Summary:
Goal of this PR is to unify cuda and hip device types in caffe2 python front end.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14221
Differential Revision: D13148564
Pulled By: bddppq
fbshipit-source-id: ef9bd2c7d238200165f217097ac5727e686d887b
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
* Update elementwise ops to support numpy style boradcast
Update elementwise ops to support numpy style boradcast
* Fix sqrt_op
* Fix compare ops
* Fix gradient test
* Fix optimizer legacy broadcast
* Fix legacy broadcast for elementwise ops
* Skip flaky test
* Fix eigen simple binary op
* Fix attention test
* Fix rnn test
* Fix LSTM test
* Fix tan grad
* Fix schema check
Summary: Adds support for backprop to While op, fixes gradient computation for Pow
Reviewed By: azzolini
Differential Revision: D6456875
fbshipit-source-id: 9f660317ad6f3898ff7d8ce43098f85c3426409b
Summary:
Adding backward pass support for If operator:
- Implemented necessary changes to Do operator and generation of gradient Do operator to properly forward gradient blobs in and out of subnet
- Using WorkspaceManager to keep track of workspaces used by Do, in case we need to have access to local blobs to compute gradients (also important for loop's backprop)
- Update to Workspace to handle blob binding from multiple parent workspaces
- Implemented generation of gradient If operator
- Unit test to build and train a net with If control op
Reviewed By: azzolini
Differential Revision: D5745096
fbshipit-source-id: 1023c90a2113716254424d1e50b9e560fe9083e5
Summary:
In some cases you have an optimized network and a normal
one. And you would like to make sure they produce same results. If
math under the hood is the same, you could do this with a very high
precision compare to a traditional numerical gradient check. One of
the application - RNNs. There we can unroll RNN into Caffe2 graph and
make sure result is the same as in the optimized version using
RecurrentNetworkOp.
Another possible application - graph transformations. We can verify
that after that nets produce same gradients (cc akyrola on memonger,
bwasti on other transformation ideas)
Reviewed By: bwasti
Differential Revision: D5200855
fbshipit-source-id: 0196af187f0c2feb33de4778ea08d0d288fe1017
Summary: this is useful to test rnn cells
Reviewed By: dzhulgakov
Differential Revision: D4720641
fbshipit-source-id: baa7df43357ed8af72ede64be3e0a642a40472df
Summary:
If we go to prod some of the sparse features might be empty or for some reason
batch might be empty. It's a good idea to be sure that we can run empty
batches.
Reviewed By: dzhulgakov
Differential Revision: D4197297
fbshipit-source-id: 1a154ebf625d1a39fd15354a154cf100f525ae9a
