[EPLB] Wire weight rearrangement for NVFP4 CuteDSL MoE

[jdebache]

Summary

Enable EPLB weight rearrangement for NVFP4 MoE on the FlashInfer CuteDSL backends (Standard and Batched). Previously CompressedTensorsW4A4Nvfp4MoEMethod returned supports_eplb = False, so any --enable-eplb invocation against an NVFP4 checkpoint failed at the FusedMoE.__init__ gate. This unblocks both online and static (--eplb-config.load_path) rearrangement on Mistral-Large-3-NVFP4 and any other NVFP4 MoE using a CuteDSL kernel.

Background

EPLB rearrangement needs two things from a quant method:

1. Registered Parameters whose leading dim is local_num_experts and is contiguous in storage, so rearrange_expert_weights_inplace can do per-expert P2P send/recv against the layer's real tensors.
2. A way to refresh any derived per-expert state that lives outside the registered Parameters (e.g. fused scale products in tensor attributes), since EPLB only touches Parameters.

NVFP4 CuteDSL violates both today:

• After process_weights_after_loading, w13_weight_scale / w2_weight_scale are strided permuted views ((32, 4, m_t, 4, k_t, E)) returned by flashinfer's convert_sf_to_mma_layout. Per its docstring, the underlying storage is (E, m_t, k_t, 32, 4, 4) contiguous — but the registered Parameter doesn't expose that.
• layer.w13_weight_scale_2 and layer.w2_weight_scale_2 are separate tensors (own storage), holding the kernel-facing (1 / w_global_scale) * input_scale fused product. The expert's process_weights_after_loading mutates them in-place via .mul_(input_scale). EPLB rearranges w13_weight_global_scale (a Parameter) without knowing about the derived fused product, so after rearrangement the kernel reads scale_2 against the old expert ordering.

Changes

1. Per-expert leading-dim view of MMA-layout scales — compressed_tensors_moe_w4a4_nvfp4.py

For the FLASHINFER_CUTEDSL backend, the inverse permute of (3, 4, 1, 5, 2, 0) is (5, 2, 4, 0, 1, 3). Applying it to the MMA view returns to the underlying (E, m_t, k_t, 32, 4, 4) contiguous layout while sharing storage. The new flow in process_weights_after_loading:

• Register the E-leading inverse-permuted view as the Parameter (visible to EPLB's get_expert_weights).
• Stash the original MMA view as layer.w{13,2}_weight_scale_mma_view (plain tensor attribute, not in named_parameters()).
• get_fused_moe_quant_config reads from _mma_view for CuteDSL so the kernel still consumes the strided MMA layout.

is_contiguous() is asserted on the inverse-permuted view to fail-fast if flashinfer ever changes the underlying storage layout.

The FLASHINFER_CUTEDSL_BATCHED backend goes through prepare_nvfp4_moe_layer_for_fi_or_cutlass, which produces E-leading contiguous scales via swizzle_blockscale. No permute trick needed; it works as-is.

2. after_eplb_rearrangement hook — fused_moe_method_base.py + eplb_state.py

• New no-op default FusedMoEMethodBase.after_eplb_rearrangement(layer).
• _run_after_eplb_rearrangement_hooks(model) iterates model.moe_layers and dispatches; called from both rearrangement paths:
  • Sync: right after _commit_eplb_maps in EplbState.rearrange.
  • Async: in _move_to_workspace once the last layer commits.
• NVFP4 override refreshes w13/w2_weight_scale_2 in place via .copy_() of (1 / w_global_scale) * input_scale, so the kernel's captured quant_config reference picks up the new expert ordering without needing to be rebuilt. input_scale is the max-broadcast scalar (invariant under permutation), so no refresh is needed for it.

3. Backend-scoped supports_eplb

_EPLB_SUPPORTED_NVFP4_BACKENDS = {FLASHINFER_CUTEDSL, FLASHINFER_CUTEDSL_BATCHED}. Other NVFP4 backends (TRTLLM pre-shuffle, Cutlass, Marlin, Emulation) still need their post-process layouts audited for per-expert leading-dim contiguity before they can be opted in — leaving them at False is the safe default rather than silently breaking them.

Test plan

End-to-end validation on a P/D-disaggregated Mistral-Large-3-675B-Instruct-2512-NVFP4 deployment on B200:

• MMLU-Pro accuracy eval — nemo-evaluator run_eval --eval_type mmlu_pro against the chat-completions endpoint after the throughput benchmark on each deployment. Accuracy: 0.7958 (single decode-worker sweep point). This is the load-bearing test for this PR: it's exactly the case the broken-rearrangement diagnosis predicted would fail — routing tables pointing at moved physical slots, the kernel reading the old fused scales, etc. would all show up as a large MMLU-Pro drop.

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