[Core][MRV2] Support eagle3 spec decode with pipeline parallel - #50514
[Core][MRV2] Support eagle3 spec decode with pipeline parallel#50514yongqinwang-cmd wants to merge 5 commits into
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This pull request has merge conflicts that must be resolved before it can be |
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Hardware validation on the exact shape this caps at (TP8 × PP2, 2×8 B200, EFA) — Kimi-K3 MXFP4 + Inferact/Kimi-K3-DSpark, 1M context, vLLM main @ e578de3 (cu130 wheel 0.26.1rc1.dev292) with this PR applied at setup time:
Two things to share beyond "it works":
Happy to rerun the same gates on the PR head once it's rebased (remote-synchronized needles / accept measurements are cheap on this rig), and to act as the hardware validator if you want a pp=2 multimodal or multi-request soak for the merge review. |
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Thanks for the PR! I resolved the conflict - cc @zixi-qi |
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Thanks for the PR! The high level approach looks good, left a few comments in the code. Additionally:
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| # Locate the embedding tensor: prefer the shard index, else scan the shards. | ||
| key = None | ||
| shard_path = None | ||
| index_path = os.path.join(model_dir, "model.safetensors.index.json") |
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Does this work when the model is not cached locally?
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I changed the code such that now it runs the model's hf_to_vllm_mapper over the checkpoint names and matches on the parameter name, keeping the original name to read the tensor.
| # been freed writes to a slot nobody reads, and add_requests zeroes | ||
| # the row before any reuse, whereas the -1 sentinels in the filtered | ||
| # `idx_mapping` would alias the last row. | ||
| outputs["draft_update"] = (slot.draft_tokens, slot.idx_mapping) |
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I think we should use the filtered idex_mapping instead of this unfiltered one. Does below explanation make sense to you?
A pending PP entry is consumed pp_size steps after it is received. During that delay, a request can finish and its state index can be reassigned to a new request. The generation check correctly replaces that row with -1 in the sampled-token mapping, but draft_update uses the original unfiltered mapping.
In a mixed batch, this writes the finished request’s stale drafts into the new request that now owns the same index. add_requests zeroing does not prevent this because the deferred write can occur after the index has been reused and zeroed.
| # The drafter is instantiated only on the last pipeline stage and is | ||
| # never itself pipelined, so it must not inherit the target's PP | ||
| # size; doing so would require the draft architecture to implement | ||
| # SupportsPP. This is a no-op for previously working setups, since | ||
| # speculative decoding under PP > 1 was rejected outright before. |
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Overall the additional comments in this PR can be a bit too verbose. Would be great if you could go through all the comments and make them as concise as possible and remove ones where the code already demonstrates the intent clearly.
| if pp_size > 2: | ||
| # The aux forwarding itself is size-agnostic: every stage | ||
| # derives what it owes downstream from the same rule, and | ||
| # the accounting is unit-tested up to pp=8. What has not | ||
| # been exercised on hardware is a *middle* stage, which | ||
| # pp>2 introduces and which must both adopt upstream taps | ||
| # and contribute its own to the same payload. Given that | ||
| # the failure mode of this feature is silently degraded | ||
| # acceptance rather than a crash, refuse rather than let it | ||
| # run unvalidated. Lifting this needs an end-to-end | ||
| # acceptance-rate comparison at pp>2, not just a boot test. | ||
| raise NotImplementedError( | ||
| f"{self.speculative_config.method} with pipeline parallel " | ||
| f"is currently supported only up to pipeline_parallel_size=2, " | ||
| f"got {pp_size}." | ||
| ) |
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From the comments here:
The aux forwarding itself is size-agnostic: every stage
Do I understand correctly that PP > 2 is technically supposed to be supported but the guard is added here because it is not tested? If so I think we should test it and remove this guard
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Originally designed for PP=2, per your comment, i re-worked this PR so that when PP>2, previous PP stages will forward their auxiliary data to the last rank (validated on both EAGLE3 on Llama-3.2-1B
and DSpark on DeepSeek-V4-Flash . This comment has more details about the exact design. My design ensures minimal communication during the drafting for the best itl.
| # receive() unconditionally allocates max_sample_len columns, but | ||
| # the non-spec sampler path (num_draft_tokens == 0) returns width 1, | ||
| # so an unpadded broadcast leaves the peer waiting on a larger count | ||
| # than the root sends. NCCL does not diagnose the mismatch: the root | ||
| # completes and the receiver hangs until the watchdog fires. Pad so | ||
| # both sides agree. post_update reads each row with | ||
| # sampled_tokens.stride(0) and stops at num_sampled, so the pad | ||
| # columns are never observed. |
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can the comments here be more concise?
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This pull request has merge conflicts that must be resolved before it can be |
Address PR vllm-project#50514 review feedback: keep-mask draft_update, HF-resolve embed load, concise comments, Llama/Qwen/DSv4 opt-in, EAGLE3×PP=2 e2e, and remove the pp>2 guard. Aux taps no longer chain through IntermediateTensors.
Three guards refused the drafter under PP -- the draft config inherited the target's pipeline size, so a draft model without SupportsPP was rejected; the V2 runner raised for eagle3/dflash/dspark because their aux taps may sit on an earlier stage; and the DSpark loader aliases the target's embed_tokens, which is a PPMissingLayer anywhere but the first stage. Past those, warmup deadlocked: PPHandler.receive always allocates [num_reqs, max_sample_len] while the sampler returns a single column when there are no drafts to verify, so broadcast sent a narrower tensor, the ranks disagreed on the element count, and the stage that had moved on wedged in its next device sync. Give the draft pipeline_parallel_size=1 since it is built on the last stage only, let a model declare that it carries its EAGLE3 aux taps across the handoff, build the target's embedding on the last stage rather than draft with uninitialized weights, pin one broadcast payload width on both sides and send the next step's drafts in it through the filtered index mapping, and declare the AMD DSpark head's draft_id_to_target_id. Decode TPOT 43.9 -> 35.2 ms and output throughput 20.0 -> 24.3 tok/s against the same runner without speculation, at 19.8% draft acceptance. Upstream adds pipeline-parallel support for these drafters in vllm-project#50514; drop this commit when rebasing onto a tag that already contains it.
Upstream refuses this outright -- vllm/v1/worker/gpu/spec_decode/dspark/utils.py on main still raises NotImplementedError when the PP group is larger than one, and SpeculativeConfig hands the draft the target's pipeline_parallel_size, which asks K3DSparkForCausalLM for a SupportsPP it does not declare. That is the error all seven of our earlier PP2 arms died on. The support lives in our vLLM fork as 2111011d33 (adopting vllm-project/vllm#50514), 540423f2c7 and 503820ebdd: the draft stops inheriting PP size and runs whole on the last stage, aux hidden states relay between stages as IntermediateTensors behind a Kimi-K3-only opt-in, the last stage loads the real embedding table instead of aliasing a PPMissingLayer, and the draft loader drops fastsafetensors under PP because its collective runs over group.WORLD. Applied with patch(1) after a --dry-run, and verified afterwards that the refusal is gone and the draft's PP size is pinned to 1. A mismatch fails the job rather than running a half-patched engine; the fork's base is 38a466e7b6 and the image is 5894fdf98, so drift will eventually break it and should be loud when it does. Any arm using this must ship with a GSM8K run. The adopting commit records that draft tokens previously failed to reach earlier stages and the first rank embedded PLACEHOLDER_TOKEN_ID(-1) -- silent corruption that ignore_eos perf arms cannot see.
…llelism vllm-project#50514 (yongqinwang-cmd)를 우리 베이스 38a466e 위에 적용한다. 우리 자체 구현(브랜치 misunp/k3-dspark-pp)과 설계가 같고 — aux를 IntermediateTensors로 릴레이, 모델별 opt-in 플래그, Kimi-K3만 opt-in, 드래프트 config의 PP 상속 제거, 마지막 스테이지 드래프터 임베딩 — 우리가 5판을 태우고도 못 찾은 두 가지를 더 담고 있다: 1. broadcast 폭 불일치. receive()는 항상 max_sample_len 열을 받는데 broadcast()는 샘플러가 만든 폭을 보내고 non-spec 경로는 폭 1이다. NCCL은 개수 불일치를 진단하지 않아 root는 완료하고 peer는 watchdog까지 멈춘다 — 우리 블로커 #7의 진짜 원인이다. 2. draft 토큰이 앞 스테이지에 도달하지 않아 첫 랭크가 PLACEHOLDER_TOKEN_ID(-1)를 임베딩했다. 조용한 오염이라 서빙에 도달했어도 못 봤을 것이다. 충돌 1건 해소: KimiLinearModel이 우리 베이스에서 SupportsQuant와 packed_modules_mapping을 갖게 되어, 양쪽을 유지하고 PR의 opt-in 플래그를 더했다. PR 테스트 12개 통과. pipeline_parallel_size <= 2 캡이 있으며 우리 TP4xPP2가 그 안이다. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> Signed-off-by: misunp <misunp@nvidia.com> (cherry picked from commit 2111011d331343cc22d02bc87020069edc22de6f)
Retest attempt on a 2-node TP2×PP2 topology — inconclusive, with one scope questionWe tried to retest this on our lab pool after the rebase. We did not get a clean validation result, and I want to report the attempt honestly rather than let silence read as "no problems found." Topology (this is the part that may be useful to you): DeepSeek-V4-Flash NVFP4, What happened Spec-ON failed at config validation, before any of the runtime paths this PR touches: Spec-OFF never served either, but for an unrelated reason — cross-node distributed init died after ~10 min with The scope question We ran So the question: is in-model MTP ( Caveat on the above, stated plainly We cannot currently confirm our test image carried this PR. One arm's engine log reports stock Offer If it would help, we're happy to run this topology against the PR head as a validation target — particularly for defect #3 in your description (the |
Root cause of AIB 63781036, where a PP=2 prefiller and a PP=1 decoder answered none of 1319 GSM8K questions. Prefill ran; decode never logged an engine step; every PUSH_REG registration timed out at 480 s. DSpark numbers its draft layers from the end of the target's, using get_num_layers(parallel_config) -- which is end-start, the PP-*local* count. At PP=2 over 93 layers stage 1 reports 46, so its draft registered model.layers.46..50 while the PP=1 peer registered 93..97, and 46..50 also collided with that stage's own target layers 47..92. Member routing transfers by layer name, so the two engines disagreed about what "model.layers.46.self_attn" is and the planner refused it, correctly, 859 times. Symmetric PP passed only because both sides computed the same wrong number. The decode-PP2 arms are green for that reason, not because they were right; their 0.907/0.932 stand but the mechanism behind them did not. Upstream has the same expression in cohere_eagle and deepseek_eagle3. It was unreachable while spec decode under PP was refused; adopting vllm-project#50514 is what made it reachable, so it belongs to this branch to get right. Those two are left alone -- different models, nothing here can test them -- but an upstream PR has to say so. Three more from a fresh review, all real: pp_rank's fallback divided the global rank by TP alone. The documented layout is ExternalDP x DP x PP x PCP x TP, so with DP>1 -- how K3 runs with EP -- it returned a stage index off by a multiple of pp_size, cached for the process lifetime. Now divides by PCP*TP and wraps by pp_size. The pull connector inherited decode-side PP when the blanket refusal came out, and it cannot do it: its handshake never exchanges pp_size, so handles resolve at stage 0 unconditionally, and consumers_per_producer counts TP alone -- a PP-sharded consumer would let the producer free blocks after one stage's notif while another was still reading. Neither symptom is loud. It refuses at startup now, which is what the docs already said. _tracks_region_members keyed off _is_hma_required, a property of this stage's layers, so a stage holding only full-attention layers advertised no members while its hybrid peer required them. Widened to cover packed and PP as well: advertising names a peer ignores costs a list in the handshake; withholding them is a dead engine. This is the third time these two gates have disagreed. 332 passed. The gemma-3 gated-repo 401 fails identically without the patch. AI assistance was used for this change. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
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This pull request has merge conflicts that must be resolved before it can be |
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Hi @yongqinwang-cmd sorry for the delay, would you mind to resolve the merge conflict again and then I will rerun CI |
Correction: the build was patched — retracting my caveat, which makes the trace above a real resultIn my previous comment I said we couldn't confirm our image carried this PR, because one arm's engine log reported stock Our image is built as Verified directly inside the image instead (built from PR head So the PR is applied, and the if self.draft_model_config:
self.draft_model_config.verify_with_parallel_config(
self.draft_parallel_config
)With One observation offered as a question, not a claim: I could not find Re-test with an in-scope drafter ( |
Correction #2 — our image was only partially patched. Please disregard the SupportsPP result.I got this wrong twice and the third check settles it. Retracting my previous comment: our test image did not contain your behavioural changes, so the What actually happened. Our image is built So the image picked up files your PR adds while missing edits your PR makes to files that already exist — which is the worst possible state, because it imports cleanly and looks patched. Our build's tripwire only asserted that the new modules import, and a partially-applied PR passes that check. Consequences for what I posted earlier:
Also confirmed: we re-fetched We're rebuilding as a direct overlay onto the installed package rather than an editable install, with a tripwire that asserts the content of your changes is what resolves at runtime — not merely that the new modules import. Then we'll run |
The discount I added to compute_need_sampled_mask was a no-op in the case it was written for. num_computed_tokens is inflated by the *previous* step's drafts -- the comment says so itself -- but the subtrahend was num_draft_tokens_per_req, this step's count. A request given four drafts and then none gets a discount of zero, still reads as finishing early, and the last rank stops broadcasting while the other ranks repeat a stale token. That is the corruption the discount exists to prevent, arriving through the discount. The reverse mismatch (three then four) over-subtracts, which is harmless. Only one direction is safe, so subtract a bound that is always at least the inflation: num_speculative_steps, passed in from the handler. The existing max(..., prefill_len) clamp absorbs it. The price is stated and tested rather than assumed: a request stays in the broadcast for up to num_speculative_steps extra steps past max_seq_len, then drops out. One redundant collective against a corrupted token. produces_sample deliberately keeps the undiscounted count -- it asks whether this step's tokens complete the prefill, and an inflated count there errs toward broadcasting, the safe direction again. 9 tests, covering the broken combination directly, every inflation the scheduler can produce, the bounded tail, and no-speculation. Not covered: tests/v1/worker/test_eagle3_aux_hidden_states_pp.py, which this branch adopted with vllm-project#50514, fails 11 of 12 in the container against an unpatched nightly and cannot be validated by copying modules into an installed wheel -- adding more modules breaks the rest. It needs a real build. I had not run it before now. Signed-off-by: Mi Sun Park <misunp@nvidia.com> Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Resolve conflicts after the DSpark lazy-import refactor and Qwen3 DFlash model subclassing while preserving embedding sharing and global draft-layer numbering. Co-authored-by: Cursor <noreply@cursor.com> Signed-off-by: Yongqin Wang <yongqinwang@roblox.com>
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@zixi-qi The merge conflict is resolved again at Local checks: 33 unit tests passed; Ruff check and format are clean. Would you mind rerunning CI? Thanks! |
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/ci run |
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✅ Triggered Buildkite CI #85077 for commit |
Build-verified result: the PP guard works correctly on a 2-node TCP-sockets topologyFollowing up my retractions above — this run is build-verified, and it's a positive result for the PR. Setup. Rebuilt as Topology. Baseline (spec-OFF), Qwen3.6-35B-A3B NVFP4, serves and is coherent:
Batching scales only 1.35× from c=1 to c=8 here, which is what a PP pipeline looks like when it's latency-bound on sockets. Spec-ON — your guard fired, correctly and fast: That is the right outcome — Request: please add
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Three guards refused the drafter under PP -- the draft config inherited the target's pipeline size, so a draft model without SupportsPP was rejected; the V2 runner raised for eagle3/dflash/dspark because their aux taps may sit on an earlier stage; and the DSpark loader aliases the target's embed_tokens, which is a PPMissingLayer anywhere but the first stage. Past those, warmup deadlocked: PPHandler.receive always allocates [num_reqs, max_sample_len] while the sampler returns a single column when there are no drafts to verify, so broadcast sent a narrower tensor, the ranks disagreed on the element count, and the stage that had moved on wedged in its next device sync. Give the draft pipeline_parallel_size=1 since it is built on the last stage only, let a model declare that it carries its EAGLE3 aux taps across the handoff, build the target's embedding on the last stage rather than draft with uninitialized weights, pin one broadcast payload width on both sides and send the next step's drafts in it through the filtered index mapping, and declare the AMD DSpark head's draft_id_to_target_id. Decode TPOT 43.9 -> 35.2 ms and output throughput 20.0 -> 24.3 tok/s against the same runner without speculation, at 19.8% draft acceptance. Upstream adds pipeline-parallel support for these drafters in vllm-project#50514; drop this commit when rebasing onto a tag that already contains it.
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Does the ci results good good to you? I check all failed tests are unrelated to this PR. |
Yeah CI looks good, thanks for the rebase! |
njhill
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Thanks @yongqinwang-cmd @zixi-qi
One question, is "taps" a standard/well-known term? I found it a bit confusing
Also the PR description seems to be quite out of date / incorrect
| # Pair the last rank's post-propose draft send. | ||
| if self.num_speculative_steps > 0: | ||
| self.pp_handler.receive_drafts(input_batch) |
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Can we absorb this into pp_handler.receive above?
| self.pp_handler.broadcast_drafts( | ||
| self.req_states.draft_tokens[input_batch.idx_mapping], | ||
| input_batch, | ||
| ) |
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Let's do the indexing inside the method if/when needed
| ) | |
| self.pp_handler.broadcast_drafts(self.req_states.draft_tokens, input_batch) |
| if self.use_pp and not supports_aux_hidden_states_over_pp(self.model): | ||
| raise ValueError( | ||
| f"{self.speculative_config.method} with pipeline parallel " | ||
| f"is not supported by {type(self.model).__name__}: it does " | ||
| "not forward auxiliary hidden states across pipeline stages." | ||
| ) | ||
| if self.use_pp: | ||
| self.aux_pp_relay_keys = aux_pp_relay_keys(self.model) |
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| if self.use_pp and not supports_aux_hidden_states_over_pp(self.model): | |
| raise ValueError( | |
| f"{self.speculative_config.method} with pipeline parallel " | |
| f"is not supported by {type(self.model).__name__}: it does " | |
| "not forward auxiliary hidden states across pipeline stages." | |
| ) | |
| if self.use_pp: | |
| self.aux_pp_relay_keys = aux_pp_relay_keys(self.model) | |
| if self.use_pp: | |
| if not supports_aux_hidden_states_over_pp(self.model): | |
| raise ValueError( | |
| f"{self.speculative_config.method} with pipeline parallel " | |
| f"is not supported by {type(self.model).__name__}: it does " | |
| "not forward auxiliary hidden states across pipeline stages." | |
| ) | |
| self.aux_pp_relay_keys = aux_pp_relay_keys(self.model) |
| if self.use_pp and not supports_aux_hidden_states_over_pp(self.model): | ||
| raise ValueError( | ||
| f"{self.speculative_config.method} with pipeline parallel " | ||
| f"is not supported by {type(self.model).__name__}: it does " | ||
| "not forward auxiliary hidden states across pipeline stages." |
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Actually could we change this method to be like verify_supports_aux_hidden_states_over_pp and have it raise the exception itself, to simplify the code here
| outputs = self.pp_handler.get_prev_sampled_outputs() | ||
| if outputs is not None: | ||
| # Land with the matching sampled tokens so _prepare_inputs | ||
| # splices real draft ids instead of placeholders. | ||
| draft_update = outputs.pop("draft_update", None) | ||
| if draft_update is not None: | ||
| draft_tokens, draft_idx_mapping = draft_update | ||
| self.req_states.draft_tokens[draft_idx_mapping] = draft_tokens |
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Could we have get_prev_sampled_outputs take self.req_states.draft_tokens as a draft_tokens_to_update input arg and then move this logic into that method?
| if self.aux_pp_relay_keys: | ||
| # The forward packs only local taps; pass the upstream ones on. | ||
| received = model_inputs["intermediate_tensors"] | ||
| assert output_intermediate_tensors is not None | ||
| output_intermediate_tensors = IntermediateTensors( | ||
| output_intermediate_tensors.tensors | ||
| | {k: received[k] for k in self.aux_pp_relay_keys} | ||
| ) |
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Can we move this logic into PPHandler, then aux_pp_relay_keys can be a field within that class instead. i.e. a method that takes intermediate_tensors and output_intermediate_tensors to update, returns new output_intermediate_tensors.
| _aux_upstream_total_cached: int = 0 | ||
|
|
||
| def _set_aux_hidden_state_layers(self, layers: tuple[int, ...]) -> None: | ||
| self.aux_hidden_state_layers = layers |
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this must be ordered I think
| self.aux_hidden_state_layers = layers | |
| self.aux_hidden_state_layers = tuple(sorted(layers)) |
njhill
changed the title
Three guards refused the drafter under PP -- the draft config inherited the target's pipeline size, so a draft model without SupportsPP was rejected; the V2 runner raised for eagle3/dflash/dspark because their aux taps may sit on an earlier stage; and the DSpark loader aliases the target's embed_tokens, which is a PPMissingLayer anywhere but the first stage. Past those, warmup deadlocked: PPHandler.receive always allocates [num_reqs, max_sample_len] while the sampler returns a single column when there are no drafts to verify, so broadcast sent a narrower tensor, the ranks disagreed on the element count, and the stage that had moved on wedged in its next device sync. Give the draft pipeline_parallel_size=1 since it is built on the last stage only, let a model declare that it carries its EAGLE3 aux taps across the handoff, build the target's embedding on the last stage rather than draft with uninitialized weights, pin one broadcast payload width on both sides and send the next step's drafts in it through the filtered index mapping, and declare the AMD DSpark head's draft_id_to_target_id. Decode TPOT 43.9 -> 35.2 ms and output throughput 20.0 -> 24.3 tok/s against the same runner without speculation, at 19.8% draft acceptance. Upstream adds pipeline-parallel support for these drafters in vllm-project#50514; drop this commit when rebasing onto a tag that already contains it.
7 participants
Purpose
EAGLE3-style speculative decoding (
eagle3/dflash/dspark) is rejected outrightwhen pipeline parallelism is enabled:
The drafter runs on the last PP rank, but it consumes auxiliary hidden states tapped from
target layers that may live on earlier stages, and those tensors were dropped at the stage
boundary. This PR forwards them and lifts the guard.
Lifting the guard alone is not sufficient. The code behind it had never executed, so it hid
a stack of latent defects in the shared PP path — all of them reachable only once spec decode
and PP run together, which means they were unobservable rather than latent-but-live:
Draft tokens never reached the non-last ranks. Only the last rank runs the drafter, so
req_states.draft_tokensis written nowhere else — yet the first rank ownsembed_tokensand builds the embeddings for the whole pipeline. It was embedding the scheduler's
PLACEHOLDER_TOKEN_ID(-1) into the draft slots, so the last rank verified real proposalsagainst logits computed from placeholders. Silent corruption. Fixed by broadcasting the
proposals from the last rank on the existing deferred slot, which has identical production
and consumption timing.
compute_need_sampled_maskended the broadcast early. The scheduler advancesnum_computed_tokensby the full scheduled width up front and rolls the rejected part backin
update_from_output, which under PP lands after the next batch is scheduled. Readingthe inflated count marked requests as finishing up to
num_speculative_tokensearly, afterwhich the last rank stopped broadcasting and the other ranks'
last_sampled_tokensfroze,repeating a stale token.
The sampled-token broadcast width could mismatch.
receive()always allocatesmax_sample_lencolumns whilebroadcast()sent whatever width the sampler produced, andthe non-spec path returns width 1. NCCL does not diagnose the count mismatch: the root
completes and the peer hangs until the watchdog fires, so any PP + spec-decode run
deadlocked on its first prefill. Fixed by padding the send side;
post_updatestops atnum_sampled, so the pad columns are never read.Design
Aux-state forwarding is added generically on
EagleModelMixin. A stage packs its own tapsplus those inherited from upstream into the
IntermediateTensorspayload, and sizes its recvbuffers from the same rule — a tap
ais upstream iffa <= start_layer— so send and recvcounts agree with no negotiation.
Models opt in with
supports_aux_hidden_states_over_pp. The previous blanket error is retainedfor those that do not, now raised with the offending model named. Kimi-K3 opts in; no other
model does, so EAGLE3 and dflash under PP remain gated and untested.
Two smaller changes ride along, both no-ops for configurations that worked before (spec decode
under PP > 1 was rejected outright): the DSpark drafter gets a real vocab embedding on the last
stage, where the target's is a
PPMissingLayer; and the draft parallel config stops inheritingthe target's PP size, since the drafter is instantiated only on the last stage and is never
itself pipelined.
Scope: capped at
pipeline_parallel_size <= 2GPUModelRunner.load_modelraisesNotImplementedErrorabove 2.This reflects validation coverage, not a known defect. The forwarding rule is size-agnostic by
construction and the accounting is unit-tested up to pp=8. But pp>2 is the first topology with
a middle stage, which must both adopt upstream taps and contribute its own to the same
payload, and no such run has happened on hardware.
The conservative default is deliberate because this feature does not fail loudly: a drafter fed
mis-ordered or missing taps still emits syntactically valid proposals that simply get rejected
more often, so the only symptom is a depressed acceptance rate. Lifting the cap should require
an acceptance-rate comparison at pp>2, not just a successful boot. Happy to drop the cap if
maintainers would rather have the untested-but-general path available.
Note on commit 1
The first commit is an independent bugfix —
MambaHybridModelState.postprocess_statefeeds anint32
idx_mappingtoindex_fill_, which accepts only int64:This is reachable on main today with no speculative decoding involved. Its only caller is
the non-last-PP-rank path in
GPUModelRunner.execute_model, so any Mamba-hybrid model (Jamba,Falcon-H1, Nemotron-H, Qwen3-Next, MiniMax, Kimi Linear) crashes under
pipeline_parallel_size >= 2with a chunked-prefill batch. It is included here because Kimi-K3is a hybrid model and hits it under PP. Glad to split it into its own PR if preferred.
Test Plan
Unit. A CPU test for the aux accounting invariant, no distributed init required:
12 cases over pp ∈ {1,2,3,4,6,8}, exercising the real
EagleModelMixinand realget_pp_indices. Covers tap ordering and the boundary-tap double-count hazard (a tap landingexactly on
start_layermust be counted as upstream by exactly one stage). The parametrizationintentionally runs past the pp<=2 cap, since the accounting is general and this is what a future
enablement would build on.
End-to-end. Kimi-K3 (93 layers, DSpark drafter,
num_speculative_tokens=7) at TP8 × PP2 on2× 8×B200 with EFA. Two checks:
speculative decoding disabled. Speculative decoding is supposed to be output-neutral, so any
divergence is a bug.
degrades acceptance before it produces visibly wrong text.
Throughput measured on 128 fixed GSM8K prompts, same seed, concurrency 1 → 32.
Test Result
Unit: 12 passed.
ruff checkandruff format --checkclean.Correctness: greedy output is token-for-token identical to the same topology without
speculative decoding.
Acceptance: 2.32 of 7 draft tokens per step.
Throughput (output tok/s, GSM8K, 128 prompts):