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[Core][Diffusion] Add reduced sampling support for DiffusionGemma - #56210
Ruiqiu-Zheng wants to merge 1 commit into
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@njhill @yewentao256 — this is an early Draft stacked on #54332. When convenient, I’d appreciate an early design review of the Model Runner V2 pre-logits fail-closed integration and the exact-RNG Diffusion sampler semantics. The Diffusion-only path has source-bound TP8 correctness/component validation; I’m not requesting any CI/ |
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Update: Current head is now the standalone DiffusionGemma-specific commit The exact head has source-bound TP8 correctness PASS on Ascend 910B2, including matched self-conditioning embedding max-abs |
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| vocab_end: int, | ||
| device: torch.device, | ||
| ) -> torch.Tensor: | ||
| full = torch.rand((*shape, vocab_size), device=device, dtype=torch.float32) |
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On platforms using the default Inductor backend, this eager torch.rand call does not produce or consume the same RNG stream as the torch.rand_like inside the compiled _compiled_sample_step. Consequently, eligible TP requests can return different Gumbel samples and advance the globally seeded RNG differently from the dense fallback, defeating vLLM's reproducibility guarantee. The equivalence test hides this because it explicitly invokes _compiled_sample_step.__wrapped__; the local path needs to use the same compiled RNG context or otherwise reproduce its generator behavior.
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| max_values = values.max(dim=-1, keepdim=True).values | ||
| tied_indices = torch.where(values == max_values, indices, vocab_size) | ||
| return tied_indices.min(dim=-1).values |
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If any TP shard's local maximum is NaN, the gathered max_values is NaN and every values == max_values comparison is false, so this fallback selects vocab_size as the token ID. That out-of-range ID is stored in the diffusion canvas/argmax history and can later fail embedding lookup. The dense argmax path still returns an in-range index for the same malformed logits, so the distributed reduction should explicitly handle NaNs or otherwise ensure the sentinel can never escape.
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This pull request has merge conflicts that must be resolved before it can be |
Signed-off-by: Ruiqiu Zheng <191817791+Ruiqiu-Zheng@users.noreply.github.com>
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Summary
This PR adds a DiffusionGemma-specific vocabulary-stationary TP sampling path, keeping vocabulary-sharded logits local through the supported custom diffusion sampler instead of materializing replicated full-vocabulary logits and FP32 sampler intermediates on every TP rank.
The Diffusion-specific path:
LogitsProcessorwhile preserving FP32 semantics;Request tiling and TP-local self-conditioning matmul/all-reduce are already upstream and are not reintroduced here. This PR also does not add a second generic reduced-sampling framework.
Scope / fallback
The initial scope is deliberately narrow: TP > 1, one active request, and standard contiguous/equal vocabulary shards.
Grammar, requested logprobs/logprob token IDs, top-k, top-p, min-p, multi-request sampling, TP1, and unsupported shard geometries select the existing dense
compute_logitspath beforecompute_local_logitsis attempted.Exact RNG preservation is intentional; RNG compression is out of scope.
Motivation
DiffusionGemma sampling operates over
[num_decode, canvas_length, vocab_size]. The full logits plus FP32 scaled/Gumbel/probability intermediates create a substantial replicated-memory floor per TP rank. This is related to the sampler memory pressure discussed in #50699. Request tiling reduces the multi-request multiplier but does not eliminate the single-request full-vocabulary floor.Validation
Source-bound TP8 component/integration validation used Ascend 910B2 with
CL=256,V=262144, andH=2816, with real BF16 local LM-head projections producing[256, 32768]logits per rank.randint, RNG state, and cross-rank RNG synchronization matched.0.00390625(acceptance threshold<= 0.005).compute_logitsbefore local capture.Source-bound component measurements:
20.187 msdense vs12.496 mslocal (~38.1% lower);2,523,837,440bytes dense vs980,324,864bytes local (~61.2% lower).These are source-bound component measurements, not a full-model E2E or production performance claim.
Focused numerical-equivalence, eligibility, static, and source-level rebase-equivalence checks also passed. Standard local pytest collection was unavailable on the development host because
tblibwas missing; CI results are pending.Related work
#54332 (
[Core] Add reduced sampling for tensor-parallel decoding) is related generic reduced-sampling work, but this PR does not depend on it. The implementation here remains deliberately DiffusionGemma-specific and does not introduce a competing generic reduced-sampling framework.