Drop 0.11.0 support (#4377)

There is a lot hack code for v0.11.0, which makes the code hard to
upgrade to newer vLLM version. Since v0.11.0 will release soon. Let's
drop v0.11.0 support first. Then we'll upgrade to v0.11.2 soon.


- vLLM version: v0.11.0
- vLLM main:
2918c1b49c

Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>

vllm_ascend/lora/punica_npu.py

@@ -349,64 +349,3 @@ class PunicaWrapperNPU(PunicaWrapperBase):
         bgmv_expand(buffer, lora_b_stacked, y, indices, add_inputs=True)
 
         y = y.view_as(y_org)
-
-
-class PunicaWrapperNPU0110(PunicaWrapperNPU):
-    # NOTE: remove me when 0.11.0 id dropped
-    def add_lora_linear(  # type: ignore[override]
-            self,
-            y: torch.Tensor,
-            x: torch.Tensor,
-            lora_a_stacked: Tuple[torch.Tensor, ...],
-            lora_b_stacked: Tuple[torch.Tensor, ...],
-            lora_bias_stacked: Optional[Tuple[torch.Tensor, ...]],
-            scale: float,
-            output_slices: Tuple[int, ...],
-            *,
-            buffer: Optional[Tuple[torch.Tensor, ...]] = None,
-            **kwargs) -> None:
-        """
-        Applicable to linear-related lora.
-
-        Semantics:
-            for i in range(len(lora_a_stacked)):
-                y[i] += (
-                    x[i].unsqueeze(0)
-                    @ lora_a_stacked[indices[i], layer_idx, :, :]
-                    @ lora_b_stacked[indices[i], layer_idx, :, :]
-                    * scale
-                    ).squeeze(0)+lora_bias_stacked[i]
-
-        Args:
-            y (torch.Tensor): Output tensor. Will be changed in-place.
-            x (torch.Tensor): Input tensor
-            lora_a_stacked (Tuple[torch.Tensor, ...]): lora_a's weight.
-            lora_b_stacked (Tuple[torch.Tensor, ...]): lora_b's weight.
-            lora_bias_stacked (Optional[Tuple[torch.Tensor, ...]]): lora's bias.
-            scale (float): Scaling factor.
-            output_slices (Tuple[int, ...]): Every slice's size.
-            buffer (Optional[Tuple[torch.Tensor, ...]]): Defaults to None.
-        """
-
-        assert len(lora_a_stacked) == len(lora_b_stacked) == len(output_slices)
-        if lora_bias_stacked is not None:
-            assert len(lora_bias_stacked) == len(output_slices)
-            y = self._apply_bias(self.token_lora_indices, y, output_slices,
-                                 lora_bias_stacked)
-
-        if buffer is None:
-            r = lora_b_stacked[0].size(-1)
-            # We set the buffer to be float32 by default, consistent with the
-            # triton op
-            buffer = tuple(
-                torch.zeros(
-                    (x.size(0), r), dtype=torch.float32, device=x.device)
-                for _ in range(len(output_slices)))
-        self.add_shrink(buffer, x, lora_a_stacked, scale, **kwargs)
-        self.add_expand(y,
-                        buffer,
-                        lora_b_stacked,
-                        None,
-                        output_slices,
-                        add_inputs=True,
-                        **kwargs)