def _default_initializer_for_determining(tensor):
assert not tensor.is_determined
undetermined_tensor = tensor._undetermined_tensor
variable_name = id_util.UniqueStr("tensor_")
blob = None
@global_function_or_identity()
def job():
nonlocal blob
with tensor._placement_scope():
blob = flow.get_variable(
name=variable_name,
shape=tuple(undetermined_tensor.shape),
dtype=undetermined_tensor.dtype,
initializer=undetermined_tensor.data_initializer,
)
job()
if undetermined_tensor.is_consistent:
determined_tensor = oneflow_api.ConsistentTensor(
undetermined_tensor.shape,
undetermined_tensor.dtype,
undetermined_tensor.sbp,
undetermined_tensor.placement,
undetermined_tensor.is_lazy,
undetermined_tensor.requires_grad,
True,
undetermined_tensor.retain_grad,
)
else:
determined_tensor = oneflow_api.LocalTensor(
undetermined_tensor.shape,
undetermined_tensor.dtype,
undetermined_tensor.device,
undetermined_tensor.is_lazy,
undetermined_tensor.requires_grad,
True,
undetermined_tensor.retain_grad,
)
determined_tensor._set_blob_object(blob.blob_object)
return determined_tensor
def _numpy_initializer_for_determining(tensor):
assert not tensor.is_determined
undetermined_tensor = tensor._undetermined_tensor
assert undetermined_tensor.numpy_data is not None
variable_name = id_util.UniqueStr("tensor_")
@global_function_or_identity()
def set_numpy_data():
with tensor._placement_scope():
flow.get_variable(
name=variable_name,
shape=tuple(undetermined_tensor.shape),
dtype=undetermined_tensor.dtype,
initializer=undetermined_tensor.data_initializer,
)
set_numpy_data()
flow.load_variables({variable_name: undetermined_tensor.numpy_data})
blob = flow.get_all_variables()[variable_name]
if undetermined_tensor.is_consistent:
determined_tensor = oneflow_api.ConsistentTensor(
undetermined_tensor.shape,
undetermined_tensor.dtype,
undetermined_tensor.sbp,
undetermined_tensor.placement,
undetermined_tensor.is_lazy,
undetermined_tensor.requires_grad,
True,
undetermined_tensor.retain_grad,
)
else:
determined_tensor = oneflow_api.LocalTensor(
undetermined_tensor.shape,
undetermined_tensor.dtype,
undetermined_tensor.device,
undetermined_tensor.is_lazy,
undetermined_tensor.requires_grad,
True,
undetermined_tensor.retain_grad,
)
determined_tensor._set_blob_object(blob.blob_object)
return determined_tensor
def _default_initializer_for_determining(undetermined_tensor):
assert not undetermined_tensor.is_consistent
variable_name = id_util.UniqueStr("tensor_")
blob = flow.get_variable(
name=variable_name,
shape=tuple(undetermined_tensor.shape),
dtype=undetermined_tensor.dtype,
initializer=undetermined_tensor.data_initializer,
)
determined_tensor = oneflow_api.LocalTensor(
undetermined_tensor.shape,
undetermined_tensor.dtype,
undetermined_tensor.device,
undetermined_tensor.is_lazy,
undetermined_tensor.requires_grad,
True,
undetermined_tensor.retain_grad,
)
determined_tensor._set_blob_object(blob.blob_object)
return determined_tensor, variable_name
def _initialized_job(
shape=None,
dtype=None,
device=None,
requires_grad=None,
retain_grad=None,
is_lazy=False,
numpy_data=None,
):
assert numpy_data is not None
variable_name = id_util.UniqueStr("tensor_")
@global_function_or_identity()
def set_data():
flow.get_variable(
name=variable_name,
shape=tuple(shape),
dtype=dtype,
initializer=flow.zeros_initializer(dtype=dtype),
)
if not is_lazy:
set_data()
flow.load_variables({variable_name: numpy_data})
blob = flow.get_all_variables()[variable_name]
determined_tensor = oneflow_api.LocalTensor(
shape,
dtype,
device,
is_lazy,
requires_grad,
True,
retain_grad,
)
determined_tensor._set_blob_object(blob.blob_object)
return determined_tensor