Fixing error messages for check_ops.py and control_flow_state.py
PiperOrigin-RevId: 396064202
Change-Id: Iac225d4427836134fbebea86a1df45e28a41aa44
diff --git a/tensorflow/python/kernel_tests/check_ops_test.py b/tensorflow/python/kernel_tests/check_ops_test.py
index 435da22..17ea8c2 100644
--- a/tensorflow/python/kernel_tests/check_ops_test.py
+++ b/tensorflow/python/kernel_tests/check_ops_test.py
@@ -135,7 +135,7 @@
@test_util.run_in_graph_and_eager_modes
def test_non_iterable_object_raises(self):
non_iterable = 1234
- with self.assertRaisesRegex(TypeError, "to be iterable"):
+ with self.assertRaisesRegex(TypeError, "to be an iterable"):
check_ops.assert_proper_iterable(non_iterable)
@test_util.run_in_graph_and_eager_modes
@@ -1177,13 +1177,13 @@
desired_rank = 2
with ops.control_dependencies(
[check_ops.assert_rank(tensor, desired_rank)]):
- with self.assertRaisesOpError("my_tensor.*rank"):
+ with self.assertRaisesOpError("my_tensor.* must have rank"):
array_ops.identity(tensor).eval(feed_dict={tensor: [1, 2]})
@test_util.run_in_graph_and_eager_modes
def test_raises_if_rank_is_not_scalar_static(self):
tensor = constant_op.constant([1, 2], name="my_tensor")
- with self.assertRaisesRegex(ValueError, "Rank must be a scalar"):
+ with self.assertRaisesRegex(ValueError, "Argument `rank` must be a scalar"):
check_ops.assert_rank(tensor, np.array([], dtype=np.int32))
@test_util.run_deprecated_v1
@@ -1192,7 +1192,7 @@
tensor = constant_op.constant(
[1, 2], dtype=dtypes.float32, name="my_tensor")
rank_tensor = array_ops.placeholder(dtypes.int32, name="rank_tensor")
- with self.assertRaisesOpError("Rank must be a scalar"):
+ with self.assertRaisesOpError("Rank must be a scalar."):
with ops.control_dependencies(
[check_ops.assert_rank(tensor, rank_tensor)]):
array_ops.identity(tensor).eval(feed_dict={rank_tensor: [1, 2]})
@@ -1221,7 +1221,7 @@
@test_util.run_in_graph_and_eager_modes
def test_rank_zero_tensor_raises_if_rank_mismatch_static_rank(self):
tensor_rank0 = constant_op.constant(42, name="my_tensor")
- with self.assertRaisesRegex(ValueError, "fail.*must have rank.*in.*1.*2"):
+ with self.assertRaisesRegex(ValueError, "fail.*must have rank.*1.*2"):
with ops.control_dependencies([
check_ops.assert_rank_in(tensor_rank0, (1, 2), message="fail")]):
self.evaluate(array_ops.identity(tensor_rank0))
@@ -1296,7 +1296,8 @@
desired_ranks = (
np.array(1, dtype=np.int32),
np.array((2, 1), dtype=np.int32))
- with self.assertRaisesRegex(ValueError, "Rank must be a scalar"):
+ with self.assertRaisesRegex(
+ ValueError, "Argument `ranks` must contain scalar tensors."):
check_ops.assert_rank_in(tensor, desired_ranks)
@test_util.run_deprecated_v1
@@ -1550,15 +1551,15 @@
constant_op.constant([[111], [232]], dtypes.int64),
constant_op.constant([23.4, -43.2], dtypes.float16),
constant_op.constant([500], dtypes.int64))
- with self.assertRaisesRegexp(TypeError, "must be of type.*float32"):
+ with self.assertRaisesRegex(TypeError, "must be of type.*float32"):
check_ops.assert_type(sparse_float16, dtypes.float32)
def test_raise_when_tf_type_is_not_dtype(self):
# Test case for GitHub issue:
# https://github.com/tensorflow/tensorflow/issues/45975
value = constant_op.constant(0.0)
- with self.assertRaisesRegexp(TypeError,
- "Cannot convert.*to a TensorFlow DType"):
+ with self.assertRaisesRegex(TypeError,
+ "Cannot convert.*to a TensorFlow DType"):
check_ops.assert_type(value, (dtypes.float32,))
@@ -1572,8 +1573,8 @@
(x, ("N", "Q")),
(y, ("N", "D")),
]
- regex = (r"Specified by tensor .* dimension 0. "
- r"Tensor .* dimension 0 must have size 3. "
+ regex = (r"Specified by tensor .* dimension 0. "
+ r"Tensor .* dimension 0 must have size 3. "
r"Received size 2")
self.raises_static_error(shapes=shapes, regex=regex)
@@ -1598,8 +1599,8 @@
(x, (3, "Q")),
(y, (3, "D")),
]
- regex = (r"Specified explicitly. "
- r"Tensor .* dimension 0 must have size 3. "
+ regex = (r"Specified explicitly. "
+ r"Tensor .* dimension 0 must have size 3. "
r"Received size 2")
self.raises_static_error(shapes=shapes, regex=regex)
@@ -1622,8 +1623,8 @@
shapes = [
(x, ()),
]
- regex = (r"Specified explicitly. "
- r"Tensor .* dimension 0 must have size 1. "
+ regex = (r"Specified explicitly. "
+ r"Tensor .* dimension 0 must have size 1. "
r"Received size 2")
self.raises_static_error(shapes=shapes, regex=regex)
@@ -1633,8 +1634,8 @@
shapes = [
(x, (2,)),
]
- regex = (r"Specified explicitly. "
- r"Tensor .* dimension 0 must have size 2. "
+ regex = (r"Specified explicitly. "
+ r"Tensor .* dimension 0 must have size 2. "
r"Received size 1")
self.raises_static_error(shapes=shapes, regex=regex)
@@ -1643,8 +1644,8 @@
scalar = array_ops.constant(5, name="rank_zero")
x = array_ops.ones([2, 2], name="x")
shapes = [(scalar, ("a",)), (x, ("a", 2))]
- regex = (r"Specified by tensor .* dimension 0. "
- r"Tensor .* dimension 0 must have size 1. "
+ regex = (r"Specified by tensor .* dimension 0. "
+ r"Tensor .* dimension 0 must have size 1. "
r"Received size 2")
self.raises_static_error(shapes=shapes, regex=regex)
@@ -1652,9 +1653,9 @@
def test_raise_not_iterable(self):
x = array_ops.constant([1, 2], name="x")
shapes = [(x, 2)]
- regex = (r"Tensor .*. "
- r"Specified shape must be an iterable. "
- r"An iterable has the attribute `__iter__` or `__getitem__`. "
+ regex = (r"Tensor .*. "
+ r"Specified shape must be an iterable. "
+ r"An iterable has the attribute `__iter__` or `__getitem__`. "
r"Received specified shape: 2")
self.raises_static_error(shapes=shapes, regex=regex)
@@ -1696,7 +1697,7 @@
def raises_static_rank_error(shapes, x, correct_rank, actual_rank):
for shape in shapes:
- regex = (r"Tensor .* must have rank %d. Received rank %d" %
+ regex = (r"Tensor .* must have rank %d. Received rank %d" %
(correct_rank, actual_rank))
self.raises_static_error(shapes=[(x, shape)], regex=regex)
@@ -1787,8 +1788,8 @@
for shapes in styles:
self.raises_static_error(
shapes=shapes,
- regex=(r"Specified by tensor .* dimension 0. "
- "Tensor .* dimension 0 must have size 2. "
+ regex=(r"Specified by tensor .* dimension 0. "
+ "Tensor .* dimension 0 must have size 2. "
"Received size 1"))
@test_util.run_in_graph_and_eager_modes
@@ -1814,8 +1815,8 @@
for shapes in styles:
self.raises_static_error(
shapes=shapes,
- regex=(r"Specified explicitly. "
- "Tensor .* dimension 0 must have size 2. "
+ regex=(r"Specified explicitly. "
+ "Tensor .* dimension 0 must have size 2. "
"Received size 1"))
@test_util.run_in_graph_and_eager_modes
@@ -1850,8 +1851,8 @@
(x, "3Q"),
(y, "*3D"),
]
- regex = (r"Specified explicitly. "
- r"Tensor .* dimension -2 must have size 3. "
+ regex = (r"Specified explicitly. "
+ r"Tensor .* dimension -2 must have size 3. "
r"Received size 2")
self.raises_static_error(shapes=s1, regex=regex)
self.raises_static_error(shapes=s2, regex=regex)
@@ -1881,7 +1882,7 @@
s2 = [
(x, "N*Q"),
]
- regex = (r"Tensor .* specified shape index .*. "
+ regex = (r"Tensor .* specified shape index .*. "
r"Symbol `...` or `\*` for a variable number of "
r"unspecified dimensions is only allowed as the first entry")
self.raises_static_error(shapes=s1, regex=regex)
@@ -1949,7 +1950,7 @@
constant_op.constant([[111], [232]], dtypes.int64),
constant_op.constant([23.4, -43.2], dtypes.float32),
constant_op.constant([500], dtypes.int64))
- with self.assertRaisesRegexp(ValueError, r"dimension 0 must have size 499"):
+ with self.assertRaisesRegex(ValueError, r"dimension 0 must have size 499"):
assertion = check_ops.assert_shapes([(sparse_float, [499])])
with ops.control_dependencies([assertion]):
out = array_ops.identity(sparse_float)
@@ -1983,7 +1984,7 @@
constant_op.constant([[5, 6], [7, 8]], dtypes.int64),
constant_op.constant([23, -43], dtypes.int32),
constant_op.constant([30, 40], dtypes.int64))
- with self.assertRaisesRegexp(ValueError, r"dimension 1 must have size 41"):
+ with self.assertRaisesRegex(ValueError, r"dimension 1 must have size 41"):
assertion = check_ops.assert_shapes([(sparse_int, [None, 41])])
with ops.control_dependencies([assertion]):
out = array_ops.identity(sparse_int)
@@ -2008,7 +2009,7 @@
constant_op.constant([[5, 6], [7, 8]], dtypes.int64),
constant_op.constant([23, -43], dtypes.int32),
constant_op.constant([30, 40], dtypes.int64))
- with self.assertRaisesRegexp(ValueError, r"dimension 1 must have size 30"):
+ with self.assertRaisesRegex(ValueError, r"dimension 1 must have size 30"):
assertion = check_ops.assert_shapes([(sparse_int, ["D", "D"])])
with ops.control_dependencies([assertion]):
out = array_ops.identity(sparse_int)
@@ -2040,7 +2041,7 @@
constant_op.constant([[5, 6], [7, 8]], dtypes.int64),
constant_op.constant([23, -43], dtypes.int32),
constant_op.constant([30, 40], dtypes.int64))
- with self.assertRaisesRegexp(ValueError, r"dimension 1 must have size 30"):
+ with self.assertRaisesRegex(ValueError, r"dimension 1 must have size 30"):
assertion = check_ops.assert_shapes([(sparse_scalar, []),
(sparse_2d, ["N", "N"])])
with ops.control_dependencies([assertion]):
@@ -2067,7 +2068,7 @@
constant_op.constant([[5, 6], [7, 8]], dtypes.int64),
constant_op.constant([23, -43], dtypes.int32),
constant_op.constant([30, 40], dtypes.int64))
- with self.assertRaisesRegexp(ValueError, r"dimension 1 must have size 30"):
+ with self.assertRaisesRegex(ValueError, r"dimension 1 must have size 30"):
assertion = check_ops.assert_shapes([(dense_scalar, []),
(sparse_2d, ["N", "N"])])
with ops.control_dependencies([assertion]):
diff --git a/tensorflow/python/ops/check_ops.py b/tensorflow/python/ops/check_ops.py
index aa36eb7..10a72b8 100644
--- a/tensorflow/python/ops/check_ops.py
+++ b/tensorflow/python/ops/check_ops.py
@@ -453,12 +453,12 @@
)
if isinstance(values, unintentional_iterables):
raise TypeError(
- 'Expected argument "values" to be a "proper" iterable. Found: %s' %
- type(values))
+ 'Expected argument `values` to be a "proper" iterable. Found: '
+ f'{type(values)}')
if not hasattr(values, '__iter__'):
raise TypeError(
- 'Expected argument "values" to be iterable. Found: %s' % type(values))
+ f'Expected argument `values` to be an iterable. Found: {type(values)}')
@tf_export('debugging.assert_negative', v1=[])
@@ -1123,7 +1123,8 @@
rank_static = tensor_util.constant_value(rank)
if rank_static is not None:
if rank_static.ndim != 0:
- raise ValueError('Rank must be a scalar.')
+ raise ValueError('Argument `rank` must be a scalar tensor. Received '
+ f'tensor {rank} of rank: {rank_static}')
x_rank_static = x.get_shape().ndims
if x_rank_static is not None:
@@ -1137,7 +1138,7 @@
# Add the condition that `rank` must have rank zero. Prevents the bug where
# someone does assert_rank(x, [n]), rather than assert_rank(x, n).
if rank_static is None:
- this_data = ['Rank must be a scalar. Received rank: ', rank]
+ this_data = [f'Rank must be a scalar. Received rank: {rank}']
rank_check = assert_rank(rank, 0, data=this_data)
condition = control_flow_ops.with_dependencies([rank_check], condition)
@@ -1234,8 +1235,8 @@
except ValueError as e:
if e.args[0] == 'Static rank condition failed':
raise ValueError(
- '%sTensor %s must have rank %d. Received rank %d, shape %s' %
- (message, name, e.args[2], e.args[1], x.get_shape()))
+ f'{message}Tensor x = {name} must have rank {e.args[2]}. Received '
+ f'rank {e.args[1]}, shape {x.get_shape()}')
else:
raise
@@ -1335,8 +1336,8 @@
except ValueError as e:
if e.args[0] == 'Static rank condition failed':
raise ValueError(
- '%sTensor %s must have rank at least %d. Received rank %d, '
- 'shape %s' % (message, name, e.args[2], e.args[1], x.get_shape()))
+ f'{message}Tensor x = {name} must have rank at least {e.args[2]}. '
+ f'Received rank {e.args[1]}, shape {x.get_shape()}')
else:
raise
@@ -1387,7 +1388,8 @@
if not any(r is None for r in ranks_static):
for rank_static in ranks_static:
if rank_static.ndim != 0:
- raise ValueError('Rank must be a scalar.')
+ raise ValueError('Argument `ranks` must contain scalar tensors. '
+ f'Received tensor of rank: {rank_static}')
x_rank_static = x.get_shape().ndims
if x_rank_static is not None:
@@ -1402,7 +1404,7 @@
# someone does assert_rank(x, [n]), rather than assert_rank(x, n).
for rank, rank_static in zip(ranks, ranks_static):
if rank_static is None:
- this_data = ['Rank must be a scalar. Received rank: ', rank]
+ this_data = [f'Rank must be a scalar. Received rank: {rank}']
rank_check = assert_rank(rank, 0, data=this_data)
condition = control_flow_ops.with_dependencies([rank_check], condition)
@@ -1499,8 +1501,8 @@
except ValueError as e:
if e.args[0] == 'Static rank condition failed':
raise ValueError(
- '%sTensor %s must have rank in %s. Received rank %d, '
- 'shape %s' % (message, name, e.args[2], e.args[1], x.get_shape()))
+ f'{message}Tensor x = {name} must have rank {e.args[2]}. Received '
+ f'rank {e.args[1]}, shape {x.get_shape()}')
else:
raise
@@ -1560,8 +1562,8 @@
else:
name = x.name
err_msg = (
- '%sExpected "x" to be integer type. Found: %s of dtype %s'
- % (_message_prefix(message), name, x.dtype))
+ f'{_message_prefix(message)}Expected argument `x` to be integer type.'
+ f' Found: {name} of dtype {x.dtype}')
raise TypeError(err_msg)
return control_flow_ops.no_op('statically_determined_was_integer')
@@ -1627,13 +1629,13 @@
tensor = ops.convert_to_tensor(tensor, name='tensor')
if tensor.dtype != tf_type:
if context.executing_eagerly():
- raise TypeError('%s tensor must be of type %s' % (message, tf_type))
+ raise TypeError(f'{message} Expected type of argument `tensor` must be '
+ f'of type {tf_type}, obtained {tensor.dtype}')
else:
+ tensor_name = tensor.name if hasattr(tensor, 'name') else ''
raise TypeError(
- '%s%s must be of type %s' %
- (_message_prefix(message),
- tensor.name if hasattr(tensor, 'name') else '',
- tf_type))
+ f'{_message_prefix(message)} Expected type of argument `tensor`, '
+ f'{tensor_name} must be of type {tf_type}, obtained {tensor.dtype}')
return control_flow_ops.no_op('statically_determined_correct_type')
@@ -1880,11 +1882,9 @@
)
if not is_iterable:
raise ValueError(
- '%s'
- 'Tensor %s. Specified shape must be an iterable. '
- 'An iterable has the attribute `__iter__` or `__getitem__`. '
- 'Received specified shape: %s' %
- (message_prefix, tensor_name(tensor), symbolic_shape))
+ f'{message_prefix} Tensor {tensor_name(tensor)}. Specified shape '
+ 'must be an iterable. An iterable has the attribute `__iter__` or '
+ f'`__getitem__`. Received specified shape: {symbolic_shape}')
# We convert this into a tuple to handle strings, lists and numpy arrays
symbolic_shape_tuple = tuple(symbolic_shape)
@@ -1896,11 +1896,10 @@
if i != 0:
raise ValueError(
- '%s'
- 'Tensor %s specified shape index %d. '
- 'Symbol `...` or `*` for a variable number of '
- 'unspecified dimensions is only allowed as the first entry' %
- (message_prefix, tensor_name(tensor), i))
+ f'{message_prefix} Tensor {tensor_name(tensor)} specified shape '
+ f'index {i}. Symbol `...` or `*` for a variable number of '
+ 'unspecified dimensions is only allowed as the first entry of '
+ f'argument `shapes` (={shapes})')
tensors_specified_innermost = True
@@ -1984,11 +1983,10 @@
if _has_known_value(actual_size) and _has_known_value(specified_size):
if int(actual_size) != int(specified_size):
raise ValueError(
- '%s%s. Tensor %s dimension %s must have size %d. '
- 'Received size %d, shape %s' %
- (message_prefix, size_check_message, tensor_name(sizes.x),
- tensor_dim, specified_size, actual_size,
- sizes.x.get_shape()))
+ f'{message_prefix}{size_check_message}. Tensor '
+ f'{tensor_name(sizes.x)} dimension {tensor_dim} must have '
+ f'size {specified_size}. Received size {actual_size}, '
+ f'shape {sizes.x.get_shape()}')
# No dynamic assertion needed
continue
@@ -2024,7 +2022,8 @@
"""Gets the difference x[1:] - x[:-1]."""
x = array_ops.reshape(x, [-1])
if not is_numeric_tensor(x):
- raise TypeError('Expected x to be numeric, instead found: %s' % x)
+ raise TypeError(f'Expected argument `x` to be numeric, instead found: {x} '
+ f'with dtype {x.dtype}')
# If x has less than 2 elements, there is nothing to compare. So return [].
is_shorter_than_two = math_ops.less(array_ops.size(x), 2)
@@ -2186,10 +2185,11 @@
expected_type = item_type
original_item_str = item.name if hasattr(item, 'name') else str(item)
elif expected_type != item_type:
- raise ValueError('%s, type=%s, must be of the same type (%s)%s.' % (
- item.name if hasattr(item, 'name') else str(item),
- item_type, expected_type,
- (' as %s' % original_item_str) if original_item_str else ''))
+ item_name = item.name if hasattr(item, 'name') else str(item)
+ item_str = (' as %s' % original_item_str if original_item_str else '')
+ raise ValueError(
+ f'{item_name}, type={item_type}, must be of the same type '
+ f'({expected_type}){item_str}.')
return expected_type # Should be unreachable
else:
return expected_type
@@ -2226,7 +2226,8 @@
if not dtype:
dtype = dtypes.float32
elif not dtype.is_floating:
- raise ValueError('Expected floating point type, got %s.' % dtype)
+ raise ValueError('Expected floating point type for argument `tensors`, '
+ f'got {dtype}')
return dtype
@@ -2281,11 +2282,11 @@
message = _message_prefix(message)
if shape.ndims != 0:
if context.executing_eagerly():
- raise ValueError('%sExpected scalar shape, saw shape: %s.'
- % (message, shape,))
+ raise ValueError(f'{message}Expected scalar shape for argument `tensor`'
+ f', saw shape: {shape}.')
else:
- raise ValueError('%sExpected scalar shape for %s, saw shape: %s.'
- % (message, tensor.name, shape))
+ raise ValueError(f'{message}Expected scalar shape for argument `tensor`'
+ f' {tensor.name}, saw shape: {shape}.')
return tensor
diff --git a/tensorflow/python/ops/control_flow_state.py b/tensorflow/python/ops/control_flow_state.py
index 5f0838a..fea80be 100644
--- a/tensorflow/python/ops/control_flow_state.py
+++ b/tensorflow/python/ops/control_flow_state.py
@@ -69,9 +69,9 @@
max_iter = while_ctxt.maximum_iterations
if max_iter is None:
raise ValueError(
- "Cannot create a gradient accumulator for tensor '%s' inside "
- "XLA while_loop because maximum_iterations was not passed to "
- "the tf.while_loop call ('%s')." % (value_name, while_ctxt.name))
+ f"Cannot create a gradient accumulator for tensor '{value_name}' "
+ "inside XLA while_loop because maximum_iterations was not passed to "
+ f"the tf.while_loop call ('{while_ctxt.name}').")
# pylint: disable=protected-access
max_iter_ctxt = max_iter.op._get_control_flow_context()
@@ -88,13 +88,13 @@
const_max_iter = tensor_util.constant_value(max_iter)
if const_max_iter is None:
raise ValueError(
- "Cannot create a gradient accumulator for tensor '%s' inside XLA "
- "while_loop. maximum_iterations tensor '%s' for while_loop context "
- "'%s' must be statically known (e.g. a constant value or known "
+ f"Cannot create a gradient accumulator for tensor '{value_name}' "
+ "inside XLA while_loop. maximum_iterations tensor "
+ f"'{max_iter.name}' for while_loop context '{while_ctxt.name}' "
+ "must be statically known (e.g. a constant value or known "
"shape dimension), or be defined at or outside the while loop "
- "context '%s' (currently defined in '%s')." %
- (value_name, max_iter.name, while_ctxt.name, curr_ctxt_name,
- max_iter_ctxt.name))
+ f"context '{curr_ctxt_name}' (currently defined in "
+ f"'{max_iter_ctxt.name}').")
max_size *= const_max_iter
# Find the next outer WhileContext (or stop if we reach the
@@ -156,8 +156,10 @@
outer_forward_ctxt = outer_grad_state.forward_context
else:
if not hasattr(forward_ctxt, "outer_context"):
- raise ValueError("Failed to call gradients on a while loop without"
- "properly serializing graph via MetaGraphDef")
+ raise ValueError("Expected `outer_context` attribute on argument "
+ "`forward_ctxt`. Failed to call gradients on a while "
+ "loop without properly serializing graph via "
+ "MetaGraphDef.")
outer_forward_ctxt = forward_ctxt.outer_context
# Add the forward loop counter.
@@ -364,7 +366,9 @@
else:
# value is in a cond context within the forward context.
if not isinstance(value_ctxt, control_flow_ops.CondContext):
- raise TypeError("value_ctxt is not a CondContext: %s" % value_ctxt)
+ raise TypeError(
+ "OutputContext for `value.op` is not a CondContext: "
+ f"{type(value_ctxt)}")
if dead_branch:
# The special case for creating a zero tensor for a dead
# branch of a switch. See _ControlFlowState.ZerosLikeV1WhileLoop().
