[v0.9.1][bugfix] fix accuracy prolem for deepseek V3/R1 models with torchair graph in long sequence predictions (#1332)

### What this PR does / why we need it?
Fix the issue of insufficient cached cosine and sine length in MLA's
TorchAir graph mode, which causes accuracy deviation during
long-sequence inference.

### Does this PR introduce _any_ user-facing change?
No.

### How was this patch tested?
We tested the accuracy of this patch with DeepSeek R1 e2e becnhmark
serving, and get 83.33 sore for AIME2024 dataset with DP4TP4EP16
setting.

![image](https://github.com/user-attachments/assets/517c63bf-164a-493f-a3cd-6ecae84f502e)

Signed-off-by: linfeng-yuan <1102311262@qq.com>

Author: linfeng-yuan

vllm_ascend/attention/mla_v1.py

@@ -1076,7 +1076,7 @@ def forward(
             decode_k_nope = None
             assert attn_metadata.decode is not None
             if self.running_in_graph:
-                seq_len = self.rotary_emb.max_position_embeddings
+                seq_len = self.rotary_emb.max_position_embeddings * self.rotary_emb.scaling_factor
                 cos = self.rotary_emb.cos_cached[:seq_len].to(
                     dtype=decode_hs_or_q_c.dtype)
                 sin = self.rotary_emb.sin_cached[:seq_len].to(
@@ -1121,7 +1121,7 @@ def forward(
             prefill_q_nope = prefill_q[..., :self.qk_nope_head_dim]
             if self.torchair_graph_enabled:
                 num_tokens = prefill_hs_or_q_c.shape[0]
-                seq_len = self.rotary_emb.max_position_embeddings
+                seq_len = self.rotary_emb.max_position_embeddings * self.rotary_emb.scaling_factor
                 cos = self.rotary_emb.cos_cached[:seq_len].to(
                     dtype=prefill_q_pe.dtype)
                 sin = self.rotary_emb.sin_cached[:seq_len].to(