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train.py
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train.py
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import torch
import dgl
import time
import os
import pandas as pd
from collections import defaultdict
from torch import optim
from torch.optim.lr_scheduler import MultiStepLR
from torch.nn.utils import clip_grad_value_
from torch.utils.tensorboard import SummaryWriter
from utils import save_model, load_model, get_model_attribute, get_last_checkpoint
from models.gcn.helper import legal_perms_sampler
from models.graph_rnn.train import evaluate_loss as eval_loss_graph_rnn
from models.dgmg.train import evaluate_loss as eval_loss_dgmg
# from models.gran.model import evaluate_loss as eval_loss_gran
from models.graphgen.train import evaluate_loss as eval_loss_graphgen
from torch.utils.data._utils.collate import default_collate as collate
def evaluate_loss(args, model, gcn, processor, sample_perm, graphs, feature_map, i_epoch):
'''
:param args:
:param model:
:param gcn:
:param processor:
:param graphs: [{‘G’:networkx, 'dG':dglgraph}]
:param feature_map:
:return:
'''
# get the batch of dGs
batch_G = [graph['G'] for graph in graphs]
real_batch_size = len(batch_G)
# gcn feed-forward
#get number of node labels
len_node_vec = len(feature_map['node_forward'])
# ------ update Dec 4: sequential decision ------
if args.enable_gcn:
# use gcn for sampling
# for idx_m in range(args.sample_size):
if args.note != 'Graphgen':
batch_perms, ll_q, log_repetitions = gcn(graphs, args.sample_size)
elif args.note == 'Graphgen':
#dfs_code_list used for p(g)
batch_perms, ll_q, log_repetitions, dfs_code_list = gcn(graphs, args.sample_size)
# log_repetitions[:, idx_m].copy_(log_repetition_m)
# ll_q[:, idx_m].copy_(ll_q_m)
# batch_perms.append(batch_perm_m)
else:
log_repetitions = torch.empty((real_batch_size, args.sample_size), requires_grad=False) # (N, M)
ll_q = torch.empty((real_batch_size, args.sample_size), device=args.device)
# adopt uniform sampling
batch_num_nodes = [graph.number_of_nodes() for graph in batch_G]
n_node = sum(batch_num_nodes)
batch_perms = [[] for _ in range(args.sample_size)]
batch_nodes = torch.ones(n_node)
parameterizations = torch.split(batch_nodes, batch_num_nodes)
for idx_g in range(len(batch_G)):
# sample permutation
batch_perm_g, ll_q_g, log_reps = sample_perm(graphs[idx_g]['G'], params=parameterizations[idx_g],
device=args.device, M=args.sample_size,
nobfs=args.nobfs, max_cr_iteration=args.max_cr_iteration)
# batch_perms.append(perms)
log_repetitions[idx_g].copy_(log_reps)
ll_q[idx_g].copy_(ll_q_g)
for idx_m in range(args.sample_size):
batch_perms[idx_m].append(batch_perm_g[idx_m])
# n_node = batch_G[b].number_of_nodes()
# params = torch.ones(n_node).unsqueeze(0).expand(args.sample_size, n_node)
# u = torch.distributions.utils.clamp_probs(torch.rand_like(params))
# z = params - torch.log(-torch.log(u))
# batch_perm_b = torch.sort(z, descending=True, dim=-1)[1]
#
# for b in range(real_batch_size):
# batch_perms[b].extend(batch_perm_b[b])
log_repetitions = log_repetitions.to(args.device)
# if args.enable_gcn:
# # do gcn sampling
# batch_nodes = gcn.forward(batch_dG, batch_dGX) # (N, V*)
# else:
# # do uniform sampling
nll_p = torch.empty((real_batch_size, args.sample_size), device=args.device)
if args.note == 'GraphRNN':
# data process and training for graphRNN
for m in range(args.sample_size):
data = [processor(graph, perms) for graph, perms in zip(batch_G, batch_perms[m])]
data = collate(data)
nll_p_m = eval_loss_graph_rnn(args, model, data, feature_map)
# nll_prob = eval_loss_graph_rnn(args, model, data, feature_map)
nll_p[:, m].copy_(nll_p_m)
if args.note == 'GRAN':
# data process and training for graphRNN
for m in range(args.sample_size):
data = [processor(graph, perms) for graph, perms in zip(batch_G, batch_perms[m])]
data = processor.collate_fn(data)
nll_p_m = eval_loss_gran(args, model, data[0])
# nll_prob = eval_loss_graph_rnn(args, model, data, feature_map)
nll_p[:, m].copy_(nll_p_m)
elif args.note == 'DGMG':
# TODO: data process and training for DGMG
# for i_g, graph in enumerate(batch_G):
# data = [processor(graph, perms[i_g]) for perms in batch_perms]
# # data = processor.collate_batch(data)
# nll_p_g = eval_loss_dgmg(model['dgmg'], data)
# nll_p[i_g, :].copy_(nll_p_g)
assert args.batch_size == 1
for m in range(args.sample_size):
data = [processor(graph, perms) for graph, perms in zip(batch_G, batch_perms[m])]
# data = processor.collate_batch(data)
nll_p_m = eval_loss_dgmg(model['dgmg'], data)
# nll_prob = eval_loss_graph_rnn(args, model, data, feature_map)
nll_p[:, m].copy_(nll_p_m)
if args.note == 'Graphgen':
for m in range(args.sample_size):
data = [processor(code, feature_map) for code in dfs_code_list[m::args.sample_size]]
data = collate(data)
nll_p_m = eval_loss_graphgen(args, model, data, feature_map)
nll_p[:,m].copy_(nll_p_m)
# log p_hat(G, pi) = log p(G|pi)p(pi) - log rep
ll_p_hat = -nll_p - log_repetitions
# print(ll_p_hat)
# fake loss for q's gradient estimation
if not args.enable_gcn:
fake_nll_q = 0
else:
fake_nll_q = -torch.mean(torch.mean((ll_p_hat.detach()-ll_q.detach()) * ll_q, dim=1))
nll_p = -torch.mean(torch.mean(ll_p_hat, dim=1))
# compute elbo ( no gradient computation involved) elbo = 1/M * sum_{i=1}^M elbo_i
elbo = torch.mean(ll_p_hat.detach()-ll_q.detach())
ll_q = torch.mean(ll_q)
# print(entropy)
return nll_p, fake_nll_q, elbo, ll_q
def train_epoch(
epoch, args, model, gcn, dataloader_train, processor, sample_perm,
optimizer, scheduler, feature_map, log_history, summary_writer=None):
# Set training mode for modules
for _, net in model.items():
net.train()
if args.enable_gcn:
gcn.train()
batch_count = len(dataloader_train)
total_loss = 0.0
for batch_id, graphs in enumerate(dataloader_train):
for _, net in model.items():
net.zero_grad()
if args.enable_gcn:
gcn.zero_grad()
st = time.time()
nll_p, fake_nll_q, elbo, ll_q = evaluate_loss(args, model, gcn, processor, sample_perm, graphs, feature_map, epoch)
loss = nll_p + fake_nll_q
loss.backward()
gradient = gcn.node_readout.FC_layers[0].bias.grad
total_loss += elbo.data.item()
spent = time.time() - st
if batch_id % args.print_interval == 0:
print('epoch {} batch {}: elbo is {}, llq is {}, time spent is {}.'.format(epoch, batch_id, elbo, ll_q, spent), flush=True)
log_history['batch_elbo'].append(elbo.data.item())
log_history['batch_time'].append(spent)
# Update params of rnn and mlp
for _, opt in optimizer.items():
opt.step()
for _, sched in scheduler.items():
sched.step()
if args.log_tensorboard:
summary_writer.add_scalar('{} {} Loss/train batch'.format(
args.note, args.graph_type), loss, batch_id + batch_count * epoch)
return total_loss / batch_count
def test_data(epoch, args, model, gcn, dataloader_validate, processor, sample_perm, feature_map):
for _, net in model.items():
net.eval()
gcn.eval()
batch_count = len(dataloader_validate)
with torch.no_grad():
total_loss = 0.0
ll_qs = 0.0
for _, graphs in enumerate(dataloader_validate):
loss_model, loss_gcn, elbo, ll_q = evaluate_loss(args, model, gcn, processor, sample_perm, graphs, feature_map, epoch)
# loss = loss_model + loss_gcn
# total_loss += loss.data.item()
ll_qs += ll_q
total_loss += elbo.data.item()
return total_loss / batch_count, ll_qs
# Main training function
def train(args, model, gcn, feature_map, dataloader_train, dataloader_validate, processor, sample_perm):
optimizer = {}
for name, net in model.items():
optimizer['optimizer_' + name] = optim.Adam(
filter(lambda p: p.requires_grad, net.parameters()), lr=args.lr,
weight_decay=5e-5)
if args.enable_gcn:
optimizer['optimizer_gcn'] = optim.Adam(gcn.parameters(), lr=args.lr*5, weight_decay=5e-5)
scheduler = {}
for name, net in model.items():
scheduler['scheduler_' + name] = MultiStepLR(
optimizer['optimizer_' + name], milestones=args.milestones,
gamma=args.gamma)
if args.enable_gcn:
scheduler['scheduler_gcn'] = MultiStepLR(
optimizer['optimizer_gcn'], milestones=args.milestones,
gamma=args.gamma)
log_history = defaultdict(list)
if args.load_model:
fname, epoch = get_last_checkpoint(args, epoch=args.epochs_end)
load_model(path=fname, device=args.device, model=model, gcn=gcn, optimizer=optimizer, scheduler=scheduler)
print('Model loaded')
df_iter = pd.read_csv(os.path.join(args.logging_iter_path))
df_epoch = pd.read_csv(os.path.join(args.logging_epoch_path))
log_history['batch_elbo'] = df_iter['batch_elbo'].tolist()
log_history['batch_time'] = df_iter['batch_time'].tolist()
log_history['train_elbo'] = df_epoch['train_elbo'].tolist()
log_history['valid_elbo'] = df_epoch['valid_elbo'].tolist()
else:
epoch = 0
if args.log_tensorboard:
writer = SummaryWriter(
log_dir=args.tensorboard_path+ ' ' + args.time, flush_secs=5)
else:
writer = None
while epoch < args.epochs:
# train
loss = train_epoch(
epoch, args, model, gcn, dataloader_train, processor, sample_perm, optimizer, scheduler, feature_map, log_history, writer)
epoch += 1
if args.log_tensorboard:
writer.add_scalar('{} {} Loss/train'.format(args.note, args.graph_type), loss, epoch)
print('Epoch: {}/{}, train loss: {:.6f}'.format(epoch, args.epochs, loss))
# validate
loss_validate, entropys_validate = test_data(epoch, args, model, gcn, dataloader_validate, processor, sample_perm, feature_map)
entropys_validate = entropys_validate / args.sample_size
if args.log_tensorboard:
writer.add_scalar('{} {} Loss/validate'.format(args.note, args.graph_type), loss_validate, epoch)
print('Epoch: {}/{}, validation loss: {:.6f} entropy: {:.6f}'.format(epoch, args.epochs, loss_validate, entropys_validate), flush=True)
# save model
save_model(epoch, args, model, gcn, optimizer, scheduler, feature_map=feature_map)
print('Model Saved - Epoch: {}/{}, train loss: {:.6f}'.format(epoch, args.epochs, loss))
log_history['train_elbo'].append(loss)
log_history['valid_elbo'].append(loss_validate)
# save logging history
df_iter = pd.DataFrame()
df_epoch = pd.DataFrame()
df_iter['batch_elbo'] = log_history['batch_elbo']
df_iter['batch_time'] = log_history['batch_time']
df_epoch['train_elbo'] = log_history['train_elbo']
df_epoch['valid_elbo'] = log_history['valid_elbo']
df_iter.to_csv(args.logging_iter_path, index=False)
df_epoch.to_csv(args.logging_epoch_path, index=False)