Merge pull request #87 from microsoft/longnet/longvit

Release LongNet and LongViT

README.md

@@ -20,6 +20,7 @@ Fundamental research to develop new architectures for foundation models and A(G)
 
 ## News
 
+- December, 2023: [LongNet](torchscale/model/LongNet.py) and [LongViT](examples/longvit/README.md) released
 - October, 2023: Update RMSNorm and SwiGLU as the default module in RetNet
 - November, 2022: TorchScale 0.1.1 released [[Paper](https://arxiv.org/abs/2211.13184)] [[PyPI](https://pypi.org/project/torchscale/)]
 
@@ -37,6 +38,18 @@ cd torchscale
 pip install -e .
 ```
 
+For faster training install [Flash Attention](https://github.com/Dao-AILab/flash-attention) for Turing, Ampere, Ada, or Hopper GPUs:
+```
+pip install flash-attn
+```
+or [xFormers](https://github.com/facebookresearch/xformers) for Volta, Turing, Ampere, Ada, or Hopper GPUs:
+```
+# cuda 11.8 version
+pip3 install -U xformers --index-url https://download.pytorch.org/whl/cu118
+# cuda 12.1 version
+pip3 install -U xformers --index-url https://download.pytorch.org/whl/cu121
+```
+
 ## Getting Started
 
 It takes only several lines of code to create a model with the above fundamental research features enabled. Here is how to quickly obtain a BERT-like encoder:
@@ -85,6 +98,21 @@ It takes only several lines of code to create a RetNet model:
 >>> print(retnet)
 ```
 
+For LongNet models ([Flash Attention](https://github.com/Dao-AILab/flash-attention) required):
+```python
+>>> import torch
+>>> from torchscale.architecture.config import EncoderConfig, DecoderConfig
+>>> from torchscale.model.longnet import LongNetEncoder, LongNetDecoder
+
+# Creating a LongNet encoder with the dilated pattern of segment_length=[2048,4096] and dilated_ratio=[1,2]
+>>> config = EncoderConfig(vocab_size=64000, segment_length='[2048,4096]', dilated_ratio='[1,2]', flash_attention=True)
+>>> longnet = LongNetEncoder(config)
+
+# Creating a LongNet decoder with the dilated pattern of segment_length=[2048,4096] and dilated_ratio=[1,2]
+>>> config = DecoderConfig(vocab_size=64000, segment_length='[2048,4096]', dilated_ratio='[1,2]', flash_attention=True)
+>>> longnet = LongNetDecoder(config)
+```
+
 ## Key Features
 
 - [DeepNorm to improve the training stability of Post-LayerNorm Transformers](https://arxiv.org/abs/2203.00555)
@@ -142,6 +170,8 @@ We have examples of how to use TorchScale in the following scenarios/tasks:
 
 - Vision
 
+  * [LongViT](examples/longvit/README.md)
+
   * ViT/BEiT [In progress]
 
 - Speech
@@ -228,6 +258,26 @@ If you find this repository useful, please consider citing our work:
 }
 ```
 
+```
+@article{longnet,
+  author={Jiayu Ding and Shuming Ma and Li Dong and Xingxing Zhang and Shaohan Huang and Wenhui Wang and Nanning Zheng and Furu Wei},
+  title     = {{LongNet}: Scaling Transformers to 1,000,000,000 Tokens},
+  journal   = {ArXiv},
+  volume    = {abs/2307.02486},
+  year      = {2023}
+}
+```
+
+```
+@article{longvit,
+  title     = {When an Image is Worth 1,024 x 1,024 Words: A Case Study in Computational Pathology},
+  author    = {Wenhui Wang and Shuming Ma and Hanwen Xu and Naoto Usuyama and Jiayu Ding and Hoifung Poon and Furu Wei},
+  journal   = {ArXiv},
+  volume    = {abs/2312.03558},
+  year      = {2023}
+}
+```
+
 ## Contributing
 
 This project welcomes contributions and suggestions.  Most contributions require you to agree to a

examples/fairseq/README.md

@@ -251,6 +251,45 @@ python -m torch.distributed.launch --nproc_per_node=2 --nnodes=1 train.py \
     --use-xmoe
 ```
 
+### LongNet Model
+
+```bash
+cd examples/fairseq/
+python -m torch.distributed.launch --nproc_per_node=2 --nnodes=1 train.py \
+    ${PATH_TO_DATA} \
+    --num-workers 2 \
+    --activation-fn gelu \
+    --share-decoder-input-output-embed \
+    --validate-interval-updates 1000 \
+    --save-interval-updates 1000 \
+    --no-epoch-checkpoints \
+    --memory-efficient-fp16 \
+    --fp16-init-scale 4 \
+    --arch lm_base \
+    --task language_modeling \
+    --sample-break-mode none \
+    --tokens-per-sample 4096 \
+    --optimizer adam --adam-betas "(0.9, 0.98)" \
+    --adam-eps 1e-08 \
+    --clip-norm 0.0 \
+    --lr 5e-4 \
+    --lr-scheduler polynomial_decay \
+    --warmup-updates 750 \
+    --dropout 0.1 \
+    --attention-dropout 0.1 \
+    --weight-decay 0.01 \
+    --batch-size 4 \
+    --update-freq 1 \
+    --required-batch-size-multiple 1 \
+    --total-num-update 50000 \
+    --max-update 50000 \
+    --seed 1 \
+    --ddp-backend=c10d \
+    --flash-attention \
+    --segment-length [2048,4096] \
+    --dilated-ratio [1,2]
+```
+
 ## Example: Machine Translation
 
 ### Data Format

examples/fairseq/models/language_modeling.py

@@ -25,6 +25,7 @@
 
 from torchscale.architecture.config import DecoderConfig
 from torchscale.architecture.decoder import Decoder
+from torchscale.model.LongNet import LongNetDecoder
 
 DEFAULT_MAX_TARGET_POSITIONS = 1024
 logger = logging.getLogger(__name__)
@@ -196,6 +197,19 @@ class LanguageConfig(FairseqDataclass):
     xpos_scale_base: Optional[int] = field(
         default=512,
     )
+    flash_attention: Optional[bool] = field(
+        default=False,
+    )
+    seq_parallel: Optional[bool] = field(
+        default=False,
+    )
+    segment_length: Optional[str] = field(
+        default='',
+    )
+    dilated_ratio: Optional[str] = field(
+        default='',
+    )
+
 
 
 @register_model("lm", dataclass=LanguageConfig)
@@ -256,7 +270,13 @@ def build_model(cls, args, task):
         config = DecoderConfig()
         config.override(args)
 
-        decoder = LMDecoder(
+        if args.segment_length != '':
+            assert args.dilated_ratio != ''
+            DECODER_CLASS = LongNetLMDecoder
+        else:
+            DECODER_CLASS = LMDecoder
+
+        decoder = DECODER_CLASS(
             config,
             embed_tokens,
             embed_positions,
@@ -291,6 +311,25 @@ def reorder_incremental_state_scripting(
                 incremental_state[module][key] = result
 
 
+class LongNetLMDecoder(LongNetDecoder, FairseqIncrementalDecoder):
+    def forward(self, src_tokens, **kwargs):
+        self_attn_padding_mask = src_tokens.eq(self.dictionary.pad())
+        return super().forward(src_tokens, self_attn_padding_mask, **kwargs)
+
+    def max_positions(self):
+        return self.embed_positions.max_positions
+
+    def reorder_incremental_state_scripting(
+        self,
+        incremental_state,
+        new_order,
+    ):
+        for module in incremental_state:
+            for key in incremental_state[module]:
+                result = incremental_state[module][key].index_select(0, new_order)
+                incremental_state[module][key] = result
+
+
 @register_model_architecture("lm", "lm_base")
 def base_lm_architecture(args):
     # backward compatibility for older model checkpoints

examples/longvit/README.md

@@ -0,0 +1,71 @@
+# [(LongViT) When an Image is Worth 1,024 x 1,024 Words: A Case Study in Computational Pathology](https://arxiv.org/abs/2312.03558)
+
+**LongViT** is a vision Transformer that can process gigapixel images (e.g., 32,768x32,768 images) in an end-to-end manner. We split the image into millions of patches and employ [LongNet](https://arxiv.org/abs/2307.02486) to directly model the extremely long sequence. We apply LongViT in the field of computational pathology and achieve remarkable performance on cancer subtyping and survival prediction tasks.
+
+
+## Setup
+```
+pip install -r requirements.txt
+pip install git+https://github.com/shumingma/fairseq.git@moe
+pip install -v -U git+https://github.com/facebookresearch/[email protected]#egg=xformers
+```
+
+
+## Pretraining
+
+We perform self-supervised pretraining on TCGA diagnostic slides using [DINO](https://arxiv.org/abs/2104.14294) objective. The detailed instructions can be found at [`get_started_for_tcga_pretraining.md`](get_started/get_started_for_tcga_pretraining.md).
+
+The link to the pretrained LongViT model on TCGA diagnostic slides:
+   - [`LongViT`](https://conversationhub.blob.core.windows.net/beit-share-public/longvit/longvit_small_patch32_1024.pth?sv=2021-10-04&st=2023-06-08T11%3A16%3A02Z&se=2033-06-09T11%3A16%3A00Z&sr=c&sp=r&sig=N4pfCVmSeq4L4tS8QbrFVsX6f6q844eft8xSuXdxU48%3D): #layer=12; hidden=384; FFN factor=4x; #head=16; patch=32x32
+
+
+## Fine-tuning on Subtyping Classification
+
+We perform finetuning on cancer subtyping on images with sizes up to 32,768x32,768 (1M patches). The detailed instructions can be found at [`get_started_for_tcga_subtyping.md`](get_started/get_started_for_tcga_subtyping.md).
+
+
+## Fine-tuning on Survival Prediction
+
+We perform finetuning on survival prediction on images with sizes up to 32,768x32,768 (1M patches). The detailed instructions can be found at [`get_started_for_tcga_survival_prediction.md`](get_started/get_started_for_tcga_survival_prediction.md).
+
+
+## Citation
+
+If you find this repository useful, please consider citing our work:
+```
+@article{longvit,
+  title={When an Image is Worth 1,024 x 1,024 Words: A Case Study in Computational Pathology},
+  author={Wang, Wenhui and Ma, Shuming and Xu, Hanwen and Usuyama, Naoto and Ding, Jiayu and Poon, Hoifung and Wei, Furu},
+  journal={arXiv preprint arXiv:2312.03558},
+  year={2023}
+}
+
+@article{longnet,
+  title={LongNet: Scaling transformers to 1,000,000,000 tokens},
+  author={Ding, Jiayu and Ma, Shuming and Dong, Li and Zhang, Xingxing and Huang, Shaohan and Wang, Wenhui and Zheng, Nanning and Wei, Furu},
+  journal={arXiv preprint arXiv:2307.02486},
+  year={2023}
+}
+
+@article{torchscale,
+  title={TorchScale: Transformers at scale},
+  author={Ma, Shuming and Wang, Hongyu and Huang, Shaohan and Wang, Wenhui and Chi, Zewen and Dong, Li and Benhaim, Alon and Patra, Barun and Chaudhary, Vishrav and Song, Xia and others},
+  journal={arXiv preprint arXiv:2211.13184},
+  year={2022}
+}
+```
+
+
+## Acknowledgement
+
+This repository is built using the [BEiT-3](https://github.com/microsoft/unilm/tree/master/beit3), the [MCAT](https://github.com/mahmoodlab/MCAT), the [DINO](https://github.com/facebookresearch/dino), the [HIPT](https://github.com/mahmoodlab/HIPT) repository and the [timm](https://github.com/rwightman/pytorch-image-models) library.
+
+
+## License
+This project is licensed under the license found in the LICENSE file in the root directory of this source tree.
+
+[Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct)
+
+### Contact Information
+
+For help or issues using LongViT models, please submit a GitHub issue.

examples/longvit/data_preprocessing/cache_transformed_images.py

@@ -0,0 +1,78 @@
+import os
+import sys
+import torch
+import random
+import argparse
+from PIL import Image, ImageFilter, ImageOps
+from multiprocessing import Pool, cpu_count
+from timm.data.transforms import RandomResizedCropAndInterpolation
+import torchvision.transforms as transforms  
+
+Image.MAX_IMAGE_PIXELS = 6400000000
+
+
+def build_transform(input_size):
+    train_interpolation = "bicubic"
+    t = [
+        RandomResizedCropAndInterpolation(input_size, scale=(0.5, 1.0), interpolation=train_interpolation), 
+        transforms.RandomHorizontalFlip(),
+    ]
+    t = transforms.Compose(t)
+
+    return t
+
+
+def pil_loader(path):
+    with open(path, "rb") as f:
+        img = Image.open(f)
+        return img.convert("RGB")
+
+
+def save_image(transformed_img, output_image_path):
+    if isinstance(transformed_img, torch.Tensor):
+        transformed_img = transforms.ToPILImage()(transformed_img)
+    transformed_img.save(output_image_path)
+
+
+def get_image_files(input_dir):  
+    for root, _, files in os.walk(input_dir):  
+        for file in files:
+            if file.lower().endswith(('.png', '.jpg', '.jpeg', '.gif', '.bmp')):  
+                yield os.path.join(root, file)
+
+
+def transform_and_save_crops(args):
+    input_path, input_dir, output_dir, transform = args
+    print(input_path)
+    file_basename = os.path.basename(input_path)
+
+    img = pil_loader(input_path)
+    transformed_img = transform(img)
+    output_image_path = os.path.join(output_dir, file_basename)
+    save_image(transformed_img, output_image_path)
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser(description='Save transformed images in a directory.')
+    parser.add_argument('input_dir', help='Path to the input directory.')
+    parser.add_argument('output_dir', help='Path to the output directory.')
+    parser.add_argument('-p', '--processes', type=int, default=cpu_count(), help='Number of processes to use. Default: number of CPU cores')
+    parser.add_argument('--input_size', type=int, default=16384, help='input image size')
+    args = parser.parse_args()
+
+    input_dir = args.input_dir
+    output_dir = args.output_dir
+    num_processes = args.processes
+    input_size = args.input_size
+    print("num_processes: {}".format(num_processes))
+    print("input_size: {}".format(input_size))
+
+    transform = build_transform(input_size=input_size)
+
+    image_files = list(get_image_files(input_dir))
+    task_args = [(file, input_dir, output_dir, transform) for file in image_files]
+
+    os.makedirs(output_dir, exist_ok=True)
+
+    with Pool(processes=num_processes) as pool:
+        pool.map(transform_and_save_crops, task_args)

examples/longvit/data_preprocessing/convert_wsi_to_images.py

@@ -0,0 +1,45 @@
+import os
+import glob
+import argparse
+import openslide
+
+from PIL import Image
+from concurrent.futures import ProcessPoolExecutor
+
+
+def convert_wsi_to_images(slide_path, image_path, target_size, level=0):
+    slide = openslide.open_slide(slide_path)
+    level_dims = slide.level_dimensions
+    region = slide.read_region((0,0), level, level_dims[level])
+    region = region.convert("RGB")
+    print("convert: {}({}) -> {}".format(slide_path, region.size, image_path))  
+    resized_img = region.resize((target_size, target_size), Image.BICUBIC)
+    resized_img.save(image_path)
+
+
+def process_slides(input_folder, output_folder, target_size, level=0):
+    if not os.path.exists(output_folder):
+        os.makedirs(output_folder)
+
+    slide_paths = glob.glob(os.path.join(input_folder, "*.svs"))
+
+    with ProcessPoolExecutor(max_workers=1) as executor:
+        for slide_path in slide_paths:
+            image_path = os.path.join(output_folder, os.path.basename(slide_path).split(".svs")[0] + ".jpg")  
+            executor.submit(convert_wsi_to_images, slide_path, image_path, target_size, level=level)
+
+
+if __name__ == "__main__":  
+    parser = argparse.ArgumentParser(description="Convert slides into images")  
+    parser.add_argument("input_folder", type=str, help="")
+    parser.add_argument("output_folder", type=str, help="")
+    parser.add_argument("target_size", type=int, help="")
+    parser.add_argument("level", type=int, help="")
+
+    args = parser.parse_args()
+    input_folder = args.input_folder
+    output_folder = args.output_folder
+    target_size = args.target_size
+    level = args.level
+
+    process_slides(input_folder, output_folder, target_size, level=level)