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https://github.com/PaddlePaddle/FastDeploy.git
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ddb06ff83f
Co-authored-by: gongweibao <gognweibao@baidu.com>
119 lines
4.9 KiB
C++
119 lines
4.9 KiB
C++
/***************************************************************************************************
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* Copyright (c) 2017 - 2022 NVIDIA CORPORATION & AFFILIATES. All rights
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*reserved. SPDX-License-Identifier: BSD-3-Clause
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*
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* Redistribution and use in source and binary forms, with or without
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* modification, are permitted provided that the following conditions are met:
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*
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* 1. Redistributions of source code must retain the above copyright notice,
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*this list of conditions and the following disclaimer.
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*
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* 2. Redistributions in binary form must reproduce the above copyright notice,
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* this list of conditions and the following disclaimer in the documentation
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* and/or other materials provided with the distribution.
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*
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* 3. Neither the name of the copyright holder nor the names of its
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* contributors may be used to endorse or promote products derived from
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* this software without specific prior written permission.
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*
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* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
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* AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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* IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
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*ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
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*LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
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*CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
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*SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
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*INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
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*CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
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*ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
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*POSSIBILITY OF SUCH DAMAGE.
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*
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**************************************************************************************************/
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/*! \file
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\brief Templates exposing architecture support for multiply-add operations
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*/
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#pragma once
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#include "cutlass_extensions/weight_only_quant_op.h"
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/////////////////////////////////////////////////////////////////////////////////////////////////
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namespace cutlass {
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namespace arch {
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// Tag which triggers MMA which will trigger
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struct OpMultiplyAddDequantizeInterleavedBToA;
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/*
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Below we have extra tags to signal what kind of dequantization we want to do
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(per col, scale only fine grained, finegrained with zero). This still lets us
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the existing template infrastructure (incl. that in CUTLASS). However, we
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split out the template below into OpMultiplyAddDequantizeInterleavedBToA along
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with the quantization op before instantiating the GEMM pieces.
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Note that this is somewhat of a hack, but it SIGNIFICANTLY reduces the amount
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of code we need to duplicate.
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*/
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struct OpMultiplyAddDequantizeInterleavedBToA_percol_scale;
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struct OpMultiplyAddDequantizeInterleavedBToA_fine_scale;
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struct OpMultiplyAddDequantizeInterleavedBToA_fine_scalebias;
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// The default just forwards the original operator
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template <typename MmaOp, WeightOnlyQuantOp QuantOp_>
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struct TagOperator {
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using TaggedOperator = MmaOp;
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};
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// Specializations below attach more information to the operator
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template <>
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struct TagOperator<OpMultiplyAddDequantizeInterleavedBToA,
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WeightOnlyQuantOp::PER_COLUMN_SCALE_ONLY> {
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using TaggedOperator = OpMultiplyAddDequantizeInterleavedBToA_percol_scale;
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};
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template <>
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struct TagOperator<OpMultiplyAddDequantizeInterleavedBToA,
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WeightOnlyQuantOp::FINEGRAINED_SCALE_ONLY> {
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using TaggedOperator = OpMultiplyAddDequantizeInterleavedBToA_fine_scale;
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};
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template <>
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struct TagOperator<OpMultiplyAddDequantizeInterleavedBToA,
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WeightOnlyQuantOp::FINEGRAINED_SCALE_AND_ZEROS> {
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using TaggedOperator = OpMultiplyAddDequantizeInterleavedBToA_fine_scalebias;
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};
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// Here we instantiate some structs to "detag" the tagged operator. It splits it
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// back to the original operator + the extra information. If no extra info was
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// tagged, the dequant op per column scaling as a default.
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template <typename TaggedMmaOp>
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struct DetagOperator {
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using Operator = TaggedMmaOp;
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static constexpr WeightOnlyQuantOp QuantOp =
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WeightOnlyQuantOp::PER_COLUMN_SCALE_ONLY;
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};
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template <>
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struct DetagOperator<OpMultiplyAddDequantizeInterleavedBToA_percol_scale> {
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using Operator = OpMultiplyAddDequantizeInterleavedBToA;
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static constexpr WeightOnlyQuantOp QuantOp =
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WeightOnlyQuantOp::PER_COLUMN_SCALE_ONLY;
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};
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template <>
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struct DetagOperator<OpMultiplyAddDequantizeInterleavedBToA_fine_scale> {
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using Operator = OpMultiplyAddDequantizeInterleavedBToA;
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static constexpr WeightOnlyQuantOp QuantOp =
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WeightOnlyQuantOp::FINEGRAINED_SCALE_ONLY;
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};
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template <>
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struct DetagOperator<OpMultiplyAddDequantizeInterleavedBToA_fine_scalebias> {
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using Operator = OpMultiplyAddDequantizeInterleavedBToA;
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static constexpr WeightOnlyQuantOp QuantOp =
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WeightOnlyQuantOp::FINEGRAINED_SCALE_AND_ZEROS;
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};
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} // namespace arch
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} // namespace cutlass
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