mirror of https://github.com/hpcaitech/ColossalAI
aibig-modeldata-parallelismdeep-learningdistributed-computingfoundation-modelsheterogeneous-traininghpcinferencelarge-scalemodel-parallelismpipeline-parallelism
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55 lines
1.5 KiB
55 lines
1.5 KiB
2 years ago
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import torch
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from colossalai.utils.model.utils import call_to_str
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class LayerSpec:
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"""
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"""
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def __init__(self, typename, *module_args, **module_kwargs):
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self.typename = typename
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self.module_args = module_args
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self.module_kwargs = module_kwargs
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self.children = None
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self._param_count = 0
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if not issubclass(typename, torch.nn.Module):
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raise RuntimeError('LayerSpec only supports torch.nn.Module types.')
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def __repr__(self):
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return call_to_str(self.typename.__name__, self.module_args, self.module_kwargs)
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@property
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def param_count(self):
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return self._param_count
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def build(self):
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"""Build the stored specification."""
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recovered_args = []
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for obj in self.module_args:
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if isinstance(obj, LayerSpec):
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obj = obj.build()
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recovered_args.append(obj)
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recovered_args = tuple(recovered_args)
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recovered_kwargs = {}
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for k, v in self.module_kwargs.items():
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if isinstance(v, LayerSpec):
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v = v.build()
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recovered_kwargs[k] = v
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return self.typename(*recovered_args, **recovered_kwargs)
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def set_children(self, children):
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self.children = children
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def count_params(self):
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self._param_count = 0
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layer = self.build()
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for param in layer.parameters():
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self._param_count += param.numel()
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return self._param_count
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def reset_param_count(self):
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self._param_count = 0
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