[tf.data] Graduating `tf.data.experimental.AUTOTUNE` to core API.
PiperOrigin-RevId: 330845881
Change-Id: I0480031d39753f5115e9dae5f5a6ae5b42e19bb0
diff --git a/RELEASE.md b/RELEASE.md
index f35c2ed..6c9f5b1 100644
--- a/RELEASE.md
+++ b/RELEASE.md
@@ -107,6 +107,8 @@
* `tf.data.Dataset.from_generator` now supports Ragged and Sparse tensors
with a new `output_signature` argument, which allows `from_generator` to
produce any type describable by a `tf.TypeSpec`.
+ * `tf.data.experimental.AUTOTUNE` is now available in the core API as
+ `tf.data.AUTOTUNE`.
* `tf.image`:
* Added deterministic `tf.image.stateless_random_*` functions for each
`tf.image.random_*` function. Added a new op
diff --git a/tensorflow/python/data/__init__.py b/tensorflow/python/data/__init__.py
index 39cbd3d..04ea1e6 100644
--- a/tensorflow/python/data/__init__.py
+++ b/tensorflow/python/data/__init__.py
@@ -23,6 +23,7 @@
# pylint: disable=unused-import
from tensorflow.python.data import experimental
+from tensorflow.python.data.ops.dataset_ops import AUTOTUNE
from tensorflow.python.data.ops.dataset_ops import Dataset
from tensorflow.python.data.ops.dataset_ops import INFINITE as INFINITE_CARDINALITY
from tensorflow.python.data.ops.dataset_ops import make_initializable_iterator
diff --git a/tensorflow/python/data/experimental/ops/batching.py b/tensorflow/python/data/experimental/ops/batching.py
index 4d2bfbb..3d04de8 100644
--- a/tensorflow/python/data/experimental/ops/batching.py
+++ b/tensorflow/python/data/experimental/ops/batching.py
@@ -176,7 +176,7 @@
num_parallel_calls: (Optional.) A `tf.int32` scalar `tf.Tensor`,
representing the number of elements to process in parallel. If not
specified, `batch_size * num_parallel_batches` elements will be processed
- in parallel. If the value `tf.data.experimental.AUTOTUNE` is used, then
+ in parallel. If the value `tf.data.AUTOTUNE` is used, then
the number of parallel calls is set dynamically based on available CPU.
Returns:
@@ -237,7 +237,7 @@
num_parallel_calls: (Optional.) A `tf.int32` scalar `tf.Tensor`,
representing the number of elements to process in parallel. If not
specified, `batch_size * num_parallel_batches` elements will be processed
- in parallel. If the value `tf.data.experimental.AUTOTUNE` is used, then
+ in parallel. If the value `tf.data.AUTOTUNE` is used, then
the number of parallel calls is set dynamically based on available CPU.
Returns:
diff --git a/tensorflow/python/data/experimental/ops/interleave_ops.py b/tensorflow/python/data/experimental/ops/interleave_ops.py
index a3cc75a..4c16d35 100644
--- a/tensorflow/python/data/experimental/ops/interleave_ops.py
+++ b/tensorflow/python/data/experimental/ops/interleave_ops.py
@@ -37,7 +37,7 @@
@deprecation.deprecated(
None,
"Use `tf.data.Dataset.interleave(map_func, cycle_length, block_length, "
- "num_parallel_calls=tf.data.experimental.AUTOTUNE)` instead. If sloppy "
+ "num_parallel_calls=tf.data.AUTOTUNE)` instead. If sloppy "
"execution is desired, use `tf.data.Options.experimental_deterministic`.")
@tf_export("data.experimental.parallel_interleave")
def parallel_interleave(map_func,
diff --git a/tensorflow/python/data/ops/dataset_ops.py b/tensorflow/python/data/ops/dataset_ops.py
index e3cce9c..5e1981c 100644
--- a/tensorflow/python/data/ops/dataset_ops.py
+++ b/tensorflow/python/data/ops/dataset_ops.py
@@ -94,6 +94,8 @@
# A constant that can be used to enable auto-tuning.
AUTOTUNE = -1
+tf_export("data.AUTOTUNE").export_constant(__name__, "AUTOTUNE")
+# TODO(b/168128531): Deprecate and remove this symbol.
tf_export("data.experimental.AUTOTUNE").export_constant(__name__, "AUTOTUNE")
# Constants representing infinite and unknown cardinalities.
@@ -1700,7 +1702,7 @@
>>> dataset = Dataset.range(1, 6) # ==> [ 1, 2, 3, 4, 5 ]
>>> dataset = dataset.map(lambda x: x + 1,
- ... num_parallel_calls=tf.data.experimental.AUTOTUNE,
+ ... num_parallel_calls=tf.data.AUTOTUNE,
... deterministic=False)
Args:
@@ -1708,7 +1710,7 @@
num_parallel_calls: (Optional.) A `tf.int32` scalar `tf.Tensor`,
representing the number elements to process asynchronously in parallel.
If not specified, elements will be processed sequentially. If the value
- `tf.data.experimental.AUTOTUNE` is used, then the number of parallel
+ `tf.data.AUTOTUNE` is used, then the number of parallel
calls is set dynamically based on available CPU.
deterministic: (Optional.) A boolean controlling whether determinism
should be traded for performance by allowing elements to be produced out
@@ -1821,7 +1823,7 @@
... "/var/data/file3.txt", "/var/data/file4.txt"]
>>> dataset = tf.data.Dataset.from_tensor_slices(filenames)
>>> dataset = dataset.interleave(lambda x: tf.data.TFRecordDataset(x),
- ... cycle_length=4, num_parallel_calls=tf.data.experimental.AUTOTUNE,
+ ... cycle_length=4, num_parallel_calls=tf.data.AUTOTUNE,
... deterministic=False)
Args:
@@ -1829,7 +1831,7 @@
cycle_length: (Optional.) The number of input elements that will be
processed concurrently. If not set, the tf.data runtime decides what it
should be based on available CPU. If `num_parallel_calls` is set to
- `tf.data.experimental.AUTOTUNE`, the `cycle_length` argument identifies
+ `tf.data.AUTOTUNE`, the `cycle_length` argument identifies
the maximum degree of parallelism.
block_length: (Optional.) The number of consecutive elements to produce
from each input element before cycling to another input element. If not
@@ -1838,7 +1840,7 @@
threadpool, which is used to fetch inputs from cycle elements
asynchronously and in parallel. The default behavior is to fetch inputs
from cycle elements synchronously with no parallelism. If the value
- `tf.data.experimental.AUTOTUNE` is used, then the number of parallel
+ `tf.data.AUTOTUNE` is used, then the number of parallel
calls is set dynamically based on available CPU.
deterministic: (Optional.) A boolean controlling whether determinism
should be traded for performance by allowing elements to be produced out
@@ -2574,7 +2576,7 @@
num_parallel_calls: (Optional.) A `tf.int32` scalar `tf.Tensor`,
representing the number elements to process asynchronously in parallel.
If not specified, elements will be processed sequentially. If the value
- `tf.data.experimental.AUTOTUNE` is used, then the number of parallel
+ `tf.data.AUTOTUNE` is used, then the number of parallel
calls is set dynamically based on available CPU.
deterministic: (Optional.) A boolean controlling whether determinism
should be traded for performance by allowing elements to be produced out
diff --git a/tensorflow/tools/api/golden/v1/tensorflow.data.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.data.pbtxt
index 1d2af01..6b64ad6 100644
--- a/tensorflow/tools/api/golden/v1/tensorflow.data.pbtxt
+++ b/tensorflow/tools/api/golden/v1/tensorflow.data.pbtxt
@@ -1,6 +1,10 @@
path: "tensorflow.data"
tf_module {
member {
+ name: "AUTOTUNE"
+ mtype: "<type \'int\'>"
+ }
+ member {
name: "Dataset"
mtype: "<type \'type\'>"
}
diff --git a/tensorflow/tools/api/golden/v2/tensorflow.data.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.data.pbtxt
index b6ac6f8..ead3f80 100644
--- a/tensorflow/tools/api/golden/v2/tensorflow.data.pbtxt
+++ b/tensorflow/tools/api/golden/v2/tensorflow.data.pbtxt
@@ -1,6 +1,10 @@
path: "tensorflow.data"
tf_module {
member {
+ name: "AUTOTUNE"
+ mtype: "<type \'int\'>"
+ }
+ member {
name: "Dataset"
mtype: "<type \'type\'>"
}
