Summary:
There was a bug in the uniqueness check that only made the first run
unique
Reviewed By: duc0
Differential Revision: D13013504
fbshipit-source-id: ecf7526d0fafd7968f1301734123f93968efef46
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/13812
Original commit changeset: 2cf95bdc5ed8
Looks like in iOS, `uint64_t` is not the same as `size_t`. :( Fixed it here.
Reviewed By: houseroad
Differential Revision: D13017390
fbshipit-source-id: d33854ce341225aba372fb945c3704edc14f9411
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/13745
We need to support types beside `int64` and `float`.
Reviewed By: bddppq, rdzhabarov
Differential Revision: D12967258
fbshipit-source-id: 688076e6f504b2bf24bba89714df87a678c5638a
Summary:
Add a markdown document summarizing the coverage of serialized operator tests. This currently only takes into account what has been covered by the tests with respect to the entire registry of c2 operators.
Next, we will break down the coverage by which operators have unit tests associated with them, which have hypothesis tests, and which have tests more specifically calling assertReferenceChecks.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/13703
Reviewed By: dzhulgakov
Differential Revision: D12970810
Pulled By: ajyu
fbshipit-source-id: 4f0cd057b1cf734371333e24d26cbab630a170e1
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/13377
* Enable junk fill for the default CPU allocator. The first diff only enables this for the tests. A second diff will change the default of zero-fill to false.
* Fix tests to use 64-bit counters that IterOp and LearningRateOp demands.
* Fix kernels that uses uninitialized memory.
Reviewed By: salexspb
Differential Revision: D10866512
fbshipit-source-id: 17860e77e63a203edf46d0da0335608f77884821
Summary:
I was hitting this error:
caffe2/caffe2/operators/stats_put_ops.h:66:25: runtime error: 9.22337e+18 is outside the range of representable values of type 'long'
So, the assignment from int64_t to float loses some precision and because of that we overflow.
Reproduced this issue with this diff D12945013
Reviewed By: mlappelbaum, jdshi-fb
Differential Revision: D12927086
fbshipit-source-id: 7eae7fe25ab49d5ac15279335bd5b1fa89d6e683
Summary: Adding Fetching Real number representation for int8 tensor in workpace.py
Reviewed By: harouwu
Differential Revision: D12936556
fbshipit-source-id: f8756a37bce21c93d44d52faf5da9c9bd6473f4a
Summary:
We updated the description of upsample_op in onnx: https://github.com/onnx/onnx/pull/1467
Therefore, we need to support the new upsample_op in caffe2-onnx backend as well.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/13272
Reviewed By: houseroad
Differential Revision: D12833656
Pulled By: zrphercule
fbshipit-source-id: 21af5282abaae12d2d044e4018a2b152aff79917
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/12733
Conv in NHWC layout only works for 2D images. This has been a pain point when implementing quantized 3D convolution because we need NHWC layout for best performance (note that NHWC layout in general gives better performance in CPU not just for quantized operators). For example, our quantized ops have a functionality to measure quantized error operator by operator but this needs running a shadow fp32 operator, but this is not easy when there's no 3D conv in NHWC layout is available (currently we're doing layout conversion on the fly for the shadow fp32 operator which is error prone). Some of Caffe2 frameworks like brew generates error when we try to create a 3D conv op in NHWC layout. This was also a blocker for using aibench because aibench is using brew.
i-am-not-moving-c2-to-c10
Reviewed By: houseroad
Differential Revision: D10333829
fbshipit-source-id: 2d203ee1db833cd3f9d39353219e3894b46c4389
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/13554
D10233252 broke ROCM test.
We don't have group conv in NHWC for hip yet and this diff omits related tests.
Reviewed By: hyuen
Differential Revision: D12917880
fbshipit-source-id: 9baf36a8cb061ee8cf393b2c438a2d1460ce5cd8
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/12428
Group conv in NHWC layout was enabled in CPU after D7547497.
In D7547497, unit test of group conv in NHWC layout in CPU was enabled in group_conv_test.py but not in conv_test.py . This diff also enables it in conv_test.py .
Reviewed By: BIT-silence
Differential Revision: D10233252
fbshipit-source-id: aeeaf3eedc60e1cf6321b5a1dbe6a561e3aacbde
Summary:
Essentially makes cuDNN to think of those kernels like of Nx1 ones.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/12902
Reviewed By: BIT-silence
Differential Revision: D10852862
Pulled By: soumith
fbshipit-source-id: 7416cf6d131177340d21cbf1d42c1daa6c7cad8c
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/13437
revert
transform the NCHW Convolution operators to NHWC and the tensors around these operators
Reviewed By: bwasti
Differential Revision: D12871789
fbshipit-source-id: 6509a29fa1654424d22904df0d3e60f8cd9c0ec7
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/13436
revert
Add a utility function to convert a list of caffe2_pb2.Argument to a dictionary.
Reviewed By: bwasti
Differential Revision: D12871811
fbshipit-source-id: 486ad09f3f37723c92a946c486ce3e24a649b4e6
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/13429
Made the SSA transformation idempotent. This ensures that if a caffe2 graph is already in SSA form, the name of the ONNX models inputs/outputs match these of the caffe2 graph.
Avoid evaluating the model by running it if the shapes of all the blobs are present in the value_info map. This speeds up the conversion and decrease its memory usage in the case of medium to large nets.
Reviewed By: abadams
Differential Revision: D12873354
fbshipit-source-id: d695b28e610562afa9a41c2d4da05be212ccb488
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/13332
Add a utility function to convert a list of caffe2_pb2.Argument to a dictionary.
Reviewed By: bwasti
Differential Revision: D10861211
fbshipit-source-id: da2fcc3e3b4dbf8decbe14a8e2d5621b3fcc377f
Summary: Made the clangr rule more robust and it discovered more callsites.
Reviewed By: smessmer
Differential Revision: D12825017
fbshipit-source-id: 3be1eeb7ea697b36ef89e78ba64c0ee1259439c4
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/13206
Add has device option for checking if a node has a device option set
Reviewed By: bwasti
Differential Revision: D12815365
fbshipit-source-id: 58477df93777f470cfb30cd75f02a659a7017b7c
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/13132
Expose more of the C++ API to python
Reviewed By: duc0
Differential Revision: D10855086
fbshipit-source-id: 98cc89bc72ef91ed1c59c1a19688e047765cf90b
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/13203
Minor changes in the test workflow to run the model on CPUs
Reviewed By: stephenyan1231
Differential Revision: D9925797
fbshipit-source-id: b7b1fb2658ab68b1ffc2b1f7b314958ea4732b32
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/13004
Implement BucketWeighted model layer, which learns a weight for each possible score in an IdScoreList. Here, we assume that the scores in the IdScoreList have already been converted into the appropriate 'buckets'. If this is not done, then essentially each score represents its own bucket.
We assume that the scores/buckets are integers, and if max_score is not set, we assume that the maximum cardinality of the score is less than or equal to the cardinality of the ids.
Reviewed By: chonglinsun
Differential Revision: D10413186
fbshipit-source-id: 743e643a1b36adf124502a8b6b29976158cdb130
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/12843
This adds a cuda implementation for the UpsampleBilinearOp and UpsampleBilinearGradientOp.
The CUDA code is based off of the corresponding ResizeNearest operators but with bilinear interpolation logic taken from the CPU implementation.
Reviewed By: houseroad
Differential Revision: D10453776
fbshipit-source-id: b29ac330b72465974ddb27c0587bca590773fdec
