tfdbg.DebugDumpDir

Class DebugDumpDir

Defined in tensorflow/python/debug/lib/debug_data.py.

See the guide: TensorFlow Debugger > Classes for debug-dump data and directories

Data set from a debug-dump directory on filesystem.

An instance of DebugDumpDir contains all DebugTensorDatum instances in a tfdbg dump root directory.

Properties

core_metadata

Metadata about the Session.run() call from the core runtime.

Of the three counters available in the return value, global_step is supplied by the caller of the debugged Session.run(), while session_run_index and executor_step_index are determined by the state of the core runtime, automatically. For the same fetch list, feed keys and debug tensor watch options, the same executor will be used and executor_step_index should increase by one at a time. However, runs with different fetch lists, feed keys and debug_tensor watch options that all share the same Session object can lead to gaps in session_run_index.

Returns:

If core metadata are loaded, a namedtuple with the fields: global_step: A global step count supplied by the caller of Session.run(). It is optional to the caller. If the caller did not supply this parameter, its value will be -1. session_run_index: A sorted index for Run() calls to the underlying TensorFlow Session object. executor_step_index: A counter for invocations of a given runtime executor. The same executor is re-used for the same fetched tensors, target nodes, input feed keys and debug tensor watch options. input_names: Names of the input (feed) Tensors. output_names: Names of the output (fetched) Tensors. target_nodes: Names of the target nodes. If the core metadata have not been loaded, None. If more than one core metadata files exist, return a list of the nametuple described above.

dumped_tensor_data

Retrieve dumped tensor data.

python_graph

Get the Python graph.

Returns:

If the Python graph has been set, returns a tf.Graph object. Otherwise, returns None.

run_feed_keys_info

Get a str representation of the feed_dict used in the Session.run() call.

Returns:

If the information is available from one Session.run call, a str obtained from repr(feed_dict). If the information is available from multiple Session.run calls, a list of str obtained from repr(feed_dict). If the information is not available, None.

run_fetches_info

Get a str representation of the fetches used in the Session.run() call.

Returns:

If the information is available from one Session.run call, a str obtained from repr(fetches). If the information is available from multiple Session.run calls, a list of str from repr(fetches). If the information is not available, None.

size

Total number of dumped tensors in the dump root directory.

Returns:

(int) The total number of dumped tensors in the dump root directory.

t0

Absolute timestamp of the first dumped tensor across all devices.

Returns:

(int) absolute timestamp of the first dumped tensor, in microseconds.

Methods

__init__

__init__(
    dump_root,
    partition_graphs=None,
    validate=True
)

DebugDumpDir constructor.

Args:

• dump_root: (str) path to the dump root directory.
• partition_graphs: A repeated field of GraphDefs representing the partition graphs executed by the TensorFlow runtime.
• validate: (bool) whether the dump files are to be validated against the partition graphs.

Raises:

• IOError: If dump_root does not exist as a directory.
• ValueError: If more than one core metadata file is found under the dump root directory.

debug_watch_keys

debug_watch_keys(
    node_name,
    device_name=None
)

Get all tensor watch keys of given node according to partition graphs.

Args:

• node_name: (str) name of the node.
• device_name: (str) name of the device. If there is only one device or if node_name exists on only one device, this argument is optional.

Returns:

(list of str) all debug tensor watch keys. Returns an empty list if the node name does not correspond to any debug watch keys.

Raises:

LookupError: If debug watch information has not been loaded from partition graphs yet.

devices

devices()

Get the list of device names.

Returns:

(list of str) names of the devices.

find

find(
    predicate,
    first_n=0,
    device_name=None,
    exclude_node_names=None
)

Find dumped tensor data by a certain predicate.

Args:

• predicate: A callable that takes two input arguments:

  python def predicate(debug_tensor_datum, tensor): # returns a bool

  where debug_tensor_datum is an instance of DebugTensorDatum, which carries the metadata, such as the Tensor's node name, output slot timestamp, debug op name, etc.; and tensor is the dumped tensor value as a numpy.ndarray. first_n: (int) return only the first n DebugTensotDatum instances (in time order) for which the predicate returns True. To return all the DebugTensotDatum instances, let first_n be <= 0. device_name: optional device name. * exclude_node_names: Optional regular expression to exclude nodes with names matching the regular expression.

Returns:

A list of all DebugTensorDatum objects in this DebugDumpDir object for which predicate returns True, sorted in ascending order of the timestamp.

find_some_path

find_some_path(
    src_node_name,
    dst_node_name,
    include_control=True,
    include_reversed_ref=False,
    device_name=None
)

Find a path between a source node and a destination node.

Limitation: the source and destination are required to be on the same device, i.e., this method does not yet take into account Send/Recv nodes across devices.

TODO(cais): Make this method work across device edges by tracing Send/Recv nodes.

Args:

• src_node_name: (str) name of the source node or name of an output tensor of the node.
• dst_node_name: (str) name of the destination node or name of an output tensor of the node.
• include_control: (bool) whrther control edges are considered in the graph tracing.
• include_reversed_ref: Whether a ref input, say from A to B, is to be also considered as an input from B to A. The rationale is that ref inputs generally let the recipient (e.g., B in this case) mutate the value of the source (e.g., A in this case). So the reverse direction of the ref edge reflects the direction of information flow.
• device_name: (str) name of the device. If there is only one device or if node_name exists on only one device, this argument is optional.

Returns:

A path from the src_node_name to dst_node_name, as a list of str, if it exists. The list includes src_node_name as the first item and dst_node_name as the last. If such a path does not exist, None.

Raises:

• ValueError: If the source and destination nodes are not on the same device.

get_dump_sizes_bytes

get_dump_sizes_bytes(
    node_name,
    output_slot,
    debug_op,
    device_name=None
)

Get the sizes of the dump files for a debug-dumped tensor.

Unit of the file size: byte.

Args:

• node_name: (str) name of the node that the tensor is produced by.
• output_slot: (int) output slot index of tensor.
• debug_op: (str) name of the debug op.
• device_name: (str) name of the device. If there is only one device or if the specified debug_watch_key exists on only one device, this argument is optional.

Returns:

(list of int): list of dump file sizes in bytes.

Raises:

• WatchKeyDoesNotExistInDebugDumpDirError: If the tensor watch key does not exist in the debug dump data.

get_rel_timestamps

get_rel_timestamps(
    node_name,
    output_slot,
    debug_op,
    device_name=None
)

Get the relative timestamp from for a debug-dumped tensor.

Relative timestamp means (absolute timestamp - t0), where t0 is the absolute timestamp of the first dumped tensor in the dump root. The tensor may be dumped multiple times in the dump root directory, so a list of relative timestamps (numpy.ndarray) is returned.

Args:

• node_name: (str) name of the node that the tensor is produced by.
• output_slot: (int) output slot index of tensor.
• debug_op: (str) name of the debug op.
• device_name: (str) name of the device. If there is only one device or if the specified debug_watch_key exists on only one device, this argument is optional.

Returns:

(list of int) list of relative timestamps.

Raises:

• WatchKeyDoesNotExistInDebugDumpDirError: If the tensor watch key does not exist in the debug dump data.

get_tensor_file_paths

get_tensor_file_paths(
    node_name,
    output_slot,
    debug_op,
    device_name=None
)

Get the file paths from a debug-dumped tensor.

Args:

• node_name: (str) name of the node that the tensor is produced by.
• output_slot: (int) output slot index of tensor.
• debug_op: (str) name of the debug op.
• device_name: (str) name of the device. If there is only one device or if the specified debug_watch_key exists on only one device, this argument is optional.

Returns:

List of file path(s) loaded. This is a list because each debugged tensor may be dumped multiple times.

Raises:

• WatchKeyDoesNotExistInDebugDumpDirError: If the tensor does not exist in the debug-dump data.

get_tensors

get_tensors(
    node_name,
    output_slot,
    debug_op,
    device_name=None
)

Get the tensor value from for a debug-dumped tensor.

The tensor may be dumped multiple times in the dump root directory, so a list of tensors (numpy.ndarray) is returned.

Args:

• node_name: (str) name of the node that the tensor is produced by.
• output_slot: (int) output slot index of tensor.
• debug_op: (str) name of the debug op.
• device_name: (str) name of the device. If there is only one device or if the specified debug_watch_key exists on only one device, this argument is optional.

Returns:

List of tensors (numpy.ndarray) loaded from the debug-dump file(s).

Raises:

• WatchKeyDoesNotExistInDebugDumpDirError: If the tensor does not exist in the debug-dump data.

loaded_partition_graphs

loaded_partition_graphs()

Test whether partition graphs have been loaded.

node_attributes

node_attributes(
    node_name,
    device_name=None
)

Get the attributes of a node.

Args:

• node_name: Name of the node in question.
• device_name: (str) name of the device. If there is only one device or if node_name exists on only one device, this argument is optional.

Returns:

Attributes of the node.

Raises:

• LookupError: If no partition graphs have been loaded.

node_device

node_device(node_name)

Get the names of the devices that has nodes of the specified name.

Args:

• node_name: (str) name of the node.

Returns:

(str or list of str) name of the device(s) on which the node of the given name is found. Returns a str if there is only one such device, otherwise return a list of str.

Raises:

• LookupError: If node inputs and control inputs have not been loaded from partition graphs yet.
• ValueError: If the node does not exist in partition graphs.

node_exists

node_exists(
    node_name,
    device_name=None
)

Test if a node exists in the partition graphs.

Args:

• node_name: (str) name of the node to be checked.
• device_name: optional device name. If None, will search for the node on all available devices. Otherwise, search for the node only on the given device.

Returns:

A boolean indicating whether the node exists.

Raises:

• LookupError: If no partition graphs have been loaded yet.
• ValueError: If device_name is specified but cannot be found.

node_inputs

node_inputs(
    node_name,
    is_control=False,
    device_name=None
)

Get the inputs of given node according to partition graphs.

Args:

• node_name: Name of the node.
• is_control: (bool) Whether control inputs, rather than non-control inputs, are to be returned.
• device_name: (str) name of the device. If there is only one device or if node_name exists on only one device, this argument is optional.

Returns:

(list of str) inputs to the node, as a list of node names.

Raises:

• LookupError: If node inputs and control inputs have not been loaded from partition graphs yet.

node_op_type

node_op_type(
    node_name,
    device_name=None
)

Get the op type of given node.

Args:

• node_name: (str) name of the node.
• device_name: (str) name of the device. If there is only one device or if node_name exists on only one device, this argument is optional.

Returns:

(str) op type of the node.

Raises:

• LookupError: If node op types have not been loaded from partition graphs yet.

node_recipients

node_recipients(
    node_name,
    is_control=False,
    device_name=None
)

Get recipient of the given node's output according to partition graphs.

Args:

• node_name: (str) name of the node.
• is_control: (bool) whether control outputs, rather than non-control outputs, are to be returned.
• device_name: (str) name of the device. If there is only one device or if node_name exists on only one device, this argument is optional.

Returns:

(list of str) all inputs to the node, as a list of node names.

Raises:

• LookupError: If node inputs and control inputs have not been loaded from partition graphs yet.

node_traceback

node_traceback(element_name)

Try to retrieve the Python traceback of node's construction.

Args:

• element_name: (str) Name of a graph element (node or tensor).

Returns:

(list) The traceback list object as returned by the extract_trace method of Python's traceback module.

Raises:

• LookupError: If Python graph is not available for traceback lookup.
• KeyError: If the node cannot be found in the Python graph loaded.

nodes

nodes(device_name=None)

Get a list of all nodes from the partition graphs.

Args:

• device_name: (str) name of device. If None, all nodes from all available devices will be included.

Returns:

All nodes' names, as a list of str.

Raises:

• LookupError: If no partition graphs have been loaded.
• ValueError: If specified node name does not exist.

partition_graphs

partition_graphs()

Get the partition graphs.

Returns:

Partition graphs as a list of GraphDef.

Raises:

• LookupError: If no partition graphs have been loaded.

reconstructed_non_debug_partition_graphs

reconstructed_non_debug_partition_graphs()

Reconstruct partition graphs with the debugger-inserted ops stripped.

The reconstructed partition graphs are identical to the original (i.e., non-debugger-decorated) partition graphs except in the following respects: 1) The exact names of the runtime-inserted internal nodes may differ. These include _Send, _Recv, _HostSend, _HostRecv, _Retval ops. 2) As a consequence of 1, the nodes that receive input directly from such send- and recv-type ops will have different input names. 3) The parallel_iteration attribute of while-loop Enter ops are set to 1.

Returns:

A dict mapping device names (strs) to reconstructed tf.GraphDefs.

set_python_graph

set_python_graph(python_graph)

Provide Python Graph object to the wrapper.

Unlike the partition graphs, which are protobuf GraphDef objects, Graph is a Python object and carries additional information such as the traceback of the construction of the nodes in the graph.

Args:

• python_graph: (ops.Graph) The Python Graph object.

transitive_inputs

transitive_inputs(
    node_name,
    include_control=True,
    include_reversed_ref=False,
    device_name=None
)

Get the transitive inputs of given node according to partition graphs.

Args:

• node_name: Name of the node.
• include_control: Include control inputs (True by default).
• include_reversed_ref: Whether a ref input, say from A to B, is to be also considered as an input from B to A. The rationale is that ref inputs generally let the recipient (e.g., B in this case) mutate the value of the source (e.g., A in this case). So the reverse direction of the ref edge reflects the direction of information flow.
• device_name: (str) name of the device. If there is only one device or if node_name exists on only one device, this argument is optional.

Returns:

(list of str) all transitive inputs to the node, as a list of node names.

Raises:

• LookupError: If node inputs and control inputs have not been loaded from partition graphs yet.

watch_key_to_data

watch_key_to_data(
    debug_watch_key,
    device_name=None
)

Get all DebugTensorDatum instances corresponding to a debug watch key.

Args:

• debug_watch_key: (str) debug watch key.
• device_name: (str) name of the device. If there is only one device or if the specified debug_watch_key exists on only one device, this argument is optional.

Returns:

A list of DebugTensorDatum instances that correspond to the debug watch key. If the watch key does not exist, returns an empty list.

Raises:

• ValueError: If there are multiple devices that have the debug_watch_key, but device_name is not specified.