def inference(features):
global h_output
print("\nRunning Inference...")
eval_start_time = time.time()
# Copy inputs
cuda.memcpy_htod_async(d_inputs[0], features["input_ids"], stream)
cuda.memcpy_htod_async(d_inputs[1], features["segment_ids"], stream)
cuda.memcpy_htod_async(d_inputs[2], features["input_mask"], stream)
# Run inference
context.execute_async_v2(bindings=[int(d_inp) for d_inp in d_inputs] + [int(d_output)], stream_handle=stream.handle)
# Transfer predictions back from GPU
cuda.memcpy_dtoh_async(h_output, d_output, stream)
# Synchronize the stream
stream.synchronize()
h_output = h_output.transpose((1,0,2,3,4))
eval_time_elapsed = time.time() - eval_start_time
print("------------------------")
print("Running inference in {:.3f} Sentences/Sec".format(1.0/eval_time_elapsed))
print("------------------------")
for index, batch in enumerate(h_output):
# Data Post-processing
start_logits = batch[:, 0]
end_logits = batch[:, 1]
prediction, nbest_json, scores_diff_json = dp.get_predictions(doc_tokens, features,
start_logits, end_logits, args.n_best_size, args.max_answer_length)
print("Processing output {:} in batch".format(index))
print("Answer: '{}'".format(prediction))
print("With probability: {:.3f}%".format(nbest_json[0]['probability'] * 100.0))
def predict(self, inputs: Tuple[Text, List[Text]]) -> List[Any]:
import helpers.data_processing as dp
from polygraphy.backend.trt import TrtRunner
import numpy as np
import collections
import time
def question_features(tokens, question):
# Extract features from the paragraph and question
return dp.convert_example_to_features(
tokens, question, self._trtModel.tokenizer,
self._trtModel.max_seq_length, self._trtModel.doc_stride,
self._trtModel.max_query_length)
features = []
doc_tokens = dp.convert_doc_tokens(inputs[0])
ques_list = inputs[1]
batch_size = len(ques_list)
if batch_size < 16:
# Pad the input batch to batch_size to match the model expected input.
pad = [ques_list[0]] * (16 - batch_size)
ques_list.extend(pad)
for question_text in ques_list:
features.append(question_features(doc_tokens, question_text)[0])
input_ids_batch = np.dstack(
[feature.input_ids for feature in features]).squeeze()
segment_ids_batch = np.dstack(
[feature.segment_ids for feature in features]).squeeze()
input_mask_batch = np.dstack(
[feature.input_mask for feature in features]).squeeze()
inputs = {
"input_ids": input_ids_batch,
"input_mask": input_mask_batch,
"segment_ids": segment_ids_batch
}
output = self._trtModel.infer_context.infer(inputs)
start_logits = output['cls_squad_logits'][:, :, 0, :, :]
end_logits = output['cls_squad_logits'][:, :, 1, :, :]
networkOutputs = [
self._NetworkOutput(start_logits=start_logits[i, :],
end_logits=end_logits[i, :],
feature_index=0)
for i in range(self._batch_size)
]
predictions = []
for feature, networkOutput in zip(features, networkOutputs):
prediction, _, _ = dp.get_predictions(
doc_tokens, [feature], [networkOutput],
self._trtModel.n_best_size, self._trtModel.max_answer_length)
predictions.append(prediction)
return [
"[Q]: " + ques + " [A]:" + prediction
for ques, prediction in zip(ques_list, predictions)
]
def inference(features, tokens):
global h_output
_NetworkOutput = collections.namedtuple( # pylint: disable=invalid-name
"NetworkOutput",
["start_logits", "end_logits", "feature_index"])
networkOutputs = []
eval_time_elapsed = 0
for feature_index, feature in enumerate(features):
# Copy inputs
input_ids_batch = np.dstack([feature.input_ids] * args.batch_size).squeeze()
segment_ids_batch = np.dstack([feature.segment_ids] * args.batch_size).squeeze()
input_mask_batch = np.dstack([feature.input_mask] * args.batch_size).squeeze()
input_ids = cuda.register_host_memory(np.ascontiguousarray(input_ids_batch.ravel()))
segment_ids = cuda.register_host_memory(np.ascontiguousarray(segment_ids_batch.ravel()))
input_mask = cuda.register_host_memory(np.ascontiguousarray(input_mask_batch.ravel()))
eval_start_time = time.time()
cuda.memcpy_htod_async(d_inputs[0], input_ids, stream)
cuda.memcpy_htod_async(d_inputs[1], segment_ids, stream)
cuda.memcpy_htod_async(d_inputs[2], input_mask, stream)
# Run inference
context.execute_async_v2(bindings=[0 for i in range(binding_idx_offset)] + [int(d_inp) for d_inp in d_inputs] + [int(d_output)], stream_handle=stream.handle)
# Synchronize the stream
stream.synchronize()
eval_time_elapsed += (time.time() - eval_start_time)
# Transfer predictions back from GPU
cuda.memcpy_dtoh_async(h_output, d_output, stream)
stream.synchronize()
# Only retrieve and post-process the first batch
batch = h_output[0]
networkOutputs.append(_NetworkOutput(
start_logits = np.array(batch.squeeze()[:, 0]),
end_logits = np.array(batch.squeeze()[:, 1]),
feature_index = feature_index
))
eval_time_elapsed /= len(features)
# Total number of n-best predictions to generate in the nbest_predictions.json output file
n_best_size = 20
# The maximum length of an answer that can be generated. This is needed
# because the start and end predictions are not conditioned on one another
max_answer_length = 30
prediction, nbest_json, scores_diff_json = dp.get_predictions(tokens, features,
networkOutputs, args.n_best_size, args.max_answer_length)
return eval_time_elapsed, prediction, nbest_json
def inference(features, tokens):
global h_output
_NetworkOutput = collections.namedtuple( # pylint: disable=invalid-name
"NetworkOutput",
["start_logits", "end_logits", "feature_index"])
networkOutputs = []
eval_time_elapsed = 0
for feature_index, feature in enumerate(features):
# Copy inputs
input_ids = cuda.register_host_memory(
np.ascontiguousarray(feature.input_ids.ravel()))
segment_ids = cuda.register_host_memory(
np.ascontiguousarray(feature.segment_ids.ravel()))
input_mask = cuda.register_host_memory(
np.ascontiguousarray(feature.input_mask.ravel()))
eval_start_time = time.time()
cuda.memcpy_htod_async(d_inputs[0], input_ids, stream)
cuda.memcpy_htod_async(d_inputs[1], segment_ids, stream)
cuda.memcpy_htod_async(d_inputs[2], input_mask, stream)
# Run inference
context.execute_async_v2(
bindings=[int(d_inp)
for d_inp in d_inputs] + [int(d_output)],
stream_handle=stream.handle)
# Synchronize the stream
stream.synchronize()
eval_time_elapsed += (time.time() - eval_start_time)
# Transfer predictions back from GPU
cuda.memcpy_dtoh_async(h_output, d_output, stream)
stream.synchronize()
for index, batch in enumerate(h_output):
# Data Post-processing
networkOutputs.append(
_NetworkOutput(
start_logits=np.array(batch.squeeze()[:, 0]),
end_logits=np.array(batch.squeeze()[:, 1]),
feature_index=feature_index))
eval_time_elapsed /= len(features)
prediction, nbest_json, scores_diff_json = dp.get_predictions(
tokens, features, networkOutputs, args.n_best_size,
args.max_answer_length)
return eval_time_elapsed, prediction, nbest_json
def inference(features, tokens):
_NetworkOutput = collections.namedtuple( # pylint: disable=invalid-name
"NetworkOutput", ["start_logits", "end_logits", "feature_index"])
networkOutputs = []
eval_time_elapsed = 0
for feature_index, feature in enumerate(features):
# Copy inputs
input_ids = np.ascontiguousarray(feature.input_ids.ravel())
segment_ids = np.ascontiguousarray(feature.segment_ids.ravel())
input_mask = np.ascontiguousarray(feature.input_mask.ravel())
eval_start_time = time.time()
# Run inference
h_output = bert.run(input_ids, segment_ids, input_mask)
eval_time_elapsed += (time.time() - eval_start_time)
# Data Post-processing
if len(h_output.shape) == 1:
S = int(h_output.shape[0] / 2)
networkOutputs.append(
_NetworkOutput(start_logits=np.array(h_output[0:S]),
end_logits=np.array(h_output[S:S * 2]),
feature_index=feature_index))
else:
for index, batch in enumerate(h_output):
networkOutputs.append(
_NetworkOutput(
start_logits=np.array(batch.squeeze()[:, 0]),
end_logits=np.array(batch.squeeze()[:, 1]),
feature_index=feature_index))
eval_time_elapsed /= len(features)
# Total number of n-best predictions to generate in the nbest_predictions.json output file
n_best_size = 20
# The maximum length of an answer that can be generated. This is needed
# because the start and end predictions are not conditioned on one another
max_answer_length = 30
prediction, nbest_json, scores_diff_json = dp.get_predictions(
tokens, features, networkOutputs, args.n_best_size,
args.max_answer_length)
return eval_time_elapsed, prediction, nbest_json
def transformation():
"""Do an inference on a single batch of data. In this sample server, we take data as CSV, convert
it to a pandas data frame for internal use and then convert the predictions back to CSV (which really
just means one prediction per line, since there's a single column.
"""
# Convert from json
if (not flask.request.content_type == 'application/json'):
return flask.Response(
response=
'This predictor only supports json data. We have a request of type '
+ flask.request.content_type,
status=415,
mimetype='text/plain')
print("Getting request.")
json_data = flask.request.get_json(force=True)
short_paragraph_text = json_data["short_paragraph_text"]
question_text = json_data["question_text"]
print("Got request, starting prediction.")
# Do prediction
h_output, doc_tokens, features, sentences_sec = ScoringService.predict(
short_paragraph_text, question_text)
print("Finished prediction.")
result = ""
for index, batch in enumerate(h_output):
start_logits = batch[:, 0]
end_logits = batch[:, 1]
# The total number of n-best predictions to generate in the nbest_predictions.json output file
n_best_size = 20
# The maximum length of an answer that can be generated. This is needed
# because the start and end predictions are not conditioned on one another
max_answer_length = 30
(prediction, nbest_json, scores_diff_json) = \
dp.get_predictions(doc_tokens, features, start_logits, end_logits, n_best_size, max_answer_length)
result += "Answer: '{}'".format(
prediction) + " with prob: {:.3f}% ".format(
nbest_json[0]['probability'] *
100.0) + "at {:.3f} Sentences/Sec.".format(sentences_sec)
return flask.Response(response=result, status=200, mimetype='text/plain')
def inference(features, tokens):
global h_output
_NetworkOutput = collections.namedtuple( # pylint: disable=invalid-name
"NetworkOutput",
["start_logits", "end_logits", "feature_index"])
networkOutputs = []
eval_time_elapsed = 0
for feature_index, feature in enumerate(features):
# Copy inputs
B = 1
S = np.sum(feature.input_mask)
input_ids = feature.input_ids[0:S]
segment_ids = feature.segment_ids[0:S]
cu_seq_lens = np.array([0, S], dtype=np.int32)
if context.get_binding_shape(0)[0] != S:
context.set_binding_shape(0, (S, ))
if context.get_binding_shape(1)[0] != S:
context.set_binding_shape(1, (S, ))
if context.get_binding_shape(2)[0] != 2:
context.set_binding_shape(2, (2, ))
if context.get_binding_shape(3)[0] != S:
context.set_binding_shape(3, (S, ))
h_input_ids = cuda.register_host_memory(
np.ascontiguousarray(input_ids.ravel()))
h_segment_ids = cuda.register_host_memory(
np.ascontiguousarray(segment_ids.ravel()))
h_cu_seq_lens = cuda.register_host_memory(
np.ascontiguousarray(cu_seq_lens.ravel()))
eval_start_time = time.time()
cuda.memcpy_htod_async(d_inputs[0], h_input_ids, stream)
cuda.memcpy_htod_async(d_inputs[1], h_segment_ids, stream)
cuda.memcpy_htod_async(d_inputs[2], h_cu_seq_lens, stream)
# Run inference
context.execute_async_v2(
bindings=[int(d_inp)
for d_inp in d_inputs] + [int(d_output)],
stream_handle=stream.handle)
# Synchronize the stream
stream.synchronize()
eval_time_elapsed += (time.time() - eval_start_time)
# Transfer predictions back from GPU
cuda.memcpy_dtoh_async(h_output, d_output, stream)
stream.synchronize()
# Only retrieve and post-process the first batch
networkOutputs.append(
_NetworkOutput(start_logits=np.array(h_output[0:S]),
end_logits=np.array(h_output[S:S * 2]),
feature_index=feature_index))
eval_time_elapsed /= len(features)
# Total number of n-best predictions to generate in the nbest_predictions.json output file
n_best_size = 20
# The maximum length of an answer that can be generated. This is needed
# because the start and end predictions are not conditioned on one another
max_answer_length = 30
prediction, nbest_json, scores_diff_json = dp.get_predictions(
tokens, features, networkOutputs, args.n_best_size,
args.max_answer_length)
return eval_time_elapsed, prediction, nbest_json
