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gpt2_hubconf.py
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from pytorch_transformers.tokenization_gpt2 import GPT2Tokenizer
from pytorch_transformers.modeling_gpt2 import (
GPT2Model,
GPT2LMHeadModel,
GPT2DoubleHeadsModel
)
# A lot of models share the same param doc. Use a decorator
# to save typing
gpt2_docstring = """
Params:
pretrained_model_name_or_path: either:
- a str with the name of a pre-trained model to load selected in the list of:
. `gpt2`, `gpt2-medium`
- a path or url to a pretrained model archive containing:
. `gpt2_config.json` a configuration file for the model
. `pytorch_model.bin` a PyTorch dump of a GPT2Model instance
- a path or url to a pretrained model archive containing:
. `gpt2_config.json` a configuration file for the model
. a TensorFlow checkpoint with trained weights
from_tf: should we load the weights from a locally saved TensorFlow checkpoint
cache_dir: an optional path to a folder in which the pre-trained models will be cached.
state_dict: an optional state dictionary (collections.OrderedDict object) to use instead of pre-trained models
*inputs, **kwargs: additional input for the specific GPT-2 class
"""
def _append_from_pretrained_docstring(docstr):
def docstring_decorator(fn):
fn.__doc__ = fn.__doc__ + docstr
return fn
return docstring_decorator
def gpt2Tokenizer(*args, **kwargs):
"""
Instantiate a GPT-2 BPE tokenizer for OpenAI GPT-2 from a pre-trained/customized vocab file.
Peculiarities:
- Byte-level BPE
Args:
pretrained_model_name_or_path: Path to pretrained model archive
or one of pre-trained vocab configs below.
* gpt2
Keyword args:
special_tokens: Special tokens in vocabulary that are not pretrained ([SEP], [CLS]...)
Default: None
max_len: An artificial maximum length to truncate tokenized sequences to;
Effective maximum length is always the minimum of this
value (if specified) and the underlying BERT model's
sequence length.
Default: None
Example:
import torch
tokenizer = torch.hub.load('huggingface/pytorch-transformers', 'gpt2Tokenizer', 'gpt2')
text = "Who was Jim Henson ?"
indexed_tokens = tokenizer.encode(tokenized_text)
"""
tokenizer = GPT2Tokenizer.from_pretrained(*args, **kwargs)
return tokenizer
@_append_from_pretrained_docstring(gpt2_docstring)
def gpt2Model(*args, **kwargs):
"""
gpt2Model is the basic OpenAI GPT-2 Transformer model based on
identical stacked masked self-attention blocks and pre-trained
on large scale dataset using language modeling signal.
Example:
# Load the tokenizer
import torch
tokenizer = torch.hub.load('huggingface/pytorch-transformers', 'gpt2Tokenizer', 'gpt2')
# Prepare tokenized input
text_1 = "Who was Jim Henson ?"
text_2 = "Jim Henson was a puppeteer"
indexed_tokens_1 = tokenizer.encode(text_1)
indexed_tokens_2 = tokenizer.encode(text_2)
tokens_tensor_1 = torch.tensor([indexed_tokens_1])
tokens_tensor_2 = torch.tensor([indexed_tokens_2])
# Load gpt2Model
model = torch.hub.load('huggingface/pytorch-transformers', 'gpt2Model', 'gpt2')
model.eval()
# Predict hidden states features for each layer
# past can be used to reuse precomputed hidden state in a subsequent predictions
with torch.no_grad():
hidden_states_1, past = model(tokens_tensor_1)
hidden_states_2, past = model(tokens_tensor_2, past=past)
"""
model = GPT2Model.from_pretrained(*args, **kwargs)
return model
@_append_from_pretrained_docstring(gpt2_docstring)
def gpt2LMHeadModel(*args, **kwargs):
"""
gpt2LMHeadModel is the OpenAI GPT-2 Transformer model with the
tied (pre-trained) language modeling head on top.
Example:
# Load the tokenizer
import torch
tokenizer = torch.hub.load('huggingface/pytorch-transformers', 'gpt2Tokenizer', 'gpt2')
# Prepare tokenized input
text_1 = "Who was Jim Henson ?"
text_2 = "Jim Henson was a puppeteer"
indexed_tokens_1 = tokenizer.encode(text_1)
indexed_tokens_2 = tokenizer.encode(text_2)
tokens_tensor_1 = torch.tensor([indexed_tokens_1])
tokens_tensor_2 = torch.tensor([indexed_tokens_2])
# Load gpt2LMHeadModel
model = torch.hub.load('huggingface/pytorch-transformers', 'gpt2LMHeadModel', 'gpt2')
model.eval()
# Predict hidden states features for each layer
# past can be used to reuse precomputed hidden state in a subsequent predictions
with torch.no_grad():
predictions_1, past = model(tokens_tensor_1)
predictions_2, past = model(tokens_tensor_2, past=past)
# Get the predicted last token
predicted_index = torch.argmax(predictions_2[0, -1, :]).item()
predicted_token = tokenizer.decode([predicted_index])
assert predicted_token == ' who'
"""
model = GPT2LMHeadModel.from_pretrained(*args, **kwargs)
return model
@_append_from_pretrained_docstring(gpt2_docstring)
def gpt2DoubleHeadsModel(*args, **kwargs):
"""
gpt2DoubleHeadsModel is the OpenAI GPT-2 Transformer model with the
tied (pre-trained) language modeling head and a multiple choice
classification head (only initialized, not pre-trained).
Example:
# Load the tokenizer
import torch
tokenizer = torch.hub.load('huggingface/pytorch-transformers', 'gpt2Tokenizer', 'gpt2')
# Prepare tokenized input
text1 = "Who was Jim Henson ? Jim Henson was a puppeteer"
text2 = "Who was Jim Henson ? Jim Henson was a mysterious young man"
tokenized_text1 = tokenizer.tokenize(text1)
tokenized_text2 = tokenizer.tokenize(text2)
indexed_tokens1 = tokenizer.convert_tokens_to_ids(tokenized_text1)
indexed_tokens2 = tokenizer.convert_tokens_to_ids(tokenized_text2)
tokens_tensor = torch.tensor([[indexed_tokens1, indexed_tokens2]])
mc_token_ids = torch.LongTensor([[len(tokenized_text1)-1, len(tokenized_text2)-1]])
# Load gpt2DoubleHeadsModel
model = torch.hub.load('huggingface/pytorch-transformers', 'gpt2DoubleHeadsModel', 'gpt2')
model.eval()
# Predict hidden states features for each layer
with torch.no_grad():
lm_logits, multiple_choice_logits, presents = model(tokens_tensor, mc_token_ids)
"""
model = GPT2DoubleHeadsModel.from_pretrained(*args, **kwargs)
return model