| import torch, os, gc |
| from safetensors import safe_open |
| from contextlib import contextmanager |
| from einops import rearrange, repeat |
| import torch.nn as nn |
| import torch.nn.functional as F |
| from tqdm import tqdm |
| import time |
| import hashlib |
|
|
| CACHE_T = 2 |
|
|
| @contextmanager |
| def init_weights_on_device(device = torch.device("meta"), include_buffers :bool = False): |
| |
| old_register_parameter = torch.nn.Module.register_parameter |
| if include_buffers: |
| old_register_buffer = torch.nn.Module.register_buffer |
| |
| def register_empty_parameter(module, name, param): |
| old_register_parameter(module, name, param) |
| if param is not None: |
| param_cls = type(module._parameters[name]) |
| kwargs = module._parameters[name].__dict__ |
| kwargs["requires_grad"] = param.requires_grad |
| module._parameters[name] = param_cls(module._parameters[name].to(device), **kwargs) |
|
|
| def register_empty_buffer(module, name, buffer, persistent=True): |
| old_register_buffer(module, name, buffer, persistent=persistent) |
| if buffer is not None: |
| module._buffers[name] = module._buffers[name].to(device) |
| |
| def patch_tensor_constructor(fn): |
| def wrapper(*args, **kwargs): |
| kwargs["device"] = device |
| return fn(*args, **kwargs) |
|
|
| return wrapper |
| |
| if include_buffers: |
| tensor_constructors_to_patch = { |
| torch_function_name: getattr(torch, torch_function_name) |
| for torch_function_name in ["empty", "zeros", "ones", "full"] |
| } |
| else: |
| tensor_constructors_to_patch = {} |
| |
| try: |
| torch.nn.Module.register_parameter = register_empty_parameter |
| if include_buffers: |
| torch.nn.Module.register_buffer = register_empty_buffer |
| for torch_function_name in tensor_constructors_to_patch.keys(): |
| setattr(torch, torch_function_name, patch_tensor_constructor(getattr(torch, torch_function_name))) |
| yield |
| finally: |
| torch.nn.Module.register_parameter = old_register_parameter |
| if include_buffers: |
| torch.nn.Module.register_buffer = old_register_buffer |
| for torch_function_name, old_torch_function in tensor_constructors_to_patch.items(): |
| setattr(torch, torch_function_name, old_torch_function) |
|
|
| def load_state_dict_from_folder(file_path, torch_dtype=None): |
| state_dict = {} |
| for file_name in os.listdir(file_path): |
| if "." in file_name and file_name.split(".")[-1] in [ |
| "safetensors", "bin", "ckpt", "pth", "pt" |
| ]: |
| state_dict.update(load_state_dict(os.path.join(file_path, file_name), torch_dtype=torch_dtype)) |
| return state_dict |
|
|
|
|
| def load_state_dict(file_path, torch_dtype=None): |
| if file_path.endswith(".safetensors"): |
| return load_state_dict_from_safetensors(file_path, torch_dtype=torch_dtype) |
| else: |
| return load_state_dict_from_bin(file_path, torch_dtype=torch_dtype) |
|
|
|
|
| def load_state_dict_from_safetensors(file_path, torch_dtype=None): |
| state_dict = {} |
| with safe_open(file_path, framework="pt", device="cpu") as f: |
| for k in f.keys(): |
| state_dict[k] = f.get_tensor(k) |
| if torch_dtype is not None: |
| state_dict[k] = state_dict[k].to(torch_dtype) |
| return state_dict |
|
|
|
|
| def load_state_dict_from_bin(file_path, torch_dtype=None): |
| state_dict = torch.load(file_path, map_location="cpu", weights_only=True) |
| if torch_dtype is not None: |
| for i in state_dict: |
| if isinstance(state_dict[i], torch.Tensor): |
| state_dict[i] = state_dict[i].to(torch_dtype) |
| return state_dict |
|
|
|
|
| def search_for_embeddings(state_dict): |
| embeddings = [] |
| for k in state_dict: |
| if isinstance(state_dict[k], torch.Tensor): |
| embeddings.append(state_dict[k]) |
| elif isinstance(state_dict[k], dict): |
| embeddings += search_for_embeddings(state_dict[k]) |
| return embeddings |
|
|
|
|
| def search_parameter(param, state_dict): |
| for name, param_ in state_dict.items(): |
| if param.numel() == param_.numel(): |
| if param.shape == param_.shape: |
| if torch.dist(param, param_) < 1e-3: |
| return name |
| else: |
| if torch.dist(param.flatten(), param_.flatten()) < 1e-3: |
| return name |
| return None |
|
|
|
|
| def build_rename_dict(source_state_dict, target_state_dict, split_qkv=False): |
| matched_keys = set() |
| with torch.no_grad(): |
| for name in source_state_dict: |
| rename = search_parameter(source_state_dict[name], target_state_dict) |
| if rename is not None: |
| print(f'"{name}": "{rename}",') |
| matched_keys.add(rename) |
| elif split_qkv and len(source_state_dict[name].shape)>=1 and source_state_dict[name].shape[0]%3==0: |
| length = source_state_dict[name].shape[0] // 3 |
| rename = [] |
| for i in range(3): |
| rename.append(search_parameter(source_state_dict[name][i*length: i*length+length], target_state_dict)) |
| if None not in rename: |
| print(f'"{name}": {rename},') |
| for rename_ in rename: |
| matched_keys.add(rename_) |
| for name in target_state_dict: |
| if name not in matched_keys: |
| print("Cannot find", name, target_state_dict[name].shape) |
|
|
|
|
| def search_for_files(folder, extensions): |
| files = [] |
| if os.path.isdir(folder): |
| for file in sorted(os.listdir(folder)): |
| files += search_for_files(os.path.join(folder, file), extensions) |
| elif os.path.isfile(folder): |
| for extension in extensions: |
| if folder.endswith(extension): |
| files.append(folder) |
| break |
| return files |
|
|
|
|
| def convert_state_dict_keys_to_single_str(state_dict, with_shape=True): |
| keys = [] |
| for key, value in state_dict.items(): |
| if isinstance(key, str): |
| if isinstance(value, torch.Tensor): |
| if with_shape: |
| shape = "_".join(map(str, list(value.shape))) |
| keys.append(key + ":" + shape) |
| keys.append(key) |
| elif isinstance(value, dict): |
| keys.append(key + "|" + convert_state_dict_keys_to_single_str(value, with_shape=with_shape)) |
| keys.sort() |
| keys_str = ",".join(keys) |
| return keys_str |
|
|
|
|
| def split_state_dict_with_prefix(state_dict): |
| keys = sorted([key for key in state_dict if isinstance(key, str)]) |
| prefix_dict = {} |
| for key in keys: |
| prefix = key if "." not in key else key.split(".")[0] |
| if prefix not in prefix_dict: |
| prefix_dict[prefix] = [] |
| prefix_dict[prefix].append(key) |
| state_dicts = [] |
| for prefix, keys in prefix_dict.items(): |
| sub_state_dict = {key: state_dict[key] for key in keys} |
| state_dicts.append(sub_state_dict) |
| return state_dicts |
|
|
| def hash_state_dict_keys(state_dict, with_shape=True): |
| keys_str = convert_state_dict_keys_to_single_str(state_dict, with_shape=with_shape) |
| keys_str = keys_str.encode(encoding="UTF-8") |
| return hashlib.md5(keys_str).hexdigest() |
|
|
| def clean_vram(): |
| gc.collect() |
| if torch.cuda.is_available(): |
| torch.cuda.empty_cache() |
| torch.cuda.ipc_collect() |
| if torch.backends.mps.is_available(): |
| torch.mps.empty_cache() |
|
|
| def get_device_list(): |
| devs = ["auto"] |
| try: |
| if hasattr(torch, "cuda") and hasattr(torch.cuda, "is_available") and torch.cuda.is_available(): |
| devs += [f"cuda:{i}" for i in range(torch.cuda.device_count())] |
| except Exception: |
| pass |
| try: |
| if hasattr(torch, "mps") and hasattr(torch.mps, "is_available") and torch.backends.mps.is_available(): |
| devs += [f"mps:{i}" for i in range(torch.mps.device_count())] |
| except Exception: |
| pass |
| return devs |
|
|
| class RMS_norm(nn.Module): |
| |
| def __init__(self, dim, channel_first=True, images=True, bias=False): |
| super().__init__() |
| broadcastable_dims = (1, 1, 1) if not images else (1, 1) |
| shape = (dim, *broadcastable_dims) if channel_first else (dim,) |
| |
| self.channel_first = channel_first |
| self.scale = dim**0.5 |
| self.gamma = nn.Parameter(torch.ones(shape)) |
| self.bias = nn.Parameter(torch.zeros(shape)) if bias else 0. |
| |
| def forward(self, x): |
| return F.normalize( |
| x, dim=(1 if self.channel_first else |
| -1)) * self.scale * self.gamma + self.bias |
| |
| class CausalConv3d(nn.Conv3d): |
| """ |
| Causal 3d convolusion. |
| """ |
| |
| def __init__(self, *args, **kwargs): |
| super().__init__(*args, **kwargs) |
| self._padding = (self.padding[2], self.padding[2], self.padding[1], |
| self.padding[1], 2 * self.padding[0], 0) |
| self.padding = (0, 0, 0) |
| |
| def forward(self, x, cache_x=None): |
| padding = list(self._padding) |
| if cache_x is not None and self._padding[4] > 0: |
| cache_x = cache_x.to(x.device) |
| |
| x = torch.cat([cache_x, x], dim=2) |
| padding[4] -= cache_x.shape[2] |
| |
| x = F.pad(x, padding, mode='replicate') |
| |
| |
| return super().forward(x) |
| |
| class PixelShuffle3d(nn.Module): |
| def __init__(self, ff, hh, ww): |
| super().__init__() |
| self.ff = ff |
| self.hh = hh |
| self.ww = ww |
| |
| def forward(self, x): |
| |
| return rearrange(x, |
| 'b c (f ff) (h hh) (w ww) -> b (c ff hh ww) f h w', |
| ff=self.ff, hh=self.hh, ww=self.ww) |
| |
| class Buffer_LQ4x_Proj(nn.Module): |
| |
| def __init__(self, in_dim, out_dim, layer_num=30): |
| super().__init__() |
| self.ff = 1 |
| self.hh = 16 |
| self.ww = 16 |
| self.hidden_dim1 = 2048 |
| self.hidden_dim2 = 3072 |
| self.layer_num = layer_num |
| |
| self.pixel_shuffle = PixelShuffle3d(self.ff, self.hh, self.ww) |
| |
| self.conv1 = CausalConv3d(in_dim*self.ff*self.hh*self.ww, self.hidden_dim1, (4, 3, 3), stride=(2, 1, 1), padding=(1, 1, 1)) |
| self.norm1 = RMS_norm(self.hidden_dim1, images=False) |
| self.act1 = nn.SiLU() |
| |
| self.conv2 = CausalConv3d(self.hidden_dim1, self.hidden_dim2, (4, 3, 3), stride=(2, 1, 1), padding=(1, 1, 1)) |
| self.norm2 = RMS_norm(self.hidden_dim2, images=False) |
| self.act2 = nn.SiLU() |
| |
| self.linear_layers = nn.ModuleList([nn.Linear(self.hidden_dim2, out_dim) for _ in range(layer_num)]) |
| |
| self.clip_idx = 0 |
| |
| def forward(self, video): |
| self.clear_cache() |
| |
| |
| t = video.shape[2] |
| iter_ = 1 + (t - 1) // 4 |
| first_frame = video[:, :, :1, :, :].repeat(1, 1, 3, 1, 1) |
| video = torch.cat([first_frame, video], dim=2) |
| |
| |
| out_x = [] |
| for i in range(iter_): |
| x = self.pixel_shuffle(video[:,:,i*4:(i+1)*4,:,:]) |
| cache1_x = x[:, :, -CACHE_T:, :, :].clone() |
| self.cache['conv1'] = cache1_x |
| x = self.conv1(x, self.cache['conv1']) |
| x = self.norm1(x) |
| x = self.act1(x) |
| cache2_x = x[:, :, -CACHE_T:, :, :].clone() |
| self.cache['conv2'] = cache2_x |
| if i == 0: |
| continue |
| x = self.conv2(x, self.cache['conv2']) |
| x = self.norm2(x) |
| x = self.act2(x) |
| out_x.append(x) |
| out_x = torch.cat(out_x, dim = 2) |
| |
| out_x = rearrange(out_x, 'b c f h w -> b (f h w) c') |
| outputs = [] |
| for i in range(self.layer_num): |
| outputs.append(self.linear_layers[i](out_x)) |
| return outputs |
| |
| def clear_cache(self): |
| self.cache = {} |
| self.cache['conv1'] = None |
| self.cache['conv2'] = None |
| self.clip_idx = 0 |
| |
| def stream_forward(self, video_clip): |
| if self.clip_idx == 0: |
| |
| first_frame = video_clip[:, :, :1, :, :].repeat(1, 1, 3, 1, 1) |
| video_clip = torch.cat([first_frame, video_clip], dim=2) |
| x = self.pixel_shuffle(video_clip) |
| cache1_x = x[:, :, -CACHE_T:, :, :].clone() |
| self.cache['conv1'] = cache1_x |
| x = self.conv1(x, self.cache['conv1']) |
| x = self.norm1(x) |
| x = self.act1(x) |
| cache2_x = x[:, :, -CACHE_T:, :, :].clone() |
| self.cache['conv2'] = cache2_x |
| self.clip_idx += 1 |
| return None |
| else: |
| x = self.pixel_shuffle(video_clip) |
| cache1_x = x[:, :, -CACHE_T:, :, :].clone() |
| self.cache['conv1'] = cache1_x |
| x = self.conv1(x, self.cache['conv1']) |
| x = self.norm1(x) |
| x = self.act1(x) |
| cache2_x = x[:, :, -CACHE_T:, :, :].clone() |
| self.cache['conv2'] = cache2_x |
| x = self.conv2(x, self.cache['conv2']) |
| x = self.norm2(x) |
| x = self.act2(x) |
| out_x = rearrange(x, 'b c f h w -> b (f h w) c') |
| outputs = [] |
| for i in range(self.layer_num): |
| outputs.append(self.linear_layers[i](out_x)) |
| self.clip_idx += 1 |
| return outputs |
|
|
| class Causal_LQ4x_Proj(nn.Module): |
| |
| def __init__(self, in_dim, out_dim, layer_num=30): |
| super().__init__() |
| self.ff = 1 |
| self.hh = 16 |
| self.ww = 16 |
| self.hidden_dim1 = 2048 |
| self.hidden_dim2 = 3072 |
| self.layer_num = layer_num |
| |
| self.pixel_shuffle = PixelShuffle3d(self.ff, self.hh, self.ww) |
| |
| self.conv1 = CausalConv3d(in_dim*self.ff*self.hh*self.ww, self.hidden_dim1, (4, 3, 3), stride=(2, 1, 1), padding=(1, 1, 1)) |
| self.norm1 = RMS_norm(self.hidden_dim1, images=False) |
| self.act1 = nn.SiLU() |
| |
| self.conv2 = CausalConv3d(self.hidden_dim1, self.hidden_dim2, (4, 3, 3), stride=(2, 1, 1), padding=(1, 1, 1)) |
| self.norm2 = RMS_norm(self.hidden_dim2, images=False) |
| self.act2 = nn.SiLU() |
| |
| self.linear_layers = nn.ModuleList([nn.Linear(self.hidden_dim2, out_dim) for _ in range(layer_num)]) |
| |
| self.clip_idx = 0 |
| |
| def forward(self, video): |
| self.clear_cache() |
| |
| |
| t = video.shape[2] |
| iter_ = 1 + (t - 1) // 4 |
| first_frame = video[:, :, :1, :, :].repeat(1, 1, 3, 1, 1) |
| video = torch.cat([first_frame, video], dim=2) |
| |
| |
| out_x = [] |
| for i in range(iter_): |
| x = self.pixel_shuffle(video[:,:,i*4:(i+1)*4,:,:]) |
| cache1_x = x[:, :, -CACHE_T:, :, :].clone() |
| x = self.conv1(x, self.cache['conv1']) |
| self.cache['conv1'] = cache1_x |
| x = self.norm1(x) |
| x = self.act1(x) |
| cache2_x = x[:, :, -CACHE_T:, :, :].clone() |
| if i == 0: |
| self.cache['conv2'] = cache2_x |
| continue |
| x = self.conv2(x, self.cache['conv2']) |
| self.cache['conv2'] = cache2_x |
| x = self.norm2(x) |
| x = self.act2(x) |
| out_x.append(x) |
| out_x = torch.cat(out_x, dim = 2) |
| out_x = rearrange(out_x, 'b c f h w -> b (f h w) c') |
| outputs = [] |
| for i in range(self.layer_num): |
| outputs.append(self.linear_layers[i](out_x)) |
| return outputs |
| |
| def clear_cache(self): |
| self.cache = {} |
| self.cache['conv1'] = None |
| self.cache['conv2'] = None |
| self.clip_idx = 0 |
| |
| def stream_forward(self, video_clip): |
| if self.clip_idx == 0: |
| |
| first_frame = video_clip[:, :, :1, :, :].repeat(1, 1, 3, 1, 1) |
| video_clip = torch.cat([first_frame, video_clip], dim=2) |
| x = self.pixel_shuffle(video_clip) |
| cache1_x = x[:, :, -CACHE_T:, :, :].clone() |
| x = self.conv1(x, self.cache['conv1']) |
| self.cache['conv1'] = cache1_x |
| x = self.norm1(x) |
| x = self.act1(x) |
| cache2_x = x[:, :, -CACHE_T:, :, :].clone() |
| self.cache['conv2'] = cache2_x |
| self.clip_idx += 1 |
| return None |
| else: |
| x = self.pixel_shuffle(video_clip) |
| cache1_x = x[:, :, -CACHE_T:, :, :].clone() |
| x = self.conv1(x, self.cache['conv1']) |
| self.cache['conv1'] = cache1_x |
| x = self.norm1(x) |
| x = self.act1(x) |
| cache2_x = x[:, :, -CACHE_T:, :, :].clone() |
| x = self.conv2(x, self.cache['conv2']) |
| self.cache['conv2'] = cache2_x |
| x = self.norm2(x) |
| x = self.act2(x) |
| out_x = rearrange(x, 'b c f h w -> b (f h w) c') |
| outputs = [] |
| for i in range(self.layer_num): |
| outputs.append(self.linear_layers[i](out_x)) |
| self.clip_idx += 1 |
| return outputs |
