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[veomni] fix: support the trainer-side rollout weight quantizer in the VeOmni engine - #8369
Since #8287 (unify QAT and low-precision rollout), init_model hands the actor engine a rollout weight quantizer whenever the rollout is quantized — for a checkpoint whose quantization_config is FP8 (e.g. DeepSeek-V4-Flash-Base), dispatch_rollout_weight_quantizer returns an Fp8ConfigWeightQuantizer — and calls engine.set_rollout_weight_quantizer, which only the Megatron engine implements. Every VeOmni-engine run on such a checkpoint, including examples/grpo_trainer/run_deepseek_v4_veomni.sh with its default model, therefore stops in init_model:
NotImplementedError: VeOmniEngineWithLMHead does not support trainer-side rollout quantization
This PR implements it the way the Megatron engine does: the engine keeps the quantizer and applies it to the per-tensor stream of get_per_tensor_param. For DeepSeek-V4, VeOmni's checkpoint converter already exports the release FP8/FP4 layout, and the quantizer is a pass-through for that model type (as for Megatron-Bridge's export); other models' BF16 streams get quantized to what the rollout loads.
Format the PR title as [{modules}] {type}: {description}
Test
tests/workers/test_veomni_rollout_quantizer_on_cpu.py: the exported stream is returned unchanged without a quantizer and passes through it otherwise; the checkpoint's hf_model export stays unquantized, and initialize() hands the checkpoint manager that unquantized export.
tests/utils/test_rollout_weight_quantizer_dispatch_on_cpu.py: a rollout.quantization override for DeepSeek-V4 is rejected whether or not the checkpoint is quantized, and so is delta_sharded sync with a quantized rollout; DeepSeek-V4's native config still passes through, and an override for another model is dispatched.
No API or config change. VeOmniEngine now implements the existing set_rollout_weight_quantizer hook, so a run with model_engine=veomni on a checkpoint whose quantization_config is FP8 (such as run_deepseek_v4_veomni.sh with its default model) gets past init_model. Two combinations the trainer cannot sync now fail at init_model with an explicit error, for both engines: a rollout.quantization override for DeepSeek-V4, and checkpoint_engine.backend='delta_sharded' with a quantized rollout.
Design & Code Changes
VeOmniEngine.set_rollout_weight_quantizer stores the quantizer; get_per_tensor_param applies it to the export of _get_hf_export_per_tensor_param (converter path and state-dict path). The checkpoint manager's hf_model export calls _get_hf_export_per_tensor_param directly, so saved checkpoints keep the trainer's weights.
dispatch_rollout_weight_quantizer rejects rollout.quantization overrides for DeepSeek-V4. Both engines export it under its checkpoint names (e.g. ffn.experts, which the override's mlp.experts MoE target does not match) and, for a quantized checkpoint, in its own FP8/FP4 layout, which CompressedTensorsWeightQuantizer passes through unchanged.
It also rejects delta_sharded sync for a quantized rollout: after the seed, that backend streams get_per_tensor_param_delta_shard, which bypasses the quantizer. vLLM's delta weight transfer already refuses quantized models; SGLang's delta loader has no such check.
Checklist Before Submitting
Read the Contribute Guide.
Apply pre-commit checks.
Add / Update the documentation: not needed.
Add unit or end-to-end test(s): tests/workers/test_veomni_rollout_quantizer_on_cpu.py and tests/utils/test_rollout_weight_quantizer_dispatch_on_cpu.py (CPU CI).
Once the PR is ready for CI, send a message in the ci-request channel of the verl Slack workspace.
recipe submodule: not changed, so there is no submodule reference to update.
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📝 Summary
Summary by CodeRabbit
New Features
Rollout weight synchronization can apply a configured quantizer; without one, weights pass through unchanged.
Checkpoint exports provide unquantized weights, while per-tensor synchronization can provide quantized weights.
Bug Fixes
DeepSeek-V4 checkpoints pass through native quantization, while explicit quantization overrides return an unsupported-operation error.
Quantized rollouts using delta_sharded synchronization now return an unsupported-operation error instead of proceeding with an unsupported configuration.
The change adds DeepSeek-V4 handling to rollout quantizer dispatch and optional rollout weight quantization in VeOmni. Checkpoint export uses unquantized weights, while per-tensor synchronization can use quantized weights.
Dispatch resolves native quantization configuration and model types before processing overrides. It rejects overrides for DeepSeek-V4 and quantized rollouts with the delta_sharded backend. Tests cover native passthrough, rejection, and dispatch for another model.
Apply quantization to VeOmni rollout weights verl/workers/engine/veomni/transformer_impl.py, tests/workers/test_veomni_rollout_quantizer_on_cpu.py
VeOmniEngine initializes an optional quantizer and provides a setter. Per-tensor synchronization applies the quantizer when configured. Checkpoint export uses a separate unquantized path. Tests cover both paths and configured or unset quantizers.
Priority: Normal
Merge Risk: Minimal · previous review 00118
The change has no established merge-blocking behavior risk; the remaining test-coverage gap is already a published finding.
Pre-merge checks: 4 · 1
Failed checks: 1 warning
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Docstring coverage is 25.00% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff. Documentation-only edits and deletions within surv…
Write docstrings for the functions missing them to satisfy the coverage threshold.
Passed checks: 4 passed
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Description check
The description clearly explains the VeOmni quantizer support, dispatch restrictions, implementation details, and validation performed.
Title check
The title is concise and accurately identifies the main change: support for the trainer-side rollout weight quantizer in the VeOmni engine.
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Full details: Docstring Coverage
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Resolution
Write docstrings for the functions missing them to satisfy the coverage threshold.
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Actionable comments posted: 3
🪄 Fix comments: Fix CodeRabbit comments on this PR
Prompt to fix review comments
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Inline comments:
Review comments at @verl/workers/engine/veomni/transformer_impl.py:
- Line 695: Reject the combination of delta_sharded synchronization and rollout
quantization in the parameter synchronization path around _quantize_for_rollout,
before sending the seed weights. Keep both modes available independently until
delta synchronization supports quantized weights.
- Line 657: In the converter export path around converter.export_weights and
_quantize_for_rollout, reject DeepSeek-V4 when rollout.quantization.linear or
rollout.quantization.moe selects CompressedTensorsWeightQuantizer. Do not return
unchanged FP8/FP4 tensors and scale entries for this unsupported layout.
- Line 705: Add a separate unquantized parameter-export path for HF checkpoint
saving, and update the export helper used by
FSDPCheckpointManager.save_checkpoint to use it. Keep
VeOmniEngine.get_per_tensor_param quantized for rollout weight synchronization.
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Actionable comments posted: 1
🪄 Fix comments: Fix CodeRabbit comments on this PR
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Inline comments:
Review comments at @verl/utils/qat/compressed_tensors.py:
- Line 560: Update the override handling guarded by _DEEPSEEK_V4_MODEL_TYPES to
reject MoE-only overrides when native is absent, until MOE_TARGET and the
rollout configuration support VeOmni’s DeepSeek-V4 expert names; do not allow
quantization to proceed without applying the requested scales.
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The current manager wiring is correct: FSDPCheckpointManager receives _get_hf_export_per_tensor_param. However, this test only calls the two engine callbacks directly. It does not construct the manager or execute its hf_model save path. A future change to wire the manager to get_per_tensor_param would therefore leave this test passing while saving quantized hf_model weights.
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Review comment at @tests/workers/test_veomni_rollout_quantizer_on_cpu.py around
lines 51 - 63:
Extend test_checkpoint_export_skips_the_rollout_weight_quantizer to construct
FSDPCheckpointManager with the engine’s _get_hf_export_per_tensor_param callback
and exercise its hf_model save path; assert that the saved weights remain
unquantized, so wiring the manager to get_per_tensor_param would fail this test.
Prompt to fix review comments
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Nitpick comments:
Review comments at @tests/workers/test_veomni_rollout_quantizer_on_cpu.py:
- Around line 51-63: Extend
test_checkpoint_export_skips_the_rollout_weight_quantizer to construct
FSDPCheckpointManager with the engine’s _get_hf_export_per_tensor_param callback
and exercise its hf_model save path; assert that the saved weights remain
unquantized, so wiring the manager to get_per_tensor_param would fail this test.
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@coderabbitai Nitpick on test_veomni_rollout_quantizer_on_cpu.py (manager-level export test): added test_initialize_wires_the_checkpoint_manager_to_the_unquantized_export in 00118f8. It runs VeOmniEngine.initialize() with the model build, offload and FSDPCheckpointManager stubbed, and asserts that the manager gets _get_hf_export_per_tensor_param; wiring it to get_per_tensor_param fails the test. Together with the existing callback test, this pins an unquantized hf_model export without building an FSDP model.
For best results, initiate chat on the files or code changes.
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🏁 Script executed:
#!/bin/bashset -euo pipefail
fd -a '^test_veomni_rollout_quantizer_on_cpu\.py$'
ast-grep outline tests/workers/test_veomni_rollout_quantizer_on_cpu.py --items all
sed -n '1,260p' tests/workers/test_veomni_rollout_quantizer_on_cpu.py
ast-grep outline verl/workers/engine/veomni/transformer_impl.py --match 'initialize|_get_hf_export_per_tensor_param|get_per_tensor_param' --items all
rg -n -C 8 'FSDPCheckpointManager\(|def _get_hf_export_per_tensor_param|def get_per_tensor_param' verl/workers/engine/veomni/transformer_impl.py
Length of output: 8342
@xiaohong42 Thanks. I inspected the added test. It calls VeOmniEngine.initialize() and checks that the checkpoint manager receives _get_hf_export_per_tensor_param. Using get_per_tensor_param instead would fail that assertion.
Together with the existing callback test, this addresses the manager-level export coverage concern without building an FSDP model. I did not run the tests.
📝 I couldn't check this conversation for new learnings, so no new learning was saved.
… AMD ROCm out of the box (#8371)
### What does this PR do?
Makes `examples/grpo_trainer/run_deepseek_v4_veomni.sh` run unchanged on
AMD gfx942 and gfx950 clusters: no `export` before `ray start`, no extra
overrides. Its training parameters are untouched. Three issues, one per
file:
1. **vLLM rollouts of FP8 models fail on ROCm since #8308.**
`set_default_vllm_quantization_backends` defaults `linear_backend` /
`moe_backend` to `deep_gemm` for every non-NVFP4 quantized rollout.
DeepGEMM has no ROCm kernels:
```
ValueError: FP8 MoE backend DEEPGEMM does not support the deployment
configuration since kernel does not support current device rocm.
```
The default is now skipped on ROCm; vLLM's automatic selection picks the
AITER / Triton kernels.
2. **Rollout weight sync silently corrupts DeepSeek-V4 on ROCm with
AITER.** vLLM B-preshuffles DeepSeek-V4's attention and shared-expert
FP8 weights once after loading; a weight sync writes checkpoint-layout
data into them while the forward keeps reading them as shuffled.
vllm-project/vllm#63604 adds `VLLM_ROCM_DSV4_BPRESHUFFLE`; the ROCm
platform's `rollout_env_vars` now defaults it to `0` for verl's rollout
servers (verl always updates rollout weights at runtime). Users can
still override it. vLLM releases since 0.27 preshuffle and ignore the
variable, so where the recipe enables AITER (gfx94x) it now refuses an
importable vLLM that does not define it. `PlatformROCm` now also
recognizes a HIP build of torch when `rocm-smi` is not installed;
platform detection used to fall back to `nvidia` there and drop this
default.
3. **Recipe worker environment**, passed through
`ray_kwargs.ray_init.runtime_env.env_vars` so that it reaches every
worker even on a cluster started with a plain `ray start` (each value
overridable from the launching shell). Keys that a `ray job submit
--runtime-env` already sets are left to the job, since Ray rejects the
same key in `ray.init`'s runtime env.
- `OMP_NUM_THREADS` = physical cores (at most the CPUs the launching
process may use, e.g. under a Slurm cpuset) / GPUs per node: Ray pins it
to 1 in its workers, and with the recipe's `enable_fsdp_offload` the
gradient accumulation and the AdamW step run on the host.
- ROCm: `NCCL_DEBUG=VERSION`. At verl's default `WARN`, RCCL 2.30 logs
`ncclAllToAll is deprecated` for every all-to-all (40 lines for 20 calls
on 2 ranks), i.e. for every MoE dispatch/combine of every layer.
- gfx94x: `VLLM_ROCM_USE_AITER=1`. vLLM's DeepSeek-V4 sparse-attention
indexer has no other ROCm path below gfx950. ROCm is detected from the
torch build and the architecture from the launching node's KFD topology,
so no visible GPU is needed. When launching from a node without AMD
GPUs, set `VLLM_ROCM_USE_AITER=1` explicitly; an explicit value is
always forwarded.
### Checklist Before Starting
- [x] Search for similar PRs:
https://github.com/verl-project/verl/pulls?q=rocm+deepseek+v4
- [x] Format the PR title as `[{modules}] {type}: {description}`
### Test
`tests/utils/test_vllm_rocm_rollout_defaults_on_cpu.py` (CPU): DeepGEMM
stays the CUDA default and is not set on ROCm; the ROCm rollout
environment defaults `VLLM_ROCM_DSV4_BPRESHUFFLE=0` and honors an
override; a HIP build is detected as `amd` without `rocm-smi` and
without a visible GPU (before the fix: `nvidia`).
`tests/special_sanity/test_deepseek_v4_veomni_recipe_env.py` runs the
recipe with the trainer launch stubbed out and checks the worker
environment it passes: CUDA gets only `OMP_NUM_THREADS`; ROCm gets
`NCCL_DEBUG=VERSION` and forwards explicit values even when the GPU
architecture is unknown; a gfx942 KFD topology turns on
`VLLM_ROCM_USE_AITER=1` and a gfx950 one does not; with AITER on, a vLLM
without `VLLM_ROCM_DSV4_BPRESHUFFLE` is refused, with AITER off it is
not; `OMP_NUM_THREADS` follows the CPU affinity; keys of a job runtime
env are left to the job.
End to end: the recipe as is, on 2 nodes per architecture. The only
overrides are `trainer.nnodes=2` (the recipe defaults to 8 nodes) and
the recipe's own `HDFS_ROOT` / `DATA_ROOT`; `WANDB_MODE=offline` (no
wandb credentials on these machines). Ray was started with only
`NCCL_SOCKET_IFNAME` / `GLOO_SOCKET_IFNAME`, plus `NCCL_IB_DISABLE=1` on
the gfx942 pair, whose RDMA is unusable (host driver issue), so RCCL
runs over TCP there. On both architectures the run trains, and rollout
and trainer token probabilities stay consistent at every step (verl's
`training/rollout_actor_probs_pearson_corr` and `rollout_corr/kl`).
Depends on #8369 (FP8 checkpoints with the VeOmni engine), #8370 (fused
AdamW under FSDP2 CPU offload: without it the 2-node run exceeds host
memory) and, on ROCm, ByteDance-Seed/VeOmni#1363 (TileLang kernels and
FP8 QAT on gfx942/gfx950), vllm-project/vllm#63604,
tile-ai/tilelang#3665 and deepseek-ai/TileKernels#43. The end-to-end
runs used the image's tilelang 0.1.10 and tile-kernels 1.0.0 with the
equivalent fixes applied (0.1.10 also needs the vectorized-`exp2` and
fp32-MFMA HIP fixes that tilelang 0.1.15 already has), VeOmni at verl's
pin `10a26aca` with the changes of VeOmni#1363, and vLLM with #63604.
### API and Usage Example
No API or config change. On a ROCm cluster the recipe is launched as on
CUDA. The worker variables it adds take the launching shell's value when
one is set:
```bash
bash examples/grpo_trainer/run_deepseek_v4_veomni.sh trainer.nnodes=2
VLLM_ROCM_USE_AITER=0 bash examples/grpo_trainer/run_deepseek_v4_veomni.sh trainer.nnodes=2 # override a recipe default
```
### Design & Code Changes
- `verl/utils/vllm/vllm_quant_utils.py`: no DeepGEMM default on ROCm;
`docs/low_precision/qat.md` says so.
- `verl/plugin/platform/platform_rocm.py`: `VLLM_ROCM_DSV4_BPRESHUFFLE`
in `rollout_env_vars`, default `0`; ROCm is detected from the HIP build
when `rocm-smi` is missing.
- `examples/grpo_trainer/run_deepseek_v4_veomni.sh`: the worker
environment block above, and the refusal of a vLLM without
`VLLM_ROCM_DSV4_BPRESHUFFLE` under AITER (the header lists the
requirement).
### Checklist Before Submitting
- [x] Read the Contribute Guide.
- [x] Apply pre-commit checks.
- [x] Add / Update the documentation: recipe comments.
- [x] Add unit or end-to-end test(s): the CPU and sanity tests above;
the recipe was run end to end.
- [ ] Once the PR is ready for CI, send a message in the `ci-request`
channel of the verl Slack workspace.
- [x] `recipe` submodule: not changed (the recipe edited here is
`examples/grpo_trainer/run_deepseek_v4_veomni.sh`), so there is no
submodule reference to update.
---------
Co-authored-by: Cursor <cursoragent@cursor.com>
Co-authored-by: wuxibin <wuxibin@bytedance.com>
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What does this PR do?
Since #8287 (unify QAT and low-precision rollout),
init_modelhands the actor engine a rollout weight quantizer whenever the rollout is quantized — for a checkpoint whosequantization_configis FP8 (e.g. DeepSeek-V4-Flash-Base),dispatch_rollout_weight_quantizerreturns anFp8ConfigWeightQuantizer— and callsengine.set_rollout_weight_quantizer, which only the Megatron engine implements. Every VeOmni-engine run on such a checkpoint, includingexamples/grpo_trainer/run_deepseek_v4_veomni.shwith its default model, therefore stops ininit_model:This PR implements it the way the Megatron engine does: the engine keeps the quantizer and applies it to the per-tensor stream of
get_per_tensor_param. For DeepSeek-V4, VeOmni's checkpoint converter already exports the release FP8/FP4 layout, and the quantizer is a pass-through for that model type (as for Megatron-Bridge's export); other models' BF16 streams get quantized to what the rollout loads.Checklist Before Starting
[{modules}] {type}: {description}Test
tests/workers/test_veomni_rollout_quantizer_on_cpu.py: the exported stream is returned unchanged without a quantizer and passes through it otherwise; the checkpoint'shf_modelexport stays unquantized, andinitialize()hands the checkpoint manager that unquantized export.tests/utils/test_rollout_weight_quantizer_dispatch_on_cpu.py: arollout.quantizationoverride for DeepSeek-V4 is rejected whether or not the checkpoint is quantized, and so isdelta_shardedsync with a quantized rollout; DeepSeek-V4's native config still passes through, and an override for another model is dispatched.run_deepseek_v4_veomni.shwith its defaults (DeepSeek-V4-Flash-Base, FP8) gets pastinit_modeland trains on two nodes each of AMD gfx942 and gfx950. The trainer exports the FP8 weights through this path at every weight sync, and rollout and trainer token probabilities stay consistent after each sync.API and Usage Example
No API or config change.
VeOmniEnginenow implements the existingset_rollout_weight_quantizerhook, so a run withmodel_engine=veomnion a checkpoint whosequantization_configis FP8 (such asrun_deepseek_v4_veomni.shwith its default model) gets pastinit_model. Two combinations the trainer cannot sync now fail atinit_modelwith an explicit error, for both engines: arollout.quantizationoverride for DeepSeek-V4, andcheckpoint_engine.backend='delta_sharded'with a quantized rollout.Design & Code Changes
VeOmniEngine.set_rollout_weight_quantizerstores the quantizer;get_per_tensor_paramapplies it to the export of_get_hf_export_per_tensor_param(converter path and state-dict path). The checkpoint manager'shf_modelexport calls_get_hf_export_per_tensor_paramdirectly, so saved checkpoints keep the trainer's weights.dispatch_rollout_weight_quantizerrejectsrollout.quantizationoverrides for DeepSeek-V4. Both engines export it under its checkpoint names (e.g.ffn.experts, which the override'smlp.expertsMoE target does not match) and, for a quantized checkpoint, in its own FP8/FP4 layout, whichCompressedTensorsWeightQuantizerpasses through unchanged.delta_shardedsync for a quantized rollout: after the seed, that backend streamsget_per_tensor_param_delta_shard, which bypasses the quantizer. vLLM's delta weight transfer already refuses quantized models; SGLang's delta loader has no such check.Checklist Before Submitting
tests/workers/test_veomni_rollout_quantizer_on_cpu.pyandtests/utils/test_rollout_weight_quantizer_dispatch_on_cpu.py(CPU CI).ci-requestchannel of the verl Slack workspace.recipesubmodule: not changed, so there is no submodule reference to update.