mirror of https://github.com/hpcaitech/ColossalAI
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89 lines
3.8 KiB
89 lines
3.8 KiB
from typing import List, Union, Mapping, Dict, Any
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import torch.optim as optim
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from torch import Tensor
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from colossalai.tensor.colo_tensor import ColoTensor
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class ColoOptimizer(optim.Optimizer):
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def __init__(self, named_params: Mapping[str, Union[Tensor, ColoTensor]], optimizer_class, *optimizer_args,
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**optimizer_kwargs):
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"""
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ColoOptimizer collects all tensors in type of ColoTensor and torch.Tensor,
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then use these tensors as ``params`` for optimizers
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Args:
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named_params (Dict[str, Union[Tensor, ShardedTensor]]) : a Dict
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of parameters, where key is the parameter key, value is either
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Tensor or ColoTensor. This usually used in
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conjunction with model.named_parameters(), the same as PyTorch.
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optimizer_class (torch.optim.Optimizer): the Optimizer to use
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locally, i.e. torch.optim.SGD, torch.optim.Adagrad, etc.
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*optimizer_args: the arguments to initialize the optimizer.
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**optimizer_kwargs: the key-word arguments to initialize the optimizer.
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"""
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tensors: List[Tensor] = []
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for value in named_params.values():
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tensors.append(value)
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self.named_params = named_params
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self._optim = optimizer_class(tensors, *optimizer_args, **optimizer_kwargs)
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self.param_groups = self._optim.param_groups
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self.state = self._optim.state
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def zero_grad(self, set_to_none: bool = False): # type: ignore[override]
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r"""Sets the gradients of all optimized :class:`torch.Tensor` s to zero.
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Args:
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set_to_none (bool): instead of setting to zero, set the grads to None.
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This will in general have lower memory footprint, and can modestly improve performance.
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However, it changes certain behaviors. For example:
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1. When the user tries to access a gradient and perform manual ops on it,
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a None attribute or a Tensor full of 0s will behave differently.
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2. If the user requests ``zero_grad(set_to_none=True)`` followed by a backward pass, ``.grad``\ s
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are guaranteed to be None for params that did not receive a gradient.
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3. ``torch.optim`` optimizers have a different behavior if the gradient is 0 or None
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(in one case it does the step with a gradient of 0 and in the other it skips
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the step altogether).
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"""
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self._optim.zero_grad(set_to_none)
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def step(self, closure=None):
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r"""Performs a single optimization step (parameter update).
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Args:
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closure (callable): A closure that reevaluates the model and
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returns the loss. Optional for most optimizers.
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.. note::
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Unless otherwise specified, this function should not modify the
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``.grad`` field of the parameters.
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"""
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self._optim.step(closure)
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def state_dict(self) -> Dict[str, Any]:
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"""
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Returned state and param_groups will contain parameter keys
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instead of parameter indices like torch.optim.Optimizer.
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"""
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# TODO: implement state_dict
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raise NotImplementedError("ColoOptimizer state_dict not implemented yet!")
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def load_state_dict(self, state_dict: Mapping[str, Any]):
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r"""Loads the ColoOptimizer state.
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Args:
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state_dict (dict): ColoOptimizer state. Should be an object returned
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from a call to :meth:`state_dict`.
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"""
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# TODO: implement load_state_dict
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raise NotImplementedError("ColoOptimizer load_state_dict not implemented yet!")
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def add_param_group(self, param_group: Any):
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r"""Add a new param group
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"""
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# TODO: implement add_param_group
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raise NotImplementedError("ColoOptimizer add_param_group not implemented yet!")
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