pin update

.ci/docker/ci_commit_pins/pytorch.txt

@@ -1 +1 @@
-19eff28ff3f19b50da46f5a9ff5f4d4d213806fe
+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)"

.ci/scripts/test_llava.sh

@@ -41,7 +41,6 @@ EXECUTORCH_COMMON_CMAKE_ARGS="                      \
         -DEXECUTORCH_BUILD_KERNELS_OPTIMIZED=ON     \
         -DEXECUTORCH_BUILD_KERNELS_QUANTIZED=ON     \
         -DEXECUTORCH_BUILD_XNNPACK=ON               \
-        -DEXECUTORCH_DO_NOT_USE_CXX11_ABI=ON        \
         -DEXECUTORCH_XNNPACK_SHARED_WORKSPACE=ON"
 
 cmake_install_executorch_libraries() {

CMakeLists.txt

@@ -145,12 +145,6 @@ if(EXECUTORCH_ENABLE_EVENT_TRACER)
   add_definitions(-DET_EVENT_TRACER_ENABLED)
 endif()
 
-option(EXECUTORCH_DO_NOT_USE_CXX11_ABI "Define _GLIBCXX_USE_CXX11_ABI=0 if ON"
-       OFF
-)
-if(EXECUTORCH_DO_NOT_USE_CXX11_ABI)
-  add_definitions(-D_GLIBCXX_USE_CXX11_ABI=0)
-endif()
 # -ffunction-sections -fdata-sections: breaks function and data into sections so
 # they can be properly gc'd. -s: strip symbol. -fno-exceptions -fno-rtti:
 # disables exceptions and runtime type.
@@ -772,13 +766,6 @@ if(EXECUTORCH_BUILD_PYBIND)
       -frtti
       -fexceptions
   )
-  if(EXECUTORCH_DO_NOT_USE_CXX11_ABI)
-      # libtorch is built with the old ABI, so we need to do the same for any
-      # .cpp files that include torch, c10, or ATen targets. Note that PyTorch
-      # nightly binary is built with _GLIBCXX_USE_CXX11_ABI set to 0 while its
-      # CI build sets this to 1 (default)
-    list(APPEND _pybind_compile_options -D_GLIBCXX_USE_CXX11_ABI=0)
-  endif()
 
   # util lib
   add_library(

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 +81,6 @@ if(LLAVA_RUNNER_NO_TORCH_DUMMY_IMAGE)
 else()
   find_package(Torch CONFIG REQUIRED)
 endif()
-add_definitions(-D_GLIBCXX_USE_CXX11_ABI=0)
 
 #
 # 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 = "dev20241112"
+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",
 ]

third-party/TARGETS

@@ -298,7 +298,6 @@ prebuilt_cxx_library(
     name = "libtorch",
     shared_lib = ":libtorch_gen[libtorch]",
     exported_preprocessor_flags = [
-        "-D_GLIBCXX_USE_CXX11_ABI=0",  # `libtorch` is built without CXX11_ABI so any target depends on it need to use the same build config.
         "-I$(location :libtorch_gen[include])",  # include header directories
         "-I$(location :libtorch_gen[include])/torch/csrc/api/include",  # include header directories
     ],
@@ -318,7 +317,6 @@ prebuilt_cxx_library(
     name = "libtorch_python",
     shared_lib = ":libtorch_gen[libtorch_python]",
     exported_preprocessor_flags = [
-        "-D_GLIBCXX_USE_CXX11_ABI=0",  # `libtorch` is built without CXX11_ABI so any target depends on it need to use the same build config.
         "-I$(location :libtorch_gen[include])",  # include header directories
         "-I$(location :libtorch_gen[include])/torch/csrc/api/include",  # include header directories
     ],

third-party/gtest_defs.bzl

@@ -4,9 +4,7 @@ COMPILER_FLAGS = [
     "-std=c++17",
 ]
 COMPILER_FLAGS_ATEN = [
-    "-std=c++17",
-    "-D_GLIBCXX_USE_CXX11_ABI=0",  # `libtorch` is built without CXX11_ABI so gtest needs to be compiled in the same way
-]
+    "-std=c++17",]
 
 # define_gtest_targets
 def define_gtest_targets():