From a53bcfcef4ffb4082e78b0affe659441f77e1434 Mon Sep 17 00:00:00 2001
From: qqingzheng <2533221180@qq.com>
Date: Sun, 17 Mar 2024 02:46:33 +0800
Subject: [PATCH] [feat] add casual vqvae

---
 opensora/models/ae/__init__.py                |   8 +-
 opensora/models/ae/videobase/__init__.py      |   8 +-
 .../ae/videobase/causal_vqvae/__init__.py     |   4 +
 .../causal_vqvae/configuration_causalvqvae.py |  28 +
 .../causal_vqvae/dataset_causalvqvae.py       | 153 ++++
 .../causal_vqvae/modeling_causalvqvae.py      | 848 ++++++++++++++++++
 .../causal_vqvae/trainer_causalvqvae.py       |  30 +
 opensora/train/train_causalvqvae.py           |  69 ++
 scripts/casual_vqvae/train.sh                 |  28 +
 9 files changed, 1174 insertions(+), 2 deletions(-)
 create mode 100644 opensora/models/ae/videobase/causal_vqvae/__init__.py
 create mode 100644 opensora/models/ae/videobase/causal_vqvae/configuration_causalvqvae.py
 create mode 100644 opensora/models/ae/videobase/causal_vqvae/dataset_causalvqvae.py
 create mode 100644 opensora/models/ae/videobase/causal_vqvae/modeling_causalvqvae.py
 create mode 100644 opensora/models/ae/videobase/causal_vqvae/trainer_causalvqvae.py
 create mode 100644 opensora/train/train_causalvqvae.py
 create mode 100644 scripts/casual_vqvae/train.sh

diff --git a/opensora/models/ae/__init__.py b/opensora/models/ae/__init__.py
index d5f25d2a8..33fd72469 100644
--- a/opensora/models/ae/__init__.py
+++ b/opensora/models/ae/__init__.py
@@ -2,7 +2,13 @@
 from .videobase import videobase_ae, videovae, videovqvae, videobase_ae_stride, videobase_ae_channel
 from .videobase import (
     VQVAEConfiguration,
-    VQVAEModel
+    VQVAEModel,
+    VQVAEDataset,
+    VQVAETrainer,
+    CausalVQVAEModel,
+    CausalVQVAEConfiguration,
+    CausalVQVAEDataset,
+    CausalVQVAETrainer
 )
 
 ae_stride_config = {}
diff --git a/opensora/models/ae/videobase/__init__.py b/opensora/models/ae/videobase/__init__.py
index d81c4c2bb..dd9a16ad6 100644
--- a/opensora/models/ae/videobase/__init__.py
+++ b/opensora/models/ae/videobase/__init__.py
@@ -5,7 +5,13 @@
     VQVAEConfiguration,
     VQVAEModel,
     VQVAETrainer,
-    VQVAEDataset, VideoGPTVQVAEWrapper
+    VQVAEDataset, VideoGPTVQVAEWrapper,
+)
+from .causal_vqvae import (
+    CausalVQVAEConfiguration,
+    CausalVQVAEDataset,
+    CausalVQVAETrainer,
+    CausalVQVAEModel
 )
 
 
diff --git a/opensora/models/ae/videobase/causal_vqvae/__init__.py b/opensora/models/ae/videobase/causal_vqvae/__init__.py
new file mode 100644
index 000000000..d36f25f61
--- /dev/null
+++ b/opensora/models/ae/videobase/causal_vqvae/__init__.py
@@ -0,0 +1,4 @@
+from .configuration_causalvqvae import CausalVQVAEConfiguration
+from .modeling_causalvqvae import CausalVQVAEModel
+from .trainer_causalvqvae import CausalVQVAETrainer
+from .dataset_causalvqvae import CausalVQVAEDataset
\ No newline at end of file
diff --git a/opensora/models/ae/videobase/causal_vqvae/configuration_causalvqvae.py b/opensora/models/ae/videobase/causal_vqvae/configuration_causalvqvae.py
new file mode 100644
index 000000000..e8602e4c3
--- /dev/null
+++ b/opensora/models/ae/videobase/causal_vqvae/configuration_causalvqvae.py
@@ -0,0 +1,28 @@
+from ..configuration_videobase import VideoBaseConfiguration
+from typing import Union, Tuple
+
+class CausalVQVAEConfiguration(VideoBaseConfiguration):
+    def __init__(
+        self,
+        embedding_dim: int = 256,
+        n_codes: int = 2048,
+        n_hiddens: int = 240,
+        n_res_layers: int = 4,
+        resolution: int = 128,
+        sequence_length: int = 16,
+        time_downsample: int = 4,
+        spatial_downsample: int = 8,
+        no_pos_embd: bool = True,
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+
+        self.embedding_dim = embedding_dim
+        self.n_codes = n_codes
+        self.n_hiddens = n_hiddens
+        self.n_res_layers = n_res_layers
+        self.resolution = resolution
+        self.sequence_length = sequence_length
+        self.time_downsample = time_downsample
+        self.spatial_downsample = spatial_downsample
+        self.no_pos_embd = no_pos_embd
diff --git a/opensora/models/ae/videobase/causal_vqvae/dataset_causalvqvae.py b/opensora/models/ae/videobase/causal_vqvae/dataset_causalvqvae.py
new file mode 100644
index 000000000..cac23f1c9
--- /dev/null
+++ b/opensora/models/ae/videobase/causal_vqvae/dataset_causalvqvae.py
@@ -0,0 +1,153 @@
+import os.path as osp
+import math
+import pickle
+import warnings
+
+import glob
+from PIL import Image
+import torch
+import torch.utils.data as data
+import torch.distributed as dist
+import torch.nn.functional as F
+from torchvision.datasets.video_utils import VideoClips
+import torchvision.transforms as transforms
+
+# Copied from https://github.com/wilson1yan/VideoGPT
+class CausalVQVAEDataset(data.Dataset):
+    """ Generic dataset for videos files stored in folders
+    Returns BCTHW videos in the range [-0.5, 0.5] """
+    video_exts = ['avi', 'mp4', 'webm']
+    image_exts = ['png', 'jpg', 'jpeg']
+    def __init__(self, video_folder, sequence_length, image_folder=None, train=True, resolution=64):
+        """
+        Args:
+            data_folder: path to the folder with videos. The folder
+                should contain a 'train' and a 'test' directory,
+                each with corresponding videos stored
+            sequence_length: length of extracted video sequences
+        """
+        super().__init__()
+        if image_folder is not None:
+            raise NotImplementedError("Image training is not supported now.")
+        
+        self.train = train
+        self.sequence_length = sequence_length
+        self.resolution = resolution
+
+        files = []
+        video_files = []
+        image_files = []
+        for data_folder in [video_folder, image_folder]:
+            if data_folder is None:
+                continue
+            folder = data_folder
+            video_files += sum([glob.glob(osp.join(folder, '**', f'*.{ext}'), recursive=True)
+                         for ext in self.video_exts], [])
+            image_files += sum([glob.glob(osp.join(folder, '**', f'*.{ext}'), recursive=True)
+                         for ext in self.image_exts], [])
+        files = video_files + image_files
+        # hacky way to compute # of classes (count # of unique parent directories)
+        # self.classes = list(set([get_parent_dir(f) for f in files]))
+        # self.classes.sort()
+        # self.class_to_label = {c: i for i, c in enumerate(self.classes)}
+
+        warnings.filterwarnings('ignore')
+        if len(video_files) != 0:
+            cache_file = osp.join(folder, f"metadata_{sequence_length}.pkl")
+            if not osp.exists(cache_file):
+                clips = VideoClips(video_files, sequence_length, num_workers=32)
+                if dist.is_initialized() and dist.get_rank() == 0:
+                    pickle.dump(clips.metadata, open(cache_file, 'wb'))
+            else:
+                metadata = pickle.load(open(cache_file, 'rb'))
+                clips = VideoClips(video_files, sequence_length,
+                                   _precomputed_metadata=metadata)
+
+            self._clips = clips
+            self._clips_num = self._clips.num_clips()
+        else:
+            self._clips = None
+            self._clips_num = 0
+        self.image_files = image_files
+
+    @property
+    def n_classes(self):
+        return len(self.classes)
+
+    def __len__(self):
+        return self._clips_num + len(self.image_files)
+
+    def __getitem__(self, idx):
+        resolution = self.resolution
+        if idx < self._clips_num:
+            video, _, _, idx = self._clips.get_clip(idx)
+            video = preprocess(video, resolution)
+            class_name = get_parent_dir(self._clips.video_paths[idx])
+        else:
+            idx -= self._clips_num
+            image = Image.open(self.image_files[idx])
+            video = preprocess_image(image, resolution, self.sequence_length)
+        # label = self.class_to_label[class_name]
+        return dict(video=video, label="")
+
+# Copied from https://github.com/wilson1yan/VideoGPT
+def get_parent_dir(path):
+    return osp.basename(osp.dirname(path))
+
+# Copied from https://github.com/wilson1yan/VideoGPT
+def preprocess(video, resolution, sequence_length=None):
+    # video: THWC, {0, ..., 255}
+    video = video.permute(0, 3, 1, 2).float() / 255. # TCHW
+    t, c, h, w = video.shape
+
+    # temporal crop
+    if sequence_length is not None:
+        assert sequence_length <= t
+        video = video[:sequence_length]
+
+    # scale shorter side to resolution
+    scale = resolution / min(h, w)
+    if h < w:
+        target_size = (resolution, math.ceil(w * scale))
+    else:
+        target_size = (math.ceil(h * scale), resolution)
+    video = F.interpolate(video, size=target_size, mode='bilinear',
+                          align_corners=False)
+
+    # center crop
+    t, c, h, w = video.shape
+    w_start = (w - resolution) // 2
+    h_start = (h - resolution) // 2
+    video = video[:, :, h_start:h_start + resolution, w_start:w_start + resolution]
+    video = video.permute(1, 0, 2, 3).contiguous() # CTHW
+
+    video -= 0.5
+
+    return video
+
+
+def preprocess_image(image, resolution, sequence_length=1):
+    # image: HWC, {0, ..., 255}
+    image = image.convert("RGB")
+    w,h = image.size
+    scale = resolution / min(h, w)
+    if h < w:
+        target_size = (resolution, math.ceil(w * scale))
+    else:
+        target_size = (math.ceil(h * scale), resolution)
+    image = image.resize(target_size)
+    
+    image = transforms.ToTensor()(image)
+    image = image.float()
+    c, h, w = image.shape
+    w_start = (w - resolution) // 2
+    h_start = (h - resolution) // 2
+    image = image[:, h_start:h_start + resolution, w_start:w_start + resolution]
+    image -= 0.5
+    c, h, w = image.shape
+    new_image = torch.zeros((c, sequence_length, h, w))
+    new_image = new_image.to(image.device)
+    new_image[:, :1, :, :] = image.unsqueeze(1)
+    new_image = new_image.contiguous()
+    
+    return new_image
\ No newline at end of file
diff --git a/opensora/models/ae/videobase/causal_vqvae/modeling_causalvqvae.py b/opensora/models/ae/videobase/causal_vqvae/modeling_causalvqvae.py
new file mode 100644
index 000000000..65f3f98a9
--- /dev/null
+++ b/opensora/models/ae/videobase/causal_vqvae/modeling_causalvqvae.py
@@ -0,0 +1,848 @@
+from ..modeling_videobase import VideoBaseAE
+import torch
+from torch import nn, Tensor
+import numpy as np
+import torch.distributed as dist
+import torch.nn.functional as F
+import math
+import os
+import json
+from typing import Tuple, Dict, Union
+from .configuration_causalvqvae import CausalVQVAEConfiguration
+from einops import rearrange, pack, unpack
+
+# Copied from https://github.com/wilson1yan/VideoGPT
+def view_range(x, i, j, shape):
+    shape = tuple(shape)
+
+    n_dims = len(x.shape)
+    if i < 0:
+        i = n_dims + i
+
+    if j is None:
+        j = n_dims
+    elif j < 0:
+        j = n_dims + j
+
+    assert 0 <= i < j <= n_dims
+
+    x_shape = x.shape
+    target_shape = x_shape[:i] + shape + x_shape[j:]
+    return x.view(target_shape)
+
+
+# Copied from https://github.com/wilson1yan/VideoGPT
+def shift_dim(x, src_dim=-1, dest_dim=-1, make_contiguous=True):
+    n_dims = len(x.shape)
+    if src_dim < 0:
+        src_dim = n_dims + src_dim
+    if dest_dim < 0:
+        dest_dim = n_dims + dest_dim
+    assert 0 <= src_dim < n_dims and 0 <= dest_dim < n_dims
+    dims = list(range(n_dims))
+    del dims[src_dim]
+    permutation = []
+    ctr = 0
+    for i in range(n_dims):
+        if i == dest_dim:
+            permutation.append(src_dim)
+        else:
+            permutation.append(dims[ctr])
+            ctr += 1
+    x = x.permute(permutation)
+    if make_contiguous:
+        x = x.contiguous()
+    return x
+
+
+# Copied from https://github.com/wilson1yan/VideoGPT
+def scaled_dot_product_attention(q, k, v, mask=None, attn_dropout=0.0, training=True):
+    # Performs scaled dot-product attention over the second to last dimension dn
+
+    # (b, n_head, d1, ..., dn, d)
+    attn = torch.matmul(q, k.transpose(-1, -2))
+    attn = attn / np.sqrt(q.shape[-1])
+    if mask is not None:
+        attn = attn.masked_fill(mask == 0, float("-inf"))
+    attn_float = F.softmax(attn, dim=-1)
+    attn = attn_float.type_as(attn)  # b x n_head x d1 x ... x dn x d
+    attn = F.dropout(attn, p=attn_dropout, training=training)
+
+    a = torch.matmul(attn, v)  # b x n_head x d1 x ... x dn x d
+
+    return a
+
+def is_odd(n):
+    return not n % 2 == 0
+
+def maybe_del_attr_(o, attr):
+    if hasattr(o, attr):
+        delattr(o, attr)
+
+def cast_tuple(t, length = 1):
+    return t if isinstance(t, tuple) else ((t,) * length)
+
+class SpatialDownsample2x(torch.nn.Module):
+    def __init__(
+        self,
+        chan_in,
+        chan_out,
+        kernel_size: Union[int, Tuple[int]] = (4,4),
+        stride: Union[int, Tuple[int]] = (2,2)
+    ):
+        super().__init__()
+        kernel_size = cast_tuple(kernel_size, 2)
+        stride = cast_tuple(stride, 2)
+        
+        self.chan_in = chan_in
+        self.chan_out = chan_out
+        self.kernel_size = kernel_size
+        
+        total_pad = tuple([k - s for k, s in zip(kernel_size, stride)])
+        pad_input = []
+        for p in total_pad[::-1]:
+            pad_input.append((p // 2 + p % 2, p // 2))
+        pad_input = sum(pad_input, tuple())
+        self.pad_input = pad_input
+        
+        self.conv = torch.nn.Conv2d(self.chan_in, self.chan_out, self.kernel_size, stride=stride)
+        
+    def forward(self, x):
+        x = F.pad(x, self.pad_input)
+        x = rearrange(x, "b c f h w -> b f c h w")
+        x, ps = pack([x], "* c h w")
+        x = self.conv(x)
+        x = unpack(x, ps, "* c h w")[0]
+        x = rearrange(x, "b f c h w -> b c f h w")
+        return x
+
+class SpatialUpsample2x(torch.nn.Module):
+    def __init__(
+        self,
+        chan_in,
+        chan_out,
+        kernel_size: Union[int, Tuple[int]] = (3,3),
+        stride: Union[int, Tuple[int]] = (1,1)
+    ):
+        super().__init__()
+        self.chan_in = chan_in
+        self.chan_out = chan_out
+        self.kernel_size = kernel_size
+        self.conv = torch.nn.Conv2d(self.chan_in, self.chan_out, self.kernel_size, stride=stride, padding=tuple([(k - 1) // 2 for k in kernel_size]))
+        
+    def forward(self, x):
+        x = rearrange(x, "b c f h w -> b f c h w")
+        x, ps = pack([x], "* c h w")
+        x = torch.nn.functional.interpolate(x, scale_factor=2.0, mode="nearest")
+        x = self.conv(x)
+        x = unpack(x, ps, "* c h w")[0]
+        x = rearrange(x, "b f c h w -> b c f h w")
+        return x
+
+class TimeDownsample2x(nn.Module):
+    def __init__(
+        self,
+        chan_in,
+        chan_out,
+        kernel_size: int = 4,
+    ):
+        super().__init__()
+        self.chan_in = chan_in
+        self.chan_out = chan_out
+        self.kernel_size = kernel_size
+        self.conv = CausalConv3d(chan_in, chan_out, kernel_size, stride=2)
+        
+    def forward(self, x):
+        return self.conv(x)
+
+class TimeUpsample2x(nn.Module):
+    def __init__(
+        self,
+        chan_in,
+        chan_out,
+        kernel_size: int = 3,
+    ):
+        super().__init__()
+        self.chan_in = chan_in
+        self.chan_out = chan_out
+        self.kernel_size = kernel_size
+        self.conv = CausalConv3d(chan_in, chan_out, kernel_size, stride=1)
+        
+    def forward(self, x):
+        x = rearrange(x, "b c f h w -> b c h w f")
+        x, ps = pack([x], "b * f")
+        if x.size(-1) > 1:
+            x = torch.concat((x[:,:,:1], F.interpolate(x[:,:,1:], scale_factor=2.0, mode="linear")), dim=-1)
+        else:
+            x = x
+        x = unpack(x, ps, "b * f")[0]
+        x = rearrange(x, "b c h w f -> b c f h w")
+        x = self.conv(x)
+        return x
+
+class CausalConv3d(nn.Module):
+    def __init__(
+        self,
+        chan_in,
+        chan_out,
+        kernel_size: Union[int, Tuple[int, int, int]],
+        **kwargs
+    ):
+        super().__init__()
+        kernel_size = cast_tuple(kernel_size, 3)
+        self.time_kernel_size = kernel_size[0]
+        stride = kwargs.pop('stride', 1)
+        stride = (stride, 1, 1)
+        total_pad = tuple([k - s for k, s in zip(kernel_size[1:], stride[1:])])
+        pad_input = []
+        for p in total_pad[::-1]:
+            pad_input.append((p // 2 + p % 2, p // 2))
+        pad_input = sum(pad_input, tuple())
+        pad_input += (0, 0)
+        self.padding = pad_input
+        self.conv = nn.Conv3d(chan_in, chan_out, kernel_size, stride = stride, **kwargs)
+
+    def forward(self, x):
+        x = F.pad(x, self.padding)
+        first_frame_pad = x[:, :, :1, : ,:].repeat((1,1,self.time_kernel_size - 1,1,1))
+        x = torch.concatenate((first_frame_pad, x), dim=2)
+        return self.conv(x)
+
+# Modified from https://github.com/wilson1yan/VideoGPT
+class AxialBlock(nn.Module):
+    def __init__(self, n_hiddens, n_head):
+        super().__init__()
+        kwargs = dict(
+            shape=(0,) * 3,
+            dim_q=n_hiddens,
+            dim_kv=n_hiddens,
+            n_head=n_head,
+            n_layer=1,
+            causal=False,
+            attn_type="axial",
+        )
+        self.attn_w = MultiHeadAttention(attn_kwargs=dict(axial_dim=-2), **kwargs)
+        self.attn_h = MultiHeadAttention(attn_kwargs=dict(axial_dim=-3), **kwargs)
+        kwargs['causal'] = True
+        self.attn_t = MultiHeadAttention(attn_kwargs=dict(axial_dim=-4), **kwargs)
+
+    def forward(self, x):
+        x = shift_dim(x, 1, -1)
+        x = self.attn_w(x, x, x) + self.attn_h(x, x, x) + self.attn_t(x, x, x)
+        x = shift_dim(x, -1, 1)
+        return x
+
+# Copied from https://github.com/wilson1yan/VideoGPT
+class AttentionResidualBlock(nn.Module):
+    def __init__(self, n_hiddens, n_heads: int = 2):
+        super().__init__()
+        self.block = nn.Sequential(
+            nn.BatchNorm3d(n_hiddens),
+            nn.ReLU(),
+            CausalConv3d(n_hiddens, n_hiddens // 2, 3, bias=False),
+            nn.BatchNorm3d(n_hiddens // 2),
+            nn.ReLU(),
+            CausalConv3d(n_hiddens // 2, n_hiddens, 1, bias=False),
+            nn.BatchNorm3d(n_hiddens),
+            nn.ReLU(),
+            AxialBlock(n_hiddens, n_heads),
+        )
+
+    def forward(self, x):
+        return x + self.block(x)
+
+# Copied from https://github.com/wilson1yan/VideoGPT
+class Codebook(nn.Module):
+    def __init__(self, n_codes, embedding_dim):
+        super().__init__()
+        self.register_buffer("embeddings", torch.randn(n_codes, embedding_dim))
+        self.register_buffer("N", torch.zeros(n_codes))
+        self.register_buffer("z_avg", self.embeddings.data.clone())
+
+        self.n_codes = n_codes
+        self.embedding_dim = embedding_dim
+        self._need_init = True
+
+    def _tile(self, x):
+        d, ew = x.shape
+        if d < self.n_codes:
+            n_repeats = (self.n_codes + d - 1) // d
+            std = 0.01 / np.sqrt(ew)
+            x = x.repeat(n_repeats, 1)
+            x = x + torch.randn_like(x) * std
+        return x
+
+    def _init_embeddings(self, z):
+        # z: [b, c, t, h, w]
+        self._need_init = False
+        flat_inputs = shift_dim(z, 1, -1).flatten(end_dim=-2)
+        y = self._tile(flat_inputs)
+
+        d = y.shape[0]
+        _k_rand = y[torch.randperm(y.shape[0])][: self.n_codes]
+        if dist.is_initialized():
+            dist.broadcast(_k_rand, 0)
+        self.embeddings.data.copy_(_k_rand)
+        self.z_avg.data.copy_(_k_rand)
+        self.N.data.copy_(torch.ones(self.n_codes))
+
+    def forward(self, z):
+        # z: [b, c, t, h, w]
+        if self._need_init and self.training:
+            self._init_embeddings(z)
+        flat_inputs = shift_dim(z, 1, -1).flatten(end_dim=-2)
+        distances = (
+            (flat_inputs**2).sum(dim=1, keepdim=True)
+            - 2 * flat_inputs @ self.embeddings.t()
+            + (self.embeddings.t() ** 2).sum(dim=0, keepdim=True)
+        )
+
+        encoding_indices = torch.argmin(distances, dim=1)
+        encode_onehot = F.one_hot(encoding_indices, self.n_codes).type_as(flat_inputs)
+        encoding_indices = encoding_indices.view(z.shape[0], *z.shape[2:])
+
+        embeddings = F.embedding(encoding_indices, self.embeddings)
+        embeddings = shift_dim(embeddings, -1, 1)
+
+        commitment_loss = 0.25 * F.mse_loss(z, embeddings.detach())
+
+        # EMA codebook update
+        if self.training:
+            n_total = encode_onehot.sum(dim=0)
+            encode_sum = flat_inputs.t() @ encode_onehot
+            if dist.is_initialized():
+                dist.all_reduce(n_total)
+                dist.all_reduce(encode_sum)
+
+            self.N.data.mul_(0.99).add_(n_total, alpha=0.01)
+            self.z_avg.data.mul_(0.99).add_(encode_sum.t(), alpha=0.01)
+
+            n = self.N.sum()
+            weights = (self.N + 1e-7) / (n + self.n_codes * 1e-7) * n
+            encode_normalized = self.z_avg / weights.unsqueeze(1)
+            self.embeddings.data.copy_(encode_normalized)
+
+            y = self._tile(flat_inputs)
+            _k_rand = y[torch.randperm(y.shape[0])][: self.n_codes]
+            if dist.is_initialized():
+                dist.broadcast(_k_rand, 0)
+
+            usage = (self.N.view(self.n_codes, 1) >= 1).float()
+            self.embeddings.data.mul_(usage).add_(_k_rand * (1 - usage))
+
+        embeddings_st = (embeddings - z).detach() + z
+
+        avg_probs = torch.mean(encode_onehot, dim=0)
+        perplexity = torch.exp(-torch.sum(avg_probs * torch.log(avg_probs + 1e-10)))
+
+        return dict(
+            embeddings=embeddings_st,
+            encodings=encoding_indices,
+            commitment_loss=commitment_loss,
+            perplexity=perplexity,
+        )
+
+    def dictionary_lookup(self, encodings):
+        embeddings = F.embedding(encodings, self.embeddings)
+        return embeddings
+
+# Modified from https://github.com/wilson1yan/VideoGPT
+class Encoder(nn.Module):
+    def __init__(self, n_hiddens, n_res_layers, time_downsample, spatial_downsample):
+        super().__init__()
+        spatial_downsample = int(math.log2(spatial_downsample))
+        self.spatial_conv = nn.ModuleList()
+        for i in range(spatial_downsample):
+            in_channels = 3 if i == 0 else n_hiddens
+            conv = SpatialDownsample2x(in_channels, n_hiddens)
+            self.spatial_conv.append(conv)
+        self.spatial_res_stack = nn.Sequential(
+            *[AttentionResidualBlock(n_hiddens) for _ in range(n_res_layers)],
+            nn.BatchNorm3d(n_hiddens),
+            nn.ReLU(),
+        )
+        time_downsample = int(math.log2(time_downsample))
+        self.time_conv = nn.ModuleList()
+        for i in range(time_downsample):
+            conv = TimeDownsample2x(n_hiddens, n_hiddens)
+            self.time_conv.append(conv)
+        self.time_res_stack = nn.Sequential(
+            *[AttentionResidualBlock(n_hiddens) for _ in range(n_res_layers)],
+            nn.BatchNorm3d(n_hiddens),
+            nn.ReLU(),
+        )
+
+    def forward(self, x):
+        h = x
+        for conv in self.spatial_conv:
+            h = F.relu(conv(h))
+        h = self.spatial_res_stack(h)
+        for conv in self.time_conv:
+            h = F.relu(conv(h))
+        h = self.time_res_stack(h)
+        return h
+
+# Copied from https://github.com/wilson1yan/VideoGPT
+class MultiHeadAttention(nn.Module):
+    def __init__(
+        self, shape, dim_q, dim_kv, n_head, n_layer, causal, attn_type, attn_kwargs
+    ):
+        super().__init__()
+        self.causal = causal
+        self.shape = shape
+
+        self.d_k = dim_q // n_head
+        self.d_v = dim_kv // n_head
+        self.n_head = n_head
+
+        self.w_qs = nn.Linear(dim_q, n_head * self.d_k, bias=False)  # q
+        self.w_qs.weight.data.normal_(std=1.0 / np.sqrt(dim_q))
+
+        self.w_ks = nn.Linear(dim_kv, n_head * self.d_k, bias=False)  # k
+        self.w_ks.weight.data.normal_(std=1.0 / np.sqrt(dim_kv))
+
+        self.w_vs = nn.Linear(dim_kv, n_head * self.d_v, bias=False)  # v
+        self.w_vs.weight.data.normal_(std=1.0 / np.sqrt(dim_kv))
+
+        self.fc = nn.Linear(n_head * self.d_v, dim_q, bias=True)  # c
+        self.fc.weight.data.normal_(std=1.0 / np.sqrt(dim_q * n_layer))
+
+        if attn_type == "full":
+            self.attn = FullAttention(shape, causal, **attn_kwargs)
+        elif attn_type == "axial":
+            self.attn = AxialAttention(len(shape), causal=causal, **attn_kwargs)
+        elif attn_type == "sparse":
+            self.attn = SparseAttention(shape, n_head, causal, **attn_kwargs)
+
+        self.cache = None
+
+    def forward(self, q, k, v, decode_step=None, decode_idx=None):
+        """Compute multi-head attention
+        Args
+            q, k, v: a [b, d1, ..., dn, c] tensor or
+                     a [b, 1, ..., 1, c] tensor if decode_step is not None
+
+        Returns
+            The output after performing attention
+        """
+
+        # compute k, q, v
+        d_k, d_v, n_head = self.d_k, self.d_v, self.n_head
+        q = view_range(self.w_qs(q), -1, None, (n_head, d_k))
+        k = view_range(self.w_ks(k), -1, None, (n_head, d_k))
+        v = view_range(self.w_vs(v), -1, None, (n_head, d_v))
+
+        # b x n_head x seq_len x d
+        # (b, *d_shape, n_head, d) -> (b, n_head, *d_shape, d)
+        q = shift_dim(q, -2, 1)
+        k = shift_dim(k, -2, 1)
+        v = shift_dim(v, -2, 1)
+
+        # fast decoding
+        if decode_step is not None:
+            if decode_step == 0:
+                if self.causal:
+                    k_shape = (q.shape[0], n_head, *self.shape, self.d_k)
+                    v_shape = (q.shape[0], n_head, *self.shape, self.d_v)
+                    self.cache = dict(
+                        k=torch.zeros(k_shape, dtype=k.dtype, device=q.device),
+                        v=torch.zeros(v_shape, dtype=v.dtype, device=q.device),
+                    )
+                else:
+                    # cache only once in the non-causal case
+                    self.cache = dict(k=k.clone(), v=v.clone())
+            if self.causal:
+                idx = (
+                    slice(None, None),
+                    slice(None, None),
+                    *[slice(i, i + 1) for i in decode_idx],
+                )
+                self.cache["k"][idx] = k
+                self.cache["v"][idx] = v
+            k, v = self.cache["k"], self.cache["v"]
+
+        a = self.attn(q, k, v, decode_step, decode_idx)
+
+        # (b, *d_shape, n_head, d) -> (b, *d_shape, n_head * d)
+        a = shift_dim(a, 1, -2).flatten(start_dim=-2)
+        a = self.fc(a)  # (b x seq_len x embd_dim)
+
+        return a
+
+# Copied from https://github.com/wilson1yan/VideoGPT
+class Decoder(nn.Module):
+    def __init__(self, n_hiddens, n_res_layers, time_downsample, spatial_downsample):
+        super().__init__()
+        self.time_res_stack = nn.Sequential(
+            *[AttentionResidualBlock(n_hiddens) for _ in range(n_res_layers)],
+            nn.BatchNorm3d(n_hiddens),
+            nn.ReLU(),
+        )
+        time_downsample = int(math.log2(time_downsample))
+        self.time_conv = nn.ModuleList()
+        for i in range(time_downsample):
+            convt = TimeUpsample2x(n_hiddens, n_hiddens)
+            self.time_conv.append(convt)
+        self.spatial_res_stack = nn.Sequential(
+            *[AttentionResidualBlock(n_hiddens) for _ in range(n_res_layers)],
+            nn.BatchNorm3d(n_hiddens),
+            nn.ReLU(),
+        )
+        spatial_downsample = int(math.log2(spatial_downsample))
+        self.spatial_conv = nn.ModuleList()
+        for i in range(spatial_downsample):
+            out_channels = 3 if i == spatial_downsample - 1 else n_hiddens
+            convt = SpatialUpsample2x(n_hiddens, out_channels)
+            self.spatial_conv.append(convt)
+
+    def forward(self, x):
+        h = self.time_res_stack(x)
+        for conv in self.time_conv:
+            h = F.relu(conv(h))
+        h = self.spatial_res_stack(h)
+        for i, conv in enumerate(self.spatial_conv):
+            h = conv(h)
+            if i < len(self.spatial_conv) - 1:
+                h = F.relu(h)
+        return h
+
+# Copied from https://github.com/wilson1yan/VideoGPT
+class FullAttention(nn.Module):
+    def __init__(self, shape, causal, attn_dropout):
+        super().__init__()
+        self.causal = causal
+        self.attn_dropout = attn_dropout
+
+        seq_len = np.prod(shape)
+        if self.causal:
+            self.register_buffer("mask", torch.tril(torch.ones(seq_len, seq_len)))
+
+    def forward(self, q, k, v, decode_step, decode_idx):
+        mask = self.mask if self.causal else None
+        if decode_step is not None and mask is not None:
+            mask = mask[[decode_step]]
+
+        old_shape = q.shape[2:-1]
+        q = q.flatten(start_dim=2, end_dim=-2)
+        k = k.flatten(start_dim=2, end_dim=-2)
+        v = v.flatten(start_dim=2, end_dim=-2)
+
+        out = scaled_dot_product_attention(
+            q, k, v, mask=mask, attn_dropout=self.attn_dropout, training=self.training
+        )
+
+        return view_range(out, 2, 3, old_shape)
+
+# Copied from https://github.com/wilson1yan/VideoGPT
+class AxialAttention(nn.Module):
+    def __init__(self, n_dim, axial_dim, causal=False):
+        super().__init__()
+        if axial_dim < 0:
+            axial_dim = 2 + n_dim + 1 + axial_dim
+        else:
+            axial_dim += 2  # account for batch, head, dim
+        self.causal = causal
+        self.axial_dim = axial_dim
+
+    def forward(self, q, k, v, decode_step, decode_idx):
+        # batch, head, frame, height, width, dim
+        q = shift_dim(q, self.axial_dim, -2).flatten(end_dim=-3)
+        k = shift_dim(k, self.axial_dim, -2).flatten(end_dim=-3)
+        v = shift_dim(v, self.axial_dim, -2)
+        
+        old_shape = list(v.shape)
+        v = v.flatten(end_dim=-3)
+        
+        if self.causal:
+            mask = torch.tril(torch.ones(q.shape[-2], q.shape[-2])) if self.causal else None
+            if decode_step is not None and mask is not None:
+                mask = mask[[decode_step]]
+            mask = mask.to(q.device)
+        else:
+            mask = None
+            
+        out = scaled_dot_product_attention(q, k, v, mask=mask, training=self.training)
+        out = out.view(*old_shape)
+        out = shift_dim(out, -2, self.axial_dim)
+        return out
+
+# Copied from https://github.com/wilson1yan/VideoGPT
+class StridedSparsityConfig(object):
+    """
+    Strided Sparse configuration specified in https://arxiv.org/abs/1904.10509 that
+    generalizes to arbitrary dimensions
+    """
+
+    def __init__(self, shape, n_head, causal, block, num_local_blocks):
+        self.n_head = n_head
+        self.shape = shape
+        self.causal = causal
+        self.block = block
+        self.num_local_blocks = num_local_blocks
+
+        assert self.num_local_blocks >= 1, "Must have at least 1 local block"
+        assert self.seq_len % self.block == 0, "seq len must be divisible by block size"
+
+        self._block_shape = self._compute_block_shape()
+        self._block_shape_cum = self._block_shape_cum_sizes()
+
+    @property
+    def seq_len(self):
+        return np.prod(self.shape)
+
+    @property
+    def num_blocks(self):
+        return self.seq_len // self.block
+
+    def set_local_layout(self, layout):
+        num_blocks = self.num_blocks
+        for row in range(0, num_blocks):
+            end = min(row + self.num_local_blocks, num_blocks)
+            for col in range(
+                max(0, row - self.num_local_blocks), (row + 1 if self.causal else end)
+            ):
+                layout[:, row, col] = 1
+        return layout
+
+    def set_global_layout(self, layout):
+        num_blocks = self.num_blocks
+        n_dim = len(self._block_shape)
+        for row in range(num_blocks):
+            assert self._to_flattened_idx(self._to_unflattened_idx(row)) == row
+            cur_idx = self._to_unflattened_idx(row)
+            # no strided attention over last dim
+            for d in range(n_dim - 1):
+                end = self._block_shape[d]
+                for i in range(0, (cur_idx[d] + 1 if self.causal else end)):
+                    new_idx = list(cur_idx)
+                    new_idx[d] = i
+                    new_idx = tuple(new_idx)
+
+                    col = self._to_flattened_idx(new_idx)
+                    layout[:, row, col] = 1
+
+        return layout
+
+    def make_layout(self):
+        layout = torch.zeros(
+            (self.n_head, self.num_blocks, self.num_blocks), dtype=torch.int64
+        )
+        layout = self.set_local_layout(layout)
+        layout = self.set_global_layout(layout)
+        return layout
+
+    def make_sparse_attn_mask(self):
+        block_layout = self.make_layout()
+        assert block_layout.shape[1] == block_layout.shape[2] == self.num_blocks
+
+        num_dense_blocks = block_layout.sum().item()
+        attn_mask = torch.ones(num_dense_blocks, self.block, self.block)
+        counter = 0
+        for h in range(self.n_head):
+            for i in range(self.num_blocks):
+                for j in range(self.num_blocks):
+                    elem = block_layout[h, i, j].item()
+                    if elem == 1:
+                        assert i >= j
+                        if i == j:  # need to mask within block on diagonals
+                            attn_mask[counter] = torch.tril(attn_mask[counter])
+                        counter += 1
+        assert counter == num_dense_blocks
+
+        return attn_mask.unsqueeze(0)
+
+    def get_non_block_layout_row(self, block_layout, row):
+        block_row = row // self.block
+        block_row = block_layout[:, [block_row]]  # n_head x 1 x n_blocks
+        block_row = block_row.repeat_interleave(self.block, dim=-1)
+        block_row[:, :, row + 1 :] = 0.0
+        return block_row
+
+    ############# Helper functions ##########################
+
+    def _compute_block_shape(self):
+        n_dim = len(self.shape)
+        cum_prod = 1
+        for i in range(n_dim - 1, -1, -1):
+            cum_prod *= self.shape[i]
+            if cum_prod > self.block:
+                break
+        assert cum_prod % self.block == 0
+        new_shape = (*self.shape[:i], cum_prod // self.block)
+
+        assert np.prod(new_shape) == np.prod(self.shape) // self.block
+
+        return new_shape
+
+    def _block_shape_cum_sizes(self):
+        bs = np.flip(np.array(self._block_shape))
+        return tuple(np.flip(np.cumprod(bs)[:-1])) + (1,)
+
+    def _to_flattened_idx(self, idx):
+        assert len(idx) == len(
+            self._block_shape
+        ), f"{len(idx)} != {len(self._block_shape)}"
+        flat_idx = 0
+        for i in range(len(self._block_shape)):
+            flat_idx += idx[i] * self._block_shape_cum[i]
+        return flat_idx
+
+    def _to_unflattened_idx(self, flat_idx):
+        assert flat_idx < np.prod(self._block_shape)
+        idx = []
+        for i in range(len(self._block_shape)):
+            idx.append(flat_idx // self._block_shape_cum[i])
+            flat_idx %= self._block_shape_cum[i]
+        return tuple(idx)
+
+# Copied from https://github.com/wilson1yan/VideoGPT
+class SparseAttention(nn.Module):
+    ops = dict()
+    attn_mask = dict()
+    block_layout = dict()
+
+    def __init__(
+        self, shape, n_head, causal, num_local_blocks=4, block=32, attn_dropout=0.0
+    ):  # does not use attn_dropout
+        super().__init__()
+        self.causal = causal
+        self.shape = shape
+
+        self.sparsity_config = StridedSparsityConfig(
+            shape=shape,
+            n_head=n_head,
+            causal=causal,
+            block=block,
+            num_local_blocks=num_local_blocks,
+        )
+
+        if self.shape not in SparseAttention.block_layout:
+            SparseAttention.block_layout[self.shape] = (
+                self.sparsity_config.make_layout()
+            )
+        if causal and self.shape not in SparseAttention.attn_mask:
+            SparseAttention.attn_mask[self.shape] = (
+                self.sparsity_config.make_sparse_attn_mask()
+            )
+
+    def get_ops(self):
+        try:
+            from deepspeed.ops.sparse_attention import MatMul, Softmax
+        except:
+            raise Exception(
+                "Error importing deepspeed. Please install using `DS_BUILD_SPARSE_ATTN=1 pip install deepspeed`"
+            )
+        if self.shape not in SparseAttention.ops:
+            sparsity_layout = self.sparsity_config.make_layout()
+            sparse_dot_sdd_nt = MatMul(
+                sparsity_layout,
+                self.sparsity_config.block,
+                "sdd",
+                trans_a=False,
+                trans_b=True,
+            )
+
+            sparse_dot_dsd_nn = MatMul(
+                sparsity_layout,
+                self.sparsity_config.block,
+                "dsd",
+                trans_a=False,
+                trans_b=False,
+            )
+
+            sparse_softmax = Softmax(sparsity_layout, self.sparsity_config.block)
+
+            SparseAttention.ops[self.shape] = (
+                sparse_dot_sdd_nt,
+                sparse_dot_dsd_nn,
+                sparse_softmax,
+            )
+        return SparseAttention.ops[self.shape]
+
+    def forward(self, q, k, v, decode_step, decode_idx):
+        if self.training and self.shape not in SparseAttention.ops:
+            self.get_ops()
+
+        SparseAttention.block_layout[self.shape] = SparseAttention.block_layout[
+            self.shape
+        ].to(q)
+        if self.causal:
+            SparseAttention.attn_mask[self.shape] = (
+                SparseAttention.attn_mask[self.shape].to(q).type_as(q)
+            )
+        attn_mask = SparseAttention.attn_mask[self.shape] if self.causal else None
+
+        old_shape = q.shape[2:-1]
+        q = q.flatten(start_dim=2, end_dim=-2)
+        k = k.flatten(start_dim=2, end_dim=-2)
+        v = v.flatten(start_dim=2, end_dim=-2)
+
+        if decode_step is not None:
+            mask = self.sparsity_config.get_non_block_layout_row(
+                SparseAttention.block_layout[self.shape], decode_step
+            )
+            out = scaled_dot_product_attention(
+                q, k, v, mask=mask, training=self.training
+            )
+        else:
+            if q.shape != k.shape or k.shape != v.shape:
+                raise Exception("SparseAttention only support self-attention")
+            sparse_dot_sdd_nt, sparse_dot_dsd_nn, sparse_softmax = self.get_ops()
+            scaling = float(q.shape[-1]) ** -0.5
+
+            attn_output_weights = sparse_dot_sdd_nt(q, k)
+            if attn_mask is not None:
+                attn_output_weights = attn_output_weights.masked_fill(
+                    attn_mask == 0, float("-inf")
+                )
+            attn_output_weights = sparse_softmax(attn_output_weights, scale=scaling)
+
+            out = sparse_dot_dsd_nn(attn_output_weights, v)
+
+        return view_range(out, 2, 3, old_shape)
+
+class CausalVQVAEModel(VideoBaseAE):
+
+    def __init__(self, config: CausalVQVAEConfiguration):
+        super().__init__()
+        self.config = config
+        self.embedding_dim = config.embedding_dim
+        self.n_codes = config.n_codes
+        self.encoder = Encoder(config.n_hiddens, config.n_res_layers, config.time_downsample, config.spatial_downsample)
+        self.decoder = Decoder(config.n_hiddens, config.n_res_layers, config.time_downsample, config.spatial_downsample)
+        self.pre_vq_conv = CausalConv3d(config.n_hiddens, config.embedding_dim, 1)
+        self.post_vq_conv = CausalConv3d(config.embedding_dim, config.n_hiddens, 1)
+        self.codebook = Codebook(config.n_codes, config.embedding_dim)
+
+    def forward(self, x):
+        z = self.pre_vq_conv(self.encoder(x))
+        vq_output = self.codebook(z)
+        x_recon = self.decoder(self.post_vq_conv(vq_output["embeddings"]))
+        recon_loss = F.mse_loss(x_recon, x) / 0.06
+        return recon_loss, x_recon, vq_output
+
+    def encode(self, x: Tensor, include_embeddings: bool = False) -> Union[Tuple[Tensor, Tensor], Tensor]:
+        h = self.pre_vq_conv(self.encoder(x))
+        vq_output: Dict[str, Tensor] = self.codebook(h)
+        if include_embeddings:
+            return vq_output["encodings"], vq_output["embeddings"]
+        else:
+            return vq_output["encodings"]
+
+    def decode(self, encodings: Tensor) -> Tensor:
+        h = F.embedding(encodings, self.codebook.embeddings)
+        h = self.post_vq_conv(shift_dim(h, -1, 1))
+        return self.decoder(h)
+
+    @classmethod
+    def load_from_checkpoint(cls, model_path):
+        with open(os.path.join(model_path, "config.json"), "r") as file:
+            config = json.load(file)
+        state_dict = torch.load(os.path.join(model_path, "pytorch_model.bin"), map_location="cpu")
+        model = cls(config=CausalVQVAEConfiguration(**config))
+        model.load_state_dict(state_dict)
+        return model
+            
+    @classmethod
+    def download_and_load_model(cls, model_name, cache_dir=None):
+        raise NotImplementedError()
diff --git a/opensora/models/ae/videobase/causal_vqvae/trainer_causalvqvae.py b/opensora/models/ae/videobase/causal_vqvae/trainer_causalvqvae.py
new file mode 100644
index 000000000..b60cc9a4b
--- /dev/null
+++ b/opensora/models/ae/videobase/causal_vqvae/trainer_causalvqvae.py
@@ -0,0 +1,30 @@
+from transformers import Trainer
+import torch.nn.functional as F
+from typing import Optional
+import os
+import torch
+from transformers.utils import WEIGHTS_NAME
+import json
+
+class CausalVQVAETrainer(Trainer):
+
+    def compute_loss(self, model, inputs, return_outputs=False):
+        model = model.module
+        x = inputs.get("video")
+        z = model.pre_vq_conv(model.encoder(x))
+        vq_output = model.codebook(z)
+        x_recon = model.decoder(model.post_vq_conv(vq_output["embeddings"]))
+        recon_loss = F.mse_loss(x_recon, x) / 0.06
+        commitment_loss = vq_output['commitment_loss']
+        loss = recon_loss + commitment_loss
+        return loss
+
+    def _save(self, output_dir: Optional[str] = None, state_dict=None):
+        output_dir = output_dir if output_dir is not None else self.args.output_dir
+        os.makedirs(output_dir, exist_ok=True)
+        if state_dict is None:
+            state_dict = self.model.state_dict()
+        with open(os.path.join(output_dir, "config.json"), "w") as file:
+            json.dump(self.model.config.to_dict(), file)
+        torch.save(state_dict, os.path.join(output_dir, WEIGHTS_NAME))
+        torch.save(self.args, os.path.join(output_dir, "training_args.bin"))
diff --git a/opensora/train/train_causalvqvae.py b/opensora/train/train_causalvqvae.py
new file mode 100644
index 000000000..11a370da7
--- /dev/null
+++ b/opensora/train/train_causalvqvae.py
@@ -0,0 +1,69 @@
+import sys
+sys.path.append(".")
+
+from opensora.models.ae.videobase import (
+    CausalVQVAEModel,
+    CausalVQVAEConfiguration,
+    CausalVQVAEDataset,
+    CausalVQVAETrainer,
+)
+
+import argparse
+from typing import Optional
+from accelerate.utils import set_seed
+from transformers import HfArgumentParser, TrainingArguments
+from dataclasses import dataclass, field, asdict
+
+
+@dataclass
+class CausalVQVAEArgument:
+    embedding_dim: int = field(default=256),
+    n_codes: int = field(default=2048),
+    n_hiddens: int = field(default=240),
+    n_res_layers: int = field(default=4),
+    resolution: int = field(default=128),
+    sequence_length: int = field(default=16),
+    time_downsample: int = field(default=4),
+    spatial_downsample: int = field(default=8),
+    no_pos_embd: bool = True,
+    video_data_path: str = field(default=None, metadata={"help": "data path"})
+    image_data_path: str = field(default=None, metadata={"help": "not implemented"})
+
+@dataclass
+class CausalVQVAETrainingArgument(TrainingArguments):
+    remove_unused_columns: Optional[bool] = field(
+        default=False, metadata={"help": "Remove columns not required by the model when using an nlp.Dataset."}
+    )
+
+def train(args, vqvae_args: CausalVQVAEArgument, training_args: CausalVQVAETrainingArgument):
+    # Load Config
+    config = CausalVQVAEConfiguration(
+        embedding_dim=vqvae_args.embedding_dim,
+        n_codes=vqvae_args.n_codes,
+        n_hiddens=vqvae_args.n_hiddens,
+        n_res_layers=vqvae_args.n_res_layers,
+        resolution=vqvae_args.resolution,
+        sequence_length=vqvae_args.sequence_length,
+        time_downsample=vqvae_args.time_downsample,
+        spatial_downsample=vqvae_args.spatial_downsample,
+        no_pos_embd=vqvae_args.no_pos_embd
+    )
+    # Load Model
+    if args.resume_from_checkpoint:
+        model = CausalVQVAEModel.load_from_checkpoint(args.resume_from_checkpoint)
+    else:
+        model = CausalVQVAEModel(config)
+    # Load Dataset
+    dataset = CausalVQVAEDataset(args.video_data_path, image_folder=None, sequence_length=args.sequence_length, resolution=config.resolution)
+    # Load Trainer
+    trainer = CausalVQVAETrainer(model, training_args, train_dataset=dataset)
+    trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)
+    trainer.save_model()
+
+if __name__ == "__main__":
+    parser = HfArgumentParser((CausalVQVAEArgument, CausalVQVAETrainingArgument))
+    vqvae_args, training_args = parser.parse_args_into_dataclasses()
+    args = argparse.Namespace(**vars(vqvae_args), **vars(training_args))
+    set_seed(args.seed)
+
+    train(args, vqvae_args, training_args)
diff --git a/scripts/casual_vqvae/train.sh b/scripts/casual_vqvae/train.sh
new file mode 100644
index 000000000..1f127e537
--- /dev/null
+++ b/scripts/casual_vqvae/train.sh
@@ -0,0 +1,28 @@
+accelerate launch \
+  --config_file scripts/accelerate_configs/default_config.yaml \
+  opensora/train/train_causalvqvae.py \
+  --do_train \
+  --seed 1234 \
+  --video_data_path ../Open-Sora-Plan/UCF-101 \
+  --per_device_train_batch_size 16 \
+  --gradient_accumulation_steps 2 \
+  --learning_rate 7e-4 \
+  --weight_decay 0. \
+  --max_steps 100000 \
+  --embedding_dim 4 \
+  --lr_scheduler_type cosine \
+  --max_grad_norm 1.0 \
+  --save_strategy steps \
+  --save_total_limit 5 \
+  --logging_steps 5 \
+  --save_steps 1000 \
+  --n_codes 1024 \
+  --n_hiddens 240 \
+  --n_res_layers 3 \
+  --time_downsample 4 \
+  --spatial_downsample 8 \
+  --bf16 \
+  --resolution 64 \
+  --sequence_length 13 \
+  --output_dir results/vqvae_causal \
+  --report_to tensorboard
\ No newline at end of file