class Miner():
def __init__(self, config: Munch = None, **kwargs):
if config == None:
config = Miner.default_config()
bittensor.config.Config.update_with_kwargs(config.miner, kwargs)
Miner.check_config(config)
self.config = config
# ---- Neuron ----
self.neuron = bittensor.neuron.Neuron(self.config)
# ---- Model ----
self.model = FFNNSynapse(
config) # Feedforward neural network with PKMRouter.
self.device = torch.device(
"cuda" if torch.cuda.is_available() else "cpu")
self.model.to(self.device) # Set model to device
# ---- Optimizer ----
self.optimizer = optim.SGD(self.model.parameters(),
lr=self.config.miner.learning_rate,
momentum=self.config.miner.momentum)
self.scheduler = torch.optim.lr_scheduler.StepLR(self.optimizer,
step_size=10.0,
gamma=0.1)
# ---- Model Load/Save tools ----
self.model_toolbox = ModelToolbox(FFNNSynapse, optim.SGD)
# ---- Dataset ----
self.train_data = torchvision.datasets.MNIST(
root=self.config.miner.root_dir + "datasets/",
train=True,
download=True,
transform=transforms.ToTensor())
self.trainloader = torch.utils.data.DataLoader(
self.train_data,
batch_size=self.config.miner.batch_size_train,
shuffle=True,
num_workers=2)
self.test_data = torchvision.datasets.MNIST(
root=self.config.miner.root_dir + "datasets/",
train=False,
download=True,
transform=transforms.ToTensor())
self.testloader = torch.utils.data.DataLoader(
self.test_data,
batch_size=self.config.miner.batch_size_test,
shuffle=False,
num_workers=2)
# ---- Tensorboard ----
self.global_step = 0
self.tensorboard = SummaryWriter(log_dir=self.config.miner.full_path)
if self.config.miner.record_log:
logger.add(
self.config.miner.full_path + "/{}_{}.log".format(
self.config.miner.name, self.config.miner.trial_uid),
format="{time:YYYY-MM-DD at HH:mm:ss} | {level} | {message}")
@staticmethod
def default_config() -> Munch:
parser = argparse.ArgumentParser()
Miner.add_args(parser)
config = bittensor.config.Config.to_config(parser)
return config
@staticmethod
def add_args(parser: argparse.ArgumentParser):
parser.add_argument('--miner.learning_rate',
default=0.01,
type=float,
help='Training initial learning rate.')
parser.add_argument('--miner.momentum',
default=0.9,
type=float,
help='Training initial momentum for SGD.')
parser.add_argument('--miner.n_epochs',
default=int(sys.maxsize),
type=int,
help='Number of training epochs.')
parser.add_argument(
'--miner.epoch_length',
default=int(sys.maxsize),
type=int,
help='Iterations of training per epoch (or dataset EOF)')
parser.add_argument('--miner.batch_size_train',
default=64,
type=int,
help='Training batch size.')
parser.add_argument('--miner.batch_size_test',
default=64,
type=int,
help='Testing batch size.')
parser.add_argument('--miner.log_interval',
default=150,
type=int,
help='Batches until miner prints log statements.')
parser.add_argument(
'--miner.sync_interval',
default=10,
type=int,
help='Batches before we we sync with chain and emit new weights.')
parser.add_argument(
'--miner.root_dir',
default='~/.bittensor/miners/',
type=str,
help='Root path to load and save data associated with each miner')
parser.add_argument(
'--miner.name',
default='mnist',
type=str,
help='Trials for this miner go in miner.root / miner.name')
parser.add_argument(
'--miner.trial_uid',
default=str(time.time()).split('.')[0],
type=str,
help='Saved models go in miner.root_dir / miner.name / miner.uid')
parser.add_argument('--miner.record_log',
default=False,
help='Record all logs when running this miner')
parser.add_argument(
'--miner.config_file',
type=str,
help=
'config file to run this neuron, if not using cmd line arguments.')
bittensor.neuron.Neuron.add_args(parser)
FFNNSynapse.add_args(parser)
@staticmethod
def check_config(config: Munch):
assert config.miner.log_interval > 0, "log_interval dimension must be positive"
assert config.miner.momentum > 0 and config.miner.momentum < 1, "momentum must be a value between 0 and 1"
assert config.miner.batch_size_train > 0, "batch_size_train must be a positive value"
assert config.miner.batch_size_test > 0, "batch_size_test must be a positive value"
assert config.miner.learning_rate > 0, "learning rate must be be a positive value."
full_path = '{}/{}/{}/'.format(config.miner.root_dir,
config.miner.name,
config.miner.trial_uid)
config.miner.full_path = os.path.expanduser(full_path)
if not os.path.exists(config.miner.full_path):
os.makedirs(config.miner.full_path)
# --- Main loop ----
def run(self):
# ---- Subscribe neuron ----
with self.neuron:
# ---- Weights ----
self.row = self.neuron.metagraph.row.to(self.model.device)
# --- Loop for epochs ---
self.best_test_loss = math.inf
self.global_step = 0
for self.epoch in range(self.config.miner.n_epochs):
# ---- Serve ----
self.neuron.axon.serve(self.model)
# ---- Train ----
self.train()
self.scheduler.step()
# If model has borked for some reason, we need to make sure it doesn't emit weights
# Instead, reload into previous version of model
if torch.any(
torch.isnan(
torch.cat([
param.view(-1)
for param in self.model.parameters()
]))):
self.model, self.optimizer = self.model_toolbox.load_model(
self.config)
continue
# ---- Test ----
test_loss, test_accuracy = self.test()
# ---- Emit ----
self.neuron.metagraph.set_weights(
self.row, wait_for_inclusion=True
) # Sets my row-weights on the chain.
# ---- Sync ----
self.neuron.metagraph.sync(
) # Pulls the latest metagraph state (with my update.)
self.row = self.neuron.metagraph.row.to(self.device)
# --- Display Epoch ----
print(self.neuron.axon.__full_str__())
print(self.neuron.dendrite.__full_str__())
print(self.neuron.metagraph)
# ---- Update Tensorboard ----
self.neuron.dendrite.__to_tensorboard__(
self.tensorboard, self.global_step)
self.neuron.metagraph.__to_tensorboard__(
self.tensorboard, self.global_step)
self.neuron.axon.__to_tensorboard__(self.tensorboard,
self.global_step)
# ---- Save ----
if test_loss < self.best_test_loss:
self.best_test_loss = test_loss # Update best loss.
self.model_toolbox.save_model(
self.config.miner.full_path, {
'epoch': self.epoch,
'model_state_dict': self.model.state_dict(),
'loss': self.best_test_loss,
'optimizer_state_dict':
self.optimizer.state_dict(),
})
self.tensorboard.add_scalar('Test loss', test_loss,
self.global_step)
# ---- Train epoch ----
def train(self):
# ---- Init training state ----
self.model.train() # Turn on dropout etc.
for batch_idx, (images, targets) in enumerate(self.trainloader):
if batch_idx >= self.config.miner.epoch_length:
break
self.global_step += 1
# ---- Remote Forward pass ----
output = self.model.remote_forward(
neuron=self.neuron,
images=images.to(self.device),
targets=torch.LongTensor(targets).to(self.device),
)
# ---- Remote Backward pass ----
loss = output.remote_target_loss + output.local_target_loss + output.distillation_loss
loss.backward() # Accumulates gradients on the model.
self.optimizer.step() # Applies accumulated gradients.
self.optimizer.zero_grad(
) # Zeros out gradients for next accummulation
# ---- Train weights ----
batch_weights = torch.mean(output.router.weights, axis=0).to(
self.model.device) # Average over batch.
self.row = (
1 -
0.03) * self.row + 0.03 * batch_weights # Moving avg update.
self.row = F.normalize(self.row, p=1,
dim=0) # Ensure normalization.
# ---- Step Logs + Tensorboard ----
processed = ((batch_idx + 1) * self.config.miner.batch_size_train)
progress = (100. * processed) / len(self.train_data)
logger.info(
'GS: {}\t Epoch: {} [{}/{} ({})]\tLoss: {}\tAcc: {}\tAxon: {}\tDendrite: {}',
colored('{}'.format(self.global_step), 'blue'),
colored('{}'.format(self.epoch), 'blue'),
colored('{}'.format(processed), 'green'),
colored('{}'.format(len(self.train_data)), 'red'),
colored('{:.2f}%'.format(progress), 'green'),
colored('{:.4f}'.format(output.local_target_loss.item()),
'green'),
colored('{:.4f}'.format(output.local_accuracy.item()),
'green'), self.neuron.axon, self.neuron.dendrite)
self.tensorboard.add_scalar('Rloss',
output.remote_target_loss.item(),
self.global_step)
self.tensorboard.add_scalar('Lloss',
output.local_target_loss.item(),
self.global_step)
self.tensorboard.add_scalar('Dloss',
output.distillation_loss.item(),
self.global_step)
# --- Test epoch ----
def test(self):
with torch.no_grad(
): # Turns off gradient computation for inference speed up.
self.model.eval() # Turns off Dropoutlayers, BatchNorm etc.
loss = 0.0
accuracy = 0.0
for _, (images, labels) in enumerate(self.testloader):
# ---- Local Forward pass ----
outputs = self.model.local_forward(
images=images.to(self.device),
targets=torch.LongTensor(labels).to(self.device),
)
loss += outputs.local_target_loss.item()
accuracy += outputs.local_accuracy.item()
return loss / len(self.testloader), accuracy / len(self.testloader)
class Session():
def __init__(self, config: Munch):
if config == None:
config = Session.default_config()
self.config = config
# ---- Neuron ----
self.neuron = Neuron(self.config)
# ---- Model ----
self.model = FFNNSynapse(
config) # Feedforward neural network with PKMRouter.
self.device = torch.device(
"cuda" if torch.cuda.is_available() else "cpu")
self.model.to(self.device) # Set model to device
# ---- Optimizer ----
self.optimizer = optim.SGD(self.model.parameters(),
lr=self.config.miner.learning_rate,
momentum=self.config.miner.momentum)
self.scheduler = torch.optim.lr_scheduler.StepLR(self.optimizer,
step_size=10.0,
gamma=0.1)
# ---- Dataset ----
self.train_data = torchvision.datasets.MNIST(
root=self.config.miner.root_dir + "datasets/",
train=True,
download=True,
transform=transforms.ToTensor())
self.trainloader = torch.utils.data.DataLoader(
self.train_data,
batch_size=self.config.miner.batch_size_train,
shuffle=True,
num_workers=2)
self.test_data = torchvision.datasets.MNIST(
root=self.config.miner.root_dir + "datasets/",
train=False,
download=True,
transform=transforms.ToTensor())
self.testloader = torch.utils.data.DataLoader(
self.test_data,
batch_size=self.config.miner.batch_size_test,
shuffle=False,
num_workers=2)
# ---- Tensorboard ----
self.global_step = 0
self.tensorboard = SummaryWriter(log_dir=self.config.miner.full_path)
@staticmethod
def default_config() -> Munch:
parser = argparse.ArgumentParser()
Session.add_args(parser)
config = Config.to_config(parser)
Session.check_config(config)
return config
@staticmethod
def add_args(parser: argparse.ArgumentParser):
parser.add_argument('--session.learning_rate',
default=0.01,
type=float,
help='Training initial learning rate.')
parser.add_argument('--session.momentum',
default=0.9,
type=float,
help='Training initial momentum for SGD.')
parser.add_argument('--session.batch_size_train',
default=64,
type=int,
help='Training batch size.')
parser.add_argument('--session.batch_size_test',
default=64,
type=int,
help='Testing batch size.')
parser.add_argument(
'--session.log_interval',
default=150,
type=int,
help='Batches until session prints log statements.')
parser.add_argument(
'--session.sync_interval',
default=150,
type=int,
help='Batches before we we sync with chain and emit new weights.')
parser.add_argument(
'--session.root_dir',
default='data/',
type=str,
help='Root path to load and save data associated with each session'
)
parser.add_argument(
'--session.name',
default='mnist',
type=str,
help='Trials for this session go in session.root / session.name')
parser.add_argument(
'--session.uid',
default=str(time.time()).split('.')[0],
type=str,
help=
'Saved models go in session.root_dir / session.name / session.uid')
Neuron.add_args(parser)
FFNNSynapse.add_args(parser)
@staticmethod
def check_config(config: Munch):
assert config.miner.log_interval > 0, "log_interval dimension must be positive"
assert config.miner.momentum > 0 and config.miner.momentum < 1, "momentum must be a value between 0 and 1"
assert config.miner.batch_size_train > 0, "batch_size_train must be a positive value"
assert config.miner.batch_size_test > 0, "batch_size_test must be a positive value"
assert config.miner.learning_rate > 0, "learning rate must be be a positive value."
full_path = '{}/{}/{}/'.format(config.miner.root_dir,
config.miner.name, config.miner.uid)
config.miner.full_path = full_path
if not os.path.exists(config.miner.full_path):
os.makedirs(config.miner.full_path)
FFNNSynapse.check_config(config)
Neuron.check_config(config)
# --- Main loop ----
def run(self):
# ---- Subscribe neuron ----
with self.neuron:
# ---- Loop forever ----
start_time = time.time()
self.epoch = -1
self.best_test_loss = math.inf
self.global_step = 0
self.weights = self.neuron.metagraph.row # Trained weights.
while True:
self.epoch += 1
# ---- Emit ----
self.neuron.metagraph.set_weights(
self.weights, wait_for_inclusion=True
) # Sets my row-weights on the chain.
# ---- Sync ----
self.neuron.metagraph.sync(
) # Pulls the latest metagraph state (with my update.)
self.weights = self.neuron.metagraph.row.to(self.device)
# ---- Train ----
self.train()
self.scheduler.step()
# ---- Test ----
test_loss, test_accuracy = self.test()
# ---- Test checks ----
time_elapsed = time.time() - start_time
assert test_accuracy > 0.8
assert test_loss < 0.2
assert len(self.neuron.metagraph.state.neurons) > 0
assert time_elapsed < 300 # 1 epoch of MNIST should take less than 5 mins.
# ---- End test ----
break
# ---- Train epoch ----
def train(self):
# ---- Init training state ----
self.model.train() # Turn on dropout etc.
for batch_idx, (images, targets) in enumerate(self.trainloader):
self.global_step += 1
# ---- Remote Forward pass ----
output = self.model.remote_forward(
neuron=self.neuron,
images=images.to(self.device),
targets=torch.LongTensor(targets).to(self.device),
)
# ---- Remote Backward pass ----
loss = output.remote_target_loss + output.local_target_loss + output.distillation_loss
loss.backward() # Accumulates gradients on the model.
self.optimizer.step() # Applies accumulated gradients.
self.optimizer.zero_grad(
) # Zeros out gradients for next accummulation
# ---- Train weights ----
batch_weights = torch.mean(output.router.weights,
axis=0) # Average over batch.
self.weights = (
1 - 0.03
) * self.weights + 0.03 * batch_weights # Moving avg update.
self.weights = F.normalize(self.weights, p=1,
dim=0) # Ensure normalization.
# ---- Step Logs + Tensorboard ----
processed = ((batch_idx + 1) * self.config.miner.batch_size_train)
progress = (100. * processed) / len(self.train_data)
logger.info(
'GS: {}\t Epoch: {} [{}/{} ({})]\t Loss: {}\t Acc: {}\t Axon: {}\t Dendrite: {}',
colored('{}'.format(self.global_step), 'blue'),
colored('{}'.format(self.epoch), 'blue'),
colored('{}'.format(processed), 'green'),
colored('{}'.format(len(self.train_data)), 'red'),
colored('{:.2f}%'.format(progress), 'green'),
colored('{:.4f}'.format(output.local_target_loss.item()),
'green'),
colored('{:.4f}'.format(output.local_accuracy.item()),
'green'), self.neuron.axon, self.neuron.dendrite)
self.tensorboard.add_scalar('Rloss',
output.remote_target_loss.item(),
self.global_step)
self.tensorboard.add_scalar('Lloss',
output.local_target_loss.item(),
self.global_step)
self.tensorboard.add_scalar('Dloss',
output.distillation_loss.item(),
self.global_step)
# --- Test epoch ----
def test(self):
with torch.no_grad(
): # Turns off gradient computation for inference speed up.
self.model.eval() # Turns off Dropoutlayers, BatchNorm etc.
loss = 0.0
accuracy = 0.0
for _, (images, labels) in enumerate(self.testloader):
# ---- Local Forward pass ----
outputs = self.model.local_forward(
images=images.to(self.device),
targets=torch.LongTensor(labels).to(self.device),
)
loss += outputs.local_target_loss.item()
accuracy += outputs.local_accuracy.item()
return loss / len(self.testloader), accuracy / len(self.testloader)