75de3fa172
### What this PR does / why we need it? Update doc Signed-off-by: hfadzxy <starmoon_zhang@163.com>
123 lines
4.0 KiB
Markdown
123 lines
4.0 KiB
Markdown
# Single NPU (Qwen2-Audio 7B)
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## Run vllm-ascend on Single NPU
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### Offline Inference on Single NPU
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Run docker container:
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```{code-block} bash
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:substitutions:
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# Update the vllm-ascend image
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export IMAGE=quay.io/ascend/vllm-ascend:|vllm_ascend_version|
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docker run --rm \
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--name vllm-ascend \
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--device /dev/davinci0 \
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--device /dev/davinci_manager \
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--device /dev/devmm_svm \
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--device /dev/hisi_hdc \
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-v /usr/local/dcmi:/usr/local/dcmi \
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-v /usr/local/bin/npu-smi:/usr/local/bin/npu-smi \
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-v /usr/local/Ascend/driver/lib64/:/usr/local/Ascend/driver/lib64/ \
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-v /usr/local/Ascend/driver/version.info:/usr/local/Ascend/driver/version.info \
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-v /etc/ascend_install.info:/etc/ascend_install.info \
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-v /root/.cache:/root/.cache \
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-p 8000:8000 \
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-it $IMAGE bash
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```
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Set up environment variables:
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```bash
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# Load model from ModelScope to speed up download
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export VLLM_USE_MODELSCOPE=True
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# Set `max_split_size_mb` to reduce memory fragmentation and avoid out of memory
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export PYTORCH_NPU_ALLOC_CONF=max_split_size_mb:256
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```
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:::{note}
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`max_split_size_mb` prevents the native allocator from splitting blocks larger than this size (in MB). This can reduce fragmentation and may allow some borderline workloads to complete without running out of memory. You can find more details [<u>here</u>](https://www.hiascend.com/document/detail/zh/CANNCommunityEdition/800alpha003/apiref/envref/envref_07_0061.html).
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:::
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Install packages required for audio processing:
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```bash
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pip config set global.index-url https://mirrors.tuna.tsinghua.edu.cn/pypi/web/simple
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pip install librosa soundfile
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```
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Run the following script to execute offline inference on a single NPU:
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```python
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from vllm import LLM, SamplingParams
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from vllm.assets.audio import AudioAsset
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from vllm.utils import FlexibleArgumentParser
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# If network issues prevent AudioAsset from fetching remote audio files, retry or check your network.
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audio_assets = [AudioAsset("mary_had_lamb"), AudioAsset("winning_call")]
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question_per_audio_count = {
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1: "What is recited in the audio?",
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2: "What sport and what nursery rhyme are referenced?"
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}
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def prepare_inputs(audio_count: int):
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audio_in_prompt = "".join([
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f"Audio {idx+1}: <|audio_bos|><|AUDIO|><|audio_eos|>\n"
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for idx in range(audio_count)
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])
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question = question_per_audio_count[audio_count]
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prompt = ("<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n"
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"<|im_start|>user\n"
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f"{audio_in_prompt}{question}<|im_end|>\n"
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"<|im_start|>assistant\n")
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mm_data = {
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"audio":
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[asset.audio_and_sample_rate for asset in audio_assets[:audio_count]]
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}
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# Merge text prompt and audio data into inputs
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inputs = {"prompt": prompt, "multi_modal_data": mm_data}
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return inputs
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def main(audio_count: int):
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# NOTE: The default `max_num_seqs` and `max_model_len` may result in OOM on
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# lower-end GPUs.
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# Unless specified, these settings have been tested to work on a single L4.
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# `limit_mm_per_prompt`: the max num items for each modality per prompt.
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llm = LLM(model="Qwen/Qwen2-Audio-7B-Instruct",
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max_model_len=4096,
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max_num_seqs=5,
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limit_mm_per_prompt={"audio": audio_count})
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inputs = prepare_inputs(audio_count)
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sampling_params = SamplingParams(temperature=0.2,
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max_tokens=64,
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stop_token_ids=None)
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outputs = llm.generate(inputs, sampling_params=sampling_params)
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for o in outputs:
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generated_text = o.outputs[0].text
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print(generated_text)
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if __name__ == "__main__":
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audio_count = 2
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main(audio_count)
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```
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If you run this script successfully, you can see the info shown below:
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```bash
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The sport referenced is baseball, and the nursery rhyme is 'Mary Had a Little Lamb'.
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```
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### Online Serving on Single NPU
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Currently, vLLM's OpenAI-Compatible server doesn't support audio inputs. Find more details [<u>here</u>](https://github.com/vllm-project/vllm/issues/19977).
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