Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/26125
We already had some optimization implementation using AVX2 for improve the quantized kernel performance. In this diff, we want to enable the runtime dispatch.
Test Plan:
Sandcastle build and test
Also test with a python binary calling into vectorized op.
torch.__config__.show()
PyTorch built with:
- GCC 4.2
- clang 8.0.20181009
- Intel(R) Math Kernel Library Version 2017.0.3 Product Build 20170413 for Intel(R) 64 architecture applications
- Intel(R) MKL-DNN v0.18.1 (Git Hash N/A)
- OpenMP 1
- **CPU capability usage: AVX2**
- Build settings:
Reviewed By: jamesr66a
Differential Revision: D17337251
fbshipit-source-id: 8e22d10011a12a4eaf54cea3485353eb1811d828
Summary:
**This PR is BC-breaking in the following way:**
In RMSpropOptions:
1. learning_rate is renamed to lr.
**Test plan before 1.5 release:**
Test that in 1.5 we can load a C++ RMSprop optimizer that was serialized in 1.4, and their states are the same.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/33450
Differential Revision: D20366623
Pulled By: anjali411
fbshipit-source-id: 83250be9b583a766927e0e22a4de8b0765379451
Summary:
One example in the current docs for `torch::nn::ModuleList` doesn't compile, and this PR fixes it.
Fixes https://github.com/pytorch/pytorch/issues/32414.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/34463
Test Plan: Imported from GitHub, without a `Test Plan:` line.
Differential Revision: D20331120
Pulled By: yf225
fbshipit-source-id: 50bb078fe1a900c9114d5434e92dc40ee13b52bf
Summary:
The init-list form of `at::indexing::Slice` (i.e. `tensor.index({{1, None, 2}, ...})` instead of `tensor.index({Slice(1, None, 2), ...})`) in C++ API can be easily confused with the list-form indexing in Python API (e.g. `tensor[[1, 3, 2], ...]`), which is not good from readability perspective. This PR removes the init-list form of `at::indexing::Slice` to make the API less confusing.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/34255
Test Plan: Imported from GitHub, without a `Test Plan:` line.
Differential Revision: D20290166
Pulled By: yf225
fbshipit-source-id: abbcbeca0b179219e5e1f196a33ef8aec87ebb76
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/34035
Bug for the conditon check in https://github.com/pytorch/pytorch/pull/24342, realized we don't have tests in either
python or cpp to catch this, so added testes for both python and cpp.
Thanks hczhu on capturing it!
Test Plan: Imported from OSS
Differential Revision: D20198837
Pulled By: wanchaol
fbshipit-source-id: 33846a14c0a8e7aac2e8328189d10c38a0d7e6ee
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
Summary:
Most of the function implementation and test code are translated from the Python version.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/33652
Differential Revision: D20052211
Pulled By: yf225
fbshipit-source-id: ce6767db54364f91ef4f06674239a12278c2752a
Summary:
This PR adds the following items:
- **1st item**: `ArrayRef<TensorIndex>` and `std::initializer_list<TensorIndex>` overloads for `Tensor::index` and `Tensor::index_put_`, to be used specifically for multi-dim indexing purpose.
Design rationale:
* C++ `Tensor::index` and `Tensor::index_put_` are both existing tensor APIs, and they currently (before this PR) only accept a list of tensors (i.e. `ArrayRef<Tensor>`) as indices. If we change their signatures to also accept non-tensors as indices (i.e. `ArrayRef<TensorIndex>`, and `TensorIndex` is convertible from `Tensor` / `Slice` / `None` / `Ellipsis`), it would slow down the original code path (since now it has to go through more steps), which is undesirable.
To get around this problem, the proposed solution is to keep the original `ArrayRef<Tensor>` overload, and add `ArrayRef<TensorIndex>` and `std::initializer_list<TensorIndex>` overloads to `Tensor::index` and `Tensor::index_put_`. This way, the original code path won’t be affected, and the tensor multi-dim indexing API is only used when the user explicitly pass an `ArrayRef<TensorIndex>` or a braced-init-list of `TensorIndex`-convertible types to `Tensor::index` and `Tensor::index_put_` .
Note that the above proposed solution would still affect perf for the user’s original `Tensor::index` or `Tensor::index_put_` call sites that use a braced-init-list of tensors as input, e.g. `tensor.index({...})` or `tensor.index_put_({...}, value)`, since now such function calls would take the multi-dim indexing path instead of the original advanced indexing path. However, there are only two instances of this in our codebase (one in ATen cpp test, one in a C++ API nn init function), and they can be easily changed to explicitly use `ArrayRef<Tensor>` as input (I changed them in this PR). For external user’s code, since this is part of the C++ frontend which is still considered experimental, we will only talk about this change in the release note, and ask users to switch to using `ArrayRef<Tensor>` explicitly if they want to keep using the original advanced indexing code path.
- **2nd item**: Mechanisms for parsing `ArrayRef<TensorIndex>` indices and performing indexing operations (mirroring the functions in `torch/csrc/autograd/python_variable_indexing.cpp`).
- **3rd item**: Simple tests to demonstrate that the `Tensor::index()` and `Tensor::index_put_()` APIs work. I will add more tests after the first few PRs are reviewed.
- **4th item**: Merge Python/C++ indexing code paths, for code simplicity. I tested locally and found that there is no perf regression resulting from the merge. I will get more concrete numbers for common use cases when we settle on the overall design.
This PR supersedes https://github.com/pytorch/pytorch/pull/30425.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/32841
Differential Revision: D19919692
Pulled By: yf225
fbshipit-source-id: 7467e64f97fc0e407624809dd183c95ea16b1482
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/33027
This PR allows default arguments in module's forward method to be skipped when module is used in `torch::nn::Sequential`, by introducing the `FORWARD_HAS_DEFAULT_ARGS` macro and requiring that all modules that have default arguments in its forward method must have a corresponding `FORWARD_HAS_DEFAULT_ARGS` macro call.
Fixes issue mentioned in https://github.com/pytorch/pytorch/issues/30931#issuecomment-564144468.
Test Plan: Imported from OSS
Differential Revision: D19777815
Pulled By: yf225
fbshipit-source-id: 73282fcf63377530063e0092a9d84b6c139d2e32
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/33026
This PR contains necessary changes to prepare for https://github.com/pytorch/pytorch/pull/33027. It exposes the following classes to public:
1. `torch::nn::AnyValue`, because if the user has optional arguments in their module's forward method, they must also use the `FORWARD_HAS_DEFAULT_ARGS` macro and pass in the default values for those optional arguments wrapped by `torch::nn::AnyValue`.
2. `torch::nn::AnyModuleHolder`, because `torch::nn::Module` needs to declare it as a friend class for it to be able to access `torch::nn::Module`'s protected methods such as `_forward_has_default_args` / `_forward_num_required_args` / `_forward_populate_default_args`.
Test Plan: Imported from OSS
Differential Revision: D19777814
Pulled By: yf225
fbshipit-source-id: 1c9d5aa24f0689154752c426a83ee98f64c9d02f
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/33068
The version counter is already tracked if we use pytorch's functions but not if the user unpack the Tensor and modifies it by hand or with a third party library.
Test Plan: Imported from OSS
Differential Revision: D19791564
Pulled By: albanD
fbshipit-source-id: a73c0f73d8fd0c0e5bf838f14bed54fa66937840
Summary:
This test case had been using the tensor
```
1 2 3 4
5 6 7 8
9 10 11 12
13 14 15 16
```
which is not an invertible tensor and causes the test case to fail, even if magma gets initialized just fine. This change uses a tensor that is invertible, and the inverse doesn't include any elements that are close to zero to avoid floating point rounding errors.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/32547
Differential Revision: D19572316
Pulled By: ngimel
fbshipit-source-id: 1baf3f8601b2ba69fdd6678d7a3d86772d01edbe
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/31990
This PR does three things:
- Add a new `allow_rebase_history` flag to the differentiable views. If set, trying to rebase their history will raise an error.
- Make sure that the codegen functions verify this flag before doing inplace operations so that they fail before doing the inplace modification.
- Make sure the codegen functions set this flag properly when we don't support rebasing the history of the output.
The codegen change can be found [here](4bf180caa0).
Test Plan: Imported from OSS
Differential Revision: D19409649
Pulled By: albanD
fbshipit-source-id: a2b41c2d231e952ecfe162bdb6bad620ac595703
Summary:
Currently, libtorch build and test are not running in macOS CI. This PR fixes the issue.
**Test Plan:**
Check that libtorch build and test are running again in macOS CI.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/32072
Differential Revision: D19391909
Pulled By: yf225
fbshipit-source-id: 1ab345b099869f78e1124f1a8bd185fa51371b6a
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/30424
`at::indexing::TensorIndex` is used for converting C++ tensor indices such as `{None, "...", Ellipsis, 0, true, {1, None, 2}, torch::tensor({1, 2})}` into its equivalent `std::vector<TensorIndex>`, so that further tensor indexing operations can be performed using the supplied indices.
Test Plan: Imported from OSS
Differential Revision: D18695902
Pulled By: yf225
fbshipit-source-id: d73e14a411cdbec815866b02e75ffd71a9186e89
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/31222
- When constructing torch::from_blob() in the case where the deleter is a nop, switch to using a nullptr context in the DataPtr (with a nop deleter)
- No real extra memory/cpu requirements here, actually saves a minor alloc.
Why? Trying to get a signal that a Tensor might contain non-owned memory from
torch::from_blob(), by detecting the nullptr context.
ghstack-source-id: 96336078
Test Plan:
buck test mode/dev caffe2/test/cpp/api/...
buck test mode/dev-nosan caffe2/test/...
Differential Revision: D18992119
fbshipit-source-id: 4eea642f82d0858b57fdfc6995364a760c10567d
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/29219
We added class constant in previous PRs, this PR allows access to
class constant in the object API
Test Plan:
build/bin/test_jit
python test/test_jit.py
Imported from OSS
Differential Revision: D18846851
fbshipit-source-id: 888a6517d5f747d1f8ced283c0c2c30b2f6c72c6
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/31011
`getAttribute` is supposed to throw when there the attribute is not
found rather than return a `nullptr`.
Test Plan:
.
Imported from OSS
Differential Revision: D18898417
fbshipit-source-id: 0fe7d824b978ad19bb5ef094d3aa560e9fc57f87
Summary:
Fixes https://github.com/pytorch/pytorch/issues/29161.
I looked a bit at the code changes related to this and think I have all of the use cases of `DeprecatedTypeProperties` covered in the message, but suggestions from someone with more context on this would be very much appreciated :)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/30281
Differential Revision: D18830818
Pulled By: ezyang
fbshipit-source-id: 1a7fcee15354ae09e6644577e7fa33bd26acfe20
Summary:
The original design of `torch::nn::utils::clip_grad_norm_` / `clip_grad_value_` takes input by non-const reference, which prevents users from passing rvalue reference as input into the functions. This PR changes the functions to take input by value, which matches the Python version's semantics, and also adheres to the C++ API convention that if a function modifies its input in-place, it should take that input by value.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/30216
Differential Revision: D18632543
Pulled By: yf225
fbshipit-source-id: 97a09d6467f982fe9c8120f483a9c07fcf13699e
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/30146
This PR fixes naming for kl_div and binary_cross_entropy functional options, to be more consistent with the naming scheme of other functional options.
Test Plan: Imported from OSS
Differential Revision: D18618971
Pulled By: yf225
fbshipit-source-id: 2af62c1a0ace2cd0c36c2f1071639bf131d8fe61
Summary:
Hi yf225,
I have a few doubts related to implementation:
1) What tests do I have to write?
2) What does _load_state_from_dict does?
3) Do I need to override reset() function as I can not see it's utility?
4) InstanceNormOptions could be removed with BatchNormOptions, but I find that
`track_running_status` is not defined instead `stateful` is defined.
InstanceNorm{1,2,3}d https://github.com/pytorch/pytorch/issues/25883
Pull Request resolved: https://github.com/pytorch/pytorch/pull/28790
Differential Revision: D18588666
Pulled By: yf225
fbshipit-source-id: bb9b81f01f62c3fc8765fa0ba0716768087ee155
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/30112
Currently, we have torch::nn functionals that takes `input` as `Tensor&` in order to be able to in-place change `input`'s value. We likely shouldn't do this because it will prevent the following use case:
```cpp
F::elu(torch::tensor(1), F::ELUFuncOptions().inplace(true))
```
The solution is to change the type of `input` to `Tensor`, so that we can pass an rvalue into the functional.
Test Plan: Imported from OSS
Differential Revision: D18601580
Pulled By: yf225
fbshipit-source-id: 639a86eb62f6c986b0f20bf7e201983e83126e73
