69 lines
2.2 KiB
Python
69 lines
2.2 KiB
Python
import torch
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import torch.nn.functional as F
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import torch_npu
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from vllm.model_executor.layers.layernorm import RMSNormGated
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from vllm_ascend.ops.layernorm import AscendGemmaRMSNorm, AscendRMSNorm
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class AscendRMSNorm310(AscendRMSNorm):
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def forward_oot(
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self,
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x: torch.Tensor,
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residual: torch.Tensor | None = None,
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) -> torch.Tensor | tuple[torch.Tensor, torch.Tensor]:
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if residual is not None:
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x, _, residual = torch_npu.npu_add_rms_norm(x, residual, self.weight, self.variance_epsilon)
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if self.bias is not None:
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x.add_(self.bias)
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return x, residual
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x, _ = torch_npu.npu_rms_norm(x, self.weight, self.variance_epsilon)
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if self.bias is not None:
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x.add_(self.bias)
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return x
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class AscendGemmaRMSNorm310(AscendGemmaRMSNorm):
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def forward_oot(
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self,
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x: torch.Tensor,
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residual: torch.Tensor | None = None,
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) -> torch.Tensor | tuple[torch.Tensor, torch.Tensor]:
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if residual is not None:
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orig_dtype = residual.dtype
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x = x + residual.to(x.dtype)
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residual = x.to(orig_dtype)
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x, _ = torch_npu.npu_rms_norm(x, 1.0 + self.weight, self.variance_epsilon)
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return x, residual
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x, _ = torch_npu.npu_rms_norm(x, 1.0 + self.weight, self.variance_epsilon)
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return x
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class AscendRMSNormGated310(RMSNormGated):
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def _apply_activation(self, z: torch.Tensor) -> torch.Tensor:
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if self.activation == "sigmoid":
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return torch.sigmoid(z)
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if self.activation in ("silu", "swish"):
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return F.silu(z)
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raise AssertionError(f"Unsupported activation: {self.activation}")
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def forward_oot(
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self,
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x: torch.Tensor,
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z: torch.Tensor | None = None,
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) -> torch.Tensor:
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if self.group_size is not None:
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return super().forward_native(x, z)
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if z is not None and not self.norm_before_gate:
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x = torch.mul(x, self._apply_activation(z))
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x, _ = torch_npu.npu_rms_norm(x, self.weight, self.eps)
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if z is not None and self.norm_before_gate:
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x = torch.mul(x, self._apply_activation(z))
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return x
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