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Refactor torch device types out of od and into _types #829

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merged 13 commits into from
Jul 26, 2023
Merged

Refactor torch device types out of od and into _types #829

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3267557
Refactor torch device types out of od and into _types
mauicv Jul 11, 2023
38ca48b
Update types for device throughout detect
mauicv Jul 11, 2023
24e0bc8
Update saving to account for torch.device
mauicv Jul 11, 2023
65b910f
Update doc string
mauicv Jul 11, 2023
3b79f49
Remove redundant logic in _types
mauicv Jul 11, 2023
d21a882
Add saving test for torch device logic
mauicv Jul 20, 2023
aaa781b
Add pydantic validation for supported torch devices
mauicv Jul 21, 2023
15afda1
Merge branch 'master' into feature/refactor-device-types
mauicv Jul 21, 2023
59ba0c9
Fix save device config docstrings
mauicv Jul 21, 2023
69322a0
Address pr comments
mauicv Jul 21, 2023
0b6cdd3
Add test for device save
mauicv Jul 21, 2023
2d05b32
Fix optional dependency tests
mauicv Jul 21, 2023
a3519f3
Minor change
mauicv Jul 26, 2023
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8 changes: 5 additions & 3 deletions alibi_detect/cd/classifier.py
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Expand Up @@ -3,6 +3,7 @@
from alibi_detect.utils.frameworks import has_pytorch, has_tensorflow, \
BackendValidator, Framework
from alibi_detect.base import DriftConfigMixin
from alibi_detect.utils._types import TorchDeviceType


from sklearn.base import ClassifierMixin
Expand Down Expand Up @@ -43,7 +44,7 @@ def __init__(
epochs: int = 3,
verbose: int = 0,
train_kwargs: Optional[dict] = None,
device: Optional[str] = None,
device: TorchDeviceType = None,
dataset: Optional[Callable] = None,
dataloader: Optional[Callable] = None,
input_shape: Optional[tuple] = None,
Expand Down Expand Up @@ -122,8 +123,9 @@ def __init__(
Optional additional kwargs when fitting the classifier. Only relevant for 'tensorflow' and
'pytorch' backends.
device
Device type used. The default None tries to use the GPU and falls back on CPU if needed.
Can be specified by passing either 'cuda', 'gpu' or 'cpu'. Only relevant for 'pytorch' backend.
Device type used. The default tries to use the GPU and falls back on CPU if needed.
Can be specified by passing either ``'cuda'``, ``'gpu'``, ``'cpu'`` or an instance of
``torch.device``. Only relevant for 'pytorch' backend.
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Just out of curiosity, if you update the intersphinx_mapping like pytorch/pytorch#10400 and then reference torch.device like :py:class:torch.device (should be in backticks), does it work?

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Can I leave this to explore in a separate issue? This PR already has a much wider scope than initially intended! 😅

dataset
Dataset object used during training. Only relevant for 'tensorflow' and 'pytorch' backends.
dataloader
Expand Down
8 changes: 5 additions & 3 deletions alibi_detect/cd/context_aware.py
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Expand Up @@ -4,6 +4,7 @@
from alibi_detect.utils.frameworks import has_pytorch, has_tensorflow, BackendValidator, Framework
from alibi_detect.utils.warnings import deprecated_alias
from alibi_detect.base import DriftConfigMixin
from alibi_detect.utils._types import TorchDeviceType

if has_pytorch:
from alibi_detect.cd.pytorch.context_aware import ContextMMDDriftTorch
Expand Down Expand Up @@ -32,7 +33,7 @@ def __init__(
prop_c_held: float = 0.25,
n_folds: int = 5,
batch_size: Optional[int] = 256,
device: Optional[str] = None,
device: TorchDeviceType = None,
input_shape: Optional[tuple] = None,
data_type: Optional[str] = None,
verbose: bool = False
Expand Down Expand Up @@ -77,8 +78,9 @@ def __init__(
batch_size
If not None, then compute batches of MMDs at a time (rather than all at once).
device
Device type used. The default None tries to use the GPU and falls back on CPU if needed.
Can be specified by passing either 'cuda', 'gpu' or 'cpu'. Only relevant for 'pytorch' backend.
Device type used. The default tries to use the GPU and falls back on CPU if needed.
Can be specified by passing either ``'cuda'``, ``'gpu'``, ``'cpu'`` or an instance of
``torch.device``. Only relevant for 'pytorch' backend.
input_shape
Shape of input data.
data_type
Expand Down
8 changes: 5 additions & 3 deletions alibi_detect/cd/keops/learned_kernel.py
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Expand Up @@ -11,6 +11,7 @@
from alibi_detect.utils.pytorch import get_device, predict_batch
from alibi_detect.utils.pytorch.data import TorchDataset
from alibi_detect.utils.frameworks import Framework
from alibi_detect.utils._types import TorchDeviceType


class LearnedKernelDriftKeops(BaseLearnedKernelDrift):
Expand Down Expand Up @@ -38,7 +39,7 @@ def __init__(
num_workers: int = 0,
verbose: int = 0,
train_kwargs: Optional[dict] = None,
device: Optional[str] = None,
device: TorchDeviceType = None,
dataset: Callable = TorchDataset,
dataloader: Callable = DataLoader,
input_shape: Optional[tuple] = None,
Expand Down Expand Up @@ -108,8 +109,9 @@ def __init__(
train_kwargs
Optional additional kwargs when training the kernel.
device
Device type used. The default None tries to use the GPU and falls back on CPU if needed.
Can be specified by passing either 'cuda', 'gpu' or 'cpu'. Relevant for 'pytorch' and 'keops' backends.
Device type used. The default tries to use the GPU and falls back on CPU if needed.
Can be specified by passing either ``'cuda'``, ``'gpu'``, ``'cpu'`` or an instance of
``torch.device``. Relevant for 'pytorch' and 'keops' backends.
dataset
Dataset object used during training.
dataloader
Expand Down
8 changes: 5 additions & 3 deletions alibi_detect/cd/keops/mmd.py
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Expand Up @@ -7,6 +7,7 @@
from alibi_detect.utils.keops.kernels import GaussianRBF
from alibi_detect.utils.pytorch import get_device
from alibi_detect.utils.frameworks import Framework
from alibi_detect.utils._types import TorchDeviceType

logger = logging.getLogger(__name__)

Expand All @@ -25,7 +26,7 @@ def __init__(
configure_kernel_from_x_ref: bool = True,
n_permutations: int = 100,
batch_size_permutations: int = 1000000,
device: Optional[str] = None,
device: TorchDeviceType = None,
input_shape: Optional[tuple] = None,
data_type: Optional[str] = None
) -> None:
Expand Down Expand Up @@ -63,8 +64,9 @@ def __init__(
batch_size_permutations
KeOps computes the n_permutations of the MMD^2 statistics in chunks of batch_size_permutations.
device
Device type used. The default None tries to use the GPU and falls back on CPU if needed.
Can be specified by passing either 'cuda', 'gpu' or 'cpu'.
Device type used. The default tries to use the GPU and falls back on CPU if needed.
Can be specified by passing either ``'cuda'``, ``'gpu'``, ``'cpu'`` or an instance of
``torch.device``.
input_shape
Shape of input data.
data_type
Expand Down
8 changes: 5 additions & 3 deletions alibi_detect/cd/learned_kernel.py
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Expand Up @@ -3,6 +3,7 @@
from alibi_detect.utils.frameworks import has_pytorch, has_tensorflow, has_keops, BackendValidator, Framework
from alibi_detect.utils.warnings import deprecated_alias
from alibi_detect.base import DriftConfigMixin
from alibi_detect.utils._types import TorchDeviceType

if has_pytorch:
from torch.utils.data import DataLoader
Expand Down Expand Up @@ -44,7 +45,7 @@ def __init__(
num_workers: int = 0,
verbose: int = 0,
train_kwargs: Optional[dict] = None,
device: Optional[str] = None,
device: TorchDeviceType = None,
dataset: Optional[Callable] = None,
dataloader: Optional[Callable] = None,
input_shape: Optional[tuple] = None,
Expand Down Expand Up @@ -117,8 +118,9 @@ def __init__(
train_kwargs
Optional additional kwargs when training the kernel.
device
Device type used. The default None tries to use the GPU and falls back on CPU if needed.
Can be specified by passing either 'cuda', 'gpu' or 'cpu'. Relevant for 'pytorch' and 'keops' backends.
Device type used. The default tries to use the GPU and falls back on CPU if needed.
Can be specified by passing either ``'cuda'``, ``'gpu'``, ``'cpu'`` or an instance of
``torch.device``. Relevant for 'pytorch' and 'keops' backends.
dataset
Dataset object used during training.
dataloader
Expand Down
8 changes: 5 additions & 3 deletions alibi_detect/cd/lsdd.py
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Expand Up @@ -3,6 +3,7 @@
from alibi_detect.utils.frameworks import has_pytorch, has_tensorflow, BackendValidator, Framework
from alibi_detect.utils.warnings import deprecated_alias
from alibi_detect.base import DriftConfigMixin
from alibi_detect.utils._types import TorchDeviceType

if has_pytorch:
from alibi_detect.cd.pytorch.lsdd import LSDDDriftTorch
Expand All @@ -26,7 +27,7 @@ def __init__(
n_permutations: int = 100,
n_kernel_centers: Optional[int] = None,
lambda_rd_max: float = 0.2,
device: Optional[str] = None,
device: TorchDeviceType = None,
input_shape: Optional[tuple] = None,
data_type: Optional[str] = None
) -> None:
Expand Down Expand Up @@ -68,8 +69,9 @@ def __init__(
The maximum relative difference between two estimates of LSDD that the regularization parameter
lambda is allowed to cause. Defaults to 0.2 as in the paper.
device
Device type used. The default None tries to use the GPU and falls back on CPU if needed.
Can be specified by passing either 'cuda', 'gpu' or 'cpu'. Only relevant for 'pytorch' backend.
Device type used. The default tries to use the GPU and falls back on CPU if needed.
Can be specified by passing either ``'cuda'``, ``'gpu'``, ``'cpu'`` or an instance of
``torch.device``. Only relevant for 'pytorch' backend.
input_shape
Shape of input data.
data_type
Expand Down
9 changes: 6 additions & 3 deletions alibi_detect/cd/lsdd_online.py
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Expand Up @@ -3,6 +3,8 @@
from typing import Any, Callable, Dict, Optional, Union
from alibi_detect.utils.frameworks import has_pytorch, has_tensorflow, BackendValidator, Framework
from alibi_detect.base import DriftConfigMixin
from alibi_detect.utils._types import TorchDeviceType

if has_pytorch:
from alibi_detect.cd.pytorch.lsdd_online import LSDDDriftOnlineTorch

Expand All @@ -23,7 +25,7 @@ def __init__(
n_bootstraps: int = 1000,
n_kernel_centers: Optional[int] = None,
lambda_rd_max: float = 0.2,
device: Optional[str] = None,
device: TorchDeviceType = None,
verbose: bool = True,
input_shape: Optional[tuple] = None,
data_type: Optional[str] = None
Expand Down Expand Up @@ -68,8 +70,9 @@ def __init__(
The maximum relative difference between two estimates of LSDD that the regularization parameter
lambda is allowed to cause. Defaults to 0.2 as in the paper.
device
Device type used. The default None tries to use the GPU and falls back on CPU if needed.
Can be specified by passing either 'cuda', 'gpu' or 'cpu'. Only relevant for 'pytorch' backend.
Device type used. The default tries to use the GPU and falls back on CPU if needed.
Can be specified by passing either ``'cuda'``, ``'gpu'``, ``'cpu'`` or an instance of
``torch.device``. Only relevant for 'pytorch' backend.
verbose
Whether or not to print progress during configuration.
input_shape
Expand Down
8 changes: 5 additions & 3 deletions alibi_detect/cd/mmd.py
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Expand Up @@ -4,6 +4,7 @@
from alibi_detect.utils.frameworks import has_pytorch, has_tensorflow, has_keops, BackendValidator, Framework
from alibi_detect.utils.warnings import deprecated_alias
from alibi_detect.base import DriftConfigMixin
from alibi_detect.utils._types import TorchDeviceType

if has_pytorch:
from alibi_detect.cd.pytorch.mmd import MMDDriftTorch
Expand Down Expand Up @@ -33,7 +34,7 @@ def __init__(
configure_kernel_from_x_ref: bool = True,
n_permutations: int = 100,
batch_size_permutations: int = 1000000,
device: Optional[str] = None,
device: TorchDeviceType = None,
input_shape: Optional[tuple] = None,
data_type: Optional[str] = None
) -> None:
Expand Down Expand Up @@ -74,8 +75,9 @@ def __init__(
KeOps computes the n_permutations of the MMD^2 statistics in chunks of batch_size_permutations.
Only relevant for 'keops' backend.
device
Device type used. The default None tries to use the GPU and falls back on CPU if needed.
Can be specified by passing either 'cuda', 'gpu' or 'cpu'. Only relevant for 'pytorch' backend.
Device type used. The default tries to use the GPU and falls back on CPU if needed.
Can be specified by passing either ``'cuda'``, ``'gpu'``, ``'cpu'`` or an instance of
``torch.device``. Only relevant for 'pytorch' backend.
input_shape
Shape of input data.
data_type
Expand Down
8 changes: 5 additions & 3 deletions alibi_detect/cd/mmd_online.py
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Expand Up @@ -3,6 +3,7 @@
from typing import Any, Callable, Dict, Optional, Union
from alibi_detect.utils.frameworks import has_pytorch, has_tensorflow, BackendValidator, Framework
from alibi_detect.base import DriftConfigMixin
from alibi_detect.utils._types import TorchDeviceType

if has_pytorch:
from alibi_detect.cd.pytorch.mmd_online import MMDDriftOnlineTorch
Expand All @@ -23,7 +24,7 @@ def __init__(
kernel: Optional[Callable] = None,
sigma: Optional[np.ndarray] = None,
n_bootstraps: int = 1000,
device: Optional[str] = None,
device: TorchDeviceType = None,
verbose: bool = True,
input_shape: Optional[tuple] = None,
data_type: Optional[str] = None
Expand Down Expand Up @@ -61,8 +62,9 @@ def __init__(
more accurately the desired ERT will be targeted. Should ideally be at least an order of magnitude
larger than the ERT.
device
Device type used. The default None tries to use the GPU and falls back on CPU if needed.
Can be specified by passing either 'cuda', 'gpu' or 'cpu'. Only relevant for 'pytorch' backend.
Device type used. The default tries to use the GPU and falls back on CPU if needed.
Can be specified by passing either ``'cuda'``, ``'gpu'``, ``'cpu'`` or an instance of
``torch.device``. Only relevant for 'pytorch' backend.
verbose
Whether or not to print progress during configuration.
input_shape
Expand Down
15 changes: 9 additions & 6 deletions alibi_detect/cd/model_uncertainty.py
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Expand Up @@ -8,6 +8,7 @@
from alibi_detect.cd.utils import encompass_batching, encompass_shuffling_and_batch_filling
from alibi_detect.utils.frameworks import BackendValidator, Framework
from alibi_detect.base import DriftConfigMixin
from alibi_detect.utils._types import TorchDeviceType

logger = logging.getLogger(__name__)

Expand All @@ -26,7 +27,7 @@ def __init__(
margin_width: float = 0.1,
batch_size: int = 32,
preprocess_batch_fn: Optional[Callable] = None,
device: Optional[str] = None,
device: TorchDeviceType = None,
tokenizer: Optional[Callable] = None,
max_len: Optional[int] = None,
input_shape: Optional[tuple] = None,
Expand Down Expand Up @@ -69,8 +70,9 @@ def __init__(
Optional batch preprocessing function. For example to convert a list of objects to a batch which can be
processed by the model.
device
Device type used. The default None tries to use the GPU and falls back on CPU if needed.
Can be specified by passing either 'cuda', 'gpu' or 'cpu'. Only relevant for 'pytorch' backend.
Device type used. The default tries to use the GPU and falls back on CPU if needed.
Can be specified by passing either ``'cuda'``, ``'gpu'``, ``'cpu'`` or an instance of
``torch.device``. Only relevant for 'pytorch' backend.
tokenizer
Optional tokenizer for NLP models.
max_len
Expand Down Expand Up @@ -179,7 +181,7 @@ def __init__(
n_evals: int = 25,
batch_size: int = 32,
preprocess_batch_fn: Optional[Callable] = None,
device: Optional[str] = None,
device: TorchDeviceType = None,
tokenizer: Optional[Callable] = None,
max_len: Optional[int] = None,
input_shape: Optional[tuple] = None,
Expand Down Expand Up @@ -222,8 +224,9 @@ def __init__(
Optional batch preprocessing function. For example to convert a list of objects to a batch which can be
processed by the model.
device
Device type used. The default None tries to use the GPU and falls back on CPU if needed.
Can be specified by passing either 'cuda', 'gpu' or 'cpu'. Only relevant for 'pytorch' backend.
Device type used. The default tries to use the GPU and falls back on CPU if needed.
Can be specified by passing either ``'cuda'``, ``'gpu'``, ``'cpu'`` or an instance of
``torch.device``. Only relevant for 'pytorch' backend.
tokenizer
Optional tokenizer for NLP models.
max_len
Expand Down
8 changes: 5 additions & 3 deletions alibi_detect/cd/pytorch/classifier.py
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Expand Up @@ -13,6 +13,7 @@
from alibi_detect.utils.pytorch.prediction import predict_batch
from alibi_detect.utils.warnings import deprecated_alias
from alibi_detect.utils.frameworks import Framework
from alibi_detect.utils._types import TorchDeviceType


class ClassifierDriftTorch(BaseClassifierDrift):
Expand Down Expand Up @@ -40,7 +41,7 @@ def __init__(
epochs: int = 3,
verbose: int = 0,
train_kwargs: Optional[dict] = None,
device: Optional[str] = None,
device: TorchDeviceType = None,
dataset: Callable = TorchDataset,
dataloader: Callable = DataLoader,
input_shape: Optional[tuple] = None,
Expand Down Expand Up @@ -108,8 +109,9 @@ def __init__(
train_kwargs
Optional additional kwargs when fitting the classifier.
device
Device type used. The default None tries to use the GPU and falls back on CPU if needed.
Can be specified by passing either 'cuda', 'gpu' or 'cpu'.
Device type used. The default tries to use the GPU and falls back on CPU if needed.
Can be specified by passing either ``'cuda'``, ``'gpu'``, ``'cpu'`` or an instance of
``torch.device``.
dataset
Dataset object used during training.
dataloader
Expand Down
8 changes: 5 additions & 3 deletions alibi_detect/cd/pytorch/context_aware.py
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Expand Up @@ -8,6 +8,7 @@
from alibi_detect.utils.warnings import deprecated_alias
from alibi_detect.utils.frameworks import Framework
from alibi_detect.cd._domain_clf import _SVCDomainClf
from alibi_detect.utils._types import TorchDeviceType
from tqdm import tqdm

logger = logging.getLogger(__name__)
Expand All @@ -32,7 +33,7 @@ def __init__(
prop_c_held: float = 0.25,
n_folds: int = 5,
batch_size: Optional[int] = 256,
device: Optional[str] = None,
device: TorchDeviceType = None,
input_shape: Optional[tuple] = None,
data_type: Optional[str] = None,
verbose: bool = False,
Expand Down Expand Up @@ -75,8 +76,9 @@ def __init__(
batch_size
If not None, then compute batches of MMDs at a time (rather than all at once).
device
Device type used. The default None tries to use the GPU and falls back on CPU if needed.
Can be specified by passing either 'cuda', 'gpu' or 'cpu'. Only relevant for 'pytorch' backend.
Device type used. The default tries to use the GPU and falls back on CPU if needed.
Can be specified by passing either ``'cuda'``, ``'gpu'``, ``'cpu'`` or an instance of
``torch.device``. Only relevant for 'pytorch' backend.
input_shape
Shape of input data.
data_type
Expand Down
8 changes: 5 additions & 3 deletions alibi_detect/cd/pytorch/learned_kernel.py
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Expand Up @@ -12,6 +12,7 @@
from alibi_detect.utils.pytorch.data import TorchDataset
from alibi_detect.utils.warnings import deprecated_alias
from alibi_detect.utils.frameworks import Framework
from alibi_detect.utils._types import TorchDeviceType


class LearnedKernelDriftTorch(BaseLearnedKernelDrift):
Expand Down Expand Up @@ -39,7 +40,7 @@ def __init__(
num_workers: int = 0,
verbose: int = 0,
train_kwargs: Optional[dict] = None,
device: Optional[str] = None,
device: TorchDeviceType = None,
dataset: Callable = TorchDataset,
dataloader: Callable = DataLoader,
input_shape: Optional[tuple] = None,
Expand Down Expand Up @@ -108,8 +109,9 @@ def __init__(
train_kwargs
Optional additional kwargs when training the kernel.
device
Device type used. The default None tries to use the GPU and falls back on CPU if needed.
Can be specified by passing either 'cuda', 'gpu' or 'cpu'. Only relevant for 'pytorch' backend.
Device type used. The default tries to use the GPU and falls back on CPU if needed.
Can be specified by passing either ``'cuda'``, ``'gpu'``, ``'cpu'`` or an instance of
``torch.device``. Only relevant for 'pytorch' backend.
dataset
Dataset object used during training.
dataloader
Expand Down
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