Optimize Transposed Convolution for CL backend (FP32/16)

This patch optimizes transposed convolution for CL backend by rewriting it in a single kernel instead of three (flip_kernel + upsample + conv). The new kernel skips the upsampling step which reduces the input space of convolution by stride_x * stride_y, resulting in significant performance improvement. It also skips the kernel flipping by traversing the weights accordingly, thus reduces the memory footprint.

Resolves: COMPMID-5676
Signed-off-by: Gunes Bayir <gunes.bayir@arm.com>
Change-Id: I8a333212dc7c5f7f0597aa58b0d56d44814baa14
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/8588
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Gian Marco Iodice <gianmarco.iodice@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Benchmark: Arm Jenkins <bsgcomp@arm.com>

1 files changed, 49 insertions, 3 deletions

diff --git a/tests/validation/CL/DeconvolutionLayer.cpp b/tests/validation/CL/DeconvolutionLayer.cpp
index 15962b588d..01d0dd8caa 100644
--- a/tests/validation/CL/DeconvolutionLayer.cpp
+++ b/tests/validation/CL/DeconvolutionLayer.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2017-2021 Arm Limited.
+ * Copyright (c) 2017-2022 Arm Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -70,6 +70,10 @@ const auto data3x3_asymm = datasets::SmallDeconvolutionShapes() * framework::dat
 const auto data3x3_precommit = datasets::SmallDeconvolutionShapes() * framework::dataset::make("StrideX", 1, 2) * framework::dataset::make("StrideY", 1, 2) * framework::dataset::make("PadX", 0, 2)
                                * framework::dataset::make("PadY", 0, 2) * framework::dataset::make("NumKernels", { 3 });
 
+const auto data3x3_precommit_large_channels = datasets::SmallDeconvolutionShapesWithLargerChannels() * framework::dataset::make("StrideX", 2) * framework::dataset::make("StrideY",
+                                              2) * framework::dataset::make("PadX", 1)
+                                              * framework::dataset::make("PadY", 2) * framework::dataset::make("NumKernels", { 5 });
+
 const auto data2x2_precommit = datasets::SmallDeconvolutionShapes() * framework::dataset::make("StrideX", 2) * framework::dataset::make("StrideY", 2) * framework::dataset::make("PadX", 1)
                                * framework::dataset::make("PadY", 1) * framework::dataset::make("NumKernels", { 3 });
 
@@ -90,9 +94,15 @@ DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(zip(zip(
     framework::dataset::make("InputInfo",   { TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32),   // Mismatching data type
                                               TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32),   // Invalid weights shape
                                               TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F16),   // Non supported data type
-                                              TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32),  // Invalid bias shape
+                                              TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32),   // Invalid bias shape
                                               TensorInfo(TensorShape(13U, 11U, 4U, 3U), 1, DataType::F32), // Window shrink
                                               TensorInfo(TensorShape(32U, 16U, 2U), 1, DataType::F32),
+                                              TensorInfo(TensorShape(2U, 13U, 27U), 1, DataType::F32, DataLayout::NHWC),   // Mismatching data type
+                                              TensorInfo(TensorShape(2U, 13U, 27U), 1, DataType::F32, DataLayout::NHWC),   // Invalid weights shape
+                                              TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F16, DataLayout::NHWC),   // Non supported data type
+                                              TensorInfo(TensorShape(2U, 13U, 27U), 1, DataType::F32, DataLayout::NHWC),   // Invalid bias shape
+                                              TensorInfo(TensorShape(4U, 11U, 13U, 3U), 1, DataType::F32, DataLayout::NHWC), // Window shrink
+                                              TensorInfo(TensorShape(2U, 16U, 32U), 1, DataType::F32, DataLayout::NHWC),
                                             }),
     framework::dataset::make("WeightsInfo", { TensorInfo(TensorShape(3U, 3U, 2U, 2U), 1, DataType::F16),
                                               TensorInfo(TensorShape(3U, 3U, 2U, 4U), 1, DataType::F32),
@@ -100,6 +110,12 @@ DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(zip(zip(
                                               TensorInfo(TensorShape(3U, 2U, 2U, 2U), 1, DataType::F32),
                                               TensorInfo(TensorShape(3U, 3U, 4U), 1, DataType::F32),
                                               TensorInfo(TensorShape(1U, 1U, 2U, 4U), 1, DataType::F32),
+                                              TensorInfo(TensorShape(2U, 3U, 3U, 2U), 1, DataType::F16, DataLayout::NHWC),
+                                              TensorInfo(TensorShape(2U, 3U, 3U, 4U), 1, DataType::F32, DataLayout::NHWC),
+                                              TensorInfo(TensorShape(3U, 3U, 2U, 2U), 1, DataType::F16, DataLayout::NHWC),
+                                              TensorInfo(TensorShape(2U, 2U, 3U, 2U), 1, DataType::F32, DataLayout::NHWC),
+                                              TensorInfo(TensorShape(4U, 3U, 3U), 1, DataType::F32, DataLayout::NHWC),
+                                              TensorInfo(TensorShape(2U, 2U, 2U, 4U), 1, DataType::F32, DataLayout::NHWC),
                                             })),
     framework::dataset::make("BiasInfo",    { TensorInfo(TensorShape(1U), 1, DataType::F16),
                                               TensorInfo(TensorShape(1U), 1, DataType::F32),
@@ -107,6 +123,12 @@ DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(zip(zip(
                                               TensorInfo(TensorShape(25U, 11U), 1, DataType::F32),
                                               TensorInfo(TensorShape(1U), 1, DataType::F32),
                                               TensorInfo(TensorShape(4U), 1, DataType::F32),
+                                              TensorInfo(TensorShape(1U), 1, DataType::F16, DataLayout::NHWC),
+                                              TensorInfo(TensorShape(1U), 1, DataType::F32, DataLayout::NHWC),
+                                              TensorInfo(TensorShape(1U), 1, DataType::F32, DataLayout::NHWC),
+                                              TensorInfo(TensorShape(25U, 11U), 1, DataType::F32, DataLayout::NHWC),
+                                              TensorInfo(TensorShape(1U), 1, DataType::F32, DataLayout::NHWC),
+                                              TensorInfo(TensorShape(4U), 1, DataType::F32, DataLayout::NHWC),
                                             })),
     framework::dataset::make("OutputInfo",  { TensorInfo(TensorShape(25U, 11U, 2U), 1, DataType::F16),
                                               TensorInfo(TensorShape(25U, 10U, 2U), 1, DataType::F32),
@@ -114,6 +136,12 @@ DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(zip(zip(
                                               TensorInfo(TensorShape(13U, 13U, 2U), 1, DataType::F32),
                                               TensorInfo(TensorShape(11U, 9U, 1U, 3U), 1, DataType::F32),
                                               TensorInfo(TensorShape(32U, 16U, 4U), 1, DataType::F32),
+                                              TensorInfo(TensorShape(2U, 11U, 25U), 1, DataType::F16, DataLayout::NHWC),
+                                              TensorInfo(TensorShape(2U, 10U, 25U), 1, DataType::F32, DataLayout::NHWC),
+                                              TensorInfo(TensorShape(25U, 11U, 2U), 1, DataType::F32, DataLayout::NHWC),
+                                              TensorInfo(TensorShape(2U, 13U, 13U), 1, DataType::F32, DataLayout::NHWC),
+                                              TensorInfo(TensorShape(1U, 9U, 11U, 3U), 1, DataType::F32, DataLayout::NHWC),
+                                              TensorInfo(TensorShape(4U, 43U, 91U), 1, DataType::F32, DataLayout::NHWC),
                                             })),
     framework::dataset::make("PadStrideInfo", { PadStrideInfo(1, 1, 0, 0),
                                                 PadStrideInfo(1, 1, 0, 0),
@@ -121,8 +149,15 @@ DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(zip(zip(
                                                 PadStrideInfo(1, 1, 0, 0),
                                                 PadStrideInfo(1, 1, 1, 1),
                                                 PadStrideInfo(1, 1, 0, 0),
+                                                PadStrideInfo(1, 1, 0, 0),
+                                                PadStrideInfo(1, 1, 0, 0),
+                                                PadStrideInfo(1, 1, 0, 0),
+                                                PadStrideInfo(1, 1, 0, 0),
+                                                PadStrideInfo(1, 1, 1, 1),
+                                                PadStrideInfo(3, 3, 2, 2),
                                            })),
-    framework::dataset::make("Expected", { false, false, false, false, false, true })),
+    framework::dataset::make("Expected", { false, false, false, false, false, true,            // NCHW
+                                           false, false, false, false, false, true })),        // NHWC
     input_info, weights_info, bias_info, output_info, pad_info, expected)
 {
     bool is_valid = bool(CLDeconvolutionLayer::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), pad_info));
@@ -171,6 +206,17 @@ FIXTURE_DATA_TEST_CASE(RunSmall, CLDeconvolutionLayerFixture3x3<float>, framewor
     // Validate output
     validate(CLAccessor(_target), _reference, tolerance_fp32);
 }
+
+FIXTURE_DATA_TEST_CASE(RunSmallWithLargeChannels, CLDeconvolutionLayerFixture3x3<float>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(data3x3_precommit_large_channels,
+                       framework::dataset::make("DataType",
+                                                DataType::F32)),
+                       data_layouts_dataset),
+                       framework::dataset::make("AddBias", { true })))
+{
+    // Validate output
+    validate(CLAccessor(_target), _reference, tolerance_fp32);
+}
+
 FIXTURE_DATA_TEST_CASE(RunAsymm, CLDeconvolutionLayerAsymmFixture3x3<float>, framework::DatasetMode::NIGHTLY, combine(combine(combine(data3x3_asymm, framework::dataset::make("DataType",
                                                                                                                       DataType::F32)),
                                                                                                                       data_layouts_dataset),