From ef051427df4387ab056c02eddba03c1c6a110fa0 Mon Sep 17 00:00:00 2001
From: Kohya S <52813779+kohya-ss@users.noreply.github.com>
Date: Mon, 16 Feb 2026 07:58:15 +0900
Subject: [PATCH] fix: `str is not "no"` to `str != "no"`

---
 library/deepspeed_utils.py | 27 +++++++++++++--------------
 1 file changed, 13 insertions(+), 14 deletions(-)

diff --git a/library/deepspeed_utils.py b/library/deepspeed_utils.py
index a8a05c3a..4daeb254 100644
--- a/library/deepspeed_utils.py
+++ b/library/deepspeed_utils.py
@@ -96,7 +96,7 @@ def prepare_deepspeed_plugin(args: argparse.Namespace):
     deepspeed_plugin.deepspeed_config["train_batch_size"] = (
         args.train_batch_size * args.gradient_accumulation_steps * int(os.environ["WORLD_SIZE"])
     )
-    
+
     deepspeed_plugin.set_mixed_precision(args.mixed_precision)
     if args.mixed_precision.lower() == "fp16":
         deepspeed_plugin.deepspeed_config["fp16"]["initial_scale_power"] = 0  # preventing overflow.
@@ -125,18 +125,18 @@ def prepare_deepspeed_model(args: argparse.Namespace, **models):
     class DeepSpeedWrapper(torch.nn.Module):
         def __init__(self, **kw_models) -> None:
             super().__init__()
-            
+
             self.models = torch.nn.ModuleDict()
-            
-            wrap_model_forward_with_torch_autocast = args.mixed_precision is not "no"
+
+            wrap_model_forward_with_torch_autocast = args.mixed_precision != "no"
 
             for key, model in kw_models.items():
                 if isinstance(model, list):
                     model = torch.nn.ModuleList(model)
-                                            
+
                 if wrap_model_forward_with_torch_autocast:
-                    model = self.__wrap_model_with_torch_autocast(model)  
-                
+                    model = self.__wrap_model_with_torch_autocast(model)
+
                 assert isinstance(
                     model, torch.nn.Module
                 ), f"model must be an instance of torch.nn.Module, but got {key} is {type(model)}"
@@ -151,7 +151,7 @@ def prepare_deepspeed_model(args: argparse.Namespace, **models):
             return model
 
         def __wrap_model_forward_with_torch_autocast(self, model):
-            
+
             assert hasattr(model, "forward"), f"model must have a forward method."
 
             forward_fn = model.forward
@@ -161,20 +161,19 @@ def prepare_deepspeed_model(args: argparse.Namespace, **models):
                     device_type = model.device.type
                 except AttributeError:
                     logger.warning(
-                            "[DeepSpeed] model.device is not available. Using get_preferred_device() "
-                            "to determine the device_type for torch.autocast()."
-                    )                    
+                        "[DeepSpeed] model.device is not available. Using get_preferred_device() "
+                        "to determine the device_type for torch.autocast()."
+                    )
                     device_type = get_preferred_device().type
 
-                with torch.autocast(device_type = device_type):
+                with torch.autocast(device_type=device_type):
                     return forward_fn(*args, **kwargs)
 
             model.forward = forward
             return model
-        
+
         def get_models(self):
             return self.models
-        
 
     ds_model = DeepSpeedWrapper(**models)
     return ds_model