/*
 * Copyright (c) 2018 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/CLWinogradConvolutionLayer.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"

using namespace arm_compute;

namespace
{
Size2D winograd_output_tile(const Size2D &input_dims, const Size2D &kernel_dims)
{
 Size2D output_tile = Size2D{};

 if(kernel_dims == Size2D(3U, 3U))
 {
 output_tile = (input_dims.width <= 4 && input_dims.height <= 4) ? Size2D(2U, 2U) : Size2D(4U, 4U);
 }
 else if(kernel_dims == Size2D(5U, 5U))
 {
 output_tile = Size2D(4U, 4U);
 }

 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))
 };

 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

CLWinogradConvolutionLayer::CLWinogradConvolutionLayer(std::shared_ptr<IMemoryManager> memory_manager)
 : _memory_group(memory_manager), _batched_mm(memory_manager), _input_transform(), _filter_transform(), _output_transform(), _activationlayer_function(), _input0(), _input1(), _batched_mm_output(),
 _original_weights(nullptr), _is_prepared(false), _is_activationlayer_enabled(false)
{
}

void CLWinogradConvolutionLayer::configure(ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, 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);

 // 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);

 // Check if the Winograd configuration requires fast math
 if(!enable_fast_math)
 {
 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(input, &_input0, winograd_info);

 // Configure filter transform
 _filter_transform.configure(weights, &_input1, winograd_info);

 // Configure batched matrix multiply
 _batched_mm.configure(&_input0, &_input1, nullptr, &_batched_mm_output, 1.0f, 0.0f, GEMMInfo(false, false, true /* Reshape weights only for the first run*/));

 // Configure output transform
 _output_transform.configure(&_batched_mm_output, biases, output, winograd_info);

 // Configure activation layer
 _is_activationlayer_enabled = act_info.enabled();
 if(_is_activationlayer_enabled)
 {
 _activationlayer_function.configure(output, nullptr, act_info);
 }

 // Allocate temporary tensors
 _input0.allocator()->allocate();
 _batched_mm_output.allocator()->allocate();
}

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);

 // Check if the Winograd configuration requires fast math
 if(!enable_fast_math)
 {
 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*/)));

 // Configure output transform
 ARM_COMPUTE_RETURN_ON_ERROR(CLWinogradOutputTransformKernel::validate(&batched_mm_output, biases, output, winograd_info));

 // Validate Activation Layer
 if(act_info.enabled())
 {
 ARM_COMPUTE_RETURN_ON_ERROR(CLActivationLayer::validate(output, nullptr, act_info));
 }

 return Status{};
}

void CLWinogradConvolutionLayer::run()
{
 prepare();

 _memory_group.acquire();

 // Run input transform
 _input_transform.run();

 // Run batched matrix multiplication
 _batched_mm.run();

 // Run output transform
 CLScheduler::get().enqueue(_output_transform);

 if(_is_activationlayer_enabled)
 {
 _activationlayer_function.run();
 }

 _memory_group.release();
}

void CLWinogradConvolutionLayer::prepare()
{
 if(!_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;
 }
}