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# pylint: disable=invalid-name,unused-variable
"""dense schedule on ARM Mali GPU"""
from tvm import te
from tvm import autotvm

from .. import nn
from ..util import traverse_inline



@autotvm.register_topi_compute('dense.mali')
def dense(_, data, weight, bias=None, out_dtype=None):
    """Dense operator on Mali"""
    return nn.dense(data, weight, bias, out_dtype)


@autotvm.register_topi_schedule('dense.mali')
def schedule_dense(cfg, outs):
    """Schedule for dense operator.

    Parameters
    ----------
    cfg: ConfigEntity
        The config entity for this template
    outs: Array of Tensor
        The computation graph description of dense
        in the format of an array of tensors.

    Returns
    -------
    s: Schedule
        The computation schedule for dense.
    """
    outs = [outs] if isinstance(outs, te.tensor.Tensor) else outs
    s = te.create_schedule([x.op for x in outs])

    def _callback(op):
        if op.tag == 'dense':
            vec_size = [1, 2, 4, 8, 16]
            max_unroll = 32

            dense_out = op.output(0)
            output = outs[0]

            y, x = s[output].op.axis
            c = s[dense_out].op.reduce_axis[0]

            ##### space definition begin #####
            cfg.define_split('tile_y', y, num_outputs=3)
            cfg.define_split('tile_x', x, num_outputs=3)
            cfg.define_split('c_unroll', c, num_outputs=2, max_factor=64)

            # fallback support
            if cfg.is_fallback:
                ref_log = autotvm.tophub.load_reference_log(
                    'mali', 'rk3399', 'dense.mali')
                cfg.fallback_with_reference_log(ref_log)
            ##### space definition end #####

            if dense_out.op in s.outputs:
                dense_out = s.cache_write(output, 'local')

            by, ty, yi = cfg['tile_y'].apply(s, output, y)
            bx, tx, xi = cfg['tile_x'].apply(s, output, x)

            s[output].bind(by, te.thread_axis('blockIdx.y'))
            s[output].bind(bx, te.thread_axis('blockIdx.x'))
            s[output].bind(ty, te.thread_axis('threadIdx.y'))
            s[output].bind(tx, te.thread_axis('threadIdx.x'))

            if cfg['tile_y'].size[-1] < max_unroll:
                s[output].unroll(yi)
            if cfg['tile_x'].size[-1] in vec_size:
                s[output].vectorize(xi)
            s[dense_out].compute_at(s[output], tx)

            k = s[dense_out].op.reduce_axis[0]
            y, x = s[dense_out].op.axis
            k, k_unroll = cfg['c_unroll'].apply(s, dense_out, k)
            s[dense_out].reorder(k, k_unroll, y, x)
            s[dense_out].unroll(k_unroll)
            if cfg['tile_y'].size[-1] < max_unroll:
                s[dense_out].unroll(y)
            if cfg['tile_x'].size[-1] in vec_size:
                s[dense_out].vectorize(x)

    traverse_inline(s, outs[0].op, _callback)
    return s


def fuse_and_bind(s, tensor, axis=None, num_thread=None):
    """ fuse all the axis and bind to GPU threads """
    # TODO(@comaniac): figure out where this function is used.
    axis = axis or s[tensor].op.axis
    fused = s[tensor].fuse(*axis)
    bx, tx = s[tensor].split(fused, num_thread)
    s[tensor].bind(bx, te.thread_axis("blockIdx.x"))
    s[tensor].bind(tx, te.thread_axis("threadIdx.x"))
    return bx, tx
