[DO NOT MERGE] 2.7 RC Test

.ci/docker/requirements.txt

@@ -36,7 +36,7 @@ datasets
 transformers
 torchmultimodal-nightly # needs to be updated to stable as soon as it's avaialable
 onnx
-onnxscript
+onnxscript>=0.2.2
 onnxruntime
 evaluate
 accelerate>=0.20.1
@@ -69,5 +69,5 @@ pycocotools
 semilearn==0.3.2
 torchao==0.5.0
 segment_anything==1.0
-torchrec==1.0.0; platform_system == "Linux"
+torchrec==1.1.0; platform_system == "Linux"
 fbgemm-gpu==1.1.0; platform_system == "Linux"

.jenkins/build.sh

@@ -22,10 +22,14 @@ sudo apt-get install -y pandoc
 #Install PyTorch Nightly for test.
 # Nightly - pip install --pre torch torchvision torchaudio -f https://download.pytorch.org/whl/nightly/cu102/torch_nightly.html
 # Install 2.5 to merge all 2.4 PRs - uncomment to install nightly binaries (update the version as needed).
-# sudo pip uninstall -y torch torchvision torchaudio torchtext torchdata
-# 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 fbgemm-gpu
+#sudo pip3 install --pre fbgemm-gpu --index-url https://download.pytorch.org/whl/nightly/cu126/
+#pip install tensordict-nightly
+#pip install torchrl-nightly
+#sudo pip3 install fbgemm-gpu==1.1.0 torchrec==1.0.0 --no-cache-dir --index-url https://download.pytorch.org/whl/test/cu126
+
 
 # Install two language tokenizers for Translation with TorchText tutorial
 python -m spacy download en_core_web_sm

.jenkins/validate_tutorials_built.py

@@ -50,7 +50,14 @@
     "intermediate_source/flask_rest_api_tutorial",
     "intermediate_source/text_to_speech_with_torchaudio",
     "intermediate_source/tensorboard_profiler_tutorial", # reenable after 2.0 release.
-    "advanced_source/semi_structured_sparse" # reenable after 3303 is fixed.
+    "advanced_source/semi_structured_sparse", # reenable after 3303 is fixed.
+    "intermediate_source/mario_rl_tutorial", # reenable after 3302 is fixed
+    "intermediate_source/reinforcement_ppo", # reenable after 3302 is fixed
+    "intermediate_source/pinmem_nonblock", # reenable after 3302 is fixed
+    "intermediate_source/dqn_with_rnn_tutorial", # reenable after 3302 is fixed
+    "advanced_source/pendulum", # reenable after 3302 is fixed
+    "advanced_source/coding_ddpg", # reenable after 3302 is fixed
+    "intermediate_source/torchrec_intro_tutorial" # reenable after 3302 is fixed
 ]
 
 def tutorial_source_dirs() -> List[Path]:

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")