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/*
* Copyright (c) 2018-2021 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.
*/
#ifndef ARM_COMPUTE_CL_WINOGRADCONV2D_H
#define ARM_COMPUTE_CL_WINOGRADCONV2D_H
#include "arm_compute/runtime/CL/CLTensor.h"
#include "src/core/CL/kernels/CLFillBorderKernel.h"
#include "src/core/gpu/cl/ClCompileContext.h"
#include "src/runtime/gpu/cl/IClOperator.h"
#include "src/runtime/gpu/cl/operators/ClGemm.h"
namespace arm_compute
{
class CLCompileContext;
class ITensorInfo;
namespace opencl
{
namespace kernels
{
class ClWinogradInputTransformKernel;
class ClWinogradFilterTransformKernel;
class ClWinogradOutputTransformKernel;
} // kernels
/** Basic function to execute Winograd-based convolution on OpenCL. This function calls the following OpenCL functions/kernels:
*
* -# @ref kernels::ClWinogradInputTransformKernel
* -# @ref kernels::ClWinogradFilterTransformKernel (only once)
* -# @ref ClGemm
* -# @ref kernels::ClWinogradOutputTransformKernel
*
*/
class ClWinogradConv2d : public IClOperator
{
public:
/** Default constructor */
ClWinogradConv2d();
/** Default destructor */
~ClWinogradConv2d();
/** Prevent instances of this class from being copied (As this class contains pointers) */
ClWinogradConv2d(const ClWinogradConv2d &) = delete;
/** Default move constructor */
ClWinogradConv2d(ClWinogradConv2d &&) = default;
/** Prevent instances of this class from being copied (As this class contains pointers) */
ClWinogradConv2d &operator=(const ClWinogradConv2d &) = delete;
/** Default move assignment operator */
ClWinogradConv2d &operator=(ClWinogradConv2d &&) = default;
/** Set the input and output tensors.
*
* Valid data layouts:
* - NHWC
* - NCHW
*
* Valid data type configurations:
* |src0 |src1 |src2 |dst |
* |:--------------|:--------------|:------|:--------------|
* |F16 |F16 |F16 |F16 |
* |F32 |F32 |F32 |F32 |
*
* @note: This function only works with 3x3,3x1,1x3,5x5,5x1,1x5,7x1 and 1x7 kernels along with unit strides for both NCHW and NHWC data layout
* @note Some Winograd configurations (i.e. F(4x4, 5x5)) are supported only with enable_fast_math = true
*
* @param[in] compile_context The compile context to be used.
* @param[in] src Source tensor info. 3 lower dimensions represent a single input [width, height, IFM],
* while every optional dimension from 4 and above represent a batch of inputs.
* Data types supported: F16/F32.
* @param[in] weights Weights tensor info. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM]. Data type supported:Same as @p src.
* @param[in] biases Biases tensor info. Shared biases supported. Biases are 1D tensor with dimensions [OFM].Data type supported: Same as @p src
* @param[out] dst Destination tensor info. 3 lower dimensions represent a single output [width, height, OFM], while the rest represent batch of outputs.
* Data types supported: Same as @p src.
* @param[in] conv_info Contains padding and stride information described in @ref PadStrideInfo.
* @param[in] act_info (Optional) Activation layer information in case of a fused activation.
* @param[in] enable_fast_math (Optional) Enable fast math computation. In case this flag were set, the function could dispatch the fastest implementation
* available which may introduce a drop of accuracy as well. Default is false
*/
void configure(const ClCompileContext &compile_context, ITensorInfo *src, ITensorInfo *weights, ITensorInfo *biases, ITensorInfo *dst, const PadStrideInfo &conv_info,
const ActivationLayerInfo &act_info = ActivationLayerInfo(), bool enable_fast_math = false);
/** Static function to check if given info will lead to a valid configuration
*
* Similar to ClWinogradConv2d::configure()
*
* @return a status
*/
static Status validate(const ITensorInfo *src, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *dst, const PadStrideInfo &conv_info,
const ActivationLayerInfo &act_info = ActivationLayerInfo(), bool enable_fast_math = false);
// Inherited method overridden
void run(ITensorPack &tensors) override;
void prepare(ITensorPack &tensors) override;
experimental::MemoryRequirements workspace() const override;
private:
ClGemm _batched_mm;
std::unique_ptr<kernels::ClWinogradInputTransformKernel> _input_transform;
std::unique_ptr<kernels::ClWinogradFilterTransformKernel> _filter_transform;
std::unique_ptr<kernels::ClWinogradOutputTransformKernel> _output_transform;
CLFillBorderKernel _border_handler;
TensorInfo _input0;
TensorInfo _input1;
TensorInfo _batched_mm_output;
bool _is_prepared;
experimental::MemoryRequirements _aux_mem{};
};
} // namespace opencl
} // namespace arm_compute
#endif /* ARM_COMPUTE_CL_WINOGRADCONV2D_H */
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