mirror of
https://github.com/PaddlePaddle/FastDeploy.git
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126 lines
3.9 KiB
Python
126 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 fastdeploy.model_executor.layers.quantization.ops import (
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cutlass_scaled_mm,
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scaled_fp8_quant,
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)
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from fastdeploy.model_executor.layers.quantization.quant_base import (
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QuantConfigBase,
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QuantMethodBase,
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)
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class WFP8AFP8Config(QuantConfigBase):
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"""
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Quantization config for weight and activation with FP8.
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"""
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def __init__(self, weight_scale_dict, act_scale_dict) -> 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.quant_max_bound = 448
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self.quant_min_bound = -448
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self.quant_round_type = 1
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def name(self) -> str:
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""" """
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return "wfp8afp8"
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@classmethod
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def from_config(cls, config: dict) -> "WFP8AFP8Config":
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""" """
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weight_scale_dict = config.get("weight_scale_dict", None)
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act_scale_dict = config.get("act_scale_dict", None)
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return cls(weight_scale_dict, act_scale_dict)
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def get_quant_method(self, layer) -> Optional[QuantMethodBase]:
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""" """
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return WFP8AFP8LinearMethod(self)
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class WFP8AFP8LinearMethod(QuantMethodBase):
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"""
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Weight and activation quantization method for linear layer with FP8
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"""
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def __init__(
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self,
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quant_config: WFP8AFP8Config,
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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, **extra_weight_attrs):
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""" """
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layer.weight_shape.reverse()
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layer.weight_dtype = "float8_e4m3fn"
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# TODO(YuanRisheng): set weight logic should be moved to process_loaded_weights func
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self.skip_quant = False
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layer.create_parameter(
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shape=layer.weight_shape,
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dtype=layer.weight_dtype,
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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.weight_scale = layer.create_parameter(
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shape=[1],
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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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def process_loaded_weights(self, layer, weights) -> None:
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""" """
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if self.skip_quant:
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weight_tensor = weights.cast(layer._dtype)
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layer.weight.set_value(weight_tensor)
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return
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if weights.dtype != paddle.float8_e4m3fn:
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self.use_per_token_if_dynamic = True
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weight_tensor = weights.transpose([1, 0]).contiguous()
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qweight, weight_scale = scaled_fp8_quant(
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weight_tensor,
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use_per_token_if_dynamic=False,
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)
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layer.weight.copy_(qweight, False)
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layer.weight_scale.set_value(weight_scale)
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def apply(self, layer, x):
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""" """
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if self.skip_quant:
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linear_out = paddle.matmul(x, layer.weight, False, True)
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return linear_out
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if self.use_per_token_if_dynamic:
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out_type = x.dtype
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a_q, a_scales = scaled_fp8_quant(x, use_per_token_if_dynamic=self.use_per_token_if_dynamic)
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linear_out = cutlass_scaled_mm(
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a_q,
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layer.weight,
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a_scales,
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layer.weight_scale,
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out_type,
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layer.bias,
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)
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else:
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raise NotImplementedError
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return linear_out
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