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bb2b5cc | 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 | # Copyright 2025 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 .base import HfQuantizer
from .quantizers_utils import get_module_from_name
if TYPE_CHECKING:
from ..modeling_utils import PreTrainedModel
from ..utils.quantization_config import FPQuantConfig
from ..utils import is_fp_quant_available, is_qutlass_available, is_torch_available, is_torch_xpu_available, logging
from ..utils.quantization_config import QuantizationConfigMixin
if is_torch_available():
import torch
logger = logging.get_logger(__name__)
class FPQuantHfQuantizer(HfQuantizer):
"""
Quantizer for the FP-Quant method. Enables the loading of prequantized models and in-flight quantization of full-precision models.
"""
requires_calibration = False
is_qat_trainable = True
quantization_config: "FPQuantConfig"
def __init__(self, quantization_config: QuantizationConfigMixin, **kwargs):
super().__init__(quantization_config, **kwargs)
def validate_environment(self, device_map, **kwargs):
if not torch.cuda.is_available() and not is_torch_xpu_available():
raise NotImplementedError(
"FPQuant quantization is only supported on GPU or Intel XPU. Please use a different quantizer."
)
if not is_qutlass_available() and not self.quantization_config.pseudoquantization:
raise ImportError(
"Using `fp_quant` with real quantization requires a **Blackwell GPU** and qutlass: `git clone https://github.com/IST-DASLab/qutlass.git && cd qutlass && pip install --no-build-isolation .`. You can use `FPQuantConfig(pseudoquantization=True, ...)` to use Triton-based pseudo-quantization. It doesn't provide any speedups but emulates the quantization behavior of the real quantization."
)
if (
self.quantization_config.pseudoquantization
and self.quantization_config.forward_dtype == "nvfp4"
and torch.cuda.is_available()
and torch.cuda.get_device_capability()[0] < 9
):
raise ValueError(
"NVFP4 pseudoquantization requires a GPU with compute capability >= 9.0 (Hopper or newer) "
"because the Triton kernel uses the `fp8e4nv` type. Please use `forward_dtype='mxfp4'` instead, "
"or use a GPU with compute capability >= 9.0."
)
if self.quantization_config.pseudoquantization:
logger.warning(
"Using pseudo-quantization for FP-Quant. This doesn't provide any speedups but emulates the quantization behavior of the real quantization."
)
if not is_fp_quant_available():
raise ImportError("Using `fp_quant` quantization requires fp_quant: `pip install fp_quant`")
if device_map is None and not self.quantization_config.pseudoquantization:
raise ValueError(
"You are attempting to load a FPQuant model without setting device_map."
" Please set device_map comprised of 'cuda' devices."
)
elif isinstance(device_map, dict):
if (
not self.quantization_config.pseudoquantization
and len(device_map) > 1
and "cpu" in device_map.values()
or "disk" in device_map.values()
):
raise ValueError(
"You are attempting to load a FPQuant 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 != torch.bfloat16:
logger.warning_once(
f"Setting dtype to {dtype}, but only bfloat16 is supported right now. Overwriting torch_dtype to bfloat16."
)
dtype = torch.bfloat16
return dtype
def param_needs_quantization(self, model: "PreTrainedModel", param_name: str, **kwargs) -> bool:
from fp_quant import FPQuantLinear
module, tensor_name = get_module_from_name(model, param_name)
if isinstance(module, FPQuantLinear) and tensor_name in ["weight", "qweight", "dqweight"]:
# Only quantize weights of FPQuantLinear modules that are not already quantized
return True
else:
return False
def _process_model_before_weight_loading(
self,
model: "PreTrainedModel",
**kwargs,
):
from fp_quant import replace_with_fp_quant_linear
from ..integrations.fp_quant import adapt_fp_quant_config
replace_with_fp_quant_linear(
model,
fp_quant_linear_config=adapt_fp_quant_config(self.quantization_config),
)
@property
def is_trainable(self, model: Optional["PreTrainedModel"] = None):
trainable = self.quantization_config.store_master_weights
if not trainable:
logger.warning(
"You are attempting to train a model with FPQuant quantization. This is only supported when `store_master_weights=True`. Please set `store_master_weights=True` to train the model."
)
return trainable
def is_serializable(self):
return True
def get_quantize_ops(self):
from ..integrations.fp_quant import FpQuantQuantize
return FpQuantQuantize(self)
def get_weight_conversions(self):
from ..core_model_loading import WeightConverter
from ..integrations.fp_quant import FpQuantDeserialize
if self.pre_quantized:
if self.quantization_config.pseudoquantization:
return [
WeightConverter(
source_patterns=[".dqweight"],
target_patterns=".dqweight",
operations=[FpQuantDeserialize(self)],
),
]
else:
return [
WeightConverter(
source_patterns=[".qweight"],
target_patterns=".qweight",
operations=[FpQuantDeserialize(self)],
),
]
return []
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