support cp ,fix qwen3.5 gdn sp#138
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meichangsu1 merged 7 commits intomodelscope:mainfrom Apr 21, 2026
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This pull request significantly enhances sequence parallelism support by implementing ZigZag Ring Attention for long-sequence training and Ulysses-style sequence parallelism for Qwen3.5 linear attention. It also introduces multimodal deepstack patching for Qwen3-VL and refactors the SequenceParallel strategy to better handle complex device meshes and packed/varlen inputs. Feedback focuses on improving code maintainability and robustness, specifically by grouping attributes in the SequenceParallel constructor, removing redundant logic and unused imports, replacing deprecated inspection methods, and centralizing duplicated loss-gathering logic.
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- Refactor linear attention sequence parallel import error message into a constantt - Fix token counting in TransformersModel by using raw DP/FSDP world size instead of data_world_size - Enhance Framework.gather_object to check distributed initialization before accessing world size - Add test utility for creating padded labels in sequence parallel tests
- Add `num_tokens` field to `ModelOutput` TypedDict for explicit token denominator - Update `LossOutput` to use `OutputType` for `num_tokens` instead of `int` - Refactor `LossMetric` to prefer `num_tokens` from outputs, with fallback to labels - Remove `_get_raw_dp_fsdp_world_size` helper and use `_device_mesh._get_dp_fsdp_world_size` - Use `InputProcessor.postprocess_tensor_sp` for loss tensor gathering in TransformersModel - Simplify sequence-parallel loss normalization by relying on output `num_tokens`
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tastelikefeet
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This PR adds context parallel and Qwen3.5 Gated DeltaNet sequence parallel support to the transformers stack, and refactors sequence parallel into a package-based implementation.
Main changes:
sequence_parallel.pyintosequence_parallel/and add shared utilities.linear_attention_sp.py;Ring attention is not supported for this path yet.sp_fsdp_dense.tests/moe/test_expert_parallel_qwen3_fsdp_sp.py.Experiment results