90d3b0fb18
* Updates for 3.2.1 release. * Minor fix in gemm op profiler for raster order. * Add scheduler mapping for raster order in the kernels.
628 lines
15 KiB
C++
628 lines
15 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 Define basic numeric operators
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This is inspired by the Standard Library's <functional> header.
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*/
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/*
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Note: CUTLASS 3x increases the host compiler requirements to C++17. However, certain
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existing integrations of CUTLASS require C++11 host compilers.
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Until this requirement can be lifted, certain headers with this annotation are required
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to be remain consistent with C++11 syntax.
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C++11 compatibility is enforced by `cutlass_test_unit_core_cpp11`.
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*/
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#pragma once
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#include "cutlass/cutlass.h"
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#include "cutlass/half.h"
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#include "cutlass/tfloat32.h"
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#include "cutlass/bfloat16.h"
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#if defined(CUTLASS_ARCH_WMMA_ENABLED)
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#include <mma.h>
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#endif // defined(CUTLASS_ARCH_WMMA_ENABLED)
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#ifdef _MSC_VER
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// Provides support for alternate operators such as 'and', 'or', ...
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#include <iso646.h>
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#endif // _MSC_VER
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namespace cutlass {
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/////////////////////////////////////////////////////////////////////////////////////////////////
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template <typename T>
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struct absolute_value_op {
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CUTLASS_HOST_DEVICE
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T operator()(T lhs) const {
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return abs(lhs);
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}
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};
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template <>
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struct absolute_value_op<float> {
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CUTLASS_HOST_DEVICE
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float operator()(float lhs) const { return fabs(lhs); }
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};
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template <typename T>
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struct plus {
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CUTLASS_HOST_DEVICE
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T operator()(T lhs, T const &rhs) const {
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lhs += rhs;
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return lhs;
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}
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};
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template <typename T>
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struct minus {
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CUTLASS_HOST_DEVICE
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T operator()(T lhs, T const &rhs) const {
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lhs -= rhs;
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return lhs;
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}
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};
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template <typename T>
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struct multiplies {
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CUTLASS_HOST_DEVICE
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T operator()(T lhs, T const &rhs) const {
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lhs *= rhs;
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return lhs;
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}
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};
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template <typename T>
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struct scale {
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T const scaling_factor_;
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CUTLASS_HOST_DEVICE
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scale(float scaling_factor) : scaling_factor_(scaling_factor) {
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}
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T operator()(T const &rhs) const {
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T result = rhs * scaling_factor_;
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return result;
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}
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};
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#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 530
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/// Partial specializations needed when __CUDA_NO_HALF2_OPERATORS__ is set
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template<>
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struct plus<__half2> {
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CUTLASS_HOST_DEVICE
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__half2 operator()(__half2 lhs, __half2 const &rhs) const {
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return __hadd2(lhs, rhs);
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}
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};
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template<>
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struct minus<__half2> {
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CUTLASS_HOST_DEVICE
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__half2 operator()(__half2 lhs, __half2 const &rhs) const {
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return __hsub2(lhs, rhs);
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}
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};
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template<>
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struct multiplies<__half2> {
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CUTLASS_HOST_DEVICE
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__half2 operator()(__half2 lhs, __half2 const &rhs) const {
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return __hmul2(lhs, rhs);
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}
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};
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/// Partial specializations needed when __CUDA_NO_HALF_OPERATORS__ is set
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template<>
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struct plus<__half> {
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CUTLASS_HOST_DEVICE
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__half operator()(__half lhs, __half const &rhs) const {
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return __hadd(lhs, rhs);
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}
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};
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template<>
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struct minus<__half> {
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CUTLASS_HOST_DEVICE
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__half operator()(__half lhs, __half const &rhs) const {
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return __hsub(lhs, rhs);
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}
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};
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template<>
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struct multiplies<__half> {
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CUTLASS_HOST_DEVICE
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__half operator()(__half lhs, __half const &rhs) const {
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return __hmul(lhs, rhs);
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}
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};
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#endif // defined(__CUDA_ARCH__)
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/// Squares with optional conversion
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template <typename T, typename Output = T>
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struct square {
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CUTLASS_HOST_DEVICE
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Output operator()(T lhs) const {
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multiplies<Output> mul_op;
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Output y = Output(lhs);
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return mul_op(y, y);
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}
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};
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/// Returns the magnitude squared of an element.
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template <typename T, typename Output = T>
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struct magnitude_squared {
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CUTLASS_HOST_DEVICE
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Output operator()(T lhs) const {
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multiplies<Output> mul_op;
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Output y = Output(lhs);
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return mul_op(y, y);
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}
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};
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/// Computes the square of a difference with optional conversion
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template <typename T, typename Output = T>
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struct square_difference {
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CUTLASS_HOST_DEVICE
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Output operator()(T lhs, T rhs) const {
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multiplies<Output> mul_op;
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Output y = Output(lhs) - Output(rhs);
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return mul_op(y, y);
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}
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};
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/// Computes the square of a difference with optional conversion
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template <typename T, typename Output = T>
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struct magnitude_squared_difference {
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CUTLASS_HOST_DEVICE
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Output operator()(T lhs, T rhs) const {
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multiplies<Output> mul_op;
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Output y = Output(lhs) - Output(rhs);
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return mul_op(y, y);
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}
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};
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/// Divides
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template <typename T>
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struct divides {
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CUTLASS_HOST_DEVICE
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T operator()(T lhs, T const &rhs) const {
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lhs /= rhs;
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return lhs;
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}
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};
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/// Negate
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template <typename T>
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struct negate {
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CUTLASS_HOST_DEVICE
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T operator()(T lhs) const {
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return -lhs;
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}
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};
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/// Greater equal
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template <typename T>
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struct greater_equal {
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CUTLASS_HOST_DEVICE
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bool operator()(T const &lhs, T const &rhs) const {
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return (lhs >= rhs);
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}
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};
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/// Greater
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template <typename T>
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struct greater {
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CUTLASS_HOST_DEVICE
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bool operator()(T const &lhs, T const &rhs) const {
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return (lhs > rhs);
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}
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};
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/// Less equal
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template <typename T>
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struct less_equal {
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CUTLASS_HOST_DEVICE
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bool operator()(T const &lhs, T const &rhs) const {
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return (lhs <= rhs);
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}
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};
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/// Less
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template <typename T>
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struct less {
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CUTLASS_HOST_DEVICE
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bool operator()(T const &lhs, T const &rhs) const {
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return (lhs < rhs);
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}
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};
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template <typename T, bool PropogateNaN = false>
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struct maximum {
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CUTLASS_HOST_DEVICE
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T operator()(T const &lhs, T const &rhs) const {
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return (lhs < rhs ? rhs : lhs);
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}
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};
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// Maximum with nan propogation
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// To propgate the NANs, the "max" of a two element that contains NaNs should also return a NaN
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template <typename T>
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struct maximum<T, true> {
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CUTLASS_HOST_DEVICE
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T operator()(T const &lhs, T const &rhs) const {
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#if defined(__CUDA_ARCH__)
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return lhs > rhs or isnan(lhs) ? lhs : rhs;
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#else
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return lhs > rhs or std::isnan(lhs) ? lhs : rhs;
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#endif
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}
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};
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template <>
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struct maximum<float, false> {
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CUTLASS_HOST_DEVICE
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float operator()(float const &lhs, float const &rhs) const {
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return fmaxf(lhs, rhs);
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}
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};
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template <>
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struct maximum<float, true> {
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CUTLASS_HOST_DEVICE
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float operator()(float const lhs, float const rhs) const {
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float res;
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#if defined(__CUDA_ARCH__) && (__CUDA_ARCH__ >= 800)
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asm volatile("max.NaN.f32 %0, %1, %2;\n" : "=f"(res) : "f"(lhs), "f"(rhs));
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#elif defined(__CUDA_ARCH__)
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res = lhs > rhs or isnan(lhs) ? lhs : rhs;
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#else
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res = lhs > rhs or std::isnan(lhs) ? lhs : rhs;
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#endif
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return res;
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}
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};
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template <typename T>
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using maximum_with_nan_propogation = maximum<T, true>;
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template <typename T, bool PropogateNaN = false>
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struct minimum{
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CUTLASS_HOST_DEVICE
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T operator()(T const &lhs, T const &rhs) const {
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return (rhs < lhs ? rhs : lhs);
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}
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};
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template <typename T>
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struct minimum<T, true> {
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CUTLASS_HOST_DEVICE
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T operator()(T const &lhs, T const &rhs) const {
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#if defined(__CUDA_ARCH__)
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return lhs < rhs or isnan(lhs) ? lhs : rhs;
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#else
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return lhs < rhs or std::isnan(lhs) ? lhs : rhs;
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#endif
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}
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};
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template <>
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struct minimum<float, false> {
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CUTLASS_HOST_DEVICE
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float operator()(float const &lhs, float const &rhs) const {
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return fminf(lhs, rhs);
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}
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};
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template <typename T, bool PropogateNaN = false>
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struct maximum_absolute_value {
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CUTLASS_HOST_DEVICE
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float operator()(T const &lhs, T const &rhs) const {
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absolute_value_op<T> abs_op;
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maximum<T, PropogateNaN> max_op;
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return max_op(abs_op(lhs), abs_op(rhs));
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}
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};
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// assumes the left operand is already an absolute value
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template <typename T, bool PropogateNaN = false>
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struct maximum_absolute_value_reduction {
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CUTLASS_HOST_DEVICE
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float operator()(T const &lhs, T const &rhs) const {
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absolute_value_op<T> abs_op;
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maximum<T, PropogateNaN> max_op;
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return max_op(lhs, abs_op(rhs));
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}
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};
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/// Fused multiply-add
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template <typename A, typename B = A, typename C = A>
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struct multiply_add {
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CUTLASS_HOST_DEVICE
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C operator()(A const &a, B const &b, C const &c) const {
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return C(a) * C(b) + c;
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}
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};
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/// Fused multiply-add
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template <typename A, typename B = A, typename C = A>
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struct multiply_add_relu0 {
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CUTLASS_HOST_DEVICE
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C operator()(A const &a, B const &b, C const &c) const {
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maximum<C> mx;
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return mx(C(a) * C(b) + c, C(0));
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}
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};
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/// Fused multiply-add
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template <typename T>
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struct and_add {
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CUTLASS_HOST_DEVICE
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T operator()(T const &a, T const &b, T const &c) const {
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return ((a & b) + c);
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}
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};
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/// Fused multiply-add
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template <typename T>
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struct xor_add {
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CUTLASS_HOST_DEVICE
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T operator()(T const &a, T const &b, T const &c) const {
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return ((a ^ b) + c);
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}
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};
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template <typename T>
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struct conjugate {
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CUTLASS_HOST_DEVICE
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T operator()(T const &a) const {
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return a;
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}
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};
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template <typename T>
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struct first {
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CUTLASS_HOST_DEVICE
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T operator()(T const & first, T const &...) const {
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return first;
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}
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};
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/////////////////////////////////////////////////////////////////////////////////////////////////
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template <typename T>
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struct logical_and {
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CUTLASS_HOST_DEVICE
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T operator()(T const &a, T const &b) const {
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return ((a && b) ? T(1) : T());
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}
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};
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template <typename T>
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struct logical_or {
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CUTLASS_HOST_DEVICE
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T operator()(T const &a, T const &b) const {
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return ((a || b) ? T(1) : T());
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}
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};
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template <typename T>
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struct logical_not {
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CUTLASS_HOST_DEVICE
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T operator()(T const &a) const {
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return T(!(a));
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}
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};
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/////////////////////////////////////////////////////////////////////////////////////////////////
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template <typename T>
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struct bit_and {
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CUTLASS_HOST_DEVICE
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T operator()(T const &a, T const &b) const {
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return a & b;
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}
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};
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template <typename T>
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struct bit_or {
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CUTLASS_HOST_DEVICE
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T operator()(T const &a, T const &b) const {
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return a | b;
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}
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};
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template <typename T>
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struct bit_not {
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CUTLASS_HOST_DEVICE
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T operator()(T const &a) const {
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return ~a;
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}
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};
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template <typename T>
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struct bit_xor {
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CUTLASS_HOST_DEVICE
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T operator()(T const &a, T const &b) const {
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return a ^ b;
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}
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};
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//////////////////////////////////////////////////////////////////////////////////////////////////
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/// Atomic reductions
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template <typename T>
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struct atomic_add
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{
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CUTLASS_DEVICE
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void operator()(T *ptr, const T &data)
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{
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#if defined(__CUDA_ARCH__)
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atomicAdd(ptr, data);
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#endif
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}
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};
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template<>
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struct atomic_add<double>
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{
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CUTLASS_DEVICE
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void operator()(double *ptr, const double &data)
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{
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#if !defined(__CUDA_ARCH__)
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CUTLASS_UNUSED(ptr);
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CUTLASS_UNUSED(data);
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#elif (__CUDA_ARCH__ >= 600)
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atomicAdd(ptr, data);
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#else
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// Use CAS loop
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unsigned long long int* ptr_int = reinterpret_cast<unsigned long long int*>(ptr);
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unsigned long long int old_int = *ptr_int;
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unsigned long long int assumed_int;
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do {
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double update = data + __longlong_as_double(old_int);
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assumed_int = old_int;
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old_int = atomicCAS(ptr_int, assumed_int, __double_as_longlong(update));
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} while (assumed_int != old_int);
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#endif // (__CUDA_ARCH__ >= 600)
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}
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};
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template<>
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struct atomic_add<half2>
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{
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CUTLASS_DEVICE
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void operator()(half2 *ptr, const half2 &data)
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{
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#if !defined(__CUDA_ARCH__) || (defined(__CUDA_ARCH__) && (__CUDA_ARCH__ < 600))
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CUTLASS_UNUSED(ptr);
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CUTLASS_UNUSED(data);
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#else
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// Vector-2 atomic reduction requires .target sm_60 or higher
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uint32_t word = reinterpret_cast<const uint32_t&>(data);
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asm volatile ("red.gpu.global.add.noftz.f16x2 [%0], %1;\n" : : "l"(ptr), "r"(word));
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#endif // (__CUDA_ARCH__ >= 600)
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}
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};
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template <typename T>
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using red [[deprecated("use atomic_add instead")]] = atomic_add<T>;
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template <typename T>
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struct atomic_maximum {
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CUTLASS_DEVICE
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T operator()(T *ptr, T value) const {
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#if defined(__CUDA_ARCH__)
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return atomicMax(ptr, value);
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#else
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CUTLASS_UNUSED(ptr);
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CUTLASS_UNUSED(value);
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CUTLASS_NOT_IMPLEMENTED();
|
|
return 0;
|
|
#endif
|
|
}
|
|
};
|
|
|
|
template <>
|
|
struct atomic_maximum<float> {
|
|
CUTLASS_DEVICE
|
|
float operator()(float *ptr, float value) const {
|
|
#if defined(__CUDA_ARCH__)
|
|
return !signbit(value) ?
|
|
__int_as_float(atomicMax((int*)ptr, __float_as_int(value))) :
|
|
__uint_as_float(atomicMin((unsigned int*)ptr, __float_as_uint(value)));
|
|
#else
|
|
CUTLASS_UNUSED(ptr);
|
|
CUTLASS_UNUSED(value);
|
|
CUTLASS_NOT_IMPLEMENTED();
|
|
return 0;
|
|
#endif
|
|
}
|
|
};
|
|
|
|
|
|
/////////////////////////////////////////////////////////////////////////////////////////////////
|
|
//
|
|
// Partial specializations for nvcuda::wmma::fragment<Use, m, n, k, T, Layout>
|
|
//
|
|
/////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
#if defined(CUTLASS_ARCH_WMMA_ENABLED)
|
|
|
|
template<typename Use, int m, int n, int k, typename T, typename Layout>
|
|
struct plus<nvcuda::wmma::fragment<Use, m, n, k, T, Layout>>
|
|
{
|
|
using Fragment = nvcuda::wmma::fragment<Use, m, n, k, T, Layout>;
|
|
using ElementType = typename Fragment::element_type;
|
|
|
|
CUTLASS_HOST_DEVICE
|
|
Fragment operator()(Fragment const &lhs, Fragment const &rhs) const
|
|
{
|
|
Fragment result;
|
|
plus<ElementType> scalar_op;
|
|
|
|
ElementType *result_elts = reinterpret_cast<ElementType*>(&result);
|
|
const ElementType *lhs_elts = reinterpret_cast<const ElementType*>(&lhs);
|
|
const ElementType *rhs_elts = reinterpret_cast<const ElementType*>(&rhs);
|
|
|
|
CUTLASS_PRAGMA_UNROLL
|
|
for (int i = 0; i < Fragment::num_elements; i++) {
|
|
result_elts[i] = scalar_op(lhs_elts[i], rhs_elts[i]);
|
|
}
|
|
|
|
return result;
|
|
}
|
|
};
|
|
|
|
#endif // defined(CUTLASS_ARCH_WMMA_ENABLED)
|
|
|
|
|
|
|
|
/////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
} // namespace cutlass
|
|
|
|
/////////////////////////////////////////////////////////////////////////////////////////////////
|