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Remove some stale forward compatibility dates

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PiperOrigin-RevId: 283092973
Change-Id: Ia708a9c04a032e1222c7d56ad4936e263424fbdd
This commit is contained in:
Gaurav Jain 2019-11-29 15:54:44 -08:00 committed by TensorFlower Gardener
parent 427c708efa
commit 14a0c12dc2
3 changed files with 13 additions and 46 deletions
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26
tensorflow/python/eager/function.py
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@ -34,7 +34,6 @@ from tensorflow.core.framework import attr_value_pb2
from tensorflow.core.framework import function_pb2
from tensorflow.python import pywrap_tensorflow
from tensorflow.python import _pywrap_utils
from tensorflow.python.compat import compat as fwd_compat
from tensorflow.python.eager import backprop
from tensorflow.python.eager import backprop_util
from tensorflow.python.eager import context
@ -1030,18 +1029,8 @@ class _TapeGradientFunctions(object):
with ops.get_default_graph()._override_gradient_function( # pylint: disable=protected-access
{"PartitionedCall": gradient_function,
"StatefulPartitionedCall": gradient_function}):
# Previously, we relyed on "_gradient_op_type" attribute to restore a
# function gradient in function_deserialization.py, So add a dummy
# value "PartitionedCallUnused" for the forward compatibility.
if fwd_compat.forward_compatible(2019, 11, 16):
forward_outputs = forward_function.call(context.context(),
forward_inputs)
else:
with ops.get_default_graph().gradient_override_map(
{"PartitionedCall": "PartitionedCallUnused",
"StatefulPartitionedCall": "PartitionedCallUnused"}):
forward_outputs = forward_function.call(context.context(),
forward_inputs)
forward_outputs = forward_function.call(context.context(),
forward_inputs)
py_backward, _ = self._wrap_backward_function(
self._func_graph, backward_function, forward_outputs)
# We will never request backward tape gradients for this operation
@ -1703,16 +1692,7 @@ class ConcreteFunction(object):
with ops.get_default_graph()._override_gradient_function( # pylint: disable=protected-access
{"PartitionedCall": self._get_gradient_function(),
"StatefulPartitionedCall": self._get_gradient_function()}):
# Previously, we relyed on "_gradient_op_type" attribute to restore a
# function gradient in function_deserialization.py. So add a dummy
# value "PartitionedCallUnused" for the forward compatibility.
if fwd_compat.forward_compatible(2019, 11, 16):
flat_outputs = forward_function.call(ctx, args_with_tangents)
else:
with ops.get_default_graph().gradient_override_map(
{"PartitionedCall": "PartitionedCallUnused",
"StatefulPartitionedCall": "PartitionedCallUnused"}):
flat_outputs = forward_function.call(ctx, args_with_tangents)
flat_outputs = forward_function.call(ctx, args_with_tangents)
forward_backward.record(flat_outputs)
return self._build_call_outputs(flat_outputs)

22
tensorflow/python/ops/math_grad.py
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@ -204,20 +204,14 @@ def _SumGrad(op, grad):
input_shape = array_ops.shape(op.inputs[0])
if compat.forward_compatible(2019, 10, 23):
if not op.get_attr("keep_dims"):
with ops.colocate_with(input_shape):
# TODO(apassos) remove this once device placement for eager ops makes
# more sense.
output_shape_kept_dims = math_ops.reduced_shape(input_shape,
op.inputs[1])
grad = array_ops.reshape(grad, output_shape_kept_dims)
return [array_ops.broadcast_to(grad, input_shape), None]
with ops.colocate_with(input_shape):
output_shape_kept_dims = math_ops.reduced_shape(input_shape, op.inputs[1])
tile_scaling = _safe_shape_div(input_shape, output_shape_kept_dims)
grad = array_ops.reshape(grad, output_shape_kept_dims)
return [array_ops.tile(grad, tile_scaling), None]
if not op.get_attr("keep_dims"):
with ops.colocate_with(input_shape):
# TODO(apassos) remove this once device placement for eager ops makes
# more sense.
output_shape_kept_dims = math_ops.reduced_shape(input_shape,
op.inputs[1])
grad = array_ops.reshape(grad, output_shape_kept_dims)
return [array_ops.broadcast_to(grad, input_shape), None]
def _MinOrMaxGrad(op, grad):

11
tensorflow/python/ops/math_ops.py
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@ -75,7 +75,6 @@ import six
from six.moves import builtins
from six.moves import xrange # pylint: disable=redefined-builtin
from tensorflow.python.compat import compat as fwd_compat
from tensorflow.python.eager import context
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import dtypes
@ -1364,10 +1363,7 @@ def tensor_equals(self, other):
g = getattr(self, "graph", None)
if (ops.Tensor._USE_EQUALITY and ops.executing_eagerly_outside_functions() and
(g is None or g._building_function)): # pylint: disable=protected-access
if fwd_compat.forward_compatible(2019, 9, 25):
return gen_math_ops.equal(self, other, incompatible_shape_error=False)
else:
return gen_math_ops.equal(self, other)
return gen_math_ops.equal(self, other, incompatible_shape_error=False)
else:
# In legacy graph mode, tensor equality is object equality
return self is other
@ -1378,10 +1374,7 @@ def tensor_not_equals(self, other):
if other is None:
return True
if ops.Tensor._USE_EQUALITY and ops.executing_eagerly_outside_functions():
if fwd_compat.forward_compatible(2019, 9, 25):
return gen_math_ops.not_equal(self, other, incompatible_shape_error=False)
else:
return gen_math_ops.not_equal(self, other)
return gen_math_ops.not_equal(self, other, incompatible_shape_error=False)
else:
# In legacy graph mode, tensor equality is object equality
return self is not other