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import argparse
import time
from PIL import Image
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, LlamaTokenizer
from transformers import StoppingCriteria, StoppingCriteriaList
import dataclasses
from enum import auto, Enum
from typing import List, Tuple, Any
from medomni.common.registry import registry
import ipdb
class SeparatorStyle(Enum):
"""Different separator style."""
SINGLE = auto()
TWO = auto()
@dataclasses.dataclass
class Conversation:
"""A class that keeps all conversation history."""
system: str
roles: List[str]
messages: List[List[str]]
offset: int
# system_img: List[Image.Image] = []
sep_style: SeparatorStyle = SeparatorStyle.SINGLE
sep: str = "###"
sep2: str = None
skip_next: bool = False
conv_id: Any = None
def get_prompt(self):
if self.sep_style == SeparatorStyle.SINGLE:
ret = self.system + self.sep
for role, message in self.messages:
if message:
ret += role + ": " + message + self.sep
else:
ret += role + ":"
return ret
elif self.sep_style == SeparatorStyle.TWO:
seps = [self.sep, self.sep2]
ret = self.system + seps[0]
for i, (role, message) in enumerate(self.messages):
if message:
ret += role + ": " + message + seps[i % 2]
else:
ret += role + ":"
return ret
else:
raise ValueError(f"Invalid style: {self.sep_style}")
def append_message(self, role, message):
self.messages.append([role, message])
def to_gradio_chatbot(self):
ret = []
for i, (role, msg) in enumerate(self.messages[self.offset:]):
if i % 2 == 0:
ret.append([msg, None])
else:
ret[-1][-1] = msg
return ret
def copy(self):
return Conversation(
system=self.system,
roles=self.roles,
messages=[[x, y] for x, y in self.messages],
offset=self.offset,
sep_style=self.sep_style,
sep=self.sep,
sep2=self.sep2,
conv_id=self.conv_id)
def dict(self):
return {
"system": self.system,
# "system_img": self.system_img,
"roles": self.roles,
"messages": self.messages,
"offset": self.offset,
"sep": self.sep,
"sep2": self.sep2,
"conv_id": self.conv_id,
}
class StoppingCriteriaSub(StoppingCriteria):
def __init__(self, stops=[], encounters=1):
super().__init__()
self.stops = stops
def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor):
for stop in self.stops:
if torch.all((stop == input_ids[0][-len(stop):])).item():
return True
return False
CONV_VISION = Conversation(
system="Give the following image: <Img>ImageContent</Img>. "
"You will be able to see the image once I provide it to you. Act as a clinician and answer my questions.",
# "You will be able to see the image once I provide it to you. Please answer my questions.",
#system="",
roles=("Human", "Assistant"),
messages=[],
offset=2,
sep_style=SeparatorStyle.SINGLE,
sep="###",
)
class Chat:
def __init__(self, model, vis_processor, device='cuda:0'):
self.device = device
self.model = model
self.vis_processor = vis_processor
stop_words_ids = [torch.tensor([835]).to(self.device),
torch.tensor([2277, 29937]).to(self.device)] # '###' can be encoded in two different ways.
self.stopping_criteria = StoppingCriteriaList([StoppingCriteriaSub(stops=stop_words_ids)])
def ask(self, text, conv):
if len(conv.messages) > 0 and conv.messages[-1][0] == conv.roles[0] \
and conv.messages[-1][1][-6:] == '</Img>': # last message is image.
conv.messages[-1][1] = ' '.join([conv.messages[-1][1], text])
else:
conv.append_message(conv.roles[0], text) # commented by hy on 5.9
def answer(self, conv, img_list, max_new_tokens=300, num_beams=1, min_length=1, top_p=0.9,
repetition_penalty=1.0, length_penalty=1, temperature=1.0, max_length=2000):
conv.append_message(conv.roles[1], None)
embs = self.get_context_emb(conv, img_list)
current_max_len = embs.shape[1] + max_new_tokens
if current_max_len - max_length > 0:
print('Warning: The number of tokens in current conversation exceeds the max length. '
'The model will not see the contexts outside the range.')
begin_idx = max(0, current_max_len - max_length)
embs = embs[:, begin_idx:]
with torch.autocast("cuda"):
outputs = self.model.llama_model.generate(
inputs_embeds=embs,
max_new_tokens=max_new_tokens,
stopping_criteria=self.stopping_criteria,
num_beams=num_beams,
do_sample=True,
min_length=min_length,
top_p=top_p,
repetition_penalty=repetition_penalty,
length_penalty=length_penalty,
temperature=temperature,
)
output_token = outputs[0]
if output_token[0] == 0: # the model might output a unknow token <unk> at the beginning. remove it
output_token = output_token[1:]
if output_token[0] == 1: # some users find that there is a start token <s> at the beginning. remove it
output_token = output_token[1:]
output_text = self.model.llama_tokenizer.decode(output_token, add_special_tokens=False)
output_text = output_text.split('###')[0] # remove the stop sign '###'
output_text = output_text.split('Assistant:')[-1].strip()
conv.messages[-1][1] = output_text # commented by hy on 5.9
#---5.9.2023---
conv.messages = []
conv.append_message(conv.roles[0], "<Img><ImageHere></Img>")
return output_text, output_token.cpu().numpy()
def upload_img(self, image, conv, img_list):
if isinstance(image, str): # is a image path
raw_image = Image.open(image).convert('RGB')
image = self.vis_processor(raw_image).unsqueeze(0).to(self.device)
elif isinstance(image, Image.Image):
raw_image = image
image = self.vis_processor(raw_image).unsqueeze(0).to(self.device)
elif isinstance(image, torch.Tensor):
if len(image.shape) == 3:
image = image.unsqueeze(0)
image = image.to(self.device)
image_emb, _ = self.model.encode_img(image)
img_list.append(image_emb)
conv.append_message(conv.roles[0], "<Img><ImageHere></Img>")
msg = "Received."
return msg
def get_context_emb(self, conv, img_list):
prompt = conv.get_prompt()
prompt_segs = prompt.split('<ImageHere>')
assert len(prompt_segs) == len(img_list) + 1, "Unmatched numbers of image placeholders and images."
#seg_tokens = []
#for i, seg in enumerate(prompt_segs):
# if i == 1:
# prompt_ids = self.model.llama_tokenizer(
# seg,
# return_tensors="pt",
# add_special_tokens=i == 0
# ).to(self.device).input_ids
# seg_tokens.append(prompt_ids)
# else:
# prompt_ids = self.model.llama_tokenizer(seg, return_tensors="pt", add_special_tokens=i == 0).to(self.device).input_ids
# seg_tokens.append(prompt_ids)
seg_tokens = [
self.model.llama_tokenizer(
seg, return_tensors="pt", add_special_tokens=i == 0).to(self.device).input_ids
# only add bos to the first seg
for i, seg in enumerate(prompt_segs)
]
seg_embs = [self.model.llama_model.model.embed_tokens(seg_t) for seg_t in seg_tokens]
# seg_embs = [self.model.llama_model.model.base_model.embed_tokens(seg_t) for seg_t in seg_tokens] # LoRA
mixed_embs = [emb for pair in zip(seg_embs[:-1], img_list) for emb in pair] + [seg_embs[-1]]
mixed_embs = torch.cat(mixed_embs, dim=1)
return mixed_embs
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