Method forward

inference/models/mpt.py:246–254

(
        self,
        x: torch.Tensor,
        start_pos: int,
        mask: Optional[torch.Tensor],
    )

        self.norm_2 = LPLayerNorm(args.d_model, eps=1e-6)

    def forward(
        self,
        x: torch.Tensor,
        start_pos: int,
        mask: Optional[torch.Tensor],
    ):
        h = x + self.attn.forward(self.norm_1(x), start_pos, mask)
        out = h + self.ffn.forward(self.norm_2(h))
        return out


class Transformer(nn.Module):