File size: 8,277 Bytes
6f0b660 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 |
# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING, Optional
from ..utils.logging import tqdm
from .base import HfQuantizer
from .quantizers_utils import get_module_from_name
if TYPE_CHECKING:
from ..modeling_utils import PreTrainedModel
from ..utils import is_accelerate_available, is_flute_available, is_hadamard_available, is_torch_available, logging
from ..utils.quantization_config import QuantizationConfigMixin
if is_torch_available():
import torch
logger = logging.get_logger(__name__)
class HiggsHfQuantizer(HfQuantizer):
"""
Quantizer of the HIGGS method. Enables the loading of prequantized models and in-flight quantization of full-precision models.
"""
requires_calibration = False
requires_parameters_quantization = True
required_packages = ["flute-kernel", "fast_hadamard_transform"]
def __init__(self, quantization_config: QuantizationConfigMixin, **kwargs):
super().__init__(quantization_config, **kwargs)
self.quantization_config = quantization_config
def validate_environment(self, device_map, **kwargs):
if not torch.cuda.is_available():
raise NotImplementedError("HIGGS quantization is only supported on GPU. Please use a different quantizer.")
if not is_accelerate_available():
raise ImportError("Using `higgs` quantization requires Accelerate: `pip install accelerate`")
if not is_flute_available():
raise ImportError("Using `higgs` quantization requires FLUTE: `pip install flute-kernel>=0.3.0`")
if not is_hadamard_available():
raise ImportError(
"Using `higgs` quantization requires fast_hadamard_transform: `pip install fast_hadamard_transform`"
)
if device_map is None:
raise ValueError(
"You are attempting to load a HIGGS model without setting device_map."
" Please set device_map comprised of 'cuda' devices."
)
elif isinstance(device_map, dict) and ("cpu" in device_map.values() or "disk" in device_map.values()):
raise ValueError(
"You are attempting to load a HIGGS model with a device_map that contains a CPU or disk device."
" This is not supported. Please remove the CPU or disk device from the device_map."
)
def update_dtype(self, dtype: "torch.dtype") -> "torch.dtype":
if dtype is None:
logger.info("`dtype` is None. Setting `dtype=torch.float16` for FLUTE compatibility.")
dtype = torch.float16
elif dtype != torch.float16 and dtype != torch.bfloat16:
raise ValueError(
f"Invalid `dtype` {dtype}. HIGGS quantization only supports `dtype=torch.float16` or `dtype=torch.bfloat16`."
)
return dtype
def create_quantized_param(
self,
model: "PreTrainedModel",
param_value: "torch.Tensor",
param_name: str,
target_device: "torch.device",
**kwargs,
):
from ..integrations import quantize_with_higgs
flute_dict = quantize_with_higgs(
param_value.to(target_device),
self.quantization_config.bits,
self.quantization_config.p,
self.quantization_config.group_size,
self.quantization_config.hadamard_size,
)
del param_value
module, _ = get_module_from_name(model, param_name)
module_name = ".".join(param_name.split(".")[:-1])
for key, value in flute_dict.items():
if key in module._parameters:
module._parameters[key] = torch.nn.Parameter(value, requires_grad=False)
elif key in module._buffers:
module._buffers[key] = torch.nn.Buffer(value)
elif key == "tune_metadata":
module.tune_metadata = value
self.quantization_config.tune_metadata[module_name] = value.to_dict()
else:
raise ValueError(f"Unexpected key {key} in module {module}")
def _process_model_before_weight_loading(
self,
model: "PreTrainedModel",
keep_in_fp32_modules: Optional[list[str]] = None,
**kwargs,
):
from ..integrations import replace_with_higgs_linear
self.modules_to_not_convert = self.get_modules_to_not_convert(
model, self.quantization_config.modules_to_not_convert, keep_in_fp32_modules
)
replace_with_higgs_linear(
model,
quantization_config=self.quantization_config,
modules_to_not_convert=self.modules_to_not_convert,
)
model.config.quantization_config = self.quantization_config
def _process_model_after_weight_loading(self, model: "PreTrainedModel", **kwargs):
from flute.tune import TuneMetaData, maybe_tune_and_repack
from flute.utils import make_workspace_streamk
from ..integrations import HiggsLinear
flute_workspaces = {}
flute_modules = {name: module for name, module in model.named_modules() if isinstance(module, HiggsLinear)}
for name, module in tqdm(flute_modules.items(), desc="Repacking HIGGS modules", leave=False):
# Every HiggsLinear needs a "workspace": a buffer for the unpacking operation.
# This buffer needs to be on the same device as the weights, but can be reused across modules otherwise.
if module.weight.device not in flute_workspaces:
flute_workspaces[module.weight.device] = make_workspace_streamk(device=module.weight.device)
module.workspace = flute_workspaces[module.weight.device]
# FLUTE weights are packed in a way that is optimized for a specific number of SMs (GPU streaming multiprocessors).
# If the model is loaded on a different device than the one it was saved on, we need to repack the weights.
module.tune_metadata = TuneMetaData.from_dict(self.quantization_config.tune_metadata[name])
module.weight.data, module.tune_metadata = maybe_tune_and_repack(
weight=module.weight.data,
scales=module.scales.data,
metadata=module.tune_metadata,
)
self.quantization_config.tune_metadata[name] = module.tune_metadata.to_dict()
def update_missing_keys(self, model, missing_keys: list[str], prefix: str) -> list[str]:
from ..integrations import HiggsLinear
higgs_names = {name for name, module in model.named_modules() if isinstance(module, HiggsLinear)}
def should_update(key: str) -> bool:
if key.endswith(".weight") or key.endswith(".bias"):
return False
full_key = f"{prefix}.{key}"
return any(name in key or name in full_key for name in higgs_names)
return [key for key in missing_keys if not should_update(key)]
@property
def is_trainable(self) -> bool:
return False
def is_serializable(self, safe_serialization=None):
return True
def param_needs_quantization(self, model: "PreTrainedModel", param_name: str, **kwargs) -> bool:
from ..integrations import HiggsLinear
module, tensor_name = get_module_from_name(model, param_name)
if isinstance(module, HiggsLinear) and tensor_name == "weight":
# Only quantize weights of HiggsLinear modules that are not already quantized
return True
else:
return False
def _dequantize(self, model):
from ..integrations import dequantize_higgs
model = dequantize_higgs(model)
return model
|