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Update end_to_end_test.py #409

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80 changes: 8 additions & 72 deletions tools/gpt/end_to_end_test.py
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Original file line number Diff line number Diff line change
Expand Up @@ -10,7 +10,7 @@
from transformers import AutoTokenizer
from utils import utils

if __name__ == '__main__':
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument('-v',
'--verbose',
Expand All @@ -29,8 +29,8 @@
type=str,
required=False,
default='http',
help='Protocol ("http"/"grpc") used to ' +
'communicate with inference service. Default is "http".')
choices=['http', 'grpc'],
help='Protocol ("http"/"grpc") used to communicate with inference service. Default is "http".')
parser.add_argument('-c',
'--concurrency',
type=int,
Expand Down Expand Up @@ -65,14 +65,11 @@
type=str,
required=True,
help='Specify tokenizer directory')
return parser.parse_args()

FLAGS = parser.parse_args()
if (FLAGS.protocol != "http") and (FLAGS.protocol != "grpc"):
print(
"unexpected protocol \"{}\", expects \"http\" or \"grpc\"".format(
FLAGS.protocol))
exit(1)

def main():
FLAGS = parse_args()

if FLAGS.url is None:
FLAGS.url = "localhost:8000" if FLAGS.protocol == "http" else "localhost:8001"

Expand Down Expand Up @@ -188,65 +185,4 @@
runtime_top_p = FLAGS.topp * np.ones([input0_data.shape[0], 1]).astype(
np.float32)
temperature = 1.0 * np.ones([input0_data.shape[0], 1]).astype(
np.float32)
len_penalty = 1.0 * np.ones([input0_data.shape[0], 1]).astype(
np.float32)
repetition_penalty = 1.0 * np.ones([input0_data.shape[0], 1]).astype(
np.float32)
random_seed = 0 * np.ones([input0_data.shape[0], 1]).astype(np.uint64)
output_log_probs = True * np.ones([input0_data.shape[0], 1
]).astype(bool)
beam_width = (FLAGS.beam_width *
np.ones([input0_data.shape[0], 1])).astype(np.int32)
pad_ids = pad_id * \
np.ones([input0_data.shape[0], 1]).astype(np.int32)
end_ids = end_id * \
np.ones([input0_data.shape[0], 1]).astype(np.int32)
min_length = 1 * \
np.ones([input0_data.shape[0], 1]).astype(np.int32)
presence_penalty = 0.0 * \
np.ones([input0_data.shape[0], 1]).astype(np.float32)
frequency_penalty = 0.0 * \
np.ones([input0_data.shape[0], 1]).astype(np.float32)
inputs = [
utils.prepare_tensor("text_input", input0_data, FLAGS.protocol),
utils.prepare_tensor("max_tokens", output0_len, FLAGS.protocol),
utils.prepare_tensor("bad_words", bad_words_list, FLAGS.protocol),
utils.prepare_tensor("stop_words", stop_words_list,
FLAGS.protocol),
utils.prepare_tensor("pad_id", pad_ids, FLAGS.protocol),
utils.prepare_tensor("end_id", end_ids, FLAGS.protocol),
utils.prepare_tensor("beam_width", beam_width, FLAGS.protocol),
utils.prepare_tensor("top_k", runtime_top_k, FLAGS.protocol),
utils.prepare_tensor("top_p", runtime_top_p, FLAGS.protocol),
utils.prepare_tensor("temperature", temperature, FLAGS.protocol),
utils.prepare_tensor("length_penalty", len_penalty,
FLAGS.protocol),
utils.prepare_tensor("repetition_penalty", repetition_penalty,
FLAGS.protocol),
utils.prepare_tensor("min_length", min_length, FLAGS.protocol),
utils.prepare_tensor("presence_penalty", presence_penalty,
FLAGS.protocol),
utils.prepare_tensor("frequency_penalty", frequency_penalty,
FLAGS.protocol),
utils.prepare_tensor("random_seed", random_seed, FLAGS.protocol),
utils.prepare_tensor("output_log_probs", output_log_probs,
FLAGS.protocol),
]

try:
result = client.infer(model_name, inputs)
ensemble_output0 = result.as_numpy("text_output")
print("============After ensemble============")
batch_size = len(input0)
ensemble_output0 = ensemble_output0.reshape([-1, batch_size
]).T.tolist()
ensemble_output0 = [[char.decode('UTF-8') for char in line]
for line in ensemble_output0]
ensemble_output0 = [''.join(line) for line in ensemble_output0]
for line in ensemble_output0:
print(f"{line}")
except Exception as e:
print(e)

assert output0 == ensemble_output0