from time import time import torch import torch.nn as nn import torch.nn.functional as F from transformers import GPT2Model, GPT2Config, GPT2LMHeadModel, GPT2PreTrainedModel from transformers.modeling_outputs import CausalLMOutputWithCrossAttentions from transformers.utils.model_parallel_utils import get_device_map, assert_device_map from models.tacotron2.text import symbols from trainer.networks import register_model from utils.audio import plot_spectrogram 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=3, padding=1), nn.GroupNorm(chan//8, chan), nn.ReLU(), nn.Conv1d(chan, chan, kernel_size=3, padding=1), nn.GroupNorm(chan//8, 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=3, padding=1), ResBlock(channels//4), nn.Conv1d(channels//4, channels//2, kernel_size=3, stride=2, padding=1), nn.GroupNorm(channels//16, channels//2), nn.ReLU(), ResBlock(channels//2), nn.Conv1d(channels//2, channels, kernel_size=3, stride=2, padding=1), nn.GroupNorm(channels//8, channels), nn.ReLU(), ResBlock(channels) ) def forward(self, x): return self.encoder(x) class GPT2InferenceModel(GPT2PreTrainedModel): def __init__(self, config, gpt, text_pos_emb, norm, linear): super().__init__(config) self.transformer = gpt self.text_pos_embedding = text_pos_emb self.lm_head = nn.Sequential(norm, linear) # Model parallel self.model_parallel = False self.device_map = None self.cached_mel_emb = None def parallelize(self, device_map=None): self.device_map = ( get_device_map(len(self.transformer.h), range(torch.cuda.device_count())) if device_map is None else device_map ) assert_device_map(self.device_map, len(self.transformer.h)) self.transformer.parallelize(self.device_map) self.lm_head = self.lm_head.to(self.transformer.first_device) self.model_parallel = True def deparallelize(self): self.transformer.deparallelize() self.transformer = self.transformer.to("cpu") self.lm_head = self.lm_head.to("cpu") self.model_parallel = False torch.cuda.empty_cache() def get_output_embeddings(self): return self.lm_head def set_output_embeddings(self, new_embeddings): self.lm_head = new_embeddings def store_mel_emb(self, mel_emb): self.cached_mel_emb = mel_emb def prepare_inputs_for_generation(self, input_ids, past=None, **kwargs): token_type_ids = kwargs.get("token_type_ids", None) # only last token for inputs_ids if past is defined in kwargs if past: input_ids = input_ids[:, -1].unsqueeze(-1) if token_type_ids is not None: token_type_ids = token_type_ids[:, -1].unsqueeze(-1) attention_mask = kwargs.get("attention_mask", None) position_ids = kwargs.get("position_ids", None) if attention_mask is not None and position_ids is None: # create position_ids on the fly for batch generation position_ids = attention_mask.long().cumsum(-1) - 1 position_ids.masked_fill_(attention_mask == 0, 1) if past: position_ids = position_ids[:, -1].unsqueeze(-1) else: position_ids = None return { "input_ids": input_ids, "past_key_values": past, "use_cache": kwargs.get("use_cache"), "position_ids": position_ids, "attention_mask": attention_mask, "token_type_ids": token_type_ids, } def forward( self, input_ids=None, past_key_values=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None, encoder_hidden_states=None, encoder_attention_mask=None, labels=None, use_cache=None, output_attentions=None, output_hidden_states=None, return_dict=None, ): assert self.cached_mel_emb is not None assert inputs_embeds is None # Not supported by this inference model. assert labels is None # Training not supported by this inference model. return_dict = return_dict if return_dict is not None else self.config.use_return_dict # Create embedding mel_len = self.cached_mel_emb.shape[1] if input_ids.shape[1] != 1: text_inputs = input_ids[:, mel_len:] text_emb = self.transformer.get_input_embeddings()(text_inputs) text_emb = text_emb + self.text_pos_embedding(torch.arange(text_emb.shape[1], device=text_emb.device)) if self.cached_mel_emb.shape[0] != text_emb.shape[0]: mel_emb = self.cached_mel_emb.repeat_interleave(text_emb.shape[0]//self.cached_mel_emb.shape[0], 0) else: mel_emb = self.cached_mel_emb emb = torch.cat([mel_emb, text_emb], dim=1) else: emb = self.transformer.get_input_embeddings()(input_ids) + \ self.text_pos_embedding(torch.tensor(attention_mask.shape[1]-mel_len, device=attention_mask.device)).unsqueeze(0).unsqueeze(0) transformer_outputs = self.transformer( inputs_embeds=emb, past_key_values=past_key_values, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) hidden_states = transformer_outputs[0] # Set device for model parallelism if self.model_parallel: torch.cuda.set_device(self.transformer.first_device) hidden_states = hidden_states.to(self.lm_head.weight.device) lm_logits = self.lm_head(hidden_states) if not return_dict: return (lm_logits,) + transformer_outputs[1:] return CausalLMOutputWithCrossAttentions( loss=None, logits=lm_logits, past_key_values=transformer_outputs.past_key_values, hidden_states=transformer_outputs.hidden_states, attentions=transformer_outputs.attentions, cross_attentions=transformer_outputs.cross_attentions, ) @staticmethod def _reorder_cache(past, beam_idx): """ This function is used to re-order the :obj:`past_key_values` cache if :meth:`~transformers.PreTrainedModel.beam_search` or :meth:`~transformers.PreTrainedModel.beam_sample` is called. This is required to match :obj:`past_key_values` with the correct beam_idx at every generation step. """ return tuple( tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past) for layer_past in past ) class GptAsrHf2(nn.Module): NUMBER_SYMBOLS = len(symbols) START_TOKEN = NUMBER_SYMBOLS NUMBER_TEXT_TOKENS = NUMBER_SYMBOLS+2 def __init__(self, layers=8, model_dim=512, heads=8, max_symbols_per_phrase=800, max_mel_frames=3000, checkpointing=True): 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_config = 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, gradient_checkpointing=checkpointing, use_cache=not checkpointing) self.gpt = GPT2Model(self.gpt_config) 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, get_attns=False): # Pad front remove last element to set up next token prediction. Pad at front is the "START" token. text_inputs = F.pad(text_targets, (1,0), value=self.START_TOKEN)[:, :-1] text_emb = self.gpt.get_input_embeddings()(text_inputs) text_emb = text_emb + self.text_pos_embedding(torch.arange(text_emb.shape[1], device=text_inputs.device)) mel_emb = self.mel_encoder(mel_inputs) 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) gpt_out = self.gpt(inputs_embeds=emb, return_dict=True, output_attentions=get_attns) if get_attns: return gpt_out.attentions enc = gpt_out.last_hidden_state text_logits = self.final_norm(enc[:, mel_emb.shape[1]:]) 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, return_attentions=False): text_targets = F.pad(text_targets, (0,1)) # Pad the targets with a <0> so that all have a "stop" token. text_logits = self.get_logits(mel_inputs, text_targets, get_attns=return_attentions) if return_attentions: return text_logits # These weren't really the logits. loss_text = F.cross_entropy(text_logits, text_targets.long()) return loss_text.mean(), text_logits def inference(self, mel_inputs, cond_text=None, do_sample=False, temperature=1.0, num_beams=8): if not hasattr(self, 'inference_model'): self.inference_model = GPT2InferenceModel(self.gpt_config, self.gpt, self.text_pos_embedding, self.final_norm, self.text_head) mel_emb = self.mel_encoder(mel_inputs) assert mel_emb.shape[-1] <= self.max_mel_frames 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)) self.inference_model.store_mel_emb(mel_emb) # "fake_inputs" are stand-ins for the MEL frames, which will be injected with the prep_inputs function above. if cond_text is None: fake_inputs = torch.full((mel_inputs.shape[0],self.max_mel_frames+1,), fill_value=1, dtype=torch.long, device=mel_inputs.device) fake_inputs[:,-1] = self.NUMBER_SYMBOLS else: cond_used = 10 fake_inputs = torch.full((mel_inputs.shape[0],self.max_mel_frames+1+cond_used,), fill_value=1, dtype=torch.long, device=mel_inputs.device) fake_inputs[:,-1-cond_used] = self.NUMBER_SYMBOLS fake_inputs[:, -cond_used:] = cond_text[:, :cond_used] gen = self.inference_model.generate(fake_inputs, do_sample=do_sample, bos_token_id=self.NUMBER_SYMBOLS, pad_token_id=0, eos_token_id=0, max_length=self.max_symbols_per_phrase+self.max_mel_frames, temperature=temperature, num_beams=num_beams, use_cache=True) return gen[:, self.max_mel_frames:] @register_model def register_gpt_asr_hf2(opt_net, opt): return GptAsrHf2(**opt_get(opt_net, ['kwargs'], {})) # Quick script that loads a model and halves the number of layers, then saves that model. def distill(): gpt = GptAsrHf2(max_symbols_per_phrase=250, max_mel_frames=1400, layers=12, model_dim=512, heads=8) gpt.load_state_dict(torch.load('X:\\dlas\\experiments\\train_gpt_asr_mass_hf\\models\\48000_gpt_ema.pth')) rc = 0 i = 0 while i < len(gpt.gpt.h): if rc % 2 != 0: del gpt.gpt.h[i] else: i += 1 rc += 1 torch.save(gpt.state_dict(), 'X:\\dlas\\experiments\\train_gpt_asr_mass_hf\\models\\48000_gpt_distilled.pth') if __name__ == '__main__': #distill() gpt = GptAsrHf2(max_symbols_per_phrase=250, max_mel_frames=1400, layers=16, model_dim=512, heads=8) l = gpt(torch.randn(2,80,800), torch.randint(high=len(symbols), size=(2,100))) #start = time() #gpt.inference(torch.randn(1,80,350), num_beams=1) #print(f"Elapsed: {time()-start}")