MiniGPT-4/minigpt4/conversation/response.py
2023-08-19 17:49:51 +08:00

107 lines
4.5 KiB
Python

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 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
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 answer(self, embs, 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):
# embs = self.get_context_emb(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:]
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()
return output_text, output_token.cpu().numpy()
def upload_img(self, image):
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)
return image_emb
def get_context_emb(self, text_list, img_list):
system = "Give the following image: <Img>ImageContent</Img>. You will be able to see the image once I provide it to you. Please answer my questions." + "###"
prompt = "Human" + ": " + "<Img><ImageHere></Img> " + text_list + "###"
prompt = system + prompt
prompt_segs = prompt.split('<ImageHere>')
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]
# [1, 42, 4096]
# [1, 13, 4096]
# print(seg_embs[:-1].shape)
# print(seg_embs[-1].shape)
mixed_embs = torch.cat([seg_embs[0], img_list, seg_embs[1]], dim=1)
# mixed_embs = torch.cat(mixed_embs, dim=1)
return mixed_embs