diff options
Diffstat (limited to 'tests/validation/CL/UNIT/dynamic_fusion/ClCompositeKernel.cpp')
| -rw-r--r-- | tests/validation/CL/UNIT/dynamic_fusion/ClCompositeKernel.cpp | 643 |
1 files changed, 643 insertions, 0 deletions
diff --git a/tests/validation/CL/UNIT/dynamic_fusion/ClCompositeKernel.cpp b/tests/validation/CL/UNIT/dynamic_fusion/ClCompositeKernel.cpp new file mode 100644 index 0000000000..c4e7033914 --- /dev/null +++ b/tests/validation/CL/UNIT/dynamic_fusion/ClCompositeKernel.cpp @@ -0,0 +1,643 @@ +/* + * Copyright (c) 2022 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. + */ + +#if defined(ENABLE_EXPERIMENTAL_DYNAMIC_FUSION) + +#include "src/gpu/cl/kernels/experimental/dynamic_fusion/ClCompositeKernel.h" + +#include "src/core/utils/helpers/float_ops.h" +#include "src/gpu/cl/kernels/ClElementwiseKernel.h" +#include "src/gpu/cl/kernels/ClGemmMatrixMultiplyNativeKernel.h" +#include "tests/CL/CLAccessor.h" +#include "tests/framework/Macros.h" +#include "tests/framework/datasets/Datasets.h" +#include "tests/validation/Validation.h" +#include "tests/validation/reference/ElementwiseOperations.h" +#include "tests/validation/reference/GEMM.h" + +#include "arm_compute/core/utils/misc/ShapeCalculator.h" +#include "src/core/AccessWindowStatic.h" +#include "src/core/helpers/AutoConfiguration.h" +#include "src/core/helpers/WindowHelpers.h" + +#include <chrono> + +using namespace arm_compute::experimental::dynamic_fusion; + +namespace arm_compute +{ +namespace test +{ +namespace validation +{ +namespace +{ +/** Macros which measures the wall clock time, and records it into a map measurement_map with name clock_name */ +#define TICK(clock_name) \ + auto clock_name##_tick = std::chrono::high_resolution_clock::now(); +#define TOCK(clock_name, measurement_map) \ + auto clock_name##_tock = std::chrono::high_resolution_clock::now(); \ + measurement_map["\"" #clock_name "\""] = duration_cast<microseconds>(clock_name##_tock - clock_name##_tick); +#define TOCK_AVG(clock_name, measurement_map, num_iterations) \ + auto clock_name##_tock = std::chrono::high_resolution_clock::now(); \ + measurement_map["\"" #clock_name "\""] = duration_cast<microseconds>((clock_name##_tock - clock_name##_tick) / (num_iterations)); + +template <typename T, typename U> +void fill(U &&tensor, int seed) +{ + static_assert(std::is_floating_point<T>::value || std::is_same<T, half>::value, "Only floating point data types supported."); + using DistributionType = typename std::conditional<std::is_same<T, half>::value, arm_compute::utils::uniform_real_distribution_16bit<T>, std::uniform_real_distribution<T>>::type; + + DistributionType distribution{ T(-1.0f), T(1.0f) }; + library->fill(tensor, distribution, seed); + + // Fill border with infinity in order to check the presence of NaN values (i.e. inf * 0) + DistributionType distribution_inf{ T(std::numeric_limits<float>::infinity()), T(std::numeric_limits<float>::infinity()) }; + library->fill_borders_with_garbage(tensor, distribution_inf, seed); +} + +using ElementsProcessed = Steps; +std::pair<Status, Window> mock_gemm_native_validate_and_configure_window(ITensorInfo *src0, ITensorInfo *src1, ITensorInfo *src2, ITensorInfo *dst, const GEMMLHSMatrixInfo &lhs_info, + const GEMMRHSMatrixInfo &rhs_info, + const GEMMKernelInfo &gemm_info, ElementsProcessed &num_elements_processed) +{ + unsigned int &num_elems_processed_per_iteration_x = num_elements_processed[0]; + unsigned int &num_elems_processed_per_iteration_y = num_elements_processed[1]; + bool reinterpret_input_as_3d = gemm_info.reinterpret_input_as_3d; + bool reinterpret_output_as_3d = gemm_info.depth_output_gemm3d != 0; + + Window win{}; + Window win_out{}; + bool window_changed = false; + + // In case both input and dst have to be reinterpreted as 3D tensors, + // force reinterpret_input_as_3d and reinterpret_output_as_3d to be false. + if(reinterpret_input_as_3d == reinterpret_output_as_3d) + { + reinterpret_output_as_3d = false; + } + + // dst tensor auto initialization if not yet initialized + auto_init_if_empty(*dst, src0->clone()->set_tensor_shape(misc::shape_calculator::compute_mm_shape(*src0, *src1, gemm_info))); + + TensorInfo tmp_info(*dst); + + if(reinterpret_output_as_3d) + { + // Since the dst tensor has to be reinterpreted as 3D and the execute window is based on a 2D GEMM, + // the window needs to be constructed on the 2D collapsed version of the tensor + TensorShape tmp_shape(dst->tensor_shape()); + tmp_shape.collapse(2U, 1U); + tmp_info.set_tensor_shape(tmp_shape); + } + + // Configure kernel window + num_elems_processed_per_iteration_x = rhs_info.n0; + num_elems_processed_per_iteration_y = lhs_info.m0; + + win = calculate_max_window(tmp_info, Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y)); + win_out = calculate_max_window(*dst, Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y)); + + AccessWindowStatic src0_access(src0, 0, 0, + src0->dimension(0), + src0->dimension(1)); + AccessWindowStatic src1_access(src1, 0, 0, + ceil_to_multiple(src1->dimension(0), num_elems_processed_per_iteration_x), + src1->dimension(1)); + AccessWindowStatic dst_access(dst, 0, 0, + dst->dimension(0), + dst->dimension(1)); + + if(src2 != nullptr) + { + const int bias_processed_per_iteration_x = num_elems_processed_per_iteration_x; + + AccessWindowStatic src2_access(src2, 0, 0, + ceil_to_multiple(src2->dimension(0), bias_processed_per_iteration_x), + src2->dimension(1)); + + window_changed = update_window_and_padding(win, src0_access, src1_access, src2_access) || // window used by the execute_window_loop + update_window_and_padding(win_out, dst_access); // window used to update the padding requirements of dst tensor + } + else + { + window_changed = update_window_and_padding(win, src0_access, src1_access) || // window used by the execute_window_loop + update_window_and_padding(win_out, dst_access); // window used to update the padding requirements of dst tensor + } + + // Collapse along the Z direction + // This collapse needs to be here in order to tune the Z dimension of LWS + Window collapsed = win; + const unsigned int dimension_to_collapse = std::min(static_cast<unsigned int>(dst->num_dimensions()), 2u); + collapsed = win.collapse(win, dimension_to_collapse); + + Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{}; + return std::make_pair(err, collapsed); +} + +void set_build_options(ClKernelCode &cl_code, GemmNativeDescriptor gemm_native_desc, + const TensorInfo &t_lhs_info, + const TensorInfo &t_rhs_info, + const TensorInfo *t_bias_info, + const TensorInfo &t_dst_info) +{ + CLBuildOptions ref_cl_build_options; + { + // If reinterpret_input_as_3d = reinterpret_output_as_3d = true, + // we will dispatch a batched-GEMM to reduce the complexity of the address calculation within the OpenCL kernel. + // This means that the actual m used by the kernel is given by dst->dimension(1) and not by gemm_info.m + auto reinterpret_input_as_3d = gemm_native_desc.reinterpret_input_as_3d; + auto reinterpret_output_as_3d = gemm_native_desc.depth_output_gemm3d != 0; + auto _slide_matrix_b = (t_rhs_info.num_dimensions() >= t_lhs_info.num_dimensions()); + auto _use_dummy_work_items = false; + // In case both input and dst have to be reinterpreted as 3D tensors, + // force reinterpret_input_as_3d and reinterpret_output_as_3d to be false. + if(reinterpret_input_as_3d == reinterpret_output_as_3d) + { + reinterpret_input_as_3d = false; + reinterpret_output_as_3d = false; + } + + const unsigned int internal_m = reinterpret_output_as_3d ? gemm_native_desc.m : t_dst_info.dimension(1); + + const unsigned int h_gemm_3d = reinterpret_output_as_3d ? t_dst_info.dimension(1) : t_lhs_info.dimension(1); + const unsigned int d_gemm_3d = reinterpret_output_as_3d ? t_dst_info.dimension(2) : t_lhs_info.dimension(2); + + // Calculate partial (store instead of load) M0 and partial N0 for the partial blocks at the end of a row/column if any. This is to avoid padding. + const unsigned int partial_store_m0 = internal_m % gemm_native_desc.lhs_info.m0; + const unsigned int partial_store_n0 = gemm_native_desc.n % gemm_native_desc.rhs_info.n0; + + // Shrink M0 to be always <= M (internal_m) to prevent out-of-bounds reads. + const unsigned int internal_m0 = std::min(internal_m, gemm_native_desc.lhs_info.m0); + + ref_cl_build_options.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(t_dst_info.data_type())); + ref_cl_build_options.add_option_if(!(helpers::float_ops::is_one(gemm_native_desc.alpha)), "-DALPHA=" + float_to_string_with_full_precision(gemm_native_desc.alpha)); + ref_cl_build_options.add_option_if(t_bias_info != nullptr, "-DBETA=" + float_to_string_with_full_precision(gemm_native_desc.beta)); + ref_cl_build_options.add_option_if(helpers::float_ops::is_one(gemm_native_desc.beta), "-DUNIT_BETA"); + ref_cl_build_options.add_option_if(gemm_native_desc.broadcast_bias, "-DBROADCAST_BIAS"); + ref_cl_build_options.add_option_if(reinterpret_input_as_3d, "-DREINTERPRET_INPUT_AS_3D"); + ref_cl_build_options.add_option_if(reinterpret_output_as_3d, "-DREINTERPRET_OUTPUT_AS_3D"); + ref_cl_build_options.add_option_if(reinterpret_input_as_3d || reinterpret_output_as_3d, "-DHEIGHT_GEMM3D=" + support::cpp11::to_string(h_gemm_3d)); + ref_cl_build_options.add_option_if(reinterpret_input_as_3d || reinterpret_output_as_3d, "-DDEPTH_GEMM3D=" + support::cpp11::to_string(d_gemm_3d)); + ref_cl_build_options.add_option_if(!_slide_matrix_b, "-DMATRIX_B_DEPTH=" + support::cpp11::to_string(t_rhs_info.dimension(2))); + ref_cl_build_options.add_option_if(_use_dummy_work_items, "-DDUMMY_WORK_ITEMS"); + ref_cl_build_options.add_option("-DM=" + support::cpp11::to_string(internal_m)); + ref_cl_build_options.add_option("-DN=" + support::cpp11::to_string(gemm_native_desc.n)); + ref_cl_build_options.add_option("-DK=" + support::cpp11::to_string(gemm_native_desc.k)); + ref_cl_build_options.add_option("-DM0=" + support::cpp11::to_string(internal_m0)); + ref_cl_build_options.add_option("-DN0=" + support::cpp11::to_string(gemm_native_desc.rhs_info.n0)); + ref_cl_build_options.add_option("-DK0=" + support::cpp11::to_string(gemm_native_desc.rhs_info.k0)); + ref_cl_build_options.add_option("-DPARTIAL_STORE_M0=" + support::cpp11::to_string(partial_store_m0)); + ref_cl_build_options.add_option("-DPARTIAL_STORE_N0=" + support::cpp11::to_string(partial_store_n0)); + // Manually add PostOps + { + ref_cl_build_options.add_option("-DOP=ADD_X_POS_1"); + ref_cl_build_options.add_option("-DP2_ELTWISE_ARG1_HEIGHT=" + support::cpp11::to_string(t_dst_info.dimension(1))); + ref_cl_build_options.add_option("-DP2_ELTWISE_ARG1_WIDTH=" + support::cpp11::to_string(t_dst_info.dimension(0))); + } + } + cl_code.build_options = ref_cl_build_options; +} +} // namespace + +TEST_SUITE(CL) +TEST_SUITE(UNIT) +TEST_SUITE(DYNAMIC_FUSION) +TEST_SUITE(ClCompositeKernel) +TEST_SUITE(Validate) + +TEST_CASE(MoveNet_SubGraph_1, framework::DatasetMode::ALL) +{ + /* Computation: + * out = add(addend, gemm_native(lhs, rhs, bias)) (non-broadcast) + */ + const auto data_type = DataType::F32; + const auto m = 5U; + const auto n = 4U; + const auto k = 3U; + const auto t_lhs_shape = TensorShape(k, m); + const auto t_rhs_shape = TensorShape(n, k); + const auto t_dst_shape = TensorShape(n, m); + auto t_lhs_info = TensorInfo(t_lhs_shape, 1, data_type); + auto t_rhs_info = TensorInfo(t_rhs_shape, 1, data_type); + const auto t_bias_info = TensorInfo(TensorShape(), 1, DataType::F32); + auto t_dst_info = TensorInfo(t_dst_shape, 1, data_type); + + const ClTensorDescriptor t_lhs_desc{ &t_lhs_info, 2 }; + const ClTensorDescriptor t_rhs_desc{ &t_rhs_info, 2 }; + const ClTensorDescriptor t_bias_desc{ &t_bias_info, 2 }; + const ClTensorDescriptor t_addend_desc{ &t_dst_info, 2 }; + const ClTensorDescriptor t_dst_desc{ &t_dst_info, 2 }; + + ClKernelBlueprint bp; + ArgumentID tid_lhs; + ArgumentID tid_rhs; + ArgumentID tid_l0_bias = g_arg_placeholder; + ArgumentID tid_l1_addend; + ArgumentID tid_dst; + auto st = add_tensor_argument(bp, t_lhs_desc, tid_lhs); + st = add_tensor_argument(bp, t_rhs_desc, tid_rhs); + st = add_tensor_argument(bp, t_addend_desc, tid_l1_addend); + st = add_tensor_argument(bp, t_dst_desc, tid_dst); + + const auto common_kernel_desc = ClKernelComponentDescriptor{}; + const GemmNativeDescriptor gemm_native_desc{ 1.0, 1.0, m, n, k }; + const GEMMKernelInfo gemm_info{ m, n, k, 0, false, false, false, false, ActivationLayerInfo{}, 1, 1, gemm_native_desc.lhs_info, gemm_native_desc.rhs_info, 0, 0 }; + const EltwiseAddDescriptor eltwise_add_desc{ ConvertPolicy::WRAP }; + const TileDescriptor store_tile_info{}; + + ArgumentID tid_acc; + st = add_tensor_intermed(bp, tid_acc); + st = add_kcomp_gemm_native(bp, common_kernel_desc, gemm_native_desc, tid_lhs, tid_rhs, tid_l0_bias, tid_acc); + + st = add_kcomp_eltwise_add(bp, common_kernel_desc, EltwiseAddDescriptor{}, tid_l1_addend, tid_acc, tid_acc); + st = add_kcomp_store(bp, common_kernel_desc, tid_acc, tid_dst, StoreType::StoreBlockBoundaryAware); + + ClKernelCode cl_code; + + st = set_tile_info(bp, store_tile_info); + st = build(cl_code, ClCodeBuilderContext{ GpuInfo{ GPUTarget::G71 } }, bp); + + set_build_options(cl_code, gemm_native_desc, t_lhs_info, t_rhs_info, nullptr, t_dst_info); + ElementsProcessed num_elements_processed{}; + auto win_config = mock_gemm_native_validate_and_configure_window(&t_lhs_info, &t_rhs_info, nullptr, &t_dst_info, gemm_native_desc.lhs_info, gemm_native_desc.rhs_info, gemm_info, + num_elements_processed); + ARM_COMPUTE_ERROR_THROW_ON(win_config.first); + cl_code.window = win_config.second; + + ClExecutionDescriptor exec_desc; + st = tune_static(exec_desc, cl_code); + + CLScheduler::get().default_init(); + ClCompositeKernel kernel; + kernel.configure(CLKernelLibrary::get().get_compile_context(), cl_code); + + // Construct tensors + CLTensor t_lhs{}; + CLTensor t_rhs{}; + CLTensor t_l1_addend{}; + CLTensor t_dst{}; + // Init tensors + { + t_lhs.allocator()->init(t_lhs_info); + t_rhs.allocator()->init(t_rhs_info); + t_l1_addend.allocator()->init(t_dst_info); + t_dst.allocator()->init(t_dst_info); + } + // "Pack" tensors + TensorBinding tensors({ { tid_lhs, &t_lhs }, + { tid_rhs, &t_rhs }, + { tid_l1_addend, &t_l1_addend }, + { tid_dst, &t_dst } + }); + // Allocate and fill tensors + { + t_lhs.allocator()->allocate(); + t_rhs.allocator()->allocate(); + t_l1_addend.allocator()->allocate(); + t_dst.allocator()->allocate(); + fill<float>(CLAccessor(t_lhs), 0); + fill<float>(CLAccessor(t_rhs), 1); + fill<float>(CLAccessor(t_l1_addend), 2); + } + + CLScheduler::get().enqueue_op(kernel, tensors, exec_desc, true); + + // Create reference + SimpleTensor<float> ref_t_lhs{ t_lhs_shape, data_type, 1 }; + SimpleTensor<float> ref_t_rhs{ t_rhs_shape, data_type, 1 }; + SimpleTensor<float> ref_t_bias_placeholder{ t_dst_shape, data_type, 1 }; + SimpleTensor<float> ref_t_l1_addend{ t_dst_shape, data_type, 1 }; + + // Fill reference + fill<float>(ref_t_lhs, 0); + fill<float>(ref_t_rhs, 1); + fill<float>(ref_t_l1_addend, 2); + const auto ref_t_dst = reference::arithmetic_operation( + ArithmeticOperation::ADD, + ref_t_l1_addend, + reference::gemm(ref_t_lhs, ref_t_rhs, ref_t_bias_placeholder, gemm_native_desc.alpha, 0.f /* To disable bias */), + data_type, + eltwise_add_desc.convert_policy); + + RelativeTolerance<float> tolerance_f32(0.001f); /**< Tolerance value for comparing reference's output against implementation's output for floating point data types */ + validate(CLAccessor(t_dst), ref_t_dst, tolerance_f32); +} + +TEST_SUITE_END() // Validate + +TEST_SUITE(Benchmark) +TEST_CASE(MoveNet_SubGraph_1, framework::DatasetMode::ALL) +{ + using std::chrono::duration_cast; + using std::chrono::microseconds; + const int num_iterations = 200; + std::map<std::string, std::chrono::microseconds> measurements; + /* Computation: + * out = add(addend, gemm_native(lhs, rhs, bias)) + */ + const auto data_type = DataType::F32; + const unsigned int m = 12 * 12; + const unsigned int n = 64; + const unsigned int k = 384; + const auto t_lhs_shape = TensorShape(k, m); + const auto t_rhs_shape = TensorShape(n, k); + const auto t_dst_shape = TensorShape(n, m); + auto t_lhs_info = TensorInfo(t_lhs_shape, 1, data_type); + auto t_rhs_info = TensorInfo(t_rhs_shape, 1, data_type); + auto t_bias_info = TensorInfo(TensorShape(), 1, data_type); + auto t_l0_dst_info = TensorInfo(t_dst_shape, 1, data_type); // Intermediate tensor for cond3 + auto t_l1_rhs_info = TensorInfo(t_dst_shape, 1, data_type); + auto t_dst_info = TensorInfo(t_dst_shape, 1, data_type); + + const auto common_kernel_desc = ClKernelComponentDescriptor{}; + const GemmNativeDescriptor gemm_native_desc{ 1.0, 0.0, m, n, k }; + const GEMMKernelInfo gemm_info{ m, n, k, 0, false, false, false, false, ActivationLayerInfo{}, 1, 1, gemm_native_desc.lhs_info, gemm_native_desc.rhs_info, 0, 0 }; + const EltwiseAddDescriptor eltwise_add_desc{ ConvertPolicy::WRAP }; + const TileDescriptor store_tile_info{}; + + // Create reference + SimpleTensor<float> ref_t_lhs{ t_lhs_shape, data_type, 1 }; + SimpleTensor<float> ref_t_rhs{ t_rhs_shape, data_type, 1 }; + SimpleTensor<float> ref_t_bias_placeholder{ t_dst_shape, data_type, 1 }; + SimpleTensor<float> ref_t_l1_addend{ t_dst_shape, data_type, 1 }; + + // Fill reference + fill<float>(ref_t_lhs, 0); + fill<float>(ref_t_rhs, 1); + fill<float>(ref_t_l1_addend, 2); + const auto ref_t_dst = reference::arithmetic_operation( + ArithmeticOperation::ADD, + ref_t_l1_addend, + reference::gemm(ref_t_lhs, ref_t_rhs, ref_t_bias_placeholder, gemm_native_desc.alpha, 0.f /* To disable bias */), + data_type, + eltwise_add_desc.convert_policy); + + CLScheduler::get().default_init(); + + /* Condition 0: Dynamic Fused Kernel */ + CLTensor cond0_t_dst{}; + { + TICK(cond0_0_startup_time); + + ClKernelBlueprint bp; + ArgumentID tid_lhs; + ArgumentID tid_rhs; + ArgumentID tid_l0_bias = g_arg_placeholder; + ArgumentID tid_l1_addend; + ArgumentID tid_dst; + + const ClTensorDescriptor t_lhs_desc{ &t_lhs_info, 2 }; + const ClTensorDescriptor t_rhs_desc{ &t_rhs_info, 2 }; + const ClTensorDescriptor t_bias_desc{ &t_bias_info, 2 }; + const ClTensorDescriptor t_addend_desc{ &t_dst_info, 2 }; + const ClTensorDescriptor t_dst_desc{ &t_dst_info, 2 }; + + ClKernelCode cl_code; + TICK(cond0_build_time) + auto st = add_tensor_argument(bp, t_lhs_desc, tid_lhs); + st = add_tensor_argument(bp, t_rhs_desc, tid_rhs); + st = add_tensor_argument(bp, t_addend_desc, tid_l1_addend); + st = add_tensor_argument(bp, t_dst_desc, tid_dst); + + ArgumentID tid_acc; + st = add_tensor_intermed(bp, tid_acc); + st = add_kcomp_gemm_native(bp, common_kernel_desc, gemm_native_desc, tid_lhs, tid_rhs, tid_l0_bias, tid_acc); + + st = add_kcomp_eltwise_add(bp, common_kernel_desc, EltwiseAddDescriptor{}, tid_l1_addend, tid_acc, tid_acc); + + st = add_kcomp_store(bp, common_kernel_desc, tid_acc, tid_dst, StoreType::StoreBlockBoundaryAware); + + st = set_tile_info(bp, store_tile_info); + st = build(cl_code, ClCodeBuilderContext{ GpuInfo{ GPUTarget::G71 } }, bp); + set_build_options(cl_code, gemm_native_desc, t_lhs_info, t_rhs_info, nullptr, t_dst_info); + ElementsProcessed num_elements_processed{}; + auto win_config = mock_gemm_native_validate_and_configure_window(&t_lhs_info, &t_rhs_info, nullptr, &t_dst_info, gemm_native_desc.lhs_info, gemm_native_desc.rhs_info, gemm_info, + num_elements_processed); + ARM_COMPUTE_ERROR_THROW_ON(win_config.first); + cl_code.window = win_config.second; + TOCK(cond0_build_time, measurements) + + TICK(cond0_tune_time) + ClExecutionDescriptor exec_desc; + st = tune_static(exec_desc, cl_code); + TOCK(cond0_tune_time, measurements) + + TICK(cond0_configure_time) + ClCompositeKernel kernel; + kernel.configure(CLKernelLibrary::get().get_compile_context(), cl_code); + TOCK(cond0_configure_time, measurements) + + // Construct tensors + CLTensor t_lhs{}; + CLTensor t_rhs{}; + CLTensor t_l1_addend{}; + + // Init tensors + { + t_lhs.allocator()->init(t_lhs_info); + t_rhs.allocator()->init(t_rhs_info); + t_l1_addend.allocator()->init(t_dst_info); + cond0_t_dst.allocator()->init(t_dst_info); + } + // Allocate tensors + { + t_lhs.allocator()->allocate(); + t_rhs.allocator()->allocate(); + t_l1_addend.allocator()->allocate(); + cond0_t_dst.allocator()->allocate(); + fill<float>(CLAccessor(t_lhs), 0); + fill<float>(CLAccessor(t_rhs), 1); + fill<float>(CLAccessor(t_l1_addend), 2); + } + + // "Pack" tensors + TensorBinding tensors({ { tid_lhs, &t_lhs }, { tid_rhs, &t_rhs }, { tid_l1_addend, &t_l1_addend }, { tid_dst, &cond0_t_dst } }); + + CLScheduler::get().enqueue_op(kernel, tensors, exec_desc, true); + CLScheduler::get().sync(); + TOCK(cond0_0_startup_time, measurements) + + TICK(cond0_1_latency) + for(int i = 0; i < num_iterations; ++i) + { + CLScheduler::get().enqueue_op(kernel, tensors, exec_desc, true); + } + CLScheduler::get().sync(); + TOCK_AVG(cond0_1_latency, measurements, num_iterations) + } + /* Condition 1: Dynamic Unfused Kernel */ + /* Condition 2: Static Fused Kernel (current) */ + CLTensor cond2_t_dst{}; + { + TICK(cond2_0_startup_time); + arm_compute::opencl::kernels::ClGemmMatrixMultiplyNativeKernel l0_gemm_mm; + + TICK(cond2_configure_time); + experimental::PostOpList<ITensorInfo *> post_ops; + post_ops.push_back_op<experimental::PostOpEltwiseAdd<ITensorInfo *>>(&t_dst_info, 1, eltwise_add_desc.convert_policy); + GEMMKernelInfo gemm_info{ m, n, k, 0, false, false, false, false, ActivationLayerInfo{}, 1, 1, gemm_native_desc.lhs_info, gemm_native_desc.rhs_info, 0, 0, post_ops }; + l0_gemm_mm.configure(CLKernelLibrary::get().get_compile_context(), &t_lhs_info, &t_rhs_info, nullptr, &t_dst_info, gemm_native_desc.alpha, gemm_native_desc.beta, gemm_native_desc.lhs_info, + gemm_native_desc.rhs_info, gemm_info); + TOCK(cond2_configure_time, measurements); + + // Construct tensors + CLTensor t_lhs{}; + CLTensor t_rhs{}; + CLTensor t_l1_addend{}; + + // Init tensors + { + t_lhs.allocator()->init(t_lhs_info); + t_rhs.allocator()->init(t_rhs_info); + t_l1_addend.allocator()->init(t_dst_info); + cond2_t_dst.allocator()->init(t_dst_info); + } + // Allocate tensors + { + t_lhs.allocator()->allocate(); + t_rhs.allocator()->allocate(); + t_l1_addend.allocator()->allocate(); + cond2_t_dst.allocator()->allocate(); + fill<float>(CLAccessor(t_lhs), 0); + fill<float>(CLAccessor(t_rhs), 1); + fill<float>(CLAccessor(t_l1_addend), 2); + } + + // "Pack" tensors + ITensorPack tensors + { + { ACL_SRC_0, &t_lhs }, + { ACL_SRC_1, &t_rhs }, + { EXPERIMENTAL_ACL_POST_OP_ARG_FIRST, &t_l1_addend }, + { ACL_DST, &cond2_t_dst }, + }; + CLScheduler::get().enqueue_op(l0_gemm_mm, tensors, true); + CLScheduler::get().sync(); + TOCK(cond2_0_startup_time, measurements); + + TICK(cond2_1_latency); + for(int i = 0; i < num_iterations; ++i) + { + CLScheduler::get().enqueue_op(l0_gemm_mm, tensors, true); + } + CLScheduler::get().sync(); + TOCK_AVG(cond2_1_latency, measurements, num_iterations); + } + /* Condition 3: Static Unfused Kernel (current) */ + CLTensor cond3_t_dst{}; + { + TICK(cond3_0_startup_time); + arm_compute::opencl::kernels::ClGemmMatrixMultiplyNativeKernel l0_gemm_mm; + arm_compute::opencl::kernels::ClSaturatedArithmeticKernel l1_add; + + TICK(cond3_configure_time); + GEMMKernelInfo gemm_info{ m, n, k, 0, false, false, false, false, ActivationLayerInfo{}, 1, 1, gemm_native_desc.lhs_info, gemm_native_desc.rhs_info, 0, 0 }; + l0_gemm_mm.configure(CLKernelLibrary::get().get_compile_context(), &t_lhs_info, &t_rhs_info, nullptr, &t_l0_dst_info, gemm_native_desc.alpha, gemm_native_desc.beta, gemm_native_desc.lhs_info, + gemm_native_desc.rhs_info, gemm_info); + l1_add.configure(CLKernelLibrary::get().get_compile_context(), ArithmeticOperation::ADD, &t_l0_dst_info, &t_l1_rhs_info, &t_dst_info, eltwise_add_desc.convert_policy); + TOCK(cond3_configure_time, measurements); + + // Construct tensors + CLTensor t_lhs{}; + CLTensor t_rhs{}; + CLTensor t_l0_dst{}; + CLTensor t_l1_addend{}; + + // Init tensors + { + t_lhs.allocator()->init(t_lhs_info); + t_rhs.allocator()->init(t_rhs_info); + t_l0_dst.allocator()->init(t_l0_dst_info); + t_l1_addend.allocator()->init(t_dst_info); + cond3_t_dst.allocator()->init(t_dst_info); + } + // Allocate tensors + { + t_lhs.allocator()->allocate(); + t_rhs.allocator()->allocate(); + t_l0_dst.allocator()->allocate(); + t_l1_addend.allocator()->allocate(); + cond3_t_dst.allocator()->allocate(); + fill<float>(CLAccessor(t_lhs), 0); + fill<float>(CLAccessor(t_rhs), 1); + fill<float>(CLAccessor(t_l1_addend), 2); + } + + // "Pack" tensors + ITensorPack tensors_l0 + { + { ACL_SRC_0, &t_lhs }, + { ACL_SRC_1, &t_rhs }, + { ACL_DST, &t_l0_dst }, + }; + ITensorPack tensors_l1 + { + { ACL_SRC_0, &t_l0_dst }, + { ACL_SRC_1, &t_l1_addend }, + { ACL_DST, &cond3_t_dst }, + }; + CLScheduler::get().enqueue_op(l0_gemm_mm, tensors_l0, true); + CLScheduler::get().enqueue_op(l1_add, tensors_l1, true); + CLScheduler::get().sync(); + TOCK(cond3_0_startup_time, measurements); + + TICK(cond3_1_latency); + for(int i = 0; i < num_iterations; ++i) + { + CLScheduler::get().enqueue_op(l0_gemm_mm, tensors_l0, true); + CLScheduler::get().enqueue_op(l1_add, tensors_l1, true); + } + CLScheduler::get().sync(); + TOCK_AVG(cond3_1_latency, measurements, num_iterations); + } + + RelativeTolerance<float> tolerance_f32(0.001f); /**< Tolerance value for comparing reference's output against implementation's output for floating point data types */ + std::cout << "cond0 validation: " << std::endl; + validate(CLAccessor(cond0_t_dst), ref_t_dst, tolerance_f32); + std::cout << "cond2 validation: " << std::endl; + validate(CLAccessor(cond2_t_dst), ref_t_dst, tolerance_f32); + std::cout << "cond3 validation: " << std::endl; + validate(CLAccessor(cond3_t_dst), ref_t_dst, tolerance_f32); + + /* Report */ + std::cout << "Performance comparison (gemm native + add)" << std::endl; + std::cout << "cond0: dynamic fusion module" << std::endl; + std::cout << "cond2: static fused with post ops" << std::endl; + std::cout << "cond3: static unfused" << std::endl; + for(auto m : measurements) + { + std::cout << m.first << ": " << m.second.count() << "us" << std::endl; + } +} +TEST_SUITE_END() // Benchmark +TEST_SUITE_END() // ClCompositeKernel +TEST_SUITE_END() // DYNAMIC_FUSION +TEST_SUITE_END() // UNIT +TEST_SUITE_END() // CL +} // namespace validation +} // namespace test +} // namespace arm_compute + +#endif // defined(ENABLE_EXPERIMENTAL_DYNAMIC_FUSION)
\ No newline at end of file |