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# Copyright The Lightning AI team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Utilities used for collections."""
from abc import ABC
from functools import partial
from typing import TYPE_CHECKING, Any, Callable, Union
import torch
from lightning_utilities.core.apply_func import apply_to_collection
from torch import Tensor
from lightning.fabric.utilities.imports import _NUMPY_AVAILABLE
from lightning.fabric.utilities.types import _DEVICE
if TYPE_CHECKING:
import numpy as np
_BLOCKING_DEVICE_TYPES = ("cpu", "mps")
def _from_numpy(value: "np.ndarray", device: _DEVICE) -> Tensor:
return torch.from_numpy(value).to(device)
CONVERSION_DTYPES: list[tuple[Any, Callable[[Any, Any], Tensor]]] = [
# bool -> uint8 as bool -> torch.bool triggers RuntimeError: Unsupported data type for NCCL process group
(bool, partial(torch.tensor, dtype=torch.uint8)),
(int, partial(torch.tensor, dtype=torch.int)),
(float, partial(torch.tensor, dtype=torch.float)),
]
if _NUMPY_AVAILABLE:
import numpy as np
CONVERSION_DTYPES.append((np.ndarray, _from_numpy))
class _TransferableDataType(ABC):
"""A custom type for data that can be moved to a torch device via ``.to(...)``.
Example:
>>> isinstance(dict, _TransferableDataType)
False
>>> isinstance(torch.rand(2, 3), _TransferableDataType)
True
>>> class CustomObject:
... def __init__(self):
... self.x = torch.rand(2, 2)
... def to(self, device):
... self.x = self.x.to(device)
... return self
>>> isinstance(CustomObject(), _TransferableDataType)
True
"""
@classmethod
def __subclasshook__(cls, subclass: Any) -> Union[bool, Any]:
if cls is _TransferableDataType:
to = getattr(subclass, "to", None)
return callable(to)
return NotImplemented
def move_data_to_device(batch: Any, device: _DEVICE) -> Any:
"""Transfers a collection of data to the given device. Any object that defines a method ``to(device)`` will be
moved and all other objects in the collection will be left untouched.
Args:
batch: A tensor or collection of tensors or anything that has a method ``.to(...)``.
See :func:`apply_to_collection` for a list of supported collection types.
device: The device to which the data should be moved
Return:
the same collection but with all contained tensors residing on the new device.
See Also:
- :meth:`torch.Tensor.to`
- :class:`torch.device`
"""
if isinstance(device, str):
device = torch.device(device)
def batch_to(data: Any) -> Any:
kwargs = {}
# Don't issue non-blocking transfers to CPU
# Same with MPS due to a race condition bug: https://github.com/pytorch/pytorch/issues/83015
if isinstance(data, Tensor) and isinstance(device, torch.device) and device.type not in _BLOCKING_DEVICE_TYPES:
kwargs["non_blocking"] = True
data_output = data.to(device, **kwargs)
if data_output is not None:
return data_output
# user wrongly implemented the `_TransferableDataType` and forgot to return `self`.
return data
return apply_to_collection(batch, dtype=_TransferableDataType, function=batch_to)
def convert_to_tensors(data: Any, device: _DEVICE) -> Any:
# convert non-tensors
for src_dtype, conversion_func in CONVERSION_DTYPES:
data = apply_to_collection(data, src_dtype, conversion_func, device=device)
return move_data_to_device(data, device)
def convert_tensors_to_scalars(data: Any) -> Any:
"""Recursively walk through a collection and convert single-item tensors to scalar values.
Raises:
ValueError:
If tensors inside ``metrics`` contains multiple elements, hence preventing conversion to a scalar.
"""
def to_item(value: Tensor) -> Union[int, float, bool]:
if value.numel() != 1:
raise ValueError(
f"The metric `{value}` does not contain a single element, thus it cannot be converted to a scalar."
)
return value.item()
return apply_to_collection(data, Tensor, to_item)
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