Summary:
Enable Gelu bf16/fp32 in CPU path using Mkldnn implementation. User doesn't need to_mkldnn() explicitly. New Gelu fp32 performs better than original one.
Add Gelu backward for https://github.com/pytorch/pytorch/pull/53615.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/58525
Reviewed By: ejguan
Differential Revision: D29940369
Pulled By: ezyang
fbshipit-source-id: df9598262ec50e5d7f6e96490562aa1b116948bf
Summary:
As GoogleTest `TEST` macro is non-compliant with it as well as `DEFINE_DISPATCH`
All changes but the ones to `.clang-tidy` are generated using following script:
```
for i in `find . -type f -iname "*.c*" -or -iname "*.h"|xargs grep cppcoreguidelines-avoid-non-const-global-variables|cut -f1 -d:|sort|uniq`; do sed -i "/\/\/ NOLINTNEXTLINE(cppcoreguidelines-avoid-non-const-global-variables)/d" $i; done
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/62008
Reviewed By: driazati, r-barnes
Differential Revision: D29838584
Pulled By: malfet
fbshipit-source-id: 1b2f8602c945bd4ce50a9bfdd204755556e31d13
Summary:
Fixes https://github.com/pytorch/pytorch/issues/27655
This PR adds a C++ and Python version of ReflectionPad3d with structured kernels. The implementation uses lambdas extensively to better share code from the backward and forward pass.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/59791
Reviewed By: gchanan
Differential Revision: D29242015
Pulled By: jbschlosser
fbshipit-source-id: 18e692d3b49b74082be09f373fc95fb7891e1b56
Summary:
This is an automatic change generated by the following script:
```
#!/usr/bin/env python3
from subprocess import check_output, check_call
import os
def get_compiled_files_list():
import json
with open("build/compile_commands.json") as f:
data = json.load(f)
files = [os.path.relpath(node['file']) for node in data]
for idx, fname in enumerate(files):
if fname.startswith('build/') and fname.endswith('.DEFAULT.cpp'):
files[idx] = fname[len('build/'):-len('.DEFAULT.cpp')]
return files
def run_clang_tidy(fname):
check_call(["python3", "tools/clang_tidy.py", "-c", "build", "-x", fname,"-s"])
changes = check_output(["git", "ls-files", "-m"])
if len(changes) == 0:
return
check_call(["git", "commit","--all", "-m", f"NOLINT stubs for {fname}"])
def main():
git_files = check_output(["git", "ls-files"]).decode("ascii").split("\n")
compiled_files = get_compiled_files_list()
for idx, fname in enumerate(git_files):
if fname not in compiled_files:
continue
if fname.startswith("caffe2/contrib/aten/"):
continue
print(f"[{idx}/{len(git_files)}] Processing {fname}")
run_clang_tidy(fname)
if __name__ == "__main__":
main()
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/56892
Reviewed By: H-Huang
Differential Revision: D27991944
Pulled By: malfet
fbshipit-source-id: 5415e1eb2c1b34319a4f03024bfaa087007d7179
Summary:
This PR adds a `padding_idx` parameter to `nn.EmbeddingBag` and `nn.functional.embedding_bag`. As with `nn.Embedding`'s `padding_idx` argument, if an embedding's index is equal to `padding_idx` it is ignored, so it is not included in the reduction.
This PR does not add support for `padding_idx` for quantized or ONNX `EmbeddingBag` for opset10/11 (opset9 is supported). In these cases, an error is thrown if `padding_idx` is provided.
Fixes https://github.com/pytorch/pytorch/issues/3194
Pull Request resolved: https://github.com/pytorch/pytorch/pull/49237
Reviewed By: walterddr, VitalyFedyunin
Differential Revision: D26948258
Pulled By: jbschlosser
fbshipit-source-id: 3ca672f7e768941f3261ab405fc7597c97ce3dfc
Summary:
Context: https://github.com/pytorch/pytorch/pull/53299#discussion_r587882857
These are the only hand-written parts of this diff:
- the addition to `.github/workflows/lint.yml`
- the file endings changed in these four files (to appease FB-internal land-blocking lints):
- `GLOSSARY.md`
- `aten/src/ATen/core/op_registration/README.md`
- `scripts/README.md`
- `torch/csrc/jit/codegen/fuser/README.md`
The rest was generated by running this command (on macOS):
```
git grep -I -l ' $' -- . ':(exclude)**/contrib/**' ':(exclude)third_party' | xargs gsed -i 's/ *$//'
```
I looked over the auto-generated changes and didn't see anything that looked problematic.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/53406
Test Plan:
This run (after adding the lint but before removing existing trailing spaces) failed:
- https://github.com/pytorch/pytorch/runs/2043032377
This run (on the tip of this PR) succeeded:
- https://github.com/pytorch/pytorch/runs/2043296348
Reviewed By: walterddr, seemethere
Differential Revision: D26856620
Pulled By: samestep
fbshipit-source-id: 3f0de7f7c2e4b0f1c089eac9b5085a58dd7e0d97
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/45377
This PR adds a C++ implementation of the TripletMarginWithDistanceLoss, for which the Python implementation was introduced in PR #43680. It's based on PR #44072, but I'm resubmitting this to unlink it from Phabricator.
Test Plan: Imported from OSS
Reviewed By: izdeby
Differential Revision: D24003973
fbshipit-source-id: 2d9ada7260a6f27425ff2fdbbf623dad0fb79405
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/44433
Not entirely sure why, but changing the type of beta from `float` to `double in autocast_mode.cpp and FunctionsManual.h fixes my compiler errors, failing instead at link time
fixing some type errors, updated fn signature in a few more files
removing my usage of Scalar, making beta a double everywhere instead
Test Plan: Imported from OSS
Reviewed By: mrshenli
Differential Revision: D23636720
Pulled By: bdhirsh
fbshipit-source-id: caea2a1f8dd72b3b5fd1d72dd886b2fcd690af6d
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/35163
This PR is BC-breaking in the following way:
Renaming:
- `torch::nn::functional::MultiLabelMarginLossFuncOptions` -> `torch::nn::functional::MultilabelMarginLossFuncOptions`
- `torch::nn::functional::MultiLabelSoftMarginLossFuncOptions` -> `torch::nn::functional::MultilabelSoftMarginLossFuncOptions`
Reason for renaming: to be consistent with the corresponding functional name after camel case to snake case conversion (e.g. the `multilabel_margin_loss` functional should use `MultilabelMarginLossFuncOptions` as options)
Test Plan: Imported from OSS
Differential Revision: D20582598
Pulled By: yf225
fbshipit-source-id: 0f5bdb8249d901b310875a14320449a2fdfa8ecd
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/35025
This PR fixes `F::interpolate` and `torch::nn::Upsample` implementation to match the Python API implementation.
**This PR is BC-breaking in the following way:**
There are changes to `UpsampleOptions` and `InterpolateFuncOptions`:
- `size` is changed from `std::vector<int64_t>` to `c10::optional<std::vector<int64_t>>`. If you want to pass a list of `int64_t` to this argument, you must pass it as `std::vector<int64_t>`.
- `scale_factor` is changed from `std::vector<double>` to `c10::optional<std::vector<double>>`. If you want to pass a list of `double` to this argument, you must pass it as `std::vector<double>`.
**TODO**: cherry-pick this PR into v1.5 release branch.
Test Plan: Imported from OSS
Differential Revision: D20559892
Pulled By: yf225
fbshipit-source-id: ac18609e351a9f2931eaeced8966b9491b2995f7
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
Summary:
Hi yf225 , I have added **NLLLoss and CrossEntropyLoss.**
```
Also, while using log_softmax in cross_entropy_loss, I am getting an error
../caffe2/../torch/csrc/api/include/torch/nn/functional/loss.h:537:63: error: no matching function for call to log_softmax(const at::Tensor&)’
const Tensor& log_softmax_input = torch::log_softmax(input);
aten/src/ATen/Functions.h:5551:22: note: candidate: at::Tensor at::log_softmax(const at::Tensor&, int64_t, c10::optional<c10::ScalarType>)
static inline Tensor log_softmax(const Tensor & self, int64_t dim, c10::optional<ScalarType> dtype) {
^~~~~~~~~~~
aten/src/ATen/Functions.h:5551:22: note: candidate expects 3 arguments, 1 provided
```
I think the other two parameters should be optional as in python frontend(shown in documentation here at https://pytorch.org/docs/stable/nn.functional.html#torch.nn.functional.log_softmax ). Rest, there were no errors in build and tests have passed
Pull Request resolved: https://github.com/pytorch/pytorch/pull/29812
Differential Revision: D18548249
Pulled By: yf225
fbshipit-source-id: 2ab350abd2a6f498d4dba2345f51ad87471f3038
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/29673
Following https://github.com/pytorch/pytorch/pull/29364 and https://github.com/pytorch/pytorch/pull/29404, this PR makes `F::EmbeddingFuncOptions` and `F::EmbeddingBagFuncOptions` separate classes from `torch::nn::EmbeddingOptions` and `torch::nn::EmbeddingBagOptions`, so that it's easier to enforce that arguments such as `num_embeddings` and `embedding_dim` are required for `torch::nn::EmbeddingOptions` and `torch::nn::EmbeddingBagOptions`.
Test Plan: Imported from OSS
Differential Revision: D18462540
Pulled By: yf225
fbshipit-source-id: f2abf431e48675b0a9d7f6f398cdb90ff9037c35
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
Summary:
This PR changes the implementation of C++ Conv{1,2,3}d layers to exactly match the Python version, and add F::conv{1,2,3}d functionals. For more thorough testing, I will rely on the parity test mechanism which uses values from `common_nn.py` to generate the inputs and options that we are interested in testing.
This PR is BC-breaking in the following way:
In `Conv{1,2,3}dOptions`:
- `with_bias` is renamed to `bias`.
- `input_channels` is renamed to `in_channels`.
- `output_channels` is renamed to `out_channels`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/28917
Differential Revision: D18471526
Pulled By: yf225
fbshipit-source-id: 7a33f60654ad93cc2e043245e7ff9e0ef9da15b3
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/29404
This PR makes all non-input arguments to functionals part of its options parameters, so that we won't break backward compatibility even if we add or reorder some of the non-input arguments to functionals in the future.
Test Plan: Imported from OSS
Differential Revision: D18378526
Pulled By: yf225
fbshipit-source-id: f5cf6bdfb844e75bf94fdee58c121e0955631b6e
Summary:
Fixes https://github.com/pytorch/pytorch/issues/17662
I'm not sure if `arange` needs to be in python_arg_parser at all, given the schemas in native_functions.yaml. In any case this at least fixes the dytpe mismatch.
In follow up PRs I will try to handle some of the other ops that do type inference at the python level, like randint.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/27629
Differential Revision: D17885939
Pulled By: eellison
fbshipit-source-id: f97a8bc722b7ab77de1c42a992e49a4a3175ad60
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/29364
Currently, we use `torch::nn::*Options` both as module options and functional options. However, this makes it very hard to manage the parameters in `torch::nn::*Options`, because a module's constructor can take a different set of arguments than the module's equivalent functional (e.g. `torch.nn.BatchNorm1d` takes `num_features, eps=1e-5, momentum=0.1, affine=True,
track_running_stats=True`, while `F::batch_norm` takes `running_mean, running_var, weight=None, bias=None, training=False, momentum=0.1, eps=1e-5`).
This PR resolves the above problem by making `F::*FuncOptions` a different class from `torch::nn::*Options` when necessary (i.e. when a module's constructor takes a different set of arguments than the module's equivalent functional). In the rest of the cases where the module constructor takes the same set of arguments as the module's equivalent functional, `F::*FuncOptions` is an alias of `torch::nn::*Options`.
Also as part of this PR, we change all functional options to pass-by-value, to make the semantics consistent across all functionals.
Test Plan: Imported from OSS
Differential Revision: D18376977
Pulled By: yf225
fbshipit-source-id: 8d9c240d93bfd5af0165b6884fdc912476b1d06b
