# <EcoSys> This function is copied from tensorflow.contrib.slim.learning. We then add cuda profiler start and stop calls so that nvprof is feasible.

import os
import time

from tensorflow.core.protobuf import config_pb2
from tensorflow.python.client import timeline
from tensorflow.python.lib.io import file_io
from tensorflow.python.platform import tf_logging as logging

def train_step(sess, train_op, global_step, train_step_kwargs):
  """Function that takes a gradient step and specifies whether to stop.
  Args:
    sess: The current session.
    train_op: An `Operation` that evaluates the gradients and returns the
      total loss.
    global_step: A `Tensor` representing the global training step.
    train_step_kwargs: A dictionary of keyword arguments.
  Returns:
    The total loss and a boolean indicating whether or not to stop training.
  Raises:
    ValueError: if 'should_trace' is in `train_step_kwargs` but `logdir` is not.
  """
  start_time = time.time()

  trace_run_options = None
  run_metadata = None
  if 'should_trace' in train_step_kwargs:
    if 'logdir' not in train_step_kwargs:
      raise ValueError('logdir must be present in train_step_kwargs when '
                       'should_trace is present')
    if sess.run(train_step_kwargs['should_trace']):
      trace_run_options = config_pb2.RunOptions(
          trace_level=config_pb2.RunOptions.FULL_TRACE)
      run_metadata = config_pb2.RunMetadata()

  total_loss, np_global_step = sess.run([train_op, global_step],
                                        options=trace_run_options,
                                        run_metadata=run_metadata)
  time_elapsed = time.time() - start_time

  if 'nvprof_on' in train_step_kwargs:
    import numba.cuda as cuda
    if np_global_step == train_step_kwargs['nvprof_start_step']:
      cuda.profile_start()
    if np_global_step == train_step_kwargs['nvprof_stop_step']:
      cuda.profile_stop()

  if run_metadata is not None:
    tl = timeline.Timeline(run_metadata.step_stats)
    trace = tl.generate_chrome_trace_format()
    trace_filename = os.path.join(train_step_kwargs['logdir'],
                                  'tf_trace-%d.json' % np_global_step)
    logging.info('Writing trace to %s', trace_filename)
    file_io.write_string_to_file(trace_filename, trace)
    if 'summary_writer' in train_step_kwargs:
      train_step_kwargs['summary_writer'].add_run_metadata(run_metadata,
                                                           'run_metadata-%d' %
                                                           np_global_step)

  if 'should_log' in train_step_kwargs:
    if sess.run(train_step_kwargs['should_log']):
      logging.info('global step %d: loss = %.4f (%.3f sec/step)',
                   np_global_step, total_loss, time_elapsed)

  # TODO(nsilberman): figure out why we can't put this into sess.run. The
  # issue right now is that the stop check depends on the global step. The
  # increment of global step often happens via the train op, which used
  # created using optimizer.apply_gradients.
  #
  # Since running `train_op` causes the global step to be incremented, one
  # would expected that using a control dependency would allow the
  # should_stop check to be run in the same session.run call:
  #
  #   with ops.control_dependencies([train_op]):
  #     should_stop_op = ...
  #
  # However, this actually seems not to work on certain platforms.
  if 'should_stop' in train_step_kwargs:
    should_stop = sess.run(train_step_kwargs['should_stop'])
  else:
    should_stop = False

  return total_loss, should_stop

# </EcoSys>

# <EcoSys> This function is copied from tensorflow.contrib.slim.learning. We then add cuda profiler start and stop calls so that nvprof is feasible.

import os
import time

from tensorflow.core.protobuf import config_pb2
from tensorflow.python.client import timeline
from tensorflow.python.lib.io import file_io
from tensorflow.python.platform import tf_logging as logging

def train_step(sess, train_op, global_step, train_step_kwargs):
  """Function that takes a gradient step and specifies whether to stop.
  Args:
    sess: The current session.
    train_op: An `Operation` that evaluates the gradients and returns the
      total loss.
    global_step: A `Tensor` representing the global training step.
    train_step_kwargs: A dictionary of keyword arguments.
  Returns:
    The total loss and a boolean indicating whether or not to stop training.
  Raises:
    ValueError: if 'should_trace' is in `train_step_kwargs` but `logdir` is not.
  """
  start_time = time.time()

  trace_run_options = None
  run_metadata = None
  if 'should_trace' in train_step_kwargs:
    if 'logdir' not in train_step_kwargs:
      raise ValueError('logdir must be present in train_step_kwargs when '
                       'should_trace is present')
    if sess.run(train_step_kwargs['should_trace']):
      trace_run_options = config_pb2.RunOptions(
          trace_level=config_pb2.RunOptions.FULL_TRACE)
      run_metadata = config_pb2.RunMetadata()

  total_loss, np_global_step = sess.run([train_op, global_step],
                                        options=trace_run_options,
                                        run_metadata=run_metadata)
  time_elapsed = time.time() - start_time

  if 'nvprof_on' in train_step_kwargs:
    import numba.cuda as cuda
    if np_global_step == train_step_kwargs['nvprof_start_step']:
      cuda.profile_start()
    if np_global_step == train_step_kwargs['nvprof_stop_step']:
      cuda.profile_stop()

  if run_metadata is not None:
    tl = timeline.Timeline(run_metadata.step_stats)
    trace = tl.generate_chrome_trace_format()
    trace_filename = os.path.join(train_step_kwargs['logdir'],
                                  'tf_trace-%d.json' % np_global_step)
    logging.info('Writing trace to %s', trace_filename)
    file_io.write_string_to_file(trace_filename, trace)
    if 'summary_writer' in train_step_kwargs:
      train_step_kwargs['summary_writer'].add_run_metadata(run_metadata,
                                                           'run_metadata-%d' %
                                                           np_global_step)

  if 'should_log' in train_step_kwargs:
    if sess.run(train_step_kwargs['should_log']):
      logging.info('global step %d: loss = %.4f (%.3f sec/step)',
                   np_global_step, total_loss, time_elapsed)

  # TODO(nsilberman): figure out why we can't put this into sess.run. The
  # issue right now is that the stop check depends on the global step. The
  # increment of global step often happens via the train op, which used
  # created using optimizer.apply_gradients.
  #
  # Since running `train_op` causes the global step to be incremented, one
  # would expected that using a control dependency would allow the
  # should_stop check to be run in the same session.run call:
  #
  #   with ops.control_dependencies([train_op]):
  #     should_stop_op = ...
  #
  # However, this actually seems not to work on certain platforms.
  if 'should_stop' in train_step_kwargs:
    should_stop = sess.run(train_step_kwargs['should_stop'])
  else:
    should_stop = False

  return total_loss, should_stop

# </EcoSys>

# <EcoSys> This function is copied from tensorflow.contrib.slim.learning. We then add cuda profiler start and stop calls so that nvprof is feasible. Function that takes a gradient step and specifies whether to stop.   Args:     sess: The current session.     train_op: An `Operation` that evaluates the gradients and returns the       total loss.     global_step: A `Tensor` representing the global training step.     train_step_kwargs: A dictionary of keyword arguments.   Returns:     The total loss and a boolean indicating whether or not to stop training.   Raises:     ValueError: if 'should_trace' is in `train_step_kwargs` but `logdir` is not. # TODO(nsilberman): figure out why we can't put this into sess.run. The # issue right now is that the stop check depends on the global step. The # increment of global step often happens via the train op, which used # created using optimizer.apply_gradients. # # Since running `train_op` causes the global step to be incremented, one # would expected that using a control dependency would allow the # should_stop check to be run in the same session.run call: # #   with ops.control_dependencies([train_op]): #     should_stop_op = ... # # However, this actually seems not to work on certain platforms. # </EcoSys>