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check_accuracy() — pytorch Function Reference

Architecture documentation for the check_accuracy() function in kernels.py from the pytorch codebase.

Function python DynamoBenchmarks Configuration calls 2

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Dependency Diagram

graph TD
  cf57d9a8_fc49_42e9_c1eb_53ac4b8190b5["check_accuracy()"]
  d4938e53_a1f5_c58c_a1e1_efc656eb80f1["clone_inputs()"]
  cf57d9a8_fc49_42e9_c1eb_53ac4b8190b5 -->|calls| d4938e53_a1f5_c58c_a1e1_efc656eb80f1
  bf14c1d3_0a90_a5c6_58ff_3e6211ae65ce["check_accuracy()"]
  cf57d9a8_fc49_42e9_c1eb_53ac4b8190b5 -->|calls| bf14c1d3_0a90_a5c6_58ff_3e6211ae65ce
  style cf57d9a8_fc49_42e9_c1eb_53ac4b8190b5 fill:#6366f1,stroke:#818cf8,color:#fff

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Source Code

benchmarks/dynamo/genai_layers/kernels.py lines 97–118

    def check_accuracy(self, args, kwargs) -> None:
        res = {}
        for backend in self.available_backends:
            args_ref, kwargs_ref = self.clone_inputs(args, kwargs)
            res[backend] = getattr(self, backend)(args_ref, kwargs_ref)()
        gold = res["eager"]
        for backend in self.available_backends:
            if backend == "eager":
                continue
            if backend == "quack":
                # quack's cross_entropy only returns float32 loss output.
                # Need to convert it to the same dtype as gold for comparison.
                res[backend] = res[backend].to(gold.dtype)
            try:
                torch.testing.assert_close(res[backend], gold)
                print(
                    f"Accuracy check \033[92m✓ succeed\033[0m for {backend} backend on {self.name} kernel"
                )
            except Exception as e:
                print(
                    f"Accuracy check \033[91m✗ failed\033[0m for {backend} backend on {self.name} kernel. Error {e}"
                )

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Frequently Asked Questions

What does check_accuracy() do?
check_accuracy() is a function in the pytorch codebase.
What does check_accuracy() call?
check_accuracy() calls 2 function(s): check_accuracy, clone_inputs.

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