tests/python/relax/test_tvmscript_parser_op_index.py

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# to you under the Apache License, Version 2.0 (the
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# with the License.  You may obtain a copy of the License at
#
#   http://www.apache.org/licenses/LICENSE-2.0
#
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from typing import Optional, Union

import tvm
import tvm.script
import tvm.testing
from tvm import IRModule, relax
from tvm.script import relax as R


def _check(
    parsed: Union[relax.Function, IRModule],
    expect: Optional[Union[relax.Function, IRModule]],
):
    test = parsed.script(show_meta=True)
    roundtrip_mod = tvm.script.from_source(test)
    tvm.ir.assert_structural_equal(parsed, roundtrip_mod)
    if expect:
        tvm.ir.assert_structural_equal(parsed, expect)


def test_take():
    @R.function
    def foo(
        x: R.Tensor((2, 3, 4), "float32"), indices: R.Tensor((3,), "int64")
    ) -> R.Tensor((2, 3, 3), "float32"):
        gv: R.Tensor((2, 3, 3), "float32") = R.take(x, indices, axis=2)
        return gv

    x = relax.Var("x", R.Tensor((2, 3, 4), "float32"))
    indices = relax.Var("indices", R.Tensor((3,), "int64"))
    bb = relax.BlockBuilder()
    with bb.function("foo", [x, indices]):
        gv = bb.emit(relax.op.take(x, indices, axis=2))
        bb.emit_func_output(gv)

    _check(foo, bb.get()["foo"])


def test_strided_slice():
    @R.function
    def foo(x: R.Tensor((8, 9, 10, 10), "float32")) -> R.Tensor((4, 9, 10, 3), "float32"):
        gv: R.Tensor((4, 9, 10, 3), "float32") = R.strided_slice(
            x,
            axes=[0, 1, -1],
            begin=[1, 0, 8],
            end=[8, 9, 0],
            strides=[2, 1, -3],
        )
        return gv

    bb = relax.BlockBuilder()
    x = relax.Var("x", R.Tensor((8, 9, 10, 10), "float32"))
    with bb.function("foo", [x]):
        gv = bb.emit(
            relax.op.strided_slice(
                x, axes=[0, 1, -1], begin=[1, 0, 8], end=[8, 9, 0], strides=[2, 1, -3]
            )
        )
        bb.emit_func_output(gv)

    _check(foo, bb.get()["foo"])


if __name__ == "__main__":
    tvm.testing.main()