From 2d9de0a3fa6ad858e70040124f362799a962bb6a Mon Sep 17 00:00:00 2001 From: Giorgio Arena Date: Thu, 15 Mar 2018 17:58:20 +0000 Subject: COMPMID-1009 Support 4x4 output tile for Winograd Filter Transform on OpenCL. Change-Id: I68c6453e0f192de659582404f109a89616b9fbb9 Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/124811 Tested-by: Jenkins Reviewed-by: Georgios Pinitas Reviewed-by: Gian Marco Iodice --- tests/validation/CL/Winograd.cpp | 37 +++++--- tests/validation/fixtures/WinogradLayerFixture.h | 16 ++-- tests/validation/reference/Winograd.cpp | 116 ++++++++++++++--------- tests/validation/reference/Winograd.h | 2 +- 4 files changed, 105 insertions(+), 66 deletions(-) (limited to 'tests') diff --git a/tests/validation/CL/Winograd.cpp b/tests/validation/CL/Winograd.cpp index aa668fa575..07a52f8ebc 100644 --- a/tests/validation/CL/Winograd.cpp +++ b/tests/validation/CL/Winograd.cpp @@ -147,12 +147,12 @@ TEST_SUITE_END() // InputTransform TEST_SUITE(FilterTransform) // *INDENT-OFF* // clang-format off -DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip( +DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip( framework::dataset::make("InputInfo",{ TensorInfo(TensorShape(3U, 3U, 5U, 3U), 1, DataType::F16), // F16 not supported TensorInfo(TensorShape(3U, 3U, 5U, 3U), 1, DataType::QASYMM8), // QASYMM8 not supported TensorInfo(TensorShape(5U, 5U, 5U, 3U), 1, DataType::F32), // Kernel size not supported - TensorInfo(TensorShape(3U, 3U), 1, DataType::F32), // valid + TensorInfo(TensorShape(3U, 3U), 1, DataType::F32), // Output tile not supported TensorInfo(TensorShape(3U, 3U, 5U, 3U), 1, DataType::F32), // valid TensorInfo(TensorShape(3U, 3U, 37U, 2U), 1, DataType::F32), // valid TensorInfo(TensorShape(3U, 3U, 37U, 22U), 1, DataType::F32) // valid @@ -164,12 +164,21 @@ DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip( TensorInfo(TensorShape(1U, 1U, 16U), 1, DataType::F32), TensorInfo(TensorShape(3U, 5U, 16U), 1, DataType::F32), TensorInfo(TensorShape(2U, 37U, 16U), 1, DataType::F32), - TensorInfo(TensorShape(22U, 37U, 16U), 1, DataType::F32) + TensorInfo(TensorShape(22U, 37U, 36U), 1, DataType::F32) })), - framework::dataset::make("Expected", { false, false, false, true, true, true, true })), - input_info, output_info, expected) + framework::dataset::make("OutputTile", { + Size2D(2U, 2U), + Size2D(2U, 2U), + Size2D(2U, 2U), + Size2D(3U, 3U), + Size2D(2U, 2U), + Size2D(2U, 2U), + Size2D(4U, 4U) + })), + framework::dataset::make("Expected", { false, false, false, false, true, true, true })), + input_info, output_info, output_tile, expected) { - ARM_COMPUTE_EXPECT(bool(CLWinogradFilterTransformKernel::validate(&input_info.clone()->set_is_resizable(false), &output_info.clone()->set_is_resizable(false))) == expected, framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(bool(CLWinogradFilterTransformKernel::validate(&input_info.clone()->set_is_resizable(false), &output_info.clone()->set_is_resizable(false), output_tile)) == expected, framework::LogLevel::ERRORS); } // clang-format on // *INDENT-ON* @@ -177,13 +186,14 @@ DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip( using CLWinogradFilterTransform = CLSynthetizeFunctionWithZeroConstantBorder; using CLWinogradFilterTransformFixture = WinogradFilterTransformValidationFixture; -DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, combine(framework::dataset::concat(datasets::SmallWinogradFilterTransformDataset(), datasets::LargeWinogradFilterTransformDataset()), +DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, combine(combine(framework::dataset::concat(datasets::SmallWinogradFilterTransformDataset(), datasets::LargeWinogradFilterTransformDataset()), + framework::dataset::make("OutputTile", { Size2D(2U, 2U), Size2D(4U, 4U) })), framework::dataset::make("DataType", { DataType::F32 })), - shape_a, is_nchw_format, data_type) + shape_a, is_nchw_format, output_tile, data_type) { ARM_COMPUTE_UNUSED(is_nchw_format); - TensorShape shape_b = compute_winograd_filter_transform_shape(TensorInfo(shape_a, 1, data_type)); + TensorShape shape_b = compute_winograd_filter_transform_shape(TensorInfo(shape_a, 1, data_type), output_tile); // Create tensors CLTensor a = create_tensor(shape_a, data_type); @@ -194,16 +204,19 @@ DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, combine(framework::da // Create and configure function CLWinogradFilterTransform winograd_filter_transform; - winograd_filter_transform.configure(&a, &b); + winograd_filter_transform.configure(&a, &b, output_tile); } -FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradFilterTransformFixture, framework::DatasetMode::ALL, combine(datasets::SmallWinogradFilterTransformDataset(), framework::dataset::make("DataType", { DataType::F32 }))) +FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradFilterTransformFixture, framework::DatasetMode::ALL, combine(combine(datasets::SmallWinogradFilterTransformDataset(), framework::dataset::make("OutputTile", { Size2D(2U, 2U), Size2D(4U, 4U) })), + framework::dataset::make("DataType", { DataType::F32 }))) { // Validate output validate(CLAccessor(_target), _reference, tolerance_f32); } -FIXTURE_DATA_TEST_CASE(RunLarge, CLWinogradFilterTransformFixture, framework::DatasetMode::NIGHTLY, combine(datasets::LargeWinogradFilterTransformDataset(), framework::dataset::make("DataType", { DataType::F32 }))) +FIXTURE_DATA_TEST_CASE(RunLarge, CLWinogradFilterTransformFixture, framework::DatasetMode::NIGHTLY, combine(combine(datasets::LargeWinogradFilterTransformDataset(), + framework::dataset::make("OutputTile", { Size2D(2U, 2U), Size2D(4U, 4U) })), + framework::dataset::make("DataType", { DataType::F32 }))) { // Validate output validate(CLAccessor(_target), _reference, tolerance_f32); diff --git a/tests/validation/fixtures/WinogradLayerFixture.h b/tests/validation/fixtures/WinogradLayerFixture.h index 9811c28008..c427f8d20e 100644 --- a/tests/validation/fixtures/WinogradLayerFixture.h +++ b/tests/validation/fixtures/WinogradLayerFixture.h @@ -225,12 +225,12 @@ class WinogradFilterTransformValidationFixture : public framework::Fixture { public: template - void setup(TensorShape input_shape, bool is_nchw_format, DataType data_type) + void setup(TensorShape input_shape, bool is_nchw_format, Size2D output_tile, DataType data_type) { - TensorShape output_shape = compute_winograd_filter_transform_shape(TensorInfo(input_shape, 1, data_type)); + TensorShape output_shape = compute_winograd_filter_transform_shape(TensorInfo(input_shape, 1, data_type), output_tile); - _target = compute_target(input_shape, output_shape, is_nchw_format, data_type); - _reference = compute_reference(input_shape, output_shape, is_nchw_format, data_type); + _target = compute_target(input_shape, output_shape, is_nchw_format, output_tile, data_type); + _reference = compute_reference(input_shape, output_shape, is_nchw_format, output_tile, data_type); } protected: @@ -254,7 +254,7 @@ protected: } } - TensorType compute_target(const TensorShape &input_shape, const TensorShape &output_shape, bool is_nchw_format, DataType data_type) + TensorType compute_target(const TensorShape &input_shape, const TensorShape &output_shape, bool is_nchw_format, const Size2D &output_tile, DataType data_type) { ARM_COMPUTE_UNUSED(is_nchw_format); @@ -264,7 +264,7 @@ protected: // Create and configure function FunctionType filter_transform; - filter_transform.configure(&src, &dst); + filter_transform.configure(&src, &dst, output_tile); ARM_COMPUTE_EXPECT(src.info()->is_resizable(), framework::LogLevel::ERRORS); ARM_COMPUTE_EXPECT(dst.info()->is_resizable(), framework::LogLevel::ERRORS); @@ -284,7 +284,7 @@ protected: return dst; } - SimpleTensor compute_reference(const TensorShape &input_shape, const TensorShape &output_shape, bool is_nchw_format, DataType data_type) + SimpleTensor compute_reference(const TensorShape &input_shape, const TensorShape &output_shape, bool is_nchw_format, const Size2D &output_tile, DataType data_type) { ARM_COMPUTE_UNUSED(is_nchw_format); @@ -294,7 +294,7 @@ protected: // Fill reference fill(src, 0, -1.f, 1.f); - return reference::winograd_filter_transform(src, output_shape); + return reference::winograd_filter_transform(src, output_shape, output_tile); } TensorType _target{}; diff --git a/tests/validation/reference/Winograd.cpp b/tests/validation/reference/Winograd.cpp index c760663b22..ad0dcbd958 100644 --- a/tests/validation/reference/Winograd.cpp +++ b/tests/validation/reference/Winograd.cpp @@ -39,40 +39,74 @@ namespace reference namespace { template -void winograd_filter_transform3x3(const SimpleTensor &in, SimpleTensor &out) +void winograd_filter_transform3x3(const SimpleTensor &in, SimpleTensor &out, const Size2D &output_tile) { + const bool is_2x2 = (output_tile.width == 2); + const unsigned int transf_side = is_2x2 ? 4u : 6u; + // Simple tensor for the 3x3 input tile SimpleTensor input_tile{ TensorShape(3u, 3u), in.data_type(), 1 }; // Simple tensor for the transformation matrix - SimpleTensor trans_matrix{ TensorShape(3u, 4u), in.data_type(), 1 }; + SimpleTensor trans_matrix{ TensorShape(3u, transf_side), in.data_type(), 1 }; // Simple tensor for the transformation matrix transpose - SimpleTensor trans_matrix_transposed{ TensorShape(4u, 3u), in.data_type(), 1 }; + SimpleTensor trans_matrix_transposed{ TensorShape(transf_side, 3u), in.data_type(), 1 }; - // Simple tensor for the 4x3 temporary tile - SimpleTensor tmp_tile{ TensorShape(3u, 4u), in.data_type(), 1 }; + // Simple tensor for the 3xSide temporary tile + SimpleTensor tmp_tile{ TensorShape(3u, transf_side), in.data_type(), 1 }; - // Simple tensor for the 4x4 output tile - SimpleTensor output_tile{ TensorShape(4u, 4u), in.data_type(), 1 }; + // Simple tensor for the SidexSide output tile + SimpleTensor transf_tile{ TensorShape(transf_side, transf_side), in.data_type(), 1 }; - // Initialize transformation matrix - // 1 | 0 | 0 - // 0.5 | 0.5 | 0.5 - // 0.5 |-0.5 | 0.5 - // 0 | 0 | 1 - trans_matrix[0 + 0 * 3] = 1.0f; - trans_matrix[1 + 0 * 3] = 0.0f; - trans_matrix[2 + 0 * 3] = 0.0f; - trans_matrix[0 + 1 * 3] = 0.5f; - trans_matrix[1 + 1 * 3] = 0.5f; - trans_matrix[2 + 1 * 3] = 0.5f; - trans_matrix[0 + 2 * 3] = 0.5f; - trans_matrix[1 + 2 * 3] = -0.5f; - trans_matrix[2 + 2 * 3] = 0.5f; - trans_matrix[0 + 3 * 3] = 0.0f; - trans_matrix[1 + 3 * 3] = 0.0f; - trans_matrix[2 + 3 * 3] = 1.0f; + if(is_2x2) + { + // Initialize 3x4 transformation matrix + // 1 | 0 | 0 + // 0.5 | 0.5 | 0.5 + // 0.5 |-0.5 | 0.5 + // 0 | 0 | 1 + trans_matrix[0 + 0 * 3] = 1.0f; + trans_matrix[1 + 0 * 3] = 0.0f; + trans_matrix[2 + 0 * 3] = 0.0f; + trans_matrix[0 + 1 * 3] = 0.5f; + trans_matrix[1 + 1 * 3] = 0.5f; + trans_matrix[2 + 1 * 3] = 0.5f; + trans_matrix[0 + 2 * 3] = 0.5f; + trans_matrix[1 + 2 * 3] = -0.5f; + trans_matrix[2 + 2 * 3] = 0.5f; + trans_matrix[0 + 3 * 3] = 0.0f; + trans_matrix[1 + 3 * 3] = 0.0f; + trans_matrix[2 + 3 * 3] = 1.0f; + } + else + { + // Initialize 3x6 transformation matrix + // 1/4 | 0 | 0 + // -1/6 | -1/6 | -1/6 + // -1/6 | 1/6 | -1/6 + // 1/24 | 1/12 | 1/6 + // 1/24 | -1/12 | 1/6 + // 0 | 0 | 1 + trans_matrix[0 + 0 * 3] = 1.0f / 4.0f; + trans_matrix[1 + 0 * 3] = 0.0f; + trans_matrix[2 + 0 * 3] = 0.0f; + trans_matrix[0 + 1 * 3] = -1.0f / 6.0f; + trans_matrix[1 + 1 * 3] = -1.0f / 6.0f; + trans_matrix[2 + 1 * 3] = -1.0f / 6.0f; + trans_matrix[0 + 2 * 3] = -1.0f / 6.0f; + trans_matrix[1 + 2 * 3] = 1.0f / 6.0f; + trans_matrix[2 + 2 * 3] = -1.0f / 6.0f; + trans_matrix[0 + 3 * 3] = 1.0f / 24.0f; + trans_matrix[1 + 3 * 3] = 1.0f / 12.0f; + trans_matrix[2 + 3 * 3] = 1.0f / 6.0f; + trans_matrix[0 + 4 * 3] = 1.0f / 24.0f; + trans_matrix[1 + 4 * 3] = -1.0f / 12.0f; + trans_matrix[2 + 4 * 3] = 1.0f / 6.0f; + trans_matrix[0 + 5 * 3] = 0.0f; + trans_matrix[1 + 5 * 3] = 0.0f; + trans_matrix[2 + 5 * 3] = 1.0f; + } // Transpose the transformation matrix transpose_matrix(trans_matrix, trans_matrix_transposed); @@ -94,26 +128,18 @@ void winograd_filter_transform3x3(const SimpleTensor &in, SimpleTensor &ou matrix_multiply(trans_matrix, input_tile, tmp_tile); // Second transformation - matrix_multiply(tmp_tile, trans_matrix_transposed, output_tile); + matrix_multiply(tmp_tile, trans_matrix_transposed, transf_tile); // Store the 4x4 output tile across the 16 channels - const int output_offset = w + z * num_filters; - out[output_offset + 0 * num_filters * num_channels] = output_tile[0 + 0 * 4]; - out[output_offset + 1 * num_filters * num_channels] = output_tile[1 + 0 * 4]; - out[output_offset + 2 * num_filters * num_channels] = output_tile[2 + 0 * 4]; - out[output_offset + 3 * num_filters * num_channels] = output_tile[3 + 0 * 4]; - out[output_offset + 4 * num_filters * num_channels] = output_tile[0 + 1 * 4]; - out[output_offset + 5 * num_filters * num_channels] = output_tile[1 + 1 * 4]; - out[output_offset + 6 * num_filters * num_channels] = output_tile[2 + 1 * 4]; - out[output_offset + 7 * num_filters * num_channels] = output_tile[3 + 1 * 4]; - out[output_offset + 8 * num_filters * num_channels] = output_tile[0 + 2 * 4]; - out[output_offset + 9 * num_filters * num_channels] = output_tile[1 + 2 * 4]; - out[output_offset + 10 * num_filters * num_channels] = output_tile[2 + 2 * 4]; - out[output_offset + 11 * num_filters * num_channels] = output_tile[3 + 2 * 4]; - out[output_offset + 12 * num_filters * num_channels] = output_tile[0 + 3 * 4]; - out[output_offset + 13 * num_filters * num_channels] = output_tile[1 + 3 * 4]; - out[output_offset + 14 * num_filters * num_channels] = output_tile[2 + 3 * 4]; - out[output_offset + 15 * num_filters * num_channels] = output_tile[3 + 3 * 4]; + const int output_offset = w + z * num_filters; + + for(unsigned int out_h = 0, out_pos = 0; out_h < transf_side; ++out_h) + { + for(unsigned int out_w = 0; out_w < transf_side; ++out_w, ++out_pos) + { + out[output_offset + out_pos * num_filters * num_channels] = transf_tile[out_w + out_h * transf_side]; + } + } } } } @@ -314,7 +340,7 @@ SimpleTensor winograd_input_transform(const SimpleTensor &src, const Tenso } template -SimpleTensor winograd_filter_transform(const SimpleTensor &in, const TensorShape &output_shape) +SimpleTensor winograd_filter_transform(const SimpleTensor &in, const TensorShape &output_shape, const Size2D &output_tile) { ARM_COMPUTE_ERROR_ON_MSG(in.data_layout() != DataLayout::NCHW, "Only supported NCHW data format"); @@ -324,7 +350,7 @@ SimpleTensor winograd_filter_transform(const SimpleTensor &in, const Tenso switch(in.shape()[0]) { case 3: - winograd_filter_transform3x3(in, out); + winograd_filter_transform3x3(in, out, output_tile); break; default: ARM_COMPUTE_ERROR("Only supported 3x3 kernel"); @@ -358,7 +384,7 @@ SimpleTensor winograd_output_transform(const SimpleTensor &in, const Tenso } template SimpleTensor winograd_input_transform(const SimpleTensor &src, const TensorShape &dst_shape, const PadStrideInfo &conv_info, const Size2D &kernel_dims); -template SimpleTensor winograd_filter_transform(const SimpleTensor &in, const TensorShape &output_shape); +template SimpleTensor winograd_filter_transform(const SimpleTensor &in, const TensorShape &output_shape, const Size2D &output_tile); template SimpleTensor winograd_output_transform(const SimpleTensor &in, const TensorShape &output_shape, const Size2D &kernel_dims, const Size2D &num_tiles); } // namespace reference } // namespace validation diff --git a/tests/validation/reference/Winograd.h b/tests/validation/reference/Winograd.h index fa1a7f3f61..62e136b09d 100644 --- a/tests/validation/reference/Winograd.h +++ b/tests/validation/reference/Winograd.h @@ -40,7 +40,7 @@ template SimpleTensor winograd_input_transform(const SimpleTensor &src, const TensorShape &dst_shape, const PadStrideInfo &conv_info, const Size2D &kernel_dims); template -SimpleTensor winograd_filter_transform(const SimpleTensor &in, const TensorShape &output_shape); +SimpleTensor winograd_filter_transform(const SimpleTensor &in, const TensorShape &output_shape, const Size2D &output_tile); template SimpleTensor winograd_output_transform(const SimpleTensor &in, const TensorShape &output_shape, const Size2D &kernel_dims, const Size2D &num_tiles); -- cgit v1.2.1