Summary:
Set value as tensor of 1 element instead of scalar, according to ONNX spec.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18199
Reviewed By: dzhulgakov
Differential Revision: D14542588
Pulled By: houseroad
fbshipit-source-id: 70dc978d870ebe6ef37c519ba4a20061c3f07372
Summary:
More ops for https://github.com/pytorch/pytorch/issues/394. ~~Also need to rebase after landing #16186, because we need to update the whitelist of the new unit test added in #16186.~~
cc: ezyang
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17093
Differential Revision: D14620068
Pulled By: ezyang
fbshipit-source-id: deec5ffc9bf7624e0350c85392ee59789bad4237
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18160
When exposing a c10 operator to the caffe2 frontend, don't use the operator schema but use the operator name instead.
This allows us to get rid of the existing mechanism for operator schema registration in a diff stacked on top.
Reviewed By: dzhulgakov
Differential Revision: D14513420
fbshipit-source-id: 6b08a9c6d9497eaf18b62361dd44bc07c7b4b76b
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18242
ghimport-source-id: b949d312a48226a34f90304162e910acee7c95cd
Stack from [ghstack](https://github.com/ezyang/ghstack):
* **#18242 Test running a CUDA build on CPU machine.**
* #18362 Add ability to query if built with CUDA and MKL-DNN.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
Differential Revision: D14584429
fbshipit-source-id: b54de5b33f0c795a7d9605d30576cdf9b74050fd
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18485
I don't know how (1) we landed the wrong version of the patch and (2) how
this passed the push blocking test
Reviewed By: pjh5
Differential Revision: D14621961
fbshipit-source-id: 0a3953d7adcdc79727a61c2acff65f436dcafe55
Summary:
This PR adds a Global Site Tag to the site.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17690
Differential Revision: D14620816
Pulled By: zou3519
fbshipit-source-id: c02407881ce08340289123f5508f92381744e8e3
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18165
ghimport-source-id: 55cb3fb63a25c2faab1725b4ec14c688bf45bd38
Stack from [ghstack](https://github.com/ezyang/ghstack):
* #18166 Bool Tensor for CUDA
* **#18165 Resolved comments from Bool Tensor for CPU PR**
-------
------------
This is a follow up PR that resolves some additional feedback on one the of previous Bool Tensor PRs.
gchanan, here is a list of almost all the comments from the original PR with respective fixes and replies:
**[utils/python_scalars.h]** why is this converting from uint8_t and not bool? (comment?)
When i was adding this, i was testing by creating a tensor and then calling its .tolist(). it worked for bool and uint8_t equally good so i left uint8_t as thought it makes more sense as we are calling PyBool_FromLong. �Changing it to bool.
**[ATen/Dispatch.h]**better name?.
fixed.
**[test/test_torch.py]** what about other factories, such as full? (and more).
There is a test that goes through the factory methods - test_tensor_factories_empty. i added some bool cases above it and added a comment that once CUDA will be done, i will unite them and it will iterate not just between CUDA and CPU but also all types. ��Adding all bool cases now. Will unite in CUDA PR.
**[generic/THTensorMath.h]** any changes in this file actually needed?
Bad merge. Fixed.
**[TH/THTensor.h]** this generates code for random, clampedRandom, and cappedRandom -- do we have tests for all of these with bool?
Added
**[c10/core/ScalarType.h]** I'm not very confident about the lack of Bool here -- can you look at the call sites and see what makes sense to do here?
Added bool to the macro and created a similar one without for a single case which fails the build with errors:
_./torch/csrc/jit/symbolic_variable.h:79:20: error: ambiguous overload for ‘operator*’ (operand types are ‘const torch::jit::SymbolicVariable’ and ‘torch::jit::Value*’)
return (*this) * insertConstant(rhs);_
Differential Revision: D14605105
fbshipit-source-id: abf82d50e8f8c50b386545ac068268651b28496d
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18445
ghimport-source-id: 30d018737bf6989bc68b7e3676f44e0ca6141fde
Stack from [ghstack](https://github.com/ezyang/ghstack):
* #18242 Test running a CUDA build on CPU machine.
* **#18445 Unify cudaGetDeviceCount implementations.**
I went about doing this by searching for calls to cudaGetDeviceCount,
and then methodically replacing them with references to c10::cuda::device_count()
or at::cuda::device_count().
There is a point to doing this: the various implementations wildly differed
in their handling of what to do when cudaGetDeviceCount returns an error.
The final standardized behavior is that **all errors are swallowed** and
we return device count of zero. This indirectly fixes running CUDA builds
on CPU, which was broken in #17847.
I added 'noexcept' to the 'deviceCount' virtual method on DeviceGuardImpl.
This is a BC-breaking change for anyone inheriting from DeviceGuardImpl
but all you need to do is put 'noexcept' on your method and it is backwards
compatible with older libtorch.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
Differential Revision: D14612189
fbshipit-source-id: 3c8d186e3dd623c0e27625212c7ce30f75d943cb
Summary:
`SobolEngine` is a quasi-random sampler used to sample points evenly between [0,1]. Here we use direction numbers to generate these samples. The maximum supported dimension for the sampler is 1111.
Documentation has been added, tests have been added based on Balandat 's references. The implementation is an optimized / tensor-ized implementation of Balandat 's implementation in Cython as provided in #9332.
This closes#9332 .
cc: soumith Balandat
Pull Request resolved: https://github.com/pytorch/pytorch/pull/10505
Reviewed By: zou3519
Differential Revision: D9330179
Pulled By: ezyang
fbshipit-source-id: 01d5588e765b33b06febe99348f14d1e7fe8e55d
Summary:
Simplify or eliminate boolean and/or expressions, optimize unwrapping a value that cannot be None, and optimize using `is` with a None and a non-None value
Since peephole optimize is now introducing constants, i added another constant propagation pass after running it.
Previously i had a PR that did this & optimized shape ops - i will add the shape optimizations in a separate PR.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18259
Differential Revision: D14602749
Pulled By: eellison
fbshipit-source-id: 1c3f5a67067d8dfdf55d7b78dcb616472ea8a267
Summary:
Fixes: https://github.com/pytorch/pytorch/issues/12598
This PR was originally authorized by ptrblck at https://github.com/pytorch/pytorch/pull/15495, but since there was no update for months after the request change, I clone that branch and resolve the code reviews here. Hope everything is good now. Especially, the implementation of count is changed from ptrblck's original algorithm to the one ngimel suggest, i.e. using `unique_by_key` and `adjacent_difference`.
The currently implementation of `_unique_dim` is VERY slow for computing inverse index and counts, see https://github.com/pytorch/pytorch/issues/18405. I will refactor `_unique_dim` in a later PR. For this PR, please allow me to keep the implementation as is.
cc: ptrblck ezyang ngimel colesbury
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18391
Reviewed By: soumith
Differential Revision: D14605905
Pulled By: VitalyFedyunin
fbshipit-source-id: 555f5a12a8e28c38b10dfccf1b6bb16c030bfdce
Summary:
Dropout is now eligible for fusion, and generated fused kernels are just as fast as dropout in ATen. Change its lowering in symbolic script so that it can actually be fused. Still special-cased for cuda, because without fusion this lowering is less efficient than current (bernoulli_ * input). Testing is covered by the test case that ailzhang added (test_dropout_cuda).
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18375
Differential Revision: D14611938
Pulled By: soumith
fbshipit-source-id: 11b18f4784e6c9265e382a8f8deca7add8df3b37
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18155
- Make a python decorator caffe2_flaky for caffe2 operator unit tests.
- The environment variable CAFFE2_RUN_FLAKY_TESTS are now used to mark flaky test mode
During test run,
- If flaky tests mode are on, only flaky tests are run
- If flaky tests mode are off, only non-flaky tests are run
Mark ctc_beam_search_decoder_op_test as flaky
Reviewed By: ezyang, salexspb
Differential Revision: D14468816
fbshipit-source-id: dceb4a48daeb5437ad9cc714bef3343e9761f3a4
Summary:
This PR did two things:
1. Enable scalar->float specialization in symbolic script, so AD formula that contains scalar in the schema, should write `float` instead.
2. add addcmul, lerp to AD and fuser.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18081
Differential Revision: D14490493
Pulled By: wanchaol
fbshipit-source-id: b3b86d960d5f051b30733bc908b19786111cdaa4
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18362
ghimport-source-id: 374b7ab97e2d6a894368007133201f510539296f
Stack from [ghstack](https://github.com/ezyang/ghstack):
* #18242 Test running a CUDA build on CPU machine.
* **#18362 Add ability to query if built with CUDA and MKL-DNN.**
Fixes#18108.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
Differential Revision: D14584430
fbshipit-source-id: 7605a1ac4e8f2a7c70d52e5a43ad7f03f0457473
Summary:
This is to fix#16141 and similar issues.
The idea is to track a reference to every shared CUDA Storage and deallocate memory only after a consumer process deallocates received Storage.
ezyang Done with cleanup. Same (insignificantly better) performance as in file-per-share solution, but handles millions of shared tensors easily. Note [ ] documentation in progress.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16854
Differential Revision: D13994490
Pulled By: VitalyFedyunin
fbshipit-source-id: 565148ec3ac4fafb32d37fde0486b325bed6fbd1
Summary:
Also asserts in storage_initialized that there is a storage.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18347
Differential Revision: D14582028
Pulled By: gchanan
fbshipit-source-id: df3f5d181188f39e361839169fd054539c3b2839
Summary:
There's no reason we can't check this, but I'm punting on implementing it for now. But it currently segfaults, so this is an improvements.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18338
Differential Revision: D14580308
Pulled By: gchanan
fbshipit-source-id: 44d4cafeab12e1beeb3453a2d4068d221c2e9c4f
Summary:
Previously it would look for the Config even if it was not written.
Fixed#18419
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18421
Differential Revision: D14597139
Pulled By: ezyang
fbshipit-source-id: c212cbf5dc91564c12d9d07e507c8285e11c6bdf
Summary:
This reverts commit 7cc7ed1322405ba3c627b9c5661a330f92c4183d.
I think it's better to sort out the issues raised in #18407 firs. I'm sorry for not stopping it earlier.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18411
Differential Revision: D14594937
Pulled By: soumith
fbshipit-source-id: 3c90b7fa7694e2f59e55607acecde4a47af801ea
Summary:
Sorry for not sending these fixes in a single PR. I found this compiler warning when I was working on something else, and I just go to GitHub and modify the file directly for convenience...
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18406
Differential Revision: D14594180
Pulled By: soumith
fbshipit-source-id: 92f48513bc62fbe2c67c759d68830a973296e43b
Summary:
To address the issue of broadcasting giving the wrong result in `nn.MSELoss()` as mentioned here https://github.com/pytorch/pytorch/issues/16045 . In particular, the issue often arises when computing the loss between tensors with shapes (n, 1) and (n,)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18349
Differential Revision: D14594176
Pulled By: soumith
fbshipit-source-id: f23ae68a4bf42f3554ad7678a314ba2c7532a6db
Summary:
Previously, we would continue to run requires grad on a loop body when the outputs and inputs disagreed. This adds a check so that we don't continue running if the results haven't changed since the last run.
Fix for https://github.com/pytorch/pytorch/issues/18320
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18361
Differential Revision: D14584332
Pulled By: eellison
fbshipit-source-id: 696b225f80a2036318540946428b525985a9e735
Summary:
This specializes optional tensor inputs to either a DimensionedTensorType or, when None is passed,
UndefinedTensor (aka AutogradZeroTensorType).
This works because we already have different specs and thus separate plans for the two cases.
It enhances the shape analysis - because now unwrapped optional tensors will have DimensionedTensorType with appropriate shape and required grad etc.
Also, when combined with "if-pruning" (which I understand #18259 works towards), we actually get much nicer concrete graphs, too.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18360
Differential Revision: D14590577
Pulled By: soumith
fbshipit-source-id: cac204a506d1d38b15703cbcc67a6b75fd4979f4
Summary:
Currently, `THPVariable_Wrap(…)` and `THPVariable_NewWithVar(…)` depend on the existence of `pyobj_` in the autograd metadata of a Variable to convert the Variable to a Python tensor. However, after the Variable/Tensor merge, there will be Variables that don't contain autograd metadata, and to allow the conversion from non-autograd-meta Variable to a Python tensor we need to store the `pyobj_` outside of autograd metadata and in a place where it will always be available.
This PR makes it possible by moving `pyobj_` into TensorImpl, so that `THPVariable_Wrap(…)` and `THPVariable_NewWithVar(…)` can always access a Variable's `pyobj_` and convert the Variable to a Python tensor.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18225
Differential Revision: D14562616
Pulled By: yf225
fbshipit-source-id: 18d4aaace70eee6120abaf9276036d1f8f51b18d
