Fix Caffe2_CPU_INCLUDE with Caffe2_GPU_INCLUDE. The expanding parent scope should be with the same variable name. The compilation in certain build configurations is corrected with this fix.
Fixes #ISSUE_NUMBER
Pull Request resolved: https://github.com/pytorch/pytorch/pull/87030
Approved by: https://github.com/kit1980
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/57366
We often get error messages such as
```
Model failed AOT (glow ahead-of-time compilation) with exception: Error during AOT optimization (non-provisioned addNetwork):
Non-recoverable device error when adding network:
Error code: PARTITIONER_ERROR
Error message: Did not find a partition with an SLS node
Error return stack:
--------------------------------------------------------------------------------
glow/glow/lib/Partitioner/Partitioner.cpp:1244
--------------------------------------------------------------------------------
glow/glow/lib/Runtime/HostManager/HostManager.cpp:375
--------------------------------------------------------------------------------
```
This makes the error message more clear by checking for the number of OnnixifiOp created before going into Glow. The check is enabled with the `verify_only_single_subnet` flag, and is disabled by default.
Test Plan: Unit tests pass
Reviewed By: khabinov
Differential Revision: D28097674
fbshipit-source-id: 0eefd8f6ec1a82546b759be8e541256bf271a673
Summary:
Since caffe2 and torch have been consolidated, CAFFE2_API should be merged with TORCH_API. Addresses a TODO.
Manually edited some references of the removed `CAFFE2_API`:
* `CONTRIBUTING.md`
* `caffe2/proto/CMakeLists.txt`
* `cmake/ProtoBuf.cmake`
* `c10/macros/Export.h`
* `torch/csrc/WindowsTorchApiMacro.h`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/49496
Reviewed By: malfet, samestep
Differential Revision: D25600726
Pulled By: janeyx99
fbshipit-source-id: 7e068d959e397ac183c097d7e9a9afeca5ddd782
Summary:
This PR makes Caffe2 compatible with TensorRT 6. To make sure it works well, new unit test is added. This test checks PyTorch->ONNX->TRT6 inference flow for all classification models from TorhchVision Zoo.
Note on CMake changes: it has to be done in order to import onnx-tensorrt project. See https://github.com/pytorch/pytorch/issues/18524 for details.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/26426
Reviewed By: hl475
Differential Revision: D17495965
Pulled By: houseroad
fbshipit-source-id: 3e8dbe8943f5a28a51368fd5686c8d6e86e7f693
Summary:
After fixing https://github.com/pytorch/pytorch/issues/20774 the TRT build was broken
Because of missing annotations, pybind_state_gpu.so was missing symbols, but pybind_state.so did not. It caused a weird combination when trying to import pybind_state_gpu first left system in semi-initialized state and lead to sigsev.
Minimal repro:
```
>>> import ctypes
>>> ctypes.CDLL('/var/lib/jenkins/.local/lib/python2.7/site-packages/caffe2/python/caffe2_pybind11_state_gpu.so')
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/usr/lib/python2.7/ctypes/__init__.py", line 362, in __init__
self._handle = _dlopen(self._name, mode)
OSError: /var/lib/jenkins/.local/lib/python2.7/site-packages/caffe2/python/caffe2_pybind11_state_gpu.so: undefined symbol: _ZN6caffe219TensorRTTransformer9TransformEPNS_9WorkspaceEPNS_6NetDefERKSt13unordered_mapINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEENS_11TensorShapeESt4hashISB_ESt8equal_toISB_ESaISt4pairIKSB_SC_EEE
>>> ctypes.CDLL('/var/lib/jenkins/.local/lib/python2.7/site-packages/caffe2/python/caffe2_pybind11_state.so')
Segmentation fault (core dumped)
```
Too lazy to repro locally, let's see if CI passes
Pull Request resolved: https://github.com/pytorch/pytorch/pull/21775
Differential Revision: D15829605
Pulled By: dzhulgakov
fbshipit-source-id: 1adb2bde56b0cd68f84cfca67bc050adcf787cd9
Summary:
There are still a few work to be done:
- Move logging and unify AT_WARN with LOG(ERROR).
- A few header files are still being plumbed through, need cleaning.
- caffe2::EnforceNotMet aliasing is not done yet.
- need to unify the macros. See c10/util/Exception.h
This is mainly a codemod and not causing functional changes. If you find your job failing and trace back to this diff, usually it can be fixed by the following approaches:
(1) add //caffe2/c10:c10 to your dependency (or transitive dependency).
(2) change objects such as at::Error, at::Optional to the c10 namespace.
(3) change functions to the c10 namespace. Especially, caffe2::MakeString is not overridden by the unified c10::str function. Nothing else changes.
Please kindly consider not reverting this diff - it involves multiple rounds of rebasing and the fix is usually simple. Contact jiayq@ or AI Platform Dev for details.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/12354
Reviewed By: orionr
Differential Revision: D10238910
Pulled By: Yangqing
fbshipit-source-id: 7794d5bf2797ab0ca6ebaccaa2f7ebbd50ff8f32
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:
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:
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:
ONNX-TensorRT is still using old opset (<7). Patch it for now.
Future fix would be expose versioning in onnx exporter.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/9285
Reviewed By: houseroad
Differential Revision: D8775268
Pulled By: yinghai
fbshipit-source-id: c272073f80cce35ebd971e44ec9472e3c8fd4b9e
* Add support to TensorRT
* Removed License header
* Bind input/output by position
* Comments
* More comments
* Add benchmark
* Add warning for performance degradation on large batch
* Address comments
* comments
