Merge pull request #1 from saibit-tech/dev/xy/align_dtype_using_mixed_precision

Fix: try aligning dtype of matrixes when training with deepspeed and mixed-precision is set to bf16 or fp16

library/deepspeed_utils.py

@@ -94,6 +94,7 @@ 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.
@@ -122,18 +123,48 @@ 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"
 
             for key, model in kw_models.items():
                 if isinstance(model, list):
                     model = torch.nn.ModuleList(model)
+                                            
                 assert isinstance(
                     model, torch.nn.Module
                 ), f"model must be an instance of torch.nn.Module, but got {key} is {type(model)}"
+
+                if wrap_model_forward_with_torch_autocast:
+                    model = self.__wrap_model_with_torch_autocast(model)  
+                
                 self.models.update(torch.nn.ModuleDict({key: model}))
 
+        def __wrap_model_with_torch_autocast(self, model):
+            if isinstance(model, torch.nn.ModuleList):
+                model = [self.__wrap_model_forward_with_torch_autocast(m) for m in model]
+            else:
+                model = self.__wrap_model_forward_with_torch_autocast(model)
+            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
+            
+            def forward(*args, **kwargs):
+                device_type = "cuda" if torch.cuda.is_available() else "cpu"
+                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

library/train_util.py

@@ -5495,6 +5495,11 @@ def load_target_model(args, weight_dtype, accelerator, unet_use_linear_projectio
 
 
 def patch_accelerator_for_fp16_training(accelerator):
+    
+    from accelerate import DistributedType
+    if accelerator.distributed_type == DistributedType.DEEPSPEED:
+        return
+    
     org_unscale_grads = accelerator.scaler._unscale_grads_
 
     def _unscale_grads_replacer(optimizer, inv_scale, found_inf, allow_fp16):

requirements.txt

@@ -1,6 +1,7 @@
 accelerate==0.33.0
 transformers==4.44.0
-diffusers[torch]==0.25.0
+diffusers==0.25.0
+deepspeed==0.16.7
 ftfy==6.1.1
 # albumentations==1.3.0
 opencv-python==4.8.1.78