Remove attention logging code

comfy/ldm/modules/attention.py

@@ -132,114 +132,12 @@ class FeedForward(nn.Module):
 def Normalize(in_channels, dtype=None, device=None):
     return torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True, dtype=dtype, device=device)
 
-import inspect
-LOG_ATTN_CALLS = False
-LOG_CONTENTS = {}
-
-def save_log_contents():
-    import folder_paths
-    output_dir = folder_paths.get_output_directory()
-
-    # Create attn_logs directory if it doesn't exist
-    attn_logs_dir = os.path.join(output_dir, "attn_logs")
-    os.makedirs(attn_logs_dir, exist_ok=True)
-
-    # Generate timestamp filename (down to second)
-    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
-    filename = f"{timestamp}.json"
-    filepath = os.path.join(attn_logs_dir, filename)
-
-    # Save LOG_CONTENTS as JSON file
-    try:
-        with open(filepath, 'w', encoding='utf-8') as f:
-            json.dump(list(LOG_CONTENTS.values()), f, indent=2, ensure_ascii=False)
-        logging.info(f"Saved attention log contents to {filepath}")
-    except Exception as e:
-        logging.error(f"Failed to save attention log contents: {e}")
-
-def get_class_from_frame(frame):
-    # Check for 'self' (instance method) or 'cls' (classmethod)
-    if 'self' in frame.f_locals:
-        return frame.f_locals['self'].__class__.__name__
-    elif 'cls' in frame.f_locals:
-        return frame.f_locals['cls'].__name__
-    return None
-
-def has_transformer_options_passed(frame):
-    if 'transformer_options' in frame.f_locals.keys():
-        if frame.f_locals['transformer_options']:
-            return True
-    return False
 
 def wrap_attn(func):
     @functools.wraps(func)
     def wrapper(*args, **kwargs):
         remove_attn_wrapper_key = False
         try:
-            if LOG_ATTN_CALLS:
-                continue_to_add = True
-                to_add = 1000
-                logged_stack = []
-                logged_stack_to_index = -1
-
-                frame = inspect.currentframe()
-                try:
-                    # skip wrapper, start at actual wrapped function
-                    frame = frame.f_back
-
-                    while frame and continue_to_add and to_add > 0:
-                        code = frame.f_code
-                        filename = code.co_filename
-                        function = code.co_name
-                        lineno = frame.f_lineno
-
-                        if function == "_calc_cond_batch_outer":
-                            break
-                        if 'venv' in filename:
-                            frame = frame.f_back
-                            continue
-                        elif 'ComfyUI' not in filename:
-                            frame = frame.f_back
-                            continue
-                        elif 'execution.py' in filename:
-                            frame = frame.f_back
-                            continue
-                        elif 'patcher_extension.py' in filename:
-                            frame = frame.f_back
-                            continue
-
-                        to_add -= 1
-                        cls_name = get_class_from_frame(frame)
-                        log_string = f"{filename}:{lineno}"
-                        if cls_name:
-                            log_string += f":{cls_name}.{function}"
-                        else:
-                            log_string += f":{function}"
-
-                        if has_transformer_options_passed(frame):
-                            log_string += ":✅"
-                            if logged_stack_to_index == -1:
-                                logged_stack_to_index = len(logged_stack)
-                        else:
-                            log_string += ":❌"
-
-                        logged_stack.append(log_string)
-
-                        # move up the stack
-                        frame = frame.f_back
-
-                    # check if we get what we want from transformer_options
-                    t_check = "❌❌❌"
-                    transformer_options = kwargs.get("transformer_options", None)
-                    if transformer_options is not None:
-                        if "optimized_attention_override" in transformer_options:
-                            t_check = "✅✅✅"
-
-                    LOG_CONTENTS["|".join(logged_stack)] = (t_check, logged_stack_to_index, logged_stack)
-
-                finally:
-                    # Important: break ref cycles so tensors aren't pinned
-                    del frame
             if "_inside_attn_wrapper" not in kwargs:
                 transformer_options = kwargs.get("transformer_options", None)
                 remove_attn_wrapper_key = True