[Spec Decode] DFlash2: local convolution + candidate selector - #52816
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Hi where is this coming from? Is there a model checkpoint we can use to validate this? Or a whitepaper discussing the architecture? |
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Hi @benchislett — sorry for the confusion. We opened the PR before the checkpoints and blog went public. Here they are: |
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FYI using Qwen3.8-27B bf16 with the published DFlash2 drafter on sm120, i got a crash under concurrent (n=4) workloads with this branch. Haven't been able to reproduce but will update if I can reliably. Relevant logs: |
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Port of upstream vllm-project#52816. DFlash2 extends the DFlash drafter with a learned candidate selector: the draft head proposes top_k candidates per step, and a selector scores paths over them instead of committing to a single greedy chain. Reconciled against this fork's adaptive-verification work. Upstream's DFlash2Speculator._generate_draft was written against the upstream DFlashSpeculator signature; this fork's parent takes two extra parameters (is_profile, num_query_per_req) and resolves the step count via _speculative_steps_for_query_len when a query length is supplied. The override now takes the same parameters and resolves the step count identically, threading the resolved value through _sample_path and _cache_draft_logits (both previously hard-coded self.num_speculative_steps as the kernel num_steps) and slicing the draft_tokens copy to match the parent's [:num_reqs, :steps] write. Without this, a DFlash2 draft under adaptive verification would run kernels over the static step count while the parent had planned for a different one. Selection is architecture-based: init_speculator routes method=dflash with DFlash2DraftModel in architectures to DFlash2Speculator, and V2 is forced for those checkpoints because the candidate selector exists only on the V2 speculator -- on V1 the same checkpoint drafts through DFlashProposer, which never calls the selector, silently degrading to DFlash1. Co-authored-by: OMP Agent <noreply@omp.local> Signed-off-by: Naadir Jeewa <naadir@randomvariable.co.uk>
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…_cls The source fix from this PR landed independently as vllm-project#53435 (a9a17e7), so this is rebased down to the part that has no equivalent on main. `test_dflash2_model_decoder_layer_cls` from vllm-project#53435 builds a model and checks `isinstance(model.layers[0], DFlash2Qwen3DecoderLayer)`. This adds a cheap static tripwire on top: it inspects the source of `DFlashQwen3Model.__init__` and asserts the layers are built through `self.decoder_layer_cls(`, never by naming `DFlashQwen3DecoderLayer` directly. That is the exact regression that caused vllm-project#53428 -- vllm-project#52816 introduced the indirection, vllm-project#52560 re-hardcoded the class, and DFlash2 drafts silently got plain DFlash layers until weight loading failed on `layers.0.attention_conv`. The tripwire catches a third occurrence without a model build. Also annotates `decoder_layer_cls` as `type[nn.Module]` and documents why the indirection exists, so the next person editing the constructor sees it. Verified: the assertions hold on current main and fail on 2f55ef2, the commit that removed the indirection. Related: vllm-project#53428, vllm-project#53435 Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
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…roject#52816) Signed-off-by: caowanlu <caowanlu@xcoresigma.com>
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## Summary This draft adds experimental DFlash2 training and checkpoint support to Speculators: - registers the `dflash2` model and config; - adds identity-initialized, block-local grouped dynamic convolutions around attention and MLP sublayers; - adds a low-rank, predecessor-conditioned selector that reranks unary top-K candidates without another draft-backbone forward pass; - trains the selector with an alpha-weighted K-way CE term over unary top-K; a missing hard target is injected only into the loss candidate set, while strict validation metrics retain serving semantics; - reports unary candidate recall/mass, teacher-forced selector accuracy, self-conditioned conditional path accuracy/accepted length, and unary top-K oracle accepted length separately; - adds CLI/config resolution, optimizer routing, docs, focused unit coverage, and an executable paired DFlash2/DSpark smoke launcher. DFlash2 currently requires the full verifier vocabulary and `sample_from_anchor=False`, matching the current inference contract. ## Attribution and experimental scope The architecture and checkpoint tensor contract are adapted from Z Lab's MIT-licensed implementation pinned at [`07ebd93`](https://github.com/z-lab/dflash/blob/07ebd93db9f472af339b644bb70221ad8428328a/dflash/model.py). The Z Lab copyright, license text, and pinned-source attribution are retained in the adapted source. The public DFlash2 materials describe inference but do not publish the training loss or complete recipe. The split unary + selector objective in this PR is therefore an experimental Speculators-native baseline. It does **not** claim to reproduce Z Lab's unpublished objective, parameterization, acceptance rate, or checkpoint quality. This prototype also trains materialized full-vocabulary predecessor and successor codebooks directly. Public sources do not establish whether those tensors were free parameters during training, so this should not be read as a reproduction of the selector parameter count reported in the DFlash2 blog. ## Serving integration status The emitted tensor names/shapes match the public Z Lab/vLLM contract. The current serving PRs expect convolution/selector fields under nested `dflash_config`, whereas Speculators saves them as flat model-config fields. A small adapter/conversion is still required for direct loading. A local adapter on top of [vLLM PR #52816](vllm-project/vllm#52816) has loaded and served multiple locally trained checkpoints through the V2 runner. That adapter is not yet part of the upstream vLLM PR. SGLang loading remains unvalidated. ## Duplicate-work check Checked on 2026-08-19: - open upstream PR search for `DFlash2`: none; - open upstream PR search for `candidate selector`: none; - upstream issue search for the same area: none; - existing PR search in the fork: none. The linked vLLM PR is complementary serving support, not duplicate Speculators training work. ## Validation ```bash CUDA_VISIBLE_DEVICES=7 .venv/bin/python -m pytest \ tests/unit/models/test_dflash2_model_definitions.py \ tests/unit/train/config/test_schema.py \ tests/unit/train/config/test_resolution.py -q ``` Result: `79 passed, 22 warnings in 33.44s`. The warnings are existing environment/deprecation warnings. Also passed: ```text ruff check (13 changed Python files) ruff format --check (13 changed Python files) bash -n examples/train/dflash2_dspark_qwen3_4b_offline_smoke.sh git diff --check ``` The tests include scalar convolution parity, block-boundary isolation, BF16 forward/backward with nonzero finite gradients on every new parameter, selector formula/top-K/path behavior, checkpoint key/config round trips, algorithm defaults, optimizer routing, and an end-to-end stubbed check that the paired launcher resolves matching common training knobs. After selecting the concrete smoke weight below, its two launcher contract tests were rerun: `2 passed`; Bash syntax, Ruff, and whitespace checks remained green. The resume fix added after the first two hero epochs was validated with: ```bash .venv/bin/python -m pytest \ tests/unit/train/test_trainer_scheduler.py \ tests/unit/train/test_checkpoint.py -q ``` Result: `16 passed`. Ruff check/format and `git diff --check` also passed. ## Matched current-objective smoke and selector-weight ablation Matched setup: Qwen3-4B, 128 ShareGPT rows, 100 steps / 5 epochs, block size 8 (7 proposals), target taps 1/9/17/25/33, sequence length 1024, maximum 128 anchors, full vocabulary, CE 0.1 + TV 0.9, AdamW at `6e-4`. | Model / selector alpha | CE | Unary recall@16 | Teacher-forced selector acc. | Self-conditioned accepted length | vLLM accepted length | |---|---:|---:|---:|---:|---:| | DFlash2, 0.1 | 4.5470 | 0.2467 | 0.0919 | 1.1272 | 1.0983 | | DFlash2, 0.25 | 5.0601 | 0.2102 | 0.0717 | 1.1042 | not run | | DFlash2, 1.0 | 5.4888 | 0.1596 | 0.0482 | 1.0987 | 1.0600 | | DSpark | 4.1701 | — | — | 1.0317* | 1.0983 | `*` DSpark's offline accepted-length metric is analytically defined differently, so the vLLM counters are the comparable runtime result. The identical eight-prompt vLLM functional smoke produced: - DFlash2 alpha 0.1: 68 accepted / 4,844 proposed, per-position `[63, 5, 0, 0, 0, 0, 0]`, mean 1.0983; - DSpark: 68 / 4,844, `[62, 4, 2, 0, 0, 0, 0]`, mean 1.0983; - DFlash2 alpha 1.0: 43 / 5,019, `[42, 1, 0, 0, 0, 0, 0]`, mean 1.0600. This is a tiny quality/functional smoke, not a throughput or model-quality claim. Based on it, the concrete paired recipe now passes `--selector-loss-alpha 0.1`; the generic experimental config default remains explicit at 1.0. ## Full-data run completed The exact Magpie + UltraChat slice referenced by the Qwen3-4B DSpark recipe was downloaded and fully audited: - 507,864 unique rows from `inference-optimization/Qwen3-8B-Regenerated-Collection@65d219d6b40bb27c45afe16665147a1d3fa21069`; - 39/39 valid Arrow shards; - 1,618,498,917 total tokens and 1,547,655,657 supervised tokens; - no read errors, nulls, length mismatches, invalid IDs, non-binary masks, or zero-supervision rows; - exact full recomputation of `token_freq.pt`. The responses are Qwen3-8B regenerated trajectories, as stated by the reference DSpark card; Qwen3-4B only renders/tokenizes and teacher-forces them here. They are not Qwen3-4B on-policy. This preparation uses a 16,384-token window to retain the full corpus; the published card used 4,096, which would retain only 75.60% of these supervised tokens. DFlash2 versus DSpark remains internally apples-to-apples when both consume this same artifact, but this is not an exact published-checkpoint reproduction. A 20-step exact-shape calibration completed end to end on 8×B300, including online hidden-state generation, 4-GPU DDP, checkpointing, validation, W&B sync, artifact hashing, and clean shutdown. Its validation smoke was finite (loss 1.1541, unary recall@16 0.2993, self-conditioned accepted length 1.0630, oracle top-16 length 1.4657). [Calibration W&B run](https://wandb.ai/mgoin/speculators-dflash2/runs/qwen3-4b-dflash2-openperfectblend-3ep-v1-calibration-20). All three full-data epochs completed successfully: | Epoch | Loss | Unary recall@16 | Teacher-forced selector acc. | Self-conditioned accepted length | Oracle top-16 length | |---|---:|---:|---:|---:|---:| | 1 | 0.3711 | 0.8286 | 0.6174 | 3.7355 | 6.0761 | | 2 | 0.3356 | 0.8538 | 0.6582 | 4.0017 | 6.3510 | | 3 | 0.3248 | 0.8608 | 0.6711 | 4.0827 | 6.4279 | The portable checkpoints and exact validation metrics are public at [epoch 1](https://huggingface.co/mgoin/Qwen3-4B-speculator.dflash2/tree/epoch-1), [epoch 2](https://huggingface.co/mgoin/Qwen3-4B-speculator.dflash2/tree/epoch-2), and [epoch 3](https://huggingface.co/mgoin/Qwen3-4B-speculator.dflash2/tree/epoch-3). The final epoch-3 model SHA-256 is `459f75b6da6a70b7d5630408196e2203798af0ca33db23bb1d628d8c9212e805`. A utilization audit at the start of epoch 3 showed the four verifier engines were arrival-starved and effectively processed one prompt per iteration. The run was checkpointed at epoch index 2, local step 2,621, global step 55,056, then resumed from an isolated copy with one verifier GPU and the same four DDP trainer ranks. The data order, sequence length, anchors, optimizer, schedule, and model configuration were unchanged. Training completed at global step 78,646; validation, checkpointing, all 28 manifest hashes, W&B sync, and shutdown succeeded. All 458,653 continuation verifier requests returned HTTP 200. [Original W&B run](https://wandb.ai/mgoin/speculators-dflash2/runs/qwen3-4b-dflash2-openperfectblend-3ep-v1); [DP1 continuation](https://wandb.ai/mgoin/speculators-dflash2/runs/qwen3-4b-dflash2-openperfectblend-3ep-dp1-resume-step55056-v1). The restart audit also found a scheduler-resume bug: constructing the scheduler overwrote the optimizer learning rate, and loading scheduler state did not restore it. Commit `0a1b3e0` restores every optimizer param-group LR from `scheduler.get_last_lr()` after state load and adds a regression test. Without the fix, the first resumed optimizer update would have used a near-zero LR. ## Epoch-2 vLLM evaluation The public [epoch-2 checkpoint](https://huggingface.co/mgoin/Qwen3-4B-speculator.dflash2/tree/epoch-2) (weights SHA-256 `14aadebbce68b3898a903e3adae607846a6d3572c9b15fbe93e5196c2d9a14ba`) was evaluated with the vLLM V2 runner. The exact serving lineage was: - vLLM DFlash2 PR #52816 head `19c9351904df4c63042671bc67a866ca48dc7d6f`; - [Ben Chislett's probabilistic-drafting safety fix](vllm-project/vllm@31840cf), exact commit `31840cf3ead3632f3c99db4a24e4aba39ad54ef6`; - local Speculators flat-config adapter `9c6917525ff8a621542cb29bf7766d14331a8024`. The server resolved `DFlash2DraftModel`, method `dflash`, greedy drafting, and seven speculative tokens. ### GSM8K Full 1,319-question, 5-shot evaluation; greedy decoding, 256-token cap, seed 42, concurrency 128: | Metric | Qwen3-4B baseline | DFlash2 epoch 2 | |---|---:|---:| | Accuracy | 85.82% (1,132/1,319) | 86.05% (1,135/1,319) | | Invalid rate | 0.076% | 0.076% | | Wall time | 8.73 s | 6.75 s | | Questions/s | 151.05 | 195.29 (1.293×) | | Output tokens/s | 17,488.92 | 22,888.23 (1.309×) | | Draft-token acceptance | — | 47.49% | | Mean accepted length | — | 4.324 | ### SPEED-Bench qualitative All 880 qualitative rows; concurrency 8, temperature 0, thinking disabled, and a 1,024-token output cap: | Metric | Qwen3-4B baseline | DFlash2 epoch 2 | |---|---:|---:| | Completed / failed | 880 / 0 | 880 / 0 | | Requests/s | 5.944 | 12.798 (2.153×) | | Output tokens/s | 2,898.25 | 6,210.74 (2.143×) | | Total tokens/s | 4,824.84 | 10,358.72 (2.147×) | | Mean TTFT | 17.82 ms | 19.89 ms | | Mean TPOT | 2.724 ms | 1.350 ms | | Mean ITL | 2.719 ms | 3.953 ms | | Mean end-to-end latency | 1,335.51 ms | 620.92 ms | | Draft-token acceptance | — | 31.35% | | Mean accepted length | — | 3.194 | This is the native vLLM **single-turn projection** of SPEED-Bench: the current loader consumes only `messages[0].content`. The 185 multi-turn rows therefore omit 258 later turns, so this is not a complete multi-turn qualitative result. ### SPEED-Bench throughput_2k All 1,536 prompts; concurrency 32, 4,096 generated tokens per prompt, `ignore_eos`, temperature 0, thinking disabled, and `max_model_len=32768`. Both variants consumed the same 3,168,640 input tokens and emitted exactly 6,291,456 output tokens: | Metric | Qwen3-4B baseline | DFlash2 epoch 2 | |---|---:|---:| | Completed / failed | 1,536 / 0 | 1,536 / 0 | | Wall time | 1,068.14 s | 427.16 s (2.501× faster) | | Requests/s | 1.438 | 3.596 (2.501×) | | Output tokens/s | 5,890.10 | 14,728.61 (2.501×) | | Total tokens/s | 8,856.61 | 22,146.55 (2.501×) | | Mean TTFT | 266.98 ms | 56.07 ms | | Mean TPOT | 5.369 ms | 2.102 ms | | Mean ITL | 5.372 ms | 8.123 ms | | Mean end-to-end latency | 22,251.81 ms | 8,664.21 ms | | Draft-token acceptance | — | 40.95% | | Mean accepted length | — | 3.866 | The initial servers used `max_model_len=16384`. The fail-closed harness rejected both result files because exactly row 817 requires 13,353 input + 4,096 output = 17,449 tokens; baseline and DFlash2 each completed 1,535/1,536 and returned the same 400. The clean 32K rerun processed that row and all other rows successfully. This was a benchmark context-limit configuration finding, not a model or speculative-decoding failure. All throughput measurements above are preliminary cross-GPU runs: baseline and DFlash2 ran concurrently on separate otherwise-free B300 GPUs with identical server/client settings. The 2.501× throughput result has identical token counts, but a final publication-quality claim should use sequential same-GPU or swapped-GPU runs. Epoch 3 is now public and fully validated offline. Its separate end-to-end vLLM evaluation is still pending; the serving results above remain explicitly scoped to epoch 2. ## AI assistance disclosure AI assistance was used for implementation, public-source inspection, testing, documentation, experiment orchestration, and preparation of this PR. The human submitter will review every changed line, validate the final results, and remains responsible for understanding and defending the design and implementation. --------- Signed-off-by: mgoin <mgoin64@gmail.com> Signed-off-by: Fynn Schmitt-Ulms <fschmitt@redhat.com> Co-authored-by: OpenAI Codex <codex@openai.com> Co-authored-by: Fynn Schmitt-Ulms <fschmitt@redhat.com> Co-authored-by: shanjiaz <zsjwpianpian@gmail.com>
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[Spec Decode] DFlash2: local convolution + candidate selector vllm-project#52816 合入目标 commit 依赖的前置更新，共 3 个 Signed-off-by: caowanlu <caowanlu@xcoresigma.com> Co-authored-by: kx <1670186653@qq.com> Co-authored-by: Giancarlo Delfin <32987265+TheEpicDolphin@users.noreply.github.com> Co-authored-by: Zihan Zhang <tiancaizhangdaxian@sjtu.edu.cn>
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…at would draft the published checkpoint as DFlash1 (mudler#1314) (mudler#1321) DFlash2 is a second DFlash architecture rather than a change to DFlash, and this engine has no route for it. Upstream carries it in two open pull requests ([vllm#52816](vllm-project/vllm#52816) at head `19c9351904df4c63042671bc67a866ca48dc7d6f`, plus the stacked guard fix [vllm#52883](vllm-project/vllm#52883)): DFlash1 gains two subclass seams and keeps every behaviour, so a `DFlashDraftModel` checkpoint resolves exactly as it does today, while `DFlash2DraftModel` adds a grouped dynamic depthwise convolution inside each draft block and a candidate selector that replaces the independent per-slot argmax with a scored path walk over the target head's top-K. This is the spec half of a two-pull-request row, the shape the developer chose at row claim. No production code lands here. `SPEC-DFLASH2` enters the engine matrix as `READY`, which is what a helper dispatch needs before W1 can start. The spec takes its shapes from the published `z-lab/Qwen3.8-27B-DFlash2` checkpoint rather than from the diff. Its safetensors header, range-read on 2026-08-19, is DFlash1's tensor set plus `layers.N.{attention,mlp}_conv.*` and three `candidate_selector` tensors, of which the two codebooks are `(248320, 256)` bf16 each: about 254 MB resident that the DFlash1 lane never allocates. It records one defect that raises nothing. That config declares all five layers `sliding_attention` AND `is_causal false`, while our causality resolution mirrors the OLD upstream rule, so every layer would run causal. The draft would emit plausible tokens, a token gate against our own output would see nothing, and only acceptance would move, which the lossless verify hides. Upstream changes `_dflash_layer_causal` to read `is_causal` first, in the same commit that adds the architecture. Three further decisions are argued rather than assumed. FlashInfer's 3380-line radix top-k is not ported: our shape is K=16 over a 248320 vocabulary for about 224 rows, and `src/vt/cuda/cuda_sample.cu:297-506` already carries the same sort-free pivot-bracket threshold search, so what is owed is emitting the surviving pairs rather than a new kernel. The path walk runs on device from the first landing, because the identical sequential shape in DSpark shipped host-side and measured 28% of the 27B draft step (mudler#436) before it had to be moved. And the GGUF drafter arm lands in the same wave rather than as a follow-on row. BEYOND-PIN by developer decision: the parity pin `555967922` does not carry the architecture at all, anchors cite the pull-request head, and this row does not advance the pin. It is the posture `SPEC-DSPARK-QWEN3-ROUTING` already takes toward vllm#52197. Records: issue mudler#1314 in the index, the `SPEC-DFLASH2` row with its section and total counts, `ENGINE_ROWS` 164 to 165 with its justification paragraph, and the `STATUS.md` projection. The `ENGINE_ROWS` comment block shifted five `ENG-RECORD-ANCHOR-RATCHET` citations into the same file by twelve lines, and those anchors are repaired here rather than left for the ratchet to catch. Gates: `scripts/agent-ready.py` all green at `d72848e06`, including `check-agent-record` at ENGINE=165 with anchor rot unchanged at 38, and `tests/scripts/test_agent_record.py` 97 passed. FOLLOWING_AGENTS_PROTOCOL Following-Agents-Protocol: true AI-Assisted: true Assisted-by: AGENT:deepseek-v4-flash [edit bash]
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Port of upstream vllm-project#52816. DFlash2 extends the DFlash drafter with a learned candidate selector: the draft head proposes top_k candidates per step, and a selector scores paths over them instead of committing to a single greedy chain. Reconciled against this fork's adaptive-verification work. Upstream's DFlash2Speculator._generate_draft was written against the upstream DFlashSpeculator signature; this fork's parent takes two extra parameters (is_profile, num_query_per_req) and resolves the step count via _speculative_steps_for_query_len when a query length is supplied. The override now takes the same parameters and resolves the step count identically, threading the resolved value through _sample_path and _cache_draft_logits (both previously hard-coded self.num_speculative_steps as the kernel num_steps) and slicing the draft_tokens copy to match the parent's [:num_reqs, :steps] write. Without this, a DFlash2 draft under adaptive verification would run kernels over the static step count while the parent had planned for a different one. Selection is architecture-based: init_speculator routes method=dflash with DFlash2DraftModel in architectures to DFlash2Speculator, and V2 is forced for those checkpoints because the candidate selector exists only on the V2 speculator -- on V1 the same checkpoint drafts through DFlashProposer, which never calls the selector, silently degrading to DFlash1. Co-authored-by: OMP Agent <noreply@omp.local> Signed-off-by: Naadir Jeewa <naadir@randomvariable.co.uk>
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Port of upstream vllm-project#52816. DFlash2 extends the DFlash drafter with a learned candidate selector: the draft head proposes top_k candidates per step, and a selector scores paths over them instead of committing to a single greedy chain. Reconciled against this fork's adaptive-verification work. Upstream's DFlash2Speculator._generate_draft was written against the upstream DFlashSpeculator signature; this fork's parent takes two extra parameters (is_profile, num_query_per_req) and resolves the step count via _speculative_steps_for_query_len when a query length is supplied. The override now takes the same parameters and resolves the step count identically, threading the resolved value through _sample_path and _cache_draft_logits (both previously hard-coded self.num_speculative_steps as the kernel num_steps) and slicing the draft_tokens copy to match the parent's [:num_reqs, :steps] write. Without this, a DFlash2 draft under adaptive verification would run kernels over the static step count while the parent had planned for a different one. Selection is architecture-based: init_speculator routes method=dflash with DFlash2DraftModel in architectures to DFlash2Speculator, and V2 is forced for those checkpoints because the candidate selector exists only on the V2 speculator -- on V1 the same checkpoint drafts through DFlashProposer, which never calls the selector, silently degrading to DFlash1. Co-authored-by: OMP Agent <noreply@omp.local> Signed-off-by: Naadir Jeewa <naadir@randomvariable.co.uk>
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Two additions to the DFlash drafter, carried by a separate architecture:
a checkpoint declaring
DFlash2DraftModelgets them, and every existingDFlashDraftModelcheckpoint resolves to the class it resolves to today,untouched by this PR.
Grouped dynamic depthwise convolution inside each block, so a proposal
position can see the ones before it without another backbone pass.
out[i,c] = Σ_t (base[t,c] + δ[i,t,g(c)]) · x[i−t,c], taps zero across the blockboundary. Wraps each sublayer in and out from one projection of its input.
Sized by
conv_kernel_sizeandconv_group_sizein the draft config.Candidate selector. Instead of an independent argmax per slot, keep the
target head's top-K per slot, score adjacent transitions
edge(p→c) = ⟨A[p] ⊙ project(h), B[c]⟩ + unary[c], and walk the best path fromthe verified anchor. At T>0 it walks by inverse CDF and returns q over the K
candidates for the lossless verify. Sized by
selector_rankandselector_top_kin the draft config.The selector runs after the draft model's forward and carries its own
@support_torch_compile. The path walk is one Triton program per request: aslot's K scores stay in registers, and the slot-to-slot dependency is a loop
inside the program rather than a kernel per slot. The vocabulary top-k, the
selector's largest single cost, uses FlashInfer's radix kernel where FlashInfer
is available and
torch.topkotherwise.DFlash2 runs on the V2 model runner, which is where its speculator lives.
use_v2_model_runnerselects V2 for a DFlash2 draft, as it already does forDSpark: the V1
DFlashProposerhas no candidate selector, so a DFlash2checkpoint reaching it would draft as DFlash1 without raising.
Results
Qwen/Qwen3.8-27Bon one H200, againstRadixArk/Qwen3.8-27B-DSparkand autoregressive decoding. Seven draft tokens per verification step for both
drafters. Qwen3.8's recommended sampling — temperature 1.0, top-p 0.95, top-k 20
— with
xhighreasoning effort and 4096 max new tokens; prompt formatting fromz-lab/dflash.Acceptance length, GSM8K
Mean over per-request acceptance, the statistic the model cards report. vLLM's
counters give a pooled ratio — total accepted tokens over total drafts — so the
per-request values are recovered at concurrency 1, where a counter delta around
a single request belongs to that request alone.
Acceptance does not move with concurrency: DFlash 2's pooled ratio reads 4.65,
4.82 and 4.74 at concurrency 1, 8 and 32.
Throughput, GSM8K
Output tokens over end-to-end wall time. Each point is 128 × concurrency
requests split over four shards, each shard one H200 serving at the stated
concurrency; the four shard rates are averaged, not summed, so a cell is a
single-GPU number measured four times. A warmup of
concurrencyrequests runsbefore the clock starts and is dropped, as in
z-lab/dflash.All three methods are served one after another inside the same container, so a
difference between them is never a difference between GPUs. Spread across the
four shards is 2.1–2.5% for autoregressive and 2.6–15.2% for the drafters, which
is the shape to expect: a drafter's rate depends on which prompts it drew and
autoregressive decoding has no such freedom.
What the conv and the selector cost
Each component timed alone in its own CUDA graph at the shapes the draft runs
(5 layers, hidden 5120, vocab 248320, block 8, K=16), against the measured
serving step — draft proposal plus target verification — taken from the run
above as
pooled acceptance × concurrency / tok s.The selector's own math — the lattice and the walk — is 0.020 ms and flat in
batch; the rest of that column is the vocabulary top-k, where FlashInfer's radix
kernel is 1.9×
torch.topkat batch 1 and 4.5× at batch 32.The convolution is 20 small calls over
[8, 5120]tensors, so it islaunch-bound rather than FLOP-bound: 0.477 ms as eager modules, 0.0096 ms
compiled as a single graph across all 20 calls, and 0.113 ms — the figure above
— with each call compiled on its own, which is what the model's compiled region
gives it, since attention and the MLP run between
prepareandfinish. Ahand-fused kernel would collect most of the remaining 12×.
Test Result
The three DFlash v1 files pass unchanged.
test_dflash_causality.pyis editedhere, in the same change that touches
_dflash_layer_causal, so its updatedtests do not by themselves speak to v1 compatibility; the versions of those
tests as they stood before this change also pass against this tree.
Serving results are the tables above: GSM8K at three concurrencies on
Qwen/Qwen3.8-27B, run through vLLM's OpenAI server, 128 × concurrency requestsper point.
Not a duplicate
gh pr list --repo vllm-project/vllm --state open --search "DFlash in:title"returns work on the existing DFlash v1 path — adaptive K (#52559), SWA (#47511),
fused cache insert (#46911), profiling annotations (#52782) — and
--search "dflash2"returns nothing. This adds the DFlash2 drafter, which noneof those touch.
AI assistance was used for this change.