add clean_memory_on_device and use it from training

fine_tune.py

@@ -10,7 +10,7 @@ import toml
 from tqdm import tqdm
 
 import torch
-from library.device_utils import init_ipex, clean_memory
+from library.device_utils import init_ipex, clean_memory_on_device
 init_ipex()
 
 from accelerate.utils import set_seed
@@ -156,7 +156,7 @@ def train(args):
         with torch.no_grad():
             train_dataset_group.cache_latents(vae, args.vae_batch_size, args.cache_latents_to_disk, accelerator.is_main_process)
         vae.to("cpu")
-        clean_memory()
+        clean_memory_on_device(accelerator.device)
 
         accelerator.wait_for_everyone()
 

library/device_utils.py

@@ -31,6 +31,21 @@ def clean_memory():
         torch.mps.empty_cache()
 
 
+def clean_memory_on_device(device: torch.device):
+    r"""
+    Clean memory on the specified device, will be called from training scripts.
+    """
+    gc.collect()
+
+    # device may "cuda" or "cuda:0", so we need to check the type of device
+    if device.type == "cuda":
+        torch.cuda.empty_cache()
+    if device.type == "xpu":
+        torch.xpu.empty_cache()
+    if device.type == "mps":
+        torch.mps.empty_cache()
+
+
 @functools.lru_cache(maxsize=None)
 def get_preferred_device() -> torch.device:
     r"""

library/sdxl_train_util.py

@@ -4,7 +4,7 @@ import os
 from typing import Optional
 
 import torch
-from library.device_utils import init_ipex, clean_memory
+from library.device_utils import init_ipex, clean_memory_on_device
 init_ipex()
 
 from accelerate import init_empty_weights
@@ -50,7 +50,7 @@ def load_target_model(args, accelerator, model_version: str, weight_dtype):
                 unet.to(accelerator.device)
                 vae.to(accelerator.device)
 
-            clean_memory()
+            clean_memory_on_device(accelerator.device)
         accelerator.wait_for_everyone()
 
     return load_stable_diffusion_format, text_encoder1, text_encoder2, vae, unet, logit_scale, ckpt_info

library/train_util.py

@@ -32,7 +32,7 @@ import toml
 from tqdm import tqdm
 
 import torch
-from library.device_utils import init_ipex, clean_memory
+from library.device_utils import init_ipex, clean_memory_on_device
 
 init_ipex()
 
@@ -2285,7 +2285,7 @@ def cache_batch_latents(
                 info.latents_flipped = flipped_latent
 
     if not HIGH_VRAM:
-        clean_memory()
+        clean_memory_on_device(vae.device)
 
 
 def cache_batch_text_encoder_outputs(
@@ -4026,7 +4026,7 @@ def load_target_model(args, weight_dtype, accelerator, unet_use_linear_projectio
                 unet.to(accelerator.device)
                 vae.to(accelerator.device)
 
-            clean_memory()
+            clean_memory_on_device(accelerator.device)
         accelerator.wait_for_everyone()
 
     return text_encoder, vae, unet, load_stable_diffusion_format
@@ -4695,7 +4695,7 @@ def sample_images_common(
     distributed_state = PartialState() # for multi gpu distributed inference. this is a singleton, so it's safe to use it here
     
     org_vae_device = vae.device  # CPUにいるはず
-    vae.to(distributed_state.device)
+    vae.to(distributed_state.device)    # distributed_state.device is same as accelerator.device
 
     # unwrap unet and text_encoder(s)
     unet = accelerator.unwrap_model(unet)
@@ -4752,7 +4752,11 @@ def sample_images_common(
 
     # save random state to restore later
     rng_state = torch.get_rng_state()
-    cuda_rng_state = torch.cuda.get_rng_state() if torch.cuda.is_available() else None # TODO mps etc. support
+    cuda_rng_state = None
+    try:
+        cuda_rng_state = torch.cuda.get_rng_state() if torch.cuda.is_available() else None
+    except Exception:
+        pass
     
     if distributed_state.num_processes <= 1:    
         # If only one device is available, just use the original prompt list. We don't need to care about the distribution of prompts.
@@ -4774,8 +4778,10 @@ def sample_images_common(
     # clear pipeline and cache to reduce vram usage
     del pipeline
 
-    with torch.cuda.device(torch.cuda.current_device()):
+    # I'm not sure which of these is the correct way to clear the memory, but accelerator's device is used in the pipeline, so I'm using it here.
-        torch.cuda.empty_cache()
+    # with torch.cuda.device(torch.cuda.current_device()):
+    #     torch.cuda.empty_cache()
+    clean_memory_on_device(accelerator.device)
     
     torch.set_rng_state(rng_state)
     if cuda_rng_state is not None:
@@ -4870,10 +4876,6 @@ def sample_image_inference(accelerator: Accelerator, args: argparse.Namespace, p
         pass
 # endregion
 
-    # # clear pipeline and cache to reduce vram usage
-    # del pipeline
-    # torch.cuda.empty_cache()
-
 
 
 # region 前処理用

sdxl_train.py

@@ -10,7 +10,7 @@ import toml
 from tqdm import tqdm
 
 import torch
-from library.device_utils import init_ipex, clean_memory
+from library.device_utils import init_ipex, clean_memory_on_device
 init_ipex()
 
 from accelerate.utils import set_seed
@@ -250,7 +250,7 @@ def train(args):
         with torch.no_grad():
             train_dataset_group.cache_latents(vae, args.vae_batch_size, args.cache_latents_to_disk, accelerator.is_main_process)
         vae.to("cpu")
-        clean_memory()
+        clean_memory_on_device(accelerator.device)
 
         accelerator.wait_for_everyone()
 
@@ -403,7 +403,7 @@ def train(args):
         # 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)
-        clean_memory()
+        clean_memory_on_device(accelerator.device)
     else:
         # make sure Text Encoders are on GPU
         text_encoder1.to(accelerator.device)

sdxl_train_control_net_lllite.py

@@ -14,7 +14,7 @@ import toml
 from tqdm import tqdm
 
 import torch
-from library.device_utils import init_ipex, clean_memory
+from library.device_utils import init_ipex, clean_memory_on_device
 init_ipex()
 
 from torch.nn.parallel import DistributedDataParallel as DDP
@@ -162,7 +162,7 @@ def train(args):
                 accelerator.is_main_process,
             )
         vae.to("cpu")
-        clean_memory()
+        clean_memory_on_device(accelerator.device)
 
         accelerator.wait_for_everyone()
 
@@ -287,7 +287,7 @@ def train(args):
         # 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)
-        clean_memory()
+        clean_memory_on_device(accelerator.device)
     else:
         # make sure Text Encoders are on GPU
         text_encoder1.to(accelerator.device)

sdxl_train_control_net_lllite_old.py

@@ -11,7 +11,7 @@ import toml
 from tqdm import tqdm
 
 import torch
-from library.device_utils import init_ipex, clean_memory
+from library.device_utils import init_ipex, clean_memory_on_device
 init_ipex()
 
 from torch.nn.parallel import DistributedDataParallel as DDP
@@ -161,7 +161,7 @@ def train(args):
                 accelerator.is_main_process,
             )
         vae.to("cpu")
-        clean_memory()
+        clean_memory_on_device(accelerator.device)
 
         accelerator.wait_for_everyone()
 
@@ -260,7 +260,7 @@ def train(args):
         # 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)
-        clean_memory()
+        clean_memory_on_device(accelerator.device)
     else:
         # make sure Text Encoders are on GPU
         text_encoder1.to(accelerator.device)

sdxl_train_network.py

@@ -1,7 +1,7 @@
 import argparse
 
 import torch
-from library.device_utils import init_ipex, clean_memory
+from library.device_utils import init_ipex, clean_memory_on_device
 init_ipex()
 
 from library import sdxl_model_util, sdxl_train_util, train_util
@@ -64,7 +64,7 @@ class SdxlNetworkTrainer(train_network.NetworkTrainer):
                 org_unet_device = unet.device
                 vae.to("cpu")
                 unet.to("cpu")
-                clean_memory()
+                clean_memory_on_device(accelerator.device)
 
             # When TE is not be trained, it will not be prepared so we need to use explicit autocast
             with accelerator.autocast():
@@ -79,7 +79,7 @@ class SdxlNetworkTrainer(train_network.NetworkTrainer):
 
             text_encoders[0].to("cpu", dtype=torch.float32)  # Text Encoder doesn't work with fp16 on CPU
             text_encoders[1].to("cpu", dtype=torch.float32)
-            clean_memory()
+            clean_memory_on_device(accelerator.device)
 
             if not args.lowram:
                 print("move vae and unet back to original device")

train_controlnet.py

@@ -11,7 +11,7 @@ import toml
 from tqdm import tqdm
 
 import torch
-from library.device_utils import init_ipex, clean_memory
+from library.device_utils import init_ipex, clean_memory_on_device
 init_ipex()
 
 from torch.nn.parallel import DistributedDataParallel as DDP
@@ -217,8 +217,8 @@ def train(args):
                 accelerator.is_main_process,
             )
         vae.to("cpu")
-        clean_memory()
+        clean_memory_on_device(accelerator.device)
-
+        
         accelerator.wait_for_everyone()
 
     if args.gradient_checkpointing:

train_db.py

@@ -11,7 +11,7 @@ import toml
 from tqdm import tqdm
 
 import torch
-from library.device_utils import init_ipex, clean_memory
+from library.device_utils import init_ipex, clean_memory_on_device
 init_ipex()
 
 from accelerate.utils import set_seed
@@ -136,7 +136,7 @@ def train(args):
         with torch.no_grad():
             train_dataset_group.cache_latents(vae, args.vae_batch_size, args.cache_latents_to_disk, accelerator.is_main_process)
         vae.to("cpu")
-        clean_memory()
+        clean_memory_on_device(accelerator.device)
 
         accelerator.wait_for_everyone()
 

train_network.py

@@ -12,7 +12,7 @@ import toml
 from tqdm import tqdm
 
 import torch
-from library.device_utils import init_ipex, clean_memory
+from library.device_utils import init_ipex, clean_memory_on_device
 init_ipex()
 
 from torch.nn.parallel import DistributedDataParallel as DDP
@@ -265,7 +265,7 @@ class NetworkTrainer:
             with torch.no_grad():
                 train_dataset_group.cache_latents(vae, args.vae_batch_size, args.cache_latents_to_disk, accelerator.is_main_process)
             vae.to("cpu")
-            clean_memory()
+            clean_memory_on_device(accelerator.device)
 
             accelerator.wait_for_everyone()
 

train_textual_inversion.py

@@ -7,7 +7,7 @@ import toml
 from tqdm import tqdm
 
 import torch
-from library.device_utils import init_ipex, clean_memory
+from library.device_utils import init_ipex, clean_memory_on_device
 init_ipex()
 
 from accelerate.utils import set_seed
@@ -361,7 +361,7 @@ class TextualInversionTrainer:
             with torch.no_grad():
                 train_dataset_group.cache_latents(vae, args.vae_batch_size, args.cache_latents_to_disk, accelerator.is_main_process)
             vae.to("cpu")
-            clean_memory()
+            clean_memory_on_device(accelerator.device)
 
             accelerator.wait_for_everyone()
 

train_textual_inversion_XTI.py

@@ -8,7 +8,7 @@ from multiprocessing import Value
 from tqdm import tqdm
 
 import torch
-from library.device_utils import init_ipex, clean_memory
+from library.device_utils import init_ipex, clean_memory_on_device
 init_ipex()
 
 from accelerate.utils import set_seed
@@ -284,7 +284,7 @@ def train(args):
         with torch.no_grad():
             train_dataset_group.cache_latents(vae, args.vae_batch_size, args.cache_latents_to_disk, accelerator.is_main_process)
         vae.to("cpu")
-        clean_memory()
+        clean_memory_on_device(accelerator.device)
 
         accelerator.wait_for_everyone()