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# pylint: disable=invalid-name
"""The templates for cuda conv2d operators"""
import tvm
from tvm import autotvm
from ..util import get_const_tuple

def schedule_direct_cuda(cfg, s, conv):
    """schedule optimized for batch size = 1"""

    ##### space definition begin #####
    n, f, y, x = s[conv].op.axis
    rc, ry, rx = s[conv].op.reduce_axis
    cfg.define_split("tile_f", f, num_outputs=4)
    cfg.define_split("tile_y", y, num_outputs=4)
    cfg.define_split("tile_x", x, num_outputs=4)
    cfg.define_split("tile_rc", rc, num_outputs=2)
    cfg.define_split("tile_ry", ry, num_outputs=2)
    cfg.define_split("tile_rx", rx, num_outputs=2)
    cfg.define_knob("auto_unroll_max_step", [0, 512, 1500])

    target = tvm.target.current_target()
    if target.target_name in ['nvptx', 'rocm']:
        cfg.define_knob("unroll_explicit", [1])
    else:
        cfg.define_knob("unroll_explicit", [0, 1])

    # fallback support
    if cfg.is_fallback:
        ref_log = autotvm.tophub.load_reference_log(
            target.target_name, target.model, 'conv2d', 'direct')
        cfg.fallback_with_reference_log(ref_log)
    ##### space definition end #####

    pad_data, kernel = s[conv].op.input_tensors

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

    if conv.op in s.outputs:
        output = conv
        OL = s.cache_write(conv, 'local')
    else:
        output = s.outputs[0].output(0)
        s[conv].set_scope('local')
        OL = conv

    # create cache stage
    AA = s.cache_read(pad_data, 'shared', [OL])
    WW = s.cache_read(kernel, 'shared', [OL])

    # tile and bind spatial axes
    n, f, y, x = s[output].op.axis
    kernel_scope, n = s[output].split(n, nparts=1)

    bf, vf, tf, fi = cfg["tile_f"].apply(s, output, f)
    by, vy, ty, yi = cfg["tile_y"].apply(s, output, y)
    bx, vx, tx, xi = cfg["tile_x"].apply(s, output, x)

    bf = s[output].fuse(n, bf)
    s[output].bind(bf, tvm.thread_axis("blockIdx.z"))
    s[output].bind(by, tvm.thread_axis("blockIdx.y"))
    s[output].bind(bx, tvm.thread_axis("blockIdx.x"))
    s[output].bind(vf, tvm.thread_axis("vthread"))
    s[output].bind(vy, tvm.thread_axis("vthread"))
    s[output].bind(vx, tvm.thread_axis("vthread"))
    s[output].bind(tf, tvm.thread_axis("threadIdx.z"))
    s[output].bind(ty, tvm.thread_axis("threadIdx.y"))
    s[output].bind(tx, tvm.thread_axis("threadIdx.x"))
    s[output].reorder(bf, by, bx, vf, vy, vx, tf, ty, tx, fi, yi, xi)
    s[OL].compute_at(s[output], tx)

    # tile reduction axes
    n, f, y, x = s[OL].op.axis
    rc, ry, rx = s[OL].op.reduce_axis
    rco, rci = cfg['tile_rc'].apply(s, OL, rc)
    ryo, ryi = cfg['tile_rx'].apply(s, OL, ry)
    rxo, rxi = cfg['tile_ry'].apply(s, OL, rx)
    s[OL].reorder(rco, ryo, rxo, rci, ryi, rxi, n, f, y, x)

    s[AA].compute_at(s[OL], rxo)
    s[WW].compute_at(s[OL], rxo)

    # cooperative fetching
    for load in [AA, WW]:
        n, f, y, x = s[load].op.axis
        fused = s[load].fuse(n, f, y, x)
        tz, fused = s[load].split(fused, nparts=cfg["tile_f"].size[2])
        ty, fused = s[load].split(fused, nparts=cfg["tile_y"].size[2])
        tx, fused = s[load].split(fused, nparts=cfg["tile_x"].size[2])
        s[load].bind(tz, tvm.thread_axis("threadIdx.z"))
        s[load].bind(ty, tvm.thread_axis("threadIdx.y"))
        s[load].bind(tx, tvm.thread_axis("threadIdx.x"))

    # unroll
    s[output].pragma(kernel_scope, 'auto_unroll_max_step', cfg['auto_unroll_max_step'].val)
    s[output].pragma(kernel_scope, 'unroll_explicit', cfg['unroll_explicit'].val)

    N, CO, OH, OW = get_const_tuple(output.shape)
    _, KH, KW, CI = get_const_tuple(kernel.shape)
    cfg.add_flop(2 * N * OH * OW * CO * CI * KH * KW)
