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author | SiCong Li <sicong.li@arm.com> | 2022-11-09 15:57:48 +0000 |
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committer | SiCong Li <sicong.li@arm.com> | 2022-11-22 14:09:34 +0000 |
commit | 31df05a1870662a7288fbaeb6fbc7fc458bb5a73 (patch) | |
tree | e75a132b8b5fd21cbceec8d0aa88da893e9c4f43 /tests/validation/CL/UNIT/dynamic_fusion/ArbitraryElementwiseFusion.cpp | |
parent | 73bb6b7ad80801e56633ad4ea12b0404b586a979 (diff) | |
download | ComputeLibrary-31df05a1870662a7288fbaeb6fbc7fc458bb5a73.tar.gz |
Remove dynamic fusion prototype with tests and examples
Public headers of the new experimental dynamic fusion can be found in arm_compute/dynamic_fusion/
New examples on how to use the interface can be found in tests/validation/dynamic_fusion/gpu/Integration.cpp
Resolves COMPMID-5683
Change-Id: I7ccb902a227fb487562df15fc3c30118d1d95bbd
Signed-off-by: SiCong Li <sicong.li@arm.com>
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/8671
Reviewed-by: Jakub Sujak <jakub.sujak@arm.com>
Reviewed-by: Gunes Bayir <gunes.bayir@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Benchmark: Arm Jenkins <bsgcomp@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Diffstat (limited to 'tests/validation/CL/UNIT/dynamic_fusion/ArbitraryElementwiseFusion.cpp')
-rw-r--r-- | tests/validation/CL/UNIT/dynamic_fusion/ArbitraryElementwiseFusion.cpp | 394 |
1 files changed, 0 insertions, 394 deletions
diff --git a/tests/validation/CL/UNIT/dynamic_fusion/ArbitraryElementwiseFusion.cpp b/tests/validation/CL/UNIT/dynamic_fusion/ArbitraryElementwiseFusion.cpp deleted file mode 100644 index 1b1e8aa761..0000000000 --- a/tests/validation/CL/UNIT/dynamic_fusion/ArbitraryElementwiseFusion.cpp +++ /dev/null @@ -1,394 +0,0 @@ -/* - * 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. - */ -#ifdef ENABLE_EXPERIMENTAL_DYNAMIC_FUSION - -#include "src/core/experimental/dynamic_fusion/ClKernelBuildingAPI.h" -#include "src/core/utils/helpers/float_ops.h" -#include "tests/CL/CLAccessor.h" -#include "tests/framework/Macros.h" -#include "tests/validation/Validation.h" -#include "tests/validation/reference/ConvolutionLayer.h" -#include "tests/validation/reference/ElementwiseOperations.h" -#include "tests/validation/reference/Permute.h" - -#include "arm_compute/runtime/experimental/ClCompositeOperator.h" -#include "tests/validation/reference/Floor.h" - -#include "arm_compute/core/ITensor.h" -#include "arm_compute/runtime/CL/CLTensor.h" -#include "tests/validation/CL/UNIT/dynamic_fusion/Utils.h" - -using namespace arm_compute::experimental::dynamic_fusion; -using namespace arm_compute::test::validation::utils; - -namespace arm_compute -{ -namespace test -{ -namespace validation -{ -TEST_SUITE(CL) -TEST_SUITE(UNIT) -TEST_SUITE(DYNAMIC_FUSION) -TEST_SUITE(ArbitraryFusion) - -TEST_CASE(ElementwiseBroadcasting, framework::DatasetMode::ALL) -{ - // Test elementwise broadcasting - const auto data_type = DataType::F32; - const auto data_layout = DataLayout::NHWC; - - const auto input_shape = TensorShape(7, 9, 5); - const auto rhs_shape = TensorShape(7, 1, 1); - const auto dst_shape = TensorShape(7, 9, 5); - - // Tensor Info - auto input_info = TensorInfo(input_shape, 1, data_type, data_layout); - auto addend_info = TensorInfo(rhs_shape, 1, data_type, data_layout); - auto dst_info = TensorInfo(); - - ElementwiseDescriptor add_desc{ ArithmeticOperation::ADD }; - - CLScheduler::get().default_reinit(); - const auto cl_compile_ctx = CLKernelLibrary::get().get_compile_context(); - OperatorGraph op_graph; - - const auto op_input = add_tensor(op_graph, input_info); - const auto op_addend = add_tensor(op_graph, addend_info); - const auto op_dst = add_tensor(op_graph, dst_info); - - add_op_elementwise_op(op_graph, add_desc, op_input, op_addend, op_dst); - - const ClWorkloadContext workload_ctx{ GpuInfo{ CLScheduler::get().target() } }; - ClWorkload workload; - build(workload, op_graph, workload_ctx); - - ClCompositeOperator op; - op.configure(cl_compile_ctx, workload); - - // Construct tensors - CLTensor t_input{}; - CLTensor t_addend{}; - CLTensor t_dst{}; - - // Init tensors - t_input.allocator()->init(input_info); - t_addend.allocator()->init(addend_info); - t_dst.allocator()->init(dst_info); - - // Allocate and fill tensors - t_input.allocator()->allocate(); - t_addend.allocator()->allocate(); - t_dst.allocator()->allocate(); - - // Fill - fill<float>(CLAccessor(t_input), 0, library.get()); - fill<float>(CLAccessor(t_addend), 1, library.get()); - - // Pack tensors - OpTensorBinding bp_tensors({ { op_input, &t_input }, - { op_addend, &t_addend }, - { op_dst, &t_dst } - }); - - // Populate prepare and run pack-maps (including allocating aux tensors) - ClAuxTensorData aux_tensor_data{}; - TensorPackMap prepare_pack_map{}; - TensorPackMap run_pack_map{}; - bind_tensors(aux_tensor_data, prepare_pack_map, run_pack_map, workload, bp_tensors); - - op.prepare(prepare_pack_map); - op.run(run_pack_map); - - // Create reference - SimpleTensor<float> ref_input{ input_shape, data_type, 1, QuantizationInfo(), DataLayout::NHWC }; - SimpleTensor<float> ref_addend{ rhs_shape, data_type, 1, QuantizationInfo(), DataLayout::NHWC }; - - // Fill reference - fill<float>(ref_input, 0, library.get()); - fill<float>(ref_addend, 1, library.get()); - - auto ref_input_nchw = reference::permute(ref_input, PermutationVector(1U, 2U, 0U)); - auto ref_addend_nchw = reference::permute(ref_addend, PermutationVector(1U, 2U, 0U)); - - auto dst_shape_nchw = dst_shape; - permute(dst_shape_nchw, PermutationVector(1U, 2U, 0U)); - - auto ref_t_dst_nchw = reference::arithmetic_operation( - ArithmeticOperation::ADD, - ref_input_nchw, - ref_addend_nchw, - data_type, - ConvertPolicy{}); - - const auto ref_t_dst = reference::permute(ref_t_dst_nchw, PermutationVector(2U, 0U, 1U)); - - RelativeTolerance<float> tolerance_f32(0.001f); - validate(CLAccessor(t_dst), ref_t_dst_nchw, tolerance_f32); -} -TEST_CASE(DivFloor, framework::DatasetMode::ALL) -{ - // x = floor(div(input, input2)) - const auto data_type = DataType::F32; - const auto eltwise_info = ElementwiseDescriptor{ ArithmeticOperation::DIV }; - - // Tensor Values - const auto width = 7U; - const auto height = 6U; - - // Shapes - const auto input1_shape = TensorShape(width, height); - const auto input2_shape = TensorShape(width, height); - const auto dst_shape = TensorShape(width, height); - - // Create reference - SimpleTensor<float> ref_src_nhwc{ input1_shape, data_type, 1, QuantizationInfo(), DataLayout::NHWC }; - SimpleTensor<float> ref_src2_nhwc{ input2_shape, data_type, 1, QuantizationInfo(), DataLayout::NHWC }; - - // Fill reference - fill<float>(ref_src_nhwc, 0, library.get()); - fill<float>(ref_src2_nhwc, 1, library.get()); - - auto ref_src = reference::permute(ref_src_nhwc, PermutationVector(1U, 2U, 0U)); - auto ref_src2 = reference::permute(ref_src2_nhwc, PermutationVector(1U, 2U, 0U)); - - TensorShape dst_shape_nchw{ dst_shape }; - permute(dst_shape_nchw, PermutationVector(1U, 2U, 0U)); - - const auto ref_dst_nchw = reference::floor_layer(reference::arithmetic_operation( - ArithmeticOperation::DIV, - ref_src, - ref_src2, - data_type, - ConvertPolicy::SATURATE)); - - const auto ref_t_dst = reference::permute(ref_dst_nchw, PermutationVector(2U, 0U, 1U)); - - // Tensor Info - auto input1_info = TensorInfo(input1_shape, 1, data_type, DataLayout::NHWC); - auto input2_info = TensorInfo(input2_shape, 1, data_type, DataLayout::NHWC); - auto dst_info = TensorInfo(); - auto acc_info = TensorInfo(); // Intermediate tensor for division - - // Initialise Scheduler - CLScheduler::get().default_reinit(); - const auto cl_compile_ctx = CLKernelLibrary::get().get_compile_context(); - OperatorGraph op_graph; - - // add tensors - auto op_input1 = add_tensor(op_graph, input1_info); - auto op_input2 = add_tensor(op_graph, input2_info); - auto op_acc = add_tensor(op_graph, acc_info); - auto op_dst = add_tensor(op_graph, dst_info); - - add_op_elementwise_op(op_graph, eltwise_info, op_input1, op_input2, op_acc); - add_op_floor(op_graph, FloorDescriptor(), op_acc, op_dst); - - const ClWorkloadContext workload_ctx{ GpuInfo{ CLScheduler::get().target() } }; - ClWorkload workload; - build(workload, op_graph, workload_ctx); - - ClCompositeOperator op; - op.configure(cl_compile_ctx, workload); - - // Configure and add tensors. - CLTensor t_input1{}; - CLTensor t_input2{}; - CLTensor t_dst{}; - - // Init Tensors - t_input1.allocator()->init(input1_info); - t_input2.allocator()->init(input2_info); - t_dst.allocator()->init(dst_info); - - // Allocate and fill tensors - t_input1.allocator()->allocate(); - t_input2.allocator()->allocate(); - t_dst.allocator()->allocate(); - - fill<float>(CLAccessor(t_input1), 0, library.get()); - fill<float>(CLAccessor(t_input2), 1, library.get()); - - // "Pack" tensors - OpTensorBinding bp_tensors({ { op_input1, &t_input1 }, - { op_input2, &t_input2 }, - { op_dst, &t_dst } - }); - - // Populate prepare and run pack-maps (including allocating aux tensors) - ClAuxTensorData aux_tensor_data{}; - TensorPackMap prepare_pack_map{}; - TensorPackMap run_pack_map{}; - bind_tensors(aux_tensor_data, prepare_pack_map, run_pack_map, workload, bp_tensors); - - op.prepare(prepare_pack_map); - op.run(run_pack_map); - - 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_dst_nchw, tolerance_f32); -} -TEST_CASE(Dconv2dAddDiv, framework::DatasetMode::ALL) -{ - // output = div(divend, add(addend, conv2d1x1(direct_conv)(input, weights, bias))) - const auto data_type = DataType::F32; - const auto data_layout = DataLayout::NHWC; - - const auto input_shape = TensorShape(384, 12, 12); - const auto weight_shape = TensorShape(384, 1, 1, 16); - const auto dst_shape = TensorShape(16, 12, 12); - - // Tensor Info - auto input_info = TensorInfo(input_shape, 1, data_type, data_layout); - auto weight_info = TensorInfo(weight_shape, 1, data_type, data_layout); - auto addend_info = TensorInfo(dst_shape, 1, data_type, data_layout); - auto divend_info = TensorInfo(dst_shape, 1, data_type, data_layout); - auto acc_info = TensorInfo(); // Intermediate tensor for conv - auto acc_1_info = TensorInfo(); - auto dst_info = TensorInfo(); - - Conv2dDescriptor conv2d_desc{}; - ElementwiseDescriptor add_desc{ ArithmeticOperation::ADD }; - ElementwiseDescriptor div_desc{ ArithmeticOperation::DIV }; - - CLScheduler::get().default_reinit(); - const auto cl_compile_ctx = CLKernelLibrary::get().get_compile_context(); - OperatorGraph op_graph; - - const auto op_input = add_tensor(op_graph, input_info); - const auto op_weight = add_tensor(op_graph, weight_info); - const auto op_addend = add_tensor(op_graph, addend_info); - const auto op_divend = add_tensor(op_graph, divend_info); - const auto op_acc = add_tensor(op_graph, acc_info); // temp accumulator; TensorInfo to be inferred - const auto op_acc_1 = add_tensor(op_graph, acc_1_info); // temp accumulator; TensorInfo to be inferred - const auto op_dst = add_tensor(op_graph, dst_info); - - auto conv2d = add_op_conv2d(op_graph, conv2d_desc, op_input, op_weight, op_acc); - force_conv2d_method(op_graph, conv2d, ConvolutionMethod::DIRECT); - add_op_elementwise_op(op_graph, add_desc, op_acc, op_addend, op_acc_1); - add_op_elementwise_op(op_graph, div_desc, op_acc_1, op_divend, op_dst); - - const ClWorkloadContext workload_ctx{ GpuInfo{ CLScheduler::get().target() } }; - ClWorkload workload; - build(workload, op_graph, workload_ctx); - - ClCompositeOperator op; - op.configure(cl_compile_ctx, workload); - - // Construct tensors - CLTensor t_input{}; - CLTensor t_weight{}; - CLTensor t_addend{}; - CLTensor t_divend{}; - CLTensor t_dst{}; - - // Init tensors - t_input.allocator()->init(input_info); - t_weight.allocator()->init(weight_info); - t_divend.allocator()->init(divend_info); - t_addend.allocator()->init(addend_info); - t_dst.allocator()->init(dst_info); - - // Allocate and fill tensors - t_input.allocator()->allocate(); - t_weight.allocator()->allocate(); - t_divend.allocator()->allocate(); - t_addend.allocator()->allocate(); - t_dst.allocator()->allocate(); - - // Fill - fill<float>(CLAccessor(t_input), 0, library.get()); - fill<float>(CLAccessor(t_weight), 1, library.get()); - fill<float>(CLAccessor(t_addend), 2, library.get()); - fill<float>(CLAccessor(t_divend), 3, library.get()); - - // Pack tensors - OpTensorBinding bp_tensors({ { op_input, &t_input }, - { op_weight, &t_weight }, - { op_addend, &t_addend }, - { op_divend, &t_divend }, - { op_dst, &t_dst } - }); - - // Populate prepare and run pack-maps (including allocating aux tensors) - ClAuxTensorData aux_tensor_data{}; - TensorPackMap prepare_pack_map{}; - TensorPackMap run_pack_map{}; - bind_tensors(aux_tensor_data, prepare_pack_map, run_pack_map, workload, bp_tensors); - - op.prepare(prepare_pack_map); - op.run(run_pack_map); - - // Create reference - SimpleTensor<float> ref_input{ input_shape, data_type, 1, QuantizationInfo(), DataLayout::NHWC }; - SimpleTensor<float> ref_weight{ weight_shape, data_type, 1, QuantizationInfo(), DataLayout::NHWC }; - SimpleTensor<float> ref_bias_placeholder{ dst_shape, data_type, 1, QuantizationInfo(), DataLayout::NHWC }; - SimpleTensor<float> ref_addend{ dst_shape, data_type, 1, QuantizationInfo(), DataLayout::NHWC }; - SimpleTensor<float> ref_divend{ dst_shape, data_type, 1, QuantizationInfo(), DataLayout::NHWC }; - - // Fill reference - fill<float>(ref_input, 0, library.get()); - fill<float>(ref_weight, 1, library.get()); - fill<float>(ref_addend, 2, library.get()); - fill<float>(ref_divend, 3, library.get()); - - auto ref_input_nchw = reference::permute(ref_input, PermutationVector(1U, 2U, 0U)); - auto ref_weight_nchw = reference::permute(ref_weight, PermutationVector(1U, 2U, 0U)); - auto ref_bias_placeholder_nchw = reference::permute(ref_bias_placeholder, PermutationVector(1U, 2U, 0U)); - auto ref_addend_nchw = reference::permute(ref_addend, PermutationVector(1U, 2U, 0U)); - auto ref_divend_nchw = reference::permute(ref_divend, PermutationVector(1U, 2U, 0U)); - - auto dst_shape_nchw = dst_shape; - permute(dst_shape_nchw, PermutationVector(1U, 2U, 0U)); - - PadStrideInfo legacy_pad_stride(conv2d_desc.stride.x(), conv2d_desc.stride.y(), conv2d_desc.pad.left, conv2d_desc.pad.right, conv2d_desc.pad.top, conv2d_desc.pad.bottom, DimensionRoundingType{}); - auto ref_acc_nchw = reference::arithmetic_operation( - ArithmeticOperation::ADD, - ref_addend_nchw, - reference::convolution_layer(ref_input_nchw, ref_weight_nchw, ref_bias_placeholder_nchw, dst_shape_nchw, legacy_pad_stride, conv2d_desc.dilation), - data_type, - ConvertPolicy{}); - - auto ref_t_dst_nchw = reference::arithmetic_operation( - ArithmeticOperation::DIV, - ref_acc_nchw, - ref_divend_nchw, - data_type, - ConvertPolicy{}); - - const auto ref_t_dst = reference::permute(ref_t_dst_nchw, PermutationVector(2U, 0U, 1U)); - - RelativeTolerance<float> tolerance_f32(0.001f); - validate(CLAccessor(t_dst), ref_t_dst_nchw, tolerance_f32); -} - -TEST_SUITE_END() // ArbitraryFusion -TEST_SUITE_END() // DYNAMIC_FUSION -TEST_SUITE_END() // UNIT -TEST_SUITE_END() // CL - -} // namespace validation -} // namespace test -} // namespace arm_compute - -#endif /* ENABLE_EXPERIMENTAL_DYNAMIC_FUSION */ |