diff options
| -rw-r--r-- | src/core/CL/cl_kernels/reduction_operation.cl | 168 | ||||
| -rw-r--r-- | src/core/CL/kernels/CLReductionOperationKernel.cpp | 81 | ||||
| -rw-r--r-- | tests/benchmark/CL/ArgMinMax.cpp | 56 | ||||
| -rw-r--r-- | tests/benchmark/fixtures/ArgMinMaxFixture.h | 84 |
4 files changed, 42 insertions, 347 deletions
diff --git a/src/core/CL/cl_kernels/reduction_operation.cl b/src/core/CL/cl_kernels/reduction_operation.cl index db034f0c3a..5a4bb9ff4c 100644 --- a/src/core/CL/cl_kernels/reduction_operation.cl +++ b/src/core/CL/cl_kernels/reduction_operation.cl @@ -36,10 +36,6 @@ #endif // defined(WIDTH) #endif // FLOAT_DATA_TYPE -#if defined(DATA_TYPE) - -#if defined(OPERATION) && defined(WIDTH) - /** Calculate square sum of a vector * * @param[in] input Pointer to the first pixel. @@ -95,112 +91,10 @@ inline DATA_TYPE product(__global const DATA_TYPE *input) return (in.s0 * in.s1); } - -#if defined(DATA_TYPE_OUTPUT) - -#if defined(ARG_MAX) -/** Find index maximum value of a vector - * - * @param[in] input Pointer to the first value. - * - * @return index of the vector. - */ -inline DATA_TYPE_OUTPUT arg_idx_max(__global const DATA_TYPE *input, const int x_idx) -{ -#if defined(MULTI_ACCESS_X) - - int x_elem = x_idx * 16; - const int x_goback = select(0, 16 - WIDTH % 16, x_elem + 16 > WIDTH); - x_elem -= x_goback; - - VEC_DATA_TYPE(DATA_TYPE, 16) - in = vload16(0, input - x_goback); - VEC_DATA_TYPE(DATA_TYPE_OUTPUT, 16) - res = { 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15 }; - - VEC_DATA_TYPE(COND_DATA_TYPE, 8) - idx_sel = (in.s01234567 > in.s89abcdef) || (in.s01234567 == in.s89abcdef && CONVERT((res.s01234567 < res.s89abcdef), VEC_DATA_TYPE(COND_DATA_TYPE, 8))); - in.s01234567 = select(in.s89abcdef, in.s01234567, idx_sel); - res.s01234567 = select(res.s89abcdef, res.s01234567, CONVERT(idx_sel, int8)); - - idx_sel.s0123 = (in.s0123 > in.s4567) || (in.s0123 == in.s4567 && CONVERT((res.s0123 < res.s4567), VEC_DATA_TYPE(COND_DATA_TYPE, 4))); - in.s0123 = select(in.s4567, in.s0123, idx_sel.s0123); - res.s0123 = select(res.s4567, res.s0123, CONVERT(idx_sel.s0123, int4)); - - idx_sel.s01 = (in.s01 > in.s23) || (in.s01 == in.s23 && CONVERT((res.s01 < res.s23), VEC_DATA_TYPE(COND_DATA_TYPE, 2))); - in.s01 = select(in.s23, in.s01, idx_sel.s01); - res.s01 = select(res.s23, res.s01, CONVERT(idx_sel.s01, int2)); - - idx_sel.s0 = (in.s0 > in.s1) || (in.s0 == in.s1 && CONVERT((res.s0 < res.s1), COND_DATA_TYPE)); - res.s0 = select(res.s1, res.s0, CONVERT(idx_sel.s0, int)); - - return res.s0 + x_elem; -#else // defined(MULTI_ACCESS_X) - - DATA_TYPE_OUTPUT res = 0; - for(DATA_TYPE_OUTPUT x_v = res + 1; x_v < WIDTH; ++x_v) - { - res = select(res, x_v, *(input + x_v) > *(input + res)); - } - - return res; -#endif // defined(MULTI_ACCESS_X) -} -#endif // defined(ARG_MAX) - -#if defined(ARG_MIN) -/** Find index minimum value of a vector - * - * @param[in] input Pointer to the first value. - * - * @return index of the vector. - */ -inline DATA_TYPE_OUTPUT arg_idx_min(__global const DATA_TYPE *input, const int x_idx) -{ -#if defined(MULTI_ACCESS_X) - - int x_elem = x_idx * 16; - const int x_goback = select(0, 16 - WIDTH % 16, x_elem + 16 > WIDTH); - x_elem -= x_goback; - - VEC_DATA_TYPE(DATA_TYPE, 16) - in = vload16(0, input - x_goback); - VEC_DATA_TYPE(DATA_TYPE_OUTPUT, 16) - res = { 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15 }; - - VEC_DATA_TYPE(COND_DATA_TYPE, 8) - idx_sel = (in.s01234567 < in.s89abcdef) || (in.s01234567 == in.s89abcdef && CONVERT((res.s01234567 < res.s89abcdef), VEC_DATA_TYPE(COND_DATA_TYPE, 8))); - in.s01234567 = select(in.s89abcdef, in.s01234567, idx_sel); - res.s01234567 = select(res.s89abcdef, res.s01234567, CONVERT(idx_sel, int8)); - - idx_sel.s0123 = (in.s0123 < in.s4567) || (in.s0123 == in.s4567 && CONVERT((res.s0123 < res.s4567), VEC_DATA_TYPE(COND_DATA_TYPE, 4))); - in.s0123 = select(in.s4567, in.s0123, idx_sel.s0123); - res.s0123 = select(res.s4567, res.s0123, CONVERT(idx_sel.s0123, int4)); - - idx_sel.s01 = (in.s01 < in.s23) || (in.s01 == in.s23 && CONVERT((res.s01 < res.s23), VEC_DATA_TYPE(COND_DATA_TYPE, 2))); - in.s01 = select(in.s23, in.s01, idx_sel.s01); - res.s01 = select(res.s23, res.s01, CONVERT(idx_sel.s01, int2)); - - idx_sel.s0 = (in.s0 < in.s1) || (in.s0 == in.s1 && CONVERT((res.s0 < res.s1), COND_DATA_TYPE)); - res.s0 = select(res.s1, res.s0, CONVERT(idx_sel.s0, int)); - - return res.s0 + x_elem; -#else // defined(MULTI_ACCESS_X) - - DATA_TYPE_OUTPUT res = 0; - for(DATA_TYPE_OUTPUT x_v = res + 1; x_v < WIDTH; ++x_v) - { - res = select(res, x_v, *(input + x_v) < * (input + res)); - } - return res; -#endif // defined(MULTI_ACCESS_X) -} -#endif // defined(ARG_MIN) - +#if defined(OPERATION) /** This kernel performs parallel reduction given an operation on x-axis. * * @note The data type must be passed at compile time using -DDATA_TYPE: e.g. -DDATA_TYPE=float - * @note The data type of the output must be passed at compile time using -DDATA_TYPE_OUTPUT: e.g. -DDATA_TYPE_OUTPUT=uint * @note The operation we want to perform must be passed at compile time using -DOPERATION e.g. -DOPERATION=square_sum * @note The mean flag must be passed at compile time using -DMEAN if we want to compute the mean value * @note The product flag must be passed at compile time using -DPROD if we want to compute the product, otherwise sum will be used @@ -223,7 +117,7 @@ inline DATA_TYPE_OUTPUT arg_idx_min(__global const DATA_TYPE *input, const int x __kernel void reduction_operation_x( IMAGE_DECLARATION(src), IMAGE_DECLARATION(partial_res), - __local DATA_TYPE_OUTPUT *local_results) + __local DATA_TYPE *local_results) { Image src = CONVERT_TO_IMAGE_STRUCT(src); Image partial_res = CONVERT_TO_IMAGE_STRUCT(partial_res); @@ -231,17 +125,9 @@ __kernel void reduction_operation_x( unsigned int lsize = get_local_size(0); unsigned int lid = get_local_id(0); - const uint x_idx = get_global_id(0); - const uint y_idx = get_global_id(1); - for(unsigned int y = 0; y < get_local_size(1); ++y) { -#if defined(ARG_MAX) || defined(ARG_MIN) - local_results[lid] = OPERATION((__global DATA_TYPE *)offset(&src, 0, y), x_idx); -#else // defined(ARG_MAX) || defined(ARG_MIN) - local_results[lid] = OPERATION((__global DATA_TYPE *)offset(&src, 0, y)); -#endif // defined(ARG_MAX) || defined(ARG_MIN) - + local_results[lid] = OPERATION((__global DATA_TYPE *)offset(&src, 0, y)); barrier(CLK_LOCAL_MEM_FENCE); // Perform parallel reduction @@ -251,26 +137,9 @@ __kernel void reduction_operation_x( { #if defined(PROD) local_results[lid] *= local_results[lid + i]; -#elif defined(ARG_MAX) - __global DATA_TYPE *src_in_row = src_ptr + src_offset_first_element_in_bytes + y_idx * src_step_y; - DATA_TYPE tmp0 = *(src_in_row + local_results[lid]); - DATA_TYPE tmp1 = *(src_in_row + local_results[lid + i]); - local_results[lid] = select( - local_results[lid], - local_results[lid + i], - ((tmp0 == tmp1) && (local_results[lid + i] < local_results[lid])) || (tmp0 < tmp1)); - -#elif defined(ARG_MIN) - __global DATA_TYPE *src_in_row = src_ptr + src_offset_first_element_in_bytes + y_idx * src_step_y; - DATA_TYPE tmp0 = *(src_in_row + local_results[lid]); - DATA_TYPE tmp1 = *(src_in_row + local_results[lid + i]); - local_results[lid] = select( - local_results[lid], - local_results[lid + i], - ((tmp0 == tmp1) && (local_results[lid + i] < local_results[lid])) || (tmp0 > tmp1)); -#else // !defined(PROD) && !defined(ARG_MAX) && !defined(ARG_MIN) +#else // !defined(PROD) local_results[lid] += local_results[lid + i]; -#endif // !defined(PROD) && !defined(ARG_MAX) && !defined(ARG_MIN) +#endif // defined(PROD) } barrier(CLK_LOCAL_MEM_FENCE); } @@ -283,22 +152,16 @@ __kernel void reduction_operation_x( local_results[0] /= WIDTH; } #endif // defined(MEAN) && defined(WIDTH) - ((__global DATA_TYPE_OUTPUT *)offset(&partial_res, get_group_id(0), y))[0] = local_results[0]; + ((__global DATA_TYPE *)offset(&partial_res, get_group_id(0), y))[0] = local_results[0]; } } } - -#endif // defined(DATA_TYPE_OUTPUT) - -#endif // defined(OPERATION) && defined(WIDTH) - -#if defined(DATA_TYPE_PROMOTED) +#endif // defined(OPERATION) #if defined(WIDTH) /** This kernel performs reduction on x-axis. (Non parallel) * * @note The data type must be passed at compile time using -DDATA_TYPE: e.g. -DDATA_TYPE=float - * @note The data type of the intermediate results must be passed at compile time using -DDATA_TYPE_PROMOTED: e.g. -DDATA_TYPE_PROMOTED=uint * @note The width size must be passed at compile time using -DWIDTH e.g. -DWIDTH=128 * @note The product flag must be passed at compile time using -DPROD if we want to compute the product, otherwise sum will be used * @note In case of ARG_MIN and ARG_MAX the condition data type must be passed at compile time using -DCOND_DATA_TYPE e.g. -DCOND_DATA_TYPE=short @@ -328,7 +191,13 @@ __kernel void reduction_operation_non_parallel_x( for(unsigned int x = 1; x < WIDTH; ++x) { DATA_TYPE_PROMOTED in = *((__global DATA_TYPE *)vector_offset(&src, x)); -#if defined(MIN) +#if defined(ARG_MAX) + indx = select(indx, x, ISGREATER(in, res)); + res = select(res, in, CONVERT(ISGREATER(in, res), COND_DATA_TYPE)); +#elif defined(ARG_MIN) + indx = select(indx, x, ISLESS(in, res)); + res = select(res, in, CONVERT(ISLESS(in, res), COND_DATA_TYPE)); +#elif defined(MIN) res = select(res, in, CONVERT(ISLESS(in, res), COND_DATA_TYPE)); #elif defined(MAX) res = select(res, in, CONVERT(ISGREATER(in, res), COND_DATA_TYPE)); @@ -357,7 +226,6 @@ __kernel void reduction_operation_non_parallel_x( /** This kernel performs reduction on y-axis. * * @note The input data type must be passed at compile time using -DDATA_TYPE: e.g. -DDATA_TYPE=float - * @note The data type of the intermediate results must be passed at compile time using -DDATA_TYPE_PROMOTED: e.g. -DDATA_TYPE_PROMOTED=uint * @note The height size must be passed at compile time using -DHEIGHT e.g. -DHEIGHT=128 * * @param[in] src_ptr Pointer to the source tensor. Supported data types: QASYMM8/S32/F16/F32 @@ -435,7 +303,6 @@ __kernel void reduction_operation_y( /** This kernel performs reduction on z-axis. * * @note The data type must be passed at compile time using -DDATA_TYPE: e.g. -DDATA_TYPE=float - * @note The data type of the intermediate results must be passed at compile time using -DDATA_TYPE_PROMOTED: e.g. -DDATA_TYPE_PROMOTED=uint * @note The depth size must be passed at compile time using -DDEPTH e.g. -DDEPTH=128 * * @param[in] input_ptr Pointer to the source tensor. Supported data types: QASYMM8/S32/F16/F32 @@ -533,9 +400,8 @@ __kernel void reduction_operation_z( /** This kernel performs reduction on w-axis. * * @note The data type must be passed at compile time using -DDATA_TYPE: e.g. -DDATA_TYPE=float - * @note The data type of the intermediate results must be passed at compile time using -DDATA_TYPE_PROMOTED: e.g. -DDATA_TYPE_PROMOTED=uint * @note The batch size must be passed at compile time using -DBATCH e.g. -DBATCH=128 - * @note The depth size must be passed at compile time using -DDEPTH e.g. -DDEPTH=128 + * @note The depth size must be passed at compile time using -DBATCH e.g. -DDEPTH=128 * * @param[in] input_ptr Pointer to the source tensor. Supported data types: QASYMM8/S32/F16/F32 * @param[in] input_stride_x Stride of the source tensor in X dimension (in bytes) @@ -616,7 +482,3 @@ __kernel void reduction_operation_w( #endif // defined(ARG_MAX) || defined(ARG_MIN) } #endif /* defined(BATCH) && defined(DEPTH) */ - -#endif /* defined(DATA_TYPE_PROMOTED) */ - -#endif /* defined(DATA_TYPE) */
\ No newline at end of file diff --git a/src/core/CL/kernels/CLReductionOperationKernel.cpp b/src/core/CL/kernels/CLReductionOperationKernel.cpp index b26d1eeb91..8e92b591d1 100644 --- a/src/core/CL/kernels/CLReductionOperationKernel.cpp +++ b/src/core/CL/kernels/CLReductionOperationKernel.cpp @@ -40,9 +40,8 @@ namespace arm_compute { namespace { -// OpenCL kernel requires input width to be a multiple of 16 for x-axis in order to use vector operations. -// And also to use a power of 2 to -constexpr unsigned int border_val = 16; +// OpenCL kernel requires input width to be a power of 2 for x-axis. +constexpr unsigned int border_val = 64; Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, unsigned int axis, ReductionOperation op, unsigned int width) { @@ -90,7 +89,8 @@ std::tuple<Status, Window> validate_and_configure_window(ITensorInfo *input, ITe const unsigned int num_elems_processed_per_iteration = (is_data_type_quantized(input->data_type()) && (axis == 0)) ? 1 : 16; Window win = calculate_max_window(*input, Steps(num_elems_processed_per_iteration)); bool window_changed = false; - const bool is_serial_op = (op == ReductionOperation::MIN || op == ReductionOperation::MAX || is_data_type_quantized(input->data_type())); + const bool is_serial_op = (op == ReductionOperation::ARG_IDX_MAX || op == ReductionOperation::ARG_IDX_MIN || op == ReductionOperation::MIN + || op == ReductionOperation::MAX || is_data_type_quantized(input->data_type())); switch(axis) { @@ -105,7 +105,7 @@ std::tuple<Status, Window> validate_and_configure_window(ITensorInfo *input, ITe } else { - const unsigned int border_width = ((input->dimension(0) % border_val) != 0 && !is_arg_min_max) ? border_val - input->dimension(0) % border_val : 0; + const unsigned int border_width = ((input->dimension(0) % border_val) != 0) ? border_val - input->dimension(0) % border_val : 0; AccessWindowStatic input_access(input, 0, 0, input->dimension(0) + border_width, 1); AccessWindowHorizontal output_access(output, 0, 1); window_changed = update_window_and_padding(win, input_access, output_access); @@ -148,8 +148,6 @@ void CLReductionOperationKernel::configure(const ICLTensor *input, ICLTensor *ou ARM_COMPUTE_ERROR_ON_NULLPTR(input, output); ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), output->info(), axis, op, width)); - auto win_config = validate_and_configure_window(input->info(), output->info(), axis, op); - ARM_COMPUTE_ERROR_THROW_ON(std::get<0>(win_config)); _input = input; _output = output; @@ -186,11 +184,7 @@ void CLReductionOperationKernel::configure(const ICLTensor *input, ICLTensor *ou build_opts.add_option(("-DOPERATION=sum")); break; case ReductionOperation::ARG_IDX_MAX: - build_opts.add_option(("-DOPERATION=arg_idx_max")); - break; case ReductionOperation::ARG_IDX_MIN: - build_opts.add_option(("-DOPERATION=arg_idx_min")); - break; case ReductionOperation::MIN: case ReductionOperation::MAX: break; @@ -204,56 +198,30 @@ void CLReductionOperationKernel::configure(const ICLTensor *input, ICLTensor *ou // Create kernel cl::NDRange lws_hint = CLKernelLibrary::get().default_ndrange(); std::string kernel_axis_name; - const bool is_serial_op = (op == ReductionOperation::MIN || op == ReductionOperation::MAX + const bool is_serial_op = (op == ReductionOperation::ARG_IDX_MAX || op == ReductionOperation::ARG_IDX_MIN || op == ReductionOperation::MIN || op == ReductionOperation::MAX || is_data_type_quantized(input->info()->data_type())); - - const bool is_arg_min_max = (op == ReductionOperation::ARG_IDX_MIN || op == ReductionOperation::ARG_IDX_MAX); switch(axis) { case 0: { - build_opts.add_option("-DDATA_TYPE_OUTPUT=" + get_cl_type_from_data_type(output->info()->data_type())); - build_opts.add_option("-DCOND_DATA_TYPE=" + get_cl_select_type_from_data_type(input->info()->data_type())); if(is_serial_op) { build_opts.add_option("-DWIDTH=" + support::cpp11::to_string(input->info()->dimension(0))); + build_opts.add_option_if_else(_input->info()->data_type() == DataType::F16, "-DCOND_DATA_TYPE=short", "-DCOND_DATA_TYPE=int"); kernel_axis_name = "non_parallel_x"; } else { - if(op == ReductionOperation::MEAN_SUM) - { - build_opts.add_option("-DWIDTH=" + support::cpp11::to_string(width)); - } - else - { - build_opts.add_option("-DWIDTH=" + support::cpp11::to_string(input->info()->dimension(0))); - } - kernel_axis_name = "x"; - if(is_arg_min_max) - { - const bool multi_access_x = (_input->info()->tensor_shape().x() > 16); - build_opts.add_option_if(multi_access_x, "-DMULTI_ACCESS_X"); - - const unsigned int width_leftover = input->info()->dimension(0) % 16; - const unsigned int border_width = (width_leftover != 0) ? 16 - width_leftover : 0; - const unsigned int num_of_threads = ((input->info()->dimension(0) + border_width) / 16); - - // Set the number of WG based on the input size. If input width is < 128 - // we can use fewer threads than 8 per workgroup - lws_hint = cl::NDRange(std::min(8U, num_of_threads)); - _border_size = BorderSize(0, 0, 0, 0); - } - else - { - const unsigned int width_leftover = input->info()->dimension(0) % border_val; - const unsigned int border_width = (width_leftover != 0) ? border_val - width_leftover : 0; - const unsigned int num_of_threads = ((input->info()->dimension(0) + border_width) / 16); - // Set the number of WG based on the input size. If input width is < 128 - // we can use fewer threads than 8 per workgroup - lws_hint = cl::NDRange(std::min(8U, num_of_threads)); - _border_size = BorderSize(0, border_width, 0, 0); - } + build_opts.add_option_if(op == ReductionOperation::MEAN_SUM, "-DWIDTH=" + support::cpp11::to_string(width)); + const unsigned int width_leftover = input->info()->dimension(0) % border_val; + const unsigned int border_width = (width_leftover != 0) ? border_val - width_leftover : 0; + const unsigned int num_of_threads = ((input->info()->dimension(0) + border_width) / 16); + kernel_axis_name = "x"; + + // Set the number of WG based on the input size. If input width is < 128 + // we can use fewer threads than 8. + lws_hint = cl::NDRange(std::min(8U, num_of_threads)); + _border_size = BorderSize(0, border_width, 0, 0); } } break; @@ -276,6 +244,10 @@ void CLReductionOperationKernel::configure(const ICLTensor *input, ICLTensor *ou _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel("reduction_operation_" + kernel_axis_name, build_opts.options())); // Configure kernel window + auto win_config = validate_and_configure_window(_input->info(), _output->info(), axis, op); + + ARM_COMPUTE_ERROR_THROW_ON(std::get<0>(win_config)); + ICLKernel::configure_internal(std::get<1>(win_config), lws_hint); } @@ -291,8 +263,9 @@ void CLReductionOperationKernel::run(const Window &window, cl::CommandQueue &que { ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(IKernel::window(), window); - const bool is_arg_min_max = (_op == ReductionOperation::ARG_IDX_MIN || _op == ReductionOperation::ARG_IDX_MAX); - const bool is_serial_op = (_op == ReductionOperation::MIN || _op == ReductionOperation::MAX || is_data_type_quantized(_input->info()->data_type())); + + const bool is_serial_op = (_op == ReductionOperation::ARG_IDX_MAX || _op == ReductionOperation::ARG_IDX_MIN || _op == ReductionOperation::MIN || _op == ReductionOperation::MAX + || is_data_type_quantized(_input->info()->data_type())); switch(_reduction_axis) { case 0: @@ -327,11 +300,11 @@ void CLReductionOperationKernel::run(const Window &window, cl::CommandQueue &que Window out_slice = out_window.first_slice_window_2D(); // Reshape window - const unsigned int border_width = ((in_slice.x().end() % border_val) != 0 && !is_arg_min_max) ? border_val - in_slice.x().end() % border_val : 0; + const unsigned int border_width = ((in_slice.x().end() % border_val) != 0) ? border_val - in_slice.x().end() % border_val : 0; in_slice.set(Window::DimX, Window::Dimension(in_slice.x().start(), in_slice.x().end() + border_width, in_slice.x().step())); // Set local sums buffer - unsigned int local_res_size = lws_hint()[0] * _output->info()->element_size(); + unsigned int local_res_size = lws_hint()[0] * _input->info()->element_size(); _kernel.setArg(num_arguments_per_2D_tensor() * 2, local_res_size, nullptr); do @@ -403,4 +376,4 @@ void CLReductionOperationKernel::run(const Window &window, cl::CommandQueue &que ARM_COMPUTE_ERROR("Not supported"); } } -} // namespace arm_compute
\ No newline at end of file +} // namespace arm_compute diff --git a/tests/benchmark/CL/ArgMinMax.cpp b/tests/benchmark/CL/ArgMinMax.cpp deleted file mode 100644 index 25a4a05d44..0000000000 --- a/tests/benchmark/CL/ArgMinMax.cpp +++ /dev/null @@ -1,56 +0,0 @@ -/* - * Copyright (c) 2019 ARM Limited. - * - * SPDX-License-Identifier: MIT - * - * Permission is hereby granted, free of charge, to any person obtaining a copy - * of this software and associated documentation files (the "Software"), to - * deal in the Software without restriction, including without limitation the - * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or - * sell copies of the Software, and to permit persons to whom the Software is - * furnished to do so, subject to the following conditions: - * - * The above copyright notice and this permission notice shall be included in all - * copies or substantial portions of the Software. - * - * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR - * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, - * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE - * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER - * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, - * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE - * SOFTWARE. - */ -#include "arm_compute/core/Types.h" -#include "arm_compute/runtime/CL/CLTensor.h" -#include "arm_compute/runtime/CL/CLTensorAllocator.h" -#include "arm_compute/runtime/CL/functions/CLArgMinMaxLayer.h" - -#include "tests/CL/CLAccessor.h" -#include "tests/benchmark/fixtures/ArgMinMaxFixture.h" -#include "tests/datasets/ShapeDatasets.h" -#include "tests/datasets/SplitDataset.h" -#include "tests/framework/Asserts.h" -#include "tests/framework/Macros.h" - -namespace arm_compute -{ -namespace test -{ -namespace benchmark -{ -TEST_SUITE(CL) - -using CLArgMinMaxBenchmarkFixture = ArgMinMaxBenchmarkFixture<CLTensor, CLArgMinMaxLayer, CLAccessor>; - -REGISTER_FIXTURE_DATA_TEST_CASE(ArgMinMax, CLArgMinMaxBenchmarkFixture, framework::DatasetMode::PRECOMMIT, - framework::dataset::combine(framework::dataset::combine(framework::dataset::combine( - datasets::Large3DShapes(), - framework::dataset::make("DataType", { DataType::F32 })), - framework::dataset::make("Axis", { 0, 1 })), - framework::dataset::make("ReductionOperation", { ReductionOperation::ARG_IDX_MAX, ReductionOperation::ARG_IDX_MIN }))); - -TEST_SUITE_END() // CL -} // namespace benchmark -} // namespace test -} // namespace arm_compute diff --git a/tests/benchmark/fixtures/ArgMinMaxFixture.h b/tests/benchmark/fixtures/ArgMinMaxFixture.h deleted file mode 100644 index f1a0c5ab4b..0000000000 --- a/tests/benchmark/fixtures/ArgMinMaxFixture.h +++ /dev/null @@ -1,84 +0,0 @@ -/* - * Copyright (c) 2019 ARM Limited. - * - * SPDX-License-Identifier: MIT - * - * Permission is hereby granted, free of charge, to any person obtaining a copy - * of this software and associated documentation files (the "Software"), to - * deal in the Software without restriction, including without limitation the - * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or - * sell copies of the Software, and to permit persons to whom the Software is - * furnished to do so, subject to the following conditions: - * - * The above copyright notice and this permission notice shall be included in all - * copies or substantial portions of the Software. - * - * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR - * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, - * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE - * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER - * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, - * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE - * SOFTWARE. - */ -#ifndef ARM_COMPUTE_TEST_ARGMINMAXFIXTURE -#define ARM_COMPUTE_TEST_ARGMINMAXFIXTURE - -#include "arm_compute/core/TensorShape.h" -#include "arm_compute/core/Types.h" -#include "tests/Globals.h" -#include "tests/Utils.h" -#include "tests/framework/Fixture.h" - -namespace arm_compute -{ -namespace test -{ -namespace benchmark -{ -/** Fixture that can be used for NEON and CL */ -template <typename TensorType, typename Function, typename Accessor> -class ArgMinMaxBenchmarkFixture : public framework::Fixture -{ -public: - template <typename...> - void setup(TensorShape shape, DataType data_type, int axis, ReductionOperation op) - { - // Create tensors - src = create_tensor<TensorType>(shape, data_type); - dst = create_tensor<TensorType>(shape, DataType::U32); - - // Create and configure function - argminmax_layer.configure(&src, axis, &dst, op); - - // Allocate tensors - src.allocator()->allocate(); - dst.allocator()->allocate(); - } - - void run() - { - argminmax_layer.run(); - } - - void sync() - { - sync_if_necessary<TensorType>(); - sync_tensor_if_necessary<TensorType>(dst); - } - - void teardown() - { - src.allocator()->free(); - dst.allocator()->free(); - } - -private: - TensorType src{}; - TensorType dst{}; - Function argminmax_layer{}; -}; -} // namespace benchmark -} // namespace test -} // namespace arm_compute -#endif /* ARM_COMPUTE_TEST_ARGMINMAXFIXTURE */ |