COMPMID-992 Implement CL RNN function

Change-Id: I8dbada5fabedbb8523e433ba73d504bd15b81466
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/125787
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>
Tested-by: Jenkins <bsgcomp@arm.com>

1 files changed, 109 insertions, 0 deletions

diff --git a/src/runtime/CL/functions/CLRNNLayer.cpp b/src/runtime/CL/functions/CLRNNLayer.cpp
new file mode 100644
index 0000000000..75eac0959f
--- /dev/null
+++ b/src/runtime/CL/functions/CLRNNLayer.cpp
@@ -0,0 +1,109 @@
+/*
+ * 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/CLRNNLayer.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;
+
+CLRNNLayer::CLRNNLayer(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()
+{
+}
+
+Status CLRNNLayer::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  = 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_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, true, false));
+    ARM_COMPUTE_RETURN_ON_ERROR(CLGEMM::validate(hidden_state, recurrent_weights, nullptr, &shape_info, 1.f, 0.f));
+    ARM_COMPUTE_RETURN_ON_ERROR(CLArithmeticAdditionKernel::validate(&shape_info, &shape_info, &shape_info, ConvertPolicy::SATURATE));
+    ARM_COMPUTE_RETURN_ON_ERROR(CLActivationLayerKernel::validate(&shape_info, &shape_info, info));
+
+    return Status{};
+}
+
+void CLRNNLayer::configure(const ICLTensor *input, const ICLTensor *weights, const ICLTensor *recurrent_weights, const ICLTensor *bias, ICLTensor *hidden_state, ICLTensor *output,
+                           ActivationLayerInfo &info)
+{
+    const int   idx_height = get_data_layout_dimension_index(input->info()->data_layout(), DataLayoutDimension::HEIGHT);
+    TensorShape shape      = compute_rnn_shape(recurrent_weights->info(), hidden_state->info()->dimension(idx_height));
+
+    _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, true, false);
+
+    _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 CLRNNLayer::run()
+{
+    _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();
+}
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