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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_CLRNN_LAYER_H
#define ARM_COMPUTE_CLRNN_LAYER_H
#include "arm_compute/runtime/CL/functions/CLActivationLayer.h"
#include "arm_compute/runtime/CL/functions/CLCopy.h"
#include "arm_compute/runtime/CL/functions/CLElementwiseOperations.h"
#include "arm_compute/runtime/CL/functions/CLFullyConnectedLayer.h"
#include "arm_compute/runtime/CL/functions/CLGEMM.h"
#include "arm_compute/runtime/CL/ICLSimpleFunction.h"
#include <memory>
namespace arm_compute
{
class ICLTensor;
/** Basic function to run @ref CLRNNLayer */
class CLRNNLayer : public IFunction
{
public:
/** Default constructor */
CLRNNLayer(std::shared_ptr<IMemoryManager> memory_manager = nullptr);
/** Prevent instances of this class from being copied */
CLRNNLayer(const CLRNNLayer &) = delete;
/** Prevent instances of this class from being copied */
CLRNNLayer &operator=(const CLRNNLayer &) = delete;
/** Default destructor */
~CLRNNLayer();
/** Initialize the function
*
* Valid data layouts:
* - NHWC
* - NCHW
*
* Valid data type configurations:
* |src0 |src1 |src2 |src3 |dst0 |dst1 |
* |:------|:------|:------|:------|:------|:------|
* |F16 |F16 |F16 |F16 |F16 |F16 |
* |F32 |F32 |F32 |F32 |F32 |F32 |
*
* @param[in] input Input is a 2-D tensor of shape [input_size, batch_size]. Data types supported: F16/F32
* @param[in] weights Weights tensor of shape [input_size, num_units] that multiplies the input. Data types supported: Same as @p input
* @param[in] recurrent_weights Weights tensor of shape [num_units, num_units] that multiplies the current 'state'. Data types supported: Same as @p input
* @param[in] bias Bias vector of shape [num_units]. Data types supported: Same as @p input
* @param[out] output Output tensor of shape [num_units, batch_size]. Data types supported: Same as @p input
* @param[in,out] hidden_state Output tensor of shape [num_units, batch_size]. Data types supported: Same as @p input
* @param[in] info Activation layer parameter.
*/
void configure(const ICLTensor *input,
const ICLTensor *weights,
const ICLTensor *recurrent_weights,
const ICLTensor *bias,
ICLTensor *hidden_state,
ICLTensor *output,
ActivationLayerInfo &info);
/** Initialize the function
*
* @param[in] compile_context The compile context to be used.
* @param[in] input Input is a 2-D tensor of shape [input_size, batch_size]. Data types supported: F16/F32
* @param[in] weights Weights tensor of shape [input_size, num_units] that multiplies the input. Data types supported: Same as @p input
* @param[in] recurrent_weights Weights tensor of shape [num_units, num_units] that multiplies the current 'state'. Data types supported: Same as @p input
* @param[in] bias Bias vector of shape [num_units]. Data types supported: Same as @p input
* @param[out] output Output tensor of shape [num_units, batch_size]. Data types supported: Same as @p input
* @param[in,out] hidden_state Output tensor of shape [num_units, batch_size]. Data types supported: Same as @p input
* @param[in] info Activation layer parameter.
*/
void configure(const CLCompileContext &compile_context,
const ICLTensor *input,
const ICLTensor *weights,
const ICLTensor *recurrent_weights,
const ICLTensor *bias,
ICLTensor *hidden_state,
ICLTensor *output,
ActivationLayerInfo &info);
/** Initialize the function
*
* @param[in] input Input is a 2-D tensor of shape [input_size, batch_size]. Data types supported: F16/F32
* @param[in] weights Weights tensor of shape [input_size, num_units] that multiplies the input. Data types supported: Same as @p input
* @param[in] recurrent_weights Weights tensor of shape [num_units, num_units] that multiplies the current 'state'. Data types supported: Same as @p input
* @param[in] bias Bias vector of shape [num_units]. Data types supported: Same as @p input
* @param[in] output Output tensor of shape [num_units, batch_size]. Data types supported: Same as @p input
* @param[in] hidden_state Output tensor of shape [num_units, batch_size]. Data types supported: Same as @p input
* @param[in] info Activation layer parameter.
*
* @return a status
*/
static Status validate(const ITensorInfo *input,
const ITensorInfo *weights,
const ITensorInfo *recurrent_weights,
const ITensorInfo *bias,
const ITensorInfo *hidden_state,
const ITensorInfo *output,
const ActivationLayerInfo &info);
// Inherited methods overridden:
void run() override;
void prepare() override;
private:
MemoryGroup _memory_group;
CLGEMM _gemm_state_f;
CLArithmeticAddition _add_kernel;
CLActivationLayer _activation;
CLFullyConnectedLayer _fully_connected_kernel;
CLCopy _copy;
CLTensor _fully_connected_out;
CLTensor _gemm_output;
CLTensor _add_output;
bool _is_prepared;
};
} // namespace arm_compute
#endif /* ARM_COMPUTE_CLRNN_LAYER_H */
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