fix: honor is_act_and_mul for FP8 MoE w13 weight allocation (#44489) - #44491
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…ject#44489) Non-gated MoE (is_act_and_mul=False) only needs 1× intermediate_size for w13_weight/w13_bias, not 2×. The unquantized path already handles this correctly; the FP8 path was hardcoding 2× regardless. On NemotronH 128-expert non-gated MoE this saves ~14 GiB VRAM, allowing the model to fit on 44 GiB GPUs with FP8 quantization.
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Hardware-verified on GIGABYTE AI TOP ATOM (GB10, 119 GiB VRAM). DGX Test ResultsAllocating actual FP8 tensors (torch.float8_e4m3fn, num_experts=128, intermediate=1856, hidden=2688, 23 MoE layers):
44 GiB GPU Fit Check (L40S with 8192 ctx, 8 concurrent seqs)
Real allocation test on DGX confirmed all 23 layers allocate successfully in both paths — the FP8 tensors fit in VRAM exactly as calculated. |
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Closing in favor of #44498 by @littlecircle0730 which also patches Thanks @littlecircle0730 for the more complete fix! |
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Fixes #44489 — FP8 MoE over-allocates w13_weight on non-gated MoE by hardcoding 2× intermediate_size.
Root cause:
Fp8MoEMethod.create_weightshardcodes2 * intermediate_size_per_partitionfor w13_weight/w13_bias regardless of whether the MoE is gated (act_and_mul). Non-gated MoE only needs 1×.Fix: Check
self.moe.is_act_and_muland use the correct dimension — mirroring the unquantized path which already handles this correctly.Impact: On NemotronH 128-expert non-gated MoE, this saves ~14 GiB VRAM with FP8 quantization, allowing the model to fit on 44 GiB GPUs.
Tested: NGMI for CI — logic is identical to the existing unquantized path.