mirror of https://github.com/hpcaitech/ColossalAI
99 lines
3.5 KiB
Python
99 lines
3.5 KiB
Python
from .utils import InsertPostInitMethodToModuleSubClasses
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import torch
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from colossalai.tensor import ColoTensor, ColoParameter
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from colossalai.nn.parallel.layers import register_colo_module, \
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ColoLinear, ColoEmbedding
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from torch import nn
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from typing import Iterator, Tuple, Union
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# find named_params includes replica
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def _named_params_with_replica(
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module: nn.Module,
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prefix: str = '',
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recurse: bool = True,
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) -> Iterator[Tuple[str, Union[nn.Parameter, ColoTensor]]]:
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modules = module.named_modules(prefix=prefix) if recurse else [(prefix, module)]
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for mod_prefix, mod in modules:
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for name, val in mod._parameters.items():
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if val is None:
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continue
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name = mod_prefix + ('.' if mod_prefix else '') + name
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yield name, val
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def ColoModulize(module):
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"""
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Replacing the parameters() and named_parameters() with our customized ones
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"""
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module._colo_visited = True
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class ColoInitContext(InsertPostInitMethodToModuleSubClasses):
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def __init__(self, lazy_memory_allocate: bool = False, device: torch.device = torch.device('cpu')):
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"""
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Args:
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lazy_memory_allocate (bool, optional): whether to allocate memory for the parameter tensors. Defaults to False.
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device (torch.device, optional): the device parameters initialized are resident on. Defaults to torch.device('cpu').
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"""
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super().__init__()
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self._lazy_memory_allocate = lazy_memory_allocate
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self._device = device
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self._register_colo_modules()
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def _register_colo_modules(self):
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register_colo_module(torch.nn.Linear, ColoLinear())
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register_colo_module(torch.nn.Embedding, ColoEmbedding())
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def _post_init_method(self, module: torch.nn.Module, *args, **kwargs):
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"""
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The function to call at the end of the constructor of each module.
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FIXME(fjr) The module may be passed to this function multiple times?
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"""
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if hasattr(module, '_colo_visited'):
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return
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name_list = []
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for name, param in _named_params_with_replica(module):
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if isinstance(param, ColoTensor):
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continue
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split = name.rfind('.')
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if split >= 0: # param in submodule
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module_name = name[:split]
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param_name = name[split + 1:]
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else:
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module_name = '' # param in current module
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param_name = name
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name_list.append((module_name, param_name))
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replaced_tensors = dict(
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) # record mapping between (torch.Tensor, ColoTensor) to distinguish the same reference
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for module_name, param_name in name_list:
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submodule = module.get_submodule(module_name)
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param = submodule.get_parameter(param_name)
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if param in replaced_tensors:
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colo_param = replaced_tensors[param]
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else:
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save_torch_payload = True if not self._lazy_memory_allocate else False
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# detaching tensor is necessary for optimizers.
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requires_grad = param.requires_grad
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colo_param = ColoParameter(param.to(self._device), requires_grad=requires_grad)
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# add mapping record
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replaced_tensors[param] = colo_param
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delattr(submodule, param_name)
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setattr(submodule, param_name, colo_param)
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colo_param.shared_param_modules.append(submodule)
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module.to(self._device)
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ColoModulize(module)
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