def build_model(rank):
model = RecurTreeGen(cmd_args).to(cmd_args.device)
if rank == 0 and cmd_args.model_dump is not None and os.path.isfile(
cmd_args.model_dump):
print('loading from', cmd_args.model_dump)
model.load_state_dict(torch.load(cmd_args.model_dump))
for param in model.parameters():
dist.broadcast(param.data, 0)
return model
def main(rank):
if cmd_args.gpu >= 0:
set_device(rank)
else:
set_device(-1)
setup_treelib(cmd_args)
model = RecurTreeGen(cmd_args).to(cmd_args.device)
if rank == 0 and cmd_args.model_dump is not None and os.path.isfile(
cmd_args.model_dump):
print('loading from', cmd_args.model_dump)
model.load_state_dict(torch.load(cmd_args.model_dump))
optimizer = optim.Adam(model.parameters(),
lr=cmd_args.learning_rate,
weight_decay=1e-4)
for param in model.parameters():
dist.broadcast(param.data, 0)
graphs = []
with open(os.path.join(cmd_args.data_dir, 'train-graphs.pkl'), 'rb') as f:
while True:
try:
g = cp.load(f)
TreeLib.InsertGraph(g)
except:
break
graphs.append(g)
for epoch in range(cmd_args.num_epochs):
pbar = range(cmd_args.epoch_save)
if rank == 0:
pbar = tqdm(pbar)
g = graphs[0]
graph_ids = [0]
blksize = cmd_args.blksize
if blksize < 0 or blksize > len(g):
blksize = len(g)
for e_it in pbar:
optimizer.zero_grad()
num_stages = len(g) // (blksize * cmd_args.num_proc) + (
len(g) % (blksize * cmd_args.num_proc) > 0)
states_prev = [None, None]
list_caches = []
prev_rank_last = None
for stage in range(num_stages):
local_st, local_num, rank_last = get_stage_stats(
stage, blksize, rank, g)
if local_num <= 0:
break
with torch.no_grad():
fn_hc_bot, h_buf_list, c_buf_list = model.forward_row_trees(
graph_ids,
list_node_starts=[local_st],
num_nodes=local_num)
if stage and rank == 0:
states_prev = recv_states(num_expect(local_st),
prev_rank_last)
if rank:
num_recv = num_expect(local_st)
states_prev = recv_states(num_recv, rank - 1)
_, next_states = model.row_tree.forward_train(
*(fn_hc_bot(0)), h_buf_list[0], c_buf_list[0],
*states_prev)
list_caches.append(states_prev)
if rank != rank_last:
send_states(next_states, rank + 1)
elif stage + 1 < num_stages:
send_states(next_states, 0)
prev_rank_last = rank_last
tot_ll = torch.zeros(1).to(cmd_args.device)
for stage in range(num_stages - 1, -1, -1):
local_st, local_num, rank_last = get_stage_stats(
stage, blksize, rank, g)
if local_num <= 0:
continue
prev_states = list_caches[stage]
if prev_states[0] is not None:
for x in prev_states:
x.requires_grad = True
ll, cur_states = model.forward_train(
graph_ids,
list_node_starts=[local_st],
num_nodes=local_num,
prev_rowsum_states=prev_states)
tot_ll = tot_ll + ll
loss = -ll / len(g)
if stage + 1 == num_stages and rank == rank_last:
loss.backward()
else:
top_grad = recv_states(
cur_states[0].shape[0],
rank + 1 if rank != rank_last else 0)
torch.autograd.backward([loss, *cur_states],
[None, *top_grad])
if prev_states[0] is not None:
grads = [x.grad.detach() for x in prev_states]
dst = rank - 1 if rank else cmd_args.num_proc - 1
send_states(grads, dst)
dist.all_reduce(tot_ll.data, op=dist.ReduceOp.SUM)
for param in model.parameters():
if param.grad is None:
param.grad = param.data.new(param.data.shape).zero_()
dist.all_reduce(param.grad.data, op=dist.ReduceOp.SUM)
if cmd_args.grad_clip > 0:
torch.nn.utils.clip_grad_norm_(model.parameters(),
max_norm=cmd_args.grad_clip)
optimizer.step()
if rank == 0:
loss = -tot_ll.item() / len(g)
pbar.set_description('epoch %.2f, loss: %.4f' %
(epoch +
(e_it + 1) / cmd_args.epoch_save, loss))
torch.save(
model.state_dict(),
os.path.join(cmd_args.save_dir, 'epoch-%d.ckpt' % epoch))
print('done rank', rank)
setup_treelib(cmd_args)
train_graphs, stats = load_graphs(
os.path.join(cmd_args.data_dir, 'train-graphs.pkl'),
os.path.join(cmd_args.data_dir, 'train-graph-stats.pkl'))
max_left = 0
max_right = 0
for n, m in stats:
max_left = max(max_left, n)
max_right = max(max_right, m)
max_num_nodes = max(max_left, max_right)
print('max # nodes:', max_num_nodes, 'max_left:', max_left, 'max_right:',
max_right)
cmd_args.max_num_nodes = max_num_nodes
model = RecurTreeGen(cmd_args).to(cmd_args.device)
if cmd_args.model_dump is not None and os.path.isfile(cmd_args.model_dump):
print('loading from', cmd_args.model_dump)
model.load_state_dict(torch.load(cmd_args.model_dump))
if cmd_args.eval_folder is not None:
g_idx = 0
eval_dir = os.path.join(cmd_args.data_dir,
'../sat-%s' % cmd_args.eval_folder)
print('loading eval from', eval_dir)
test_graphs, stats = load_graphs(
os.path.join(eval_dir, 'test-graphs.pkl'),
os.path.join(eval_dir, 'test-graph-stats.pkl'))
out_dir = os.path.join(
cmd_args.save_dir, '%s-pred_formulas-e-%d-g-%.2f' %
(cmd_args.eval_folder, cmd_args.epoch_load, cmd_args.greedy_frac))
import numpy as np
import random
import networkx as nx
from bigg.common.configs import cmd_args, set_device
from bigg.model.tree_clib.tree_lib import setup_treelib, TreeLib
from bigg.model.tree_model import RecurTreeGen
if __name__ == '__main__':
random.seed(cmd_args.seed)
torch.manual_seed(cmd_args.seed)
np.random.seed(cmd_args.seed)
set_device(cmd_args.gpu)
setup_treelib(cmd_args)
train_graphs = [nx.barabasi_albert_graph(10, 2)]
TreeLib.InsertGraph(train_graphs[0])
max_num_nodes = max([len(gg.nodes) for gg in train_graphs])
cmd_args.max_num_nodes = max_num_nodes
model = RecurTreeGen(cmd_args).to(cmd_args.device)
optimizer = optim.Adam(model.parameters(),
lr=cmd_args.learning_rate,
weight_decay=1e-4)
for i in range(2):
optimizer.zero_grad()
ll, _ = model.forward_train([0])
loss = -ll / max_num_nodes
print('iter', i, 'loss', loss.item())
loss.backward()
optimizer.step()
return graphs
if __name__ == '__main__':
random.seed(cmd_args.seed)
torch.manual_seed(cmd_args.seed)
np.random.seed(cmd_args.seed)
set_device(cmd_args.gpu)
setup_treelib(cmd_args)
train_graphs = load_graphs(
os.path.join(cmd_args.data_dir, 'train-graphs.pkl'))
max_num_nodes = max([len(gg.nodes) for gg in train_graphs])
cmd_args.max_num_nodes = max_num_nodes
model = RecurTreeGen(cmd_args).to(cmd_args.device)
if cmd_args.model_dump is not None and os.path.isfile(cmd_args.model_dump):
print('loading from', cmd_args.model_dump)
model.load_state_dict(torch.load(cmd_args.model_dump))
if cmd_args.phase != 'train':
# get num nodes dist
num_node_dist = get_node_dist(train_graphs)
gt_graphs = load_graphs(
os.path.join(cmd_args.data_dir, '%s-graphs.pkl' % cmd_args.phase))
print('# gt graphs', len(gt_graphs))
gen_graphs = []
with torch.no_grad():
for _ in tqdm(range(cmd_args.num_test_gen)):
num_nodes = np.argmax(np.random.multinomial(1, num_node_dist))
_, pred_edges, _ = model(num_nodes, display=cmd_args.display)