Merge remote-tracking branch 'upstream/master' into context_tf2

official/nlp/transformer/beam_search_v1_test.py → official/nlp/modeling/ops/beam_search_test.py

@@ -14,33 +14,19 @@
 # ==============================================================================
 """Test beam search helper methods."""
 
-import tensorflow.compat.v1 as tf
+import tensorflow as tf
 
-from official.nlp.transformer import beam_search_v1 as beam_search
+from official.nlp.modeling.ops import beam_search
 
 
 class BeamSearchHelperTests(tf.test.TestCase):
 
-  def setUp(self):
-    super(BeamSearchHelperTests, self).setUp()
-    tf.compat.v1.disable_eager_execution()
-
   def test_expand_to_beam_size(self):
     x = tf.ones([7, 4, 2, 5])
     x = beam_search._expand_to_beam_size(x, 3)
-    with self.session() as sess:
-      shape = sess.run(tf.shape(x))
+    shape = tf.shape(x)
     self.assertAllEqual([7, 3, 4, 2, 5], shape)
 
-  def test_shape_list(self):
-    y = tf.compat.v1.placeholder(dtype=tf.int32, shape=[])
-    x = tf.ones([7, y, 2, 5])
-    shape = beam_search._shape_list(x)
-    self.assertIsInstance(shape[0], int)
-    self.assertIsInstance(shape[1], tf.Tensor)
-    self.assertIsInstance(shape[2], int)
-    self.assertIsInstance(shape[3], int)
-
   def test_get_shape_keep_last_dim(self):
     y = tf.constant(4.0)
     x = tf.ones([7, tf.cast(tf.sqrt(y), tf.int32), 2, 5])
@@ -51,16 +37,12 @@ class BeamSearchHelperTests(tf.test.TestCase):
   def test_flatten_beam_dim(self):
     x = tf.ones([7, 4, 2, 5])
     x = beam_search._flatten_beam_dim(x)
-    with self.session() as sess:
-      shape = sess.run(tf.shape(x))
-    self.assertAllEqual([28, 2, 5], shape)
+    self.assertAllEqual([28, 2, 5], tf.shape(x))
 
   def test_unflatten_beam_dim(self):
     x = tf.ones([28, 2, 5])
     x = beam_search._unflatten_beam_dim(x, 7, 4)
-    with self.session() as sess:
-      shape = sess.run(tf.shape(x))
-    self.assertAllEqual([7, 4, 2, 5], shape)
+    self.assertAllEqual([7, 4, 2, 5], tf.shape(x))
 
   def test_gather_beams(self):
     x = tf.reshape(tf.range(24), [2, 3, 4])
@@ -73,9 +55,6 @@ class BeamSearchHelperTests(tf.test.TestCase):
     #                  [20 21 22 23]]]
 
     y = beam_search._gather_beams(x, [[1, 2], [0, 2]], 2, 2)
-    with self.session() as sess:
-      y = sess.run(y)
-
     self.assertAllEqual([[[4, 5, 6, 7],
                           [8, 9, 10, 11]],
                          [[12, 13, 14, 15],
@@ -87,9 +66,6 @@ class BeamSearchHelperTests(tf.test.TestCase):
     x_scores = [[0, 1, 1], [1, 0, 1]]
 
     y = beam_search._gather_topk_beams(x, x_scores, 2, 2)
-    with self.session() as sess:
-      y = sess.run(y)
-
     self.assertAllEqual([[[4, 5, 6, 7],
                           [8, 9, 10, 11]],
                          [[12, 13, 14, 15],

official/nlp/nhnet/models.py

@@ -31,7 +31,7 @@ from official.nlp.modeling.layers import multi_channel_attention
 from official.nlp.nhnet import configs
 from official.nlp.nhnet import decoder
 from official.nlp.nhnet import utils
-from official.nlp.transformer import beam_search
+from official.nlp.modeling.ops import beam_search
 
 
 def embedding_linear(embedding_matrix, x):

official/nlp/tasks/masked_lm.py

@@ -40,8 +40,9 @@ class MaskedLMConfig(cfg.TaskConfig):
 class MaskedLMTask(base_task.Task):
   """Mock task object for testing."""
 
-  def build_model(self):
-    return bert.instantiate_pretrainer_from_cfg(self.task_config.model)
+  def build_model(self, params=None):
+    params = params or self.task_config.model
+    return bert.instantiate_pretrainer_from_cfg(params)
 
   def build_losses(self,
                    labels,

official/nlp/transformer/beam_search.py

-# Copyright 2018 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.
-# ==============================================================================
-"""Beam search in TF v2."""
-
-import tensorflow as tf
-
-from official.nlp.transformer import beam_search_v1 as v1
-
-_StateKeys = v1._StateKeys  # pylint: disable=protected-access
-
-
-class SequenceBeamSearchV2(v1.SequenceBeamSearch):
-  """Implementation of beam search loop in v2."""
-
-  def search(self, initial_ids, initial_cache):
-    """Beam search for sequences with highest scores."""
-    state, state_shapes = self._create_initial_state(initial_ids, initial_cache)
-
-    finished_state = tf.nest.map_structure(
-        tf.stop_gradient,
-        tf.while_loop(self._continue_search,
-                      self._search_step,
-                      loop_vars=[state],
-                      shape_invariants=[state_shapes],
-                      parallel_iterations=1))
-    finished_state = finished_state[0]
-
-    alive_seq = finished_state[_StateKeys.ALIVE_SEQ]
-    alive_log_probs = finished_state[_StateKeys.ALIVE_LOG_PROBS]
-    finished_seq = finished_state[_StateKeys.FINISHED_SEQ]
-    finished_scores = finished_state[_StateKeys.FINISHED_SCORES]
-    finished_flags = finished_state[_StateKeys.FINISHED_FLAGS]
-
-    # 2.0 changes tf.where behavior. Should make parameters broadcastable.
-    finished_cond = tf.reduce_any(finished_flags, 1, name="finished_cond")
-    seq_cond = _expand_to_same_rank(finished_cond, finished_seq)
-    score_cond = _expand_to_same_rank(finished_cond, finished_scores)
-
-    # Account for corner case where there are no finished sequences for a
-    # particular batch item. In that case, return alive sequences for that batch
-    # item.
-    finished_seq = tf.where(seq_cond, finished_seq, alive_seq)
-    finished_scores = tf.where(
-        score_cond, finished_scores, alive_log_probs)
-    return finished_seq, finished_scores
-
-
-def sequence_beam_search(symbols_to_logits_fn,
-                         initial_ids,
-                         initial_cache,
-                         vocab_size,
-                         beam_size,
-                         alpha,
-                         max_decode_length,
-                         eos_id,
-                         padded_decode=False,
-                         dtype="float32"):
-  """Search for sequence of subtoken ids with the largest probability.
-
-  Args:
-    symbols_to_logits_fn: A function that takes in ids, index, and cache as
-      arguments. The passed in arguments will have shape:
-        ids -> A tensor with shape [batch_size * beam_size, index].
-        index -> A scalar.
-        cache -> A nested dictionary of tensors [batch_size * beam_size, ...].
-      The function must return a tuple of logits and new cache:
-        logits -> A tensor with shape [batch * beam_size, vocab_size].
-        new cache -> A nested dictionary with the same shape/structure as the
-          inputted cache.
-    initial_ids: An int32 tensor with shape [batch_size]. Starting ids for
-      each batch item.
-    initial_cache: A dictionary, containing starting decoder variables
-      information.
-    vocab_size: An integer, the size of tokens.
-    beam_size: An integer, the number of beams.
-    alpha: A float, defining the strength of length normalization.
-    max_decode_length: An integer, the maximum length to decoded a sequence.
-    eos_id: An integer, ID of eos token, used to determine when a sequence has
-      finished.
-    padded_decode: A bool, indicating if max_sequence_length padding is used
-      for beam search.
-    dtype: A tensorflow data type used for score computation. The default is
-      tf.float32.
-
-  Returns:
-    Top decoded sequences [batch_size, beam_size, max_decode_length]
-    sequence scores [batch_size, beam_size]
-  """
-  batch_size = (
-      initial_ids.shape.as_list()[0] if padded_decode else
-      tf.shape(initial_ids)[0])
-  sbs = SequenceBeamSearchV2(symbols_to_logits_fn, vocab_size, batch_size,
-                             beam_size, alpha, max_decode_length, eos_id,
-                             padded_decode, dtype)
-  return sbs.search(initial_ids, initial_cache)
-
-
-def _expand_to_same_rank(tensor, target):
-  """Expands a given tensor to target's rank to be broadcastable.
-
-  Args:
-    tensor: input tensor to tile. Shape: [b, d1, ..., da]
-    target: target tensor. Shape: [b, d1, ..., da, ..., dn]
-
-  Returns:
-    Tiled tensor of shape [b, d1, ..., da, 1, ..., 1] with same rank of target.
-
-  Raises:
-    ValueError, if the shape rank of rank tensor/target is None.
-  """
-  if tensor.shape.rank is None:
-    raise ValueError("Expect rank for tensor shape, but got None.")
-  if target.shape.rank is None:
-    raise ValueError("Expect rank for target shape, but got None.")
-
-  with tf.name_scope("expand_rank"):
-    diff_rank = target.shape.rank - tensor.shape.rank
-    for _ in range(diff_rank):
-      tensor = tf.expand_dims(tensor, -1)
-    return tensor

official/nlp/transformer/embedding_layer.py

@@ -43,6 +43,7 @@ class EmbeddingSharedWeights(tf.keras.layers.Layer):
       self.shared_weights = self.add_weight(
           "weights",
           shape=[self.vocab_size, self.hidden_size],
+          dtype=tf.float32,
           initializer=tf.random_normal_initializer(
               mean=0., stddev=self.hidden_size**-0.5))
     super(EmbeddingSharedWeights, self).build(input_shape)

official/nlp/transformer/transformer.py

@@ -23,8 +23,8 @@ from __future__ import print_function
 
 import tensorflow as tf
 from official.nlp.modeling.layers import position_embedding
+from official.nlp.modeling.ops import beam_search
 from official.nlp.transformer import attention_layer
-from official.nlp.transformer import beam_search
 from official.nlp.transformer import embedding_layer
 from official.nlp.transformer import ffn_layer
 from official.nlp.transformer import metrics

official/recommendation/ncf_common.py

@@ -94,7 +94,7 @@ def parse_flags(flags_obj):
       "beta2": flags_obj.beta2,
       "epsilon": flags_obj.epsilon,
       "match_mlperf": flags_obj.ml_perf,
-      "epochs_between_evals": FLAGS.epochs_between_evals,
+      "epochs_between_evals": flags_obj.epochs_between_evals,
       "keras_use_ctl": flags_obj.keras_use_ctl,
       "hr_threshold": flags_obj.hr_threshold,
       "stream_files": flags_obj.tpu is not None,

official/recommendation/ncf_input_pipeline.py

@@ -25,10 +25,8 @@ import tensorflow.compat.v2 as tf
 # pylint: enable=g-bad-import-order
 
 from official.recommendation import constants as rconst
-from official.recommendation import movielens
 from official.recommendation import data_pipeline
-
-NUM_SHARDS = 16
+from official.recommendation import movielens
 
 
 def create_dataset_from_tf_record_files(input_file_pattern,
@@ -36,17 +34,15 @@ def create_dataset_from_tf_record_files(input_file_pattern,
                                         batch_size,
                                         is_training=True):
   """Creates dataset from (tf)records files for training/evaluation."""
-
-  files = tf.data.Dataset.list_files(input_file_pattern, shuffle=is_training)
-
-  def make_dataset(files_dataset, shard_index):
-    """Returns dataset for sharded tf record files."""
   if pre_batch_size != batch_size:
     raise ValueError("Pre-batch ({}) size is not equal to batch "
                      "size ({})".format(pre_batch_size, batch_size))
-    files_dataset = files_dataset.shard(NUM_SHARDS, shard_index)
-    dataset = files_dataset.interleave(
+
+  files = tf.data.Dataset.list_files(input_file_pattern, shuffle=is_training)
+
+  dataset = files.interleave(
       tf.data.TFRecordDataset,
+      cycle_length=16,
       num_parallel_calls=tf.data.experimental.AUTOTUNE)
   decode_fn = functools.partial(
       data_pipeline.DatasetManager.deserialize,
@@ -54,14 +50,7 @@ def create_dataset_from_tf_record_files(input_file_pattern,
       is_training=is_training)
   dataset = dataset.map(
       decode_fn, num_parallel_calls=tf.data.experimental.AUTOTUNE)
-    return dataset
 
-  dataset = tf.data.Dataset.range(NUM_SHARDS)
-  map_fn = functools.partial(make_dataset, files)
-  dataset = dataset.interleave(
-      map_fn,
-      cycle_length=NUM_SHARDS,
-      num_parallel_calls=tf.data.experimental.AUTOTUNE)
   dataset = dataset.prefetch(tf.data.experimental.AUTOTUNE)
   return dataset
 

official/vision/detection/modeling/architecture/fpn.py

@@ -28,7 +28,7 @@ import functools
 
 import tensorflow as tf
 
-from tensorflow.python.keras import backend
+from official.vision.detection.modeling.architecture import keras_utils
 from official.vision.detection.modeling.architecture import nn_ops
 from official.vision.detection.ops import spatial_transform_ops
 
@@ -120,7 +120,7 @@ class Fpn(object):
           'The minimum backbone level %d should be '%(min(input_levels)) +
           'less or equal to FPN minimum level %d.:'%(self._min_level))
     backbone_max_level = min(max(input_levels), self._max_level)
-    with backend.get_graph().as_default(), tf.name_scope('fpn'):
+    with keras_utils.maybe_enter_backend_graph(), tf.name_scope('fpn'):
       # Adds lateral connections.
       feats_lateral = {}
       for level in range(self._min_level, backbone_max_level + 1):

official/vision/detection/modeling/architecture/heads.py

@@ -22,7 +22,8 @@ import functools
 
 import numpy as np
 import tensorflow as tf
-from tensorflow.python.keras import backend
+
+from official.vision.detection.modeling.architecture import keras_utils
 from official.vision.detection.modeling.architecture import nn_ops
 from official.vision.detection.ops import spatial_transform_ops
 
@@ -127,7 +128,7 @@ class RpnHead(tf.keras.layers.Layer):
     scores_outputs = {}
     box_outputs = {}
 
-    with backend.get_graph().as_default(), tf.name_scope('rpn_head'):
+    with keras_utils.maybe_enter_backend_graph(), tf.name_scope('rpn_head'):
       for level in range(self._min_level, self._max_level + 1):
         scores_output, box_output = self._shared_rpn_heads(
             features[level], self._anchors_per_location, level, is_training)
@@ -249,7 +250,8 @@ class FastrcnnHead(tf.keras.layers.Layer):
         predictions.
     """
 
-    with backend.get_graph().as_default(), tf.name_scope('fast_rcnn_head'):
+    with keras_utils.maybe_enter_backend_graph(), tf.name_scope(
+        'fast_rcnn_head'):
       # reshape inputs beofre FC.
       _, num_rois, height, width, filters = roi_features.get_shape().as_list()
 
@@ -368,7 +370,7 @@ class MaskrcnnHead(tf.keras.layers.Layer):
         boxes is not 4.
     """
 
-    with backend.get_graph().as_default():
+    with keras_utils.maybe_enter_backend_graph():
       with tf.name_scope('mask_head'):
         _, num_rois, height, width, filters = roi_features.get_shape().as_list()
         net = tf.reshape(roi_features, [-1, height, width, filters])
@@ -552,7 +554,8 @@ class RetinanetHead(object):
     """Returns outputs of RetinaNet head."""
     class_outputs = {}
     box_outputs = {}
-    with backend.get_graph().as_default(), tf.name_scope('retinanet_head'):
+    with keras_utils.maybe_enter_backend_graph(), tf.name_scope(
+        'retinanet_head'):
       for level in range(self._min_level, self._max_level + 1):
         features = fpn_features[level]
 
@@ -644,7 +647,7 @@ class ShapemaskPriorHead(object):
       detection_priors: A float Tensor of shape [batch_size * num_instances,
         mask_size, mask_size, 1].
     """
-    with backend.get_graph().as_default(), tf.name_scope('prior_mask'):
+    with keras_utils.maybe_enter_backend_graph(), tf.name_scope('prior_mask'):
       batch_size, num_instances, _ = boxes.get_shape().as_list()
       outer_boxes = tf.cast(outer_boxes, tf.float32)
       boxes = tf.cast(boxes, tf.float32)
@@ -807,7 +810,7 @@ class ShapemaskCoarsemaskHead(object):
       mask_outputs: instance mask prediction as a float Tensor of shape
         [batch_size, num_instances, mask_size, mask_size].
     """
-    with backend.get_graph().as_default(), tf.name_scope('coarse_mask'):
+    with keras_utils.maybe_enter_backend_graph(), tf.name_scope('coarse_mask'):
       # Transform detection priors to have the same dimension as features.
       detection_priors = tf.expand_dims(detection_priors, axis=-1)
       detection_priors = self._coarse_mask_fc(detection_priors)
@@ -939,7 +942,7 @@ class ShapemaskFinemaskHead(object):
     """
     # Extract the foreground mean features
     # with tf.variable_scope('fine_mask', reuse=tf.AUTO_REUSE):
-    with backend.get_graph().as_default(), tf.name_scope('fine_mask'):
+    with keras_utils.maybe_enter_backend_graph(), tf.name_scope('fine_mask'):
       mask_probs = tf.nn.sigmoid(mask_logits)
       # Compute instance embedding for hard average.
       binary_mask = tf.cast(tf.greater(mask_probs, 0.5), features.dtype)

official/vision/detection/modeling/architecture/keras_utils.py

+# Copyright 2019 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.
+# ==============================================================================
+"""Util functions to integrate with Keras internals."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+
+from tensorflow.python.keras import backend
+
+try:
+  from tensorflow.python.keras.engine import keras_tensor  # pylint: disable=g-import-not-at-top,unused-import
+except ImportError:
+  keras_tensor = None
+
+
+class NoOpContextManager(object):
+
+  def __enter__(self):
+    pass
+
+  def __exit__(self, *args):
+    pass
+
+
+def maybe_enter_backend_graph():
+  if (keras_tensor is not None) and keras_tensor.keras_tensors_enabled():
+    return NoOpContextManager()
+  else:
+    return backend.get_graph().as_default()

official/vision/detection/modeling/architecture/resnet.py

@@ -25,7 +25,7 @@ from __future__ import print_function
 
 from absl import logging
 import tensorflow as tf
-from tensorflow.python.keras import backend
+from official.vision.detection.modeling.architecture import keras_utils
 from official.vision.detection.modeling.architecture import nn_ops
 
 # TODO(b/140112644): Refactor the code with Keras style, i.e. build and call.
@@ -90,7 +90,7 @@ class Resnet(object):
       The values are corresponding feature hierarchy in ResNet with shape
       [batch_size, height_l, width_l, num_filters].
     """
-    with backend.get_graph().as_default():
+    with keras_utils.maybe_enter_backend_graph():
       with tf.name_scope('resnet%s' % self._resnet_depth):
         return self._resnet_fn(inputs, is_training)
 

official/vision/detection/modeling/architecture/spinenet.py

@@ -24,8 +24,8 @@ import math
 from absl import logging
 import tensorflow as tf
 
-from tensorflow.python.keras import backend
 from official.modeling import tf_utils
+from official.vision.detection.modeling.architecture import keras_utils
 from official.vision.detection.modeling.architecture import nn_blocks
 
 layers = tf.keras.layers
@@ -486,7 +486,7 @@ class SpineNetBuilder(object):
     self._norm_epsilon = norm_epsilon
 
   def __call__(self, inputs, is_training=None):
-    with backend.get_graph().as_default():
+    with keras_utils.maybe_enter_backend_graph():
       model = SpineNet(
           input_specs=self._input_specs,
           min_level=self._min_level,

official/vision/detection/modeling/maskrcnn_model.py

@@ -20,13 +20,13 @@ from __future__ import print_function
 
 import tensorflow as tf
 
-from tensorflow.python.keras import backend
 from official.vision.detection.dataloader import anchor
 from official.vision.detection.dataloader import mode_keys
 from official.vision.detection.evaluation import factory as eval_factory
 from official.vision.detection.modeling import base_model
 from official.vision.detection.modeling import losses
 from official.vision.detection.modeling.architecture import factory
+from official.vision.detection.modeling.architecture import keras_utils
 from official.vision.detection.ops import postprocess_ops
 from official.vision.detection.ops import roi_ops
 from official.vision.detection.ops import spatial_transform_ops
@@ -297,7 +297,7 @@ class MaskrcnnModel(base_model.Model):
   def build_model(self, params, mode):
     if self._keras_model is None:
       input_layers = self.build_input_layers(self._params, mode)
-      with backend.get_graph().as_default():
+      with keras_utils.maybe_enter_backend_graph():
         outputs = self.model_outputs(input_layers, mode)
 
         model = tf.keras.models.Model(

official/vision/detection/modeling/retinanet_model.py

@@ -20,12 +20,12 @@ from __future__ import print_function
 
 import tensorflow as tf
 
-from tensorflow.python.keras import backend
 from official.vision.detection.dataloader import mode_keys
 from official.vision.detection.evaluation import factory as eval_factory
 from official.vision.detection.modeling import base_model
 from official.vision.detection.modeling import losses
 from official.vision.detection.modeling.architecture import factory
+from official.vision.detection.modeling.architecture import keras_utils
 from official.vision.detection.ops import postprocess_ops
 
 
@@ -120,7 +120,7 @@ class RetinanetModel(base_model.Model):
 
   def build_model(self, params, mode=None):
     if self._keras_model is None:
-      with backend.get_graph().as_default():
+      with keras_utils.maybe_enter_backend_graph():
         outputs = self.model_outputs(self._input_layer, mode)
 
         model = tf.keras.models.Model(

official/vision/detection/modeling/shapemask_model.py

@@ -20,13 +20,13 @@ from __future__ import print_function
 
 import tensorflow as tf
 
-from tensorflow.python.keras import backend
 from official.vision.detection.dataloader import anchor
 from official.vision.detection.dataloader import mode_keys
 from official.vision.detection.evaluation import factory as eval_factory
 from official.vision.detection.modeling import base_model
 from official.vision.detection.modeling import losses
 from official.vision.detection.modeling.architecture import factory
+from official.vision.detection.modeling.architecture import keras_utils
 from official.vision.detection.ops import postprocess_ops
 from official.vision.detection.utils import box_utils
 
@@ -265,7 +265,7 @@ class ShapeMaskModel(base_model.Model):
   def build_model(self, params, mode):
     if self._keras_model is None:
       input_layers = self.build_input_layers(self._params, mode)
-      with backend.get_graph().as_default():
+      with keras_utils.maybe_enter_backend_graph():
         outputs = self.model_outputs(input_layers, mode)
 
         model = tf.keras.models.Model(

official/vision/image_classification/resnet/common.py

@@ -255,7 +255,7 @@ def define_keras_flags(
       name='tpu', default='', help='TPU address to connect to.')
   flags.DEFINE_integer(
       name='steps_per_loop',
-      default=500,
+      default=None,
       help='Number of steps per training loop. Only training step happens '
       'inside the loop. Callbacks will not be called inside. Will be capped at '
       'steps per epoch.')

official/vision/image_classification/resnet/resnet_ctl_imagenet_main.py

@@ -125,7 +125,7 @@ def run(flags_obj):
 
   per_epoch_steps, train_epochs, eval_steps = get_num_train_iterations(
       flags_obj)
-  if not flags_obj.steps_per_loop:
+  if flags_obj.steps_per_loop is None:
     steps_per_loop = per_epoch_steps
   elif flags_obj.steps_per_loop > per_epoch_steps:
     steps_per_loop = per_epoch_steps