DL-Art-School/codes/models/gpt_voice/gpt_asr_hf.py

314 lines
13 KiB
Python
Raw Normal View History

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),
2021-11-02 00:43:11 +00:00
nn.GroupNorm(chan//8, chan),
nn.ReLU(),
nn.Conv1d(chan, chan, kernel_size=3, padding=1),
2021-11-02 00:43:11 +00:00
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=5, padding=2),
ResBlock(channels//4),
ResBlock(channels//4),
nn.Conv1d(channels//4, channels//2, kernel_size=3, stride=2, padding=1),
2021-11-02 00:43:11 +00:00
nn.GroupNorm(channels//16, channels//2),
nn.ReLU(),
ResBlock(channels//2),
ResBlock(channels//2),
nn.Conv1d(channels//2, channels, kernel_size=3, stride=2, padding=1),
2021-11-02 00:43:11 +00:00
nn.GroupNorm(channels//8, channels),
nn.ReLU(),
ResBlock(channels),
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(text_emb.shape[0], 1, 1)
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 GptAsrHf(nn.Module):
NUMBER_SYMBOLS = len(symbols)
NUMBER_TEXT_TOKENS = NUMBER_SYMBOLS+1
2021-11-02 00:43:11 +00:00
def __init__(self, layers=8, model_dim=512, heads=8, max_symbols_per_phrase=200, max_mel_frames=1000, 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,
2021-11-02 00:43:11 +00:00
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):
# 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, 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((1,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((1,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=False)
return gen[:, self.max_mel_frames:]
@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)
'''