9434f24ded
### What this PR does / why we need it? This PR adds the initial multi modal model for nightly test, including 3 cases for Qwen2.5-vl-7b acc/perf test on A3, we need test them daily. It also inclues 8 cases for deepseek-r1-0528-w8a8 func, acc and perf tests ### Does this PR introduce _any_ user-facing change? No ### How was this patch tested? by running the test - vLLM version: v0.11.0rc3 - vLLM main: https://github.com/vllm-project/vllm/commit/v0.11.0 --------- Signed-off-by: jiangyunfan1 <jiangyunfan1@h-partners.com>
137 lines
4.3 KiB
Python
137 lines
4.3 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-R1-0528-W8A8",
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]
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MODES = [
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"torchair",
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"single",
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"aclgraph",
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"no_chunkprefill",
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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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"case_type": "performance",
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"dataset_path": "vllm-ascend/GSM8K-in3500-bs400",
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"request_conf": "vllm_api_stream_chat",
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"dataset_conf": "gsm8k/gsm8k_gen_0_shot_cot_str_perf",
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"num_prompts": 400,
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"max_out_len": 1500,
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"batch_size": 1000,
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"baseline": 1,
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"threshold": 0.97
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}]
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@pytest.mark.asyncio
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@pytest.mark.parametrize("model", MODELS)
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@pytest.mark.parametrize("mode", MODES)
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async def test_models(model: str, mode: str) -> None:
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port = get_open_port()
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env_dict = {
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"OMP_NUM_THREADS": "10",
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"OMP_PROC_BIND": "false",
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"HCCL_BUFFSIZE": "1024",
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"PYTORCH_NPU_ALLOC_CONF": "expandable_segments: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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additional_config = {
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"ascend_scheduler_config": {
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"enabled": False
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},
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"torchair_graph_config": {
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"enabled": True,
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"enable_multistream_moe": False,
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"enable_multistream_mla": True,
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"graph_batch_sizes": [16],
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"use_cached_graph": True
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},
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"chunked_prefill_for_mla": True,
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"enable_weight_nz_layout": True
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}
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server_args = [
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"--quantization", "ascend", "--data-parallel-size", "2",
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"--tensor-parallel-size", "8", "--enable-expert-parallel", "--port",
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str(port), "--seed", "1024", "--max-model-len", "36864",
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"--max-num-batched-tokens", "4096", "--max-num-seqs", "16",
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"--trust-remote-code", "--gpu-memory-utilization", "0.9",
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"--speculative-config",
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json.dumps(speculative_config)
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]
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if mode == "single":
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server_args.append("--enforce-eager")
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additional_config["torchair_graph_config"] = {"enabled": False}
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if mode == "aclgraph":
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additional_config["torchair_graph_config"] = {"enabled": False}
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if mode == "no_chunkprefill":
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additional_config["ascend_scheduler_config"] = {"enabled": True}
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i = server_args.index("--max-num-batched-tokens") + 1
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server_args[i] = "36864"
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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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if mode in ["single", "no_chunkprefill"]:
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return
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# aisbench test
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run_aisbench_cases(model, port, aisbench_cases)
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