def testRaggedMapOnStructure_RaggedOutputs(self):
batman = ragged_factory_ops.constant([[1, 2, 3], [4], [5, 6, 7]])
# [[10, 20, 30], [40], [50, 60, 70]]
robin = ragged_functional_ops.map_flat_values(mo.multiply, batman, 10)
features = {'batman': batman, 'robin': robin}
def _increment(f):
return {
'batman': f['batman'] + 1,
'robin': f['robin'] + 1,
}
output = ragged_map_ops.map_fn(
fn=_increment,
elems=features,
infer_shape=False,
dtype={
'batman':
ragged_tensor.RaggedTensorType(dtype=dtypes.int32,
ragged_rank=1),
'robin':
ragged_tensor.RaggedTensorType(dtype=dtypes.int32,
ragged_rank=1)
},
)
self.assertAllEqual(output['batman'], [[2, 3, 4], [5], [6, 7, 8]])
self.assertAllEqual(output['robin'],
[[11, 21, 31], [41], [51, 61, 71]])
def _ragged_substr(text_input, begin, end):
text_input_flat = None
if ragged_tensor.is_ragged(text_input):
text_input_flat = text_input.flat_values
else:
text_input_flat = text_input
def _ragged_tile(x):
input_text, indices = x
multiple = math_ops.reduce_sum(indices.row_lengths())
return array_ops.tile([input_text], [multiple])
broadcasted_text = ragged_map_ops.map_fn(
_ragged_tile,
(text_input_flat, begin),
dtype=ragged_tensor.RaggedTensorType(dtype=dtypes.string,
ragged_rank=1),
infer_shape=False,
)
size = math_ops.sub(array_ops.squeeze(end.flat_values),
array_ops.squeeze(begin.flat_values))
new_tokens = string_ops.substr_v2(broadcasted_text,
array_ops.squeeze(begin.flat_values),
size)
return begin.with_flat_values(new_tokens.flat_values)
def testMismatchRaggedRank2(self):
elems = ragged_factory_ops.constant([[1, 2, 3], [4, 5], [6, 7]])
fn = lambda x: ragged_tensor.RaggedTensor.from_row_starts(x, [0])
with self.assertRaisesRegex(
ValueError, r'(?s)Expected `fn` to return.*But it returned.*'):
_ = ragged_map_ops.map_fn(fn,
elems,
dtype=ragged_tensor.RaggedTensorType(
dtype=dtypes.int64, ragged_rank=10))
def testMismatchRaggedRank(self):
elems = ragged_factory_ops.constant([[[1, 2, 3]], [[4, 5], [6, 7]]])
fn = lambda x: ragged_math_ops.reduce_sum(x, axis=0)
with self.assertRaisesRegex(
ValueError, r'(?s)Expected `fn` to return.*But it returned.*'):
_ = ragged_map_ops.map_fn(fn,
elems,
dtype=ragged_tensor.RaggedTensorType(
dtype=dtypes.int64, ragged_rank=23))
def testMismatchRaggedRank2(self):
elems = ragged_factory_ops.constant([[1, 2, 3], [4, 5], [6, 7]])
fn = lambda x: ragged_tensor.RaggedTensor.from_row_starts(x, [0])
with self.assertRaisesWithLiteralMatch(
ValueError,
r'The declared ragged rank (10) mismatches the result (2)'):
_ = ragged_map_ops.map_fn(fn,
elems,
dtype=ragged_tensor.RaggedTensorType(
dtype=dtypes.int64, ragged_rank=10))
def testMismatchRaggedRank(self):
elems = ragged_factory_ops.constant([[[1, 2, 3]], [[4, 5], [6, 7]]])
fn = lambda x: ragged_math_ops.reduce_sum(x, axis=0)
with self.assertRaisesWithLiteralMatch(
ValueError,
r'The declared ragged rank (23) mismatches the result (1)'):
_ = ragged_map_ops.map_fn(fn,
elems,
dtype=ragged_tensor.RaggedTensorType(
dtype=dtypes.int64, ragged_rank=23))
def getTokenWord(self, text, token_starts, token_ends):
def _FindSubstr(input_tensor):
text, token_start, token_length = input_tensor
return tf.strings.substr(text, token_start, token_length)
token_lengths = token_ends - token_starts
token_word = ragged_map_ops.map_fn(
_FindSubstr, (text, token_starts, token_lengths),
dtype=ragged_tensor.RaggedTensorType(dtype=tf.string, ragged_rank=1),
infer_shape=False)
return token_word
def testZip(self):
x = ragged_factory_ops.constant(
[[10, 20], [30, 40], [50, 60], [70], [80, 90, 100]], dtypes.int64)
y = array_ops.expand_dims(mo.range(x.nrows(), dtype=dtypes.int64), axis=1)
def _zip(foo):
y_val, x_val = foo
bar = backend.tile(y_val, array_ops.shape(x_val))
return array_ops.stack([bar, x_val], axis=1)
output = ragged_map_ops.map_fn(
_zip, (y, x),
dtype=ragged_tensor.RaggedTensorType(dtype=dtypes.int64, ragged_rank=1),
infer_shape=False)
self.assertRaggedEqual(
output, [[[0, 10], [0, 20]], [[1, 30], [1, 40]], [[2, 50], [2, 60]],
[[3, 70]], [[4, 80], [4, 90], [4, 100]]])
def testBatchGather(self):
tokens = ragged_factory_ops.constant([['hello', '.', 'there'],
['merhaba'],
['bonjour', '.', 'ca va', '?']])
indices = ragged_factory_ops.constant([[0, 2], [0], [0, 2]])
def gather(x):
tokens_val, indices_val = x
return array_ops.gather(tokens_val, indices_val)
data = tokens, indices
out = ragged_map_ops.map_fn(gather,
data,
dtype=ragged_tensor.RaggedTensorType(
dtype=dtypes.string, ragged_rank=1),
infer_shape=False)
self.assertAllEqual(
out, [[b'hello', b'there'], [b'merhaba'], [b'bonjour', b'ca va']])
def get_segments(self, sentences):
"""Extracts the next sentence label from sentences.
Args:
sentences: A `RaggedTensor` of strings w/ shape [batch, (num_sentences)].
Returns:
A tuple of (segment_a, segment_b, is_next_sentence) where:
segment_a: A `Tensor` of strings w/ shape [total_num_sentences] that
contains all the original sentences.
segment_b: A `Tensor` with shape [num_sentences] that contains
either the subsequent sentence of `segment_a` or a randomly injected
sentence.
is_next_sentence: A `Tensor` of bool w/ shape [num_sentences]
that contains whether or not `segment_b` is truly a subsequent sentence
or not.
"""
next_sentence = ragged_map_ops.map_fn(
functools.partial(manip_ops.roll, axis=0, shift=-1),
sentences,
dtype=ragged_tensor.RaggedTensorType(dtypes.string, 1),
infer_shape=False)
random_sentence = sentences.with_flat_values(
self._shuffle_fn(sentences.flat_values))
is_next_sentence_labels = (self._random_fn(sentences.flat_values.shape)
> self._random_next_sentence_threshold)
is_next_sentence = sentences.with_flat_values(is_next_sentence_labels)
# Randomly decide if we should use next sentence or throw in a random
# sentence.
segment_two = ragged_where_op.where(is_next_sentence,
x=next_sentence,
y=random_sentence)
# Get rid of the docs dimensions
sentences = sentences.merge_dims(-2, -1)
segment_two = segment_two.merge_dims(-2, -1)
is_next_sentence = is_next_sentence.merge_dims(-2, -1)
is_next_sentence = math_ops.cast(is_next_sentence, dtypes.int64)
return sentences, segment_two, is_next_sentence
class RaggedMapOpTest(test_util.TensorFlowTestCase, parameterized.TestCase):
@parameterized.parameters([
# The following test sets map over a RaggedTensor and apply a
# transformation that returns with shape:
# [d1, (d2)] -> [d1]
dict(
fn=mo.reduce_mean,
elems=[[1, 2, 3], [4, 5], [6, 7]],
elems_dtype=dtypes.int32,
expected_output=[2, 4, 6],
result_dtype=dtypes.int32,
),
dict(
fn=string_ops.reduce_join,
elems=[['foo', 'bar', 'baz'], ['a'], ['b', 'c']],
expected_output=[b'foobarbaz', b'a', b'bc'],
elems_dtype=dtypes.string,
result_dtype=dtypes.string,
),
# [d1, (d2)] -> [d1, 2]
dict(
fn=lambda x: array_ops.stack([mo.reduce_mean(x),
mo.reduce_sum(x)]),
# fn=self.stack_mean_and_sum,
elems=[[1, 2, 3], [4, 5], [6, 7]],
expected_output=[[2, 6], [4.5, 9], [6.5, 13]],
elems_dtype=dtypes.float32,
result_dtype=dtypes.float32,
expected_ragged_rank=0,
),
# [d1, (d2)] -> [d1, (d2)]
dict(
fn=lambda x: x + np.int64(1),
elems=[[1, 2, 3], [4, 5], [6, 7]],
expected_output=[[2, 3, 4], [5, 6], [7, 8]],
elems_dtype=dtypes.int64,
result_dtype=ragged_tensor.RaggedTensorType(dtype=dtypes.int64,
ragged_rank=1),
),
# [d1, (d2), d3] -> [d1, (d2), d3]
dict(
fn=lambda x: x + np.int64(1),
elems=[[[1, 2], [3, 4]], [], [[5, 6], [7, 8], [9, 0]]],
elems_ragged_rank=1,
expected_ragged_rank=1,
result_dtype=ragged_tensor.RaggedTensorType(dtype=dtypes.int64,
ragged_rank=1),
expected_output=[[[2, 3], [4, 5]], [], [[6, 7], [8, 9], [10, 1]]],
),
# [d1, (d2)] -> [d1, (d2), (d3)]
dict(
fn=lambda x: ragged_tensor.RaggedTensor.from_row_starts(x, [0]),
elems=[[1, 2, 3], [4, 5], [6, 7]],
expected_output=[[[1, 2, 3]], [[4, 5]], [[6, 7]]],
result_dtype=ragged_tensor.RaggedTensorType(dtype=dtypes.int64,
ragged_rank=2),
),
# [d1, (d2), (d3)] -> [d1, (d2), (d3)]
dict(
fn=lambda x: ragged_functional_ops.map_flat_values(mo.add, x, 1),
elems=[[[1, 2, 3]], [[4, 5], [6, 7]]],
expected_output=[[[2, 3, 4]], [[5, 6], [7, 8]]],
result_dtype=ragged_tensor.RaggedTensorType(dtype=dtypes.int64,
ragged_rank=2),
),
# [d1, (d2), (d3)] -> [d1, (d2)]
dict(
fn=lambda x: ragged_math_ops.reduce_sum(x, axis=1),
elems=[[[1, 2, 3]], [[4, 5], [6, 7]]],
expected_output=[[6], [9, 13]],
result_dtype=ragged_tensor.RaggedTensorType(dtype=dtypes.int64,
ragged_rank=1),
),
# [d1, (d2), (d3)] -> [d1, (d3)]
dict(
fn=lambda x: ragged_math_ops.reduce_sum(x, axis=0),
elems=[[[1, 2, 3]], [[4, 5], [6, 7]]],
expected_output=[[1, 2, 3], [10, 12]],
result_dtype=ragged_tensor.RaggedTensorType(dtype=dtypes.int64,
ragged_rank=1),
),
# [d1, (d2), (d3)] -> [d1]
dict(
fn=ragged_math_ops.reduce_sum,
elems=[[[1, 2, 3]], [[4, 5], [6, 7]]],
expected_output=[6, 22],
result_dtype=dtypes.int64,
),
# [d1] -> [d1, (d2)]
dict(
fn=mo.range,
elems=[4, 0, 2],
expected_output=[[0, 1, 2, 3], [], [0, 1]],
result_dtype=ragged_tensor.RaggedTensorType(dtype=dtypes.int64,
ragged_rank=1),
),
# [d1] -> [d1, (d2), (d3)]
dict(
fn=lambda x: ragged_math_ops.range(mo.range(x)),
elems=[5, 0, 3],
expected_output=[[[], [0], [0, 1], [0, 1, 2], [0, 1, 2, 3]], [],
[[], [0], [0, 1]]],
result_dtype=ragged_tensor.RaggedTensorType(dtype=dtypes.int64,
ragged_rank=2),
),
# [d1, (d2), (d3), (d4a), (d5)] -> [d1, (d2), (d3), (d4b), (d5)]
dict(
fn=lambda x: x + np.int64(1),
elems=[[[[[1, 2, 3]], [[4], [5]]]], [[[[6, 7]]], [[[8], []]]]],
expected_output=[[[[[2, 3, 4]], [[5], [6]]]],
[[[[7, 8]]], [[[9], []]]]],
result_dtype=ragged_tensor.RaggedTensorType(dtype=dtypes.int64,
ragged_rank=4),
),
])
def testRaggedMap(
self,
fn,
elems,
expected_output,
expected_ragged_rank=None,
result_ragged_rank=None,
elems_ragged_rank=None,
elems_dtype=dtypes.int64,
result_dtype=None,
infer_shape=True,
):
elems = ragged_factory_ops.constant(elems, elems_dtype,
elems_ragged_rank)
output = ragged_map_ops.map_fn(fn=fn,
elems=elems,
dtype=result_dtype,
infer_shape=infer_shape)
expected_rt = ragged_factory_ops.constant(
expected_output, ragged_rank=expected_ragged_rank)
self.assertAllEqual(expected_rt, output)
def testRaggedMapOnStructure(self):
batman = ragged_factory_ops.constant([[1, 2, 3], [4], [5, 6, 7]])
# [[10, 20, 30], [40], [50, 60, 70]]
robin = ragged_functional_ops.map_flat_values(mo.multiply, batman, 10)
features = {'batman': batman, 'robin': robin}
def _reduce_sum_from_all(f):
return mo.reduce_sum(f['batman']) + mo.reduce_sum(f['robin'])
output = ragged_map_ops.map_fn(
fn=_reduce_sum_from_all,
elems=features,
dtype=dtypes.int32,
)
self.assertAllEqual(output, [66, 44, 198])
# Test mapping over a dict of RTs can produce a dict of RTs.
def testRaggedMapOnStructure_RaggedOutputs(self):
batman = ragged_factory_ops.constant([[1, 2, 3], [4], [5, 6, 7]])
# [[10, 20, 30], [40], [50, 60, 70]]
robin = ragged_functional_ops.map_flat_values(mo.multiply, batman, 10)
features = {'batman': batman, 'robin': robin}
def _increment(f):
return {
'batman': f['batman'] + 1,
'robin': f['robin'] + 1,
}
output = ragged_map_ops.map_fn(
fn=_increment,
elems=features,
infer_shape=False,
dtype={
'batman':
ragged_tensor.RaggedTensorType(dtype=dtypes.int32,
ragged_rank=1),
'robin':
ragged_tensor.RaggedTensorType(dtype=dtypes.int32,
ragged_rank=1)
},
)
self.assertAllEqual(output['batman'], [[2, 3, 4], [5], [6, 7, 8]])
self.assertAllEqual(output['robin'],
[[11, 21, 31], [41], [51, 61, 71]])
def testZip(self):
x = ragged_factory_ops.constant(
[[10, 20], [30, 40], [50, 60], [70], [80, 90, 100]], dtypes.int64)
y = array_ops.expand_dims(mo.range(x.nrows(out_type=dtypes.int64)),
axis=1)
def _zip(foo):
y_val, x_val = foo
bar = array_ops.tile(y_val, array_ops.shape(x_val))
return array_ops.stack([bar, x_val], axis=1)
output = ragged_map_ops.map_fn(_zip, (y, x),
dtype=ragged_tensor.RaggedTensorType(
dtype=dtypes.int64, ragged_rank=1),
infer_shape=False)
self.assertAllEqual(
output,
[[[0, 10], [0, 20]], [[1, 30], [1, 40]], [[2, 50], [2, 60]],
[[3, 70]], [[4, 80], [4, 90], [4, 100]]])
def testBatchGather(self):
tokens = ragged_factory_ops.constant([['hello', '.', 'there'],
['merhaba'],
['bonjour', '.', 'ca va', '?']])
indices = ragged_factory_ops.constant([[0, 2], [0], [0, 2]])
def gather(x):
tokens_val, indices_val = x
return array_ops.gather(tokens_val, indices_val)
data = tokens, indices
out = ragged_map_ops.map_fn(gather,
data,
dtype=ragged_tensor.RaggedTensorType(
dtype=dtypes.string, ragged_rank=1),
infer_shape=False)
self.assertAllEqual(
out, [[b'hello', b'there'], [b'merhaba'], [b'bonjour', b'ca va']])
def testMismatchRaggedRank(self):
elems = ragged_factory_ops.constant([[[1, 2, 3]], [[4, 5], [6, 7]]])
fn = lambda x: ragged_math_ops.reduce_sum(x, axis=0)
with self.assertRaisesRegex(
ValueError, r'(?s)Expected `fn` to return.*But it returned.*'):
_ = ragged_map_ops.map_fn(fn,
elems,
dtype=ragged_tensor.RaggedTensorType(
dtype=dtypes.int64, ragged_rank=23))
def testMismatchRaggedRank2(self):
elems = ragged_factory_ops.constant([[1, 2, 3], [4, 5], [6, 7]])
fn = lambda x: ragged_tensor.RaggedTensor.from_row_starts(x, [0])
with self.assertRaisesRegex(
ValueError, r'(?s)Expected `fn` to return.*But it returned.*'):
_ = ragged_map_ops.map_fn(fn,
elems,
dtype=ragged_tensor.RaggedTensorType(
dtype=dtypes.int64, ragged_rank=10))
def testMapOnSparseTensor(self):
s = sparse_tensor.SparseTensor(
indices=[[0, 0], [0, 1], [1, 0], [1, 1]],
values=[0, 5, 0, 4],
dense_shape=[2, 2],
)
t2 = ragged_tensor.RaggedTensor.from_sparse(s)
id_t2 = ragged_map_ops.map_fn(
lambda x: x,
t2,
)
self.assertAllEqual(id_t2, [[0, 5], [0, 4]])
def testDefaultAttrValues(self):
ragged_map_ops.map_fn(fn=lambda x: x,
elems=ragged_factory_ops.constant([[7]]),
dtype=ragged_tensor.RaggedTensorType(
dtype=dtypes.int32, ragged_rank=1))
