derek.holloway
7502bf64a4
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255 lines
10 KiB
C++
255 lines
10 KiB
C++
/*******************************************************************************
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*
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* MIT License
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*
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* Copyright (c) 2023 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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#pragma once
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namespace test {
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template <typename DLModule>
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void ComputeCPUBNInference(DLModule& dl_module)
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{
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int size{0};
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miopenGetTensorDescriptorSize(&dl_module.input.desc, &size);
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// In case of NxCxDxHxW
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auto ReshapeIfNeeded = [size](auto& desc) {
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if(size == 5)
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{
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desc = miopen::BuildReshaped4DTensorDescriptor(desc);
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}
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};
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ReshapeIfNeeded(dl_module.input.desc);
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ReshapeIfNeeded(dl_module.out_ref.desc);
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ReshapeIfNeeded(dl_module.scale.desc);
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ReshapeIfNeeded(dl_module.shift.desc);
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ReshapeIfNeeded(dl_module.estMean.desc);
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ReshapeIfNeeded(dl_module.estVariance.desc);
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if(dl_module.bn_mode == miopenBNSpatial)
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{
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batchNormSpatialHostInference(dl_module.input,
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dl_module.out_ref,
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dl_module.scale,
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dl_module.shift,
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dl_module.epsilon,
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dl_module.estMean,
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dl_module.estVariance,
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dl_module.useInverseVariance);
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}
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else if(dl_module.bn_mode == miopenBNPerActivation)
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{
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batchNormPerActivHostInference(dl_module.input,
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dl_module.out_ref,
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dl_module.scale,
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dl_module.shift,
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dl_module.epsilon,
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dl_module.estMean,
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dl_module.estVariance,
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dl_module.useInverseVariance);
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}
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else
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{
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std::cout << "\nUnknown inference batch miopenBatchNormMode_t\n";
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exit(EXIT_FAILURE);
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}
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}
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template <typename DLModule>
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void ComputeCPUBNBwd(DLModule& dl_module)
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{
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int size{0};
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miopenGetTensorDescriptorSize(&dl_module.input.desc, &size);
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// In case of NxCxDxHxW
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auto ReshapeIfNeeded = [size](auto& desc) {
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if(size == 5)
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{
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desc = miopen::BuildReshaped4DTensorDescriptor(desc);
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}
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};
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ReshapeIfNeeded(dl_module.input.desc);
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ReshapeIfNeeded(dl_module.dy.desc);
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ReshapeIfNeeded(dl_module.out_ref.desc);
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ReshapeIfNeeded(dl_module.bnScale.desc);
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ReshapeIfNeeded(dl_module.dScale_ref.desc);
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ReshapeIfNeeded(dl_module.dBias_ref.desc);
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ReshapeIfNeeded(dl_module.savedMean.desc);
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ReshapeIfNeeded(dl_module.savedInvVar.desc);
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if(dl_module.bn_mode == miopenBNSpatial)
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{
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batchNormSpatialHostBwdTrain(dl_module.input,
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dl_module.dy,
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dl_module.out_ref,
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dl_module.bnScale,
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dl_module.bnBias,
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dl_module.dScale_ref,
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dl_module.dBias_ref,
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dl_module.savedMean,
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dl_module.savedInvVar,
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dl_module.activ_mode,
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dl_module.activ_beta,
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dl_module.activ_alpha);
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}
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else if(dl_module.bn_mode == miopenBNPerActivation)
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{
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batchNormPerActHostBwdTrain(dl_module.input,
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dl_module.dy,
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dl_module.out_ref,
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dl_module.bnScale,
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dl_module.dScale_ref,
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dl_module.dBias_ref,
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dl_module.savedMean,
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dl_module.savedInvVar);
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}
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else
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{
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std::cout << "\nUnknown BwdTrain batch miopenBatchNormMode_t\n";
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exit(EXIT_FAILURE);
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}
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}
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template <typename DLModule>
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void ComputeCPUBNFwdTrain(DLModule& dl_module)
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{
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int size{0};
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miopenGetTensorDescriptorSize(&dl_module.input.desc, &size);
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// In case of NxCxDxHxW
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auto ReshapeIfNeeded = [size](auto& desc) {
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if(size == 5)
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{
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desc = miopen::BuildReshaped4DTensorDescriptor(desc);
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}
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};
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ReshapeIfNeeded(dl_module.input.desc);
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ReshapeIfNeeded(dl_module.out_ref.desc);
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ReshapeIfNeeded(dl_module.scale.desc);
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ReshapeIfNeeded(dl_module.shift.desc);
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ReshapeIfNeeded(dl_module.saveMean_ref.desc);
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ReshapeIfNeeded(dl_module.saveVariance_ref.desc);
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ReshapeIfNeeded(dl_module.runMean_ref.desc);
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ReshapeIfNeeded(dl_module.runVariance_ref.desc);
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if(dl_module.bn_mode == miopenBNSpatial)
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{
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batchNormSpatialHostFwdTrain(dl_module.input,
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dl_module.out_ref,
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dl_module.scale,
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dl_module.shift,
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dl_module.epsilon,
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dl_module.averageFactor,
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dl_module.saveMean_ref,
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dl_module.saveVariance_ref,
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dl_module.runMean_ref,
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dl_module.runVariance_ref);
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}
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else if(dl_module.bn_mode == miopenBNPerActivation)
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{
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batchNormPerActHostFwdTrain(dl_module.input,
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dl_module.out_ref,
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dl_module.scale,
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dl_module.shift,
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dl_module.epsilon,
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dl_module.averageFactor,
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dl_module.saveMean_ref,
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dl_module.saveVariance_ref,
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dl_module.runMean_ref,
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dl_module.runVariance_ref);
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}
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else
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{
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std::cout << "\nUnknown FwdTrain batch miopenBatchNormMode_t\n";
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exit(EXIT_FAILURE);
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}
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}
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template <typename T, typename U = double>
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void CompareTensor(const tensor<T>& output,
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const tensor<U>& out_ref,
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const double threshold = std::numeric_limits<T>::epsilon())
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{
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EXPECT_FALSE(miopen::range_zero(out_ref)) << "CPU data is all zeros";
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EXPECT_FALSE(miopen::range_zero(output)) << "GPU data is all zeros";
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EXPECT_FALSE(miopen::find_idx(output, miopen::not_finite) >= 0)
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<< "Non finite number found in the GPU data";
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EXPECT_TRUE(miopen::range_distance(out_ref) == miopen::range_distance(output));
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auto error = miopen::rms_range(out_ref, output);
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EXPECT_FALSE(miopen::find_idx(out_ref, miopen::not_finite) >= 0)
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<< "Non finite number found in the CPU data";
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EXPECT_TRUE(error < threshold)
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<< "Error beyond tolerance Error:" << error << ", Threshold: " << threshold;
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}
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} // namespace test
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namespace test {
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namespace FusionPlan {
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template <typename DLModule>
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void InitFusionPlan(miopen::FusionPlanDescriptor& fusePlanDesc, DLModule& dl_module)
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{
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fusePlanDesc = miopen::FusionPlanDescriptor(miopenVerticalFusion, dl_module.GetInputDesc());
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}
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template <typename DLModule>
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void AddBnInfer(miopen::FusionPlanDescriptor& fusePlanDesc,
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miopen::OperatorArgs& params,
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DLModule& dl_module)
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{
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auto bnOp = std::make_shared<miopen::BatchNormInferenceFusionOpDescriptor>(dl_module.bn_mode,
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dl_module.bn_desc);
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EXPECT_EQ(fusePlanDesc.AddOp(bnOp), miopenStatusSuccess);
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bnOp->SetArgs(params,
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&dl_module.alpha,
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&dl_module.beta,
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dl_module.scale_dev.get(),
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dl_module.shift_dev.get(),
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dl_module.estMean_dev.get(),
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dl_module.estVariance_dev.get(),
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dl_module.epsilon);
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}
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template <typename DLModule>
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void AddActiv(miopen::FusionPlanDescriptor& fusePlanDesc,
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miopen::OperatorArgs& params,
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DLModule& dl_module,
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miopenActivationMode_t activ_mode)
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{
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auto activOp = std::make_shared<miopen::ActivFwdFusionOpDescriptor>(activ_mode);
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EXPECT_EQ(fusePlanDesc.AddOp(activOp), miopenStatusSuccess);
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EXPECT_EQ(activOp->SetArgs(params,
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&dl_module.alpha,
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&dl_module.beta,
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dl_module.activ_alpha,
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dl_module.activ_beta,
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dl_module.activ_gamma),
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miopenStatusSuccess);
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}
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inline bool Skip(const miopen::Handle& handle)
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{
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const std::string arch = handle.GetDeviceName();
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bool skip_test = (arch != "gfx908" && arch != "gfx90a");
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return skip_test;
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}
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} // namespace FusionPlan
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} // namespace test
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