def forward(ctx, pos, flat_netpin, netpin_start, pin2net_map, net_weights,
net_mask, pin_mask, gamma, num_threads):
"""
@param pos pin location (x array, y array), not cell location
@param flat_netpin flat netpin map, length of #pins
@param netpin_start starting index in netpin map for each net, length of #nets+1, the last entry is #pins
@param pin2net_map pin2net map
@param net_weights weight of nets
@param net_mask whether to compute wirelength, 1 means to compute, 0 means to ignore
@param pin_mask whether compute gradient for a pin, 1 means to fill with zero, 0 means to compute
@param gamma the smaller, the closer to HPWL
"""
if pos.is_cuda:
output = weighted_average_wirelength_cuda.forward(
pos.view(pos.numel()), flat_netpin, netpin_start, pin2net_map,
net_weights, net_mask, gamma)
else:
output = weighted_average_wirelength_cpp.forward(
pos.view(pos.numel()), flat_netpin, netpin_start, net_weights,
net_mask, gamma, num_threads)
ctx.flat_netpin = flat_netpin
ctx.netpin_start = netpin_start
ctx.pin2net_map = pin2net_map
ctx.net_weights = net_weights
ctx.net_mask = net_mask
ctx.pin_mask = pin_mask
ctx.gamma = gamma
ctx.pos = pos
ctx.num_threads = num_threads
return output
def forward(ctx, pos, flat_netpin, netpin_start, pin2net_map, net_weights,
net_mask, pin_mask, inv_gamma, num_threads):
"""
@param pos pin location (x array, y array), not cell location
@param flat_netpin flat netpin map, length of #pins
@param netpin_start starting index in netpin map for each net, length of #nets+1, the last entry is #pins
@param pin2net_map pin2net map
@param net_weights weight of nets
@param net_mask whether to compute wirelength, 1 means to compute, 0 means to ignore
@param pin_mask whether compute gradient for a pin, 1 means to fill with zero, 0 means to compute
@param inv_gamma 1/gamma, the larger, the closer to HPWL
"""
tt = time.time()
if pos.is_cuda:
output = weighted_average_wirelength_cuda.forward(
pos.view(pos.numel()), flat_netpin, netpin_start, pin2net_map,
net_weights, net_mask, inv_gamma)
else:
output = weighted_average_wirelength_cpp.forward(
pos.view(pos.numel()), flat_netpin, netpin_start, net_weights,
net_mask, inv_gamma, num_threads)
ctx.flat_netpin = flat_netpin
ctx.netpin_start = netpin_start
ctx.pin2net_map = pin2net_map
ctx.net_weights = net_weights
ctx.net_mask = net_mask
ctx.pin_mask = pin_mask
ctx.inv_gamma = inv_gamma
ctx.pos = pos
ctx.num_threads = num_threads
ctx.exp_xy = output[1]
ctx.exp_nxy = output[2]
ctx.exp_xy_sum = output[3]
ctx.exp_nxy_sum = output[4]
ctx.xyexp_xy_sum = output[5]
ctx.xyexp_nxy_sum = output[6]
if pos.is_cuda:
torch.cuda.synchronize()
logger.debug("wirelength forward %.3f ms" %
((time.time() - tt) * 1000))
return output[0]