--- /dev/null
+/*
+ * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
+ * 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.
+ */
+#ifndef __ARM_COMPUTE_CLRNN_LAYER_EX_H__
+#define __ARM_COMPUTE_CLRNN_LAYER_EX_H__
+
+#include "arm_compute/core/CL/kernels/CLActivationLayerKernel.h"
+#include "arm_compute/core/CL/kernels/CLCopyKernel.h"
+#include "arm_compute/core/CL/kernels/CLElementwiseOperationKernel.h"
+#include "arm_compute/runtime/CL/ICLSimpleFunction.h"
+#include "arm_compute/runtime/CL/functions/CLFullyConnectedLayer.h"
+#include "arm_compute/runtime/CL/functions/CLGEMM.h"
+
+namespace arm_compute
+{
+class ICLTensor;
+
+/** Basic function to run @ref CLRNNLayerEx */
+class CLRNNLayerEx : public IFunction
+{
+public:
+ /** Default constructor */
+ CLRNNLayerEx(std::shared_ptr<IMemoryManager> memory_manager = nullptr);
+ /** 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[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] 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:
+ CLMemoryGroup _memory_group;
+ CLGEMM _gemm_state_f;
+ CLSaturatedArithmeticOperationKernel _add_kernel;
+ CLActivationLayerKernel _activation_kernel;
+ CLFullyConnectedLayer _fully_connected_kernel;
+ CLCopyKernel _copy_kernel;
+ CLTensor _fully_connected_out;
+ CLTensor _gemm_output;
+ CLTensor _add_output;
+ bool _is_prepared;
+};
+}
+#endif /* __ARM_COMPUTE_CLRNN_LAYER_EX_H__ */
--- /dev/null
+/*
+ * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
+ * 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/CLRNNLayerEx.h"
+
+#include "arm_compute/core/Helpers.h"
+#include "arm_compute/core/Types.h"
+#include "arm_compute/core/Utils.h"
+#include "arm_compute/core/utils/misc/ShapeCalculator.h"
+#include "arm_compute/runtime/CL/CLScheduler.h"
+#include "support/ToolchainSupport.h"
+
+#include <utility>
+
+using namespace arm_compute;
+using namespace arm_compute::misc::shape_calculator;
+
+CLRNNLayerEx::CLRNNLayerEx(std::shared_ptr<IMemoryManager> memory_manager)
+ : _memory_group(std::move(memory_manager)), _gemm_state_f(), _add_kernel(),
+ _activation_kernel(), _fully_connected_kernel(), _copy_kernel(), _fully_connected_out(),
+ _gemm_output(), _add_output(), _is_prepared(false)
+{
+}
+
+Status CLRNNLayerEx::validate(const ITensorInfo *input, const ITensorInfo *weights,
+ const ITensorInfo *recurrent_weights, const ITensorInfo *bias,
+ const ITensorInfo *hidden_state, const ITensorInfo *output,
+ const ActivationLayerInfo &info)
+{
+ const int idx_width = 0;
+ const int idx_height = 1;
+ ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, weights, recurrent_weights, bias, hidden_state,
+ output);
+ ARM_COMPUTE_RETURN_ERROR_ON(input->dimension(idx_width) != weights->dimension(idx_width));
+ ARM_COMPUTE_RETURN_ERROR_ON(weights->dimension(idx_height) !=
+ recurrent_weights->dimension(idx_width));
+ ARM_COMPUTE_RETURN_ERROR_ON(recurrent_weights->dimension(idx_width) !=
+ recurrent_weights->dimension(1));
+ ARM_COMPUTE_RETURN_ERROR_ON(bias->num_dimensions() != 1);
+ ARM_COMPUTE_RETURN_ERROR_ON(bias->dimension(idx_width) != weights->dimension(idx_height));
+ ARM_COMPUTE_RETURN_ERROR_ON(hidden_state->dimension(idx_width) != weights->dimension(idx_height));
+ ARM_COMPUTE_RETURN_ERROR_ON(hidden_state->dimension(idx_height) != input->dimension(idx_height));
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(output->tensor_shape(),
+ hidden_state->tensor_shape());
+
+ auto shape_info =
+ TensorInfo(compute_rnn_shape(recurrent_weights, hidden_state->dimension(idx_height)), 1,
+ input->data_type());
+
+ ARM_COMPUTE_RETURN_ON_ERROR(CLFullyConnectedLayer::validate(input, weights, bias, &shape_info));
+ ARM_COMPUTE_RETURN_ON_ERROR(
+ CLGEMM::validate(hidden_state, recurrent_weights, nullptr, &shape_info, 1.f, 0.f));
+ ARM_COMPUTE_RETURN_ON_ERROR(CLSaturatedArithmeticOperationKernel::validate(
+ ArithmeticOperation::ADD, &shape_info, &shape_info, &shape_info, ConvertPolicy::SATURATE));
+ ARM_COMPUTE_RETURN_ON_ERROR(CLActivationLayerKernel::validate(&shape_info, &shape_info, info));
+
+ return Status{};
+}
+
+void CLRNNLayerEx::configure(const ICLTensor *input, const ICLTensor *weights,
+ const ICLTensor *recurrent_weights, const ICLTensor *bias,
+ ICLTensor *hidden_state, ICLTensor *output, ActivationLayerInfo &info)
+{
+ ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, recurrent_weights, bias, hidden_state, output);
+ ARM_COMPUTE_ERROR_THROW_ON(CLRNNLayerEx::validate(input->info(), weights->info(),
+ recurrent_weights->info(), bias->info(),
+ hidden_state->info(), output->info(), info));
+
+ const int idx_height = 1;
+ TensorShape shape =
+ compute_rnn_shape(recurrent_weights->info(), hidden_state->info()->dimension(idx_height));
+
+ _is_prepared = false;
+
+ _fully_connected_out.allocator()->init(TensorInfo(shape, 1, input->info()->data_type()));
+ _gemm_output.allocator()->init(TensorInfo(shape, 1, input->info()->data_type()));
+
+ // Manage intermediate buffers and configure
+ _memory_group.manage(&_fully_connected_out);
+ _fully_connected_kernel.configure(input, weights, bias, &_fully_connected_out);
+
+ _memory_group.manage(&_gemm_output);
+ _gemm_state_f.configure(hidden_state, recurrent_weights, nullptr, &_gemm_output, 1.f, 0.f);
+
+ _add_output.allocator()->init(TensorInfo(shape, 1, input->info()->data_type()));
+ _memory_group.manage(&_add_output);
+
+ _add_kernel.configure(ArithmeticOperation::ADD, &_fully_connected_out, &_gemm_output,
+ &_add_output, ConvertPolicy::SATURATE);
+
+ _fully_connected_out.allocator()->allocate();
+ _gemm_output.allocator()->allocate();
+
+ _activation_kernel.configure(&_add_output, hidden_state, info);
+ _add_output.allocator()->allocate();
+
+ _copy_kernel.configure(hidden_state, output);
+}
+
+void CLRNNLayerEx::run()
+{
+ prepare();
+
+ _memory_group.acquire();
+
+ _fully_connected_kernel.run();
+ _gemm_state_f.run();
+ CLScheduler::get().enqueue(_add_kernel);
+ CLScheduler::get().enqueue(_activation_kernel);
+
+ // copy hidden out to output
+ CLScheduler::get().enqueue(_copy_kernel);
+
+ _memory_group.release();
+}
+
+void CLRNNLayerEx::prepare()
+{
+ if (!_is_prepared)
+ {
+ _fully_connected_kernel.prepare();
+ _gemm_state_f.prepare();
+
+ _is_prepared = true;
+ }
+}
projects / platform / core / ml / nnfw.git / commitdiff