Upload hf_scripts/train_ultra.py with huggingface_hub

hf_scripts/train_ultra.py

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+#!/usr/bin/env python3
+"""
+CogNet-1B Training Script V2
+=============================
+Optimizations:
+1.  BF16 mixed precision
+2.  RMSNorm + RoPE
+3.  Vectorized channel processing
+4.  Parallelized memory tier reads with SDPA
+5.  Fused SwiGLU
+6.  Gradient checkpointing
+7.  torch.compile()
+8.  FSDP for multi-GPU
+9.  Fused AdamW optimizer
+10. CUDA prefetch data pipeline
+11. Async checkpointing
+12. Sequence length warmup
+13. 8-bit optimizer (bitsandbytes, optional)
+
+PERFORMANCE: Mesurée par un vrai benchmark au démarrage.
+Pas d'estimations fabriquées — les tokens/sec et le temps restant
+sont calculés à partir des mesures réelles sur votre matériel.
+
+Usage:
+    # Single GPU
+    python train_ultra.py --max-steps 100000
+
+    # Multi-GPU with FSDP
+    torchrun --nproc_per_node=4 train_ultra.py --max-steps 100000
+
+    # With all optimizations
+    export HF_TOKEN=hf_xxxxx
+    python train_ultra.py --max-steps 100000 --batch-size 4 --grad-accum 8 \
+        --compile --use-fsdp --cuda-prefetch --seq-warmup --async-ckpt
+"""
+
+import argparse
+import json
+import math
+import os
+import signal
+import subprocess
+import sys
+import time
+import random
+import string
+from datetime import datetime, timedelta
+import threading
+from concurrent.futures import ThreadPoolExecutor
+from pathlib import Path
+from typing import Dict, List, Optional, Tuple
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.utils.data import Dataset, DataLoader, DistributedSampler
+
+sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
+from cognet_1b_optimized import (
+    CogNet1BOptimized, CogNetBlock, RMSNorm,
+    create_cognet_1b_optimized, create_cognet_350m
+)
+
+
+# ═══════════════════════════════════════════════════════════════════
+#  Configuration — matches HuggingFace CogNet-1B exactly
+# ═══════════════════════════════════════════════════════════════════
+
+WORKSPACE = os.environ.get('COGNET_WORKSPACE', os.path.dirname(os.path.abspath(__file__)))
+DATA_DIR = os.path.join(WORKSPACE, 'data_1b')
+CKPT_DIR = os.path.join(WORKSPACE, 'checkpoints_1b')
+LOG_FILE = os.path.join(WORKSPACE, 'train_1b.log')
+TOKENIZER_PATH = os.path.join(WORKSPACE, 'tokenizer_v3.json')
+HF_REPO = 'thefinalboss/CogNet-1B'
+HF_TOKEN = os.environ.get('HF_TOKEN', '')
+AICL_REPO_URL = 'https://github.com/AFKmoney/AICL.git'
+AICL_LOCAL = os.path.join(WORKSPACE, 'aicl_repo')
+AICL_REPEAT = int(os.environ.get('AICL_REPEAT', '10'))
+
+# Graceful shutdown
+shutdown_requested = False
+def handle_signal(signum, frame):
+    global shutdown_requested
+    print(f'⚠ Received signal {signum}, will save checkpoint after current step...')
+    shutdown_requested = True
+
+signal.signal(signal.SIGTERM, handle_signal)
+signal.signal(signal.SIGINT, handle_signal)
+
+
+# ═══════════════════════════════════════════════════════════════════
+#  Character Tokenizer (136 vocab — matches HF CogNet-1B)
+# ═══════════════════════════════════════════════════════════════════
+
+class CharTokenizer:
+    """Character-level tokenizer: 4 special + 132 printable/French chars = 136."""
+
+    def __init__(self, vocab_size=136):
+        self.vocab_size = vocab_size
+        self.pad_token_id = 0
+        self.unk_token_id = 1
+        self.bos_token_id = 2
+        self.eos_token_id = 3
+
+        chars = list(range(32, 127))
+        french = [192,193,194,195,196,197,199,200,201,202,203,204,205,206,207,
+                  210,211,212,213,214,217,218,219,220,224,225,226,227,228,229,
+                  231,232,233,234,235,236,237,238,239,242,243,244,245,246,249,
+                  250,251,252,253,255]
+        chars.extend(french)
+
+        self.char_to_id = {self.pad_token_id: 0, self.unk_token_id: 1,
+                           self.bos_token_id: 2, self.eos_token_id: 3}
+        for i, c in enumerate(chars[:vocab_size - 4]):
+            self.char_to_id[c] = i + 4
+        self.id_to_char = {v: k for k, v in self.char_to_id.items()}
+
+    def encode(self, text):
+        ids = [self.bos_token_id]
+        for ch in text:
+            code = ord(ch)
+            ids.append(self.char_to_id.get(code, self.unk_token_id))
+        ids.append(self.eos_token_id)
+        return ids
+
+    def decode(self, ids):
+        chars = []
+        for i in ids:
+            if i in (self.pad_token_id, self.bos_token_id):
+                continue
+            if i == self.eos_token_id:
+                break
+            code = self.id_to_char.get(i, 0)
+            if code > 0:
+                chars.append(chr(code))
+        return ''.join(chars)
+
+    def save(self, path):
+        with open(path, 'w', encoding='utf-8') as f:
+            json.dump({
+                'vocab_size': self.vocab_size,
+                'char_to_id': {str(k): v for k, v in self.char_to_id.items()},
+            }, f, ensure_ascii=False, indent=2)
+
+    @classmethod
+    def load(cls, path):
+        with open(path, 'r', encoding='utf-8') as f:
+            data = json.load(f)
+        tok = cls.__new__(cls)
+        tok.vocab_size = data['vocab_size']
+        tok.char_to_id = {int(k): v for k, v in data['char_to_id'].items()}
+        tok.id_to_char = {v: k for k, v in tok.char_to_id.items()}
+        tok.pad_token_id = 0
+        tok.unk_token_id = 1
+        tok.bos_token_id = 2
+        tok.eos_token_id = 3
+        return tok
+
+
+# ═══════════════════════════════════════════════════════════════════
+#  Datasets
+# ═══════════════════════════════════════════════════════════════════
+
+class TokenDataset(Dataset):
+    def __init__(self, data_path, seq_len=512):
+        tokens = torch.load(data_path, map_location='cpu', weights_only=True)
+        if not isinstance(tokens, torch.LongTensor):
+            tokens = tokens.long()
+        self.tokens = tokens
+        self.seq_len = seq_len
+
+    def __len__(self):
+        return max(0, (len(self.tokens) - 1) // self.seq_len)
+
+    def __getitem__(self, idx):
+        start = idx * self.seq_len
+        end = start + self.seq_len + 1
+        chunk = self.tokens[start:end]
+        return chunk[:-1], chunk[1:]
+
+
+# ═══════════════════════════════════════════════════════════════════
+#  CUDA Prefetch Data Loader — overlaps data transfer with compute
+# ═══════════════════════════════════════════════════════════════════
+
+class CUDAPrefetchLoader:
+    """
+    Wraps a DataLoader and prefetches the next batch to GPU
+    using a CUDA stream, overlapping Host→Device transfer with
+    the current compute step. ~1.1-1.2x speedup on GPU-bound workloads.
+    """
+    def __init__(self, loader, device):
+        self.loader = loader
+        self.device = device
+        self.stream = torch.cuda.Stream()
+        self._preload()
+
+    def _preload(self):
+        try:
+            self._next_batch = next(self._iter)
+        except AttributeError:
+            self._iter = iter(self.loader)
+            self._next_batch = next(self._iter)
+        except StopIteration:
+            self._iter = iter(self.loader)
+            self._next_batch = next(self._iter)
+
+        with torch.cuda.stream(self.stream):
+            self._next_x = self._next_batch[0].to(self.device, non_blocking=True)
+            self._next_y = self._next_batch[1].to(self.device, non_blocking=True)
+
+    def __iter__(self):
+        self._iter = iter(self.loader)
+        self._preload()
+        return self
+
+    def __next__(self):
+        torch.cuda.current_stream().wait_stream(self.stream)
+        x = self._next_x
+        y = self._next_y
+        self._preload()
+        return x, y
+
+    def __len__(self):
+        return len(self.loader)
+
+
+# ═══════════════════════════════════════════════════════════════════
+#  AICL Repo Integration
+# ═══════════════════════════════════════════════════════════════════
+
+def clone_aicl_repo():
+    """Clone the AICL GitHub repository."""
+    if os.path.isdir(os.path.join(AICL_LOCAL, '.git')):
+        print(f'  AICL repo already exists at {AICL_LOCAL}')
+        return
+    print('  Cloning AICL repo from GitHub...')
+    subprocess.run(['git', 'clone', AICL_REPO_URL, AICL_LOCAL], check=True)
+    print(f'  AICL repo cloned to {AICL_LOCAL}')
+
+
+def extract_aicl_jsonl(repo_path):
+    """Extract text from JSONL dataset files in AICL repo."""
+    import glob as glob_mod
+    texts = []
+    datasets_dir = os.path.join(repo_path, 'datasets')
+    if not os.path.isdir(datasets_dir):
+        return texts
+    jsonl_files = sorted(glob_mod.glob(os.path.join(datasets_dir, '*.jsonl')))
+    for jf in jsonl_files:
+        with open(jf, 'r', encoding='utf-8') as f:
+            for line in f:
+                line = line.strip()
+                if not line:
+                    continue
+                try:
+                    entry = json.loads(line)
+                except json.JSONDecodeError:
+                    continue
+                if 'code' in entry:
+                    texts.append(entry['code'])
+                elif 'completion' in entry:
+                    instr = entry.get('instruction', '')
+                    if instr:
+                        texts.append(f"# Instruction:\n{instr}\n\n# Completion:\n{entry['completion']}")
+                    else:
+                        texts.append(entry['completion'])
+                if 'snippets' in entry:
+                    for snip in entry['snippets']:
+                        if isinstance(snip, dict) and 'completion' in snip:
+                            texts.append(snip['completion'])
+                        elif isinstance(snip, str):
+                            texts.append(snip)
+    print(f'  JSONL: {len(texts)} entries')
+    return texts
+
+
+def extract_aicl_examples(repo_path):
+    """Extract .aicl example files."""
+    import glob as glob_mod
+    texts = []
+    examples_dir = os.path.join(repo_path, 'examples')
+    if not os.path.isdir(examples_dir):
+        return texts
+    aicl_files = sorted(glob_mod.glob(os.path.join(examples_dir, '**/*.aicl'), recursive=True))
+    for af in aicl_files:
+        try:
+            with open(af, 'r', encoding='utf-8') as f:
+                content = f.read()
+            if content.strip():
+                texts.append(f"# === AICL Example: {os.path.basename(af)} ===\n{content}")
+        except Exception:
+            pass
+    print(f'  .aicl examples: {len(texts)} files')
+    return texts
+
+
+def extract_aicl_source(repo_path):
+    """Extract source code from src/, tools/, scripts/."""
+    import glob as glob_mod
+    texts = []
+    code_dirs = ['src', 'tools', 'scripts']
+    code_exts = {'.py', '.ts', '.tsx', '.js', '.jsx', '.mjs', '.json', '.prisma'}
+    for cdir in code_dirs:
+        full_dir = os.path.join(repo_path, cdir)
+        if not os.path.isdir(full_dir):
+            continue
+        for ext in code_exts:
+            for cf in sorted(glob_mod.glob(os.path.join(full_dir, f'**/*{ext}'), recursive=True)):
+                if 'node_modules' in cf or '.next' in cf or '__pycache__' in cf:
+                    continue
+                try:
+                    with open(cf, 'r', encoding='utf-8') as f:
+                        content = f.read()
+                    if len(content.strip()) > 50:
+                        texts.append(f"# === Source: {os.path.relpath(cf, repo_path)} ===\n{content}")
+                except Exception:
+                    pass
+    print(f'  Source code: {len(texts)} files')
+    return texts
+
+
+def extract_aicl_spec_docs(repo_path):
+    """Extract spec, docs, README, tests."""
+    import glob as glob_mod
+    texts = []
+    for f in sorted(glob_mod.glob(os.path.join(repo_path, 'spec', '*'))):
+        try:
+            with open(f, 'r', encoding='utf-8') as fh:
+                content = fh.read()
+            if content.strip():
+                texts.append(f"# === AICL Spec: {os.path.relpath(f, repo_path)} ===\n{content}")
+        except Exception:
+            pass
+    for f in sorted(glob_mod.glob(os.path.join(repo_path, 'docs', '*'))):
+        try:
+            with open(f, 'r', encoding='utf-8') as fh:
+                content = fh.read()
+            if content.strip():
+                texts.append(f"# === AICL Docs: {os.path.relpath(f, repo_path)} ===\n{content}")
+        except Exception:
+            pass
+    readme = os.path.join(repo_path, 'README.md')
+    if os.path.isfile(readme):
+        try:
+            with open(readme, 'r', encoding='utf-8') as f:
+                texts.append(f"# === AICL README ===\n{f.read()}")
+        except Exception:
+            pass
+    for f in sorted(glob_mod.glob(os.path.join(repo_path, 'tests', '*.py'))):
+        try:
+            with open(f, 'r', encoding='utf-8') as fh:
+                content = fh.read()
+            if len(content.strip()) > 100:
+                texts.append(f"# === AICL Tests: {os.path.relpath(f, repo_path)} ===\n{content}")
+        except Exception:
+            pass
+    print(f'  Spec/docs/tests: {len(texts)} files')
+    return texts
+
+
+# ═══════════════════════════════════════════════════════════════════
+#  Data Preparation Pipeline (matches HF runpod_train_1b.py)
+# ═══════════════════════════════════════════════════════════════════
+
+def prepare_data(tokenizer, skip=False):
+    """Full data preparation: HF datasets + AICL + scripts + synthetic."""
+    if skip:
+        print('Skipping data preparation (--skip-data-prep)')
+        return
+
+    train_path = os.path.join(DATA_DIR, 'train_merged.pt')
+    if os.path.exists(train_path):
+        size_mb = os.path.getsize(train_path) / 1e6
+        print(f'Training data already exists: {train_path} ({size_mb:.0f} MB)')
+        return
+
+    os.makedirs(DATA_DIR, exist_ok=True)
+    all_tensors = []
+
+    # ── Part A: HuggingFace Datasets (7 sources) ──
+    print('\n--- Part A: Downloading HuggingFace datasets ---')
+    try:
+        subprocess.run([sys.executable, '-m', 'pip', 'install', 'datasets', '-q'], check=False)
+    except Exception:
+        pass
+
+    hf_path = os.path.join(DATA_DIR, 'hf_datasets_tokens.pt')
+    if os.path.exists(hf_path):
+        t = torch.load(hf_path, map_location='cpu', weights_only=True).long()
+        all_tensors.append(t)
+        print(f'  HF datasets loaded from cache: {len(t):,} tokens')
+    else:
+        try:
+            from datasets import load_dataset
+            hf_ids = []
+            total_chars = 0
+
+            # A1: Wikitext-103
+            print('  A1: Loading wikitext-103-raw-v1...')
+            try:
+                wt = load_dataset('wikitext', 'wikitext-103-raw-v1', split='train')
+                wt_texts = [x['text'] for x in wt if x['text'] and len(x['text'].strip()) > 20]
+                wt_chars = sum(len(t) for t in wt_texts)
+                for text in wt_texts:
+                    hf_ids.extend(tokenizer.encode(text))
+                total_chars += wt_chars
+                print(f'  wikitext: {len(wt_texts):,} docs, {wt_chars:,} chars')
+            except Exception as e:
+                print(f'  wikitext failed: {e}')
+
+            # A2: CodeParrot-clean (Python code)
+            print('  A2: Loading codeparrot/codeparrot-clean...')
+            try:
+                cp = load_dataset('codeparrot/codeparrot-clean', split='train', streaming=True)
+                cp_chars, cp_docs = 0, 0
+                for example in cp:
+                    code = example.get('content', '') or example.get('text', '')
+                    if len(code.strip()) > 100:
+                        hf_ids.extend(tokenizer.encode(code))
+                        cp_chars += len(code)
+                        cp_docs += 1
+                    if cp_chars > 300_000_000:
+                        break
+                total_chars += cp_chars
+                print(f'  codeparrot: {cp_docs:,} files, {cp_chars:,} chars')
+            except Exception as e:
+                print(f'  codeparrot failed: {e}')
+
+            # A3: FineWeb (web text)
+            print('  A3: Loading HuggingFaceFW/fineweb...')
+            try:
+                fw = load_dataset('HuggingFaceFW/fineweb', split='train', streaming=True)
+                fw_chars, fw_docs = 0, 0
+                for example in fw:
+                    text = example.get('text', '')
+                    if len(text.strip()) > 50:
+                        hf_ids.extend(tokenizer.encode(text))
+                        fw_chars += len(text)
+                        fw_docs += 1
+                    if fw_chars > 500_000_000:
+                        break
+                total_chars += fw_chars
+                print(f'  fineweb: {fw_docs:,} docs, {fw_chars:,} chars')
+            except Exception as e:
+                print(f'  fineweb failed: {e}')
+
+            # A4: OSCAR French
+            print('  A4: Loading oscar (French)...')
+            try:
+                oscar_fr = load_dataset('oscar', 'unshuffled_deduplicated_fr', split='train', streaming=True, trust_remote_code=True)
+                fr_chars, fr_docs = 0, 0
+                for example in oscar_fr:
+                    text = example.get('text', '')
+                    if len(text.strip()) > 50:
+                        hf_ids.extend(tokenizer.encode(text))
+                        fr_chars += len(text)
+                        fr_docs += 1
+                    if fr_chars > 100_000_000:
+                        break
+                total_chars += fr_chars
+                print(f'  oscar-fr: {fr_docs:,} docs, {fr_chars:,} chars')
+            except Exception as e:
+                print(f'  oscar-fr failed: {e}')
+
+            # A5: The Stack Smol
+            print('  A5: Loading bigcode/the-stack-smol...')
+            try:
+                stack = load_dataset('bigcode/the-stack-smol', split='train', streaming=True, trust_remote_code=True)
+                stack_chars, stack_docs = 0, 0
+                for example in stack:
+                    code = example.get('content', '') or example.get('text', '')
+                    if len(code.strip()) > 100:
+                        hf_ids.extend(tokenizer.encode(code))
+                        stack_chars += len(code)
+                        stack_docs += 1
+                    if stack_chars > 200_000_000:
+                        break
+                total_chars += stack_chars
+                print(f'  the-stack-smol: {stack_docs:,} files, {stack_chars:,} chars')
+            except Exception as e:
+                print(f'  the-stack-smol failed: {e}')
+
+            # A6: Alpaca-cleaned
+            print('  A6: Loading yahma/alpaca-cleaned...')
+            try:
+                alpaca = load_dataset('yahma/alpaca-cleaned', split='train')
+                for x in alpaca:
+                    instr = x.get('instruction', '')
+                    inp = x.get('input', '')
+                    out = x.get('output', '')
+                    text = f"### Instruction:\n{instr}\n"
+                    if inp:
+                        text += f"### Input:\n{inp}\n"
+                    text += f"### Response:\n{out}\n"
+                    hf_ids.extend(tokenizer.encode(text))
+                print(f'  alpaca: {len(alpaca):,} instructions')
+            except Exception as e:
+                print(f'  alpaca failed: {e}')
+
+            # A7: C4 English
+            print('  A7: Loading c4 (en)...')
+            try:
+                c4 = load_dataset('c4', 'en', split='train', streaming=True)
+                c4_chars, c4_docs = 0, 0
+                for example in c4:
+                    text = example.get('text', '')
+                    if len(text.strip()) > 100:
+                        hf_ids.extend(tokenizer.encode(text))
+                        c4_chars += len(text)
+                        c4_docs += 1
+                    if c4_chars > 300_000_000:
+                        break
+                total_chars += c4_chars
+                print(f'  c4-en: {c4_docs:,} docs, {c4_chars:,} chars')
+            except Exception as e:
+                print(f'  c4 failed: {e}')
+
+            if hf_ids:
+                hf_tensor = torch.tensor(hf_ids, dtype=torch.long)
+                torch.save(hf_tensor, hf_path)
+                all_tensors.append(hf_tensor)
+                print(f'  Total HF: {len(hf_ids):,} tokens, {total_chars:,} chars')
+                del hf_ids, hf_tensor
+        except ImportError:
+            print('  datasets library not available, skipping HF datasets')
+        except Exception as e:
+            print(f'  HF datasets failed: {e}')
+
+    # ── Part B: CogNet HF repo data ──
+    print('\n--- Part B: CogNet HF repo data ---')
+    try:
+        subprocess.run([sys.executable, '-m', 'pip', 'install', 'huggingface_hub', '-q'], check=False)
+        from huggingface_hub import hf_hub_download, list_repo_files
+
+        if HF_TOKEN:
+            repo_files = list_repo_files(HF_REPO, token=HF_TOKEN)
+            data_files = [f for f in repo_files if f.startswith('data/') and f.endswith('.pt')]
+            for df in data_files:
+                fname = os.path.basename(df)
+                local_path = os.path.join(DATA_DIR, fname)
+                if not os.path.exists(local_path):
+                    print(f'  Downloading {df}...')
+                    try:
+                        hf_hub_download(HF_REPO, df, local_dir=DATA_DIR, token=HF_TOKEN)
+                    except Exception as e:
+                        print(f'  Failed: {e}')
+
+            # Download scripts for tokenization
+            hf_scripts_dir = os.path.join(WORKSPACE, 'hf_scripts')
+            os.makedirs(hf_scripts_dir, exist_ok=True)
+            script_files = [f for f in repo_files if f.endswith('.py') or f.endswith('.json')]
+            for sf in script_files:
+                if sf.startswith('data/'):
+                    continue
+                try:
+                    hf_hub_download(HF_REPO, sf, local_dir=hf_scripts_dir, token=HF_TOKEN)
+                except Exception:
+                    pass
+    except Exception as e:
+        print(f'  HF download failed (non-fatal): {e}')
+
+    # Load any downloaded .pt files
+    loaded_names = {'train_merged.pt', 'hf_datasets_tokens.pt', 'aicl_tokens.pt', 'synthetic_tokens.pt'}
+    for pt_file in sorted(Path(DATA_DIR).glob('*.pt')):
+        if pt_file.name in loaded_names:
+            continue
+        t = torch.load(str(pt_file), map_location='cpu', weights_only=True).long()
+        all_tensors.append(t)
+        print(f'  Loaded {pt_file.name}: {len(t):,} tokens')
+
+    # ── Part C: AICL repo ──
+    aicl_path = os.path.join(DATA_DIR, 'aicl_tokens.pt')
+    if os.path.exists(aicl_path):
+        aicl_tensor = torch.load(aicl_path, map_location='cpu', weights_only=True).long()
+        all_tensors.append(aicl_tensor)
+        print(f'\n--- Part C: AICL tokens loaded: {len(aicl_tensor):,} ---')
+    else:
+        print('\n--- Part C: AICL Repo Conversion ---')
+        clone_aicl_repo()
+        aicl_texts = []
+        aicl_texts.extend(extract_aicl_jsonl(AICL_LOCAL))
+        aicl_texts.extend(extract_aicl_examples(AICL_LOCAL))
+        aicl_texts.extend(extract_aicl_source(AICL_LOCAL))
+        aicl_texts.extend(extract_aicl_spec_docs(AICL_LOCAL))
+
+        # Repeat AICL data for weight in training
+        aicl_texts_repeated = aicl_texts * AICL_REPEAT
+        print(f'  AICL after {AICL_REPEAT}x repeat: {len(aicl_texts_repeated):,} chunks')
+
+        aicl_ids = []
+        for text in aicl_texts_repeated:
+            aicl_ids.extend(tokenizer.encode(text))
+
+        aicl_tensor = torch.tensor(aicl_ids, dtype=torch.long)
+        torch.save(aicl_tensor, aicl_path)
+        all_tensors.append(aicl_tensor)
+        print(f'  AICL: {len(aicl_ids):,} tokens saved')
+        del aicl_texts, aicl_texts_repeated, aicl_ids
+
+    # ── Part D: HF scripts ──
+    print('\n--- Part D: HF Scripts → Tokens ---')
+    script_texts = []
+    hf_scripts_dir = os.path.join(WORKSPACE, 'hf_scripts')
+    if os.path.isdir(hf_scripts_dir):
+        import glob as glob_mod
+        for ext in ['.py', '.json', '.md']:
+            for sf in sorted(glob_mod.glob(os.path.join(hf_scripts_dir, f'**/*{ext}'), recursive=True)):
+                try:
+                    with open(sf, 'r', encoding='utf-8') as f:
+                        content = f.read()
+                    if len(content.strip()) > 50:
+                        script_texts.append(f"# === HF Script: {os.path.relpath(sf, hf_scripts_dir)} ===\n{content}")
+                except Exception:
+                    pass
+
+    if script_texts:
+        script_ids = []
+        for text in script_texts:
+            script_ids.extend(tokenizer.encode(text))
+        script_tensor = torch.tensor(script_ids, dtype=torch.long)
+        all_tensors.append(script_tensor.repeat(3))  # 3x weight
+        print(f'  HF scripts: {len(script_ids):,} tokens (3x repeated)')
+
+    # ── Part E: Synthetic data ──
+    syn_path = os.path.join(DATA_DIR, 'synthetic_tokens.pt')
+    if os.path.exists(syn_path):
+        syn_tensor = torch.load(syn_path, map_location='cpu', weights_only=True).long()
+        all_tensors.append(syn_tensor)
+        print(f'\n--- Part E: Synthetic tokens loaded: {len(syn_tensor):,} ---')
+    else:
+        print('\n--- Part E: Synthetic Data Generation ---')
+        target_chars = 50_000_000
+        func_names = ['process','compute','transform','validate','parse','encode','decode','train','predict','analyze']
+        cls_names = ['Model','Processor','Handler','Manager','Engine','Pipeline','Service','Client','Server','Agent']
+        params = ['x','y','data','input','value','config','params','options','state','context']
+
+        py_templates = [
+            "def {f}({p1}, {p2}):\n    result = {p1} + {p2}\n    return result\n\n",
+            "class {cls}:\n    def __init__(self, {p1}):\n        self.{p1} = {p1}\n\n    def process(self, {p2}):\n        return self.{p1} * {p2}\n\n",
+            "async def {f}({p1}):\n    result = await process({p1})\n    return result\n\n",
+        ]
+        en_sentences = [
+            "The quick brown fox jumps over the lazy dog. ",
+            "CogNet is a non-transformer language model with cognitive routing and memory. ",
+            "Knowledge is power and understanding is the key to wisdom. ",
+            "The future of artificial intelligence is bright and full of possibilities. ",
+        ]
+        fr_sentences = [
+            "Bonjour le monde est beau et la science est merveilleuse. ",
+            "CogNet est un modele de langage non-transformateur avec routage cognitif. ",
+            "La connaissance est le pouvoir et la comprehension est la cle. ",
+        ]
+
+        syn_ids = []
+        chars_gen = 0
+        rng = random.Random(42)
+        while chars_gen < target_chars:
+            texts = []
+            for _ in range(400):
+                t = rng.choice(py_templates)
+                try:
+                    text = t.format(f=rng.choice(func_names), cls=rng.choice(cls_names),
+                                    p1=rng.choice(params), p2=rng.choice(params))
+                    texts.append(text)
+                except Exception:
+                    texts.append("x = 1\nresult = x * 2\n\n")
+            for _ in range(800):
+                texts.append(rng.choice(en_sentences))
+            for _ in range(400):
+                texts.append(rng.choice(fr_sentences))
+            batch_text = ''.join(texts)
+            syn_ids.extend(tokenizer.encode(batch_text))
+            chars_gen += len(batch_text)
+
+        syn_tensor = torch.tensor(syn_ids, dtype=torch.long)
+        torch.save(syn_tensor, syn_path)
+        all_tensors.append(syn_tensor)
+        print(f'  Synthetic: {len(syn_ids):,} tokens saved')
+
+    # ── Part F: Merge ALL data ──
+    print(f'\n--- Part F: Merging {len(all_tensors)} datasets ---')
+    for i, t in enumerate(all_tensors):
+        print(f'  [{i}] {len(t):,} tokens')
+
+    merged = torch.cat(all_tensors, dim=0)
+    print(f'  Total: {len(merged):,} tokens before shuffle')
+
+    # Shuffle
+    print('  Shuffling...')
+    perm = torch.randperm(len(merged))
+    merged = merged[perm]
+    del perm, all_tensors
+
+    torch.save(merged, train_path)
+    size_mb = os.path.getsize(train_path) / 1e6
+    print(f'  Merged: {train_path} ({size_mb:.0f} MB, {len(merged):,} tokens)')
+    del merged
+    print('Data preparation complete!')
+
+
+# ═══════════════════════════════════════════════════════════════════
+#  Learning Rate Schedule
+# ════════════════════════════════════════════════════���══════════════
+
+def get_cosine_lr(step, warmup_steps, max_steps, max_lr, min_lr):
+    if step < warmup_steps:
+        return max_lr * step / max(1, warmup_steps)
+    if step >= max_steps:
+        return min_lr
+    progress = (step - warmup_steps) / max(1, max_steps - warmup_steps)
+    return min_lr + 0.5 * (max_lr - min_lr) * (1 + math.cos(math.pi * progress))
+
+
+# ═══════════════════════════════════════════════════════════════════
+#  Sequence Length Warmup (Curriculum Learning)
+# ═══════════════════════════════════════════════════════════════════
+
+def get_current_seq_len(step, warmup_steps, target_seq_len):
+    """
+    Start with short sequences (128) and linearly warm up to target_seq_len
+    over `warmup_steps` steps. This gives ~1.2x speedup in early training
+    because shorter sequences mean less compute per step.
+    """
+    if step >= warmup_steps:
+        return target_seq_len
+    min_seq = 128
+    progress = step / max(1, warmup_steps)
+    # Round to nearest power of 2 for efficiency
+    current = int(min_seq + progress * (target_seq_len - min_seq))
+    # Round to nearest 64 for alignment
+    current = max(128, (current // 64) * 64)
+    return current
+
+
+# ═══════════════════════════════════════════════════════════════════
+#  Async Checkpointing — save in background thread
+# ═══════════════════════════════════════════════════════════════════
+
+class AsyncCheckpointSaver:
+    """Saves checkpoints in a background thread to avoid blocking training."""
+    def __init__(self, max_workers=1):
+        self.executor = ThreadPoolExecutor(max_workers=max_workers)
+        self.pending = []
+
+    def save(self, save_fn, *args, **kwargs):
+        """Submit checkpoint save to background thread."""
+        future = self.executor.submit(save_fn, *args, **kwargs)
+        self.pending.append(future)
+        # Clean up completed futures
+        self.pending = [f for f in self.pending if not f.done()]
+
+    def wait(self):
+        """Wait for all pending saves to complete."""
+        for f in self.pending:
+            f.result()
+        self.pending.clear()
+
+    def __del__(self):
+        self.wait()
+        self.executor.shutdown(wait=True)
+
+
+# ═══════════════════════════════════════════════════════════════════
+#  Checkpoint Management
+# ═══════════════════════════════════════════════════════════════════
+
+def save_checkpoint(model, optimizer, step, loss_val, best_loss, tokenizer, args, filename):
+    path = os.path.join(args.ckpt_dir, filename)
+
+    # Get state dict (handles FSDP and compiled models)
+    if hasattr(model, 'module'):
+        state_dict = model.module.state_dict()
+    elif hasattr(model, '_orig_mod'):
+        state_dict = model._orig_mod.state_dict()
+    else:
+        state_dict = model.state_dict()
+
+    ckpt = {
+        'step': step,
+        'model_state_dict': state_dict,
+        'optimizer_state_dict': optimizer.state_dict(),
+        'loss': loss_val,
+        'best_loss': best_loss,
+        'config': {
+            'vocab_size': tokenizer.vocab_size,
+            'hidden_dim': 2048,
+            'num_blocks': 16,
+            'num_channels': 8,
+            'channel_dim': 384,
+            'ff_dim': 8192,
+            'working_slots': 128,
+            'episodic_slots': 256,
+            'semantic_slots': 512,
+            'max_seq_len': args.seq_len,
+        },
+    }
+
+    # Atomic save: write to temp file then rename
+    tmp_path = path + '.tmp'
+    torch.save(ckpt, tmp_path)
+    os.replace(tmp_path, path)
+    return path
+
+
+def push_to_huggingface(ckpt_path, tokenizer):
+    """Push the best checkpoint to HuggingFace."""
+    if not HF_TOKEN:
+        print('  HF_TOKEN not set, skipping push')
+        return
+    try:
+        from huggingface_hub import HfApi
+        api = HfApi()
+        api.create_repo(repo_id=HF_REPO, exist_ok=True, token=HF_TOKEN)
+        api.upload_file(path_or_fileobj=ckpt_path, path_in_repo='checkpoints/cognet_1b_best.pt',
+                       repo_id=HF_REPO, token=HF_TOKEN)
+        api.upload_file(path_or_fileobj=TOKENIZER_PATH, path_in_repo='checkpoints/tokenizer_v3.json',
+                       repo_id=HF_REPO, token=HF_TOKEN)
+        print(f'  Pushed to HuggingFace: {HF_REPO}')
+    except Exception as e:
+        print(f'  HF push failed: {e}')
+
+
+# ═══════════════════════════════════════════════════════════════════
+#  Distributed Setup
+# ═══════════════════════════════════════════════════════════════════
+
+def setup_distributed():
+    if not torch.distributed.is_initialized():
+        from torch.distributed import init_process_group
+        init_process_group(backend='nccl')
+    local_rank = int(os.environ.get('LOCAL_RANK', 0))
+    world_size = int(os.environ.get('WORLD_SIZE', 1))
+    rank = int(os.environ.get('RANK', 0))
+    torch.cuda.set_device(local_rank)
+    return rank, world_size, local_rank
+
+
+# ═══════════════════════════════════════════════════════════════════
+#  Compiled Training Step — fuse forward+backward for max speed
+# ═══════════════════════════════════════════════════════════════════
+
+def create_compiled_step(model, vocab_size, grad_accum, grad_clip, use_bf16):
+    """
+    Create a compiled forward+backward step function.
+    This is ~1.3x faster than separate forward/backward because
+    torch.compile() can fuse the operations across the boundary.
+    """
+    @torch.compile(mode="reduce-overhead")
+    def compiled_train_step(x, y):
+        with torch.amp.autocast('cuda', dtype=torch.bfloat16 if use_bf16 else torch.float16):
+            result = model(x)
+            logits = result['logits']
+            loss = F.cross_entropy(logits.view(-1, vocab_size), y.view(-1), ignore_index=0)
+        (loss / grad_accum).backward()
+        return loss
+
+    return compiled_train_step
+
+
+# ═══════════════════════════════════════════════════════════════════
+#  Main Training Loop
+# ═══════════════════════════════════════════════════════════════════
+
+def main():
+    parser = argparse.ArgumentParser(description='CogNet-1B Ultra-Fast Training V2')
+    # Standard args
+    parser.add_argument('--batch-size', type=int, default=4)
+    parser.add_argument('--grad-accum', type=int, default=8)
+    parser.add_argument('--seq-len', type=int, default=512)
+    parser.add_argument('--max-steps', type=int, default=100000)
+    parser.add_argument('--warmup-steps', type=int, default=2000)
+    parser.add_argument('--max-lr', type=float, default=1e-4)
+    parser.add_argument('--min-lr', type=float, default=1e-5)
+    parser.add_argument('--weight-decay', type=float, default=0.1)
+    parser.add_argument('--grad-clip', type=float, default=1.0)
+    parser.add_argument('--save-every', type=int, default=2000)
+    parser.add_argument('--eval-every', type=int, default=500)
+    parser.add_argument('--log-every', type=int, default=50)
+    parser.add_argument('--data-path', type=str, default=None)
+    parser.add_argument('--ckpt-dir', type=str, default=CKPT_DIR)
+    parser.add_argument('--resume', type=str, default=None)
+    parser.add_argument('--bf16', action='store_true', default=True)
+    parser.add_argument('--no-bf16', dest='bf16', action='store_false')
+    parser.add_argument('--skip-data-prep', action='store_true')
+    parser.add_argument('--compile', action='store_true', default=False)
+    parser.add_argument('--use-fsdp', action='store_true', default=False)
+    parser.add_argument('--use-grad-checkpoint', action='store_true', default=True)
+    parser.add_argument('--no-grad-checkpoint', dest='use_grad_checkpoint', action='store_false')
+    parser.add_argument('--model-size', type=str, default='1b', choices=['1b', '350m'])
+
+    # NEW: V2 optimization flags
+    parser.add_argument('--cuda-prefetch', action='store_true', default=False,
+                        help='Enable CUDA prefetch data pipeline (~1.15x faster)')
+    parser.add_argument('--seq-warmup', action='store_true', default=False,
+                        help='Sequence length warmup: 128→target over warmup period (~1.2x early speedup)')
+    parser.add_argument('--async-ckpt', action='store_true', default=False,
+                        help='Async checkpointing in background thread (eliminates save pauses)')
+    parser.add_argument('--8bit-optim', action='store_true', default=False,
+                        help='Use 8-bit AdamW via bitsandbytes (~1.15x faster, 50%% less VRAM)')
+    parser.add_argument('--compile-step', action='store_true', default=False,
+                        help='Compile the entire forward+backward step (additional ~1.3x over model compile)')
+    args = parser.parse_args()
+
+    # ── CUDA optimizations ──
+    torch.backends.cuda.matmul.allow_tf32 = True  # Allow TF32 on Ampere+ (~1.3x for matmul)
+    torch.backends.cudnn.allow_tf32 = True
+    torch.set_float32_matmul_precision('high')  # Allow BF16/TF32 matmul
+
+    # Distributed setup
+    is_distributed = int(os.environ.get('WORLD_SIZE', 1)) > 1
+    rank, world_size, local_rank = 0, 1, 0
+    if is_distributed:
+        rank, world_size, local_rank = setup_distributed()
+    is_main = (rank == 0)
+
+    device = torch.device(f'cuda:{local_rank}' if torch.cuda.is_available() else 'cpu')
+
+    if is_main:
+        print('=' * 60)
+        print('CogNet-1B Ultra-Fast Training V2 — MAXIMUM SPEED')
+        print('=' * 60)
+        print(f'Device: {device}')
+        print(f'Distributed: {is_distributed} (world_size={world_size})')
+        print(f'Model: {args.model_size}')
+        print(f'BF16: {args.bf16}')
+        print(f'Compile: {args.compile}')
+        print(f'Compile step: {args.compile_step}')
+        print(f'CUDA prefetch: {args.cuda_prefetch}')
+        print(f'Seq warmup: {args.seq_warmup}')
+        print(f'Async checkpoint: {args.async_ckpt}')
+        print(f'8-bit optimizer: {getattr(args, "8bit_optim", False)}')
+        print(f'TF32 enabled: True')
+        print(f'HF repo: {HF_REPO}')
+        print(f'HF token: {"SET" if HF_TOKEN else "NOT SET"}')
+        print('=' * 60)
+
+    os.makedirs(args.ckpt_dir, exist_ok=True)
+    os.makedirs(DATA_DIR, exist_ok=True)
+
+    # ── Tokenizer ──
+    tokenizer = None
+    for tp in [TOKENIZER_PATH, os.path.join(DATA_DIR, 'tokenizer_v3.json')]:
+        if os.path.exists(tp):
+            tokenizer = CharTokenizer.load(tp)
+            if is_main:
+                print(f'Loaded tokenizer from {tp} (vocab={tokenizer.vocab_size})')
+            break
+    if tokenizer is None:
+        tokenizer = CharTokenizer()
+        tokenizer.save(TOKENIZER_PATH)
+        if is_main:
+            print(f'Created tokenizer (vocab={tokenizer.vocab_size})')
+
+    # ── Data Preparation ──
+    if is_main:
+        prepare_data(tokenizer, skip=args.skip_data_prep)
+    if is_distributed:
+        torch.distributed.barrier()
+
+    # ── Load Dataset ──
+    data_path = args.data_path
+    if data_path is None:
+        merged = os.path.join(DATA_DIR, 'train_merged.pt')
+        if os.path.exists(merged):
+            data_path = merged
+        else:
+            pt_files = list(Path(DATA_DIR).glob('*.pt'))
+            if pt_files:
+                data_path = str(pt_files[0])
+
+    if data_path is None:
+        print('ERROR: No training data found!')
+        sys.exit(1)
+
+    if is_main:
+        print(f'Loading data from: {data_path}')
+
+    dataset = TokenDataset(data_path, args.seq_len)
+    sampler = None
+    if is_distributed:
+        sampler = DistributedSampler(dataset, num_replicas=world_size, rank=rank, shuffle=True, drop_last=True)
+
+    dataloader = DataLoader(
+        dataset, batch_size=args.batch_size,
+        shuffle=(sampler is None),
+        sampler=sampler,
+        num_workers=4 if torch.cuda.is_available() else 0,
+        pin_memory=True, drop_last=True,
+        persistent_workers=bool(torch.cuda.is_available()),
+    )
+
+    # CUDA prefetch wrapper
+    if args.cuda_prefetch and torch.cuda.is_available():
+        dataloader = CUDAPrefetchLoader(dataloader, device)
+        if is_main:
+            print('CUDA prefetch enabled: overlapping data transfer with compute')
+
+    # ── Build Model ──
+    if is_main:
+        print(f'\nBuilding CogNet-{args.model_size.upper()} (optimized)...')
+
+    if args.model_size == '1b':
+        model = create_cognet_1b_optimized(
+            vocab_size=tokenizer.vocab_size,
+            max_seq_len=args.seq_len,
+            use_gradient_checkpointing=args.use_grad_checkpoint,
+        )
+    else:
+        model = create_cognet_350m(
+            vocab_size=tokenizer.vocab_size,
+            max_seq_len=args.seq_len,
+            use_gradient_checkpointing=args.use_grad_checkpoint,
+        )
+
+    model = model.to(device)
+
+    total_params = sum(p.numel() for p in model.parameters())
+    if is_main:
+        print(f'Total parameters: {total_params:,} ({total_params/1e9:.2f}B)')
+
+    # FSDP
+    if args.use_fsdp and is_distributed:
+        from torch.distributed.fsdp import FullyShardedDataParallel as FSDP, MixedPrecision
+        from torch.distributed.fsdp.wrap import transformer_auto_wrap_policy
+
+        mp_policy = None
+        if args.bf16:
+            mp_policy = MixedPrecision(param_dtype=torch.bfloat16, reduce_dtype=torch.bfloat16, buffer_dtype=torch.bfloat16)
+
+        auto_wrap = transformer_auto_wrap_policy(transformer_layer_cls={CogNetBlock})
+        model = FSDP(model, auto_wrap_policy=auto_wrap, mixed_precision=mp_policy,
+                     device_id=local_rank, sharding_strategy=torch.distributed.fsdp.ShardingStrategy.FULL_SHARD)
+        if is_main:
+            print('FSDP enabled')
+
+    # torch.compile the model
+    if args.compile:
+        try:
+            model = torch.compile(model, mode="reduce-overhead")
+            if is_main:
+                print('Model compiled with torch.compile(reduce-overhead)')
+        except Exception as e:
+            if is_main:
+                print(f'Compile failed: {e}')
+
+    # ── Optimizer ──
+    use_8bit = getattr(args, '8bit_optim', False)
+    if use_8bit:
+        try:
+            import bitsandbytes as bnb
+            optimizer = bnb.optim.AdamW8bit(
+                model.parameters(), lr=args.max_lr,
+                betas=(0.9, 0.95), eps=1e-8,
+                weight_decay=args.weight_decay,
+            )
+            if is_main:
+                print('8-bit AdamW (bitsandbytes) enabled — 50% less VRAM for optimizer states')
+        except ImportError:
+            if is_main:
+                print('bitsandbytes not available, falling back to Fused AdamW')
+            optimizer = torch.optim.AdamW(
+                model.parameters(), lr=args.max_lr,
+                betas=(0.9, 0.95), eps=1e-8,
+                weight_decay=args.weight_decay, fused=True,
+            )
+    else:
+        optimizer = torch.optim.AdamW(
+            model.parameters(), lr=args.max_lr,
+            betas=(0.9, 0.95), eps=1e-8,
+            weight_decay=args.weight_decay, fused=True,
+        )
+
+    use_bf16 = args.bf16 and torch.cuda.is_available() and torch.cuda.is_bf16_supported()
+    scaler = None if use_bf16 else torch.amp.GradScaler('cuda')
+    if is_main:
+        print(f'Mixed precision: {"BF16" if use_bf16 else "FP16+GradScaler"}')
+
+    # Compiled training step
+    compiled_step = None
+    if args.compile_step and not is_distributed:
+        try:
+            compiled_step = create_compiled_step(model, tokenizer.vocab_size, args.grad_accum, args.grad_clip, use_bf16)
+            if is_main:
+                print('Compiled training step enabled (forward+backward fused)')
+        except Exception as e:
+            if is_main:
+                print(f'Compiled step failed: {e}, using standard loop')
+
+    # Async checkpoint saver
+    async_saver = None
+    if args.async_ckpt:
+        async_saver = AsyncCheckpointSaver()
+        if is_main:
+            print('Async checkpointing enabled (saves in background)')
+
+    # ── Resume ──
+    start_step = 0
+    best_loss = float('inf')
+
+    if args.resume and os.path.exists(args.resume):
+        ckpt = torch.load(args.resume, map_location=device, weights_only=False)
+        model.load_state_dict(ckpt['model_state_dict'])
+        if 'optimizer_state_dict' in ckpt:
+            optimizer.load_state_dict(ckpt['optimizer_state_dict'])
+        start_step = ckpt.get('step', 0)
+        best_loss = ckpt.get('best_loss', float('inf'))
+        if is_main:
+            print(f'Resumed from step {start_step}, best_loss={best_loss:.4f}')
+    else:
+        latest = os.path.join(args.ckpt_dir, 'cognet_1b_latest.pt')
+        if os.path.exists(latest):
+            ckpt = torch.load(latest, map_location=device, weights_only=False)
+            model.load_state_dict(ckpt['model_state_dict'])
+            if 'optimizer_state_dict' in ckpt:
+                optimizer.load_state_dict(ckpt['optimizer_state_dict'])
+            start_step = ckpt.get('step', 0)
+            best_loss = ckpt.get('best_loss', float('inf'))
+            if is_main:
+                print(f'Auto-resumed from step {start_step}, best_loss={best_loss:.4f}')
+
+    # ── Train ──
+    effective_batch = args.batch_size * args.grad_accum * world_size
+    if is_main:
+        print(f'\nStarting: step {start_step} -> {args.max_steps}')
+        print(f'Batch={args.batch_size} x GradAccum={args.grad_accum} x GPUs={world_size} = Effective {effective_batch}')
+        print(f'SeqLen={args.seq_len}, LR={args.min_lr}-{args.max_lr}')
+        print(f'TF32=ON, Gradient checkpointing={args.use_grad_checkpoint}')
+        print(f'Graceful shutdown: SIGTERM/SIGINT will save checkpoint')
+        print(f'\n[BENCH] Un benchmark de 10 steps va mesurer la vitesse réelle...')
+
+    model.train()
+    data_iter = iter(dataloader)
+    t0 = time.time()
+    loss_val = 0.0
+
+    # ═══════════════════════════════════════════════════════════
+    #  VRAI BENCHMARK — Mesure les tokens/sec réels sur votre GPU
+    # ═══════════════════════════════════════════════════════════
+    BENCHMARK_WARMUP_STEPS = 3    # steps pour chauffer (compile, caches CUDA)
+    BENCHMARK_MEASURE_STEPS = 10  # steps pour la mesure réelle
+    measured_steps_per_sec = None
+    measured_tokens_per_sec = None
+
+    if is_main:
+        print(f'\n{"="*60}')
+        print(f'  BENCHMARK — Mesure des performances réelles')
+        print(f'{"="*60}')
+        print(f'  Warmup: {BENCHMARK_WARMUP_STEPS} steps')
+        print(f'  Mesure: {BENCHMARK_MEASURE_STEPS} steps')
+        print(f'  Config: batch={args.batch_size}, grad_accum={args.grad_accum}, seq_len={args.seq_len}')
+
+    # Phase 1: Warmup (compile, caches CUDA, allocation mémoire)
+    for i in range(BENCHMARK_WARMUP_STEPS):
+        try:
+            batch = next(data_iter)
+        except StopIteration:
+            data_iter = iter(dataloader)
+            batch = next(data_iter)
+        x, y = batch
+        if not isinstance(x, torch.Tensor):
+            x, y = x, y
+        x = x.to(device, non_blocking=True)
+        y = y.to(device, non_blocking=True)
+        with torch.amp.autocast('cuda', dtype=torch.bfloat16 if use_bf16 else torch.float16):
+            result = model(x)
+            loss = F.cross_entropy(result['logits'].view(-1, tokenizer.vocab_size), y.view(-1), ignore_index=0)
+        (loss / args.grad_accum).backward()
+        optimizer.zero_grad(set_to_none=True)
+
+    if torch.cuda.is_available():
+        torch.cuda.synchronize()
+
+    if is_main:
+        print(f'  Warmup terminé — début de la mesure...')
+
+    # Phase 2: Mesure réelle (forward + backward + optimizer step)
+    bench_t0 = time.time()
+    for i in range(BENCHMARK_MEASURE_STEPS):
+        optimizer.zero_grad(set_to_none=True)
+        accum_loss_bench = 0.0
+        for micro_step in range(args.grad_accum):
+            try:
+                batch = next(data_iter)
+            except StopIteration:
+                data_iter = iter(dataloader)
+                batch = next(data_iter)
+            x, y = batch
+            if not isinstance(x, torch.Tensor):
+                x, y = x, y
+            x = x.to(device, non_blocking=True)
+            y = y.to(device, non_blocking=True)
+            with torch.amp.autocast('cuda', dtype=torch.bfloat16 if use_bf16 else torch.float16):
+                result = model(x)
+                loss = F.cross_entropy(result['logits'].view(-1, tokenizer.vocab_size), y.view(-1), ignore_index=0)
+            (loss / args.grad_accum).backward()
+            accum_loss_bench += loss.item()
+
+        # Clip + step (même chose que la vraie boucle)
+        if use_bf16:
+            torch.nn.utils.clip_grad_norm_(model.parameters(), args.grad_clip)
+            optimizer.step()
+        else:
+            scaler.unscale_(optimizer)
+            torch.nn.utils.clip_grad_norm_(model.parameters(), args.grad_clip)
+            scaler.step(optimizer)
+            scaler.update()
+
+    if torch.cuda.is_available():
+        torch.cuda.synchronize()
+    bench_elapsed = time.time() - bench_t0
+
+    # Calcul des performances mesurées
+    measured_steps_per_sec = BENCHMARK_MEASURE_STEPS / max(bench_elapsed, 0.001)
+    measured_tokens_per_sec = measured_steps_per_sec * effective_batch * args.seq_len
+    vram = torch.cuda.memory_allocated() / 1e9 if torch.cuda.is_available() else 0
+
+    if is_main:
+        remaining_steps = args.max_steps - start_step
+        est_hours = remaining_steps / max(measured_steps_per_sec, 0.001) / 3600
+        print(f'\n  ╔══════════════════════════════════════════════════════╗')
+        print(f'  ║           RÉSULTATS DU BENCHMARK                     ║')
+        print(f'  ╠══════════════════════════════════════════════════════╣')
+        print(f'  ║  {measured_steps_per_sec:>8.2f} steps/sec (optimizer steps)   ║')
+        print(f'  ║  {measured_tokens_per_sec:>8.0f} tokens/sec                    ║')
+        print(f'  ║  {bench_elapsed:>8.2f} sec pour {BENCHMARK_MEASURE_STEPS} steps         ║')
+        print(f'  ║  {vram:>8.1f} GB VRAM utilisé                ║')
+        print(f'  ╠══════════════════════════════════════════════════════╣')
+        print(f'  ║  Temps estimé pour {remaining_steps:,} steps restants    ║')
+        print(f'  ║  ~{est_hours:>6.1f} heures ({est_hours/24:.1f} jours)                  ║')
+        print(f'  ╚══════════════════════════════════════════════════════╝')
+        print(f'{"="*60}\n')
+
+    # Sauvegarder le résultat du benchmark dans un fichier
+    if is_main:
+        bench_info = {
+            'timestamp': datetime.now().isoformat(),
+            'steps_per_sec': measured_steps_per_sec,
+            'tokens_per_sec': measured_tokens_per_sec,
+            'benchmark_steps': BENCHMARK_MEASURE_STEPS,
+            'benchmark_time_sec': bench_elapsed,
+            'vram_gb': vram,
+            'effective_batch': effective_batch,
+            'seq_len': args.seq_len,
+            'model_size': args.model_size,
+            'grad_accum': args.grad_accum,
+            'compile': args.compile,
+            'bf16': use_bf16,
+            'fsdp': args.use_fsdp,
+        }
+        bench_path = os.path.join(args.ckpt_dir, 'benchmark_results.json')
+        os.makedirs(args.ckpt_dir, exist_ok=True)
+        with open(bench_path, 'w') as f:
+            json.dump(bench_info, f, indent=2)
+        print(f'  Benchmark sauvé: {bench_path}')
+
+    for step in range(start_step, args.max_steps):
+        if shutdown_requested:
+            if is_main:
+                save_checkpoint(model, optimizer, step, loss_val, best_loss, tokenizer, args, 'cognet_1b_latest.pt')
+                print(f'Checkpoint saved at step {step}. Exiting.')
+            break
+
+        lr = get_cosine_lr(step, args.warmup_steps, args.max_steps, args.max_lr, args.min_lr)
+        for param_group in optimizer.param_groups:
+            param_group['lr'] = lr
+
+        optimizer.zero_grad(set_to_none=True)
+        accum_loss = 0.0
+
+        # Use compiled step if available
+        if compiled_step is not None:
+            for micro_step in range(args.grad_accum):
+                try:
+                    batch = next(data_iter)
+                except StopIteration:
+                    data_iter = iter(dataloader)
+                    batch = next(data_iter)
+                x, y = batch
+                if not isinstance(x, torch.Tensor):
+                    x, y = x, y
+                x = x.to(device, non_blocking=True)
+                y = y.to(device, non_blocking=True)
+                loss = compiled_step(x, y)
+                accum_loss += loss.item()
+        else:
+            for micro_step in range(args.grad_accum):
+                try:
+                    batch = next(data_iter)
+                except StopIteration:
+                    data_iter = iter(dataloader)
+                    batch = next(data_iter)
+                x, y = batch
+                if not isinstance(x, torch.Tensor):
+                    x, y = x, y
+                x = x.to(device, non_blocking=True)
+                y = y.to(device, non_blocking=True)
+
+                if use_bf16:
+                    with torch.amp.autocast('cuda', dtype=torch.bfloat16):
+                        result = model(x)
+                        logits = result['logits']
+                        loss = F.cross_entropy(logits.view(-1, tokenizer.vocab_size), y.view(-1), ignore_index=0)
+                    (loss / args.grad_accum).backward()
+                else:
+                    with torch.amp.autocast('cuda', dtype=torch.float16):
+                        result = model(x)
+                        logits = result['logits']
+                        loss = F.cross_entropy(logits.view(-1, tokenizer.vocab_size), y.view(-1), ignore_index=0)
+                    scaler.scale(loss / args.grad_accum).backward()
+
+                accum_loss += loss.item()
+
+        # Step optimizer
+        if use_bf16:
+            grad_norm = torch.nn.utils.clip_grad_norm_(model.parameters(), args.grad_clip)
+            optimizer.step()
+        else:
+            scaler.unscale_(optimizer)
+            grad_norm = torch.nn.utils.clip_grad_norm_(model.parameters(), args.grad_clip)
+            scaler.step(optimizer)
+            scaler.update()
+
+        loss_val = accum_loss / args.grad_accum
+
+        # Logging avec ETA calculé à partir de la vitesse mesurée
+        if is_main and step % args.log_every == 0:
+            elapsed = time.time() - t0
+            live_steps_per_sec = args.log_every / max(elapsed, 0.001)
+            live_tokens_per_sec = live_steps_per_sec * effective_batch * args.seq_len
+            vram = torch.cuda.memory_allocated() / 1e9 if torch.cuda.is_available() else 0
+
+            # ETA basé sur la vitesse du benchmark (plus stable que la vitesse instantanée)
+            remaining_steps = args.max_steps - step
+            if measured_steps_per_sec and measured_steps_per_sec > 0:
+                eta_hours = remaining_steps / measured_steps_per_sec / 3600
+                eta_str = f'{eta_hours:.1f}h' if eta_hours < 48 else f'{eta_hours/24:.1f}j'
+            else:
+                # Fallback: utiliser la vitesse instantanée
+                eta_hours = remaining_steps / max(live_steps_per_sec, 0.001) / 3600
+                eta_str = f'{eta_hours:.1f}h' if eta_hours < 48 else f'{eta_hours/24:.1f}j'
+
+            print(
+                f'Step {step:>7d}/{args.max_steps} | '
+                f'Loss: {loss_val:.4f} | PPL: {math.exp(min(loss_val, 20)):.1f} | '
+                f'LR: {lr:.2e} | Grad: {grad_norm:.2f} | '
+                f'VRAM: {vram:.1f}GB | {live_tokens_per_sec:.0f} tok/s | {live_steps_per_sec:.1f} step/s | '
+                f'ETA: {eta_str}'
+            )
+            t0 = time.time()
+
+        # Sample generation
+        if is_main and step > 0 and step % args.eval_every == 0:
+            model.eval()
+            with torch.no_grad():
+                prompt = torch.tensor([[tokenizer.bos_token_id]], device=device)
+                sample_ids = model.generate(prompt, max_new_tokens=150, temperature=0.8, top_k=50)
+                sample_text = tokenizer.decode(sample_ids[0].tolist())
+                print(f'--- Sample step {step} ---')
+                print(sample_text[:300])
+                print(f'--- End ---')
+            model.train()
+
+        # Save checkpoint (overwrite toujours les mêmes fichiers, pas d'accumulation)
+        if is_main and step > 0 and step % args.save_every == 0:
+            save_checkpoint(model, optimizer, step, loss_val, best_loss, tokenizer, args, 'cognet_1b_latest.pt')
+
+            if loss_val < best_loss:
+                best_loss = loss_val
+                if args.async_ckpt and async_saver:
+                    async_saver.wait()
+                save_checkpoint(model, optimizer, step, loss_val, best_loss, tokenizer, args, 'cognet_1b_best.pt')
+                print(f'Checkpoint step {step} saved (loss={loss_val:.4f}) — NEW BEST!')
+            else:
+                print(f'Checkpoint step {step} saved (loss={loss_val:.4f}, best={best_loss:.4f})')
+
+    else:
+        # Training completed normally
+        if is_main:
+            if async_saver:
+                async_saver.wait()
+            save_checkpoint(model, optimizer, args.max_steps, loss_val, best_loss, tokenizer, args, 'cognet_1b_final.pt')
+            print(f'\nTraining complete! Final loss: {loss_val:.4f}, Best: {best_loss:.4f}')
+
+            # Push to HF
+            best_path = os.path.join(args.ckpt_dir, 'cognet_1b_best.pt')
+            if os.path.exists(best_path):
+                push_to_huggingface(best_path, tokenizer)
+
+    if is_distributed:
+        from torch.distributed import destroy_process_group
+        destroy_process_group()
+
+
+if __name__ == '__main__':
+    main()