Commit 95279e32 authored by Jaikrishnan Menon's avatar Jaikrishnan Menon

CPU Direct Execution: Implement logical and relational op kernels

parent 5dc3867b
/*******************************************************************************
* 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 ElementType>
void equal(void* input0, void* input1, 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<char, 1, Eigen::RowMajor>> out(
static_cast<char*>(output), out_dims);
Eigen::TensorMap<Eigen::Tensor<ElementType, 1, Eigen::RowMajor>> in0(
static_cast<ElementType*>(input0), in_dims);
Eigen::TensorMap<Eigen::Tensor<ElementType, 1, Eigen::RowMajor>> in1(
static_cast<ElementType*>(input1), in_dims);
out.device(eigen::global_thread_pool_device) =
(in0 == in1).template cast<char>();
}
}
}
}
}
/*******************************************************************************
* 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 ElementType>
void greater(void* input0, void* input1, 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<char, 1, Eigen::RowMajor>> out(
static_cast<char*>(output), out_dims);
Eigen::TensorMap<Eigen::Tensor<ElementType, 1, Eigen::RowMajor>> in0(
static_cast<ElementType*>(input0), in_dims);
Eigen::TensorMap<Eigen::Tensor<ElementType, 1, Eigen::RowMajor>> in1(
static_cast<ElementType*>(input1), in_dims);
out.device(eigen::global_thread_pool_device) =
(in0 > in1).template cast<char>();
}
}
}
}
}
/*******************************************************************************
* 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 ElementType>
void greater_eq(void* input0, void* input1, 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<char, 1, Eigen::RowMajor>> out(
static_cast<char*>(output), out_dims);
Eigen::TensorMap<Eigen::Tensor<ElementType, 1, Eigen::RowMajor>> in0(
static_cast<ElementType*>(input0), in_dims);
Eigen::TensorMap<Eigen::Tensor<ElementType, 1, Eigen::RowMajor>> in1(
static_cast<ElementType*>(input1), in_dims);
out.device(eigen::global_thread_pool_device) =
(in0 >= in1).template cast<char>();
}
}
}
}
}
/*******************************************************************************
* 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 ElementType>
void less(void* input0, void* input1, 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<char, 1, Eigen::RowMajor>> out(
static_cast<char*>(output), out_dims);
Eigen::TensorMap<Eigen::Tensor<ElementType, 1, Eigen::RowMajor>> in0(
static_cast<ElementType*>(input0), in_dims);
Eigen::TensorMap<Eigen::Tensor<ElementType, 1, Eigen::RowMajor>> in1(
static_cast<ElementType*>(input1), in_dims);
out.device(eigen::global_thread_pool_device) =
(in0 < in1).template cast<char>();
}
}
}
}
}
/*******************************************************************************
* 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 ElementType>
void less_eq(void* input0, void* input1, 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<char, 1, Eigen::RowMajor>> out(
static_cast<char*>(output), out_dims);
Eigen::TensorMap<Eigen::Tensor<ElementType, 1, Eigen::RowMajor>> in0(
static_cast<ElementType*>(input0), in_dims);
Eigen::TensorMap<Eigen::Tensor<ElementType, 1, Eigen::RowMajor>> in1(
static_cast<ElementType*>(input1), in_dims);
out.device(eigen::global_thread_pool_device) =
(in0 <= in1).template cast<char>();
}
}
}
}
}
/*******************************************************************************
* 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 ElementType>
void log(void* input0, 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<ElementType, 1, Eigen::RowMajor>> out(
static_cast<ElementType*>(output), out_dims);
Eigen::TensorMap<Eigen::Tensor<ElementType, 1, Eigen::RowMajor>> in0(
static_cast<ElementType*>(input0), in_dims);
out.device(eigen::global_thread_pool_device) = in0.log();
}
}
}
}
}
/*******************************************************************************
* 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 ElementType>
void logical_not(void* input0, 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<char, 1, Eigen::RowMajor>> out(
static_cast<char*>(output), out_dims);
Eigen::TensorMap<Eigen::Tensor<ElementType, 1, Eigen::RowMajor>> in0(
static_cast<ElementType*>(input0), in_dims);
out.device(eigen::global_thread_pool_device) =
(in0 == ElementType(0)).template cast<char>();
}
}
}
}
}
/*******************************************************************************
* 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 ElementType>
void not_equal(void* input0, void* input1, 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<char, 1, Eigen::RowMajor>> out(
static_cast<char*>(output), out_dims);
Eigen::TensorMap<Eigen::Tensor<ElementType, 1, Eigen::RowMajor>> in0(
static_cast<ElementType*>(input0), in_dims);
Eigen::TensorMap<Eigen::Tensor<ElementType, 1, Eigen::RowMajor>> in1(
static_cast<ElementType*>(input1), in_dims);
out.device(eigen::global_thread_pool_device) =
(in0 != in1).template cast<char>();
}
}
}
}
}
/*******************************************************************************
* 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 ElementType>
void subtract(void* input0, void* input1, 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<ElementType, 1, Eigen::RowMajor>> out(
static_cast<ElementType*>(output), out_dims);
Eigen::TensorMap<Eigen::Tensor<ElementType, 1, Eigen::RowMajor>> in0(
static_cast<ElementType*>(input0), in_dims);
Eigen::TensorMap<Eigen::Tensor<ElementType, 1, Eigen::RowMajor>> in1(
static_cast<ElementType*>(input1), in_dims);
out.device(eigen::global_thread_pool_device) = in0 - in1;
}
}
}
}
}
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