326 lines
13 KiB
C++
326 lines
13 KiB
C++
/***************************************************************************************************
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* Copyright (c) 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 operations with a generic element-wise activation
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function. Scaling factors are applied to operands A, B, and C. The pre-activation auxiliary
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output is also returned.
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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_generic.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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/// Aux = ((alpha * scale_a * scale_b) * accumulator) + ((beta * scale_c) * source) + bias
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/// D = activation(Aux)
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///
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template <
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template<typename T> class ActivationFunctor,
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typename ElementOutput_, ///< Data type used to load and store tensors
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typename ElementAuxOutput_, ///< Data type used to store auxiliary output
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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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bool IsHeavy = false
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>
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class LinearCombinationGenericWithScalingAndAbsMax {
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public:
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using ElementOutput = ElementOutput_;
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using ElementAuxOutput = ElementAuxOutput_;
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using ElementAccumulator = ElementAccumulator_;
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using ElementCompute = ElementCompute_;
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using ElementScalingFactor = ElementAccumulator_;
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/// Data type used for absolute maximum value
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using ElementAbsmax = float;
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static bool const kIsScalingAndAmaxAuxOutputNeeded = (platform::is_same<ElementAuxOutput, cutlass::float_e4m3_t>::value ||
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platform::is_same<ElementAuxOutput, cutlass::float_e5m2_t>::value);
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static bool const kIsScalingAndAmaxOutputNeeded = (platform::is_same<ElementOutput, cutlass::float_e4m3_t>::value ||
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platform::is_same<ElementOutput, cutlass::float_e5m2_t>::value);
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static bool const kIsHeavy = IsHeavy;
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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 FragmentAuxOutput = Array<ElementAuxOutput, 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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struct ActivationParams
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: LinearCombinationGenericParams<ElementCompute>,
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GenericActivationTraits<ActivationFunctor<ElementCompute>>::Arguments {
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using LinearCombinationGenericParams<ElementCompute>::LinearCombinationGenericParams;
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};
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ActivationParams activation;
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ElementScalingFactor const* scale_a_ptr = nullptr; ///< pointer to a scalar - if not null, loads it from memory
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ElementScalingFactor const* scale_b_ptr = nullptr; ///< pointer to b scalar - if not null, loads it from memory
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ElementScalingFactor const* scale_c_ptr = nullptr; ///< pointer to c scalar - if not null, loads it from memory
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ElementScalingFactor const* scale_d_ptr = nullptr; ///< pointer to d scalar - if not null, loads it from memory
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ElementScalingFactor const* scale_aux_ptr = nullptr; ///< pointer to aux scalar - if not null, loads it from memory
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ElementAbsmax * abs_max_aux_ptr = nullptr; ///< pointer to location to store amax of Aux
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ElementAbsmax * abs_max_D_ptr = nullptr; ///< pointer to location to store amax of D
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CUTLASS_HOST_DEVICE
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Params() :
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scale_a_ptr(nullptr),
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scale_b_ptr(nullptr),
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scale_c_ptr(nullptr),
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scale_d_ptr(nullptr),
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scale_aux_ptr(nullptr),
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abs_max_aux_ptr(nullptr),
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abs_max_D_ptr(nullptr) {}
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CUTLASS_HOST_DEVICE
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Params(ActivationParams activation_params,
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ElementScalingFactor const* scale_a_ptr,
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ElementScalingFactor const* scale_b_ptr,
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ElementScalingFactor const* scale_c_ptr,
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ElementScalingFactor const* scale_d_ptr,
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ElementScalingFactor const* scale_aux_ptr,
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ElementAbsmax * abs_max_aux_ptr,
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ElementAbsmax * abs_max_D_ptr) :
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activation(activation_params),
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scale_a_ptr(scale_a_ptr),
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scale_b_ptr(scale_b_ptr),
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scale_c_ptr(scale_c_ptr),
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scale_d_ptr(scale_d_ptr),
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scale_aux_ptr(scale_aux_ptr),
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abs_max_aux_ptr(abs_max_aux_ptr),
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abs_max_D_ptr(abs_max_D_ptr) {}
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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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Params params_;
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bool skip_elementwise_;
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// Scaling factors for output and auxiliary output
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ElementCompute scale_d_;
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ElementCompute scale_aux_;
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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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LinearCombinationGenericWithScalingAndAbsMax(Params const ¶ms) :
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params_(params),
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skip_elementwise_(false),
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scale_d_(ElementCompute(params.scale_d_ptr ? *(params.scale_d_ptr) : ElementScalingFactor(1))),
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scale_aux_(ElementCompute(params.scale_aux_ptr ? *(params.scale_aux_ptr) : ElementScalingFactor(1)))
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{
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params_.activation.alpha = (params.activation.alpha_ptr ? *params.activation.alpha_ptr : params.activation.alpha);
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params_.activation.beta = (params.activation.beta_ptr ? *params.activation.beta_ptr : params.activation.beta);
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auto scale_a =
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ElementCompute(params.scale_a_ptr ? *(params.scale_a_ptr) : ElementScalingFactor(1));
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auto scale_b =
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ElementCompute(params.scale_b_ptr ? *(params.scale_b_ptr) : ElementScalingFactor(1));
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auto scale_c =
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ElementCompute(params.scale_c_ptr ? *(params.scale_c_ptr) : ElementScalingFactor(1));
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multiplies<ElementCompute> multiply;
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params_.activation.alpha = multiply(params.activation.alpha, multiply(scale_a, scale_b));
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params_.activation.beta = multiply(params.activation.beta, scale_c);
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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 params_.activation.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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params_.activation.beta = ElementCompute(1);
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}
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// Only the final partition should perform the activation function
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// and scale the output and auxiliary output values.
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if (k_partition != k_partition_count - 1) {
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skip_elementwise_ = true;
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scale_d_ = ElementCompute(1.);
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scale_aux_ = ElementCompute(1.);
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}
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}
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/// Computes linear scaling:
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/// Aux = (alpha * scale_a * scale_b * accumulator) + (beta * scale_c * source) + bias
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/// D = activation(Aux)
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CUTLASS_HOST_DEVICE
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void operator()(
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FragmentCompute& output,
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FragmentCompute& aux_output,
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FragmentAccumulator const &accumulator,
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FragmentCompute const& bias,
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FragmentOutput const &source) {
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// Convert source to interal compute numeric type
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NumericArrayConverter<ElementCompute, ElementOutput, kCount, Round> source_converter;
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NumericArrayConverter<ElementCompute, ElementAccumulator, kCount, Round> accumulator_converter;
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FragmentCompute converted_source = source_converter(source);
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FragmentCompute converted_accumulator = accumulator_converter(accumulator);
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// Perform binary operations
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FragmentCompute intermediate;
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multiplies<FragmentCompute> multiply;
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plus<FragmentCompute> add;
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multiply_add<FragmentCompute> mul_add_accumulator;
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ActivationFunctor<FragmentCompute> activation;
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if (Scale == ScaleType::NoBetaScaling) {
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intermediate = converted_source;
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intermediate = mul_add_accumulator(params_.activation.alpha, converted_accumulator, intermediate);
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} else if (Scale == ScaleType::Nothing) {
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intermediate = converted_accumulator;
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} else {
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intermediate = multiply(params_.activation.beta, converted_source);
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intermediate = mul_add_accumulator(params_.activation.alpha, converted_accumulator, intermediate);
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}
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intermediate = add(intermediate, bias);
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aux_output = intermediate;
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if constexpr (GenericActivationTraits<ActivationFunctor<ElementCompute>>::IsArgumentsNeeded) {
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output = skip_elementwise_ ? intermediate : activation(intermediate, params_.activation);
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} else {
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output = skip_elementwise_ ? intermediate : activation(intermediate);
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}
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}
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/// Computes linear scaling:
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/// Aux = (alpha * scale_a * scale_b * accumulator) + bias
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/// D = activation(Aux)
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CUTLASS_DEVICE
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void operator()(
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FragmentCompute& output,
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FragmentCompute& aux_output,
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FragmentAccumulator const &accumulator,
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FragmentCompute const& bias) {
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// Convert source to interal compute numeric type
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NumericArrayConverter<ElementCompute, ElementAccumulator, kCount, Round> accumulator_converter;
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FragmentCompute converted_accumulator = accumulator_converter(accumulator);
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// Perform binary operations
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FragmentCompute intermediate;
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multiplies<FragmentCompute> multiply;
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plus<FragmentCompute> add;
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ActivationFunctor<FragmentCompute> activation;
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if (Scale == ScaleType::Nothing) {
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intermediate = converted_accumulator;
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} else {
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intermediate = multiply(params_.activation.alpha, converted_accumulator);
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}
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intermediate = add(intermediate, bias);
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aux_output = intermediate;
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if constexpr (GenericActivationTraits<ActivationFunctor<FragmentCompute>>::IsArgumentsNeeded) {
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output = skip_elementwise_ ? intermediate : activation(intermediate, params_.activation);
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} else {
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output = skip_elementwise_ ? intermediate : activation(intermediate);
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}
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}
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CUTLASS_HOST_DEVICE
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ElementAbsmax* get_ptr_output_abs_max() const {
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return params_.abs_max_D_ptr;
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}
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CUTLASS_HOST_DEVICE
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ElementAbsmax* get_ptr_aux_output_abs_max() const {
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return params_.abs_max_aux_ptr;
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}
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CUTLASS_HOST_DEVICE
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ElementCompute get_scale_d() const {
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return scale_d_;
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}
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CUTLASS_HOST_DEVICE
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ElementCompute get_scale_aux() const {
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return scale_aux_;
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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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