CPU Direct Execution: Implement Concat

src/ngraph/runtime/cpu/builder/concat.cpp

+/*******************************************************************************
+* Copyright 2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#include "ngraph/op/concat.hpp"
+#include "ngraph/runtime/cpu/cpu_builder.hpp"
+#include "ngraph/runtime/cpu/kernel/concat.hpp"
+
+using namespace std;
+using namespace ngraph;
+
+namespace ngraph
+{
+    namespace runtime
+    {
+        namespace cpu
+        {
+            template <>
+            void Builder::BUILDER_DECL(ngraph::op::Concat)
+            {
+                auto axis =
+                    (dynamic_cast<const ngraph::op::Concat*>(node))->get_concatenation_axis();
+
+                auto& functors = external_function->get_functors();
+                auto& tensor_data = external_function->get_tensor_data();
+                std::function<decltype(runtime::cpu::kernel::concat<float, 1>)> kernel;
+
+                SELECT_KERNEL_BY_RANK(kernel,
+                                      out[0].get_element_type(),
+                                      out[0].get_shape().size(),
+                                      runtime::cpu::kernel::concat);
+
+                vector<reference_wrapper<void*>> arg_tensors;
+                vector<Shape> arg_shapes;
+                for (auto& arg : args)
+                {
+                    if (shape_size(arg.get_shape()))
+                    {
+                        arg_tensors.emplace_back(tensor_data[arg.get_name()]);
+                        arg_shapes.emplace_back(arg.get_shape());
+                    }
+                }
+
+                auto& out_tensor = tensor_data[out[0].get_name()];
+                auto out_shape = out[0].get_shape();
+
+                auto functor =
+                    [&, kernel, arg_tensors, arg_shapes, out_shape, axis](CPURuntimeContext* ctx) {
+                        kernel(arg_tensors, arg_shapes, out_tensor, out_shape, axis);
+                    };
+                functors.emplace_back(functor);
+            }
+
+            REGISTER_OP_BUILDER(Concat);
+        }
+    }
+}

src/ngraph/runtime/cpu/kernel/concat.hpp

+/*******************************************************************************
+* Copyright 2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#pragma once
+#include <functional>
+#include <iostream>
+#include <vector>
+
+#define EIGEN_USE_THREADS
+#include <unsupported/Eigen/CXX11/Tensor>
+
+#include "ngraph/runtime/cpu/kernel/eigen_thread_pool.hpp"
+#include "ngraph/shape.hpp"
+
+namespace ngraph
+{
+    namespace runtime
+    {
+        namespace cpu
+        {
+            namespace kernel
+            {
+                template <typename ElementType, int Rank>
+                void concat(std::vector<std::reference_wrapper<void*>> inputs,
+                            std::vector<Shape> input_shapes,
+                            void* output,
+                            Shape output_shape,
+                            size_t axis)
+                {
+                    Eigen::array<Eigen::Index, Rank> out_dims;
+                    for (int i = 0; i < Rank; i++)
+                    {
+                        out_dims[i] = output_shape[i];
+                    }
+                    Eigen::TensorMap<Eigen::Tensor<ElementType, Rank, Eigen::RowMajor>> out(
+                        static_cast<ElementType*>(output), out_dims);
+
+                    Eigen::array<Eigen::Index, Rank> in_dims, concat_pos;
+                    concat_pos.fill(0);
+
+                    for (int i = 0; i < input_shapes.size(); i++)
+                    {
+                        for (int j = 0; j < Rank; j++)
+                        {
+                            in_dims[j] = input_shapes[i][j];
+                        }
+
+                        Eigen::TensorMap<Eigen::Tensor<ElementType, Rank, Eigen::RowMajor>> in(
+                            static_cast<ElementType*>(inputs[i].get()), in_dims);
+                        out.slice(concat_pos, in_dims).device(eigen::global_thread_pool_device) =
+                            in;
+                        concat_pos[axis] += in_dims[axis];
+                    }
+                }
+            }
+        }
+    }
+}