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372 lines
13 KiB
C++
372 lines
13 KiB
C++
/*******************************************************************************
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*
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* MIT License
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*
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* Copyright (c) 2025 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/batchnorm/solvers.hpp>
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#include <miopen/batchnorm/problem_description.hpp>
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#include <miopen/tensor.hpp>
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#include <miopen/activ.hpp>
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#include <miopen/handle.hpp>
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#include <miopen/execution_context.hpp>
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#include <miopen/datatype.hpp>
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using namespace miopen;
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using namespace miopen::batchnorm;
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using namespace miopen::solver::batchnorm;
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namespace {
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struct ApplicabilityParams
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{
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miopenDataType_t x_type;
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miopenDataType_t y_type;
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miopenDataType_t scale_type;
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miopenDataType_t bias_type;
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miopenDataType_t mean_type;
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miopenDataType_t var_type;
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bool expect_applicable;
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std::string test_name;
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friend std::ostream& operator<<(std::ostream& os, const ApplicabilityParams& p)
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{
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os << "ApplicabilityParams(";
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os << "x_type=" << GetDataType(p.x_type) << ", ";
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os << "y_type=" << GetDataType(p.y_type) << ", ";
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os << "scale_type=" << GetDataType(p.scale_type) << ", ";
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os << "bias_type=" << GetDataType(p.bias_type) << ", ";
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os << "mean_type=" << GetDataType(p.mean_type) << ", ";
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os << "var_type=" << GetDataType(p.var_type) << ", ";
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os << "expect_applicable=" << (p.expect_applicable ? "true" : "false") << ", ";
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os << "test_name=" << p.test_name;
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os << ")";
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return os;
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}
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};
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TensorDescriptor MakeTensorDesc(miopenDataType_t type)
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{
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return TensorDescriptor(type, {1, 2, 3, 4});
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}
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ActivationDescriptor MakeActivationDesc(miopenActivationMode_t mode = miopenActivationPASTHRU)
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{
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ActivationDescriptor desc(mode, 1.0, 1.0, 1.0);
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return desc;
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}
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ExecutionContext MakeContext()
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{
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ExecutionContext ctx;
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return ctx;
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}
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std::vector<ApplicabilityParams> GetParams()
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{
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std::vector<ApplicabilityParams> params;
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// Valid cases
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for(auto dt : {miopenFloat, miopenHalf, miopenBFloat16})
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{
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params.push_back({dt,
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dt,
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miopenFloat,
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miopenFloat,
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miopenFloat,
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miopenFloat,
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true,
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"X_Y_" + GetDataType(dt) + "_Stats_" + GetDataType(miopenFloat)});
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}
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// Invalid: X & Y are invalid types
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params.push_back({miopenInt8,
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miopenInt8,
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miopenFloat,
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miopenFloat,
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miopenFloat,
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miopenFloat,
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false,
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"X_INT8_Y_INT8"});
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params.push_back({miopenInt32,
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miopenInt32,
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miopenFloat,
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miopenFloat,
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miopenFloat,
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miopenFloat,
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false,
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"X_INT32_Y_INT32"});
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// Invalid: X & Y are mixed
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params.push_back({miopenFloat,
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miopenHalf,
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miopenFloat,
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miopenFloat,
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miopenFloat,
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miopenFloat,
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false,
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"X_FP32_Y_FP16"});
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params.push_back({miopenHalf,
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miopenBFloat16,
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miopenFloat,
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miopenFloat,
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miopenFloat,
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miopenFloat,
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false,
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"X_FP16_Y_BFP16"});
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params.push_back({miopenBFloat16,
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miopenFloat,
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miopenFloat,
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miopenFloat,
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miopenFloat,
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miopenFloat,
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false,
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"X_BFP16_Y_FP32"});
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// Invalid: stats not FP32
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params.push_back({miopenFloat,
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miopenFloat,
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miopenHalf,
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miopenFloat,
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miopenFloat,
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miopenFloat,
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false,
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"Scale_FP16"});
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params.push_back({miopenFloat,
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miopenFloat,
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miopenFloat,
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miopenHalf,
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miopenFloat,
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miopenFloat,
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false,
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"Bias_FP16"});
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params.push_back({miopenFloat,
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miopenFloat,
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miopenFloat,
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miopenFloat,
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miopenHalf,
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miopenFloat,
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false,
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"Mean_FP16"});
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params.push_back({miopenFloat,
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miopenFloat,
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miopenFloat,
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miopenFloat,
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miopenFloat,
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miopenHalf,
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false,
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"Var_FP16"});
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// Invalid: all stats not FP32
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params.push_back({miopenHalf,
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miopenHalf,
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miopenHalf,
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miopenHalf,
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miopenHalf,
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miopenHalf,
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false,
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"AllStats_FP16"});
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return params;
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}
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} // namespace
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class CPU_BatchNormFwdTrainingSpatialApplicabilityTest_NONE
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: public ::testing::TestWithParam<ApplicabilityParams>
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{
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};
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TEST_P(CPU_BatchNormFwdTrainingSpatialApplicabilityTest_NONE, IsApplicable)
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{
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const auto& p = GetParam();
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auto ctx = MakeContext();
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auto xDesc = MakeTensorDesc(p.x_type);
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auto yDesc = MakeTensorDesc(p.y_type);
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auto scaleDesc = MakeTensorDesc(p.scale_type);
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auto biasDesc = MakeTensorDesc(p.bias_type);
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auto meanDesc = MakeTensorDesc(p.mean_type);
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auto varDesc = MakeTensorDesc(p.var_type);
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auto actDesc = MakeActivationDesc();
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ProblemDescription problem(miopenBNSpatial,
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xDesc,
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yDesc,
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scaleDesc,
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biasDesc,
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meanDesc,
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varDesc,
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1.0,
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1e-5,
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true,
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true,
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1,
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actDesc);
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BnFwdTrainingSpatial solver;
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EXPECT_EQ(solver.IsApplicable(ctx, problem), p.expect_applicable) << p.test_name;
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}
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INSTANTIATE_TEST_SUITE_P(Smoke,
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CPU_BatchNormFwdTrainingSpatialApplicabilityTest_NONE,
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::testing::ValuesIn(GetParams()));
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class CPU_BatchNormFwdTrainingPerActivationApplicabilityTest_NONE
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: public ::testing::TestWithParam<ApplicabilityParams>
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{
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};
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TEST_P(CPU_BatchNormFwdTrainingPerActivationApplicabilityTest_NONE, IsApplicable)
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{
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const auto& p = GetParam();
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auto ctx = MakeContext();
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auto xDesc = MakeTensorDesc(p.x_type);
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auto yDesc = MakeTensorDesc(p.y_type);
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auto scaleDesc = MakeTensorDesc(p.scale_type);
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auto biasDesc = MakeTensorDesc(p.bias_type);
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auto meanDesc = MakeTensorDesc(p.mean_type);
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auto varDesc = MakeTensorDesc(p.var_type);
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auto actDesc = MakeActivationDesc();
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ProblemDescription problem(miopenBNPerActivation,
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xDesc,
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yDesc,
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scaleDesc,
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biasDesc,
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meanDesc,
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varDesc,
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1.0,
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1e-5,
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true,
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true,
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1,
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actDesc);
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BnFwdTrainingPerActivation solver;
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EXPECT_EQ(solver.IsApplicable(ctx, problem), p.expect_applicable) << p.test_name;
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}
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INSTANTIATE_TEST_SUITE_P(Smoke,
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CPU_BatchNormFwdTrainingPerActivationApplicabilityTest_NONE,
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::testing::ValuesIn(GetParams()));
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class CPU_BatchNormFwdInferenceApplicabilityTest_NONE
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: public ::testing::TestWithParam<ApplicabilityParams>
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{
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};
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TEST_P(CPU_BatchNormFwdInferenceApplicabilityTest_NONE, IsApplicable)
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{
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const auto& p = GetParam();
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auto ctx = MakeContext();
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auto xDesc = MakeTensorDesc(p.x_type);
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auto yDesc = MakeTensorDesc(p.y_type);
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auto scaleDesc = MakeTensorDesc(p.scale_type);
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auto biasDesc = MakeTensorDesc(p.bias_type);
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auto meanDesc = MakeTensorDesc(p.mean_type);
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auto varDesc = MakeTensorDesc(p.var_type);
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auto actDesc = MakeActivationDesc();
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ProblemDescription problem(
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miopenBNSpatial, xDesc, yDesc, scaleDesc, biasDesc, meanDesc, varDesc, 1e-5, actDesc);
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BnFwdInference solver;
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EXPECT_EQ(solver.IsApplicable(ctx, problem), p.expect_applicable) << p.test_name;
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}
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INSTANTIATE_TEST_SUITE_P(Smoke,
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CPU_BatchNormFwdInferenceApplicabilityTest_NONE,
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::testing::ValuesIn(GetParams()));
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class CPU_BatchNormBwdTrainingSpatialApplicabilityTest_NONE
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: public ::testing::TestWithParam<ApplicabilityParams>
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{
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};
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TEST_P(CPU_BatchNormBwdTrainingSpatialApplicabilityTest_NONE, IsApplicable)
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{
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const auto& p = GetParam();
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auto ctx = MakeContext();
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auto xDesc = MakeTensorDesc(p.x_type);
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auto dyDesc = MakeTensorDesc(p.y_type);
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auto dxDesc = MakeTensorDesc(p.y_type); // dx usually same type as y
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auto scaleDesc = MakeTensorDesc(p.scale_type);
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auto biasDesc = MakeTensorDesc(p.bias_type);
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auto meanDesc = MakeTensorDesc(p.mean_type);
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auto varDesc = MakeTensorDesc(p.var_type);
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auto actDesc = MakeActivationDesc();
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ProblemDescription problem(miopenBNSpatial,
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xDesc,
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dyDesc,
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dxDesc,
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scaleDesc,
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biasDesc,
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meanDesc,
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varDesc,
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1e-5,
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false,
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1,
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actDesc);
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BnBwdTrainingSpatial solver;
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EXPECT_EQ(solver.IsApplicable(ctx, problem), p.expect_applicable) << p.test_name;
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}
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INSTANTIATE_TEST_SUITE_P(Smoke,
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CPU_BatchNormBwdTrainingSpatialApplicabilityTest_NONE,
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::testing::ValuesIn(GetParams()));
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class CPU_BatchNormBwdTrainingPerActivationApplicabilityTest_NONE
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: public ::testing::TestWithParam<ApplicabilityParams>
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{
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};
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TEST_P(CPU_BatchNormBwdTrainingPerActivationApplicabilityTest_NONE, IsApplicable)
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{
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const auto& p = GetParam();
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auto ctx = MakeContext();
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auto xDesc = MakeTensorDesc(p.x_type);
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auto dyDesc = MakeTensorDesc(p.y_type);
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auto dxDesc = MakeTensorDesc(p.y_type); // dx usually same type as y
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auto scaleDesc = MakeTensorDesc(p.scale_type);
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auto biasDesc = MakeTensorDesc(p.bias_type);
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auto meanDesc = MakeTensorDesc(p.mean_type);
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auto varDesc = MakeTensorDesc(p.var_type);
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auto actDesc = MakeActivationDesc();
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ProblemDescription problem(miopenBNPerActivation,
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xDesc,
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dyDesc,
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dxDesc,
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scaleDesc,
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biasDesc,
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meanDesc,
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varDesc,
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1e-5,
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false,
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1,
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actDesc);
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BnBwdTrainingPerActivation solver;
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EXPECT_EQ(solver.IsApplicable(ctx, problem), p.expect_applicable) << p.test_name;
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}
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INSTANTIATE_TEST_SUITE_P(Smoke,
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CPU_BatchNormBwdTrainingPerActivationApplicabilityTest_NONE,
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::testing::ValuesIn(GetParams()));
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