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# 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 Context that captures profile and performs profiling/dumping.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import threading
from tensorflow.core.protobuf import config_pb2
from tensorflow.python.client import session
from tensorflow.python.framework import errors
from tensorflow.python.platform import gfile
from tensorflow.python.profiler import model_analyzer
from tensorflow.python.profiler import tfprof_logger
def _profiled_init(self, target='', graph=None, config=None):
"""Overwrites the session.__init__."""
self._profiler_init_internal(target, graph, config) # pylint: disable=protected-access
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
# pylint: enable=protected-access
class ProfileContext(object):
"""A Context that captures RunMetadata and performs profiling.
```python
# Auto profiling at step 1, 100 and 1000.:
with tf.contrib.tfprof.ProfileContext() as pctx:
# Create the profiling options.
opts = tf.profiler.ProfileOptionBuilder.time_and_memory()
# Run profiling at certain steps. Multiple ones can be added.
pctx.add_auto_profiling('op', opts, [1, 100, 1000])
# Or dump the profile files at certain steps.
pctx.add_auto_profile_dump('/tmp/profiles', [1000])
# Run train/eval loop.
train_loop().
# Alternatively, enable and capture RunMetadata of next step.
with tf.contrib.tfprof.ProfileContext() as pctx:
pctx.capture_next_run_meta()
opts = tf.profiler.ProfileOptionBuilder.time_and_memory()
_ = session.run(train_op)
tf.profiler.profile(session.graph,
run_meta=pctx.run_meta(),
cmd='op',
options=opts)
```
"""
def __init__(self):
self._lock = threading.Lock()
self._capture_next_step = False
self._step = 0
self._auto_profiles = []
self._auto_dumps = []
self._run_meta = None
def add_auto_profiling(self, cmd, profile_options, profile_steps):
"""Runs profiling at some steps with provided command and options.
Args:
cmd: The profiling commands.
profile_options: The profiling options.
profile_steps: A list/set of integers. The profiling command and options
will be run automatically at these integer steps. Each step is
a session.run.
"""
with self._lock:
self._auto_profiles.append((cmd, profile_options, profile_steps))
def add_auto_profile_dump(self, outdir, dump_steps):
"""Dumps profiles at some steps to the directory.
Args:
outdir: The directory to dump the profile files.
dump_steps: A list/set of integers. The profile files will be dump at
these integer steps. Each step is a session.run.
"""
with self._lock:
self._auto_dumps.append((outdir, dump_steps))
def capture_next_run_meta(self):
"""Enables tracing and captures RunMetadata at next session.run.
The captured RunMetadata can be retrieved via run_meta(). It
will be cleared one step later.
"""
with self._lock:
self._capture_next_step = True
def run_meta(self):
"""Returns the RunMetadata captured at previous session.run.
Needs to call capture_next_run_meta() before session.run to enable
capturing.
"""
with self._lock:
assert self._run_meta, 'Need to call capture_next_run_meta()'
return self._run_meta
def _is_capture_enforced(self):
with self._lock:
return self._capture_next_step
def _add_run_meta(self, run_meta):
with self._lock:
self._run_meta = run_meta
self._capture_next_step = False
def _new_step(self):
with self._lock:
self._run_meta = None
self._step += 1
def _profile_candidates(self):
to_profile = []
with self._lock:
for auto_prof in self._auto_profiles:
_, _, prof_steps = auto_prof
if self._step - 1 in prof_steps:
to_profile.append(auto_prof)
return to_profile
def _dump_candidates(self):
to_dump = []
with self._lock:
for auto_dump in self._auto_dumps:
_, dump_steps = auto_dump
if self._step - 1 in dump_steps:
to_dump.append(auto_dump)
return to_dump
def __enter__(self):
self.old_run = getattr(session.BaseSession, 'run', None)
self.old_init = getattr(session.BaseSession, '__init__', None)
if not self.old_run:
raise errors.InternalError(None, None, 'BaseSession misses run method.')
elif not self.old_init:
raise errors.InternalError(None, None,
'BaseSession misses __init__ method.')
elif getattr(session.BaseSession, '_profiler_run_internal', None):
raise errors.InternalError(None, None,
'Already in context or context not cleaned.')
elif getattr(session.BaseSession, '_profiler_init_internal', None):
raise errors.InternalError(None, None,
'Already in context or context not cleaned.')
else:
setattr(session.BaseSession, 'run', _profiled_run)
setattr(session.BaseSession, '__init__', _profiled_init)
setattr(session.BaseSession, '_profiler_run_internal', self.old_run)
setattr(session.BaseSession, '_profiler_init_internal', self.old_init)
setattr(session.BaseSession, 'profile_context', self)
return self
def __exit__(self, exec_type, exec_value, exec_tb):
setattr(session.BaseSession, 'run', self.old_run)
setattr(session.BaseSession, '__init__', self.old_init)
setattr(session.BaseSession, '_profiler_run_internal', None)
setattr(session.BaseSession, '_profiler_init_internal', None)
setattr(session.BaseSession, 'profile_context', None)
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