remove args

Author: angelayi

.ci/docker/ci_commit_pins/pytorch.txt

@@ -1 +1 @@
-80ca6dd892613fd4f1dee9040b8273ddeadb1c50
+2ea4b56ec872424e486c4fe2d55da061067a2ed3

.ci/docker/common/install_pytorch.sh

@@ -26,7 +26,7 @@ install_pytorch_and_domains() {
 
   chown -R ci-user .
 
-  export _GLIBCXX_USE_CXX11_ABI=0
+  export _GLIBCXX_USE_CXX11_ABI=1
   # Then build and install PyTorch
   conda_run python setup.py bdist_wheel
   pip_install "$(echo dist/*.whl)"

examples/models/llama3_2_vision/text_decoder/test/test_text_decoder.py

@@ -74,8 +74,6 @@ def test_llama3_2_text_decoder_aoti(self) -> None:
         with tempfile.TemporaryDirectory() as tmpdir:
             path = torch._inductor.aoti_compile_and_package(
                 ep,
-                model.get_example_inputs(),
-                kwargs=model.get_example_kwarg_inputs(),
                 package_path=os.path.join(tmpdir, "text_decoder.pt2"),
             )
             encoder_aoti = torch._inductor.aoti_load_package(path)

examples/models/llama3_2_vision/vision_encoder/test/test_vision_encoder.py

@@ -36,7 +36,6 @@ def test_flamingo_vision_encoder(self) -> None:
         with tempfile.TemporaryDirectory() as tmpdir:
             path = torch._inductor.aoti_compile_and_package(
                 ep,
-                model.get_example_inputs(),
                 package_path=os.path.join(tmpdir, "vision_encoder.pt2"),
             )
             print(path)

examples/models/llava/CMakeLists.txt

@@ -81,7 +81,7 @@ if(LLAVA_RUNNER_NO_TORCH_DUMMY_IMAGE)
 else()
   find_package(Torch CONFIG REQUIRED)
 endif()
-add_definitions(-D_GLIBCXX_USE_CXX11_ABI=0)
+add_definitions(-D_GLIBCXX_USE_CXX11_ABI=1)
 
 #
 # llava_main: test binary to run llava, with tokenizer and sampler integrated

extension/llm/export/builder.py

@@ -10,9 +10,11 @@
 
 # pyre-unsafe
 
+import contextlib
 import logging
 from enum import Enum
 from typing import Any, Callable, Dict, List, Optional
+from unittest.mock import patch
 
 import torch
 from executorch.backends.transforms.duplicate_dynamic_quant_chain import (
@@ -184,15 +186,23 @@ def export(self) -> "LLMEdgeManager":
         # 2. torch.no_grad() is for getting rid of the dropout (not sure why training ops will show up)
         with torch.nn.attention.sdpa_kernel([SDPBackend.MATH]), torch.no_grad():
             if hasattr(self.args, "qnn") and self.args.qnn:
-                # TODO: this is temporary and export_for_training doesn't work with qnn either. We need a
-                # functional graph. See issue https://github.com/pytorch/executorch/pull/4627 for more details
-                exported_module = torch.export.export(
-                    self.model,
-                    self.example_inputs,
-                    self.example_kwarg_inputs,
-                    dynamic_shapes=dynamic_shape,
-                    strict=True,
-                )
+                # TODO: this is temporary, as qnn flow does not work with new, non-functional export IR.
+                # See issue: https://github.com/pytorch/executorch/issues/7373
+
+                with patch.object(
+                    torch._utils_internal,
+                    "export_training_ir_rollout_check",
+                    return_value=False,
+                ):
+                    # TODO: this is temporary and export_for_training doesn't work with qnn either. We need a
+                    # functional graph. See issue https://github.com/pytorch/executorch/pull/4627 for more details
+                    exported_module = torch.export.export(
+                        self.model,
+                        self.example_inputs,
+                        self.example_kwarg_inputs,
+                        dynamic_shapes=dynamic_shape,
+                        strict=True,
+                    )
             else:
                 logging.info("Exporting with:")
                 logging.info(f"inputs: {self.example_inputs}")
@@ -354,15 +364,25 @@ def export_to_edge(self) -> "LLMEdgeManager":
             if self.pre_autograd_graph_module is None:
                 # Run export() if it didn't run
                 self.export()
-            self.edge_manager = export_to_edge(
-                self.pre_autograd_graph_module,  # pyre-fixme[6]
-                self.example_inputs,
-                example_kwarg_inputs=self.example_kwarg_inputs,
-                dynamic_shapes=dynamic_shape,
-                edge_constant_methods=self.metadata,
-                edge_compile_config=edge_config,
-                verbose=self.verbose,
-            )
+
+            override_export_behaviour = contextlib.nullcontext()
+            if hasattr(self.args, "qnn") and self.args.qnn:
+                override_export_behaviour = patch.object(
+                    torch._utils_internal,
+                    "export_training_ir_rollout_check",
+                    return_value=False,
+                )
+
+            with override_export_behaviour:
+                self.edge_manager = export_to_edge(
+                    self.pre_autograd_graph_module,  # pyre-fixme[6]
+                    self.example_inputs,
+                    example_kwarg_inputs=self.example_kwarg_inputs,
+                    dynamic_shapes=dynamic_shape,
+                    edge_constant_methods=self.metadata,
+                    edge_compile_config=edge_config,
+                    verbose=self.verbose,
+                )
         return self
 
     def to_backend(self, partitioners: Optional[List[Partitioner]]) -> "LLMEdgeManager":

extension/llm/modules/test/test_position_embeddings.py

@@ -177,7 +177,6 @@ def test_tiled_token_positional_embedding_aoti(self):
         with tempfile.TemporaryDirectory() as tmpdir:
             path = torch._inductor.aoti_compile_and_package(
                 tpe_ep,
-                (self.x, self.aspect_ratio),
                 package_path=os.path.join(tmpdir, "tpe.pt2"),
             )
             tpe_aoti = load_package(path)

install_requirements.py

@@ -112,7 +112,7 @@ def python_is_compatible():
 # NOTE: If a newly-fetched version of the executorch repo changes the value of
 # NIGHTLY_VERSION, you should re-run this script to install the necessary
 # package versions.
-NIGHTLY_VERSION = "dev20241206"
+NIGHTLY_VERSION = "dev20241218"
 
 # The pip repository that hosts nightly torch packages.
 TORCH_NIGHTLY_URL = "https://download.pytorch.org/whl/nightly/cpu"
@@ -124,7 +124,7 @@ def python_is_compatible():
     # been installed on CI before this step, so pip won't reinstall them
     f"torch==2.6.0.{NIGHTLY_VERSION}" if USE_PYTORCH_NIGHTLY else "torch",
     (
-        f"torchvision==0.20.0.{NIGHTLY_VERSION}"
+        f"torchvision==0.22.0.{NIGHTLY_VERSION}"
         if USE_PYTORCH_NIGHTLY
         else "torchvision"
     ),  # For testing.
@@ -135,7 +135,7 @@ def python_is_compatible():
 # TODO: Make each example publish its own requirements.txt
 EXAMPLES_REQUIREMENTS = [
     "timm==1.0.7",
-    f"torchaudio==2.5.0.{NIGHTLY_VERSION}" if USE_PYTORCH_NIGHTLY else "torchaudio",
+    f"torchaudio==2.6.0.{NIGHTLY_VERSION}" if USE_PYTORCH_NIGHTLY else "torchaudio",
     "torchsr==1.0.4",
     "transformers==4.46.1",
 ]