f10acddb78
Ascend scheduler was added for non chunk prefill case before, since that the npu ops didn't work well with chunked prefill. Now the ops with chunked prefill work better, it's time to remove the ascend scheduler to use vLLM default scheduler. - vLLM version: v0.11.2 --------- Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
122 lines
3.8 KiB
Python
122 lines
3.8 KiB
Python
# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
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# Copyright 2023 The vLLM team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# This file is a part of the vllm-ascend project.
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#
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import json
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from typing import Any
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import openai
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import pytest
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from vllm.utils import get_open_port
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from tests.e2e.conftest import RemoteOpenAIServer
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from tools.aisbench import run_aisbench_cases
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MODELS = [
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"vllm-ascend/DeepSeek-R1-W8A8",
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]
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prompts = [
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"San Francisco is a",
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]
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api_keyword_args = {
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"max_tokens": 10,
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}
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aisbench_cases = [{
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"case_type": "accuracy",
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"dataset_path": "vllm-ascend/gsm8k-lite",
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"request_conf": "vllm_api_general_chat",
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"dataset_conf": "gsm8k/gsm8k_gen_0_shot_cot_chat_prompt",
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"max_out_len": 32768,
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"batch_size": 32,
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"baseline": 95,
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"threshold": 5
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}]
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@pytest.mark.asyncio
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@pytest.mark.parametrize("model", MODELS)
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async def test_models(model: str) -> None:
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port = get_open_port()
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env_dict = {
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"OMP_NUM_THREADS": "100",
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"OMP_PROC_BIND": "false",
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"HCCL_BUFFSIZE": "200",
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"VLLM_ASCEND_ENABLE_MLAPO": "1",
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"VLLM_RPC_TIMEOUT": "3600000",
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"VLLM_EXECUTE_MODEL_TIMEOUT_SECONDS": "3600000",
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"DISABLE_L2_CACHE": "1",
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"DYNAMIC_EPLB": "true",
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}
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speculative_config = {
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"num_speculative_tokens": 1,
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"method": "deepseek_mtp"
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}
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compilation_config = {
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"cudagraph_capture_sizes": [24],
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"cudagraph_mode": "FULL_DECODE_ONLY"
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}
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additional_config: dict[str, Any] = {
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"torchair_graph_config": {
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"enabled": True
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},
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"enable_shared_expert_dp": False,
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"multistream_overlap_shared_expert": False,
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"dynamic_eplb": True,
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"num_iterations_eplb_update": 14000,
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"num_wait_worker_iterations": 30,
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"init_redundancy_expert": 0,
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"gate_eplb": False
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}
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server_args = [
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"--quantization", "ascend", "--seed", "1024",
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"--no-enable-prefix-caching", "--data-parallel-size", "4",
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"--tensor-parallel-size", "4", "--enable-expert-parallel", "--port",
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str(port), "--max-model-len", "40000", "--max-num-batched-tokens",
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"4096", "--max-num-seqs", "12", "--trust-remote-code",
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"--gpu-memory-utilization", "0.92"
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]
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server_args.extend(
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["--speculative-config",
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json.dumps(speculative_config)])
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server_args.extend(
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["--compilation-config",
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json.dumps(compilation_config)])
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server_args.extend(["--additional-config", json.dumps(additional_config)])
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request_keyword_args: dict[str, Any] = {
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**api_keyword_args,
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}
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with RemoteOpenAIServer(model,
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server_args,
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server_port=port,
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env_dict=env_dict,
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auto_port=False) as server:
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client = server.get_async_client()
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batch = await client.completions.create(
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model=model,
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prompt=prompts,
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**request_keyword_args,
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)
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choices: list[openai.types.CompletionChoice] = batch.choices
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assert choices[0].text, "empty response"
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print(choices)
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# aisbench test
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run_aisbench_cases(model,
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port,
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aisbench_cases,
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server_args=server_args)
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