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/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
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 "tensorflow/contrib/lite/graph_info.h"
#include <algorithm>
namespace tflite {
namespace {
// Provide a range iterable wrapper for TfLiteIntArray* (C lists that TfLite
// C api uses. Can't use the google array_view, since we can't depend on even
// absl for embedded device reasons.
// TODO(aselle): Move this into central utilities.
class TfLiteIntArrayView {
public:
// Construct a view of a TfLiteIntArray*. Note, `int_array` should be non-null
// and this view does not take ownership of it.
explicit TfLiteIntArrayView(const TfLiteIntArray* int_array)
: int_array_(int_array) {}
typedef const int* const_iterator;
const_iterator begin() const { return int_array_->data; }
const_iterator end() const { return &int_array_->data[int_array_->size]; }
TfLiteIntArrayView(const TfLiteIntArrayView&) = default;
TfLiteIntArrayView& operator=(const TfLiteIntArrayView& rhs) = default;
private:
const TfLiteIntArray* int_array_;
};
// Helper class that actually performs partitioning by subgraph.
// Outputs to a provided `subgraphs` structure.
//
// Example usage:
// PartitionGraphIntoIndependentSubgraphsImpl partitioner(
// info, nodes_to_part, subgraphs);
// partitioner.Partition();
class PartitionGraphIntoIndependentSubgraphsImpl {
public:
PartitionGraphIntoIndependentSubgraphsImpl(
const GraphInfo* info, const TfLiteIntArray* nodes_to_partition,
std::vector<Subgraph>* subgraphs)
: info_(info),
subgraphs_(subgraphs),
node_type_(info->num_nodes(), Subgraph::kTfNonPartition) {
// Populate the node_type_ map.
for (auto node_index : TfLiteIntArrayView(nodes_to_partition)) {
node_type_[node_index] = Subgraph::kTfPartition;
}
}
// Actually partition the graph.
void Partition() {
// Initialize here to make Partition() re-entrant.
subgraphs_->clear();
tensor_epochs_.clear();
tensor_epochs_.resize(info_->num_tensors(), kEpochAlwaysReady);
node_epochs_.clear();
node_epochs_.resize(info_->num_nodes(), kEpochNotReady);
// Set computed tensors to be kEpochNotReady (initializer set everything to
// AlwaysReady).
for (int node_index = 0; node_index < info_->num_nodes(); node_index++) {
const TfLiteNode& node = info_->node(node_index);
for (int output_tensor_index : TfLiteIntArrayView(node.outputs)) {
tensor_epochs_[output_tensor_index] = kEpochNotReady;
}
}
// Do a graph traversal where each iteration in the loop is an epoch
// that corresponds to a subgraph that only contains nodes that are of
// the same node_type_.
while (true) {
BuildSubgraph();
if (subgraphs_->back().nodes.empty()) {
subgraphs_->pop_back();
break;
}
}
// Mark model outputs as subgraph outputs. All the rest have already been
// identified.
for (int output_index : info_->outputs()) {
int output_epoch = tensor_epochs_[output_index];
Subgraph& output_subgraph = (*subgraphs_)[output_epoch];
output_subgraph.output_tensors.push_back(output_index);
}
// Make sure every subgraph's inputs and outputs are unique. Since the
// list of inputs and outputs is generated in a way that produces
// duplicates.
for (Subgraph& subgraph : *subgraphs_) {
// Sort and uniquefy using standard library algorithms.
auto uniquefy = [](std::vector<int>* items) {
std::sort(items->begin(), items->end());
auto last = std::unique(items->begin(), items->end());
items->erase(last, items->end());
};
uniquefy(&subgraph.input_tensors);
uniquefy(&subgraph.output_tensors);
}
}
private:
// Special integer values needed for tensor_epochs_ and node_epochs_.
enum {
// The node or tensor is not ready to be assigned an epoch. e.g. a node's
// inputs have not all been assigned epochs.
kEpochNotReady = -1,
// Used for tensor_epochs_. This means that the tensor is always ready.
// e.g. an input to the whole model or a constant that has no dependencies.
kEpochAlwaysReady = -2
};
// Updates the node `node_index` and returns true if it is assigned to an
// epoch. False is returned if the node is already set to an epoch, its inputs
// are not all assigned to epochs, or if it cannot be assigned to the current
// epoch since the epoch's node_type doesn't match.
bool UpdateNode(int node_index) {
const TfLiteNode& node = info_->node(node_index);
Subgraph& current_subgraph = subgraphs_->back();
int current_epoch = subgraphs_->size() - 1;
// Check if node is already done.
if (node_epochs_[node_index] != kEpochNotReady) {
return false;
}
// See if all dependencies of this node are already assigned to a
// subgraph.
for (int input_tensor_index : TfLiteIntArrayView(node.inputs)) {
if (tensor_epochs_[input_tensor_index] == kEpochNotReady) {
return false;
}
}
// When we are starting a new epoch, the first ready node defines
// the type of that epoch.
if (current_subgraph.type == Subgraph::kTfUnexplored) {
current_subgraph.type = node_type_[node_index];
}
// The node gets assigned to this epoch if it is the same type as
// the epoch's assigned type. Note, if this is the current ready
// node encountered during this epoch, this condition will be
// automatically true.
if (current_subgraph.type == node_type_[node_index]) {
node_epochs_[node_index] = current_epoch;
current_subgraph.nodes.push_back(node_index);
// All outputs of this node now are assigned to this epoch as
// well.
for (int output_tensor_index : TfLiteIntArrayView(node.outputs)) {
tensor_epochs_[output_tensor_index] = current_epoch;
}
// Look at our inputs one more time to update that tensor's
// epochs' outputs
for (int input_tensor_index : TfLiteIntArrayView(node.inputs)) {
int input_epoch = tensor_epochs_[input_tensor_index];
int node_epoch = current_epoch;
if (input_epoch != node_epoch) {
current_subgraph.input_tensors.push_back(input_tensor_index);
// Set inputs to be outputs of the subgraph where they reside.
// the if condition makes sure inputs to the whole computation
// are not included (i.e. those initialized to -2 above).
if (input_epoch >= 0) {
Subgraph& input_subgraph = (*subgraphs_)[input_epoch];
input_subgraph.output_tensors.push_back(input_tensor_index);
}
}
}
return true;
} else {
return false;
}
}
// Completely populates the current subgraph by doing graph traversal
void BuildSubgraph() {
subgraphs_->emplace_back(Subgraph());
// loop until no more nodes can be updated.
while (true) {
bool did_something = false;
for (int node_index = 0; node_index < info_->num_nodes(); node_index++) {
if (UpdateNode(node_index)) {
did_something = true;
}
}
if (!did_something) return;
}
}
// Temporary data needed for partitioning.
const GraphInfo* info_;
// List of subgraphs to populate
std::vector<Subgraph>* subgraphs_;
std::vector<Subgraph::Type> node_type_;
// Maps from tensor index to the epoch in which it is assigned. Also special
// negative values of kEpochNotAssigned if not assigned, kEpochNotReady if it
// is an input or constant.
std::vector<int> tensor_epochs_;
// Maps from tensor index to the epoch in which it is assigned. Also special
// negative values of kEpochNotAssigned if not assigned.
std::vector<int> node_epochs_;
};
} // namespace
TfLiteStatus PartitionGraphIntoIndependentSubgraphs(
const GraphInfo* info, const TfLiteIntArray* nodes_to_partition,
std::vector<Subgraph>* subgraphs) {
PartitionGraphIntoIndependentSubgraphsImpl(info, nodes_to_partition,
subgraphs)
.Partition();
return kTfLiteOk;
}
} // namespace tflite
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