835b4c8f1d
aclgraph is stable and fast now. Let's drop torchair graph mode now.
TODO: some logic to adapt torchair should be cleaned up as well. We'll
do it in the following PR.
- vLLM version: v0.12.0
- vLLM main:
ad32e3e19c
Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
Co-authored-by: Mengqing Cao <cmq0113@163.com>
116 lines
3.7 KiB
Python
116 lines
3.7 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 = {"num_speculative_tokens": 1, "method": "mtp"}
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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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"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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