Tensor Attributes

Each local oneflow.Tensor has a oneflow.dtype, oneflow.device, and global oneflow.Tensor has a oneflow.dtype, oneflow.placement, oneflow.sbp.

oneflow.dtype

class oneflow.dtype

A oneflow.dtype is an object that represents the data type of a oneflow.Tensor. Oneflow has eight different data types:

Data type

dtype

CPU tensor

GPU tensor

Boolean

oneflow.bool

oneflow.BoolTensor

oneflow.cuda.BoolTensor

8-bit integer (unsigned)

oneflow.uint8

oneflow.ByteTensor

oneflow.cuda.ByteTensor

8-bit integer (signed)

oneflow.int8

oneflow.CharTensor

oneflow.cuda.CharTensor

64-bit floating point

oneflow.float64 or oneflow.double

oneflow.DoubleTensor

oneflow.cuda.DoubleTensor

32-bit floating point

oneflow.float32 or oneflow.float

oneflow.FloatTensor

oneflow.cuda.FloatTensor

16-bit floating point

oneflow.float16 or oneflow.half

oneflow.HalfTensor

oneflow.cuda.HalfTensor

32-bit integer (signed)

oneflow.int32 or oneflow.int

oneflow.IntTensor

oneflow.cuda.IntTensor

64-bit integer (signed)

oneflow.int64 or oneflow.long

oneflow.LongTensor

oneflow.cuda.LongTensor

To find out if a oneflow.dtype is a floating point data type, the property is_floating_point can be used, which returns True if the data type is a floating point data type.

When the dtypes of inputs to an arithmetic operation (add, sub, div, mul) differ, we promote by finding the minimum dtype that satisfies the following rules:

  • If the type of a scalar operand is of a higher category than tensor operands (where complex > floating > integral > boolean), we promote to a type with sufficient size to hold all scalar operands of that category.

  • If a zero-dimension tensor operand has a higher category than dimensioned operands, we promote to a type with sufficient size and category to hold all zero-dim tensor operands of that category.

  • If there are no higher-category zero-dim operands, we promote to a type with sufficient size and category to hold all dimensioned operands.

A floating point scalar operand has dtype oneflow.get_default_dtype() and an integral non-boolean scalar operand has dtype oneflow.int64. Unlike numpy, we do not inspect values when determining the minimum dtypes of an operand. Quantized and complex types are not yet supported.

Promotion Examples:

>>> float_tensor = oneflow.ones(1, dtype=oneflow.float)
>>> double_tensor = oneflow.ones(1, dtype=oneflow.double)
>>> int_tensor = oneflow.ones(1, dtype=oneflow.int)
>>> long_tensor = oneflow.ones(1, dtype=oneflow.long)
>>> uint_tensor = oneflow.ones(1, dtype=oneflow.uint8)
>>> double_tensor = oneflow.ones(1, dtype=oneflow.double)
>>> bool_tensor = oneflow.ones(1, dtype=oneflow.bool)
# zero-dim tensors
>>> long_zerodim = oneflow.tensor(1, dtype=oneflow.long)
>>> int_zerodim = oneflow.tensor(1, dtype=oneflow.int)

>>> a,b=oneflow.tensor(5),oneflow.tensor(5)
>>> oneflow.add(a, b).dtype
oneflow.int64
# 5 is an int64, but does not have higher category than int_tensor so is not considered.
>>> (int_tensor + 5).dtype
oneflow.int32
>>> (int_tensor + long_zerodim).dtype
oneflow.int64
>>> (long_tensor + int_tensor).dtype
oneflow.int64
>>> (bool_tensor + long_tensor).dtype
oneflow.int64
>>> (bool_tensor + uint_tensor).dtype
oneflow.uint8
>>> (float_tensor + double_tensor).dtype
oneflow.float64
>>> (bool_tensor + int_tensor).dtype
oneflow.int32
# Since long is a different kind than float, result dtype only needs to be large enough
# to hold the float.
>>> oneflow.add(long_tensor, float_tensor).dtype
oneflow.float32
When the output tensor of an arithmetic operation is specified, we allow casting to its dtype except that:
  • An integral output tensor cannot accept a floating point tensor.

  • A boolean output tensor cannot accept a non-boolean tensor.

  • A non-complex output tensor cannot accept a complex tensor

Casting Examples:

# allowed:
>>> float_tensor *= float_tensor
>>> float_tensor *= int_tensor
>>> float_tensor *= uint_tensor
>>> float_tensor *= bool_tensor
>>> int_tensor *= uint_tensor

# disallowed (RuntimeError: result type can't be cast to the desired output type):
>>> float_tensor *= double_tensor
>>> int_tensor *= float_tensor
>>> int_tensor *= long_tensor
>>> uint_tensor *= int_tensor
>>> bool_tensor *= int_tensor
>>> bool_tensor *= uint_tensor

oneflow.device

class oneflow.device

A oneflow.device is an object representing the device on which a oneflow.Tensor is or will be allocated.

The oneflow.device contains a device type ('cpu' or 'cuda') and optional device ordinal for the device type. If the device ordinal is not present, this object will always represent the current device for the device type, even after oneflow.cuda.set_device() is called; e.g., a oneflow.Tensor constructed with device 'cuda' is equivalent to 'cuda:X' where X is the result of oneflow.cuda.current_device().

A oneflow.Tensor’s device can be accessed via the Tensor.device property.

A oneflow.device can be constructed via a string or via a string and device ordinal

Via a string:

>>> oneflow.device('cuda:0')
device(type='cuda', index=0)

>>> oneflow.device('cpu')
device(type='cpu', index=0)

>>> oneflow.device('cuda')  # current cuda device
device(type='cuda', index=0)

Via a string and device ordinal:

>>> oneflow.device('cuda', 0)
device(type='cuda', index=0)

>>> oneflow.device('cpu', 0)
device(type='cpu', index=0)

Note

The oneflow.device argument in functions can generally be substituted with a string. This allows for fast prototyping of code.

>>> # Example of a function that takes in a oneflow.device
>>> cuda1 = oneflow.device('cuda:1')
>>> oneflow.randn((2,3), device=cuda1)
>>> # You can substitute the oneflow.device with a string
>>> oneflow.randn((2,3), device='cuda:1')

Note

For legacy reasons, a device can be constructed via a single device ordinal, which is treated as a cuda device. This matches Tensor.get_device(), which returns an ordinal for cuda tensors and is not supported for cpu tensors.

>>> oneflow.device(1)
device(type='cuda', index=1)

Note

Methods which take a device will generally accept a (properly formatted) string or (legacy) integer device ordinal, i.e. the following are all equivalent:

>>> oneflow.randn((2,3), device=oneflow.device('cuda:1'))
>>> oneflow.randn((2,3), device='cuda:1')
>>> oneflow.randn((2,3), device=1)  # legacy

oneflow.placement

class oneflow.placement

A oneflow.placement is an object representing the device group on which a oneflow.Tensor is or will be allocated. The oneflow.placement contains a device type (‘cpu’ or ‘cuda’) and corresponding device sequence.

A oneflow.Tensor’s placement can be accessed via the Tensor.placement property.

A oneflow.placement can be constructed in several ways:

>>> import oneflow as flow

>>> p = flow.placement(type="cuda", ranks=[0, 1, 2, 3])
>>> p
oneflow.placement(type="cuda", ranks=[0, 1, 2, 3])
>>> p = flow.placement(type="cuda", ranks=[[0, 1], [2, 3]])
>>> p
oneflow.placement(type="cuda", ranks=[[0, 1], [2, 3]])

oneflow.placement.all

oneflow.placement.all(device_type)oneflow.placement

Returns a placement that contains all available devices.

Parameters

device_type (str) – cuda or cpu

For examples:

# Runs on 4 ranks
import oneflow as flow

p = flow.placement.all("cuda") # oneflow.placement(type="cuda", ranks=[0, 1, 2, 3])
p = flow.placement.all("cpu") # oneflow.placement(type="cpu", ranks=[0, 1, 2, 3])

oneflow.env.all_device_placement

oneflow.env.all_device_placement(device_type)oneflow.placement

Returns a placement that contains all available devices.

Note

It is recommended to use oneflow.placement.all instead of this function.

Parameters

device_type (str) – cuda or cpu

For examples:

# Runs on 4 ranks
import oneflow as flow

p = flow.env.all_device_placement("cuda") # oneflow.placement(type="cuda", ranks=[0, 1, 2, 3])
p = flow.env.all_device_placement("cpu") # oneflow.placement(type="cpu", ranks=[0, 1, 2, 3])

oneflow.sbp.sbp

class oneflow.sbp.sbp

A oneflow.sbp is an object representing that how the data of the global tensor is distributed across the ranks of the Tensor placement.

oneflow.sbp includes three types:

  • oneflow.sbp.split(dim)

    Indicates that the global tensor is evenly divided according to the dimension dim and distributed on each rank.

  • oneflow.sbp.broadcast()

    Indicates that the global tensor is replicated on each rank.

  • oneflow.sbp.partial_sum()

    Indicates that the value of the global tensor is element-wise sum of the local tensors distributed in each rank.

A oneflow.Tensor’s sbp can be accessed via the Tensor.sbp property.

A oneflow.sbp can be constructed in several ways:

>>> import oneflow as flow

>>> s = flow.sbp.split(0)
>>> s
oneflow.sbp.split(dim=0)
>>> b = flow.sbp.broadcast()
>>> b
oneflow.sbp.broadcast
>>> p = flow.sbp.partial_sum()
>>> p
oneflow.sbp.partial_sum