first commit

app.py

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+import gradio as gr
+
+from demo import VideoCLSModel
+
+
+sample_videos = [
+    [
+        "data/svitt-ego-demo/0/video/2d560d56-dc47-4c76-8d41-889c8aa55d66-converted.mp4",
+        "data/svitt-ego-demo/0/video/eb5cb2b0-59e6-45da-af1b-ba86c7ab0b54-converted.mp4",
+        "data/svitt-ego-demo/0/video/0a3097fc-baed-4d11-a4c9-30f07eb91af6-converted.mp4",
+        "data/svitt-ego-demo/0/video/1a870d5d-5787-4098-ad8d-fe7343c43698-converted.mp4",
+        "data/svitt-ego-demo/0/video/014b473f-aec0-49c7-b394-abc7309ca3c7-converted.mp4",
+    ],
+    [
+        "data/svitt-ego-demo/1/video/029eeb9a-8853-48a4-a1dc-e8868b58adf3-converted.mp4",
+        "data/svitt-ego-demo/1/video/968139e2-987e-4615-a2d4-fa2e683bae8a-converted.mp4",
+        "data/svitt-ego-demo/1/video/fb9fda68-f264-465d-9208-19876f5ef90f-converted.mp4",
+        "data/svitt-ego-demo/1/video/53da674a-089d-428a-a719-e322b2de002b-converted.mp4",
+        "data/svitt-ego-demo/1/video/060e07d8-e818-4f9c-9d6b-6504f5fd42a3-converted.mp4",
+    ],
+    [
+        "data/svitt-ego-demo/2/video/fa2f1291-3796-41a6-8f7b-6e7c1491b9b2-converted.mp4",
+        "data/svitt-ego-demo/2/video/8d83478f-c5d2-4ac3-a823-e1b2ac7594d7-converted.mp4",
+        "data/svitt-ego-demo/2/video/5f6f87ea-e1c3-4868-bb60-22c9e874d056-converted.mp4",
+        "data/svitt-ego-demo/2/video/77718528-2de9-48b4-b6b8-e7c602032afb-converted.mp4",
+        "data/svitt-ego-demo/2/video/9abbf7f4-68f0-4f52-812f-df2a3df48f7b-converted.mp4",
+    ],
+    [
+        "data/svitt-ego-demo/3/video/2a6b3d10-8da9-4f0e-a681-59ba48a55dbf-converted.mp4",
+        "data/svitt-ego-demo/3/video/5afd7421-fb6b-4c65-a09a-716f79a7a935-converted.mp4",
+        "data/svitt-ego-demo/3/video/f7aec252-bd4f-4696-8de5-ef7b871e2194-converted.mp4",
+        "data/svitt-ego-demo/3/video/84d6855a-242b-44a6-b48d-2db302b5ea7a-converted.mp4",
+        "data/svitt-ego-demo/3/video/81fff27c-97c0-483a-ad42-47fa947977a9-converted.mp4",
+    ],
+]
+sample_text = [
+    "drops the palm fronds on the ground",
+    "stands up",
+    "throws nuts in a bowl",
+    "puts the speaker and notepad in both hands on a seat",
+]
+sample_text_dict = {
+    "drops the palm fronds on the ground": 0,
+    "stands up": 1,
+    "throws nuts in a bowl": 2,
+    "puts the speaker and notepad in both hands on a seat": 3,
+}
+num_samples = len(sample_videos[0])
+labels = [f"video-{i}" for i in range(num_samples)]
+
+def main():
+    svitt = VideoCLSModel(
+        "configs/ego_mcq/svitt.yml", 
+        sample_videos,
+    )
+    def predict(text):
+        idx = sample_text_dict[text]
+        ft_action, gt_action = svitt.predict(idx, text)
+        return labels[gt_action], labels[ft_action]
+
+    with gr.Blocks() as demo:
+        gr.Markdown(
+            """
+            # SViTT-Ego for Multiple Choice Question
+            Choose a sample query and click predict to view the results.
+            """
+        )
+        with gr.Row():        
+            with gr.Column():
+                videos = [gr.Video(label=labels[i], format='mp4', height=256, min_width=340) for i in range(num_samples)]
+            with gr.Column():
+                text = gr.Text(label="Query", visible=False)
+                label = gr.Text(label="Ground Truth")
+                ours = gr.Text(label="SViTT-Ego prediction")
+        btn = gr.Button("Predict", variant="primary")
+        btn.click(predict, inputs=[text], outputs=[label, ours])
+        inputs = [text]
+        inputs.extend(videos)
+        gr.Examples(examples=[[sample_text[i], x[0], x[1], x[2], x[3], x[4]] for i, x in enumerate(sample_videos)], inputs=inputs)
+
+    demo.launch(share=True)
+
+
+if __name__ == "__main__":
+    main()
+    

ckpt/svitt-ego.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:5d66ad807fb618b1e99da476d54238555eb51925afa65e444ba43dc5c235db1e
+size 2500535422

configs/base.yml

@@ -0,0 +1,21 @@
+model:
+    pretrain: ""
+    resume: ""
+    timesformer_freeze_space: false
+    drop_path_rate: 0.1
+    dropout_ratio: 0.5
+    freeze_vis_backbone: false
+    freeze_txt_backbone: false
+    use_vn_classifier: false
+
+data:
+    dataset: ek100_mir
+    root: datasets/EK100/video_ht256px
+    metadata: datasets/EK100/epic-kitchens-100-annotations/retrieval_annotations/EPIC_100_retrieval_train.csv
+    metadata_val: datasets/EK100/epic-kitchens-100-annotations/retrieval_annotations/EPIC_100_retrieval_test.csv
+    relevancy_path: datasets/EK100/epic-kitchens-100-annotations/retrieval_annotations/relevancy/caption_relevancy_EPIC_100_retrieval_test.pkl
+    clip_length: 16
+    clip_stride: 4
+    sparse_sample: false
+    num_crops: 1
+    num_clips: 1

configs/config_bert.json

@@ -0,0 +1,21 @@
+{
+  "architectures": [
+    "BertForMaskedLM"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "type_vocab_size": 2,
+  "vocab_size": 30522,
+  "fusion_layer": 9,
+  "encoder_width": 768
+}

configs/ego_mcq/multiple-choice-question.yaml

@@ -0,0 +1,37 @@
+
+text_encoder: bert-base-uncased
+bert_config: configs/config_bert.json
+vit_type: beit  # items in ${vit_zoo}
+vit_zoo:  # from huggingface
+  beit: microsoft/beit-base-patch16-224-pt22k-ft22k
+vit_name_or_pretrained_path: ${vit_zoo[${vit_type}]}
+
+vision_encoder_args:
+  token_keep_rate: 0.7
+  token_keep_strategy: cls_attn
+  token_drop_loc: [3, 6, 9]
+  sparse_local_attn: 1
+  sparse_random_attn: 5
+  attn_block_size: 56
+
+image_res: 224
+embed_dim: 256
+video_input:
+  num_frames: 4
+  reader: decord  # one of [decord, av]
+  sample_type: rand
+  num_frames_test: 16  # num_frames during inference/test
+  sample_type_test: middle
+max_txt_l:
+  image: 32
+  video: 32
+  
+batch_size:
+  image: 8
+  video: 8
+batch_size_test:
+  image: 8
+  video: 8
+k_test: 128
+temp: 0.18
+mlm_prob: 0.5

configs/ego_mcq/svitt.yml

@@ -0,0 +1,9 @@
+model:
+    pretrain: ckpt/svitt-ego.pth
+    freeze_vis_backbone: true
+    freeze_txt_backbone: true
+    num_frames: 4
+    config: configs/ego_mcq/multiple-choice-question.yaml
+
+data:
+    root: data/svitt-ego-demo

data/svitt-ego-demo/0/meta.json

@@ -0,0 +1 @@
+{"text": "#C C drops the palm fronds on the ground", "text_ops": ["#C C picks a bowl with  fruit from person O", "#C C turns to the woman X on his right", "#C C picks the pocket knife ", "#C C removes the paint from the wall.", "#C C drops the palm fronds on the ground"], "correct": 4, "type": 1, "meta": {"raw_captions": "#C C drops the palm fronds on the ground", "paths": [["/datasets/ego4d/egovlp/full_scale_256_chunked/2d560d56-dc47-4c76-8d41-889c8aa55d66/4.mp4", "/datasets/ego4d/egovlp/full_scale_256_chunked/2d560d56-dc47-4c76-8d41-889c8aa55d66/4.mp4"], ["/datasets/ego4d/egovlp/full_scale_256_chunked/eb5cb2b0-59e6-45da-af1b-ba86c7ab0b54/0.mp4", "/datasets/ego4d/egovlp/full_scale_256_chunked/eb5cb2b0-59e6-45da-af1b-ba86c7ab0b54/0.mp4"], ["/datasets/ego4d/egovlp/full_scale_256_chunked/0a3097fc-baed-4d11-a4c9-30f07eb91af6/0.mp4", "/datasets/ego4d/egovlp/full_scale_256_chunked/0a3097fc-baed-4d11-a4c9-30f07eb91af6/0.mp4"], ["/datasets/ego4d/egovlp/full_scale_256_chunked/1a870d5d-5787-4098-ad8d-fe7343c43698/0.mp4", "/datasets/ego4d/egovlp/full_scale_256_chunked/1a870d5d-5787-4098-ad8d-fe7343c43698/0.mp4"], ["/datasets/ego4d/egovlp/full_scale_256_chunked/014b473f-aec0-49c7-b394-abc7309ca3c7/0.mp4", "/datasets/ego4d/egovlp/full_scale_256_chunked/014b473f-aec0-49c7-b394-abc7309ca3c7/0.mp4"]]}}

data/svitt-ego-demo/0/tensors.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:3a572098fa6fb25b4a465dc138a3341f6b22cfc525f7e772b53bc17bd171e56d
+size 12042987

data/svitt-ego-demo/1/meta.json

@@ -0,0 +1 @@
+{"text": "#C C stands up", "text_ops": ["#C C walks a round", "#C C stands up", "#O person Y pushes the door ", "#C C picks pastry cloth", "#C C holds the wire"], "correct": 1, "type": 1, "meta": {"raw_captions": "#C C holds the wire", "paths": [["/datasets/ego4d/egovlp/full_scale_256_chunked/029eeb9a-8853-48a4-a1dc-e8868b58adf3/0.mp4", "/datasets/ego4d/egovlp/full_scale_256_chunked/029eeb9a-8853-48a4-a1dc-e8868b58adf3/0.mp4"], ["/datasets/ego4d/egovlp/full_scale_256_chunked/968139e2-987e-4615-a2d4-fa2e683bae8a/4.mp4", "/datasets/ego4d/egovlp/full_scale_256_chunked/968139e2-987e-4615-a2d4-fa2e683bae8a/4.mp4"], ["/datasets/ego4d/egovlp/full_scale_256_chunked/fb9fda68-f264-465d-9208-19876f5ef90f/0.mp4", "/datasets/ego4d/egovlp/full_scale_256_chunked/fb9fda68-f264-465d-9208-19876f5ef90f/0.mp4"], ["/datasets/ego4d/egovlp/full_scale_256_chunked/53da674a-089d-428a-a719-e322b2de002b/1.mp4", "/datasets/ego4d/egovlp/full_scale_256_chunked/53da674a-089d-428a-a719-e322b2de002b/1.mp4"], ["/datasets/ego4d/egovlp/full_scale_256_chunked/060e07d8-e818-4f9c-9d6b-6504f5fd42a3/0.mp4", "/datasets/ego4d/egovlp/full_scale_256_chunked/060e07d8-e818-4f9c-9d6b-6504f5fd42a3/0.mp4"]]}}

data/svitt-ego-demo/1/tensors.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:053afd65ae1250bfc609d34c561c51fdde4812251b233f2f246f68b66da84533
+size 12042987

data/svitt-ego-demo/2/meta.json

@@ -0,0 +1 @@
+{"text": "#C C throws nuts in a bowl.", "text_ops": ["#C C throws nuts in a bowl.", "#O The woman Z places her right hand on the table.", "#O The woman T touches a card on the table with her left hand.", "#C C joins the pieces of dough together on the tray.", "#O A woman X walks forward"], "correct": 0, "type": 1, "meta": {"raw_captions": "#O A woman X walks forward", "paths": [["/datasets/ego4d/egovlp/full_scale_256_chunked/fa2f1291-3796-41a6-8f7b-6e7c1491b9b2/0.mp4", "/datasets/ego4d/egovlp/full_scale_256_chunked/fa2f1291-3796-41a6-8f7b-6e7c1491b9b2/0.mp4"], ["/datasets/ego4d/egovlp/full_scale_256_chunked/8d83478f-c5d2-4ac3-a823-e1b2ac7594d7/1.mp4", "/datasets/ego4d/egovlp/full_scale_256_chunked/8d83478f-c5d2-4ac3-a823-e1b2ac7594d7/1.mp4"], ["/datasets/ego4d/egovlp/full_scale_256_chunked/5f6f87ea-e1c3-4868-bb60-22c9e874d056/0.mp4", "/datasets/ego4d/egovlp/full_scale_256_chunked/5f6f87ea-e1c3-4868-bb60-22c9e874d056/0.mp4"], ["/datasets/ego4d/egovlp/full_scale_256_chunked/77718528-2de9-48b4-b6b8-e7c602032afb/4.mp4", "/datasets/ego4d/egovlp/full_scale_256_chunked/77718528-2de9-48b4-b6b8-e7c602032afb/4.mp4"], ["/datasets/ego4d/egovlp/full_scale_256_chunked/9abbf7f4-68f0-4f52-812f-df2a3df48f7b/1.mp4", "/datasets/ego4d/egovlp/full_scale_256_chunked/9abbf7f4-68f0-4f52-812f-df2a3df48f7b/1.mp4"]]}}

data/svitt-ego-demo/2/tensors.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:381ef1a03e5685229295a3a6a327ef39b0489b0da5fe9ba3754c6101f6c39ec6
+size 12042987

data/svitt-ego-demo/3/meta.json

@@ -0,0 +1 @@
+{"text": "#C C puts the speaker and notepad in both hands on a seat.", "text_ops": ["#C C picks a chaff from the pan of ingredients", "#C C switches his left hand grip on the broom", "#C C cuts the dough on the tray with the scraper in his right hand.", "#C C pulls the wire mesh.", "#C C puts the speaker and notepad in both hands on a seat."], "correct": 4, "type": 1, "meta": {"raw_captions": "#C C puts the speaker and notepad in both hands on a seat.", "paths": [["/datasets/ego4d/egovlp/full_scale_256_chunked/2a6b3d10-8da9-4f0e-a681-59ba48a55dbf/2.mp4", "/datasets/ego4d/egovlp/full_scale_256_chunked/2a6b3d10-8da9-4f0e-a681-59ba48a55dbf/2.mp4"], ["/datasets/ego4d/egovlp/full_scale_256_chunked/5afd7421-fb6b-4c65-a09a-716f79a7a935/1.mp4", "/datasets/ego4d/egovlp/full_scale_256_chunked/5afd7421-fb6b-4c65-a09a-716f79a7a935/1.mp4"], ["/datasets/ego4d/egovlp/full_scale_256_chunked/f7aec252-bd4f-4696-8de5-ef7b871e2194/1.mp4", "/datasets/ego4d/egovlp/full_scale_256_chunked/f7aec252-bd4f-4696-8de5-ef7b871e2194/1.mp4"], ["/datasets/ego4d/egovlp/full_scale_256_chunked/84d6855a-242b-44a6-b48d-2db302b5ea7a/0.mp4", "/datasets/ego4d/egovlp/full_scale_256_chunked/84d6855a-242b-44a6-b48d-2db302b5ea7a/0.mp4"], ["/datasets/ego4d/egovlp/full_scale_256_chunked/81fff27c-97c0-483a-ad42-47fa947977a9/9.mp4", "/datasets/ego4d/egovlp/full_scale_256_chunked/81fff27c-97c0-483a-ad42-47fa947977a9/9.mp4"]]}}

data/svitt-ego-demo/3/tensors.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:3ace4bcc2911dc78fc7ef6802c9c2b392207e42811e7449e628adf39ffa5f84c
+size 12042987

demo.py

@@ -0,0 +1,165 @@
+### demo.py
+# Define model classes for inference.
+###
+import json
+import torch
+import torch.nn as nn
+import torch.backends.cudnn as cudnn
+from einops import rearrange
+from transformers import BertTokenizer
+from torchvision import transforms
+from torchvision.transforms._transforms_video import (
+    NormalizeVideo, 
+)
+
+from svitt.model import SViTT
+from svitt.config import load_cfg, setup_config
+from svitt.base_dataset import read_frames_cv2_egoclip
+
+
+class VideoModel(nn.Module):
+    """ Base model for video understanding based on SViTT architecture. """
+    def __init__(self, config):
+        """ Initializes the model.
+        Parameters:
+            config: config file
+        """
+        super(VideoModel, self).__init__()
+        self.cfg = load_cfg(config)
+        self.model = self.build_model()
+        self.templates = ['{}']
+        self.dataset = self.cfg['data']['dataset']
+        self.eval()
+
+    def build_model(self):
+        cfg = self.cfg
+        if cfg['model'].get('pretrain', False):
+            ckpt_path = cfg['model']['pretrain']
+        else:
+            raise Exception('no checkpoint found')
+        
+        if cfg['model'].get('config', False):
+            config_path = cfg['model']['config']
+        else:
+            raise Exception('no model config found')
+        
+        self.model_cfg = setup_config(config_path)
+        self.tokenizer = BertTokenizer.from_pretrained(self.model_cfg.text_encoder)
+        model = SViTT(config=self.model_cfg, tokenizer=self.tokenizer)
+
+        print(f"Loading checkpoint from {ckpt_path}")
+        checkpoint = torch.load(ckpt_path, map_location="cpu")
+        state_dict = checkpoint["model"]
+
+        # fix for zero-shot evaluation
+        for key in list(state_dict.keys()):
+            if "bert" in key:
+                encoder_key = key.replace("bert.", "")
+                state_dict[encoder_key] = state_dict[key]
+                    
+        if torch.cuda.is_available():
+            model.cuda()
+                        
+        model.load_state_dict(state_dict, strict=False)
+
+        return model
+
+    def eval(self):
+        cudnn.benchmark = True
+        for p in self.model.parameters():
+            p.requires_grad = False
+        self.model.eval()
+
+
+class VideoCLSModel(VideoModel):
+    """ Video model for video classification tasks (Charades-Ego, EGTEA). """
+    def __init__(self, config, sample_videos):
+        super(VideoCLSModel, self).__init__(config)
+        self.sample_videos = sample_videos
+        self.video_transform = self.init_video_transform()
+        
+    #def load_data(self, idx=None):
+    #    filename = f"{self.cfg['data']['root']}/{idx}/tensors.pt"
+    #    return torch.load(filename)
+    def init_video_transform(self,
+            input_res=224,
+            center_crop=256,
+            norm_mean=(0.485, 0.456, 0.406),
+            norm_std=(0.229, 0.224, 0.225),
+        ):
+        print('Video Transform is used!')
+        normalize = NormalizeVideo(mean=norm_mean, std=norm_std)
+        return transforms.Compose(
+            [
+                transforms.Resize(center_crop),
+                transforms.CenterCrop(center_crop),
+                transforms.Resize(input_res),
+                normalize,
+            ]
+        )
+    
+    def load_data(self, idx):
+        num_frames = self.model_cfg.video_input.num_frames
+        video_paths = self.sample_videos[idx]
+        clips = [None] * len(video_paths)
+        for i, path in enumerate(video_paths):
+            imgs = read_frames_cv2_egoclip(path, num_frames, 'uniform')
+            imgs = imgs.transpose(0, 1) 
+            imgs = self.video_transform(imgs)
+            imgs = imgs.transpose(0, 1)  
+            clips[i] = imgs
+        return torch.stack(clips)
+    
+    def load_meta(self, idx=None):
+        filename = f"{self.cfg['data']['root']}/{idx}/meta.json"
+        with open(filename, "r") as f:
+            meta = json.load(f)
+        return meta
+        
+    @torch.no_grad()
+    def get_text_features(self, text):
+        print('=> Extracting text features')
+        embeddings = self.tokenizer(
+            text, 
+            padding="max_length", 
+            truncation=True,
+            max_length=self.model_cfg.max_txt_l.video, 
+            return_tensors="pt",
+        )
+        _, class_embeddings = self.model.encode_text(embeddings)
+        return class_embeddings
+
+    @torch.no_grad()
+    def forward(self, idx, text=None):
+        print('=> Start forwarding')
+        meta = self.load_meta(idx)
+        clips = self.load_data(idx)
+        if text is None:
+            text = meta["text"][4:]
+        text_features = self.get_text_features(text)
+        target = meta["correct"]
+
+        # encode images
+        pooled_image_feat_all = []
+        for i in range(clips.shape[0]):
+        
+            images = clips[i,:].unsqueeze(0)
+            bsz = images.shape[0]
+
+            _, pooled_image_feat, *outputs = self.model.encode_image(images) 
+            if pooled_image_feat.ndim == 3:
+                pooled_image_feat = rearrange(pooled_image_feat, '(b k) n d -> b (k n) d', b=bsz)
+            else:
+                pooled_image_feat = rearrange(pooled_image_feat, '(b k) d -> b k d', b=bsz)
+            
+            pooled_image_feat_all.append(pooled_image_feat)
+        
+        pooled_image_feat_all = torch.cat(pooled_image_feat_all, dim=0)
+        similarity = self.model.get_sim(pooled_image_feat_all, text_features)[0]
+        return similarity.argmax(), target
+
+    @torch.no_grad()
+    def predict(self, idx, text=None):
+        output, target = self.forward(idx, text)
+        return output.numpy(), target
+

requirements.txt

@@ -0,0 +1,14 @@
+gradio
+torch
+torchvision
+scikit-learn
+eva-decord
+timm
+einops
+ftfy
+regex
+transformers
+omegaconf
+zCurve
+numpy-hilbert-curve
+opencv-python-headless

svitt/base_dataset.py

@@ -0,0 +1,56 @@
+import random
+
+import cv2
+import torch
+import numpy as np
+
+
+def sample_frames_start_end(num_frames, start, end, sample='rand', fix_start=None):
+    acc_samples = min(num_frames, end)
+    intervals = np.linspace(start=start, stop=end, num=acc_samples + 1).astype(int)
+    ranges = [(interv, intervals[idx + 1] - 1) for idx, interv in enumerate(intervals[:-1])]
+    if sample == 'rand':
+        frame_idxs = [random.choice(range(x[0], x[1])) for x in ranges]
+    elif fix_start is not None:
+        frame_idxs = [x[0] + fix_start for x in ranges]
+    elif sample == 'uniform':
+        frame_idxs = [(x[0] + x[1]) // 2 for x in ranges]
+    else:
+        raise NotImplementedError
+    return frame_idxs
+
+def read_frames_cv2_egoclip(
+        video_path, 
+        num_frames, 
+        sample,
+    ):
+    
+    cap = cv2.VideoCapture(video_path)
+    vlen = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
+    assert (cap.isOpened())
+
+    # get indexes of sampled frames
+    start_f = 0
+    end_f = vlen
+    frame_idxs = sample_frames_start_end(num_frames, start_f, end_f, sample=sample)
+
+    frames = []
+    for index in frame_idxs:
+        _index = index % (600 * 30)
+        _index = min(_index, vlen)
+        cap.set(cv2.CAP_PROP_POS_FRAMES, _index - 1)
+        ret, frame = cap.read()
+
+        if ret:
+            frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+            frame = torch.from_numpy(frame)
+            # (H x W x C) to (C x H x W)
+            frame = frame.permute(2, 0, 1)
+            frames.append(frame)
+
+    while len(frames) < num_frames: # complete the frame
+        frames.append(frames[-1])
+
+    frames = torch.stack(frames).float() / 255
+    cap.release()
+    return frames

svitt/config.py

@@ -0,0 +1,36 @@
+
+import yaml
+from omegaconf import OmegaConf, DictConfig
+
+def load_base_cfg():
+    with open('configs/base.yml', 'r') as fp:
+        cfg = yaml.load(fp, Loader=yaml.SafeLoader)
+    return cfg
+
+def load_cfg(cfg_file):
+    cfg = load_base_cfg()
+    with open(cfg_file, 'r') as fp:
+        exp_cfg = yaml.load(fp, Loader=yaml.SafeLoader)
+
+    cfg['model'].update(exp_cfg.get('model', {}))
+    cfg['data'].update(exp_cfg.get('data', {}))
+    return cfg
+
+def convert_types(config):
+    """Convert `'None'` (str) --> `None` (None). Only supports top-level"""
+    for k, v in config.items():
+        if isinstance(v, DictConfig):
+            setattr(config, k, convert_types(v))
+
+        # TODO convert types in ListConfig, right now they are ignored
+        # if isinstance(v, ListConfig):
+        #     new_v = ListConfig()
+
+        if v in ["None", "none"]:
+            setattr(config, k, None)
+    return config
+
+def setup_config(config_path):
+    yaml_config = OmegaConf.load(config_path)
+    config = convert_types(yaml_config)
+    return config

svitt/model.py

@@ -0,0 +1,340 @@
+from svitt.utils import (
+    interpolate_pos_embed,
+    interpolate_pos_relative_bias_beit_3d,
+)
+from omegaconf import OmegaConf
+from transformers import ViTModel, ViTConfig
+from svitt.sparse_config import BertConfig, BeitConfig
+from svitt.sparse_xbeit import BeitModel
+from svitt.sparse_xbert import BertModel, BertForMaskedLM
+
+import torch
+from torch import nn
+import torch.nn.functional as F
+
+
+class SViTT(nn.Module):
+    """Common utils shared by pretraining and downstream retrieval"""
+    def __init__(self, config=None, tokenizer=None, pretrain=True, **kwargs):
+        super().__init__()
+        self.config = config
+        self.tokenizer = tokenizer
+        self.embed_dim = config.embed_dim
+        self.vision_width = 768
+        self.text_width = 768
+        self.pretrain = pretrain
+
+        self.vision_encoder, self.vision_layernorm = self.build_vision_encoder()
+        self.text_encoder = self.build_text_encoder()
+
+        self.vision_proj = nn.Linear(self.vision_width, self.embed_dim)
+        self.text_proj = nn.Linear(self.text_width, self.embed_dim)
+
+        self.temp = nn.Parameter(torch.ones([]) * config.temp)
+        self.itm_head = nn.Linear(self.text_width, 2)
+
+
+    def build_text_encoder(self):
+
+        bert_config = BertConfig.from_json_file(self.config.bert_config)
+
+        # Override params for sparse vision encoder
+        model_args = getattr(self.config, 'text_encoder_args', {})
+        if model_args:
+            model_args = OmegaConf.to_object(model_args)
+            bert_config.update(model_args)
+
+        if self.pretrain:
+            text_encoder, _ = BertForMaskedLM.from_pretrained(
+                self.config.text_encoder, config=bert_config,
+                output_loading_info=True
+            )
+        else:
+            text_encoder, _ = BertModel.from_pretrained(
+                self.config.text_encoder, config=bert_config,
+                add_pooling_layer=False, output_loading_info=True
+            )
+        return text_encoder
+
+    def build_vision_encoder(self):
+        # if self.config.vit_type in ["beit", "deit", "vit", "vit32"]:
+        if self.config.vit_type in ["beit"]:
+            vision_encoder = self.build_huggingface_vit_with_image_size(
+                self.config.vit_name_or_pretrained_path, self.config.image_res,)
+        else:
+            raise ValueError(f"Unknown vit type {self.config.vit_type}")
+
+        # add layernorm for normalizing BEiT outputs hidden states
+        vision_layernorm = None
+        if self.config.vit_type == "beit":
+            vision_layernorm = nn.LayerNorm(self.vision_width, eps=1e-12)
+        return vision_encoder, vision_layernorm
+
+    # @classmethod
+    # def build_huggingface_vit_with_image_size(cls, model_card: str, image_size: int):
+    def build_huggingface_vit_with_image_size(self, model_card: str, image_size: int):
+        """Build a vit model from huggingface hub, also interpolate pos_embed when needed.
+
+        Args:
+            model_card: name in huggingface hub, e.g., `facebook/deit-base-patch16-224`
+            image_size: new image size, may be different from pre-training image_size of `model_card`
+
+        ref: https://github.com/huggingface/transformers/issues/12167#issuecomment-861356232
+        """
+        is_beit = "beit" in model_card
+        if "beit" in model_card:
+            model_cls, config_cls = BeitModel, BeitConfig
+        elif "deit" in model_card or "vit" in model_card:
+            # the deit model we use is loaded in vit arch,
+            # see https://huggingface.co/facebook/deit-base-patch16-224#how-to-use
+            model_cls, config_cls = ViTModel, ViTConfig
+        else:
+            raise ValueError(f"Unexpected model_card: {model_card}")
+
+        # BEiT uses average pooled tokens instead of [CLS] used by other models
+        tmp_model = model_cls.from_pretrained(model_card, add_pooling_layer=is_beit)
+        state_dict = tmp_model.state_dict()
+        del tmp_model
+
+        # Override params for sparse vision encoder
+        model_args = getattr(self.config, 'vision_encoder_args', {})
+        if model_args:
+            model_args = OmegaConf.to_object(model_args)
+        model_config = config_cls.from_pretrained(
+            model_card, 
+            image_size=image_size, 
+            **model_args,
+        )
+        model = model_cls(config=model_config, add_pooling_layer=is_beit, num_frames=self.config.video_input.num_frames)
+        if is_beit:
+            # interpolate relative pos bias
+            state_dict = interpolate_pos_relative_bias_beit_3d(
+                state_dict_old=state_dict,
+                state_dict_new=model.state_dict(),
+                patch_shape_new=model.window_size
+            )
+        else:
+            # interpolate pos_embed and load weights to new model
+            state_dict["embeddings.position_embeddings"] = interpolate_pos_embed(
+                pos_embed_old=state_dict["embeddings.position_embeddings"],
+                pos_embed_new=model.embeddings.position_embeddings,
+                num_patches_new=model.embeddings.patch_embeddings.num_patches
+            )
+        msg = model.load_state_dict(state_dict, strict=False)
+        return model
+
+    def get_text_encoder(self):
+        """get text encoder, used for text and cross-modal encoding"""
+        encoder = self.text_encoder
+        return encoder.bert if hasattr(encoder, "bert") else encoder
+
+    def encode_image(self, video, output_token_idx=False, output_attentions=False):
+        video_embeds = self.vision_encoder(video, output_token_idx=output_token_idx, output_attentions=output_attentions)  # (bsz, seq_len, d)
+        if self.vision_layernorm is not None:  # only for BEiT mean-pooling
+            video_embeds.last_hidden_state = self.vision_layernorm(video_embeds.last_hidden_state)
+        if output_token_idx:
+            token_idx = video_embeds.token_idx
+        
+        if output_attentions:
+            attentions = video_embeds.attentions
+            
+        if self.config.vit_type == "beit":
+            pooled_video_embeds = video_embeds.pooler_output  # (bsz*num_frms, d)
+            video_embeds = video_embeds.last_hidden_state  # (bsz*num_frms, L, d)
+        else:
+            video_embeds = video_embeds.last_hidden_state
+            pooled_video_embeds = video_embeds[:, 0]
+        
+        outputs = (video_embeds, pooled_video_embeds)
+
+        if output_token_idx:
+            outputs += (token_idx,)
+        
+        if output_attentions:
+            outputs += (attentions,)
+
+        return outputs
+
+    def _encode_image(self, image):
+        bsz, num_frms, c, h, w = image.shape  # `num_frms` could be changing for image (=1) or video (e.g., =4)
+        image = image.view(bsz*num_frms, c, h, w)
+        image_embeds = self.vision_encoder(image)
+        if self.vision_layernorm is not None:  # only for BEiT mean-pooling
+            image_embeds.last_hidden_state = self.vision_layernorm(image_embeds.last_hidden_state)
+
+        if self.config.vit_type == "beit":
+            pooled_image_embeds = image_embeds.pooler_output  # (bsz*num_frms, d)
+            image_embeds = image_embeds.last_hidden_state  # (bsz*num_frms, L, d)
+        else:
+            image_embeds = image_embeds.last_hidden_state
+            pooled_image_embeds = image_embeds[:, 0]
+
+        image_embeds = image_embeds.view(bsz, num_frms, -1, self.vision_width)  # (bsz, num_frms, L, d)
+        pooled_image_embeds = pooled_image_embeds.view(bsz, num_frms, self.vision_width) \
+            if pooled_image_embeds is not None else None  # (bsz, num_frms, d)
+        return image_embeds, pooled_image_embeds
+
+    def encode_text(self, text):
+        text_output = self.get_text_encoder()(
+            text.input_ids,
+            attention_mask=text.attention_mask,
+            return_dict=True,
+            mode='text'
+        )
+        text_embeds = text_output.last_hidden_state 
+        pooled_text_embeds = text_embeds[:, 0] 
+        return text_embeds, pooled_text_embeds
+
+    @torch.no_grad()
+    def clip_contrastive_temperature(self, min_val=0.001, max_val=0.5):
+        """Seems only used during pre-training"""
+        self.temp.clamp_(min_val, max_val)
+
+    @torch.no_grad()
+    def get_mask(self, sim, idx=None, normalize=False):
+        """
+        sim: (N, N)
+        idx: (N, )
+        normalize: bool, make row sum equal to 1
+        """
+        if idx is not None:
+            idx = idx.view(-1, 1)
+            mask = torch.eq(idx, idx.T).to(sim.dtype)
+            if normalize:  
+                mask = mask / mask.sum(1, keepdim=True)
+        else:
+            mask = torch.zeros_like(sim)
+            mask.fill_diagonal_(1)
+        return mask  # `1` mark valid/matched location
+
+    def get_contrastive_loss(self, pooled_image_embeds, pooled_text_embeds, idx=None):
+        sim_i2t, sim_t2i = self.get_sim(
+            pooled_image_embeds, pooled_text_embeds, t=self.temp)
+
+        with torch.no_grad():
+            sim_i2t_targets = self.get_mask(sim_i2t, idx=idx, normalize=True)
+            sim_t2i_targets = sim_i2t_targets
+
+        loss_i2t = -torch.sum(
+            F.log_softmax(sim_i2t, dim=1) * sim_i2t_targets, dim=1).mean()
+        loss_t2i = -torch.sum(
+            F.log_softmax(sim_t2i, dim=1) * sim_t2i_targets, dim=1).mean()
+
+        loss_ita = (loss_i2t + loss_t2i) / 2
+        return loss_ita, sim_i2t, sim_t2i
+
+    def get_sim(self, pooled_image_embeds, pooled_text_embeds, t=1):
+        """
+        Args:
+            pooled_image_embeds: (bsz, num_frms, d)
+            pooled_text_embeds: (bsz, d)
+            t: temperature
+        """
+        image_proj = self.vision_proj
+        text_proj = self.text_proj
+
+        image_feat = F.normalize(image_proj(pooled_image_embeds), dim=-1)
+        text_feat = F.normalize(text_proj(pooled_text_embeds), dim=-1)
+
+        if image_feat.ndim == 3:
+            sim_i2t = torch.einsum("mld,nd->mln", image_feat, text_feat).mean(1) / t  # (N, N)
+        else:
+            sim_i2t = torch.einsum("md,nd ->mn", image_feat, text_feat) / t  # (N, N)
+        sim_t2i = sim_i2t.T
+        return sim_i2t, sim_t2i
+
+    def get_itm_loss(self, 
+            sim_i2t, 
+            sim_t2i, 
+            text_embeds, 
+            text_atts, 
+            image_embeds, 
+            image_atts, 
+            idx=None,
+        ):
+        """
+        sim_i2t, sim_t2i: (N, N)
+        text_embeds, text_atts, image_embeds, image_atts: (N, *)
+        idx: (N, )
+        """
+        bsz = len(sim_i2t)
+
+        with torch.no_grad():
+            weights_i2t = F.softmax(sim_i2t+1e-4, dim=1)  # (N, N)
+            weights_t2i = F.softmax(sim_t2i+1e-4, dim=1)
+
+            mask = self.get_mask(sim_i2t, idx=idx).bool()
+            weights_i2t.masked_fill_(mask, 0)
+            weights_t2i.masked_fill_(mask, 0)
+
+        # select a negative image for each text
+        if self.config.itm_hard_neg:
+            img_neg_indices = torch.multinomial(weights_t2i, 1).squeeze() #RuntimeError: invalid multinomial distribution (sum of probabilities <= 0) 
+        else:
+            img_neg_indices = self.get_rand_indices(mask, 1).squeeze()            
+
+        image_embeds_neg = image_embeds[img_neg_indices]
+
+        # select a negative text for each image
+        if self.config.itm_hard_neg:
+            txt_neg_indices = torch.multinomial(weights_i2t, 1).squeeze()
+        else:
+            txt_neg_indices = self.get_rand_indices(mask, 1).squeeze()
+
+        text_embeds_neg = text_embeds[txt_neg_indices]
+        text_atts_neg = text_atts[txt_neg_indices]  # (N, L, d)
+
+        # embedding on local gpu
+        _text_embeds = text_embeds  
+        _text_atts = text_atts
+        _image_embeds = image_embeds
+        _image_atts = image_atts
+        # concat embeddings
+        text_embeds_all = torch.cat([_text_embeds, _text_embeds, text_embeds_neg], dim=0)
+        text_atts_all = torch.cat([_text_atts, _text_atts, text_atts_neg], dim=0)
+        image_embeds_all = torch.cat([_image_embeds, image_embeds_neg, _image_embeds], dim=0)
+        image_atts_all = torch.cat([_image_atts, _image_atts, _image_atts], dim=0)
+
+        text_encoder = self.get_text_encoder()
+        output = text_encoder(
+            encoder_embeds=text_embeds_all,
+            attention_mask=text_atts_all,
+            encoder_hidden_states=image_embeds_all,
+            encoder_attention_mask=image_atts_all,
+            return_dict=True,
+            mode='fusion',
+        )
+
+        itm_embeds = output.last_hidden_state[:, 0]  # pos (N, d) + neg (2N, d)
+
+        loss_itm = self._get_itm_loss(itm_embeds, enc=self.itm_head)
+        itm_embeds_pos = itm_embeds[:bsz]  # (N, d)
+
+        return loss_itm, itm_embeds_pos
+
+    def _get_itm_loss(self, itm_embeds, enc):
+        """
+        itm_embeds: (3*N, D)
+        enc: nn.Module that projects cls_embeds
+        """
+        itm_scores = enc(itm_embeds)  # (3*N, 2)
+        bs = itm_scores.size(0) // 3
+        itm_labels = itm_scores.new_ones(3*bs, dtype=torch.long)
+        itm_labels[bs:] = 0
+        loss_itm = F.cross_entropy(itm_scores, itm_labels)
+        return loss_itm
+
+    def get_rand_indices(self, mask, k):
+        """
+        Args:
+            mask: (N, L) 0 indicates the positions that we can sample, 1 otherwise
+            k: #indices to sample at each row
+        Returns:
+            (N, k) indices
+        """
+        mask = mask.float()
+        mask = mask - 10000 * mask
+        mask += torch.randn_like(mask)
+        _, indices = torch.sort(mask, dim=1, descending=True)
+        indices = indices[:, :k].contiguous()  
+        return indices

svitt/sparse_config.py

@@ -0,0 +1,351 @@
+# coding=utf-8
+# Copyright Microsoft Research and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from collections import OrderedDict
+from typing import Mapping
+
+from transformers.configuration_utils import PretrainedConfig
+from transformers.onnx import OnnxConfig
+
+
+BERT_PRETRAINED_CONFIG_ARCHIVE_MAP = {
+    "bert-base-uncased": "https://huggingface.co/bert-base-uncased/resolve/main/config.json",
+    "bert-large-uncased": "https://huggingface.co/bert-large-uncased/resolve/main/config.json",
+    "bert-base-cased": "https://huggingface.co/bert-base-cased/resolve/main/config.json",
+    "bert-large-cased": "https://huggingface.co/bert-large-cased/resolve/main/config.json",
+    "bert-base-multilingual-uncased": "https://huggingface.co/bert-base-multilingual-uncased/resolve/main/config.json",
+    "bert-base-multilingual-cased": "https://huggingface.co/bert-base-multilingual-cased/resolve/main/config.json",
+    "bert-base-chinese": "https://huggingface.co/bert-base-chinese/resolve/main/config.json",
+    "bert-base-german-cased": "https://huggingface.co/bert-base-german-cased/resolve/main/config.json",
+    "bert-large-uncased-whole-word-masking": "https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/config.json",
+    "bert-large-cased-whole-word-masking": "https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/config.json",
+    "bert-large-uncased-whole-word-masking-finetuned-squad": "https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/config.json",
+    "bert-large-cased-whole-word-masking-finetuned-squad": "https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/config.json",
+    "bert-base-cased-finetuned-mrpc": "https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/config.json",
+    "bert-base-german-dbmdz-cased": "https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/config.json",
+    "bert-base-german-dbmdz-uncased": "https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/config.json",
+    "cl-tohoku/bert-base-japanese": "https://huggingface.co/cl-tohoku/bert-base-japanese/resolve/main/config.json",
+    "cl-tohoku/bert-base-japanese-whole-word-masking": "https://huggingface.co/cl-tohoku/bert-base-japanese-whole-word-masking/resolve/main/config.json",
+    "cl-tohoku/bert-base-japanese-char": "https://huggingface.co/cl-tohoku/bert-base-japanese-char/resolve/main/config.json",
+    "cl-tohoku/bert-base-japanese-char-whole-word-masking": "https://huggingface.co/cl-tohoku/bert-base-japanese-char-whole-word-masking/resolve/main/config.json",
+    "TurkuNLP/bert-base-finnish-cased-v1": "https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/config.json",
+    "TurkuNLP/bert-base-finnish-uncased-v1": "https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/config.json",
+    "wietsedv/bert-base-dutch-cased": "https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/config.json",
+    # See all BERT models at https://huggingface.co/models?filter=bert
+}
+
+
+class BertConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`BertModel`] or a
+    [`TFBertModel`]. It is used to instantiate a BERT model according to the specified arguments,
+    defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration
+    to that of the BERT [bert-base-uncased](https://huggingface.co/bert-base-uncased) architecture.
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model
+    outputs. Read the documentation from [`PretrainedConfig`] for more information.
+
+
+    Args:
+        vocab_size (`int`, *optional*, defaults to 30522):
+            Vocabulary size of the BERT model. Defines the number of different tokens that can be represented by the
+            `inputs_ids` passed when calling [`BertModel`] or
+            [`TFBertModel`].
+        hidden_size (`int`, *optional*, defaults to 768):
+            Dimensionality of the encoder layers and the pooler layer.
+        num_hidden_layers (`int`, *optional*, defaults to 12):
+            Number of hidden layers in the Transformer encoder.
+        num_attention_heads (`int`, *optional*, defaults to 12):
+            Number of attention heads for each attention layer in the Transformer encoder.
+        intermediate_size (`int`, *optional*, defaults to 3072):
+            Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder.
+        hidden_act (`str` or `Callable`, *optional*, defaults to `"gelu"`):
+            The non-linear activation function (function or string) in the encoder and pooler. If string,
+            `"gelu"`, `"relu"`, `"silu"` and `"gelu_new"` are supported.
+        hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
+            The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
+        attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1):
+            The dropout ratio for the attention probabilities.
+        max_position_embeddings (`int`, *optional*, defaults to 512):
+            The maximum sequence length that this model might ever be used with. Typically set this to something large
+            just in case (e.g., 512 or 1024 or 2048).
+        type_vocab_size (`int`, *optional*, defaults to 2):
+            The vocabulary size of the `token_type_ids` passed when calling [`BertModel`] or
+            [`TFBertModel`].
+        initializer_range (`float`, *optional*, defaults to 0.02):
+            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+        layer_norm_eps (`float`, *optional*, defaults to 1e-12):
+            The epsilon used by the layer normalization layers.
+        position_embedding_type (`str`, *optional*, defaults to `"absolute"`):
+            Type of position embedding. Choose one of `"absolute"`, `"relative_key"`,
+            `"relative_key_query"`. For positional embeddings use `"absolute"`. For more information on
+            `"relative_key"`, please refer to [Self-Attention with Relative Position Representations (Shaw et al.)](https://arxiv.org/abs/1803.02155). For more information on `"relative_key_query"`, please refer to
+            *Method 4* in [Improve Transformer Models with Better Relative Position Embeddings (Huang et al.)](https://arxiv.org/abs/2009.13658).
+        use_cache (`bool`, *optional*, defaults to `True`):
+            Whether or not the model should return the last key/values attentions (not used by all models). Only
+            relevant if `config.is_decoder=True`.
+        classifier_dropout (`float`, *optional*):
+            The dropout ratio for the classification head.
+
+    Examples:
+
+    ```python
+    >>> from transformers import BertModel, BertConfig
+
+    >>> # Initializing a BERT bert-base-uncased style configuration
+    >>> configuration = BertConfig()
+
+    >>> # Initializing a model from the bert-base-uncased style configuration
+    >>> model = BertModel(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+    model_type = "bert"
+
+    def __init__(
+        self,
+        vocab_size=30522,
+        hidden_size=768,
+        num_hidden_layers=12,
+        num_attention_heads=12,
+        intermediate_size=3072,
+        hidden_act="gelu",
+        hidden_dropout_prob=0.1,
+        attention_probs_dropout_prob=0.1,
+        max_position_embeddings=512,
+        type_vocab_size=2,
+        initializer_range=0.02,
+        layer_norm_eps=1e-12,
+        pad_token_id=0,
+        position_embedding_type="absolute",
+        use_cache=True,
+        classifier_dropout=None,
+        token_keep_rate=1,
+        token_keep_strategy='cls_attn',
+        token_drop_loc=[9],
+        **kwargs
+    ):
+        super().__init__(pad_token_id=pad_token_id, **kwargs)
+
+        self.vocab_size = vocab_size
+        self.hidden_size = hidden_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.hidden_act = hidden_act
+        self.intermediate_size = intermediate_size
+        self.hidden_dropout_prob = hidden_dropout_prob
+        self.attention_probs_dropout_prob = attention_probs_dropout_prob
+        self.max_position_embeddings = max_position_embeddings
+        self.type_vocab_size = type_vocab_size
+        self.initializer_range = initializer_range
+        self.layer_norm_eps = layer_norm_eps
+        self.position_embedding_type = position_embedding_type
+        self.use_cache = use_cache
+        self.classifier_dropout = classifier_dropout
+        self.token_keep_rate = token_keep_rate
+        self.token_keep_strategy = token_keep_strategy
+        self.token_drop_loc = token_drop_loc
+
+
+class BertOnnxConfig(OnnxConfig):
+    @property
+    def inputs(self) -> Mapping[str, Mapping[int, str]]:
+        return OrderedDict(
+            [
+                ("input_ids", {0: "batch", 1: "sequence"}),
+                ("attention_mask", {0: "batch", 1: "sequence"}),
+                ("token_type_ids", {0: "batch", 1: "sequence"}),
+            ]
+        )
+
+
+BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP = {
+    "microsoft/beit-base-patch16-224-in22k": "https://huggingface.co/microsoft/beit-base-patch16-224-in22k/resolve/main/config.json",
+    # See all BEiT models at https://huggingface.co/models?filter=beit
+}
+
+
+class BeitConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`BeitModel`]. It is used to
+    instantiate an BEiT model according to the specified arguments, defining the model architecture. Instantiating a
+    configuration with the defaults will yield a similar configuration to that of the BEiT
+    [microsoft/beit-base-patch16-224-in22k](https://huggingface.co/microsoft/beit-base-patch16-224-in22k)
+    architecture.
+
+    Args:
+        vocab_size (`int`, *optional*, defaults to 8092):
+            Vocabulary size of the BEiT model. Defines the number of different image tokens that can be used during
+            pre-training.
+        hidden_size (`int`, *optional*, defaults to 768):
+            Dimensionality of the encoder layers and the pooler layer.
+        num_hidden_layers (`int`, *optional*, defaults to 12):
+            Number of hidden layers in the Transformer encoder.
+        num_attention_heads (`int`, *optional*, defaults to 12):
+            Number of attention heads for each attention layer in the Transformer encoder.
+        intermediate_size (`int`, *optional*, defaults to 3072):
+            Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
+        hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
+            The non-linear activation function (function or string) in the encoder and pooler. If string,
+            `"gelu"`, `"relu"`, `"selu"` and `"gelu_new"` are supported.
+        hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
+            The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
+        attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1):
+            The dropout ratio for the attention probabilities.
+        initializer_range (`float`, *optional*, defaults to 0.02):
+            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+        layer_norm_eps (`float`, *optional*, defaults to 1e-12):
+            The epsilon used by the layer normalization layers.
+        image_size (`int`, *optional*, defaults to `224`):
+            The size (resolution) of each image.
+        patch_size (`int`, *optional*, defaults to `16`):
+            The size (resolution) of each patch.
+        num_channels (`int`, *optional*, defaults to `3`):
+            The number of input channels.
+        use_mask_token (`bool`, *optional*, defaults to `False`):
+            Whether to use a mask token for masked image modeling.
+        use_absolute_position_embeddings (`bool`, *optional*, defaults to `False`):
+            Whether to use BERT-style absolute position embeddings.
+        use_relative_position_bias (`bool`, *optional*, defaults to `False`):
+            Whether to use T5-style relative position embeddings in the self-attention layers.
+        use_shared_relative_position_bias (`bool`, *optional*, defaults to `False`):
+            Whether to use the same relative position embeddings across all self-attention layers of the Transformer.
+        layer_scale_init_value (`float`, *optional*, defaults to 0.1):
+            Scale to use in the self-attention layers. 0.1 for base, 1e-5 for large. Set 0 to disable layer scale.
+        drop_path_rate (`float`, *optional*, defaults to 0.1):
+            Stochastic depth rate per sample (when applied in the main path of residual layers).
+        use_mean_pooling (`bool`, *optional*, defaults to `True`):
+            Whether to mean pool the final hidden states of the patches instead of using the final hidden state of the
+            CLS token, before applying the classification head.
+        out_indices (`List[int]`, *optional*, defaults to `[3, 5, 7, 11]`):
+            Indices of the feature maps to use for semantic segmentation.
+        pool_scales (`Tuple[int]`, *optional*, defaults to `[1, 2, 3, 6]`):
+            Pooling scales used in Pooling Pyramid Module applied on the last feature map.
+        use_auxiliary_head (`bool`, *optional*, defaults to `True`):
+            Whether to use an auxiliary head during training.
+        auxiliary_loss_weight (`float`, *optional*, defaults to 0.4):
+            Weight of the cross-entropy loss of the auxiliary head.
+        auxiliary_channels (`int`, *optional*, defaults to 256):
+            Number of channels to use in the auxiliary head.
+        auxiliary_num_convs (`int`, *optional*, defaults to 1):
+            Number of convolutional layers to use in the auxiliary head.
+        auxiliary_concat_input (`bool`, *optional*, defaults to `False`):
+            Whether to concatenate the output of the auxiliary head with the input before the classification layer.
+        semantic_loss_ignore_index (`int`, *optional*, defaults to 255):
+            The index that is ignored by the loss function of the semantic segmentation model.
+
+    Example:
+
+    ```python
+    >>> from transformers import BeitModel, BeitConfig
+
+    >>> # Initializing a BEiT beit-base-patch16-224-in22k style configuration
+    >>> configuration = BeitConfig()
+
+    >>> # Initializing a model from the beit-base-patch16-224-in22k style configuration
+    >>> model = BeitModel(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+    model_type = "beit"
+
+    def __init__(
+        self,
+        vocab_size=8192,
+        hidden_size=768,
+        num_hidden_layers=12,
+        num_attention_heads=12,
+        intermediate_size=3072,
+        hidden_act="gelu",
+        hidden_dropout_prob=0.0,
+        attention_probs_dropout_prob=0.0,
+        initializer_range=0.02,
+        layer_norm_eps=1e-12,
+        is_encoder_decoder=False,
+        image_size=224,
+        patch_size=16,
+        num_channels=3,
+        use_mask_token=False,
+        use_absolute_position_embeddings=False,
+        use_relative_position_bias=False,
+        use_shared_relative_position_bias=False,
+        layer_scale_init_value=0.1,
+        drop_path_rate=0.1,
+        use_mean_pooling=True,
+        out_indices=[3, 5, 7, 11],
+        pool_scales=[1, 2, 3, 6],
+        use_auxiliary_head=True,
+        auxiliary_loss_weight=0.4,
+        auxiliary_channels=256,
+        auxiliary_num_convs=1,
+        auxiliary_concat_input=False,
+        semantic_loss_ignore_index=255,
+        token_keep_rate=1,
+        token_keep_strategy='cls_attn',
+        token_drop_loc=[3, 6, 9],
+        sparse_random_attn=None,
+        sparse_local_attn=1,
+        attn_block_size=1,
+        num_cls_tokens=1,
+        token_3d_order='none',
+        **kwargs
+    ):
+        super().__init__(**kwargs)
+
+        self.vocab_size = vocab_size
+        self.hidden_size = hidden_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.intermediate_size = intermediate_size
+        self.hidden_act = hidden_act
+        self.hidden_dropout_prob = hidden_dropout_prob
+        self.attention_probs_dropout_prob = attention_probs_dropout_prob
+        self.initializer_range = initializer_range
+        self.layer_norm_eps = layer_norm_eps
+
+        self.image_size = image_size
+        self.patch_size = patch_size
+        self.num_channels = num_channels
+        self.use_mask_token = use_mask_token
+        self.use_absolute_position_embeddings = use_absolute_position_embeddings
+        self.use_relative_position_bias = use_relative_position_bias
+        self.use_shared_relative_position_bias = use_shared_relative_position_bias
+        self.layer_scale_init_value = layer_scale_init_value
+        self.drop_path_rate = drop_path_rate
+        self.use_mean_pooling = use_mean_pooling
+        # decode head attributes (semantic segmentation)
+        self.out_indices = out_indices
+        self.pool_scales = pool_scales
+        # auxiliary head attributes (semantic segmentation)
+        self.use_auxiliary_head = use_auxiliary_head
+        self.auxiliary_loss_weight = auxiliary_loss_weight
+        self.auxiliary_channels = auxiliary_channels
+        self.auxiliary_num_convs = auxiliary_num_convs
+        self.auxiliary_concat_input = auxiliary_concat_input
+        self.semantic_loss_ignore_index = semantic_loss_ignore_index
+
+        # node sparsification
+        self.token_keep_rate = token_keep_rate
+        self.token_keep_strategy = token_keep_strategy
+        self.token_drop_loc = token_drop_loc
+        # edge sparsification
+        self.sparse_random_attn = sparse_random_attn
+        self.sparse_local_attn = sparse_local_attn
+        self.attn_block_size = attn_block_size
+        self.num_cls_tokens = num_cls_tokens
+        # token order
+        self.token_3d_order = token_3d_order

svitt/sparse_xbeit.py

@@ -0,0 +1,1585 @@
+# coding=utf-8
+# Copyright 2021 Google AI, Ross Wightman, The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+""" PyTorch BEiT model. """
+
+
+import collections.abc
+import math
+import numpy as np
+from dataclasses import dataclass
+from typing import Optional, Tuple
+import zCurve
+import hilbert
+
+import torch
+import torch.utils.checkpoint
+from torch import nn
+from torch.nn import CrossEntropyLoss, MSELoss
+from einops import rearrange, repeat
+
+from transformers.activations import ACT2FN
+from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward, replace_return_docstrings
+from transformers.modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling, MaskedLMOutput, SequenceClassifierOutput
+from transformers.modeling_utils import PreTrainedModel, find_pruneable_heads_and_indices, prune_linear_layer
+from svitt.sparse_config import BeitConfig
+
+
+_CONFIG_FOR_DOC = "BeitConfig"
+_CHECKPOINT_FOR_DOC = "microsoft/beit-base-patch16-224"
+
+BEIT_PRETRAINED_MODEL_ARCHIVE_LIST = [
+    "microsoft/beit-base-patch16-224",
+    # See all BEiT models at https://huggingface.co/models?filter=beit
+]
+
+
+@dataclass
+class BeitModelOutputWithPooling(BaseModelOutputWithPooling):
+    """
+    Class for outputs of :class:`~transformers.BeitModel`.
+
+    Args:
+        last_hidden_state (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`):
+            Sequence of hidden-states at the output of the last layer of the model.
+        pooler_output (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, hidden_size)`):
+            Average of the last layer hidden states of the patch tokens (excluding the `[CLS]` token) if
+            `config.use_mean_pooling` is set to True. If set to False, then the final hidden state of the `[CLS]` token
+            will be returned.
+        hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
+            Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer)
+            of shape :obj:`(batch_size, sequence_length, hidden_size)`.
+
+            Hidden-states of the model at the output of each layer plus the initial embedding outputs.
+        attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
+            Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape :obj:`(batch_size, num_heads,
+            sequence_length, sequence_length)`.
+
+            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
+            heads.
+    """
+    token_idx: Optional[Tuple[torch.LongTensor]] = None
+
+
+@dataclass
+class BeitModelOutput(BaseModelOutput):
+    token_idx: Optional[Tuple[torch.LongTensor]] = None
+
+
+# Inspired by
+# https://github.com/rwightman/pytorch-image-models/blob/b9bd960a032c75ca6b808ddeed76bee5f3ed4972/timm/models/layers/helpers.py
+# From PyTorch internals
+def to_2tuple(x):
+    if isinstance(x, collections.abc.Iterable):
+        return x
+    return (x, x)
+
+
+# Based on https://github.com/rwightman/pytorch-image-models/blob/a2727c1bf78ba0d7b5727f5f95e37fb7f8866b1f/timm/models/layers/drop.py
+def drop_path(x, drop_prob: float = 0.0, training: bool = False):
+    """
+    Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
+
+    Comment by Ross Wightman: This is the same as the DropConnect impl I created for EfficientNet, etc networks,
+    however, the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper...
+    See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted for changing the
+    layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use 'survival rate' as the
+    argument.
+    """
+    if drop_prob == 0.0 or not training:
+        return x
+    keep_prob = 1 - drop_prob
+    shape = (x.shape[0],) + (1,) * (x.ndim - 1)  # work with diff dim tensors, not just 2D ConvNets
+    random_tensor = keep_prob + torch.rand(shape, dtype=x.dtype, device=x.device)
+    random_tensor.floor_()  # binarize
+    output = x.div(keep_prob) * random_tensor
+    return output
+
+
+class DropPath(nn.Module):
+    """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks)."""
+
+    def __init__(self, drop_prob=None):
+        super().__init__()
+        self.drop_prob = drop_prob
+
+    def forward(self, x):
+        return drop_path(x, self.drop_prob, self.training)
+
+    def extra_repr(self) -> str:
+        return "p={}".format(self.drop_prob)
+
+
+# Based on timm implementation, which can be found here:
+# https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py
+class BeitEmbeddings(nn.Module):
+    """
+    Construct the CLS token, position and patch embeddings. Optionally, also the mask token.
+
+    """
+
+    def __init__(self, config):
+        super().__init__()
+
+        self.cls_token = nn.Parameter(torch.zeros(1, 1, config.hidden_size))
+        if config.use_mask_token:
+            self.mask_token = nn.Parameter(torch.zeros(1, 1, config.hidden_size))
+        else:
+            self.mask_token = None
+        self.patch_embeddings = PatchEmbeddings(
+            image_size=config.image_size,
+            patch_size=config.patch_size,
+            num_channels=config.num_channels,
+            embed_dim=config.hidden_size,
+        )
+        num_patches = self.patch_embeddings.num_patches
+        if config.use_absolute_position_embeddings:
+            self.position_embeddings = nn.Parameter(torch.zeros(1, num_patches + 1, config.hidden_size))
+        else:
+            self.position_embeddings = None
+        self.dropout = nn.Dropout(config.hidden_dropout_prob)
+
+    def forward(self, pixel_values, bool_masked_pos=None):
+
+        if pixel_values.ndim == 5:  # video input=
+            embeddings = self.patch_embeddings(pixel_values.flatten(0, 1))
+            embeddings = rearrange(embeddings, '(b m) n d -> b (m n) d', m=pixel_values.shape[1])
+        else:  # image input
+            embeddings = self.patch_embeddings(pixel_values)
+            
+        batch_size, seq_len, _ = embeddings.size()
+
+        cls_tokens = self.cls_token.expand(batch_size, -1, -1)
+        if bool_masked_pos is not None:
+            mask_tokens = self.mask_token.expand(batch_size, seq_len, -1)
+            # replace the masked visual tokens by mask_tokens
+            w = bool_masked_pos.unsqueeze(-1).type_as(mask_tokens)
+            embeddings = embeddings * (1 - w) + mask_tokens * w
+
+        embeddings = torch.cat((cls_tokens, embeddings), dim=1)
+        if self.position_embeddings is not None:
+            embeddings = embeddings + self.position_embeddings
+        embeddings = self.dropout(embeddings)
+
+        return embeddings
+
+
+# Based on timm implementation, which can be found here:
+# https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py
+class PatchEmbeddings(nn.Module):
+    """
+    Image to Patch Embedding.
+    """
+
+    def __init__(self, image_size=224, patch_size=16, num_channels=3, embed_dim=768):
+        super().__init__()
+        image_size = to_2tuple(image_size)
+        patch_size = to_2tuple(patch_size)
+        num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
+        patch_shape = (image_size[0] // patch_size[0], image_size[1] // patch_size[1])
+        self.image_size = image_size
+        self.patch_size = patch_size
+        self.num_patches = num_patches
+        self.patch_shape = patch_shape
+
+        self.projection = nn.Conv2d(num_channels, embed_dim, kernel_size=patch_size, stride=patch_size)
+
+    def forward(self, pixel_values):
+        batch_size, num_channels, height, width = pixel_values.shape
+        # FIXME look at relaxing size constraints
+        if height != self.image_size[0] or width != self.image_size[1]:
+            raise ValueError(
+                f"Input image size ({height}*{width}) doesn't match model ({self.image_size[0]}*{self.image_size[1]})."
+            )
+        x = self.projection(pixel_values).flatten(2).transpose(1, 2)
+
+        return x
+
+
+class BeitSelfAttention(nn.Module):
+    def __init__(self, config, window_size=None):
+        super().__init__()
+        if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
+            raise ValueError(
+                f"The hidden size {config.hidden_size,} is not a multiple of the number of attention "
+                f"heads {config.num_attention_heads}."
+            )
+
+        self.num_attention_heads = config.num_attention_heads
+        self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
+        self.all_head_size = self.num_attention_heads * self.attention_head_size
+
+        self.query = nn.Linear(config.hidden_size, self.all_head_size)
+        self.key = nn.Linear(config.hidden_size, self.all_head_size, bias=False)
+        self.value = nn.Linear(config.hidden_size, self.all_head_size)
+
+        self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
+
+        # sparse params
+        self.random_attn = config.sparse_random_attn
+        self.local_attn = config.sparse_local_attn
+        self.block_size = config.attn_block_size
+        self.num_cls_tokens = config.num_cls_tokens
+        if self.local_attn is not None and self.random_attn is not None:
+            self.num_kv_blocks = self.local_attn + self.random_attn
+
+        if window_size:
+            self.relative_position_bias = BeitRelativePositionBias3D(config, window_size=window_size)
+        else:
+            self.relative_position_bias = None
+    
+    def split_heads(self, x):
+        return rearrange(x, 'b n (h d) -> b h n d', h=self.num_attention_heads)
+    
+    def join_heads(self, x):
+        return rearrange(x, 'b h n d -> b n (h d)')
+    
+    def blockify(self, x):
+        assert x.dim() == 4, f"Unsupported input shape {x.shape}"
+        seq_len = x.shape[2]
+        if seq_len % self.block_size > 0:  # seq_len not divisible by block_size, zero pad
+            pad_len = self.block_size - seq_len % self.block_size
+            x = nn.functional.pad(x, (0, 0, 0, pad_len))
+        else:
+            pad_len = 0
+        x = rearrange(x, 'b h (m n) d -> b h m n d', n=self.block_size)
+        return x, pad_len
+    
+    def dense_attention(self, q, k, v, head_mask=None, relative_position_bias=None, q_idx=None, k_idx=None):
+        # q, k, v: (bsz, num_heads, seq_len, dims)
+        assert k.shape[2] == v.shape[2], "Key and value shapes mismatch"
+        sim = torch.einsum('b h i d, b h j d -> b h i j', q, k)
+        sim = sim / math.sqrt(self.attention_head_size)
+
+        # Add relative position bias if present.
+        if self.relative_position_bias is not None:
+            if q_idx is not None and q_idx.ndim == 2:
+                assert k_idx is not None and len(q_idx) == len(k_idx)
+                bias = torch.stack([
+                    self.relative_position_bias(from_idx=q_idx_, to_idx=k_idx_)
+                    for q_idx_, k_idx_ in zip(q_idx, k_idx)
+                ])
+            else:
+                bias = self.relative_position_bias(from_idx=q_idx, to_idx=k_idx).unsqueeze(0)
+            sim = sim + bias
+
+        # Add shared relative position bias if provided.
+        if relative_position_bias is not None:
+            sim = sim + relative_position_bias
+
+        # Normalize the attention scores to probabilities.
+        attn = sim.softmax(dim=-1)
+        attn = self.dropout(attn)
+        if head_mask is not None:
+            attn = attn * head_mask
+
+        out = torch.einsum('b h i j, b h j d -> b h i d', attn, v)
+        return out, attn
+    
+    def _sparse_attn_relative_position_bias(self, q_idx, pad_q, attn_idx, group_len):
+        q_idx_blk = nn.functional.pad(q_idx, (0, pad_q)).view(-1, self.block_size)
+        attn_idx_flt = rearrange(q_idx_blk[attn_idx], 'm n j -> m (n j)')  # (seq_len, num_kv_blocks * group_len)
+        cls_idx = torch.arange(self.num_cls_tokens, device=q_idx.device)
+        cls_idx = repeat(cls_idx, 'n -> m n', m=len(attn_idx_flt))
+        attn_idx_flt = torch.cat((cls_idx, attn_idx_flt), dim=1)
+        attn_idx_flt = repeat(attn_idx_flt, 'm n -> (m i) n', i=group_len)
+        if pad_q > 0:
+            attn_idx_flt = attn_idx_flt[:-pad_q]
+        bias_flt = self.relative_position_bias(from_idx=q_idx, to_idx=attn_idx_flt)
+        if pad_q > 0:
+            bias_flt = nn.functional.pad(bias_flt, (0, 0, 0, pad_q))
+        return rearrange(bias_flt, 'h (m i) n -> h m i n', i=group_len)  # num_heads, seq_len, group_len, (num_kv_blocks * group_len + num_cls_tokens)
+    
+    def sparse_attention(self, q, k, v, head_mask=None, relative_position_bias=None, q_idx=None, mimic_full=False):
+        assert self.local_attn == 0 or self.local_attn % 2 == 1, "Even local window size not supported"
+        assert k.shape[2] == v.shape[2], "Key and value shapes mismatch"
+
+        
+        if not mimic_full:
+            cls_k, k = k[..., :self.num_cls_tokens, :], k[..., self.num_cls_tokens:, :]  # cls_k: (bsz, num_heads, num_cls_tokens, dims)
+            cls_v, v = v[..., :self.num_cls_tokens, :], v[..., self.num_cls_tokens:, :]
+
+        # pad token sequence to multiples of block_size
+        if mimic_full:
+            bsz, num_heads, seq_len, dims = q.shape
+        else:
+            q, pad_q = self.blockify(q)  # q: (bsz, num_heads, seq_len, group_len, dims)
+            k, pad_k = self.blockify(k)
+            v, pad_v = self.blockify(v)
+            bsz, num_heads, seq_len, group_len, dims = q.shape
+
+            # global attention
+            cls_sim = torch.einsum('b h n i d, b h j d -> b h n i j', q, cls_k)  # (bsz, num_heads, seq_len, group_len, num_cls_tokens)
+
+        if mimic_full:
+            sim = torch.einsum('b h i d, b h j d -> b h i j', q, k)
+            sim = sim / math.sqrt(self.attention_head_size)
+            sim = sim + self.relative_position_bias(from_idx=q_idx).unsqueeze(0)
+        
+        else:
+            # initialize empty sim matrix
+            sim = torch.empty((bsz, num_heads, seq_len, self.num_kv_blocks, group_len, group_len), device=q.device)
+            attn_idx = torch.zeros((seq_len, self.num_kv_blocks), dtype=torch.int64, device=q.device)
+
+            # local window attention
+            cnt = 0
+            if self.local_attn > 0:
+                num_rolls = self.local_attn // 2
+                for r in range(-num_rolls, num_rolls + 1):
+                    sim[..., cnt, :, :] = torch.einsum('b h n i d, b h n j d -> b h n i j', q, k.roll(-r, dims=2))
+                    attn_idx[:, cnt] = torch.arange(seq_len, device=q.device).roll(r)
+                    cnt += 1
+            
+            # random attention
+            if self.random_attn > 0:
+                # generate random attention pattern
+                rand = torch.rand((seq_len, seq_len), device=q.device)
+                if self.local_attn > 0:
+                    # avoid overlap with local attention
+                    for r in range(-num_rolls, num_rolls + 1):
+                        tgt_idx = list(i % seq_len for i in range(r, seq_len + r))
+                        rand[range(seq_len), tgt_idx] = 0
+                _, idx = rand.topk(self.random_attn, dim=-1)  # seq_len, random_attn
+                idx, _ = torch.sort(idx, dim=1)
+                attn_idx[:, cnt:] = idx
+
+                idx_ = repeat(idx, 'n m -> b h n m i d', b=bsz, h=num_heads, i=group_len, d=dims)
+
+                for r in range(self.random_attn):
+                    sim[..., cnt, :, :] = torch.einsum('b h n i d, b h n j d -> b h n i j', q, k.gather(2, idx_[..., r, :, :]))
+                    cnt += 1
+
+            sim = rearrange(sim, 'b h m n i j -> b h m i (n j)')  # (bsz, num_heads, seq_len, group_len, num_kv_blocks * group_len)
+            sim = torch.cat((cls_sim, sim), -1)
+            sim = sim / math.sqrt(self.attention_head_size)
+
+            # Add relative position bias if present.
+            # NOTE: we assume q and k (excluding cls) use same token indexing, for relative position embedding
+            if self.relative_position_bias is not None:
+                assert q_idx is not None, "query index required for relative position bias"
+                if q_idx.ndim == 2:
+                    # different indices for each sample
+                    bias = torch.stack([
+                        self._sparse_attn_relative_position_bias(q_idx_, pad_q, attn_idx, group_len)
+                        for q_idx_ in q_idx
+                    ])
+                else:
+                    bias = self._sparse_attn_relative_position_bias(q_idx, pad_q, attn_idx, group_len).unsqueeze(0)
+                sim = sim + bias
+
+        # Add shared relative position bias if provided.
+        if relative_position_bias is not None:
+            raise NotImplementedError
+            sim = sim + relative_position_bias
+
+        attn = sim.softmax(dim=-1)
+        attn = self.dropout(attn)
+        if head_mask is not None:
+            attn = attn * head_mask
+
+        # block attention
+        if mimic_full:
+            out = torch.einsum('b h i j, b h j d -> b h i d', attn, v)
+
+        else:
+            out = torch.empty((bsz, num_heads, seq_len, group_len, dims), device=q.device)
+            for m in range(seq_len):
+                v_row = torch.index_select(v, 2, attn_idx[m])
+                v_row = rearrange(v_row, 'b h n j d -> b h (n j) d')  # (bsz, num_heads, num_kv_blocks * group_len, dims)
+                v_row = torch.cat((cls_v, v_row), 2)
+                out[..., m, :, :] = torch.einsum('b h i j, b h j d -> b h i d', attn[..., m, :, :], v_row)
+            out = rearrange(out, 'b h n i d -> b h (n i) d')
+            if pad_q > 0:
+                out = out[..., :-pad_q, :]
+
+        return out, attn
+        
+    def forward(self, hidden_states, head_mask=None, output_attentions=False, relative_position_bias=None, token_idx=None):
+        # compute qkv
+        q = self.split_heads(self.query(hidden_states))
+        k = self.split_heads(self.key(hidden_states))
+        v = self.split_heads(self.value(hidden_states))
+        
+        # combine local token_idx with cls tokens
+        # NOTE: assume token_idx starts from 0
+        cls_q_idx = torch.arange(self.num_cls_tokens, device=q.device)
+        if token_idx is not None:
+            if token_idx.ndim == 2:
+                cls_q_idx = repeat(cls_q_idx, 'n -> b n', b=q.shape[0])
+            all_token_idx = torch.cat((cls_q_idx, token_idx + self.num_cls_tokens), dim=-1)
+        else:
+            all_token_idx = None
+
+        if self.random_attn is None:
+            outputs, attention_probs = self.dense_attention(q, k, v, head_mask=head_mask,
+                                                            relative_position_bias=relative_position_bias,
+                                                            q_idx=all_token_idx,
+                                                            k_idx=all_token_idx)
+            cls_attention_probs = attention_probs[..., :self.num_cls_tokens, :]
+
+        else:
+            cls_q, q = q[..., :self.num_cls_tokens, :], q[..., self.num_cls_tokens:, :]
+
+            # dense global attention (num_cls_tokens, seq_len)
+            cls_outputs, cls_attention_probs = self.dense_attention(cls_q, k, v, head_mask=head_mask,
+                                                                    relative_position_bias=relative_position_bias,
+                                                                    q_idx=cls_q_idx,
+                                                                    k_idx=all_token_idx)
+
+            # sparse local attention (local_seq_len, seq_len)
+            if token_idx is None:
+                token_idx = torch.arange(q.shape[-2], device=q.device)
+            outputs, attention_probs = self.sparse_attention(q, k, v, head_mask=head_mask,
+                                                             relative_position_bias=relative_position_bias,
+                                                             q_idx=token_idx + self.num_cls_tokens)
+
+            outputs = torch.cat((cls_outputs, outputs), dim=2)
+        
+        outputs = self.join_heads(outputs)
+
+        outputs = (outputs, cls_attention_probs) if output_attentions else (outputs,)
+
+        return outputs
+
+
+class BeitSelfOutput(nn.Module):
+    """
+    The residual connection is defined in BeitLayer instead of here (as is the case with other models), due to the
+    layernorm applied before each block.
+    """
+
+    def __init__(self, config):
+        super().__init__()
+        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
+        self.dropout = nn.Dropout(config.hidden_dropout_prob)
+
+    def forward(self, hidden_states, input_tensor, gamma=None):
+        hidden_states = self.dense(hidden_states)
+        hidden_states = self.dropout(hidden_states)
+
+        return hidden_states
+
+
+class BeitAttention(nn.Module):
+    def __init__(self, config, window_size=None):
+        super().__init__()
+        self.attention = BeitSelfAttention(config, window_size=window_size)
+        self.output = BeitSelfOutput(config)
+        self.pruned_heads = set()
+
+    def prune_heads(self, heads):
+        if len(heads) == 0:
+            return
+        heads, index = find_pruneable_heads_and_indices(
+            heads, self.attention.num_attention_heads, self.attention.attention_head_size, self.pruned_heads
+        )
+
+        # Prune linear layers
+        self.attention.query = prune_linear_layer(self.attention.query, index)
+        self.attention.key = prune_linear_layer(self.attention.key, index)
+        self.attention.value = prune_linear_layer(self.attention.value, index)
+        self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)
+
+        # Update hyper params and store pruned heads
+        self.attention.num_attention_heads = self.attention.num_attention_heads - len(heads)
+        self.attention.all_head_size = self.attention.attention_head_size * self.attention.num_attention_heads
+        self.pruned_heads = self.pruned_heads.union(heads)
+
+    def forward(self, hidden_states, head_mask=None, output_attentions=False, relative_position_bias=None, token_idx=None):
+        self_outputs = self.attention(hidden_states, head_mask, output_attentions, relative_position_bias, token_idx)
+
+        attention_output = self.output(self_outputs[0], hidden_states)
+
+        outputs = (attention_output,) + self_outputs[1:]  # add attentions if we output them
+        return outputs
+
+
+class BeitIntermediate(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
+        if isinstance(config.hidden_act, str):
+            self.intermediate_act_fn = ACT2FN[config.hidden_act]
+        else:
+            self.intermediate_act_fn = config.hidden_act
+
+    def forward(self, hidden_states):
+        hidden_states = self.dense(hidden_states)
+        hidden_states = self.intermediate_act_fn(hidden_states)
+
+        return hidden_states
+
+
+class BeitOutput(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
+        self.dropout = nn.Dropout(config.hidden_dropout_prob)
+
+    def forward(self, hidden_states):
+        hidden_states = self.dense(hidden_states)
+        hidden_states = self.dropout(hidden_states)
+
+        return hidden_states
+
+
+class BeitLayer(nn.Module):
+    """This corresponds to the Block class in the timm implementation."""
+
+    def __init__(self, config, window_size=None, drop_path_rate=0.0, 
+                 token_keep_rate=1.0):
+        super().__init__()
+        self.chunk_size_feed_forward = config.chunk_size_feed_forward
+        self.seq_len_dim = 1
+        self.attention = BeitAttention(config, window_size=window_size)
+        self.intermediate = BeitIntermediate(config)
+        self.output = BeitOutput(config)
+        self.layernorm_before = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+        self.drop_path = DropPath(drop_path_rate) if drop_path_rate > 0.0 else nn.Identity()
+        self.layernorm_after = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+
+        # sparse params
+        self.token_keep_rate = token_keep_rate
+        self.token_keep_strategy = config.token_keep_strategy
+        self.num_cls_tokens = config.num_cls_tokens
+
+        init_values = config.layer_scale_init_value
+        if init_values > 0:
+            self.lambda_1 = nn.Parameter(init_values * torch.ones((config.hidden_size)), requires_grad=True)
+            self.lambda_2 = nn.Parameter(init_values * torch.ones((config.hidden_size)), requires_grad=True)
+        else:
+            self.lambda_1, self.lambda_2 = None, None
+    
+    def sparsify(self, x, attn):
+        x_cls, x_ = x[:, :self.num_cls_tokens], x[:, self.num_cls_tokens:]
+        assert 0 < self.token_keep_rate <= 1, "Expected keep rate in range (0, 1]"
+        left_tokens = math.ceil(self.token_keep_rate * x_.size(1))
+
+        if self.token_keep_strategy == 'cls_attn':
+            if len(attn.shape) == 4:
+                attn = attn.mean(1)  # pool over attention heads
+            cls_attn = attn[:, 0, self.num_cls_tokens:]
+            _, idx = torch.topk(cls_attn, left_tokens, dim=1)  # [B, left_tokens]
+
+        elif self.token_keep_strategy == 'random':
+            rand = torch.rand(x_.shape[:2], device=x_.device)
+            _, idx = torch.topk(rand, left_tokens, dim=1)  # [B, left_tokens]
+
+        else:
+            raise NotImplementedError(f"Sparse strategy {self.token_keep_strategy} is not implemented")
+
+        idx, _ = torch.sort(idx, dim=1)
+        index = idx.unsqueeze(-1).expand(-1, -1, x_.size(-1))  # [B, left_tokens, C]
+        outputs = torch.cat((x_cls, x_.gather(1, index)), dim=1).contiguous()
+        return outputs, idx
+
+    def forward(self, hidden_states, head_mask=None, output_attentions=False, relative_position_bias=None, token_idx=None):
+        self_attention_outputs = self.attention(
+            self.layernorm_before(hidden_states),  # in BEiT, layernorm is applied before self-attention
+            head_mask,
+            output_attentions=(output_attentions or self.token_keep_rate < 1),
+            relative_position_bias=relative_position_bias,
+            token_idx=token_idx
+        )
+        attention_output = self_attention_outputs[0]
+        outputs = self_attention_outputs[1:]  # add self attentions if we output attention weights
+
+        # apply lambda_1 if present
+        if self.lambda_1 is not None:
+            attention_output = self.lambda_1 * attention_output
+
+        # first residual connection
+        hidden_states = self.drop_path(attention_output) + hidden_states
+
+        # in BEiT, layernorm is also applied after self-attention
+        layer_output = self.layernorm_after(hidden_states)
+
+        layer_output = self.intermediate(layer_output)
+        layer_output = self.output(layer_output)
+
+        if self.lambda_2 is not None:
+            layer_output = self.lambda_2 * layer_output
+
+        # second residual connection
+        layer_output = self.drop_path(layer_output) + hidden_states
+
+        # node sparsification
+        if self.token_keep_rate < 1:
+            layer_output, token_keep_idx = self.sparsify(layer_output, outputs[0])
+            if token_idx is not None:
+                if token_idx.ndim == 1:
+                    token_idx = repeat(token_idx, 'n -> b n', b=len(token_keep_idx))
+                token_keep_idx = token_idx.gather(1, token_keep_idx)
+            outputs = outputs + (token_keep_idx,)
+
+        outputs = (layer_output,) + outputs
+
+        return outputs
+
+
+class BeitRelativePositionBias(nn.Module):
+    def __init__(self, config, window_size):
+        super().__init__()
+        self.window_size = window_size
+        self.num_relative_distance = (2 * window_size[0] - 1) * (2 * window_size[1] - 1) + 3
+        self.relative_position_bias_table = nn.Parameter(
+            torch.zeros(self.num_relative_distance, config.num_attention_heads)
+        )  # 2*Wh-1 * 2*Ww-1, nH
+        # cls to token & token 2 cls & cls to cls
+
+        # get pair-wise relative position index for each token inside the window
+        coords_h = torch.arange(window_size[0])
+        coords_w = torch.arange(window_size[1])
+        coords = torch.stack(torch.meshgrid([coords_h, coords_w]))  # 2, Wh, Ww
+        coords_flatten = torch.flatten(coords, 1)  # 2, Wh*Ww
+        relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :]  # 2, Wh*Ww, Wh*Ww
+        relative_coords = relative_coords.permute(1, 2, 0).contiguous()  # Wh*Ww, Wh*Ww, 2
+        relative_coords[:, :, 0] += window_size[0] - 1  # shift to start from 0
+        relative_coords[:, :, 1] += window_size[1] - 1
+        relative_coords[:, :, 0] *= 2 * window_size[1] - 1
+        relative_position_index = torch.zeros(
+            size=(window_size[0] * window_size[1] + 1,) * 2, dtype=relative_coords.dtype
+        )
+        relative_position_index[1:, 1:] = relative_coords.sum(-1)  # Wh*Ww, Wh*Ww
+        relative_position_index[0, 0:] = self.num_relative_distance - 3
+        relative_position_index[0:, 0] = self.num_relative_distance - 2
+        relative_position_index[0, 0] = self.num_relative_distance - 1
+
+        self.register_buffer("relative_position_index", relative_position_index, persistent=False)
+
+    def forward(self):
+        relative_position_bias = self.relative_position_bias_table[self.relative_position_index.view(-1)].view(
+            self.window_size[0] * self.window_size[1] + 1, self.window_size[0] * self.window_size[1] + 1, -1
+        )  # Wh*Ww,Wh*Ww,nH
+
+        return relative_position_bias.permute(2, 0, 1).contiguous()  # nH, Wh*Ww, Wh*Ww
+
+
+class BeitRelativePositionBias3D(nn.Module):
+    """
+    3D relative position bias
+    """
+    def __init__(self, config, window_size, num_cls_tokens=1):
+        super().__init__()
+        self.window_size = window_size
+        self.num_cls_tokens = num_cls_tokens
+        
+        relative_size = [w * 2 - 1 for w in window_size]
+        self.num_relative_distance = np.prod(relative_size) + 2 * num_cls_tokens + num_cls_tokens ** 2
+
+        self.relative_position_bias_table = nn.Parameter(
+            torch.zeros(self.num_relative_distance, config.num_attention_heads)
+        )
+
+        # get pair-wise relative position index for each token inside the window
+        coords_range = [torch.arange(w) for w in window_size]
+        coords_flatten = torch.stack(torch.meshgrid(coords_range)).flatten(1)
+        relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :]
+        relative_coords = relative_coords.permute(1, 2, 0).contiguous()
+        
+        for i, w in enumerate(window_size):
+            relative_coords[:, :, i] += w - 1  # shift to start from 0
+        
+        for i, r in enumerate(relative_size[1:]):
+            relative_coords[:, :, :i + 1] *= r
+
+        self.seq_len = np.prod(window_size) + num_cls_tokens
+        relative_position_index = torch.zeros((self.seq_len, self.seq_len), dtype=relative_coords.dtype)
+        relative_position_index[num_cls_tokens:, num_cls_tokens:] = relative_coords.sum(-1)
+        
+        start = np.prod(relative_size)
+        cls2loc = torch.arange(num_cls_tokens).unsqueeze(1) + start
+        relative_position_index[:num_cls_tokens, num_cls_tokens:] = cls2loc
+        start += num_cls_tokens
+
+        loc2cls = torch.arange(num_cls_tokens).unsqueeze(0) + start
+        relative_position_index[num_cls_tokens:, :num_cls_tokens] = loc2cls
+        start += num_cls_tokens
+
+        cls2cls = torch.arange(num_cls_tokens ** 2).view(num_cls_tokens, num_cls_tokens) + start
+        relative_position_index[:num_cls_tokens, :num_cls_tokens] = cls2cls
+
+        self.register_buffer("relative_position_index", relative_position_index)
+
+    def forward(self, from_idx=None, to_idx=None):
+        """
+        from_idx: indices of query tokens (1-dim)
+        to_idx: indices of key/value tokens (1-dim, or 2-dim w/ one row per query)
+        """
+        attn_idx = self.relative_position_index
+
+        # query indices
+        if from_idx is not None:
+            attn_idx = attn_idx[from_idx]
+
+        # key indices
+        if to_idx is not None:
+            assert to_idx.ndim in (1, 2), "to_idx must be 1- or 2-dimensional tensors"
+            if to_idx.ndim == 1:
+                attn_idx = attn_idx[:, to_idx]
+            else:
+                attn_idx = attn_idx.gather(1, to_idx)
+
+        rows, cols = attn_idx.shape
+        relative_position_bias = self.relative_position_bias_table[attn_idx.flatten()]
+        relative_position_bias = rearrange(relative_position_bias, '(i j) h -> h i j', i=rows, j=cols)
+        return relative_position_bias.contiguous()
+
+
+class BeitEncoder(nn.Module):
+    def __init__(self, config, window_size=None):
+        super().__init__()
+        self.config = config
+        if config.use_shared_relative_position_bias:
+            self.relative_position_bias = BeitRelativePositionBias3D(config, window_size=window_size)
+        else:
+            self.relative_position_bias = None
+
+        self._register_token_order(window_size)
+
+        # stochastic depth decay rule
+        dpr = [x.item() for x in torch.linspace(0, config.drop_path_rate, config.num_hidden_layers)]
+
+        # node sparsification
+        token_keep_rate = [1] * config.num_hidden_layers
+        for loc in config.token_drop_loc:
+            token_keep_rate[loc] = config.token_keep_rate
+        
+        self.layer = nn.ModuleList(
+            [
+                BeitLayer(
+                    config,
+                    window_size=window_size if config.use_relative_position_bias else None,
+                    drop_path_rate=dpr[i], token_keep_rate=token_keep_rate[i]
+                )
+                for i in range(config.num_hidden_layers)
+            ]
+        )
+
+        self.gradient_checkpointing = False
+    
+    def _register_token_order(self, shape):
+        if self.config.token_3d_order == 'none':
+            order = None
+        elif self.config.token_3d_order == 'zcurve':
+            nbits = max(shape).bit_length()
+            coords = list(np.ndindex(*shape))
+            order = zCurve.par_interlace(coords, len(shape), nbits)
+            order = torch.tensor(np.argsort(order))
+        elif self.config.token_3d_order == 'hilbert':
+            nbits = max(shape).bit_length()
+            coords = list(np.ndindex(*shape))
+            order = hilbert.encode(np.stack(coords), len(shape), nbits)
+            order = torch.tensor(np.argsort(order))
+        else:
+            raise NotImplementedError(f"Token ordering {self.config.token_3d_order} not supported")
+
+        if order is not None:
+            self.register_buffer('token_order', order, persistent=False)
+        else:
+            self.token_order = None
+
+    def forward(
+        self,
+        hidden_states,
+        head_mask=None,
+        output_attentions=False,
+        output_hidden_states=False,
+        output_token_idx=False,
+        return_dict=True,
+    ):
+        all_hidden_states = () if output_hidden_states else None
+        all_self_attentions = () if output_attentions else None
+        all_token_idx = () if output_token_idx else None
+
+        token_idx = self.token_order
+        if token_idx is not None:
+            cls_states, local_states = hidden_states[:, :self.config.num_cls_tokens], hidden_states[:, self.config.num_cls_tokens:]
+            local_states = torch.index_select(local_states, dim=1, index=token_idx)
+            hidden_states = torch.cat((cls_states, local_states), 1)
+
+        for i, layer_module in enumerate(self.layer):
+            if output_hidden_states:
+                all_hidden_states = all_hidden_states + (hidden_states,)
+
+            layer_head_mask = head_mask[i] if head_mask is not None else None
+
+            if self.gradient_checkpointing and self.training:
+
+                def create_custom_forward(module):
+                    def custom_forward(*inputs):
+                        return module(*inputs, output_attentions)
+
+                    return custom_forward
+
+                layer_outputs = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(layer_module),
+                    hidden_states,
+                    layer_head_mask,
+                )
+            else:
+                relative_position_bias = (
+                    self.relative_position_bias() if self.relative_position_bias is not None else None
+                )
+                layer_outputs = layer_module(hidden_states, layer_head_mask, output_attentions, relative_position_bias, token_idx)
+
+            hidden_states = layer_outputs[0]
+
+            if layer_module.token_keep_rate < 1:
+                token_idx = layer_outputs[-1]
+
+                if output_token_idx:
+                    all_token_idx = all_token_idx + (token_idx,)
+
+            if output_attentions:
+                all_self_attentions = all_self_attentions + (layer_outputs[1],)
+
+        if output_hidden_states:
+            all_hidden_states = all_hidden_states + (hidden_states,)
+
+        if not return_dict:
+            return tuple(v for v in [hidden_states, all_hidden_states, all_self_attentions] if v is not None)
+        return BeitModelOutput(
+            last_hidden_state=hidden_states,
+            hidden_states=all_hidden_states,
+            attentions=all_self_attentions,
+            token_idx=all_token_idx
+        )
+
+
+class BeitPreTrainedModel(PreTrainedModel):
+    """
+    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
+    models.
+    """
+
+    config_class = BeitConfig
+    base_model_prefix = "beit"
+    supports_gradient_checkpointing = True
+
+    def _init_weights(self, module):
+        """Initialize the weights"""
+        if isinstance(module, (nn.Linear, nn.Conv2d, nn.ConvTranspose2d)):
+            # Slightly different from the TF version which uses truncated_normal for initialization
+            # cf https://github.com/pytorch/pytorch/pull/5617
+            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
+            if module.bias is not None:
+                module.bias.data.zero_()
+        elif isinstance(module, nn.Embedding):
+            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
+            if module.padding_idx is not None:
+                module.weight.data[module.padding_idx].zero_()
+        elif isinstance(module, nn.LayerNorm):
+            module.bias.data.zero_()
+            module.weight.data.fill_(1.0)
+
+    def _set_gradient_checkpointing(self, module, value=False):
+        if isinstance(module, BeitEncoder):
+            module.gradient_checkpointing = value
+
+
+BEIT_START_DOCSTRING = r"""
+    This model is a PyTorch `torch.nn.Module <https://pytorch.org/docs/stable/nn.html#torch.nn.Module>`_ subclass. Use
+    it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
+    behavior.
+
+    Parameters:
+        config (:class:`~transformers.BeitConfig`): Model configuration class with all the parameters of the model.
+            Initializing with a config file does not load the weights associated with the model, only the
+            configuration. Check out the :meth:`~transformers.PreTrainedModel.from_pretrained` method to load the model
+            weights.
+"""
+
+BEIT_INPUTS_DOCSTRING = r"""
+    Args:
+        pixel_values (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, num_channels, height, width)`):
+            Pixel values. Pixel values can be obtained using :class:`~transformers.BeitFeatureExtractor`. See
+            :meth:`transformers.BeitFeatureExtractor.__call__` for details.
+
+        head_mask (:obj:`torch.FloatTensor` of shape :obj:`(num_heads,)` or :obj:`(num_layers, num_heads)`, `optional`):
+            Mask to nullify selected heads of the self-attention modules. Mask values selected in ``[0, 1]``:
+
+            - 1 indicates the head is **not masked**,
+            - 0 indicates the head is **masked**.
+
+        output_attentions (:obj:`bool`, `optional`):
+            Whether or not to return the attentions tensors of all attention layers. See ``attentions`` under returned
+            tensors for more detail.
+        output_hidden_states (:obj:`bool`, `optional`):
+            Whether or not to return the hidden states of all layers. See ``hidden_states`` under returned tensors for
+            more detail.
+        return_dict (:obj:`bool`, `optional`):
+            Whether or not to return a :class:`~transformers.file_utils.ModelOutput` instead of a plain tuple.
+"""
+
+
+@add_start_docstrings(
+    "The bare Beit Model transformer outputting raw hidden-states without any specific head on top.",
+    BEIT_START_DOCSTRING,
+)
+class BeitModel(BeitPreTrainedModel):
+    def __init__(self, config, add_pooling_layer=True, num_frames=None):
+        super().__init__(config)
+        self.config = config
+
+        self.embeddings = BeitEmbeddings(config)
+        self.window_size = self.embeddings.patch_embeddings.patch_shape
+        if num_frames is not None:
+            self.window_size = (num_frames,) + self.window_size
+        self.encoder = BeitEncoder(config, window_size=self.window_size)
+
+        self.layernorm = (
+            nn.Identity() if config.use_mean_pooling else nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+        )
+        self.pooler = BeitPooler(config) if add_pooling_layer else None
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.embeddings.patch_embeddings
+
+    def _prune_heads(self, heads_to_prune):
+        """
+        Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
+        class PreTrainedModel
+        """
+        for layer, heads in heads_to_prune.items():
+            self.encoder.layer[layer].attention.prune_heads(heads)
+
+    @add_start_docstrings_to_model_forward(BEIT_INPUTS_DOCSTRING)
+    @replace_return_docstrings(output_type=BeitModelOutputWithPooling, config_class=_CONFIG_FOR_DOC)
+    def forward(
+        self,
+        pixel_values=None,
+        bool_masked_pos=None,
+        head_mask=None,
+        output_attentions=None,
+        output_hidden_states=None,
+        output_token_idx=None,
+        return_dict=None,
+    ):
+        r"""
+        Returns:
+
+        Examples::
+
+            >>> from transformers import BeitFeatureExtractor, BeitModel
+            >>> from PIL import Image
+            >>> import requests
+
+            >>> url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
+            >>> image = Image.open(requests.get(url, stream=True).raw)
+
+            >>> feature_extractor = BeitFeatureExtractor.from_pretrained('microsoft/beit-base-patch16-224-pt22k-ft22k')
+            >>> model = BeitModel.from_pretrained('microsoft/beit-base-patch16-224-pt22k-ft22k')
+
+            >>> inputs = feature_extractor(images=image, return_tensors="pt")
+            >>> outputs = model(**inputs)
+            >>> last_hidden_states = outputs.last_hidden_state
+        """
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        if pixel_values is None:
+            raise ValueError("You have to specify pixel_values")
+
+        # Prepare head mask if needed
+        # 1.0 in head_mask indicate we keep the head
+        # attention_probs has shape bsz x n_heads x N x N
+        # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
+        # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
+        head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
+
+        embedding_output = self.embeddings(pixel_values, bool_masked_pos)
+
+        encoder_outputs = self.encoder(
+            embedding_output,
+            head_mask=head_mask,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            output_token_idx=output_token_idx,
+            return_dict=return_dict,
+        )
+        sequence_output = encoder_outputs[0]
+        sequence_output = self.layernorm(sequence_output)
+        pooled_output = self.pooler(sequence_output) if self.pooler is not None else None
+
+        if not return_dict:
+            return (sequence_output, pooled_output) + encoder_outputs[1:]
+
+        return BeitModelOutputWithPooling(
+            last_hidden_state=sequence_output,
+            pooler_output=pooled_output,
+            hidden_states=encoder_outputs.hidden_states,
+            attentions=encoder_outputs.attentions,
+            token_idx=encoder_outputs.token_idx,
+        )
+
+
+class BeitPooler(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.layernorm = (
+            nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) if config.use_mean_pooling else None
+        )
+
+    def forward(self, hidden_states):
+        if self.layernorm is not None:
+            # Mean pool the final hidden states of the patch tokens
+            patch_tokens = hidden_states[:, 1:, :]
+            pooled_output = self.layernorm(patch_tokens.mean(1))
+        else:
+            # Pool by simply taking the final hidden state of the [CLS] token
+            pooled_output = hidden_states[:, 0]
+
+        return pooled_output
+
+
+@add_start_docstrings(
+    "Beit Model transformer with a 'language' modeling head on top (to predict visual tokens).",
+    BEIT_START_DOCSTRING,
+)
+class BeitForMaskedImageModeling(BeitPreTrainedModel):
+    def __init__(self, config):
+        super().__init__(config)
+
+        self.num_labels = config.num_labels
+        self.beit = BeitModel(config, add_pooling_layer=False)
+
+        # Classifier head
+        self.layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    @add_start_docstrings_to_model_forward(BEIT_INPUTS_DOCSTRING)
+    @replace_return_docstrings(output_type=MaskedLMOutput, config_class=_CONFIG_FOR_DOC)
+    def forward(
+        self,
+        pixel_values=None,
+        bool_masked_pos=None,
+        head_mask=None,
+        labels=None,
+        output_attentions=None,
+        output_hidden_states=None,
+        return_dict=None,
+    ):
+        r"""
+        bool_masked_pos (:obj:`torch.BoolTensor` of shape :obj:`(batch_size, num_patches)`):
+            Boolean masked positions. Indicates which patches are masked (1) and which aren't (0).
+
+        labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`):
+            Labels for computing the image classification/regression loss. Indices should be in :obj:`[0, ...,
+            config.num_labels - 1]`. If :obj:`config.num_labels == 1` a regression loss is computed (Mean-Square loss),
+            If :obj:`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+
+        Returns:
+
+        Examples::
+
+            >>> from transformers import BeitFeatureExtractor, BeitForMaskedImageModeling
+            >>> from PIL import Image
+            >>> import requests
+
+            >>> url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
+            >>> image = Image.open(requests.get(url, stream=True).raw)
+
+            >>> feature_extractor = BeitFeatureExtractor.from_pretrained('microsoft/beit-base-patch16-224-pt22k')
+            >>> model = BeitForMaskedImageModeling.from_pretrained('microsoft/beit-base-patch16-224-pt22k')
+
+            >>> inputs = feature_extractor(images=image, return_tensors="pt")
+            >>> outputs = model(**inputs)
+            >>> logits = outputs.logits
+        """
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        outputs = self.beit(
+            pixel_values,
+            bool_masked_pos=bool_masked_pos,
+            head_mask=head_mask,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        sequence_output = outputs[0]
+        sequence_output = self.layernorm(sequence_output)
+        prediction_scores = self.lm_head(sequence_output[:, 1:])
+
+        masked_lm_loss = None
+        if labels is not None:
+            loss_fct = CrossEntropyLoss()  # -100 index = padding token
+            masked_lm_loss = loss_fct(prediction_scores[bool_masked_pos], labels)
+
+        if not return_dict:
+            output = (prediction_scores,) + outputs[2:]
+            return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
+
+        return MaskedLMOutput(
+            loss=masked_lm_loss,
+            logits=prediction_scores,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+
+@add_start_docstrings(
+    """
+    Beit Model transformer with an image classification head on top (a linear layer on top of the average of the final
+    hidden states of the patch tokens) e.g. for ImageNet.
+    """,
+    BEIT_START_DOCSTRING,
+)
+class BeitForImageClassification(BeitPreTrainedModel):
+    def __init__(self, config):
+        super().__init__(config)
+
+        self.num_labels = config.num_labels
+        self.beit = BeitModel(config, add_pooling_layer=True)
+
+        # Classifier head
+        self.classifier = nn.Linear(config.hidden_size, config.num_labels) if config.num_labels > 0 else nn.Identity()
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    @add_start_docstrings_to_model_forward(BEIT_INPUTS_DOCSTRING)
+    @replace_return_docstrings(output_type=SequenceClassifierOutput, config_class=_CONFIG_FOR_DOC)
+    def forward(
+        self,
+        pixel_values=None,
+        head_mask=None,
+        labels=None,
+        output_attentions=None,
+        output_hidden_states=None,
+        return_dict=None,
+    ):
+        r"""
+        labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`):
+            Labels for computing the image classification/regression loss. Indices should be in :obj:`[0, ...,
+            config.num_labels - 1]`. If :obj:`config.num_labels == 1` a regression loss is computed (Mean-Square loss),
+            If :obj:`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+
+        Returns:
+
+        Examples::
+
+            >>> from transformers import BeitFeatureExtractor, BeitForImageClassification
+            >>> from PIL import Image
+            >>> import requests
+
+            >>> url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
+            >>> image = Image.open(requests.get(url, stream=True).raw)
+
+            >>> feature_extractor = BeitFeatureExtractor.from_pretrained('microsoft/beit-base-patch16-224')
+            >>> model = BeitForImageClassification.from_pretrained('microsoft/beit-base-patch16-224')
+
+            >>> inputs = feature_extractor(images=image, return_tensors="pt")
+            >>> outputs = model(**inputs)
+            >>> logits = outputs.logits
+            >>> # model predicts one of the 1000 ImageNet classes
+            >>> predicted_class_idx = logits.argmax(-1).item()
+            >>> print("Predicted class:", model.config.id2label[predicted_class_idx])
+        """
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        outputs = self.beit(
+            pixel_values,
+            head_mask=head_mask,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        pooled_output = outputs.pooler_output if return_dict else outputs[1]
+
+        logits = self.classifier(pooled_output)
+
+        loss = None
+        if labels is not None:
+            if self.num_labels == 1:
+                #  We are doing regression
+                loss_fct = MSELoss()
+                loss = loss_fct(logits.view(-1), labels.view(-1))
+            else:
+                loss_fct = CrossEntropyLoss()
+                loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
+
+        if not return_dict:
+            output = (logits,) + outputs[2:]
+            return ((loss,) + output) if loss is not None else output
+
+        return SequenceClassifierOutput(
+            loss=loss,
+            logits=logits,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+
+class BeitConvModule(nn.Module):
+    """
+    A convolutional block that bundles conv/norm/activation layers. This block simplifies the usage of convolution
+    layers, which are commonly used with a norm layer (e.g., BatchNorm) and activation layer (e.g., ReLU).
+
+    Based on OpenMMLab's implementation, found in https://github.com/open-mmlab/mmsegmentation.
+    """
+
+    def __init__(self, in_channels, out_channels, kernel_size, padding=0, bias=False, dilation=1):
+        super().__init__()
+        self.conv = nn.Conv2d(
+            in_channels=in_channels,
+            out_channels=out_channels,
+            kernel_size=kernel_size,
+            padding=padding,
+            bias=bias,
+            dilation=dilation,
+        )
+        self.bn = nn.BatchNorm2d(out_channels)
+        self.activation = nn.ReLU()
+
+    def forward(self, input):
+        output = self.conv(input)
+        output = self.bn(output)
+        output = self.activation(output)
+
+        return output
+
+
+class BeitPyramidPoolingModule(nn.ModuleList):
+    """
+    Pyramid Pooling Module (PPM) used in PSPNet.
+
+    Args:
+        pool_scales (tuple[int]): Pooling scales used in Pooling Pyramid
+            Module.
+        in_channels (int): Input channels.
+        channels (int): Channels after modules, before conv_seg.
+        align_corners (bool): align_corners argument of F.interpolate.
+
+    Based on OpenMMLab's implementation, found in https://github.com/open-mmlab/mmsegmentation.
+    """
+
+    def __init__(self, pool_scales, in_channels, channels, align_corners):
+        super().__init__()
+        self.pool_scales = pool_scales
+        self.align_corners = align_corners
+        self.in_channels = in_channels
+        self.channels = channels
+        for pool_scale in pool_scales:
+            self.append(
+                nn.Sequential(
+                    nn.AdaptiveAvgPool2d(pool_scale),
+                    BeitConvModule(self.in_channels, self.channels, kernel_size=1),
+                )
+            )
+
+    def forward(self, x):
+        ppm_outs = []
+        for ppm in self:
+            ppm_out = ppm(x)
+            upsampled_ppm_out = nn.functional.interpolate(
+                ppm_out, size=x.size()[2:], mode="bilinear", align_corners=self.align_corners
+            )
+            ppm_outs.append(upsampled_ppm_out)
+        return ppm_outs
+
+
+class BeitUperHead(nn.Module):
+    """
+    Unified Perceptual Parsing for Scene Understanding. This head is the implementation of `UPerNet
+    <https://arxiv.org/abs/1807.10221>`_.
+
+    Based on OpenMMLab's implementation, found in https://github.com/open-mmlab/mmsegmentation.
+    """
+
+    def __init__(self, config):
+        super().__init__()
+
+        self.pool_scales = config.pool_scales  # e.g. (1, 2, 3, 6)
+        self.in_channels = [config.hidden_size] * 4  # e.g. [768, 768, 768, 768]
+        self.channels = config.hidden_size
+        self.align_corners = False
+        self.classifier = nn.Conv2d(self.channels, config.num_labels, kernel_size=1)
+
+        # PSP Module
+        self.psp_modules = BeitPyramidPoolingModule(
+            self.pool_scales,
+            self.in_channels[-1],
+            self.channels,
+            align_corners=self.align_corners,
+        )
+        self.bottleneck = BeitConvModule(
+            self.in_channels[-1] + len(self.pool_scales) * self.channels,
+            self.channels,
+            kernel_size=3,
+            padding=1,
+        )
+        # FPN Module
+        self.lateral_convs = nn.ModuleList()
+        self.fpn_convs = nn.ModuleList()
+        for in_channels in self.in_channels[:-1]:  # skip the top layer
+            l_conv = BeitConvModule(in_channels, self.channels, kernel_size=1)
+            fpn_conv = BeitConvModule(self.channels, self.channels, kernel_size=3, padding=1)
+            self.lateral_convs.append(l_conv)
+            self.fpn_convs.append(fpn_conv)
+
+        self.fpn_bottleneck = BeitConvModule(
+            len(self.in_channels) * self.channels,
+            self.channels,
+            kernel_size=3,
+            padding=1,
+        )
+
+    def psp_forward(self, inputs):
+        x = inputs[-1]
+        psp_outs = [x]
+        psp_outs.extend(self.psp_modules(x))
+        psp_outs = torch.cat(psp_outs, dim=1)
+        output = self.bottleneck(psp_outs)
+
+        return output
+
+    def forward(self, encoder_hidden_states):
+        # build laterals
+        laterals = [lateral_conv(encoder_hidden_states[i]) for i, lateral_conv in enumerate(self.lateral_convs)]
+
+        laterals.append(self.psp_forward(encoder_hidden_states))
+
+        # build top-down path
+        used_backbone_levels = len(laterals)
+        for i in range(used_backbone_levels - 1, 0, -1):
+            prev_shape = laterals[i - 1].shape[2:]
+            laterals[i - 1] = laterals[i - 1] + nn.functional.interpolate(
+                laterals[i], size=prev_shape, mode="bilinear", align_corners=self.align_corners
+            )
+
+        # build outputs
+        fpn_outs = [self.fpn_convs[i](laterals[i]) for i in range(used_backbone_levels - 1)]
+        # append psp feature
+        fpn_outs.append(laterals[-1])
+
+        for i in range(used_backbone_levels - 1, 0, -1):
+            fpn_outs[i] = nn.functional.interpolate(
+                fpn_outs[i], size=fpn_outs[0].shape[2:], mode="bilinear", align_corners=self.align_corners
+            )
+        fpn_outs = torch.cat(fpn_outs, dim=1)
+        output = self.fpn_bottleneck(fpn_outs)
+        output = self.classifier(output)
+
+        return output
+
+
+class BeitFCNHead(nn.Module):
+    """
+    Fully Convolution Networks for Semantic Segmentation. This head is implemented of `FCNNet
+    <https://arxiv.org/abs/1411.4038>`_.
+
+    Args:
+        config (BeitConfig): Configuration.
+        in_channels
+        kernel_size (int): The kernel size for convs in the head. Default: 3.
+        dilation (int): The dilation rate for convs in the head. Default: 1.
+
+
+    Based on OpenMMLab's implementation, found in https://github.com/open-mmlab/mmsegmentation.
+    """
+
+    def __init__(self, config, in_index=2, kernel_size=3, dilation=1):
+        super().__init__()
+        self.in_channels = config.hidden_size
+        self.channels = config.auxiliary_channels
+        self.num_convs = config.auxiliary_num_convs
+        self.concat_input = config.auxiliary_concat_input
+        self.in_index = in_index
+
+        conv_padding = (kernel_size // 2) * dilation
+        convs = []
+        convs.append(
+            BeitConvModule(
+                self.in_channels, self.channels, kernel_size=kernel_size, padding=conv_padding, dilation=dilation
+            )
+        )
+        for i in range(self.num_convs - 1):
+            convs.append(
+                BeitConvModule(
+                    self.channels, self.channels, kernel_size=kernel_size, padding=conv_padding, dilation=dilation
+                )
+            )
+        if self.num_convs == 0:
+            self.convs = nn.Identity()
+        else:
+            self.convs = nn.Sequential(*convs)
+        if self.concat_input:
+            self.conv_cat = BeitConvModule(
+                self.in_channels + self.channels, self.channels, kernel_size=kernel_size, padding=kernel_size // 2
+            )
+
+        self.classifier = nn.Conv2d(self.channels, config.num_labels, kernel_size=1)
+
+    def forward(self, encoder_hidden_states):
+        # just take the relevant feature maps
+        hidden_states = encoder_hidden_states[self.in_index]
+        output = self.convs(hidden_states)
+        if self.concat_input:
+            output = self.conv_cat(torch.cat([hidden_states, output], dim=1))
+        output = self.classifier(output)
+        return output
+
+
+@add_start_docstrings(
+    """
+    Beit Model transformer with a semantic segmentation head on top e.g. for ADE20k, CityScapes.
+    """,
+    BEIT_START_DOCSTRING,
+)
+class BeitForSemanticSegmentation(BeitPreTrainedModel):
+    def __init__(self, config):
+        super().__init__(config)
+
+        self.num_labels = config.num_labels
+        self.beit = BeitModel(config, add_pooling_layer=False)
+
+        # FPNs
+        self.fpn1 = nn.Sequential(
+            nn.ConvTranspose2d(config.hidden_size, config.hidden_size, kernel_size=2, stride=2),
+            nn.BatchNorm2d(config.hidden_size),
+            nn.GELU(),
+            nn.ConvTranspose2d(config.hidden_size, config.hidden_size, kernel_size=2, stride=2),
+        )
+        self.fpn2 = nn.Sequential(
+            nn.ConvTranspose2d(config.hidden_size, config.hidden_size, kernel_size=2, stride=2),
+        )
+        self.fpn3 = nn.Identity()
+        self.fpn4 = nn.MaxPool2d(kernel_size=2, stride=2)
+
+        # Semantic segmentation head(s)
+        self.decode_head = BeitUperHead(config)
+        self.auxiliary_head = BeitFCNHead(config) if config.use_auxiliary_head else None
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def compute_loss(self, logits, auxiliary_logits, labels):
+        # upsample logits to the images' original size
+        upsampled_logits = nn.functional.interpolate(
+            logits, size=labels.shape[-2:], mode="bilinear", align_corners=False
+        )
+        if auxiliary_logits is not None:
+            upsampled_auxiliary_logits = nn.functional.interpolate(
+                auxiliary_logits, size=labels.shape[-2:], mode="bilinear", align_corners=False
+            )
+        # compute weighted loss
+        loss_fct = CrossEntropyLoss(ignore_index=self.config.semantic_loss_ignore_index)
+        main_loss = loss_fct(upsampled_logits, labels)
+        auxiliary_loss = loss_fct(upsampled_auxiliary_logits, labels)
+        loss = main_loss + self.config.auxiliary_loss_weight * auxiliary_loss
+
+        return loss
+
+    @add_start_docstrings_to_model_forward(BEIT_INPUTS_DOCSTRING)
+    @replace_return_docstrings(output_type=SequenceClassifierOutput, config_class=_CONFIG_FOR_DOC)
+    def forward(
+        self,
+        pixel_values=None,
+        head_mask=None,
+        labels=None,
+        output_attentions=None,
+        output_hidden_states=None,
+        return_dict=None,
+    ):
+        r"""
+        labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, height, width)`, `optional`):
+            Ground truth semantic segmentation maps for computing the loss. Indices should be in :obj:`[0, ...,
+            config.num_labels - 1]`. If :obj:`config.num_labels > 1`, a classification loss is computed
+            (Cross-Entropy).
+
+        Returns:
+
+        Examples::
+
+            >>> from transformers import BeitFeatureExtractor, BeitForSemanticSegmentation
+            >>> from PIL import Image
+            >>> import requests
+
+            >>> url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
+            >>> image = Image.open(requests.get(url, stream=True).raw)
+
+            >>> feature_extractor = BeitFeatureExtractor.from_pretrained('microsoft/beit-base-finetuned-ade-640-640')
+            >>> model = BeitForSemanticSegmentation.from_pretrained('microsoft/beit-base-finetuned-ade-640-640')
+
+            >>> inputs = feature_extractor(images=image, return_tensors="pt")
+            >>> outputs = model(**inputs)
+            >>> # logits are of shape (batch_size, num_labels, height/4, width/4)
+            >>> logits = outputs.logits
+        """
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+
+        outputs = self.beit(
+            pixel_values,
+            head_mask=head_mask,
+            output_attentions=output_attentions,
+            output_hidden_states=True,  # we need the intermediate hidden states
+            return_dict=return_dict,
+        )
+
+        encoder_hidden_states = outputs.hidden_states if return_dict else outputs[2]
+
+        # only keep certain features, and reshape
+        # note that we do +1 as the encoder_hidden_states also includes the initial embeddings
+        features = [feature for idx, feature in enumerate(encoder_hidden_states) if idx + 1 in self.config.out_indices]
+        batch_size = pixel_values.shape[0]
+        patch_resolution = self.config.image_size // self.config.patch_size
+        features = [
+            x[:, 1:, :].permute(0, 2, 1).reshape(batch_size, -1, patch_resolution, patch_resolution) for x in features
+        ]
+
+        # apply FPNs
+        ops = [self.fpn1, self.fpn2, self.fpn3, self.fpn4]
+        for i in range(len(features)):
+            features[i] = ops[i](features[i])
+
+        logits = self.decode_head(features)
+        auxiliary_logits = None
+        if self.auxiliary_head is not None:
+            auxiliary_logits = self.auxiliary_head(features)
+
+        loss = None
+        if labels is not None:
+            if self.config.num_labels == 1:
+                raise ValueError("The number of labels should be greater than one")
+            else:
+                loss = self.compute_loss(logits, auxiliary_logits, labels)
+
+        if not return_dict:
+            if output_hidden_states:
+                output = (logits,) + outputs[2:]
+            else:
+                output = (logits,) + outputs[3:]
+            return ((loss,) + output) if loss is not None else output
+
+        return SequenceClassifierOutput(
+            loss=loss,
+            logits=logits,
+            hidden_states=outputs.hidden_states if output_hidden_states else None,
+            attentions=outputs.attentions,
+        )

svitt/sparse_xbert.py

@@ -0,0 +1,2039 @@
+# coding=utf-8
+# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
+# Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""PyTorch BERT model. """
+
+import math
+import os
+import warnings
+from dataclasses import dataclass
+from typing import Optional, Tuple
+
+import torch
+from torch import Tensor, device, nn
+import torch.utils.checkpoint
+from torch import nn
+from torch.nn import CrossEntropyLoss, MSELoss
+import torch.nn.functional as F
+
+from transformers.activations import ACT2FN
+from transformers.file_utils import (
+    ModelOutput,
+    add_start_docstrings,
+    add_start_docstrings_to_model_forward,
+    replace_return_docstrings,
+)
+from transformers.modeling_outputs import (
+    BaseModelOutputWithPastAndCrossAttentions,
+    BaseModelOutputWithPoolingAndCrossAttentions,
+    CausalLMOutputWithCrossAttentions,
+    MaskedLMOutput,
+    MultipleChoiceModelOutput,
+    NextSentencePredictorOutput,
+    QuestionAnsweringModelOutput,
+    SequenceClassifierOutput,
+    TokenClassifierOutput,
+)
+from transformers.modeling_utils import (
+    PreTrainedModel,
+    apply_chunking_to_forward,
+    find_pruneable_heads_and_indices,
+    prune_linear_layer,
+)
+from svitt.sparse_config import BertConfig
+
+import transformers
+transformers.logging.set_verbosity_error()
+
+
+_CONFIG_FOR_DOC = "BertConfig"
+_TOKENIZER_FOR_DOC = "BertTokenizer"
+
+BERT_PRETRAINED_MODEL_ARCHIVE_LIST = [
+    "bert-base-uncased",
+    "bert-large-uncased",
+    "bert-base-cased",
+    "bert-large-cased",
+    "bert-base-multilingual-uncased",
+    "bert-base-multilingual-cased",
+    "bert-base-chinese",
+    "bert-base-german-cased",
+    "bert-large-uncased-whole-word-masking",
+    "bert-large-cased-whole-word-masking",
+    "bert-large-uncased-whole-word-masking-finetuned-squad",
+    "bert-large-cased-whole-word-masking-finetuned-squad",
+    "bert-base-cased-finetuned-mrpc",
+    "bert-base-german-dbmdz-cased",
+    "bert-base-german-dbmdz-uncased",
+    "cl-tohoku/bert-base-japanese",
+    "cl-tohoku/bert-base-japanese-whole-word-masking",
+    "cl-tohoku/bert-base-japanese-char",
+    "cl-tohoku/bert-base-japanese-char-whole-word-masking",
+    "TurkuNLP/bert-base-finnish-cased-v1",
+    "TurkuNLP/bert-base-finnish-uncased-v1",
+    "wietsedv/bert-base-dutch-cased",
+    # See all BERT models at https://huggingface.co/models?filter=bert
+]
+
+
+@dataclass
+class BertModelOutputWithPastAndCrossAttentions(BaseModelOutputWithPastAndCrossAttentions):
+    token_idx: Optional[Tuple[torch.LongTensor]] = None
+
+
+@dataclass
+class BertModelOutputWithPoolingAndCrossAttentions(BaseModelOutputWithPoolingAndCrossAttentions):
+    token_idx: Optional[Tuple[torch.LongTensor]] = None
+
+
+def load_tf_weights_in_bert(model, config, tf_checkpoint_path):
+    """Load tf checkpoints in a pytorch model."""
+    try:
+        import re
+
+        import numpy as np
+        import tensorflow as tf
+    except ImportError:
+        print(
+            "Loading a TensorFlow model in PyTorch, requires TensorFlow to be installed. Please see "
+            "https://www.tensorflow.org/install/ for installation instructions."
+        )
+        raise
+    tf_path = os.path.abspath(tf_checkpoint_path)
+    # Load weights from TF model
+    init_vars = tf.train.list_variables(tf_path)
+    names = []
+    arrays = []
+    for name, shape in init_vars:
+        array = tf.train.load_variable(tf_path, name)
+        names.append(name)
+        arrays.append(array)
+
+    for name, array in zip(names, arrays):
+        name = name.split("/")
+        # adam_v and adam_m are variables used in AdamWeightDecayOptimizer to calculated m and v
+        # which are not required for using pretrained model
+        if any(
+            n in ["adam_v", "adam_m", "AdamWeightDecayOptimizer",
+                  "AdamWeightDecayOptimizer_1", "global_step"]
+            for n in name
+        ):
+            continue
+        pointer = model
+        for m_name in name:
+            if re.fullmatch(r"[A-Za-z]+_\d+", m_name):
+                scope_names = re.split(r"_(\d+)", m_name)
+            else:
+                scope_names = [m_name]
+            if scope_names[0] == "kernel" or scope_names[0] == "gamma":
+                pointer = getattr(pointer, "weight")
+            elif scope_names[0] == "output_bias" or scope_names[0] == "beta":
+                pointer = getattr(pointer, "bias")
+            elif scope_names[0] == "output_weights":
+                pointer = getattr(pointer, "weight")
+            elif scope_names[0] == "squad":
+                pointer = getattr(pointer, "classifier")
+            else:
+                try:
+                    pointer = getattr(pointer, scope_names[0])
+                except AttributeError:
+                    continue
+            if len(scope_names) >= 2:
+                num = int(scope_names[1])
+                pointer = pointer[num]
+        if m_name[-11:] == "_embeddings":
+            pointer = getattr(pointer, "weight")
+        elif m_name == "kernel":
+            array = np.transpose(array)
+        try:
+            assert (
+                pointer.shape == array.shape
+            ), f"Pointer shape {pointer.shape} and array shape {array.shape} mismatched"
+        except AssertionError as e:
+            e.args += (pointer.shape, array.shape)
+            raise
+        pointer.data = torch.from_numpy(array)
+    return model
+
+
+class BertEmbeddings(nn.Module):
+    """Construct the embeddings from word, position and token_type embeddings."""
+
+    def __init__(self, config):
+        super().__init__()
+        self.word_embeddings = nn.Embedding(
+            config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
+        self.position_embeddings = nn.Embedding(
+            config.max_position_embeddings, config.hidden_size)
+        self.token_type_embeddings = nn.Embedding(
+            config.type_vocab_size, config.hidden_size)
+
+        # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
+        # any TensorFlow checkpoint file
+        self.LayerNorm = nn.LayerNorm(
+            config.hidden_size, eps=config.layer_norm_eps)
+        self.dropout = nn.Dropout(config.hidden_dropout_prob)
+
+        # position_ids (1, len position emb) is contiguous in memory and exported when serialized
+        self.register_buffer("position_ids", torch.arange(
+            config.max_position_embeddings).expand((1, -1)))
+        self.position_embedding_type = getattr(
+            config, "position_embedding_type", "absolute")
+
+        self.config = config
+
+    def forward(
+        self, input_ids=None, token_type_ids=None, position_ids=None, inputs_embeds=None, past_key_values_length=0
+    ):
+        if input_ids is not None:
+            input_shape = input_ids.size()
+        else:
+            input_shape = inputs_embeds.size()[:-1]
+
+        seq_length = input_shape[1]
+
+        if position_ids is None:
+            position_ids = self.position_ids[:,
+                                             past_key_values_length: seq_length + past_key_values_length]
+
+        if token_type_ids is None:
+            token_type_ids = torch.zeros(
+                input_shape, dtype=torch.long, device=self.position_ids.device)
+
+        if inputs_embeds is None:
+            inputs_embeds = self.word_embeddings(input_ids)
+
+        token_type_embeddings = self.token_type_embeddings(token_type_ids)
+
+        embeddings = inputs_embeds + token_type_embeddings
+        if self.position_embedding_type == "absolute":
+            position_embeddings = self.position_embeddings(position_ids)
+            embeddings += position_embeddings
+        embeddings = self.LayerNorm(embeddings)
+        embeddings = self.dropout(embeddings)
+        return embeddings
+
+
+class BertSelfAttention(nn.Module):
+    def __init__(self, config, is_cross_attention):
+        super().__init__()
+        self.config = config
+        if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
+            raise ValueError(
+                "The hidden size (%d) is not a multiple of the number of attention "
+                "heads (%d)" % (config.hidden_size, config.num_attention_heads)
+            )
+
+        self.num_attention_heads = config.num_attention_heads
+        self.attention_head_size = int(
+            config.hidden_size / config.num_attention_heads)
+        self.all_head_size = self.num_attention_heads * self.attention_head_size
+
+        self.query = nn.Linear(config.hidden_size, self.all_head_size)
+        if is_cross_attention:
+            self.key = nn.Linear(config.encoder_width, self.all_head_size)
+            self.value = nn.Linear(config.encoder_width, self.all_head_size)
+        else:
+            self.key = nn.Linear(config.hidden_size, self.all_head_size)
+            self.value = nn.Linear(config.hidden_size, self.all_head_size)
+
+        self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
+        self.position_embedding_type = getattr(
+            config, "position_embedding_type", "absolute")
+        if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
+            self.max_position_embeddings = config.max_position_embeddings
+            self.distance_embedding = nn.Embedding(
+                2 * config.max_position_embeddings - 1, self.attention_head_size)
+        self.save_attention = False
+
+    def save_attn_gradients(self, attn_gradients):
+        self.attn_gradients = attn_gradients
+
+    def get_attn_gradients(self):
+        return self.attn_gradients
+
+    def save_attention_map(self, attention_map):
+        self.attention_map = attention_map
+
+    def get_attention_map(self):
+        return self.attention_map
+
+    def transpose_for_scores(self, x):
+        new_x_shape = x.size()[
+            :-1] + (self.num_attention_heads, self.attention_head_size)
+        x = x.view(*new_x_shape)
+        return x.permute(0, 2, 1, 3)
+
+    def forward(
+        self,
+        hidden_states,
+        attention_mask=None,
+        head_mask=None,
+        encoder_hidden_states=None,
+        encoder_attention_mask=None,
+        past_key_value=None,
+        output_attentions=False,
+    ):
+        mixed_query_layer = self.query(hidden_states)
+
+        # If this is instantiated as a cross-attention module, the keys
+        # and values come from an encoder; the attention mask needs to be
+        # such that the encoder's padding tokens are not attended to.
+        is_cross_attention = encoder_hidden_states is not None
+
+        if is_cross_attention:
+            key_layer = self.transpose_for_scores(
+                self.key(encoder_hidden_states))
+            value_layer = self.transpose_for_scores(
+                self.value(encoder_hidden_states))
+            attention_mask = encoder_attention_mask
+        elif past_key_value is not None:
+            key_layer = self.transpose_for_scores(self.key(hidden_states))
+            value_layer = self.transpose_for_scores(self.value(hidden_states))
+            key_layer = torch.cat([past_key_value[0], key_layer], dim=2)
+            value_layer = torch.cat([past_key_value[1], value_layer], dim=2)
+        else:
+            key_layer = self.transpose_for_scores(self.key(hidden_states))
+            value_layer = self.transpose_for_scores(self.value(hidden_states))
+
+        query_layer = self.transpose_for_scores(mixed_query_layer)
+
+        past_key_value = (key_layer, value_layer)
+
+        # Take the dot product between "query" and "key" to get the raw attention scores.
+        attention_scores = torch.matmul(
+            query_layer, key_layer.transpose(-1, -2))
+
+        if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
+            seq_length = hidden_states.size()[1]
+            position_ids_l = torch.arange(
+                seq_length, dtype=torch.long, device=hidden_states.device).view(-1, 1)
+            position_ids_r = torch.arange(
+                seq_length, dtype=torch.long, device=hidden_states.device).view(1, -1)
+            distance = position_ids_l - position_ids_r
+            positional_embedding = self.distance_embedding(
+                distance + self.max_position_embeddings - 1)
+            positional_embedding = positional_embedding.to(
+                dtype=query_layer.dtype)  # fp16 compatibility
+
+            if self.position_embedding_type == "relative_key":
+                relative_position_scores = torch.einsum(
+                    "bhld,lrd->bhlr", query_layer, positional_embedding)
+                attention_scores = attention_scores + relative_position_scores
+            elif self.position_embedding_type == "relative_key_query":
+                relative_position_scores_query = torch.einsum(
+                    "bhld,lrd->bhlr", query_layer, positional_embedding)
+                relative_position_scores_key = torch.einsum(
+                    "bhrd,lrd->bhlr", key_layer, positional_embedding)
+                attention_scores = attention_scores + \
+                    relative_position_scores_query + relative_position_scores_key
+
+        attention_scores = attention_scores / \
+            math.sqrt(self.attention_head_size)
+        if attention_mask is not None:
+            # Apply the attention mask is (precomputed for all layers in BertModel forward() function)
+            attention_scores = attention_scores + attention_mask
+
+        # Normalize the attention scores to probabilities.
+        attention_probs = nn.Softmax(dim=-1)(attention_scores)
+
+        if is_cross_attention and self.save_attention:
+            self.save_attention_map(attention_probs)
+            attention_probs.register_hook(self.save_attn_gradients)
+
+        # This is actually dropping out entire tokens to attend to, which might
+        # seem a bit unusual, but is taken from the original Transformer paper.
+        attention_probs_dropped = self.dropout(attention_probs)
+
+        # Mask heads if we want to
+        if head_mask is not None:
+            attention_probs_dropped = attention_probs_dropped * head_mask
+
+        context_layer = torch.matmul(attention_probs_dropped, value_layer)
+
+        context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
+        new_context_layer_shape = context_layer.size()[
+            :-2] + (self.all_head_size,)
+        context_layer = context_layer.view(*new_context_layer_shape)
+
+        # added `attention_scores` to return tuple
+        outputs = (context_layer, attention_probs, attention_scores) if output_attentions else (
+            context_layer,)
+
+        outputs = outputs + (past_key_value,)
+        return outputs
+
+
+class BertSelfOutput(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
+        self.LayerNorm = nn.LayerNorm(
+            config.hidden_size, eps=config.layer_norm_eps)
+        self.dropout = nn.Dropout(config.hidden_dropout_prob)
+
+    def forward(self, hidden_states, input_tensor):
+        hidden_states = self.dense(hidden_states)
+        hidden_states = self.dropout(hidden_states)
+        hidden_states = self.LayerNorm(hidden_states + input_tensor)
+        return hidden_states
+
+
+class BertAttention(nn.Module):
+    def __init__(self, config, is_cross_attention=False):
+        super().__init__()
+        self.self = BertSelfAttention(config, is_cross_attention)
+        self.output = BertSelfOutput(config)
+        self.pruned_heads = set()
+
+    def prune_heads(self, heads):
+        if len(heads) == 0:
+            return
+        heads, index = find_pruneable_heads_and_indices(
+            heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads
+        )
+
+        # Prune linear layers
+        self.self.query = prune_linear_layer(self.self.query, index)
+        self.self.key = prune_linear_layer(self.self.key, index)
+        self.self.value = prune_linear_layer(self.self.value, index)
+        self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)
+
+        # Update hyper params and store pruned heads
+        self.self.num_attention_heads = self.self.num_attention_heads - \
+            len(heads)
+        self.self.all_head_size = self.self.attention_head_size * \
+            self.self.num_attention_heads
+        self.pruned_heads = self.pruned_heads.union(heads)
+
+    def forward(
+        self,
+        hidden_states,
+        attention_mask=None,
+        head_mask=None,
+        encoder_hidden_states=None,
+        encoder_attention_mask=None,
+        past_key_value=None,
+        output_attentions=False,
+    ):
+        self_outputs = self.self(
+            hidden_states,
+            attention_mask,
+            head_mask,
+            encoder_hidden_states,
+            encoder_attention_mask,
+            past_key_value,
+            output_attentions,
+        )
+        attention_output = self.output(self_outputs[0], hidden_states)
+        # add attentions if we output them
+        outputs = (attention_output,) + self_outputs[1:]
+        return outputs  # (context_layer, attention_probs, attention_scores, past_key_value,)
+
+
+class BertIntermediate(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
+        if isinstance(config.hidden_act, str):
+            self.intermediate_act_fn = ACT2FN[config.hidden_act]
+        else:
+            self.intermediate_act_fn = config.hidden_act
+
+    def forward(self, hidden_states):
+        hidden_states = self.dense(hidden_states)
+        hidden_states = self.intermediate_act_fn(hidden_states)
+        return hidden_states
+
+
+class BertOutput(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
+        self.LayerNorm = nn.LayerNorm(
+            config.hidden_size, eps=config.layer_norm_eps)
+        self.dropout = nn.Dropout(config.hidden_dropout_prob)
+
+    def forward(self, hidden_states, input_tensor):
+        hidden_states = self.dense(hidden_states)
+        hidden_states = self.dropout(hidden_states)
+        hidden_states = self.LayerNorm(hidden_states + input_tensor)
+        return hidden_states
+
+
+class BertLayer(nn.Module):
+    def __init__(self, config, layer_num, token_keep_rate=1.0):
+        super().__init__()
+        self.config = config
+        self.chunk_size_feed_forward = config.chunk_size_feed_forward
+        self.seq_len_dim = 1
+        self.attention = BertAttention(config)
+
+        self.has_cross_attention = (layer_num >= config.fusion_layer)
+        if self.has_cross_attention:
+            self.layer_num = layer_num
+            self.crossattention = BertAttention(
+                config, is_cross_attention=True)
+
+            # sparse params
+            self.token_keep_rate = token_keep_rate
+            self.token_keep_strategy = config.token_keep_strategy
+            self.encoder_num_cls_tokens = 1  # multiple cls tokens
+
+        self.intermediate = BertIntermediate(config)
+        self.output = BertOutput(config)
+    
+    def sparsify(self, x, attn, mask=None):
+        x_cls, x_ = x[:, :self.encoder_num_cls_tokens], x[:, self.encoder_num_cls_tokens:]
+        assert 0 < self.token_keep_rate <= 1, "Expected keep rate in range (0, 1]"
+        left_tokens = math.ceil(self.token_keep_rate * x_.size(1))
+        if len(attn.shape) == 4:
+            attn = attn.mean(1)  # pool over attention heads
+
+        if self.token_keep_strategy == 'cls_attn':
+            cls_attn = attn[:, 0, self.encoder_num_cls_tokens:]
+            _, idx = torch.topk(cls_attn, left_tokens, dim=1)  # [B, left_tokens]
+
+        elif self.token_keep_strategy == 'avg_attn':
+            avg_attn = attn.mean(1)[:, self.encoder_num_cls_tokens:]
+            _, idx = torch.topk(avg_attn, left_tokens, dim=1)  # [B, left_tokens]
+
+        elif self.token_keep_strategy == 'random':
+            rand = torch.rand(x_.shape[:2], device=x_.device)
+            _, idx = torch.topk(rand, left_tokens, dim=1)  # [B, left_tokens]
+
+        else:
+            raise NotImplementedError(f"Sparse strategy {self.token_keep_strategy} is not implemented")
+
+        idx, _ = torch.sort(idx, dim=1)
+        index = idx.unsqueeze(-1).expand(-1, -1, x_.size(-1))  # [B, left_tokens, C]
+        outputs = torch.cat((x_cls, x_.gather(1, index)), dim=1).contiguous()
+        if mask is not None:
+            mask_cls, mask_ = mask[..., :self.encoder_num_cls_tokens], mask[..., self.encoder_num_cls_tokens:]
+            index = idx.unsqueeze(1).unsqueeze(1)  # [B, 1, 1, left_tokens]
+            mask = torch.cat((mask_cls, mask_.gather(-1, index)), dim=-1).contiguous()
+        return outputs, mask, idx
+
+    def forward(
+        self,
+        hidden_states,
+        attention_mask=None,
+        head_mask=None,
+        encoder_hidden_states=None,
+        encoder_attention_mask=None,
+        past_key_value=None,
+        output_attentions=False,
+    ):
+        # decoder uni-directional self-attention cached key/values tuple is at positions 1,2
+        self_attn_past_key_value = past_key_value[:
+                                                  2] if past_key_value is not None else None
+        self_attention_outputs = self.attention(
+            hidden_states,
+            attention_mask,
+            head_mask,
+            output_attentions=output_attentions,
+            past_key_value=self_attn_past_key_value,
+        )  # (context_layer, attention_probs, attention_scores, past_key_value,)
+        attention_output = self_attention_outputs[0]
+
+        outputs = self_attention_outputs[1:-1]
+        present_key_value = self_attention_outputs[-1]
+
+        if self.has_cross_attention:
+            assert encoder_hidden_states is not None, "encoder_hidden_states must be given for cross-attention layers"
+            output_attentions = (output_attentions or self.token_keep_rate < 1)
+
+            if type(encoder_hidden_states) == list:
+                cross_attention_outputs = self.crossattention(
+                    attention_output,
+                    attention_mask,
+                    head_mask,
+                    encoder_hidden_states[(
+                        self.layer_num-self.config.fusion_layer) % len(encoder_hidden_states)],
+                    encoder_attention_mask[(
+                        self.layer_num-self.config.fusion_layer) % len(encoder_hidden_states)],
+                    output_attentions=output_attentions,
+                )
+                attention_output = cross_attention_outputs[0]
+                outputs = outputs + cross_attention_outputs[1:-1]
+
+            else:
+                cross_attention_outputs = self.crossattention(
+                    attention_output,
+                    attention_mask,
+                    head_mask,
+                    encoder_hidden_states,
+                    encoder_attention_mask,
+                    output_attentions=output_attentions,
+                )  # (context_layer, attention_probs, attention_scores, past_key_value,)
+                attention_output = cross_attention_outputs[0]
+
+                # add cross attentions if we output attention weights
+                outputs = outputs + cross_attention_outputs[1:-1]
+                
+                # node sparsification
+                if self.token_keep_rate < 1:
+                    encoder_hidden_states, encoder_attention_mask, token_keep_idx = self.sparsify(
+                        encoder_hidden_states, cross_attention_outputs[1], encoder_attention_mask)
+                    outputs = outputs + (encoder_hidden_states, encoder_attention_mask, token_keep_idx)
+
+        layer_output = apply_chunking_to_forward(
+            self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output
+        )
+        outputs = (layer_output,) + outputs
+
+        outputs = outputs + (present_key_value,)
+
+        return outputs
+
+    def feed_forward_chunk(self, attention_output):
+        intermediate_output = self.intermediate(attention_output)
+        layer_output = self.output(intermediate_output, attention_output)
+        return layer_output
+
+
+class BertEncoder(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.config = config
+
+        # node sparsification
+        token_keep_rate = [1] * config.num_hidden_layers
+        for loc in config.token_drop_loc:
+            token_keep_rate[loc] = config.token_keep_rate
+
+        self.layer = nn.ModuleList([BertLayer(config, i, token_keep_rate[i])
+                                   for i in range(config.num_hidden_layers)])
+
+    def forward(
+        self,
+        hidden_states,
+        attention_mask=None,
+        head_mask=None,
+        encoder_hidden_states=None,
+        encoder_attention_mask=None,
+        past_key_values=None,
+        use_cache=None,
+        output_attentions=False,
+        output_hidden_states=False,
+        output_token_idx=False,
+        return_dict=True,
+        mode='multi_modal',
+        normalize_attention=True  
+    ):
+        all_hidden_states = () if output_hidden_states else None
+        all_self_attentions = () if output_attentions else None
+        all_cross_attentions = () if output_attentions else None
+        all_token_idx = () if output_token_idx else None
+
+        next_decoder_cache = () if use_cache else None
+
+        if mode == 'text':
+            start_layer = 0
+            output_layer = self.config.fusion_layer
+
+        elif mode == 'fusion':
+            start_layer = self.config.fusion_layer
+            output_layer = self.config.num_hidden_layers
+
+        elif mode == 'multi_modal':
+            start_layer = 0
+            output_layer = self.config.num_hidden_layers
+
+        for i in range(start_layer, output_layer):
+            layer_module = self.layer[i]
+            if output_hidden_states:
+                all_hidden_states = all_hidden_states + (hidden_states,)
+
+            layer_head_mask = head_mask[i] if head_mask is not None else None
+            past_key_value = past_key_values[i] if past_key_values is not None else None
+
+            if getattr(self.config, "gradient_checkpointing", False) and self.training:
+
+                if use_cache:
+                    use_cache = False
+
+                def create_custom_forward(module):
+                    def custom_forward(*inputs):
+                        return module(*inputs, past_key_value, output_attentions)
+
+                    return custom_forward
+
+                layer_outputs = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(layer_module),
+                    hidden_states,
+                    attention_mask,
+                    layer_head_mask,
+                    encoder_hidden_states,
+                    encoder_attention_mask,
+                )
+            else:
+                layer_outputs = layer_module(
+                    hidden_states,
+                    attention_mask,
+                    layer_head_mask,
+                    encoder_hidden_states,
+                    encoder_attention_mask,
+                    past_key_value,
+                    output_attentions,
+                )  # (context_layer, attention_probs, attention_scores, past_key_value,)
+            hidden_states = layer_outputs[0]
+            # update visual sequence
+            if mode == 'fusion' and layer_module.token_keep_rate < 1:
+                encoder_hidden_states, encoder_attention_mask, token_idx = layer_outputs[-4:-1]
+
+                if output_token_idx:
+                    all_token_idx = all_token_idx + (token_idx,)
+
+            if use_cache:
+                next_decoder_cache += (layer_outputs[-1],)
+            if output_attentions:
+                # whether to output normalized attention, 
+                # note for unnormalized attention, there is a mask added
+                offset = int(normalize_attention)
+                all_self_attentions = all_self_attentions + (layer_outputs[2-offset], )
+                if hasattr(layer_module, "crossattention"):
+                    all_cross_attentions = all_cross_attentions + (layer_outputs[4-offset], )
+
+        if output_hidden_states:
+            all_hidden_states = all_hidden_states + (hidden_states,)
+
+        if not return_dict:
+            return tuple(
+                v
+                for v in [
+                    hidden_states,
+                    next_decoder_cache,
+                    all_hidden_states,
+                    all_self_attentions,
+                    all_cross_attentions,
+                ]
+                if v is not None
+            )
+        return BertModelOutputWithPastAndCrossAttentions(
+            last_hidden_state=hidden_states,
+            past_key_values=next_decoder_cache,
+            hidden_states=all_hidden_states,
+            attentions=all_self_attentions,
+            cross_attentions=all_cross_attentions,
+            token_idx=all_token_idx
+        )
+
+
+class BertPooler(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
+        self.activation = nn.Tanh()
+
+    def forward(self, hidden_states):
+        # We "pool" the model by simply taking the hidden state corresponding
+        # to the first token.
+        first_token_tensor = hidden_states[:, 0]
+        pooled_output = self.dense(first_token_tensor)
+        pooled_output = self.activation(pooled_output)
+        return pooled_output
+
+
+class BertPredictionHeadTransform(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
+        if isinstance(config.hidden_act, str):
+            self.transform_act_fn = ACT2FN[config.hidden_act]
+        else:
+            self.transform_act_fn = config.hidden_act
+        self.LayerNorm = nn.LayerNorm(
+            config.hidden_size, eps=config.layer_norm_eps)
+
+    def forward(self, hidden_states):
+        hidden_states = self.dense(hidden_states)
+        hidden_states = self.transform_act_fn(hidden_states)
+        hidden_states = self.LayerNorm(hidden_states)
+        return hidden_states
+
+
+class BertLMPredictionHead(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.transform = BertPredictionHeadTransform(config)
+
+        # The output weights are the same as the input embeddings, but there is
+        # an output-only bias for each token.
+        self.decoder = nn.Linear(
+            config.hidden_size, config.vocab_size, bias=False)
+
+        self.bias = nn.Parameter(torch.zeros(config.vocab_size))
+
+        # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`
+        self.decoder.bias = self.bias
+
+    def forward(self, hidden_states):
+        hidden_states = self.transform(hidden_states)
+        hidden_states = self.decoder(hidden_states)
+        return hidden_states
+
+
+class BertOnlyMLMHead(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.predictions = BertLMPredictionHead(config)
+
+    def forward(self, sequence_output):
+        prediction_scores = self.predictions(sequence_output)
+        return prediction_scores
+
+
+class BertOnlyNSPHead(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.seq_relationship = nn.Linear(config.hidden_size, 2)
+
+    def forward(self, pooled_output):
+        seq_relationship_score = self.seq_relationship(pooled_output)
+        return seq_relationship_score
+
+
+class BertPreTrainingHeads(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.predictions = BertLMPredictionHead(config)
+        self.seq_relationship = nn.Linear(config.hidden_size, 2)
+
+    def forward(self, sequence_output, pooled_output):
+        prediction_scores = self.predictions(sequence_output)
+        seq_relationship_score = self.seq_relationship(pooled_output)
+        return prediction_scores, seq_relationship_score
+
+
+class BertPreTrainedModel(PreTrainedModel):
+    """
+    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
+    models.
+    """
+
+    config_class = BertConfig
+    load_tf_weights = load_tf_weights_in_bert
+    base_model_prefix = "bert"
+    _keys_to_ignore_on_load_missing = [r"position_ids"]
+
+    def _init_weights(self, module):
+        """ Initialize the weights """
+        if isinstance(module, (nn.Linear, nn.Embedding)):
+            # Slightly different from the TF version which uses truncated_normal for initialization
+            # cf https://github.com/pytorch/pytorch/pull/5617
+            module.weight.data.normal_(
+                mean=0.0, std=self.config.initializer_range)
+        elif isinstance(module, nn.LayerNorm):
+            module.bias.data.zero_()
+            module.weight.data.fill_(1.0)
+        if isinstance(module, nn.Linear) and module.bias is not None:
+            module.bias.data.zero_()
+
+
+@dataclass
+class BertForPreTrainingOutput(ModelOutput):
+    """
+    Output type of :class:`~transformers.BertForPreTraining`.
+    Args:
+        loss (`optional`, returned when ``labels`` is provided, ``torch.FloatTensor`` of shape :obj:`(1,)`):
+            Total loss as the sum of the masked language modeling loss and the next sequence prediction
+            (classification) loss.
+        prediction_logits (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, config.vocab_size)`):
+            Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
+        seq_relationship_logits (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, 2)`):
+            Prediction scores of the next sequence prediction (classification) head (scores of True/False continuation
+            before SoftMax).
+        hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
+            Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer)
+            of shape :obj:`(batch_size, sequence_length, hidden_size)`.
+            Hidden-states of the model at the output of each layer plus the initial embedding outputs.
+        attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
+            Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape :obj:`(batch_size, num_heads,
+            sequence_length, sequence_length)`.
+            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
+            heads.
+    """
+
+    loss: Optional[torch.FloatTensor] = None
+    prediction_logits: torch.FloatTensor = None
+    seq_relationship_logits: torch.FloatTensor = None
+    hidden_states: Optional[Tuple[torch.FloatTensor]] = None
+    attentions: Optional[Tuple[torch.FloatTensor]] = None
+
+
+BERT_START_DOCSTRING = r"""
+    This model inherits from :class:`~transformers.PreTrainedModel`. Check the superclass documentation for the generic
+    methods the library implements for all its model (such as downloading or saving, resizing the input embeddings,
+    pruning heads etc.)
+    This model is also a PyTorch `torch.nn.Module <https://pytorch.org/docs/stable/nn.html#torch.nn.Module>`__
+    subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to
+    general usage and behavior.
+    Parameters:
+        config (:class:`~transformers.BertConfig`): Model configuration class with all the parameters of the model.
+            Initializing with a config file does not load the weights associated with the model, only the
+            configuration. Check out the :meth:`~transformers.PreTrainedModel.from_pretrained` method to load the model
+            weights.
+"""
+
+BERT_INPUTS_DOCSTRING = r"""
+    Args:
+        input_ids (:obj:`torch.LongTensor` of shape :obj:`({0})`):
+            Indices of input sequence tokens in the vocabulary.
+            Indices can be obtained using :class:`~transformers.BertTokenizer`. See
+            :meth:`transformers.PreTrainedTokenizer.encode` and :meth:`transformers.PreTrainedTokenizer.__call__` for
+            details.
+            `What are input IDs? <../glossary.html#input-ids>`__
+        attention_mask (:obj:`torch.FloatTensor` of shape :obj:`({0})`, `optional`):
+            Mask to avoid performing attention on padding token indices. Mask values selected in ``[0, 1]``:
+            - 1 for tokens that are **not masked**,
+            - 0 for tokens that are **masked**.
+            `What are attention masks? <../glossary.html#attention-mask>`__
+        token_type_ids (:obj:`torch.LongTensor` of shape :obj:`({0})`, `optional`):
+            Segment token indices to indicate first and second portions of the inputs. Indices are selected in ``[0,
+            1]``:
+            - 0 corresponds to a `sentence A` token,
+            - 1 corresponds to a `sentence B` token.
+            `What are token type IDs? <../glossary.html#token-type-ids>`_
+        position_ids (:obj:`torch.LongTensor` of shape :obj:`({0})`, `optional`):
+            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range ``[0,
+            config.max_position_embeddings - 1]``.
+            `What are position IDs? <../glossary.html#position-ids>`_
+        head_mask (:obj:`torch.FloatTensor` of shape :obj:`(num_heads,)` or :obj:`(num_layers, num_heads)`, `optional`):
+            Mask to nullify selected heads of the self-attention modules. Mask values selected in ``[0, 1]``:
+            - 1 indicates the head is **not masked**,
+            - 0 indicates the head is **masked**.
+        inputs_embeds (:obj:`torch.FloatTensor` of shape :obj:`({0}, hidden_size)`, `optional`):
+            Optionally, instead of passing :obj:`input_ids` you can choose to directly pass an embedded representation.
+            This is useful if you want more control over how to convert :obj:`input_ids` indices into associated
+            vectors than the model's internal embedding lookup matrix.
+        output_attentions (:obj:`bool`, `optional`):
+            Whether or not to return the attentions tensors of all attention layers. See ``attentions`` under returned
+            tensors for more detail.
+        output_hidden_states (:obj:`bool`, `optional`):
+            Whether or not to return the hidden states of all layers. See ``hidden_states`` under returned tensors for
+            more detail.
+        return_dict (:obj:`bool`, `optional`):
+            Whether or not to return a :class:`~transformers.file_utils.ModelOutput` instead of a plain tuple.
+"""
+
+
+@add_start_docstrings(
+    "The bare Bert Model transformer outputting raw hidden-states without any specific head on top.",
+    BERT_START_DOCSTRING,
+)
+class BertModel(BertPreTrainedModel):
+    """
+    The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of
+    cross-attention is added between the self-attention layers, following the architecture described in `Attention is
+    all you need <https://arxiv.org/abs/1706.03762>`__ by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit,
+    Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin.
+    argument and :obj:`add_cross_attention` set to :obj:`True`; an :obj:`encoder_hidden_states` is then expected as an
+    input to the forward pass.
+    """
+
+    def __init__(self, config, add_pooling_layer=True):
+        super().__init__(config)
+        self.config = config
+
+        self.embeddings = BertEmbeddings(config)
+
+        self.encoder = BertEncoder(config)
+
+        self.pooler = BertPooler(config) if add_pooling_layer else None
+
+        self.init_weights()
+
+    def get_input_embeddings(self):
+        return self.embeddings.word_embeddings
+
+    def set_input_embeddings(self, value):
+        self.embeddings.word_embeddings = value
+
+    def _prune_heads(self, heads_to_prune):
+        """
+        Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
+        class PreTrainedModel
+        """
+        for layer, heads in heads_to_prune.items():
+            self.encoder.layer[layer].attention.prune_heads(heads)
+
+    def get_extended_attention_mask(self, attention_mask: Tensor, input_shape: Tuple[int], device: device, is_decoder: bool) -> Tensor:
+        """
+        Makes broadcastable attention and causal masks so that future and masked tokens are ignored.
+
+        Arguments:
+            attention_mask (:obj:`torch.Tensor`):
+                Mask with ones indicating tokens to attend to, zeros for tokens to ignore.
+            input_shape (:obj:`Tuple[int]`):
+                The shape of the input to the model.
+            device: (:obj:`torch.device`):
+                The device of the input to the model.
+
+        Returns:
+            :obj:`torch.Tensor` The extended attention mask, with a the same dtype as :obj:`attention_mask.dtype`.
+        """
+        # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
+        # ourselves in which case we just need to make it broadcastable to all heads.
+        if attention_mask.dim() == 3:
+            extended_attention_mask = attention_mask[:, None, :, :]
+        elif attention_mask.dim() == 2:
+            # Provided a padding mask of dimensions [batch_size, seq_length]
+            # - if the model is a decoder, apply a causal mask in addition to the padding mask
+            # - if the model is an encoder, make the mask broadcastable to [batch_size, num_heads, seq_length, seq_length]
+            if is_decoder:
+                batch_size, seq_length = input_shape
+                seq_ids = torch.arange(seq_length, device=device)
+                causal_mask = seq_ids[None, None, :].repeat(
+                    batch_size, seq_length, 1) <= seq_ids[None, :, None]
+                # in case past_key_values are used we need to add a prefix ones mask to the causal mask
+                # causal and attention masks must have same type with pytorch version < 1.3
+                causal_mask = causal_mask.to(attention_mask.dtype)
+
+                if causal_mask.shape[1] < attention_mask.shape[1]:
+                    prefix_seq_len = attention_mask.shape[1] - \
+                        causal_mask.shape[1]
+                    causal_mask = torch.cat(
+                        [
+                            torch.ones(
+                                (batch_size, seq_length,
+                                 prefix_seq_len), device=device, dtype=causal_mask.dtype
+                            ),
+                            causal_mask,
+                        ],
+                        axis=-1,
+                    )
+
+                extended_attention_mask = causal_mask[:, None,
+                                                      :, :] * attention_mask[:, None, None, :]
+            else:
+                extended_attention_mask = attention_mask[:, None, None, :]
+        else:
+            raise ValueError(
+                "Wrong shape for input_ids (shape {}) or attention_mask (shape {})".format(
+                    input_shape, attention_mask.shape
+                )
+            )
+
+        # Since attention_mask is 1.0 for positions we want to attend and 0.0 for
+        # masked positions, this operation will create a tensor which is 0.0 for
+        # positions we want to attend and -10000.0 for masked positions.
+        # Since we are adding it to the raw scores before the softmax, this is
+        # effectively the same as removing these entirely.
+        extended_attention_mask = extended_attention_mask.to(
+            dtype=self.dtype)  # fp16 compatibility
+        extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0
+        return extended_attention_mask
+
+    def forward(
+        self,
+        input_ids=None,
+        attention_mask=None,
+        token_type_ids=None,
+        position_ids=None,
+        head_mask=None,
+        inputs_embeds=None,
+        encoder_embeds=None,
+        encoder_hidden_states=None,
+        encoder_attention_mask=None,
+        past_key_values=None,
+        use_cache=None,
+        output_attentions=None,
+        output_hidden_states=None,
+        output_token_idx=None,
+        return_dict=None,
+        is_decoder=False,
+        mode='multi_modal',
+        normalize_attention=True,
+    ):
+        r"""
+        encoder_hidden_states  (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`):
+            Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
+            the model is configured as a decoder.
+        encoder_attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
+            Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
+            the cross-attention if the model is configured as a decoder. Mask values selected in ``[0, 1]``:
+            - 1 for tokens that are **not masked**,
+            - 0 for tokens that are **masked**.
+        past_key_values (:obj:`tuple(tuple(torch.FloatTensor))` of length :obj:`config.n_layers` with each tuple having 4 tensors of shape :obj:`(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
+            Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
+            If :obj:`past_key_values` are used, the user can optionally input only the last :obj:`decoder_input_ids`
+            (those that don't have their past key value states given to this model) of shape :obj:`(batch_size, 1)`
+            instead of all :obj:`decoder_input_ids` of shape :obj:`(batch_size, sequence_length)`.
+        use_cache (:obj:`bool`, `optional`):
+            If set to :obj:`True`, :obj:`past_key_values` key value states are returned and can be used to speed up
+            decoding (see :obj:`past_key_values`).
+        """
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        if is_decoder:
+            use_cache = use_cache if use_cache is not None else self.config.use_cache
+        else:
+            use_cache = False
+
+        if input_ids is not None and inputs_embeds is not None:
+            raise ValueError(
+                "You cannot specify both input_ids and inputs_embeds at the same time")
+        elif input_ids is not None:
+            input_shape = input_ids.size()
+            batch_size, seq_length = input_shape
+            device = input_ids.device
+        elif inputs_embeds is not None:
+            input_shape = inputs_embeds.size()[:-1]
+            batch_size, seq_length = input_shape
+            device = inputs_embeds.device
+        elif encoder_embeds is not None:
+            input_shape = encoder_embeds.size()[:-1]
+            batch_size, seq_length = input_shape
+            device = encoder_embeds.device
+        else:
+            raise ValueError(
+                "You have to specify either input_ids or inputs_embeds or encoder_embeds")
+
+        # past_key_values_length
+        past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0
+
+        if attention_mask is None:
+            attention_mask = torch.ones(
+                ((batch_size, seq_length + past_key_values_length)), device=device)
+        if token_type_ids is None:
+            token_type_ids = torch.zeros(
+                input_shape, dtype=torch.long, device=device)
+
+        # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
+        # ourselves in which case we just need to make it broadcastable to all heads.
+        extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape,
+                                                                                 device, is_decoder)
+
+        # If a 2D or 3D attention mask is provided for the cross-attention
+        # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
+        if encoder_hidden_states is not None:
+            if type(encoder_hidden_states) == list:
+                encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states[0].size(
+                )
+            else:
+                encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size()
+            encoder_hidden_shape = (
+                encoder_batch_size, encoder_sequence_length)
+
+            if type(encoder_attention_mask) == list:
+                encoder_extended_attention_mask = [
+                    self.invert_attention_mask(mask) for mask in encoder_attention_mask]
+            elif encoder_attention_mask is None:
+                encoder_attention_mask = torch.ones(
+                    encoder_hidden_shape, device=device)
+                encoder_extended_attention_mask = self.invert_attention_mask(
+                    encoder_attention_mask)
+            else:
+                encoder_extended_attention_mask = self.invert_attention_mask(
+                    encoder_attention_mask)
+        else:
+            encoder_extended_attention_mask = None
+
+        # Prepare head mask if needed
+        # 1.0 in head_mask indicate we keep the head
+        # attention_probs has shape bsz x n_heads x N x N
+        # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
+        # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
+        head_mask = self.get_head_mask(
+            head_mask, self.config.num_hidden_layers)
+
+        if encoder_embeds is None:
+            embedding_output = self.embeddings(
+                input_ids=input_ids,
+                position_ids=position_ids,
+                token_type_ids=token_type_ids,
+                inputs_embeds=inputs_embeds,
+                past_key_values_length=past_key_values_length,
+            )
+        else:
+            embedding_output = encoder_embeds
+
+        encoder_outputs = self.encoder(
+            embedding_output,
+            attention_mask=extended_attention_mask,
+            head_mask=head_mask,
+            encoder_hidden_states=encoder_hidden_states,
+            encoder_attention_mask=encoder_extended_attention_mask,
+            past_key_values=past_key_values,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            output_token_idx=output_token_idx,
+            return_dict=return_dict,
+            mode=mode,
+            normalize_attention=normalize_attention,
+
+        )
+        sequence_output = encoder_outputs[0]
+        pooled_output = self.pooler(
+            sequence_output) if self.pooler is not None else None
+
+        if not return_dict:
+            return (sequence_output, pooled_output) + encoder_outputs[1:]
+
+        return BertModelOutputWithPoolingAndCrossAttentions(
+            last_hidden_state=sequence_output,
+            pooler_output=pooled_output,
+            past_key_values=encoder_outputs.past_key_values,
+            hidden_states=encoder_outputs.hidden_states,
+            attentions=encoder_outputs.attentions,
+            cross_attentions=encoder_outputs.cross_attentions,
+            token_idx=encoder_outputs.token_idx,
+        )
+
+
+@add_start_docstrings(
+    """
+    Bert Model with two heads on top as done during the pretraining: a `masked language modeling` head and a `next
+    sentence prediction (classification)` head.
+    """,
+    BERT_START_DOCSTRING,
+)
+class BertForPreTraining(BertPreTrainedModel):
+    def __init__(self, config):
+        super().__init__(config)
+
+        self.bert = BertModel(config)
+        self.cls = BertPreTrainingHeads(config)
+
+        self.init_weights()
+
+    def get_output_embeddings(self):
+        return self.cls.predictions.decoder
+
+    def set_output_embeddings(self, new_embeddings):
+        self.cls.predictions.decoder = new_embeddings
+
+    @add_start_docstrings_to_model_forward(BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
+    @replace_return_docstrings(output_type=BertForPreTrainingOutput, config_class=_CONFIG_FOR_DOC)
+    def forward(
+        self,
+        input_ids=None,
+        attention_mask=None,
+        token_type_ids=None,
+        position_ids=None,
+        head_mask=None,
+        inputs_embeds=None,
+        labels=None,
+        next_sentence_label=None,
+        output_attentions=None,
+        output_hidden_states=None,
+        return_dict=None,
+    ):
+        r"""
+        labels (:obj:`torch.LongTensor` of shape ``(batch_size, sequence_length)``, `optional`):
+            Labels for computing the masked language modeling loss. Indices should be in ``[-100, 0, ...,
+            config.vocab_size]`` (see ``input_ids`` docstring) Tokens with indices set to ``-100`` are ignored
+            (masked), the loss is only computed for the tokens with labels in ``[0, ..., config.vocab_size]``
+        next_sentence_label (``torch.LongTensor`` of shape ``(batch_size,)``, `optional`):
+            Labels for computing the next sequence prediction (classification) loss. Input should be a sequence pair
+            (see :obj:`input_ids` docstring) Indices should be in ``[0, 1]``:
+            - 0 indicates sequence B is a continuation of sequence A,
+            - 1 indicates sequence B is a random sequence.
+        kwargs (:obj:`Dict[str, any]`, optional, defaults to `{}`):
+            Used to hide legacy arguments that have been deprecated.
+        Returns:
+        Example::
+            >>> from transformers import BertTokenizer, BertForPreTraining
+            >>> import torch
+            >>> tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
+            >>> model = BertForPreTraining.from_pretrained('bert-base-uncased')
+            >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
+            >>> outputs = model(**inputs)
+            >>> prediction_logits = outputs.prediction_logits
+            >>> seq_relationship_logits = outputs.seq_relationship_logits
+        """
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        outputs = self.bert(
+            input_ids,
+            attention_mask=attention_mask,
+            token_type_ids=token_type_ids,
+            position_ids=position_ids,
+            head_mask=head_mask,
+            inputs_embeds=inputs_embeds,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        sequence_output, pooled_output = outputs[:2]
+        prediction_scores, seq_relationship_score = self.cls(
+            sequence_output, pooled_output)
+
+        total_loss = None
+        if labels is not None and next_sentence_label is not None:
+            loss_fct = CrossEntropyLoss()
+            masked_lm_loss = loss_fct(
+                prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))
+            next_sentence_loss = loss_fct(
+                seq_relationship_score.view(-1, 2), next_sentence_label.view(-1))
+            total_loss = masked_lm_loss + next_sentence_loss
+
+        if not return_dict:
+            output = (prediction_scores, seq_relationship_score) + outputs[2:]
+            return ((total_loss,) + output) if total_loss is not None else output
+
+        return BertForPreTrainingOutput(
+            loss=total_loss,
+            prediction_logits=prediction_scores,
+            seq_relationship_logits=seq_relationship_score,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+
+@add_start_docstrings(
+    """Bert Model with a `language modeling` head on top for CLM fine-tuning. """, BERT_START_DOCSTRING
+)
+class BertLMHeadModel(BertPreTrainedModel):
+
+    _keys_to_ignore_on_load_unexpected = [r"pooler"]
+    _keys_to_ignore_on_load_missing = [
+        r"position_ids", r"predictions.decoder.bias"]
+
+    def __init__(self, config):
+        super().__init__(config)
+
+        self.bert = BertModel(config, add_pooling_layer=False)
+        self.cls = BertOnlyMLMHead(config)
+
+        self.init_weights()
+
+    def get_output_embeddings(self):
+        return self.cls.predictions.decoder
+
+    def set_output_embeddings(self, new_embeddings):
+        self.cls.predictions.decoder = new_embeddings
+
+    @add_start_docstrings_to_model_forward(BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
+    @replace_return_docstrings(output_type=CausalLMOutputWithCrossAttentions, config_class=_CONFIG_FOR_DOC)
+    def forward(
+        self,
+        input_ids=None,
+        attention_mask=None,
+        token_type_ids=None,
+        position_ids=None,
+        head_mask=None,
+        inputs_embeds=None,
+        encoder_hidden_states=None,
+        encoder_attention_mask=None,
+        labels=None,
+        past_key_values=None,
+        use_cache=None,
+        output_attentions=None,
+        output_hidden_states=None,
+        return_dict=None,
+        is_decoder=True,
+        reduction='mean',
+        mode='multi_modal',
+        normalize_attention=True,
+        soft_labels=None,
+        alpha=0,
+        return_logits=False,
+    ):
+        r"""
+        encoder_hidden_states  (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`):
+            Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
+            the model is configured as a decoder.
+        encoder_attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
+            Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
+            the cross-attention if the model is configured as a decoder. Mask values selected in ``[0, 1]``:
+            - 1 for tokens that are **not masked**,
+            - 0 for tokens that are **masked**.
+        labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
+            Labels for computing the left-to-right language modeling loss (next word prediction). Indices should be in
+            ``[-100, 0, ..., config.vocab_size]`` (see ``input_ids`` docstring) Tokens with indices set to ``-100`` are
+            ignored (masked), the loss is only computed for the tokens with labels n ``[0, ..., config.vocab_size]``
+        past_key_values (:obj:`tuple(tuple(torch.FloatTensor))` of length :obj:`config.n_layers` with each tuple having 4 tensors of shape :obj:`(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
+            Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
+            If :obj:`past_key_values` are used, the user can optionally input only the last :obj:`decoder_input_ids`
+            (those that don't have their past key value states given to this model) of shape :obj:`(batch_size, 1)`
+            instead of all :obj:`decoder_input_ids` of shape :obj:`(batch_size, sequence_length)`.
+        use_cache (:obj:`bool`, `optional`):
+            If set to :obj:`True`, :obj:`past_key_values` key value states are returned and can be used to speed up
+            decoding (see :obj:`past_key_values`).
+        Returns:
+        Example::
+            >>> from transformers import BertTokenizer, BertLMHeadModel, BertConfig
+            >>> import torch
+            >>> tokenizer = BertTokenizer.from_pretrained('bert-base-cased')
+            >>> config = BertConfig.from_pretrained("bert-base-cased")
+            >>> model = BertLMHeadModel.from_pretrained('bert-base-cased', config=config)
+            >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
+            >>> outputs = model(**inputs)
+            >>> prediction_logits = outputs.logits
+        """
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+        if labels is not None:
+            use_cache = False
+
+        outputs = self.bert(
+            input_ids,
+            attention_mask=attention_mask,
+            token_type_ids=token_type_ids,
+            position_ids=position_ids,
+            head_mask=head_mask,
+            inputs_embeds=inputs_embeds,
+            encoder_hidden_states=encoder_hidden_states,
+            encoder_attention_mask=encoder_attention_mask,
+            past_key_values=past_key_values,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+            is_decoder=is_decoder,
+            mode=mode,
+            normalize_attention=normalize_attention,
+        )
+
+        sequence_output = outputs[0]
+        prediction_scores = self.cls(sequence_output)
+
+        if return_logits:
+            return prediction_scores[:, :-1, :].contiguous()
+
+        lm_loss = None
+        if labels is not None:
+            # we are doing next-token prediction; shift prediction scores and input ids by one
+            shifted_prediction_scores = prediction_scores[:,
+                                                          :-1, :].contiguous()
+            labels = labels[:, 1:].contiguous()
+            loss_fct = CrossEntropyLoss(reduction=reduction)
+            lm_loss = loss_fct(
+                shifted_prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))
+            lm_loss = lm_loss.view(prediction_scores.size(0), -1).sum(1)
+
+        if soft_labels is not None:
+            loss_distill = - \
+                torch.sum(F.log_softmax(shifted_prediction_scores,
+                          dim=1)*soft_labels, dim=-1)
+            loss_distill = (loss_distill * (labels != -100)).sum(1)
+            lm_loss = (1-alpha)*lm_loss + alpha*loss_distill
+
+        if not return_dict:
+            output = (prediction_scores,) + outputs[2:]
+            return ((lm_loss,) + output) if lm_loss is not None else output
+
+        return CausalLMOutputWithCrossAttentions(
+            loss=lm_loss,
+            logits=prediction_scores,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+            cross_attentions=outputs.cross_attentions,
+        )
+
+    def prepare_inputs_for_generation(self, input_ids, past=None, attention_mask=None, **model_kwargs):
+        input_shape = input_ids.shape
+        # if model is used as a decoder in encoder-decoder model, the decoder attention mask is created on the fly
+        if attention_mask is None:
+            attention_mask = input_ids.new_ones(input_shape)
+
+        # cut decoder_input_ids if past is used
+        if past is not None:
+            input_ids = input_ids[:, -1:]
+
+        return {
+            "input_ids": input_ids,
+            "attention_mask": attention_mask,
+            "past_key_values": past,
+            "encoder_hidden_states": model_kwargs.get("encoder_hidden_states", None),
+            "encoder_attention_mask": model_kwargs.get("encoder_attention_mask", None),
+            "is_decoder": True,
+        }
+
+    def _reorder_cache(self, past, beam_idx):
+        reordered_past = ()
+        for layer_past in past:
+            reordered_past += (tuple(past_state.index_select(0, beam_idx)
+                               for past_state in layer_past),)
+        return reordered_past
+
+
+@dataclass
+class MaskedLMOutputWithDistill(MaskedLMOutput):
+    loss_aux: Optional[torch.FloatTensor] = None
+    loss_distill: Optional[torch.FloatTensor] = None
+
+
+@add_start_docstrings("""Bert Model with a `language modeling` head on top. """, BERT_START_DOCSTRING)
+class BertForMaskedLM(BertPreTrainedModel):
+
+    _keys_to_ignore_on_load_unexpected = [r"pooler"]
+    _keys_to_ignore_on_load_missing = [
+        r"position_ids", r"predictions.decoder.bias"]
+
+    def __init__(self, config):
+        super().__init__(config)
+
+        self.bert = BertModel(config, add_pooling_layer=False)
+        self.cls = BertOnlyMLMHead(config)
+
+        self.init_weights()
+
+    def tie_aux_decoder_weights(self, module, aux_modules):
+        """Tie decoder weights of all `aux_modules` to `module`, (not bias)"""
+        for m in aux_modules:
+            m.predictions.decoder.weight = module.predictions.decoder.weight
+
+    def get_output_embeddings(self):
+        return self.cls.predictions.decoder
+
+    def set_output_embeddings(self, new_embeddings):
+        self.cls.predictions.decoder = new_embeddings
+
+    def forward(
+        self,
+        input_ids=None,
+        attention_mask=None,
+        token_type_ids=None,
+        position_ids=None,
+        head_mask=None,
+        inputs_embeds=None,
+        encoder_embeds=None,
+        encoder_hidden_states=None,
+        encoder_attention_mask=None,
+        labels=None,
+        output_attentions=None,
+        output_hidden_states=None,
+        return_dict=None,
+        is_decoder=False,
+        mode='multi_modal',
+        normalize_attention=True,
+        soft_labels=None,
+        alpha=0,
+        return_logits=False,
+    ):
+        r"""
+        labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
+            Labels for computing the masked language modeling loss. Indices should be in ``[-100, 0, ...,
+            config.vocab_size]`` (see ``input_ids`` docstring) Tokens with indices set to ``-100`` are ignored
+            (masked), the loss is only computed for the tokens with labels in ``[0, ..., config.vocab_size]``
+        """
+
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        outputs = self.bert(
+            input_ids,
+            attention_mask=attention_mask,
+            token_type_ids=token_type_ids,
+            position_ids=position_ids,
+            head_mask=head_mask,
+            inputs_embeds=inputs_embeds,
+            encoder_embeds=encoder_embeds,
+            encoder_hidden_states=encoder_hidden_states,
+            encoder_attention_mask=encoder_attention_mask,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+            is_decoder=is_decoder,
+            mode=mode,
+            normalize_attention=normalize_attention
+        )
+
+        sequence_output = outputs[0]
+        prediction_scores = self.cls(sequence_output)
+
+        if return_logits:
+            return prediction_scores
+
+        masked_lm_loss = None
+        masked_lm_loss_aux = 0.
+        if labels is not None:
+            loss_fct = CrossEntropyLoss()  # -100 index = padding token
+            masked_lm_loss = loss_fct(
+                prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))
+
+        if soft_labels is not None:
+            loss_distill = - \
+                torch.sum(F.log_softmax(prediction_scores, dim=1)
+                          * soft_labels, dim=-1)
+            loss_distill = loss_distill[labels != -100].mean()
+            masked_lm_loss = (1-alpha)*masked_lm_loss + alpha*loss_distill
+
+        if not return_dict:
+            output = (prediction_scores,) + outputs[2:]
+            return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
+
+        # changed from MaskedLMOutput to MaskedLMOutputWithDistill
+        return MaskedLMOutputWithDistill(
+            loss=masked_lm_loss,
+            loss_aux=masked_lm_loss_aux,
+            logits=prediction_scores,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+    def prepare_inputs_for_generation(self, input_ids, attention_mask=None, **model_kwargs):
+        input_shape = input_ids.shape
+        effective_batch_size = input_shape[0]
+
+        #  add a dummy token
+        assert self.config.pad_token_id is not None, "The PAD token should be defined for generation"
+        attention_mask = torch.cat(
+            [attention_mask, attention_mask.new_zeros((attention_mask.shape[0], 1))], dim=-1)
+        dummy_token = torch.full(
+            (effective_batch_size, 1), self.config.pad_token_id, dtype=torch.long, device=input_ids.device
+        )
+        input_ids = torch.cat([input_ids, dummy_token], dim=1)
+
+        return {"input_ids": input_ids, "attention_mask": attention_mask}
+
+
+@add_start_docstrings(
+    """Bert Model with a `next sentence prediction (classification)` head on top. """,
+    BERT_START_DOCSTRING,
+)
+class BertForNextSentencePrediction(BertPreTrainedModel):
+    def __init__(self, config):
+        super().__init__(config)
+
+        self.bert = BertModel(config)
+        self.cls = BertOnlyNSPHead(config)
+
+        self.init_weights()
+
+    @add_start_docstrings_to_model_forward(BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
+    @replace_return_docstrings(output_type=NextSentencePredictorOutput, config_class=_CONFIG_FOR_DOC)
+    def forward(
+        self,
+        input_ids=None,
+        attention_mask=None,
+        token_type_ids=None,
+        position_ids=None,
+        head_mask=None,
+        inputs_embeds=None,
+        labels=None,
+        output_attentions=None,
+        output_hidden_states=None,
+        return_dict=None,
+        **kwargs
+    ):
+        r"""
+        labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`):
+            Labels for computing the next sequence prediction (classification) loss. Input should be a sequence pair
+            (see ``input_ids`` docstring). Indices should be in ``[0, 1]``:
+            - 0 indicates sequence B is a continuation of sequence A,
+            - 1 indicates sequence B is a random sequence.
+        Returns:
+        Example::
+            >>> from transformers import BertTokenizer, BertForNextSentencePrediction
+            >>> import torch
+            >>> tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
+            >>> model = BertForNextSentencePrediction.from_pretrained('bert-base-uncased')
+            >>> prompt = "In Italy, pizza served in formal settings, such as at a restaurant, is presented unsliced."
+            >>> next_sentence = "The sky is blue due to the shorter wavelength of blue light."
+            >>> encoding = tokenizer(prompt, next_sentence, return_tensors='pt')
+            >>> outputs = model(**encoding, labels=torch.LongTensor([1]))
+            >>> logits = outputs.logits
+            >>> assert logits[0, 0] < logits[0, 1] # next sentence was random
+        """
+
+        if "next_sentence_label" in kwargs:
+            warnings.warn(
+                "The `next_sentence_label` argument is deprecated and will be removed in a future version, use `labels` instead.",
+                FutureWarning,
+            )
+            labels = kwargs.pop("next_sentence_label")
+
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        outputs = self.bert(
+            input_ids,
+            attention_mask=attention_mask,
+            token_type_ids=token_type_ids,
+            position_ids=position_ids,
+            head_mask=head_mask,
+            inputs_embeds=inputs_embeds,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        pooled_output = outputs[1]
+
+        seq_relationship_scores = self.cls(pooled_output)
+
+        next_sentence_loss = None
+        if labels is not None:
+            loss_fct = CrossEntropyLoss()
+            next_sentence_loss = loss_fct(
+                seq_relationship_scores.view(-1, 2), labels.view(-1))
+
+        if not return_dict:
+            output = (seq_relationship_scores,) + outputs[2:]
+            return ((next_sentence_loss,) + output) if next_sentence_loss is not None else output
+
+        return NextSentencePredictorOutput(
+            loss=next_sentence_loss,
+            logits=seq_relationship_scores,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+
+@add_start_docstrings(
+    """
+    Bert Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled
+    output) e.g. for GLUE tasks.
+    """,
+    BERT_START_DOCSTRING,
+)
+class BertForSequenceClassification(BertPreTrainedModel):
+    def __init__(self, config):
+        super().__init__(config)
+        self.num_labels = config.num_labels
+
+        self.bert = BertModel(config)
+        self.dropout = nn.Dropout(config.hidden_dropout_prob)
+        self.classifier = nn.Linear(config.hidden_size, config.num_labels)
+
+        self.init_weights()
+
+    def forward(
+        self,
+        input_ids=None,
+        attention_mask=None,
+        token_type_ids=None,
+        position_ids=None,
+        head_mask=None,
+        inputs_embeds=None,
+        labels=None,
+        output_attentions=None,
+        output_hidden_states=None,
+        return_dict=None,
+    ):
+        r"""
+        labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`):
+            Labels for computing the sequence classification/regression loss. Indices should be in :obj:`[0, ...,
+            config.num_labels - 1]`. If :obj:`config.num_labels == 1` a regression loss is computed (Mean-Square loss),
+            If :obj:`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+        """
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        outputs = self.bert(
+            input_ids,
+            attention_mask=attention_mask,
+            token_type_ids=token_type_ids,
+            position_ids=position_ids,
+            head_mask=head_mask,
+            inputs_embeds=inputs_embeds,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        pooled_output = outputs[1]
+
+        pooled_output = self.dropout(pooled_output)
+        logits = self.classifier(pooled_output)
+
+        loss = None
+        if labels is not None:
+            if self.num_labels == 1:
+                #  We are doing regression
+                loss_fct = MSELoss()
+                loss = loss_fct(logits.view(-1), labels.view(-1))
+            else:
+                loss_fct = CrossEntropyLoss()
+                loss = loss_fct(
+                    logits.view(-1, self.num_labels), labels.view(-1))
+
+        if not return_dict:
+            output = (logits,) + outputs[2:]
+            return ((loss,) + output) if loss is not None else output
+
+        return SequenceClassifierOutput(
+            loss=loss,
+            logits=logits,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+
+@add_start_docstrings(
+    """
+    Bert Model with a multiple choice classification head on top (a linear layer on top of the pooled output and a
+    softmax) e.g. for RocStories/SWAG tasks.
+    """,
+    BERT_START_DOCSTRING,
+)
+class BertForMultipleChoice(BertPreTrainedModel):
+    def __init__(self, config):
+        super().__init__(config)
+
+        self.bert = BertModel(config)
+        self.dropout = nn.Dropout(config.hidden_dropout_prob)
+        self.classifier = nn.Linear(config.hidden_size, 1)
+
+        self.init_weights()
+
+    def forward(
+        self,
+        input_ids=None,
+        attention_mask=None,
+        token_type_ids=None,
+        position_ids=None,
+        head_mask=None,
+        inputs_embeds=None,
+        labels=None,
+        output_attentions=None,
+        output_hidden_states=None,
+        return_dict=None,
+    ):
+        r"""
+        labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`):
+            Labels for computing the multiple choice classification loss. Indices should be in ``[0, ...,
+            num_choices-1]`` where :obj:`num_choices` is the size of the second dimension of the input tensors. (See
+            :obj:`input_ids` above)
+        """
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+        num_choices = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1]
+
+        input_ids = input_ids.view(-1, input_ids.size(-1)
+                                   ) if input_ids is not None else None
+        attention_mask = attention_mask.view(
+            -1, attention_mask.size(-1)) if attention_mask is not None else None
+        token_type_ids = token_type_ids.view(
+            -1, token_type_ids.size(-1)) if token_type_ids is not None else None
+        position_ids = position_ids.view(-1, position_ids.size(-1)
+                                         ) if position_ids is not None else None
+        inputs_embeds = (
+            inputs_embeds.view(-1, inputs_embeds.size(-2),
+                               inputs_embeds.size(-1))
+            if inputs_embeds is not None
+            else None
+        )
+
+        outputs = self.bert(
+            input_ids,
+            attention_mask=attention_mask,
+            token_type_ids=token_type_ids,
+            position_ids=position_ids,
+            head_mask=head_mask,
+            inputs_embeds=inputs_embeds,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        pooled_output = outputs[1]
+
+        pooled_output = self.dropout(pooled_output)
+        logits = self.classifier(pooled_output)
+        reshaped_logits = logits.view(-1, num_choices)
+
+        loss = None
+        if labels is not None:
+            loss_fct = CrossEntropyLoss()
+            loss = loss_fct(reshaped_logits, labels)
+
+        if not return_dict:
+            output = (reshaped_logits,) + outputs[2:]
+            return ((loss,) + output) if loss is not None else output
+
+        return MultipleChoiceModelOutput(
+            loss=loss,
+            logits=reshaped_logits,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+
+@add_start_docstrings(
+    """
+    Bert Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for
+    Named-Entity-Recognition (NER) tasks.
+    """,
+    BERT_START_DOCSTRING,
+)
+class BertForTokenClassification(BertPreTrainedModel):
+
+    _keys_to_ignore_on_load_unexpected = [r"pooler"]
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.num_labels = config.num_labels
+
+        self.bert = BertModel(config, add_pooling_layer=False)
+        self.dropout = nn.Dropout(config.hidden_dropout_prob)
+        self.classifier = nn.Linear(config.hidden_size, config.num_labels)
+
+        self.init_weights()
+
+    def forward(
+        self,
+        input_ids=None,
+        attention_mask=None,
+        token_type_ids=None,
+        position_ids=None,
+        head_mask=None,
+        inputs_embeds=None,
+        labels=None,
+        output_attentions=None,
+        output_hidden_states=None,
+        return_dict=None,
+    ):
+        r"""
+        labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
+            Labels for computing the token classification loss. Indices should be in ``[0, ..., config.num_labels -
+            1]``.
+        """
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        outputs = self.bert(
+            input_ids,
+            attention_mask=attention_mask,
+            token_type_ids=token_type_ids,
+            position_ids=position_ids,
+            head_mask=head_mask,
+            inputs_embeds=inputs_embeds,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        sequence_output = outputs[0]
+
+        sequence_output = self.dropout(sequence_output)
+        logits = self.classifier(sequence_output)
+
+        loss = None
+        if labels is not None:
+            loss_fct = CrossEntropyLoss()
+            # Only keep active parts of the loss
+            if attention_mask is not None:
+                active_loss = attention_mask.view(-1) == 1
+                active_logits = logits.view(-1, self.num_labels)
+                active_labels = torch.where(
+                    active_loss, labels.view(-1), torch.tensor(
+                        loss_fct.ignore_index).type_as(labels)
+                )
+                loss = loss_fct(active_logits, active_labels)
+            else:
+                loss = loss_fct(
+                    logits.view(-1, self.num_labels), labels.view(-1))
+
+        if not return_dict:
+            output = (logits,) + outputs[2:]
+            return ((loss,) + output) if loss is not None else output
+
+        return TokenClassifierOutput(
+            loss=loss,
+            logits=logits,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+
+@add_start_docstrings(
+    """
+    Bert Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear
+    layers on top of the hidden-states output to compute `span start logits` and `span end logits`).
+    """,
+    BERT_START_DOCSTRING,
+)
+class BertForQuestionAnswering(BertPreTrainedModel):
+
+    _keys_to_ignore_on_load_unexpected = [r"pooler"]
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.num_labels = config.num_labels
+
+        self.bert = BertModel(config, add_pooling_layer=False)
+        self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
+
+        self.init_weights()
+
+    def forward(
+        self,
+        input_ids=None,
+        attention_mask=None,
+        token_type_ids=None,
+        position_ids=None,
+        head_mask=None,
+        inputs_embeds=None,
+        start_positions=None,
+        end_positions=None,
+        output_attentions=None,
+        output_hidden_states=None,
+        return_dict=None,
+    ):
+        r"""
+        start_positions (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`):
+            Labels for position (index) of the start of the labelled span for computing the token classification loss.
+            Positions are clamped to the length of the sequence (:obj:`sequence_length`). Position outside of the
+            sequence are not taken into account for computing the loss.
+        end_positions (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`):
+            Labels for position (index) of the end of the labelled span for computing the token classification loss.
+            Positions are clamped to the length of the sequence (:obj:`sequence_length`). Position outside of the
+            sequence are not taken into account for computing the loss.
+        """
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        outputs = self.bert(
+            input_ids,
+            attention_mask=attention_mask,
+            token_type_ids=token_type_ids,
+            position_ids=position_ids,
+            head_mask=head_mask,
+            inputs_embeds=inputs_embeds,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        sequence_output = outputs[0]
+
+        logits = self.qa_outputs(sequence_output)
+        start_logits, end_logits = logits.split(1, dim=-1)
+        start_logits = start_logits.squeeze(-1)
+        end_logits = end_logits.squeeze(-1)
+
+        total_loss = None
+        if start_positions is not None and end_positions is not None:
+            # If we are on multi-GPU, split add a dimension
+            if len(start_positions.size()) > 1:
+                start_positions = start_positions.squeeze(-1)
+            if len(end_positions.size()) > 1:
+                end_positions = end_positions.squeeze(-1)
+            # sometimes the start/end positions are outside our model inputs, we ignore these terms
+            ignored_index = start_logits.size(1)
+            start_positions.clamp_(0, ignored_index)
+            end_positions.clamp_(0, ignored_index)
+
+            loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
+            start_loss = loss_fct(start_logits, start_positions)
+            end_loss = loss_fct(end_logits, end_positions)
+            total_loss = (start_loss + end_loss) / 2
+
+        if not return_dict:
+            output = (start_logits, end_logits) + outputs[2:]
+            return ((total_loss,) + output) if total_loss is not None else output
+
+        return QuestionAnsweringModelOutput(
+            loss=total_loss,
+            start_logits=start_logits,
+            end_logits=end_logits,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )

svitt/tokenization_bert.py

@@ -0,0 +1,546 @@
+# coding=utf-8
+# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Tokenization classes for Bert."""
+
+
+import collections
+import os
+import unicodedata
+from typing import List, Optional, Tuple
+
+from transformers.tokenization_utils import PreTrainedTokenizer, _is_control, _is_punctuation, _is_whitespace
+from transformers.utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+VOCAB_FILES_NAMES = {"vocab_file": "vocab.txt"}
+
+PRETRAINED_VOCAB_FILES_MAP = {
+    "vocab_file": {
+        "bert-base-uncased": "https://huggingface.co/bert-base-uncased/resolve/main/vocab.txt",
+        "bert-large-uncased": "https://huggingface.co/bert-large-uncased/resolve/main/vocab.txt",
+        "bert-base-cased": "https://huggingface.co/bert-base-cased/resolve/main/vocab.txt",
+        "bert-large-cased": "https://huggingface.co/bert-large-cased/resolve/main/vocab.txt",
+        "bert-base-multilingual-uncased": "https://huggingface.co/bert-base-multilingual-uncased/resolve/main/vocab.txt",
+        "bert-base-multilingual-cased": "https://huggingface.co/bert-base-multilingual-cased/resolve/main/vocab.txt",
+        "bert-base-chinese": "https://huggingface.co/bert-base-chinese/resolve/main/vocab.txt",
+        "bert-base-german-cased": "https://huggingface.co/bert-base-german-cased/resolve/main/vocab.txt",
+        "bert-large-uncased-whole-word-masking": "https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/vocab.txt",
+        "bert-large-cased-whole-word-masking": "https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/vocab.txt",
+        "bert-large-uncased-whole-word-masking-finetuned-squad": "https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/vocab.txt",
+        "bert-large-cased-whole-word-masking-finetuned-squad": "https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/vocab.txt",
+        "bert-base-cased-finetuned-mrpc": "https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/vocab.txt",
+        "bert-base-german-dbmdz-cased": "https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/vocab.txt",
+        "bert-base-german-dbmdz-uncased": "https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/vocab.txt",
+        "TurkuNLP/bert-base-finnish-cased-v1": "https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/vocab.txt",
+        "TurkuNLP/bert-base-finnish-uncased-v1": "https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/vocab.txt",
+        "wietsedv/bert-base-dutch-cased": "https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/vocab.txt",
+    }
+}
+
+PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
+    "bert-base-uncased": 512,
+    "bert-large-uncased": 512,
+    "bert-base-cased": 512,
+    "bert-large-cased": 512,
+    "bert-base-multilingual-uncased": 512,
+    "bert-base-multilingual-cased": 512,
+    "bert-base-chinese": 512,
+    "bert-base-german-cased": 512,
+    "bert-large-uncased-whole-word-masking": 512,
+    "bert-large-cased-whole-word-masking": 512,
+    "bert-large-uncased-whole-word-masking-finetuned-squad": 512,
+    "bert-large-cased-whole-word-masking-finetuned-squad": 512,
+    "bert-base-cased-finetuned-mrpc": 512,
+    "bert-base-german-dbmdz-cased": 512,
+    "bert-base-german-dbmdz-uncased": 512,
+    "TurkuNLP/bert-base-finnish-cased-v1": 512,
+    "TurkuNLP/bert-base-finnish-uncased-v1": 512,
+    "wietsedv/bert-base-dutch-cased": 512,
+}
+
+PRETRAINED_INIT_CONFIGURATION = {
+    "bert-base-uncased": {"do_lower_case": True},
+    "bert-large-uncased": {"do_lower_case": True},
+    "bert-base-cased": {"do_lower_case": False},
+    "bert-large-cased": {"do_lower_case": False},
+    "bert-base-multilingual-uncased": {"do_lower_case": True},
+    "bert-base-multilingual-cased": {"do_lower_case": False},
+    "bert-base-chinese": {"do_lower_case": False},
+    "bert-base-german-cased": {"do_lower_case": False},
+    "bert-large-uncased-whole-word-masking": {"do_lower_case": True},
+    "bert-large-cased-whole-word-masking": {"do_lower_case": False},
+    "bert-large-uncased-whole-word-masking-finetuned-squad": {"do_lower_case": True},
+    "bert-large-cased-whole-word-masking-finetuned-squad": {"do_lower_case": False},
+    "bert-base-cased-finetuned-mrpc": {"do_lower_case": False},
+    "bert-base-german-dbmdz-cased": {"do_lower_case": False},
+    "bert-base-german-dbmdz-uncased": {"do_lower_case": True},
+    "TurkuNLP/bert-base-finnish-cased-v1": {"do_lower_case": False},
+    "TurkuNLP/bert-base-finnish-uncased-v1": {"do_lower_case": True},
+    "wietsedv/bert-base-dutch-cased": {"do_lower_case": False},
+}
+
+
+def load_vocab(vocab_file):
+    """Loads a vocabulary file into a dictionary."""
+    vocab = collections.OrderedDict()
+    with open(vocab_file, "r", encoding="utf-8") as reader:
+        tokens = reader.readlines()
+    for index, token in enumerate(tokens):
+        token = token.rstrip("\n")
+        vocab[token] = index
+    return vocab
+
+
+def whitespace_tokenize(text):
+    """Runs basic whitespace cleaning and splitting on a piece of text."""
+    text = text.strip()
+    if not text:
+        return []
+    tokens = text.split()
+    return tokens
+
+
+class BertTokenizer(PreTrainedTokenizer):
+    r"""
+    Construct a BERT tokenizer. Based on WordPiece.
+    This tokenizer inherits from :class:`~transformers.PreTrainedTokenizer` which contains most of the main methods.
+    Users should refer to this superclass for more information regarding those methods.
+    Args:
+        vocab_file (:obj:`str`):
+            File containing the vocabulary.
+        do_lower_case (:obj:`bool`, `optional`, defaults to :obj:`True`):
+            Whether or not to lowercase the input when tokenizing.
+        do_basic_tokenize (:obj:`bool`, `optional`, defaults to :obj:`True`):
+            Whether or not to do basic tokenization before WordPiece.
+        never_split (:obj:`Iterable`, `optional`):
+            Collection of tokens which will never be split during tokenization. Only has an effect when
+            :obj:`do_basic_tokenize=True`
+        unk_token (:obj:`str`, `optional`, defaults to :obj:`"[UNK]"`):
+            The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
+            token instead.
+        sep_token (:obj:`str`, `optional`, defaults to :obj:`"[SEP]"`):
+            The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
+            sequence classification or for a text and a question for question answering. It is also used as the last
+            token of a sequence built with special tokens.
+        pad_token (:obj:`str`, `optional`, defaults to :obj:`"[PAD]"`):
+            The token used for padding, for example when batching sequences of different lengths.
+        cls_token (:obj:`str`, `optional`, defaults to :obj:`"[CLS]"`):
+            The classifier token which is used when doing sequence classification (classification of the whole sequence
+            instead of per-token classification). It is the first token of the sequence when built with special tokens.
+        mask_token (:obj:`str`, `optional`, defaults to :obj:`"[MASK]"`):
+            The token used for masking values. This is the token used when training this model with masked language
+            modeling. This is the token which the model will try to predict.
+        tokenize_chinese_chars (:obj:`bool`, `optional`, defaults to :obj:`True`):
+            Whether or not to tokenize Chinese characters.
+            This should likely be deactivated for Japanese (see this `issue
+            <https://github.com/huggingface/transformers/issues/328>`__).
+        strip_accents: (:obj:`bool`, `optional`):
+            Whether or not to strip all accents. If this option is not specified, then it will be determined by the
+            value for :obj:`lowercase` (as in the original BERT).
+    """
+
+    vocab_files_names = VOCAB_FILES_NAMES
+    pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
+    pretrained_init_configuration = PRETRAINED_INIT_CONFIGURATION
+    max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
+
+    def __init__(
+        self,
+        vocab_file,
+        do_lower_case=True,
+        do_basic_tokenize=True,
+        never_split=None,
+        unk_token="[UNK]",
+        sep_token="[SEP]",
+        pad_token="[PAD]",
+        cls_token="[CLS]",
+        mask_token="[MASK]",
+        tokenize_chinese_chars=True,
+        strip_accents=None,
+        **kwargs
+    ):
+        super().__init__(
+            do_lower_case=do_lower_case,
+            do_basic_tokenize=do_basic_tokenize,
+            never_split=never_split,
+            unk_token=unk_token,
+            sep_token=sep_token,
+            pad_token=pad_token,
+            cls_token=cls_token,
+            mask_token=mask_token,
+            tokenize_chinese_chars=tokenize_chinese_chars,
+            strip_accents=strip_accents,
+            **kwargs,
+        )
+
+        if not os.path.isfile(vocab_file):
+            raise ValueError(
+                "Can't find a vocabulary file at path '{}'. To load the vocabulary from a Google pretrained "
+                "model use `tokenizer = BertTokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`".format(
+                    vocab_file)
+            )
+        self.vocab = load_vocab(vocab_file)
+        self.ids_to_tokens = collections.OrderedDict(
+            [(ids, tok) for tok, ids in self.vocab.items()])
+        self.do_basic_tokenize = do_basic_tokenize
+        if do_basic_tokenize:
+            self.basic_tokenizer = BasicTokenizer(
+                do_lower_case=do_lower_case,
+                never_split=never_split,
+                tokenize_chinese_chars=tokenize_chinese_chars,
+                strip_accents=strip_accents,
+            )
+        self.wordpiece_tokenizer = WordpieceTokenizer(
+            vocab=self.vocab, unk_token=self.unk_token)
+
+    @property
+    def do_lower_case(self):
+        return self.basic_tokenizer.do_lower_case
+
+    @property
+    def vocab_size(self):
+        return len(self.vocab)
+
+    def get_vocab(self):
+        return dict(self.vocab, **self.added_tokens_encoder)
+
+    def _tokenize(self, text):
+        split_tokens = []
+        if self.do_basic_tokenize:
+            for token in self.basic_tokenizer.tokenize(text, never_split=self.all_special_tokens):
+
+                # If the token is part of the never_split set
+                if token in self.basic_tokenizer.never_split:
+                    split_tokens.append(token)
+                else:
+                    split_tokens += self.wordpiece_tokenizer.tokenize(token)
+        else:
+            split_tokens = self.wordpiece_tokenizer.tokenize(text)
+        return split_tokens
+
+    def _convert_token_to_id(self, token):
+        """ Converts a token (str) in an id using the vocab. """
+        return self.vocab.get(token, self.vocab.get(self.unk_token))
+
+    def _convert_id_to_token(self, index):
+        """Converts an index (integer) in a token (str) using the vocab."""
+        return self.ids_to_tokens.get(index, self.unk_token)
+
+    def convert_tokens_to_string(self, tokens):
+        """ Converts a sequence of tokens (string) in a single string. """
+        out_string = " ".join(tokens).replace(" ##", "").strip()
+        return out_string
+
+    def build_inputs_with_special_tokens(
+        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
+    ) -> List[int]:
+        """
+        Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
+        adding special tokens. A BERT sequence has the following format:
+        - single sequence: ``[CLS] X ``
+        - pair of sequences: ``[CLS] A [SEP] B [SEP]``
+        Args:
+            token_ids_0 (:obj:`List[int]`):
+                List of IDs to which the special tokens will be added.
+            token_ids_1 (:obj:`List[int]`, `optional`):
+                Optional second list of IDs for sequence pairs.
+        Returns:
+            :obj:`List[int]`: List of `input IDs <../glossary.html#input-ids>`__ with the appropriate special tokens.
+        """
+        if token_ids_1 is None:
+            return [self.cls_token_id] + token_ids_0
+        cls = [self.cls_token_id]
+        sep = [self.sep_token_id]
+        return cls + token_ids_0 + sep + token_ids_1 + sep
+
+    def get_special_tokens_mask(
+        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
+    ) -> List[int]:
+        """
+        Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
+        special tokens using the tokenizer ``prepare_for_model`` method.
+        Args:
+            token_ids_0 (:obj:`List[int]`):
+                List of IDs.
+            token_ids_1 (:obj:`List[int]`, `optional`):
+                Optional second list of IDs for sequence pairs.
+            already_has_special_tokens (:obj:`bool`, `optional`, defaults to :obj:`False`):
+                Whether or not the token list is already formatted with special tokens for the model.
+        Returns:
+            :obj:`List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
+        """
+
+        if already_has_special_tokens:
+            if token_ids_1 is not None:
+                raise ValueError(
+                    "You should not supply a second sequence if the provided sequence of "
+                    "ids is already formatted with special tokens for the model."
+                )
+            return list(map(lambda x: 1 if x in [self.sep_token_id, self.cls_token_id] else 0, token_ids_0))
+
+        if token_ids_1 is not None:
+            return [1] + ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) + [1]
+        return [1] + ([0] * len(token_ids_0)) + [1]
+
+    def create_token_type_ids_from_sequences(
+        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
+    ) -> List[int]:
+        """
+        Create a mask from the two sequences passed to be used in a sequence-pair classification task. A BERT sequence
+        pair mask has the following format:
+        ::
+            0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1
+            | first sequence    | second sequence |
+        If :obj:`token_ids_1` is :obj:`None`, this method only returns the first portion of the mask (0s).
+        Args:
+            token_ids_0 (:obj:`List[int]`):
+                List of IDs.
+            token_ids_1 (:obj:`List[int]`, `optional`):
+                Optional second list of IDs for sequence pairs.
+        Returns:
+            :obj:`List[int]`: List of `token type IDs <../glossary.html#token-type-ids>`_ according to the given
+            sequence(s).
+        """
+        sep = [self.sep_token_id]
+        cls = [self.cls_token_id]
+        if token_ids_1 is None:
+            return len(cls + token_ids_0 + sep) * [0]
+        return len(cls + token_ids_0 + sep) * [0] + len(token_ids_1 + sep) * [1]
+
+    def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
+        index = 0
+        if os.path.isdir(save_directory):
+            vocab_file = os.path.join(
+                save_directory, (filename_prefix + "-" if filename_prefix else "") +
+                VOCAB_FILES_NAMES["vocab_file"]
+            )
+        else:
+            vocab_file = (filename_prefix +
+                          "-" if filename_prefix else "") + save_directory
+        with open(vocab_file, "w", encoding="utf-8") as writer:
+            for token, token_index in sorted(self.vocab.items(), key=lambda kv: kv[1]):
+                if index != token_index:
+                    logger.warning(
+                        "Saving vocabulary to {}: vocabulary indices are not consecutive."
+                        " Please check that the vocabulary is not corrupted!".format(
+                            vocab_file)
+                    )
+                    index = token_index
+                writer.write(token + "\n")
+                index += 1
+        return (vocab_file,)
+
+
+class BasicTokenizer(object):
+    """
+    Constructs a BasicTokenizer that will run basic tokenization (punctuation splitting, lower casing, etc.).
+    Args:
+        do_lower_case (:obj:`bool`, `optional`, defaults to :obj:`True`):
+            Whether or not to lowercase the input when tokenizing.
+        never_split (:obj:`Iterable`, `optional`):
+            Collection of tokens which will never be split during tokenization. Only has an effect when
+            :obj:`do_basic_tokenize=True`
+        tokenize_chinese_chars (:obj:`bool`, `optional`, defaults to :obj:`True`):
+            Whether or not to tokenize Chinese characters.
+            This should likely be deactivated for Japanese (see this `issue
+            <https://github.com/huggingface/transformers/issues/328>`__).
+        strip_accents: (:obj:`bool`, `optional`):
+            Whether or not to strip all accents. If this option is not specified, then it will be determined by the
+            value for :obj:`lowercase` (as in the original BERT).
+    """
+
+    def __init__(self, do_lower_case=True, never_split=None, tokenize_chinese_chars=True, strip_accents=None):
+        if never_split is None:
+            never_split = []
+        self.do_lower_case = do_lower_case
+        self.never_split = set(never_split)
+        self.tokenize_chinese_chars = tokenize_chinese_chars
+        self.strip_accents = strip_accents
+
+    def tokenize(self, text, never_split=None):
+        """
+        Basic Tokenization of a piece of text. Split on "white spaces" only, for sub-word tokenization, see
+        WordPieceTokenizer.
+        Args:
+            **never_split**: (`optional`) list of str
+                Kept for backward compatibility purposes. Now implemented directly at the base class level (see
+                :func:`PreTrainedTokenizer.tokenize`) List of token not to split.
+        """
+        # union() returns a new set by concatenating the two sets.
+        never_split = self.never_split.union(
+            set(never_split)) if never_split else self.never_split
+        text = self._clean_text(text)
+
+        # This was added on November 1st, 2018 for the multilingual and Chinese
+        # models. This is also applied to the English models now, but it doesn't
+        # matter since the English models were not trained on any Chinese data
+        # and generally don't have any Chinese data in them (there are Chinese
+        # characters in the vocabulary because Wikipedia does have some Chinese
+        # words in the English Wikipedia.).
+        if self.tokenize_chinese_chars:
+            text = self._tokenize_chinese_chars(text)
+        orig_tokens = whitespace_tokenize(text)
+        split_tokens = []
+        for token in orig_tokens:
+            if token not in never_split:
+                if self.do_lower_case:
+                    token = token.lower()
+                    if self.strip_accents is not False:
+                        token = self._run_strip_accents(token)
+                elif self.strip_accents:
+                    token = self._run_strip_accents(token)
+            split_tokens.extend(self._run_split_on_punc(token, never_split))
+
+        output_tokens = whitespace_tokenize(" ".join(split_tokens))
+        return output_tokens
+
+    def _run_strip_accents(self, text):
+        """Strips accents from a piece of text."""
+        text = unicodedata.normalize("NFD", text)
+        output = []
+        for char in text:
+            cat = unicodedata.category(char)
+            if cat == "Mn":
+                continue
+            output.append(char)
+        return "".join(output)
+
+    def _run_split_on_punc(self, text, never_split=None):
+        """Splits punctuation on a piece of text."""
+        if never_split is not None and text in never_split:
+            return [text]
+        chars = list(text)
+        i = 0
+        start_new_word = True
+        output = []
+        while i < len(chars):
+            char = chars[i]
+            if _is_punctuation(char):
+                output.append([char])
+                start_new_word = True
+            else:
+                if start_new_word:
+                    output.append([])
+                start_new_word = False
+                output[-1].append(char)
+            i += 1
+
+        return ["".join(x) for x in output]
+
+    def _tokenize_chinese_chars(self, text):
+        """Adds whitespace around any CJK character."""
+        output = []
+        for char in text:
+            cp = ord(char)
+            if self._is_chinese_char(cp):
+                output.append(" ")
+                output.append(char)
+                output.append(" ")
+            else:
+                output.append(char)
+        return "".join(output)
+
+    def _is_chinese_char(self, cp):
+        """Checks whether CP is the codepoint of a CJK character."""
+        # This defines a "chinese character" as anything in the CJK Unicode block:
+        #   https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
+        #
+        # Note that the CJK Unicode block is NOT all Japanese and Korean characters,
+        # despite its name. The modern Korean Hangul alphabet is a different block,
+        # as is Japanese Hiragana and Katakana. Those alphabets are used to write
+        # space-separated words, so they are not treated specially and handled
+        # like the all of the other languages.
+        if (
+            (cp >= 0x4E00 and cp <= 0x9FFF)
+            or (cp >= 0x3400 and cp <= 0x4DBF)  #
+            or (cp >= 0x20000 and cp <= 0x2A6DF)  #
+            or (cp >= 0x2A700 and cp <= 0x2B73F)  #
+            or (cp >= 0x2B740 and cp <= 0x2B81F)  #
+            or (cp >= 0x2B820 and cp <= 0x2CEAF)  #
+            or (cp >= 0xF900 and cp <= 0xFAFF)
+            or (cp >= 0x2F800 and cp <= 0x2FA1F)  #
+        ):  #
+            return True
+
+        return False
+
+    def _clean_text(self, text):
+        """Performs invalid character removal and whitespace cleanup on text."""
+        output = []
+        for char in text:
+            cp = ord(char)
+            if cp == 0 or cp == 0xFFFD or _is_control(char):
+                continue
+            if _is_whitespace(char):
+                output.append(" ")
+            else:
+                output.append(char)
+        return "".join(output)
+
+
+class WordpieceTokenizer(object):
+    """Runs WordPiece tokenization."""
+
+    def __init__(self, vocab, unk_token, max_input_chars_per_word=100):
+        self.vocab = vocab
+        self.unk_token = unk_token
+        self.max_input_chars_per_word = max_input_chars_per_word
+
+    def tokenize(self, text):
+        """
+        Tokenizes a piece of text into its word pieces. This uses a greedy longest-match-first algorithm to perform
+        tokenization using the given vocabulary.
+        For example, :obj:`input = "unaffable"` wil return as output :obj:`["un", "##aff", "##able"]`.
+        Args:
+          text: A single token or whitespace separated tokens. This should have
+            already been passed through `BasicTokenizer`.
+        Returns:
+          A list of wordpiece tokens.
+        """
+
+        output_tokens = []
+        for token in whitespace_tokenize(text):
+            chars = list(token)
+            if len(chars) > self.max_input_chars_per_word:
+                output_tokens.append(self.unk_token)
+                continue
+
+            is_bad = False
+            start = 0
+            sub_tokens = []
+            while start < len(chars):
+                end = len(chars)
+                cur_substr = None
+                while start < end:
+                    substr = "".join(chars[start:end])
+                    if start > 0:
+                        substr = "##" + substr
+                    if substr in self.vocab:
+                        cur_substr = substr
+                        break
+                    end -= 1
+                if cur_substr is None:
+                    is_bad = True
+                    break
+                sub_tokens.append(cur_substr)
+                start = end
+
+            if is_bad:
+                output_tokens.append(self.unk_token)
+            else:
+                output_tokens.extend(sub_tokens)
+        return output_tokens

svitt/utils.py

@@ -0,0 +1,235 @@
+import torch
+import torch.nn.functional as F
+import torch.nn as nn
+from scipy import interpolate
+import numpy as np
+from einops import rearrange, repeat
+
+
+def _init_transformer_weights(module, initializer_range=0.02):
+    """Initialize the weights. Copied from transformers ViT/Bert model init"""
+    if isinstance(module, (nn.Linear, nn.Conv2d)):
+        # Slightly different from the TF version which uses truncated_normal for initialization
+        # cf https://github.com/pytorch/pytorch/pull/5617
+        module.weight.data.normal_(mean=0.0, std=initializer_range)
+        if module.bias is not None:
+            module.bias.data.zero_()
+    elif isinstance(module, nn.Embedding):
+        module.weight.data.normal_(mean=0.0, std=initializer_range)
+        if module.padding_idx is not None:
+            module.weight.data[module.padding_idx].zero_()
+    elif isinstance(module, nn.LayerNorm):
+        module.bias.data.zero_()
+        module.weight.data.fill_(1.0)
+
+
+def interpolate_pos_embed(pos_embed_old, pos_embed_new, num_patches_new):
+    """
+    Args:
+        pos_embed_old: (1, L_old, d), pre-trained
+        pos_embed_new: (1, L_new, d), newly initialized, to be replaced by interpolated weights
+        num_patches_new:
+    """
+    # interpolate position embedding
+    embedding_size = pos_embed_old.shape[-1]
+    num_extra_tokens = pos_embed_new.shape[-2] - num_patches_new
+    # height (== width) for the checkpoint position embedding
+    orig_size = int((pos_embed_old.shape[-2] - num_extra_tokens) ** 0.5)
+    # height (== width) for the new position embedding
+    new_size = int(num_patches_new ** 0.5)
+
+    if orig_size != new_size:
+        # class_token and dist_token are kept unchanged
+        # the extra tokens seems always at the beginning of the position embedding
+        extra_tokens = pos_embed_old[:, :num_extra_tokens]
+        # only the position tokens are interpolated
+        pos_tokens = pos_embed_old[:, num_extra_tokens:]
+        pos_tokens = pos_tokens.reshape(
+            -1, orig_size, orig_size, embedding_size).permute(0, 3, 1, 2)
+        pos_tokens = torch.nn.functional.interpolate(
+            pos_tokens, size=(new_size, new_size), mode='bicubic', align_corners=False)
+        pos_tokens = pos_tokens.permute(0, 2, 3, 1).flatten(1, 2)
+        interpolated_pos_embed = torch.cat((extra_tokens, pos_tokens), dim=1)
+        return interpolated_pos_embed
+    else:
+        return pos_embed_old
+
+
+def interpolate_pos_relative_bias_beit(state_dict_old, state_dict_new, patch_shape_new):
+    """
+    Args:
+        state_dict_old: loaded state dict
+        state_dict_new: state dict for model with new image size
+        patch_shape_new: new model patch_shape
+    ref: https://github.com/microsoft/unilm/blob/master/beit/run_class_finetuning.py
+    """
+    all_keys = list(state_dict_old.keys())
+    for key in all_keys:
+        if "relative_position_index" in key:
+            state_dict_old.pop(key)
+
+        if "relative_position_bias_table" in key:
+            rel_pos_bias = state_dict_old[key]
+            src_num_pos, num_attn_heads = rel_pos_bias.size()
+            dst_num_pos, _ = state_dict_new[key].size()
+            dst_patch_shape = patch_shape_new
+            if dst_patch_shape[0] != dst_patch_shape[1]:
+                raise NotImplementedError()
+            num_extra_tokens = dst_num_pos - (dst_patch_shape[0] * 2 - 1) * (dst_patch_shape[1] * 2 - 1)
+            src_size = int((src_num_pos - num_extra_tokens) ** 0.5)
+            dst_size = int((dst_num_pos - num_extra_tokens) ** 0.5)
+            if src_size != dst_size:
+                extra_tokens = rel_pos_bias[-num_extra_tokens:, :]
+                rel_pos_bias = rel_pos_bias[:-num_extra_tokens, :]
+
+                def geometric_progression(a, r, n):
+                    return a * (1.0 - r ** n) / (1.0 - r)
+
+                left, right = 1.01, 1.5
+                while right - left > 1e-6:
+                    q = (left + right) / 2.0
+                    gp = geometric_progression(1, q, src_size // 2)
+                    if gp > dst_size // 2:
+                        right = q
+                    else:
+                        left = q
+
+                # if q > 1.090307:
+                #     q = 1.090307
+
+                dis = []
+                cur = 1
+                for i in range(src_size // 2):
+                    dis.append(cur)
+                    cur += q ** (i + 1)
+
+                r_ids = [-_ for _ in reversed(dis)]
+
+                x = r_ids + [0] + dis
+                y = r_ids + [0] + dis
+
+                t = dst_size // 2.0
+                dx = np.arange(-t, t + 0.1, 1.0)
+                dy = np.arange(-t, t + 0.1, 1.0)
+
+                all_rel_pos_bias = []
+
+                for i in range(num_attn_heads):
+                    z = rel_pos_bias[:, i].view(src_size, src_size).float().numpy()
+                    f = interpolate.interp2d(x, y, z, kind='cubic')
+                    all_rel_pos_bias.append(
+                        torch.Tensor(f(dx, dy)).contiguous().view(-1, 1).to(rel_pos_bias.device))
+
+                rel_pos_bias = torch.cat(all_rel_pos_bias, dim=-1)
+
+                new_rel_pos_bias = torch.cat((rel_pos_bias, extra_tokens), dim=0)
+                state_dict_old[key] = new_rel_pos_bias
+    return state_dict_old
+
+
+def interpolate_pos_relative_bias_beit_3d(state_dict_old, state_dict_new, patch_shape_new, src_t_size=1):
+    """
+    Args:
+        state_dict_old: loaded state dict
+        state_dict_new: state dict for model with new image size
+        patch_shape_new: new model patch_shape
+    ref: https://github.com/microsoft/unilm/blob/master/beit/run_class_finetuning.py
+    """
+    all_keys = list(state_dict_old.keys())
+    for key in all_keys:
+        if "relative_position_index" in key:
+            state_dict_old.pop(key)
+
+        if "relative_position_bias_table" in key:
+            src_num_pos, num_attn_heads = state_dict_old[key].size()
+            dst_num_pos, _ = state_dict_new[key].size()
+            if src_num_pos == dst_num_pos:
+                continue
+
+            num_extra_tokens = dst_num_pos - np.prod([w * 2 - 1 for w in patch_shape_new])
+                
+            src_s_size = int((src_num_pos - num_extra_tokens) / src_t_size)
+            src_size = int(src_s_size ** 0.5)
+            dst_size = patch_shape_new[-1] * 2 - 1
+
+            if src_size != dst_size:
+                # Spatial interpolation
+                rel_pos_bias = state_dict_old[key]
+                extra_tokens = rel_pos_bias[-num_extra_tokens:, :]
+                rel_pos_bias = rel_pos_bias[:-num_extra_tokens, :]
+
+                def geometric_progression(a, r, n):
+                    return a * (1.0 - r ** n) / (1.0 - r)
+
+                left, right = 1.01, 1.5
+                while right - left > 1e-6:
+                    q = (left + right) / 2.0
+                    gp = geometric_progression(1, q, src_size // 2)
+                    if gp > dst_size // 2:
+                        right = q
+                    else:
+                        left = q
+
+                # if q > 1.090307:
+                #     q = 1.090307
+
+                dis = []
+                cur = 1
+                for i in range(src_size // 2):
+                    dis.append(cur)
+                    cur += q ** (i + 1)
+
+                r_ids = [-_ for _ in reversed(dis)]
+
+                x = r_ids + [0] + dis
+                y = r_ids + [0] + dis
+
+                t = dst_size // 2.0
+                dx = np.arange(-t, t + 0.1, 1.0)
+                dy = np.arange(-t, t + 0.1, 1.0)
+
+                all_rel_pos_bias = []
+
+                for i in range(num_attn_heads):
+                    z = rel_pos_bias[:, i].view(src_size, src_size).float().numpy()
+                    f = interpolate.interp2d(x, y, z, kind='cubic')
+                    all_rel_pos_bias.append(
+                        torch.Tensor(f(dx, dy)).contiguous().view(-1, 1).to(rel_pos_bias.device))
+
+                rel_pos_bias = torch.cat(all_rel_pos_bias, dim=-1)
+
+                new_rel_pos_bias = torch.cat((rel_pos_bias, extra_tokens), dim=0)
+                state_dict_old[key] = new_rel_pos_bias
+
+            dst_t_size = patch_shape_new[0] * 2 - 1
+            if src_t_size != dst_t_size:
+                # Temporal interpolation                
+                rel_pos_bias = state_dict_old[key]
+                extra_tokens = rel_pos_bias[-num_extra_tokens:, :]
+                rel_pos_bias = rel_pos_bias[:-num_extra_tokens, :]
+                
+                if src_t_size == 1:
+                    rel_pos_bias = repeat(rel_pos_bias, 's d -> (t s) d', t=dst_t_size)
+                else:
+                    rel_pos_bias = rearrange(rel_pos_bias, '(t s) d -> s d t', t=src_t_size)
+                    rel_pos_bias = F.interpolate(rel_pos_bias, dst_t_size, mode='nearest')
+                    rel_pos_bias = rearrange(rel_pos_bias, 's d t -> (t s) d')
+                new_rel_pos_bias = torch.cat((rel_pos_bias, extra_tokens), dim=0)
+                state_dict_old[key] = new_rel_pos_bias
+
+    return state_dict_old
+
+
+def tile(x, dim, n_tile):
+    init_dim = x.size(dim)
+    repeat_idx = [1] * x.dim()
+    repeat_idx[dim] = n_tile
+    x = x.repeat(*repeat_idx)
+    order_index = torch.LongTensor(np.concatenate(
+        [init_dim * np.arange(n_tile) + i for i in range(init_dim)]))
+    return torch.index_select(x, dim, order_index.to(x.device))
+
+
+def mask_logits(target, mask):
+    return target * mask + (1 - mask) * (-1e10)
+