class NASGrammarModel():
def __init__(self, grammar, device, hparams=stgs.VAE_HPARAMS):
"""
Load trained encoder/decoder and grammar model
:param grammar: A nas_grammar.Grammar object
:param hparams: dict, hyperparameters for the VAE and the grammar model
"""
self._grammar = grammar
self.device = device
self.hp = hparams
self.max_len = self.hp['max_len']
self._productions = self._grammar.GCFG.productions()
self._prod_map = make_prod_map(grammar.GCFG)
self._parser = nltk.ChartParser(grammar.GCFG)
self._tokenize = make_tokenizer(grammar.GCFG)
self._n_chars = len(self._productions)
self._lhs_map = grammar.lhs_map
self.vae = NA_VAE(self.hp)
self.vae.eval()
@staticmethod
def _pop_or_nothing(stack):
# Tries to pop item at top of stack S, unless there is nothing.
try:
return stack.pop()
except:
return 'Nothing'
@staticmethod
def _prods_to_sent(prods):
# converts a list of productions into a sentence
seq = [prods[0].lhs()]
for prod in prods:
if str(prod.lhs()) == 'Nothing':
break
for i, s in enumerate(seq):
if s == prod.lhs():
seq = seq[:i] + list(prod.rhs()) + seq[i + 1:]
break
try:
return ''.join(seq)
except:
return ''
def encode(self, sents):
"""
Returns the mean of the distribution, which is the predicted latent vector, for a one-hot vector of production
rules.
"""
one_hot = make_one_hot(self._grammar.GCFG, self._tokenize,
self._prod_map,
sents, self.max_len, self._n_chars).transpose(
2, 1) # (1, batch, max_len, n_chars)
one_hot = one_hot.to(self.device)
self.vae.eval()
with torch.no_grad():
mu_1, logvar_1, mu_2, logvar_2, z2 = self.vae.encode(one_hot)
z1 = self.vae.reparameterize(mu_1, logvar_1)
debed_z1 = self.vae.debed_1(z1)
debed_z2 = self.vae.debed_2(z2)
z = torch.cat([debed_z1, debed_z2], dim=1)
return z, one_hot # (batch, latent_sz)
def _sample_using_masks(self, unmasked, logs=True):
"""
Samples a one-hot vector from unmasked selection, masking at each timestep.
/!\ This is probably where we will diverge from the Grammar-VAE paper because we need to introduce
conditions on the nodes that can be selected as input for each layer, and the Agg types also
depend on the node values selected (Agg == '-' iff ND == '-').
:param unmasked: The output of the VAE's decoder, so a collection of logit vectors (i.e. before
softmax); size (batch, timesteps, max_length)
"""
x_hat = np.zeros_like(unmasked)
# Create a stack (data structure) for each input in the batch, i.e. each sentence
S = np.empty((unmasked.shape[0], ),
dtype=object) # dimension 0 == number of sentences
for i in range(S.shape[0]):
S[i] = [str(self._grammar.start_index)
] # initialise each stack with the start symbol 'S'
# Loop over time axis, sampling values and updating masks at every step
for t in range(unmasked.shape[2]):
next_nonterminal = [
self._lhs_map[self._pop_or_nothing(a)] for a in S
]
mask = self._grammar.masks[
next_nonterminal] # get indices of valid productions for next symbol
if logs:
masked_output = np.multiply(np.exp(unmasked[..., t]),
mask) + 1e-100
else:
masked_output = np.multiply(unmasked[..., t], mask) + 1e-100
# This comes from Kusner et al. 2016 - GANs for Sequences of Discrete Elements with the
# Gumbel-Softmax Distribution, using work done in Jang et al. 2017 - Categorical
# Reparameteterization with Gumbel-Softmax, which itself uses the Gumber-Max trick presented
# in Maddison et al. 2014 - A* Sampling. y ~ Softmax(h) is equivalent to setting
# y=one_hot(argmax((h_i + g_i))) where g_i are independently sampled from Gumbel(0, 1)
sampled_output = np.argmax(np.add(
np.random.gumbel(size=masked_output.shape),
np.log(masked_output)),
axis=-1)
# Fill the highest-probability production rule with 1., all others are 0.
x_hat[np.arange(unmasked.shape[0]), sampled_output, t] = 1.
# Collect non-terminals in RHS of the selected production and push them onto stack in reverse order
rhs = [
filter(
lambda a: (isinstance(a, nltk.grammar.Nonterminal) and
(str(a) != 'None')), self._productions[i].rhs())
for i in sampled_output
] # single output per sentence
for i in range(S.shape[0]):
S[i].extend(list(map(str, rhs[i]))[::-1])
if not S.any(): break # stop when stack is empty
return x_hat
def decode(self, z=None, one_hot=None):
"""
Sample from the grammar decoder using the CFG-based mask, and return a sequence of production rules.
:param z: latent vector representing the sentence, of dimensions (batch, latent_sz). If None, must provide
argument one_hot (for testing purposes, mainly).
:param one_hot: If provided, decode the one_hot matrix directly instead of the decoded latent vector.
"""
if z is None: # testing purposes
unmasked = one_hot
logs = False
else: # normal regime
logs = True
#assert z.ndim == 2 # (batch, latent_sz)
self.vae.eval()
with torch.no_grad():
unmasked = self.vae.decode(
z).detach().cpu().numpy() # (batch, max_len, n_char)
assert unmasked.shape[1:] == (self._n_chars, self.max_len), \
print(f'umasked_shape[1:] == {unmasked.shape[1:]}, expected {(self._n_chars, self.max_len)}.')
x_hat = self._sample_using_masks(unmasked, logs)
# Convert from one-hot to sequence of production rules
prod_seq = [[
self._productions[x_hat[index, :, t].argmax()]
for t in range(x_hat.shape[2])
] for index in range(x_hat.shape[0])]
return [self._prods_to_sent(prods) for prods in prod_seq], x_hat
class IntegratedPredictor(pl.LightningModule):
def __init__(
self,
grammar_mdl: NASGrammarModel,
pretrained_vae: bool,
freeze_vae: bool,
vae_hparams: dict = stgs.VAE_HPARAMS,
pred_hparams: dict = stgs.PRED_HPARAMS,
):
super().__init__()
# make separate Namespaces for convenience
self.vae_hparams, self.pred_hparams = vae_hparams, pred_hparams
# make Namespace of combined hyperparameters for compatibility with PL:
self.hparams = {}
for k, v in vae_hparams.items():
self.hparams['_'.join(['vae', k])] = v
for k, v in pred_hparams.items():
self.hparams['_'.join(['pred', k])] = v
self.hparams = Namespace(**self.hparams)
self.vae = NA_VAE(self.vae_hparams)
if pretrained_vae:
self.vae.load_state_dict(torch.load(self.hparams.vae_weights_path))
if freeze_vae:
self.vae.freeze()
print('VAE encoder frozen.')
self.predictor = PerfPredictor(grammar_mdl, self.pred_hparams)
def forward(self, batch) -> torch.Tensor:
one_hot, n_layers = batch
mu_1, logvar_1, mu_2, logvar_2, z2 = self.vae.encode(
one_hot.squeeze(1))
z1 = self.vae.reparameterize(mu_1, logvar_1)
debed_z1 = self.vae.debed_1(z1)
debed_z2 = self.vae.debed_2(z2)
z = torch.cat([debed_z1, debed_z2], dim=1)
return self.predictor(z)
def mixup_inputs(self, batch, alpha=1.0):
"""
Returns mixed pairs of inputs and targets within a batch, and a lambda value sampled from a beta
distribution.
"""
one_hot, n_layers, y_true = batch
if alpha > 0:
lam = np.random.beta(alpha, alpha)
else:
lam = 1.
idx = torch.randperm(y_true.size(0))
mixed_onehot = lam * one_hot + (1 - lam) * one_hot[idx, ...]
mixed_n_layers = lam * n_layers + (1 - lam) * n_layers[idx]
return mixed_onehot, mixed_n_layers, y_true, y_true[idx], lam
def mixup_criterion(self, criterion, y_pred, y_true_a, y_true_b, lam):
return lam * criterion(y_pred, y_true_a) + (1 - lam) * criterion(
y_pred, y_true_b)
def loss_function(self, y_pred: torch.Tensor,
y_true: torch.Tensor) -> torch.Tensor:
return self.predictor.loss_function(y_pred, y_true)
def configure_optimizers(self):
return self.predictor.configure_optimizers()
def training_step(self, batch, batch_idx):
one_hot, n_layers, y_true = batch
if one_hot.dim() < 3: one_hot.unsqueeze_(0)
if n_layers.dim() < 2: n_layers.unsqueeze_(0)
if y_true.dim() < 2: y_true.unsqueeze_(0)
if self.hparams.pred_mixup:
one_hot, n_layers, y_true_a, y_true_b, lam = self.mixup_inputs(
batch, self.hparams.pred_mixup_alpha)
y_pred = self.forward((one_hot, n_layers))
if self.hparams.pred_mixup:
loss_val = self.mixup_criterion(self.loss_function, y_pred,
y_true_a, y_true_b, lam)
else:
loss_val = self.loss_function(y_pred, y_true)
lr = torch.tensor(
self.predictor.optim.param_groups[0]["lr"]).type_as(loss_val)
step = torch.tensor(self.global_step).type_as(lr)
if self.trainer.use_dp or self.trainer.use_ddp2:
loss_val.unsqueeze_(0)
lr.unsqueeze_(0)
step.unsqueeze_(0)
logs = {"loss": loss_val.sqrt(), "lr": lr, "step": step}
p_bar = {"global_step": step}
return {
"loss": loss_val.sqrt(),
"lr": lr,
"log": logs,
"global_step": step,
"progress_bar": p_bar,
}
def test_step(self, batch, batch_idx):
one_hot, n_layers, y_true = batch
y_pred = self.forward((one_hot, n_layers))
loss_val = self.loss_function(y_pred, y_true)
return {'test_loss': loss_val.sqrt()}
def test_epoch_end(self, outputs):
test_loss_mean = torch.stack([x['test_loss'] for x in outputs]).mean()
return {'test_loss': test_loss_mean}
def prepare_data(self):
try:
tr_set = SentenceRetrieverWithVaeTraining(
stgs.PRED_BATCH_PATH / "train.csv",
grammar_mdl=self.predictor.grammar_mdl)
self.tst_set = SentenceRetrieverWithVaeTraining(
stgs.PRED_BATCH_PATH / "test.csv",
grammar_mdl=self.predictor.grammar_mdl)
except:
tr_set = SentenceRetrieverWithVaeTraining(
stgs.PRED_BATCH_PATH / "fitnessbatch.csv",
grammar_mdl=self.predictor.grammar_mdl,
)
if self.hparams.pred_val_set_pct > 0.0:
val_len = int(self.hp.val_set_pct * len(tr_set))
self.tr_set, self.val_set = random_split(
tr_set, [len(tr_set) - val_len, val_len])
else:
self.tr_set, self.val_set = tr_set, None
def train_dataloader(self) -> DataLoader:
return DataLoader(
self.tr_set,
batch_size=self.hparams.pred_batch_sz,
shuffle=True,
drop_last=False,
num_workers=self.hparams.pred_num_workers,
)
def test_dataloader(self):
return DataLoader(self.tst_set,
batch_size=1,
shuffle=False,
drop_last=False,
num_workers=self.hparams.pred_num_workers)
