mirror of
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110 lines
3.9 KiB
Python
110 lines
3.9 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 typing import Optional
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import paddle
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from paddlenlp.utils.log import logger
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import fastdeploy
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from fastdeploy.platforms.utils import convert_to_npu_dequant_scale
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from .quant_base import QuantConfigBase, QuantMethodBase
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class W8A8Config(QuantConfigBase):
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"""
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quantization config for weight 8bits and activation 8bits
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"""
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def __init__(self, weight_scale_dict, act_scale_dict,
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use_gemm_dequant) -> None:
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super().__init__()
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self.weight_scale_dict = weight_scale_dict
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self.act_scale_dict = act_scale_dict
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self.use_gemm_dequant = use_gemm_dequant
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def get_name(self) -> str:
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return "w8a8"
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@classmethod
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def from_config(cls, config: dict) -> "W8A8Config":
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weight_scale_dict = config["weight_scale_dict"]
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act_scale_dict = config["act_scale_dict"]
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use_gemm_dequant = config["use_gemm_dequant"]
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return cls(weight_scale_dict, act_scale_dict, use_gemm_dequant)
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def get_quant_method(self, layer) -> Optional[QuantMethodBase]:
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return W8A8LinearMethod(self)
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class W8A8LinearMethod(QuantMethodBase):
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"""
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quantization method for weight 8bits and activation 8bits of linear layer
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"""
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def __init__(
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self,
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quant_config: W8A8Config,
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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_scale = self.quant_config.weight_scale_dict.get(
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layer.prefix + ".weight_quanter")
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in_scale = self.quant_config.act_scale_dict.get(layer.prefix +
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".activation_quanter")
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self.skip_quant = False
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if weight_scale is None or in_scale is None:
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self.skip_quant = True
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return
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max_range = 127.0
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linear_out_scale = paddle.to_tensor(
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weight_scale /
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(max_range * max_range * in_scale)).astype("float32")
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layer.linear_out_scale = layer.create_parameter(
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shape=[layer.embed_dim],
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dtype="float32",
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is_bias=False,
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default_initializer=paddle.nn.initializer.Constant(0),
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)
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layer.linear_out_scale.set_value(
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convert_to_npu_dequant_scale(linear_out_scale))
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def process_loaded_weights(self, layer, weights) -> None:
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if self.skip_quant:
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logger.debug(f"{layer.prefix} skip quant")
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weight_tensor = weights.cast(layer._dtype)
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layer.linear_weight.set_value(weight_tensor)
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else:
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weight_tensor = weights.transpose([1, 0])
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weight_tensor = paddle.cast(weight_tensor, layer.weight_dtype)
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layer.linear_weight.set_value(weight_tensor)
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def apply(self, layer, x):
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if self.skip_quant:
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linear_out = paddle.matmul(x, layer.linear_weight, False, True)
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return linear_out
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if self.quant_config.use_gemm_dequant:
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linear_out = fastdeploy.model_executor.ops.gpu.gemm_dequant(
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x, layer.linear_weight, layer.linear_out_scale, layer._dtype)
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else:
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linear_out = paddle.matmul(x, layer.linear_weight, False, True)
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linear_out = fastdeploy.model_executor.ops.gpu.dequant_int8(
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linear_out, layer.linear_out_scale, layer._dtype)
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return linear_out
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