CPU Direct Execution: Implement tensor type conversions

src/ngraph/runtime/cpu/builder/convert.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/convert.hpp"
+#include "ngraph/runtime/cpu/cpu_builder.hpp"
+#include "ngraph/runtime/cpu/kernel/convert.hpp"
+
+using namespace std;
+using namespace ngraph;
+
+namespace ngraph
+{
+    namespace runtime
+    {
+        namespace cpu
+        {
+            template <>
+            void Builder::BUILDER_DECL(ngraph::op::Convert)
+            {
+                auto& functors = external_function->get_functors();
+                auto& tensor_data = external_function->get_tensor_data();
+
+                auto& arg_tensor = tensor_data[args[0].get_name()];
+                auto& out_tensor = tensor_data[out[0].get_name()];
+
+                auto element_count = out[0].get_size();
+
+                std::function<decltype(runtime::cpu::kernel::convert<float, int>)> kernel;
+
+                if (out[0].get_element_type() == element::boolean)
+                {
+                    SELECT_KERNEL(
+                        kernel, args[0].get_element_type(), runtime::cpu::kernel::convert_to_i8);
+                }
+                else if (out[0].get_element_type() == element::f32)
+                {
+                    SELECT_KERNEL(kernel,
+                                  args[0].get_element_type(),
+                                  runtime::cpu::kernel::convert_to_float32);
+                }
+                else if (out[0].get_element_type() == element::f64)
+                {
+                    SELECT_KERNEL(kernel,
+                                  args[0].get_element_type(),
+                                  runtime::cpu::kernel::convert_to_float64);
+                }
+                else if (out[0].get_element_type() == element::i8)
+                {
+                    SELECT_KERNEL(
+                        kernel, args[0].get_element_type(), runtime::cpu::kernel::convert_to_i8);
+                }
+                else if (out[0].get_element_type() == element::i16)
+                {
+                    SELECT_KERNEL(
+                        kernel, args[0].get_element_type(), runtime::cpu::kernel::convert_to_i16);
+                }
+                else if (out[0].get_element_type() == element::i32)
+                {
+                    SELECT_KERNEL(
+                        kernel, args[0].get_element_type(), runtime::cpu::kernel::convert_to_i32);
+                }
+                else if (out[0].get_element_type() == element::i64)
+                {
+                    SELECT_KERNEL(
+                        kernel, args[0].get_element_type(), runtime::cpu::kernel::convert_to_i64);
+                }
+                else if (out[0].get_element_type() == element::u8)
+                {
+                    SELECT_KERNEL(
+                        kernel, args[0].get_element_type(), runtime::cpu::kernel::convert_to_u8);
+                }
+                else if (out[0].get_element_type() == element::u16)
+                {
+                    SELECT_KERNEL(
+                        kernel, args[0].get_element_type(), runtime::cpu::kernel::convert_to_u16);
+                }
+                else if (out[0].get_element_type() == element::u32)
+                {
+                    SELECT_KERNEL(
+                        kernel, args[0].get_element_type(), runtime::cpu::kernel::convert_to_u32);
+                }
+                else if (out[0].get_element_type() == element::u64)
+                {
+                    SELECT_KERNEL(
+                        kernel, args[0].get_element_type(), runtime::cpu::kernel::convert_to_u64);
+                }
+                else
+                {
+                    throw ngraph_error("Cannot convert from an invalid input element type");
+                }
+
+                auto functor = [&, kernel, element_count](CPURuntimeContext* ctx) {
+                    kernel(arg_tensor, out_tensor, element_count);
+                };
+                functors.emplace_back(functor);
+            }
+
+            REGISTER_OP_BUILDER(Convert);
+        }
+    }
+}

src/ngraph/runtime/cpu/kernel/convert.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
+
+#define EIGEN_USE_THREADS
+#include <unsupported/Eigen/CXX11/Tensor>
+
+#include "ngraph/runtime/cpu/kernel/eigen_thread_pool.hpp"
+
+namespace ngraph
+{
+    namespace runtime
+    {
+        namespace cpu
+        {
+            namespace kernel
+            {
+                template <typename InputElementType, typename OutputElementType>
+                void convert(void* input, void* output, size_t count)
+                {
+                    Eigen::array<Eigen::Index, 1> out_dims, in_dims;
+
+                    out_dims[0] = in_dims[0] = count;
+
+                    Eigen::TensorMap<Eigen::Tensor<OutputElementType, 1, Eigen::RowMajor>> out(
+                        static_cast<OutputElementType*>(output), out_dims);
+                    Eigen::TensorMap<Eigen::Tensor<InputElementType, 1, Eigen::RowMajor>> in(
+                        static_cast<InputElementType*>(input), in_dims);
+
+                    out.device(eigen::global_thread_pool_device) =
+                        in.template cast<OutputElementType>();
+                }
+
+                template <typename InputElementType>
+                void convert_to_float32(void* input, void* output, size_t count)
+                {
+                    convert<InputElementType, float>(input, output, count);
+                }
+
+                template <typename InputElementType>
+                void convert_to_float64(void* input, void* output, size_t count)
+                {
+                    convert<InputElementType, double>(input, output, count);
+                }
+
+                template <typename InputElementType>
+                void convert_to_i8(void* input, void* output, size_t count)
+                {
+                    convert<InputElementType, int8_t>(input, output, count);
+                }
+
+                template <typename InputElementType>
+                void convert_to_i16(void* input, void* output, size_t count)
+                {
+                    convert<InputElementType, int16_t>(input, output, count);
+                }
+
+                template <typename InputElementType>
+                void convert_to_i32(void* input, void* output, size_t count)
+                {
+                    convert<InputElementType, int32_t>(input, output, count);
+                }
+
+                template <typename InputElementType>
+                void convert_to_i64(void* input, void* output, size_t count)
+                {
+                    convert<InputElementType, int64_t>(input, output, count);
+                }
+
+                template <typename InputElementType>
+                void convert_to_u8(void* input, void* output, size_t count)
+                {
+                    convert<InputElementType, uint8_t>(input, output, count);
+                }
+
+                template <typename InputElementType>
+                void convert_to_u16(void* input, void* output, size_t count)
+                {
+                    convert<InputElementType, uint16_t>(input, output, count);
+                }
+
+                template <typename InputElementType>
+                void convert_to_u32(void* input, void* output, size_t count)
+                {
+                    convert<InputElementType, uint32_t>(input, output, count);
+                }
+
+                template <typename InputElementType>
+                void convert_to_u64(void* input, void* output, size_t count)
+                {
+                    convert<InputElementType, uint64_t>(input, output, count);
+                }
+            }
+        }
+    }
+}