[discrete diffusion] Add dflash pipeline#13699
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kashif wants to merge 7 commits intohuggingface:mainfrom
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[discrete diffusion] Add dflash pipeline#13699kashif wants to merge 7 commits intohuggingface:mainfrom
kashif wants to merge 7 commits intohuggingface:mainfrom
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Adds DFlashPipeline + DFlashTokenDiffusionScheduler for block-diffusion speculative decoding with a draft DFlash model and a target causal LM. Verified against the six bug patterns surfaced in the LLaDA2 review (huggingface#13598). DFlash sidesteps most of them by being batch_size=1 only and relying on the causal default for attention; the applicable patterns (huggingface#3 callback bindings, huggingface#4 EOS at first generated position, huggingface#6 inner progress-bar config preservation) are pinned by regression tests. Public surface mirrors the LLaDA2 / SDAR / IDLM conventions: lazy import, dummy objects, scheduler + output dataclass, pipeline + output dataclass, fast tests for both, scheduler doc page, pipeline doc page. Sample/train scripts under examples/discrete_diffusion/.
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- Training: `position_ids` must span `[0, start + block_size)` so the draft's attention RoPE cos/sin covers both `k_ctx` (target_hidden, length `start`) and `k_noise` (noise_embedding, length `block_size`). Previously we passed only `arange(start, start + block_size)` which triggered a K-side broadcast mismatch on the very first batch. - Docs/examples: target loads as plain Qwen3 / Qwen3.5 (no remote code), but the draft's custom DFlashDraftModel class lives in the Hub repo's `auto_map`, so `trust_remote_code=True` is required for draft loads only. Updated the example docstring, pipeline doc page, sample script, train script, and the GPU verify script. Smoke-tested via srun on z-lab/Qwen3.5-4B-DFlash + Qwen/Qwen3.5-4B (H100): 3 steps complete, final checkpoint saved.
…rgets
The pipeline previously short-circuited to `draft.spec_generate(...)` when
the draft model exposed it (e.g. z-lab/Qwen3-8B-DFlash-b16). That path is
the upstream `dflash_generate` loop, which calls `past_key_values_target.crop()`
unconditionally — fine for full-attention targets, but on hybrid targets it
silently corrupts the linear-attention recurrent state.
Confirmed in transformers 5.8.0.dev0 at cache_utils.py:759-761:
def crop(self, max_length: int):
# We don't crop the linear attention cache, so simply do nothing here
pass
`LinearAttentionCacheLayerMixin.crop` is documented as a no-op, so any
verify loop that relies on `cache.crop()` for rollback is wrong on hybrid
attention targets. Our explicit loop already handles this via
`DFlashTokenDiffusionScheduler.snapshot_cache` / `restore_cache` plus an
accepted-prefix re-forward, and reduces to a plain `.crop()` on full-attn
targets.
Verified end-to-end on GPU after the removal:
- z-lab/Qwen3.5-4B-DFlash + Qwen/Qwen3.5-4B (hybrid attn): "2 + 2 equals 4."
- z-lab/Qwen3-8B-DFlash-b16 + Qwen/Qwen3-8B (full attn): "2 + 2 equals 4."
Fast tests: 43 passed.
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What does this PR do?
Added the DFlash pipeline as a stanalone PR extracted from #12911 and requires huggingface/transformers#45846
Fixes # (issue)
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