[MRv2][PCP] Carry the row plan in attention metadata; fix mixed prefill+decode crash

[LucasWilkinson]

Follow-up to the PCP-GQA work on pcp-gqa, continuing the simplification pass from #1 (closed after you absorbed it). Two commits.

1. Row plan travels as attention metadata, not forward context

pcp_row_plan and pcp_has_prefill are per-step properties of the batch, so they can be built where the rest of the attention metadata is built, instead of being stashed into forward_context.additional_kwargs by a dedicated model-runner hook.

PCPManager.partition_batch now sets both on the rank-local InputBatch, and they flow InputBatch -> CommonAttentionMetadata -> FlashAttentionMetadata like every other field. FlashAttentionImpl reads them off attn_metadata. PCPManager.populate_forward_context() and its call site in GPUModelRunner are removed.

2. Two things that fell out of (1)

The mixed prefill+decode crash is fixed. build() was calling _build_dcp_context_lens on PCP+DCP prefill/mixed steps, but those steps never read its per-request output -- the row plan carries its own per-row context lengths. A rank-local batch holds up to two rows per prefilling request, so a batch of 8 requests produced 14 local rows and the write into the max_num_seqs-sized _dcp_context_kv_lens buffer raised:

RuntimeError: The expanded size of the tensor (8) must match the existing size (14)
  at non-singleton dimension 0.  Target sizes: [8].  Tensor sizes: [14]
  ... flash_attn.py, line 527, in _build_dcp_context_lens

which kills the engine. Because the builder can now see the plan, it simply skips the call. I checked the alternative fix (sizing the buffer 2 * max_num_seqs) against this one: both produce byte-identical generations, confirming the value really was dead on those steps. Skipping is less code and less per-step work.

The write-gathered K/V no longer needs a forward-context entry. do_kv_cache_update and _forward_pcp_dcp run on the same FlashAttentionImpl instance for a given layer (attn_layer.impl in both unified_kv_cache_update and unified_attention_with_output), so the pcp_gathered_kv:{layer_name} key was doing work the object identity already does. It is now instance state, consumed and cleared by forward(), which preserves the kv-sharing staleness guarantee the layer keying was there for.

Net effect: no PCP code path modifies the forward context.

Not a duplicate

This targets your pcp-gqa branch, not vllm-project/vllm:main; it is a refactor of code that exists only in vllm-project#49564. It supersedes nothing open -- #1 in this fork is closed.

Testing

Blackwell (FA4), Qwen3-0.6B, enforce_eager, greedy, compared against a tp1 baseline on the same build:

Config	Before	After
tp1 control	OK	OK
tp1 pcp2 dcp2, prefill-only	OK, 1/6 prompts differ	unchanged
tp1 mixed prefill+decode	OK	OK
tp1 pcp2 dcp2, mixed prefill+decode	crash (8-vs-14 above)	OK, 4/8 prompts differ

Commands:

python cp_check.py    Qwen/Qwen3-0.6B --tp 1 --pcp 2 --dcp 2   # 6 prompts, 32 tokens
python mixed_check.py Qwen/Qwen3-0.6B --tp 1 --pcp 2 --dcp 2   # 8 prompts, 128 tokens, max_num_seqs=8

The mixed-batch divergences are single-token flips deep into greedy decode after 30-160 identical characters ("toward"/"towards", "Step-by-Step"/"Step-by-step"), i.e. near-ties resolving differently under a different reduction order rather than breakage. A string compare cannot prove that, so I ran 5-shot gsm8k (200 problems): tp1 scores 0.440 strict / 0.435 flexible, tp1 pcp2 dcp2 scores 0.425 / 0.425, both ± 0.035. The 1.0-1.5pp gap is 2-3 problems and sits inside one standard error, so the divergence looks like reduction-order numerics. At n=200 that rules out a large regression but not a subtle one; see the comment below for details and the command.

Unrelated and unchanged by these commits: DeepSeek-V2-Lite under pcp2 dcp2 still fails at startup inside the MoE topk_softmax kernel, before attention runs.

AI assistance

These changes were developed with AI assistance (Claude Code). I have reviewed every changed line and ran the tests above myself.

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[LucasWilkinson]

gsm8k numbers, following up on the accuracy question I flagged as open.

5-shot, 200 problems, Qwen3-0.6B, Blackwell/FA4, enforce_eager, lm_eval --model vllm:

Config	strict-match	flexible-extract
tp1	0.440 ± 0.035	0.435 ± 0.035
tp1 pcp2 dcp2	0.425 ± 0.035	0.425 ± 0.035

A 1.0-1.5pp gap, i.e. 2-3 problems out of 200 and well inside one standard error. Together with the shape of the text divergences (single-token flips at near-ties after 30-160 identical characters), this is consistent with CP reduction-order numerics rather than a correctness bug.

Stating the limit plainly: at n=200 the stderr is ±3.5pp, so this rules out a large regression but would not catch a subtle one costing a point or two. If you want a firmer bound before merging, the same run at --limit 1000 on a larger model would tighten it considerably.

Reproduce:

lm_eval --model vllm \
  --model_args pretrained=Qwen/Qwen3-0.6B,tensor_parallel_size=1,prefill_context_parallel_size=2,decode_context_parallel_size=2,max_model_len=4096,gpu_memory_utilization=0.30,enforce_eager=True \
  --tasks gsm8k --num_fewshot 5 --limit 200 --batch_size auto

[LucasWilkinson]

Pushed two more commits, both verified on GPU (Blackwell/FA4, Qwen3-0.6B, tp1 pcp2 dcp2). The branch is now four commits.

97aad2056e — drop pcp_has_prefill

The slot mapping PCPManager hands the attention layer is already the gathered one, with every entry this rank must not write masked to PAD_SLOT_ID (_convert_to_gathered_slot_mappings), and reshape_and_cache_flash skips negative slots. Decode rows are replicated across PCP ranks with only rank 0 unmasked, so the duplicate writes the flag was avoiding were already suppressed — gathering unconditionally is correct. The flag was never load-bearing for correctness.

Removing it also leaves _get_attn_metadata_for_layer with no callers, so that goes too, and with it the last forward-context access in the PCP path. flash_attn.py no longer imports get_forward_context at all.

This has a measured cost, so flagging it rather than burying it: a pure-decode step now runs a redundant K/V all-gather per layer (decode rows are replicated, so it gathers pcp identical copies and the mask discards all but one). Decode-dominated throughput, three interleaved runs per arm, --input-len 32 --output-len 512 --num-prompts 64:

	output tok/s	mean
always gather (this branch)	528.6, 551.1, 558.8	546.2
skip on pure decode (parent)	577.3, 571.4, 573.5	574.1

~5% down, and the ranges do not overlap. Correct but not free — say the word if you would rather keep the gate and I will restore it off the row plan.

ff2f1e9bb6 — row plan out of InputBatch

It now travels the same route PCPManager's other per-step outputs already take: runner to model_state.prepare_attn to build_attn_metadata. InputBatch is untouched by PCP entirely. Costs an optional param on ModelState.prepare_attn and its overrides.

Still in progress

Removing PCPRowPlan altogether by folding PCP+DCP prefill into _forward_with_dcp — re-splitting each row as [0, row_start) from the cache plus its own new tokens locally, which makes the suffix indexing and the gathered-K/V stash unnecessary. It is about -310 lines and currently failing (the gathered rows overflow the max_num_seqs-sized _dcp_context_kv_lens), so it is deliberately not in this push. Will follow up once it passes.

[LucasWilkinson]

PCPRowPlan is gone. Pushed as 4c0dc92d9a; the branch is five commits and now -298 lines against pcp-gqa.

What made it removable

The plan existed because of how the PCP+DCP prefill split each row. Re-splitting it at the row's own start makes both halves ordinary:

	cached context	new tokens
before	[0, num_computed) from cache	[req_start, row_end) from a write-gathered K/V buffer + suffix_kv_idx
now	[0, row_start) from cache	[row_start, row_end) — this row's own, already local

Earlier chunks' new tokens are in the cache by the time attention runs (unified_kv_cache_update orders the write), so they are simply context. And a local row's seq_len is its end position, so seq_lens - query_lens is exactly row_start — which _build_dcp_context_lens already computes. The new-token half becomes key/value directly, which is what pure DCP has always done.

What survives is the query gather the LSE combine requires (every DCP rank must evaluate the same rows; DualChunkSwap gives each a different set). That is an all-gather of the padded query buffer with a fixed rank-major layout, so PCPManager now describes it as an ordinary CommonAttentionMetadata and the builder turns it into a nested FlashAttentionMetadata. A rank takes its own rows back as the contiguous slice [pcp_rank * padded, + n].

Deleted: PCPRowPlan, PCPPrefixPlan, build_cp_row_plan (233 lines), _forward_pcp_dcp (116 lines), the write-gathered K/V stash, and every index tensor (suffix_kv_idx, q_local_idx, q_restore_idx, local_token_idx, local_out_idx). _forward_with_dcp now covers pure DCP, PCP+DCP decode and PCP+DCP prefill.

Testing

This one changes what the kernels compute, so I ran the eval rather than relying on string compare. gsm8k 5-shot, 200 problems:

	strict	flexible
tp1 baseline	0.440	0.440
tp1 pcp2 dcp2, unified path	0.430	0.435
tp1 pcp2 dcp2, row-plan path (previous commit)	0.425	0.425

Inside one standard error (±0.035), and slightly ahead of the path it replaces. Mixed prefill+decode runs clean. Same caveat as before: at n=200 this rules out a large regression, not a 1-2pp one.

Two traps worth knowing about if you review this

• _dcp_context_kv_lens is sized max_num_seqs, but the gathered batch has pcp times as many rows. Same class of bug as the mixed-batch crash in c9cb78b809, one level up.
• The max_dcp_context_kv_len == 0 early return in _forward_with_dcp has to consult the gathered metadata. The rank-local view deliberately leaves that unbuilt, so the original check silently skipped the context attention altogether — every row attended only its own tokens and produced fluent, wrong output. It only surfaced because the harness diffs against a baseline instead of checking that the run succeeded.