Merge branch 'main' into fix_getting_started_cpp_example

Author: mergennachin

CONTRIBUTING.md

@@ -1,7 +1,6 @@
 Thank you for your interest in contributing to ExecuTorch! We want to make
 it easy to contribute to this project.
 
- 
 
 ## Dev Install
 
@@ -91,7 +90,7 @@ executorch
 │   └── <a href="runtime/platform">platform</a> - Layer between architecture specific code and portable C++.
 ├── <a href="schema">schema</a> - ExecuTorch PTE file format flatbuffer schemas.
 ├── <a href="scripts">scripts</a> - Utility scripts for building libs, size management, dependency management, etc.
-├── <a href="shim">shim</a> - Compatibility layer between OSS and Internal builds.
+├── <a href="shim_et">shim_et</a> - Compatibility layer between OSS and Internal builds.
 ├── <a href="test">test</a> - Broad scoped end-to-end tests.
 ├── <a href="third-party">third-party</a> - Third-party dependencies.
 ├── <a href="tools">tools</a> - Tools for building ExecuTorch from source, for different built tools (CMake, Buck).
@@ -192,9 +191,6 @@ in the Github repo.
 
 ## Coding Style
 
-Goal: Encourage standards that make it easier to read, edit, maintain, and debug
-the ExecuTorch code.
-
 ### lintrunner
 
 We use [`lintrunner`](https://pypi.org/project/lintrunner/) to help make sure the
@@ -259,7 +255,7 @@ toolchains, and having access to relatively modern C++ features.
 
 #### C/C++ standard library usage
 
-**Restricted usage of the C++ standard library.**
+**Restricted usage of the C++ standard library**
 
 Rationale: ExecuTorch is intended to be portable to bare-metal systems that lack
 certain features, like dynamic memory, threading, and locking, required by parts
@@ -280,7 +276,7 @@ careful to also manually destroy objects initialized in this way.
 
 #### C++ language features
 
-**Exceptions: Do not use.**
+**Exceptions: Do not use**
 - Rationale: Exceptions are not widely supported on some classes of
   microcontrollers and DSPs, and they can significantly increase binary size.
 
@@ -289,12 +285,12 @@ must work with threading**
 - Rationale: The core runtime must work on systems that do not have threading
   support.
 
-**RTTI, dynamic_cast, and `<typeid>`: Do not use.**
+**RTTI, dynamic_cast, and `<typeid>`: Do not use**
 - Rationale: RTTI adds extra data to every virtual class. ExecuTorch doesn't
   have a strong need for `dynamic_cast` and friends, so it's better to reduce
   the binary size.
 
-**Templates and template metaprogramming: Be careful and avoid if possible.**
+**Templates and template metaprogramming: Be careful and avoid if possible**
 - Rationale: Most templating results in code generation, and is one of the most
   common sources of binary bloat. Some use of templates is fine (e.g. an
   `ArrayRef<T>`, or code that handles multiple `ScalarType` types), but for the
@@ -359,7 +355,7 @@ docs](https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/
 for basics.
 
 1. Push your branch to your fork of `pytorch/executorch`. Most people do not
-  have permission to push a branch directoy to the upstream repo.
+  have permission to push a branch directory to the upstream repo.
 1. Create your PR
    - Use the `main` branch as the base.
    - Give the PR a clear and descriptive title. It will become the title of the

README.md

@@ -49,9 +49,9 @@ Key value propositions of ExecuTorch are:
 ## Getting Started
 To get started you can:
 
-- Visit the [Step by Step Tutorial](https://pytorch.org/executorch/main/index.html) on getting things running locally and deploy a model to a device
+- Visit the [Step by Step Tutorial](https://pytorch.org/executorch/main/index.html) to get things running locally and deploy a model to a device
 - Use this [Colab Notebook](https://pytorch.org/executorch/stable/getting-started-setup.html#quick-setup-colab-jupyter-notebook-prototype) to start playing around right away
-- Jump straight into LLMs use cases by following specific instructions for [Llama](./examples/models/llama/README.md) and [Llava](./examples/models/llava/README.md)
+- Jump straight into LLM use cases by following specific instructions for [Llama](./examples/models/llama/README.md) and [Llava](./examples/models/llava/README.md)
 
 ## Feedback and Engagement
 

backends/arm/test/models/test_mobilenet_v3_arm.py

@@ -46,7 +46,7 @@ def test_mv3_tosa_BI():
         aten_op=[],
         exir_op=[],
         use_to_edge_transform_and_lower=True,
-        atol=0.3,
+        atol=0.5,
         qtol=1,
     )
     pipeline.run()
@@ -63,7 +63,7 @@ def test_mv3_u55_BI():
         exir_ops=[],
         run_on_fvp=True,
         use_to_edge_transform_and_lower=True,
-        atol=0.3,
+        atol=0.5,
         qtol=1,
     )
     pipeline.run()
@@ -80,7 +80,7 @@ def test_mv3_u85_BI():
         exir_ops=[],
         run_on_fvp=True,
         use_to_edge_transform_and_lower=True,
-        atol=0.3,
+        atol=0.5,
         qtol=1,
     )
     pipeline.run()

backends/arm/test/ops/test_sigmoid_16bit.py

@@ -81,7 +81,7 @@ def forward(self, x):
 
 
 @common.parametrize("test_data", test_data_suite)
-@pytest.mark.flaky(reruns=5)
+@pytest.mark.flaky(reruns=32)  # Flaky due to Vela bug: MLBEDSW-10642
 def test_sigmoid_tosa_BI(test_data):
     pipeline = TosaPipelineBI(
         Sigmoid(), (test_data(),), Sigmoid.aten_op, Sigmoid.exir_op
@@ -97,7 +97,7 @@ def test_sigmoid_tosa_BI(test_data):
         "ramp": "AssertionError: Output 0 does not match reference output. MLETORCH-787"
     },
 )
-@pytest.mark.flaky(reruns=5)
+@pytest.mark.flaky(reruns=32)  # Flaky due to Vela bug: MLBEDSW-10642
 def test_sigmoid_add_sigmoid_tosa_BI(test_data):
     pipeline = TosaPipelineBI(
         SigmoidAddSigmoid(), (test_data(),), Sigmoid.aten_op, Sigmoid.exir_op
@@ -110,6 +110,7 @@ def test_sigmoid_add_sigmoid_tosa_BI(test_data):
     "test_data",
     test_data_suite,
 )
+@pytest.mark.flaky(reruns=32)  # Flaky due to Vela bug: MLBEDSW-10642
 def test_sigmoid_tosa_u55(test_data):
     pipeline = OpNotSupportedPipeline(
         Sigmoid(), (test_data(),), "TOSA-0.80+BI+u55", {Sigmoid.exir_op: 1}
@@ -122,6 +123,7 @@ def test_sigmoid_tosa_u55(test_data):
     "test_data",
     test_data_suite,
 )
+@pytest.mark.flaky(reruns=32)  # Flaky due to Vela bug: MLBEDSW-10642
 def test_sigmoid_add_sigmoid_tosa_u55(test_data):
     pipeline = OpNotSupportedPipeline(
         SigmoidAddSigmoid(),
@@ -135,7 +137,7 @@ def test_sigmoid_add_sigmoid_tosa_u55(test_data):
 
 
 @common.parametrize("test_data", test_data_suite)
-@pytest.mark.flaky(reruns=5)
+@pytest.mark.flaky(reruns=32)  # Flaky due to Vela bug: MLBEDSW-10642
 @common.XfailIfNoCorstone320
 def test_sigmoid_tosa_u85(test_data):
     pipeline = EthosU85PipelineBI(
@@ -152,7 +154,7 @@ def test_sigmoid_tosa_u85(test_data):
         "ramp": "AssertionError: Output 0 does not match reference output.",
     },
 )
-@pytest.mark.flaky(reruns=5)
+@pytest.mark.flaky(reruns=32)  # Flaky due to Vela bug: MLBEDSW-10642
 @common.XfailIfNoCorstone320
 def test_sigmoid_add_sigmoid_tosa_u85(test_data):
     pipeline = EthosU85PipelineBI(

backends/arm/test/ops/test_sigmoid_32bit.py

@@ -97,7 +97,7 @@ def forward(self, x):
 
 
 @common.parametrize("test_data", test_data_suite)
-@pytest.mark.flaky(reruns=5)
+@pytest.mark.flaky(reruns=32)  # Flaky due to Vela bug: MLBEDSW-10642
 def test_sigmoid_tosa_BI(test_data):
     pipeline = TosaPipelineBI(
         Sigmoid(),
@@ -110,7 +110,7 @@ def test_sigmoid_tosa_BI(test_data):
 
 
 @common.parametrize("test_data", test_data_suite)
-@pytest.mark.flaky(reruns=5)
+@pytest.mark.flaky(reruns=32)  # Flaky due to Vela bug: MLBEDSW-10642
 def test_sigmoid_add_sigmoid_tosa_BI(test_data):
     pipeline = TosaPipelineBI(
         SigmoidAddSigmoid(),
@@ -123,6 +123,7 @@ def test_sigmoid_add_sigmoid_tosa_BI(test_data):
 
 
 @common.parametrize("test_data", test_data_suite)
+@pytest.mark.flaky(reruns=32)  # Flaky due to Vela bug: MLBEDSW-10642
 def test_sigmoid_tosa_u55(test_data):
     pipeline = OpNotSupportedPipeline(
         Sigmoid(), (test_data(),), "TOSA-0.80+BI+u55", {Sigmoid.exir_op: 1}
@@ -132,6 +133,7 @@ def test_sigmoid_tosa_u55(test_data):
 
 
 @common.parametrize("test_data", test_data_suite)
+@pytest.mark.flaky(reruns=32)  # Flaky due to Vela bug: MLBEDSW-10642
 def test_sigmoid_add_sigmoid_tosa_u55(test_data):
     pipeline = OpNotSupportedPipeline(
         SigmoidAddSigmoid(),
@@ -145,7 +147,7 @@ def test_sigmoid_add_sigmoid_tosa_u55(test_data):
 
 
 @common.parametrize("test_data", test_data_suite)
-@pytest.mark.flaky(reruns=5)
+@pytest.mark.flaky(reruns=32)  # Flaky due to Vela bug: MLBEDSW-10642
 @common.XfailIfNoCorstone320
 def test_sigmoid_tosa_u85(test_data):
     pipeline = EthosU85PipelineBI(
@@ -162,7 +164,7 @@ def test_sigmoid_tosa_u85(test_data):
         "ramp": "AssertionError: Output 0 does not match reference output.",
     },
 )
-@pytest.mark.flaky(reruns=5)
+@pytest.mark.flaky(reruns=32)  # Flaky due to Vela bug: MLBEDSW-10642
 @common.XfailIfNoCorstone320
 def test_sigmoid_add_sigmoid_tosa_u85(test_data):
     pipeline = EthosU85PipelineBI(

backends/qualcomm/_passes/decompose_einsum.py

@@ -8,6 +8,8 @@
 from executorch.exir.pass_base import ExportPass, PassResult
 from torch.fx.experimental.proxy_tensor import make_fx
 
+from .utils import copy_nn_module_stack
+
 
 class DecomposeEinsum(ExportPass):
     """
@@ -36,6 +38,7 @@ def call(self, graph_module: torch.fx.GraphModule) -> PassResult:
                         remap[f"arg1_{i+1}"] = arg
 
                     for decomposed_node in decomposed_module.graph.nodes:
+                        copy_nn_module_stack(node, decomposed_node)
                         # This is the arg[0] equation string, which is not required anymore after decomposition
                         if "arg0" in decomposed_node.name:
                             continue

backends/qualcomm/_passes/decompose_linalg_vector_norm.py

@@ -8,6 +8,8 @@
 from executorch.exir import to_edge
 from executorch.exir.pass_base import ExportPass, PassResult
 
+from .utils import copy_nn_module_stack
+
 
 class LinalgVectorNorm(torch.nn.Module):
     def __init__(self, exp, dim, keepdim):
@@ -62,6 +64,7 @@ def call(self, graph_module: torch.fx.GraphModule) -> PassResult:
                     remap = {"x": node.args[0]}
 
                     for decomposed_node in decomposed_module.graph.nodes:
+                        copy_nn_module_stack(node, decomposed_node)
                         # no need to copy existent 'output'
                         if decomposed_node.op == "output":
                             for user in node.users.copy():

backends/qualcomm/_passes/utils.py

@@ -121,6 +121,14 @@ def get_passes_dependency_for_capture_program():
     }
 
 
+def copy_nn_module_stack(src, target):
+    """
+    Copy meta["nn_module_stack"] from src node to target node if existing.
+    """
+    if value := src.meta.get("nn_module_stack"):
+        target.meta["nn_module_stack"] = value
+
+
 def is_float_tensor(node: torch.fx.Node) -> bool:
     if "val" not in node.meta or not isinstance(node.meta["val"], FakeTensor):
         return False