大改

vllm_npu/torchair/torchair_worker.py

@@ -0,0 +1,63 @@
+#
+# 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.
+import torch
+from vllm.logger import logger
+
+import vllm_npu.envs as envs_ascend
+from vllm_npu.ascend_config import get_ascend_config
+from vllm_npu.torchair.torchair_model_runner import NPUTorchairModelRunner
+from vllm_npu.torchair.utils import (check_kv_cache_bytes_cache_exist,
+                                        delete_torchair_cache_file,
+                                        read_kv_cache_bytes_from_file)
+from vllm_npu.worker.worker_v1 import NPUWorker
+
+
+class NPUTorchairWorker(NPUWorker):
+    """Torchair worker bases on NPUWorker. Only torchair specified code should be added in this class."""
+
+    def determine_available_memory(self) -> int:
+        """Override determine_available_memory to use cached torchair kv_cache_bytes."""
+
+        available_kv_cache_memory = super().determine_available_memory()
+        ascend_config = get_ascend_config()
+        if ascend_config.enable_shared_expert_dp:
+            return available_kv_cache_memory
+        if ascend_config.torchair_graph_config.use_cached_kv_cache_bytes:
+            if check_kv_cache_bytes_cache_exist():
+                old_kv_cache_bytes = read_kv_cache_bytes_from_file(
+                    torch.distributed.get_rank())
+                if 0 < old_kv_cache_bytes <= available_kv_cache_memory:
+                    logger.info(
+                        f"Use cached torchair kv_cache_bytes: {old_kv_cache_bytes}"
+                    )
+                    self.model_runner.new_kv_cache_bytes = old_kv_cache_bytes
+                    return old_kv_cache_bytes
+                else:
+                    logger.info(
+                        "Cached torchair kv_cache_bytes is too big, invalidate old torchair_cache"
+                    )
+                    delete_torchair_cache_file()
+            bytes_floating_tolerance = 1024 * 1024 * envs_ascend.vllm_npu_KV_CACHE_MEGABYTES_FLOATING_TOLERANCE
+            available_kv_cache_memory -= bytes_floating_tolerance
+            logger.info(f"Use new kv_cache_bytes: {available_kv_cache_memory}")
+            self.model_runner.new_kv_cache_bytes = available_kv_cache_memory
+        return available_kv_cache_memory
+
+    def init_device(self):
+        """Override init_device to init torchair model runner"""
+        device = self._init_device()
+        # Init ModelRunner here, so that we have access to self.device.
+        self.model_runner = NPUTorchairModelRunner(self.vllm_config, device)