gclm_train_example.py

print("Starting...")

###############################################
# CONFIGURATION — CUSTOMIZE EVERYTHING HERE
###############################################

# ---- data / vocab ----
TXT_PATH = "data.txt"
TOKENIZER_NAME = "gpt2"
REDUCE_VOCAB = True
VOCAB_SAVE_PATH = "vocab_map.pt"

# ---- training ----
EPOCHS = 25
MICRO_BATCH_SIZE = 1
GRAD_ACCUM_STEPS = 8
LEARNING_RATE = 3e-4

# ---- model ----
D_MODEL = 256
N_LAYERS = 4
MAX_SEQ_LEN = 8192

LOCAL_KERNEL_SIZE = 5
GLOBAL_KERNEL_SIZE = 256
USE_GLOBAL_EVERY_N_LAYERS = 2

# ---- FFT conv ----
FFT_SIZE = 1024   # must be power of 2 and > GLOBAL_KERNEL_SIZE

# ---- checkpointing ----
SAVE_PATH = "model.pt"
SAVE_N_EPOCHS = 1

# ---- device ----
USE_DEVICE = "cuda"
USE_AMP = True
USE_ACTIVATION_CHECKPOINTING = False

# ---- torch.compile ----
COMPILE = False
COMPILE_MODE = "reduce-overhead"
COMPILE_BACKEND = "eager"

###############################################
# END CONFIG
###############################################

import os

# Windows cannot use expandable_segments — only enable on Linux.
if os.name != "nt":
    os.environ.setdefault("PYTORCH_CUDA_ALLOC_CONF", "expandable_segments:True")

import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import Dataset, DataLoader
from tqdm import tqdm
import tiktoken

# performance settings
torch.set_float32_matmul_precision("high")
torch.backends.cuda.matmul.allow_tf32 = True
torch.backends.cudnn.allow_tf32 = True

###############################################################
# SPECIAL TOKENS
###############################################################

PAD_ID = 0
SEP_ID = 1
EOS_ID = 2
OFFSET = 3

###############################################################
# VOCAB
###############################################################

def build_dataset_vocab(txt_path, tokenizer, save_path):
    text = open(txt_path, "r", encoding="utf-8").read()
    token_ids = tokenizer.encode(text)
    used = sorted(set(token_ids))

    id2new = {tok: i + OFFSET for i, tok in enumerate(used)}

    torch.save({
        "used_tokens": used,
        "id2new": id2new,
        "PAD_ID": PAD_ID,
        "SEP_ID": SEP_ID,
        "EOS_ID": EOS_ID,
    }, save_path)

    print(f"[OK] Vocab size: {len(used) + OFFSET}")
    return used, id2new


###############################################################
# DATASET
###############################################################

class RemappedTextDataset(Dataset):
    def __init__(self, path, tokenizer, id2new, max_len):
        text = open(path, "r", encoding="utf-8").read()
        raw = tokenizer.encode(text)
        self.ids = [id2new.get(i, PAD_ID) for i in raw] + [EOS_ID]
        self.max_len = max_len

    def __len__(self):
        return len(self.ids) - self.max_len - 1

    def __getitem__(self, i):
        x = self.ids[i:i+self.max_len]
        y = self.ids[i+1:i+self.max_len+1]
        return torch.tensor(x), torch.tensor(y)


###############################################################
# GLOBAL + LOCAL CONVOLUTION
###############################################################

class GlobalConv1D(nn.Module):
    def __init__(self, d_model, kernel_size, fft_size):
        super().__init__()
        self.kernel = nn.Parameter(torch.randn(d_model, kernel_size) * 0.01)
        self.kernel_size = kernel_size
        self.fft_size = fft_size

    def forward(self, x):
        B, C, T = x.shape
        K = min(self.kernel_size, T)

        overlap = K - 1
        block = self.fft_size - overlap

        x = F.pad(x, (overlap, 0))
        k = self.kernel[:, :K]
        k = F.pad(k, (0, self.fft_size - K))
        k_f = torch.fft.rfft(k, n=self.fft_size)

        outs = []
        pos = 0
        while pos < T:
            seg = x[..., pos:pos+self.fft_size]
            if seg.shape[-1] < self.fft_size:
                seg = F.pad(seg, (0, self.fft_size - seg.shape[-1]))

            y = torch.fft.irfft(
                torch.fft.rfft(seg, n=self.fft_size) * k_f.unsqueeze(0),
                n=self.fft_size
            )
            outs.append(y[..., overlap:overlap+block])
            pos += block

        return torch.cat(outs, dim=-1)[..., :T]


class LocalConv1D(nn.Module):
    def __init__(self, d_model, k):
        super().__init__()
        self.k = k
        self.dw = nn.Conv1d(d_model, d_model, k, groups=d_model)
        self.pw = nn.Conv1d(d_model, d_model, 1)

    def forward(self, x):
        x = F.pad(x, (self.k - 1, 0))
        return self.pw(F.relu(self.dw(x)))


class Block(nn.Module):
    def __init__(self, d_model, use_global):
        super().__init__()
        self.use_global = use_global

        self.ln1 = nn.LayerNorm(d_model)
        self.local = LocalConv1D(d_model, LOCAL_KERNEL_SIZE)

        if use_global:
            self.ln2 = nn.LayerNorm(d_model)
            self.global_conv = GlobalConv1D(d_model, GLOBAL_KERNEL_SIZE, FFT_SIZE)

        self.ln3 = nn.LayerNorm(d_model)
        self.ff = nn.Sequential(
            nn.Linear(d_model, d_model*4),
            nn.GELU(),
            nn.Linear(d_model*4, d_model)
        )

    def forward(self, x):
        x = x + self.local(self.ln1(x).transpose(1,2)).transpose(1,2)
        if self.use_global:
            x = x + self.global_conv(self.ln2(x).transpose(1,2)).transpose(1,2)
        return x + self.ff(self.ln3(x))


class GCLM(nn.Module):
    def __init__(self, vocab):
        super().__init__()
        self.emb = nn.Embedding(vocab, D_MODEL)
        self.pos = nn.Embedding(MAX_SEQ_LEN, D_MODEL)

        self.layers = nn.ModuleList([
            Block(D_MODEL, i % USE_GLOBAL_EVERY_N_LAYERS == 0)
            for i in range(N_LAYERS)
        ])

        self.ln = nn.LayerNorm(D_MODEL)
        self.head = nn.Linear(D_MODEL, vocab)

    def forward(self, x):
        T = x.size(1)
        h = self.emb(x) + self.pos(torch.arange(T, device=x.device))
        for layer in self.layers:
            h = layer(h)
        return self.head(self.ln(h))


###############################################################
# TRAINING LOOP
###############################################################

def train():
    device = USE_DEVICE if torch.cuda.is_available() else "cpu"
    print("[INFO] Device:", device)

    tok = tiktoken.get_encoding(TOKENIZER_NAME)
    used, id2new = build_dataset_vocab(TXT_PATH, tok, VOCAB_SAVE_PATH)
    vocab = len(used) + OFFSET

    ds = RemappedTextDataset(TXT_PATH, tok, id2new, MAX_SEQ_LEN)
    dl = DataLoader(ds, batch_size=MICRO_BATCH_SIZE, shuffle=True)

    model = GCLM(vocab).to(device)

    # 🔁 RESUME IF CHECKPOINT EXISTS
    if os.path.exists(SAVE_PATH):
        model.load_state_dict(torch.load(SAVE_PATH, map_location=device))
        print(f"[RESUME] Loaded existing checkpoint from {SAVE_PATH}")

    if device == "cuda" and COMPILE:
        print("[INFO] Compiling model with torch.compile...")
        model = torch.compile(
            model,
            mode=COMPILE_MODE,
            fullgraph=False,
            backend=COMPILE_BACKEND
        )

    opt = torch.optim.AdamW(model.parameters(), lr=LEARNING_RATE)
    loss_fn = nn.CrossEntropyLoss(ignore_index=PAD_ID)

    scaler = torch.amp.GradScaler("cuda", enabled=(device=="cuda" and USE_AMP))

    for ep in range(EPOCHS):
        print(f"\nEpoch {ep+1}/{EPOCHS}")
        opt.zero_grad(set_to_none=True)

        for i, (x, y) in enumerate(tqdm(dl)):
            x, y = x.to(device), y.to(device)

            with torch.amp.autocast("cuda", enabled=(device=="cuda" and USE_AMP)):
                logits = model(x)
                loss = loss_fn(logits.reshape(-1, vocab), y.reshape(-1))
                loss = loss / GRAD_ACCUM_STEPS

            scaler.scale(loss).backward()

            if (i+1) % GRAD_ACCUM_STEPS == 0:
                scaler.step(opt)
                scaler.update()
                opt.zero_grad(set_to_none=True)

        if SAVE_N_EPOCHS and (ep+1) % SAVE_N_EPOCHS == 0:
            torch.save(model.state_dict(), SAVE_PATH)
            print("[OK] Saved checkpoint.")

    torch.save(model.state_dict(), SAVE_PATH)
    print("[DONE] Training complete.")


###############################################################
# ENTRY POINT
###############################################################

if __name__ == "__main__":
    train()