Summary:
…done once
This allow no-op build to work correctly even when BUILD_CAFFE2_OPS is on.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14982
Differential Revision: D13413960
Pulled By: zdevito
fbshipit-source-id: 6e5412a8c375af8a47c76f548cdd31cff15f3853
Summary:
Currently in caffe2, one cannot properly fetch the content of Int8 blobs.
Upon digging the source code, it turns out that the relevant source code is not being compiled. Adding the source to CMakeLists.txt fixes this issue.
First time ever doing a pull request. Please let me know if there's any rule I should follow. Thanks.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15047
Differential Revision: D13417583
Pulled By: bddppq
fbshipit-source-id: dd39575971a3012635edbf97a045d80e4b62a8eb
Summary:
fix auto grad summing for IfOp where intermediate output needs renaming.
Bug before this diff:
- we only renames the output of IfOp without changing the subnet ops output
- this results in blob not found error
the unittest provides an example
this diff fix that for IfOp
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14772
Differential Revision: D13327090
Pulled By: harouwu
fbshipit-source-id: ec40ee88526ace3619c54551e223dd71158a02f8
Summary:
This will let us install tests and other Caffe2 python code as a part of running Caffe2 tests in PyTorch.
Broken out of https://github.com/pytorch/pytorch/pull/13733/
cc pjh5 yf225
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14898
Reviewed By: pjh5
Differential Revision: D13381123
Pulled By: orionr
fbshipit-source-id: 0ec96629b0570f6cc2abb1d1d6fce084e7464dbe
Summary:
This pull request contains changes for:
1. Added MIOpen RNN API miopenGetRNNLayerBiasSize and miopenGetRNNLayerParamSize.
2. Fixed usage of API miopenGetRNNLayerParam.
3. Modifying the RNN test to run using MIOpen engine.
Differential Revision: D13355699
Pulled By: bddppq
fbshipit-source-id: 6f750657f8049c5446eca893880b397804120b69
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/13756
This implements general Gather operator for arbitrary axis, sharing the code with BatchGather.
- CPU gather & batch gather logic is now shared through caffe2::gather_helper, for any axis.
- Shared CUDA kernel moved to gather_op.cuh, for any axis.
- Gradients of axis > 0 delegate to BatchGatherGradientOp which now has axis argument.
- BatchGatherOp doc strings updated to have correct rank (q + (r -1)) and output.
- Added tests for axis == 2.
GatherOp supports index wrapping for axis == 0 by default, which was earlier for ONNX.
This diff also extends it to work in Cuda kernel. Added "wrap_indices" argument which specifies
wheather this wrapping should be done; set it to true if you'd like wrapping for any axis.
TBD: Update gradients to support negative indices (separate diff).
TBD: Once we have operator versioning, we'd like to update GatherOp to NOT support axis 0 wrapping
by default, but rather do it only if wrap_indices is set.
Reviewed By: dzhulgakov
Differential Revision: D12983815
fbshipit-source-id: 8add9d67b47fe8c5ba7a335f581ca0530b205cd7
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/14268
Removes the need for Context in Tensor by doing simple dispatch for CopyBytes. It'd eventually be subsumed by Roy Li's changes of proper copy_ op, but before that is done, let's get a clear logic of how copies are implemented and clean up some craft in CopyFrom implementation.
Note, that with these changes, one can probably can get rid of Context::CopyFromCPU/CopyToCPU, but it's a matter for follow up diffs.
This diff doesn't change the API of Tensor yet, but relies on the fact that passing `Context` to CopyFrom makes copy async if the device is CUDA and doesn't have any effect otherwise (that's how Context methods are implemented).
This doesn't change semantics of copy async implementation - as before it blindly calls cudaMemcpyAsync which probably means that it can be misused if invoked separately outside of operator body. I'll leave it for the follow up copy_ unification.
For Extend() we always do async copy - it makes sense as it's an in-place device-device operation and only any further op would be observable.
Note: there are now three ways of invoking copy in C2 code - templated CopyBytes, virtual CopyFromCPU/etc, and double-dispatch free method here. Hopefully we can get rid of the second one.
Also, please advise whether it's c10-worthy :)
Reviewed By: ezyang
Differential Revision: D13117987
fbshipit-source-id: a6772d6dcf3effaf06717da3a656fc9873b310b5
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14342
Sometimes, when we are creating a TaskGroup, we are in fact creating a TaskGroup for a distributed job. In some cases, we may want to register a few nets as "remote" to a TaskGroup. The remote net should have sufficient attributes on where they should be executed later on.
This diff adds the remote net attribute to the TaskGroup class. It exposes two minimal functionalities: adding a remote net, and getting all remote nets added to a TaskGroup.
Reviewed By: d4l3k
Differential Revision: D13188320
fbshipit-source-id: efe947aec30817e9512a5e18be985713b9356bdc
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/14196
Pull Request resolved: https://github.com/pytorch/pytorch/pull/13641
FeedTensor function used to take a pointer to Tensor and feed the content using Resize
and mutable_data, but since Tensor is a pointer now, we can just return a Tensor instead.
Reviewed By: dzhulgakov
Differential Revision: D13091163
fbshipit-source-id: 9abf2fd320baca76e050530c500dd29f8e2d0211
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:
Add "axis" and "axis_w" arguments in FC to support customized axix to reduce dim.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/12971
Reviewed By: bddppq
Differential Revision: D12850675
Pulled By: yinghai
fbshipit-source-id: f1cde163201bd7add53b8475329db1f038a73019
Summary:
xw285cornell
- To make hip files to have unique filename extension we change hip files from _hip.cc to .hip (it's the only blessing option other than .cu in hipcc 3d51a1fb01/bin/hipcc (L552)).
- Change to use host compiler to compile .cc|.cpp files. Previously we use hcc to compile them which is unnecessary
- Change the hipify script to not replace "gpu" with "hip" in the filename of the generated hipified files. Previously we do this because hcc has a bug when linking files that have same filename. We have now changed to use host linker to do linking so this is unnecessary anymore.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14036
Reviewed By: xw285cornell
Differential Revision: D13091813
Pulled By: bddppq
fbshipit-source-id: ea3d887751d8abb39d75f5d5104aa66ce66b9ee0
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/13795
Add ability for dot string generation for a single subgraph and python bindings (which is pretty useful for model exploration in Python)
Restructure DotGenerator class a bit to make it easy to implement this feature
Reviewed By: bwasti
Differential Revision: D13010512
fbshipit-source-id: 825665438394b7e6968ab6da167b477af82a7b62
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14074
expose inducesEdges and addNode to python's NNSubgraph. This make it easy to manually construct a NNSubgraph in python
Reviewed By: bwasti
Differential Revision: D13092885
fbshipit-source-id: a94ed0b318162e27e3a4b5a4954eb6d169da7405
Summary:
Currently after performing export it gives two entries of externel_input
of input data in predict_net proto because it extends the externel_input
twice once seperately using input blob and one it is extendind all the entries
of external_input from proto in which input blob is already included
Signed-off-by: Parth Raichura <parth.raichura@softnautics.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/12979
Differential Revision: D12916349
Pulled By: soumith
fbshipit-source-id: 4d4a1c68c0936f8de3f4e380aea1393fe193cd2d
Summary:
Avoid false failure by checking for the presence of the test data in setup.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/13627
Differential Revision: D13090324
Pulled By: ezyang
fbshipit-source-id: e85571943d168c0007212d7b1a5b99ffa0c39235
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/13825
async_polling was an intermediate step towards async_scheduling and is not used
Reviewed By: yinghai
Differential Revision: D13019059
fbshipit-source-id: eee6ba53e7f476ddb481afba3bf1768303864d32
Summary:
This pull request contains changes for:
1. Removing ConvTranspose related changes from caffe2/operators/hip/conv_op_miopen.cc
2. Adding the file caffe2/operators/hip/conv_transpose_op_miopen.cc
3. Modifying the tests to run convTranspose op using MIOpen engine
Differential Revision: D13055099
Pulled By: bddppq
fbshipit-source-id: ca284f8f9a073005b22013c375cc958257815865
Summary: Currently Lambdarank applies exponential emphasis on relevance, i.e., g=2^rel when calculating dcg, this diff adds options that supports g=rel in the loss function.
Reviewed By: itomatik
Differential Revision: D9891514
fbshipit-source-id: 64730d467a665670edd37e6dc1c077987991d1a8
