mirror of
https://github.com/ggerganov/llama.cpp.git
synced 2024-12-24 10:24:35 +00:00
convert.py : advanced option (#2753)
* Allow convert.py to convert to q8_0 Fix issue with bounded_parallel_map and greedy consuming iterator Display elapsed time during conversion * Add --concurrency option Minor improvements to help text Clean up bounded_parallel_map function a bit * Massive speed improvement thanks to Cebtenzzre * Refactor types
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Kerfuffle
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206
convert.py
206
convert.py
@ -3,6 +3,7 @@
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import gguf
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import argparse
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import concurrent.futures
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from concurrent.futures import ThreadPoolExecutor, ProcessPoolExecutor
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import copy
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import enum
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import faulthandler
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@ -17,13 +18,14 @@ 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 time
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import zipfile
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import numpy as np
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from abc import ABCMeta, abstractmethod
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from 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, Literal, Optional, Sequence, Tuple, TypeVar, Union)
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from typing import (IO, TYPE_CHECKING, Any, Callable, Dict, Generator, Iterable, List, Literal, Optional, Sequence, Set, Tuple, TypeVar, Union)
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from sentencepiece import SentencePieceProcessor # type: ignore
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if TYPE_CHECKING:
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@ -37,30 +39,70 @@ NDArray: 'TypeAlias' = 'np.ndarray[Any, Any]'
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ARCH=gguf.MODEL_ARCH.LLAMA
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NAMES=gguf.MODEL_TENSOR_NAMES[ARCH]
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DEFAULT_CONCURRENCY = 8
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#
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# data types
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#
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@dataclass(frozen=True)
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class UnquantizedDataType:
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class DataType:
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name: str
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dtype: 'np.dtype[Any]'
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valid_conversions: List[str]
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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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def elements_to_bytes(self, n_elements: int) -> int:
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return n_elements * self.dtype.itemsize
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DataType = Union[UnquantizedDataType]
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@dataclass(frozen=True)
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class UnquantizedDataType(DataType):
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pass
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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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DT_F16 = UnquantizedDataType('F16', dtype = np.dtype(np.float16), valid_conversions = ['F32', 'Q8_0'])
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DT_F32 = UnquantizedDataType('F32', dtype = np.dtype(np.float32), valid_conversions = ['F16', 'Q8_0'])
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DT_I32 = UnquantizedDataType('I32', dtype = np.dtype(np.int16), valid_conversions = [])
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DT_BF16 = UnquantizedDataType('BF16', dtype = np.dtype(np.uint16), valid_conversions = ['F32', 'F16', 'Q8_0'])
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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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@dataclass(frozen=True)
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class QuantizedDataType(DataType):
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block_size: int
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quantized_dtype: 'np.dtype[Any]'
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ggml_type: gguf.GGMLQuantizationType
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def quantize(self, arr: NDArray) -> NDArray:
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raise NotImplementedError(f'Quantization for {self.name} not implemented')
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def elements_to_bytes(self, n_elements: int) -> int:
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assert n_elements % self.block_size == 0, f'Invalid number of elements {n_elements} for {self.name} with block size {self.block_size}'
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return self.quantized_dtype.itemsize * (n_elements // self.block_size)
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@dataclass(frozen=True)
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class Q8_0QuantizedDataType(QuantizedDataType):
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# Mini Q8_0 quantization in Python!
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def quantize(self, arr: NDArray) -> NDArray:
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assert arr.size % self.block_size == 0 and arr.size != 0, f'Bad array size {arr.size}'
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assert arr.dtype == np.float32, f'Bad array type {arr.dtype}'
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n_blocks = arr.size // self.block_size
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blocks = arr.reshape((n_blocks, self.block_size))
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# Much faster implementation of block quantization contributed by @Cebtenzzre
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def quantize_blocks_q8_0(blocks: NDArray) -> Iterable[Tuple[Any, Any]]:
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d = abs(blocks).max(axis = 1) / np.float32(127)
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with np.errstate(divide = 'ignore'):
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qs = (blocks / d[:, None]).round()
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qs[d == 0] = 0
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yield from zip(d, qs)
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return np.fromiter(quantize_blocks_q8_0(blocks), count = n_blocks, dtype = self.quantized_dtype)
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DT_Q8_0 = Q8_0QuantizedDataType('Q8_0',
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dtype = np.dtype(np.float32), valid_conversions = [],
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ggml_type = gguf.GGMLQuantizationType.Q8_0, block_size = 32,
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quantized_dtype = np.dtype([('d', '<f2'), ('qs', 'i1', (32,))]))
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# Quantized types skipped here because they may also map to np.float32
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NUMPY_TYPE_TO_DATA_TYPE: Dict['np.dtype[Any]', DataType] = {}
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for dt in (DT_BF16, DT_F16, DT_F32, DT_I32):
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if dt.dtype in NUMPY_TYPE_TO_DATA_TYPE:
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raise ValueError(f'Invalid duplicate data type {dt}')
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NUMPY_TYPE_TO_DATA_TYPE[dt.dtype] = dt
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SAFETENSORS_DATA_TYPES: Dict[str, DataType] = {
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'BF16': DT_BF16,
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@ -73,20 +115,22 @@ SAFETENSORS_DATA_TYPES: Dict[str, DataType] = {
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# TODO: rename to LLAMAFileType
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# TODO: move to `gguf.py`
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class GGMLFileType(enum.IntEnum):
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AllF32 = 0
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MostlyF16 = 1 # except 1d tensors
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AllF32 = 0
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MostlyF16 = 1 # except 1d tensors
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MostlyQ8_0 = 7 # except 1d tensors
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def type_for_tensor(self, name: str, tensor: 'LazyTensor') -> DataType:
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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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else:
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dt = GGML_FILE_TYPE_TO_DATA_TYPE.get(self)
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if dt is None:
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raise ValueError(self)
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# 1D tensors are always F32.
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return dt if len(tensor.shape) > 1 else DT_F32
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GGML_FILE_TYPE_TO_DATA_TYPE: Dict[GGMLFileType, DataType] = {
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GGMLFileType.AllF32 : DT_F32,
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GGMLFileType.MostlyF16 : DT_F16,
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GGMLFileType.MostlyQ8_0: DT_Q8_0,
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}
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#
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# hparams loading
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@ -415,7 +459,7 @@ class UnquantizedTensor(Tensor):
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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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dtype = data_type.dtype
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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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@ -454,22 +498,6 @@ def load_unquantized(lazy_tensor: 'LazyTensor', expected_dtype: Any = None, conv
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GGMLCompatibleTensor = Union[UnquantizedTensor]
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class DeferredPermutedTensor(Tensor):
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def __init__(self, base: Tensor, n_head: int, n_head_kv: 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, self.n_head_kv)
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def to_ggml(self) -> GGMLCompatibleTensor:
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return self.base.to_ggml().permute(self.n_head, self.n_head_kv)
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def permute(self, n_head: int, n_head_kv: int) -> Tensor:
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raise Exception("shouldn't permute twice")
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@dataclass
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class LazyTensor:
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_load: Callable[[], Tensor]
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@ -479,7 +507,9 @@ class LazyTensor:
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def load(self) -> Tensor:
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ret = self._load()
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assert ret.data_type == self.data_type, (self.data_type, ret.data_type, self.description)
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# Should be okay if it maps to the same numpy type?
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assert ret.data_type == self.data_type or (self.data_type.dtype == ret.data_type.dtype), \
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(self.data_type, ret.data_type, self.description)
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return ret
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def astype(self, data_type: DataType) -> 'LazyTensor':
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@ -490,8 +520,8 @@ class LazyTensor:
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return LazyTensor(load, self.shape, data_type, f'convert({data_type}) {self.description}')
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def validate_conversion_to(self, data_type: DataType) -> None:
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if data_type == self.data_type:
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return
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if data_type != self.data_type and data_type.name not in self.data_type.valid_conversions:
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raise ValueError(f'Cannot validate conversion from {self.data_type} to {data_type}.')
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LazyModel = Dict[str, LazyTensor]
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@ -617,9 +647,7 @@ class LazyUnpickler(pickle.Unpickler):
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info = self.zip_file.getinfo(filename)
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def load(offset: int, elm_count: int) -> NDArray:
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dtype = DATA_TYPE_TO_NUMPY.get(data_type)
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if dtype is None:
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raise Exception("tensor stored in unsupported format")
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dtype = data_type.dtype
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fp = self.zip_file.open(info)
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fp.seek(offset * dtype.itemsize)
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size = elm_count * dtype.itemsize
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@ -683,7 +711,7 @@ def lazy_load_safetensors_file(fp: IO[bytes], path: Path) -> ModelPlus:
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def convert(info: Dict[str, Any]) -> LazyTensor:
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data_type = SAFETENSORS_DATA_TYPES[info['dtype']]
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numpy_dtype = DATA_TYPE_TO_NUMPY[data_type]
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numpy_dtype = data_type.dtype
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shape: List[int] = info['shape']
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begin, end = info['data_offsets']
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assert 0 <= begin <= end <= len(byte_buf)
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@ -723,23 +751,35 @@ def lazy_load_file(path: Path) -> ModelPlus:
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In = TypeVar('In')
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Out = TypeVar('Out')
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def bounded_parallel_map(func: Callable[[In], Out], iterable: Iterable[In], concurrency: int) -> Iterable[Out]:
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def bounded_parallel_map(func: Callable[[In], Out], iterable: Iterable[In], concurrency: int, max_workers: Optional[int] = None, factory: Callable = ThreadPoolExecutor) -> Iterable[Out]:
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'''Parallel map, but with backpressure. If the caller doesn't call `next`
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fast enough, this will stop calling `func` at some point rather than
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letting results pile up in memory. Specifically, there is a max of one
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output value buffered per thread.'''
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with concurrent.futures.ThreadPoolExecutor() as executor:
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if concurrency < 2:
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yield from map(func, iterable)
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# Not reached.
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iterable = iter(iterable)
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with factory(max_workers = max_workers) as executor:
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futures: List[concurrent.futures.Future[Out]] = []
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items_rev = list(iterable)[::-1]
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for i in range(min(concurrency, len(items_rev))):
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futures.append(executor.submit(func, items_rev.pop()))
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done = False
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for _ in range(concurrency):
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try:
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futures.append(executor.submit(func, next(iterable)))
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except StopIteration:
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done = True
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break
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while futures:
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result = futures.pop(0).result()
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if items_rev:
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futures.append(executor.submit(func, items_rev.pop()))
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while not done and len(futures) < concurrency:
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try:
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futures.append(executor.submit(func, next(iterable)))
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except StopIteration:
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done = True
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break
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yield result
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def check_vocab_size(params: Params, vocab: Vocab) -> None:
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if params.n_vocab != vocab.vocab_size:
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assert isinstance(vocab, BpeVocab) or isinstance(vocab, SentencePieceVocab)
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@ -804,12 +844,11 @@ class OutputFile:
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self.gguf.add_token_types(toktypes)
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def add_tensor_info(self, name: str, tensor: LazyTensor) -> None:
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n_elements = 1
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for dim in tensor.shape:
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n_elements *= dim
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data_type = DATA_TYPE_TO_NUMPY[tensor.data_type]
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data_nbytes = n_elements * data_type.itemsize
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self.gguf.add_tensor_info(name, tensor.shape, data_type, data_nbytes)
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n_elements = int(np.prod(tensor.shape))
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raw_dtype = getattr(tensor.data_type, 'ggml_type', None)
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data_type = getattr(tensor.data_type, 'quantized_type', None) or tensor.data_type.dtype
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data_nbytes = tensor.data_type.elements_to_bytes(n_elements)
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self.gguf.add_tensor_info(name, tensor.shape, data_type, data_nbytes, raw_dtype = raw_dtype)
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def write_meta(self) -> None:
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self.gguf.write_header_to_file()
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@ -835,7 +874,20 @@ class OutputFile:
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of.close()
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@staticmethod
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def write_all(fname_out: Path, params: Params, model: LazyModel, vocab: Vocab) -> None:
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def do_item(item: Tuple[str, LazyTensor]) -> Tuple[DataType, NDArray]:
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name, lazy_tensor = item
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tensor = lazy_tensor.load().to_ggml()
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return (lazy_tensor.data_type, tensor.ndarray)
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@staticmethod
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def maybe_do_quantize(item: Tuple[DataType, NDArray]) -> NDArray:
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dt, arr = item
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if not isinstance(dt, QuantizedDataType):
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return arr
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return dt.quantize(arr)
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@staticmethod
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def write_all(fname_out: Path, ftype: GGMLFileType, params: Params, model: LazyModel, vocab: Vocab, concurrency: int = DEFAULT_CONCURRENCY) -> None:
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check_vocab_size(params, vocab)
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of = OutputFile(fname_out)
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@ -851,16 +903,19 @@ class OutputFile:
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of.write_meta()
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of.write_tensor_info()
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def do_item(item: Tuple[str, LazyTensor]) -> NDArray:
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name, lazy_tensor = item
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return lazy_tensor.load().to_ggml().ndarray
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# tensor data
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ndarrays = bounded_parallel_map(do_item, model.items(), concurrency=8)
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ndarrays_inner = bounded_parallel_map(OutputFile.do_item, model.items(), concurrency = concurrency)
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if ftype == GGMLFileType.MostlyQ8_0:
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ndarrays = bounded_parallel_map(OutputFile.maybe_do_quantize, ndarrays_inner, concurrency = concurrency, max_workers = concurrency, factory = ProcessPoolExecutor)
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else:
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ndarrays = map(OutputFile.maybe_do_quantize, ndarrays_inner)
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start = time.time()
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for i, ((name, lazy_tensor), ndarray) in enumerate(zip(model.items(), ndarrays)):
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elapsed = time.time() - start
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size = ' x '.join(f"{dim:6d}" for dim in lazy_tensor.shape)
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padi = len(str(len(model)))
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print(f"[{i+1:{padi}d}/{len(model)}] Writing tensor {name:38s} | size {size:16} | type {lazy_tensor.data_type}")
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print(f"[{i+1:{padi}d}/{len(model)}] Writing tensor {name:38s} | size {size:16} | type {lazy_tensor.data_type.name:4} | T+{int(elapsed):4}")
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of.gguf.write_tensor_data(ndarray)
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of.close()
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@ -872,6 +927,8 @@ def pick_output_type(model: LazyModel, output_type_str: Optional[str]) -> GGMLFi
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return GGMLFileType.AllF32
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if output_type_str == "f16" or (output_type_str is None and wq_type in (DT_F16, DT_BF16)):
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return GGMLFileType.MostlyF16
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if output_type_str == "q8_0":
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return GGMLFileType.MostlyQ8_0
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name_to_type = {name: lazy_tensor.data_type for (name, lazy_tensor) in model.items()}
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@ -918,7 +975,7 @@ def convert_model_names(model: LazyModel, params: Params) -> LazyModel:
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print(f"skipping tensor {name_new}")
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continue
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else:
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print(f"{name:48s} -> {name_new:40s} | {lazy_tensor.data_type} | {lazy_tensor.shape}")
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print(f"{name:48s} -> {name_new:40s} | {lazy_tensor.data_type.name:6s} | {lazy_tensor.shape}")
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out[name_new] = lazy_tensor
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return out
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@ -1023,6 +1080,7 @@ def default_outfile(model_paths: List[Path], file_type: GGMLFileType) -> Path:
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namestr = {
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GGMLFileType.AllF32: "f32",
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GGMLFileType.MostlyF16: "f16",
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GGMLFileType.MostlyQ8_0:"q8_0",
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}[file_type]
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ret = model_paths[0].parent / f"ggml-model-{namestr}.gguf"
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if ret in model_paths:
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@ -1046,12 +1104,13 @@ def main(args_in: Optional[List[str]] = None) -> None:
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parser.add_argument("--dump", action="store_true", help="don't convert, just show what's in the model")
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parser.add_argument("--dump-single", action="store_true", help="don't convert, just show what's in a single model file")
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parser.add_argument("--vocab-only", action="store_true", help="extract only the vocab")
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parser.add_argument("--outtype", choices=["f32", "f16"], help="output format (default: based on input)")
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parser.add_argument("--outtype", choices=["f32", "f16", "q8_0"], help="output format - note: q8_0 may be very slow (default: f16 or f32 based on input)")
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parser.add_argument("--vocab-dir", type=Path, help="directory containing tokenizer.model, if separate from model file")
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parser.add_argument("--outfile", type=Path, help="path to write to; default: based on input")
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parser.add_argument("model", type=Path, help="directory containing model file, or model file itself (*.pth, *.pt, *.bin)")
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parser.add_argument("--vocabtype", choices=["spm", "bpe"], help="vocab format (default: spm)", default="spm")
|
||||
parser.add_argument("--ctx", type=int, help="model training context (default: based on input)")
|
||||
parser.add_argument("--concurrency", type=int, help=f"concurrency used for conversion (default: {DEFAULT_CONCURRENCY})", default = DEFAULT_CONCURRENCY)
|
||||
args = parser.parse_args(args_in)
|
||||
|
||||
if args.dump_single:
|
||||
@ -1073,6 +1132,7 @@ def main(args_in: Optional[List[str]] = None) -> None:
|
||||
params.ftype = {
|
||||
"f32": GGMLFileType.AllF32,
|
||||
"f16": GGMLFileType.MostlyF16,
|
||||
"q8_0": GGMLFileType.MostlyQ8_0,
|
||||
}[args.outtype]
|
||||
|
||||
print(f"params = {params}")
|
||||
@ -1104,7 +1164,7 @@ def main(args_in: Optional[List[str]] = None) -> None:
|
||||
params.ftype = ftype
|
||||
print(f"Writing {outfile}, format {ftype}")
|
||||
|
||||
OutputFile.write_all(outfile, params, model, vocab)
|
||||
OutputFile.write_all(outfile, ftype, params, model, vocab, concurrency = args.concurrency)
|
||||
print(f"Wrote {outfile}")
|
||||
|
||||
|
||||
|
||||
Loading…
Reference in New Issue
Block a user