Python model_analyzer.profile函数代码示例

  def testSelectEverything(self):
    ops.reset_default_graph()
    outfile = os.path.join(test.get_temp_dir(), 'dump')
    opts = (builder(builder.trainable_variables_parameter())
            .with_file_output(outfile)
            .with_accounted_types(['.*'])
            .select(['params', 'float_ops', 'occurrence', 'device', 'op_types',
                     'input_shapes']).build())

    rewriter_config = rewriter_config_pb2.RewriterConfig(
        disable_model_pruning=True)
    graph_options = config_pb2.GraphOptions(rewrite_options=rewriter_config)
    config = config_pb2.ConfigProto(graph_options=graph_options)
    with session.Session(config=config) as sess, ops.device('/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.profile(
          sess.graph, run_meta, options=opts)

  def testTrackPersistentBytes(self):
    ops.reset_default_graph()
    a = array_ops.constant(np.ones((100, 100)))
    b = array_ops.constant(np.ones((100, 100)))
    c = a * b

    with session.Session() as sess:
      run_options = config_pb2.RunOptions(
          trace_level=config_pb2.RunOptions.FULL_TRACE)
      run_metadata = config_pb2.RunMetadata()
      sess.run(c, options=run_options, run_metadata=run_metadata)

      options = option_builder.ProfileOptionBuilder.time_and_memory()
      options['min_bytes'] = 0
      options['select'] = ('bytes', 'peak_bytes', 'output_bytes',
                           'residual_bytes')
      ret = model_analyzer.profile(
          sess.graph, run_meta=run_metadata, cmd='scope', options=options)

      run_metadata = config_pb2.RunMetadata()
      sess.run(c, options=run_options, run_metadata=run_metadata)
      ret2 = model_analyzer.profile(
          sess.graph, run_meta=run_metadata, cmd='scope', options=options)

      n = lib.SearchTFProfNode(ret, 'mul')
      n2 = lib.SearchTFProfNode(ret2, 'mul')
      self.assertGreater(n.peak_bytes, 0)
      self.assertGreater(n.output_bytes, 0)
      self.assertGreater(n.residual_bytes, 0)
      self.assertEqual(n.peak_bytes, n2.peak_bytes)
      self.assertEqual(n.output_bytes, n2.output_bytes)
      self.assertEqual(n.residual_bytes, n2.residual_bytes)

  def testSelectEverthingDetail(self):
    ops.reset_default_graph()
    dev = '/gpu:0' if test.is_gpu_available() else '/cpu:0'
    outfile = os.path.join(test.get_temp_dir(), 'dump')
    opts = (builder(builder.trainable_variables_parameter())
            .with_file_output(outfile)
            .with_accounted_types(['.*'])
            .select(['micros', 'bytes', 'params', 'float_ops', 'occurrence',
                     'device', 'op_types', 'input_shapes']).build())

    config = config_pb2.ConfigProto()
    with session.Session(config=config) as sess, ops.device(dev):
      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.profile(
          sess.graph, run_meta, options=opts)

      with gfile.Open(outfile, 'r') as f:
        # pylint: disable=line-too-long
        outputs = f.read().split('\n')

        self.assertEqual(outputs[0],
                         'node name | # parameters | # float_ops | requested bytes | total execution time | accelerator execution time | cpu execution time | assigned devices | op types | op count (run|defined) | input shapes')
        for o in outputs[1:]:
          if o.find('Conv2D ') > 0:
            metrics = o[o.find('(') +1: o.find(')')].split(',')
            # Make sure time is profiled.
            gap = 1 if test.is_gpu_available() else 2
            for i in range(3, 6, gap):
              mat = re.search('(.*)[um]s/(.*)[um]s', metrics[i])
              self.assertGreater(float(mat.group(1)), 0.0)
              self.assertGreater(float(mat.group(2)), 0.0)
            # Make sure device is profiled.
            if test.is_gpu_available():
              self.assertTrue(metrics[6].find('gpu') > 0)
              self.assertFalse(metrics[6].find('cpu') > 0)
            else:
              self.assertFalse(metrics[6].find('gpu') > 0)
              self.assertTrue(metrics[6].find('cpu') > 0)
            # Make sure float_ops is profiled.
            mat = re.search('(.*)k/(.*)k flops', metrics[1].strip())
            self.assertGreater(float(mat.group(1)), 0.0)
            self.assertGreater(float(mat.group(2)), 0.0)
            # Make sure op_count is profiled.
            self.assertEqual(metrics[8].strip(), '1/1|1/1')
            # Make sure input_shapes is profiled.
            self.assertEqual(metrics[9].strip(), '0:2x6x6x3|1:3x3x3x6')

          if o.find('DW (3x3x3x6') > 0:
            metrics = o[o.find('(') +1: o.find(')')].split(',')
            mat = re.search('(.*)/(.*) params', metrics[1].strip())
            self.assertGreater(float(mat.group(1)), 0.0)
            self.assertGreater(float(mat.group(2)), 0.0)

  def testSelectEverything(self):
    ops.reset_default_graph()
    outfile = os.path.join(test.get_temp_dir(), 'dump')
    opts = (builder(builder.trainable_variables_parameter())
            .with_file_output(outfile)
            .with_accounted_types(['.*'])
            .select(['params', 'float_ops', 'occurrence', 'device', 'op_types',
                     'input_shapes']).build())

    rewriter_config = rewriter_config_pb2.RewriterConfig(
        disable_model_pruning=True)
    graph_options = config_pb2.GraphOptions(rewrite_options=rewriter_config)
    config = config_pb2.ConfigProto(graph_options=graph_options)
    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.profile(
          sess.graph, run_meta, options=opts)

      with gfile.Open(outfile, 'r') as f:
        # pylint: disable=line-too-long
        self.assertEqual(
            'node name | # parameters | # float_ops | assigned devices | op types | op count (run|defined) | input shapes\n_TFProfRoot (--/451 params, --/10.44k flops, _kTFScopeParent, --/7|--/35, )\n  Conv2D (0/0 params, 5.83k/5.83k flops, /job:localhost/replica:0/task:0/cpu:0, /job:localhost/replica:0/task:0/cpu:0|Conv2D, 1/1|1/1, 0:2x6x6x3|1:3x3x3x6)\n  Conv2D_1 (0/0 params, 4.61k/4.61k flops, /job:localhost/replica:0/task:0/cpu:0, /job:localhost/replica:0/task:0/cpu:0|Conv2D, 1/1|1/1, 0:2x3x3x6|1:2x2x6x12)\n  DW (3x3x3x6, 162/162 params, 0/0 flops, /job:localhost/replica:0/task:0/cpu:0, /job:localhost/replica:0/task:0/cpu:0|VariableV2|_trainable_variables, 1/2|1/10, )\n    DW/Assign (0/0 params, 0/0 flops, Assign, 0/0|1/1, 0:3x3x3x6|1:3x3x3x6)\n    DW/Initializer (0/0 params, 0/0 flops, _kTFScopeParent, 0/0|1/7, )\n      DW/Initializer/random_normal (0/0 params, 0/0 flops, Add, 0/0|1/6, 0:3x3x3x6|1:1)\n        DW/Initializer/random_normal/RandomStandardNormal (0/0 params, 0/0 flops, RandomStandardNormal, 0/0|1/1, 0:4)\n        DW/Initializer/random_normal/mean (0/0 params, 0/0 flops, Const, 0/0|1/1, )\n        DW/Initializer/random_normal/mul (0/0 params, 0/0 flops, Mul, 0/0|1/1, 0:3x3x3x6|1:1)\n        DW/Initializer/random_normal/shape (0/0 params, 0/0 flops, Const, 0/0|1/1, )\n        DW/Initializer/random_normal/stddev (0/0 params, 0/0 flops, Const, 0/0|1/1, )\n    DW/read (0/0 params, 0/0 flops, /job:localhost/replica:0/task:0/cpu:0, /job:localhost/replica:0/task:0/cpu:0|Identity, 1/1|1/1, 0:3x3x3x6)\n  DW2 (2x2x6x12, 288/288 params, 0/0 flops, /job:localhost/replica:0/task:0/cpu:0, /job:localhost/replica:0/task:0/cpu:0|VariableV2|_trainable_variables, 1/2|1/10, )\n    DW2/Assign (0/0 params, 0/0 flops, Assign, 0/0|1/1, 0:2x2x6x12|1:2x2x6x12)\n    DW2/Initializer (0/0 params, 0/0 flops, _kTFScopeParent, 0/0|1/7, )\n      DW2/Initializer/random_normal (0/0 params, 0/0 flops, Add, 0/0|1/6, 0:2x2x6x12|1:1)\n        DW2/Initializer/random_normal/RandomStandardNormal (0/0 params, 0/0 flops, RandomStandardNormal, 0/0|1/1, 0:4)\n        DW2/Initializer/random_normal/mean (0/0 params, 0/0 flops, Const, 0/0|1/1, )\n        DW2/Initializer/random_normal/mul (0/0 params, 0/0 flops, Mul, 0/0|1/1, 0:2x2x6x12|1:1)\n        DW2/Initializer/random_normal/shape (0/0 params, 0/0 flops, Const, 0/0|1/1, )\n        DW2/Initializer/random_normal/stddev (0/0 params, 0/0 flops, Const, 0/0|1/1, )\n    DW2/read (0/0 params, 0/0 flops, /job:localhost/replica:0/task:0/cpu:0, /job:localhost/replica:0/task:0/cpu:0|Identity, 1/1|1/1, 0:2x2x6x12)\n  ScalarW (1, 1/1 params, 0/0 flops, VariableV2|_trainable_variables, 0/0|1/10, )\n    ScalarW/Assign (0/0 params, 0/0 flops, Assign, 0/0|1/1, 0:1|1:1)\n    ScalarW/Initializer (0/0 params, 0/0 flops, _kTFScopeParent, 0/0|1/7, )\n      ScalarW/Initializer/random_normal (0/0 params, 0/0 flops, Add, 0/0|1/6, 0:1|1:1)\n        ScalarW/Initializer/random_normal/RandomStandardNormal (0/0 params, 0/0 flops, RandomStandardNormal, 0/0|1/1, 0:0)\n        ScalarW/Initializer/random_normal/mean (0/0 params, 0/0 flops, Const, 0/0|1/1, )\n        ScalarW/Initializer/random_normal/mul (0/0 params, 0/0 flops, Mul, 0/0|1/1, 0:1|1:1)\n        ScalarW/Initializer/random_normal/shape (0/0 params, 0/0 flops, Const, 0/0|1/1, )\n        ScalarW/Initializer/random_normal/stddev (0/0 params, 0/0 flops, Const, 0/0|1/1, )\n    ScalarW/read (0/0 params, 0/0 flops, Identity, 0/0|1/1, 0:1)\n  init (0/0 params, 0/0 flops, NoOp, 0/0|1/1, 0:1|1:3x3x3x6|2:2x2x6x12)\n  zeros (0/0 params, 0/0 flops, /job:localhost/replica:0/task:0/cpu:0, /job:localhost/replica:0/task:0/cpu:0|Const, 1/1|1/1, )\n',
            f.read())

  def testSimpleCodeView(self):
    ops.reset_default_graph()
    outfile = os.path.join(test.get_temp_dir(), 'dump')
    # TODO(xpan): Test 'micros'. Since the execution time changes each run,
    # it's a bit difficult to test it now.
    opts = (builder(builder.trainable_variables_parameter())
            .with_file_output(outfile)
            .with_accounted_types(['.*'])
            .with_node_names(show_name_regexes=['.*model_analyzer_testlib.*'])
            .account_displayed_op_only(False)
            .select(['bytes', 'params', 'float_ops', 'num_hidden_ops', 'device',
                     'input_shapes']).build())

    with session.Session() as sess:
      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.profile(
          sess.graph, run_meta, cmd='code', options=opts)

      with gfile.Open(outfile, 'r') as f:
        # pylint: disable=line-too-long
        self.assertEqual(
            'node name | output bytes | # parameters | # float_ops | assigned devices | input',
            f.read()[0:80])

def _profiled_run(self,
                  fetches,
                  feed_dict=None,
                  options=None,
                  run_metadata=None):
  """Overwrites the session.run()."""
  # pylint: disable=protected-access
  # Count the session steps.
  self.profile_context._new_step()
  # Fast path if no need for profiling.
  to_profiles = self.profile_context._profile_candidates()
  to_dumps = self.profile_context._dump_candidates()
  if (not to_profiles and not to_dumps and
      not self.profile_context._is_capture_enforced()):
    return self._profiler_run_internal(
        fetches, feed_dict, options, run_metadata)

  # Enable tracing, perform auto profiling or auto dump.
  if not run_metadata:
    run_metadata = config_pb2.RunMetadata()

  if not options:
    options = config_pb2.RunOptions(
        trace_level=config_pb2.RunOptions.FULL_TRACE)
    old_trace_level = options.trace_level
  else:
    old_trace_level = options.trace_level
    options.trace_level = config_pb2.RunOptions.FULL_TRACE

  ret = self._profiler_run_internal(fetches, feed_dict, options, run_metadata)

  if self.profile_context._capture_next_step:
    self.profile_context._add_run_meta(run_metadata)

  for to_dump in to_dumps:
    outdir, _ = to_dump
    if not gfile.Exists(outdir):
      gfile.MakeDirs(outdir)
    with gfile.Open(os.path.join(outdir, 'graph.pbtxt'), 'w') as f:
      f.write('%s' % self.graph.as_graph_def(add_shapes=True))
    with gfile.Open(os.path.join(outdir, 'run_metadata'), 'w') as f:
      f.write(run_metadata.SerializeToString())
    tfprof_logger.write_op_log(
        self.graph, outdir, run_meta=run_metadata, add_trace=True)

  for to_prof in to_profiles:
    cmd, opts, _ = to_prof
    model_analyzer.profile(
        self.graph, run_meta=run_metadata, cmd=cmd, options=opts)

  # Restore to default.
  options.trace_level = old_trace_level
  return ret

  def testComplexCodeView(self):
    ops.reset_default_graph()
    outfile = os.path.join(test.get_temp_dir(), 'dump')
    opts = (builder(builder.trainable_variables_parameter())
            .with_file_output(outfile)
            .with_accounted_types(['.*'])
            .with_node_names(show_name_regexes=
                             ['.*model_analyzer_testlib.py.*'])
            .account_displayed_op_only(False)
            .select(['params', 'float_ops']).build())

    with session.Session() as sess:
      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.profile(
          sess.graph, run_meta, cmd='code', options=opts)

      # pylint: disable=line-too-long
      with gfile.Open(outfile, 'r') as f:
        lines = f.read().split('\n')
        result = '\n'.join([l[:min(len(l), 80)] for l in lines])
        self.assertEqual('node name | # parameters | # float_ops\n_TFProfRoot (--/2.84k params, --/91.04k flops)\n  model_analyzer_testlib.py:58:BuildFullModel:seq.append(array_... (0/1.80k para\n    model_analyzer_testlib.py:35:BuildSmallModel:image = array_ops... (0/0 param\n    model_analyzer_testlib.py:39:BuildSmallModel:initializer=init_... (0/4 param\n    model_analyzer_testlib.py:43:BuildSmallModel:initializer=init_... (0/648 par\n    model_analyzer_testlib.py:44:BuildSmallModel:x = nn_ops.conv2d... (0/0 param\n    model_analyzer_testlib.py:48:BuildSmallModel:initializer=init_... (0/1.15k p\n    model_analyzer_testlib.py:49:BuildSmallModel:x = nn_ops.conv2d... (0/0 param\n  model_analyzer_testlib.py:58:BuildFullModel:seq.append(array_... (gradient) (0\n    model_analyzer_testlib.py:44:BuildSmallModel:x = nn_ops.conv2d... (gradient)\n    model_analyzer_testlib.py:49:BuildSmallModel:x = nn_ops.conv2d... (gradient)\n  model_analyzer_testlib.py:62:BuildFullModel:cell, array_ops.c... (0/1.04k para\n  model_analyzer_testlib.py:62:BuildFullModel:cell, array_ops.c... (gradient) (0\n  model_analyzer_testlib.py:64:BuildFullModel:target = array_op... (0/0 params, \n  model_analyzer_testlib.py:65:BuildFullModel:loss = nn_ops.l2_... (0/0 params, \n  model_analyzer_testlib.py:65:BuildFullModel:loss = nn_ops.l2_... (gradient) (0\n  model_analyzer_testlib.py:67:BuildFullModel:return sgd_op.min... (0/0 params, \n',
                         result)

      self.assertLess(0, tfprof_node.total_exec_micros)
      self.assertEqual(2844, tfprof_node.total_parameters)
      self.assertEqual(91040, tfprof_node.total_float_ops)
      self.assertEqual(8, len(tfprof_node.children))
      self.assertEqual('_TFProfRoot', tfprof_node.name)
      self.assertEqual(
          'model_analyzer_testlib.py:58:BuildFullModel:seq.append(array_...',
          tfprof_node.children[0].name)
      self.assertEqual(
          'model_analyzer_testlib.py:58:BuildFullModel:seq.append(array_... (gradient)',
          tfprof_node.children[1].name)
      self.assertEqual(
          'model_analyzer_testlib.py:62:BuildFullModel:cell, array_ops.c...',
          tfprof_node.children[2].name)
      self.assertEqual(
          'model_analyzer_testlib.py:62:BuildFullModel:cell, array_ops.c... (gradient)',
          tfprof_node.children[3].name)
      self.assertEqual(
          'model_analyzer_testlib.py:64:BuildFullModel:target = array_op...',
          tfprof_node.children[4].name)
      self.assertEqual(
          'model_analyzer_testlib.py:65:BuildFullModel:loss = nn_ops.l2_...',
          tfprof_node.children[5].name)
      self.assertEqual(
          'model_analyzer_testlib.py:65:BuildFullModel:loss = nn_ops.l2_... (gradient)',
          tfprof_node.children[6].name)
      self.assertEqual(
          'model_analyzer_testlib.py:67:BuildFullModel:return sgd_op.min...',
          tfprof_node.children[7].name)

def _run_model():
  x = random_ops.random_normal(shape=[1, SIZE])
  w = random_ops.random_normal(shape=[SIZE, 2 * SIZE])
  y = math_ops.matmul(x, w)

  config = config_pb2.ConfigProto()
  config.graph_options.rewrite_options.arithmetic_optimization = (
      rewriter_config_pb2.RewriterConfig.OFF)
  with session.Session(config=config) as sess:
    run_metadata = config_pb2.RunMetadata()
    opts = builder.time_and_memory()
    opts['min_micros'] = 0
    opts['min_bytes'] = 0
    opts['order_by'] = 'name'
    opts['output'] = 'none'
    _ = sess.run(y,
                 options=config_pb2.RunOptions(
                     trace_level=config_pb2.RunOptions.FULL_TRACE),
                 run_metadata=run_metadata)
    tfprof_node = model_analyzer.profile(
        sess.graph,
        run_meta=run_metadata,
        options=opts)

    return tfprof_node, run_metadata

  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', 'device'
    ]
    opts['output'] = 'none'

    with session.Session() as sess:
      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.profile(
          sess.graph, run_meta, cmd='code', 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))

  def testTimeline(self):
    ops.reset_default_graph()
    outfile = os.path.join(test.get_temp_dir(), 'timeline')
    opts = (builder(builder.trainable_variables_parameter())
            .with_max_depth(100000)
            .with_step(0)
            .with_timeline_output(outfile)
            .with_accounted_types(['.*']).build())

    with session.Session() as sess:
      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)

      _ = model_analyzer.profile(
          sess.graph, run_meta, cmd='graph', options=opts)

      with gfile.Open(outfile, 'r') as f:
        # Test that a json file is created.
        # TODO(xpan): tfprof Timeline isn't quite correct on Windows.
        # Investigate why.
        if os.name != 'nt':
          self.assertLess(1000, len(f.read()))
        else:
          self.assertLess(1, len(f.read()))

  def testTimeline(self):
    ops.reset_default_graph()
    opts = model_analyzer.TRAINABLE_VARS_PARAMS_STAT_OPTIONS.copy()
    outfile = os.path.join(test.get_temp_dir(), 'timeline')
    opts['output'] = 'timeline:outfile=' + outfile
    opts['account_type_regexes'] = ['.*']
    opts['max_depth'] = 100000
    opts['step'] = 0

    with session.Session() as sess:
      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)

      _ = model_analyzer.profile(
          sess.graph, run_meta, cmd='graph', options=opts)

      with gfile.Open(outfile, 'r') as f:
        # Test that a json file is created.
        # TODO(xpan): tfprof Timeline isn't quite correct on Windows.
        # Investigate why.
        if os.name != 'nt':
          self.assertLess(1000, len(f.read()))
        else:
          self.assertLess(1, len(f.read()))

  def testCodeViewLeafGraphNode(self):
    ops.reset_default_graph()
    opts = (builder(builder.trainable_variables_parameter())
            .with_empty_output()
            .with_accounted_types(['.*'])
            .account_displayed_op_only(False)
            .select(['bytes', 'params', 'float_ops', 'device']).build())

    with session.Session() as sess:
      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.profile(
          sess.graph, run_meta, cmd='code', 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))

  def testDumpToFile(self):
    ops.reset_default_graph()
    opts = model_analyzer.TRAINABLE_VARS_PARAMS_STAT_OPTIONS.copy()
    outfile = os.path.join(test.get_temp_dir(), 'dump')
    opts['output'] = 'file:outfile=' + outfile

    with session.Session() as sess:
      _ = lib.BuildSmallModel()
      model_analyzer.profile(sess.graph, options=opts)

      with gfile.Open(outfile, 'r') as f:
        self.assertEqual(u'node name | # parameters\n'
                         '_TFProfRoot (--/451 params)\n'
                         '  DW (3x3x3x6, 162/162 params)\n'
                         '  DW2 (2x2x6x12, 288/288 params)\n'
                         '  ScalarW (1, 1/1 params)\n',
                         f.read())

  def testDumpToFile(self):
    ops.reset_default_graph()
    outfile = os.path.join(test.get_temp_dir(), 'dump')
    opts = builder(builder.trainable_variables_parameter()
                  ).with_file_output(outfile).build()

    with session.Session() as sess:
      _ = lib.BuildSmallModel()
      model_analyzer.profile(sess.graph, options=opts)

      with gfile.Open(outfile, 'r') as f:
        self.assertEqual(u'node name | # parameters\n'
                         '_TFProfRoot (--/451 params)\n'
                         '  DW (3x3x3x6, 162/162 params)\n'
                         '  DW2 (2x2x6x12, 288/288 params)\n'
                         '  ScalarW (1, 1/1 params)\n',
                         f.read())

  def testOpView(self):
    ops.reset_default_graph()
    outfile = os.path.join(test.get_temp_dir(), 'dump')

    opts = (builder(builder.trainable_variables_parameter())
            .with_file_output(outfile)
            .with_accounted_types(['.*'])
            .with_min_occurrence(10)
            .order_by('occurrence')
            .select(['params', 'micros', 'bytes',
                     'peak_bytes', 'residual_bytes',
                     'output_bytes', 'occurrence', 'input_shapes']).build())

    with session.Session() as sess:
      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.profile(
          sess.graph, run_meta, cmd='op', options=opts)

      with gfile.Open(outfile, 'r') as f:
        # pylint: disable=line-too-long
        self.assertEqual(
            'nodename|requestedbytes|peakbytes|residualbytes|outputbytes|totalexecutiontime|acceleratorexecutiontime|cpuexecutiontime|#parameters|opoccurrence(run|defined)|inputshapes',
            lib.CheckAndRemoveDoc(f.read()).replace('\t',
                                                    '').replace(' ', '')[0:170])
        # pylint: enable=line-too-long

      total_children = 0
      last_occurrence = 1e32
      input_shapes = 0
      last_total_micros = tfprof_node.total_exec_micros
      last_micros = tfprof_node.exec_micros
      while tfprof_node.children:
        for gnode in tfprof_node.graph_nodes:
          input_shapes += len(gnode.input_shapes)
        self.assertEqual(len(tfprof_node.children), 1)
        tfprof_node = tfprof_node.children[0]

        self.assertEqual(
            last_total_micros, tfprof_node.total_exec_micros + last_micros)
        last_total_micros = tfprof_node.total_exec_micros
        last_micros = tfprof_node.exec_micros

        total_children += 1
        self.assertLessEqual(len(tfprof_node.graph_nodes), last_occurrence)
        last_occurrence = len(tfprof_node.graph_nodes)

      self.assertGreater(input_shapes, 0)