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[feat] Enable chunked prefill for llava-onevision (#2412)
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,173 @@ | ||
| """ | ||
| Usage: | ||
| python3 -m unittest test_vision_chunked_prefill.TestVisionChunkedPrefill.test_chunked_prefill | ||
| """ | ||
|
|
||
| import base64 | ||
| import io | ||
| import os | ||
| import unittest | ||
| from concurrent.futures import ThreadPoolExecutor | ||
| from typing import Union | ||
|
|
||
| import numpy as np | ||
| import requests | ||
| from decord import VideoReader, cpu | ||
| from PIL import Image | ||
|
|
||
| from sglang.srt.utils import kill_process_tree | ||
| from sglang.test.test_utils import ( | ||
| DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH, | ||
| DEFAULT_URL_FOR_TEST, | ||
| popen_launch_server, | ||
| ) | ||
|
|
||
|
|
||
| class TestVisionChunkedPrefill(unittest.TestCase): | ||
| def prepare_video_messages(self, video_path, max_frames_num=8): | ||
| vr = VideoReader(video_path, ctx=cpu(0)) | ||
| total_frame_num = len(vr) | ||
| uniform_sampled_frames = np.linspace( | ||
| 0, total_frame_num - 1, max_frames_num, dtype=int | ||
| ) | ||
| frame_idx = uniform_sampled_frames.tolist() | ||
| frames = vr.get_batch(frame_idx).asnumpy() | ||
|
|
||
| base64_frames = [] | ||
| for frame in frames: | ||
| pil_img = Image.fromarray(frame) | ||
| buff = io.BytesIO() | ||
| pil_img.save(buff, format="JPEG") | ||
| base64_str = base64.b64encode(buff.getvalue()).decode("utf-8") | ||
| base64_frames.append(base64_str) | ||
|
|
||
| messages = [{"role": "user", "content": []}] | ||
| frame_format = { | ||
| "type": "image_url", | ||
| "image_url": {"url": "data:image/jpeg;base64,{}"}, | ||
| "modalities": "video", | ||
| } | ||
|
|
||
| for base64_frame in base64_frames: | ||
| frame_format["image_url"]["url"] = "data:image/jpeg;base64,{}".format( | ||
| base64_frame | ||
| ) | ||
| messages[0]["content"].append(frame_format.copy()) | ||
|
|
||
| prompt = {"type": "text", "text": "Please describe the video briefly."} | ||
| messages[0]["content"].append(prompt) | ||
|
|
||
| return messages | ||
|
|
||
| def get_prompt_from_messages(self, messages): | ||
| text = ( | ||
| "<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n" | ||
| "<|im_start|>user\n" | ||
| ) | ||
| image_data = [] | ||
| for content in messages[0]["content"]: | ||
| if content["type"] == "image_url": | ||
| text += "<image>\n" | ||
| image_data.append(content["image_url"]["url"]) | ||
| text += "Please describe the video briefly.<|im_end|>\n<|im_start|>assistant\n" | ||
| return text, image_data | ||
|
|
||
| def generate(self, text, image_data): | ||
| response = requests.post( | ||
| self.base_url + "/generate", | ||
| json={ | ||
| "text": text, | ||
| "image_data": image_data, | ||
| "sampling_params": { | ||
| "temperature": 0, | ||
| "max_new_tokens": 32, | ||
| "no_stop_trim": True, | ||
| "skip_special_tokens": False, | ||
| }, | ||
| "modalities": ["multi-images"], | ||
| }, | ||
| ).json() | ||
| return response["text"] | ||
|
|
||
| def generate_for_video(self, batch, num_frame) -> Union[str, list[str]]: | ||
| # prepare the video input about Steven introducing ipod nano | ||
| url = "https://raw.githubusercontent.com/evolvinglmms-lab/sglang/dev/onevision_local/assets/jobs.mp4" | ||
| cache_dir = os.path.expanduser("~/.cache") | ||
| file_path = os.path.join(cache_dir, "jobs.mp4") | ||
| os.makedirs(cache_dir, exist_ok=True) | ||
| if not os.path.exists(file_path): | ||
| response = requests.get(url) | ||
| response.raise_for_status() | ||
| with open(file_path, "wb") as f: | ||
| f.write(response.content) | ||
|
|
||
| if not batch: | ||
| assert isinstance(num_frame, int) | ||
| messages = self.prepare_video_messages(file_path, max_frames_num=num_frame) | ||
| text, image_data = self.get_prompt_from_messages(messages) | ||
| return self.generate(text, image_data) | ||
| else: | ||
| assert isinstance(num_frame, list) | ||
| func_args = [] | ||
| for max_frames_num in num_frame: | ||
| messages = self.prepare_video_messages( | ||
| file_path, | ||
| max_frames_num=max_frames_num, | ||
| ) | ||
| text, image_data = self.get_prompt_from_messages(messages) | ||
| func_args.append((text, image_data)) | ||
|
|
||
| with ThreadPoolExecutor(max_workers=10) as executor: | ||
| responses = list(executor.map(lambda p: self.generate(*p), func_args)) | ||
|
|
||
| return responses | ||
|
|
||
| def run_generate(self, chunked_prefill_size, batch, num_frame): | ||
| # launch server | ||
| model = "lmms-lab/llava-onevision-qwen2-7b-ov" | ||
| # model = "meta-llama/Llama-3.2-11B-Vision-Instruct" | ||
| self.base_url = DEFAULT_URL_FOR_TEST | ||
| process = popen_launch_server( | ||
| model, | ||
| self.base_url, | ||
| timeout=DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH, | ||
| other_args=[ | ||
| "--chunked-prefill-size", | ||
| f"{chunked_prefill_size}", | ||
| ], | ||
| ) | ||
| try: | ||
| return self.generate_for_video(batch, num_frame) | ||
| finally: | ||
| kill_process_tree(process.pid) | ||
|
|
||
| def test_chunked_prefill(self): | ||
| output_chunked = self.run_generate( | ||
| chunked_prefill_size=1024, batch=False, num_frame=1 | ||
| ) | ||
| output_no_chunked = self.run_generate( | ||
| chunked_prefill_size=-1, batch=False, num_frame=1 | ||
| ) | ||
|
|
||
| print("output with chunked prefill:") | ||
| print(output_chunked) | ||
| print("output without chunked prefill:") | ||
| print(output_no_chunked) | ||
| assert output_chunked == output_no_chunked | ||
|
|
||
| output_chunked = self.run_generate( | ||
| chunked_prefill_size=1024, batch=True, num_frame=[2, 6, 8, 10] | ||
| ) | ||
| output_no_chunked = self.run_generate( | ||
| chunked_prefill_size=-1, batch=True, num_frame=[2, 6, 8, 10] | ||
| ) | ||
|
|
||
| print("output with chunked prefill:") | ||
| print(output_chunked) | ||
| print("output without chunked prefill:") | ||
| print(output_no_chunked) | ||
| assert output_chunked == output_no_chunked | ||
|
|
||
|
|
||
| if __name__ == "__main__": | ||
| unittest.main() |