mirror of
https://git.adityakumar.xyz/llama.cpp.git
synced 2024-11-08 15:09:44 +00:00
1264 lines
51 KiB
Python
Executable file
1264 lines
51 KiB
Python
Executable file
#!/usr/bin/env python
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import argparse
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import concurrent.futures
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import copy
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import enum
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import faulthandler
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import functools
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import io
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import itertools
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import json
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import math
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import mmap
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import pickle
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import re
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import signal
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import struct
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import sys
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import zipfile
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from abc import ABCMeta, abstractmethod
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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, Iterable, List,
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Literal, Optional, Sequence, Tuple, TypeVar, Union)
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import numpy as np
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from sentencepiece import SentencePieceProcessor # type: ignore
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if TYPE_CHECKING:
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from typing_extensions 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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@dataclass(frozen=True)
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class UnquantizedDataType:
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name: str
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DT_F16 = UnquantizedDataType('F16')
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DT_F32 = UnquantizedDataType('F32')
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DT_I32 = UnquantizedDataType('I32')
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DT_BF16 = UnquantizedDataType('BF16')
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@dataclass(frozen=True)
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class QuantizedDataType:
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groupsize: int
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have_addends: bool
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have_g_idx: bool
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DT_Q4_0 = QuantizedDataType(groupsize=32, have_addends=False, have_g_idx=False)
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DT_Q4_1 = QuantizedDataType(groupsize=32, have_addends=True, have_g_idx=False)
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DataType = Union[UnquantizedDataType, QuantizedDataType]
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DATA_TYPE_TO_FTYPE: Dict[DataType, int] = {
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DT_F32: 0,
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DT_F16: 1,
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DT_Q4_0: 2,
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DT_Q4_1: 3,
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}
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FTYPE_TO_DATA_TYPE: Dict[int, DataType] = \
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{ftype: dtype for (dtype, ftype) in DATA_TYPE_TO_FTYPE.items()}
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DATA_TYPE_TO_NUMPY: Dict[DataType, 'np.dtype[Any]'] = {
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DT_BF16: np.dtype(np.uint16),
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DT_F16: np.dtype(np.float16),
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DT_F32: np.dtype(np.float32),
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DT_I32: np.dtype(np.int32),
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}
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NUMPY_TYPE_TO_DATA_TYPE: Dict['np.dtype[Any]', DataType] = \
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{dtype: data_type for (data_type, dtype) in DATA_TYPE_TO_NUMPY.items()}
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class GGMLFileType(enum.Enum):
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AllF32 = 0
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MostlyF16 = 1 # except 1d tensors
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MostlyQ4_0 = 2 # except 1d tensors
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MostlyQ4_1 = 3 # except 1d tensors
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PerLayerIsQ4_1 = 4 # but tok_embeddings.weight and output.weight are F16
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def type_for_tensor(self, name: str, tensor: 'LazyTensor') -> DataType:
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if len(tensor.shape) == 1:
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# 1D tensors are always F32.
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return DT_F32
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elif self == GGMLFileType.AllF32:
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return DT_F32
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elif self == GGMLFileType.MostlyF16:
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return DT_F16
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elif self == GGMLFileType.MostlyQ4_0:
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return DT_Q4_0
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elif self == GGMLFileType.MostlyQ4_1:
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return DT_Q4_1
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elif self == GGMLFileType.PerLayerIsQ4_1:
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if name in ('output.weight', 'tok_embeddings.weight'):
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return DT_F16
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else:
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return DT_Q4_1
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else:
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raise ValueError(self)
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def make_tensors_list() -> List[str]:
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ret = [
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'tok_embeddings.weight',
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'norm.weight',
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'output.weight',
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]
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for i in range(80): # maximum number of layer
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ret += [
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f'layers.{i}.attention.wq.weight',
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f'layers.{i}.attention.wk.weight',
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f'layers.{i}.attention.wv.weight',
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f'layers.{i}.attention.wo.weight',
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f'layers.{i}.attention_norm.weight',
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f'layers.{i}.feed_forward.w1.weight',
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f'layers.{i}.feed_forward.w2.weight',
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f'layers.{i}.feed_forward.w3.weight',
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f'layers.{i}.ffn_norm.weight',
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]
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return ret
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TENSORS_LIST = make_tensors_list()
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TENSORS_SET = set(TENSORS_LIST)
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def find_n_mult(n_ff: int, n_embd: int) -> int:
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# hardcoded magic range
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for n_mult in range(256, 1, -1):
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calc_ff = (((8*n_embd) // 3 + n_mult - 1) // n_mult)*n_mult
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if calc_ff == n_ff:
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return n_mult
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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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@dataclass
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class Params:
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n_vocab: int
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n_embd: int
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n_mult: int
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n_head: int
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n_layer: int
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@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
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# try transformer naming first
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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)
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elif "model.layers.0.self_attn.W_pack.weight" in model: # next: try baichuan naming
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n_layer=next(i for i in itertools.count() if f"model.layers.{i}.self_attn.W_pack.weight" not in model)
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else:
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n_layer=next(i for i in itertools.count() if f"layers.{i}.attention.wq.weight" not in model)
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if n_layer < 1:
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raise Exception("failed to guess 'n_layer'. This model is unknown or unsupported.\n"
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"Suggestion: provide 'config.json' of the model in the same directory containing model files.")
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n_head=n_embd // 128 # guessed
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return Params(
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n_vocab=n_vocab,
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n_embd=n_embd,
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n_mult=256,
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n_head=n_head,
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n_layer=n_layer,
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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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config = json.load(open(config_path))
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n_vocab = config["vocab_size"];
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n_embd = config["hidden_size"];
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n_head = config["num_attention_heads"];
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n_layer = config["num_hidden_layers"];
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n_ff = config["intermediate_size"];
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n_mult = find_n_mult(n_ff, n_embd);
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return Params(
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n_vocab=n_vocab,
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n_embd=n_embd,
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n_mult=n_mult,
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n_head=n_head,
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n_layer=n_layer,
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)
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@staticmethod
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def load(model_plus: 'ModelPlus') -> 'Params':
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orig_config_path = model_plus.paths[0].parent / "params.json"
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hf_transformer_config_path = model_plus.paths[0].parent / "config.json"
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if hf_transformer_config_path.exists():
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params = Params.loadHFTransformerJson(model_plus.model, hf_transformer_config_path)
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else:
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params = Params.guessed(model_plus.model)
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print(f'params: n_vocab:{params.n_vocab} n_embd:{params.n_embd} n_mult:{params.n_mult} n_head:{params.n_head} n_layer:{params.n_layer}')
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return params
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class SentencePieceVocab:
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def __init__(self, fname_tokenizer: Path, fname_added_tokens: Optional[Path]) -> None:
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self.sentencepiece_tokenizer = SentencePieceProcessor(str(fname_tokenizer))
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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))
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else:
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added_tokens = {}
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vocab_size: int = self.sentencepiece_tokenizer.vocab_size()
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expected_ids = list(range(vocab_size, vocab_size + len(added_tokens)))
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actual_ids = sorted(added_tokens.values())
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if expected_ids != actual_ids:
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raise Exception(f"Expected added token IDs to be sequential and start at {len(added_tokens)}; got {actual_ids}")
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items = sorted(added_tokens.items(), key=lambda text_idx: text_idx[1])
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self.added_tokens_list = [text for (text, idx) in items]
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self.vocab_size_base: int = vocab_size
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self.vocab_size: int = self.vocab_size_base + len(self.added_tokens_list)
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self.fname_tokenizer = fname_tokenizer
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self.fname_added_tokens = fname_added_tokens
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def sentencepiece_tokens(self) -> Iterable[Tuple[bytes, float]]:
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tokenizer = self.sentencepiece_tokenizer
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for i in range(tokenizer.vocab_size()):
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text: bytes
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if tokenizer.is_unknown(i):
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text = " \u2047 ".encode("utf-8")
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elif tokenizer.is_control(i):
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text = b""
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elif tokenizer.is_byte(i):
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piece = tokenizer.id_to_piece(i)
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if len(piece) != 6:
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raise Exception(f"Invalid token: {piece}")
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byte_value = int(piece[3:-1], 16)
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text = struct.pack("B", byte_value)
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else:
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text = tokenizer.id_to_piece(i).replace("\u2581", " ").encode("utf-8")
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score: float = tokenizer.get_score(i)
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yield text, score
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def added_tokens(self) -> Iterable[Tuple[bytes, float]]:
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for text in self.added_tokens_list:
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score = -1000.0
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yield text.encode("utf-8"), score
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def all_tokens(self) -> Iterable[Tuple[bytes, float]]:
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yield from self.sentencepiece_tokens()
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yield from self.added_tokens()
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def __repr__(self) -> str:
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return f"<SentencePieceVocab with {self.vocab_size_base} base tokens and {len(self.added_tokens_list)} added tokens>"
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class GGMLVocab:
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def __init__(self, tokens: List[Tuple[bytes, float]]):
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self.tokens = tokens
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self.vocab_size = len(tokens)
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def all_tokens(self) -> Iterable[Tuple[bytes, float]]:
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return self.tokens
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def __repr__(self) -> str:
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return f"<GGMLVocab with {self.vocab_size} tokens>"
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Vocab = Union[SentencePieceVocab, GGMLVocab]
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def permute(weights: NDArray, n_head: int) -> NDArray:
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return (weights.reshape(n_head, 2, weights.shape[0] // n_head // 2, *weights.shape[1:])
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.swapaxes(1, 2)
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.reshape(weights.shape))
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def dequantize_q4(qvalues_pack32: NDArray, scales: NDArray, addends: Optional[NDArray], g_idx: Optional[NDArray]) -> NDArray:
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# First reinterpret each row from a list of int32s containing 8 values each
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# to a list of uint8s containing 2 values each.
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qvalues_pack8 = qvalues_pack32.view(np.uint8)
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# Then split out the two values per int8 (which requires an actual
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# conversion because numpy doesn't natively support int4s).
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qvalues = np.zeros([qvalues_pack8.shape[0], qvalues_pack8.shape[1] * 2], dtype=np.uint8)
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qvalues[:, 0::2] = qvalues_pack8 & 0xf
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qvalues[:, 1::2] = qvalues_pack8 >> 4
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assert addends is None or addends.shape == scales.shape
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assert qvalues.shape[0] == scales.shape[0]
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assert qvalues.shape[1] % scales.shape[1] == 0
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if g_idx is None:
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repeat_count = qvalues.shape[1] // scales.shape[1]
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scales = scales[:, :, np.newaxis]
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if addends is not None:
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addends = addends[:, :, np.newaxis]
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# Reshape so that the below computation broadcasts over scales and addends:
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qvalues.shape = (qvalues.shape[0], scales.shape[1], int(repeat_count))
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else:
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# In this case the scale and addend is selected for each column by g_idx:
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assert addends is not None
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scales = scales[:, g_idx]
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addends = addends[:, g_idx]
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if addends is None:
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# Q4_0
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qvalues = qvalues.view(np.int8)
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qvalues -= 8
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# And do the actual 'value = scale * qvalue + addend' computation.
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values = scales * qvalues
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if addends is not None:
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values += addends
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if g_idx is None:
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values.shape = (values.shape[0], values.shape[1] * values.shape[2])
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return values
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class Tensor(metaclass=ABCMeta):
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data_type: DataType
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@abstractmethod
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def astype(self, data_type: DataType) -> 'Tensor': ...
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@abstractmethod
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def permute(self, n_head: int) -> 'Tensor': ...
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@abstractmethod
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def permute_part(self, n_part: int, n_head: int) -> 'UnquantizedTensor': ...
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@abstractmethod
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def part(self, n_part: int) -> 'UnquantizedTensor': ...
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@abstractmethod
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def to_ggml(self) -> 'GGMLCompatibleTensor': ...
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def bf16_to_fp32(bf16_arr: np.ndarray) -> np.ndarray:
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assert bf16_arr.dtype == np.uint16, f"Input array should be of dtype uint16, but got {bf16_arr.dtype}"
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fp32_arr = bf16_arr.astype(np.uint32) << 16
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return fp32_arr.view(np.float32)
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class UnquantizedTensor(Tensor):
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def __init__(self, ndarray: NDArray) -> None:
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assert isinstance(ndarray, np.ndarray)
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self.ndarray = ndarray
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self.data_type = NUMPY_TYPE_TO_DATA_TYPE[ndarray.dtype]
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def astype(self, data_type: DataType) -> Tensor:
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dtype = DATA_TYPE_TO_NUMPY[data_type]
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if self.data_type == DT_BF16:
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self.ndarray = bf16_to_fp32(self.ndarray)
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return UnquantizedTensor(self.ndarray.astype(dtype))
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def to_ggml(self) -> 'UnquantizedTensor':
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return self
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def permute_part(self, n_part: int, n_head: int) -> 'UnquantizedTensor':
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r = self.ndarray.shape[0] // 3
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return UnquantizedTensor(permute(self.ndarray[r * n_part : r * n_part + r, ...], n_head))
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def part(self, n_part: int) -> 'UnquantizedTensor':
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r = self.ndarray.shape[0] // 3
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return UnquantizedTensor(self.ndarray[r * n_part : r * n_part + r, ...])
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def permute(self, n_head: int) -> 'UnquantizedTensor':
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return UnquantizedTensor(permute(self.ndarray, n_head))
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def load_unquantized(lazy_tensor: 'LazyTensor', expected_dtype: Any = None, convert: bool = False) -> NDArray:
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tensor = lazy_tensor.load()
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assert isinstance(tensor, UnquantizedTensor)
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# double-check:
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actual_shape = list(tensor.ndarray.shape)
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assert actual_shape == lazy_tensor.shape, (actual_shape, lazy_tensor.shape)
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if expected_dtype is not None and expected_dtype != tensor.ndarray.dtype:
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if convert:
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tensor.ndarray = tensor.ndarray.astype(expected_dtype)
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else:
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raise ValueError(f'expected this tensor to have dtype {expected_dtype}, got {tensor.ndarray.dtype}')
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return tensor.ndarray
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class GGMLQuantizedTensor(Tensor):
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data_type: QuantizedDataType
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def __init__(self, ndarray: NDArray, shape: List[int], data_type: DataType) -> None:
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rows, columns = shape
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assert data_type in (DT_Q4_1, DT_Q4_0) # for now
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assert isinstance(data_type, QuantizedDataType) # redundant, but mypy complains without this
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assert columns % data_type.groupsize == 0
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words_in_block = 6 if data_type == DT_Q4_1 else 5
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self.ndarray = ndarray.view(dtype=np.uint32).reshape((rows, columns // data_type.groupsize, words_in_block))
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self.shape = shape[:]
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self.data_type = data_type
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def astype(self, data_type: DataType) -> Tensor:
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if data_type == self.data_type:
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return self
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scales = self.ndarray[:, :, 0].view(np.float32)
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if self.data_type.have_addends:
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addends = self.ndarray[:, :, 1].view(np.float32)
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else:
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addends = None
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qweights = self.ndarray[:, :, -4:].reshape([self.shape[0], self.shape[1] // 8])
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dq = dequantize_q4(qweights, scales, addends, g_idx=None)
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return UnquantizedTensor(dq).astype(data_type)
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def to_ggml(self) -> 'GGMLQuantizedTensor':
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return self
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def permute(self, n_head: int) -> 'GGMLQuantizedTensor':
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return GGMLQuantizedTensor(permute(self.ndarray, n_head), self.shape, self.data_type)
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GGMLCompatibleTensor = Union[UnquantizedTensor, GGMLQuantizedTensor]
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class DeferredPermutedTensor(Tensor):
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def __init__(self, base: Tensor, n_head: int) -> None:
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self.base = base
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self.n_head = n_head
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self.data_type = self.base.data_type
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def astype(self, data_type: DataType) -> Tensor:
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return self.base.astype(data_type).permute(self.n_head)
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def to_ggml(self) -> GGMLCompatibleTensor:
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return self.base.to_ggml().permute(self.n_head)
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def permute(self, n_head: int) -> Tensor:
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raise Exception("shouldn't permute twice")
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class GPTQForLLaMaQuantizedTensor(Tensor):
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def __init__(self, model: 'LazyModel', namebase: str) -> None:
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qweight = load_unquantized(model[f"{namebase}.qweight"], np.int32)
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scales = load_unquantized(model[f"{namebase}.scales"], np.float32, convert=True)
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bias = model.get(f"{namebase}.bias")
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if bias is not None:
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# Q4_1 does not support bias; good thing the bias is always all zeros.
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assert not np.any(load_unquantized(bias))
|
|
|
|
if f"{namebase}.zeros" in model:
|
|
zeros = load_unquantized(model[f"{namebase}.zeros"], np.float32)
|
|
else:
|
|
qzeros = load_unquantized(model[f"{namebase}.qzeros"], np.int32)
|
|
assert qzeros.dtype == np.int32
|
|
zeros = dequantize_q4(qzeros, scales, scales, g_idx=None)
|
|
assert zeros.dtype == np.float32
|
|
|
|
assert zeros.shape == scales.shape
|
|
|
|
# Output is transposed compared to the input, and addends have their sign flipped.
|
|
# Scales and zeros similarly must be transposed but only for newer
|
|
# versions of GPTQ-for-LLaMa; the older versions can be identified by
|
|
# having shape (n_embd, 1).
|
|
qweight = qweight.T
|
|
if scales.shape[1] != 1:
|
|
scales = scales.T
|
|
zeros = zeros.T
|
|
|
|
# Output also has signs flipped for the addends.
|
|
self.qweight = qweight
|
|
self.scales = scales
|
|
self.addends = -zeros
|
|
|
|
self.g_idx: Optional[NDArray]
|
|
if f"{namebase}.g_idx" in model:
|
|
self.g_idx = load_unquantized(model[f"{namebase}.g_idx"], np.int32)
|
|
assert self.g_idx.shape == (qweight.shape[1] * 8,)
|
|
else:
|
|
self.g_idx = None
|
|
|
|
self.shape = [self.qweight.shape[0], self.qweight.shape[1] * 8]
|
|
self.data_type = QuantizedDataType(groupsize=self.groupsize(), have_addends=True,
|
|
have_g_idx=(self.g_idx is not None))
|
|
|
|
def inspect(self, row: int, col: int) -> None:
|
|
'''For debugging.'''
|
|
qweight = (self.qweight[row, col // 8] >> (4 * (col & 7))) & 0xf
|
|
if self.g_idx is not None:
|
|
group = self.g_idx[col]
|
|
else:
|
|
group = int(col // self.groupsize())
|
|
scale = self.scales[row, group]
|
|
addend = self.addends[row, group]
|
|
with np.printoptions(precision=None, suppress=True):
|
|
print(f'scale:{scale} addend:{addend} qweight:{qweight}')
|
|
print('possible values:', np.arange(16) * scale + addend)
|
|
print('actual value:', qweight * scale + addend)
|
|
|
|
def astype(self, data_type: DataType) -> Tensor:
|
|
if isinstance(data_type, QuantizedDataType):
|
|
assert self.g_idx is None and data_type.have_addends is True and data_type.have_g_idx is False
|
|
return self.regroup(data_type.groupsize)
|
|
|
|
dequantized = dequantize_q4(np.ascontiguousarray(self.qweight), self.scales, self.addends, self.g_idx)
|
|
return UnquantizedTensor(dequantized).astype(data_type)
|
|
|
|
def groupsize(self) -> int:
|
|
assert self.addends.shape == self.scales.shape
|
|
assert self.shape[1] % self.scales.shape[1] == 0
|
|
return self.shape[1] // self.scales.shape[1]
|
|
|
|
def regroup(self, new_groupsize: int = 32) -> 'GPTQForLLaMaQuantizedTensor':
|
|
# Old versions of GPTQ-for-LLaMa shared scales and addends between all the
|
|
# columns in a row. Newer versions share them between every set of N
|
|
# columns in a row, where N is the `groupsize` parameter, usually 128. The
|
|
# output format shares them between every set of 32 columns. To handle
|
|
# this, duplicate scales and addends for every smaller group.
|
|
# (In the above, 'row' and 'column' are in the sense of the output.)
|
|
assert self.g_idx is None
|
|
old_groupsize = self.groupsize()
|
|
assert old_groupsize >= new_groupsize and old_groupsize % new_groupsize == 0, old_groupsize
|
|
ret = copy.copy(self)
|
|
ret.addends = self.addends.repeat(old_groupsize // new_groupsize, axis=1)
|
|
ret.scales = self.scales.repeat(old_groupsize // new_groupsize, axis=1)
|
|
ret.data_type = QuantizedDataType(groupsize=new_groupsize, have_addends=True, have_g_idx=False)
|
|
return ret
|
|
|
|
def permute(self, n_head: int) -> Tensor:
|
|
return DeferredPermutedTensor(self, n_head)
|
|
|
|
def to_ggml(self) -> GGMLQuantizedTensor:
|
|
# The output format looks like this:
|
|
# For each row:
|
|
# For each group of 32 columns:
|
|
# - addend (float32, 4 bytes)
|
|
# - scale (float32, 4 bytes)
|
|
# - weights (int4 * 32, 16 bytes)
|
|
|
|
if self.groupsize() != 32:
|
|
raise Exception("should have been regrouped before converting to ggml")
|
|
|
|
# Since the output format is mixed between integers and floats, we have
|
|
# to hackily view the floats as int32s just so numpy will let us
|
|
# concatenate them.
|
|
addends_view = self.addends.view(dtype=np.int32)[:, :, np.newaxis]
|
|
scales_view = self.scales.view(dtype=np.int32)[:, :, np.newaxis]
|
|
|
|
# Split into groups of 4 columns (i.e. 32 columns of quantized data):
|
|
grouped = self.qweight.reshape([self.qweight.shape[0], self.qweight.shape[1] // 4, 4])
|
|
|
|
# And concatenate:
|
|
grouped = np.concatenate([scales_view, addends_view, grouped], axis=2, casting='no')
|
|
|
|
return GGMLQuantizedTensor(grouped, self.shape, DT_Q4_1)
|
|
|
|
|
|
@dataclass
|
|
class LazyTensor:
|
|
_load: Callable[[], Tensor]
|
|
shape: List[int]
|
|
data_type: DataType
|
|
description: str
|
|
|
|
def load(self) -> Tensor:
|
|
ret = self._load()
|
|
assert ret.data_type == self.data_type, (self.data_type, ret.data_type, self.description)
|
|
return ret
|
|
|
|
def astype(self, data_type: DataType) -> 'LazyTensor':
|
|
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:
|
|
if data_type == self.data_type:
|
|
return
|
|
if isinstance(data_type, QuantizedDataType):
|
|
if not isinstance(self.data_type, QuantizedDataType):
|
|
raise Exception(f"Can't turn an unquantized tensor into a quantized type ({data_type})")
|
|
if self.data_type.have_g_idx:
|
|
sys.stderr.write(
|
|
"Error: Input uses the newer GPTQ-for-LLaMa format (using g_idx), "
|
|
"which is not yet natively supported by GGML. "
|
|
"For now you can still convert this model by passing `--outtype f16` to dequantize, "
|
|
"but that will result in a much larger output file for no quality benefit.\n")
|
|
sys.exit(1)
|
|
assert not data_type.have_g_idx and self.data_type.have_addends and data_type.have_addends
|
|
|
|
|
|
LazyModel = Dict[str, LazyTensor]
|
|
|
|
|
|
@dataclass
|
|
class ModelPlus:
|
|
model: LazyModel
|
|
paths: List[Path] # Where this was read from.
|
|
format: Literal['ggml', 'torch', 'safetensors']
|
|
vocab: Optional[Vocab] # For GGML models (which have vocab built in), the vocab.
|
|
|
|
|
|
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]
|
|
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]
|
|
concatenated: NDArray = np.concatenate(ndarrays, axis=axis)
|
|
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}
|
|
|
|
|
|
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()
|
|
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
|
|
# don't split indivdual tensors between files.
|
|
model: LazyModel = {}
|
|
for mp in models_plus:
|
|
model.update(mp.model)
|
|
else:
|
|
model = merge_sharded([mp.model for mp in models_plus])
|
|
|
|
return ModelPlus(model, paths, format, vocab)
|
|
|
|
|
|
def permute_lazy(lazy_tensor: LazyTensor, n_head: int) -> LazyTensor:
|
|
def load() -> Tensor:
|
|
return lazy_tensor.load().permute(n_head)
|
|
return LazyTensor(load, lazy_tensor.shape, lazy_tensor.data_type, f'permute({n_head}) ' + lazy_tensor.description)
|
|
|
|
def permute_part_lazy(lazy_tensor: LazyTensor, n_part: int, n_head: int) -> LazyTensor:
|
|
def load() -> Tensor:
|
|
return lazy_tensor.load().permute_part(n_part, n_head)
|
|
s = lazy_tensor.shape.copy()
|
|
s[0] = s[0] // 3
|
|
return LazyTensor(load, s, lazy_tensor.data_type, f'permute({n_head}) ' + lazy_tensor.description)
|
|
|
|
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)
|
|
|
|
def convert_transformers_to_orig(model: LazyModel, params: Params) -> LazyModel:
|
|
out: LazyModel = {}
|
|
out["tok_embeddings.weight"] = model["model.embed_tokens.weight"]
|
|
out["norm.weight"] = model["model.norm.weight"]
|
|
out["output.weight"] = model["lm_head.weight"]
|
|
|
|
for i in itertools.count():
|
|
if f"model.layers.{i}.self_attn.q_proj.weight" in model:
|
|
out[f"layers.{i}.attention.wq.weight"] = permute_lazy(model[f"model.layers.{i}.self_attn.q_proj.weight"], params.n_head)
|
|
out[f"layers.{i}.attention.wk.weight"] = permute_lazy(model[f"model.layers.{i}.self_attn.k_proj.weight"], params.n_head)
|
|
out[f"layers.{i}.attention.wv.weight"] = model[f"model.layers.{i}.self_attn.v_proj.weight"]
|
|
elif f"model.layers.{i}.self_attn.W_pack.weight" in model:
|
|
out[f"layers.{i}.attention.wq.weight"] = permute_part_lazy(model[f"model.layers.{i}.self_attn.W_pack.weight"], 0, params.n_head)
|
|
out[f"layers.{i}.attention.wk.weight"] = permute_part_lazy(model[f"model.layers.{i}.self_attn.W_pack.weight"], 1, params.n_head)
|
|
out[f"layers.{i}.attention.wv.weight"] = part_lazy(model[f"model.layers.{i}.self_attn.W_pack.weight"], 2)
|
|
else:
|
|
break
|
|
|
|
out[f"layers.{i}.attention.wo.weight"] = model[f"model.layers.{i}.self_attn.o_proj.weight"]
|
|
|
|
out[f"layers.{i}.feed_forward.w1.weight"] = model[f"model.layers.{i}.mlp.gate_proj.weight"]
|
|
out[f"layers.{i}.feed_forward.w2.weight"] = model[f"model.layers.{i}.mlp.down_proj.weight"]
|
|
out[f"layers.{i}.feed_forward.w3.weight"] = model[f"model.layers.{i}.mlp.up_proj.weight"]
|
|
|
|
out[f"layers.{i}.attention_norm.weight"] = model[f"model.layers.{i}.input_layernorm.weight"]
|
|
out[f"layers.{i}.ffn_norm.weight"] = model[f"model.layers.{i}.post_attention_layernorm.weight"]
|
|
return out
|
|
|
|
|
|
def handle_quantization(model: LazyModel) -> LazyModel:
|
|
'''Convert a model with entries for 'foo.qweight', 'foo.scales', etc.
|
|
(which resolve to UnquantizedTensors with the raw data) to one with entries
|
|
for 'foo.weight' (which resolve to QuantizedTensors).
|
|
'''
|
|
def convert(name: str) -> Tuple[str, LazyTensor]:
|
|
if name.endswith(".qweight"):
|
|
namebase = name.rsplit('.', 1)[0]
|
|
orig_name = namebase + ".weight"
|
|
|
|
lazy_tensor = model[name]
|
|
assert len(lazy_tensor.shape) == 2
|
|
real_shape = [lazy_tensor.shape[1], lazy_tensor.shape[0] * 8]
|
|
|
|
# Calculate type. This replicates the logic in
|
|
# GPTQForLLaMaQuantizedTensor (which is executed when the modelis
|
|
# actually loaded).
|
|
lazy_scales = model[f"{namebase}.scales"]
|
|
scales_width = 1 if lazy_scales.shape[1] == 1 else lazy_scales.shape[0]
|
|
assert real_shape[1] % scales_width == 0
|
|
groupsize = real_shape[1] // scales_width
|
|
have_g_idx = f"{namebase}.g_idx" in model
|
|
data_type = QuantizedDataType(groupsize=groupsize, have_addends=True, have_g_idx=have_g_idx)
|
|
|
|
def load() -> Tensor:
|
|
return GPTQForLLaMaQuantizedTensor(model, namebase)
|
|
|
|
return (orig_name, LazyTensor(load, real_shape, data_type, '[quantized]'))
|
|
else:
|
|
return (name, model[name])
|
|
return dict(convert(name) for name in model)
|
|
|
|
# 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]
|
|
filename = self.data_base_path + '/' + filename_stem
|
|
info = self.zip_file.getinfo(filename)
|
|
|
|
def load(offset: int, elm_count: int) -> NDArray:
|
|
dtype = DATA_TYPE_TO_NUMPY.get(data_type)
|
|
if dtype is None:
|
|
raise Exception("tensor stored in unsupported format")
|
|
fp = self.zip_file.open(info)
|
|
fp.seek(offset * dtype.itemsize)
|
|
size = elm_count * dtype.itemsize
|
|
data = fp.read(size)
|
|
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)
|
|
|
|
# @staticmethod
|
|
def lazy_rebuild_tensor_v2(storage: Any, storage_offset: Any, size: Any, stride: Any,
|
|
# pyright: ignore[reportSelfClsParameterName]
|
|
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)
|
|
|
|
# @staticmethod
|
|
def rebuild_from_type_v2(func, new_type, args, state):
|
|
return func(*args)
|
|
|
|
CLASSES: Dict[Any, Any] = {
|
|
('torch._tensor', '_rebuild_from_type_v2'): rebuild_from_type_v2,
|
|
('torch._utils', '_rebuild_tensor_v2'): lazy_rebuild_tensor_v2,
|
|
('torch', 'BFloat16Storage'): LazyStorageKind(DT_BF16),
|
|
('torch', 'HalfStorage'): LazyStorageKind(DT_F16),
|
|
('torch', 'FloatStorage'): LazyStorageKind(DT_F32),
|
|
('torch', 'IntStorage'): LazyStorageKind(DT_I32),
|
|
('torch', 'Tensor'): LazyTensor,
|
|
}
|
|
|
|
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()
|
|
as_dict = dict(model.items())
|
|
return ModelPlus(model=as_dict, paths=[path], format='torch', vocab=None)
|
|
|
|
|
|
SAFETENSORS_DATA_TYPES: Dict[str, DataType] = {
|
|
'BF16': DT_BF16,
|
|
'F16': DT_F16,
|
|
'F32': DT_F32,
|
|
'I32': DT_I32,
|
|
}
|
|
|
|
|
|
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))
|
|
# 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:
|
|
data_type = SAFETENSORS_DATA_TYPES[info['dtype']]
|
|
numpy_dtype = DATA_TYPE_TO_NUMPY[data_type]
|
|
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
|
|
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)
|
|
model = {name: convert(info) for (name, info) in header.items() if name != '__metadata__'}
|
|
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:
|
|
raise Exception("unexpectedly reached end of file")
|
|
return ret
|
|
|
|
|
|
def lazy_load_ggml_file(fp: io.BufferedReader, path: Path) -> ModelPlus:
|
|
magic = must_read(fp, 4)[::-1]
|
|
if magic in (b'ggmf', b'ggjt'):
|
|
version, = struct.unpack("i", must_read(fp, 4))
|
|
assert version == 1
|
|
else:
|
|
assert magic == b'ggml'
|
|
version = None
|
|
n_vocab, n_embd, n_mult, n_head, n_layer, rot, file_type = struct.unpack('<7i', must_read(fp, 28))
|
|
|
|
tokens: List[Tuple[bytes, float]] = []
|
|
for i in range(n_vocab):
|
|
if i == 32000:
|
|
# HACK: GPT4All messed with the format without changing the magic
|
|
# number. Specifically, they changed the vocab section to contain
|
|
# `n_vocab - 1` tokens instead of `n_vocab` (i.e. omitting the
|
|
# extra pad token). Try to detect if we're reading a file like
|
|
# this.
|
|
orig_pos = fp.tell()
|
|
fp.seek(20, io.SEEK_CUR)
|
|
is_gpt4all = fp.read(21) == b'tok_embeddings.weight'
|
|
fp.seek(orig_pos)
|
|
if is_gpt4all:
|
|
break
|
|
|
|
length, = struct.unpack("i", must_read(fp, 4))
|
|
text = must_read(fp, length)
|
|
if magic != b'ggml':
|
|
score, = struct.unpack("f", must_read(fp, 4))
|
|
tokens.append((text, score))
|
|
vocab = GGMLVocab(tokens) if magic != b'ggml' else None
|
|
|
|
model: LazyModel = {}
|
|
# Use mmap for the actual data to avoid race conditions with the file offset.
|
|
off = fp.raw.tell()
|
|
mapped = memoryview(mmap.mmap(fp.fileno(), 0, access=mmap.ACCESS_READ))
|
|
fp.raw.seek(off) # needed on Windows
|
|
|
|
def read_tensor() -> None: # this is a function so that variables captured in `load` don't change
|
|
shape_len, name_len, ftype = struct.unpack("iii", must_read(fp, 12))
|
|
assert 0 <= shape_len <= 3
|
|
shape: List[int] = list(struct.unpack(f"{shape_len}i", must_read(fp, 4 * shape_len)))
|
|
shape = shape[::-1]
|
|
name = must_read(fp, name_len).decode('utf-8')
|
|
data_type = FTYPE_TO_DATA_TYPE[ftype]
|
|
|
|
if magic == b'ggjt':
|
|
fp.seek((fp.tell() + 31) & -32)
|
|
|
|
if data_type == DT_Q4_1:
|
|
# See GPTQForLLaMaQuantizedTensor.ggml_ndarray()
|
|
size = 24 * (shape[1] // 32) * shape[0]
|
|
elif data_type == DT_Q4_0:
|
|
size = 20 * (shape[1] // 32) * shape[0]
|
|
else:
|
|
numpy_dtype = DATA_TYPE_TO_NUMPY[data_type]
|
|
elm_count = math.prod(shape)
|
|
size = elm_count * numpy_dtype.itemsize
|
|
offset = fp.tell()
|
|
buf = mapped[offset:offset+size]
|
|
fp.seek(size, io.SEEK_CUR)
|
|
|
|
def load() -> Tensor:
|
|
if isinstance(data_type, QuantizedDataType):
|
|
ndarray = np.frombuffer(buf, dtype=np.uint32)
|
|
return GGMLQuantizedTensor(ndarray, shape, data_type)
|
|
else:
|
|
return UnquantizedTensor(np.frombuffer(buf, dtype=numpy_dtype).reshape(shape))
|
|
description = f'ggml offset={offset} type={data_type} path={path}'
|
|
model[name] = LazyTensor(load, shape, data_type, description)
|
|
|
|
while fp.read(1) != b'':
|
|
fp.seek(-1, io.SEEK_CUR)
|
|
read_tensor()
|
|
|
|
return ModelPlus(model=model, paths=[path], format='ggml', vocab=vocab)
|
|
|
|
|
|
@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 first8[2:4] == b'gg':
|
|
# GGML format
|
|
return lazy_load_ggml_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')
|
|
|
|
|
|
def bounded_parallel_map(func: Callable[[In], Out], iterable: Iterable[In], concurrency: int) -> 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
|
|
output value buffered per thread.'''
|
|
with concurrent.futures.ThreadPoolExecutor() as executor:
|
|
futures: List[concurrent.futures.Future[Out]] = []
|
|
items_rev = list(iterable)[::-1]
|
|
for i in range(min(concurrency, len(items_rev))):
|
|
futures.append(executor.submit(func, items_rev.pop()))
|
|
while futures:
|
|
result = futures.pop(0).result()
|
|
if items_rev:
|
|
futures.append(executor.submit(func, items_rev.pop()))
|
|
yield result
|
|
|
|
|
|
def check_vocab_size(params: Params, vocab: Vocab) -> None:
|
|
if params.n_vocab != vocab.vocab_size:
|
|
# GGMLVocab comes from the same file as the model so shouldn't mismatch:
|
|
assert isinstance(vocab, SentencePieceVocab)
|
|
if params.n_vocab == vocab.vocab_size_base:
|
|
print("Ignoring added_tokens.json since model matches vocab size without it.")
|
|
vocab.added_tokens_list = []
|
|
vocab.vocab_size = vocab.vocab_size_base
|
|
return
|
|
msg = f"Vocab size mismatch (model has {params.n_vocab}, but {vocab.fname_tokenizer}"
|
|
if vocab.fname_added_tokens is not None:
|
|
msg += f" combined with {vocab.fname_added_tokens}"
|
|
msg += f" has {vocab.vocab_size})."
|
|
if vocab.vocab_size < params.n_vocab < vocab.vocab_size + 20 and vocab.fname_added_tokens is None:
|
|
msg += f" Most likely you are missing added_tokens.json (should be in {vocab.fname_tokenizer.parent})."
|
|
raise Exception(msg)
|
|
|
|
|
|
class OutputFile:
|
|
def __init__(self, fname_out: Path) -> None:
|
|
self.fout = open(fname_out, "wb")
|
|
|
|
def write_file_header(self, params: Params, file_type: GGMLFileType) -> None:
|
|
self.fout.write(b"ggjt"[::-1]) # magic
|
|
values = [
|
|
1, # file version
|
|
params.n_vocab,
|
|
params.n_embd,
|
|
params.n_mult,
|
|
params.n_head,
|
|
params.n_layer,
|
|
params.n_embd // params.n_head, # rot (obsolete)
|
|
file_type.value,
|
|
]
|
|
self.fout.write(struct.pack("i" * len(values), *values))
|
|
|
|
def write_tensor_header(self, name: str, shape: Sequence[int], data_type: DataType) -> None:
|
|
sname = name.encode('utf-8')
|
|
self.fout.write(struct.pack("iii", len(shape), len(sname), DATA_TYPE_TO_FTYPE[data_type]))
|
|
self.fout.write(struct.pack("i" * len(shape), *shape[::-1]))
|
|
self.fout.write(sname)
|
|
self.fout.seek((self.fout.tell() + 31) & -32)
|
|
|
|
def write_vocab(self, vocab: Vocab) -> None:
|
|
for text, score in vocab.all_tokens():
|
|
self.fout.write(struct.pack("i", len(text)))
|
|
self.fout.write(text)
|
|
self.fout.write(struct.pack("f", score))
|
|
|
|
@staticmethod
|
|
def write_vocab_only(fname_out: Path, vocab: Vocab) -> None:
|
|
of = OutputFile(fname_out)
|
|
params = Params(n_vocab=vocab.vocab_size, n_embd=0, n_mult=0,
|
|
n_head=1, n_layer=0)
|
|
of = OutputFile(fname_out)
|
|
of.write_file_header(params, file_type=GGMLFileType.AllF32)
|
|
of.write_vocab(vocab)
|
|
of.fout.close()
|
|
|
|
@staticmethod
|
|
def write_all(fname_out: Path, params: Params, file_type: GGMLFileType, model: LazyModel, vocab: Vocab) -> None:
|
|
check_vocab_size(params, vocab)
|
|
of = OutputFile(fname_out)
|
|
of.write_file_header(params, file_type)
|
|
print("Writing vocab...")
|
|
of.write_vocab(vocab)
|
|
|
|
def do_item(item: Tuple[str, LazyTensor]) -> NDArray:
|
|
name, lazy_tensor = item
|
|
return lazy_tensor.load().to_ggml().ndarray
|
|
|
|
ndarrays = bounded_parallel_map(do_item, model.items(), concurrency=8)
|
|
for i, ((name, lazy_tensor), ndarray) in enumerate(zip(model.items(), ndarrays)):
|
|
size = ' x '.join(f"{dim:6d}" for dim in lazy_tensor.shape)
|
|
padi = len(str(len(model)))
|
|
print(f"[{i+1:{padi}d}/{len(model)}] Writing tensor {name:38s} | size {size:16} | type {lazy_tensor.data_type}")
|
|
of.write_tensor_header(name, lazy_tensor.shape, lazy_tensor.data_type)
|
|
ndarray.tofile(of.fout)
|
|
of.fout.close()
|
|
|
|
|
|
def pick_output_type(model: LazyModel, output_type_str: Optional[str]) -> GGMLFileType:
|
|
wq_type = model["layers.0.attention.wq.weight"].data_type
|
|
if output_type_str == "f32" or (output_type_str is None and wq_type in (DT_F32, DT_BF16)):
|
|
return GGMLFileType.AllF32
|
|
if output_type_str == "f16" or (output_type_str is None and wq_type == DT_F16):
|
|
return GGMLFileType.MostlyF16
|
|
if output_type_str == "q4_1" or (output_type_str is None and isinstance(wq_type, QuantizedDataType) and
|
|
wq_type.have_addends):
|
|
if isinstance(model["output.weight"].data_type, QuantizedDataType):
|
|
return GGMLFileType.MostlyQ4_1
|
|
else:
|
|
return GGMLFileType.PerLayerIsQ4_1
|
|
if output_type_str == "q4_0" or (output_type_str is None and isinstance(wq_type, QuantizedDataType)):
|
|
return GGMLFileType.MostlyQ4_0
|
|
name_to_type = {name: lazy_tensor.data_type for (name, lazy_tensor) in model.items()}
|
|
raise Exception(f"Unexpected combination of types: {name_to_type}")
|
|
|
|
|
|
def do_necessary_conversions(model: LazyModel, params: Params) -> LazyModel:
|
|
model = handle_quantization(model)
|
|
|
|
if "lm_head.weight" in model:
|
|
model = convert_transformers_to_orig(model, params)
|
|
model = filter_and_sort_tensors(model)
|
|
|
|
return model
|
|
|
|
|
|
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()}
|
|
|
|
|
|
def nth_multifile_path(path: Path, n: int) -> Optional[Path]:
|
|
'''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]] = [
|
|
# - 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
|
|
|
|
|
|
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] = []
|
|
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():
|
|
# Check if it's a set of safetensors files first
|
|
files = list(path.glob("model-00001-of-*.safetensors"))
|
|
if not files:
|
|
# Try the PyTorch patterns too, with lower priority
|
|
globs = ["consolidated.00.pth", "pytorch_model-00001-of-*.bin", "*.pt", "pytorch_model.bin"]
|
|
files = [file for glob in globs for file in path.glob(glob)]
|
|
if not files:
|
|
# Try GGML too, but with lower priority, since if both a non-GGML
|
|
# model and a GGML model exist in the same directory, we assume the
|
|
# latter was converted from the former.
|
|
files = list(path.glob("ggml-model*.bin*"))
|
|
if not files:
|
|
raise Exception(f"Can't find model in directory {path}")
|
|
if len(files) > 1:
|
|
raise Exception(f"Found multiple models in {path}, not sure which to pick: {files}")
|
|
path = files[0]
|
|
|
|
paths = find_multifile_paths(path)
|
|
models_plus: List[ModelPlus] = []
|
|
for path in paths:
|
|
print(f"Loading model file {path}")
|
|
models_plus.append(lazy_load_file(path))
|
|
|
|
model_plus = merge_multifile_models(models_plus)
|
|
return model_plus
|
|
|
|
|
|
def filter_and_sort_tensors(model: LazyModel) -> LazyModel:
|
|
return {name: model[name] for name in TENSORS_LIST if name in model}
|
|
|
|
|
|
def load_vocab(path: Path) -> SentencePieceVocab:
|
|
# 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.
|
|
if path.is_dir():
|
|
path2 = path / "tokenizer.model"
|
|
# Use `.parent` instead of /.. to handle the symlink case better.
|
|
path3 = path.parent / "tokenizer.model"
|
|
if path2.exists():
|
|
path = path2
|
|
elif path3.exists():
|
|
path = path3
|
|
else:
|
|
raise FileNotFoundError(
|
|
f"Could not find tokenizer.model in {path} or its parent; "
|
|
"if it's in another directory, pass the directory as --vocab-dir")
|
|
added_tokens_path = path.parent / "added_tokens.json"
|
|
print(f"Loading vocab file {path}")
|
|
return SentencePieceVocab(path, added_tokens_path if added_tokens_path.exists() else None)
|
|
|
|
|
|
def default_outfile(model_paths: List[Path], file_type: GGMLFileType) -> Path:
|
|
namestr = {
|
|
GGMLFileType.AllF32: "f32",
|
|
GGMLFileType.MostlyF16: "f16",
|
|
GGMLFileType.MostlyQ4_0: "q4_0",
|
|
GGMLFileType.MostlyQ4_1: "q4_1",
|
|
GGMLFileType.PerLayerIsQ4_1: "q4_1",
|
|
}[file_type]
|
|
ret = model_paths[0].parent / f"ggml-model-{namestr}.bin"
|
|
if ret in model_paths:
|
|
sys.stderr.write(
|
|
f"Error: Default output path ({ret}) would overwrite the input. "
|
|
"Please explicitly specify a path using --outfile.\n")
|
|
sys.exit(1)
|
|
return ret
|
|
|
|
|
|
def do_dump_model(model_plus: ModelPlus) -> None:
|
|
print(f"model_plus.paths = {model_plus.paths!r}")
|
|
print(f"model_plus.format = {model_plus.format!r}")
|
|
print(f"model_plus.vocab = {model_plus.vocab!r}")
|
|
for name, lazy_tensor in model_plus.model.items():
|
|
print(f"{name}: shape={lazy_tensor.shape} type={lazy_tensor.data_type}; {lazy_tensor.description}")
|
|
|
|
|
|
def main(args_in: Optional[List[str]] = 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")
|
|
parser.add_argument("--vocab-only", action="store_true", help="extract only the vocab")
|
|
parser.add_argument("--outtype", choices=["f32", "f16", "q4_1", "q4_0"], help="output format (default: based on input)")
|
|
parser.add_argument("--vocab-dir", type=Path, help="directory containing tokenizer.model, if separate from model file")
|
|
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)")
|
|
args = parser.parse_args(args_in)
|
|
|
|
vocab: Vocab
|
|
if args.dump_single:
|
|
model_plus = lazy_load_file(args.model)
|
|
do_dump_model(model_plus)
|
|
elif args.vocab_only:
|
|
vocab = load_vocab(args.vocab_dir or args.model)
|
|
assert args.outfile, "need --outfile if using --vocab-only"
|
|
outfile = args.outfile
|
|
OutputFile.write_vocab_only(outfile, vocab)
|
|
print(f"Wrote {outfile}")
|
|
else:
|
|
model_plus = load_some_model(args.model)
|
|
if args.dump:
|
|
do_dump_model(model_plus)
|
|
return
|
|
if model_plus.vocab is not None and args.vocab_dir is None:
|
|
vocab = model_plus.vocab
|
|
else:
|
|
vocab_dir = args.vocab_dir if args.vocab_dir else model_plus.paths[0].parent
|
|
vocab = load_vocab(vocab_dir)
|
|
params = Params.load(model_plus)
|
|
model = model_plus.model
|
|
model = do_necessary_conversions(model, params)
|
|
output_type = pick_output_type(model, args.outtype)
|
|
model = convert_to_output_type(model, output_type)
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outfile = args.outfile or default_outfile(model_plus.paths, output_type)
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OutputFile.write_all(outfile, params, output_type, model, vocab)
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print(f"Wrote {outfile}")
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if __name__ == '__main__':
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main()
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