Automated rollback of change 155258346

Change: 155292907

7 files changed, 60 insertions, 283 deletions

diff --git a/tensorflow/contrib/tfprof/README.md b/tensorflow/contrib/tfprof/README.md
index d891ecdc9a..c7ff4a2921 100644
--- a/tensorflow/contrib/tfprof/README.md
+++ b/tensorflow/contrib/tfprof/README.md
@@ -11,12 +11,7 @@ Consultants: Jon Shlens, Pete Warden
 1.  Measure model parameters, float operations, tensor shapes.
 2.  Measure op execution times, requested memory size and device placement.
 3.  Inspect checkpoint tensors' shapes and their values.
-4.  3 ways to view and explore TensorFlow model profiles
-
-    *  Organize by Python code call stack.
-    *  Organize by TensorFlow operation name scope hierarchies.
-    *  Organize by TensorFlow operation inputs/outputs graph.
-
+4.  Explore model based on name scope or graph structure.
 5.  Selectively grouping/filtering/accounting/ordering ops.
 
 tfprof can be used as Python API, Interactive CLI and One-shot Script.
@@ -33,8 +28,7 @@ param_stats = tf.contrib.tfprof.model_analyzer.print_model_analysis(
     tfprof_options=tf.contrib.tfprof.model_analyzer.
         TRAINABLE_VARS_PARAMS_STAT_OPTIONS)
 
-# param_stats is tensorflow.tfprof.TFGraphNodeProto proto.
-# It organize the statistics
+# param_stats is tensorflow.tfprof.TFProfNode proto. It organize the statistics
 # of each graph node in tree scructure. Let's print the root below.
 sys.stdout.write('total_params: %d\n' % param_stats.total_parameters)
 ```
diff --git a/tensorflow/contrib/tfprof/python/tools/tfprof/BUILD b/tensorflow/contrib/tfprof/python/tools/tfprof/BUILD
index 22bca93c87..818c2d2cbf 100644
--- a/tensorflow/contrib/tfprof/python/tools/tfprof/BUILD
+++ b/tensorflow/contrib/tfprof/python/tools/tfprof/BUILD
@@ -23,31 +23,14 @@ py_test(
     srcs_version = "PY2AND3",
     deps = [
         ":model_analyzer",
-        ":model_analyzer_testlib",
-        "//tensorflow/python:client",
-        "//tensorflow/python:client_testlib",
-        "//tensorflow/python:framework_for_generated_wrappers",
-        "//tensorflow/python:platform",
-        "//tensorflow/python:variables",
-    ],
-)
-
-py_library(
-    name = "model_analyzer_testlib",
-    srcs = ["model_analyzer_testlib.py"],
-    srcs_version = "PY2AND3",
-    deps = [
-        ":model_analyzer",
-        "//tensorflow/contrib/rnn:rnn_py",
         "//tensorflow/core:protos_all_py",
         "//tensorflow/python:array_ops",
+        "//tensorflow/python:client",
+        "//tensorflow/python:client_testlib",
         "//tensorflow/python:framework_for_generated_wrappers",
         "//tensorflow/python:init_ops",
-        "//tensorflow/python:math_ops",
         "//tensorflow/python:nn_ops",
         "//tensorflow/python:platform",
-        "//tensorflow/python:rnn",
-        "//tensorflow/python:training",
         "//tensorflow/python:variable_scope",
         "//tensorflow/python:variables",
     ],
diff --git a/tensorflow/contrib/tfprof/python/tools/tfprof/model_analyzer.py b/tensorflow/contrib/tfprof/python/tools/tfprof/model_analyzer.py
index 13b407d815..cc94fd65b5 100644
--- a/tensorflow/contrib/tfprof/python/tools/tfprof/model_analyzer.py
+++ b/tensorflow/contrib/tfprof/python/tools/tfprof/model_analyzer.py
@@ -123,7 +123,7 @@ def print_model_analysis(graph,
   """Print model statistics.
 
     Prints the model statistics to stdout. Also returns the results
-    in a TFGraphNodeProto proto. See go/tfprof or run tfprof tool:
+    in a TFProfNode proto. See go/tfprof or run tfprof tool:
     'bazel run third_party/tensorflow/tools/tfprof help'
 
     Examples:
@@ -142,19 +142,15 @@ def print_model_analysis(graph,
               'micros' and 'bytes'.
     op_log: tensorflow::tfprof::OpLog proto. users can use this proto to
             group together ops and use a op_type to select the group.
-    tfprof_cmd: string. Either 'scope', 'graph', 'code'.
-                'scope' view organize outputs using ops' name scope.
-                'graph' view organize outputs using op's inputs/outputs.
-                'code' view organize outputs using Python call stack.
+    tfprof_cmd: string. Either 'scope' or 'graph'. 'scope' view organize
+                ops using their name scopes. 'graph' view organize ops using
+                their graph inputs.
     tfprof_options: See 'tfprof help' for details.
   Returns:
-    If tfprof_cmd is 'scope' or 'graph', returns TFGraphNodeProto proto.
-    If tfprof_cmd is 'code', returns TFCodeNodeProto proto.
-    Side effect: a formatted output to stdout.
+    TFProfNode proto. Side effect: a formatted output to stdout.
   """
   # pylint: disable=protected-access
-  op_log = tfprof_logger._merge_default_with_oplog(
-      graph, op_log, run_meta, add_trace=tfprof_cmd == 'code')
+  op_log = tfprof_logger._merge_default_with_oplog(graph, op_log, run_meta)
   # pylint: enable=protected-access
   opts = tfprof_options_pb2.OptionsProto()
   opts.max_depth = tfprof_options['max_depth']
@@ -182,24 +178,11 @@ def print_model_analysis(graph,
   opts.dump_to_file = tfprof_options['dump_to_file']
 
   run_meta_str = run_meta.SerializeToString() if run_meta else b''
+  op_log_str = op_log.SerializeToString() if op_log else b''
 
-  if tfprof_cmd == 'code':
-    tfprof_node = tfprof_output_pb2.TFCodeNodeProto()
-    tfprof_node.ParseFromString(
-        print_mdl.PrintModelAnalysis(
-            graph.as_graph_def().SerializeToString(),
-            run_meta_str,
-            op_log.SerializeToString(),
-            tfprof_cmd.encode('utf-8'),
-            opts.SerializeToString()))
-  else:
-    tfprof_node = tfprof_output_pb2.TFGraphNodeProto()
-    tfprof_node.ParseFromString(
-        print_mdl.PrintModelAnalysis(
-            graph.as_graph_def().SerializeToString(),
-            run_meta_str,
-            op_log.SerializeToString(),
-            tfprof_cmd.encode('utf-8'),
-            opts.SerializeToString()))
-
+  tfprof_node = tfprof_output_pb2.TFProfNode()
+  tfprof_node.ParseFromString(
+      print_mdl.PrintModelAnalysis(
+          graph.as_graph_def().SerializeToString(), run_meta_str, op_log_str,
+          tfprof_cmd.encode('utf-8'), opts.SerializeToString()))
   return tfprof_node
diff --git a/tensorflow/contrib/tfprof/python/tools/tfprof/model_analyzer_test.py b/tensorflow/contrib/tfprof/python/tools/tfprof/model_analyzer_test.py
index ac0d46d4ae..66b9267cbe 100644
--- a/tensorflow/contrib/tfprof/python/tools/tfprof/model_analyzer_test.py
+++ b/tensorflow/contrib/tfprof/python/tools/tfprof/model_analyzer_test.py
@@ -18,27 +18,49 @@ from __future__ import division
 from __future__ import print_function
 
 import os
+
 from tensorflow.core.protobuf import config_pb2
 from tensorflow.python.client import session
+from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import ops
+from tensorflow.python.ops import array_ops
+from tensorflow.python.ops import init_ops
+from tensorflow.python.ops import nn_ops
+from tensorflow.python.ops import variable_scope
 from tensorflow.python.ops import variables
 from tensorflow.python.platform import gfile
 from tensorflow.python.platform import test
 
 # XXX: this depends on pywrap_tensorflow and must come later
 from tensorflow.contrib.tfprof.python.tools.tfprof import model_analyzer
-from tensorflow.contrib.tfprof.python.tools.tfprof import model_analyzer_testlib as lib
 
 
 class PrintModelAnalysisTest(test.TestCase):
 
+  def _BuildSmallModel(self):
+    image = array_ops.zeros([2, 6, 6, 3])
+    _ = variable_scope.get_variable(
+        'ScalarW', [],
+        dtypes.float32,
+        initializer=init_ops.random_normal_initializer(stddev=0.001))
+    kernel = variable_scope.get_variable(
+        'DW', [3, 3, 3, 6],
+        dtypes.float32,
+        initializer=init_ops.random_normal_initializer(stddev=0.001))
+    x = nn_ops.conv2d(image, kernel, [1, 2, 2, 1], padding='SAME')
+    kernel = variable_scope.get_variable(
+        'DW2', [2, 2, 6, 12],
+        dtypes.float32,
+        initializer=init_ops.random_normal_initializer(stddev=0.001))
+    x = nn_ops.conv2d(x, kernel, [1, 2, 2, 1], padding='SAME')
+    return x
+
   def testDumpToFile(self):
-    ops.reset_default_graph()
     opts = model_analyzer.TRAINABLE_VARS_PARAMS_STAT_OPTIONS
     opts['dump_to_file'] = os.path.join(test.get_temp_dir(), 'dump')
 
     with session.Session() as sess, ops.device('/cpu:0'):
-      _ = lib.BuildSmallModel()
+      _ = self._BuildSmallModel()
       model_analyzer.print_model_analysis(sess.graph, tfprof_options=opts)
 
       with gfile.Open(opts['dump_to_file'], 'r') as f:
@@ -49,7 +71,6 @@ class PrintModelAnalysisTest(test.TestCase):
                          f.read())
 
   def testSelectEverything(self):
-    ops.reset_default_graph()
     opts = model_analyzer.TRAINABLE_VARS_PARAMS_STAT_OPTIONS
     opts['dump_to_file'] = os.path.join(test.get_temp_dir(), 'dump')
     opts['account_type_regexes'] = ['.*']
@@ -57,10 +78,8 @@ class PrintModelAnalysisTest(test.TestCase):
         'bytes', 'params', 'float_ops', 'num_hidden_ops', 'device', 'op_types'
     ]
 
-    config = config_pb2.ConfigProto(
-        graph_options=config_pb2.GraphOptions(build_cost_model=1))
-    with session.Session(config=config) as sess, ops.device('/cpu:0'):
-      x = lib.BuildSmallModel()
+    with session.Session() as sess, ops.device('/cpu:0'):
+      x = self._BuildSmallModel()
 
       sess.run(variables.global_variables_initializer())
       run_meta = config_pb2.RunMetadata()
@@ -79,121 +98,6 @@ class PrintModelAnalysisTest(test.TestCase):
             f.read())
         # pylint: enable=line-too-long
 
-  def testSimpleCodeView(self):
-    ops.reset_default_graph()
-    opts = model_analyzer.TRAINABLE_VARS_PARAMS_STAT_OPTIONS.copy()
-    opts['dump_to_file'] = os.path.join(test.get_temp_dir(), 'dump')
-    opts['account_type_regexes'] = ['.*']
-    opts['show_name_regexes'] = ['.*model_analyzer_testlib.*']
-    opts['account_displayed_op_only'] = False
-    # TODO(xpan): Test 'micros'. Since the execution time changes each run,
-    # it's a bit difficult to test it now.
-    opts['select'] = [
-        'bytes', 'params', 'float_ops', 'num_hidden_ops', 'device',
-    ]
-
-    config = config_pb2.ConfigProto(
-        graph_options=config_pb2.GraphOptions(build_cost_model=1))
-    with session.Session(config=config) as sess, ops.device('/cpu:0'):
-      x = lib.BuildSmallModel()
-
-      sess.run(variables.global_variables_initializer())
-      run_meta = config_pb2.RunMetadata()
-      _ = sess.run(x,
-                   options=config_pb2.RunOptions(
-                       trace_level=config_pb2.RunOptions.FULL_TRACE),
-                   run_metadata=run_meta)
-
-      model_analyzer.print_model_analysis(
-          sess.graph, run_meta, tfprof_cmd='code', tfprof_options=opts)
-
-      with gfile.Open(opts['dump_to_file'], 'r') as f:
-        # pylint: disable=line-too-long
-        self.assertEqual(
-            '_TFProfRoot (0/451 params, 0/10.44k flops, 0B/5.28KB)\n  model_analyzer_testlib.py:33:BuildSmallModel:image = array_ops... (0/0 params, 0/0 flops, 0B/864B)\n  model_analyzer_testlib.py:37:BuildSmallModel:initializer=init_... (0/1 params, 0/0 flops, 0B/0B)\n  model_analyzer_testlib.py:41:BuildSmallModel:initializer=init_... (0/162 params, 0/0 flops, 0B/1.30KB)\n  model_analyzer_testlib.py:42:BuildSmallModel:x = nn_ops.conv2d... (0/0 params, 0/5.83k flops, 0B/432B)\n  model_analyzer_testlib.py:46:BuildSmallModel:initializer=init_... (0/288 params, 0/0 flops, 0B/2.30KB)\n  model_analyzer_testlib.py:47:BuildSmallModel:x = nn_ops.conv2d... (0/0 params, 0/4.61k flops, 0B/384B)\n',
-            f.read())
-        # pylint: enable=line-too-long
-
-  def testComplexCodeView(self):
-    ops.reset_default_graph()
-    opts = model_analyzer.TRAINABLE_VARS_PARAMS_STAT_OPTIONS.copy()
-    opts['dump_to_file'] = os.path.join(test.get_temp_dir(), 'dump')
-    opts['account_type_regexes'] = ['.*']
-    opts['show_name_regexes'] = ['.*model_analyzer_testlib.py.*']
-    opts['account_displayed_op_only'] = False
-    opts['select'] = [
-        'bytes', 'params', 'float_ops', 'num_hidden_ops', 'device',
-    ]
-
-    config = config_pb2.ConfigProto(
-        graph_options=config_pb2.GraphOptions(build_cost_model=1))
-    with session.Session(config=config) as sess, ops.device('/cpu:0'):
-      x = lib.BuildFullModel()
-
-      sess.run(variables.global_variables_initializer())
-      run_meta = config_pb2.RunMetadata()
-      _ = sess.run(x,
-                   options=config_pb2.RunOptions(
-                       trace_level=config_pb2.RunOptions.FULL_TRACE),
-                   run_metadata=run_meta)
-
-      tfprof_node = model_analyzer.print_model_analysis(
-          sess.graph, run_meta, tfprof_cmd='code', tfprof_options=opts)
-
-      # pylint: disable=line-too-long
-      with gfile.Open(opts['dump_to_file'], 'r') as f:
-        self.assertEqual(
-            '_TFProfRoot (0/2.84k params, 0/54.08k flops, 0B/241.58KB)\n  model_analyzer_testlib.py:56:BuildFullModel:seq.append(array_... (0/1.80k params, 0/41.76k flops, 0B/20.08KB)\n    model_analyzer_testlib.py:33:BuildSmallModel:image = array_ops... (0/0 params, 0/0 flops, 0B/864B)\n    model_analyzer_testlib.py:37:BuildSmallModel:initializer=init_... (0/4 params, 0/0 flops, 0B/0B)\n    model_analyzer_testlib.py:41:BuildSmallModel:initializer=init_... (0/648 params, 0/0 flops, 0B/5.18KB)\n    model_analyzer_testlib.py:42:BuildSmallModel:x = nn_ops.conv2d... (0/0 params, 0/23.33k flops, 0B/1.73KB)\n    model_analyzer_testlib.py:46:BuildSmallModel:initializer=init_... (0/1.15k params, 0/0 flops, 0B/9.22KB)\n    model_analyzer_testlib.py:47:BuildSmallModel:x = nn_ops.conv2d... (0/0 params, 0/18.43k flops, 0B/1.54KB)\n  model_analyzer_testlib.py:60:BuildFullModel:cell, array_ops.c... (0/1.04k params, 0/4.13k flops, 0B/24.86KB)\n  model_analyzer_testlib.py:62:BuildFullModel:target = array_op... (0/0 params, 0/0 flops, 0B/0B)\n  model_analyzer_testlib.py:63:BuildFullModel:loss = nn_ops.l2_... (0/0 params, 0/0 flops, 0B/528B)\n  model_analyzer_testlib.py:65:BuildFullModel:return sgd_op.min... (0/0 params, 0/8.19k flops, 0B/196.12KB)\n',
-            f.read())
-
-      self.assertEqual(241584, tfprof_node.total_requested_bytes)
-      self.assertLess(0, tfprof_node.total_exec_micros)
-      self.assertEqual(2844, tfprof_node.total_parameters)
-      self.assertEqual(54080, tfprof_node.total_float_ops)
-      self.assertEqual(5, len(tfprof_node.children))
-      self.assertEqual('_TFProfRoot', tfprof_node.name)
-      self.assertEqual('model_analyzer_testlib.py:56:BuildFullModel:seq.append(array_...',
-                       tfprof_node.children[0].name)
-      self.assertEqual('model_analyzer_testlib.py:60:BuildFullModel:cell, array_ops.c...',
-                       tfprof_node.children[1].name)
-      self.assertEqual('model_analyzer_testlib.py:62:BuildFullModel:target = array_op...',
-                       tfprof_node.children[2].name)
-      self.assertEqual('model_analyzer_testlib.py:63:BuildFullModel:loss = nn_ops.l2_...',
-                       tfprof_node.children[3].name)
-      self.assertEqual('model_analyzer_testlib.py:65:BuildFullModel:return sgd_op.min...',
-                       tfprof_node.children[4].name)
-      # pylint: enable=line-too-long
-
-  def testCodeViewLeafGraphNode(self):
-    ops.reset_default_graph()
-    opts = model_analyzer.TRAINABLE_VARS_PARAMS_STAT_OPTIONS.copy()
-    opts['account_type_regexes'] = ['.*']
-    opts['account_displayed_op_only'] = False
-    opts['select'] = [
-        'bytes', 'params', 'float_ops', 'num_hidden_ops', 'device',
-    ]
-
-    config = config_pb2.ConfigProto(
-        graph_options=config_pb2.GraphOptions(build_cost_model=1))
-    with session.Session(config=config) as sess, ops.device('/cpu:0'):
-      x = lib.BuildSmallModel()
-
-      sess.run(variables.global_variables_initializer())
-      run_meta = config_pb2.RunMetadata()
-      _ = sess.run(x,
-                   options=config_pb2.RunOptions(
-                       trace_level=config_pb2.RunOptions.FULL_TRACE),
-                   run_metadata=run_meta)
-
-      tfprof_node = model_analyzer.print_model_analysis(
-          sess.graph, run_meta, tfprof_cmd='code', tfprof_options=opts)
-
-      leaf = tfprof_node
-      while leaf.children:
-        self.assertEqual(0, len(leaf.graph_nodes))
-        leaf = leaf.children[0]
-      self.assertEqual(1, len(leaf.graph_nodes))
-
 
 if __name__ == '__main__':
   test.main()
diff --git a/tensorflow/contrib/tfprof/python/tools/tfprof/model_analyzer_testlib.py b/tensorflow/contrib/tfprof/python/tools/tfprof/model_analyzer_testlib.py
deleted file mode 100644
index 81bac84b8c..0000000000
--- a/tensorflow/contrib/tfprof/python/tools/tfprof/model_analyzer_testlib.py
+++ /dev/null
@@ -1,67 +0,0 @@
-# Copyright 2016 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.
-# ==============================================================================
-"""A test lib that defines some models."""
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-from tensorflow.contrib.rnn.python.ops.core_rnn_cell import BasicRNNCell
-from tensorflow.python.framework import dtypes
-from tensorflow.python.ops import array_ops
-from tensorflow.python.ops import init_ops
-from tensorflow.python.ops import math_ops
-from tensorflow.python.ops import nn_ops
-from tensorflow.python.ops import rnn
-from tensorflow.python.ops import variable_scope
-from tensorflow.python.training import gradient_descent
-
-
-def BuildSmallModel():
-  """Build a small forward conv model."""
-  image = array_ops.zeros([2, 6, 6, 3])
-  _ = variable_scope.get_variable(
-      'ScalarW', [],
-      dtypes.float32,
-      initializer=init_ops.random_normal_initializer(stddev=0.001))
-  kernel = variable_scope.get_variable(
-      'DW', [3, 3, 3, 6],
-      dtypes.float32,
-      initializer=init_ops.random_normal_initializer(stddev=0.001))
-  x = nn_ops.conv2d(image, kernel, [1, 2, 2, 1], padding='SAME')
-  kernel = variable_scope.get_variable(
-      'DW2', [2, 2, 6, 12],
-      dtypes.float32,
-      initializer=init_ops.random_normal_initializer(stddev=0.001))
-  x = nn_ops.conv2d(x, kernel, [1, 2, 2, 1], padding='SAME')
-  return x
-
-
-def BuildFullModel():
-  """Build the full model with conv,rnn,opt."""
-  seq = []
-  for i in xrange(4):
-    with variable_scope.variable_scope('inp_%d' % i):
-      seq.append(array_ops.reshape(BuildSmallModel(), [2, 1, -1]))
-
-  cell = BasicRNNCell(16, 48)
-  out = rnn.dynamic_rnn(
-      cell, array_ops.concat(seq, axis=1), dtype=dtypes.float32)[0]
-
-  target = array_ops.ones_like(out)
-  loss = nn_ops.l2_loss(math_ops.reduce_mean(target - out))
-  sgd_op = gradient_descent.GradientDescentOptimizer(1e-2)
-  return sgd_op.minimize(loss)
-
-
diff --git a/tensorflow/contrib/tfprof/python/tools/tfprof/print_model_analysis_test.py b/tensorflow/contrib/tfprof/python/tools/tfprof/print_model_analysis_test.py
index aa133d3142..f0ac36c66a 100644
--- a/tensorflow/contrib/tfprof/python/tools/tfprof/print_model_analysis_test.py
+++ b/tensorflow/contrib/tfprof/python/tools/tfprof/print_model_analysis_test.py
@@ -96,13 +96,12 @@ class PrintModelAnalysisTest(test.TestCase):
 
     with session.Session() as sess, ops.device('/cpu:0'):
       _ = self._BuildSmallModel()
-      tfprof_pb = tfprof_output_pb2.TFGraphNodeProto()
+      tfprof_pb = tfprof_output_pb2.TFProfNode()
       tfprof_pb.ParseFromString(
-          print_mdl.PrintModelAnalysis(
-              sess.graph.as_graph_def().SerializeToString(),
-              b'', b'', b'scope', opts.SerializeToString()))
+          print_mdl.PrintModelAnalysis(sess.graph.as_graph_def(
+          ).SerializeToString(), b'', b'', b'scope', opts.SerializeToString()))
 
-      expected_pb = tfprof_output_pb2.TFGraphNodeProto()
+      expected_pb = tfprof_output_pb2.TFProfNode()
       text_format.Merge(r"""name: "_TFProfRoot"
       exec_micros: 0
       requested_bytes: 0
diff --git a/tensorflow/contrib/tfprof/python/tools/tfprof/tfprof_logger.py b/tensorflow/contrib/tfprof/python/tools/tfprof/tfprof_logger.py
index cd3912bbfb..e8cf84b6c7 100644
--- a/tensorflow/contrib/tfprof/python/tools/tfprof/tfprof_logger.py
+++ b/tensorflow/contrib/tfprof/python/tools/tfprof/tfprof_logger.py
@@ -62,13 +62,12 @@ def _fill_missing_graph_shape(graph, run_meta):
   return graph
 
 
-def _get_logged_ops(graph, run_meta=None, add_trace=False):
+def _get_logged_ops(graph, run_meta=None):
   """Extract trainable model parameters and FLOPs for ops from a Graph.
 
   Args:
     graph: tf.Graph.
     run_meta: RunMetadata proto used to complete shape information.
-    add_trace: Whether to add op trace information.
   Returns:
     logged_ops: dict mapping from op_name to OpLogEntry.
   """
@@ -77,32 +76,21 @@ def _get_logged_ops(graph, run_meta=None, add_trace=False):
 
   op_missing_shape = 0
   logged_ops = {}
-  for op in graph.get_operations():
+  graph_def = graph.as_graph_def()
+  for node in graph_def.node:
     try:
-      stats = ops.get_stats_for_node_def(
-          graph, op.node_def, REGISTERED_FLOP_STATS)
+      stats = ops.get_stats_for_node_def(graph, node, REGISTERED_FLOP_STATS)
     except ValueError:
       # Catch Exception When shape is incomplete. Skip it.
       op_missing_shape += 1
       stats = None
 
-    entry = tfprof_log_pb2.OpLogEntry()
-    entry.name = op.name
-    add_entry = False
-    if stats and stats.value:
+    if not stats or not stats.value:
+      continue
+    if node.name not in logged_ops:
+      entry = tfprof_log_pb2.OpLogEntry()
+      entry.name = node.name
       entry.float_ops = int(stats.value)
-      add_entry = True
-
-    if add_trace:
-      for tb in op.traceback:
-        trace = entry.code_def.traces.add()
-        trace.file = tb[0] if tb[0] else 'none'
-        trace.lineno = tb[1] if tb[1] else -1
-        trace.function = tb[2] if tb[2] else 'none'
-        trace.line = tb[3] if tb[3] else 'none'
-      add_entry = True
-
-    if add_entry:
       logged_ops[entry.name] = entry
 
   for v in graph.get_collection(ops.GraphKeys.TRAINABLE_VARIABLES):
@@ -120,21 +108,18 @@ def _get_logged_ops(graph, run_meta=None, add_trace=False):
   return logged_ops
 
 
-def _merge_default_with_oplog(graph, op_log=None,
-                              run_meta=None,
-                              add_trace=False):
+def _merge_default_with_oplog(graph, op_log=None, run_meta=None):
   """Merge the tfprof default extra info with caller's op_log.
 
   Args:
     graph: tf.Graph.
     op_log: OpLog proto.
     run_meta: RunMetadata proto used to complete shape information.
-    add_trace: Whether to add op trace information.
   Returns:
     tmp_op_log: Merged OpLog proto.
   """
   tmp_op_log = tfprof_log_pb2.OpLog()
-  logged_ops = _get_logged_ops(graph, run_meta, add_trace=add_trace)
+  logged_ops = _get_logged_ops(graph, run_meta)
   if not op_log:
     tmp_op_log.log_entries.extend(logged_ops.values())
   else:
@@ -146,16 +131,13 @@ def _merge_default_with_oplog(graph, op_log=None,
         all_ops[op_name].types.extend(entry.types)
         if entry.float_ops > 0 and all_ops[op_name].float_ops == 0:
           all_ops[op_name].float_ops = entry.float_ops
-        if entry.code_def.traces and not all_ops[op_name].code_def.traces:
-          all_ops[op_name].code_def.MergeFrom(entry.code_def)
       else:
         all_ops[op_name] = entry
     tmp_op_log.log_entries.extend(all_ops.values())
   return tmp_op_log
 
 
-def write_op_log(graph, log_dir, op_log=None, run_meta=None,
-                 add_trace=False):
+def write_op_log(graph, log_dir, op_log=None, run_meta=None):
   """Log provided 'op_log', and add additional model information below.
 
     The API also assigns ops in tf.trainable_variables() an op type called
@@ -172,9 +154,8 @@ def write_op_log(graph, log_dir, op_log=None, run_meta=None,
         one is created.
     run_meta: (Optional) RunMetadata proto that helps flops computation using
         run time shape information.
-    add_trace: Whether to add op trace information. Used to support "code" view.
   """
-  op_log = _merge_default_with_oplog(graph, op_log, run_meta, add_trace)
+  op_log = _merge_default_with_oplog(graph, op_log, run_meta)
 
   with gfile.Open(os.path.join(log_dir, 'tfprof_log'), 'w') as log:
     log.write(op_log.SerializeToString())