This commit is contained in:
yumoqing 2025-06-05 15:15:29 +00:00
parent b8104f5ca1
commit 8d812bf42f
5 changed files with 238 additions and 2 deletions

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@ -0,0 +1,51 @@
from time import time
from transformers import TextIteratorStreamer
class BaseChatLLM:
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()
session[key] = messages
def get_streamer(self):
return TextIteratorStreamer(
tokenizer=self.tokenizer,
skip_special_tokens=True,
skip_prompt=True
)
def output_generator(self, streamer):
all_txt = ''
t1 = time()
i = 0
for txt in streamer:
if i == 0:
t2 = time()
i += 1
yield {
'done': False,
'text': txt
}
t3 = time()
unk = self.tokenizer(all_txt, return_tensors="pt")
print(f'{unk=};')
output_tokens = len(unk["input_ids"][0])
yield {
'done': True,
'text': '',
'response_time': t2 - t1,
'finish_time': t3 - t1,
'output_token': output_tokens
}

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@ -106,7 +106,7 @@ class TransformersChatEngine:
if not self.output_json:
return text
input_tokens = inputs["input_ids"].shape[1]
outputi_ids.sequences.shape[1] - input_tokens
output_tokens = len(self.tokenizer(text, return_tensors="pt")["input_ids"][0])
return {
'content':text,
'input_tokens': input_tokens,

133
llmengine/gemma3_it.py Normal file
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#!/share/vllm-0.8.5/bin/python
# pip install accelerate
import threading
from time import time
from appPublic.worker import awaitify
from ahserver.serverenv import get_serverenv
from transformers import AutoProcessor, Gemma3ForConditionalGeneration, TextIteratorStreamer
from PIL import Image
import requests
import torch
from llmengine.base_chat_llm import BaseChatLLM
class Gemma3LLM(BaseChatLLM):
def __init__(self, model_id):
self.model = Gemma3ForConditionalGeneration.from_pretrained(
model_id, device_map="auto"
).eval()
self.processor = AutoProcessor.from_pretrained(model_id)
self.tokenizer = self.processor.tokenizer
self.messages = []
self.model_id = model_id
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]
streamer = self.get_streamer()
generate_kwargs = dict(
**inputs,
streamer=streamer,
max_new_tokens=512,
do_sample=True,
eos_token_id=self.tokenizer.eos_token_id
)
thread = threading.Thread(target=self.model.generate,
kwargs=generate_kwargs)
thread.start()
for d in self.output_generator(streamer):
if d['done']:
d['input_tokens'] = input_len
yield d
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))
all_txt = ''
for d in self._gen(messages):
all_txt += d['text']
d['text'] = all_txt
messages.append(self._build_assistant_message(all_txt))
self.set_session_message(request, messages)
return d
async def strem_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))
all_txt = ''
for d in self._gen(messages):
yield d
all_txt += d['text']
data = await f(messages)
messages.append(self._build_assistant_message(all_txt))
self.set_session_messages(request, messages)
def _generate(self, prompt, image_path=None, sys_prompt=None):
messages = self.messages
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))
all_txt = ''
ld = None
for d in self._gen(messages):
all_txt += d['text']
ld = d
ld['text'] = all_txt
messages.append(self._build_assistant_message(all_txt))
return ld
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)

52
llmengine/medgemma3_it.py Normal file
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# pip install accelerate
import time
from transformers import AutoProcessor, AutoModelForImageTextToText
from PIL import Image
import requests
import torch
model_id = "google/medgemma-4b-it"
class MedgemmaLLM:
def __init__(self, model_id):
self.model = AutoModelForImageTextToText.from_pretrained(
model_id,
torch_dtype=torch.bfloat16,
device_map="auto",
)
self.processor = AutoProcessor.from_pretrained(model_id)
self.model_id = model_id
# Image attribution: Stillwaterising, CC0, via Wikimedia Commons
image_url = "https://upload.wikimedia.org/wikipedia/commons/c/c8/Chest_Xray_PA_3-8-2010.png"
image = Image.open(requests.get(image_url, headers={"User-Agent": "example"}, stream=True).raw)
messages = [
{
"role": "system",
"content": [{"type": "text", "text": "You are an expert radiologist."}]
},
{
"role": "user",
"content": [
{"type": "text", "text": "Describe this X-ray"},
{"type": "image", "image": image}
]
}
]
inputs = processor.apply_chat_template(
messages, add_generation_prompt=True, tokenize=True,
return_dict=True, return_tensors="pt"
).to(model.device, dtype=torch.bfloat16)
input_len = inputs["input_ids"].shape[-1]
with torch.inference_mode():
generation = model.generate(**inputs, max_new_tokens=200, do_sample=False)
generation = generation[0][input_len:]
decoded = processor.decode(generation, skip_special_tokens=True)
print(decoded)

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@ -6,7 +6,7 @@ import argparse
def get_args():
parser = argparse.ArgumentParser(description="Example script using argparse")
parser.add_argument('--gpus', '-g', type=str, required=False, default='0', help='Identify GPU id, default is 0, comma split')
parser.add_argument("--stream", action="store_true", help="是否流式输出")
parser.add_argument("--stream", action="store_true", help="是否流式输出", default=True)
parser.add_argument('modelpath', type=str, help='Path to model folder')
args = parser.parse_args()
return args