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/* 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/framework/common_shape_fns.h"
#include "tensorflow/core/framework/op.h"
#include "tensorflow/core/framework/op_def_builder.h"
#include "tensorflow/core/framework/shape_inference.h"
namespace tensorflow {
// --------------------------------------------------------------------------
// The ops in this section can be composed to define an input
// pipeline. Each op produces a DT_VARIANT tensor that represents
// a DAG of "dataset" objects. An "dataset" object can be converted
// to a stateful "iterator" by passing the "dataset" to the
// "MakeIterator" op.
//
// TODO(b/65524810): DT_VARIANT tensors that represent "dataset" objects are
// not presently serializable. To avoid issues with constant folding, ensure
// that any "source dataset" ops (i.e. ops that output a dataset and do not
// take one as input) are marked "stateful".
REGISTER_OP("IgnoreErrorsDataset")
.Input("input_dataset: variant")
.Output("handle: variant")
.Attr("output_types: list(type) >= 1")
.Attr("output_shapes: list(shape) >= 1")
.SetShapeFn(shape_inference::ScalarShape)
.Doc(R"doc(
Creates a dataset that contains the elements of `input_dataset` ignoring errors.
)doc");
REGISTER_OP("MapAndBatchDataset")
.Input("input_dataset: variant")
.Input("other_arguments: Targuments")
.Input("batch_size: int64")
.Input("num_parallel_batches: int64")
.Output("handle: variant")
.Attr("f: func")
.Attr("Targuments: list(type) >= 0")
.Attr("output_types: list(type) >= 1")
.Attr("output_shapes: list(shape) >= 1")
.SetShapeFn(shape_inference::ScalarShape)
.Doc(R"doc(
Creates a dataset that applies `f` to the outputs of `input_dataset` and then
batches `batch_size` of them.
Unlike a "MapDataset", which applies `f` sequentially, this dataset invokes up
to `batch_size * num_parallel_batches` copies of `f` in parallel.
batch_size: A scalar representing the number of elements to accumulate in a
batch. It determines the number of concurrent invocations of `f` that process
elements from `input_dataset` in parallel.
num_parallel_batches: A scalar representing the number of batches to create in
parallel. Processing multiple batches in parallel benefits workloads prone to
stragglers.
)doc");
REGISTER_OP("ScanDataset")
.Input("input_dataset: variant")
.Input("initial_state: Tstate")
.Input("other_arguments: Targuments")
.Output("handle: variant")
.Attr("f: func")
.Attr("Tstate: list(type) >= 1")
.Attr("Targuments: list(type) >= 0")
.Attr("output_types: list(type) >= 1")
.Attr("output_shapes: list(shape) >= 1")
.SetShapeFn(shape_inference::ScalarShape)
.Doc(R"doc(
Creates a dataset successively reduces `f` over the elements of `input_dataset`.
)doc");
REGISTER_OP("ParallelInterleaveDataset")
.Input("input_dataset: variant")
.Input("other_arguments: Targuments")
.Input("cycle_length: int64")
.Input("block_length: int64")
.Input("sloppy: bool")
.Output("handle: variant")
.Attr("f: func")
.Attr("Targuments: list(type) >= 0")
.Attr("output_types: list(type) >= 1")
.Attr("output_shapes: list(shape) >= 1")
.SetShapeFn(shape_inference::ScalarShape)
.Doc(R"doc(
Creates a dataset that applies `f` to the outputs of `input_dataset`.
The resulting dataset is similar to the `InterleaveDataset`, with the exception
that if retrieving the next value from a dataset would cause the requester to
block, it will skip that input dataset. This dataset is especially useful
when loading data from a variable-latency datastores (e.g. HDFS, GCS), as it
allows the training step to proceed so long as some data is available.
!! WARNING !! This dataset is not deterministic!
f: A function mapping elements of `input_dataset`, concatenated with
`other_arguments`, to a Dataset variant that contains elements matching
`output_types` and `output_shapes`.
)doc");
REGISTER_OP("GroupByWindowDataset")
.Input("input_dataset: variant")
.Input("key_func_other_arguments: Tkey_func_other_arguments")
.Input("reduce_func_other_arguments: Treduce_func_other_arguments")
.Input(
"window_size_func_other_arguments: Twindow_size_func_other_arguments")
.Output("handle: variant")
.Attr("key_func: func")
.Attr("reduce_func: func")
.Attr("window_size_func: func")
.Attr("Tkey_func_other_arguments: list(type) >= 0")
.Attr("Treduce_func_other_arguments: list(type) >= 0")
.Attr("Twindow_size_func_other_arguments: list(type) >= 0")
.Attr("output_types: list(type) >= 1")
.Attr("output_shapes: list(shape) >= 1")
.SetShapeFn(shape_inference::ScalarShape)
.Doc(R"doc(
Creates a dataset that computes a windowed group-by on `input_dataset`.
// TODO(mrry): Support non-int64 keys.
key_func: A function mapping an element of `input_dataset`, concatenated
with `key_func_other_arguments` to a scalar value of type DT_INT64.
)doc");
REGISTER_OP("DenseToSparseBatchDataset")
.Input("input_dataset: variant")
.Input("batch_size: int64")
.Input("row_shape: int64")
.Output("handle: variant")
// NOTE(mrry): the 0th and 2nd elements will be DT_INT64.
.Attr("output_types: list(type) >= 1")
// NOTE(mrry): the 1st and 2nd elements will be vectors.
.Attr("output_shapes: list(shape) >= 1")
.SetShapeFn(shape_inference::ScalarShape)
.Doc(R"doc(
Creates a dataset that yields a SparseTensor for each element of the input.
input_dataset: A handle to an input dataset. Must have a single component.
batch_size: A scalar representing the number of elements to accumulate in a
batch.
row_shape: A vector representing the dense shape of each row in the produced
SparseTensor. The shape may be partially specified, using `-1` to indicate
that a particular dimension should use the maximum size of all batch elements.
)doc");
REGISTER_OP("SqlDataset")
.Input("driver_name: string")
.Input("data_source_name: string")
.Input("query: string")
.Output("handle: variant")
.Attr("output_types: list(type) >= 1")
.Attr("output_shapes: list(shape) >= 1")
.SetIsStateful() // TODO(b/65524810): Source dataset ops must be marked
// stateful to inhibit constant folding.
.SetShapeFn(shape_inference::ScalarShape)
.Doc(R"doc(
Creates a dataset that executes a SQL query and emits rows of the result set.
driver_name: The database type. Currently, the only supported type is 'sqlite'.
data_source_name: A connection string to connect to the database.
query: A SQL query to execute.
)doc");
REGISTER_OP("DatasetToSingleElement")
.Input("dataset: variant")
.Output("components: output_types")
.Attr("output_types: list(type) >= 1")
.Attr("output_shapes: list(shape) >= 1")
.SetShapeFn([](shape_inference::InferenceContext* c) {
shape_inference::ShapeHandle unused;
TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 0, &unused));
std::vector<PartialTensorShape> output_shapes;
TF_RETURN_IF_ERROR(c->GetAttr("output_shapes", &output_shapes));
if (output_shapes.size() != c->num_outputs()) {
return errors::InvalidArgument(
"`output_shapes` must be the same length as `output_types` (",
output_shapes.size(), " vs. ", c->num_outputs());
}
for (size_t i = 0; i < output_shapes.size(); ++i) {
shape_inference::ShapeHandle output_shape_handle;
TF_RETURN_IF_ERROR(c->MakeShapeFromPartialTensorShape(
output_shapes[i], &output_shape_handle));
c->set_output(static_cast<int>(i), output_shape_handle);
}
return Status::OK();
})
.Doc(R"doc(
Outputs the single element from the given dataset.
dataset: A handle to a dataset that contains a single element.
components: The components of the single element of `input`.
)doc");
REGISTER_OP("SerializeIterator")
.Input("resource_handle: resource")
.Output("serialized: variant")
.SetShapeFn(shape_inference::ScalarShape)
.Doc(R"doc(
Converts the given `resource_handle` representing an iterator to a variant tensor.
resource_handle: A handle to an iterator resource.
serialized: A variant tensor storing the state of the iterator contained in the
resource.
)doc");
REGISTER_OP("DeserializeIterator")
.Input("resource_handle: resource")
.Input("serialized: variant")
.SetShapeFn(shape_inference::NoOutputs)
.Doc(R"doc(
Converts the given variant tensor to an iterator and stores it in the given resource.
resource_handle: A handle to an iterator resource.
serialized: A variant tensor storing the state of the iterator contained in the
resource.
)doc");
} // namespace tensorflow
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