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Copyright (c) Meta Platforms, Inc. and affiliates.
"""
import math
import os
import random
# import shutil
import numpy as np
import torch
# import torch.nn as nn
# import torch.nn.functional as F
from torch.optim import Optimizer
from utils.manifold_utils import MANIFOLD_BUCKET, pathmgr
def init_seed(seed):
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
np.random.seed(seed)
random.seed(seed)
def weight_parameters(module):
return [param for name, param in module.named_parameters() if ".weight" in name]
def bias_parameters(module):
return [param for name, param in module.named_parameters() if ".bias" in name]
def other_parameters(module):
return [
param
for name, param in module.named_parameters()
if ".bias" not in name and ".weight" not in name
]
def load_checkpoint(model_path):
# weights = torch.load(model_path)
if "manifold" not in model_path:
model_path = os.path.join("manifold://" + MANIFOLD_BUCKET, model_path)
with pathmgr.open(model_path, "rb") as f:
for i in range(3):
try:
weights = torch.load(f)
break
except Exception:
if i == 2:
raise Exception
epoch = None
if "epoch" in weights:
epoch = weights.pop("epoch")
if "state_dict" in weights:
state_dict = weights["state_dict"]
else:
state_dict = weights
return epoch, state_dict
def save_checkpoint(save_path, states, file_prefixes, is_best, filename="ckpt.pth.tar"):
def run_one_sample(save_path, state, prefix, is_best, filename):
# torch.save(state, os.path.join(save_path, "{}_{}".format(prefix, filename)))
if "manifold" not in save_path:
save_path = os.path.join("manifold://" + MANIFOLD_BUCKET, save_path)
save_path = os.path.join(save_path, "{}_{}".format(prefix, filename))
with pathmgr.open(save_path, "wb") as f:
for i in range(3):
try:
torch.save(state, f)
return
except Exception:
if i == 2:
raise Exception
if not isinstance(file_prefixes, str):
for (prefix, state) in zip(file_prefixes, states):
run_one_sample(save_path, state, prefix, is_best, filename)
else:
run_one_sample(save_path, states, file_prefixes, is_best, filename)
def restore_model(model, pretrained_file):
epoch, weights = load_checkpoint(pretrained_file)
model_keys = set(model.state_dict().keys())
weight_keys = set(weights.keys())
# load weights by name
weights_not_in_model = sorted(weight_keys - model_keys)
model_not_in_weights = sorted(model_keys - weight_keys)
if len(model_not_in_weights):
print("Warning: There are weights in model but not in pre-trained.")
for key in model_not_in_weights:
print(key)
weights[key] = model.state_dict()[key]
if len(weights_not_in_model):
print("Warning: There are pre-trained weights not in model.")
for key in weights_not_in_model:
print(key)
from collections import OrderedDict
new_weights = OrderedDict()
for key in model_keys:
new_weights[key] = weights[key]
weights = new_weights
model.load_state_dict(weights)
return model
class AdamW(Optimizer):
"""Implements AdamW algorithm.
It has been proposed in `Fixing Weight Decay Regularization in Adam`_.
Arguments:
params (iterable): iterable of parameters to optimize or dicts defining
parameter groups
lr (float, optional): learning rate (default: 1e-3)
betas (Tuple[float, float], optional): coefficients used for computing
running averages of gradient and its square (default: (0.9, 0.999))
eps (float, optional): term added to the denominator to improve
numerical stability (default: 1e-8)
weight_decay (float, optional): weight decay (L2 penalty) (default: 0)
.. Fixing Weight Decay Regularization in Adam:
https://arxiv.org/abs/1711.05101
"""
def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8, weight_decay=0):
defaults = {"lr": lr, "betas": betas, "eps": eps, "weight_decay": weight_decay}
super(AdamW, self).__init__(params, defaults)
def step(self, closure=None):
"""Performs a single optimization step.
Arguments:
closure (callable, optional): A closure that reevaluates the model
and returns the loss.
"""
loss = None
if closure is not None:
loss = closure()
for group in self.param_groups:
for p in group["params"]:
if p.grad is None:
continue
grad = p.grad.data
if grad.is_sparse:
raise RuntimeError(
"AdamW does not support sparse gradients, please consider SparseAdam instead"
)
state = self.state[p]
# State initialization
if len(state) == 0:
state["step"] = 0
# Exponential moving average of gradient values
state["exp_avg"] = torch.zeros_like(p.data)
# Exponential moving average of squared gradient values
state["exp_avg_sq"] = torch.zeros_like(p.data)
exp_avg, exp_avg_sq = state["exp_avg"], state["exp_avg_sq"]
beta1, beta2 = group["betas"]
state["step"] += 1
# according to the paper, this penalty should come after the bias correction
# if group['weight_decay'] != 0:
# grad = grad.add(group['weight_decay'], p.data)
# Decay the first and second moment running average coefficient
exp_avg.mul_(beta1).add_(1 - beta1, grad)
exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad)
denom = exp_avg_sq.sqrt().add_(group["eps"])
bias_correction1 = 1 - beta1 ** state["step"]
bias_correction2 = 1 - beta2 ** state["step"]
step_size = group["lr"] * math.sqrt(bias_correction2) / bias_correction1
p.data.addcdiv_(-step_size, exp_avg, denom)
if group["weight_decay"] != 0:
p.data.add_(-group["weight_decay"], p.data)
return loss
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