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feat(dynamo): support python based dynamo vLLM workers administration - #3629
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Replaces the three switches `model.fsdp_cpu_offload`, `model.optim_cpu_offload` and `model.full_offload` with one field: ```toml [model] offload = "optimizer" # default; also "full", "none" ``` This applies to both the RL trainer and SFT. The `fsdp` switch is not migrated: the mode is removed outright (rationale below). The `[model.full_offload] numa_bind` knob is dropped: no recipe, deployment, template or doc sets it. Full offload now always pins each rank's CPUs to its GPU's NUMA node. That was already the default. Ranks whose GPU reports no NUMA node still skip binding with a warning. On hosts without GPU sysfs topology (`/sys/bus/pci/devices/<bus>/numa_node`), binding is no longer optional: full offload fails there instead of being switched off. Why: the three switches could only be used one at a time, enforced by `cpu_offload_mutual_exclusion`. Because `optim_cpu_offload` defaulted to `true`, enabling full offload also meant writing `optim_cpu_offload = false`, otherwise validation failed (see the old `configs/advanced/deepseek-v4-flash/sft.toml` comment). With one value the conflicting combinations cannot be expressed, so that validator is deleted. The default behaviour does not change: optimizer-state offload stays on. Why drop `fsdp`: the mode was the stock torch FSDP2 `CPUOffloadPolicy` with no in-tree config, example, or test user; Muon rejected it and it broke NCCL weight broadcast. It was kept only for long-context runs whose fp32 masters/moments no longer fit on GPU (the reason it was restored in #3052); the `full` mode added in #3234 keeps FP32 masters, moments, and gradients in host RAM, covering that memory pressure instead. Gone with it: the `CPUOffloadPolicy` selection in `setup_fsdp`, CPU-side model init in `setup_model`/`load_dcp_from_hf`, `_move_buffers_to_cuda`, and the fsdp leg of the Gloo special case. Changes: - `ModelConfig.offload: Literal["none", "optimizer", "full"] = "optimizer"` replaces the three fields. `OptimizerInBackwardOffloadConfig` (and with it `numa_bind`) and its bool-normalizing `BeforeValidator` are gone. - Validators in `TrainerConfig` and `SFTConfig`: - full offload accepts only AdamW or SignSGD and disables clipping, now reading `offload == "full"`; - the `Muon optimizer does not support FSDP CPU offload` validators are deleted with the mode. - Runtime: - `setup_optimizer(..., offload=...)` replaces `cpu_offload` + `full_offload_config`, and its "both enabled" check is gone. - `FullCPUOffloadOptimizer` drops its unused `offload_config` argument. - `setup_full_cpu_optimizer_offload()` takes no arguments and always binds. - `model.py`: FSDP always uses the default `OffloadPolicy()`; CPU-side model init in `setup_model`/`load_dcp_from_hf` and `_move_buffers_to_cuda` are gone. - Tomls: `glm-4.5-air/swe-budget.toml` and `ci/integration/reverse-text-sft/full-offload-resume.toml` → `offload = "full"`; `glm-5.3/sft/h200/16k-12-node.toml` → `offload = "none"`; `glm-5.3/sft/h200/base.toml` → `offload = "optimizer"`. Also updated `docs/scaling.md`, the `deepseek-v4-flash/sft.toml` comment, and `skills/training/start-run/SKILL.md`, which documented the removed fields. Breaking: the old keys now fail with `extra_forbidden`, and `offload = "fsdp"` fails the `Literal`. There is no shim. Interaction with #3849 (one trainer-common config base): #3849 moves the full-offload and Muon/FSDP validators from `TrainerConfig`/`SFTConfig` onto `BaseTrainerConfig`. This PR edits the full-offload validator's condition and deletes the Muon/FSDP one, so the conflict is textual only: keep #3849's single copy of the full-offload validators with `self.model.offload == "full"` and drop its Muon/FSDP copy. Whichever lands second takes a small rebase. Other open PRs that set or read the old keys need a rebase: - tomls: #3883, #3879, #3766, #3765, #3729, #3629, #3627, #3616, #3544 - code: #3885 (`optim_cpu_offload`), #3739 (`full_offload` validators), #3724, #3719, #3699, #3610, #3487 (`fsdp_cpu_offload` / `enable_gloo`) Test plan - `pytest tests/unit/orchestrator tests/unit/test_configs.py tests/unit/train/test_cpu_adam.py tests/unit/train/test_state_offload.py -m "not gpu"`: 336 passed, 1 skipped (Qwen3-VL snapshot not cached on this host), 4 deselected (GPU; this host has no GPU). The existing offload config tests are updated to the new field. `test_load_configs` covers the 4 edited tomls. - `cli(TrainerConfig|SFTConfig, ...)`: `--model.offload full|none` and the default (`optimizer`) resolve as expected; `optim_cpu_offload` and `offload = "fsdp"` are rejected. - `import prime_rl.trainer.rl.train, prime_rl.trainer.sft.train` works. - `ruff check` and `ruff format --check` on the changed Python files: clean. - Not run: GPU or multi-node training with `offload = "full"`. <!-- CURSOR_SUMMARY --> --- > [!NOTE] > **Medium Risk** > Breaking TOML/API surface touches distributed training memory paths (FSDP offload, optimizer-in-backward, Gloo); behavior should match prior modes when mapped correctly, but mis-migrated configs will fail at parse time. > > **Overview** > **Breaking config change:** `model.fsdp_cpu_offload`, `model.optim_cpu_offload`, and `model.full_offload` are replaced by a single **`model.offload`** setting (`"optimizer"` default, or `"full"`, `"fsdp"`, `"none"`). RL and SFT share the same field; example TOMLs and scaling docs are updated accordingly. > > The mutual-exclusion validator between the old flags is removed because only one mode can be expressed. **`[model.full_offload]` / `numa_bind` is dropped** — `"full"` always runs NUMA binding and CPU optimizer thread setup via parameterless `setup_full_cpu_optimizer_offload()`. Runtime paths (`setup_optimizer`, FSDP CPU offload in `model.py`, Gloo when offload is `"full"` or `"fsdp"`) key off the new enum; `FullCPUOffloadOptimizer` no longer takes an offload config object. > > Default training behavior is unchanged: optimizer-state CPU offload remains the default (`"optimizer"`). Old TOML keys are not aliased and should fail validation. > > <sup>Reviewed by [Cursor Bugbot](https://cursor.com/bugbot) for commit 35da73a. Bugbot is set up for automated code reviews on this repo. Configure [here](https://www.cursor.com/dashboard/bugbot).</sup> <!-- /CURSOR_SUMMARY --> --------- Co-authored-by: sami jaghouar <sami.jaghouar@gmail.com>
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Summary
dynamo.vllmworkers through their advertised engine routes while preserving the collective-RPC sidecar contract.examples/extra/dynamo/basic/andexamples/extra/dynamo/vlm/nemotron-3.5-super/, with focused Dynamo administration tests.Why
Python Dynamo workers advertise a different administration contract from the existing collective-RPC sidecar. Prime-RL must discover the correct worker topology and routes, translate weight-update operations, and use Dynamo's versioned adapter names consistently. Dynamo generation also needs matching renderer-based evaluation requests and adapter retention aligned with the off-policy window.
Dependency and scope
Shared support #3735 must merge first. Both PRs target main because an upstream PR cannot use a fork-only branch as its base. This PR intentionally depends on the shared renderer and LoRA interface hooks; its standalone LoRA path is incomplete until the prerequisite lands.
Backend-independent configuration, routed-expert completeness checks (including
missing_routed_expert_branches), rollout rejection handling, generic adapter-model propagation, and their tests have moved to that prerequisite. The remaining production diff is limited to Dynamo administration, Dynamo factory wiring, and Dynamo-specific orchestrator selection. This PR retains the tensor-parallel runtime behavior previously developed in #3618.Validation
7caf94fa4); only a native LoRA regression test was added.