Pearl#13551
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zhaoyangwang-nvidia wants to merge 2 commits intoNVIDIA:mainfrom
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Signed-off-by: ZhaoyangWang <zhaoyangw@nvidia.com>
Implement CPU-initiated libibverbs RDMA transport to offload the draft
model to a separate GPU, replacing local draft inference with a remote
RDMA peer. The target model (TRT-LLM) writes accumulated output tokens
to the draft server via RDMA Write; the draft server returns speculative
tokens via RDMA Write back.
Key changes:
- rdma_draft_offload.py: ibverbs RC QP client with GPUDirect MR registration,
QP state machine (RESET->INIT->RTR->RTS), and per-round request/response
- rdma_draft_protocol.py: fixed-size binary protocol (32B header + 256B tokens,
MAGIC-checked, 4096B total) for target<->draft RDMA buffers
- draft_target.py: RDMA offload path in DraftTargetOneModelWorker.forward(),
output token history accumulation, warmup pre-connection
- llm_args.py: DraftTargetDecodingConfig RDMA fields; allow speculative_model=None
when draft_offload_enabled=True
- model_loader.py: skip draft weight loading when draft_offload_enabled
- modeling_speculative.py: skip draft model instantiation; thread is_warmup
- _util.py: skip separate draft KV cache when draft_offload_enabled
- model_engine.py: pass is_warmup flag through to model forward inputs
- .gitignore: ignore cmake-created symlinks deep_ep/deep_gemm/flash_mla
Signed-off-by: ZhaoyangWang <zhaoyangw@nvidia.com>
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Please review the following before submitting your PR:
PR description clearly explains what and why. If using CodeRabbit's summary, please make sure it makes sense.
PR Follows TRT-LLM CODING GUIDELINES to the best of your knowledge.
Test cases are provided for new code paths (see test instructions)
Any new dependencies have been scanned for license and vulnerabilities
CODEOWNERS updated if ownership changes
Documentation updated as needed
Update tava architecture diagram if there is a significant design change in PR.
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