[Refactor] Provide a framework to accommodate operators for different hardware devices (#5735)

come from: https://github.com/vllm-project/vllm-ascend/issues/5463

Reason:

During the iteration process of the hardware version, there may be a
large number of iterations for the operators, which can lead to
short-term compatibility differences. Therefore, an intermediate
adaptation layer is provided to accommodate the short-term differences
in operators.


- vLLM version: v0.13.0
- vLLM main:
2f4e6548ef

---------

Signed-off-by: weijinqian_v1 <weijinqian@huawei.com>
Signed-off-by: weijinqian0 <1184188277@qq.com>
Co-authored-by: weijinqian_v1 <weijinqian@huawei.com>

vllm_ascend/attention/attention_v1.py

@@ -43,9 +43,9 @@ from vllm_ascend.attention.utils import (AscendCommonAttentionMetadata,
 from vllm_ascend.compilation.acl_graph import (
     get_draft_graph_params, get_graph_params,
     update_draft_graph_params_workspaces, update_graph_params_workspaces)
+from vllm_ascend.device.device_op import DeviceOperator
 from vllm_ascend.ops.flashcomm2_oshard_manager import flashcomm2_oshard_manager
-from vllm_ascend.utils import (AscendDeviceType, get_ascend_device_type,
+from vllm_ascend.utils import weak_ref_tensors
-                               weak_ref_tensors)
 
 # default max value of sliding window size
 SWA_INT_MAX = 2147483647
@@ -693,27 +693,14 @@ class AscendAttentionBackendImpl(AttentionImpl):
                 self.key_cache, self.value_cache = kv_cache[0], kv_cache[1]
             slots = attn_metadata.slot_mapping
             encoder_decoder = (self.attn_type == AttentionType.ENCODER_DECODER)
-            if get_ascend_device_type() == AscendDeviceType.A5:
+            DeviceOperator.reshape_and_cache(
-                # TODO: Once eagle running to here, it may has error because of the 0 dim of slot_mapping.
-                # Should check if the 0 dim of slot_mapping must equal to the 0 dim of key.
-                # If it's necessary, the slots should be sliced.
-                torch_npu.npu_scatter_pa_kv_cache(
-                    key=key[:attn_metadata.num_actual_tokens]
-                    if not encoder_decoder else key,
-                    value=value[:attn_metadata.num_actual_tokens].contiguous()
-                    if not encoder_decoder else value,
-                    key_cache=self.key_cache,
-                    value_cache=self.value_cache,
-                    slot_mapping=slots)
-            else:
-                torch_npu._npu_reshape_and_cache(
                 key=key[:attn_metadata.num_actual_tokens]
                 if not encoder_decoder else key,
                 value=value[:attn_metadata.num_actual_tokens]
                 if not encoder_decoder else value,
                 key_cache=self.key_cache,
                 value_cache=self.value_cache,
-                    slot_indices=slots[:attn_metadata.num_actual_tokens]
+                slot_mapping=slots[:attn_metadata.num_actual_tokens]
                 if not encoder_decoder else slots)
             if self.is_kv_producer:
                 attn_metadata.reshape_cache_event.record()

vllm_ascend/device/device_op.py

@@ -0,0 +1,56 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
+#
+# 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.
+#
+from typing import Optional, Type
+
+import torch_npu
+
+from vllm_ascend.utils import AscendDeviceType, get_ascend_device_type
+
+
+class BaseDeviceAdaptor(object):
+
+    @classmethod
+    def reshape_and_cache(cls, key, value, key_cache, value_cache,
+                          slot_mapping):
+        torch_npu._npu_reshape_and_cache(key=key,
+                                         value=value,
+                                         key_cache=key_cache,
+                                         value_cache=value_cache,
+                                         slot_indices=slot_mapping)
+
+
+class A5DeviceAdaptor(BaseDeviceAdaptor):
+
+    @classmethod
+    def reshape_and_cache(cls, key, value, key_cache, value_cache,
+                          slot_mapping):
+        torch_npu.npu_scatter_pa_kv_cache(key=key,
+                                          value=value.contiguous(),
+                                          key_cache=key_cache,
+                                          value_cache=value_cache,
+                                          slot_mapping=slot_mapping)
+
+
+def get_device_adaptor():
+    ascend_device_type = get_ascend_device_type()
+    if ascend_device_type == AscendDeviceType.A5:
+        return A5DeviceAdaptor
+    return BaseDeviceAdaptor
+
+
+DeviceOperator: Optional[Type['BaseDeviceAdaptor']] = get_device_adaptor()