[Lint]Style: Convert vllm-ascend/ to ruff format(Batch #10) (#6173)

### What this PR does / why we need it?
**Scope of Changes**:
| File Path |
| :--- |
|`vllm_ascend/ops/layer_shard_linear.py`|
|`vllm_ascend/ops/linear.py`|
|`vllm_ascend/ops/linear_op.py`|
|`vllm_ascend/worker/worker.py`|
| ` vllm_ascend/patch/worker/patch_bert.py` |
| ` vllm_ascend/patch/worker/patch_deepseek.py` |
| ` vllm_ascend/patch/worker/patch_distributed.py` |
| ` vllm_ascend/patch/worker/patch_module.py` |
| ` vllm_ascend/patch/worker/patch_multimodal_merge.py` |
| ` vllm_ascend/patch/worker/patch_qwen3_next.py` |
| ` vllm_ascend/patch/worker/patch_qwen3_next_mtp.py` |
| ` vllm_ascend/patch/worker/patch_rejection_sampler.py` |
| ` vllm_ascend/patch/worker/patch_rope.py` |
| ` vllm_ascend/patch/worker/patch_triton.py` |
| ` vllm_ascend/patch/worker/patch_unquantized_gemm.py` |
| ` vllm_ascend/patch/worker/patch_v2_egale.py` |
|` vllm_ascend/worker/npu_input_batch.py`|
|` vllm_ascend/worker/v2/aclgraph_utils.py`|
|` vllm_ascend/worker/v2/attn_utils.py`|
|` vllm_ascend/worker/v2/model_runner.py`|
|` vllm_ascend/worker/v2/sample/gumbel.py`|
|` vllm_ascend/worker/v2/sample/penalties.py`|
|` vllm_ascend/worker/v2/sample/sampler.py`|
|` vllm_ascend/worker/v2/spec_decode/__init__.py`|
|` vllm_ascend/worker/v2/spec_decode/eagle.py`|
|` vllm_ascend/worker/v2/states.py`|
### Does this PR introduce _any_ user-facing change?

### How was this patch tested?

- vLLM version: v0.14.0
- vLLM main:
d68209402d

Signed-off-by: MrZ20 <2609716663@qq.com>
Signed-off-by: SILONG ZENG <2609716663@qq.com>
Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
Co-authored-by: wangxiyuan <wangxiyuan1007@gmail.com>

vllm_ascend/worker/v2/sample/gumbel.py

@@ -76,8 +76,7 @@ def _gumbel_sample_kernel(
     idx = tl.argmax(logits, axis=0)
     token_id = block_idx * BLOCK_SIZE + idx
     value = tl.max(logits, axis=0)
-    tl.store(local_argmax_ptr + req_idx * local_argmax_stride + block_idx,
-             token_id)
+    tl.store(local_argmax_ptr + req_idx * local_argmax_stride + block_idx, token_id)
     tl.store(local_max_ptr + req_idx * local_max_stride + block_idx, value)
 
 

vllm_ascend/worker/v2/sample/penalties.py

@@ -68,8 +68,7 @@ def _penalties_and_temperature_kernel(
     if use_penalty:
         req_state_idx = tl.load(idx_mapping_ptr + batch_idx)
         output_bin_counts = tl.load(
-            output_bin_counts_ptr + req_state_idx * output_bin_counts_stride +
-            block,
+            output_bin_counts_ptr + req_state_idx * output_bin_counts_stride + block,
             mask=mask,
         )
         # to use vector core, if use > 0 will use scalar to slow down performance
@@ -77,11 +76,9 @@ def _penalties_and_temperature_kernel(
 
         # Apply repetition penalties.
         if use_rep_penalty:
-            packed_block = block_idx * BLOCK_SIZE // 32 + tl.arange(
-                0, BLOCK_SIZE // 32)
+            packed_block = block_idx * BLOCK_SIZE // 32 + tl.arange(0, BLOCK_SIZE // 32)
             packed_mask = tl.load(
-                prompt_bin_mask_ptr + req_state_idx * prompt_bin_mask_stride +
-                packed_block,
+                prompt_bin_mask_ptr + req_state_idx * prompt_bin_mask_stride + packed_block,
                 mask=packed_block < tl.cdiv(vocab_size, 32),
             )
             # the compiler itself does not optimize right-shift operations, so we change the same func
@@ -97,8 +94,7 @@ def _penalties_and_temperature_kernel(
             prompt_bin_mask = prompt_bin_mask.reshape(BLOCK_SIZE)
 
             # If token appears in prompt or output, apply, otherwise use 1.0 for no-op.
-            scale = tl.where(prompt_bin_mask | output_bin_mask, rep_penalty,
-                             1.0)
+            scale = tl.where(prompt_bin_mask | output_bin_mask, rep_penalty, 1.0)
             # If logits are positive, divide by penalty, otherwise multiply by penalty.
             logits *= tl.where(logits > 0, 1.0 / scale, scale)
 

vllm_ascend/worker/v2/sample/sampler.py

@@ -16,18 +16,16 @@
 #
 
 import torch
-from vllm.v1.sample.ops.topk_topp_sampler import apply_top_k_top_p
 from vllm.v1.sample.metadata import SamplingMetadata
+from vllm.v1.sample.ops.topk_topp_sampler import apply_top_k_top_p
 from vllm.v1.worker.gpu.sample.min_p import apply_min_p
 from vllm.v1.worker.gpu.sample.sampler import Sampler
 
 from vllm_ascend.worker.v2.sample.gumbel import gumbel_sample
-from vllm_ascend.worker.v2.sample.penalties import \
-    apply_penalties_and_temperature
+from vllm_ascend.worker.v2.sample.penalties import apply_penalties_and_temperature
 
 
 class AscendSampler(Sampler):
-
     def sample(
         self,
         logits: torch.Tensor,
@@ -45,8 +43,7 @@ class AscendSampler(Sampler):
         if sampling_metadata.min_p is not None:
             apply_min_p(logits, sampling_metadata.min_p)
         # Apply top_k and/or top_p. This might return a new tensor.
-        logits = apply_top_k_top_p(logits, sampling_metadata.top_k,
-                                   sampling_metadata.top_p)
+        logits = apply_top_k_top_p(logits, sampling_metadata.top_k, sampling_metadata.top_p)
 
         sampled = gumbel_sample(
             logits,