573 lines
20 KiB
C++
573 lines
20 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 with a maximum operation used by epilogues.
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*/
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#pragma once
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#include "cutlass/half.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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#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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namespace detail {
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/// Single source of truth for whether to unroll for `LinearCombinationClamp()`
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constexpr bool LinearCombinationReluIsHeavy() {
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return false;
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}
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}
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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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///< 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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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 LinearCombinationRelu {
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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 FragmentCompute = Array<ElementCompute, kCount>;
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using FragmentScaleBias = 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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static bool const kIsHeavy = detail::LinearCombinationReluIsHeavy();
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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 threshold; ///< minimum value that is output
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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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threshold(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 = ElementCompute(0),
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ElementCompute threshold = ElementCompute(0)
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): alpha(alpha), beta(beta), threshold(threshold), 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 = nullptr,
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ElementCompute threshold = ElementCompute(0)
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): alpha(0), beta(0), threshold(threshold), 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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ElementCompute alpha_;
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ElementCompute beta_;
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ElementCompute threshold_;
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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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LinearCombinationRelu(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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if (Scale == ScaleType::NoBetaScaling) return true;
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if (Scale == ScaleType::OnlyAlphaScaling) return false;
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if (Scale == ScaleType::OnlyAlphaPerChannelScaling) return false;
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if (Scale == ScaleType::Nothing) return false;
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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<ElementCompute const &>(allones);
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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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FragmentCompute converted_source = source_converter(source);
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FragmentCompute converted_accumulator = accumulator_converter(accumulator);
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// Perform binary operations
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FragmentCompute intermediate;
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multiplies<FragmentCompute> mul_add_source;
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multiply_add<FragmentCompute> mul_add_accumulator;
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ReLu<FragmentCompute> relu;
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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_, 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 = relu(threshold_, intermediate);
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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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FragmentCompute converted_accumulator = accumulator_converter(accumulator);
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// Perform binary operations
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FragmentCompute intermediate;
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multiplies<FragmentCompute> mul_accumulator;
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ReLu<FragmentCompute> relu;
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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 = relu(threshold_, intermediate);
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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 per-channel linear scaling and bias : D = scale * accumulator + bias
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/// Scale and Bias are from input Fragment
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CUTLASS_HOST_DEVICE
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FragmentOutput operator()(
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FragmentAccumulator const &accumulator,
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FragmentScaleBias const &scale,
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FragmentScaleBias const &bias) 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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FragmentCompute converted_accumulator = accumulator_converter(accumulator);
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// Perform per-channel scale and bias
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FragmentCompute intermediate;
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multiply_add<FragmentCompute> mul_add_accumulator;
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if(Scale == ScaleType::OnlyAlphaPerChannelScaling)
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intermediate = mul_add_accumulator(scale, converted_accumulator, bias); // D = scale * Accum + bias
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else
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intermediate = mul_add_accumulator(alpha_, converted_accumulator, bias); // D = alpha * Accum + bias
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ReLu<FragmentCompute> relu;
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// Compute threshold optionally
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intermediate = relu(threshold_, intermediate);
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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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// Conditional guards to enable partial specialization for packed integers
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#if defined(__CUDA_ARCH__) && (__CUDA_ARCH__ >= 720) && ((__CUDACC_VER_MAJOR__ > 10) || ((__CUDACC_VER_MAJOR__ >= 10) && (__CUDACC_VER_MINOR__ >= 2)))
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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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/// Special handling for int types
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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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ScaleType::Kind Scale, ///< Control Alpha and Beta scaling
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FloatRoundStyle Round
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>
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class LinearCombinationRelu <ElementOutput_, Count, int, float, Scale, Round> {
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public:
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using ElementOutput = ElementOutput_;
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using ElementAccumulator = int;
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using ElementCompute = float;
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static bool const kIsHeavy = detail::LinearCombinationReluIsHeavy();
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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 FragmentCompute = Array<ElementCompute, kCount>;
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using FragmentScaleBias = 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; ///< scales source tensor
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ElementCompute threshold; ///< minimum value that is output
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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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threshold(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 = ElementCompute(0),
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ElementCompute threshold = ElementCompute(0)
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): alpha(alpha), beta(beta), threshold(threshold), 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 = nullptr,
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ElementCompute threshold = ElementCompute(0)
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): alpha(0), beta(0), threshold(threshold), 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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ElementCompute alpha_;
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ElementCompute beta_;
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ElementCompute threshold_;
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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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LinearCombinationRelu(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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if (Scale == ScaleType::NoBetaScaling) return true;
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if (Scale == ScaleType::OnlyAlphaScaling) return false;
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if (Scale == ScaleType::OnlyAlphaPerChannelScaling) return false;
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if (Scale == ScaleType::Nothing) return false;
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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<ElementCompute const &>(allones);
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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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FragmentCompute converted_source = source_converter(source);
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FragmentCompute converted_accumulator = accumulator_converter(accumulator);
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// Perform binary operations
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FragmentCompute intermediate;
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multiplies<FragmentCompute> mul_add_source;
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multiply_add<FragmentCompute> mul_add_accumulator;
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ReLu<FragmentCompute> relu;
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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_, 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 = relu(threshold_, intermediate);
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if (cutlass::platform::numeric_limits<ElementOutput>::is_integer) {
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// Convert floats back to INT
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FragmentAccumulator scaled_accumulator;
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NumericArrayConverter<int, ElementCompute, kCount, Round> compute_converter;
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scaled_accumulator = compute_converter(intermediate);
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// Convert to destination numeric type
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NumericArrayConverter<ElementOutput, int, kCount, Round>
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destination_converter;
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return destination_converter(scaled_accumulator);
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} else {
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NumericArrayConverter<ElementOutput, ElementCompute, kCount, Round>
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destination_converter;
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return destination_converter(intermediate);
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}
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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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FragmentCompute converted_accumulator = accumulator_converter(accumulator);
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// Perform binary operations
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FragmentCompute intermediate;
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multiplies<FragmentCompute> mul_accumulator;
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ReLu<FragmentCompute> relu;
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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 = relu(threshold_, intermediate);
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if (cutlass::platform::numeric_limits<ElementOutput>::is_integer) {
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// Convert floats back to INT
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FragmentAccumulator scaled_accumulator;
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NumericArrayConverter<int, ElementCompute, kCount, Round> compute_converter;
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scaled_accumulator = compute_converter(intermediate);
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// Convert to destination numeric type
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NumericArrayConverter<ElementOutput, int, kCount, Round>
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destination_converter;
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return destination_converter(scaled_accumulator);
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} else {
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NumericArrayConverter<ElementOutput, ElementCompute, kCount, Round>
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destination_converter;
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return destination_converter(intermediate);
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}
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}
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/// Computes per-channel linear scaling and bias : D = scale * accumulator + bias
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/// Scale and Bias are from input Fragment
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CUTLASS_HOST_DEVICE
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FragmentOutput operator()(
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FragmentAccumulator const &accumulator,
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FragmentScaleBias const &scale,
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FragmentScaleBias const &bias) 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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FragmentCompute converted_accumulator = accumulator_converter(accumulator);
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// Perform per-channel scale and bias
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FragmentCompute intermediate;
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multiply_add<FragmentCompute> mul_add_accumulator;
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if(Scale == ScaleType::OnlyAlphaPerChannelScaling)
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intermediate = mul_add_accumulator(scale, converted_accumulator, bias); // D = scale * Accum + bias
|
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else
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|
intermediate = mul_add_accumulator(alpha_, converted_accumulator, bias); // D = alpha * Accum + bias
|
|
|
|
ReLu<FragmentCompute> relu;
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|
|
|
// Compute threshold optionally
|
|
intermediate = relu(threshold_, intermediate);
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|
|
|
if (cutlass::platform::numeric_limits<ElementOutput>::is_integer) {
|
|
// Convert floats back to INT
|
|
FragmentAccumulator scaled_accumulator;
|
|
|
|
NumericArrayConverter<int, ElementCompute, kCount, Round> compute_converter;
|
|
|
|
scaled_accumulator = compute_converter(intermediate);
|
|
|
|
// Convert to destination numeric type
|
|
NumericArrayConverter<ElementOutput, int, kCount, Round>
|
|
destination_converter;
|
|
|
|
return destination_converter(scaled_accumulator);
|
|
} else {
|
|
NumericArrayConverter<ElementOutput, ElementCompute, kCount, Round>
|
|
destination_converter;
|
|
return destination_converter(intermediate);
|
|
}
|
|
}
|
|
};
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|
|
|
#endif // Conditional guards to enable partial specialization for packed integers
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|
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/////////////////////////////////////////////////////////////////////////////////////////////////
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|
|
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} // namespace thread
|
|
} // namespace epilogue
|
|
} // namespace cutlass
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|
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/////////////////////////////////////////////////////////////////////////////////////////////////
|