text: # pylint: disable=invalid-name
  """Compute definition for conv2d with cuda backend"""
  import tvm
  from tvm import autotvm
  from tvm.contrib import cudnn
  
  from .. import nn, generic
  from ..util import get_const_tuple, traverse_inline
  
  from .conv2d_direct import schedule_direct_cuda
  from .conv2d_winograd import winograd_cuda, schedule_winograd_cuda
  from .conv2d_int8 import conv2d_NCHWc_int8, schedule_conv2d_NCHWc_int8
  
  
  @autotvm.register_topi_compute(nn.conv2d, ['cuda', 'gpu'], ['direct', 'winograd', 'int8'])
  def conv2d_cuda(cfg, data, kernel, strides, padding, dilation, layout='NCHW', out_dtype='float32'):
      """Conv2D operator for cuda backend.
  
      Parameters
      ----------
      cfg: ConfigEntity
          The config for this template
  
      data : tvm.Tensor
          4-D with shape [batch, in_channel, in_height, in_width] or
          5-D with shape [batch, ic_chunk, in_height, in_width, ic_block]
  
      kernel : tvm.Tensor
          4-D with shape [num_filter, in_channel, filter_height, filter_width] or
          6-D with shape [num_filter_chunk, in_channel_chunk, filter_height,
          filter_width, num_filter_block, in_channel_block]
  
      strides : int or a list/tuple of two ints
          stride size, or [stride_height, stride_width]
  
      padding : int or a list/tuple of two ints
          padding size, or [pad_height, pad_width]
  
      dilation: int or a list/tuple of two ints
          dilation size, or [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]
      """
      target = tvm.target.current_target()
  
      if "cudnn" in target.libs:
          if layout == 'NCHW':
              tensor_format = 0 # CUDNN_TENSOR_NCHW
              N, _, H, W = get_const_tuple(data.shape)
          elif layout == 'NHWC':
              tensor_format = 1 # CUDNN_TENSOR_NHWC
              N, H, W, _ = get_const_tuple(data.shape)
          else:
              raise ValueError("Unsupported layout %s in cudnn" % layout)
          CO, CI, KH, KW = get_const_tuple(kernel.shape)
  
          # handle dilation
          stride_h, stride_w = (strides, strides) if isinstance(strides, int) else strides
          pad_h, pad_w = (padding, padding) if isinstance(padding, int) else padding
          dilation_h, dilation_w = (dilation, dilation) if isinstance(dilation, int) else dilation
  
          OH = (H + 2 * pad_h - KH) // stride_h + 1
          OW = (W + 2 * pad_w - KW) // stride_w + 1
          cfg.add_flop(2 * N * OH * OW * CO * CI * ((KH - 1) * dilation_h + 1) *\
                      ((KW - 1) * dilation_w + 1))
  
          return cudnn.conv2d_forward(data,
                                      kernel,
                                      stride_h,
                                      stride_w,
                                      pad_h,
                                      pad_w,
                                      dilation_h,
                                      dilation_w,
                                      conv_mode=1,
                                      tensor_format=tensor_format,
                                      algo=-1)  # let CUDNN choose the best algo
  
      if cfg.template_key == 'winograd':
          return winograd_cuda(cfg, data, kernel, strides, padding, dilation, layout, out_dtype,
                               pre_computed=False)
      if cfg.template_key == 'int8':
          return conv2d_NCHWc_int8(cfg, data, kernel, strides, padding, dilation, layout, out_dtype)
  
      if layout == 'NCHW':
          return nn.conv2d_nchw(data, kernel, strides, padding, dilation, out_dtype)
      elif layout == 'HWCN':
          return nn.conv2d_hwcn(data, kernel, strides, padding, dilation, out_dtype)
      else:
          raise ValueError("not support this layout {} yet".format(layout))
  
  
  @autotvm.register_topi_schedule(generic.schedule_conv2d_nchw, ["cuda", "gpu"],
                                  ["direct", 'winograd', "int8"])
  def schedule_conv2d_nchw_cuda(cfg, outs):
      """TOPI schedule callback of conv2d for cuda gpu
  
      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.
      """
      target = tvm.target.current_target()
      if 'cudnn' in target.libs:
          return generic.schedule_extern(outs)
  
      outs = [outs] if isinstance(outs, tvm.tensor.Tensor) else outs
      s = tvm.create_schedule([x.op for x in outs])
  
      def _callback(op):
          if op.tag == 'conv2d_nchw':
              schedule_direct_cuda(cfg, s, op.output(0))
          if op.tag == 'conv2d_nchw_winograd':
              schedule_winograd_cuda(cfg, s, op.output(0), pre_computed=False)
          if op.tag == "conv2d_NCHWc_int8":
              schedule_conv2d_NCHWc_int8(cfg, s, op.output(0))
  
      traverse_inline(s, outs[0].op, _callback)
      return s
  