1 files changed, 146 insertions, 0 deletions
diff --git a/runtimes/libs/ARMComputeEx/src/runtime/NEON/functions/NERNNLayerEx.cpp b/runtimes/libs/ARMComputeEx/src/runtime/NEON/functions/NERNNLayerEx.cpp
new file mode 100644
index 000000000..84411c266
--- /dev/null
+++ b/runtimes/libs/ARMComputeEx/src/runtime/NEON/functions/NERNNLayerEx.cpp
@@ -0,0 +1,146 @@
+/*
+ * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
+ * 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/NEON/functions/NERNNLayerEx.h"
+
+#include "arm_compute/core/Error.h"
+#include "arm_compute/core/TensorInfo.h"
+#include "arm_compute/core/Types.h"
+#include "arm_compute/core/Validate.h"
+#include "arm_compute/core/utils/misc/ShapeCalculator.h"
+#include "arm_compute/runtime/NEON/NEScheduler.h"
+
+namespace arm_compute
+{
+NERNNLayerEx::NERNNLayerEx(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 NERNNLayerEx::validate(const ITensorInfo *input, const ITensorInfo *weights,
+                              const ITensorInfo *recurrent_weights, const ITensorInfo *bias,
+                              const ITensorInfo *hidden_state, const ITensorInfo *output,
+                              const ActivationLayerInfo &info)
+{
+  ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, weights, recurrent_weights, bias, hidden_state,
+                                      output);
+
+  const int idx_width = 0;
+  const int idx_height = 1;
+  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(idx_height));
+  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(misc::shape_calculator::compute_rnn_shape(
+                                   recurrent_weights, hidden_state->dimension(idx_height)),
+                               1, input->data_type());
+
+  ARM_COMPUTE_RETURN_ON_ERROR(NEFullyConnectedLayer::validate(input, weights, bias, &shape_info));
+  ARM_COMPUTE_RETURN_ON_ERROR(NEArithmeticAdditionKernel::validate(
+      &shape_info, &shape_info, &shape_info, ConvertPolicy::SATURATE));
+  ARM_COMPUTE_RETURN_ON_ERROR(NEActivationLayerKernel::validate(&shape_info, &shape_info, info));
+
+  return Status{};
+}
+
+void NERNNLayerEx::configure(const ITensor *input, const ITensor *weights,
+                             const ITensor *recurrent_weights, const ITensor *bias,
+                             ITensor *hidden_state, ITensor *output, ActivationLayerInfo &info)
+{
+  ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, recurrent_weights, bias, hidden_state, output);
+  ARM_COMPUTE_ERROR_THROW_ON(NERNNLayerEx::validate(input->info(), weights->info(),
+                                                    recurrent_weights->info(), bias->info(),
+                                                    hidden_state->info(), output->info(), info));
+
+  const int idx_height = 1;
+  TensorShape shape = misc::shape_calculator::compute_rnn_shape(
+      recurrent_weights->info(), hidden_state->info()->dimension(idx_height));
+
+  _is_prepared = false;
+
+  // Manage intermediate buffers and configure
+  _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(&_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 NERNNLayerEx::run()
+{
+  prepare();
+
+  MemoryGroupResourceScope scope_mg(_memory_group);
+
+  _fully_connected_kernel.run();
+
+  _gemm_state_f.run();
+
+  NEScheduler::get().schedule(&_add_kernel, Window::DimY);
+  NEScheduler::get().schedule(&_activation_kernel, Window::DimY);
+
+  // copy hidden out to output
+  NEScheduler::get().schedule(&_copy_kernel, Window::DimY);
+}
+
+void NERNNLayerEx::prepare()
+{
+  if (!_is_prepared)
+  {
+    _fully_connected_kernel.prepare();
+    _gemm_state_f.prepare();
+
+    _is_prepared = true;
+  }
+}
+} // namespace arm_compute