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.
This commit is contained in:
Cebtenzzre
parent
f30937a5a2
commit
ef87d46d68
7 changed files with 92 additions and 93 deletions
|
|
@ -9,7 +9,7 @@ import os
|
|||
import struct
|
||||
import sys
|
||||
from pathlib import Path
|
||||
from typing import Any, List
|
||||
from typing import Any
|
||||
|
||||
import gguf
|
||||
import numpy as np
|
||||
|
|
@ -117,9 +117,9 @@ gguf_writer.add_file_type(ftype)
|
|||
|
||||
print("gguf: get tokenizer metadata")
|
||||
|
||||
tokens: List[bytearray] = []
|
||||
scores: List[float] = []
|
||||
toktypes: List[int] = []
|
||||
tokens: list[bytearray] = []
|
||||
scores: list[float] = []
|
||||
toktypes: list[int] = []
|
||||
|
||||
tokenizer_json_file = dir_model / 'tokenizer.json'
|
||||
if not tokenizer_json_file.is_file():
|
||||
|
|
|
|||
|
|
@ -9,7 +9,7 @@ import os
|
|||
import struct
|
||||
import sys
|
||||
from pathlib import Path
|
||||
from typing import Any, List
|
||||
from typing import Any
|
||||
|
||||
import gguf
|
||||
import numpy as np
|
||||
|
|
@ -114,7 +114,7 @@ gguf_writer.add_layer_norm_eps(hparams["layer_norm_eps"])
|
|||
|
||||
print("gguf: get tokenizer metadata")
|
||||
|
||||
tokens: List[bytearray] = []
|
||||
tokens: list[bytearray] = []
|
||||
|
||||
tokenizer_json_file = dir_model / 'tokenizer.json'
|
||||
if not tokenizer_json_file.is_file():
|
||||
|
|
|
|||
|
|
@ -11,7 +11,7 @@ import os
|
|||
import struct
|
||||
import sys
|
||||
from pathlib import Path
|
||||
from typing import TYPE_CHECKING, Any, List
|
||||
from typing import TYPE_CHECKING, Any
|
||||
|
||||
import gguf
|
||||
import numpy as np
|
||||
|
|
@ -132,9 +132,9 @@ if "rope_scaling" in hparams and hparams["rope_scaling"] != None and "factor" in
|
|||
|
||||
print("gguf: get tokenizer metadata")
|
||||
|
||||
tokens: List[bytes] = []
|
||||
scores: List[float] = []
|
||||
toktypes: List[int] = []
|
||||
tokens: list[bytes] = []
|
||||
scores: list[float] = []
|
||||
toktypes: list[int] = []
|
||||
|
||||
tokenizer_model_file = dir_model / 'tokenizer.model'
|
||||
if not tokenizer_model_file.is_file():
|
||||
|
|
|
|||
|
|
@ -9,7 +9,7 @@ import os
|
|||
import struct
|
||||
import sys
|
||||
from pathlib import Path
|
||||
from typing import TYPE_CHECKING, Any, List, Optional
|
||||
from typing import TYPE_CHECKING, Any
|
||||
|
||||
import gguf
|
||||
import numpy as np
|
||||
|
|
@ -25,7 +25,7 @@ NDArray: TypeAlias = 'np.ndarray[Any, Any]'
|
|||
# https://github.com/huggingface/transformers/blob/main/src/transformers/models/llama/convert_llama_weights_to_hf.py
|
||||
|
||||
|
||||
def reverse_hf_permute(weights: NDArray, n_head: int, n_kv_head: Optional[int] = None) -> NDArray:
|
||||
def reverse_hf_permute(weights: NDArray, n_head: int, n_kv_head: int | None = None) -> NDArray:
|
||||
if n_kv_head is not None and n_head != n_kv_head:
|
||||
n_head //= n_kv_head
|
||||
|
||||
|
|
@ -139,9 +139,9 @@ if "rope_scaling" in hparams and hparams["rope_scaling"] != None and "factor" in
|
|||
|
||||
print("gguf: get tokenizer metadata")
|
||||
|
||||
tokens: List[bytes] = []
|
||||
scores: List[float] = []
|
||||
toktypes: List[int] = []
|
||||
tokens: list[bytes] = []
|
||||
scores: list[float] = []
|
||||
toktypes: list[int] = []
|
||||
|
||||
tokenizer_model_file = dir_model / 'tokenizer.model'
|
||||
if not tokenizer_model_file.is_file():
|
||||
|
|
|
|||
|
|
@ -6,12 +6,12 @@ import os
|
|||
import re
|
||||
import struct
|
||||
import sys
|
||||
from typing import Any, BinaryIO, Dict, Sequence
|
||||
from typing import Any, BinaryIO, Sequence
|
||||
|
||||
import numpy as np
|
||||
import torch
|
||||
|
||||
NUMPY_TYPE_TO_FTYPE: Dict[str, int] = {"float32": 0, "float16": 1}
|
||||
NUMPY_TYPE_TO_FTYPE: dict[str, int] = {"float32": 0, "float16": 1}
|
||||
|
||||
|
||||
HF_SUBLAYER_TO_GGML = {
|
||||
|
|
@ -48,7 +48,7 @@ def translate_tensor_name(t: str) -> str:
|
|||
sys.exit(1)
|
||||
|
||||
|
||||
def write_file_header(fout: BinaryIO, params: Dict[str, Any]) -> None:
|
||||
def write_file_header(fout: BinaryIO, params: dict[str, Any]) -> None:
|
||||
fout.write(b"ggla"[::-1]) # magic (ggml lora)
|
||||
fout.write(struct.pack("i", 1)) # file version
|
||||
fout.write(struct.pack("i", params["r"]))
|
||||
|
|
|
|||
97
convert.py
97
convert.py
|
|
@ -23,8 +23,7 @@ from abc import ABCMeta, abstractmethod
|
|||
from concurrent.futures import ProcessPoolExecutor, ThreadPoolExecutor
|
||||
from dataclasses import dataclass
|
||||
from pathlib import Path
|
||||
from typing import (IO, TYPE_CHECKING, Any, Callable, Dict, Generator, Iterable, List, Literal, Optional, Sequence, Set,
|
||||
Tuple, Type, TypeVar, Union)
|
||||
from typing import IO, TYPE_CHECKING, Any, Callable, Generator, Iterable, Literal, Sequence, TypeVar
|
||||
|
||||
import gguf
|
||||
import numpy as np
|
||||
|
|
@ -50,7 +49,7 @@ DEFAULT_CONCURRENCY = 8
|
|||
class DataType:
|
||||
name: str
|
||||
dtype: np.dtype[Any]
|
||||
valid_conversions: List[str]
|
||||
valid_conversions: list[str]
|
||||
|
||||
def elements_to_bytes(self, n_elements: int) -> int:
|
||||
return n_elements * self.dtype.itemsize
|
||||
|
|
@ -86,7 +85,7 @@ class Q8_0QuantizedDataType(QuantizedDataType):
|
|||
n_blocks = arr.size // self.block_size
|
||||
blocks = arr.reshape((n_blocks, self.block_size))
|
||||
# Much faster implementation of block quantization contributed by @Cebtenzzre
|
||||
def quantize_blocks_q8_0(blocks: NDArray) -> Iterable[Tuple[Any, Any]]:
|
||||
def quantize_blocks_q8_0(blocks: NDArray) -> Iterable[tuple[Any, Any]]:
|
||||
d = abs(blocks).max(axis = 1) / np.float32(127)
|
||||
with np.errstate(divide = 'ignore'):
|
||||
qs = (blocks / d[:, None]).round()
|
||||
|
|
@ -100,13 +99,13 @@ DT_Q8_0 = Q8_0QuantizedDataType('Q8_0',
|
|||
quantized_dtype = np.dtype([('d', '<f2'), ('qs', 'i1', (32,))]))
|
||||
|
||||
# Quantized types skipped here because they may also map to np.float32
|
||||
NUMPY_TYPE_TO_DATA_TYPE: Dict[np.dtype[Any], DataType] = {}
|
||||
NUMPY_TYPE_TO_DATA_TYPE: dict[np.dtype[Any], DataType] = {}
|
||||
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
|
||||
|
||||
SAFETENSORS_DATA_TYPES: Dict[str, DataType] = {
|
||||
SAFETENSORS_DATA_TYPES: dict[str, DataType] = {
|
||||
'BF16': DT_BF16,
|
||||
'F16': DT_F16,
|
||||
'F32': DT_F32,
|
||||
|
|
@ -128,7 +127,7 @@ class GGMLFileType(enum.IntEnum):
|
|||
# 1D tensors are always F32.
|
||||
return dt if len(tensor.shape) > 1 else DT_F32
|
||||
|
||||
GGML_FILE_TYPE_TO_DATA_TYPE: Dict[GGMLFileType, DataType] = {
|
||||
GGML_FILE_TYPE_TO_DATA_TYPE: dict[GGMLFileType, DataType] = {
|
||||
GGMLFileType.AllF32 : DT_F32,
|
||||
GGMLFileType.MostlyF16 : DT_F16,
|
||||
GGMLFileType.MostlyQ8_0: DT_Q8_0,
|
||||
|
|
@ -150,13 +149,13 @@ class Params:
|
|||
n_head_kv: int
|
||||
f_norm_eps: float
|
||||
|
||||
f_rope_freq_base: Optional[float] = None
|
||||
f_rope_scale: Optional[float] = None
|
||||
f_rope_freq_base: float | None = None
|
||||
f_rope_scale: float | None = None
|
||||
|
||||
ftype: Optional[GGMLFileType] = None
|
||||
ftype: GGMLFileType | None = None
|
||||
|
||||
# path to the directory containing the model files
|
||||
path_model: Optional[Path] = None
|
||||
path_model: Path | None = None
|
||||
|
||||
@staticmethod
|
||||
def find_n_mult(n_ff: int, n_embd: int) -> int:
|
||||
|
|
@ -316,9 +315,9 @@ class Params:
|
|||
#
|
||||
|
||||
class BpeVocab:
|
||||
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.bpe_tokenizer = json.loads(open(str(fname_tokenizer), encoding="utf-8").read())
|
||||
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:
|
||||
|
|
@ -337,7 +336,7 @@ 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
|
||||
byte_encoder = tokenization_gpt2.bytes_to_unicode()
|
||||
|
|
@ -347,12 +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()
|
||||
|
||||
|
|
@ -361,9 +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:
|
||||
|
|
@ -382,7 +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)
|
||||
|
|
@ -406,19 +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
|
||||
|
|
@ -498,13 +497,13 @@ def load_unquantized(lazy_tensor: LazyTensor, expected_dtype: Any = None, conver
|
|||
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
|
||||
|
||||
|
|
@ -527,24 +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
|
||||
|
|
@ -572,7 +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()
|
||||
|
|
@ -676,7 +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__'),
|
||||
|
|
@ -709,15 +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
|
||||
|
|
@ -756,7 +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
|
||||
|
|
@ -765,13 +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:
|
||||
|
|
@ -895,13 +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
|
||||
|
|
@ -942,7 +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):
|
||||
|
|
@ -962,7 +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
|
||||
|
||||
|
|
@ -997,12 +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.
|
||||
|
|
@ -1018,11 +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:
|
||||
|
|
@ -1053,7 +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))
|
||||
|
|
@ -1062,7 +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.
|
||||
|
|
@ -1093,7 +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",
|
||||
|
|
@ -1116,7 +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")
|
||||
|
|
|
|||
|
|
@ -10,7 +10,7 @@ import tempfile
|
|||
from enum import IntEnum, auto
|
||||
from io import BufferedWriter
|
||||
from pathlib import Path
|
||||
from typing import IO, Any, BinaryIO, Callable, Dict, List, Optional, Sequence, Tuple, Union
|
||||
from typing import IO, Any, BinaryIO, Callable, Sequence
|
||||
|
||||
import numpy as np
|
||||
|
||||
|
|
@ -105,7 +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",
|
||||
|
|
@ -114,7 +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",
|
||||
|
|
@ -160,7 +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,
|
||||
|
|
@ -169,7 +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
|
||||
|
|
@ -205,7 +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
|
||||
|
|
@ -300,9 +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 = {}
|
||||
|
|
@ -323,7 +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
|
||||
|
|
@ -334,13 +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
|
||||
|
|
@ -434,10 +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()
|
||||
|
|
@ -533,7 +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)
|
||||
|
||||
|
|
@ -563,7 +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")
|
||||
|
|
@ -582,7 +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)
|
||||
|
|
@ -602,7 +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))
|
||||
|
|
@ -728,13 +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]):
|
||||
|
|
@ -758,11 +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, ...] = tuple(('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:
|
||||
|
|
@ -823,7 +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
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue