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126 lines
4.1 KiB
Python
126 lines
4.1 KiB
Python
"""
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# Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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from abc import abstractmethod
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from typing import Optional
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import paddle
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from paddle.nn.quant import weight_only_linear, weight_quantize
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from fastdeploy.platforms import current_platform
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from .quant_base import QuantConfigBase, QuantMethodBase
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class WeightOnlyConfig(QuantConfigBase):
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"""
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Quantization config for weight only
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Args:
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weight_only_linear_arch: The architecture of weight only linear layer
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algo: The quant algorithm("weight_only_int8" or "weight_only_int4") used for weight only linear layer
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"""
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def __init__(
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self,
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weight_only_linear_arch: int,
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algo: str,
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) -> None:
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super().__init__()
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self.weight_only_linear_arch = weight_only_linear_arch
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self.algo = algo
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def get_name(self) -> str:
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return "weight_only"
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@classmethod
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def from_config(cls, config: dict) -> "WeightOnlyConfig":
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weight_only_linear_arch = config["weight_only_linear_arch"]
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algo = config["algo"]
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return cls(weight_only_linear_arch, algo)
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def get_quant_method(self, layer) -> Optional[QuantMethodBase]:
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if current_platform.is_xpu():
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from fastdeploy.model_executor.layers.backends import XPUWeightOnlyLinearMethod
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return XPUWeightOnlyLinearMethod(self)
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else:
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return GPUWeightOnlyLinearMethod(self)
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class WeightOnlyLinearMethod(QuantMethodBase):
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"""
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Weight only quantization method for linear layer
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"""
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def __init__(
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self,
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quant_config: WeightOnlyConfig,
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) -> None:
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super().__init__()
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self.quant_config = quant_config
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def create_weights(self, layer):
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weight_only_scale_name = layer.prefix + ".weight_only_scale"
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linear_weight_scale_shape = [layer.embed_dim]
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if hasattr(layer, "linear_weight_shape"):
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if isinstance(layer.linear_weight_shape, list):
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layer_weight_shape = layer.linear_weight_shape
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linear_weight_scale_shape = layer_weight_shape[:1]
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layer.linear_weight_scale = layer.create_parameter(
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shape=linear_weight_scale_shape,
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dtype=layer._dtype,
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is_bias=False,
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)
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@abstractmethod
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def process_loaded_weights(self, layer, weights) -> None:
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raise NotImplementedError
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def apply(self, layer, x):
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linear_out = weight_only_linear(
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x,
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weight=layer.linear_weight,
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bias=layer.linear_bias if layer.add_bias else None,
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weight_scale=layer.linear_weight_scale,
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weight_dtype=layer.weight_dtype,
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arch=self.quant_config.weight_only_linear_arch,
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)
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return linear_out
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class GPUWeightOnlyLinearMethod(WeightOnlyLinearMethod):
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"""
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Weight only quantization method for linear layer on GPU
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The weights are loaded in the BF16 numerical format. After loading, the quantization coefficients will be computed,
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and the weights will be quantized to int8 or int4.
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"""
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def __init__(
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self,
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quant_config: WeightOnlyConfig,
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) -> None:
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super().__init__(quant_config)
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def process_loaded_weights(self, layer, weight) -> None:
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quanted_weight_tensor, weight_scale_tensor = weight_quantize(
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weight,
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algo=self.quant_config.algo,
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arch=self.quant_config.weight_only_linear_arch,
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)
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layer.linear_weight.set_value(quanted_weight_tensor)
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layer.linear_weight_scale.set_value(
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weight_scale_tensor.astype(paddle.get_default_dtype()))
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