Fix attn mask ignore logic in training-time trace (#32613)

* fix attn mask logic for training-time trace

* add test

* fix

* fix

* fix

* fix

* fix

* format

* [run-slow] llama

* avoid accelearate

* [run-slow] llama

tests/models/gemma/test_modeling_gemma.py

@@ -321,6 +321,9 @@ class GemmaModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixi
     # used in `test_torch_compile`
     _torch_compile_test_ckpt = "google/gemma-2b"
 
+    # used in `test_torch_compile_for_training`
+    _torch_compile_train_cls = GemmaForCausalLM if is_torch_available() else None
+
     # TODO (ydshieh): Check this. See https://app.circleci.com/pipelines/github/huggingface/transformers/79245/workflows/9490ef58-79c2-410d-8f51-e3495156cf9c/jobs/1012146
     def is_pipeline_test_to_skip(
         self, pipeline_test_casse_name, config_class, model_architecture, tokenizer_name, processor_name
@@ -808,7 +811,7 @@ class GemmaIntegrationTest(unittest.TestCase):
 
         prompts = ["Hello I am doing", "Hi today"]
         tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b", pad_token="</s>", padding_side="right")
-        model = GemmaForCausalLM.from_pretrained("google/gemma-2b", device_map="sequential", torch_dtype=torch.float16)
+        model = GemmaForCausalLM.from_pretrained("google/gemma-2b", device_map=torch_device, torch_dtype=torch.float16)
         inputs = tokenizer(prompts, return_tensors="pt", padding=True).to(model.device)
 
         # Dynamic Cache