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Before this PR, we didn't check that types in a schema were valid. This is because TorchScript treats unknown types as type variables. This PR checks types in a schema for the TORCH_LIBRARY APIs. To do this, we add an `allow_typevars` flag to parseSchema so that TorchScript can use allow_typevars=True. We also add some error messages for common mistakes (e.g. using int64_t or double in schema). Test Plan: - new tests Differential Revision: [D56432690](https://our.internmc.facebook.com/intern/diff/D56432690) Pull Request resolved: https://github.com/pytorch/pytorch/pull/124520 Approved by: https://github.com/albanD
24 lines
704 B
C++
24 lines
704 B
C++
#pragma once
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#include <ATen/core/function_schema.h>
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#include <c10/macros/Macros.h>
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#include <string>
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#include <variant>
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namespace torch {
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namespace jit {
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// allow_typevars: If true, we assume that lowercase types that we don't
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// understand are type variables. This is only needed for TorchScript (and not
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// not needed for custom ops).
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TORCH_API std::variant<c10::OperatorName, c10::FunctionSchema> parseSchemaOrName(
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const std::string& schemaOrName,
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bool allow_typevars = true);
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TORCH_API c10::FunctionSchema parseSchema(
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const std::string& schema,
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bool allow_typevars = true);
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TORCH_API c10::OperatorName parseName(const std::string& name);
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} // namespace jit
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} // namespace torch
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