Supra-50M-Base/train.py

"""
© SupraLabs 2026 - Official pretraining code for PROJECT CHIMERA - 50M Llama
"""

import os
os.environ["PYTORCH_ALLOC_CONF"] = "expandable_segments:True"
os.environ["CUDA_VISIBLE_DEVICES"] = "0"

print("[*] Loading libraries...")
import torch
import math
import numpy as np
from datasets import load_dataset
from tokenizers import ByteLevelBPETokenizer
from transformers import (
    LlamaConfig,
    LlamaForCausalLM,
    PreTrainedTokenizerFast,
    Trainer,
    TrainingArguments,
)
from torch.utils.data import Dataset
from tqdm import tqdm

print("[*] Loading tokenizer...")
fast_tokenizer = ByteLevelBPETokenizer(
    "custom_llama_tokenizer-vocab.json",
    "custom_llama_tokenizer-merges.txt"
)
tokenizer = PreTrainedTokenizerFast(
    tokenizer_object=fast_tokenizer,
    bos_token="<s>",
    eos_token="</s>",
    unk_token="<unk>",
    pad_token="<pad>",
)

TOKEN_BIN     = "tokens.bin"
TARGET_TOKENS = 20_000_000_000
SEQ_LEN       = 1024
BATCH_TEXTS   = 1000
FLUSH_EVERY   = 1_000_000


def build_token_bin(fast_tokenizer, path=TOKEN_BIN, target_tokens=TARGET_TOKENS):
    if os.path.exists(path) and os.path.getsize(path) >= target_tokens * 2:
        print(f"[=] Reusing existing token file: {path}")
        return

    print(f"[*] Streaming + tokenizing {target_tokens:,} tokens → {path}")
    mm = np.memmap(path, dtype=np.uint16, mode="w+", shape=(target_tokens,))

    dataset = load_dataset(
        "HuggingFaceFW/fineweb-edu", "sample-100BT",
        split="train", streaming=True
    )

    written = 0
    buf = []
    texts = []
    pbar = tqdm(total=target_tokens, desc="[*] Gathering tokens", unit="tok")

    def flush_buf():
        nonlocal written, buf
        if not buf:
            return False
        n = min(len(buf), target_tokens - written)
        mm[written:written + n] = np.asarray(buf[:n], dtype=np.uint16)
        written += n
        pbar.update(n)
        del buf[:n]
        return written >= target_tokens

    for example in dataset:
        texts.append(example["text"])
        if len(texts) >= BATCH_TEXTS:
            encs = fast_tokenizer.encode_batch(texts)
            texts.clear()
            for e in encs:
                buf.extend(e.ids)
            if len(buf) >= FLUSH_EVERY:
                if flush_buf():
                    break

    if written < target_tokens and texts:
        encs = fast_tokenizer.encode_batch(texts)
        for e in encs:
            buf.extend(e.ids)
    if written < target_tokens:
        flush_buf()

    pbar.close()
    mm.flush()
    del mm
    print(f"[+] Wrote {written:,} tokens to {path} "
          f"({os.path.getsize(path)/1e6:.1f} MB)")


class MemmapDataset(Dataset):
    def __init__(self, path, total_tokens, seq_len=SEQ_LEN):
        self.path     = path
        self.seq_len  = seq_len
        self.n_chunks = total_tokens // seq_len
        self._data    = None  # lazy open (Multiprocessing-safe)

    @property
    def data(self):
        if self._data is None:
            self._data = np.memmap(
                self.path, dtype=np.uint16, mode="r",
                shape=(self.n_chunks * self.seq_len,)
            )
        return self._data

    def __len__(self):
        return self.n_chunks

    def __getitem__(self, idx):
        s   = idx * self.seq_len
        arr = np.asarray(self.data[s:s + self.seq_len], dtype=np.int64)
        ids = torch.from_numpy(arr)
        return {"input_ids": ids, "labels": ids.clone()}


def collate_fn(batch):
    input_ids = torch.stack([b["input_ids"] for b in batch])
    labels    = torch.stack([b["labels"]    for b in batch])
    return {"input_ids": input_ids, "labels": labels}


print(f"[*] Preparing {TARGET_TOKENS:,} tokens (streaming, memmap-backed)...")
build_token_bin(fast_tokenizer, TOKEN_BIN, TARGET_TOKENS)
dataset = MemmapDataset(TOKEN_BIN, TARGET_TOKENS, seq_len=SEQ_LEN)
print(f"[+] Dataset ready: {len(dataset):,} chunks of {SEQ_LEN} tokens")

print("[*] Setting up model...")
config = LlamaConfig(
    vocab_size=32_000,
    hidden_size=512,
    intermediate_size=1408,
    num_hidden_layers=12,
    num_attention_heads=8,
    num_key_value_heads=4,
    max_position_embeddings=1024,
    rope_theta=10_000,
    tie_word_embeddings=True,
    pad_token_id=tokenizer.pad_token_id,
    bos_token_id=tokenizer.bos_token_id,
    eos_token_id=tokenizer.eos_token_id,
)
model = LlamaForCausalLM(config)
print(f"[*] Model parameters: {model.num_parameters():,}")

print("[*] Defining training arguments...")
training_args = TrainingArguments(
    output_dir="./Chimera",
    num_train_epochs=1,
    per_device_train_batch_size=32,
    gradient_accumulation_steps=4,
    save_steps=500,
    save_total_limit=2,
    logging_steps=100,
    weight_decay=0.1,
    fp16=False,
    bf16=True,
    push_to_hub=False,
    report_to="none",
    dataloader_num_workers=os.cpu_count() // 2,
    dataloader_pin_memory=True,
    learning_rate=6e-4,
    lr_scheduler_type="cosine",
    warmup_ratio=0.02,
    max_grad_norm=1.0,
    optim="adamw_torch_fused",
    adam_beta1=0.9,
    adam_beta2=0.95,
    torch_compile=True,
)

trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=dataset,
    data_collator=collate_fn,
)

print("[*] Starting training...")
trainer.train()
trainer.save_model("./Chimera-FINAL")
tokenizer.save_pretrained("./Chimera-FINAL")
print("[*] Training finished.")