Optimize CL DeconvolutionLayer-Part II: Add CLDirectDeconvolution function to be used by CLDeconvolution.

This is only a code refactoring (no optimizations have been added)

Change-Id: I78488f4aecfe1cce93c31dba31489dcee4c85c67
Signed-off-by: giuros01 <giuseppe.rossini@arm.com>
Reviewed-on: https://review.mlplatform.org/c/895
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Gian Marco Iodice <gianmarco.iodice@arm.com>

2 files changed, 221 insertions, 149 deletions

diff --git a/src/runtime/CL/functions/CLDeconvolutionLayer.cpp b/src/runtime/CL/functions/CLDeconvolutionLayer.cpp
index 9da02c10ad..2c17473fc7 100644
--- a/src/runtime/CL/functions/CLDeconvolutionLayer.cpp
+++ b/src/runtime/CL/functions/CLDeconvolutionLayer.cpp
@@ -23,188 +23,62 @@
  */
 #include "arm_compute/runtime/CL/functions/CLDeconvolutionLayer.h"
 
-#include "arm_compute/core/Helpers.h"
 #include "arm_compute/core/Utils.h"
 #include "arm_compute/core/Validate.h"
 #include "arm_compute/core/utils/misc/ShapeCalculator.h"
+#include "arm_compute/core/utils/quantization/AsymmHelpers.h"
 #include "arm_compute/runtime/CL/CLScheduler.h"
-#include "arm_compute/runtime/CPP/CPPScheduler.h"
 
+#include <cmath>
 #include <memory>
 #include <tuple>
 
 using namespace arm_compute;
 using namespace arm_compute::misc::shape_calculator;
 
-CLDeconvolutionLayer::CLDeconvolutionLayer(std::shared_ptr<IMemoryManager> memory_manager) // NOLINT
-    : _memory_group(std::move(memory_manager)),
-      _scale_f(),
-      _conv_f(),
-      _flip_weights(),
-      _scaled_output(),
-      _original_weights(nullptr),
-      _weights_flipped(),
-      _is_prepared(false)
+CLDeconvolutionLayer::CLDeconvolutionLayer(std::shared_ptr<IMemoryManager> memory_manager)
+    : _memory_manager(std::move(memory_manager)), _function()
 {
 }
 
-Status CLDeconvolutionLayer::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *bias, ITensorInfo *output, const PadStrideInfo &info,
-                                      unsigned int inner_border_right, unsigned int inner_border_top, const WeightsInfo &weights_info)
-{
-    ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, weights, output);
-    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::F16, DataType::F32);
-    ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT(input, weights);
-
-    const DataLayout data_layout = input->data_layout();
-
-    const size_t idx_w = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH);
-    const size_t idx_h = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT);
-    const size_t idx_c = get_data_layout_dimension_index(data_layout, DataLayoutDimension::CHANNEL);
-
-    ARM_COMPUTE_RETURN_ERROR_ON(weights->dimension(idx_w) != weights->dimension(idx_h));
-    ARM_COMPUTE_RETURN_ERROR_ON(weights->dimension(idx_w) < 1);
-    ARM_COMPUTE_RETURN_ERROR_ON(!info.padding_is_symmetric());
-
-    const unsigned int stride_x = info.stride().first;
-    const unsigned int stride_y = info.stride().second;
-
-    ARM_COMPUTE_RETURN_ERROR_ON_MSG(inner_border_right > stride_x - 1, "inner_border_right must be smaller than stride_x");
-    ARM_COMPUTE_RETURN_ERROR_ON_MSG(inner_border_top > stride_y - 1, "inner_border_top must be smaller than stride_y");
-
-    auto out_dims = deconvolution_output_dimensions(input->dimension(idx_w), input->dimension(idx_h), weights->dimension(idx_w), weights->dimension(idx_h),
-                                                    info.pad().first, info.pad().second, stride_x, stride_y);
-
-    const TensorShape output_shape = compute_deconvolution_output_shape(out_dims, *input, *weights);
-
-    ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output, weights);
-
-    if(bias != nullptr)
-    {
-        if(is_data_type_quantized_asymmetric(input->data_type()))
-        {
-            ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(bias, 1, DataType::S32);
-        }
-        else
-        {
-            ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, bias);
-        }
-        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT(input, bias);
-    }
-
-    ARM_COMPUTE_RETURN_ERROR_ON_MSG(output->dimension(idx_w) != output_shape[idx_w], "Output's width is invalid.");
-    ARM_COMPUTE_RETURN_ERROR_ON_MSG(output->dimension(idx_h) != output_shape[idx_h], "Output's height is invalid.");
-    ARM_COMPUTE_RETURN_ERROR_ON_MSG(output->dimension(idx_c) != output_shape[idx_c], "Output's depth is invalid.");
-
-    unsigned int        padx            = 0;
-    unsigned int        pady            = 0;
-    const TensorShape   scale_out_shape = compute_deconvolution_upsampled_shape(*input, *weights, stride_x, stride_y, inner_border_right, inner_border_top, out_dims, padx, pady);
-    TensorInfo          scale_out_info(input->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(scale_out_shape).set_data_layout(data_layout));
-    const PadStrideInfo conv_info(1, 1, 0, 0, 0, 0, DimensionRoundingType::CEIL);
-
-    ARM_COMPUTE_RETURN_ON_ERROR(CLDeconvolutionLayerUpsample::validate(input, &scale_out_info, BorderSize(inner_border_right, inner_border_top), info));
-    ARM_COMPUTE_RETURN_ON_ERROR(CLConvolutionLayer::validate(&scale_out_info, weights, bias, output, conv_info, weights_info));
-
-    return Status{};
-}
-
-void CLDeconvolutionLayer::configure(ICLTensor *input, ICLTensor *weights, const ICLTensor *bias, ICLTensor *output, const PadStrideInfo &info,
+void CLDeconvolutionLayer::configure(ICLTensor *input, ICLTensor *weights, const ICLTensor *bias, ICLTensor *output, const PadStrideInfo &deconv_info,
                                      unsigned int inner_border_right, unsigned int inner_border_top, const WeightsInfo &weights_info)
 {
+    ARM_COMPUTE_UNUSED(inner_border_right, inner_border_top);
     ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, output);
+    auto f = arm_compute::support::cpp14::make_unique<CLDirectDeconvolutionLayer>();
+    f->configure(input, weights, bias, output, deconv_info, weights_info);
+    _function = std::move(f);
+}
 
-    const unsigned int stride_x = info.stride().first;
-    const unsigned int stride_y = info.stride().second;
-
-    const DataLayout data_layout = input->info()->data_layout();
-
-    const size_t idx_w = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH);
-    const size_t idx_h = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT);
-
-    _original_weights = weights;
-    _weights_flipped.allocator()->init(weights->info()->clone()->set_data_layout(data_layout));
-    _flip_weights.configure(weights, &_weights_flipped);
-
-    auto out_dims = deconvolution_output_dimensions(input->info()->dimension(idx_w), input->info()->dimension(idx_h), weights->info()->dimension(idx_w), weights->info()->dimension(idx_h),
-                                                    info.pad().first, info.pad().second, stride_x, stride_y);
-
-    const TensorShape output_shape = compute_deconvolution_output_shape(out_dims, *input->info(), *weights->info());
-
-    // Output auto initialization if not yet initialized
-    auto_init_if_empty(*output->info(), input->info()->clone()->set_tensor_shape(output_shape).set_data_layout(data_layout));
-
-    // Perform validation step
-    ARM_COMPUTE_ERROR_THROW_ON(CLDeconvolutionLayer::validate(input->info(), weights->info(), bias == nullptr ? nullptr : bias->info(), output->info(), info, inner_border_right, inner_border_top));
-
-    _is_prepared = weights_info.retain_internal_weights();
-
-    _memory_group.manage(&_scaled_output);
-
-    // Find the upsampled dimensions and the padding needed for the convolution with stride 1 in order to match output shape
-    unsigned int      padx            = 0;
-    unsigned int      pady            = 0;
-    const TensorShape scale_out_shape = compute_deconvolution_upsampled_shape(*input->info(), *weights->info(), stride_x, stride_y, inner_border_right, inner_border_top, out_dims, padx, pady);
-
-    TensorInfo scale_out_info(scale_out_shape, 1, input->info()->data_type(), input->info()->quantization_info());
-    scale_out_info.set_data_layout(data_layout);
-    _scaled_output.allocator()->init(scale_out_info);
-
-    // configure scale function
-    const PadStrideInfo upsample_info(stride_x, stride_y, padx / 2, pady / 2);
-    _scale_f.configure(input, &_scaled_output, BorderSize(inner_border_top, inner_border_right), upsample_info);
-
-    // setup the function to convolve the upscaled output
-    const PadStrideInfo conv_info(1, 1, 0, 0, 0, 0, DimensionRoundingType::CEIL);
-    _conv_f.configure(&_scaled_output, &_weights_flipped, bias, output, conv_info, weights_info);
-    _scaled_output.allocator()->allocate();
+Status CLDeconvolutionLayer::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *bias, ITensorInfo *output, const PadStrideInfo &deconv_info,
+                                      unsigned int inner_border_right, unsigned int inner_border_top, const WeightsInfo &weights_info)
+{
+    ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, weights, output);
+    ARM_COMPUTE_UNUSED(inner_border_right, inner_border_top);
+    ARM_COMPUTE_RETURN_ON_ERROR(CLDirectDeconvolutionLayer::validate(input, weights, bias, output, deconv_info, weights_info));
+    return Status{};
 }
 
-void CLDeconvolutionLayer::configure(ICLTensor *input, ICLTensor *weights, const ICLTensor *bias, ICLTensor *output, const PadStrideInfo &info,
+void CLDeconvolutionLayer::configure(ICLTensor *input, ICLTensor *weights, const ICLTensor *bias, ICLTensor *output, const PadStrideInfo &deconv_info,
                                      const WeightsInfo &weights_info)
 {
-    configure(input, weights, bias, output, info, 0, 0, weights_info);
+    configure(input, weights, bias, output, deconv_info, 0, 0, weights_info);
 }
 
-Status CLDeconvolutionLayer::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *bias, ITensorInfo *output, const PadStrideInfo &info,
+Status CLDeconvolutionLayer::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *bias, ITensorInfo *output, const PadStrideInfo &deconv_info,
                                       const WeightsInfo &weights_info)
 {
-    return CLDeconvolutionLayer::validate(input, weights, bias, output, info, 0, 0, weights_info);
+    return CLDeconvolutionLayer::validate(input, weights, bias, output, deconv_info, 0, 0, weights_info);
 }
 
 void CLDeconvolutionLayer::run()
 {
     prepare();
-
-    _memory_group.acquire();
-
-    _scale_f.run();
-    _conv_f.run();
-
-    _memory_group.release();
+    _function->run();
 }
 
 void CLDeconvolutionLayer::prepare()
 {
-    if(!_is_prepared)
-    {
-        ARM_COMPUTE_ERROR_ON(!_original_weights->is_used());
-
-        // Run weights flipping and mark original weights tensor as unused
-        _weights_flipped.allocator()->allocate();
-        _weights_flipped.map(true);
-        _original_weights->map(CLScheduler::get().queue(), true);
-        CPPScheduler::get().schedule(&_flip_weights, Window::DimZ);
-        _weights_flipped.unmap();
-        _original_weights->unmap(CLScheduler::get().queue());
-        _original_weights->mark_as_unused();
-
-        // Prepare convolution
-        _conv_f.prepare();
-
-        if(!_weights_flipped.is_used())
-        {
-            _weights_flipped.allocator()->free();
-        }
-
-        _is_prepared = true;
-    }
+    _function->prepare();
 }
diff --git a/src/runtime/CL/functions/CLDirectDeconvolutionLayer.cpp b/src/runtime/CL/functions/CLDirectDeconvolutionLayer.cpp
new file mode 100644
index 0000000000..c01588a164
--- /dev/null
+++ b/src/runtime/CL/functions/CLDirectDeconvolutionLayer.cpp
@@ -0,0 +1,198 @@
+/*
+ * Copyright (c) 2019 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 "arm_compute/runtime/CL/functions/CLDirectDeconvolutionLayer.h"
+
+#include "arm_compute/core/Helpers.h"
+#include "arm_compute/core/Utils.h"
+#include "arm_compute/core/Validate.h"
+#include "arm_compute/core/utils/misc/ShapeCalculator.h"
+#include "arm_compute/runtime/CL/CLScheduler.h"
+#include "arm_compute/runtime/CPP/CPPScheduler.h"
+#include "utils/TypePrinter.h"
+
+#include <memory>
+#include <tuple>
+
+namespace arm_compute
+{
+using namespace arm_compute::misc::shape_calculator;
+
+CLDirectDeconvolutionLayer::CLDirectDeconvolutionLayer(std::shared_ptr<IMemoryManager> memory_manager) // NOLINT
+    : _memory_group(std::move(memory_manager)),
+      _scale_f(),
+      _conv_f(),
+      _flip_weights(),
+      _scaled_output(),
+      _original_weights(nullptr),
+      _weights_flipped(),
+      _is_prepared(false)
+{
+}
+
+Status CLDirectDeconvolutionLayer::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *bias, ITensorInfo *output, const PadStrideInfo &info,
+                                            const WeightsInfo &weights_info)
+{
+    ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, weights, output);
+    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::F16, DataType::F32);
+    ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT(input, weights);
+
+    const DataLayout data_layout = input->data_layout();
+
+    const size_t idx_w = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH);
+    const size_t idx_h = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT);
+    const size_t idx_c = get_data_layout_dimension_index(data_layout, DataLayoutDimension::CHANNEL);
+
+    ARM_COMPUTE_RETURN_ERROR_ON(weights->dimension(idx_w) != weights->dimension(idx_h));
+    ARM_COMPUTE_RETURN_ERROR_ON(weights->dimension(idx_w) < 1);
+    ARM_COMPUTE_RETURN_ERROR_ON(!info.padding_is_symmetric());
+
+    const unsigned int stride_x = info.stride().first;
+    const unsigned int stride_y = info.stride().second;
+
+    auto out_dims = deconvolution_output_dimensions(input->dimension(idx_w), input->dimension(idx_h), weights->dimension(idx_w), weights->dimension(idx_h),
+                                                    info.pad().first, info.pad().second, stride_x, stride_y);
+
+    const TensorShape output_shape = compute_deconvolution_output_shape(out_dims, *input, *weights);
+
+    ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output, weights);
+
+    if(bias != nullptr)
+    {
+        if(is_data_type_quantized_asymmetric(input->data_type()))
+        {
+            ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(bias, 1, DataType::S32);
+        }
+        else
+        {
+            ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, bias);
+        }
+        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT(input, bias);
+    }
+
+    ARM_COMPUTE_RETURN_ERROR_ON_MSG(output->dimension(idx_w) != output_shape[idx_w], "Output's width is invalid.");
+    ARM_COMPUTE_RETURN_ERROR_ON_MSG(output->dimension(idx_h) != output_shape[idx_h], "Output's height is invalid.");
+    ARM_COMPUTE_RETURN_ERROR_ON_MSG(output->dimension(idx_c) != output_shape[idx_c], "Output's depth is invalid.");
+
+    unsigned int        padx            = 0;
+    unsigned int        pady            = 0;
+    const TensorShape   scale_out_shape = compute_deconvolution_upsampled_shape(*input, *weights, stride_x, stride_y, 0, 0, out_dims, padx, pady);
+    TensorInfo          scale_out_info(input->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(scale_out_shape).set_data_layout(data_layout));
+    const PadStrideInfo conv_info(1, 1, 0, 0, 0, 0, DimensionRoundingType::CEIL);
+
+    ARM_COMPUTE_RETURN_ON_ERROR(CLDeconvolutionLayerUpsample::validate(input, &scale_out_info, BorderSize(), info));
+    ARM_COMPUTE_RETURN_ON_ERROR(CLConvolutionLayer::validate(&scale_out_info, weights, bias, output, conv_info, weights_info));
+
+    return Status{};
+}
+
+void CLDirectDeconvolutionLayer::configure(ICLTensor *input, ICLTensor *weights, const ICLTensor *bias, ICLTensor *output, const PadStrideInfo &info,
+                                           const WeightsInfo &weights_info)
+{
+    ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, output);
+
+    const unsigned int stride_x = info.stride().first;
+    const unsigned int stride_y = info.stride().second;
+
+    const DataLayout data_layout = input->info()->data_layout();
+
+    const size_t idx_w = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH);
+    const size_t idx_h = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT);
+
+    _original_weights = weights;
+    _weights_flipped.allocator()->init(weights->info()->clone()->set_data_layout(data_layout));
+    _flip_weights.configure(weights, &_weights_flipped);
+
+    auto out_dims = deconvolution_output_dimensions(input->info()->dimension(idx_w), input->info()->dimension(idx_h), weights->info()->dimension(idx_w), weights->info()->dimension(idx_h),
+                                                    info.pad().first, info.pad().second, stride_x, stride_y);
+
+    const TensorShape output_shape = compute_deconvolution_output_shape(out_dims, *input->info(), *weights->info());
+
+    // Output auto initialization if not yet initialized
+    auto_init_if_empty(*output->info(), input->info()->clone()->set_tensor_shape(output_shape).set_data_layout(data_layout));
+
+    // Perform validation step
+    ARM_COMPUTE_ERROR_THROW_ON(CLDirectDeconvolutionLayer::validate(input->info(), weights->info(), bias == nullptr ? nullptr : bias->info(), output->info(), info));
+
+    _is_prepared = weights_info.retain_internal_weights();
+
+    _memory_group.manage(&_scaled_output);
+
+    // Find the upsampled dimensions and the padding needed for the convolution with stride 1 in order to match output shape
+    unsigned int      padx            = 0;
+    unsigned int      pady            = 0;
+    const TensorShape scale_out_shape = compute_deconvolution_upsampled_shape(*input->info(), *weights->info(), stride_x, stride_y, 0, 0, out_dims, padx, pady);
+
+    TensorInfo scale_out_info(scale_out_shape, 1, input->info()->data_type(), input->info()->quantization_info());
+    scale_out_info.set_data_layout(data_layout);
+    _scaled_output.allocator()->init(scale_out_info);
+
+    // configure scale function
+    const PadStrideInfo upsample_info(stride_x, stride_y, padx / 2, pady / 2);
+    _scale_f.configure(input, &_scaled_output, BorderSize(), upsample_info);
+
+    // setup the function to convolve the upscaled output
+    const PadStrideInfo conv_info(1, 1, 0, 0, 0, 0, DimensionRoundingType::CEIL);
+    _conv_f.configure(&_scaled_output, &_weights_flipped, bias, output, conv_info, weights_info);
+    _scaled_output.allocator()->allocate();
+}
+
+void CLDirectDeconvolutionLayer::run()
+{
+    prepare();
+
+    _memory_group.acquire();
+
+    _scale_f.run();
+    _conv_f.run();
+
+    _memory_group.release();
+}
+
+void CLDirectDeconvolutionLayer::prepare()
+{
+    if(!_is_prepared)
+    {
+        ARM_COMPUTE_ERROR_ON(!_original_weights->is_used());
+
+        // Run weights flipping and mark original weights tensor as unused
+        _weights_flipped.allocator()->allocate();
+        _weights_flipped.map(true);
+        _original_weights->map(CLScheduler::get().queue(), true);
+        CPPScheduler::get().schedule(&_flip_weights, Window::DimZ);
+        _weights_flipped.unmap();
+        _original_weights->unmap(CLScheduler::get().queue());
+        _original_weights->mark_as_unused();
+
+        // Prepare convolution
+        _conv_f.prepare();
+
+        if(!_weights_flipped.is_used())
+        {
+            _weights_flipped.allocator()->free();
+        }
+
+        _is_prepared = true;
+    }
+}
+} // namespace arm_compute