[booster] added the accelerator implementation (#3159)

colossalai/booster/accelerator.py

@@ -3,12 +3,52 @@ import torch.nn as nn
 
 __all__ = ['Accelerator']
 
+_supported_devices = [
+    'cpu',
+    'cuda',
+
+    # To be supported
+    # 'xpu',
+    # 'npu',
+    # 'tpu',
+]
+
 
 class Accelerator:
+    """
+    Accelerator is an abstraction for the hardware device that is used to run the model.
 
-    def __init__(self, device: torch.device):
+    Args:
+        device (str): The device to be used. Currently only support 'cpu' and 'gpu'.
+    """
+
+    def __init__(self, device: str):
         self.device = device
 
-    def setup_model(self, model: nn.Module) -> nn.Module:
-        # TODO: implement this method
-        pass
+        assert self.device in _supported_devices, f"Device {self.device} is not supported yet, supported devices include {_supported_devices}"
+
+    def bind(self):
+        """
+        Set the default device for the current process.
+        """
+        if self.device == 'cpu':
+            pass
+        elif self.device == 'cuda':
+            # TODO(FrankLeeeee): use global environment to check if it is a dist job
+            # if is_distributed:
+            #     local_rank = EnvTable().get_local_rank()
+            #     torch.cuda.set_device(torch.device(f'cuda:{local_rank}'))
+            torch.cuda.set_device(torch.device('cuda'))
+            pass
+        else:
+            raise ValueError(f"Device {self.device} is not supported yet")
+
+    def configure_model(self, model: nn.Module) -> nn.Module:
+        """
+        Move the model to the device.
+
+        Args:
+            model (nn.Module): The model to be moved.
+        """
+        model = model.to(torch.device(self.device))
+        return model

colossalai/booster/booster.py

@@ -8,6 +8,7 @@ from torch.optim import Optimizer
 from torch.optim.lr_scheduler import _LRScheduler as LRScheduler
 from torch.utils.data import DataLoader
 
+from .accelerator import Accelerator
 from .mixed_precision import MixedPrecision, mixed_precision_factory
 from .plugin import Plugin
 
@@ -51,9 +52,16 @@ class Booster:
     """
 
     def __init__(self,
-                 device: Union[str, torch.device] = 'cuda',
+                 device: str = 'cuda',
                  mixed_precision: Union[MixedPrecision, str] = None,
                  plugin: Optional[Plugin] = None) -> None:
+        # TODO(FrankLeeeee): add plugin control logic
+        # if self.plugin is not None and self.plugin.control_accelerator:
+        #     ...
+        # create acclerator
+        self.acceleartor = Accelerator(device)
+        self.acceleartor.set_default_device()
+
         # validate and set precision
         if isinstance(MixedPrecision, str):
             # the user will take the default arguments for amp training
@@ -78,6 +86,11 @@ class Booster:
             lr_scheduler (LRScheduler): The lr_scheduler to be boosted.
             dataloader (DataLoader): The dataloader to be boosted.
         """
+        # TODO(FrankLeeeee): add plugin control logic
+        # if self.plugin is not None and self.plugin.control_accelerator:
+        #     ...
+        model = self.acceleartor.configure_model(model)
+
         # TODO(FrankLeeeee): consider multi-model and multi-optimizer case
         # TODO(lsg): Add plugin control logic
         # e.g.

tests/test_booster/test_accelerator.py

@@ -0,0 +1,13 @@
+import pytest
+import torch.nn as nn
+from torchvision.models import resnet18
+
+from colossalai.booster.accelerator import Accelerator
+
+
+@pytest.mark.parametrize('device', ['cpu', 'cuda'])
+def test_accelerator(device):
+    acceleartor = Accelerator(device)
+    model = nn.Linear(8, 8)
+    model = acceleartor.configure_model(model)
+    assert next(model.parameters()).device.type == device

tests/test_fx/test_tracer/test_torchrec_model/test_dlrm_model.py

@@ -56,6 +56,7 @@ def test_torchrec_dlrm_models():
         data = data_gen_fn()
 
         # dlrm_interactionarch is not supported
+        # TODO(FrankLeeeee): support this model
         if name == 'dlrm_interactionarch':
             continue