From 555b7e52ad98ff90a0c3458731078b9261849b14 Mon Sep 17 00:00:00 2001 From: James Betker Date: Thu, 18 Nov 2021 20:02:24 -0700 Subject: [PATCH] Add rev2 of GptAsrHf --- codes/models/gpt_voice/gpt_asr_hf2.py | 310 ++++++++++++++++++++++++++ 1 file changed, 310 insertions(+) create mode 100644 codes/models/gpt_voice/gpt_asr_hf2.py diff --git a/codes/models/gpt_voice/gpt_asr_hf2.py b/codes/models/gpt_voice/gpt_asr_hf2.py new file mode 100644 index 00000000..95e78e89 --- /dev/null +++ b/codes/models/gpt_voice/gpt_asr_hf2.py @@ -0,0 +1,310 @@ +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.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) + NUMBER_TEXT_TOKENS = NUMBER_SYMBOLS+1 + + 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_targets = F.pad(text_targets, (1,0), value=self.NUMBER_SYMBOLS)[:, :-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 = 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}")