support multi gpu in caching text encoder outputs

README.md

@@ -25,7 +25,10 @@ The feature of SDXL training is now available in sdxl branch as an experimental
 Summary of the feature:
 
 - `sdxl_train.py` is a script for SDXL fine-tuning. The usage is almost the same as `fine_tune.py`, but it also supports DreamBooth dataset.
-  - `prepare_buckets_latents.py` now supports SDXL fine-tuning.
+  - `--full_bf16` option is added. This option enables the full bfloat16 training. This option is useful to reduce the GPU memory usage.
+    - However, bitsandbytes==0.35 doesn't seem to support this. Please use a newer version of bitsandbytes or another optimizer.
+    - I cannot find bitsandbytes>0.35.0 that works correctly on Windows.
+- `prepare_buckets_latents.py` now supports SDXL fine-tuning.
 - `sdxl_train_network.py` is a script for LoRA training for SDXL. The usage is almost the same as `train_network.py`.
 - Both scripts has following additional options:
   - `--cache_text_encoder_outputs`: Cache the outputs of the text encoders. This option is useful to reduce the GPU memory usage. This option cannot be used with options for shuffling or dropping the captions.
@@ -64,6 +67,7 @@ learning_rate = 4e-7 # SDXL original learning rate
 - [ ] Support Textual Inversion training.
 - [ ] Support `--weighted_captions` option.
 - [ ] Change `--output_config` option to continue the training.
+- [ ] Extend `--full_bf16` for all the scripts.
 
 ## About requirements.txt
 

library/sdxl_train_util.py

@@ -319,7 +319,7 @@ def save_sd_model_on_epoch_end_or_stepwise(
 
 # TextEncoderの出力をキャッシュする
 # weight_dtypeを指定するとText Encoderそのもの、およひ出力がweight_dtypeになる
-def cache_text_encoder_outputs(args, accelerator, tokenizers, text_encoders, data_loader, weight_dtype):
+def cache_text_encoder_outputs(args, accelerator, tokenizers, text_encoders, dataset, weight_dtype):
     print("caching text encoder outputs")
 
     tokenizer1, tokenizer2 = tokenizers
@@ -332,9 +332,9 @@ def cache_text_encoder_outputs(args, accelerator, tokenizers, text_encoders, dat
 
     text_encoder1_cache = {}
     text_encoder2_cache = {}
-    for batch in tqdm(data_loader):
-        input_ids1_batch = batch["input_ids"]
-        input_ids2_batch = batch["input_ids2"]
+    for batch in tqdm(dataset):
+        input_ids1_batch = batch["input_ids"].to(accelerator.device)
+        input_ids2_batch = batch["input_ids2"].to(accelerator.device)
 
         # split batch to avoid OOM
         # TODO specify batch size by args

sdxl_train.py

@@ -204,12 +204,25 @@ def train(args):
             text_encoder2.gradient_checkpointing_enable()
         training_models.append(text_encoder1)
         training_models.append(text_encoder2)
+
+        text_encoder1_cache = None
+        text_encoder2_cache = None
+
+        # set require_grad=True later
     else:
         text_encoder1.requires_grad_(False)
         text_encoder2.requires_grad_(False)
         text_encoder1.eval()
         text_encoder2.eval()
 
+        # TextEncoderの出力をキャッシュする
+        if args.cache_text_encoder_outputs:
+            # Text Encodes are eval and no grad
+            text_encoder1_cache, text_encoder2_cache = sdxl_train_util.cache_text_encoder_outputs(
+                args, accelerator, (tokenizer1, tokenizer2), (text_encoder1, text_encoder2), train_dataset_group, None
+            )
+            accelerator.wait_for_everyone()
+
     if not cache_latents:
         vae.requires_grad_(False)
         vae.eval()
@@ -289,23 +302,16 @@ def train(args):
         (unet,) = train_util.transform_models_if_DDP([unet])
         text_encoder1.to(weight_dtype)
         text_encoder2.to(weight_dtype)
-        text_encoder1.eval()
-        text_encoder2.eval()
 
-    # TextEncoderの出力をキャッシュする
+    # TextEncoderの出力をキャッシュするときにはCPUへ移動する
     if args.cache_text_encoder_outputs:
-        text_encoder1_cache, text_encoder2_cache = sdxl_train_util.cache_text_encoder_outputs(
-            args, accelerator, (tokenizer1, tokenizer2), (text_encoder1, text_encoder2), train_dataloader, None
-        )
-        accelerator.wait_for_everyone()
-        # Text Encoder doesn't work on CPU with fp16
+        # move Text Encoders for sampling images. Text Encoder doesn't work on CPU with fp16
         text_encoder1.to("cpu", dtype=torch.float32)
         text_encoder2.to("cpu", dtype=torch.float32)
         if torch.cuda.is_available():
             torch.cuda.empty_cache()
     else:
-        text_encoder1_cache = None
-        text_encoder2_cache = None
+        # make sure Text Encoders are on GPU
         text_encoder1.to(accelerator.device)
         text_encoder2.to(accelerator.device)
 

sdxl_train_network.py

@@ -47,7 +47,7 @@ class SdxlNetworkTrainer(train_network.NetworkTrainer):
         return args.cache_text_encoder_outputs
 
     def cache_text_encoder_outputs_if_needed(
-        self, args, accelerator, unet, vae, tokenizers, text_encoders, data_loader, weight_dtype
+        self, args, accelerator, unet, vae, tokenizers, text_encoders, dataset, weight_dtype
     ):
         if args.cache_text_encoder_outputs:
             if not args.lowram:
@@ -61,7 +61,7 @@ class SdxlNetworkTrainer(train_network.NetworkTrainer):
                     torch.cuda.empty_cache()
 
             text_encoder1_cache, text_encoder2_cache = sdxl_train_util.cache_text_encoder_outputs(
-                args, accelerator, tokenizers, text_encoders, data_loader, weight_dtype
+                args, accelerator, tokenizers, text_encoders, dataset, weight_dtype
             )
             accelerator.wait_for_everyone()
             text_encoders[0].to("cpu", dtype=torch.float32) # Text Encoder doesn't work with fp16 on CPU

train_network.py

@@ -255,6 +255,11 @@ class NetworkTrainer:
 
             accelerator.wait_for_everyone()
 
+        # 必要ならテキストエンコーダーの出力をキャッシュする: Text Encoderはcpuまたはgpuへ移される
+        self.cache_text_encoder_outputs_if_needed(
+            args, accelerator, unet, vae, tokenizers, text_encoders, train_dataset_group, weight_dtype
+        )
+
         # prepare network
         net_kwargs = {}
         if args.network_args is not None:
@@ -419,11 +424,6 @@ class NetworkTrainer:
             vae.eval()
             vae.to(accelerator.device, dtype=vae_dtype)
 
-        # 必要ならテキストエンコーダーの出力をキャッシュする: Text Encoderはcpuまたはgpuへ移される
-        self.cache_text_encoder_outputs_if_needed(
-            args, accelerator, unet, vae, tokenizers, text_encoders, train_dataloader, weight_dtype
-        )
-
         # 実験的機能：勾配も含めたfp16学習を行う　PyTorchにパッチを当ててfp16でのgrad scaleを有効にする
         if args.full_fp16:
             train_util.patch_accelerator_for_fp16_training(accelerator)