mirror of
https://github.com/ggerganov/llama.cpp.git
synced 2024-11-11 13:30:35 +00:00
convert-hf : support bfloat16 conversion (#7158)
* convert-hf : support bfloat16 conversion * gguf-py : flake8 fixes * convert-hf : add missing space after comma * convert-hf : get bit-exact same output as ./quantize The quantization version was missing. * convert-hf : don't round bf16 NANs * convert-hf : save some memory with np.int16 intermediate bf16 weights * convert-hf : more closely match llama.cpp with which weights to keep in f32 * convert-hf : add --outtype auto-f16 A reason for this to exist is for model quantizers who want an initial GGUF with the most fidelity to the original model while still using a 16-bit float type instead of 32-bit floats. * convert-hf : remove a semicolon because flake8 doesn't like it It's a reflex from when programming in C/C++, I guess. * convert-hf : support outtype templating in outfile name * convert-hf : rename --outtype auto-f16 to --outtype auto
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@ -12,7 +12,7 @@ import sys
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from enum import IntEnum
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from pathlib import Path
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from hashlib import sha256
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from typing import TYPE_CHECKING, Any, Callable, ContextManager, Iterable, Iterator, Sequence, TypeVar, cast, overload
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from typing import TYPE_CHECKING, Any, Callable, ContextManager, Iterable, Iterator, Sequence, TypeVar, cast
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import numpy as np
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import torch
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@ -48,7 +48,6 @@ class Model:
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dir_model: Path
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ftype: int
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fname_out: Path
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is_big_endian: bool
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endianess: gguf.GGUFEndian
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use_temp_file: bool
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@ -56,20 +55,20 @@ class Model:
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part_names: list[str]
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is_safetensors: bool
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hparams: dict[str, Any]
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gguf_writer: gguf.GGUFWriter
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block_count: int
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tensor_map: gguf.TensorNameMap
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tensor_names: set[str] | None
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fname_out: Path
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gguf_writer: gguf.GGUFWriter
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# subclasses should define this!
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model_arch: gguf.MODEL_ARCH
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def __init__(self, dir_model: Path, ftype: int, fname_out: Path, is_big_endian: bool, use_temp_file: bool, eager: bool):
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if self.__class__ == Model:
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raise TypeError(f"{self.__class__.__name__!r} should not be directly instantiated")
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def __init__(self, dir_model: Path, ftype: gguf.LlamaFileType, fname_out: Path, is_big_endian: bool, use_temp_file: bool, eager: bool):
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if type(self) is Model:
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raise TypeError(f"{type(self).__name__!r} should not be directly instantiated")
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self.dir_model = dir_model
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self.ftype = ftype
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self.fname_out = fname_out
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self.is_big_endian = is_big_endian
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self.endianess = gguf.GGUFEndian.BIG if is_big_endian else gguf.GGUFEndian.LITTLE
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self.use_temp_file = use_temp_file
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@ -79,10 +78,23 @@ class Model:
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if not self.is_safetensors:
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self.part_names = Model.get_model_part_names(self.dir_model, ".bin")
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self.hparams = Model.load_hparams(self.dir_model)
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self.gguf_writer = gguf.GGUFWriter(fname_out, gguf.MODEL_ARCH_NAMES[self.model_arch], endianess=self.endianess, use_temp_file=self.use_temp_file)
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self.block_count = self.find_hparam(["n_layers", "num_hidden_layers", "n_layer"])
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self.tensor_map = gguf.get_tensor_name_map(self.model_arch, self.block_count)
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self.tensor_names = None
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if self.ftype == gguf.LlamaFileType.GUESSED:
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# NOTE: can't use field "torch_dtype" in config.json, because some finetunes lie.
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_, first_tensor = next(self.get_tensors())
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if first_tensor.dtype == torch.float16:
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logger.info(f"choosing --outtype f16 from first tensor type ({first_tensor.dtype})")
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self.ftype = gguf.LlamaFileType.MOSTLY_F16
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else:
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logger.info(f"choosing --outtype bf16 from first tensor type ({first_tensor.dtype})")
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self.ftype = gguf.LlamaFileType.MOSTLY_BF16
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ftype_up: str = self.ftype.name.partition("_")[2].upper()
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ftype_lw: str = ftype_up.lower()
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# allow templating the file name with the output ftype, useful with the "auto" ftype
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self.fname_out = fname_out.parent / fname_out.name.format(ftype_lw, outtype=ftype_lw, ftype=ftype_lw, OUTTYPE=ftype_up, FTYPE=ftype_up)
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self.gguf_writer = gguf.GGUFWriter(self.fname_out, gguf.MODEL_ARCH_NAMES[self.model_arch], endianess=self.endianess, use_temp_file=self.use_temp_file)
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@classmethod
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def __init_subclass__(cls):
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@ -142,14 +154,27 @@ class Model:
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raise ValueError(f"Mismatch between weight map and model parts for tensor names: {sym_diff}")
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def format_tensor_name(self, key: gguf.MODEL_TENSOR, bid: int | None = None, suffix: str = ".weight") -> str:
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name: str = gguf.TENSOR_NAMES[key]
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if key not in gguf.MODEL_TENSORS[self.model_arch]:
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raise ValueError(f"Missing {key!r} for MODEL_TENSORS of {self.model_arch!r}")
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name: str = gguf.TENSOR_NAMES[key]
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if "{bid}" in name:
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assert bid is not None
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name = name.format(bid=bid)
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return name + suffix
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def match_model_tensor_name(self, name: str, key: gguf.MODEL_TENSOR, bid: int | None, suffix: str = ".weight") -> bool:
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if key not in gguf.MODEL_TENSORS[self.model_arch]:
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return False
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key_name: str = gguf.TENSOR_NAMES[key]
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if "{bid}" in key_name:
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if bid is None:
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return False
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key_name = key_name.format(bid=bid)
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else:
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if bid is not None:
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return False
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return name == (key_name + suffix)
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def map_tensor_name(self, name: str, try_suffixes: Sequence[str] = (".weight", ".bias")) -> str:
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new_name = self.tensor_map.get_name(key=name, try_suffixes=try_suffixes)
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if new_name is None:
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@ -215,6 +240,23 @@ class Model:
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return False
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def write_tensors(self):
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# same as ggml_compute_fp32_to_bf16 in ggml-impl.h
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def np_fp32_to_bf16(n: np.ndarray):
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# force nan to quiet
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n = np.where((n & 0x7fffffff) > 0x7f800000, (n & 0xffff0000) | (64 << 16), n)
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# flush subnormals to zero
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n = np.where((n & 0x7f800000) == 0, n & 0x80000000, n)
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# round to nearest even
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n = (n + (0x7fff + ((n >> 16) & 1))) >> 16
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return n.astype(np.int16)
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# Doing this row-wise is much, much faster than element-wise, hence the signature
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v_fp32_to_bf16 = np.vectorize(np_fp32_to_bf16, otypes=[np.int16], signature="(n)->(n)")
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if self.lazy:
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# TODO: find a way to implicitly wrap np.vectorize functions
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# NOTE: the type is changed to reflect otypes passed to np.vectorize above
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v_fp32_to_bf16 = gguf.LazyNumpyTensor._wrap_fn(v_fp32_to_bf16, meta_noop=np.int16)
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max_name_len = max(len(s) for _, s in self.tensor_map.mapping.values()) + len(".weight,")
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for name, data_torch in self.get_tensors():
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@ -239,35 +281,60 @@ class Model:
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data: np.ndarray = data # type hint
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n_dims = len(data.shape)
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data_dtype = data.dtype
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# if f32 desired, convert any float16 to float32
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if self.ftype == 0 and data_dtype == np.float16:
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data = data.astype(np.float32)
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data_qtype: gguf.GGMLQuantizationType | None = None
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# when both are True, f32 should win
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extra_f32 = self.extra_f32_tensors(name, new_name, bid, n_dims)
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extra_f16 = self.extra_f16_tensors(name, new_name, bid, n_dims)
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# Most of the codebase that takes in 1D tensors or norms only handles F32 tensors
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extra_f32 = extra_f32 or n_dims == 1 or new_name.endswith("_norm.weight")
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# Conditions should closely match those in llama_model_quantize_internal in llama.cpp
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extra_f32 = any(cond for cond in (
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extra_f32,
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n_dims == 1,
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new_name.endswith("_norm.weight"),
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))
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# Some tensor types are always in float32
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extra_f32 = extra_f32 or any(self.match_model_tensor_name(new_name, key, bid) for key in (
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gguf.MODEL_TENSOR.FFN_GATE_INP,
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gguf.MODEL_TENSOR.POS_EMBD,
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gguf.MODEL_TENSOR.TOKEN_TYPES,
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))
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# if f16 desired, convert any float32 2-dim weight tensors to float16
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extra_f16 = extra_f16 or (name.endswith(".weight") and n_dims >= 2)
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extra_f16 = any(cond for cond in (
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extra_f16,
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(name.endswith(".weight") and n_dims >= 2),
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))
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# when both extra_f32 and extra_f16 are False, convert to float32 by default
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if self.ftype == 1 and data_dtype == np.float16 and (extra_f32 or not extra_f16):
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data = data.astype(np.float32)
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if self.ftype != gguf.LlamaFileType.ALL_F32 and extra_f16 and not extra_f32:
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if self.ftype == gguf.LlamaFileType.MOSTLY_F16:
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if data_dtype != np.float16:
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data = data.astype(np.float16)
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data_qtype = gguf.GGMLQuantizationType.F16
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if self.ftype == 1 and data_dtype == np.float32 and extra_f16 and not extra_f32:
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data = data.astype(np.float16)
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elif self.ftype == gguf.LlamaFileType.MOSTLY_BF16:
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if data_dtype != np.float32:
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data = data.astype(np.float32)
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data = v_fp32_to_bf16(data.view(np.int32))
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assert data.dtype == np.int16
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data_qtype = gguf.GGMLQuantizationType.BF16
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else: # by default, convert to float32
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if data_dtype != np.float32:
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data = data.astype(np.float32)
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data_qtype = gguf.GGMLQuantizationType.F32
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assert data_qtype is not None
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# reverse shape to make it similar to the internal ggml dimension order
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shape_str = f"{{{', '.join(str(n) for n in reversed(data.shape))}}}"
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# n_dims is implicit in the shape
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logger.info(f"{f'%-{max_name_len}s' % f'{new_name},'} {old_dtype} --> {data.dtype}, shape = {shape_str}")
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logger.info(f"{f'%-{max_name_len}s' % f'{new_name},'} {old_dtype} --> {data_qtype.name}, shape = {shape_str}")
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self.gguf_writer.add_tensor(new_name, data)
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self.gguf_writer.add_tensor(new_name, data, raw_dtype=data_qtype)
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def write(self):
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self.write_tensors()
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@ -2044,12 +2111,6 @@ class BertModel(Model):
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return [(self.map_tensor_name(name), data_torch)]
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def extra_f32_tensors(self, name: str, new_name: str, bid: int | None, n_dims: int) -> bool:
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del new_name, bid, n_dims # unused
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# not used with get_rows, must be F32
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return name == "embeddings.token_type_embeddings.weight"
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@Model.register("NomicBertModel")
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class NomicBertModel(BertModel):
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@ -2339,92 +2400,40 @@ class JinaBertV2Model(BertModel):
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# tree of lazy tensors
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class LazyTorchTensor:
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_meta: Tensor
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_data: Tensor | None
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_args: tuple
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_func: Callable[[tuple], Tensor] | None
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def __init__(self, *, meta: Tensor, data: Tensor | None = None, args: tuple = (), func: Callable[[tuple], Tensor] | None = None):
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self._meta = meta
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self._data = data
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self._args = args
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self._func = func
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@staticmethod
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def _recurse_apply(o: Any, fn: Callable[[Any], Any]) -> Any:
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# TODO: dict and set
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if isinstance(o, (list, tuple)):
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L = []
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for item in o:
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L.append(LazyTorchTensor._recurse_apply(item, fn))
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if isinstance(o, tuple):
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L = tuple(L)
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return L
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elif isinstance(o, LazyTorchTensor):
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return fn(o)
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else:
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return o
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def _wrap_fn(self, fn: Callable, use_self: bool = False) -> Callable[[Any], LazyTorchTensor]:
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def wrapped_fn(*args, **kwargs):
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if kwargs is None:
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kwargs = {}
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args = ((self,) if use_self else ()) + args
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meta_args = LazyTorchTensor._recurse_apply(args, lambda t: t._meta)
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return LazyTorchTensor(meta=fn(*meta_args, **kwargs), args=args, func=lambda a: fn(*a, **kwargs))
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return wrapped_fn
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def __getattr__(self, __name: str) -> Any:
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meta_attr = getattr(self._meta, __name)
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if callable(meta_attr):
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return self._wrap_fn(getattr(torch.Tensor, __name), use_self=True)
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elif isinstance(meta_attr, torch.Tensor):
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# for things like self.T
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return self._wrap_fn(lambda s: getattr(s, __name))(self)
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else:
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return meta_attr
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class LazyTorchTensor(gguf.LazyBase):
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_tensor_type = torch.Tensor
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# to keep the type-checker happy
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dtype: torch.dtype
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shape: torch.Size
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# only used when converting a torch.Tensor to a np.ndarray
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_dtype_map: dict[torch.dtype, type] = {
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torch.float16: np.float16,
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torch.float32: np.float32,
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}
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def numpy(self) -> gguf.LazyTensor:
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def numpy(self) -> gguf.LazyNumpyTensor:
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dtype = self._dtype_map[self.dtype]
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return gguf.LazyTensor(lambda: LazyTorchTensor.to_eager(self).numpy(), dtype=dtype, shape=self.shape)
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return gguf.LazyNumpyTensor(
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meta=np.lib.stride_tricks.as_strided(np.zeros(1, dtype), self.shape, (0 for _ in self.shape)),
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lazy=self._lazy,
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args=(self,),
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func=(lambda s: s[0].numpy())
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)
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@overload
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@staticmethod
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def to_eager(t: Tensor | LazyTorchTensor) -> Tensor: ...
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@overload
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@staticmethod
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def to_eager(t: tuple) -> tuple: ...
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@staticmethod
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def to_eager(t: Any) -> Any:
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def simple_to_eager(_t: LazyTorchTensor) -> Tensor:
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# wake up the lazy tensor
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if _t._data is None and _t._func is not None:
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# recurse into its arguments
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_t._args = LazyTorchTensor.to_eager(_t._args)
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_t._data = _t._func(_t._args)
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if _t._data is not None:
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return _t._data
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else:
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raise ValueError(f"Could not compute lazy tensor {_t!r} with args {_t._args!r}")
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# recurse into lists and/or tuples, keeping their structure
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return LazyTorchTensor._recurse_apply(t, simple_to_eager)
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@staticmethod
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def from_eager(t: Tensor) -> Tensor:
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if (t.__class__ == LazyTorchTensor):
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@classmethod
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def eager_to_meta(cls, t: Tensor) -> Tensor:
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if t.is_meta:
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return t
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return LazyTorchTensor(meta=t.detach().to("meta"), data=t) # type: ignore
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return t.detach().to("meta")
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@classmethod
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def meta_with_dtype(cls, m: Tensor, dtype: torch.dtype) -> Tensor:
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m = m.detach()
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if not m.is_meta:
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m = m.to("meta")
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m.dtype = dtype
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return m
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@classmethod
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def __torch_function__(cls, func, types, args=(), kwargs=None):
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@ -2435,28 +2444,8 @@ class LazyTorchTensor:
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if func is torch.Tensor.numpy:
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return args[0].numpy()
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if func is torch.equal:
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eager_args = LazyTorchTensor.to_eager(args)
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return func(*eager_args, **kwargs)
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return LazyTorchTensor._wrap_fn(args[0], func)(*args, **kwargs)
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# special methods bypass __getattr__, so they need to be added manually
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# ref: https://docs.python.org/3/reference/datamodel.html#special-lookup
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# NOTE: LazyTorchTensor can't be a subclass of Tensor (and then be used
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# as self._meta is currently used), because then the following
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# operations would by default not be wrapped, and so not propagated
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# when the tensor is made eager.
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# It's better to get non-silent errors for not-yet-supported operators.
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# TODO: add more when needed to avoid clutter, or find a more concise way
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def __neg__(self, *args): # mamba
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return self._wrap_fn(torch.Tensor.__neg__)(self, *args)
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def __add__(self, *args): # gemma
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return self._wrap_fn(torch.Tensor.__add__)(self, *args)
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def __getitem__(self, *args): # bloom falcon refact internlm2
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return self._wrap_fn(torch.Tensor.__getitem__)(self, *args)
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return LazyTorchTensor._wrap_fn(func)(*args, **kwargs)
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def parse_args() -> argparse.Namespace:
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@ -2472,11 +2461,11 @@ def parse_args() -> argparse.Namespace:
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)
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parser.add_argument(
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"--outfile", type=Path,
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help="path to write to; default: based on input",
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help="path to write to; default: based on input. {ftype} will be replaced by the outtype.",
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)
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parser.add_argument(
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"--outtype", type=str, choices=["f32", "f16"], default="f16",
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help="output format - use f32 for float32, f16 for float16",
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"--outtype", type=str, choices=["f32", "f16", "bf16", "auto"], default="f16",
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help="output format - use f32 for float32, f16 for float16, bf16 for bfloat16, auto for the highest-fidelity 16-bit float type depending on the first loaded tensor type",
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)
|
||||
parser.add_argument(
|
||||
"--bigendian", action="store_true",
|
||||
@ -2530,16 +2519,18 @@ def main() -> None:
|
||||
logger.error(f'Error: {args.model} is not a directory')
|
||||
sys.exit(1)
|
||||
|
||||
ftype_map = {
|
||||
"f32": gguf.GGMLQuantizationType.F32,
|
||||
"f16": gguf.GGMLQuantizationType.F16,
|
||||
ftype_map: dict[str, gguf.LlamaFileType] = {
|
||||
"f32": gguf.LlamaFileType.ALL_F32,
|
||||
"f16": gguf.LlamaFileType.MOSTLY_F16,
|
||||
"bf16": gguf.LlamaFileType.MOSTLY_BF16,
|
||||
"auto": gguf.LlamaFileType.GUESSED,
|
||||
}
|
||||
|
||||
if args.outfile is not None:
|
||||
fname_out = args.outfile
|
||||
else:
|
||||
# output in the same directory as the model by default
|
||||
fname_out = dir_model / f'ggml-model-{args.outtype}.gguf'
|
||||
fname_out = dir_model / 'ggml-model-{ftype}.gguf'
|
||||
|
||||
logger.info(f"Loading model: {dir_model.name}")
|
||||
|
||||
@ -2555,14 +2546,16 @@ def main() -> None:
|
||||
logger.info("Set model tokenizer")
|
||||
model_instance.set_vocab()
|
||||
|
||||
model_instance.gguf_writer.add_quantization_version(gguf.GGML_QUANT_VERSION)
|
||||
|
||||
if args.vocab_only:
|
||||
logger.info(f"Exporting model vocab to '{fname_out}'")
|
||||
logger.info(f"Exporting model vocab to '{model_instance.fname_out}'")
|
||||
model_instance.write_vocab()
|
||||
else:
|
||||
logger.info(f"Exporting model to '{fname_out}'")
|
||||
logger.info(f"Exporting model to '{model_instance.fname_out}'")
|
||||
model_instance.write()
|
||||
|
||||
logger.info(f"Model successfully exported to '{fname_out}'")
|
||||
logger.info(f"Model successfully exported to '{model_instance.fname_out}'")
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
|
||||
@ -1,4 +1,5 @@
|
||||
from .constants import *
|
||||
from .lazy import *
|
||||
from .gguf_reader import *
|
||||
from .gguf_writer import *
|
||||
from .tensor_mapping import *
|
||||
|
||||
@ -10,6 +10,7 @@ from typing import Any
|
||||
GGUF_MAGIC = 0x46554747 # "GGUF"
|
||||
GGUF_VERSION = 3
|
||||
GGUF_DEFAULT_ALIGNMENT = 32
|
||||
GGML_QUANT_VERSION = 2 # GGML_QNT_VERSION from ggml.h
|
||||
|
||||
#
|
||||
# metadata keys
|
||||
@ -838,6 +839,49 @@ class GGMLQuantizationType(IntEnum):
|
||||
BF16 = 30
|
||||
|
||||
|
||||
# TODO: add GGMLFileType from ggml_ftype in ggml.h
|
||||
|
||||
|
||||
# from llama_ftype in llama.h
|
||||
# ALL VALUES SHOULD BE THE SAME HERE AS THEY ARE OVER THERE.
|
||||
class LlamaFileType(IntEnum):
|
||||
ALL_F32 = 0
|
||||
MOSTLY_F16 = 1 # except 1d tensors
|
||||
MOSTLY_Q4_0 = 2 # except 1d tensors
|
||||
MOSTLY_Q4_1 = 3 # except 1d tensors
|
||||
MOSTLY_Q4_1_SOME_F16 = 4 # tok_embeddings.weight and output.weight are F16
|
||||
# MOSTLY_Q4_2 = 5 # support has been removed
|
||||
# MOSTLY_Q4_3 = 6 # support has been removed
|
||||
MOSTLY_Q8_0 = 7 # except 1d tensors
|
||||
MOSTLY_Q5_0 = 8 # except 1d tensors
|
||||
MOSTLY_Q5_1 = 9 # except 1d tensors
|
||||
MOSTLY_Q2_K = 10 # except 1d tensors
|
||||
MOSTLY_Q3_K_S = 11 # except 1d tensors
|
||||
MOSTLY_Q3_K_M = 12 # except 1d tensors
|
||||
MOSTLY_Q3_K_L = 13 # except 1d tensors
|
||||
MOSTLY_Q4_K_S = 14 # except 1d tensors
|
||||
MOSTLY_Q4_K_M = 15 # except 1d tensors
|
||||
MOSTLY_Q5_K_S = 16 # except 1d tensors
|
||||
MOSTLY_Q5_K_M = 17 # except 1d tensors
|
||||
MOSTLY_Q6_K = 18 # except 1d tensors
|
||||
MOSTLY_IQ2_XXS = 19 # except 1d tensors
|
||||
MOSTLY_IQ2_XS = 20 # except 1d tensors
|
||||
MOSTLY_Q2_K_S = 21 # except 1d tensors
|
||||
MOSTLY_IQ3_XS = 22 # except 1d tensors
|
||||
MOSTLY_IQ3_XXS = 23 # except 1d tensors
|
||||
MOSTLY_IQ1_S = 24 # except 1d tensors
|
||||
MOSTLY_IQ4_NL = 25 # except 1d tensors
|
||||
MOSTLY_IQ3_S = 26 # except 1d tensors
|
||||
MOSTLY_IQ3_M = 27 # except 1d tensors
|
||||
MOSTLY_IQ2_S = 28 # except 1d tensors
|
||||
MOSTLY_IQ2_M = 29 # except 1d tensors
|
||||
MOSTLY_IQ4_XS = 30 # except 1d tensors
|
||||
MOSTLY_IQ1_M = 31 # except 1d tensors
|
||||
MOSTLY_BF16 = 32 # except 1d tensors
|
||||
|
||||
GUESSED = 1024 # not specified in the model file
|
||||
|
||||
|
||||
class GGUFEndian(IntEnum):
|
||||
LITTLE = 0
|
||||
BIG = 1
|
||||
|
||||
@ -7,7 +7,7 @@ import struct
|
||||
import tempfile
|
||||
from enum import Enum, auto
|
||||
from io import BufferedWriter
|
||||
from typing import IO, Any, Callable, Sequence, Mapping
|
||||
from typing import IO, Any, Sequence, Mapping
|
||||
from string import ascii_letters, digits
|
||||
|
||||
import numpy as np
|
||||
@ -28,47 +28,6 @@ from .constants import (
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class LazyTensor:
|
||||
data: Callable[[], np.ndarray[Any, Any]]
|
||||
# to avoid too deep recursion
|
||||
functions: list[Callable[[np.ndarray[Any, Any]], np.ndarray[Any, Any]]]
|
||||
dtype: np.dtype[Any]
|
||||
shape: tuple[int, ...]
|
||||
|
||||
def __init__(self, data: Callable[[], np.ndarray[Any, Any]], *, dtype: type, shape: tuple[int, ...]):
|
||||
self.data = data
|
||||
self.functions = []
|
||||
self.dtype = np.dtype(dtype)
|
||||
self.shape = shape
|
||||
|
||||
def astype(self, dtype: type, **kwargs) -> LazyTensor:
|
||||
self.functions.append(lambda n: n.astype(dtype, **kwargs))
|
||||
self.dtype = np.dtype(dtype)
|
||||
return self
|
||||
|
||||
@property
|
||||
def nbytes(self) -> int:
|
||||
size = 1
|
||||
for n in self.shape:
|
||||
size *= n
|
||||
return size * self.dtype.itemsize
|
||||
|
||||
def tofile(self, *args, **kwargs) -> None:
|
||||
data = self.data()
|
||||
for f in self.functions:
|
||||
data = f(data)
|
||||
assert data.shape == self.shape
|
||||
assert data.dtype == self.dtype
|
||||
assert data.nbytes == self.nbytes
|
||||
self.functions = []
|
||||
self.data = lambda: data
|
||||
data.tofile(*args, **kwargs)
|
||||
|
||||
def byteswap(self, *args, **kwargs) -> LazyTensor:
|
||||
self.functions.append(lambda n: n.byteswap(*args, **kwargs))
|
||||
return self
|
||||
|
||||
|
||||
class WriterState(Enum):
|
||||
EMPTY = auto()
|
||||
HEADER = auto()
|
||||
@ -79,7 +38,7 @@ class WriterState(Enum):
|
||||
class GGUFWriter:
|
||||
fout: BufferedWriter
|
||||
temp_file: tempfile.SpooledTemporaryFile[bytes] | None
|
||||
tensors: list[np.ndarray[Any, Any] | LazyTensor]
|
||||
tensors: list[np.ndarray[Any, Any]]
|
||||
_simple_value_packing = {
|
||||
GGUFValueType.UINT8: "B",
|
||||
GGUFValueType.INT8: "b",
|
||||
@ -278,7 +237,7 @@ class GGUFWriter:
|
||||
self.ti_data_count += 1
|
||||
|
||||
def add_tensor(
|
||||
self, name: str, tensor: np.ndarray[Any, Any] | LazyTensor, raw_shape: Sequence[int] | None = None,
|
||||
self, name: str, tensor: np.ndarray[Any, Any], raw_shape: Sequence[int] | None = None,
|
||||
raw_dtype: GGMLQuantizationType | None = None,
|
||||
) -> None:
|
||||
if self.endianess == GGUFEndian.BIG:
|
||||
@ -303,7 +262,7 @@ class GGUFWriter:
|
||||
if pad != 0:
|
||||
fp.write(bytes([0] * pad))
|
||||
|
||||
def write_tensor_data(self, tensor: np.ndarray[Any, Any] | LazyTensor) -> None:
|
||||
def write_tensor_data(self, tensor: np.ndarray[Any, Any]) -> None:
|
||||
if self.state is not WriterState.TI_DATA:
|
||||
raise ValueError(f'Expected output file to contain tensor info, got {self.state}')
|
||||
|
||||
@ -391,7 +350,7 @@ class GGUFWriter:
|
||||
def add_name(self, name: str) -> None:
|
||||
self.add_string(Keys.General.NAME, name)
|
||||
|
||||
def add_quantization_version(self, quantization_version: GGMLQuantizationType) -> None:
|
||||
def add_quantization_version(self, quantization_version: int) -> None:
|
||||
self.add_uint32(
|
||||
Keys.General.QUANTIZATION_VERSION, quantization_version)
|
||||
|
||||
|
||||
225
gguf-py/gguf/lazy.py
Normal file
225
gguf-py/gguf/lazy.py
Normal file
@ -0,0 +1,225 @@
|
||||
from __future__ import annotations
|
||||
from abc import ABC, ABCMeta, abstractmethod
|
||||
|
||||
import logging
|
||||
from typing import Any, Callable
|
||||
from collections import deque
|
||||
|
||||
import numpy as np
|
||||
from numpy.typing import DTypeLike
|
||||
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class LazyMeta(ABCMeta):
|
||||
|
||||
def __new__(cls, name: str, bases: tuple[type, ...], namespace: dict[str, Any], **kwargs):
|
||||
def __getattr__(self, __name: str) -> Any:
|
||||
meta_attr = getattr(self._meta, __name)
|
||||
if callable(meta_attr):
|
||||
return type(self)._wrap_fn(
|
||||
(lambda s, *args, **kwargs: getattr(s, __name)(*args, **kwargs)),
|
||||
use_self=self,
|
||||
)
|
||||
elif isinstance(meta_attr, self._tensor_type):
|
||||
# e.g. self.T with torch.Tensor should still be wrapped
|
||||
return type(self)._wrap_fn(lambda s: getattr(s, __name))(self)
|
||||
else:
|
||||
# no need to wrap non-tensor properties,
|
||||
# and they likely don't depend on the actual contents of the tensor
|
||||
return meta_attr
|
||||
|
||||
namespace["__getattr__"] = __getattr__
|
||||
|
||||
# need to make a builder for the wrapped wrapper to copy the name,
|
||||
# or else it fails with very cryptic error messages,
|
||||
# because somehow the same string would end up in every closures
|
||||
def mk_wrap(op_name: str, *, meta_noop: bool = False):
|
||||
# need to wrap the wrapper to get self
|
||||
def wrapped_special_op(self, *args, **kwargs):
|
||||
return type(self)._wrap_fn(
|
||||
getattr(type(self)._tensor_type, op_name),
|
||||
meta_noop=meta_noop,
|
||||
)(self, *args, **kwargs)
|
||||
return wrapped_special_op
|
||||
|
||||
# special methods bypass __getattr__, so they need to be added manually
|
||||
# ref: https://docs.python.org/3/reference/datamodel.html#special-lookup
|
||||
# NOTE: doing this from a metaclass is very convenient
|
||||
# TODO: make this even more comprehensive
|
||||
for binary_op in (
|
||||
"lt", "le", "eq", "ne", "ge", "gt", "not"
|
||||
"abs", "add", "and", "floordiv", "invert", "lshift", "mod", "mul", "matmul",
|
||||
"neg", "or", "pos", "pow", "rshift", "sub", "truediv", "xor",
|
||||
"iadd", "iand", "ifloordiv", "ilshift", "imod", "imul", "ior", "irshift", "isub", "ixor",
|
||||
"radd", "rand", "rfloordiv", "rmul", "ror", "rpow", "rsub", "rtruediv", "rxor",
|
||||
):
|
||||
attr_name = f"__{binary_op}__"
|
||||
# the result of these operators usually has the same shape and dtype as the input,
|
||||
# so evaluation on the meta tensor can be skipped.
|
||||
namespace[attr_name] = mk_wrap(attr_name, meta_noop=True)
|
||||
|
||||
for special_op in (
|
||||
"getitem", "setitem", "len",
|
||||
):
|
||||
attr_name = f"__{special_op}__"
|
||||
namespace[attr_name] = mk_wrap(attr_name, meta_noop=False)
|
||||
|
||||
return super().__new__(cls, name, bases, namespace, **kwargs)
|
||||
|
||||
|
||||
# Tree of lazy tensors
|
||||
class LazyBase(ABC, metaclass=LazyMeta):
|
||||
_tensor_type: type
|
||||
_meta: Any
|
||||
_data: Any | None
|
||||
_lazy: deque[LazyBase] # shared within a graph, to avoid deep recursion when making eager
|
||||
_args: tuple
|
||||
_func: Callable[[tuple], Any] | None
|
||||
|
||||
def __init__(self, *, meta: Any, data: Any | None = None, lazy: deque[LazyBase] | None = None, args: tuple = (), func: Callable[[tuple], Any] | None = None):
|
||||
super().__init__()
|
||||
self._meta = meta
|
||||
self._data = data
|
||||
self._lazy = lazy if lazy is not None else deque()
|
||||
self._args = args
|
||||
self._func = func
|
||||
assert self._func is not None or self._data is not None
|
||||
if self._data is None:
|
||||
self._lazy.append(self)
|
||||
|
||||
def __init_subclass__(cls) -> None:
|
||||
if "_tensor_type" not in cls.__dict__:
|
||||
raise TypeError(f"property '_tensor_type' must be defined for {cls!r}")
|
||||
return super().__init_subclass__()
|
||||
|
||||
@staticmethod
|
||||
def _recurse_apply(o: Any, fn: Callable[[Any], Any]) -> Any:
|
||||
# TODO: dict and set
|
||||
if isinstance(o, (list, tuple)):
|
||||
L = []
|
||||
for item in o:
|
||||
L.append(LazyBase._recurse_apply(item, fn))
|
||||
if isinstance(o, tuple):
|
||||
L = tuple(L)
|
||||
return L
|
||||
elif isinstance(o, LazyBase):
|
||||
return fn(o)
|
||||
else:
|
||||
return o
|
||||
|
||||
@classmethod
|
||||
def _wrap_fn(cls, fn: Callable, *, use_self: LazyBase | None = None, meta_noop: bool | DTypeLike = False) -> Callable[[Any], Any]:
|
||||
def wrapped_fn(*args, **kwargs):
|
||||
if kwargs is None:
|
||||
kwargs = {}
|
||||
args = ((use_self,) if use_self is not None else ()) + args
|
||||
|
||||
meta_args = LazyBase._recurse_apply(args, lambda t: t._meta)
|
||||
|
||||
if isinstance(meta_noop, bool) and not meta_noop:
|
||||
try:
|
||||
res = fn(*meta_args, **kwargs)
|
||||
except NotImplementedError:
|
||||
# running some operations on PyTorch's Meta tensors can cause this exception
|
||||
res = None
|
||||
else:
|
||||
# some operators don't need to actually run on the meta tensors
|
||||
assert len(args) > 0
|
||||
res = args[0]
|
||||
assert isinstance(res, cls)
|
||||
res = res._meta
|
||||
# allow operations to override the dtype
|
||||
if meta_noop is not True:
|
||||
res = cls.meta_with_dtype(res, meta_noop)
|
||||
|
||||
if isinstance(res, cls._tensor_type):
|
||||
def collect_replace(t: LazyBase):
|
||||
if collect_replace.shared_lazy is None:
|
||||
collect_replace.shared_lazy = t._lazy
|
||||
else:
|
||||
collect_replace.shared_lazy.extend(t._lazy)
|
||||
t._lazy = collect_replace.shared_lazy
|
||||
|
||||
# emulating a static variable
|
||||
collect_replace.shared_lazy = None
|
||||
|
||||
LazyBase._recurse_apply(args, collect_replace)
|
||||
|
||||
shared_lazy = collect_replace.shared_lazy
|
||||
|
||||
return cls(meta=cls.eager_to_meta(res), lazy=shared_lazy, args=args, func=lambda a: fn(*a, **kwargs))
|
||||
else:
|
||||
del res # not needed
|
||||
# non-tensor return likely relies on the contents of the args
|
||||
# (e.g. the result of torch.equal)
|
||||
eager_args = cls.to_eager(args)
|
||||
return fn(*eager_args, **kwargs)
|
||||
return wrapped_fn
|
||||
|
||||
@classmethod
|
||||
def to_eager(cls, t: Any) -> Any:
|
||||
def simple_to_eager(_t: LazyBase) -> Any:
|
||||
def already_eager_to_eager(_t: LazyBase) -> Any:
|
||||
assert _t._data is not None
|
||||
return _t._data
|
||||
|
||||
while _t._data is None:
|
||||
lt = _t._lazy.popleft()
|
||||
if lt._data is not None:
|
||||
raise ValueError(f"{lt} did not belong in the lazy queue")
|
||||
assert lt._func is not None
|
||||
lt._args = cls._recurse_apply(lt._args, already_eager_to_eager)
|
||||
lt._data = lt._func(lt._args)
|
||||
# sanity check
|
||||
assert lt._data.dtype == lt._meta.dtype
|
||||
assert lt._data.shape == lt._meta.shape
|
||||
|
||||
return _t._data
|
||||
|
||||
# recurse into lists and/or tuples, keeping their structure
|
||||
return cls._recurse_apply(t, simple_to_eager)
|
||||
|
||||
@classmethod
|
||||
def eager_to_meta(cls, t: Any) -> Any:
|
||||
return cls.meta_with_dtype(t, t.dtype)
|
||||
|
||||
# must be overridden, meta tensor init is backend-specific
|
||||
@classmethod
|
||||
@abstractmethod
|
||||
def meta_with_dtype(cls, m: Any, dtype: Any) -> Any: pass
|
||||
|
||||
@classmethod
|
||||
def from_eager(cls, t: Any) -> Any:
|
||||
if type(t) is cls:
|
||||
# already eager
|
||||
return t
|
||||
elif isinstance(t, cls._tensor_type):
|
||||
return cls(meta=cls.eager_to_meta(t), data=t)
|
||||
else:
|
||||
return TypeError(f"{type(t)!r} is not compatible with {cls._tensor_type!r}")
|
||||
|
||||
|
||||
class LazyNumpyTensor(LazyBase):
|
||||
_tensor_type = np.ndarray
|
||||
|
||||
@classmethod
|
||||
def meta_with_dtype(cls, m: np.ndarray[Any, Any], dtype: DTypeLike) -> np.ndarray[Any, Any]:
|
||||
# The initial idea was to use np.nan as the fill value,
|
||||
# but non-float types like np.int16 can't use that.
|
||||
# So zero it is.
|
||||
cheat = np.zeros(1, dtype)
|
||||
return np.lib.stride_tricks.as_strided(cheat, m.shape, (0 for _ in m.shape))
|
||||
|
||||
def astype(self, dtype, *args, **kwargs):
|
||||
meta = type(self).meta_with_dtype(self._meta, dtype)
|
||||
full_args = (self, dtype,) + args
|
||||
# very important to pass the shared _lazy deque, or else there's an infinite loop somewhere.
|
||||
return type(self)(meta=meta, args=full_args, lazy=self._lazy, func=(lambda a: a[0].astype(*a[1:], **kwargs)))
|
||||
|
||||
def tofile(self, *args, **kwargs):
|
||||
eager = LazyNumpyTensor.to_eager(self)
|
||||
return eager.tofile(*args, **kwargs)
|
||||
|
||||
# TODO: __array_function__
|
||||
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Reference in New Issue
Block a user