Better Shape Function Registration #3237
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Summary: X-link: facebookresearch/FBGEMM#338 With the increasing use of torch.compile both internally and in open source, I wanted to take some time to think about how to best make our FBGEMM custom ops compile compatible. This diff attempts to implement a testable and scalable way of registering custom shape functions easily. `torch.compile` requires that custom operators have shape functions registered, as they are needed for tracing. In FBGEMM, we have inconsistently registered such shape functions. This diff attempts to clean up and register a good number of commonly used operators. Notably, pytorch allows two methods of registering shape functions for custom ops. In CPP, you can use a Meta function, or in python you can use `register_fake`. It turns out `register_fake` is the recommended and more powerful approach. For example, it is needed for ops that cross devices (such as the car ops) and is needed when exporting a traced graph. This diff thus focuses on the register_fake method and converts a handful of Meta registrations to it. My hope is that this can provide an easily extensible way of registering shape functions for other kernel authors. Reviewed By: jianyuh Differential Revision: D64147797
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Summary: X-link: facebookresearch/FBGEMM#338 With the increasing use of torch.compile both internally and in open source, I wanted to take some time to think about how to best make our FBGEMM custom ops compile compatible. This diff attempts to implement a testable and scalable way of registering custom shape functions easily. `torch.compile` requires that custom operators have shape functions registered, as they are needed for tracing. In FBGEMM, we have inconsistently registered such shape functions. This diff attempts to clean up and register a good number of commonly used operators. Notably, pytorch allows two methods of registering shape functions for custom ops. In CPP, you can use a Meta function, or in python you can use `register_fake`. It turns out `register_fake` is the recommended and more powerful approach. For example, it is needed for ops that cross devices (such as the car ops) and is needed when exporting a traced graph. This diff thus focuses on the register_fake method and converts a handful of Meta registrations to it. My hope is that this can provide an easily extensible way of registering shape functions for other kernel authors. Reviewed By: jianyuh, jiawenliu64 Differential Revision: D64147797
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Summary: X-link: facebookresearch/FBGEMM#338 With the increasing use of torch.compile both internally and in open source, I wanted to take some time to think about how to best make our FBGEMM custom ops compile compatible. This diff attempts to implement a testable and scalable way of registering custom shape functions easily. `torch.compile` requires that custom operators have shape functions registered, as they are needed for tracing. In FBGEMM, we have inconsistently registered such shape functions. This diff attempts to clean up and register a good number of commonly used operators. Notably, pytorch allows two methods of registering shape functions for custom ops. In CPP, you can use a Meta function, or in python you can use `register_fake`. It turns out `register_fake` is the recommended and more powerful approach. For example, it is needed for ops that cross devices (such as the car ops) and is needed when exporting a traced graph. This diff thus focuses on the register_fake method and converts a handful of Meta registrations to it. My hope is that this can provide an easily extensible way of registering shape functions for other kernel authors. Reviewed By: jianyuh, jiawenliu64 Differential Revision: D64147797
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Summary: X-link: facebookresearch/FBGEMM#338 With the increasing use of torch.compile both internally and in open source, I wanted to take some time to think about how to best make our FBGEMM custom ops compile compatible. This diff attempts to implement a testable and scalable way of registering custom shape functions easily. `torch.compile` requires that custom operators have shape functions registered, as they are needed for tracing. In FBGEMM, we have inconsistently registered such shape functions. This diff attempts to clean up and register a good number of commonly used operators. Notably, pytorch allows two methods of registering shape functions for custom ops. In CPP, you can use a Meta function, or in python you can use `register_fake`. It turns out `register_fake` is the recommended and more powerful approach. For example, it is needed for ops that cross devices (such as the car ops) and is needed when exporting a traced graph. This diff thus focuses on the register_fake method and converts a handful of Meta registrations to it. My hope is that this can provide an easily extensible way of registering shape functions for other kernel authors. Reviewed By: jianyuh, jiawenliu64 Differential Revision: D64147797
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Summary: X-link: facebookresearch/FBGEMM#338 With the increasing use of torch.compile both internally and in open source, I wanted to take some time to think about how to best make our FBGEMM custom ops compile compatible. This diff attempts to implement a testable and scalable way of registering custom shape functions easily. `torch.compile` requires that custom operators have shape functions registered, as they are needed for tracing. In FBGEMM, we have inconsistently registered such shape functions. This diff attempts to clean up and register a good number of commonly used operators. Notably, pytorch allows two methods of registering shape functions for custom ops. In CPP, you can use a Meta function, or in python you can use `register_fake`. It turns out `register_fake` is the recommended and more powerful approach. For example, it is needed for ops that cross devices (such as the car ops) and is needed when exporting a traced graph. This diff thus focuses on the register_fake method and converts a handful of Meta registrations to it. My hope is that this can provide an easily extensible way of registering shape functions for other kernel authors. Reviewed By: jianyuh, jiawenliu64 Differential Revision: D64147797
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Summary: X-link: facebookresearch/FBGEMM#338 With the increasing use of torch.compile both internally and in open source, I wanted to take some time to think about how to best make our FBGEMM custom ops compile compatible. This diff attempts to implement a testable and scalable way of registering custom shape functions easily. `torch.compile` requires that custom operators have shape functions registered, as they are needed for tracing. In FBGEMM, we have inconsistently registered such shape functions. This diff attempts to clean up and register a good number of commonly used operators. Notably, pytorch allows two methods of registering shape functions for custom ops. In CPP, you can use a Meta function, or in python you can use `register_fake`. It turns out `register_fake` is the recommended and more powerful approach. For example, it is needed for ops that cross devices (such as the car ops) and is needed when exporting a traced graph. This diff thus focuses on the register_fake method and converts a handful of Meta registrations to it. My hope is that this can provide an easily extensible way of registering shape functions for other kernel authors. Reviewed By: jianyuh, jiawenliu64 Differential Revision: D64147797
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Summary: X-link: pytorch#3237 Pull Request resolved: facebookresearch/FBGEMM#338 With the increasing use of torch.compile both internally and in open source, I wanted to take some time to think about how to best make our FBGEMM custom ops compile compatible. This diff attempts to implement a testable and scalable way of registering custom shape functions easily. `torch.compile` requires that custom operators have shape functions registered, as they are needed for tracing. In FBGEMM, we have inconsistently registered such shape functions. This diff attempts to clean up and register a good number of commonly used operators. Notably, pytorch allows two methods of registering shape functions for custom ops. In CPP, you can use a Meta function, or in python you can use `register_fake`. It turns out `register_fake` is the recommended and more powerful approach. For example, it is needed for ops that cross devices (such as the car ops) and is needed when exporting a traced graph. This diff thus focuses on the register_fake method and converts a handful of Meta registrations to it. My hope is that this can provide an easily extensible way of registering shape functions for other kernel authors. Reviewed By: jianyuh, jiawenliu64 Differential Revision: D64147797 fbshipit-source-id: 73b25394b3d65730ddb5cb1614536c007f918638
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Summary:
torch.compilerequires that custom operators have shape functions registered, as they are needed for tracing.In FBGEMM, we have inconsistently registered such shape functions. This diff attempts to clean up and register a good number of commonly used operators.
Notably, pytorch allows two methods of registering shape functions for custom ops. In CPP, you can use a Meta function, or in python you can use
register_fake. It turns outregister_fakeis the recommended and more powerful approach. For example, it is needed for ops that cross devices (such as the car ops) and is needed when exporting a traced graph.This diff thus focuses on the register_fake method and converts a handful of Meta registrations to it. My hope is that this can provide an easily extensible way of registering shape functions for other kernel authors.
Differential Revision: D64147797