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()