Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/34072
This diff helps check all the ops not supported by lite_interpreter.
Helpful mainly to find all the ops that need to be added instead of adding them
one by one.
Test Plan:
buck run caffe2/binaries:lite_interpreter_model_load --
--model=<bytecode-model-path>
Reviewed By: iseeyuan
Differential Revision: D20194092
fbshipit-source-id: 0d596cd0204308027194af7ed738551d0c32a374
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/33265
This removes the need for isinstance to keep trace of list and tuple
separately by introducing AnyListType and AnyTupleType into the JIT
type system to be the common supertype of any lists or tuples.
This allows us to remove the weird flags from the interpreter for
the isinstance operator.
Test Plan: Imported from OSS
Differential Revision: D19883933
Pulled By: zdevito
fbshipit-source-id: f998041b42d8b4554c5b99f4d95d1d42553c4d81
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/32889
Common primitive ops that have special inputs make it very hard to
serialize the bytecode for mobile because information about how the
op behaves is hidden in the Node*. This changes how we handle the following
ops so that they are encoded as their own interpreter bytecodes.
```
USES NODE: prim::TupleUnpack(...) -> (...)
USES NODE: prim::TupleSlice(...) -> (...)
USES NODE: prim::TupleConstruct(...) -> (...)
USES NODE: prim::ListUnpack(...) -> (...)
USES NODE: prim::ListConstruct(...) -> (...)
USES NODE: prim::DictConstruct(...) -> (...)
USES NODE: prim::Constant() -> (...)
USES NODE: prim::isinstance(...) -> (...)
USES NODE: prim::CreateObject(...) -> (...)
USES NODE: prim::fork(...) -> (...)
USES NODE: aten::warn(str message, *, int stacklevel=2) -> () # need stack level information, so ideally in interpreter so it can look at the stack
```
This leaves a state where the _only_ remaining Node*-consuming builtins
are things that are only introduced during JIT optimization and will
not appear in mobile code.
Serialization of bytecode can now be made to directly write the CodeImpl
object without modification.
Test Plan: Imported from OSS
Differential Revision: D19673157
Pulled By: zdevito
fbshipit-source-id: 7b8c633d38a4c783b250fbdb222705e71a83ad26
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/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
