1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
|
/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/
#include "tensorflow/core/common_runtime/function.h"
#include "tensorflow/core/framework/partial_tensor_shape.h"
#include "tensorflow/core/framework/tensor.h"
#include "tensorflow/core/kernels/data/captured_function.h"
#include "tensorflow/core/kernels/data/dataset.h"
#include "tensorflow/core/lib/random/random.h"
namespace tensorflow {
namespace data {
namespace {
// See documentation in ../ops/dataset_ops.cc for a high-level
// description of the following op.
class MapDatasetOp : public UnaryDatasetOpKernel {
public:
explicit MapDatasetOp(OpKernelConstruction* ctx)
: UnaryDatasetOpKernel(ctx),
graph_def_version_(ctx->graph_def_version()) {
OP_REQUIRES_OK(ctx, ctx->GetAttr("f", &func_));
OP_REQUIRES_OK(ctx, ctx->GetAttr("output_types", &output_types_));
OP_REQUIRES_OK(ctx, ctx->GetAttr("output_shapes", &output_shapes_));
OP_REQUIRES_OK(ctx, ctx->GetAttr("use_inter_op_parallelism",
&use_inter_op_parallelism_));
}
void MakeDataset(OpKernelContext* ctx, DatasetBase* input,
DatasetBase** output) override {
OpInputList inputs;
OP_REQUIRES_OK(ctx, ctx->input_list("other_arguments", &inputs));
std::vector<Tensor> other_arguments;
other_arguments.reserve(inputs.size());
for (const Tensor& t : inputs) {
other_arguments.push_back(t);
}
std::unique_ptr<CapturedFunction> captured_func;
OP_REQUIRES_OK(ctx, CapturedFunction::Create(
func_, std::move(other_arguments),
use_inter_op_parallelism_, &captured_func));
*output = new Dataset(ctx, input, func_, std::move(captured_func),
output_types_, output_shapes_);
}
private:
class Dataset : public DatasetBase {
public:
Dataset(OpKernelContext* ctx, const DatasetBase* input,
const NameAttrList& func,
std::unique_ptr<CapturedFunction> captured_func,
const DataTypeVector& output_types,
const std::vector<PartialTensorShape>& output_shapes)
: DatasetBase(DatasetContext(ctx)),
input_(input),
func_(func),
captured_func_(std::move(captured_func)),
output_types_(output_types),
output_shapes_(output_shapes) {
input_->Ref();
}
~Dataset() override { input_->Unref(); }
std::unique_ptr<IteratorBase> MakeIteratorInternal(
const string& prefix) const override {
return std::unique_ptr<IteratorBase>(
new Iterator({this, strings::StrCat(prefix, "::Map")}));
}
const DataTypeVector& output_dtypes() const override {
return output_types_;
}
const std::vector<PartialTensorShape>& output_shapes() const override {
return output_shapes_;
}
string DebugString() const override { return "MapDatasetOp::Dataset"; }
protected:
Status AsGraphDefInternal(SerializationContext* ctx,
DatasetGraphDefBuilder* b,
Node** output) const override {
TF_RETURN_IF_ERROR(b->AddFunction(ctx, func_.name()));
Node* input_graph_node = nullptr;
TF_RETURN_IF_ERROR(b->AddInputDataset(ctx, input_, &input_graph_node));
DataTypeVector other_arguments_types;
other_arguments_types.reserve(captured_func_->captured_inputs().size());
std::vector<Node*> other_arguments;
other_arguments.reserve(captured_func_->captured_inputs().size());
for (const Tensor& t : captured_func_->captured_inputs()) {
Node* node;
TF_RETURN_IF_ERROR(b->AddTensor(t, &node));
other_arguments.emplace_back(node);
other_arguments_types.emplace_back(t.dtype());
}
AttrValue f;
b->BuildAttrValue(func_, &f);
AttrValue other_arguments_types_attr;
b->BuildAttrValue(other_arguments_types, &other_arguments_types_attr);
TF_RETURN_IF_ERROR(b->AddDataset(
this, {std::make_pair(0, input_graph_node)}, // Single tensor inputs.
{std::make_pair(1, other_arguments)}, // Tensor list inputs.
{std::make_pair("f", f),
std::make_pair("Targuments", other_arguments_types_attr)}, // Attrs
output));
return Status::OK();
}
private:
class Iterator : public DatasetIterator<Dataset> {
public:
explicit Iterator(const Params& params)
: DatasetIterator<Dataset>(params) {}
Status Initialize(IteratorContext* ctx) override {
TF_RETURN_IF_ERROR(
dataset()->input_->MakeIterator(ctx, prefix(), &input_impl_));
return dataset()->captured_func_->Instantiate(ctx);
}
Status GetNextInternal(IteratorContext* ctx,
std::vector<Tensor>* out_tensors,
bool* end_of_sequence) override {
// NOTE(mrry): This method is thread-safe as long as
// `input_impl_` and `f` are thread-safe. However, if multiple
// threads enter this method, outputs may be observed in a
// non-deterministic order.
std::vector<Tensor> args;
TF_RETURN_IF_ERROR(input_impl_->GetNext(ctx, &args, end_of_sequence));
if (*end_of_sequence) {
return Status::OK();
}
// TODO(mrry): Avoid blocking a threadpool thread. We will need to
// stack-rip the iterators and use async kernels.
Status s =
dataset()->captured_func_->Run(ctx, std::move(args), out_tensors);
if (errors::IsOutOfRange(s)) {
// `f` may deliberately raise `errors::OutOfRange` to indicate
// that we should terminate the iteration early.
*end_of_sequence = true;
return Status::OK();
} else {
return s;
}
}
protected:
Status SaveInternal(IteratorStateWriter* writer) override {
TF_RETURN_IF_ERROR(SaveInput(writer, input_impl_));
return Status::OK();
}
Status RestoreInternal(IteratorContext* ctx,
IteratorStateReader* reader) override {
TF_RETURN_IF_ERROR(RestoreInput(ctx, reader, input_impl_));
return Status::OK();
}
private:
std::unique_ptr<IteratorBase> input_impl_;
};
const DatasetBase* const input_;
const NameAttrList func_;
const std::unique_ptr<CapturedFunction> captured_func_;
const DataTypeVector output_types_;
const std::vector<PartialTensorShape> output_shapes_;
};
const int graph_def_version_;
DataTypeVector output_types_;
std::vector<PartialTensorShape> output_shapes_;
NameAttrList func_;
bool use_inter_op_parallelism_;
};
REGISTER_KERNEL_BUILDER(Name("MapDataset").Device(DEVICE_CPU), MapDatasetOp);
} // namespace
} // namespace data
} // namespace tensorflow
|
