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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/core/grappler/optimizers/data/noop_elimination.h"
#include "tensorflow/core/framework/attr_value.pb.h"
#include "tensorflow/core/framework/node_def.pb.h"
#include "tensorflow/core/grappler/clusters/cluster.h"
#include "tensorflow/core/grappler/grappler_item.h"
#include "tensorflow/core/grappler/mutable_graph_view.h"
#include "tensorflow/core/grappler/op_types.h"
#include "tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.h"
#include "tensorflow/core/grappler/optimizers/data/graph_utils.h"
#include "tensorflow/core/grappler/utils.h"
#include "tensorflow/core/platform/protobuf.h"
namespace tensorflow {
namespace grappler {
namespace {
bool IsTakeAll(const NodeDef& take_node, const GraphView& graph) {
if (take_node.op() != "TakeDataset") return false;
const NodeDef& count_node = *graph.GetNode(take_node.input(1));
// We are looking only for 'take' with negative count.
return count_node.attr().at("value").tensor().int64_val(0) < 0;
}
bool IsSkipNone(const NodeDef& skip_node, const GraphView& graph) {
if (skip_node.op() != "SkipDataset") return false;
const NodeDef& count_node = *graph.GetNode(skip_node.input(1));
// We are looking only for skip(0) nodes.
return count_node.attr().at("value").tensor().int64_val(0) == 0;
}
bool IsRepeatOne(const NodeDef& repeat_node, const GraphView& graph) {
if (repeat_node.op() != "RepeatDataset") return false;
const NodeDef& count_node = *graph.GetNode(repeat_node.input(1));
// We are looking only for repeat(1) nodes.
return count_node.attr().at("value").tensor().int64_val(0) == 1;
}
bool IsNoOp(const NodeDef& node, const GraphView& graph) {
return IsTakeAll(node, graph) || IsSkipNone(node, graph) ||
IsRepeatOne(node, graph);
}
} // namespace
Status NoOpElimination::Optimize(Cluster* cluster, const GrapplerItem& item,
GraphDef* output) {
*output = item.graph;
MutableGraphView graph(output);
std::set<string> nodes_to_delete;
for (const NodeDef& node : item.graph.node()) {
if (!IsNoOp(node, graph)) continue;
GraphView::InputPort input_port = graph.GetInputPort(node.name(), 0);
NodeDef* const parent = graph.GetRegularFanin(input_port).node;
graph.ReplaceInput(node, *parent);
nodes_to_delete.insert(node.name());
}
graph.DeleteNodes(nodes_to_delete);
return Status::OK();
}
void NoOpElimination::Feedback(Cluster* cluster, const GrapplerItem& item,
const GraphDef& optimize_output, double result) {
// no-op
}
REGISTER_GRAPH_OPTIMIZER_AS(NoOpElimination, "noop_elimination");
} // end namespace grappler
} // end namespace tensorflow
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