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Derive backend modes from worker roles so configurations cannot drift, and make AIPerf setup tolerate system-installed blinker.
Disable unstable overlap scheduling and decode CUDA graphs so multinode TP benchmarks complete reliably.
Use repository-standard headers so the multinode additions pass CI-only validation.
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Included review availability: Your plan provides up to 12 included reviews per hour; 11 remain after this review. 📝 WalkthroughWalkthroughAI Dynamo now supports Slurm multinode workers with topology validation, distributed vLLM and SGLang launch handling, new test scenarios, updated documentation, and stricter AIPerf success validation. ChangesAI Dynamo multinode execution
Priority: ➖ Normal Estimated code review effort: 4 (Complex) | ~60 minutes Change: Feature Merge Risk: 🟡 Moderate · up to Pin the WideEP model revision before merging to prevent future upstream model-code changes from executing in the test environment. Add the zero-request regression test to protect the new AIPerf success rule. 🚥 Pre-merge checks | ✅ 4✅ Passed checks (4 passed)
✨ Finishing Touches🧪 Generate unit tests (beta)
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Actionable comments posted: 4
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Inline comments:
In `@conf/experimental/ai_dynamo/test_scenario/sglang_wideep_slurm.toml`:
- Line 27: Pin the Qwen model to a reviewed immutable commit revision in the
HFModel download configuration and propagate the same revision to both worker
commands that enable --trust-remote-code; if remote code is unnecessary, remove
that flag instead. Keep the model identifier and existing worker configuration
unchanged.
In `@src/cloudai/workloads/ai_dynamo/ai_dynamo.py`:
- Around line 723-733: Extract the shared worker-topology validation rules from
constraint_check and AIDynamoSlurmCommandGenStrategy._validate_multinode_worker
into one Python helper returning the per-node GPU footprint and an optional
failure reason; have constraint_check return False for a reason and the Slurm
strategy raise ValueError with it. Preserve the standalone
_validate_worker_topology implementation in ai_dynamo.sh while keeping all
existing checks, including node divisibility, SGLang and vLLM executor modes, DP
divisibility, and per-node GPU footprint.
In `@src/cloudai/workloads/ai_dynamo/ai_dynamo.sh`:
- Around line 398-406: Update _set_worker_disaggregation_modes so the
no-prefill-nodes branch assigns the SGLang disaggregation mode as null instead
of agg; preserve the existing prefill and decode assignments when prefill nodes
are configured.
In `@tests/workloads/ai_dynamo/test_report_gen_strategy.py`:
- Line 270: Add a separate zero-request report fixture containing “Request
Count,0.00”, then invoke the existing AIDynamoTestDefinition success evaluation
and assert that was_run_successful returns False, ensuring the request_count <=
0 branch is covered without changing the existing error-request report test.
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📒 Files selected for processing (16)
conf/experimental/ai_dynamo/test/sglang.tomlconf/experimental/ai_dynamo/test/vllm.tomlconf/experimental/ai_dynamo/test_scenario/sglang_hicache.tomlconf/experimental/ai_dynamo/test_scenario/sglang_multinode_worker_slurm.tomlconf/experimental/ai_dynamo/test_scenario/sglang_wideep_slurm.tomlconf/experimental/ai_dynamo/test_scenario/vllm_lmcache.tomlconf/experimental/ai_dynamo/test_scenario/vllm_multinode_worker_slurm.tomlconf/experimental/ai_dynamo/test_scenario/vllm_wideep_slurm.tomldoc/workloads/ai_dynamo.rstsrc/cloudai/workloads/ai_dynamo/ai_dynamo.pysrc/cloudai/workloads/ai_dynamo/ai_dynamo.shsrc/cloudai/workloads/ai_dynamo/slurm_command_gen_strategy.pytests/ref_data/ai-dynamo.sbatchtests/test_acceptance.pytests/workloads/ai_dynamo/test_command_gen_strategy_slurm.pytests/workloads/ai_dynamo/test_report_gen_strategy.py
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@juntaowww I need some time to properly review the changes, will try to finish it until end of the week |
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| dynamo_version: str = Field( | ||
| default="f7e468c7e8ff0d1426db987564e60572167e8464", | ||
| default=DYNAMO_COMMIT, |
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please don't change this one. we must support already working configs on users' side. or you're 100% bumped commit will remain backwards compatible?
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looks like deepep_path should be configurable as well then
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reverted to make sure be compatible
| if [[ ${prefill_config["gpus-per-worker"]} -eq 0 ]] || [[ ${decode_config["gpus-per-worker"]} -eq 0 ]]; then | ||
| log "ERROR: Invalid TP/PP configuration" | ||
| exit 1 | ||
| fi | ||
| if [[ ${prefill_config["gpus-per-worker"]} -gt $num_gpus ]] \ | ||
| || [[ ${decode_config["gpus-per-worker"]} -gt $num_gpus ]]; then | ||
| log "ERROR: Worker GPU footprint exceeds the ${num_gpus} GPU(s) available on this node" | ||
| exit 1 | ||
| fi |
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I wonder if this isn't already a part of some validation in ai_dynamo.py or slurm_command_gen_strategy.py?
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indeed, removed as in python file the checks are already done
| done | ||
| } | ||
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| _set_worker_disaggregation_modes() { |
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This introduces a backward-compatibility regression for existing vLLM configurations. Before this PR, the default prefill command was:
cmd = "python3 -m dynamo.vllm --is-prefill-worker"This function now also adds --disaggregation-mode prefill, producing a command containing both role selectors. Dynamo exits before engine initialization with:
ValueError: Cannot combine --is-prefill-worker/--is-decode-worker with --disaggregation-mode. Use only --disaggregation-mode.
I reproduced this with the unchanged pre-PR configurations. All vLLM Slurm cases failed identically: single-node, shared-node, multinode, and LMCache. Equivalent SGLang cases passed because their existing disaggregation-mode value is stored in the argument map and gets overwritten rather than duplicated.
Updating the checked-in configuration to remove --is-prefill-worker migrates that configuration, but does not preserve compatibility for existing user configurations. The tests also replace the legacy command, so they do not cover this upgrade path.
Please preserve legacy role selectors: when cmd already contains --is-prefill-worker or --is-decode-worker, do not inject a conflicting --disaggregation-mode, or normalize the command safely. Please also add a regression test using the unchanged pre-PR vLLM configuration.
This failure occurs with nodes-per-worker=1, so it breaks existing workloads unrelated to the new multi-node-worker functionality.
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updated to ensure backward compatibility
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| [cmd_args] | ||
| docker_image_url = "nvcr.io/nvidia/ai-dynamo/vllm-runtime:1.1.1" | ||
| docker_image_url = "nvcr.io/nvidia/ai-dynamo/vllm-runtime:1.3.1" |
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please keep the existing configs as is. only add new scenarios pointing to the existing test tomls, overriding specific values
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reverted and the bump only appear on scenario files
| local rendered_config="${LMCACHE_CONFIG_FILE}.tmp.${SLURM_NODEID:-0}" | ||
| if ! FRONTEND_NODE="$frontend_node" \ | ||
| FRONTEND_IP="$frontend_ip" \ | ||
| FRONTEND_IP="$frontend_address" \ |
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what is the justification for this change? now FRONTEND_IP is not an ip but hostname
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reverted, as the changes were not necessary
| @property | ||
| def final_env_vars(self) -> dict[str, str | list[str]]: | ||
| env_vars = super().final_env_vars | ||
| env_vars["DYNAMO_NODELIST"] = "$(scontrol show hostname $SLURM_JOB_NODELIST | tr -s '\\n' ',' | sed 's/,$//')" |
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could you please make this env var respects the value provided by user if any (and if empty - keep the definition you added)
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added a conditional check and make sure it respect the users settings
2 participants
Summary
CloudAI previously treated num-nodes as independent backend workers and could not group multiple nodes into one distributed vLLM or SGLang worker.
This PR adds the optional
nodes-per-workersetting, derives backend ranks and rendezvous arguments, supports multi-node TP/PP and WideEP layouts, infers disaggregation roles, and validates topology constraints. Omittingnodes-per-workerpreserves the existing one-worker-per-node behavior.It also updates AI Dynamo to 1.3.1, adds dedicated examples and documentation, preserves backend address overrides, and rejects AIPerf reports with zero successful requests.
Test Plan
manual runs + tests.
Additional Notes