COMPMID-935 - Implementing Convolution with Winograd on OpenCL (part 4)

Implemented Winograd Output Transform (2x2,3x3) on OpenCL
Implemented CLWinogradConvolutionLayer on OpenCL

Change-Id: I6a113fc5f052ca07f878d2b800d2ab003f84af65
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/125148
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>
Tested-by: Jenkins <bsgcomp@arm.com>

1 files changed, 174 insertions, 5 deletions

diff --git a/tests/validation/CL/Winograd.cpp b/tests/validation/CL/Winograd.cpp
index 0b21ed2577..aa668fa575 100644
--- a/tests/validation/CL/Winograd.cpp
+++ b/tests/validation/CL/Winograd.cpp
@@ -22,17 +22,22 @@
  * SOFTWARE.
  */
 #include "arm_compute/core/CL/kernels/CLWinogradFilterTransformKernel.h"
+#include "arm_compute/core/CL/kernels/CLWinogradOutputTransformKernel.h"
 #include "arm_compute/core/Types.h"
 #include "arm_compute/core/utils/misc/ShapeCalculator.h"
 #include "arm_compute/runtime/CL/CLTensor.h"
 #include "arm_compute/runtime/CL/CLTensorAllocator.h"
+#include "arm_compute/runtime/CL/functions/CLWinogradConvolutionLayer.h"
 #include "arm_compute/runtime/CL/functions/CLWinogradInputTransform.h"
 #include "tests/CL/CLAccessor.h"
 #include "tests/CL/Helper.h"
 #include "tests/PaddingCalculator.h"
+#include "tests/datasets/LargeConvolutionLayerDataset.h"
 #include "tests/datasets/ShapeDatasets.h"
+#include "tests/datasets/SmallConvolutionLayerDataset.h"
 #include "tests/datasets/WinogradFilterTransformDataset.h"
 #include "tests/datasets/WinogradInputTransformDataset.h"
+#include "tests/datasets/WinogradOutputTransformDataset.h"
 #include "tests/framework/Asserts.h"
 #include "tests/framework/Macros.h"
 #include "tests/framework/datasets/Datasets.h"
@@ -47,7 +52,7 @@ namespace validation
 {
 namespace
 {
-constexpr AbsoluteTolerance<float> tolerance_f32(0.0001f);
+constexpr AbsoluteTolerance<float> tolerance_f32(0.001f);
 } // namespace
 
 using namespace arm_compute::misc::shape_calculator;
@@ -65,9 +70,9 @@ DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(zip(
                                                                                         TensorInfo(TensorShape(53U, 21U, 5U, 3U), 1, DataType::QASYMM8), // QASYMM8 not supported
                                                                                         TensorInfo(TensorShape(53U, 21U, 5U, 3U), 1, DataType::F32),     // Kernel size not supported
                                                                                         TensorInfo(TensorShape(53U, 21U, 5U, 3U), 1, DataType::F32),     // Strides not supported
-                                                                                        TensorInfo(TensorShape(53U, 33U, 4U), 1, DataType::F32),         // valid
-                                                                                        TensorInfo(TensorShape(34U, 42U, 7U, 3U), 1, DataType::F32),     // valid
-                                                                                        TensorInfo(TensorShape(31U, 37U, 37U), 1, DataType::F32)         // valid
+                                                                                        TensorInfo(TensorShape(53U, 33U, 4U), 1, DataType::F32),         // Padding needed
+                                                                                        TensorInfo(TensorShape(34U, 42U, 7U, 3U), 1, DataType::F32),     // Padding needed
+                                                                                        TensorInfo(TensorShape(31U, 37U, 37U), 1, DataType::F32)         // Padding needed
                                                                                     }),
                                                 framework::dataset::make("OutputInfo", {
                                                                                         TensorInfo(TensorShape(5U, 5U, 16U, 3U), 1, DataType::F16),
@@ -96,7 +101,7 @@ DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(zip(
                                                                                         Size2D(3U, 3U),
                                                                                         Size2D(3U, 3U)
                                                                                     })),
-                                                framework::dataset::make("Expected", { false, false, false, false, true, true, true })),
+                                                framework::dataset::make("Expected", { false, false, false, false, false, false, false })),
                                             input_info, output_info, conv_info, kernel_dims, expected)
 {
     ARM_COMPUTE_EXPECT(bool(CLWinogradInputTransform::validate(&input_info.clone()->set_is_resizable(false), &output_info.clone()->set_is_resizable(false), conv_info, kernel_dims)) == expected, framework::LogLevel::ERRORS);
@@ -203,8 +208,172 @@ FIXTURE_DATA_TEST_CASE(RunLarge, CLWinogradFilterTransformFixture, framework::Da
     // Validate output
     validate(CLAccessor(_target), _reference, tolerance_f32);
 }
+
 TEST_SUITE_END() // FilterTransform
 
+TEST_SUITE(OutputTransform)
+// *INDENT-OFF*
+// clang-format off
+DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(zip(zip(zip(
+                                                framework::dataset::make("InputInfo",{
+                                                                                        TensorInfo(TensorShape(24U, 49U, 16U, 5U), 1, DataType::F16),        // F16 not supported
+                                                                                        TensorInfo(TensorShape(128U, 3136U, 16U, 5U), 1, DataType::QASYMM8), // QASYMM8 not supported
+                                                                                        TensorInfo(TensorShape(256U, 784U, 16U, 5U), 1, DataType::F32),      // Kernel size not supported
+                                                                                        TensorInfo(TensorShape(512U, 169U, 16U, 5U), 1, DataType::F32),      // Valid
+                                                                                        TensorInfo(TensorShape(13U, 6U, 16U, 4U), 1, DataType::F32),         // Padding needed
+                                                                                        TensorInfo(TensorShape(7U, 16U, 16U, 7U), 1, DataType::F32),         // Valid
+                                                                                        TensorInfo(TensorShape(1U, 442U, 16U, 37U), 1, DataType::F32)        // Wrong number of tiles
+                                                                                    }),
+                                                framework::dataset::make("BiasInfo", {
+                                                                                        TensorInfo(TensorShape(24U), 1, DataType::F16),
+                                                                                        TensorInfo(TensorShape(128U), 1, DataType::QASYMM8),
+                                                                                        TensorInfo(TensorShape(256U), 1, DataType::F32),
+                                                                                        TensorInfo(TensorShape(512U), 1, DataType::F32),
+                                                                                        TensorInfo(TensorShape(13U), 1, DataType::F32),
+                                                                                        TensorInfo(TensorShape(7U), 1, DataType::F32),
+                                                                                        TensorInfo(TensorShape(1U), 1, DataType::F32)
+                                                                                    })),
+                                                framework::dataset::make("OutputInfo", {
+                                                                                        TensorInfo(TensorShape(14U, 14U, 24U, 5U), 1, DataType::F16),
+                                                                                        TensorInfo(TensorShape(112U, 112U, 128U, 5U), 1, DataType::QASYMM8),
+                                                                                        TensorInfo(TensorShape(55U, 55U, 256U, 5U), 1, DataType::F32),
+                                                                                        TensorInfo(TensorShape(26U, 26U, 512U, 5U), 1, DataType::F32),
+                                                                                        TensorInfo(TensorShape(5U, 4U, 13U, 4U), 1, DataType::F32),
+                                                                                        TensorInfo(TensorShape(8U, 8U, 7U, 7U), 1, DataType::F32),
+                                                                                        TensorInfo(TensorShape(51U, 33U, 1U, 37U), 1, DataType::F32)
+                                                                                    })),
+                                                framework::dataset::make("KernelDims", {
+                                                                                        Size2D(3U, 3U),
+                                                                                        Size2D(3U, 3U),
+                                                                                        Size2D(5U, 5U),
+                                                                                        Size2D(3U, 3U),
+                                                                                        Size2D(3U, 3U),
+                                                                                        Size2D(3U, 3U),
+                                                                                        Size2D(3U, 3U)
+                                                                                    })),
+                                                framework::dataset::make("OutputDims", {
+                                                                                        Size2D(14U, 14U),
+                                                                                        Size2D(112U, 112U),
+                                                                                        Size2D(55U, 55U),
+                                                                                        Size2D(26U, 26U),
+                                                                                        Size2D(5U, 4U),
+                                                                                        Size2D(8U, 8U),
+                                                                                        Size2D(51U, 33U)
+                                                                                    })),
+                                                framework::dataset::make("NumTiles", {
+                                                                                        Size2D(7U, 7U),
+                                                                                        Size2D(56U, 56U),
+                                                                                        Size2D(28U, 28U),
+                                                                                        Size2D(13U, 13U),
+                                                                                        Size2D(3U, 2U),
+                                                                                        Size2D(4U, 4U),
+                                                                                        Size2D(26U, 16U)
+                                                                                    })),
+                                                framework::dataset::make("Expected", { false, false, false, true, false, true, false })),
+                                            input_info, bias_info, output_info, kernel_dims, output_dims, num_tiles, expected)
+{
+    ARM_COMPUTE_EXPECT(bool(CLWinogradOutputTransformKernel::validate(&input_info.clone()->set_is_resizable(false), &bias_info.clone()->set_is_resizable(false), &output_info.clone()->set_is_resizable(false), kernel_dims, output_dims, num_tiles)) == expected, framework::LogLevel::ERRORS);
+}
+// clang-format on
+// *INDENT-ON*
+
+using CLWinogradOutputTransform        = CLSynthetizeFunctionWithZeroConstantBorder<CLWinogradOutputTransformKernel, 0>;
+using CLWinogradOutputTransformFixture = WinogradOutputTransformValidationFixture<CLTensor, CLAccessor, CLWinogradOutputTransform, float>;
+
+DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, combine(framework::dataset::concat(datasets::SmallWinogradOutputTransformDataset(), datasets::LargeWinogradOutputTransformDataset()),
+                                                                   framework::dataset::make("DataType", { DataType::F32 })),
+               shape_a, kernel_dims, output_convolved_dims, num_tiles, data_layout, data_type)
+{
+    TensorShape shape_b = compute_winograd_output_transform_shape(TensorInfo(shape_a, 1, data_type), output_convolved_dims, data_layout);
+
+    // Create tensors
+    CLTensor a = create_tensor<CLTensor>(shape_a, data_type);
+    CLTensor b = create_tensor<CLTensor>(shape_b, data_type);
+
+    ARM_COMPUTE_EXPECT(a.info()->is_resizable(), framework::LogLevel::ERRORS);
+    ARM_COMPUTE_EXPECT(b.info()->is_resizable(), framework::LogLevel::ERRORS);
+
+    // Create and configure function
+    CLWinogradOutputTransform winograd_output_transform;
+    winograd_output_transform.configure(&a, nullptr, &b, kernel_dims, output_convolved_dims, num_tiles);
+}
+
+FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradOutputTransformFixture, framework::DatasetMode::ALL, combine(datasets::SmallWinogradOutputTransformDataset(), framework::dataset::make("DataType", { DataType::F32 })))
+{
+    // Validate output
+    validate(CLAccessor(_target), _reference, tolerance_f32);
+}
+
+FIXTURE_DATA_TEST_CASE(RunLarge, CLWinogradOutputTransformFixture, framework::DatasetMode::NIGHTLY, combine(datasets::LargeWinogradOutputTransformDataset(), framework::dataset::make("DataType", { DataType::F32 })))
+{
+    // Validate output
+    validate(CLAccessor(_target), _reference, tolerance_f32);
+}
+
+TEST_SUITE_END() // OutputTransform
+
+TEST_SUITE(ConvolutionLayer)
+// *INDENT-OFF*
+// clang-format off
+DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(zip(zip(
+                                                framework::dataset::make("InputInfo", {
+                                                                                        TensorInfo(TensorShape(17U, 31U, 2U), 1, DataType::F16),     // FP16 not supported
+                                                                                        TensorInfo(TensorShape(17U, 31U, 2U), 1, DataType::F32),     // Datatype mismatch
+                                                                                        TensorInfo(TensorShape(23U, 27U, 5U, 4U), 1, DataType::F32), // Stride y not supported
+                                                                                        TensorInfo(TensorShape(16U, 16U, 8U), 1, DataType::F32),     // Padding needed
+                                                                                        TensorInfo(TensorShape(33U, 27U, 7U, 4U), 1, DataType::F32)  // Kernel size not supported
+                                                                                      }),
+                                                framework::dataset::make("WeightsInfo", {
+                                                                                        TensorInfo(TensorShape(3U, 3U, 2U, 19U), 1, DataType::F32),
+                                                                                        TensorInfo(TensorShape(3U, 3U, 2U, 19U), 1, DataType::QASYMM8),
+                                                                                        TensorInfo(TensorShape(3U, 3U, 5U, 21U), 1, DataType::F32),
+                                                                                        TensorInfo(TensorShape(3U, 3U, 8U, 16U), 1, DataType::F32),
+                                                                                        TensorInfo(TensorShape(5U, 5U, 7U, 16U), 1, DataType::F16)
+                                                                                        })),
+                                                framework::dataset::make("BiasesInfo", {
+                                                                                        TensorInfo(TensorShape(19U), 1, DataType::F32),
+                                                                                        TensorInfo(TensorShape(19U), 1, DataType::F32),
+                                                                                        TensorInfo(TensorShape(21U), 1, DataType::F32),
+                                                                                        TensorInfo(TensorShape(16U), 1, DataType::F32),
+                                                                                        TensorInfo(TensorShape(16U), 1, DataType::F32)
+                                                                                       })),
+                                                framework::dataset::make("OutputInfo", {
+                                                                                        TensorInfo(TensorShape(17U, 31U, 19U), 1, DataType::F32),
+                                                                                        TensorInfo(TensorShape(15U, 15U, 19U), 1, DataType::F32),
+                                                                                        TensorInfo(TensorShape(21U, 25U, 21U, 4U), 1, DataType::F32),
+                                                                                        TensorInfo(TensorShape(16U, 16U, 16U), 1, DataType::F32),
+                                                                                        TensorInfo(TensorShape(11U, 12U, 16U, 4U), 1, DataType::F32)
+                                                                                       })),
+                                                framework::dataset::make("ConvInfo", {
+                                                                                        PadStrideInfo(1, 1, 1, 1),
+                                                                                        PadStrideInfo(1, 1, 1, 1),
+                                                                                        PadStrideInfo(1, 2, 0, 0),
+                                                                                        PadStrideInfo(1, 1, 1, 1),
+                                                                                        PadStrideInfo(1, 1, 1, 0)
+                                                                                                                 })),
+                                                framework::dataset::make("Expected", { false, false, false, false, false })),
+               input_info, weights_info, bias_info, output_info, conv_info, expected)
+{
+    ARM_COMPUTE_EXPECT(bool(CLWinogradConvolutionLayer::validate(&input_info.clone()->set_is_resizable(false), &weights_info.clone()->set_is_resizable(false), &bias_info.clone()->set_is_resizable(false), &output_info.clone()->set_is_resizable(false), conv_info)) == expected, framework::LogLevel::ERRORS);
+}
+// clang-format on
+// *INDENT-ON*
+
+using CLWinogradConvolutionLayerFixture = WinogradConvolutionLayerValidationFixture<CLTensor, CLAccessor, CLWinogradConvolutionLayer, float>;
+FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradConvolutionLayerFixture, framework::DatasetMode::PRECOMMIT, combine(datasets::SmallWinogradConvolutionLayer3x3Dataset(),
+                                                                                                               framework::dataset::make("DataType", { DataType::F32 })))
+{
+    // Validate output
+    validate(CLAccessor(_target), _reference, tolerance_f32);
+}
+
+FIXTURE_DATA_TEST_CASE(RunLarge, CLWinogradConvolutionLayerFixture, framework::DatasetMode::NIGHTLY, combine(datasets::LargeWinogradConvolutionLayer3x3Dataset(), framework::dataset::make("DataType", { DataType::F32 })))
+{
+    // Validate output
+    validate(CLAccessor(_target), _reference, tolerance_f32);
+}
+TEST_SUITE_END() // ConvolutionLayer
+
 TEST_SUITE_END() // Winograd
 TEST_SUITE_END() // CL
 } // namespace validation