TensorFlow.NET/src/TensorFlowNET.Core/Checkpoint/SaveUtil.cs at master · feelsyt/TensorFlow.NET · GitHub

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using OneOf;

using System;

using System.Collections.Generic;

using System.Diagnostics;

using System.Linq;

using System.Text;

using Tensorflow.Train;

using Tensorflow.Training;

using Tensorflow.Common.Extensions;

using pbc = global::Google.Protobuf.Collections;

namespace Tensorflow.Checkpoint

{

internal record class TrackableData(

// A trackable in the root Trackable object graph.

Trackable trackable,

// The index at which the Trackable appears in TrackableObjectGraph.nodes.

int node_id,

// The BFS-generated path from the root object / used to generate readable checkpoint keys.

string object_name,

// A list of ObjectReference for each child connected to this Trackable.

pbc::RepeatedField<global::Tensorflow.TrackableObjectGraph.Types.TrackableObject.Types.ObjectReference> children_proto,

// A list of SlotVariableReference to save to the object (only valid for Optimizer objects).

pbc::RepeatedField<global::Tensorflow.TrackableObjectGraph.Types.TrackableObject.Types.SlotVariableReference> slot_variable_proto,

// The object to save to checkpoint. Usually this is the same as `trackable`,

// but can differ when the the caller wants to specify a different object to

// save. For example, when saving checkpoints asynchronously, variables are

// copied to the CPU. `object_to_save` is set as the copied variable.

Trackable object_to_save

);

public static class SaveUtil

{

public static (IDictionary<Trackable, IDictionary<string, IDictionary<string, OneOf<Tensor, SaveSpec>>>>, IDictionary<Tensor, object>, IDictionary<string, IDictionary<string, Trackable>>, TrackableObjectGraph)

serialize_graph_view(ObjectGraphView graph_view, IDictionary<Trackable, Trackable>? object_map = null, bool call_with_mapped_captures = false, object? cache = null)

{

var (trackable_data, node_ids) = gather_trackable_data(graph_view, object_map);

var (tensor_trackables, pystate_trackables, registered_trackables) = split_trackables(trackable_data);

var object_graph_proto = fill_object_graph_proto(trackable_data);

var serialized_tensors = get_and_write_tensors_to_serialize(tensor_trackables, node_ids, call_with_mapped_captures, cache, object_graph_proto);

var registered_savers = get_and_write_registered_savers(registered_trackables, object_graph_proto);

Dictionary<Tensor, object> feed_additions;

if(cache is null)

{

feed_additions = null;

serialized_tensors = serialized_tensors.Concat(get_and_write_tensors_to_serialize(pystate_trackables, node_ids, call_with_mapped_captures,

cache, object_graph_proto)).ToDictionary(x => x.Key, x => x.Value);

}

else

{

feed_additions = null;

// TODO: deal with cache.

throw new NotFiniteNumberException();

}

CheckPointUtils.add_checkpoint_values_check(object_graph_proto);

return (serialized_tensors, feed_additions, registered_savers, object_graph_proto);

}

private static (IList<TrackableData>, IDictionary<Trackable, int>) gather_trackable_data(ObjectGraphView graph_view, IDictionary<Trackable, Trackable>? object_map)

{

var (trackable_objects, node_paths) = graph_view.breadth_first_traversal();

Dictionary<Trackable, string> object_names = new();

foreach(var pair in node_paths)

{

object_names[pair.Key] = TrackableUtils.object_path_to_string(pair.Value);

}

Dictionary<Trackable, int> node_ids = new();

for(int i = 0; i < trackable_objects.Count; i++)

{

node_ids[trackable_objects[i]] = i;

}

var slot_variables = CheckPointUtils.serialize_slot_variables(trackable_objects, node_ids, object_names);

List<TrackableData> trackable_data = new();

foreach(var trackable in trackable_objects)

{

pbc::RepeatedField<global::Tensorflow.TrackableObjectGraph.Types.TrackableObject.Types.ObjectReference> children_proto = new();

foreach(var child in graph_view.list_children(trackable))

{

children_proto.Add(new TrackableObjectGraph.Types.TrackableObject.Types.ObjectReference()

{

NodeId = node_ids[child.Refer],

LocalName = child.Name

});

}

slot_variables.TryGetValue(trackable, out var slot_variable);

trackable_data.Add(new TrackableData(

trackable: trackable,

node_id: node_ids[trackable],

object_name: object_names[trackable],

children_proto: children_proto,

slot_variable_proto: slot_variable??new pbc.RepeatedField<TrackableObjectGraph.Types.TrackableObject.Types.SlotVariableReference>(),

object_to_save: CheckPointUtils.get_mapped_trackable(trackable, object_map)

));

}

return (trackable_data, node_ids);

}

private static TrackableObjectGraph fill_object_graph_proto(IList<TrackableData> trackable_data)

{

TrackableObjectGraph object_graph_proto = new();

for(int i = 0; i < trackable_data.Count; i++)

{

var td = trackable_data[i];

Debug.Assert(td.node_id == i);

TrackableObjectGraph.Types.TrackableObject trackable_object = new();

trackable_object.SlotVariables.AddRange(td.slot_variable_proto);

trackable_object.Children.AddRange(td.children_proto);

object_graph_proto.Nodes.Add(trackable_object);

}

return object_graph_proto;

}

/// <summary>

/// Creates dictionary of tensors to checkpoint, and updates the proto.

/// </summary>

/// <param name="tensor_trackables"></param>

/// <param name="node_ids"></param>

/// <param name="call_with_mapped_captures"></param>

/// <param name="cache"></param>

/// <param name="object_graph_proto"></param>

private static IDictionary<Trackable, IDictionary<string, IDictionary<string, OneOf<Tensor, SaveSpec>>>> get_and_write_tensors_to_serialize(IList<TrackableData> tensor_trackables, IDictionary<Trackable, int> node_ids,

bool call_with_mapped_captures, object? cache, TrackableObjectGraph object_graph_proto)

{

Dictionary<Trackable, IDictionary<string, IDictionary<string, OneOf<Tensor, SaveSpec>>>> serialized_tensors = new();

foreach(var td in tensor_trackables)

{

// TODO: deal with cache.

var legacy_name = SaveableCompat.get_saveable_name(td.object_to_save) ?? "";

Trackable trackable = null;

IDictionary<string, IDictionary<string, OneOf<Tensor, SaveSpec>>> tensor_dict;

if(!saveable_object_util.trackable_has_serialize_to_tensor(td.object_to_save) || legacy_name.Length > 0)

{

(trackable, tensor_dict) = get_tensors_from_legacy_saveable(td, node_ids, call_with_mapped_captures, object_graph_proto);

}

else

{

tensor_dict = get_tensors_from_trackable(td, call_with_mapped_captures, object_graph_proto);

trackable = td.object_to_save;

}

if(trackable is not null)

{

serialized_tensors[trackable] = tensor_dict;

}

else

{

serialized_tensors[Trackable.None] = tensor_dict;

}

return serialized_tensors;

}

private static IDictionary<string, IDictionary<string, OneOf<Tensor, SaveSpec>>> get_tensors_from_trackable(TrackableData trackable_data, bool call_with_mapped_captures, TrackableObjectGraph object_graph_proto)

{

var trackable = trackable_data.object_to_save;

// TODO: complete it. Note that actually `call_with_mapped_captures` is of function type.

IDictionary<string, IDictionary<string, OneOf<Tensor, SaveSpec>>> ret_tensor_dict;

if (call_with_mapped_captures)

{

throw new NotImplementedException();

}

else

{

ret_tensor_dict = trackable.serialize_to_tensors();

}

Dictionary<string, IDictionary<string, OneOf<Tensor, SaveSpec>>> tensor_dict = new();

foreach(var pair in ret_tensor_dict)

{

var local_name = TrackableUtils.escape_local_name(pair.Key);

var maybe_tensor = pair.Value;

var checkpoint_key = TrackableUtils.checkpoint_key(trackable_data.object_name, local_name);

tensor_dict[checkpoint_key] = maybe_tensor;

foreach(var key in maybe_tensor.Keys)

{

if (maybe_tensor[key].IsTypeOrDeriveFrom<SaveSpec>())

{

maybe_tensor[key].AsT1.name = local_name + maybe_tensor[key].AsT1.name;

}

if(object_graph_proto is not null)

{

object_graph_proto.Nodes[trackable_data.node_id].Attributes.Add(new TrackableObjectGraph.Types.TrackableObject.Types.SerializedTensor()

{

Name = local_name,

CheckpointKey = checkpoint_key,

FullName = CheckPointUtils.get_full_name(trackable)

});

}

return tensor_dict;

}

/// <summary>

/// Gets tensors to serialize from a Trackable with legacy SaveableObjects.

/// </summary>

/// <param name="trackable_data"></param>

/// <param name="node_ids"></param>

/// <param name="call_with_mapped_captures"></param>

/// <param name="object_graph_proto"></param>

/// <returns></returns>

private static (Trackable, IDictionary<string, IDictionary<string, OneOf<Tensor, SaveSpec>>>) get_tensors_from_legacy_saveable(TrackableData trackable_data, IDictionary<Trackable, int> node_ids,

bool call_with_mapped_captures, TrackableObjectGraph object_graph_proto)

{

Dictionary<Trackable, string> object_names = new();

object_names[trackable_data.trackable] = trackable_data.object_name;

Dictionary<Trackable, Trackable> object_map = new();

object_map[trackable_data.trackable] = trackable_data.object_to_save;

var (checkpoint_factory_map, _) = SaveUtilV1.get_checkpoint_factories_and_keys(object_names, object_map);

var (named_saveable_objects, _) = SaveUtilV1.generate_saveable_objects(checkpoint_factory_map, object_graph_proto, node_ids, object_map,

call_with_mapped_captures, saveables_cache: null);

var trackable = new SaveableCompatibilityConverter(trackable_data.object_to_save, named_saveable_objects);

return (trackable, trackable.serialize_to_tensors());

}

private static IDictionary<string, IDictionary<string, Trackable>> get_and_write_registered_savers(IDictionary<string, IList<TrackableData>> registered_trackables, TrackableObjectGraph object_graph_proto)

{

Dictionary<string, IDictionary<string, Trackable>> registered_savers = new();

foreach(var pair in registered_trackables)

{

foreach(var td in pair.Value)

{

if (registered_savers.ContainsKey(pair.Key))

{

registered_savers[pair.Key] = new Dictionary<string, Trackable>();

}

else

{

registered_savers[pair.Key][td.object_name] = td.object_to_save;

}

var object_proto = object_graph_proto.Nodes[td.node_id];

// TODO: add APIs and complete it. Now the `TrackableObjectGraph.Types.TrackableObject` lacks `registered_savers`.

}

return registered_savers;

}

private static (IList<TrackableData>, IList<TrackableData>, IDictionary<string, IList<TrackableData>>) split_trackables(IEnumerable<TrackableData> trackable_data)

{

List<TrackableData> tensor_trackables = new();

List<TrackableData> py_state_trackables = new(); // skip the process of `PyState` for the lack of API. This is only a pleceholder.

Dictionary<string, IList<TrackableData>> registered_trackables = new();

foreach(var td in trackable_data)

{

// TODO: deal with registration.

tensor_trackables.Add(td);

}

return (tensor_trackables, py_state_trackables, registered_trackables);

}

TensorFlow.NET/src/TensorFlowNET.Core/Checkpoint/SaveUtil.cs at master · feelsyt/TensorFlow.NET · GitHub

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