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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,80 @@ | ||
| from safetensors.torch import safe_open | ||
| import torch | ||
| import torch.nn as nn | ||
| from torch.nn.utils.parametrizations import weight_norm | ||
|
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||
|
|
||
| class Embed(nn.Module): | ||
| def __init__( | ||
| self, hidden_size: int, num_audio_tokens: int, num_text_tokens: int, num_vq=4 | ||
| ): | ||
| super().__init__() | ||
|
|
||
| self.num_vq = num_vq | ||
| self.num_audio_tokens = num_audio_tokens | ||
|
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||
| self.model_dim = hidden_size | ||
| self.emb_code = nn.ModuleList( | ||
| [nn.Embedding(num_audio_tokens, self.model_dim) for _ in range(num_vq)], | ||
| ) | ||
| self.emb_text = nn.Embedding(num_text_tokens, self.model_dim) | ||
|
|
||
| self.head_text = weight_norm( | ||
| nn.Linear(self.model_dim, num_text_tokens, bias=False), | ||
| name="weight", | ||
| ) | ||
| self.head_code = nn.ModuleList( | ||
| [ | ||
| weight_norm( | ||
| nn.Linear(self.model_dim, num_audio_tokens, bias=False), | ||
| name="weight", | ||
| ) | ||
| for _ in range(self.num_vq) | ||
| ], | ||
| ) | ||
|
|
||
| @torch.inference_mode() | ||
| def from_pretrained(self, filename: str): | ||
| state_dict_tensors = {} | ||
| with safe_open(filename, framework="pt") as f: | ||
| for k in f.keys(): | ||
| state_dict_tensors[k] = f.get_tensor(k) | ||
| self.load_state_dict(state_dict_tensors) | ||
|
|
||
| def __call__( | ||
| self, input_ids: torch.Tensor, text_mask: torch.Tensor | ||
| ) -> torch.Tensor: | ||
| """ | ||
| get_emb | ||
| """ | ||
| return super().__call__(input_ids, text_mask) | ||
|
|
||
| @torch.inference_mode() | ||
| def forward(self, input_ids: torch.Tensor, text_mask: torch.Tensor) -> torch.Tensor: | ||
| """ | ||
| get_emb | ||
| """ | ||
| device = next(self.parameters()).device | ||
| emb_text: torch.Tensor = self.emb_text( | ||
| input_ids[text_mask].narrow(1, 0, 1).squeeze_(1).to(device) | ||
| ) | ||
|
|
||
| text_mask_inv = text_mask.logical_not().to(device) | ||
| masked_input_ids: torch.Tensor = input_ids[text_mask_inv].to(device) | ||
|
|
||
| emb_code = [ | ||
| self.emb_code[i](masked_input_ids[:, i]) for i in range(self.num_vq) | ||
| ] | ||
| emb_code = torch.stack(emb_code, 2).sum(2) | ||
|
|
||
| emb = torch.zeros( | ||
| (input_ids.shape[:-1]) + (emb_text.shape[-1],), | ||
| device=emb_text.device, | ||
| dtype=emb_text.dtype, | ||
| ) | ||
| emb[text_mask] = emb_text | ||
| emb[text_mask_inv] = emb_code.to(emb.dtype) | ||
|
|
||
| del emb_text, emb_code, text_mask_inv | ||
|
|
||
| return emb |
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