Fix device id

tutorials/developer_api_guide/tensor_parallel.py

@@ -85,7 +85,7 @@ def _(func, types, args, kwargs):
 class M(torch.nn.Module):
     def __init__(self, in_features, out_features, **kwargs) -> None:
         super().__init__(**kwargs)
-        self.linear = torch.nn.Linear(in_features, out_features, bias=False, device="cuda")
+        self.linear = torch.nn.Linear(in_features, out_features, bias=False)
 
     def forward(self, x: torch.Tensor) -> torch.Tensor:
         return self.linear(x)
@@ -144,10 +144,15 @@ def main():
     # To make sure different ranks create the same module
     torch.manual_seed(5)
 
+    # Get rank and device
+    world_size = int(os.environ["WORLD_SIZE"])
+    rank = int(os.environ["RANK"])
+    device = torch.device(f"cuda:{rank % torch.cuda.device_count()}")
+
     # Original model
-    proj_up = M(1024, 2048)
-    proj_dn = M(2048, 1024)
-    example_input = 100 * torch.randn(128, 1024, device="cuda")
+    proj_up = M(1024, 2048).to(device)
+    proj_dn = M(2048, 1024).to(device)
+    example_input = 100 * torch.randn(128, 1024, device=device)
     y = proj_dn(proj_up(example_input))
 
     # Quantize the model
@@ -157,8 +162,6 @@ def main():
     print("Quantization works!")
 
     # Create a device mesh
-    world_size = int(os.environ["WORLD_SIZE"])
-    rank = int(os.environ["RANK"])
     dist.init_process_group(backend="nccl")
     mesh = dist.init_device_mesh("cuda", (world_size,))