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ngraph
Commit 6b1ea2a1 authored by Robert Kimball's avatar Robert Kimball Committed by Scott Cyphers
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remove unused files (#143)

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graveyard/ngraph.cpp deleted 100644 → 0
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// ----------------------------------------------------------------------------
// 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
// ----------------------------------------------------------------------------
#include "ngraph.hpp"
#include "log.hpp"
NGraph* create_ngraph_object()
{
return new NGraph();
}
void destroy_ngraph_object(NGraph* pObj)
{
delete pObj;
}
void NGraph::add_params(const std::vector<std::string>& paramList)
{
NGRAPH_INFO << "Adding parameters";
m_params.insert(m_params.end(), paramList.begin(), paramList.end());
}
const std::vector<std::string>& NGraph::get_params() const
{
return m_params;
}
graveyard/ngraph.hpp deleted 100644 → 0
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#pragma once
// ----------------------------------------------------------------------------
// 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
// ----------------------------------------------------------------------------
#include <string>
#include <vector>
class NGraph
{
public:
void add_params(const std::vector<std::string>& paramList);
const std::vector<std::string>& get_params() const;
std::string get_name() const { return "NGraph Implementation Object"; }
private:
std::vector<std::string> m_params;
};
// Factory methods
extern "C" NGraph* create_ngraph_object();
extern "C" void destroy_ngraph_object(NGraph* pObj);
// FUnction pointers to the factory methods
typedef NGraph* (*CreateNGraphObjPfn)();
typedef void (*DestroyNGraphObjPfn)(NGraph*);
graveyard/src/names.cpp deleted 100644 → 0
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// ----------------------------------------------------------------------------
// 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
// ----------------------------------------------------------------------------
#include <sstream>
#include "names.hpp"
using namespace ngraph;
size_t NameableValue::__counter = 0;
std::map<std::string, NameableValue> NameableValue::__all_names;
NameableValue::NameableValue(const std::string& name,
const std::string& graph_label_type,
const std::string& doc_string)
: m_name{name}
, m_doc_string{doc_string}
{
auto glt = m_name;
if (graph_label_type.size() > 0)
{
glt = graph_label_type;
}
{
std::stringstream ss;
ss << glt << "[" << m_name << "]";
m_graph_label = ss.str();
}
}
const std::string& NameableValue::graph_label()
{
return m_graph_label;
}
const std::string& NameableValue::name()
{
return m_name;
}
void NameableValue::name(const std::string& name)
{
// if name == type(self).__name__ or name in NameableValue.__all_names:
// while True:
// c_name = "{}_{}".format(name, type(self).__counter)
// if c_name not in NameableValue.__all_names:
// name = c_name
// break
// type(self).__counter += 1
// NameableValue.__all_names[name] = self
// self.__name = name
}
const std::string& NameableValue::short_name()
{
// sn = self.name.split('_')[0]
// if sn.find('.') != -1:
// sn = sn.split('.')[1]
// return sn
static const std::string x = "unimplemented";
return x;
}
NameableValue& NameableValue::named(const std::string& name)
{
m_name = name;
return *this;
}
graveyard/src/names.hpp deleted 100644 → 0
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// ----------------------------------------------------------------------------
// 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
#include <map>
#include <string>
namespace ngraph
{
//================================================================================================
// NameableValue
// An Axis labels a dimension of a tensor. The op-graph uses
// the identity of Axis objects to pair and specify dimensions in
// symbolic expressions. This system has several advantages over
// using the length and position of the axis as in other frameworks:
//
// 1) Convenience. The dimensions of tensors, which may be nested
// deep in a computation graph, can be specified without having to
// calculate their lengths.
//
// 2) Safety. Axis labels are analogous to types in general-purpose
// programming languages, allowing objects to interact only when
// they are permitted to do so in advance. In symbolic computation,
// this prevents interference between axes that happen to have the
// same lengths but are logically distinct, e.g. if the number of
// training examples and the number of input features are both 50.
//
// TODO: Please add to the list...
//
// Arguments:
// length: The length of the axis.
// batch: Whether the axis is a batch axis.
// recurrent: Whether the axis is a recurrent axis.
//================================================================================================
class NameableValue
{
public:
//!-----------------------------------------------------------------------------------
//! NameableValue
//! An object that can be named.
//!
//! Arguments:
//! graph_label_type: A label that should be used when drawing the graph. Defaults to
//! the class name.
//! name (str): The name of the object.
//! **kwargs: Parameters for related classes.
//!
//! Attributes:
//! graph_label_type: A label that should be used when drawing the graph.
//! id: Unique id for this object.
//!-----------------------------------------------------------------------------------
NameableValue(const std::string& name,
const std::string& graph_label_type = "",
const std::string& doc_string = "");
//!-----------------------------------------------------------------------------------
//! graph_label
//! The label used for drawings of the graph.
//!-----------------------------------------------------------------------------------
const std::string& graph_label();
//!-----------------------------------------------------------------------------------
//! name
//! Sets the object name to a unique name based on name.
//!
//! Arguments:
//! name: Prefix for the name
//!-----------------------------------------------------------------------------------
const std::string& name();
//!-----------------------------------------------------------------------------------
//! name
//!-----------------------------------------------------------------------------------
void name(const std::string& name);
//!-----------------------------------------------------------------------------------
//! short_name
//!-----------------------------------------------------------------------------------
const std::string& short_name();
//!-----------------------------------------------------------------------------------
//! named
//!-----------------------------------------------------------------------------------
NameableValue& named(const std::string& name);
static size_t __counter;
static std::map<std::string, NameableValue> __all_names;
std::string m_name;
std::string m_graph_label;
std::string m_short_name;
std::string m_doc_string;
};
} // end namespace ngraph
graveyard/src/strides.cpp deleted 100644 → 0
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#include <algorithm>
#include <iostream>
#include "strides.hpp"
#include "util.hpp"
using namespace std;
//================================================================================================
//
//================================================================================================
ngraph::tensor_size::tensor_size()
: m_tree{}
, m_element_type{element_type_float}
{
}
ngraph::tensor_size::tensor_size(size_t s, ElementType et)
: m_tree{s}
, m_element_type{et}
{
}
ngraph::tensor_size::tensor_size(const std::initializer_list<scalar_tree>& list, ElementType et)
: m_tree{list}
, m_element_type{et}
{
}
ngraph::tensor_size::tensor_size(const std::vector<size_t>& list, const ElementType& et)
: m_tree{list}
, m_element_type{et}
{
}
ngraph::tensor_stride ngraph::tensor_size::full_strides() const
{
tensor_stride result{*this};
vector<size_t*> value_pointer_list;
vector<size_t> size_list;
scalar_tree::traverse_tree(result.m_tree, [&](size_t* value) {
value_pointer_list.push_back(value);
size_list.push_back(*value);
});
int index = value_pointer_list.size() - 1;
*value_pointer_list[index] = result.m_element_type.size();
for (index--; index >= 0; index--)
{
*value_pointer_list[index] = *value_pointer_list[index + 1] * size_list[index + 1];
}
return result;
}
ngraph::tensor_stride ngraph::tensor_size::strides() const
{
return full_strides().strides();
}
ngraph::tensor_size ngraph::tensor_size::sizes() const
{
vector<size_t> tmp;
if (m_tree.is_list())
{
for (auto s : m_tree.get_list())
{
tmp.push_back(s.reduce([](size_t a, size_t b) { return a * b; }));
}
}
else
{
tmp.push_back(m_tree.get_value());
}
return tensor_size(tmp, m_element_type);
}
std::ostream& ngraph::operator<<(std::ostream& out, const ngraph::tensor_size& s)
{
out << s.m_tree;
return out;
}
//================================================================================================
//
//================================================================================================
ngraph::tensor_stride::tensor_stride()
: m_tree{}
, m_element_type{element_type_float}
{
}
ngraph::tensor_stride::tensor_stride(const tensor_size& s)
: m_tree{}
, m_element_type{s.m_element_type}
{
m_tree = s.m_tree;
}
ngraph::tensor_stride::tensor_stride(const std::vector<size_t>& list, const ElementType& et)
: m_tree{}
, m_element_type{et}
{
m_tree = list;
}
ngraph::tensor_stride ngraph::tensor_stride::reduce_strides() const
{
vector<size_t> tmp;
if (m_tree.is_list())
{
for (auto s : m_tree.get_list())
{
tmp.push_back(s.reduce([](size_t a, size_t b) { return min(a, b); }));
}
}
else
{
tmp.push_back(m_tree.get_value());
}
return tensor_stride(tmp, m_element_type);
}
ngraph::tensor_stride ngraph::tensor_stride::full_strides() const
{
return *this;
}
ngraph::tensor_stride ngraph::tensor_stride::strides() const
{
return reduce_strides();
}
std::ostream& ngraph::operator<<(std::ostream& out, const ngraph::tensor_stride& s)
{
out << s.m_tree;
return out;
}
graveyard/src/strides.hpp deleted 100644 → 0
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#pragma once
#include <cstdio>
#include <initializer_list>
#include <vector>
#include "element_type.hpp"
#include "tree.hpp"
namespace ngraph
{
class tensor_size;
class tensor_stride;
}
//================================================================================================
//
//================================================================================================
class ngraph::tensor_size
{
friend class tensor_stride;
public:
tensor_size();
tensor_size(size_t s, ElementType et = element_type_float);
tensor_size(const std::initializer_list<scalar_tree>& list,
ElementType et = element_type_float);
const ElementType& get_type() const { return m_element_type; }
tensor_stride full_strides() const;
tensor_stride strides() const;
tensor_size sizes() const;
tensor_size operator[](size_t index) const;
friend std::ostream& operator<<(std::ostream& out, const tensor_size& s);
private:
tensor_size(const std::vector<size_t>&, const ElementType&);
scalar_tree m_tree;
ElementType m_element_type;
};
//================================================================================================
//
//================================================================================================
class ngraph::tensor_stride
{
friend class tensor_size;
public:
tensor_stride();
const ElementType& get_type() const { return m_element_type; }
tensor_stride full_strides() const;
tensor_stride strides() const;
tensor_stride reduce_strides() const;
tensor_stride operator[](size_t index) const;
friend std::ostream& operator<<(std::ostream& out, const tensor_stride& s);
private:
tensor_stride(const tensor_size&);
tensor_stride(const std::vector<size_t>&, const ElementType&);
scalar_tree m_tree;
ElementType m_element_type;
};
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graveyard/src/transformers/mock.hpp deleted 100644 → 0
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// ----------------------------------------------------------------------------
// 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
#include <map>
#include <memory>
#include <sstream>
#include <string>
#include <type_traits>
#include <vector>
#include "element_type.hpp"
namespace ngraph
{
class ExecutionState;
class Op;
// class TensorDescription;
class ComputationOp;
using computation_op_ptr = std::shared_ptr<ComputationOp>;
using op_ptr = std::shared_ptr<Op>;
using scalar_t = float;
//================================================================================================
// TensorInterface
//================================================================================================
class TensorInterface
{
public:
virtual ~TensorInterface() {}
virtual const ElementType& element_type() const = 0;
virtual std::string value_string() const = 0;
};
//================================================================================================
// Tensor
//================================================================================================
template <typename T>
class Tensor : public TensorInterface
{
public:
Tensor(const T& val)
: m_value{val}
, m_element_type{element_type_float}
{
}
virtual ~Tensor() {}
const ElementType& element_type() const override { return m_element_type; }
std::string value_string() const override
{
std::string rc = "WTF";
if (std::is_floating_point<T>::value)
{
std::stringstream ss;
ss << m_value;
rc = ss.str();
}
return rc;
}
private:
T m_value;
ElementType m_element_type;
};
//================================================================================================
// Transformer
//================================================================================================
class Transformer
{
public:
virtual ~Transformer() {}
virtual ExecutionState& execution_state() = 0;
};
//================================================================================================
// TensorDescription
//================================================================================================
// class TensorDescription
// {
// public:
// virtual ~TensorDescription();
// virtual axes_key_t axes_key() const = 0;
// virtual std::string name() const = 0;
// virtual std::vector<size_t> shape() const = 0;
// virtual std::shared_ptr<TensorDescription> base() = 0;
// virtual ElementType element_type() const = 0;
// virtual size_t tensor_size() = 0;
// virtual bool is_persistent() = 0;
// virtual bool is_input() = 0;
// };
//================================================================================================
// Op
//================================================================================================
// class Op
// {
// // Any operation that can be in an AST.
// // Arguments:
// // args: Values used by this node.
// // const: The value of a constant Op, or None,
// // constant (bool): The Op is constant. Default False.
// // forward: If not None, the node to use instead of this node.
// // metadata: String key value dictionary for frontend metadata.
// // kwargs: Args defined in related classes.
// // Attributes:
// // const: The value of a constant.
// // constant (bool): The value is constant.
// // control_deps (OrderedSet): Ops in addtion to args that must run before this op.
// // persistent (bool): The value will be retained from computation to computation and
// // not shared. Always True if reference is set.
// // metadata: Dictionary with of string keys and values used for attaching
// // arbitrary metadata to nodes.
// // trainable: The value is trainable.
// public:
// virtual ~Op() {}
// virtual std::string name() const = 0;
// virtual tensor_description_ptr tensor_description() = 0;
// virtual op_ptr tensor() = 0;
// virtual bool is_tensor_op() = 0;
// virtual bool is_state_op() const = 0;
// virtual bool is_sequencing_op() const = 0;
// virtual op_ptr effective_tensor_op() = 0;
// virtual const std::vector<op_ptr>& all_deps() const = 0;
// // ops
// // TODO support multiple types
// static op_ptr constant(float value)
// {
// op_ptr = make_shared<LiteralScalarOp>(value);
// }
// };
//================================================================================================
// TensorOp
//================================================================================================
// class TensorOp : public Op
// {
// public:
// std::string name() const override { return "TensorOp"; }
// tensor_description_ptr tensor_description() override { return nullptr; }
// op_ptr tensor() override { return nullptr; }
// bool is_tensor_op() override { return true; }
// bool is_state_op() const override { return false; }
// op_ptr effective_tensor_op() override { return nullptr; }
// const std::vector<op_ptr>& all_deps() const override { return m_all_deps; }
// private:
// std::vector<op_ptr> m_all_deps;
// };
} // end of namespace ngraph
graveyard/src/transformers/mock_transformer.cpp deleted 100644 → 0
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// ----------------------------------------------------------------------------
// 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
// ----------------------------------------------------------------------------
#include "mock_transformer.hpp"
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src/ngraph/CMakeLists.txt
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#include "ngraph/tree.hpp"
#include "ngraph/util.hpp"
//================================================================================================
//
//================================================================================================
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