def on_value_event(self, event):
"""Implementation of the tensor value-carrying Event proto callback.
Writes the Event proto to the file system for testing. The path written to
follows the same pattern as the file:// debug URLs of tfdbg, i.e., the
name scope of the op becomes the directory structure under the dump root
directory.
Args:
event: The Event proto carrying a tensor value.
Returns:
If the debug node belongs to the set of currently activated breakpoints,
a `EventReply` proto will be returned.
"""
if self._dump_dir:
self._write_value_event(event)
else:
value = event.summary.value[0]
tensor_value = debug_data.load_tensor_from_event(event)
self._event_listener_servicer.debug_tensor_values[value.node_name].append(
tensor_value)
items = event.summary.value[0].node_name.split(":")
node_name = items[0]
output_slot = int(items[1])
debug_op = items[2]
if ((node_name, output_slot, debug_op) in
self._event_listener_servicer.breakpoints):
return debug_service_pb2.EventReply()
def on_value_event(self, event):
"""Implementation of the tensor value-carrying Event proto callback.
Writes the Event proto to the file system for testing. The path written to
follows the same pattern as the file:// debug URLs of tfdbg, i.e., the
name scope of the op becomes the directory structure under the dump root
directory.
Args:
event: The Event proto carrying a tensor value.
Returns:
If the debug node belongs to the set of currently activated breakpoints,
a `EventReply` proto will be returned.
"""
if self._dump_dir:
self._write_value_event(event)
else:
value = event.summary.value[0]
tensor_value = debug_data.load_tensor_from_event(event)
self._event_listener_servicer.debug_tensor_values[value.node_name].append(
tensor_value)
items = event.summary.value[0].node_name.split(":")
node_name = items[0]
output_slot = int(items[1])
debug_op = items[2]
if ((node_name, output_slot, debug_op) in
self._event_listener_servicer.breakpoints):
return debug_service_pb2.EventReply()
def on_value_event(self, event):
"""Records the summary values based on an updated message from the
debugger.
Logs an error message if writing the event to disk fails.
Args:
event: The Event proto to be processed.
"""
if not event.summary.value:
logger.info("The summary of the event lacks a value.")
return None
# The node name property in the event proto is actually a watch key, which
# is a concatenation of several pieces of data.
watch_key = event.summary.value[0].node_name
tensor_value = debug_data.load_tensor_from_event(event)
device_name = _extract_device_name_from_event(event)
node_name, output_slot, debug_op = event.summary.value[
0
].node_name.split(":")
maybe_base_expanded_node_name = self._run_states.get_maybe_base_expanded_node_name(
node_name, self._run_key, device_name
)
self._tensor_store.add(watch_key, tensor_value)
self._outgoing_channel.put(
_comm_tensor_data(
device_name,
node_name,
maybe_base_expanded_node_name,
output_slot,
debug_op,
tensor_value,
event.wall_time,
)
)
logger.info("on_value_event(): waiting for client ack (tensors)...")
self._incoming_channel.get()
logger.info("on_value_event(): client ack received (tensor).")
# Determine if the particular debug watch key is in the current list of
# breakpoints. If it is, send an EventReply() to unblock the debug op.
if self._is_debug_node_in_breakpoints(event.summary.value[0].node_name):
logger.info(
"Sending empty EventReply for breakpoint: %s",
event.summary.value[0].node_name,
)
# TODO(cais): Support receiving and sending tensor value from front-end.
return debug_service_pb2.EventReply()
return None
def on_value_event(self, event):
"""Implementation of the tensor value-carrying Event proto callback.
Writes the Event proto to the file system for testing. The path written to
follows the same pattern as the file:// debug URLs of tfdbg, i.e., the
name scope of the op becomes the directory structure under the dump root
directory.
Args:
event: The Event proto carrying a tensor value.
"""
if self._dump_dir:
self._write_value_event(event)
else:
value = event.summary.value[0]
self._event_listener_servicer.debug_tensor_values[value.node_name].append(
debug_data.load_tensor_from_event(event))
def on_value_event(self, event):
"""Implementation of the tensor value-carrying Event proto callback.
Writes the Event proto to the file system for testing. The path written to
follows the same pattern as the file:// debug URLs of tfdbg, i.e., the
name scope of the op becomes the directory structure under the dump root
directory.
Args:
event: The Event proto carrying a tensor value.
"""
if self._dump_dir:
self._write_value_event(event)
else:
value = event.summary.value[0]
self._event_listener_servicer.debug_tensor_values[value.node_name].append(
debug_data.load_tensor_from_event(event))
def on_value_event(self, event):
"""Records the summary values based on an updated message from the debugger.
Logs an error message if writing the event to disk fails.
Args:
event: The Event proto to be processed.
"""
if not event.summary.value:
tf.logging.info('The summary of the event lacks a value.')
return None
# The node name property in the event proto is actually a watch key, which
# is a concatenation of several pieces of data.
watch_key = event.summary.value[0].node_name
tensor_value = debug_data.load_tensor_from_event(event)
device_name = _extract_device_name_from_event(event)
node_name, output_slot, debug_op = (
event.summary.value[0].node_name.split(':'))
maybe_base_expanded_node_name = (
self._run_states.get_maybe_base_expanded_node_name(node_name,
self._run_key,
device_name))
self._tensor_store.add(watch_key, tensor_value)
self._outgoing_channel.put(_comm_tensor_data(
device_name, node_name, maybe_base_expanded_node_name, output_slot,
debug_op, tensor_value, event.wall_time))
tf.logging.info('on_value_event(): waiting for client ack (tensors)...')
self._incoming_channel.get()
tf.logging.info('on_value_event(): client ack received (tensor).')
# Determine if the particular debug watch key is in the current list of
# breakpoints. If it is, send an EventReply() to unblock the debug op.
if self._is_debug_node_in_breakpoints(event.summary.value[0].node_name):
tf.logging.info('Sending empty EventReply for breakpoint: %s',
event.summary.value[0].node_name)
# TODO(cais): Support receiving and sending tensor value from front-end.
return debug_service_pb2.EventReply()
return None
