Port CLWinogradConvolutionLayer with ClWinogradConv2d

Port CLWinogradInputTransformKernel
Port CLWinogradFilterTransformKernel
Port CLWinogradOutputTransformKernel

Resolves: COMPMID-4504

Change-Id: I3177dda0b9c2f56b36cb317027e94abe8d47229e
Signed-off-by: Manuel Bottini <manuel.bottini@arm.com>
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/5680
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>

2 files changed, 42 insertions, 231 deletions

diff --git a/src/runtime/CL/functions/CLWinogradConvolutionLayer.cpp b/src/runtime/CL/functions/CLWinogradConvolutionLayer.cpp
index 6b8b00414a..f758c3d0b3 100644
--- a/src/runtime/CL/functions/CLWinogradConvolutionLayer.cpp
+++ b/src/runtime/CL/functions/CLWinogradConvolutionLayer.cpp
@@ -23,79 +23,34 @@
  */
 #include "arm_compute/runtime/CL/functions/CLWinogradConvolutionLayer.h"
 
+#include "arm_compute/core/CL/CLKernelLibrary.h"
 #include "arm_compute/core/CL/ICLTensor.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 "src/core/CL/kernels/CLFillBorderKernel.h"
-#include "src/core/CL/kernels/CLWinogradFilterTransformKernel.h"
-#include "src/core/CL/kernels/CLWinogradOutputTransformKernel.h"
+#include "arm_compute/core/KernelDescriptors.h"
+#include "src/core/CL/ICLKernel.h"
+#include "src/core/helpers/MemoryHelpers.h"
+#include "src/runtime/gpu/cl/operators/ClWinogradConv2d.h"
+#include "support/Cast.h"
 
-using namespace arm_compute;
-
-namespace
+namespace arm_compute
 {
-Size2D winograd_output_tile(const Size2D &input_dims, const Size2D &kernel_dims, DataLayout data_layout)
+struct CLWinogradConvolutionLayer::Impl
 {
-    Size2D output_tile = Size2D{};
-
-    const unsigned int kernel_max_dim = std::max(kernel_dims.width, kernel_dims.height);
-
-    // Check if the input spatial dimensions are smaller than 4
-    const bool is_input_lt4_nchw = (input_dims.width <= 4 && input_dims.height <= 4) && (data_layout == DataLayout::NCHW);
-
-    if(kernel_max_dim == 3U)
-    {
-        if(kernel_dims == Size2D(3U, 3U))
-        {
-            output_tile = is_input_lt4_nchw ? Size2D(2U, 2U) : Size2D(4U, 4U);
-        }
-        else if(kernel_dims == Size2D(3U, 1U))
-        {
-            output_tile = is_input_lt4_nchw ? Size2D(2U, 1U) : Size2D(4U, 1U);
-        }
-        else
-        {
-            output_tile = is_input_lt4_nchw ? Size2D(1U, 2U) : Size2D(1U, 4U);
-        }
-    }
-    else if(kernel_max_dim == 5U)
-    {
-        output_tile = Size2D(kernel_dims.width == 1 ? 1U : 4U,
-                             kernel_dims.height == 1 ? 1U : 4U);
-    }
-    else if(kernel_max_dim == 7U)
-    {
-        output_tile = Size2D(kernel_dims.width == 1 ? 1U : 2U,
-                             kernel_dims.height == 1 ? 1U : 2U);
-    }
-
-    return output_tile;
-}
-
-bool check_support_fast_math(const Size2D &output_tile, const Size2D &kernel_size)
-{
-    // Check if we want to configure a Winograd configuration which requires fast math
-    using WinogradConfiguration = std::pair<std::pair<int, int>, std::pair<int, int>>;
-
-    std::vector<WinogradConfiguration> fast_math_winograd =
-    {
-        WinogradConfiguration(std::pair<int, int>(4, 4), std::pair<int, int>(5, 5)),
-        WinogradConfiguration(std::pair<int, int>(2, 2), std::pair<int, int>(7, 7))
-    };
-
-    auto p = std::make_pair(std::pair<int, int>(output_tile.width, output_tile.height),
-                            std::pair<int, int>(kernel_size.width, kernel_size.height));
-
-    return std::find(fast_math_winograd.begin(), fast_math_winograd.end(), p) != fast_math_winograd.end();
-}
-} // namespace
+    const ICLTensor                          *src{ nullptr };
+    const ICLTensor                          *weights{ nullptr };
+    const ICLTensor                          *biases{ nullptr };
+    ICLTensor                                *dst{ nullptr };
+    std::unique_ptr<opencl::ClWinogradConv2d> op{ nullptr };
+    ITensorPack                               run_pack{};
+    ITensorPack                               prep_pack{};
+    MemoryGroup                               memory_group{};
+    WorkspaceData<CLTensor>                   workspace_tensors{};
+    bool                                      is_prepared{ false };
+};
 
 CLWinogradConvolutionLayer::CLWinogradConvolutionLayer(std::shared_ptr<IMemoryManager> memory_manager)
-    : _memory_group(memory_manager), _batched_mm(memory_manager), _input_transform(), _filter_transform(std::make_unique<CLWinogradFilterTransformKernel>()),
-      _output_transform(std::make_unique<CLWinogradOutputTransformKernel>()), _input0(), _input1(), _batched_mm_output(), _original_weights(nullptr), _is_prepared(false)
+    : _impl(std::make_unique<Impl>())
 {
+    _impl->memory_group = MemoryGroup(memory_manager);
 }
 
 CLWinogradConvolutionLayer::~CLWinogradConvolutionLayer() = default;
@@ -110,139 +65,45 @@ void CLWinogradConvolutionLayer::configure(const CLCompileContext &compile_conte
                                            const PadStrideInfo       &conv_info,
                                            const ActivationLayerInfo &act_info, bool enable_fast_math)
 {
-    // Get indices for the width and height
-    const size_t idx_width  = get_data_layout_dimension_index(input->info()->data_layout(), DataLayoutDimension::WIDTH);
-    const size_t idx_height = get_data_layout_dimension_index(input->info()->data_layout(), DataLayoutDimension::HEIGHT);
+    _impl->src     = input;
+    _impl->weights = weights;
+    _impl->biases  = biases;
+    _impl->dst     = output;
 
-    // Input shape, kernel size and output tile
-    const Size2D input_dims  = Size2D(input->info()->tensor_shape()[idx_width], input->info()->tensor_shape()[idx_height]);
-    const Size2D kernel_size = Size2D(weights->info()->tensor_shape()[idx_width], weights->info()->tensor_shape()[idx_height]);
-    const Size2D output_tile = winograd_output_tile(input_dims, kernel_size, input->info()->data_layout());
+    _impl->op = std::make_unique<opencl::ClWinogradConv2d>();
+    _impl->op->configure(compile_context, input->info(), weights->info(), (biases != nullptr ? biases->info() : nullptr), output->info(), conv_info, act_info, enable_fast_math);
 
-    // Check if the Winograd configuration requires fast math
-    if(!enable_fast_math)
+    _impl->run_pack =
     {
-        ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32); //disable winograd for fp16 if fast math is false.
-        ARM_COMPUTE_ERROR_ON_MSG(check_support_fast_math(output_tile, kernel_size), "This Winograd configuration requires enable_fast_math=true");
-    }
-    const WinogradInfo winograd_info = WinogradInfo(output_tile,
-                                                    kernel_size,
-                                                    input_dims,
-                                                    conv_info,
-                                                    input->info()->data_layout());
-
-    _is_prepared      = false;
-    _original_weights = weights;
-
-    // Manage intermediate tensors
-    _memory_group.manage(&_input0);
-    _memory_group.manage(&_batched_mm_output);
-
-    // Do not manage _input1 as it contains the weights
-
-    // Configure input transform
-    _input_transform.configure(compile_context, input, &_input0, winograd_info);
-
-    // Configure filter transform
-    _filter_transform->configure(compile_context, weights, &_input1, winograd_info);
-
-    // Configure batched matrix multiply
-    _batched_mm.configure(compile_context, &_input0, &_input1, nullptr, &_batched_mm_output, 1.0f, 0.0f, GEMMInfo(false, false, true /* Reshape weights only for the first run*/, 0, false, false,
-                                                                                                                  GEMMLowpOutputStageInfo(),
-                                                                                                                  (input->info()->data_type() == DataType::F16)));
-
-    // Configure output transform
-    _output_transform->configure(compile_context, &_batched_mm_output, biases, output, winograd_info, act_info);
+        { TensorType::ACL_SRC_0, _impl->src },
+        { TensorType::ACL_SRC_1, _impl->weights },
+        { TensorType::ACL_SRC_2, _impl->biases },
+        { TensorType::ACL_DST, _impl->dst }
+    };
 
-    // Allocate temporary tensors
-    _input0.allocator()->allocate();
-    _batched_mm_output.allocator()->allocate();
+    _impl->prep_pack         = { { TensorType::ACL_SRC_1, _impl->weights } };
+    _impl->workspace_tensors = manage_workspace<CLTensor>(_impl->op->workspace(), _impl->memory_group, _impl->run_pack, _impl->prep_pack);
 }
 
 Status CLWinogradConvolutionLayer::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info,
                                             const ActivationLayerInfo &act_info, bool enable_fast_math)
 {
-    // Get indeces for the width and height
-    const size_t idx_width  = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::WIDTH);
-    const size_t idx_height = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::HEIGHT);
-
-    // Input shape, kernel size and output tile
-    const Size2D input_dims  = Size2D(input->tensor_shape()[idx_width], input->tensor_shape()[idx_height]);
-    const Size2D kernel_size = Size2D(weights->tensor_shape()[idx_width], weights->tensor_shape()[idx_height]);
-    const Size2D output_tile = winograd_output_tile(input_dims, kernel_size, input->data_layout());
-
-    ARM_COMPUTE_RETURN_ERROR_ON_MSG(((conv_info.pad_left() > (kernel_size.x() / 2u)) || (conv_info.pad_right() > (kernel_size.x() / 2u))), "Winograd only supports padding up to half kernel size");
-    ARM_COMPUTE_RETURN_ERROR_ON_MSG(((conv_info.pad_top() > (kernel_size.y() / 2u)) || (conv_info.pad_bottom() > (kernel_size.y() / 2u))), "Winograd only supports padding up to half kernel size");
-
-    // Check if the Winograd configuration requires fast math
-    if(!enable_fast_math)
-    {
-        ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32); //disable winograd for fp16 if fast math is false.
-        ARM_COMPUTE_RETURN_ERROR_ON_MSG(check_support_fast_math(output_tile, kernel_size), "This Winograd configuration requires enable_fast_math=true");
-    }
-
-    const WinogradInfo winograd_info = WinogradInfo(output_tile,
-                                                    kernel_size,
-                                                    input_dims,
-                                                    conv_info,
-                                                    input->data_layout());
-
-    // Validate input transform
-    const TensorShape input0_shape = misc::shape_calculator::compute_winograd_input_transform_shape(*input, winograd_info);
-    const TensorInfo  input0       = input->clone()->set_tensor_shape(input0_shape);
-    ARM_COMPUTE_RETURN_ON_ERROR(CLWinogradInputTransform::validate(input, &input0, winograd_info));
-
-    // Validate filter transform
-    const TensorShape input1_shape = misc::shape_calculator::compute_winograd_filter_transform_shape(*weights, winograd_info);
-    const TensorInfo  input1       = weights->clone()->set_tensor_shape(input1_shape);
-    ARM_COMPUTE_RETURN_ON_ERROR(CLWinogradFilterTransformKernel::validate(weights, &input1, winograd_info));
-
-    // Validate batched matrix multiply
-    TensorShape batched_mm_output_shape = input0.tensor_shape();
-    batched_mm_output_shape[0]          = input1.tensor_shape()[0];
-    const TensorInfo batched_mm_output  = input0.clone()->set_tensor_shape(batched_mm_output_shape);
-    ARM_COMPUTE_RETURN_ON_ERROR(CLGEMM::validate(&input0, &input1, nullptr, &batched_mm_output, 1.0f, 0.0f, GEMMInfo(false, false, true /* Reshape weights only for the first run*/, 0, false, false,
-                                                                                                                     GEMMLowpOutputStageInfo(), (input->data_type() == DataType::F16))));
-
-    // Configure output transform
-    ARM_COMPUTE_RETURN_ON_ERROR(CLWinogradOutputTransformKernel::validate(&batched_mm_output, biases, output, winograd_info, act_info));
-
-    return Status{};
+    return opencl::ClWinogradConv2d::validate(input, weights, biases, output, conv_info, act_info, enable_fast_math);
 }
 
 void CLWinogradConvolutionLayer::run()
 {
+    MemoryGroupResourceScope scope_mg(_impl->memory_group);
     prepare();
-
-    MemoryGroupResourceScope scope_mg(_memory_group);
-
-    // Run input transform
-    _input_transform.run();
-
-    // Run batched matrix multiplication
-    _batched_mm.run();
-
-    // Run output transform
-    CLScheduler::get().enqueue(*_output_transform);
+    _impl->op->run(_impl->run_pack);
 }
 
 void CLWinogradConvolutionLayer::prepare()
 {
-    if(!_is_prepared)
+    if(!_impl->is_prepared)
     {
-        // Run filter transform and mark original weights as unused
-        _input1.allocator()->allocate();
-        CLScheduler::get().enqueue(*_filter_transform, false);
-        _original_weights->mark_as_unused();
-
-        // Prepare GEMM and release reshaped weights if marked unused by CLGEMM
-        _batched_mm.prepare();
-        if(!_input1.is_used())
-        {
-            _input1.allocator()->free();
-        }
-
-        CLScheduler::get().queue().finish();
-        _is_prepared = true;
+        _impl->op->prepare(_impl->prep_pack);
+        _impl->is_prepared = true;
     }
 }
+} // namespace arm_compute
\ No newline at end of file
diff --git a/src/runtime/CL/functions/CLWinogradInputTransform.cpp b/src/runtime/CL/functions/CLWinogradInputTransform.cpp
deleted file mode 100644
index 6d5a692bc3..0000000000
--- a/src/runtime/CL/functions/CLWinogradInputTransform.cpp
+++ /dev/null
@@ -1,50 +0,0 @@
-/*
- * Copyright (c) 2018-2020 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/CLWinogradInputTransform.h"
-
-#include "arm_compute/core/CL/ICLTensor.h"
-#include "arm_compute/core/Error.h"
-#include "src/core/CL/kernels/CLFillBorderKernel.h"
-#include "src/core/CL/kernels/CLWinogradInputTransformKernel.h"
-
-using namespace arm_compute;
-
-void CLWinogradInputTransform::configure(ICLTensor *input, ICLTensor *output, const WinogradInfo &winograd_info)
-{
-    configure(CLKernelLibrary::get().get_compile_context(), input, output, winograd_info);
-}
-
-void CLWinogradInputTransform::configure(const CLCompileContext &compile_context, ICLTensor *input, ICLTensor *output, const WinogradInfo &winograd_info)
-{
-    auto k = std::make_unique<CLWinogradInputTransformKernel>();
-    k->configure(compile_context, input, output, winograd_info);
-    _kernel = std::move(k);
-    _border_handler->configure(compile_context, input, _kernel->border_size(), BorderMode::CONSTANT, PixelValue());
-}
-
-Status CLWinogradInputTransform::validate(const ITensorInfo *input, const ITensorInfo *output, const WinogradInfo &winograd_info)
-{
-    ARM_COMPUTE_RETURN_ON_ERROR(CLWinogradInputTransformKernel::validate(input, output, winograd_info));
-    return Status{};
-}