tensor
Functions:
Name | Description |
---|---|
cast_to_device |
Make a deep copy of value, casting all tensors to the given device and dtype. |
collect_devices |
Collect all devices in the given value. |
collect_floating_point_dtypes |
Collect all floating point dtypes in the given value. |
hash_tensor_data |
Compute the hash of the tensor's data represented as a string. |
cast_to_device
¶
cast_to_device(
value: T,
*,
device: str | int | device | None = None,
dtype: dtype | None = None,
) -> T
Make a deep copy of value, casting all tensors to the given device and dtype.
Adapted from: https://github.com/pytorch/pytorch/blob/49444c3e546bf240bed24a101e747422d1f8a0ee/torch/optim/optimizer.py#L209C1-L225C29.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
|
T
|
The value to recursively copy and cast. |
required |
|
str | int | device | None
|
The device to cast tensors to. |
None
|
|
dtype | None
|
The dtype to cast tensors. Only applied to floating point tensors. |
None
|
Returns:
Type | Description |
---|---|
T
|
The copied and casted value. |
collect_devices
¶
collect_floating_point_dtypes
¶
collect_floating_point_dtypes(
value: Tensor
| dict[Any, Any]
| UserDict[Any, Any]
| Iterable[Any]
| Any,
) -> set[torch.dtype]
Collect all floating point dtypes in the given value.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
|
Tensor | dict[Any, Any] | UserDict[Any, Any] | Iterable[Any] | Any
|
The value to recursively collect floating point dtypes from. |
required |
Returns:
Type | Description |
---|---|
set[torch.dtype]
|
The set of all floating point dtypes in the given value. |
hash_tensor_data
¶
Compute the hash of the tensor's data represented as a string.
Note
Since 0 and -0 have different byte representations, they will produce different hash values.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
|
Tensor
|
The tensor whose data to hash. |
required |
Returns:
Type | Description |
---|---|
int
|
The hash of the tensor's data. |
Changed in version 0.76.0: Moved hash_tensor to its own utility and renamed for clarity that the hash operates on the tensor data.