[Model] Adapt the MiniCPM-2B model

Signed-off-by: Wang Kunpeng <1289706727@qq.com>

vllm_ascend/__init__.py

@@ -18,4 +18,6 @@
 
 def register():
     """Register the NPU platform."""
+    # To ensure that the module is correctly replaced, add it at the beginning
+    import vllm_ascend.patch_module  # noqa: F401
     return "vllm_ascend.platform.NPUPlatform"

vllm_ascend/patch/__init__.py

@@ -0,0 +1,17 @@
+#
+# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
+# This file is a part of the vllm-ascend project.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+import vllm_ascend.patch.patch_minicpm  # noqa

vllm_ascend/patch/patch_minicpm.py

@@ -0,0 +1,40 @@
+#
+# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
+# This file is a part of the vllm-ascend project.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+import torch
+
+from vllm.attention import AttentionMetadata
+from vllm.model_executor.models.minicpm import MiniCPMAttention
+
+
+def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        kv_cache: torch.Tensor,
+        attn_metadata: AttentionMetadata,
+) -> torch.Tensor:
+    qkv, _ = self.qkv_proj(hidden_states)
+    q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
+    q, k = self.rotary_emb(positions, q, k)
+    attn_output = self.attn(q, k, v, kv_cache, attn_metadata)
+    output, _ = self.o_proj(attn_output)
+    return output
+
+
+# The type conversion in the forward function is deleted to support the rope operator.
+MiniCPMAttention.forward = forward

vllm_ascend/patch_module.py

@@ -0,0 +1,21 @@
+#
+# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
+# This file is a part of the vllm-ascend project.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+import sys
+import types
+
+# prevent errors caused by triton not supported
+sys.modules['vllm.model_executor.layers.fused_moe.fused_moe'] = types.ModuleType('fused_moe_module')

vllm_ascend/worker.py

@@ -68,8 +68,9 @@ def __init__(
         is_driver_worker: bool = False,
         model_runner_cls: Optional[Type[ModelRunnerBase]] = None,
     ) -> None:
-        # Register ops when worker init.
+        # Register ops and patch when worker init.
         from vllm_ascend import ops  # noqa: F401
+        from vllm_ascend import patch  # noqa: F401
 
         WorkerBase.__init__(self, vllm_config=vllm_config)
         # Try to import mindie_turbo to accelerate vLLM inference.