def test_stack_rnn_cell(self):
x, y = gen_data.random_training_set(batch_size=bz)
d_model = 128
hidden_size = 16
stack_width = 10
stack_depth = 20
num_layers = 1
num_dir = 2
encoder = Encoder(gen_data.n_characters, d_model,
gen_data.char2idx[gen_data.pad_symbol])
x = encoder(x)
rnn_cells = []
in_dim = d_model
cell_type = 'gru'
for _ in range(num_layers):
rnn_cells.append(
StackRNNCell(in_dim,
hidden_size,
has_stack=True,
unit_type=cell_type,
stack_depth=stack_depth,
stack_width=stack_width))
in_dim = hidden_size * num_dir
rnn_cells = torch.nn.ModuleList(rnn_cells)
h0 = init_hidden(num_layers=num_layers,
batch_size=bz,
hidden_size=hidden_size,
num_dir=num_dir)
c0 = init_hidden(num_layers=num_layers,
batch_size=bz,
hidden_size=hidden_size,
num_dir=num_dir)
s0 = init_stack(bz, stack_width, stack_depth)
seq_length = x.shape[0]
hidden_outs = torch.zeros(num_layers, num_dir, seq_length, bz,
hidden_size)
if cell_type == 'lstm':
cell_outs = torch.zeros(num_layers, num_dir, seq_length, bz,
hidden_size)
assert 0 <= num_dir <= 2
for l in range(num_layers):
for d in range(num_dir):
h, c, stack = h0[l, d, :], c0[l, d, :], s0
if d == 0:
indices = range(x.shape[0])
else:
indices = reversed(range(x.shape[0]))
for i in indices:
x_t = x[i, :, :]
hx, stack = rnn_cells[l](x_t, h, c, stack)
if cell_type == 'lstm':
hidden_outs[l, d, i, :, :] = hx[0]
cell_outs[l, d, i, :, :] = hx[1]
else:
hidden_outs[l, d, i, :, :] = hx
def forward(self, inp, return_logits=False):
"""
Evaluates the input to determine its reward.
Argument
:param inp: list / tuple
[0] Input from encoder of shape (seq_len, batch_size, embed_dim)
[1] SMILES validity flag
:param return_logits:
:return: tensor
Reward of shape (batch_size, 1)
"""
x = inp[0]
seq_len, batch_size = x.shape[:2]
# Project embedding to a low dimension space (assumes the input has a higher dimension)
x = self.proj_net(x)
# Construct initial states
hidden = init_hidden(self.num_layers, batch_size, self.hidden_size,
self.num_dir, x.device)
hidden_ = init_hidden(1, batch_size, self.hidden_size, 1,
x.device).view(batch_size, self.hidden_size)
if self.has_cell:
cell = init_cell(self.num_layers, batch_size, self.hidden_size,
self.num_dir, x.device)
cell_ = init_cell(1, batch_size, self.hidden_size, 1,
x.device).view(batch_size, self.hidden_size)
hidden, hidden_ = (hidden, cell), (hidden_, cell_)
# Apply base rnn
output, hidden = self.base_rnn(x, hidden)
# Additive attention, see: http://arxiv.org/abs/1409.0473
if self.use_attention:
for i in range(seq_len):
h = hidden_[0] if self.has_cell else hidden_
s = h.unsqueeze(0).expand(seq_len, *h.shape)
x_ = torch.cat([output, s], dim=-1)
logits = self.attn_linear(x_.contiguous().view(
-1, x_.shape[-1]))
wts = torch.softmax(logits.view(seq_len, batch_size).t(),
-1).unsqueeze(2)
x_ = x_.permute(1, 2, 0)
ctx = x_.bmm(wts).squeeze(dim=2)
hidden_ = self.post_rnn(ctx, hidden_)
rw_x = hidden_[0] if self.has_cell else hidden_
else:
rw_x = output[-1]
logits = rw_x
if self.v_flag:
rw_x = torch.cat([rw_x, inp[-1]], dim=-1)
reward = self.reward_net(rw_x)
if return_logits:
return reward, logits
return reward
def get_initial_states(batch_size, hidden_size, num_layers, stack_depth, stack_width, unit_type):
hidden = init_hidden(num_layers=num_layers, batch_size=batch_size, hidden_size=hidden_size, num_dir=1,
dvc=device)
if unit_type == 'lstm':
cell = init_cell(num_layers=num_layers, batch_size=batch_size, hidden_size=hidden_size, num_dir=1,
dvc=device)
else:
cell = None
stack = init_stack(batch_size, stack_width, stack_depth, dvc=device)
return hidden, cell, stack
def forward(self, x):
"""
Critic net
:param x: tensor
x.shape structure is (seq. len, batch, dim)
:return: tensor
(seq_len/states, batch, 1)
"""
if isinstance(x, (list, tuple)):
x = x[0]
batch_size = x.shape[1]
hidden = init_hidden(self.num_layers, batch_size, self.hidden_size, 2,
x.device)
if self.has_cell:
cell = init_cell(self.num_layers, batch_size, self.hidden_size, 2,
x.device)
hidden = (hidden, cell)
x, _ = self.rnn(x, hidden)
x = self.linear(self.norm(x))
return x
def train(model,
optimizer,
gen_data,
rnn_args,
n_iters=5000,
sim_data_node=None,
epoch_ckpt=(1, 2.0),
tb_writer=None,
is_hsearch=False):
tb_writer = None # tb_writer()
start = time.time()
best_model_wts = model.state_dict()
best_score = -10000
best_epoch = -1
terminate_training = False
e_avg = ExpAverage(.01)
num_batches = math.ceil(gen_data.file_len / gen_data.batch_size)
n_epochs = math.ceil(n_iters / num_batches)
grad_stats = GradStats(model, beta=0.)
# learning rate decay schedulers
# scheduler = sch.StepLR(optimizer, step_size=500, gamma=0.01)
# pred_loss functions
criterion = nn.CrossEntropyLoss(
ignore_index=gen_data.char2idx[gen_data.pad_symbol])
# sub-nodes of sim data resource
loss_lst = []
train_loss_node = DataNode(label="train_loss", data=loss_lst)
metrics_dict = {}
metrics_node = DataNode(label="validation_metrics", data=metrics_dict)
train_scores_lst = []
train_scores_node = DataNode(label="train_score",
data=train_scores_lst)
scores_lst = []
scores_node = DataNode(label="validation_score", data=scores_lst)
# add sim data nodes to parent node
if sim_data_node:
sim_data_node.data = [
train_loss_node, train_scores_node, metrics_node, scores_node
]
try:
# Main training loop
tb_idx = {'train': Count(), 'val': Count(), 'test': Count()}
epoch_losses = []
epoch_scores = []
for epoch in range(6):
phase = 'train'
# Iterate through mini-batches
# with TBMeanTracker(tb_writer, 10) as tracker:
with grad_stats:
for b in trange(0,
num_batches,
desc=f'{phase} in progress...'):
inputs, labels = gen_data.random_training_set()
batch_size, seq_len = inputs.shape[:2]
optimizer.zero_grad()
# track history if only in train
with torch.set_grad_enabled(phase == "train"):
# Create hidden states for each layer
hidden_states = []
for _ in range(rnn_args['num_layers']):
hidden = init_hidden(
num_layers=1,
batch_size=batch_size,
hidden_size=rnn_args['hidden_size'],
num_dir=rnn_args['num_dir'],
dvc=rnn_args['device'])
if rnn_args['has_cell']:
cell = init_cell(
num_layers=1,
batch_size=batch_size,
hidden_size=rnn_args['hidden_size'],
num_dir=rnn_args['num_dir'],
dvc=rnn_args['device'])
else:
cell = None
if rnn_args['has_stack']:
stack = init_stack(batch_size,
rnn_args['stack_width'],
rnn_args['stack_depth'],
dvc=rnn_args['device'])
else:
stack = None
hidden_states.append((hidden, cell, stack))
# forward propagation
outputs = model([inputs] + hidden_states)
predictions = outputs[0]
predictions = predictions.permute(1, 0, -1)
predictions = predictions.contiguous().view(
-1, predictions.shape[-1])
labels = labels.contiguous().view(-1)
# calculate loss
loss = criterion(predictions, labels)
# metrics
eval_dict = {}
score = IreleasePretrain.evaluate(
eval_dict, predictions, labels)
# TBoard info
# tracker.track("%s/loss" % phase, loss.item(), tb_idx[phase].IncAndGet())
# tracker.track("%s/score" % phase, score, tb_idx[phase].i)
# for k in eval_dict:
# tracker.track('{}/{}'.format(phase, k), eval_dict[k], tb_idx[phase].i)
# backward pass
loss.backward()
optimizer.step()
# for epoch stats
epoch_losses.append(loss.item())
# for sim data resource
train_scores_lst.append(score)
loss_lst.append(loss.item())
# for epoch stats
epoch_scores.append(score)
print("\t{}: Epoch={}/{}, batch={}/{}, "
"pred_loss={:.4f}, accuracy: {:.2f}, sample: {}".
format(
time_since(start),
epoch + 1, n_epochs, b + 1, num_batches,
loss.item(), eval_dict['accuracy'],
generate_smiles(generator=model,
gen_data=gen_data,
init_args=rnn_args,
num_samples=1)))
IreleasePretrain.save_model(
model,
'./model_dir/',
name=f'irelease-pretrained_stack-rnn_gru_'
f'{date_label}_epoch_{epoch}')
# End of mini=batch iterations.
except RuntimeError as e:
print(str(e))
duration = time.time() - start
print('\nModel training duration: {:.0f}m {:.0f}s'.format(
duration // 60, duration % 60))
return {
'model': model,
'score': round(np.mean(epoch_scores), 3),
'epoch': n_epochs
}
def train(generator,
optimizer,
rnn_args,
pretrained_net_path=None,
pretrained_net_name=None,
n_iters=5000,
sim_data_node=None,
tb_writer=None,
is_hsearch=False,
is_pretraining=True,
grad_clipping=None):
expert_model = rnn_args['expert_model']
tb_writer = tb_writer()
best_model_wts = generator.state_dict()
best_score = -1000
best_epoch = -1
demo_data_gen = rnn_args['demo_data_gen']
unbiased_data_gen = rnn_args['unbiased_data_gen']
prior_data_gen = rnn_args['prior_data_gen']
score_exp_avg = ExpAverage(beta=0.6)
exp_type = rnn_args['exp_type']
if is_pretraining:
num_batches = math.ceil(prior_data_gen.file_len /
prior_data_gen.batch_size)
else:
num_batches = math.ceil(demo_data_gen.file_len /
demo_data_gen.batch_size)
n_epochs = math.ceil(n_iters / num_batches)
grad_stats = GradStats(generator, beta=0.)
# learning rate decay schedulers
# scheduler = sch.StepLR(optimizer, step_size=500, gamma=0.01)
# pred_loss functions
criterion = nn.CrossEntropyLoss(
ignore_index=prior_data_gen.char2idx[prior_data_gen.pad_symbol])
# sub-nodes of sim data resource
loss_lst = []
train_loss_node = DataNode(label="train_loss", data=loss_lst)
# collect mean predictions
unbiased_smiles_mean_pred, biased_smiles_mean_pred, gen_smiles_mean_pred = [], [], []
unbiased_smiles_mean_pred_data_node = DataNode(
'baseline_mean_vals', unbiased_smiles_mean_pred)
biased_smiles_mean_pred_data_node = DataNode('biased_mean_vals',
biased_smiles_mean_pred)
gen_smiles_mean_pred_data_node = DataNode('gen_mean_vals',
gen_smiles_mean_pred)
if sim_data_node:
sim_data_node.data = [
unbiased_smiles_mean_pred_data_node,
biased_smiles_mean_pred_data_node,
gen_smiles_mean_pred_data_node, train_loss_node
]
# load pretrained model
if pretrained_net_path and pretrained_net_name:
print('Loading pretrained model...')
weights = StackRNNBaseline.load_model(pretrained_net_path,
pretrained_net_name)
generator.load_state_dict(weights)
print('Pretrained model loaded successfully!')
start = time.time()
try:
demo_score = np.mean(
expert_model(
demo_data_gen.random_training_set_smiles(1000))[1])
baseline_score = np.mean(
expert_model(
unbiased_data_gen.random_training_set_smiles(1000))[1])
step_idx = Count()
gen_data = prior_data_gen if is_pretraining else demo_data_gen
n_epochs = 2
with TBMeanTracker(tb_writer, 1) as tracker:
mode = 'Pretraining' if is_pretraining else 'Fine tuning'
for epoch in range(n_epochs):
epoch_losses = []
epoch_mean_preds = []
epoch_per_valid = []
with grad_stats:
for b in trange(
0,
num_batches,
desc=
f'Epoch {epoch + 1}/{n_epochs}, {mode} in progress...'
):
inputs, labels = gen_data.random_training_set()
inputs = inputs.to(device)
labels = labels.to(device)
batch_size, seq_len = inputs.shape[:2]
optimizer.zero_grad()
# track history if only in train
# with torch.set_grad_enabled(phase == "train"):
# Create hidden states for each layer
hidden_states = []
for _ in range(rnn_args['num_layers']):
hidden = init_hidden(
num_layers=1,
batch_size=batch_size,
hidden_size=rnn_args['hidden_size'],
num_dir=rnn_args['num_dir'],
dvc=rnn_args['device'])
if rnn_args['has_cell']:
cell = init_cell(
num_layers=1,
batch_size=batch_size,
hidden_size=rnn_args['hidden_size'],
num_dir=rnn_args['num_dir'],
dvc=rnn_args['device'])
else:
cell = None
if rnn_args['has_stack']:
stack = init_stack(batch_size,
rnn_args['stack_width'],
rnn_args['stack_depth'],
dvc=rnn_args['device'])
else:
stack = None
hidden_states.append((hidden, cell, stack))
# forward propagation
outputs = generator([inputs] + hidden_states)
predictions = outputs[0]
predictions = predictions.permute(1, 0, -1)
predictions = predictions.contiguous().view(
-1, predictions.shape[-1])
labels = labels.contiguous().view(-1)
# calculate loss
loss = criterion(predictions, labels)
if grad_clipping:
torch.nn.utils.clip_grad_norm_(
generator.parameters(), grad_clipping)
optimizer.step()
# for sim data resource
n_to_generate = 200
with torch.set_grad_enabled(False):
samples = generate_smiles(
generator,
demo_data_gen,
rnn_args,
num_samples=n_to_generate)
samples_pred = expert_model(samples)[1]
# metrics
eval_dict = {}
eval_score = StackRNNBaseline.evaluate(
eval_dict, samples,
demo_data_gen.random_training_set_smiles(1000))
# TBoard info
tracker.track('loss', loss.item(),
step_idx.IncAndGet())
for k in eval_dict:
tracker.track(f'{k}', eval_dict[k], step_idx.i)
mean_preds = np.mean(samples_pred)
epoch_mean_preds.append(mean_preds)
if exp_type == 'drd2':
per_qualified = float(
len([v for v in samples_pred if v >= 0.8
])) / len(samples_pred)
score = mean_preds
elif exp_type == 'logp':
per_qualified = np.sum(
(samples_pred >= 1.0)
& (samples_pred < 5.0)) / len(samples_pred)
score = mean_preds
elif exp_type == 'jak2_max':
per_qualified = np.sum(
(samples_pred >=
demo_score)) / len(samples_pred)
diff = mean_preds - demo_score
score = np.exp(diff)
elif exp_type == 'jak2_min':
per_qualified = np.sum(
(samples_pred <=
demo_score)) / len(samples_pred)
diff = demo_score - mean_preds
score = np.exp(diff)
else: # pretraining
score = -loss.item()
per_qualified = 0.
per_valid = len(samples_pred) / n_to_generate
epoch_per_valid.append(per_valid)
unbiased_smiles_mean_pred.append(
float(baseline_score))
biased_smiles_mean_pred.append(float(demo_score))
gen_smiles_mean_pred.append(float(mean_preds))
tb_writer.add_scalars(
'qsar_score', {
'sampled': mean_preds,
'baseline': baseline_score,
'demo_data': demo_score
}, step_idx.i)
tb_writer.add_scalars(
'SMILES stats', {
'per. of valid': per_valid,
'per. of qualified': per_qualified
}, step_idx.i)
avg_len = np.nanmean([len(s) for s in samples])
tracker.track('Average SMILES length', avg_len,
step_idx.i)
score_exp_avg.update(score)
if score_exp_avg.value > best_score:
best_model_wts = copy.deepcopy(
generator.state_dict())
best_score = score_exp_avg.value
best_epoch = epoch
if step_idx.i > 0 and step_idx.i % 1000 == 0:
smiles = generate_smiles(generator=generator,
gen_data=gen_data,
init_args=rnn_args,
num_samples=3)
print(f'Sample SMILES = {smiles}')
# End of mini=batch iterations.
print(
f'{time_since(start)}: Epoch {epoch + 1}/{n_epochs}, loss={np.mean(epoch_losses)},'
f'Mean value of predictions = {np.mean(epoch_mean_preds)}, '
f'% of valid SMILES = {np.mean(epoch_per_valid)}')
except ValueError as e:
print(str(e))
duration = time.time() - start
print('Model training duration: {:.0f}m {:.0f}s'.format(
duration // 60, duration % 60))
generator.load_state_dict(best_model_wts)
return {
'model': generator,
'score': round(best_score, 3),
'epoch': best_epoch
}