Summary:
similar to softmax there are issues of getting nan randomly
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17170
Differential Revision: D14110515
Pulled By: bddppq
fbshipit-source-id: 5c97661184d45a02122fd69d35a839fdf4520c8c
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17158
Because of Reshape op, batch size can be changed. This diff addresses first order issue raised from multiple batch size system. We need to export different real_batch_size for different max_batch_size input and attach it to the right output.
It also fixes a false exception.
Reviewed By: ipiszy
Differential Revision: D14099541
fbshipit-source-id: 0fa9e86826f417a11d2b5dd2ee60dff64a7ce8c4
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17046
As we are moving to use bound shape inference, we can remove the awkward fake inference run path and make the code cleaner.
Reviewed By: ipiszy
Differential Revision: D14061501
fbshipit-source-id: b3ace98b3dabef3c3359086a0bb1410518cefa26
Summary:
For >2D input, previously the code uses static shape captured during tracing and reshape before/after `Gemm`.
Now we add `-1` to the first `Reshape`, and uses `Shape(X) => Slice(outer) => Concat(with -1 for inner) => Reshape` for the second.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16184
Differential Revision: D14070754
Pulled By: ezyang
fbshipit-source-id: 86c69e9b254945b3406c07e122e57a00dfeba3df
Summary:
The second input (`lengths`) is not supported.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16727
Differential Revision: D14054105
Pulled By: houseroad
fbshipit-source-id: 36b8d00460f9623696439e1bd2a6bc60b7bb263c
Summary:
This PR is a follow up of #15460, it did the following things:
* remove the undefined tensor semantic in jit script/tracing mode
* change ATen/JIT schema for at::index and other index related ops with `Tensor?[]` to align with what at::index is really doing and to adopt `optional[tensor]` in JIT
* change python_print to correctly print the exported script
* register both TensorList and ListOfOptionalTensor in JIT ATen ops to support both
* Backward compatibility for `torch.jit.annotate(Tensor, None)`
List of follow ups:
* remove the undefined tensor semantic in jit autograd, autodiff and grad_of
* remove prim::Undefined fully
For easy reviews, please turn on `hide white space changes` in diff settings.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16379
Differential Revision: D13855677
Pulled By: wanchaol
fbshipit-source-id: 0e21c14d7de250c62731227c81bfbfb7b7da20ab
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14143
ConvTranspose has a per-operator attribute rename, which meant that the
global attribute rename for kernels => kernel_shape was not applied.
Changing the behavior so that the global renames always apply, but per-op
renames can override those for specific attributes.
Note: The python frontend path isn't actually used for ConvTranspose, but I
thought it would be good to make it consistent.
Reviewed By: yinghai
Differential Revision: D13113395
fbshipit-source-id: cd3f124b4b5c753a506d297138b7d002b51bfb38
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14214
This is to pick up the residual task of T36325466 to make sure that input/output binding of c2 Onnxifi op is positional.
Reviewed By: dzhulgakov
Differential Revision: D13134470
fbshipit-source-id: d1b916dade65c79133b86507cd54ea5166fa6810
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/13798
The semantics of C2 and ONNX Concat is a bit different. C2 concat accepts "add_axis" arg and will raise the dim if so. It's equivalent of attaching a Reshape after plain concat in ONNX.
Reviewed By: rdzhabarov
Differential Revision: D13012867
fbshipit-source-id: da23e555bae709fd2a373b04dcb9db4e984ae315
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:
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/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/13007
No reason to use the hook if it's set, this helps fbcode traces.
This slightly pessimizes the stack trace for ATen functions,
because we are no longer skipping all of the frames we should.
This is probably OK.
Reviewed By: Yangqing
Differential Revision: D10518499
fbshipit-source-id: be54e490df3c3fde7ff894b5b1473442ffc7ded3
Summary:
- exhaustive_search attribute will be blacklisted so it
will be discarded from the coverted onnx model. At present
it throws error while verifying the onnx model
Signed-off-by: Parth Raichura <parth.raichura@softnautics.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/12805
Differential Revision: D10502374
Pulled By: ezyang
fbshipit-source-id: 0926dfa3237a8a431184e7f7250146e5b0cbfb85
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/12900
Workspace sometimes will be populated with input tensors for shape inference but net.external_input() is not a reliable way to tell weights from input in the workspace. We say in some usecases where net.external_input() is empty. In this case, we need to give user an option to provide input hint.
Reviewed By: bddppq
Differential Revision: D10476822
fbshipit-source-id: 1a3fa2df69b959d5b952a7824eba9e6c713f4f07
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/12685
In this diff, we push the fake run of the net into the ONNXIFI transformer, because
1. We cannot do shape inference for every op
2. Since the net has been SSA rewritten, we cannot use shape info from outer workspace directly.
In addition, this diff adds input shape info when querying the `onnxBackendCompatibility` function.
Reviewed By: bddppq
Differential Revision: D10390164
fbshipit-source-id: 80475444da2170c814678ed0ed3298e28a1fba92
Summary:
Changes in this PR:
1. Intermediate Docker image is shared from build stage to test stage through ECR, in order to fix the Caffe2 flaky CUDA tests.
2. There are ~7 Caffe2 operator tests that are only flaky in `caffe2_py2_gcc4_8_ubuntu14_04_test` on CPU. Disabling those tests on that config only, which is okay to do because we are still running those tests in other test jobs.
After this PR is merged, CircleCI will be running on master automatically, and will be running on PRs if the author rebased their PR onto the newest master (which we will ask all the authors to do when we switch off Jenkins for Linux).
Pull Request resolved: https://github.com/pytorch/pytorch/pull/12389
Differential Revision: D10224267
Pulled By: yf225
fbshipit-source-id: dd1a90a425c3d13b870d3d328cb301eee2e6e2cd
Summary:
If block is missing control inputs when do caffe2 net execution, this PR add them back and remove the un-SSA semantics
jamesr66a
Pull Request resolved: https://github.com/pytorch/pytorch/pull/12224
Differential Revision: D10135408
Pulled By: wanchaol
fbshipit-source-id: 746c870bde54ed4ca627167361db1b3f36cd235c
Summary:
Original commit changeset: f5614a5d2607
D9986213 is causing Multifeed Aggregator a [huge performance different](https://our.intern.facebook.com/intern/ads/analyze_canary/412951953278781781/) and is blocking aggregator push since last Friday night: https://fburl.com/feedtools/b6izvwjz
We need to land this revert ASAP to unblock aggregator push.
Reviewed By: orionr
Differential Revision: D10123245
fbshipit-source-id: d83da8e00a1250f5d09811a0a587c127e377aab2
