def handler(event, context):
start_time = time.time()
# dataset setting
file = event['file']
data_bucket = event['data_bucket']
dataset_type = event['dataset_type']
assert dataset_type == "dense_libsvm"
n_features = event['n_features']
n_classes = event['n_classes']
n_workers = event['n_workers']
worker_index = event['worker_index']
tmp_table_name = event['tmp_table_name']
merged_table_name = event['merged_table_name']
key_col = event['key_col']
# training setting
model_name = event['model']
optim = event['optim']
sync_mode = event['sync_mode']
assert model_name.lower() in MLModel.Linear_Models
assert optim.lower() == Optimization.ADMM
assert sync_mode.lower() in [
Synchronization.Reduce, Synchronization.Reduce_Scatter
]
# hyper-parameter
learning_rate = event['lr']
batch_size = event['batch_size']
n_epochs = event['n_epochs']
valid_ratio = event['valid_ratio']
n_admm_epochs = event['n_admm_epochs']
lam = event['lambda']
rho = event['rho']
print('data bucket = {}'.format(data_bucket))
print("file = {}".format(file))
print('number of workers = {}'.format(n_workers))
print('worker index = {}'.format(worker_index))
print('model = {}'.format(model_name))
print('optimization = {}'.format(optim))
print('sync mode = {}'.format(sync_mode))
s3_storage = S3Storage()
dynamo_client = dynamo_operator.get_client()
tmp_table = DynamoTable(dynamo_client, tmp_table_name)
merged_table = DynamoTable(dynamo_client, merged_table_name)
communicator = DynamoCommunicator(dynamo_client, tmp_table, merged_table,
key_col, n_workers, worker_index)
# Read file from s3
read_start = time.time()
lines = s3_storage.load(file,
data_bucket).read().decode('utf-8').split("\n")
print("read data cost {} s".format(time.time() - read_start))
parse_start = time.time()
dataset = libsvm_dataset.from_lines(lines, n_features, dataset_type)
print("parse data cost {} s".format(time.time() - parse_start))
preprocess_start = time.time()
# Creating data indices for training and validation splits:
dataset_size = len(dataset)
indices = list(range(dataset_size))
split = int(np.floor(valid_ratio * dataset_size))
shuffle_dataset = True
random_seed = 100
if shuffle_dataset:
np.random.seed(random_seed)
np.random.shuffle(indices)
train_indices, val_indices = indices[split:], indices[:split]
# Creating data samplers and loaders:
train_sampler = SubsetRandomSampler(train_indices)
valid_sampler = SubsetRandomSampler(val_indices)
train_loader = torch.utils.data.DataLoader(dataset,
batch_size=batch_size,
sampler=train_sampler)
n_train_batch = len(train_loader)
validation_loader = torch.utils.data.DataLoader(dataset,
batch_size=batch_size,
sampler=valid_sampler)
print("preprocess data cost {} s, dataset size = {}".format(
time.time() - preprocess_start, dataset_size))
model = linear_models.get_model(model_name, n_features, n_classes)
z, u = initialize_z_and_u(model.linear.weight.data.size())
print("size of z = {}".format(z.shape))
print("size of u = {}".format(u.shape))
# Loss and Optimizer
# Softmax is internally computed.
# Set parameters to be updated.
criterion = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)
# Training the Model
train_start = time.time()
for admm_epoch in range(n_admm_epochs):
print(">>> ADMM Epoch[{}]".format(admm_epoch))
admm_epoch_start = time.time()
admm_epoch_cal_time = 0
admm_epoch_comm_time = 0
admm_epoch_test_time = 0
for epoch in range(n_epochs):
epoch_start = time.time()
epoch_loss = 0.
for batch_index, (items, labels) in enumerate(train_loader):
batch_start = time.time()
items = Variable(items.view(-1, n_features))
labels = Variable(labels)
# Forward + Backward + Optimize
optimizer.zero_grad()
outputs = model(items)
classify_loss = criterion(outputs, labels)
epoch_loss += classify_loss.item()
u_z = torch.from_numpy(u) - torch.from_numpy(z)
loss = classify_loss
for name, param in model.named_parameters():
if name.split('.')[-1] == "weight":
loss += rho / 2.0 * torch.norm(param + u_z, p=2)
# loss = classify_loss + rho / 2.0 * torch.norm(torch.sum(model.linear.weight, u_z))
optimizer.zero_grad()
loss.backward(retain_graph=True)
optimizer.step()
epoch_cal_time = time.time() - epoch_start
admm_epoch_cal_time += epoch_cal_time
# Test the Model
test_start = time.time()
n_test_correct = 0
n_test = 0
test_loss = 0
for items, labels in validation_loader:
items = Variable(items.view(-1, n_features))
labels = Variable(labels)
outputs = model(items)
test_loss += criterion(outputs, labels).item()
_, predicted = torch.max(outputs.data, 1)
n_test += labels.size(0)
n_test_correct += (predicted == labels).sum()
epoch_test_time = time.time() - test_start
admm_epoch_test_time += epoch_test_time
print(
'Epoch: [%d/%d], Step: [%d/%d], Time: %.4f, Loss: %.4f, epoch cost %.4f, '
'cal cost %.4f s, test cost %.4f s, accuracy of the model on the %d test samples: %d %%, loss = %f'
% (epoch + 1, n_epochs, batch_index + 1, n_train_batch,
time.time() - train_start, epoch_loss,
time.time() - epoch_start, epoch_cal_time, epoch_test_time,
n_test, 100. * n_test_correct / n_test, test_loss / n_test))
sync_start = time.time()
w = model.linear.weight.data.numpy()
w_shape = w.shape
b = model.linear.bias.data.numpy()
b_shape = b.shape
u_shape = u.shape
w_b = np.concatenate((w.flatten(), b.flatten()))
u_w_b = np.concatenate((u.flatten(), w_b.flatten()))
# admm does not support async
if sync_mode == "reduce":
u_w_b_merge = communicator.reduce_epoch(u_w_b, admm_epoch)
elif sync_mode == "reduce_scatter":
u_w_b_merge = communicator.reduce_scatter_epoch(u_w_b, admm_epoch)
u_mean = u_w_b_merge[:u_shape[0] *
u_shape[1]].reshape(u_shape) / float(n_workers)
w_mean = u_w_b_merge[u_shape[0] * u_shape[1]: u_shape[0] * u_shape[1] + w_shape[0] * w_shape[1]]\
.reshape(w_shape) / float(n_workers)
b_mean = u_w_b_merge[u_shape[0] * u_shape[1] + w_shape[0] * w_shape[1]:]\
.reshape(b_shape[0]) / float(n_workers)
model.linear.weight.data = torch.from_numpy(w_mean)
model.linear.bias.data = torch.from_numpy(b_mean)
admm_epoch_comm_time += time.time() - sync_start
if worker_index == 0:
delete_start = time.time()
communicator.delete_expired_epoch(admm_epoch)
admm_epoch_comm_time += time.time() - delete_start
# z, u, r, s = update_z_u(w, z, u, rho, num_workers, lam)
# stop = check_stop(ep_abs, ep_rel, r, s, dataset_size, num_features, w, z, u, rho)
# print("stop = {}".format(stop))
# z = num_workers * rho / (2 * lam + num_workers * rho) * (w + u_mean)
z = update_z(w_mean, u_mean, rho, n_workers, lam)
u = u + model.linear.weight.data.numpy() - z
print(
"ADMM Epoch[{}] finishes, cost {} s, cal cost {} s, sync cost {} s, test cost {} s"
.format(admm_epoch,
time.time() - admm_epoch_start, admm_epoch_cal_time,
admm_epoch_comm_time, admm_epoch_test_time))
# Test the Model
n_test_correct = 0
n_test = 0
test_loss = 0
for items, labels in validation_loader:
items = Variable(items.view(-1, n_features))
labels = Variable(labels)
outputs = model(items)
test_loss += criterion(outputs, labels).item()
_, predicted = torch.max(outputs.data, 1)
n_test += labels.size(0)
n_test_correct += (predicted == labels).sum()
print(
'Train finish, time = %.4f, accuracy of the model on the %d test samples: %d %%, loss = %f'
% (time.time() - train_start, n_test, 100. * n_test_correct / n_test,
test_loss / n_test))
if worker_index == 0:
s3_storage.clear(tmp_table_name)
s3_storage.clear(merged_table_name)
end_time = time.time()
print("Elapsed time = {} s".format(end_time - start_time))
def handler(event, context):
start_time = time.time()
# dataset setting
file = event['file']
data_bucket = event['data_bucket']
dataset_type = event['dataset_type']
n_features = event['n_features']
n_classes = event['n_classes']
n_workers = event['n_workers']
worker_index = event['worker_index']
tmp_bucket = event['tmp_bucket']
merged_bucket = event['merged_bucket']
# training setting
model_name = event['model']
optim = event['optim']
sync_mode = event['sync_mode']
assert model_name.lower() in MLModel.Linear_Models
assert optim.lower() in Optimization.All
assert sync_mode.lower() in Synchronization.All
# hyper-parameter
learning_rate = event['lr']
batch_size = event['batch_size']
n_epochs = event['n_epochs']
valid_ratio = event['valid_ratio']
shuffle_dataset = True
random_seed = 100
print('bucket = {}'.format(data_bucket))
print("file = {}".format(file))
print('number of workers = {}'.format(n_workers))
print('worker index = {}'.format(worker_index))
print('model = {}'.format(model_name))
print('optimization = {}'.format(optim))
print('sync mode = {}'.format(sync_mode))
storage = S3Storage()
communicator = S3Communicator(storage, tmp_bucket, merged_bucket,
n_workers, worker_index)
# Read file from s3
read_start = time.time()
lines = storage.load(file, data_bucket).read().decode('utf-8').split("\n")
print("read data cost {} s".format(time.time() - read_start))
parse_start = time.time()
dataset = libsvm_dataset.from_lines(lines, n_features, dataset_type)
print("parse data cost {} s".format(time.time() - parse_start))
preprocess_start = time.time()
# Creating data indices for training and validation splits:
dataset_size = len(dataset)
indices = list(range(dataset_size))
split = int(np.floor(valid_ratio * dataset_size))
if shuffle_dataset:
np.random.seed(random_seed)
np.random.shuffle(indices)
train_indices, val_indices = indices[split:], indices[:split]
# Creating data samplers and loaders:
train_sampler = SubsetRandomSampler(train_indices)
valid_sampler = SubsetRandomSampler(val_indices)
train_loader = torch.utils.data.DataLoader(dataset,
batch_size=batch_size,
sampler=train_sampler)
n_train_batch = len(train_loader)
validation_loader = torch.utils.data.DataLoader(dataset,
batch_size=batch_size,
sampler=valid_sampler)
print("preprocess data cost {} s, dataset size = {}".format(
time.time() - preprocess_start, dataset_size))
model = linear_models.get_model(model_name, n_features, n_classes)
# Loss and Optimizer
# Softmax is internally computed.
# Set parameters to be updated.
criterion = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)
train_start = time.time()
# Training the Model
for epoch in range(n_epochs):
epoch_start = time.time()
epoch_cal_time = 0
epoch_sync_time = 0
epoch_loss = 0
for batch_index, (items, labels) in enumerate(train_loader):
# print("------worker {} epoch {} batch {}------".format(worker_index, epoch, batch_index))
batch_start = time.time()
items = Variable(items.view(-1, n_features))
labels = Variable(labels)
# Forward + Backward + Optimize
optimizer.zero_grad()
outputs = model(items)
loss = criterion(outputs, labels)
epoch_loss += loss.data
loss.backward()
if optim == "grad_avg":
if sync_mode == "reduce" or sync_mode == "reduce_scatter":
w_grad = model.linear.weight.grad.data.numpy()
w_grad_shape = w_grad.shape
b_grad = model.linear.bias.grad.data.numpy()
b_grad_shape = b_grad.shape
w_b_grad = np.concatenate(
(w_grad.flatten(), b_grad.flatten()))
batch_cal_time = time.time() - batch_start
epoch_cal_time += batch_cal_time
batch_sync_start = time.time()
postfix = "{}_{}".format(epoch, batch_index)
if sync_mode == "reduce":
w_b_grad_merge = communicator.reduce_batch(
w_b_grad, postfix)
elif sync_mode == "reduce_scatter":
w_b_grad_merge = communicator.reduce_scatter_batch(
w_b_grad, postfix)
w_grad_merge = w_b_grad_merge[:w_grad_shape[0] * w_grad_shape[1]]\
.reshape(w_grad_shape) / float(n_workers)
b_grad_merge = w_b_grad_merge[w_grad_shape[0] * w_grad_shape[1]:]\
.reshape(b_grad_shape[0]) / float(n_workers)
model.linear.weight.grad = Variable(
torch.from_numpy(w_grad_merge))
model.linear.bias.grad = Variable(
torch.from_numpy(b_grad_merge))
batch_sync_time = time.time() - batch_sync_start
print("one {} round cost {} s".format(
sync_mode, batch_sync_time))
epoch_sync_time += batch_sync_time
elif sync_mode == "async":
# async does step before sync
optimizer.step()
w = model.linear.weight.data.numpy()
w_shape = w.shape
b = model.linear.bias.data.numpy()
b_shape = b.shape
w_b = np.concatenate((w.flatten(), b.flatten()))
batch_cal_time = time.time() - epoch_start
epoch_cal_time += batch_cal_time
batch_sync_start = time.time()
# init model
if worker_index == 0 and epoch == 0 and batch_index == 0:
storage.save(w_b.tobytes(), Prefix.w_b_prefix,
merged_bucket)
w_b_merge = communicator.async_reduce(
w_b, Prefix.w_b_prefix)
# do not need average
w_merge = w_b_merge[:w_shape[0] *
w_shape[1]].reshape(w_shape)
b_merge = w_b_merge[w_shape[0] * w_shape[1]:].reshape(
b_shape[0])
model.linear.weight.data = torch.from_numpy(w_merge)
model.linear.bias.data = torch.from_numpy(b_merge)
batch_sync_time = time.time() - batch_sync_start
print("one {} round cost {} s".format(
sync_mode, batch_sync_time))
epoch_sync_time += batch_sync_time
if sync_mode != "async":
step_start = time.time()
optimizer.step()
epoch_cal_time += time.time() - step_start
# print('Epoch: [%d/%d], Step: [%d/%d], Time: %.4f s, Loss: %.4f, batch cost %.4f s'
# % (epoch + 1, n_epochs, batch_index + 1, n_train_batch,
# time.time() - train_start, loss.data, time.time() - batch_start))
if optim == "model_avg":
w = model.linear.weight.data.numpy()
w_shape = w.shape
b = model.linear.bias.data.numpy()
b_shape = b.shape
w_b = np.concatenate((w.flatten(), b.flatten()))
epoch_cal_time += time.time() - epoch_start
epoch_sync_start = time.time()
postfix = str(epoch)
if sync_mode == "reduce":
w_b_merge = communicator.reduce_epoch(w_b, postfix)
elif sync_mode == "reduce_scatter":
w_b_merge = communicator.reduce_scatter_epoch(w_b, postfix)
elif sync_mode == "async":
if epoch == 0:
storage.save(w_b.tobytes(), Prefix.w_b_prefix,
merged_bucket)
w_b_merge = communicator.async_reduce(w_b, Prefix.w_b_prefix)
w_merge = w_b_merge[:w_shape[0] * w_shape[1]].reshape(w_shape)
b_merge = w_b_merge[w_shape[0] * w_shape[1]:].reshape(b_shape[0])
if sync_mode == "reduce" or sync_mode == "reduce_scatter":
w_merge = w_merge / float(n_workers)
b_merge = b_merge / float(n_workers)
model.linear.weight.data = torch.from_numpy(w_merge)
model.linear.bias.data = torch.from_numpy(b_merge)
print("one {} round cost {} s".format(
sync_mode,
time.time() - epoch_sync_start))
epoch_sync_time += time.time() - epoch_sync_start
if worker_index == 0:
delete_start = time.time()
# model avg delete by epoch
if optim == "model_avg" and sync_mode != "async":
communicator.delete_expired_epoch(epoch)
elif optim == "grad_avg" and sync_mode != "async":
communicator.delete_expired_batch(epoch, batch_index)
epoch_sync_time += time.time() - delete_start
# Test the Model
test_start = time.time()
n_test_correct = 0
n_test = 0
test_loss = 0
for items, labels in validation_loader:
items = Variable(items.view(-1, n_features))
labels = Variable(labels)
outputs = model(items)
test_loss += criterion(outputs, labels).data
_, predicted = torch.max(outputs.data, 1)
n_test += labels.size(0)
n_test_correct += (predicted == labels).sum()
test_time = time.time() - test_start
print(
'Epoch: [%d/%d], Step: [%d/%d], Time: %.4f, Loss: %.4f, epoch cost %.4f: '
'calculation cost = %.4f s, synchronization cost %.4f s, test cost %.4f s, '
'accuracy of the model on the %d test samples: %d %%, loss = %f' %
(epoch + 1, n_epochs, batch_index + 1, n_train_batch, time.time() -
train_start, epoch_loss.data, time.time() - epoch_start,
epoch_cal_time, epoch_sync_time, test_time, n_test,
100. * n_test_correct / n_test, test_loss / n_test))
if worker_index == 0:
storage.clear(tmp_bucket)
storage.clear(merged_bucket)
end_time = time.time()
print("Elapsed time = {} s".format(end_time - start_time))