mirror of
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139 lines
4.9 KiB
Python
139 lines
4.9 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 fastdeploy import envs
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from fastdeploy.model_executor.layers.moe import FusedMoE
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from ..utils import get_tensor, per_block_cast_to_fp8
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from .quant_base import QuantConfigBase, QuantMethodBase
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class BlockWiseFP8Config(QuantConfigBase):
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"""
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block wise quantization config, only support fp8 quant and only supports loading weights in BF16 format.
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After loading the weights, it will automatically compute quantization sparsity and dynamically perform
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per-token quantization of activations during inference.
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"""
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def __init__(self, weight_block_size: list = [-1, -1]) -> None:
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super().__init__()
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self.weight_block_size = weight_block_size
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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.use_deep_gemm = bool(envs.FD_USE_DEEP_GEMM)
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def name(self) -> str:
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return "block_wise_fp8"
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@classmethod
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def from_config(cls, config: dict) -> "BlockWiseFP8Config":
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weight_block_size = config.get("weight_block_size", [128, 128])
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return cls(weight_block_size)
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def get_quant_method(self, layer) -> Optional[QuantMethodBase]:
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"""
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Get quantization method.
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"""
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if isinstance(layer, FusedMoE):
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if self.use_deep_gemm:
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from fastdeploy.model_executor.layers.moe.fused_moe_deepgemm_backend import (
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DeepGemmFusedMoeMethod,
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)
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return DeepGemmFusedMoeMethod(self)
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else:
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from fastdeploy.model_executor.layers.moe.fused_moe_triton_backend import (
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BlockWiseFP8MoEMethod,
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)
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return BlockWiseFP8MoEMethod(self)
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else:
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return BlockWiseFP8LinearMethod(self)
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class BlockWiseFP8LinearMethod(QuantMethodBase):
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"""
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block wise quantization method for linear
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"""
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def __init__(
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self,
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quant_config: BlockWiseFP8Config,
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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_dtype = "float8_e4m3fn"
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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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layer.weight_scale = layer.create_parameter(
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shape=[
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(layer.output_size + self.quant_config.weight_block_size[0] - 1)
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// self.quant_config.weight_block_size[0],
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(layer.input_size + self.quant_config.weight_block_size[1] - 1)
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// self.quant_config.weight_block_size[1],
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],
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dtype="float32",
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is_bias=False,
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)
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def process_loaded_weights(self, layer, weights) -> None:
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weight_tensor = weights.transpose([1, 0])
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quanted_weight_tensor, weight_block_scale_tensor = per_block_cast_to_fp8(weight_tensor)
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layer.weight.copy_(quanted_weight_tensor, False)
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layer.weight_scale.set_value(weight_block_scale_tensor)
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def process_prequanted_weights(self, layer, state_dict):
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"""
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process_prequanted_weights
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"""
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quant_weight = get_tensor(state_dict.pop(layer.weight_key))
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weight_scale = get_tensor(state_dict.pop(layer.weight_scale_key))
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quant_weight = quant_weight.transpose([1, 0]).contiguous()
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layer.weight.copy_(quant_weight.view("float8_e4m3fn"), False)
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weight_scale = weight_scale.transpose([1, 0])
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layer.weight_scale.set_value(weight_scale)
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def apply(self, layer, x):
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x, x_scale_tensor = fastdeploy.model_executor.ops.gpu.per_token_quant_padding(
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x, self.quant_config.weight_block_size[0]
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)
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linear_out = paddle.empty((x.shape[0], layer.output_size), dtype=paddle.bfloat16)
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from fastdeploy.model_executor.ops.gpu import deep_gemm
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deep_gemm.gemm_fp8_fp8_bf16_nt(
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(x, x_scale_tensor),
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(layer.weight, layer.weight_scale),
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linear_out,
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)
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if layer.with_bias:
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linear_out = paddle.add(linear_out, layer.bias)
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return linear_out
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