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| from typing import TYPE_CHECKING, Any, Optional |
|
|
| from .base import HfQuantizer |
| from .quantizers_utils import get_module_from_name |
|
|
|
|
| if TYPE_CHECKING: |
| from ..modeling_utils import PreTrainedModel |
|
|
| from ..utils import is_fp_quant_available, is_qutlass_available, is_torch_available, logging |
| from ..utils.quantization_config import QuantizationConfigMixin |
|
|
|
|
| if is_torch_available(): |
| import torch |
|
|
| logger = logging.get_logger(__name__) |
|
|
|
|
| class FPQuantHfQuantizer(HfQuantizer): |
| """ |
| Quantizer for the FP-Quant method. Enables the loading of prequantized models and in-flight quantization of full-precision models. |
| """ |
|
|
| requires_calibration = False |
| requires_parameters_quantization = True |
| is_qat_trainable = False |
| required_packages = ["fp_quant"] |
|
|
| def __init__(self, quantization_config: QuantizationConfigMixin, **kwargs): |
| super().__init__(quantization_config, **kwargs) |
| self.quantization_config = quantization_config |
|
|
| def validate_environment(self, device_map, **kwargs): |
| if not torch.cuda.is_available(): |
| raise NotImplementedError( |
| "FPQuant quantization is only supported on GPU. Please use a different quantizer." |
| ) |
|
|
| if not is_qutlass_available() and not self.quantization_config.pseudoquantization: |
| raise ImportError( |
| "Using `fp_quant` with real quantization requires a **Blackwell GPU** and qutlass: `git clone https://github.com/IST-DASLab/qutlass.git && cd qutlass && pip install --no-build-isolation .`. You can use `FPQuantConfig(pseudoquantization=True, ...)` to use Triton-based pseudo-quantization. It doesn't provide any speedups but emulates the quantization behavior of the real quantization." |
| ) |
|
|
| if self.quantization_config.pseudoquantization: |
| logger.warning( |
| "Using pseudo-quantization for FP-Quant. This doesn't provide any speedups but emulates the quantization behavior of the real quantization." |
| ) |
|
|
| if not is_fp_quant_available(): |
| raise ImportError("Using `fp_quant` quantization requires fp_quant: `pip install fp_quant`") |
|
|
| if device_map is None: |
| raise ValueError( |
| "You are attempting to load a FPQuant model without setting device_map." |
| " Please set device_map comprised of 'cuda' devices." |
| ) |
| elif isinstance(device_map, dict) and ("cpu" in device_map.values() or "disk" in device_map.values()): |
| raise ValueError( |
| "You are attempting to load a FPQuant model with a device_map that contains a CPU or disk device." |
| " This is not supported. Please remove the CPU or disk device from the device_map." |
| ) |
|
|
| def update_dtype(self, dtype: "torch.dtype") -> "torch.dtype": |
| if dtype is None: |
| logger.info("`dtype` is None. Setting `dtype=torch.bfloat16` for qutlass compatibility.") |
| dtype = torch.bfloat16 |
| elif dtype != torch.bfloat16: |
| raise ValueError(f"Invalid `dtype` {dtype}. fp_quant quantization only supports `dtype=torch.bfloat16`.") |
|
|
| return dtype |
|
|
| def create_quantized_param( |
| self, |
| model: "PreTrainedModel", |
| param_value: "torch.Tensor", |
| param_name: str, |
| target_device: "torch.device", |
| state_dict: dict[str, Any], |
| unexpected_keys: Optional[list[str]] = None, |
| ): |
| module, _ = get_module_from_name(model, param_name) |
|
|
| |
| |
| |
| |
|
|
| if param_name.endswith(".qweight"): |
| |
| module.qweight = torch.nn.Parameter( |
| param_value.to(target_device), |
| requires_grad=False, |
| ) |
| module.weight = None |
| module.dqweight = None |
| return |
|
|
| if param_name.endswith(".dqweight"): |
| |
| module.dqweight = torch.nn.Parameter(param_value.to(target_device)) |
| module.weight = None |
| module.qweight = None |
| module.scales = None |
| return |
|
|
| |
| module.weight = torch.nn.Parameter(param_value.to(target_device)) |
| |
| module.pre_forward() |
|
|
| if unexpected_keys is not None and param_name in unexpected_keys: |
| unexpected_keys.remove(param_name) |
|
|
| def _process_model_before_weight_loading( |
| self, |
| model: "PreTrainedModel", |
| **kwargs, |
| ): |
| from fp_quant import replace_with_fp_quant_linear |
|
|
| from ..integrations.fp_quant import adapt_fp_quant_config |
|
|
| replace_with_fp_quant_linear( |
| model, |
| fp_quant_linear_config=adapt_fp_quant_config(self.quantization_config), |
| ) |
| model.config.quantization_config = self.quantization_config |
|
|
| def _process_model_after_weight_loading(self, model: "PreTrainedModel", **kwargs): |
| return model |
|
|
| def update_missing_keys(self, model, missing_keys: list[str], prefix: str) -> list[str]: |
| from fp_quant import FPQuantLinear |
|
|
| fp_quant_names = {name for name, module in model.named_modules() if isinstance(module, FPQuantLinear)} |
|
|
| def should_exclude(key: str) -> bool: |
| if key.endswith(".weight") or key.endswith(".bias"): |
| return False |
| full_key = f"{prefix}.{key}" |
| return any(name in key or name in full_key for name in fp_quant_names) |
|
|
| return [key for key in missing_keys if not should_exclude(key)] |
|
|
| @property |
| def is_trainable(self, model: Optional["PreTrainedModel"] = None): |
| return False |
|
|
| def is_serializable(self, safe_serialization=None): |
| return True |
|
|
| def check_quantized_param( |
| self, |
| model: "PreTrainedModel", |
| param_value: "torch.Tensor", |
| param_name: str, |
| state_dict: dict[str, Any], |
| **kwargs, |
| ) -> bool: |
| from fp_quant import FPQuantLinear |
|
|
| module, tensor_name = get_module_from_name(model, param_name) |
| if isinstance(module, FPQuantLinear) and tensor_name in ["weight", "qweight", "dqweight"]: |
| |
| return True |
| else: |
| return False |
|
|
