453 lines
13 KiB
C++
453 lines
13 KiB
C++
/***************************************************************************************************
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* Copyright (c) 2017 - 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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* 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, this
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* 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 ARE
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* DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
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* FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
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* DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
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* SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
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* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
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* OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
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* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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*
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**************************************************************************************************/
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/*! \file
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\brief Functor performing linear combination operations used by epilogues.
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*/
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#pragma once
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#include <cuda_fp16.h>
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#include "cutlass/cutlass.h"
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#include "cutlass/numeric_types.h"
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#include "cutlass/array.h"
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#include "cutlass/functional.h"
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#include "cutlass/numeric_conversion.h"
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#include "cutlass/epilogue/thread/activation.h"
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/////////////////////////////////////////////////////////////////////////////////////////////////
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namespace cutlass {
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namespace epilogue {
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namespace thread {
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/////////////////////////////////////////////////////////////////////////////////////////////////
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namespace detail {
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template <typename Element, int ElementsPerAccess>
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struct ArrayMaximum {
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CUTLASS_HOST_DEVICE
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Array<Element, ElementsPerAccess> operator()(
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Array<Element, ElementsPerAccess> const &lhs,
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Array<Element, ElementsPerAccess> const &rhs) const {
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Array<Element, ElementsPerAccess> result;
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CUTLASS_PRAGMA_UNROLL
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for (int i = 0; i < ElementsPerAccess; ++i) {
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result[i] = fmax(lhs[i], rhs[i]);
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}
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return result;
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}
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};
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template <int ElementsPerAccess>
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struct ArrayMaximum<half_t, ElementsPerAccess> {
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CUTLASS_DEVICE
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Array<half_t, ElementsPerAccess> operator()(
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Array<half_t, ElementsPerAccess> const &lhs,
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Array<half_t, ElementsPerAccess> const &rhs) const {
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Array<half_t, ElementsPerAccess> result;
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#if __CUDA_ARCH__ >= 800
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int const kVectorCount = ElementsPerAccess / 2;
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__half2 const *lhs_ptr = reinterpret_cast<__half2 const *>(lhs.raw_data());
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__half2 const *rhs_ptr = reinterpret_cast<__half2 const *>(rhs.raw_data());
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__half2 *res_ptr = reinterpret_cast<__half2 *>(result.raw_data());
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CUTLASS_PRAGMA_UNROLL
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for (int i = 0; i < kVectorCount; ++i) {
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res_ptr[i] = __hmax2(lhs_ptr[i], rhs_ptr[i]);
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}
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#else
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__half const *lhs_ptr = reinterpret_cast<__half const *>(lhs.raw_data());
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__half const *rhs_ptr = reinterpret_cast<__half const *>(rhs.raw_data());
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__half *res_ptr = reinterpret_cast<__half *>(result.raw_data());
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CUTLASS_PRAGMA_UNROLL
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for (int i = 0; i < ElementsPerAccess; ++i) {
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res_ptr[i] = ((lhs_ptr[i] < rhs_ptr[i]) ? rhs_ptr[i] : lhs_ptr[i]);
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}
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#endif
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return result;
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}
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CUTLASS_DEVICE
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Array<half_t, ElementsPerAccess> operator()(
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Array<half_t, ElementsPerAccess> const &lhs,
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half_t const &rhs) const {
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Array<half_t, ElementsPerAccess> result;
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#if __CUDA_ARCH__ >= 800
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int const kVectorCount = ElementsPerAccess / 2;
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__half rhs_raw = reinterpret_cast<__half const &>(rhs);
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__half2 rhs_pair = __half2half2(rhs_raw);
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__half2 const *lhs_ptr = reinterpret_cast<__half2 const *>(lhs.raw_data());
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__half2 *res_ptr = reinterpret_cast<__half2 *>(result.raw_data());
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CUTLASS_PRAGMA_UNROLL
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for (int i = 0; i < kVectorCount; ++i) {
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res_ptr[i] = __hmax2(lhs_ptr[i], rhs_pair);
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}
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#else
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__half const *lhs_ptr = reinterpret_cast<__half const *>(lhs.raw_data());
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__half const rhs_raw = reinterpret_cast<__half const &>(rhs);
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__half *res_ptr = reinterpret_cast<__half *>(result.raw_data());
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CUTLASS_PRAGMA_UNROLL
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for (int i = 0; i < ElementsPerAccess; ++i) {
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res_ptr[i] = ((lhs_ptr[i] < rhs_raw) ? rhs_raw : lhs_ptr[i]);
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}
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#endif
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return result;
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}
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};
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/////////////////////////////////////////////////////////////////////////////////////////////////
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template <typename Element, int ElementsPerAccess>
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struct ReluConditional {
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CUTLASS_HOST_DEVICE
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void operator()(
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bool conditional[],
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Array<Element, ElementsPerAccess> const &fragment,
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Element threshold) const {
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CUTLASS_PRAGMA_UNROLL
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for (int i = 0; i < ElementsPerAccess; ++i) {
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conditional[i] = !(fragment[i] < threshold);
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}
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}
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};
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template <int ElementsPerAccess>
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struct ReluConditional<half_t, ElementsPerAccess> {
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CUTLASS_DEVICE
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void operator()(
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bool conditional[],
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Array<half_t, ElementsPerAccess> const &fragment,
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half_t threshold) const {
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__half y = reinterpret_cast<__half const &>(threshold);
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__half const *x = reinterpret_cast<__half const *>(fragment.raw_data());
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CUTLASS_PRAGMA_UNROLL
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for (int i = 0; i < ElementsPerAccess; ++i) {
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conditional[i] = !__hlt(x[i], y);
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}
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}
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};
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} // namespace detail
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/////////////////////////////////////////////////////////////////////////////////////////////////
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/// This is a partial specialization for fused Bias and ReLU. It supports the option of packing
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/// ReLU conditionals in a bit vector that may be used by backwards passes as an optimization.
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///
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/// This class can only be used with cutlass::epilogue::threadblock::EpilogueWithBroadcast<>.
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///
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/// This base class is meant to define the concept required of the
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/// EpilogueWithBroadcast::OutputOp
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template <
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typename ElementC_,
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typename ElementAccumulator_,
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typename ElementCompute_,
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typename ElementZ_,
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int ElementsPerAccess,
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bool StoreT = true,
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typename ElementVector_ = ElementC_
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>
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class LinearCombinationBiasRelu {
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public:
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using ElementOutput = ElementC_;
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using ElementC = ElementC_;
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using ElementAccumulator = ElementAccumulator_;
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using ElementCompute = ElementCompute_;
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using ElementZ = ElementZ_;
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using ElementVector = ElementVector_;
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using ElementT = uint1b_t;
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static int const kElementsPerAccess = ElementsPerAccess;
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static int const kCount = kElementsPerAccess;
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using ElementwiseOp = ReLu<ElementCompute>;
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using BinaryOp = plus<ElementCompute>;
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// Indicates that this epilogue applies only one binary operation
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static bool const kIsSingleSource = true;
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using FragmentAccumulator = Array<ElementAccumulator, kElementsPerAccess>;
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using FragmentCompute = Array<ElementCompute, kElementsPerAccess>;
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using FragmentC = Array<ElementOutput, kElementsPerAccess>;
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using FragmentZ = Array<ElementZ, kElementsPerAccess>;
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using FragmentT = Array<ElementT, kElementsPerAccess>;
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/// If true, the 'Z' tensor is stored
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static bool const kStoreZ = true;
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/// If true, the 'T' tensor is stored
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static bool const kStoreT = StoreT;
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/// Host-constructable parameters structure
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struct Params {
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ElementCompute alpha; ///< scales accumulators
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ElementCompute beta; ///< scales source tensor
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ElementCompute const *alpha_ptr; ///< pointer to accumulator scalar - if not null, loads it from memory
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ElementCompute const *beta_ptr; ///< pointer to source scalar - if not null, loads it from memory
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ElementZ threshold; ///< ReLu threshold
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//
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// Methods
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//
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//
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// Methods
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//
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CUTLASS_HOST_DEVICE
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Params():
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alpha(ElementCompute(1)),
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beta(ElementCompute()),
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alpha_ptr(nullptr),
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beta_ptr(nullptr),
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threshold(ElementCompute()) { }
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CUTLASS_HOST_DEVICE
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Params(
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ElementCompute alpha,
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ElementCompute beta,
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ElementCompute threshold_ = ElementCompute()
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):
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alpha(alpha), beta(beta), alpha_ptr(nullptr), beta_ptr(nullptr) {
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NumericConverter<ElementZ, ElementCompute> convert_threshold;
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threshold = convert_threshold(threshold_);
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}
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CUTLASS_HOST_DEVICE
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Params(
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ElementCompute alpha
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): alpha(alpha), beta(0), alpha_ptr(nullptr), beta_ptr(nullptr), threshold(ElementZ()) {
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}
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CUTLASS_HOST_DEVICE
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Params(
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ElementCompute const *alpha_ptr,
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ElementCompute const *beta_ptr,
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ElementCompute threshold_ = ElementCompute()
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): alpha(0), beta(0), alpha_ptr(alpha_ptr), beta_ptr(beta_ptr) {
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NumericConverter<ElementZ, ElementCompute> convert_threshold;
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threshold = convert_threshold(threshold_);
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}
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CUTLASS_HOST_DEVICE
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Params(
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ElementCompute const *alpha_ptr
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): alpha(0), beta(0), alpha_ptr(alpha_ptr), beta_ptr(nullptr), threshold(ElementZ()) {
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}
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};
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private:
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//
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// Data members
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//
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ElementCompute alpha_;
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ElementCompute beta_;
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ElementZ threshold_;
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public:
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//
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// Methods
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//
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/// Constructor from Params
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CUTLASS_HOST_DEVICE
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LinearCombinationBiasRelu(Params const ¶ms) {
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alpha_ = (params.alpha_ptr ? *params.alpha_ptr : params.alpha);
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beta_ = (params.beta_ptr ? *params.beta_ptr : params.beta);
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threshold_ = params.threshold;
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}
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/// Returns true if source is needed
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CUTLASS_HOST_DEVICE
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bool is_source_needed() const {
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return beta_ != ElementCompute(0);
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}
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/// Functionally required for serial reduction in the epilogue
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CUTLASS_HOST_DEVICE
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void set_k_partition(int k_partition, int k_partition_count) {
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if (k_partition) {
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beta_ = ElementCompute(1);
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}
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if (k_partition != k_partition_count - 1) {
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// set to NaN to make ReLU no-op for all except last k partitions
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int64_t allones = -1;
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threshold_ = reinterpret_cast<ElementZ const &>(allones);
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}
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}
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/// Applies the operation when is_source_needed() is true
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CUTLASS_HOST_DEVICE
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void operator()(
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FragmentZ &frag_Z,
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FragmentT &frag_T,
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FragmentAccumulator const &AB,
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FragmentC const &frag_C,
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FragmentCompute const &V) const {
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BinaryOp binary_op;
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FragmentCompute tmp_Accum = NumericArrayConverter<ElementCompute, ElementAccumulator, kElementsPerAccess>()(AB);
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FragmentCompute tmp_C = NumericArrayConverter<ElementCompute, ElementC, kElementsPerAccess>()(frag_C);
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FragmentCompute result_Z;
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bool conditions[kElementsPerAccess];
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CUTLASS_PRAGMA_UNROLL
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for (int i = 0; i < kElementsPerAccess; ++i) {
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ElementCompute z = alpha_ * tmp_Accum[i];
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z += beta_ * tmp_C[i];
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z = binary_op(z, V[i]);
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result_Z[i] = z;
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}
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NumericArrayConverter<ElementZ, ElementCompute, kElementsPerAccess> convert_z;
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frag_Z = convert_z(result_Z);
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//
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// Compute condition
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//
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detail::ReluConditional<ElementZ, kElementsPerAccess> relu_conditional;
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relu_conditional(conditions, frag_Z, threshold_);
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detail::ArrayMaximum<ElementZ, kElementsPerAccess> maximum_op;
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frag_Z = maximum_op(frag_Z, threshold_);
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if (kStoreT) {
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PackPredicates<kElementsPerAccess> pack_predicates;
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frag_T = pack_predicates(conditions);
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}
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}
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/// Applies the operation when is_source_needed() is false
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CUTLASS_HOST_DEVICE
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void operator()(
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FragmentZ &frag_Z,
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FragmentT &frag_T,
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FragmentAccumulator const &AB,
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FragmentCompute const &V) const {
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BinaryOp binary_op;
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FragmentCompute tmp_Accum = NumericArrayConverter<ElementCompute, ElementAccumulator, kElementsPerAccess>()(AB);
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FragmentCompute result_Z;
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bool conditions[kElementsPerAccess];
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CUTLASS_PRAGMA_UNROLL
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for (int i = 0; i < kElementsPerAccess; ++i) {
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ElementCompute z = binary_op(alpha_ * tmp_Accum[i], V[i]);
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result_Z[i] = z;
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}
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NumericArrayConverter<ElementZ, ElementCompute, kElementsPerAccess> convert_z;
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frag_Z = convert_z(result_Z);
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//
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// Compute condition
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//
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detail::ReluConditional<ElementZ, kElementsPerAccess> relu_conditional;
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relu_conditional(conditions, frag_Z, threshold_);
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detail::ArrayMaximum<ElementZ, kElementsPerAccess> maximum_op;
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frag_Z = maximum_op(frag_Z, threshold_);
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//
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// Compute conditions
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//
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//
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// Store
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//
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if (kStoreT) {
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PackPredicates<kElementsPerAccess> pack_predicates;
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frag_T = pack_predicates(conditions);
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}
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}
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};
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/////////////////////////////////////////////////////////////////////////////////////////////////
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} // namespace thread
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} // namespace epilogue
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} // namespace cutlass
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/////////////////////////////////////////////////////////////////////////////////////////////////
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