def test_lm_generate_xlm_mlm_en_2048(self):
model = XLMWithLMHeadModel.from_pretrained("xlm-mlm-en-2048")
model.to(torch_device)
input_ids = torch.tensor([[14, 447]],
dtype=torch.long,
device=torch_device) # the president
expected_output_ids = [
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
] # the president the president the president the president the president the president the president the president the president the president
# TODO(PVP): this and other input_ids I tried for generation give pretty bad results. Not sure why. Model might just not be made for auto-regressive inference
output_ids = model.generate(input_ids, do_sample=False)
self.assertListEqual(output_ids[0].cpu().numpy().tolist(),
expected_output_ids)
def load_model_tokenizer(self, pretrained):
""" Load transformer model and tokenizer for given pre-trained name
:param pretrained: pre-trained name
:return: model, tokenizer
"""
model = None
tokenizer = None
if self.method == "T5":
if pretrained in T5_PRETRAINED_MODELS:
model = T5ForConditionalGeneration.from_pretrained(pretrained)
tokenizer = T5Tokenizer.from_pretrained(pretrained)
elif self.method == "BART":
if pretrained in BART_PRETRAINED_MODELS:
model = BartForConditionalGeneration.from_pretrained(pretrained)
tokenizer = BartTokenizer.from_pretrained(pretrained)
elif self.method == "GPT-2":
if pretrained in GPT2_PRETRAINED_MODELS:
model = GPT2LMHeadModel.from_pretrained(pretrained)
model.config.max_length = self.max_length
tokenizer = GPT2Tokenizer.from_pretrained(pretrained)
elif self.method == "XLM":
if pretrained in XLM_PRETRAINED_MODELS:
model = XLMWithLMHeadModel.from_pretrained(pretrained)
model.config.max_length = self.max_length
tokenizer = XLMTokenizer.from_pretrained(pretrained)
else:
pass
return model, tokenizer
def test_lm_generate_xlm_mlm_en_2048(self):
model = XLMWithLMHeadModel.from_pretrained("xlm-mlm-en-2048")
input_ids = torch.Tensor([[1, 14, 2232, 26,
1]]).long() # The dog is cute
expected_output_ids = [
1,
14,
2232,
26,
1,
567,
26,
32,
149,
149,
149,
149,
149,
149,
149,
149,
149,
149,
149,
149,
] # The dog is nothing is it!!!!!!!!!!!! TODO (PVP): this sentence (and others I tried) does not make much sense, there seems to be a problem with xlm language generation.
torch.manual_seed(0)
output_ids = model.generate(input_ids)
self.assertListEqual(output_ids[0].tolist(), expected_output_ids)
filename = "SUBTLEXus74286wordstextversion.txt"
vocab = get_vocab(filename, 3000)
rw_vocab = get_vocab(filename, 10000)
filename2 = "SUBTLEX-US frequency list with PoS information text version.txt"
pos_dict = get_pos_dict(filename2)
GPT2 = ModelInfo(GPT2LMHeadModel.from_pretrained('gpt2', return_dict=True),
GPT2Tokenizer.from_pretrained('gpt2'), "Ġ", vocab, "GTP2")
Roberta = ModelInfo(
RobertaForCausalLM.from_pretrained('roberta-base', return_dict=True),
RobertaTokenizer.from_pretrained('roberta-base'), "_", vocab, "Roberta")
XLM = ModelInfo(
XLMWithLMHeadModel.from_pretrained('xlm-mlm-xnli15-1024',
return_dict=True),
XLMTokenizer.from_pretrained('xlm-mlm-xnli15-1024'), "_", vocab, "XLM")
T5 = ModelInfo(
T5ForConditionalGeneration.from_pretrained("t5-base", return_dict=True),
T5Tokenizer.from_pretrained("t5-base"), "_", vocab, "T5")
Albert = ModelInfo(
AlbertForMaskedLM.from_pretrained('albert-base-v2', return_dict=True),
AlbertTokenizer.from_pretrained('albert-base-v2'), "_", vocab, "Albert")
TXL = ModelInfo(TransfoXLLMHeadModel.from_pretrained('transfo-xl-wt103'),
TransfoXLTokenizer.from_pretrained('transfo-xl-wt103'), "_",
vocab, "TXL")
if __name__ == "__main__":
tokenizer = RobertaTokenizerFast.from_pretrained('roberta-base', do_lower_case=False)
model = RobertaForMaskedLM.from_pretrained('./multi-label_LM/multi-label_RoBerta_e10_b16', config=config)
# 12-layer, 768-hidden, 12-heads, 125M parameters, roberta-base using the bert-base architecture
elif args.LM == 'XLM':
from transformers import XLMConfig, XLMTokenizer, XLMWithLMHeadModel
config = XLMConfig(vocab_size=64139,
emb_dim=1024,
max_position_embeddings=512,
n_heads=8,
n_layers=6,
)
tokenizer = XLMTokenizer.from_pretrained('xlm-mlm-enfr-1024', do_lower_case=False)
model = XLMWithLMHeadModel.from_pretrained('./multi-label_LM/multi-label_XLM_e10_b16', config=config)
# 6-layer, 1024-hidden, 8-heads
# XLM English-French model trained on the concatenation of English and French wikipedia
else:
print('need to define LM from Bert,RoBerta,XLM')
print(model)
def freeze_layer_fun(freeze_layer):
for name, param in model.named_parameters():
if freeze_layer in name:
print(name)
param.requires_grad = False
else:
pass
tokenizer = XLMTokenizer.from_pretrained('xlm-mlm-xnli15-1024', do_lower_case=False)
model = XLMWithLMHeadModel.from_pretrained('xlm-mlm-xnli15-1024', config=config)
# 12-layer, 1024-hidden, 8-heads
# XLM Model pre-trained with MLM on the 15 XNLI languages.
'''
config = XLMConfig(
vocab_size=64139,
emb_dim=1024,
max_position_embeddings=512,
n_heads=8,
n_layers=6,
)
tokenizer = XLMTokenizer.from_pretrained('xlm-mlm-enfr-1024',
do_lower_case=False)
model = XLMWithLMHeadModel.from_pretrained('xlm-mlm-enfr-1024',
config=config)
#6-layer, 1024-hidden, 8-heads
# XLM English-French model trained on the concatenation of English and French wikipedia
else:
print('need to define LM from Bert,RoBerta,XLM')
'''
from transformers import BertTokenizerFast, BertConfig, BertForMaskedLM, AdamW
tokenizer = BertTokenizerFast.from_pretrained('bert-base-cased', do_lower_case=False)
'''
train_inputs = torch.Tensor()
train_masks = torch.Tensor()
for sent in sentences_train:
encoded_sent = tokenizer.encode_plus(
train, val, test = Multi30k.splits(root='../../data',
exts=('.en', '.de'),
fields=(en_text, de_text))
en_text.build_vocab(train, max_size=30000, min_freq=3)
de_text.build_vocab(train, max_size=30000, min_freq=3)
vocab_en = en_text.vocab
vocab_de = de_text.vocab
pad_idx = vocab_de.stoi['<pad>']
train_ldr, val_ldr, test_ldr = BucketIterator.splits((train, val, test),
batch_size=5)
# load model
xlm = XLMWithLMHeadModel.from_pretrained('xlm-mlm-ende-1024')
xlm.transformer.embeddings = nn.Embedding(len(vocab_en),
xlm.config.emb_dim,
padding_idx=pad_idx)
xlm.pred_layer.proj = nn.Linear(xlm.config.emb_dim,
len(vocab_de),
bias=True)
xlm.cuda()
xent = nn.CrossEntropyLoss()
batch = next(iter(train_ldr))
src, trg = batch.src.to(0), batch.trg.to(0)
def mt_loss(out, target):
# only compute loss for non-padding indices
import torch
from transformers import XLMTokenizer, XLMWithLMHeadModel
import logging
logging.basicConfig(level=logging.INFO)
tokenizer = XLMTokenizer.from_pretrained('xlm-mlm-17-1280')
model = XLMWithLMHeadModel.from_pretrained('xlm-mlm-17-1280')
input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).unsqueeze(
0) # Batch size 1
outputs = model(input_ids)
last_hidden_states = outputs[
0] # The last hidden-state is the first element of the output tuple
print(last_hidden_states)
print(last_hidden_states.shape)
predicted_index = torch.argmax(last_hidden_states).item()
print(predicted_index)
predicted_token = tokenizer.convert_ids_to_tokens([predicted_index])
print(predicted_token)