1e4703cbab
* Support parallel split K mode for porfiling Signed-off-by: Peter Han <fujun.han@iluvatar.ai> * Parallel Split K support 1. find gemm kernel by preference key 2. switch m n for redution kernel Signed-off-by: Peter Han <fujun.han@iluvatar.ai> * parallel splitk for fp16 gemm * add one missing file Co-authored-by: Haicheng Wu <haichengw@nvidia.com>
1207 lines
41 KiB
Plaintext
1207 lines
41 KiB
Plaintext
/***************************************************************************************************
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* Copyright (c) 2017-2022, NVIDIA CORPORATION. All rights reserved.
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*
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* Redistribution and use in source and binary forms, with or without modification, are permitted
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* provided that the following conditions are met:
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* * Redistributions of source code must retain the above copyright notice, this list of
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* conditions and the following disclaimer.
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* * Redistributions in binary form must reproduce the above copyright notice, this list of
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* conditions and the following disclaimer in the documentation and/or other materials
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* provided with the distribution.
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* * Neither the name of the NVIDIA CORPORATION nor the names of its contributors may be used
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* to endorse or promote products derived from this software without specific prior written
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* permission.
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*
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* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR
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* IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
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* FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE
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* FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
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* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
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* OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
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* STRICT LIABILITY, 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 Execution environment
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*/
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#include <iostream>
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#include <stdexcept>
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#include <iomanip>
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#include <ios>
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#include "cutlass/core_io.h"
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#include "cublas_helpers.h"
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#include "gemm_operation_profiler.h"
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#include "gpu_timer.h"
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#include "cutlass/library/singleton.h"
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#include "cutlass/library/library.h"
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#include "cutlass/library/handle.h"
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/////////////////////////////////////////////////////////////////////////////////////////////////
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namespace cutlass {
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namespace profiler {
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/////////////////////////////////////////////////////////////////////////////////////////////////
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/// Ctor
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GemmOperationProfiler::GemmOperationProfiler(Options const &options):
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OperationProfiler(
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options,
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library::OperationKind::kGemm,
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{
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{ArgumentTypeID::kEnumerated, {"gemm_kind"}, "Variant of GEMM (gemm, batched, array, universal, planar_complex, planar_complex_array)"},
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{ArgumentTypeID::kEnumerated, {"split_k_mode"}, "Variant of split K mode(serial, parallel)"},
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{ArgumentTypeID::kInteger, {"m", "problem-size::m"}, "M dimension of the GEMM problem space"},
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{ArgumentTypeID::kInteger, {"n", "problem-size::n"}, "N dimension of the GEMM problem space"},
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{ArgumentTypeID::kInteger, {"k", "problem-size::k"}, "K dimension of the GEMM problem space"},
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{ArgumentTypeID::kTensor, {"A"}, "Tensor storing the A operand"},
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{ArgumentTypeID::kTensor, {"B"}, "Tensor storing the B operand"},
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{ArgumentTypeID::kTensor, {"C"}, "Tensor storing the C operand"},
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{ArgumentTypeID::kScalar, {"alpha", "epilogue::alpha"}, "Epilogue scalar alpha"},
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{ArgumentTypeID::kScalar, {"beta", "epilogue::beta"}, "Epilogue scalar beta"},
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{ArgumentTypeID::kInteger, {"split_k_slices", "split-k-slices"}, "Number of partitions of K dimension"},
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{ArgumentTypeID::kInteger, {"batch_count", "batch-count"}, "Number of GEMMs computed in one batch"},
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},
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{ library::Provider::kCUBLAS}
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) {
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description_ = " General matrix-matrix product. D = alpha * A*B + beta * C";
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}
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/// Destructor
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GemmOperationProfiler::~GemmOperationProfiler() {
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}
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/// Prints usage statement for the math function
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void GemmOperationProfiler::print_usage(std::ostream &out) const {
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out << "GEMM" << "\n\n";
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OperationProfiler::print_usage(out);
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}
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/// Prints examples
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void GemmOperationProfiler::print_examples(std::ostream &out) const {
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out << "\nExamples:\n\n"
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<< "Profile a particular problem size:\n"
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<< " $ cutlass_profiler --operation=Gemm --m=1024 --n=1024 --k=128\n\n"
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<< "Schmoo over problem size and beta:\n"
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<< " $ cutlass_profiler --operation=Gemm --m=1024:4096:256 --n=1024:4096:256 --k=128:8192:128 --beta=0,1,2.5\n\n"
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<< "Schmoo over accumulator types:\n"
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<< " $ cutlass_profiler --operation=Gemm --accumulator-type=f16,f32\n\n"
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<< "Run when A is f16 with column-major and B is any datatype with row-major (For column major, use column, col, or n. For row major use, row or t):\n"
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<< " $ cutlass_profiler --operation=Gemm --A=f16:column --B=*:row\n\n"
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<< "Profile a particular problem size with split K and paralell reduction:\n"
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<< " $ cutlass_profiler --operation=Gemm --split_k_mode=parallel --split_k_slices=2 --m=1024 --n=1024 --k=128\n\n"
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<< "Using various input value distribution:\n"
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<< " $ cutlass_profiler --operation=Gemm --dist=uniform,min:0,max:3\n"
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<< " $ cutlass_profiler --operation=Gemm --dist=gaussian,mean:0,stddev:3\n"
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<< " $ cutlass_profiler --operation=Gemm --dist=sequential,start:0,delta:1\n\n"
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<< "Run a kernel with cta tile size of 256x128x32 and save workspace if results are incorrect (note that --cta-tile::k=32 is default cta-tile size):\n"
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<< " $ cutlass_profiler --operation=Gemm --cta_m=256 --cta_n=128 --cta_k=32 --save-workspace=incorrect\n\n"
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<< "Test your changes to gemm kernels with a quick functional test and save results in functional-test.csv:\n"
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<< " $ cutlass_profiler --operation=Gemm \\ \n"
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<< " --m=8,56,120,136,256,264,512,520,1024,1032,4096,8192,16384 \\ \n"
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<< " --n=8,56,120,136,256,264,512,520,1024,1032,4096,8192,16384 \\ \n"
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<< " --k=8,16,32,64,128,256,288,384,504,512,520 \\ \n"
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<< " --beta=0,1,2 --profiling-iterations=1 \\ \n"
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<< " --providers=cutlass --output=functional-test.csv\n\n";
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}
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/////////////////////////////////////////////////////////////////////////////////////////////////
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#if 0
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// used this for debugging
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static std::string byte_string(std::vector<uint8_t> const &bytes) {
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std::stringstream ss;
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ss << "0x";
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for (size_t idx = bytes.size(); idx > 0; --idx) {
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ss << std::hex << std::setw(2) << std::setfill('0') << uint32_t(bytes.at(idx - 1));
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}
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return ss.str();
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}
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#endif
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Status GemmOperationProfiler::GemmProblem::parse(
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library::GemmDescription const &operation_desc,
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ProblemSpace const &problem_space,
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ProblemSpace::Problem const &problem) {
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this->mode = library::GemmUniversalMode::kGemm;
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if (!arg_as_int(this->m, "m", problem_space, problem)) {
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// default value
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this->m = 1024;
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}
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if (!arg_as_int(this->n, "n", problem_space, problem)) {
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// default value
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this->n = 1024;
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}
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if (!arg_as_int(this->k, "k", problem_space, problem)) {
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// default value
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this->k = 1024;
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}
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if (!arg_as_SplitKModeID(this->split_k_mode, "split_k_mode", problem_space, problem)) {
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// defualt value
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this->split_k_mode = library::SplitKMode::kSerial;
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}
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this->mode = library::GemmUniversalMode::kGemm;
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if(this->split_k_mode == library::SplitKMode::kParallel) {
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this->mode = library::GemmUniversalMode::kGemmSplitKParallel;
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}
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if (!arg_as_int(this->split_k_slices, "split_k_slices", problem_space, problem)) {
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// default value
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this->split_k_slices = 1;
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}
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if (!arg_as_int(this->batch_count, "batch_count", problem_space, problem)) {
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// default value
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this->batch_count = 1;
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} else if (this->batch_count > 1) {
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this->mode = library::GemmUniversalMode::kBatched;
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}
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if (this->split_k_slices > 1 && this->batch_count > 1) {
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// At least one of these must be one
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return Status::kErrorInvalidProblem;
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}
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if (!tensor_description_satisfies(operation_desc.A, "A", problem_space, problem)) {
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return Status::kErrorInvalidProblem;
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}
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if (!tensor_description_satisfies(operation_desc.B, "B", problem_space, problem)) {
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return Status::kErrorInvalidProblem;
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}
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if (!tensor_description_satisfies(operation_desc.C, "C", problem_space, problem)) {
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return Status::kErrorInvalidProblem;
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}
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if (!arg_as_scalar(
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this->alpha,
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operation_desc.element_epilogue,
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"alpha",
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problem_space,
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problem)) {
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if (!cast_from_double(this->alpha, operation_desc.element_epilogue, 1)) {
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return Status::kErrorInternal;
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}
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}
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if (!arg_as_scalar(
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this->beta,
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operation_desc.element_epilogue,
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"beta",
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problem_space,
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problem)) {
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if (!cast_from_double(this->beta, operation_desc.element_epilogue, 0)) {
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return Status::kErrorInternal;
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}
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}
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this->lda = DeviceAllocation::get_packed_layout(
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operation_desc.A.layout, {int(this->m), int(this->k)}).front();
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this->ldb = DeviceAllocation::get_packed_layout(
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operation_desc.B.layout, {int(this->k), int(this->n)}).front();
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this->ldc = DeviceAllocation::get_packed_layout(
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operation_desc.C.layout, {int(this->m), int(this->n)}).front();
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return Status::kSuccess;
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}
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/// Total number of bytes loaded
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int64_t GemmOperationProfiler::GemmProblem::bytes(library::GemmDescription const &operation_desc) const {
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// Input bytes read and Output bytes written for the gemm problem
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int64_t bytes =
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int64_t(library::sizeof_bits(operation_desc.A.element) * m / 8) * k +
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int64_t(library::sizeof_bits(operation_desc.B.element) * n / 8) * k +
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int64_t(library::sizeof_bits(operation_desc.C.element) * m / 8) * n;
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// Set is_beta_zero true if beta is zero
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bool is_beta_zero = std::all_of(beta.begin(), beta.end(), [](uint8_t i) { return i==0; });
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// Output bytes read for the gemm problem for non-zero beta values
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if (!is_beta_zero) {
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bytes += int64_t(library::sizeof_bits(operation_desc.C.element) * m / 8) * n;
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}
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bytes *= batch_count;
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return bytes;
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}
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/// Total number of flops computed
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int64_t GemmOperationProfiler::GemmProblem::flops(library::GemmDescription const &operation_desc) const {
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int64_t flops_ = (int64_t(m) * n * k + m * n) * 2 * batch_count;
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// complex-valued support
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switch (operation_desc.tile_description.math_instruction.math_operation) {
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case library::MathOperationID::kMultiplyAddComplex:
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flops_ *= 4;
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break;
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case library::MathOperationID::kMultiplyAddComplexFastF32:
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flops_ *= 4;
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break;
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case library::MathOperationID::kMultiplyAddGaussianComplex:
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flops_ *= 3;
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break;
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default: break;
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}
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return flops_;
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}
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/// Initializes a performance result
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void GemmOperationProfiler::GemmProblem::initialize_result(
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PerformanceResult &result,
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library::GemmDescription const &operation_desc,
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ProblemSpace const &problem_space) {
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result.arguments.resize(problem_space.rank());
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set_argument(result, "gemm_kind", problem_space, library::to_string(operation_desc.gemm_kind));
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set_argument(result, "split_k_mode", problem_space, library::to_string(split_k_mode));
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set_argument(result, "A", problem_space,
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std::string(library::to_string(operation_desc.A.element)) + ":" + library::to_string(operation_desc.A.layout));
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set_argument(result, "B", problem_space,
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std::string(library::to_string(operation_desc.B.element)) + ":" + library::to_string(operation_desc.B.layout));
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set_argument(result, "C", problem_space,
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std::string(library::to_string(operation_desc.C.element)) + ":" + library::to_string(operation_desc.C.layout));
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set_argument(result, "m", problem_space, m);
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set_argument(result, "n", problem_space, n);
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set_argument(result, "k", problem_space, k);
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set_argument(result, "split_k_slices", problem_space, split_k_slices);
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set_argument(result, "batch_count", problem_space, batch_count);
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set_argument(result, "alpha", problem_space,
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library::lexical_cast(alpha, operation_desc.element_epilogue));
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set_argument(result, "beta", problem_space,
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library::lexical_cast(beta, operation_desc.element_epilogue));
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}
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/////////////////////////////////////////////////////////////////////////////////////////////////
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/// Extracts the problem dimensions
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Status GemmOperationProfiler::initialize_configuration(
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Options const &options,
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PerformanceReport &report,
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DeviceContext &device_context,
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library::Operation const *operation,
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ProblemSpace const &problem_space,
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ProblemSpace::Problem const &problem) {
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library::GemmDescription const &operation_desc =
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static_cast<library::GemmDescription const &>(operation->description());
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if (operation_desc.gemm_kind != library::GemmKind::kUniversal) {
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return Status::kErrorInvalidProblem;
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}
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Status status = problem_.parse(operation_desc, problem_space, problem);
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if (status != Status::kSuccess) {
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return status;
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}
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gemm_workspace_.configuration.mode = problem_.mode;
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gemm_workspace_.configuration.problem_size.m() = int(problem_.m);
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gemm_workspace_.configuration.problem_size.n() = int(problem_.n);
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gemm_workspace_.configuration.problem_size.k() = int(problem_.k);
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gemm_workspace_.configuration.lda = problem_.lda;
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gemm_workspace_.configuration.ldb = problem_.ldb;
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gemm_workspace_.configuration.ldc = problem_.ldc;
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gemm_workspace_.configuration.ldd = problem_.ldc;
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if (problem_.mode == library::GemmUniversalMode::kBatched) {
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gemm_workspace_.configuration.batch_count = problem_.batch_count;
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}
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else {
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gemm_workspace_.configuration.batch_count = problem_.split_k_slices;
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}
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gemm_workspace_.arguments.A = nullptr;
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gemm_workspace_.arguments.B = nullptr;
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gemm_workspace_.arguments.C = nullptr;
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gemm_workspace_.arguments.D = nullptr;
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gemm_workspace_.arguments.alpha = problem_.alpha.data();
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gemm_workspace_.arguments.beta = problem_.beta.data();
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gemm_workspace_.arguments.pointer_mode = library::ScalarPointerMode::kHost;
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// initialize reduction operation for parallel splitKMode
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if (problem_.split_k_mode == library::SplitKMode::kParallel) {
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if (!initialize_reduction_configuration_(operation, problem)) {
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return Status::kErrorInternal;
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}
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}
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initialize_result_(this->model_result_, options, operation_desc, problem_space);
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return operation->can_implement(&gemm_workspace_.configuration, &gemm_workspace_.arguments);
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}
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/// Initializes the performance result
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void GemmOperationProfiler::initialize_result_(
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PerformanceResult &result,
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Options const &options,
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library::GemmDescription const &operation_desc,
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ProblemSpace const &problem_space) {
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result.provider = library::Provider::kCUTLASS;
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result.disposition = Disposition::kNotRun;
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result.status = Status::kSuccess;
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result.operation_name = operation_desc.name;
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problem_.initialize_result(result, operation_desc, problem_space);
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OperationProfiler::initialize_result_(result, operation_desc, problem_space);
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result.bytes = problem_.bytes(operation_desc);
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result.flops = problem_.flops(operation_desc);
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result.runtime = 0;
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}
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/// Initialize redution problem dimentions and library::Operation
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bool GemmOperationProfiler::initialize_reduction_configuration_(
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library::Operation const *operation,
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ProblemSpace::Problem const &problem) {
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library::GemmDescription const &gemm_desc =
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static_cast<library::GemmDescription const&>(operation->description());
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if (!cast_from_double(problem_.alpha_one, gemm_desc.element_epilogue, 1)) {
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return false;
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}
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if (!cast_from_double(problem_.beta_zero, gemm_desc.element_epilogue, 0)) {
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return false;
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}
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/// initialize library::ReductionConfiguration
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gemm_workspace_.reduction_configuration.problem_size = gemm::GemmCoord(int(problem_.n), int(problem_.m), int(problem_.k)).mn();
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gemm_workspace_.reduction_configuration.partitions = int(problem_.split_k_slices);
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gemm_workspace_.reduction_configuration.partition_stride = gemm::GemmCoord(int(problem_.n), int(problem_.m), int(problem_.k)).mn().product();
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gemm_workspace_.reduction_configuration.ldw = problem_.ldc;
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gemm_workspace_.reduction_configuration.lds = problem_.ldc;
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gemm_workspace_.reduction_configuration.ldd = problem_.ldc;
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// find reduction operation
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library::ReductionFunctionalKey reduction_key(
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library::Provider::kCUTLASS,
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gemm_desc.tile_description.math_instruction.element_accumulator, // element workspace
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gemm_desc.tile_description.math_instruction.element_accumulator, // element accumulator
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gemm_desc.C.element, // element output
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gemm_desc.element_epilogue // element coumpute
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);
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auto reduction_it = library::Singleton::get().operation_table.reduction_operations.find(reduction_key);
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if (reduction_it == library::Singleton::get().operation_table.reduction_operations.end()) {
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return false;
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}
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// initialize reduction operation required for parallel split-k operator
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reduction_op_ = reduction_it->second;
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// reduction operation found and initialized
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return true;
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}
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/// Initializes workspace
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Status GemmOperationProfiler::initialize_workspace(
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Options const &options,
|
|
PerformanceReport &report,
|
|
DeviceContext &device_context,
|
|
library::Operation const *operation,
|
|
ProblemSpace const &problem_space,
|
|
ProblemSpace::Problem const &problem) {
|
|
|
|
library::Operation const* underlying_operation = operation;
|
|
|
|
if (problem_.split_k_mode == library::SplitKMode::kParallel) {
|
|
if (!(underlying_operation = library::find_gemm_operation_for_parallel_reduction(operation))) {
|
|
return Status::kErrorNotSupported;
|
|
}
|
|
}
|
|
|
|
library::GemmDescription const &operation_desc =
|
|
static_cast<library::GemmDescription const &>(operation->description());
|
|
|
|
// Compute the number of copies of the problem to avoid L2 camping.
|
|
if (!options.profiling.workspace_count) {
|
|
int64_t bytes = problem_.bytes(operation_desc);
|
|
if (bytes < 3 * int64_t(options.device.properties.l2CacheSize)) {
|
|
gemm_workspace_.problem_count =
|
|
1 + int((3 * int64_t(options.device.properties.l2CacheSize)) / bytes);
|
|
}
|
|
else {
|
|
gemm_workspace_.problem_count = 1;
|
|
}
|
|
}
|
|
else {
|
|
gemm_workspace_.problem_count = options.profiling.workspace_count;
|
|
}
|
|
|
|
if (options.execution_mode != ExecutionMode::kDryRun) {
|
|
|
|
gemm_workspace_.A = device_context.allocate_tensor(
|
|
options,
|
|
"A",
|
|
operation_desc.A.element,
|
|
operation_desc.A.layout,
|
|
{int(problem_.m), int(problem_.k)},
|
|
{int(problem_.lda)},
|
|
problem_.batch_count * gemm_workspace_.problem_count
|
|
);
|
|
|
|
gemm_workspace_.B = device_context.allocate_tensor(
|
|
options,
|
|
"B",
|
|
operation_desc.B.element,
|
|
operation_desc.B.layout,
|
|
{int(problem_.k), int(problem_.n)},
|
|
{int(problem_.ldb)},
|
|
problem_.batch_count * gemm_workspace_.problem_count
|
|
);
|
|
|
|
gemm_workspace_.C = device_context.allocate_tensor(
|
|
options,
|
|
"C",
|
|
operation_desc.C.element,
|
|
operation_desc.C.layout,
|
|
{int(problem_.m), int(problem_.n)},
|
|
{int(problem_.ldc)},
|
|
problem_.batch_count * gemm_workspace_.problem_count
|
|
);
|
|
|
|
gemm_workspace_.Computed = device_context.allocate_tensor(
|
|
"D",
|
|
operation_desc.C.element,
|
|
operation_desc.C.layout,
|
|
{int(problem_.m), int(problem_.n)},
|
|
{int(problem_.ldc)},
|
|
problem_.batch_count * gemm_workspace_.problem_count
|
|
);
|
|
|
|
gemm_workspace_.Reference = device_context.allocate_tensor(
|
|
"Reference",
|
|
operation_desc.C.element,
|
|
operation_desc.C.layout,
|
|
{int(problem_.m), int(problem_.n)},
|
|
{int(problem_.ldc)},
|
|
problem_.batch_count * gemm_workspace_.problem_count
|
|
);
|
|
|
|
gemm_workspace_.Reference->copy_from_device(gemm_workspace_.C->data());
|
|
|
|
gemm_workspace_.arguments.batch_stride_A = gemm_workspace_.A->batch_stride();
|
|
gemm_workspace_.arguments.batch_stride_B = gemm_workspace_.B->batch_stride();
|
|
gemm_workspace_.arguments.batch_stride_C = gemm_workspace_.C->batch_stride();
|
|
gemm_workspace_.arguments.batch_stride_D = gemm_workspace_.Computed->batch_stride();
|
|
}
|
|
|
|
//
|
|
// Initialize the CUTLASS operation
|
|
//
|
|
Status status = Status::kSuccess;
|
|
|
|
if (options.profiling.provider_enabled(library::Provider::kCUTLASS)) {
|
|
|
|
if (options.execution_mode != ExecutionMode::kDryRun) {
|
|
|
|
uint64_t workspace_size = underlying_operation->get_host_workspace_size(&gemm_workspace_.configuration);
|
|
gemm_workspace_.host_workspace.resize(workspace_size, 0);
|
|
|
|
workspace_size = underlying_operation->get_device_workspace_size(&gemm_workspace_.configuration,
|
|
&gemm_workspace_.arguments);
|
|
gemm_workspace_.device_workspace.reset(library::NumericTypeID::kU8, workspace_size);
|
|
|
|
status = underlying_operation->initialize(
|
|
&gemm_workspace_.configuration,
|
|
gemm_workspace_.host_workspace.data(),
|
|
gemm_workspace_.device_workspace.data());
|
|
|
|
if (status != Status::kSuccess) {
|
|
return status;
|
|
}
|
|
|
|
if (problem_.split_k_mode == library::SplitKMode::kParallel) {
|
|
workspace_size = reduction_op_->get_host_workspace_size(&gemm_workspace_.reduction_configuration);
|
|
gemm_workspace_.reduction_host_workspace.resize(workspace_size, 0);
|
|
|
|
status = reduction_op_->initialize(
|
|
&gemm_workspace_.reduction_configuration,
|
|
gemm_workspace_.reduction_host_workspace.data(),
|
|
nullptr);
|
|
|
|
if (status != Status::kSuccess) {
|
|
return status;
|
|
}
|
|
}
|
|
}
|
|
|
|
//
|
|
// If CUTLASS is enabled, generate a result for it
|
|
//
|
|
results_.push_back(model_result_);
|
|
results_.back().provider = library::Provider::kCUTLASS;
|
|
results_.back().op_kind = library::OperationKind::kGemm;
|
|
results_.back().disposition = Disposition::kNotRun;
|
|
|
|
for(auto provider : verification_providers_) {
|
|
results_.back().verification_map[provider] = Disposition::kNotRun;
|
|
}
|
|
}
|
|
|
|
return status;
|
|
}
|
|
|
|
/////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
/// Verifies CUTLASS against references
|
|
bool GemmOperationProfiler::verify_cutlass(
|
|
Options const &options,
|
|
PerformanceReport &report,
|
|
DeviceContext &device_context,
|
|
library::Operation const *operation,
|
|
ProblemSpace const &problem_space,
|
|
ProblemSpace::Problem const &problem) {
|
|
|
|
if (!options.profiling.provider_enabled(library::Provider::kCUTLASS)) {
|
|
return true;
|
|
}
|
|
|
|
if (options.execution_mode == ExecutionMode::kDryRun) {
|
|
return true;
|
|
}
|
|
|
|
// Initialize structure containing GEMM arguments
|
|
gemm_workspace_.arguments.A = gemm_workspace_.A->data();
|
|
gemm_workspace_.arguments.B = gemm_workspace_.B->data();
|
|
gemm_workspace_.arguments.C = gemm_workspace_.C->data();
|
|
gemm_workspace_.arguments.D = gemm_workspace_.Computed->data();
|
|
gemm_workspace_.arguments.alpha = problem_.alpha.data();
|
|
gemm_workspace_.arguments.beta = problem_.beta.data();
|
|
gemm_workspace_.arguments.pointer_mode = library::ScalarPointerMode::kHost;
|
|
gemm_workspace_.arguments.batch_stride_A = gemm_workspace_.A->batch_stride();
|
|
gemm_workspace_.arguments.batch_stride_B = gemm_workspace_.B->batch_stride();
|
|
gemm_workspace_.arguments.batch_stride_C = gemm_workspace_.C->batch_stride();
|
|
gemm_workspace_.arguments.batch_stride_D = gemm_workspace_.Computed->batch_stride();
|
|
|
|
if (problem_.split_k_mode == library::SplitKMode::kParallel) {
|
|
gemm_workspace_.arguments.D = gemm_workspace_.device_workspace.data();
|
|
gemm_workspace_.arguments.alpha = problem_.alpha_one.data();
|
|
gemm_workspace_.arguments.beta = problem_.beta_zero.data();
|
|
|
|
gemm_workspace_.reduction_arguments.workspace = gemm_workspace_.device_workspace.data();
|
|
gemm_workspace_.reduction_arguments.source = gemm_workspace_.C->data();
|
|
gemm_workspace_.reduction_arguments.destination = gemm_workspace_.Computed->data();
|
|
gemm_workspace_.reduction_arguments.alpha = problem_.alpha.data();
|
|
gemm_workspace_.reduction_arguments.beta = problem_.beta.data();
|
|
gemm_workspace_.reduction_arguments.pointer_mode = library::ScalarPointerMode::kHost;
|
|
}
|
|
|
|
//
|
|
// Run the CUTLASS operation
|
|
//
|
|
|
|
// initialize gemm underlying operation to handle parallel reduction
|
|
library::Operation const * underlying_operation = operation;
|
|
|
|
if (problem_.split_k_mode == library::SplitKMode::kParallel) {
|
|
if (!(underlying_operation = library::find_gemm_operation_for_parallel_reduction(operation))) {
|
|
results_.back().disposition = Disposition::kFailed;
|
|
return false;
|
|
}
|
|
}
|
|
|
|
results_.back().status = underlying_operation->run(
|
|
&gemm_workspace_.arguments,
|
|
gemm_workspace_.host_workspace.data(),
|
|
gemm_workspace_.device_workspace.data());
|
|
|
|
if (results_.back().status != Status::kSuccess) {
|
|
results_.back().disposition = Disposition::kFailed;
|
|
return false;
|
|
}
|
|
|
|
// Run parallel reduction kernel for parallel split_k_mode
|
|
if (problem_.split_k_mode == library::SplitKMode::kParallel) {
|
|
results_.back().status = reduction_op_->run(
|
|
&gemm_workspace_.reduction_arguments,
|
|
gemm_workspace_.reduction_host_workspace.data(),
|
|
nullptr);
|
|
|
|
if (results_.back().status != Status::kSuccess) {
|
|
results_.back().disposition = Disposition::kFailed;
|
|
return false;
|
|
}
|
|
}
|
|
|
|
cudaError_t result = cudaDeviceSynchronize();
|
|
if (result != cudaSuccess) {
|
|
results_.back().disposition = Disposition::kFailed;
|
|
return false;
|
|
}
|
|
|
|
// CUTLASS op ran the but not yet verified against any verification provider
|
|
results_.back().disposition = Disposition::kNotVerified;
|
|
|
|
//
|
|
// Run verification providers
|
|
//
|
|
|
|
if (options.verification.enabled) {
|
|
|
|
#if CUTLASS_ENABLE_CUBLAS
|
|
if (options.verification.provider_enabled(library::Provider::kCUBLAS)) {
|
|
|
|
// Guard against unsupported cases
|
|
auto const & gemm_desc = static_cast<library::GemmDescription const &>(operation->description());
|
|
|
|
if (cublas_satisfies(gemm_desc) == Status::kSuccess) {
|
|
|
|
// call cublas verification if supported
|
|
verify_with_cublas_(
|
|
options,
|
|
report,
|
|
device_context,
|
|
operation,
|
|
problem_space,
|
|
problem);
|
|
}
|
|
|
|
else {
|
|
// set verification map for cublas to not supported
|
|
results_.back().verification_map[library::Provider::kCUBLAS] = Disposition::kNotSupported;
|
|
}
|
|
}
|
|
#endif // #if CUTLASS_ENABLE_CUBLAS
|
|
|
|
verify_with_reference_(options, report, device_context, operation, problem_space, problem);
|
|
|
|
// Update disposition to worst case verification outcome among all
|
|
// verification providers which are supported
|
|
bool is_any_verification_run_passed = false;
|
|
for(auto &m : results_.back().verification_map) {
|
|
if(m.second == Disposition::kFailed || m.second == Disposition::kIncorrect) {
|
|
results_.back().disposition = m.second;
|
|
return true;
|
|
}
|
|
if(!is_any_verification_run_passed && m.second == Disposition::kPassed) {
|
|
is_any_verification_run_passed = true;
|
|
}
|
|
}
|
|
|
|
if(is_any_verification_run_passed) {
|
|
results_.back().disposition = Disposition::kPassed;
|
|
}
|
|
}
|
|
|
|
// Return true means continue profiling
|
|
return true;
|
|
}
|
|
|
|
///////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
/// Verifies CUTLASS against references
|
|
bool GemmOperationProfiler::verify_with_cublas_(
|
|
Options const &options,
|
|
PerformanceReport &report,
|
|
DeviceContext &device_context,
|
|
library::Operation const *operation,
|
|
ProblemSpace const &problem_space,
|
|
ProblemSpace::Problem const &problem) {
|
|
|
|
|
|
#if CUTLASS_ENABLE_CUBLAS
|
|
|
|
library::GemmDescription const &gemm_desc =
|
|
static_cast<library::GemmDescription const &>(operation->description());
|
|
|
|
//
|
|
// Construct cuBLAS operators
|
|
//
|
|
|
|
CublasCreate handle;
|
|
cublasStatus_t status = handle.get_cublas_create_status();
|
|
|
|
if (status != CUBLAS_STATUS_SUCCESS) {
|
|
|
|
results_.back().verification_map[library::Provider::kCUBLAS] = get_cutlass_disposition(status);
|
|
return true;
|
|
}
|
|
|
|
std::vector<cublasGemmAlgo_t> algorithms;
|
|
|
|
detail::select_cublas_algorithms(
|
|
algorithms,
|
|
options,
|
|
gemm_desc);
|
|
|
|
if (algorithms.empty()) {
|
|
// no algorithm selected
|
|
return true;
|
|
}
|
|
|
|
//
|
|
// Initialize state
|
|
//
|
|
|
|
try {
|
|
|
|
//
|
|
// Construct dispatcher to cublasGemmEx()
|
|
//
|
|
|
|
// Initialize structure containing GEMM arguments
|
|
gemm_workspace_.arguments.A = gemm_workspace_.A->data();
|
|
gemm_workspace_.arguments.batch_stride_A = gemm_workspace_.A->batch_stride();
|
|
gemm_workspace_.arguments.B = gemm_workspace_.B->data();
|
|
gemm_workspace_.arguments.batch_stride_B = gemm_workspace_.B->batch_stride();
|
|
gemm_workspace_.arguments.C = gemm_workspace_.Reference->data();
|
|
gemm_workspace_.arguments.batch_stride_C = gemm_workspace_.Reference->batch_stride();
|
|
gemm_workspace_.arguments.D = gemm_workspace_.Reference->data();
|
|
gemm_workspace_.arguments.batch_stride_D = gemm_workspace_.Reference->batch_stride();
|
|
gemm_workspace_.arguments.alpha = problem_.alpha.data();
|
|
gemm_workspace_.arguments.beta = problem_.beta.data();
|
|
gemm_workspace_.arguments.pointer_mode = library::ScalarPointerMode::kHost;
|
|
|
|
detail::cublasGemmExDispatcher gemm_op(
|
|
gemm_desc,
|
|
gemm_workspace_.configuration,
|
|
gemm_workspace_.arguments,
|
|
algorithms.front()
|
|
);
|
|
|
|
if (gemm_op.status != Status::kSuccess) {
|
|
results_.back().verification_map[library::Provider::kCUBLAS] = Disposition::kNotRun;
|
|
return true;
|
|
}
|
|
|
|
results_.back().status = Status::kSuccess;
|
|
|
|
status = gemm_op(handle);
|
|
|
|
// Handle errors
|
|
if (status != CUBLAS_STATUS_SUCCESS) {
|
|
|
|
results_.back().verification_map[library::Provider::kCUBLAS] = get_cutlass_disposition(status);
|
|
return true;
|
|
}
|
|
|
|
//
|
|
// Verify results
|
|
//
|
|
|
|
results_.back().verification_map[library::Provider::kCUBLAS] = compare_tensors(
|
|
options,
|
|
*gemm_workspace_.Computed,
|
|
*gemm_workspace_.Reference,
|
|
gemm_workspace_.Computed->batch_stride()
|
|
);
|
|
|
|
// Save workspace if incorrect
|
|
if (options.verification.save_workspace == SaveWorkspace::kIncorrect &&
|
|
results_.back().verification_map[library::Provider::kCUBLAS] == Disposition::kIncorrect) {
|
|
|
|
save_workspace(
|
|
device_context,
|
|
options,
|
|
gemm_desc,
|
|
library::Provider::kCUTLASS,
|
|
library::Provider::kCUBLAS);
|
|
}
|
|
}
|
|
catch (...) {
|
|
results_.back().verification_map[library::Provider::kCUBLAS] = Disposition::kFailed;
|
|
}
|
|
|
|
#endif
|
|
|
|
// Return true means continue profiling
|
|
return true;
|
|
}
|
|
|
|
/////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
/// Verifies CUTLASS against host and device references
|
|
bool GemmOperationProfiler::verify_with_reference_(
|
|
Options const &options,
|
|
PerformanceReport &report,
|
|
DeviceContext &device_context,
|
|
library::Operation const *operation,
|
|
ProblemSpace const &problem_space,
|
|
ProblemSpace::Problem const &problem) {
|
|
|
|
library::GemmDescription const &gemm_desc =
|
|
static_cast<library::GemmDescription const &>(operation->description());
|
|
|
|
//
|
|
// Initialize state
|
|
//
|
|
|
|
library::Provider references[] = {
|
|
library::Provider::kReferenceDevice,
|
|
library::Provider::kReferenceHost
|
|
};
|
|
|
|
for (auto provider : references) {
|
|
|
|
// Skip providers that are not enabled
|
|
if (!options.verification.provider_enabled(provider)) {
|
|
continue;
|
|
}
|
|
|
|
void *ptr_A = gemm_workspace_.A->data();
|
|
void *ptr_B = gemm_workspace_.B->data();
|
|
void *ptr_C = gemm_workspace_.C->data();
|
|
void *ptr_D = gemm_workspace_.Reference->data();
|
|
|
|
// To support the host-side reference, conditionally allocate and
|
|
// copy tensors to host memory.
|
|
std::vector<uint8_t> host_data_A;
|
|
std::vector<uint8_t> host_data_B;
|
|
std::vector<uint8_t> host_data_C;
|
|
std::vector<uint8_t> host_data_D;
|
|
|
|
if (provider == library::Provider::kReferenceHost) {
|
|
|
|
host_data_A.resize(gemm_workspace_.A->bytes());
|
|
ptr_A = host_data_A.data();
|
|
gemm_workspace_.A->copy_to_host(ptr_A);
|
|
|
|
host_data_B.resize(gemm_workspace_.B->bytes());
|
|
ptr_B = host_data_B.data();
|
|
gemm_workspace_.B->copy_to_host(ptr_B);
|
|
|
|
host_data_C.resize(gemm_workspace_.C->bytes());
|
|
ptr_C = host_data_C.data();
|
|
gemm_workspace_.C->copy_to_host(ptr_C);
|
|
|
|
host_data_D.resize(gemm_workspace_.Reference->bytes());
|
|
ptr_D = host_data_D.data();
|
|
}
|
|
|
|
//
|
|
// Launch
|
|
//
|
|
|
|
library::Handle handle;
|
|
|
|
handle.set_provider(provider);
|
|
|
|
Status status = handle.gemm_universal(
|
|
problem_.mode,
|
|
gemm_workspace_.configuration.problem_size.m(),
|
|
gemm_workspace_.configuration.problem_size.n(),
|
|
gemm_workspace_.configuration.problem_size.k(),
|
|
gemm_desc.tile_description.math_instruction.element_accumulator,
|
|
gemm_desc.element_epilogue,
|
|
|
|
problem_.alpha.data(),
|
|
|
|
gemm_desc.A.element,
|
|
gemm_desc.A.layout,
|
|
gemm_desc.transform_A,
|
|
ptr_A,
|
|
int(gemm_workspace_.configuration.lda),
|
|
|
|
gemm_desc.B.element,
|
|
gemm_desc.B.layout,
|
|
gemm_desc.transform_B,
|
|
ptr_B,
|
|
int(gemm_workspace_.configuration.ldb),
|
|
|
|
problem_.beta.data(),
|
|
|
|
gemm_desc.C.element,
|
|
ptr_C,
|
|
int(gemm_workspace_.configuration.ldc),
|
|
|
|
ptr_D,
|
|
int(gemm_workspace_.configuration.ldd),
|
|
|
|
gemm_workspace_.configuration.batch_count,
|
|
gemm_workspace_.A->batch_stride(),
|
|
gemm_workspace_.B->batch_stride(),
|
|
gemm_workspace_.C->batch_stride(),
|
|
gemm_workspace_.Reference->batch_stride()
|
|
);
|
|
|
|
if (status != Status::kSuccess) {
|
|
results_.back().verification_map[provider] = Disposition::kNotRun;
|
|
return true;
|
|
}
|
|
|
|
results_.back().status = status;
|
|
|
|
if (provider == library::Provider::kReferenceHost) {
|
|
gemm_workspace_.Reference->copy_from_host(ptr_D);
|
|
}
|
|
|
|
//
|
|
// Verify results
|
|
//
|
|
|
|
results_.back().verification_map[provider] = compare_tensors(
|
|
options,
|
|
*gemm_workspace_.Computed,
|
|
*gemm_workspace_.Reference,
|
|
gemm_workspace_.Computed->batch_stride()
|
|
);
|
|
|
|
// Save workspace if incorrect
|
|
if (options.verification.save_workspace == SaveWorkspace::kIncorrect &&
|
|
results_.back().verification_map[provider] == Disposition::kIncorrect) {
|
|
|
|
save_workspace(
|
|
device_context,
|
|
options,
|
|
gemm_desc,
|
|
library::Provider::kCUTLASS,
|
|
provider);
|
|
}
|
|
}
|
|
|
|
return true;
|
|
}
|
|
|
|
/////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
/// Measures performance results
|
|
bool GemmOperationProfiler::profile(
|
|
Options const &options,
|
|
PerformanceReport &report,
|
|
DeviceContext &device_context,
|
|
library::Operation const *operation,
|
|
ProblemSpace const &problem_space,
|
|
ProblemSpace::Problem const &problem) {
|
|
|
|
if (options.profiling.provider_enabled(library::Provider::kCUTLASS)) {
|
|
|
|
// Initialize structure containing GEMM arguments
|
|
gemm_workspace_.arguments.A = gemm_workspace_.A->data();
|
|
gemm_workspace_.arguments.B = gemm_workspace_.B->data();
|
|
gemm_workspace_.arguments.C = gemm_workspace_.C->data();
|
|
gemm_workspace_.arguments.D = gemm_workspace_.Computed->data();
|
|
gemm_workspace_.arguments.alpha = problem_.alpha.data();
|
|
gemm_workspace_.arguments.beta = problem_.beta.data();
|
|
gemm_workspace_.arguments.pointer_mode = library::ScalarPointerMode::kHost;
|
|
gemm_workspace_.arguments.batch_stride_A = gemm_workspace_.A->batch_stride();
|
|
gemm_workspace_.arguments.batch_stride_B = gemm_workspace_.B->batch_stride();
|
|
gemm_workspace_.arguments.batch_stride_C = gemm_workspace_.C->batch_stride();
|
|
gemm_workspace_.arguments.batch_stride_D = gemm_workspace_.Computed->batch_stride();
|
|
|
|
if (problem_.split_k_mode == library::SplitKMode::kParallel) {
|
|
gemm_workspace_.arguments.D = gemm_workspace_.device_workspace.data();
|
|
gemm_workspace_.arguments.alpha = problem_.alpha_one.data();
|
|
gemm_workspace_.arguments.beta = problem_.beta_zero.data();
|
|
|
|
gemm_workspace_.reduction_arguments.workspace = gemm_workspace_.device_workspace.data();
|
|
gemm_workspace_.reduction_arguments.source = gemm_workspace_.C->data();
|
|
gemm_workspace_.reduction_arguments.destination = gemm_workspace_.Computed->data();
|
|
gemm_workspace_.reduction_arguments.alpha = problem_.alpha.data();
|
|
gemm_workspace_.reduction_arguments.beta = problem_.beta.data();
|
|
gemm_workspace_.reduction_arguments.pointer_mode = library::ScalarPointerMode::kHost;
|
|
}
|
|
|
|
results_.back().status = profile_cutlass_(
|
|
results_.back().runtime,
|
|
options,
|
|
operation,
|
|
&gemm_workspace_.arguments,
|
|
gemm_workspace_.host_workspace.data(),
|
|
gemm_workspace_.device_workspace.data()
|
|
);
|
|
}
|
|
return true;
|
|
}
|
|
|
|
/////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
/// Method to profile a CUTLASS Operation
|
|
Status GemmOperationProfiler::profile_cutlass_(
|
|
double &runtime,
|
|
Options const &options,
|
|
library::Operation const *operation,
|
|
void *arguments,
|
|
void *host_workspace,
|
|
void *device_workspace) {
|
|
|
|
GpuTimer timer;
|
|
|
|
// initialize gemm underlying operation to handle parallel reduction
|
|
library::Operation const * underlying_operation = operation;
|
|
|
|
if (problem_.split_k_mode == library::SplitKMode::kParallel) {
|
|
if (!(underlying_operation = library::find_gemm_operation_for_parallel_reduction(operation))) {
|
|
return Status::kErrorNotSupported;
|
|
}
|
|
}
|
|
|
|
//
|
|
// Optional sleep to limit power consumption and thermals
|
|
//
|
|
|
|
sleep(options.profiling.sleep_duration);
|
|
|
|
//
|
|
// Warmup loop
|
|
//
|
|
|
|
Status status;
|
|
|
|
for (int iteration = 0; iteration < options.profiling.warmup_iterations; ++iteration) {
|
|
|
|
int problem_idx = (iteration % gemm_workspace_.problem_count) * problem_.batch_count;
|
|
|
|
gemm_workspace_.arguments.A = gemm_workspace_.A->batch_data(problem_idx);
|
|
gemm_workspace_.arguments.B = gemm_workspace_.B->batch_data(problem_idx);
|
|
gemm_workspace_.arguments.C = gemm_workspace_.C->batch_data(problem_idx);
|
|
gemm_workspace_.arguments.D = gemm_workspace_.Computed->batch_data(problem_idx);
|
|
|
|
if (problem_.split_k_mode == library::SplitKMode::kParallel) {
|
|
gemm_workspace_.arguments.D = gemm_workspace_.device_workspace.data();
|
|
|
|
gemm_workspace_.reduction_arguments.workspace = gemm_workspace_.device_workspace.data();
|
|
gemm_workspace_.reduction_arguments.source = gemm_workspace_.C->batch_data(problem_idx);
|
|
gemm_workspace_.reduction_arguments.destination = gemm_workspace_.Computed->batch_data(problem_idx);
|
|
}
|
|
|
|
// Execute the CUTLASS operation
|
|
status = underlying_operation->run(
|
|
&gemm_workspace_.arguments,
|
|
host_workspace,
|
|
device_workspace);
|
|
|
|
if (status != Status::kSuccess) {
|
|
return status;
|
|
}
|
|
|
|
// Run parallel reduction kernel for parallel split_k_mode
|
|
if (problem_.split_k_mode == library::SplitKMode::kParallel) {
|
|
status = reduction_op_->run(
|
|
&gemm_workspace_.reduction_arguments,
|
|
gemm_workspace_.reduction_host_workspace.data(),
|
|
nullptr);
|
|
|
|
if (status != Status::kSuccess) {
|
|
return status;
|
|
}
|
|
}
|
|
}
|
|
|
|
//
|
|
// Initialize GPU timer
|
|
//
|
|
|
|
timer.start();
|
|
|
|
//
|
|
// Profiling loop
|
|
//
|
|
|
|
int Iterations = options.profiling.iterations;
|
|
|
|
int iteration = 0;
|
|
for (; iteration < Iterations; ++iteration) {
|
|
|
|
// Iterate over copies of the problem in memory
|
|
int workspace_idx = options.profiling.warmup_iterations + iteration;
|
|
int problem_idx = (workspace_idx % gemm_workspace_.problem_count) * problem_.batch_count;
|
|
|
|
gemm_workspace_.arguments.A = gemm_workspace_.A->batch_data(problem_idx);
|
|
gemm_workspace_.arguments.B = gemm_workspace_.B->batch_data(problem_idx);
|
|
gemm_workspace_.arguments.C = gemm_workspace_.C->batch_data(problem_idx);
|
|
gemm_workspace_.arguments.D = gemm_workspace_.Computed->batch_data(problem_idx);
|
|
|
|
if (problem_.split_k_mode == library::SplitKMode::kParallel) {
|
|
gemm_workspace_.arguments.D = gemm_workspace_.device_workspace.data();
|
|
|
|
gemm_workspace_.reduction_arguments.workspace = gemm_workspace_.device_workspace.data();
|
|
gemm_workspace_.reduction_arguments.source = gemm_workspace_.C->batch_data(problem_idx);
|
|
gemm_workspace_.reduction_arguments.destination = gemm_workspace_.Computed->batch_data(problem_idx);
|
|
}
|
|
|
|
status = underlying_operation->run(
|
|
arguments,
|
|
host_workspace,
|
|
device_workspace);
|
|
|
|
if (status != Status::kSuccess) {
|
|
return status;
|
|
}
|
|
|
|
// Run parallel reduction kernel for parallel split_k_mode
|
|
if (problem_.split_k_mode == library::SplitKMode::kParallel) {
|
|
status = reduction_op_->run(
|
|
&gemm_workspace_.reduction_arguments,
|
|
gemm_workspace_.reduction_host_workspace.data(),
|
|
nullptr);
|
|
|
|
if (status != Status::kSuccess) {
|
|
return status;
|
|
}
|
|
}
|
|
}
|
|
|
|
//
|
|
// Wait for completion
|
|
//
|
|
|
|
timer.stop_and_wait();
|
|
|
|
//
|
|
// Update performance result
|
|
//
|
|
|
|
runtime = timer.duration(iteration);
|
|
|
|
return status;
|
|
}
|
|
|
|
/////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
} // namespace profiler
|
|
} // namespace cutlass
|
|
|
|
/////////////////////////////////////////////////////////////////////////////////////////////////
|