Manish GuptaandManish Gupta <manigupta@nvidia.com>, Haicheng Wu <haichengw@nvidia.com>, Dustyn Blasig <dblasig@nvidia.com>, Andrew Kerr <akerr@nvidia.com>
6615010cd0
CUTLASS 2.4 (Implicit GEMM Convolution) Co-authored-by: Manish Gupta <manigupta@nvidia.com>, Haicheng Wu <haichengw@nvidia.com>, Dustyn Blasig <dblasig@nvidia.com>, Andrew Kerr <akerr@nvidia.com>
230 lines
8.9 KiB
C++
230 lines
8.9 KiB
C++
/***************************************************************************************************
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* Copyright (c) 2017-2020, 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 on planar-complex arrays
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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/complex.h"
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#include "cutlass/array_planar_complex.h"
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#include "cutlass/functional.h"
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#include "cutlass/numeric_conversion.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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/// Applies a linear combination operator to arrays of planar-complex elements.
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///
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/// D = alpha * accumulator + beta * source + uniform
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///
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/// Note, as with most CUTLASS components for planar complex, the template arguments describe
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/// the underlying real data type.
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template <
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typename ElementOutput_, ///< Data type used to load and store tensors
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int Count, ///< Number of elements computed per operation
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typename ElementAccumulator_ = ElementOutput_, ///< Accumulator data type
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typename ElementCompute_ = ElementOutput_, ///< Data type used to compute linear combination
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FloatRoundStyle Round = FloatRoundStyle::round_to_nearest
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>
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class LinearCombinationPlanarComplex {
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public:
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using ElementOutput = ElementOutput_;
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using ElementAccumulator = ElementAccumulator_;
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using ElementCompute = ElementCompute_;
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static int const kCount = Count;
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using FragmentOutput = ArrayPlanarComplex<ElementOutput, kCount>;
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using FragmentAccumulator = ArrayPlanarComplex<ElementAccumulator, kCount>;
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using ComputeFragment = ArrayPlanarComplex<ElementCompute, kCount>;
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static FloatRoundStyle const kRound = Round;
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/// Host-constructable parameters structure
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struct Params {
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complex<ElementCompute> alpha; ///< scales accumulators
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complex<ElementCompute> beta; ///< scales source tensor
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complex<ElementCompute> const *alpha_ptr; ///< pointer to accumulator scalar - if not null, loads it from memory
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complex<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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complex<ElementCompute> alpha,
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complex<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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complex<ElementCompute> const *alpha_ptr,
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complex<ElementCompute> const *beta_ptr
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): alpha(complex<ElementCompute>()), beta(complex<ElementCompute>()), alpha_ptr(alpha_ptr), beta_ptr(beta_ptr) {
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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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complex<ElementCompute> alpha_;
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complex<ElementCompute> beta_;
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public:
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/// Constructs the function object, possibly loading from pointers in host memory
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CUTLASS_HOST_DEVICE
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LinearCombinationPlanarComplex(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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}
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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_.real() != ElementCompute(0) || beta_.imag() != 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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}
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/// Computes linear scaling: D = alpha * accumulator + beta * source
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CUTLASS_HOST_DEVICE
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FragmentOutput operator()(
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FragmentAccumulator const &accumulator,
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FragmentOutput const &source) const {
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// Convert source to interal compute numeric type
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NumericArrayConverter<ElementCompute, ElementOutput, kCount, Round> source_converter;
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NumericArrayConverter<ElementCompute, ElementAccumulator, kCount, Round> accumulator_converter;
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ComputeFragment converted_source(
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source_converter(source.real),
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source_converter(source.imag));
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ComputeFragment converted_accumulator(
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accumulator_converter(accumulator.real),
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accumulator_converter(accumulator.imag));
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// Perform binary operations
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ComputeFragment intermediate;
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multiplies<Array<ElementCompute, kCount> > mul_op;
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multiply_add<Array<ElementCompute, kCount> > mul_add_op;
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// complex multiply: I = beta * C
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intermediate.real = mul_op(beta_.real(), converted_source.real);
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intermediate.imag = mul_op(beta_.real(), converted_source.imag);
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intermediate.real = mul_add_op(-beta_.imag(), converted_source.imag, intermediate.real);
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intermediate.imag = mul_add_op( beta_.imag(), converted_source.real, intermediate.imag);
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// complex multiply-add: I = alpha * AB + I
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intermediate.real = mul_add_op(alpha_.real(), converted_accumulator.real, intermediate.real);
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intermediate.imag = mul_add_op(alpha_.real(), converted_accumulator.imag, intermediate.imag);
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intermediate.real = mul_add_op(-alpha_.imag(), converted_accumulator.imag, intermediate.real);
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intermediate.imag = mul_add_op( alpha_.imag(), converted_accumulator.real, intermediate.imag);
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// Convert to destination numeric type
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NumericArrayConverter<ElementOutput, ElementCompute, kCount, Round> destination_converter;
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return FragmentOutput(
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destination_converter(intermediate.real),
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destination_converter(intermediate.imag));
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}
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/// Computes linear scaling: D = alpha * accumulator + beta * source
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CUTLASS_HOST_DEVICE
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FragmentOutput operator()(
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FragmentAccumulator const &accumulator) const {
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// Convert source to interal compute numeric type
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NumericArrayConverter<ElementCompute, ElementAccumulator, kCount, Round> accumulator_converter;
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ComputeFragment converted_accumulator(
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accumulator_converter(accumulator.real),
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accumulator_converter(accumulator.imag));
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// Perform binary operations
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ComputeFragment intermediate;
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multiplies<Array<ElementCompute, kCount> > mul_op;
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multiply_add<Array<ElementCompute, kCount> > mul_add_op;
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// complex multiply-add: I = alpha * AB + I
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intermediate.real = mul_add_op(alpha_.real(), converted_accumulator.real);
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intermediate.imag = mul_add_op(alpha_.real(), converted_accumulator.imag);
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intermediate.real = mul_add_op(-alpha_.imag(), converted_accumulator.imag, intermediate.real);
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intermediate.imag = mul_add_op( alpha_.imag(), converted_accumulator.real, intermediate.imag);
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// Convert to destination numeric type
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NumericArrayConverter<ElementOutput, ElementCompute, kCount, Round> destination_converter;
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return FragmentOutput(
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destination_converter(intermediate.real),
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destination_converter(intermediate.imag));
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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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