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new autoregressive check-in

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This commit is contained in:
James Betker 2022-04-07 22:18:56 -07:00
parent 33e4bc7907
commit 73e9929825
3 changed files with 1309 additions and 70 deletions
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4
api_new_autoregressive.py
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@ -134,8 +134,8 @@ class TextToSpeech:
self.tokenizer = VoiceBpeTokenizer()
download_models()
self.autoregressive = AutoregressiveCodegen(512, 12).cpu().eval()
self.autoregressive.load_state_dict(torch.load('D:\\dlas\\experiments\\train_autoregressive_codegen\\models\\23000_codegen_ema.pth'))
self.autoregressive = AutoregressiveCodegen(1024, 16).cpu().eval()
self.autoregressive.load_state_dict(torch.load('X:\\dlas\\experiments\\train_autoregressive_codegen\\models\\11000_codegen_ema.pth'))
self.clip = VoiceCLIP(dim_text=512, dim_speech=512, dim_latent=512, num_text_tokens=256, text_enc_depth=12,
text_seq_len=350, text_heads=8,

114
models/new_autoregressive.py
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@ -1,11 +1,9 @@
import functools
import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers import GPT2PreTrainedModel, GPT2Config
from models.xtransformers import TransformerWrapper, Encoder, Decoder
from transformers.modeling_outputs import CausalLMOutputWithCrossAttentions
from x_transformers import TransformerWrapper, Encoder, Decoder
from models.arch_util import AttentionBlock
@ -87,8 +85,8 @@ class InferenceModel(GPT2PreTrainedModel):
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
hidden_states = self.transformer.decoder(input_ids, context=self.context, return_embeddings=True)
logits = self.transformer.decoder.transformer.to_logits(hidden_states)
hidden_states = self.transformer.decoder(input_ids, full_context=self.context, return_embeddings=True)
logits = self.transformer.decoder.to_logits(hidden_states)
if not return_dict:
return (logits, )
@ -157,54 +155,22 @@ class ConditioningEncoder(nn.Module):
return h.mean(dim=2)
class CheckpointedLayer(nn.Module):
"""
Wraps a module. When forward() is called, passes kwargs that require_grad through torch.checkpoint() and bypasses
checkpoint for all other args.
"""
def __init__(self, wrap):
super().__init__()
self.wrap = wrap
def forward(self, x, *args, **kwargs):
for k, v in kwargs.items():
assert not (isinstance(v, torch.Tensor) and v.requires_grad) # This would screw up checkpointing.
partial = functools.partial(self.wrap, **kwargs)
return torch.utils.checkpoint.checkpoint(partial, x, *args)
class CheckpointedXTransformerWrapper(nn.Module):
"""
Wraps a TransformerWrapper and applies CheckpointedLayer to each layer.
"""
def __init__(self, checkpoint=True, **xtransformer_kwargs):
super().__init__()
self.transformer = TransformerWrapper(**xtransformer_kwargs)
if not checkpoint:
return
for i in range(len(self.transformer.attn_layers.layers)):
n, b, r = self.transformer.attn_layers.layers[i]
self.transformer.attn_layers.layers[i] = nn.ModuleList([n, CheckpointedLayer(b), r])
def forward(self, x, **kwargs):
return self.transformer(x, **kwargs)
class AutoregressiveCodegen(nn.Module):
def __init__(self, model_dim, depth, num_text_tokens=256, num_mel_tokens=8194, max_text_tokens=4000,
max_mel_tokens=4000, dropout=.1):
def __init__(self, model_dim, depth, num_text_tokens=256, num_mel_tokens=8194, dropout=.1):
super().__init__()
assert depth >= 8 # This is the minimum bound to support the context interleaving that happens later.
self.START_TOKEN=8192
self.STOP_TOKEN=8193
self.max_mel_tokens = max_mel_tokens
self.minicoder = ConditioningEncoder(80, model_dim, do_checkpointing=False)
self.encoder = CheckpointedXTransformerWrapper(
self.max_text_token_id = num_text_tokens
self.max_mel_token_id = num_mel_tokens
self.mel_embedding = ConditioningEncoder(80, model_dim, do_checkpointing=False)
self.encoder = TransformerWrapper(
num_tokens=num_text_tokens,
max_seq_len=max_text_tokens,
use_pos_emb=False,
max_seq_len=-1,
attn_layers = Encoder(
depth=depth//2,
depth=depth,
heads=model_dim//64,
dim=model_dim,
attn_dropout=dropout,
@ -213,11 +179,14 @@ class AutoregressiveCodegen(nn.Module):
ff_glu=True,
ff_mult=1,
rotary_pos_emb=True,
rel_pos_bias=True,
attn_rel_pos_bias=True,
))
self.decoder = CheckpointedXTransformerWrapper(
self.encoder.norm = nn.Identity() # This layer and the next are unused.
self.encoder.to_logits = nn.Identity()
self.decoder = TransformerWrapper(
num_tokens=num_mel_tokens,
max_seq_len=max_mel_tokens,
use_pos_emb=False,
max_seq_len=-1,
attn_layers=Decoder(
depth=depth,
heads=model_dim//64,
@ -228,18 +197,21 @@ class AutoregressiveCodegen(nn.Module):
ff_glu=True,
ff_mult=1,
rotary_pos_emb=True,
rel_pos_bias=True,
cross_attend=True,
attn_rel_pos_bias=True,
))
def get_grad_norm_parameter_groups(self):
return {
'encoder': list(self.encoder.parameters()),
'decoder': list(self.decoder.parameters()),
'minicoder': list(self.minicoder.parameters()),
'minicoder': list(self.mel_embedding.parameters()),
}
def forward(self, text_codes, conditioning_signal, mel_codes, wav_lengths, return_loss=True):
assert text_codes.max() < self.max_text_token_id and text_codes.min() >= 0, f'Invalid text code encountered: {text_codes.max()}, {text_codes.min()}'
assert mel_codes.max() < self.max_mel_token_id and mel_codes.min() >= 0, f'Invalid mel code encountered: {mel_codes.max()}, {mel_codes.min()}'
# Format mel_codes with a stop token on the end.
mel_lengths = wav_lengths // 1024 + 1
for b in range(mel_codes.shape[0]):
@ -251,42 +223,50 @@ class AutoregressiveCodegen(nn.Module):
conditioning_signal = conditioning_signal.unsqueeze(1)
cond_embs = []
for i in range(conditioning_signal.shape[1]):
cond_embs.append(self.minicoder(conditioning_signal[:, i]))
cond_embs.append(self.mel_embedding(conditioning_signal[:, i]))
cond_emb = torch.stack(cond_embs, dim=1).mean(dim=1, keepdim=True)
enc_text = self.encoder(text_codes, return_embeddings=True)
context = torch.cat([cond_emb, enc_text], dim=1)
_, enc_text = self.encoder(text_codes, return_hiddens=True)
# Interleave cond_emb into the first few contexts.
full_context = enc_text
full_context[1] = cond_emb
full_context[3] = cond_emb
full_context[6] = cond_emb
# Execute the decoder
dec_inputs = F.pad(mel_codes, (1,0), value=self.START_TOKEN)[:, :-1]
dec = self.decoder(dec_inputs, context=context)
dec = self.decoder(dec_inputs, full_context=full_context)
if not return_loss:
return dec
loss_mel = F.cross_entropy(dec.permute(0,2,1), mel_codes)
return loss_mel
def generate(self, conditioning_signal, text_codes, **hf_generate_kwargs):
if not hasattr(self, 'inference_model'):
self.inference_model = InferenceModel(self)
def generate(self, conditioning_signal, text_codes, max_tokens=256, **hf_generate_kwargs):
inference_model = InferenceModel(self)
# Build the context
if len(conditioning_signal.shape) != 4:
conditioning_signal = conditioning_signal.unsqueeze(1)
cond_embs = []
for i in range(conditioning_signal.shape[1]):
cond_embs.append(self.minicoder(conditioning_signal[:, i]))
cond_embs.append(self.mel_embedding(conditioning_signal[:, i]))
cond_emb = torch.stack(cond_embs, dim=1).mean(dim=1, keepdim=True)
enc_text = self.encoder(text_codes, return_embeddings=True)
context = torch.cat([cond_emb, enc_text], dim=1)
self.inference_model.store_context(context)
_, enc_text = self.encoder(text_codes, return_hiddens=True)
# Interleave cond_emb into the first few contexts.
full_context = enc_text
full_context[1] = cond_emb
full_context[3] = cond_emb
full_context[6] = cond_emb
inference_model.store_context(full_context)
gen = self.inference_model.generate(bos_token_id=self.START_TOKEN, pad_token_id=self.STOP_TOKEN, eos_token_id=self.STOP_TOKEN,
max_length=250, output_attentions=False, return_dict_in_generate=True,
gen = inference_model.generate(bos_token_id=self.START_TOKEN, pad_token_id=self.STOP_TOKEN, eos_token_id=self.STOP_TOKEN,
max_length=max_tokens, output_attentions=False, return_dict_in_generate=True,
**hf_generate_kwargs)
return gen.sequences
if __name__ == '__main__':
codegen = AutoregressiveCodegen(1024, 20)
codegen.generate(torch.randn((1,80,120)), torch.randint(0,256,(1,200)))
codegen = AutoregressiveCodegen(256, 10)
torch.save(codegen.state_dict(), 'sample.pth')
#codegen.generate(torch.randn((1,80,120)), torch.randint(0,256,(1,200)))
codegen(torch.randint(0,256, (2,200)),
torch.randn(2,80,120),
torch.randint(0,8192, (2,350)),

1259
models/xtransformers.py Normal file
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