COMPMID-1067 NEON RNN FP32 / FP16

Change-Id: I440df2b2af512fd874651baf28428caa6f8e0b41
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/134433
Tested-by: Jenkins <bsgcomp@arm.com>
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>

1 files changed, 125 insertions, 0 deletions

diff --git a/src/runtime/NEON/functions/NERNNLayer.cpp b/src/runtime/NEON/functions/NERNNLayer.cpp
new file mode 100644
index 0000000000..08017e20c3
--- /dev/null
+++ b/src/runtime/NEON/functions/NERNNLayer.cpp
@@ -0,0 +1,125 @@
+/*
+ * 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/NEON/functions/NERNNLayer.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
+{
+NERNNLayer::NERNNLayer(std::shared_ptr<IMemoryManager> memory_manager)
+    : _memory_group(std::move(memory_manager)), _gemm_state_f(), _add_kernel(), _activation_kernel(), _fully_connected_kernel(), _fully_connected_out(), _gemm_output(), _add_output(), _hidden_state(),
+      _output()
+{
+}
+
+Status NERNNLayer::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  = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::WIDTH);
+    const int idx_height = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::HEIGHT);
+    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, true, false));
+    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 NERNNLayer::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(NERNNLayer::validate(input->info(), weights->info(), recurrent_weights->info(), bias->info(), hidden_state->info(), output->info(), info));
+
+    _hidden_state = hidden_state;
+    _output       = output;
+
+    const int   idx_height = get_data_layout_dimension_index(input->info()->data_layout(), DataLayoutDimension::HEIGHT);
+    TensorShape shape      = misc::shape_calculator::compute_rnn_shape(recurrent_weights->info(), hidden_state->info()->dimension(idx_height));
+
+    // Manage intermediate buffers and configure
+    _fully_connected_out.allocator()->init(TensorInfo(shape, 1, input->info()->data_type()));
+    _memory_group.manage(&_fully_connected_out);
+    _fully_connected_kernel.configure(input, weights, bias, &_fully_connected_out, true, false);
+
+    _gemm_output.allocator()->init(TensorInfo(shape, 1, input->info()->data_type()));
+    _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();
+}
+
+void NERNNLayer::run()
+{
+    _memory_group.acquire();
+
+    _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
+    Window hidden_state_window;
+    Window output_window;
+    hidden_state_window.use_tensor_dimensions(_hidden_state->info()->tensor_shape(), Window::DimY);
+    output_window.use_tensor_dimensions(_output->info()->tensor_shape(), Window::DimY);
+
+    Iterator hidden_state_it(_hidden_state, output_window);
+    Iterator output_it(_output, output_window);
+
+    execute_window_loop(output_window, [&](const Coordinates & id)
+    {
+        memcpy(output_it.ptr(), hidden_state_it.ptr(), _output->info()->dimension(0) * _output->info()->element_size());
+    },
+    hidden_state_it, output_it);
+
+    _memory_group.release();
+}
+} // namespace arm_compute