1 files changed, 0 insertions, 80 deletions
diff --git a/tensorflow/core/grappler/optimizers/constant_folding_test.cc b/tensorflow/core/grappler/optimizers/constant_folding_test.cc
index 43f84b1ddf..a1dee6d2fb 100644
--- a/tensorflow/core/grappler/optimizers/constant_folding_test.cc
+++ b/tensorflow/core/grappler/optimizers/constant_folding_test.cc
@@ -14,7 +14,6 @@ limitations under the License.
 ==============================================================================*/
 
 #include "tensorflow/core/grappler/optimizers/constant_folding.h"
-#include "tensorflow/cc/ops/array_ops_internal.h"
 #include "tensorflow/cc/ops/standard_ops.h"
 #include "tensorflow/core/framework/node_def.pb.h"
 #include "tensorflow/core/framework/tensor_testutil.h"
@@ -839,85 +838,6 @@ TEST_F(ConstantFoldingTest, Packing) {
   // size needed to naively encode 1000 floats folded twice).
   EXPECT_GT(8000, output.ByteSizeLong());
 }
-
-TEST_F(ConstantFoldingTest, ConstantMaterialization) {
-  tensorflow::Scope s = tensorflow::Scope::NewRootScope();
-  Output a =
-      ops::Placeholder(s.WithOpName("a"), DT_FLOAT,
-                       ops::Placeholder::Shape(PartialTensorShape({-1, -1})));
-  Output b = ops::Square(s.WithOpName("b"), a);
-  Output c = ops::Mul(s.WithOpName("c"), a, b);
-  Output d = ops::Shape(s.WithOpName("d"), a);
-  Output e = ops::Shape(s.WithOpName("e"), b);
-
-  auto f = ops::internal::BroadcastGradientArgs(s.WithOpName("f"), d, e);
-  Output o1 = ops::Identity(s.WithOpName("o1"), f.r0);
-  Output o2 = ops::Identity(s.WithOpName("o2"), f.r1);
-
-  Output g = ops::Placeholder(s.WithOpName("g"), DT_FLOAT,
-                              ops::Placeholder::Shape(PartialTensorShape({1})));
-  Output h = ops::Shape(s.WithOpName("h"), g);
-  auto i = ops::internal::BroadcastGradientArgs(s.WithOpName("i"), d, h);
-  Output p1 = ops::Identity(s.WithOpName("p1"), i.r0);
-  Output p2 = ops::Identity(s.WithOpName("p2"), i.r1);
-
-  GrapplerItem item;
-  TF_CHECK_OK(s.ToGraphDef(&item.graph));
-
-  ConstantFolding fold(RewriterConfig::AGGRESSIVE, nullptr /* cpu_device */);
-  GraphDef output;
-  Status status = fold.Optimize(nullptr, item, &output);
-  TF_EXPECT_OK(status);
-
-  // Run a second time to make sure the optimization is idempotent.
-  item.graph.Swap(&output);
-  status = fold.Optimize(nullptr, item, &output);
-  TF_EXPECT_OK(status);
-
-  int found = 0;
-  for (const auto& node : output.node()) {
-    if (node.name() == "o1") {
-      ++found;
-      EXPECT_EQ(1, node.input_size());
-      EXPECT_EQ("ConstantFolding/f-0", node.input(0));
-    } else if (node.name() == "o2") {
-      ++found;
-      EXPECT_EQ(1, node.input_size());
-      EXPECT_EQ("ConstantFolding/f-1", node.input(0));
-    } else if (node.name() == "ConstantFolding/f-0") {
-      ++found;
-      EXPECT_EQ("Const", node.op());
-      EXPECT_EQ(1, node.input_size());
-      EXPECT_EQ("^f", node.input(0));
-      EXPECT_EQ(0, TensorShape(node.attr().at("value").tensor().tensor_shape())
-                       .num_elements());
-    } else if (node.name() == "ConstantFolding/f-1") {
-      ++found;
-      EXPECT_EQ("Const", node.op());
-      EXPECT_EQ(1, node.input_size());
-      EXPECT_EQ("^f", node.input(0));
-      EXPECT_EQ(0, TensorShape(node.attr().at("value").tensor().tensor_shape())
-                       .num_elements());
-    } else if (node.name() == "p1") {
-      ++found;
-      EXPECT_EQ(1, node.input_size());
-      EXPECT_EQ("ConstantFolding/i-0", node.input(0));
-    } else if (node.name() == "p2") {
-      ++found;
-      EXPECT_EQ(1, node.input_size());
-      EXPECT_EQ("i:1", node.input(0));
-    } else if (node.name() == "ConstantFolding/i-0") {
-      ++found;
-      EXPECT_EQ("Const", node.op());
-      EXPECT_EQ(1, node.input_size());
-      EXPECT_EQ("^i", node.input(0));
-      EXPECT_EQ(0, TensorShape(node.attr().at("value").tensor().tensor_shape())
-                       .num_elements());
-    }
-  }
-  EXPECT_EQ(7, found);
-}
-
 }  // namespace
 }  // namespace grappler
 }  // namespace tensorflow