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Commit d73f92c4 authored by Adam Procter's avatar Adam Procter Committed by adstraw
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Remove XLA compatibility figleaves (will be moved to ngraph-tensorflow-bridge) (#704)

parent b4c672f2
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src/ngraph/CMakeLists.txt
View file @ d73f92c4
...@@ -19,7 +19,6 @@ set (SRC ...@@ -19,7 +19,6 @@ set (SRC
builder/autobroadcast.cpp builder/autobroadcast.cpp
builder/numpy_transpose.cpp builder/numpy_transpose.cpp
builder/reduce_ops.cpp builder/reduce_ops.cpp
builder/xla_tuple.cpp
coordinate_transform.cpp coordinate_transform.cpp
descriptor/input.cpp descriptor/input.cpp
descriptor/layout/dense_tensor_view_layout.cpp descriptor/layout/dense_tensor_view_layout.cpp
......
src/ngraph/builder/xla_tuple.cpp deleted 100644 → 0
View file @ b4c672f2
/*******************************************************************************
* Copyright 2017-2018 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 <memory>
#include "ngraph/builder/xla_tuple.hpp"
#include "ngraph/descriptor/primary_tensor_view.hpp"
#include "ngraph/descriptor/tensor_view.hpp"
#include "ngraph/except.hpp"
#include "ngraph/op/parameter.hpp"
#include "ngraph/runtime/call_frame.hpp"
#include "ngraph/type/element_type.hpp"
#include "ngraph/type/type.hpp"
using namespace std;
using namespace ngraph;
xla::op::Tuple::Tuple(const NodeVector& nodes)
: Node("Tuple", NodeVector{})
, m_elements(nodes)
{
}
std::shared_ptr<Node> xla::op::Tuple::copy_with_new_args(const NodeVector& new_args) const
{
return make_shared<Tuple>(new_args);
}
const NodeVector& xla::op::Tuple::get_elements() const
{
return m_elements;
}
size_t xla::op::Tuple::get_tuple_size() const
{
return m_elements.size();
}
shared_ptr<Node> xla::op::Tuple::get_tuple_element(size_t i)
{
return m_elements.at(i);
}
shared_ptr<Node> xla::op::get_tuple_element(shared_ptr<Node> node, size_t i)
{
shared_ptr<xla::op::Tuple> tuple = dynamic_pointer_cast<xla::op::Tuple>(node);
if (tuple == nullptr)
{
throw ngraph_error("get_tuple_element called on a non-tuple");
}
return tuple->get_tuple_element(i);
}
namespace
{
// Add the node to nodes if it's not a Tuple, otherwise add nodes for the elements of the tuple.
template <typename T>
void flatten(vector<shared_ptr<T>>& nodes, shared_ptr<Node> node)
{
auto xla_tuple = dynamic_pointer_cast<xla::op::Tuple>(node);
if (xla_tuple == nullptr)
{
auto t_node = dynamic_pointer_cast<T>(node);
if (t_node == nullptr)
{
throw ngraph_error("Invalid node type type encountered");
}
nodes.push_back(t_node);
}
else
{
for (auto element : xla_tuple->get_elements())
{
flatten<T>(nodes, element);
}
}
}
// Collect a vector of the non-Tuple nodes that underly nodes
template <typename T>
vector<shared_ptr<T>> flatten(const NodeVector& nodes)
{
vector<shared_ptr<T>> result;
for (auto node : nodes)
{
flatten<T>(result, node);
}
return result;
}
}
xla::XLAFunction::XLAFunction(const NodeVector& results,
const NodeVector& parameters,
const string& name)
: Function(flatten<Node>(results), flatten<ngraph::op::Parameter>(parameters), name)
{
}
xla::XLATuple::XLATuple(const XLAValues& elements)
: runtime::TensorView(make_shared<descriptor::PrimaryTensorView>(
make_shared<ngraph::TensorViewType>(element::f32, Shape{}),
"XLATuple",
false,
false,
false))
, m_elements(elements)
{
}
const vector<shared_ptr<runtime::TensorView>>& xla::XLATuple::get_elements() const
{
return m_elements;
}
size_t xla::XLATuple::get_tuple_size() const
{
return m_elements.size();
}
shared_ptr<runtime::TensorView> xla::XLATuple::get_tuple_element(size_t i) const
{
return m_elements.at(i);
}
void xla::XLATuple::write(const void* p, size_t tensor_offset, size_t n)
{
throw ngraph_error("Cannot write to a tuple");
}
void xla::XLATuple::read(void* p, size_t tensor_offset, size_t n) const
{
throw ngraph_error("Cannot read from a tuple");
}
std::shared_ptr<runtime::TensorView> xla::get_tuple_element(std::shared_ptr<xla::XLATuple> tuple,
size_t i)
{
return tuple->get_tuple_element(i);
}
namespace
{
// Collect the real tensors, expanding the tensors that are really tuples
void flatten(runtime::TensorViewPtrs& tensors, shared_ptr<runtime::TensorView> tensor)
{
auto xla_tuple = dynamic_pointer_cast<xla::XLATuple>(tensor);
if (xla_tuple == nullptr)
{
tensors.push_back(tensor);
}
else
{
for (auto element : xla_tuple->get_elements())
{
flatten(tensors, element);
}
}
}
// Return a vector of the real tensors underlying a vector of tensors which may contain tuples.
runtime::TensorViewPtrs flatten(const runtime::TensorViewPtrs& tensors)
{
runtime::TensorViewPtrs result;
for (auto tensor : tensors)
{
flatten(result, tensor);
}
return result;
}
}
void xla::call(shared_ptr<runtime::CallFrame> call_frame,
const runtime::TensorViewPtrs& outputs,
const runtime::TensorViewPtrs& inputs)
{
runtime::TensorViewPtrs flat_outputs(flatten(outputs));
runtime::TensorViewPtrs flat_inputs(flatten(inputs));
call_frame->tensor_call(flat_outputs, flat_inputs);
}
src/ngraph/builder/xla_tuple.hpp deleted 100644 → 0
View file @ b4c672f2
/*******************************************************************************
* Copyright 2017-2018 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 <memory>
#include <vector>
#include "ngraph/function.hpp"
#include "ngraph/node.hpp"
#include "ngraph/runtime/backend.hpp"
#include "ngraph/runtime/tensor_view.hpp"
#include "ngraph/type/element_type.hpp"
namespace ngraph
{
namespace runtime
{
class CallFrame;
}
namespace xla
{
namespace op
{
/// A special Op for constructing graphs with XLA tuples.
/// Can only be used as an argument to the get_tuple_element function, which returns the node
/// that was used when the tuple was constructed; the constructed graph should have no Tuple
/// nodes in it.
class Tuple : public Node
{
public:
Tuple(const NodeVector& nodes);
std::shared_ptr<Node> get_tuple_element(size_t i);
size_t get_tuple_size() const;
const NodeVector& get_elements() const;
virtual std::shared_ptr<Node>
copy_with_new_args(const NodeVector& new_args) const override;
protected:
NodeVector m_elements;
};
std::shared_ptr<Node> get_tuple_element(std::shared_ptr<Node> tuple, size_t i);
}
/// Extends functions to let results include xla::op::Tuple, and paramaters to include
/// xla::op::Tuple of op::Parameter trees.
class XLAFunction : public Function
{
public:
XLAFunction(const NodeVector& results,
const NodeVector& parameters,
const std::string& name = "");
};
using XLAValues = std::vector<std::shared_ptr<runtime::TensorView>>;
/// An XLATuple is a implemented as an extension of a float scalar so that it fits in nicely
/// with the nGraph type hierarchy.
class XLATuple : public runtime::TensorView
{
public:
XLATuple(const XLAValues& elements);
const ngraph::runtime::TensorViewPtrs& get_elements() const;
std::shared_ptr<runtime::TensorView> get_tuple_element(size_t i) const;
size_t get_tuple_size() const;
virtual void write(const void* p, size_t tensor_offset, size_t n) override;
virtual void read(void* p, size_t tensor_offset, size_t n) const override;
protected:
std::vector<std::shared_ptr<runtime::TensorView>> m_elements;
};
/// Convenience function for making a runtime tuple.
inline std::shared_ptr<XLATuple> make_tuple(const XLAValues& elements)
{
return std::make_shared<XLATuple>(elements);
}
/// Convenience function for accessing a tuple element.
std::shared_ptr<runtime::TensorView> get_tuple_element(std::shared_ptr<XLATuple> xla_tuple,
size_t i);
/// Invoke a call frame where some arguments might be XLATuples
void call(std::shared_ptr<runtime::CallFrame> call_frame,
const ngraph::runtime::TensorViewPtrs& outputs,
const ngraph::runtime::TensorViewPtrs& inputs);
}
}
src/ngraph/ngraph.hpp
View file @ d73f92c4
...@@ -43,12 +43,6 @@ ...@@ -43,12 +43,6 @@
/// @brief Convenience functions that create addional graph nodes to implement commonly-used /// @brief Convenience functions that create addional graph nodes to implement commonly-used
/// recipes, for example auto-broadcast. /// recipes, for example auto-broadcast.
/// @namespace ngraph::xla
/// @brief Code to facilitate nGraph's support for XLA/HLO.
/// @namespace ngraph::xla::op
/// @brief Operators specific to nGraph's support for XLA/HLO.
#include "ngraph/builder/autobroadcast.hpp" #include "ngraph/builder/autobroadcast.hpp"
#include "ngraph/builder/numpy_transpose.hpp" #include "ngraph/builder/numpy_transpose.hpp"
#include "ngraph/builder/reduce_ops.hpp" #include "ngraph/builder/reduce_ops.hpp"
......
src/ngraph/serializer.cpp
View file @ d73f92c4
...@@ -842,9 +842,6 @@ static shared_ptr<ngraph::Function> ...@@ -842,9 +842,6 @@ static shared_ptr<ngraph::Function>
{ {
node = make_shared<op::Tanh>(args[0]); node = make_shared<op::Tanh>(args[0]);
} }
// else if (node_op == "XLAGetTupleElement")
// {
// }
else else
{ {
stringstream ss; stringstream ss;
...@@ -1239,9 +1236,6 @@ static json write(const Node& n, bool binary_constant_data) ...@@ -1239,9 +1236,6 @@ static json write(const Node& n, bool binary_constant_data)
else if (node_op == "Tanh") else if (node_op == "Tanh")
{ {
} }
else if (node_op == "XLAGetTupleElement")
{
}
return node; return node;
} }
src/ngraph/util.hpp
View file @ d73f92c4
...@@ -260,7 +260,7 @@ namespace ngraph ...@@ -260,7 +260,7 @@ namespace ngraph
}; };
/** /**
* This utility takes forward-propogation and back-propogation XLAunctions * This utility takes forward-propogation and back-propagation functions
* and turns them into clone functions where the intermediate values of * and turns them into clone functions where the intermediate values of
* the forward prop are added to the output of fprop and the input of the bprop * the forward prop are added to the output of fprop and the input of the bprop
* to avoid repeat calcualtions. * to avoid repeat calcualtions.
......
test/CMakeLists.txt
View file @ d73f92c4
...@@ -28,7 +28,6 @@ set (SRC ...@@ -28,7 +28,6 @@ set (SRC
backend_debug_api.cpp backend_debug_api.cpp
builder.cpp builder.cpp
builder_autobroadcast.cpp builder_autobroadcast.cpp
builder_xla.cpp
build_graph.cpp build_graph.cpp
copy.cpp copy.cpp
core_fusion.cpp core_fusion.cpp
......
test/builder_xla.cpp deleted 100644 → 0
View file @ b4c672f2
/*******************************************************************************
* Copyright 2017-2018 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 <vector>
#include "gtest/gtest.h"
#include "ngraph/builder/xla_tuple.hpp"
#include "ngraph/ngraph.hpp"
#include "util/test_tools.hpp"
using namespace std;
using namespace ngraph;
TEST(builder_xla, simple)
{
Shape shape{2, 2};
auto pA = make_shared<op::Parameter>(element::f32, shape);
auto pB = make_shared<op::Parameter>(element::f32, shape);
auto pC = make_shared<op::Parameter>(element::f32, shape);
auto ABC = make_shared<xla::op::Tuple>(NodeVector{pA, pB, pC});
auto A = xla::op::get_tuple_element(ABC, 0);
auto B = xla::op::get_tuple_element(ABC, 1);
auto C = xla::op::get_tuple_element(ABC, 2);
auto f = make_shared<xla::XLAFunction>(
NodeVector{make_shared<xla::op::Tuple>(NodeVector{(A + B) * C})}, NodeVector{ABC});
auto manager = runtime::Manager::get("INTERPRETER");
auto external = manager->compile(f);
auto backend = manager->allocate_backend();
auto cf = backend->make_call_frame(external);
// Create some tensors for input/output
auto a = backend->make_primary_tensor_view(element::f32, shape);
copy_data(a, vector<float>{1, 2, 3, 4});
auto b = backend->make_primary_tensor_view(element::f32, shape);
copy_data(b, vector<float>{5, 6, 7, 8});
auto c = backend->make_primary_tensor_view(element::f32, shape);
copy_data(c, vector<float>{9, 10, 11, 12});
auto abc = xla::make_tuple({a, b, c});
auto bac = xla::make_tuple({b, a, c});
auto acb = xla::make_tuple({a, c, b});
auto result = backend->make_primary_tensor_view(element::f32, shape);
auto result_tuple = xla::make_tuple({result});
xla::call(cf, {result_tuple}, {abc});
EXPECT_EQ((vector<float>{54, 80, 110, 144}), read_vector<float>(result));
xla::call(cf, {result_tuple}, {bac});
EXPECT_EQ((vector<float>{54, 80, 110, 144}), read_vector<float>(result));
xla::call(cf, {result_tuple}, {acb});
EXPECT_EQ((vector<float>{50, 72, 98, 128}), read_vector<float>(result));
}
TEST(builder_xla, empty_tuple_interpreter)
{
auto empty_tuple = make_shared<xla::op::Tuple>(NodeVector{});
auto f = make_shared<xla::XLAFunction>(NodeVector{empty_tuple}, NodeVector{});
auto manager = runtime::Manager::get("INTERPRETER");
auto external = manager->compile(f);
auto backend = manager->allocate_backend();
auto cf = backend->make_call_frame(external);
xla::call(cf, {}, {});
}
TEST(builder_xla, empty_tuple_cpu)
{
auto empty_tuple = make_shared<xla::op::Tuple>(NodeVector{});
auto f = make_shared<xla::XLAFunction>(NodeVector{empty_tuple}, NodeVector{});
auto manager = runtime::Manager::get("CPU");
auto external = manager->compile(f);
auto backend = manager->allocate_backend();
auto cf = backend->make_call_frame(external);
xla::call(cf, {}, {});
}
test/includes.cpp
View file @ d73f92c4
...@@ -35,7 +35,6 @@ TEST(DISABLED_include, complete) ...@@ -35,7 +35,6 @@ TEST(DISABLED_include, complete)
"ngraph/builder/autobroadcast.hpp", "ngraph/builder/autobroadcast.hpp",
"ngraph/builder/numpy_transpose.hpp", "ngraph/builder/numpy_transpose.hpp",
"ngraph/builder/reduce_ops.hpp", "ngraph/builder/reduce_ops.hpp",
"ngraph/builder/xla_tuple.hpp",
"ngraph/codegen/code_writer.hpp", "ngraph/codegen/code_writer.hpp",
"ngraph/codegen/compiler.hpp", "ngraph/codegen/compiler.hpp",
"ngraph/codegen/execution_engine.hpp", "ngraph/codegen/execution_engine.hpp",
......
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