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import torch.nn as nn
from typing import Union
from copy import deepcopy
from omegaconf import DictConfig
from collections import OrderedDict
from lightning import LightningModule
import warnings
from jutils import instantiate_from_config
from jutils import load_partial_from_config
from jutils import exists, freeze, default
from patch_flow.log_utils import log_images
from patch_flow.metrics import ImageMetricTracker
from patch_flow.diagonal_gaussian import DiagonalGaussian
from torchmetrics.aggregation import CatMetric
def un_normalize_ims(ims):
"""Convert from [-1, 1] to [0, 255]"""
ims = ((ims * 127.5) + 127.5).clip(0, 255).to(torch.uint8)
return ims
@torch.no_grad()
def update_ema(ema_model, model, decay=0.9999):
"""
Step the EMA model towards the current model.
"""
ema_params = OrderedDict(ema_model.named_parameters())
model_params = OrderedDict(model.named_parameters())
for name, param in model_params.items():
if not param.requires_grad:
continue
# unwrap DDP
if name.startswith("module."):
name = name.replace("module.", "")
ema_params[name].mul_(decay).add_(param.data, alpha=1 - decay)
def instantiate_if_needed(config_or_obj):
if isinstance(config_or_obj, nn.Module):
return config_or_obj
elif isinstance(config_or_obj, dict) or isinstance(config_or_obj, DictConfig):
return instantiate_from_config(config_or_obj)
else:
raise ValueError(f"Expected nn.Module or config dict, got {type(config_or_obj)}")
# ===================================================================================================
class LatentFlowTrainer(LightningModule):
def __init__(
self,
model: Union[dict, DictConfig, nn.Module],
first_stage: Union[dict, DictConfig, nn.Module],
flow: Union[dict, DictConfig, object],
# learning
lr: float = 1e-4,
weight_decay: float = 0.0,
ema_rate: float = 0.9999,
lr_scheduler_cfg: dict = None,
# logging
sample_kwargs: dict = None,
):
super().__init__()
# flow logic
self.flow = instantiate_if_needed(flow)
# unet/transformer model
self.model = instantiate_if_needed(model)
# EMA of unet/transformer model
self.ema_model = None
self.ema_rate = ema_rate
if ema_rate > 0:
if isinstance(model, nn.Module):
warnings.warn("EMA model with deepcopy, might run into issues with compile.")
self.ema_model = deepcopy(self.model)
else:
self.ema_model = instantiate_if_needed(model)
self.ema_model.load_state_dict(self.model.state_dict())
freeze(self.ema_model)
self.ema_model.eval()
update_ema(self.ema_model, self.model, decay=0) # ensure EMA is in sync
# first stage autoencoder
self.first_stage = instantiate_if_needed(first_stage)
self.first_stage.eval().to(self.device)
freeze(self.first_stage)
# training parameters
self.lr = lr
self.weight_decay = weight_decay
self.lr_scheduler_cfg = lr_scheduler_cfg
# visualization
self.sample_kwargs = sample_kwargs or {}
self.generator = torch.Generator()
# evaluation
self.metric_tracker = ImageMetricTracker().to(self.device)
# SD3 & Meta Movie Gen show that val loss correlates with human quality
# and compute the loss in equidistant segments in (0, 1) to reduce variance
self.val_losses = CatMetric().to(self.device) # sync across GPUs
self.val_images = None
self.val_epochs = 0
self.save_hyperparameters()
# signal handler for slurm, flag to make sure the signal
# is not handled at an incorrect state, e.g. during weights update
def configure_optimizers(self):
opt = torch.optim.AdamW(
[p for p in self.parameters() if p.requires_grad], lr=self.lr, weight_decay=self.weight_decay
)
out = dict(optimizer=opt)
if exists(self.lr_scheduler_cfg):
sch = load_partial_from_config(self.lr_scheduler_cfg)
sch = sch(optimizer=opt)
out["lr_scheduler"] = sch
return out
def on_train_batch_end(self, outputs, batch, batch_idx):
# first checking for trainer ensures that the module can be also used with accelerate
if exists(self._trainer) and exists(self.lr_scheduler_cfg):
self.lr_schedulers().step()
if exists(self.ema_model):
update_ema(self.ema_model, self.model, decay=self.ema_rate)
# ===================================================================================================
# training logic
@torch.no_grad()
def encode(self, x):
return self.first_stage.encode(x) if exists(self.first_stage) else x
@torch.no_grad()
def decode(self, z):
return self.first_stage.decode(z) if exists(self.first_stage) else z
def forward(self, batch):
ims = batch["image"]
latent = batch.get("latent", None)
if not exists(latent):
latent = self.encode(ims)
label = batch.get("label", None)
# compute loss
loss = self.flow.training_losses(model=self.model, x1=latent, y=label)
return loss
# ===================================================================================================
# validation
def validation_step(self, batch, batch_idx):
ims = batch["image"]
label = batch.get("label", None)
latent = batch.get("latent", None)
if latent is None:
latent = self.encode(ims)
bs = ims.shape[0]
g = self.generator.manual_seed(batch_idx + self.global_rank * 16102024)
noise = torch.randn(latent.shape, generator=g, dtype=ims.dtype).to(ims.device)
sample_model = self.ema_model if exists(self.ema_model) else self.model
# flow models val loss shows correlation with human quality
if hasattr(self.flow, "validation_losses"):
latent = default(latent, self.encode(ims))
_, val_loss_per_segment = self.flow.validation_losses(model=sample_model, x1=latent, x0=noise, y=label)
self.val_losses.update(val_loss_per_segment.unsqueeze(0))
# sample images
samples = self.flow.generate(model=sample_model, x=noise, y=label, **self.sample_kwargs)
samples = self.decode(samples)
# metrics
self.metric_tracker(ims, samples)
# save the images for visualization
if self.val_images is None:
real_ims = un_normalize_ims(ims)
fake_ims = un_normalize_ims(samples)
self.val_images = {
"real": real_ims[:20],
"fake": fake_ims[:20],
}
def on_validation_epoch_end(self):
# visualization
for key, ims in self.val_images.items():
log_images(self.logger, ims, f"val/{key}/samples", stack="row", split=4, step=self.global_step)
# reset val images
self.val_images = None
# compute metrics
metrics = self.metric_tracker.aggregate()
for k, v in metrics.items():
self.log(f"val/{k}", v, sync_dist=True)
self.metric_tracker.reset()
# compute val loss if available (Flow models)
if len(self.val_losses.value) > 0:
val_losses = self.val_losses.compute() # (N batches, segments)
val_losses = val_losses.mean(0) # mean per segment
for i, loss in enumerate(val_losses):
self.log(f"val/loss_segment_{i}", loss, sync_dist=True)
self.log("val/loss", val_losses.mean(), sync_dist=True)
self.val_losses.reset()
# log some information
self.val_epochs += 1
self.print(f"Val epoch {self.val_epochs:,} | Optimizer step {self.global_step:,}")
metric_str = " | ".join([f"{k}: {v:.4f}" for k, v in metrics.items()])
self.print(metric_str)
# ===================================================================================================
class LatentPatchForcingTrainer(LatentFlowTrainer):
def __init__(self, *args, uncertainty_weight: float = 0.01, **kwargs):
super().__init__(*args, **kwargs)
self.uncertainty_weight = uncertainty_weight
assert (
hasattr(self.model, "predict_uncertainty") and self.model.predict_uncertainty
), "Model should be PatchForcingDiT with predict_uncertainty=True."
def forward(self, batch):
ims = batch["image"]
latent = batch.get("latent", None)
if not exists(latent):
latent = self.encode(ims)
label = batch.get("label", None)
# compute loss
xt, ut, t = self.flow.get_interpolants(x1=latent)
vt, logvar_theta = self.model(x=xt, t=t, y=label, return_uncertainty=True)
# fm loss
fm_loss = (vt - ut).square().mean()
# uncertainty loss following SRM
sigma_theta = torch.exp(0.5 * logvar_theta)
pred_theta = DiagonalGaussian(mean=vt.detach(), std=sigma_theta)
sigma_loss = pred_theta.nll(ut).mean()
loss = fm_loss + self.uncertainty_weight * sigma_loss
loss_dict = {"flow_loss": fm_loss, "sigma_loss": sigma_loss}
return loss, loss_dict
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