init v0.23.0

Signed-off-by: Sun Ruoxi <sunruoxi@4paradigm.com>

vllm_ascend/worker/v2/states.py

@@ -0,0 +1,71 @@
+# Adapt from https://github.com/vllm-project/vllm/blob/main/vllm/v1/worker/gpu/states.py
+# 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.
+#
+
+import torch
+from vllm.v1.worker.gpu.states import RequestState
+
+
+class AscendRequestState(RequestState):
+    """Request state for Ascend NPUs."""
+
+    def __init__(
+        self,
+        max_num_reqs: int,
+        max_model_len: int,
+        max_num_batched_tokens: int,
+        num_speculative_steps: int,
+        vocab_size: int,
+        device: torch.device,
+    ):
+        super().__init__(
+            max_num_reqs,
+            max_model_len,
+            max_num_batched_tokens,
+            num_speculative_steps,
+            vocab_size,
+            device,
+        )
+        # vllm gpu_model_runner_v2 deprecate the seqs_lens_cpu attribute,
+        # because they think most attention backends do not need it.
+        # However, Ascend attention backend muse uses seqs_lens_cpu,
+        # so we keep num_computed_tokens_cpu here, seq_lens_cpu need to be
+        # calculated by num_computed_tokens_cpu + decode_token_per_req outside.
+        self.num_computed_tokens_cpu: torch.Tensor = torch.zeros(
+            self.max_num_reqs,
+            dtype=torch.int32,
+            device="cpu",
+        )
+
+    def add_request(
+        self,
+        req_id,
+        prompt_len,
+        all_token_ids,
+        num_computed_tokens,
+        max_tokens=None,
+    ):
+        super().add_request(
+            req_id,
+            prompt_len,
+            all_token_ids,
+            num_computed_tokens,
+            max_tokens=max_tokens,
+        )
+        req_idx = self.req_id_to_index[req_id]
+        self.num_computed_tokens_cpu[req_idx] = num_computed_tokens