update v2 demo

demo.py

@@ -7,10 +7,12 @@ import torch
 import torch.backends.cudnn as cudnn
 import gradio as gr
 
+from transformers import StoppingCriteriaList
+
 from minigpt4.common.config import Config
 from minigpt4.common.dist_utils import get_rank
 from minigpt4.common.registry import registry
-from minigpt4.conversation.conversation import Chat, CONV_VISION_Vicuna0, CONV_VISION_LLama2
+from minigpt4.conversation.conversation import Chat, CONV_VISION_Vicuna0, CONV_VISION_LLama2, StoppingCriteriaSub
 
 # imports modules for registration
 from minigpt4.datasets.builders import *
@@ -66,7 +68,12 @@ CONV_VISION = conv_dict[model_config.model_type]
 
 vis_processor_cfg = cfg.datasets_cfg.cc_sbu_align.vis_processor.train
 vis_processor = registry.get_processor_class(vis_processor_cfg.name).from_config(vis_processor_cfg)
-chat = Chat(model, vis_processor, device='cuda:{}'.format(args.gpu_id))
+
+stop_words_ids = [[835], [2277, 29937]]
+stop_words_ids = [torch.tensor(ids).to(device='cuda:{}'.format(args.gpu_id)) for ids in stop_words_ids]
+stopping_criteria = StoppingCriteriaList([StoppingCriteriaSub(stops=stop_words_ids)])
+
+chat = Chat(model, vis_processor, device='cuda:{}'.format(args.gpu_id), stopping_criteria=stopping_criteria)
 print('Initialization Finished')
 
 
@@ -89,6 +96,7 @@ def upload_img(gr_img, text_input, chat_state):
     chat_state = CONV_VISION.copy()
     img_list = []
     llm_message = chat.upload_img(gr_img, chat_state, img_list)
+    chat.encode_img(img_list)
     return gr.update(interactive=False), gr.update(interactive=True, placeholder='Type and press Enter'), gr.update(value="Start Chatting", interactive=False), chat_state, img_list
 
 
@@ -124,7 +132,7 @@ with gr.Blocks() as demo:
     gr.Markdown(article)
 
     with gr.Row():
-        with gr.Column(scale=0.5):
+        with gr.Column(scale=1):
             image = gr.Image(type="pil")
             upload_button = gr.Button(value="Upload & Start Chat", interactive=True, variant="primary")
             clear = gr.Button("Restart")
@@ -147,7 +155,7 @@ with gr.Blocks() as demo:
                 label="Temperature",
             )
 
-        with gr.Column():
+        with gr.Column(scale=2):
             chat_state = gr.State()
             img_list = gr.State()
             chatbot = gr.Chatbot(label='MiniGPT-4')

demo_v2.py

@@ -1,37 +1,23 @@
 import argparse
 import os
 import random
-import requests
-from io import BytesIO
-from threading import Thread
 from collections import defaultdict
 
 import cv2
-from termcolor import colored
-from textwrap import wrap
-from torchvision.transforms import functional as F
 import re
 
 import numpy as np
 from PIL import Image
 import torch
-import torch.backends.cudnn as cudnn
 import html
 import gradio as gr
-from transformers import TextIteratorStreamer
 
+import torch.backends.cudnn as cudnn
 
-import minigpt4.tasks as tasks
 from minigpt4.common.config import Config
-from minigpt4.common.dist_utils import get_rank, init_distributed_mode
-from minigpt4.common.logger import setup_logger
-from minigpt4.common.optims import (
-    LinearWarmupCosineLRScheduler,
-    LinearWarmupStepLRScheduler,
-)
+
 from minigpt4.common.registry import registry
-from minigpt4.common.utils import now
-from minigpt4.conversation.conversation import Conversation, SeparatorStyle, StoppingCriteriaList, StoppingCriteriaSub
+from minigpt4.conversation.conversation import Conversation, SeparatorStyle, Chat
 
 # imports modules for registration
 from minigpt4.datasets.builders import *
@@ -40,17 +26,22 @@ from minigpt4.processors import *
 from minigpt4.runners import *
 from minigpt4.tasks import *
 
-parser = argparse.ArgumentParser(description="Demo")
-parser.add_argument("--cfg-path", required=True, help="path to configuration file.")
-parser.add_argument(
-    "--options",
-    nargs="+",
-    help="override some settings in the used config, the key-value pair "
-         "in xxx=yyy format will be merged into config file (deprecate), "
-         "change to --cfg-options instead.",
-)
 
-import torch.backends.cudnn as cudnn
+def parse_args():
+    parser = argparse.ArgumentParser(description="Demo")
+    parser.add_argument("--cfg-path", default='eval_configs/minigptv2_eval.yaml',
+                        help="path to configuration file.")
+    parser.add_argument("--gpu-id", type=int, default=0, help="specify the gpu to load the model.")
+    parser.add_argument(
+        "--options",
+        nargs="+",
+        help="override some settings in the used config, the key-value pair "
+             "in xxx=yyy format will be merged into config file (deprecate), "
+             "change to --cfg-options instead.",
+    )
+    args = parser.parse_args()
+    return args
+
 
 random.seed(42)
 np.random.seed(42)
@@ -60,19 +51,18 @@ cudnn.benchmark = False
 cudnn.deterministic = True
 
 print('Initializing Chat')
-cfg = Config(parser.parse_args(['--cfg-path', 'eval_configs/minigpt4_object_detection_448x448_llama2.yaml']))
-cfg.model_cfg.ckpt = "/home/zhud/c2090/minigpt4_ckpt/448_conversation_correct_best_v7_ablation1_v5_v6/20231007035/checkpoint_35.pth"
-cfg.model_cfg.lora_r = 64
-cfg.model_cfg.lora_alpha = 16
+args = parse_args()
+cfg = Config(args)
 
-device = 'cuda'
+device = 'cuda:{}'.format(args.gpu_id)
 
 model_config = cfg.model_cfg
+model_config.device_8bit = args.gpu_id
 model_cls = registry.get_model_class(model_config.arch)
 model = model_cls.from_config(model_config).to(device)
 bounding_box_size = 100
 
-vis_processor_cfg = cfg.datasets_cfg.coco.vis_processor.train
+vis_processor_cfg = cfg.datasets_cfg.cc_sbu_align.vis_processor.train
 vis_processor = registry.get_processor_class(vis_processor_cfg.name).from_config(vis_processor_cfg)
 
 model = model.eval()
@@ -484,6 +474,7 @@ def gradio_answer(chatbot, chat_state, img_list, temperature):
 
 
 def gradio_stream_answer(chatbot, chat_state, img_list, temperature):
+    print('chat state', chat_state)
     if not isinstance(img_list[0], torch.Tensor):
         chat.encode_img(img_list)
     streamer = chat.stream_answer(conv=chat_state,
@@ -498,7 +489,7 @@ def gradio_stream_answer(chatbot, chat_state, img_list, temperature):
         chatbot[-1][1] = output
         yield chatbot, chat_state
     #     print('message: ', chat_state.messages)
-    chat_state.messages[-1][1] = reverse_escape(output) + '</s>'
+    chat_state.messages[-1][1] = '</s>'
     return chatbot, chat_state
 
 
@@ -538,102 +529,6 @@ def gradio_taskselect(idx):
     return prompt_list[idx], instruct_list[idx]
 
 
-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([2]).to(self.device)]
-        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)
-
-    def answer_prepare(self, conv, img_list, max_new_tokens=300, num_beams=1, min_length=1, top_p=0.9,
-                       repetition_penalty=1.05, 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:]
-
-        generation_kwargs = dict(
-            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,
-        )
-        return generation_kwargs
-
-    def answer(self, conv, img_list, **kargs):
-        generation_kwargs = self.answer_prepare(conv, img_list, **kargs)
-
-        output_token = self.model.llama_model.generate(**generation_dict)[0]
-        output_text = self.model.llama_tokenizer.decode(output_token, skip_special_tokens=True)
-        conv.messages[-1][1] = output_text
-        return output_text, output_token.cpu().numpy()
-
-    def stream_answer(self, conv, img_list, **kargs):
-        generation_kwargs = self.answer_prepare(conv, img_list, **kargs)
-        streamer = TextIteratorStreamer(self.model.llama_tokenizer, skip_special_tokens=True)
-        generation_kwargs['streamer'] = streamer
-        thread = Thread(target=self.model.llama_model.generate, kwargs=generation_kwargs)
-        thread.start()
-        return streamer
-
-    def encode_img(self, img_list):
-        image = img_list[0]
-        img_list.pop(0)
-        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)
-
-    def upload_img(self, image, conv, img_list):
-        conv.append_message(conv.roles[0], "<Img><ImageHere></Img>")
-        img_list.append(image)
-        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 = [
-            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.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
-
 
 
 chat = Chat(model, vis_processor, device=device)

environment.yml

@@ -7,12 +7,12 @@ dependencies:
   - python=3.9
   - cudatoolkit
   - pip
-  - pytorch=1.12.1
+  - pytorch=2.0.0
   - pytorch-mutex=1.0=cuda
   - torchaudio=0.12.1
   - torchvision=0.13.1
   - pip:
-    - accelerate==0.16.0
+    - accelerate==0.20.3
     - aiohttp==3.8.4
     - aiosignal==1.3.1
     - async-timeout==4.0.2
@@ -25,7 +25,7 @@ dependencies:
     - filelock==3.9.0
     - fonttools==4.38.0
     - frozenlist==1.3.3
-    - huggingface-hub==0.13.4
+    - huggingface-hub==0.18.0
     - importlib-resources==5.12.0
     - kiwisolver==1.4.4
     - matplotlib==3.7.0
@@ -40,7 +40,7 @@ dependencies:
     - regex==2022.10.31
     - tokenizers==0.13.2
     - tqdm==4.64.1
-    - transformers==4.28.0
+    - transformers==4.32.0
     - timm==0.6.13
     - spacy==3.5.1
     - webdataset==0.2.48
@@ -53,11 +53,10 @@ dependencies:
     - iopath==0.1.10
     - decord==0.6.0
     - tenacity==8.2.2
-    - peft
+    - peft==0.2.0
     - pycocoevalcap
     - sentence-transformers
     - umap-learn
     - notebook
-    - gradio==3.24.1
-    - gradio-client==0.0.8
+    - gradio==3.47.1
     - wandb

minigpt4/conversation/conversation.py

@@ -1,10 +1,11 @@
 import argparse
 import time
+from threading import Thread
 from PIL import Image
 
 import torch
 from transformers import AutoTokenizer, AutoModelForCausalLM, LlamaTokenizer
-from transformers import StoppingCriteria, StoppingCriteriaList
+from transformers import StoppingCriteria, StoppingCriteriaList, TextIteratorStreamer
 
 import dataclasses
 from enum import auto, Enum
@@ -129,13 +130,16 @@ CONV_VISION_LLama2 = Conversation(
 
 
 class Chat:
-    def __init__(self, model, vis_processor, device='cuda:0'):
+    def __init__(self, model, vis_processor, device='cuda:0', stopping_criteria=None):
         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)])
+
+        if stopping_criteria is not None:
+            self.stopping_criteria = stopping_criteria
+        else:
+            stop_words_ids = [torch.tensor([2]).to(self.device)]
+            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] \
@@ -144,8 +148,8 @@ class Chat:
         else:
             conv.append_message(conv.roles[0], text)
 
-    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):
+    def answer_prepare(self, conv, img_list, max_new_tokens=300, num_beams=1, min_length=1, top_p=0.9,
+                       repetition_penalty=1.05, length_penalty=1, temperature=1.0, max_length=2000):
         conv.append_message(conv.roles[1], None)
         embs = self.get_context_emb(conv, img_list)
 
@@ -154,10 +158,9 @@ class Chat:
             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(
+        generation_kwargs = dict(
             inputs_embeds=embs,
             max_new_tokens=max_new_tokens,
             stopping_criteria=self.stopping_criteria,
@@ -169,18 +172,31 @@ class Chat:
             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)
+        return generation_kwargs
+
+    def answer(self, conv, img_list, **kargs):
+        generation_dict = self.answer_prepare(conv, img_list, **kargs)
+
+        output_token = self.model.llama_model.generate(**generation_dict)[0]
+        output_text = self.model.llama_tokenizer.decode(output_token, skip_special_tokens=True)
+
         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):
+    def stream_answer(self, conv, img_list, **kargs):
+        generation_kwargs = self.answer_prepare(conv, img_list, **kargs)
+        streamer = TextIteratorStreamer(self.model.llama_tokenizer, skip_special_tokens=True)
+        generation_kwargs['streamer'] = streamer
+        thread = Thread(target=self.model.llama_model.generate, kwargs=generation_kwargs)
+        thread.start()
+        return streamer
+
+    def encode_img(self, img_list):
+        image = img_list[0]
+        img_list.pop(0)
         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)
@@ -194,9 +210,12 @@ class Chat:
 
         image_emb, _ = self.model.encode_img(image)
         img_list.append(image_emb)
+
+    def upload_img(self, image, conv, img_list):
         conv.append_message(conv.roles[0], "<Img><ImageHere></Img>")
+        img_list.append(image)
         msg = "Received."
-        # self.conv.append_message(self.conv.roles[1], msg)
+
         return msg
 
     def get_context_emb(self, conv, img_list):
@@ -209,7 +228,9 @@ class Chat:
             # 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]
+        print('debug device: ', self.device)
+        print('debug model device: ', self.model.device)
+        seg_embs = [self.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

minigpt4/models/base_model.py

@@ -169,7 +169,7 @@ class BaseModel(nn.Module):
         return visual_encoder, ln_vision
 
     def init_llm(cls, llama_model_path, low_resource=False, low_res_device=0, lora_r=0,
-                 **lora_kargs):
+                 lora_target_modules=["q_proj","v_proj"], **lora_kargs):
         logging.info('Loading LLAMA')
         llama_tokenizer = LlamaTokenizer.from_pretrained(llama_model_path, use_fast=False)
         llama_tokenizer.pad_token = "$$"
@@ -193,6 +193,7 @@ class BaseModel(nn.Module):
                 r=lora_r,
                 bias="none",
                 task_type="CAUSAL_LM",
+                target_modules=lora_target_modules,
                 **lora_kargs
             )
             llama_model = get_peft_model(llama_model, loraconfig)