Package upstream model with reproducible local MMEB evaluation

bb2b5cc verified 7 days ago

6.87 kB

# 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 []

	# 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 []