mirror of
https://git.datalinker.icu/vllm-project/vllm.git
synced 2025-12-10 03:15:20 +08:00
102 lines
3.3 KiB
Python
102 lines
3.3 KiB
Python
# SPDX-License-Identifier: Apache-2.0
|
||
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
|
||
|
||
# NOTE To avoid overloading the CI pipeline, this test script will
|
||
# not be triggered on CI and is primarily intended for local testing
|
||
# and verification.
|
||
|
||
import vllm
|
||
from vllm.lora.request import LoRARequest
|
||
|
||
from ..utils import multi_gpu_test
|
||
|
||
MODEL_PATH = "deepseek-ai/DeepSeek-V2-Lite-Chat"
|
||
|
||
PROMPT_TEMPLATE = "<｜begin▁of▁sentence｜>You are a helpful assistant.\n\nUser: {context}\n\nAssistant:" # noqa: E501
|
||
|
||
|
||
def generate_and_test(llm: vllm.LLM, lora_path: str, lora_id: int):
|
||
prompts = [
|
||
PROMPT_TEMPLATE.format(context="Who are you?"),
|
||
]
|
||
sampling_params = vllm.SamplingParams(temperature=0, max_tokens=64)
|
||
outputs = llm.generate(
|
||
prompts,
|
||
sampling_params,
|
||
lora_request=LoRARequest(str(lora_id), lora_id, lora_path) if lora_id else None,
|
||
)
|
||
# Print the outputs.
|
||
generated_texts: list[str] = []
|
||
for output in outputs:
|
||
prompt = output.prompt
|
||
generated_text = output.outputs[0].text.strip()
|
||
generated_texts.append(generated_text)
|
||
print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
|
||
# return generated_texts
|
||
expected_lora_output = [
|
||
"I am \u5f20\u5b50\u8c6a, an AI assistant developed by \u9648\u58eb\u680b.", # noqa: E501
|
||
]
|
||
for i in range(len(expected_lora_output)):
|
||
assert generated_texts[i].startswith(expected_lora_output[i])
|
||
|
||
|
||
def test_deepseekv2_lora(deepseekv2_lora_files):
|
||
# We enable enforce_eager=True here to reduce VRAM usage for lora-test CI,
|
||
# Otherwise, the lora-test will fail due to CUDA OOM.
|
||
llm = vllm.LLM(
|
||
MODEL_PATH,
|
||
max_model_len=1024,
|
||
enable_lora=True,
|
||
max_loras=4,
|
||
enforce_eager=True,
|
||
trust_remote_code=True,
|
||
enable_chunked_prefill=True,
|
||
)
|
||
generate_and_test(llm, deepseekv2_lora_files, 1)
|
||
|
||
|
||
def test_deepseekv2(deepseekv2_lora_files):
|
||
# We enable enforce_eager=True here to reduce VRAM usage for lora-test CI,
|
||
# Otherwise, the lora-test will fail due to CUDA OOM.
|
||
llm = vllm.LLM(
|
||
MODEL_PATH,
|
||
max_model_len=1024,
|
||
enable_lora=True,
|
||
max_loras=4,
|
||
enforce_eager=True,
|
||
trust_remote_code=True,
|
||
)
|
||
generate_and_test(llm, deepseekv2_lora_files, 1)
|
||
|
||
|
||
@multi_gpu_test(num_gpus=2)
|
||
def test_deepseekv2_tp2(deepseekv2_lora_files):
|
||
# We enable enforce_eager=True here to reduce VRAM usage for lora-test CI,
|
||
# Otherwise, the lora-test will fail due to CUDA OOM.
|
||
llm = vllm.LLM(
|
||
MODEL_PATH,
|
||
max_model_len=1024,
|
||
enable_lora=True,
|
||
max_loras=4,
|
||
enforce_eager=True,
|
||
trust_remote_code=True,
|
||
tensor_parallel_size=2,
|
||
)
|
||
generate_and_test(llm, deepseekv2_lora_files, 2)
|
||
|
||
|
||
@multi_gpu_test(num_gpus=4)
|
||
def test_deepseekv2_tp4(deepseekv2_lora_files):
|
||
# We enable enforce_eager=True here to reduce VRAM usage for lora-test CI,
|
||
# Otherwise, the lora-test will fail due to CUDA OOM.
|
||
llm = vllm.LLM(
|
||
MODEL_PATH,
|
||
max_model_len=1024,
|
||
enable_lora=True,
|
||
max_loras=4,
|
||
enforce_eager=True,
|
||
trust_remote_code=True,
|
||
tensor_parallel_size=4,
|
||
)
|
||
generate_and_test(llm, deepseekv2_lora_files, 2)
|