This function is called in `normalize_function` which is in a fairly hot path for
`FakeTensor` dispatch. In this simple benchmark I see `normalize_function`
improve from 92 us to 17 us just by caching this signature object.
```python
import torch
from torch._subclasses import FakeTensorMode
from torch.fx.operator_schemas import normalize_function
aten = torch._ops.ops.aten
%timeit normalize_function(
aten.empty_strided.default, args=((100, 100), (100, 1)),
kwargs=dict(device="cuda"), normalize_to_only_use_kwargs=True)
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/112327
Approved by: https://github.com/lezcano
In 3.11 bytecode size is not constant, so in order to get from `f_lasti` to opcode index, one need to search for the closes offset in disassembled instructions.
Update `_patch_function` to construct code with all the properties that exist in 3.11 runtime.
Update `_torchscript_schema_to_signature` to mark `from` named arg as positional argument only, as this is a reserved keyword in Python and as such checked by `inspect` package in 3.11
Pull Request resolved: https://github.com/pytorch/pytorch/pull/92895
Approved by: https://github.com/albanD
Continuation after https://github.com/pytorch/pytorch/pull/90163.
Here is a script I used to find all the non-existing arguments in the docstrings (the script can give false positives in presence of *args/**kwargs or decorators):
_Edit:_
I've realized that the indentation is wrong for the last `break` in the script, so the script only gives output for a function if the first docstring argument is wrong. I'll create a separate PR if I find more issues with corrected script.
``` python
import ast
import os
import docstring_parser
for root, dirs, files in os.walk('.'):
for name in files:
if root.startswith("./.git/") or root.startswith("./third_party/"):
continue
if name.endswith(".py"):
full_name = os.path.join(root, name)
with open(full_name, "r") as source:
tree = ast.parse(source.read())
for node in ast.walk(tree):
if isinstance(node, ast.FunctionDef):
all_node_args = node.args.args
if node.args.vararg is not None:
all_node_args.append(node.args.vararg)
if node.args.kwarg is not None:
all_node_args.append(node.args.kwarg)
if node.args.posonlyargs is not None:
all_node_args.extend(node.args.posonlyargs)
if node.args.kwonlyargs is not None:
all_node_args.extend(node.args.kwonlyargs)
args = [a.arg for a in all_node_args]
docstring = docstring_parser.parse(ast.get_docstring(node))
doc_args = [a.arg_name for a in docstring.params]
clean_doc_args = []
for a in doc_args:
clean_a = ""
for c in a.split()[0]:
if c.isalnum() or c == '_':
clean_a += c
if clean_a:
clean_doc_args.append(clean_a)
doc_args = clean_doc_args
for a in doc_args:
if a not in args:
print(full_name, node.lineno, args, doc_args)
break
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/90505
Approved by: https://github.com/malfet, https://github.com/ZainRizvi
Previously, we were taking the `.op` from OpOverload/OpOverloadPacket and looking for a mapping in `_jit_builtins` for their signature. Those will only exist for operators on the public api, not the overload packets, e.g. `torch.resize_as_` not `torch.ops.aten.resize_as_` (as least in this case, and im pretty sure generally). The OpOverloads/OpOverloadPackets have schemas stored on them so we can just use those directly.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/77182
Approved by: https://github.com/anjali411
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/76469
Broken by Original commit changeset: 450e86c4e08a
Original Phabricator Diff: D35874477
Test Plan: Added unit test coverage to test_fx_experimental
Reviewed By: albanD
Differential Revision: D35978105
fbshipit-source-id: f22670b3b00a86777a26feaf4cb911595d150a17
(cherry picked from commit 91868b1e872c19d58d96a6c80a5e78dc6ffe4c7b)
The new `test_public_bindings.py` test means `__module__` will be set
correctly in future, even for functions bound from C++. Instead, just
test directly that the function is of the `BuiltinFunctionType` which
only passes for functions exported with the CPython API.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/75896
Approved by: https://github.com/albanD
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/71443
cogwheel test inline_cvr_infer_canary_pyper_model_publish is timing out.
The convert_fx call takes > 20 mins for local and local_ro sub modules, which used to take ~ 2 mins.
Test Plan:
Fblearn flow run
* the following cmd took 1113 seconds before the diff and 5002 seconds after.
flow-cli clone-locally 320014219 --run-as-secure-group pytorch_at_scale --operators pyper_model_publish_workflow.pyper_model_publish_workflow.process_torch_package_model_files.process_non_sparse_parameters[0]
Cogwheel test
* Cogwheel test with packages in B3588 (the last good run) took 4694.48s
* Cogwheel test with packages in B3590 (the first timeout) took 13975.83s
* Cogwheel test with the following packages took 4535.04s
* all packages in B3588 except the model publish
* the model publish built with D33469839 (043e84b3d2) reversed (created D33633570)
Reviewed By: albanD, jerryzh168
Differential Revision: D33633570
fbshipit-source-id: dc5e777c48a90c551641a3f79126461f6a60449e
(cherry picked from commit 03ab65023a)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/67254
Fixes https://github.com/pytorch/pytorch/issues/65997
BC breaking:
`output = torch.ops._test.leaky_relu(self=torch.tensor(-1.0))` now fails with the error `TypeError: __call__() got multiple values for argument 'self'` since we call into `OpOverloadBundle`'s `__call__` method that has `self` bound to it as its first argument.
Follow up work:
1. disallow `default` as an overload name for aten operators.
2. Add a method to obtain a list of all overloads (exclude the ones registered by JIT)
3. Add methods/properties to `OpOverload` to access more schema information (types of input and output args etc)
cc ezyang gchanan
Test Plan: Imported from OSS
Reviewed By: pbelevich
Differential Revision: D33469839
Pulled By: anjali411
fbshipit-source-id: c3fc43460f1c7c9651c64b4d46337be21c400621
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/67254
Fixes https://github.com/pytorch/pytorch/issues/65997
TODO: disallow `default` as an overload name for aten operators.
BC breaking:
`output = torch.ops._test.leaky_relu(self=torch.tensor(-1.0))` now fails with the error `TypeError: __call__() got multiple values for argument 'self'` since we call into `OpOverloadBundle`'s `__call__` method that has `self` bound to it as its first argument.
cc ezyang gchanan
Test Plan: Imported from OSS
Reviewed By: albanD
Differential Revision: D33262228
Pulled By: anjali411
fbshipit-source-id: 600dbf511514ea9b41aea3e6b1bc1102dab08909
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/57183
Previously, if it was unable to support matching against a type, it would throw an error.
However, this exposes the user to arbitrary Torchscript schemas, which may or may not be problematic. Although we may support these in the future, for now we just return False (which will simply eliminate that schema from the candidates).
Test Plan: T89661626 and T89664016
Reviewed By: spaugh, khabinov
Differential Revision: D28072018
fbshipit-source-id: 83017d1e96d19912163edc74a5e43b2816783218
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/56991
Original commit changeset: c5aa5f61a215
Diff: D27987746 (267b554b6f)
Test Plan: `buck test` under the glow-buck target is the target that this reversion is intended to fix
Reviewed By: jfix71
Differential Revision: D28019659
fbshipit-source-id: 37584ff404fc9195b309a5a6afdb4edbc2b4f088
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/56790
If the argument doesn't match `List[int]`, this code falls through to
`issubclass(argument_type, List[int])` which is invalid and raises a
`TypeError`. If this happens during the processing of a `Union` (e.g.
`Optional`), the other union types aren't given the chance to match against the
signature.
This also stop normalize_function from indescriminately swallowing exceptions,
which let this bug go unnoticed.
Test Plan: Imported from OSS
Reviewed By: ngimel
Differential Revision: D27987746
Pulled By: mruberry
fbshipit-source-id: c5aa5f61a215f0f39925e7053f33bff4b5d5acc2
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/55995
Normalization is kind of broken currently. But making default arguments visible still appears to work, and is nice functionality to still be able to rely on/use. Adds an option to `NormalizeArgs`'s `__init__` called `normalize_to_only_use_kwargs` which defaults to true, which if set to false will keep using the same signature as provided, but additionally set kwargs in kwargs.
Test Plan: Added test to `test_fx_experimental`.
Reviewed By: 842974287
Differential Revision: D27759448
fbshipit-source-id: 620061fcf46d8549ac70b62aede8b6740aee3778
Summary:
Commandeered from https://github.com/pytorch/pytorch/pull/54563
Primary changes from first PR:
1. Refactored primary `normalize_function` logic into `operator_schemas.py` so that non-FX users can use it.
2. Refactored tests a bit, and added a path to call `normalize_function` directly.
3. Moved check for `boolean_dispatch` so that `torch.lu` also gets properly handled.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/55992
Reviewed By: mruberry
Differential Revision: D27774396
Pulled By: Chillee
fbshipit-source-id: 7f65632e1d608e4abd55aec5ccbfdc3f67f52b8e
