302 lines
12 KiB
C++
302 lines
12 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 Epilogue functor specialized for residual blocks in deep neural networks.
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*/
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#pragma once
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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/detail.hpp"
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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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/// Models a residual block of the form: UnaryOp(BinaryOp(BinaryOp(ActivationOp(TensorOp(X) + bias), residual1), residual2))
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template <typename ElementOutput_, typename ElementAccumulator_,
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typename ElementCompute_, typename ElementC_, int ElementsPerAccess,
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template <typename T> class ActivationOp_,
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template <typename T> class BinaryOp1_,
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template <typename T> class UnaryOp_,
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template <typename T> class BinaryOp2_ = detail::NoOp,
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bool StoreT_ = false,
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typename ElementVector_ = ElementC_>
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class LinearCombinationResidualBlock {
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public:
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static bool const kIsSingleSource = false;
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using ElementOutput = ElementC_;
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using ElementC = ElementC_;
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using ElementAccumulator = ElementAccumulator_;
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using ElementCompute = ElementCompute_;
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using ElementVector = ElementVector_;
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static int const kElementsPerAccess = ElementsPerAccess;
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static int const kCount = kElementsPerAccess;
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using UnaryOp = UnaryOp_<Array<ElementCompute, kCount>>;
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using BinaryOp1 = BinaryOp1_<Array<ElementCompute, kCount>>;
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using BinaryOp2 = BinaryOp2_<Array<ElementCompute, kCount>>;
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using ActivationOp = ActivationOp_<Array<ElementCompute, kCount>>;
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using FragmentAccumulator = Array<ElementAccumulator, kElementsPerAccess>;
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using FragmentCompute = Array<ElementCompute, kElementsPerAccess>;
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using FragmentC = Array<ElementC, kElementsPerAccess>;
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using FragmentOutput = Array<ElementOutput, kElementsPerAccess>;
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using ElementZ = ElementOutput_;
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using ElementT = ElementZ;
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using FragmentZ = Array<ElementZ, kElementsPerAccess>;
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using FragmentT = Array<ElementT, kElementsPerAccess>;
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static bool const kIsHeavy = true;
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static bool const kStoreZ = true;
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static bool const kStoreT = StoreT_;
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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 residual input
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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 residual scalar - if not null, loads it from memory
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CUTLASS_HOST_DEVICE
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Params() : alpha(ElementCompute(1)), beta(ElementCompute(1)) {}
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CUTLASS_HOST_DEVICE
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Params(ElementCompute alpha, ElementCompute beta)
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: alpha(alpha), beta(beta) {}
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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(0), beta(0), alpha_ptr(alpha_ptr), beta_ptr(beta_ptr) {}
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};
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private:
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ElementCompute alpha_;
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ElementCompute beta_;
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bool skip_elementwise_;
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public:
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/// Constructor from Params
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CUTLASS_HOST_DEVICE
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LinearCombinationResidualBlock(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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skip_elementwise_ = false;
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}
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/// The "source" tensor corresponds to the residual input
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CUTLASS_HOST_DEVICE
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bool is_source_needed() const { return true; }
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/// Functionally required for serial reduction in the epilogue
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/// IMPORTANT: Split-k is supported only when ActivationOp is Identity.
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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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skip_elementwise_ = true;
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}
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}
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/// Applies the operation UnaryOp(BinaryOp(BinaryOp(ActivationOp(AB + bias), residual1), residual2))
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CUTLASS_HOST_DEVICE
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void operator()(FragmentOutput &frag_Z, FragmentOutput &, FragmentAccumulator const &AB,
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FragmentC const &residual1, FragmentC const &residual2,
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FragmentCompute const &bias) const {
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UnaryOp unary_op;
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BinaryOp1 binary_op1;
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BinaryOp2 binary_op2;
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ActivationOp activation;
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FragmentCompute tmp_Accum =
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NumericArrayConverter<ElementCompute, ElementAccumulator, kElementsPerAccess>()(AB);
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FragmentCompute tmp_residual1 =
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NumericArrayConverter<ElementCompute, ElementC, kElementsPerAccess>()(residual1);
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FragmentCompute tmp_residual2 =
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NumericArrayConverter<ElementCompute, ElementC, kElementsPerAccess>()(residual2);
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FragmentCompute z =
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binary_op2(binary_op1(activation(alpha_ * tmp_Accum + bias), beta_ * tmp_residual1), beta_ * tmp_residual2);
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FragmentCompute result_Z = skip_elementwise_ ? z : unary_op(z);
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NumericArrayConverter<ElementOutput, ElementCompute, kElementsPerAccess> convert_z;
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frag_Z = convert_z(result_Z);
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}
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/// Should never be called
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CUTLASS_HOST_DEVICE
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void operator()(FragmentOutput &, FragmentOutput &, FragmentAccumulator const &,
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FragmentCompute const &) const {}
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};
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/// Models a residual block of the form: UnaryOp(BinaryOp(ActivationOp(TensorOp(X) + bias), residual))
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template <typename ElementOutput_, typename ElementAccumulator_,
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typename ElementCompute_, typename ElementC_, int ElementsPerAccess,
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template <typename T> class ActivationOp_,
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template <typename T> class BinaryOp1_,
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template <typename T> class UnaryOp_,
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bool StoreT_,
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typename ElementVector_>
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class LinearCombinationResidualBlock<ElementOutput_, ElementAccumulator_,
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ElementCompute_, ElementC_, ElementsPerAccess,
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ActivationOp_, BinaryOp1_, UnaryOp_,
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detail::NoOp, StoreT_, ElementVector_> {
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public:
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static bool const kIsSingleSource = true;
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using ElementOutput = ElementC_;
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using ElementC = ElementC_;
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using ElementAccumulator = ElementAccumulator_;
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using ElementCompute = ElementCompute_;
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using ElementVector = ElementVector_;
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static int const kElementsPerAccess = ElementsPerAccess;
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static int const kCount = kElementsPerAccess;
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using UnaryOp = UnaryOp_<Array<ElementCompute, kCount>>;
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using BinaryOp = BinaryOp1_<Array<ElementCompute, kCount>>;
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using ActivationOp = ActivationOp_<Array<ElementCompute, kCount>>;
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using FragmentAccumulator = Array<ElementAccumulator, kElementsPerAccess>;
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using FragmentCompute = Array<ElementCompute, kElementsPerAccess>;
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using FragmentC = Array<ElementC, kElementsPerAccess>;
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using FragmentOutput = Array<ElementOutput, kElementsPerAccess>;
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using ElementZ = ElementOutput_;
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using ElementT = ElementZ;
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using FragmentZ = Array<ElementZ, kElementsPerAccess>;
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using FragmentT = Array<ElementT, kElementsPerAccess>;
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static bool const kIsHeavy = true;
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static bool const kStoreZ = true;
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static bool const kStoreT = StoreT_;
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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 residual input
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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 residual scalar - if not null, loads it from memory
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CUTLASS_HOST_DEVICE
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Params() : alpha(ElementCompute(1)), beta(ElementCompute(1)) {}
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CUTLASS_HOST_DEVICE
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Params(ElementCompute alpha, ElementCompute beta)
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: alpha(alpha), beta(beta) {}
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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(0), beta(0), alpha_ptr(alpha_ptr), beta_ptr(beta_ptr) {}
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};
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private:
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ElementCompute alpha_;
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ElementCompute beta_;
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bool skip_elementwise_;
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public:
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/// Constructor from Params
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CUTLASS_HOST_DEVICE
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LinearCombinationResidualBlock(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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skip_elementwise_ = false;
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}
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/// The "source" tensor corresponds to the residual input
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CUTLASS_HOST_DEVICE
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bool is_source_needed() const { return true; }
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/// Functionally required for serial reduction in the epilogue
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/// IMPORTANT: Split-k is supported only when ActivationOp is Identity.
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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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skip_elementwise_ = true;
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}
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}
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/// Applies the operation UnaryOp(BinaryOp(ActivationOp(AB + bias), residual))
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CUTLASS_HOST_DEVICE
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void operator()(FragmentOutput &frag_Z, FragmentOutput &, FragmentAccumulator const &AB,
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FragmentC const &residual,
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FragmentCompute const &bias) const {
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UnaryOp unary_op;
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BinaryOp binary_op;
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ActivationOp activation;
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FragmentCompute tmp_Accum =
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NumericArrayConverter<ElementCompute, ElementAccumulator, kElementsPerAccess>()(AB);
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FragmentCompute tmp_residual =
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NumericArrayConverter<ElementCompute, ElementC, kElementsPerAccess>()(residual);
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FragmentCompute z =
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binary_op(activation(alpha_ * tmp_Accum + bias), beta_ * tmp_residual);
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FragmentCompute result_Z = skip_elementwise_ ? z : unary_op(z);
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NumericArrayConverter<ElementOutput, ElementCompute, kElementsPerAccess> convert_z;
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frag_Z = convert_z(result_Z);
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}
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/// Should never be called
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CUTLASS_HOST_DEVICE
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void operator()(FragmentOutput &, FragmentOutput &, FragmentAccumulator const &,
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FragmentCompute const &) const {}
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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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