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tools/train_utils/optimization/fastai_optim.py
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264
tools/train_utils/optimization/fastai_optim.py
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# This file is modified from https://github.com/traveller59/second.pytorch
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try:
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from collections.abc import Iterable
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except:
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from collections import Iterable
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import torch
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from torch import nn
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from torch._utils import _unflatten_dense_tensors
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from torch.nn.utils import parameters_to_vector
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bn_types = (nn.BatchNorm1d, nn.BatchNorm2d, nn.BatchNorm3d, nn.SyncBatchNorm)
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def split_bn_bias(layer_groups):
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"Split the layers in `layer_groups` into batchnorm (`bn_types`) and non-batchnorm groups."
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split_groups = []
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for l in layer_groups:
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l1, l2 = [], []
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for c in l.children():
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if isinstance(c, bn_types):
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l2.append(c)
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else:
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l1.append(c)
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split_groups += [nn.Sequential(*l1), nn.Sequential(*l2)]
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return split_groups
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def get_master(layer_groups, flat_master: bool = False):
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"Return two lists, one for the model parameters in FP16 and one for the master parameters in FP32."
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split_groups = split_bn_bias(layer_groups)
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model_params = [[param for param in lg.parameters() if param.requires_grad] for lg in split_groups]
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if flat_master:
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master_params = []
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for lg in model_params:
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if len(lg) != 0:
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mp = parameters_to_vector([param.data.float() for param in lg])
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mp = torch.nn.Parameter(mp, requires_grad=True)
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if mp.grad is None: mp.grad = mp.new(*mp.size())
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master_params.append([mp])
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else:
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master_params.append([])
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return model_params, master_params
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else:
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master_params = [[param.clone().float().detach() for param in lg] for lg in model_params]
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for mp in master_params:
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for param in mp: param.requires_grad = True
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return model_params, master_params
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def model_g2master_g(model_params, master_params, flat_master: bool = False) -> None:
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"Copy the `model_params` gradients to `master_params` for the optimizer step."
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if flat_master:
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for model_group, master_group in zip(model_params, master_params):
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if len(master_group) != 0:
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master_group[0].grad.data.copy_(parameters_to_vector([p.grad.data.float() for p in model_group]))
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else:
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for model_group, master_group in zip(model_params, master_params):
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for model, master in zip(model_group, master_group):
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if model.grad is not None:
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if master.grad is None: master.grad = master.data.new(*master.data.size())
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master.grad.data.copy_(model.grad.data)
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else:
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master.grad = None
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def master2model(model_params, master_params, flat_master: bool = False) -> None:
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"Copy `master_params` to `model_params`."
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if flat_master:
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for model_group, master_group in zip(model_params, master_params):
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if len(model_group) != 0:
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for model, master in zip(model_group, _unflatten_dense_tensors(master_group[0].data, model_group)):
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model.data.copy_(master)
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else:
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for model_group, master_group in zip(model_params, master_params):
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for model, master in zip(model_group, master_group): model.data.copy_(master.data)
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def listify(p=None, q=None):
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"Make `p` listy and the same length as `q`."
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if p is None:
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p = []
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elif isinstance(p, str):
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p = [p]
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elif not isinstance(p, Iterable):
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p = [p]
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n = q if type(q) == int else len(p) if q is None else len(q)
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if len(p) == 1: p = p * n
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assert len(p) == n, f'List len mismatch ({len(p)} vs {n})'
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return list(p)
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def trainable_params(m: nn.Module):
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"Return list of trainable params in `m`."
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res = filter(lambda p: p.requires_grad, m.parameters())
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return res
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def is_tuple(x) -> bool: return isinstance(x, tuple)
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# copy from fastai.
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class OptimWrapper():
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"Basic wrapper around `opt` to simplify hyper-parameters changes."
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def __init__(self, opt, wd, true_wd: bool = False, bn_wd: bool = True):
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self.opt, self.true_wd, self.bn_wd = opt, true_wd, bn_wd
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self.opt_keys = list(self.opt.param_groups[0].keys())
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self.opt_keys.remove('params')
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self.read_defaults()
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self.wd = wd
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@classmethod
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def create(cls, opt_func, lr,
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layer_groups, **kwargs):
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"Create an `optim.Optimizer` from `opt_func` with `lr`. Set lr on `layer_groups`."
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split_groups = split_bn_bias(layer_groups)
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opt = opt_func([{'params': trainable_params(l), 'lr': 0} for l in split_groups])
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opt = cls(opt, **kwargs)
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opt.lr, opt.opt_func = listify(lr, layer_groups), opt_func
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return opt
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def new(self, layer_groups):
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"Create a new `OptimWrapper` from `self` with another `layer_groups` but the same hyper-parameters."
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opt_func = getattr(self, 'opt_func', self.opt.__class__)
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split_groups = split_bn_bias(layer_groups)
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opt = opt_func([{'params': trainable_params(l), 'lr': 0} for l in split_groups])
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return self.create(opt_func, self.lr, layer_groups, wd=self.wd, true_wd=self.true_wd, bn_wd=self.bn_wd)
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def __repr__(self) -> str:
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return f'OptimWrapper over {repr(self.opt)}.\nTrue weight decay: {self.true_wd}'
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# Pytorch optimizer methods
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def step(self) -> None:
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"Set weight decay and step optimizer."
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# weight decay outside of optimizer step (AdamW)
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if self.true_wd:
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for lr, wd, pg1, pg2 in zip(self._lr, self._wd, self.opt.param_groups[::2], self.opt.param_groups[1::2]):
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for p in pg1['params']:
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# When some parameters are fixed: Shaoshuai Shi
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if p.requires_grad is False:
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continue
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p.data.mul_(1 - wd * lr)
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if self.bn_wd:
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for p in pg2['params']:
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# When some parameters are fixed: Shaoshuai Shi
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if p.requires_grad is False:
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continue
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p.data.mul_(1 - wd * lr)
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self.set_val('weight_decay', listify(0, self._wd))
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self.opt.step()
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def zero_grad(self) -> None:
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"Clear optimizer gradients."
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self.opt.zero_grad()
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# Passthrough to the inner opt.
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def __getattr__(self, k: str):
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return getattr(self.opt, k, None)
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def clear(self):
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"Reset the state of the inner optimizer."
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sd = self.state_dict()
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sd['state'] = {}
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self.load_state_dict(sd)
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# Hyperparameters as properties
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@property
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def lr(self) -> float:
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return self._lr[-1]
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@lr.setter
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def lr(self, val: float) -> None:
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self._lr = self.set_val('lr', listify(val, self._lr))
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@property
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def mom(self) -> float:
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return self._mom[-1]
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@mom.setter
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def mom(self, val: float) -> None:
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if 'momentum' in self.opt_keys:
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self.set_val('momentum', listify(val, self._mom))
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elif 'betas' in self.opt_keys:
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self.set_val('betas', (listify(val, self._mom), self._beta))
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self._mom = listify(val, self._mom)
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@property
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def beta(self) -> float:
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return None if self._beta is None else self._beta[-1]
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@beta.setter
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def beta(self, val: float) -> None:
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"Set beta (or alpha as makes sense for given optimizer)."
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if val is None: return
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if 'betas' in self.opt_keys:
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self.set_val('betas', (self._mom, listify(val, self._beta)))
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elif 'alpha' in self.opt_keys:
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self.set_val('alpha', listify(val, self._beta))
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self._beta = listify(val, self._beta)
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@property
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def wd(self) -> float:
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return self._wd[-1]
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@wd.setter
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def wd(self, val: float) -> None:
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"Set weight decay."
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if not self.true_wd: self.set_val('weight_decay', listify(val, self._wd), bn_groups=self.bn_wd)
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self._wd = listify(val, self._wd)
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# Helper functions
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def read_defaults(self) -> None:
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"Read the values inside the optimizer for the hyper-parameters."
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self._beta = None
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if 'lr' in self.opt_keys: self._lr = self.read_val('lr')
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if 'momentum' in self.opt_keys: self._mom = self.read_val('momentum')
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if 'alpha' in self.opt_keys: self._beta = self.read_val('alpha')
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if 'betas' in self.opt_keys: self._mom, self._beta = self.read_val('betas')
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if 'weight_decay' in self.opt_keys: self._wd = self.read_val('weight_decay')
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def set_val(self, key: str, val, bn_groups: bool = True):
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"Set `val` inside the optimizer dictionary at `key`."
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if is_tuple(val): val = [(v1, v2) for v1, v2 in zip(*val)]
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for v, pg1, pg2 in zip(val, self.opt.param_groups[::2], self.opt.param_groups[1::2]):
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pg1[key] = v
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if bn_groups: pg2[key] = v
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return val
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def read_val(self, key: str):
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"Read a hyperparameter `key` in the optimizer dictionary."
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val = [pg[key] for pg in self.opt.param_groups[::2]]
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if is_tuple(val[0]): val = [o[0] for o in val], [o[1] for o in val]
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return val
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class FastAIMixedOptim(OptimWrapper):
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@classmethod
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def create(cls, opt_func, lr,
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layer_groups, model, flat_master=False, loss_scale=512.0, **kwargs):
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"Create an `optim.Optimizer` from `opt_func` with `lr`. Set lr on `layer_groups`."
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opt = OptimWrapper.create(opt_func, lr, layer_groups, **kwargs)
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opt.model_params, opt.master_params = get_master(layer_groups, flat_master)
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opt.flat_master = flat_master
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opt.loss_scale = loss_scale
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opt.model = model
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# Changes the optimizer so that the optimization step is done in FP32.
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# opt = self.learn.opt
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mom, wd, beta = opt.mom, opt.wd, opt.beta
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lrs = [lr for lr in opt._lr for _ in range(2)]
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opt_params = [{'params': mp, 'lr': lr} for mp, lr in zip(opt.master_params, lrs)]
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opt.opt = opt_func(opt_params)
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opt.mom, opt.wd, opt.beta = mom, wd, beta
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return opt
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def step(self):
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model_g2master_g(self.model_params, self.master_params, self.flat_master)
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for group in self.master_params:
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for param in group: param.grad.div_(self.loss_scale)
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super(FastAIMixedOptim, self).step()
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self.model.zero_grad()
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# Update the params from master to model.
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master2model(self.model_params, self.master_params, self.flat_master)
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