50 lines
1.6 KiB
Python
50 lines
1.6 KiB
Python
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import torch
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from torch import nn
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import torch.nn.functional as F
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from transformers import GPT2Config, GPT2Model
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from trainer.networks import register_model
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from utils.util import opt_get
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class Mel2VecCodesGpt(nn.Module):
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def __init__(self, dim, layers, num_groups=8, num_vectors=8):
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super().__init__()
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self.num_groups = num_groups
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self.config = GPT2Config(vocab_size=1, n_positions=8192, n_embd=dim, n_layer=layers, n_head=dim//64,
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n_inner=dim*2)
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self.gpt = GPT2Model(self.config)
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del self.gpt.wte # Unused, we'll do our own embeddings.
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self.embeddings = nn.ModuleList([nn.Embedding(num_vectors, dim//num_groups) for _ in range(num_groups)])
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self.heads = nn.ModuleList([nn.Linear(dim, num_vectors) for _ in range(num_groups)])
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def forward(self, codes):
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assert codes.shape[-1] == self.num_groups
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inputs = codes[:, :-1]
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targets = codes[:, 1:]
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h = [embedding(inputs[:, :, i]) for i, embedding in enumerate(self.embeddings)]
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h = torch.cat(h, dim=-1)
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h = self.gpt(inputs_embeds=h, return_dict=True).last_hidden_state
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losses = 0
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for i, head in enumerate(self.heads):
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logits = head(h).permute(0,2,1)
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loss = F.cross_entropy(logits, targets[:,:,i])
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losses = losses + loss
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return losses / self.num_groups
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@register_model
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def register_music_gpt(opt_net, opt):
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return Mel2VecCodesGpt(**opt_get(opt_net, ['kwargs'], {}))
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if __name__ == '__main__':
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model = Mel2VecCodesGpt(512, 8)
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codes = torch.randint(0,8, (2,300,8))
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model(codes)
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