DTypeConfig¶
- class torch.ao.quantization.backend_config.DTypeConfig(input_dtype=None, output_dtype=None, weight_dtype=None, bias_dtype=None, is_dynamic=None)[source]¶
Config for the set of supported input/output activation, weight, and bias data types for the patterns defined in
BackendConfig
.Example usage:
>>> dtype_config1 = DTypeConfig( ... input_dtype=torch.quint8, ... output_dtype=torch.quint8, ... weight_dtype=torch.qint8, ... bias_dtype=torch.float) >>> dtype_config2 = DTypeConfig( ... input_dtype=DTypeWithConstraints( ... dtype=torch.quint8, ... quant_min_lower_bound=0, ... quant_max_upper_bound=255, ... ), ... output_dtype=DTypeWithConstraints( ... dtype=torch.quint8, ... quant_min_lower_bound=0, ... quant_max_upper_bound=255, ... ), ... weight_dtype=DTypeWithConstraints( ... dtype=torch.qint8, ... quant_min_lower_bound=-128, ... quant_max_upper_bound=127, ... ), ... bias_dtype=torch.float) >>> dtype_config1.input_dtype torch.quint8 >>> dtype_config2.input_dtype torch.quint8 >>> dtype_config2.input_dtype_with_constraints DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=0, quant_max_upper_bound=255, scale_min_lower_bound=None, scale_max_upper_bound=None)
- classmethod from_dict(dtype_config_dict)[source]¶
- Create a
DTypeConfig
from a dictionary with the following items (all optional): “input_dtype”: torch.dtype or
DTypeWithConstraints
“output_dtype”: torch.dtype orDTypeWithConstraints
“weight_dtype”: torch.dtype orDTypeWithConstraints
“bias_type”: torch.dtype “is_dynamic”: bool
- Return type:
- Create a