[DO NOT MERGE] 2.7 RC Test

.jenkins/build.sh

@@ -26,8 +26,8 @@ sudo apt-get install -y pandoc
 # sudo pip3 install torch==2.6.0 torchvision --no-cache-dir --index-url https://download.pytorch.org/whl/test/cu124
 # sudo pip uninstall -y fbgemm-gpu torchrec
 # sudo pip3 install fbgemm-gpu==1.1.0 torchrec==1.0.0 --no-cache-dir --index-url https://download.pytorch.org/whl/test/cu124
-sudo pip uninstall -y torch torchvision torchaudio torchtext torchdata torchrl tensordict
-pip3 install torch==2.7.0 torchvision torchaudio --no-cache-dir --index-url https://download.pytorch.org/whl/test/cu126
+# sudo pip uninstall -y torch torchvision torchaudio torchtext torchdata torchrl tensordict
+# pip3 install torch==2.7.0 torchvision torchaudio --no-cache-dir --index-url https://download.pytorch.org/whl/test/cu126
 #sudo pip uninstall -y fbgemm-gpu
 # Install two language tokenizers for Translation with TorchText tutorial
 python -m spacy download en_core_web_sm

intermediate_source/torch_export_tutorial.py

@@ -995,7 +995,7 @@ def forward(self, x):
 #    with torch.no_grad():
 #        pt2_path = torch._inductor.aoti_compile_and_package(ep)
 #
-#    # Load and run the .so file in Python.
+#    # Load and run the .pt2 file in Python.
 #    # To load and run it in a C++ environment, see:
 #    # https://pytorch.org/docs/main/torch.compiler_aot_inductor.html
 #    aoti_compiled = torch._inductor.aoti_load_package(pt2_path)

recipes_source/torch_export_aoti_python.py

@@ -176,7 +176,7 @@
 model_path = os.path.join(os.getcwd(), "resnet18.pt2")
 
 compiled_model = torch._inductor.aoti_load_package(model_path)
-example_inputs = (torch.randn(2, 3, 224, 224, device=device),)
+example_inputs = torch.randn(2, 3, 224, 224, device=device)
 
 with torch.inference_mode():
     output = compiled_model(example_inputs)
@@ -238,11 +238,11 @@ def timed(fn):
 
 torch._dynamo.reset()
 
-model = torch._inductor.aoti_load_package(model_path)
-example_inputs = (torch.randn(1, 3, 224, 224, device=device),)
+compiled_model = torch._inductor.aoti_load_package(model_path)
+example_inputs = torch.randn(1, 3, 224, 224, device=device)
 
 with torch.inference_mode():
-    _, time_taken = timed(lambda: model(example_inputs))
+    _, time_taken = timed(lambda: compiled_model(example_inputs))
     print(f"Time taken for first inference for AOTInductor is {time_taken:.2f} ms")