Improve device support and add support for Apple Silicon chipset (`mp…

…s`) (AdaptiveMotorControlLab#34)

* add to() method to move cebra models (sklearn API) from devices

* better name of test

* assign self.device_ if it exists only

* modify check_device() to allow GPU id specification

* adapt test given the possibility of specifying GPU ids

* add support for mps device

* add mps to _set_device() in io

* add mps logic when cuda_if_available + fix test for torch versions < 1.12

* fix test when cuda is not available

* fix test when pytorch < 1.12

cebra/helper.py

@@ -21,6 +21,7 @@
 
 import numpy as np
 import numpy.typing as npt
+import pkg_resources
 import requests
 import torch
 
@@ -61,6 +62,17 @@ def download_file_from_zip_url(url, file="montblanc_tracks.h5"):
     return pathlib.Path(foldername) / "data" / file
 
 
+def _is_mps_availabe(torch):
+    available = False
+    if pkg_resources.parse_version(
+            torch.__version__) >= pkg_resources.parse_version("1.12"):
+        if torch.backends.mps.is_available():
+            if torch.backends.mps.is_built():
+                available = True
+
+    return available
+
+
 def _is_integer(y: Union[npt.NDArray, torch.Tensor]) -> bool:
     """Check if the values in ``y`` are :py:class:`int`.
 

cebra/integrations/sklearn/cebra.py

@@ -1256,3 +1256,52 @@ def load(cls,
             raise RuntimeError("Model loaded from file is not compatible with "
                                "the current CEBRA version.")
         return model
+
+    def to(self, device: Union[str, torch.device]):
+        """Moves the cebra model to the specified device.
+
+        Args:
+            device: The device to move the cebra model to. This can be a string representing
+                    the device ('cpu','cuda', cuda:device_id, or 'mps') or a torch.device object.
+
+        Returns:
+            The cebra model instance.
+
+        Example:
+
+            >>> import cebra
+            >>> import numpy as np
+            >>> dataset =  np.random.uniform(0, 1, (1000, 30))
+            >>> cebra_model = cebra.CEBRA(max_iterations=10, device = "cuda_if_available")
+            >>> cebra_model.fit(dataset)
+            CEBRA(max_iterations=10)
+            >>> cebra_model = cebra_model.to("cpu")
+        """
+
+        if not isinstance(device, (str, torch.device)):
+            raise TypeError(
+                "The 'device' parameter must be a string or torch.device object."
+            )
+
+        if (not device == 'cpu') and (not device.startswith('cuda')) and (
+                not device == 'mps'):
+            raise ValueError(
+                "The 'device' parameter must be a valid device string or device object."
+            )
+
+        if isinstance(device, str):
+            device = torch.device(device)
+
+        if (not device.type == 'cpu') and (
+                not device.type.startswith('cuda')) and (not device == 'mps'):
+            raise ValueError(
+                "The 'device' parameter must be a valid device string or device object."
+            )
+
+        if hasattr(self, "device_"):
+            self.device_ = device
+
+        self.device = device
+        self.solver_.model.to(device)
+
+        return self

cebra/integrations/sklearn/utils.py

@@ -15,6 +15,8 @@
 import sklearn.utils.validation as sklearn_utils_validation
 import torch
 
+import cebra.helper
+
 
 def update_old_param(old: dict, new: dict, kwargs: dict, default) -> tuple:
     """Handle deprecated arguments of a function until they are replaced.
@@ -114,16 +116,50 @@ def check_device(device: str) -> str:
         device: The device to return, if possible.
 
     Returns:
-        Either cuda or cpu depending on {device} and availability in the environment.
+        Either cuda, cuda:device_id, mps, or cpu depending on {device} and availability in the environment.
     """
+
     if device == "cuda_if_available":
         if torch.cuda.is_available():
             return "cuda"
+        elif cebra.helper._is_mps_availabe(torch):
+            return "mps"
         else:
             return "cpu"
-    elif device in ["cuda", "cpu"]:
+    elif device.startswith("cuda:") and len(device) > 5:
+        cuda_device_id = device[5:]
+        if cuda_device_id.isdigit():
+            device_count = torch.cuda.device_count()
+            device_id = int(cuda_device_id)
+            if device_id < device_count:
+                return f"cuda:{device_id}"
+            else:
+                raise ValueError(
+                    f"CUDA device {device_id} is not available. Available device IDs are 0 to {device_count - 1}."
+                )
+        else:
+            raise ValueError(
+                f"Invalid CUDA device ID format. Please use 'cuda:device_id' where '{cuda_device_id}' is an integer."
+            )
+    elif device == "cuda" and torch.cuda.is_available():
+        return "cuda:0"
+    elif device == "cpu":
         return device
-    raise ValueError(f"Device needs to be cuda or cpu, but got {device}.")
+    elif device == "mps":
+        if not torch.backends.mps.is_available():
+            if not torch.backends.mps.is_built():
+                raise ValueError(
+                    "MPS not available because the current PyTorch install was not "
+                    "built with MPS enabled.")
+            else:
+                raise ValueError(
+                    "MPS not available because the current MacOS version is not 12.3+ "
+                    "and/or you do not have an MPS-enabled device on this machine."
+                )
+
+        return device
+
+    raise ValueError(f"Device needs to be cuda, cpu or mps, but got {device}.")
 
 
 def check_fitted(model: "cebra.models.Model") -> bool:

cebra/io.py

@@ -75,7 +75,7 @@ def _set_device(self, device):
             return
         if not isinstance(device, str):
             device = device.type
-        if device not in ("cpu", "cuda"):
+        if device not in ("cpu", "cuda", "mps"):
             if device.startswith("cuda"):
                 _, id_ = device.split(":")
                 if int(id_) >= torch.cuda.device_count():