Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/66744
Modified loops in files under fbsource/fbcode/caffe2/ from the format
`for(TYPE var=x0;var<x_max;x++)`
to the format
`for(const auto var: irange(xmax))`
This was achieved by running r-barnes's loop upgrader script (D28874212) with some modification to exclude all files under /torch/jit and a number of reversions or unused variable suppression warnings added by hand.
Test Plan: Sandcastle
Reviewed By: ngimel
Differential Revision: D31705358
fbshipit-source-id: d6ea350cbaa8f452fc78f238160e5374be637a48
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/66234
Modified loops in files under fbsource/fbcode/caffe2/ from the format
`for(TYPE var=x0;var<x_max;x++)`
to the format
`for(const auto var: irange(xmax))`
This was achieved by running r-barnes's loop upgrader script (D28874212) with some modification to exclude all files under /torch/jit and a number of reversions or unused variable suppression warnings added by hand.
bypass_size_limit
allow-large-files
Test Plan: Sandcastle
Reviewed By: ngimel
Differential Revision: D30652629
fbshipit-source-id: 0ae6c4bbbb554bad42e372792a6430e1acf15e3e
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:
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:
Slightly modified Adam, following the python implementation, and the `ProducesPyTorchValues` tests pass. I had a problem with another test though (see commit c1a6241676ab84fc531c1c3a10f964aa5704092e), it seems that optimizing for two steps with the same optimizer vs optimizing for two steps using freshly initialized objects will produce the same output.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/40009
Differential Revision: D22096053
Pulled By: glaringlee
fbshipit-source-id: a31a8f5488cb37c53752ddf15436efabdba67dc4
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/36984
Follow LOG(WARNING) format for c++ side warnings in order to play well with larger services, especially when using glog. I need to hook up into GLOG internals a bit in order to override FILE/LINE without having to change the whole thing to be macros, but it seems to be stable between glog versions.
Note, this also changes caffe2_log_level to warning by default - I think it's a much better default when compiling without glog (or maybe even have info).
With glog output, stderr capture doesn't work any more in tests. That's why we instead use c10-level warnings capture.
Test Plan:
Run unittest in both glog and non-glog build mode:
glog:
```
W0416 12:06:49.778215 3311666 exception_test.cpp:23] Warning: I'm a warning (function TestBody)
```
no-glog:
```
[W exception_test.cpp:23] Warning: I'm a warning (function TestBody)
```
Reviewed By: ilia-cher
Differential Revision: D21151351
fbshipit-source-id: fa926d9e480db5ff696990dad3d80f79ef79f24a
Summary:
1. Removed LossClosureOptimizer, and merged Optimizer into OptimizerBase (and renamed the merged class to Optimizer)
2. Merged the LBFGS-specific serialize test function and the generic test_serialize_optimizer function.
3. BC-compatibility serialization test for LBFGS
4. Removed mentions of parameters_ in optimizer.cpp, de-virtualize all functions
5. Made defaults_ optional argument in all optimizers except SGD
**TODO**: add BC-breaking notes for this PR
Pull Request resolved: https://github.com/pytorch/pytorch/pull/34957
Test Plan: Imported from GitHub, without a `Test Plan:` line.
Differential Revision: D20678162
Pulled By: yf225
fbshipit-source-id: 74e062e42d86dc118f0fbaddd794e438b2eaf35a
Summary:
1. Removed LossClosureOptimizer, and merged Optimizer into OptimizerBase (and renamed the merged class to Optimizer)
2. Merged the LBFGS-specific serialize test function and the generic test_serialize_optimizer function.
3. BC-compatibility serialization test for LBFGS
4. Removed mentions of parameters_ in optimizer.cpp, de-virtualize all functions
5. Made defaults_ optional argument in all optimizers except SGD
**TODO**: add BC-breaking notes for this PR
Pull Request resolved: https://github.com/pytorch/pytorch/pull/34957
Differential Revision: D20645945
Pulled By: yf225
fbshipit-source-id: 383588065bf1859b38f0ad0a25d93d41e153c96e
Summary:
1. Removed LossClosureOptimizer, and merged Optimizer into OptimizerBase (and renamed the merged class to Optimizer)
2. Merged the LBFGS-specific serialize test function and the generic test_serialize_optimizer function.
3. BC-compatibility serialization test for LBFGS
4. Removed mentions of parameters_ in optimizer.cpp, de-virtualize all functions
5. Made defaults_ optional argument in all optimizers except SGD
Pull Request resolved: https://github.com/pytorch/pytorch/pull/34957
Test Plan: Imported from GitHub, without a `Test Plan:` line.
Differential Revision: D20518647
Pulled By: anjali411
fbshipit-source-id: 4760d1d29df1784e2d01e2a476d2a08e9df4ea1c
Summary:
Follow-ups after this PR:
* Remove `LossClosureOptimizer`, and merge `Optimizer` into `OptimizerBase` (and rename the merged class to Optimizer)
* Merge the LBFGS-specific serialize test function and the generic `test_serialize_optimizer` function, possibly by passing a bool `has_only_global_state` flag into the `test_serialize_optimizer` function to denote whether `size()` should be equal to 1 or 2?
* https://github.com/pytorch/pytorch/pull/34564#discussion_r393780303
* It seems that we don't have the equivalent `XORConvergence_LBFGS` test like the other optimizers, and it would be good to add one
* Remove mentions of `parameters_` in optimizer.cpp, de-virtualize all functions, and remove the `OptimizerBase(std::vector<Tensor> parameters)` constructor from `OptimizerBase`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/34564
Test Plan: Imported from GitHub, without a `Test Plan:` line.
Differential Revision: D20495701
Pulled By: anjali411
fbshipit-source-id: 6d35286d2decb6f7dff93d9d3e57515770666622
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:
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:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/28828
This updates torch::script::Module to more closely match the behavior
of nn.Module. In particular, it implements the (optionally recurisive)
iterators that retrieve submodules, parameters, and buffers and makes
their names match the python versions.
This also removes the individual accessors for Parameter, Module, Buffer, etc.
and replaces them with a single `attr` function which is equivalent to
writing `a.foo` in Python (`setattr` emulates `a.foo = v`).
As we build out the user-facing API for TorchScript values this will end
up matching how an attribute is accessed on general objects.
This PR preservers the python bindings for script::Module by emulating the
old API at the binding level. A followup will clean up the usage to more
directly match the C++ API.
Test Plan: Imported from OSS
Differential Revision: D18197611
Pulled By: zdevito
fbshipit-source-id: 7ee4dcbb258605d1c988314b05d938423f1ccee5
Summary:
Before, we would only give the key we are looking for (i.e. typically
just "No such serialized tensor 'weight'", no matter for which submodule
we were looking for a weight.
Now we error with "No such serialized tensor '0.conv1.weight'" or
similar.
The analogous information is added to missing module error messages.
I threw in a test, and it saved me already...
Pull Request resolved: https://github.com/pytorch/pytorch/pull/28499
Differential Revision: D18122442
Pulled By: yf225
fbshipit-source-id: a134b6d06ca33de984a11d6fea923244bcd9fb95
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/28039
Right now, torch::save() uses std::ostream, which results in unnecessary
data copies in practice. Similar for torch::load().
Adding a std::function<size_t(const void*, size_t)> as an output option,
parallel to the existing filename and std::ostream apis, gives users the
flexibility to emit directly to a backing store.
For a simple case of appending the output to a std::string, we observe
significant benchmark savings (on order of -50%), even with the
minor std::function<> dispatch overhead. The main reason is that
std::ostringstream effectively requires 2 extra copies of the data
beyond a simple string.append lambda.
We also provide a parallel api for the load(), though this one is
slightly more complex due to the need to do arbitrary position reads.
Test Plan:
buck test mode/dev-nosan caffe2/test/...
(Basic serialization test in caffe2/test/cpp/api/serialize.cpp)
Benchmark in experimental/jeremyl/c2/SerializationBench.cpp, with D17823443
(1M time goes from 90ms -> 40ms, albeit with crc patch applied)
Differential Revision: D17939034
fbshipit-source-id: 344cce46f74b6438cb638a8cfbeccf4e1aa882d7
Summary:
Right now, torch::save() uses std::ostream, which results in unnecessary
data copies in practice. Similar for torch::load().
Adding a std::function<size_t(const void*, size_t)> as an output option,
parallel to the existing filename and std::ostream apis, gives users the
flexibility to emit directly to a backing store.
For a simple case of appending the output to a std::string, we observe
significant benchmark savings (on order of -50%), even with the
minor std::function<> dispatch overhead. The main reason is that
std::ostringstream effectively requires 2 extra copies of the data
beyond a simple string.append lambda.
We also provide a parallel api for the load(), though this one is
slightly more complex due to the need to do arbitrary position reads.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/27586
Test Plan:
buck test mode/dev-nosan caffe2/test/...
(Basic serialization test in caffe2/test/cpp/api/serialize.cpp)
Benchmark in experimental/jeremyl/c2/SerializationBench.cpp, with D17823443
(1M time goes from 90ms -> 40ms, albeit with crc patch applied)
Differential Revision: D17822962
Pulled By: jjlilley
fbshipit-source-id: d344a7e59707f3b30d42280fbab78f87399e4d10
Summary:
This PR fixes https://github.com/pytorch/pytorch/issues/24192 by including the private field `iteration_` in SGD optimizer serialization. Under the hood, `iteration_` is serialized into an `IValue`, then stored in a JIT module as an attribute.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/26906
Differential Revision: D17628359
Pulled By: yf225
fbshipit-source-id: beec1367459e973a1c9080dc86f502e4c7bc5ebd
Summary:
This PR aims to re-organize C++ API `torch::nn` folder structure in the following way:
- Every module in `torch/csrc/api/include/torch/nn/modules/` (except `any.h`, `named_any.h`, `modulelist.h`, `sequential.h`, `embedding.h`) has a strictly equivalent Python file in `torch/nn/modules/`. For example:
`torch/csrc/api/include/torch/nn/modules/pooling.h` -> `torch/nn/modules/pooling.py`
`torch/csrc/api/include/torch/nn/modules/conv.h` -> `torch/nn/modules/conv.py`
`torch/csrc/api/include/torch/nn/modules/batchnorm.h` -> `torch/nn/modules/batchnorm.py`
`torch/csrc/api/include/torch/nn/modules/sparse.h` -> `torch/nn/modules/sparse.py`
- Containers such as `any.h`, `named_any.h`, `modulelist.h`, `sequential.h` are moved into `torch/csrc/api/include/torch/nn/modules/container/`, because their implementations are too long to be combined into one file (like `torch/nn/modules/container.py` in Python API)
- `embedding.h` is not renamed to `sparse.h` yet, because we have another work stream that works on API parity for Embedding and EmbeddingBag, and renaming the file would cause conflict. After the embedding API parity work is done, we will rename `embedding.h` to `sparse.h` to match the Python file name, and move the embedding options out to options/ folder.
- `torch/csrc/api/include/torch/nn/functional/` is added, and the folder structure mirrors that of `torch/csrc/api/include/torch/nn/modules/`. For example, `torch/csrc/api/include/torch/nn/functional/pooling.h` contains the functions for pooling, which are then used by the pooling modules in `torch/csrc/api/include/torch/nn/modules/pooling.h`.
- `torch/csrc/api/include/torch/nn/options/` is added, and the folder structure mirrors that of `torch/csrc/api/include/torch/nn/modules/`. For example, `torch/csrc/api/include/torch/nn/options/pooling.h` contains MaxPoolOptions, which is used by both MaxPool modules in `torch/csrc/api/include/torch/nn/modules/pooling.h`, and max_pool functions in `torch/csrc/api/include/torch/nn/functional/pooling.h`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/26262
Differential Revision: D17422426
Pulled By: yf225
fbshipit-source-id: c413d2a374ba716dac81db31516619bbd879db7f
Summary:
Currently, the Python API doesn't serialize layers that don't have weights (such as `nn.ReLU` and `nn.MaxPool2d`e.g. in https://github.com/pytorch/vision/blob/master/torchvision/models/densenet.py#L80-L81). If one saves a model that contains weight-less layers in Python and tries to load it into C++, the C++ module loading code (`torch::load(...)`) will throw an error complaining that the expected layers are not found in the serialized file (e.g. https://github.com/pytorch/vision/pull/728#issuecomment-480974175). This PR solves the problem by ignoring layers that are not serializable (which currently only include `nn::Functional`) in the C++ module serialization code (`torch::save(...)` and `torch::load(...)`), and the user is expected to use `nn::Functional` to wrap the weight-less layers so that they can be ignored when serializing / deserializing.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/19740
Differential Revision: D15100575
Pulled By: yf225
fbshipit-source-id: 956481a2355d1de45341585abedda05e35d2ee8b
Summary:
In the distributed training development work, we need to be able to serialize a `std::vector` of `torch::Tensor`s. This PR adds support for serializing `std::vector<torch::Tensor>`.
cc. mrshenli
Pull Request resolved: https://github.com/pytorch/pytorch/pull/19677
Differential Revision: D15069860
Pulled By: yf225
fbshipit-source-id: 505147e5f5fea78be1bf60fb8418bc187dbc2a98
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:
Fixes a bug where (de-)/serializing a hierarchy of submodules where one submodule doesn't have any parameters, but its submodules do, doesn't get properly loaded. This had to do with the fact that the old protobuf format couldn't store empty parameters.
Fixes https://github.com/pytorch/pytorch/issues/14891
soumith ezyang ebetica
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15033
Differential Revision: D13411322
Pulled By: goldsborough
fbshipit-source-id: 2ef73b2aa93fa9e46b1cbe1fd47d9f134d6016d5
Summary:
This is a stack PR based on https://github.com/pytorch/pytorch/pull/14454.
It enables the restoring the storage to appropriate device.
~~[TODO]: add/modify appropriate tests~~ Done
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14711
Reviewed By: dzhulgakov
Differential Revision: D13315746
Pulled By: houseroad
fbshipit-source-id: fe6f24a45c35e88fd1a2eebc09950d4430fac185
Summary:
This is a pre-cursor diff to Python <-> C++ frontend integration -- I have a follow-up PR coming for that. This PR changes the C++ frontend module interface to replace the custom "cursor"s I introduced some time ago with `OrderedDict`. I introduced cursors at the time as a convenient way of applying functions and query operations on a modules' parameters, buffers and modules, allowing things like `module.parameters().map(my_func)`. However, I noticed that (1) this functionality is easily implement-able on top of a regular data structure and (2) more importantly, using OrderedDicts is much, much easier for Python integration. This is especially true given that ScriptModule today also uses OrderedDict. Since C++ frontend modules and ScriptModules will soon too share as many implementation details as possible, it is overall the best move to ditch the custom cursor datastructure and pervasively use OrderedDict everywhere.
For this I did:
1. Changed the C++ frontend module interface to more closely match the Python one by providing `parameters()`, `named_parameters()` and other methods Python provides. This is very important for the following diff which binds these into Python for inter-op with Python modules.
2. In lieu of the `Cursor::apply()` method I added `nn::Module::apply`. This again is one more unifying step between Python and C++, since Python modules have an apply function too.
3. Deleted all uses of Cursor.
4. Tidied and beefed up the `OrderedDict` class. In particular, I made `OrderedDict::Item` store an `std::pair` under the hood, because that is trivial to bind into Python and saved me a lot of headaches. `key` and `value` become methods instead of fields, which they should have been from the very start anyway because it allows exactly these kinds of changes, as per usual good software engineering principle of encapsulation.
5. Added many tests for the OrderedDict use in `nn::Module`.
ebetica ezyang
Pull Request resolved: https://github.com/pytorch/pytorch/pull/13427
Differential Revision: D12894092
Pulled By: goldsborough
fbshipit-source-id: 715770c95a9643753a1db26d7f9da9a78619a15d
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
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:
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
Summary:
This PR serves two purposes:
1. Design an abstraction over a serialization scheme for C++ modules, optimizers and tensors in general,
2. Add serialization to the ONNX/PyTorch proto format.
This is currently a rough prototype I coded up today, to get quick feedback.
For this I propose the following serialization interface within the C++ API:
```cpp
namespace torch { namespace serialize {
class Reader {
public:
virtual ~Reader() = default;
virtual void read(const std::string& key, Tensor& tensor, bool is_buffer = false) = 0;
virtual void finish() { }
};
class Writer {
public:
virtual ~Reader() = default;
virtual void writer(const std::string& key, const Tensor& tensor, bool is_buffer = false) = 0;
virtual void finish() { }
};
}} // namespace torch::serialize
```
There are then subclasses of these two for (1) Cereal and (2) Protobuf (called the "DefaultWriter" and "DefaultReader" to hide the implementation details). See `torch/serialize/cereal.h` and `torch/serialize/default.h`. This abstraction and subclassing for these two allows us to:
1. Provide a cereal-less serialization forward that we can ship and iterate on going forward,
2. Provide no-friction backwards compatibility with existing C++ API uses, mainly StarCraft.
The user-facing API is (conceptually):
```cpp
void torch::save(const Module& module, Writer& writer);
void torch::save(const Optimizer& optimizer, Writer& writer);
void torch::read(Module& module, Reader& reader);
void torch::read(Optimizer& optimizer, Reader& reader);
```
with implementations for both optimizers and modules that write into the `Writer` and read from the `Reader`
ebetica ezyang zdevito dzhulgakov
Pull Request resolved: https://github.com/pytorch/pytorch/pull/11619
Differential Revision: D9984664
Pulled By: goldsborough
fbshipit-source-id: e03afaa646221546e7f93bb8dfe3558e384a5847
