Add load option for loading SavedModel from specific io_device for distributed training.
A new class LoadOptions is created similar to the existing SavedOptions. The option experimental_io_device is the only option added at this time and usd to set the io_device when loading a SavedModel for distributed training. PiperOrigin-RevId: 316557681 Change-Id: If3f1eae18b09085ff11dc8a6882fabcb18f5f48e
This commit is contained in:
Ken Franko
TensorFlower Gardener
parent
5d4c6e105f
commit
67fb07ba9f
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@ -2078,7 +2078,11 @@ class Model(base_layer.Layer, version_utils.ModelVersionSelector):
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save_relative_paths=True,
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all_model_checkpoint_paths=[filepath])
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def load_weights(self, filepath, by_name=False, skip_mismatch=False):
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def load_weights(self,
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filepath,
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by_name=False,
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skip_mismatch=False,
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options=None):
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"""Loads all layer weights, either from a TensorFlow or an HDF5 weight file.
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If `by_name` is False weights are loaded based on the network's
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@ -2108,6 +2112,8 @@ class Model(base_layer.Layer, version_utils.ModelVersionSelector):
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skip_mismatch: Boolean, whether to skip loading of layers where there is
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a mismatch in the number of weights, or a mismatch in the shape of
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the weight (only valid when `by_name=True`).
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options: Optional `tf.train.CheckpointOptions` object that specifies
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options for loading weights.
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Returns:
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When loading a weight file in TensorFlow format, returns the same status
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@ -2145,7 +2151,7 @@ class Model(base_layer.Layer, version_utils.ModelVersionSelector):
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# The checkpoint is not readable in TensorFlow format. Try HDF5.
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save_format = 'h5'
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if save_format == 'tf':
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status = self._trackable_saver.restore(filepath)
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status = self._trackable_saver.restore(filepath, options)
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if by_name:
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raise NotImplementedError(
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'Weights may only be loaded based on topology into Models when '
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@ -135,7 +135,7 @@ def save_model(model,
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@keras_export('keras.models.load_model')
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def load_model(filepath, custom_objects=None, compile=True): # pylint: disable=redefined-builtin
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def load_model(filepath, custom_objects=None, compile=True, options=None): # pylint: disable=redefined-builtin
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"""Loads a model saved via `model.save()`.
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Usage:
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@ -162,6 +162,8 @@ def load_model(filepath, custom_objects=None, compile=True): # pylint: disable=
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considered during deserialization.
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compile: Boolean, whether to compile the model
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after loading.
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options: Optional `tf.saved_model.LoadOptions` object that specifies
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options for loading from SavedModel.
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Returns:
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A Keras model instance. If the original model was compiled, and saved with
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@ -182,7 +184,7 @@ def load_model(filepath, custom_objects=None, compile=True): # pylint: disable=
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filepath = path_to_string(filepath)
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if isinstance(filepath, six.string_types):
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loader_impl.parse_saved_model(filepath)
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return saved_model_load.load(filepath, compile)
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return saved_model_load.load(filepath, compile, options)
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raise IOError(
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'Unable to load model. Filepath is not an hdf5 file (or h5py is not '
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@ -90,7 +90,7 @@ KERAS_OBJECT_IDENTIFIERS = (
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'_tf_keras_rnn_layer')
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def load(path, compile=True): # pylint: disable=redefined-builtin
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def load(path, compile=True, options=None): # pylint: disable=redefined-builtin
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"""Loads Keras objects from a SavedModel.
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Any Keras layer or model saved to the SavedModel will be loaded back
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@ -107,13 +107,18 @@ def load(path, compile=True): # pylint: disable=redefined-builtin
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Args:
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path: Path to SavedModel.
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compile: If true, compile the model after loading it.
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options: Optional `tf.saved_model.LoadOptions` object that specifies
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options for loading from SavedModel.
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Returns:
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Object loaded from SavedModel.
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"""
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# TODO(kathywu): Add saving/loading of optimizer, compiled losses and metrics.
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# TODO(kathywu): Add code to load from objects that contain all endpoints
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model = tf_load.load_internal(path, loader_cls=KerasObjectLoader)
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model = tf_load.load_internal(
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path, options=options, loader_cls=KerasObjectLoader)
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# pylint: disable=protected-access
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if isinstance(model, training_lib.Model) and compile:
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@ -348,6 +348,7 @@ py_library(
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deps = [
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":constants",
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":function_deserialization",
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":load_options",
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":load_v1_in_v2",
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":loader",
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":nested_structure_coder",
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@ -522,6 +523,13 @@ py_library(
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],
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)
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py_library(
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name = "load_options",
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srcs = ["load_options.py"],
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deps = [
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],
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)
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py_library(
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name = "method_name_updater",
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srcs = ["method_name_updater.py"],
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@ -37,11 +37,13 @@ from tensorflow.python.ops import lookup_ops
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from tensorflow.python.ops import resource_variable_ops
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from tensorflow.python.ops import variables
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from tensorflow.python.saved_model import function_deserialization
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from tensorflow.python.saved_model import load_options
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from tensorflow.python.saved_model import load_v1_in_v2
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from tensorflow.python.saved_model import loader_impl
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from tensorflow.python.saved_model import nested_structure_coder
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from tensorflow.python.saved_model import revived_types
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from tensorflow.python.saved_model import utils_impl as saved_model_utils
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from tensorflow.python.training.saving import checkpoint_options
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from tensorflow.python.training.tracking import base
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from tensorflow.python.training.tracking import graph_view
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from tensorflow.python.training.tracking import tracking
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@ -105,7 +107,8 @@ class _WrapperFunction(function.ConcreteFunction):
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class Loader(object):
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"""Helper class to load an object-based SavedModel."""
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def __init__(self, object_graph_proto, saved_model_proto, export_dir):
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def __init__(self, object_graph_proto, saved_model_proto, export_dir,
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ckpt_options):
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meta_graph = saved_model_proto.meta_graphs[0]
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self._asset_file_def = meta_graph.asset_file_def
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self._operation_attributes = {
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@ -115,6 +118,7 @@ class Loader(object):
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self._concrete_functions = (
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function_deserialization.load_function_def_library(
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meta_graph.graph_def.library))
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self._checkpoint_options = ckpt_options
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for name, concrete_function in self._concrete_functions.items():
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# Wrap all the concrete function so that they are capable of dealing with
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@ -306,9 +310,10 @@ class Loader(object):
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with ops.device("CPU"):
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saver._file_prefix_placeholder = constant_op.constant(variables_path)
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if self._expect_partial_checkpoint:
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load_status = saver.restore(variables_path).expect_partial()
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load_status = saver.restore(variables_path,
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self._checkpoint_options).expect_partial()
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else:
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load_status = saver.restore(variables_path)
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load_status = saver.restore(variables_path, self._checkpoint_options)
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load_status.assert_existing_objects_matched()
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checkpoint = load_status._checkpoint
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@ -491,7 +496,7 @@ def _call_attribute(instance, *args, **kwargs):
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@tf_export("saved_model.load", v1=["saved_model.load_v2"])
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def load(export_dir, tags=None):
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def load(export_dir, tags=None, options=None):
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"""Load a SavedModel from `export_dir`.
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Signatures associated with the SavedModel are available as functions:
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@ -569,6 +574,8 @@ def load(export_dir, tags=None):
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tags: A tag or sequence of tags identifying the MetaGraph to load. Optional
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if the SavedModel contains a single MetaGraph, as for those exported from
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`tf.saved_model.save`.
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options: Optional, `tf.saved_model.LoadOptions` object that specifies
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options for loading.
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Returns:
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A trackable object with a `signatures` attribute mapping from signature
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@ -579,11 +586,12 @@ def load(export_dir, tags=None):
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Raises:
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ValueError: If `tags` don't match a MetaGraph in the SavedModel.
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"""
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return load_internal(export_dir, tags)
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return load_internal(export_dir, tags, options)
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def load_internal(export_dir, tags=None, loader_cls=Loader):
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def load_internal(export_dir, tags=None, options=None, loader_cls=Loader):
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"""Loader implementation."""
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options = options or load_options.LoadOptions()
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if tags is not None and not isinstance(tags, set):
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# Supports e.g. tags=SERVING and tags=[SERVING]. Sets aren't considered
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# sequences for nest.flatten, so we put those through as-is.
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@ -602,10 +610,12 @@ def load_internal(export_dir, tags=None, loader_cls=Loader):
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"it, pass 'None', or pass matching tags.")
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.format(export_dir, meta_graph_def.meta_info_def.tags, tags))
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object_graph_proto = meta_graph_def.object_graph_def
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ckpt_options = checkpoint_options.CheckpointOptions(
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experimental_io_device=options.experimental_io_device)
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with ops.init_scope():
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loader = loader_cls(object_graph_proto,
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saved_model_proto,
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export_dir)
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loader = loader_cls(object_graph_proto, saved_model_proto, export_dir,
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ckpt_options)
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root = loader.get(0)
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if isinstance(loader, Loader):
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root.graph_debug_info = loader.adjust_debug_info_func_names(debug_info)
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@ -0,0 +1,57 @@
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# Copyright 2020 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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"""Options for saving SavedModels."""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from tensorflow.python.util.tf_export import tf_export
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@tf_export("saved_model.LoadOptions", v1=[])
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class LoadOptions(object):
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"""Options for loading a SavedModel.
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This function may be used in the `options` argument in functions that
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load a SavedModel (`tf.saved_model.load`, `tf.keras.models.load_model`).
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"""
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# Define object attributes in __slots__ for improved memory and performance.
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__slots__ = ("experimental_io_device",)
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def __init__(self,
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experimental_io_device=None):
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"""Creates an object that stores options for SavedModel loading.
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Args:
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experimental_io_device: string. Applies in a distributed setting.
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Tensorflow device to use to access the filesystem. If `None` (default)
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then for each variable the filesystem is accessed from the CPU:0 device
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of the host where that variable is assigned. If specified, the
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filesystem is instead accessed from that device for all variables.
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This is for example useful if you want to load from a local directory,
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such as "/tmp" when running in a distributed setting. In that case
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pass a device for the host where the "/tmp" directory is accessible.
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Example:
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load_options = tf.saved_model.LoadOptions(experimental_io_device=
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'/job:localhost')
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restoredmodel = tf.keras.models.load_model(saved_model_path,
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options=load_options)
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"""
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self.experimental_io_device = experimental_io_device
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@ -56,6 +56,7 @@ from tensorflow.python.ops import variables
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from tensorflow.python.ops.ragged import ragged_factory_ops
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from tensorflow.python.ops.ragged import ragged_tensor
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from tensorflow.python.saved_model import load
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from tensorflow.python.saved_model import load_options
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from tensorflow.python.saved_model import save
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from tensorflow.python.saved_model import tag_constants
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from tensorflow.python.training import monitored_session
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@ -1788,6 +1789,12 @@ class LoadTest(test.TestCase, parameterized.TestCase):
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self.assertAllEqual(imported2.f(rt, 2), [[3, 4], [5]])
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self.assertAllEqual(imported2.f(rt, 3), [[4, 5], [6]])
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def test_accepts_io_device(self, cycles):
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options = load_options.LoadOptions()
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self.assertIsNone(options.experimental_io_device)
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options = load_options.LoadOptions(experimental_io_device="/job:localhost")
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self.assertEqual("/job:localhost", options.experimental_io_device)
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class SingleCycleTests(test.TestCase, parameterized.TestCase):
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@ -258,7 +258,7 @@ tf_class {
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}
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member_method {
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name: "load_weights"
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argspec: "args=[\'self\', \'filepath\', \'by_name\', \'skip_mismatch\'], varargs=None, keywords=None, defaults=[\'False\', \'False\'], "
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argspec: "args=[\'self\', \'filepath\', \'by_name\', \'skip_mismatch\', \'options\'], varargs=None, keywords=None, defaults=[\'False\', \'False\', \'None\'], "
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}
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member_method {
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name: "make_predict_function"
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@ -264,7 +264,7 @@ tf_class {
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}
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member_method {
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name: "load_weights"
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argspec: "args=[\'self\', \'filepath\', \'by_name\', \'skip_mismatch\'], varargs=None, keywords=None, defaults=[\'False\', \'False\'], "
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argspec: "args=[\'self\', \'filepath\', \'by_name\', \'skip_mismatch\', \'options\'], varargs=None, keywords=None, defaults=[\'False\', \'False\', \'None\'], "
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}
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member_method {
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name: "make_predict_function"
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@ -259,7 +259,7 @@ tf_class {
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}
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member_method {
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name: "load_weights"
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argspec: "args=[\'self\', \'filepath\', \'by_name\', \'skip_mismatch\'], varargs=None, keywords=None, defaults=[\'False\', \'False\'], "
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argspec: "args=[\'self\', \'filepath\', \'by_name\', \'skip_mismatch\', \'options\'], varargs=None, keywords=None, defaults=[\'False\', \'False\', \'None\'], "
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}
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member_method {
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name: "make_predict_function"
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@ -259,7 +259,7 @@ tf_class {
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}
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member_method {
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name: "load_weights"
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argspec: "args=[\'self\', \'filepath\', \'by_name\', \'skip_mismatch\'], varargs=None, keywords=None, defaults=[\'False\', \'False\'], "
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argspec: "args=[\'self\', \'filepath\', \'by_name\', \'skip_mismatch\', \'options\'], varargs=None, keywords=None, defaults=[\'False\', \'False\', \'None\'], "
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}
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member_method {
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name: "make_predict_function"
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@ -258,7 +258,7 @@ tf_class {
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}
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member_method {
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name: "load_weights"
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argspec: "args=[\'self\', \'filepath\', \'by_name\', \'skip_mismatch\'], varargs=None, keywords=None, defaults=[\'False\', \'False\'], "
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argspec: "args=[\'self\', \'filepath\', \'by_name\', \'skip_mismatch\', \'options\'], varargs=None, keywords=None, defaults=[\'False\', \'False\', \'None\'], "
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}
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member_method {
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name: "make_predict_function"
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@ -264,7 +264,7 @@ tf_class {
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}
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member_method {
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name: "load_weights"
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argspec: "args=[\'self\', \'filepath\', \'by_name\', \'skip_mismatch\'], varargs=None, keywords=None, defaults=[\'False\', \'False\'], "
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argspec: "args=[\'self\', \'filepath\', \'by_name\', \'skip_mismatch\', \'options\'], varargs=None, keywords=None, defaults=[\'False\', \'False\', \'None\'], "
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}
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member_method {
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name: "make_predict_function"
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@ -14,7 +14,7 @@ tf_module {
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}
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member_method {
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name: "load_model"
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argspec: "args=[\'filepath\', \'custom_objects\', \'compile\'], varargs=None, keywords=None, defaults=[\'None\', \'True\'], "
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argspec: "args=[\'filepath\', \'custom_objects\', \'compile\', \'options\'], varargs=None, keywords=None, defaults=[\'None\', \'True\', \'None\'], "
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}
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member_method {
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name: "model_from_config"
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@ -182,7 +182,7 @@ tf_module {
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}
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member_method {
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name: "load_v2"
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argspec: "args=[\'export_dir\', \'tags\'], varargs=None, keywords=None, defaults=[\'None\'], "
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argspec: "args=[\'export_dir\', \'tags\', \'options\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
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}
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member_method {
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name: "main_op_with_restore"
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@ -258,7 +258,7 @@ tf_class {
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}
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member_method {
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name: "load_weights"
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argspec: "args=[\'self\', \'filepath\', \'by_name\', \'skip_mismatch\'], varargs=None, keywords=None, defaults=[\'False\', \'False\'], "
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argspec: "args=[\'self\', \'filepath\', \'by_name\', \'skip_mismatch\', \'options\'], varargs=None, keywords=None, defaults=[\'False\', \'False\', \'None\'], "
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}
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member_method {
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name: "make_predict_function"
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@ -264,7 +264,7 @@ tf_class {
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}
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member_method {
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name: "load_weights"
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argspec: "args=[\'self\', \'filepath\', \'by_name\', \'skip_mismatch\'], varargs=None, keywords=None, defaults=[\'False\', \'False\'], "
|
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argspec: "args=[\'self\', \'filepath\', \'by_name\', \'skip_mismatch\', \'options\'], varargs=None, keywords=None, defaults=[\'False\', \'False\', \'None\'], "
|
||||
}
|
||||
member_method {
|
||||
name: "make_predict_function"
|
||||
|
|
|
|||
|
|
@ -259,7 +259,7 @@ tf_class {
|
|||
}
|
||||
member_method {
|
||||
name: "load_weights"
|
||||
argspec: "args=[\'self\', \'filepath\', \'by_name\', \'skip_mismatch\'], varargs=None, keywords=None, defaults=[\'False\', \'False\'], "
|
||||
argspec: "args=[\'self\', \'filepath\', \'by_name\', \'skip_mismatch\', \'options\'], varargs=None, keywords=None, defaults=[\'False\', \'False\', \'None\'], "
|
||||
}
|
||||
member_method {
|
||||
name: "make_predict_function"
|
||||
|
|
|
|||
|
|
@ -259,7 +259,7 @@ tf_class {
|
|||
}
|
||||
member_method {
|
||||
name: "load_weights"
|
||||
argspec: "args=[\'self\', \'filepath\', \'by_name\', \'skip_mismatch\'], varargs=None, keywords=None, defaults=[\'False\', \'False\'], "
|
||||
argspec: "args=[\'self\', \'filepath\', \'by_name\', \'skip_mismatch\', \'options\'], varargs=None, keywords=None, defaults=[\'False\', \'False\', \'None\'], "
|
||||
}
|
||||
member_method {
|
||||
name: "make_predict_function"
|
||||
|
|
|
|||
|
|
@ -258,7 +258,7 @@ tf_class {
|
|||
}
|
||||
member_method {
|
||||
name: "load_weights"
|
||||
argspec: "args=[\'self\', \'filepath\', \'by_name\', \'skip_mismatch\'], varargs=None, keywords=None, defaults=[\'False\', \'False\'], "
|
||||
argspec: "args=[\'self\', \'filepath\', \'by_name\', \'skip_mismatch\', \'options\'], varargs=None, keywords=None, defaults=[\'False\', \'False\', \'None\'], "
|
||||
}
|
||||
member_method {
|
||||
name: "make_predict_function"
|
||||
|
|
|
|||
|
|
@ -264,7 +264,7 @@ tf_class {
|
|||
}
|
||||
member_method {
|
||||
name: "load_weights"
|
||||
argspec: "args=[\'self\', \'filepath\', \'by_name\', \'skip_mismatch\'], varargs=None, keywords=None, defaults=[\'False\', \'False\'], "
|
||||
argspec: "args=[\'self\', \'filepath\', \'by_name\', \'skip_mismatch\', \'options\'], varargs=None, keywords=None, defaults=[\'False\', \'False\', \'None\'], "
|
||||
}
|
||||
member_method {
|
||||
name: "make_predict_function"
|
||||
|
|
|
|||
|
|
@ -14,7 +14,7 @@ tf_module {
|
|||
}
|
||||
member_method {
|
||||
name: "load_model"
|
||||
argspec: "args=[\'filepath\', \'custom_objects\', \'compile\'], varargs=None, keywords=None, defaults=[\'None\', \'True\'], "
|
||||
argspec: "args=[\'filepath\', \'custom_objects\', \'compile\', \'options\'], varargs=None, keywords=None, defaults=[\'None\', \'True\', \'None\'], "
|
||||
}
|
||||
member_method {
|
||||
name: "model_from_config"
|
||||
|
|
|
|||
|
|
@ -0,0 +1,13 @@
|
|||
path: "tensorflow.saved_model.LoadOptions"
|
||||
tf_class {
|
||||
is_instance: "<class \'tensorflow.python.saved_model.load_options.LoadOptions\'>"
|
||||
is_instance: "<type \'object\'>"
|
||||
member {
|
||||
name: "experimental_io_device"
|
||||
mtype: "<type \'member_descriptor\'>"
|
||||
}
|
||||
member_method {
|
||||
name: "__init__"
|
||||
argspec: "args=[\'self\', \'experimental_io_device\'], varargs=None, keywords=None, defaults=[\'None\'], "
|
||||
}
|
||||
}
|
||||
|
|
@ -44,6 +44,10 @@ tf_module {
|
|||
name: "GPU"
|
||||
mtype: "<type \'str\'>"
|
||||
}
|
||||
member {
|
||||
name: "LoadOptions"
|
||||
mtype: "<type \'type\'>"
|
||||
}
|
||||
member {
|
||||
name: "PREDICT_INPUTS"
|
||||
mtype: "<type \'str\'>"
|
||||
|
|
@ -110,7 +114,7 @@ tf_module {
|
|||
}
|
||||
member_method {
|
||||
name: "load"
|
||||
argspec: "args=[\'export_dir\', \'tags\'], varargs=None, keywords=None, defaults=[\'None\'], "
|
||||
argspec: "args=[\'export_dir\', \'tags\', \'options\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
|
||||
}
|
||||
member_method {
|
||||
name: "save"
|
||||
|
|
|
|||
Loading…
Reference in New Issue