def __init__(self,
contexts=None,
fill_context_embeddings=True,
device=torch.device('cuda' if torch.cuda.is_available() else 'cpu')):
super(LongQAModel, self).__init__()
self.device = device
self.c_model = DPRContextEncoder.from_pretrained('facebook/dpr-ctx_encoder-single-nq-base').to(device)
self.c_tokenizer = DPRContextEncoderTokenizer.from_pretrained('facebook/dpr-ctx_encoder-single-nq-base')
self.q_model = DPRQuestionEncoder.from_pretrained('facebook/dpr-question_encoder-single-nq-base').to(device)
self.q_tokenizer = DPRQuestionEncoderTokenizer.from_pretrained('facebook/dpr-question_encoder-single-nq-base')
self.r_model = DPRReader.from_pretrained('facebook/dpr-reader-single-nq-base').to(device)
self.r_tokenizer = DPRReaderTokenizerFast.from_pretrained('facebook/dpr-reader-single-nq-base')
self.contexts = contexts
# Not enough time to load context embeddings in AWS SageMaker,
# but can fill weights from saved state dict after loading model.
if not self.contexts:
with open('code/contexts.json') as f:
self.contexts = json.load(f)
# output_features = self.c_model.ctx_encoder.bert_model.pooler.dense.out_features
# self.context_embeddings = nn.Parameter(torch.zeros(len(self.contexts), output_features)).to(device)
# else:
context_embeddings = []
with torch.no_grad():
for context in self.contexts:
input_ids = self.c_tokenizer(context, return_tensors='pt').to(device)["input_ids"]
output = self.c_model(input_ids)
context_embeddings.append(output.pooler_output)
self.context_embeddings = nn.Parameter(torch.cat(context_embeddings, dim=0)).to(device)
print('cwd!:', os.getcwd())
print(os.listdir('code'))
self.noise_remover = joblib.load('code/filter_model.sav')
def get_model(
pretrained_model_name_or_path: str = 'facebook/dpr-reader-single-nq-base',
device: Optional[str] = None,
) -> DPRReader:
device = device or ('cuda' if torch.cuda.is_available() else 'cpu')
device = torch.device(device)
return DPRReader.from_pretrained(pretrained_model_name_or_path).to(device).eval()
def test_reader_inference(self):
tokenizer = DPRReaderTokenizer.from_pretrained("facebook/dpr-reader-single-nq-base")
model = DPRReader.from_pretrained("facebook/dpr-reader-single-nq-base")
model.to(torch_device)
encoded_inputs = tokenizer(
questions="What is love ?",
titles="Haddaway",
texts="What Is Love is a song recorded by the artist Haddaway",
padding=True,
return_tensors="pt",
)
encoded_inputs.to(torch_device)
outputs = model(**encoded_inputs)
# compare the actual values for a slice.
expected_start_logits = torch.tensor(
[[-10.3005, -10.7765, -11.4872, -11.6841, -11.9312, -10.3002, -9.8544, -11.7378, -12.0821, -10.2975]],
dtype=torch.float,
device=torch_device,
)
expected_end_logits = torch.tensor(
[[-11.0684, -11.7041, -11.5397, -10.3465, -10.8791, -6.8443, -11.9959, -11.0364, -10.0096, -6.8405]],
dtype=torch.float,
device=torch_device,
)
self.assertTrue(torch.allclose(outputs.start_logits[:, :10], expected_start_logits, atol=1e-4))
self.assertTrue(torch.allclose(outputs.end_logits[:, :10], expected_end_logits, atol=1e-4))
def __init__(
self,
dpr_fn: str,
tokenizer_fn: str,
tokenizer_max_len: int,
):
self.dpr = DPRReader.from_pretrained(dpr_fn)
self.tokenizer_max_len = tokenizer_max_len
self.tokenizer = DPRReaderTokenizer.from_pretrained(
tokenizer_fn, max_len=tokenizer_max_len)
device = 'cuda' if cuda_is_available() else 'cpu'
self.dpr.to(device)
self.device = device
def __init__(self):
self.context_tokenizer = DPRContextEncoderTokenizer.from_pretrained(
'facebook/dpr-ctx_encoder-single-nq-base')
self.context_model = DPRContextEncoder.from_pretrained(
'facebook/dpr-ctx_encoder-single-nq-base', return_dict=True)
self.query_tokenizer = DPRQuestionEncoderTokenizer.from_pretrained(
'facebook/dpr-question_encoder-single-nq-base')
self.query_encoder = DPRQuestionEncoder.from_pretrained(
"facebook/dpr-question_encoder-single-nq-base")
self.reader_tokenizer = DPRReaderTokenizer.from_pretrained(
'facebook/dpr-reader-single-nq-base')
self.reader_model = DPRReader.from_pretrained(
'facebook/dpr-reader-single-nq-base', return_dict=True)
self.vector_length = 768
def test_model_from_pretrained(self):
for model_name in DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = DPRContextEncoder.from_pretrained(model_name)
self.assertIsNotNone(model)
for model_name in DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = DPRContextEncoder.from_pretrained(model_name)
self.assertIsNotNone(model)
for model_name in DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = DPRQuestionEncoder.from_pretrained(model_name)
self.assertIsNotNone(model)
for model_name in DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = DPRReader.from_pretrained(model_name)
self.assertIsNotNone(model)
def __init__(self,
model_name: str,
tokenizer_name: str = None,
span_selection_rules=None,
num_spans: int = 1,
max_answer_length: int = 10,
num_spans_per_passage: int = 10,
batch_size: int = 16,
device: str = 'cuda:0'):
if span_selection_rules is None:
span_selection_rules = [DprSelection()]
self.device = device
self.model = DPRReader.from_pretrained(model_name).to(
self.device).eval()
if tokenizer_name:
self.tokenizer = DPRReaderTokenizer.from_pretrained(tokenizer_name)
else:
self.tokenizer = DPRReaderTokenizer.from_pretrained(model_name)
self.span_selection_rules = span_selection_rules
self.num_spans = num_spans
self.max_answer_length = max_answer_length
self.num_spans_per_passage = num_spans_per_passage
self.batch_size = batch_size
class DPRReader:
reader_tokenizer = DPRReaderTokenizer.from_pretrained(
'facebook/dpr-reader-single-nq-base')
reader_model = DPRReader.from_pretrained(
'facebook/dpr-reader-single-nq-base', return_dict=True)
MAX_TOKENS = 512
MAX_TOKENS_QUESTION = 30
MAX_TOKENS_DOCUMENT = MAX_TOKENS - MAX_TOKENS_QUESTION - 2 # [SEP] and [CLS]
def __init__(self):
self.device = 'cuda' if torch.cuda.is_available() else 'cpu'
if self.device == 'cuda':
self.reader_model = self.reader_model.cuda()
def _reconstruct_tokens(self, bert_tokens):
output_string = ''
for token in bert_tokens:
if token[:2] == '##':
output_string += token[2:]
else:
output_string += ' '
output_string += token
return output_string[1:]
def get_token_length(self, string):
tokens = self.reader_tokenizer.encode(string)
return len(tokens)
def chunk_document(self, document, re_consolidate=True):
'''Chunks up a long document into optimally large pieces so that they
can be passed to BERT. Activating `re_consolidate` will put the chunks
back together to make them as large as possible for improved
performance.
'''
document_length = self.get_token_length(document)
if document_length > self.MAX_TOKENS_DOCUMENT:
approved_chunks = []
paragraphs = document.split('\n')
paragraphs = [par for par in paragraphs if par]
for paragraph in paragraphs:
paragraph_length = self.get_token_length(paragraph)
if paragraph_length > self.MAX_TOKENS_DOCUMENT:
sentences = paragraph.split('.')
sentences = [sen for sen in sentences if sen]
for sentence in sentences:
sentence_length = self.get_token_length(sentence)
if sentence_length > self.MAX_TOKENS_DOCUMENT:
print("Ignoring overlong sentence.")
else:
approved_chunks.append(sentence)
else:
approved_chunks.append(paragraph)
if re_consolidate:
lengths = [
self.get_token_length(chunk) for chunk in approved_chunks
]
consolidated_chunks = []
running_length = 0
current_chunk = ''
for chunk, length in zip(approved_chunks, lengths):
if (running_length + length) < self.MAX_TOKENS_DOCUMENT:
current_chunk += chunk
running_length += length
else:
consolidated_chunks.append(current_chunk)
current_chunk = chunk
running_length = length
return consolidated_chunks
else:
return approved_chunks
else:
return [document]
def read_documents(self, question: str, documents: List[str],
titles: List[str]):
encoded_inputs = self.reader_tokenizer(questions=question,
titles=titles,
texts=documents,
return_tensors='pt',
padding=True)
input_ids = encoded_inputs['input_ids']
encoded_inputs = encoded_inputs.to(self.device) #TODO Figure this out?
outputs = self.reader_model(**encoded_inputs)
start_logits = outputs.start_logits
end_logits = outputs.end_logits
relevance_logits = outputs.relevance_logits
responses = []
for i in range(len(documents)):
title = titles[i]
document = documents[i]
start = start_logits[i]
end = end_logits[i]
relevance = relevance_logits[i]
inp_ids = input_ids[i]
input_tokens = self.reader_tokenizer.convert_ids_to_tokens(inp_ids)
answer_start = int(start.argmax())
answer_end = int(end.argmax())
relevance = float(relevance.max())
answer_tokens = input_tokens[answer_start:answer_end + 1]
answer_str = self._reconstruct_tokens(answer_tokens)
response = {
'answer': answer_str,
'relevance': relevance,
'title': title,
'document': document
}
responses.append(response)
response = responses.sort(key=lambda x: -x['relevance'])
return responses
def read_chunked_document(self, question: str, document: str, title: str):
chunked_docs = self.chunk_document(document)
titles_list = [title for i in range(len(chunked_docs))]
return self.read_documents(question, chunked_docs, titles_list)
class DPRReader(DocumentChunker):
'''
Class for "reading" retrieved documents with DPR, which performs two
functions: re-ranking them and providing candidate answers to the question.
'''
reader_tokenizer = DPRReaderTokenizer.from_pretrained(
'facebook/dpr-reader-single-nq-base')
reader_model = DPRReader.from_pretrained(
'facebook/dpr-reader-single-nq-base', return_dict=True)
def __init__(self):
super(DocumentChunker).__init__()
self.device = 'cuda' if torch.cuda.is_available() else 'cpu'
if self.device == 'cuda':
self.reader_model = self.reader_model.cuda()
def _reconstruct_tokens(self, bert_tokens: List[str]):
'''
Utility function for reassembling WordPiece tokens into
human-readable strings.
'''
output_string = ''
for token in bert_tokens:
if token[:2] == '##':
output_string += token[2:]
else:
output_string += ' '
output_string += token
return output_string[1:]
def read_documents(self, question: str, documents: List[str],
titles: List[str]):
'''
Reads a series of `documents` and `titles` and rates their relevance
to the `question` as well as proposes an answer.
Args:
question (str):
The question string (e.g. `who is bill gates?`)
documents (List[str]):
List of documents to rate/propose an answer from.
titles (List[str]):
List of the titles of those documents
'''
assert len(documents) == len(titles)
encoded_inputs = self.reader_tokenizer(questions=question,
titles=titles,
texts=documents,
return_tensors='pt',
padding=True)
input_ids = encoded_inputs['input_ids']
encoded_inputs = encoded_inputs.to(self.device)
outputs = self.reader_model(**encoded_inputs)
start_logits = outputs.start_logits
end_logits = outputs.end_logits
relevance_logits = outputs.relevance_logits
responses = []
for i in range(len(documents)):
title = titles[i]
document = documents[i]
start = start_logits[i]
end = end_logits[i]
relevance = relevance_logits[i]
inp_ids = input_ids[i]
input_tokens = self.reader_tokenizer.convert_ids_to_tokens(inp_ids)
answer_start = int(start.argmax())
answer_end = int(end.argmax())
relevance = float(relevance.max())
answer_tokens = input_tokens[answer_start:answer_end + 1]
answer_str = self._reconstruct_tokens(answer_tokens)
response = {
'answer': answer_str,
'relevance': relevance,
'title': title,
'document': document
}
responses.append(response)
return responses
def read_chunked_document(self, question: str, document: str, title: str):
'''
Read a single document that may be exceed the maximum length BERT
can handle, so chunk it up into pieces.
For args see DPRReader.read_documents()
'''
chunked_docs = self.chunk_document(document)
titles_list = [title for i in range(len(chunked_docs))]
return self.read_documents(question, chunked_docs, titles_list)
def __init__(self):
self.r_encoder = DPRReader.from_pretrained(
"facebook/dpr-reader-single-nq-base").to(Config.device)
self.r_tokenizer = DPRReaderTokenizerFast.from_pretrained(
"facebook/dpr-reader-single-nq-base")
