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trainer.py
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trainer.py
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import os
from math import ceil
from contextlib import contextmanager, nullcontext
from functools import partial, wraps
from collections.abc import Iterable
import torch
from torch import nn
import torch.nn.functional as F
from torch.utils.data import random_split, DataLoader
from torch.optim import Adam
from torch.optim.lr_scheduler import CosineAnnealingLR, LambdaLR
from torch.cuda.amp import autocast, GradScaler
import pytorch_warmup as warmup
from imagen_pytorch.imagen_pytorch import Imagen, NullUnet
from imagen_pytorch.elucidated_imagen import ElucidatedImagen
from imagen_pytorch.data import cycle
from imagen_pytorch.version import __version__
from packaging import version
import numpy as np
from ema_pytorch import EMA
from accelerate import Accelerator, DistributedType, DistributedDataParallelKwargs
from fsspec.core import url_to_fs
from fsspec.implementations.local import LocalFileSystem
# helper functions
def exists(val):
return val is not None
def default(val, d):
if exists(val):
return val
return d() if callable(d) else d
def cast_tuple(val, length = 1):
if isinstance(val, list):
val = tuple(val)
return val if isinstance(val, tuple) else ((val,) * length)
def find_first(fn, arr):
for ind, el in enumerate(arr):
if fn(el):
return ind
return -1
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
def num_to_groups(num, divisor):
groups = num // divisor
remainder = num % divisor
arr = [divisor] * groups
if remainder > 0:
arr.append(remainder)
return arr
# url to fs, bucket, path - for checkpointing to cloud
def url_to_bucket(url):
if '://' not in url:
return url
_, suffix = url.split('://')
if prefix in {'gs', 's3'}:
return suffix.split('/')[0]
else:
raise ValueError(f'storage type prefix "{prefix}" is not supported yet')
# decorators
def eval_decorator(fn):
def inner(model, *args, **kwargs):
was_training = model.training
model.eval()
out = fn(model, *args, **kwargs)
model.train(was_training)
return out
return inner
def cast_torch_tensor(fn, cast_fp16 = False):
@wraps(fn)
def inner(model, *args, **kwargs):
device = kwargs.pop('_device', model.device)
cast_device = kwargs.pop('_cast_device', True)
should_cast_fp16 = cast_fp16 and model.cast_half_at_training
kwargs_keys = kwargs.keys()
all_args = (*args, *kwargs.values())
split_kwargs_index = len(all_args) - len(kwargs_keys)
all_args = tuple(map(lambda t: torch.from_numpy(t) if exists(t) and isinstance(t, np.ndarray) else t, all_args))
if cast_device:
all_args = tuple(map(lambda t: t.to(device) if exists(t) and isinstance(t, torch.Tensor) else t, all_args))
if should_cast_fp16:
all_args = tuple(map(lambda t: t.half() if exists(t) and isinstance(t, torch.Tensor) and t.dtype != torch.bool else t, all_args))
args, kwargs_values = all_args[:split_kwargs_index], all_args[split_kwargs_index:]
kwargs = dict(tuple(zip(kwargs_keys, kwargs_values)))
out = fn(model, *args, **kwargs)
return out
return inner
# gradient accumulation functions
def split_iterable(it, split_size):
accum = []
for ind in range(ceil(len(it) / split_size)):
start_index = ind * split_size
accum.append(it[start_index: (start_index + split_size)])
return accum
def split(t, split_size = None):
if not exists(split_size):
return t
if isinstance(t, torch.Tensor):
return t.split(split_size, dim = 0)
if isinstance(t, Iterable):
return split_iterable(t, split_size)
return TypeError
def find_first(cond, arr):
for el in arr:
if cond(el):
return el
return None
def split_args_and_kwargs(*args, split_size = None, **kwargs):
all_args = (*args, *kwargs.values())
len_all_args = len(all_args)
first_tensor = find_first(lambda t: isinstance(t, torch.Tensor), all_args)
assert exists(first_tensor)
batch_size = len(first_tensor)
split_size = default(split_size, batch_size)
num_chunks = ceil(batch_size / split_size)
dict_len = len(kwargs)
dict_keys = kwargs.keys()
split_kwargs_index = len_all_args - dict_len
split_all_args = [split(arg, split_size = split_size) if exists(arg) and isinstance(arg, (torch.Tensor, Iterable)) else ((arg,) * num_chunks) for arg in all_args]
chunk_sizes = num_to_groups(batch_size, split_size)
for (chunk_size, *chunked_all_args) in tuple(zip(chunk_sizes, *split_all_args)):
chunked_args, chunked_kwargs_values = chunked_all_args[:split_kwargs_index], chunked_all_args[split_kwargs_index:]
chunked_kwargs = dict(tuple(zip(dict_keys, chunked_kwargs_values)))
chunk_size_frac = chunk_size / batch_size
yield chunk_size_frac, (chunked_args, chunked_kwargs)
# imagen trainer
def imagen_sample_in_chunks(fn):
@wraps(fn)
def inner(self, *args, max_batch_size = None, **kwargs):
if not exists(max_batch_size):
return fn(self, *args, **kwargs)
if self.imagen.unconditional:
batch_size = kwargs.get('batch_size')
batch_sizes = num_to_groups(batch_size, max_batch_size)
outputs = [fn(self, *args, **{**kwargs, 'batch_size': sub_batch_size}) for sub_batch_size in batch_sizes]
else:
outputs = [fn(self, *chunked_args, **chunked_kwargs) for _, (chunked_args, chunked_kwargs) in split_args_and_kwargs(*args, split_size = max_batch_size, **kwargs)]
if isinstance(outputs[0], torch.Tensor):
return torch.cat(outputs, dim = 0)
return list(map(lambda t: torch.cat(t, dim = 0), list(zip(*outputs))))
return inner
def restore_parts(state_dict_target, state_dict_from):
for name, param in state_dict_from.items():
if name not in state_dict_target:
continue
if param.size() == state_dict_target[name].size():
state_dict_target[name].copy_(param)
else:
print(f"layer {name}({param.size()} different than target: {state_dict_target[name].size()}")
return state_dict_target
class ImagenTrainer(nn.Module):
locked = False
def __init__(
self,
imagen = None,
imagen_checkpoint_path = None,
use_ema = True,
lr = 1e-4,
eps = 1e-8,
beta1 = 0.9,
beta2 = 0.99,
max_grad_norm = None,
group_wd_params = True,
warmup_steps = None,
cosine_decay_max_steps = None,
only_train_unet_number = None,
fp16 = False,
precision = None,
split_batches = True,
dl_tuple_output_keywords_names = ('images', 'text_embeds', 'text_masks', 'cond_images'),
verbose = True,
split_valid_fraction = 0.025,
split_valid_from_train = False,
split_random_seed = 42,
checkpoint_path = None,
checkpoint_every = None,
checkpoint_fs = None,
fs_kwargs: dict = None,
max_checkpoints_keep = 20,
**kwargs
):
super().__init__()
assert not ImagenTrainer.locked, 'ImagenTrainer can only be initialized once per process - for the sake of distributed training, you will now have to create a separate script to train each unet (or a script that accepts unet number as an argument)'
assert exists(imagen) ^ exists(imagen_checkpoint_path), 'either imagen instance is passed into the trainer, or a checkpoint path that contains the imagen config'
# determine filesystem, using fsspec, for saving to local filesystem or cloud
self.fs = checkpoint_fs
if not exists(self.fs):
fs_kwargs = default(fs_kwargs, {})
self.fs, _ = url_to_fs(default(checkpoint_path, './'), **fs_kwargs)
assert isinstance(imagen, (Imagen, ElucidatedImagen))
ema_kwargs, kwargs = groupby_prefix_and_trim('ema_', kwargs)
# elucidated or not
self.is_elucidated = isinstance(imagen, ElucidatedImagen)
# create accelerator instance
accelerate_kwargs, kwargs = groupby_prefix_and_trim('accelerate_', kwargs)
assert not (fp16 and exists(precision)), 'either set fp16 = True or forward the precision ("fp16", "bf16") to Accelerator'
accelerator_mixed_precision = default(precision, 'fp16' if fp16 else 'no')
self.accelerator = Accelerator(**{
'split_batches': split_batches,
'mixed_precision': accelerator_mixed_precision,
'kwargs_handlers': [DistributedDataParallelKwargs(find_unused_parameters = True)]
, **accelerate_kwargs})
ImagenTrainer.locked = self.is_distributed
# cast data to fp16 at training time if needed
self.cast_half_at_training = accelerator_mixed_precision == 'fp16'
# grad scaler must be managed outside of accelerator
grad_scaler_enabled = fp16
# imagen, unets and ema unets
self.imagen = imagen
self.num_unets = len(self.imagen.unets)
self.use_ema = use_ema and self.is_main
self.ema_unets = nn.ModuleList([])
# keep track of what unet is being trained on
# only going to allow 1 unet training at a time
self.ema_unet_being_trained_index = -1 # keeps track of which ema unet is being trained on
# data related functions
self.train_dl_iter = None
self.train_dl = None
self.valid_dl_iter = None
self.valid_dl = None
self.dl_tuple_output_keywords_names = dl_tuple_output_keywords_names
# auto splitting validation from training, if dataset is passed in
self.split_valid_from_train = split_valid_from_train
assert 0 <= split_valid_fraction <= 1, 'split valid fraction must be between 0 and 1'
self.split_valid_fraction = split_valid_fraction
self.split_random_seed = split_random_seed
# be able to finely customize learning rate, weight decay
# per unet
lr, eps, warmup_steps, cosine_decay_max_steps = map(partial(cast_tuple, length = self.num_unets), (lr, eps, warmup_steps, cosine_decay_max_steps))
for ind, (unet, unet_lr, unet_eps, unet_warmup_steps, unet_cosine_decay_max_steps) in enumerate(zip(self.imagen.unets, lr, eps, warmup_steps, cosine_decay_max_steps)):
optimizer = Adam(
unet.parameters(),
lr = unet_lr,
eps = unet_eps,
betas = (beta1, beta2),
**kwargs
)
if self.use_ema:
self.ema_unets.append(EMA(unet, **ema_kwargs))
scaler = GradScaler(enabled = grad_scaler_enabled)
scheduler = warmup_scheduler = None
if exists(unet_cosine_decay_max_steps):
scheduler = CosineAnnealingLR(optimizer, T_max = unet_cosine_decay_max_steps)
if exists(unet_warmup_steps):
warmup_scheduler = warmup.LinearWarmup(optimizer, warmup_period = unet_warmup_steps)
if not exists(scheduler):
scheduler = LambdaLR(optimizer, lr_lambda = lambda step: 1.0)
# set on object
setattr(self, f'optim{ind}', optimizer) # cannot use pytorch ModuleList for some reason with optimizers
setattr(self, f'scaler{ind}', scaler)
setattr(self, f'scheduler{ind}', scheduler)
setattr(self, f'warmup{ind}', warmup_scheduler)
# gradient clipping if needed
self.max_grad_norm = max_grad_norm
# step tracker and misc
self.register_buffer('steps', torch.tensor([0] * self.num_unets))
self.verbose = verbose
# automatic set devices based on what accelerator decided
self.imagen.to(self.device)
self.to(self.device)
# checkpointing
assert not (exists(checkpoint_path) ^ exists(checkpoint_every))
self.checkpoint_path = checkpoint_path
self.checkpoint_every = checkpoint_every
self.max_checkpoints_keep = max_checkpoints_keep
self.can_checkpoint = self.is_local_main if isinstance(checkpoint_fs, LocalFileSystem) else self.is_main
if exists(checkpoint_path) and self.can_checkpoint:
bucket = url_to_bucket(checkpoint_path)
if not self.fs.exists(bucket):
self.fs.mkdir(bucket)
self.load_from_checkpoint_folder()
# only allowing training for unet
self.only_train_unet_number = only_train_unet_number
self.prepared = False
def prepare(self):
assert not self.prepared, f'The trainer is allready prepared'
self.validate_and_set_unet_being_trained(self.only_train_unet_number)
self.prepared = True
# computed values
@property
def device(self):
return self.accelerator.device
@property
def is_distributed(self):
return not (self.accelerator.distributed_type == DistributedType.NO and self.accelerator.num_processes == 1)
@property
def is_main(self):
return self.accelerator.is_main_process
@property
def is_local_main(self):
return self.accelerator.is_local_main_process
@property
def unwrapped_unet(self):
return self.accelerator.unwrap_model(self.unet_being_trained)
# optimizer helper functions
def get_lr(self, unet_number):
self.validate_unet_number(unet_number)
unet_index = unet_number - 1
optim = getattr(self, f'optim{unet_index}')
return optim.param_groups[0]['lr']
# function for allowing only one unet from being trained at a time
def validate_and_set_unet_being_trained(self, unet_number = None):
if exists(unet_number):
self.validate_unet_number(unet_number)
assert not exists(self.only_train_unet_number) or self.only_train_unet_number == unet_number, 'you cannot only train on one unet at a time. you will need to save the trainer into a checkpoint, and resume training on a new unet'
self.only_train_unet_number = unet_number
self.imagen.only_train_unet_number = unet_number
if not exists(unet_number):
return
self.wrap_unet(unet_number)
def wrap_unet(self, unet_number):
if hasattr(self, 'one_unet_wrapped'):
return
unet = self.imagen.get_unet(unet_number)
unet_index = unet_number - 1
optimizer = getattr(self, f'optim{unet_index}')
scheduler = getattr(self, f'scheduler{unet_index}')
if self.train_dl:
self.unet_being_trained, self.train_dl, optimizer = self.accelerator.prepare(unet, self.train_dl, optimizer)
else:
self.unet_being_trained, optimizer = self.accelerator.prepare(unet, optimizer)
if exists(scheduler):
scheduler = self.accelerator.prepare(scheduler)
setattr(self, f'optim{unet_index}', optimizer)
setattr(self, f'scheduler{unet_index}', scheduler)
self.one_unet_wrapped = True
# hacking accelerator due to not having separate gradscaler per optimizer
def set_accelerator_scaler(self, unet_number):
def patch_optimizer_step(accelerated_optimizer, method):
def patched_step(*args, **kwargs):
accelerated_optimizer._accelerate_step_called = True
return method(*args, **kwargs)
return patched_step
unet_number = self.validate_unet_number(unet_number)
scaler = getattr(self, f'scaler{unet_number - 1}')
self.accelerator.scaler = scaler
for optimizer in self.accelerator._optimizers:
optimizer.scaler = scaler
optimizer._accelerate_step_called = False
optimizer._optimizer_original_step_method = optimizer.optimizer.step
optimizer._optimizer_patched_step_method = patch_optimizer_step(optimizer, optimizer.optimizer.step)
# helper print
def print(self, msg):
if not self.is_main:
return
if not self.verbose:
return
return self.accelerator.print(msg)
# validating the unet number
def validate_unet_number(self, unet_number = None):
if self.num_unets == 1:
unet_number = default(unet_number, 1)
assert 0 < unet_number <= self.num_unets, f'unet number should be in between 1 and {self.num_unets}'
return unet_number
# number of training steps taken
def num_steps_taken(self, unet_number = None):
if self.num_unets == 1:
unet_number = default(unet_number, 1)
return self.steps[unet_number - 1].item()
def print_untrained_unets(self):
print_final_error = False
for ind, (steps, unet) in enumerate(zip(self.steps.tolist(), self.imagen.unets)):
if steps > 0 or isinstance(unet, NullUnet):
continue
self.print(f'unet {ind + 1} has not been trained')
print_final_error = True
if print_final_error:
self.print('when sampling, you can pass stop_at_unet_number to stop early in the cascade, so it does not try to generate with untrained unets')
# data related functions
def add_train_dataloader(self, dl = None):
if not exists(dl):
return
assert not exists(self.train_dl), 'training dataloader was already added'
assert not self.prepared, f'You need to add the dataset before preperation'
self.train_dl = dl
def add_valid_dataloader(self, dl):
if not exists(dl):
return
assert not exists(self.valid_dl), 'validation dataloader was already added'
assert not self.prepared, f'You need to add the dataset before preperation'
self.valid_dl = dl
def add_train_dataset(self, ds = None, *, batch_size, **dl_kwargs):
if not exists(ds):
return
assert not exists(self.train_dl), 'training dataloader was already added'
valid_ds = None
if self.split_valid_from_train:
train_size = int((1 - self.split_valid_fraction) * len(ds))
valid_size = len(ds) - train_size
ds, valid_ds = random_split(ds, [train_size, valid_size], generator = torch.Generator().manual_seed(self.split_random_seed))
self.print(f'training with dataset of {len(ds)} samples and validating with randomly splitted {len(valid_ds)} samples')
dl = DataLoader(ds, batch_size = batch_size, **dl_kwargs)
self.add_train_dataloader(dl)
if not self.split_valid_from_train:
return
self.add_valid_dataset(valid_ds, batch_size = batch_size, **dl_kwargs)
def add_valid_dataset(self, ds, *, batch_size, **dl_kwargs):
if not exists(ds):
return
assert not exists(self.valid_dl), 'validation dataloader was already added'
dl = DataLoader(ds, batch_size = batch_size, **dl_kwargs)
self.add_valid_dataloader(dl)
def create_train_iter(self):
assert exists(self.train_dl), 'training dataloader has not been registered with the trainer yet'
if exists(self.train_dl_iter):
return
self.train_dl_iter = cycle(self.train_dl)
def create_valid_iter(self):
assert exists(self.valid_dl), 'validation dataloader has not been registered with the trainer yet'
if exists(self.valid_dl_iter):
return
self.valid_dl_iter = cycle(self.valid_dl)
def train_step(self, *, unet_number = None, **kwargs):
if not self.prepared:
self.prepare()
self.create_train_iter()
kwargs = {'unet_number': unet_number, **kwargs}
loss = self.step_with_dl_iter(self.train_dl_iter, **kwargs)
self.update(unet_number = unet_number)
return loss
@torch.no_grad()
@eval_decorator
def valid_step(self, **kwargs):
if not self.prepared:
self.prepare()
self.create_valid_iter()
context = self.use_ema_unets if kwargs.pop('use_ema_unets', False) else nullcontext
with context():
loss = self.step_with_dl_iter(self.valid_dl_iter, **kwargs)
return loss
def step_with_dl_iter(self, dl_iter, **kwargs):
dl_tuple_output = cast_tuple(next(dl_iter))
model_input = dict(list(zip(self.dl_tuple_output_keywords_names, dl_tuple_output)))
loss = self.forward(**{**kwargs, **model_input})
return loss
# checkpointing functions
@property
def all_checkpoints_sorted(self):
glob_pattern = os.path.join(self.checkpoint_path, '*.pt')
checkpoints = self.fs.glob(glob_pattern)
sorted_checkpoints = sorted(checkpoints, key = lambda x: int(str(x).split('.')[-2]), reverse = True)
return sorted_checkpoints
def load_from_checkpoint_folder(self, last_total_steps = -1):
if last_total_steps != -1:
filepath = os.path.join(self.checkpoint_path, f'checkpoint.{last_total_steps}.pt')
self.load(filepath)
return
sorted_checkpoints = self.all_checkpoints_sorted
if len(sorted_checkpoints) == 0:
self.print(f'no checkpoints found to load from at {self.checkpoint_path}')
return
last_checkpoint = sorted_checkpoints[0]
self.load(last_checkpoint)
def save_to_checkpoint_folder(self):
self.accelerator.wait_for_everyone()
if not self.can_checkpoint:
return
total_steps = int(self.steps.sum().item())
filepath = os.path.join(self.checkpoint_path, f'checkpoint.{total_steps}.pt')
self.save(filepath)
if self.max_checkpoints_keep <= 0:
return
sorted_checkpoints = self.all_checkpoints_sorted
checkpoints_to_discard = sorted_checkpoints[self.max_checkpoints_keep:]
for checkpoint in checkpoints_to_discard:
self.fs.rm(checkpoint)
# saving and loading functions
def save(
self,
path,
overwrite = True,
without_optim_and_sched = False,
**kwargs
):
self.accelerator.wait_for_everyone()
if not self.can_checkpoint:
return
fs = self.fs
assert not (fs.exists(path) and not overwrite)
self.reset_ema_unets_all_one_device()
save_obj = dict(
model = self.imagen.state_dict(),
version = __version__,
steps = self.steps.cpu(),
**kwargs
)
save_optim_and_sched_iter = range(0, self.num_unets) if not without_optim_and_sched else tuple()
for ind in save_optim_and_sched_iter:
scaler_key = f'scaler{ind}'
optimizer_key = f'optim{ind}'
scheduler_key = f'scheduler{ind}'
warmup_scheduler_key = f'warmup{ind}'
scaler = getattr(self, scaler_key)
optimizer = getattr(self, optimizer_key)
scheduler = getattr(self, scheduler_key)
warmup_scheduler = getattr(self, warmup_scheduler_key)
if exists(scheduler):
save_obj = {**save_obj, scheduler_key: scheduler.state_dict()}
if exists(warmup_scheduler):
save_obj = {**save_obj, warmup_scheduler_key: warmup_scheduler.state_dict()}
save_obj = {**save_obj, scaler_key: scaler.state_dict(), optimizer_key: optimizer.state_dict()}
if self.use_ema:
save_obj = {**save_obj, 'ema': self.ema_unets.state_dict()}
# determine if imagen config is available
if hasattr(self.imagen, '_config'):
self.print(f'this checkpoint is commandable from the CLI - "imagen --model {str(path)} \"<prompt>\""')
save_obj = {
**save_obj,
'imagen_type': 'elucidated' if self.is_elucidated else 'original',
'imagen_params': self.imagen._config
}
#save to path
with fs.open(path, 'wb') as f:
torch.save(save_obj, f)
self.print(f'checkpoint saved to {path}')
def load(self, path, only_model = False, strict = True, noop_if_not_exist = False):
fs = self.fs
if noop_if_not_exist and not fs.exists(path):
self.print(f'trainer checkpoint not found at {str(path)}')
return
assert fs.exists(path), f'{path} does not exist'
self.reset_ema_unets_all_one_device()
# to avoid extra GPU memory usage in main process when using Accelerate
with fs.open(path) as f:
loaded_obj = torch.load(f, map_location='cpu')
if version.parse(__version__) != version.parse(loaded_obj['version']):
self.print(f'loading saved imagen at version {loaded_obj["version"]}, but current package version is {__version__}')
try:
self.imagen.load_state_dict(loaded_obj['model'], strict = strict)
except RuntimeError:
print("Failed loading state dict. Trying partial load")
self.imagen.load_state_dict(restore_parts(self.imagen.state_dict(),
loaded_obj['model']))
if only_model:
return loaded_obj
self.steps.copy_(loaded_obj['steps'])
for ind in range(0, self.num_unets):
scaler_key = f'scaler{ind}'
optimizer_key = f'optim{ind}'
scheduler_key = f'scheduler{ind}'
warmup_scheduler_key = f'warmup{ind}'
scaler = getattr(self, scaler_key)
optimizer = getattr(self, optimizer_key)
scheduler = getattr(self, scheduler_key)
warmup_scheduler = getattr(self, warmup_scheduler_key)
if exists(scheduler) and scheduler_key in loaded_obj:
scheduler.load_state_dict(loaded_obj[scheduler_key])
if exists(warmup_scheduler) and warmup_scheduler_key in loaded_obj:
warmup_scheduler.load_state_dict(loaded_obj[warmup_scheduler_key])
if exists(optimizer):
try:
optimizer.load_state_dict(loaded_obj[optimizer_key])
scaler.load_state_dict(loaded_obj[scaler_key])
except:
self.print('could not load optimizer and scaler, possibly because you have turned on mixed precision training since the last run. resuming with new optimizer and scalers')
if self.use_ema:
assert 'ema' in loaded_obj
try:
self.ema_unets.load_state_dict(loaded_obj['ema'], strict = strict)
except RuntimeError:
print("Failed loading state dict. Trying partial load")
self.ema_unets.load_state_dict(restore_parts(self.ema_unets.state_dict(),
loaded_obj['ema']))
self.print(f'checkpoint loaded from {path}')
return loaded_obj
# managing ema unets and their devices
@property
def unets(self):
return nn.ModuleList([ema.ema_model for ema in self.ema_unets])
def get_ema_unet(self, unet_number = None):
if not self.use_ema:
return
unet_number = self.validate_unet_number(unet_number)
index = unet_number - 1
if isinstance(self.unets, nn.ModuleList):
unets_list = [unet for unet in self.ema_unets]
delattr(self, 'ema_unets')
self.ema_unets = unets_list
if index != self.ema_unet_being_trained_index:
for unet_index, unet in enumerate(self.ema_unets):
unet.to(self.device if unet_index == index else 'cpu')
self.ema_unet_being_trained_index = index
return self.ema_unets[index]
def reset_ema_unets_all_one_device(self, device = None):
if not self.use_ema:
return
device = default(device, self.device)
self.ema_unets = nn.ModuleList([*self.ema_unets])
self.ema_unets.to(device)
self.ema_unet_being_trained_index = -1
@torch.no_grad()
@contextmanager
def use_ema_unets(self):
if not self.use_ema:
output = yield
return output
self.reset_ema_unets_all_one_device()
self.imagen.reset_unets_all_one_device()
self.unets.eval()
trainable_unets = self.imagen.unets
self.imagen.unets = self.unets # swap in exponential moving averaged unets for sampling
output = yield
self.imagen.unets = trainable_unets # restore original training unets
# cast the ema_model unets back to original device
for ema in self.ema_unets:
ema.restore_ema_model_device()
return output
def print_unet_devices(self):
self.print('unet devices:')
for i, unet in enumerate(self.imagen.unets):
device = next(unet.parameters()).device
self.print(f'\tunet {i}: {device}')
if not self.use_ema:
return
self.print('\nema unet devices:')
for i, ema_unet in enumerate(self.ema_unets):
device = next(ema_unet.parameters()).device
self.print(f'\tema unet {i}: {device}')
# overriding state dict functions
def state_dict(self, *args, **kwargs):
self.reset_ema_unets_all_one_device()
return super().state_dict(*args, **kwargs)
def load_state_dict(self, *args, **kwargs):
self.reset_ema_unets_all_one_device()
return super().load_state_dict(*args, **kwargs)
# encoding text functions
def encode_text(self, text, **kwargs):
return self.imagen.encode_text(text, **kwargs)
# forwarding functions and gradient step updates
def update(self, unet_number = None):
unet_number = self.validate_unet_number(unet_number)
self.validate_and_set_unet_being_trained(unet_number)
self.set_accelerator_scaler(unet_number)
index = unet_number - 1
unet = self.unet_being_trained
optimizer = getattr(self, f'optim{index}')
scaler = getattr(self, f'scaler{index}')
scheduler = getattr(self, f'scheduler{index}')
warmup_scheduler = getattr(self, f'warmup{index}')
# set the grad scaler on the accelerator, since we are managing one per u-net
if exists(self.max_grad_norm):
self.accelerator.clip_grad_norm_(unet.parameters(), self.max_grad_norm)
optimizer.step()
optimizer.zero_grad()
if self.use_ema:
ema_unet = self.get_ema_unet(unet_number)
ema_unet.update()
# scheduler, if needed
maybe_warmup_context = nullcontext() if not exists(warmup_scheduler) else warmup_scheduler.dampening()
with maybe_warmup_context:
if exists(scheduler) and not self.accelerator.optimizer_step_was_skipped: # recommended in the docs
scheduler.step()
self.steps += F.one_hot(torch.tensor(unet_number - 1, device = self.steps.device), num_classes = len(self.steps))
if not exists(self.checkpoint_path):
return
total_steps = int(self.steps.sum().item())
if total_steps % self.checkpoint_every:
return
self.save_to_checkpoint_folder()
@torch.no_grad()
@cast_torch_tensor
@imagen_sample_in_chunks
def sample(self, *args, **kwargs):
context = nullcontext if kwargs.pop('use_non_ema', False) else self.use_ema_unets
self.print_untrained_unets()
if not self.is_main:
kwargs['use_tqdm'] = False
with context():
output = self.imagen.sample(*args, device = self.device, **kwargs)
return output
@partial(cast_torch_tensor, cast_fp16 = True)
def forward(
self,
*args,
unet_number = None,
max_batch_size = None,
**kwargs
):
unet_number = self.validate_unet_number(unet_number)
self.validate_and_set_unet_being_trained(unet_number)
self.set_accelerator_scaler(unet_number)
assert not exists(self.only_train_unet_number) or self.only_train_unet_number == unet_number, f'you can only train unet #{self.only_train_unet_number}'
total_loss = 0.
for chunk_size_frac, (chunked_args, chunked_kwargs) in split_args_and_kwargs(*args, split_size = max_batch_size, **kwargs):
with self.accelerator.autocast():
loss = self.imagen(*chunked_args, unet = self.unet_being_trained, unet_number = unet_number, **chunked_kwargs)
loss = loss * chunk_size_frac
total_loss += loss.item()
if self.training:
self.accelerator.backward(loss)
return total_loss