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[Feature] Add DWDP (Distributed Weight Data Parallelism) for MoE prefill - #29778
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/rerun-test test_disaggregation_dwdp_gpt_oss.py test_disaggregation_dwdp_mimo.py |
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/rerun-test test_disaggregation_dwdp_gpt_oss.py test_disaggregation_dwdp_mimo.py |
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We might add a document later for DWDP |
Summary
Add DWDP as a new parallelism strategy for MoE prefill. Instead of EP all-to-all token dispatch, each rank prefetches peer expert weights via NVLink P2P and computes all experts locally, eliminating all cross-rank synchronization.
Based on the DWDP paper (arXiv 2604.01621), ported from TensorRT-LLM's implementation using CUDA VMM composite virtual addresses.
Key Design
dp_attention,dp_lm_head,SCHEDULER_SKIP_ALL_GATHER; idle ranks skip forward entirelyUsage
Results (4x B200, gpt-oss-120b, prefill-only)
DWDP4 vs DEP4 (EP4 + allgather), same total tokens:
bench_serving at saturation (CONC=128, ISL=8K): DWDP4 506K tok/s vs DEP4 329K tok/s (1.54x).
CI States
Latest PR Test (Base): ❌ Run #29770352071
Latest PR Test (Extra): ✅ Run #29799031259