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convert.py
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#!/usr/bin/env python
import argparse
import concurrent.futures
import copy
import enum
import faulthandler
import functools
import io
import itertools
import json
import math
import mmap
import pickle
import re
import signal
import struct
import sys
import zipfile
from abc import ABCMeta, abstractmethod
from dataclasses import dataclass
from pathlib import Path
from typing import (IO, TYPE_CHECKING, Any, Callable, Dict, Iterable, List,
Literal, Optional, Sequence, Tuple, TypeVar, Union)
import numpy as np
from sentencepiece import SentencePieceProcessor # type: ignore
if TYPE_CHECKING:
from typing_extensions import TypeAlias
if hasattr(faulthandler, 'register') and hasattr(signal, 'SIGUSR1'):
faulthandler.register(signal.SIGUSR1)
NDArray: 'TypeAlias' = 'np.ndarray[Any, Any]'
@dataclass(frozen=True)
class UnquantizedDataType:
name: str
DT_F16 = UnquantizedDataType('F16')
DT_F32 = UnquantizedDataType('F32')
DT_I32 = UnquantizedDataType('I32')
DT_BF16 = UnquantizedDataType('BF16')
@dataclass(frozen=True)
class QuantizedDataType:
groupsize: int
have_addends: bool
have_g_idx: bool
DT_Q4_0 = QuantizedDataType(groupsize=32, have_addends=False, have_g_idx=False)
DT_Q4_1 = QuantizedDataType(groupsize=32, have_addends=True, have_g_idx=False)
DataType = Union[UnquantizedDataType, QuantizedDataType]
DATA_TYPE_TO_FTYPE: Dict[DataType, int] = {
DT_F32: 0,
DT_F16: 1,
DT_Q4_0: 2,
DT_Q4_1: 3,
}
FTYPE_TO_DATA_TYPE: Dict[int, DataType] = \
{ftype: dtype for (dtype, ftype) in DATA_TYPE_TO_FTYPE.items()}
DATA_TYPE_TO_NUMPY: Dict[DataType, 'np.dtype[Any]'] = {
DT_BF16: np.dtype(np.uint16),
DT_F16: np.dtype(np.float16),
DT_F32: np.dtype(np.float32),
DT_I32: np.dtype(np.int32),
}
NUMPY_TYPE_TO_DATA_TYPE: Dict['np.dtype[Any]', DataType] = \
{dtype: data_type for (data_type, dtype) in DATA_TYPE_TO_NUMPY.items()}
class GGMLFileType(enum.Enum):
AllF32 = 0
MostlyF16 = 1 # except 1d tensors
MostlyQ4_0 = 2 # except 1d tensors
MostlyQ4_1 = 3 # except 1d tensors
PerLayerIsQ4_1 = 4 # but tok_embeddings.weight and output.weight are F16
def type_for_tensor(self, name: str, tensor: 'LazyTensor') -> DataType:
if len(tensor.shape) == 1:
# 1D tensors are always F32.
return DT_F32
elif self == GGMLFileType.AllF32:
return DT_F32
elif self == GGMLFileType.MostlyF16:
return DT_F16
elif self == GGMLFileType.MostlyQ4_0:
return DT_Q4_0
elif self == GGMLFileType.MostlyQ4_1:
return DT_Q4_1
elif self == GGMLFileType.PerLayerIsQ4_1:
if name in ('output.weight', 'tok_embeddings.weight'):
return DT_F16
else:
return DT_Q4_1
else:
raise ValueError(self)
def make_tensors_list() -> List[str]:
ret = [
'tok_embeddings.weight',
'norm.weight',
'output.weight',
]
for i in range(80): # maximum number of layer
ret += [
f'layers.{i}.attention.wq.weight',
f'layers.{i}.attention.wk.weight',
f'layers.{i}.attention.wv.weight',
f'layers.{i}.attention.wo.weight',
f'layers.{i}.attention_norm.weight',
f'layers.{i}.feed_forward.w1.weight',
f'layers.{i}.feed_forward.w2.weight',
f'layers.{i}.feed_forward.w3.weight',
f'layers.{i}.ffn_norm.weight',
]
return ret
TENSORS_LIST = make_tensors_list()
TENSORS_SET = set(TENSORS_LIST)
def find_n_mult(n_ff: int, n_embd: int) -> int:
# hardcoded magic range
for n_mult in range(256, 1, -1):
calc_ff = (((8*n_embd) // 3 + n_mult - 1) // n_mult)*n_mult
if calc_ff == n_ff:
return n_mult
raise Exception(f"failed to find n_mult for (n_ff={n_ff}, n_embd={n_embd}).")
@dataclass
class Params:
n_vocab: int
n_embd: int
n_mult: int
n_head: int
n_layer: int
@staticmethod
def guessed(model: 'LazyModel') -> 'Params':
# try transformer naming first
n_vocab, n_embd = model["model.embed_tokens.weight"].shape if "model.embed_tokens.weight" in model else model["tok_embeddings.weight"].shape
# try transformer naming first
if "model.layers.0.self_attn.q_proj.weight" in model:
n_layer=next(i for i in itertools.count() if f"model.layers.{i}.self_attn.q_proj.weight" not in model)
elif "model.layers.0.self_attn.W_pack.weight" in model: # next: try baichuan naming
n_layer=next(i for i in itertools.count() if f"model.layers.{i}.self_attn.W_pack.weight" not in model)
else:
n_layer=next(i for i in itertools.count() if f"layers.{i}.attention.wq.weight" not in model)
if n_layer < 1:
raise Exception("failed to guess 'n_layer'. This model is unknown or unsupported.\n"
"Suggestion: provide 'config.json' of the model in the same directory containing model files.")
n_head=n_embd // 128 # guessed
return Params(
n_vocab=n_vocab,
n_embd=n_embd,
n_mult=256,
n_head=n_head,
n_layer=n_layer,
)
@staticmethod
def loadHFTransformerJson(model: 'LazyModel', config_path: 'Path') -> 'Params':
config = json.load(open(config_path))
n_vocab = config["vocab_size"];
n_embd = config["hidden_size"];
n_head = config["num_attention_heads"];
n_layer = config["num_hidden_layers"];
n_ff = config["intermediate_size"];
n_mult = find_n_mult(n_ff, n_embd);
return Params(
n_vocab=n_vocab,
n_embd=n_embd,
n_mult=n_mult,
n_head=n_head,
n_layer=n_layer,
)
@staticmethod
def load(model_plus: 'ModelPlus') -> 'Params':
orig_config_path = model_plus.paths[0].parent / "params.json"
hf_transformer_config_path = model_plus.paths[0].parent / "config.json"
if hf_transformer_config_path.exists():
params = Params.loadHFTransformerJson(model_plus.model, hf_transformer_config_path)
else:
params = Params.guessed(model_plus.model)
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}')
return params
class SentencePieceVocab:
def __init__(self, fname_tokenizer: Path, fname_added_tokens: Optional[Path]) -> None:
self.sentencepiece_tokenizer = SentencePieceProcessor(str(fname_tokenizer))
added_tokens: Dict[str, int]
if fname_added_tokens is not None:
added_tokens = json.load(open(fname_added_tokens))
else:
added_tokens = {}
vocab_size: int = self.sentencepiece_tokenizer.vocab_size()
expected_ids = list(range(vocab_size, vocab_size + len(added_tokens)))
actual_ids = sorted(added_tokens.values())
if expected_ids != actual_ids:
raise Exception(f"Expected added token IDs to be sequential and start at {len(added_tokens)}; got {actual_ids}")
items = sorted(added_tokens.items(), key=lambda text_idx: text_idx[1])
self.added_tokens_list = [text for (text, idx) in items]
self.vocab_size_base: int = vocab_size
self.vocab_size: int = self.vocab_size_base + len(self.added_tokens_list)
self.fname_tokenizer = fname_tokenizer
self.fname_added_tokens = fname_added_tokens
def sentencepiece_tokens(self) -> Iterable[Tuple[bytes, float]]:
tokenizer = self.sentencepiece_tokenizer
for i in range(tokenizer.vocab_size()):
text: bytes
if tokenizer.is_unknown(i):
text = " \u2047 ".encode("utf-8")
elif tokenizer.is_control(i):
text = b""
elif tokenizer.is_byte(i):
piece = tokenizer.id_to_piece(i)
if len(piece) != 6:
raise Exception(f"Invalid token: {piece}")
byte_value = int(piece[3:-1], 16)
text = struct.pack("B", byte_value)
else:
text = tokenizer.id_to_piece(i).replace("\u2581", " ").encode("utf-8")
score: float = tokenizer.get_score(i)
yield text, score
def added_tokens(self) -> Iterable[Tuple[bytes, float]]:
for text in self.added_tokens_list:
score = -1000.0
yield text.encode("utf-8"), score
def all_tokens(self) -> Iterable[Tuple[bytes, float]]:
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>"
class GGMLVocab:
def __init__(self, tokens: List[Tuple[bytes, float]]):
self.tokens = tokens
self.vocab_size = len(tokens)
def all_tokens(self) -> Iterable[Tuple[bytes, float]]:
return self.tokens
def __repr__(self) -> str:
return f"<GGMLVocab with {self.vocab_size} tokens>"
Vocab = Union[SentencePieceVocab, GGMLVocab]
def permute(weights: NDArray, n_head: int) -> NDArray:
return (weights.reshape(n_head, 2, weights.shape[0] // n_head // 2, *weights.shape[1:])
.swapaxes(1, 2)
.reshape(weights.shape))
def dequantize_q4(qvalues_pack32: NDArray, scales: NDArray, addends: Optional[NDArray], g_idx: Optional[NDArray]) -> NDArray:
# First reinterpret each row from a list of int32s containing 8 values each
# to a list of uint8s containing 2 values each.
qvalues_pack8 = qvalues_pack32.view(np.uint8)
# Then split out the two values per int8 (which requires an actual
# conversion because numpy doesn't natively support int4s).
qvalues = np.zeros([qvalues_pack8.shape[0], qvalues_pack8.shape[1] * 2], dtype=np.uint8)
qvalues[:, 0::2] = qvalues_pack8 & 0xf
qvalues[:, 1::2] = qvalues_pack8 >> 4
assert addends is None or addends.shape == scales.shape
assert qvalues.shape[0] == scales.shape[0]
assert qvalues.shape[1] % scales.shape[1] == 0
if g_idx is None:
repeat_count = qvalues.shape[1] // scales.shape[1]
scales = scales[:, :, np.newaxis]
if addends is not None:
addends = addends[:, :, np.newaxis]
# Reshape so that the below computation broadcasts over scales and addends:
qvalues.shape = (qvalues.shape[0], scales.shape[1], int(repeat_count))
else:
# In this case the scale and addend is selected for each column by g_idx:
assert addends is not None
scales = scales[:, g_idx]
addends = addends[:, g_idx]
if addends is None:
# Q4_0
qvalues = qvalues.view(np.int8)
qvalues -= 8
# And do the actual 'value = scale * qvalue + addend' computation.
values = scales * qvalues
if addends is not None:
values += addends
if g_idx is None:
values.shape = (values.shape[0], values.shape[1] * values.shape[2])
return values
class Tensor(metaclass=ABCMeta):
data_type: DataType
@abstractmethod
def astype(self, data_type: DataType) -> 'Tensor': ...
@abstractmethod
def permute(self, n_head: int) -> 'Tensor': ...
@abstractmethod
def permute_part(self, n_part: int, n_head: int) -> 'UnquantizedTensor': ...
@abstractmethod
def part(self, n_part: int) -> 'UnquantizedTensor': ...
@abstractmethod
def to_ggml(self) -> 'GGMLCompatibleTensor': ...
def bf16_to_fp32(bf16_arr: np.ndarray) -> np.ndarray:
assert bf16_arr.dtype == np.uint16, f"Input array should be of dtype uint16, but got {bf16_arr.dtype}"
fp32_arr = bf16_arr.astype(np.uint32) << 16
return fp32_arr.view(np.float32)
class UnquantizedTensor(Tensor):
def __init__(self, ndarray: NDArray) -> None:
assert isinstance(ndarray, np.ndarray)
self.ndarray = ndarray
self.data_type = NUMPY_TYPE_TO_DATA_TYPE[ndarray.dtype]
def astype(self, data_type: DataType) -> Tensor:
dtype = DATA_TYPE_TO_NUMPY[data_type]
if self.data_type == DT_BF16:
self.ndarray = bf16_to_fp32(self.ndarray)
return UnquantizedTensor(self.ndarray.astype(dtype))
def to_ggml(self) -> 'UnquantizedTensor':
return self
def permute_part(self, n_part: int, n_head: int) -> 'UnquantizedTensor':
r = self.ndarray.shape[0] // 3
return UnquantizedTensor(permute(self.ndarray[r * n_part : r * n_part + r, ...], n_head))
def part(self, n_part: int) -> 'UnquantizedTensor':
r = self.ndarray.shape[0] // 3
return UnquantizedTensor(self.ndarray[r * n_part : r * n_part + r, ...])
def permute(self, n_head: int) -> 'UnquantizedTensor':
return UnquantizedTensor(permute(self.ndarray, n_head))
def load_unquantized(lazy_tensor: 'LazyTensor', expected_dtype: Any = None, convert: bool = False) -> NDArray:
tensor = lazy_tensor.load()
assert isinstance(tensor, UnquantizedTensor)
# double-check:
actual_shape = list(tensor.ndarray.shape)
assert actual_shape == lazy_tensor.shape, (actual_shape, lazy_tensor.shape)
if expected_dtype is not None and expected_dtype != tensor.ndarray.dtype:
if convert:
tensor.ndarray = tensor.ndarray.astype(expected_dtype)
else:
raise ValueError(f'expected this tensor to have dtype {expected_dtype}, got {tensor.ndarray.dtype}')
return tensor.ndarray
class GGMLQuantizedTensor(Tensor):
data_type: QuantizedDataType
def __init__(self, ndarray: NDArray, shape: List[int], data_type: DataType) -> None:
rows, columns = shape
assert data_type in (DT_Q4_1, DT_Q4_0) # for now
assert isinstance(data_type, QuantizedDataType) # redundant, but mypy complains without this
assert columns % data_type.groupsize == 0
words_in_block = 6 if data_type == DT_Q4_1 else 5
self.ndarray = ndarray.view(dtype=np.uint32).reshape((rows, columns // data_type.groupsize, words_in_block))
self.shape = shape[:]
self.data_type = data_type
def astype(self, data_type: DataType) -> Tensor:
if data_type == self.data_type:
return self
scales = self.ndarray[:, :, 0].view(np.float32)
if self.data_type.have_addends:
addends = self.ndarray[:, :, 1].view(np.float32)
else:
addends = None
qweights = self.ndarray[:, :, -4:].reshape([self.shape[0], self.shape[1] // 8])
dq = dequantize_q4(qweights, scales, addends, g_idx=None)
return UnquantizedTensor(dq).astype(data_type)
def to_ggml(self) -> 'GGMLQuantizedTensor':
return self
def permute(self, n_head: int) -> 'GGMLQuantizedTensor':
return GGMLQuantizedTensor(permute(self.ndarray, n_head), self.shape, self.data_type)
GGMLCompatibleTensor = Union[UnquantizedTensor, GGMLQuantizedTensor]
class DeferredPermutedTensor(Tensor):
def __init__(self, base: Tensor, n_head: int) -> None:
self.base = base
self.n_head = n_head
self.data_type = self.base.data_type
def astype(self, data_type: DataType) -> Tensor:
return self.base.astype(data_type).permute(self.n_head)
def to_ggml(self) -> GGMLCompatibleTensor:
return self.base.to_ggml().permute(self.n_head)
def permute(self, n_head: int) -> Tensor:
raise Exception("shouldn't permute twice")
class GPTQForLLaMaQuantizedTensor(Tensor):
def __init__(self, model: 'LazyModel', namebase: str) -> None:
qweight = load_unquantized(model[f"{namebase}.qweight"], np.int32)
scales = load_unquantized(model[f"{namebase}.scales"], np.float32, convert=True)
bias = model.get(f"{namebase}.bias")
if bias is not None:
# Q4_1 does not support bias; good thing the bias is always all zeros.
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: