from_pretrained orchestration + distributed save/load#45409
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3outeille merged 12 commits intomoe-sequence-parallelfrom Apr 14, 2026
Merged
from_pretrained orchestration + distributed save/load#454093outeille merged 12 commits intomoe-sequence-parallelfrom
3outeille merged 12 commits intomoe-sequence-parallelfrom
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- Add gather_full_state_dict() for DTensor→full tensor saving - Add convert_strided_to_shard() / restore_strided_from_shard() for DCP - Add _redistribute_dtensor() helper - Full distributed_config integration in from_pretrained/save_pretrained - Rename apply_fsdp2 → apply_fully_shard_data_parallel - save_optimizer() / load_optimizer() in distributed/utils - Trainer integration with distributed_config - Updated FSDP and TP tests for new orchestration API - DTensor shard-on-read test updates
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# Conflicts: # src/transformers/distributed/utils.py
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* MoE expert parallelism + sequence parallelism - Add PackedColwiseParallel for fused gate_up_proj weights - Add MoEExpertsParallel with per-expert DTensor sharding - Add PrepareModuleInputOutput for SP allgather/split hooks - Add _AllReduceBackward for MoE routing weight gradients - Extend TPStyle with moe_experts, packed_colwise, activation, module kinds - _StridedShard handling in core_model_loading for interleaved weights - MoE model configs: mixtral, deepseek_v3, qwen3 with SP plans - DTensor rotary_pos_emb guard for mixtral * Fix ruff linting and formatting * Fix ruff formatting in core_model_loading.py * Restore _IdentityOp accidentally removed in 25a1f48 The _IdentityOp class (added by PR #44983) was accidentally deleted during the MoE expert parallelism work. It is needed by finegrained_fp8.py and metal_quantization.py as a pass-through reverse_op for dequantize operations. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * Backport new TP/FSDP API + fix DTensor imports in Copied-from models * from_pretrained orchestration + distributed save/load (#45409) * from_pretrained orchestration + save/load - Add gather_full_state_dict() for DTensor→full tensor saving - Add convert_strided_to_shard() / restore_strided_from_shard() for DCP - Add _redistribute_dtensor() helper - Full distributed_config integration in from_pretrained/save_pretrained - Rename apply_fsdp2 → apply_fully_shard_data_parallel - save_optimizer() / load_optimizer() in distributed/utils - Trainer integration with distributed_config - Updated FSDP and TP tests for new orchestration API - DTensor shard-on-read test updates * revert distributed utils * eaaea * all tests for core modeling are passing * populate import from init for tp * ruff * ruff --------- Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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Summary
distributed_configintegration infrom_pretrained()— mesh creation, apply TP + FSDP, attachmodel.device_meshgather_full_state_dict()for streaming DTensor→full tensor saving (rank 0 only)convert_strided_to_shard()/restore_strided_from_shard()for DCP compatibility with_StridedShardsave_optimizer()/load_optimizer()indistributed/utils.pyapply_fsdp2→apply_fully_shard_data_paralleldistributed_configPart of the distributed training API chain: #44989
Chain:
main ← #44989 ← #44083 ← #44974 ← #45028 ← #45408 ← this PRReview question
Does
from_pretrainedwire things up in the right order? Is save/load round-trip correct?Test plan
from_pretrainedwith distributed_configgather_full_state_dict()roundtrip verificationsave_optimizer()/load_optimizer()roundtrip