def build_rmst_wl(self, params, placedb, pin_pos_op, device):
"""
@brief compute rectilinear minimum spanning tree wirelength with flute
@param params parameters
@param placedb placement database
@param pin_pos_op the op to compute pin locations according to cell locations
@param device cpu or cuda
"""
# wirelength cost
POWVFILE = os.path.abspath(
os.path.join(os.path.dirname(__file__),
"../../thirdparty/flute-3.1/POWV9.dat"))
POSTFILE = os.path.abspath(
os.path.join(os.path.dirname(__file__),
"../../thirdparty/flute-3.1/POST9.dat"))
print("POWVFILE = %s" % (POWVFILE))
print("POSTFILE = %s" % (POSTFILE))
wirelength_for_pin_op = rmst_wl.RMSTWL(
flat_netpin=torch.from_numpy(placedb.flat_net2pin_map).to(device),
netpin_start=torch.from_numpy(
placedb.flat_net2pin_start_map).to(device),
ignore_net_degree=params.ignore_net_degree,
POWVFILE=POWVFILE,
POSTFILE=POSTFILE)
# wirelength for position
def build_wirelength_op(pos):
pin_pos = pin_pos_op(pos)
wls = wirelength_for_pin_op(pin_pos.clone().cpu(),
self.read_lut_flag)
self.read_lut_flag = False
return wls
return build_wirelength_op
def test_rmst_wlRandom(self):
dtype = np.float64
pin_pos = np.array(
[[0.0, 0.0], [1.0, 2.0], [1.5, 0.2], [0.5, 3.1], [0.6, 1.1]],
dtype=dtype)
net2pin_map = np.array([np.array([0, 4]), np.array([1, 2, 3])])
pin_x = pin_pos[:, 0]
pin_y = pin_pos[:, 1]
# construct flat_net2pin_map and flat_net2pin_start_map
# flat netpin map, length of #pins
flat_net2pin_map = np.zeros(len(pin_pos), dtype=np.int32)
# starting index in netpin map for each net, length of #nets+1, the last entry is #pins
flat_net2pin_start_map = np.zeros(len(net2pin_map) + 1, dtype=np.int32)
count = 0
for i in range(len(net2pin_map)):
flat_net2pin_map[count:count +
len(net2pin_map[i])] = net2pin_map[i]
flat_net2pin_start_map[i] = count
count += len(net2pin_map[i])
flat_net2pin_start_map[len(net2pin_map)] = len(pin_pos)
print("flat_net2pin_map = ", flat_net2pin_map)
print("flat_net2pin_start_map = ", flat_net2pin_start_map)
# test cpu
print(np.transpose(pin_pos))
pin_pos_var = Variable(torch.from_numpy(pin_pos))
print(pin_pos_var)
# clone is very important, because the custom op cannot deep copy the data
pin_pos_var = torch.t(pin_pos_var).contiguous()
#pdb.set_trace()
project_path = os.path.abspath(
os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
POWVFILE = os.path.join(project_path, "thirdparty/flute/POWV9.dat")
POSTFILE = os.path.join(project_path, "thirdparty/flute/POST9.dat")
custom = rmst_wl.RMSTWL(torch.from_numpy(flat_net2pin_map),
torch.from_numpy(flat_net2pin_start_map),
torch.tensor(len(flat_net2pin_map)),
POWVFILE=POWVFILE,
POSTFILE=POSTFILE)
rmst_wl_value = custom.forward(pin_pos_var, read_lut_flag=True)
print("rmst_wl_value = ", rmst_wl_value.data.numpy())
rmst_wl_value = custom.forward(pin_pos_var, read_lut_flag=False)
print("rmst_wl_value = ", rmst_wl_value.data.numpy())
