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212 lines
11 KiB
C++
212 lines
11 KiB
C++
/*******************************************************************************
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*
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* MIT License
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*
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* Copyright (c) 2024 Advanced Micro Devices, Inc.
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*
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* Permission is hereby granted, free of charge, to any person obtaining a copy
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* of this software and associated documentation files (the "Software"), to deal
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* in the Software without restriction, including without limitation the rights
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* to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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* copies of the Software, and to permit persons to whom the Software is
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* furnished to do so, subject to the following conditions:
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*
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* The above copyright notice and this permission notice shall be included in all
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* copies or substantial portions of the Software.
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*
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* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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* SOFTWARE.
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*
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*******************************************************************************/
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#include <gtest/gtest.h>
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#include <miopen/miopen.h>
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#include <miopen/errors.hpp>
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#include "bn_test_data.hpp"
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#include "get_handle.hpp"
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// Test that batch norm training APIs reject invalid inputs matching PyTorch behavior
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// PyTorch errors:
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// - Spatial: "Expected more than 1 value per channel when training"
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// - PerActivation: "Expected more than 1 value per channel when training" (N > 1)
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struct GPU_BNInputValidation2D_FP32
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: public testing::TestWithParam<
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std::tuple<BN2DTestCase, miopenTensorLayout_t, miopenBatchNormMode_t>>
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{
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};
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struct GPU_BNInputValidation3D_FP32
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: public testing::TestWithParam<
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std::tuple<BN3DTestCase, miopenTensorLayout_t, miopenBatchNormMode_t>>
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{
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};
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TEST_P(GPU_BNInputValidation2D_FP32, RejectsInvalidInput)
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{
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auto [config, layout, bn_mode] = GetParam();
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auto&& handle = get_handle();
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float alpha = 1.0f, beta = 0.0f;
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if(config.Direction == miopen::batchnorm::Direction::ForwardTraining)
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{
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BNFwdTrainTestData<float, float, float, float, float, double, BN2DTestCase> data;
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data.SetUpImpl(config, bn_mode, layout);
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auto status = miopenBatchNormalizationForwardTraining(&handle,
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bn_mode,
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&alpha,
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&beta,
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&data.input.desc,
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data.in_dev.get(),
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&data.output.desc,
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data.out_dev.get(),
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&data.scale.desc,
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data.scale_dev.get(),
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data.shift_dev.get(),
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data.averageFactor,
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data.runMean_dev.get(),
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data.runVariance_dev.get(),
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data.epsilon,
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data.saveMean_dev.get(),
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data.saveVariance_dev.get());
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const char* mode_str = (bn_mode == miopenBNSpatial) ? "Spatial" : "PerActivation";
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const char* layout_str = (layout == miopenTensorNCHW) ? "NCHW" : "NHWC";
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EXPECT_EQ(status, miopenStatusBadParm)
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<< "ForwardTraining should reject " << mode_str << " mode with " << layout_str
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<< " layout: N=" << config.N << " H=" << config.H << " W=" << config.W
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<< " (N*H*W=" << (config.N * config.H * config.W) << ")";
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}
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else if(config.Direction == miopen::batchnorm::Direction::Backward)
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{
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BNBwdTestData<float, float, float, float, float, float, double, BN2DTestCase> data;
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data.SetUpImpl(config, bn_mode, layout);
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auto status = miopenBatchNormalizationBackward(&handle,
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bn_mode,
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&alpha,
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&beta,
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&alpha,
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&beta,
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&data.input.desc,
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data.in_dev.get(),
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&data.dy.desc,
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data.dy_dev.get(),
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&data.output.desc,
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data.out_dev.get(),
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&data.bnScale.desc,
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data.bnScale_dev.get(),
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data.dScale_dev.get(),
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data.dBias_dev.get(),
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data.epsilon,
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data.savedMean_dev.get(),
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data.savedInvVar_dev.get());
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const char* mode_str = (bn_mode == miopenBNSpatial) ? "Spatial" : "PerActivation";
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const char* layout_str = (layout == miopenTensorNCHW) ? "NCHW" : "NHWC";
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EXPECT_EQ(status, miopenStatusBadParm)
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<< "Backward should reject " << mode_str << " mode with " << layout_str
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<< " layout: N=" << config.N << " H=" << config.H << " W=" << config.W
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<< " (N*H*W=" << (config.N * config.H * config.W) << ")";
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}
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}
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TEST_P(GPU_BNInputValidation3D_FP32, RejectsInvalidInput)
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{
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auto [config, layout, bn_mode] = GetParam();
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auto&& handle = get_handle();
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float alpha = 1.0f, beta = 0.0f;
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if(config.Direction == miopen::batchnorm::Direction::ForwardTraining)
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{
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BNFwdTrainTestData<float, float, float, float, float, double, BN3DTestCase> data;
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data.SetUpImpl(config, bn_mode, layout);
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auto status = miopenBatchNormalizationForwardTraining(&handle,
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bn_mode,
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&alpha,
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&beta,
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&data.input.desc,
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data.in_dev.get(),
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&data.output.desc,
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data.out_dev.get(),
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&data.scale.desc,
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data.scale_dev.get(),
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data.shift_dev.get(),
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data.averageFactor,
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data.runMean_dev.get(),
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data.runVariance_dev.get(),
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data.epsilon,
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data.saveMean_dev.get(),
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data.saveVariance_dev.get());
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const char* mode_str = (bn_mode == miopenBNSpatial) ? "Spatial" : "PerActivation";
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const char* layout_str = (layout == miopenTensorNCDHW) ? "NCDHW" : "NDHWC";
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EXPECT_EQ(status, miopenStatusBadParm)
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<< "ForwardTraining 3D should reject " << mode_str << " mode with " << layout_str
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<< " layout: N=" << config.N << " D=" << config.D << " H=" << config.H
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<< " W=" << config.W << " (N*D*H*W=" << (config.N * config.D * config.H * config.W)
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<< ")";
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}
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else if(config.Direction == miopen::batchnorm::Direction::Backward)
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{
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BNBwdTestData<float, float, float, float, float, float, double, BN3DTestCase> data;
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data.SetUpImpl(config, bn_mode, layout);
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auto status = miopenBatchNormalizationBackward(&handle,
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bn_mode,
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&alpha,
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&beta,
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&alpha,
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&beta,
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&data.input.desc,
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data.in_dev.get(),
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&data.dy.desc,
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data.dy_dev.get(),
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&data.output.desc,
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data.out_dev.get(),
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&data.bnScale.desc,
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data.bnScale_dev.get(),
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data.dScale_dev.get(),
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data.dBias_dev.get(),
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data.epsilon,
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data.savedMean_dev.get(),
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data.savedInvVar_dev.get());
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const char* mode_str = (bn_mode == miopenBNSpatial) ? "Spatial" : "PerActivation";
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const char* layout_str = (layout == miopenTensorNCDHW) ? "NCDHW" : "NDHWC";
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EXPECT_EQ(status, miopenStatusBadParm)
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<< "Backward 3D should reject " << mode_str << " mode with " << layout_str
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<< " layout: N=" << config.N << " D=" << config.D << " H=" << config.H
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<< " W=" << config.W << " (N*D*H*W=" << (config.N * config.D * config.H * config.W)
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<< ")";
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}
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}
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// Instantiate tests for 2D with all combinations of layouts and modes
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INSTANTIATE_TEST_SUITE_P(
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Smoke,
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GPU_BNInputValidation2D_FP32,
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testing::Combine(testing::ValuesIn(Network2DInvalidTraining<BN2DTestCase>()),
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testing::ValuesIn({miopenTensorNCHW, miopenTensorNHWC}),
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testing::ValuesIn({miopenBNSpatial, miopenBNPerActivation})));
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// Instantiate tests for 3D with all combinations of layouts and modes
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INSTANTIATE_TEST_SUITE_P(
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Smoke,
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GPU_BNInputValidation3D_FP32,
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testing::Combine(testing::ValuesIn(Network3DInvalidTraining<BN3DTestCase>()),
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testing::ValuesIn({miopenTensorNCDHW, miopenTensorNDHWC}),
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testing::ValuesIn({miopenBNSpatial, miopenBNPerActivation})));
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