331 lines
12 KiB
C++
331 lines
12 KiB
C++
/***************************************************************************************************
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* Copyright (c) 2017 - 2023 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 used by epilogues.
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*/
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#pragma once
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#include "cutlass/cutlass.h"
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#include "cutlass/numeric_types.h"
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#include "cutlass/array.h"
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#include "cutlass/functional.h"
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#include "cutlass/numeric_conversion.h"
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#include "cutlass/epilogue/thread/scale_type.h"
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#include "cutlass/epilogue/thread/linear_combination_params.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
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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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typename ElementSource_ = ElementOutput_
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>
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class LinearCombination {
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public:
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using ElementOutput = ElementOutput_;
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using ElementSource = ElementSource_;
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using ElementAccumulator = ElementAccumulator_;
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using ElementCompute = ElementCompute_;
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using ElementC = ElementSource_;
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using ElementD = ElementOutput_;
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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 FragmentSource = Array<ElementSource, kCount>;
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using FragmentAccumulator = Array<ElementAccumulator, kCount>;
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using FragmentCompute = Array<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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{
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ElementCompute alpha; ///< scales accumulators
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ElementCompute beta; ///< scales source tensor
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ElementCompute const *alpha_ptr; ///< pointer to accumulator scalar - if not null, loads it from memory
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ElementCompute const *beta_ptr; ///< pointer to source scalar - if not null, loads it from memory
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CUTLASS_HOST_DEVICE
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Params():
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alpha(ElementCompute(1)),
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beta(ElementCompute(0)),
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alpha_ptr(nullptr),
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beta_ptr(nullptr) { }
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CUTLASS_HOST_DEVICE
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Params(
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ElementCompute alpha,
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ElementCompute beta
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):
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alpha(alpha), beta(beta), alpha_ptr(nullptr), 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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):
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alpha(alpha), beta(0), alpha_ptr(nullptr), beta_ptr(nullptr) { }
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CUTLASS_HOST_DEVICE
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Params(
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ElementCompute const *alpha_ptr,
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ElementCompute const *beta_ptr
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):
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alpha(0), beta(0), alpha_ptr(alpha_ptr), beta_ptr(beta_ptr) { }
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CUTLASS_HOST_DEVICE
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Params(
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ElementCompute const *alpha_ptr
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):
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alpha(0), beta(0), alpha_ptr(alpha_ptr), beta_ptr(nullptr) { }
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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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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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LinearCombination(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::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_ != 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 intermediate: X = beta * source
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CUTLASS_HOST_DEVICE
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FragmentCompute compute_intermediate(
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FragmentSource const &source) const {
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// Convert source to internal compute numeric type
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NumericArrayConverter<ElementCompute, ElementSource, kCount, Round> source_converter;
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FragmentCompute converted_source = source_converter(source);
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if (Scale == ScaleType::NoBetaScaling) {
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return converted_source;
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}
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else {
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multiplies<FragmentCompute> mul_source;
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return mul_source(beta_, converted_source);
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}
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}
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/// Computes linear scaling with intermediate: D = alpha * accumulator + X
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CUTLASS_HOST_DEVICE
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FragmentOutput with_intermediate(
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FragmentAccumulator const& accumulator,
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FragmentCompute const& intermediate) const {
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// Convert accumulator to internal compute numeric type
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NumericArrayConverter<ElementCompute, ElementAccumulator, kCount, Round> accumulator_converter;
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// Convert to destination numeric type
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NumericArrayConverter<ElementOutput, ElementCompute, kCount, Round> destination_converter;
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FragmentCompute converted_accumulator = accumulator_converter(accumulator);
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if (Scale == ScaleType::Nothing) {
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return destination_converter(converted_accumulator);
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} else {
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// Perform binary operations
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multiply_add<FragmentCompute> mul_add_accumulator;
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FragmentCompute computed_output = mul_add_accumulator(alpha_, converted_accumulator, intermediate);
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return destination_converter(computed_output);
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}
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}
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/// Computes linear scaling with source: 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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FragmentSource const &source) const {
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// Convert source to internal compute numeric type
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NumericArrayConverter<ElementCompute, ElementSource, kCount, Round> source_converter;
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NumericArrayConverter<ElementCompute, ElementAccumulator, kCount, Round> accumulator_converter;
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// Convert to destination numeric type
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NumericArrayConverter<ElementOutput, ElementCompute, kCount, Round> destination_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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if (Scale == ScaleType::Nothing)
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return destination_converter(converted_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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if (Scale == ScaleType::NoBetaScaling)
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intermediate = converted_source;
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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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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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// Convert to destination numeric type
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NumericArrayConverter<ElementOutput, ElementCompute, kCount, Round> destination_converter;
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FragmentCompute converted_accumulator = accumulator_converter(accumulator);
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if (Scale == ScaleType::Nothing)
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return destination_converter(converted_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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intermediate = mul_accumulator(alpha_, converted_accumulator); // D = alpha * Accum
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return destination_converter(intermediate);
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}
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//
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// Specializations for scalar (for use with cute::collective::DefaultEpilogue)
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//
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CUTLASS_HOST_DEVICE
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ElementD operator()(ElementAccumulator const accumulator, ElementC const source) const {
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// Convert everything to Compute type, do compute, and then store to output type
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NumericConverter<ElementCompute, ElementAccumulator, Round> accumulator_converter;
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[[maybe_unused]] NumericConverter<ElementCompute, ElementC, Round> source_converter;
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NumericConverter<ElementD, ElementCompute, Round> destination_converter;
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// Convert to destination numeric type
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ElementCompute converted_accumulator = accumulator_converter(accumulator);
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if constexpr (Scale == ScaleType::Nothing) {
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return destination_converter(converted_accumulator);
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}
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// Perform binary operations
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ElementCompute intermediate;
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multiplies<ElementCompute> multiply;
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multiply_add<ElementCompute> madd;
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if constexpr (Scale == ScaleType::NoBetaScaling) {
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intermediate = source_converter(source);
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}
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else {
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intermediate = multiply(beta_, source); // X = beta * C + uniform
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}
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intermediate = madd(alpha_, converted_accumulator, intermediate); // D = alpha * Accum + X
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return destination_converter(intermediate);
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}
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CUTLASS_HOST_DEVICE
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ElementD operator()(ElementAccumulator const accumulator) const {
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// Convert everything to Compute type, do compute, and then store to output type
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NumericConverter<ElementCompute, ElementAccumulator, Round> accumulator_converter;
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NumericConverter<ElementD, ElementCompute, Round> destination_converter;
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ElementCompute converted_accumulator = accumulator_converter(accumulator);
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// Convert to destination numeric type
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if constexpr (Scale == ScaleType::Nothing) {
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return destination_converter(converted_accumulator);
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}
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// Perform binary operations
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ElementCompute intermediate;
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multiplies<ElementCompute> multiply;
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intermediate = multiply(alpha_, accumulator); // D = alpha * Accum
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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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