possible compile fixes

model_loading.py

@@ -1,9 +1,41 @@
 import os
-import torch
-import torch.nn as nn
 import json
 import folder_paths
 import comfy.model_management as mm
+from typing import Union
+
+def patched_write_atomic(
+    path_: str,
+    content: Union[str, bytes],
+    make_dirs: bool = False,
+    encode_utf_8: bool = False,
+) -> None:
+    # Write into temporary file first to avoid conflicts between threads
+    # Avoid using a named temporary file, as those have restricted permissions
+    from pathlib import Path
+    import os
+    import shutil
+    import threading
+    assert isinstance(
+        content, (str, bytes)
+    ), "Only strings and byte arrays can be saved in the cache"
+    path = Path(path_)
+    if make_dirs:
+        path.parent.mkdir(parents=True, exist_ok=True)
+    tmp_path = path.parent / f".{os.getpid()}.{threading.get_ident()}.tmp"
+    write_mode = "w" if isinstance(content, str) else "wb"
+    with tmp_path.open(write_mode, encoding="utf-8" if encode_utf_8 else None) as f:
+        f.write(content)
+    shutil.copy2(src=tmp_path, dst=path) #changed to allow overwriting cache files
+    os.remove(tmp_path)
+try:
+    import torch._inductor.codecache
+    torch._inductor.codecache.write_atomic = patched_write_atomic
+except:
+    pass
+
+import torch
+import torch.nn as nn
 
 from diffusers.models import AutoencoderKLCogVideoX
 from diffusers.schedulers import CogVideoXDDIMScheduler

nodes.py

@@ -5,7 +5,7 @@ import comfy.model_management as mm
 from einops import rearrange
 from contextlib import nullcontext
 
-from .utils import log, check_diffusers_version
+from .utils import log, check_diffusers_version, print_memory
 check_diffusers_version()
 from diffusers.schedulers import (
     CogVideoXDDIMScheduler, 
@@ -864,6 +864,10 @@ class CogVideoSampler:
 
         # if sigmas is not None:
         #     sigma_list = sigmas.tolist()
+        try:
+            torch.cuda.reset_peak_memory_stats(device)
+        except:
+            pass
   
         autocastcondition = not pipeline["onediff"] or not dtype == torch.float32
         autocast_context = torch.autocast(mm.get_autocast_device(device), dtype=dtype) if autocastcondition else nullcontext()
@@ -898,8 +902,13 @@ class CogVideoSampler:
                 if (hasattr, block, "cached_hidden_states") and block.cached_hidden_states is not None:
                     block.cached_hidden_states = None
                     block.cached_encoder_hidden_states = None
-                    
+
+        print_memory(device)
         mm.soft_empty_cache()
+        try:
+            torch.cuda.reset_peak_memory_stats(device)
+        except:
+            pass
 
         return (pipeline, {"samples": latents})
 

utils.py

@@ -1,5 +1,5 @@
 import importlib.metadata
-
+import torch
 import logging
 logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
 log = logging.getLogger(__name__)
@@ -19,4 +19,12 @@ def remove_specific_blocks(model, block_indices_to_remove):
     new_blocks = [block for i, block in enumerate(transformer_blocks) if i not in block_indices_to_remove]
     model.transformer_blocks = nn.ModuleList(new_blocks)
     
-    return model
+    return model
+
+def print_memory(device):
+    memory = torch.cuda.memory_allocated(device) / 1024**3
+    max_memory = torch.cuda.max_memory_allocated(device) / 1024**3
+    max_reserved = torch.cuda.max_memory_reserved(device) / 1024**3
+    log.info(f"Allocated memory: {memory=:.3f} GB")
+    log.info(f"Max allocated memory: {max_memory=:.3f} GB")
+    log.info(f"Max reserved memory: {max_reserved=:.3f} GB")