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
