def _to_batched_tensor_list(self, value):
if self._dense_shape.merge_with(
tensor_util.constant_value_as_shape(
value.dense_shape)).ndims == 0:
raise ValueError(
"Unbatching a sparse tensor is only supported for rank >= 1")
return [
sparse_ops.serialize_many_sparse(value, out_type=dtypes.variant)
]
def benchmarkVeryLarge2DFloatSparseTensor(self):
np.random.seed(127)
num_elements = 10000
batch_size = 64
indices_batch = np.random.randint(batch_size,
size=num_elements,
dtype=np.int64)
indices_value = np.arange(num_elements, dtype=np.int64)
indices = np.asarray(sorted(zip(indices_batch, indices_value)),
dtype=np.int64)
values = ["feature_value_for_embedding_lookup"] * num_elements
shape = np.asarray([batch_size, num_elements], dtype=np.int64)
with session.Session(config=benchmark.benchmark_config()) as sess:
with ops.device("/cpu:0"):
indices = variables.Variable(indices)
values = variables.Variable(values)
shape = variables.Variable(shape)
st = sparse_tensor_lib.SparseTensor(indices, values, shape)
st_handles = add_many_sparse_to_tensors_map(st)
st_roundtrip = take_many_sparse_from_tensors_map(
sparse_map_op=st_handles.op, sparse_handles=st_handles)
st_roundtrip_op = st_roundtrip.values.op
st_serialized = sparse_ops.serialize_many_sparse(st)
st_deserialized = sparse_ops.deserialize_many_sparse(
st_serialized, dtype=values.dtype)
st_deserialized_op = st_deserialized.values.op
variables.global_variables_initializer().run()
st_roundtrip_values = self.evaluate(st_roundtrip)
st_deserialized_values = self.evaluate(st_deserialized)
np.testing.assert_equal(st_roundtrip_values.values,
st_deserialized_values.values)
np.testing.assert_equal(st_roundtrip_values.indices,
st_deserialized_values.indices)
np.testing.assert_equal(st_roundtrip_values.dense_shape,
st_deserialized_values.dense_shape)
self.run_op_benchmark(
sess,
st_roundtrip_op,
min_iters=2000,
name="benchmark_very_large_2d_float_st_tensor_maps")
self.run_op_benchmark(
sess,
st_deserialized_op,
min_iters=2000,
name="benchmark_very_large_2d_float_st_serialization")
def benchmarkVeryLarge2DFloatSparseTensor(self):
np.random.seed(127)
num_elements = 10000
batch_size = 64
indices_batch = np.random.randint(
batch_size, size=num_elements, dtype=np.int64)
indices_value = np.arange(num_elements, dtype=np.int64)
indices = np.asarray(
sorted(zip(indices_batch, indices_value)), dtype=np.int64)
values = ["feature_value_for_embedding_lookup"] * num_elements
shape = np.asarray([batch_size, num_elements], dtype=np.int64)
with session.Session(config=benchmark.benchmark_config()) as sess:
with ops.device("/cpu:0"):
indices = variables.Variable(indices)
values = variables.Variable(values)
shape = variables.Variable(shape)
st = sparse_tensor_lib.SparseTensor(indices, values, shape)
st_handles = add_many_sparse_to_tensors_map(st)
st_roundtrip = take_many_sparse_from_tensors_map(
sparse_map_op=st_handles.op, sparse_handles=st_handles)
st_roundtrip_op = st_roundtrip.values.op
st_serialized = sparse_ops.serialize_many_sparse(st)
st_deserialized = sparse_ops.deserialize_many_sparse(
st_serialized, dtype=values.dtype)
st_deserialized_op = st_deserialized.values.op
variables.global_variables_initializer().run()
st_roundtrip_values = sess.run(st_roundtrip)
st_deserialized_values = sess.run(st_deserialized)
np.testing.assert_equal(st_roundtrip_values.values,
st_deserialized_values.values)
np.testing.assert_equal(st_roundtrip_values.indices,
st_deserialized_values.indices)
np.testing.assert_equal(st_roundtrip_values.dense_shape,
st_deserialized_values.dense_shape)
self.run_op_benchmark(
sess,
st_roundtrip_op,
min_iters=2000,
name="benchmark_very_large_2d_float_st_tensor_maps")
self.run_op_benchmark(
sess,
st_deserialized_op,
min_iters=2000,
name="benchmark_very_large_2d_float_st_serialization")
def serialize_many_sparse_tensors(tensors):
"""Serializes many sparse tensors into a batch.
Args:
tensors: a tensor structure to serialize.
Returns:
`tensors` with any sparse tensors replaced by the serialized batch.
"""
ret = nest.pack_sequence_as(tensors, [
sparse_ops.serialize_many_sparse(tensor, out_type=dtypes.variant)
if sparse_tensor.is_sparse(tensor) else tensor
for tensor in nest.flatten(tensors)
])
return ret
def serialize_many_sparse_tensors(tensors):
"""Serializes many sparse tensors into a batch.
Args:
tensors: a tensor structure to serialize.
Returns:
`tensors` with any sparse tensors replaced by the serialized batch.
"""
ret = nest.pack_sequence_as(tensors, [
sparse_ops.serialize_many_sparse(tensor, out_type=dtypes.variant)
if sparse_tensor.is_sparse(tensor) else tensor
for tensor in nest.flatten(tensors)
])
return ret
def testSerializeManyDeserializeManyRoundTrip(self):
with self.test_session(use_gpu=False) as sess:
# N == 4 because shape_value == [4, 5]
indices_value = np.array([[0, 0], [0, 1], [2, 0]], dtype=np.int64)
values_value = np.array([b"a", b"b", b"c"])
shape_value = np.array([4, 5], dtype=np.int64)
sparse_tensor = self._SparseTensorPlaceholder(dtype=dtypes.string)
serialized = sparse_ops.serialize_many_sparse(sparse_tensor)
deserialized = sparse_ops.deserialize_many_sparse(
serialized, dtype=dtypes.string)
serialized_value, deserialized_value = sess.run(
[serialized, deserialized],
feed_dict={
sparse_tensor.indices: indices_value,
sparse_tensor.values: values_value,
sparse_tensor.dense_shape: shape_value
})
self.assertEqual(serialized_value.shape, (4, 3))
self.assertAllEqual(deserialized_value.indices, indices_value)
self.assertAllEqual(deserialized_value.values, values_value)
self.assertAllEqual(deserialized_value.dense_shape, shape_value)
def testSerializeManyDeserializeManyRoundTrip(self):
with self.test_session(use_gpu=False) as sess:
# N == 4 because shape_value == [4, 5]
indices_value = np.array([[0, 0], [0, 1], [2, 0]], dtype=np.int64)
values_value = np.array([b"a", b"b", b"c"])
shape_value = np.array([4, 5], dtype=np.int64)
sparse_tensor = self._SparseTensorPlaceholder(dtype=dtypes.string)
serialized = sparse_ops.serialize_many_sparse(sparse_tensor)
deserialized = sparse_ops.deserialize_many_sparse(
serialized, dtype=dtypes.string)
serialized_value, deserialized_value = sess.run(
[serialized, deserialized],
feed_dict={
sparse_tensor.indices: indices_value,
sparse_tensor.values: values_value,
sparse_tensor.dense_shape: shape_value
})
self.assertEqual(serialized_value.shape, (4, 3))
self.assertAllEqual(deserialized_value.indices, indices_value)
self.assertAllEqual(deserialized_value.values, values_value)
self.assertAllEqual(deserialized_value.dense_shape, shape_value)
def _maybe_serialize(t):
if not isinstance(t, ops.SparseTensor):
return t
return (sparse_ops.serialize_many_sparse(t) if enqueue_many
else sparse_ops.serialize_sparse(t))
def _maybe_serialize(t):
if not isinstance(t, ops.SparseTensor):
return t
return (sparse_ops.serialize_many_sparse(t)
if enqueue_many else sparse_ops.serialize_sparse(t))
def _maybe_serialize(t, is_sparse):
if not is_sparse:
return t
return (sparse_ops.serialize_many_sparse(t) if enqueue_many
else sparse_ops.serialize_sparse(t))
def _maybe_serialize(t, is_sparse):
if not is_sparse:
return t
return (sparse_ops.serialize_many_sparse(t)
if enqueue_many else sparse_ops.serialize_sparse(t))
def _to_batched_tensor_list(self, value):
if self._dense_shape.merge_with(
tensor_util.constant_value_as_shape(value.dense_shape)).ndims == 0:
raise ValueError(
"Unbatching a sparse tensor is only supported for rank >= 1")
return [sparse_ops.serialize_many_sparse(value, out_type=dtypes.variant)]
