def __init__(self, model_name, num_labels: int, clf_dropout=0.2):
super().__init__()
self.transformer = GPT2Model.from_pretrained(model_name)
self.dropout = nn.Dropout(clf_dropout)
self.linear = nn.Linear(self.transformer.config.n_embd * 2, num_labels)
nn.init.normal_(self.linear.weight, std=0.02)
nn.init.normal_(self.linear.bias, 0)
def extract_gpt2_hidden_activations(
text_path, save_activs_to): #, mode="full_model", focus_layers=[]):
# read in text samples to pass through single layer of gpt2 model
text_inputs = []
with open(text_path, "rb") as infile:
text_inputs = pickle.load(infile)
# num_inputs = len(text_inputs)
# Load pre-trained model tokenizer (vocabulary)
tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
# get the hidden activations - assumes a gpu is available
layer_activs = []
for text in text_inputs:
# tokenize text
indexed_tokens = tokenizer.encode(text)
tokens_tensor = torch.tensor([indexed_tokens]).to('cuda')
# set up model
model = GPT2Model.from_pretrained('gpt2')
model.eval()
model.to('cuda')
# grab the hidden activations and save them to layer_actives
with torch.no_grad():
hidden, _ = model(tokens_tensor)
layer_activs.append(hidden.cpu().numpy().squeeze())
# clear gpu memory in preparation for next text sample
torch.cuda.empty_cache()
# save layer dimensions
with open(save_activs_to, "wb") as outfile:
pickle.dump(layer_activs, outfile)
pass
def download_model(name):
if not name in MODELS:
raise Exception(str(name) + ' not a model in the list')
if not exists(PATH):
print("# ", str(PATH), "not found, creating dir.")
mkdir(PATH)
print('# Downloading model: ' + str(name))
name_path = MODEL_PATH_DICT[name]
if name == 'word2vec':
if not exists(join(PATH, name_path)):
wget.download(
'https://s3.amazonaws.com/dl4j-distribution/GoogleNews-vectors-negative300.bin.gz'
)
shutil.move(name_path, join(PATH, name_path))
print('# Downloaded word2vec')
else:
print('# Already downloaded')
if name == 'glove':
if not exists(join(PATH, name_path)):
wget.download(
'http://nlp.stanford.edu/data/wordvecs/glove.840B.300d.zip')
zip = zipfile.ZipFile('./glove.840B.300d.zip')
zip.extractall()
_ = glove2word2vec('./glove.840B.300d.txt', join(PATH, name_path))
print('# Downloaded glove')
else:
print('# Already downloaded')
if name == 'dict2vec':
if not exists(join(PATH, name_path)):
wget.download(
'https://dict2vec.s3.amazonaws.com/dict2vec300.tar.bz2')
tar = tarfile.open("dict2vec300.tar.bz2")
tar.extractall()
tar.close()
shutil.move(name_path, join(PATH, name_path))
print('# Downloaded dict2vec')
else:
print('# Already downloaded')
if name == 'conceptnet':
if not exists(join(PATH, name_path)):
wget.download(
'https://conceptnet.s3.amazonaws.com/downloads/2019/numberbatch/numberbatch-en-19.08.txt.gz'
)
shutil.move(name_path, join(PATH, name_path))
print('# Downloaded Conceptnet Numberbatch')
else:
print('# Already downloaded')
if name == 'bert' or name == 'bert-context':
_ = BertTokenizer.from_pretrained('bert-large-uncased')
_ = BertModel.from_pretrained(
'bert-large-uncased').embeddings.word_embeddings.weight.data.numpy(
)
print('# Downloaded bert')
if name == 'gpt2' or name == 'gpt2-context':
_ = GPT2Tokenizer.from_pretrained('gpt2')
_ = GPT2LMHeadModel.from_pretrained('gpt2')
_ = GPT2Model.from_pretrained('gpt2')
print('# Downloaded gpt-2')
def construct_encoder(self):
model = GPT2Model.from_pretrained(self.model_name)
model.cuda()
model = torch.nn.DataParallel(model)
model.eval()
tokenizer = GPT2Tokenizer.from_pretrained(self.model_name)
print("Model and tokenzier are constructed!")
return model, tokenizer
def __init__(self, pretrain_path, dropout=0.1):
super(Gpt2Model, self).__init__()
self.bert = GPT2Model.from_pretrained(pretrain_path)
self.dropout = nn.Dropout(dropout)
self.aux_head = nn.Sequential(
OrderedDict([
('dropout', nn.Dropout(dropout)),
('clf', nn.Linear(self.bert.config.n_embd, 6)),
]))
self.main_head = nn.Sequential(
OrderedDict([('dropout', nn.Dropout(dropout)),
('clf', nn.Linear(self.bert.config.n_embd, 1))]))
def from_opt(cls, opt):
if 'pretrained' not in opt:
return cls(opt['n_vocab'], d_word_vec=opt['d_word_vec'], d_model=opt['d_model'], len_max_seq=opt['len_max_seq'],
n_layer=opt['n_layer'], d_inner=opt['d_inner'], n_head=opt['n_head'], slf_attn=opt['slf_attn'],
d_k=opt['d_k'], d_v=opt['d_v'], feat_vocab=opt['feat_vocab'], d_feat_vec=opt['d_feat_vec'],
layer_attn=opt['layer_attn'], slf_attn_mask=opt['mask_slf_attn'],
dropout=opt['dropout'], attn_dropout=opt['attn_dropout'])
elif opt['pretrained'].count('bert'):
pretrained = BertModel.from_pretrained(opt['pretrained'])
return cls(opt['n_vocab'], pretrained=pretrained, layer_attn=opt['layer_attn'], model_name='bert')
elif opt['pretrained'].count('gpt2'):
pretrained = GPT2Model.from_pretrained(opt['pretrained'])
return cls(opt['n_vocab'], pretrained=pretrained, model_name='gpt2')
else:
raise ValueError("Other pretrained models haven't been supported yet")
def __init__(self, cuda_device=-1):
super(GPT2Embedder, self).__init__()
self.cuda_device = 'cpu' if cuda_device == -1 else f'cuda:{cuda_device}'
# Load pre-trained model tokenizer (vocabulary)
self.enc = GPT2Tokenizer.from_pretrained('gpt2')
# Load pre-trained model (weights)
self.model = GPT2Model.from_pretrained('gpt2')
self.model.to(self.cuda_device)
self.model.eval(
) # we only use the evaluation mode of the pretrained model
self._bos_id = self.enc.encoder['<|endoftext|>']
self._bos_past = None
def fetch_objects():
bert = BertModel.from_pretrained(
'bert-base-uncased').embeddings.position_embeddings.weight.data
gpt = OpenAIGPTModel.from_pretrained(
'openai-gpt').positions_embed.weight.data
gpt2 = GPT2Model.from_pretrained('gpt2').wpe.weight.data
bert_tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
gpt_tokenizer = OpenAIGPTTokenizer.from_pretrained('openai-gpt')
gpt2_tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
return {
'bert': bert,
'gpt': gpt,
'gpt2': gpt2
}, {
'bert': bert_tokenizer,
'gpt': gpt_tokenizer,
'gpt2': gpt2_tokenizer
}
def extract_gpt2_hidden_word_representations(
word, save_activs_to): #, mode="full_model", focus_layers=[]):
# text_inputs = []
# with open(text_path, "rb") as infile:
# text_inputs = pickle.load(infile)
# num_inputs = len(text_inputs)
# Load pre-trained model tokenizer (vocabulary)
tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
# get the hidden activations for word -- assumes gpu is available
word_vec = None # initialize word vector object
# tokenize word
indexed_tokens = tokenizer.encode(word)
num_tokens = len(indexed_tokens)
tokens_tensor = torch.tensor([indexed_tokens]).to('cuda')
# set up model
model = GPT2Model.from_pretrained('gpt2')
model.eval()
model.to('cuda')
# get word_vec
with torch.no_grad():
# get token-wise activations
hidden, _ = model(tokens_tensor)
hidden_np = hidden.cpu().numpy()
# identify hidden layer dimension that represents different tokens
# seq_dim = hidden_np.shape.index(num_tokens)
seq_dim = 1 # we know that the dimension corresponding to tokens is the 2nd dimension, indexed by 1
# sum the hidden layer element-wise along the token dimension to get word vector representation
word_vec = np.sum(hidden_np, axis=seq_dim).squeeze()
# clear gpu memory
torch.cuda.empty_cache()
# save word vector
with open(save_activs_to, "wb") as outfile:
pickle.dump(word_vec, outfile)
pass
def transform(self, X):
# Load pre-trained model tokenizer (vocabulary)
tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
# Load pre-trained model (weights)
model = GPT2Model.from_pretrained('gpt2', cache_dir='tmp/gpt2/')
model.eval()
output = []
for idx, row in tqdm(X.iterrows(), total=len(X)):
# Encode some inputs
indexed_tokens_1 = tokenizer.encode(row.text)
# If you have a GPU, put everything on cuda
# Convert inputs to PyTorch tensors
tokens_tensor_1 = torch.tensor([indexed_tokens_1])
tokens_tensor_1 = tokens_tensor_1.to('cuda')
model.to('cuda')
# Predict hidden states features for each layer
with torch.no_grad():
hidden_states_1, past = model(tokens_tensor_1)
tokens = [
tokenizer.decoder[token].replace('Ġ', '')
for token in indexed_tokens_1
]
output.append([tokens, hidden_states_1.cpu()[0]])
output = pd.DataFrame(output, columns=['tokens', 'layer_-1'])
res = []
for idx, row in X.iterrows():
res.append(self.get_sample_props(output.loc[idx], **row)[1:])
res = pd.DataFrame(res,
columns=[
'tokens', 'pronoun_offset_token',
'a_offset_token', 'b_offset_token', 'a_span',
'b_span', 'pronoun_token', 'a_tokens',
'b_tokens', 'bert', 'cls'
])
cols = set(X.columns).difference(res.columns)
return {'X': pd.concat([X[cols], res], axis=1)}
def load_model_fromlist(name):
if not name in MODELS:
raise Exception(str(name) + ' not a model in the list')
print('# Loading model: ' + str(name))
name_path = MODEL_PATH_DICT[name]
if name == 'word2vec':
if not exists(join(PATH, name_path)): download_model(name)
return (gensim.models.KeyedVectors.load_word2vec_format(join(
PATH, name_path),
binary=True))
if name == 'glove':
if not exists(join(PATH, name_path)): download_model(name)
return (gensim.models.KeyedVectors.load_word2vec_format(
join(PATH, name_path)))
if name == 'dict2vec':
if not exists(join(PATH, name_path)): download_model(name)
return (gensim.models.KeyedVectors.load_word2vec_format(
join(PATH, name_path), binary=False, unicode_errors="ignore"))
if name == 'conceptnet':
if not exists(join(PATH, name_path)): download_model(name)
return (gensim.models.KeyedVectors.load_word2vec_format(
join(PATH, name_path)))
if name == 'bert':
tokenizer = BertTokenizer.from_pretrained('bert-large-uncased')
model = BertModel.from_pretrained(
'bert-large-uncased').embeddings.word_embeddings.weight.data.numpy(
)
return ([model, tokenizer])
if name == 'bert-context':
tokenizer = BertTokenizer.from_pretrained('bert-large-uncased')
model = BertModel.from_pretrained('bert-large-uncased',
output_hidden_states=True)
return ([model, tokenizer])
if name == 'gpt2':
tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
model = GPT2LMHeadModel.from_pretrained(
'gpt2').transformer.wte.weight.data.numpy()
return ([model, tokenizer])
if name == 'gpt2-context':
tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
model = GPT2Model.from_pretrained('gpt2', output_hidden_states=True)
return ([model, tokenizer])
def __init__(self, model_name, add_dense=True, trainable=False):
super().__init__()
self.model_name = model_name
self.add_dense = add_dense
self.trainable = trainable
if self.model_name == 'GPT':
self.encoder = OpenAIGPTModel.from_pretrained('openai-gpt')
elif self.model_name == 'GPT-2':
self.encoder = GPT2Model.from_pretrained('gpt2')
else:
raise NotImplementedError(f'{self.model_name} -- No such model')
if not self.trainable:
for p in self.encoder.parameters():
p.requires_grad = False
if self.add_dense:
self.dense = nn.Linear(in_features=768, out_features=128)
def Get_GPT2_Representation(self, examples):
for i, example in enumerate(examples):
# example.gpt2_mat = np.zeros((pb.fgt_maxlength,768))
# continue
if (self.gpt2_tokenizer == None):
self.gpt2_tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
text = example.fgt_channels[0]
indexed_tokens = self.gpt2_tokenizer.encode(text)
tokens_tensor = torch.tensor([indexed_tokens])
if (self.gpt2 == None):
self.gpt2 = GPT2Model.from_pretrained('gpt2')
self.gpt2.eval()
with torch.no_grad():
hidden_states, past = self.gpt2(tokens_tensor) # (1, 5, 768)
shape = np.array(hidden_states).shape
representation, sum = [], 0
a, b = shape[1], shape[2]
representation = np.zeros((a, b))
for layer in hidden_states:
for words in layer.numpy():
representation += words
sum += 1
if (sum > 0):
representation = representation * 1.0 / sum
representation = list(representation)
while (len(representation) < pb.fgt_maxlength):
representation.append(np.zeros(b))
example.gpt2_mat = representation[0:pb.fgt_maxlength]
print("{:.2%}".format(i * 1.0 / len(examples)))
def __init__(self,
n_layers,
in_size,
out_size,
embed_size,
dropout=0.5,
initialEmbW=None,
rnn_type='lstm',
attention=None,
q_size=-1,
embedding_init=None,
weights_init=None,
elmo_init=False,
elmo_num_outputs=1,
finetune_elmo=False,
bert_init=False,
bert_model=None,
finetune_bert=False,
add_word_emb=True):
"""Initialize encoder with structure parameters
Args:
n_layers (int): Number of layers.
in_size (int): Dimensionality of input vectors.
out_size (int) : Dimensionality of hidden vectors to be output.
embed_size (int): Dimensionality of word embedding.
dropout (float): Dropout ratio.
"""
# TODO
conv_out_size = 512
super(LSTMEncoder, self).__init__()
self.embed = nn.Embedding(in_size, embed_size)
if embedding_init is not None:
self.embed.weight.data.copy_(torch.from_numpy(embedding_init))
elif weights_init is not None:
self.embed.weight.data.copy_(
torch.from_numpy(weights_init['embed']))
self.elmo_init = elmo_init
self.bert_init = bert_init
self.bert_model = bert_model
self.add_word_emb = add_word_emb
if elmo_init:
options_file = "https://allennlp.s3.amazonaws.com/models/elmo/2x4096_512_2048cnn_2xhighway/elmo_2x4096_512_2048cnn_2xhighway_options.json"
weight_file = "https://allennlp.s3.amazonaws.com/models/elmo/2x4096_512_2048cnn_2xhighway/elmo_2x4096_512_2048cnn_2xhighway_weights.hdf5"
self.elmo = Elmo(options_file,
weight_file,
elmo_num_outputs,
requires_grad=finetune_elmo)
elmo_layer = [
nn.Linear(elmo_num_outputs * 1024, out_size),
nn.ReLU()
]
self.elmo_layer = nn.Sequential(*elmo_layer)
elif bert_init:
if 'bert' in bert_model:
self.bert = BertModel.from_pretrained(bert_model)
elif 'openai-gpt' in bert_model:
self.bert = OpenAIGPTModel.from_pretrained(bert_model)
elif 'gpt2' in bert_model:
self.bert = GPT2Model.from_pretrained(bert_model)
elif 'transfo-xl' in bert_model:
self.bert = TransfoXLModel.from_pretrained(bert_model)
self.finetune_bert = finetune_bert
if not finetune_bert:
for param in self.bert.parameters():
param.requires_grad = False
if bert_model in ['bert-base-uncased', 'openai-gpt', 'gpt2']:
bert_in = 768
elif bert_model in [
'bert-large-uncased', 'gpt2-medium', 'transfo-xl-wt103'
]:
bert_in = 1024
bert_layer = [nn.Linear(bert_in, out_size), nn.ReLU()]
self.bert_layer = nn.Sequential(*bert_layer)
if rnn_type == 'lstm':
self.lstm = nn.LSTM(embed_size,
out_size,
n_layers,
batch_first=True,
dropout=dropout)
elif rnn_type == 'gru':
self.lstm = nn.GRU(embed_size,
out_size,
n_layers,
batch_first=True,
dropout=dropout)
self.attention = attention
if attention == 'conv' or attention == 'conv_sum':
conv_in_size = out_size
self.conv1 = nn.Conv1d(in_channels=conv_in_size,
out_channels=conv_out_size,
kernel_size=1,
padding=0)
self.conv2 = nn.Conv1d(in_channels=conv_out_size,
out_channels=2,
kernel_size=1,
padding=0)
if weights_init is not None:
self.conv1.weight.data.copy_(
torch.from_numpy(weights_init['conv1']))
self.conv2.weight.data.copy_(
torch.from_numpy(weights_init['conv2']))
elif attention == 'c_conv_sum':
hidden_size = 512
conv_hidden_size = 256
layers = [
weight_norm(nn.Linear(out_size, hidden_size), dim=None),
nn.ReLU()
]
self.c_fa = nn.Sequential(*layers)
layers = [
weight_norm(nn.Linear(q_size, hidden_size), dim=None),
nn.ReLU()
]
self.q_fa = nn.Sequential(*layers)
layers = [
nn.Conv2d(in_channels=hidden_size,
out_channels=conv_hidden_size,
kernel_size=1),
nn.ReLU(),
nn.Conv2d(in_channels=conv_hidden_size,
out_channels=1,
kernel_size=1)
]
self.cq_att = nn.Sequential(*layers)
if weights_init is not None:
self.c_fa[0].weight.data.copy_(
torch.from_numpy(weights_init['c_fa']))
self.q_fa[0].weight.data.copy_(
torch.from_numpy(weights_init['q_fa']))
self.cq_att[0].weight.data.copy_(
torch.from_numpy(weights_init['cq_att_conv1']))
self.cq_att[2].weight.data.copy_(
torch.from_numpy(weights_init['cq_att_conv2']))
def test_model_from_pretrained(self):
cache_dir = "/tmp/pytorch_pretrained_bert_test/"
for model_name in list(PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
model = GPT2Model.from_pretrained(model_name, cache_dir=cache_dir)
shutil.rmtree(cache_dir)
self.assertIsNotNone(model)
def _init_model_tokenizer(self):
from pytorch_pretrained_bert import GPT2Model, GPT2Tokenizer
self._tokenizer = GPT2Tokenizer.from_pretrained(self.model_dir)
self._model = GPT2Model.from_pretrained(self.model_dir)
self._model.eval()
def __init__(self,
n_layers,
in_size,
out_size,
embed_size,
in_size_hier,
hidden_size,
proj_size,
dropout=0.5,
initialEmbW=None,
independent=False,
rnn_type='lstm',
classifier='baseline',
states_att=False,
state_size=-1,
embedding_init=None,
weights_init=None,
elmo_init=False,
elmo_num_outputs=1,
finetune_elmo=False,
bert_init=False,
bert_model=None,
finetune_bert=False,
add_word_emb=True,
pretrained_all=True):
"""Initialize encoder with structure parameters
Args:
n_layers (int): Number of layers.
in_size (int): Dimensionality of input vectors.
out_size (int): Dimensionality of output vectors.
embed_size (int): Dimensionality of word embedding.
hidden_size (int) : Dimensionality of hidden vectors.
proj_size (int) : Dimensionality of projection before softmax.
dropout (float): Dropout ratio.
"""
#TODO
att_size = 128
self.rnn_type = rnn_type
self.classifier = classifier
super(HLSTMDecoder, self).__init__()
self.embed = nn.Embedding(in_size, embed_size)
if embedding_init is not None:
self.embed.weight.data.copy_(torch.from_numpy(embedding_init))
elif weights_init is not None:
self.embed.weight.data.copy_(
torch.from_numpy(weights_init['embed']))
if rnn_type == 'lstm':
self.lstm = nn.LSTM(embed_size + in_size_hier,
hidden_size,
n_layers,
batch_first=True,
dropout=dropout)
elif rnn_type == 'gru':
self.lstm = nn.GRU(embed_size + in_size_hier,
hidden_size,
n_layers,
batch_first=True,
dropout=dropout)
if weights_init is not None:
lstm_wt = weights_init['lstm']
for k, v in lstm_wt.items():
self.lstm.__getattr__(k).data.copy_(torch.from_numpy(v))
self.elmo_init = elmo_init
self.bert_init = bert_init
self.pretrained_all = pretrained_all
self.bert_model = bert_model
self.add_word_emb = add_word_emb
if False:
#if pretrained_all and elmo_init:
options_file = "https://allennlp.s3.amazonaws.com/models/elmo/2x4096_512_2048cnn_2xhighway/elmo_2x4096_512_2048cnn_2xhighway_options.json"
weight_file = "https://allennlp.s3.amazonaws.com/models/elmo/2x4096_512_2048cnn_2xhighway/elmo_2x4096_512_2048cnn_2xhighway_weights.hdf5"
self.elmo = Elmo(options_file,
weight_file,
elmo_num_outputs,
requires_grad=finetune_elmo)
elmo_layer = [
nn.Linear(elmo_num_outputs * 1024, out_size),
nn.ReLU()
]
self.elmo_layer = nn.Sequential(*elmo_layer)
elif False:
#elif pretrained_all and bert_init:
if 'bert' in bert_model:
self.bert = BertModel.from_pretrained(bert_model)
elif 'openai-gpt' in bert_model:
self.bert = OpenAIGPTModel.from_pretrained(bert_model)
elif 'gpt2' in bert_model:
self.bert = GPT2Model.from_pretrained(bert_model)
elif 'transfo-xl' in bert_model:
self.bert = TransfoXLModel.from_pretrained(bert_model)
self.finetune_bert = finetune_bert
if not finetune_bert:
for param in self.bert.parameters():
param.requires_grad = False
if bert_model in ['bert-base-uncased', 'openai-gpt', 'gpt2']:
bert_in = 768
elif bert_model in [
'bert-large-uncased', 'gpt2-medium', 'transfo-xl-wt103'
]:
bert_in = 1024
bert_layer = [nn.Linear(bert_in, out_size), nn.ReLU()]
self.bert_layer = nn.Sequential(*bert_layer)
self.n_layers = n_layers
self.dropout = dropout
self.independent = independent
self.states_att = states_att
if states_att:
self.ecW = nn.Linear(state_size, att_size)
self.ysW = nn.Linear(hidden_size, att_size)
hidden_size += state_size
if classifier == 'baseline':
layers = [
nn.Linear(hidden_size, proj_size),
nn.Linear(proj_size, out_size)
]
self.y_classifier = nn.Sequential(*layers)
elif classifier == 'weighted_norm':
layers = [
weight_norm(nn.Linear(hidden_size, proj_size), dim=None),
nn.ReLU(),
weight_norm(nn.Linear(proj_size, out_size), dim=None)
]
self.y_classifier = nn.Sequential(*layers)
elif classifier == 'logit':
layers = [
weight_norm(nn.Linear(hidden_size, proj_size), dim=None),
nn.ReLU(),
nn.Linear(proj_size, out_size)
]
self.classifier_txt = nn.Sequential(*layers)
layers = [
weight_norm(nn.Linear(hidden_size, 2048), dim=None),
nn.ReLU(),
nn.Linear(2048, out_size)
]
self.classifier_ft = nn.Sequential(*layers)
if weights_init is not None:
self.classifier_txt[0].weight.data.copy_(
torch.from_numpy(weights_init['classifier_txt']))
self.classifier_ft[0].weight.data.copy_(
torch.from_numpy(weights_init['classifier_ft']))
print(tokenizer.decode(indexed_tokens_1)) # Who was Jim Henson ?
print(tokenizer.decode([8727, 373, 474, 320])) # who was jim
print(tokenizer.decode([30963, 1559, 5633])) # henson ?
print(tokenizer.decode([30963])) # hen
print(tokenizer.decode([508, 8727, 373])) # whowho was
indexed_tokens_1 = tokenizer.convert_tokens_to_ids(text_1) # AttributeError: 'GPT2Tokenizer' object has no attribute 'convert_tokens_to_ids'
indexed_tokens_2 = tokenizer.encode(text_2); print(indexed_tokens_2) # [18050, 367, 19069, 373, 257, 13595, 14471, 263]
## Convert inputs to PyTorch tensors
tokens_tensor_1 = torch.tensor([indexed_tokens_1]); print(tokens_tensor_1)
tokens_tensor_2 = torch.tensor([indexed_tokens_2])
##################################################################
## GPT2Model
## Load pre-trained model (weights)
model = GPT2Model.from_pretrained('/Users/coder352/datasets/WordVec/pytorch_pretrained_bert/gpt2/')
model.eval()
# INFO:pytorch_pretrained_bert.modeling_gpt2:Model config {
# "initializer_range": 0.02,
# "layer_norm_epsilon": 1e-05,
# "n_ctx": 1024,
# "n_embd": 768,
# "n_head": 12,
# "n_layer": 12,
# "n_positions": 1024,
# "vocab_size": 50257
# }
## Predict hidden states features for each layer
with torch.no_grad():
hidden_states_1, past = model(tokens_tensor_1)
def main():
"""Main training program."""
print('Evaluate GPT2 model')
# Disable CuDNN.
torch.backends.cudnn.enabled = False
# Timer.
timers = Timers()
# Arguments.
args = get_args()
# Pytorch distributed.
initialize_distributed(args)
# Random seeds for reproducability.
set_random_seed(args.seed)
# Data stuff.
eval_data = get_eval_data(args)
# Model, optimizer, and learning rate.
if args.eval_hf:
from pytorch_pretrained_bert import GPT2LMHeadModel
from pytorch_pretrained_bert import GPT2Model as HFGPT2Model
if args.num_layers == 24:
model_path = args.load
#model_path = '/home/universal-lm-data.cosmos549/repos/gpt2_mp/models/345M'
hfmodel = HFGPT2Model.from_pretrained(model_path, cache_dir='gpt2_weights', from_tf=True).cuda()
model = GPT2LMHeadModel(hfmodel.config)
model.transformer.load_state_dict(hfmodel.state_dict())
model.cuda()
else:
model = GPT2LMHeadModel.from_pretrained('gpt2', cache_dir='gpt2_weights').cuda()
else:
if args.load_openai:
from utils import move_weights
model_path = args.load
args.load = None
model = setup_model(args)
from pytorch_pretrained_bert import GPT2LMHeadModel
from pytorch_pretrained_bert import GPT2Model as HFGPT2Model
model_path = 'gpt2'
from_tf = False
print('loading openai weights')
model.cpu()
if args.num_layers == 24:
#model_path = '/home/universal-lm-data.cosmos549/repos/gpt2_mp/models/345M'
hfmodel = HFGPT2Model.from_pretrained(model_path, cache_dir='gpt2_weights', from_tf=True)
gpt2model = GPT2LMHeadModel(hfmodel.config)
gpt2model.transformer.load_state_dict(hfmodel.state_dict())
gpt2model
else:
gpt2model = GPT2LMHeadModel.from_pretrained('gpt2', cache_dir='gpt2_weights')
model2fill = model
while isinstance(model2fill, (DDP, FP16_Module)):
model2fill = model2fill.module
move_weights(model2fill, gpt2model)
model.cuda()
else:
model = setup_model(args)
# Run on test data.
prefix = "wiki" #os.path.basename(args.valid_data)
evaluate_and_print_results(prefix, eval_data,
model, args, timers)
def __init__(self,
n_wlayers,
n_slayers,
in_size,
out_size,
embed_size,
hidden_size,
dropout=0.5,
ignore_label=None,
initialEmbW=None,
independent=False,
rnn_type='lstm',
embedding_init=None,
weights_init=None,
elmo_init=False,
elmo_num_outputs=1,
finetune_elmo=False,
bert_init=False,
bert_model=None,
finetune_bert=False,
add_word_emb=True,
pretrained_all=True,
concat_his=False):
"""Initialize encoder with structure parameters
Args:
n_layers (int): Number of layers.
in_size (int): Dimensionality of input vectors.
out_size (int) : Dimensionality of hidden vectors to be output.
embed_size (int): Dimensionality of word embedding.
dropout (float): Dropout ratio.
"""
super(HLSTMEncoder, self).__init__()
self.embed = nn.Embedding(in_size, embed_size)
if embedding_init is not None:
self.embed.weight.data.copy_(torch.from_numpy(embedding_init))
elif weights_init is not None:
self.embed.weight.data.copy_(
torch.from_numpy(weights_init['embed']))
if rnn_type == 'lstm':
self.wlstm = nn.LSTM(embed_size,
hidden_size,
n_wlayers,
batch_first=True,
dropout=dropout)
self.slstm = nn.LSTM(hidden_size,
out_size,
n_slayers,
batch_first=True,
dropout=dropout)
elif rnn_type == 'gru':
self.wlstm = nn.GRU(embed_size,
hidden_size,
n_wlayers,
batch_first=True,
dropout=dropout)
self.slstm = nn.GRU(hidden_size,
out_size,
n_slayers,
batch_first=True,
dropout=dropout)
self.elmo_init = elmo_init
self.bert_init = bert_init
self.pretrained_all = pretrained_all
self.concat_his = concat_his
self.bert_model = bert_model
self.add_word_emb = add_word_emb
if pretrained_all and elmo_init:
options_file = "https://allennlp.s3.amazonaws.com/models/elmo/2x4096_512_2048cnn_2xhighway/elmo_2x4096_512_2048cnn_2xhighway_options.json"
weight_file = "https://allennlp.s3.amazonaws.com/models/elmo/2x4096_512_2048cnn_2xhighway/elmo_2x4096_512_2048cnn_2xhighway_weights.hdf5"
self.elmo = Elmo(options_file,
weight_file,
elmo_num_outputs,
requires_grad=finetune_elmo)
elmo_layer = [
nn.Linear(elmo_num_outputs * 1024, out_size),
nn.ReLU()
]
self.elmo_layer = nn.Sequential(*elmo_layer)
elif pretrained_all and bert_init:
if 'bert' in bert_model:
self.bert = BertModel.from_pretrained(bert_model)
elif 'openai-gpt' in bert_model:
self.bert = OpenAIGPTModel.from_pretrained(bert_model)
elif 'gpt2' in bert_model:
self.bert = GPT2Model.from_pretrained(bert_model)
elif 'transfo-xl' in bert_model:
self.bert = TransfoXLModel.from_pretrained(bert_model)
self.finetune_bert = finetune_bert
if not finetune_bert:
for param in self.bert.parameters():
param.requires_grad = False
if bert_model in ['bert-base-uncased', 'openai-gpt', 'gpt2']:
bert_in = 768
elif bert_model in [
'bert-large-uncased', 'gpt2-medium', 'transfo-xl-wt103'
]:
bert_in = 1024
bert_layer = [nn.Linear(bert_in, out_size), nn.ReLU()]
self.bert_layer = nn.Sequential(*bert_layer)
self.independent = independent
self.rnn_type = rnn_type