Improved CustomOp API with schema inference #100127
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This PR changes the CustomOp API. There are now two ways to create a
CustomOp object.
Method 1: with no schema string. We will infer what the schema string is
from your type annotations
```py
@custom_op("customlib::foo")
def foo(x: Tensor) -> Tensor:
...
```
Method 2: with a schema string, if the inference doesn't work well.
```py
@custom_op("customlib::foo", "(Tensor x) -> Tensor")
def foo(x):
...
```
Some details:
- We support most combinations of {Tensor, Number, int, float, bool} and
{Optional[typ], Tuple[typ, ...]} as inputs. The combinations we support are mostly
from me reading native_functions.yaml.
- We support only Tensor or Tuple of Tensor of fixed size returns.
- A lot of this PR is input validation for both of the above two
methods. For example, when a user provides a manual schema string, then
their function must not have any type annotations and the number of args
and arg names must match the schema.
Test Plan:
- new tests
[ghstack-poisoned]
🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/100127
Note: Links to docs will display an error until the docs builds have been completed. ✅ No FailuresAs of commit 0833eda: This comment was automatically generated by Dr. CI and updates every 15 minutes. |
zou3519
added a commit
that referenced
this pull request
Apr 26, 2023
This PR changes the CustomOp API. There are now two ways to create a
CustomOp object.
Method 1: with no schema string. We will infer what the schema string is
from your type annotations
```py
custom_op("customlib::foo")
def foo(x: Tensor) -> Tensor:
...
```
Method 2: with a schema string, if the inference doesn't work well.
```py
custom_op("customlib::foo", "(Tensor x) -> Tensor")
def foo(x):
...
```
Some details:
- We support most combinations of {Tensor, Number, int, float, bool} and
{Optional[typ], Tuple[typ, ...]} as inputs. The combinations we support are mostly
from me reading native_functions.yaml.
- We support only Tensor or Tuple of Tensor of fixed size returns.
- A lot of this PR is input validation for both of the above two
methods. For example, when a user provides a manual schema string, then
their function must not have any type annotations and the number of args
and arg names must match the schema.
Test Plan:
- new tests
ghstack-source-id: 727f2c6
Pull Request resolved: #100127
ezyang
reviewed
Apr 27, 2023
torch/_custom_op.py
Outdated
| This API is used as a decorator (see examples). | ||
| Arguments: | ||
| TODO(rzou) |
ezyang
approved these changes
Apr 27, 2023
This PR changes the CustomOp API. There are now two ways to create a
CustomOp object.
Method 1: with no schema string. We will infer what the schema string is
from your type annotations
```py
custom_op("customlib::foo")
def foo(x: Tensor) -> Tensor:
...
```
Method 2: with a schema string, if the inference doesn't work well.
```py
custom_op("customlib::foo", "(Tensor x) -> Tensor")
def foo(x):
...
```
Some details:
- We support most combinations of {Tensor, Number, int, float, bool} and
{Optional[typ], Tuple[typ, ...]} as inputs. The combinations we support are mostly
from me reading native_functions.yaml.
- We support only Tensor or Tuple of Tensor of fixed size returns.
- A lot of this PR is input validation for both of the above two
methods. For example, when a user provides a manual schema string, then
their function must not have any type annotations and the number of args
and arg names must match the schema.
Test Plan:
- new tests
[ghstack-poisoned]
This PR changes the CustomOp API. There are now two ways to create a
CustomOp object.
Method 1: with no schema string. We will infer what the schema string is
from your type annotations
```py
custom_op("customlib::foo")
def foo(x: Tensor) -> Tensor:
...
```
Method 2: with a schema string, if the inference doesn't work well.
```py
custom_op("customlib::foo", "(Tensor x) -> Tensor")
def foo(x):
...
```
Some details:
- We support most combinations of {Tensor, Number, int, float, bool} and
{Optional[typ], Tuple[typ, ...]} as inputs. The combinations we support are mostly
from me reading native_functions.yaml.
- We support only Tensor or Tuple of Tensor of fixed size returns.
- A lot of this PR is input validation for both of the above two
methods. For example, when a user provides a manual schema string, then
their function must not have any type annotations and the number of args
and arg names must match the schema.
Test Plan:
- new tests
[ghstack-poisoned]
This PR changes the CustomOp API. There are now two ways to create a
CustomOp object.
Method 1: with no schema string. We will infer what the schema string is
from your type annotations
```py
custom_op("customlib::foo")
def foo(x: Tensor) -> Tensor:
...
```
Method 2: with a schema string, if the inference doesn't work well.
```py
custom_op("customlib::foo", "(Tensor x) -> Tensor")
def foo(x):
...
```
Some details:
- We support most combinations of {Tensor, Number, int, float, bool} and
{Optional[typ], Tuple[typ, ...]} as inputs. The combinations we support are mostly
from me reading native_functions.yaml.
- We support only Tensor or Tuple of Tensor of fixed size returns.
- A lot of this PR is input validation for both of the above two
methods. For example, when a user provides a manual schema string, then
their function must not have any type annotations and the number of args
and arg names must match the schema.
Test Plan:
- new tests
[ghstack-poisoned]
This PR changes the CustomOp API. There are now two ways to create a
CustomOp object.
Method 1: with no schema string. We will infer what the schema string is
from your type annotations
```py
custom_op("customlib::foo")
def foo(x: Tensor) -> Tensor:
...
```
Method 2: with a schema string, if the inference doesn't work well.
```py
custom_op("customlib::foo", "(Tensor x) -> Tensor")
def foo(x):
...
```
Some details:
- We support most combinations of {Tensor, Number, int, float, bool} and
{Optional[typ], Tuple[typ, ...]} as inputs. The combinations we support are mostly
from me reading native_functions.yaml.
- We support only Tensor or Tuple of Tensor of fixed size returns.
- A lot of this PR is input validation for both of the above two
methods. For example, when a user provides a manual schema string, then
their function must not have any type annotations and the number of args
and arg names must match the schema.
Test Plan:
- new tests
[ghstack-poisoned]
zou3519
added a commit
that referenced
this pull request
Apr 27, 2023
This PR changes the CustomOp API. There are now two ways to create a
CustomOp object.
Method 1: with no schema string. We will infer what the schema string is
from your type annotations
```py
custom_op("customlib::foo")
def foo(x: Tensor) -> Tensor:
...
```
Method 2: with a schema string, if the inference doesn't work well.
```py
custom_op("customlib::foo", "(Tensor x) -> Tensor")
def foo(x):
...
```
Some details:
- We support most combinations of {Tensor, Number, int, float, bool} and
{Optional[typ], Tuple[typ, ...]} as inputs. The combinations we support are mostly
from me reading native_functions.yaml.
- We support only Tensor or Tuple of Tensor of fixed size returns.
- A lot of this PR is input validation for both of the above two
methods. For example, when a user provides a manual schema string, then
their function must not have any type annotations and the number of args
and arg names must match the schema.
Test Plan:
- new tests
ghstack-source-id: ac5ff4b
Pull Request resolved: #100127
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Merge startedYour change will be merged once all checks pass (ETA 0-4 Hours). Learn more about merging in the wiki. Questions? Feedback? Please reach out to the PyTorch DevX Team |
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Stack from ghstack:
This PR changes the CustomOp API. There are now two ways to create a
CustomOp object.
Method 1: with no schema string. We will infer what the schema string is
from your type annotations
Method 2: with a schema string, if the inference doesn't work well.
Some details:
{Optional[typ], Tuple[typ, ...]} as inputs. The combinations we support are mostly
from me reading native_functions.yaml.
methods. For example, when a user provides a manual schema string, then
their function must not have any type annotations and the number of args
and arg names must match the schema.
Test Plan: