Add a new `deepep_v2` all2all backend that uses the DeepEP v2
ElasticBuffer API (NCCL GIN backend). This provides a unified
dispatch/combine interface that works for both intra-node and
inter-node expert parallelism with analytical SM calculation.
Key changes:
- New DeepEPV2PrepareAndFinalize class using do_expand=True for
per-expert-contiguous layout with weighted reduction in combine
- DeepEPV2All2AllManager with ElasticBuffer handle caching and
theoretical SM calculation via get_theoretical_num_sms()
- NCCL >= 4.30.4 version gating in has_deep_ep_v2() since the
GIN backend requires a newer NCCL than PyTorch typically bundles
- FP8 block-quantized dispatch support
- DBO (micro-batching) support with async prepare/finalize
- Environment variables: VLLM_DEEPEP_V2_ALLOW_HYBRID_MODE,
VLLM_DEEPEP_V2_PREFER_OVERLAP, VLLM_DEEPEP_V2_ALLOW_MULTIPLE_REDUCTION
- Update DeepEP install script to pin v2.0 release (b306af06af)
- Comprehensive multi-process test suite
Usage: --all2all-backend=deepep_v2 --enable-expert-parallel
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Signed-off-by: Tyler Michael Smith <tlrmchlsmth@gmail.com>
