tests/python/relax/test_optimize_layout_transform.py

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"""Tests to validate relax optimize layout tranform pass."""

import numpy as np
import pytest
import tvm.testing
from tvm import relax
from tvm.ir.base import assert_structural_equal
from tvm.relax.transform import DeadCodeElimination, FuseTIR, OptimizeLayoutTransform
from tvm.script import ir as I, tir as T, relax as R


def _run_pass_compare_output(Before, Expected):
    After = tvm.ir.transform.Sequential(
        [
            OptimizeLayoutTransform(),
            DeadCodeElimination(),
            FuseTIR(),
        ]
    )(Before)

    tvm.ir.assert_structural_equal(Expected, After)


def test_optimize_transform_layout_pass_one_arg():
    @I.ir_module
    class Before:
        @T.prim_func(private=True)
        def relax_add_replacement(
            arg0: T.Buffer((4, 4), "float32"),
            arg1: T.Buffer((4, 4), "float32"),
            output: T.Buffer((4, 4), "float32"),
        ):
            T.func_attr({"operator_name": "relax.add"})
            # with T.block("root"):
            for ax0, ax1 in T.grid(4, 4):
                with T.block("T_add"):
                    v_ax0, v_ax1 = T.axis.remap("SS", [ax0, ax1])
                    T.reads(arg0[v_ax0, v_ax1], arg1[v_ax0, v_ax1])
                    T.writes(output[v_ax0, v_ax1])
                    output[v_ax0, v_ax1] = arg0[v_ax0, v_ax1] + arg1[v_ax0, v_ax1]

        @R.function
        def main(
            x: R.Tensor((16,), dtype="float32"), y: R.Tensor((16,), dtype="float32")
        ) -> R.Tensor((16,), dtype="float32"):
            with R.dataflow():
                lv: R.Tensor((4, 4), dtype="float32") = R.layout_transform(
                    x, index_map=lambda i: (i // 4, i % 4), pad_value=None
                )
                lv1: R.Tensor((4, 4), dtype="float32") = R.layout_transform(
                    y, index_map=lambda i: (i // 4, i % 4), pad_value=None
                )
                lv2 = R.call_tir(
                    Before.relax_add_replacement,
                    (lv, lv1),
                    out_sinfo=R.Tensor((4, 4), dtype="float32"),
                )
                lv0: R.Tensor((16,), dtype="float32") = R.layout_transform(
                    lv2, index_map=lambda axis0, axis1: (axis0 * 4 + axis1,), pad_value=None
                )
                lv3: R.Tensor((4, 4), dtype="float32") = R.layout_transform(
                    lv0, index_map=lambda i: (i // 4, i % 4), pad_value=None
                )
                lv4: R.Tensor((4, 4), dtype="float32") = R.layout_transform(
                    y, index_map=lambda i: (i // 4, i % 4), pad_value=None
                )
                lv5 = R.call_tir(
                    Before.relax_add_replacement,
                    (lv4, lv3),
                    out_sinfo=R.Tensor((4, 4), dtype="float32"),
                )
                lv2_1: R.Tensor((16,), dtype="float32") = R.layout_transform(
                    lv5, index_map=lambda axis0, axis1: (axis0 * 4 + axis1,), pad_value=None
                )
                gv: R.Tensor((16,), dtype="float32") = lv2_1
                R.output(gv)
            return gv

    @I.ir_module
    class Expected:
        @T.prim_func(private=True)
        def relax_add_replacement(
            arg0: T.Buffer((4, 4), "float32"),
            arg1: T.Buffer((4, 4), "float32"),
            output: T.Buffer((4, 4), "float32"),
        ):
            T.func_attr({"operator_name": "relax.add"})
            # with T.block("root"):
            for ax0, ax1 in T.grid(4, 4):
                with T.block("T_add"):
                    v_ax0, v_ax1 = T.axis.remap("SS", [ax0, ax1])
                    T.reads(arg0[v_ax0, v_ax1], arg1[v_ax0, v_ax1])
                    T.writes(output[v_ax0, v_ax1])
                    output[v_ax0, v_ax1] = arg0[v_ax0, v_ax1] + arg1[v_ax0, v_ax1]

        @R.function
        def main(
            x: R.Tensor((16,), dtype="float32"), y: R.Tensor((16,), dtype="float32")
        ) -> R.Tensor((16,), dtype="float32"):
            with R.dataflow():
                lv: R.Tensor((4, 4), dtype="float32") = R.layout_transform(
                    x, index_map=lambda i: (i // 4, i % 4), pad_value=None
                )
                lv1: R.Tensor((4, 4), dtype="float32") = R.layout_transform(
                    y, index_map=lambda i: (i // 4, i % 4), pad_value=None
                )
                lv2 = R.call_tir(
                    Expected.relax_add_replacement,
                    (lv, lv1),
                    out_sinfo=R.Tensor((4, 4), dtype="float32"),
                )
                lv5 = R.call_tir(
                    Expected.relax_add_replacement,
                    (lv1, lv2),
                    out_sinfo=R.Tensor((4, 4), dtype="float32"),
                )
                gv: R.Tensor((16,), dtype="float32") = R.layout_transform(
                    lv5, index_map=lambda axis0, axis1: (axis0 * 4 + axis1,), pad_value=None
                )
                R.output(gv)
            return gv

    _run_pass_compare_output(Before, Expected)


def test_optimize_transform_layout_pass_two_args():
    @I.ir_module
    class Before:
        @T.prim_func(private=True)
        def relax_add_replacement(
            arg0: T.Buffer((4, 4), "float32"),
            arg1: T.Buffer((4, 4), "float32"),
            output: T.Buffer((4, 4), "float32"),
        ):
            T.func_attr({"operator_name": "relax.add"})
            # with T.block("root"):
            for ax0, ax1 in T.grid(4, 4):
                with T.block("T_add"):
                    v_ax0, v_ax1 = T.axis.remap("SS", [ax0, ax1])
                    T.reads(arg0[v_ax0, v_ax1], arg1[v_ax0, v_ax1])
                    T.writes(output[v_ax0, v_ax1])
                    output[v_ax0, v_ax1] = arg0[v_ax0, v_ax1] + arg1[v_ax0, v_ax1]

        @R.function
        def main(
            x: R.Tensor((16,), dtype="float32"),
            y: R.Tensor((16,), dtype="float32"),
            z: R.Tensor((16,), dtype="float32"),
        ) -> R.Tensor((16,), dtype="float32"):
            with R.dataflow():
                lv: R.Tensor((4, 4), dtype="float32") = R.layout_transform(
                    x, index_map=lambda i: (i // 4, i % 4), pad_value=None
                )
                lv1: R.Tensor((4, 4), dtype="float32") = R.layout_transform(
                    y, index_map=lambda i: (i // 4, i % 4), pad_value=None
                )
                lv2: R.Tensor((4, 4), dtype="float32") = R.layout_transform(
                    z, index_map=lambda i: (i // 4, i % 4), pad_value=None
                )
                lv3 = R.call_tir(
                    Before.relax_add_replacement,
                    (lv, lv1),
                    out_sinfo=R.Tensor((4, 4), dtype="float32"),
                )
                lv4 = R.call_tir(
                    Before.relax_add_replacement,
                    (lv, lv2),
                    out_sinfo=R.Tensor((4, 4), dtype="float32"),
                )
                lv5: R.Tensor((16,), dtype="float32") = R.layout_transform(
                    lv3, index_map=lambda axis0, axis1: (axis0 * 4 + axis1,), pad_value=None
                )
                lv6: R.Tensor((16,), dtype="float32") = R.layout_transform(
                    lv4, index_map=lambda axis0, axis1: (axis0 * 4 + axis1,), pad_value=None
                )
                lv7: R.Tensor((4, 4), dtype="float32") = R.layout_transform(
                    lv5, index_map=lambda i: (i // 4, i % 4), pad_value=None
                )
                lv8: R.Tensor((4, 4), dtype="float32") = R.layout_transform(
                    lv6, index_map=lambda i: (i // 4, i % 4), pad_value=None
                )
                lv9 = R.call_tir(
                    Before.relax_add_replacement,
                    (lv7, lv8),
                    out_sinfo=R.Tensor((4, 4), dtype="float32"),
                )
                lv10: R.Tensor((16,), dtype="float32") = R.layout_transform(
                    lv9, index_map=lambda axis0, axis1: (axis0 * 4 + axis1,), pad_value=None
                )
                gv: R.Tensor((16,), dtype="float32") = lv10
                R.output(gv)
            return gv

    @I.ir_module
    class Expected:
        @T.prim_func(private=True)
        def relax_add_replacement(
            arg0: T.Buffer((4, 4), "float32"),
            arg1: T.Buffer((4, 4), "float32"),
            output: T.Buffer((4, 4), "float32"),
        ):
            T.func_attr({"operator_name": "relax.add"})
            # with T.block("root"):
            for ax0, ax1 in T.grid(4, 4):
                with T.block("T_add"):
                    v_ax0, v_ax1 = T.axis.remap("SS", [ax0, ax1])
                    T.reads(arg0[v_ax0, v_ax1], arg1[v_ax0, v_ax1])
                    T.writes(output[v_ax0, v_ax1])
                    output[v_ax0, v_ax1] = arg0[v_ax0, v_ax1] + arg1[v_ax0, v_ax1]

        @R.function
        def main(
            x: R.Tensor((16,), dtype="float32"),
            y: R.Tensor((16,), dtype="float32"),
            z: R.Tensor((16,), dtype="float32"),
        ) -> R.Tensor((16,), dtype="float32"):
            with R.dataflow():
                lv: R.Tensor((4, 4), dtype="float32") = R.layout_transform(
                    x, index_map=lambda i: (i // 4, i % 4), pad_value=None
                )
                lv1: R.Tensor((4, 4), dtype="float32") = R.layout_transform(
                    y, index_map=lambda i: (i // 4, i % 4), pad_value=None
                )
                lv2: R.Tensor((4, 4), dtype="float32") = R.layout_transform(
                    z, index_map=lambda i: (i // 4, i % 4), pad_value=None
                )
                lv3 = R.call_tir(
                    Expected.relax_add_replacement,
                    (lv, lv1),
                    out_sinfo=R.Tensor((4, 4), dtype="float32"),
                )
                lv4 = R.call_tir(
                    Expected.relax_add_replacement,
                    (lv, lv2),
                    out_sinfo=R.Tensor((4, 4), dtype="float32"),
                )
                lv5 = R.call_tir(
                    Expected.relax_add_replacement,
                    (lv3, lv4),
                    out_sinfo=R.Tensor((4, 4), dtype="float32"),
                )
                gv: R.Tensor((16,), dtype="float32") = R.layout_transform(
                    lv5, index_map=lambda axis0, axis1: (axis0 * 4 + axis1,), pad_value=None
                )
                R.output(gv)
            return gv

    _run_pass_compare_output(Before, Expected)


def test_tranform_layout_tir_remove_pad_transform_layout():
    @I.ir_module
    class Before:
        @T.prim_func(private=True)
        def relax_relu_replacement(
            arg0: T.Buffer((16,), "float32"), output: T.Buffer((16,), "float32")
        ):
            T.func_attr({"operator_name": "relax.relu"})
            # with T.block("root"):
            for ax0 in range(16):
                with T.block("T_add"):
                    v_ax0 = T.axis.spatial(16, ax0)
                    T.reads(arg0[v_ax0])
                    T.writes(output[v_ax0])
                    output[v_ax0] = T.max(arg0[v_ax0], T.float32(0))

        @T.prim_func(private=True)
        def remove_pad(var_input: T.handle, var_output: T.handle):
            T.func_attr({"operator_name": "remove_pad", "tir.noalias": T.bool(True)})
            p0 = T.int64()
            input = T.match_buffer(var_input, (p0,))
            i0 = T.int64()
            output = T.match_buffer(var_output, (i0,))
            # with T.block("root"):
            for ax0 in range(i0):
                with T.block("output"):
                    v_ax0 = T.axis.spatial(i0, ax0)
                    T.reads(input[v_ax0])
                    T.writes(output[v_ax0])
                    output[v_ax0] = input[v_ax0]

        @R.function
        def main(x: R.Tensor((14,), dtype="float32")) -> R.Tensor((14,), dtype="float32"):
            with R.dataflow():
                lv: R.Tensor((16,), dtype="float32") = R.layout_transform(
                    x,
                    index_map=T.index_map(lambda i: (i % 16,)),
                    pad_value=None,
                    axis_separators=[],
                )
                lv1 = R.call_tir(
                    Before.relax_relu_replacement,
                    (lv,),
                    out_sinfo=R.Tensor((16,), dtype="float32"),
                )
                lv2: R.Tensor((16,), dtype="float32") = R.layout_transform(
                    lv1,
                    index_map=T.index_map(lambda axis0: (axis0,)),
                    pad_value=None,
                    axis_separators=[],
                )
                lv_1 = R.call_tir(
                    Before.remove_pad, (lv2,), out_sinfo=R.Tensor((14,), dtype="float32")
                )
                lv3: R.Tensor((16,), dtype="float32") = R.layout_transform(
                    lv_1,
                    index_map=T.index_map(lambda i: (i % 16,)),
                    pad_value=None,
                    axis_separators=[],
                )
                lv4 = R.call_tir(
                    Before.relax_relu_replacement,
                    (lv3,),
                    out_sinfo=R.Tensor((16,), dtype="float32"),
                )
                lv5: R.Tensor((16,), dtype="float32") = R.layout_transform(
                    lv4,
                    index_map=T.index_map(lambda axis0: (axis0,)),
                    pad_value=None,
                    axis_separators=[],
                )
                lv_2 = R.call_tir(
                    Before.remove_pad, (lv5,), out_sinfo=R.Tensor((14,), dtype="float32")
                )
                gv: R.Tensor((14,), dtype="float32") = lv_2
                R.output(gv)
            return gv

    @I.ir_module
    class Expected:
        @T.prim_func(private=True)
        def relax_relu_replacement(
            arg0: T.Buffer((16,), "float32"), output: T.Buffer((16,), "float32")
        ):
            T.func_attr({"operator_name": "relax.relu"})
            # with T.block("root"):
            for ax0 in range(16):
                with T.block("T_add"):
                    v_ax0 = T.axis.spatial(16, ax0)
                    T.reads(arg0[v_ax0])
                    T.writes(output[v_ax0])
                    output[v_ax0] = T.max(arg0[v_ax0], T.float32(0))

        @T.prim_func(private=True)
        def remove_pad(var_input: T.handle, var_output: T.handle):
            T.func_attr({"operator_name": "remove_pad", "tir.noalias": T.bool(True)})
            p0 = T.int64()
            input = T.match_buffer(var_input, (p0,))
            i0 = T.int64()
            output = T.match_buffer(var_output, (i0,))
            # with T.block("root"):
            for ax0 in range(i0):
                with T.block("output"):
                    v_ax0 = T.axis.spatial(i0, ax0)
                    T.reads(input[v_ax0])
                    T.writes(output[v_ax0])
                    output[v_ax0] = input[v_ax0]

        @R.function
        def main(x: R.Tensor((14,), dtype="float32")) -> R.Tensor((14,), dtype="float32"):
            with R.dataflow():
                lv: R.Tensor((16,), dtype="float32") = R.layout_transform(
                    x,
                    index_map=T.index_map(lambda i: (i % 16,)),
                    pad_value=None,
                    axis_separators=[],
                )
                lv1 = R.call_tir(
                    Expected.relax_relu_replacement,
                    (lv,),
                    out_sinfo=R.Tensor((16,), dtype="float32"),
                )
                lv4 = R.call_tir(
                    Expected.relax_relu_replacement,
                    (lv1,),
                    out_sinfo=R.Tensor((16,), dtype="float32"),
                )
                lv5: R.Tensor((16,), dtype="float32") = R.layout_transform(
                    lv4,
                    index_map=T.index_map(lambda axis0: (axis0,)),
                    pad_value=None,
                    axis_separators=[],
                )
                gv = R.call_tir(
                    Expected.remove_pad, (lv5,), out_sinfo=R.Tensor((14,), dtype="float32")
                )
                R.output(gv)
            return gv

    _run_pass_compare_output(Before, Expected)


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