def indexed_slices_reduce_sum(
indices: input_blob_util.ArgBlobDef,
values: input_blob_util.ArgBlobDef,
name: Optional[str] = None,
) -> Tuple[remote_blob_util.BlobDef]:
op_conf = op_conf_util.OperatorConf()
if name is None:
op_conf.name = id_util.UniqueStr("IndexedSlicesReduceSum_")
else:
op_conf.name = name
op_conf.indexed_slices_reduce_sum_conf.x_indices = indices.unique_name
op_conf.indexed_slices_reduce_sum_conf.x_values = values.unique_name
op_conf.indexed_slices_reduce_sum_conf.y_indices = "y_indices"
op_conf.indexed_slices_reduce_sum_conf.y_values = "y_values"
op_conf.indexed_slices_reduce_sum_conf.num_unique = "num_unique"
interpret_util.Forward(op_conf)
y_indices_lbi = logical_blob_id_util.LogicalBlobId()
y_indices_lbi.op_name = op_conf.name
y_indices_lbi.blob_name = "y_indices"
y_values_lbi = logical_blob_id_util.LogicalBlobId()
y_values_lbi.op_name = op_conf.name
y_values_lbi.blob_name = "y_values"
num_unique_lbi = logical_blob_id_util.LogicalBlobId()
num_unique_lbi.op_name = op_conf.name
num_unique_lbi.blob_name = "num_unique"
return (
remote_blob_util.RemoteBlob(y_indices_lbi),
remote_blob_util.RemoteBlob(y_values_lbi),
remote_blob_util.RemoteBlob(num_unique_lbi),
)
def dynamic_binary_split(
x: input_blob_util.ArgBlobDef,
base_shift: int = 2,
out_num: int = 2,
name: Optional[str] = None,
) -> List[remote_blob_util.BlobDef]:
op_conf = op_conf_util.OperatorConf()
if name is None:
op_conf.name = id_util.UniqueStr("DynamicBinarySplit_")
else:
op_conf.name = name
obns = []
out_remote_blobs = []
for i in range(out_num):
obns.append("out_" + str(i))
setattr(op_conf.dynamic_binary_split_conf, "in", x.unique_name)
# op_conf.dynamic_binary_split_conf.in = x.unique_name
op_conf.dynamic_binary_split_conf.out[:] = obns
op_conf.dynamic_binary_split_conf.base_shift = base_shift
interpret_util.Forward(op_conf)
for i in range(out_num):
out_lbi = logical_blob_id_util.LogicalBlobId()
out_lbi.op_name = op_conf.name
out_lbi.blob_name = obns[i]
out_remote_blobs.append(remote_blob_util.RemoteBlob(out_lbi))
return out_remote_blobs
def elem_cnt(
input_blob: remote_blob_util.BlobDef,
axis: Optional[Sequence[int]] = None,
dtype: Optional[dtype_util.dtype] = None,
name: Optional[str] = None,
) -> remote_blob_util.BlobDef:
op_conf = op_conf_util.OperatorConf()
setattr(
op_conf,
"name",
name if name is not None else id_util.UniqueStr("ShapeElemCnt_"),
)
op_conf.shape_elem_cnt_conf.x = input_blob.unique_name
if axis is None:
op_conf.shape_elem_cnt_conf.exclude_axis_conf.SetInParent()
else:
assert isinstance(axis, (tuple, list))
op_conf.shape_elem_cnt_conf.include_axis_conf.axis.extend(axis)
if dtype is not None:
op_conf.shape_elem_cnt_conf.data_type = dtype.oneflow_proto_dtype
op_conf.shape_elem_cnt_conf.y = "y"
interpret_util.Forward(op_conf)
out_lbi = logical_blob_id_util.LogicalBlobId()
out_lbi.op_name = op_conf.name
out_lbi.blob_name = "y"
return remote_blob_util.RemoteBlob(out_lbi)
def broadcast_to_compatible_with(
x: remote_blob_util.BlobDef,
compatible: Sequence[remote_blob_util.BlobDef],
name: Optional[str] = None,
) -> remote_blob_util.BlobDef:
r"""Returns a 'Blob' with the shape can be broadcasted by other shapes
Args:
x (remote_blob_util.BlobDef): a 'Blob'
compatible (Sequence[remote_blob_util.BlobDef]): Sequence of different shape
name (Optional[str], optional): This operator's name. Defaults to None.
Returns:
remote_blob_util.BlobDef: A 'Blob' with the biggest shape
"""
assert isinstance(compatible, (list, tuple))
if name is None:
name = id_util.UniqueStr("BroadcastToCompatibleWith_")
op_conf = op_conf_util.OperatorConf()
setattr(op_conf, "name", name)
setattr(op_conf.broadcast_to_compatible_with_conf, "x", x.unique_name)
setattr(op_conf.broadcast_to_compatible_with_conf, "y", "y")
op_conf.broadcast_to_compatible_with_conf.compatible.extend(
[cp.unique_name for cp in compatible])
interpret_util.Forward(op_conf)
ret_lbi = logical_blob_id_util.LogicalBlobId()
ret_lbi.op_name = op_conf.name
ret_lbi.blob_name = "y"
return remote_blob_util.RemoteBlob(ret_lbi)
def constant_like(
like: remote_blob_util.BlobDef,
value: Union[int, float],
dtype: Optional[dtype_util.dtype] = None,
name: Optional[str] = None,
) -> remote_blob_util.BlobDef:
op_conf = op_conf_util.OperatorConf()
setattr(
op_conf,
"name",
name if name is not None else id_util.UniqueStr("ConstantLike_"),
)
setattr(op_conf.constant_like_conf, "like", like.unique_name)
if isinstance(value, int):
op_conf.constant_like_conf.int_operand = value
elif isinstance(value, float):
op_conf.constant_like_conf.float_operand = value
else:
raise NotImplementedError
if dtype is not None:
setattr(op_conf.constant_like_conf, "data_type",
dtype.oneflow_proto_dtype)
setattr(op_conf.constant_like_conf, "out", "out")
interpret_util.Forward(op_conf)
out_lbi = logical_blob_id_util.LogicalBlobId()
setattr(out_lbi, "op_name", op_conf.name)
setattr(out_lbi, "blob_name", "out")
return remote_blob_util.RemoteBlob(out_lbi)
def tensor_buffer_to_tensor_list(
input: remote_blob_util.BlobDef,
shape: Sequence[int],
dtype: dtype_util.dtype,
name: Optional[str] = None,
) -> remote_blob_util.BlobDef:
if name is None:
name = id_util.UniqueStr("TensorBufferToList_")
op_conf = op_conf_util.OperatorConf()
setattr(op_conf, "name", name)
setattr(op_conf.tensor_buffer_to_tensor_list_conf, "in", input.unique_name)
setattr(op_conf.tensor_buffer_to_tensor_list_conf, "out", "out")
op_conf.tensor_buffer_to_tensor_list_conf.shape.dim[:] = list(shape)
setattr(
op_conf.tensor_buffer_to_tensor_list_conf,
"data_type",
dtype.oneflow_proto_dtype,
)
interpret_util.Forward(op_conf)
lbi = logical_blob_id_util.LogicalBlobId()
lbi.op_name = op_conf.name
lbi.blob_name = "out"
return remote_blob_util.RemoteBlob(lbi)
def argwhere(
condition: remote_blob_util.BlobDef,
dtype: Optional[dtype_util.dtype] = None,
name: Optional[str] = None,
) -> remote_blob_util.BlobDef:
if name is None:
name = id_util.UniqueStr("ArgWhere_")
op_conf = op_conf_util.OperatorConf()
setattr(op_conf, "name", name)
setattr(op_conf.arg_where_conf, "in", condition.unique_name)
setattr(op_conf.arg_where_conf, "out", "out")
setattr(op_conf.arg_where_conf, "out_size", "out_size")
if dtype is not None:
setattr(op_conf.arg_where_conf, "data_type", dtype.oneflow_proto_dtype)
interpret_util.Forward(op_conf)
arg_where_out_lbi = logical_blob_id_util.LogicalBlobId()
setattr(arg_where_out_lbi, "op_name", op_conf.name)
setattr(arg_where_out_lbi, "blob_name", "out")
arg_where_out_size_lbi = logical_blob_id_util.LogicalBlobId()
setattr(arg_where_out_size_lbi, "op_name", op_conf.name)
setattr(arg_where_out_size_lbi, "blob_name", "out_size")
arg_where_out = remote_blob_util.RemoteBlob(arg_where_out_lbi)
arg_where_out_size = remote_blob_util.RemoteBlob(arg_where_out_size_lbi)
return sync_dynamic_resize(arg_where_out, arg_where_out_size)
def lazy_system_assign(ref, value, validate_shape=None, use_locking=None, name=None):
op_conf = _SystemAssignOpConf(ref, value, name=name)
device_tag, machine_device_ids = oneflow_api.GetDeviceTagAndMachineDeviceIds(
ref.parallel_conf
)
with oneflow.scope.placement(device_tag, machine_device_ids):
interpret_util.Forward(op_conf)
return ref
def tensor_list_split(
input_tensor_list: oneflow_api.BlobDesc,
name: Optional[str] = None) -> Tuple[oneflow_api.BlobDesc]:
"""This operator splits the input `TensorList`.
Args:
input_tensor_list (oneflow_api.BlobDesc): The input `TensorList`.
name (Optional[str], optional): The name for the operation. Defaults to None.
Returns:
Tuple[oneflow_api.BlobDesc]: A Tuple of `ListNumpy`.
For example:
.. code-block:: python
import oneflow as flow
import numpy as np
import oneflow.typing as tp
from typing import Tuple
func_config = flow.FunctionConfig()
func_config.default_data_type(flow.float)
func_config.default_logical_view(flow.scope.mirrored_view())
@flow.global_function(function_config=func_config)
def tensorList_split_Job(x: tp.ListListNumpy.Placeholder(shape=(2, 5, 4), dtype=flow.float32),
) -> Tuple[tp.ListNumpy, tp.ListNumpy]:
return flow.tensor_list_split(x)
x = np.random.rand(1, 3, 2).astype(np.float32)
y = np.random.rand(1, 2, 2).astype(np.float32)
out = tensorList_split_Job([[x, y]])
# out[0][0].shape (3, 2)
# out[1][0].shape (2, 2)
"""
if name is None:
name = id_util.UniqueStr("TensorListSplit_")
output_size = input_tensor_list.shape[0]
op_conf = op_conf_util.OperatorConf()
setattr(op_conf, "name", name)
setattr(op_conf.tensor_list_split_conf, "in",
input_tensor_list.unique_name)
op_conf.tensor_list_split_conf.out.extend(
["out_{}".format(i) for i in range(output_size)])
interpret_util.Forward(op_conf)
ret = []
for i in range(output_size):
out_lbi = logical_blob_id_util.LogicalBlobId()
setattr(out_lbi, "op_name", op_conf.name)
setattr(out_lbi, "blob_name", "out_{}".format(i))
ret.append(remote_blob_util.RemoteBlob(out_lbi))
return tuple(ret)
def tensor_list_to_tensor_buffer(
input: oneflow_api.BlobDesc,
name: Optional[str] = None) -> oneflow_api.BlobDesc:
"""This operator converts `TensorList` to `TensorBuffer`.
Refer to `Concept Explanation <https://docs.oneflow.org/basics_topics/concept_explanation.html#3tensorbuffer-tensorlist>`_
for more about TensorList.
Args:
input (oneflow_api.BlobDesc): The input `TensorList`.
name (Optional[str], optional): The name for the operation. Defaults to None.
Returns:
oneflow_api.BlobDesc: The result Blob.
For example:
.. code-block:: python
import oneflow as flow
import numpy as np
import oneflow.typing as tp
func_config = flow.FunctionConfig()
func_config.default_data_type(flow.float)
func_config.default_logical_view(flow.scope.mirrored_view())
@flow.global_function(function_config=func_config)
def tensorList_to_tensorBuffer_Job(x: tp.ListListNumpy.Placeholder(shape=(2, 5, 4), dtype=flow.float32),
) -> tp.ListListNumpy:
x = flow.tensor_list_to_tensor_buffer(input=x)
return flow.tensor_buffer_to_tensor_list(x,
shape=(5, 4),
dtype=flow.float32)
x = np.random.rand(1, 3, 2).astype(np.float32)
y = np.random.rand(1, 2, 2).astype(np.float32)
out = tensorList_to_tensorBuffer_Job([[x, y]])
# out[0][0].shape (1, 3, 2)
"""
if name is None:
name = id_util.UniqueStr("TensorListToBuffer_")
op_conf = op_conf_util.OperatorConf()
setattr(op_conf, "name", name)
setattr(op_conf.tensor_list_to_tensor_buffer_conf, "in", input.unique_name)
setattr(op_conf.tensor_list_to_tensor_buffer_conf, "out", "out")
interpret_util.Forward(op_conf)
lbi = logical_blob_id_util.LogicalBlobId()
lbi.op_name = op_conf.name
lbi.blob_name = "out"
return remote_blob_util.RemoteBlob(lbi)
def image_decoder_random_crop_resize(
input_blob: oneflow_api.BlobDesc,
target_width: int,
target_height: int,
num_attempts: Optional[int] = None,
seed: Optional[int] = None,
random_area: Optional[Sequence[float]] = None,
random_aspect_ratio: Optional[Sequence[float]] = None,
num_workers: Optional[int] = None,
warmup_size: Optional[int] = None,
max_num_pixels: Optional[int] = None,
name: Optional[str] = None,
) -> Tuple[oneflow_api.BlobDesc]:
if name is None:
name = id_util.UniqueStr("ImageDecoderRandomCropResize_")
op_conf = op_conf_util.OperatorConf()
op_conf.name = name
setattr(op_conf.image_decoder_random_crop_resize_conf, "in",
input_blob.unique_name)
op_conf.image_decoder_random_crop_resize_conf.out = "out"
op_conf.image_decoder_random_crop_resize_conf.target_width = target_width
op_conf.image_decoder_random_crop_resize_conf.target_height = target_height
if num_attempts is not None:
op_conf.image_decoder_random_crop_resize_conf.num_attempts = num_attempts
if seed is not None:
op_conf.image_decoder_random_crop_resize_conf.seed = seed
if random_area is not None:
assert len(random_area) == 2
op_conf.image_decoder_random_crop_resize_conf.random_area_min = random_area[
0]
op_conf.image_decoder_random_crop_resize_conf.random_area_max = random_area[
1]
if random_aspect_ratio is not None:
assert len(random_aspect_ratio) == 2
op_conf.image_decoder_random_crop_resize_conf.random_aspect_ratio_min = random_aspect_ratio[
0]
op_conf.image_decoder_random_crop_resize_conf.random_aspect_ratio_max = random_aspect_ratio[
1]
if num_workers is not None:
op_conf.image_decoder_random_crop_resize_conf.num_workers = num_workers
if warmup_size is not None:
op_conf.image_decoder_random_crop_resize_conf.warmup_size = warmup_size
if max_num_pixels is not None:
op_conf.image_decoder_random_crop_resize_conf.max_num_pixels = max_num_pixels
interpret_util.Forward(op_conf)
lbi = logical_blob_id_util.LogicalBlobId()
lbi.op_name = op_conf.name
lbi.blob_name = "out"
return remote_blob_util.RemoteBlob(lbi)
def lazy_system_assign(ref, value, validate_shape=None, use_locking=None, name=None):
op_conf = _SystemAssignOpConf(ref, value, name=name)
(
device_tag,
machine_device_ids,
hierarchy,
) = oneflow._oneflow_internal.GetDeviceTagAndMachineDeviceIdsAndHierarchy(
ref.parallel_conf
)
if hierarchy is not None:
hierarchy = tuple(hierarchy.dim())
with oneflow.scope.placement(device_tag, machine_device_ids, hierarchy):
interpret_util.Forward(op_conf)
return ref
def tensor_list_to_tensor_buffer(
input: remote_blob_util.BlobDef,
name: Optional[str] = None) -> remote_blob_util.BlobDef:
if name is None:
name = id_util.UniqueStr("TensorListToBuffer_")
op_conf = op_conf_util.OperatorConf()
setattr(op_conf, "name", name)
setattr(op_conf.tensor_list_to_tensor_buffer_conf, "in", input.unique_name)
setattr(op_conf.tensor_list_to_tensor_buffer_conf, "out", "out")
interpret_util.Forward(op_conf)
lbi = logical_blob_id_util.LogicalBlobId()
lbi.op_name = op_conf.name
lbi.blob_name = "out"
return remote_blob_util.RemoteBlob(lbi)
def square_sum(
x: input_blob_util.ArgBlobDef, name: Optional[str] = None
) -> remote_blob_util.BlobDef:
op_conf = op_conf_util.OperatorConf()
if name is None:
op_conf.name = id_util.UniqueStr("SquareSum_")
else:
op_conf.name = name
op_conf.square_sum_conf.x = x.unique_name
op_conf.square_sum_conf.y = "y"
interpret_util.Forward(op_conf)
lbi = logical_blob_id_util.LogicalBlobId()
lbi.op_name = op_conf.name
lbi.blob_name = "y"
return remote_blob_util.RemoteBlob(lbi)
def dynamic_binary_concat(
input_blob_list: Sequence[remote_blob_util.BlobDef],
source_blob: input_blob_util.ArgBlobDef,
source_sbp: str = "S:0",
name: Optional[str] = None,
) -> remote_blob_util.BlobDef:
op_conf = op_conf_util.OperatorConf()
if name is None:
op_conf.name = id_util.UniqueStr("DynamicBinaryConcat_")
else:
op_conf.name = name
in_lbns = []
for in_blob in input_blob_list:
in_lbns.append(in_blob.unique_name)
getattr(op_conf.dynamic_binary_concat_conf, "in").extend(in_lbns)
# op_conf.dynamic_binary_concat_conf.in[:] = in_lbns
op_conf.dynamic_binary_concat_conf.out = "out"
op_conf.dynamic_binary_concat_conf.out_data_type = (
source_blob.dtype.oneflow_proto_dtype)
op_conf.dynamic_binary_concat_conf.out_shape.dim.extend(
list(source_blob.shape))
if source_blob.batch_axis is not None:
op_conf.dynamic_binary_concat_conf.out_batch_axis.value = source_blob.batch_axis
else:
op_conf.dynamic_binary_concat_conf.out_batch_axis.SetInParent()
if "S" in source_sbp:
axis = int(source_sbp.split(":")[-1])
op_conf.dynamic_binary_concat_conf.out_sbp.split_parallel.axis = axis
elif "B" in source_sbp:
op_conf.dynamic_binary_concat_conf.out_sbp.broadcast_parallel.SetInParent(
)
elif "P" in source_sbp:
op_conf.dynamic_binary_concat_conf.out_sbp.partial_sum_parallel.SetInParent(
)
else:
print("Error! invalid sbp str:", source_sbp)
op_conf.dynamic_binary_concat_conf.out_sbp.SetInParent()
interpret_util.Forward(op_conf)
out_lbi = logical_blob_id_util.LogicalBlobId()
out_lbi.op_name = op_conf.name
out_lbi.blob_name = "out"
return remote_blob_util.RemoteBlob(out_lbi)
def reshape(
x: remote_blob_util.BlobDef, shape: Sequence[int], name: Optional[str] = None
) -> remote_blob_util.BlobDef:
r"""Reshapes a blob.
Args:
x: A `Blob`.
shape: Shape of the output blob.
name: A name for the operation (optional).
Returns:
A `Blob`, has the same type as `x`.
"""
x = flow.cast_to_current_logical_view(x)
assert isinstance(shape, tuple) or isinstance(shape, list)
shape = list(shape)
assert all(dim == -1 or dim > 0 for dim in shape)
assert shape.count(-1) <= 1
if not x.is_dynamic:
if name is None:
name = id_util.UniqueStr("Reshape_")
return (
flow.user_op_builder(name)
.Op("reshape")
.Input("in", [x])
.Output("out")
.Attr("shape", infer_shape(x, shape))
.Build()
.InferAndTryRun()
.RemoteBlobList()[0]
)
else:
op_conf = op_conf_util.OperatorConf()
setattr(
op_conf,
"name",
name if name is not None else id_util.UniqueStr("DynamicReshape_"),
)
setattr(op_conf.dynamic_reshape_conf, "in", x.unique_name)
op_conf.dynamic_reshape_conf.shape.dim.extend(list(shape))
setattr(op_conf.dynamic_reshape_conf, "out", "out")
interpret_util.Forward(op_conf)
lbi = logical_blob_id_util.LogicalBlobId()
lbi.op_name = op_conf.name
lbi.blob_name = "out"
return remote_blob_util.RemoteBlob(lbi)
def elem_cnt(
inputs: remote_blob_util.BlobDef,
dtype: Optional[dtype_util.dtype] = None,
name: Optional[str] = None,
) -> remote_blob_util.BlobDef:
op_conf = op_conf_util.OperatorConf()
setattr(op_conf, "name",
name if name is not None else id_util.UniqueStr("ElemCnt_"))
op_conf.shape_elem_cnt_conf.x = inputs.unique_name
op_conf.shape_elem_cnt_conf.exclude_axis_conf.SetInParent()
if dtype is not None:
op_conf.shape_elem_cnt_conf.data_type = dtype.oneflow_proto_dtype
op_conf.shape_elem_cnt_conf.y = "y"
interpret_util.Forward(op_conf)
out_lbi = logical_blob_id_util.LogicalBlobId()
setattr(out_lbi, "op_name", op_conf.name)
setattr(out_lbi, "blob_name", "y")
return remote_blob_util.RemoteBlob(out_lbi)
def dynamic_reshape(
x: remote_blob_util.BlobDef, shape: Sequence[int], name: Optional[str] = None
) -> remote_blob_util.BlobDef:
assert isinstance(shape, tuple) or isinstance(shape, list)
shape = list(shape)
op_conf = op_conf_util.OperatorConf()
setattr(
op_conf,
"name",
name if name is not None else id_util.UniqueStr("DynamicReshape_"),
)
setattr(op_conf.dynamic_reshape_conf, "in", x.unique_name)
op_conf.dynamic_reshape_conf.shape.dim.extend(list(shape))
setattr(op_conf.dynamic_reshape_conf, "out", "out")
interpret_util.Forward(op_conf)
lbi = logical_blob_id_util.LogicalBlobId()
lbi.op_name = op_conf.name
lbi.blob_name = "out"
return remote_blob_util.RemoteBlob(lbi)
def tensor_list_split(
input_tensor_list: remote_blob_util.BlobDef,
name: Optional[str] = None) -> Tuple[remote_blob_util.BlobDef]:
if name is None:
name = id_util.UniqueStr("TensorListSplit_")
output_size = input_tensor_list.shape[0]
op_conf = op_conf_util.OperatorConf()
setattr(op_conf, "name", name)
setattr(op_conf.tensor_list_split_conf, "in",
input_tensor_list.unique_name)
op_conf.tensor_list_split_conf.out.extend(
["out_{}".format(i) for i in range(output_size)])
interpret_util.Forward(op_conf)
ret = []
for i in range(output_size):
out_lbi = logical_blob_id_util.LogicalBlobId()
setattr(out_lbi, "op_name", op_conf.name)
setattr(out_lbi, "blob_name", "out_{}".format(i))
ret.append(remote_blob_util.RemoteBlob(out_lbi))
return tuple(ret)
def stack(
inputs: Sequence[remote_blob_util.BlobDef], axis: int, name: Optional[str] = None
) -> remote_blob_util.BlobDef:
if not isinstance(inputs, (list, tuple)):
inputs = [inputs]
if axis < 0:
axis = axis + len(inputs[0].shape)
assert axis == 0, "Only support dim0 stack now."
op_conf = op_conf_util.OperatorConf()
setattr(op_conf, "name", name or id_util.UniqueStr("Stack_"))
getattr(op_conf.stack_conf, "in").extend([input.unique_name for input in inputs])
setattr(op_conf.stack_conf, "axis", axis)
setattr(op_conf.stack_conf, "out", "out")
interpret_util.Forward(op_conf)
lbi = logical_blob_id_util.LogicalBlobId()
lbi.op_name = op_conf.name
lbi.blob_name = "out"
return remote_blob_util.RemoteBlob(lbi)
def sync_dynamic_resize(
inputs: remote_blob_util.BlobDef,
size: remote_blob_util.BlobDef,
name: Optional[str] = None,
) -> remote_blob_util.BlobDef:
op_conf = op_conf_util.OperatorConf()
setattr(
op_conf,
"name",
name if name is not None else id_util.UniqueStr("SyncDynamicResize_"),
)
setattr(op_conf.sync_dynamic_resize_conf, "in", inputs.unique_name)
setattr(op_conf.sync_dynamic_resize_conf, "size", size.unique_name)
setattr(op_conf.sync_dynamic_resize_conf, "axis", 0)
setattr(op_conf.sync_dynamic_resize_conf, "out", "out")
setattr(op_conf.sync_dynamic_resize_conf, "eager", flow.eager_execution_enabled())
interpret_util.Forward(op_conf)
out_lbi = logical_blob_id_util.LogicalBlobId()
setattr(out_lbi, "op_name", op_conf.name)
setattr(out_lbi, "blob_name", "out")
return remote_blob_util.RemoteBlob(out_lbi)
def unique_with_counts(
x: input_blob_util.ArgBlobDef,
out_idx: flow.dtype = flow.int32,
name: Optional[str] = None,
) -> Tuple[oneflow_api.BlobDesc]:
op_conf = op_conf_util.OperatorConf()
if name is None:
op_conf.name = id_util.UniqueStr("UniqueWithCounts_")
else:
op_conf.name = name
op_conf.unique_with_counts_conf.x = x.unique_name
op_conf.unique_with_counts_conf.y = "y"
op_conf.unique_with_counts_conf.idx = "idx"
op_conf.unique_with_counts_conf.count = "count"
op_conf.unique_with_counts_conf.num_unique = "num_unique"
op_conf.unique_with_counts_conf.out_idx = oneflow_api.deprecated.GetProtoDtype4OfDtype(
out_idx
)
interpret_util.Forward(op_conf)
y_lbi = logical_blob_id_util.LogicalBlobId()
y_lbi.op_name = op_conf.name
y_lbi.blob_name = "y"
idx_lbi = logical_blob_id_util.LogicalBlobId()
idx_lbi.op_name = op_conf.name
idx_lbi.blob_name = "idx"
count_lbi = logical_blob_id_util.LogicalBlobId()
count_lbi.op_name = op_conf.name
count_lbi.blob_name = "count"
num_unique_lbi = logical_blob_id_util.LogicalBlobId()
num_unique_lbi.op_name = op_conf.name
num_unique_lbi.blob_name = "num_unique"
return (
remote_blob_util.RemoteBlob(y_lbi),
remote_blob_util.RemoteBlob(idx_lbi),
remote_blob_util.RemoteBlob(count_lbi),
remote_blob_util.RemoteBlob(num_unique_lbi),
)
def identity(x: remote_blob_util.BlobDef,
name: Optional[str] = None) -> remote_blob_util.BlobDef:
r"""Return a `Blob` has identical content and data type to input `Blob`. Analogous to `tf.identity <https://www.tensorflow.org/api_docs/python/tf/identity>`_
Args:
input: a `Blob`
name: name of this operator. `None` by default
Returns:
A `Blob`
"""
if name is None:
name = id_util.UniqueStr("Identity_")
op_conf = op_conf_util.OperatorConf()
op_conf.name = name
setattr(op_conf.identity_conf, "in", x.unique_name)
op_conf.identity_conf.out = "out"
interpret_util.Forward(op_conf)
lbi = logical_blob_id_util.LogicalBlobId()
lbi.op_name = op_conf.name
lbi.blob_name = "out"
return remote_blob_util.RemoteBlob(lbi)
def broadcast_to_compatible_with(
x: remote_blob_util.BlobDef,
compatible: Sequence[remote_blob_util.BlobDef],
name: Optional[str] = None,
) -> remote_blob_util.BlobDef:
assert isinstance(compatible, (list, tuple))
if name is None:
name = id_util.UniqueStr("BroadcastToCompatibleWith_")
op_conf = op_conf_util.OperatorConf()
setattr(op_conf, "name", name)
setattr(op_conf.broadcast_to_compatible_with_conf, "x", x.unique_name)
setattr(op_conf.broadcast_to_compatible_with_conf, "y", "y")
op_conf.broadcast_to_compatible_with_conf.compatible.extend(
[cp.unique_name for cp in compatible]
)
interpret_util.Forward(op_conf)
ret_lbi = logical_blob_id_util.LogicalBlobId()
ret_lbi.op_name = op_conf.name
ret_lbi.blob_name = "y"
return remote_blob_util.RemoteBlob(ret_lbi)
def identity_n(
inputs: Iterable[remote_blob_util.BlobDef], name: Optional[str] = None
) -> List[remote_blob_util.BlobDef]:
op_conf = op_conf_util.OperatorConf()
setattr(
op_conf, "name", name if name is not None else id_util.UniqueStr("IdentityN_"),
)
assert len(inputs) > 1
out_bns = []
for idx, blob in enumerate(inputs):
getattr(op_conf.tuple_identity_conf, "in").append(blob.unique_name)
out_bn = "out_" + str(idx)
getattr(op_conf.tuple_identity_conf, "out").append(out_bn)
out_bns.append(out_bn)
interpret_util.Forward(op_conf)
def bn_to_remote_blob(bn):
lbi = logical_blob_id_util.LogicalBlobId()
lbi.op_name = op_conf.name
lbi.blob_name = bn
return remote_blob_util.RemoteBlob(lbi)
return list(map(bn_to_remote_blob, out_bns))
def elem_cnt(
input_blob: remote_blob_util.BlobDef,
axis: Optional[Sequence[int]] = None,
dtype: Optional[dtype_util.dtype] = None,
name: Optional[str] = None,
) -> remote_blob_util.BlobDef:
r"""Reduce shape emements count accross 'axis'
Args:
input_blob (remote_blob_util.BlobDef): a 'Blob'
axis (Optional[Sequence[int]], optional): The axis need to be reduced. Defaults to None.
dtype (Optional[int], optional): A OneFlow data type (flow.int32). Defaults to None.
name (Optional[str], optional): This operator's name. Defaults to None.
Returns:
remote_blob_util.BlobDef: A 'Blob'
"""
op_conf = op_conf_util.OperatorConf()
setattr(
op_conf,
"name",
name if name is not None else id_util.UniqueStr("ShapeElemCnt_"),
)
op_conf.shape_elem_cnt_conf.x = input_blob.unique_name
if axis is None:
op_conf.shape_elem_cnt_conf.exclude_axis_conf.SetInParent()
else:
assert isinstance(axis, (tuple, list))
op_conf.shape_elem_cnt_conf.include_axis_conf.axis.extend(axis)
if dtype is not None:
op_conf.shape_elem_cnt_conf.data_type = dtype.oneflow_proto_dtype
op_conf.shape_elem_cnt_conf.y = "y"
interpret_util.Forward(op_conf)
out_lbi = logical_blob_id_util.LogicalBlobId()
out_lbi.op_name = op_conf.name
out_lbi.blob_name = "y"
return remote_blob_util.RemoteBlob(out_lbi)
def constant_like(
like: oneflow._oneflow_internal.BlobDesc,
value: Union[int, float],
dtype: Optional[flow.dtype] = None,
name: Optional[str] = None,
) -> oneflow._oneflow_internal.BlobDesc:
"""This operator creates a constant Blob that has the same shape as `like`.
Args:
like (oneflow._oneflow_internal.BlobDesc): A Blob.
value (Union[int, float]): The constant value of Blob.
dtype (Optional[flow.dtype], optional): The data type of Blob. Defaults to None.
name (Optional[str], optional): The name for the operation. Defaults to None.
Raises:
NotImplementedError: The data type of value should be int or float.
Returns:
oneflow._oneflow_internal.BlobDesc: The result Blob.
For example:
.. code-block:: python
import oneflow as flow
import numpy as np
import oneflow.typing as tp
@flow.global_function()
def constant_like_Job() -> tp.Numpy:
constant_blob = flow.constant(value=1.5,
shape=(1, 3, 3),
dtype=flow.float)
constant_like_blob = flow.constant_like(like=constant_blob,
value=5.5,
dtype=flow.float)
return constant_like_blob
out = constant_like_Job()
# out [[[5.5 5.5 5.5]
# [5.5 5.5 5.5]
# [5.5 5.5 5.5]]]
"""
op_conf = op_conf_util.OperatorConf()
setattr(
op_conf,
"name",
name if name is not None else id_util.UniqueStr("ConstantLike_"),
)
setattr(op_conf.constant_like_conf, "like", like.unique_name)
if isinstance(value, int):
op_conf.constant_like_conf.int_operand = value
elif isinstance(value, float):
op_conf.constant_like_conf.float_operand = value
else:
raise NotImplementedError
if dtype is not None:
setattr(
op_conf.constant_like_conf,
"data_type",
oneflow._oneflow_internal.deprecated.GetProtoDtype4OfDtype(dtype),
)
setattr(op_conf.constant_like_conf, "out", "out")
interpret_util.Forward(op_conf)
out_lbi = logical_blob_id_util.LogicalBlobId()
setattr(out_lbi, "op_name", op_conf.name)
setattr(out_lbi, "blob_name", "out")
return remote_blob_util.RemoteBlob(out_lbi)
def InferAndTryRun(self):
interpret_util.Forward(self.op_conf_)
return self
def tensor_buffer_to_tensor_list(
input: oneflow_api.BlobDesc,
shape: Sequence[int],
dtype: dtype_util.dtype,
name: Optional[str] = None,
) -> oneflow_api.BlobDesc:
"""This operator converts `TensorBuffer` to `TensorList`.
Refer to `Concept Explanation <https://docs.oneflow.org/basics_topics/concept_explanation.html#3tensorbuffer-tensorlist>`_
for more about TensorList.
Args:
input (oneflow_api.BlobDesc): The input Tensor Buffer.
shape (Sequence[int]): The shape of input Tensor Buffer.
dtype (dtype_util.dtype): The data type.
name (Optional[str], optional): The name for the operation. Defaults to None.
Returns:
oneflow_api.BlobDesc: The result Blob.
For example:
.. code-block:: python
import oneflow as flow
import numpy as np
import oneflow.typing as tp
@flow.global_function()
def tensorBuffer_to_tensorList_Job(x: tp.Numpy.Placeholder(shape=(4, 16, 64, 64), dtype=flow.float32),
) -> tp.ListListNumpy:
x = flow.tensor_to_tensor_buffer(x,
instance_dims=3)
out = flow.tensor_buffer_to_tensor_list(input=x,
shape=(16, 64, 64),
dtype=flow.float32)
return out
x = np.random.randn(4, 16, 64, 64).astype(np.float32)
out = tensorBuffer_to_tensorList_Job(x)
# out[0][0].shape (1, 16, 64, 64)
"""
if name is None:
name = id_util.UniqueStr("TensorBufferToList_")
op_conf = op_conf_util.OperatorConf()
setattr(op_conf, "name", name)
setattr(op_conf.tensor_buffer_to_tensor_list_conf, "in", input.unique_name)
setattr(op_conf.tensor_buffer_to_tensor_list_conf, "out", "out")
op_conf.tensor_buffer_to_tensor_list_conf.shape.dim[:] = list(shape)
setattr(
op_conf.tensor_buffer_to_tensor_list_conf,
"data_type",
dtype.oneflow_proto_dtype,
)
interpret_util.Forward(op_conf)
lbi = logical_blob_id_util.LogicalBlobId()
lbi.op_name = op_conf.name
lbi.blob_name = "out"
return remote_blob_util.RemoteBlob(lbi)
