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Zero
Running
on
Zero
| """ | |
| ein notation: | |
| b - batch | |
| n - sequence | |
| nt - text sequence | |
| nw - raw wave length | |
| d - dimension | |
| """ | |
| from __future__ import annotations | |
| import torch | |
| from torch import nn | |
| from einops import repeat | |
| from x_transformers.x_transformers import RotaryEmbedding | |
| from model.modules import ( | |
| TimestepEmbedding, | |
| ConvPositionEmbedding, | |
| MMDiTBlock, | |
| AdaLayerNormZero_Final, | |
| precompute_freqs_cis, get_pos_embed_indices, | |
| ) | |
| # text embedding | |
| class TextEmbedding(nn.Module): | |
| def __init__(self, out_dim, text_num_embeds): | |
| super().__init__() | |
| self.text_embed = nn.Embedding(text_num_embeds + 1, out_dim) # will use 0 as filler token | |
| self.precompute_max_pos = 1024 | |
| self.register_buffer("freqs_cis", precompute_freqs_cis(out_dim, self.precompute_max_pos), persistent=False) | |
| def forward(self, text: int['b nt'], drop_text = False) -> int['b nt d']: | |
| text = text + 1 | |
| if drop_text: | |
| text = torch.zeros_like(text) | |
| text = self.text_embed(text) | |
| # sinus pos emb | |
| batch_start = torch.zeros((text.shape[0],), dtype=torch.long) | |
| batch_text_len = text.shape[1] | |
| pos_idx = get_pos_embed_indices(batch_start, batch_text_len, max_pos=self.precompute_max_pos) | |
| text_pos_embed = self.freqs_cis[pos_idx] | |
| text = text + text_pos_embed | |
| return text | |
| # noised input & masked cond audio embedding | |
| class AudioEmbedding(nn.Module): | |
| def __init__(self, in_dim, out_dim): | |
| super().__init__() | |
| self.linear = nn.Linear(2 * in_dim, out_dim) | |
| self.conv_pos_embed = ConvPositionEmbedding(out_dim) | |
| def forward(self, x: float['b n d'], cond: float['b n d'], drop_audio_cond = False): | |
| if drop_audio_cond: | |
| cond = torch.zeros_like(cond) | |
| x = torch.cat((x, cond), dim = -1) | |
| x = self.linear(x) | |
| x = self.conv_pos_embed(x) + x | |
| return x | |
| # Transformer backbone using MM-DiT blocks | |
| class MMDiT(nn.Module): | |
| def __init__(self, *, | |
| dim, depth = 8, heads = 8, dim_head = 64, dropout = 0.1, ff_mult = 4, | |
| text_num_embeds = 256, mel_dim = 100, | |
| ): | |
| super().__init__() | |
| self.time_embed = TimestepEmbedding(dim) | |
| self.text_embed = TextEmbedding(dim, text_num_embeds) | |
| self.audio_embed = AudioEmbedding(mel_dim, dim) | |
| self.rotary_embed = RotaryEmbedding(dim_head) | |
| self.dim = dim | |
| self.depth = depth | |
| self.transformer_blocks = nn.ModuleList( | |
| [ | |
| MMDiTBlock( | |
| dim = dim, | |
| heads = heads, | |
| dim_head = dim_head, | |
| dropout = dropout, | |
| ff_mult = ff_mult, | |
| context_pre_only = i == depth - 1, | |
| ) | |
| for i in range(depth) | |
| ] | |
| ) | |
| self.norm_out = AdaLayerNormZero_Final(dim) # final modulation | |
| self.proj_out = nn.Linear(dim, mel_dim) | |
| def forward( | |
| self, | |
| x: float['b n d'], # nosied input audio | |
| cond: float['b n d'], # masked cond audio | |
| text: int['b nt'], # text | |
| time: float['b'] | float[''], # time step | |
| drop_audio_cond, # cfg for cond audio | |
| drop_text, # cfg for text | |
| mask: bool['b n'] | None = None, | |
| ): | |
| batch = x.shape[0] | |
| if time.ndim == 0: | |
| time = repeat(time, ' -> b', b = batch) | |
| # t: conditioning (time), c: context (text + masked cond audio), x: noised input audio | |
| t = self.time_embed(time) | |
| c = self.text_embed(text, drop_text = drop_text) | |
| x = self.audio_embed(x, cond, drop_audio_cond = drop_audio_cond) | |
| seq_len = x.shape[1] | |
| text_len = text.shape[1] | |
| rope_audio = self.rotary_embed.forward_from_seq_len(seq_len) | |
| rope_text = self.rotary_embed.forward_from_seq_len(text_len) | |
| for block in self.transformer_blocks: | |
| c, x = block(x, c, t, mask = mask, rope = rope_audio, c_rope = rope_text) | |
| x = self.norm_out(x, t) | |
| output = self.proj_out(x) | |
| return output | |