from time import time import torch import torch.nn as nn import torch.nn.functional as F from munch import munchify from transformers import GPT2Model, GPT2Config from models.gpt_voice.lucidrains_gpt import Transformer from models.tacotron2.taco_utils import get_mask_from_lengths from models.tacotron2.text import symbols, sequence_to_text from trainer.networks import register_model from utils.util import opt_get class ResBlock(nn.Module): def __init__(self, chan): super().__init__() self.net = nn.Sequential( nn.Conv1d(chan, chan, kernel_size=5, padding = 2), nn.BatchNorm1d(chan), nn.ReLU(), nn.Conv1d(chan, chan, kernel_size=5, padding = 2), nn.BatchNorm1d(chan) ) def forward(self, x): return F.relu(self.net(x) + x) class MelEncoder(nn.Module): def __init__(self, channels, mel_channels=80): super().__init__() self.channels = channels self.encoder = nn.Sequential(nn.Conv1d(mel_channels, channels//4, kernel_size=7, padding=3), ResBlock(channels//4), ResBlock(channels//4), nn.Conv1d(channels//4, channels//2, kernel_size=5, stride=2, padding=2), nn.BatchNorm1d(channels//2), nn.ReLU(), ResBlock(channels//2), ResBlock(channels//2), ResBlock(channels//2), nn.Conv1d(channels//2, channels, kernel_size=5, stride=2, padding=2), ResBlock(channels), ResBlock(channels), ResBlock(channels) ) def forward(self, x): return self.encoder(x) class GptAsrHf(nn.Module): NUMBER_SYMBOLS = len(symbols) NUMBER_TEXT_TOKENS = NUMBER_SYMBOLS+1 def __init__(self, layers=8, model_dim=512, heads=8, max_symbols_per_phrase=200, max_mel_frames=1000): super().__init__() self.max_mel_frames = max_mel_frames // 4 # Mel frames are reduced by a factor of 4 during encoding. self.max_symbols_per_phrase = max_symbols_per_phrase self.model_dim = model_dim self.max_mel_frames = self.max_mel_frames self.mel_encoder = MelEncoder(model_dim) self.text_pos_embedding = nn.Embedding(self.max_symbols_per_phrase + 1, model_dim) self.mel_pos_embedding = nn.Embedding(self.max_mel_frames, model_dim) seq_length = 2+self.max_symbols_per_phrase+self.max_mel_frames self.gpt = GPT2Model(GPT2Config(vocab_size=self.NUMBER_TEXT_TOKENS, n_positions=seq_length, n_ctx=seq_length, n_embd=model_dim, n_layer=layers, n_head=heads)) self.final_norm = nn.LayerNorm(model_dim) self.text_head = nn.Linear(model_dim, self.NUMBER_TEXT_TOKENS) def get_logits(self, mel_inputs, text_targets): # Pad front and back. Pad at front is the "START" token. text_targets = F.pad(text_targets, (1,0), value=self.NUMBER_SYMBOLS) text_targets = F.pad(text_targets, (0, self.max_symbols_per_phrase - text_targets.shape[1])) text_emb = self.gpt.get_input_embeddings()(text_targets) text_emb = text_emb + self.text_pos_embedding(torch.arange(text_emb.shape[1], device=text_targets.device)) mel_emb = self.mel_encoder(mel_inputs) mel_emb = F.pad(mel_emb, (0, self.max_mel_frames - mel_emb.shape[-1])) mel_emb = mel_emb.permute(0,2,1).contiguous() mel_emb = mel_emb + self.mel_pos_embedding(torch.arange(mel_emb.shape[1], device=mel_emb.device)) emb = torch.cat([mel_emb, text_emb], dim=1) enc = self.gpt(inputs_embeds=emb, return_dict=True).last_hidden_state text_logits = self.final_norm(enc[:, self.max_mel_frames:]) text_logits = self.text_head(text_logits) text_logits = text_logits.permute(0,2,1) return text_logits def forward(self, mel_inputs, text_targets): text_logits = self.get_logits(mel_inputs, text_targets) loss_text = F.cross_entropy(text_logits[:,:,:-1], text_targets[:,1:].long()) return loss_text.mean(), text_logits @register_model def register_gpt_asr_hf(opt_net, opt): return GptAsrHf(**opt_get(opt_net, ['kwargs'], {})) # Quick script that loads a model and halves the number of layers, then saves that model. def distill(): gpt = GptAsrHf(max_symbols_per_phrase=250, max_mel_frames=1400, layers=12, model_dim=768, heads=12) gpt.load_state_dict(torch.load('../experiments/train_gpt_asr_mass/models/21500_mel_gen.pth')) rc = 0 i = 0 while i < len(gpt.gpt.layers.layers): if rc % 2 != 0: del gpt.gpt.layers.layers[i] else: i += 1 rc += 1 torch.save(gpt.state_dict(), '../experiments/train_gpt_asr_mass/models/21500_mel_gen_distilled.pth') if __name__ == '__main__': gpt = GptAsrHf(max_symbols_per_phrase=100, max_mel_frames=200, layers=6, model_dim=256, heads=2) l = gpt(torch.randn(2,80,800), torch.randint(high=len(symbols), size=(2,100))) ''' with torch.no_grad(): t = torch.randn(1,80,800).cuda() start = time() s = gpt.inference_beam_topk(t) print(time()-start) start = time() o = gpt.inference_beam_topk(t, fn='inference_beam_opt') print(time()-start) '''