#!/usr/bin/env python3
"""#19 Hardware optimizations — throughput boost (accuracy-neutral).

- torch.compile (PyTorch 2.0+)
- FlashAttention-3 (Dao 2024)
- Liger Kernel (LinkedIn 2024)
- bfloat16 mixed precision
- Gradient checkpointing
- Fused optimizers (Apex)

Accuracy kazancı: 0 (sadece iteration hız artışı → daha fazla deney → dolaylı)
"""

OPTIMIZATION_RECIPE = {
    "torch_compile": {
        "enabled": True,
        "mode": "max-autotune",
        "gain": "1.5-2× throughput (backbone inference)",
        "code": "model = torch.compile(model, mode='max-autotune')",
    },
    "flash_attention_3": {
        "enabled": True,
        "gain": "2-3× attention speedup, ~30% memory save",
        "install": "pip install flash-attn --no-build-isolation",
        "note": "FlashAttention-3 H100-spesifik; A100'de FlashAttention-2"
    },
    "liger_kernel": {
        "enabled": True,
        "gain": "~20% throughput on LLMs/ViTs (Linkedin 2024)",
        "install": "pip install liger-kernel",
        "modules": ["LigerRMSNorm", "LigerCrossEntropy", "LigerFusedLinearCrossEntropy"]
    },
    "bf16": {
        "enabled": True,
        "gain": "50% memory reduce, 2× throughput on A100",
        "code": "torch.set_float32_matmul_precision('medium')"
    },
    "gradient_checkpointing": {
        "enabled": False,
        "note": "Sadece memory kısıtı varsa. Accuracy neutral."
    },
    "fused_adamw": {
        "enabled": True,
        "gain": "~5% optimizer step speedup",
        "code": "torch.optim.AdamW(fused=True)"
    },
    "channels_last": {
        "enabled": True,
        "code": "model = model.to(memory_format=torch.channels_last)"
    }
}

if __name__ == '__main__':
    import json
    from pathlib import Path
    out = Path("/arf/scratch/stakan/hitit-proje/datasets/processed/hw_optimizations.json")
    with open(out, 'w') as f:
        json.dump(OPTIMIZATION_RECIPE, f, indent=2, ensure_ascii=False)
    print(f"HW opt recipe yazıldı: {out}")
    print("\nAktifleştirme:")
    for k, v in OPTIMIZATION_RECIPE.items():
        if isinstance(v, dict) and v.get('enabled'):
            print(f"  ✓ {k}")