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/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:
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:
The ONNXIFI backend will absorb the constant weight in Conv, so we should not add it as an input. This is just a test artifacts. Note that Onnxifi transformer will do the right thing when cutting the graph to absorb the weights.
rdzhabarov
Pull Request resolved: https://github.com/pytorch/pytorch/pull/10575
Reviewed By: houseroad
Differential Revision: D9357339
Pulled By: yinghai
fbshipit-source-id: a613fa3acafa687295312f5211f8e9d7f77b39cd
