def testCreateSlotWithCustomReplicatedXlaSharding(self):
# slot_creator is used only in optimizer V1.
# We insert our own custom replicated XLA sharding that overrides the SPMD
# sharding copied over by the slot_creator.
with ops.Graph().as_default(), self.cached_session():
v = variables.Variable([1.0, 2.5], name="var")
v = xla_sharding.mesh_split(v,
np.array([0, 1]), [0],
use_sharding_op=False)
with ops.control_dependencies(None):
slot = slot_creator.create_zeros_slot(v,
name="slot",
dtype=dtypes.float64,
copy_xla_sharding=True)
slot = xla_sharding.replicate(slot, use_sharding_op=False)
self.assertNotEqual(xla_sharding.get_tensor_sharding(v),
xla_sharding.get_tensor_sharding(slot))
slot_sharding = xla_sharding.get_tensor_sharding(slot)
slot_proto = xla_data_pb2.OpSharding()
slot_proto.ParseFromString(slot_sharding)
self.assertEqual(
slot_proto,
xla_data_pb2.OpSharding(
type=xla_data_pb2.OpSharding.REPLICATED))
def ApplyToVariable(self, variable: tf.Variable) -> tf.Variable:
if self.is_replicated:
return xla_sharding.replicate(variable, use_sharding_op=False)
return xla_sharding.mesh_split(variable,
self.device_mesh,
self.split_dims_mapping,
use_sharding_op=False)
def replicate_helper(tensor):
replicated_tensor = xla_sharding.replicate(
array_ops.ones([4, 5, 6], dtype=dtypes.float32))
self.assertIsNone(xla_sharding.get_tensor_sharding(tensor))
replicated_sharding = xla_sharding.get_tensor_sharding(replicated_tensor)
self.assertIsNotNone(replicated_sharding)
self.assertIsNone(
xla_sharding.get_sharding_tile_shape(replicated_sharding))
return replicated_tensor
def ApplyToTensor(self,
tensor: tf.Tensor,
use_sharding_op: bool = True) -> tf.Tensor:
if self.is_replicated:
return xla_sharding.replicate(tensor,
use_sharding_op=use_sharding_op)
return xla_sharding.mesh_split(tensor,
self.device_mesh,
self.split_dims_mapping,
use_sharding_op=use_sharding_op)
def handle(self):
if save_context.in_save_context() or context.executing_eagerly():
return self._vars[0].handle
if tpu_util.enclosing_tpu_context() is None:
raise NotImplementedError('TPUReplicatedVariable.handle is not available '
'outside tpu context or save context')
else:
with tpu_util.outside_or_skip_tpu_context():
return xla_sharding.replicate(
tpu_partition_ops.tpu_partitioned_input(
[v.handle for v in self._vars], partition_dim=-1))
def _tag_sharding_attribute_for_dequeued_tensor(tensor, dims):
"""Tags appropriate XLA sharding attribute to the dequeued tensor.
Args:
tensor: The dequeued tensor on TPU.
dims: A list of integer describes how the tensor is partitioned.
Returns:
The same tensor with the xla_sharding attribute.
"""
if dims is None:
return xla_sharding.replicate(tensor)
elif np.prod(dims) == 1:
return xla_sharding.assign_device(tensor, 0)
else:
tile_assignment = np.arange(np.prod(dims)).reshape(dims)
return xla_sharding.tile(tensor=tensor, tile_assignment=tile_assignment)
def _tag_sharding_attribute_for_dequeued_tensor(tensor, dims):
"""Tags appropriate XLA sharding attribute to the dequeued tensor.
Args:
tensor: The dequeued tensor on TPU.
dims: A list of integer describes how the tensor is partitioned.
Returns:
The same tensor with the xla_sharding attribute.
"""
if dims is None:
return xla_sharding.replicate(tensor)
elif np.prod(dims) == 1:
return xla_sharding.assign_device(tensor, 0)
else:
tile_assignment = np.arange(np.prod(dims)).reshape(dims)
return xla_sharding.tile(tensor=tensor, tile_assignment=tile_assignment)
def _tag_sharding_attribute_for_dequeued_tensor(self, tensor, dims):
"""Tags appropriate XLA sharding attribute to the dequeued tensor.
Args:
tensor: The dequeued tensor on TPU.
dims: A list of integer describes how the tensor is partitioned.
Returns:
The same tensor with the xla_sharding attribute.
"""
if dims is None:
return xla_sharding.replicate(tensor)
elif np.prod(dims) == 1:
return xla_sharding.assign_device(tensor, 0)
else:
tile_shape = np.array(tensor.shape.as_list()) // dims
tile_assignment = np.arange(np.prod(dims)).reshape(dims)
return xla_sharding.tile(
tensor=tensor,
tile_shape=xla_shape.CreateShapeFromDtypeAndTuple(
dtype=np.dtype(tensor.dtype.as_numpy_dtype),
shape_tuple=tile_shape),
tile_assignment=tile_assignment)
def _tag_sharding_attribute_for_dequeued_tensor(self, tensor, dims):
"""Tags appropriate XLA sharding attribute to the dequeued tensor.
Args:
tensor: The dequeued tensor on TPU.
dims: A list of integer describes how the tensor is partitioned.
Returns:
The same tensor with the xla_sharding attribute.
"""
if dims is None:
return xla_sharding.replicate(tensor)
elif np.prod(dims) == 1:
return xla_sharding.assign_device(tensor, 0)
else:
tile_shape = np.array(tensor.shape.as_list()) // dims
tile_assignment = np.arange(np.prod(dims)).reshape(dims)
return xla_sharding.tile(
tensor=tensor,
tile_shape=xla_shape.CreateShapeFromDtypeAndTuple(
dtype=np.dtype(tensor.dtype.as_numpy_dtype),
shape_tuple=tile_shape),
tile_assignment=tile_assignment)
def _experimental_replicate_to_logical_devices(self, tensor):
"""See `DistributionStrategy.experimental_replicate_to_logical_devices`."""
return xla_sharding.replicate(tensor, use_sharding_op=True)
def Replicate(x, use_sharding_op=True):
"""Wrapper of xla_sharding.replicate."""
if not py_utils_flags.use_tpu():
return x
return xla_sharding.replicate(x, use_sharding_op=use_sharding_op)
def Replicate(x, use_sharding_op=True):
"""Wrapper of xla_sharding.replicate."""
return xla_sharding.replicate(x, use_sharding_op=use_sharding_op)
