diff options
Diffstat (limited to 'tests/validation/CL/GEMMMatrixMultiplyReshapedOnlyRHS.cpp')
| -rw-r--r-- | tests/validation/CL/GEMMMatrixMultiplyReshapedOnlyRHS.cpp | 89 |
1 files changed, 89 insertions, 0 deletions
diff --git a/tests/validation/CL/GEMMMatrixMultiplyReshapedOnlyRHS.cpp b/tests/validation/CL/GEMMMatrixMultiplyReshapedOnlyRHS.cpp index 14b1c66e2c..0456ca2017 100644 --- a/tests/validation/CL/GEMMMatrixMultiplyReshapedOnlyRHS.cpp +++ b/tests/validation/CL/GEMMMatrixMultiplyReshapedOnlyRHS.cpp @@ -185,6 +185,69 @@ bool validate_configuration(unsigned int m_value, unsigned int n_value, unsigned CLGEMMMatrixMultiplyReshapedOnlyRHS gemm; return bool(gemm.validate(&lhs, &rhs_reshaped, &bias, &dst, alpha, beta, 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, unsigned int h0_value, + bool i_value_rhs, bool t_value_rhs, bool export_to_cl_image, bool broadcast_bias, bool input_as_3d, unsigned int depth_output_gemm3d, const ActivationLayerInfo &act_info, + DataType dt_input0, DataType dt_input1, DataType dt_input2, DataType dt_output, float alpha, float beta) +{ + 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; + rhs_info.h0 = h0_value; + rhs_info.interleave = i_value_rhs; + rhs_info.transpose = t_value_rhs; + rhs_info.export_to_cl_image = export_to_cl_image; + + GEMMKernelInfo kernel_info; + kernel_info.m = M; + kernel_info.n = N; + kernel_info.k = K; + kernel_info.depth_output_gemm3d = depth_output_gemm3d; + kernel_info.reinterpret_input_as_3d = input_as_3d; + 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 rhs_shape_reshaped = compute_rhs_reshaped_shape(TensorInfo(rhs_shape, 1, dt_input1), + rhs_info); + + const TensorShape dst_shape = compute_mm_shape(TensorInfo(lhs_shape, 1, dt_input0), + TensorInfo(rhs_shape_reshaped, 1, dt_input1), + kernel_info); + + const TensorShape bias_shape(N, + M, // Correct calculation should be: broadcast_bias? 1 : M, it's wrong here on purpose just for validation test + broadcast_bias? 1 : b_value); + + // Create tensors + CLTensor lhs = create_tensor<CLTensor>(lhs_shape, dt_input0); + CLTensor rhs_reshaped = create_tensor<CLTensor>(rhs_shape_reshaped, dt_input1); + CLTensor bias = create_tensor<CLTensor>(bias_shape, dt_input2); + CLTensor dst = create_tensor<CLTensor>(dst_shape, dt_output); + + ARM_COMPUTE_EXPECT(lhs.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(rhs_reshaped.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); + + // Validate zero-padding + CLGEMMMatrixMultiplyReshapedOnlyRHS gemm; + + gemm.configure(&lhs, &rhs_reshaped, &bias, &dst, alpha, beta, lhs_info, rhs_info, kernel_info); + + return dst.info()->padding().empty(); +} } // namespace TEST_SUITE(CL) @@ -236,6 +299,32 @@ b_value, m0_value, n0_value, k0_value, broadcast_bias, input_as_3d, depth_output ARM_COMPUTE_EXPECT(status == expected_value, framework::LogLevel::ERRORS); } +/** 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 + */ +DATA_TEST_CASE(ValidateZeroPadding, framework::DatasetMode::ALL, zip(zip(zip(zip( +framework::dataset::make("M", { 24, 64, 101, 1 }), +framework::dataset::make("N", { 48, 29, 16, 122 })), +framework::dataset::make("M0", { 4, 8, 7, 2 })), +framework::dataset::make("N0", { 4, 4, 16, 3 })), +framework::dataset::make("export_to_cl_image", { false, true, true, false })), +m_value, n_value, m0_value, n0_value, export_to_cl_image) +{ + constexpr DataType dt = DataType::F32; + // Disable export_to_cl_image if the target platform does not support the OpenCL cl_khr_image2d_from_buffer extension + bool actual_export_to_cl_image = image2d_from_buffer_supported(CLKernelLibrary::get().get_device()) && export_to_cl_image; + + bool status = validate_zero_padding(m_value, n_value, 23, 1, m0_value, n0_value, 4, 1, false, false, actual_export_to_cl_image, false, 0, 0, ActivationLayerInfo(), dt, dt, dt, dt, 1.0f, 1.0f); + ARM_COMPUTE_EXPECT(status, framework::LogLevel::ERRORS); +} + TEST_SUITE(Float) TEST_SUITE(FP32) |