Fix the bug that Trainer cannot correctly call `torch_jit_model_eva…

…l` (huggingface#35722)

Fix the bug that the accelerator.autocast does not pass parameters correctly when calling torch_jit_model_eval (huggingface#35706)

src/transformers/trainer.py

@@ -230,6 +230,7 @@
     from accelerate import __version__ as accelerate_version
     from accelerate.state import AcceleratorState
     from accelerate.utils import (
+        AutocastKwargs,
         DistributedDataParallelKwargs,
         DistributedType,
         load_fsdp_model,
@@ -1832,7 +1833,8 @@ def torch_jit_model_eval(self, model, dataloader, training=False):
                 # remove mixed precision hooks from the model
                 if original_forward:
                     jit_model.forward = original_forward
-                with self.accelerator.autocast(cache_enabled=False), torch.no_grad():
+                autocast_handler = AutocastKwargs(cache_enabled=False)
+                with self.accelerator.autocast(autocast_handler=autocast_handler), torch.no_grad():
                     if version.parse(version.parse(torch.__version__).base_version) >= version.parse("2.0.0"):
                         if isinstance(example_batch, dict):
                             jit_model = torch.jit.trace(jit_model, example_kwarg_inputs=example_batch, strict=False)