Add MethodMeta object for python visibility #5571
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🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/5571
Note: Links to docs will display an error until the docs builds have been completed. ✅ No FailuresAs of commit 8b907f7 with merge base d2ba238 ( This comment was automatically generated by Dr. CI and updates every 15 minutes. |
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Summary: Pull Request resolved: pytorch#5571 Some clients and consumers of the Executorch program files (.pte) were requesting ways to access metadata like the sizes of tensors and the number of bytes they needed. When I told them how to access them in C++, they requested Python wrappers since they had processing scripts written in Python. Add some implementations of MethodMeta and TensorInfo methods. Note that these become more expensive than in C++ because they need to allocate python objects, but I doubt these are used in performance-sensitive applications anyway. And dealing with lifetimes of mixed C++/Python objects is complex, so I favored simple lifetimes. Differential Revision: D63288433
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Summary: Pull Request resolved: pytorch#5571 Some clients and consumers of the Executorch program files (.pte) were requesting ways to access metadata like the sizes of tensors and the number of bytes they needed. When I told them how to access them in C++, they requested Python wrappers since they had processing scripts written in Python. Add some implementations of MethodMeta and TensorInfo methods. Note that these become more expensive than in C++ because they need to allocate python objects, but I doubt these are used in performance-sensitive applications anyway. And dealing with lifetimes of mixed C++/Python objects is complex, so I favored simple lifetimes. Differential Revision: D63288433
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Summary: Pull Request resolved: pytorch#5571 Some clients and consumers of the Executorch program files (.pte) were requesting ways to access metadata like the sizes of tensors and the number of bytes they needed. When I told them how to access them in C++, they requested Python wrappers since they had processing scripts written in Python. Add some implementations of MethodMeta and TensorInfo methods. Note that these become more expensive than in C++ because they need to allocate python objects, but I doubt these are used in performance-sensitive applications anyway. And dealing with lifetimes of mixed C++/Python objects is complex, so I favored simple lifetimes. Differential Revision: D63288433
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Summary: Pull Request resolved: pytorch#5571 Some clients and consumers of the Executorch program files (.pte) were requesting ways to access metadata like the sizes of tensors and the number of bytes they needed. When I told them how to access them in C++, they requested Python wrappers since they had processing scripts written in Python. Add some implementations of MethodMeta and TensorInfo methods. Note that these become more expensive than in C++ because they need to allocate python objects, but I doubt these are used in performance-sensitive applications anyway. And dealing with lifetimes of mixed C++/Python objects is complex, so I favored simple lifetimes. Reviewed By: dbort Differential Revision: D63288433
Summary: Pull Request resolved: pytorch#5571 Some clients and consumers of the Executorch program files (.pte) were requesting ways to access metadata like the sizes of tensors and the number of bytes they needed. When I told them how to access them in C++, they requested Python wrappers since they had processing scripts written in Python. Add some implementations of MethodMeta and TensorInfo methods. Note that these become more expensive than in C++ because they need to allocate python objects, but I doubt these are used in performance-sensitive applications anyway. And dealing with lifetimes of mixed C++/Python objects is complex, so I favored simple lifetimes. Reviewed By: dbort Differential Revision: D63288433
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Summary: Pull Request resolved: #5571 Some clients and consumers of the Executorch program files (.pte) were requesting ways to access metadata like the sizes of tensors and the number of bytes they needed. When I told them how to access them in C++, they requested Python wrappers since they had processing scripts written in Python. Add some implementations of MethodMeta and TensorInfo methods. Note that these become more expensive than in C++ because they need to allocate python objects, but I doubt these are used in performance-sensitive applications anyway. And dealing with lifetimes of mixed C++/Python objects is complex, so I favored simple lifetimes. Reviewed By: dbort Differential Revision: D63288433 fbshipit-source-id: af775120a8ebd9bf455671a8ce1f158259aa50e6
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Summary: Pull Request resolved: #5571 Some clients and consumers of the Executorch program files (.pte) were requesting ways to access metadata like the sizes of tensors and the number of bytes they needed. When I told them how to access them in C++, they requested Python wrappers since they had processing scripts written in Python. Add some implementations of MethodMeta and TensorInfo methods. Note that these become more expensive than in C++ because they need to allocate python objects, but I doubt these are used in performance-sensitive applications anyway. And dealing with lifetimes of mixed C++/Python objects is complex, so I favored simple lifetimes. Reviewed By: dbort Differential Revision: D63288433 fbshipit-source-id: af775120a8ebd9bf455671a8ce1f158259aa50e6
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* Add MethodMeta object for python visibility (#5571) Summary: Pull Request resolved: #5571 Some clients and consumers of the Executorch program files (.pte) were requesting ways to access metadata like the sizes of tensors and the number of bytes they needed. When I told them how to access them in C++, they requested Python wrappers since they had processing scripts written in Python. Add some implementations of MethodMeta and TensorInfo methods. Note that these become more expensive than in C++ because they need to allocate python objects, but I doubt these are used in performance-sensitive applications anyway. And dealing with lifetimes of mixed C++/Python objects is complex, so I favored simple lifetimes. Reviewed By: dbort Differential Revision: D63288433 fbshipit-source-id: af775120a8ebd9bf455671a8ce1f158259aa50e6 * Add mapping from C++ program::verification to Python (#5915) Summary: As titled. This enables `portable_lib._load_for_executorch[_from_buffer]` to accept `Program::Verification` argument. See added test, now we can do something like: ``` from executorch.extension.pybindings.portable_lib import Verification module = load_fn( exported_program.buffer, enable_etdump=False, debug_buffer_size=0, program_verification=Verification.Minimal, ) ``` Pull Request resolved: #5915 Test Plan: See unit test Reviewed By: dbort Differential Revision: D63987538 Pulled By: larryliu0820 fbshipit-source-id: b68d8d1149e2d46b90544679707f420179e72b19 * Find portable_lib.so in pip package during cmake build (#5961) Summary: * Rename `_portable_lib.cpython-3.<distribution info>.so` to `_portable_lib.so` so it can be found by CMake `find_library()`. This can be achieved by setting `SETUPTOOLS_EXT_SUFFIX`. * Since `executorch-config.cmake` is also being used to find installed libraries such as `executorch.a`, `xnnpack_backend.a`, add a condition to tell if `executorch-config.cmake` is being used in cmake-out or site-packages. Pull Request resolved: #5961 Reviewed By: metascroy Differential Revision: D64014291 Pulled By: larryliu0820 fbshipit-source-id: 2757f2883d3f836e9efd45676f792c12f742e63d * Improve pip package build (#5965) Summary: Addressing comments in #5961. * Separate out `executorch-wheel-config.cmake` from `executorch-config.cmake`. * Hardcode the envrionment variable `SETUPTOOLS_EXT_SUFFIX` in `setup.py`. Pull Request resolved: #5965 Reviewed By: dbort Differential Revision: D64017947 Pulled By: larryliu0820 fbshipit-source-id: 0bdff5e2d2ec5873540d1b701595c7a316e84e80 * Let find_package(executorch) find the correct include directory (#6102) Summary: There's a typo in `executorch-wheel-config.cmake` that points to the wrong `include` path: ``` <site-packages>/executorch/share/cmake/include ``` Where it actually should be ``` <site-packages>/executorch/include ``` Fixing this issue. Verified it on [build_torchao_ops.sh](https://github.com/pytorch/ao/blob/main/torchao/experimental/build_torchao_ops.sh) Pull Request resolved: #6102 Reviewed By: lucylq Differential Revision: D64189337 Pulled By: larryliu0820 fbshipit-source-id: 13033587f5499537623995b8f9457fb47d780340 * New Runtime pybind API (#6063) Summary: Based on this proposal: https://docs.google.com/document/d/10Q4-pt97inQQtFf-FjjwhMaDXXCfk1zGy6V6EkygNUY/edit#heading=h.fcrpnrtb6cud Historically our pybinding APIs are not following the same C++ modeling (Program, Method etc) and hence it's hard to use and easy to hit footguns - for example, if we load the program and return it from a python method, it goes out of the scope and releases the memory. This effort is to create Pybind APIs that resembles C++ objects so it's less confusing to the users. Add the following python classes: * `Runtime`: a singleton object hosting methods like `load_program`. Returns a `Program` object when calling `load_program`. Also exposes the operator registry * `Program`: each pte file should have one `Program` object. Most important method is `load_method` which returns a `Method` object. It has a property `method_names` where we can inspect what methods are inside this .pte file. * `Method`: one object per method name in a given `Program`. Exposes `execute` which takes in pytree flattened torch tensors as input and return pytree flattened output. It also exposes `MethodMeta` for users to inspect more information regarding input/output of this method. Pull Request resolved: #6063 Reviewed By: dbort Differential Revision: D64132360 Pulled By: larryliu0820 fbshipit-source-id: a2f35edc5fd8c200df0812a693e454d66d6a907e * Lint * Fix test_pybindings.py --------- Co-authored-by: Riley Dulin <dulinr@meta.com>
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Summary:
Some clients and consumers of the Executorch program files (.pte) were
requesting ways to access metadata like the sizes of tensors and the number
of bytes they needed.
When I told them how to access them in C++, they requested Python wrappers
since they had processing scripts written in Python.
Add some implementations of MethodMeta and TensorInfo methods.
Note that these become more expensive than in C++ because they need to
allocate python objects, but I doubt these are used in
performance-sensitive applications anyway. And dealing with
lifetimes of mixed C++/Python objects is complex, so I favored simple lifetimes.
Differential Revision: D63288433