Add v1 to the names of Keras files that are legacy-training-loop-specific.

PiperOrigin-RevId: 331851473
Change-Id: I18dfafc0e30627bb328ce945d822d8be80672e3e

tensorflow/python/keras/engine/BUILD

@@ -33,10 +33,10 @@ py_library(
         "saving.py",
         "sequential.py",
         "training.py",
-        "training_arrays.py",
-        "training_distributed.py",
-        "training_eager.py",
-        "training_generator.py",
+        "training_arrays_v1.py",
+        "training_distributed_v1.py",
+        "training_eager_v1.py",
+        "training_generator_v1.py",
         "training_utils.py",
         "training_v1.py",
     ],

tensorflow/python/keras/engine/base_preprocessing_layer.py

@@ -31,7 +31,7 @@ from tensorflow.python.framework import errors
 from tensorflow.python.framework import ops
 from tensorflow.python.framework import sparse_tensor
 from tensorflow.python.keras import backend as K
-from tensorflow.python.keras.engine import training_generator
+from tensorflow.python.keras.engine import training_generator_v1
 from tensorflow.python.keras.engine.base_layer import Layer
 from tensorflow.python.keras.utils import tf_utils
 from tensorflow.python.ops import sparse_ops
@@ -175,7 +175,7 @@ class CombinerPreprocessingLayer(PreprocessingLayer):
       next_data = self._get_dataset_iterator(
           dataset_ops.Dataset.from_tensor_slices(data).batch(512))
     else:
-      generator, _ = training_generator.convert_to_generator_like(
+      generator, _ = training_generator_v1.convert_to_generator_like(
           data, batch_size=512)
       # If the data is not a dataset, we can iterate over it using next(foo);
       # here, we wrap that into a callable.

tensorflow/python/keras/engine/training_distributed_v1.py

@@ -33,7 +33,7 @@ from tensorflow.python.keras import backend as K
 from tensorflow.python.keras import callbacks as cbks
 from tensorflow.python.keras.distribute import distributed_training_utils as dist_utils
 from tensorflow.python.keras.engine import partial_batch_padding_handler as padding_util
-from tensorflow.python.keras.engine import training_arrays
+from tensorflow.python.keras.engine import training_arrays_v1
 from tensorflow.python.keras.engine import training_utils
 from tensorflow.python.keras.utils.generic_utils import Progbar
 from tensorflow.python.keras.utils.mode_keys import ModeKeys
@@ -669,7 +669,7 @@ class DistributionSingleWorkerTrainingLoop(training_utils.TrainingLoop):
             validation_steps=validation_steps,
             validation_freq=validation_freq)
 
-    return training_arrays.fit_loop(
+    return training_arrays_v1.fit_loop(
         model,
         dataset,
         batch_size=batch_size,
@@ -717,7 +717,7 @@ class DistributionSingleWorkerTrainingLoop(training_utils.TrainingLoop):
         return experimental_tpu_test_loop(
             model, dataset, verbose=verbose, steps=steps, callbacks=callbacks)
 
-    return training_arrays.test_loop(
+    return training_arrays_v1.test_loop(
         model,
         inputs=dataset,
         batch_size=batch_size,
@@ -751,7 +751,7 @@ class DistributionSingleWorkerTrainingLoop(training_utils.TrainingLoop):
       if not context.executing_eagerly():
         return experimental_tpu_predict_loop(
             model, dataset, verbose=verbose, steps=steps, callbacks=callbacks)
-    return training_arrays.predict_loop(
+    return training_arrays_v1.predict_loop(
         model,
         dataset,
         batch_size=batch_size,

tensorflow/python/keras/engine/training_generator_test.py

@@ -34,7 +34,7 @@ from tensorflow.python.keras import metrics as metrics_module
 from tensorflow.python.keras import testing_utils
 from tensorflow.python.keras.engine import input_layer
 from tensorflow.python.keras.engine import training
-from tensorflow.python.keras.engine import training_generator
+from tensorflow.python.keras.engine import training_generator_v1
 from tensorflow.python.keras.optimizer_v2 import rmsprop
 from tensorflow.python.keras.utils import data_utils
 from tensorflow.python.platform import test
@@ -527,7 +527,7 @@ class TestConvertToGeneratorLike(test.TestCase, parameterized.TestCase):
         isinstance(data, (dataset_ops.DatasetV2, iterator_ops.Iterator))):
       return
 
-    generator, steps = training_generator.convert_to_generator_like(
+    generator, steps = training_generator_v1.convert_to_generator_like(
         data, batch_size=2, steps_per_epoch=expected_batches)
     self.assertEqual(steps, expected_batches)
 

tensorflow/python/keras/engine/training_v1.py

@@ -45,10 +45,10 @@ from tensorflow.python.keras import optimizers
 from tensorflow.python.keras.distribute import distributed_training_utils
 from tensorflow.python.keras.engine import base_layer
 from tensorflow.python.keras.engine import training as training_lib
-from tensorflow.python.keras.engine import training_arrays
-from tensorflow.python.keras.engine import training_distributed
-from tensorflow.python.keras.engine import training_eager
-from tensorflow.python.keras.engine import training_generator
+from tensorflow.python.keras.engine import training_arrays_v1
+from tensorflow.python.keras.engine import training_distributed_v1
+from tensorflow.python.keras.engine import training_eager_v1
+from tensorflow.python.keras.engine import training_generator_v1
 from tensorflow.python.keras.engine import training_utils
 from tensorflow.python.keras.mixed_precision.experimental import loss_scale_optimizer
 from tensorflow.python.keras.optimizer_v2 import optimizer_v2
@@ -582,25 +582,25 @@ class Model(training_lib.Model):
     # Case 1: distribution strategy.
     if self._distribution_strategy:
       if self._in_multi_worker_mode():
-        return training_distributed.DistributionMultiWorkerTrainingLoop(
-            training_distributed.DistributionSingleWorkerTrainingLoop())
+        return training_distributed_v1.DistributionMultiWorkerTrainingLoop(
+            training_distributed_v1.DistributionSingleWorkerTrainingLoop())
       else:
-        return training_distributed.DistributionSingleWorkerTrainingLoop()
+        return training_distributed_v1.DistributionSingleWorkerTrainingLoop()
 
     # Case 2: generator-like. Input is Python generator, or Sequence object,
     # or a non-distributed Dataset or iterator in eager execution.
     if data_utils.is_generator_or_sequence(inputs):
-      return training_generator.GeneratorOrSequenceTrainingLoop()
+      return training_generator_v1.GeneratorOrSequenceTrainingLoop()
     if training_utils.is_eager_dataset_or_iterator(inputs):
-      return training_generator.EagerDatasetOrIteratorTrainingLoop()
+      return training_generator_v1.EagerDatasetOrIteratorTrainingLoop()
 
     # Case 3: Symbolic tensors or Numpy array-like.
     # This includes Datasets and iterators in graph mode (since they
     # generate symbolic tensors).
     if self.run_eagerly:
-      return training_generator.GeneratorLikeTrainingLoop()
+      return training_generator_v1.GeneratorLikeTrainingLoop()
     else:
-      return training_arrays.ArrayLikeTrainingLoop()
+      return training_arrays_v1.ArrayLikeTrainingLoop()
 
   def fit(self,
           x=None,
@@ -1062,7 +1062,7 @@ class Model(training_lib.Model):
     # for each replica by `self._distribution_strategy` and the same code path
     # as Eager is expected to be taken.
     if self.run_eagerly or self._distribution_strategy:
-      output_dict = training_eager.train_on_batch(
+      output_dict = training_eager_v1.train_on_batch(
           self,
           x,
           y,
@@ -1141,7 +1141,7 @@ class Model(training_lib.Model):
     # If `self._distribution_strategy` is True, then we are in a replica context
     # at this point.
     if self.run_eagerly or self._distribution_strategy:
-      output_dict = training_eager.test_on_batch(
+      output_dict = training_eager_v1.test_on_batch(
           self,
           x,
           y,