log loss per image (#7278)

* log loss per image

* add commandline param for per image loss logging

* style

* debug-loss -> debug_loss

---------
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>

examples/text_to_image/train_text_to_image_lora_sdxl.py

@@ -425,6 +425,11 @@ def parse_args(input_args=None):
         default=4,
         help=("The dimension of the LoRA update matrices."),
     )
+    parser.add_argument(
+        "--debug_loss",
+        action="store_true",
+        help="debug loss for each image, if filenames are awailable in the dataset",
+    )
 
     if input_args is not None:
         args = parser.parse_args(input_args)
@@ -603,6 +608,7 @@ def main(args):
     # Move unet, vae and text_encoder to device and cast to weight_dtype
     # The VAE is in float32 to avoid NaN losses.
     unet.to(accelerator.device, dtype=weight_dtype)
+
     if args.pretrained_vae_model_name_or_path is None:
         vae.to(accelerator.device, dtype=torch.float32)
     else:
@@ -890,13 +896,17 @@ def main(args):
         tokens_one, tokens_two = tokenize_captions(examples)
         examples["input_ids_one"] = tokens_one
         examples["input_ids_two"] = tokens_two
+        if args.debug_loss:
+            fnames = [os.path.basename(image.filename) for image in examples[image_column] if image.filename]
+            if fnames:
+                examples["filenames"] = fnames
         return examples
 
     with accelerator.main_process_first():
         if args.max_train_samples is not None:
             dataset["train"] = dataset["train"].shuffle(seed=args.seed).select(range(args.max_train_samples))
         # Set the training transforms
-        train_dataset = dataset["train"].with_transform(preprocess_train)
+        train_dataset = dataset["train"].with_transform(preprocess_train, output_all_columns=True)
 
     def collate_fn(examples):
         pixel_values = torch.stack([example["pixel_values"] for example in examples])
@@ -905,7 +915,7 @@ def main(args):
         crop_top_lefts = [example["crop_top_lefts"] for example in examples]
         input_ids_one = torch.stack([example["input_ids_one"] for example in examples])
         input_ids_two = torch.stack([example["input_ids_two"] for example in examples])
-        return {
+        result = {
             "pixel_values": pixel_values,
             "input_ids_one": input_ids_one,
             "input_ids_two": input_ids_two,
@@ -913,6 +923,11 @@ def main(args):
             "crop_top_lefts": crop_top_lefts,
         }
 
+        filenames = [example["filenames"] for example in examples if "filenames" in example]
+        if filenames:
+            result["filenames"] = filenames
+        return result
+
     # DataLoaders creation:
     train_dataloader = torch.utils.data.DataLoader(
         train_dataset,
@@ -1105,7 +1120,9 @@ def main(args):
                     loss = F.mse_loss(model_pred.float(), target.float(), reduction="none")
                     loss = loss.mean(dim=list(range(1, len(loss.shape)))) * mse_loss_weights
                     loss = loss.mean()
-
+                if args.debug_loss and "filenames" in batch:
+                    for fname in batch["filenames"]:
+                        accelerator.log({"loss_for_" + fname: loss}, step=global_step)
                 # Gather the losses across all processes for logging (if we use distributed training).
                 avg_loss = accelerator.gather(loss.repeat(args.train_batch_size)).mean()
                 train_loss += avg_loss.item() / args.gradient_accumulation_steps