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fix global steps tracking & --save_steps intermittent saves (#6, #8) #11

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merged 2 commits into from
Dec 10, 2022
Merged

fix global steps tracking & --save_steps intermittent saves (#6, #8) #11

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211605a
fix global steps tracking & --save_steps intermittent saves (#6, #8)
timh Dec 9, 2022
259795d
guard against calling unwrap_model with keep_fp32_wrapper arg on olde…
timh Dec 9, 2022
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29 changes: 21 additions & 8 deletions train_lora_dreambooth.py
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Original file line number Diff line number Diff line change
Expand Up @@ -6,6 +6,7 @@
import itertools
import math
import os
import inspect
from pathlib import Path
from typing import Optional

Expand Down Expand Up @@ -828,13 +829,14 @@ def collate_fn(examples):
)
progress_bar.set_description("Steps")
global_step = 0
last_save = 0

for epoch in range(args.num_train_epochs):
unet.train()
if args.train_text_encoder:
text_encoder.train()
for step, batch in enumerate(train_dataloader):

for step, batch in enumerate(train_dataloader):
# Convert images to latent space
latents = vae.encode(
batch["pixel_values"].to(dtype=weight_dtype)
Expand Down Expand Up @@ -908,21 +910,32 @@ def collate_fn(examples):
progress_bar.update(1)
optimizer.zero_grad()

# Checks if the accelerator has performed an optimization step behind the scenes
if accelerator.sync_gradients:

global_step += 1

if global_step % args.save_steps == 0:
# Checks if the accelerator has performed an optimization step behind the scenes
if accelerator.sync_gradients:
if args.save_steps and global_step - last_save >= args.save_steps:
if accelerator.is_main_process:
# newer versions of accelerate allow the 'keep_fp32_wrapper' arg. without passing
# it, the models will be unwrapped, and when they are then used for further training,
# we will crash. pass this, but only to newer versions of accelerate. fixes
# https://github.com/huggingface/diffusers/issues/1566
accepts_keep_fp32_wrapper = "keep_fp32_wrapper" in set(
inspect.signature(accelerator.unwrap_model).parameters.keys()
)
extra_args = {"keep_fp32_wrapper": True} if accepts_keep_fp32_wrapper else {}
pipeline = StableDiffusionPipeline.from_pretrained(
args.pretrained_model_name_or_path,
unet=accelerator.unwrap_model(unet),
text_encoder=accelerator.unwrap_model(text_encoder),
unet=accelerator.unwrap_model(unet, **extra_args),
text_encoder=accelerator.unwrap_model(text_encoder, **extra_args),
revision=args.revision,
)

save_lora_weight(pipeline.unet, args.output_dir + "/lora_weight.pt")
filename = f"{args.output_dir}/lora_weight_e{epoch}_s{global_step}.pt"
print(f"save weights {filename}")
save_lora_weight(pipeline.unet, filename)

last_save = global_step

logs = {"loss": loss.detach().item(), "lr": lr_scheduler.get_last_lr()[0]}
progress_bar.set_postfix(**logs)
Expand Down