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# Copyright 2022, Lefebvre Dalloz Services
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import json
import zlib
import requests
from transformers import AutoTokenizer, PreTrainedTokenizer, TensorType
from transformer_deploy.benchmarks.utils import print_timings, setup_logging, track_infer_time
setup_logging()
tokenizer: PreTrainedTokenizer = AutoTokenizer.from_pretrained("philschmid/MiniLM-L6-H384-uncased-sst2")
tokens = tokenizer(
text="This live event is great. I will sign-up for Infinity.",
max_length=16,
truncation=True,
return_tensors=TensorType.NUMPY,
)
# https://github.com/triton-inference-server/server/blob/main/docs/protocol/extension_classification.md
url = "http://127.0.0.1:8000/v2/models/transformer_onnx_model/versions/1/infer"
message = {
"id": "42",
"inputs": [
{
"name": "input_ids",
"shape": tokens["input_ids"].shape,
"datatype": "INT64",
"data": tokens["input_ids"].tolist(),
},
{
"name": "token_type_ids",
"shape": tokens["token_type_ids"].shape,
"datatype": "INT64",
"data": tokens["token_type_ids"].tolist(),
},
{
"name": "attention_mask",
"shape": tokens["attention_mask"].shape,
"datatype": "INT64",
"data": tokens["attention_mask"].tolist(),
},
],
"outputs": [
{
"name": "output",
"parameters": {"binary_data": False},
}
],
}
time_buffer = list()
session = requests.Session()
for _ in range(10000):
bytes_message = bytes(json.dumps(message), encoding="raw_unicode_escape")
request_body = zlib.compress(bytes_message)
_ = session.post(
url,
data=request_body,
headers={
"Content-Encoding": "gzip",
"Accept-Encoding": "gzip",
"Inference-Header-Content-Length": str(len(bytes_message)),
},
)
for _ in range(100):
with track_infer_time(time_buffer):
bytes_message = bytes(json.dumps(message), encoding="raw_unicode_escape")
request_body = zlib.compress(bytes_message)
_ = session.post(
url,
data=request_body,
headers={
"Content-Encoding": "gzip",
"Accept-Encoding": "gzip",
"Inference-Header-Content-Length": str(len(bytes_message)),
},
)
print_timings(name="triton (onnx backend) - requests", timings=time_buffer)
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