Manish Gupta
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/***************************************************************************************************
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* Copyright (c) 2017-2021, NVIDIA CORPORATION. All rights reserved.
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*
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* Redistribution and use in source and binary forms, with or without modification, are permitted
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* provided that the following conditions are met:
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* * Redistributions of source code must retain the above copyright notice, this list of
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* conditions and the following disclaimer.
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* * Redistributions in binary form must reproduce the above copyright notice, this list of
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* conditions and the following disclaimer in the documentation and/or other materials
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* provided with the distribution.
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* * Neither the name of the NVIDIA CORPORATION nor the names of its contributors may be used
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* to endorse or promote products derived from this software without specific prior written
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* permission.
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*
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* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR
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* IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
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* FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE
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* FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
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* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
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* OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
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* STRICT LIABILITY, OR TOR (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 "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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/// 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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typename ElementT_,
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int ElementsPerAccess,
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typename ElementwiseOp_ = Identity<ElementCompute_>,
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typename BinaryOp_ = plus<ElementCompute_>
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>
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class LinearCombinationBiasElementwise {
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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 ElementT = ElementT_;
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static int const kElementsPerAccess = ElementsPerAccess;
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static int const kCount = kElementsPerAccess;
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using ElementwiseOp = ElementwiseOp_;
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using BinaryOp = BinaryOp_;
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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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using FragmentOutput = FragmentZ;
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static bool const kIsHeavy = ElementwiseOp::kIsHeavy;
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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 = true;
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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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//
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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(0)),
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alpha_ptr(nullptr),
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beta_ptr(nullptr) { }
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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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): alpha(alpha), beta(beta), alpha_ptr(nullptr), beta_ptr(nullptr) {
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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) {
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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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): alpha(0), beta(0), alpha_ptr(alpha_ptr), beta_ptr(beta_ptr) {
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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) {
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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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bool skip_elementwise_;
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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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LinearCombinationBiasElementwise(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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skip_elementwise_ = false;
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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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skip_elementwise_ = true;
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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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ElementwiseOp elementwise_op;
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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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FragmentCompute result_T;
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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] + beta_ * tmp_C[i], V[i]);
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result_Z[i] = z;
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result_T[i] = skip_elementwise_ ? z : elementwise_op(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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NumericArrayConverter<ElementT, ElementCompute, kElementsPerAccess> convert_t;
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frag_T = convert_t(result_T);
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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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ElementwiseOp elementwise_op;
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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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FragmentCompute result_T;
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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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result_T[i] = skip_elementwise_ ? z : elementwise_op(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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NumericArrayConverter<ElementT, ElementCompute, kElementsPerAccess> convert_t;
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frag_T = convert_t(result_T);
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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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