def load_model(model_path, device):
"""
Load the pretrained model states and prepare the model for sentiment analysis.
Parameters
----------
model_path: str
Path to the pretrained model states binary file.
device: torch.device
Device to load the model on.
Returns
-------
model: BertForSequenceClassification
Model with the loaded pretrained states
"""
config = BertConfig(vocab_size=30522, type_vocab_size=2)
model = BertForSequenceClassification(config, 2, [11])
model_states = torch.load(model_path, map_location=device)
model.load_state_dict(model_states)
model.eval()
return model
def load_deprecated_model(model_path):
"""
Load the pretrained model states and prepare the model for sentiment analysis on CPU.
This method returns a custom BertForSequenceClassification model that allows it to work
with LayerIntegratedGradients and LayerIntermediateGradients.
Parameters
----------
model_path: str
Path to the pretrained model states binary file.
Returns
-------
model: BertForSequenceClassification
Model with the loaded pretrained states.
"""
config = BertConfig(vocab_size=30522, type_vocab_size=2)
model = BertForSequenceClassification(config, 2, [11])
model_states = torch.load(model_path, map_location=torch.device("cpu"))
model.load_state_dict(model_states)
model.eval()
return model
def main():
parser = argparse.ArgumentParser()
## Required parameters
parser.add_argument("--data_dir",
default=None,
type=str,
required=True,
help="The input data dir. Should contain the .tsv files (or other data files) for the task.")
parser.add_argument("--task_name",
default=None,
type=str,
required=True,
help="The name of the task to train.")
parser.add_argument("--output_dir",
default=None,
type=str,
required=True,
help="The output directory where the model checkpoints will be written.")
parser.add_argument("--bert_config_file",
default=None,
type=str,
required=True,
help="Path to the configuration file for the BERT model.")
## Other parameters
parser.add_argument("--init_checkpoint",
default=None,
type=str,
help="Initial checkpoint (usually from a pre-trained BERT model).")
parser.add_argument("--max_seq_length",
default=128,
type=int,
help="The maximum total input sequence length after WordPiece tokenization. \n"
"Sequences longer than this will be truncated, and sequences shorter \n"
"than this will be padded.")
parser.add_argument("--do_train",
default=False,
action='store_true',
help="Whether to run training.")
parser.add_argument("--do_eval",
default=False,
action='store_true',
help="Whether to run eval on the dev set.")
parser.add_argument("--discr",
default=False,
action='store_true',
help="Whether to do discriminative fine-tuning.")
parser.add_argument("--train_batch_size",
default=32,
type=int,
help="Total batch size for training.")
parser.add_argument("--eval_batch_size",
default=8,
type=int,
help="Total batch size for eval.")
parser.add_argument("--learning_rate",
default=5e-5,
type=float,
help="The initial learning rate for Adam.")
parser.add_argument("--num_train_epochs",
default=3.0,
type=float,
help="Total number of training epochs to perform.")
parser.add_argument("--warmup_proportion",
default=0.1,
type=float,
help="Proportion of training to perform linear learning rate warmup for. "
"E.g., 0.1 = 10%% of training.")
parser.add_argument("--save_checkpoints_steps",
default=1000,
type=int,
help="How often to save the model checkpoint.")
parser.add_argument("--no_cuda",
default=False,
action='store_true',
help="Whether not to use CUDA when available")
parser.add_argument("--accumulate_gradients",
type=int,
default=1,
help="Number of steps to accumulate gradient on (divide the batch_size and accumulate)")
parser.add_argument("--local_rank",
type=int,
default=-1,
help="local_rank for distributed training on gpus")
parser.add_argument('--seed',
type=int,
default=42,
help="random seed for initialization")
parser.add_argument('--gradient_accumulation_steps',
type=int,
default=1,
help="Number of updates steps to accumualte before performing a backward/update pass.")
parser.add_argument('--layers',
type=int,
nargs='+',
default=[-2],
help="choose the layers that used for downstream tasks, "
"-2 means use pooled output, -1 means all layer,"
"else means the detail layers. default is -2")
parser.add_argument('--num_datas',
default=None,
type=int,
help="the number of data examples")
parser.add_argument('--num_test_datas',
default=None,
type=int,
help="the number of data examples"
)
parser.add_argument('--pooling_type',
default=None,
type=str,
choices=[None, 'mean', 'max'])
args = parser.parse_args()
processors = {
"sst": SSTProcessor,
}
if args.local_rank == -1 or args.no_cuda:
device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu")
n_gpu = torch.cuda.device_count()
else:
device = torch.device("cuda", args.local_rank)
n_gpu = 1
# Initializes the distributed backend which will take care of sychronizing nodes/GPUs
torch.distributed.init_process_group(backend='nccl')
logger.info("device %s n_gpu %d distributed training %r", device, n_gpu, bool(args.local_rank != -1))
if args.accumulate_gradients < 1:
raise ValueError("Invalid accumulate_gradients parameter: {}, should be >= 1".format(
args.accumulate_gradients))
args.train_batch_size = int(args.train_batch_size / args.accumulate_gradients)
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
if n_gpu > 0:
torch.cuda.manual_seed_all(args.seed)
if not args.do_train and not args.do_eval:
raise ValueError("At least one of `do_train` or `do_eval` must be True.")
if os.path.exists(args.output_dir) and os.listdir(args.output_dir):
raise ValueError("Output directory ({}) already exists and is not empty.".format(args.output_dir))
os.makedirs(args.output_dir, exist_ok=True)
summary_writer = SummaryWriter(args.output_dir)
task_name = args.task_name.lower()
if task_name not in processors:
raise ValueError("Task not found: %s" % (task_name))
processor = processors[task_name]()
label_list = processor.get_labels()
tokenizer = BertTokenizer.from_pretrained("bert-large-cased")
bert_config = BertConfig.from_json_file(args.bert_config_file)
model = BertForSequenceClassification(bert_config, len(label_list), args.layers, pooling=args.pooling_type)
model.to(device)
if args.local_rank != -1:
model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.local_rank],
output_device=args.local_rank)
elif n_gpu > 1:
model = torch.nn.DataParallel(model)
no_decay = ['bias', 'gamma', 'beta']
optimizer_parameters = [
{'params': [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)], 'weight_decay_rate': 0.01},
{'params': [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)], 'weight_decay_rate': 0.0}
]
optimizer = AdamW(optimizer_parameters,
lr=args.learning_rate,
correct_bias=False)
global_step = 0
global_train_step = 0
all_examples = processor.get_all_examples(args.data_dir)
all_features = convert_examples_to_features(
all_examples, label_list, args.max_seq_length, tokenizer)
all_input_ids = all_features['input_ids']
all_input_mask = all_features['attention_mask']
all_segment_ids = all_features['token_type_ids']
all_label_ids = all_features['labels']
all_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label_ids)
train_data, eval_data = random_split(all_data, [100000, 12428])
eval_dataloader = DataLoader(eval_data, batch_size=args.eval_batch_size, shuffle=False)
if args.do_train:
logger.info("***** Running training *****")
logger.info(" Batch size = %d", args.train_batch_size)
if args.local_rank == -1:
train_sampler = RandomSampler(train_data)
else:
train_sampler = DistributedSampler(train_data)
train_dataloader = DataLoader(train_data, sampler=train_sampler, batch_size=args.train_batch_size)
print("TOTAL STEPS: ", (len(train_dataloader)*int(args.num_train_epochs)))
epoch=0
for _ in trange(int(args.num_train_epochs), desc="Epoch"):
epoch+=1
model.train()
tr_loss = 0
nb_tr_examples, nb_tr_steps = 0, 0
for step, batch in enumerate(tqdm(train_dataloader, desc="Iteration")):
batch = tuple(t.to(device) for t in batch)
input_ids, input_mask, token_type_ids, label_ids = batch
loss, _ = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=label_ids)
if n_gpu > 1:
loss = loss.mean() # mean() to average on multi-gpu.
if args.gradient_accumulation_steps > 1:
loss = loss / args.gradient_accumulation_steps
loss.backward()
tr_loss += loss.item()
nb_tr_examples += input_ids.size(0)
nb_tr_steps += 1
if (step + 1) % args.gradient_accumulation_steps == 0:
optimizer.step() # We have accumulated enought gradients
# scheduler.step()
summary_writer.add_scalar('Loss/train', loss.item(), global_step)
# possibly comment this out
max_grad_norm = 1.0
_clip_grad_norm(optimizer_parameters, max_grad_norm)
model.zero_grad()
global_step += 1
model.eval()
eval_loss, eval_accuracy = 0, 0
pos_eval_prec, pos_eval_recall, pos_eval_f1 = 0, 0, 0
neg_eval_prec, neg_eval_recall, neg_eval_f1 = 0, 0, 0
nb_eval_steps, nb_eval_examples = 0, 0
with open(os.path.join(args.output_dir, "results_ep"+str(epoch)+".txt"),"w") as f:
for input_ids, input_mask, segment_ids, label_ids in tqdm(eval_dataloader, desc="Evaluate"):
input_ids = input_ids.to(device)
input_mask = input_mask.to(device)
segment_ids = segment_ids.to(device)
label_ids = label_ids.to(device)
with torch.no_grad():
tmp_eval_loss, logits = model(input_ids, token_type_ids=segment_ids, attention_mask=input_mask, labels=label_ids)
logits = logits.detach().cpu().numpy()
label_ids = label_ids.detach().to('cpu').numpy()
outputs = np.argmax(logits, axis=1)
for output in outputs:
f.write(str(output)+"\n")
tmp_eval_accuracy=np.sum(outputs == label_ids)
tmp_eval_prec, tmp_eval_recall, tmp_eval_f1 = get_analytics_neg_sent(outputs, label_ids)
eval_loss += tmp_eval_loss.mean().item()
eval_accuracy += tmp_eval_accuracy
neg_eval_prec += tmp_eval_prec
neg_eval_recall += tmp_eval_recall
neg_eval_f1 += tmp_eval_f1
tmp_eval_prec, tmp_eval_recall, tmp_eval_f1 = get_analytics_pos_sent(outputs, label_ids)
pos_eval_prec += tmp_eval_prec
pos_eval_recall += tmp_eval_recall
pos_eval_f1 += tmp_eval_f1
global_train_step += 1
summary_writer.add_scalar("Loss/test", tmp_eval_loss.mean().item(), global_train_step)
summary_writer.add_scalar("Accuracy/test", tmp_eval_accuracy, global_train_step)
nb_eval_examples += input_ids.size(0)
nb_eval_steps += 1
eval_loss = eval_loss / nb_eval_steps
eval_accuracy = eval_accuracy / nb_eval_examples
pos_eval_prec = pos_eval_prec / nb_eval_steps
pos_eval_recall = pos_eval_recall / nb_eval_steps
pos_eval_f1 = pos_eval_f1 / nb_eval_steps
neg_eval_prec = neg_eval_prec / nb_eval_steps
neg_eval_recall = neg_eval_recall / nb_eval_steps
neg_eval_f1 = neg_eval_f1 / nb_eval_steps
result = {'eval_loss': eval_loss,
'eval_accuracy': eval_accuracy,
'global_step': global_step,
'loss': tr_loss/nb_tr_steps,
'pos_eval_precision': pos_eval_prec,
'neg_eval_precision': neg_eval_prec,
'pos_eval_recall': pos_eval_recall,
'neg_eval_recall': neg_eval_recall,
'pos_eval_f1': pos_eval_f1,
'neg_eval_f1': neg_eval_f1}
summary_writer.add_scalar("Epoch_loss/train", tr_loss, epoch)
summary_writer.add_scalar("Epoch_loss/test", eval_loss, epoch)
summary_writer.add_scalar("Epoch_accuracy/test", eval_accuracy, epoch)
summary_writer.add_scalar("Epoch_positive_precision/test", pos_eval_prec, epoch)
summary_writer.add_scalar("Epoch_negative_precision/test", neg_eval_prec, epoch)
summary_writer.add_scalar("Epoch_positive_recall/test", pos_eval_recall, epoch)
summary_writer.add_scalar("Epoch_negative_recall/test", neg_eval_recall, epoch)
summary_writer.add_scalar("Epoch_positive_f1/test", pos_eval_f1, epoch)
summary_writer.add_scalar("Epoch_negative_f1/test", neg_eval_f1, epoch)
output_eval_file = os.path.join(args.output_dir, "eval_results_ep"+str(epoch)+".txt")
print("output_eval_file=",output_eval_file)
with open(output_eval_file, "w") as writer:
logger.info("***** Eval results *****")
for key in sorted(result.keys()):
logger.info(" %s = %s", key, str(result[key]))
writer.write("%s = %s\n" % (key, str(result[key])))
print("Saving model")
torch.save(model.module.state_dict(), os.path.join(args.output_dir, "sst2-finetuned-bert-model_"+str(epoch)+".pth"))
def main():
parser = argparse.ArgumentParser()
## Required parameters
parser.add_argument(
"--data_dir",
default=None,
type=str,
help=
"The input data dir. Should contain the .tsv files (or other data files) for the task."
)
parser.add_argument("--task_name",
default=None,
type=str,
required=True,
help="The name of the task to train.")
parser.add_argument(
"--bert_model",
default=None,
type=str,
required=True,
help="Bert pre-trained model selected in the list: bert-base-uncased, "
"bert-large-uncased, bert-base-cased, bert-large-cased, bert-base-multilingual-uncased, "
"bert-base-multilingual-cased, bert-base-chinese.")
parser.add_argument(
"--max_seq_length",
default=128,
type=int,
help=
"The maximum total input sequence length after WordPiece tokenization. \n"
"Sequences longer than this will be truncated, and sequences shorter \n"
"than this will be padded.")
parser.add_argument("--eval_batch_size",
default=32,
type=int,
help="Total batch size for eval.")
parser.add_argument(
"--do_lower_case",
action='store_true',
help="Set this flag if you are using an uncased model.")
parser.add_argument(
"--model_dir",
default=None,
type=str,
required=True,
help=
"The output directory where the model predictions and checkpoints will be written."
)
parser.add_argument("--output_file",
default=None,
type=str,
required=True,
help="The output results will be written.")
parser.add_argument("--data_file",
default='',
type=str,
help="The input directory of input data file.")
parser.add_argument("--no_cuda",
action='store_true',
help="Whether not to use CUDA when available")
parser.add_argument("--local_rank",
type=int,
default=-1,
help="local_rank for distributed training on gpus")
parser.add_argument('--seed',
type=int,
default=42,
help="random seed for initialization")
parser.add_argument(
'--fp16',
action='store_true',
help="Whether to use 16-bit float precision instead of 32-bit")
parser.add_argument("--weights",
default='',
type=int,
help="The output results will be written.")
args = parser.parse_args()
if args.local_rank == -1 or args.no_cuda:
device = torch.device("cuda" if torch.cuda.is_available()
and not args.no_cuda else "cpu")
n_gpu = torch.cuda.device_count()
else:
torch.cuda.set_device(args.local_rank)
device = torch.device("cuda", args.local_rank)
n_gpu = 1
# Initializes the distributed backend which will take care of sychronizing nodes/GPUs
torch.distributed.init_process_group(backend='nccl')
logger.info(
"device: {} n_gpu: {}, distributed training: {}, 16-bits training: {}".
format(device, n_gpu, bool(args.local_rank != -1), args.fp16))
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
if n_gpu > 0:
torch.cuda.manual_seed_all(args.seed)
task_name = args.task_name.lower()
if task_name not in processors:
raise ValueError("Task not found: %s" % (task_name))
processor = processors[task_name]()
num_labels = num_labels_task[task_name]
label_list = processor.get_labels()
tokenizer = BertTokenizer.from_pretrained(args.bert_model,
do_lower_case=args.do_lower_case)
eval_examples = processor.get_dev_examples(args.data_file)
eval_features = convert_examples_to_features(eval_examples, label_list,
args.max_seq_length,
tokenizer)
logger.info("***** Running evaluation *****")
logger.info(" Num examples = %d", len(eval_examples))
logger.info(" Batch size = %d", args.eval_batch_size)
all_input_ids = torch.tensor([f.input_ids for f in eval_features],
dtype=torch.long)
all_input_mask = torch.tensor([f.input_mask for f in eval_features],
dtype=torch.long)
all_segment_ids = torch.tensor([f.segment_ids for f in eval_features],
dtype=torch.long)
all_label_ids = torch.tensor([f.label_id for f in eval_features],
dtype=torch.long)
eval_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids,
all_label_ids)
# Run prediction for full data
eval_sampler = SequentialSampler(eval_data)
eval_dataloader = DataLoader(eval_data,
sampler=eval_sampler,
batch_size=args.eval_batch_size)
for i in range(args.weights):
print("epoch:{}".format(i))
#if args.weights != '':
#WEIGHTS_NAME = "epoch"+str(args.weights)+"_"+WEIGHTS_NAME
output_model_file = os.path.join(args.model_dir,
"epoch" + str(i) + "_" + WEIGHTS_NAME)
#else:
#output_model_file = os.path.join(args.model_dir, WEIGHTS_NAME)
output_config_file = os.path.join(args.model_dir, CONFIG_NAME)
config = BertConfig(output_config_file)
model = BertForSequenceClassification(config, num_labels=num_labels)
#model = BertForLMClassification(config, num_labels=num_labels)
#model = BertForDClassifier(config, num_labels=num_labels)
model.load_state_dict(torch.load(output_model_file))
model.to(device)
model.eval()
eval_loss, eval_accuracy = 0, 0
nb_eval_steps, nb_eval_examples = 0, 0
for input_ids, input_mask, segment_ids, label_ids in tqdm(
eval_dataloader, desc="Evaluating"):
input_ids = input_ids.to(device)
input_mask = input_mask.to(device)
segment_ids = segment_ids.to(device)
label_ids = label_ids.to(device)
with torch.no_grad():
tmp_eval_loss = model(input_ids, segment_ids, input_mask,
label_ids)
logits = model(input_ids, segment_ids, input_mask)
logits = logits.detach().cpu().numpy()
label_ids = label_ids.to('cpu').numpy()
tmp_eval_accuracy = accuracy(logits, label_ids)
eval_loss += tmp_eval_loss.mean().item()
eval_accuracy += tmp_eval_accuracy
nb_eval_examples += input_ids.size(0)
nb_eval_steps += 1
eval_loss = eval_loss / nb_eval_steps
eval_accuracy = eval_accuracy / nb_eval_examples
result = {'eval_loss': eval_loss, 'eval_accuracy': eval_accuracy}
output_eval_file = args.output_file
with open(output_eval_file, "a") as writer:
logger.info("***** Eval results *****")
for key in sorted(result.keys()):
logger.info(" %s = %s", key, str(result[key]))
writer.write("%s = %s\n" % (key, str(result[key])))
def load_bert_model(model_path, device, num_cuda_devs):
"""
Load the pretrained BERT model states and prepare the model for sentiment analysis on CPU.
This method returns a custom BertForSequenceClassification model that allows it to work
with LayerIntegratedGradients and LayerIntermediateGradients.
Loads a slighly different parallel model if num_cuda_devs > 1.
Parameters
----------
model_path: str
Path to the pretrained model states binary file.
device: torch.device
Device to load the model on.
num_cuda_devs: int
Determines how model parallel is used.
Returns
-------
model: BertForSequenceClassification
Model with the loaded pretrained states.
tokenizer: BertTokenizer
Instance of the tokenizer for BERT models.
"""
config = BertConfig(vocab_size=30522, type_vocab_size=2)
if num_cuda_devs < 2:
model = BertForSequenceClassification(config, 2, [11])
model_states = torch.load(model_path, map_location=torch.device("cpu"))
model.load_state_dict(model_states)
model.eval()
model.to(device) # puts it on cuda:0 or cpu, less work
else:
if num_cuda_devs == 2:
embed_device = "cuda:0"
encoder_device1 = "cuda:1"
encoder_device2 = "cuda:1"
encoder_device3 = "cuda:0"
pooler_device = "cuda:0"
elif num_cuda_devs == 3:
embed_device = "cuda:0"
encoder_device1 = "cuda:0"
encoder_device2 = "cuda:1"
encoder_device3 = "cuda:2"
pooler_device = "cuda:0"
else:
# 4 cuda devices
embed_device = "cuda:0"
encoder_device1 = "cuda:1"
encoder_device2 = "cuda:2"
encoder_device3 = "cuda:3"
pooler_device = "cuda:0"
model = BertForSequenceClassificationParallel(
config,
2, [11],
embeddings_device=embed_device,
encoder_device1=encoder_device1,
encoder_device2=encoder_device2,
encoder_device3=encoder_device3,
pooler_device=pooler_device)
model_states = torch.load(model_path, map_location="cpu")
model.load_state_dict(model_states, strict=False)
model.eval()
# override the embeddings layer
weight = torch.zeros((30525, 768), dtype=torch.float32)
weight[0:30522, :] = model.bert.embeddings.word_embeddings.weight
weight[30523, :] = torch.rand(768, dtype=torch.float32)
weight[30524, :] = torch.randn(768, dtype=torch.float32)
weight = weight.to(model.bert.embeddings.word_embeddings.weight.device)
model.bert.embeddings.word_embeddings.weight = nn.Parameter(weight)
# override the config number of embeddings
model.bert.embeddings.word_embeddings.num_embeddings += 3
tokenizer = BertTokenizer.from_pretrained("bert-large-uncased")
return model, tokenizer