232 lines
8.2 KiB
C++
232 lines
8.2 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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#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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#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 an array of elements.
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///
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/// D = alpha * accumulator + beta * source + uniform
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///
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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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ScaleType::Kind Scale = ScaleType::Default, ///< Control Alpha and Beta scaling
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FloatRoundStyle Round = FloatRoundStyle::round_to_nearest
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>
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class LinearCombinationLeakyRelu {
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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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static const ScaleType::Kind kScale = Scale;
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using FragmentOutput = Array<ElementOutput, kCount>;
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using FragmentAccumulator = Array<ElementAccumulator, kCount>;
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using ComputeFragment = Array<ElementCompute, kCount>;
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using FragmentSource = Array<ElementOutput, 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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ElementCompute alpha; ///< scales accumulators
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ElementCompute beta_bias; ///< scales bias tensor
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ElementCompute leaky_alpha; ///< leaky_alpha
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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_bias(ElementCompute(0)),
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leaky_alpha(ElementCompute(1))
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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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ElementCompute beta_bias,
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ElementCompute leaky_alpha = ElementCompute(1)
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): alpha(alpha), beta_bias(beta_bias), leaky_alpha(leaky_alpha) {
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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_bias_;
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ElementCompute leaky_alpha_recip_;
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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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LinearCombinationLeakyRelu(Params const ¶ms) {
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alpha_ = (params.alpha);
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beta_bias_ = (params.beta_bias);
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leaky_alpha_recip_ = (ElementCompute(params.leaky_alpha));
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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::NoBetaScaling) return true;
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if (Scale == ScaleType::OnlyAlphaScaling) return false;
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if (Scale == ScaleType::Nothing) return false;
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return beta_bias_ != 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) {
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if (k_partition) {
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beta_bias_ = ElementCompute(1);
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}
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}
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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_bias_ = 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 = source_converter(source);
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ComputeFragment converted_accumulator = accumulator_converter(accumulator);
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// Perform binary operations
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ComputeFragment intermediate;
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multiplies<ComputeFragment> mul_add_source;
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multiply_add<ComputeFragment> mul_add_accumulator;
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LeakyReLU<ComputeFragment> leakyrelu;
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if (Scale == ScaleType::NoBetaScaling) {
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intermediate = converted_source;
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intermediate = mul_add_accumulator(alpha_, converted_accumulator, intermediate); // D = alpha * Accum + X
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} else if (Scale == ScaleType::Nothing) {
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intermediate = converted_accumulator;
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} else {
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intermediate = mul_add_source(beta_bias_, converted_source); // X = beta * C + uniform
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intermediate = mul_add_accumulator(alpha_, converted_accumulator, intermediate); // D = alpha * Accum + X
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}
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// Compute threshold optionally
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intermediate = leakyrelu(intermediate, leaky_alpha_recip_);
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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 destination_converter(intermediate);
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}
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/// Computes linear scaling: D = alpha * accumulator
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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 = accumulator_converter(accumulator);
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// Perform binary operations
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ComputeFragment intermediate;
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multiplies<ComputeFragment> mul_accumulator;
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LeakyReLU<ComputeFragment> leakyrelu;
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//printf("in doing with bias");
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if (Scale == ScaleType::Nothing) {
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intermediate = converted_accumulator;
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} else {
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intermediate = mul_accumulator(alpha_, converted_accumulator); // D = alpha * Accum
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}
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// Compute threshold optionally
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intermediate = leakyrelu(intermediate, leaky_alpha_recip_);
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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 destination_converter(intermediate);
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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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