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Unverified Commit 10a78dda authored by Robert Kimball's avatar Robert Kimball Committed by GitHub
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update hybrid to new api (#2428)

parent b7484420
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Showing with 274 additions and 206 deletions
src/ngraph/runtime/hybrid/CMakeLists.txt
View file @ 10a78dda
......@@ -16,6 +16,7 @@
add_library(hybrid_base STATIC
hybrid_backend.cpp
hybrid_executable.cpp
hybrid_util.cpp
pass/default_placement.cpp
pass/dump.cpp
......
src/ngraph/runtime/hybrid/hybrid_backend.cpp
View file @ 10a78dda
......@@ -19,6 +19,7 @@
#include "ngraph/pass/manager.hpp"
#include "ngraph/pass/visualize_tree.hpp"
#include "ngraph/runtime/host_tensor.hpp"
#include "ngraph/runtime/hybrid/hybrid_executable.hpp"
#include "ngraph/runtime/hybrid/hybrid_util.hpp"
#include "ngraph/runtime/hybrid/pass/default_placement.hpp"
#include "ngraph/runtime/hybrid/pass/dump.hpp"
......@@ -53,17 +54,6 @@ shared_ptr<runtime::Tensor> runtime::hybrid::HybridBackend::create_tensor(
return (*it)->create_tensor(element_type, shape, memory_pointer);
}
static void node_modifiers(const Node& node, vector<string>& attributes)
{
vector<string> colors = {"\"#A0FFA0\"", "\"#FFF790\""};
if (node.get_placement_index() < colors.size())
{
string color = colors[node.get_placement_index()];
attributes.push_back("style=filled");
attributes.push_back("fillcolor=" + color);
}
}
shared_ptr<runtime::Executable>
runtime::hybrid::HybridBackend::compile(shared_ptr<Function> func,
bool enable_performance_collection)
......@@ -72,177 +62,7 @@ shared_ptr<runtime::Executable>
m_backend_list, func, enable_performance_collection, m_debug_enabled);
}
runtime::hybrid::HybridExecutable::HybridExecutable(
const std::vector<std::shared_ptr<runtime::Backend>>& backend_list,
const shared_ptr<Function>& func,
bool enable_performance_collection,
bool debug_enabled)
: m_function{func}
, m_backend_list{backend_list}
, m_debug_enabled{debug_enabled}
{
{
// Run placement pass
ngraph::pass::Manager pass_manager;
pass_manager.register_pass<runtime::hybrid::pass::DefaultPlacement>(m_backend_list);
pass_manager.register_pass<runtime::hybrid::pass::FixGetOutputElement>();
pass_manager.register_pass<runtime::hybrid::pass::Liveness>();
pass_manager.register_pass<runtime::hybrid::pass::Dump>("graph.dump");
// pass_manager.register_pass<runtime::hybrid::pass::MemoryLayout>();
if (m_debug_enabled)
{
pass_manager.register_pass<ngraph::pass::VisualizeTree>("graph.png", node_modifiers);
}
pass_manager.run_passes(m_function);
// Split function to sub_functions
tie(m_sub_functions, m_map_parameter_to_result) =
runtime::hybrid::split_function_by_placement(m_function);
// Compile subfunctions in corresponding backends
size_t subfunction_number = 0;
for (shared_ptr<Function>& sub_function : m_sub_functions)
{
size_t placement = sub_function->get_placement();
if (m_debug_enabled)
{
string name = "subfunction_" + to_string(subfunction_number++);
ngraph::pass::Manager pm;
pm.register_pass<ngraph::pass::VisualizeTree>(name + ".png", node_modifiers);
pm.register_pass<runtime::hybrid::pass::Dump>(name + ".dump");
pm.run_passes(sub_function);
}
auto backend = m_backend_list[placement];
shared_ptr<Executable> exec = backend->compile(sub_function);
m_executable_map[sub_function] = exec;
// Compile will replace nodes so we need to make one more pass through all
// ops to reset placement
for (auto op : sub_function->get_ops())
{
op->set_placement_index(placement);
}
}
}
set_parameters_and_results(*func);
}
bool runtime::hybrid::HybridExecutable::call(const vector<shared_ptr<runtime::Tensor>>& outputs,
const vector<shared_ptr<runtime::Tensor>>& inputs)
{
bool rc = true;
using node_map_t = unordered_map<shared_ptr<Node>, shared_ptr<runtime::Tensor>>;
// Parameter and result node in sub_function maps to one Tensor
node_map_t map_node_to_tensor;
for (size_t i = 0; i < inputs.size(); ++i)
{
map_node_to_tensor[m_function->get_parameters()[i]] = inputs[i];
}
for (size_t i = 0; i < outputs.size(); ++i)
{
map_node_to_tensor[m_function->get_results()[i]] = outputs[i];
}
// Call subfunctions
for (const shared_ptr<Function>& sub_function : m_sub_functions)
{
// Init backend
size_t placement = sub_function->get_placement();
auto backend = m_backend_list[placement];
// Prepare parameter Tensors
vector<shared_ptr<runtime::Tensor>> parameters;
for (const shared_ptr<op::Parameter>& parameter_node : sub_function->get_parameters())
{
auto it = map_node_to_tensor.find(parameter_node);
if (it != map_node_to_tensor.end())
{
if (it->second->get_parent() == backend.get())
{
parameters.push_back(it->second);
}
else
{
auto parameter = backend->create_tensor(parameter_node->get_element_type(),
parameter_node->get_shape());
parameter->copy_from(*(it->second));
parameters.push_back(parameter);
}
}
else
{
// Handle temporary tensors that go between subgraphs
auto result_node = m_map_parameter_to_result.at(parameter_node);
auto result = map_node_to_tensor.at(result_node);
auto parameter = backend->create_tensor(parameter_node->get_element_type(),
parameter_node->get_shape());
parameter->copy_from(*result);
map_node_to_tensor[parameter_node] = parameter;
parameters.push_back(parameter);
}
}
// Prepare result Tensors
vector<shared_ptr<runtime::Tensor>> results;
map<runtime::Tensor*, runtime::Tensor*> copy_back;
for (const shared_ptr<op::Result>& result_node : sub_function->get_results())
{
auto it = map_node_to_tensor.find(result_node);
if (it != map_node_to_tensor.end())
{
if (it->second->get_parent() == backend.get())
{
results.push_back(it->second);
}
else
{
auto result = backend->create_tensor(result_node->get_element_type(),
result_node->get_shape());
results.push_back(result);
copy_back.insert({result.get(), it->second.get()});
}
}
else
{
// Handle temporary tensors that go between subgraphs
auto result = backend->create_tensor(result_node->get_element_type(),
result_node->get_shape());
map_node_to_tensor[result_node] = result;
results.push_back(result);
}
}
// Call
auto exec = m_executable_map[sub_function];
exec->call(results, parameters);
// Need to copy any results to the correct device
for (const auto& p : copy_back)
{
p.second->copy_from(*p.first);
}
}
return rc;
}
bool runtime::hybrid::HybridBackend::is_supported(const Node& node) const
{
return true;
}
size_t runtime::hybrid::HybridExecutable::get_placement(const runtime::Tensor* t)
{
size_t index = 0;
for (const shared_ptr<ngraph::runtime::Backend>& be : m_backend_list)
{
if (t->get_parent() == be.get())
{
return index;
}
index++;
}
return -1;
}
src/ngraph/runtime/hybrid/hybrid_backend.hpp
View file @ 10a78dda
......@@ -30,7 +30,6 @@ namespace ngraph
namespace hybrid
{
class HybridBackend;
class HybridExecutable;
}
}
}
......@@ -59,27 +58,3 @@ private:
std::vector<std::shared_ptr<runtime::Backend>> m_backend_list;
bool m_debug_enabled = false;
};
class ngraph::runtime::hybrid::HybridExecutable : public runtime::Executable
{
public:
HybridExecutable(const std::vector<std::shared_ptr<runtime::Backend>>& backend_list,
const std::shared_ptr<Function>& func,
bool enable_performance_collection = false,
bool debug_enabled = false);
bool call(const std::vector<std::shared_ptr<ngraph::runtime::Tensor>>& outputs,
const std::vector<std::shared_ptr<ngraph::runtime::Tensor>>& inputs) override;
private:
std::shared_ptr<ngraph::Function> m_function;
std::vector<std::shared_ptr<ngraph::Function>> m_sub_functions;
std::unordered_map<std::shared_ptr<ngraph::op::Parameter>, std::shared_ptr<ngraph::op::Result>>
m_map_parameter_to_result;
std::vector<std::shared_ptr<runtime::Backend>> m_backend_list;
bool m_debug_enabled = false;
std::unordered_map<std::shared_ptr<Function>, std::shared_ptr<Executable>> m_executable_map;
size_t get_placement(const runtime::Tensor* t);
};
src/ngraph/runtime/hybrid/hybrid_executable.cpp 0 → 100644
View file @ 10a78dda
//*****************************************************************************
// Copyright 2017-2019 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/runtime/hybrid/hybrid_executable.hpp"
#include "ngraph/graph_util.hpp"
#include "ngraph/pass/manager.hpp"
#include "ngraph/pass/visualize_tree.hpp"
#include "ngraph/runtime/host_tensor.hpp"
#include "ngraph/runtime/hybrid/hybrid_backend.hpp"
#include "ngraph/runtime/hybrid/hybrid_util.hpp"
#include "ngraph/runtime/hybrid/pass/default_placement.hpp"
#include "ngraph/runtime/hybrid/pass/dump.hpp"
#include "ngraph/runtime/hybrid/pass/fix_get_output_element.hpp"
#include "ngraph/runtime/hybrid/pass/liveness.hpp"
#include "ngraph/runtime/hybrid/pass/memory_layout.hpp"
#include "ngraph/runtime/tensor.hpp"
using namespace ngraph;
using namespace std;
static void node_modifiers(const Node& node, vector<string>& attributes)
{
vector<string> colors = {"\"#A0FFA0\"", "\"#FFF790\""};
if (node.get_placement_index() < colors.size())
{
string color = colors[node.get_placement_index()];
attributes.push_back("style=filled");
attributes.push_back("fillcolor=" + color);
}
}
runtime::hybrid::HybridExecutable::HybridExecutable(
const std::vector<std::shared_ptr<runtime::Backend>>& backend_list,
const shared_ptr<Function>& func,
bool enable_performance_collection,
bool debug_enabled)
: m_function{func}
, m_backend_list{backend_list}
, m_debug_enabled{debug_enabled}
{
{
// Run placement pass
ngraph::pass::Manager pass_manager;
pass_manager.register_pass<runtime::hybrid::pass::DefaultPlacement>(m_backend_list);
pass_manager.register_pass<runtime::hybrid::pass::FixGetOutputElement>();
pass_manager.register_pass<runtime::hybrid::pass::Liveness>();
pass_manager.register_pass<runtime::hybrid::pass::Dump>("graph.dump");
// pass_manager.register_pass<runtime::hybrid::pass::MemoryLayout>();
if (m_debug_enabled)
{
pass_manager.register_pass<ngraph::pass::VisualizeTree>("graph.png", node_modifiers);
}
pass_manager.run_passes(m_function);
// Split function to sub_functions
tie(m_sub_functions, m_map_parameter_to_result) =
runtime::hybrid::split_function_by_placement(m_function);
// Compile subfunctions in corresponding backends
size_t subfunction_number = 0;
for (shared_ptr<Function>& sub_function : m_sub_functions)
{
size_t placement = sub_function->get_placement();
if (m_debug_enabled)
{
string name = "subfunction_" + to_string(subfunction_number++);
ngraph::pass::Manager pm;
pm.register_pass<ngraph::pass::VisualizeTree>(name + ".png", node_modifiers);
pm.register_pass<runtime::hybrid::pass::Dump>(name + ".dump");
pm.run_passes(sub_function);
}
auto backend = m_backend_list[placement];
shared_ptr<Executable> exec = backend->compile(sub_function);
m_executable_map[sub_function] = exec;
// Compile will replace nodes so we need to make one more pass through all
// ops to reset placement
for (auto op : sub_function->get_ops())
{
op->set_placement_index(placement);
}
}
}
set_parameters_and_results(*func);
}
bool runtime::hybrid::HybridExecutable::call(const vector<shared_ptr<runtime::Tensor>>& outputs,
const vector<shared_ptr<runtime::Tensor>>& inputs)
{
bool rc = true;
using node_map_t = unordered_map<shared_ptr<Node>, shared_ptr<runtime::Tensor>>;
// Parameter and result node in sub_function maps to one Tensor
node_map_t map_node_to_tensor;
for (size_t i = 0; i < inputs.size(); ++i)
{
map_node_to_tensor[m_function->get_parameters()[i]] = inputs[i];
}
for (size_t i = 0; i < outputs.size(); ++i)
{
map_node_to_tensor[m_function->get_results()[i]] = outputs[i];
}
// Call subfunctions
for (const shared_ptr<Function>& sub_function : m_sub_functions)
{
// Init backend
size_t placement = sub_function->get_placement();
auto backend = m_backend_list[placement];
// Prepare parameter Tensors
vector<shared_ptr<runtime::Tensor>> parameters;
for (const shared_ptr<op::Parameter>& parameter_node : sub_function->get_parameters())
{
auto it = map_node_to_tensor.find(parameter_node);
if (it != map_node_to_tensor.end())
{
if (it->second->get_parent() == backend.get())
{
parameters.push_back(it->second);
}
else
{
auto parameter = backend->create_tensor(parameter_node->get_element_type(),
parameter_node->get_shape());
parameter->copy_from(*(it->second));
parameters.push_back(parameter);
}
}
else
{
// Handle temporary tensors that go between subgraphs
auto result_node = m_map_parameter_to_result.at(parameter_node);
auto result = map_node_to_tensor.at(result_node);
auto parameter = backend->create_tensor(parameter_node->get_element_type(),
parameter_node->get_shape());
parameter->copy_from(*result);
map_node_to_tensor[parameter_node] = parameter;
parameters.push_back(parameter);
}
}
// Prepare result Tensors
vector<shared_ptr<runtime::Tensor>> results;
map<runtime::Tensor*, runtime::Tensor*> copy_back;
for (const shared_ptr<op::Result>& result_node : sub_function->get_results())
{
auto it = map_node_to_tensor.find(result_node);
if (it != map_node_to_tensor.end())
{
if (it->second->get_parent() == backend.get())
{
results.push_back(it->second);
}
else
{
auto result = backend->create_tensor(result_node->get_element_type(),
result_node->get_shape());
results.push_back(result);
copy_back.insert({result.get(), it->second.get()});
}
}
else
{
// Handle temporary tensors that go between subgraphs
auto result = backend->create_tensor(result_node->get_element_type(),
result_node->get_shape());
map_node_to_tensor[result_node] = result;
results.push_back(result);
}
}
// Call
auto exec = m_executable_map[sub_function];
exec->call(results, parameters);
// Need to copy any results to the correct device
for (const auto& p : copy_back)
{
p.second->copy_from(*p.first);
}
}
return rc;
}
size_t runtime::hybrid::HybridExecutable::get_placement(const runtime::Tensor* t)
{
size_t index = 0;
for (const shared_ptr<ngraph::runtime::Backend>& be : m_backend_list)
{
if (t->get_parent() == be.get())
{
return index;
}
index++;
}
return -1;
}
src/ngraph/runtime/hybrid/hybrid_executable.hpp 0 → 100644
View file @ 10a78dda
//*****************************************************************************
// Copyright 2017-2019 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 <map>
#include <memory>
#include <string>
#include <vector>
#include "ngraph/runtime/backend.hpp"
namespace ngraph
{
namespace runtime
{
namespace hybrid
{
class HybridExecutable;
}
}
}
class ngraph::runtime::hybrid::HybridExecutable : public runtime::Executable
{
public:
HybridExecutable(const std::vector<std::shared_ptr<runtime::Backend>>& backend_list,
const std::shared_ptr<Function>& func,
bool enable_performance_collection = false,
bool debug_enabled = false);
bool call(const std::vector<std::shared_ptr<ngraph::runtime::Tensor>>& outputs,
const std::vector<std::shared_ptr<ngraph::runtime::Tensor>>& inputs) override;
private:
std::shared_ptr<ngraph::Function> m_function;
std::vector<std::shared_ptr<ngraph::Function>> m_sub_functions;
std::unordered_map<std::shared_ptr<ngraph::op::Parameter>, std::shared_ptr<ngraph::op::Result>>
m_map_parameter_to_result;
std::vector<std::shared_ptr<runtime::Backend>> m_backend_list;
bool m_debug_enabled = false;
std::unordered_map<std::shared_ptr<Function>, std::shared_ptr<Executable>> m_executable_map;
size_t get_placement(const runtime::Tensor* t);
};
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