mirror of
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119 lines
3.9 KiB
Python
119 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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import fastdeploy
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from ..moe import FusedMoE
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from .quant_base import QuantConfigBase, QuantMethodBase
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QUANT_SCALING_FACTOR = 448
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class W4AFP8Config(QuantConfigBase):
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"""
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quantization config for weight 4bits and activation fp8
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"""
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def __init__(self, weight_scale_dict, act_scale_dict, is_permuted, hadamard_block_size) -> 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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self.is_permuted = is_permuted
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self.hadamard_block_size = hadamard_block_size
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def name(self) -> str:
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return "w4afp8"
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@classmethod
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def from_config(cls, config: dict) -> "W4AFP8Config":
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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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is_permuted = config.get("is_permuted", True)
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hadamard_block_size = config.get("hadamard_block_size", 128)
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return cls(weight_scale_dict, act_scale_dict, is_permuted, hadamard_block_size)
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def get_quant_method(self, layer) -> Optional[QuantMethodBase]:
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if isinstance(layer, FusedMoE):
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from fastdeploy.model_executor.layers.moe.fused_moe_cutlass_backend import (
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CutlassW4AFP8MoEMethod,
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)
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return CutlassW4AFP8MoEMethod(self)
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return W4AFP8LinearMethod(self)
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class W4AFP8LinearMethod(QuantMethodBase):
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"""
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W4 AFP8 quant method for linear
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"""
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def __init__(
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self,
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quant_config: W4AFP8Config,
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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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layer.weight_shape.reverse()
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layer.weight_shape[0] //= 2
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layer.weight_dtype = "int8"
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layer.weight = 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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def process_loaded_weights(self, layer, weights) -> None:
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(
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quanted_weight_tensor,
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weight_scale_tensor,
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) = fastdeploy.model_executor.ops.gpu.scaled_gemm_f8_i4_f16_weight_quantize(
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paddle.cast(weights, "float32").cpu(),
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groupsize=-1,
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scale_dtype="float16",
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)
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weight_scale_tensor = paddle.view(weight_scale_tensor, layer._dtype)
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layer.weight.set_value(quanted_weight_tensor)
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layer.weight_scale.set_value(weight_scale_tensor)
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def apply(self, layer, x):
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linear_out = fastdeploy.model_executor.ops.gpu.scaled_gemm_f8_i4_f16(
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x,
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layer.weight,
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layer.weight_scale,
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zero_points=None,
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bias=layer.bias if layer.add_bias else None,
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out_scale=self.quant_config.weight_scale_dict.get(layer.prefix + ".weight_scale")
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/ (
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self.quant_config.act_scale_dict.get(layer.prefix + ".activation_scale")
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* QUANT_SCALING_FACTOR
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* QUANT_SCALING_FACTOR
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),
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groupsize=0,
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out_dtype=layer._dtype,
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)
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return linear_out
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