DL-Art-School/codes/models/audio/tts/transformer_builders.py

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"""
A list of functions that map a unified set of arguments to a fully built transformer. Also includes some testing
utilities for measuring parameter count, FLOPS, and general performance of each type.
Every function contains the following arguments:
layers: Net number of layers in the transformer.
model_dim: Hidden dimensionality of the model.
heads: Number of attention heads.
max_mel_seq_len: Maximum mel sequence length to attend to.
max_text_seq_len: Maximum text sequence length to attend to.
checkpointing: Whether or not the underlying implementation should support gradient checkpointing.
Returns:
(model, global_mel_pos_embedding, global_text_pos_embedding, local_mel_pos_embedding, local_text_pos_embedding)
model: The transformer model
global_mel_pos_embedding: A global embedding function (that takes the MEL sequence as input) which should be added on to the MEL embeddings.
global_text_pos_embedding: The global embedding function for text tokens.
local_mel_pos_embedding: A local embedding function which, if not None, should be concatenated with the local text position embeddings and fed to the transformer.
local_text_pos_embedding: The local embedding function for text positions which will be None if local_mel_pos_embedding=None.
"""
import functools
import random
from time import time
import torch
import torch.nn as nn
from tqdm import tqdm
def null_position_embeddings(range, dim):
return torch.zeros((range.shape[0], range.shape[1], dim), device=range.device)
class LearnedPositionEmbeddings(nn.Module):
def __init__(self, seq_len, model_dim, init=.02, relative=True):
super().__init__()
self.emb = nn.Embedding(seq_len, model_dim)
# Initializing this way is standard for GPT-2
self.emb.weight.data.normal_(mean=0.0, std=init)
self.relative = relative
self.seq_len = seq_len
def forward(self, x):
sl = x.shape[1]
if self.relative:
start = random.randint(sl, self.seq_len) - sl
return self.emb(torch.arange(start, start+sl, device=x.device))
else:
return self.emb(torch.arange(0, sl, device=x.device))
def get_fixed_embedding(self, ind, dev):
return self.emb(torch.tensor([ind], device=dev)).unsqueeze(0)
def build_hf_gpt_transformer(layers, model_dim, heads, max_mel_seq_len, max_text_seq_len, checkpointing):
"""
GPT-2 implemented by the HuggingFace library.
"""
from transformers import GPT2Config, GPT2Model
gpt_config = GPT2Config(vocab_size=256, # Unused.
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n_positions=1,
n_ctx=1,
n_embd=model_dim,
n_layer=layers,
n_head=heads,
gradient_checkpointing=checkpointing,
use_cache=not checkpointing)
gpt = GPT2Model(gpt_config)
# Override the built in positional embeddings
del gpt.wpe
gpt.wpe = functools.partial(null_position_embeddings, dim=model_dim)
# Built-in token embeddings are unused.
del gpt.wte
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mel_pos_emb = LearnedPositionEmbeddings(max_mel_seq_len, model_dim) if max_mel_seq_len != -1 else functools.partial(null_position_embeddings, dim=model_dim)
text_pos_emb = LearnedPositionEmbeddings(max_text_seq_len, model_dim) if max_mel_seq_len != -1 else functools.partial(null_position_embeddings, dim=model_dim)
return gpt, mel_pos_emb, text_pos_emb, None, None
def build_lr_performer(layers, model_dim, heads, max_mel_seq_len, max_text_seq_len, checkpointing):
"""
lucidrains Performer implementation, https://github.com/lucidrains/performer-pytorch
"""
from models.lucidrains.performer.performer_pytorch import Performer
model = Performer(dim=model_dim, depth=layers, heads=heads, dim_head=model_dim, causal=True)
return model
def build_lr_reformer(layers, model_dim, heads, max_mel_seq_len, max_text_seq_len, checkpointing):
"""
lucidrains Reformer implementation, https://github.com/lucidrains/reformer-pytorch
"""
pass
def build_lr_xformer(layers, model_dim, heads, max_mel_seq_len, max_text_seq_len, checkpointing):
"""
lucidrains x-transformer implementation, https://github.com/lucidrains/x-transformers
"""
pass
def test_all_performance(**kwargs):
transformer_builders = [#build_hf_gpt_transformer,
build_lr_performer,]
# build_lr_reformer,
# build_lr_xformer]
for builder in transformer_builders:
model = builder(**kwargs)
start = time()
args = torch.randint(0, 8192, (16,450))
for k in tqdm(range(10)):
model(args)
stop = time()
print(f"Model: {str(builder)}; Elapsed: {stop-start}")
if __name__ == '__main__':
test_all_performance(layers=12, model_dim=512, heads=8, num_tokens=8192, max_seq_len=1000, checkpointing=False)