fix: preserve RAY_EXPERIMENTAL_NOSET_CUDA_VISIBLE_DEVICES to prevent NCCL NVSwitch bugs

[dmvevents]

Fixes #1963. Related: #1961.

Summary

worker_groups.py removes RAY_EXPERIMENTAL_NOSET_CUDA_VISIBLE_DEVICES from the worker environment (line 501), which forces Ray to set per-actor CUDA_VISIBLE_DEVICES masking (e.g., CUDA_VISIBLE_DEVICES=3 for the 4th GPU). This triggers three confirmed NCCL bugs on NVSwitch topologies (H200 P5en, H100 P5):

1. cuMem import penalty (NVIDIA/nccl#1749) — p2pMap() iterates over all devices when importing cuMem handles with non-overlapping CUDA_VISIBLE_DEVICES. Causes 3,660ms first-operation penalty vs 1.5ms with torchrun.

2. NVLS rank ordering corruption (NVIDIA/nccl#1906) — allgather in nvls.cc is missing a user rank table when GPU indices are permuted by CUDA_VISIBLE_DEVICES. Causes hang or silent data corruption on NVSwitch systems.

3. Multi-channel P2P hang at >8M elements — AllReduce hangs for tensors larger than ~32MB even with NCCL_CUMEM_ENABLE=0 and NCCL_NVLS_ENABLE=0.

Fix

Preserve RAY_EXPERIMENTAL_NOSET_CUDA_VISIBLE_DEVICES=1 instead of removing it. This tells Ray not to mask CUDA_VISIBLE_DEVICES, so each worker sees all GPUs. Workers use explicit torch.cuda.set_device(local_rank) instead, which mirrors torchrun behavior and works correctly on both NVSwitch and NVLink topologies.

Benchmarks (same hardware, same NCCL)

Method	AllReduce 4KB	AllReduce 933MB
torchrun (no GPU masking)	1.5ms	1.5ms
Ray (GPU masking forced)	3,660ms	HANGS forever

Both tests: P5en.48xlarge (8x H200, NVSwitch), NCCL 2.27.5, EFA, same container.

Test plan

• Verified fix on 4x P5en.48xlarge (32x H200) — multi-node GRPO training completes successfully
• Verified no regression on P5.48xlarge (H100, NVLink PXN) — works as before
• LOCAL_RANK is already set in worker_env_vars (line 492), so workers can torch.cuda.set_device() correctly

Summary by CodeRabbit

• Bug Fixes
  • Improved distributed training GPU configuration to prevent unintended CUDA device masking and properly preserve GPU device visibility settings across worker processes.

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[coderabbitai]

📝 Walkthrough

Walkthrough

The change modifies environment variable handling in worker initialization to explicitly preserve the RAY_EXPERIMENTAL_NOSET_CUDA_VISIBLE_DEVICES flag instead of removing it, preventing Ray from applying per-actor GPU masking that causes NCCL issues on H200/NVSwitch hardware.

Changes

Cohort / File(s)	Summary
Ray Environment Variable Handling nemo_rl/distributed/worker_groups.py	Replaced removal of RAY_EXPERIMENTAL_NOSET_CUDA_VISIBLE_DEVICES with explicit assignment of "1" to preserve Ray's no-GPU-masking behavior. Maintains removal of other Ray-specific environment variables (RAY_CLIENT_MODE, RAY_JOB_ID, RAY_LD_PRELOAD, RAY_RAYLET_PID, RAY_USAGE_STATS_ENABLED).

Estimated code review effort

🎯 1 (Trivial) | ⏱️ ~3 minutes

🚥 Pre-merge checks | ✅ 5 | ❌ 1

❌ Failed checks (1 warning)

Check name	Status	Explanation	Resolution
Test Results For Major Changes	⚠️ Warning	PR fixes critical GPU/NCCL issue on NVSwitch hardware affecting performance and correctness, but test results are not formally documented in PR description despite being mentioned in objectives.	Add formal Testing section to PR description documenting P5en and P5 benchmark results, regression testing outcomes, and GRPO multi-node training verification details.

✅ Passed checks (5 passed)

Check name	Status	Explanation
Description Check	✅ Passed	Check skipped - CodeRabbit’s high-level summary is enabled.
Title check	✅ Passed	The title accurately reflects the main change: preserving RAY_EXPERIMENTAL_NOSET_CUDA_VISIBLE_DEVICES to prevent NCCL NVSwitch bugs, which is the core fix in the changeset.
Linked Issues check	✅ Passed	The PR directly addresses issue #1963 by preserving RAY_EXPERIMENTAL_NOSET_CUDA_VISIBLE_DEVICES=1, preventing Ray GPU masking and avoiding the three confirmed NCCL bugs on NVSwitch hardware, meeting all stated coding objectives.
Out of Scope Changes check	✅ Passed	All changes are scoped to the specific issue: modification of worker environment handling in _create_workers_from_bundle_indices to preserve the environment variable while removing unrelated Ray env vars unchanged.
Docstring Coverage	✅ Passed	Docstring coverage is 100.00% which is sufficient. The required threshold is 80.00%.

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✨ Finishing Touches

🧪 Generate unit tests (beta)

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[coderabbitai]

🧹 Nitpick comments (1)

nemo_rl/distributed/worker_groups.py (1)

506-506: Use setdefault() to preserve caller-provided overrides while maintaining the safe default.

Line 506 unconditionally overwrites any value for RAY_EXPERIMENTAL_NOSET_CUDA_VISIBLE_DEVICES, contradicting the comment which says "Preserve". The current merge pattern (lines 430–433) respects caller-provided env_vars, and the pattern of explicit pop() calls (lines 508–512) shows selective env var management. Using setdefault() aligns the implementation with both the comment's intent and the method's design pattern of respecting caller values while providing a safe default for this critical NCCL bug workaround.

♻️ Suggested refactor

-                worker_env_vars["RAY_EXPERIMENTAL_NOSET_CUDA_VISIBLE_DEVICES"] = "1"
+                worker_env_vars.setdefault(
+                    "RAY_EXPERIMENTAL_NOSET_CUDA_VISIBLE_DEVICES", "1"
+                )

🤖 Prompt for AI Agents

Verify each finding against the current code and only fix it if needed.

In `@nemo_rl/distributed/worker_groups.py` at line 506, The code unconditionally
overwrites RAY_EXPERIMENTAL_NOSET_CUDA_VISIBLE_DEVICES on worker_env_vars which
violates the "Preserve" intent; change the assignment to use
worker_env_vars.setdefault("RAY_EXPERIMENTAL_NOSET_CUDA_VISIBLE_DEVICES", "1")
so that any caller-provided value is preserved while still providing the safe
default for the NCCL workaround (locate the usage of worker_env_vars and the
RAY_EXPERIMENTAL_NOSET_CUDA_VISIBLE_DEVICES symbol in worker_groups.py).

🤖 Prompt for all review comments with AI agents

Verify each finding against the current code and only fix it if needed.

Nitpick comments:
In `@nemo_rl/distributed/worker_groups.py`:
- Line 506: The code unconditionally overwrites
RAY_EXPERIMENTAL_NOSET_CUDA_VISIBLE_DEVICES on worker_env_vars which violates
the "Preserve" intent; change the assignment to use
worker_env_vars.setdefault("RAY_EXPERIMENTAL_NOSET_CUDA_VISIBLE_DEVICES", "1")
so that any caller-provided value is preserved while still providing the safe
default for the NCCL workaround (locate the usage of worker_env_vars and the
RAY_EXPERIMENTAL_NOSET_CUDA_VISIBLE_DEVICES symbol in worker_groups.py).

---

ℹ️ Review info

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📥 Commits

Reviewing files that changed from the base of the PR and between 69d1872 and 42f95f6.

📒 Files selected for processing (1)

• nemo_rl/distributed/worker_groups.py

[yuki-97]

hi @guyueh1 @terrykong , could you help to take a review to check if this will have some side effects?

[terrykong]

Review Summary

The approach (preserving RAY_EXPERIMENTAL_NOSET_CUDA_VISIBLE_DEVICES=1) is sound and consistent with how vLLM and SGLang workers already behave. However, there is a critical regression in the default DTensor V1 path and several secondary issues that need attention before merge.

Historical context: The original pop() was introduced in PR #432 by @hemildesai for ray job submit support. After investigation, the KubeRay risk is low — NeMo-RL uses whole-node GPU allocations, and vLLM/SGLang already set this env var to 1.

[terrykong]

Critical: DTensor V1 LOCAL_RANK=0 regression

This unconditional = "1" makes all GPUs visible to every worker. However, dtensor_policy_worker.py:320-329 has a LOCAL_RANK=0 hack that assumes only 1 GPU is visible:

# torch==2.8 uses LOCAL_RANK to set the device here
# but CUDA_VISIBLE_DEVICES is set to only 1 gpu, so we need to temporarily set LOCAL_RANK to 0.
prev_local_rank = os.environ["LOCAL_RANK"]
os.environ["LOCAL_RANK"] = "0"
device_mesh = torch.distributed.device_mesh.init_device_mesh(...)

With all GPUs visible, init_device_mesh calls set_device(0) for every worker → all workers fight over GPU 0 → OOM. DTensor V1 is the default (lm_policy.py:113: _v2=False). DTensor V2 is unaffected (uses FSDP2Manager which reads real LOCAL_RANK).

Fix: Remove the LOCAL_RANK=0 hack (lines 323-324). With all GPUs visible, init_device_mesh should use the real LOCAL_RANK=bundle_idx.

[terrykong]

This comment is inaccurate — no production nemo_rl code calls torch.cuda.set_device(local_rank). Device binding happens implicitly via init_device_mesh / Megatron internals reading the LOCAL_RANK env var.

Suggested change

	# Workers use explicit torch.cuda.set_device(local_rank) instead.
	# Workers rely on LOCAL_RANK env var for device selection via
	# init_device_mesh / Megatron internals.

[terrykong]

Additional Findings

vLLM Non-Parallel GPU Collision Risk

vllm_worker.py:362-364 — Non-parallel vLLM workers (TP=1) see all 8 GPUs but don't explicitly select a device. Multiple DP replicas on one node may collide on GPU 0. SGLang handles this via base_gpu_id (sglang_worker.py:120,123) — vLLM's non-parallel path lacks equivalent logic.

Stale Comments

These comments reference CUDA_VISIBLE_DEVICES masking behavior that no longer applies with this change:

• dtensor_policy_worker.py:321: "CUDA_VISIBLE_DEVICES is set to only 1 gpu"
• sglang_generation.py:60: "Each server will see logical GPUs 0-N (via CUDA_VISIBLE_DEVICES set by Ray)"
• sglang_generation.py:156: Same stale reference

Megatron Seed Computation Change

megatron_policy_worker.py:777-780 — torch.cuda.device_count() returns 8 instead of 1, changing seed values. The new computation is actually more correct semantically (proper node_idx), but breaks reproducibility vs. previous runs. Worth noting in the PR description.

[terrykong]

Re: CodeRabbit's setdefault() suggestion — disagree.

The unconditional = "1" is intentional. This is a safety invariant for NCCL correctness, not a default. If a caller had RAY_EXPERIMENTAL_NOSET_CUDA_VISIBLE_DEVICES=0 in their env, setdefault() would silently not apply the fix, defeating the entire purpose of the PR.

Every other site in the codebase that sets this variable uses unconditional assignment:

• vllm_worker.py:109
• sglang_worker.py:120,123

The word "Preserve" in the comment means "keep this env var present" (vs. the old pop()), not "defer to caller-provided values."

[terrykong]

hey @dmvevents. the above review was claude generated so we should just use it as a guide. i think this change makes sense, but we'll need to run the nightly CI to make sure this doesn't break anything since this change has a large impact