From 36a559e49a3d5b832b1cffd47f2298f452616bb9 Mon Sep 17 00:00:00 2001 From: Michalis Spyrou Date: Tue, 20 Mar 2018 10:30:58 +0000 Subject: COMPMID-992 Implement CL RNN function Change-Id: I8dbada5fabedbb8523e433ba73d504bd15b81466 Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/125787 Reviewed-by: Georgios Pinitas Tested-by: Jenkins --- src/runtime/CL/functions/CLRNNLayer.cpp | 109 ++++++++++++++++++++++++++++++++ 1 file changed, 109 insertions(+) create mode 100644 src/runtime/CL/functions/CLRNNLayer.cpp (limited to 'src/runtime/CL/functions/CLRNNLayer.cpp') 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 + +using namespace arm_compute; +using namespace arm_compute::misc::shape_calculator; + +CLRNNLayer::CLRNNLayer(std::shared_ptr 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(); +} \ No newline at end of file -- cgit v1.2.1