def tiny_gru_receiver_generator():
return core.RnnReceiverDeterministic(
Receiver(n_hidden=50, n_outputs=n_dim),
opts.vocab_size + 1,
opts.receiver_emb,
hidden_size=50,
cell="gru",
)
def __init__(self, opts):
super(OrigReceiver, self).__init__()
n_dim = opts.n_attributes * opts.n_values
receiver = Receiver(n_hidden=opts.hidden, n_outputs=n_dim)
self.receiver = core.RnnReceiverDeterministic(
receiver,
opts.vocab_size + 1,
opts.receiver_emb,
opts.hidden,
cell="gru",
)
def main(params):
opts = get_params(params)
print(opts)
device = opts.device
train_data = SphereData(n_points=int(opts.n_points))
train_loader = DataLoader(train_data,
batch_size=opts.batch_size,
shuffle=True)
test_data = SphereData(n_points=int(1e3))
test_loader = DataLoader(train_data,
batch_size=opts.batch_size,
shuffle=False)
sender = CircleSender(opts.vocab_size)
assert opts.lenses in [0, 1]
if opts.lenses == 1:
sender = torch.nn.Sequential(Lenses(math.pi / 4), sender)
receiver = Receiver(n_hidden=opts.receiver_hidden,
n_dim=2,
inner_layers=opts.receiver_layers)
receiver = core.RnnReceiverDeterministic(
receiver,
opts.vocab_size + 1, # exclude eos = 0
opts.receiver_emb,
opts.receiver_hidden,
cell=opts.receiver_cell,
num_layers=opts.cell_layers)
game = core.SenderReceiverRnnReinforce(sender,
receiver,
diff_loss,
receiver_entropy_coeff=0.05,
sender_entropy_coeff=0.0)
optimizer = core.build_optimizer(receiver.parameters())
loss = game.loss
trainer = core.Trainer(
game=game,
optimizer=optimizer,
train_data=train_loader,
validation_data=test_loader,
callbacks=[core.ConsoleLogger(as_json=True, print_train_loss=True)],
grad_norm=1.0)
trainer.train(n_epochs=opts.n_epochs)
core.close()
)
game = core.SenderReceiverRnnGS(sender, receiver, loss)
elif opts.mode.lower() == "rf-deterministic":
sender = core.RnnSenderReinforce(
sender,
opts.vocab_size,
opts.sender_embedding,
opts.sender_hidden,
cell=opts.sender_cell,
max_len=opts.max_len,
force_eos=False,
)
receiver = core.RnnReceiverDeterministic(
receiver,
opts.vocab_size,
opts.receiver_embedding,
opts.receiver_hidden,
cell=opts.receiver_cell,
)
game = core.SenderReceiverRnnReinforce(
sender,
receiver,
loss,
sender_entropy_coeff=opts.sender_entropy_coeff,
receiver_entropy_coeff=opts.receiver_entropy_coeff,
length_cost=opts.length_cost,
)
elif opts.mode.lower() == "rf":
sender = core.RnnSenderReinforce(
sender,
opts.vocab_size,
def main(params):
opts = get_params(params)
print(opts, flush=True)
device = opts.device
force_eos = opts.force_eos == 1
if opts.probs == 'uniform':
probs = np.ones(opts.n_features)
elif opts.probs == 'powerlaw':
probs = 1 / np.arange(1, opts.n_features+1, dtype=np.float32)
else:
probs = np.array([float(x) for x in opts.probs.split(',')], dtype=np.float32)
probs /= probs.sum()
print('the probs are: ', probs, flush=True)
train_loader = OneHotLoader(n_features=opts.n_features, batch_size=opts.batch_size,
batches_per_epoch=opts.batches_per_epoch, probs=probs)
# single batches with 1s on the diag
test_loader = UniformLoader(opts.n_features)
if opts.sender_cell == 'transformer':
sender = Sender(n_features=opts.n_features, n_hidden=opts.sender_embedding)
sender = core.TransformerSenderReinforce(agent=sender, vocab_size=opts.vocab_size,
embed_dim=opts.sender_embedding, max_len=opts.max_len,
num_layers=opts.sender_num_layers, num_heads=opts.sender_num_heads,
hidden_size=opts.sender_hidden,
force_eos=opts.force_eos,
generate_style=opts.sender_generate_style,
causal=opts.causal_sender)
else:
sender = Sender(n_features=opts.n_features, n_hidden=opts.sender_hidden)
sender = core.RnnSenderReinforce(sender,
opts.vocab_size, opts.sender_embedding, opts.sender_hidden,
cell=opts.sender_cell, max_len=opts.max_len, num_layers=opts.sender_num_layers,
force_eos=force_eos)
if opts.receiver_cell == 'transformer':
receiver = Receiver(n_features=opts.n_features, n_hidden=opts.receiver_embedding)
receiver = core.TransformerReceiverDeterministic(receiver, opts.vocab_size, opts.max_len,
opts.receiver_embedding, opts.receiver_num_heads, opts.receiver_hidden,
opts.receiver_num_layers, causal=opts.causal_receiver)
else:
receiver = Receiver(n_features=opts.n_features, n_hidden=opts.receiver_hidden)
receiver = core.RnnReceiverDeterministic(receiver, opts.vocab_size, opts.receiver_embedding,
opts.receiver_hidden, cell=opts.receiver_cell,
num_layers=opts.receiver_num_layers)
empty_logger = LoggingStrategy.minimal()
game = core.SenderReceiverRnnReinforce(sender, receiver, loss, sender_entropy_coeff=opts.sender_entropy_coeff,
receiver_entropy_coeff=opts.receiver_entropy_coeff,
train_logging_strategy=empty_logger,
length_cost=opts.length_cost)
optimizer = core.build_optimizer(game.parameters())
callbacks = [EarlyStopperAccuracy(opts.early_stopping_thr),
core.ConsoleLogger(as_json=True, print_train_loss=True)]
if opts.checkpoint_dir:
checkpoint_name = f'{opts.name}_vocab{opts.vocab_size}_rs{opts.random_seed}_lr{opts.lr}_shid{opts.sender_hidden}_rhid{opts.receiver_hidden}_sentr{opts.sender_entropy_coeff}_reg{opts.length_cost}_max_len{opts.max_len}'
callbacks.append(core.CheckpointSaver(checkpoint_path=opts.checkpoint_dir, prefix=checkpoint_name))
trainer = core.Trainer(game=game, optimizer=optimizer, train_data=train_loader, validation_data=test_loader, callbacks=callbacks)
trainer.train(n_epochs=opts.n_epochs)
game.logging_strategy = LoggingStrategy.maximal() # now log everything
dump(trainer.game, opts.n_features, device, False)
core.close()
def main(params):
print(torch.cuda.is_available())
opts = get_params(params)
print(opts, flush=True)
device = opts.device
force_eos = opts.force_eos == 1
if opts.probs == 'uniform':
probs = np.ones(opts.n_features)
elif opts.probs == 'powerlaw':
probs = 1 / np.arange(1, opts.n_features+1, dtype=np.float32)
#elif opts.probs == "creneau":
# ones = np.ones(int(opts.n_features/2))
# tens = 10*np.ones(opts.n_features-int(opts.n_features/2))
# probs = np.concatenate((tens,ones),axis=0)
#elif opts.probs == "toy":
# fives = 5*np.ones(int(opts.n_features/10))
# ones = np.ones(opts.n_features-int(opts.n_features/10))
# probs = np.concatenate((fives,ones),axis=0)
#elif opts.probs == "escalier":
# ones = np.ones(int(opts.n_features/4))
# tens = 10*np.ones(int(opts.n_features/4))
# huns = 100*np.ones(int(opts.n_features/4))
# thous = 1000*np.ones(opts.n_features-3*int(opts.n_features/4))
# probs = np.concatenate((thous,huns,tens,ones),axis=0)
else:
probs = np.array([float(x) for x in opts.probs.split(',')], dtype=np.float32)
probs /= probs.sum()
print('the probs are: ', probs, flush=True)
train_loader = OneHotLoader(n_features=opts.n_features, batch_size=opts.batch_size,
batches_per_epoch=opts.batches_per_epoch, probs=probs)
# single batches with 1s on the diag
test_loader = UniformLoader(opts.n_features)
if opts.sender_cell == 'transformer':
sender = Sender(n_features=opts.n_features, n_hidden=opts.sender_embedding)
sender = core.TransformerSenderReinforce(agent=sender, vocab_size=opts.vocab_size,
embed_dim=opts.sender_embedding, max_len=opts.max_len,
num_layers=opts.sender_num_layers, num_heads=opts.sender_num_heads,
hidden_size=opts.sender_hidden,
force_eos=opts.force_eos,
generate_style=opts.sender_generate_style,
causal=opts.causal_sender)
else:
sender = Sender(n_features=opts.n_features, n_hidden=opts.sender_hidden)
sender = core.RnnSenderReinforce(sender,
opts.vocab_size, opts.sender_embedding, opts.sender_hidden,
cell=opts.sender_cell, max_len=opts.max_len, num_layers=opts.sender_num_layers,
force_eos=force_eos)
if opts.receiver_cell == 'transformer':
receiver = Receiver(n_features=opts.n_features, n_hidden=opts.receiver_embedding)
receiver = core.TransformerReceiverDeterministic(receiver, opts.vocab_size, opts.max_len,
opts.receiver_embedding, opts.receiver_num_heads, opts.receiver_hidden,
opts.receiver_num_layers, causal=opts.causal_receiver)
else:
receiver = Receiver(n_features=opts.n_features, n_hidden=opts.receiver_hidden)
if not opts.impatient:
receiver = Receiver(n_features=opts.n_features, n_hidden=opts.receiver_hidden)
receiver = core.RnnReceiverDeterministic(receiver, opts.vocab_size, opts.receiver_embedding,
opts.receiver_hidden, cell=opts.receiver_cell,
num_layers=opts.receiver_num_layers)
else:
receiver = Receiver(n_features=opts.receiver_hidden, n_hidden=opts.vocab_size)
# If impatient 1
receiver = RnnReceiverImpatient(receiver, opts.vocab_size, opts.receiver_embedding,
opts.receiver_hidden, cell=opts.receiver_cell,
num_layers=opts.receiver_num_layers, max_len=opts.max_len, n_features=opts.n_features)
# If impatient 2
#receiver = RnnReceiverImpatient2(receiver, opts.vocab_size, opts.receiver_embedding,
# opts.receiver_hidden, cell=opts.receiver_cell,
# num_layers=opts.receiver_num_layers, max_len=opts.max_len, n_features=opts.n_features)
if not opts.impatient:
game = core.SenderReceiverRnnReinforce(sender, receiver, loss, sender_entropy_coeff=opts.sender_entropy_coeff,
receiver_entropy_coeff=opts.receiver_entropy_coeff,
length_cost=opts.length_cost,unigram_penalty=opts.unigram_pen,reg=opts.reg)
else:
game = SenderImpatientReceiverRnnReinforce(sender, receiver, loss_impatient, sender_entropy_coeff=opts.sender_entropy_coeff,
receiver_entropy_coeff=opts.receiver_entropy_coeff,
length_cost=opts.length_cost,unigram_penalty=opts.unigram_pen,reg=opts.reg)
optimizer = core.build_optimizer(game.parameters())
trainer = core.Trainer(game=game, optimizer=optimizer, train_data=train_loader,
validation_data=test_loader, callbacks=[EarlyStopperAccuracy(opts.early_stopping_thr)])
for epoch in range(int(opts.n_epochs)):
print("Epoch: "+str(epoch))
if epoch%100==0:
trainer.optimizer.defaults["lr"]/=2
trainer.train(n_epochs=1)
if opts.checkpoint_dir:
trainer.save_checkpoint(name=f'{opts.name}_vocab{opts.vocab_size}_rs{opts.random_seed}_lr{opts.lr}_shid{opts.sender_hidden}_rhid{opts.receiver_hidden}_sentr{opts.sender_entropy_coeff}_reg{opts.length_cost}_max_len{opts.max_len}')
if not opts.impatient:
acc_vec,messages=dump(trainer.game, opts.n_features, device, False,epoch)
else:
acc_vec,messages=dump_impatient(trainer.game, opts.n_features, device, False,epoch)
# ADDITION TO SAVE MESSAGES
all_messages=[]
for x in messages:
x = x.cpu().numpy()
all_messages.append(x)
all_messages = np.asarray(all_messages)
if epoch%50==0:
torch.save(sender.state_dict(), opts.dir_save+"/sender/sender_weights"+str(epoch)+".pth")
torch.save(receiver.state_dict(), opts.dir_save+"/receiver/receiver_weights"+str(epoch)+".pth")
#print(acc_vec)
np.save(opts.dir_save+'/messages/messages_'+str((epoch))+'.npy', all_messages)
np.save(opts.dir_save+'/accuracy/accuracy_'+str((epoch))+'.npy', acc_vec)
core.close()
def small_gru_receiver_generator(): return \
core.RnnReceiverDeterministic(
Receiver(n_hidden=100, n_outputs=n_dim),
opts.vocab_size + 1, opts.receiver_emb, hidden_size=100, cell='gru')
def tiny_gru_receiver_generator(): return \
def main(params):
opts = get_params(params)
if opts.validation_batch_size == 0:
opts.validation_batch_size = opts.batch_size
print(opts, flush=True)
# the following if statement controls aspects specific to the two game tasks: loss, input data and architecture of the Receiver
# (the Sender is identical in both cases, mapping a single input attribute-value vector to a variable-length message)
if opts.game_type == "discri":
# the game object we will encounter below takes as one of its mandatory arguments a loss: a loss in EGG is expected to take as arguments the sender input,
# the message, the Receiver input, the Receiver output and the labels (although some of these elements might not actually be used by a particular loss);
# together with the actual loss computation, the loss function can return a dictionary with other auxiliary statistics: in this case, accuracy
def loss(
_sender_input,
_message,
_receiver_input,
receiver_output,
labels,
_aux_input,
):
# in the discriminative case, accuracy is computed by comparing the index with highest score in Receiver output (a distribution of unnormalized
# probabilities over target poisitions) and the corresponding label read from input, indicating the ground-truth position of the target
acc = (receiver_output.argmax(dim=1) == labels).detach().float()
# similarly, the loss computes cross-entropy between the Receiver-produced target-position probability distribution and the labels
loss = F.cross_entropy(receiver_output, labels, reduction="none")
return loss, {"acc": acc}
# the input data are read into DataLodaer objects, which are pytorch constructs implementing standard data processing functionalities, such as shuffling
# and batching
# within our games, we implement dataset classes, such as AttValDiscriDataset, to read the input text files and convert the information they contain
# into the form required by DataLoader
# look at the definition of the AttValDiscrDataset (the class to read discrimination game data) in data_readers.py for further details
# note that, for the training dataset, we first instantiate the AttValDiscriDataset object and then feed it to DataLoader, whereas for the
# validation data (confusingly called "test" data due to code heritage inertia) we directly declare the AttValDiscriDataset when instantiating
# DataLoader: the reason for this difference is that we need the train_ds object to retrieve the number of features of the input vectors
train_ds = AttValDiscriDataset(path=opts.train_data,
n_values=opts.n_values)
train_loader = DataLoader(train_ds,
batch_size=opts.batch_size,
shuffle=True,
num_workers=1)
test_loader = DataLoader(
AttValDiscriDataset(path=opts.validation_data,
n_values=opts.n_values),
batch_size=opts.validation_batch_size,
shuffle=False,
num_workers=1,
)
# note that the number of features retrieved here concerns inputs after they are converted to 1-hot vectors
n_features = train_ds.get_n_features()
# we define here the core of the Receiver for the discriminative game, see the architectures.py file for details:
# note that this will be embedded in a wrapper below to define the full agent
receiver = DiscriReceiver(n_features=n_features,
n_hidden=opts.receiver_hidden)
else: # reco game
def loss(sender_input, _message, _receiver_input, receiver_output,
labels, _aux_input):
# in the case of the recognition game, for each attribute we compute a different cross-entropy score
# based on comparing the probability distribution produced by the Receiver over the values of each attribute
# with the corresponding ground truth, and then averaging across attributes
# accuracy is instead computed by considering as a hit only cases where, for each attribute, the Receiver
# assigned the largest probability to the correct value
# most of this function consists of the usual pytorch madness needed to reshape tensors in order to perform these computations
n_attributes = opts.n_attributes
n_values = opts.n_values
batch_size = sender_input.size(0)
receiver_output = receiver_output.view(batch_size * n_attributes,
n_values)
receiver_guesses = receiver_output.argmax(dim=1)
correct_samples = ((receiver_guesses == labels.view(-1)).view(
batch_size, n_attributes).detach())
acc = (torch.sum(correct_samples, dim=-1) == n_attributes).float()
labels = labels.view(batch_size * n_attributes)
loss = F.cross_entropy(receiver_output, labels, reduction="none")
loss = loss.view(batch_size, -1).mean(dim=1)
return loss, {"acc": acc}
# again, see data_readers.py in this directory for the AttValRecoDataset data reading class
train_loader = DataLoader(
AttValRecoDataset(
path=opts.train_data,
n_attributes=opts.n_attributes,
n_values=opts.n_values,
),
batch_size=opts.batch_size,
shuffle=True,
num_workers=1,
)
test_loader = DataLoader(
AttValRecoDataset(
path=opts.validation_data,
n_attributes=opts.n_attributes,
n_values=opts.n_values,
),
batch_size=opts.validation_batch_size,
shuffle=False,
num_workers=1,
)
# the number of features for the Receiver (input) and the Sender (output) is given by n_attributes*n_values because
# they are fed/produce 1-hot representations of the input vectors
n_features = opts.n_attributes * opts.n_values
# we define here the core of the receiver for the discriminative game, see the architectures.py file for details
# this will be embedded in a wrapper below to define the full architecture
receiver = RecoReceiver(n_features=n_features,
n_hidden=opts.receiver_hidden)
# we are now outside the block that defined game-type-specific aspects of the games: note that the core Sender architecture
# (see architectures.py for details) is shared by the two games (it maps an input vector to a hidden layer that will be use to initialize
# the message-producing RNN): this will also be embedded in a wrapper below to define the full architecture
sender = Sender(n_hidden=opts.sender_hidden, n_features=n_features)
# now, we instantiate the full sender and receiver architectures, and connect them and the loss into a game object
# the implementation differs slightly depending on whether communication is optimized via Gumbel-Softmax ('gs') or Reinforce ('rf', default)
if opts.mode.lower() == "gs":
# in the following lines, we embed the Sender and Receiver architectures into standard EGG wrappers that are appropriate for Gumbel-Softmax optimization
# the Sender wrapper takes the hidden layer produced by the core agent architecture we defined above when processing input, and uses it to initialize
# the RNN that generates the message
sender = core.RnnSenderGS(
sender,
vocab_size=opts.vocab_size,
embed_dim=opts.sender_embedding,
hidden_size=opts.sender_hidden,
cell=opts.sender_cell,
max_len=opts.max_len,
temperature=opts.temperature,
)
# the Receiver wrapper takes the symbol produced by the Sender at each step (more precisely, in Gumbel-Softmax mode, a function of the overall probability
# of non-eos symbols upt to the step is used), maps it to a hidden layer through a RNN, and feeds this hidden layer to the
# core Receiver architecture we defined above (possibly with other Receiver input, as determined by the core architecture) to generate the output
receiver = core.RnnReceiverGS(
receiver,
vocab_size=opts.vocab_size,
embed_dim=opts.receiver_embedding,
hidden_size=opts.receiver_hidden,
cell=opts.receiver_cell,
)
game = core.SenderReceiverRnnGS(sender, receiver, loss)
# callback functions can be passed to the trainer object (see below) to operate at certain steps of training and validation
# for example, the TemperatureUpdater (defined in callbacks.py in the core directory) will update the Gumbel-Softmax temperature hyperparameter
# after each epoch
callbacks = [
core.TemperatureUpdater(agent=sender, decay=0.9, minimum=0.1)
]
else: # NB: any other string than gs will lead to rf training!
# here, the interesting thing to note is that we use the same core architectures we defined above, but now we embed them in wrappers that are suited to
# Reinforce-based optmization
sender = core.RnnSenderReinforce(
sender,
vocab_size=opts.vocab_size,
embed_dim=opts.sender_embedding,
hidden_size=opts.sender_hidden,
cell=opts.sender_cell,
max_len=opts.max_len,
)
receiver = core.RnnReceiverDeterministic(
receiver,
vocab_size=opts.vocab_size,
embed_dim=opts.receiver_embedding,
hidden_size=opts.receiver_hidden,
cell=opts.receiver_cell,
)
game = core.SenderReceiverRnnReinforce(
sender,
receiver,
loss,
sender_entropy_coeff=opts.sender_entropy_coeff,
receiver_entropy_coeff=0,
)
callbacks = []
# we are almost ready to train: we define here an optimizer calling standard pytorch functionality
optimizer = core.build_optimizer(game.parameters())
# in the following statement, we finally instantiate the trainer object with all the components we defined (the game, the optimizer, the data
# and the callbacks)
if opts.print_validation_events == True:
# we add a callback that will print loss and accuracy after each training and validation pass (see ConsoleLogger in callbacks.py in core directory)
# if requested by the user, we will also print a detailed log of the validation pass after full training: look at PrintValidationEvents in
# language_analysis.py (core directory)
trainer = core.Trainer(
game=game,
optimizer=optimizer,
train_data=train_loader,
validation_data=test_loader,
callbacks=callbacks + [
core.ConsoleLogger(print_train_loss=True, as_json=True),
core.PrintValidationEvents(n_epochs=opts.n_epochs),
],
)
else:
trainer = core.Trainer(
game=game,
optimizer=optimizer,
train_data=train_loader,
validation_data=test_loader,
callbacks=callbacks +
[core.ConsoleLogger(print_train_loss=True, as_json=True)],
)
# and finally we train!
trainer.train(n_epochs=opts.n_epochs)
def main(params):
opts = get_params(params)
print(opts)
device = opts.device
n_a, n_v = opts.n_a, opts.n_v
opts.vocab_size = n_v
train_data = AttributeValueData(n_attributes=n_a,
n_values=n_v,
mul=1,
mode='train')
train_loader = DataLoader(train_data,
batch_size=opts.batch_size,
shuffle=True)
test_data = AttributeValueData(n_attributes=n_a,
n_values=n_v,
mul=1,
mode='test')
test_loader = DataLoader(test_data,
batch_size=opts.batch_size,
shuffle=False)
print(f'# Size of train {len(train_data)} test {len(test_data)}')
if opts.language == 'identity':
sender = IdentitySender(n_a, n_v)
elif opts.language == 'rotated':
sender = RotatedSender(n_a, n_v)
else:
assert False
receiver = Receiver(n_hidden=opts.receiver_hidden,
n_dim=n_a * n_v,
inner_layers=opts.receiver_layers)
receiver = core.RnnReceiverDeterministic(
receiver,
opts.vocab_size + 1, # exclude eos = 0
opts.receiver_emb,
opts.receiver_hidden,
cell=opts.receiver_cell,
num_layers=opts.cell_layers)
diff_loss = DiffLoss(n_a, n_v, loss_type=opts.loss_type)
game = core.SenderReceiverRnnReinforce(sender,
receiver,
diff_loss,
receiver_entropy_coeff=0.05,
sender_entropy_coeff=0.0)
optimizer = core.build_optimizer(receiver.parameters())
loss = game.loss
early_stopper = core.EarlyStopperAccuracy(1.0, validation=False)
trainer = core.Trainer(game=game,
optimizer=optimizer,
train_data=train_loader,
validation_data=test_loader,
callbacks=[
core.ConsoleLogger(as_json=True,
print_train_loss=True),
early_stopper
],
grad_norm=1.0)
trainer.train(n_epochs=opts.n_epochs)
core.close()
def main(params):
opts = get_params(params)
print(opts, flush=True)
device = opts.device
force_eos = opts.force_eos == 1
if opts.probs == 'uniform':
probs = np.ones(opts.n_features)
elif opts.probs == 'powerlaw':
probs = 1 / np.arange(1, opts.n_features + 1, dtype=np.float32)
else:
probs = np.array([float(x) for x in opts.probs.split(',')],
dtype=np.float32)
probs /= probs.sum()
train_loader = OneHotLoader(n_features=opts.n_features,
batch_size=opts.batch_size,
batches_per_epoch=opts.batches_per_epoch,
probs=probs)
# single batches with 1s on the diag
test_loader = UniformLoader(opts.n_features)
if opts.sender_cell == 'transformer':
sender = Sender(n_features=opts.n_features,
n_hidden=opts.sender_embedding)
sender = core.TransformerSenderReinforce(
agent=sender,
vocab_size=opts.vocab_size,
embed_dim=opts.sender_embedding,
max_len=opts.max_len,
num_layers=opts.sender_num_layers,
num_heads=opts.sender_num_heads,
hidden_size=opts.sender_hidden,
force_eos=opts.force_eos,
generate_style=opts.sender_generate_style,
causal=opts.causal_sender)
else:
sender = Sender(n_features=opts.n_features,
n_hidden=opts.sender_hidden)
sender = core.RnnSenderReinforce(sender,
opts.vocab_size,
opts.sender_embedding,
opts.sender_hidden,
cell=opts.sender_cell,
max_len=opts.max_len,
num_layers=opts.sender_num_layers,
force_eos=force_eos)
if opts.receiver_cell == 'transformer':
receiver = Receiver(n_features=opts.n_features,
n_hidden=opts.receiver_embedding)
receiver = core.TransformerReceiverDeterministic(
receiver,
opts.vocab_size,
opts.max_len,
opts.receiver_embedding,
opts.receiver_num_heads,
opts.receiver_hidden,
opts.receiver_num_layers,
causal=opts.causal_receiver)
else:
receiver = Receiver(n_features=opts.n_features,
n_hidden=opts.receiver_hidden)
if not opts.impatient:
receiver = Receiver(n_features=opts.n_features,
n_hidden=opts.receiver_hidden)
receiver = core.RnnReceiverDeterministic(
receiver,
opts.vocab_size,
opts.receiver_embedding,
opts.receiver_hidden,
cell=opts.receiver_cell,
num_layers=opts.receiver_num_layers)
else:
receiver = Receiver(n_features=opts.receiver_hidden,
n_hidden=opts.vocab_size)
# If impatient 1
receiver = RnnReceiverImpatient(
receiver,
opts.vocab_size,
opts.receiver_embedding,
opts.receiver_hidden,
cell=opts.receiver_cell,
num_layers=opts.receiver_num_layers,
max_len=opts.max_len,
n_features=opts.n_features)
sender.load_state_dict(
torch.load(opts.sender_weights, map_location=torch.device('cpu')))
receiver.load_state_dict(
torch.load(opts.receiver_weights, map_location=torch.device('cpu')))
if not opts.impatient:
game = core.SenderReceiverRnnReinforce(
sender,
receiver,
loss,
sender_entropy_coeff=opts.sender_entropy_coeff,
receiver_entropy_coeff=opts.receiver_entropy_coeff,
length_cost=opts.length_cost,
unigram_penalty=opts.unigram_pen)
else:
game = SenderImpatientReceiverRnnReinforce(
sender,
receiver,
loss,
sender_entropy_coeff=opts.sender_entropy_coeff,
receiver_entropy_coeff=opts.receiver_entropy_coeff,
length_cost=opts.length_cost,
unigram_penalty=opts.unigram_pen)
optimizer = core.build_optimizer(game.parameters())
trainer = core.Trainer(
game=game,
optimizer=optimizer,
train_data=train_loader,
validation_data=test_loader,
callbacks=[EarlyStopperAccuracy(opts.early_stopping_thr)])
# Debut test position
position_sieve = np.zeros((opts.n_features, opts.max_len))
for position in range(opts.max_len):
dataset = [[torch.eye(opts.n_features).to(device), None]]
if opts.impatient:
sender_inputs, messages, receiver_inputs, receiver_outputs, _ = \
dump_test_position_impatient(trainer.game,
dataset,
position=position,
voc_size=opts.vocab_size,
gs=False,
device=device,
variable_length=True)
else:
sender_inputs, messages, receiver_inputs, receiver_outputs, _ = \
dump_test_position(trainer.game,
dataset,
position=position,
voc_size=opts.vocab_size,
gs=False,
device=device,
variable_length=True)
acc_pos = []
for sender_input, message, receiver_output in zip(
sender_inputs, messages, receiver_outputs):
input_symbol = sender_input.argmax()
output_symbol = receiver_output.argmax()
acc = (input_symbol == output_symbol).float().item()
acc_pos.append(acc)
acc_pos = np.array(acc_pos)
position_sieve[:, position] = acc_pos
# Put -1 for position after message_length
_, messages = dump(trainer.game, opts.n_features, device, False)
# Convert messages to numpy array
messages_np = []
for x in messages:
x = x.cpu().numpy()
messages_np.append(x)
for i in range(len(messages_np)):
# Message i
message_i = messages_np[i]
id_0 = np.where(message_i == 0)[0]
if id_0.shape[0] > 0:
for j in range(id_0[0] + 1, opts.max_len):
position_sieve[i, j] = -1
np.save("analysis/position_sieve.npy", position_sieve)
core.close()
def main(params):
opts = get_params(params)
print(opts, flush=True)
device = opts.device
force_eos = opts.force_eos == 1
if opts.probs == 'uniform':
probs = np.ones(opts.n_features)
elif opts.probs == 'powerlaw':
probs = 1 / np.arange(1, opts.n_features+1, dtype=np.float32)
elif opts.probs == 'perso':
probs = opts.n_features+1 - np.arange(1, opts.n_features+1, dtype=np.float32)
else:
probs = np.array([float(x) for x in opts.probs.split(',')], dtype=np.float32)
probs /= probs.sum()
print('the probs are: ', probs, flush=True)
train_loader = OneHotLoader(n_features=opts.n_features, batch_size=opts.batch_size,
batches_per_epoch=opts.batches_per_epoch, probs=probs)
# single batches with 1s on the diag
test_loader = UniformLoader(opts.n_features)
if opts.sender_cell == 'transformer':
sender = Sender(n_features=opts.n_features, n_hidden=opts.sender_embedding)
sender = core.TransformerSenderReinforce(agent=sender, vocab_size=opts.vocab_size,
embed_dim=opts.sender_embedding, max_len=opts.max_len,
num_layers=opts.sender_num_layers, num_heads=opts.sender_num_heads,
hidden_size=opts.sender_hidden,
force_eos=opts.force_eos,
generate_style=opts.sender_generate_style,
causal=opts.causal_sender)
else:
sender = Sender(n_features=opts.n_features, n_hidden=opts.sender_hidden)
sender = core.RnnSenderReinforce(sender,
opts.vocab_size, opts.sender_embedding, opts.sender_hidden,
cell=opts.sender_cell, max_len=opts.max_len, num_layers=opts.sender_num_layers,
force_eos=force_eos)
if opts.receiver_cell == 'transformer':
receiver = Receiver(n_features=opts.n_features, n_hidden=opts.receiver_embedding)
receiver = core.TransformerReceiverDeterministic(receiver, opts.vocab_size, opts.max_len,
opts.receiver_embedding, opts.receiver_num_heads, opts.receiver_hidden,
opts.receiver_num_layers, causal=opts.causal_receiver)
else:
receiver = Receiver(n_features=opts.n_features, n_hidden=opts.receiver_hidden)
receiver = core.RnnReceiverDeterministic(receiver, opts.vocab_size, opts.receiver_embedding,
opts.receiver_hidden, cell=opts.receiver_cell,
num_layers=opts.receiver_num_layers)
game = core.SenderReceiverRnnReinforce(sender, receiver, loss, sender_entropy_coeff=opts.sender_entropy_coeff,
receiver_entropy_coeff=opts.receiver_entropy_coeff,
length_cost=opts.length_cost)
optimizer = core.build_optimizer(game.parameters())
trainer = core.Trainer(game=game, optimizer=optimizer, train_data=train_loader,
validation_data=test_loader,
callbacks=[EarlyStopperAccuracy(opts.early_stopping_thr),
core.ConsoleLogger(as_json=True, print_train_loss=True)])
""" mode accuracy chope a chaque epoch
accs=[]
all_messages,acc=dump(trainer.game, opts.n_features, device, False)
np.save('messages_0.npy', all_messages)
accs.append(acc)
for i in range(int(opts.n_epochs)):
print(i)
trainer.train(n_epochs=1)
all_messages,acc=dump(trainer.game, opts.n_features, device, False)
np.save('messages_'+str((i+1))+'.npy', all_messages)
accs.append(acc)
np.save('accuracy.npy',accs)
"""
trainer.train(n_epochs=opts.n_epochs)
#if opts.checkpoint_dir:
#trainer.save_checkpoint(name=f'{opts.name}_vocab{opts.vocab_size}_rs{opts.random_seed}_lr{opts.lr}_shid{opts.sender_hidden}_rhid{opts.receiver_hidden}_sentr{opts.sender_entropy_coeff}_reg{opts.length_cost}_max_len{opts.max_len}')
#for i in range(30):
# for k in range(30):
# if i<k:
# all_messages=dump(trainer.game, opts.n_features, device, False,pos_m=i,pos_M=k)
all_messages=dump(trainer.game, opts.n_features, device, False)
print(all_messages)
#freq=np.zeros(30)
#for message in all_messages[0]:
# if i in range(message.shape[0]):
# freq[int(message[i])]+=1
#print(freq)
core.close()
def main(params):
opts = get_params(params)
print(opts)
device = opts.device
train_loader = OneHotLoader(n_bits=opts.n_bits,
bits_s=opts.bits_s,
bits_r=opts.bits_r,
batch_size=opts.batch_size,
batches_per_epoch=opts.n_examples_per_epoch /
opts.batch_size)
test_loader = UniformLoader(n_bits=opts.n_bits,
bits_s=opts.bits_s,
bits_r=opts.bits_r)
test_loader.batch = [x.to(device) for x in test_loader.batch]
if not opts.variable_length:
sender = Sender(n_bits=opts.n_bits,
n_hidden=opts.sender_hidden,
vocab_size=opts.vocab_size)
if opts.mode == 'gs':
sender = core.GumbelSoftmaxWrapper(agent=sender,
temperature=opts.temperature)
receiver = Receiver(n_bits=opts.n_bits,
n_hidden=opts.receiver_hidden)
receiver = core.SymbolReceiverWrapper(
receiver,
vocab_size=opts.vocab_size,
agent_input_size=opts.receiver_hidden)
game = core.SymbolGameGS(sender, receiver, diff_loss)
elif opts.mode == 'rf':
sender = core.ReinforceWrapper(agent=sender)
receiver = Receiver(n_bits=opts.n_bits,
n_hidden=opts.receiver_hidden)
receiver = core.SymbolReceiverWrapper(
receiver,
vocab_size=opts.vocab_size,
agent_input_size=opts.receiver_hidden)
receiver = core.ReinforceDeterministicWrapper(agent=receiver)
game = core.SymbolGameReinforce(
sender,
receiver,
diff_loss,
sender_entropy_coeff=opts.sender_entropy_coeff)
elif opts.mode == 'non_diff':
sender = core.ReinforceWrapper(agent=sender)
receiver = ReinforcedReceiver(n_bits=opts.n_bits,
n_hidden=opts.receiver_hidden)
receiver = core.SymbolReceiverWrapper(
receiver,
vocab_size=opts.vocab_size,
agent_input_size=opts.receiver_hidden)
game = core.SymbolGameReinforce(
sender,
receiver,
non_diff_loss,
sender_entropy_coeff=opts.sender_entropy_coeff,
receiver_entropy_coeff=opts.receiver_entropy_coeff)
else:
if opts.mode != 'rf':
print('Only mode=rf is supported atm')
opts.mode = 'rf'
if opts.sender_cell == 'transformer':
receiver = Receiver(n_bits=opts.n_bits,
n_hidden=opts.receiver_hidden)
sender = Sender(
n_bits=opts.n_bits,
n_hidden=opts.sender_hidden,
vocab_size=opts.sender_hidden) # TODO: not really vocab
sender = core.TransformerSenderReinforce(
agent=sender,
vocab_size=opts.vocab_size,
embed_dim=opts.sender_emb,
max_len=opts.max_len,
num_layers=1,
num_heads=1,
hidden_size=opts.sender_hidden)
else:
receiver = Receiver(n_bits=opts.n_bits,
n_hidden=opts.receiver_hidden)
sender = Sender(
n_bits=opts.n_bits,
n_hidden=opts.sender_hidden,
vocab_size=opts.sender_hidden) # TODO: not really vocab
sender = core.RnnSenderReinforce(agent=sender,
vocab_size=opts.vocab_size,
embed_dim=opts.sender_emb,
hidden_size=opts.sender_hidden,
max_len=opts.max_len,
cell=opts.sender_cell)
if opts.receiver_cell == 'transformer':
receiver = Receiver(n_bits=opts.n_bits, n_hidden=opts.receiver_emb)
receiver = core.TransformerReceiverDeterministic(
receiver,
opts.vocab_size,
opts.max_len,
opts.receiver_emb,
num_heads=1,
hidden_size=opts.receiver_hidden,
num_layers=1)
else:
receiver = Receiver(n_bits=opts.n_bits,
n_hidden=opts.receiver_hidden)
receiver = core.RnnReceiverDeterministic(receiver,
opts.vocab_size,
opts.receiver_emb,
opts.receiver_hidden,
cell=opts.receiver_cell)
game = core.SenderReceiverRnnGS(sender, receiver, diff_loss)
game = core.SenderReceiverRnnReinforce(
sender,
receiver,
diff_loss,
sender_entropy_coeff=opts.sender_entropy_coeff,
receiver_entropy_coeff=opts.receiver_entropy_coeff)
optimizer = torch.optim.Adam([
dict(params=sender.parameters(), lr=opts.sender_lr),
dict(params=receiver.parameters(), lr=opts.receiver_lr)
])
loss = game.loss
intervention = CallbackEvaluator(test_loader,
device=device,
is_gs=opts.mode == 'gs',
loss=loss,
var_length=opts.variable_length,
input_intervention=True)
trainer = core.Trainer(game=game,
optimizer=optimizer,
train_data=train_loader,
validation_data=test_loader,
callbacks=[
core.ConsoleLogger(as_json=True),
EarlyStopperAccuracy(opts.early_stopping_thr),
intervention
])
trainer.train(n_epochs=opts.n_epochs)
core.close()
def main(params):
opts = get_params(params)
device = torch.device("cuda" if opts.cuda else "cpu")
train_loader = OneHotLoader(n_features=opts.n_features,
batch_size=opts.batch_size,
batches_per_epoch=opts.batches_per_epoch)
test_loader = OneHotLoader(n_features=opts.n_features,
batch_size=opts.batch_size,
batches_per_epoch=opts.batches_per_epoch,
seed=7)
sender = Sender(n_hidden=opts.sender_hidden, n_features=opts.n_features)
receiver = Receiver(n_features=opts.n_features,
n_hidden=opts.receiver_hidden)
if opts.mode.lower() == 'rf':
sender = core.RnnSenderReinforce(sender,
opts.vocab_size,
opts.sender_embedding,
opts.sender_hidden,
cell=opts.sender_cell,
max_len=opts.max_len)
receiver = core.RnnReceiverDeterministic(receiver,
opts.vocab_size,
opts.receiver_embedding,
opts.receiver_hidden,
cell=opts.receiver_cell)
game = core.SenderReceiverRnnReinforce(
sender,
receiver,
loss,
sender_entropy_coeff=opts.sender_entropy_coeff,
receiver_entropy_coeff=opts.receiver_entropy_coeff)
callbacks = []
elif opts.mode.lower() == 'gs':
sender = core.RnnSenderGS(sender,
opts.vocab_size,
opts.sender_embedding,
opts.sender_hidden,
cell=opts.sender_cell,
max_len=opts.max_len,
temperature=opts.temperature)
receiver = core.RnnReceiverGS(receiver,
opts.vocab_size,
opts.receiver_embedding,
opts.receiver_hidden,
cell=opts.receiver_cell)
game = core.SenderReceiverRnnGS(sender, receiver, loss)
callbacks = [
core.TemperatureUpdater(agent=sender, decay=0.9, minimum=0.1)
]
else:
raise NotImplementedError(f'Unknown training mode, {opts.mode}')
optimizer = torch.optim.Adam([{
'params': game.sender.parameters(),
'lr': opts.sender_lr
}, {
'params': game.receiver.parameters(),
'lr': opts.receiver_lr
}])
trainer = core.Trainer(game=game,
optimizer=optimizer,
train_data=train_loader,
validation_data=test_loader,
callbacks=callbacks +
[core.ConsoleLogger(as_json=True)])
trainer.train(n_epochs=opts.n_epochs)
core.close()
def main(params):
opts = get_params(params)
print(opts, flush=True)
# For compatibility, after https://github.com/facebookresearch/EGG/pull/130
# the meaning of `length` changed a bit. Before it included the EOS symbol; now
# it doesn't. To ensure that hyperparameters/CL arguments do not change,
# we subtract it here.
opts.max_len -= 1
device = opts.device
if opts.probs == "uniform":
probs = np.ones(opts.n_features)
elif opts.probs == "powerlaw":
probs = 1 / np.arange(1, opts.n_features + 1, dtype=np.float32)
else:
probs = np.array([float(x) for x in opts.probs.split(",")], dtype=np.float32)
probs /= probs.sum()
print("the probs are: ", probs, flush=True)
train_loader = OneHotLoader(
n_features=opts.n_features,
batch_size=opts.batch_size,
batches_per_epoch=opts.batches_per_epoch,
probs=probs,
)
# single batches with 1s on the diag
test_loader = UniformLoader(opts.n_features)
if opts.sender_cell == "transformer":
sender = Sender(n_features=opts.n_features, n_hidden=opts.sender_embedding)
sender = core.TransformerSenderReinforce(
agent=sender,
vocab_size=opts.vocab_size,
embed_dim=opts.sender_embedding,
max_len=opts.max_len,
num_layers=opts.sender_num_layers,
num_heads=opts.sender_num_heads,
hidden_size=opts.sender_hidden,
generate_style=opts.sender_generate_style,
causal=opts.causal_sender,
)
else:
sender = Sender(n_features=opts.n_features, n_hidden=opts.sender_hidden)
sender = core.RnnSenderReinforce(
sender,
opts.vocab_size,
opts.sender_embedding,
opts.sender_hidden,
cell=opts.sender_cell,
max_len=opts.max_len,
num_layers=opts.sender_num_layers,
)
if opts.receiver_cell == "transformer":
receiver = Receiver(
n_features=opts.n_features, n_hidden=opts.receiver_embedding
)
receiver = core.TransformerReceiverDeterministic(
receiver,
opts.vocab_size,
opts.max_len,
opts.receiver_embedding,
opts.receiver_num_heads,
opts.receiver_hidden,
opts.receiver_num_layers,
causal=opts.causal_receiver,
)
else:
receiver = Receiver(n_features=opts.n_features, n_hidden=opts.receiver_hidden)
receiver = core.RnnReceiverDeterministic(
receiver,
opts.vocab_size,
opts.receiver_embedding,
opts.receiver_hidden,
cell=opts.receiver_cell,
num_layers=opts.receiver_num_layers,
)
empty_logger = LoggingStrategy.minimal()
game = core.SenderReceiverRnnReinforce(
sender,
receiver,
loss,
sender_entropy_coeff=opts.sender_entropy_coeff,
receiver_entropy_coeff=opts.receiver_entropy_coeff,
train_logging_strategy=empty_logger,
length_cost=opts.length_cost,
)
optimizer = core.build_optimizer(game.parameters())
callbacks = [
EarlyStopperAccuracy(opts.early_stopping_thr),
core.ConsoleLogger(as_json=True, print_train_loss=True),
]
if opts.checkpoint_dir:
checkpoint_name = f"{opts.name}_vocab{opts.vocab_size}_rs{opts.random_seed}_lr{opts.lr}_shid{opts.sender_hidden}_rhid{opts.receiver_hidden}_sentr{opts.sender_entropy_coeff}_reg{opts.length_cost}_max_len{opts.max_len}"
callbacks.append(
core.CheckpointSaver(
checkpoint_path=opts.checkpoint_dir, prefix=checkpoint_name
)
)
trainer = core.Trainer(
game=game,
optimizer=optimizer,
train_data=train_loader,
validation_data=test_loader,
callbacks=callbacks,
)
trainer.train(n_epochs=opts.n_epochs)
game.logging_strategy = LoggingStrategy.maximal() # now log everything
dump(trainer.game, opts.n_features, device, False)
core.close()
shuffle=True)
test_loader = DataLoader(test,
batch_size=opts.batch_size,
drop_last=False,
shuffle=False)
sender = core.RnnSenderReinforce(agent=Sender(opts.n_features * 2,
opts.sender_hidden),
vocab_size=opts.vocab_size,
embed_dim=opts.sender_embedding,
hidden_size=opts.sender_hidden,
max_len=opts.max_len,
force_eos=True,
cell=opts.rnn_cell)
receiver = core.RnnReceiverDeterministic(agent=Receiver(
opts.n_features * 2, opts.receiver_hidden),
vocab_size=opts.vocab_size,
embed_dim=opts.receiver_embedding,
hidden_size=opts.receiver_hidden,
cell=opts.rnn_cell)
neptune.init('tomekkorbak/template-transfer2')
with neptune.create_experiment(params=vars(opts),
upload_source_files=get_filepaths(),
tags=['buffled_berkeley']) as experiment:
print(os.environ)
compositional_game = core.SenderReceiverRnnReinforce(
sender,
receiver,
loss,
sender_entropy_coeff=opts.sender_entropy_coeff,
receiver_entropy_coeff=opts.receiver_entropy_coeff,
length_cost=opts.length_cost)
def main(params):
import copy
opts = get_params(params)
device = opts.device
full_data = enumerate_attribute_value(opts.n_attributes, opts.n_values)
if opts.density_data > 0:
sampled_data = select_subset_V2(
full_data, opts.density_data, opts.n_attributes, opts.n_values
)
full_data = copy.deepcopy(sampled_data)
train, generalization_holdout = split_holdout(full_data)
train, uniform_holdout = split_train_test(train, 0.1)
generalization_holdout, train, uniform_holdout, full_data = [
one_hotify(x, opts.n_attributes, opts.n_values)
for x in [generalization_holdout, train, uniform_holdout, full_data]
]
train, validation = ScaledDataset(train, opts.data_scaler), ScaledDataset(train, 1)
generalization_holdout, uniform_holdout, full_data = (
ScaledDataset(generalization_holdout),
ScaledDataset(uniform_holdout),
ScaledDataset(full_data),
)
generalization_holdout_loader, uniform_holdout_loader, full_data_loader = [
DataLoader(x, batch_size=opts.batch_size)
for x in [generalization_holdout, uniform_holdout, full_data]
]
train_loader = DataLoader(train, batch_size=opts.batch_size)
validation_loader = DataLoader(validation, batch_size=len(validation))
n_dim = opts.n_attributes * opts.n_values
if opts.receiver_cell in ["lstm", "rnn", "gru"]:
receiver = Receiver(n_hidden=opts.receiver_hidden, n_outputs=n_dim)
receiver = core.RnnReceiverDeterministic(
receiver,
opts.vocab_size + 1,
opts.receiver_emb,
opts.receiver_hidden,
cell=opts.receiver_cell,
)
else:
raise ValueError(f"Unknown receiver cell, {opts.receiver_cell}")
if opts.sender_cell in ["lstm", "rnn", "gru"]:
sender = Sender(n_inputs=n_dim, n_hidden=opts.sender_hidden)
sender = core.RnnSenderReinforce(
agent=sender,
vocab_size=opts.vocab_size,
embed_dim=opts.sender_emb,
hidden_size=opts.sender_hidden,
max_len=opts.max_len,
cell=opts.sender_cell,
)
else:
raise ValueError(f"Unknown sender cell, {opts.sender_cell}")
sender = PlusOneWrapper(sender)
loss = DiffLoss(opts.n_attributes, opts.n_values)
baseline = {
"no": core.baselines.NoBaseline,
"mean": core.baselines.MeanBaseline,
"builtin": core.baselines.BuiltInBaseline,
}[opts.baseline]
game = core.SenderReceiverRnnReinforce(
sender,
receiver,
loss,
sender_entropy_coeff=opts.sender_entropy_coeff,
receiver_entropy_coeff=0.0,
length_cost=0.0,
baseline_type=baseline,
)
optimizer = torch.optim.Adam(game.parameters(), lr=opts.lr)
metrics_evaluator = Metrics(
validation.examples,
opts.device,
opts.n_attributes,
opts.n_values,
opts.vocab_size + 1,
freq=opts.stats_freq,
)
loaders = []
loaders.append(
(
"generalization hold out",
generalization_holdout_loader,
DiffLoss(opts.n_attributes, opts.n_values, generalization=True),
)
)
loaders.append(
(
"uniform holdout",
uniform_holdout_loader,
DiffLoss(opts.n_attributes, opts.n_values),
)
)
holdout_evaluator = Evaluator(loaders, opts.device, freq=0)
early_stopper = EarlyStopperAccuracy(opts.early_stopping_thr, validation=True)
trainer = core.Trainer(
game=game,
optimizer=optimizer,
train_data=train_loader,
validation_data=validation_loader,
callbacks=[
core.ConsoleLogger(as_json=True, print_train_loss=False),
early_stopper,
metrics_evaluator,
holdout_evaluator,
],
)
trainer.train(n_epochs=opts.n_epochs)
last_epoch_interaction = early_stopper.validation_stats[-1][1]
validation_acc = last_epoch_interaction.aux["acc"].mean()
uniformtest_acc = holdout_evaluator.results["uniform holdout"]["acc"]
# Train new agents
if validation_acc > 0.99:
def _set_seed(seed):
import random
import numpy as np
random.seed(seed)
torch.manual_seed(seed)
np.random.seed(seed)
if torch.cuda.is_available():
torch.cuda.manual_seed_all(seed)
core.get_opts().preemptable = False
core.get_opts().checkpoint_path = None
# freeze Sender and probe how fast a simple Receiver will learn the thing
def retrain_receiver(receiver_generator, sender):
receiver = receiver_generator()
game = core.SenderReceiverRnnReinforce(
sender,
receiver,
loss,
sender_entropy_coeff=0.0,
receiver_entropy_coeff=0.0,
)
optimizer = torch.optim.Adam(receiver.parameters(), lr=opts.lr)
early_stopper = EarlyStopperAccuracy(
opts.early_stopping_thr, validation=True
)
trainer = core.Trainer(
game=game,
optimizer=optimizer,
train_data=train_loader,
validation_data=validation_loader,
callbacks=[early_stopper, Evaluator(loaders, opts.device, freq=0)],
)
trainer.train(n_epochs=opts.n_epochs // 2)
accs = [x[1]["acc"] for x in early_stopper.validation_stats]
return accs
frozen_sender = Freezer(copy.deepcopy(sender))
def gru_receiver_generator():
return core.RnnReceiverDeterministic(
Receiver(n_hidden=opts.receiver_hidden, n_outputs=n_dim),
opts.vocab_size + 1,
opts.receiver_emb,
hidden_size=opts.receiver_hidden,
cell="gru",
)
def small_gru_receiver_generator():
return core.RnnReceiverDeterministic(
Receiver(n_hidden=100, n_outputs=n_dim),
opts.vocab_size + 1,
opts.receiver_emb,
hidden_size=100,
cell="gru",
)
def tiny_gru_receiver_generator():
return core.RnnReceiverDeterministic(
Receiver(n_hidden=50, n_outputs=n_dim),
opts.vocab_size + 1,
opts.receiver_emb,
hidden_size=50,
cell="gru",
)
def nonlinear_receiver_generator():
return NonLinearReceiver(
n_outputs=n_dim,
vocab_size=opts.vocab_size + 1,
max_length=opts.max_len,
n_hidden=opts.receiver_hidden,
)
for name, receiver_generator in [
("gru", gru_receiver_generator),
("nonlinear", nonlinear_receiver_generator),
("tiny_gru", tiny_gru_receiver_generator),
("small_gru", small_gru_receiver_generator),
]:
for seed in range(17, 17 + 3):
_set_seed(seed)
accs = retrain_receiver(receiver_generator, frozen_sender)
accs += [1.0] * (opts.n_epochs // 2 - len(accs))
auc = sum(accs)
print(
json.dumps(
{
"mode": "reset",
"seed": seed,
"receiver_name": name,
"auc": auc,
}
)
)
print("---End--")
core.close()
def main(params):
opts = get_params(params)
print(opts, flush=True)
device = opts.device
force_eos = opts.force_eos == 1
if opts.probs == 'uniform':
probs = np.ones(opts.n_features)
elif opts.probs == 'powerlaw':
probs = 1 / np.arange(1, opts.n_features+1, dtype=np.float32)
else:
probs = np.array([float(x) for x in opts.probs.split(',')], dtype=np.float32)
probs /= probs.sum()
train_loader = OneHotLoader(n_features=opts.n_features, batch_size=opts.batch_size,
batches_per_epoch=opts.batches_per_epoch, probs=probs)
# single batches with 1s on the diag
test_loader = UniformLoader(opts.n_features)
if opts.sender_cell == 'transformer':
sender = Sender(n_features=opts.n_features, n_hidden=opts.sender_embedding)
sender = core.TransformerSenderReinforce(agent=sender, vocab_size=opts.vocab_size,
embed_dim=opts.sender_embedding, max_len=opts.max_len,
num_layers=opts.sender_num_layers, num_heads=opts.sender_num_heads,
hidden_size=opts.sender_hidden,
force_eos=opts.force_eos,
generate_style=opts.sender_generate_style,
causal=opts.causal_sender)
else:
sender = Sender(n_features=opts.n_features, n_hidden=opts.sender_hidden)
sender = core.RnnSenderReinforce(sender,
opts.vocab_size, opts.sender_embedding, opts.sender_hidden,
cell=opts.sender_cell, max_len=opts.max_len, num_layers=opts.sender_num_layers,
force_eos=force_eos)
if opts.receiver_cell == 'transformer':
receiver = Receiver(n_features=opts.n_features, n_hidden=opts.receiver_embedding)
receiver = core.TransformerReceiverDeterministic(receiver, opts.vocab_size, opts.max_len,
opts.receiver_embedding, opts.receiver_num_heads, opts.receiver_hidden,
opts.receiver_num_layers, causal=opts.causal_receiver)
else:
receiver = Receiver(n_features=opts.n_features, n_hidden=opts.receiver_hidden)
if not opts.impatient:
receiver = Receiver(n_features=opts.n_features, n_hidden=opts.receiver_hidden)
receiver = core.RnnReceiverDeterministic(receiver, opts.vocab_size, opts.receiver_embedding,
opts.receiver_hidden, cell=opts.receiver_cell,
num_layers=opts.receiver_num_layers)
else:
receiver = Receiver(n_features=opts.receiver_hidden, n_hidden=opts.vocab_size)
# If impatient 1
receiver = RnnReceiverImpatient(receiver, opts.vocab_size, opts.receiver_embedding,
opts.receiver_hidden, cell=opts.receiver_cell,
num_layers=opts.receiver_num_layers, max_len=opts.max_len, n_features=opts.n_features)
# If impatient 2
#receiver = RnnReceiverImpatient2(receiver, opts.vocab_size, opts.receiver_embedding,
# opts.receiver_hidden, cell=opts.receiver_cell,
# num_layers=opts.receiver_num_layers, max_len=opts.max_len, n_features=opts.n_features)
sender.load_state_dict(torch.load(opts.sender_weights,map_location=torch.device('cpu')))
receiver.load_state_dict(torch.load(opts.receiver_weights,map_location=torch.device('cpu')))
if not opts.impatient:
game = core.SenderReceiverRnnReinforce(sender, receiver, loss, sender_entropy_coeff=opts.sender_entropy_coeff,
receiver_entropy_coeff=opts.receiver_entropy_coeff,
length_cost=opts.length_cost,unigram_penalty=opts.unigram_pen)
else:
game = SenderImpatientReceiverRnnReinforce(sender, receiver, loss, sender_entropy_coeff=opts.sender_entropy_coeff,
receiver_entropy_coeff=opts.receiver_entropy_coeff,
length_cost=opts.length_cost,unigram_penalty=opts.unigram_pen)
optimizer = core.build_optimizer(game.parameters())
trainer = core.Trainer(game=game, optimizer=optimizer, train_data=train_loader,
validation_data=test_loader, callbacks=[EarlyStopperAccuracy(opts.early_stopping_thr)])
# Test impose message
if not opts.impatient:
acc_vec,messages=dump(trainer.game, opts.n_features, device, False)
else:
acc_vec,messages=dump_impatient(trainer.game, opts.n_features, device, False,save_dir=opts.save_dir)
all_messages=[]
for x in messages:
x = x.cpu().numpy()
all_messages.append(x)
all_messages = np.asarray(all_messages)
messages=-1*np.ones((opts.n_features,opts.max_len))
for i in range(len(all_messages)):
for j in range(all_messages[i].shape[0]):
messages[i,j]=all_messages[i][j]
np.save(opts.save_dir+"messages_analysis.npy",messages)
core.close()
def main(params):
import copy
opts = get_params(params)
device = opts.device
train_loader, validation_loader = get_dsprites_dataloader(
path_to_data='egg/zoo/data_loaders/data/dsprites.npz',
batch_size=opts.batch_size,
subsample=opts.subsample,
image=False)
n_dim = opts.n_attributes
if opts.receiver_cell in ['lstm', 'rnn', 'gru']:
receiver = Receiver(n_hidden=opts.receiver_hidden, n_outputs=n_dim)
receiver = core.RnnReceiverDeterministic(receiver,
opts.vocab_size + 1,
opts.receiver_emb,
opts.receiver_hidden,
cell=opts.receiver_cell)
else:
raise ValueError(f'Unknown receiver cell, {opts.receiver_cell}')
if opts.sender_cell in ['lstm', 'rnn', 'gru']:
sender = Sender(n_inputs=n_dim, n_hidden=opts.sender_hidden)
sender = core.RnnSenderReinforce(agent=sender,
vocab_size=opts.vocab_size,
embed_dim=opts.sender_emb,
hidden_size=opts.sender_hidden,
max_len=opts.max_len,
force_eos=False,
cell=opts.sender_cell)
else:
raise ValueError(f'Unknown sender cell, {opts.sender_cell}')
sender = PlusOneWrapper(sender)
loss = DiffLoss(opts.n_attributes, opts.n_values)
baseline = {
'no': core.baselines.NoBaseline,
'mean': core.baselines.MeanBaseline,
'builtin': core.baselines.BuiltInBaseline
}[opts.baseline]
game = core.SenderReceiverRnnReinforce(
sender,
receiver,
loss,
sender_entropy_coeff=opts.sender_entropy_coeff,
receiver_entropy_coeff=0.0,
length_cost=0.0,
baseline_type=baseline)
optimizer = torch.optim.Adam(game.parameters(), lr=opts.lr)
latent_values, _ = zip(*[batch for batch in validation_loader])
latent_values = latent_values[0]
metrics_evaluator = Metrics(latent_values,
opts.device,
opts.n_attributes,
opts.n_values,
opts.vocab_size + 1,
freq=opts.stats_freq)
loaders = []
#loaders.append(("generalization hold out", generalization_holdout_loader, DiffLoss(
#opts.n_attributes, opts.n_values, generalization=True)))
loaders.append(("uniform holdout", validation_loader,
DiffLoss(opts.n_attributes, opts.n_values)))
holdout_evaluator = Evaluator(loaders, opts.device, freq=0)
early_stopper = EarlyStopperAccuracy(opts.early_stopping_thr,
validation=True)
trainer = core.Trainer(game=game,
optimizer=optimizer,
train_data=train_loader,
validation_data=validation_loader,
callbacks=[
core.ConsoleLogger(as_json=True,
print_train_loss=False),
early_stopper, metrics_evaluator,
holdout_evaluator
])
trainer.train(n_epochs=opts.n_epochs)
last_epoch_interaction = early_stopper.validation_stats[-1][1]
validation_acc = last_epoch_interaction.aux['acc'].mean()
uniformtest_acc = holdout_evaluator.results['uniform holdout']['acc']
# Train new agents
if validation_acc > 0.99:
def _set_seed(seed):
import random
import numpy as np
random.seed(seed)
torch.manual_seed(seed)
np.random.seed(seed)
if torch.cuda.is_available():
torch.cuda.manual_seed_all(seed)
core.get_opts().preemptable = False
core.get_opts().checkpoint_path = None
# freeze Sender and probe how fast a simple Receiver will learn the thing
def retrain_receiver(receiver_generator, sender):
receiver = receiver_generator()
game = core.SenderReceiverRnnReinforce(sender,
receiver,
loss,
sender_entropy_coeff=0.0,
receiver_entropy_coeff=0.0)
optimizer = torch.optim.Adam(receiver.parameters(), lr=opts.lr)
early_stopper = EarlyStopperAccuracy(opts.early_stopping_thr,
validation=True)
trainer = core.Trainer(game=game,
optimizer=optimizer,
train_data=train_loader,
validation_data=validation_loader,
callbacks=[
early_stopper,
Evaluator(loaders, opts.device, freq=0)
])
trainer.train(n_epochs=opts.n_epochs // 2)
accs = [x[1]['acc'] for x in early_stopper.validation_stats]
return accs
frozen_sender = Freezer(copy.deepcopy(sender))
def gru_receiver_generator(): return \
core.RnnReceiverDeterministic(Receiver(n_hidden=opts.receiver_hidden, n_outputs=n_dim),
opts.vocab_size + 1, opts.receiver_emb, hidden_size=opts.receiver_hidden, cell='gru')
def small_gru_receiver_generator(): return \
core.RnnReceiverDeterministic(
Receiver(n_hidden=100, n_outputs=n_dim),
opts.vocab_size + 1, opts.receiver_emb, hidden_size=100, cell='gru')
def tiny_gru_receiver_generator(): return \
core.RnnReceiverDeterministic(
Receiver(n_hidden=50, n_outputs=n_dim),
opts.vocab_size + 1, opts.receiver_emb, hidden_size=50, cell='gru')
def nonlinear_receiver_generator(): return \
NonLinearReceiver(n_outputs=n_dim, vocab_size=opts.vocab_size + 1,
max_length=opts.max_len, n_hidden=opts.receiver_hidden)
for name, receiver_generator in [
('gru', gru_receiver_generator),
('nonlinear', nonlinear_receiver_generator),
('tiny_gru', tiny_gru_receiver_generator),
('small_gru', small_gru_receiver_generator),
]:
for seed in range(17, 17 + 3):
_set_seed(seed)
accs = retrain_receiver(receiver_generator, frozen_sender)
accs += [1.0] * (opts.n_epochs // 2 - len(accs))
auc = sum(accs)
print(
json.dumps({
"mode": "reset",
"seed": seed,
"receiver_name": name,
"auc": auc
}))
print('---End--')
core.close()
