Bugfixes for the training example

examples/training_ddpm.py

@@ -1,10 +1,10 @@
-import random
+import os
 
-import numpy as np
 import torch
+import PIL.Image
+import argparse
 import torch.nn.functional as F
 
-import PIL.Image
 from accelerate import Accelerator
 from datasets import load_dataset
 from diffusers import DDPM, DDPMScheduler, UNetModel
@@ -14,7 +14,6 @@ from torchvision.transforms import (
     Lambda,
     RandomCrop,
     RandomHorizontalFlip,
-    RandomVerticalFlip,
     Resize,
     ToTensor,
 )
@@ -22,119 +21,126 @@ from tqdm.auto import tqdm
 from transformers import get_linear_schedule_with_warmup
 
 
-def set_seed(seed):
-    # torch.backends.cudnn.deterministic = True
-    # torch.backends.cudnn.benchmark = False
-    torch.manual_seed(seed)
-    torch.cuda.manual_seed_all(seed)
-    np.random.seed(seed)
-    random.seed(seed)
+def main(args):
+    accelerator = Accelerator(mixed_precision=args.mixed_precision)
+
+    model = UNetModel(
+        attn_resolutions=(16,),
+        ch=128,
+        ch_mult=(1, 2, 4, 8),
+        dropout=0.0,
+        num_res_blocks=2,
+        resamp_with_conv=True,
+        resolution=64,
+    )
+    noise_scheduler = DDPMScheduler(timesteps=1000)
+    optimizer = torch.optim.Adam(model.parameters(), lr=1e-4)
+
+    num_epochs = 100
+    batch_size = 16
+    gradient_accumulation_steps = 1
+
+    augmentations = Compose(
+        [
+            Resize(64, interpolation=InterpolationMode.BILINEAR),
+            RandomCrop(64),
+            RandomHorizontalFlip(),
+            ToTensor(),
+            Lambda(lambda x: x * 2 - 1),
+        ]
+    )
+    dataset = load_dataset("huggan/pokemon", split="train")
+
+    def transforms(examples):
+        images = [augmentations(image.convert("RGB")) for image in examples["image"]]
+        return {"input": images}
+
+    dataset.set_transform(transforms)
+    train_dataloader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=True)
+
+    lr_scheduler = get_linear_schedule_with_warmup(
+        optimizer=optimizer,
+        num_warmup_steps=500,
+        num_training_steps=(len(train_dataloader) * num_epochs) // gradient_accumulation_steps,
+    )
+
+    model, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
+        model, optimizer, train_dataloader, lr_scheduler
+    )
+
+    for epoch in range(num_epochs):
+        model.train()
+        with tqdm(total=len(train_dataloader), unit="ba") as pbar:
+            pbar.set_description(f"Epoch {epoch}")
+            for step, batch in enumerate(train_dataloader):
+                clean_images = batch["input"]
+                noisy_images = torch.empty_like(clean_images)
+                noise_samples = torch.empty_like(clean_images)
+                bsz = clean_images.shape[0]
+
+                timesteps = torch.randint(0, noise_scheduler.timesteps, (bsz,), device=clean_images.device).long()
+                for idx in range(bsz):
+                    noise = torch.randn(clean_images.shape[1:]).to(clean_images.device)
+                    noise_samples[idx] = noise
+                    noisy_images[idx] = noise_scheduler.forward_step(clean_images[idx], noise, timesteps[idx])
+
+                if step % gradient_accumulation_steps != 0:
+                    with accelerator.no_sync(model):
+                        output = model(noisy_images, timesteps)
+                        # predict the noise
+                        loss = F.mse_loss(output, noise_samples)
+                        accelerator.backward(loss)
+                else:
+                    output = model(noisy_images, timesteps)
+                    loss = F.mse_loss(output, noise_samples)
+                    accelerator.backward(loss)
+                    torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
+                    optimizer.step()
+                    lr_scheduler.step()
+                    optimizer.zero_grad()
+                pbar.update(1)
+                pbar.set_postfix(loss=loss.detach().item(), lr=optimizer.param_groups[0]["lr"])
+
+                optimizer.step()
+
+        torch.distributed.barrier()
+        if args.local_rank in [-1, 0]:
+            model.eval()
+            with torch.no_grad():
+                pipeline = DDPM(unet=model.module, noise_scheduler=noise_scheduler)
+                generator = torch.Generator()
+                generator = generator.manual_seed(0)
+                # run pipeline in inference (sample random noise and denoise)
+                image = pipeline(generator=generator)
+
+                # process image to PIL
+                image_processed = image.cpu().permute(0, 2, 3, 1)
+                image_processed = (image_processed + 1.0) * 127.5
+                image_processed = image_processed.type(torch.uint8).numpy()
+                image_pil = PIL.Image.fromarray(image_processed[0])
+
+                # save image
+                pipeline.save_pretrained("./pokemon-ddpm")
+                image_pil.save(f"./pokemon-ddpm/test_{epoch}.png")
+        torch.distributed.barrier()
 
 
-set_seed(0)
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Simple example of training script.")
+    parser.add_argument("--local_rank", type=int)
+    parser.add_argument(
+        "--mixed_precision",
+        type=str,
+        default="no",
+        choices=["no", "fp16", "bf16"],
+        help="Whether to use mixed precision. Choose"
+             "between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
+             "and an Nvidia Ampere GPU.",
+    )
 
-accelerator = Accelerator()
+    args = parser.parse_args()
+    env_local_rank = int(os.environ.get("LOCAL_RANK", -1))
+    if env_local_rank != -1 and env_local_rank != args.local_rank:
+        args.local_rank = env_local_rank
 
-model = UNetModel(
-    attn_resolutions=(16,),
-    ch=128,
-    ch_mult=(1, 2, 2, 2),
-    dropout=0.0,
-    num_res_blocks=2,
-    resamp_with_conv=True,
-    resolution=32,
-)
-noise_scheduler = DDPMScheduler(timesteps=1000)
-optimizer = torch.optim.Adam(model.parameters(), lr=3e-4)
-
-num_epochs = 100
-batch_size = 64
-gradient_accumulation_steps = 2
-
-augmentations = Compose(
-    [
-        Resize(32, interpolation=InterpolationMode.BILINEAR),
-        RandomHorizontalFlip(),
-        RandomVerticalFlip(),
-        RandomCrop(32),
-        ToTensor(),
-        Lambda(lambda x: x * 2 - 1),
-    ]
-)
-dataset = load_dataset("huggan/flowers-102-categories", split="train")
-
-
-def transforms(examples):
-    images = [augmentations(image.convert("RGB")) for image in examples["image"]]
-    return {"input": images}
-
-
-dataset.set_transform(transforms)
-train_dataloader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=True)
-
-lr_scheduler = get_linear_schedule_with_warmup(
-    optimizer=optimizer,
-    num_warmup_steps=500,
-    num_training_steps=(len(train_dataloader) * num_epochs) // gradient_accumulation_steps,
-)
-
-model, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
-    model, optimizer, train_dataloader, lr_scheduler
-)
-
-for epoch in range(num_epochs):
-    model.train()
-    pbar = tqdm(total=len(train_dataloader), unit="ba")
-    pbar.set_description(f"Epoch {epoch}")
-    losses = []
-    for step, batch in enumerate(train_dataloader):
-        clean_images = batch["input"]
-        noisy_images = torch.empty_like(clean_images)
-        noise_samples = torch.empty_like(clean_images)
-        bsz = clean_images.shape[0]
-
-        timesteps = torch.randint(0, noise_scheduler.timesteps, (bsz,), device=clean_images.device).long()
-        for idx in range(bsz):
-            noise = torch.randn((3, 32, 32)).to(clean_images.device)
-            noise_samples[idx] = noise
-            noisy_images[idx] = noise_scheduler.forward_step(clean_images[idx], noise, timesteps[idx])
-
-        if step % gradient_accumulation_steps == 0:
-            with accelerator.no_sync(model):
-                output = model(noisy_images, timesteps)
-                # predict the noise
-                loss = F.l1_loss(output, noise_samples)
-                accelerator.backward(loss)
-        else:
-            output = model(noisy_images, timesteps)
-            loss = F.l1_loss(output, clean_images)
-            accelerator.backward(loss)
-            torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
-            optimizer.step()
-            lr_scheduler.step()
-            optimizer.zero_grad()
-        loss = loss.detach().item()
-        losses.append(loss)
-        pbar.update(1)
-        pbar.set_postfix(loss=loss, avg_loss=np.mean(losses), lr=optimizer.param_groups[0]["lr"])
-
-        optimizer.step()
-
-    # eval
-    model.eval()
-    with torch.no_grad():
-        pipeline = DDPM(unet=model, noise_scheduler=noise_scheduler)
-        generator = torch.Generator()
-        generator = generator.manual_seed(0)
-        # run pipeline in inference (sample random noise and denoise)
-        image = pipeline(generator=generator)
-
-        # process image to PIL
-        image_processed = image.cpu().permute(0, 2, 3, 1)
-        image_processed = (image_processed + 1.0) * 127.5
-        image_processed = image_processed.type(torch.uint8).numpy()
-        image_pil = PIL.Image.fromarray(image_processed[0])
-
-        # save image
-        pipeline.save_pretrained("./flowers-ddpm")
-        image_pil.save(f"./flowers-ddpm/test_{epoch}.png")
+    main(args)