578 lines
20 KiB
Plaintext
578 lines
20 KiB
Plaintext
/***************************************************************************************************
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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 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 "cublas_helpers.h"
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#include "sparse_gemm_operation_profiler.h"
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#include "gpu_timer.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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SparseGemmOperationProfiler::SparseGemmOperationProfiler(Options const &options):
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OperationProfiler(
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options,
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library::OperationKind::kSparseGemm,
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{
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{ArgumentTypeID::kEnumerated, {"gemm_kind"}, "Variant of GEMM (e.g. sparse, ...)"},
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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::kTensor, {"E"}, "Tensor storing the E 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"}, "Number of partitions of K dimension"},
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{ArgumentTypeID::kInteger, {"batch_count"}, "Number of GEMMs computed in one batch"},
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}
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) {
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description_ = " Structured sparse GEMM. D = alpha * A*B + beta * C";
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}
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/// Destructor
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SparseGemmOperationProfiler::~SparseGemmOperationProfiler() {
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}
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/// Prints usage statement for the math function
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void SparseGemmOperationProfiler::print_usage(std::ostream &out) const {
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out << "Sparse 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 SparseGemmOperationProfiler::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=SparseGemm --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=SparseGemm --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=SparseGemm --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=SparseGemm --A=f16:column --B=*:row\n\n"
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<< "Using various input value distribution:\n"
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<< " $ cutlass_profiler --operation=SparseGemm --dist=uniform,min:0,max:3\n"
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<< " $ cutlass_profiler --operation=SparseGemm --dist=gaussian,mean:0,stddev:3\n"
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<< " $ cutlass_profiler --operation=SparseGemm --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=SparseGemm --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=SparseGemm \\ \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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Status SparseGemmOperationProfiler::SparseGemmProblem::parse(
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library::SparseGemmDescription const &operation_desc,
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ProblemSpace const &problem_space,
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ProblemSpace::Problem const &problem) {
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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_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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}
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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 (!tensor_description_satisfies(operation_desc.E, "E", 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->elements_per_128b =
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128 / library::sizeof_bits(operation_desc.A.element);
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this->lda = DeviceAllocation::get_packed_layout(
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operation_desc.A.layout,
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{int(this->m), int(this->k) / int(this->sparse)})
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.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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this->lde =
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DeviceAllocation::get_packed_layout(
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operation_desc.E.layout,
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{int(this->m), int(this->k / this->sparse / this->elements_per_128b)})
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.front();
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return Status::kSuccess;
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}
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/// Initializes a performance result
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void SparseGemmOperationProfiler::SparseGemmProblem::initialize_result(
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PerformanceResult &result,
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library::SparseGemmDescription 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, "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, "E", problem_space,
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std::string(library::to_string(operation_desc.E.element)) + ":" + library::to_string(operation_desc.E.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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/// Extracts the problem dimensions
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Status SparseGemmOperationProfiler::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::SparseGemmDescription const &operation_desc =
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static_cast<library::SparseGemmDescription const &>(operation->description());
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if (operation_desc.gemm_kind != library::GemmKind::kSparse) {
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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.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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gemm_workspace_.configuration.lde = problem_.lde;
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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.E = 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_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 SparseGemmOperationProfiler::initialize_result_(
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PerformanceResult &result,
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Options const &options,
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library::SparseGemmDescription 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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// Input bytes read and Output bytes written for the gemm problem
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result.bytes =
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int64_t(library::sizeof_bits(operation_desc.A.element) * problem_.m / 8) *
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problem_.k / problem_.sparse +
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int64_t(library::sizeof_bits(operation_desc.B.element) * problem_.n / 8) *
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problem_.k +
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int64_t(library::sizeof_bits(operation_desc.C.element) * problem_.m / 8) *
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problem_.n +
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int64_t(library::sizeof_bits(operation_desc.E.element) * problem_.m / 8) *
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problem_.k / problem_.sparse / problem_.elements_per_128b;
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// Set is_beta_zero true if beta is zero
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bool is_beta_zero = std::all_of(problem_.beta.begin(), problem_.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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result.bytes += int64_t(library::sizeof_bits(operation_desc.C.element) * problem_.m / 8) * problem_.n;
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}
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result.flops = 2 * (problem_.m * problem_.n * problem_.k + problem_.m * problem_.n);
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result.runtime = 0;
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}
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/// Initializes workspace
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Status SparseGemmOperationProfiler::initialize_workspace(
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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::SparseGemmDescription const &operation_desc =
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static_cast<library::SparseGemmDescription const &>(operation->description());
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if (options.execution_mode != ExecutionMode::kDryRun) {
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int seed_shift = 0;
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gemm_workspace_.A = device_context.allocate_tensor(
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options,
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"A",
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operation_desc.A.element,
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operation_desc.A.layout,
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{int(problem_.m), int(problem_.k) / int(problem_.sparse)},
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{int(problem_.lda)},
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1, // batch_count
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seed_shift++
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);
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gemm_workspace_.B = device_context.allocate_tensor(
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options,
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"B",
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operation_desc.B.element,
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operation_desc.B.layout,
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{int(problem_.k), int(problem_.n)},
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{int(problem_.ldb)},
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1, // batch_count
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seed_shift++
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);
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gemm_workspace_.C = device_context.allocate_tensor(
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options,
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"C",
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operation_desc.C.element,
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operation_desc.C.layout,
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{int(problem_.m), int(problem_.n)},
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{int(problem_.ldc)},
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1, // batch_count
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seed_shift++
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);
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gemm_workspace_.Computed = device_context.allocate_tensor(
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"D",
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operation_desc.C.element,
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operation_desc.C.layout,
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{int(problem_.m), int(problem_.n)},
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{int(problem_.ldc)}
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);
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gemm_workspace_.E = device_context.allocate_sparsemeta_tensor(
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options,
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"E",
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operation_desc.E.element,
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operation_desc.E.layout,
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operation_desc.A.element,
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{int(problem_.m), int(problem_.k) / int(problem_.sparse) / int(problem_.elements_per_128b)},
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{int(problem_.lde)},
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1, // batch_count
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seed_shift++
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);
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gemm_workspace_.Reference = device_context.allocate_tensor(
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"Reference",
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operation_desc.C.element,
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operation_desc.C.layout,
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{int(problem_.m), int(problem_.n)},
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{int(problem_.ldc)}
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);
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gemm_workspace_.Reference->copy_from_device(gemm_workspace_.C->data());
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}
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//
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// Initialize the CUTLASS operation
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//
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Status status = Status::kSuccess;
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if (options.profiling.provider_enabled(library::Provider::kCUTLASS)) {
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if (options.execution_mode != ExecutionMode::kDryRun) {
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uint64_t workspace_size = operation->get_host_workspace_size(&gemm_workspace_.configuration);
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gemm_workspace_.host_workspace.resize(workspace_size, 0);
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workspace_size = operation->get_device_workspace_size(&gemm_workspace_.configuration);
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gemm_workspace_.device_workspace.reset(library::NumericTypeID::kU8, workspace_size);
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status = operation->initialize(
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&gemm_workspace_.configuration,
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gemm_workspace_.host_workspace.data(),
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gemm_workspace_.device_workspace.data());
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}
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//
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// If CUTLASS is enabled, generate a result for it
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//
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results_.push_back(model_result_);
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results_.back().provider = library::Provider::kCUTLASS;
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results_.back().op_kind = library::OperationKind::kSparseGemm;
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results_.back().disposition = Disposition::kNotRun;
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for(auto &verification_provider : options.verification.providers) {
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results_.back().verification_map[verification_provider] = Disposition::kNotRun;
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}
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}
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return status;
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}
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/////////////////////////////////////////////////////////////////////////////////////////////////
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/// Verifies CUTLASS against references
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bool SparseGemmOperationProfiler::verify_cutlass(
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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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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.E = gemm_workspace_.E->data();
|
|
gemm_workspace_.arguments.alpha = problem_.alpha.data();
|
|
gemm_workspace_.arguments.beta = problem_.beta.data();
|
|
gemm_workspace_.arguments.pointer_mode = library::ScalarPointerMode::kHost;
|
|
|
|
//
|
|
// Run the CUTLASS operation
|
|
//
|
|
|
|
results_.back().status = 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;
|
|
}
|
|
|
|
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) {
|
|
|
|
// 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;
|
|
}
|
|
|
|
///////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
/// Measures performance results
|
|
bool SparseGemmOperationProfiler::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.E = gemm_workspace_.E->data();
|
|
gemm_workspace_.arguments.alpha = problem_.alpha.data();
|
|
gemm_workspace_.arguments.beta = problem_.beta.data();
|
|
gemm_workspace_.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;
|
|
}
|
|
|
|
/////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
} // namespace profiler
|
|
} // namespace cutlass
|
|
|
|
/////////////////////////////////////////////////////////////////////////////////////////////////
|