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https://github.com/ROCm/composable_kernel.git
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105 lines
4.0 KiB
C++
105 lines
4.0 KiB
C++
// Copyright (c) Advanced Micro Devices, Inc., or its affiliates.
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// SPDX-License-Identifier: MIT
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#pragma once
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#include <gtest/gtest.h>
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#include "ck/utility/data_type.hpp"
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#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
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#include "ck/utility/number.hpp"
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#include "ck/library/utility/literals.hpp"
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#include "ck/library/utility/host_tensor.hpp"
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#include "ck/library/utility/host_tensor_generator.hpp"
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#include "ck/tensor_operation/gpu/element/unary_element_wise_operation.hpp"
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#include "ck/library/utility/device_memory.hpp"
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#include "ck/library/utility/fill.hpp"
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#include "ck/tensor_operation/gpu/device/device_gemm_mx.hpp"
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#include "ck/library/tensor_operation_instance/gpu/gemm_mx.hpp"
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#include "ck/library/reference_tensor_operation/cpu/reference_mx_gemm.hpp"
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#include "ck/library/utility/check_err.hpp"
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#include "profiler/profile_gemm_mx_impl.hpp"
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namespace ck {
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namespace test {
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namespace {
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using Row = ck::tensor_layout::gemm::RowMajor;
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using Col = ck::tensor_layout::gemm::ColumnMajor;
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} // namespace
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template <typename Tuple>
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class TestGemmMX : public testing::Test
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{
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using Row = ck::tensor_layout::gemm::RowMajor;
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using F32 = float;
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using ScaleType = e8m0_bexp_t;
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protected:
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using ALayout = std::tuple_element_t<0, Tuple>;
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using BLayout = std::tuple_element_t<1, Tuple>;
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using CLayout = Row;
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using ADataType = std::tuple_element_t<2, Tuple>;
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using BDataType = std::tuple_element_t<3, Tuple>;
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using CDataType = std::tuple_element_t<4, Tuple>;
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using AccDataType = float;
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public:
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static constexpr index_t ScaleBlockSize = std::tuple_element_t<5, Tuple>{};
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static constexpr bool verify_ = true;
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static constexpr int init_method_ = 2; // decimal value initialization
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static constexpr bool log_ = false;
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static constexpr bool bench_ = false; // measure kernel performance
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std::vector<int> k_batches_;
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void SetUp() override { k_batches_ = {1}; }
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void Run(const int M,
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const int N,
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const int K,
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const int StrideA,
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const int StrideB,
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const int StrideC)
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{
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for(auto kb : k_batches_)
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{
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RunSingle(M, N, K, StrideA, StrideB, StrideC, kb);
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}
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}
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void RunSingle(const int M,
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const int N,
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const int K,
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const int StrideA,
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const int StrideB,
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const int StrideC,
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int kbatch = 1,
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int n_warmup = 10,
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int n_iter = 10)
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{
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bool pass = ck::profiler::profile_gemm_mx_impl<ADataType,
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BDataType,
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CDataType,
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ALayout,
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BLayout,
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CLayout,
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ScaleBlockSize>(verify_,
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init_method_,
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log_,
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bench_,
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M,
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N,
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K,
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StrideA,
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StrideB,
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StrideC,
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kbatch,
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n_warmup,
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n_iter);
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EXPECT_TRUE(pass);
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}
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};
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} // namespace test
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} // namespace ck
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