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# Optimization
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Advanced optimization techniques including quantization, mixed precision training, hardware acceleration, and memory efficiency improvements for both inference and training workflows.
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## Capabilities
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### Quantization
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Reduce model memory footprint and increase inference speed through various quantization techniques.
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```python { .api }
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class BitsAndBytesConfig:
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    def __init__(
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        self,
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        load_in_8bit: bool = False,
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        load_in_4bit: bool = False,
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        llm_int8_threshold: float = 6.0,
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        llm_int8_skip_modules: Optional[List[str]] = None,
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        llm_int8_enable_fp32_cpu_offload: bool = False,
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        llm_int8_has_fp16_weight: bool = False,
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        bnb_4bit_compute_dtype: Optional[torch.dtype] = None,
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        bnb_4bit_quant_type: str = "fp4",
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        bnb_4bit_use_double_quant: bool = False,
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        bnb_4bit_quant_storage: Optional[torch.dtype] = None,
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        **kwargs
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    ):
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        """
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        Configuration for BitsAndBytes quantization.
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        Args:
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            load_in_8bit: Enable 8-bit quantization
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            load_in_4bit: Enable 4-bit quantization
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            llm_int8_threshold: Threshold for outlier detection
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            llm_int8_skip_modules: Modules to skip quantization
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            llm_int8_enable_fp32_cpu_offload: Offload fp32 weights to CPU
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            llm_int8_has_fp16_weight: Model has fp16 weights
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            bnb_4bit_compute_dtype: Compute dtype for 4-bit
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            bnb_4bit_quant_type: Quantization type ("fp4", "nf4")
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            bnb_4bit_use_double_quant: Use double quantization
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            bnb_4bit_quant_storage: Storage dtype for quantized weights
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        """
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class GPTQConfig:
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    def __init__(
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        self,
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        bits: int = 4,
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        tokenizer: Optional[Union[str, PreTrainedTokenizer]] = None,
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        dataset: Optional[Union[str, List[str]]] = None,
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        group_size: int = 128,
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        damp_percent: float = 0.1,
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        desc_act: bool = False,
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        static_groups: bool = False,
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        sym: bool = True,
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        true_sequential: bool = True,
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        model_name_or_path: Optional[str] = None,
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        model_seqlen: Optional[int] = None,
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        block_name_to_quantize: Optional[str] = None,
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        module_name_preceding_first_block: Optional[List[str]] = None,
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        batch_size: int = 1,
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        pad_token_id: Optional[int] = None,
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        use_exllama: Optional[bool] = None,
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        max_input_length: Optional[int] = None,
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        exllama_config: Optional[Dict[str, Any]] = None,
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        cache_block_outputs: bool = True,
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        modules_in_block_to_quantize: Optional[List[List[str]]] = None,
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        **kwargs
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    ):
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        """
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        Configuration for GPTQ quantization.
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        Args:
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            bits: Number of bits for quantization
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            tokenizer: Tokenizer for calibration dataset
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            dataset: Calibration dataset name or samples
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            group_size: Group size for quantization
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            damp_percent: Damping percentage
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            desc_act: Use descending activation order
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            static_groups: Use static groups
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            sym: Use symmetric quantization
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            true_sequential: Use true sequential quantization
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            model_seqlen: Model sequence length
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            batch_size: Batch size for calibration
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            use_exllama: Use ExLlama kernels
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            max_input_length: Maximum input length
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        """
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class AwqConfig:
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    def __init__(
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        self,
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        bits: int = 4,
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        group_size: int = 128,
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        zero_point: bool = True,
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        version: str = "GEMM",
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        backend: str = "autoawq",
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        do_fuse: Optional[bool] = None,
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        fuse_max_seq_len: Optional[int] = None,
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        modules_to_fuse: Optional[Dict] = None,
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        **kwargs
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    ):
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        """
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        Configuration for AWQ quantization.
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        Args:
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            bits: Number of bits for quantization
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            group_size: Group size for quantization
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            zero_point: Use zero point quantization
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            version: AWQ version ("GEMM", "GEMV")
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            backend: Backend implementation
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            do_fuse: Enable module fusion
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            fuse_max_seq_len: Maximum sequence length for fusion
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            modules_to_fuse: Specific modules to fuse
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        """
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```
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### Mixed Precision Training
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Optimize training speed and memory usage with mixed precision techniques.
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```python { .api }
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class TrainingArguments:
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    """Training arguments with mixed precision options."""
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    def __init__(
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        self,
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        # Mixed precision options
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        fp16: bool = False,
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        bf16: bool = False,
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        fp16_opt_level: str = "O1",
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        fp16_backend: str = "auto",
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        fp16_full_eval: bool = False,
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        bf16_full_eval: bool = False,
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        tf32: Optional[bool] = None,
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        dataloader_pin_memory: bool = True,
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        # Other training args...
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        **kwargs
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    ):
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        """
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        Configure mixed precision training.
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        Args:
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            fp16: Enable 16-bit floating point training
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            bf16: Enable bfloat16 training (better numerical stability)
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            fp16_opt_level: Optimization level for Apex ("O0", "O1", "O2", "O3")
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            fp16_backend: Backend for fp16 ("auto", "apex", "amp")
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            fp16_full_eval: Use fp16 for evaluation
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            bf16_full_eval: Use bf16 for evaluation
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            tf32: Enable TensorFloat-32 on Ampere GPUs
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            dataloader_pin_memory: Pin memory for faster data transfer
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        """
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```
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### Gradient Checkpointing
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Trade computation for memory by recomputing activations during backward pass.
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```python { .api }
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class PreTrainedModel:
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    """Model with gradient checkpointing support."""
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    def gradient_checkpointing_enable(
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        self,
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        gradient_checkpointing_kwargs: Optional[Dict] = None
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    ) -> None:
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        """
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        Enable gradient checkpointing for the model.
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        Args:
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            gradient_checkpointing_kwargs: Additional arguments for checkpointing
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        """
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    def gradient_checkpointing_disable(self) -> None:
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        """Disable gradient checkpointing."""
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class TrainingArguments:
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    def __init__(
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        self,
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        gradient_checkpointing: bool = False,
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        gradient_checkpointing_kwargs: Optional[Dict] = None,
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        **kwargs
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    ):
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        """
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        Configure gradient checkpointing in training.
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        Args:
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            gradient_checkpointing: Enable gradient checkpointing
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            gradient_checkpointing_kwargs: Additional checkpointing options
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        """
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```
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### Memory Optimization
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Advanced memory management techniques for large models.
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```python { .api }
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def enable_memory_efficient_attention():
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    """Enable memory-efficient attention implementations."""
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def get_memory_footprint_mb(
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    model: torch.nn.Module,
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    return_buffers: bool = True
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) -> int:
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    """
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    Get model memory footprint in MB.
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    Args:
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        model: PyTorch model
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        return_buffers: Include buffer memory
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    Returns:
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        Memory footprint in megabytes
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    """
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class DeepSpeedConfig:
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    """Configuration for DeepSpeed optimization."""
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    @staticmethod
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    def get_config(
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        stage: int = 2,
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        offload_optimizer: bool = False,
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        offload_param: bool = False,
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        reduce_bucket_size: int = 200000000,
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        stage3_prefetch_bucket_size: int = 200000000,
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        stage3_param_persistence_threshold: int = 1000000,
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        **kwargs
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    ) -> Dict[str, Any]:
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        """
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        Get DeepSpeed configuration dictionary.
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        Args:
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            stage: ZeRO stage (1, 2, or 3)
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            offload_optimizer: Offload optimizer states to CPU
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            offload_param: Offload parameters to CPU
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            reduce_bucket_size: Gradient reduction bucket size
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            stage3_prefetch_bucket_size: Parameter prefetch bucket size
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            stage3_param_persistence_threshold: Parameter persistence threshold
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        Returns:
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            DeepSpeed configuration dictionary
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        """
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```
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### Hardware Acceleration
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Optimize for specific hardware platforms and accelerators.
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```python { .api }
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class BetterTransformer:
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    """Flash Attention and other optimized kernels."""
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    @staticmethod
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    def transform(
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        model: PreTrainedModel,
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        keep_original_model: bool = False,
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        **kwargs
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    ) -> PreTrainedModel:
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        """
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        Apply BetterTransformer optimizations.
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        Args:
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            model: Model to optimize
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            keep_original_model: Keep reference to original model
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        Returns:
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            Optimized model with fast attention
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        """
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    @staticmethod  
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    def reverse(model: PreTrainedModel) -> PreTrainedModel:
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        """Reverse BetterTransformer optimizations."""
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# PyTorch 2.0 Compilation
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def torch_compile_model(
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    model: PreTrainedModel,
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    backend: str = "inductor",
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    mode: str = "default",
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    **kwargs
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) -> PreTrainedModel:
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    """
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    Compile model with PyTorch 2.0 torch.compile.
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    Args:
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        model: Model to compile
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        backend: Compilation backend ("inductor", "aot_eager", etc.)
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        mode: Compilation mode ("default", "reduce-overhead", "max-autotune")
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    Returns:
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        Compiled model
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    """
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class TrainingArguments:
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    def __init__(
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        self,
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        torch_compile: bool = False,
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        torch_compile_backend: Optional[str] = None,
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        torch_compile_mode: Optional[str] = None,
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        **kwargs
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    ):
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        """
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        Configure PyTorch compilation in training.
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        Args:
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            torch_compile: Enable torch.compile
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            torch_compile_backend: Compilation backend
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            torch_compile_mode: Compilation mode
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        """
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```
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### Model Parallelism
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Distribute large models across multiple devices.
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```python { .api }
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class TrainingArguments:
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    def __init__(
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        self,
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        # Data parallelism
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        local_rank: int = -1,
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        ddp_backend: Optional[str] = None,
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        ddp_timeout: Optional[int] = 1800,
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        ddp_find_unused_parameters: Optional[bool] = None,
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        # Model parallelism  
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        fsdp: str = "",
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        fsdp_min_num_params: int = 0,
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        fsdp_config: Optional[str] = None,
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        fsdp_transformer_layer_cls_to_wrap: Optional[str] = None,
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        # Pipeline parallelism
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        deepspeed: Optional[str] = None,
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        **kwargs
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    ):
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        """
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        Configure distributed training strategies.
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        Args:
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            local_rank: Local rank for distributed training
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            ddp_backend: Distributed data parallel backend
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            ddp_timeout: DDP timeout in seconds
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            fsdp: Fully Sharded Data Parallel configuration
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            fsdp_min_num_params: Minimum parameters for FSDP wrapping
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            deepspeed: DeepSpeed configuration file path
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        """
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def load_model_with_device_map(
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    model_name: str,
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    device_map: Union[str, Dict] = "auto",
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    max_memory: Optional[Dict] = None,
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    offload_folder: Optional[str] = None,
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    **kwargs
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) -> PreTrainedModel:
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    """
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    Load model with automatic device mapping.
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    Args:
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        model_name: Model name or path
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        device_map: Device mapping strategy or custom mapping
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        max_memory: Maximum memory per device
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        offload_folder: Folder for offloaded weights
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    Returns:
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        Model distributed across available devices
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    """
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```
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### Inference Optimization
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Optimize models specifically for inference workloads.
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```python { .api }
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def optimize_model_for_inference(
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    model: PreTrainedModel,
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    optimize_for_latency: bool = True,
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    optimize_for_throughput: bool = False,
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    use_bettertransformer: bool = True,
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    use_torch_compile: bool = True,
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    **kwargs
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) -> PreTrainedModel:
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    """
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    Apply inference-specific optimizations.
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    Args:
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        model: Model to optimize
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        optimize_for_latency: Optimize for low latency
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        optimize_for_throughput: Optimize for high throughput
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        use_bettertransformer: Apply BetterTransformer
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        use_torch_compile: Use torch.compile
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    Returns:
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        Optimized model for inference
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    """
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class StaticCache:
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    """Static key-value cache for improved inference performance."""
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    def __init__(
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        self, 
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        config: PretrainedConfig,
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        max_batch_size: int,
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        max_cache_len: int,
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        device: torch.device,
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        dtype: torch.dtype = torch.float16
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    ):
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        """
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        Initialize static cache.
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        Args:
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            config: Model configuration
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            max_batch_size: Maximum batch size
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            max_cache_len: Maximum cache length
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            device: Device for cache tensors
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            dtype: Data type for cache
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        """
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```
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### Caching and Hub Integration
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Optimize model loading and sharing through intelligent caching.
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```python { .api }
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def cached_file(
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    path_or_repo_id: Union[str, os.PathLike],
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    filename: str,
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    cache_dir: Union[str, os.PathLike] = None,
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    force_download: bool = False,
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    resume_download: bool = False,
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    proxies: Optional[Dict[str, str]] = None,
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    token: Union[bool, str] = None,
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    revision: Optional[str] = None,
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    local_files_only: bool = False,
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    **kwargs
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) -> Optional[str]:
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    """
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    Download and cache file from Hugging Face Hub.
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    Args:
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        path_or_repo_id: Repository ID or local path
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        filename: File to download
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        cache_dir: Custom cache directory
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        force_download: Force fresh download
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        resume_download: Resume interrupted download
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        token: Authentication token
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        revision: Model revision/branch
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        local_files_only: Only use local files
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    Returns:
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        Path to cached file
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    """
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def clean_files_cache(
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    cache_dir: Optional[Union[str, os.PathLike]] = None,
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    token: Union[bool, str] = None
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) -> None:
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    """Clean up cached files to free disk space."""
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def scan_cache_dir(
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    cache_dir: Union[str, os.PathLike] = None
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) -> Dict[str, Any]:
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    """Scan cache directory and return usage statistics."""
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```
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## Optimization Examples
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Common optimization patterns for different use cases:
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
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import torch
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# 8-bit quantization
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quantization_config = BitsAndBytesConfig(load_in_8bit=True)
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model = AutoModelForCausalLM.from_pretrained(
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    "facebook/opt-6.7b",
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    quantization_config=quantization_config,
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    device_map="auto"
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)
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# 4-bit quantization with NF4
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quantization_config = BitsAndBytesConfig(
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    load_in_4bit=True,
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    bnb_4bit_quant_type="nf4",
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    bnb_4bit_use_double_quant=True,
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    bnb_4bit_compute_dtype=torch.bfloat16
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)
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model = AutoModelForCausalLM.from_pretrained(
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    "microsoft/DialoGPT-large",
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    quantization_config=quantization_config
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)
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# Mixed precision training
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from transformers import TrainingArguments, Trainer
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training_args = TrainingArguments(
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    output_dir="./results",
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    bf16=True,  # Use bfloat16 for better stability
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    gradient_checkpointing=True,  # Save memory
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    dataloader_pin_memory=True,  # Faster data loading
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    torch_compile=True,  # PyTorch 2.0 compilation
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)
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trainer = Trainer(
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    model=model,
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    args=training_args,
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    train_dataset=train_dataset
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)
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# BetterTransformer optimization
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from transformers import BetterTransformer
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# Apply optimizations
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model = BetterTransformer.transform(model)
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# Use optimized model for inference
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outputs = model.generate(**inputs, max_new_tokens=50)
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# DeepSpeed training
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training_args = TrainingArguments(
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    output_dir="./results",
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    deepspeed="ds_config.json",  # DeepSpeed config file
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    gradient_checkpointing=True,
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    fp16=True
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)
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# Device mapping for large models
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model = AutoModelForCausalLM.from_pretrained(
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    "microsoft/DialoGPT-large",
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    device_map="auto",  # Automatic device placement
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    max_memory={0: "10GB", 1: "10GB"},  # Memory limits per GPU
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    offload_folder="./offload"  # Offload unused weights
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)
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# Static cache for faster inference
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from transformers import StaticCache
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cache = StaticCache(
536
    config=model.config,
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    max_batch_size=4,
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    max_cache_len=512,
539
    device=model.device,
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    dtype=torch.float16
541
)
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# Use cache during generation
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outputs = model.generate(
545
    **inputs,
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    past_key_values=cache,
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    use_cache=True,
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    max_new_tokens=50
549
)
550
```
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## Performance Recommendations
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**For Training:**
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- Use `bf16=True` for better numerical stability than `fp16`
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- Enable `gradient_checkpointing=True` for large models
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- Use `torch_compile=True` with PyTorch 2.0+ for speed improvements
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- Consider DeepSpeed for very large models
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**For Inference:**
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- Apply BetterTransformer optimizations for attention speedup
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- Use 8-bit or 4-bit quantization for memory-constrained environments
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- Enable `torch_compile=True` for repeated inference patterns
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- Use static caching for batch inference scenarios
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**For Memory Optimization:**
567
- Use `device_map="auto"` for automatic multi-GPU placement
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- Set appropriate `max_memory` limits per device
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- Enable gradient checkpointing during training
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- Consider CPU offloading for extremely large models