feat(moe): EP experts forward收纳到kernel底下实现多impls管理，并且添加 NPU grouped matmul的forward分支。#252
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werwrewe wants to merge 1 commit into
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feat(moe): EP experts forward收纳到kernel底下实现多impls管理，并且添加 NPU grouped matmul的forward分支。#252werwrewe wants to merge 1 commit into
werwrewe wants to merge 1 commit into
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kevssim
reviewed
Jul 23, 2026
| # observed in practice as garbage split sizes. Force a device sync before | ||
| # reading gathered results back on the host. | ||
| if num_local_tokens_per_expert.device.type == 'npu': | ||
| torch.npu.synchronize() |
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最好使用平台无关的封装：
from twinkle import torch_util
torch_util.synchronize()
kevssim
reviewed
Jul 23, 2026
| # all-to-all, causing EP collective order divergence. | ||
| return permuted_tokens | ||
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| if _ep_experts_can_use_npu_gmm(self): |
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更好的做法是把gmm定义成一个接口，有不同的impl，然后在expert_parallel.py中直接调用接口，隐藏impl细节。
werwrewe
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kernelize 的 NPU patch 无法生效，导致NPU forward被ep forward覆盖。考虑在ep forward中单独给NPU提供forward。ep forward原实现逐 expert Python 循环，在 EP 闭包内部复用现有 NPU 组件（GmmFunction + npu_swiglu + _get_cached_expert_weights）替换循环实现并行化。每次前向做资格检查，不满足条件自动回退原循环并打印一次 WARNING 说明原因。
端到端验证： loss 曲线与原实现一致，使用ep_fsdp2_lora_qwen3_5_moe.sh 设备2*910B 模型qwen3.5-35B-A3B 每 12 step 约 200s -> 60s。
二编：
改动代码结构，原有EP experts forward也收纳到kernel底下ops里面并且实现多impls管理。测试两种ep forward分支（NPU和逐expert循环）都能跑通，使用ep_fsdp2_lora_qwen3_5_moe.sh 设备2*910B batch8 模型qwen3.5-35B-A3B 16steps NPU时间185s，逐expert循环393s。