Remove expired forward compatibility horizons

tensorflow/compiler/tests/fused_batchnorm_test.py

@@ -23,7 +23,6 @@ import numpy as np
 
 from tensorflow.compiler.tests import test_utils
 from tensorflow.compiler.tests import xla_test
-from tensorflow.python.compat import compat
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import gen_nn_ops
 from tensorflow.python.ops import gradient_checker
@@ -132,9 +131,6 @@ class FusedBatchNormTest(xla_test.XLATestCase, parameterized.TestCase):
 
   def _testLearning(self, use_gradient_checker, data_format,
                     exponential_avg_factor):
-    if not compat.forward_compatible(2020, 3,
-                                     6) and exponential_avg_factor != 1.0:
-      self.skipTest("running average not available.")
     channel = 3
     x_shape = [2, 2, 6, channel]
     scale_shape = [channel]

tensorflow/python/keras/layers/normalization.py

@@ -18,7 +18,6 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-from tensorflow.python.compat import compat
 from tensorflow.python.distribute import distribution_strategy_context
 from tensorflow.python.framework import constant_op
 from tensorflow.python.framework import dtypes
@@ -547,7 +546,6 @@ class BatchNormalizationBase(Layer):
     # after fixing graph pattern matching and enabling fused_batch_norm to
     # take exponential_avg_factor as a tensor input.
     use_fused_avg_updates = (
-        compat.forward_compatible(2020, 3, 6) and
         ops.executing_eagerly_outside_functions() and
         isinstance(self.momentum, (float, int)) and
         device_context.enclosing_tpu_context() is None)

tensorflow/python/ops/nn_fused_batchnorm_test.py

@@ -20,7 +20,6 @@ from __future__ import print_function
 
 import numpy as np
 
-from tensorflow.python.compat import compat
 from tensorflow.python.framework import constant_op
 from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import test_util
@@ -380,13 +379,7 @@ class BatchNormalizationTest(test.TestCase):
     use_gpu_vals = [False]
     if test.is_gpu_available(cuda_only=True):
       use_gpu_vals += [True]
-    factors = [
-        1.0,
-    ]
-    if compat.forward_compatible(2020, 3, 6):
-      factors += [
-          0.6,
-      ]
+    factors = [1.0, 0.6]
     for dtype in [np.float16, np.float32]:
       for use_gpu in use_gpu_vals:
         for data_format in ['NHWC', 'NCHW']:

tensorflow/python/ops/nn_impl.py

@@ -20,7 +20,6 @@ from __future__ import print_function
 
 import math
 
-from tensorflow.python.compat import compat
 from tensorflow.python.distribute import distribution_strategy_context as ds
 from tensorflow.python.framework import constant_op
 from tensorflow.python.framework import dtypes
@@ -1458,7 +1457,7 @@ def batch_normalization(x,
       `tf.nn.moments(..., keepdims=False)` during training, or running averages
       thereof during inference.
 
-  See equation 11 in Algorithm 2 of source: 
+  See equation 11 in Algorithm 2 of source:
   [Batch Normalization: Accelerating Deep Network Training by
   Reducing Internal Covariate Shift; S. Ioffe, C. Szegedy]
   (http://arxiv.org/abs/1502.03167).
@@ -1589,31 +1588,18 @@ def fused_batch_norm(
   min_epsilon = 1.001e-5
   epsilon = epsilon if epsilon > min_epsilon else min_epsilon
 
-  if compat.forward_compatible(2020, 3, 6):
-    y, running_mean, running_var, _, _, _ = gen_nn_ops.fused_batch_norm_v3(
-        x,
-        scale,
-        offset,
-        mean,
-        variance,
-        epsilon=epsilon,
-        exponential_avg_factor=exponential_avg_factor,
-        data_format=data_format,
-        is_training=is_training,
-        name=name)
-    return y, running_mean, running_var
-  else:
-    y, running_mean, running_var, _, _, _ = gen_nn_ops.fused_batch_norm_v3(
-        x,
-        scale,
-        offset,
-        mean,
-        variance,
-        epsilon=epsilon,
-        data_format=data_format,
-        is_training=is_training,
-        name=name)
-    return y, running_mean, running_var
+  y, running_mean, running_var, _, _, _ = gen_nn_ops.fused_batch_norm_v3(
+      x,
+      scale,
+      offset,
+      mean,
+      variance,
+      epsilon=epsilon,
+      exponential_avg_factor=exponential_avg_factor,
+      data_format=data_format,
+      is_training=is_training,
+      name=name)
+  return y, running_mean, running_var
 
 
 @tf_export(v1=["nn.batch_norm_with_global_normalization"])