Summary:
Create an uninitialized ivalue. This will be needed for Breaks & Continues to match up if block outputs of values that are guaranteed not to be used but need to escape the block scope. It is not exposed to users.
Was previously part of final returns but I was asked to make a separate PR for it.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/21387
Differential Revision: D15745124
Pulled By: eellison
fbshipit-source-id: ae6a6f766b4a70a71b9033987a630cfbf044e296
Summary:
This changes our compiler so it first emits Loads & Stores, and then transforms the graph to SSA in a follow up pass. When a variable is set, we emit a prim::Store, and when a variable is referenced, we emit a prim::Load.
```
a = 1
print(a)
```
becomes:
```
%a.1 : int = prim::Constant[value=1]()
prim::Store[name="a"](%a.1)
%a : int = prim::Load[name="a"]()
prim::Print(%a)
```
In the follow up pass, convertToSSA, the values are turned into SSA form with the Loads & Stores removed. This change will enable breaks and continues because you can transform the graph with the variable naming information still intact.
There are still some remaining jitter and edge cases issues that I have to look through, but I think is still ready for eview.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/21101
Differential Revision: D15723353
Pulled By: eellison
fbshipit-source-id: 3269934d4bc24ddaf3a87fdd20620b0f954d83d0
Summary:
cc ezyang this is meant to fix the fuser failures on master
Pull Request resolved: https://github.com/pytorch/pytorch/pull/21252
Differential Revision: D15594283
Pulled By: jamesr66a
fbshipit-source-id: 85f37e78b2de051c92ade3fe4c44c7530b4542e5
Summary:
Resolves https://github.com/pytorch/lockdown/issues/29
Examples:
```
import torch
torch.jit.script
def foobar(x):
return torch.blargh(xyz)
==
RuntimeError:
object has no attribute blargh:
at compile.py:5:12
torch.jit.script
def foo(x):
return torch.blargh(x)
~~~~~~~~~~~~ <--- HERE
```
It also gets the correct column number in the case where the original source file has common leading whitespace in front of the callable:
```
import torch
with torch.no_grad():
torch.jit.script
def foo(x):
return torch.blargh(x)
==
RuntimeError:
object has no attribute blargh:
at compile_leading.py:6:24
torch.jit.script
def foo(x):
return torch.blargh(x)
~~~~~~~~~~~~ <--- HERE
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/20898
Differential Revision: D15552424
Pulled By: jamesr66a
fbshipit-source-id: 78d0f0de03f7ccbf3e7ea193a1b4eced57ea5d69
Summary:
This PR makes `torch.save` call out to the pickler which saves a tensor in the same format that `torch.save()` does, the file looks like `| pickle archive 1 (includes sizes, strides, requires_grad, etc...) | pickle archive 2 (list of tensor keys) | tensor binary data |` and can be read back in with `torch.load(my_file, pickle_module=torch.jit._pickle)`
Fixes#18003
Unpickling in the JIT for things such as model parallelism will be a follow up PR
](https://our.intern.facebook.com/intern/diff/15015160/)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18154
Pulled By: driazati
Differential Revision: D15015160
fbshipit-source-id: ef76a44b8c243f4794cd7e245ec8305e965bc59f
Summary:
Canonicalize the ordering of outputs of if and loop nodes based on their first usage. Previously we were able to canonicalize output order by sorting on variable name, but this breaks down with outputs added in an early return pass.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/20015
Differential Revision: D15266066
Pulled By: eellison
fbshipit-source-id: ba5340c068a68b1ffc73f056db194b92d3274dc4
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:
Make it possible to construct a pinned memory tensor without creating a storage first and without calling pin_memory() function. It is also faster, as copy operation is unnecessary.
Supported functions:
```python
torch.rand_like(t, pin_memory=True)
torch.randn_like(t, pin_memory=True)
torch.empty_like(t, pin_memory=True)
torch.full_like(t, 4, pin_memory=True)
torch.zeros_like(t, pin_memory=True)
torch.ones_like(t, pin_memory=True)
torch.tensor([10,11], pin_memory=True)
torch.randn(3, 5, pin_memory=True)
torch.rand(3, pin_memory=True)
torch.zeros(3, pin_memory=True)
torch.randperm(3, pin_memory=True)
torch.empty(6, pin_memory=True)
torch.ones(6, pin_memory=True)
torch.eye(6, pin_memory=True)
torch.arange(3, 5, pin_memory=True)
```
Part of the bigger: `Remove Storage` plan.
Now compatible with both torch scripts:
` _1 = torch.zeros([10], dtype=6, layout=0, device=torch.device("cpu"), pin_memory=False)`
and
` _1 = torch.zeros([10], dtype=6, layout=0, device=torch.device("cpu"))`
Same checked for all similar functions `rand_like`, `empty_like` and others
It is fixed version of #18455
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18952
Differential Revision: D14801792
Pulled By: VitalyFedyunin
fbshipit-source-id: 8dbc61078ff7a637d0ecdb95d4e98f704d5450ba
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18711
ghimport-source-id: c9caedc0660b2b7ba3730cd0e1a2e0e9c3cf422b
Stack from [ghstack](https://github.com/ezyang/ghstack):
* **#18711 [jit] fix side-effects and aliasing for custom ops**
Previously we didn't track aliasing, mutation, or side effects for
custom ops. This PR adds in guards with the most conservative
assumptions possible: the op will
1) have side effects,
2) write to everything
3) produce a wildcard.
In order to tell whether a given operator is a custom op, this PR introduces
the concept of a "reserved" namespace (basically all our builtin namespaces).
Custom ops live in non-reserved namespaces, so a check on the namespace
is sufficient to tell whether a schema/node is "custom" or not.
This is just to get things correct for now. Follow-ups to this:
- Users should be able to specify aliasing/mutability without having to learn
the whole alias annotation schema.
- Relax assumptions a bit. In particular outputs can only alias input tensors,
they don't have to be wildcards.
Fixes#18490
Differential Revision: D14730978
fbshipit-source-id: 540b47a24ccf24145051609bdcc99c97e46e0fe0
Summary:
Make it possible to construct a pinned memory tensor without creating a storage first and without calling pin_memory() function. It is also faster, as copy operation is unnecessary.
Supported functions:
```python
torch.rand_like(t, pin_memory=True)
torch.randn_like(t, pin_memory=True)
torch.empty_like(t, pin_memory=True)
torch.full_like(t, 4, pin_memory=True)
torch.zeros_like(t, pin_memory=True)
torch.ones_like(t, pin_memory=True)
torch.tensor([10,11], pin_memory=True)
torch.randn(3, 5, pin_memory=True)
torch.rand(3, pin_memory=True)
torch.zeros(3, pin_memory=True)
torch.randperm(3, pin_memory=True)
torch.empty(6, pin_memory=True)
torch.ones(6, pin_memory=True)
torch.eye(6, pin_memory=True)
torch.arange(3, 5, pin_memory=True)
```
Part of the bigger: `Remove Storage` plan.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18455
Reviewed By: ezyang
Differential Revision: D14672084
Pulled By: VitalyFedyunin
fbshipit-source-id: 9d0997ec00f59500ee018f8b851934d334012124
Summary:
This defines a generic counters API that users can utilize to provide monitoring functionality in e.g. a production service. We expose both counters for runtime internals as well as a TorchScript API to create user-defined counters. Synopsis of the API:
- `torch/csrc/jit/script/logging.h` specifies the externally-facing API in C++
- `torch/jit/_logging.py` specifies the Python API
We use an interface, `LoggerBase`, to define the interactions between users and a logging backend. Implementing a subclass of `LoggerBase` allows the user to handle these events in a custom way, such as logging into a DB or calling into an infra-specific counters API.
From the frontend perspective, we can create log events in two ways:
1. We provide an `add_stat_value(name, val)` function. This calls into the Logger backend with a key/value pair. For example, we might call `add_stat_value('foo', 1)` to bump an event counter.
2. We provide a `time_point()` function to record a timestamp in nanoseconds. This can be used in conjunction with `add_stat_value` to record runtime wall clock durations.
Examples of frontend usage can be found in `test_jit.py TestLogging`.
We provide a trivial `LockingLogger` implementation as an example and for testing purposes. It is likely not ready for production usage. It demonstrates that a backend implementing the API can do things like specify aggregation types and report these aggregate stats via the `get_counters()` API.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18235
Differential Revision: D14545060
Pulled By: jamesr66a
fbshipit-source-id: 04099543a1898cfdd411511e46e03d5dce9b4881
Summary:
Simplify or eliminate boolean and/or expressions, optimize unwrapping a value that cannot be None, and optimize using `is` with a None and a non-None value
Since peephole optimize is now introducing constants, i added another constant propagation pass after running it.
Previously i had a PR that did this & optimized shape ops - i will add the shape optimizations in a separate PR.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18259
Differential Revision: D14602749
Pulled By: eellison
fbshipit-source-id: 1c3f5a67067d8dfdf55d7b78dcb616472ea8a267
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:
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:
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:
The method will be used in IRParser and in NetDef converter.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17372
Differential Revision: D14172494
Pulled By: ZolotukhinM
fbshipit-source-id: 96cae8422bc73c3c2eb27524f44ec1ee8cae92f3
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 removes curly braces from the outputs (we have indentation to indicate scopes), also adds ':' after graph and blocks declaration and removes ';' from the return line. ".expect" tests are updated to keep up with it.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16986
Differential Revision: D14062540
Pulled By: ZolotukhinM
fbshipit-source-id: 7f8e2d11619152a21ef7f1f7f8579c49392c3eca
Summary:
Fixes: https://github.com/pytorch/pytorch/issues/16233
The following changes are made:
- Modify `TupleType` to store optional field names
- Modify schema matching to return fill in those field names when creating `TupleType` as return type.
- Modify codegen of JIT to copy field names to schema string
- Modify `SchemaParser` to set field names of returned schema.
- Modify `SimpleValue::attr` to emit tuple indexing for named tuple.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16253
Reviewed By: ezyang
Differential Revision: D13954298
Pulled By: zdevito
fbshipit-source-id: 247d483d78a0c9c12d1ba36e1f1ec6c3f1a3007b
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16751
This was made more complicated by the fact that ivalue::IntList
is a thing. So I had to fix all of the sites where we referring
to IValue post facto.
The following codemods were run, in this order:
```
codemod -m -d . --extensions cc,cpp,cu,cuh,h,hpp,py,cwrap,yaml,in IntList IntArrayRef
codemod -m -d . --extensions cc,cpp,cu,cuh,h,hpp,py,cwrap,yaml,in IntArrayRef::create IntList::create
codemod -m -d . --extensions cc,cpp,cu,cuh,h,hpp,py,cwrap,yaml,in ivalue::IntArrayRef ivalue::IntList
codemod -m -d . --extensions cc,cpp,cu,cuh,h,hpp,py,cwrap,yaml,in Tag::IntArrayRef Tag::IntList
codemod -m -d . --extensions cc,cpp,cu,cuh,h,hpp,py,cwrap,yaml,in isIntArrayRef isIntList
codemod -m -d . --extensions cc,cpp,cu,cuh,h,hpp,py,cwrap,yaml,in toIntArrayRef toIntList
codemod -m -d . --extensions cc,cpp,cu,cuh,h,hpp,py,cwrap,yaml,in 'Shared<IntArrayRef>' 'Shared<IntList>'
codemod -m -d . --extensions cc,cpp,cu,cuh,h,hpp,py,cwrap,yaml,in 'intrusive_ptr<IntArrayRef>' 'intrusive_ptr<IntList>'
```
Some manual fixups were done afterwards; they can be reviewed separately
at https://github.com/pytorch/pytorch/pull/16752
Reviewed By: dzhulgakov
Differential Revision: D13954363
fbshipit-source-id: b5c40aacba042402155a2f5a229fa6db7992ac64
Summary:
Adds a decorator `torch.jit.ignore` for Python functions that tells the compiler to skip over these Python values, putting a `prim::Error` in their place which always throws an exception when run.
This lets you have Python-only code in your model in an explicit way, which is useful for debugging, and still be able to save/load the model.
Fixes#15815
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16055
Differential Revision: D13797286
Pulled By: driazati
fbshipit-source-id: 29d36776608ec101649a702952fc6ff3c27655b1
