mirror of
https://github.com/ggerganov/llama.cpp.git
synced 2024-11-11 13:30:35 +00:00
convert : fix python 3.8 support, modernize type annotations (#2916)
* convert : fix python 3.8 support * convert : sort imports * convert : fix required parameters in convert-llama-ggmlv3-to-gguf * convert : fix mypy errors in convert-llama-ggmlv3-to-gguf * convert : use PEP 585 generics and PEP 604 unions Now that we have `from __future__ import annotations`, we can use this modern syntax in Python 3.7 instead of restricting support to Python 3.9 or 3.10 respectively. * gguf.py : a tuple is already a tuple * add mypy.ini * convert : add necessary `type: ignore` comments * gguf-py: bump version
This commit is contained in:
Cebtenzzre
GitHub
parent
8afe228000
commit
92d0b751a7
@ -1,18 +1,21 @@
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#!/usr/bin/env python3
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# HF falcon--> gguf conversion
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import gguf
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import os
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import sys
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import struct
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from __future__ import annotations
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import argparse
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import json
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import os
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import struct
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import sys
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from pathlib import Path
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from typing import Any
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import gguf
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import numpy as np
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import torch
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import argparse
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from transformers import AutoTokenizer # type: ignore[import]
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from typing import Any, List
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from pathlib import Path
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from transformers import AutoTokenizer
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def bytes_to_unicode():
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# ref: https://github.com/openai/gpt-2/blob/master/src/encoder.py
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@ -114,9 +117,9 @@ gguf_writer.add_file_type(ftype)
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print("gguf: get tokenizer metadata")
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tokens: List[bytearray] = []
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scores: List[float] = []
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toktypes: List[int] = []
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tokens: list[bytearray] = []
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scores: list[float] = []
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toktypes: list[int] = []
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tokenizer_json_file = dir_model / 'tokenizer.json'
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if not tokenizer_json_file.is_file():
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@ -1,18 +1,20 @@
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#!/usr/bin/env python3
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# HF gptneox--> gguf conversion
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import gguf
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import os
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import sys
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import struct
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from __future__ import annotations
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import argparse
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import json
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import os
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import struct
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import sys
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from pathlib import Path
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from typing import Any
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import gguf
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import numpy as np
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import torch
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import argparse
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from typing import Any, List
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from pathlib import Path
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from transformers import AutoTokenizer
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from transformers import AutoTokenizer # type: ignore[import]
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# ref: https://github.com/openai/gpt-2/blob/master/src/encoder.py
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@ -112,7 +114,7 @@ gguf_writer.add_layer_norm_eps(hparams["layer_norm_eps"])
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print("gguf: get tokenizer metadata")
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tokens: List[bytearray] = []
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tokens: list[bytearray] = []
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tokenizer_json_file = dir_model / 'tokenizer.json'
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if not tokenizer_json_file.is_file():
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@ -3,22 +3,25 @@
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# Only models with a single datafile are supported, like 7B
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# HF files required in the model dir: config.json tokenizer_config.json tokenizer.json tokenizer.model
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import gguf
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import os
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import sys
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import struct
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from __future__ import annotations
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import argparse
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import json
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import os
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import struct
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import sys
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from pathlib import Path
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from typing import TYPE_CHECKING, Any
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import gguf
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import numpy as np
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import torch
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import argparse
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from sentencepiece import SentencePieceProcessor # type: ignore[import]
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from typing import Any, List, TypeAlias
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from pathlib import Path
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from sentencepiece import SentencePieceProcessor
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if TYPE_CHECKING:
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from typing import TypeAlias
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#NDArray = np.ndarray[Any, Any]
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# compatible with python < 3.9
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NDArray: 'TypeAlias' = 'np.ndarray[Any, Any]'
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NDArray: TypeAlias = 'np.ndarray[Any, Any]'
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def count_model_parts(dir_model: Path) -> int:
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@ -129,9 +132,9 @@ if "rope_scaling" in hparams and hparams["rope_scaling"] != None and "factor" in
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print("gguf: get tokenizer metadata")
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tokens: List[bytes] = []
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scores: List[float] = []
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toktypes: List[int] = []
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tokens: list[bytes] = []
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scores: list[float] = []
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toktypes: list[int] = []
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tokenizer_model_file = dir_model / 'tokenizer.model'
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if not tokenizer_model_file.is_file():
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@ -1,10 +1,14 @@
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#!/usr/bin/env python3
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import sys, struct, math, argparse
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from __future__ import annotations
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import argparse
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import math
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import struct
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import sys
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from pathlib import Path
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import numpy as np
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import gguf
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import numpy as np
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# Note: Does not support GGML_QKK_64
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QK_K = 256
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@ -72,7 +76,7 @@ class Vocab:
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class Tensor:
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def __init__(self):
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self.name = None
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self.dims = ()
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self.dims: tuple[int, ...] = ()
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self.dtype = None
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self.start_offset = 0
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self.len_bytes = np.int64(0)
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@ -119,7 +123,7 @@ class GGMLV3Model:
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offset += hp.load(data, offset)
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vocab = Vocab()
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offset += vocab.load(data, offset, hp.n_vocab)
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tensors = []
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tensors: list[Tensor] = []
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tensor_map = {}
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while offset < len(data):
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tensor = Tensor()
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@ -305,8 +309,8 @@ def handle_metadata(cfg, hp):
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def handle_args():
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parser = argparse.ArgumentParser(description = 'Convert GGMLv3 models to GGUF')
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parser.add_argument('--input', '-i', type = Path, help = 'Input GGMLv3 filename')
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parser.add_argument('--output', '-o', type = Path, help ='Output GGUF filename')
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parser.add_argument('--input', '-i', type = Path, required = True, help = 'Input GGMLv3 filename')
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parser.add_argument('--output', '-o', type = Path, required = True, help ='Output GGUF filename')
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parser.add_argument('--name', help = 'Set model name')
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parser.add_argument('--desc', help = 'Set model description')
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parser.add_argument('--gqa', type = int, default = 1, help = 'grouped-query attention factor (use 8 for LLaMA2 70B)')
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@ -1,28 +1,31 @@
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#!/usr/bin/env python3
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# HF llama --> gguf conversion
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import gguf
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import os
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import sys
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import struct
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from __future__ import annotations
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import argparse
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import json
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import os
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import struct
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import sys
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from pathlib import Path
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from typing import TYPE_CHECKING, Any
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import gguf
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import numpy as np
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import torch
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import argparse
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from sentencepiece import SentencePieceProcessor # type: ignore[import]
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from typing import Any, List, Optional, TypeAlias
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from pathlib import Path
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from sentencepiece import SentencePieceProcessor
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if TYPE_CHECKING:
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from typing import TypeAlias
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#NDArray = np.ndarray[Any, Any]
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# compatible with python < 3.9
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NDArray: 'TypeAlias' = 'np.ndarray[Any, Any]'
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NDArray: TypeAlias = 'np.ndarray[Any, Any]'
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# reverse HF permute back to original pth layout
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# https://github.com/huggingface/transformers/blob/main/src/transformers/models/llama/convert_llama_weights_to_hf.py
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def reverse_hf_permute(weights: NDArray, n_head: int, n_kv_head: Optional[int] = None) -> NDArray:
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def reverse_hf_permute(weights: NDArray, n_head: int, n_kv_head: int | None = None) -> NDArray:
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if n_kv_head is not None and n_head != n_kv_head:
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n_head //= n_kv_head
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@ -136,9 +139,9 @@ if "rope_scaling" in hparams and hparams["rope_scaling"] != None and "factor" in
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print("gguf: get tokenizer metadata")
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tokens: List[bytes] = []
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scores: List[float] = []
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toktypes: List[int] = []
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tokens: list[bytes] = []
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scores: list[float] = []
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toktypes: list[int] = []
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tokenizer_model_file = dir_model / 'tokenizer.model'
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if not tokenizer_model_file.is_file():
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@ -1,15 +1,17 @@
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#!/usr/bin/env python3
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from __future__ import annotations
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import json
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import os
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import re
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import struct
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import sys
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from typing import Any, Dict, Sequence, BinaryIO
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from typing import Any, BinaryIO, Sequence
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import numpy as np
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import torch
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NUMPY_TYPE_TO_FTYPE: Dict[str, int] = {"float32": 0, "float16": 1}
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NUMPY_TYPE_TO_FTYPE: dict[str, int] = {"float32": 0, "float16": 1}
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HF_SUBLAYER_TO_GGML = {
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@ -46,7 +48,7 @@ def translate_tensor_name(t: str) -> str:
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sys.exit(1)
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def write_file_header(fout: BinaryIO, params: Dict[str, Any]) -> None:
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def write_file_header(fout: BinaryIO, params: dict[str, Any]) -> None:
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fout.write(b"ggla"[::-1]) # magic (ggml lora)
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fout.write(struct.pack("i", 1)) # file version
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fout.write(struct.pack("i", params["r"]))
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149
convert.py
149
convert.py
@ -1,9 +1,8 @@
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#!/usr/bin/env python3
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from __future__ import annotations
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import gguf
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import argparse
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import concurrent.futures
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from concurrent.futures import ThreadPoolExecutor, ProcessPoolExecutor
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import copy
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import enum
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import faulthandler
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@ -20,21 +19,23 @@ import struct
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import sys
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import time
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import zipfile
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import numpy as np
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from abc import ABCMeta, abstractmethod
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from concurrent.futures import ProcessPoolExecutor, ThreadPoolExecutor
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from dataclasses import dataclass
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from pathlib import Path
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from typing import (IO, TYPE_CHECKING, Any, Callable, Dict, Generator, Iterable, List, Literal, Optional, Sequence, Set, Tuple, Type, TypeVar, Union)
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from sentencepiece import SentencePieceProcessor # type: ignore
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from typing import IO, TYPE_CHECKING, Any, Callable, Generator, Iterable, Literal, Sequence, TypeVar
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import gguf
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import numpy as np
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from sentencepiece import SentencePieceProcessor # type: ignore[import]
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if TYPE_CHECKING:
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from typing_extensions import TypeAlias
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from typing import TypeAlias
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if hasattr(faulthandler, 'register') and hasattr(signal, 'SIGUSR1'):
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faulthandler.register(signal.SIGUSR1)
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NDArray: 'TypeAlias' = 'np.ndarray[Any, Any]'
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NDArray: TypeAlias = 'np.ndarray[Any, Any]'
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ARCH=gguf.MODEL_ARCH.LLAMA
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NAMES=gguf.MODEL_TENSOR_NAMES[ARCH]
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@ -47,8 +48,8 @@ DEFAULT_CONCURRENCY = 8
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@dataclass(frozen=True)
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class DataType:
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name: str
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dtype: 'np.dtype[Any]'
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valid_conversions: List[str]
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dtype: np.dtype[Any]
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valid_conversions: list[str]
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def elements_to_bytes(self, n_elements: int) -> int:
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return n_elements * self.dtype.itemsize
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@ -65,7 +66,7 @@ DT_BF16 = UnquantizedDataType('BF16', dtype = np.dtype(np.uint16), valid_convers
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@dataclass(frozen=True)
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class QuantizedDataType(DataType):
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block_size: int
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quantized_dtype: 'np.dtype[Any]'
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quantized_dtype: np.dtype[Any]
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ggml_type: gguf.GGMLQuantizationType
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def quantize(self, arr: NDArray) -> NDArray:
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@ -84,7 +85,7 @@ class Q8_0QuantizedDataType(QuantizedDataType):
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n_blocks = arr.size // self.block_size
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blocks = arr.reshape((n_blocks, self.block_size))
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# Much faster implementation of block quantization contributed by @Cebtenzzre
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def quantize_blocks_q8_0(blocks: NDArray) -> Iterable[Tuple[Any, Any]]:
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def quantize_blocks_q8_0(blocks: NDArray) -> Iterable[tuple[Any, Any]]:
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d = abs(blocks).max(axis = 1) / np.float32(127)
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with np.errstate(divide = 'ignore'):
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qs = (blocks / d[:, None]).round()
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@ -98,13 +99,13 @@ DT_Q8_0 = Q8_0QuantizedDataType('Q8_0',
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quantized_dtype = np.dtype([('d', '<f2'), ('qs', 'i1', (32,))]))
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# Quantized types skipped here because they may also map to np.float32
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NUMPY_TYPE_TO_DATA_TYPE: Dict['np.dtype[Any]', DataType] = {}
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NUMPY_TYPE_TO_DATA_TYPE: dict[np.dtype[Any], DataType] = {}
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for dt in (DT_BF16, DT_F16, DT_F32, DT_I32):
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if dt.dtype in NUMPY_TYPE_TO_DATA_TYPE:
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raise ValueError(f'Invalid duplicate data type {dt}')
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NUMPY_TYPE_TO_DATA_TYPE[dt.dtype] = dt
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SAFETENSORS_DATA_TYPES: Dict[str, DataType] = {
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SAFETENSORS_DATA_TYPES: dict[str, DataType] = {
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'BF16': DT_BF16,
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'F16': DT_F16,
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'F32': DT_F32,
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@ -119,14 +120,14 @@ class GGMLFileType(enum.IntEnum):
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MostlyF16 = 1 # except 1d tensors
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MostlyQ8_0 = 7 # except 1d tensors
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def type_for_tensor(self, name: str, tensor: 'LazyTensor') -> DataType:
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def type_for_tensor(self, name: str, tensor: LazyTensor) -> DataType:
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dt = GGML_FILE_TYPE_TO_DATA_TYPE.get(self)
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if dt is None:
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raise ValueError(self)
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# 1D tensors are always F32.
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return dt if len(tensor.shape) > 1 else DT_F32
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GGML_FILE_TYPE_TO_DATA_TYPE: Dict[GGMLFileType, DataType] = {
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GGML_FILE_TYPE_TO_DATA_TYPE: dict[GGMLFileType, DataType] = {
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GGMLFileType.AllF32 : DT_F32,
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GGMLFileType.MostlyF16 : DT_F16,
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GGMLFileType.MostlyQ8_0: DT_Q8_0,
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@ -148,13 +149,13 @@ class Params:
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n_head_kv: int
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f_norm_eps: float
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f_rope_freq_base: Optional[float] = None
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f_rope_scale: Optional[float] = None
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f_rope_freq_base: float | None = None
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f_rope_scale: float | None = None
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ftype: Optional[GGMLFileType] = None
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ftype: GGMLFileType | None = None
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# path to the directory containing the model files
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path_model: Optional['Path'] = None
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path_model: Path | None = None
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@staticmethod
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def find_n_mult(n_ff: int, n_embd: int) -> int:
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@ -166,7 +167,7 @@ class Params:
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raise Exception(f"failed to find n_mult for (n_ff={n_ff}, n_embd={n_embd}).")
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@staticmethod
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def guessed(model: 'LazyModel') -> 'Params':
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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
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@ -202,7 +203,7 @@ class Params:
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)
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@staticmethod
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def loadHFTransformerJson(model: 'LazyModel', config_path: 'Path') -> 'Params':
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def loadHFTransformerJson(model: LazyModel, config_path: Path) -> Params:
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config = json.load(open(config_path))
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n_vocab = config["vocab_size"]
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@ -247,7 +248,7 @@ class Params:
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# LLaMA v2 70B params.json
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# {"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
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@staticmethod
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def loadOriginalParamsJson(model: 'LazyModel', config_path: 'Path') -> 'Params':
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def loadOriginalParamsJson(model: LazyModel, config_path: Path) -> Params:
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config = json.load(open(config_path))
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n_vocab = config["vocab_size"] if "vocab_size" in config else -1
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@ -291,7 +292,7 @@ class Params:
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)
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@staticmethod
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def load(model_plus: 'ModelPlus') -> 'Params':
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def load(model_plus: ModelPlus) -> Params:
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hf_config_path = model_plus.paths[0].parent / "config.json"
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orig_config_path = model_plus.paths[0].parent / "params.json"
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@ -314,9 +315,9 @@ class Params:
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#
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class BpeVocab:
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def __init__(self, fname_tokenizer: Path, fname_added_tokens: Optional[Path]) -> None:
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def __init__(self, fname_tokenizer: Path, fname_added_tokens: Path | None) -> None:
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self.bpe_tokenizer = json.loads(open(str(fname_tokenizer), encoding="utf-8").read())
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added_tokens: Dict[str, int]
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added_tokens: dict[str, int]
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if fname_added_tokens is not None:
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added_tokens = json.load(open(fname_added_tokens, encoding="utf-8"))
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else:
|
||||
@ -335,9 +336,9 @@ class BpeVocab:
|
||||
self.fname_tokenizer = fname_tokenizer
|
||||
self.fname_added_tokens = fname_added_tokens
|
||||
|
||||
def bpe_tokens(self) -> Iterable[Tuple[bytes, float, gguf.TokenType]]:
|
||||
def bpe_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]:
|
||||
tokenizer = self.bpe_tokenizer
|
||||
from transformers.models.gpt2 import tokenization_gpt2
|
||||
from transformers.models.gpt2 import tokenization_gpt2 # type: ignore[import]
|
||||
byte_encoder = tokenization_gpt2.bytes_to_unicode()
|
||||
byte_decoder = {v: k for k, v in byte_encoder.items()}
|
||||
for i, item in enumerate(tokenizer):
|
||||
@ -345,12 +346,12 @@ class BpeVocab:
|
||||
score: float = -i
|
||||
yield text, score, gguf.TokenType.USER_DEFINED
|
||||
|
||||
def added_tokens(self) -> Iterable[Tuple[bytes, float, gguf.TokenType]]:
|
||||
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]]:
|
||||
def all_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]:
|
||||
yield from self.bpe_tokens()
|
||||
yield from self.added_tokens()
|
||||
|
||||
@ -359,9 +360,9 @@ class BpeVocab:
|
||||
|
||||
|
||||
class SentencePieceVocab:
|
||||
def __init__(self, fname_tokenizer: Path, fname_added_tokens: Optional[Path]) -> None:
|
||||
def __init__(self, fname_tokenizer: Path, fname_added_tokens: Path | None) -> None:
|
||||
self.sentencepiece_tokenizer = SentencePieceProcessor(str(fname_tokenizer))
|
||||
added_tokens: Dict[str, int]
|
||||
added_tokens: dict[str, int]
|
||||
if fname_added_tokens is not None:
|
||||
added_tokens = json.load(open(fname_added_tokens, encoding="utf-8"))
|
||||
else:
|
||||
@ -380,7 +381,7 @@ class SentencePieceVocab:
|
||||
self.fname_tokenizer = fname_tokenizer
|
||||
self.fname_added_tokens = fname_added_tokens
|
||||
|
||||
def sentencepiece_tokens(self) -> Iterable[Tuple[bytes, float, gguf.TokenType]]:
|
||||
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)
|
||||
@ -404,19 +405,19 @@ class SentencePieceVocab:
|
||||
|
||||
yield text, score, toktype
|
||||
|
||||
def added_tokens(self) -> Iterable[Tuple[bytes, float, gguf.TokenType]]:
|
||||
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]]:
|
||||
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>"
|
||||
|
||||
Vocab = Union[BpeVocab, SentencePieceVocab]
|
||||
Vocab: TypeAlias = 'BpeVocab | SentencePieceVocab'
|
||||
|
||||
#
|
||||
# data loading
|
||||
@ -436,15 +437,15 @@ class Tensor(metaclass=ABCMeta):
|
||||
data_type: DataType
|
||||
|
||||
@abstractmethod
|
||||
def astype(self, data_type: DataType) -> 'Tensor': ...
|
||||
def astype(self, data_type: DataType) -> Tensor: ...
|
||||
@abstractmethod
|
||||
def permute(self, n_head: int, n_head_kv: int) -> 'Tensor': ...
|
||||
def permute(self, n_head: int, n_head_kv: int) -> Tensor: ...
|
||||
@abstractmethod
|
||||
def permute_part(self, n_part: int, n_head: int, n_head_kv: int) -> 'UnquantizedTensor': ...
|
||||
def permute_part(self, n_part: int, n_head: int, n_head_kv: int) -> UnquantizedTensor: ...
|
||||
@abstractmethod
|
||||
def part(self, n_part: int) -> 'UnquantizedTensor': ...
|
||||
def part(self, n_part: int) -> UnquantizedTensor: ...
|
||||
@abstractmethod
|
||||
def to_ggml(self) -> 'GGMLCompatibleTensor': ...
|
||||
def to_ggml(self) -> GGMLCompatibleTensor: ...
|
||||
|
||||
|
||||
def bf16_to_fp32(bf16_arr: np.ndarray[Any, np.dtype[np.uint16]]) -> NDArray:
|
||||
@ -465,22 +466,22 @@ class UnquantizedTensor(Tensor):
|
||||
self.ndarray = bf16_to_fp32(self.ndarray)
|
||||
return UnquantizedTensor(self.ndarray.astype(dtype))
|
||||
|
||||
def to_ggml(self) -> 'UnquantizedTensor':
|
||||
def to_ggml(self) -> UnquantizedTensor:
|
||||
return self
|
||||
|
||||
def permute_part(self, n_part: int, n_head: int, n_head_kv: int) -> 'UnquantizedTensor':
|
||||
def permute_part(self, n_part: int, n_head: int, n_head_kv: int) -> UnquantizedTensor:
|
||||
r = self.ndarray.shape[0] // 3
|
||||
return UnquantizedTensor(permute(self.ndarray[r * n_part : r * n_part + r, ...], n_head, n_head_kv))
|
||||
|
||||
def part(self, n_part: int) -> 'UnquantizedTensor':
|
||||
def part(self, n_part: int) -> UnquantizedTensor:
|
||||
r = self.ndarray.shape[0] // 3
|
||||
return UnquantizedTensor(self.ndarray[r * n_part : r * n_part + r, ...])
|
||||
|
||||
def permute(self, n_head: int, n_head_kv: int) -> 'UnquantizedTensor':
|
||||
def permute(self, n_head: int, n_head_kv: int) -> UnquantizedTensor:
|
||||
return UnquantizedTensor(permute(self.ndarray, n_head, n_head_kv))
|
||||
|
||||
|
||||
def load_unquantized(lazy_tensor: 'LazyTensor', expected_dtype: Any = None, convert: bool = False) -> NDArray:
|
||||
def load_unquantized(lazy_tensor: LazyTensor, expected_dtype: Any = None, convert: bool = False) -> NDArray:
|
||||
tensor = lazy_tensor.load()
|
||||
assert isinstance(tensor, UnquantizedTensor)
|
||||
|
||||
@ -496,13 +497,13 @@ def load_unquantized(lazy_tensor: 'LazyTensor', expected_dtype: Any = None, conv
|
||||
return tensor.ndarray
|
||||
|
||||
|
||||
GGMLCompatibleTensor = Union[UnquantizedTensor]
|
||||
GGMLCompatibleTensor = UnquantizedTensor
|
||||
|
||||
|
||||
@dataclass
|
||||
class LazyTensor:
|
||||
_load: Callable[[], Tensor]
|
||||
shape: List[int]
|
||||
shape: list[int]
|
||||
data_type: DataType
|
||||
description: str
|
||||
|
||||
@ -513,7 +514,7 @@ class LazyTensor:
|
||||
(self.data_type, ret.data_type, self.description)
|
||||
return ret
|
||||
|
||||
def astype(self, data_type: DataType) -> 'LazyTensor':
|
||||
def astype(self, data_type: DataType) -> LazyTensor:
|
||||
self.validate_conversion_to(data_type)
|
||||
|
||||
def load() -> Tensor:
|
||||
@ -525,24 +526,24 @@ class LazyTensor:
|
||||
raise ValueError(f'Cannot validate conversion from {self.data_type} to {data_type}.')
|
||||
|
||||
|
||||
LazyModel = Dict[str, LazyTensor]
|
||||
LazyModel = dict[str, LazyTensor]
|
||||
|
||||
|
||||
@dataclass
|
||||
class ModelPlus:
|
||||
model: LazyModel
|
||||
paths: List[Path] # Where this was read from.
|
||||
paths: list[Path] # Where this was read from.
|
||||
format: Literal['ggml', 'torch', 'safetensors', 'none']
|
||||
vocab: Optional[Vocab] # For GGML models (which have vocab built in), the vocab.
|
||||
vocab: Vocab | None # For GGML models (which have vocab built in), the vocab.
|
||||
|
||||
|
||||
def merge_sharded(models: List[LazyModel]) -> LazyModel:
|
||||
def merge_sharded(models: list[LazyModel]) -> LazyModel:
|
||||
# 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:
|
||||
lazy_tensors: List[LazyTensor] = [model[name] for model in models]
|
||||
lazy_tensors: list[LazyTensor] = [model[name] for model in models]
|
||||
if len(lazy_tensors) == 1:
|
||||
# only one file; don't go through this procedure since there might
|
||||
# be quantized tensors
|
||||
@ -570,7 +571,7 @@ def merge_sharded(models: List[LazyModel]) -> LazyModel:
|
||||
return {name: convert(name) for name in names}
|
||||
|
||||
|
||||
def merge_multifile_models(models_plus: List[ModelPlus]) -> ModelPlus:
|
||||
def merge_multifile_models(models_plus: list[ModelPlus]) -> ModelPlus:
|
||||
formats = set(mp.format for mp in models_plus)
|
||||
assert len(formats) == 1, "different formats?"
|
||||
format = formats.pop()
|
||||
@ -674,7 +675,7 @@ class LazyUnpickler(pickle.Unpickler):
|
||||
def rebuild_from_type_v2(func, new_type, args, state):
|
||||
return func(*args)
|
||||
|
||||
CLASSES: Dict[Tuple[str, str], Any] = {
|
||||
CLASSES: dict[tuple[str, str], Any] = {
|
||||
# getattr used here as a workaround for mypy not being smart enough to detrmine
|
||||
# the staticmethods have a __func__ attribute.
|
||||
('torch._tensor', '_rebuild_from_type_v2'): getattr(rebuild_from_type_v2, '__func__'),
|
||||
@ -707,15 +708,15 @@ def lazy_load_torch_file(outer_fp: IO[bytes], path: Path) -> ModelPlus:
|
||||
|
||||
def lazy_load_safetensors_file(fp: IO[bytes], path: Path) -> ModelPlus:
|
||||
header_size, = struct.unpack('<Q', fp.read(8))
|
||||
header: Dict[str, Dict[str, Any]] = json.loads(fp.read(header_size))
|
||||
header: dict[str, dict[str, Any]] = json.loads(fp.read(header_size))
|
||||
# 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))
|
||||
byte_buf = mapped[8 + header_size:]
|
||||
|
||||
def convert(info: Dict[str, Any]) -> LazyTensor:
|
||||
def convert(info: dict[str, Any]) -> LazyTensor:
|
||||
data_type = SAFETENSORS_DATA_TYPES[info['dtype']]
|
||||
numpy_dtype = data_type.dtype
|
||||
shape: List[int] = info['shape']
|
||||
shape: list[int] = info['shape']
|
||||
begin, end = info['data_offsets']
|
||||
assert 0 <= begin <= end <= len(byte_buf)
|
||||
assert end - begin == math.prod(shape) * numpy_dtype.itemsize
|
||||
@ -754,7 +755,7 @@ def lazy_load_file(path: Path) -> ModelPlus:
|
||||
In = TypeVar('In')
|
||||
Out = TypeVar('Out')
|
||||
|
||||
def bounded_parallel_map(func: Callable[[In], Out], iterable: Iterable[In], concurrency: int, max_workers: Optional[int] = None, use_processpool_executor: bool = False) -> Iterable[Out]:
|
||||
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]:
|
||||
'''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
|
||||
@ -763,13 +764,13 @@ def bounded_parallel_map(func: Callable[[In], Out], iterable: Iterable[In], conc
|
||||
yield from map(func, iterable)
|
||||
# Not reached.
|
||||
iterable = iter(iterable)
|
||||
executor_class: Union[Type[ThreadPoolExecutor], Type[ProcessPoolExecutor]]
|
||||
executor_class: type[ThreadPoolExecutor] | type[ProcessPoolExecutor]
|
||||
if use_processpool_executor:
|
||||
executor_class = ProcessPoolExecutor
|
||||
else:
|
||||
executor_class = ThreadPoolExecutor
|
||||
with executor_class(max_workers = max_workers) as executor:
|
||||
futures: List[concurrent.futures.Future[Out]] = []
|
||||
futures: list[concurrent.futures.Future[Out]] = []
|
||||
done = False
|
||||
for _ in range(concurrency):
|
||||
try:
|
||||
@ -893,13 +894,13 @@ class OutputFile:
|
||||
of.close()
|
||||
|
||||
@staticmethod
|
||||
def do_item(item: Tuple[str, LazyTensor]) -> Tuple[DataType, NDArray]:
|
||||
def do_item(item: tuple[str, LazyTensor]) -> tuple[DataType, NDArray]:
|
||||
name, lazy_tensor = item
|
||||
tensor = lazy_tensor.load().to_ggml()
|
||||
return (lazy_tensor.data_type, tensor.ndarray)
|
||||
|
||||
@staticmethod
|
||||
def maybe_do_quantize(item: Tuple[DataType, NDArray]) -> NDArray:
|
||||
def maybe_do_quantize(item: tuple[DataType, NDArray]) -> NDArray:
|
||||
dt, arr = item
|
||||
if not isinstance(dt, QuantizedDataType):
|
||||
return arr
|
||||
@ -940,7 +941,7 @@ class OutputFile:
|
||||
|
||||
of.close()
|
||||
|
||||
def pick_output_type(model: LazyModel, output_type_str: Optional[str]) -> GGMLFileType:
|
||||
def pick_output_type(model: LazyModel, output_type_str: str | None) -> GGMLFileType:
|
||||
wq_type = model[NAMES[gguf.MODEL_TENSOR.ATTN_Q].format(bid=0)+".weight"].data_type
|
||||
|
||||
if output_type_str == "f32" or (output_type_str is None and wq_type == DT_F32):
|
||||
@ -960,7 +961,7 @@ def convert_to_output_type(model: LazyModel, output_type: GGMLFileType) -> LazyM
|
||||
|
||||
def convert_model_names(model: LazyModel, params: Params) -> LazyModel:
|
||||
tmap = gguf.TensorNameMap(ARCH, params.n_layer)
|
||||
should_skip: Set[gguf.MODEL_TENSOR] = set(gguf.MODEL_TENSOR_SKIP.get(ARCH, []))
|
||||
should_skip: set[gguf.MODEL_TENSOR] = set(gguf.MODEL_TENSOR_SKIP.get(ARCH, []))
|
||||
|
||||
tmp = model
|
||||
|
||||
@ -995,12 +996,12 @@ def convert_model_names(model: LazyModel, params: Params) -> LazyModel:
|
||||
|
||||
return out
|
||||
|
||||
def nth_multifile_path(path: Path, n: int) -> Optional[Path]:
|
||||
def nth_multifile_path(path: Path, n: int) -> Path | None:
|
||||
'''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:
|
||||
patterns: List[Tuple[str, str]] = [
|
||||
patterns: list[tuple[str, str]] = [
|
||||
# - 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.
|
||||
@ -1016,11 +1017,11 @@ def nth_multifile_path(path: Path, n: int) -> Optional[Path]:
|
||||
return None
|
||||
|
||||
|
||||
def find_multifile_paths(path: Path) -> List[Path]:
|
||||
def find_multifile_paths(path: Path) -> list[Path]:
|
||||
'''Given any path belonging to a multi-file model (e.g. foo.bin.1), return
|
||||
the whole list of paths in the model.
|
||||
'''
|
||||
ret: List[Path] = []
|
||||
ret: list[Path] = []
|
||||
for i in itertools.count():
|
||||
nth_path = nth_multifile_path(path, i)
|
||||
if nth_path is None:
|
||||
@ -1051,7 +1052,7 @@ def load_some_model(path: Path) -> ModelPlus:
|
||||
path = files[0]
|
||||
|
||||
paths = find_multifile_paths(path)
|
||||
models_plus: List[ModelPlus] = []
|
||||
models_plus: list[ModelPlus] = []
|
||||
for path in paths:
|
||||
print(f"Loading model file {path}")
|
||||
models_plus.append(lazy_load_file(path))
|
||||
@ -1060,7 +1061,7 @@ def load_some_model(path: Path) -> ModelPlus:
|
||||
return model_plus
|
||||
|
||||
|
||||
def load_vocab(path: Path, vocabtype: Optional[str]) -> Union[BpeVocab, SentencePieceVocab]:
|
||||
def load_vocab(path: Path, vocabtype: str | None) -> Vocab:
|
||||
# Be extra-friendly and accept either a file or a directory. Also, if it's
|
||||
# a directory, it might be the model directory, and tokenizer.model might
|
||||
# be in the parent of that.
|
||||
@ -1091,7 +1092,7 @@ def load_vocab(path: Path, vocabtype: Optional[str]) -> Union[BpeVocab, Sentence
|
||||
raise ValueError(f"Unsupported vocabulary type {vocabtype}")
|
||||
|
||||
|
||||
def default_outfile(model_paths: List[Path], file_type: GGMLFileType) -> Path:
|
||||
def default_outfile(model_paths: list[Path], file_type: GGMLFileType) -> Path:
|
||||
namestr = {
|
||||
GGMLFileType.AllF32: "f32",
|
||||
GGMLFileType.MostlyF16: "f16",
|
||||
@ -1114,7 +1115,7 @@ def do_dump_model(model_plus: ModelPlus) -> None:
|
||||
print(f"{name}: shape={lazy_tensor.shape} type={lazy_tensor.data_type}; {lazy_tensor.description}")
|
||||
|
||||
|
||||
def main(args_in: Optional[List[str]] = None) -> None:
|
||||
def main(args_in: list[str] | None = None) -> None:
|
||||
parser = argparse.ArgumentParser(description="Convert a LLaMa model to a GGML compatible file")
|
||||
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")
|
||||
|
||||
@ -1,16 +1,18 @@
|
||||
#!/usr/bin/env python3
|
||||
import shutil
|
||||
import sys
|
||||
import struct
|
||||
import tempfile
|
||||
import numpy as np
|
||||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
import os
|
||||
from pathlib import Path
|
||||
|
||||
import shutil
|
||||
import struct
|
||||
import sys
|
||||
import tempfile
|
||||
from enum import IntEnum, auto
|
||||
from io import BufferedWriter
|
||||
from typing import Any, BinaryIO, Callable, IO, Dict, List, Optional, Sequence, Tuple, Union
|
||||
from pathlib import Path
|
||||
from typing import IO, Any, BinaryIO, Callable, Sequence
|
||||
|
||||
import numpy as np
|
||||
|
||||
#
|
||||
# constants
|
||||
@ -103,7 +105,7 @@ class MODEL_TENSOR(IntEnum):
|
||||
FFN_NORM : int = auto()
|
||||
|
||||
|
||||
MODEL_ARCH_NAMES: Dict[MODEL_ARCH, str] = {
|
||||
MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
|
||||
MODEL_ARCH.LLAMA: "llama",
|
||||
MODEL_ARCH.FALCON: "falcon",
|
||||
MODEL_ARCH.GPT2: "gpt2",
|
||||
@ -112,7 +114,7 @@ MODEL_ARCH_NAMES: Dict[MODEL_ARCH, str] = {
|
||||
MODEL_ARCH.MPT: "mpt",
|
||||
}
|
||||
|
||||
MODEL_TENSOR_NAMES: Dict[MODEL_ARCH, Dict[MODEL_TENSOR, str]] = {
|
||||
MODEL_TENSOR_NAMES: dict[MODEL_ARCH, dict[MODEL_TENSOR, str]] = {
|
||||
MODEL_ARCH.LLAMA: {
|
||||
MODEL_TENSOR.TOKEN_EMBD: "token_embd",
|
||||
MODEL_TENSOR.OUTPUT_NORM: "output_norm",
|
||||
@ -158,7 +160,7 @@ MODEL_TENSOR_NAMES: Dict[MODEL_ARCH, Dict[MODEL_TENSOR, str]] = {
|
||||
}
|
||||
|
||||
# tensors that will not be serialized
|
||||
MODEL_TENSOR_SKIP: Dict[MODEL_ARCH, List[MODEL_TENSOR]] = {
|
||||
MODEL_TENSOR_SKIP: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
|
||||
MODEL_ARCH.LLAMA: [
|
||||
MODEL_TENSOR.ROPE_FREQS,
|
||||
MODEL_TENSOR.ATTN_ROT_EMBD,
|
||||
@ -167,7 +169,7 @@ MODEL_TENSOR_SKIP: Dict[MODEL_ARCH, List[MODEL_TENSOR]] = {
|
||||
|
||||
|
||||
class TensorNameMap:
|
||||
mappings_cfg: Dict[MODEL_TENSOR, Tuple[str, ...]] = {
|
||||
mappings_cfg: dict[MODEL_TENSOR, tuple[str, ...]] = {
|
||||
# Token embeddings
|
||||
MODEL_TENSOR.TOKEN_EMBD: (
|
||||
"gpt_neox.embed_in", # gptneox
|
||||
@ -203,7 +205,7 @@ class TensorNameMap:
|
||||
),
|
||||
}
|
||||
|
||||
block_mappings_cfg: Dict[MODEL_TENSOR, Tuple[str, ...]] = {
|
||||
block_mappings_cfg: dict[MODEL_TENSOR, tuple[str, ...]] = {
|
||||
# Attention norm
|
||||
MODEL_TENSOR.ATTN_NORM: (
|
||||
"gpt_neox.layers.{bid}.input_layernorm", # gptneox
|
||||
@ -298,9 +300,9 @@ class TensorNameMap:
|
||||
),
|
||||
}
|
||||
|
||||
mapping: Dict[str, Tuple[MODEL_TENSOR, str]]
|
||||
mapping: dict[str, tuple[MODEL_TENSOR, str]]
|
||||
|
||||
tensor_names: Dict[MODEL_TENSOR, str]
|
||||
tensor_names: dict[MODEL_TENSOR, str]
|
||||
|
||||
def __init__(self, arch: MODEL_ARCH, n_blocks: int):
|
||||
mapping = self.mapping = {}
|
||||
@ -321,7 +323,7 @@ class TensorNameMap:
|
||||
key = key.format(bid = bid)
|
||||
mapping[key] = (tensor, tensor_name)
|
||||
|
||||
def get_type_and_name(self, key: str, try_suffixes: Sequence[str]) -> Optional[Tuple[MODEL_TENSOR, str]]:
|
||||
def get_type_and_name(self, key: str, try_suffixes: Sequence[str]) -> tuple[MODEL_TENSOR, str] | None:
|
||||
result = self.mapping.get(key)
|
||||
if result is not None:
|
||||
return result
|
||||
@ -332,13 +334,13 @@ class TensorNameMap:
|
||||
return (result[0], result[1] + suffix)
|
||||
return None
|
||||
|
||||
def get_name(self, key: str, try_suffixes: Sequence[str]) -> Optional[str]:
|
||||
def get_name(self, key: str, try_suffixes: Sequence[str]) -> str | None:
|
||||
result = self.get_type_and_name(key, try_suffixes = try_suffixes)
|
||||
if result is None:
|
||||
return None
|
||||
return result[1]
|
||||
|
||||
def get_type(self, key: str, try_suffixes: Sequence[str]) -> Optional[MODEL_TENSOR]:
|
||||
def get_type(self, key: str, try_suffixes: Sequence[str]) -> MODEL_TENSOR | None:
|
||||
result = self.get_type_and_name(key, try_suffixes = try_suffixes)
|
||||
if result is None:
|
||||
return None
|
||||
@ -432,10 +434,10 @@ class GGUFWriter:
|
||||
ti_data = b""
|
||||
ti_data_count = 0
|
||||
use_temp_file: bool
|
||||
temp_file: Optional[tempfile.SpooledTemporaryFile[bytes]] = None
|
||||
tensors: List[Tuple[np.ndarray[Any, Any], int]]
|
||||
temp_file: tempfile.SpooledTemporaryFile[bytes] | None = None
|
||||
tensors: list[tuple[np.ndarray[Any, Any], int]]
|
||||
|
||||
def __init__(self, path: Union[os.PathLike[str], str], arch: str, use_temp_file = True):
|
||||
def __init__(self, path: os.PathLike[str] | str, arch: str, use_temp_file = True):
|
||||
self.fout = open(path, "wb")
|
||||
self.arch = arch
|
||||
self.add_architecture()
|
||||
@ -531,7 +533,7 @@ class GGUFWriter:
|
||||
GGUFValueType.FLOAT64: "<d",
|
||||
GGUFValueType.BOOL: "?" ,
|
||||
}
|
||||
def add_val(self, val: Any, vtype: Optional[GGUFValueType] = None, add_vtype: bool = True):
|
||||
def add_val(self, val: Any, vtype: GGUFValueType | None = None, add_vtype: bool = True):
|
||||
if vtype is None:
|
||||
vtype = GGUFValueType.get_type(val)
|
||||
|
||||
@ -561,7 +563,7 @@ class GGUFWriter:
|
||||
def ggml_pad(x: int, n: int) -> int:
|
||||
return ((x + n - 1) // n) * n
|
||||
|
||||
def add_tensor_info(self, name: str, tensor_shape: Sequence[int], tensor_dtype: Union[np.dtype[np.float16], np.dtype[np.float32]], tensor_nbytes: int, raw_dtype: Optional[GGMLQuantizationType] = None):
|
||||
def add_tensor_info(self, name: str, tensor_shape: Sequence[int], tensor_dtype: np.dtype[np.float16] | np.dtype[np.float32], tensor_nbytes: int, raw_dtype: GGMLQuantizationType | None = None):
|
||||
assert raw_dtype is not None or tensor_dtype in (np.float32, np.float16), "Only F32 and F16 tensors are supported for now"
|
||||
|
||||
encoded_name = name.encode("utf8")
|
||||
@ -580,7 +582,7 @@ class GGUFWriter:
|
||||
self.offset_tensor += GGUFWriter.ggml_pad(tensor_nbytes, self.data_alignment)
|
||||
self.ti_data_count += 1
|
||||
|
||||
def add_tensor(self, name: str, tensor: np.ndarray[Any, Any], raw_shape: Optional[Sequence[int]] = None, raw_dtype: Optional[GGMLQuantizationType] = None):
|
||||
def add_tensor(self, name: str, tensor: np.ndarray[Any, Any], raw_shape: Sequence[int] | None = None, raw_dtype: GGMLQuantizationType | None = None):
|
||||
if self.use_temp_file and self.temp_file is None:
|
||||
fp = tempfile.SpooledTemporaryFile(mode="w+b", max_size=256*1024*1024)
|
||||
fp.seek(0)
|
||||
@ -600,7 +602,7 @@ class GGUFWriter:
|
||||
if pad != 0:
|
||||
self.temp_file.write(bytes([0] * pad))
|
||||
|
||||
def write_padding(self, fp: BinaryIO, n: int, align: Optional[int] = None):
|
||||
def write_padding(self, fp: BinaryIO, n: int, align: int | None = None):
|
||||
pad = GGUFWriter.ggml_pad(n, align if align is not None else self.data_alignment) - n
|
||||
if pad != 0:
|
||||
fp.write(bytes([0] * pad))
|
||||
@ -726,13 +728,13 @@ class GGUFWriter:
|
||||
def add_tokenizer_model(self, model: str):
|
||||
self.add_string(KEY_TOKENIZER_MODEL, model)
|
||||
|
||||
def add_token_list(self, tokens: Union[Sequence[str], Sequence[bytes], Sequence[bytearray]]):
|
||||
def add_token_list(self, tokens: Sequence[str] | Sequence[bytes] | Sequence[bytearray]):
|
||||
self.add_array(KEY_TOKENIZER_LIST, tokens)
|
||||
|
||||
def add_token_merges(self, merges: Union[Sequence[str], Sequence[bytes], Sequence[bytearray]]):
|
||||
def add_token_merges(self, merges: Sequence[str] | Sequence[bytes] | Sequence[bytearray]):
|
||||
self.add_array(KEY_TOKENIZER_MERGES, merges)
|
||||
|
||||
def add_token_types(self, types: Union[Sequence[TokenType], Sequence[int]]):
|
||||
def add_token_types(self, types: Sequence[TokenType] | Sequence[int]):
|
||||
self.add_array(KEY_TOKENIZER_TOKEN_TYPE, types)
|
||||
|
||||
def add_token_scores(self, scores: Sequence[float]):
|
||||
@ -756,11 +758,11 @@ class GGUFWriter:
|
||||
|
||||
class SpecialVocab:
|
||||
load_merges: bool = False
|
||||
merges: List[str] = []
|
||||
special_token_types: Tuple[str, ...] = tuple(('bos', 'eos', 'unk', 'sep', 'pad'))
|
||||
special_token_ids: Dict[str, int] = {}
|
||||
merges: list[str] = []
|
||||
special_token_types: tuple[str, ...] = ('bos', 'eos', 'unk', 'sep', 'pad')
|
||||
special_token_ids: dict[str, int] = {}
|
||||
|
||||
def __init__(self, path: Path, load_merges: bool = False, special_token_types: Optional[Tuple[str, ...]] = None):
|
||||
def __init__(self, path: Path, load_merges: bool = False, special_token_types: tuple[str, ...] | None = None):
|
||||
self.special_token_ids = {}
|
||||
self.load_merges = load_merges
|
||||
if special_token_types is not None:
|
||||
@ -821,7 +823,7 @@ class SpecialVocab:
|
||||
print(f'gguf: Adding {len(self.merges)} merge(s).')
|
||||
gw.add_token_merges(self.merges)
|
||||
for typ, tokid in self.special_token_ids.items():
|
||||
handler: Optional[Callable[[int], None]] = getattr(gw, f'add_{typ}_token_id', None)
|
||||
handler: Callable[[int], None] | None = getattr(gw, f'add_{typ}_token_id', None)
|
||||
if handler is None:
|
||||
print(f'gguf: WARNING: No handler for special token type {typ} with id {tokid} - skipping')
|
||||
continue
|
||||
|
||||
@ -1,6 +1,6 @@
|
||||
[tool.poetry]
|
||||
name = "gguf"
|
||||
version = "0.2.1"
|
||||
version = "0.3.1"
|
||||
description = "Write ML models in GGUF for GGML"
|
||||
authors = ["GGML <ggml@ggml.ai>"]
|
||||
packages = [
|
||||
|
||||
Loading…
Reference in New Issue
Block a user