bugfix
This commit is contained in:
parent
8d812bf42f
commit
789bc750a2
@ -1,23 +1,61 @@
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import threading
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import torch
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from time import time
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from transformers import TextIteratorStreamer
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from appPublic.log import debug
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class BaseChatLLM:
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async def get_session_key(self):
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def get_session_key(self):
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return self.model_id + ':messages'
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async def get_session_messages(self, request):
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f = get_serverenv('get_session')
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session = await f(request)
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def _get_session_messages(self, session):
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key = self.get_session_key()
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messages = session.get(key) or []
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return messages
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async def set_session_messages(self, request, messages):
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f = get_serverenv('get_session')
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session = await f(request)
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def _set_session_messages(self, session, messages):
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key = self.get_session_key()
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session[key] = messages
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def _build_assistant_message(self, prompt):
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return {
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"role":"assistant",
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"content":[{"type": "text", "text": prompt}]
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}
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def _build_sys_message(self, prompt):
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return {
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"role":"system",
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"content":[{"type": "text", "text": prompt}]
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}
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def _build_user_message(self, prompt, image_path=None,
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video_path=None, audio_path=None):
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contents = [
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{
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"type":"text", "text": prompt
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}
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]
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if image_path:
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contents.append({
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"type": "image",
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"image": image_path
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})
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if video_path:
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contents.append({
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"type": "video",
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"video":video_path
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})
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if audio_path:
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contents.append({
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"tyoe": "audio",
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"audio": audio_path
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})
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return {
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"role": "user",
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"content": contents
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}
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def get_streamer(self):
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return TextIteratorStreamer(
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tokenizer=self.tokenizer,
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@ -30,22 +68,121 @@ class BaseChatLLM:
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t1 = time()
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i = 0
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for txt in streamer:
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if txt == '':
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continue
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if i == 0:
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t2 = time()
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i += 1
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all_txt += txt
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yield {
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'done': False,
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'text': txt
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}
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t3 = time()
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unk = self.tokenizer(all_txt, return_tensors="pt")
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print(f'{unk=};')
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t = all_txt
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unk = self.tokenizer(t, return_tensors="pt")
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output_tokens = len(unk["input_ids"][0])
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yield {
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d = {
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'done': True,
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'text': '',
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'text': all_txt,
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'response_time': t2 - t1,
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'finish_time': t3 - t1,
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'output_token': output_tokens
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}
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# debug(f'{all_txt=}, {d=}')
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yield d
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def _generator(self, session, prompt,
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image_path=None,
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video_path=None,
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audio_path=None,
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sys_prompt=None):
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messages = self._get_session_messages(session)
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if sys_prompt:
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messages.append(self._build_sys_message(sys_prompt))
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messages.append(self._build_user_message(prompt, image_path=image_path))
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# debug(f'{messages=}')
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for d in self._gen(messages):
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if d['done']:
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# debug(f'++++++++++++++{d=}')
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messages.append(self._build_assistant_message(d['text']))
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yield d
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self._set_session_messages(session, messages)
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def generate(self, session, prompt,
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image_path=None,
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video_path=None,
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audio_path=None,
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sys_prompt=None):
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for d in self._generator(session, prompt,
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image_path=image_path,
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video_path=video_path,
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audio_path=audio_path,
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sys_prompt=sys_prompt):
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if d['done']:
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return d
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def stream_generate(self, session, prompt,
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image_path=None,
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video_path=None,
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audio_path=None,
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sys_prompt=None):
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for d in self._generator(session, prompt,
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image_path=image_path,
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video_path=video_path,
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audio_path=audio_path,
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sys_prompt=sys_prompt):
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yield d
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async def async_generate(self, session, prompt,
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image_path=None,
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video_path=None,
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audio_path=None,
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sys_prompt=None):
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return self.generate(session, prompt,
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image_path=image_path,
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video_path=video_path,
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audio_path=audio_path,
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sys_prompt=sys_prompt)
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async def async_stream_generate(self, session, prompt,
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image_path=None,
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video_path=None,
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audio_path=None,
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sys_prompt=None):
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for d in self._generator(session, prompt,
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image_path=image_path,
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video_path=video_path,
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audio_path=audio_path,
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sys_prompt=sys_prompt):
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yield d
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def build_kwargs(self, inputs, streamer):
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generate_kwargs = dict(
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**inputs,
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streamer=streamer,
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max_new_tokens=512,
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do_sample=True,
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eos_token_id=self.tokenizer.eos_token_id
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)
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return generate_kwargs
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def _messages2inputs(self, messages):
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return self.processor.apply_chat_template(
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messages, add_generation_prompt=True,
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tokenize=True,
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return_dict=True, return_tensors="pt"
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).to(self.model.device, dtype=torch.bfloat16)
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def _gen(self, messages):
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inputs = self._messages2inputs(messages)
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input_len = inputs["input_ids"].shape[-1]
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streamer = self.get_streamer()
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kwargs = self.build_kwargs(inputs, streamer)
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thread = threading.Thread(target=self.model.generate,
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kwargs=kwargs)
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thread.start()
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for d in self.output_generator(streamer):
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if d['done']:
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d['input_tokens'] = input_len
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# debug(f'{d=}\n')
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yield d
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@ -21,105 +21,9 @@ class Gemma3LLM(BaseChatLLM):
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self.messages = []
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self.model_id = model_id
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def _build_assistant_message(self, prompt):
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return {
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"role":"assistant",
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"content":[{"type": "text", "text": prompt}]
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}
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def _build_sys_message(self, prompt):
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return {
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"role":"system",
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"content":[{"type": "text", "text": prompt}]
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}
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def _build_user_message(self, prompt, image_path=None):
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contents = [
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{
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"type":"text", "text": prompt
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}
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]
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if image_path:
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contents.append({
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"type": "image",
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"image": image_path
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})
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return {
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"role": "user",
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"content": contents
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}
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def _gen(self, messages):
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t1 = time()
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inputs = self.processor.apply_chat_template(
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messages, add_generation_prompt=True,
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tokenize=True,
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return_dict=True, return_tensors="pt"
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).to(self.model.device, dtype=torch.bfloat16)
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input_len = inputs["input_ids"].shape[-1]
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streamer = self.get_streamer()
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generate_kwargs = dict(
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**inputs,
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streamer=streamer,
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max_new_tokens=512,
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do_sample=True,
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eos_token_id=self.tokenizer.eos_token_id
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)
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thread = threading.Thread(target=self.model.generate,
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kwargs=generate_kwargs)
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thread.start()
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for d in self.output_generator(streamer):
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if d['done']:
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d['input_tokens'] = input_len
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yield d
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async def generate(self, request, prompt,
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image_path=None,
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sys_prompt=None):
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messages = self.get_session_messages(request)
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if sys_prompt and len(messages) == 0:
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messages.append(self._build_sys_message(sys_prompt))
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messages.append(self._build_user_message(prompt, image_path=image_path))
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all_txt = ''
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for d in self._gen(messages):
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all_txt += d['text']
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d['text'] = all_txt
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messages.append(self._build_assistant_message(all_txt))
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self.set_session_message(request, messages)
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return d
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async def strem_generate(self, request, prompt,
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image_path=None,
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sys_prompt=None):
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messages = self.get_session_messages(request)
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if sys_prompt and len(messages) == 0:
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messages.append(self._build_sys_message(sys_prompt))
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messages.append(self._build_user_message(prompt, image_path=image_path))
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all_txt = ''
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for d in self._gen(messages):
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yield d
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all_txt += d['text']
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data = await f(messages)
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messages.append(self._build_assistant_message(all_txt))
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self.set_session_messages(request, messages)
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def _generate(self, prompt, image_path=None, sys_prompt=None):
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messages = self.messages
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if sys_prompt and len(messages) == 0:
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messages.append(self._build_sys_message(sys_prompt))
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messages.append(self._build_user_message(prompt, image_path=image_path))
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all_txt = ''
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ld = None
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for d in self._gen(messages):
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all_txt += d['text']
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ld = d
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ld['text'] = all_txt
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messages.append(self._build_assistant_message(all_txt))
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return ld
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if __name__ == '__main__':
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gemma3 = Gemma3LLM('/share/models/google/gemma-3-4b-it')
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session = {}
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while True:
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print('input prompt')
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p = input()
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@ -128,6 +32,11 @@ if __name__ == '__main__':
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break;
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print('input image path')
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imgpath=input()
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t = gemma3._generate(p, image_path=imgpath)
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print(t)
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for d in gemma3.stream_generate(session, p, image_path=imgpath):
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if not d['done']:
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print(d['text'], end='', flush=True)
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else:
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x = {k:v for k,v in d.items() if k != 'text'}
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print(f'\n{x}\n')
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@ -4,11 +4,11 @@ from transformers import AutoProcessor, AutoModelForImageTextToText
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from PIL import Image
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import requests
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import torch
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from llmengine.base_chat_llm import BaseChatLLM
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model_id = "google/medgemma-4b-it"
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class MedgemmaLLM:
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class MedgemmaLLM(BaseChatLLM):
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def __init__(self, model_id):
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self.model = AutoModelForImageTextToText.from_pretrained(
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model_id,
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@ -16,37 +16,36 @@ class MedgemmaLLM:
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device_map="auto",
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)
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self.processor = AutoProcessor.from_pretrained(model_id)
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self.tokenizer = self.processor.tokenizer
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self.model_id = model_id
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# Image attribution: Stillwaterising, CC0, via Wikimedia Commons
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image_url = "https://upload.wikimedia.org/wikipedia/commons/c/c8/Chest_Xray_PA_3-8-2010.png"
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image = Image.open(requests.get(image_url, headers={"User-Agent": "example"}, stream=True).raw)
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def _messages2inputs(self, messages):
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inputs = self.processor.apply_chat_template(
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messages,
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add_generation_prompt=True,
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tokenize=True,
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return_dict=True,
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return_tensors="pt"
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).to(self.model.device, dtype=torch.bfloat16)
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return inputs
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messages = [
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{
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"role": "system",
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"content": [{"type": "text", "text": "You are an expert radiologist."}]
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},
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{
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"role": "user",
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"content": [
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{"type": "text", "text": "Describe this X-ray"},
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{"type": "image", "image": image}
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]
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}
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]
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if __name__ == '__main__':
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med = MedgemmaLLM('/share/models/google/medgemma-4b-it')
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session = {}
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while True:
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print(f'chat with {med.model_id}')
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print('input prompt')
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p = input()
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if p:
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if p == 'q':
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break;
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print('input image path')
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imgpath=input()
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for d in med.stream_generate(session, p, image_path=imgpath):
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if not d['done']:
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print(d['text'], end='', flush=True)
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else:
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x = {k:v for k,v in d.items() if k != 'text'}
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print(f'\n{x}\n')
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inputs = processor.apply_chat_template(
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messages, add_generation_prompt=True, tokenize=True,
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return_dict=True, return_tensors="pt"
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).to(model.device, dtype=torch.bfloat16)
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input_len = inputs["input_ids"].shape[-1]
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with torch.inference_mode():
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generation = model.generate(**inputs, max_new_tokens=200, do_sample=False)
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generation = generation[0][input_len:]
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decoded = processor.decode(generation, skip_special_tokens=True)
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print(decoded)
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75
llmengine/qwen3.py
Normal file
75
llmengine/qwen3.py
Normal file
@ -0,0 +1,75 @@
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#!/share/vllm-0.8.5/bin/python
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# pip install accelerate
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from appPublic.worker import awaitify
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from ahserver.serverenv import get_serverenv
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from transformers import AutoModelForCausalLM, AutoTokenizer
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from PIL import Image
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import torch
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from llmengine.base_chat_llm import BaseChatLLM
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class Qwen3LLM(BaseChatLLM):
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def __init__(self, model_id):
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self.tokenizer = AutoTokenizer.from_pretrained(model_id)
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self.model = AutoModelForCausalLM.from_pretrained(
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model_id,
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torch_dtype="auto",
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device_map="auto"
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)
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self.model_id = model_id
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def _build_assistant_message(self, prompt):
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return {
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"role":"assistant",
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"content":prompt
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}
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def _build_sys_message(self, prompt):
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return {
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"role":"system",
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"content": prompt
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}
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def _build_user_message(self, prompt, **kw):
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return {
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"role":"user",
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"content": prompt
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}
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def build_kwargs(self, inputs, streamer):
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generate_kwargs = dict(
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**inputs,
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streamer=streamer,
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max_new_tokens=32768,
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do_sample=True,
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eos_token_id=self.tokenizer.eos_token_id
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)
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return generate_kwargs
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def _messages2inputs(self, messages):
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text = self.tokenizer.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True,
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enable_thinking=True
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)
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return self.tokenizer([text], return_tensors="pt").to(self.model.device)
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if __name__ == '__main__':
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q3 = Qwen3LLM('/share/models/Qwen/Qwen3-32B')
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session = {}
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while True:
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print('input prompt')
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p = input()
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if p:
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if p == 'q':
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break;
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for d in q3.stream_generate(session, p):
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if not d['done']:
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print(d['text'], end='', flush=True)
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else:
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x = {k:v for k,v in d.items() if k != 'text'}
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print(f'\n{x}\n')
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0
test/ds-r1-8b
Executable file
0
test/ds-r1-8b
Executable file
119
test/gemma-3-4b-it
Executable file
119
test/gemma-3-4b-it
Executable file
@ -0,0 +1,119 @@
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#!/share/vllm-0.8.5/bin/python
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# pip install accelerate
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import torch
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lfrom time import time
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from appPublic.worker import awaitify
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from ahserver.serverenv import get_serverenv
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from transformers import AutoProcessor, Gemma3ForConditionalGeneration
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from PIL import Image
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import requests
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import torch
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class Gemma3LLM:
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def __init__(self, model_id):
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self.model = Gemma3ForConditionalGeneration.from_pretrained(
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model_id, device_map="auto"
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).eval()
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self.processor = AutoProcessor.from_pretrained(model_id)
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self.messages = []
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self.model_id = model_id
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|
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async def get_session_key(self):
|
||||
return self.model_id + ':messages'
|
||||
|
||||
async def get_session_messages(self, request):
|
||||
f = get_serverenv('get_session')
|
||||
session = await f(request)
|
||||
key = self.get_session_key()
|
||||
messages = session.get(key) or []
|
||||
return messages
|
||||
|
||||
async def set_session_messages(self, request, messages):
|
||||
f = get_serverenv('get_session')
|
||||
session = await f(request)
|
||||
key = self.get_session_key()
|
||||
await session[key] = messages
|
||||
|
||||
def _generate(self, request, prompt, image_path=None, sys_prompt=None):
|
||||
if sys_prompt:
|
||||
sys_message = self._build_sys_message(sys_prompt)
|
||||
self.messages.append(sys_message)
|
||||
user_message = self._build_user_message(prompt, image_path=image_path)
|
||||
self.messages.append(user_message)
|
||||
data = self._gen(self.messages)
|
||||
self.messages.append(self._build_assistant_message(data['text']))
|
||||
|
||||
def _build_assistant_message(self, prompt):
|
||||
return {
|
||||
"role":"assistant",
|
||||
"content":[{"type": "text", "text": prompt}]
|
||||
}
|
||||
|
||||
def _build_sys_message(self, prompt):
|
||||
return {
|
||||
"role":"system",
|
||||
"content":[{"type": "text", "text": prompt}]
|
||||
}
|
||||
|
||||
def _build_user_message(self, prompt, image_path=None):
|
||||
contents = [
|
||||
{
|
||||
"type":"text", "text": prompt
|
||||
}
|
||||
]
|
||||
if image_path:
|
||||
contents.append({
|
||||
"type": "image",
|
||||
"image": image_path
|
||||
})
|
||||
|
||||
return {
|
||||
"role": "user",
|
||||
"content": contents
|
||||
}
|
||||
|
||||
def _gen(self, messages):
|
||||
t1 = time()
|
||||
inputs = self.processor.apply_chat_template(
|
||||
messages, add_generation_prompt=True,
|
||||
tokenize=True,
|
||||
return_dict=True, return_tensors="pt"
|
||||
).to(self.model.device, dtype=torch.bfloat16)
|
||||
input_len = inputs["input_ids"].shape[-1]
|
||||
with torch.inference_mode():
|
||||
generation = self.model.generate(**inputs, max_new_tokens=1000, do_sample=True)
|
||||
generation = generation[0][input_len:]
|
||||
decoded = self.processor.decode(generation, skip_special_tokens=True)
|
||||
t2 = time()
|
||||
return {
|
||||
"role": "assistant",
|
||||
"input_tokens": input_len,
|
||||
"output_token": len(generation),
|
||||
"timecost": t2 - t1,
|
||||
"text": decoded
|
||||
}
|
||||
|
||||
async def generate(self, request, prompt, image_path=None, sys_prompt=None):
|
||||
messages = self.get_session_messages(request)
|
||||
if sys_prompt and len(messages) == 0:
|
||||
messages.append(self._build_sys_message(sys_prompt))
|
||||
messages.append(self._build_user_message(prompt, image_path=image_path))
|
||||
f = awaitify(self._gen)
|
||||
data = await f(messages)
|
||||
messages.append(self._build_assistant_message(data['text']))
|
||||
self.set_session_message(request, messages)
|
||||
|
||||
if __name__ == '__main__':
|
||||
gemma3 = Gemma3LLM('/share/models/google/gemma-3-4b-it')
|
||||
while True:
|
||||
print('input prompt')
|
||||
p = input()
|
||||
if p:
|
||||
if p == 'q':
|
||||
break;
|
||||
print('input image path')
|
||||
imgpath=input()
|
||||
t = gemma3._generate(p, image_path=imgpath)
|
||||
print(t)
|
||||
|
3
test/gemma3.sh
Executable file
3
test/gemma3.sh
Executable file
@ -0,0 +1,3 @@
|
||||
#!/usr/bin/bash
|
||||
|
||||
CUDA_VISIBLE_DEVICES=1 /share/vllm-0.8.5/bin/python -m llmengine.gemma3_it
|
3
test/medgemma3.sh
Executable file
3
test/medgemma3.sh
Executable file
@ -0,0 +1,3 @@
|
||||
#!/usr/bin/bash
|
||||
|
||||
CUDA_VISIBLE_DEVICES=0 /share/vllm-0.8.5/bin/python -m llmengine.medgemma3_it
|
3
test/qwen3.sh
Executable file
3
test/qwen3.sh
Executable file
@ -0,0 +1,3 @@
|
||||
#!/usr/bin/bash
|
||||
|
||||
CUDA_VISIBLE_DEVICES=2,3,4,5,6,7 /share/vllm-0.8.5/bin/python -m llmengine.qwen3
|
Loading…
Reference in New Issue
Block a user