def setUp(self):
model = ModelInputDict()
sp_net_config = supernet(expand_ratio=[0.5, 1.0])
self.model = Convert(sp_net_config).convert(model)
self.images = paddle.randn(shape=[2, 3, 32, 32], dtype='float32')
self.images2 = {
'data': paddle.randn(shape=[2, 12, 32, 32], dtype='float32')
}
default_run_config = {'skip_layers': ['conv1.0', 'conv2.0']}
self.run_config = RunConfig(**default_run_config)
self.ofa_model = OFA(self.model, run_config=self.run_config)
self.ofa_model._clear_search_space(self.images, data=self.images2)
def init_config(self):
default_run_config = {
'train_batch_size': 1,
'n_epochs': [[2, 5]],
'init_learning_rate': [[0.003, 0.001]],
'dynamic_batch_size': [1],
'total_images': 1,
}
self.run_config = RunConfig(**default_run_config)
default_distill_config = {
'teacher_model': self.teacher_model,
'mapping_layers': ['models.3.fn'],
}
self.distill_config = DistillConfig(**default_distill_config)
self.elastic_order = None
def init_config(self):
default_run_config = {
'train_batch_size': 1,
'eval_batch_size': 1,
'n_epochs': [[2, 5]],
'init_learning_rate': [[0.003, 0.001]],
'dynamic_batch_size': [1],
'total_images': 1,
}
self.run_config = RunConfig(**default_run_config)
default_distill_config = {
'lambda_distill': 0.01,
'teacher_model': self.teacher_model,
}
self.distill_config = DistillConfig(**default_distill_config)
def init_config(self):
default_run_config = {
'train_batch_size': 1,
'eval_batch_size': 1,
'n_epochs': [[1], [2, 3], [4, 5]],
'init_learning_rate': [[0.001], [0.003, 0.001], [0.003, 0.001]],
'dynamic_batch_size': [1, 1, 1],
'total_images': 1,
'elastic_depth': (5, 15, 24)
}
self.run_config = RunConfig(**default_run_config)
default_distill_config = {
'lambda_distill': 0.01,
'teacher_model': self.teacher_model,
'mapping_layers': ['models.0.fn']
}
self.distill_config = DistillConfig(**default_distill_config)
def test_ofa():
model = Model()
teacher_model = Model()
default_run_config = {
'train_batch_size': 256,
'n_epochs': [[1], [2, 3], [4, 5]],
'init_learning_rate': [[0.001], [0.003, 0.001], [0.003, 0.001]],
'dynamic_batch_size': [1, 1, 1],
'total_images': 50000, #1281167,
'elastic_depth': (2, 5, 8)
}
run_config = RunConfig(**default_run_config)
default_distill_config = {
'lambda_distill': 0.01,
'teacher_model': teacher_model,
'mapping_layers': ['models.0.fn']
}
distill_config = DistillConfig(**default_distill_config)
ofa_model = OFA(model, run_config, distill_config=distill_config)
train_dataset = paddle.vision.datasets.MNIST(mode='train',
backend='cv2',
transform=transform)
train_loader = paddle.io.DataLoader(train_dataset,
places=place,
feed_list=[image, label],
drop_last=True,
batch_size=64)
start_epoch = 0
for idx in range(len(run_config.n_epochs)):
cur_idx = run_config.n_epochs[idx]
for ph_idx in range(len(cur_idx)):
cur_lr = run_config.init_learning_rate[idx][ph_idx]
adam = paddle.optimizer.Adam(
learning_rate=cur_lr,
parameter_list=(ofa_model.parameters() +
ofa_model.netAs_param))
for epoch_id in range(start_epoch,
run_config.n_epochs[idx][ph_idx]):
for batch_id, data in enumerate(train_loader()):
dy_x_data = np.array([
x[0].reshape(1, 28, 28) for x in data
]).astype('float32')
y_data = np.array([x[1] for x in data
]).astype('int64').reshape(-1, 1)
img = paddle.dygraph.to_variable(dy_x_data)
label = paddle.dygraph.to_variable(y_data)
label.stop_gradient = True
for model_no in range(run_config.dynamic_batch_size[idx]):
output, _ = ofa_model(img, label)
loss = F.mean(output)
dis_loss = ofa_model.calc_distill_loss()
loss += dis_loss
loss.backward()
if batch_id % 10 == 0:
print(
'epoch: {}, batch: {}, loss: {}, distill loss: {}'
.format(epoch_id, batch_id,
loss.numpy()[0],
dis_loss.numpy()[0]))
### accumurate dynamic_batch_size network of gradients for same batch of data
### NOTE: need to fix gradients accumulate in PaddlePaddle
adam.minimize(loss)
adam.clear_gradients()
start_epoch = run_config.n_epochs[idx][ph_idx]
sp_config = supernet(expand_ratio=args.width_mult_list)
model = Convert(sp_config).convert(model)
utils.set_state_dict(model, origin_weights)
del origin_weights
teacher_model = ErnieModelForSequenceClassification.from_pretrained(
args.from_pretrained, num_labels=3, name='teacher')
setattr(teacher_model, 'return_additional_info', True)
default_run_config = {
'n_epochs': [[4 * args.epoch], [6 * args.epoch]],
'init_learning_rate': [[args.lr], [args.lr]],
'elastic_depth': args.depth_mult_list,
'dynamic_batch_size': [[1, 1], [1, 1]]
}
run_config = RunConfig(**default_run_config)
model_cfg = get_config(args.from_pretrained)
default_distill_config = {'teacher_model': teacher_model}
distill_config = DistillConfig(**default_distill_config)
ofa_model = OFA(model,
run_config,
distill_config=distill_config,
elastic_order=['width', 'depth'])
### suppose elastic width first
if args.reorder_weight:
head_importance, neuron_importance = compute_neuron_head_importance(
args, ofa_model.model, tokenizer, dev_ds, place, model_cfg)
def do_train(args):
paddle.set_device("gpu" if args.n_gpu else "cpu")
if paddle.distributed.get_world_size() > 1:
paddle.distributed.init_parallel_env()
set_seed(args)
args.task_name = args.task_name.lower()
dataset_class, metric_class = TASK_CLASSES[args.task_name]
args.model_type = args.model_type.lower()
model_class, tokenizer_class = MODEL_CLASSES[args.model_type]
train_ds = dataset_class.get_datasets(['train'])
tokenizer = tokenizer_class.from_pretrained(args.model_name_or_path)
trans_func = partial(convert_example,
tokenizer=tokenizer,
label_list=train_ds.get_labels(),
max_seq_length=args.max_seq_length)
train_ds = train_ds.apply(trans_func, lazy=True)
train_batch_sampler = paddle.io.DistributedBatchSampler(
train_ds, batch_size=args.batch_size, shuffle=True)
batchify_fn = lambda samples, fn=Tuple(
Pad(axis=0, pad_val=tokenizer.pad_token_id), # input
Pad(axis=0, pad_val=tokenizer.pad_token_id), # segment
Stack(), # length
Stack(dtype="int64" if train_ds.get_labels() else "float32") # label
): [data for i, data in enumerate(fn(samples)) if i != 2]
train_data_loader = DataLoader(dataset=train_ds,
batch_sampler=train_batch_sampler,
collate_fn=batchify_fn,
num_workers=0,
return_list=True)
if args.task_name == "mnli":
dev_dataset_matched, dev_dataset_mismatched = dataset_class.get_datasets(
["dev_matched", "dev_mismatched"])
dev_dataset_matched = dev_dataset_matched.apply(trans_func, lazy=True)
dev_dataset_mismatched = dev_dataset_mismatched.apply(trans_func,
lazy=True)
dev_batch_sampler_matched = paddle.io.BatchSampler(
dev_dataset_matched, batch_size=args.batch_size, shuffle=False)
dev_data_loader_matched = DataLoader(
dataset=dev_dataset_matched,
batch_sampler=dev_batch_sampler_matched,
collate_fn=batchify_fn,
num_workers=0,
return_list=True)
dev_batch_sampler_mismatched = paddle.io.BatchSampler(
dev_dataset_mismatched, batch_size=args.batch_size, shuffle=False)
dev_data_loader_mismatched = DataLoader(
dataset=dev_dataset_mismatched,
batch_sampler=dev_batch_sampler_mismatched,
collate_fn=batchify_fn,
num_workers=0,
return_list=True)
else:
dev_dataset = dataset_class.get_datasets(["dev"])
dev_dataset = dev_dataset.apply(trans_func, lazy=True)
dev_batch_sampler = paddle.io.BatchSampler(dev_dataset,
batch_size=args.batch_size,
shuffle=False)
dev_data_loader = DataLoader(dataset=dev_dataset,
batch_sampler=dev_batch_sampler,
collate_fn=batchify_fn,
num_workers=0,
return_list=True)
num_labels = 1 if train_ds.get_labels() == None else len(
train_ds.get_labels())
# Step1: Initialize the origin BERT model.
model = model_class.from_pretrained(args.model_name_or_path,
num_classes=num_labels)
if paddle.distributed.get_world_size() > 1:
model = paddle.DataParallel(model)
# Step2: Convert origin model to supernet.
sp_config = supernet(expand_ratio=args.width_mult_list)
model = Convert(sp_config).convert(model)
# Use weights saved in the dictionary to initialize supernet.
weights_path = os.path.join(args.model_name_or_path,
'model_state.pdparams')
origin_weights = paddle.load(weights_path)
model.set_state_dict(origin_weights)
# Step3: Define teacher model.
teacher_model = model_class.from_pretrained(args.model_name_or_path,
num_classes=num_labels)
new_dict = utils.utils.remove_model_fn(teacher_model, origin_weights)
teacher_model.set_state_dict(new_dict)
del origin_weights, new_dict
default_run_config = {'elastic_depth': args.depth_mult_list}
run_config = RunConfig(**default_run_config)
# Step4: Config about distillation.
mapping_layers = ['bert.embeddings']
for idx in range(model.bert.config['num_hidden_layers']):
mapping_layers.append('bert.encoder.layers.{}'.format(idx))
default_distill_config = {
'lambda_distill': args.lambda_rep,
'teacher_model': teacher_model,
'mapping_layers': mapping_layers,
}
distill_config = DistillConfig(**default_distill_config)
# Step5: Config in supernet training.
ofa_model = OFA(model,
run_config=run_config,
distill_config=distill_config,
elastic_order=['depth'])
#elastic_order=['width'])
criterion = paddle.nn.loss.CrossEntropyLoss() if train_ds.get_labels(
) else paddle.nn.loss.MSELoss()
metric = metric_class()
if args.task_name == "mnli":
dev_data_loader = (dev_data_loader_matched, dev_data_loader_mismatched)
lr_scheduler = paddle.optimizer.lr.LambdaDecay(
args.learning_rate,
lambda current_step, num_warmup_steps=args.warmup_steps,
num_training_steps=args.max_steps if args.max_steps > 0 else
(len(train_data_loader) * args.num_train_epochs): float(
current_step) / float(max(1, num_warmup_steps))
if current_step < num_warmup_steps else max(
0.0,
float(num_training_steps - current_step) / float(
max(1, num_training_steps - num_warmup_steps))))
optimizer = paddle.optimizer.AdamW(
learning_rate=lr_scheduler,
epsilon=args.adam_epsilon,
parameters=ofa_model.model.parameters(),
weight_decay=args.weight_decay,
apply_decay_param_fun=lambda x: x in [
p.name for n, p in ofa_model.model.named_parameters()
if not any(nd in n for nd in ["bias", "norm"])
])
global_step = 0
tic_train = time.time()
for epoch in range(args.num_train_epochs):
# Step6: Set current epoch and task.
ofa_model.set_epoch(epoch)
ofa_model.set_task('depth')
for step, batch in enumerate(train_data_loader):
global_step += 1
input_ids, segment_ids, labels = batch
for depth_mult in args.depth_mult_list:
for width_mult in args.width_mult_list:
# Step7: Broadcast supernet config from width_mult,
# and use this config in supernet training.
net_config = utils.dynabert_config(ofa_model, width_mult,
depth_mult)
ofa_model.set_net_config(net_config)
logits, teacher_logits = ofa_model(
input_ids, segment_ids, attention_mask=[None, None])
rep_loss = ofa_model.calc_distill_loss()
if args.task_name == 'sts-b':
logit_loss = 0.0
else:
logit_loss = soft_cross_entropy(
logits, teacher_logits.detach())
loss = rep_loss + args.lambda_logit * logit_loss
loss.backward()
optimizer.step()
lr_scheduler.step()
ofa_model.model.clear_gradients()
if global_step % args.logging_steps == 0:
if (not args.n_gpu > 1) or paddle.distributed.get_rank() == 0:
logger.info(
"global step %d, epoch: %d, batch: %d, loss: %f, speed: %.2f step/s"
% (global_step, epoch, step, loss, args.logging_steps /
(time.time() - tic_train)))
tic_train = time.time()
if global_step % args.save_steps == 0:
if args.task_name == "mnli":
evaluate(teacher_model,
criterion,
metric,
dev_data_loader_matched,
width_mult=100)
evaluate(teacher_model,
criterion,
metric,
dev_data_loader_mismatched,
width_mult=100)
else:
evaluate(teacher_model,
criterion,
metric,
dev_data_loader,
width_mult=100)
for depth_mult in args.depth_mult_list:
for width_mult in args.width_mult_list:
net_config = utils.dynabert_config(
ofa_model, width_mult, depth_mult)
ofa_model.set_net_config(net_config)
tic_eval = time.time()
if args.task_name == "mnli":
acc = evaluate(ofa_model, criterion, metric,
dev_data_loader_matched, width_mult,
depth_mult)
evaluate(ofa_model, criterion, metric,
dev_data_loader_mismatched, width_mult,
depth_mult)
print("eval done total : %s s" %
(time.time() - tic_eval))
else:
acc = evaluate(ofa_model, criterion, metric,
dev_data_loader, width_mult,
depth_mult)
print("eval done total : %s s" %
(time.time() - tic_eval))
if (not args.n_gpu > 1
) or paddle.distributed.get_rank() == 0:
output_dir = os.path.join(args.output_dir,
"model_%d" % global_step)
if not os.path.exists(output_dir):
os.makedirs(output_dir)
# need better way to get inner model of DataParallel
model_to_save = model._layers if isinstance(
model, paddle.DataParallel) else model
model_to_save.save_pretrained(output_dir)
tokenizer.save_pretrained(output_dir)
def init_config(self):
default_run_config = {'skip_layers': ['branch2.0']}
self.run_config = RunConfig(**default_run_config)
def __call__(self, model, param_state_dict):
paddleslim = try_import('paddleslim')
from paddleslim.nas.ofa import OFA, RunConfig, utils
from paddleslim.nas.ofa.convert_super import Convert, supernet
task = self.ofa_config['task']
expand_ratio = self.ofa_config['expand_ratio']
skip_neck = self.ofa_config['skip_neck']
skip_head = self.ofa_config['skip_head']
run_config = self.ofa_config['RunConfig']
if 'skip_layers' in run_config:
skip_layers = run_config['skip_layers']
else:
skip_layers = []
# supernet config
sp_config = supernet(expand_ratio=expand_ratio)
# convert to supernet
model = Convert(sp_config).convert(model)
skip_names = []
if skip_neck:
skip_names.append('neck.')
if skip_head:
skip_names.append('head.')
for name, sublayer in model.named_sublayers():
for n in skip_names:
if n in name:
skip_layers.append(name)
run_config['skip_layers'] = skip_layers
run_config = RunConfig(**run_config)
# build ofa model
ofa_model = OFA(model, run_config=run_config)
ofa_model.set_epoch(0)
ofa_model.set_task(task)
input_spec = [{
"image": paddle.ones(
shape=[1, 3, 640, 640], dtype='float32'),
"im_shape": paddle.full(
[1, 2], 640, dtype='float32'),
"scale_factor": paddle.ones(
shape=[1, 2], dtype='float32')
}]
ofa_model._clear_search_space(input_spec=input_spec)
ofa_model._build_ss = True
check_ss = ofa_model._sample_config('expand_ratio', phase=None)
# tokenize the search space
ofa_model.tokenize()
# check token map, search cands and search space
logger.info('Token map is {}'.format(ofa_model.token_map))
logger.info('Search candidates is {}'.format(ofa_model.search_cands))
logger.info('The length of search_space is {}, search_space is {}'.
format(len(ofa_model._ofa_layers), ofa_model._ofa_layers))
# set model state dict into ofa model
utils.set_state_dict(ofa_model.model, param_state_dict)
return ofa_model