[hubert-marek]

Compiled Qwen 3.8 training fails on a single packed sequence with ConstraintViolationError: the ngram/position-learning path specializes cu_seqlens to length 2, but mark_dynamic forbids that specialization.

Use maybe_mark_dynamic for this dimension so Dynamo can specialize the single-sequence case while attempting dynamic shapes for other packed-sequence counts. Shape guards remain active. This changes only Qwen 3.8; it does not change embedding sharding, model math, or other architectures.

Targets main, which already contains the affected text-model path. Multimodal support in #3916 is independent of this fix.

Validation on an H200 compute allocation with PyTorch 2.13.0+cu130:

• The unmodified strict annotation reproduces the same ConstraintViolationError in the real ngram module.
• The optional annotation passes 10 compiled/eager PLE comparisons: one, two, four, one and three packed sequences, both with and without non-reentrant activation checkpointing. Outputs, input/parameter gradients and AdamW parameter updates match within rtol=2e-4, atol=2e-5.
• Ruff check, formatting check and git diff --check pass.

The isolated probe uses small real PLE/ngram modules, not the full distributed model. These checks do not establish a successful full-model distributed compiled update.

---

Note

Low Risk
Single-line Dynamo annotation change in one model; validated for numerical parity across packed-sequence shapes without altering training math.

Overview
Fixes compiled Qwen 3.8 training when a batch is a single packed sequence: Torch Dynamo was hitting ConstraintViolationError because the PLE/ngram path specializes cu_seqlens length (e.g. to 2) while mark_dynamic blocked that specialization.

In Qwen3_8FlashNextTextModel.forward, cu_seqlens dim 0 is annotated with torch._dynamo.maybe_mark_dynamic instead of mark_dynamic, so Dynamo can specialize the one-sequence case while still treating other packed-sequence counts as dynamic. Scope is Qwen 3.8 Flash Next only; model math, sharding, and other architectures are unchanged.

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[chatgpt-codex-connector]

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Review	Status	Commit	Review trigger
📝 Code Review	✅ Completed 2026-10-15T11:12:39.354800Z	13beb48	New commits

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[garrett361]

lgtm