TensorFlow.NET/src/TensorFlowNET.Core/Graphs/FuncGraph.cs at master · ekumachidi/TensorFlow.NET · GitHub

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using Google.Protobuf;

using System;

using System.Buffers;

using System.Diagnostics;

using System.Linq;

using Tensorflow.Eager;

using Tensorflow.Exceptions;

using Tensorflow.Framework;

using Tensorflow.Framework.Models;

using Tensorflow.Functions;

using Tensorflow.NumPy;

using Tensorflow.Operations;

using Tensorflow.Util;

using static Tensorflow.Binding;

namespace Tensorflow.Graphs;

/// <summary>

/// Graph representing a function body.

/// </summary>

public class FuncGraph : Graph, IDisposable

{

internal SafeFuncGraphHandle _func_graph_handle;

internal HashSet<Tensor> _resource_tensor_inputs;

internal HashSet<WeakReference<IVariableV1>> _watched_variables;

internal IEnumerable<WeakReference<IVariableV1>> _weak_variables;

internal object[] _structured_outputs;

internal Dictionary<long, string> _output_names;

public string FuncName => _graph_key;

public Tensors Inputs { get; set; } = new Tensors();

public Tensors Outputs { get; set; } = new Tensors();

public Tensors FlatStructuredOutputs

{

get

{

List<Tensor> res = new();

foreach(var obj in _structured_outputs)

{

if(obj is Tensor tensor)

{

res.Add(tensor);

}

else if(obj is IEnumerable<Tensor> tensors)

{

res.AddRange(tensors);

}

else

{

throw new TypeError("The structured outputs member should be tensor or tensors.");

}

return res;

}

public string Name { get; set; }

public IEnumerable<IVariableV1> Variables

{

get

{

return _weak_variables.Select(v =>

{

if (v.TryGetTarget(out var target))

{

return target;

}

else

{

throw new AssertionError("Called a function referencing variables which have been deleted. " +

"This likely means that function-local variables were created and " +

"not referenced elsewhere in the program. This is generally a " +

"mistake; consider storing variables in an object attribute on first call.");

}

});

}

internal set

{

_weak_variables = value.Select(x => new WeakReference<IVariableV1>(x));

}

public IEnumerable<IVariableV1> TrainableVariables => Variables.Where(v => v.Trainable);

public Dictionary<string, AttrValue> Attrs { get; set; }

internal Dictionary<long, (Tensor, Tensor)> _captures

= new Dictionary<long, (Tensor, Tensor)>();

public Tensor[] external_captures

=> _captures.Select(x => x.Value.Item1).ToArray();

public (Tensor, Tensor)[] captures

=> _captures.Values.Select(x => x).ToArray();

public Tensor[] internal_captures

=> _captures.Select(x => x.Value.Item2).ToArray();

public Tensor[] captured_inputs

=> external_captures;

/// <summary>

/// Construct a new FuncGraph.

/// </summary>

public FuncGraph(string name) : base()

{

outer_graph = ops.get_default_graph();

while (outer_graph.building_function)

outer_graph = outer_graph.OuterGraph;

_graph_key = Name = name;

building_function = true;

_weak_variables = new List<WeakReference<IVariableV1>>();

_resource_tensor_inputs = new HashSet<Tensor>();

_watched_variables = new HashSet<WeakReference<IVariableV1>>();

}

public FuncGraph(SafeGraphHandle handle, string name, Dictionary<string, AttrValue> attrs) : base()

{

outer_graph = ops.get_default_graph();

while (outer_graph.building_function)

outer_graph = outer_graph.OuterGraph;

_graph_key = Name = name;

building_function = true;

Attrs = attrs;

// Will to test if FuncGraph has memory leak

// c_api.TF_DeleteGraph(_handle);

_handle = handle;

_weak_variables = new List<WeakReference<IVariableV1>>();

_resource_tensor_inputs = new HashSet<Tensor>();

_watched_variables = new HashSet<WeakReference<IVariableV1>>();

}

public void replace_capture(Tensor tensor, Tensor placeholder)

{

_captures[tensor.Id] = (tensor, placeholder);

}

public unsafe void ToGraph(Operation[] opers,

Tensor[] inputs, Tensor[] outputs,

string[] output_names)

{

var status = new Status();

if (output_names is null)

{

output_names = new string[0];

};

_func_graph_handle = c_api.TF_GraphToFunction(_handle,

_graph_key,

false,

opers.Length,

opers.Select(x => (IntPtr)x).ToArray(),

inputs.Length,

inputs.Select(x => new TF_Output(x.op, 0)).ToArray(),

outputs.Length,

outputs.Select(x => new TF_Output(x.op, 0)).ToArray(),

output_names.Length != outputs.Length ? null : output_names,

IntPtr.Zero,

null,

status);

status.Check(true);

SetAttrs();

// c_api.TF_GraphCopyFunction(outer_graph, _func_graph_handle, IntPtr.Zero, status.Handle);

// status.Check(true);

c_api.TFE_ContextAddFunction(tf.Context, _func_graph_handle, status);

status.Check(true);

_graph_key = c_api.StringPiece(c_api.TF_FunctionName(_func_graph_handle));

Inputs = inputs;

// mark_as_return

Outputs = outputs;// .Select(x => array_ops.identity(x)).ToArray();

}

public override Operation create_op(string op_type, Tensor[] inputs, TF_DataType[] dtypes, TF_DataType[] input_types = null, string name = null, Dictionary<string, AttrValue> attrs = null, OpDef op_def = null, bool compute_device = true)

{

foreach(var (i, inp) in enumerate(inputs))

inputs[i] = capture(inp);

return base.create_op(op_type, inputs, dtypes, input_types, name, attrs, op_def, compute_device);

}

const int _EAGER_CONST_THRESHOLD = 128;

public Tensor capture(Tensor tensor, string name = null, Shape shape = null)

{

if(tensor is EagerTensor or NDArray)

{

if (name == null)

name = ops.uid().ToString();

// Small EagerTensors are captured with Const ops

if (dtypes.is_value_dtype(tensor.dtype)

&& (tensor.rank == 0 || tensor.size < _EAGER_CONST_THRESHOLD))

return capture_eager_tensor(tensor, name);

// Large EagerTensors and resources are captured with Placeholder ops

return _capture_helper(tensor, name, shape: shape);

}

if(tensor.graph != this)

{

if (name == null)

name = tensor.op.name;

var inner_graph = tensor.graph;

while(inner_graph != null && inner_graph is FuncGraph inner_func_graph)

{

if (inner_graph == this)

throw new InaccessibleTensorError($"The tensor '{tensor.name}' cannot be accessed here: it is defined" +

" in another function or code block. Use return values," +

" explicit Python locals or TensorFlow collections to access" +

$" it. Defined in: {tensor.graph.graph_key}; accessed from: {graph_key}.");

inner_graph = inner_func_graph.outer_graph;

}

return _capture_helper(tensor, name);

}

return tensor;

}

public void watch_variable(IVariableV1 v)

{

if (_resource_tensor_inputs.Contains(v.Handle))

{

return;

}

_watched_variables.Add(new WeakReference<IVariableV1>(v));

//this = this.outer_graph;

}

Tensor capture_eager_tensor(Tensor tensor, string name)

{

Tensor graph_const = null;

if (!_captures.ContainsKey(tensor.Id))

{

graph_const = tf_with(ops.control_dependencies(null), ctl

=> constant_op.constant(tensor.numpy(), dtype: tensor.dtype, shape: tensor.shape, name: name));

add_capture(tensor, graph_const);

}

else

{

graph_const = _captures[tensor.Id].Item2;

}

BackwardFunction _backward_function_wrapper = (output_grads, unneeded_gradients) =>

{

return output_grads;

};

tf.Runner.RecordGradient("captured_value",

new[] { graph_const }, null,

new[] { tensor },

getBackwardFunction: _backward_function_wrapper

/*getForwardFunction: forward_function*/);

return graph_const;

}

Tensor _capture_helper(Tensor tensor, string name, Shape shape = null)

{

Tensor placeholder = null;

if (!_captures.ContainsKey(tensor.Id))

{

placeholder = _create_substitute_placeholder(tensor,

name: name,

dtype: tensor.dtype,

shape: shape);

add_capture(tensor, placeholder);

}

else

{

placeholder = _captures[tensor.Id].Item2;

}

BackwardFunction _backward_function_wrapper = (output_grads, unneeded_gradients) =>

{

return output_grads;

};

tf.Runner.RecordGradient("captured_value",

new[] { placeholder }, null,

new[] { tensor },

getBackwardFunction: _backward_function_wrapper

/*getForwardFunction: forward_function*/);

return placeholder;

}

void add_capture(Tensor tensor, Tensor placeholder)

{

_captures.Add(tensor.Id, (tensor, placeholder));

Inputs.Add(placeholder);

}

Tensor pop_capture(Tensor tensor)

{

if(_captures.TryGetValue(tensor.Id, out var capture))

{

_captures.Remove(tensor.Id);

return capture.Item2;

}

else

{

return null;

}

Tensor _create_substitute_placeholder(Tensor value,

string name = null,

TF_DataType dtype = TF_DataType.DtInvalid,

Shape shape = null)

{

if (shape is null)

shape = value.shape;

if (dtype == TF_DataType.DtInvalid)

dtype = value.dtype;

var placeholder = tf_with(ops.control_dependencies(null), ctl

=> array_ops.placeholder(dtype, shape: shape, name: name));

// custom_gradient.copy_handle_data(value, placeholder)

return placeholder;

}

void SetAttrs()

{

if (Attrs == null)

return;

foreach (var (_name, attr_value) in enumerate(Attrs))

{

var serialized = attr_value.ToByteArray();

c_api.TF_FunctionSetAttrValueProto(_func_graph_handle, _name, serialized, serialized.Length, tf.Status);

tf.Status.Check(true);

}

public override Graph as_default()

{

tf.Context.graph_mode(isFunc: true);

ops.set_default_graph(this);

return this;

}

public override void Exit()

{

tf.Context.restore_mode();

ops.pop_graph();

}

public void Dispose()

{

c_api.TFE_ContextRemoveFunction(tf.Context, _graph_key, tf.Status);

}

public static FuncGraph func_graph_from_func(string name, Func<object[], object[]> func,

object[] args, Dictionary<string, object> kwargs, TensorSpec[] signature = null,

FuncGraph func_graph = null, bool autograph = false, object autograph_options = null,

bool add_control_dependencies = true, string[] arg_names = null,

Tensor op_return_value = null, bool capture_by_value = false,

bool acd_record_initial_resource_uses = false)

{

if(func_graph is null)

{

func_graph = new FuncGraph(name);

}

// TODO(Rinne): deal with control dependencies.

func_graph.as_default();

var current_scope = variable_scope.get_variable_scope();

var default_use_resource = current_scope.use_resource;

current_scope.use_resource = true;

if(signature is not null)

{

args = signature;

kwargs = new Dictionary<string, object>();

}

var func_args = _get_defun_inputs_from_args(args, arg_names);

var func_kwargs = _get_defun_inputs_from_kwargs(kwargs);

if(func_kwargs is not null && func_kwargs.Count > 0)

{

throw new NotImplementedException("The keyword args has not been supported in `func_graph_from_func`.");

}

foreach(var arg in nest.flatten<object>(new object[] { func_args, func_kwargs }))

{

if(arg is Tensor tensor && tensor.dtype == dtypes.resource)

{

func_graph._resource_tensor_inputs.Add(tensor);

}

else if (arg is ResourceVariable variable)

{

func_graph._resource_tensor_inputs.Add(variable.Handle);

}

// skip the assignment of `func_graph.structured_input_signature`.

var flat_func_args = nest.flatten(func_args as object);

var flat_func_kwargs = nest.flatten(func_kwargs as object);

func_graph.Inputs = new Tensors(flat_func_args.concat(flat_func_kwargs)

.Where(x => x is Tensor).Select(x => (Tensor)x).ToArray());

//var func_args_before = nest.pack_sequence_as(func_args, flat_func_args, true);

//var func_kwargs_before = nest.pack_sequence_as(func_kwargs, flat_func_kwargs, true);

Tensor convert(object x)

{

if (x is null) return null;

Tensor res = null;

if(op_return_value is not null && x is Operation)

{

tf_with(ops.control_dependencies(new object[] { x }), _ =>

{

res = array_ops.identity(op_return_value);

});

}

else if(x is not TensorArray)

{

Debug.Assert(x is Tensor);

res = ops.convert_to_tensor_or_composite(x as Tensor);

}

else

{

throw new NotImplementedException($"The `TensorArray` is not supported here currently.");

}

if (add_control_dependencies)

{

// TODO(Rinne): `x = deps_ctx.mark_as_return(x)`.

}

return res;

}

if (autograph)

{

throw new NotImplementedException("The autograph of `func_graph_from_func` has not been supported.");

}

var func_outputs = func(func_args);

func_outputs = variable_utils.convert_variables_to_tensors(func_outputs);

func_outputs = func_outputs.Select(x => convert(x)).ToArray();

// TODO(Rinne): `check_func_mutation`.

current_scope.use_resource = default_use_resource;

var graph_variables = func_graph._watched_variables.ToList();

HashSet<IVariableV1> arg_variables = new HashSet<IVariableV1>();

List<Tensor> inputs = new();

foreach(var arg in composite_tensor_utils.flatten_with_variables(func_args))

{

if(arg is BaseResourceVariable variable)

{

var resource_placeholder = func_graph.pop_capture(variable.Handle);

if(resource_placeholder is null)

{

continue;

}

Debug.Assert(variable is IVariableV1);

arg_variables.Add(variable as IVariableV1);

inputs.Add(resource_placeholder);

}

else if(arg is Tensor tensor)

{

inputs.Add(tensor);

}

var variables = graph_variables.Select(v =>

{

if (v.TryGetTarget(out var target))

{

return target;

}

else

{

return null;

}

}).Where(v => v is not null && !arg_variables.Contains(v));

func_graph.Inputs = inputs.Concat(func_graph.internal_captures).ToArray();

func_graph._structured_outputs = func_outputs;

func_graph.Outputs.AddRange(func_graph.FlatStructuredOutputs.Where(x => x is not null)

.Select(x => func_graph.capture(x)));

func_graph.Variables = variables;

func_graph.Exit();

if (add_control_dependencies)

{

// TODO(Rinne): implement it.

}

return func_graph;

}

private static object[] _get_defun_inputs_from_args(object[] args, string[] names)

{

return _get_defun_inputs(args, names, args) as object[];

}

private static Dictionary<string, object> _get_defun_inputs_from_kwargs(Dictionary<string, object> kwargs)

{

// TODO(Rinne): implement it.

Debug.Assert(kwargs is null || kwargs.Count == 0);

return kwargs;

//string[] names;

//object[] args;

//if(kwargs is not null && kwargs.Count > 0)

//{

// var sorted_kwargs = kwargs.OrderBy(x => x.Key);

// names = sorted_kwargs.Select(x => x.Key).ToArray();

// args = sorted_kwargs.Select(x => x.Value).ToArray();

//}

//else

//{

// names = new string[0];

// args = new object[0];

//}

//return _get_defun_inputs(args, names, kwargs) as Dictionary<string, object>;

}

private static object _get_defun_inputs(object[] args, string[] names, object structured_args)

{

List<object> function_inputs = new();

if(names is null)

{

names = new string[args.Length];

}

foreach(var (arg_value, name) in zip(args, names))

{

foreach(var val in composite_tensor_utils.flatten_with_variables_or_variable_specs(arg_value))

{

function_inputs.Add(_get_defun_input(val, name));

}

return nest.pack_sequence_as(structured_args, nest.flatten<object>(function_inputs), true);

}

private static object _get_defun_input(object arg, string name)

{

var func_graph = ops.get_default_graph() as FuncGraph;

Debug.Assert(func_graph is not null);

if (arg is Tensor tensor)

{

Tensor placeholder;

try

{

placeholder = GraphOnlyOps.graph_placeholder(tensor.dtype, tensor.shape, name);

}

catch (ValueError ex)

{

tf.Logger.Warning(ex.ToString());

placeholder = GraphOnlyOps.graph_placeholder(tensor.dtype, tensor.shape);

}

handle_data_util.copy_handle_data(tensor, placeholder);

if (name is not null)

{

placeholder.op._set_attr("_user_specified_name", new AttrValue()

{

S = tf.compat.as_bytes(name)

});

}

return placeholder;

}

else if (arg is TensorSpec spec)

{

string requested_name;

if (!string.IsNullOrEmpty(spec.name))

{

requested_name = spec.name;

}

else

{

requested_name = name;

}

Tensor placeholder;

try

{

placeholder = GraphOnlyOps.graph_placeholder(spec.dtype, spec.shape, requested_name);

}

catch (ValueError)

{

// TODO(Rinne): Add warning here.

placeholder = GraphOnlyOps.graph_placeholder(spec.dtype, spec.shape);

}

if (name is not null)

{

placeholder.op._set_attr("_user_specified_name", new AttrValue()

{

S = tf.compat.as_bytes(requested_name)

});

}

return placeholder;

}

else if (arg is BaseResourceVariable variable)

{

var placeholder = func_graph.capture(variable.Handle, name);

placeholder.op._set_attr("_user_specified_name", new AttrValue()

{

S = tf.compat.as_bytes(name)

});

return arg;

}

// TODO(Rinne): deal with `VariableSpec`.

else

{

return arg;

}

TensorFlow.NET/src/TensorFlowNET.Core/Graphs/FuncGraph.cs at master · ekumachidi/TensorFlow.NET · GitHub

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