Module ktrain.torch_base

Expand source code
import warnings
class TorchBase:
    """
    Utility methods for working pretrained Torch models
    """

    def __init__(self, device, quantize=False, min_transformers_version=None):
        if min_transformers_version is not None:
            import transformers
            from packaging import version
            if version.parse(transformers.__version__) < version.parse(min_transformers_version):
                raise Exception(f'This feature requires transformers>={min_transformers_version}. '+\
                                 'It is usually safe for you to manually upgrade transformers even if ktrain installed a lower version.')
        try:
            import torch
        except (ImportError, OSError):
            raise Exception('This capability in ktrain requires PyTorch to be installed. Please install for your environment: '+\
                            'https://pytorch.org/get-started/locally/')
        self.quantize = quantize
        self.torch_device = device
        if self.torch_device is None: self.torch_device = 'cuda' if torch.cuda.is_available() else 'cpu'


    def quantize_model(self, model):
        """
        quantize a model
        """
        import torch
        if self.torch_device == 'cpu':
            return torch.quantization.quantize_dynamic(model, {torch.nn.Linear}, dtype=torch.qint8)
        elif self.torch_device != 'cpu':
            return model.half()


    def device_to_id(self, device_str=None):
        device_str = self.torch_device if device_str is None else device_str
        if device_str.lower() == 'cpu':
            return -1
        elif device_str.lower() == 'cuda':
            return 0
        elif device_str.lower().startswith('cuda:'):
            _, device_id = device_str.split(':')[1]
            device_id = int(device_id)
            return device_id
        else:
            warnings.warn('Could not determine device ID - defaulting to -1')
            return -1

Classes

class TorchBase (device, quantize=False, min_transformers_version=None)

Utility methods for working pretrained Torch models

Expand source code
class TorchBase:
    """
    Utility methods for working pretrained Torch models
    """

    def __init__(self, device, quantize=False, min_transformers_version=None):
        if min_transformers_version is not None:
            import transformers
            from packaging import version
            if version.parse(transformers.__version__) < version.parse(min_transformers_version):
                raise Exception(f'This feature requires transformers>={min_transformers_version}. '+\
                                 'It is usually safe for you to manually upgrade transformers even if ktrain installed a lower version.')
        try:
            import torch
        except (ImportError, OSError):
            raise Exception('This capability in ktrain requires PyTorch to be installed. Please install for your environment: '+\
                            'https://pytorch.org/get-started/locally/')
        self.quantize = quantize
        self.torch_device = device
        if self.torch_device is None: self.torch_device = 'cuda' if torch.cuda.is_available() else 'cpu'


    def quantize_model(self, model):
        """
        quantize a model
        """
        import torch
        if self.torch_device == 'cpu':
            return torch.quantization.quantize_dynamic(model, {torch.nn.Linear}, dtype=torch.qint8)
        elif self.torch_device != 'cpu':
            return model.half()


    def device_to_id(self, device_str=None):
        device_str = self.torch_device if device_str is None else device_str
        if device_str.lower() == 'cpu':
            return -1
        elif device_str.lower() == 'cuda':
            return 0
        elif device_str.lower().startswith('cuda:'):
            _, device_id = device_str.split(':')[1]
            device_id = int(device_id)
            return device_id
        else:
            warnings.warn('Could not determine device ID - defaulting to -1')
            return -1

Subclasses

Methods

def device_to_id(self, device_str=None)
Expand source code
def device_to_id(self, device_str=None):
    device_str = self.torch_device if device_str is None else device_str
    if device_str.lower() == 'cpu':
        return -1
    elif device_str.lower() == 'cuda':
        return 0
    elif device_str.lower().startswith('cuda:'):
        _, device_id = device_str.split(':')[1]
        device_id = int(device_id)
        return device_id
    else:
        warnings.warn('Could not determine device ID - defaulting to -1')
        return -1
def quantize_model(self, model)

quantize a model

Expand source code
def quantize_model(self, model):
    """
    quantize a model
    """
    import torch
    if self.torch_device == 'cpu':
        return torch.quantization.quantize_dynamic(model, {torch.nn.Linear}, dtype=torch.qint8)
    elif self.torch_device != 'cpu':
        return model.half()