From 44e0562f9497fb79f042427307472a87d266d90a Mon Sep 17 00:00:00 2001 From: Haotian Liu Date: Thu, 5 Oct 2023 20:08:03 -0500 Subject: [PATCH] Release LLaVA-1.5 --- README.md | 37 +++--- docs/LoRA.md | 9 ++ docs/MODEL_ZOO.md | 41 ++++-- docs/Release_Notes.md | 31 ----- images/llava_v1_5_radar.jpg | Bin 0 -> 324397 bytes llava/conversation.py | 19 ++- llava/mm_utils.py | 27 +++- llava/model/llava_arch.py | 15 ++- llava/model/multimodal_projector/builder.py | 51 ++++++++ llava/serve/gradio_web_server.py | 56 ++++----- llava/train/llama_flash_attn_monkey_patch.py | 87 ++++++------- llava/train/llava_trainer.py | 124 ++++++++++++++++++- llava/train/train.py | 23 +++- pyproject.toml | 2 +- 14 files changed, 368 insertions(+), 154 deletions(-) delete mode 100644 docs/Release_Notes.md create mode 100644 images/llava_v1_5_radar.jpg create mode 100644 llava/model/multimodal_projector/builder.py diff --git a/README.md b/README.md index 9320fb30a..ba6d9fb5c 100644 --- a/README.md +++ b/README.md @@ -2,9 +2,12 @@ *Visual instruction tuning towards large language and vision models with GPT-4 level capabilities.* -[[Project Page](https://llava-vl.github.io/)] [[Paper](https://arxiv.org/abs/2304.08485)] [[Demo](https://llava.hliu.cc/)] [[Data](https://github.com/haotian-liu/LLaVA/blob/main/docs/Data.md)] [[Model Zoo](https://github.com/haotian-liu/LLaVA/blob/main/docs/MODEL_ZOO.md)] +[[Project Page](https://llava-vl.github.io/)] [[Demo](https://llava.hliu.cc/)] [[Data](https://github.com/haotian-liu/LLaVA/blob/main/docs/Data.md)] [[Model Zoo](https://github.com/haotian-liu/LLaVA/blob/main/docs/MODEL_ZOO.md)] -**Visual Instruction Tuning**
+**Improved Baselines with Visual Instruction Tuning** [[Paper](https://arxiv.org/abs/2310.03744)]
+[Haotian Liu](https://hliu.cc), [Chunyuan Li](https://chunyuan.li/), [Yuheng Li](https://yuheng-li.github.io/), [Yong Jae Lee](https://pages.cs.wisc.edu/~yongjaelee/) + +**Visual Instruction Tuning** (NeurIPS 2023, **Oral**) [[Paper](https://arxiv.org/abs/2304.08485)]
[Haotian Liu*](https://hliu.cc), [Chunyuan Li*](https://chunyuan.li/), [Qingyang Wu](https://scholar.google.ca/citations?user=HDiw-TsAAAAJ&hl=en/), [Yong Jae Lee](https://pages.cs.wisc.edu/~yongjaelee/) (*Equal Contribution)

@@ -14,22 +17,20 @@ ## Release +- [10/5] 🔥 LLaVA-1.5 is out! Achieving SoTA on 11 benchmarks, with just simple modifications to the original LLaVA, utilizes all public data, completes training in ~1 day on a single 8-A100 node, and surpasses methods like Qwen-VL-Chat that use billion-scale data. Check out the [technical report](#), and explore the [demo](https://llava.hliu.cc/)! Models are available in [Model Zoo](https://github.com/haotian-liu/LLaVA/blob/main/docs/MODEL_ZOO.md), with training and evaluation scripts coming in the next week! - [9/26] LLaVA is improved with reinforcement learning from human feedback (RLHF) to improve fact grounding and reduce hallucination. Check out the new SFT and RLHF checkpoints at project [[LLavA-RLHF]](https://llava-rlhf.github.io/) -- [9/22] [LLaVA](https://arxiv.org/abs/2304.08485) is accpeted by NeurIPS 2023 as oral presentation, and [LLaVA-Med](https://arxiv.org/abs/2306.00890) is accpeted by NeurIPS 2023 Datasets and Benchmarks Track as spotlight presentation. +- [9/22] [LLaVA](https://arxiv.org/abs/2304.08485) is accpeted by NeurIPS 2023 as **oral presentation**, and [LLaVA-Med](https://arxiv.org/abs/2306.00890) is accpeted by NeurIPS 2023 Datasets and Benchmarks Track as **spotlight presentation**. - [9/20] We summarize our emprical study of training 33B and 65B LLaVA mdoels in a [note](https://arxiv.org/abs/2309.09958). Further, if you are interested in the comprehensive review, evolution and trend of multimodal foundation models, please check out our recent survey paper [``Multimodal Foundation Models: From Specialists to General-Purpose Assistants''.](https://arxiv.org/abs/2309.10020)

-- [7/19] 🔥 We release a major upgrade, including support for LLaMA-2, LoRA training, 4-/8-bit inference, higher resolution (336x336), and a lot more. We release [LLaVA Bench](https://github.com/haotian-liu/LLaVA/blob/main/docs/LLaVA_Bench.md) for benchmarking open-ended visual chat with results from Bard and Bing-Chat. We also support and verify training with RTX 3090 and RTX A6000. Check out [LLaVA-from-LLaMA-2](https://github.com/haotian-liu/LLaVA/blob/main/docs/LLaVA_from_LLaMA2.md), [release notes](https://github.com/haotian-liu/LLaVA/blob/main/docs/Release_Notes.md#7192023), and our [model zoo](https://github.com/haotian-liu/LLaVA/blob/main/docs/MODEL_ZOO.md)! +- [7/19] 🔥 We release a major upgrade, including support for LLaMA-2, LoRA training, 4-/8-bit inference, higher resolution (336x336), and a lot more. We release [LLaVA Bench](https://github.com/haotian-liu/LLaVA/blob/main/docs/LLaVA_Bench.md) for benchmarking open-ended visual chat with results from Bard and Bing-Chat. We also support and verify training with RTX 3090 and RTX A6000. Check out [LLaVA-from-LLaMA-2](https://github.com/haotian-liu/LLaVA/blob/main/docs/LLaVA_from_LLaMA2.md), and our [model zoo](https://github.com/haotian-liu/LLaVA/blob/main/docs/MODEL_ZOO.md)! - [6/26] [CVPR 2023 Tutorial](https://vlp-tutorial.github.io/) on **Large Multimodal Models: Towards Building and Surpassing Multimodal GPT-4**! Please check out [[Slides](https://datarelease.blob.core.windows.net/tutorial/vision_foundation_models_2023/slides/Chunyuan_cvpr2023_tutorial_lmm.pdf)] [[Notes](https://arxiv.org/abs/2306.14895)] [[YouTube](https://youtu.be/mkI7EPD1vp8)] [[Bilibli](https://www.bilibili.com/video/BV1Ng4y1T7v3/)]. - [6/11] We released the preview for the most requested feature: DeepSpeed and LoRA support! Please see documentations [here](./docs/LoRA.md). - [6/1] We released **LLaVA-Med: Large Language and Vision Assistant for Biomedicine**, a step towards building biomedical domain large language and vision models with GPT-4 level capabilities. Checkout the [paper](https://arxiv.org/abs/2306.00890) and [page](https://github.com/microsoft/LLaVA-Med). -- [5/13] Interested in quantifying the emerged **zero-shot OCR** performance of LLaVA and open-sourced LMM? Please check out the paper ["On the Hidden Mystery of OCR in Large Multimodal Models"](https://arxiv.org/abs/2305.07895), where LLaVA consistently outperforms miniGPT4 on 17 out of 18 datasets, despite LlaVA being trained with an order of magnitude smaller training data. - [5/6] We are releasing [LLaVA-Lighting-MPT-7B-preview](https://huggingface.co/liuhaotian/LLaVA-Lightning-MPT-7B-preview), based on MPT-7B-Chat! See [here](#LLaVA-MPT-7b) for more details. - [5/2] 🔥 We are releasing LLaVA-Lighting! Train a lite, multimodal GPT-4 with just $40 in 3 hours! See [here](#train-llava-lightning) for more details. -- [5/2] We upgrade LLaVA package to v0.1 to support Vicuna v0 and v1 checkpoints, please upgrade following instructions [here](#install). -- [4/30] Our checkpoint with Vicuna-7b-v0 has been released [here](#llava-7b)! This checkpoint is more accessible and device friendly. Stay tuned for a major upgrade next week! - [4/27] Thanks to the community effort, LLaVA-13B with 4-bit quantization allows you to run on a GPU with as few as 12GB VRAM! Try it out [here](https://github.com/oobabooga/text-generation-webui/tree/main/extensions/llava). - [4/17] 🔥 We released **LLaVA: Large Language and Vision Assistant**. We propose visual instruction tuning, towards building large language and vision models with GPT-4 level capabilities. Checkout the [paper](https://arxiv.org/abs/2304.08485) and [demo](https://llava.hliu.cc/). @@ -106,7 +107,7 @@ You just launched the Gradio web interface. Now, you can open the web interface This is the actual *worker* that performs the inference on the GPU. Each worker is responsible for a single model specified in `--model-path`. ```Shell -python -m llava.serve.model_worker --host 0.0.0.0 --controller http://localhost:10000 --port 40000 --worker http://localhost:40000 --model-path ./checkpoints/LLaVA-13B-v0 +python -m llava.serve.model_worker --host 0.0.0.0 --controller http://localhost:10000 --port 40000 --worker http://localhost:40000 --model-path liuhaotian/llava-v1.5-13b ``` Wait until the process finishes loading the model and you see "Uvicorn running on ...". Now, refresh your Gradio web UI, and you will see the model you just launched in the model list. @@ -120,7 +121,7 @@ python -m llava.serve.model_worker --host 0.0.0.0 --controller http://localhost: If the VRAM of your GPU is less than 24GB (e.g., RTX 3090, RTX 4090, etc.), you may try running it with multiple GPUs. Our latest code base will automatically try to use multiple GPUs if you have more than one GPU. You can specify which GPUs to use with `CUDA_VISIBLE_DEVICES`. Below is an example of running with the first two GPUs. ```Shell -CUDA_VISIBLE_DEVICES=0,1 python -m llava.serve.model_worker --host 0.0.0.0 --controller http://localhost:10000 --port 40000 --worker http://localhost:40000 --model-path ./checkpoints/LLaVA-13B-v0 +CUDA_VISIBLE_DEVICES=0,1 python -m llava.serve.model_worker --host 0.0.0.0 --controller http://localhost:10000 --port 40000 --worker http://localhost:40000 --model-path liuhaotian/llava-v1.5-13b ``` #### Launch a model worker (4-bit, 8-bit inference, quantized) @@ -128,7 +129,7 @@ CUDA_VISIBLE_DEVICES=0,1 python -m llava.serve.model_worker --host 0.0.0.0 --con You can launch the model worker with quantized bits (4-bit, 8-bit), which allows you to run the inference with reduced GPU memory footprint, potentially allowing you to run on a GPU with as few as 12GB VRAM. Note that inference with quantized bits may not be as accurate as the full-precision model. Simply append `--load-4bit` or `--load-8bit` to the **model worker** command that you are executing. Below is an example of running with 4-bit quantization. ```Shell -python -m llava.serve.model_worker --host 0.0.0.0 --controller http://localhost:10000 --port 40000 --worker http://localhost:40000 --model-path liuhaotian/llava-llama-2-13b-chat-lightning-preview --load-4bit +python -m llava.serve.model_worker --host 0.0.0.0 --controller http://localhost:10000 --port 40000 --worker http://localhost:40000 --model-path liuhaotian/llava-v1.5-13b --load-4bit ``` #### Launch a model worker (LoRA weights, unmerged) @@ -143,11 +144,11 @@ python -m llava.serve.model_worker --host 0.0.0.0 --controller http://localhost: ### CLI Inference -Chat about images using LLaVA without the need of Gradio interface. It also supports multiple GPUs, 4-bit and 8-bit quantized inference. With 4-bit quantization, for our LLaVA-Lightning-MPT-7B, it uses less than 8GB VRAM on a single GPU. +Chat about images using LLaVA without the need of Gradio interface. It also supports multiple GPUs, 4-bit and 8-bit quantized inference. With 4-bit quantization, for our LLaVA-1.5-7B, it uses less than 8GB VRAM on a single GPU. ```Shell python -m llava.serve.cli \ - --model-path liuhaotian/LLaVA-Lightning-MPT-7B-preview \ + --model-path liuhaotian/llava-v1.5-7b \ --image-file "https://llava-vl.github.io/static/images/view.jpg" \ --load-4bit ``` @@ -167,13 +168,13 @@ We use a similar set of hyperparameters as Vicuna in finetuning. Both hyperpara | Hyperparameter | Global Batch Size | Learning rate | Epochs | Max length | Weight decay | | --- | ---: | ---: | ---: | ---: | ---: | -| LLaVA-13B | 128 | 2e-3 | 1 | 2048 | 0 | +| LLaVA-13B | 256 | 1e-3 | 1 | 2048 | 0 | 2. Finetuning | Hyperparameter | Global Batch Size | Learning rate | Epochs | Max length | Weight decay | | --- | ---: | ---: | ---: | ---: | ---: | -| LLaVA-13B | 32 | 2e-5 | 3 | 2048 | 0 | +| LLaVA-13B | 128 | 2e-5 | 1 | 2048 | 0 | ### Prepare Vicuna checkpoints @@ -332,6 +333,14 @@ Please check out the documentation [here](https://github.com/haotian-liu/LLaVA/b If you find LLaVA useful for your research and applications, please cite using this BibTeX: ```bibtex + +@misc{liu2023llava, + title={Improved Baselines with Visual Instruction Tuning}, + author={Liu, Haotian and Li, Chunyuan and Li, Yuheng and Lee, Yong Jae}, + publisher={arXiv:2310.03744}, + year={2023}, +} + @misc{liu2023llava, title={Visual Instruction Tuning}, author={Liu, Haotian and Li, Chunyuan and Wu, Qingyang and Lee, Yong Jae}, diff --git a/docs/LoRA.md b/docs/LoRA.md index 8a0684e46..369fe9257 100644 --- a/docs/LoRA.md +++ b/docs/LoRA.md @@ -35,3 +35,12 @@ Please see sample training scripts for [LoRA](https://github.com/haotian-liu/LLa We provide sample DeepSpeed configs, [`zero3.json`](https://github.com/haotian-liu/LLaVA/blob/main/scripts/zero3.json) is more like PyTorch FSDP, and [`zero3_offload.json`](https://github.com/haotian-liu/LLaVA/blob/main/scripts/zero3_offload.json) can further save memory consumption by offloading parameters to CPU. `zero3.json` is usually faster than `zero3_offload.json` but requires more GPU memory, therefore, we recommend trying `zero3.json` first, and if you run out of GPU memory, try `zero3_offload.json`. You can also tweak the `per_device_train_batch_size` and `gradient_accumulation_steps` in the config to save memory, and just to make sure that `per_device_train_batch_size` and `gradient_accumulation_steps` remains the same. If you are having issues with ZeRO-3 configs, and there are enough VRAM, you may try [`zero2.json`](https://github.com/haotian-liu/LLaVA/blob/main/scripts/zero2.json). This consumes slightly more memory than ZeRO-3, and behaves more similar to PyTorch FSDP, while still supporting parameter-efficient tuning. + +## Create Merged Checkpoints + +```Shell +python scripts/merge_lora_weights.py \ + --model-path /path/to/lora_model \ + --model-base /path/to/base_model \ + --save-model-path /path/to/merge_model +``` diff --git a/docs/MODEL_ZOO.md b/docs/MODEL_ZOO.md index 9c314bbbe..21a52a297 100644 --- a/docs/MODEL_ZOO.md +++ b/docs/MODEL_ZOO.md @@ -1,11 +1,30 @@ # Model Zoo -We'll add more model checkpoints into model zoo very soon. Stay tuned! +**To Use LLaVA-1.5 checkpoints, your llava package version must be newer than 1.1.0. [Instructions](https://github.com/haotian-liu/LLaVA#upgrade-to-latest-code-base) on how to upgrade.** If you are interested in including any other details in Model Zoo, please open an issue :) The model weights below are *merged* weights. You do not need to apply delta. The usage of LLaVA checkpoints should comply with the base LLM's model license: [LLaMA](https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md). +## LLaVA-v1.5 + +| Version | Size | Schedule | Checkpoint | VQAv2 | GQA | VizWiz | SQA | T-VQA | POPE | MME | MM-Bench | MM-Bench-CN | SEED | LLaVA-Bench-Wild | MM-Vet | +|----------|----------|-----------|-----------|---|---|---|---|---|---|---|---|---|---|---|---| +| LLaVA-1.5 | 7B | full_ft-1e | [liuhaotian/llava-v1.5-7b](https://huggingface.co/liuhaotian/llava-v1.5-7b) | 78.5 | 62.0 | 50.0 | 66.8 | 58.2 | 85.9 | 1510.7 | 64.3 | 58.3 | 58.6 | 63.4 | 30.5 | +| LLaVA-1.5 | 13B | full_ft-1e | [liuhaotian/llava-v1.5-13b](https://huggingface.co/liuhaotian/llava-v1.5-13b) | 80.0 | 63.3 | 53.6 | 71.6 | 61.3 | 85.9 | 1531.3 | 67.7 | 63.6 | 61.6 | 70.7 | 35.4 | +| LLaVA-1.5 | 7B | lora-1e | coming soon | +| LLaVA-1.5 | 13B | lora-1e | coming soon | + +

+
+ LLaVA-1.5 achieves SoTA performance across 11 benchmarks. +

+ + +## LLaVA-v1 + +*Note: We recommend using the most capable LLaVA-v1.5 series above for the best performance.* + | Base LLM | Vision Encoder | Pretrain Data | Pretraining schedule | Finetuning Data | Finetuning schedule | LLaVA-Bench-Conv | LLaVA-Bench-Detail | LLaVA-Bench-Complex | LLaVA-Bench-Overall | Download | |----------|----------------|---------------|----------------------|-----------------|--------------------|------------------|--------------------|---------------------|---------------------|---------------------| | Vicuna-13B-v1.3 | CLIP-L-336px | LCS-558K | 1e | LLaVA-Instruct-80K | proj-1e, lora-1e | 64.3 | 55.9 | 81.7 | 70.1 | [LoRA](https://huggingface.co/liuhaotian/llava-v1-0719-336px-lora-vicuna-13b-v1.3) [LoRA-Merged](https://huggingface.co/liuhaotian/llava-v1-0719-336px-lora-merge-vicuna-13b-v1.3) | @@ -28,16 +47,18 @@ When using these projector weights to instruction tune your LMM, please make sur --mm_use_im_patch_token False ``` -| Base LLM | Vision Encoder | Pretrain Data | Pretraining schedule | Download | +| Base LLM | Vision Encoder | Projection | Pretrain Data | Pretraining schedule | Download | |----------|----------------|---------------|----------------------|----------| -| LLaMA-2-13B-Chat | CLIP-L-336px | LCS-558K | 1e | [projector](https://huggingface.co/liuhaotian/llava-336px-pretrain-llama-2-13b-chat) | -| LLaMA-2-7B-Chat | CLIP-L-336px | LCS-558K | 1e | [projector](https://huggingface.co/liuhaotian/llava-336px-pretrain-llama-2-7b-chat) | -| LLaMA-2-13B-Chat | CLIP-L | LCS-558K | 1e | [projector](https://huggingface.co/liuhaotian/llava-pretrain-llama-2-13b-chat) | -| LLaMA-2-7B-Chat | CLIP-L | LCS-558K | 1e | [projector](https://huggingface.co/liuhaotian/llava-pretrain-llama-2-7b-chat) | -| Vicuna-13B-v1.3 | CLIP-L-336px | LCS-558K | 1e | [projector](https://huggingface.co/liuhaotian/llava-336px-pretrain-vicuna-13b-v1.3) | -| Vicuna-7B-v1.3 | CLIP-L-336px | LCS-558K | 1e | [projector](https://huggingface.co/liuhaotian/llava-336px-pretrain-vicuna-7b-v1.3) | -| Vicuna-13B-v1.3 | CLIP-L | LCS-558K | 1e | [projector](https://huggingface.co/liuhaotian/llava-pretrain-vicuna-13b-v1.3) | -| Vicuna-7B-v1.3 | CLIP-L | LCS-558K | 1e | [projector](https://huggingface.co/liuhaotian/llava-pretrain-vicuna-7b-v1.3) | +| Vicuna-13B-v1.5 | CLIP-L-336px | MLP-2x | LCS-558K | 1e | [projector](https://huggingface.co/liuhaotian/llava-v1.5-mlp2x-336px-pretrain-vicuna-13b-v1.5) | +| Vicuna-7B-v1.5 | CLIP-L-336px | MLP-2x | LCS-558K | 1e | [projector](https://huggingface.co/liuhaotian/llava-v1.5-mlp2x-336px-pretrain-vicuna-7b-v1.5) | +| LLaMA-2-13B-Chat | CLIP-L-336px | Linear | LCS-558K | 1e | [projector](https://huggingface.co/liuhaotian/llava-336px-pretrain-llama-2-13b-chat) | +| LLaMA-2-7B-Chat | CLIP-L-336px | Linear | LCS-558K | 1e | [projector](https://huggingface.co/liuhaotian/llava-336px-pretrain-llama-2-7b-chat) | +| LLaMA-2-13B-Chat | CLIP-L | Linear | LCS-558K | 1e | [projector](https://huggingface.co/liuhaotian/llava-pretrain-llama-2-13b-chat) | +| LLaMA-2-7B-Chat | CLIP-L | Linear | LCS-558K | 1e | [projector](https://huggingface.co/liuhaotian/llava-pretrain-llama-2-7b-chat) | +| Vicuna-13B-v1.3 | CLIP-L-336px | Linear | LCS-558K | 1e | [projector](https://huggingface.co/liuhaotian/llava-336px-pretrain-vicuna-13b-v1.3) | +| Vicuna-7B-v1.3 | CLIP-L-336px | Linear | LCS-558K | 1e | [projector](https://huggingface.co/liuhaotian/llava-336px-pretrain-vicuna-7b-v1.3) | +| Vicuna-13B-v1.3 | CLIP-L | Linear | LCS-558K | 1e | [projector](https://huggingface.co/liuhaotian/llava-pretrain-vicuna-13b-v1.3) | +| Vicuna-7B-v1.3 | CLIP-L | Linear | LCS-558K | 1e | [projector](https://huggingface.co/liuhaotian/llava-pretrain-vicuna-7b-v1.3) | ## Science QA Checkpoints diff --git a/docs/Release_Notes.md b/docs/Release_Notes.md deleted file mode 100644 index be30b3cf2..000000000 --- a/docs/Release_Notes.md +++ /dev/null @@ -1,31 +0,0 @@ -# Release Notes - -We document release notes here for each update. - -### 7/19/2023 - -The first major update since our initial release! - -- Added **LLaMA-2** support -- **Full LoRA support**. To make model training more accessible, we release a set of model weights based on LoRA, which supports training on academic resources (e.g. 4x A6000s, or 8x 3090s, **without the need of CPU offloading**) -- A more versatile design for training large multimodal models, including swapping different language models, vision encoders, and more coming soon -- Support higher resolution input using CLIP-ViT-L-336px as the vision encoder for a more detailed visual understanding -- Ablate and clean up some design choices to make the training simpler and smoother -- Full DeepSpeed support -- Improved model checkpoint saving during pretraining stage to save disk space -- Improved WebUI interface -- Improved support for inference with multiple-GPUs -- Support inference with 4-bit and 8-bit quantization -- Support interactive CLI inference - -We train all models in this release using LLaVA-LCS-558K for pretraining and LLaVA-Instruct-80K for instruction tuning, to maintain an efficient and affordable training budget. **The full training (including both pretraining and finetuning) can be completed within 6 hours on 8x 3090s.** - -*We hope this release further benefits the community and makes large multimodal models more accessible.* - -#### Detailed Changes (7/19/2023) - -- Tokenization. We remove the dependency of the additional tokens (``, ``, ``), so that during the pretraining stage, the tokenizer does not change at all and we only update the linear projector weights. -- Prompt. - - Pretraining. 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zkk&RobRS<$&ZFxQD)x_r6%sk0mjP(;-9NURVJ;07II2Z&^zA)}GG z<@Yxmn9P)>`rJ|2c5`3vKqm7tFz^FP2y4yw{UOTJYcY+-Al6t2)`_Zbo4y!SpOE*S zu3B7oDEaE1D=9%aCQTD>qQ8uEBmXoo)uJ;|X?QNAD>0tM1;XtJ(D#)woCw(2TtP%; z0S$BHd*&Kb@r>-Ga1-aG?l!=XUVy{EzTcIxM5GCQC2l^q3k5oHNK+^hR0bIoQ2(_M zAryo}x7Ip_eGNV~(Pe;nw0Xi8?%ErOs4Z(E0A(R!WnmG8NFMoyuPI%7<14U|^4s-q z7{%c3NSyyb+$QRq4mmoukr($lXAgv$Rg|8(DiIM2;sw6Y7b4YOJZ>mH=N$YnM(BLQdghsMp5{*YEyy*FvcSBS1oSI6p8 z;Vr0d^d_Ll;4t@x6JNmD@Xt%jt8VeVj{?d+UQNPsGt!|-`aaxv5J1pKkMLgbZ@S-T(eK^f)@6Pd4-5|x7GpQjswxj}I`Cz-{5gDbN0}8#_P1x-!jH8%Zk)tnZ=W#O&$gpu h`cFuE|Anr>`TKv39^T)?DE>*S$^YNJgZ=k!{~JlSU2p&Z literal 0 HcmV?d00001 diff --git a/llava/conversation.py b/llava/conversation.py index fcf2b2561..6ee30cf99 100644 --- a/llava/conversation.py +++ b/llava/conversation.py @@ -129,7 +129,7 @@ def expand2square(pil_img, background_color=(122, 116, 104)): result.paste(pil_img, ((height - width) // 2, 0)) return result image = expand2square(image) - elif image_process_mode == "Crop": + elif image_process_mode in ["Default", "Crop"]: pass elif image_process_mode == "Resize": image = image.resize((336, 336)) @@ -141,11 +141,12 @@ def expand2square(pil_img, background_color=(122, 116, 104)): shortest_edge = int(min(max_len / aspect_ratio, min_len, min_hw)) longest_edge = int(shortest_edge * aspect_ratio) W, H = image.size - if H > W: - H, W = longest_edge, shortest_edge - else: - H, W = shortest_edge, longest_edge - image = image.resize((W, H)) + if longest_edge != max(image.size): + if H > W: + H, W = longest_edge, shortest_edge + else: + H, W = shortest_edge, longest_edge + image = image.resize((W, H)) if return_pil: images.append(image) else: @@ -178,10 +179,8 @@ def to_gradio_chatbot(self): image.save(buffered, format="JPEG") img_b64_str = base64.b64encode(buffered.getvalue()).decode() img_str = f'user upload image' - ret.append([img_str, None]) - msg = msg.replace('', '').strip() - if len(msg) > 0: - ret.append([msg, None]) + msg = img_str + msg.replace('', '').strip() + ret.append([msg, None]) else: ret.append([msg, None]) else: diff --git a/llava/mm_utils.py b/llava/mm_utils.py index 992021e5a..2662b91db 100644 --- a/llava/mm_utils.py +++ b/llava/mm_utils.py @@ -11,8 +11,33 @@ def load_image_from_base64(image): return Image.open(BytesIO(base64.b64decode(image))) +def expand2square(pil_img, background_color): + width, height = pil_img.size + if width == height: + return pil_img + elif width > height: + result = Image.new(pil_img.mode, (width, width), background_color) + result.paste(pil_img, (0, (width - height) // 2)) + return result + else: + result = Image.new(pil_img.mode, (height, height), background_color) + result.paste(pil_img, ((height - width) // 2, 0)) + return result + + def process_images(images, image_processor, model_cfg): - return image_processor(images, return_tensors='pt')['pixel_values'] + image_aspect_ratio = getattr(model_cfg, "image_aspect_ratio", None) + new_images = [] + if image_aspect_ratio == 'pad': + for image in images: + image = expand2square(image, tuple(int(x*255) for x in image_processor.image_mean)) + image = image_processor.preprocess(image, return_tensors='pt')['pixel_values'][0] + new_images.append(image) + else: + return image_processor(images, return_tensors='pt')['pixel_values'] + if all(x.shape == new_images[0].shape for x in new_images): + new_images = torch.stack(new_images, dim=0) + return new_images def tokenizer_image_token(prompt, tokenizer, image_token_index=IMAGE_TOKEN_INDEX, return_tensors=None): diff --git a/llava/model/llava_arch.py b/llava/model/llava_arch.py index 20d017dab..fd538c937 100644 --- a/llava/model/llava_arch.py +++ b/llava/model/llava_arch.py @@ -19,6 +19,7 @@ import torch.nn as nn from .multimodal_encoder.builder import build_vision_tower +from .multimodal_projector.builder import build_vision_projector from llava.constants import IGNORE_INDEX, IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_PATCH_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN @@ -30,7 +31,7 @@ def __init__(self, config): if hasattr(config, "mm_vision_tower"): self.vision_tower = build_vision_tower(config, delay_load=True) - self.mm_projector = nn.Linear(config.mm_hidden_size, config.hidden_size) + self.mm_projector = build_vision_projector(config) def get_vision_tower(self): vision_tower = getattr(self, 'vision_tower', None) @@ -54,12 +55,12 @@ def initialize_vision_modules(self, model_args, fsdp=None): self.vision_tower = vision_tower self.config.use_mm_proj = True + self.config.mm_projector_type = getattr(model_args, 'mm_projector_type', 'linear') self.config.mm_hidden_size = vision_tower.hidden_size self.config.mm_vision_select_layer = mm_vision_select_layer self.config.mm_vision_select_feature = mm_vision_select_feature - if not hasattr(self, 'mm_projector'): - self.mm_projector = nn.Linear(self.config.mm_hidden_size, self.config.hidden_size) + self.mm_projector = build_vision_projector(self.config) if pretrain_mm_mlp_adapter is not None: mm_projector_weights = torch.load(pretrain_mm_mlp_adapter, map_location='cpu') @@ -107,8 +108,12 @@ def prepare_inputs_labels_for_multimodal( for batch_idx, cur_input_ids in enumerate(input_ids): if (cur_input_ids == IMAGE_TOKEN_INDEX).sum() == 0: # multimodal LLM, but the current sample is not multimodal - cur_input_embeds = self.get_model().embed_tokens(cur_input_ids) - cur_input_embeds = cur_input_embeds + (0. * self.get_model().mm_projector(vision_tower.dummy_feature)).sum() + # FIXME: this is a hacky fix, for deepspeed zero3 to work + half_len = cur_input_ids.shape[0] // 2 + cur_image_features = image_features[cur_image_idx] + cur_input_embeds_1 = self.get_model().embed_tokens(cur_input_ids[:half_len]) + cur_input_embeds_2 = self.get_model().embed_tokens(cur_input_ids[half_len:]) + cur_input_embeds = torch.cat([cur_input_embeds_1, cur_image_features[0:0], cur_input_embeds_2], dim=0) new_input_embeds.append(cur_input_embeds) if labels is not None: new_labels.append(labels[batch_idx]) diff --git a/llava/model/multimodal_projector/builder.py b/llava/model/multimodal_projector/builder.py new file mode 100644 index 000000000..31cd4f48e --- /dev/null +++ b/llava/model/multimodal_projector/builder.py @@ -0,0 +1,51 @@ +import torch +import torch.nn as nn +import re + + +class IdentityMap(nn.Module): + def __init__(self): + super().__init__() + + def forward(self, x, *args, **kwargs): + return x + + @property + def config(self): + return {"mm_projector_type": 'identity'} + + +class SimpleResBlock(nn.Module): + def __init__(self, channels): + super().__init__() + self.pre_norm = nn.LayerNorm(channels) + + self.proj = nn.Sequential( + nn.Linear(channels, channels), + nn.GELU(), + nn.Linear(channels, channels) + ) + def forward(self, x): + x = self.pre_norm(x) + return x + self.proj(x) + + +def build_vision_projector(config, delay_load=False, **kwargs): + projector_type = getattr(config, 'mm_projector_type', 'linear') + + if projector_type == 'linear': + return nn.Linear(config.mm_hidden_size, config.hidden_size) + + mlp_gelu_match = re.match(r'^mlp(\d+)x_gelu$', projector_type) + if mlp_gelu_match: + mlp_depth = int(mlp_gelu_match.group(1)) + modules = [nn.Linear(config.mm_hidden_size, config.hidden_size)] + for _ in range(1, mlp_depth): + modules.append(nn.GELU()) + modules.append(nn.Linear(config.hidden_size, config.hidden_size)) + return nn.Sequential(*modules) + + if projector_type == 'identity': + return IdentityMap() + + raise ValueError(f'Unknown projector type: {projector_type}') diff --git a/llava/serve/gradio_web_server.py b/llava/serve/gradio_web_server.py index d5d2ed7c7..f298bde47 100644 --- a/llava/serve/gradio_web_server.py +++ b/llava/serve/gradio_web_server.py @@ -66,27 +66,18 @@ def load_demo(url_params, request: gr.Request): value=model, visible=True) state = default_conversation.copy() - return (state, - dropdown_update, - gr.Chatbot.update(visible=True), - gr.Textbox.update(visible=True), - gr.Button.update(visible=True), - gr.Row.update(visible=True), - gr.Accordion.update(visible=True)) + return state, dropdown_update def load_demo_refresh_model_list(request: gr.Request): logger.info(f"load_demo. ip: {request.client.host}") models = get_model_list() state = default_conversation.copy() - return (state, gr.Dropdown.update( - choices=models, - value=models[0] if len(models) > 0 else ""), - gr.Chatbot.update(visible=True), - gr.Textbox.update(visible=True), - gr.Button.update(visible=True), - gr.Row.update(visible=True), - gr.Accordion.update(visible=True)) + dropdown_update = gr.Dropdown.update( + choices=models, + value=models[0] if len(models) > 0 else "" + ) + return state, dropdown_update def vote_last_response(state, vote_type, model_selector, request: gr.Request): @@ -306,10 +297,17 @@ def http_bot(state, model_selector, temperature, top_p, max_new_tokens, request: The service is a research preview intended for non-commercial use only, subject to the model [License](https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md) of LLaMA, [Terms of Use](https://openai.com/policies/terms-of-use) of the data generated by OpenAI, and [Privacy Practices](https://chrome.google.com/webstore/detail/sharegpt-share-your-chatg/daiacboceoaocpibfodeljbdfacokfjb) of ShareGPT. Please contact us if you find any potential violation. """) +block_css = """ + +#buttons button { + min-width: min(120px,100%); +} + +""" def build_demo(embed_mode): - textbox = gr.Textbox(show_label=False, placeholder="Enter text and press ENTER", visible=False, container=False) - with gr.Blocks(title="LLaVA", theme=gr.themes.Base()) as demo: + textbox = gr.Textbox(show_label=False, placeholder="Enter text and press ENTER", container=False) + with gr.Blocks(title="LLaVA", theme=gr.themes.Default(), css=block_css) as demo: state = gr.State() if not embed_mode: @@ -327,9 +325,9 @@ def build_demo(embed_mode): imagebox = gr.Image(type="pil") image_process_mode = gr.Radio( - ["Crop", "Resize", "Pad"], - value="Crop", - label="Preprocess for non-square image") + ["Crop", "Resize", "Pad", "Default"], + value="Default", + label="Preprocess for non-square image", visible=False) cur_dir = os.path.dirname(os.path.abspath(__file__)) gr.Examples(examples=[ @@ -337,19 +335,19 @@ def build_demo(embed_mode): [f"{cur_dir}/examples/waterview.jpg", "What are the things I should be cautious about when I visit here?"], ], inputs=[imagebox, textbox]) - with gr.Accordion("Parameters", open=False, visible=False) as parameter_row: + with gr.Accordion("Parameters", open=False) as parameter_row: temperature = gr.Slider(minimum=0.0, maximum=1.0, value=0.2, step=0.1, interactive=True, label="Temperature",) top_p = gr.Slider(minimum=0.0, maximum=1.0, value=0.7, step=0.1, interactive=True, label="Top P",) max_output_tokens = gr.Slider(minimum=0, maximum=1024, value=512, step=64, interactive=True, label="Max output tokens",) - with gr.Column(scale=6): - chatbot = gr.Chatbot(elem_id="chatbot", label="LLaVA Chatbot", visible=False, height=550) + with gr.Column(scale=8): + chatbot = gr.Chatbot(elem_id="chatbot", label="LLaVA Chatbot", height=550) with gr.Row(): with gr.Column(scale=8): textbox.render() - with gr.Column(scale=1, min_width=60): - submit_btn = gr.Button(value="Submit", visible=False) - with gr.Row(visible=False) as button_row: + with gr.Column(scale=1, min_width=50): + submit_btn = gr.Button(value="Send", variant="primary") + with gr.Row(elem_id="buttons") as button_row: upvote_btn = gr.Button(value="👍 Upvote", interactive=False) downvote_btn = gr.Button(value="👎 Downvote", interactive=False) flag_btn = gr.Button(value="⚠️ Flag", interactive=False) @@ -384,12 +382,10 @@ def build_demo(embed_mode): [state, chatbot] + btn_list) if args.model_list_mode == "once": - demo.load(load_demo, [url_params], [state, model_selector, - chatbot, textbox, submit_btn, button_row, parameter_row], + demo.load(load_demo, [url_params], [state, model_selector], _js=get_window_url_params) elif args.model_list_mode == "reload": - demo.load(load_demo_refresh_model_list, None, [state, model_selector, - chatbot, textbox, submit_btn, button_row, parameter_row]) + demo.load(load_demo_refresh_model_list, None, [state, model_selector]) else: raise ValueError(f"Unknown model list mode: {args.model_list_mode}") diff --git a/llava/train/llama_flash_attn_monkey_patch.py b/llava/train/llama_flash_attn_monkey_patch.py index c81389836..31db2eff8 100644 --- a/llava/train/llama_flash_attn_monkey_patch.py +++ b/llava/train/llama_flash_attn_monkey_patch.py @@ -1,13 +1,10 @@ -from typing import List, Optional, Tuple -import logging +from typing import Optional, Tuple +import warnings import torch -from torch import nn import transformers -from transformers.models.llama.modeling_llama import apply_rotary_pos_emb - -from einops import rearrange +from transformers.models.llama.modeling_llama import apply_rotary_pos_emb, repeat_kv try: from flash_attn.flash_attn_interface import flash_attn_unpadded_qkvpacked_func @@ -25,10 +22,11 @@ def forward( output_attentions: bool = False, use_cache: bool = False, ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: - """Input shape: Batch x Time x Channel + if output_attentions: + warnings.warn( + "Output attentions is not supported for patched `LlamaAttention`, returning `None` instead." + ) - attention_mask: [bsz, q_len] - """ bsz, q_len, _ = hidden_states.size() query_states = ( @@ -38,68 +36,61 @@ def forward( ) key_states = ( self.k_proj(hidden_states) - .view(bsz, q_len, self.num_heads, self.head_dim) + .view(bsz, q_len, self.num_key_value_heads, self.head_dim) .transpose(1, 2) ) value_states = ( self.v_proj(hidden_states) - .view(bsz, q_len, self.num_heads, self.head_dim) + .view(bsz, q_len, self.num_key_value_heads, self.head_dim) .transpose(1, 2) - ) - # [bsz, q_len, nh, hd] - # [bsz, nh, q_len, hd] + ) # shape: (b, num_heads, s, head_dim) kv_seq_len = key_states.shape[-2] - assert past_key_value is None, "past_key_value is not supported" + if past_key_value is not None: + kv_seq_len += past_key_value[0].shape[-2] cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len) query_states, key_states = apply_rotary_pos_emb( query_states, key_states, cos, sin, position_ids ) - # [bsz, nh, t, hd] - assert not output_attentions, "output_attentions is not supported" - assert not use_cache, "use_cache is not supported" - - # Flash attention codes from - # https://github.com/HazyResearch/flash-attention/blob/main/flash_attn/flash_attention.py - - # transform the data into the format required by flash attention - qkv = torch.stack( - [query_states, key_states, value_states], dim=2 - ) # [bsz, nh, 3, q_len, hd] - qkv = qkv.transpose(1, 3) # [bsz, q_len, 3, nh, hd] - # We have disabled _prepare_decoder_attention_mask in LlamaModel - # the attention_mask should be the same as the key_padding_mask + + if past_key_value is not None: + # reuse k, v + key_states = torch.cat([past_key_value[0], key_states], dim=2) + value_states = torch.cat([past_key_value[1], value_states], dim=2) + + past_key_value = (key_states, value_states) if use_cache else None + + # repeat k/v heads if n_kv_heads < n_heads + key_states = repeat_kv(key_states, self.num_key_value_groups) + value_states = repeat_kv(value_states, self.num_key_value_groups) + + # Transform the data into the format required by flash attention + qkv = torch.stack([query_states, key_states, value_states], dim=2) + qkv = qkv.transpose(1, 3) # shape: [b, s, 3, num_heads, head_dim] key_padding_mask = attention_mask if key_padding_mask is None: - qkv = rearrange(qkv, "b s ... -> (b s) ...") - max_s = q_len + qkv = qkv.reshape(-1, 3, self.num_heads, self.head_dim) cu_q_lens = torch.arange( 0, (bsz + 1) * q_len, step=q_len, dtype=torch.int32, device=qkv.device ) + max_s = q_len output = flash_attn_unpadded_qkvpacked_func( qkv, cu_q_lens, max_s, 0.0, softmax_scale=None, causal=True ) - output = rearrange(output, "(b s) ... -> b s ...", b=bsz) + output = output.view(bsz, q_len, -1) else: - nheads = qkv.shape[-2] - x = rearrange(qkv, "b s three h d -> b s (three h d)") - x_unpad, indices, cu_q_lens, max_s = unpad_input(x, key_padding_mask) - x_unpad = rearrange( - x_unpad, "nnz (three h d) -> nnz three h d", three=3, h=nheads - ) + qkv = qkv.reshape(bsz, q_len, -1) + qkv, indices, cu_q_lens, max_s = unpad_input(qkv, key_padding_mask) + qkv = qkv.view(-1, 3, self.num_heads, self.head_dim) output_unpad = flash_attn_unpadded_qkvpacked_func( - x_unpad, cu_q_lens, max_s, 0.0, softmax_scale=None, causal=True - ) - output = rearrange( - pad_input( - rearrange(output_unpad, "nnz h d -> nnz (h d)"), indices, bsz, q_len - ), - "b s (h d) -> b s h d", - h=nheads, + qkv, cu_q_lens, max_s, 0.0, softmax_scale=None, causal=True ) - return self.o_proj(rearrange(output, "b s h d -> b s (h d)")), None, None + output_unpad = output_unpad.reshape(-1, self.num_heads * self.head_dim) + output = pad_input(output_unpad, indices, bsz, q_len) + + return self.o_proj(output), None, past_key_value # Disable the transformation of the attention mask in LlamaModel as the flash attention @@ -114,7 +105,7 @@ def _prepare_decoder_attention_mask( def replace_llama_attn_with_flash_attn(): cuda_major, cuda_minor = torch.cuda.get_device_capability() if cuda_major < 8: - logging.warning( + warnings.warn( "Flash attention is only supported on A100 or H100 GPU during training due to head dim > 64 backward." "ref: https://github.com/HazyResearch/flash-attention/issues/190#issuecomment-1523359593" ) diff --git a/llava/train/llava_trainer.py b/llava/train/llava_trainer.py index 27cd42bd1..ec6f40613 100644 --- a/llava/train/llava_trainer.py +++ b/llava/train/llava_trainer.py @@ -1,8 +1,13 @@ import os import torch +from torch.utils.data import Sampler + from transformers import Trainer -from typing import Optional +from transformers.trainer import ( + has_length, +) +from typing import List, Optional def maybe_zero_3(param, ignore_status=False, name=None): @@ -25,8 +30,123 @@ def get_mm_adapter_state_maybe_zero_3(named_params, keys_to_match): return to_return +def split_to_even_chunks(indices, lengths, num_chunks): + """ + Split a list of indices into `chunks` chunks of roughly equal lengths. + """ + + if len(indices) % num_chunks != 0: + return [indices[i::num_chunks] for i in range(num_chunks)] + + num_indices_per_chunk = len(indices) // num_chunks + + chunks = [[] for _ in range(num_chunks)] + chunks_lengths = [0 for _ in range(num_chunks)] + for index in indices: + shortest_chunk = chunks_lengths.index(min(chunks_lengths)) + chunks[shortest_chunk].append(index) + chunks_lengths[shortest_chunk] += lengths[index] + if len(chunks[shortest_chunk]) == num_indices_per_chunk: + chunks_lengths[shortest_chunk] = float("inf") + + return chunks + + +def get_modality_length_grouped_indices(lengths, batch_size, world_size, generator=None): + # We need to use torch for the random part as a distributed sampler will set the random seed for torch. + assert all(l != 0 for l in lengths), "Should not have zero length." + mm_indices, mm_lengths = zip(*[(i, l) for i, l in enumerate(lengths) if l > 0]) + lang_indices, lang_lengths = zip(*[(i, -l) for i, l in enumerate(lengths) if l < 0]) + + assert len(mm_indices) > 0, "Should have at least one multimodal sample." + assert len(lang_indices) > 0, "Should have at least one language sample." + + mm_shuffle = [mm_indices[i] for i in get_length_grouped_indices(mm_lengths, batch_size, world_size, generator=None)] + lang_shuffle = [lang_indices[i] for i in get_length_grouped_indices(lang_lengths, batch_size, world_size, generator=None)] + megabatch_size = world_size * batch_size + mm_megabatches = [mm_shuffle[i : i + megabatch_size] for i in range(0, len(mm_shuffle), megabatch_size)] + lang_megabatches = [lang_shuffle[i : i + megabatch_size] for i in range(0, len(lang_shuffle), megabatch_size)] + + last_mm = mm_megabatches[-1] + last_lang = lang_megabatches[-1] + additional_batch = last_mm + last_lang + megabatches = mm_megabatches[:-1] + lang_megabatches[:-1] + megabatch_indices = torch.randperm(len(megabatches), generator=generator) + megabatches = [megabatches[i] for i in megabatch_indices] + + if len(additional_batch) >= megabatch_size: + megabatches = [additional_batch[:megabatch_size]] + megabatches + additional_batch = additional_batch[megabatch_size:] + + if len(additional_batch) > 0: + megabatches.append(additional_batch) + + return [i for megabatch in megabatches for i in megabatch] + + +def get_length_grouped_indices(lengths, batch_size, world_size, generator=None, merge=True): + # We need to use torch for the random part as a distributed sampler will set the random seed for torch. + indices = torch.randperm(len(lengths), generator=generator) + megabatch_size = world_size * batch_size + megabatches = [indices[i : i + megabatch_size].tolist() for i in range(0, len(lengths), megabatch_size)] + megabatches = [sorted(megabatch, key=lambda i: lengths[i], reverse=True) for megabatch in megabatches] + megabatches = [split_to_even_chunks(megabatch, lengths, world_size) for megabatch in megabatches] + + return [i for megabatch in megabatches for batch in megabatch for i in batch] + + +class LengthGroupedSampler(Sampler): + r""" + Sampler that samples indices in a way that groups together features of the dataset of roughly the same length while + keeping a bit of randomness. + """ + + def __init__( + self, + batch_size: int, + world_size: int, + lengths: Optional[List[int]] = None, + generator=None, + group_by_modality: bool = False, + ): + if lengths is None: + raise ValueError("Lengths must be provided.") + + self.batch_size = batch_size + self.world_size = world_size + self.lengths = lengths + self.generator = generator + self.group_by_modality = group_by_modality + + def __len__(self): + return len(self.lengths) + + def __iter__(self): + if self.group_by_modality: + indices = get_modality_length_grouped_indices(self.lengths, self.batch_size, self.world_size, generator=self.generator) + else: + indices = get_length_grouped_indices(self.lengths, self.batch_size, self.world_size, generator=self.generator) + return iter(indices) + + class LLaVATrainer(Trainer): + def _get_train_sampler(self) -> Optional[torch.utils.data.Sampler]: + if self.train_dataset is None or not has_length(self.train_dataset): + return None + + if self.args.group_by_modality_length: + lengths = self.train_dataset.modality_lengths + return LengthGroupedSampler( + # self.args.train_batch_size * self.args.gradient_accumulation_steps, # TODO: seems that we should not have gradient_accumulation_steps + self.args.train_batch_size, + world_size=self.args.world_size, + lengths=lengths, + group_by_modality=True, + ) + else: + return super()._get_train_sampler() + def _save_checkpoint(self, model, trial, metrics=None): if getattr(self.args, 'tune_mm_mlp_adapter', False): from transformers.trainer_utils import PREFIX_CHECKPOINT_DIR @@ -36,7 +156,7 @@ def _save_checkpoint(self, model, trial, metrics=None): output_dir = os.path.join(run_dir, checkpoint_folder) # Only save Adapter - keys_to_match = ['mm_projector'] + keys_to_match = ['mm_projector', 'vision_resampler'] if getattr(self.args, "use_im_start_end", False): keys_to_match.extend(['embed_tokens', 'embed_in']) diff --git a/llava/train/train.py b/llava/train/train.py index c1dbee137..0d198f7e3 100644 --- a/llava/train/train.py +++ b/llava/train/train.py @@ -54,6 +54,7 @@ class ModelArguments: vision_tower: Optional[str] = field(default=None) mm_vision_select_layer: Optional[int] = field(default=-1) # default to the last layer pretrain_mm_mlp_adapter: Optional[str] = field(default=None) + mm_projector_type: Optional[str] = field(default='linear') mm_use_im_start_end: bool = field(default=False) mm_use_im_patch_token: bool = field(default=True) mm_vision_select_feature: Optional[str] = field(default="patch") @@ -102,6 +103,7 @@ class TrainingArguments(transformers.TrainingArguments): lora_dropout: float = 0.05 lora_weight_path: str = "" lora_bias: str = "none" + group_by_modality_length: bool = field(default=False) def maybe_zero_3(param, ignore_status=False, name=None): @@ -140,7 +142,7 @@ def get_peft_state_maybe_zero_3(named_params, bias): to_return[bias_name] = t else: raise NotImplementedError - to_return = {k: maybe_zero_3(v, name=k) for k, v in to_return.items()} + to_return = {k: maybe_zero_3(v, ignore_status=True) for k, v in to_return.items()} return to_return @@ -636,6 +638,23 @@ def __init__(self, data_path: str, def __len__(self): return len(self.list_data_dict) + @property + def lengths(self): + length_list = [] + for sample in self.list_data_dict: + img_tokens = 128 if 'image' in sample else 0 + length_list.append(sum(len(conv['value'].split()) for conv in sample['conversations']) + img_tokens) + return length_list + + @property + def modality_lengths(self): + length_list = [] + for sample in self.list_data_dict: + cur_len = sum(len(conv['value'].split()) for conv in sample['conversations']) + cur_len = cur_len if 'image' in sample else -cur_len + length_list.append(cur_len) + return length_list + def __getitem__(self, i) -> Dict[str, torch.Tensor]: sources = self.list_data_dict[i] if isinstance(i, int): @@ -856,7 +875,7 @@ def make_inputs_require_grad(module, input, output): ) vision_tower = model.get_vision_tower() - vision_tower.to(dtype=torch.float16, device=training_args.device) + vision_tower.to(dtype=torch.bfloat16 if training_args.bf16 else torch.float16, device=training_args.device) data_args.image_processor = vision_tower.image_processor data_args.is_multimodal = True diff --git a/pyproject.toml b/pyproject.toml index 6b7209c86..25c7bf00d 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta" [project] name = "llava" -version = "1.0.2" +version = "1.1.0" description = "Towards GPT-4 like large language and visual assistant." readme = "README.md" requires-python = ">=3.8"