Summary:
It might need some cleaning up and might be missing some features, but it should be already working for most cases.
This PR is based on top of PR16986 (so please review only the last commit here).
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16987
Differential Revision: D14074577
Pulled By: ZolotukhinM
fbshipit-source-id: 712b598f423265655f574bb9903e2066628eaad3
Summary:
Currently the converters are very straightforward, i.e. there is no code for trying to
preserve semantics, we're purely perform conversion from one format to another.
Two things that we might want to add/change:
1. Add semantic conversion as well (but probably it would be a good idea to keep
it separate as a temporary thing).
2. Make sure we don't mess with value names, as they are crucial for current
uses of NetDefs.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17123
Differential Revision: D14090244
Pulled By: ZolotukhinM
fbshipit-source-id: 07175fa9235582e1d1da5f10a42a5c1280b1b394
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16534
All c10 ops from the c10 dispatcher are now automatically registered with JIT
Reviewed By: dzhulgakov
Differential Revision: D13869275
fbshipit-source-id: 5ab5dec5b983fe661f977f9d29d8036768cdcab6
Summary:
This just moves thing around to make AliasTracker independently testable and keep things a little more separate. Follow-on PRs will change the interfaces of AliasDb and AliasTracker to be more clearly distinct.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16588
Differential Revision: D13891894
Pulled By: suo
fbshipit-source-id: c5b590b5fdd462afefe743e499034068bf35784a
Summary:
This puts stubs in the autograd profiler for the use of cuda APIs allowing the cuda parts of libtorch to be linked separately from the CPU parts.
This also edits the buck build.
Previous:
For GPU builds:
_C -> csrc -> caffe2
For CPU builds:
_C -> csrc-cpu -> caffe2
Now:
GPU:
_C -> libtorch_cuda -> (libtorch -> caffe2, for CPU)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15898
Reviewed By: ailzhang
Differential Revision: D13617991
Pulled By: zdevito
fbshipit-source-id: 6d84a50bb356a54b4217f93219902755601b00e1
Summary:
This improves the error message for "unknown builtin op" to suggest similarly named ops.
Currently it prints out all operators with a name within two edits.
Related issue: https://github.com/pytorch/pytorch/issues/13409
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15183
Differential Revision: D13578509
Pulled By: eellison
fbshipit-source-id: 5c73408eda1f7aa456f5bd28790c34df0c76aeca
Summary:
This PR allows a subclass of programs that have return statements that are not final in the graph.
`final_returns.h` contains the a comment describing how this is accomplished.
To minimize complexity in `compiler.cpp`, this pass is done as an AST-to-AST rewrite before the compiler runs.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15463
Differential Revision: D13538962
Pulled By: zdevito
fbshipit-source-id: 67105ca873351825b4a364092ab1873779f3e462
Summary:
This separates the different parts of compiler.cpp to make their relationship more clear. In particular it adds:
* sugared_value.{h,cpp} - all the public SugaredValues that the compiler defines and a few that were inside compiler.cpp
* type_parser.{h, cpp} - Turns TreeRef's defining types into TypePtr
* schema_matching.{h, cpp} - infrastructure for matching arguments against overloaded schema and emitting builtin operators with a particular schema.
Retains:
* compiler.{h, cpp} - now responsible simply for the `defineMethodsInModule` infra structure.
Some utility functions like inlineCallTo have moved to ir.h.
Only thing that is not a move is some changes in module.h/cpp that remove multiple returns from `Method::emit_call_to`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15355
Reviewed By: suo, wanchaol
Differential Revision: D13507524
Pulled By: zdevito
fbshipit-source-id: 69ec936a9ff1a383c12a883616346b219c72e393
Summary:
This PR enables autodiff to use the forward/backward graph compiled from python code, instead of using symbolic gradients(modifying the original graph directly).
We put the map in a separate .h file for now to wait for the native_functions.yaml and derivatives.yaml merge. This should ideally go into native_functions.yaml eventually.
This PR should be enough to unblock us for now, we can start writing gradients for aten functions in python.
Differential Revision: D13494635
Pulled By: ailzhang
fbshipit-source-id: f8d51a15243ac46afd09d930c573ccdfcd9fdaaf
