Create gclm_train_example.py

gclm_train_example.py

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+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()