Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/49054
These are some followups from the first pyi codegen PR. Still maintaining byte-for-byte compatibility in this one.
- Separated `argument_str() with a pyi flag into two functions, `argument_str()` and `argument_str_pyi()`
- Added a notes section for pyi at the top of `python.py`
- Added a `Python Interface` section that I moved the free-standing pyi functions to
Test Plan: Imported from OSS
Reviewed By: ljk53
Differential Revision: D25410848
Pulled By: bdhirsh
fbshipit-source-id: db83a80af900c32b5e32d67ce27767f6e7c2adfb
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/49043
Previously, this function had nontrivial algorithmic content,
but after #48195, this was just a swiss army knife for pasting
together arguments while maintaining structure. I added some
more properties for Arguments for convenient access in this way,
and then inlined the implementation of group_arguments into all of its call
sites, simplifying whenever contextual. This might be controversial, but I
think the resulting code is easier to understand.
You may notice that there is some modest code duplication between
dispatcher.cpparguments_exprs and CppSignature.argument_packs.
This is a known problem and I will be attempting to fix it in
a follow up PR.
Confirmed to be byte-for-byte compatible.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
Test Plan: Imported from OSS
Reviewed By: H-Huang
Differential Revision: D25455885
Pulled By: ezyang
fbshipit-source-id: 8fbe066e8c3cb7ee8adb5b87296ec5bd7b49e01f
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/49042
I want the names positional and kwarg_only to give the unflat
representation (e.g., preserving TensorOptionsArguments in the
returned Union). So I regret my original naming choice when
I moved grouping to model. This renames them to have flat_ prefix
and also adds a flat_non_out argument for cases where you just
want to look at non-out arguments.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
Test Plan: Imported from OSS
Reviewed By: H-Huang
Differential Revision: D25455884
Pulled By: ezyang
fbshipit-source-id: f923f8881267a3e3e8e9521519412f7cc25034fc
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/47712
This adds a faithful API for ops with out arguments, as described in https://docs.google.com/document/d/1h7nBibRwkRLQ8rsPhfALlwWR0QbkdQm30u4ZBwmaps8/edit# .
After this, an op will generate the following overloads for the C++ API:
```cpp
// Generated from the aten::abs operator (NOT from aten::abs.out)
Tensor at::abs(Tensor& self)
// Generated from the aten::abs.out operator
Tensor& at::abs(Tensor& self, Tensor& out)
Tensor& at::abs_out(Tensor& out, Tensor& self)
```
This is an important step towards making those ops c10-full (it allows VariableType, XLA and other backends to ignore reordering and just call through with the same argument order), but this does not make any of those ops c10-full yet.
It enables the faithful API independent from c10-fullness. That means the API is more consistent with the same API for all ops and making an op c10-full in the future will not trigger future C++ API changes.
ghstack-source-id: 118068091
Test Plan: waitforsandcastle
Reviewed By: ezyang
Differential Revision: D24835252
fbshipit-source-id: dedfabd07140fc8347bbf16ff219aad3b20f2870
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/48754
The goal of this PR is to kill Declarations.yaml in the pyi codegen, in favor of native_functions + the existing python object model.
**High-level design**
Since the python signatures used by the `python_arg_parser` are “supposed” to resemble the corresponding pyi type hint signatures, I re-used the existing python object model that Jiakai defined in `tools/codegen/api/python.py`. This means that the pyi codegen now reads `native_functions.yaml`, parses it into a bunch of `PythonSignatureGroup` objects, and emits corresponding method + function variants of type-hint signatures for each one, respectively into `__init__.pyi` and `_VariableFunctions.pyi`.
What makes this uglier is that pyi and the python arg parser have a number of differences in how they’re emitted. I expressed that through a `pyi` flag on the `PythonSignature` dataclass, that tells it whether or not to print itself as a pyi vs. arg_parser signature.
One thing worth noting is how pyi generates signatures differently for native / deprecated op signatures.
For native ops:
- The pyi codegen fuses functional and out variants of each op into a single signature with an optional `out` argument. Ops without an `out` variant just get an ordinary functional signature.
- Some ops that fit certain criteria also get a second “varargs” signature - basically ops with a single positional argument of type List[int].
For deprecated signatures:
- Functional and out variants are not fused - they each get their own signature entry
- There are no varargs signatures
This is currently implemented through the `signature_str()` and `signature_str_vararg()` methods on the `PythonSignature`/`PythonSignatureDeprecated` classes. `signature_str()` knows how to print itself with/without out arguments, differently for native/deprecated ops. `signature_str_vararg()` optionally returns a vararg variant of the signature if one exists.
**Calling out the gap between python_arg_parser vs. pyi**
The two formats are notably different, so I don’t think we can expect to unify them completely. That said, I encountered a number of differences in the pyi codegen that looked wrong- I tried to call them out in the PR, to be removed later. Just as an example, looking at the `svd` signature in the python_arg_parser vs. the pyi type hint:
python_arg_parser
```
Static PythonArgParser parser({
“svd(Tensor input, bool some=True, bool compute_uv=True, *, TensorList[3] out=None”,
}, /*traceable=*/true);
```
Pyi
```
def svd(input: Tensor, some: _bool=True, compute_uv: _bool=True, *, out: Optional[Tensor]=None) -> namedtuple_U_S_V: …
```
The two have obvious syntactic differences that we probably don’t plan on changing: the python_arg_parser doesn’t include `def` or return types, and it includes the type hint before the variable name. But the type of `out` in pyi is probably wrong, since `svd` has multiple output params. I tried to clearly call out any instances of the pyi codegen diverging in a way that looks buggy, so we can clean it up in a later PR (see the comments for details).
Another particularly ugly “bug” that I kept in to maintain byte-for-byte compatibility is the fact that the pyi codegen groups operator overloads together. It turns out that the only reason it does this (as far as I can tell) is because is tacks on an out argument to signatures that don’t have one, if ANY overloads of that op have an out variant.
E.g. consider the pyi type hints generated for `nanmedian` in `_VF.pyi`:
```
overload
def nanmedian(input: Tensor, *, out: Optional[Tensor]=None) -> Tensor: ...
overload
def nanmedian(input: Tensor, dim: _int, keepdim: _bool=False, *, out: Optional[Tensor]=None) -> namedtuple_values_indices: ...
overload
def nanmedian(input: Tensor, dim: Union[str, ellipsis, None], keepdim: _bool=False, *, out: Optional[Tensor]=None) -> namedtuple_values_indices: ...
```
And the corresponding native_functions.yaml entries:
```
- func: nanmedian(Tensor self) -> Tensor
- func: nanmedian.dim(Tensor self, int dim, bool keepdim=False) -> (Tensor values, Tensor indices)
- func: nanmedian.dim_values(Tensor self, int dim, bool keepdim=False, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices)
- func: nanmedian.names_dim(Tensor self, Dimname dim, bool keepdim=False) -> (Tensor values, Tensor indices)
- func: nanmedian.names_dim_values(Tensor self, Dimname dim, bool keepdim=False, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!)
```
Signature 2 corresponds to entries 2 and 3 in native_functions, and Signature 3 corresponds to entries 4 and 5. But signature 1 has an optional out argument, even though entry 1 in native_functions.yaml has no out variant.
I’d like to delete that logic in a later PR- that will also have the added benefit no longer requiring to group overloads together in the pyi codegen. We can just operate independently on each PythonSignatureGroup.
**More detailed accounting of the changes**
Per file:
gen_python_functions.py
- `load_signatures()` can now skip deprecated signatures. Needed because pyi only includes deprecated functions, and skips their method variants (maybe we should add them in…?)
- Moved `namedtuple_fieldnames` into python.cpp
- `group_overloads()` can now opt to not sort the overloads (needed for byte-for-byte compact, pyi doesn’t sort for some reason)
Python.py:
- Gave `PythonSignature`and `PythonSignatureDeprecated` a `pyi` flag that tells it whether or not to print itself in pyi vs. python_arg_parser format
- Added a `PythonReturns` dataclass , which is now a member of PythonSignature. It is only used by pyi. I found this useful because python returns need to know how to deal with named tuple returns properly. I also moved `namedtuple_fieldnames` into this file from gen_python_functions
gen_pyi.py
- Merged `get_py_torch_functions` and `get_py_variable_methods` into a single function, since they’re very similar
- Lifted out all of the pyi type hint type-mapping mess and dropped it into python.py. This required updating the mapping to deal with NativeFunction objects instead of the outputs of Declarations.yaml (this was most of the logic in `type_to_python`, `arg_to_type_hint`, and `generate_type_hints`). `generate_type_hints` is now a small orchestration function that gathers the different signatures for each PythonSignatureGroup.
- NamedTuples are now generated by calling `PythonReturn.named_tuple()` (in `generate_named_tuples()`), rather than appending to a global list
A lot of hardcoded pyi signatures still live in `gen_pyi.py`. I didn’t look to closely into whether or not any of that can be removed as part of this PR.
Test Plan: Imported from OSS
Reviewed By: ljk53
Differential Revision: D25343802
Pulled By: bdhirsh
fbshipit-source-id: f73e99e1afef934ff41e4aca3dabf34273459a52
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/48182
I'm planning to add a bunch more argument fields following
https://github.com/pytorch/pytorch/pull/45890#discussion_r503646917 and
it will be a lot more convenient if the arguments get to live
in their own dedicated struct. Type checker will tell you if
I've done it wrong. No change to output.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
Test Plan: Imported from OSS
Reviewed By: ljk53
Differential Revision: D25057897
Pulled By: ezyang
fbshipit-source-id: dd377181dad6ab0c894d19d83408b7812775a691
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/47977
Avoid calling CppSignatureGroup api - python signature shouldn't depend on
cpp signature. Still use cpp.group_arguments() to group TensorOptions.
Confirmed byte-for-byte compatible with the old codegen:
```
Run it before and after this PR:
.jenkins/pytorch/codegen-test.sh <baseline_output_dir>
.jenkins/pytorch/codegen-test.sh <test_output_dir>
Then run diff to compare the generated files:
diff -Naur <baseline_output_dir> <test_output_dir>
```
Test Plan: Imported from OSS
Reviewed By: ezyang
Differential Revision: D24976334
Pulled By: ljk53
fbshipit-source-id: 5df5a7bbfd2b8cb460153e5bea4d91e65f716390
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/47745
This is a relatively small codegen. Reintroduced 'simple_type' to preserve
old codegen output.
It depends on some methods defined in gen_python_functions.py - next PR will
clean up the remaining Declarations.yaml methods in gen_python_functions.py.
Confirmed byte-for-byte compatible with the old codegen:
```
Run it before and after this PR:
.jenkins/pytorch/codegen-test.sh <baseline_output_dir>
.jenkins/pytorch/codegen-test.sh <test_output_dir>
Then run diff to compare the generated files:
diff -Naur <baseline_output_dir> <test_output_dir>
```
Differential Revision: D24885068
Test Plan: Imported from OSS
Reviewed By: ezyang
Pulled By: ljk53
fbshipit-source-id: c0fbd726bcc450c3c7fe232c23e5b31779d0b65f
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/46978
Refactored and added type annotations to the most part of the file.
Some top-level codegen functions are called by other codegen scripts.
Will migrate them in subsequent PRs.
Test Plan: Imported from OSS
Reviewed By: ezyang
Differential Revision: D24589210
Pulled By: ljk53
fbshipit-source-id: e0c7e5b3672b41983f321400c2e2330d1462e76e
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/46977
Clean up a few TODOs in the new python binding codegen.
Get rid of the _simple_type() hack and the uses of cpp_type_str.
Now python argument type strings and PythonArgParser unpacking methods
are directly generated from the original Type model.
Test Plan: Imported from OSS
Reviewed By: ezyang
Differential Revision: D24589209
Pulled By: ljk53
fbshipit-source-id: b2a6c3911d58eae49c031d319c8ea6f804e2cfde
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/46976
Technically, it's not semantic preserving, e.g.: emition of
'requires_grad' is no longer gated by 'has_tensor_return' - there is no
guarantee that is_like_or_new_function should all have tensor return.
But the output is identical so there might be some invariant - could
also add assertion to fail loudly when it's broken.
Test Plan: Imported from OSS
Reviewed By: ezyang
Differential Revision: D24589211
Pulled By: ljk53
fbshipit-source-id: 47c7e43b080e4e67a526fde1a8a53aae99df4432
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/46244
- What does the generated binding code do?
The Python binding codegen produces code that takes the input list of
PyObjects, finds the matching ATen C++ function using PythonArgParser,
converts the PyObjects into C++ types and calls the ATen C++ function:
```
+--------+ parsing +------------------------+ binding +-----------------------+
| PyObjs | ---------> | PythonArgParser Output | ---------> | Cpp Function Dispatch |
+--------+ +------------------------+ +-----------------------+
```
- Are Python arguments 1-1 mapped to C++ arguments?
Python arguments might be reordered, packed, unpacked when binding to
C++ arguments, as illustrated below:
```
// Binding - Reorder & Packing
// aten::empty.names(int[] size, *, Dimname[]? names, ScalarType? dtype=None, Layout? layout=None,
Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor
Python Args Cpp Args
-----------------------------------------------------------
0: size size
1: names names
2: memory_format -------+
3: dtype -----+-|--> options
4: layout / |
5: device / +--> memory_format
6: pin_memory /
7: requires_grad -+
// Binding - Unpacking
// aten::max.names_dim(Tensor self, Dimname dim, bool keepdim=False) -> (Tensor values, Tensor indices)
Python Args Cpp Args
-----------------------------------------------------------
+----> max
/-----> max_values
0: input / self
1: dim / dim
2: keepdim / keepdim
3: out -----+
```
- Why do we want to rewrite the python binding codegen?
The old codegen takes Declarations.yaml as input. It doesn't distinguish
between Python arguments and C++ arguments - they are all mixed together
as a bag of non-typed dict objects. Different methods process these arg
objects and add new attributes for various different purposes. It's not so
obvious to figure out the semantics of these attributes. The complicated
binding logic happens implicitly and scatteredly.
```
+--------------------+
| Native Functions |
+--------------------+
|
|
v
+--------------------+
| Cpp Signatures |
+--------------------+
|
|
v
+--------------------+
| Declarations.yaml |
+--------------------+
| +-------------------------------------+
| +-------> | PythonArgParser Schema |
| | +-------------------------------------+
| | .
| | .
v | .
+--------------------+ +-------------------------------------+
| NonTyped Args Objs | --> | PythonArgParser -> Cpp Args Binding |
+--------------------+ +-------------------------------------+
| .
| .
| .
| +-------------------------------------+
+-------> | Cpp Function Dispatch |
+-------------------------------------+
```
This PR leverages the new immutable data models introduced in the new
aten codegen. It introduces dedicated data models for python schema.
This way, we can not only avoid subtle Declaration.yaml conversions but
also decouple the generation of python schema, python to c++ binding and
c++ function call.
The ultimate state will be like the following diagram:
```
+-------------------+ +-------------------------------------+
+-------> | Python Signatures | --> | PythonArgParser Schema |
| +-------------------+ +-------------------------------------+
| | .
| | .
| | .
+------------------+ | +-------------------------------------+
| Native Functions | +-------> | PythonArgParser -> Cpp Args Binding |
+------------------+ | +-------------------------------------+
| | .
| | .
| | .
| +-------------------+ +-------------------------------------+
+-------> | Cpp Signatures | --> | Cpp Function Dispatch |
+-------------------+ +-------------------------------------+
```
This PR has migrated the core binding logic from
tools/autograd/gen_python_functions.py to tools/codegen/api/python.py.
It produces the byte-for-byte same results (tested with #46243).
Will migrate the rest of gen_python_functions.py in subsequent PRs.
Test Plan: Imported from OSS
Reviewed By: bhosmer
Differential Revision: D24388874
Pulled By: ljk53
fbshipit-source-id: f88b6df4e917cf90d868a2bbae2d5ffb680d1841
