Summary:
Following on #19747, this implements most of the `torch.jit.script()` changes laid out in #20939.
Still to do:
* Accessing a method from Python does not add it as a `ScriptMethod` (so only `export`ed methods and `forward` are compiled)
* Calling a method other than `forward` on a submodule doesn't work
](https://our.intern.facebook.com/intern/diff/15560490/)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/20708
Pulled By: driazati
Differential Revision: D15560490
fbshipit-source-id: cc7ef3a1c2772eff9beba5f3e66546d2b7d7198a
Summary:
Following on #19747, this implements most of the `torch.jit.script()` changes laid out in #20939.
Still to do:
* Accessing a method from Python does not add it as a `ScriptMethod` (so only `export`ed methods and `forward` are compiled)
* Calling a method other than `forward` on a submodule doesn't work
](https://our.intern.facebook.com/intern/diff/15546045/)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/20708
Pulled By: driazati
Differential Revision: D15546045
fbshipit-source-id: c2c8fe179088ffbdad47198e799a456560655b86
Summary:
Stack from [ghstack](https://github.com/ezyang/ghstack):
* **#19578 [jit] Try to script all Python functions**
This adds the `torch.jit._enable_recursive_script` context manager, which will try to compile any Python functions it sees. It's hidden behind an internal context manager for now since it's incomplete (doesn't work for script_methods/Python submodules). If it can't compile the Python function it outputs an error.
](https://our.intern.facebook.com/intern/diff/15386727/)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/19578
Pulled By: driazati
Differential Revision: D15386727
fbshipit-source-id: 4e308f67677b8e9fccfc525a91bb2f4585062048
Summary:
Fix for https://github.com/pytorch/pytorch/issues/16962
This needs fixing because we turn lists into tuples when constantify a module, so indexing into a Tuple of one type with a non-constant integer is quite common.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/20081
Differential Revision: D15205893
Pulled By: eellison
fbshipit-source-id: 61d74ee071ad0aad98e46fe807d6f6cc5f6abd2f
Summary:
* adds TORCH_API and AT_CUDA_API in places
* refactor code generation Python logic to separate
caffe2/torch outputs
* fix hip and asan
* remove profiler_cuda from hip
* fix gcc warnings for enums
* Fix PythonOp::Kind
Pull Request resolved: https://github.com/pytorch/pytorch/pull/19554
Differential Revision: D15082727
Pulled By: kostmo
fbshipit-source-id: 83a8a99717f025ab44b29608848928d76b3147a4
Summary:
This PR propagates where we use first-class modules objects into the compiler. This creates a transitionary state where:
* compiler.cpp creates Graphs where `self` is a Module class and attributes/parameters/buffers/submodules are looked up with `prim::GetAttr`
* GraphExecutor still runs "lowered graphs" where the self object has been removed by a compiler pass `lower_first_class_method`.
* Tracing still creates "lowered graphs", and a pass "lift_lowered_method" creates a first-class method graph for things.
* This PR separates out Method and Function. A script::Function is a pure Graph with no `self` bound. Similar to Python, a script::Method is just a bound `self` and its underlying `script::Function`.
* This PR also separates CompilationUnit from Module. A CompilationUnit is just a list of named script::Functions. Class's have a CompilationUnit holding the class methods, and Modules also have a CompilationUnit holding their Methods. This avoids the weird circular case Module --has a-> Class -> has a -> Module ...
Details:
* In this transitionary state, we maintain two copies of a Graph, first-class module and lowered. Th first-class one has a self argument that is the module's class type. The lowered one is the lowered graph that uses the initial_ivalues inputs.
* When defining lowered methods using `_defined_lowered` we immediately create the first-class equivalent. The reverse is done lazily, creating lowered_methods on demand from the class.
* The two way conversions will be deleted in a future PR when the executor itself runs first-class objects. However this requires more changes to (1) the traces, (2) the python bindings, and (3) the onnx export pass and would make this PR way to large.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/19167
Differential Revision: D14891966
Pulled By: zdevito
fbshipit-source-id: 0b5f03118aa65448a15c7a7818e64089ec93d7ea
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18469
ghimport-source-id: 73cb8b58f43f10b1dcfca805fd5b25c4fa977632
Stack from [ghstack](https://github.com/ezyang/ghstack):
* **#18469 Create Object that represents a Module**
* #18468 slots with explicit value/setValue make more sense in future patches
* #18467 Make Object hold its ClassType
* #18379 Enforce single parent for script submodules
* #18378 Unify namespace of script::Module
* #18314 Add ability to specialize class types to ArgumentSpec
* #18226 Add Slot type to abstract the raw pointers being used for slots.
This changes the underlying storage for script::Module to hold
a ivalue::Object which has slots for all the parameters and attributes.
NamedIValue and Slot are now merged together into one class Slot that stores
the tuple (ivalue::Object, offset) and can be used to read the name, type,
or value of the slot and also to set the value. This cleans up a bunch
of client uses.
This PR does not actually use the module object in any generated code.
A future PR will switch how code is generated to treat modules as
first class.
Differential Revision: D14613508
fbshipit-source-id: d853a7559f58d244de2ef54a781427fcd1060ed0
Summary:
This refactor lets us track the types of initial values added onto a `Method`. The main motivation for this is the change in `module.cpp`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18252
Differential Revision: D14673459
Pulled By: driazati
fbshipit-source-id: 21200180c47f25bb70898771adfb569856e6c34a
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18379
ghimport-source-id: 9895ecc1ff7897e98853dc00675341f36726e7c7
Stack from [ghstack](https://github.com/ezyang/ghstack):
* **#18379 Enforce single parent for script submodules**
* #18378 Unify namespace of script::Module
* #18314 Add ability to specialize class types to ArgumentSpec
* #18226 Add Slot type to abstract the raw pointers being used for slots.
The assumption that a ScriptModule has a single parent is present in
our serialization format, and likely a few other places. It is not
enforced on creation of script module hierarchies though, meaning that
problems associated with (e.g. replicating a module twice in the output
format) will not be caught until much later in the development cycle.
This patch enforces the property when a submodule is registered.
It also removes NamedModule since it is no longer necessary in this regime.
This will also allow the easy discover of a modules fully-qualified name
without needing to traverse the Module hierarchy.
Differential Revision: D14603722
fbshipit-source-id: 63ab5d0cccf7d66c7833e0adf9023024ca9607cb
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18378
ghimport-source-id: 55c29bb436a2153d29ff2f4488d99d8863c187b1
Stack from [ghstack](https://github.com/ezyang/ghstack):
* #18379 Enforce single parent for script submodules
* **#18378 Unify namespace of script::Module**
* #18314 Add ability to specialize class types to ArgumentSpec
* #18226 Add Slot type to abstract the raw pointers being used for slots.
This removes individual OrderedDicts in favor of a single unified
namespace for all things in a script::Module. This removes a whole
class of bugs where both a method and an parameter could get the
same name, for instance.
Since we no longer have to expose OrderedDict::Item objects, a lot of
downstream code can be simplified.
We no longer now double-store names (both in the key of the dictionary,
and in the object itself).
Differential Revision: D14603723
fbshipit-source-id: b5f7551b3074679623edd6ea70269830353b4d4c
Summary:
* adds attributes to `ScriptModule.__getattr__` so they can be accessed in Python after re-importing
* full support for all the possible values for an `int64_t`
* this necessitated a bunch more `pushWhatever` functions, so re-introduced a templated version to cut down on duplicate code
* tests to validate references / value sharing works
* adds `torch.jit.Unpickler` which people can use to de-serialize the pickle files into Python / have a quick reference on how to do this without PyTorch
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18188
Differential Revision: D14527490
Pulled By: driazati
fbshipit-source-id: efd15579cc04aa2e28c4b2c9490d82d849dee559
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18226
ghimport-source-id: b9ec8651212875b30971cc6859d2ddec6559ae3a
If modules become first-class IValues, then the slots will no longer be raw pointers but (IValue, index) pairs. This commit inserts the Slot abstraction so that this change can be made in later patches.
Stack from [ghstack](https://github.com/ezyang/ghstack):
* **#18226 Add Slot type to abstract the raw pointers being used for slots.**
Differential Revision: D14542022
fbshipit-source-id: b81d7f4334c983d663e7551bda82df43680d7c5f
Summary:
Stack:
⚫ **#17856 [jit] support serialization of classes** [💛](https://our.intern.facebook.com/intern/diff/D14402599/)
Add support for saving/loading TorchScript modules that depend on user-defned classes.
We track class dependencies the same we track tensor constants, then write them
all out such that we can just compile them in order before compiling the module
hierarchy.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17856
Reviewed By: shannonzhu
Differential Revision: D14461599
Pulled By: suo
fbshipit-source-id: 7115f87e069fd00dc8381d7de9997864fef7ea9f
Summary:
Similar to `nn.Parameter`s, this PR lets you store any `IValue` on a module as an attribute on a `ScriptModule` (only from the Python front-end currently). To mark something as an attribute, it should wrapped in `jit.Attribute(value, type)` (ex. `self.table = torch.jit.Attribute(table, Dict[str, torch.Tensor])`)
Followup Work:
* (de)serializing for use in C++
* change `self.training` to be a `bool` attribute instead of a buffer
* mutable attributes
* string frontend support
* documentation
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17309
Differential Revision: D14354316
Pulled By: driazati
fbshipit-source-id: 67e08ab5229366b67fbc837e67b58831a4fb3318
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17528
as title. register_prim_ops is messy because someone ruined clang-format, but I figured it's okay to include here since this is such a mechanical change
Reviewed By: driazati
Differential Revision: D14236943
fbshipit-source-id: c2b22845837b7f830015510e48ec2ee5202fa407
Summary:
See comment inside of code. This fixes a bug where sometimes we would try to avoid printing long lines but would inadvertently reorder the expressions, which can change the semantics of the program
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17557
Differential Revision: D14250608
Pulled By: zdevito
fbshipit-source-id: d44996af4e90fe9ab9508d13cd04adbfc7bb5d1c
Summary:
First pass at user defined types. The following is contained in this PR:
- `UserType` type, which contains a reference to a module with all methods for the type, and a separate namespace for data attributes (map of name -> TypePtr).
- `UserTypeRegistry`, similar to the operator registry
- `UserObject` which is the runtime representation of the user type (just a map of names -> IValues)
- `UserTypeValue` SugaredValue, to manage getattr and setattr while generating IR, plus compiler.cpp changes to make that work.
- Frontend changes to get `torch.jit.script` to work as a class decorator
- `ClassDef` node in our AST.
- primitive ops for object creation, setattr, and getattr, plus alias analysis changes to make mutation safe.
Things that definitely need to get done:
- Import/export, python_print support
- String frontend doesn't understand class definitions yet
- Python interop (using a user-defined type outside TorchScript) is completely broken
- Static methods (without `self`) don't work
Things that are nice but not essential:
- Method definition shouldn't matter (right now you can only reference a method that's already been defined)
- Class definitions can only contain defs, no other expressions are supported.
Things I definitely won't do initially:
- Polymorphism/inheritance
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17314
Differential Revision: D14194065
Pulled By: suo
fbshipit-source-id: c5434afdb9b39f84b7c85a9fdc2891f8250b5025
Summary:
Bunch of random stuff I came across while doing UDT stuff. Putting in a separate PR to avoid noise
- fix up the alias analysis list ops to include fork/wait
- improve dump() for aliasDb to print writes
- Move BuiltinFunction::call() to sugaredvalue with the rest of the methods
- formatting and includes
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17288
Differential Revision: D14147105
Pulled By: suo
fbshipit-source-id: 62e2a922a1726b684347365dc42c72188f154e9c
Summary:
Trying to land again, make prim::None into a case of prim::Constant. Reverted the previous landing because it broke an important onnx export test.
https://github.com/pytorch/pytorch/pull/16160
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17186
Differential Revision: D14115304
Pulled By: eellison
fbshipit-source-id: 161435fc30460b4e116cdd62c7b2e5b94581dcb7
Summary:
This change simplifies analysis done on constants since prim::None does not need to be handled separately now. To check if a constant node is None, use node->isNone().
Next step will be to remove prim::Undefined.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16160
Differential Revision: D14109636
Pulled By: eellison
fbshipit-source-id: d26fd383976163a2ddd4c24984bd672a541cc876
Summary:
This PR is a follow up of #15460, it did the following things:
* remove the undefined tensor semantic in jit script/tracing mode
* change ATen/JIT schema for at::index and other index related ops with `Tensor?[]` to align with what at::index is really doing and to adopt `optional[tensor]` in JIT
* change python_print to correctly print the exported script
* register both TensorList and ListOfOptionalTensor in JIT ATen ops to support both
* Backward compatibility for `torch.jit.annotate(Tensor, None)`
List of follow ups:
* remove the undefined tensor semantic in jit autograd, autodiff and grad_of
* remove prim::Undefined fully
For easy reviews, please turn on `hide white space changes` in diff settings.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16379
Differential Revision: D13855677
Pulled By: wanchaol
fbshipit-source-id: 0e21c14d7de250c62731227c81bfbfb7b7da20ab
