Summary:
libshm_manager doesn't need to depend on all of libtorch. It only uses tiny tempfile.h which can be moved to c10. I could just duplicate the file too, but it's not worth it as c10 is small enough.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17019
Differential Revision: D14052688
Pulled By: dzhulgakov
fbshipit-source-id: 8797d15f8c7c49c49d40b7ab2f43aa3bf6becb0c
Summary:
This PR adds Windows support for the C++ frontend. A lot of declarations were missing `TORCH_API` macros, and lots of code just did not compile on MSVC.
ebetica ezyang orionr
Pull Request resolved: https://github.com/pytorch/pytorch/pull/11716
Reviewed By: orionr
Differential Revision: D13038253
Pulled By: goldsborough
fbshipit-source-id: c8e5a45efd26117aeb99e768b56fcd5a89fcb9f8
Summary:
This PR implements a DataLoader API for the C++ frontend.
The components present in this API largely match the Python API. It consists of:
- `Dataset`s: Conceptually a function from a set of indices to a batch of examples;
- `Transform`s: A functional transformation of a dataset. A `Map<D, T>` for Dataset `D` and transform `T` is itself a dataset;
- `Sampler`s: Specify a strategy for generating indices for a new batch;
- A `DataLoader`, with the ability to automatically parallelize fetching of samples across multiple worker threads;
Note that collation functions fall naturally out of the `Map<Dataset, Transform>` abstraction.
Things that are missing right now that maybe should be added:
- Memory pinning for CUDA tensors
The API was designed to be generalizable to almost any kind of dataset, transform or sampling strategy, while providing a convenient API out of the box. To achieve this, it is quite heavily templatized on various possible input types.
There are many parts to this PR! Right now, I would like feedback on:
- Your impression of the general usability of the API;
- Your impression of which parts seem too complex or overthought;
- The implementation of the parallelization aspects of the DataLoader. I've followed the Python implementation in some matters, but also differ in others. I think my implementation is a little cleaner and decouples components slightly better than the Python dataloader.
I haven't added too many comments yet, as this is fresh out of the oven. Let me know if anything is unclear from the code itself.
There also aren't any tests yet. I will write a comprehensive test suite once we agree on the API and implementation.
apaszke ezyang The controller you requested could not be found. pietern
Pull Request resolved: https://github.com/pytorch/pytorch/pull/11918
Reviewed By: ezyang
Differential Revision: D9998881
Pulled By: goldsborough
fbshipit-source-id: 22cf357b63692bea42ddb1cc2abc71dae5030aea
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/12792
This is a follow up diff after D10238910.
Only non-codemod change is the removal of ATen/Error.h and ATen/core/Error.h. Other files are basically changing the inclusion path + clang format for inclusion order.
Reviewed By: bddppq
Differential Revision: D10437824
fbshipit-source-id: 7f885f80ab5827468d1351cfb2765d0e3f555a69
Summary:
This PR does three things:
1. Add support for serializing to `ostream` and deserializing from `istream`s in addition to files. This is after https://github.com/pytorch/pytorch/pull/11932 added support for streams in `torch::jit::ExportModule` and `torch::jit::load`.
2. Update the internal interface for how things get serialized into archives (e.g. use the more idiomatic `operator<<` instead of a `save` method). *The external interface does not change*.
3. Add documentation.
ezyang ebetica
Pull Request resolved: https://github.com/pytorch/pytorch/pull/12421
Reviewed By: ezyang
Differential Revision: D10248529
Pulled By: goldsborough
fbshipit-source-id: 6cde6abd0174e3fbf3579c05376a32db0b53755f
Summary:
In our #better-engineering quest of removing all uses of catch in favor of gtest, this PR ports JIT tests to gtest. After #11846 lands, we will be able to delete catch.
I don't claim to use/write these tests much (though I wrote the custom operator tests) so please do scrutinize whether you will want to write tests in the way I propose. Basically:
1. One function declaration per "test case" in test/cpp/jit/test.h
2. One definition in test/cpp/jit/test.cpp
3. If you want to be able to run it in Python, add it to `runJitTests()` which is called from Python tests
4. If you want to be able to run it in C++, add a `JIT_TEST` line in test/cpp/jit/gtest.cpp
Notice also I was able to share support code between C++ frontend and JIT tests, which is healthy.
ezyang apaszke zdevito
Pull Request resolved: https://github.com/pytorch/pytorch/pull/12030
Differential Revision: D10207745
Pulled By: goldsborough
fbshipit-source-id: d4bae087e4d03818b72b8853cd5802d79a4cf32e
