Set torch default dtype in a context manager (vllm-project#971)

Signed-off-by: Antoni Baum <[email protected]>

vllm/model_executor/model_loader.py

@@ -1,4 +1,5 @@
 """Utilities for selecting and loading models."""
+import contextlib
 from typing import Type
 
 import torch
@@ -30,6 +31,15 @@
 }
 
 
+@contextlib.contextmanager
+def _set_default_torch_dtype(dtype: torch.dtype):
+    """Sets the default torch dtype to the given dtype."""
+    old_dtype = torch.get_default_dtype()
+    torch.set_default_dtype(dtype)
+    yield
+    torch.set_default_dtype(old_dtype)
+
+
 def _get_model_architecture(config: PretrainedConfig) -> Type[nn.Module]:
     architectures = getattr(config, "architectures", [])
     for arch in architectures:
@@ -42,19 +52,18 @@ def _get_model_architecture(config: PretrainedConfig) -> Type[nn.Module]:
 
 def get_model(model_config: ModelConfig) -> nn.Module:
     model_class = _get_model_architecture(model_config.hf_config)
-    torch.set_default_dtype(model_config.dtype)
-
-    # Create a model instance.
-    # The weights will be initialized as empty tensors.
-    model = model_class(model_config.hf_config)
-    if model_config.use_dummy_weights:
-        model = model.cuda()
-        # NOTE(woosuk): For accurate performance evaluation, we assign
-        # random values to the weights.
-        initialize_dummy_weights(model)
-    else:
-        # Load the weights from the cached or downloaded files.
-        model.load_weights(model_config.model, model_config.download_dir,
-                           model_config.use_np_weights)
-        model = model.cuda()
+    with _set_default_torch_dtype(model_config.dtype):
+        # Create a model instance.
+        # The weights will be initialized as empty tensors.
+        model = model_class(model_config.hf_config)
+        if model_config.use_dummy_weights:
+            model = model.cuda()
+            # NOTE(woosuk): For accurate performance evaluation, we assign
+            # random values to the weights.
+            initialize_dummy_weights(model)
+        else:
+            # Load the weights from the cached or downloaded files.
+            model.load_weights(model_config.model, model_config.download_dir,
+                               model_config.use_np_weights)
+            model = model.cuda()
     return model.eval()