def main(n_aggregation, dim_feature, n_epochs, batch_size, eps):
W = np.random.normal(0, 0.4, [dim_feature, dim_feature])
A = np.random.normal(0, 0.4, dim_feature)
b = np.array([0.])
model = GraphNeuralNetwork(W, A, b, n_aggregation=n_aggregation)
optimizer = Adam(model)
dataset = util.get_train_data('../../datasets')
train_data, valid_data = util.random_split(dataset, train_ratio=0.5)
print('train_size: %d, valid_size: %d' %
(len(train_data), len(valid_data)))
for epoch in range(n_epochs):
train_loss = util.AverageMeter()
train_acc = util.AverageMeter()
for graphs, labels in util.get_shuffled_batches(
train_data, batch_size):
grads_flat = 0
for graph, label in zip(graphs, labels):
x = np.zeros([len(graph), dim_feature])
x[:, 0] = 1
grads_flat += calc_grads(model, graph, x, label,
bce_with_logit, eps) / batch_size
outputs = model(graph, x)
train_loss.update(bce_with_logit(outputs, label), 1)
train_acc.update((sigmoid(outputs) > 0.5) == label, 1)
optimizer.update(grads_flat)
valid_loss, valid_acc = test(model, valid_data, dim_feature)
print(
'epoch: %d, train_loss: %f, train_acc: %f, valid_loss: %f, vald_acc: %f'
% (epoch, train_loss.avg, train_acc.avg, valid_loss, valid_acc))
def train():
graphs, labels = load_data("datasets/train")
train_inputs, train_targets, val_inputs, val_targets = utils.split_train_val(
graphs, labels, val_rate=0.3)
model = GNNModel(8)
loss_func = BinaryCrossEntropy()
optimizer = Adam()
batch_generator = utils.BatchGenerator(batch_size=32)
min_loss = 100000
for epoch in range(50):
print(f"Epoch{epoch + 1}")
train_losses = []
for inputs, targets in batch_generator.generator(
train_inputs, train_targets):
train_loss, loss_grad = loss_func(model,
inputs,
targets,
is_grad=True)
optimizer.update(model, loss_grad)
train_losses.append(train_loss)
train_mean_loss = np.mean(train_losses)
pred = np.array([model.predict(input_)
for input_ in train_inputs]).squeeze()
train_accuracy = accuracy(pred, train_targets)
val_losses = []
for inputs, targets in batch_generator.generator(
val_inputs, val_targets):
val_loss, _ = loss_func(model, inputs, targets, is_grad=False)
val_losses.append(val_loss)
val_mean_loss = np.mean(val_losses)
pred = np.array([model.predict(input_)
for input_ in val_inputs]).squeeze()
val_accuracy = accuracy(pred, val_targets)
if min(min_loss, val_mean_loss) < min_loss:
min_loss = val_mean_loss
print(
f"Train loss: {train_mean_loss}\tTrain accuracy: {train_accuracy}"
)
print(
f"Validation loss: {val_mean_loss}\tValidation accuracy: {val_accuracy}"
)
print("")
def get_optimizer(model, args):
"""Set up the optimizer."""
# Build parameter groups (weight decay and non-decay).
while isinstance(model, (DDP, FP16_Module)):
model = model.module
layers = model.model.bert.encoder.layer
pooler = model.model.bert.pooler
lmheads = model.model.cls.predictions
nspheads = model.model.cls.seq_relationship
embeddings = model.model.bert.embeddings
param_groups = []
param_groups += list(get_params_for_weight_decay_optimization(layers))
param_groups += list(get_params_for_weight_decay_optimization(pooler))
param_groups += list(get_params_for_weight_decay_optimization(nspheads))
param_groups += list(get_params_for_weight_decay_optimization(embeddings))
param_groups += list(
get_params_for_weight_decay_optimization(lmheads.transform))
param_groups[1]['params'].append(lmheads.bias)
# Use Adam.
optimizer = Adam(param_groups, lr=args.lr, weight_decay=args.weight_decay)
# Wrap into fp16 optimizer.
if args.fp16:
optimizer = FP16_Optimizer(optimizer,
static_loss_scale=args.loss_scale,
dynamic_loss_scale=args.dynamic_loss_scale,
dynamic_loss_args={
'scale_window': args.loss_scale_window,
'min_scale': args.min_scale,
'delayed_shift': args.hysteresis
})
return optimizer
def __init__(self, args):
super(PPO, self).__init__()
# saved path
self.saved_path = args.saved_path
if not os.path.exists(self.saved_path):
os.makedirs(self.saved_path)
# neural networks
self.actor_critic = ActorCritic(action_size=args.action_size, hidden_size=args.hidden_size,
extra_hidden=args.extra_hidden, enlargement=args.enlargement,
recurrent=args.recurrent, device=args.device).to(args.device)
# args
self.rank = args.rank
self.device = args.device
self.num_steps = args.num_steps
self.num_envs = args.num_envs
self.num_rollouts = args.num_rollouts
self.render = args.render
self.action_size = args.action_size
self.update_epochs = args.update_epochs
self.batch_size = args.batch_size
self.clip_range = args.clip_range
self.max_grad_norm = args.max_grad_norm
self.gamma = args.gamma
self.lamda = args.lamda
self.coeff_ent = args.coeff_ent
# optimizer
self.optimizer = Adam(
self.actor_critic.parameters(), args.learning_rate)
# batch
self.sample_envs = self.batch_size // self.num_steps
def main(n_aggregation, dim_feature, n_epochs, batch_size, eps, outputfile):
W = np.random.normal(0, 0.4, [dim_feature, dim_feature])
A = np.random.normal(0, 0.4, dim_feature)
b = np.array([0.])
model = GraphNeuralNetwork(W, A, b, n_aggregation=n_aggregation)
optimizer = Adam(model)
# Training
train_data = util.get_train_data('../../datasets')
print('train_size: %d' % len(train_data))
for epoch in range(n_epochs):
train_loss = util.AverageMeter()
train_acc = util.AverageMeter()
for graphs, labels in util.get_shuffled_batches(
train_data, batch_size):
grads_flat = 0
for graph, label in zip(graphs, labels):
x = np.zeros([len(graph), dim_feature])
x[:, 0] = 1
grads_flat += calc_grads(model, graph, x, label,
bce_with_logit, eps) / batch_size
outputs = model(graph, x)
train_loss.update(bce_with_logit(outputs, label), 1)
train_acc.update((sigmoid(outputs) > 0.5) == label, 1)
optimizer.update(grads_flat)
print('epoch: %d, train_loss: %f, train_acc: %f' %
(epoch, train_loss.avg, train_acc.avg))
# Prediction
test_data = util.get_test_data('../../datasets')
with open(outputfile, 'w') as o:
for graph in test_data:
x = np.zeros([len(graph), dim_feature])
x[:, 0] = 1
logit = model(graph, x)
pred = sigmoid(logit) > 0.5
o.write(str(int(pred[0])) + '\n')
def train(seq, dataloader, epochs=10):
criterion = CrossEntropyLoss(seq)
optimizer = Adam(seq)
for epoch in range(epochs):
epoch_loss = 0.0
epoch_accuracy = 0.0
n_batch = 0
for batch, labels in dataloader:
n_batch += 1
outputs = seq(batch)
loss = criterion(outputs, labels)
accuracy = accuracy_score(outputs.argmax(axis=1), labels)
loss.backward()
optimizer.step()
epoch_loss += loss
epoch_accuracy += accuracy
print("Epoch {}/{} - loss: {:%.5f} accuracy: {:%.5f}".format(
epoch + 1, epochs, epoch_loss / n_batch, epoch_accuracy / n_batch))
print("Finished training !")
def main():
args = parse_args()
with open(args.input, 'r') as fp:
data_loader = DataLoader(fp.read(), batch_size=args.seq_length)
rnn = RNN()
params = init_params(data_loader.vocab_size, hidden_size=args.hidden_size)
optimizer = Adam(params, lr=args.lr)
it = 0
for epoch in range(args.num_epochs):
hidden_state = np.zeros((1, args.hidden_size))
for x, y in data_loader:
if it % args.sample_every == 0:
one_hot = sample(rnn, hidden_state, x[0], params,
args.sample_size)
generated_text = data_loader.decode(one_hot)
print(generated_text)
loss, hidden_state, dparams = rnn_training_step(
rnn, hidden_state, x, y, params)
if it % args.print_every == 0:
print('iteration: {}, loss: {}'.format(it, loss))
optimizer.step(dparams)
it += 1
参考：http://glowingpython.blogspot.jp/2012/02/convolution-with-numpy.html
"""
window_len = 11
s = np.r_[x[window_len - 1:0:-1], x, x[-1:-window_len:-1]]
w = np.kaiser(window_len, 2)
y = np.convolve(w / w.sum(), s, mode='valid')
return y[5:len(y) - 5]
##定义几种优化optimizer
optimizers = OrderedDict()
optimizers['SGD'] = SGD()
optimizers['momentum'] = momentum()
optimizers['adagrad'] = adagrad()
optimizers['Adam'] = Adam()
##提取数据
(x_train, t_train), (x_test, t_test) = load_mnist(normalize=True,
one_hot_label=True)
#设置参数,初始化网络
train_size = x_train.shape[0]
batch_size = 100
iter_num = 1000
train_loss = {}
networks = {}
for key in optimizers.keys():
networks[key] = backnet(input_size=784, hidden_size=50, output_size=10)
def main(args, local_rank):
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(name)s - %(message)s',
datefmt='%m/%d/%Y %H:%M:%S',
level=logging.INFO)
vocabs = dict()
vocabs['src'] = Vocab(args.src_vocab, 0, [BOS, EOS])
vocabs['tgt'] = Vocab(args.tgt_vocab, 0, [BOS, EOS])
if args.world_size == 1 or (dist.get_rank() == 0):
logger.info(args)
for name in vocabs:
logger.info("vocab %s, size %d, coverage %.3f", name,
vocabs[name].size, vocabs[name].coverage)
set_seed(19940117)
#device = torch.device('cpu')
torch.cuda.set_device(local_rank)
device = torch.device('cuda', local_rank)
if args.resume_ckpt:
model = MatchingModel.from_pretrained(vocabs, args.resume_ckpt)
else:
model = MatchingModel.from_params(vocabs, args.layers, args.embed_dim,
args.ff_embed_dim, args.num_heads,
args.dropout, args.output_dim,
args.bow)
if args.world_size > 1:
set_seed(19940117 + dist.get_rank())
model = model.to(device)
if args.resume_ckpt:
dev_data = DataLoader(vocabs,
args.dev_data,
args.dev_batch_size,
addition=args.additional_negs)
acc = validate(model, dev_data, device)
logger.info("initialize from %s, initial acc %.2f", args.resume_ckpt,
acc)
optimizer = Adam(model.parameters(),
lr=args.lr,
betas=(0.9, 0.98),
eps=1e-9)
lr_schedule = get_linear_schedule_with_warmup(optimizer, args.warmup_steps,
args.total_train_steps)
train_data = DataLoader(vocabs,
args.train_data,
args.per_gpu_train_batch_size,
worddrop=args.worddrop,
addition=args.additional_negs)
global_step, step, epoch = 0, 0, 0
tr_stat = Statistics()
logger.info("start training")
model.train()
while global_step <= args.total_train_steps:
for batch in train_data:
batch = move_to_device(batch, device)
loss, acc, bsz = model(batch['src_tokens'], batch['tgt_tokens'],
args.label_smoothing)
tr_stat.update({
'loss': loss.item() * bsz,
'nsamples': bsz,
'acc': acc * bsz
})
tr_stat.step()
loss.backward()
step += 1
if not (step % args.gradient_accumulation_steps
== -1 % args.gradient_accumulation_steps):
continue
if args.world_size > 1:
average_gradients(model)
torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
optimizer.step()
lr_schedule.step()
optimizer.zero_grad()
global_step += 1
if args.world_size == 1 or (dist.get_rank() == 0):
if global_step % args.print_every == -1 % args.print_every:
logger.info("epoch %d, step %d, loss %.3f, acc %.3f",
epoch, global_step,
tr_stat['loss'] / tr_stat['nsamples'],
tr_stat['acc'] / tr_stat['nsamples'])
tr_stat = Statistics()
if global_step > args.warmup_steps and global_step % args.eval_every == -1 % args.eval_every:
dev_data = DataLoader(vocabs,
args.dev_data,
args.dev_batch_size,
addition=args.additional_negs)
acc = validate(model, dev_data, device)
logger.info("epoch %d, step %d, dev, dev acc %.2f", epoch,
global_step, acc)
save_path = '%s/epoch%d_batch%d_acc%.2f' % (
args.ckpt, epoch, global_step, acc)
model.save(args, save_path)
model.train()
if global_step > args.total_train_steps:
break
epoch += 1
logger.info('rank %d, finish training after %d steps', local_rank,
global_step)
def main(args, local_rank):
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(name)s - %(message)s',
datefmt='%m/%d/%Y %H:%M:%S',
level=logging.INFO)
vocabs = dict()
vocabs['src'] = Vocab(args.src_vocab, 0, [BOS, EOS])
vocabs['tgt'] = Vocab(args.tgt_vocab, 0, [BOS, EOS])
if args.world_size == 1 or (dist.get_rank() == 0):
logger.info(args)
for name in vocabs:
logger.info("vocab %s, size %d, coverage %.3f", name,
vocabs[name].size, vocabs[name].coverage)
set_seed(19940117)
#device = torch.device('cpu')
torch.cuda.set_device(local_rank)
device = torch.device('cuda', local_rank)
if args.arch == 'vanilla':
model = Generator(vocabs, args.embed_dim, args.ff_embed_dim,
args.num_heads, args.dropout, args.enc_layers,
args.dec_layers, args.label_smoothing)
elif args.arch == 'mem':
model = MemGenerator(vocabs, args.embed_dim, args.ff_embed_dim,
args.num_heads, args.dropout, args.mem_dropout,
args.enc_layers, args.dec_layers,
args.mem_enc_layers, args.label_smoothing,
args.use_mem_score)
elif args.arch == 'rg':
logger.info("start building model")
logger.info("building retriever")
retriever = Retriever.from_pretrained(
args.num_retriever_heads,
vocabs,
args.retriever,
args.nprobe,
args.topk,
local_rank,
use_response_encoder=(args.rebuild_every > 0))
logger.info("building retriever + generator")
model = RetrieverGenerator(vocabs, retriever, args.share_encoder,
args.embed_dim, args.ff_embed_dim,
args.num_heads, args.dropout,
args.mem_dropout, args.enc_layers,
args.dec_layers, args.mem_enc_layers,
args.label_smoothing)
if args.resume_ckpt:
model.load_state_dict(torch.load(args.resume_ckpt)['model'])
else:
global_step = 0
if args.world_size > 1:
set_seed(19940117 + dist.get_rank())
model = model.to(device)
retriever_params = [
v for k, v in model.named_parameters() if k.startswith('retriever.')
]
other_params = [
v for k, v in model.named_parameters()
if not k.startswith('retriever.')
]
optimizer = Adam([{
'params': retriever_params,
'lr': args.embed_dim**-0.5 * 0.1
}, {
'params': other_params,
'lr': args.embed_dim**-0.5
}],
betas=(0.9, 0.98),
eps=1e-9)
lr_schedule = get_inverse_sqrt_schedule_with_warmup(
optimizer, args.warmup_steps, args.total_train_steps)
train_data = DataLoader(vocabs,
args.train_data,
args.per_gpu_train_batch_size,
for_train=True,
rank=local_rank,
num_replica=args.world_size)
model.eval()
#dev_data = DataLoader(vocabs, cur_dev_data, args.dev_batch_size, for_train=False)
#bleu = validate(device, model, dev_data, beam_size=5, alpha=0.6, max_time_step=10)
step, epoch = 0, 0
tr_stat = Statistics()
logger.info("start training")
model.train()
best_dev_bleu = 0.
while global_step <= args.total_train_steps:
for batch in train_data:
#step_start = time.time()
batch = move_to_device(batch, device)
if args.arch == 'rg':
loss, acc = model(
batch,
update_mem_bias=(global_step >
args.update_retriever_after))
else:
loss, acc = model(batch)
tr_stat.update({
'loss': loss.item() * batch['tgt_num_tokens'],
'tokens': batch['tgt_num_tokens'],
'acc': acc
})
tr_stat.step()
loss.backward()
#step_cost = time.time() - step_start
#print ('step_cost', step_cost)
step += 1
if not (step % args.gradient_accumulation_steps
== -1 % args.gradient_accumulation_steps):
continue
if args.world_size > 1:
average_gradients(model)
torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
optimizer.step()
lr_schedule.step()
optimizer.zero_grad()
global_step += 1
if args.world_size == 1 or (dist.get_rank() == 0):
if global_step % args.print_every == -1 % args.print_every:
logger.info("epoch %d, step %d, loss %.3f, acc %.3f",
epoch, global_step,
tr_stat['loss'] / tr_stat['tokens'],
tr_stat['acc'] / tr_stat['tokens'])
tr_stat = Statistics()
if global_step % args.eval_every == -1 % args.eval_every:
model.eval()
max_time_step = 256 if global_step > 2 * args.warmup_steps else 5
bleus = []
for cur_dev_data in args.dev_data:
dev_data = DataLoader(vocabs,
cur_dev_data,
args.dev_batch_size,
for_train=False)
bleu = validate(device,
model,
dev_data,
beam_size=5,
alpha=0.6,
max_time_step=max_time_step)
bleus.append(bleu)
bleu = sum(bleus) / len(bleus)
logger.info("epoch %d, step %d, dev bleu %.2f", epoch,
global_step, bleu)
if bleu > best_dev_bleu:
testbleus = []
for cur_test_data in args.test_data:
test_data = DataLoader(vocabs,
cur_test_data,
args.dev_batch_size,
for_train=False)
testbleu = validate(device,
model,
test_data,
beam_size=5,
alpha=0.6,
max_time_step=max_time_step)
testbleus.append(testbleu)
testbleu = sum(testbleus) / len(testbleus)
logger.info("epoch %d, step %d, test bleu %.2f", epoch,
global_step, testbleu)
torch.save({
'args': args,
'model': model.state_dict()
}, '%s/best.pt' % (args.ckpt, ))
if not args.only_save_best:
torch.save(
{
'args': args,
'model': model.state_dict()
},
'%s/epoch%d_batch%d_devbleu%.2f_testbleu%.2f' %
(args.ckpt, epoch, global_step, bleu,
testbleu))
best_dev_bleu = bleu
model.train()
if args.rebuild_every > 0 and (global_step % args.rebuild_every
== -1 % args.rebuild_every):
model.retriever.drop_index()
torch.cuda.empty_cache()
next_index_dir = '%s/batch%d' % (args.ckpt, global_step)
if args.world_size == 1 or (dist.get_rank() == 0):
model.retriever.rebuild_index(next_index_dir)
dist.barrier()
else:
dist.barrier()
model.retriever.update_index(next_index_dir, args.nprobe)
if global_step > args.total_train_steps:
break
epoch += 1
logger.info('rank %d, finish training after %d steps', local_rank,
global_step)
##初始值给定,
init_pos=(-7.0,2.0)
params={}
params['x']=init_pos[0]
params['y']=init_pos[1]
grads={}
grads['x']=0
grads['y']=0
optimizers=OrderedDict()##有序字典
optimizers['SGD']=SGD(lr=0.95)
optimizers['momentum']=momentum(lr=0.1)
optimizers['adagrad']=adagrad(lr=1.5)
optimizers['Adam']= Adam(lr=0.3)
idx=1##图的位置分布
for key in optimizers.keys():##取每个key键值
##尝试每种优化方法
##初始化参数
optimizer=optimizers[key]
params['x']=init_pos[0]
params['y']=init_pos[1]
x_history=[]
y_history=[]
##定义梯度的来源以及梯度下降过程
for i in range(30):
x_history.append(params['x'])
def foo(mod, op, d):
if (op[0] == "linear"):
xx = Linear(d)
# rnncell, lstmcell, grucell
elif (mod[0] in ["LSTMCell", "GRUCell"]) and (op[0] == "forward"):
xx = RNNCell(d)
elif op[0] in [
"conv1d",
"conv2d",
]:
xx = Conv(d)
elif (op[0] in Pointwise.ops):
xx = Pointwise(d)
elif (op[0] in Convert.ops):
xx = Convert(d)
elif op[0] in ["__matmul__", "matmul"]:
xx = Matmul(d)
elif op[0] == "embedding":
xx = Embedding(d)
#reduction
elif op[0] == "sum":
xx = Sum(d)
elif op[0] == "mean":
xx = Mean(d)
elif op[0] == "norm":
xx = Norm(d)
elif op[0] == "dropout":
xx = Dropout(d)
#Index, Slice, Join, Mutate
elif (op[0] == "cat"):
xx = Cat(d)
elif (op[0] == "reshape"):
xx = Reshape(d)
elif (op[0] == "masked_scatter_"):
xx = MaskedScatter(d)
elif (op[0] == "gather"):
xx = Gather(d)
elif (op[0] == "nonzero"):
xx = Nonzero(d)
elif (op[0] == "index_select"):
xx = IndexSelect(d)
elif (op[0] == "masked_select"):
xx = MaskedSelect(d)
#blas
elif op[0] in ["addmm", "addmm_"]:
xx = Addmm(d)
elif op[0] == "mm":
xx = Mm(d)
elif op[0] == "bmm":
xx = Bmm(d)
#softmax
elif op[0] == "softmax":
xx = Softmax(d)
elif op[0] == "log_softmax":
xx = LogSoftmax(d)
#loss
elif op[0] == "mse_loss":
xx = MSELoss(d)
#optimizers
elif op[0] == "adam":
xx = Adam(d)
#normalization
elif op[0] == "batch_norm":
xx = BatchNorm(d)
#random
elif op[0] == "randperm":
xx = RandPerm(d)
#misc
elif op[0] == "copy_":
xx = Copy(d)
elif op[0] == "clone":
xx = Clone(d)
elif op[0] == "contiguous":
xx = Contiguous(d)
elif op[0] == "any":
xx = Any(d)
elif (op[0] in Activation.ops):
xx = Activation(d)
elif op[0] == "to":
xx = Convert(d)
else:
xx = Foo(d)
return xx
scores, deprocess(targets, n_bits))
# multinomial sampling needs to be processed to [-1,1] at generation
generate_fn = partial(pixelcnn.generate_fn,
preprocess_fn=preprocess,
n_bits=args.n_bits)
optimizer = RMSprop(model.parameters(), lr=args.lr, polyak=args.polyak)
scheduler = None
elif args.model == 'pixelcnnpp':
from generative_models_toolbox.algos.pixelcnn import pixelcnnpp
model = pixelcnnpp.PixelCNNpp(args.image_dims, args.n_channels,
args.n_res_layers, args.n_logistic_mix,
args.n_cond_classes).to(args.device)
loss_fn = pixelcnnpp.loss_fn
generate_fn = pixelcnnpp.generate_fn
optimizer = Adam(model.parameters(),
lr=args.lr,
betas=(0.95, 0.9995),
polyak=args.polyak)
scheduler = torch.optim.lr_scheduler.ExponentialLR(
optimizer, args.lr_decay)
elif args.model == 'pixelsnail':
from generative_models_toolbox.algos.pixelcnn import pixelsnail, pixelcnnpp
model = pixelsnail.PixelSNAIL(args.image_dims, args.n_channels,
args.n_res_layers, args.attn_n_layers,
args.attn_nh, args.attn_dq, args.attn_dv,
args.attn_drop_rate, args.n_logistic_mix,
args.n_cond_classes).to(args.device)
loss_fn = pixelcnnpp.loss_fn
generate_fn = pixelcnnpp.generate_fn
optimizer = Adam(model.parameters(),
lr=args.lr,
betas=(0.95, 0.9995),
images = np.asarray(images)
img_viewer_examples(images, labels.tolist()[0], greyscale= True)
model = Model()
model.add(Dense(784, 90))
model.add(ReLU())
model.add(Dense(90, 45))
model.add(ReLU())
model.add(Dense(45, 10))
model.set_loss(CrossEntropyLoss())
optimizer = Adam(model.parameters(), learning_rate = 0.01)
lr_schedular = StepLR(optimizer, step_size = 1, gamma=0.1)
# weights path
path = "./checkpoints/Linear_MINST_weights.sav"
# model = load_weights(path)
epochs = 6
for epoch in range(epochs):
i = 0
for image, label in dataloader:
if epoch == 5:
model.graph()
image = image/255
i = i + 1
def run(hparams,
model,
train_dataloader,
valid_dataloader,
device,
out_dir='checkpoints'):
learning_rate = hparams['learning_rate']
accumulate_step = hparams['accumulate_step']
lr_schedule = hparams['lr_schedule']
warmup_steps = hparams['warmup_steps']
warmup_proportion = hparams['warmup_proportion']
n_embd = hparams['n_embd']
num_optim_steps = hparams['num_optim_steps']
train_batch_size = hparams['train_batch_size']
valid_step = hparams['valid_step']
no_token_id = hparams['no_token_id']
model_parameters = filter(lambda p: p.requires_grad, model.parameters())
total_params = sum([np.prod(p.size()) for p in model_parameters])
logger.info('Number of parameter = {}'.format(total_params))
param_optimizer = list(model.named_parameters())
no_decay = ['bias', 'ln']
optimizer_grouped_parameters = [{
'params':
[p for n, p in param_optimizer if not any(nd in n for nd in no_decay)],
'weight_decay':
0.01
}, {
'params':
[p for n, p in param_optimizer if any(nd in n for nd in no_decay)],
'weight_decay':
0.0
}]
optimizer = Adam(optimizer_grouped_parameters,
learning_rate,
max_grad_norm=1.0)
step = 0
global_step = 0
epoch = 0
while True:
model.train()
(tr_loss, tr_ppl, mean_ppl, nb_tr_examples,
nb_tr_steps) = 0.0, 0.0, 0.0, 0, 0
n_token_real, n_token_total = 0, 0
pbar = tqdm.tqdm(enumerate(train_dataloader),
total=len(train_dataloader))
for i, batch in pbar:
batch = tuple(t.cuda() for t in batch)
input_ids, position_ids, token_type_ids, label_ids, *_ = batch
if no_token_id:
token_type_ids = None
loss, ppl = model(input_ids, position_ids, token_type_ids,
label_ids)
loss = loss.mean()
loss = loss / (train_batch_size / input_ids.shape[0])
loss.backward()
nb_tr_steps += 1
tr_loss += float(
loss.sum().item()) * (train_batch_size / input_ids.shape[0])
if ppl.sum().item() < 1000000:
tr_ppl += ppl.sum().item()
else:
tr_ppl += mean_ppl
mean_loss = tr_loss / nb_tr_steps
mean_ppl = tr_ppl / nb_tr_steps
n_token_total += input_ids.shape[0] * input_ids.shape[1]
n_token_real += (input_ids != 0).sum().item()
#gradient update
step += 1
if step % accumulate_step == 0:
set_lr(optimizer, global_step, lr_schedule, learning_rate,
warmup_steps, warmup_proportion, n_embd,
num_optim_steps)
optimizer.step()
optimizer.zero_grad()
global_step += 1
print(
'epoch: {}, global_step: {}, step: {}, mean_loss: {}, mean_ppl:{}'
.format(epoch + 1, global_step + 1, step + 1, mean_loss,
mean_ppl),
file=train_logger)
if global_step % valid_step == 0:
print('Saving model...')
torch.save(
{
'model': model.state_dict(),
'epoch': epoch,
'hparams': hparams,
},
os.path.join(out_dir,
f'GPT2-pretrain-step-{global_step}.pkl'))
eval_loss, eval_ppl = valid(model, valid_dataloader, epoch,
device)
print('{},{},{},{},{}'.format(epoch + 1, global_step + 1,
step + 1, eval_loss,
eval_ppl),
file=valid_logger)
logger.info('current learning rate: ' +
str(optimizer.param_groups[0]['lr']))
model.train()
if global_step >= num_optim_steps:
break
if (step + 1) % CACHE_EMPTY_STEP == 0:
torch.cuda.empty_cache()
if global_step >= num_optim_steps:
break
epoch += 1
train_logger.close()
valid_logger.close()
"Please install apex from https://www.github.com/nvidia/apex "
"to use distributed and fp16 training.")
optimizer = FusedAdam(optimizer_grouped_parameters,
lr=args.learning_rate,
bias_correction=False,
max_grad_norm=1.0)
if args.loss_scale == 0:
optimizer = FP16_Optimizer(optimizer, dynamic_loss_scale=True,
verbose=False)
else:
optimizer = FP16_Optimizer(optimizer,
static_loss_scale=args.loss_scale,
verbose=False)
else:
optimizer = Adam(optimizer_grouped_parameters, args.learning_rate,
max_grad_norm=1.0)
#########################################################################
# Training !
##########################################################################
if args.local_rank == -1 or get_rank() == 0:
with open(join(log_dir, 'train_log.txt'), 'a+', buffering=1) as train_logger:
print('epoch,global_step,step,mean_loss,mean_ppl,n_token_real,'
'n_token_total,epoch_time', file=train_logger)
with open(join(log_dir, 'eval_log.txt'), 'a+', buffering=1) as eval_logger:
print('epoch,global_step,step,eval_loss,eval_ppl', file=eval_logger)
global_step = 0
step = 0
return ll
def likelihood(x,y):
out = model(x)
#return -1*((y - out)**2).sum(1)
return log_normal(y,out,zero+np.log(9)).sum(1)
def lossf(x,y):
ll = likelihood(x,y).sum() + prior() + bnn.params.merged_sampler.logdet
return -ll/float(n)
L = 32
adam = Adam(bnn.params.parameters(), 0.001)
T = 2500
x1, x2 = -6, 6
y1, y2 = -100, 100
for i in range(T):
adam.zero_grad()
bnn.params.sample()
loss = lossf(X_,Y_)
loss.backward()
adam.step()