Add mrope op fusion (#3509)

### What this PR does / why we need it?
Add mrope fusion op for qwen2.5-vl. This mrope operator dosen't support
Qwen3-VL currently. Thus could only take affect in qwen2.5-vl

- vLLM version: v0.11.0rc3
- vLLM main: https://github.com/vllm-project/vllm/commit/v0.11.0

---------

Signed-off-by: shaopeng666 <shaopeng666@noreply.gitcode.com>
Co-authored-by: shaopeng666 <shaopeng666@noreply.gitcode.com>

tests/ut/ops/test_rotary_embedding.py

@@ -6,13 +6,14 @@ import torch
 from transformers.configuration_utils import PretrainedConfig
 from vllm.config import ModelConfig, VllmConfig
 from vllm.model_executor.layers.rotary_embedding import (
-    DeepseekScalingRotaryEmbedding, RotaryEmbedding)
+    DeepseekScalingRotaryEmbedding, MRotaryEmbedding, RotaryEmbedding)
 
 from tests.ut.base import TestBase
 from vllm_ascend.ascend_forward_context import set_ascend_forward_context
 from vllm_ascend.ops.rotary_embedding import _custom_rotary_embedding_enabled
 
 MODEL = "Qwen3-0.6B"
+MODEL_VL = "Qwen/Qwen2.5-VL-3B-Instruct"
 MAX_NUM_BATCHED_TOKEND = 10000
 
 
@@ -376,3 +377,86 @@ class TestAscendDeepseekScalingRotaryEmbedding(TestBase):
                 expected,
                 places=6,
                 msg=f"Failed for scale={scale}, mscale={mscale}")
+
+
+class TestAscendMRotaryEmbedding(unittest.TestCase):
+
+    def setUp(self):
+        # Common setup for tests
+        self.number_tokens = 3
+        self.num_head = 8
+        self.num_kvhead = 8
+        self.head_size = 128
+        self.max_position_embeddings = 128000
+        self.is_neox_style = True
+        self.rope_theta = 1000000.0
+        self.positions_1d = torch.tensor([1, 2, 3])
+        self.positions_2d = torch.randint(1, 10, (3, self.number_tokens))
+
+        self.query = torch.randn(
+            (self.number_tokens, self.num_head * self.head_size),
+            dtype=torch.bfloat16)
+        self.key = torch.randn(
+            (self.number_tokens, self.num_kvhead * self.head_size),
+            dtype=torch.bfloat16)
+
+        # Qwen2.5-VL mrope section case
+        self.mrope_section = [16, 24, 24]
+
+        self.layer = MRotaryEmbedding(self.head_size,
+                                      self.head_size,
+                                      self.max_position_embeddings,
+                                      base=self.rope_theta,
+                                      is_neox_style=self.is_neox_style,
+                                      dtype=torch.bfloat16,
+                                      mrope_section=self.mrope_section)
+
+        self.mock_config = MagicMock()
+        self.mock_config.torchair_graph_config.enabled = False
+
+    def _create_vllm_config(self):
+        vllm_config = VllmConfig()
+        model_config = ModelConfig(MODEL_VL,
+                                   tokenizer=MODEL_VL,
+                                   max_model_len=MAX_NUM_BATCHED_TOKEND)
+        model_config.hf_config = PretrainedConfig()
+        vllm_config.model_config = model_config
+        return vllm_config
+
+    @patch('torch_npu.npu_mrope')
+    @patch('vllm.config.ModelConfig.__post_init__', MagicMock())
+    @patch('vllm.config.VllmConfig.__post_init__', MagicMock())
+    @patch('vllm.distributed.parallel_state._DP', MagicMock(world_size=1))
+    @patch('vllm.distributed.parallel_state._TP', MagicMock(world_size=1))
+    def test_forward_oot_1d_positions(self, mock_npu_mrope):
+        mock_npu_mrope.return_value = (torch.zeros_like(self.query),
+                                       torch.zeros_like(self.key))
+
+        vllm_config = self._create_vllm_config()
+        with set_ascend_forward_context(None, vllm_config):
+            result_q, result_k = self.layer.forward_oot(
+                self.positions_1d, self.query, self.key)
+
+        mock_npu_mrope.assert_called_once()
+        self.assertFalse(torch.isnan(result_q).any().item())
+        self.assertFalse(torch.isnan(result_k).any().item())
+        self.assertEqual(result_q.shape, self.query.shape)
+
+    @patch('torch_npu.npu_mrope')
+    @patch('vllm.config.ModelConfig.__post_init__', MagicMock())
+    @patch('vllm.config.VllmConfig.__post_init__', MagicMock())
+    @patch('vllm.distributed.parallel_state._DP', MagicMock(world_size=1))
+    @patch('vllm.distributed.parallel_state._TP', MagicMock(world_size=1))
+    def test_forward_oot_2d_positions(self, mock_npu_mrope):
+        mock_npu_mrope.return_value = (torch.zeros_like(self.query),
+                                       torch.zeros_like(self.key))
+
+        vllm_config = self._create_vllm_config()
+        with set_ascend_forward_context(None, vllm_config):
+            result_q, result_k = self.layer.forward_oot(
+                self.positions_2d, self.query, self.key)
+
+        mock_npu_mrope.assert_called_once()
+        self.assertFalse(torch.isnan(result_q).any().item())
+        self.assertFalse(torch.isnan(result_k).any().item())
+        self.assertEqual(result_q.shape, self.query.shape)

vllm_ascend/ops/rotary_embedding.py

@@ -22,7 +22,7 @@ import torch
 import torch_npu
 from vllm.forward_context import get_forward_context
 from vllm.model_executor.layers.rotary_embedding import (
-    DeepseekScalingRotaryEmbedding, RotaryEmbedding,
+    DeepseekScalingRotaryEmbedding, MRotaryEmbedding, RotaryEmbedding,
     YaRNScalingRotaryEmbedding)
 
 from vllm_ascend.platform import NPUPlatform
@@ -395,3 +395,37 @@ class AscendDeepseekScalingRotaryEmbedding(DeepseekScalingRotaryEmbedding):
         q_pe, k_pe = _rope_forward_oot(self, positions, query, key,
                                        is_neox_style, offsets)
         return q_pe, k_pe
+
+
+class AscendMRotaryEmbedding(MRotaryEmbedding):
+
+    def forward_oot(
+        self,
+        positions: torch.Tensor,
+        query: torch.Tensor,
+        key: torch.Tensor,
+    ):
+        if self.mrope_section != [16, 24, 24]:
+            return super().forward_oot(positions, query, key)
+
+        import torch_npu
+        mrope_section = [0, 0, 0
+                         ] if positions.ndim == 1 else self.mrope_section
+
+        if self.cos_sin_cache.device != query.device:  # type: ignore
+            self.cos_sin_cache = self.cos_sin_cache.to(  # type: ignore
+                query.device)  # type: ignore
+
+        if self.cos_sin_cache.dtype != query.dtype:  # type: ignore
+            self.cos_sin_cache = self.cos_sin_cache.to(  # type: ignore
+                query.dtype)  # type: ignore
+
+        query, key = torch_npu.npu_mrope(positions,
+                                         query.contiguous(),
+                                         key.contiguous(),
+                                         self.cos_sin_cache.contiguous(),
+                                         self.head_size,
+                                         mrope_section=mrope_section,
+                                         rotary_mode='half')
+
+        return query, key

vllm_ascend/utils.py

@@ -517,8 +517,8 @@ def register_ascend_customop(vllm_config: Optional[VllmConfig] = None):
                                         AscendReplicatedLinear,
                                         AscendRowParallelLinear)
     from vllm_ascend.ops.rotary_embedding import (
-        AscendDeepseekScalingRotaryEmbedding, AscendRotaryEmbedding,
-        AscendYaRNRotaryEmbedding)
+        AscendDeepseekScalingRotaryEmbedding, AscendMRotaryEmbedding,
+        AscendRotaryEmbedding, AscendYaRNRotaryEmbedding)
     from vllm_ascend.ops.vocab_parallel_embedding import (
         AscendLogitsProcessor, AscendParallelLMHead,
         AscendVocabParallelEmbedding)
@@ -528,6 +528,7 @@ def register_ascend_customop(vllm_config: Optional[VllmConfig] = None):
         "QuickGELU": AscendQuickGELU,
         "SiluAndMul": AscendSiluAndMul,
         "RotaryEmbedding": AscendRotaryEmbedding,
+        "MRotaryEmbedding": AscendMRotaryEmbedding,
         "ColumnParallelLinear": AscendColumnParallelLinear,
         "RowParallelLinear": AscendRowParallelLinear,
         "YaRNScalingRotaryEmbedding": AscendYaRNRotaryEmbedding,