# 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. # ============================================================================== """gRPC debug server in Python.""" # pylint: disable=g-bad-import-order import collections import json import queue import threading import time from concurrent import futures import grpc from tensorflow.core.debug import debug_service_pb2 from tensorflow.core.framework import graph_pb2 from tensorflow.python.debug.lib import debug_graphs from tensorflow.python.debug.lib import debug_service_pb2_grpc from tensorflow.python.platform import tf_logging as logging from tensorflow.python.util import compat DebugWatch = collections.namedtuple("DebugWatch", ["node_name", "output_slot", "debug_op"]) def _state_change(new_state, node_name, output_slot, debug_op): state_change = debug_service_pb2.EventReply.DebugOpStateChange() state_change.state = new_state state_change.node_name = node_name state_change.output_slot = output_slot state_change.debug_op = debug_op return state_change class EventListenerBaseStreamHandler: """Per-stream handler of EventListener gRPC streams.""" def __init__(self): """Constructor of EventListenerBaseStreamHandler.""" def on_core_metadata_event(self, event): """Callback for core metadata. Args: event: The Event proto that carries a JSON string in its `log_message.message` field. Returns: `None` or an `EventReply` proto to be sent back to the client. If `None`, an `EventReply` proto construct with the default no-arg constructor will be sent back to the client. """ raise NotImplementedError( "on_core_metadata_event() is not implemented in the base servicer " "class") def on_graph_def(self, graph_def, device_name, wall_time): """Callback for Event proto received through the gRPC stream. This Event proto carries a GraphDef, encoded as bytes, in its graph_def field. Args: graph_def: A GraphDef object. device_name: Name of the device on which the graph was created. wall_time: An epoch timestamp (in microseconds) for the graph. Returns: `None` or an `EventReply` proto to be sent back to the client. If `None`, an `EventReply` proto construct with the default no-arg constructor will be sent back to the client. """ raise NotImplementedError( "on_graph_def() is not implemented in the base servicer class") def on_value_event(self, event): """Callback for Event proto received through the gRPC stream. This Event proto carries a Tensor in its summary.value[0] field. Args: event: The Event proto from the stream to be processed. """ raise NotImplementedError( "on_value_event() is not implemented in the base servicer class") class EventListenerBaseServicer(debug_service_pb2_grpc.EventListenerServicer): """Base Python class for gRPC debug server.""" def __init__(self, server_port, stream_handler_class): """Constructor. Args: server_port: (int) Port number to bind to. stream_handler_class: A class of the base class `EventListenerBaseStreamHandler` that will be used to constructor stream handler objects during `SendEvents` calls. """ self._server_port = server_port self._stream_handler_class = stream_handler_class self._server_lock = threading.Lock() self._server_started = False self._stop_requested = False self._debug_ops_state_change_queue = queue.Queue() self._gated_grpc_debug_watches = set() self._breakpoints = set() def SendEvents(self, request_iterator, context): """Implementation of the SendEvents service method. This method receives streams of Event protos from the client, and processes them in ways specified in the on_event() callback. The stream is bi-directional, but currently only the client-to-server stream (i.e., the stream from the debug ops to the server) is used. Args: request_iterator: The incoming stream of Event protos. context: Server context. Raises: ValueError: If there are more than one core metadata events. Yields: An empty stream of responses. """ core_metadata_count = 0 # A map from GraphDef hash to a list of received chunks. graph_def_chunks = {} tensor_chunks = {} stream_handler = None for event in request_iterator: if not stream_handler: stream_handler = self._stream_handler_class() if event.summary and event.summary.value: # An Event proto carrying a tensor value. maybe_tensor_event = self._process_tensor_event_in_chunks( event, tensor_chunks) if maybe_tensor_event: event_reply = stream_handler.on_value_event(maybe_tensor_event) if event_reply is not None: yield self._process_debug_op_state_changes(event_reply) else: # Non-tensor-value Event. if event.graph_def: # GraphDef-carrying Event. maybe_graph_def, maybe_device_name, maybe_wall_time = ( self._process_encoded_graph_def_in_chunks( event, graph_def_chunks)) if maybe_graph_def: reply = stream_handler.on_graph_def( maybe_graph_def, maybe_device_name, maybe_wall_time) yield self._process_debug_op_state_changes(reply) elif event.log_message.message: # Core metadata-carrying Event. core_metadata_count += 1 if core_metadata_count > 1: raise ValueError( "Expected one core metadata event; received multiple") reply = stream_handler.on_core_metadata_event(event) yield self._process_debug_op_state_changes(reply) def _process_debug_op_state_changes(self, event_reply=None): """Dequeue and process all the queued debug-op state change protos. Include all the debug-op state change protos in a `EventReply` proto. Args: event_reply: An `EventReply` to add the `DebugOpStateChange` protos to, or `None`. Returns: An `EventReply` proto with the dequeued `DebugOpStateChange` protos (if any) added. """ if event_reply is None: event_reply = debug_service_pb2.EventReply() while not self._debug_ops_state_change_queue.empty(): state_change = self._debug_ops_state_change_queue.get() debug_node_key = (state_change.node_name, state_change.output_slot, state_change.debug_op) if (state_change.state == debug_service_pb2.EventReply.DebugOpStateChange.READ_WRITE): logging.info("Adding breakpoint %s:%d:%s", state_change.node_name, state_change.output_slot, state_change.debug_op) self._breakpoints.add(debug_node_key) elif (state_change.state == debug_service_pb2.EventReply.DebugOpStateChange.READ_ONLY): logging.info("Adding watchpoint %s:%d:%s", state_change.node_name, state_change.output_slot, state_change.debug_op) if debug_node_key in self._breakpoints: self._breakpoints.discard(debug_node_key) elif (state_change.state == debug_service_pb2.EventReply.DebugOpStateChange.DISABLED): logging.info("Removing watchpoint or breakpoint: %s:%d:%s", state_change.node_name, state_change.output_slot, state_change.debug_op) if debug_node_key in self._breakpoints: self._breakpoints.discard(debug_node_key) else: logging.warn( "Attempting to remove a non-existent debug node key: %s", debug_node_key) new_state_change = event_reply.debug_op_state_changes.add() new_state_change.CopyFrom(state_change) return event_reply def _process_tensor_event_in_chunks(self, event, tensor_chunks): """Possibly reassemble event chunks. Due to gRPC's message size limit, a large tensor can be encapsulated in multiple Event proto chunks to be sent through the debugger stream. This method keeps track of the chunks that have arrived, reassemble all chunks corresponding to a tensor when they have arrived and return the reassembled Event proto. Args: event: The single Event proto that has arrived. tensor_chunks: A dict used to keep track of the Event protos that have arrived but haven't been reassembled. Returns: If all Event protos corresponding to a tensor have arrived, returns the reassembled Event proto. Otherwise, return None. """ value = event.summary.value[0] debugger_plugin_metadata = json.loads( compat.as_text(value.metadata.plugin_data.content)) device_name = debugger_plugin_metadata["device"] num_chunks = debugger_plugin_metadata["numChunks"] chunk_index = debugger_plugin_metadata["chunkIndex"] if num_chunks <= 1: return event debug_node_name = value.node_name timestamp = int(event.wall_time) tensor_key = "%s_%s_%d" % (device_name, debug_node_name, timestamp) if tensor_key not in tensor_chunks: tensor_chunks[tensor_key] = [None] * num_chunks chunks = tensor_chunks[tensor_key] if value.tensor.tensor_content: chunks[chunk_index] = value.tensor elif value.tensor.string_val: chunks[chunk_index] = event if None not in chunks: if value.tensor.tensor_content: event.summary.value[0].tensor.tensor_content = b"".join( chunk.tensor_content for chunk in chunks) del tensor_chunks[tensor_key] return event elif value.tensor.string_val: merged_event = chunks[0] for chunk in chunks[1:]: merged_event.summary.value[0].tensor.string_val.extend( list(chunk.summary.value[0].tensor.string_val)) return merged_event def _process_encoded_graph_def_in_chunks(self, event, graph_def_chunks): """Process an Event proto containing a chunk of encoded GraphDef. Args: event: the Event proto containing the chunk of encoded GraphDef. graph_def_chunks: A dict mapping keys for GraphDefs (i.e., ",,") to a list of chunks of encoded GraphDefs. Returns: If all chunks of the GraphDef have arrived, return decoded GraphDef proto, device name, wall_time. Otherwise, return None, None, None. """ graph_def = graph_pb2.GraphDef() index_bar_0 = event.graph_def.find(b"|") index_bar_1 = event.graph_def.find(b"|", index_bar_0 + 1) index_bar_2 = event.graph_def.find(b"|", index_bar_1 + 1) graph_def_hash_device_timestamp = event.graph_def[:index_bar_0] chunk_index = int(event.graph_def[index_bar_0 + 1 : index_bar_1]) num_chunks = int(event.graph_def[index_bar_1 + 1 : index_bar_2]) if graph_def_hash_device_timestamp not in graph_def_chunks: graph_def_chunks[graph_def_hash_device_timestamp] = [None] * num_chunks graph_def_chunks[graph_def_hash_device_timestamp][ chunk_index] = event.graph_def[index_bar_2 + 1:] if all(graph_def_chunks[graph_def_hash_device_timestamp]): device_name = graph_def_hash_device_timestamp.split(b",")[1] wall_time = int(graph_def_hash_device_timestamp.split(b",")[2]) graph_def.ParseFromString( b"".join(graph_def_chunks[graph_def_hash_device_timestamp])) del graph_def_chunks[graph_def_hash_device_timestamp] self._process_graph_def(graph_def) return graph_def, device_name, wall_time else: return None, None, None def _process_graph_def(self, graph_def): for node_def in graph_def.node: if (debug_graphs.is_debug_node(node_def.name) and node_def.attr["gated_grpc"].b): node_name, output_slot, _, debug_op = ( debug_graphs.parse_debug_node_name(node_def.name)) self._gated_grpc_debug_watches.add( DebugWatch(node_name, output_slot, debug_op)) def run_server(self, blocking=True): """Start running the server. Args: blocking: If `True`, block until `stop_server()` is invoked. Raises: ValueError: If server stop has already been requested, or if the server has already started running. """ self._server_lock.acquire() try: if self._stop_requested: raise ValueError("Server has already stopped") if self._server_started: raise ValueError("Server has already started running") no_max_message_sizes = [("grpc.max_receive_message_length", -1), ("grpc.max_send_message_length", -1)] self.server = grpc.server(futures.ThreadPoolExecutor(max_workers=10), options=no_max_message_sizes) debug_service_pb2_grpc.add_EventListenerServicer_to_server(self, self.server) self.server.add_insecure_port("[::]:%d" % self._server_port) self.server.start() self._server_started = True finally: self._server_lock.release() if blocking: while not self._stop_requested: time.sleep(1.0) def stop_server(self, grace=1.0): """Request server stopping. Once stopped, server cannot be stopped or started again. This method is non-blocking. Call `wait()` on the returned event to block until the server has completely stopped. Args: grace: Grace period in seconds to be used when calling `server.stop()`. Raises: ValueError: If server stop has already been requested, or if the server has not started running yet. Returns: A threading.Event that will be set when the server has completely stopped. """ self._server_lock.acquire() try: if not self._server_started: raise ValueError("Server has not started running") if self._stop_requested: raise ValueError("Server has already stopped") self._stop_requested = True return self.server.stop(grace=grace) finally: self._server_lock.release() def request_watch(self, node_name, output_slot, debug_op, breakpoint=False): # pylint: disable=redefined-builtin """Request enabling a debug tensor watchpoint or breakpoint. This will let the server send a EventReply to the client side (i.e., the debugged TensorFlow runtime process) to request adding a watch key (i.e., ::) to the list of enabled watch keys. The list applies only to debug ops with the attribute gated_grpc=True. To disable the watch, use `request_unwatch()`. Args: node_name: (`str`) name of the node that the to-be-watched tensor belongs to, e.g., "hidden/Weights". output_slot: (`int`) output slot index of the tensor to watch. debug_op: (`str`) name of the debug op to enable. This should not include any attribute substrings. breakpoint: (`bool`) Iff `True`, the debug op will block and wait until it receives an `EventReply` response from the server. The `EventReply` proto may carry a TensorProto that modifies the value of the debug op's output tensor. """ self._debug_ops_state_change_queue.put( _state_change( debug_service_pb2.EventReply.DebugOpStateChange.READ_WRITE if breakpoint else debug_service_pb2.EventReply.DebugOpStateChange.READ_ONLY, node_name, output_slot, debug_op)) def request_unwatch(self, node_name, output_slot, debug_op): """Request disabling a debug tensor watchpoint or breakpoint. This is the opposite of `request_watch()`. Args: node_name: (`str`) name of the node that the to-be-watched tensor belongs to, e.g., "hidden/Weights". output_slot: (`int`) output slot index of the tensor to watch. debug_op: (`str`) name of the debug op to enable. This should not include any attribute substrings. """ self._debug_ops_state_change_queue.put( _state_change( debug_service_pb2.EventReply.DebugOpStateChange.DISABLED, node_name, output_slot, debug_op)) @property def breakpoints(self): """Get a set of the currently-activated breakpoints. Returns: A `set` of 3-tuples: (node_name, output_slot, debug_op), e.g., {("MatMul", 0, "DebugIdentity")}. """ return self._breakpoints def gated_grpc_debug_watches(self): """Get the list of debug watches with attribute gated_grpc=True. Since the server receives `GraphDef` from the debugged runtime, it can only return such debug watches that it has received so far. Returns: A `list` of `DebugWatch` `namedtuples` representing the debug watches with gated_grpc=True. Each `namedtuple` element has the attributes: `node_name` as a `str`, `output_slot` as an `int`, `debug_op` as a `str`. """ return list(self._gated_grpc_debug_watches) def SendTracebacks(self, request, context): """Base implementation of the handling of SendTracebacks calls. The base implementation does nothing with the incoming request. Override in an implementation of the server if necessary. Args: request: A `CallTraceback` proto, containing information about the type (e.g., graph vs. eager execution) and source-code traceback of the call and (any) associated `tf.Graph`s. context: Server context. Returns: A `EventReply` proto. """ return debug_service_pb2.EventReply() def SendSourceFiles(self, request, context): """Base implementation of the handling of SendSourceFiles calls. The base implementation does nothing with the incoming request. Override in an implementation of the server if necessary. Args: request: A `DebuggedSourceFiles` proto, containing the path, content, size and last-modified timestamp of source files. context: Server context. Returns: A `EventReply` proto. """ return debug_service_pb2.EventReply()