[6/N][refactor]delete torchair in rotary ops (#2581)

### What this PR does / why we need it?
After moved torchair related rope ops into torchair_ops, split the
torchair from the origin rope ops to make the code clean.

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

### How was this patch tested?
vLLM version: main
vLLM main:
ab9f2cfd19


- vLLM version: v0.10.1.1
- vLLM main:
81eea3d348

Signed-off-by: hust17yixuan <303660421@qq.com>

vllm_ascend/ops/rotary_embedding.py

@@ -24,7 +24,6 @@ import torch_npu
 from vllm.model_executor.layers.rotary_embedding import (
     DeepseekScalingRotaryEmbedding, RotaryEmbedding)
 
-from vllm_ascend.ascend_config import get_ascend_config
 from vllm_ascend.platform import NPUPlatform
 from vllm_ascend.utils import enable_custom_op, is_310p
 
@@ -43,15 +42,6 @@ def rope_forward_oot(
     is_neox_style_override: Optional[bool] = None,
     is_qwen_torchair: Optional[bool] = False,
 ) -> Tuple[torch.Tensor, torch.Tensor]:
-    if get_ascend_config(
-    ).torchair_graph_config.enabled and not is_qwen_torchair:
-        return self.forward_native(
-            positions,
-            query,
-            key,
-            offsets,
-        )
-
     query_shape, key_shape = query.shape, key.shape
     if self.cos_sin_cache.device != query.device:
         self.cos_sin_cache = self.cos_sin_cache.to(query.device)
@@ -120,11 +110,6 @@ class AscendRotaryEmbedding(RotaryEmbedding):
     ) -> None:
         super().__init__(head_size, rotary_dim, max_position_embeddings, base,
                          is_neox_style, dtype)
-        if get_ascend_config().torchair_graph_config.enabled:
-            set_cos_sin_cache(self,
-                              seq_len=max_position_embeddings,
-                              device="npu",
-                              dtype=dtype)
 
     def forward_oot(
         self,
@@ -137,42 +122,9 @@ class AscendRotaryEmbedding(RotaryEmbedding):
         is_prefill: Optional[bool] = True,
         is_qwen_torchair: Optional[bool] = False,
     ):
-        if get_ascend_config().torchair_graph_config.enabled \
-                and is_qwen_torchair and not is_prefill:
-            if max_seq_len is not None and torch.gt(
-                    max_seq_len, self.max_position_embeddings):
-                set_cos_sin_cache(self,
-                                  seq_len=max_seq_len,
-                                  device=query.device,
-                                  dtype=torch.float32)
-
-            # bsnd/bnsd
-            if positions is not None:
-                cos = self.embed(positions, self.cos)
-                sin = self.embed(positions, self.sin)
-                self.cos_embed = cos
-                self.sin_embed = sin
-            else:
-                cos = self.cos_embed
-                sin = self.sin_embed
-
-            query = query.view(*query.shape[:-1], -1,
-                               self.head_size).contiguous()
-            key = key.view(*key.shape[:-1], -1, self.head_size).contiguous()
-
-            cos = cos.unsqueeze(-2).unsqueeze(-2)
-            sin = sin.unsqueeze(-2).unsqueeze(-2)
-
-            query = query.unsqueeze(1)
-            key = key.unsqueeze(1)
-
-            q_embed, k_embed = torch_npu.npu_apply_rotary_pos_emb(
-                query, key, cos, sin)
-            return q_embed.flatten(-2), k_embed.flatten(-2)
-        else:
-            return rope_forward_oot(self, positions, query, key, offsets,
-                                    is_neox_style_override,
-                                    is_qwen_torchair)  # type: ignore
+        return rope_forward_oot(self, positions, query, key, offsets,
+                                is_neox_style_override,
+                                is_qwen_torchair)  # type: ignore
 
 
 class AscendDeepseekScalingRotaryEmbedding(DeepseekScalingRotaryEmbedding):