train_belief_state.py

from x_transformers import TransformerWrapper, Decoder, BeliefStateWrapper
from x_transformers.autoregressive_wrapper import AutoregressiveWrapper

import random
import tqdm
import gzip
import numpy as np
import torch
import torch.optim as optim
from torch.nn import functional as F
from torch.utils.data import DataLoader, Dataset

# constants

NUM_BATCHES = int(1e5)
BATCH_SIZE = 2
GRADIENT_ACCUMULATE_EVERY = 8
LEARNING_RATE = 1e-4
VALIDATE_EVERY  = 100
GENERATE_EVERY  = 500
GENERATE_LENGTH = 256
SEQ_LEN = 256

FORWARD_BACKWARD_SAME_MODEL = True

# helpers

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)))

# instantiate GPT-like decoder model for forward and backwards

forward_model = TransformerWrapper(
    num_tokens = 256,
    max_seq_len = SEQ_LEN,
    attn_layers = Decoder(
        dim = 512,
        depth = 6,
        heads = 8,
        rotary_pos_emb = True
    )
)

backward_model = None

if not FORWARD_BACKWARD_SAME_MODEL:
    backward_model = TransformerWrapper(
        num_tokens = 256,
        max_seq_len = SEQ_LEN,
        attn_layers = Decoder(
            dim = 512,
            depth = 4, # do a smaller backwards
            heads = 8,
            rotary_pos_emb = True
        )
    )

model = BeliefStateWrapper(
    forward_decoder = forward_model,
    backward_decoder = backward_model
)

model.cuda()

# prepare enwik8 data

with gzip.open('./data/enwik8.gz') as file:
    data = np.frombuffer(file.read(int(95e6)), dtype=np.uint8).copy()
    train_x, valid_x = np.split(data, [int(90e6)])
    data_train, data_val = torch.from_numpy(train_x), torch.from_numpy(valid_x)

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, (1,))
        full_seq = self.data[rand_start: rand_start + self.seq_len + 1].long()
        return full_seq.cuda()

    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  = cycle(DataLoader(train_dataset, batch_size = BATCH_SIZE, drop_last = True))
val_loader    = cycle(DataLoader(val_dataset, batch_size = BATCH_SIZE, drop_last = True))

# optimizer

optim = torch.optim.Adam(model.parameters(), lr=LEARNING_RATE)

# training

for i in tqdm.tqdm(range(NUM_BATCHES), mininterval = 10., desc = 'training'):
    model.train()

    for __ in range(GRADIENT_ACCUMULATE_EVERY):
        loss = model(next(train_loader))
        (loss / GRADIENT_ACCUMULATE_EVERY).backward()

    print(f'training loss: {loss.item()}')
    torch.nn.utils.clip_grad_norm_(model.parameters(), 0.5)
    optim.step()
    optim.zero_grad()

    if i % VALIDATE_EVERY == 0:
        model.eval()
        with torch.no_grad():
            loss = model(next(val_loader))
            print(f'validation loss: {loss.item()}')

    if i % GENERATE_EVERY == 0:
        model.eval()
        inp = random.choice(val_dataset)[:-1]
        prime = decode_tokens(inp)

        print(f'%s \n\n %s', (prime, '*' * 100))

        print('forwards:\n')

        sample = model.generate_with_suffix_cond(
            prompts = inp,
            seq_len = GENERATE_LENGTH,
            cache_kv = True
        )

        output_str = decode_tokens(sample)
        print(output_str)

        print('\nbackwards:\n')

        sample = model.generate_with_suffix_cond(
            prompts = inp,
            seq_len = GENERATE_LENGTH,
            cache_kv = True,
            decode_backwards = True
        )

        output_str = decode_tokens(sample.flip(0))
        print(output_str)