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/*
* Copyright (c) 2018-2019 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/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, DataLayout data_layout)
{
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
CLWinogradConvolutionLayer::CLWinogradConvolutionLayer(std::shared_ptr<IMemoryManager> memory_manager)
: _memory_group(memory_manager), _batched_mm(memory_manager), _input_transform(), _filter_transform(), _output_transform(), _input0(), _input1(), _batched_mm_output(), _original_weights(nullptr),
_is_prepared(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, input->info()->data_layout());
// Check if the Winograd configuration requires fast math
if(!enable_fast_math)
{
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(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*/, 0, false, false, GEMMLowpOutputStageInfo(),
(input->info()->data_type() == DataType::F16)));
// Configure output transform
_output_transform.configure(&_batched_mm_output, biases, output, winograd_info, 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, input->data_layout());
// 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));
// 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);
_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;
}
}
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