[Bugfix] Fix seq_lens reset issue causing performance degradation (#6158)

### What this PR does / why we need it?
Now `seq_lens` was not being reset correctly after each step due to
missing code that clears the sequence lengths. As a result, when
processing a smaller batch after a larger batch, the `seq_lens` from the
larger batch was still carried over. This caused the attention operator
to compute using an unnecessarily larger sequence length, leading to an
increased computation load and performance degradation.



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


### How was this patch tested?

- vLLM version: v0.13.0
- vLLM main:
d68209402d

Signed-off-by: ZYang6263 <zy626375@gmail.com>

vllm_ascend/worker/model_runner_v1.py

@@ -974,6 +974,8 @@ class NPUModelRunner(GPUModelRunner):
                                 1:pad_size +
                                 1] * self.uniform_decode_query_len + last_query_loc
                         self.query_start_loc.copy_to_gpu(num_reqs_padded + 1)
+                        self.seq_lens.np[num_reqs:].fill(0)
+                        self.seq_lens.copy_to_gpu(num_reqs_padded)             
 
                     # So we are trying to simulate the behavior of GPUModelRunner's
                     # prepare_inputs for uniform decode mode by padding query_start_loc