709 lines
18 KiB
C++
709 lines
18 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 This extends the contents of cutlass/functional.h with frequently used activation functions.
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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/constants.h"
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#include "cutlass/complex.h"
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#include "cutlass/array.h"
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#include "cutlass/half.h"
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#include "cutlass/functional.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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template <typename T>
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struct LinearCombinationGenericParams {
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T alpha; ///< scales accumulators
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T beta; ///< scales source tensor
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T const *alpha_ptr; ///< pointer to accumulator scalar - if not null, loads it from memory
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T 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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LinearCombinationGenericParams():
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alpha(T(1)),
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beta(T(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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LinearCombinationGenericParams(
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T alpha,
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T beta = T(0)
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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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LinearCombinationGenericParams(
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T const *alpha_ptr,
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T const *beta_ptr = nullptr
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): alpha(0), beta(0), alpha_ptr(alpha_ptr), beta_ptr(beta_ptr) { }
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};
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/////////////////////////////////////////////////////////////////////////////////////////////////
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// Identity operator
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template <typename T>
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struct Identity {
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static const bool kIsHeavy=false;
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CUTLASS_HOST_DEVICE
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T operator()(T value) const {
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return value;
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}
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using Params = LinearCombinationGenericParams<T>;
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CUTLASS_HOST_DEVICE
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T operator()(T const &value, Params const ¶ms_) const {
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return this->operator()(value);
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}
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};
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template <typename T, int N>
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struct Identity<Array<T, N> > {
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CUTLASS_HOST_DEVICE
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Array<T, N> operator()(Array<T, N> const &value) const {
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return value;
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}
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using Params = LinearCombinationGenericParams<T>;
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CUTLASS_HOST_DEVICE
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Array<T, N> operator()(Array<T, N> const &value, Params const ¶ms_) const {
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return this->operator()(value);
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}
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};
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/// ReLu operator - propagates NaNs
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/// Always put threshold in the right hand side of max to propagate NaN.
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template <typename T>
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struct ReLu {
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static const bool kIsHeavy=false;
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CUTLASS_HOST_DEVICE
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T operator()(T const & threshold, T value) const {
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maximum<T> mx;
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return mx(value, threshold);
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}
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CUTLASS_HOST_DEVICE
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T operator()(T value) const {
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maximum<T> mx;
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return mx(value, T(0));
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}
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/// Host-constructable parameters structure
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using Params = LinearCombinationGenericParams<T>;
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CUTLASS_HOST_DEVICE
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T operator()(T value, Params const ¶ms_) const {
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return this->operator()(value);
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}
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};
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template <typename T>
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using ReLU = ReLu<T>;
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template <typename T, int N>
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struct ReLu<Array<T, N>> {
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static const bool kIsHeavy=false;
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CUTLASS_HOST_DEVICE
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Array<T, N> operator()(T const & threshold, Array<T, N> const &frag) const {
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maximum<Array<T, N>> mx;
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return mx(frag, threshold);
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}
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CUTLASS_HOST_DEVICE
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Array<T, N> operator()(Array<T, N> const &frag) const {
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maximum<Array<T, N>> mx;
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return mx(frag, T(0));
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}
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/// Host-constructable parameters structure
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using Params = LinearCombinationGenericParams<T>;
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CUTLASS_HOST_DEVICE
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Array<T, N> operator()(Array<T, N> const &frag, Params const ¶ms_) const {
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return this->operator()(frag);
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}
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};
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// Leaky Relu operator
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template <typename T>
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struct LeakyReLU {
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struct Params: LinearCombinationGenericParams<T> {
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T leaky_alpha; ///< leaky_alpha
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// Methods
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using LinearCombinationGenericParams<T>::LinearCombinationGenericParams;
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CUTLASS_HOST_DEVICE
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Params():
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LinearCombinationGenericParams<T>(),
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leaky_alpha(T(1)) {}
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CUTLASS_HOST_DEVICE
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Params(
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T alpha,
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T beta,
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T leaky_alpha = T(1)
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): LinearCombinationGenericParams<T>(alpha, beta), leaky_alpha(leaky_alpha) {}
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};
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CUTLASS_HOST_DEVICE
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T operator()(T const &value, T const & alpha_recip) const {
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T res = value > T(0) ? value : value * alpha_recip;
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return res;
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}
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CUTLASS_HOST_DEVICE
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T operator()(T const &value, Params const ¶ms_) const {
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this->operator()(value, params_.leaky_alpha);
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}
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};
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template <typename T, int N>
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struct LeakyReLU<Array<T, N> > {
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struct Params: LinearCombinationGenericParams<T> {
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T leaky_alpha; ///< leaky_alpha
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using LinearCombinationGenericParams<T>::LinearCombinationGenericParams;
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// Methods
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CUTLASS_HOST_DEVICE
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Params():
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LinearCombinationGenericParams<T>(),
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leaky_alpha(T(1)) {}
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CUTLASS_HOST_DEVICE
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Params(
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T alpha,
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T beta,
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T leaky_alpha = T(1)
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): LinearCombinationGenericParams<T>(alpha, beta), leaky_alpha(leaky_alpha) {}
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};
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CUTLASS_HOST_DEVICE
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Array<T, N> operator()(Array<T, N> const &value, T const & alpha_recip) const {
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Array<T, N> y;
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LeakyReLU<T> leaky_op;
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CUTLASS_PRAGMA_UNROLL
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for (int i = 0; i < int(value.size()); ++i) {
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y[i] = leaky_op(value[i], alpha_recip);
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}
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return y;
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}
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CUTLASS_HOST_DEVICE
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Array<T, N> operator()(Array<T, N> const &value, Params const ¶ms_) const {
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return this->operator()(value, params_.leaky_alpha);
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}
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};
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// Tanh operator
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template <typename T>
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struct Tanh {
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CUTLASS_HOST_DEVICE
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T operator()(T const &scalar) const {
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return fast_tanh(scalar);
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}
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using Params = LinearCombinationGenericParams<T>;
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CUTLASS_HOST_DEVICE
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T operator()(T const &scalar, Params const ¶ms_) const {
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return this->operator()(scalar);
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}
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};
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template <typename T, int N>
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struct Tanh<Array<T, N> > {
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CUTLASS_HOST_DEVICE
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Array<T, N> operator()(Array<T, N> const &value) const {
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Array<T, N> y;
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Tanh<T> tanh_op;
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CUTLASS_PRAGMA_UNROLL
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for (int i = 0; i < N; ++i) {
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y[i] = tanh_op(value[i]);
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}
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return y;
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}
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using Params = LinearCombinationGenericParams<T>;
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CUTLASS_HOST_DEVICE
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Array<T, N> operator()(Array<T, N> const &value, Params const ¶ms_) const {
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return this->operator()(value);
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}
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};
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template <int N>
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struct Tanh<Array<half_t, N>> {
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using T = half_t;
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CUTLASS_HOST_DEVICE
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Array<T, N> operator()(Array<T, N> const& z) const {
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fast_tanh_op<Array<T, N>> tanh;
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return tanh(z);
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}
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using Params = LinearCombinationGenericParams<T>;
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CUTLASS_HOST_DEVICE
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Array<T, N> operator()(Array<T, N> const &value, Params const ¶ms_) const {
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return this->operator()(value);
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}
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};
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// Sigmoid operator
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template <typename T>
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struct Sigmoid {
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CUTLASS_HOST_DEVICE
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T operator()(T const &scalar) const {
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return T(1) / (T(1) + fast_exp(-scalar));
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}
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using Params = LinearCombinationGenericParams<T>;
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CUTLASS_HOST_DEVICE
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T operator()(T const &scalar, Params const ¶ms_) const {
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return this->operator()(scalar);
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}
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};
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template <typename T, int N>
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struct Sigmoid<Array<T, N> > {
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CUTLASS_HOST_DEVICE
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Array<T, N> operator()(Array<T, N> const &value) const {
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Array<T, N> y;
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Sigmoid<T> sigmoid_op;
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CUTLASS_PRAGMA_UNROLL
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for (int i = 0; i < N; ++i) {
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y[i] = sigmoid_op(value[i]);
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}
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return y;
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}
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using Params = LinearCombinationGenericParams<T>;
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CUTLASS_HOST_DEVICE
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Array<T, N> operator()(Array<T, N> const &value, Params const ¶ms_) const {
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return this->operator()(value);
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}
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};
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template <int N>
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struct Sigmoid<Array<half_t, N>> {
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using T = half_t;
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CUTLASS_HOST_DEVICE
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Array<T, N> operator()(Array<T, N> const& z) const {
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plus<Array<T, N>> add;
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#if defined(CUTLASS_USE_TANH_FOR_SIGMOID)
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multiplies<Array<T, N>> mul;
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fast_tanh_op<Array<T, N>> tanh;
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return mul(add(tanh(mul(z, cutlass::constants::half<T>())), cutlass::constants::one<T>()),
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cutlass::constants::half<T>());
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#else
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divides<Array<T, N>> div;
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negate<Array<T, N>> neg;
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fast_exp_op<Array<T, N>> fast_exp;
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return div(cutlass::constants::one<T>(),
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add(cutlass::constants::one<T>(),
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fast_exp(neg(z))));
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#endif
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}
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using Params = LinearCombinationGenericParams<T>;
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CUTLASS_HOST_DEVICE
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Array<T, N> operator()(Array<T, N> const &z, Params const ¶ms_) const {
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return this->operator()(z);
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}
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};
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// SiLu (swish) operator introduced by Elfwing et al. in the following paper
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// "Sigmoid-Weighted Linear Units for Neural Network Function Approximation in Reinforcement Learning" (2017)
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// https://arxiv.org/pdf/1702.03118.pdf
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// It is used in EfficientNet and YOLOv5, for example.
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// Reference: https://pytorch.org/docs/stable/generated/torch.nn.SiLU.html
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template <typename T>
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struct SiLu {
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CUTLASS_HOST_DEVICE
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T operator()(T const &scalar) const {
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Sigmoid<T> sigmoid;
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return scalar * sigmoid(scalar);
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}
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using Params = LinearCombinationGenericParams<T>;
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CUTLASS_HOST_DEVICE
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T operator()(T const &scalar, Params const ¶ms_) const {
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return this->operator()(scalar);
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}
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};
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template <typename T, int N>
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struct SiLu<Array<T, N>> {
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CUTLASS_HOST_DEVICE
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Array<T, N> operator()(Array<T, N> const &value) const {
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Sigmoid<Array<T, N>> sigmoid_op;
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multiplies<Array<T, N>> mul;
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return mul(value, sigmoid_op(value));
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}
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using Params = LinearCombinationGenericParams<T>;
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CUTLASS_HOST_DEVICE
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Array<T, N> operator()(Array<T, N> const &value, Params const ¶ms_) const {
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return this->operator()(value);
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}
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};
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// Hardswish operator introduced by Howard et al. in the following paper
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// "Searching for MobileNetV3" (2019)
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// https://arxiv.org/pdf/1905.02244.pdf
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// It is used in models based on MobilenetNetV3.
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// Reference: https://pytorch.org/docs/stable/generated/torch.nn.Hardswish.html
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template <typename T>
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struct HardSwish {
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CUTLASS_HOST_DEVICE
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T operator()(T const &x) const {
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minimum<T> mn;
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maximum<T> mx;
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T relu6 = mn(mx(x + T(3), T(0)), T(6));
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return x * relu6 / T(6);
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}
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using Params = LinearCombinationGenericParams<T>;
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CUTLASS_HOST_DEVICE
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T operator()(T const &x, Params const ¶ms_) const {
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return this->operator()(x);
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}
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};
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template <>
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struct HardSwish<float> {
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using T = float;
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CUTLASS_HOST_DEVICE
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T operator()(T const &x) const {
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minimum<T> mn;
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maximum<T> mx;
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T relu6 = mn(mx(x + T(3), T(0)), T(6));
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return x * relu6 * 0.16666667f;
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}
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using Params = LinearCombinationGenericParams<T>;
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CUTLASS_HOST_DEVICE
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T operator()(T const &x, Params const ¶ms_) const {
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return this->operator()(x);
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}
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};
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template <typename T, int N>
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struct HardSwish<Array<T, N> > {
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CUTLASS_HOST_DEVICE
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Array<T, N> operator()(Array<T, N> const &value) const {
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Array<T, N> y;
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HardSwish<T> hardswish_op;
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CUTLASS_PRAGMA_UNROLL
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for (int i = 0; i < N; ++i) {
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y[i] = hardswish_op(value[i]);
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}
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return y;
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}
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using Params = LinearCombinationGenericParams<T>;
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CUTLASS_HOST_DEVICE
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Array<T, N> operator()(Array<T, N> const &x, Params const ¶ms_) const {
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return this->operator()(x);
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}
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};
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template <int N>
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struct HardSwish<Array<half_t, N> > {
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using T = half_t;
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CUTLASS_HOST_DEVICE
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Array<T, N> operator()(Array<T, N> const &value) const {
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minimum<Array<T, N> > mn;
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maximum<Array<T, N> > mx;
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multiplies<Array<T, N> > mul;
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plus<Array<T, N> > add;
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return mul(mul(mn(mx(add(value, T(3)), T(0)), T(6)), value), T(0.16666667f));
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}
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using Params = LinearCombinationGenericParams<T>;
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CUTLASS_HOST_DEVICE
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Array<T, N> operator()(Array<T, N> const &x, Params const ¶ms_) const {
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return this->operator()(x);
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}
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};
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//
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// GELU function definitions implemented as described by
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// Hendrycks, D., and Gimpel, K. in
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// "Gaussian Error Linear Units (GELUs)." (2020)
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// https://arxiv.org/pdf/1606.08415.pdf
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//
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// Floating-point constants are Taylor coefficients described in the paper.
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//
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// GELU operator
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template <typename T>
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struct GELU {
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CUTLASS_HOST_DEVICE
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T operator()(T const &scalar) const {
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return T(cutlass::constants::half<T>() * scalar *
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(cutlass::constants::one<T>() + (T)erff((float)(scalar * cutlass::constants::half_root_two<T>()))));
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}
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using Params = LinearCombinationGenericParams<T>;
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CUTLASS_HOST_DEVICE
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T operator()(T const &scalar, Params const ¶ms_) const {
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return this->operator()(scalar);
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}
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};
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template <>
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struct GELU<float> {
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CUTLASS_HOST_DEVICE
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float operator()(float const &scalar) const {
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return cutlass::constants::half<float>() * scalar *
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(cutlass::constants::one<float>() + erff(scalar * cutlass::constants::half_root_two<float>() ));
|
|
}
|
|
|
|
using Params = LinearCombinationGenericParams<float>;
|
|
|
|
CUTLASS_HOST_DEVICE
|
|
float operator()(float const &scalar, Params const ¶ms_) const {
|
|
return this->operator()(scalar);
|
|
}
|
|
};
|
|
|
|
template <>
|
|
struct GELU<double> {
|
|
CUTLASS_HOST_DEVICE
|
|
double operator()(double const &scalar) const {
|
|
return cutlass::constants::half<double>() * scalar *
|
|
(cutlass::constants::one<double>() + erf( scalar * cutlass::constants::half_root_two<double>() ));
|
|
}
|
|
|
|
using Params = LinearCombinationGenericParams<double>;
|
|
|
|
CUTLASS_HOST_DEVICE
|
|
double operator()(double const &scalar, Params const ¶ms_) const {
|
|
return this->operator()(scalar);
|
|
}
|
|
};
|
|
|
|
template <typename T, int N>
|
|
struct GELU<Array<T, N> > {
|
|
CUTLASS_HOST_DEVICE
|
|
Array<T, N> operator()(Array<T, N> const &value) const {
|
|
Array<T, N> y;
|
|
GELU<T> gelu_op;
|
|
|
|
CUTLASS_PRAGMA_UNROLL
|
|
for (int i = 0; i < N; ++i) {
|
|
y[i] = gelu_op(value[i]);
|
|
}
|
|
|
|
return y;
|
|
}
|
|
|
|
using Params = LinearCombinationGenericParams<T>;
|
|
|
|
CUTLASS_HOST_DEVICE
|
|
Array<T, N> operator()(Array<T, N> const &value, Params const ¶ms_) const {
|
|
return this->operator()(value);
|
|
}
|
|
};
|
|
|
|
// GELU operator implemented using the Taylor series approximation
|
|
template <typename T>
|
|
struct GELU_taylor {
|
|
static const bool kIsHeavy=true;
|
|
CUTLASS_HOST_DEVICE
|
|
T operator()(T const &z) const {
|
|
|
|
T k0 = T(0.7978845608028654);
|
|
T k1 = T(0.044715);
|
|
|
|
return T(cutlass::constants::half<T>() * z *
|
|
(cutlass::constants::one<T>() + fast_tanh(k0 * z * (cutlass::constants::one<T>() + k1 * z * z))));
|
|
}
|
|
|
|
using Params = LinearCombinationGenericParams<T>;
|
|
|
|
CUTLASS_HOST_DEVICE
|
|
T operator()(T const &scalar, Params const ¶ms_) const {
|
|
return this->operator()(scalar);
|
|
}
|
|
};
|
|
|
|
template <int N>
|
|
struct GELU_taylor<Array<half_t, N> > {
|
|
static const bool kIsHeavy=true;
|
|
CUTLASS_HOST_DEVICE
|
|
Array<half_t, N> operator()(Array<half_t, N> const &z) const {
|
|
|
|
using T = half_t;
|
|
Array<half_t, N> y;
|
|
|
|
half_t k0 = half_t(0.7978845608028654);
|
|
half_t k1 = half_t(0.044715);
|
|
|
|
multiply_add<Array<half_t, N>> fma;
|
|
multiplies<Array<half_t, N>> mul;
|
|
plus<Array<half_t, N>> add;
|
|
|
|
fast_tanh_op<Array<half_t, N>> tanh;
|
|
|
|
Array<half_t, N> u = mul(mul(k0, z), fma(mul(k1, z), z, cutlass::constants::one<T>()));
|
|
|
|
y = mul(mul(z, cutlass::constants::half<T>()), add(cutlass::constants::one<T>(), tanh(u)));
|
|
|
|
return y;
|
|
}
|
|
|
|
using Params = LinearCombinationGenericParams<half_t>;
|
|
|
|
CUTLASS_HOST_DEVICE
|
|
Array<half_t, N> operator()(Array<half_t, N> const &value, Params const ¶ms_) const {
|
|
return this->operator()(value);
|
|
}
|
|
};
|
|
|
|
template <typename T, int N>
|
|
struct GELU_taylor<Array<T, N> > {
|
|
static const bool kIsHeavy=true;
|
|
CUTLASS_HOST_DEVICE
|
|
Array<T, N> operator()(Array<T, N> const &value) const {
|
|
Array<T, N> y;
|
|
GELU_taylor<T> gelu_op;
|
|
|
|
CUTLASS_PRAGMA_UNROLL
|
|
for (int i = 0; i < N; ++i) {
|
|
y[i] = gelu_op(value[i]);
|
|
}
|
|
|
|
return y;
|
|
}
|
|
|
|
using Params = LinearCombinationGenericParams<T>;
|
|
|
|
CUTLASS_HOST_DEVICE
|
|
Array<T, N> operator()(Array<T, N> const &value, Params const ¶ms_) const {
|
|
return this->operator()(value);
|
|
}
|
|
};
|
|
|
|
/// Computes backwards pass for GELU operator assuming d_t is the layer gradient and
|
|
/// z is computed from the forward pass.
|
|
template <typename T>
|
|
struct dGELU {
|
|
CUTLASS_HOST_DEVICE
|
|
T operator()(T const &d_t, T const &z) const {
|
|
|
|
T k0 = T(0.7978845608028654);
|
|
T k1 = T(0.044715);
|
|
T k2 = T(0.1070322243);
|
|
|
|
T tanh_out = fast_tanh(k0 * z * (1 + k1 * z * z));
|
|
|
|
T ff = constants::half<T>() * z * ((1 - tanh_out * tanh_out) * (k0 + k2 * z * z)) +
|
|
constants::half<T>() * (1 + tanh_out);
|
|
|
|
return ff * d_t;
|
|
}
|
|
};
|
|
|
|
template <typename T, int N>
|
|
struct dGELU<Array<T, N> > {
|
|
CUTLASS_HOST_DEVICE
|
|
Array<T, N> operator()(Array<T, N> const &d_t, Array<T, N> const &z) const {
|
|
Array<T, N> y;
|
|
dGELU<T> gelu_op;
|
|
|
|
CUTLASS_PRAGMA_UNROLL
|
|
for (int i = 0; i < N; ++i) {
|
|
y[i] = gelu_op(d_t[i], z[i]);
|
|
}
|
|
|
|
return y;
|
|
}
|
|
};
|
|
|
|
/////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
} // namespace thread
|
|
} // namespace epilogue
|
|
} // namespace cutlass
|
|
|
|
/////////////////////////////////////////////////////////////////////////////////////////////////
|