mirror of https://github.com/hpcaitech/ColossalAI
197 lines
7.1 KiB
Python
197 lines
7.1 KiB
Python
import importlib
|
|
|
|
import torch
|
|
from torch import optim
|
|
import numpy as np
|
|
|
|
from inspect import isfunction
|
|
from PIL import Image, ImageDraw, ImageFont
|
|
|
|
|
|
def log_txt_as_img(wh, xc, size=10):
|
|
# wh a tuple of (width, height)
|
|
# xc a list of captions to plot
|
|
b = len(xc)
|
|
txts = list()
|
|
for bi in range(b):
|
|
txt = Image.new("RGB", wh, color="white")
|
|
draw = ImageDraw.Draw(txt)
|
|
font = ImageFont.truetype('data/DejaVuSans.ttf', size=size)
|
|
nc = int(40 * (wh[0] / 256))
|
|
lines = "\n".join(xc[bi][start:start + nc] for start in range(0, len(xc[bi]), nc))
|
|
|
|
try:
|
|
draw.text((0, 0), lines, fill="black", font=font)
|
|
except UnicodeEncodeError:
|
|
print("Cant encode string for logging. Skipping.")
|
|
|
|
txt = np.array(txt).transpose(2, 0, 1) / 127.5 - 1.0
|
|
txts.append(txt)
|
|
txts = np.stack(txts)
|
|
txts = torch.tensor(txts)
|
|
return txts
|
|
|
|
|
|
def ismap(x):
|
|
if not isinstance(x, torch.Tensor):
|
|
return False
|
|
return (len(x.shape) == 4) and (x.shape[1] > 3)
|
|
|
|
|
|
def isimage(x):
|
|
if not isinstance(x,torch.Tensor):
|
|
return False
|
|
return (len(x.shape) == 4) and (x.shape[1] == 3 or x.shape[1] == 1)
|
|
|
|
|
|
def exists(x):
|
|
return x is not None
|
|
|
|
|
|
def default(val, d):
|
|
if exists(val):
|
|
return val
|
|
return d() if isfunction(d) else d
|
|
|
|
|
|
def mean_flat(tensor):
|
|
"""
|
|
https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/nn.py#L86
|
|
Take the mean over all non-batch dimensions.
|
|
"""
|
|
return tensor.mean(dim=list(range(1, len(tensor.shape))))
|
|
|
|
|
|
def count_params(model, verbose=False):
|
|
total_params = sum(p.numel() for p in model.parameters())
|
|
if verbose:
|
|
print(f"{model.__class__.__name__} has {total_params*1.e-6:.2f} M params.")
|
|
return total_params
|
|
|
|
|
|
def instantiate_from_config(config):
|
|
if not "target" in config:
|
|
if config == '__is_first_stage__':
|
|
return None
|
|
elif config == "__is_unconditional__":
|
|
return None
|
|
raise KeyError("Expected key `target` to instantiate.")
|
|
return get_obj_from_str(config["target"])(**config.get("params", dict()))
|
|
|
|
|
|
def get_obj_from_str(string, reload=False):
|
|
module, cls = string.rsplit(".", 1)
|
|
if reload:
|
|
module_imp = importlib.import_module(module)
|
|
importlib.reload(module_imp)
|
|
return getattr(importlib.import_module(module, package=None), cls)
|
|
|
|
|
|
class AdamWwithEMAandWings(optim.Optimizer):
|
|
# credit to https://gist.github.com/crowsonkb/65f7265353f403714fce3b2595e0b298
|
|
def __init__(self, params, lr=1.e-3, betas=(0.9, 0.999), eps=1.e-8, # TODO: check hyperparameters before using
|
|
weight_decay=1.e-2, amsgrad=False, ema_decay=0.9999, # ema decay to match previous code
|
|
ema_power=1., param_names=()):
|
|
"""AdamW that saves EMA versions of the parameters."""
|
|
if not 0.0 <= lr:
|
|
raise ValueError("Invalid learning rate: {}".format(lr))
|
|
if not 0.0 <= eps:
|
|
raise ValueError("Invalid epsilon value: {}".format(eps))
|
|
if not 0.0 <= betas[0] < 1.0:
|
|
raise ValueError("Invalid beta parameter at index 0: {}".format(betas[0]))
|
|
if not 0.0 <= betas[1] < 1.0:
|
|
raise ValueError("Invalid beta parameter at index 1: {}".format(betas[1]))
|
|
if not 0.0 <= weight_decay:
|
|
raise ValueError("Invalid weight_decay value: {}".format(weight_decay))
|
|
if not 0.0 <= ema_decay <= 1.0:
|
|
raise ValueError("Invalid ema_decay value: {}".format(ema_decay))
|
|
defaults = dict(lr=lr, betas=betas, eps=eps,
|
|
weight_decay=weight_decay, amsgrad=amsgrad, ema_decay=ema_decay,
|
|
ema_power=ema_power, param_names=param_names)
|
|
super().__init__(params, defaults)
|
|
|
|
def __setstate__(self, state):
|
|
super().__setstate__(state)
|
|
for group in self.param_groups:
|
|
group.setdefault('amsgrad', False)
|
|
|
|
@torch.no_grad()
|
|
def step(self, closure=None):
|
|
"""Performs a single optimization step.
|
|
Args:
|
|
closure (callable, optional): A closure that reevaluates the model
|
|
and returns the loss.
|
|
"""
|
|
loss = None
|
|
if closure is not None:
|
|
with torch.enable_grad():
|
|
loss = closure()
|
|
|
|
for group in self.param_groups:
|
|
params_with_grad = []
|
|
grads = []
|
|
exp_avgs = []
|
|
exp_avg_sqs = []
|
|
ema_params_with_grad = []
|
|
state_sums = []
|
|
max_exp_avg_sqs = []
|
|
state_steps = []
|
|
amsgrad = group['amsgrad']
|
|
beta1, beta2 = group['betas']
|
|
ema_decay = group['ema_decay']
|
|
ema_power = group['ema_power']
|
|
|
|
for p in group['params']:
|
|
if p.grad is None:
|
|
continue
|
|
params_with_grad.append(p)
|
|
if p.grad.is_sparse:
|
|
raise RuntimeError('AdamW does not support sparse gradients')
|
|
grads.append(p.grad)
|
|
|
|
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, memory_format=torch.preserve_format)
|
|
# Exponential moving average of squared gradient values
|
|
state['exp_avg_sq'] = torch.zeros_like(p, memory_format=torch.preserve_format)
|
|
if amsgrad:
|
|
# Maintains max of all exp. moving avg. of sq. grad. values
|
|
state['max_exp_avg_sq'] = torch.zeros_like(p, memory_format=torch.preserve_format)
|
|
# Exponential moving average of parameter values
|
|
state['param_exp_avg'] = p.detach().float().clone()
|
|
|
|
exp_avgs.append(state['exp_avg'])
|
|
exp_avg_sqs.append(state['exp_avg_sq'])
|
|
ema_params_with_grad.append(state['param_exp_avg'])
|
|
|
|
if amsgrad:
|
|
max_exp_avg_sqs.append(state['max_exp_avg_sq'])
|
|
|
|
# update the steps for each param group update
|
|
state['step'] += 1
|
|
# record the step after step update
|
|
state_steps.append(state['step'])
|
|
|
|
optim._functional.adamw(params_with_grad,
|
|
grads,
|
|
exp_avgs,
|
|
exp_avg_sqs,
|
|
max_exp_avg_sqs,
|
|
state_steps,
|
|
amsgrad=amsgrad,
|
|
beta1=beta1,
|
|
beta2=beta2,
|
|
lr=group['lr'],
|
|
weight_decay=group['weight_decay'],
|
|
eps=group['eps'],
|
|
maximize=False)
|
|
|
|
cur_ema_decay = min(ema_decay, 1 - state['step'] ** -ema_power)
|
|
for param, ema_param in zip(params_with_grad, ema_params_with_grad):
|
|
ema_param.mul_(cur_ema_decay).add_(param.float(), alpha=1 - cur_ema_decay)
|
|
|
|
return loss |