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
This commit is contained in:
compilade
GitHub
parent
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commit
5a419926b0
5 changed files with 404 additions and 182 deletions
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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",
|
||||
)
|
||||
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__':
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue