def advise(graph, run_meta=None, tfprof_options=ALL_ADVICE): # pylint: disable=dangerous-default-value
"""Auto profile and advise.
Builds profiles and automatically check anormalies of various
aspects. See go/tfprof or README for examples and tutorials.
Args:
graph: tf.Graph.
run_meta: tensorflow::RunMetadata proto. Allows auto-profile
time and memroy.
tfprof_options: see ALL_ADVICE example above.
Returns:
Returns AdviceProto proto
"""
# pylint: disable=protected-access
op_log = tfprof_logger._merge_default_with_oplog(
graph, None, run_meta, add_trace=True)
# pylint: enable=protected-access
run_meta_str = run_meta.SerializeToString() if run_meta else b''
opts = _build_advisor_options(tfprof_options)
ret = tfprof_output_pb2.AdviceProto()
ret.ParseFromString(
print_mdl.PrintModelAnalysis(
graph.as_graph_def(add_shapes=True).SerializeToString(),
run_meta_str,
op_log.SerializeToString(),
'advise'.encode('utf-8'),
opts.SerializeToString()))
return ret
def print_model_analysis(graph,
run_meta=None,
op_log=None,
tfprof_cmd='scope',
tfprof_options=TRAINABLE_VARS_PARAMS_STAT_OPTIONS):
"""Print model statistics.
See go/tfprof or README for examples and tutorials.
Run tfprof tool for help:
'bazel run third_party/tensorflow/tools/tfprof help'
Args:
graph: tf.Graph.
run_meta: tensorflow::RunMetadata proto. When provided, also shows valid
timing and memory information when 'select' option contains
'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 'op', 'scope', 'graph', 'code'.
'op' view organize outputs using operation type. (e.g. MatMul)
'scope' view organize outputs using graph node name scope.
'graph' view organize outputs using graph node inputs/outputs.
'code' view organize outputs using Python call stack.
tfprof_options: See 'tfprof help' for details.
Returns:
If tfprof_cmd is 'scope' or 'graph', returns TFGraphNodeProto proto.
If tfprof_cmd is 'op' or 'code', returns TFMultiGraphNodeProto proto.
Side effect: stdout/file/timeline.json depending on tfprof_options['output']
"""
# pylint: disable=protected-access
op_log = tfprof_logger._merge_default_with_oplog(
graph, op_log, run_meta, add_trace=tfprof_cmd == 'code')
# pylint: enable=protected-access
opts = _build_options(tfprof_options)
run_meta_str = run_meta.SerializeToString() if run_meta else b''
if tfprof_cmd == 'code' or tfprof_cmd == 'op':
tfprof_node = tfprof_output_pb2.TFMultiGraphNodeProto()
tfprof_node.ParseFromString(
print_mdl.PrintModelAnalysis(
graph.as_graph_def(add_shapes=True).SerializeToString(),
run_meta_str, op_log.SerializeToString(),
tfprof_cmd.encode('utf-8'), opts.SerializeToString()))
elif tfprof_cmd == 'graph' or tfprof_cmd == 'scope':
tfprof_node = tfprof_output_pb2.TFGraphNodeProto()
tfprof_node.ParseFromString(
print_mdl.PrintModelAnalysis(
graph.as_graph_def(add_shapes=True).SerializeToString(),
run_meta_str, op_log.SerializeToString(),
tfprof_cmd.encode('utf-8'), opts.SerializeToString()))
else:
raise errors.InvalidArgumentError(
None, None, 'unknown tfprof_cmd: %s\n' % tfprof_cmd)
return tfprof_node
def add_step(self, step, run_meta):
"""Add statistics of a step.
Args:
step: A step uint64 used to identify the RunMetadata. Must be different
across different AddStep() calls.
run_meta: RunMetadata proto that contains statistics of a session run.
"""
# pylint: disable=protected-access
op_log = tfprof_logger._merge_default_with_oplog(
self._graph, run_meta=run_meta, add_trace=False,
add_trainable_var=False)
# pylint: enable=protected-access
print_mdl.AddStep(
step, run_meta.SerializeToString(), op_log.SerializeToString())
def add_step(self, step, run_meta):
"""Add statistics of a step.
Args:
step: A step uint64 used to identify the RunMetadata. Must be different
across different AddStep() calls.
run_meta: RunMetadata proto that contains statistics of a session run.
"""
# pylint: disable=protected-access
op_log = tfprof_logger._merge_default_with_oplog(
self._graph, run_meta=run_meta, add_trace=False,
add_trainable_var=False)
# pylint: enable=protected-access
print_mdl.AddStep(
step, run_meta.SerializeToString(), op_log.SerializeToString())
def __init__(self, graph, op_log=None):
"""Constructor.
Args:
graph: tf.Graph.
op_log: optional. tensorflow::tfprof::OpLog proto. Used to define
extra op types.
"""
self._graph = graph
# pylint: disable=protected-access
op_log = tfprof_logger._merge_default_with_oplog(self._graph,
op_log=op_log)
# pylint: enable=protected-access
print_mdl.NewProfiler(self._graph.as_graph_def().SerializeToString(),
op_log.SerializeToString())
def __init__(self, graph, op_log=None):
"""Constructor.
Args:
graph: tf.Graph.
op_log: optional. tensorflow::tfprof::OpLog proto. Used to define
extra op types.
"""
self._graph = graph
# pylint: disable=protected-access
op_log = tfprof_logger._merge_default_with_oplog(
self._graph, op_log=op_log)
# pylint: enable=protected-access
print_mdl.NewProfiler(
self._graph.as_graph_def(add_shapes=True).SerializeToString(),
op_log.SerializeToString())
def print_model_analysis(graph,
run_meta=None,
op_log=None,
tfprof_cmd='scope',
tfprof_options=TRAINABLE_VARS_PARAMS_STAT_OPTIONS):
"""Print model statistics.
See go/tfprof or README for examples and tutorials.
Run tfprof tool for help:
'bazel run third_party/tensorflow/tools/tfprof help'
Args:
graph: tf.Graph.
run_meta: tensorflow::RunMetadata proto. When provided, also shows valid
timing and memory information when 'select' option contains
'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 'op', 'scope', 'graph', 'code'.
'op' view organize outputs using operation type. (e.g. MatMul)
'scope' view organize outputs using graph node name scope.
'graph' view organize outputs using graph node inputs/outputs.
'code' view organize outputs using Python call stack.
tfprof_options: See 'tfprof help' for details.
Returns:
If tfprof_cmd is 'scope' or 'graph', returns TFGraphNodeProto proto.
If tfprof_cmd is 'op' or 'code', returns TFMultiGraphNodeProto proto.
Side effect: stdout/file/timeline.json depending on tfprof_options['output']
"""
# pylint: disable=protected-access
op_log = tfprof_logger._merge_default_with_oplog(
graph, op_log, run_meta, add_trace=tfprof_cmd == 'code')
# pylint: enable=protected-access
opts = _build_options(tfprof_options)
run_meta_str = run_meta.SerializeToString() if run_meta else b''
if tfprof_cmd == 'code' or tfprof_cmd == 'op':
tfprof_node = tfprof_output_pb2.TFMultiGraphNodeProto()
tfprof_node.ParseFromString(
print_mdl.PrintModelAnalysis(
graph.as_graph_def(add_shapes=True).SerializeToString(),
run_meta_str,
op_log.SerializeToString(),
tfprof_cmd.encode('utf-8'),
opts.SerializeToString()))
elif tfprof_cmd == 'graph' or tfprof_cmd == 'scope':
tfprof_node = tfprof_output_pb2.TFGraphNodeProto()
tfprof_node.ParseFromString(
print_mdl.PrintModelAnalysis(
graph.as_graph_def(add_shapes=True).SerializeToString(),
run_meta_str,
op_log.SerializeToString(),
tfprof_cmd.encode('utf-8'),
opts.SerializeToString()))
else:
raise errors.InvalidArgumentError(
None, None, 'unknown tfprof_cmd: %s\n' % tfprof_cmd)
return tfprof_node
def print_model_analysis(graph,
run_meta=None,
op_log=None,
tfprof_cmd='scope',
tfprof_options=TRAINABLE_VARS_PARAMS_STAT_OPTIONS):
"""Print model statistics.
Prints the model statistics to stdout. Also returns the results
in a TFGraphNodeProto proto. See go/tfprof or run tfprof tool:
'bazel run third_party/tensorflow/tools/tfprof help'
Examples:
Show the parameter/shape statistics of tf.trainable_variables().
print_model_analysis(sess.graph).
Show number of float ops. Only ops with RegisterStatistics defined
are counted.
show_float_op_opts = model_analyzer.FLOAT_OPS_OPTIONS
print_model_analysis(sess.graph, tfprof_options=show_float_op_opts)
Args:
graph: tf.Graph.
run_meta: tensorflow::RunMetadata proto. When provided, also shows valid
timing and memory information when 'select' option contains
'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_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.
"""
# pylint: disable=protected-access
op_log = tfprof_logger._merge_default_with_oplog(
graph, op_log, run_meta, add_trace=tfprof_cmd == 'code')
# pylint: enable=protected-access
opts = tfprof_options_pb2.OptionsProto()
opts.max_depth = tfprof_options['max_depth']
opts.min_bytes = tfprof_options['min_bytes']
opts.min_micros = tfprof_options['min_micros']
opts.min_params = tfprof_options['min_params']
opts.min_float_ops = tfprof_options['min_float_ops']
for p in tfprof_options['device_regexes']:
opts.device_regexes.append(p)
opts.order_by = tfprof_options['order_by']
for p in tfprof_options['account_type_regexes']:
opts.account_type_regexes.append(p)
for p in tfprof_options['start_name_regexes']:
opts.start_name_regexes.append(p)
for p in tfprof_options['trim_name_regexes']:
opts.trim_name_regexes.append(p)
for p in tfprof_options['show_name_regexes']:
opts.show_name_regexes.append(p)
for p in tfprof_options['hide_name_regexes']:
opts.hide_name_regexes.append(p)
opts.account_displayed_op_only = tfprof_options['account_displayed_op_only']
for p in tfprof_options['select']:
opts.select.append(p)
opts.output = tfprof_options['output']
opts.dump_to_file = tfprof_options['dump_to_file']
run_meta_str = run_meta.SerializeToString() if run_meta 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()))
return tfprof_node
