forked from lucidrains/DALLE2-pytorch
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train.py
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train.py
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import copy
from functools import partial
import torch
from torch import nn
from torch.cuda.amp import autocast, GradScaler
from dalle2_pytorch.dalle2_pytorch import Decoder
from dalle2_pytorch.optimizer import get_optimizer
# helper functions
def exists(val):
return val is not None
def cast_tuple(val, length = 1):
return val if isinstance(val, tuple) else ((val,) * length)
def pick_and_pop(keys, d):
values = list(map(lambda key: d.pop(key), keys))
return dict(zip(keys, values))
def group_dict_by_key(cond, d):
return_val = [dict(),dict()]
for key in d.keys():
match = bool(cond(key))
ind = int(not match)
return_val[ind][key] = d[key]
return (*return_val,)
def string_begins_with(prefix, str):
return str.startswith(prefix)
def group_by_key_prefix(prefix, d):
return group_dict_by_key(partial(string_begins_with, prefix), d)
def groupby_prefix_and_trim(prefix, d):
kwargs_with_prefix, kwargs = group_dict_by_key(partial(string_begins_with, prefix), d)
kwargs_without_prefix = dict(map(lambda x: (x[0][len(prefix):], x[1]), tuple(kwargs_with_prefix.items())))
return kwargs_without_prefix, kwargs
# exponential moving average wrapper
class EMA(nn.Module):
def __init__(
self,
model,
beta = 0.99,
update_after_step = 1000,
update_every = 10,
):
super().__init__()
self.beta = beta
self.online_model = model
self.ema_model = copy.deepcopy(model)
self.update_after_step = update_after_step # only start EMA after this step number, starting at 0
self.update_every = update_every
self.register_buffer('initted', torch.Tensor([False]))
self.register_buffer('step', torch.tensor([0.]))
def update(self):
self.step += 1
if self.step <= self.update_after_step or (self.step % self.update_every) != 0:
return
if not self.initted:
self.ema_model.state_dict(self.online_model.state_dict())
self.initted.data.copy_(torch.Tensor([True]))
self.update_moving_average(self.ema_model, self.online_model)
def update_moving_average(self, ma_model, current_model):
def calculate_ema(beta, old, new):
if not exists(old):
return new
return old * beta + (1 - beta) * new
for current_params, ma_params in zip(current_model.parameters(), ma_model.parameters()):
old_weight, up_weight = ma_params.data, current_params.data
ma_params.data = calculate_ema(self.beta, old_weight, up_weight)
for current_buffer, ma_buffer in zip(current_model.buffers(), ma_model.buffers()):
new_buffer_value = calculate_ema(self.beta, ma_buffer, current_buffer)
ma_buffer.copy_(new_buffer_value)
def __call__(self, *args, **kwargs):
return self.ema_model(*args, **kwargs)
# trainers
class DecoderTrainer(nn.Module):
def __init__(
self,
decoder,
use_ema = True,
lr = 3e-4,
wd = 1e-2,
max_grad_norm = None,
amp = False,
**kwargs
):
super().__init__()
assert isinstance(decoder, Decoder)
ema_kwargs, kwargs = groupby_prefix_and_trim('ema_', kwargs)
self.decoder = decoder
self.num_unets = len(self.decoder.unets)
self.use_ema = use_ema
if use_ema:
has_lazy_linear = any([type(module) == nn.LazyLinear for module in decoder.modules()])
assert not has_lazy_linear, 'you must set the text_embed_dim on your u-nets if you plan on doing automatic exponential moving average'
self.ema_unets = nn.ModuleList([])
self.amp = amp
# be able to finely customize learning rate, weight decay
# per unet
lr, wd = map(partial(cast_tuple, length = self.num_unets), (lr, wd))
for ind, (unet, unet_lr, unet_wd) in enumerate(zip(self.decoder.unets, lr, wd)):
optimizer = get_optimizer(
unet.parameters(),
lr = unet_lr,
wd = unet_wd,
**kwargs
)
setattr(self, f'optim{ind}', optimizer) # cannot use pytorch ModuleList for some reason with optimizers
if self.use_ema:
self.ema_unets.append(EMA(unet, **ema_kwargs))
scaler = GradScaler(enabled = amp)
setattr(self, f'scaler{ind}', scaler)
# gradient clipping if needed
self.max_grad_norm = max_grad_norm
@property
def unets(self):
return nn.ModuleList([ema.ema_model for ema in self.ema_unets])
def scale(self, loss, *, unet_number):
assert 1 <= unet_number <= self.num_unets
index = unet_number - 1
scaler = getattr(self, f'scaler{index}')
return scaler.scale(loss)
def update(self, unet_number):
assert 1 <= unet_number <= self.num_unets
index = unet_number - 1
unet = self.decoder.unets[index]
optimizer = getattr(self, f'optim{index}')
scaler = getattr(self, f'scaler{index}')
if exists(self.max_grad_norm):
scaler.unscale_(optimizer)
nn.utils.clip_grad_norm_(unet.parameters(), self.max_grad_norm)
scaler.step(optimizer)
scaler.update()
optimizer.zero_grad()
if self.use_ema:
ema_unet = self.ema_unets[index]
ema_unet.update()
@torch.no_grad()
def sample(self, *args, **kwargs):
if self.use_ema:
trainable_unets = self.decoder.unets
self.decoder.unets = self.unets # swap in exponential moving averaged unets for sampling
output = self.decoder.sample(*args, **kwargs)
if self.use_ema:
self.decoder.unets = trainable_unets # restore original training unets
return output
def forward(
self,
x,
*,
unet_number,
divisor = 1,
**kwargs
):
with autocast(enabled = self.amp):
loss = self.decoder(x, unet_number = unet_number, **kwargs)
return self.scale(loss / divisor, unet_number = unet_number)