def __init__(self, paragraphs, vocab, c_window_len):
self.paragraphs = paragraphs
self.c_tokens = [
text_to_tokens(par.lower(), vocab) for par in paragraphs
]
self.c_window_len = c_window_len
self.par_id = -1
self.get_next_paragraph()
def main():
args = build_argparser().parse_args()
paragraphs = get_paragraphs(args.input)
preprocessing_start_time = perf_counter()
vocab = load_vocab_file(args.vocab)
log.debug("Loaded vocab file from {}, get {} tokens".format(args.vocab, len(vocab)))
# get context as a string (as we might need it's length for the sequence reshape)
context = '\n'.join(paragraphs)
sentences = re.split(sentence_splitter, context)
preprocessed_sentences = [text_to_tokens(sentence, vocab) for sentence in sentences]
max_sentence_length = max([len(tokens) + 2 for tokens, _ in preprocessed_sentences])
preprocessing_total_time = (perf_counter() - preprocessing_start_time) * 1e3
source = tuple(zip(sentences, preprocessed_sentences))
if args.adapter == 'openvino':
plugin_config = get_user_config(args.device, args.num_streams, args.num_threads)
model_adapter = OpenvinoAdapter(create_core(), args.model, device=args.device, plugin_config=plugin_config,
max_num_requests=args.num_infer_requests, model_parameters = {'input_layouts': args.layout})
elif args.adapter == 'ovms':
model_adapter = OVMSAdapter(args.model)
enable_padding = not args.dynamic_shape
model = BertNamedEntityRecognition(model_adapter, {'vocab': vocab, 'input_names': args.input_names, 'enable_padding': enable_padding})
if max_sentence_length > model.max_length:
model.reshape(max_sentence_length if enable_padding else (1, max_sentence_length))
model.log_layers_info()
pipeline = AsyncPipeline(model)
next_sentence_id = 0
next_sentence_id_to_show = 0
start_time = perf_counter()
while True:
if pipeline.callback_exceptions:
raise pipeline.callback_exceptions[0]
results = pipeline.get_result(next_sentence_id_to_show)
if results:
(score, filtered_labels_id), meta = results
next_sentence_id_to_show += 1
print_raw_results(score, filtered_labels_id, meta)
continue
if pipeline.is_ready():
if next_sentence_id == len(source):
break
sentence, (c_tokens_id, c_token_s_e) = source[next_sentence_id]
pipeline.submit_data(c_tokens_id, next_sentence_id, {'sentence': sentence, 'c_token_s_e': c_token_s_e})
next_sentence_id += 1
else:
pipeline.await_any()
pipeline.await_all()
if pipeline.callback_exceptions:
raise pipeline.callback_exceptions[0]
for sentence_id in range(next_sentence_id_to_show, next_sentence_id):
results = pipeline.get_result(sentence_id)
(score, filtered_labels_id), meta = results
print_raw_results(score, filtered_labels_id, meta)
total_latency = (perf_counter() - start_time) * 1e3 + preprocessing_total_time
log.info("Metrics report:")
log.info("\tLatency: {:.1f} ms".format(total_latency))
def main():
args = build_argparser().parse_args()
paragraphs = get_paragraphs(args.input)
preprocessing_start_time = perf_counter()
vocab = load_vocab_file(args.vocab)
log.debug("Loaded vocab file from {}, get {} tokens".format(
args.vocab, len(vocab)))
# get context as a string (as we might need it's length for the sequence reshape)
context = '\n'.join(paragraphs)
visualizer = Visualizer(context, args.colors)
# encode context into token ids list
c_tokens = text_to_tokens(context.lower(), vocab)
total_latency = (perf_counter() - preprocessing_start_time) * 1e3
if args.adapter == 'openvino':
plugin_config = get_user_config(args.device, args.num_streams,
args.num_threads)
model_adapter = OpenvinoAdapter(
create_core(),
args.model,
device=args.device,
plugin_config=plugin_config,
max_num_requests=args.num_infer_requests)
elif args.adapter == 'ovms':
model_adapter = OVMSAdapter(args.model)
config = {
'vocab': vocab,
'input_names': args.input_names,
'output_names': args.output_names,
'max_answer_token_num': args.max_answer_token_num,
'squad_ver': args.model_squad_ver
}
model = BertQuestionAnswering(model_adapter, config)
if args.reshape:
# find the closest multiple of 64, if it is smaller than current network's sequence length, do reshape
new_length = min(
model.max_length,
int(
np.ceil(
(len(c_tokens[0]) + args.max_question_token_num) / 64) *
64))
if new_length < model.max_length:
try:
model.reshape(new_length)
except RuntimeError:
log.error(
"Failed to reshape the network, please retry the demo without '-r' option"
)
sys.exit(-1)
else:
log.debug(
"\tSkipping network reshaping,"
" as (context length + max question length) exceeds the current (input) network sequence length"
)
model.log_layers_info()
pipeline = AsyncPipeline(model)
if args.questions:
def questions():
for question in args.questions:
log.info("\n\tQuestion: {}".format(question))
yield question
else:
def questions():
while True:
yield input('\n\tType a question (empty string to exit): ')
for question in questions():
if not question.strip():
break
answers = []
next_window_id = 0
next_window_id_to_show = 0
start_time = perf_counter()
q_tokens_id, _ = text_to_tokens(question.lower(), vocab)
source = ContextSource(q_tokens_id, c_tokens, model.max_length)
while True:
if pipeline.callback_exceptions:
raise pipeline.callback_exceptions[0]
results = pipeline.get_result(next_window_id_to_show)
if results:
next_window_id_to_show += 1
update_answers_list(answers, results[0])
continue
if pipeline.is_ready():
if source.is_over():
break
pipeline.submit_data(source.get_data(), next_window_id, None)
next_window_id += 1
else:
pipeline.await_any()
pipeline.await_all()
for window_id in range(next_window_id_to_show, next_window_id):
results = pipeline.get_result(window_id)
while results is None:
results = pipeline.get_result(window_id)
update_answers_list(answers, results[0])
visualizer.show_answers(answers)
total_latency += (perf_counter() - start_time) * 1e3
log.info("Metrics report:")
log.info("\tLatency: {:.1f} ms".format(total_latency))
def main():
args = build_argparser().parse_args()
paragraphs = get_paragraphs(args.input)
vocab_start_time = perf_counter()
vocab = load_vocab_file(args.vocab)
log.debug("Loaded vocab file from {}, get {} tokens".format(
args.vocab, len(vocab)))
visualizer = Visualizer(args.colors)
total_latency = (perf_counter() - vocab_start_time) * 1e3
ie = create_core()
plugin_config = get_user_config(args.device, args.num_streams,
args.num_threads)
model_emb_adapter = OpenvinoAdapter(
ie,
args.model_emb,
device=args.device,
plugin_config=plugin_config,
max_num_requests=args.num_infer_requests)
model_emb = BertEmbedding(model_emb_adapter, {
'vocab': vocab,
'input_names': args.input_names_emb
})
model_emb.log_layers_info()
# reshape BertEmbedding model to infer short questions and long contexts
max_len_context = 384
max_len_question = 32
for new_length in [max_len_question, max_len_context]:
model_emb.reshape(new_length)
if new_length == max_len_question:
emb_exec_net = ie.load_network(model_emb_adapter.net, args.device)
else:
emb_pipeline = AsyncPipeline(model_emb)
if args.model_qa:
model_qa_adapter = OpenvinoAdapter(
ie,
args.model_qa,
device=args.device,
plugin_config=plugin_config,
max_num_requests=args.num_infer_requests)
config = {
'vocab': vocab,
'input_names': args.input_names_qa,
'output_names': args.output_names_qa,
'max_answer_token_num': args.max_answer_token_num,
'squad_ver': args.model_qa_squad_ver
}
model_qa = BertQuestionAnswering(model_qa_adapter, config)
model_qa.log_layers_info()
qa_pipeline = AsyncPipeline(model_qa)
log.info("\t\tStage 1 (Calc embeddings for the context)")
contexts_all = []
start_time = perf_counter()
# get context as string and then encode it into token id list
# calculate number of tokens for context in each request.
# reserve 3 positions for special tokens [CLS] q_tokens [SEP] c_tokens [SEP]
if args.model_qa:
# to make context be able to pass model_qa together with question
c_window_len = model_qa.max_length - (max_len_question + 3)
else:
# to make context be able to pass model_emb without question
c_window_len = max_len_context - 2
def calc_question_embedding(tokens_id):
num = min(max_len_question - 2, len(tokens_id))
inputs, _ = model_emb.preprocess((tokens_id[:num], max_len_question))
raw_result = emb_exec_net.infer(inputs)
return model_emb.postprocess(raw_result, None)
source = ContextSource(paragraphs, vocab, c_window_len)
next_window_id = 0
next_window_id_to_show = 0
contexts_all = []
while True:
if emb_pipeline.callback_exceptions:
raise emb_pipeline.callback_exceptions[0]
results = emb_pipeline.get_result(next_window_id_to_show)
if results:
embedding, meta = results
meta['c_data'].emb = embedding
contexts_all.append(meta['c_data'])
next_window_id_to_show += 1
continue
if emb_pipeline.is_ready():
if source.is_over():
break
c_data = source.get_data()
num = min(max_len_context - 2, len(c_data.c_tokens_id))
emb_pipeline.submit_data(
(c_data.c_tokens_id[:num], max_len_context), next_window_id,
{'c_data': c_data})
next_window_id += 1
else:
emb_pipeline.await_any()
emb_pipeline.await_all()
for window_id in range(next_window_id_to_show, next_window_id):
results = emb_pipeline.get_result(window_id)
while results is None:
results = emb_pipeline.get_result(window_id)
embedding, meta = results
meta['c_data'].emb = embedding
contexts_all.append(meta['c_data'])
next_window_id_to_show += 1
total_latency += (perf_counter() - start_time) * 1e3
context_embeddings_time = total_latency
if args.questions:
def questions():
for question in args.questions:
log.info("\n\tQuestion: {}".format(question))
yield question
else:
def questions():
while True:
yield input('\n\tType a question (empty string to exit): ')
for question in questions():
if not question.strip():
break
start_time = perf_counter()
log.info(
"\t\tStage 2 (Calc question embedding and compare with {} context embeddings)"
.format(len(contexts_all)))
q_tokens_id, _ = text_to_tokens(question.lower(), vocab)
q_emb = calc_question_embedding(q_tokens_id)
distances = [(np.linalg.norm(context.emb - q_emb, 2), context)
for context in contexts_all]
distances.sort(key=lambda x: x[0])
keep_num = min(args.best_n, len(distances))
distances_filtered = distances[:keep_num]
log.info(
"The closest {} contexts to question filtered from {} context embeddings:"
.format(keep_num, len(distances)))
visualizer.show_closest_contexts(distances_filtered)
if args.model_qa:
answers = []
next_context_id = 0
next_context_id_to_show = 0
while True:
if qa_pipeline.callback_exceptions:
raise qa_pipeline.callback_exceptions[0]
results = qa_pipeline.get_result(next_context_id_to_show)
if results:
next_context_id_to_show += 1
output, meta = results
update_answers_list(answers, output, meta['c_data'])
continue
if qa_pipeline.is_ready():
if next_context_id == len(distances_filtered):
break
_, c_data = distances_filtered[next_context_id]
qa_pipeline.submit_data((c_data, q_tokens_id),
next_context_id,
{'c_data': c_data})
next_context_id += 1
else:
qa_pipeline.await_any()
qa_pipeline.await_all()
for context_id in range(next_context_id_to_show, next_context_id):
results = qa_pipeline.get_result(context_id)
while results is None:
results = qa_pipeline.get_result(context_id)
output, meta = results
update_answers_list(answers, output, meta['c_data'])
log.info(
"\t\tStage 3 (Show top 3 answers from {} closest contexts of Stage 1)"
.format(len(answers)))
answers = sorted(answers, key=lambda x: -x[0])[:3]
visualizer.show_answers(answers)
total_latency += (perf_counter() - start_time) * 1e3
log.info("Metrics report:")
log.info("\tContext embeddings latency (stage 1): {:.1f} ms".format(
context_embeddings_time))
log.info("\tLatency (all stages): {:.1f} ms".format(total_latency))