Fix BERT and ViT perf

.github/workflows/call-perf-test.yml

@@ -136,6 +136,7 @@ jobs:
         fi
 
         if [ -n "${{ matrix.build.pyreq }}" ]; then
+          pip install torch==2.9.0 --index-url https://download.pytorch.org/whl/cpu
           pip install ${{ matrix.build.pyreq }}
         fi
         if [ "${{ matrix.build.project }}" == "tt-xla" ]; then

benchmark/tt-xla/test_encoders.py

@@ -158,7 +158,7 @@ def test_bert(output_file):
     loader = ModelLoader()
     model_info_name = loader.get_model_info().name
     print(f"\nLoading model {model_info_name}...")
-    model = loader.load_model(dtype_override=DTYPE_MAP[data_format])
+    model = loader.load_model(dtype_override=DTYPE_MAP[data_format], attn_implementation="eager")
 
     # Create function for loading raw inputs
     load_inputs_fn = get_default_inputs

benchmark/tt-xla/test_vision.py

@@ -402,7 +402,7 @@ def test_vit(output_file):
     variant = ModelVariant.BASE
     loader = ModelLoader(variant=variant)
     model_info_name = loader.get_model_info(variant=variant).name
-    model = loader.load_model(dtype_override=data_format)
+    model = loader.load_model(dtype_override=data_format, attn_implementation="eager")
     model = model.eval()
 
     def load_inputs_fn(batch_size, dtype):