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[1/N][Refactor] Refactor code to adapt with vllm main (#3612)

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### What this PR does / why we need it?
This is the step 1 of refactoring code to adapt with vllm main, and this
pr aligned with
17c540a993

1. refactor deepseek to the latest code arch as of
17c540a993
 
2. bunches of fixes due to vllm changes
- Fix `AscendScheduler` `__post_init__`, caused by
https://github.com/vllm-project/vllm/pull/25075
- Fix `AscendScheduler` init got an unexpected arg `block_size`, caused
by https://github.com/vllm-project/vllm/pull/26296
- Fix `KVCacheManager` `get_num_common_prefix_blocks` arg, caused by
https://github.com/vllm-project/vllm/pull/23485
- Fix `MLAAttention` import,caused by
https://github.com/vllm-project/vllm/pull/25103
- Fix `SharedFusedMoE` import, caused by
https://github.com/vllm-project/vllm/pull/26145
- Fix `LazyLoader` improt, caused by
https://github.com/vllm-project/vllm/pull/27022
- Fix `vllm.utils.swap_dict_values` improt, caused by
https://github.com/vllm-project/vllm/pull/26990
- Fix `Backend` enum import, caused by
https://github.com/vllm-project/vllm/pull/25893
- Fix `CompilationLevel` renaming to `CompilationMode` issue introduced
by https://github.com/vllm-project/vllm/pull/26355
- Fix fused_moe ops, caused by
https://github.com/vllm-project/vllm/pull/24097
- Fix bert model because of `inputs_embeds`, caused by
https://github.com/vllm-project/vllm/pull/25922
- Fix MRope because of `get_input_positions_tensor` to
`get_mrope_input_positions`, caused by
https://github.com/vllm-project/vllm/pull/24172
- Fix `splitting_ops` changes introduced by
https://github.com/vllm-project/vllm/pull/25845
- Fix multi-modality changes introduced by
https://github.com/vllm-project/vllm/issues/16229
- Fix lora bias dropping issue introduced by
https://github.com/vllm-project/vllm/pull/25807
- Fix structured ouput break introduced by
https://github.com/vllm-project/vllm/issues/26737

### Does this PR introduce _any_ user-facing change?

### How was this patch tested?
CI passed with existing test.


- vLLM version: v0.11.0rc3
- vLLM main: https://github.com/vllm-project/vllm/commit/v0.11.0

---------

Signed-off-by: MengqingCao <cmq0113@163.com>
Signed-off-by: Icey <1790571317@qq.com>
Co-authored-by: Icey <1790571317@qq.com>
This commit is contained in:
Mengqing Cao
2025-10-24 16:55:08 +08:00
committed by GitHub
parent ec9ec78b53
commit cea0755b07
47 changed files with 1189 additions and 493 deletions
Download Patch File Download Diff File Expand all files Collapse all files

66
vllm_ascend/lora/punica_npu.py
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@@ -262,7 +262,6 @@ class PunicaWrapperNPU(PunicaWrapperBase):
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, ...],
*,
@@ -292,10 +291,6 @@ class PunicaWrapperNPU(PunicaWrapperBase):
"""
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)
@@ -354,3 +349,64 @@ 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)