Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/19083
As we have discussed, there are too many of AdjustBatch ops and they incur reallocation overhead and affects the performance. We will eliminate these ops by
- inling the input adjust batch op into Glow
- inling the output adjust batch op into OnnxifiOp and do that only conditionally.
This is the C2 part of the change and requires change from Glow side to work e2e.
Reviewed By: rdzhabarov
Differential Revision: D14860582
fbshipit-source-id: ac2588b894bac25735babb62b1924acc559face6
Summary:
Almost there, feel free to review.
these c10 operators are exported to _caffe2 domain.
TODO:
- [x] let the onnx checker pass
- [x] test tensor list as argument
- [x] test caffe2 backend and converter
- [x] check the c10 schema can be exported to onnx
- [x] refactor the test case to share some code
- [x] fix the problem in ONNX_ATEN_FALLBACK
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18210
Reviewed By: zrphercule
Differential Revision: D14600916
Pulled By: houseroad
fbshipit-source-id: 2592a75f21098fb6ceb38c5d00ee40e9e01cd144
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:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18129
A lot of tensor interference function assume the operator passes the schema.
So call Verity to make sure this is actually the case.
Created diff before to add checking in Concat (https://github.com/pytorch/pytorch/pull/17110), but I encountered lot more places where this is assumed (for example ElementwiseOpShapeInference)
Reviewed By: mdschatz
Differential Revision: D14503933
fbshipit-source-id: cf0097b8c3e4beb1cded6b61e092a6adee4b8fcb
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18084
data_strategy parameter was not used in some of unit tests for optimizers
Reviewed By: hyuen
Differential Revision: D14487830
fbshipit-source-id: d757cd06aa2965f4c0570a4a18ba090b98820ef4
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18036
- Add macros to export c10 cuda operators to caffe2 frontend
- Instead of having a separate caffe2 registry for the c10 operator wrappers, use the existing caffe2 registries
Reviewed By: ezyang
Differential Revision: D14467495
fbshipit-source-id: 7715ed2e38d2bbe16f1446ae82c17193a3fabcb9
Summary:
Observed the test `TestGroupConvolution.test_group_convolution` to fail with the following error:
```
Falsifying example: test_group_convolution(self=<caffe2.python.operator_test.group_conv_test.TestGroupConvolution testMethod=test_group_convolution>, stride=3, pad=0, kernel=5, size=8, group=4, input_channels_per_group=7, output_channels_per_group=8, batch_size=2, order='NHWC', engine='', use_bias=False, gc=, dc=[, device_type: 1])
You can reproduce this example by temporarily adding reproduce_failure('3.59.1', b'AAAA') as a decorator on your test case
```
This example generated by hypothesis has `group=2, order='NHWC' and dc=[, device_type: 1])`.
I think this example should be skipped.
I have mimicked the change corresponding to [PR#13554](https://github.com/pytorch/pytorch/pull/13554) to skip this example.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17715
Differential Revision: D14346642
Pulled By: ezyang
fbshipit-source-id: b1f1fef09f625fdb43d31c7213854e61a96381ba
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16723
Removed obsolete argument correct_transform_coords in bbox_transform op.
* It was only for backward compatibility. We should not have models using it now.
Differential Revision: D13937430
fbshipit-source-id: 504bb066137ce408c12dc9dcc2e0a513bad9b7ee
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16691
Previous diffs already introduced a macro that registers caffe2 CPU kernels with c10.
This now also registers the CUDA kernels with it.
Reviewed By: bwasti
Differential Revision: D13901619
fbshipit-source-id: c15e5b7081ff10e5219af460779b88d6e091a6a6
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16643
The test was disabled in D13908117 because it conflicted with another diff that was about to land.
Now fixed the merge conflict and re-landing it.
Reviewed By: ezyang
Differential Revision: D13911775
fbshipit-source-id: b790f1c3a3f207916eea41ac93bc104d011f629b
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16548
With this macro, a caffe2 operator can now directly be registered with c10.
No need to write custom wrapper kernels anymore.
Differential Revision: D13877076
fbshipit-source-id: e56846238c5bb4b1989b79855fd44d5ecf089c9c
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16676
This op is used for changing batch size (first dimension) of the tensor.
Reviewed By: bertmaher, ipiszy
Differential Revision: D13929200
fbshipit-source-id: 4f2c3faec072d468be8301bf00c80d33adb3b5b3
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16785
There's no EIGEN engine implemented for DeformConv but unit test was checking it.
Reviewed By: BIT-silence
Differential Revision: D13967306
fbshipit-source-id: e29c19f59f5700fc0501c59f45d60443b87ffedc
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16478
This diff includes an example registration of a caffe2 op in torch. A previous attempt ran into a static initialization order bug.
Reviewed By: smessmer
Differential Revision: D13854304
fbshipit-source-id: ec463ce2272126d08a5163d1599361ee5b718bbc
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16630
two PRs landed concurrently - enforcing tensor constraints and refactoring c10. Since it's not a prod code - disable test and I'll let Sebastian to fix it properly.
Reviewed By: ezyang
Differential Revision: D13908117
fbshipit-source-id: 381c5626078b794afa1fc7a95cb1ea529650424c
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16246
The op schema says it returns multiple values, so let's actually return multiple values instead of one tuple.
For some reason, this did work when called from python (probably some auto-unpacking),
but once called from JIT, it segfaulted. This diff fixes that.
Reviewed By: dzhulgakov
Differential Revision: D13780147
fbshipit-source-id: fe94f82f4c53b7454f77c4484fca4ac9dc444475
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16374
this fixes the original attempt in OSS (adds to CMake and python build files)
Reviewed By: smessmer
Differential Revision: D13821061
fbshipit-source-id: 82f0dade0145fd04bdf8e3cb3954b5790e918162
