/* * Copyright (c) 2022-2023 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 "tests/AssetsLibrary.h" #include "tests/CL/CLAccessor.h" #include "tests/framework/Fixture.h" #include "tests/framework/Macros.h" #include "tests/framework/datasets/Datasets.h" #include "tests/validation/Validation.h" #include "tests/validation/reference/ConvolutionLayer.h" #include "tests/datasets/SmallConvolutionLayerDataset.h" #include "tests/validation/fixtures/dynamic_fusion/gpu/cl/DirectConv2dFixture.h" namespace arm_compute { namespace test { namespace validation { namespace { /** Tolerances from tests/validation/CL/DirectConvolutionLayer.cpp */ RelativeTolerance tolerance_f32(0.05f); /**< Tolerance value for comparing reference's output against implementation's output for DataType::F32 */ RelativeTolerance tolerance_f16(half_float::half(0.2)); /**< Tolerance value for comparing reference's output against implementation's output for DataType::F16 */ constexpr float abs_tolerance_f32(0.0001f); /**< Absolute tolerance for FP32 tests*/ constexpr float tolerance_num = 0.07f; /**< Tolerance number */ } // namespace TEST_SUITE(CL) TEST_SUITE(DYNAMIC_FUSION) /** Synced with tests/validation/CL/ConvolutionLayer.cpp * * Difference | Why the difference * f32 tolerance here is smaller | To use the same tolerance as that of DirectConv2d; lowering tolerance is safe * No quantized tests | Not supported yet * No grouped CNN tests | Not supported yet * No mixed layout tests | Not needed; only NHWC is supported * No activation/post op tests | Not needed in fusion * No ValidateConvolutionMethod | Only a single method (direct conv2d) is supported * No ReshapeWeights = true tests | Not applicable yet. This parameter only concerns gemm-based conv2d * No RunSmallWithPadding tests | Padding is removed * */ TEST_SUITE(CONV2D) template using DynamicFusionGpuConv2dFixture = DynamicFusionGpuConv2dValidationFixture; TEST_SUITE(FP32) FIXTURE_DATA_TEST_CASE(RunSmall, DynamicFusionGpuConv2dFixture, framework::DatasetMode::ALL, combine(combine(combine(datasets::SmallConvolutionLayerDataset(), framework::dataset::make("DataType", DataType::F32)), framework::dataset::make("DataLayout", { DataLayout::NHWC })), framework::dataset::make("QuantizationInfo", QuantizationInfo()))) { // Validate output validate(CLAccessor(_target), _reference, tolerance_f32); } TEST_SUITE_END() // FP32 TEST_SUITE(FP16) FIXTURE_DATA_TEST_CASE(RunSmall, DynamicFusionGpuConv2dFixture, framework::DatasetMode::ALL, combine(combine(combine(datasets::SmallConvolutionLayerDataset(), framework::dataset::make("DataType", DataType::F16)), framework::dataset::make("DataLayout", { DataLayout::NHWC })), framework::dataset::make("QuantizationInfo", QuantizationInfo()))) { // Validate output validate(CLAccessor(_target), _reference, tolerance_f16, tolerance_num); } TEST_SUITE_END() // FP16 // Tests for specific conv2d methods /** Synced with tests/validation/CL/DirectConvolutionLayer.cpp * * Difference | Why the difference * No quantized tests | Not supported yet * No Invalid output size test | Not applicable. Output is removed from the interface * No mixed layout/NCHW tests | Not needed; only NHWC is supported * No activation tests | Not needed in fusion */ TEST_SUITE(DIRECT_CONV2D) // *INDENT-OFF* // clang-format off DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(zip( framework::dataset::make("InputInfo", { TensorInfo(TensorShape(2U, 27U, 13U), 1, DataType::F32, DataLayout::NHWC), // Invalid: Mismatching data type input/weights TensorInfo(TensorShape(2U, 27U, 13U), 1, DataType::F32, DataLayout::NHWC), // Invalid: Mismatching input feature maps TensorInfo(TensorShape(2U, 27U, 13U), 1, DataType::F32, DataLayout::NHWC), // Invalid weights dimensions TensorInfo(TensorShape(2U, 27U, 13U), 1, DataType::F32, DataLayout::NHWC), // Unsupported biases size TensorInfo(TensorShape(2U, 27U, 13U), 1, DataType::F32, DataLayout::NHWC), // Unsupported biases dimensions TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32, DataLayout::NCHW), // Unsupported data layout: NCHW TensorInfo(TensorShape(2U, 32U, 16U), 1, DataType::QASYMM8, DataLayout::NHWC), // Unsupported data type: quantized TensorInfo(TensorShape(2U, 32U, 16U), 1, DataType::F32, DataLayout::NHWC), TensorInfo(TensorShape(2U, 27U, 13U), 1, DataType::F32, DataLayout::NHWC), // Arbitrary weight sizes for NHWC are supported TensorInfo(TensorShape(2U, 27U, 13U), 1, DataType::F32, DataLayout::NHWC), // Non-rectangular weights dimensions for NHWC are supported TensorInfo(TensorShape(2U, 27U, 13U), 1, DataType::F32, DataLayout::NHWC), // Strides > 2 for any kernel sizes for NHWC are supported }), framework::dataset::make("WeightsInfo",{ TensorInfo(TensorShape(2U, 3U, 3U, 4U), 1, DataType::F16, DataLayout::NHWC), TensorInfo(TensorShape(3U, 3U, 3U, 4U), 1, DataType::F32, DataLayout::NHWC), TensorInfo(TensorShape(2U, 3U, 3U, 4U, 3U), 1, DataType::F32, DataLayout::NHWC), TensorInfo(TensorShape(2U, 3U, 3U, 4U), 1, DataType::F32, DataLayout::NHWC), TensorInfo(TensorShape(2U, 3U, 3U, 4U), 1, DataType::F32, DataLayout::NHWC), TensorInfo(TensorShape(3U, 3U, 2U, 4U), 1, DataType::F32, DataLayout::NCHW), TensorInfo(TensorShape(2U, 1U, 1U, 4U), 1, DataType::QASYMM8, DataLayout::NHWC), TensorInfo(TensorShape(2U, 1U, 1U, 4U), 1, DataType::F32, DataLayout::NHWC), TensorInfo(TensorShape(2U, 13U, 13U, 4U), 1, DataType::F32, DataLayout::NHWC), TensorInfo(TensorShape(2U, 5U, 3U, 4U), 1, DataType::F32, DataLayout::NHWC), TensorInfo(TensorShape(2U, 3U, 3U, 4U), 1, DataType::F32, DataLayout::NHWC), })), framework::dataset::make("BiasesInfo",{ TensorInfo(TensorShape(4U), 1, DataType::F32, DataLayout::NHWC), TensorInfo(TensorShape(4U), 1, DataType::F32, DataLayout::NHWC), TensorInfo(TensorShape(4U), 1, DataType::F32, DataLayout::NHWC), TensorInfo(TensorShape(3U), 1, DataType::F32, DataLayout::NHWC), TensorInfo(TensorShape(4U, 2U), 1, DataType::F32, DataLayout::NHWC), TensorInfo(TensorShape(25U), 1, DataType::F32, DataLayout::NCHW), TensorInfo(TensorShape(4U), 1, DataType::QASYMM8, DataLayout::NHWC), TensorInfo(TensorShape(4U), 1, DataType::F32, DataLayout::NHWC), TensorInfo(TensorShape(4U), 1, DataType::F32, DataLayout::NHWC), TensorInfo(TensorShape(4U), 1, DataType::F32, DataLayout::NHWC), TensorInfo(TensorShape(4U), 1, DataType::F32, DataLayout::NHWC), })), framework::dataset::make("Conv2dAttributes", { Conv2dAttributes().stride({1, 1}).pad({0, 0, 0, 0}), Conv2dAttributes().stride({1, 1}).pad({0, 0, 0, 0}), Conv2dAttributes().stride({1, 1}).pad({0, 0, 0, 0}), Conv2dAttributes().stride({1, 1}).pad({0, 0, 0, 0}), Conv2dAttributes().stride({1, 1}).pad({0, 0, 0, 0}), Conv2dAttributes().stride({1, 1}).pad({0, 0, 0, 0}), Conv2dAttributes().stride({1, 1}).pad({0, 0, 0, 0}), Conv2dAttributes().stride({1, 1}).pad({0, 0, 0, 0}), Conv2dAttributes().stride({1, 1}).pad({0, 0, 0, 0}), Conv2dAttributes().stride({1, 1}).pad({0, 0, 0, 0}), Conv2dAttributes().stride({3, 3}).pad({0, 0, 0, 0}), })), framework::dataset::make("Expected", { false, false, false, false, false, false, false, true, true, true, true })), input_info, weights_info, biases_info, conv2d_attrs, expected) { auto cl_compile_ctx = CLKernelLibrary::get().get_compile_context(); auto gpu_ctx = GpuWorkloadContext{ &cl_compile_ctx }; GpuWorkloadSketch sketch{ &gpu_ctx }; const TensorInfo sketch_input_info = sketch.create_tensor_info(input_info); const TensorInfo sketch_weights_info = sketch.create_tensor_info(weights_info); const TensorInfo sketch_biases_info = sketch.create_tensor_info(biases_info); bool is_valid = bool(GpuConv2d::validate_op(sketch, &sketch_input_info, &sketch_weights_info, &sketch_biases_info, conv2d_attrs)); ARM_COMPUTE_EXPECT(is_valid == expected, framework::LogLevel::ERRORS); } template using DynamicFusionGpuDirectConv2dFixture = DynamicFusionDirectConv2dValidationFixture; TEST_SUITE(FP16) FIXTURE_DATA_TEST_CASE(RunSmall, DynamicFusionGpuDirectConv2dFixture, framework::DatasetMode::PRECOMMIT, combine(combine(combine(zip(zip(zip(zip(zip( framework::dataset::make("InputShape", { TensorShape(27U, 13U, 23U), TensorShape(19U, 5U, 16U, 4U), TensorShape(13U, 5U, 17U, 2U), TensorShape(32U, 37U, 13U) } ), framework::dataset::make("StrideX", { 1, 3, 1, 1 })), framework::dataset::make("StrideY", { 1, 3, 2, 1 })), framework::dataset::make("PadX", { 1, 3, 0, 4 })), framework::dataset::make("PadY", { 1, 3, 0, 4 })), framework::dataset::make("KernelSize", { 3, 8, 1, 9 })), framework::dataset::make("NumKernels", { 17, 3, 1, 19 })), framework::dataset::make("DataType", DataType::F16)), framework::dataset::make("DataLayout", DataLayout::NHWC))) { validate(CLAccessor(_target), _reference, tolerance_f16, tolerance_num); } FIXTURE_DATA_TEST_CASE(RunLarge, DynamicFusionGpuDirectConv2dFixture, framework::DatasetMode::NIGHTLY, combine(combine(combine(zip(zip(zip(zip(zip( framework::dataset::make("InputShape", { TensorShape(800U, 800U, 3U) } ), framework::dataset::make("StrideX", { 1 })), framework::dataset::make("StrideY", { 1 })), framework::dataset::make("PadX", { 1 })), framework::dataset::make("PadY", { 1 })), framework::dataset::make("KernelSize", { 9 })), framework::dataset::make("NumKernels", { 3 })), framework::dataset::make("DataType", DataType::F16)), framework::dataset::make("DataLayout", DataLayout::NHWC))) { validate(CLAccessor(_target), _reference, tolerance_f16, tolerance_num); } TEST_SUITE_END() // FP16 TEST_SUITE(FP32) FIXTURE_DATA_TEST_CASE(RunSmall, DynamicFusionGpuDirectConv2dFixture, framework::DatasetMode::PRECOMMIT, combine(combine(combine(zip(zip(zip(zip(zip( framework::dataset::make("InputShape", { TensorShape(27U, 13U, 23U), TensorShape(19U, 5U, 16U, 4U), TensorShape(13U, 5U, 17U, 2U), TensorShape(32U, 37U, 13U) } ), framework::dataset::make("StrideX", { 1, 3, 1, 1 })), framework::dataset::make("StrideY", { 1, 3, 2, 1 })), framework::dataset::make("PadX", { 1, 3, 0, 4 })), framework::dataset::make("PadY", { 1, 3, 0, 4 })), framework::dataset::make("KernelSize", { 3, 8, 1, 9 })), framework::dataset::make("NumKernels", { 17, 3, 1, 19 })), framework::dataset::make("DataType", DataType::F32)), framework::dataset::make("DataLayout", DataLayout::NHWC))) { validate(CLAccessor(_target), _reference, tolerance_f32, 0.0, abs_tolerance_f32); } FIXTURE_DATA_TEST_CASE(RunLarge, DynamicFusionGpuDirectConv2dFixture, framework::DatasetMode::NIGHTLY, combine(combine(combine(zip(zip(zip(zip(zip( framework::dataset::make("InputShape", { TensorShape(800U, 800U, 3U) } ), framework::dataset::make("StrideX", { 1 })), framework::dataset::make("StrideY", { 1 })), framework::dataset::make("PadX", { 1 })), framework::dataset::make("PadY", { 1 })), framework::dataset::make("KernelSize", { 9 })), framework::dataset::make("NumKernels", { 3 })), framework::dataset::make("DataType", DataType::F32)), framework::dataset::make("DataLayout", DataLayout::NHWC))) { validate(CLAccessor(_target), _reference, tolerance_f32, 0.0, abs_tolerance_f32); } // clang-format on // *INDENT-ON* TEST_SUITE_END() // FP32 TEST_SUITE_END() // DIRECT_CONV2D TEST_SUITE_END() // CONV2D TEST_SUITE_END() // DYNAMIC_FUSION TEST_SUITE_END() // CL } // namespace validation } // namespace test } // namespace arm_compute