- Some attempts to be extra user-friendly:
- The script tries to be forgiving with arguments, e.g. you can
specify either the model file itself or the directory containing
it.
- The script doesn't depend on config.json / params.json, just in
case the user downloaded files individually and doesn't have those
handy. But you still need tokenizer.model and, for Alpaca,
added_tokens.json.
- The script tries to give a helpful error message if
added_tokens.json is missing.
2023-04-14 07:03:03 +00:00
2023-08-21 20:07:43 +00:00
tmp = model
py : new conversion script (#545)
Current status: Working, except for the latest GPTQ-for-LLaMa format
that includes `g_idx`. This turns out to require changes to GGML, so
for now it only works if you use the `--outtype` option to dequantize it
back to f16 (which is pointless except for debugging).
I also included some cleanup for the C++ code.
This script is meant to replace all the existing conversion scripts
(including the ones that convert from older GGML formats), while also
adding support for some new formats. Specifically, I've tested with:
- [x] `LLaMA` (original)
- [x] `llama-65b-4bit`
- [x] `alpaca-native`
- [x] `alpaca-native-4bit`
- [x] LLaMA converted to 'transformers' format using
`convert_llama_weights_to_hf.py`
- [x] `alpaca-native` quantized with `--true-sequential --act-order
--groupsize 128` (dequantized only)
- [x] same as above plus `--save_safetensors`
- [x] GPT4All
- [x] stock unversioned ggml
- [x] ggmh
There's enough overlap in the logic needed to handle these different
cases that it seemed best to move to a single script.
I haven't tried this with Alpaca-LoRA because I don't know where to find
it.
Useful features:
- Uses multiple threads for a speedup in some cases (though the Python
GIL limits the gain, and sometimes it's disk-bound anyway).
- Combines split models into a single file (both the intra-tensor split
of the original and the inter-tensor split of 'transformers' format
files). Single files are more convenient to work with and more
friendly to future changes to use memory mapping on the C++ side. To
accomplish this without increasing memory requirements, it has some
custom loading code which avoids loading whole input files into memory
at once.
- Because of the custom loading code, it no longer depends in PyTorch,
which might make installing dependencies slightly easier or faster...
although it still depends on NumPy and sentencepiece, so I don't know
if there's any meaningful difference. In any case, I also added a
requirements.txt file to lock the dependency versions in case of any
future breaking changes.
- Type annotations checked with mypy.
- Some attempts to be extra user-friendly:
- The script tries to be forgiving with arguments, e.g. you can
specify either the model file itself or the directory containing
it.
- The script doesn't depend on config.json / params.json, just in
case the user downloaded files individually and doesn't have those
handy. But you still need tokenizer.model and, for Alpaca,
added_tokens.json.
- The script tries to give a helpful error message if
added_tokens.json is missing.
2023-04-14 07:03:03 +00:00
2024-04-03 13:07:05 +00:00
# merge experts into one tensor
if params . n_experts and params . n_experts > 0 :
for i_l in range ( params . n_layer ) :
for w in range ( 1 , 4 ) :
experts = [ ]
for e in range ( params . n_experts ) :
if f " layers. { i_l } .feed_forward.experts. { e } .w { w } .weight " in model :
experts . append ( model [ f " layers. { i_l } .feed_forward.experts. { e } .w { w } .weight " ] )
del tmp [ f " layers. { i_l } .feed_forward.experts. { e } .w { w } .weight " ]
elif f " model.layers. { i_l } .block_sparse_moe.experts. { e } .w { w } .weight " in model :
experts . append ( model [ f " model.layers. { i_l } .block_sparse_moe.experts. { e } .w { w } .weight " ] )
del tmp [ f " model.layers. { i_l } .block_sparse_moe.experts. { e } .w { w } .weight " ]
else :
raise ValueError ( f " Expert tensor not found: layers. { i_l } .feed_forward.experts. { e } .w { w } .weight " )
tmp [ f " layers. { i_l } .feed_forward.experts.w { w } .weight " ] = pack_experts_lazy ( experts )
2023-08-21 20:07:43 +00:00
# HF models permut or pack some of the tensors, so we need to undo that
for i in itertools . count ( ) :
if f " model.layers. { i } .self_attn.q_proj.weight " in model :
2024-05-03 19:36:41 +00:00
logger . debug ( f " Permuting layer { i } " )
2023-08-21 20:07:43 +00:00
tmp [ f " model.layers. { i } .self_attn.q_proj.weight " ] = permute_lazy ( model [ f " model.layers. { i } .self_attn.q_proj.weight " ] , params . n_head , params . n_head )
tmp [ f " model.layers. { i } .self_attn.k_proj.weight " ] = permute_lazy ( model [ f " model.layers. { i } .self_attn.k_proj.weight " ] , params . n_head , params . n_head_kv )
2023-11-20 10:35:47 +00:00
# tmp[f"model.layers.{i}.self_attn.v_proj.weight"] = model[f"model.layers.{i}.self_attn.v_proj.weight"]
2023-08-21 20:07:43 +00:00
elif f " model.layers. { i } .self_attn.W_pack.weight " in model :
2024-05-03 19:36:41 +00:00
logger . debug ( f " Unpacking and permuting layer { i } " )
2023-08-30 08:25:50 +00:00
tmp [ f " model.layers. { i } .self_attn.q_proj.weight " ] = permute_part_lazy ( model [ f " model.layers. { i } .self_attn.W_pack.weight " ] , 0 , params . n_head , params . n_head )
tmp [ f " model.layers. { i } .self_attn.k_proj.weight " ] = permute_part_lazy ( model [ f " model.layers. { i } .self_attn.W_pack.weight " ] , 1 , params . n_head , params . n_head_kv )
2023-08-21 20:07:43 +00:00
tmp [ f " model.layers. { i } .self_attn.v_proj.weight " ] = part_lazy ( model [ f " model.layers. { i } .self_attn.W_pack.weight " ] , 2 )
2023-08-29 09:48:41 +00:00
del tmp [ f " model.layers. { i } .self_attn.W_pack.weight " ]
2023-08-21 20:07:43 +00:00
else :
break
py : new conversion script (#545)
Current status: Working, except for the latest GPTQ-for-LLaMa format
that includes `g_idx`. This turns out to require changes to GGML, so
for now it only works if you use the `--outtype` option to dequantize it
back to f16 (which is pointless except for debugging).
I also included some cleanup for the C++ code.
This script is meant to replace all the existing conversion scripts
(including the ones that convert from older GGML formats), while also
adding support for some new formats. Specifically, I've tested with:
- [x] `LLaMA` (original)
- [x] `llama-65b-4bit`
- [x] `alpaca-native`
- [x] `alpaca-native-4bit`
- [x] LLaMA converted to 'transformers' format using
`convert_llama_weights_to_hf.py`
- [x] `alpaca-native` quantized with `--true-sequential --act-order
--groupsize 128` (dequantized only)
- [x] same as above plus `--save_safetensors`
- [x] GPT4All
- [x] stock unversioned ggml
- [x] ggmh
There's enough overlap in the logic needed to handle these different
cases that it seemed best to move to a single script.
I haven't tried this with Alpaca-LoRA because I don't know where to find
it.
Useful features:
- Uses multiple threads for a speedup in some cases (though the Python
GIL limits the gain, and sometimes it's disk-bound anyway).
- Combines split models into a single file (both the intra-tensor split
of the original and the inter-tensor split of 'transformers' format
files). Single files are more convenient to work with and more
friendly to future changes to use memory mapping on the C++ side. To
accomplish this without increasing memory requirements, it has some
custom loading code which avoids loading whole input files into memory
at once.
- Because of the custom loading code, it no longer depends in PyTorch,
which might make installing dependencies slightly easier or faster...
although it still depends on NumPy and sentencepiece, so I don't know
if there's any meaningful difference. In any case, I also added a
requirements.txt file to lock the dependency versions in case of any
future breaking changes.
- Type annotations checked with mypy.
- Some attempts to be extra user-friendly:
- The script tries to be forgiving with arguments, e.g. you can
specify either the model file itself or the directory containing
it.
- The script doesn't depend on config.json / params.json, just in
case the user downloaded files individually and doesn't have those
handy. But you still need tokenizer.model and, for Alpaca,
added_tokens.json.
- The script tries to give a helpful error message if
added_tokens.json is missing.
2023-04-14 07:03:03 +00:00
2023-08-21 20:07:43 +00:00
out : LazyModel = { }
for name , lazy_tensor in model . items ( ) :
2023-08-30 08:25:50 +00:00
tensor_type , name_new = tmap . get_type_and_name ( name , try_suffixes = ( " .weight " , " .bias " ) ) or ( None , None )
if name_new is None :
llava : support v1.6 (#5267)
* Create llava-survery-v2.py
* Update convert-image-encoder-to-gguf.py
* Update convert-image-encoder-to-gguf.py
* Rename llava-survery-v2.py to llava-surgery-v2.py
* Update convert-image-encoder-to-gguf.py
will now search for projector
* Update convert-image-encoder-to-gguf.py
whoops
* Update llava-surgery-v2.py
* Clip: Bugfix for normalization (it did not loat the 3 std and mean values)
Clip: bicubic resize function
Clip: added save-to-bmp/pil for debugging and conversion from/to 32/8 images
Clip: added normalization with FP16 precision simulation (image tensors match HF implementation, can be switched off, only used for llava-1.6)
Clip: added newline tensor, mergetype kv, image-grid kv, new resize-pad function with resolution from gridpoints
Clip: clip_image_preprocess now returns a float * vector instead of float, this way llava 1.5 and 1.6 is supported
llava: added ggml cpu graph for embedding patching, added spatial_unpad preliminary support, added a lot of comments that need to be cleaned when all is final
convert-image-encoder: fixed image-grid flattening
* whitespace corrections
* ws
* Tensors are now properly permuted.
Before the embeddings were inserted 1:1, now they are split into the 24x24 patches as in reference.
* ws
* added verbose_prompt support into cli
added stopwords for llava-1.6 into cli
* moved llava functions to llava.cpp, made clip.h C compatible API, replaced vector style functions with pointers, added a debug define to remove functions from compilation while not needed
* ws
* convert : skip unknown tensors (need for LLaVA)
* llava : update readme
* llava : fix compile warnings
* llava : style
* convert : add --skip-unknown CLI arg
* server : remove clip structs
* bugfix for non llava-1.6
It should now work with llava-1.5 as well
* clip : minor code rearrange
* llava : update readme a bit
---------
Co-authored-by: John <cmt-nct@users.noreply.github.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-02-14 07:38:35 +00:00
if skip_unknown :
2024-05-03 19:36:41 +00:00
logger . warning ( f " Unexpected tensor name: { name } - skipping " )
llava : support v1.6 (#5267)
* Create llava-survery-v2.py
* Update convert-image-encoder-to-gguf.py
* Update convert-image-encoder-to-gguf.py
* Rename llava-survery-v2.py to llava-surgery-v2.py
* Update convert-image-encoder-to-gguf.py
will now search for projector
* Update convert-image-encoder-to-gguf.py
whoops
* Update llava-surgery-v2.py
* Clip: Bugfix for normalization (it did not loat the 3 std and mean values)
Clip: bicubic resize function
Clip: added save-to-bmp/pil for debugging and conversion from/to 32/8 images
Clip: added normalization with FP16 precision simulation (image tensors match HF implementation, can be switched off, only used for llava-1.6)
Clip: added newline tensor, mergetype kv, image-grid kv, new resize-pad function with resolution from gridpoints
Clip: clip_image_preprocess now returns a float * vector instead of float, this way llava 1.5 and 1.6 is supported
llava: added ggml cpu graph for embedding patching, added spatial_unpad preliminary support, added a lot of comments that need to be cleaned when all is final
convert-image-encoder: fixed image-grid flattening
* whitespace corrections
* ws
* Tensors are now properly permuted.
Before the embeddings were inserted 1:1, now they are split into the 24x24 patches as in reference.
* ws
* added verbose_prompt support into cli
added stopwords for llava-1.6 into cli
* moved llava functions to llava.cpp, made clip.h C compatible API, replaced vector style functions with pointers, added a debug define to remove functions from compilation while not needed
* ws
* convert : skip unknown tensors (need for LLaVA)
* llava : update readme
* llava : fix compile warnings
* llava : style
* convert : add --skip-unknown CLI arg
* server : remove clip structs
* bugfix for non llava-1.6
It should now work with llava-1.5 as well
* clip : minor code rearrange
* llava : update readme a bit
---------
Co-authored-by: John <cmt-nct@users.noreply.github.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-02-14 07:38:35 +00:00
continue
2024-03-28 15:44:36 +00:00
raise ValueError ( f " Unexpected tensor name: { name } . Use --skip-unknown to ignore it (e.g. LLaVA) " )
py : new conversion script (#545)
Current status: Working, except for the latest GPTQ-for-LLaMa format
that includes `g_idx`. This turns out to require changes to GGML, so
for now it only works if you use the `--outtype` option to dequantize it
back to f16 (which is pointless except for debugging).
I also included some cleanup for the C++ code.
This script is meant to replace all the existing conversion scripts
(including the ones that convert from older GGML formats), while also
adding support for some new formats. Specifically, I've tested with:
- [x] `LLaMA` (original)
- [x] `llama-65b-4bit`
- [x] `alpaca-native`
- [x] `alpaca-native-4bit`
- [x] LLaMA converted to 'transformers' format using
`convert_llama_weights_to_hf.py`
- [x] `alpaca-native` quantized with `--true-sequential --act-order
--groupsize 128` (dequantized only)
- [x] same as above plus `--save_safetensors`
- [x] GPT4All
- [x] stock unversioned ggml
- [x] ggmh
There's enough overlap in the logic needed to handle these different
cases that it seemed best to move to a single script.
I haven't tried this with Alpaca-LoRA because I don't know where to find
it.
Useful features:
- Uses multiple threads for a speedup in some cases (though the Python
GIL limits the gain, and sometimes it's disk-bound anyway).
- Combines split models into a single file (both the intra-tensor split
of the original and the inter-tensor split of 'transformers' format
files). Single files are more convenient to work with and more
friendly to future changes to use memory mapping on the C++ side. To
accomplish this without increasing memory requirements, it has some
custom loading code which avoids loading whole input files into memory
at once.
- Because of the custom loading code, it no longer depends in PyTorch,
which might make installing dependencies slightly easier or faster...
although it still depends on NumPy and sentencepiece, so I don't know
if there's any meaningful difference. In any case, I also added a
requirements.txt file to lock the dependency versions in case of any
future breaking changes.
- Type annotations checked with mypy.
- Some attempts to be extra user-friendly:
- The script tries to be forgiving with arguments, e.g. you can
specify either the model file itself or the directory containing
it.
- The script doesn't depend on config.json / params.json, just in
case the user downloaded files individually and doesn't have those
handy. But you still need tokenizer.model and, for Alpaca,
added_tokens.json.
- The script tries to give a helpful error message if
added_tokens.json is missing.
2023-04-14 07:03:03 +00:00
2023-08-30 08:25:50 +00:00
if tensor_type in should_skip :
2024-05-03 19:36:41 +00:00
logger . debug ( f " skipping tensor { name_new } " )
2023-08-21 20:07:43 +00:00
continue
2023-08-30 08:25:50 +00:00
2024-05-03 19:36:41 +00:00
logger . debug ( f " { name : 48s } -> { name_new : 40s } | { lazy_tensor . data_type . name : 6s } | { lazy_tensor . shape } " )
2023-08-30 08:25:50 +00:00
out [ name_new ] = lazy_tensor
py : new conversion script (#545)
Current status: Working, except for the latest GPTQ-for-LLaMa format
that includes `g_idx`. This turns out to require changes to GGML, so
for now it only works if you use the `--outtype` option to dequantize it
back to f16 (which is pointless except for debugging).
I also included some cleanup for the C++ code.
This script is meant to replace all the existing conversion scripts
(including the ones that convert from older GGML formats), while also
adding support for some new formats. Specifically, I've tested with:
- [x] `LLaMA` (original)
- [x] `llama-65b-4bit`
- [x] `alpaca-native`
- [x] `alpaca-native-4bit`
- [x] LLaMA converted to 'transformers' format using
`convert_llama_weights_to_hf.py`
- [x] `alpaca-native` quantized with `--true-sequential --act-order
--groupsize 128` (dequantized only)
- [x] same as above plus `--save_safetensors`
- [x] GPT4All
- [x] stock unversioned ggml
- [x] ggmh
There's enough overlap in the logic needed to handle these different
cases that it seemed best to move to a single script.
I haven't tried this with Alpaca-LoRA because I don't know where to find
it.
Useful features:
- Uses multiple threads for a speedup in some cases (though the Python
GIL limits the gain, and sometimes it's disk-bound anyway).
- Combines split models into a single file (both the intra-tensor split
of the original and the inter-tensor split of 'transformers' format
files). Single files are more convenient to work with and more
friendly to future changes to use memory mapping on the C++ side. To
accomplish this without increasing memory requirements, it has some
custom loading code which avoids loading whole input files into memory
at once.
- Because of the custom loading code, it no longer depends in PyTorch,
which might make installing dependencies slightly easier or faster...
although it still depends on NumPy and sentencepiece, so I don't know
if there's any meaningful difference. In any case, I also added a
requirements.txt file to lock the dependency versions in case of any
future breaking changes.
- Type annotations checked with mypy.
- Some attempts to be extra user-friendly:
- The script tries to be forgiving with arguments, e.g. you can
specify either the model file itself or the directory containing
it.
- The script doesn't depend on config.json / params.json, just in
case the user downloaded files individually and doesn't have those
handy. But you still need tokenizer.model and, for Alpaca,
added_tokens.json.
- The script tries to give a helpful error message if
added_tokens.json is missing.
2023-04-14 07:03:03 +00:00
2023-08-21 20:07:43 +00:00
return out
py : new conversion script (#545)
Current status: Working, except for the latest GPTQ-for-LLaMa format
that includes `g_idx`. This turns out to require changes to GGML, so
for now it only works if you use the `--outtype` option to dequantize it
back to f16 (which is pointless except for debugging).
I also included some cleanup for the C++ code.
This script is meant to replace all the existing conversion scripts
(including the ones that convert from older GGML formats), while also
adding support for some new formats. Specifically, I've tested with:
- [x] `LLaMA` (original)
- [x] `llama-65b-4bit`
- [x] `alpaca-native`
- [x] `alpaca-native-4bit`
- [x] LLaMA converted to 'transformers' format using
`convert_llama_weights_to_hf.py`
- [x] `alpaca-native` quantized with `--true-sequential --act-order
--groupsize 128` (dequantized only)
- [x] same as above plus `--save_safetensors`
- [x] GPT4All
- [x] stock unversioned ggml
- [x] ggmh
There's enough overlap in the logic needed to handle these different
cases that it seemed best to move to a single script.
I haven't tried this with Alpaca-LoRA because I don't know where to find
it.
Useful features:
- Uses multiple threads for a speedup in some cases (though the Python
GIL limits the gain, and sometimes it's disk-bound anyway).
- Combines split models into a single file (both the intra-tensor split
of the original and the inter-tensor split of 'transformers' format
files). Single files are more convenient to work with and more
friendly to future changes to use memory mapping on the C++ side. To
accomplish this without increasing memory requirements, it has some
custom loading code which avoids loading whole input files into memory
at once.
- Because of the custom loading code, it no longer depends in PyTorch,
which might make installing dependencies slightly easier or faster...
although it still depends on NumPy and sentencepiece, so I don't know
if there's any meaningful difference. In any case, I also added a
requirements.txt file to lock the dependency versions in case of any
future breaking changes.
- Type annotations checked with mypy.
- Some attempts to be extra user-friendly:
- The script tries to be forgiving with arguments, e.g. you can
specify either the model file itself or the directory containing
it.
- The script doesn't depend on config.json / params.json, just in
case the user downloaded files individually and doesn't have those
handy. But you still need tokenizer.model and, for Alpaca,
added_tokens.json.
- The script tries to give a helpful error message if
added_tokens.json is missing.
2023-04-14 07:03:03 +00:00
2023-11-20 10:35:47 +00:00
2023-08-31 05:02:23 +00:00
def nth_multifile_path ( path : Path , n : int ) - > Path | None :
py : new conversion script (#545)
Current status: Working, except for the latest GPTQ-for-LLaMa format
that includes `g_idx`. This turns out to require changes to GGML, so
for now it only works if you use the `--outtype` option to dequantize it
back to f16 (which is pointless except for debugging).
I also included some cleanup for the C++ code.
This script is meant to replace all the existing conversion scripts
(including the ones that convert from older GGML formats), while also
adding support for some new formats. Specifically, I've tested with:
- [x] `LLaMA` (original)
- [x] `llama-65b-4bit`
- [x] `alpaca-native`
- [x] `alpaca-native-4bit`
- [x] LLaMA converted to 'transformers' format using
`convert_llama_weights_to_hf.py`
- [x] `alpaca-native` quantized with `--true-sequential --act-order
--groupsize 128` (dequantized only)
- [x] same as above plus `--save_safetensors`
- [x] GPT4All
- [x] stock unversioned ggml
- [x] ggmh
There's enough overlap in the logic needed to handle these different
cases that it seemed best to move to a single script.
I haven't tried this with Alpaca-LoRA because I don't know where to find
it.
Useful features:
- Uses multiple threads for a speedup in some cases (though the Python
GIL limits the gain, and sometimes it's disk-bound anyway).
- Combines split models into a single file (both the intra-tensor split
of the original and the inter-tensor split of 'transformers' format
files). Single files are more convenient to work with and more
friendly to future changes to use memory mapping on the C++ side. To
accomplish this without increasing memory requirements, it has some
custom loading code which avoids loading whole input files into memory
at once.
- Because of the custom loading code, it no longer depends in PyTorch,
which might make installing dependencies slightly easier or faster...
although it still depends on NumPy and sentencepiece, so I don't know
if there's any meaningful difference. In any case, I also added a
requirements.txt file to lock the dependency versions in case of any
future breaking changes.
- Type annotations checked with mypy.
- Some attempts to be extra user-friendly:
- The script tries to be forgiving with arguments, e.g. you can
specify either the model file itself or the directory containing
it.
- The script doesn't depend on config.json / params.json, just in
case the user downloaded files individually and doesn't have those
handy. But you still need tokenizer.model and, for Alpaca,
added_tokens.json.
- The script tries to give a helpful error message if
added_tokens.json is missing.
2023-04-14 07:03:03 +00:00
''' Given any path belonging to a multi-file model (e.g. foo.bin.1), return
the nth path in the model .
'''
# Support the following patterns:
2024-03-28 15:44:36 +00:00
patterns = [
py : new conversion script (#545)
Current status: Working, except for the latest GPTQ-for-LLaMa format
that includes `g_idx`. This turns out to require changes to GGML, so
for now it only works if you use the `--outtype` option to dequantize it
back to f16 (which is pointless except for debugging).
I also included some cleanup for the C++ code.
This script is meant to replace all the existing conversion scripts
(including the ones that convert from older GGML formats), while also
adding support for some new formats. Specifically, I've tested with:
- [x] `LLaMA` (original)
- [x] `llama-65b-4bit`
- [x] `alpaca-native`
- [x] `alpaca-native-4bit`
- [x] LLaMA converted to 'transformers' format using
`convert_llama_weights_to_hf.py`
- [x] `alpaca-native` quantized with `--true-sequential --act-order
--groupsize 128` (dequantized only)
- [x] same as above plus `--save_safetensors`
- [x] GPT4All
- [x] stock unversioned ggml
- [x] ggmh
There's enough overlap in the logic needed to handle these different
cases that it seemed best to move to a single script.
I haven't tried this with Alpaca-LoRA because I don't know where to find
it.
Useful features:
- Uses multiple threads for a speedup in some cases (though the Python
GIL limits the gain, and sometimes it's disk-bound anyway).
- Combines split models into a single file (both the intra-tensor split
of the original and the inter-tensor split of 'transformers' format
files). Single files are more convenient to work with and more
friendly to future changes to use memory mapping on the C++ side. To
accomplish this without increasing memory requirements, it has some
custom loading code which avoids loading whole input files into memory
at once.
- Because of the custom loading code, it no longer depends in PyTorch,
which might make installing dependencies slightly easier or faster...
although it still depends on NumPy and sentencepiece, so I don't know
if there's any meaningful difference. In any case, I also added a
requirements.txt file to lock the dependency versions in case of any
future breaking changes.
- Type annotations checked with mypy.
- Some attempts to be extra user-friendly:
- The script tries to be forgiving with arguments, e.g. you can
specify either the model file itself or the directory containing
it.
- The script doesn't depend on config.json / params.json, just in
case the user downloaded files individually and doesn't have those
handy. But you still need tokenizer.model and, for Alpaca,
added_tokens.json.
- The script tries to give a helpful error message if
added_tokens.json is missing.
2023-04-14 07:03:03 +00:00
# - x.00.pth, x.01.pth, etc.
( r ' \ .[0-9] {2} \ .pth$ ' , f ' . { n : 02 } .pth ' ) ,
# - x-00001-of-00002.bin, x-00002-of-00002.bin, etc.
( r ' -[0-9] {5} -of-(.*)$ ' , fr ' - { n : 05 } -of- \ 1 ' ) ,
# x.bin, x.bin.1, etc.
( r ' ( \ .[0-9]+)?$ ' , r ' \ 1 ' if n == 0 else fr ' \ 1. { n } ' )
]
for regex , replacement in patterns :
if re . search ( regex , path . name ) :
new_path = path . with_name ( re . sub ( regex , replacement , path . name ) )
if new_path . exists ( ) :
return new_path
return None
2023-08-31 05:02:23 +00:00
def find_multifile_paths ( path : Path ) - > list [ Path ] :
py : new conversion script (#545)
Current status: Working, except for the latest GPTQ-for-LLaMa format
that includes `g_idx`. This turns out to require changes to GGML, so
for now it only works if you use the `--outtype` option to dequantize it
back to f16 (which is pointless except for debugging).
I also included some cleanup for the C++ code.
This script is meant to replace all the existing conversion scripts
(including the ones that convert from older GGML formats), while also
adding support for some new formats. Specifically, I've tested with:
- [x] `LLaMA` (original)
- [x] `llama-65b-4bit`
- [x] `alpaca-native`
- [x] `alpaca-native-4bit`
- [x] LLaMA converted to 'transformers' format using
`convert_llama_weights_to_hf.py`
- [x] `alpaca-native` quantized with `--true-sequential --act-order
--groupsize 128` (dequantized only)
- [x] same as above plus `--save_safetensors`
- [x] GPT4All
- [x] stock unversioned ggml
- [x] ggmh
There's enough overlap in the logic needed to handle these different
cases that it seemed best to move to a single script.
I haven't tried this with Alpaca-LoRA because I don't know where to find
it.
Useful features:
- Uses multiple threads for a speedup in some cases (though the Python
GIL limits the gain, and sometimes it's disk-bound anyway).
- Combines split models into a single file (both the intra-tensor split
of the original and the inter-tensor split of 'transformers' format
files). Single files are more convenient to work with and more
friendly to future changes to use memory mapping on the C++ side. To
accomplish this without increasing memory requirements, it has some
custom loading code which avoids loading whole input files into memory
at once.
- Because of the custom loading code, it no longer depends in PyTorch,
which might make installing dependencies slightly easier or faster...
although it still depends on NumPy and sentencepiece, so I don't know
if there's any meaningful difference. In any case, I also added a
requirements.txt file to lock the dependency versions in case of any
future breaking changes.
- Type annotations checked with mypy.
- Some attempts to be extra user-friendly:
- The script tries to be forgiving with arguments, e.g. you can
specify either the model file itself or the directory containing
it.
- The script doesn't depend on config.json / params.json, just in
case the user downloaded files individually and doesn't have those
handy. But you still need tokenizer.model and, for Alpaca,
added_tokens.json.
- The script tries to give a helpful error message if
added_tokens.json is missing.
2023-04-14 07:03:03 +00:00
''' Given any path belonging to a multi-file model (e.g. foo.bin.1), return
the whole list of paths in the model .
'''
2023-08-31 05:02:23 +00:00
ret : list [ Path ] = [ ]
py : new conversion script (#545)
Current status: Working, except for the latest GPTQ-for-LLaMa format
that includes `g_idx`. This turns out to require changes to GGML, so
for now it only works if you use the `--outtype` option to dequantize it
back to f16 (which is pointless except for debugging).
I also included some cleanup for the C++ code.
This script is meant to replace all the existing conversion scripts
(including the ones that convert from older GGML formats), while also
adding support for some new formats. Specifically, I've tested with:
- [x] `LLaMA` (original)
- [x] `llama-65b-4bit`
- [x] `alpaca-native`
- [x] `alpaca-native-4bit`
- [x] LLaMA converted to 'transformers' format using
`convert_llama_weights_to_hf.py`
- [x] `alpaca-native` quantized with `--true-sequential --act-order
--groupsize 128` (dequantized only)
- [x] same as above plus `--save_safetensors`
- [x] GPT4All
- [x] stock unversioned ggml
- [x] ggmh
There's enough overlap in the logic needed to handle these different
cases that it seemed best to move to a single script.
I haven't tried this with Alpaca-LoRA because I don't know where to find
it.
Useful features:
- Uses multiple threads for a speedup in some cases (though the Python
GIL limits the gain, and sometimes it's disk-bound anyway).
- Combines split models into a single file (both the intra-tensor split
of the original and the inter-tensor split of 'transformers' format
files). Single files are more convenient to work with and more
friendly to future changes to use memory mapping on the C++ side. To
accomplish this without increasing memory requirements, it has some
custom loading code which avoids loading whole input files into memory
at once.
- Because of the custom loading code, it no longer depends in PyTorch,
which might make installing dependencies slightly easier or faster...
although it still depends on NumPy and sentencepiece, so I don't know
if there's any meaningful difference. In any case, I also added a
requirements.txt file to lock the dependency versions in case of any
future breaking changes.
- Type annotations checked with mypy.
- Some attempts to be extra user-friendly:
- The script tries to be forgiving with arguments, e.g. you can
specify either the model file itself or the directory containing
it.
- The script doesn't depend on config.json / params.json, just in
case the user downloaded files individually and doesn't have those
handy. But you still need tokenizer.model and, for Alpaca,
added_tokens.json.
- The script tries to give a helpful error message if
added_tokens.json is missing.
2023-04-14 07:03:03 +00:00
for i in itertools . count ( ) :
nth_path = nth_multifile_path ( path , i )
if nth_path is None :
break
ret . append ( nth_path )
if not ret :
# No matches. This should only happen if the file was named, e.g.,
# foo.0, and there was no file named foo. Oh well, try to process it
# as a single file.
return [ path ]
return ret
def load_some_model ( path : Path ) - > ModelPlus :
''' Load a model of any supported format. '''
# Be extra-friendly and accept either a file or a directory:
if path . is_dir ( ) :
2023-05-08 11:54:26 +00:00
# Check if it's a set of safetensors files first
2024-04-10 13:23:12 +00:00
globs = [ " model-00001-of-*.safetensors " , " model.safetensors " , " consolidated.safetensors " ]
2023-11-14 01:03:40 +00:00
files = [ file for glob in globs for file in path . glob ( glob ) ]
2023-05-08 11:54:26 +00:00
if not files :
# Try the PyTorch patterns too, with lower priority
2023-06-17 10:32:48 +00:00
globs = [ " consolidated.00.pth " , " pytorch_model-00001-of-*.bin " , " *.pt " , " pytorch_model.bin " ]
2023-05-08 11:54:26 +00:00
files = [ file for glob in globs for file in path . glob ( glob ) ]
py : new conversion script (#545)
Current status: Working, except for the latest GPTQ-for-LLaMa format
that includes `g_idx`. This turns out to require changes to GGML, so
for now it only works if you use the `--outtype` option to dequantize it
back to f16 (which is pointless except for debugging).
I also included some cleanup for the C++ code.
This script is meant to replace all the existing conversion scripts
(including the ones that convert from older GGML formats), while also
adding support for some new formats. Specifically, I've tested with:
- [x] `LLaMA` (original)
- [x] `llama-65b-4bit`
- [x] `alpaca-native`
- [x] `alpaca-native-4bit`
- [x] LLaMA converted to 'transformers' format using
`convert_llama_weights_to_hf.py`
- [x] `alpaca-native` quantized with `--true-sequential --act-order
--groupsize 128` (dequantized only)
- [x] same as above plus `--save_safetensors`
- [x] GPT4All
- [x] stock unversioned ggml
- [x] ggmh
There's enough overlap in the logic needed to handle these different
cases that it seemed best to move to a single script.
I haven't tried this with Alpaca-LoRA because I don't know where to find
it.
Useful features:
- Uses multiple threads for a speedup in some cases (though the Python
GIL limits the gain, and sometimes it's disk-bound anyway).
- Combines split models into a single file (both the intra-tensor split
of the original and the inter-tensor split of 'transformers' format
files). Single files are more convenient to work with and more
friendly to future changes to use memory mapping on the C++ side. To
accomplish this without increasing memory requirements, it has some
custom loading code which avoids loading whole input files into memory
at once.
- Because of the custom loading code, it no longer depends in PyTorch,
which might make installing dependencies slightly easier or faster...
although it still depends on NumPy and sentencepiece, so I don't know
if there's any meaningful difference. In any case, I also added a
requirements.txt file to lock the dependency versions in case of any
future breaking changes.
- Type annotations checked with mypy.
- Some attempts to be extra user-friendly:
- The script tries to be forgiving with arguments, e.g. you can
specify either the model file itself or the directory containing
it.
- The script doesn't depend on config.json / params.json, just in
case the user downloaded files individually and doesn't have those
handy. But you still need tokenizer.model and, for Alpaca,
added_tokens.json.
- The script tries to give a helpful error message if
added_tokens.json is missing.
2023-04-14 07:03:03 +00:00
if not files :
2024-03-28 15:44:36 +00:00
raise FileNotFoundError ( f " Can ' t find model in directory { path } " )
py : new conversion script (#545)
Current status: Working, except for the latest GPTQ-for-LLaMa format
that includes `g_idx`. This turns out to require changes to GGML, so
for now it only works if you use the `--outtype` option to dequantize it
back to f16 (which is pointless except for debugging).
I also included some cleanup for the C++ code.
This script is meant to replace all the existing conversion scripts
(including the ones that convert from older GGML formats), while also
adding support for some new formats. Specifically, I've tested with:
- [x] `LLaMA` (original)
- [x] `llama-65b-4bit`
- [x] `alpaca-native`
- [x] `alpaca-native-4bit`
- [x] LLaMA converted to 'transformers' format using
`convert_llama_weights_to_hf.py`
- [x] `alpaca-native` quantized with `--true-sequential --act-order
--groupsize 128` (dequantized only)
- [x] same as above plus `--save_safetensors`
- [x] GPT4All
- [x] stock unversioned ggml
- [x] ggmh
There's enough overlap in the logic needed to handle these different
cases that it seemed best to move to a single script.
I haven't tried this with Alpaca-LoRA because I don't know where to find
it.
Useful features:
- Uses multiple threads for a speedup in some cases (though the Python
GIL limits the gain, and sometimes it's disk-bound anyway).
- Combines split models into a single file (both the intra-tensor split
of the original and the inter-tensor split of 'transformers' format
files). Single files are more convenient to work with and more
friendly to future changes to use memory mapping on the C++ side. To
accomplish this without increasing memory requirements, it has some
custom loading code which avoids loading whole input files into memory
at once.
- Because of the custom loading code, it no longer depends in PyTorch,
which might make installing dependencies slightly easier or faster...
although it still depends on NumPy and sentencepiece, so I don't know
if there's any meaningful difference. In any case, I also added a
requirements.txt file to lock the dependency versions in case of any
future breaking changes.
- Type annotations checked with mypy.
- Some attempts to be extra user-friendly:
- The script tries to be forgiving with arguments, e.g. you can
specify either the model file itself or the directory containing
it.
- The script doesn't depend on config.json / params.json, just in
case the user downloaded files individually and doesn't have those
handy. But you still need tokenizer.model and, for Alpaca,
added_tokens.json.
- The script tries to give a helpful error message if
added_tokens.json is missing.
2023-04-14 07:03:03 +00:00
if len ( files ) > 1 :
2024-03-28 15:44:36 +00:00
raise ValueError ( f " Found multiple models in { path } , not sure which to pick: { files } " )
py : new conversion script (#545)
Current status: Working, except for the latest GPTQ-for-LLaMa format
that includes `g_idx`. This turns out to require changes to GGML, so
for now it only works if you use the `--outtype` option to dequantize it
back to f16 (which is pointless except for debugging).
I also included some cleanup for the C++ code.
This script is meant to replace all the existing conversion scripts
(including the ones that convert from older GGML formats), while also
adding support for some new formats. Specifically, I've tested with:
- [x] `LLaMA` (original)
- [x] `llama-65b-4bit`
- [x] `alpaca-native`
- [x] `alpaca-native-4bit`
- [x] LLaMA converted to 'transformers' format using
`convert_llama_weights_to_hf.py`
- [x] `alpaca-native` quantized with `--true-sequential --act-order
--groupsize 128` (dequantized only)
- [x] same as above plus `--save_safetensors`
- [x] GPT4All
- [x] stock unversioned ggml
- [x] ggmh
There's enough overlap in the logic needed to handle these different
cases that it seemed best to move to a single script.
I haven't tried this with Alpaca-LoRA because I don't know where to find
it.
Useful features:
- Uses multiple threads for a speedup in some cases (though the Python
GIL limits the gain, and sometimes it's disk-bound anyway).
- Combines split models into a single file (both the intra-tensor split
of the original and the inter-tensor split of 'transformers' format
files). Single files are more convenient to work with and more
friendly to future changes to use memory mapping on the C++ side. To
accomplish this without increasing memory requirements, it has some
custom loading code which avoids loading whole input files into memory
at once.
- Because of the custom loading code, it no longer depends in PyTorch,
which might make installing dependencies slightly easier or faster...
although it still depends on NumPy and sentencepiece, so I don't know
if there's any meaningful difference. In any case, I also added a
requirements.txt file to lock the dependency versions in case of any
future breaking changes.
- Type annotations checked with mypy.
- Some attempts to be extra user-friendly:
- The script tries to be forgiving with arguments, e.g. you can
specify either the model file itself or the directory containing
it.
- The script doesn't depend on config.json / params.json, just in
case the user downloaded files individually and doesn't have those
handy. But you still need tokenizer.model and, for Alpaca,
added_tokens.json.
- The script tries to give a helpful error message if
added_tokens.json is missing.
2023-04-14 07:03:03 +00:00
path = files [ 0 ]
paths = find_multifile_paths ( path )
2023-08-31 05:02:23 +00:00
models_plus : list [ ModelPlus ] = [ ]
py : new conversion script (#545)
Current status: Working, except for the latest GPTQ-for-LLaMa format
that includes `g_idx`. This turns out to require changes to GGML, so
for now it only works if you use the `--outtype` option to dequantize it
back to f16 (which is pointless except for debugging).
I also included some cleanup for the C++ code.
This script is meant to replace all the existing conversion scripts
(including the ones that convert from older GGML formats), while also
adding support for some new formats. Specifically, I've tested with:
- [x] `LLaMA` (original)
- [x] `llama-65b-4bit`
- [x] `alpaca-native`
- [x] `alpaca-native-4bit`
- [x] LLaMA converted to 'transformers' format using
`convert_llama_weights_to_hf.py`
- [x] `alpaca-native` quantized with `--true-sequential --act-order
--groupsize 128` (dequantized only)
- [x] same as above plus `--save_safetensors`
- [x] GPT4All
- [x] stock unversioned ggml
- [x] ggmh
There's enough overlap in the logic needed to handle these different
cases that it seemed best to move to a single script.
I haven't tried this with Alpaca-LoRA because I don't know where to find
it.
Useful features:
- Uses multiple threads for a speedup in some cases (though the Python
GIL limits the gain, and sometimes it's disk-bound anyway).
- Combines split models into a single file (both the intra-tensor split
of the original and the inter-tensor split of 'transformers' format
files). Single files are more convenient to work with and more
friendly to future changes to use memory mapping on the C++ side. To
accomplish this without increasing memory requirements, it has some
custom loading code which avoids loading whole input files into memory
at once.
- Because of the custom loading code, it no longer depends in PyTorch,
which might make installing dependencies slightly easier or faster...
although it still depends on NumPy and sentencepiece, so I don't know
if there's any meaningful difference. In any case, I also added a
requirements.txt file to lock the dependency versions in case of any
future breaking changes.
- Type annotations checked with mypy.
- Some attempts to be extra user-friendly:
- The script tries to be forgiving with arguments, e.g. you can
specify either the model file itself or the directory containing
it.
- The script doesn't depend on config.json / params.json, just in
case the user downloaded files individually and doesn't have those
handy. But you still need tokenizer.model and, for Alpaca,
added_tokens.json.
- The script tries to give a helpful error message if
added_tokens.json is missing.
2023-04-14 07:03:03 +00:00
for path in paths :
2024-05-03 19:36:41 +00:00
logger . info ( f " Loading model file { path } " )
py : new conversion script (#545)
Current status: Working, except for the latest GPTQ-for-LLaMa format
that includes `g_idx`. This turns out to require changes to GGML, so
for now it only works if you use the `--outtype` option to dequantize it
back to f16 (which is pointless except for debugging).
I also included some cleanup for the C++ code.
This script is meant to replace all the existing conversion scripts
(including the ones that convert from older GGML formats), while also
adding support for some new formats. Specifically, I've tested with:
- [x] `LLaMA` (original)
- [x] `llama-65b-4bit`
- [x] `alpaca-native`
- [x] `alpaca-native-4bit`
- [x] LLaMA converted to 'transformers' format using
`convert_llama_weights_to_hf.py`
- [x] `alpaca-native` quantized with `--true-sequential --act-order
--groupsize 128` (dequantized only)
- [x] same as above plus `--save_safetensors`
- [x] GPT4All
- [x] stock unversioned ggml
- [x] ggmh
There's enough overlap in the logic needed to handle these different
cases that it seemed best to move to a single script.
I haven't tried this with Alpaca-LoRA because I don't know where to find
it.
Useful features:
- Uses multiple threads for a speedup in some cases (though the Python
GIL limits the gain, and sometimes it's disk-bound anyway).
- Combines split models into a single file (both the intra-tensor split
of the original and the inter-tensor split of 'transformers' format
files). Single files are more convenient to work with and more
friendly to future changes to use memory mapping on the C++ side. To
accomplish this without increasing memory requirements, it has some
custom loading code which avoids loading whole input files into memory
at once.
- Because of the custom loading code, it no longer depends in PyTorch,
which might make installing dependencies slightly easier or faster...
although it still depends on NumPy and sentencepiece, so I don't know
if there's any meaningful difference. In any case, I also added a
requirements.txt file to lock the dependency versions in case of any
future breaking changes.
- Type annotations checked with mypy.
- Some attempts to be extra user-friendly:
- The script tries to be forgiving with arguments, e.g. you can
specify either the model file itself or the directory containing
it.
- The script doesn't depend on config.json / params.json, just in
case the user downloaded files individually and doesn't have those
handy. But you still need tokenizer.model and, for Alpaca,
added_tokens.json.
- The script tries to give a helpful error message if
added_tokens.json is missing.
2023-04-14 07:03:03 +00:00
models_plus . append ( lazy_load_file ( path ) )
model_plus = merge_multifile_models ( models_plus )
return model_plus
convert.py : fix vanilla LLaMA model conversion (#4818)
* Update Imports and Add Notes for Future Reference
- Updated import statements in `convert.py`.
- Added import for `AutoTokenizer` from `transformers` module.
- Added conditional import for `gguf` from the local directory.
- Added comments and notes for future reference.
Additional Notes:
- Noted removal of a redundant `TypeAlias` import.
- Noted the removal of a `gguf` debug statement.
- Commented on the presence of `ARCH` and `NDArray` definitions.
- Commented on cleaning up and refactoring data type definitions.
* Refine Model Hyperparameters and Params Class
- Updated type annotations to use `Optional` for clarity.
- Improved method names and attribute consistency.
- Removed unnecessary variables for better code readability.
Additional Notes:
- Highlighted the use of `Optional` for clearer intent.
- Ensured backward and forward compatibility.
* Restore BpeVocab and SentencePieceVocab classes
- Restored the BpeVocab class for handling BPE tokenization.
- Restored the SentencePieceVocab class for SentencePiece tokenization.
These classes are essential for maintaining the original behavior of the codebase.
* refactor: Standardize vocabulary handling with HfVocab
- Replaced VocabLoader with HfVocab, aligning vocabulary handling across classes.
- Updated initialization of HfVocab with local_files_only=True for AutoTokenizer.
- Introduced optional parameter fname_added_tokens for flexible added token management.
- Streamlined added token handling for clarity and conciseness.
- Maintained special tokens and IDs, enhancing token management.
- Simplified token processing methods for improved readability.
- Added a placeholder for score computation with a default value of -1000.0.
- Optimized newline token check for efficiency.
- Updated __repr__ function for clarity in representation.
- Adjusted type alias Vocab to include BpeVocab, SentencePieceVocab, and HfVocab.
- Removed redundant code related to special token handling, reverse vocabulary mapping, and vocabulary file detection.
This refactoring promotes a standardized and modular approach to vocabulary management, facilitating future integration with a VocabFactory and improving code maintainability and scalability.
* refactor: Enhance readability, functionality, and code quality
- Improved code formatting and readability for better maintainability.
- Refactored LazyUnpickler's CLASSES dictionary for clarity.
- Added print statements and warnings in check_vocab_size for user feedback.
- Removed find_vocab_file_path, as it's superseded by VocabFactory.
- Preparatory changes for upcoming classes: OutputFile and VocabFactory.
- Overall focus on code quality, error handling, and consistency.
These changes reflect a continuous effort to refine the codebase, ensuring it meets best practices and prepares for future enhancements, such as the VocabFactory.
* refactor: Update OutputFile class for enhanced model vocabulary management
- Restructured the constructor for improved readability.
- Updated `add_meta_arch` method for flexible model name determination.
- Introduced `handle_tokenizer_model` for mapping vocab types to supported tokenizer models.
- Streamlined vocabulary extraction with `extract_vocabulary_from_model`.
- Simplified vocabulary metadata addition using `add_meta_vocab`.
- Refactored `add_tensor_info` for clarity and consistency.
- Improved error handling for better user feedback.
These changes signify the development of a versatile and comprehensive `OutputFile` class, enabling efficient management of model conversion output, metadata, vocabulary, and tensor information.
* feat: Introduce VocabFactory for flexible vocabulary management in model conversion
- The VocabFactory class is added to facilitate modular vocabulary handling.
- The constructor initializes a directory path and detects vocabulary-related files.
- The _select_file method provides file paths based on vocabulary type (e.g., BPE, SentencePiece).
- _create_special_vocab generates special vocabularies, accommodating different types.
- The load_vocab method loads vocabularies, handling BPE, SentencePiece, and Hugging Face Fast Tokenizer.
- Error handling and logging enhance debugging and user feedback.
- The modular and flexible design simplifies vocabulary management and supports future extensions.
The VocabFactory class enhances code modularity and maintainability, allowing versatile vocabulary handling in the model conversion process.
* refactor: Improve code organization, argument parsing, and user interface
- Renamed 'default_outfile' to 'default_output_file' for clarity.
- Refactored argument parser setup into 'get_argument_parser' function.
- Introduced descriptive comments for each argument in the parser.
- Added '--vocab-type' argument with choices ["spm", "bpe", "hfft"] for vocabulary processing.
- Improved flag naming consistency: '--outfile' to '--out-file' and '--bigendian' to '--big-endian'.
- Enhanced error handling to prevent overwriting input data in 'default_output_file'.
- Made 'argv' in 'main' an optional parameter for flexibility.
- Introduced dynamic import for 'awq.apply_awq' based on 'args.awq_path' for conditional dependency.
These changes enhance code clarity, organization, and the user interface of the script, aligning it with Python best practices and improving maintainability.
* refactor: Further refine functionality, improve user interaction, and streamline vocabulary handling
- Renamed command-line arguments for clarity and consistency.
- Improved path resolution and import adjustments for robustness.
- Thoughtfully handled 'awq-path' and conditional logic for the weighted model.
- Enhanced model and vocabulary loading with the 'VocabFactory' class for structured and adaptable loading.
- Strengthened error handling and user feedback for a more user-friendly experience.
- Structured output file handling with clear conditions and defaults.
- Streamlined and organized the 'main' function for better logic flow.
- Passed 'sys.argv[1:]' to 'main' for adaptability and testability.
These changes solidify the script's functionality, making it more robust, user-friendly, and adaptable. The use of the 'VocabFactory' class is a notable enhancement in efficient vocabulary handling, reflecting a thoughtful and iterative approach to script development.
* chore: Apply ruff formatting to convert.py
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
* Revert to commit 0614c33
* chore: Apply flake8 formatting rules
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
* refactor: Revise `check_vocab_size` for Enhanced Clarity and Correctness
- Resolved an unreachable branch issue by reorganizing the conditional structure.
- Moved the special case check for `params.n_vocab == -1` to the top for immediate assertion.
- Flattened the conditional logic for improved clarity and predictability of the function's behavior.
These changes enhance the readability and functional correctness of the `check_vocab_size` function without altering its intended functionality.
* py : fix outfile and outtype
* py : suggest hint for missing vocab size
---------
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-01-09 18:46:46 +00:00
class VocabFactory :
2024-03-28 15:44:36 +00:00
_VOCAB_CLASSES : list [ type [ Vocab ] ] = [ SentencePieceVocab , BpeVocab , LlamaHfVocab ]
2024-03-02 17:27:26 +00:00
convert.py : fix vanilla LLaMA model conversion (#4818)
* Update Imports and Add Notes for Future Reference
- Updated import statements in `convert.py`.
- Added import for `AutoTokenizer` from `transformers` module.
- Added conditional import for `gguf` from the local directory.
- Added comments and notes for future reference.
Additional Notes:
- Noted removal of a redundant `TypeAlias` import.
- Noted the removal of a `gguf` debug statement.
- Commented on the presence of `ARCH` and `NDArray` definitions.
- Commented on cleaning up and refactoring data type definitions.
* Refine Model Hyperparameters and Params Class
- Updated type annotations to use `Optional` for clarity.
- Improved method names and attribute consistency.
- Removed unnecessary variables for better code readability.
Additional Notes:
- Highlighted the use of `Optional` for clearer intent.
- Ensured backward and forward compatibility.
* Restore BpeVocab and SentencePieceVocab classes
- Restored the BpeVocab class for handling BPE tokenization.
- Restored the SentencePieceVocab class for SentencePiece tokenization.
These classes are essential for maintaining the original behavior of the codebase.
* refactor: Standardize vocabulary handling with HfVocab
- Replaced VocabLoader with HfVocab, aligning vocabulary handling across classes.
- Updated initialization of HfVocab with local_files_only=True for AutoTokenizer.
- Introduced optional parameter fname_added_tokens for flexible added token management.
- Streamlined added token handling for clarity and conciseness.
- Maintained special tokens and IDs, enhancing token management.
- Simplified token processing methods for improved readability.
- Added a placeholder for score computation with a default value of -1000.0.
- Optimized newline token check for efficiency.
- Updated __repr__ function for clarity in representation.
- Adjusted type alias Vocab to include BpeVocab, SentencePieceVocab, and HfVocab.
- Removed redundant code related to special token handling, reverse vocabulary mapping, and vocabulary file detection.
This refactoring promotes a standardized and modular approach to vocabulary management, facilitating future integration with a VocabFactory and improving code maintainability and scalability.
* refactor: Enhance readability, functionality, and code quality
- Improved code formatting and readability for better maintainability.
- Refactored LazyUnpickler's CLASSES dictionary for clarity.
- Added print statements and warnings in check_vocab_size for user feedback.
- Removed find_vocab_file_path, as it's superseded by VocabFactory.
- Preparatory changes for upcoming classes: OutputFile and VocabFactory.
- Overall focus on code quality, error handling, and consistency.
These changes reflect a continuous effort to refine the codebase, ensuring it meets best practices and prepares for future enhancements, such as the VocabFactory.
* refactor: Update OutputFile class for enhanced model vocabulary management
- Restructured the constructor for improved readability.
- Updated `add_meta_arch` method for flexible model name determination.
- Introduced `handle_tokenizer_model` for mapping vocab types to supported tokenizer models.
- Streamlined vocabulary extraction with `extract_vocabulary_from_model`.
- Simplified vocabulary metadata addition using `add_meta_vocab`.
- Refactored `add_tensor_info` for clarity and consistency.
- Improved error handling for better user feedback.
These changes signify the development of a versatile and comprehensive `OutputFile` class, enabling efficient management of model conversion output, metadata, vocabulary, and tensor information.
* feat: Introduce VocabFactory for flexible vocabulary management in model conversion
- The VocabFactory class is added to facilitate modular vocabulary handling.
- The constructor initializes a directory path and detects vocabulary-related files.
- The _select_file method provides file paths based on vocabulary type (e.g., BPE, SentencePiece).
- _create_special_vocab generates special vocabularies, accommodating different types.
- The load_vocab method loads vocabularies, handling BPE, SentencePiece, and Hugging Face Fast Tokenizer.
- Error handling and logging enhance debugging and user feedback.
- The modular and flexible design simplifies vocabulary management and supports future extensions.
The VocabFactory class enhances code modularity and maintainability, allowing versatile vocabulary handling in the model conversion process.
* refactor: Improve code organization, argument parsing, and user interface
- Renamed 'default_outfile' to 'default_output_file' for clarity.
- Refactored argument parser setup into 'get_argument_parser' function.
- Introduced descriptive comments for each argument in the parser.
- Added '--vocab-type' argument with choices ["spm", "bpe", "hfft"] for vocabulary processing.
- Improved flag naming consistency: '--outfile' to '--out-file' and '--bigendian' to '--big-endian'.
- Enhanced error handling to prevent overwriting input data in 'default_output_file'.
- Made 'argv' in 'main' an optional parameter for flexibility.
- Introduced dynamic import for 'awq.apply_awq' based on 'args.awq_path' for conditional dependency.
These changes enhance code clarity, organization, and the user interface of the script, aligning it with Python best practices and improving maintainability.
* refactor: Further refine functionality, improve user interaction, and streamline vocabulary handling
- Renamed command-line arguments for clarity and consistency.
- Improved path resolution and import adjustments for robustness.
- Thoughtfully handled 'awq-path' and conditional logic for the weighted model.
- Enhanced model and vocabulary loading with the 'VocabFactory' class for structured and adaptable loading.
- Strengthened error handling and user feedback for a more user-friendly experience.
- Structured output file handling with clear conditions and defaults.
- Streamlined and organized the 'main' function for better logic flow.
- Passed 'sys.argv[1:]' to 'main' for adaptability and testability.
These changes solidify the script's functionality, making it more robust, user-friendly, and adaptable. The use of the 'VocabFactory' class is a notable enhancement in efficient vocabulary handling, reflecting a thoughtful and iterative approach to script development.
* chore: Apply ruff formatting to convert.py
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
* Revert to commit 0614c33
* chore: Apply flake8 formatting rules
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
* refactor: Revise `check_vocab_size` for Enhanced Clarity and Correctness
- Resolved an unreachable branch issue by reorganizing the conditional structure.
- Moved the special case check for `params.n_vocab == -1` to the top for immediate assertion.
- Flattened the conditional logic for improved clarity and predictability of the function's behavior.
These changes enhance the readability and functional correctness of the `check_vocab_size` function without altering its intended functionality.
* py : fix outfile and outtype
* py : suggest hint for missing vocab size
---------
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-01-09 18:46:46 +00:00
def __init__ ( self , path : Path ) :
self . path = path
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def _create_special_vocab ( self , vocab : BaseVocab , model_parent_path : Path ) - > gguf . SpecialVocab :
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load_merges = vocab . name == " bpe "
2024-03-28 15:44:36 +00:00
n_vocab = vocab . vocab_size if isinstance ( vocab , Vocab ) else None
convert.py : fix vanilla LLaMA model conversion (#4818)
* Update Imports and Add Notes for Future Reference
- Updated import statements in `convert.py`.
- Added import for `AutoTokenizer` from `transformers` module.
- Added conditional import for `gguf` from the local directory.
- Added comments and notes for future reference.
Additional Notes:
- Noted removal of a redundant `TypeAlias` import.
- Noted the removal of a `gguf` debug statement.
- Commented on the presence of `ARCH` and `NDArray` definitions.
- Commented on cleaning up and refactoring data type definitions.
* Refine Model Hyperparameters and Params Class
- Updated type annotations to use `Optional` for clarity.
- Improved method names and attribute consistency.
- Removed unnecessary variables for better code readability.
Additional Notes:
- Highlighted the use of `Optional` for clearer intent.
- Ensured backward and forward compatibility.
* Restore BpeVocab and SentencePieceVocab classes
- Restored the BpeVocab class for handling BPE tokenization.
- Restored the SentencePieceVocab class for SentencePiece tokenization.
These classes are essential for maintaining the original behavior of the codebase.
* refactor: Standardize vocabulary handling with HfVocab
- Replaced VocabLoader with HfVocab, aligning vocabulary handling across classes.
- Updated initialization of HfVocab with local_files_only=True for AutoTokenizer.
- Introduced optional parameter fname_added_tokens for flexible added token management.
- Streamlined added token handling for clarity and conciseness.
- Maintained special tokens and IDs, enhancing token management.
- Simplified token processing methods for improved readability.
- Added a placeholder for score computation with a default value of -1000.0.
- Optimized newline token check for efficiency.
- Updated __repr__ function for clarity in representation.
- Adjusted type alias Vocab to include BpeVocab, SentencePieceVocab, and HfVocab.
- Removed redundant code related to special token handling, reverse vocabulary mapping, and vocabulary file detection.
This refactoring promotes a standardized and modular approach to vocabulary management, facilitating future integration with a VocabFactory and improving code maintainability and scalability.
* refactor: Enhance readability, functionality, and code quality
- Improved code formatting and readability for better maintainability.
- Refactored LazyUnpickler's CLASSES dictionary for clarity.
- Added print statements and warnings in check_vocab_size for user feedback.
- Removed find_vocab_file_path, as it's superseded by VocabFactory.
- Preparatory changes for upcoming classes: OutputFile and VocabFactory.
- Overall focus on code quality, error handling, and consistency.
These changes reflect a continuous effort to refine the codebase, ensuring it meets best practices and prepares for future enhancements, such as the VocabFactory.
* refactor: Update OutputFile class for enhanced model vocabulary management
- Restructured the constructor for improved readability.
- Updated `add_meta_arch` method for flexible model name determination.
- Introduced `handle_tokenizer_model` for mapping vocab types to supported tokenizer models.
- Streamlined vocabulary extraction with `extract_vocabulary_from_model`.
- Simplified vocabulary metadata addition using `add_meta_vocab`.
- Refactored `add_tensor_info` for clarity and consistency.
- Improved error handling for better user feedback.
These changes signify the development of a versatile and comprehensive `OutputFile` class, enabling efficient management of model conversion output, metadata, vocabulary, and tensor information.
* feat: Introduce VocabFactory for flexible vocabulary management in model conversion
- The VocabFactory class is added to facilitate modular vocabulary handling.
- The constructor initializes a directory path and detects vocabulary-related files.
- The _select_file method provides file paths based on vocabulary type (e.g., BPE, SentencePiece).
- _create_special_vocab generates special vocabularies, accommodating different types.
- The load_vocab method loads vocabularies, handling BPE, SentencePiece, and Hugging Face Fast Tokenizer.
- Error handling and logging enhance debugging and user feedback.
- The modular and flexible design simplifies vocabulary management and supports future extensions.
The VocabFactory class enhances code modularity and maintainability, allowing versatile vocabulary handling in the model conversion process.
* refactor: Improve code organization, argument parsing, and user interface
- Renamed 'default_outfile' to 'default_output_file' for clarity.
- Refactored argument parser setup into 'get_argument_parser' function.
- Introduced descriptive comments for each argument in the parser.
- Added '--vocab-type' argument with choices ["spm", "bpe", "hfft"] for vocabulary processing.
- Improved flag naming consistency: '--outfile' to '--out-file' and '--bigendian' to '--big-endian'.
- Enhanced error handling to prevent overwriting input data in 'default_output_file'.
- Made 'argv' in 'main' an optional parameter for flexibility.
- Introduced dynamic import for 'awq.apply_awq' based on 'args.awq_path' for conditional dependency.
These changes enhance code clarity, organization, and the user interface of the script, aligning it with Python best practices and improving maintainability.
* refactor: Further refine functionality, improve user interaction, and streamline vocabulary handling
- Renamed command-line arguments for clarity and consistency.
- Improved path resolution and import adjustments for robustness.
- Thoughtfully handled 'awq-path' and conditional logic for the weighted model.
- Enhanced model and vocabulary loading with the 'VocabFactory' class for structured and adaptable loading.
- Strengthened error handling and user feedback for a more user-friendly experience.
- Structured output file handling with clear conditions and defaults.
- Streamlined and organized the 'main' function for better logic flow.
- Passed 'sys.argv[1:]' to 'main' for adaptability and testability.
These changes solidify the script's functionality, making it more robust, user-friendly, and adaptable. The use of the 'VocabFactory' class is a notable enhancement in efficient vocabulary handling, reflecting a thoughtful and iterative approach to script development.
* chore: Apply ruff formatting to convert.py
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
* Revert to commit 0614c33
* chore: Apply flake8 formatting rules
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
* refactor: Revise `check_vocab_size` for Enhanced Clarity and Correctness
- Resolved an unreachable branch issue by reorganizing the conditional structure.
- Moved the special case check for `params.n_vocab == -1` to the top for immediate assertion.
- Flattened the conditional logic for improved clarity and predictability of the function's behavior.
These changes enhance the readability and functional correctness of the `check_vocab_size` function without altering its intended functionality.
* py : fix outfile and outtype
* py : suggest hint for missing vocab size
---------
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-01-09 18:46:46 +00:00
return gguf . SpecialVocab (
model_parent_path ,
load_merges = load_merges ,
special_token_types = None , # Predetermined or passed as a parameter
n_vocab = n_vocab ,
)
2023-08-21 20:07:43 +00:00
2024-03-14 16:21:56 +00:00
def _create_vocab_by_path ( self , vocab_types : list [ str ] ) - > Vocab :
2024-03-28 15:44:36 +00:00
vocab_classes : dict [ str , type [ Vocab ] ] = { cls . name : cls for cls in self . _VOCAB_CLASSES }
selected_vocabs : dict [ str , type [ Vocab ] ] = { }
for vtype in vocab_types :
try :
selected_vocabs [ vtype ] = vocab_classes [ vtype ]
except KeyError :
raise ValueError ( f " Unsupported vocabulary type { vtype } " ) from None
convert.py : fix vanilla LLaMA model conversion (#4818)
* Update Imports and Add Notes for Future Reference
- Updated import statements in `convert.py`.
- Added import for `AutoTokenizer` from `transformers` module.
- Added conditional import for `gguf` from the local directory.
- Added comments and notes for future reference.
Additional Notes:
- Noted removal of a redundant `TypeAlias` import.
- Noted the removal of a `gguf` debug statement.
- Commented on the presence of `ARCH` and `NDArray` definitions.
- Commented on cleaning up and refactoring data type definitions.
* Refine Model Hyperparameters and Params Class
- Updated type annotations to use `Optional` for clarity.
- Improved method names and attribute consistency.
- Removed unnecessary variables for better code readability.
Additional Notes:
- Highlighted the use of `Optional` for clearer intent.
- Ensured backward and forward compatibility.
* Restore BpeVocab and SentencePieceVocab classes
- Restored the BpeVocab class for handling BPE tokenization.
- Restored the SentencePieceVocab class for SentencePiece tokenization.
These classes are essential for maintaining the original behavior of the codebase.
* refactor: Standardize vocabulary handling with HfVocab
- Replaced VocabLoader with HfVocab, aligning vocabulary handling across classes.
- Updated initialization of HfVocab with local_files_only=True for AutoTokenizer.
- Introduced optional parameter fname_added_tokens for flexible added token management.
- Streamlined added token handling for clarity and conciseness.
- Maintained special tokens and IDs, enhancing token management.
- Simplified token processing methods for improved readability.
- Added a placeholder for score computation with a default value of -1000.0.
- Optimized newline token check for efficiency.
- Updated __repr__ function for clarity in representation.
- Adjusted type alias Vocab to include BpeVocab, SentencePieceVocab, and HfVocab.
- Removed redundant code related to special token handling, reverse vocabulary mapping, and vocabulary file detection.
This refactoring promotes a standardized and modular approach to vocabulary management, facilitating future integration with a VocabFactory and improving code maintainability and scalability.
* refactor: Enhance readability, functionality, and code quality
- Improved code formatting and readability for better maintainability.
- Refactored LazyUnpickler's CLASSES dictionary for clarity.
- Added print statements and warnings in check_vocab_size for user feedback.
- Removed find_vocab_file_path, as it's superseded by VocabFactory.
- Preparatory changes for upcoming classes: OutputFile and VocabFactory.
- Overall focus on code quality, error handling, and consistency.
These changes reflect a continuous effort to refine the codebase, ensuring it meets best practices and prepares for future enhancements, such as the VocabFactory.
* refactor: Update OutputFile class for enhanced model vocabulary management
- Restructured the constructor for improved readability.
- Updated `add_meta_arch` method for flexible model name determination.
- Introduced `handle_tokenizer_model` for mapping vocab types to supported tokenizer models.
- Streamlined vocabulary extraction with `extract_vocabulary_from_model`.
- Simplified vocabulary metadata addition using `add_meta_vocab`.
- Refactored `add_tensor_info` for clarity and consistency.
- Improved error handling for better user feedback.
These changes signify the development of a versatile and comprehensive `OutputFile` class, enabling efficient management of model conversion output, metadata, vocabulary, and tensor information.
* feat: Introduce VocabFactory for flexible vocabulary management in model conversion
- The VocabFactory class is added to facilitate modular vocabulary handling.
- The constructor initializes a directory path and detects vocabulary-related files.
- The _select_file method provides file paths based on vocabulary type (e.g., BPE, SentencePiece).
- _create_special_vocab generates special vocabularies, accommodating different types.
- The load_vocab method loads vocabularies, handling BPE, SentencePiece, and Hugging Face Fast Tokenizer.
- Error handling and logging enhance debugging and user feedback.
- The modular and flexible design simplifies vocabulary management and supports future extensions.
The VocabFactory class enhances code modularity and maintainability, allowing versatile vocabulary handling in the model conversion process.
* refactor: Improve code organization, argument parsing, and user interface
- Renamed 'default_outfile' to 'default_output_file' for clarity.
- Refactored argument parser setup into 'get_argument_parser' function.
- Introduced descriptive comments for each argument in the parser.
- Added '--vocab-type' argument with choices ["spm", "bpe", "hfft"] for vocabulary processing.
- Improved flag naming consistency: '--outfile' to '--out-file' and '--bigendian' to '--big-endian'.
- Enhanced error handling to prevent overwriting input data in 'default_output_file'.
- Made 'argv' in 'main' an optional parameter for flexibility.
- Introduced dynamic import for 'awq.apply_awq' based on 'args.awq_path' for conditional dependency.
These changes enhance code clarity, organization, and the user interface of the script, aligning it with Python best practices and improving maintainability.
* refactor: Further refine functionality, improve user interaction, and streamline vocabulary handling
- Renamed command-line arguments for clarity and consistency.
- Improved path resolution and import adjustments for robustness.
- Thoughtfully handled 'awq-path' and conditional logic for the weighted model.
- Enhanced model and vocabulary loading with the 'VocabFactory' class for structured and adaptable loading.
- Strengthened error handling and user feedback for a more user-friendly experience.
- Structured output file handling with clear conditions and defaults.
- Streamlined and organized the 'main' function for better logic flow.
- Passed 'sys.argv[1:]' to 'main' for adaptability and testability.
These changes solidify the script's functionality, making it more robust, user-friendly, and adaptable. The use of the 'VocabFactory' class is a notable enhancement in efficient vocabulary handling, reflecting a thoughtful and iterative approach to script development.
* chore: Apply ruff formatting to convert.py
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
* Revert to commit 0614c33
* chore: Apply flake8 formatting rules
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
* refactor: Revise `check_vocab_size` for Enhanced Clarity and Correctness
- Resolved an unreachable branch issue by reorganizing the conditional structure.
- Moved the special case check for `params.n_vocab == -1` to the top for immediate assertion.
- Flattened the conditional logic for improved clarity and predictability of the function's behavior.
These changes enhance the readability and functional correctness of the `check_vocab_size` function without altering its intended functionality.
* py : fix outfile and outtype
* py : suggest hint for missing vocab size
---------
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-01-09 18:46:46 +00:00
2024-03-28 15:44:36 +00:00
for vtype , cls in selected_vocabs . items ( ) :
try :
vocab = cls ( self . path )
break
except FileNotFoundError :
pass # ignore unavailable tokenizers
else :
raise FileNotFoundError ( f " Could not find a tokenizer matching any of { vocab_types } " )
2024-05-03 19:36:41 +00:00
logger . info ( f " Loaded vocab file { vocab . fname_tokenizer !r} , type { vocab . name !r} " )
2024-03-28 15:44:36 +00:00
return vocab
2024-03-14 16:21:56 +00:00
2024-03-28 15:44:36 +00:00
def load_vocab ( self , vocab_types : list [ str ] | None , model_parent_path : Path ) - > tuple [ BaseVocab , gguf . SpecialVocab ] :
vocab : BaseVocab
if vocab_types is None :
2024-03-14 16:21:56 +00:00
vocab = NoVocab ( )
convert.py : fix vanilla LLaMA model conversion (#4818)
* Update Imports and Add Notes for Future Reference
- Updated import statements in `convert.py`.
- Added import for `AutoTokenizer` from `transformers` module.
- Added conditional import for `gguf` from the local directory.
- Added comments and notes for future reference.
Additional Notes:
- Noted removal of a redundant `TypeAlias` import.
- Noted the removal of a `gguf` debug statement.
- Commented on the presence of `ARCH` and `NDArray` definitions.
- Commented on cleaning up and refactoring data type definitions.
* Refine Model Hyperparameters and Params Class
- Updated type annotations to use `Optional` for clarity.
- Improved method names and attribute consistency.
- Removed unnecessary variables for better code readability.
Additional Notes:
- Highlighted the use of `Optional` for clearer intent.
- Ensured backward and forward compatibility.
* Restore BpeVocab and SentencePieceVocab classes
- Restored the BpeVocab class for handling BPE tokenization.
- Restored the SentencePieceVocab class for SentencePiece tokenization.
These classes are essential for maintaining the original behavior of the codebase.
* refactor: Standardize vocabulary handling with HfVocab
- Replaced VocabLoader with HfVocab, aligning vocabulary handling across classes.
- Updated initialization of HfVocab with local_files_only=True for AutoTokenizer.
- Introduced optional parameter fname_added_tokens for flexible added token management.
- Streamlined added token handling for clarity and conciseness.
- Maintained special tokens and IDs, enhancing token management.
- Simplified token processing methods for improved readability.
- Added a placeholder for score computation with a default value of -1000.0.
- Optimized newline token check for efficiency.
- Updated __repr__ function for clarity in representation.
- Adjusted type alias Vocab to include BpeVocab, SentencePieceVocab, and HfVocab.
- Removed redundant code related to special token handling, reverse vocabulary mapping, and vocabulary file detection.
This refactoring promotes a standardized and modular approach to vocabulary management, facilitating future integration with a VocabFactory and improving code maintainability and scalability.
* refactor: Enhance readability, functionality, and code quality
- Improved code formatting and readability for better maintainability.
- Refactored LazyUnpickler's CLASSES dictionary for clarity.
- Added print statements and warnings in check_vocab_size for user feedback.
- Removed find_vocab_file_path, as it's superseded by VocabFactory.
- Preparatory changes for upcoming classes: OutputFile and VocabFactory.
- Overall focus on code quality, error handling, and consistency.
These changes reflect a continuous effort to refine the codebase, ensuring it meets best practices and prepares for future enhancements, such as the VocabFactory.
* refactor: Update OutputFile class for enhanced model vocabulary management
- Restructured the constructor for improved readability.
- Updated `add_meta_arch` method for flexible model name determination.
- Introduced `handle_tokenizer_model` for mapping vocab types to supported tokenizer models.
- Streamlined vocabulary extraction with `extract_vocabulary_from_model`.
- Simplified vocabulary metadata addition using `add_meta_vocab`.
- Refactored `add_tensor_info` for clarity and consistency.
- Improved error handling for better user feedback.
These changes signify the development of a versatile and comprehensive `OutputFile` class, enabling efficient management of model conversion output, metadata, vocabulary, and tensor information.
* feat: Introduce VocabFactory for flexible vocabulary management in model conversion
- The VocabFactory class is added to facilitate modular vocabulary handling.
- The constructor initializes a directory path and detects vocabulary-related files.
- The _select_file method provides file paths based on vocabulary type (e.g., BPE, SentencePiece).
- _create_special_vocab generates special vocabularies, accommodating different types.
- The load_vocab method loads vocabularies, handling BPE, SentencePiece, and Hugging Face Fast Tokenizer.
- Error handling and logging enhance debugging and user feedback.
- The modular and flexible design simplifies vocabulary management and supports future extensions.
The VocabFactory class enhances code modularity and maintainability, allowing versatile vocabulary handling in the model conversion process.
* refactor: Improve code organization, argument parsing, and user interface
- Renamed 'default_outfile' to 'default_output_file' for clarity.
- Refactored argument parser setup into 'get_argument_parser' function.
- Introduced descriptive comments for each argument in the parser.
- Added '--vocab-type' argument with choices ["spm", "bpe", "hfft"] for vocabulary processing.
- Improved flag naming consistency: '--outfile' to '--out-file' and '--bigendian' to '--big-endian'.
- Enhanced error handling to prevent overwriting input data in 'default_output_file'.
- Made 'argv' in 'main' an optional parameter for flexibility.
- Introduced dynamic import for 'awq.apply_awq' based on 'args.awq_path' for conditional dependency.
These changes enhance code clarity, organization, and the user interface of the script, aligning it with Python best practices and improving maintainability.
* refactor: Further refine functionality, improve user interaction, and streamline vocabulary handling
- Renamed command-line arguments for clarity and consistency.
- Improved path resolution and import adjustments for robustness.
- Thoughtfully handled 'awq-path' and conditional logic for the weighted model.
- Enhanced model and vocabulary loading with the 'VocabFactory' class for structured and adaptable loading.
- Strengthened error handling and user feedback for a more user-friendly experience.
- Structured output file handling with clear conditions and defaults.
- Streamlined and organized the 'main' function for better logic flow.
- Passed 'sys.argv[1:]' to 'main' for adaptability and testability.
These changes solidify the script's functionality, making it more robust, user-friendly, and adaptable. The use of the 'VocabFactory' class is a notable enhancement in efficient vocabulary handling, reflecting a thoughtful and iterative approach to script development.
* chore: Apply ruff formatting to convert.py
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
* Revert to commit 0614c33
* chore: Apply flake8 formatting rules
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
* refactor: Revise `check_vocab_size` for Enhanced Clarity and Correctness
- Resolved an unreachable branch issue by reorganizing the conditional structure.
- Moved the special case check for `params.n_vocab == -1` to the top for immediate assertion.
- Flattened the conditional logic for improved clarity and predictability of the function's behavior.
These changes enhance the readability and functional correctness of the `check_vocab_size` function without altering its intended functionality.
* py : fix outfile and outtype
* py : suggest hint for missing vocab size
---------
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-01-09 18:46:46 +00:00
else :
2024-03-14 16:21:56 +00:00
vocab = self . _create_vocab_by_path ( vocab_types )
2024-01-20 23:14:18 +00:00
# FIXME: Respect --vocab-dir?
convert.py : fix vanilla LLaMA model conversion (#4818)
* Update Imports and Add Notes for Future Reference
- Updated import statements in `convert.py`.
- Added import for `AutoTokenizer` from `transformers` module.
- Added conditional import for `gguf` from the local directory.
- Added comments and notes for future reference.
Additional Notes:
- Noted removal of a redundant `TypeAlias` import.
- Noted the removal of a `gguf` debug statement.
- Commented on the presence of `ARCH` and `NDArray` definitions.
- Commented on cleaning up and refactoring data type definitions.
* Refine Model Hyperparameters and Params Class
- Updated type annotations to use `Optional` for clarity.
- Improved method names and attribute consistency.
- Removed unnecessary variables for better code readability.
Additional Notes:
- Highlighted the use of `Optional` for clearer intent.
- Ensured backward and forward compatibility.
* Restore BpeVocab and SentencePieceVocab classes
- Restored the BpeVocab class for handling BPE tokenization.
- Restored the SentencePieceVocab class for SentencePiece tokenization.
These classes are essential for maintaining the original behavior of the codebase.
* refactor: Standardize vocabulary handling with HfVocab
- Replaced VocabLoader with HfVocab, aligning vocabulary handling across classes.
- Updated initialization of HfVocab with local_files_only=True for AutoTokenizer.
- Introduced optional parameter fname_added_tokens for flexible added token management.
- Streamlined added token handling for clarity and conciseness.
- Maintained special tokens and IDs, enhancing token management.
- Simplified token processing methods for improved readability.
- Added a placeholder for score computation with a default value of -1000.0.
- Optimized newline token check for efficiency.
- Updated __repr__ function for clarity in representation.
- Adjusted type alias Vocab to include BpeVocab, SentencePieceVocab, and HfVocab.
- Removed redundant code related to special token handling, reverse vocabulary mapping, and vocabulary file detection.
This refactoring promotes a standardized and modular approach to vocabulary management, facilitating future integration with a VocabFactory and improving code maintainability and scalability.
* refactor: Enhance readability, functionality, and code quality
- Improved code formatting and readability for better maintainability.
- Refactored LazyUnpickler's CLASSES dictionary for clarity.
- Added print statements and warnings in check_vocab_size for user feedback.
- Removed find_vocab_file_path, as it's superseded by VocabFactory.
- Preparatory changes for upcoming classes: OutputFile and VocabFactory.
- Overall focus on code quality, error handling, and consistency.
These changes reflect a continuous effort to refine the codebase, ensuring it meets best practices and prepares for future enhancements, such as the VocabFactory.
* refactor: Update OutputFile class for enhanced model vocabulary management
- Restructured the constructor for improved readability.
- Updated `add_meta_arch` method for flexible model name determination.
- Introduced `handle_tokenizer_model` for mapping vocab types to supported tokenizer models.
- Streamlined vocabulary extraction with `extract_vocabulary_from_model`.
- Simplified vocabulary metadata addition using `add_meta_vocab`.
- Refactored `add_tensor_info` for clarity and consistency.
- Improved error handling for better user feedback.
These changes signify the development of a versatile and comprehensive `OutputFile` class, enabling efficient management of model conversion output, metadata, vocabulary, and tensor information.
* feat: Introduce VocabFactory for flexible vocabulary management in model conversion
- The VocabFactory class is added to facilitate modular vocabulary handling.
- The constructor initializes a directory path and detects vocabulary-related files.
- The _select_file method provides file paths based on vocabulary type (e.g., BPE, SentencePiece).
- _create_special_vocab generates special vocabularies, accommodating different types.
- The load_vocab method loads vocabularies, handling BPE, SentencePiece, and Hugging Face Fast Tokenizer.
- Error handling and logging enhance debugging and user feedback.
- The modular and flexible design simplifies vocabulary management and supports future extensions.
The VocabFactory class enhances code modularity and maintainability, allowing versatile vocabulary handling in the model conversion process.
* refactor: Improve code organization, argument parsing, and user interface
- Renamed 'default_outfile' to 'default_output_file' for clarity.
- Refactored argument parser setup into 'get_argument_parser' function.
- Introduced descriptive comments for each argument in the parser.
- Added '--vocab-type' argument with choices ["spm", "bpe", "hfft"] for vocabulary processing.
- Improved flag naming consistency: '--outfile' to '--out-file' and '--bigendian' to '--big-endian'.
- Enhanced error handling to prevent overwriting input data in 'default_output_file'.
- Made 'argv' in 'main' an optional parameter for flexibility.
- Introduced dynamic import for 'awq.apply_awq' based on 'args.awq_path' for conditional dependency.
These changes enhance code clarity, organization, and the user interface of the script, aligning it with Python best practices and improving maintainability.
* refactor: Further refine functionality, improve user interaction, and streamline vocabulary handling
- Renamed command-line arguments for clarity and consistency.
- Improved path resolution and import adjustments for robustness.
- Thoughtfully handled 'awq-path' and conditional logic for the weighted model.
- Enhanced model and vocabulary loading with the 'VocabFactory' class for structured and adaptable loading.
- Strengthened error handling and user feedback for a more user-friendly experience.
- Structured output file handling with clear conditions and defaults.
- Streamlined and organized the 'main' function for better logic flow.
- Passed 'sys.argv[1:]' to 'main' for adaptability and testability.
These changes solidify the script's functionality, making it more robust, user-friendly, and adaptable. The use of the 'VocabFactory' class is a notable enhancement in efficient vocabulary handling, reflecting a thoughtful and iterative approach to script development.
* chore: Apply ruff formatting to convert.py
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
* Revert to commit 0614c33
* chore: Apply flake8 formatting rules
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
* refactor: Revise `check_vocab_size` for Enhanced Clarity and Correctness
- Resolved an unreachable branch issue by reorganizing the conditional structure.
- Moved the special case check for `params.n_vocab == -1` to the top for immediate assertion.
- Flattened the conditional logic for improved clarity and predictability of the function's behavior.
These changes enhance the readability and functional correctness of the `check_vocab_size` function without altering its intended functionality.
* py : fix outfile and outtype
* py : suggest hint for missing vocab size
---------
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-01-09 18:46:46 +00:00
special_vocab = self . _create_special_vocab (
vocab ,
model_parent_path ,
)
return vocab , special_vocab
py : new conversion script (#545)
Current status: Working, except for the latest GPTQ-for-LLaMa format
that includes `g_idx`. This turns out to require changes to GGML, so
for now it only works if you use the `--outtype` option to dequantize it
back to f16 (which is pointless except for debugging).
I also included some cleanup for the C++ code.
This script is meant to replace all the existing conversion scripts
(including the ones that convert from older GGML formats), while also
adding support for some new formats. Specifically, I've tested with:
- [x] `LLaMA` (original)
- [x] `llama-65b-4bit`
- [x] `alpaca-native`
- [x] `alpaca-native-4bit`
- [x] LLaMA converted to 'transformers' format using
`convert_llama_weights_to_hf.py`
- [x] `alpaca-native` quantized with `--true-sequential --act-order
--groupsize 128` (dequantized only)
- [x] same as above plus `--save_safetensors`
- [x] GPT4All
- [x] stock unversioned ggml
- [x] ggmh
There's enough overlap in the logic needed to handle these different
cases that it seemed best to move to a single script.
I haven't tried this with Alpaca-LoRA because I don't know where to find
it.
Useful features:
- Uses multiple threads for a speedup in some cases (though the Python
GIL limits the gain, and sometimes it's disk-bound anyway).
- Combines split models into a single file (both the intra-tensor split
of the original and the inter-tensor split of 'transformers' format
files). Single files are more convenient to work with and more
friendly to future changes to use memory mapping on the C++ side. To
accomplish this without increasing memory requirements, it has some
custom loading code which avoids loading whole input files into memory
at once.
- Because of the custom loading code, it no longer depends in PyTorch,
which might make installing dependencies slightly easier or faster...
although it still depends on NumPy and sentencepiece, so I don't know
if there's any meaningful difference. In any case, I also added a
requirements.txt file to lock the dependency versions in case of any
future breaking changes.
- Type annotations checked with mypy.
- Some attempts to be extra user-friendly:
- The script tries to be forgiving with arguments, e.g. you can
specify either the model file itself or the directory containing
it.
- The script doesn't depend on config.json / params.json, just in
case the user downloaded files individually and doesn't have those
handy. But you still need tokenizer.model and, for Alpaca,
added_tokens.json.
- The script tries to give a helpful error message if
added_tokens.json is missing.
2023-04-14 07:03:03 +00:00
2024-01-20 23:14:18 +00:00
def default_outfile ( model_paths : list [ Path ] , file_type : GGMLFileType ) - > Path :
py : new conversion script (#545)
Current status: Working, except for the latest GPTQ-for-LLaMa format
that includes `g_idx`. This turns out to require changes to GGML, so
for now it only works if you use the `--outtype` option to dequantize it
back to f16 (which is pointless except for debugging).
I also included some cleanup for the C++ code.
This script is meant to replace all the existing conversion scripts
(including the ones that convert from older GGML formats), while also
adding support for some new formats. Specifically, I've tested with:
- [x] `LLaMA` (original)
- [x] `llama-65b-4bit`
- [x] `alpaca-native`
- [x] `alpaca-native-4bit`
- [x] LLaMA converted to 'transformers' format using
`convert_llama_weights_to_hf.py`
- [x] `alpaca-native` quantized with `--true-sequential --act-order
--groupsize 128` (dequantized only)
- [x] same as above plus `--save_safetensors`
- [x] GPT4All
- [x] stock unversioned ggml
- [x] ggmh
There's enough overlap in the logic needed to handle these different
cases that it seemed best to move to a single script.
I haven't tried this with Alpaca-LoRA because I don't know where to find
it.
Useful features:
- Uses multiple threads for a speedup in some cases (though the Python
GIL limits the gain, and sometimes it's disk-bound anyway).
- Combines split models into a single file (both the intra-tensor split
of the original and the inter-tensor split of 'transformers' format
files). Single files are more convenient to work with and more
friendly to future changes to use memory mapping on the C++ side. To
accomplish this without increasing memory requirements, it has some
custom loading code which avoids loading whole input files into memory
at once.
- Because of the custom loading code, it no longer depends in PyTorch,
which might make installing dependencies slightly easier or faster...
although it still depends on NumPy and sentencepiece, so I don't know
if there's any meaningful difference. In any case, I also added a
requirements.txt file to lock the dependency versions in case of any
future breaking changes.
- Type annotations checked with mypy.
- Some attempts to be extra user-friendly:
- The script tries to be forgiving with arguments, e.g. you can
specify either the model file itself or the directory containing
it.
- The script doesn't depend on config.json / params.json, just in
case the user downloaded files individually and doesn't have those
handy. But you still need tokenizer.model and, for Alpaca,
added_tokens.json.
- The script tries to give a helpful error message if
added_tokens.json is missing.
2023-04-14 07:03:03 +00:00
namestr = {
2024-01-20 23:14:18 +00:00
GGMLFileType . AllF32 : " f32 " ,
py : new conversion script (#545)
Current status: Working, except for the latest GPTQ-for-LLaMa format
that includes `g_idx`. This turns out to require changes to GGML, so
for now it only works if you use the `--outtype` option to dequantize it
back to f16 (which is pointless except for debugging).
I also included some cleanup for the C++ code.
This script is meant to replace all the existing conversion scripts
(including the ones that convert from older GGML formats), while also
adding support for some new formats. Specifically, I've tested with:
- [x] `LLaMA` (original)
- [x] `llama-65b-4bit`
- [x] `alpaca-native`
- [x] `alpaca-native-4bit`
- [x] LLaMA converted to 'transformers' format using
`convert_llama_weights_to_hf.py`
- [x] `alpaca-native` quantized with `--true-sequential --act-order
--groupsize 128` (dequantized only)
- [x] same as above plus `--save_safetensors`
- [x] GPT4All
- [x] stock unversioned ggml
- [x] ggmh
There's enough overlap in the logic needed to handle these different
cases that it seemed best to move to a single script.
I haven't tried this with Alpaca-LoRA because I don't know where to find
it.
Useful features:
- Uses multiple threads for a speedup in some cases (though the Python
GIL limits the gain, and sometimes it's disk-bound anyway).
- Combines split models into a single file (both the intra-tensor split
of the original and the inter-tensor split of 'transformers' format
files). Single files are more convenient to work with and more
friendly to future changes to use memory mapping on the C++ side. To
accomplish this without increasing memory requirements, it has some
custom loading code which avoids loading whole input files into memory
at once.
- Because of the custom loading code, it no longer depends in PyTorch,
which might make installing dependencies slightly easier or faster...
although it still depends on NumPy and sentencepiece, so I don't know
if there's any meaningful difference. In any case, I also added a
requirements.txt file to lock the dependency versions in case of any
future breaking changes.
- Type annotations checked with mypy.
- Some attempts to be extra user-friendly:
- The script tries to be forgiving with arguments, e.g. you can
specify either the model file itself or the directory containing
it.
- The script doesn't depend on config.json / params.json, just in
case the user downloaded files individually and doesn't have those
handy. But you still need tokenizer.model and, for Alpaca,
added_tokens.json.
- The script tries to give a helpful error message if
added_tokens.json is missing.
2023-04-14 07:03:03 +00:00
GGMLFileType . MostlyF16 : " f16 " ,
2024-01-20 23:14:18 +00:00
GGMLFileType . MostlyQ8_0 : " q8_0 " ,
2023-06-22 12:20:47 +00:00
} [ file_type ]
2023-08-21 20:07:43 +00:00
ret = model_paths [ 0 ] . parent / f " ggml-model- { namestr } .gguf "
py : new conversion script (#545)
Current status: Working, except for the latest GPTQ-for-LLaMa format
that includes `g_idx`. This turns out to require changes to GGML, so
for now it only works if you use the `--outtype` option to dequantize it
back to f16 (which is pointless except for debugging).
I also included some cleanup for the C++ code.
This script is meant to replace all the existing conversion scripts
(including the ones that convert from older GGML formats), while also
adding support for some new formats. Specifically, I've tested with:
- [x] `LLaMA` (original)
- [x] `llama-65b-4bit`
- [x] `alpaca-native`
- [x] `alpaca-native-4bit`
- [x] LLaMA converted to 'transformers' format using
`convert_llama_weights_to_hf.py`
- [x] `alpaca-native` quantized with `--true-sequential --act-order
--groupsize 128` (dequantized only)
- [x] same as above plus `--save_safetensors`
- [x] GPT4All
- [x] stock unversioned ggml
- [x] ggmh
There's enough overlap in the logic needed to handle these different
cases that it seemed best to move to a single script.
I haven't tried this with Alpaca-LoRA because I don't know where to find
it.
Useful features:
- Uses multiple threads for a speedup in some cases (though the Python
GIL limits the gain, and sometimes it's disk-bound anyway).
- Combines split models into a single file (both the intra-tensor split
of the original and the inter-tensor split of 'transformers' format
files). Single files are more convenient to work with and more
friendly to future changes to use memory mapping on the C++ side. To
accomplish this without increasing memory requirements, it has some
custom loading code which avoids loading whole input files into memory
at once.
- Because of the custom loading code, it no longer depends in PyTorch,
which might make installing dependencies slightly easier or faster...
although it still depends on NumPy and sentencepiece, so I don't know
if there's any meaningful difference. In any case, I also added a
requirements.txt file to lock the dependency versions in case of any
future breaking changes.
- Type annotations checked with mypy.
- Some attempts to be extra user-friendly:
- The script tries to be forgiving with arguments, e.g. you can
specify either the model file itself or the directory containing
it.
- The script doesn't depend on config.json / params.json, just in
case the user downloaded files individually and doesn't have those
handy. But you still need tokenizer.model and, for Alpaca,
added_tokens.json.
- The script tries to give a helpful error message if
added_tokens.json is missing.
2023-04-14 07:03:03 +00:00
if ret in model_paths :
2024-05-03 19:36:41 +00:00
logger . error (
2023-06-17 10:32:48 +00:00
f " Error: Default output path ( { ret } ) would overwrite the input. "
2024-05-03 19:36:41 +00:00
" Please explicitly specify a path using --outfile. " )
py : new conversion script (#545)
Current status: Working, except for the latest GPTQ-for-LLaMa format
that includes `g_idx`. This turns out to require changes to GGML, so
for now it only works if you use the `--outtype` option to dequantize it
back to f16 (which is pointless except for debugging).
I also included some cleanup for the C++ code.
This script is meant to replace all the existing conversion scripts
(including the ones that convert from older GGML formats), while also
adding support for some new formats. Specifically, I've tested with:
- [x] `LLaMA` (original)
- [x] `llama-65b-4bit`
- [x] `alpaca-native`
- [x] `alpaca-native-4bit`
- [x] LLaMA converted to 'transformers' format using
`convert_llama_weights_to_hf.py`
- [x] `alpaca-native` quantized with `--true-sequential --act-order
--groupsize 128` (dequantized only)
- [x] same as above plus `--save_safetensors`
- [x] GPT4All
- [x] stock unversioned ggml
- [x] ggmh
There's enough overlap in the logic needed to handle these different
cases that it seemed best to move to a single script.
I haven't tried this with Alpaca-LoRA because I don't know where to find
it.
Useful features:
- Uses multiple threads for a speedup in some cases (though the Python
GIL limits the gain, and sometimes it's disk-bound anyway).
- Combines split models into a single file (both the intra-tensor split
of the original and the inter-tensor split of 'transformers' format
files). Single files are more convenient to work with and more
friendly to future changes to use memory mapping on the C++ side. To
accomplish this without increasing memory requirements, it has some
custom loading code which avoids loading whole input files into memory
at once.
- Because of the custom loading code, it no longer depends in PyTorch,
which might make installing dependencies slightly easier or faster...
although it still depends on NumPy and sentencepiece, so I don't know
if there's any meaningful difference. In any case, I also added a
requirements.txt file to lock the dependency versions in case of any
future breaking changes.
- Type annotations checked with mypy.
- Some attempts to be extra user-friendly:
- The script tries to be forgiving with arguments, e.g. you can
specify either the model file itself or the directory containing
it.
- The script doesn't depend on config.json / params.json, just in
case the user downloaded files individually and doesn't have those
handy. But you still need tokenizer.model and, for Alpaca,
added_tokens.json.
- The script tries to give a helpful error message if
added_tokens.json is missing.
2023-04-14 07:03:03 +00:00
sys . exit ( 1 )
return ret
def do_dump_model ( model_plus : ModelPlus ) - > None :
2024-05-03 19:36:41 +00:00
print ( f " model_plus.paths = { model_plus . paths !r} " ) # noqa: NP100
print ( f " model_plus.format = { model_plus . format !r} " ) # noqa: NP100
print ( f " model_plus.vocab = { model_plus . vocab !r} " ) # noqa: NP100
py : new conversion script (#545)
Current status: Working, except for the latest GPTQ-for-LLaMa format
that includes `g_idx`. This turns out to require changes to GGML, so
for now it only works if you use the `--outtype` option to dequantize it
back to f16 (which is pointless except for debugging).
I also included some cleanup for the C++ code.
This script is meant to replace all the existing conversion scripts
(including the ones that convert from older GGML formats), while also
adding support for some new formats. Specifically, I've tested with:
- [x] `LLaMA` (original)
- [x] `llama-65b-4bit`
- [x] `alpaca-native`
- [x] `alpaca-native-4bit`
- [x] LLaMA converted to 'transformers' format using
`convert_llama_weights_to_hf.py`
- [x] `alpaca-native` quantized with `--true-sequential --act-order
--groupsize 128` (dequantized only)
- [x] same as above plus `--save_safetensors`
- [x] GPT4All
- [x] stock unversioned ggml
- [x] ggmh
There's enough overlap in the logic needed to handle these different
cases that it seemed best to move to a single script.
I haven't tried this with Alpaca-LoRA because I don't know where to find
it.
Useful features:
- Uses multiple threads for a speedup in some cases (though the Python
GIL limits the gain, and sometimes it's disk-bound anyway).
- Combines split models into a single file (both the intra-tensor split
of the original and the inter-tensor split of 'transformers' format
files). Single files are more convenient to work with and more
friendly to future changes to use memory mapping on the C++ side. To
accomplish this without increasing memory requirements, it has some
custom loading code which avoids loading whole input files into memory
at once.
- Because of the custom loading code, it no longer depends in PyTorch,
which might make installing dependencies slightly easier or faster...
although it still depends on NumPy and sentencepiece, so I don't know
if there's any meaningful difference. In any case, I also added a
requirements.txt file to lock the dependency versions in case of any
future breaking changes.
- Type annotations checked with mypy.
- Some attempts to be extra user-friendly:
- The script tries to be forgiving with arguments, e.g. you can
specify either the model file itself or the directory containing
it.
- The script doesn't depend on config.json / params.json, just in
case the user downloaded files individually and doesn't have those
handy. But you still need tokenizer.model and, for Alpaca,
added_tokens.json.
- The script tries to give a helpful error message if
added_tokens.json is missing.
2023-04-14 07:03:03 +00:00
for name , lazy_tensor in model_plus . model . items ( ) :
2024-05-03 19:36:41 +00:00
print ( f " { name } : shape= { lazy_tensor . shape } type= { lazy_tensor . data_type } ; { lazy_tensor . description } " ) # noqa: NP100
py : new conversion script (#545)
Current status: Working, except for the latest GPTQ-for-LLaMa format
that includes `g_idx`. This turns out to require changes to GGML, so
for now it only works if you use the `--outtype` option to dequantize it
back to f16 (which is pointless except for debugging).
I also included some cleanup for the C++ code.
This script is meant to replace all the existing conversion scripts
(including the ones that convert from older GGML formats), while also
adding support for some new formats. Specifically, I've tested with:
- [x] `LLaMA` (original)
- [x] `llama-65b-4bit`
- [x] `alpaca-native`
- [x] `alpaca-native-4bit`
- [x] LLaMA converted to 'transformers' format using
`convert_llama_weights_to_hf.py`
- [x] `alpaca-native` quantized with `--true-sequential --act-order
--groupsize 128` (dequantized only)
- [x] same as above plus `--save_safetensors`
- [x] GPT4All
- [x] stock unversioned ggml
- [x] ggmh
There's enough overlap in the logic needed to handle these different
cases that it seemed best to move to a single script.
I haven't tried this with Alpaca-LoRA because I don't know where to find
it.
Useful features:
- Uses multiple threads for a speedup in some cases (though the Python
GIL limits the gain, and sometimes it's disk-bound anyway).
- Combines split models into a single file (both the intra-tensor split
of the original and the inter-tensor split of 'transformers' format
files). Single files are more convenient to work with and more
friendly to future changes to use memory mapping on the C++ side. To
accomplish this without increasing memory requirements, it has some
custom loading code which avoids loading whole input files into memory
at once.
- Because of the custom loading code, it no longer depends in PyTorch,
which might make installing dependencies slightly easier or faster...
although it still depends on NumPy and sentencepiece, so I don't know
if there's any meaningful difference. In any case, I also added a
requirements.txt file to lock the dependency versions in case of any
future breaking changes.
- Type annotations checked with mypy.
- Some attempts to be extra user-friendly:
- The script tries to be forgiving with arguments, e.g. you can
specify either the model file itself or the directory containing
it.
- The script doesn't depend on config.json / params.json, just in
case the user downloaded files individually and doesn't have those
handy. But you still need tokenizer.model and, for Alpaca,
added_tokens.json.
- The script tries to give a helpful error message if
added_tokens.json is missing.
2023-04-14 07:03:03 +00:00
2024-01-20 23:14:18 +00:00
def main ( args_in : list [ str ] | None = None ) - > None :
2023-11-11 05:04:50 +00:00
output_choices = [ " f32 " , " f16 " ]
if np . uint32 ( 1 ) == np . uint32 ( 1 ) . newbyteorder ( " < " ) :
# We currently only support Q8_0 output on little endian systems.
output_choices . append ( " q8_0 " )
2024-03-02 17:27:26 +00:00
parser = argparse . ArgumentParser ( description = " Convert a LLaMA model to a GGML compatible file " )
llava : support v1.6 (#5267)
* Create llava-survery-v2.py
* Update convert-image-encoder-to-gguf.py
* Update convert-image-encoder-to-gguf.py
* Rename llava-survery-v2.py to llava-surgery-v2.py
* Update convert-image-encoder-to-gguf.py
will now search for projector
* Update convert-image-encoder-to-gguf.py
whoops
* Update llava-surgery-v2.py
* Clip: Bugfix for normalization (it did not loat the 3 std and mean values)
Clip: bicubic resize function
Clip: added save-to-bmp/pil for debugging and conversion from/to 32/8 images
Clip: added normalization with FP16 precision simulation (image tensors match HF implementation, can be switched off, only used for llava-1.6)
Clip: added newline tensor, mergetype kv, image-grid kv, new resize-pad function with resolution from gridpoints
Clip: clip_image_preprocess now returns a float * vector instead of float, this way llava 1.5 and 1.6 is supported
llava: added ggml cpu graph for embedding patching, added spatial_unpad preliminary support, added a lot of comments that need to be cleaned when all is final
convert-image-encoder: fixed image-grid flattening
* whitespace corrections
* ws
* Tensors are now properly permuted.
Before the embeddings were inserted 1:1, now they are split into the 24x24 patches as in reference.
* ws
* added verbose_prompt support into cli
added stopwords for llava-1.6 into cli
* moved llava functions to llava.cpp, made clip.h C compatible API, replaced vector style functions with pointers, added a debug define to remove functions from compilation while not needed
* ws
* convert : skip unknown tensors (need for LLaVA)
* llava : update readme
* llava : fix compile warnings
* llava : style
* convert : add --skip-unknown CLI arg
* server : remove clip structs
* bugfix for non llava-1.6
It should now work with llava-1.5 as well
* clip : minor code rearrange
* llava : update readme a bit
---------
Co-authored-by: John <cmt-nct@users.noreply.github.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-02-14 07:38:35 +00:00
parser . add_argument ( " --dump " , action = " store_true " , help = " don ' t convert, just show what ' s in the model " )
parser . add_argument ( " --dump-single " , action = " store_true " , help = " don ' t convert, just show what ' s in a single model file " )
parser . add_argument ( " --vocab-only " , action = " store_true " , help = " extract only the vocab " )
2024-03-14 16:21:56 +00:00
parser . add_argument ( " --no-vocab " , action = " store_true " , help = " store model without the vocab " )
llava : support v1.6 (#5267)
* Create llava-survery-v2.py
* Update convert-image-encoder-to-gguf.py
* Update convert-image-encoder-to-gguf.py
* Rename llava-survery-v2.py to llava-surgery-v2.py
* Update convert-image-encoder-to-gguf.py
will now search for projector
* Update convert-image-encoder-to-gguf.py
whoops
* Update llava-surgery-v2.py
* Clip: Bugfix for normalization (it did not loat the 3 std and mean values)
Clip: bicubic resize function
Clip: added save-to-bmp/pil for debugging and conversion from/to 32/8 images
Clip: added normalization with FP16 precision simulation (image tensors match HF implementation, can be switched off, only used for llava-1.6)
Clip: added newline tensor, mergetype kv, image-grid kv, new resize-pad function with resolution from gridpoints
Clip: clip_image_preprocess now returns a float * vector instead of float, this way llava 1.5 and 1.6 is supported
llava: added ggml cpu graph for embedding patching, added spatial_unpad preliminary support, added a lot of comments that need to be cleaned when all is final
convert-image-encoder: fixed image-grid flattening
* whitespace corrections
* ws
* Tensors are now properly permuted.
Before the embeddings were inserted 1:1, now they are split into the 24x24 patches as in reference.
* ws
* added verbose_prompt support into cli
added stopwords for llava-1.6 into cli
* moved llava functions to llava.cpp, made clip.h C compatible API, replaced vector style functions with pointers, added a debug define to remove functions from compilation while not needed
* ws
* convert : skip unknown tensors (need for LLaVA)
* llava : update readme
* llava : fix compile warnings
* llava : style
* convert : add --skip-unknown CLI arg
* server : remove clip structs
* bugfix for non llava-1.6
It should now work with llava-1.5 as well
* clip : minor code rearrange
* llava : update readme a bit
---------
Co-authored-by: John <cmt-nct@users.noreply.github.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-02-14 07:38:35 +00:00
parser . add_argument ( " --outtype " , choices = output_choices , help = " output format - note: q8_0 may be very slow (default: f16 or f32 based on input) " )
parser . add_argument ( " --vocab-dir " , type = Path , help = " directory containing tokenizer.model, if separate from model file " )
2024-03-02 17:27:26 +00:00
parser . add_argument ( " --vocab-type " , help = " vocab types to try in order, choose from ' spm ' , ' bpe ' , ' hfft ' (default: spm,hfft) " , default = " spm,hfft " )
llava : support v1.6 (#5267)
* Create llava-survery-v2.py
* Update convert-image-encoder-to-gguf.py
* Update convert-image-encoder-to-gguf.py
* Rename llava-survery-v2.py to llava-surgery-v2.py
* Update convert-image-encoder-to-gguf.py
will now search for projector
* Update convert-image-encoder-to-gguf.py
whoops
* Update llava-surgery-v2.py
* Clip: Bugfix for normalization (it did not loat the 3 std and mean values)
Clip: bicubic resize function
Clip: added save-to-bmp/pil for debugging and conversion from/to 32/8 images
Clip: added normalization with FP16 precision simulation (image tensors match HF implementation, can be switched off, only used for llava-1.6)
Clip: added newline tensor, mergetype kv, image-grid kv, new resize-pad function with resolution from gridpoints
Clip: clip_image_preprocess now returns a float * vector instead of float, this way llava 1.5 and 1.6 is supported
llava: added ggml cpu graph for embedding patching, added spatial_unpad preliminary support, added a lot of comments that need to be cleaned when all is final
convert-image-encoder: fixed image-grid flattening
* whitespace corrections
* ws
* Tensors are now properly permuted.
Before the embeddings were inserted 1:1, now they are split into the 24x24 patches as in reference.
* ws
* added verbose_prompt support into cli
added stopwords for llava-1.6 into cli
* moved llava functions to llava.cpp, made clip.h C compatible API, replaced vector style functions with pointers, added a debug define to remove functions from compilation while not needed
* ws
* convert : skip unknown tensors (need for LLaVA)
* llava : update readme
* llava : fix compile warnings
* llava : style
* convert : add --skip-unknown CLI arg
* server : remove clip structs
* bugfix for non llava-1.6
It should now work with llava-1.5 as well
* clip : minor code rearrange
* llava : update readme a bit
---------
Co-authored-by: John <cmt-nct@users.noreply.github.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-02-14 07:38:35 +00:00
parser . add_argument ( " --outfile " , type = Path , help = " path to write to; default: based on input " )
parser . add_argument ( " model " , type = Path , help = " directory containing model file, or model file itself (*.pth, *.pt, *.bin) " )
parser . add_argument ( " --ctx " , type = int , help = " model training context (default: based on input) " )
parser . add_argument ( " --concurrency " , type = int , help = f " concurrency used for conversion (default: { DEFAULT_CONCURRENCY } ) " , default = DEFAULT_CONCURRENCY )
parser . add_argument ( " --big-endian " , action = " store_true " , help = " model is executed on big endian machine " )
parser . add_argument ( " --pad-vocab " , action = " store_true " , help = " add pad tokens when model vocab expects more than tokenizer metadata provides " )
parser . add_argument ( " --skip-unknown " , action = " store_true " , help = " skip unknown tensor names instead of failing " )
2024-05-03 19:36:41 +00:00
parser . add_argument ( " --verbose " , action = " store_true " , help = " increase output verbosity " )
2024-01-20 23:14:18 +00:00
args = parser . parse_args ( args_in )
2024-05-03 19:36:41 +00:00
if args . verbose :
logging . basicConfig ( level = logging . DEBUG )
elif args . dump_single or args . dump :
# Avoid printing anything besides the dump output
logging . basicConfig ( level = logging . WARNING )
else :
logging . basicConfig ( level = logging . INFO )
2024-03-28 15:44:36 +00:00
if args . no_vocab and args . vocab_only :
raise ValueError ( " --vocab-only does not make sense with --no-vocab " )
2023-12-27 15:39:45 +00:00
py : new conversion script (#545)
Current status: Working, except for the latest GPTQ-for-LLaMa format
that includes `g_idx`. This turns out to require changes to GGML, so
for now it only works if you use the `--outtype` option to dequantize it
back to f16 (which is pointless except for debugging).
I also included some cleanup for the C++ code.
This script is meant to replace all the existing conversion scripts
(including the ones that convert from older GGML formats), while also
adding support for some new formats. Specifically, I've tested with:
- [x] `LLaMA` (original)
- [x] `llama-65b-4bit`
- [x] `alpaca-native`
- [x] `alpaca-native-4bit`
- [x] LLaMA converted to 'transformers' format using
`convert_llama_weights_to_hf.py`
- [x] `alpaca-native` quantized with `--true-sequential --act-order
--groupsize 128` (dequantized only)
- [x] same as above plus `--save_safetensors`
- [x] GPT4All
- [x] stock unversioned ggml
- [x] ggmh
There's enough overlap in the logic needed to handle these different
cases that it seemed best to move to a single script.
I haven't tried this with Alpaca-LoRA because I don't know where to find
it.
Useful features:
- Uses multiple threads for a speedup in some cases (though the Python
GIL limits the gain, and sometimes it's disk-bound anyway).
- Combines split models into a single file (both the intra-tensor split
of the original and the inter-tensor split of 'transformers' format
files). Single files are more convenient to work with and more
friendly to future changes to use memory mapping on the C++ side. To
accomplish this without increasing memory requirements, it has some
custom loading code which avoids loading whole input files into memory
at once.
- Because of the custom loading code, it no longer depends in PyTorch,
which might make installing dependencies slightly easier or faster...
although it still depends on NumPy and sentencepiece, so I don't know
if there's any meaningful difference. In any case, I also added a
requirements.txt file to lock the dependency versions in case of any
future breaking changes.
- Type annotations checked with mypy.
- Some attempts to be extra user-friendly:
- The script tries to be forgiving with arguments, e.g. you can
specify either the model file itself or the directory containing
it.
- The script doesn't depend on config.json / params.json, just in
case the user downloaded files individually and doesn't have those
handy. But you still need tokenizer.model and, for Alpaca,
added_tokens.json.
- The script tries to give a helpful error message if
added_tokens.json is missing.
2023-04-14 07:03:03 +00:00
if args . dump_single :
model_plus = lazy_load_file ( args . model )
do_dump_model ( model_plus )
2023-08-30 08:25:50 +00:00
return
2023-08-21 20:07:43 +00:00
2023-08-30 08:25:50 +00:00
if not args . vocab_only :
model_plus = load_some_model ( args . model )
else :
2024-01-20 23:14:18 +00:00
model_plus = ModelPlus ( model = { } , paths = [ args . model / ' dummy ' ] , format = ' none ' , vocab = None )
2023-08-30 08:25:50 +00:00
if args . dump :
do_dump_model ( model_plus )
return
2024-05-03 19:36:41 +00:00
2023-10-20 11:19:40 +00:00
endianess = gguf . GGUFEndian . LITTLE
convert.py : fix vanilla LLaMA model conversion (#4818)
* Update Imports and Add Notes for Future Reference
- Updated import statements in `convert.py`.
- Added import for `AutoTokenizer` from `transformers` module.
- Added conditional import for `gguf` from the local directory.
- Added comments and notes for future reference.
Additional Notes:
- Noted removal of a redundant `TypeAlias` import.
- Noted the removal of a `gguf` debug statement.
- Commented on the presence of `ARCH` and `NDArray` definitions.
- Commented on cleaning up and refactoring data type definitions.
* Refine Model Hyperparameters and Params Class
- Updated type annotations to use `Optional` for clarity.
- Improved method names and attribute consistency.
- Removed unnecessary variables for better code readability.
Additional Notes:
- Highlighted the use of `Optional` for clearer intent.
- Ensured backward and forward compatibility.
* Restore BpeVocab and SentencePieceVocab classes
- Restored the BpeVocab class for handling BPE tokenization.
- Restored the SentencePieceVocab class for SentencePiece tokenization.
These classes are essential for maintaining the original behavior of the codebase.
* refactor: Standardize vocabulary handling with HfVocab
- Replaced VocabLoader with HfVocab, aligning vocabulary handling across classes.
- Updated initialization of HfVocab with local_files_only=True for AutoTokenizer.
- Introduced optional parameter fname_added_tokens for flexible added token management.
- Streamlined added token handling for clarity and conciseness.
- Maintained special tokens and IDs, enhancing token management.
- Simplified token processing methods for improved readability.
- Added a placeholder for score computation with a default value of -1000.0.
- Optimized newline token check for efficiency.
- Updated __repr__ function for clarity in representation.
- Adjusted type alias Vocab to include BpeVocab, SentencePieceVocab, and HfVocab.
- Removed redundant code related to special token handling, reverse vocabulary mapping, and vocabulary file detection.
This refactoring promotes a standardized and modular approach to vocabulary management, facilitating future integration with a VocabFactory and improving code maintainability and scalability.
* refactor: Enhance readability, functionality, and code quality
- Improved code formatting and readability for better maintainability.
- Refactored LazyUnpickler's CLASSES dictionary for clarity.
- Added print statements and warnings in check_vocab_size for user feedback.
- Removed find_vocab_file_path, as it's superseded by VocabFactory.
- Preparatory changes for upcoming classes: OutputFile and VocabFactory.
- Overall focus on code quality, error handling, and consistency.
These changes reflect a continuous effort to refine the codebase, ensuring it meets best practices and prepares for future enhancements, such as the VocabFactory.
* refactor: Update OutputFile class for enhanced model vocabulary management
- Restructured the constructor for improved readability.
- Updated `add_meta_arch` method for flexible model name determination.
- Introduced `handle_tokenizer_model` for mapping vocab types to supported tokenizer models.
- Streamlined vocabulary extraction with `extract_vocabulary_from_model`.
- Simplified vocabulary metadata addition using `add_meta_vocab`.
- Refactored `add_tensor_info` for clarity and consistency.
- Improved error handling for better user feedback.
These changes signify the development of a versatile and comprehensive `OutputFile` class, enabling efficient management of model conversion output, metadata, vocabulary, and tensor information.
* feat: Introduce VocabFactory for flexible vocabulary management in model conversion
- The VocabFactory class is added to facilitate modular vocabulary handling.
- The constructor initializes a directory path and detects vocabulary-related files.
- The _select_file method provides file paths based on vocabulary type (e.g., BPE, SentencePiece).
- _create_special_vocab generates special vocabularies, accommodating different types.
- The load_vocab method loads vocabularies, handling BPE, SentencePiece, and Hugging Face Fast Tokenizer.
- Error handling and logging enhance debugging and user feedback.
- The modular and flexible design simplifies vocabulary management and supports future extensions.
The VocabFactory class enhances code modularity and maintainability, allowing versatile vocabulary handling in the model conversion process.
* refactor: Improve code organization, argument parsing, and user interface
- Renamed 'default_outfile' to 'default_output_file' for clarity.
- Refactored argument parser setup into 'get_argument_parser' function.
- Introduced descriptive comments for each argument in the parser.
- Added '--vocab-type' argument with choices ["spm", "bpe", "hfft"] for vocabulary processing.
- Improved flag naming consistency: '--outfile' to '--out-file' and '--bigendian' to '--big-endian'.
- Enhanced error handling to prevent overwriting input data in 'default_output_file'.
- Made 'argv' in 'main' an optional parameter for flexibility.
- Introduced dynamic import for 'awq.apply_awq' based on 'args.awq_path' for conditional dependency.
These changes enhance code clarity, organization, and the user interface of the script, aligning it with Python best practices and improving maintainability.
* refactor: Further refine functionality, improve user interaction, and streamline vocabulary handling
- Renamed command-line arguments for clarity and consistency.
- Improved path resolution and import adjustments for robustness.
- Thoughtfully handled 'awq-path' and conditional logic for the weighted model.
- Enhanced model and vocabulary loading with the 'VocabFactory' class for structured and adaptable loading.
- Strengthened error handling and user feedback for a more user-friendly experience.
- Structured output file handling with clear conditions and defaults.
- Streamlined and organized the 'main' function for better logic flow.
- Passed 'sys.argv[1:]' to 'main' for adaptability and testability.
These changes solidify the script's functionality, making it more robust, user-friendly, and adaptable. The use of the 'VocabFactory' class is a notable enhancement in efficient vocabulary handling, reflecting a thoughtful and iterative approach to script development.
* chore: Apply ruff formatting to convert.py
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
* Revert to commit 0614c33
* chore: Apply flake8 formatting rules
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
* refactor: Revise `check_vocab_size` for Enhanced Clarity and Correctness
- Resolved an unreachable branch issue by reorganizing the conditional structure.
- Moved the special case check for `params.n_vocab == -1` to the top for immediate assertion.
- Flattened the conditional logic for improved clarity and predictability of the function's behavior.
These changes enhance the readability and functional correctness of the `check_vocab_size` function without altering its intended functionality.
* py : fix outfile and outtype
* py : suggest hint for missing vocab size
---------
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-01-09 18:46:46 +00:00
if args . big_endian :
2023-10-20 11:19:40 +00:00
endianess = gguf . GGUFEndian . BIG
2023-08-21 20:07:43 +00:00
2024-05-08 12:22:32 +00:00
params = None
if args . pad_vocab or not args . vocab_only :
params = Params . load ( model_plus )
if params . n_ctx == - 1 :
if args . ctx is None :
msg = """ \
The model doesn ' t have a context size, and you didn ' t specify one with - - ctx
Please specify one with - - ctx :
- LLaMA v1 : - - ctx 2048
- LLaMA v2 : - - ctx 4096 """
parser . error ( textwrap . dedent ( msg ) )
params . n_ctx = args . ctx
if args . outtype :
params . ftype = {
" f32 " : GGMLFileType . AllF32 ,
" f16 " : GGMLFileType . MostlyF16 ,
" q8_0 " : GGMLFileType . MostlyQ8_0 ,
} [ args . outtype ]
logger . info ( f " params = { params } " )
2023-08-21 20:07:43 +00:00
convert.py : fix vanilla LLaMA model conversion (#4818)
* Update Imports and Add Notes for Future Reference
- Updated import statements in `convert.py`.
- Added import for `AutoTokenizer` from `transformers` module.
- Added conditional import for `gguf` from the local directory.
- Added comments and notes for future reference.
Additional Notes:
- Noted removal of a redundant `TypeAlias` import.
- Noted the removal of a `gguf` debug statement.
- Commented on the presence of `ARCH` and `NDArray` definitions.
- Commented on cleaning up and refactoring data type definitions.
* Refine Model Hyperparameters and Params Class
- Updated type annotations to use `Optional` for clarity.
- Improved method names and attribute consistency.
- Removed unnecessary variables for better code readability.
Additional Notes:
- Highlighted the use of `Optional` for clearer intent.
- Ensured backward and forward compatibility.
* Restore BpeVocab and SentencePieceVocab classes
- Restored the BpeVocab class for handling BPE tokenization.
- Restored the SentencePieceVocab class for SentencePiece tokenization.
These classes are essential for maintaining the original behavior of the codebase.
* refactor: Standardize vocabulary handling with HfVocab
- Replaced VocabLoader with HfVocab, aligning vocabulary handling across classes.
- Updated initialization of HfVocab with local_files_only=True for AutoTokenizer.
- Introduced optional parameter fname_added_tokens for flexible added token management.
- Streamlined added token handling for clarity and conciseness.
- Maintained special tokens and IDs, enhancing token management.
- Simplified token processing methods for improved readability.
- Added a placeholder for score computation with a default value of -1000.0.
- Optimized newline token check for efficiency.
- Updated __repr__ function for clarity in representation.
- Adjusted type alias Vocab to include BpeVocab, SentencePieceVocab, and HfVocab.
- Removed redundant code related to special token handling, reverse vocabulary mapping, and vocabulary file detection.
This refactoring promotes a standardized and modular approach to vocabulary management, facilitating future integration with a VocabFactory and improving code maintainability and scalability.
* refactor: Enhance readability, functionality, and code quality
- Improved code formatting and readability for better maintainability.
- Refactored LazyUnpickler's CLASSES dictionary for clarity.
- Added print statements and warnings in check_vocab_size for user feedback.
- Removed find_vocab_file_path, as it's superseded by VocabFactory.
- Preparatory changes for upcoming classes: OutputFile and VocabFactory.
- Overall focus on code quality, error handling, and consistency.
These changes reflect a continuous effort to refine the codebase, ensuring it meets best practices and prepares for future enhancements, such as the VocabFactory.
* refactor: Update OutputFile class for enhanced model vocabulary management
- Restructured the constructor for improved readability.
- Updated `add_meta_arch` method for flexible model name determination.
- Introduced `handle_tokenizer_model` for mapping vocab types to supported tokenizer models.
- Streamlined vocabulary extraction with `extract_vocabulary_from_model`.
- Simplified vocabulary metadata addition using `add_meta_vocab`.
- Refactored `add_tensor_info` for clarity and consistency.
- Improved error handling for better user feedback.
These changes signify the development of a versatile and comprehensive `OutputFile` class, enabling efficient management of model conversion output, metadata, vocabulary, and tensor information.
* feat: Introduce VocabFactory for flexible vocabulary management in model conversion
- The VocabFactory class is added to facilitate modular vocabulary handling.
- The constructor initializes a directory path and detects vocabulary-related files.
- The _select_file method provides file paths based on vocabulary type (e.g., BPE, SentencePiece).
- _create_special_vocab generates special vocabularies, accommodating different types.
- The load_vocab method loads vocabularies, handling BPE, SentencePiece, and Hugging Face Fast Tokenizer.
- Error handling and logging enhance debugging and user feedback.
- The modular and flexible design simplifies vocabulary management and supports future extensions.
The VocabFactory class enhances code modularity and maintainability, allowing versatile vocabulary handling in the model conversion process.
* refactor: Improve code organization, argument parsing, and user interface
- Renamed 'default_outfile' to 'default_output_file' for clarity.
- Refactored argument parser setup into 'get_argument_parser' function.
- Introduced descriptive comments for each argument in the parser.
- Added '--vocab-type' argument with choices ["spm", "bpe", "hfft"] for vocabulary processing.
- Improved flag naming consistency: '--outfile' to '--out-file' and '--bigendian' to '--big-endian'.
- Enhanced error handling to prevent overwriting input data in 'default_output_file'.
- Made 'argv' in 'main' an optional parameter for flexibility.
- Introduced dynamic import for 'awq.apply_awq' based on 'args.awq_path' for conditional dependency.
These changes enhance code clarity, organization, and the user interface of the script, aligning it with Python best practices and improving maintainability.
* refactor: Further refine functionality, improve user interaction, and streamline vocabulary handling
- Renamed command-line arguments for clarity and consistency.
- Improved path resolution and import adjustments for robustness.
- Thoughtfully handled 'awq-path' and conditional logic for the weighted model.
- Enhanced model and vocabulary loading with the 'VocabFactory' class for structured and adaptable loading.
- Strengthened error handling and user feedback for a more user-friendly experience.
- Structured output file handling with clear conditions and defaults.
- Streamlined and organized the 'main' function for better logic flow.
- Passed 'sys.argv[1:]' to 'main' for adaptability and testability.
These changes solidify the script's functionality, making it more robust, user-friendly, and adaptable. The use of the 'VocabFactory' class is a notable enhancement in efficient vocabulary handling, reflecting a thoughtful and iterative approach to script development.
* chore: Apply ruff formatting to convert.py
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
* Revert to commit 0614c33
* chore: Apply flake8 formatting rules
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
* refactor: Revise `check_vocab_size` for Enhanced Clarity and Correctness
- Resolved an unreachable branch issue by reorganizing the conditional structure.
- Moved the special case check for `params.n_vocab == -1` to the top for immediate assertion.
- Flattened the conditional logic for improved clarity and predictability of the function's behavior.
These changes enhance the readability and functional correctness of the `check_vocab_size` function without altering its intended functionality.
* py : fix outfile and outtype
* py : suggest hint for missing vocab size
---------
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-01-09 18:46:46 +00:00
model_parent_path = model_plus . paths [ 0 ] . parent
vocab_path = Path ( args . vocab_dir or args . model or model_parent_path )
vocab_factory = VocabFactory ( vocab_path )
2024-03-28 15:44:36 +00:00
vocab_types = None if args . no_vocab else args . vocab_type . split ( " , " )
vocab , special_vocab = vocab_factory . load_vocab ( vocab_types , model_parent_path )
convert.py : fix vanilla LLaMA model conversion (#4818)
* Update Imports and Add Notes for Future Reference
- Updated import statements in `convert.py`.
- Added import for `AutoTokenizer` from `transformers` module.
- Added conditional import for `gguf` from the local directory.
- Added comments and notes for future reference.
Additional Notes:
- Noted removal of a redundant `TypeAlias` import.
- Noted the removal of a `gguf` debug statement.
- Commented on the presence of `ARCH` and `NDArray` definitions.
- Commented on cleaning up and refactoring data type definitions.
* Refine Model Hyperparameters and Params Class
- Updated type annotations to use `Optional` for clarity.
- Improved method names and attribute consistency.
- Removed unnecessary variables for better code readability.
Additional Notes:
- Highlighted the use of `Optional` for clearer intent.
- Ensured backward and forward compatibility.
* Restore BpeVocab and SentencePieceVocab classes
- Restored the BpeVocab class for handling BPE tokenization.
- Restored the SentencePieceVocab class for SentencePiece tokenization.
These classes are essential for maintaining the original behavior of the codebase.
* refactor: Standardize vocabulary handling with HfVocab
- Replaced VocabLoader with HfVocab, aligning vocabulary handling across classes.
- Updated initialization of HfVocab with local_files_only=True for AutoTokenizer.
- Introduced optional parameter fname_added_tokens for flexible added token management.
- Streamlined added token handling for clarity and conciseness.
- Maintained special tokens and IDs, enhancing token management.
- Simplified token processing methods for improved readability.
- Added a placeholder for score computation with a default value of -1000.0.
- Optimized newline token check for efficiency.
- Updated __repr__ function for clarity in representation.
- Adjusted type alias Vocab to include BpeVocab, SentencePieceVocab, and HfVocab.
- Removed redundant code related to special token handling, reverse vocabulary mapping, and vocabulary file detection.
This refactoring promotes a standardized and modular approach to vocabulary management, facilitating future integration with a VocabFactory and improving code maintainability and scalability.
* refactor: Enhance readability, functionality, and code quality
- Improved code formatting and readability for better maintainability.
- Refactored LazyUnpickler's CLASSES dictionary for clarity.
- Added print statements and warnings in check_vocab_size for user feedback.
- Removed find_vocab_file_path, as it's superseded by VocabFactory.
- Preparatory changes for upcoming classes: OutputFile and VocabFactory.
- Overall focus on code quality, error handling, and consistency.
These changes reflect a continuous effort to refine the codebase, ensuring it meets best practices and prepares for future enhancements, such as the VocabFactory.
* refactor: Update OutputFile class for enhanced model vocabulary management
- Restructured the constructor for improved readability.
- Updated `add_meta_arch` method for flexible model name determination.
- Introduced `handle_tokenizer_model` for mapping vocab types to supported tokenizer models.
- Streamlined vocabulary extraction with `extract_vocabulary_from_model`.
- Simplified vocabulary metadata addition using `add_meta_vocab`.
- Refactored `add_tensor_info` for clarity and consistency.
- Improved error handling for better user feedback.
These changes signify the development of a versatile and comprehensive `OutputFile` class, enabling efficient management of model conversion output, metadata, vocabulary, and tensor information.
* feat: Introduce VocabFactory for flexible vocabulary management in model conversion
- The VocabFactory class is added to facilitate modular vocabulary handling.
- The constructor initializes a directory path and detects vocabulary-related files.
- The _select_file method provides file paths based on vocabulary type (e.g., BPE, SentencePiece).
- _create_special_vocab generates special vocabularies, accommodating different types.
- The load_vocab method loads vocabularies, handling BPE, SentencePiece, and Hugging Face Fast Tokenizer.
- Error handling and logging enhance debugging and user feedback.
- The modular and flexible design simplifies vocabulary management and supports future extensions.
The VocabFactory class enhances code modularity and maintainability, allowing versatile vocabulary handling in the model conversion process.
* refactor: Improve code organization, argument parsing, and user interface
- Renamed 'default_outfile' to 'default_output_file' for clarity.
- Refactored argument parser setup into 'get_argument_parser' function.
- Introduced descriptive comments for each argument in the parser.
- Added '--vocab-type' argument with choices ["spm", "bpe", "hfft"] for vocabulary processing.
- Improved flag naming consistency: '--outfile' to '--out-file' and '--bigendian' to '--big-endian'.
- Enhanced error handling to prevent overwriting input data in 'default_output_file'.
- Made 'argv' in 'main' an optional parameter for flexibility.
- Introduced dynamic import for 'awq.apply_awq' based on 'args.awq_path' for conditional dependency.
These changes enhance code clarity, organization, and the user interface of the script, aligning it with Python best practices and improving maintainability.
* refactor: Further refine functionality, improve user interaction, and streamline vocabulary handling
- Renamed command-line arguments for clarity and consistency.
- Improved path resolution and import adjustments for robustness.
- Thoughtfully handled 'awq-path' and conditional logic for the weighted model.
- Enhanced model and vocabulary loading with the 'VocabFactory' class for structured and adaptable loading.
- Strengthened error handling and user feedback for a more user-friendly experience.
- Structured output file handling with clear conditions and defaults.
- Streamlined and organized the 'main' function for better logic flow.
- Passed 'sys.argv[1:]' to 'main' for adaptability and testability.
These changes solidify the script's functionality, making it more robust, user-friendly, and adaptable. The use of the 'VocabFactory' class is a notable enhancement in efficient vocabulary handling, reflecting a thoughtful and iterative approach to script development.
* chore: Apply ruff formatting to convert.py
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
* Revert to commit 0614c33
* chore: Apply flake8 formatting rules
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
* refactor: Revise `check_vocab_size` for Enhanced Clarity and Correctness
- Resolved an unreachable branch issue by reorganizing the conditional structure.
- Moved the special case check for `params.n_vocab == -1` to the top for immediate assertion.
- Flattened the conditional logic for improved clarity and predictability of the function's behavior.
These changes enhance the readability and functional correctness of the `check_vocab_size` function without altering its intended functionality.
* py : fix outfile and outtype
* py : suggest hint for missing vocab size
---------
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-01-09 18:46:46 +00:00
2023-08-21 20:07:43 +00:00
if args . vocab_only :
2024-03-28 15:44:36 +00:00
assert isinstance ( vocab , Vocab )
2023-10-22 18:14:56 +00:00
if not args . outfile :
raise ValueError ( " need --outfile if using --vocab-only " )
py : new conversion script (#545)
Current status: Working, except for the latest GPTQ-for-LLaMa format
that includes `g_idx`. This turns out to require changes to GGML, so
for now it only works if you use the `--outtype` option to dequantize it
back to f16 (which is pointless except for debugging).
I also included some cleanup for the C++ code.
This script is meant to replace all the existing conversion scripts
(including the ones that convert from older GGML formats), while also
adding support for some new formats. Specifically, I've tested with:
- [x] `LLaMA` (original)
- [x] `llama-65b-4bit`
- [x] `alpaca-native`
- [x] `alpaca-native-4bit`
- [x] LLaMA converted to 'transformers' format using
`convert_llama_weights_to_hf.py`
- [x] `alpaca-native` quantized with `--true-sequential --act-order
--groupsize 128` (dequantized only)
- [x] same as above plus `--save_safetensors`
- [x] GPT4All
- [x] stock unversioned ggml
- [x] ggmh
There's enough overlap in the logic needed to handle these different
cases that it seemed best to move to a single script.
I haven't tried this with Alpaca-LoRA because I don't know where to find
it.
Useful features:
- Uses multiple threads for a speedup in some cases (though the Python
GIL limits the gain, and sometimes it's disk-bound anyway).
- Combines split models into a single file (both the intra-tensor split
of the original and the inter-tensor split of 'transformers' format
files). Single files are more convenient to work with and more
friendly to future changes to use memory mapping on the C++ side. To
accomplish this without increasing memory requirements, it has some
custom loading code which avoids loading whole input files into memory
at once.
- Because of the custom loading code, it no longer depends in PyTorch,
which might make installing dependencies slightly easier or faster...
although it still depends on NumPy and sentencepiece, so I don't know
if there's any meaningful difference. In any case, I also added a
requirements.txt file to lock the dependency versions in case of any
future breaking changes.
- Type annotations checked with mypy.
- Some attempts to be extra user-friendly:
- The script tries to be forgiving with arguments, e.g. you can
specify either the model file itself or the directory containing
it.
- The script doesn't depend on config.json / params.json, just in
case the user downloaded files individually and doesn't have those
handy. But you still need tokenizer.model and, for Alpaca,
added_tokens.json.
- The script tries to give a helpful error message if
added_tokens.json is missing.
2023-04-14 07:03:03 +00:00
outfile = args . outfile
2024-05-08 12:22:32 +00:00
if params is None :
params = Params (
n_vocab = vocab . vocab_size ,
n_embd = 1 ,
n_layer = 1 ,
n_ctx = 1 ,
n_ff = 1 ,
n_head = 1 ,
n_head_kv = 1 ,
f_norm_eps = 1e-5 ,
)
2024-01-20 23:14:18 +00:00
OutputFile . write_vocab_only ( outfile , params , vocab , special_vocab ,
endianess = endianess , pad_vocab = args . pad_vocab )
2024-05-03 19:36:41 +00:00
logger . info ( f " Wrote { outfile } " )
2023-08-30 08:25:50 +00:00
return
2023-08-21 20:07:43 +00:00
2024-03-14 16:21:56 +00:00
if model_plus . vocab is not None and args . vocab_dir is None and not args . no_vocab :
2023-08-30 08:25:50 +00:00
vocab = model_plus . vocab
convert.py : fix vanilla LLaMA model conversion (#4818)
* Update Imports and Add Notes for Future Reference
- Updated import statements in `convert.py`.
- Added import for `AutoTokenizer` from `transformers` module.
- Added conditional import for `gguf` from the local directory.
- Added comments and notes for future reference.
Additional Notes:
- Noted removal of a redundant `TypeAlias` import.
- Noted the removal of a `gguf` debug statement.
- Commented on the presence of `ARCH` and `NDArray` definitions.
- Commented on cleaning up and refactoring data type definitions.
* Refine Model Hyperparameters and Params Class
- Updated type annotations to use `Optional` for clarity.
- Improved method names and attribute consistency.
- Removed unnecessary variables for better code readability.
Additional Notes:
- Highlighted the use of `Optional` for clearer intent.
- Ensured backward and forward compatibility.
* Restore BpeVocab and SentencePieceVocab classes
- Restored the BpeVocab class for handling BPE tokenization.
- Restored the SentencePieceVocab class for SentencePiece tokenization.
These classes are essential for maintaining the original behavior of the codebase.
* refactor: Standardize vocabulary handling with HfVocab
- Replaced VocabLoader with HfVocab, aligning vocabulary handling across classes.
- Updated initialization of HfVocab with local_files_only=True for AutoTokenizer.
- Introduced optional parameter fname_added_tokens for flexible added token management.
- Streamlined added token handling for clarity and conciseness.
- Maintained special tokens and IDs, enhancing token management.
- Simplified token processing methods for improved readability.
- Added a placeholder for score computation with a default value of -1000.0.
- Optimized newline token check for efficiency.
- Updated __repr__ function for clarity in representation.
- Adjusted type alias Vocab to include BpeVocab, SentencePieceVocab, and HfVocab.
- Removed redundant code related to special token handling, reverse vocabulary mapping, and vocabulary file detection.
This refactoring promotes a standardized and modular approach to vocabulary management, facilitating future integration with a VocabFactory and improving code maintainability and scalability.
* refactor: Enhance readability, functionality, and code quality
- Improved code formatting and readability for better maintainability.
- Refactored LazyUnpickler's CLASSES dictionary for clarity.
- Added print statements and warnings in check_vocab_size for user feedback.
- Removed find_vocab_file_path, as it's superseded by VocabFactory.
- Preparatory changes for upcoming classes: OutputFile and VocabFactory.
- Overall focus on code quality, error handling, and consistency.
These changes reflect a continuous effort to refine the codebase, ensuring it meets best practices and prepares for future enhancements, such as the VocabFactory.
* refactor: Update OutputFile class for enhanced model vocabulary management
- Restructured the constructor for improved readability.
- Updated `add_meta_arch` method for flexible model name determination.
- Introduced `handle_tokenizer_model` for mapping vocab types to supported tokenizer models.
- Streamlined vocabulary extraction with `extract_vocabulary_from_model`.
- Simplified vocabulary metadata addition using `add_meta_vocab`.
- Refactored `add_tensor_info` for clarity and consistency.
- Improved error handling for better user feedback.
These changes signify the development of a versatile and comprehensive `OutputFile` class, enabling efficient management of model conversion output, metadata, vocabulary, and tensor information.
* feat: Introduce VocabFactory for flexible vocabulary management in model conversion
- The VocabFactory class is added to facilitate modular vocabulary handling.
- The constructor initializes a directory path and detects vocabulary-related files.
- The _select_file method provides file paths based on vocabulary type (e.g., BPE, SentencePiece).
- _create_special_vocab generates special vocabularies, accommodating different types.
- The load_vocab method loads vocabularies, handling BPE, SentencePiece, and Hugging Face Fast Tokenizer.
- Error handling and logging enhance debugging and user feedback.
- The modular and flexible design simplifies vocabulary management and supports future extensions.
The VocabFactory class enhances code modularity and maintainability, allowing versatile vocabulary handling in the model conversion process.
* refactor: Improve code organization, argument parsing, and user interface
- Renamed 'default_outfile' to 'default_output_file' for clarity.
- Refactored argument parser setup into 'get_argument_parser' function.
- Introduced descriptive comments for each argument in the parser.
- Added '--vocab-type' argument with choices ["spm", "bpe", "hfft"] for vocabulary processing.
- Improved flag naming consistency: '--outfile' to '--out-file' and '--bigendian' to '--big-endian'.
- Enhanced error handling to prevent overwriting input data in 'default_output_file'.
- Made 'argv' in 'main' an optional parameter for flexibility.
- Introduced dynamic import for 'awq.apply_awq' based on 'args.awq_path' for conditional dependency.
These changes enhance code clarity, organization, and the user interface of the script, aligning it with Python best practices and improving maintainability.
* refactor: Further refine functionality, improve user interaction, and streamline vocabulary handling
- Renamed command-line arguments for clarity and consistency.
- Improved path resolution and import adjustments for robustness.
- Thoughtfully handled 'awq-path' and conditional logic for the weighted model.
- Enhanced model and vocabulary loading with the 'VocabFactory' class for structured and adaptable loading.
- Strengthened error handling and user feedback for a more user-friendly experience.
- Structured output file handling with clear conditions and defaults.
- Streamlined and organized the 'main' function for better logic flow.
- Passed 'sys.argv[1:]' to 'main' for adaptability and testability.
These changes solidify the script's functionality, making it more robust, user-friendly, and adaptable. The use of the 'VocabFactory' class is a notable enhancement in efficient vocabulary handling, reflecting a thoughtful and iterative approach to script development.
* chore: Apply ruff formatting to convert.py
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
* Revert to commit 0614c33
* chore: Apply flake8 formatting rules
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
* refactor: Revise `check_vocab_size` for Enhanced Clarity and Correctness
- Resolved an unreachable branch issue by reorganizing the conditional structure.
- Moved the special case check for `params.n_vocab == -1` to the top for immediate assertion.
- Flattened the conditional logic for improved clarity and predictability of the function's behavior.
These changes enhance the readability and functional correctness of the `check_vocab_size` function without altering its intended functionality.
* py : fix outfile and outtype
* py : suggest hint for missing vocab size
---------
Signed-off-by: teleprint-me <77757836+teleprint-me@users.noreply.github.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-01-09 18:46:46 +00:00
2024-05-03 19:36:41 +00:00
logger . info ( f " Vocab info: { vocab } " )
logger . info ( f " Special vocab info: { special_vocab } " )
2024-01-20 23:14:18 +00:00
model = model_plus . model
llava : support v1.6 (#5267)
* Create llava-survery-v2.py
* Update convert-image-encoder-to-gguf.py
* Update convert-image-encoder-to-gguf.py
* Rename llava-survery-v2.py to llava-surgery-v2.py
* Update convert-image-encoder-to-gguf.py
will now search for projector
* Update convert-image-encoder-to-gguf.py
whoops
* Update llava-surgery-v2.py
* Clip: Bugfix for normalization (it did not loat the 3 std and mean values)
Clip: bicubic resize function
Clip: added save-to-bmp/pil for debugging and conversion from/to 32/8 images
Clip: added normalization with FP16 precision simulation (image tensors match HF implementation, can be switched off, only used for llava-1.6)
Clip: added newline tensor, mergetype kv, image-grid kv, new resize-pad function with resolution from gridpoints
Clip: clip_image_preprocess now returns a float * vector instead of float, this way llava 1.5 and 1.6 is supported
llava: added ggml cpu graph for embedding patching, added spatial_unpad preliminary support, added a lot of comments that need to be cleaned when all is final
convert-image-encoder: fixed image-grid flattening
* whitespace corrections
* ws
* Tensors are now properly permuted.
Before the embeddings were inserted 1:1, now they are split into the 24x24 patches as in reference.
* ws
* added verbose_prompt support into cli
added stopwords for llava-1.6 into cli
* moved llava functions to llava.cpp, made clip.h C compatible API, replaced vector style functions with pointers, added a debug define to remove functions from compilation while not needed
* ws
* convert : skip unknown tensors (need for LLaVA)
* llava : update readme
* llava : fix compile warnings
* llava : style
* convert : add --skip-unknown CLI arg
* server : remove clip structs
* bugfix for non llava-1.6
It should now work with llava-1.5 as well
* clip : minor code rearrange
* llava : update readme a bit
---------
Co-authored-by: John <cmt-nct@users.noreply.github.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-02-14 07:38:35 +00:00
model = convert_model_names ( model , params , args . skip_unknown )
2024-01-20 23:14:18 +00:00
ftype = pick_output_type ( model , args . outtype )
model = convert_to_output_type ( model , ftype )
outfile = args . outfile or default_outfile ( model_plus . paths , ftype )
2023-08-30 08:25:50 +00:00
params . ftype = ftype
2024-05-03 19:36:41 +00:00
logger . info ( f " Writing { outfile } , format { ftype } " )
2023-08-30 08:25:50 +00:00
2024-01-20 23:14:18 +00:00
OutputFile . write_all ( outfile , ftype , params , model , vocab , special_vocab ,
concurrency = args . concurrency , endianess = endianess , pad_vocab = args . pad_vocab )
2024-05-03 19:36:41 +00:00
logger . info ( f " Wrote { outfile } " )
py : new conversion script (#545)
Current status: Working, except for the latest GPTQ-for-LLaMa format
that includes `g_idx`. This turns out to require changes to GGML, so
for now it only works if you use the `--outtype` option to dequantize it
back to f16 (which is pointless except for debugging).
I also included some cleanup for the C++ code.
This script is meant to replace all the existing conversion scripts
(including the ones that convert from older GGML formats), while also
adding support for some new formats. Specifically, I've tested with:
- [x] `LLaMA` (original)
- [x] `llama-65b-4bit`
- [x] `alpaca-native`
- [x] `alpaca-native-4bit`
- [x] LLaMA converted to 'transformers' format using
`convert_llama_weights_to_hf.py`
- [x] `alpaca-native` quantized with `--true-sequential --act-order
--groupsize 128` (dequantized only)
- [x] same as above plus `--save_safetensors`
- [x] GPT4All
- [x] stock unversioned ggml
- [x] ggmh
There's enough overlap in the logic needed to handle these different
cases that it seemed best to move to a single script.
I haven't tried this with Alpaca-LoRA because I don't know where to find
it.
Useful features:
- Uses multiple threads for a speedup in some cases (though the Python
GIL limits the gain, and sometimes it's disk-bound anyway).
- Combines split models into a single file (both the intra-tensor split
of the original and the inter-tensor split of 'transformers' format
files). Single files are more convenient to work with and more
friendly to future changes to use memory mapping on the C++ side. To
accomplish this without increasing memory requirements, it has some
custom loading code which avoids loading whole input files into memory
at once.
- Because of the custom loading code, it no longer depends in PyTorch,
which might make installing dependencies slightly easier or faster...
although it still depends on NumPy and sentencepiece, so I don't know
if there's any meaningful difference. In any case, I also added a
requirements.txt file to lock the dependency versions in case of any
future breaking changes.
- Type annotations checked with mypy.
- Some attempts to be extra user-friendly:
- The script tries to be forgiving with arguments, e.g. you can
specify either the model file itself or the directory containing
it.
- The script doesn't depend on config.json / params.json, just in
case the user downloaded files individually and doesn't have those
handy. But you still need tokenizer.model and, for Alpaca,
added_tokens.json.
- The script tries to give a helpful error message if
added_tokens.json is missing.
2023-04-14 07:03:03 +00:00
2024-01-20 23:14:18 +00:00
if __name__ == ' __main__ ' :
main ( )