tests/python/tirx-transform/test_tir_transform_flatten_buffer.py

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import tvm
import tvm.testing
from tvm.script import ir as I
from tvm.script import tirx as T


def _transform():
    return tvm.transform.Sequential(
        [
            tvm.tirx.transform.FlattenBuffer(),
            tvm.tirx.transform.Simplify(),
        ]
    )


def test_elementwise():
    """2-d buffers are flattened to 1-d"""

    @I.ir_module
    class Before:
        @T.prim_func
        def main(A: T.Buffer((16, 16), "float32"), C: T.Buffer((16, 16), "float32")):
            for i in T.serial(0, 16):
                B_new = T.decl_buffer([1, 16], "float32")
                for j in T.serial(0, 16):
                    B_new[0, j] = A[i, j] + 1.0
                for j in T.serial(0, 16):
                    C[i, j] = B_new[0, j] * 2.0

    @I.ir_module
    class Expected:
        @T.prim_func
        def main(A: T.Buffer((16, 16), "float32"), C: T.Buffer((16, 16), "float32")):
            A_1 = T.decl_buffer(256, dtype="float32", data=A.data)
            C_1 = T.decl_buffer(256, dtype="float32", data=C.data)
            for i in T.serial(0, 16):
                B_new = T.decl_buffer([16], "float32")
                for j in T.serial(0, 16):
                    B_new[j] = A_1[((i * 16) + j)] + 1.0
                for j in T.serial(0, 16):
                    C_1[((i * 16) + j)] = B_new[j] * 2.0

    After = _transform()(Before)
    tvm.ir.assert_structural_equal(After, Expected)


def test_elementwise_without_decl_buffer():
    """2-d buffers are flattened to 1-d

    Like test_elementwise, but the TIR doesn't have the DeclBuffer
    node.  The T.Buffer declaration applies only during the
    parsing the TVMScript, and doesn't occur in the TIR itself.  In
    this case, the allocation should be assumed to be targeting flat
    memory, and should be flattened to a 1-d allocation.
    """

    @I.ir_module(check_well_formed=False)
    class Before:
        @T.prim_func
        def main(A: T.Buffer((16, 16), "float32"), C: T.Buffer((16, 16), "float32")):
            for i in T.serial(0, 16):
                B_new_buf = T.alloc_buffer((1, 16), "float32")
                B_new = T.Buffer([1, 16], "float32", data=B_new_buf.data)
                for j in T.serial(0, 16):
                    B_new[0, j] = A[i, j] + 1.0
                for j in T.serial(0, 16):
                    C[i, j] = B_new[0, j] * 2.0

    @I.ir_module(check_well_formed=False)
    class Expected:
        @T.prim_func
        def main(input_A: T.Buffer((16, 16), "float32"), input_C: T.Buffer((16, 16), "float32")):
            A = T.decl_buffer(256, dtype="float32", data=input_A.data)
            C = T.decl_buffer(256, dtype="float32", data=input_C.data)
            for i in T.serial(0, 16):
                B_new_buf = T.alloc_buffer((16,), "float32")
                B_new = T.Buffer(16, "float32", data=B_new_buf.data)
                for j in T.serial(0, 16):
                    B_new[j] = A[((i * 16) + j)] + 1.0
                for j in T.serial(0, 16):
                    C[((i * 16) + j)] = B_new[j] * 2.0

    After = _transform()(Before)
    tvm.ir.assert_structural_equal(After, Expected)


def test_gpu():
    """Buffer flattening may have indices based on GPU thread vars"""

    @I.ir_module
    class Before:
        @T.prim_func
        def main(A: T.Buffer((16, 16), "float32"), C: T.Buffer((16, 16), "float32")):
            i0 = T.env_thread("blockIdx.x")
            i1 = T.env_thread("threadIdx.x")
            i2 = T.env_thread("vthread")

            T.launch_thread(i0, 4)
            T.launch_thread(i1, 2)
            T.launch_thread(i2, 2)
            B = T.decl_buffer([1, 16], "float32", scope="local")
            for j in range(0, 16):
                B[0, j] = A[i0 * 4 + i1 * 2 + i2, j] + 1.0
            for j in range(0, 16):
                C[i0 * 4 + i1 * 2 + i2, j] = B[0, j] * 2.0

    @I.ir_module
    class Expected:
        @T.prim_func
        def main(A: T.Buffer((16, 16), "float32"), C: T.Buffer((16, 16), "float32")):
            A_1 = T.decl_buffer(256, dtype="float32", data=A.data)
            C_1 = T.decl_buffer(256, dtype="float32", data=C.data)

            i0 = T.env_thread("blockIdx.x")
            i1 = T.env_thread("threadIdx.x")
            i2 = T.env_thread("vthread")

            T.launch_thread(i0, 4)
            T.launch_thread(i1, 2)
            T.launch_thread(i2, 2)
            B = T.decl_buffer([16], "float32", scope="local")
            for j in range(0, 16):
                B[j] = A_1[i0 * 64 + i1 * 32 + i2 * 16 + j] + 1.0
            for j in range(0, 16):
                C_1[i0 * 64 + i1 * 32 + i2 * 16 + j] = B[j] * 2.0

    After = _transform()(Before)
    tvm.ir.assert_structural_equal(After, Expected)


def test_symbolic():
    """Dynamically-sized arrrays are flattened"""

    @I.ir_module
    class Before:
        @T.prim_func
        def main(a: T.handle, c: T.handle, n: T.int32, m: T.int32) -> None:
            A = T.match_buffer(a, (n, m), "float32")
            C = T.match_buffer(c, (n, m), "float32")

            for i in range(0, n):
                B = T.decl_buffer([m], "float32")
                for j in range(0, m):
                    B[j] = A[i, j] + 1.0
                for j in range(0, m):
                    C[i, j] = B[j] * 2.0

    @I.ir_module
    class Expected:
        @T.prim_func
        def main(a: T.handle, c: T.handle, n: T.int32, m: T.int32) -> None:
            A = T.match_buffer(a, (n, m), "float32")
            C = T.match_buffer(c, (n, m), "float32")
            A_1 = T.decl_buffer(n * m, "float32", data=A.data)
            C_1 = T.decl_buffer(n * m, "float32", data=C.data)

            for i in range(0, n):
                B = T.decl_buffer([m], "float32")
                for j in range(0, m):
                    B[j] = A_1[i * m + j] + 1.0
                for j in range(0, m):
                    C_1[i * m + j] = B[j] * 2.0

    After = _transform()(Before)
    tvm.ir.assert_structural_equal(After, Expected)


def test_fused_symbolic():
    """Dynamically-sized arrrays with fused iterator which can be flattened"""

    @I.ir_module
    class Before:
        @T.prim_func
        def main(a: T.handle, b: T.handle, n: T.int32) -> None:
            A = T.match_buffer(a, (32, n, n), "float32")
            B = T.match_buffer(b, (32, n, n), "float32")

            for i in range(0, n * n * 32):
                B[i // (n * n), (i % (n * n)) // n, i % n] = A[
                    i // (n * n), (i % (n * n)) // n, i % n
                ]

    @I.ir_module
    class Expected:
        @T.prim_func
        def main(a: T.handle, b: T.handle, n: T.int32) -> None:
            input_A = T.match_buffer(a, (32, n, n), "float32")
            input_B = T.match_buffer(b, (32, n, n), "float32")
            A = T.decl_buffer(n * n * 32, "float32", data=input_A.data)
            B = T.decl_buffer(n * n * 32, "float32", data=input_B.data)

            for i in range(0, n * n * 32):
                B[i] = A[i]

    After = _transform()(Before)
    tvm.ir.assert_structural_equal(After, Expected)


def test_fused_symbolic_with_predicate():
    """Dynamically-sized arrrays with fused iterator which can be flattened with extra predicate"""

    @I.ir_module
    class Before:
        @T.prim_func
        def main(a: T.handle, b: T.handle, n: T.int32) -> None:
            A = T.match_buffer(a, (32, n, n), "float32")
            B = T.match_buffer(b, (32, n, n), "float32")
            for bx, tx in T.grid((n * n + 1) // 2, 64):
                if bx * 64 + tx < n * n * 32:
                    B[
                        (bx * 64 + tx) // (n * n),
                        ((bx * 64 + tx) % (n * n)) // n,
                        (bx * 64 + tx) % n,
                    ] = A[
                        (bx * 64 + tx) // (n * n),
                        ((bx * 64 + tx) % (n * n)) // n,
                        (bx * 64 + tx) % n,
                    ]

    @I.ir_module
    class Expected:
        @T.prim_func
        def main(a: T.handle, b: T.handle, n: T.int32) -> None:
            input_A = T.match_buffer(a, (32, n, n), "float32")
            input_B = T.match_buffer(b, (32, n, n), "float32")
            A = T.decl_buffer(n * n * 32, "float32", data=input_A.data)
            B = T.decl_buffer(n * n * 32, "float32", data=input_B.data)

            for bx, tx in T.grid((n * n + 1) // 2, 64):
                if bx * 64 + tx < n * n * 32:
                    B[bx * 64 + tx] = A[bx * 64 + tx]

    After = _transform()(Before)
    tvm.ir.assert_structural_equal(After, Expected)


def test_multi_alloc():
    """If multiple allocations occur, all are flattened."""

    @I.ir_module
    class Before:
        @T.prim_func
        def main(A: T.Buffer((4, 32), "float32"), D: T.Buffer((4, 32), "float32")):
            for i, j in T.grid(4, 32):
                B = T.decl_buffer((4, 32), "float32", scope="global")
                C = T.decl_buffer((4, 32), "float32", scope="global")
                B[i, j] = A[i, j] + 1.0
                C[i, j] = A[i, j] + B[i, j]
                D[i, j] = C[i, j] * 2.0

    @I.ir_module
    class Expected:
        @T.prim_func
        def main(A: T.Buffer((4, 32), "float32"), D: T.Buffer((4, 32), "float32")):
            A_1 = T.decl_buffer(128, "float32", data=A.data)
            D_1 = T.decl_buffer(128, "float32", data=D.data)

            for i, j in T.grid(4, 32):
                B = T.decl_buffer([128], "float32")
                C = T.decl_buffer([128], "float32")
                B[i * 32 + j] = A_1[i * 32 + j] + 1.0
                C[i * 32 + j] = A_1[i * 32 + j] + B[i * 32 + j]
                D_1[i * 32 + j] = C[i * 32 + j] * 2.0

    After = _transform()(Before)
    tvm.ir.assert_structural_equal(After, Expected)


def test_strided():
    """Indices for flattened buffers use the specified striding."""

    @I.ir_module
    class Before:
        @T.prim_func
        def main(A: T.Buffer((16, 16), "float32"), C: T.Buffer((16, 16), "float32")):
            for i0 in T.serial(4):
                B = T.decl_buffer([4, 17], "float32")
                B_1 = T.decl_buffer([4, 16], dtype="float32", data=B.data, strides=[17, 1])
                for i1, j in T.grid(4, 16):
                    B_1[i1, j] = A[i0 * 4 + i1, j] + 1.0
                for i1, j in T.grid(4, 16):
                    C[i0 * 4 + i1, j] = B_1[i1, j] * 2.0

    @I.ir_module
    class Expected:
        @T.prim_func
        def main(A: T.Buffer((16, 16), "float32"), C: T.Buffer((16, 16), "float32")):
            A_1 = T.decl_buffer(256, dtype="float32", data=A.data)
            C_1 = T.decl_buffer(256, dtype="float32", data=C.data)
            for i0 in T.serial(0, 4):
                B = T.decl_buffer([68], "float32")
                B_1 = T.decl_buffer([68], "float32", data=B.data)
                for i1 in T.serial(0, 4):
                    for j in T.serial(0, 16):
                        B_1[i1 * 17 + j] = A_1[i0 * 64 + i1 * 16 + j] + 1.0
                for i1 in T.serial(0, 4):
                    for j in T.serial(0, 16):
                        C_1[i0 * 64 + i1 * 16 + j] = B_1[i1 * 17 + j] * 2.0

    After = _transform()(Before)
    tvm.ir.assert_structural_equal(After, Expected)


def test_boolean():
    """Boolean buffers should be replaced by a backing int8 array"""

    @I.ir_module
    class Before:
        @T.prim_func
        def main(A: T.Buffer(10, "bool"), B: T.Buffer(10, "bool")) -> None:
            for i0 in T.serial(10):
                B[i0] = A[i0]

    @I.ir_module
    class Expected:
        @T.prim_func
        def main(input_A: T.Buffer(10, "bool"), input_B: T.Buffer(10, "bool")) -> None:
            A = T.decl_buffer(10, dtype="int8", data=input_A.data)
            B = T.decl_buffer(10, dtype="int8", data=input_B.data)
            # body
            for i0 in T.serial(10):
                B[i0] = T.cast(T.cast(A[i0], "bool"), "int8")

    After = _transform()(Before)
    tvm.ir.assert_structural_equal(After, Expected)


def test_flatten_inside_block():
    """Flattening access inside a block flattens the accessed region."""

    @I.ir_module
    class Before:
        @T.prim_func
        def main():
            A = T.sblock_alloc_buffer([32, 32])
            for i, j in T.grid(32, 32):
                with T.sblock("block"):
                    T.reads(A[i, j])
                    T.evaluate(A[i, j])

    @I.ir_module
    class Expected:
        @T.prim_func
        def main():
            A = T.sblock_alloc_buffer([1024])
            for i, j in T.grid(32, 32):
                with T.sblock("block"):
                    T.reads(A[i * 32 + j])
                    T.evaluate(A[i * 32 + j])

    After = _transform()(Before)
    tvm.ir.assert_structural_equal(After, Expected)


def test_no_change_to_2d_physical_buffer():
    """Flattening preserves axis separators."""

    @I.ir_module
    class Before:
        @T.prim_func
        def main():
            A = T.sblock_alloc_buffer([32, 32], axis_separators=[1])
            for i, j in T.grid(32, 32):
                T.evaluate(A[i, j])

    Expected = Before

    After = _transform()(Before)
    tvm.ir.assert_structural_equal(After, Expected)


def test_flatten_alloc_buffer_with_axis_separators():
    """Flattening preserves axis separators"""

    @I.ir_module
    class Before:
        @T.prim_func
        def main():
            A = T.sblock_alloc_buffer([2, 3, 5, 7, 11, 13], axis_separators=[3])
            for i0, i1, i2, i3, i4, i5 in T.grid(2, 3, 5, 7, 11, 13):
                T.evaluate(A[i0, i1, i2, i3, i4, i5])

    @I.ir_module
    class Expected:
        @T.prim_func
        def main():
            A = T.sblock_alloc_buffer([30, 1001], axis_separators=[1])
            for i0, i1, i2, i3, i4, i5 in T.grid(2, 3, 5, 7, 11, 13):
                T.evaluate(A[i0 * 15 + i1 * 5 + i2, i3 * 143 + i4 * 13 + i5])

    After = _transform()(Before)
    tvm.ir.assert_structural_equal(After, Expected)


def test_flatten_decl_buffer_with_axis_separators():
    """Flattening preserves axis separators

    Like test_flatten_alloc_buffer_with_axis_separators, but the allocations
    is done using Allocate/DeclBuffer, rather than through
    BlockNode::alloc_buffers.
    """

    @I.ir_module
    class Before:
        @T.prim_func
        def main():
            A = T.decl_buffer([2, 3, 5, 7, 11, 13], axis_separators=[3])
            for i0, i1, i2, i3, i4, i5 in T.grid(2, 3, 5, 7, 11, 13):
                T.evaluate(A[i0, i1, i2, i3, i4, i5])

    @I.ir_module
    class Expected:
        @T.prim_func
        def main():
            A = T.decl_buffer([30, 1001], axis_separators=[1])
            for i0, i1, i2, i3, i4, i5 in T.grid(2, 3, 5, 7, 11, 13):
                T.evaluate(A[i0 * 15 + i1 * 5 + i2, i3 * 143 + i4 * 13 + i5])

    After = _transform()(Before)
    tvm.ir.assert_structural_equal(After, Expected)


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