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#
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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.StmtSimplify(),
        ]
    )


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

    @I.ir_module(s_tir=True)
    class Before:
        @T.prim_func(s_tir=True)
        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(s_tir=True)
    class Expected:
        @T.prim_func(s_tir=True)
        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, s_tir=True)
    class Before:
        @T.prim_func(s_tir=True)
        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, s_tir=True)
    class Expected:
        @T.prim_func(s_tir=True)
        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(s_tir=True)
    class Before:
        @T.prim_func(s_tir=True)
        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(s_tir=True)
    class Expected:
        @T.prim_func(s_tir=True)
        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(s_tir=True)
    class Before:
        @T.prim_func(s_tir=True)
        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(s_tir=True)
    class Expected:
        @T.prim_func(s_tir=True)
        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(s_tir=True)
    class Before:
        @T.prim_func(s_tir=True)
        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(s_tir=True)
    class Expected:
        @T.prim_func(s_tir=True)
        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(s_tir=True)
    class Before:
        @T.prim_func(s_tir=True)
        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(s_tir=True)
    class Expected:
        @T.prim_func(s_tir=True)
        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(s_tir=True)
    class Before:
        @T.prim_func(s_tir=True)
        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(s_tir=True)
    class Expected:
        @T.prim_func(s_tir=True)
        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(s_tir=True)
    class Before:
        @T.prim_func(s_tir=True)
        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(s_tir=True)
    class Expected:
        @T.prim_func(s_tir=True)
        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 are flattened but kept as bool (no int8 backing array)"""

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

    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(s_tir=True)
    class Before:
        @T.prim_func(s_tir=True)
        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(s_tir=True)
    class Expected:
        @T.prim_func(s_tir=True)
        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(s_tir=True)
    class Before:
        @T.prim_func(s_tir=True)
        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(s_tir=True)
    class Before:
        @T.prim_func(s_tir=True)
        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(s_tir=True)
    class Expected:
        @T.prim_func(s_tir=True)
        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(s_tir=True)
    class Before:
        @T.prim_func(s_tir=True)
        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(s_tir=True)
    class Expected:
        @T.prim_func(s_tir=True)
        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()
