Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/44337
Add a new run_method to mobile Module which is variadic (takes any number of arguments) to match full jit.
ghstack-source-id: 111909068
Test Plan: Added new unit test to test_jit test suite
Reviewed By: linbinyu, ann-ss
Differential Revision: D23585763
fbshipit-source-id: 007cf852290f03615b78c35aa6f7a21287ccff9e
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/44202
In preparation for changing mobile run_method() to be variadic, this diff:
* Implements get_method() for mobile Module, which is similar to find_method but expects the method to exist.
* Replaces calls to the current nonvariadic implementation of run_method() by calling get_method() and then invoking the operator() overload on Method objects.
ghstack-source-id: 111848222
Test Plan: CI, and all the unit tests which currently contain run_method that are being changed.
Reviewed By: iseeyuan
Differential Revision: D23436351
fbshipit-source-id: 4655ed7182d8b6f111645d69798465879b67a577
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/43965
As part of a larger effort to unify the API between the lite interpreter and full JIT:
- implement torch::jit::mobile::Method, a proxy for torch::jit::mobile::Function
- add support for overloaded operator() to mobile Method and Function
- mobile find_method now returns a c10::optional<Method> (so signature matches full jit)
- moves some implementation of Function from module.cpp to function.cpp
ghstack-source-id: 111161942
Test Plan: CI
Reviewed By: iseeyuan
Differential Revision: D23330762
fbshipit-source-id: bf0ba0d711d9566c92af31772057ecd35983ee6d
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/42880
Enable switching between and checking for training and eval mode for torch::jit::mobile::Module using train(), eval(), and is_training(), like exists for torch::jit::Module.
Test Plan: Imported from OSS
Reviewed By: iseeyuan
Differential Revision: D23063006
Pulled By: ann-ss
fbshipit-source-id: b79002148c46146b6e961cbef8aaf738bbd53cb2
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/42133
Test Plan:
We save a module with module debugging information as follows.
```
import torch
m = torch.jit.load('./detect.pt')
# Save module without debug info
m._save_for_lite_interpreter('./detect.bc')
# Save module with debug info
m._save_for_lite_interpreter('./detect.bc', _save_debug_info_in_bytecode=True)
```
Size of the file without module debugging information: 4.508 MB
Size of the file with module debugging information: 4.512 MB
Reviewed By: kimishpatel
Differential Revision: D22803740
Pulled By: taivu1998
fbshipit-source-id: c82ea62498fde36a1cfc5b073e2cea510d3b7edb
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/41376
torch::jit::mobile::Module does not currently support accessing parameters via their attribute names, but torch::jit::Module does. This diff adds an equivalent functionality to mobile::Module.
Test Plan: Imported from OSS
Reviewed By: iseeyuan
Differential Revision: D22609142
Pulled By: ann-ss
fbshipit-source-id: 1a5272ff336f99a3c0bb6194c6a6384754f47846
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/35115
This commit runs the newly added tools/clang_format.py on the JIT
codebase and includes all of the formatting changes thus produced.
Testing:
Ran the script, CI.
Test Plan: Imported from OSS
Reviewed By: eellison
Differential Revision: D20568523
Pulled By: SplitInfinity
fbshipit-source-id: e09bdb982ccf090eecfb7c7b461b8d0681eef82b
Summary:
Now that lists are no longer specialized, we can register only one operator for list ops that are generic to their element type.
This PR reorgs lists into three sets of ops:
- CREATE_GENERIC_LIST_OPS
- CREATE_SPECIALIZED_LIST_OPS
- CREATE_COMPARATOR_LIST_OPS_SPECIALIZED (we didn't bind certain specialized ops to Tensor)
This is important to land quickly because mobile is finalizing its bytecode soon, after which we could not remove these ops.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/34520
Reviewed By: iseeyuan
Differential Revision: D20429775
Pulled By: eellison
fbshipit-source-id: ae6519f9b0f731eaa2bf4ac20736317d0a66b8a0
Summary:
Now that lists are no longer specialized, we can register only one operator for list ops that are generic to their element type.
This PR reorgs lists into three sets of ops:
- CREATE_GENERIC_LIST_OPS
- CREATE_SPECIALIZED_LIST_OPS
- CREATE_COMPARATOR_LIST_OPS_SPECIALIZED (we didn't bind certain specialized ops to Tensor)
This is important to land quickly because mobile is finalizing its bytecode soon, after which we could not remove these ops.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/34520
Differential Revision: D20368543
Pulled By: eellison
fbshipit-source-id: ad0c6d70d2a6be6ff0e948d6786052167fc43e27
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/34515
Once upon a time we thought this was necessary. In reality it is not, so
removing it.
For backcompat, our public interface (defined in `api/`) still has
typedefs to the old `script::` names.
There was only one collision: `Pass` as a `Stmt` and `Pass` as a graph
transform. I renamed one of them.
Test Plan: Imported from OSS
Differential Revision: D20353503
Pulled By: suo
fbshipit-source-id: 48bb911ce75120a8c9e0c6fb65262ef775dfba93
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/33294
1. Serialize bytecode of __setstate__ and run it when loading the model.
2. One use case is quantization. To test this use case a few operators are registered temporarily for lite interpreter. The "_" prefix registration will be removed when the operators are all migrated to mobile.
Test Plan: Imported from OSS
Differential Revision: D20162898
Pulled By: iseeyuan
fbshipit-source-id: 7a3180807bf38fbce594d86993896861f12bb58c
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/33243
If a file does not exist in an archive, PyTorchStreamReader throws an exception. However, when PyTorchStreamReader is destructed another exception is thrown while processing the first exception. As a result of this double exception there is SIGABORT.
Thanks dreiss for catching this bug and suggesting the fix. It happened when he used _load_for_mobile to load a torch script file without bytecode session. A unittest is added to test this case.
Test Plan: Imported from OSS
Differential Revision: D19859205
Pulled By: iseeyuan
fbshipit-source-id: 8f96b6256f1a1f933fce1c256d64604c7e9269e4
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/30612
The first version to move prim ops to c10 registration. After the reviewers are fine with the initial changes, more operators will be moved in the same style.
Test Plan: Imported from OSS
Differential Revision: D19237648
Pulled By: iseeyuan
fbshipit-source-id: c5a519604efffb80564a556536f17d829f71d9f9
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/30060
Mobile forward() passed inputs by reference, which is different from JIT's script::module. To make it consistent, change it pass by value.
Test Plan: Imported from OSS
Differential Revision: D18587786
Pulled By: iseeyuan
fbshipit-source-id: fa398124fd0a5168f708733ff88f0ba327726f43
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/29960
Overload name is required in mobile operators with the same name but different schema. Since it's not used in JIT, it's safe to add overload names for JIT operators.
Test Plan: Imported from OSS
Differential Revision: D18555484
fbshipit-source-id: b451379af24e255d8b0c61b964ae32fd1a64ed34
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/29421
Inline graph before writing the bytecode file, so that all the instructions are emitted from the top-level methods.
Test Plan: Imported from OSS
Differential Revision: D18404180
fbshipit-source-id: 4759474a8dba3813616ebce8253bea09941f6bbb
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/27748
There's TSAN test failure. From stack it's likely related to mkldnn (https://github.com/pytorch/pytorch/issues/27497). Before the issue is resolved, disable TSAN test.
ghstack-source-id: 91761706
Test Plan: buck test mode/dev-tsan caffe2/test/cpp/jit:jit -- 'JitTest\.LiteInterpreterConv' --run-disabled
Reviewed By: bddppq
Differential Revision: D17880082
fbshipit-source-id: 251d9b9577838146231c8e122f755936edd1c281
Summary: There's TSAN test failure. From stack it's likely related to mkldnn (https://github.com/pytorch/pytorch/issues/27497). Before the issue is resolved, disable TSAN test.
Test Plan: buck test mode/dev-tsan caffe2/test/cpp/jit:jit -- 'JitTest\.LiteInterpreterConv' --run-disabled
Reviewed By: bddppq
Differential Revision: D17846079
fbshipit-source-id: 669d6385690223d83996fb14051c39df0c521dfa
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/27104
* The use case here is to replace prim::ListConstruct, which requires Node, but Node is not available in mobile lite interpreter.
* (OPN, X, N), X is the index to the vararg operator-name and operator tables. N is number of inputs. For ListConstruct example, operator name can be "aten::listconstruct" and the overloaded name is the output type ("int", "float", "bool", "tensor" and "generic").
* A vararg operator table is built with void(int input_size, Stack& stack) functions.
## Unit test
LiteInterpreterConv covers OPN instruction and conv operator.
Test Plan: Imported from OSS
Differential Revision: D17762853
fbshipit-source-id: 475aa0c6678e3760cec805862a78510913a89c83
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/25187
The bytecode export flow: dump the bytecode format for the light weighted interpreter.
* The bytecode is generated without input spec optimization. It would be more generic (input independent) with no obvious performance degradation (to be tested).
* Main API: torch::jit::script::Module::save(filename, extra_files, bool *bytecode_format* = false).
* Both bytecode and module object are exported in pickle format.
* The module object (in data.pkl) is the same as the original JIT model.
* The serializer is dependent on pickle only (no protobuf or Json).
* The major functionality is forked in ScriptModuleSerializer2::serialize().
* The test loader is test_bc_export.cpp.
* Simple APIs are added in Code and its implementation to get necessary information (instructions, operators and constants).
* Since there's no dependency on graph/node, GetAttr is promoted from an operator to first-class instruction (https://github.com/pytorch/pytorch/pull/25151) .
* Some definitions (instructions, writeArchive, etc) that are shared by full JIT and bytecode are pulled out of the local namespace (https://github.com/pytorch/pytorch/pull/25148).
The output layout looks like:
* folders of methods.
* In each method folder (for example, forward/):
* bytecode.pkl: instructions and operators
* constants{.pkl,/}: constant list in constants.pkl. If there are tensors in constants, the binary tensor files in constants/ folder.
* data{.pkl,/}: the module object, with binary tensor files in data/ folder. The same as in torchscript.
Test Plan: Imported from OSS
Differential Revision: D17076411
fbshipit-source-id: 46eb298e7320d1e585b0101effc0fcfd09219046
