[CI] optimize lint term (#5986)

### What this PR does / why we need it?
This patch purpose to optimize the lint check term. The main idea is to
reduce unnecessary installation time.
1. The installation of vllm is not must, only append the path of vllm
src to the `PATHONPATH` is effective
2. This installation of `requirements-dev.txt` is not must, we have a
pre-built image `quay.io/ascend-ci/vllm-ascend:lint` with all the
requirements installed in advance.
**NOTE**: the conditions for triggering image builds are: 1).Daily
scheduled build; 2) Build when requirements are modified; 3) Manual
build. This ensures that the dependencies in our image are up-to-date to
the greatest extent possible.
3. The `mypy` was separated from the `pre-commit` hook for performance
reasons; we found that integrating `mypy` into the `pre-commit` hook
resulted in poor performance.
4. Reduce the CPU core consumption from 16 -> 8

### Does this PR introduce _any_ user-facing change?
The end-to-end lint time was optimized from 20min/per PR to 8min/per PR
### How was this patch tested?

- vLLM version: v0.13.0
- vLLM main:
2c24bc6996

---------

Signed-off-by: wangli <wangli858794774@gmail.com>

vllm_ascend/_310p/attention/attention_v1.py

@@ -15,6 +15,7 @@
 # This file is a part of the vllm-ascend project.
 #
 
+from typing import Any
 
 import torch
 import torch_npu
@@ -23,7 +24,7 @@ from vllm_ascend._310p.attention.attention_mask import AttentionMaskBuilder, bui
 from vllm_ascend._310p.attention.metadata_builder import AscendAttentionMetadataBuilder310P
 from vllm_ascend.attention.attention_v1 import AscendAttentionBackend as _BaseBackend
 from vllm_ascend.attention.attention_v1 import AscendAttentionBackendImpl as _BaseImpl
-from vllm_ascend.attention.attention_v1 import AscendAttentionMetadataBuilder, AscendAttentionState
+from vllm_ascend.attention.attention_v1 import AscendAttentionMetadataBuilder, AscendAttentionState, AscendMetadata
 from vllm_ascend.utils import ACL_FORMAT_FRACTAL_NZ, aligned_16, nd_to_nz_2d
 
 
@@ -47,9 +48,17 @@ class AscendAttentionBackend310(_BaseBackend):
 
 
 class AscendAttentionBackendImpl310(_BaseImpl):
-    def forward_paged_attention(self, query, attn_metadata, output):
+    def forward_paged_attention(
+        self,
+        query: Any,
+        attn_metadata: AscendMetadata,
+        output: Any | None = None,
+    ) -> Any:
         if attn_metadata.seq_lens.device != query.device:
-            attn_metadata.seq_lens = attn_metadata.seq_lens.to(device=query.device, non_blocking=True)
+            attn_metadata.seq_lens = attn_metadata.seq_lens.to(
+                device=query.device,
+                non_blocking=True,
+            )
         return super().forward_paged_attention(query, attn_metadata, output)
 
     def _forward_prefill_310p_fallback(self, query, key, value, attn_metadata, output):