deepseek_v4: route NVFP4-modelopt experts to ModelOptNvFp4FusedMoE#18
deepseek_v4: route NVFP4-modelopt experts to ModelOptNvFp4FusedMoE#18vedcsolution wants to merge 1 commit into
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DeepSeek-V4-Flash-NVFP4 (NVIDIA modelopt) sets expert_dtype=fp4 but its MoE experts are NVFP4 (weight_scale_2 + input_scale), not MXFP4. DeepseekV4FP8Config previously always used Mxfp4MoEMethod for fp4 experts, causing KeyError: experts.w13_input_scale on GB10 (sm_121). Detect moe_quant_algo==NVFP4 and use the existing ModelOptNvFp4FusedMoE for experts while keeping FP8 block for linear/attn. Adjust weights mapper to not apply the MXFP4 .scale rename to NVFP4 per-expert keys.
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Oh, sorry, I missed the PR. |
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I've supported NVFP4 on the latest commit. I cherry-picked your change to keep your credit. |
2 participants
Fix
KeyError: experts.w13_input_scalewhen loading the official NVIDIA DeepSeek-V4-Flash-NVFP4 checkpoint on DGX Spark GB10 (sm_121a).Problem
DeepseekV4FP8Config.get_quant_methodassumedexpert_dtype="fp4"≡ MXFP4 and always returnedMxfp4MoEMethod, which registers single-level E8M0 scales. The nvidia/DeepSeek-V4-Flash-NVFP4 checkpoint is NVFP4-modelopt (3 scales per expert:weight_scale,weight_scale_2,input_scale), so weight loading failed withKeyError: layers.0.ffn.experts.w13_input_scale.Solution
Detect NVFP4 experts via
moe_quant_algoin config.json. Whenmoe_quant_algo=NVFP4, route the MoE expert layer to the existingModelOptNvFp4FusedMoE(frommodelopt.py) which correctly registers the two-level scales. Linear/attention/shared_expert layers remain FP8 block viaFp8Configbase — only the expert MoE method is swapped.Testing
Validated on 2× DGX Spark GB10 (sm_121a), TP=2, no Ray: model loads 46/46 shards, API responds, generation coherent ~28 tok/s. Backend: FlashInfer CUTLASS NVFP4.
w1.weight_scale_2 must match w3.weight_scale_2warning confirmed harmless (0% diff across 768 expert samples).