[Model][MiniCPM] support MiniCPM (#645)

### What this PR does / why we need it?
This pr support minicpm in branch main. see
#164


### How was this patch tested?
test locally with minicpm

---------

Signed-off-by: MengqingCao <[email protected]>

Author: MengqingCao

vllm_ascend/patch/__init__.py

@@ -96,6 +96,8 @@
 #    Related PR (if no, explain why): no related PR, we want add this ability into vllm
 #    Future Plan:
 #       Remove those patch when vllm merged them
+#
+#
 # * Worker Patch:
 # ===============
 # ** File: worker/patch_0_8_4/patch_metrics.py **
@@ -125,6 +127,20 @@
 #    Future Plan:
 #       Revert it when the related pr is merged in vllm.
 #
+# ** File: worker/patch_common/patch_minicpm.py **
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+#   1. `vllm.model_executor.models.minicpm.MiniCPMAttention.forward`
+#    Why:
+#       The forward func of MiniCPMAttention in vllm do a datatype convert
+#       (original datatype --> float32) to ensure the precision on cuda.
+#       However float32 is not supported in cann rope op, thus we keep this patch
+#    How：
+#       Removed the dtype convert operations in forward
+#    Related PR (if no, explain why): 1. refused by vllm. 2. vllm doesn't support 3. prepare to submit....
+#       NO, only for npu due to rope op.
+#    Future Plan:
+#       Keep this patch in vllm-ascend.
+#
 # ** File: worker/patch_common/patch_multi_step_worker.py **
 # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 #   1. `vllm.spec_decode.multi_step_worker.MultiStepWorker.sampler_output`
@@ -156,3 +172,15 @@
 #       - https://github.com/vllm-project/vllm-ascend/pull/395
 #    Future Plan:
 #       Revert it when the related pr is merged in vllm and vllm-ascend.
+#
+# ** File: worker/patch_0_8_4/patch_tritonplaceholder.py **
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+#   1. `triton` Module
+#    Why:
+#       Triton is not supported on npu currently, importing triton will break vllm-ascend
+#    How：
+#       ditto
+#    Related PR (if no, explain why): 1. refused by vllm. 2. vllm doesn't support 3. prepare to submit....
+#       TritonPlaceholder is only available in vllm>0.8.4
+#    Future Plan:
+#       Revert it when branch main doesn't maintain v0.8.4.

vllm_ascend/patch/worker/patch_0_8_4/__init__.py

@@ -16,3 +16,4 @@
 #
 
 import vllm_ascend.patch.worker.patch_0_8_4.patch_metrics  # noqa
+import vllm_ascend.patch.worker.patch_0_8_4.patch_tritonplaceholder  # noqa

vllm_ascend/patch/worker/patch_0_8_4/patch_tritonplaceholder.py

@@ -0,0 +1,68 @@
+#
+# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
+# Copyright 2023 The vLLM team.
+#
+# 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.
+# This file is a part of the vllm-ascend project.
+# Adapted from vllm/triton_utils/importing.py
+#
+
+import sys
+import types
+from importlib.util import find_spec
+
+from vllm.logger import logger
+
+HAS_TRITON = (
+    find_spec("triton") is not None
+    or find_spec("pytorch-triton-xpu") is not None  # Not compatible
+)
+
+if not HAS_TRITON:
+    logger.info("Triton not installed or not compatible; certain GPU-related"
+                " functions will not be available.")
+
+    class TritonPlaceholder(types.ModuleType):
+
+        def __init__(self):
+            super().__init__("triton")
+            self.jit = self._dummy_decorator("jit")
+            self.autotune = self._dummy_decorator("autotune")
+            self.heuristics = self._dummy_decorator("heuristics")
+            self.language = TritonLanguagePlaceholder()
+            logger.warning_once(
+                "Triton is not installed. Using dummy decorators. "
+                "Install it via `pip install triton` to enable kernel"
+                " compilation.")
+
+        def _dummy_decorator(self, name):
+
+            def decorator(func=None, **kwargs):
+                if func is None:
+                    return lambda f: f
+                return func
+
+            return decorator
+
+    class TritonLanguagePlaceholder(types.ModuleType):
+
+        def __init__(self):
+            super().__init__("triton.language")
+            self.constexpr = None
+            self.dtype = None
+
+    sys.modules['triton'] = TritonPlaceholder()
+    sys.modules['triton.language'] = TritonLanguagePlaceholder()
+
+if 'triton' in sys.modules:
+    logger.info("Triton module has been replaced with a placeholder.")

vllm_ascend/patch/worker/patch_common/__init__.py

@@ -16,5 +16,6 @@
 #
 
 import vllm_ascend.patch.worker.patch_common.patch_metrics  # noqa
+import vllm_ascend.patch.worker.patch_common.patch_minicpm  # noqa
 import vllm_ascend.patch.worker.patch_common.patch_multi_step_worker  # noqa
 import vllm_ascend.patch.worker.patch_common.patch_spec_decode_worker  # noqa

vllm_ascend/patch/worker/patch_common/patch_minicpm.py

@@ -0,0 +1,36 @@
+#
+# 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.model_executor.models.minicpm import MiniCPMAttention
+
+
+def forward(
+    self,
+    positions: torch.Tensor,
+    hidden_states: torch.Tensor,
+) -> 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)
+    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