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https://github.com/ROCm/composable_kernel.git
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8784a72e23
* Re-structure ckProfiler source files * Rename profiler.cpp to main.cpp * Modularize ckProfiler operations * Add description for profiler operations * Use longer name to avoid name collision * Use macro to delay expansion * Use std::move() to avoid object copying * Prohibit users from calling dtor * Use macro to eliminate redundant code * Make friend function hidden * Add missing include directive <iostream> * Fix wrong include directives * Remove int8 from batchnorm-forward instances since it is not needed for forward training and could fail test Co-authored-by: Qianfeng Zhang <Qianfeng.Zhang@amd.com>
110 lines
4.9 KiB
C++
110 lines
4.9 KiB
C++
// SPDX-License-Identifier: MIT
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// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
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#include <cstdlib>
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#include <iostream>
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#include <initializer_list>
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#include <vector>
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#include <tuple>
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#include <gtest/gtest.h>
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#include "profiler/profile_batchnorm_forward_impl.hpp"
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using F16 = ck::half_t;
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using F32 = float;
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using BF16 = ck::bhalf_t;
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using I8 = int8_t;
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using F64 = double;
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template <typename Tuple>
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class TestBatchNormFwdRank4 : public ::testing::Test
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{
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private:
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const double epsilon = std::numeric_limits<float>::epsilon();
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const double averageFactor = 0.1;
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protected:
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using XDataType = std::tuple_element_t<0, Tuple>;
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using YDataType = std::tuple_element_t<1, Tuple>;
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using AccDataType = std::tuple_element_t<2, Tuple>;
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using ScaleDataType = std::tuple_element_t<3, Tuple>;
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using BiasDataType = std::tuple_element_t<4, Tuple>;
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using MeanVarDataType = std::tuple_element_t<5, Tuple>;
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std::vector<std::vector<size_t>> list_of_lengths = {
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{128, 16, 3, 1024}, {128, 16, 6, 512}, {1, 1, 1, 1}, {4, 4, 4, 4}, {32, 32, 32, 32}};
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std::vector<int> reduceDims;
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template <int NumReduceDim>
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void Run()
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{
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for(auto& inOutLengths : list_of_lengths)
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{
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bool pass = true;
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EXPECT_FALSE(reduceDims.size() != NumReduceDim);
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pass =
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pass && ck::profiler::profile_batchnorm_forward_impl<XDataType,
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YDataType,
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AccDataType,
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ScaleDataType,
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BiasDataType,
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MeanVarDataType,
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4,
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NumReduceDim>(true,
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3,
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false,
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false,
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inOutLengths,
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reduceDims,
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true,
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true,
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epsilon,
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averageFactor);
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pass =
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pass && ck::profiler::profile_batchnorm_forward_impl<XDataType,
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YDataType,
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AccDataType,
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ScaleDataType,
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BiasDataType,
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MeanVarDataType,
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4,
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NumReduceDim>(true,
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3,
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false,
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false,
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inOutLengths,
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reduceDims,
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false,
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false,
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epsilon,
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averageFactor);
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EXPECT_TRUE(pass);
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}
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}
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};
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using KernelTypes = ::testing::Types<std::tuple<F16, F16, F32, F16, F16, F32>,
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std::tuple<F32, F32, F32, F32, F32, F32>,
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std::tuple<BF16, BF16, F32, BF16, BF16, F32>,
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std::tuple<F64, F64, F64, F64, F64, F64>>;
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TYPED_TEST_SUITE(TestBatchNormFwdRank4, KernelTypes);
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// nhwc
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TYPED_TEST(TestBatchNormFwdRank4, nhwc)
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{
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this->reduceDims = {0, 1, 2};
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this->template Run<3>();
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}
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// nchw
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TYPED_TEST(TestBatchNormFwdRank4, nchw)
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{
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this->reduceDims = {0, 2, 3};
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this->template Run<3>();
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}
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