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/*
* Copyright (c) 2017-2023 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_NEGEMMCONVOLUTIONLAYER_H
#define ARM_COMPUTE_NEGEMMCONVOLUTIONLAYER_H
#include "arm_compute/core/Types.h"
#include "arm_compute/function_info/ActivationLayerInfo.h"
#include "arm_compute/runtime/IFunction.h"
#include "arm_compute/runtime/IMemoryManager.h"
#include "arm_compute/runtime/IWeightsManager.h"
#include "arm_compute/runtime/MemoryGroup.h"
#include <memory>
namespace arm_compute
{
class ITensor;
class ITensorInfo;
/** Basic function to compute the convolution layer. This function calls the following kernels/functions:
*
* -# @ref cpu::CpuGemmConv2d
*
*/
class NEGEMMConvolutionLayer : public IFunction
{
public:
/** Constructor */
NEGEMMConvolutionLayer(const std::shared_ptr<IMemoryManager> &memory_manager = nullptr,
IWeightsManager *weights_manager = nullptr);
/** Prevent instances of this class from being copied (As this class contains pointers) */
NEGEMMConvolutionLayer(const NEGEMMConvolutionLayer &) = delete;
/** Prevent instances of this class from being moved (As this class contains non movable objects) */
NEGEMMConvolutionLayer(NEGEMMConvolutionLayer &&) = delete;
/** Prevent instances of this class from being copied (As this class contains pointers) */
NEGEMMConvolutionLayer &operator=(const NEGEMMConvolutionLayer &) = delete;
/** Prevent instances of this class from being moved (As this class contains non movable objects) */
NEGEMMConvolutionLayer &operator=(NEGEMMConvolutionLayer &&) = delete;
/** Default destructor */
~NEGEMMConvolutionLayer();
/** 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 |
* |BFLOAT16 |BFLOAT16 |BFLOAT16 |BFLOAT16 |
* |QASYMM8 |QASYMM8 |S32 |QASYMM8 |
* |QASYMM8 |QSYMM8_PER_CHANNEL |S32 |QASYMM8 |
* |QASYMM8_SIGNED |QASYMM8_SIGNED |S32 |QASYMM8_SIGNED |
* |QASYMM8_SIGNED |QSYMM8_PER_CHANNEL |S32 |QASYMM8_SIGNED |
*
* @param[in] input Source tensor. 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: QASYMM8/QASYMM8_SIGNED/BFLOAT16/F16/F32.
* @param[in] weights Weights tensor. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM].
* Data type supported: QASYMM8/QASYMM8_SIGNED/QSYMM8_PER_CHANNEL/BFLOAT16/F16/F32.
* @param[in] biases Biases tensor. Shared biases supported. Biases are 1D tensor with dimensions [OFM].
* Data type supported: Should match @p input data type, except for input of QASYMM8/QASYMM8_SIGNED type where biases should be of S32 type.
* @param[out] output Destination tensor. 3 lower dimensions represent a single output [width, height, OFM], while the rest represent batch of outputs.
* Data types supported: Same as @p input.
* @param[in] conv_info Contains padding and stride information described in @ref PadStrideInfo.
* @param[in] weights_info Specifies if the weights tensor has been reshaped with NEWeightsReshapeKernel. If this is not part of the fully connected layer the weights
* tensor has also been transposed with cpu::kernels::CpuGemmTranspose1xWKernel. Data type supported: Same as @p input.
* @param[in] dilation (Optional) Dilation, in elements, across x and y. Defaults to (1, 1).
* @param[in] act_info (Optional) Activation layer information in case of a fused activation. Only RELU, BOUNDED_RELU and LU_BOUNDED_RELU supported.
* @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
* @param[in] num_groups (Optional) Number of groups when performing a grouped convolution. num_groups != 1 is not supported
*/
void configure(const ITensor *input,
const ITensor *weights,
const ITensor *biases,
ITensor *output,
const PadStrideInfo &conv_info,
const WeightsInfo &weights_info = WeightsInfo(),
const Size2D &dilation = Size2D(1U, 1U),
const ActivationLayerInfo &act_info = ActivationLayerInfo(),
bool enable_fast_math = false,
unsigned int num_groups = 1);
/** Static function to check if given info will lead to a valid configuration of @ref NEGEMMConvolutionLayer
*
* @param[in] input 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: QASYMM8/QASYMM8_SIGNED/BFLOAT16/F16/F32.
* @param[in] weights Weights tensor info. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM].
* Data type supported: QASYMM8/QASYMM8_SIGNED/QSYMM8_PER_CHANNEL/BFLOAT16/F16/F32.
* @param[in] biases Biases tensor info. Shared biases supported. Biases are 1D tensor with dimensions [OFM].
* Data type supported: Should match @p input data type, except for input of QASYMM8/QASYMM8_SIGNED type where biases should be of S32 type.
* @param[in] output 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 input.
* @param[in] conv_info Contains padding and stride information described in @ref PadStrideInfo.
* @param[in] weights_info Specifies if the weights tensor has been reshaped with NEWeightsReshapeKernel. If this is not part of the fully connected layer the weights
* tensor has also been transposed with cpu::kernels::CpuGemmTranspose1xWKernel. Data type supported: Same as @p input.
* @param[in] dilation (Optional) Dilation, in elements, across x and y. Defaults to (1, 1).
* @param[in] act_info (Optional) Activation layer information in case of a fused activation. Only RELU, BOUNDED_RELU and LU_BOUNDED_RELU supported.
* @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
* @param[in] num_groups (Optional) Number of groups when performing a grouped convolution. num_groups != 1 is not supported
*
* @return a status
*/
static Status validate(const ITensorInfo *input,
const ITensorInfo *weights,
const ITensorInfo *biases,
const ITensorInfo *output,
const PadStrideInfo &conv_info,
const WeightsInfo &weights_info = WeightsInfo(),
const Size2D &dilation = Size2D(1U, 1U),
const ActivationLayerInfo &act_info = ActivationLayerInfo(),
bool enable_fast_math = false,
unsigned int num_groups = 1);
/** Static function to check if there is an optimized version of
* GEMM available for the input parameters.
*
* The method is intended to be used to find out the optimal
* memory layout to be used for the weights tensor when running
* variable weights execution.
*
* The user can query the database of optimised kernels in
* arm_gemm by specifying one of the enumerations of
* arm_compute::WeightFormat in the weight_format field of the input
* parameter weights_info. In case of success, the method
* writes the expected format in the output parameter
* expected_weight_format. The expected_weight_format can than be
* used in the configure method of the class for retrieving the
* best optimal kernel.
*
* Use case one - query for a specific format:
*
* WeightInfo weights_info(..., arm_compute::WeightFormat::OHWIo4, ...); // Set the value of the input query.
* if (NEGEMMConvolutionlayer::has_opt_impl(WeightFormat(), ...., weights_info, ...))
* {
* auto conv = std::unique_ptr<NEGEMMConvolutionlayer>();
* conv->configure(..., weights_info, ...); // uses the same WeightFormat the user wanted originally, OHWYo4.
* conv->run(...);
* }
*
* Use case two - query for any format that would be optimal for the GEMM to execute:
*
* WeightInfo weights_info(..., arm_compute::WeightFormat::ANY, ...); // Set the value of the input query.
* arm_compute::WeightFormat expected_wf;
* if (NEGEMMConvolutionlayer::has_opt_impl(expected_wf, ...., weights_info, ...))
* {
* auto conv = std::unique_ptr<NEGEMMConvolutionlayer>();
* // ... code to convert the layout of the weights tensor to the layout returned by has_opt_impl
* WeightInfo new_weights_info(..., expected_wf, ...); // Set the value of the WeightFormat returned by has_opt_impl.
* conv->configure(..., new_weights_info, ...);
* conv->run(...);
* }
*
* Notice that a GEMM configured with a WeightFormat other than
* UNSPECIFIED will run GEMM with variable weights mode.
*
* @param[out] expected_weight_format The arm_compute::WeightFormat expected by the kernel.
* @param[in] src Source tensor info.
* @param[in] weights Weights tensor info.
* @param[in] biases Biases tensor info. Shared biases supported.
* @param[in] dst Destination tensor info.
* @param[in] conv_info Contains padding and stride information described in @ref PadStrideInfo.
* @param[in] weights_info (optional) Specifies additional configuration parameters for the weights of the GEMM computation.
* @param[in] dilation (Optional) Dilation, in elements, across x and y. Defaults to (1, 1).
* @param[in] act_info (Optional) Activation layer information in case of a fused activation. Only RELU, BOUNDED_RELU and LU_BOUNDED_RELU supported. And no activation (i.e. Linear) which is the default value.
* @param[in] enable_fast_math (Optional) Enable fast math computation. In case this flag were set, the function could dispatch the fastest implementation
*
* @return a Status
*/
static Status has_opt_impl(arm_compute::WeightFormat &expected_weight_format,
const ITensorInfo *src,
const ITensorInfo *weights,
const ITensorInfo *biases,
const ITensorInfo *dst,
const PadStrideInfo &conv_info,
const WeightsInfo &weights_info = WeightsInfo(),
const Size2D &dilation = Size2D(1U, 1U),
const ActivationLayerInfo &act_info = ActivationLayerInfo(),
bool enable_fast_math = false);
// Inherited methods overridden:
void run() override;
void prepare() override;
private:
struct Impl;
std::unique_ptr<Impl> _impl;
};
} // namespace arm_compute
#endif /* ARM_COMPUTE_NEGEMMCONVOLUTIONLAYER_H */
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