diff options
Diffstat (limited to 'tests/validation/CL/GEMMMatrixMultiplyNative.cpp')
| -rw-r--r-- | tests/validation/CL/GEMMMatrixMultiplyNative.cpp | 72 |
1 files changed, 71 insertions, 1 deletions
diff --git a/tests/validation/CL/GEMMMatrixMultiplyNative.cpp b/tests/validation/CL/GEMMMatrixMultiplyNative.cpp index 5e4a12ddb7..bdf8248bb2 100644 --- a/tests/validation/CL/GEMMMatrixMultiplyNative.cpp +++ b/tests/validation/CL/GEMMMatrixMultiplyNative.cpp @@ -1,5 +1,5 @@ /* - * Copyright (c) 2019 Arm Limited. + * Copyright (c) 2019-2020 Arm Limited. * * SPDX-License-Identifier: MIT * @@ -164,6 +164,54 @@ void validate_configuration(unsigned int m_value, unsigned int n_value, unsigned CLGEMMMatrixMultiplyNative gemm; gemm.configure(&lhs, &rhs, &bias, &dst, 1.0f, 1.0f, lhs_info, rhs_info, kernel_info); } +/** Zero padding test */ +bool validate_zero_padding(unsigned int m_value, unsigned int n_value, unsigned int k_value, unsigned int b_value, unsigned int m0_value, unsigned int n0_value, unsigned int k0_value, bool broadcast_bias, DataType data_type, const ActivationLayerInfo &act_info) +{ + const unsigned int M = m_value; + const unsigned int N = n_value; + const unsigned int K = k_value; + + GEMMLHSMatrixInfo lhs_info; + lhs_info.m0 = m0_value; + lhs_info.k0 = k0_value; + + GEMMRHSMatrixInfo rhs_info; + rhs_info.n0 = n0_value; + rhs_info.k0 = k0_value; + + GEMMKernelInfo kernel_info; + kernel_info.m = M; + kernel_info.n = N; + kernel_info.k = K; + kernel_info.broadcast_bias = broadcast_bias; + kernel_info.activation_info = act_info; + + const TensorShape lhs_shape(K, M, b_value); + const TensorShape rhs_shape(N, K, b_value); + const TensorShape bias_shape(N, + broadcast_bias? 1 : M, + broadcast_bias? 1 : b_value); + const TensorShape dst_shape = compute_mm_shape(TensorInfo(lhs_shape, 1, data_type), + TensorInfo(rhs_shape, 1, data_type), + kernel_info); + + // Create tensors + CLTensor lhs = create_tensor<CLTensor>(lhs_shape, data_type); + CLTensor rhs = create_tensor<CLTensor>(rhs_shape, data_type); + CLTensor bias = create_tensor<CLTensor>(bias_shape, data_type); + CLTensor dst = create_tensor<CLTensor>(dst_shape, data_type); + + ARM_COMPUTE_EXPECT(lhs.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(rhs.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(bias.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(dst.info()->is_resizable(), framework::LogLevel::ERRORS); + + // Create and configure function + CLGEMMMatrixMultiplyNative gemm; + gemm.configure(&lhs, &rhs, &bias, &dst, 1.0f, 1.0f, lhs_info, rhs_info, kernel_info); + + return dst.info()->padding().empty(); +} } // namespace TEST_SUITE(CL) @@ -185,6 +233,28 @@ m_value, n_value, k_value, b_value, m0_value, n0_value, k0_value, broadcast_bias validate_configuration(m_value, n_value, k_value, b_value, m0_value, n0_value, k0_value, broadcast_bias, DataType::F32, act_value); } +/** Validate zero padding tests + * + * A series of validation tests to check that no padding is added as part of configuration for 4 different scenarios. + * + * Checks performed in order: + * - No partial blocks in both x and y dimensions + * - Partial blocks in x dimension + * - Partial blocks in y dimension + * - Partial blocks in both x and y dimensions + * - No blocks in both x and y dimensions, scalar store (N0==1) + */ +DATA_TEST_CASE(ValidateZeroPadding, framework::DatasetMode::ALL, zip(zip(zip( +framework::dataset::make("M", { 24, 64, 101, 1, 50 }), +framework::dataset::make("N", { 48, 29, 16, 122, 20 })), +framework::dataset::make("M0", { 4, 8, 7, 2, 1 })), +framework::dataset::make("N0", { 4, 4, 16, 3, 1 })), +m_value, n_value, m0_value, n0_value) +{ + bool status = validate_zero_padding(m_value, n_value, 23, 1, m0_value, n0_value, 4, false, DataType::F32, ActivationLayerInfo()); + ARM_COMPUTE_EXPECT(status, framework::LogLevel::ERRORS); +} + FIXTURE_DATA_TEST_CASE(RunSmall, CLGEMMMatrixMultiplyNativeFixture<float>, framework::DatasetMode::ALL, combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine( m_values, |