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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"
namespace tensorflow {
REGISTER_OP("DirectedInterleaveDataset")
.Input("selector_input_dataset: variant")
.Input("data_input_datasets: N * variant")
.Output("handle: variant")
.Attr("output_types: list(type) >= 1")
.Attr("output_shapes: list(shape) >= 1")
.Attr("N: int >= 1")
.SetShapeFn(shape_inference::ScalarShape)
.Doc(R"doc(
A substitute for `InterleaveDataset` on a fixed list of `N` datasets.
selector_input_dataset: A dataset of scalar `DT_INT64` elements that determines
which of the `N` data inputs should produce the next output element.
data_input_datasets: `N` datasets with the same type that will be interleaved
according to the values of `selector_input_dataset`.
)doc");
REGISTER_OP("CSVDataset")
.Input("filenames: string")
.Input("compression_type: string")
.Input("buffer_size: int64")
.Input("header: bool")
.Input("field_delim: string")
.Input("use_quote_delim: bool")
.Input("na_value: string")
.Input("select_cols: int64")
.Input("record_defaults: output_types")
.Output("handle: variant")
.Attr("output_types: list({float,double,int32,int64,string}) >= 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::InferenceContext* c) {
shape_inference::ShapeHandle unused;
// `filenames` must be a scalar or a vector.
TF_RETURN_IF_ERROR(c->WithRankAtMost(c->input(0), 1, &unused));
// `compression_type`, `buffer_size`, `header`, `field_delim`,
// `use_quote_delim`, `na_value` must be scalars
TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 0, &unused));
TF_RETURN_IF_ERROR(c->WithRank(c->input(2), 0, &unused));
TF_RETURN_IF_ERROR(c->WithRank(c->input(3), 0, &unused));
TF_RETURN_IF_ERROR(c->WithRank(c->input(4), 0, &unused));
TF_RETURN_IF_ERROR(c->WithRank(c->input(5), 0, &unused));
TF_RETURN_IF_ERROR(c->WithRank(c->input(6), 0, &unused));
// `select_cols` must be a vector
TF_RETURN_IF_ERROR(c->WithRank(c->input(7), 1, &unused));
// `record_defaults` must be lists of scalars
for (size_t i = 8; i < c->num_inputs(); ++i) {
TF_RETURN_IF_ERROR(c->WithRank(c->input(i), 1, &unused));
}
return shape_inference::ScalarShape(c);
});
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("UniqueDataset")
.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 unique elements of `input_dataset`.
)doc");
REGISTER_OP("IteratorGetDevice")
.Input("resource: resource")
.Output("device: string")
.SetShapeFn(shape_inference::ScalarShape)
.Doc(R"doc(
Returns the name of the device on which `resource` has been placed.
)doc");
REGISTER_OP("FunctionBufferingResource")
.Input("string_arg: string")
.Input("target_device: string")
.Output("resource: resource")
.Attr("shared_name: string")
.Attr("container: string")
.Attr("f: func")
.Attr("buffer_size: int")
.Attr("output_types: list(type)")
.SetShapeFn(shape_inference::UnknownShape)
.Doc(R"doc(
Creates a resource that fills up a buffer by making function calls.
string_arg: String argument to the function call.
target_device: Target device to execute the function on.
resource: Handle to the resource created.
f: Function to be executed.
buffer_size: Size of the buffer.
container: If non-empty, this resource is placed in the given container.
Otherwise, a default container is used.
shared_name: If non-empty, this resource will be shared under the given name
across multiple sessions.
output_types: The type list for the return values.
)doc");
REGISTER_OP("FunctionBufferingResourceGetNext")
.Input("function_buffer_resource: resource")
.Attr("output_types: list(type)")
.Output("output: output_types")
.SetShapeFn(shape_inference::UnknownShape)
.Doc(R"doc(
Gets the next element from a FunctionBufferingResource.
function_buffer_resource: The FunctionBufferingResource handle.
output: A list of return values.
output_types: The type list for the return values.
)doc");
REGISTER_OP("FunctionBufferingResourceReset")
.Input("function_buffer_resource: resource")
.SetShapeFn(shape_inference::UnknownShape)
.Doc(R"doc(
Resets the FunctionBufferingResource.
function_buffer_resource: The FunctionBufferingResource handle.
)doc");
REGISTER_OP("MultiDeviceIterator")
.Output("handle: resource")
.Attr("devices: list(string) >= 1")
.Attr("shared_name: string")
.Attr("container: string")
.Attr("output_types: list(type) >= 1")
.Attr("output_shapes: list(shape) >= 1")
.Doc(R"doc(
Creates a MultiDeviceIterator resource.
handle: Handle to the resource created.
devices: A list of devices the iterator works across.
shared_name: If non-empty, this resource will be shared under the given name
across multiple sessions.
container: If non-empty, this resource is placed in the given container.
Otherwise, a default container is used.
output_types: The type list for the return values.
output_shapes: The list of shapes being produced.
)doc");
REGISTER_OP("MultiDeviceIteratorInit")
.Input("dataset: variant")
.Input("multi_device_iterator: resource")
.Output("incarnation_id: int64")
.Doc(R"doc(
Initializes the multi device iterator with the given dataset.
incarnation_id: An int64 indicating which incarnation of the MultiDeviceIterator
is running.
dataset: Dataset to be iterated upon.
multi_device_iterator: A MultiDeviceIteratorResource.
)doc");
REGISTER_OP("MultiDeviceIteratorGetNextFromShard")
.Input("multi_device_iterator: resource")
.Input("shard_num: int32")
.Input("incarnation_id: int64")
.Output("components: output_types")
.Attr("output_types: list(type) >= 1")
.Attr("output_shapes: list(shape) >= 1")
.Doc(R"doc(
Gets next element for the provided shard number.
multi_device_iterator: A MultiDeviceIterator resource.
shard_num: Integer representing which shard to fetch data for.
incarnation_id: Which incarnation of the MultiDeviceIterator is running.
components: Result of the get_next on the dataset.
output_types: The type list for the return values.
output_shapes: The list of shapes being produced.
)doc");
REGISTER_OP("MultiDeviceIteratorToStringHandle")
.Input("multi_device_iterator: resource")
.Output("string_handle: string")
.Doc(R"doc(
Produces a string handle for the given MultiDeviceIterator.
multi_device_iterator: A MultiDeviceIterator resource.
string_handle: A string representing the resource.
)doc");
REGISTER_OP("MultiDeviceIteratorFromStringHandle")
.Input("string_handle: string")
.Output("multi_device_iterator: resource")
.Attr("output_types: list(type) >= 0 = []")
.Attr("output_shapes: list(shape) >= 0 = []")
.Doc(R"doc(
Generates a MultiDeviceIterator resource from its provided string handle.
string_handle: String representing the resource.
multi_device_iterator: A MultiDeviceIterator resource.
output_types: The type list for the return values.
output_shapes: The list of shapes being produced.
)doc");
REGISTER_OP("ThreadPoolDataset")
.Input("input_dataset: variant")
.Input("thread_pool: resource")
.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 uses a custom thread pool to compute `input_dataset`.
handle: A resource produced by the ThreadPoolHandle op.
)doc");
REGISTER_OP("ThreadPoolHandle")
.Output("handle: resource")
.SetShapeFn(shape_inference::ScalarShape)
.Attr("num_threads: int")
.Attr("max_intra_op_parallelism: int = 1")
.Attr("display_name: string")
.Attr("container: string = ''")
.Attr("shared_name: string = ''")
.Doc(R"doc(
Creates a custom thread pool with the given number of threads.
handle: A resource that can be consumed by one or more ThreadPoolDataset ops.
num_threads: The number of threads in the thread pool.
max_intra_op_parallelism: The maximum degree of parallelism to use within
operations that execute on this threadpool.
display_name: A human-readable name for the threads that may be visible in
some visualizations.
)doc");
REGISTER_OP("AssertNextDataset")
.Input("input_dataset: variant")
.Input("transformations: string")
.Output("handle: variant")
.Attr("output_types: list(type) >= 1")
.Attr("output_shapes: list(shape) >= 1")
.SetShapeFn([](shape_inference::InferenceContext* c) {
shape_inference::ShapeHandle unused;
// transformations should be a vector.
TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 1, &unused));
return shape_inference::ScalarShape(c);
});
} // namespace tensorflow
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