pytorch/tools/test/test_codegen.py

import dataclasses
import typing
import unittest

from tools.autograd import gen_autograd_functions
from tools.autograd import load_derivatives
import torchgen.model


class TestCreateDerivative(unittest.TestCase):
    def test_named_grads(self) -> None:
        schema = torchgen.model.FunctionSchema.parse(
            "func(Tensor a, Tensor b) -> (Tensor x, Tensor y)"
        )
        native_function = dataclasses.replace(DEFAULT_NATIVE_FUNCTION, func=schema)

        derivative = load_derivatives.create_derivative(
            native_function,
            formula="func_backward(grad_x, grad_y)",
            var_names=(),
            available_named_gradients=["grad_x", "grad_y"],
        )
        self.assertSetEqual(derivative.named_gradients, {"grad_x", "grad_y"})

    def test_non_differentiable_output(self) -> None:
        specification = "func(Tensor a, Tensor b) -> (Tensor x, bool y, Tensor z)"
        schema = torchgen.model.FunctionSchema.parse(specification)
        native_function = dataclasses.replace(DEFAULT_NATIVE_FUNCTION, func=schema)

        differentiability_info = load_derivatives.create_differentiability_info(
            defn={
                "name": specification,
                "a": "grads[0]",
                "b": "grads[2]",
            },
            functions_by_signature={schema.signature(): [native_function]},
            functions_by_schema={specification: native_function},
            op_counter=typing.Counter[str](),
        )

        self.assertSequenceEqual(
            differentiability_info.available_named_gradients,
            # grad_y is not present because y is a
            # bool and thus not differentiable.
            ["grad_x", "grad_z"],
        )

    def test_indexed_grads(self) -> None:
        schema = torchgen.model.FunctionSchema.parse(
            "func(Tensor a, Tensor b) -> (Tensor x, Tensor y)"
        )
        native_function = dataclasses.replace(DEFAULT_NATIVE_FUNCTION, func=schema)

        derivative = load_derivatives.create_derivative(
            native_function,
            formula="func_backward(grads[0], grads[1])",
            var_names=(),
            available_named_gradients=["grad_x", "grad_y"],
        )
        self.assertSetEqual(derivative.named_gradients, set())

    def test_named_grads_and_indexed_grads(self) -> None:
        specification = "func(Tensor a, Tensor b) -> (Tensor x, Tensor y)"
        schema = torchgen.model.FunctionSchema.parse(specification)
        native_function = dataclasses.replace(DEFAULT_NATIVE_FUNCTION, func=schema)

        with self.assertRaisesRegex(
            RuntimeError, 'illegally mixes use of "grad_RETURN_NAME"'
        ):
            load_derivatives.create_differentiability_info(
                defn={
                    "name": specification,
                    # Uh-oh, the derivatives reference gradients by
                    # name and by index.
                    "a": "grad_x",
                    "b": "grads[1]",
                },
                functions_by_signature={schema.signature(): [native_function]},
                functions_by_schema={specification: native_function},
                op_counter=typing.Counter[str](),
            )


class TestGenAutogradFunctions(unittest.TestCase):
    def test_non_differentiable_output_invalid_type(self) -> None:
        specification = "func(Tensor a, Tensor b) -> (Tensor x, bool y, Tensor z)"
        schema = torchgen.model.FunctionSchema.parse(specification)
        native_function = dataclasses.replace(DEFAULT_NATIVE_FUNCTION, func=schema)

        differentiability_info = load_derivatives.create_differentiability_info(
            defn={
                "name": specification,
                "a": "grad_x",
                "b": "grad_z",
            },
            functions_by_signature={schema.signature(): [native_function]},
            functions_by_schema={specification: native_function},
            op_counter=typing.Counter[str](),
        )
        definition = gen_autograd_functions.process_function(
            differentiability_info, gen_autograd_functions.FUNCTION_DEFINITION
        )
        # grad_z should map to grads[1], not grads[2] because output 1
        # (y) is not differentiable.
        assert "grad_z = grads[2]" not in definition
        assert "grad_z = grads[1]" in definition

    def test_non_differentiable_output_output_differentiability(self) -> None:
        specification = "func(Tensor a, Tensor b) -> (Tensor x, Tensor y, Tensor z)"
        schema = torchgen.model.FunctionSchema.parse(specification)
        native_function = dataclasses.replace(DEFAULT_NATIVE_FUNCTION, func=schema)

        differentiability_info = load_derivatives.create_differentiability_info(
            defn={
                "name": specification,
                "a": "grad_x",
                "b": "grad_z",
                "output_differentiability": [True, False, True],
            },
            functions_by_signature={schema.signature(): [native_function]},
            functions_by_schema={specification: native_function},
            op_counter=typing.Counter[str](),
        )
        definition = gen_autograd_functions.process_function(
            differentiability_info, gen_autograd_functions.FUNCTION_DEFINITION
        )
        # grad_z should map to grads[1], not grads[2] because output 1
        # (y) is not differentiable.
        assert "grad_z = grads[2]" not in definition
        assert "grad_z = grads[1]" in definition


# Represents the most basic NativeFunction. Use dataclasses.replace()
# to edit for use.
DEFAULT_NATIVE_FUNCTION, _ = torchgen.model.NativeFunction.from_yaml(
    {"func": "func() -> bool"}, loc=torchgen.model.Location(__file__, 1)
)


if __name__ == "__main__":
    unittest.main()