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Commit 3d5f9f5e authored by Adam Procter's avatar Adam Procter
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De-Eigenize remaining arithmetic binops

parent 8aaebcb5
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src/ngraph/runtime/kernel/divide.hpp 0 → 100644
View file @ 3d5f9f5e
// ----------------------------------------------------------------------------
// Copyright 2017 Nervana Systems Inc.
// 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
// ----------------------------------------------------------------------------
#pragma once
namespace ngraph
{
namespace runtime
{
namespace kernel
{
template <typename T>
void divide(T* arg0, T* arg1, T* out, size_t count)
{
for (size_t i = 0; i < count; i++)
{
out[i] = arg0[i] / arg1[i];
}
}
}
}
}
src/ngraph/runtime/kernel/maximum.hpp 0 → 100644
View file @ 3d5f9f5e
// ----------------------------------------------------------------------------
// Copyright 2017 Nervana Systems Inc.
// 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
// ----------------------------------------------------------------------------
#pragma once
namespace ngraph
{
namespace runtime
{
namespace kernel
{
template <typename T>
void maximum(T* arg0, T* arg1, T* out, size_t count)
{
for (size_t i = 0; i < count; i++)
{
out[i] = arg0[i] > arg1[i] ? arg0[i] : arg1[i];
}
}
}
}
}
src/ngraph/runtime/kernel/minimum.hpp 0 → 100644
View file @ 3d5f9f5e
// ----------------------------------------------------------------------------
// Copyright 2017 Nervana Systems Inc.
// 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
// ----------------------------------------------------------------------------
#pragma once
namespace ngraph
{
namespace runtime
{
namespace kernel
{
template <typename T>
void minimum(T* arg0, T* arg1, T* out, size_t count)
{
for (size_t i = 0; i < count; i++)
{
out[i] = arg0[i] < arg1[i] ? arg0[i] : arg1[i];
}
}
}
}
}
src/ngraph/runtime/kernel/multiply.hpp 0 → 100644
View file @ 3d5f9f5e
// ----------------------------------------------------------------------------
// Copyright 2017 Nervana Systems Inc.
// 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
// ----------------------------------------------------------------------------
#pragma once
namespace ngraph
{
namespace runtime
{
namespace kernel
{
template <typename T>
void multiply(T* arg0, T* arg1, T* out, size_t count)
{
for (size_t i = 0; i < count; i++)
{
out[i] = arg0[i] * arg1[i];
}
}
}
}
}
src/ngraph/runtime/kernel/power.hpp 0 → 100644
View file @ 3d5f9f5e
// ----------------------------------------------------------------------------
// Copyright 2017 Nervana Systems Inc.
// 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
// ----------------------------------------------------------------------------
#pragma once
namespace ngraph
{
namespace runtime
{
namespace kernel
{
template <typename T>
void power(T* arg0, T* arg1, T* out, size_t count)
{
for (size_t i = 0; i < count; i++)
{
out[i] = std::pow(arg0[i],arg1[i]);
}
}
}
}
}
src/ngraph/runtime/kernel/subtract.hpp 0 → 100644
View file @ 3d5f9f5e
// ----------------------------------------------------------------------------
// Copyright 2017 Nervana Systems Inc.
// 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
// ----------------------------------------------------------------------------
#pragma once
namespace ngraph
{
namespace runtime
{
namespace kernel
{
template <typename T>
void subtract(T* arg0, T* arg1, T* out, size_t count)
{
for (size_t i = 0; i < count; i++)
{
out[i] = arg0[i] - arg1[i];
}
}
}
}
}
src/ngraph/runtime/ngvm/external_function.cpp
View file @ 3d5f9f5e
......@@ -87,7 +87,7 @@
#include "ngraph/runtime/ngvm/eigen/copy.hpp"
#include "ngraph/runtime/ngvm/instruction/cos.hpp"
#include "ngraph/runtime/ngvm/instruction/cosh.hpp"
#include "ngraph/runtime/ngvm/eigen/divide.hpp"
#include "ngraph/runtime/ngvm/instruction/divide.hpp"
#include "ngraph/runtime/ngvm/eigen/dot.hpp"
#include "ngraph/runtime/ngvm/eigen/equal.hpp"
#include "ngraph/runtime/ngvm/instruction/exp.hpp"
......@@ -101,13 +101,13 @@
#include "ngraph/runtime/ngvm/eigen/matrix_slice.hpp"
#include "ngraph/runtime/ngvm/eigen/matrix_transpose.hpp"
#include "ngraph/runtime/ngvm/eigen/matrix_vector_product.hpp"
#include "ngraph/runtime/ngvm/eigen/maximum.hpp"
#include "ngraph/runtime/ngvm/eigen/minimum.hpp"
#include "ngraph/runtime/ngvm/eigen/multiply.hpp"
#include "ngraph/runtime/ngvm/instruction/maximum.hpp"
#include "ngraph/runtime/ngvm/instruction/minimum.hpp"
#include "ngraph/runtime/ngvm/instruction/multiply.hpp"
#include "ngraph/runtime/ngvm/instruction/negate.hpp"
#include "ngraph/runtime/ngvm/eigen/not.hpp"
#include "ngraph/runtime/ngvm/eigen/not_equal.hpp"
#include "ngraph/runtime/ngvm/eigen/power.hpp"
#include "ngraph/runtime/ngvm/instruction/power.hpp"
#include "ngraph/runtime/ngvm/eigen/reduce_matrix_columns.hpp"
#include "ngraph/runtime/ngvm/eigen/reduce_matrix_rows.hpp"
#include "ngraph/runtime/ngvm/eigen/reduce_to_scalar.hpp"
......@@ -120,7 +120,7 @@
#include "ngraph/runtime/ngvm/instruction/sin.hpp"
#include "ngraph/runtime/ngvm/instruction/sinh.hpp"
#include "ngraph/runtime/ngvm/instruction/sqrt.hpp"
#include "ngraph/runtime/ngvm/eigen/subtract.hpp"
#include "ngraph/runtime/ngvm/instruction/subtract.hpp"
#include "ngraph/runtime/ngvm/eigen/sum_matrix_columns.hpp"
#include "ngraph/runtime/ngvm/eigen/sum_matrix_rows.hpp"
#include "ngraph/runtime/ngvm/eigen/sum_to_scalar.hpp"
......@@ -408,12 +408,12 @@ ExternalFunction::OpMap& ExternalFunction::get_op_map()
REGISTER_SIGNED_NUMERIC_UNOP(op::Abs, eigen::AbsInstruction);
REGISTER_NUMERIC_BINOP(op::Add, instruction::AddInstruction);
REGISTER_NUMERIC_BINOP(op::Divide, eigen::DivideInstruction);
REGISTER_NUMERIC_BINOP(op::Maximum, eigen::MaximumInstruction);
REGISTER_NUMERIC_BINOP(op::Minimum, eigen::MinimumInstruction);
REGISTER_NUMERIC_BINOP(op::Multiply, eigen::MultiplyInstruction);
REGISTER_NUMERIC_BINOP(op::Power, eigen::PowerInstruction);
REGISTER_NUMERIC_BINOP(op::Subtract, eigen::SubtractInstruction);
REGISTER_NUMERIC_BINOP(op::Divide, instruction::DivideInstruction);
REGISTER_NUMERIC_BINOP(op::Maximum, instruction::MaximumInstruction);
REGISTER_NUMERIC_BINOP(op::Minimum, instruction::MinimumInstruction);
REGISTER_NUMERIC_BINOP(op::Multiply, instruction::MultiplyInstruction);
REGISTER_NUMERIC_BINOP(op::Power, instruction::PowerInstruction);
REGISTER_NUMERIC_BINOP(op::Subtract, instruction::SubtractInstruction);
REGISTER_TO_OP_MAP(op::Constant)
{
......
src/ngraph/runtime/ngvm/eigen/divide.hpp → src/ngraph/runtime/ngvm/instruction/divide.hpp
View file @ 3d5f9f5e
......@@ -14,8 +14,9 @@
#pragma once
#include "ngraph/runtime/kernel/divide.hpp"
#include "ngraph/runtime/ngvm/call_frame.hpp"
#include "ngraph/runtime/ngvm/eigen/utils.hpp"
#include "ngraph/runtime/ngvm/utils.hpp"
#include "ngraph/runtime/ngvm/instruction.hpp"
#include "ngraph/runtime/tensor_view.hpp"
......@@ -25,15 +26,15 @@ namespace ngraph
{
namespace ngvm
{
namespace eigen
namespace instruction
{
template <typename ET>
class DivideInstruction : public Instruction
{
public:
DivideInstruction(const TensorViewInfo& arg0,
const TensorViewInfo& arg1,
const TensorViewInfo& out)
const TensorViewInfo& arg1,
const TensorViewInfo& out)
: m_arg0(arg0)
, m_arg1(arg1)
, m_out(out)
......@@ -42,8 +43,13 @@ namespace ngraph
virtual void execute(CallFrame& call_frame) const override
{
EigenArray1d<ET>(call_frame, m_out) = EigenArray1d<ET>(call_frame, m_arg0) /
EigenArray1d<ET>(call_frame, m_arg1);
typename ET::type* arg0 = get_tensor_data_ptr<ET>(call_frame, m_arg0);
typename ET::type* arg1 = get_tensor_data_ptr<ET>(call_frame, m_arg1);
typename ET::type* out = get_tensor_data_ptr<ET>(call_frame, m_out);
size_t count = get_tensor_element_count(call_frame, m_arg0);
kernel::divide<typename ET::type>(arg0, arg1, out, count);
}
protected:
......
src/ngraph/runtime/ngvm/eigen/maximum.hpp → src/ngraph/runtime/ngvm/instruction/maximum.hpp
View file @ 3d5f9f5e
......@@ -14,8 +14,9 @@
#pragma once
#include "ngraph/runtime/kernel/maximum.hpp"
#include "ngraph/runtime/ngvm/call_frame.hpp"
#include "ngraph/runtime/ngvm/eigen/utils.hpp"
#include "ngraph/runtime/ngvm/utils.hpp"
#include "ngraph/runtime/ngvm/instruction.hpp"
#include "ngraph/runtime/tensor_view.hpp"
......@@ -25,13 +26,15 @@ namespace ngraph
{
namespace ngvm
{
namespace eigen
namespace instruction
{
template <typename ET>
class MaximumInstruction : public Instruction
{
public:
MaximumInstruction(TensorViewInfo arg0, TensorViewInfo arg1, TensorViewInfo out)
MaximumInstruction(const TensorViewInfo& arg0,
const TensorViewInfo& arg1,
const TensorViewInfo& out)
: m_arg0(arg0)
, m_arg1(arg1)
, m_out(out)
......@@ -40,9 +43,13 @@ namespace ngraph
virtual void execute(CallFrame& call_frame) const override
{
EigenArray1d<ET>(call_frame, m_out) =
EigenArray1d<ET>(call_frame, m_arg0)
.max(EigenArray1d<ET>(call_frame, m_arg1));
typename ET::type* arg0 = get_tensor_data_ptr<ET>(call_frame, m_arg0);
typename ET::type* arg1 = get_tensor_data_ptr<ET>(call_frame, m_arg1);
typename ET::type* out = get_tensor_data_ptr<ET>(call_frame, m_out);
size_t count = get_tensor_element_count(call_frame, m_arg0);
kernel::maximum<typename ET::type>(arg0, arg1, out, count);
}
protected:
......
src/ngraph/runtime/ngvm/eigen/minimum.hpp → src/ngraph/runtime/ngvm/instruction/minimum.hpp
View file @ 3d5f9f5e
......@@ -14,8 +14,9 @@
#pragma once
#include "ngraph/runtime/kernel/minimum.hpp"
#include "ngraph/runtime/ngvm/call_frame.hpp"
#include "ngraph/runtime/ngvm/eigen/utils.hpp"
#include "ngraph/runtime/ngvm/utils.hpp"
#include "ngraph/runtime/ngvm/instruction.hpp"
#include "ngraph/runtime/tensor_view.hpp"
......@@ -25,13 +26,15 @@ namespace ngraph
{
namespace ngvm
{
namespace eigen
namespace instruction
{
template <typename ET>
class MinimumInstruction : public Instruction
{
public:
MinimumInstruction(TensorViewInfo arg0, TensorViewInfo arg1, TensorViewInfo out)
MinimumInstruction(const TensorViewInfo& arg0,
const TensorViewInfo& arg1,
const TensorViewInfo& out)
: m_arg0(arg0)
, m_arg1(arg1)
, m_out(out)
......@@ -40,9 +43,13 @@ namespace ngraph
virtual void execute(CallFrame& call_frame) const override
{
EigenArray1d<ET>(call_frame, m_out) =
EigenArray1d<ET>(call_frame, m_arg0)
.min(EigenArray1d<ET>(call_frame, m_arg1));
typename ET::type* arg0 = get_tensor_data_ptr<ET>(call_frame, m_arg0);
typename ET::type* arg1 = get_tensor_data_ptr<ET>(call_frame, m_arg1);
typename ET::type* out = get_tensor_data_ptr<ET>(call_frame, m_out);
size_t count = get_tensor_element_count(call_frame, m_arg0);
kernel::minimum<typename ET::type>(arg0, arg1, out, count);
}
protected:
......
src/ngraph/runtime/ngvm/eigen/multiply.hpp → src/ngraph/runtime/ngvm/instruction/multiply.hpp
View file @ 3d5f9f5e
......@@ -14,9 +14,11 @@
#pragma once
#include "ngraph/runtime/kernel/multiply.hpp"
#include "ngraph/runtime/ngvm/call_frame.hpp"
#include "ngraph/runtime/ngvm/eigen/utils.hpp"
#include "ngraph/runtime/ngvm/utils.hpp"
#include "ngraph/runtime/ngvm/instruction.hpp"
#include "ngraph/runtime/tensor_view.hpp"
namespace ngraph
{
......@@ -24,15 +26,15 @@ namespace ngraph
{
namespace ngvm
{
namespace eigen
namespace instruction
{
template <typename ET>
class MultiplyInstruction : public Instruction
{
public:
MultiplyInstruction(TensorViewInfo arg0,
TensorViewInfo arg1,
TensorViewInfo out)
MultiplyInstruction(const TensorViewInfo& arg0,
const TensorViewInfo& arg1,
const TensorViewInfo& out)
: m_arg0(arg0)
, m_arg1(arg1)
, m_out(out)
......@@ -41,8 +43,13 @@ namespace ngraph
virtual void execute(CallFrame& call_frame) const override
{
EigenArray1d<ET>(call_frame, m_out) = EigenArray1d<ET>(call_frame, m_arg0) *
EigenArray1d<ET>(call_frame, m_arg1);
typename ET::type* arg0 = get_tensor_data_ptr<ET>(call_frame, m_arg0);
typename ET::type* arg1 = get_tensor_data_ptr<ET>(call_frame, m_arg1);
typename ET::type* out = get_tensor_data_ptr<ET>(call_frame, m_out);
size_t count = get_tensor_element_count(call_frame, m_arg0);
kernel::multiply<typename ET::type>(arg0, arg1, out, count);
}
protected:
......
src/ngraph/runtime/ngvm/eigen/power.hpp → src/ngraph/runtime/ngvm/instruction/power.hpp
View file @ 3d5f9f5e
......@@ -14,8 +14,9 @@
#pragma once
#include "ngraph/runtime/kernel/power.hpp"
#include "ngraph/runtime/ngvm/call_frame.hpp"
#include "ngraph/runtime/ngvm/eigen/utils.hpp"
#include "ngraph/runtime/ngvm/utils.hpp"
#include "ngraph/runtime/ngvm/instruction.hpp"
#include "ngraph/runtime/tensor_view.hpp"
......@@ -25,15 +26,15 @@ namespace ngraph
{
namespace ngvm
{
namespace eigen
namespace instruction
{
template <typename ET>
class PowerInstruction : public Instruction
{
public:
PowerInstruction(const TensorViewInfo& arg0,
const TensorViewInfo& arg1,
const TensorViewInfo& out)
const TensorViewInfo& arg1,
const TensorViewInfo& out)
: m_arg0(arg0)
, m_arg1(arg1)
, m_out(out)
......@@ -42,9 +43,13 @@ namespace ngraph
virtual void execute(CallFrame& call_frame) const override
{
EigenArray1d<ET>(call_frame, m_out) =
EigenArray1d<ET>(call_frame, m_arg0)
.pow(EigenArray1d<ET>(call_frame, m_arg1));
typename ET::type* arg0 = get_tensor_data_ptr<ET>(call_frame, m_arg0);
typename ET::type* arg1 = get_tensor_data_ptr<ET>(call_frame, m_arg1);
typename ET::type* out = get_tensor_data_ptr<ET>(call_frame, m_out);
size_t count = get_tensor_element_count(call_frame, m_arg0);
kernel::power<typename ET::type>(arg0, arg1, out, count);
}
protected:
......
src/ngraph/runtime/ngvm/eigen/subtract.hpp → src/ngraph/runtime/ngvm/instruction/subtract.hpp
View file @ 3d5f9f5e
......@@ -14,8 +14,9 @@
#pragma once
#include "ngraph/runtime/kernel/subtract.hpp"
#include "ngraph/runtime/ngvm/call_frame.hpp"
#include "ngraph/runtime/ngvm/eigen/utils.hpp"
#include "ngraph/runtime/ngvm/utils.hpp"
#include "ngraph/runtime/ngvm/instruction.hpp"
#include "ngraph/runtime/tensor_view.hpp"
......@@ -25,15 +26,15 @@ namespace ngraph
{
namespace ngvm
{
namespace eigen
namespace instruction
{
template <typename ET>
class SubtractInstruction : public Instruction
{
public:
SubtractInstruction(TensorViewInfo arg0,
TensorViewInfo arg1,
TensorViewInfo out)
SubtractInstruction(const TensorViewInfo& arg0,
const TensorViewInfo& arg1,
const TensorViewInfo& out)
: m_arg0(arg0)
, m_arg1(arg1)
, m_out(out)
......@@ -42,8 +43,13 @@ namespace ngraph
virtual void execute(CallFrame& call_frame) const override
{
EigenArray1d<ET>(call_frame, m_out) = EigenArray1d<ET>(call_frame, m_arg0) -
EigenArray1d<ET>(call_frame, m_arg1);
typename ET::type* arg0 = get_tensor_data_ptr<ET>(call_frame, m_arg0);
typename ET::type* arg1 = get_tensor_data_ptr<ET>(call_frame, m_arg1);
typename ET::type* out = get_tensor_data_ptr<ET>(call_frame, m_out);
size_t count = get_tensor_element_count(call_frame, m_arg0);
kernel::subtract<typename ET::type>(arg0, arg1, out, count);
}
protected:
......
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