Add an option to apply ModelPruner when building a grappler item and an option to provide specific feed nodes to the item builder.

PiperOrigin-RevId: 171758733

tensorflow/core/grappler/BUILD

@@ -100,6 +100,7 @@ cc_library(
         "//tensorflow/core:lib_internal",
         "//tensorflow/core:protos_all_cc",
         "//tensorflow/core/grappler/inputs:utils",
+        "//tensorflow/core/grappler/optimizers:model_pruner",
     ],
 )
 

tensorflow/core/grappler/grappler_item_builder.cc

@@ -36,6 +36,7 @@ limitations under the License.
 #include "tensorflow/core/graph/graph_constructor.h"
 #include "tensorflow/core/grappler/inputs/utils.h"
 #include "tensorflow/core/grappler/op_types.h"
+#include "tensorflow/core/grappler/optimizers/model_pruner.h"
 #include "tensorflow/core/grappler/utils.h"
 #include "tensorflow/core/lib/io/path.h"
 #include "tensorflow/core/platform/protobuf_internal.h"
@@ -133,12 +134,24 @@ Status OptimizeGraph(const GraphDef& graph_def_arg, GraphDef* output_graph_def,
       ConvertGraphDefToGraph(graph_ctor_opts, graph_def, graphptr.get()));
 
   // Optimize the graph.
-  GraphOptimizer optimizer(*optimizer_opts);
+  ::tensorflow::GraphOptimizer optimizer(*optimizer_opts);
   optimizer.Optimize(flr, env, devices[0], &graphptr, /*shape_map=*/nullptr);
   graphptr->ToGraphDef(output_graph_def);
 
   return Status::OK();
 }
+
+// Applies the same graph pruning logic to the graph as Session.Run in TF.
+// If the returned status is not OK, item state may be inconsistent.
+Status PruneGraph(GrapplerItem* item) {
+  ModelPruner pruner;
+  GraphDef pruned_graph;
+  Cluster* cluster = nullptr;  // ModelPruner doesn't check cluster.
+  TF_RETURN_IF_ERROR(pruner.Optimize(cluster, *item, &pruned_graph));
+  item->graph = std::move(pruned_graph);
+  return Status::OK();
+}
+
 }  // namespace
 
 // static
@@ -152,6 +165,18 @@ std::unique_ptr<GrapplerItem> GrapplerItemFromMetaGraphDef(
   new_item->id = id;
   new_item->graph = meta_graph.graph_def();
 
+  // Fill in feed nodes from config, if any provided.
+  for (const auto& feed_node : cfg.feed_nodes) {
+    const string feed_name = NodeName(feed_node);
+    if (feed_name.empty()) {
+      LOG(ERROR) << "Invalid feed node name " << feed_node
+                 << ", skipping this input.";
+      return nullptr;
+    }
+    LOG(INFO) << "Will use feed node " << feed_name;
+    new_item->feed.emplace_back(feed_name, Tensor());
+  }
+
   // Attempt to detect the fetch node(s).
   if (meta_graph.collection_def().count("train_op") > 0) {
     const CollectionDef& nodes = meta_graph.collection_def().at("train_op");
@@ -339,9 +364,23 @@ std::unique_ptr<GrapplerItem> GrapplerItemFromMetaGraphDef(
           }
         }
       }
+
       Tensor fake_input(type, shape);
       InitializeTensor(type, &fake_input);
-      new_item->feed.emplace_back(node.name(), fake_input);
+
+      if (cfg.feed_nodes.empty()) {
+        // No specific feed nodes were given. Assume all placeholders are fed.
+        new_item->feed.emplace_back(node.name(), fake_input);
+      } else if (cfg.feed_nodes.count(node.name()) > 0) {
+        // If specific feed nodes were given, only update their tensors.
+        auto it = find_if(new_item->feed.begin(), new_item->feed.end(),
+                          [&node](std::pair<string, Tensor>& f) {
+                            return f.first == node.name();
+                          });
+        QCHECK(it != new_item->feed.end());
+        it->second = fake_input;
+      }
+
       // Set the shape of the node in the graph. This is needed for statically
       // inferring shapes and is a no-op when dynamically inferring shapes as
       // the Placeholder shape will match the shape passed from new_item->feed.
@@ -418,6 +457,16 @@ std::unique_ptr<GrapplerItem> GrapplerItemFromMetaGraphDef(
     return nullptr;
   }
 
+  if (cfg.prune_graph) {
+    VLOG(1) << "Pruning graph...";
+    auto status = PruneGraph(new_item.get());
+    if (!status.ok()) {
+      LOG(ERROR) << "Pruning failed: " << status.error_message();
+      return nullptr;
+    }
+    VLOG(1) << "Pruning ran succesfully.";
+  }
+
   // Validate feed, fetch and init nodes
   std::unordered_set<string> nodes;
   for (const auto& node : new_item->graph.node()) {

tensorflow/core/grappler/grappler_item_builder.h

@@ -17,6 +17,7 @@ limitations under the License.
 #define TENSORFLOW_GRAPPLER_GRAPPLER_ITEM_BUILDER_H_
 
 #include <memory>
+#include <set>
 #include <string>
 #include "tensorflow/core/grappler/grappler_item.h"
 
@@ -45,6 +46,10 @@ struct ItemConfig {
   bool erase_noinline_attributes = false;
   // If non-empty, override the directory of asset paths.
   string assets_directory_override;
+  // If true, runs ModelPruner on the graph.
+  bool prune_graph = false;
+  // Override feed nodes list.
+  std::set<string> feed_nodes;
 };
 
 // Factory method for creating a GrapplerItem from a MetaGraphDef.