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import json
from copy import deepcopy
import torch
import base64
from io import BytesIO
from typing import Any, List, Dict
from PIL import Image
from transformers import AutoTokenizer, AutoModel
def chat(
model,
image_list,
msgs_list,
tokenizer,
vision_hidden_states=None,
max_new_tokens=1024,
sampling=True,
max_inp_length=2048,
system_prompt_list=None,
**kwargs
):
copy_msgs_lst = []
images_list = []
tgt_sizes_list = []
for i in range(len(msgs_list)):
msgs = msgs_list[i]
image = image_list[i]
system_prompt = system_prompt_list[i] if system_prompt_list else None
if isinstance(msgs, str):
msgs = json.loads(msgs)
copy_msgs = deepcopy(msgs)
if image is not None and isinstance(copy_msgs[0]['content'], str):
copy_msgs[0]['content'] = [image, copy_msgs[0]['content']]
images = []
tgt_sizes = []
for i, msg in enumerate(copy_msgs):
role = msg["role"]
content = msg["content"]
assert role in ["user", "assistant"]
if i == 0:
assert role == "user", "The role of first msg should be user"
if isinstance(content, str):
content = [content]
cur_msgs = []
for c in content:
if isinstance(c, Image.Image):
image = c
if model.config.slice_mode:
slice_images, image_placeholder = model.get_slice_image_placeholder(
image, tokenizer
)
cur_msgs.append(image_placeholder)
for slice_image in slice_images:
slice_image = model.transform(slice_image)
H, W = slice_image.shape[1:]
images.append(model.reshape_by_patch(slice_image))
tgt_sizes.append(
torch.Tensor([H // model.config.patch_size, W // model.config.patch_size]).type(torch.int32))
else:
images.append(model.transform(image))
cur_msgs.append(
tokenizer.im_start
+ tokenizer.unk_token * model.config.query_num
+ tokenizer.im_end
)
elif isinstance(c, str):
cur_msgs.append(c)
msg['content'] = '\n'.join(cur_msgs)
if tgt_sizes:
tgt_sizes = torch.vstack(tgt_sizes)
if system_prompt:
sys_msg = {'role': 'system', 'content': system_prompt}
copy_msgs = [sys_msg] + copy_msgs
copy_msgs_lst.append(copy_msgs)
images_list.append(images)
tgt_sizes_list.append(tgt_sizes)
input_ids_list = tokenizer.apply_chat_template(copy_msgs_lst, tokenize=True, add_generation_prompt=False)
if sampling:
generation_config = {
"top_p": 0.8,
"top_k": 100,
"temperature": 0.7,
"do_sample": True,
"repetition_penalty": 1.05
}
else:
generation_config = {
"num_beams": 3,
"repetition_penalty": 1.2,
}
generation_config.update(
(k, kwargs[k]) for k in generation_config.keys() & kwargs.keys()
)
with torch.inference_mode():
res, vision_hidden_states = model.generate(
input_id_list=input_ids_list,
max_inp_length=max_inp_length,
img_list=images_list,
tgt_sizes=tgt_sizes_list,
tokenizer=tokenizer,
max_new_tokens=max_new_tokens,
vision_hidden_states=vision_hidden_states,
return_vision_hidden_states=True,
stream=False,
**generation_config
)
return res
class EndpointHandler(): # batch
def __init__(self, path=""):
# Use a pipeline as a high-level helper
model_name = "SwordElucidator/MiniCPM-Llama3-V-2_5-int4"
model = AutoModel.from_pretrained(model_name, trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
model.eval()
self.model = model
self.tokenizer = tokenizer
def __call__(self, data: Any) -> List[List[Dict[str, float]]]:
inputs = data.pop("inputs", data)
image_list = []
msgs_list = []
for input_ in inputs:
image = input_.pop("image", None) # base64 image as bytes
question = input_.pop("question", None)
msgs = input_.pop("msgs", None)
image = Image.open(BytesIO(base64.b64decode(image)))
if not msgs:
msgs = [{'role': 'user', 'content': question}]
image_list.append(image)
msgs_list.append(msgs)
return chat(
self.model,
image_list,
msgs_list,
self.tokenizer,
)
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