Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/37094
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
Test Plan: Imported from OSS
Differential Revision: D21202892
Pulled By: ezyang
fbshipit-source-id: d59e6bffabd90cc734056bdce2cd1fe63262fab8
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/30915
Since we now have C++14, we don't need these c10::guts helpers anymore
ghstack-source-id: 95777609
Test Plan: waitforsandcastle
Differential Revision: D18869639
fbshipit-source-id: 97716f932297c64c6e814410ac47b444c33d4e2e
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/22429
Android NDK r20 removes the guard `(__ANDROID_API__ <= __ANDROID_API_O_MR1__)`, so we do it here also. There is insufficient reason to keep these decls undefined for earlier API levels. NDK r15 and earlier don't even define `__ANDROID_API_O_MR1__`, so the preprocessor defaults it to 0 and the guard evaluates as TRUE.
Reviewed By: smeenai, hlu1
Differential Revision: D16084105
fbshipit-source-id: f0857b3eb0573fe219f0d6c5e6583f89e2b5518f
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17764
Original commit changeset: f1923fdca4a1
reverted int8 ops fixes the original runtime regression.
We'll ignore the memory regression since it is flaky, see D14228484
Reviewed By: dzhulgakov
Differential Revision: D13885233
fbshipit-source-id: ccbe4b94acb44b7b4cb3ae4d73e3f6091e1e1195
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17481
Usually, feature macros are either defined or undefined and checked accordingly.
C10_MOBILE was a weird special case that was always defined but either defined to 1 or to 0.
This caused a lot of confusion for me when trying to disable something from mobile build and it also disabled it
from the server build (because I was using ifdef). Also, I found a place in the existing code base that made
that wrong assumption and used the macro wrongly, see https://fburl.com/y4icohts
Reviewed By: dzhulgakov
Differential Revision: D14214825
fbshipit-source-id: f3a155b6d43d334e8839e2b2e3c40ed2c773eab6
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16273
Previously we have SetOutputSize which accept a partially initialized Output Tensor and set it to the correct size,
the diff change this to GetOutputSize that returns the correct size instead.
e.g.
```
auto* Y = Output(0);
ConvPoolOp<Context>::SetOutputSize(X, Y, channels);
...
Y->mutable_data<T>...
```
-->
```
auto sizes = ConvPoolOp<Context>::GetOutputSize(X, channels);
auto* Y = Output(0, sizes, at::dtype<T>());
```
Reviewed By: dzhulgakov
Differential Revision: D13736281
fbshipit-source-id: 64abce3dbaed0b375098463333dfd0ea5a3b1945
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15322
caffe2 mobile opengl code is not used, deleting it to reduce complications when we perform other changes
Reviewed By: Maratyszcza
Differential Revision: D13499943
fbshipit-source-id: 6479f6b9f50f08b5ae28f8f0bc4a1c4fc3f3c3c2
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14269
Removes reference to Context proper and instead adds a bool argument for async copy (the same as `copy_`)
For CopyFrom - I haven't tweaked all callsites yet. Instead I rely on a terrible hack that pointer to context is implicitly converted to bool when passed, haha :) It's not a good code and I propose to fix it in a follow up diff (maybe using clangr tooling).
Reviewed By: ezyang
Differential Revision: D13117981
fbshipit-source-id: 7cb1dc2ba6a4c50ac26614f45ab8318ea96e3138
Summary:
Hi guys,
I'd like to build Caffe2 with more supported options in Windows with Microsoft Visual Studios.
This is the first pull request.
Running scripts/build_windows_shared.bat is able to build Caffe2 with both CMAKE_BUILD_TYPE=Debug and CMAKE_BUILD_TYPE=Release with Visual Studio 14 2015.
CUDA is 9.0, cudnn is 7.0.5, glog, gflags and lmdb are supported on my system.
Python is 3.5, Detectron works from python interface as well.
It was even possible to debug detectron code and step into caffe2_gpu.dll with pdbs built.
What is disappointing, that c10/experimental ops don't build with this Visual Studio generator, I added special option INCLUDE_EXPERIMENTAL_C10_OPS (default ON) to deal with it in build_windows_shared.bat.
After this pull request the next step is to add Visual Studio 2017 support in the script.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/13550
Reviewed By: ezyang
Differential Revision: D13042597
Pulled By: orionr
fbshipit-source-id: f313f909f599cd582a1d000eff766eef3a9fc4fc
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/13689
Now that typeid.h lives in c10/util, the include paths should reflect that.
Reviewed By: ezyang
Differential Revision: D12912237
fbshipit-source-id: e54225f049f690de77cb6d5f417994b211a6e1fb
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/12714
This is a short change to enable c10 namespace in caffe2. We did not enable
it before due to gflags global variable confusion, but it should have been
mostly cleaned now. Right now, the plan on record is that namespace caffe2 and
namespace aten will fully be supersets of namespace c10.
Most of the diff is codemod, and only two places of non-codemod is in caffe2/core/common.h, where
```
using namespace c10;
```
is added, and in Flags.h, where instead of creating aliasing variables in c10 namespace, we directly put it in the global namespace to match gflags (and same behavior if gflags is not being built with).
Reviewed By: dzhulgakov
Differential Revision: D10390486
fbshipit-source-id: 5e2df730e28e29a052f513bddc558d9f78a23b9b
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/12180
I had to fix a lot of call sites, because a lot of places assume that
you can actually get a const vector&, and if the internal representation
of sizes in a tensor is NOT a vector, it's not possible to fulfill
this API contract.
Framework changes:
- I deleted TensorImpl::dims(); caffe2::Tensor::dims() just forwards to
sizes() now.
- De-templatized SetDims; now it is an explicit list of ArrayRef and
variadic overloads. This makes implicit conversions work again,
so I don't need to explicitly list the std::vector cases too.
- As a knock-on effect, this causes Reset() to accept at::IntList as well as
const std::vector<int64_t>&
- Edited variadic overloads of SetDims to all forward to the underlying
arbitrary-dim implementation, reducing code duplication. (It's probably
marginally less efficient in the new world.)
- Replace Tensor constructor accepting const std::vector<int64_t>& with at::IntList
- Make MKLTensor accept ArrayRef along with vector in constructor and
Reset (unfortunately, no implicit conversions here, since it's templated on
index type.)
- There are a few other places, like cudnn, where I changed functions
that previously took const std::vector<int64_t>& to take at::IntList
instead.
Classification of call site changes:
- 'const std::vector<int64_t>& x_dims = x.dims()' ==>
'at::IntList x_dims = x.dims()'
- 'std::vector<int64_t> x_dims = x.dims()' ==>
'std::vector<int64_t> x_dims = x.dims().vec()' (we need a copy!)
Usually this is because we're about to mutably modify the vector
to compute some new dimension. However, it also very commonly occurs in the
form: 'x_dims_ = x.dims()' because we frequently cache sizes in operators.
- Instead of constructing std::vector<int64_t>{blah, blah}, construct an
at::IntList directly
ArrayRef changes:
- cbegin()/cend() iterators, they operate the same aas begin()/end() because
everything on ArrayRef is const.
- Moved operator<< into ArrayRef.h, so that it's always available when
working with ArrayRef. I also templated it, so it now works on an
ArrayRef of any type.
- Add operator== overload for ArrayRef, and also add variants to permit
comparison of ArrayRef with std::vector, a very common operation.
(The non-templated version of operator== can get these automatically
via implicit conversion, but with templates C++ refuses to do
any explicit conversions.)
I'm planning to audit all dims() call sites to make sure they don't
expect 'auto x = t.dims()' to give you an x whose lifetime can validly
outlive the tensor.
I opted not to do a dims() to sizes() rename, because dims() also matches
the protobufs accessor. Bad news!
Reviewed By: jerryzh168
Differential Revision: D10111759
fbshipit-source-id: a2a81dc4b92c22ad4b3b8ef4077a7e97b6479452
Summary:
This does 6 things:
- add c10/util/Registry.h as the unified registry util
- cleaned up some APIs such as export condition
- fully remove aten/core/registry.h
- fully remove caffe2/core/registry.h
- remove a bogus aten/registry.h
- unifying all macros
- set up registry testing in c10
Also, an important note that we used to mark the templated Registry class as EXPORT - this should not happen, because one should almost never export a template class. This PR fixes that.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/12077
Reviewed By: ezyang
Differential Revision: D10050771
Pulled By: Yangqing
fbshipit-source-id: 417b249b49fed6a67956e7c6b6d22374bcee24cf
Summary:
TSIA. Right now we should basically use C10_EXPORT and C10_IMPORT for explicitly marking dllexport and dllimport, as a continued effort of the C10 unification.
This is a codemod by mechanically doing the following change:
CAFFE2_{EXPORT,IMPORT} -> C10_{EXPORT,IMPORT}
AT_CORE_{EXPORT,IMPORT} -> C10_{EXPORT,IMPORT}
Pull Request resolved: https://github.com/pytorch/pytorch/pull/12019
Reviewed By: ezyang, teng-li
Differential Revision: D10016276
Pulled By: Yangqing
fbshipit-source-id: a420d62c43d1110105fc88f9e9076e28a3203164
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/12043
Re-trying D9979976, this time with all call sites fixed.
D9979976 got reverted because there was a call site that wasn't covered by sandcastle it seems.
I fixed it and used 'grep' to ensure there aren't any more call sites in fbsource.
Reviewed By: ezyang
Differential Revision: D10026392
fbshipit-source-id: cd341514a8e53a40147ea0ee3e52f63bb6444157
Summary: The controller you requested could not be found. Original commit changeset: 2ea17724e223
Differential Revision:
D10026321
Ninja: stable broken
fbshipit-source-id: faf87cb7cc0f78c2c10d4aa6fceea279cd27acd6
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/11923
This is pre-work to allow moving Blob to ATen/core, which cannot depend on caffe2 anymore.
(1) Removing the Blob -> Tensor dependency allows us to move Blob to ATen/core and use it inside IValue without having to wait for the Tensor merge to be complete.
(2) In the final Blob design, we want it to be a very small class that doesn't have any special treatment for Tensor (or to be more correct, doesn't allow storing Tensor anymore), so this is anyhow the direction we want to go.
This changes call sites that will have to be moved to IValue later, but they cannot be moved to IValue directly, because for that, IValue first needs to be able to store Blob, which in turn first needs this diff and some other changes coming up in future diffs.
Codemods:
$ codemod --extensions h,hpp,c,cpp,cc "([a-zA-Z0-9_]+)\\.IsTensorType\\(" "BlobIsTensorType(\\1, "
$ codemod --extensions h,hpp,c,cpp,cc "([a-zA-Z0-9_]+)->IsTensorType\\(" "BlobIsTensorType(*\\1, "
$ codemod --extensions h,hpp,c,cpp,cc "([a-zA-Z0-9_]+)\\.GetMutableTensor\\(" "BlobGetMutableTensor(\\1, "
$ codemod --extensions h,hpp,c,cpp,cc "([a-zA-Z0-9_]+)->GetMutableTensor\\(" "BlobGetMutableTensor(*\\1, "
It is, however, not only these codemods because regex based refactoring was only able to match a small amount of the call sites. To catch more, I wouldn've needed a AST aware tool like clangr, which I didn't figure out how to use.
Reviewed By: ezyang
Differential Revision: D9979976
fbshipit-source-id: 2ea17724e223b5b73b44f99362727759ca689e61
Summary:
Old per-API+arch headers reside in
/opt/android_ndk/r*/platforms/android-*/arch-*/usr/include/
New Unified headers reside in
/opt/android_ndk/r*/sysroot/usr/include/
Unified headers are not exactly drop-in replacements for the old ones. Old headers had some nested includes that are absent in the unified versions, so we need to explicitly include them.
Reviewed By: mzlee
Differential Revision: D9952200
fbshipit-source-id: 6515e1d1ab576069db499c3fb23a69d507279c8c
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/11167
Narrow the Blob API as preparation for merging Blob/IValue
- get rid of templated IsType and Operator::InputIsType / OutputIsType
- Use 'using' instead of 'typedef' for DestroyCall (just for readability)
Reviewed By: ezyang
Differential Revision: D9623916
fbshipit-source-id: 952f0b0cf5a525094b02e8d2798dd57a56a9e1d8
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/11254
Previously we use DeviceType in caffe2.proto directly, but it's an `enum` and have implicit conversion to int, which does not have type safety, e.g. we have to explicitly check for a device type is valid in event.h:
```
template <int d>
struct EventCreateFunctionRegisterer {
explicit EventCreateFunctionRegisterer(EventCreateFunction f) {
static_assert(d < MaxDeviceTypes, "");
Event::event_creator_[d] = f;
}
};
```
at::DeviceType is an `enum class`, and it does not have implicit conversion to int, and provides better type safety guarantees. In this diff we have done the following refactor(taking CPU as an example):
1. caffe2::DeviceType → caffe2::DeviceTypeProto
2. caffe2::CPU → caffe2::PROTO_CPU
3. caffe2::DeviceType = at::DeviceType
4. caffe2::CPU = at::DeviceType::CPU
codemod -d caffe2/caffe2 --extensions h,cc,cpp 'device_type\(\), ' 'device_type(), PROTO_'
+ some manual changes
In short, after this diff, in c++, caffe2::CPU refers to the at::DeviceType::CPU and the old proto caffe2::CPU will be caffe2::PROTO_CPU.
In python side, we have a temporary workaround that alias `caffe2_pb2.CPU = caffe2_pb2.PROOT_CPU` to make the change easier to review and this will be removed later.
Reviewed By: ezyang
Differential Revision: D9545704
fbshipit-source-id: 461a28a4ca74e616d3ee183a607078a717fd38a7
Summary: Update all the caller for the new interface
Reviewed By: highker
Differential Revision: D9323167
fbshipit-source-id: a39335ceb402db0719f5f2314085ba9a81380308
Summary:
This was used as a convenient way for us to convert c1 models. Now that conversion is more or less done, we should probably require any users who need to convert c1 models to explicitly install c1. This PR removes the explicit c1 proto (which was copied from c1) in favor of explicit installation.
Note that caffe_translator would still work properly, only difference is that now users need to install c1 separately.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/10380
Differential Revision: D9267981
Pulled By: Yangqing
fbshipit-source-id: a6ce5d9463e6567976da83f2d08b2c3d94d14390
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/10261
1. Reserve
Currently, Reserve will allocate new memory and old data in the tensor is also preserved,
and Resize is relying on this behavior in some call-site, e.g. https://github.com/pytorch/pytorch/blob/master/caffe2/operators/reservoir_sampling.cc#L103, where we should be using Extend.
We want to bring semantics of Reserve to be more aligned with std::vector, i.e. we want it to be
an optimization about memory allocation and remove the semantics about preserving the data. We'll remove the guarantee that data will be preserved after Reserve, and Extend will be the only API that preserves old data when we do in-place extension of memory. This also helps with the later refactoring on split Storage from Tensor.
Also, we'll only pass in the outer dimension to Reserve which means the later dimensions should be set before we call Reserve.
2. Extend/Shrink
Previously, Extend actually means ExtendBy and Shrink means ShrinkTo, I would like to add a ExtendTo for convenience, and change Shrink to ShrinkTo.
Old functions calling Extend is still there, although it actually means Extend by, but I think it still makes sense to have it.
3. Usage Patterns
The expected usage patterns right now is:
```
t->Resize({0, 32, 32, 32});
t->template mutable_data<T>(); // set meta_
t->Reserve(100);
auto* t_data = t->template mutable_data<T>();
// feed data to tensor using t_data
for (int i = 0; i < 100; ++i) {
t->Extend(1, 50, &context_);
// you can continue to use t_data if you have reserved enough space
// otherwise, you should call t->template mutable_data<T> again to
// get the new data pointer since Extend will allocate new memory even
// though the original data is preserved.
}
```
Reviewed By: ezyang
Differential Revision: D9128147
fbshipit-source-id: e765f6566d73deafe2abeef0b2cc0ebcbfebd096
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/9939
Pull Request resolved: https://github.com/facebookresearch/weakly-supervised-action-detection/pull/13
Pull Request resolved: https://github.com/pytorch/translate/pull/166
Pull Request resolved: https://github.com/pytorch/pytorch/pull/9125
Closes https://github.com/pytorch/pytorch/pull/9125
Use inheritance for polymorphism, and remove template parameter
This is to change the templating in call sites, the core implementations will change later
Before Caffe2 Tensor class was compile-time fixed to bind to a particular device/context. With this change, we're making it a runtime property (stored inside the tensor), but preserve the same semantics. For example, one has to specify device type in order to create a Tensor - there are no uninitialized tensors. More specifically the changes are:
1. We added an extra argument *DeviceType* to most of the constructors of the tensor, e.g. (Tensor(DeviceType type)),
2. Semantics of constructor Tensor(const Tensor<SrcContext>& src, ContextForCopy* context); is changed, in this constructor, the second context is passed in to enable us to call the templated Copy function, it could be in a different context as source and target previously, now we'll enforce that the context should have same device type as src, if it is provided.
3. To preserve 'get-or-construct' semantics of Blob, we added specialized getter Blob::GetMutableTensor that verifies both that Blob contains a Tensor and that it's of a correct type
4. Specifically, Tensor type is not default-constructible any more (as we don't have unknown device tensors) and thus some of the code handling STL containers needs to change
Note: Some changes are postponed just to keep this diff a bit smaller. Please see `TODO`s.
Reviewed By: ezyang, houseroad
Differential Revision: D9024330
fbshipit-source-id: e0b8295d2dc6ebe2963383ded5af799ad17164ba
Summary:
Pull Request resolved: https://github.com/facebookresearch/weakly-supervised-action-detection/pull/13
Pull Request resolved: https://github.com/pytorch/translate/pull/166
Pull Request resolved: https://github.com/pytorch/pytorch/pull/9125
Closes https://github.com/pytorch/pytorch/pull/9125
Use inheritance for polymorphism, and remove template parameter
This is to change the templating in call sites, the core implementations will change later
Before Caffe2 Tensor class was compile-time fixed to bind to a particular device/context. With this change, we're making it a runtime property (stored inside the tensor), but preserve the same semantics. For example, one has to specify device type in order to create a Tensor - there are no uninitialized tensors. More specifically the changes are:
1. We added an extra argument *DeviceType* to most of the constructors of the tensor, e.g. (Tensor(DeviceType type)),
2. Semantics of constructor Tensor(const Tensor<SrcContext>& src, ContextForCopy* context); is changed, in this constructor, the second context is passed in to enable us to call the templated Copy function, it could be in a different context as source and target previously, now we'll enforce that the context should have same device type as src, if it is provided.
3. To preserve 'get-or-construct' semantics of Blob, we added specialized getter Blob::GetMutableTensor that verifies both that Blob contains a Tensor and that it's of a correct type
4. Specifically, Tensor type is not default-constructible any more (as we don't have unknown device tensors) and thus some of the code handling STL containers needs to change
Note: Some changes are postponed just to keep this diff a bit smaller. Please see `TODO`s.
Reviewed By: xw285cornell
Differential Revision: D8121878
fbshipit-source-id: 4a5e9a677ba4ac82095df959851a054c81eccf81
