Rename GPUAccelerator to CUDAAccelerator

docs/source-pytorch/api_references.rst

@@ -12,7 +12,7 @@ accelerators
 
     Accelerator
     CPUAccelerator
-    GPUAccelerator
+    CUDAAccelerator
     HPUAccelerator
     IPUAccelerator
     TPUAccelerator

docs/source-pytorch/common/trainer.rst

@@ -249,8 +249,8 @@ Example::
 
     .. code-block:: python
 
-        # This is part of the built-in `GPUAccelerator`
-        class GPUAccelerator(Accelerator):
+        # This is part of the built-in `CUDAAccelerator`
+        class CUDAAccelerator(Accelerator):
             """Accelerator for GPU devices."""
 
             @staticmethod
@@ -603,8 +603,8 @@ based on the accelerator type (``"cpu", "gpu", "tpu", "ipu", "auto"``).
 
     .. code-block:: python
 
-        # This is part of the built-in `GPUAccelerator`
-        class GPUAccelerator(Accelerator):
+        # This is part of the built-in `CUDAAccelerator`
+        class CUDAAccelerator(Accelerator):
             """Accelerator for GPU devices."""
 
             @staticmethod

docs/source-pytorch/extensions/accelerator.rst

@@ -125,7 +125,7 @@ Accelerator API
 
     Accelerator
     CPUAccelerator
-    GPUAccelerator
+    CUDAAccelerator
     HPUAccelerator
     IPUAccelerator
     MPSAccelerator

src/pytorch_lightning/accelerators/__init__.py

@@ -12,7 +12,7 @@
 # limitations under the License.
 from pytorch_lightning.accelerators.accelerator import Accelerator  # noqa: F401
 from pytorch_lightning.accelerators.cpu import CPUAccelerator  # noqa: F401
-from pytorch_lightning.accelerators.gpu import GPUAccelerator  # noqa: F401
+from pytorch_lightning.accelerators.cuda import CUDAAccelerator  # noqa: F401
 from pytorch_lightning.accelerators.hpu import HPUAccelerator  # noqa: F401
 from pytorch_lightning.accelerators.ipu import IPUAccelerator  # noqa: F401
 from pytorch_lightning.accelerators.mps import MPSAccelerator  # noqa: F401

src/pytorch_lightning/accelerators/gpu.py → src/pytorch_lightning/accelerators/cuda.py

@@ -28,8 +28,8 @@
 _log = logging.getLogger(__name__)
 
 
-class GPUAccelerator(Accelerator):
-    """Accelerator for GPU devices."""
+class CUDAAccelerator(Accelerator):
+    """Accelerator for NVIDIA CUDA devices."""
 
     def setup_environment(self, root_device: torch.device) -> None:
         """
@@ -92,6 +92,12 @@ def is_available() -> bool:
 
     @classmethod
     def register_accelerators(cls, accelerator_registry: Dict) -> None:
+        accelerator_registry.register(
+            "cuda",
+            cls,
+            description=f"{cls.__class__.__name__}",
+        )
+        # temporarily enable "gpu" to point to the CUDA Accelerator
         accelerator_registry.register(
             "gpu",
             cls,

src/pytorch_lightning/loops/dataloader/evaluation_loop.py

@@ -23,7 +23,7 @@
 from torch.utils.data.dataloader import DataLoader
 
 import pytorch_lightning as pl
-from pytorch_lightning.accelerators import GPUAccelerator
+from pytorch_lightning.accelerators import CUDAAccelerator
 from pytorch_lightning.callbacks.progress.rich_progress import _RICH_AVAILABLE
 from pytorch_lightning.loops.dataloader import DataLoaderLoop
 from pytorch_lightning.loops.epoch import EvaluationEpochLoop
@@ -411,7 +411,7 @@ def _select_data_fetcher_type(trainer: "pl.Trainer") -> Type[AbstractDataFetcher
         )
         return DataLoaderIterDataFetcher
     elif os.getenv("PL_INTER_BATCH_PARALLELISM", "0") == "1":
-        if not isinstance(trainer.accelerator, GPUAccelerator):
+        if not isinstance(trainer.accelerator, CUDAAccelerator):
             raise MisconfigurationException("Inter batch parallelism is available only when using Nvidia GPUs.")
         return InterBatchParallelDataFetcher
     return DataFetcher

src/pytorch_lightning/loops/fit_loop.py

@@ -17,7 +17,7 @@
 from typing import Optional, Type
 
 import pytorch_lightning as pl
-from pytorch_lightning.accelerators import GPUAccelerator
+from pytorch_lightning.accelerators import CUDAAccelerator
 from pytorch_lightning.loops import Loop
 from pytorch_lightning.loops.epoch import TrainingEpochLoop
 from pytorch_lightning.loops.epoch.training_epoch_loop import _OUTPUTS_TYPE as _EPOCH_OUTPUTS_TYPE
@@ -340,7 +340,7 @@ def _select_data_fetcher(trainer: "pl.Trainer") -> Type[AbstractDataFetcher]:
         )
         return DataLoaderIterDataFetcher
     elif os.getenv("PL_INTER_BATCH_PARALLELISM", "0") == "1":
-        if not isinstance(trainer.accelerator, GPUAccelerator):
+        if not isinstance(trainer.accelerator, CUDAAccelerator):
             raise MisconfigurationException("Inter batch parallelism is available only when using Nvidia GPUs.")
         return InterBatchParallelDataFetcher
     return DataFetcher

src/pytorch_lightning/strategies/deepspeed.py

@@ -27,7 +27,7 @@
 from torch.optim import Optimizer
 
 import pytorch_lightning as pl
-from pytorch_lightning.accelerators.gpu import GPUAccelerator
+from pytorch_lightning.accelerators.cuda import CUDAAccelerator
 from pytorch_lightning.core.optimizer import _init_optimizers_and_lr_schedulers
 from pytorch_lightning.overrides.base import _LightningModuleWrapperBase, _LightningPrecisionModuleWrapperBase
 from pytorch_lightning.plugins.environments.cluster_environment import ClusterEnvironment
@@ -452,7 +452,7 @@ def init_deepspeed(self):
         if self.lightning_module.trainer.gradient_clip_algorithm == GradClipAlgorithmType.VALUE:
             raise MisconfigurationException("DeepSpeed does not support clipping gradients by value.")
 
-        if not isinstance(self.accelerator, GPUAccelerator):
+        if not isinstance(self.accelerator, CUDAAccelerator):
             raise MisconfigurationException(
                 f"DeepSpeed strategy is only supported on GPU but `{self.accelerator.__class__.__name__}` is used."
             )

src/pytorch_lightning/strategies/hivemind.py

@@ -172,9 +172,9 @@ def num_peers(self) -> int:
     @property
     def root_device(self) -> torch.device:
         from pytorch_lightning.accelerators.cpu import CPUAccelerator
-        from pytorch_lightning.accelerators.gpu import GPUAccelerator
+        from pytorch_lightning.accelerators.cuda import CUDAAccelerator
 
-        if isinstance(self.accelerator, GPUAccelerator):
+        if isinstance(self.accelerator, CUDAAccelerator):
             return torch.device(f"cuda:{torch.cuda.current_device()}")
         elif isinstance(self.accelerator, CPUAccelerator):
             return torch.device("cpu")

src/pytorch_lightning/trainer/connectors/accelerator_connector.py

@@ -22,7 +22,7 @@
 
 from pytorch_lightning.accelerators.accelerator import Accelerator
 from pytorch_lightning.accelerators.cpu import CPUAccelerator
-from pytorch_lightning.accelerators.gpu import GPUAccelerator
+from pytorch_lightning.accelerators.cuda import CUDAAccelerator
 from pytorch_lightning.accelerators.hpu import HPUAccelerator
 from pytorch_lightning.accelerators.ipu import IPUAccelerator
 from pytorch_lightning.accelerators.mps import MPSAccelerator
@@ -370,12 +370,12 @@ def _check_config_and_set_final_flags(
                             )
                         self._accelerator_flag = "cpu"
                     if self._strategy_flag.parallel_devices[0].type == "cuda":
-                        if self._accelerator_flag and self._accelerator_flag not in ("auto", "gpu"):
+                        if self._accelerator_flag and self._accelerator_flag not in ("auto", "cuda", "gpu"):
                             raise MisconfigurationException(
                                 f"GPU parallel_devices set through {self._strategy_flag.__class__.__name__} class,"
                                 f" but accelerator set to {self._accelerator_flag}, please choose one device type"
                             )
-                        self._accelerator_flag = "gpu"
+                        self._accelerator_flag = "cuda"
                     self._parallel_devices = self._strategy_flag.parallel_devices
 
         amp_type = amp_type if isinstance(amp_type, str) else None
@@ -475,7 +475,7 @@ def _map_deprecated_devices_specific_info_to_accelerator_and_device_flag(
                 if tpu_cores:
                     self._accelerator_flag = "tpu"
                 if gpus:
-                    self._accelerator_flag = "gpu"
+                    self._accelerator_flag = "cuda"
                 if num_processes:
                     self._accelerator_flag = "cpu"
 
@@ -497,7 +497,7 @@ def _choose_accelerator(self) -> str:
             if MPSAccelerator.is_available():
                 return "mps"
             if torch.cuda.is_available() and torch.cuda.device_count() > 0:
-                return "gpu"
+                return "cuda"
         return "cpu"
 
     def _set_parallel_devices_and_init_accelerator(self) -> None:
@@ -534,7 +534,7 @@ def _set_devices_flag_if_auto_passed(self) -> None:
             self._devices_flag = self.accelerator.auto_device_count()
 
     def _set_devices_flag_if_auto_select_gpus_passed(self) -> None:
-        if self._auto_select_gpus and isinstance(self._gpus, int) and isinstance(self.accelerator, GPUAccelerator):
+        if self._auto_select_gpus and isinstance(self._gpus, int) and isinstance(self.accelerator, CUDAAccelerator):
             self._devices_flag = pick_multiple_gpus(self._gpus)
             log.info(f"Auto select gpus: {self._devices_flag}")
 
@@ -579,8 +579,8 @@ def _choose_strategy(self) -> Union[Strategy, str]:
             return DDPStrategy.strategy_name
         if len(self._parallel_devices) <= 1:
             # TODO: Change this once gpu accelerator was renamed to cuda accelerator
-            if isinstance(self._accelerator_flag, (GPUAccelerator, MPSAccelerator)) or (
-                isinstance(self._accelerator_flag, str) and self._accelerator_flag in ("gpu", "mps")
+            if isinstance(self._accelerator_flag, (CUDAAccelerator, MPSAccelerator)) or (
+                isinstance(self._accelerator_flag, str) and self._accelerator_flag in ("cuda", "gpu", "mps")
             ):
                 device = device_parser.determine_root_gpu_device(self._parallel_devices)
             else:
@@ -609,7 +609,7 @@ def _check_strategy_and_fallback(self) -> None:
         if (
             strategy_flag in DDPFullyShardedNativeStrategy.get_registered_strategies()
             or isinstance(self._strategy_flag, DDPFullyShardedNativeStrategy)
-        ) and self._accelerator_flag != "gpu":
+        ) and self._accelerator_flag not in ("cuda", "gpu"):
             raise MisconfigurationException(
                 f"You selected strategy to be `{DDPFullyShardedNativeStrategy.strategy_name}`, "
                 "but GPU accelerator is not used."
@@ -632,7 +632,7 @@ def _handle_horovod(self) -> None:
             )
 
         hvd.init()
-        if isinstance(self.accelerator, GPUAccelerator):
+        if isinstance(self.accelerator, CUDAAccelerator):
             # Horovod assigns one local GPU per process
             self._parallel_devices = [torch.device(f"cuda:{i}") for i in range(hvd.local_size())]
         else:

src/pytorch_lightning/trainer/trainer.py

@@ -38,7 +38,7 @@
 import pytorch_lightning as pl
 from pytorch_lightning.accelerators import (
     Accelerator,
-    GPUAccelerator,
+    CUDAAccelerator,
     HPUAccelerator,
     IPUAccelerator,
     MPSAccelerator,
@@ -1735,7 +1735,7 @@ def __setup_profiler(self) -> None:
 
     def _log_device_info(self) -> None:
 
-        if GPUAccelerator.is_available():
+        if CUDAAccelerator.is_available():
             gpu_available = True
             gpu_type = " (cuda)"
         elif MPSAccelerator.is_available():
@@ -1745,7 +1745,7 @@ def _log_device_info(self) -> None:
             gpu_available = False
             gpu_type = ""
 
-        gpu_used = isinstance(self.accelerator, (GPUAccelerator, MPSAccelerator))
+        gpu_used = isinstance(self.accelerator, (CUDAAccelerator, MPSAccelerator))
         rank_zero_info(f"GPU available: {gpu_available}{gpu_type}, used: {gpu_used}")
 
         num_tpu_cores = self.num_devices if isinstance(self.accelerator, TPUAccelerator) else 0
@@ -1758,10 +1758,10 @@ def _log_device_info(self) -> None:
         rank_zero_info(f"HPU available: {_HPU_AVAILABLE}, using: {num_hpus} HPUs")
 
         # TODO: Integrate MPS Accelerator here, once gpu maps to both
-        if torch.cuda.is_available() and not isinstance(self.accelerator, GPUAccelerator):
+        if torch.cuda.is_available() and not isinstance(self.accelerator, CUDAAccelerator):
             rank_zero_warn(
                 "GPU available but not used. Set `accelerator` and `devices` using"
-                f" `Trainer(accelerator='gpu', devices={GPUAccelerator.auto_device_count()})`.",
+                f" `Trainer(accelerator='gpu', devices={CUDAAccelerator.auto_device_count()})`.",
                 category=PossibleUserWarning,
             )
 
@@ -2069,7 +2069,7 @@ def root_gpu(self) -> Optional[int]:
             "`Trainer.root_gpu` is deprecated in v1.6 and will be removed in v1.8. "
             "Please use `Trainer.strategy.root_device.index` instead."
         )
-        return self.strategy.root_device.index if isinstance(self.accelerator, GPUAccelerator) else None
+        return self.strategy.root_device.index if isinstance(self.accelerator, CUDAAccelerator) else None
 
     @property
     def tpu_cores(self) -> int:
@@ -2093,7 +2093,7 @@ def num_gpus(self) -> int:
             "`Trainer.num_gpus` was deprecated in v1.6 and will be removed in v1.8."
             " Please use `Trainer.num_devices` instead."
         )
-        return self.num_devices if isinstance(self.accelerator, GPUAccelerator) else 0
+        return self.num_devices if isinstance(self.accelerator, CUDAAccelerator) else 0
 
     @property
     def devices(self) -> int:
@@ -2109,7 +2109,7 @@ def data_parallel_device_ids(self) -> Optional[List[int]]:
             "`Trainer.data_parallel_device_ids` was deprecated in v1.6 and will be removed in v1.8."
             " Please use `Trainer.device_ids` instead."
         )
-        return self.device_ids if isinstance(self.accelerator, GPUAccelerator) else None
+        return self.device_ids if isinstance(self.accelerator, CUDAAccelerator) else None
 
     @property
     def lightning_module(self) -> "pl.LightningModule":

src/pytorch_lightning/utilities/memory.py

@@ -101,7 +101,7 @@ def get_gpu_memory_map() -> Dict[str, float]:
     r"""
     .. deprecated:: v1.5
         This function was deprecated in v1.5 in favor of
-        `pytorch_lightning.accelerators.gpu._get_nvidia_gpu_stats` and will be removed in v1.7.
+        `pytorch_lightning.accelerators.cuda._get_nvidia_gpu_stats` and will be removed in v1.7.
 
     Get the current gpu usage.