# pylint: disable=invalid-name,unused-variable,no-else-return
"""Conv2D schedule for ARM CPU"""
from __future__ import absolute_import as _abs

import warnings

import numpy as np

import tvm
from tvm import autotvm

from ..generic import schedule_conv2d_nchw, schedule_conv2d_winograd_without_weight_transform
from ..util import traverse_inline, get_const_tuple, const_matrix
from ..nn import dilate, pad, conv2d, conv2d_alter_layout, \
                 conv2d_winograd_without_weight_transform, depthwise_conv2d_nchw
from ..nn.util import get_const_int, get_pad_tuple

@autotvm.register_topi_compute(conv2d, 'arm_cpu', ['direct'])
def conv2d_arm_cpu(cfg, data, kernel, strides, padding, dilation, layout, out_dtype):
    """TOPI compute callback for conv2d

    Parameters
    ----------
    cfg: ConfigEntity
        The config for this template

    data : tvm.Tensor
        4-D with shape [batch, in_channel, in_height, in_width]

    kernel : tvm.Tensor
        4-D with shape [num_filter, in_channel, filter_height, filter_width] or
        pre-packed 5-D with shape [num_filter_chunk, in_channel, filter_height,
        filter_width, num_filter_block]

    strides : list of two ints
        [stride_height, stride_width]

    padding : list of two ints
        [pad_height, pad_width]

    dilation : list of two ints
        [dilation_height, dilation_width]

    layout : str
        layout of data

    out_dtype: str
        The output type. This is used for mixed precision.

    Returns
    -------
    output : tvm.Tensor
        4-D with shape [batch, out_channel, out_height, out_width]
    """
    return _decl_spatial_pack(cfg, data, kernel, strides, padding, dilation, layout, out_dtype,
                              num_tile=2)

@autotvm.register_topi_schedule(schedule_conv2d_nchw, 'arm_cpu', ['direct', 'winograd'])
def schedule_conv2d_nchw_arm_cpu(cfg, outs):
    """TOPI schedule callback for conv2d

    Parameters
    ----------
    cfg: ConfigEntity
        The config for this template

    outs: Array of Tensor
        The computation graph description of conv2d
        in the format of an array of tensors.

    Returns
    -------
    s: Schedule
        The computation schedule for conv2d.
    """
    s = tvm.create_schedule([x.op for x in outs])

    def _callback(op):
        # schedule conv2d
        if 'spatial_conv2d_output' in op.tag:
            output = op.output(0)
            conv = op.input_tensors[0]

            data_vec = conv.op.input_tensors[0]
            data_pad = data_vec.op.input_tensors[0]
            s[data_pad].compute_inline()

            kernel_vec = conv.op.input_tensors[1]
            if kernel_vec.op.name == 'kernel_vec':
                kernel = kernel_vec.op.input_tensors[0]
            else:
                kernel = kernel_vec
            if isinstance(kernel.op, tvm.tensor.ComputeOp) and "dilate" in kernel.op.tag:
                s[kernel].compute_inline()

            _schedule_spatial_pack(cfg, s, data_vec, kernel_vec, conv, output, outs[0])

        if 'winograd_conv2d_output' in op.tag:
            output = op.output(0)
            _schedule_winograd(cfg, s, output, outs[0])

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


def _decl_spatial_pack(cfg, data, kernel, strides, padding, dilation, layout, out_dtype, num_tile):
    assert layout == "NCHW", "Only support NCHW"
    # create workload according to raw arguments
    out_dtype = out_dtype or data.dtype
    N, CI, IH, IW = get_const_tuple(data.shape)

    if isinstance(dilation, int):
        dilation_h = dilation_w = dilation
    else:
        dilation_h, dilation_w = dilation

    if len(kernel.shape) == 4:
        pre_packed = False
        CO, _, KH, KW = get_const_tuple(kernel.shape)
    else:  # kernel tensor is pre packed
        pre_packed = True
        CO, _, KH, KW, VC = get_const_tuple(kernel.shape)
        CO = CO * VC

    dilated_kernel_h = (KH - 1) * dilation_h + 1
    dilated_kernel_w = (KW - 1) * dilation_w + 1
    pad_top, pad_left, pad_bottom, pad_right = get_pad_tuple(
        padding, (dilated_kernel_h, dilated_kernel_w))
    HSTR, WSTR = strides if isinstance(strides, (tuple, list)) else (strides, strides)
    OH = (IH + pad_top + pad_bottom - dilated_kernel_h) // HSTR + 1
    OW = (IW + pad_left + pad_right - dilated_kernel_w) // WSTR + 1
    data_pad = pad(data, [0, 0, pad_top, pad_left], [0, 0, pad_bottom, pad_right])

    # ==================== define configuration space ====================
    n, co, oh, ow = cfg.axis(N), cfg.axis(CO), cfg.axis(OH), cfg.axis(OW)
    ci, kh, kw = cfg.reduce_axis(CI), cfg.reduce_axis(KH), cfg.reduce_axis(KW)

    if num_tile == 2:     # for arm cpu
        co, vc = cfg.define_split('tile_co', co, num_outputs=2)
        oh, vh = cfg.define_split('tile_oh', oh, num_outputs=2)
        ow, vw = cfg.define_split('tile_ow', ow, num_outputs=2)
    elif num_tile == 3:   # for mali gpu
        co, _, vc = cfg.define_split('tile_co', co, num_outputs=3)
        oh, _, vh = cfg.define_split('tile_oh', oh, num_outputs=3)
        ow, _, vw = cfg.define_split('tile_ow', ow, num_outputs=3)
    else:
        raise RuntimeError("Invalid num_tile")

    cfg.define_reorder("reorder_0",
                       [n, co, oh, ow, ci, kh, kw, vh, vw, vc],
                       policy='candidate', candidate=[
                           [n, co, oh, ow, ci, kh, kw, vh, vw, vc],
                           [n, co, oh, ow, ci, kh, kw, vc, vh, vw]])

    cfg.define_annotate("ann_reduce", [kh, kw], policy='try_unroll')
    cfg.define_annotate("ann_spatial", [vh, vw, vc], policy='try_unroll_vec')

    # fallback support
    if cfg.is_fallback:
        if num_tile == 2:     # arm cpu
            ref_log = autotvm.tophub.load_reference_log('arm_cpu', 'rk3399', 'conv2d', 'direct')
            cfg.fallback_with_reference_log(ref_log)
        elif num_tile == 3:  # mali gpu
            ref_log = autotvm.tophub.load_reference_log('mali', 'rk3399', 'conv2d', 'direct')
            cfg.fallback_with_reference_log(ref_log)
    # ====================================================================

    VC = cfg["tile_co"].size[-1]
    VH = cfg["tile_oh"].size[-1]
    VW = cfg["tile_ow"].size[-1]

    kvshape = (CO // VC, CI, KH, KW, VC)
    ovshape = (N, CO // VC, OH // VH, OW // VW, VH, VW, VC)
    oshape = (N, CO, OH, OW)

    if dilation_h != 1 or dilation_w != 1:
        # undilate input data
        dvshape = (N, OH // VH, OW // VW, CI, KH, KW, VH, VW)
        data_vec = tvm.compute(dvshape, lambda n, h, w, ci, kh, kw, vh, vw:
                               data_pad[n][ci][(h*VH+vh)*HSTR+kh*dilation_h]
                               [(w*VW+vw)*WSTR+kw*dilation_w],
                               name='data_vec_undilated')
    else:
        dvshape = (N, OH // VH, OW // VW, CI, VH*HSTR + KH-1, VW*WSTR + KW-1)
        data_vec = tvm.compute(dvshape, lambda n, h, w, ci, vh, vw:
                               data_pad[n][ci][h*VH*HSTR+vh][w*VW*WSTR+vw],
                               name='data_vec')

    if pre_packed:
        kernel_vec = kernel
    else:
        kernel_vec = tvm.compute(kvshape, lambda co, ci, kh, kw, vc:
                                 kernel[co*VC+vc][ci][kh][kw],
                                 name='kernel_vec')

    ci = tvm.reduce_axis((0, CI), name='ci')
    kh = tvm.reduce_axis((0, KH), name='kh')
    kw = tvm.reduce_axis((0, KW), name='kw')

    if dilation_h != 1 or dilation_w != 1:
        conv = tvm.compute(ovshape, lambda n, co, h, w, vh, vw, vc: \
            tvm.sum(data_vec[n, h, w, ci, kh, kw, vh, vw].astype(out_dtype) *
                    kernel_vec[co, ci, kh, kw, vc].astype(out_dtype),
                    axis=[ci, kh, kw]), name='conv')
    else:
        conv = tvm.compute(ovshape, lambda n, co, h, w, vh, vw, vc: \
            tvm.sum(data_vec[n, h, w, ci, vh*HSTR+kh, vw*WSTR+kw].astype(out_dtype) *
                    kernel_vec[co, ci, kh, kw, vc].astype(out_dtype),
                    axis=[ci, kh, kw]), name='conv')

    output = tvm.compute(oshape, lambda n, co, h, w:
                         conv[n][co//VC][h//VH][w//VW][h%VH][w%VW][co%VC],
                         name='output_unpack', tag='spatial_conv2d_output')
    return output

def _schedule_spatial_pack(cfg, s, data_vec, kernel_vec,
                           conv, output, last):
    """schedule implementation"""
    n, co, oh, ow, vh, vw, vc = s[conv].op.axis
    ci, kh, kw = s[conv].op.reduce_axis

    # schedule conv
    cfg["reorder_0"].apply(s, conv, [n, co, oh, ow, ci, kh, kw, vh, vw, vc])
    cfg["ann_reduce"].apply(s, conv, [kh, kw],
                            axis_lens=[get_const_int(kh.dom.extent),
                                       get_const_int(kw.dom.extent)],
                            max_unroll=16,
                            cfg=cfg)
    cfg["ann_spatial"].apply(s, conv, [vh, vw, vc],
                             axis_lens=[cfg['tile_oh'].size[-1],
                                        cfg['tile_ow'].size[-1],
                                        cfg['tile_co'].size[-1]],
                             max_unroll=16,
                             cfg=cfg)

    # schedule fusion
    n, co, h, w = s[last].op.axis
    co, vc = cfg['tile_co'].apply(s, last, co)
    oh, vh = cfg['tile_oh'].apply(s, last, h)
    ow, vw = cfg['tile_ow'].apply(s, last, w)
    s[last].reorder(n, co, oh, ow, vh, vw, vc)
    if last != output:
        s[output].compute_inline()
        cfg["ann_spatial"].apply(s, last, [vh, vw, vc],
                                 axis_lens=[cfg['tile_oh'].size[-1],
                                            cfg['tile_ow'].size[-1],
                                            cfg['tile_co'].size[-1]],
                                 max_unroll=16,
                                 cfg=cfg)
    s[conv].compute_at(s[last], ow)

    # mark parallel
    s[last].parallel(co)

    if data_vec.op.name == 'data_vec_undilated':
        _, h, _, _, _, _, _, _ = s[data_vec].op.axis
    else:
        _, h, _, _, _, _ = s[data_vec].op.axis
    s[data_vec].parallel(h)

    if kernel_vec.op.name == 'kernel_vec':
        co, _, _, _, _ = s[kernel_vec].op.axis
        if autotvm.GLOBAL_SCOPE.in_tuning:
            # kernel packing will be pre-computed during compilation, so we skip
            # this part to make tuning records correct
            s[kernel_vec].pragma(co, 'debug_skip_region')
        else:
            s[kernel_vec].parallel(co)
    elif kernel_vec.op.name == 'kernel_vec_conv2d_transpose':  # for conv2d transpose
        co, _, _, _, _ = s[kernel_vec].op.axis
        s[kernel_vec].parallel(co)

    return s


@autotvm.register_topi_compute(conv2d, 'arm_cpu', ['winograd'])
def conv2d_arm_cpu_winograd(cfg, data, kernel, strides, padding, dilation, layout, out_dtype):
    """ TOPI compute callback. Use winograd template """
    tile_size = 4
    return _decl_winograd(cfg, data, kernel, strides, padding, dilation, layout,
                          out_dtype, tile_size)

def _decl_winograd(cfg, data, kernel, strides, padding, dilation, layout, out_dtype, tile_size):
    N, CI, IH, IW = get_const_tuple(data.shape)

    if isinstance(dilation, int):
        dilation_h = dilation_w = dilation
    else:
        dilation_h, dilation_w = dilation

    if len(kernel.shape) == 4:
        if dilation_h != 1 or dilation_w != 1:
            kernel = dilate(kernel, (1, 1, dilation_h, dilation_w))
        pre_computed = False
        CO, _, KH, KW = get_const_tuple(kernel.shape)
    else:
        assert (dilation_h, dilation_w) == (1, 1), "Does not support dilation"
        pre_computed = True
        H_CAT, W_CAT, CO, CI, VC = get_const_tuple(kernel.shape)
        CO *= VC
        KH, KW = H_CAT - tile_size + 1, W_CAT - tile_size + 1
    HSTR, WSTR = strides if isinstance(strides, (tuple, list)) else (strides, strides)
    HPAD, WPAD, _, _ = get_pad_tuple(padding, kernel)

    assert layout == 'NCHW'
    assert KH == 3 and KW == 3 and HPAD == 1 and WPAD == 1 and HSTR == 1 and WSTR == 1
    data_pad = pad(data, (0, 0, HPAD, WPAD), name="data_pad")

    if tile_size == 4:
        G_data = np.array([
            [1 / 4.0, 0, 0],
            [-1 / 6.0, -1 / 6.0, -1 / 6.0],
            [-1 / 6.0, 1 / 6.0, -1 / 6.0],
            [1 / 24.0, 1 / 12.0, 1 / 6.0],
            [1 / 24.0, -1 / 12.0, 1 / 6.0],
            [0, 0, 1]], dtype=np.float32)

        B_data = np.array([
            [4, 0, 0, 0, 0, 0],
            [0, -4, 4, -2, 2, 4],
            [-5, -4, -4, -1, -1, 0],
            [0, 1, -1, 2, -2, -5],
            [1, 1, 1, 1, 1, 0],
            [0, 0, 0, 0, 0, 1]], out_dtype)

        A_data = np.array([
            [1, 0, 0, 0],
            [1, 1, 1, 1],
            [1, -1, 1, -1],
            [1, 2, 4, 8],
            [1, -2, 4, -8],
            [0, 0, 0, 1]], out_dtype)
    elif tile_size == 2:
        G_data = np.array([
            [1, 0, 0],
            [1.0/2, 1.0/2, 1.0/2],
            [1.0/2, -1.0/2, 1.0/2],
            [0, 0, 1]], np.float32)

        B_data = np.array([
            [1, 0, 0, 0],
            [0, 1, -1, 1],
            [-1, 1, 1, 0],
            [0, 0, 0, -1]], out_dtype)

        A_data = np.array([
            [1, 0],
            [1, 1],
            [1, -1],
            [0, -1]], out_dtype)
    else:
        raise ValueError("Unsupported tile size for winograd: " + str(tile_size))

    m = A_data.shape[1]
    r = 3
    alpha = m + r - 1
    K = CO
    C = CI

    H = (IH + 2 * HPAD - 3) // HSTR + 1
    W = (IW + 2 * WPAD - 3) // WSTR + 1
    nH, nW = (H + m-1) // m, (W + m-1) // m
    P = N * nH * nW

    cfg.define_split('tile_p', cfg.axis(P), num_outputs=2, filter=lambda x: x.size[-1] <= 16)
    cfg.define_split('tile_k', cfg.axis(K), num_outputs=2, filter=lambda x: x.size[-1] <= 16)
    VP = cfg['tile_p'].size[-1]
    VK = cfg['tile_k'].size[-1]

    # pack input tile
    input_tile = tvm.compute((C, P // VP, alpha, alpha, VP),
                             lambda c, b, eps, nu, bb:
                             data_pad[(b*VP+bb) // (nH*nW)][c][(b*VP+bb) // nW % nH * m + eps]
                             [(b*VP+bb) % nW * m + nu],
                             name='d')

    # transform kernel
    if pre_computed:
        U = kernel
    else:
        G = const_matrix(G_data, 'G')
        r_kh = tvm.reduce_axis((0, KH), 'r_kh')
        r_kw = tvm.reduce_axis((0, KW), 'r_kw')
        U = tvm.compute((alpha, alpha, K // VK, C, VK), lambda eps, nu, k, c, kk:
                        tvm.sum(kernel[k * VK + kk][c][r_kh][r_kw].astype(out_dtype) *
                                G[eps][r_kh] * G[nu][r_kw], axis=[r_kh, r_kw]), name='U')

    # transform image
    B = const_matrix(B_data, 'B')
    r_eps = tvm.reduce_axis((0, alpha), 'r_eps')
    r_nu = tvm.reduce_axis((0, alpha), 'r_nu')
    V = tvm.compute((alpha, alpha, P // VP, C, VP), lambda eps, nu, b, c, bb:
                    tvm.sum(input_tile[c][b][r_eps][r_nu][bb].astype(out_dtype) *
                            B[r_eps][eps] * B[r_nu][nu], axis=[r_eps, r_nu]), name='V')

    # batch gemm
    c = tvm.reduce_axis((0, C), name='c')
    M = tvm.compute((alpha, alpha, K, P), lambda eps, nu, k, b:
                    tvm.sum(U[eps][nu][k // VK][c][k % VK] *
                            V[eps][nu][b // VP][c][b % VP], axis=c), name='M')

    # inverse transform
    A = const_matrix(A_data, 'A')
    r_eps = tvm.reduce_axis((0, alpha), 'r_eps')
    r_nu = tvm.reduce_axis((0, alpha), 'r_nu')
    Y = tvm.compute((K, P, m, m), lambda k, b, vh, vw:
                    tvm.sum(M[r_eps][r_nu][k][b] * A[r_eps][vh] * A[r_nu][vw],
                            axis=[r_eps, r_nu]), name='Y')

    # unpack output
    output = tvm.compute((N, K, H, W), lambda n, k, h, w:
                         Y[k][n * nH * nW + (h//m) * nW + w//m][h % m][w % m],
                         name='output', tag='winograd_conv2d_output')

    # we have to manually assign effective GFLOP for winograd
    cfg.add_flop(2 * N * K * H * W * KH * KW * C)
    return output

def _schedule_winograd(cfg, s, output, last):
    Y = output.op.input_tensors[0]
    M, A = Y.op.input_tensors
    U, V = M.op.input_tensors
    d, B = V.op.input_tensors
    data_pad = d.op.input_tensors[0]

    # padding
    s[data_pad].compute_inline()

    # pack input tiles
    s[d].compute_inline()

    # transform kernel
    if isinstance(U.op, tvm.tensor.ComputeOp):
        kernel, G = U.op.input_tensors
        s[G].compute_inline()
        eps, nu, k, c, kk, = s[U].op.axis
        if autotvm.GLOBAL_SCOPE.in_tuning:
            # kernel transformation will be pre-computed during compilation, so we skip
            # this part to make tuning records correct
            s[U].pragma(eps, 'debug_skip_region')
        else:
            r_kh, r_kw = s[U].op.reduce_axis
            s[U].reorder(k, c, eps, nu, r_kh, r_kw, kk)
            for axis in [eps, nu, r_kh, r_kw]:
                s[U].unroll(axis)
            s[U].vectorize(kk)
            s[U].parallel(k)

        if isinstance(kernel.op, tvm.tensor.ComputeOp) and "dilate" in kernel.op.tag:
            s[kernel].compute_inline()

    # transform image
    DD = s.cache_read(d, 'global', [V])
    s[B].compute_inline()
    eps, nu, b, c, bb = s[V].op.axis
    r_eps, r_nu = s[V].op.reduce_axis
    s[V].reorder(b, c, eps, nu, r_eps, r_nu, bb)
    for axis in [eps, nu, r_eps, r_nu]:
        s[V].unroll(axis)
    s[DD].compute_at(s[V], c)
    s[V].vectorize(bb)
    s[V].parallel(b)

    # batch gemm
    eps, nu, k, b = s[M].op.axis
    c = s[M].op.reduce_axis[0]
    cfg.define_split('tile_c', c, num_outputs=2, filter=lambda x: x.size[-1] <= 16)
    co, ci = cfg['tile_c'].apply(s, M, c)
    xo, xi = cfg['tile_p'].apply(s, M, b)
    s[M].reorder(eps, nu, xo, co, k, ci, xi)
    cfg.define_annotate('ann_reduce', [ci], policy='try_unroll')
    cfg.define_annotate('ann_spatial', [k, xi], policy='try_unroll_vec')
    cfg['ann_reduce'].apply(s, M, [ci],
                            axis_lens=[cfg['tile_c'].size[-1]],
                            max_unroll=16,
                            cfg=cfg)
    cfg['ann_spatial'].apply(s, M, [k, xi])

    # inverse transform
    s[A].compute_inline()
    k, b, vh, vw = s[Y].op.axis
    r_eps, r_nu = s[Y].op.reduce_axis
    for axis in [vh, vw, r_eps, r_nu]:
        s[Y].unroll(axis)

    # output
    n, co, h, w = s[last].op.axis
    co, coi = cfg['tile_k'].apply(s, last, co)
    s[M].compute_at(s[last], co)
    s[last].parallel(co)

    MM = s.cache_read(M, 'global', [Y])
    m = get_const_int(V.shape[0]) + 1 - 3
    ho, wo, hi, wi = s[last].tile(h, w, m, m)
    s[Y].compute_at(s[last], wo)
    s[MM].compute_at(s[last], wo)

    if output != last:
        s[output].compute_inline()


##### REGISTER TOPI COMPUTE / SCHEDULE FOR WINOGRAD WITH WEIGHT TRANSFORM #####
@autotvm.register_topi_compute(conv2d_winograd_without_weight_transform, 'arm_cpu', ['winograd'])
def conv2d_winograd_ww(cfg, data, kernel, strides, padding, dilation, layout, out_dtype, tile_size):
    """TOPI compute callback"""
    return _decl_winograd(cfg, data, kernel, strides, padding, dilation, layout, out_dtype,\
                          tile_size)


@autotvm.register_topi_schedule(schedule_conv2d_winograd_without_weight_transform,
                                'arm_cpu', ['winograd'])
def schedule_conv2d_winograd_without_weight_transform_(cfg, outs):
    """TOPI schedule callback"""
    s = tvm.create_schedule([x.op for x in outs])

    def _callback(op):
        if 'winograd_conv2d_output' in op.tag:
            output = op.output(0)
            _schedule_winograd(cfg, s, output, outs[0])

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


##### REGISTER ALTER OP LAYOUT #####
@conv2d_alter_layout.register(["arm_cpu"])
def _alter_conv2d_layout_arm(attrs, inputs, tinfos, F):
    """Alter op layout for pre-computing kernel transformation

    Parameters
    ----------
    attrs : nnvm.top.AttrDict or tvm.attrs.Attrs
        Attributes of current convolution
    inputs : nnvm.symbol or tvm.relay.Expr
        Grouped input symbols
    tinfos : list
        Input shape and dtype
    F: symbol
        The context, can be either nnvm.sym or relay.op

    Note
    ----
    Unlike other TOPI functions, this function operates on both graph level and operator level,
    so we have to pass 'F' to make it support our two versions of graph IR, NNVM and Relay.
    """
    copy_inputs = [s for s in inputs]

    new_attrs = {k: attrs[k] for k in attrs.keys()}

    dilation = attrs.get_int_tuple("dilation")
    strides = attrs.get_int_tuple("strides")
    padding = attrs.get_int_tuple("padding")
    groups = attrs.get_int('groups')
    data_layout_key = "data_layout" if "data_layout" in new_attrs else "layout"
    layout = attrs[data_layout_key]
    out_dtype = attrs["out_dtype"]
    if out_dtype == "" or out_dtype == "same":
        out_dtype = tinfos[0].dtype

    if layout != 'NCHW':
        return None
    if dilation != (1, 1):
        warnings.warn("Does not support weight pre-transform for dilated convolution.")
        return None

    data, kernel = tinfos[0:2]
    N, CI, H, W = get_const_tuple(data.shape)
    CO, _, KH, KW = get_const_tuple(kernel.shape)

    if groups == 1:
        # query config of this workload
        workload = autotvm.task.args_to_workload(
            [data, kernel, strides, padding, dilation, layout, out_dtype], conv2d)
        target = tvm.target.current_target()
        dispatch_ctx = autotvm.DispatchContext.current
        cfg = dispatch_ctx.query(target, workload)

        if cfg.is_fallback:  # if is fallback, clear query cache and return None
            autotvm.task.clear_fallback_cache(target, workload)
            return None

        if cfg.template_key == 'direct':  # pack weight tensor
            VC = cfg['tile_co'].size[-1]
            new_attrs['kernel_layout'] = 'OIHW%do' % VC

            # Store the same config for the altered operator (workload)
            new_data = data
            new_kernel = tvm.placeholder((CO // VC, CI, KH, KW, VC), dtype=kernel.dtype)
            new_workload = autotvm.task.args_to_workload(
                [new_data, new_kernel, strides, padding, dilation, 'NCHW', out_dtype], conv2d)
            dispatch_ctx.update(target, new_workload, cfg)

            return F.nn.conv2d(*copy_inputs, **new_attrs)
        else:  # pre-compute weight transformation in winograd
            if "-device=arm_cpu" in target.options:
                tile_size = 4
                VC = cfg['tile_k'].size[-1]
            else:
                from ..mali.conv2d import _pick_tile_size
                tile_size = _pick_tile_size(tinfos[0], tinfos[1])
                VC = cfg['tile_bna'].val

            weight = F.nn.contrib_conv2d_winograd_weight_transform(copy_inputs[1],
                                                                   tile_size=tile_size)
            weight = F.reshape(weight,
                               newshape=(KH + tile_size - 1, KW + tile_size - 1, CO // VC, VC, CI))
            weight = F.transpose(weight, axes=[0, 1, 2, 4, 3])

            copy_inputs[1] = weight
            new_attrs['tile_size'] = tile_size

            # Store the same config for the altered operator (workload)
            new_data = data
            new_weight = tvm.placeholder((KH + tile_size - 1, KH + tile_size -1, CO // VC, CI, VC),
                                         kernel.dtype)
            new_workload = autotvm.task.args_to_workload(
                [new_data, new_weight, strides, padding, dilation,
                 new_attrs[data_layout_key], out_dtype, tile_size],
                conv2d_winograd_without_weight_transform)
            dispatch_ctx.update(target, new_workload, cfg)

            return F.nn.contrib_conv2d_winograd_without_weight_transform(*copy_inputs, **new_attrs)
    else:
        workload = autotvm.task.args_to_workload(
            [data, kernel, strides, padding, dilation, out_dtype], depthwise_conv2d_nchw)
        target = tvm.target.current_target()
        dispatch_ctx = autotvm.DispatchContext.current
        cfg = dispatch_ctx.query(target, workload)

        if cfg.is_fallback:  # if is fallback, clear query cache and return None
            autotvm.task.clear_fallback_cache(tvm.target.current_target(), workload)
            return None
        if cfg.template_key == 'contrib_spatial_pack':
            VC = cfg['tile_co'].size[-1]
            new_attrs['kernel_layout'] = 'OIHW%do' % (cfg['tile_co'].size[-1])

            # Store the same config for the altered operator (workload)
            new_data = data
            CO, M, KH, KW = get_const_tuple(kernel.shape)
            new_kernel = tvm.placeholder((CO // VC, M, KH, KW, VC), dtype=kernel.dtype)
            new_workload = autotvm.task.args_to_workload(
                [new_data, new_kernel, strides, padding, dilation, out_dtype],
                depthwise_conv2d_nchw)
            dispatch_ctx.update(target, new_workload, cfg)

            return F.nn.conv2d(*copy_inputs, **new_attrs)
        else:
            # currently we only have contrib_spatial_pack and direct template
            # add more schedule templates.
            return None
