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on
Zero
Running
on
Zero
| import torch | |
| from einops import rearrange | |
| from torch import nn | |
| from einops import rearrange | |
| class WanRMSNorm(nn.Module): | |
| def __init__(self, dim, eps=1e-5): | |
| super().__init__() | |
| self.dim = dim | |
| self.eps = eps | |
| self.weight = nn.Parameter(torch.ones(dim)) | |
| def forward(self, x): | |
| r""" | |
| Args: | |
| x(Tensor): Shape [B, L, C] | |
| """ | |
| return self._norm(x.float()).type_as(x) * self.weight | |
| def _norm(self, x): | |
| return x * torch.rsqrt(x.pow(2).mean(dim=-1, keepdim=True) + self.eps) | |
| class DummyAdapterLayer(nn.Module): | |
| def __init__(self, layer): | |
| super().__init__() | |
| self.layer = layer | |
| def forward(self, *args, **kwargs): | |
| return self.layer(*args, **kwargs) | |
| class AudioProjModel(nn.Module): | |
| def __init__( | |
| self, | |
| seq_len=5, | |
| blocks=13, # add a new parameter blocks | |
| channels=768, # add a new parameter channels | |
| intermediate_dim=512, | |
| output_dim=1536, | |
| context_tokens=16, | |
| ): | |
| super().__init__() | |
| self.seq_len = seq_len | |
| self.blocks = blocks | |
| self.channels = channels | |
| self.input_dim = seq_len * blocks * channels # update input_dim to be the product of blocks and channels. | |
| self.intermediate_dim = intermediate_dim | |
| self.context_tokens = context_tokens | |
| self.output_dim = output_dim | |
| # define multiple linear layers | |
| self.audio_proj_glob_1 = DummyAdapterLayer(nn.Linear(self.input_dim, intermediate_dim)) | |
| self.audio_proj_glob_2 = DummyAdapterLayer(nn.Linear(intermediate_dim, intermediate_dim)) | |
| self.audio_proj_glob_3 = DummyAdapterLayer(nn.Linear(intermediate_dim, context_tokens * output_dim)) | |
| self.audio_proj_glob_norm = DummyAdapterLayer(nn.LayerNorm(output_dim)) | |
| self.initialize_weights() | |
| def initialize_weights(self): | |
| # Initialize transformer layers: | |
| def _basic_init(module): | |
| if isinstance(module, nn.Linear): | |
| torch.nn.init.xavier_uniform_(module.weight) | |
| if module.bias is not None: | |
| nn.init.constant_(module.bias, 0) | |
| self.apply(_basic_init) | |
| def forward(self, audio_embeds): | |
| video_length = audio_embeds.shape[1] | |
| audio_embeds = rearrange(audio_embeds, "bz f w b c -> (bz f) w b c") | |
| batch_size, window_size, blocks, channels = audio_embeds.shape | |
| audio_embeds = audio_embeds.view(batch_size, window_size * blocks * channels) | |
| audio_embeds = torch.relu(self.audio_proj_glob_1(audio_embeds)) | |
| audio_embeds = torch.relu(self.audio_proj_glob_2(audio_embeds)) | |
| context_tokens = self.audio_proj_glob_3(audio_embeds).reshape(batch_size, self.context_tokens, self.output_dim) | |
| context_tokens = self.audio_proj_glob_norm(context_tokens) | |
| context_tokens = rearrange(context_tokens, "(bz f) m c -> bz f m c", f=video_length) | |
| return context_tokens |