diff options
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, 394 insertions, 0 deletions
diff --git a/tests/validation/CL/UNIT/dynamic_fusion/ArbitraryElementwiseFusion.cpp b/tests/validation/CL/UNIT/dynamic_fusion/ArbitraryElementwiseFusion.cpp new file mode 100644 index 0000000000..1b1e8aa761 --- /dev/null +++ b/tests/validation/CL/UNIT/dynamic_fusion/ArbitraryElementwiseFusion.cpp @@ -0,0 +1,394 @@ +/* + * 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 */ |