Feed the test input with random numbers instead of constants to prevent

constant folding from wiping out a large part of the test network
Change: 151354452

3 files changed, 19 insertions, 11 deletions

diff --git a/tensorflow/core/grappler/costs/graph_memory_test.cc b/tensorflow/core/grappler/costs/graph_memory_test.cc
index a3c58a1d76..82c86064c6 100644
--- a/tensorflow/core/grappler/costs/graph_memory_test.cc
+++ b/tensorflow/core/grappler/costs/graph_memory_test.cc
@@ -32,7 +32,11 @@ TEST_F(GraphMemoryTest, Basic) {
   GraphMemory memory(item);
   Status s = memory.InferStatically();
   TF_CHECK_OK(s);
-  EXPECT_EQ(240, memory.GetWorstCaseMemoryUsage());
+  // 5 AddN + 1 random op each generating 10 values -> 240 bytes
+  // 4 more bytes for the mean of the distribution and 4 more for the stddev.
+  EXPECT_EQ(248, memory.GetWorstCaseMemoryUsage());
+  // If at most one op executes at a time, it needs 10 inputs values and 10
+  // output values, or 8 bytes.
   EXPECT_EQ(80, memory.GetBestCaseMemoryUsage());
 }
 
@@ -44,8 +48,10 @@ TEST_F(GraphMemoryTest, UnknownBatchSize) {
   GraphMemory memory(item);
   Status s = memory.InferStatically();
   TF_CHECK_OK(s);
-  EXPECT_EQ(24, memory.GetWorstCaseMemoryUsage());
-  EXPECT_EQ(8, memory.GetBestCaseMemoryUsage());
+  // Same maths as before, except that batch size is unknown and therefore
+  // assumed to be one.
+  EXPECT_EQ(32, memory.GetWorstCaseMemoryUsage());
+  EXPECT_EQ(12, memory.GetBestCaseMemoryUsage());
 }
 
 }  // namespace
diff --git a/tensorflow/core/grappler/costs/graph_properties_test.cc b/tensorflow/core/grappler/costs/graph_properties_test.cc
index d2f448e6d3..32683644fb 100644
--- a/tensorflow/core/grappler/costs/graph_properties_test.cc
+++ b/tensorflow/core/grappler/costs/graph_properties_test.cc
@@ -49,9 +49,9 @@ TEST_F(GraphPropertiesTest, StaticProperties) {
   TF_CHECK_OK(s);
 
   for (const auto& node : item.graph.node()) {
-    if (node.op() == "Const") {
-      // The const node has no input.
-      EXPECT_EQ(0, properties.GetInputProperties(node.name()).size());
+    if (node.op() == "RandomStandardNormal") {
+      // The node has one input (the shape of the tensor to generate).
+      EXPECT_EQ(1, properties.GetInputProperties(node.name()).size());
       // The const node has one output.
       const auto props = properties.GetOutputProperties(node.name());
       EXPECT_EQ(1, props.size());
@@ -94,12 +94,13 @@ TEST_F(GraphPropertiesTest, DynamicProperties) {
   TF_CHECK_OK(s);
 
   for (const auto& node : item.graph.node()) {
-    if (node.op() == "Const") {
-      // The constant node is missing from the cost graph
+    if (node.op() == "RandomStandardNormal") {
+      // The random node is missing from the cost graph (why ?)
       EXPECT_EQ(0, properties.GetInputProperties(node.name()).size());
     } else if (node.op() == "AddN") {
-      // Since the const node is missing, we can't infer the input properties of
-      // the first AddN node. THe other AddN have the expected properties
+      // Since the random node is missing, we can't infer the input properties
+      // of the first AddN node. The other AddN nodes have the expected
+      // properties.
       if (node.name() == "AddN") {
         const auto props = properties.GetInputProperties(node.name());
         EXPECT_EQ(1, props.size());
diff --git a/tensorflow/core/grappler/inputs/trivial_test_graph_input_yielder.cc b/tensorflow/core/grappler/inputs/trivial_test_graph_input_yielder.cc
index 8370133fc4..446ae2df64 100644
--- a/tensorflow/core/grappler/inputs/trivial_test_graph_input_yielder.cc
+++ b/tensorflow/core/grappler/inputs/trivial_test_graph_input_yielder.cc
@@ -39,7 +39,8 @@ GraphDef CreateGraphDef(int num_stages, int width, int tensor_size,
 
   // x is from the feed.
   const int batch_size = tensor_size < 0 ? 1 : tensor_size;
-  Output x = Const(s.WithOpName("x"), 0.0f, {batch_size, 1});
+  Output x =
+      RandomNormal(s.WithOpName("x"), {batch_size, 1}, DataType::DT_FLOAT);
 
   // Create stages.
   std::vector<Output> last_stage;