def testShapeMismatch(self):
x = ragged.constant([[1, 2, 3], [4, 5]])
y = ragged.constant([[1, 2, 3], [4, 5, 6]])
with self.assertRaisesRegexp(errors.InvalidArgumentError,
'Incompatible shapes'):
with self.cached_session():
ragged.add(x, y).eval()
def testUnknownRankError(self):
x = ragged.constant([[1, 2], [3]])
y = ragged.from_row_splits(
array_ops.placeholder_with_default([1, 2, 3], shape=None), x.row_splits)
with self.assertRaisesRegexp(
ValueError, r'Unable to broadcast: unknown rank'):
ragged.add(x, y)
def testShapeMismatch(self):
x = ragged.constant([[1, 2, 3], [4, 5]])
y = ragged.constant([[1, 2, 3], [4, 5, 6]])
with self.assertRaisesRegexp(errors.InvalidArgumentError,
'Incompatible shapes'):
with self.cached_session():
ragged.add(x, y).eval()
def testUnknownRankError(self):
x = ragged.constant([[1, 2], [3]])
y = ragged.from_row_splits(
array_ops.placeholder_with_default([1, 2, 3], shape=None), x.row_splits)
with self.assertRaisesRegexp(
ValueError, r'Unable to broadcast: unknown rank'):
ragged.add(x, y)
def testUnknownRankError(self):
x = ragged.constant([[1, 2], [3]])
y = ragged.from_row_splits(
array_ops.placeholder_with_default([1, 2, 3], shape=None), x.row_splits)
with self.assertRaisesRegexp(
ValueError, r'Ragged elementwise ops require that rank \(number '
r'of dimensions\) be statically known.'):
ragged.add(x, y)
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]],
expected_output=[2, 4, 6],
),
dict(
fn=string_ops.reduce_join,
elems=[['foo', 'bar', 'baz'], ['a'], ['b', 'c']],
expected_output=[b'foobarbaz', b'a', b'bc'],
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]],
dtype=dtypes.float32,
),
# [d1, (d2)] -> [d1, (d2)]
dict(
fn=lambda x: x+1,
elems=[[1, 2, 3], [4, 5], [6, 7]],
expected_output=[[2, 3, 4], [5, 6], [7, 8]],
dtype=dtypes.int64,
result_dtype=ragged.RaggedTensorType(dtype=dtypes.int64,
ragged_rank=1),
),
# [d1, (d2), d3] -> [d1, (d2), d3]
dict(
fn=lambda x: x+1,
elems=[[[1, 2], [3, 4]], [], [[5, 6], [7, 8], [9, 0]]],
elems_ragged_rank=1,
expected_ragged_rank=1,
result_dtype=ragged.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.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.RaggedTensorType(dtype=dtypes.int64,
ragged_rank=2),
),
# [d1, (d2), (d3)] -> [d1, (d2), (d3)]
dict(
fn=lambda x: ragged.map_inner_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.RaggedTensorType(dtype=dtypes.int64,
ragged_rank=2),
),
# [d1, (d2), (d3)] -> [d1, (d2)]
dict(
fn=lambda x: ragged.reduce_sum(x, axis=1),
elems=[[[1, 2, 3]], [[4, 5], [6, 7]]],
expected_output=[[6], [9, 13]],
result_dtype=ragged.RaggedTensorType(dtype=dtypes.int64,
ragged_rank=1),
),
# [d1, (d2), (d3)] -> [d1, (d3)]
dict(
fn=lambda x: ragged.reduce_sum(x, axis=0),
elems=[[[1, 2, 3]], [[4, 5], [6, 7]]],
expected_output=[[1, 2, 3], [10, 12]],
result_dtype=ragged.RaggedTensorType(dtype=dtypes.int64,
ragged_rank=1),
),
# [d1, (d2), (d3)] -> [d1]
dict(
fn=ragged.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.RaggedTensorType(dtype=dtypes.int64,
ragged_rank=1),
),
# [d1] -> [d1, (d2), (d3)]
dict(
fn=lambda x: ragged.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.RaggedTensorType(dtype=dtypes.int64,
ragged_rank=2),
),
# [d1, (d2), (d3), (d4a), (d5)] -> [d1, (d2), (d3), (d4b), (d5)]
dict(
fn=lambda x: ragged.add(x, 1),
elems=[[[[[1, 2, 3]], [[4], [5]]]], [[[[6, 7]]], [[[8], []]]]],
expected_output=[[[[[2, 3, 4]], [[5], [6]]]],
[[[[7, 8]]], [[[9], []]]]],
result_dtype=ragged.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,
dtype=dtypes.int64,
result_dtype=None,
infer_shape=False,
):
elems = ragged.constant(elems, dtype, elems_ragged_rank)
output = ragged.map_fn(
fn=fn, elems=elems, dtype=result_dtype, infer_shape=infer_shape)
expected_rt = ragged.constant(
expected_output, ragged_rank=expected_ragged_rank)
with self.test_session():
if ragged.is_ragged(expected_output):
self.assertEqual(output.ragged_rank, expected_rt.ragged_rank)
output_values = self.evaluate(output)
self.assertAllEqual(expected_output, output_values.tolist())
def testRaggedMapOnStructure(self):
batman = ragged.constant([[1, 2, 3], [4], [5, 6, 7]])
# [[10, 20, 30], [40], [50, 60, 70]]
robin = ragged.map_inner_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_fn(
fn=_reduce_sum_from_all,
elems=features,
dtype=dtypes.int32,
)
with self.test_session():
self.assertAllEqual(output.eval().tolist(), [66, 44, 198])
# Test mapping over a dict of RTs can produce a dict of RTs.
def testRaggedMapOnStructure_RaggedOutputs(self):
batman = ragged.constant([[1, 2, 3], [4], [5, 6, 7]])
# [[10, 20, 30], [40], [50, 60, 70]]
robin = ragged.map_inner_values(mo.multiply, batman, 10)
features = {'batman': batman, 'robin': robin}
def _increment(f):
return {
'batman': ragged.add(f['batman'], 1),
'robin': ragged.add(f['robin'], 1),
}
output = ragged.map_fn(
fn=_increment,
elems=features,
infer_shape=False,
dtype={
'batman':
ragged.RaggedTensorType(dtype=dtypes.int32, ragged_rank=1),
'robin':
ragged.RaggedTensorType(dtype=dtypes.int32, ragged_rank=1)
},
)
with self.test_session():
self.assertAllEqual(output['batman'].eval().tolist(),
[[2, 3, 4], [5], [6, 7, 8]])
self.assertAllEqual(output['robin'].eval().tolist(),
[[11, 21, 31], [41], [51, 61, 71]])
def testZip(self):
x = ragged.constant([[10, 20], [30, 40], [50, 60], [70], [80, 90, 100]],
dtypes.int64)
y = array_ops.expand_dims(
mo.range(ragged.nrows(x), 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_fn(
_zip, (y, x),
dtype=ragged.RaggedTensorType(dtype=dtypes.int64, ragged_rank=1),
infer_shape=False)
with self.test_session():
result = self.evaluate(output).tolist()
self.assertAllEqual(
result, [[[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.constant([['hello', '.', 'there'], ['merhaba'],
['bonjour', '.', 'ca va', '?']])
indices = ragged.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_fn(
gather,
data,
dtype=ragged.RaggedTensorType(dtype=dtypes.string, ragged_rank=1),
infer_shape=False)
with self.test_session():
self.assertAllEqual(
self.evaluate(out).tolist(),
[[b'hello', b'there'], [b'merhaba'], [b'bonjour', b'ca va']])
def testMismatchRaggedRank(self):
elems = ragged.constant([[[1, 2, 3]], [[4, 5], [6, 7]]])
fn = lambda x: ragged.reduce_sum(x, axis=0)
with self.assertRaisesWithLiteralMatch(
ValueError, r'The declared ragged rank (23) mismatches the result (1)'):
_ = ragged.map_fn(
fn,
elems,
dtype=ragged.RaggedTensorType(dtype=dtypes.int64, ragged_rank=23))
def testMismatchRaggedRank2(self):
elems = ragged.constant([[1, 2, 3], [4, 5], [6, 7]])
fn = lambda x: ragged.from_row_starts(x, [0])
with self.assertRaisesWithLiteralMatch(
ValueError, r'The declared ragged rank (10) mismatches the result (1)'):
_ = ragged.map_fn(
fn,
elems,
dtype=ragged.RaggedTensorType(dtype=dtypes.int64, ragged_rank=10))
def _increment(f):
return {
'batman': ragged.add(f['batman'], 1),
'robin': ragged.add(f['robin'], 1),
}
def testShapeMismatch(self):
x = ragged.constant([[1, 2, 3], [4, 5]])
y = ragged.constant([[1, 2, 3], [4, 5, 6]])
with self.assertRaisesRegexp(errors.InvalidArgumentError,
'Inputs must have identical ragged splits'):
ragged.add(x, y)
def testBroadcastError4(self):
x = ragged.constant([[[1]]])
y = ragged.constant([[1]])
with self.assertRaisesRegexp(
ValueError, 'Ragged elementwise ops do not support broadcasting yet'):
ragged.add(x, y)
def testBroadcastError3(self):
x = ragged.constant([[[1, 2], [3]], [[4, 5], [6]]], ragged_rank=2)
y = ragged.constant([[7, 8], [9]], ragged_rank=1)
with self.assertRaisesRegexp(
ValueError, 'Ragged elementwise ops do not support broadcasting yet'):
ragged.add(x, y)
def testBroadcastError2(self):
x = ragged.constant([[[1, 2], [3, 4]], [[5]]], ragged_rank=2)
y = ragged.constant([[[8], [3]], [[2]]], ragged_rank=1)
with self.assertRaisesRegexp(ValueError,
'Inputs must have identical ragged splits'):
ragged.add(x, y)