Add device argument for multi-backends access & Ascend NPU support

[MengqingCao]

🚀 Feature

1. Add a --device arg to determine the backend, and modify the hard code related to cuda to accelerator.
2. Add Ascend NPU support by introducing torch-npu adapter.

   Motivation & Examples

Currently, PyTorch supports many accelerators besides NVIDIA GPU, e.g., XLA devices (like TPUs), XPU, MPS and Ascend NPU. Adding a --device argument for users to specify the accelerator they would like to use is helpful. If this is acceptable for community, I would like to do this work.

Moreover, on the basis of device arg, I would like to add support for Ascend NPU backend for detectron2.

A tiny example The modify of _distributed_worker func:

def _distributed_worker(
     local_rank,
     main_func,
     world_size,
-    num_gpus_per_machine,
+    num_accelerators_per_machine,
     machine_rank,
     dist_url,
     args,
     timeout=DEFAULT_TIMEOUT,
 ):
-    has_gpu = torch.cuda.is_available()
-    if has_gpu:
-        assert num_gpus_per_machine <= torch.cuda.device_count()
-    global_rank = machine_rank * num_gpus_per_machine + local_rank
+    device = args[0].device
+    dist_backend = "gloo"
+    if "cuda" in device:
+        if torch.cuda.is_available():
+            assert num_accelerators_per_machine <= torch.cuda.device_count()
+            dist_backend = "nccl"
+    elif "npu" in device:
+        if torch.npu.is_available():
+            assert num_accelerators_per_machine <= torch.npu.device_count()
+            dist_backend = "hccl"
+    global_rank = machine_rank * num_accelerators_per_machine + local_rank
     try:
         dist.init_process_group(
-            backend="NCCL" if has_gpu else "GLOO",
+            backend=dist_backend,
             init_method=dist_url,
             init_method=dist_url,
             world_size=world_size,
             rank=global_rank,
            timeout=timeout,
        )
     except Exception as e:
        logger = logging.getLogger(__name__)
        logger.error("Process group URL: {}".format(dist_url))
        raise e

     # Setup the local process group.
-    comm.create_local_process_group(num_gpus_per_machine)
-    if has_gpu:
+    comm.create_local_process_group(num_accelerators_per_machine)
+    if torch.cuda.is_available():
         torch.cuda.set_device(local_rank)

    # synchronize is needed here to prevent a possible timeout after calling init_process_group
    # See: https://github.com/facebookresearch/maskrcnn-benchmark/issues/172
    comm.synchronize()

    main_func(*args)

Related Info

torch.device : https://pytorch.org/docs/stable/tensor_attributes.html#torch-device

torch Praviteuse1 (Registering new backend module to Pytorch) : https://pytorch.org/tutorials/advanced/privateuseone.html

[Programmer-RD-AI]

I think this is an extremely useful feature to add...!

[MengqingCao]

I think this is an extremely useful feature to add...!

Thanks for your approval, I‘ ll do this work and submit a PR soon

[Programmer-RD-AI]

ah ok great.. Create a comment here and I will checkout the PR :)