| /* |
| * 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. |
| */ |
| |
| #include "arm_compute/core/CL/CLKernelLibrary.h" |
| #include "arm_compute/core/TensorInfo.h" |
| #include "arm_compute/dynamic_fusion/runtime/gpu/cl/ClWorkloadRuntime.h" |
| #include "arm_compute/dynamic_fusion/sketch/OperatorAttributes.h" |
| #include "arm_compute/dynamic_fusion/sketch/gpu/GpuWorkloadSketch.h" |
| #include "arm_compute/dynamic_fusion/sketch/gpu/operators/GpuConv2d.h" |
| #include "arm_compute/runtime/CL/CLScheduler.h" |
| |
| #include "src/gpu/cl/operators/ClAdd.h" |
| #include "src/gpu/cl/operators/ClConv2d.h" |
| |
| #include "tests/CL/CLAccessor.h" |
| #include "tests/framework/Asserts.h" |
| #include "tests/framework/Macros.h" |
| #include "tests/validation/CL/UNIT/dynamic_fusion/Utils.h" |
| #include "tests/validation/Validation.h" |
| #include "tests/validation/reference/ConvolutionLayer.h" |
| #include "tests/validation/reference/ElementwiseOperations.h" |
| #include "tests/validation/reference/Permute.h" |
| |
| #ifdef ARM_COMPUTE_ASSERTS_ENABLED |
| #include "tests/SimpleTensorPrinter.h" |
| #endif /* ARM_COMPUTE_ASSERTS_ENABLED */ |
| |
| 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(INTEGRATION) |
| TEST_SUITE(DYNAMIC_FUSION) |
| TEST_CASE(Conv2d, framework::DatasetMode::ALL) |
| { |
| /* Computation: |
| * out = conv2d1x1(direct_conv)(input, weights, bias) |
| */ |
| CLScheduler::get().default_reinit(); |
| |
| const auto data_type = DataType::F32; |
| const auto data_layout = DataLayout::NHWC; |
| const auto t_input_shape = TensorShape(384, 12, 12); |
| const auto t_weight_shape = TensorShape(384, 1, 1, 16); |
| const auto t_dst_shape = TensorShape(16, 12, 12); |
| |
| // Create a new workload sketch |
| auto cl_compile_ctx = CLKernelLibrary::get().get_compile_context(); |
| auto gpu_ctx = GpuWorkloadContext{ &cl_compile_ctx }; |
| GpuWorkloadSketch sketch{ &gpu_ctx }; |
| |
| // Fuse conv2d |
| Conv2dAttributes conv2d_attr{}; |
| auto input_info = sketch.create_tensor_info(t_input_shape, 1, data_type, data_layout); |
| auto weight_info = sketch.create_tensor_info(TensorInfo(t_weight_shape, 1, data_type, data_layout)); |
| auto dst_info = sketch.create_tensor_info(); |
| GpuConv2d::create_op(sketch, &input_info, &weight_info, nullptr, &dst_info, conv2d_attr); |
| |
| // Configure runtime |
| ClWorkloadRuntime runtime; |
| runtime.configure(sketch); |
| |
| // (Important) Allocate auxiliary tensor memory if there are any |
| // Instead of using ACL allocated memory, the user can choose to import memory into the tensors |
| for(auto &data : runtime.get_auxiliary_tensors()) |
| { |
| CLTensor *tensor = data.first; |
| AuxMemoryInfo aux_mem_req = data.second; |
| tensor->allocator()->init(*data.first->info(), aux_mem_req.alignment); |
| tensor->allocator()->allocate(); // Use ACL allocated memory |
| // auto buf = cl::Buffer(); |
| // tensor->allocator()->import_memory(buf); // Or, import external memory |
| } |
| |
| // Construct user tensors |
| CLTensor t_input{}; |
| CLTensor t_weight{}; |
| CLTensor t_dst{}; |
| |
| // Initialize user tensors |
| t_input.allocator()->init(input_info); |
| t_weight.allocator()->init(weight_info); |
| t_dst.allocator()->init(dst_info); |
| |
| // Allocate and fill user tensors |
| // Instead of using ACL allocator, the user can choose to import memory into the tensors |
| t_input.allocator()->allocate(); |
| t_weight.allocator()->allocate(); |
| t_dst.allocator()->allocate(); |
| fill<float>(CLAccessor(t_input), 0, library.get()); |
| fill<float>(CLAccessor(t_weight), 1, library.get()); |
| |
| // Run runtime |
| runtime.run({ &t_input, &t_weight, &t_dst }); |
| |
| // Create reference |
| SimpleTensor<float> ref_t_input{ t_input_shape, data_type, 1, QuantizationInfo(), DataLayout::NHWC }; |
| SimpleTensor<float> ref_t_weight{ t_weight_shape, data_type, 1, QuantizationInfo(), DataLayout::NHWC }; |
| SimpleTensor<float> ref_t_bias_placeholder{ t_dst_shape, data_type, 1, QuantizationInfo(), DataLayout::NHWC }; |
| |
| // Fill reference |
| fill<float>(ref_t_input, 0, library.get()); |
| fill<float>(ref_t_weight, 1, library.get()); |
| |
| auto ref_t_input_nchw = reference::permute(ref_t_input, PermutationVector(1U, 2U, 0U)); |
| auto ref_t_weight_nchw = reference::permute(ref_t_weight, PermutationVector(1U, 2U, 0U)); |
| auto ref_t_bias_placeholder_nchw = reference::permute(ref_t_bias_placeholder, PermutationVector(1U, 2U, 0U)); |
| auto t_dst_shape_nchw = t_dst_shape; |
| permute(t_dst_shape_nchw, PermutationVector(1U, 2U, 0U)); |
| |
| PadStrideInfo legacy_pad_stride(conv2d_attr.stride().x(), conv2d_attr.stride().y(), conv2d_attr.pad().left, conv2d_attr.pad().right, conv2d_attr.pad().top, conv2d_attr.pad().bottom, |
| DimensionRoundingType{}); |
| auto ref_t_dst_nchw = reference::convolution_layer(ref_t_input_nchw, ref_t_weight_nchw, ref_t_bias_placeholder_nchw, t_dst_shape_nchw, legacy_pad_stride, conv2d_attr.dilation()); |
| const auto ref_t_dst = reference::permute(ref_t_dst_nchw, PermutationVector(2U, 0U, 1U)); |
| |
| 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_nchw, tolerance_f32); |
| } |
| TEST_SUITE(Invalid_Fusion_Should_Fail) |
| TEST_CASE(Multiple_Complex_Ops_0, framework::DatasetMode::ALL) |
| { |
| /* Computation: |
| * out = conv2d(conv2d(l0_input, l0_weight), l1_weight) |
| */ |
| CLScheduler::get().default_reinit(); |
| |
| const auto data_type = DataType::F32; |
| const auto data_layout = DataLayout::NHWC; |
| const auto t_input_shape = TensorShape(384, 12, 12); |
| const auto t_weight_shape = TensorShape(384, 1, 1, 16); |
| auto t_input_info = TensorInfo(t_input_shape, 1, data_type, data_layout); |
| auto t_weight_info = TensorInfo(t_weight_shape, 1, data_type, data_layout); |
| auto t_dst_info = TensorInfo(); |
| |
| Conv2dAttributes conv2d_attr{}; |
| |
| // Create a new workload sketch |
| auto cl_compile_ctx = CLKernelLibrary::get().get_compile_context(); |
| auto gpu_ctx = GpuWorkloadContext{ &cl_compile_ctx }; |
| GpuWorkloadSketch sketch{ &gpu_ctx }; |
| |
| // Create tensor infos |
| auto input_info = sketch.create_tensor_info(t_input_shape, 1, data_type, data_layout); |
| auto weight_info = sketch.create_tensor_info(TensorInfo(t_weight_shape, 1, data_type, data_layout)); |
| auto dst_info = sketch.create_tensor_info(); |
| |
| // Fuse conv2d into the workload |
| { |
| // Validate operator |
| const auto success = GpuConv2d::validate_op(sketch, &input_info, &weight_info, nullptr, &dst_info, conv2d_attr); |
| ARM_COMPUTE_EXPECT(bool(success), framework::LogLevel::ERRORS); |
| |
| GpuConv2d::create_op(sketch, &input_info, &weight_info, nullptr, &dst_info, conv2d_attr); |
| } |
| |
| // Create tensor infos |
| auto weight_info_2 = sketch.create_tensor_info(t_weight_info); |
| auto dst_info_2 = sketch.create_tensor_info(); |
| |
| // Fuse conv2d into the workload |
| { |
| // Validate operator, should fail |
| const auto success = GpuConv2d::validate_op(sketch, &dst_info, &weight_info_2, nullptr, &dst_info_2, conv2d_attr); |
| ARM_COMPUTE_EXPECT(!bool(success), framework::LogLevel::ERRORS); |
| } |
| } |
| TEST_SUITE_END() // Invalid_Fusion_Should_Fail |
| TEST_SUITE_END() // DYNAMIC_FUSION |
| TEST_SUITE_END() // INTEGRATION |
| TEST_SUITE_END() // CL |
| } // namespace validation |
| } // namespace test |
| } // namespace arm_compute |