def from_pretrained(model_id_or_path: str,
device: Optional[torch.device] = None):
torch_model = TorchBertModel.from_pretrained(model_id_or_path)
model = BertModelNoPooler.from_torch(torch_model, device)
model.config = torch_model.config
model._torch_model = torch_model # prevent destroy torch model.
return model
def __init__(self, tagset_size):
super(BertForSequenceClassification, self).__init__()
self.tagset_size = tagset_size
self.BertModel_single = BertModel.from_pretrained(pretrain_model_dir)
self.single_hidden2tag = BertClassificationHead(
bert_hidden_dim, tagset_size)
def __init__(self,
args,
tokenizer: BertTokenizer,
object_features_variant=False,
positional_embed_variant=False,
latent_transformer=False):
super().__init__()
self.args = args
self.tokenizer = tokenizer
self.image_projection = nn.Sequential(
nn.Linear(512, 768), nn.BatchNorm1d(768, momentum=0.01))
config = BertConfig.from_pretrained('bert-base-uncased')
self.tokenizer = tokenizer
self.embeddings = BertEmbeddings(config)
self.text_encoder = BertModel.from_pretrained("bert-base-uncased",
return_dict=True)
self.decoder = BertLMHeadModel.from_pretrained(
'bert-base-uncased',
is_decoder=True,
use_cache=True,
add_cross_attention=True)
if object_features_variant:
self.image_transformer = ImageTransformerEncoder(args)
self.positional_embed = True if positional_embed_variant else False
self.latent_transformer = latent_transformer
def __init__(
self,
vocab: Vocabulary,
bert_model: Union[str, BertModel],
embedding_dropout: float = 0.0,
initializer: InitializerApplicator = InitializerApplicator(),
label_smoothing: float = None,
ignore_span_metric: bool = False,
srl_eval_path: str = DEFAULT_SRL_EVAL_PATH,
**kwargs,
) -> None:
super().__init__(vocab, **kwargs)
if isinstance(bert_model, str):
self.bert_model = BertModel.from_pretrained(bert_model)
else:
self.bert_model = bert_model
self.num_classes = self.vocab.get_vocab_size("labels")
if srl_eval_path is not None:
# For the span based evaluation, we don't want to consider labels
# for verb, because the verb index is provided to the model.
self.span_metric = SrlEvalScorer(srl_eval_path,
ignore_classes=["V"])
else:
self.span_metric = None
self.tag_projection_layer = Linear(self.bert_model.config.hidden_size,
self.num_classes)
self.embedding_dropout = Dropout(p=embedding_dropout)
self._label_smoothing = label_smoothing
self.ignore_span_metric = ignore_span_metric
initializer(self)
def main():
if len(sys.argv) != 3:
print(
"Usage: \n"
" convert_huggingface_bert_to_npz model_name (bert-base-uncased) output_file"
)
exit(0)
torch.set_grad_enabled(False)
model_name = sys.argv[1]
model = BertModel.from_pretrained(model_name)
arrays = {k: v.detach() for k, v in model.named_parameters()}
q_weight_key = 'self.query.weight'
k_weight_key = 'self.key.weight'
v_weight_key = 'self.value.weight'
q_bias_key = 'self.query.bias'
k_bias_key = 'self.key.bias'
v_bias_key = 'self.value.bias'
numpy_dict = {}
for k in arrays.keys():
if k.endswith(q_weight_key):
v = torch.clone(
torch.t(
torch.cat([
arrays[k],
arrays[k[:-len(q_weight_key)] + k_weight_key],
arrays[k[:-len(q_weight_key)] + v_weight_key]
], 0).contiguous()).contiguous())
numpy_dict[k[:-len(q_weight_key)] + "qkv.weight"] = v.numpy()
elif k.endswith(q_bias_key):
v = torch.cat([
arrays[k], arrays[k[:-len(q_bias_key)] + k_bias_key],
arrays[k[:-len(q_bias_key)] + v_bias_key]
], 0).numpy()
numpy_dict[k[:-len(q_bias_key)] + 'qkv.bias'] = v
elif any((k.endswith(suffix) for suffix in (k_weight_key, v_weight_key,
k_bias_key, v_bias_key))):
continue
elif (k.endswith("attention.output.dense.weight")
or k.endswith("pooler.dense.weight")
or (k.endswith("output.dense.weight")
or k.endswith("intermediate.dense.weight"))):
numpy_dict[k] = torch.clone(torch.t(
arrays[k]).contiguous()).numpy()
else:
numpy_dict[k] = arrays[k].numpy()
del arrays
del model
numpy.savez_compressed(sys.argv[2], **numpy_dict)
def __init__(self,
bert_model_name,
trainable=False,
output_size=0,
activation=gelu,
dropout=0.0):
"""Initializes pertrained `BERT` model
Arguments:
bert_model_name {str} -- bert model name
Keyword Arguments:
output_size {float} -- output size (default: {None})
activation {nn.Module} -- activation function (default: {gelu})
dropout {float} -- dropout rate (default: {0.0})
"""
super().__init__()
self.bert_model = BertModel.from_pretrained(bert_model_name,
output_attentions=True,
output_hidden_states=True)
logger.info("Load bert model {} successfully.".format(bert_model_name))
self.output_size = output_size
if trainable:
logger.info(
"Start fine-tuning bert model {}.".format(bert_model_name))
else:
logger.info("Keep fixed bert model {}.".format(bert_model_name))
for param in self.bert_model.parameters():
param.requires_grad = trainable
if self.output_size > 0:
self.mlp = BertLinear(
input_size=self.bert_model.config.hidden_size,
output_size=self.output_size,
activation=activation)
else:
self.output_size = self.bert_model.config.hidden_size
self.mlp = lambda x: x
if dropout > 0:
self.dropout = nn.Dropout(p=dropout)
else:
self.dropout = lambda x: x
def __init__(self,
name_or_path_or_model: Union[str, BertModel],
adapter_size: int = 64,
adapter_num: int = 12,
external_param: Union[bool, List[bool]] = False,
**kwargs):
super().__init__()
if isinstance(name_or_path_or_model, str):
self.bert = BertModel.from_pretrained(name_or_path_or_model)
else:
self.bert = name_or_path_or_model
set_requires_grad(self.bert, False)
if isinstance(external_param, bool):
param_place = [external_param for _ in range(adapter_num)]
elif isinstance(external_param, list):
param_place = [False for _ in range(adapter_num)]
for i, e in enumerate(external_param, 1):
param_place[-i] = e
else:
raise ValueError("wrong type of external_param!")
self.adapters = nn.ModuleList([
nn.ModuleList([
Adapter(self.bert.config.hidden_size, adapter_size,
param_place[i]),
Adapter(self.bert.config.hidden_size, adapter_size,
param_place[i])
]) for i in range(adapter_num)
])
for i, adapters in enumerate(self.adapters, 1):
layer = self.bert.encoder.layer[-i]
layer.output = AdapterBertOutput(layer.output, adapters[0].forward)
set_requires_grad(layer.output.base.LayerNorm, True)
layer.attention.output = AdapterBertOutput(layer.attention.output,
adapters[1].forward)
set_requires_grad(layer.attention.output.base.LayerNorm, True)
self.output_dim = self.bert.config.hidden_size
def __init__(
self,
vocab: Vocabulary,
bert_model: Union[str, Dict[str, Any], BertModel],
embedding_dropout: float = 0.0,
initializer: InitializerApplicator = InitializerApplicator(),
label_smoothing: float = None,
ignore_span_metric: bool = False,
srl_eval_path: str = DEFAULT_SRL_EVAL_PATH,
**kwargs,
) -> None:
super().__init__(vocab, **kwargs)
if isinstance(bert_model, str):
self.bert_model = BertModel.from_pretrained(bert_model)
elif isinstance(bert_model, dict):
warnings.warn(
"Initializing BertModel without pretrained weights. This is fine if you're loading "
"from an AllenNLP archive, but not if you're training.",
UserWarning,
)
bert_config = BertConfig.from_dict(bert_model)
self.bert_model = BertModel(bert_config)
else:
self.bert_model = bert_model
self.num_classes = self.vocab.get_vocab_size("labels")
if srl_eval_path is not None:
# For the span based evaluation, we don't want to consider labels
# for verb, because the verb index is provided to the model.
self.span_metric = SrlEvalScorer(srl_eval_path,
ignore_classes=["V"])
else:
self.span_metric = None
self.tag_projection_layer = Linear(self.bert_model.config.hidden_size,
self.num_classes)
self.embedding_dropout = Dropout(p=embedding_dropout)
self._label_smoothing = label_smoothing
self.ignore_span_metric = ignore_span_metric
initializer(self)
def test_from_pytorch(self):
with torch.no_grad():
with self.subTest("bert-base-cased"):
tokenizer = BertTokenizerFast.from_pretrained(
"bert-base-cased")
fx_model = FlaxBertModel.from_pretrained("bert-base-cased")
pt_model = BertModel.from_pretrained("bert-base-cased")
# Check for simple input
pt_inputs = tokenizer.encode_plus(
"This is a simple input",
return_tensors=TensorType.PYTORCH)
fx_inputs = tokenizer.encode_plus(
"This is a simple input", return_tensors=TensorType.JAX)
pt_outputs = pt_model(**pt_inputs).to_tuple()
fx_outputs = fx_model(**fx_inputs)
self.assertEqual(
len(fx_outputs), len(pt_outputs),
"Output lengths differ between Flax and PyTorch")
for fx_output, pt_output in zip(fx_outputs, pt_outputs):
self.assert_almost_equals(fx_output, pt_output.numpy(),
5e-3)
def main(config):
args = config
if args.gradient_accumulation_steps < 1:
raise ValueError(
"Invalid gradient_accumulation_steps parameter: {}, should be >= 1"
.format(args.gradient_accumulation_steps))
args.train_batch_size = args.train_batch_size // args.gradient_accumulation_steps
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
if not os.path.exists(args.output_dir):
os.makedirs(args.output_dir)
processor = ATEPCProcessor()
label_list = processor.get_labels()
num_labels = len(label_list) + 1
datasets = {
'camera': "atepc_datasets/camera",
'car': "atepc_datasets/car",
'phone': "atepc_datasets/phone",
'notebook': "atepc_datasets/notebook",
'laptop': "atepc_datasets/laptop",
'restaurant': "atepc_datasets/restaurant",
'twitter': "atepc_datasets/twitter",
'mixed': "atepc_datasets/mixed",
}
pretrained_bert_models = {
'camera': "bert-base-chinese",
'car': "bert-base-chinese",
'phone': "bert-base-chinese",
'notebook': "bert-base-chinese",
'laptop': "bert-base-uncased",
'restaurant': "bert-base-uncased",
# for loading domain-adapted BERT
# 'restaurant': "../bert_pretrained_restaurant",
'twitter': "bert-base-uncased",
'mixed': "bert-base-multilingual-uncased",
}
args.bert_model = pretrained_bert_models[args.dataset]
args.data_dir = datasets[args.dataset]
def convert_polarity(examples):
for i in range(len(examples)):
polarities = []
for polarity in examples[i].polarity:
if polarity == 2:
polarities.append(1)
else:
polarities.append(polarity)
examples[i].polarity = polarities
tokenizer = BertTokenizer.from_pretrained(args.bert_model,
do_lower_case=True)
train_examples = processor.get_train_examples(args.data_dir)
eval_examples = processor.get_test_examples(args.data_dir)
num_train_optimization_steps = int(
len(train_examples) / args.train_batch_size /
args.gradient_accumulation_steps) * args.num_train_epochs
bert_base_model = BertModel.from_pretrained(args.bert_model)
bert_base_model.config.num_labels = num_labels
if args.dataset in {'camera', 'car', 'phone', 'notebook'}:
convert_polarity(train_examples)
convert_polarity(eval_examples)
model = LCF_ATEPC(bert_base_model, args=args)
else:
model = LCF_ATEPC(bert_base_model, args=args)
for arg in vars(args):
logger.info('>>> {0}: {1}'.format(arg, getattr(args, arg)))
model.to(device)
param_optimizer = list(model.named_parameters())
no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight']
optimizer_grouped_parameters = [{
'params':
[p for n, p in param_optimizer if not any(nd in n for nd in no_decay)],
'weight_decay':
0.00001
}, {
'params':
[p for n, p in param_optimizer if any(nd in n for nd in no_decay)],
'weight_decay':
0.00001
}]
optimizer = AdamW(optimizer_grouped_parameters,
lr=args.learning_rate,
weight_decay=0.00001)
eval_features = convert_examples_to_features(eval_examples, label_list,
args.max_seq_length,
tokenizer)
all_spc_input_ids = torch.tensor([f.input_ids_spc 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)
all_polarities = torch.tensor([f.polarities for f in eval_features],
dtype=torch.long)
all_valid_ids = torch.tensor([f.valid_ids for f in eval_features],
dtype=torch.long)
all_lmask_ids = torch.tensor([f.label_mask for f in eval_features],
dtype=torch.long)
eval_data = TensorDataset(all_spc_input_ids, all_input_mask,
all_segment_ids, all_label_ids, all_polarities,
all_valid_ids, all_lmask_ids)
# Run prediction for full data
eval_sampler = RandomSampler(eval_data)
eval_dataloader = DataLoader(eval_data,
sampler=eval_sampler,
batch_size=args.eval_batch_size)
def evaluate(eval_ATE=True, eval_APC=True):
# evaluate
apc_result = {'max_apc_test_acc': 0, 'max_apc_test_f1': 0}
ate_result = 0
y_true = []
y_pred = []
n_test_correct, n_test_total = 0, 0
test_apc_logits_all, test_polarities_all = None, None
model.eval()
label_map = {i: label for i, label in enumerate(label_list, 1)}
for input_ids_spc, input_mask, segment_ids, label_ids, polarities, valid_ids, l_mask in eval_dataloader:
input_ids_spc = input_ids_spc.to(device)
input_mask = input_mask.to(device)
segment_ids = segment_ids.to(device)
valid_ids = valid_ids.to(device)
label_ids = label_ids.to(device)
polarities = polarities.to(device)
l_mask = l_mask.to(device)
with torch.no_grad():
ate_logits, apc_logits = model(input_ids_spc,
segment_ids,
input_mask,
valid_ids=valid_ids,
polarities=polarities,
attention_mask_label=l_mask)
if eval_APC:
polarities = model.get_batch_polarities(polarities)
n_test_correct += (torch.argmax(
apc_logits, -1) == polarities).sum().item()
n_test_total += len(polarities)
if test_polarities_all is None:
test_polarities_all = polarities
test_apc_logits_all = apc_logits
else:
test_polarities_all = torch.cat(
(test_polarities_all, polarities), dim=0)
test_apc_logits_all = torch.cat(
(test_apc_logits_all, apc_logits), dim=0)
if eval_ATE:
if not args.use_bert_spc:
label_ids = model.get_batch_token_labels_bert_base_indices(
label_ids)
ate_logits = torch.argmax(F.log_softmax(ate_logits, dim=2),
dim=2)
ate_logits = ate_logits.detach().cpu().numpy()
label_ids = label_ids.to('cpu').numpy()
input_mask = input_mask.to('cpu').numpy()
for i, label in enumerate(label_ids):
temp_1 = []
temp_2 = []
for j, m in enumerate(label):
if j == 0:
continue
elif label_ids[i][j] == len(label_list):
y_true.append(temp_1)
y_pred.append(temp_2)
break
else:
temp_1.append(label_map.get(label_ids[i][j], 'O'))
temp_2.append(label_map.get(ate_logits[i][j], 'O'))
if eval_APC:
test_acc = n_test_correct / n_test_total
if args.dataset in {'camera', 'car', 'phone', 'notebook'}:
test_f1 = f1_score(torch.argmax(test_apc_logits_all, -1).cpu(),
test_polarities_all.cpu(),
labels=[0, 1],
average='macro')
else:
test_f1 = f1_score(torch.argmax(test_apc_logits_all, -1).cpu(),
test_polarities_all.cpu(),
labels=[0, 1, 2],
average='macro')
test_acc = round(test_acc * 100, 2)
test_f1 = round(test_f1 * 100, 2)
apc_result = {
'max_apc_test_acc': test_acc,
'max_apc_test_f1': test_f1
}
if eval_ATE:
report = classification_report(y_true, y_pred, digits=4)
tmps = report.split()
ate_result = round(float(tmps[7]) * 100, 2)
return apc_result, ate_result
def save_model(path):
# Save a trained model and the associated configuration,
# Take care of the storage!
os.makedirs(path, exist_ok=True)
model_to_save = model.module if hasattr(
model, 'module') else model # Only save the model it-self
model_to_save.save_pretrained(path)
tokenizer.save_pretrained(path)
label_map = {i: label for i, label in enumerate(label_list, 1)}
model_config = {
"bert_model": args.bert_model,
"do_lower": True,
"max_seq_length": args.max_seq_length,
"num_labels": len(label_list) + 1,
"label_map": label_map
}
json.dump(model_config, open(os.path.join(path, "config.json"), "w"))
logger.info('save model to: {}'.format(path))
def train():
train_features = convert_examples_to_features(train_examples,
label_list,
args.max_seq_length,
tokenizer)
logger.info("***** Running training *****")
logger.info(" Num examples = %d", len(train_examples))
logger.info(" Batch size = %d", args.train_batch_size)
logger.info(" Num steps = %d", num_train_optimization_steps)
all_spc_input_ids = torch.tensor(
[f.input_ids_spc for f in train_features], dtype=torch.long)
all_input_mask = torch.tensor([f.input_mask for f in train_features],
dtype=torch.long)
all_segment_ids = torch.tensor([f.segment_ids for f in train_features],
dtype=torch.long)
all_label_ids = torch.tensor([f.label_id for f in train_features],
dtype=torch.long)
all_valid_ids = torch.tensor([f.valid_ids for f in train_features],
dtype=torch.long)
all_lmask_ids = torch.tensor([f.label_mask for f in train_features],
dtype=torch.long)
all_polarities = torch.tensor([f.polarities for f in train_features],
dtype=torch.long)
train_data = TensorDataset(all_spc_input_ids, all_input_mask,
all_segment_ids, all_label_ids,
all_polarities, all_valid_ids,
all_lmask_ids)
train_sampler = SequentialSampler(train_data)
train_dataloader = DataLoader(train_data,
sampler=train_sampler,
batch_size=args.train_batch_size)
max_apc_test_acc = 0
max_apc_test_f1 = 0
max_ate_test_f1 = 0
global_step = 0
for epoch in range(int(args.num_train_epochs)):
logger.info('#' * 80)
logger.info('Train {} Epoch{}'.format(args.seed, epoch + 1,
args.data_dir))
logger.info('#' * 80)
nb_tr_examples, nb_tr_steps = 0, 0
for step, batch in enumerate(train_dataloader):
model.train()
batch = tuple(t.to(device) for t in batch)
input_ids_spc, input_mask, segment_ids, label_ids, polarities, valid_ids, l_mask = batch
loss_ate, loss_apc = model(input_ids_spc, segment_ids,
input_mask, label_ids, polarities,
valid_ids, l_mask)
loss = loss_ate + loss_apc
loss.backward()
nb_tr_examples += input_ids_spc.size(0)
nb_tr_steps += 1
optimizer.step()
optimizer.zero_grad()
global_step += 1
if global_step % args.eval_steps == 0:
if epoch >= args.num_train_epochs - 2 or args.num_train_epochs <= 2:
# evaluate in last 2 epochs
apc_result, ate_result = evaluate(
eval_ATE=not args.use_bert_spc)
# apc_result, ate_result = evaluate()
# path = '{0}/{1}_{2}_apcacc_{3}_apcf1_{4}_atef1_{5}'.format(
# args.output_dir,
# args.dataset,
# args.local_context_focus,
# round(apc_result['max_apc_test_acc'], 2),
# round(apc_result['max_apc_test_f1'], 2),
# round(ate_result, 2)
# )
# if apc_result['max_apc_test_acc'] > max_apc_test_acc or \
# apc_result['max_apc_test_f1'] > max_apc_test_f1 or \
# ate_result > max_ate_test_f1:
# save_model(path)
if apc_result['max_apc_test_acc'] > max_apc_test_acc:
max_apc_test_acc = apc_result['max_apc_test_acc']
if apc_result['max_apc_test_f1'] > max_apc_test_f1:
max_apc_test_f1 = apc_result['max_apc_test_f1']
if ate_result > max_ate_test_f1:
max_ate_test_f1 = ate_result
current_apc_test_acc = apc_result['max_apc_test_acc']
current_apc_test_f1 = apc_result['max_apc_test_f1']
current_ate_test_f1 = round(ate_result, 2)
logger.info('*' * 80)
logger.info('Train {} Epoch{}, Evaluate for {}'.format(
args.seed, epoch + 1, args.data_dir))
logger.info(
f'APC_test_acc: {current_apc_test_acc}(max: {max_apc_test_acc}) '
f'APC_test_f1: {current_apc_test_f1}(max: {max_apc_test_f1})'
)
if args.use_bert_spc:
logger.info(
f'ATE_test_F1: {current_apc_test_f1}(max: {max_apc_test_f1})'
f' (Unreliable since `use_bert_spc` is "True".)'
)
else:
logger.info(
f'ATE_test_f1: {current_ate_test_f1}(max:{max_ate_test_f1})'
)
logger.info('*' * 80)
return [max_apc_test_acc, max_apc_test_f1, max_ate_test_f1]
return train()
def test_fused_softmax():
from megatron.model.fused_softmax import FusedScaleMaskSoftmax, SoftmaxFusionTypes
from megatron.model.gpt2_model import (
gpt2_attention_mask_func as attention_mask_func, )
bert = BertModel.from_pretrained("bert-base-cased").cuda().half()
tokenizer = BertTokenizer.from_pretrained("bert-base-cased")
test_text = (
"Hello. How are you? I am fine thank you and you? yes Good. "
"hi hi hi hi hi hi hi hi hi hi hi hi hi" # 32
)
tokens = tokenizer(
[test_text] * 4,
return_tensors="pt",
)
embedding_output = bert.embeddings(
input_ids=tokens["input_ids"].cuda(),
position_ids=None,
token_type_ids=tokens["token_type_ids"].cuda(),
inputs_embeds=None,
past_key_values_length=0,
)
# (bsz, 1, 1, seq_len)
mask = bert.get_extended_attention_mask(
attention_mask=tokens["attention_mask"].cuda(),
input_shape=tokens["input_ids"].shape,
device=bert.device,
)
# (bsz, 1, seq_len, seq_len)
mask = mask.repeat(1, 1, mask.size()[-1], 1)
attention = bert.encoder.layer[0].attention.self
key_layer = attention.transpose_for_scores(attention.key(embedding_output))
query_layer = attention.transpose_for_scores(
attention.query(embedding_output))
attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
attention_scores /= math.sqrt(key_layer.size()[-1])
fused_softmax = (FusedScaleMaskSoftmax(
input_in_fp16=True,
input_in_bf16=False,
fusion_type=SoftmaxFusionTypes.general,
mask_func=attention_mask_func,
scale=None,
softmax_in_fp32=False,
).cuda().half())
fused_softmax_output = fused_softmax(
attention_scores,
(mask != 0),
)
torch_softmax = (FusedScaleMaskSoftmax(
input_in_fp16=True,
input_in_bf16=False,
mask_func=attention_mask_func,
fusion_type=SoftmaxFusionTypes.none,
scale=None,
softmax_in_fp32=False,
).cuda().half())
torch_softmax_output = torch_softmax(
attention_scores,
(mask != 0),
)
test_result = (fused_softmax_output - torch_softmax_output).abs()
while test_result.dim() != 1:
test_result = test_result.mean(dim=-1)
diff = test_result.mean(dim=-1)
if diff <= 1e-3:
print(
f"\n[Success] test_fused_softmax"
f"\n > mean_difference={diff}"
f"\n > fused_values={fused_softmax_output[-1][-1][-1][:5].tolist()}"
f"\n > torch_values={torch_softmax_output[-1][-1][-1][:5].tolist()}"
)
else:
print(
f"\n[Fail] test_fused_softmax"
f"\n > mean_difference={diff}, "
f"\n > fused_values={fused_softmax_output[-1][-1][-1][:5].tolist()}, "
f"\n > torch_values={torch_softmax_output[-1][-1][-1][:5].tolist()}"
)
####################
# Helper functions #
####################
def process(t):
return torch.stack(t).squeeze().detach().numpy()
# BERT
if __name__ == "__main__":
excluded_neurons = {0: (0, ), 1: (0, 1), 2: (0, 1, 2)}
model = BertModel.from_pretrained("bert-base-cased",
output_attentions=True,
output_values=True,
output_dense=True,
output_mlp_activations=True,
output_q_activations=True,
output_k_activations=True,
output_v_activations=True,
excluded_neurons=excluded_neurons)
tokenizer = BertTokenizer.from_pretrained("bert-base-cased")
inputs = tokenizer("Hello", return_tensors="pt")
print("### inputs ###")
print(inputs.items())
outputs = model(**inputs)
print("### values ###")
print(len(outputs["values"]))
print(outputs["values"][0].shape)
values = outputs["values"]
values = torch.stack(values).squeeze()
values = values.detach().numpy()
def __init__(self, model_name):
self.tokenizer = AutoTokenizer.from_pretrained(model_name)
self.model = BertModel.from_pretrained(model_name).eval()
self.model.cuda()
def __init__(self, args, tokenizer) -> None:
super().__init__(args, tokenizer)
self.generative_encoder = BertModel.from_pretrained("bert-base-uncased", return_dict=True)
self.latent_layer = Latent(args)