def backward(ctx, grad_pos):
tt = time.time()
if grad_pos.is_cuda:
output = weighted_average_wirelength_cuda.backward(
grad_pos, ctx.pos, ctx.exp_xy.view([-1]),
ctx.exp_nxy.view([-1]), ctx.exp_xy_sum.view([-1]),
ctx.exp_nxy_sum.view([-1]), ctx.xyexp_xy_sum.view([-1]),
ctx.xyexp_nxy_sum.view([-1]), ctx.flat_netpin,
ctx.netpin_start, ctx.pin2net_map, ctx.net_weights,
ctx.net_mask, ctx.inv_gamma)
else:
output = weighted_average_wirelength_cpp.backward(
grad_pos, ctx.pos, ctx.exp_xy.view([-1]),
ctx.exp_nxy.view([-1]), ctx.exp_xy_sum.view([-1]),
ctx.exp_nxy_sum.view([-1]), ctx.xyexp_xy_sum.view([-1]),
ctx.xyexp_nxy_sum.view([-1]), ctx.flat_netpin,
ctx.netpin_start, ctx.pin2net_map, ctx.net_weights,
ctx.net_mask, ctx.inv_gamma, ctx.num_threads)
output[:output.numel() // 2].masked_fill_(ctx.pin_mask, 0.0)
output[output.numel() // 2:].masked_fill_(ctx.pin_mask, 0.0)
if grad_pos.is_cuda:
torch.cuda.synchronize()
logger.debug("wirelength backward %.3f ms" %
((time.time() - tt) * 1000))
return output, None, None, None, None, None, None, None, None
def backward(ctx, grad_pos):
if grad_pos.is_cuda:
output = weighted_average_wirelength_cuda.backward(
grad_pos, ctx.pos, ctx.flat_netpin, ctx.netpin_start,
ctx.pin2net_map, ctx.net_weights, ctx.net_mask, ctx.gamma)
else:
output = weighted_average_wirelength_cpp.backward(
grad_pos, ctx.pos, ctx.flat_netpin, ctx.netpin_start,
ctx.net_weights, ctx.net_mask, ctx.gamma, ctx.num_threads)
output[:output.numel() // 2].masked_fill_(ctx.pin_mask, 0.0)
output[output.numel() // 2:].masked_fill_(ctx.pin_mask, 0.0)
return output, None, None, None, None, None, None, None, None