Commit 730d3cd
feat(customizer): add grpo support with gym environments (#1156)
* feat(customizer): add opensandbox installation and verification
Signed-off-by: Sam Oluwalana <soluwalana@nvidia.com>
* feat(customizer): add grpo support with gym environments
Signed-off-by: Sam Oluwalana <soluwalana@nvidia.com>
* Cleanup doc strings
Signed-off-by: Sam Oluwalana <soluwalana@nvidia.com>
* fix(rl): clear pre-existing lint and type-check debt
`ruff check` and the `ty` pre-commit hook both fail on this branch today. The
ty hook only inspects staged files, so the type errors stay invisible until a
commit happens to touch one of these files — at which point they block it.
- ruff: 8 auto-fixable errors (import sorting, unused imports) plus formatting
across services/rl.
- test_compiler: the compiler returns TypedDicts typed as
PlatformJobStepSpecParam, which is not assignable to `dict[str, Any]`, and
`spec["steps"]` is typed as an Iterable so it cannot be indexed. Loosen the
helper signatures and go through a concrete list.
- test_compiler: pass `OutputNameType.MODEL` rather than the bare string, and
drop a dead attribute branch — step env entries are TypedDicts, so they are
plain dicts at runtime.
- test_schemas: annotate the parametrized kwargs dict so the heterogeneous
bad-value fixtures stop failing per-field argument checks.
- config: suppress `unsupported-base` for the dynamic service-config base,
matching the existing precedent in services/studio/src/nmp/studio/config.py.
No behavior change.
Signed-off-by: Albert Cui <albcui@nvidia.com>
* refactor(jobs): extract job_storage_subpath into a shared helper
The Kubernetes jobs backend spells out `jobs/{workspace}/{job}` in two places
when mounting the shared job-storage PVC. Anything that hands the same data to
a second consumer mounting that PVC itself has to reproduce the layout exactly,
so put it behind one helper next to DEFAULT_JOB_STORAGE_PATH rather than have
each call site restate it.
No behavior change; the follow-up sandboxed-GRPO work is the first outside
consumer.
Signed-off-by: Albert Cui <albcui@nvidia.com>
* fix(rl): make sandboxed GRPO runnable end to end
Sandboxed GRPO is the platform default and the only mode the compiler allows
once a cluster is sandbox-capable, but nothing on that path reached a training
step. Verified against the NeMo-RL commit the image pins (NEMO_RL_REF in
docker-bake.hcl).
Sandbox provisioning
- SandboxConfig omitted environment_pvc_claim and workspace_pvc_claim, which
upstream declares required with no default under `extra="forbid"`, so the Gym
host failed validation after the model download and vLLM startup. Add the
claim/sub-path triples, sourced from a new NMP_RL_JOB_STORAGE_PVC_CLAIM
setting the compiler injects — the training container is handed the storage
path but never the claim behind it. Fail at compile time when it is unset.
- Emit job_id. Upstream defaults it to a shared constant and stamps it onto
every sandbox pod as a label, so concurrent jobs were indistinguishable.
Config compilation
- data.train.data_path pointed at the sandbox-internal /job/dataset mount, but
NeMo-RL opens it in the training master, where that path does not exist. Keep
the job-storage path; the sandbox sees the same file at its own mount.
- config_paths was written only on the colocated branch, so sandboxed Gym
started with no server instances. Read it from the manifest in both modes.
- A Gym dataset only has to ship training.jsonl, but validation was left
enabled, and upstream asserts a validation dataset exists whenever
val_period/val_at_start/val_at_end is set. Disable validation and
best-checkpoint ranking when there is no split, and say so in the log.
- Reject num_prompts_per_step * num_generations_per_prompt that is not a
multiple of batch_size at submit time; it previously asserted at the first
optimizer step, after a full rollout.
Environment packaging
- upload passed api_key= to FilesClient, which takes auth=, so --upload raised
TypeError before issuing a request.
- A validation_fraction that consumed every row silently made the training and
validation files identical, and GRPO then ranks checkpoints on its own
training data. Reject it, and bound the CLI flag to [0, 1).
- Pick the hub wheel by parsed version rather than filename order, so 0.10.0
wins over 0.9.0 and host-side generation matches what the cluster installs.
- Default the offline-install venv outside the package root: site-packages
ships .jsonl files, which the layout validator rejects, so job resume and
rerun failed on the same PVC.
- Guard the optional example_id column, and compare config_paths containment by
path components — "environment-attacker" has "environment" as a prefix.
Schema and API
- training.type has no default. It is the union discriminator, so it is already
required on the wire; defaulting it made the generated OpenAPI advertise the
field as optional while the server rejected it.
- Restore the field descriptions dropped when schemas.py was split into a
package, and document the GRPO fields. They drive `rl explain`, the OpenAPI
spec, and SDK docstrings.
- Expose GRPO max_grad_norm, which the compiler read but never populated.
- Add the environment fileset purpose and its metadata content type.
OpenSandbox operator assets
- install.sh ran under `set -x` and passed the generated API key via
--from-literal, so it reached the trace, the terminal, any CI log, and
kubectl's argv. Disable xtrace around key handling and pass a 0600 temp file.
- The crun template paired a blanket `tolerations: [{operator: Exists}]` with
only preferred anti-affinity, so shared-kernel sandboxes running untrusted
environment code could land on control-plane nodes under capacity pressure.
Add a required rule excluding them.
- verify/lib.sh took a bare command substitution for its readiness poll, so
under `set -e` one transient curl failure aborted the script and defeated
READY_TIMEOUT_S.
Signed-off-by: Albert Cui <albcui@nvidia.com>
* chore(sdk): regenerate OpenAPI spec and Python SDK
Picks up the environment fileset purpose, EnvironmentMetadataContent, and the
now-required training.type discriminator.
EnvironmentMetadataContent is pinned in $shared rather than left under
files/filesets. Every FilesetMetadata member has to live alongside it: homed
under `files`, FilesetMetadata's import of it becomes shared -> files -> shared
and the generated package fails to import. This mirrors the existing pin on
FilesetMetadata itself.
Generated by `make refresh-openapi` and `make stainless`.
Signed-off-by: Albert Cui <albcui@nvidia.com>
* fix(rl): stop selecting the V2 DTensor worker for GRPO
`policy.dtensor_cfg._v2` is not a perf toggle — it decides which Ray actor runs
policy training, and therefore which venv that actor launches under:
_v2: True -> DTensorPolicyWorkerV2 -> PY_EXECUTABLES.AUTOMODEL -> --extra automodel
_v2 unset -> DTensorPolicyWorker -> PY_EXECUTABLES.FSDP -> --extra fsdp
docker/rl/Dockerfile.nmp-rl-base deliberately does not build the automodel
extra: with mcore it is the only source of Transformer-Engine, the longest CUDA
compile in that image. The venv prefetch cannot cover for it either — the filter
is the substring "dtensor_policy_worker.DTensorPolicyWorker", which does not
match the V2 FQN (it has "_v2." where the filter expects "."). So every GRPO job
would have compiled nemo-automodel + TE on the node at startup, and failed
outright on a cluster without egress to PyPI.
This also contradicted our own docs: docker/rl/README.md states DPO and GRPO
both train on DTensorPolicyWorker / `fsdp`. DPO was already correct because it
never sets the flag.
Drop it rather than make it configurable. The documented reason to want V2 is
dp_replicate_size > 1, which we never set, and dp_replicate_size is a
parallelism concern — so if we adopt Automodel the natural interface is to
derive _v2 from a user-facing parallelism field, not an operator boolean.
Designing that knob now, before Automodel support exists, would likely pick the
wrong shape and leave a deprecated flag in the config schema and Helm values.
Add a regression test pinning the config -> actor -> venv coupling for all three
backend selections (dtensor, megatron, generation), since re-adding the flag
looks harmless in isolation. Confirmed it fails when _v2 is restored. When
Automodel is added to the image, that test is the one place to flip — and doing
so forces a look at the prefetch filters.
Signed-off-by: Albert Cui <albcui@nvidia.com>
* make lint-fix
Signed-off-by: Albert Cui <albcui@nvidia.com>
* chore(licenses): add overrides for antlr4-python3-runtime and wget
Both arrive transitively with the GRPO Gym dependencies and osv-scanner
reports each as NON-STANDARD, so `make check-licenses` failed with two
packages needing overrides.
antlr4-python3-runtime is BSD-3-Clause (already an approved license), so
it goes with the other parser runtimes. wget declares "License: Public
Domain" with no corresponding SPDX identifier, so it goes in the
"Licenses not automatically approved" block alongside the existing
Unlicense entries.
Dependency chains:
antlr4-python3-runtime <- latex2sympy2-extended <- math-verify
wget <- verifiers (NeMo Gym)
Regenerating also picks up nmp-jobs in the nemo_platform_plugin reverse
dependency list, which the job_storage_subpath refactor introduced.
Signed-off-by: Albert Cui <albcui@nvidia.com>
* Accomodate GRPO + LoRA
Signed-off-by: Sam Oluwalana <soluwalana@nvidia.com>
* lint fix
Signed-off-by: Sam Oluwalana <soluwalana@nvidia.com>
* fix(rl): select the V2 DTensor worker when GRPO uses LoRA
`Accomodate GRPO + LoRA` added lora_cfg to policy.dtensor_cfg but left `_v2`
unset, and its own test asserted `_v2 is True` — so the branch had a failing
test pointing straight at the gap.
LoRA is implemented only in DTensorPolicyWorkerV2. At the pinned NeMo-RL SHA
(ace40313), dtensor_policy_worker.py has zero references to lora, while
dtensor_policy_worker_v2.py has 23, and lm_policy.py picks between them on
`config["dtensor_cfg"].get("_v2", False)`. Handing lora_cfg to V1 therefore
does not fail — it silently trains full weights and reports success.
`_v2` is set only when LoRA is enabled, not unconditionally, because the two
workers resolve to different venvs in ray_actor_environment_registry:
V1 -> PY_EXECUTABLES.FSDP, which the image prefetches, and
V2 -> PY_EXECUTABLES.AUTOMODEL, which it does not. Full-weight GRPO keeps the
prefetched path and is unchanged; only LoRA runs take on the automodel
dependency. Until that venv is prefetched, a LoRA actor builds it on the node
at job start — slow at best, a hard failure on a deny-egress cluster. The
Dockerfile comment claiming automodel is "unused by our DPO+GRPO path" is now
false, so it is corrected to describe what LoRA needs.
Also fixes five LoRA schema tests that construct GRPOTraining without `type`.
Four failed outright, since the discriminator is required and validation
rejected them before reaching the LoRA validators under test. The fifth,
test_grpo_lora_rejects_lora_merged, passed for the wrong reason: it caught the
missing-`type` error rather than the invalid finetuning_type, and would have
passed even if lora_merged were accepted.
Adds test_lora_and_v2_stay_coupled to pin the relationship in both directions;
the existing tests each cover one case but neither fails if the two are
decoupled.
Signed-off-by: Albert Cui <albcui@nvidia.com>
* refactor(rl): omit lora_cfg entirely for full-weight GRPO
Both `lora_cfg` and `_v2` are NotRequired in NeMo-RL's DTensorConfig, and both
read sites use `.get("lora_cfg", {}).get("enabled", False)` / `.get("_v2",
False)`, so a full-weight run can leave them out rather than emit
`lora_cfg.enabled: False`. The compiled YAML now says only what applies.
Emitting the pair together also makes the coupling structural: `_v2` selects
DTensorPolicyWorkerV2, the only worker implementing LoRA, so the two keys can
no longer drift apart through an edit that touches one line and not the other.
Corrects the reasoning in the previous commit: V1 does not silently train full
weights when handed an enabled lora_cfg. lm_policy.py asserts
"LoRA is not supported for DTensorPolicyWorker V1" before building the worker,
so the failure is loud. The fix stands -- only the described failure mode was
wrong.
Tests now read the config the way NeMo-RL does, so "key omitted" and
"enabled: False" are equivalent to them and they do not pin which we emit.
Signed-off-by: Albert Cui <albcui@nvidia.com>
* style(rl): drop the LoRA/_v2 commentary from the config builder
The dtensor_cfg block reads as data again. Why `_v2` tracks LoRA is covered by
test_lora_and_v2_stay_coupled, which fails in both directions, and the image
dependency it creates is documented at the prefetch filters in
docker/rl/Dockerfile.nmp-rl-base, where acting on it belongs.
Signed-off-by: Albert Cui <albcui@nvidia.com>
* fix(rl): emit the MasterConfig fields the GRPO driver requires
A real GRPO run on dev-blue died at driver startup with four pydantic
ValidationErrors against NeMo-RL's MasterConfig:
policy.generation.top_k
policy.generation.stop_token_ids
policy.generation.stop_strings
policy.make_sequence_length_divisible_by
All four are required with no default. Values mirror upstream's reference
config examples/configs/grpo_math_1B.yaml, which sets the three generation
fields to null and make_sequence_length_divisible_by to the tensor-parallel
size. None is meaningful rather than absent: generation/__init__.py fills
stop_token_ids with [tokenizer.eos_token_id] when it sees None.
Why this survived until a live run: dpo_config already sets
make_sequence_length_divisible_by, and the three generation fields are
GRPO-only because DPO runs no vLLM, so no existing path touched them. The
compiler's tests assert individual fields rather than validating the emitted
config against MasterConfig, and MasterConfig is not importable here (it
needs torch), so nothing local could have caught it.
The cost of that gap is the reason for the added test: the failure surfaces in
the driver, after Ray is up and four pods in, minutes of GPU time after submit.
Signed-off-by: Albert Cui <albcui@nvidia.com>
* fix(rl): emit the required vllm_cfg keys for GRPO generation
Second live failure on dev-blue, one layer past the MasterConfig fix:
nmp.rl.tasks.training.errors.parser.KeyError: 'kv_cache_dtype'
raised from grpo.py where it reads generation_config["vllm_cfg"]["kv_cache_dtype"]
directly. vllm_cfg is a TypedDict (VllmSpecificArgs) whose keys MasterConfig does
not validate, so a missing member is not a config error at load — it is a KeyError
wherever vLLM first reads it, after Ray is already up.
Rather than fix the one key that failed, this adds every non-NotRequired member
we were missing. Of the eight required by the TypedDict we emitted five;
kv_cache_dtype and expert_parallel_size were absent. expert_parallel_size is read
unconditionally at vllm_generation.py:109 and vllm_worker.py:304, so it was the
next failure in line.
skip_tokenizer_init is required by the TypedDict but deliberately still omitted:
generation/__init__.py fills it in when absent, choosing based on stop_strings
and expose_http_server, and hardcoding it would override logic that exists for
VLMs. The test asserts its absence so a later "complete the required fields"
change does not silently break that.
Values match upstream's examples/configs/grpo_math_1B.yaml.
Signed-off-by: Albert Cui <albcui@nvidia.com>
* chore: rebase off of main + resolve conflicts + regenrate
Signed-off-by: anubhutiv <anubhutiv@nvidia.com>
* fix(rl): omit chat_template when the model ships none
NeMo-RL's TokenizerConfig declares chat_template: NotRequired[str] -- absent is
valid, None is not. resolve_chat_template returns None whenever the model has no
built-in template and the user supplied no override (chat_templates.py:173, right
after logging "No chat template found"), and both config builders emitted the key
unconditionally. Any such model therefore failed MasterConfig validation in the
driver with "Input should be a valid string", after Ray was already up.
Extracted as a shared helper because DPO and GRPO had the identical block and the
identical bug.
Not caught by the dev-blue run because Qwen3 ships a template; it needs a model
that does not, which is the kind of gap a single-model GPU run cannot close.
Found instead by validating the compiled config against the real MasterConfig
locally.
Signed-off-by: Albert Cui <albcui@nvidia.com>
* test(rl): gate the image build on the compiled config matching NeMo-RL
compile_grpo_config and NeMo-RL's schemas only ever share an interpreter inside
the training image: services/rl/tests cannot import MasterConfig (it needs torch)
and NeMo-RL cannot see the compiler. So the two halves of the contract were never
checked against each other, and two config-shape bugs reached a live GPU run --
each costing an image build and a redeploy to find, and each surfacing only after
Ray was already up.
Runs in the existing smoke-test stage under the smoke_nmp_rl_training marker, so
no Dockerfile change: line 82 already runs everything carrying it. Adds ~12s to a
stage that already installs pytest and runs on a CPU runner, and failing it fails
the nmp-rl-training-smoke-test bake target, so a broken image is not published.
Checks MasterConfig construction and VllmSpecificArgs.__required_keys__ (a
TypedDict pydantic does not validate the members of), for both the full-weight and
LoRA paths, plus the lora_cfg/_v2 coupling.
Running inside the image also removes the skew that makes an external validator
untrustworthy: an image with pydantic <2.13 accepts configs the deployed one
rejects, because 2.13 enforces required keys on nested TypedDicts and 2.12 does
not. Here the validator and the validated code are the same artifact.
Verified by overlaying current source onto an existing image: 5 passed, and 5
failed when kv_cache_dtype, make_sequence_length_divisible_by and _v2 were removed.
The fixture is duplicated rather than shared with services/rl/tests, which is not
copied into the image. It only needs one representative config, not the
parametrizable one the unit tests build.
Signed-off-by: Albert Cui <albcui@nvidia.com>
* chore(customizer): add automodel and mcore backends in RL image
Signed-off-by: anubhutiv <anubhutiv@nvidia.com>
* fix: no module named nemo_gym during training
Signed-off-by: anubhutiv <anubhutiv@nvidia.com>
* fix: sandbox create timeout + sandbox runtime image + fix relative config paths
Signed-off-by: anubhutiv <anubhutiv@nvidia.com>
* fix: add policy_model.yaml
Signed-off-by: anubhutiv <anubhutiv@nvidia.com>
* fix: hook up allow_internet from platform to rl
Signed-off-by: anubhutiv <anubhutiv@nvidia.com>
* fix: allow configuring the allowlisted domains
Signed-off-by: anubhutiv <anubhutiv@nvidia.com>
* fix: propagate the venv correctly
Signed-off-by: anubhutiv <anubhutiv@nvidia.com>
* fix: minor bug
Signed-off-by: anubhutiv <anubhutiv@nvidia.com>
* fix: make opensandbox server scheme configurable + conversion script fixes
Signed-off-by: anubhutiv <anubhutiv@nvidia.com>
* fix: find the correct lora adapter
Signed-off-by: anubhutiv <anubhutiv@nvidia.com>
* chore: expose val_at_start to validate baseline model
Signed-off-by: anubhutiv <anubhutiv@nvidia.com>
* expose more grpo configs + fix schema
Signed-off-by: anubhutiv <anubhutiv@nvidia.com>
* expose the sandbox resource
Signed-off-by: anubhutiv <anubhutiv@nvidia.com>
* chore: make sandbox pod ttl configurable
Signed-off-by: anubhutiv <anubhutiv@nvidia.com>
* rebase onto main + regenerate licenses and sdk
Signed-off-by: anubhutiv <anubhutiv@nvidia.com>
* chore: update RL repo ref + lint
Signed-off-by: anubhutiv <anubhutiv@nvidia.com>
* chore: expose more configs for moe models
Signed-off-by: anubhutiv <anubhutiv@nvidia.com>
* chore: address comments
Signed-off-by: anubhutiv <anubhutiv@nvidia.com>
* fix: add automodel patch
Signed-off-by: anubhutiv <anubhutiv@nvidia.com>
* fix: update how we patch
Signed-off-by: anubhutiv <anubhutiv@nvidia.com>
* chore: address comments + patch for nemotron
Signed-off-by: anubhutiv <anubhutiv@nvidia.com>
* fix: fix log_interval + address comments
Signed-off-by: anubhutiv <anubhutiv@nvidia.com>
* update licenses
Signed-off-by: anubhutiv <anubhutiv@nvidia.com>
* fix lint
Signed-off-by: anubhutiv <anubhutiv@nvidia.com>
---------
Signed-off-by: Sam Oluwalana <soluwalana@nvidia.com>
Signed-off-by: Albert Cui <albcui@nvidia.com>
Signed-off-by: anubhutiv <anubhutiv@nvidia.com>
Co-authored-by: Albert Cui <albcui@nvidia.com>
Co-authored-by: anubhutiv <anubhutiv@nvidia.com>1 parent ca93cab commit 730d3cd
97 files changed
Lines changed: 8751 additions & 2466 deletions
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