Explicitly disable P2P using launch, and pick up in state if a user will face issues.

src/accelerate/commands/launch.py

@@ -34,6 +34,7 @@
     DistributedType,
     PrepareForLaunch,
     _filter_args,
+    check_cuda_p2p_ib_support,
     is_bf16_available,
     is_deepspeed_available,
     is_npu_available,
@@ -642,6 +643,17 @@ def multi_gpu_launcher(args):
     import torch.distributed.run as distrib_run
 
     current_env = prepare_multi_gpu_env(args)
+    if not check_cuda_p2p_ib_support():
+        message = "Using RTX 3090 or 4000 series which doesn't support faster communication speedups. Ensuring P2P and IB communications are disabled."
+        warn = False
+        if "NCCL_P2P_DISABLE" not in current_env:
+            current_env["NCCL_P2P_DISABLE"] = str(1)
+            warn = True
+        if "NCCL_IB_DISABLE" not in current_env:
+            current_env["NCCL_IB_DISABLE"] = str(1)
+            warn = True
+        if warn:
+            logger.warning(message)
 
     debug = getattr(args, "debug", False)
     args = _filter_args(
@@ -668,6 +680,17 @@ def deepspeed_launcher(args):
         raise ImportError("DeepSpeed is not installed => run `pip3 install deepspeed` or build it from source.")
 
     cmd, current_env = prepare_deepspeed_cmd_env(args)
+    if not check_cuda_p2p_ib_support():
+        message = "Using RTX 3090 or 4000 series which doesn't support faster communication speedups. Ensuring P2P and IB communications are disabled."
+        warn = False
+        if "NCCL_P2P_DISABLE" not in current_env:
+            current_env["NCCL_P2P_DISABLE"] = str(1)
+            warn = True
+        if "NCCL_IB_DISABLE" not in current_env:
+            current_env["NCCL_IB_DISABLE"] = str(1)
+            warn = True
+        if warn:
+            logger.warning(message)
 
     if args.num_machines > 1 and args.deepspeed_multinode_launcher != DEEPSPEED_MULTINODE_LAUNCHERS[1]:
         with open(".deepspeed_env", "a") as f:

src/accelerate/state.py

@@ -28,6 +28,7 @@
     DistributedType,
     DynamoBackend,
     GradientAccumulationPlugin,
+    check_cuda_p2p_ib_support,
     get_ccl_version,
     get_int_from_env,
     is_ccl_available,
@@ -181,6 +182,14 @@ def __init__(self, cpu: bool = False, **kwargs):
                         self.backend = "nccl"
                     dist.init_distributed(dist_backend=self.backend, auto_mpi_discovery=False, **kwargs)
 
+                if not check_cuda_p2p_ib_support():
+                    if "NCCL_P2P_DISABLE" not in os.environ or "NCCL_IB_DISABLE" not in os.environ:
+                        raise NotImplementedError(
+                            "Using RTX 3090 or 4000 series doesn't support faster communication broadband via P2P or IB. "
+                            'Please set `NCCL_P2P_DISABLE="1"` and `NCCL_IB_DISABLE="1" or use `accelerate launch` which '
+                            "will do this automatically."
+                        )
+
                 self.num_processes = torch.distributed.get_world_size()
                 self.process_index = torch.distributed.get_rank()
                 self.local_process_index = int(os.environ.get("LOCAL_RANK", -1))
@@ -206,6 +215,13 @@ def __init__(self, cpu: bool = False, **kwargs):
                     if self.backend is None:
                         self.backend = "nccl"
                     torch.distributed.init_process_group(backend=self.backend, **kwargs)
+                if not check_cuda_p2p_ib_support():
+                    if "NCCL_P2P_DISABLE" not in os.environ or "NCCL_IB_DISABLE" not in os.environ:
+                        raise NotImplementedError(
+                            "Using RTX 3090 or 4000 series doesn't support faster communication broadband via P2P or IB. "
+                            'Please set `NCCL_P2P_DISABLE="1"` and `NCCL_IB_DISABLE="1" or use `accelerate launch` which '
+                            "will do this automatically."
+                        )
                 self.num_processes = torch.distributed.get_world_size()
                 self.process_index = torch.distributed.get_rank()
                 self.local_process_index = int(os.environ.get("LOCAL_RANK", -1))

src/accelerate/utils/__init__.py

@@ -39,6 +39,7 @@
 )
 from .environment import (
     are_libraries_initialized,
+    check_cuda_p2p_ib_support,
     get_int_from_env,
     parse_choice_from_env,
     parse_flag_from_env,

src/accelerate/utils/environment.py

@@ -16,6 +16,8 @@
 import sys
 from typing import Dict
 
+import torch
+
 
 def str_to_bool(value) -> int:
     """
@@ -57,3 +59,19 @@ def are_libraries_initialized(*library_names: str) -> Dict[str, bool]:
     Checks if any of `library_names` are imported in the environment. Will return results as a `key:bool` pair.
     """
     return [lib_name for lib_name in library_names if lib_name in sys.modules]
+
+
+def check_cuda_p2p_ib_support():
+    """
+    Checks if the devices being used have issues with P2P and IB communications, namely any consumer GPU hardware after
+    the 3090.
+    """
+    if torch.cuda.is_available():
+        # Get the first device/default
+        device_name = torch.cuda.get_device_name()
+        device_count = torch.cuda.device_count()
+        unsupported_devices = ["RTX 3090", "RTX 40"]
+        if device_count > 1:
+            if any(device in device_name for device in unsupported_devices):
+                return False
+    return True