now can run on mac m2

.gitignore

@@ -0,0 +1,3 @@
+.DS_Store
+__pycache__/
+model/

README.md

@@ -5,17 +5,14 @@
 
 <a href='https://minigpt-4.github.io'><img src='https://img.shields.io/badge/Project-Page-Green'></a>  <a href='https://arxiv.org/abs/2304.10592'><img src='https://img.shields.io/badge/Paper-Arxiv-red'></a> <a href='https://huggingface.co/spaces/Vision-CAIR/minigpt4'><img src='https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue'></a> <a href='https://huggingface.co/Vision-CAIR/MiniGPT-4'><img src='https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Model-blue'></a> [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1OK4kYsZphwt5DXchKkzMBjYF6jnkqh4R?usp=sharing) [![YouTube](https://badges.aleen42.com/src/youtube.svg)](https://www.youtube.com/watch?v=__tftoxpBAw&feature=youtu.be)
 
-
 ## News
 We now provide a pretrained MiniGPT-4 aligned with Vicuna-7B! The demo GPU memory consumption now can be as low as 12GB.
 
-
 ## Online Demo
 
 Click the image to chat with MiniGPT-4 around your images
 [![demo](figs/online_demo.png)](https://minigpt-4.github.io)
 
-
 ## Examples
   |   |   |
 :-------------------------:|:-------------------------:
@@ -24,8 +21,6 @@ Click the image to chat with MiniGPT-4 around your images
 
 More examples can be found in the [project page](https://minigpt-4.github.io).
 
-
-
 ## Introduction
 - MiniGPT-4 aligns a frozen visual encoder from BLIP-2 with a frozen LLM, Vicuna, using just one projection layer.
 - We train MiniGPT-4 with two stages. The first traditional pretraining stage is trained using roughly 5 million aligned image-text pairs in 10 hours using 4 A100s. After the first stage, Vicuna is able to understand the image. But the generation ability of Vicuna is heavilly impacted.
@@ -33,10 +28,8 @@ More examples can be found in the [project page](https://minigpt-4.github.io).
 - The second finetuning stage is trained on this dataset in a conversation template to significantly improve its generation reliability and overall usability. To our surprise, this stage is computationally efficient and takes only around 7 minutes with a single A100.
 - MiniGPT-4 yields many emerging vision-language capabilities similar to those demonstrated in GPT-4.
 
-
 ![overview](figs/overview.png)
 
-
 ## Getting Started
 ### Installation
 
@@ -51,7 +44,6 @@ conda env create -f environment.yml
 conda activate minigpt4
 ```
 
-
 **2. Prepare the pretrained Vicuna weights**
 
 The current version of MiniGPT-4 is built on the v0 versoin of Vicuna-13B.
@@ -79,12 +71,9 @@ Download the pretrained checkpoints according to the Vicuna model you prepare.
 :------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------:
  [Downlad](https://drive.google.com/file/d/1a4zLvaiDBr-36pasffmgpvH5P7CKmpze/view?usp=share_link) | [Download](https://drive.google.com/file/d/1RY9jV0dyqLX-o38LrumkKRh6Jtaop58R/view?usp=sharing)
 
-
 Then, set the path to the pretrained checkpoint in the evaluation config file
 in [eval_configs/minigpt4_eval.yaml](eval_configs/minigpt4_eval.yaml#L10) at Line 11.
 
-
-
 ### Launching Demo Locally
 
 Try out our demo [demo.py](demo.py) on your local machine by running
@@ -101,7 +90,6 @@ in 16 bit by setting low_resource to False in the config file
 
 Thanks [@WangRongsheng](https://github.com/WangRongsheng), you can also run our code on [Colab](https://colab.research.google.com/drive/1OK4kYsZphwt5DXchKkzMBjYF6jnkqh4R?usp=sharing)
 
-
 ### Training
 The training of MiniGPT-4 contains two alignment stages.
 
@@ -124,7 +112,6 @@ A MiniGPT-4 checkpoint with only stage one training can be downloaded
 [here (13B)](https://drive.google.com/file/d/1u9FRRBB3VovP1HxCAlpD9Lw4t4P6-Yq8/view?usp=share_link) or [here (7B)](https://drive.google.com/file/d/1HihQtCEXUyBM1i9DQbaK934wW3TZi-h5/view?usp=share_link).
 Compared to the model after stage two, this checkpoint generate incomplete and repeated sentences frequently.
 
-
 **2. Second finetuning stage**
 
 In the second stage, we use a small high quality image-text pair dataset created by ourselves
@@ -143,8 +130,11 @@ torchrun --nproc-per-node NUM_GPU train.py --cfg-path train_configs/minigpt4_sta
 
 After the second stage alignment, MiniGPT-4 is able to talk about the image coherently and user-friendly.
 
+## Run on Mac
 
-
+```
+pip install -r requirements.txt
+```
 
 ## Acknowledgement
 
@@ -152,7 +142,6 @@ After the second stage alignment, MiniGPT-4 is able to talk about the image cohe
 + [Lavis](https://github.com/salesforce/LAVIS) This repository is built upon Lavis!
 + [Vicuna](https://github.com/lm-sys/FastChat) The fantastic language ability of Vicuna with only 13B parameters is just amazing. And it is open-source!
 
-
 If you're using MiniGPT-4 in your research or applications, please cite using this BibTeX:
 ```bibtex
 @misc{zhu2022minigpt4,
@@ -163,7 +152,6 @@ If you're using MiniGPT-4 in your research or applications, please cite using th
 }
 ```
 
-
 ## License
 This repository is under [BSD 3-Clause License](LICENSE.md).
 Many codes are based on [Lavis](https://github.com/salesforce/LAVIS) with

demo.py

@@ -19,6 +19,7 @@ from minigpt4.processors import *
 from minigpt4.runners import *
 from minigpt4.tasks import *
 
+CUDA = torch.cuda.is_available()
 
 def parse_args():
     parser = argparse.ArgumentParser(description="Demo")
@@ -57,11 +58,13 @@ cfg = Config(args)
 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('cuda:{}'.format(args.gpu_id))
+GPU = 'cuda:{}'.format(args.gpu_id) if CUDA else None
+model = model_cls.from_config(model_config).to(GPU)
+model = torch.compile(model)
 
 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))
+chat = Chat(model, vis_processor, device=GPU)
 print('Initialization Finished')
 
 # ========================================

environment.yml

@@ -4,13 +4,13 @@ channels:
   - defaults
   - anaconda
 dependencies:
-  - python=3.9
+  - python=3.10
   - cudatoolkit
   - pip
-  - pytorch=1.12.1
+  - pytorch=2.0.0
   - pytorch-mutex=1.0=cuda
   - torchaudio=0.12.1
-  - torchvision=0.13.1
+  - torchvision=0.15.1
   - pip:
     - accelerate==0.16.0
     - aiohttp==3.8.4

minigpt4/configs/models/minigpt4.yaml

@@ -13,7 +13,7 @@ model:
   num_query_token: 32
 
   # Vicuna
-  llama_model: "/path/to/vicuna/weights/"
+  llama_model: './model/vicuna-13b' # /path/to/vicuna/weights/
 
   # generation configs
   prompt: ""

minigpt4/models/mini_gpt4.py

@@ -10,6 +10,7 @@ from minigpt4.models.blip2 import Blip2Base, disabled_train
 from minigpt4.models.modeling_llama import LlamaForCausalLM
 from transformers import LlamaTokenizer
 
+CUDA = torch.cuda.is_available()
 
 @registry.register_model("mini_gpt4")
 class MiniGPT4(Blip2Base):
@@ -86,17 +87,19 @@ class MiniGPT4(Blip2Base):
         self.llama_tokenizer = LlamaTokenizer.from_pretrained(llama_model, use_fast=False)
         self.llama_tokenizer.pad_token = self.llama_tokenizer.eos_token
 
+        torch_dtype = torch.float16 if CUDA else torch.float32
         if self.low_resource:
             self.llama_model = LlamaForCausalLM.from_pretrained(
                 llama_model,
-                torch_dtype=torch.float16,
-                load_in_8bit=True,
-                device_map={'': device_8bit}
+                torch_dtype=torch_dtype,
+                load_in_8bit=CUDA,
+                offload_folder="model/offload",
+                device_map={'': device_8bit} if CUDA else 'auto'
             )
         else:
             self.llama_model = LlamaForCausalLM.from_pretrained(
                 llama_model,
-                torch_dtype=torch.float16,
+                torch_dtype=torch_dtype,
             )
 
         for name, param in self.llama_model.named_parameters():

requirements.txt

@@ -0,0 +1,85 @@
+accelerate==0.18.0
+aiofiles==23.1.0
+aiohttp==3.8.4
+aiosignal==1.3.1
+altair==4.2.2
+antlr4-python3-runtime==4.9.3
+anyio==3.6.2
+async-timeout==4.0.2
+attrs==23.1.0
+bitsandbytes==0.38.1
+braceexpand==0.1.7
+certifi==2022.12.7
+charset-normalizer==3.1.0
+click==8.1.3
+contourpy==1.0.7
+cycler==0.11.0
+entrypoints==0.4
+eva-decord==0.6.1
+fastapi==0.95.1
+ffmpy==0.3.0
+filelock==3.12.0
+fonttools==4.39.3
+frozenlist==1.3.3
+fsspec==2023.4.0
+gradio==3.28.1
+gradio_client==0.1.4
+h11==0.14.0
+httpcore==0.17.0
+httpx==0.24.0
+huggingface-hub==0.14.1
+idna==3.4
+iopath==0.1.10
+Jinja2==3.1.2
+jsonschema==4.17.3
+kiwisolver==1.4.4
+linkify-it-py==2.0.0
+markdown-it-py==2.2.0
+MarkupSafe==2.1.2
+matplotlib==3.7.1
+mdit-py-plugins==0.3.3
+mdurl==0.1.2
+mpmath==1.3.0
+multidict==6.0.4
+networkx==3.1
+numpy==1.24.3
+omegaconf==2.3.0
+opencv-python==4.7.0.72
+orjson==3.8.11
+packaging==23.1
+pandas==2.0.1
+Pillow==9.5.0
+portalocker==2.7.0
+psutil==5.9.5
+pydantic==1.10.7
+pydub==0.25.1
+pyparsing==3.0.9
+pyrsistent==0.19.3
+python-dateutil==2.8.2
+python-multipart==0.0.6
+pytz==2023.3
+PyYAML==6.0
+regex==2023.3.23
+requests==2.29.0
+semantic-version==2.10.0
+sentencepiece==0.1.98
+six==1.16.0
+sniffio==1.3.0
+socksio==1.0.0
+starlette==0.26.1
+sympy==1.11.1
+timm==0.6.13
+tokenizers==0.13.3
+toolz==0.12.0
+torch==2.0.0
+torchvision==0.15.1
+tqdm==4.65.0
+transformers==4.28.1
+typing_extensions==4.5.0
+tzdata==2023.3
+uc-micro-py==1.0.1
+urllib3==1.26.15
+uvicorn==0.22.0
+webdataset==0.2.48
+websockets==11.0.2
+yarl==1.9.2