Apply correct inference precision implementation

modules/devices.py

@@ -132,6 +132,21 @@ patch_module_list = [
 ]
 
 
+def cast_output(result):
+    if isinstance(result, tuple):
+        result = tuple(i.to(dtype_inference) if isinstance(i, torch.Tensor) else i for i in result)
+    elif isinstance(result, torch.Tensor):
+        result = result.to(dtype_inference)
+    return result
+
+
+def autocast_with_cast_output(self, *args, **kwargs):
+    result = self.org_forward(*args, **kwargs)
+    if dtype_inference != dtype:
+        result = cast_output(result)
+    return result
+
+
 def manual_cast_forward(target_dtype):
     def forward_wrapper(self, *args, **kwargs):
         if any(
@@ -149,15 +164,7 @@ def manual_cast_forward(target_dtype):
             self.to(org_dtype)
 
         if target_dtype != dtype_inference:
-            if isinstance(result, tuple):
+            result = cast_output(result)
-                result = tuple(
-                    i.to(dtype_inference)
-                    if isinstance(i, torch.Tensor)
-                    else i
-                    for i in result
-                )
-            elif isinstance(result, torch.Tensor):
-                result = result.to(dtype_inference)
         return result
     return forward_wrapper
 
@@ -178,6 +185,20 @@ def manual_cast(target_dtype):
             module_type.forward = module_type.org_forward
 
 
+@contextlib.contextmanager
+def precision_full_with_autocast(autocast_ctx):
+    for module_type in patch_module_list:
+        org_forward = module_type.forward
+        module_type.forward = autocast_with_cast_output
+        module_type.org_forward = org_forward
+    try:
+        with autocast_ctx:
+            yield None
+    finally:
+        for module_type in patch_module_list:
+            module_type.forward = module_type.org_forward
+
+
 def autocast(disable=False):
     if disable:
         return contextlib.nullcontext()
@@ -191,6 +212,9 @@ def autocast(disable=False):
     if has_xpu() or has_mps() or cuda_no_autocast():
         return manual_cast(dtype_inference)
 
+    if dtype_inference == torch.float32 and dtype != torch.float32:
+        return precision_full_with_autocast(torch.autocast("cuda"))
+
     return torch.autocast("cuda")