Add MambaLayer FSDP support and fix TP annotation for SSM parameters#4329
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Phlip79 wants to merge 2 commits intoNVIDIA:mainfrom
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Add MambaLayer FSDP support and fix TP annotation for SSM parameters#4329Phlip79 wants to merge 2 commits intoNVIDIA:mainfrom
Phlip79 wants to merge 2 commits intoNVIDIA:mainfrom
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MambaLayer (GraphableMegatronModule) was not recognized as an FSDP sharding unit, causing its parameters to remain in the root group and defeating ZeRO-3 param sharding for Mamba and hybrid models. Additionally, MambaMixer sets tensor_model_parallel and partition_dim directly on parameters (conv1d, A_log, dt_bias, D, norm.weight) rather than on the owning module. The TP annotation logic only checked module-level attributes, so these parameters were either unclassified or misclassified by the norm-name fallback (e.g. ExtendedRMSNorm treated as replicated when actually TP-sharded). Changes: - Register MambaLayer in default fsdp_unit_modules (mcore_fsdp_adapter) and sub_modules_to_wrap (torch_fully_sharded_data_parallel) - Add param-level TP attribute fallback in _detect_parallelism_type, placed before the norm-name fallback so TP-sharded norm weights are correctly classified - Pass param through from _annotate_tensor_parallelism - Add tests for param-level TP detection, norm override, and a MambaMixer-like end-to-end annotation test Co-Authored-By: Claude Opus 4.6 (1M context) <[email protected]>
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/ok to test 0ef2848 |
ilml
approved these changes
Apr 21, 2026
shjwudp
approved these changes
Apr 22, 2026
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LGTM, thanks @Phlip79 !
- This PR sets MambaLayer as the default sharding layer.
- IIt adds MFSDP-TP compatibility for MambaLayer — the layer now sets
partition_dimon parameters directly, rather than on modules.
It would be great to make Mamba + MoE the default model for E2E tests if you're up for it — this would help us catch and track Mamba-related regressions more reliably. Reference: tests/unit_tests/distributed/megatron_fsdp/utils.py#L55
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Closing this PR in favor of #4467 which solves the same issue. |
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MambaLayer (GraphableMegatronModule) was not recognized as an FSDP sharding unit, causing its parameters to remain in the root group and defeating ZeRO-3 param sharding for Mamba and hybrid models.
Additionally, MambaMixer sets tensor_model_parallel and partition_dim directly on parameters (conv1d, A_log, dt_bias, D, norm.weight) rather than on the owning module. The TP annotation logic only checked module-level attributes, so these parameters were either unclassified or misclassified by the norm-name fallback (e.g. ExtendedRMSNorm treated as replicated when actually TP-sharded).
Changes:
Testing
Tested using Qwen3-Next
HybridModelimplementation: wandb. Set the following flags: