def best_arch_search():
model.eval()
result_df = pd.DataFrame(columns=['Genotype', 'Val_reward'])
ntokens = len(corpus.dictionary)
i = 0
hidden = model.init_hidden(eval_batch_size)
for m in range(search_arch_num):
parallel_model.sample_new_architecture()
data, targets = get_batch(val_data, i, args)
targets = targets.view(-1)
hidden = repackage_hidden(hidden)
#log_prob, hidden = parallel_model(data, hidden)
#loss = nn.functional.nll_loss(log_prob.view(-1, log_prob.size(2)), targets).data
loss, hidden = parallel_model._loss(hidden, data, targets)
reward = architect.reward_c / torch.exp(loss)
gene = parallel_model.genotype()
temp_df = pd.DataFrame([[gene, reward.item()]],
columns=['Genotype', 'Val_reward'])
result_df = result_df.append(temp_df, ignore_index=True)
i += args.bptt
if i >= search_data.size(0) - 2:
i = 0
result_df = result_df.sort_values(by='Val_reward', ascending=False)
result_df.to_csv('search_result.csv')
def evaluate(data_source, batch_size=10):
# Turn on evaluation mode which disables dropout.
model.eval()
total_loss = 0
ntokens = len(corpus.dictionary)
hidden = model.init_hidden(batch_size)
with torch.no_grad():
for i in range(0, data_source.size(0) - 1, args.bptt):
data, targets = get_batch(data_source, i, args, evaluation=True)
targets = targets.view(-1)
log_prob, hidden = parallel_model(data, hidden)
loss = nn.functional.nll_loss(log_prob.view(-1, log_prob.size(2)), targets).data
total_loss += loss * len(data)
hidden = repackage_hidden(hidden)
return total_loss.item() / len(data_source)
def evaluate(data_source, batch_size=10, data_name='dev'):
data_source = DataLoader(args.data_dir + '/dev.json',
batch_size,
opt,
vocab,
evaluation=True)
print('Evaluating Model!')
# Turn on evaluation mode which disables dropout.
model.eval()
total_loss = 0
# ntokens = len(corpus.dictionary)
# ntokens = len(vocab.word2id)
# for i in range(0, data_source.size(0) - 1, args.bptt):
predictions = []
for i in range(len(data_source)):
batch = data_source.next_batch()
batch_size = len(batch['relation'])
hidden = model.init_hidden(batch_size)[0]
# data, targets = get_batch(data_source, i, args, evaluation=True)
data = batch
targets = batch['relation']
targets = targets.view(-1)
# print('tokens: {} | hidden: {}'.format(batch['tokens'].shape, hidden.shape))
log_prob, hidden = parallel_model(data, hidden)
loss = nn.functional.nll_loss(
log_prob, targets).data # log_prob.view(-1, log_prob.size(2))
total_loss += loss * len(data)
batch_predictions = torch.argmax(log_prob, dim=-1).cpu().data.numpy()
batch_predictions = [
id2label[prediction] for prediction in batch_predictions
]
predictions += batch_predictions
# hidden = repackage_hidden(hidden)
precision, recall, f1 = scorer.score(dev_data.gold(), predictions)
logging.info('{} set | Precision: {} | Recall: {} | F1: {}'.format(
data_name, precision, recall, f1))
print('total loss: {}'.format(total_loss))
return total_loss / len(data_source)
def train():
assert (
args.batch_size % args.small_batch_size == 0
), "batch_size must be divisible by small_batch_size"
# Turn on training mode which enables dropout.
total_loss = 0
start_time = time.time()
ntokens = len(corpus.dictionary)
hidden = [
model.init_hidden(args.small_batch_size)
for _ in range(args.batch_size // args.small_batch_size)
]
hidden_valid = [
model.init_hidden(args.small_batch_size)
for _ in range(args.batch_size // args.small_batch_size)
]
batch, i = 0, 0
while i < train_data.size(0) - 1 - 1:
bptt = args.bptt if np.random.random() < 0.95 else args.bptt / 2.0
# Prevent excessively small or negative sequence lengths
# seq_len = max(5, int(np.random.normal(bptt, 5)))
# # There's a very small chance that it could select a very long sequence length resulting in OOM
# seq_len = min(seq_len, args.bptt + args.max_seq_len_delta)
seq_len = int(bptt)
lr2 = optimizer.param_groups[0]["lr"]
optimizer.param_groups[0]["lr"] = lr2 * seq_len / args.bptt
model.train()
data_valid, targets_valid = get_batch(
search_data, i % (search_data.size(0) - 1), args
)
data, targets = get_batch(train_data, i, args, seq_len=seq_len)
optimizer.zero_grad()
start, end, s_id = 0, args.small_batch_size, 0
while start < args.batch_size:
# cur_data, cur_targets = (
# data[:, start:end],
# targets[:, start:end].contiguous().view(-1),
# )
# cur_data_valid, cur_targets_valid = (
# data_valid[:, start:end],
# targets_valid[:, start:end].contiguous(),
# )
cur_data, cur_targets = (data, targets.contiguous())
cur_data_valid, cur_targets_valid = (
data_valid, targets_valid.contiguous())
# Starting each batch, we detach the hidden state from how it was previously produced.
# If we didn't, the model would try backpropagating all the way to start of the dataset.
hidden[s_id] = repackage_hidden(hidden[s_id])
hidden_valid[s_id] = repackage_hidden(hidden_valid[s_id])
hidden_valid[s_id], grad_norm = architect.step(
hidden[s_id],
cur_data,
cur_targets,
hidden_valid[s_id],
cur_data_valid,
cur_targets_valid,
optimizer,
args.unrolled,
)
# assuming small_batch_size = batch_size so we don't accumulate gradients
optimizer.zero_grad()
hidden[s_id] = repackage_hidden(hidden[s_id])
log_prob, hidden[s_id], rnn_hs, dropped_rnn_hs = parallel_model(
cur_data, hidden[s_id], return_h=True
)
raw_loss = nn.functional.nll_loss(
log_prob.view(-1, log_prob.size(2)), cur_targets
)
loss = raw_loss
# Activiation Regularization
if args.alpha > 0:
loss = loss + sum(
args.alpha * dropped_rnn_h.pow(2).mean()
for dropped_rnn_h in dropped_rnn_hs[-1:]
)
# Temporal Activation Regularization (slowness)
loss = loss + sum(
args.beta * (rnn_h[1:] - rnn_h[:-1]).pow(2).mean()
for rnn_h in rnn_hs[-1:]
)
loss *= args.small_batch_size / args.batch_size
total_loss += raw_loss.data * args.small_batch_size / args.batch_size
loss.backward()
s_id += 1
start = end
end = start + args.small_batch_size
gc.collect()
# `clip_grad_norm` helps prevent the exploding gradient problem in RNNs.
torch.nn.utils.clip_grad_norm_(model.parameters(), args.clip)
optimizer.step()
# total_loss += raw_loss.data
optimizer.param_groups[0]["lr"] = lr2
if batch % args.log_interval == 0 and batch > 0:
logging.info(parallel_model.genotype())
print(F.softmax(parallel_model.weights, dim=-1))
cur_loss = total_loss.item() / args.log_interval
elapsed = time.time() - start_time
logging.info(
"| epoch {:3d} | {:5d}/{:5d} batches | lr {:02.2f} | ms/batch {:5.2f} | "
"loss {:5.2f} | ppl {:8.2f}".format(
epoch,
batch,
len(train_data) // args.bptt,
optimizer.param_groups[0]["lr"],
elapsed * 1000 / args.log_interval,
cur_loss,
math.exp(cur_loss),
)
)
total_loss = 0
start_time = time.time()
batch += 1
i += seq_len
def train(train_data, dev_data):
assert args.batch_size % args.small_batch_size == 0, 'batch_size must be divisible by small_batch_size'
ntokens = len(vocab.word2id)
# Turn on training mode which enables dropout.
total_loss = 0
total_valid_loss = 0
start_time = time.time()
# ntokens = len(corpus.dictionary)
# batch, i = 0, 0
for batch in range(len(train_data)):
train_batch = train_data.next_batch()
dev_batch = dev_data.next_batch()
# for batch, (train_batch, dev_batch) in enumerate(zip(train_data, dev_data)):
# hidden = [model.init_hidden(args.small_batch_size) for _ in range(args.batch_size // args.small_batch_size)]
# hidden_valid = [model.init_hidden(args.small_batch_size) for _ in
# range(args.batch_size // args.small_batch_size)]
#print('hidden shape: {} | hidden valid: {} |'.format(hidden.shape, hidden_valid.shape))
# while i < train_data.size(0) - 1 - 1:
bptt = args.bptt if np.random.random() < 0.95 else args.bptt / 2.
# Prevent excessively small or negative sequence lengths
# seq_len = max(5, int(np.random.normal(bptt, 5)))
# # There's a very small chance that it could select a very long sequence length resulting in OOM
# seq_len = min(seq_len, args.bptt + args.max_seq_len_delta)
# seq_len = int(bptt)
lr2 = optimizer.param_groups[0]['lr']
optimizer.param_groups[0]['lr'] = lr2 #* seq_len / args.bptt
model.train()
# data_valid, targets_valid = get_batch(search_data, i % (search_data.size(0) - 1), args)
# data, targets = get_batch(train_data, i, args, seq_len=seq_len)
optimizer.zero_grad()
# start, end, s_id = 0, args.small_batch_size, 0
cur_data = train_batch
cur_targets = train_batch['relation']
cur_data_valid = dev_batch
cur_targets_valid = dev_batch['relation']
hidden = model.init_hidden(len(train_batch['relation']))[0]
hidden_valid = model.init_hidden(len(dev_batch['relation']))[0]
# print('Train Batch Shapes: | Hidden: {} | Tokens: {} |'.format(hidden.shape, cur_data['tokens'].shape))
# print('Dev Batch Shapes: | Hidden: {} | Tokens: {} |'.format(hidden_valid.shape, cur_data_valid['tokens'].shape))
assert hidden.shape[1] == cur_data['tokens'].shape[
0], 'Hidden shape: {} | tokens shape: {}'.format(
hidden.shape, cur_data['tokens'].shape)
assert hidden_valid.shape[1] == cur_data_valid['tokens'].shape[
0], 'Hidden shape: {} | tokens shape: {}'.format(
hidden_valid.shape, cur_data_valid['tokens'].shape)
# while start < args.batch_size:
# cur_data, cur_targets = data[:, start: end], targets[:, start: end].contiguous().view(-1)
# cur_data_valid, cur_targets_valid = data_valid[:, start: end], targets_valid[:, start: end].contiguous().view(-1)
# Starting each batch, we detach the hidden state from how it was previously produced.
# If we didn't, the model would try backpropagating all the way to start of the dataset.
# hidden[s_id] = repackage_hidden(hidden[s_id])
# hidden_valid[s_id] = repackage_hidden(hidden_valid[s_id])
#print(hidden.shape)
#hidden = repackage_hidden(hidden)
#hidden_valid = repackage_hidden(hidden_valid)
# hidden_valid[s_id], grad_norm = architect.step(
# hidden[s_id], cur_data, cur_targets,
# hidden_valid[s_id], cur_data_valid, cur_targets_valid,
# optimizer,
# args.unrolled)
hidden_valid, valid_loss = architect.step(hidden, cur_data,
cur_targets, hidden_valid,
cur_data_valid,
cur_targets_valid, optimizer,
args.unrolled)
total_valid_loss += valid_loss.data
# print('Finished architect step...')
# assuming small_batch_size = batch_size so we don't accumulate gradients
optimizer.zero_grad()
# hidden[s_id] = repackage_hidden(hidden[s_id])
#hidden = repackage_hidden(hidden)
# log_prob, hidden[s_id], rnn_hs, dropped_rnn_hs = parallel_model(cur_data, hidden[s_id], return_h=True)
# print('Entering model training...')
hidden = torch.autograd.Variable(hidden.data)
# Hidden should be all zeros
print('hidden all zeros?: (not {})'.format(torch.sum(hidden)))
log_prob, hidden, rnn_hs, dropped_rnn_hs = parallel_model(
cur_data, hidden, return_h=True)
# print('received predictions')
raw_loss = nn.functional.nll_loss(log_prob, cur_targets)
# print('received loss' )
loss = raw_loss
# Activiation Regularization
if args.alpha > 0:
loss = loss + sum(args.alpha * dropped_rnn_h.pow(2).mean()
for dropped_rnn_h in dropped_rnn_hs[-1:])
# Temporal Activation Regularization (slowness)
loss = loss + sum(args.beta * (rnn_h[1:] - rnn_h[:-1]).pow(2).mean()
for rnn_h in rnn_hs[-1:])
# loss *= args.small_batch_size / args.batch_size
total_loss += raw_loss.data # * args.small_batch_size / args.batch_size
loss.backward()
# s_id += 1
# start = end
# end = start + args.small_batch_size
# print('backpropogated...')
gc.collect()
# print('garbage collected...')
# `clip_grad_norm` helps prevent the exploding gradient problem in RNNs.
torch.nn.utils.clip_grad_norm(model.parameters(), args.clip)
# print('clipped gradients...')
optimizer.step()
# print('updated gradients...')
# total_loss += raw_loss.data
optimizer.param_groups[0]['lr'] = lr2
if batch % args.log_interval == 0: # and batch > 0:
logging.info(parallel_model.genotype())
print(F.softmax(parallel_model.weights, dim=-1))
#print('total loss: {}'.format(type(total_loss)))
#print('total loss: {}'.format(total_loss))
#print('total loss: {}'.format(total_loss.shape))
#cur_loss = total_loss[0] / args.log_interval
cur_loss = total_loss / args.log_interval
cur_valid_loss = total_valid_loss / args.log_interval
elapsed = time.time() - start_time
logging.info(
'| epoch {:3d} | {:5d}/{:5d} batches | lr {:02.2f} | ms/batch {:5.2f} | '
'loss {:5.2f} | ppl {:8.2f} | valid loss: {:5.2f} | valid ppl: {:5.2f}'
.format(epoch, batch, len(train_data),
optimizer.param_groups[0]['lr'],
elapsed * 1000 / args.log_interval, cur_loss,
math.exp(cur_loss), cur_valid_loss,
math.exp(cur_valid_loss)))
total_loss = 0
start_time = time.time()
# print('on to next batch...')
# batch += 1
# i += seq_len
print('Reached end of epoch training!')
def train_arch():
assert args.batch_size % args.small_batch_size == 0, 'batch_size must be divisible by small_batch_size'
# Turn on training mode which enables dropout.
total_loss = 0
start_time = time.time()
ntokens = len(corpus.dictionary)
hidden_valid = [
model.init_hidden(args.small_batch_size)
for _ in range(args.batch_size // args.small_batch_size)
]
batch, i = 0, 0
ep_loss = 0
model.eval()
while i < search_data.size(0) - 1 - 1:
bptt = args.bptt if np.random.random() < 0.95 else args.bptt / 2.
# Prevent excessively small or negative sequence lengths
# seq_len = max(5, int(np.random.normal(bptt, 5)))
# # There's a very small chance that it could select a very long sequence length resulting in OOM
# seq_len = min(seq_len, args.bptt + args.max_seq_len_delta)
seq_len = int(bptt)
data_valid, targets_valid = get_batch(search_data, i, args)
start, end, s_id = 0, args.small_batch_size, 0
while start < args.batch_size:
cur_data_valid, cur_targets_valid = data_valid[:, start:
end], targets_valid[:,
start:
end].contiguous(
).view(
-1
)
hidden_valid[s_id] = repackage_hidden(hidden_valid[s_id])
parallel_model.sample_new_architecture()
if i == 0:
for e in model.edge_weights:
print(F.softmax(e, dim=-1))
print(F.softmax(model.weights, dim=-1))
print(model.baseline)
if (batch + 1) % arch_opt_step == 0:
is_opt_step = True
else:
is_opt_step = False
if i == 0:
architect.optimizer.zero_grad()
hidden_valid[s_id], raw_loss = architect.step(
hidden_valid[s_id], cur_data_valid, cur_targets_valid,
is_opt_step)
raw_loss, hidden_valid[s_id] = model._loss(hidden_valid[s_id],
cur_data_valid,
cur_targets_valid)
raw_loss = raw_loss.detach()
loss = raw_loss
total_loss += raw_loss.data * args.small_batch_size / args.batch_size
ep_loss += raw_loss * len(cur_data_valid)
s_id += 1
start = end
end = start + args.small_batch_size
gc.collect()
# total_loss += raw_loss.data
if batch % args.log_interval == 0 and batch > 0:
logging.info(parallel_model.genotype())
print(F.softmax(parallel_model.weights, dim=-1))
cur_loss = total_loss.item() / args.log_interval
elapsed = time.time() - start_time
logging.info(
'| arch_epoch {:3d} | {:5d}/{:5d} batches | lr {:02.2f} | ms/batch {:5.2f} | '
'loss {:5.2f} | ppl {:8.2f}'.format(
epoch, batch,
len(search_data) // args.bptt,
optimizer.param_groups[0]['lr'],
elapsed * 1000 / args.log_interval, cur_loss,
math.exp(cur_loss)))
total_loss = 0
start_time = time.time()
batch += 1
i += seq_len
#Optimizer step for residual of valid queue
if not is_opt_step:
architect.optimizer.step()
return ep_loss.item() / len(search_data)
def train():
assert args.batch_size % args.small_batch_size == 0, 'batch_size must be divisible by small_batch_size'
# Turn on training mode which enables dropout.
total_loss = 0
start_time = time.time()
ntokens = len(corpus.dictionary)
hidden = [
model.init_hidden(args.small_batch_size)
for _ in range(args.batch_size // args.small_batch_size)
]
batch, i = 0, 0
model.train()
while i < train_data.size(0) - 1 - 1:
bptt = args.bptt if np.random.random() < 0.95 else args.bptt / 2.
# Prevent excessively small or negative sequence lengths
# seq_len = max(5, int(np.random.normal(bptt, 5)))
# # There's a very small chance that it could select a very long sequence length resulting in OOM
# seq_len = min(seq_len, args.bptt + args.max_seq_len_delta)
seq_len = int(bptt)
lr2 = optimizer.param_groups[0]['lr']
optimizer.param_groups[0]['lr'] = lr2 * seq_len / args.bptt
data, targets = get_batch(train_data, i, args, seq_len=seq_len)
start, end, s_id = 0, args.small_batch_size, 0
while start < args.batch_size:
cur_data, cur_targets = data[:,
start:end], targets[:, start:
end].contiguous(
).view(-1)
optimizer.zero_grad()
hidden[s_id] = repackage_hidden(hidden[s_id])
parallel_model.sample_new_architecture()
log_prob, hidden[s_id], rnn_hs, dropped_rnn_hs = parallel_model(
cur_data, hidden[s_id], return_h=True)
raw_loss = nn.functional.nll_loss(
log_prob.view(-1, log_prob.size(2)), cur_targets)
loss = raw_loss
# Activiation Regularization
if args.alpha > 0:
loss = loss + sum(args.alpha * dropped_rnn_h.pow(2).mean()
for dropped_rnn_h in dropped_rnn_hs[-1:])
# Temporal Activation Regularization (slowness)
loss = loss + sum(args.beta *
(rnn_h[1:] - rnn_h[:-1]).pow(2).mean()
for rnn_h in rnn_hs[-1:])
loss *= args.small_batch_size / args.batch_size
total_loss += raw_loss.data * args.small_batch_size / args.batch_size
loss.backward()
s_id += 1
start = end
end = start + args.small_batch_size
gc.collect()
# `clip_grad_norm` helps prevent the exploding gradient problem in RNNs.
torch.nn.utils.clip_grad_norm_(model.parameters(), args.clip)
optimizer.step()
# total_loss += raw_loss.data
optimizer.param_groups[0]['lr'] = lr2
if batch % args.log_interval == 0 and batch > 0:
cur_loss = total_loss.item() / args.log_interval
elapsed = time.time() - start_time
logging.info(
'| dag_epoch {:3d} | {:5d}/{:5d} batches | lr {:02.2f} | ms/batch {:5.2f} | '
'loss {:5.2f} | ppl {:8.2f}'.format(
epoch, batch,
len(train_data) // args.bptt,
optimizer.param_groups[0]['lr'],
elapsed * 1000 / args.log_interval, cur_loss,
math.exp(cur_loss)))
total_loss = 0
start_time = time.time()
batch += 1
i += seq_len
