236 lines
9.1 KiB
C++
236 lines
9.1 KiB
C++
/***************************************************************************************************
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* Copyright (c) 2017 - 2024 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 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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#include "cutlass/epilogue/thread/scale_type.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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///< Usually it is 128/sizeof_bits<ElementOutput_>,
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///< but we use 64 or 32 sometimes when there are not enough data to store
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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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ScaleType::Kind Scale = ScaleType::Default ///< Control Alpha and Beta scaling
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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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using ElementScalar = complex<ElementCompute>;
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static int const kCount = Count;
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static const ScaleType::Kind kScale = Scale;
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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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ElementScalar alpha{ElementCompute(1)}; ///< scales accumulators
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ElementScalar beta{ElementCompute(0)}; ///< scales source tensor
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ElementScalar const* alpha_ptr{nullptr}; ///< pointer to accumulator scalar - if not null, loads it from memory
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ElementScalar const* beta_ptr{nullptr}; ///< 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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Params() = default;
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CUTLASS_HOST_DEVICE
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Params(
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ElementScalar alpha,
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ElementScalar beta
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): alpha(alpha), beta(beta)
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{}
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CUTLASS_HOST_DEVICE
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Params(
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ElementScalar const *alpha_ptr,
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ElementScalar const *beta_ptr
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): 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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ElementScalar alpha_;
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ElementScalar 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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if (Scale == ScaleType::OnlyAlphaScaling) return false;
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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_op(alpha_.real(), converted_accumulator.real);
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intermediate.imag = mul_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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