Summary:
This adds support for PEP 526 style annotations on assignments in place of
`torch.jit.annotate()`, so
```python
a = torch.jit.annotate(List[int], [])
```
turns into
```python
a : List[int] = []
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/21390
Differential Revision: D15790937
Pulled By: driazati
fbshipit-source-id: 0cc204f7209a79839d330663cc6ba8320d3a4120
Summary:
This adds support for PEP 526 style annotations on assignments in place of
`torch.jit.annotate()`, so
```python
a = torch.jit.annotate(List[int], [])
```
turns into
```python
a : List[int] = []
```
](https://our.intern.facebook.com/intern/diff/15706021/)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/21390
Pulled By: driazati
Differential Revision: D15706021
fbshipit-source-id: 8bf1459f229d5fd0e16e59953b9656e85a2207fb
Summary:
Resolves https://github.com/pytorch/lockdown/issues/51
This adds support for converting simple f-string literals to calls to `string.format()`. It does not support conversion specifiers or format strings.
This also does not support the string parser frontend, since that implementation would be more involved and likely would require modifying our TorchScript AST
Pull Request resolved: https://github.com/pytorch/pytorch/pull/21037
Reviewed By: zdevito
Differential Revision: D15541183
Pulled By: jamesr66a
fbshipit-source-id: ae9df85e73f646d7219c1349f5b7683becbcef20
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:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18598
ghimport-source-id: c74597e5e7437e94a43c163cee0639b20d0d0c6a
Stack from [ghstack](https://github.com/ezyang/ghstack):
* **#18598 Turn on F401: Unused import warning.**
This was requested by someone at Facebook; this lint is turned
on for Facebook by default. "Sure, why not."
I had to noqa a number of imports in __init__. Hypothetically
we're supposed to use __all__ in this case, but I was too lazy
to fix it. Left for future work.
Be careful! flake8-2 and flake8-3 behave differently with
respect to import resolution for # type: comments. flake8-3 will
report an import unused; flake8-2 will not. For now, I just
noqa'd all these sites.
All the changes were done by hand.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
Differential Revision: D14687478
fbshipit-source-id: 30d532381e914091aadfa0d2a5a89404819663e3
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:
This provides the minimum necessary to allow derivative formulas for things that have a kwarg only specifier in their schema. Support for non-parser frontend default arguments for kwargs is not completed.
Fixes#16921
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17339
Differential Revision: D14160923
Pulled By: zdevito
fbshipit-source-id: 822e964c5a3fe2806509cf24d9f51c6dc01711c3
Summary:
This PR adds basic support (creation and indexing) for immutable dictionaries in Script. This includes Python/string frontend support and a `IValue::GenericDict` type backed by a `std::unordered_map`. Only `str`, `int`, and `float` are supported as keys, any type can be a value. Structure is pretty similar to list.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16208
Differential Revision: D13881686
Pulled By: driazati
fbshipit-source-id: 29ce9835b953c3456f57bcc2bbdf7fe0cbf941c0
Summary:
This PR changes Method (just Method not all graphs) to always have a single
return argument.
This is part 1 in a set of changes that will enable us to have better handling if early return statements.
The simplification that this change provides greatly reduces the work for the next step.
This change makes it so that Method and Python handle multiple returns in the same way:
* 0 - None
* 1 - <single value>
* many - Tuple[...]
The result is that a lot of special-case handling in compiler.cpp and its
bindings can be removed. It also fixes several bugs in return handling,
including one where return values were not always checked against their
attributed values.
Notes:
* inferTypeFrom is renamed to be more accurate and discourage use.
* This has uncovered some bugs in other components, which are noted in
the diff.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15289
Differential Revision: D13481649
Pulled By: zdevito
fbshipit-source-id: 0e2242a40bb28cca2d0e8be48bede96195e4858c
Summary:
Previously, we did not distinguish between `a = b` (simple assignment),
and `a, = b` (tuple destructuring of a singleton tuple).
The second case would fail in the string frontend, and would not unpack
in the python frontend. This patch fixes both issues and also cleans up
the error reporting for unexpected expressions on the LHS.
Will likely conflict with #13486
Pull Request resolved: https://github.com/pytorch/pytorch/pull/13656
Differential Revision: D12964566
Pulled By: zdevito
fbshipit-source-id: 992b19e5068aef59a78cd23cb0e59a9eeb7755d1
Summary:
This PR changes the compiler to correctly emit in-place operators for augmented assignments (`+=` and friends).
- To better match the Python AST structure, add an `AugAssign` tree view and make `Assign` apply only to `=` assignments.
- Emit those `AugAssign` exprs in the compiler, dispatching to in-place aten ops for tensors and lowering to simple assignments for scalar types.
- In order to preserve (suspect) ONNX export semantics, add a pass to lower the in-place operators to out-of-place operators.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/13364
Differential Revision: D12899734
Pulled By: suo
fbshipit-source-id: bec83be0062cb0235eb129aed78d6110a9e2c146
Summary:
As titled, this PR is a part of tasks to unblock exporting the standard library.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/13336
Differential Revision: D12888912
Pulled By: wanchaol
fbshipit-source-id: 6213a17a75a593ae45999994fd9562f29b7d42df
Summary:
Adding assert statements to unblock standard library.
The same limitations that apply to the existing implementation of Exceptions apply to this as well
(No control-flow logic, & we ignore the specific Exception thrown).
Pull Request resolved: https://github.com/pytorch/pytorch/pull/13408
Reviewed By: driazati
Differential Revision: D12876451
Pulled By: eellison
fbshipit-source-id: 767ba5a50ba7c5dd6a857ed4845ac076a81cf305
Summary:
This is a first step towards adding exceptions. We need minimal support in order to begin converting the torch library to weak script mode (which is the main goal here).
Some limitations (that are documented in the tests & compiler):
1. Cannot assign exceptions to variables
2. Any name after raise is being treated as a valid Exception
3. No control flow analysis yet. Below a will be undefined:
if True:
a = 1
else:
raise Exception("Hi")
return a
Pull Request resolved: https://github.com/pytorch/pytorch/pull/12789
Differential Revision: D12848936
Pulled By: eellison
fbshipit-source-id: 1f60ceef2381040486123ec797e97d65b074862d
Summary:
- In Python 2, use of `/` (regardless of int/float/Tensor) causes a compiler error if
`from __future__ import division` is not imported in the file.
- The / operator is universally set to do "true" division for integers
- Added a `prim::FloorDiv` operator because it is used in loop unrolling.
The error if users use '/' in python 2 without importing from __future__
occurs when building the JIT AST.
cc apaszke zdevito
Pull Request resolved: https://github.com/pytorch/pytorch/pull/11016
Differential Revision: D9613527
Pulled By: zou3519
fbshipit-source-id: 0cebf44d5b8c92e203167733692ad33c4ec9dac6
Summary:
Part of #10774.
This PR does the following:
- Support ast.ExtSlice in the frontend. This is done by returning a
list of ast.Index and ast.Slice.
- Support multidimensional indexing with ints and slices
The general approach is to desugar multidimensional indexing into
at::slice, at::select operations. This is exactly how normal pytorch
does indexing (by desugaring it into at::slice, at::select, and other ops).
I used [this code](https://github.com/pytorch/pytorch/blob/master/torch/csrc/autograd/python_variable_indexing.cpp) as reference.
We should be able to copy the rest of this to implement the missing
indexing features in script (indexing with ellipses, tensors, sequences, etc).
After I'm done implementing the missing indexing features in future prs, I can try to
templatize python_variable_indexing.cpp so that it can work with both JIT
script and normal pytorch indexing, but right now I'm not sure if that's
a good idea or not.
cc zdevito jamesr66a apaszke wanchaol
Pull Request resolved: https://github.com/pytorch/pytorch/pull/10787
Differential Revision: D9481402
Pulled By: zou3519
fbshipit-source-id: 78c9fa42771a037d157879e23e20b87401cf1837
Summary:
After this, all combinations of {String frontend, Python AST Frontend}{Python 3-style type annotations, MyPy-style type comments}{Script method, Script function} should properly accept type annotations.
Possible TODOs:
- Clean up the functions marked HACK
- Clean up the Subscript tree-view to better match the Python AST versions
- Can we use this for Python functions? That's the only place annotations.get_signature() is still needed
Pull Request resolved: https://github.com/pytorch/pytorch/pull/10279
Differential Revision: D9319726
Pulled By: jamesr66a
fbshipit-source-id: b13f7d4f066b0283d4fc1421a1abb9305c3b28fa
Summary:
This PR adds strings to the ast and implements them for print statements. Strings are lifted as attributes to the print node. They must be arguments to print itself, not as an argument for an object that is passed to print. If they are encountered elsewhere a NYI exception will be thrown.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/9324
Reviewed By: jramseyer
Differential Revision: D8807128
Pulled By: eellison
fbshipit-source-id: 984401ff458ed18d473c6d1bd86750e56c77d078
Summary:
Previously, the parser was emitting list literals for tuples, but the IR was representing list literals internally with TupleTypes.
For implementing most list operations, I think it will be helpful distinguish between lists (dynamic size, homogeneous types) and tuples (fixed arity, heterogeneous types)
This diff modifies the parser logic to emit tuple literals. This frees us to represent lists as ListType in the IR, while still properly mapping tuple literals to TupleTypes.
A following diff will actually switch over list literals to emit ListTypes.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/10128
Differential Revision: D9121305
Pulled By: michaelsuo
fbshipit-source-id: e0cad07ae8bac680f7f8113d10e5129d5a1a511d
Summary:
Supersedes #8925
This PR fixes#8502, it fixes the gradients problem for clamp when passing None to the function, and add support for the NoneLiteral and NoneType in script to enable clamp tests. Now we could have corner cases like:
```python
torch.jit.script
def func():
x = torch.randn(3, 3, requires_grad=True)
y = torch.clamp(x, None, 0) # max = 0
y = torch.clamp(x, min=None, max=0)
```
In both JIT and Aten, we use Scalar(NAN) as a sentinel value when passing None type to function clamp, this is the current way we used to support None type in JIT and to solve the gradient problem when user explicitly passing None into clamp.
In JIT side, we create a tensor(NAN) and undefinedTensor if we encounter None when matching the function schema, and later in the interpreter, it will translate to Scalar(NAN) if needed.
Ideally we don't need clamp_min and clamp_max in ATenNative/Autograd and could only support clamp after this change, but since bunch of other operators (e.g. Activation.cpp, Loss.cpp) is using clamp_min in several places, we will still have the functions available, but all python invocations will only call clamp instead of clamp_min/max (with calling underlying th_max/th_min in clamp).
zdevito jamesr66a
Pull Request resolved: https://github.com/pytorch/pytorch/pull/9596
Reviewed By: zdevito
Differential Revision: D8940839
Pulled By: wanchaol
fbshipit-source-id: c543a867b82e0ab8c99384773b173fdde2605d28
* docstring support for @script and @script_method
* make it python2 compatible
* improve according to review
* improve build_stmts
* use filter instead of list comprehension
* improve the way wrap is handled for script_method
* stash the original method instead
* allow dynamic attr for ScriptMethod and GraphExecutor
* a bit comment on build_Expr
* remove _build_wrap
* a bit improve on comments
* rename to __original_methods
* should be _original_methods
* Support list and tuple literals: Adds support for [a, b], (a, b) and "a, "
* Allow non-tensors to reach emitBuiltinCall, each SugaredValue::call
is now responsible for checking the types of its inputs.
Add support for calling cat with a tuple to emitBuiltinOp
* Fixes to the way script handles multiple values, and other minor fixes.
This commit improves our handling of operators that return multiple values.
Builtins are now checked so that they return the right number of values,
and support for TupleValue is extended to all things that can return
multiple values.
This resolves issues where the compiler accepted things like:
a, b = c + c
This would cause the interpreter to crash. Now each operator knows
how many results it will produce and can check it against the number
of requested inputs.
Notes:
* Allow True/False literals in constant expressions
* make handling of keyword constants more consistent to support True/False
* make parsing constants match the way we construct constants from python
* improve the error messages when accessing bad graph attributes.
* switch findTensorOp to return an optional.
* check that attribute types are correct in findTensorOp
* Check the correct number of outputs for builtins
This also changes emitExpr to return a single SugaredValue
Rather than possibly returning multiple values, emitExpr now
always returns a single value, which _might_ be a tuple. This approach
more closely follows python making the code easier to follow.
Checks for returning the right number of values are now located in
the assignment operator, and occur when unpacking the tuple.
We still pass `n_binders` to function calls so that calls into python
know how many values they should return.
* Something that works
* Tuple sugared value
* Works with commenting out input size check
* support string frontend
* Initial starred assignment
* Fix parser
* Fixup tests
* clang-format
* fix rebase error
* lint
* move star assign test to string frontend to make py2 happy
* Py2 fix: parse starargs from Call node
* Address some comments
* Fixup merge
* Remove overloaded unary operators
* Bugfix and test case
* Address a few more comments
* asValues -> asTuple
* Remove unrolledFor stuff
* Fixup getValues
* Pass CallsiteDescriptor struct and have different behavior for different call types
* Address comments and lint
* some type checks
* Address comments
* lint
* Fix mistake
Script functions can now have no return statements, empty
return statements, or return one or more values.
Additionally fix the lexer to always emit TK_NEWLINE before
TK_DEDENT, which simplifies the parser.
Before, using an unknown binary operator like `@`:
```
import torch
@torch.jit.script
def mm(x, y):
return x @ y
x = torch.randn(4, 3)
y = torch.randn(3, 2)
mm(x, y)
```
resulted in [this not-so-readable trace](https://gist.github.com/zou3519/052b8998108c4bc0fe0e7c85c6f5758e).
Now, it tells the user that the problem is an unknown binary operator:
```
NotSupportedError: unsupported binary operator: MatMult
@torch.jit.script
def mm(x, y):
return x @ y
~~~ <--- HERE
```
* Implement range for loop in script
* Fix handling of boolean constants
* Use WithInsertPoint
* Allow dynamic max trip count
* fix symbols
* Fix argument order
* fix test
* Add insert{Input,Output} APIs and use them
* Factor out condition stuff
* clang-format
* Address remaining comments
* Fix tests
* Implement script in AST frontend
Add script::Module C++ class to represent script modules
switch AST -> IR conversion to work on Modules/Methods rather than raw graphs
function-only AST -> IR conversion is just a simplified case where there is
only one module with a single method and no parameters.
introduce SugaredValue in compiler.h to represent values in scope in a script
function that are not first-class and that get desugared. This is used to
represent the module's self parameter, as well as python function calls,
and method calls on tensor
provide a Python ScriptModule that provides a nice API on top of script::Module
allowing for the definition of script modules with methods, parameters,
and submodules
Not in this PR but intended for the future:
ScriptModule actually subclasses nn.Module, with most methods implemented
Unification of tracedmodule and script module functionality into one container class.
Detailed changelog:
* Switch compiler over to using Module, but don't
use them yet.
* Remove intermediate attribute encoding in compiler
* Create SugaredValue object to handle resolution
of compiled module.
* switch to_ir to modules, implement Select
* hacky python wrappers
* Private ScriptModule
* Add `define` to script module
* Attributes use TK_LIST_LITERAL
this anticipates adding a real list literal expression to the language.
* Add a metaclass to make sure script stubs are registered
* Add a test
* Doc createResolutionCallback
* Docs and minor editing
* Address PR comments
* Document
* Fix unicode issue
Additionally:
- add support for calling functions that are not methods in the Python frontend
- add an end-to-end test for the Python frontend
- add a capture_stdout helper for checking that `print` actually works
