254 lines
9.6 KiB
C++
254 lines
9.6 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 operation, bias addition, and tensor-tensor
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elementwise operations
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*/
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#pragma once
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#include "cutlass/cutlass.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/numeric_types.h"
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#include "cutlass/epilogue/thread/activation.h"
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#include "cutlass/epilogue/thread/detail.hpp"
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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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namespace detail {
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/// Returns whether a source operand is needed for a combination of binary operation and scale
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/// type. Simple specialized checks are made for cases in which 0 is an identity element of
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/// the binary operation.
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template <class BinaryOp, class ElementCompute, ScaleType::Kind Scale>
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CUTLASS_HOST_DEVICE
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bool is_binary_op_source_needed(ElementCompute scale) {
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if constexpr (cute::is_same_v<BinaryOp, NoOp<ElementCompute>>) {
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return false;
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}
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else if constexpr (cute::is_same_v<BinaryOp, plus<ElementCompute>> || cute::is_same_v<BinaryOp, minus<ElementCompute>>) {
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// Cases for binary operators for which 0 is an identity element
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if constexpr (Scale == ScaleType::NoBetaScaling) return true;
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if constexpr (Scale == ScaleType::OnlyAlphaScaling) return false;
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if constexpr (Scale == ScaleType::Nothing) return false;
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return scale != ElementCompute(0);
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}
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return true;
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}
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} // namespace detail
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/////////////////////////////////////////////////////////////////////////////////////////////////
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/** Compute a tensor-tensor broadcast epilogue.
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*
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* @param ElementOutput_ Data type used to load and store tensors
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* @param ElementAccumulator_ Accumulator data type
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* @param ElementCompute_ Data type used to compute linear combination
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* @param ElementBias_ Data type of Bias elements
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* @param ActivationFunctor_ Fused Activation
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* @param BinaryOp0_ Binary operation to perform on O0 and C0. detail::NoOp means no operation
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* @param BinaryOp1_ Binary operation to perform on O1 and C1. detail::NoOp means no operation
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* @param UnaryOp_ Unary operation to perform on final result
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* @param Scale Controls the type of Alpha and Beta scaling to perform
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* @param Round How values should be rounded in conversions
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* @param ElementSource_ Data type used for source operands
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*
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* Computes the following:
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* O0 = alpha * accumulator + bias
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* O1 = BinaryOp0(O0, beta * C0)
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* O2 = BinaryOp1(O1, beta * C1)
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* D = UnaryOp(O2)
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*/
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template <
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class ElementOutput_,
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class ElementAccumulator_ = ElementOutput_,
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class ElementCompute_ = ElementOutput_,
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class ElementBias_ = ElementCompute_,
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template <class T> class ActivationFunctor_ = Identity,
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template <class T> class BinaryOp0_ = plus,
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template <class T> class BinaryOp1_ = detail::NoOp,
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template <class T> class UnaryOp_ = Identity,
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ScaleType::Kind Scale = ScaleType::Default,
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FloatRoundStyle Round = FloatRoundStyle::round_to_nearest,
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class ElementSource_ = ElementOutput_
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>
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class LinearCombinationTensorBroadcast {
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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 = ElementCompute;
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using ElementBias = ElementBias_;
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using ElementC = ElementSource_;
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using ElementD = ElementOutput_;
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using ElementScalingFactor = ElementAccumulator_;
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using UnaryOp = UnaryOp_<ElementCompute>;
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using BinaryOp0 = BinaryOp0_<ElementCompute>;
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using BinaryOp1 = BinaryOp1_<ElementCompute>;
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using ActivationFunctor = ActivationFunctor_<ElementCompute>;
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static constexpr int kCount = 1;
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static constexpr 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 FragmentBias = Array<ElementBias, kCount>;
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static constexpr FloatRoundStyle kRound = Round;
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using NoOpType = detail::NoOp<ElementCompute>;
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static constexpr bool IsBinaryOp0Enabled = !cute::is_same_v<BinaryOp0, NoOpType>;
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static constexpr bool IsBinaryOp1Enabled = !cute::is_same_v<BinaryOp1, NoOpType>;
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static constexpr bool IsUnaryOpEnabled = !cute::is_same_v<UnaryOp, NoOpType> && !cute::is_same_v<UnaryOp, Identity<ElementCompute>>;
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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 const* alpha_ptr = nullptr; ///< pointer to accumulator scalar - if not null, loads it from memory
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ElementCompute 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(ElementCompute const* alpha_ptr, ElementCompute const* beta_ptr)
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: alpha_ptr(alpha_ptr),
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beta_ptr(beta_ptr) {}
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CUTLASS_HOST_DEVICE
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Params(ElementCompute const* alpha_ptr)
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: alpha_ptr(alpha_ptr) {}
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CUTLASS_HOST_DEVICE
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Params(ElementCompute alpha,
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ElementCompute beta)
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: alpha(alpha),
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beta(beta) {}
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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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LinearCombinationTensorBroadcast(Params const& params)
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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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/// Returns true if source 0 is needed
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CUTLASS_HOST_DEVICE
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bool is_source0_needed() const {
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return detail::is_binary_op_source_needed<BinaryOp0, ElementCompute, Scale>(beta_);
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}
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/// Returns true if source 1 is needed
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CUTLASS_HOST_DEVICE
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bool is_source1_needed() const {
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return detail::is_binary_op_source_needed<BinaryOp1, ElementCompute, Scale>(beta_);
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}
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//
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// Specialization for scalar
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//
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CUTLASS_HOST_DEVICE
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ElementD operator()(ElementAccumulator const accumulator, ElementC const source0, ElementC source1, ElementBias const bias) {
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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<ElementCompute, ElementBias, Round> bias_converter;
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NumericConverter<ElementCompute, ElementC, Round> source_converter;
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NumericConverter<ElementD, ElementCompute, Round> destination_converter;
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ActivationFunctor act;
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multiplies<ElementCompute> mul;
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multiply_add<ElementCompute> madd;
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ElementCompute intermediate = accumulator_converter(accumulator);
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intermediate = madd(alpha_, intermediate, bias_converter(bias));
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intermediate = act(intermediate);
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// Apply BinaryOp0, if needed
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if constexpr (IsBinaryOp0Enabled) {
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BinaryOp0 bin0;
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ElementCompute converted_source = source_converter(source0);
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intermediate = bin0(intermediate, mul(beta_, converted_source));
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}
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// Apply BinaryOp1, if needed
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if constexpr (IsBinaryOp1Enabled) {
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BinaryOp1 bin1;
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ElementCompute converted_source = source_converter(source1);
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intermediate = bin1(intermediate, mul(beta_, converted_source));
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}
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// Apply UnaryOp, if needed
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if constexpr (IsUnaryOpEnabled) {
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UnaryOp unary;
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intermediate = unary(intermediate);
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}
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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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