Map speedup (#6745)

* Speed up dataset mapping

* Fix missing columns

* Remove cache files cleanup

* Update examples/text_to_image/train_text_to_image_sdxl.py

* make style

* Fix code style

* style

* Empty-Commit

---------

Co-authored-by: Sayak Paul <[email protected]>
Co-authored-by: Quentin Lhoest <[email protected]>
Co-authored-by: Quentin Lhoest <[email protected]>

Author: 3 people

examples/text_to_image/train_text_to_image_sdxl.py

@@ -35,7 +35,7 @@
 from accelerate import Accelerator
 from accelerate.logging import get_logger
 from accelerate.utils import ProjectConfiguration, set_seed
-from datasets import load_dataset
+from datasets import concatenate_datasets, load_dataset
 from huggingface_hub import create_repo, upload_folder
 from packaging import version
 from torchvision import transforms
@@ -896,13 +896,19 @@ def preprocess_train(examples):
         # details: https://github.com/huggingface/diffusers/pull/4038#discussion_r1266078401
         new_fingerprint = Hasher.hash(args)
         new_fingerprint_for_vae = Hasher.hash(vae_path)
-        train_dataset = train_dataset.map(compute_embeddings_fn, batched=True, new_fingerprint=new_fingerprint)
-        train_dataset = train_dataset.map(
+        train_dataset_with_embeddings = train_dataset.map(
+            compute_embeddings_fn, batched=True, new_fingerprint=new_fingerprint
+        )
+        train_dataset_with_vae = train_dataset.map(
             compute_vae_encodings_fn,
             batched=True,
             batch_size=args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps,
             new_fingerprint=new_fingerprint_for_vae,
         )
+        precomputed_dataset = concatenate_datasets(
+            [train_dataset_with_embeddings, train_dataset_with_vae.remove_columns(["image", "text"])], axis=1
+        )
+        precomputed_dataset = precomputed_dataset.with_transform(preprocess_train)
 
     del text_encoders, tokenizers, vae
     gc.collect()
@@ -925,7 +931,7 @@ def collate_fn(examples):
 
     # DataLoaders creation:
     train_dataloader = torch.utils.data.DataLoader(
-        train_dataset,
+        precomputed_dataset,
         shuffle=True,
         collate_fn=collate_fn,
         batch_size=args.train_batch_size,
@@ -976,7 +982,7 @@ def unwrap_model(model):
     total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
 
     logger.info("***** Running training *****")
-    logger.info(f"  Num examples = {len(train_dataset)}")
+    logger.info(f"  Num examples = {len(precomputed_dataset)}")
     logger.info(f"  Num Epochs = {args.num_train_epochs}")
     logger.info(f"  Instantaneous batch size per device = {args.train_batch_size}")
     logger.info(f"  Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}")