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"""Shared helpers for reduction schedules."""

from tvm.backend.trn.layout import is_trainium_layout
from tvm.script import tirx as T
from tvm.tirx import PrimFunc
from tvm.tirx.operator.tile_primitive import DispatchContext, fail
from tvm.tirx.operator.tile_primitive.common import ReduceOpType
from tvm.tirx.tile_primitive import TilePrimitiveCall

from ..common import init_analyzer, nki_dim
from ..dim_utils import get_reduction_dim_map
from ..instruction_generator import InstructionGenerator
from ..workspace_utils import check_workspace_buffer

reduce_ops = {ReduceOpType.SUM: "add", ReduceOpType.MAX: "max", ReduceOpType.MIN: "min"}


def generate_intermediate_buffer(
    dst_buffer_region: int, rfactor_size: int, workspace, sctx: DispatchContext
):
    """Generate an intermediate buffer for two-stage reduction if needed.

    Returns:
        Tuple[Optional[buffer], int]: The intermediate buffer and reduction factor size.
    """
    intermediate_shape = [dst_buffer_region.buffer.ty.layout.size("P"), rfactor_size]

    if "partial_reduce" in workspace:
        intermediate_buffer = workspace["partial_reduce"]
        check_workspace_buffer(intermediate_buffer, intermediate_shape, "trn.sbuf")
    else:
        assert sctx.alloc_only, (
            "Partial reduce buffer must be specified in workspace. Run tvm.tirx.trn.transform.TrnPrivateBufferAlloc first."  # noqa: E501
        )
        intermediate_buffer = T.buffer(
            intermediate_shape,
            dtype=dst_buffer_region.buffer.ty.dtype,
            scope="trn.sbuf",
            buffer_name="partial_reduce",
        )
        sctx.add_alloc_buffer(intermediate_buffer)

    return intermediate_buffer


def reduction_trn(
    op: TilePrimitiveCall, reduce_op: ReduceOpType, sctx: DispatchContext, negate: bool = False
) -> PrimFunc | None:
    """Schedule reduction operation on Trainium.

    Args:
        op: The operation call.
        reduce_op: The reduction operation type.
        sctx: The dispatch context.
        negate: Whether to negate the result.

    Returns:
        Optional[PrimFunc]: The scheduled function, or None if not applicable.
    """
    if not (sctx.is_target("trn") and sctx.scope_kind == "thread"):
        fail("requires Trainium target and thread exec_scope")

    dst_buffer_region, src_buffer_region, axes, accum = op.args[:4]
    assert not accum, "Accumulation is not supported for reduction on Trainium"
    analyzer = init_analyzer(sctx)
    assert reduce_op in reduce_ops, f"Unsupported reduce operation {reduce_op}"

    # Extract buffers
    dst = dst_buffer_region.buffer
    src = src_buffer_region.buffer
    axes = [i if i >= 0 else len(src.ty.shape) + i for i in axes]
    dim_map = get_reduction_dim_map(src_buffer_region, dst_buffer_region, axes, analyzer)

    # Layout validation
    assert all(
        [
            src.ty.layout and dst.ty.layout,
            src.scope() == "trn.sbuf" or src.scope() == "trn.psum",
            dst.scope() == "trn.sbuf",
            is_trainium_layout(src.ty.layout),
            is_trainium_layout(dst.ty.layout),
            src.ty.layout.size("P") == dst.ty.layout.size("P"),
        ]
    ), "Invalid layout"

    # Find maximum instruction size
    inst_gen = InstructionGenerator([src_buffer_region, dst_buffer_region], analyzer)
    inst_gen.link_buffer_regions(src_buffer_region, dst_buffer_region, dim_map)
    inst_repr = inst_gen.find_max_inst_size_from_one_region(src_buffer_region, axes)
    inst_size_limit = op.config.get("max_inst_size", None)
    inst_repr.bound_inst_size(inst_size_limit, analyzer)
    assert analyzer.can_prove(inst_repr.size > 1), "Instruction size must be greater than 1"

    # Get partition size and extents
    p_size = src.ty.layout.size("P")
    f_var = T.Var("F", "int32")
    p_var = T.Var("P", "int32")
    spatial_b_var = T.Var("sB", "int32")
    reduction_b_var = T.Var("rB", "int32")
    inst_gen.bind_inst_iter(src_buffer_region, f_var, inst_repr.size, inst_repr.stride, True)
    inst_gen.bind_inst_iter(src_buffer_region, p_var, p_size, 1, False)
    reduction_b_extent = inst_gen.fill_in_block_dim(src_buffer_region, reduction_b_var, axes)
    spatial_b_extent = inst_gen.fill_in_block_dim(src_buffer_region, spatial_b_var)
    # Get reduction operation code
    opcode = reduce_ops[reduce_op]

    # Generate intermediate buffer if needed
    if reduction_b_extent != 1:
        intermediate_buffer = generate_intermediate_buffer(
            dst_buffer_region, reduction_b_extent, op.workspace, sctx
        )

    # fmt: off
    # Single-stage reduction implementation
    if reduction_b_extent == 1:
        @T.prim_func
        def impl():
            for b_loop in T.serial(0, spatial_b_extent):
                with T.attr(0, "tensorized_nki_instruction", 1):
                    for p_loop in T.serial(0, p_size, annotations={nki_dim: "P"}):
                        for f_loop in T.serial(0, inst_repr.size, annotations={nki_dim: "F"}):
                            inst_gen.set_bind_map_all({p_var: p_loop, f_var: f_loop, spatial_b_var: b_loop})  # noqa: E501
                            if inst_gen.make_guard(src_buffer_region):
                                src_indices = T.meta_var(inst_gen.generate_indices(src_buffer_region))  # noqa: E501
                                dst_indices = T.meta_var(inst_gen.generate_indices(dst_buffer_region))  # noqa: E501
                                T.evaluate(T.nki.tensorreduce(dst[tuple(dst_indices)], src[tuple(src_indices)], opcode, negate, -1))  # noqa: E501
        return impl
    # Two-stage reduction implementation
    else:
        @T.prim_func
        def two_stage_reduction():
            for b_loop in T.serial(0, spatial_b_extent):
                for reduction_b_loop in T.serial(0, reduction_b_extent):
                    with T.attr(0, "tensorized_nki_instruction", 1):
                        for p_loop in T.serial(0, p_size, annotations={nki_dim: "P"}):
                            for f_loop in T.serial(0, inst_repr.size, annotations={nki_dim: "F"}):
                                inst_gen.set_bind_map_all({p_var: p_loop, f_var: f_loop, spatial_b_var: b_loop, reduction_b_var: reduction_b_loop})  # noqa: E501
                                if inst_gen.make_guard(src_buffer_region):
                                    src_indices = T.meta_var(inst_gen.generate_indices(src_buffer_region))  # noqa: E501
                                    T.evaluate(T.nki.tensorreduce(intermediate_buffer[p_loop, reduction_b_loop], src[src_indices], opcode, False, -1))  # noqa: E501
                with T.attr(0, "tensorized_nki_instruction", 1):
                    for p_loop in T.serial(0, p_size, annotations={nki_dim: "P"}):
                        for f_loop in T.serial(0, reduction_b_extent, annotations={nki_dim: "F"}):
                            inst_gen.set_bind_map(src_buffer_region, {p_var: p_loop, f_var: 0, spatial_b_var: b_loop, reduction_b_var: f_loop})  # noqa: E501
                            inst_gen.set_bind_map(dst_buffer_region, {p_var: p_loop, spatial_b_var: b_loop})  # noqa: E501
                            if inst_gen.make_guard(src_buffer_region):
                                dst_indices = T.meta_var(inst_gen.generate_indices(dst_buffer_region))  # noqa: E501
                                T.evaluate(T.nki.tensorreduce(dst[dst_indices], intermediate_buffer[p_loop, f_loop], opcode, negate, -1))  # noqa: E501
        return two_stage_reduction
    # fmt: on
