From 36debd4472839997fd3b6ec9d58530d95e3c17de Mon Sep 17 00:00:00 2001 From: Manuel Bottini Date: Mon, 22 Jul 2019 11:42:55 +0100 Subject: COMPMID-1816: Use parallel reduction on 0 axis in CL ARG_MIN/ARG_MAX Parallelization of reduction along x axes Removal of the use of padding Fast vector implementation of reduction operation Change-Id: I3a56c57b9fc1135cf8f79d1021d966ea22b084b1 Signed-off-by: Manuel Bottini Reviewed-on: https://review.mlplatform.org/c/1791 Comments-Addressed: Arm Jenkins Tested-by: Arm Jenkins Reviewed-by: Gian Marco Iodice --- src/core/CL/cl_kernels/reduction_operation.cl | 168 +++++++++++++++++++-- src/core/CL/kernels/CLReductionOperationKernel.cpp | 81 ++++++---- tests/benchmark/CL/ArgMinMax.cpp | 56 +++++++ tests/benchmark/fixtures/ArgMinMaxFixture.h | 84 +++++++++++ 4 files changed, 347 insertions(+), 42 deletions(-) create mode 100644 tests/benchmark/CL/ArgMinMax.cpp create mode 100644 tests/benchmark/fixtures/ArgMinMaxFixture.h diff --git a/src/core/CL/cl_kernels/reduction_operation.cl b/src/core/CL/cl_kernels/reduction_operation.cl index 5a4bb9ff4c..db034f0c3a 100644 --- a/src/core/CL/cl_kernels/reduction_operation.cl +++ b/src/core/CL/cl_kernels/reduction_operation.cl @@ -36,6 +36,10 @@ #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. @@ -91,10 +95,112 @@ inline DATA_TYPE product(__global const DATA_TYPE *input) return (in.s0 * in.s1); } -#if defined(OPERATION) + +#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) + /** 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 @@ -117,7 +223,7 @@ inline DATA_TYPE product(__global const DATA_TYPE *input) __kernel void reduction_operation_x( IMAGE_DECLARATION(src), IMAGE_DECLARATION(partial_res), - __local DATA_TYPE *local_results) + __local DATA_TYPE_OUTPUT *local_results) { Image src = CONVERT_TO_IMAGE_STRUCT(src); Image partial_res = CONVERT_TO_IMAGE_STRUCT(partial_res); @@ -125,9 +231,17 @@ __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) { - local_results[lid] = OPERATION((__global DATA_TYPE *)offset(&src, 0, 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) + barrier(CLK_LOCAL_MEM_FENCE); // Perform parallel reduction @@ -137,9 +251,26 @@ __kernel void reduction_operation_x( { #if defined(PROD) local_results[lid] *= local_results[lid + i]; -#else // !defined(PROD) +#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) local_results[lid] += local_results[lid + i]; -#endif // defined(PROD) +#endif // !defined(PROD) && !defined(ARG_MAX) && !defined(ARG_MIN) } barrier(CLK_LOCAL_MEM_FENCE); } @@ -152,16 +283,22 @@ __kernel void reduction_operation_x( local_results[0] /= WIDTH; } #endif // defined(MEAN) && defined(WIDTH) - ((__global DATA_TYPE *)offset(&partial_res, get_group_id(0), y))[0] = local_results[0]; + ((__global DATA_TYPE_OUTPUT *)offset(&partial_res, get_group_id(0), y))[0] = local_results[0]; } } } -#endif // defined(OPERATION) + +#endif // defined(DATA_TYPE_OUTPUT) + +#endif // defined(OPERATION) && defined(WIDTH) + +#if defined(DATA_TYPE_PROMOTED) #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 @@ -191,13 +328,7 @@ __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(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) +#if 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)); @@ -226,6 +357,7 @@ __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 @@ -303,6 +435,7 @@ __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 @@ -400,8 +533,9 @@ __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 -DBATCH e.g. -DDEPTH=128 + * @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 * @param[in] input_stride_x Stride of the source tensor in X dimension (in bytes) @@ -482,3 +616,7 @@ __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 8e92b591d1..b26d1eeb91 100644 --- a/src/core/CL/kernels/CLReductionOperationKernel.cpp +++ b/src/core/CL/kernels/CLReductionOperationKernel.cpp @@ -40,8 +40,9 @@ namespace arm_compute { namespace { -// OpenCL kernel requires input width to be a power of 2 for x-axis. -constexpr unsigned int border_val = 64; +// 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; Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, unsigned int axis, ReductionOperation op, unsigned int width) { @@ -89,8 +90,7 @@ std::tuple 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::ARG_IDX_MAX || op == ReductionOperation::ARG_IDX_MIN || op == ReductionOperation::MIN - || op == ReductionOperation::MAX || is_data_type_quantized(input->data_type())); + const bool is_serial_op = (op == ReductionOperation::MIN || op == ReductionOperation::MAX || is_data_type_quantized(input->data_type())); switch(axis) { @@ -105,7 +105,7 @@ std::tuple validate_and_configure_window(ITensorInfo *input, ITe } else { - const unsigned int border_width = ((input->dimension(0) % border_val) != 0) ? border_val - input->dimension(0) % border_val : 0; + const unsigned int border_width = ((input->dimension(0) % border_val) != 0 && !is_arg_min_max) ? 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); @@ -148,6 +148,8 @@ 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; @@ -184,7 +186,11 @@ 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; @@ -198,30 +204,56 @@ 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::ARG_IDX_MAX || op == ReductionOperation::ARG_IDX_MIN || op == ReductionOperation::MIN || op == ReductionOperation::MAX + const bool is_serial_op = (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 { - 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); + 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); + } } } break; @@ -244,10 +276,6 @@ void CLReductionOperationKernel::configure(const ICLTensor *input, ICLTensor *ou _kernel = static_cast(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); } @@ -263,9 +291,8 @@ 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_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); + const bool is_serial_op = (_op == ReductionOperation::MIN || _op == ReductionOperation::MAX || is_data_type_quantized(_input->info()->data_type())); switch(_reduction_axis) { case 0: @@ -300,11 +327,11 @@ 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) ? border_val - in_slice.x().end() % border_val : 0; + 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; 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] * _input->info()->element_size(); + unsigned int local_res_size = lws_hint()[0] * _output->info()->element_size(); _kernel.setArg(num_arguments_per_2D_tensor() * 2, local_res_size, nullptr); do @@ -376,4 +403,4 @@ void CLReductionOperationKernel::run(const Window &window, cl::CommandQueue &que ARM_COMPUTE_ERROR("Not supported"); } } -} // namespace arm_compute +} // namespace arm_compute \ No newline at end of file diff --git a/tests/benchmark/CL/ArgMinMax.cpp b/tests/benchmark/CL/ArgMinMax.cpp new file mode 100644 index 0000000000..25a4a05d44 --- /dev/null +++ b/tests/benchmark/CL/ArgMinMax.cpp @@ -0,0 +1,56 @@ +/* + * 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; + +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 new file mode 100644 index 0000000000..f1a0c5ab4b --- /dev/null +++ b/tests/benchmark/fixtures/ArgMinMaxFixture.h @@ -0,0 +1,84 @@ +/* + * 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 +class ArgMinMaxBenchmarkFixture : public framework::Fixture +{ +public: + template + void setup(TensorShape shape, DataType data_type, int axis, ReductionOperation op) + { + // Create tensors + src = create_tensor(shape, data_type); + dst = create_tensor(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(); + sync_tensor_if_necessary(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 */ -- cgit v1.2.1