Commit Graph

9 Commits

Author SHA1 Message Date
Will Feng
1494005cfd C++ tensor indexing: more indexing tests (#30427)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/30427

Test Plan: Imported from OSS

Differential Revision: D18695899

Pulled By: yf225

fbshipit-source-id: 74455fe52ef922556fabe65aefca9ec93fe2346d
2020-02-28 22:07:41 -08:00
Will Feng
5c33d98b0d Add assert_tensor_equal and assert_tensor_not_equal to test/cpp/api/support.h (#30426)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/30426

This PR adds `assert_tensor_equal` and `assert_tensor_not_equal` to `test/cpp/api/support.h`, as better functions for testing whether two tensors are equal / not equal.

Test Plan: Imported from OSS

Differential Revision: D18695900

Pulled By: yf225

fbshipit-source-id: c19b9bc4c4e84d9f444015023649d27618fcbdf5
2020-02-26 13:25:25 -08:00
Will Feng
18ec4632b3 Exclude undefined tensors in the result of Module::parameters() / named_paramters() / buffers() / named_buffers() (#30626)
Summary:
PR https://github.com/pytorch/pytorch/pull/30523 attempted to fix https://github.com/pytorch/pytorch/issues/30508 and https://github.com/pytorch/pytorch/issues/30462, but the fix wasn't complete. This PR makes the following improvements:
1. Fixes https://github.com/pytorch/pytorch/issues/30508 and https://github.com/pytorch/pytorch/issues/30462 properly by excluding undefined tensors in the result of `Module::parameters()` / `named_parameters()` / `buffers()` / `named_buffers()`, which mirrors the Python API behavior.
2. Audits all use sites of `Module::parameters_` / `buffers_` and change them to `Module::named_parameters(/*recurse=*/false)` / `named_buffers(/*recurse=*/false)` when appropriate, so that use sites of module parameters / buffers never need to worry about undefined tensors.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/30626

Differential Revision: D18777507

Pulled By: yf225

fbshipit-source-id: 55b64b69779e1186342efd3c44857f416334ed6b
2019-12-02 21:59:58 -08:00
Will Feng
2bcac59a30 Use default dtype for torch::tensor(floating_point_values) and torch::tensor(empty braced-init-list) when dtype is not specified (#29632)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/29632

This PR is BC-breaking in the following way:

Previously, C++ `torch::tensor` with a floating-point literal with no suffix (e.g. `torch::tensor(1.1)`) or a (nested) braced-init-list of
floating-point literals with no suffix (e.g. `torch::tensor({{1.1, 2.2}})` produces a tensor with dtype `at::kDouble`. After this PR, it produces a tensor with dtype `torch::get_default_dtype()`, matching Python `torch.tensor` behavior.

Test Plan: Imported from OSS

Differential Revision: D18465819

Pulled By: yf225

fbshipit-source-id: 6834fe50335c677bc3832f2a5e9cf8d1ede9f665
2019-11-13 15:17:11 -08:00
Will Feng
085bd15880 Add TORCH_WARN_ONCE, and use it in Tensor.data<T>() (#25207)
Summary:
This PR adds `TORCH_WARN_ONCE` macro, and use it in `Tensor.data<T>()`.

cc. gchanan
Pull Request resolved: https://github.com/pytorch/pytorch/pull/25207

Differential Revision: D17066263

Pulled By: yf225

fbshipit-source-id: 411c6ccc8326fb27ff885fee4638df8b5ba4d449
2019-08-27 21:42:44 -07:00
Will Feng
420b37f3c6 Deprecate tensor.data<T>(), and codemod tensor.data<T>() to tensor.data_ptr<T>() (#24886)
Summary:
This PR adds deprecation message for `tensor.data<T>()` (91d94e7d41), and changes all call sites of `tensor.data<T>()` to `tensor.data_ptr<T>()`  in PyTorch core.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/24886

Differential Revision: D16924576

Pulled By: yf225

fbshipit-source-id: 0943d6be73245c7c549c78597b74c3b07fa24440
2019-08-21 20:11:24 -07:00
Peter Goldsborough
393ad6582d Use torch:: instead of at:: in all C++ APIs (#13523)
Summary:
In TorchScript and C++ extensions we currently advocate a mix of `torch::` and `at::` namespace usage. In the C++ frontend I had instead exported all symbols from `at::` and some from `c10::` into the `torch::` namespace. This is far, far easier for users to understand, and also avoid bugs around creating tensors vs. variables. The same should from now on be true for the TorchScript C++ API (for running and loading models) and all C++ extensions.

Note that since we're just talking about typedefs, this change does not break any existing code.

Once this lands I will update stuff in `pytorch/tutorials` too.

zdevito ezyang gchanan
Pull Request resolved: https://github.com/pytorch/pytorch/pull/13523

Differential Revision: D12942787

Pulled By: goldsborough

fbshipit-source-id: 76058936bd8707b33d9e5bbc2d0705fc3d820763
2018-11-06 14:32:25 -08:00
Peter Goldsborough
db8d01b248 Move JIT tests to gtest (#12030)
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
2018-10-06 23:09:44 -07:00
Peter Goldsborough
825181ea9d Rewrite C++ API tests in gtest (#11953)
Summary:
This PR is a large codemod to rewrite all C++ API tests with GoogleTest (gtest) instead of Catch.

You can largely trust me to have correctly code-modded the tests, so it's not required to review every of the 2000+ changed lines. However, additional things I changed were:

1. Moved the cmake parts for these tests into their own `CMakeLists.txt` under `test/cpp/api` and calling `add_subdirectory` from `torch/CMakeLists.txt`
2. Fixing DataParallel tests which weren't being compiled because `USE_CUDA` wasn't correctly being set at all.
3. Updated README

ezyang ebetica
Pull Request resolved: https://github.com/pytorch/pytorch/pull/11953

Differential Revision: D9998883

Pulled By: goldsborough

fbshipit-source-id: affe3f320b0ca63e7e0019926a59076bb943db80
2018-09-21 21:28:16 -07:00