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debiased_hybrid_loss.py
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debiased_hybrid_loss.py
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import torch
from torchvision import transforms
import torch.nn.functional as F
import random
from utils.lora import extract_lora_child_module
from utils.func_utils import tensor_to_vae_latent, sample_noise
def DebiasedHybridLoss(
train_loss_temporal,
accelerator,
optimizers,
lr_schedulers,
unet,
vae,
text_encoder,
noise_scheduler,
batch,
step,
config,
random_hflip_img=False,
spatial_lora_num=1
):
mask_spatial_lora = random.uniform(0, 1) < 0.2
cache_latents = config.train.cache_latents
if not cache_latents:
latents = tensor_to_vae_latent(batch["pixel_values"], vae)
else:
latents = batch["latents"]
# Sample noise that we'll add to the latents
# use_offset_noise = use_offset_noise and not rescale_schedule
noise = sample_noise(latents, 0.1, False)
bsz = latents.shape[0]
# Sample a random timestep for each video
timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps, (bsz,), device=latents.device)
timesteps = timesteps.long()
# Add noise to the latents according to the noise magnitude at each timestep
# (this is the forward diffusion process)
noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)
# *Potentially* Fixes gradient checkpointing training.
# See: https://github.com/prigoyal/pytorch_memonger/blob/master/tutorial/Checkpointing_for_PyTorch_models.ipynb
# if kwargs.get('eval_train', False):
# unet.eval()
# text_encoder.eval()
# Encode text embeddings
token_ids = batch['prompt_ids']
encoder_hidden_states = text_encoder(token_ids)[0]
detached_encoder_state = encoder_hidden_states.clone().detach()
# Get the target for loss depending on the prediction type
if noise_scheduler.config.prediction_type == "epsilon":
target = noise
elif noise_scheduler.config.prediction_type == "v_prediction":
target = noise_scheduler.get_velocity(latents, noise, timesteps)
else:
raise ValueError(f"Unknown prediction type {noise_scheduler.config.prediction_type}")
encoder_hidden_states = detached_encoder_state
# optimization
if mask_spatial_lora:
loras = extract_lora_child_module(unet, target_replace_module=["Transformer2DModel"])
for lora_i in loras:
lora_i.scale = 0.
loss_spatial = None
else:
loras = extract_lora_child_module(unet, target_replace_module=["Transformer2DModel"])
if spatial_lora_num == 1:
for lora_i in loras:
lora_i.scale = 1.
else:
for lora_i in loras:
lora_i.scale = 0.
for lora_idx in range(0, len(loras), spatial_lora_num):
loras[lora_idx + step].scale = 1.
loras = extract_lora_child_module(unet, target_replace_module=["TransformerTemporalModel"])
if len(loras) > 0:
for lora_i in loras:
lora_i.scale = 0.
ran_idx = torch.randint(0, noisy_latents.shape[2], (1,)).item()
if random.uniform(0, 1) < random_hflip_img:
pixel_values_spatial = transforms.functional.hflip(
batch["pixel_values"][:, ran_idx, :, :, :]).unsqueeze(1)
latents_spatial = tensor_to_vae_latent(pixel_values_spatial, vae)
noise_spatial = sample_noise(latents_spatial, 0.1, False)
noisy_latents_input = noise_scheduler.add_noise(latents_spatial, noise_spatial, timesteps)
target_spatial = noise_spatial
model_pred_spatial = unet(noisy_latents_input, timesteps,
encoder_hidden_states=encoder_hidden_states).sample
loss_spatial = F.mse_loss(model_pred_spatial[:, :, 0, :, :].float(),
target_spatial[:, :, 0, :, :].float(), reduction="mean")
else:
noisy_latents_input = noisy_latents[:, :, ran_idx, :, :]
target_spatial = target[:, :, ran_idx, :, :]
model_pred_spatial = unet(noisy_latents_input.unsqueeze(2), timesteps,
encoder_hidden_states=encoder_hidden_states).sample
loss_spatial = F.mse_loss(model_pred_spatial[:, :, 0, :, :].float(),
target_spatial.float(), reduction="mean")
model_pred = unet(noisy_latents, timesteps, encoder_hidden_states=encoder_hidden_states).sample
loss_temporal = F.mse_loss(model_pred.float(), target.float(), reduction="mean")
beta = 1
alpha = (beta ** 2 + 1) ** 0.5
ran_idx = torch.randint(0, model_pred.shape[2], (1,)).item()
model_pred_decent = alpha * model_pred - beta * model_pred[:, :, ran_idx, :, :].unsqueeze(2)
target_decent = alpha * target - beta * target[:, :, ran_idx, :, :].unsqueeze(2)
loss_ad_temporal = F.mse_loss(model_pred_decent.float(), target_decent.float(), reduction="mean")
loss_temporal = loss_temporal + loss_ad_temporal
avg_loss_temporal = accelerator.gather(loss_temporal.repeat(config.train.train_batch_size)).mean()
train_loss_temporal += avg_loss_temporal.item() / config.train.gradient_accumulation_steps
if not mask_spatial_lora:
accelerator.backward(loss_spatial, retain_graph=True)
if spatial_lora_num == 1:
optimizers[1].step()
else:
optimizers[step+1].step()
accelerator.backward(loss_temporal)
optimizers[0].step()
if spatial_lora_num == 1:
lr_schedulers[1].step()
else:
lr_schedulers[1 + step].step()
lr_schedulers[0].step()
return loss_temporal, train_loss_temporal