Auto-detect NCCL network configuration - SLURM (#565)

Co-authored-by: Hossein Kavianihamedani <hosseinkh@fb.com>

Author: HosseinKaviani-H

src/forge/controller/provisioner.py

@@ -134,6 +134,23 @@ async def set_environment(proc_mesh: ProcMesh, env_vars: dict[str, str]):
     await env_setter.set_env.call(env_vars)
 
 
+def get_nccl_env_vars() -> dict[str, str]:
+    """Get NCCL environment variables by detecting network interfaces."""
+    if "NCCL_SOCKET_IFNAME" in os.environ and "NCCL_IB_DISABLE" in os.environ:
+        return {}
+
+    try:
+        interfaces = os.listdir("/sys/class/net/")
+        ib_interfaces = [i for i in interfaces if i.startswith("ib")]
+
+        return {
+            "NCCL_SOCKET_IFNAME": ",".join(ib_interfaces) if ib_interfaces else "^lo",
+            "NCCL_IB_DISABLE": "0" if ib_interfaces else "1",
+        }
+    except Exception:
+        return {"NCCL_SOCKET_IFNAME": "^lo", "NCCL_IB_DISABLE": "1"}
+
+
 class GpuManager:
     """Tracks and assigns GPU devices on a host.
 
@@ -347,11 +364,16 @@ async def get_proc_mesh(
             if with_gpus:
                 if not addr or not port:
                     addr, port = await get_remote_info(host_mesh)
-                gpu_ids = gpu_manager.get_gpus(num_procs)
+                gpu_ids: list[str] = gpu_manager.get_gpus(num_procs)
 
+                # Set PyTorch distributed environment variables
                 env_vars["MASTER_ADDR"] = addr
                 env_vars["MASTER_PORT"] = port
 
+                # Get NCCL-specific environment variables
+                nccl_vars = await get_nccl_env_vars()
+                env_vars.update(nccl_vars)
+
                 # Set the PTD world size
                 world_size = num_procs * (num_hosts or 1)
                 env_vars["WORLD_SIZE"] = str(world_size)

tests/integration_tests/test_titan_fwd_vs_hf_fwd.py

@@ -213,7 +213,7 @@ def compare_logits(
                 hf_val = hf_logits_cpu[pos].item()
                 diff_val = abs_diff[pos].item()
                 print(
-                    f"  {i+1}. Position {pos}: titan={titan_val:.6f}, hf={hf_val:.6f}, diff={diff_val:.6f}"
+                    f"  {i + 1}. Position {pos}: titan={titan_val:.6f}, hf={hf_val:.6f}, diff={diff_val:.6f}"
                 )
 
     return metrics
@@ -242,12 +242,12 @@ def compare_probabilities(
             zip(titan_top_k.values, titan_top_k.indices)
         ):
             token = tokenizer.decode([token_id.item()])
-            print(f"  {i+1}. '{token}' (id={token_id.item()}): {prob.item():.6f}")
+            print(f"  {i + 1}. '{token}' (id={token_id.item()}): {prob.item():.6f}")
 
         print("\nHugging Face Top-K:")
         for i, (prob, token_id) in enumerate(zip(hf_top_k.values, hf_top_k.indices)):
             token = tokenizer.decode([token_id.item()])
-            print(f"  {i+1}. '{token}' (id={token_id.item()}): {prob.item():.6f}")
+            print(f"  {i + 1}. '{token}' (id={token_id.item()}): {prob.item():.6f}")
 
     # Calculate overlap in top-k predictions
     titan_top_tokens = set(titan_top_k.indices.tolist())

tests/unit_tests/datasets/test_hf.py

@@ -231,10 +231,10 @@ def test_shuffling_behavior(self, dataset_factory, small_dataset_file):
         # But should contain the same set of IDs
         assert set(first_epoch_ids) == set(
             range(SMALL_DATASET_SIZE)
-        ), f"First epoch samples should be (0-{SMALL_DATASET_SIZE-1}), got {first_epoch_ids}"
+        ), f"First epoch samples should be (0-{SMALL_DATASET_SIZE - 1}), got {first_epoch_ids}"
         assert set(second_epoch_ids) == set(
             range(SMALL_DATASET_SIZE)
-        ), f"Second epoch samples should be (0-{SMALL_DATASET_SIZE-1}), got {second_epoch_ids}"
+        ), f"Second epoch samples should be (0-{SMALL_DATASET_SIZE - 1}), got {second_epoch_ids}"
 
     def test_epoch_tracking(self, dataset_factory, small_dataset_file):
         """Test that epoch number is correctly tracked across dataset restarts."""