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train.py
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train.py
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import math
import gzip
import random
import tqdm
import numpy as np
from functools import wraps, partial
import torch
from torch.optim import Adam, AdamW
from torch import Tensor
from torch.nn import Module, functional as F
from torch.utils.data import DataLoader, Dataset
from accelerate import Accelerator
from gateloop_transformer import Transformer
# constants
NUM_BATCHES = int(1e5)
BATCH_SIZE = 4
GRAD_ACCUM_EVERY = 4
LEARNING_RATE = 1e-4
WEIGHT_DECAY = 0.
VALIDATE_EVERY = 100
PRIME_LENGTH = 128
GENERATE_EVERY = 500
GENERATE_LENGTH = 512
SEQ_LEN = 256
WANDB = True
PROJECT_NAME = 'gateloop'
RUN_NAME = 'baseline gateloop'
# hf accelerate
accelerator = Accelerator(log_with = 'wandb' if WANDB else None)
# helpers
def exists(v):
return v is not None
def cycle(loader):
while True:
for data in loader:
yield data
def decode_token(token):
return str(chr(max(32, token)))
def decode_tokens(tokens):
return "".join(list(map(decode_token, tokens)))
# sampling helpers
def log(t, eps = 1e-20):
return torch.log(t.clamp(min = eps))
def gumbel_noise(t):
noise = torch.zeros_like(t).uniform_(0, 1)
return -log(-log(noise))
def gumbel_sample(t, temperature = 1., dim = -1):
return ((t / max(temperature, 1e-10)) + gumbel_noise(t)).argmax(dim = dim)
def top_k(logits, thres = 0.9):
k = math.ceil((1 - thres) * logits.shape[-1])
val, ind = torch.topk(logits, k)
probs = torch.full_like(logits, float('-inf'))
probs.scatter_(-1, ind, val)
return probs
def base_decoding(
net: Module,
prompt: Tensor,
seq_len: int,
temperature = 1.,
filter_thres = 0.9,
):
prompt_seq_len, out = prompt.shape[-1], prompt.clone()
sample_num_times = max(0, seq_len - prompt_seq_len)
for _ in range(sample_num_times):
logits = net(out)
logits = logits[:, -1]
logits = top_k(logits, thres = filter_thres)
sample = gumbel_sample(logits, temperature = temperature, dim = -1)
out = torch.cat((out, sample[..., None]), dim = -1)
return out[..., prompt_seq_len:]
# optimizer
def separate_weight_decayable_params(params):
wd_params, no_wd_params = [], []
for param in params:
param_list = no_wd_params if param.ndim < 2 else wd_params
param_list.append(param)
return wd_params, no_wd_params
def get_optimizer(
params,
lr = 1e-4,
wd = 0.,
betas = (0.9, 0.99),
eps = 1e-8,
group_wd_params = True,
**kwargs
):
opt_kwargs = dict(lr = lr, betas = betas, eps = eps)
if wd == 0:
return Adam(params, **opt_kwargs)
opt_kwargs = {'weight_decay': wd, **opt_kwargs}
if not group_wd_params:
return AdamW(params, **opt_kwargs)
wd_params, no_wd_params = separate_weight_decayable_params(params)
params = [
{'params': wd_params},
{'params': no_wd_params, 'weight_decay': 0},
]
return AdamW(params, **opt_kwargs)
# instantiate transformer
hparams = dict(
num_tokens = 256,
dim = 512,
depth = 6,
use_gate_looped_attn = True,
gate_loop_heads = 512, # in paper, they used heads == dim, but should experiment with less heads, as memory allows. we should figure out how much max-heads contributed to the performance, if any
data_dependent_rel_pos = False,
attn_softmax_normalize = True,
ablate_complex = False,
ablate_state_transition = False,
rotary_emb = False,
post_ln_norm = True
)
model = Transformer(**hparams)
# initialize experiment tracking
num_parameters = sum(p.numel() for p in model.parameters())
print(f'number of parameters: {num_parameters}')
wandb_config = {**hparams, 'num_parameters': num_parameters}
accelerator.init_trackers(PROJECT_NAME, config = wandb_config)
if WANDB and exists(RUN_NAME) and len(accelerator.trackers) > 0:
accelerator.trackers[0].run.name = RUN_NAME
# prepare enwik8 data
with gzip.open("./data/enwik8.gz") as file:
data = np.frombuffer(file.read(int(95e6)), dtype=np.uint8).copy()
np_train, np_valid = np.split(data, [int(90e6)])
data_train, data_val = torch.from_numpy(np_train), torch.from_numpy(np_valid)
class TextSamplerDataset(Dataset):
def __init__(self, data, seq_len):
super().__init__()
self.data = data
self.seq_len = seq_len
def __getitem__(self, index):
rand_start = torch.randint(0, self.data.size(0) - self.seq_len, (1,))
full_seq = self.data[rand_start : rand_start + self.seq_len + 1].long()
return full_seq
def __len__(self):
return self.data.size(0) // self.seq_len
train_dataset = TextSamplerDataset(data_train, SEQ_LEN)
val_dataset = TextSamplerDataset(data_val, SEQ_LEN)
train_loader = DataLoader(train_dataset, batch_size=BATCH_SIZE)
val_loader = DataLoader(val_dataset, batch_size=BATCH_SIZE)
# optimizer
optim = get_optimizer(
model.parameters(),
lr = LEARNING_RATE,
wd = WEIGHT_DECAY
)
# prepare
(
model,
optim,
train_loader,
val_loader
) = accelerator.prepare(
model,
optim,
train_loader,
val_loader
)
train_loader = cycle(train_loader)
val_loader = cycle(val_loader)
# training
for i in tqdm.tqdm(range(NUM_BATCHES), mininterval = 10.0, desc = "training"):
model.train()
for _ in range(GRAD_ACCUM_EVERY):
data = next(train_loader)
loss = model(data, return_loss = True)
accelerator.backward(loss / GRAD_ACCUM_EVERY)
print(f"training loss: {loss.item():.3f}")
accelerator.log(dict(loss = loss.item()), step = i)
torch.nn.utils.clip_grad_norm_(model.parameters(), 0.5)
optim.step()
optim.zero_grad()
accelerator.wait_for_everyone()
if i % VALIDATE_EVERY == 0:
model.eval()
with torch.no_grad():
valid_data = next(val_loader)
loss = model(valid_data, return_loss = True)
print(f"validation loss: {loss.item():.3f}")
accelerator.log(dict(valid_loss = loss.item()), step = i)
accelerator.wait_for_everyone()
if i % GENERATE_EVERY == 0:
model.eval()
inp = random.choice(val_dataset)[:PRIME_LENGTH]
inp = inp.to(accelerator.device)
prime = decode_tokens(inp)
print(f"%s \n\n %s", (prime, "*" * 100))
prompt = inp[None, ...]
sampled = base_decoding(model, prompt, GENERATE_LENGTH)
base_decode_output = decode_tokens(sampled[0])
print("\n\n", base_decode_output, "\n")