130 lines
4.7 KiB
Python
130 lines
4.7 KiB
Python
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# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Utilities for graph plugin."""
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from tensorboard.compat.proto import graph_pb2
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def _prefixed_op_name(prefix, op_name):
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return "%s/%s" % (prefix, op_name)
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def _prefixed_func_name(prefix, func_name):
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"""Returns function name prefixed with `prefix`.
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For function libraries, which are often created out of autographed Python
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function, are factored out in the graph vis. They are grouped under a
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function name which often has a shape of
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`__inference_[py_func_name]_[numeric_suffix]`.
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While it does not have some unique information about which graph it is from,
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creating another wrapping structure with graph prefix and "/" is less than
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ideal so we join the prefix and func_name using underscore.
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TODO(stephanwlee): add business logic to strip "__inference_" for more user
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friendlier name
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"""
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return "%s_%s" % (prefix, func_name)
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def _add_with_prepended_names(prefix, graph_to_add, destination_graph):
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for node in graph_to_add.node:
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new_node = destination_graph.node.add()
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new_node.CopyFrom(node)
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new_node.name = _prefixed_op_name(prefix, node.name)
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new_node.input[:] = [
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_prefixed_op_name(prefix, input_name) for input_name in node.input
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]
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# Remap tf.function method name in the PartitionedCall. 'f' is short for
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# function.
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if new_node.op == "PartitionedCall" and new_node.attr["f"]:
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new_node.attr["f"].func.name = _prefixed_func_name(
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prefix,
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new_node.attr["f"].func.name,
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)
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for func in graph_to_add.library.function:
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new_func = destination_graph.library.function.add()
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new_func.CopyFrom(func)
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new_func.signature.name = _prefixed_func_name(
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prefix, new_func.signature.name
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)
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for gradient in graph_to_add.library.gradient:
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new_gradient = destination_graph.library.gradient.add()
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new_gradient.CopyFrom(gradient)
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new_gradient.function_name = _prefixed_func_name(
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prefix,
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new_gradient.function_name,
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)
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new_gradient.gradient_func = _prefixed_func_name(
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prefix,
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new_gradient.gradient_func,
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)
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def merge_graph_defs(graph_defs):
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"""Merges GraphDefs by adding unique prefix, `graph_{ind}`, to names.
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All GraphDefs are expected to be of TensorBoard's.
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When collecting graphs using the `tf.summary.trace` API, node names are not
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guranteed to be unique. When non-unique names are not considered, it can
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lead to graph visualization showing them as one which creates inaccurate
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depiction of the flow of the graph (e.g., if there are A -> B -> C and D ->
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B -> E, you may see {A, D} -> B -> E). To prevent such graph, we checked
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for uniquenss while merging but it resulted in
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https://github.com/tensorflow/tensorboard/issues/1929.
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To remedy these issues, we simply "apply name scope" on each graph by
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prefixing it with unique name (with a chance of collision) to create
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unconnected group of graphs.
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In case there is only one graph def passed, it returns the original
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graph_def. In case no graph defs are passed, it returns an empty GraphDef.
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Args:
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graph_defs: TensorBoard GraphDefs to merge.
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Returns:
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TensorBoard GraphDef that merges all graph_defs with unique prefixes.
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Raises:
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ValueError in case GraphDef versions mismatch.
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"""
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if len(graph_defs) == 1:
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return graph_defs[0]
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elif len(graph_defs) == 0:
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return graph_pb2.GraphDef()
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dst_graph_def = graph_pb2.GraphDef()
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if graph_defs[0].versions.producer:
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dst_graph_def.versions.CopyFrom(graph_defs[0].versions)
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for index, graph_def in enumerate(graph_defs):
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if dst_graph_def.versions.producer != graph_def.versions.producer:
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raise ValueError("Cannot combine GraphDefs of different versions.")
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_add_with_prepended_names(
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"graph_%d" % (index + 1),
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graph_def,
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dst_graph_def,
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)
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return dst_graph_def
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