fix(minimax-h3): align Ref2AV output - #1490
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Watebear
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Sep 9, 2026
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这里应该是sparse和packed之后的计算不兼容，所以临时用for循环来替代了。效率肯定低。spare先这样么？
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tp通信的fp32，不能用 sensetive layer
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Watebear
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Sep 9, 2026
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| @torch.jit.script | ||
| def _snakebeta(hidden_states: torch.Tensor, alpha: torch.Tensor, beta: torch.Tensor) -> torch.Tensor: |
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这是个优化点，对一个多次调用的激活，做了compile的优化
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| self.proj = nn.Linear(config["hidden_size"], config["hidden_size"], bias=True) | ||
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| def shard_for_tensor_parallel(self): | ||
| if self.tp_size == 1: |
Watebear
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Sep 9, 2026
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| @ROPE_REGISTER("h3_sgl_rope") | ||
| class MiniMaxH3SGLRope(RopeTemplate): |
Watebear
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Sep 9, 2026
| sigma_from_timestep = 1.0 - timestep.to(device=sample.device, dtype=sample.dtype) | ||
| ratio = sigma_next / sigma | ||
| return ratio * sample.float() + (1.0 - ratio) * denoised.float() | ||
| state = sample.float() |
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