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 minigpt4.common.registry import registry 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, # system_img=self.system_img, 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: ImageContent. " "You will be able to see the image once I provide it to you. Please answer my questions.", 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:] == '': # last message is image. conv.messages[-1][1] = ' '.join([conv.messages[-1][1], text]) else: conv.append_message(conv.roles[0], text) def answer(self, conv, img_list, max_new_tokens=200, num_beams=5, min_length=1, top_p=0.9, repetition_penalty=1.0, length_penalty=1, temperature=1, 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 + 100 # begin_idx = max(0, current_max_len - max_length) # embs = embs[:, begin_idx:] outputs = self.model.llama_model.generate( inputs_embeds=embs, max_new_tokens=max_new_tokens, stopping_criteria=self.stopping_criteria, num_beams=num_beams, 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: 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 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], "") msg = "Received." # self.conv.append_message(self.conv.roles[1], msg) return msg def get_context_emb(self, conv, img_list): prompt = conv.get_prompt() prompt_segs = prompt.split('') assert len(prompt_segs) == len(img_list) + 1, "Unmatched numbers of image placeholders and images." 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] 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