Remove numpy_datasets from V2 strategies

PiperOrigin-RevId: 336741829
Change-Id: Ifa8f9869e2ced3383bf1d7cd5d115d137eeadea9

RELEASE.md

@@ -52,6 +52,8 @@
   to be 0. To simulate the V1 the behavior of
   tf.quantization.quantize_and_dequantize(...) use
   tf.grad_pass_through(tf.quantization.quantize_and_dequantize_v2)(...).
+* `tf.distribute.Strategy.experimental_make_numpy_dataset` is removed. Please
+  use `tf.data.Dataset.from_tensor_slices` instead.
 
 ## Known Caveats
 

tensorflow/python/distribute/BUILD

@@ -206,7 +206,6 @@ py_test(
         "//tensorflow/python/autograph/core:test_lib",
         "//tensorflow/python/data/ops:dataset_ops",
         "//tensorflow/python/distribute/cluster_resolver:cluster_resolver_lib",
-        "//third_party/py/numpy",
     ],
 )
 

tensorflow/python/distribute/distribute_lib.py

@@ -681,9 +681,7 @@ class StrategyBase(object):
     [see the
     guide](https://www.tensorflow.org/guide/distributed_training#using_tfdistributestrategy_with_custom_training_loops):
 
-      * Start by either creating a `tf.data.Dataset` normally or using
-        `tf.distribute.experimental_make_numpy_dataset` to make a dataset out of
-        a `numpy` array.
+      * Start by creating a `tf.data.Dataset` normally.
       * Use `tf.distribute.Strategy.experimental_distribute_dataset` to convert
         a `tf.data.Dataset` to something that produces "per-replica" values.
         If you want to manually specify how the dataset should be partitioned
@@ -908,39 +906,6 @@ class StrategyBase(object):
       return self.extended._make_input_fn_iterator(  # pylint: disable=protected-access
           input_fn, replication_mode=replication_mode)
 
-  @deprecation.deprecated(
-      "2020-09-30", "Please use tf.data.Dataset.from_tensor_slices instead")
-  def experimental_make_numpy_dataset(self, numpy_input):
-    """Makes a `tf.data.Dataset` from a numpy array.
-
-    This avoids adding `numpy_input` as a large constant in the graph,
-    and copies the data to the machine or machines that will be processing
-    the input.
-
-    Note that you will likely need to use `experimental_distribute_dataset`
-    with the returned dataset to further distribute it with the strategy.
-
-    Example:
-
-    >>> strategy = tf.distribute.MirroredStrategy()
-    >>> numpy_input = np.ones([10], dtype=np.float32)
-    >>> dataset = strategy.experimental_make_numpy_dataset(numpy_input)
-    >>> dataset
-    <TensorSliceDataset shapes: (), types: tf.float32>
-    >>> dataset = dataset.batch(2)
-    >>> dist_dataset = strategy.experimental_distribute_dataset(dataset)
-
-    Args:
-      numpy_input: a nest of NumPy input arrays that will be converted into a
-        dataset. Note that the NumPy arrays are stacked, as that is normal
-        `tf.data.Dataset` behavior.
-
-    Returns:
-      A `tf.data.Dataset` representing `numpy_input`.
-    """
-    return self.extended.experimental_make_numpy_dataset(
-        numpy_input, session=None)
-
   @doc_controls.do_not_generate_docs  # DEPRECATED: TF 1.x only
   def experimental_run(self, fn, input_iterator=None):
     """DEPRECATED TF 1.x ONLY."""

tensorflow/python/distribute/distribute_lib_test.py

@@ -19,7 +19,6 @@ from __future__ import division
 from __future__ import print_function
 
 from absl.testing import parameterized
-import numpy as np
 
 from tensorflow.python.autograph.core import converter_testing
 from tensorflow.python.data.ops import dataset_ops
@@ -99,10 +98,6 @@ class _TestExtended(distribute_lib.StrategyExtendedV1):
     del reduce_op, destinations, experimental_hints
     return value
 
-  def _experimental_make_numpy_dataset(self, numpy_input, session):
-    del session
-    return dataset_ops.DatasetV2.from_tensor_slices(numpy_input)
-
   def _experimental_run_steps_on_iterator(self, fn, iterator, iterations,
                                           initial_loop_values=None):
     # TODO(tomhennigan) This is missing many things (e.g. ctx.run_op).
@@ -346,13 +341,6 @@ class TestStrategyTest(test.TestCase):
       self.assertEqual(self.evaluate(x), self.evaluate(x_r))
       self.assertEqual(self.evaluate(y), self.evaluate(y_r))
 
-  @_run_in_and_out_of_scope
-  def testExperimentalMakeNumpyDataset(self, dist):
-    numpy_input = np.ones([10], dtype=np.float32)
-    dataset = dist.experimental_make_numpy_dataset(numpy_input)
-    self.assertEqual(
-        self.evaluate(dataset.reduce(0., lambda a, b: a + b)), 10.)
-
   @_run_in_and_out_of_scope
   def testExperimentalRunStepsOnIterator(self, dist):
     all_inputs = []

tensorflow/python/distribute/strategy_test_lib.py

@@ -28,6 +28,7 @@ from tensorflow.core.protobuf import config_pb2
 from tensorflow.core.util import event_pb2
 from tensorflow.python.client import session as session_lib
 from tensorflow.python.data.ops import dataset_ops
+from tensorflow.python.distribute import distribute_lib
 from tensorflow.python.distribute import distribute_utils
 from tensorflow.python.distribute import distribution_strategy_context as ds_context
 from tensorflow.python.distribute import reduce_util
@@ -429,6 +430,8 @@ class DistributionTestBase(test.TestCase):
       self.assertEqual((1,) * len(global_step_tensors), global_step_values)
 
   def _test_numpy_dataset(self, strategy, session=None, run_in_function=False):
+    if not isinstance(strategy, distribute_lib.StrategyV1):
+      self.skipTest("n/a: V1 only")
     cached_session = session or self.cached_session()
     with strategy.scope(), cached_session as sess:
       x = np.asarray([[1, 2], [6, 12], [2, 4], [5, 10], [3, 6], [4, 8]])

tensorflow/tools/api/golden/v2/tensorflow.distribute.-mirrored-strategy.pbtxt

@@ -48,10 +48,6 @@ tf_class {
     name: "experimental_local_results"
     argspec: "args=[\'self\', \'value\'], varargs=None, keywords=None, defaults=None"
   }
-  member_method {
-    name: "experimental_make_numpy_dataset"
-    argspec: "args=[\'self\', \'numpy_input\'], varargs=None, keywords=None, defaults=None"
-  }
   member_method {
     name: "experimental_run"
     argspec: "args=[\'self\', \'fn\', \'input_iterator\'], varargs=None, keywords=None, defaults=[\'None\'], "

tensorflow/tools/api/golden/v2/tensorflow.distribute.-one-device-strategy.pbtxt

@@ -48,10 +48,6 @@ tf_class {
     name: "experimental_local_results"
     argspec: "args=[\'self\', \'value\'], varargs=None, keywords=None, defaults=None"
   }
-  member_method {
-    name: "experimental_make_numpy_dataset"
-    argspec: "args=[\'self\', \'numpy_input\'], varargs=None, keywords=None, defaults=None"
-  }
   member_method {
     name: "experimental_run"
     argspec: "args=[\'self\', \'fn\', \'input_iterator\'], varargs=None, keywords=None, defaults=[\'None\'], "

tensorflow/tools/api/golden/v2/tensorflow.distribute.-strategy.pbtxt

@@ -47,10 +47,6 @@ tf_class {
     name: "experimental_local_results"
     argspec: "args=[\'self\', \'value\'], varargs=None, keywords=None, defaults=None"
   }
-  member_method {
-    name: "experimental_make_numpy_dataset"
-    argspec: "args=[\'self\', \'numpy_input\'], varargs=None, keywords=None, defaults=None"
-  }
   member_method {
     name: "experimental_run"
     argspec: "args=[\'self\', \'fn\', \'input_iterator\'], varargs=None, keywords=None, defaults=[\'None\'], "

tensorflow/tools/api/golden/v2/tensorflow.distribute.-t-p-u-strategy.pbtxt

@@ -52,10 +52,6 @@ tf_class {
     name: "experimental_local_results"
     argspec: "args=[\'self\', \'value\'], varargs=None, keywords=None, defaults=None"
   }
-  member_method {
-    name: "experimental_make_numpy_dataset"
-    argspec: "args=[\'self\', \'numpy_input\'], varargs=None, keywords=None, defaults=None"
-  }
   member_method {
     name: "experimental_replicate_to_logical_devices"
     argspec: "args=[\'self\', \'tensor\'], varargs=None, keywords=None, defaults=None"

tensorflow/tools/api/golden/v2/tensorflow.distribute.experimental.-central-storage-strategy.pbtxt

@@ -48,10 +48,6 @@ tf_class {
     name: "experimental_local_results"
     argspec: "args=[\'self\', \'value\'], varargs=None, keywords=None, defaults=None"
   }
-  member_method {
-    name: "experimental_make_numpy_dataset"
-    argspec: "args=[\'self\', \'numpy_input\'], varargs=None, keywords=None, defaults=None"
-  }
   member_method {
     name: "experimental_run"
     argspec: "args=[\'self\', \'fn\', \'input_iterator\'], varargs=None, keywords=None, defaults=[\'None\'], "

tensorflow/tools/api/golden/v2/tensorflow.distribute.experimental.-multi-worker-mirrored-strategy.pbtxt

@@ -48,10 +48,6 @@ tf_class {
     name: "experimental_local_results"
     argspec: "args=[\'self\', \'value\'], varargs=None, keywords=None, defaults=None"
   }
-  member_method {
-    name: "experimental_make_numpy_dataset"
-    argspec: "args=[\'self\', \'numpy_input\'], varargs=None, keywords=None, defaults=None"
-  }
   member_method {
     name: "experimental_run"
     argspec: "args=[\'self\', \'fn\', \'input_iterator\'], varargs=None, keywords=None, defaults=[\'None\'], "

tensorflow/tools/api/golden/v2/tensorflow.distribute.experimental.-parameter-server-strategy.pbtxt

@@ -48,10 +48,6 @@ tf_class {
     name: "experimental_local_results"
     argspec: "args=[\'self\', \'value\'], varargs=None, keywords=None, defaults=None"
   }
-  member_method {
-    name: "experimental_make_numpy_dataset"
-    argspec: "args=[\'self\', \'numpy_input\'], varargs=None, keywords=None, defaults=None"
-  }
   member_method {
     name: "experimental_run"
     argspec: "args=[\'self\', \'fn\', \'input_iterator\'], varargs=None, keywords=None, defaults=[\'None\'], "

tensorflow/tools/api/golden/v2/tensorflow.distribute.experimental.-t-p-u-strategy.pbtxt

@@ -48,10 +48,6 @@ tf_class {
     name: "experimental_local_results"
     argspec: "args=[\'self\', \'value\'], varargs=None, keywords=None, defaults=None"
   }
-  member_method {
-    name: "experimental_make_numpy_dataset"
-    argspec: "args=[\'self\', \'numpy_input\'], varargs=None, keywords=None, defaults=None"
-  }
   member_method {
     name: "experimental_run"
     argspec: "args=[\'self\', \'fn\', \'input_iterator\'], varargs=None, keywords=None, defaults=[\'None\'], "