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Diffstat (limited to 'tests/validation/CL/GEMMMatrixMultiplyReshaped.cpp')
| -rw-r--r-- | tests/validation/CL/GEMMMatrixMultiplyReshaped.cpp | 90 |
1 files changed, 90 insertions, 0 deletions
diff --git a/tests/validation/CL/GEMMMatrixMultiplyReshaped.cpp b/tests/validation/CL/GEMMMatrixMultiplyReshaped.cpp index 309a967abc..d7853f3ea7 100644 --- a/tests/validation/CL/GEMMMatrixMultiplyReshaped.cpp +++ b/tests/validation/CL/GEMMMatrixMultiplyReshaped.cpp @@ -170,11 +170,101 @@ const auto broadcast_bias_values = framework::dataset::make("broadcast_bias", { /** LHS transposed values */ const auto lhs_transpose_values = framework::dataset::make("lhs_transpose", { false, true } ); + +/** 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, 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 = false; + 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 lhs_shape_reshaped = compute_lhs_reshaped_shape(TensorInfo(lhs_shape, 1, dt_input0), + lhs_info); + 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_reshaped, 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_reshaped = create_tensor<CLTensor>(lhs_shape_reshaped, 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_reshaped.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 + CLGEMMMatrixMultiplyReshaped gemm; + + gemm.configure(&lhs_reshaped, &rhs_reshaped, &bias, &dst, alpha, beta, lhs_info, rhs_info, kernel_info); + + // Padding can be added along rhs and bias's X/Y dimension + return dst.info()->padding().empty() && lhs_reshaped.info()->padding().empty(); +} } // namespace TEST_SUITE(CL) TEST_SUITE(GEMMMatrixMultiplyReshaped) +/** Validate zero padding tests + * + * A series of validation tests to check the zero padding requirement + * + * 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 + * - Special case: partial_n0 == 9 (vstore1 should be invoked instead of vstore_partial_1) + */ +DATA_TEST_CASE(ValidateZeroPadding, framework::DatasetMode::ALL, zip(zip(zip( +framework::dataset::make("M", { 24, 64, 101, 1, 103 }), +framework::dataset::make("N", { 48, 29, 16, 121, 41 })), +framework::dataset::make("M0", { 4, 8, 4, 2, 4 })), +framework::dataset::make("N0", { 4, 4, 16, 2, 16 })), +m_value, n_value, m0_value, n0_value) +{ + constexpr DataType dt = DataType::F32; + + bool status = validate_zero_padding(m_value, n_value, 23, 1, m0_value, n0_value, 4, 1, false, false, false, 0, 0, ActivationLayerInfo(), dt, dt, dt, dt, 1.0f, 1.0f); + ARM_COMPUTE_EXPECT(status, framework::LogLevel::ERRORS); +} + // *INDENT-OFF* // clang-format off DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(zip(zip(zip(zip( |