[Qwen3.5 MoE] Add hybrid decoder model (GatedDeltaNet + full attention + MoE)

[gali-leilei]

Summary

Upstreams Qwen3.5 MoE support from the internal fork. Qwen3.5 MoE is a hybrid decoder: GatedDeltaNet (linear attention) for most layers + full attention every N layers, with MoE + gated shared expert FFN. Validated on real 35B-A3B checkpoints (HF↔DCP roundtrip: 19/19 tensors exact) and L20X GPUs (debugmodel + 35B-A3B, 10 steps, no errors).

New model: torchtitan/models/qwen3_5_moe/

• model.py: OffsetRMSNorm, RMSNormGated, GatedDeltaNet (FLA optional, torch_naive fallback), Attention (output gating + partial RoPE), TransformerBlock, Model
• parallelize.py: TP/FSDP/CP/EP support. DTensor-safe wrappers for depthwise conv1d (_DTensorSafeConv1d) and FLA kernel dispatch (_install_dtensor_safe_dispatch). Registers softplus as DTensor pointwise op.
• state_dict_adapter.py: HF↔TorchTitan conversion. Handles expert gate_up_proj [E,2I,D] ↔ w1+w3 split/concat.
• config_registry.py: debugmodel, 35b-a3b, 35b-a3b-sdpa, 35b-a3b-varlen training configs
• __init__.py: model configs (debugmodel, 35b-a3b, 122b-a10b, 397b-a17b, 397B_A19B), build(), model_registry()

Related changes

• torchtitan/models/__init__.py: add "qwen3_5_moe" to _supported_models
• tests/integration_tests/models.py: qwen3_5_moe FSDP+TP+EP integration test (4 GPUs)

Test plan

• pre-commit run --all-files — flake8, ufmt, pydoclint, codespell all pass
• Integration test on 4 GPUs: --module qwen3_5_moe --config qwen3_5_moe_debugmodel with FSDP+TP+EP

🤖 Generated with Claude Code

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