Making large AI models cheaper, faster and more accessible
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import torch
from colossalai.utils.model.utils import call_to_str
class LayerSpec:
"""
"""
def __init__(self, typename, *module_args, **module_kwargs):
self.typename = typename
self.module_args = module_args
self.module_kwargs = module_kwargs
self.children = None
self._param_count = 0
if not issubclass(typename, torch.nn.Module):
raise RuntimeError('LayerSpec only supports torch.nn.Module types.')
def __repr__(self):
return call_to_str(self.typename.__name__, self.module_args, self.module_kwargs)
@property
def param_count(self):
return self._param_count
def build(self):
"""Build the stored specification."""
recovered_args = []
for obj in self.module_args:
if isinstance(obj, LayerSpec):
obj = obj.build()
recovered_args.append(obj)
recovered_args = tuple(recovered_args)
recovered_kwargs = {}
for k, v in self.module_kwargs.items():
if isinstance(v, LayerSpec):
v = v.build()
recovered_kwargs[k] = v
return self.typename(*recovered_args, **recovered_kwargs)
def set_children(self, children):
self.children = children
def count_params(self):
self._param_count = 0
layer = self.build()
for param in layer.parameters():
self._param_count += param.numel()
return self._param_count
def reset_param_count(self):
self._param_count = 0