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end_to_end_test.py
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end_to_end_test.py
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#!/usr/bin/python
import os
import sys
sys.path.append(os.path.dirname(os.path.dirname(os.path.realpath(__file__))))
import argparse
import json
import sys
from datetime import datetime
from functools import partial
import numpy as np
from utils import utils
def callback(user_data, start_time, result, error):
user_data._completed_requests.put((result, error))
stop_time = datetime.now()
latency = (stop_time - start_time).total_seconds() * 1000.0
latency = round(latency, 3)
user_data._latencies.append(latency)
def test_functionality(client, prompts, output_lens):
print(f"[INFO] Start testing on {len(prompts)} prompts.")
for i, prompt in enumerate(prompts):
# 1. Ensemble models manually: preprocessing -> tensorrt_llm -> postprocessing
model_name = 'preprocessing'
input0 = [[prompt]]
input0_data = np.array(input0).astype(object)
output0_len = np.ones_like(input0).astype(np.int32) * output_lens[i]
bad_words_list = np.array([[""]], dtype=object)
stop_words_list = np.array([[""]], dtype=object)
inputs = [
utils.prepare_tensor("QUERY", input0_data, FLAGS.protocol),
utils.prepare_tensor("BAD_WORDS_DICT", bad_words_list,
FLAGS.protocol),
utils.prepare_tensor("STOP_WORDS_DICT", stop_words_list,
FLAGS.protocol),
utils.prepare_tensor("REQUEST_OUTPUT_LEN", output0_len,
FLAGS.protocol),
]
result = client.infer(model_name, inputs, request_id=str(i))
output0 = result.as_numpy("INPUT_ID")
output1 = result.as_numpy("REQUEST_INPUT_LEN")
output2 = result.as_numpy("REQUEST_OUTPUT_LEN")
model_name = "tensorrt_llm"
inputs = [
utils.prepare_tensor("input_ids", output0, FLAGS.protocol),
utils.prepare_tensor("input_lengths", output1, FLAGS.protocol),
utils.prepare_tensor("request_output_len", output2,
FLAGS.protocol),
]
result = client.infer(model_name, inputs, request_id=str(i))
output0 = result.as_numpy("output_ids").astype(np.int32)
seq_lengths = result.as_numpy("sequence_length")
cum_log_probs = result.as_numpy("cum_log_probs").astype(np.float32)
output_log_probs = result.as_numpy("output_log_probs").astype(
np.float32)
model_name = "postprocessing"
inputs = [
utils.prepare_tensor("TOKENS_BATCH", output0, FLAGS.protocol),
utils.prepare_tensor("SEQUENCE_LENGTH", seq_lengths,
FLAGS.protocol),
utils.prepare_tensor("CUM_LOG_PROBS", cum_log_probs,
FLAGS.protocol),
utils.prepare_tensor("OUTPUT_LOG_PROBS", output_log_probs,
FLAGS.protocol)
]
inputs[0].set_data_from_numpy(output0)
inputs[1].set_data_from_numpy(seq_lengths)
inputs[2].set_data_from_numpy(cum_log_probs)
inputs[3].set_data_from_numpy(output_log_probs)
result = client.infer(model_name, inputs, request_id=str(i))
output0 = result.as_numpy("OUTPUT")
# 2. Use ensemble model
model_name = "ensemble"
input0 = [[prompt]]
input0_data = np.array(input0).astype(object)
output0_len = np.ones_like(input0).astype(np.int32) * output_lens[i]
bad_words_list = np.array([[""]], dtype=object)
stop_words_list = np.array([[""]], dtype=object)
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),
]
result = client.infer(model_name, inputs, request_id=str(i))
# 3. Check the results between manually ensembled models and the ensemble model
ensemble_output = result.as_numpy('text_output')
ensemble_cum_log_probs = result.as_numpy('cum_log_probs')
ensemble_output_log_probs = result.as_numpy('output_log_probs')
assert output0 == ensemble_output
assert cum_log_probs == ensemble_cum_log_probs
assert (output_log_probs == ensemble_output_log_probs).all()
if FLAGS.verbose:
print('Response: {}'.format(result.get_response()))
print('Output: {}'.format(ensemble_output))
print(f"[INFO] Functionality test succeed.")
def test_performance(client, prompts, output_lens):
model_name = "ensemble"
print(f"[INFO] Warm up for benchmarking.")
for i in range(min(10, len(prompts))):
input0 = [[prompts[0]]]
input0_data = np.array(input0).astype(object)
output0_len = np.ones_like(input0).astype(np.int32) * output_lens[i]
bad_words_list = np.array([[""]], dtype=object)
stop_words_list = np.array([[""]], dtype=object)
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),
]
client.infer(model_name, inputs, request_id=str(i))
print(f"[INFO] Start benchmarking on {len(prompts)} prompts.")
latency = 0
async_requests = []
start_time = datetime.now()
user_data = utils.UserData()
for i, prompt in enumerate(prompts):
input0 = [[prompt]]
input0_data = np.array(input0).astype(object)
output0_len = np.ones_like(input0).astype(np.int32) * output_lens[i]
bad_words_list = np.array([[""]], dtype=object)
stop_words_list = np.array([[""]], dtype=object)
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),
]
if FLAGS.protocol == "http":
async_requests.append(
client.async_infer(model_name, inputs, request_id=str(i)))
elif FLAGS.protocol == "grpc":
async_requests.append(
client.async_infer(model_name,
inputs,
callback=partial(callback, user_data,
datetime.now()),
request_id=str(i)))
if FLAGS.protocol == "http":
utils.get_http_results(async_requests)
elif FLAGS.protocol == "grpc":
utils.get_grpc_results(user_data, len(prompts))
else:
raise RuntimeError("Invalid protocol")
stop_time = datetime.now()
latency = (stop_time - start_time).total_seconds() * 1000.0
latency = round(latency, 3)
print(f"[INFO] Total Latency: {latency} ms")
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('-v',
'--verbose',
action="store_true",
required=False,
default=False,
help='Enable verbose output')
parser.add_argument('-u',
'--url',
type=str,
required=False,
help='Inference server URL.')
parser.add_argument(
'-i',
'--protocol',
type=str,
required=False,
default='http',
choices=['http', 'grpc'],
help='Protocol ("http"/"grpc") used to ' +
'communicate with inference service. Default is "http".')
parser.add_argument('-c',
'--concurrency',
type=int,
default=128,
required=False,
help='Specify concurrency')
parser.add_argument('--max-input-len',
type=int,
required=True,
help='Specify max input length')
parser.add_argument('--dataset',
type=str,
required=True,
help='Dataset path used for the test.')
FLAGS = parser.parse_args()
if FLAGS.url is None:
FLAGS.url = "localhost:8000" if FLAGS.protocol == "http" else "localhost:8001"
try:
client = utils.create_inference_server_client(
FLAGS.protocol,
FLAGS.url,
concurrency=FLAGS.concurrency,
verbose=FLAGS.verbose)
except Exception as e:
print("Encountered error: " + str(e))
sys.exit(1)
prompts = []
output_lens = []
with open(FLAGS.dataset, 'r') as f:
data_dict = json.load(f)
for req in data_dict:
prompt = req['input'] + ' ' + req['instruction']
output = req['output']
# 1.3 is a magic number that converts number of words to number of tokens
if int(len(prompt.split(' ')) / 1.3) > FLAGS.max_input_len:
continue
prompts.append(prompt)
# 1.3 is a magic number that converts number of words to number of tokens
output_lens.append(int(len(output.split(' ')) * 1.3))
test_functionality(client, prompts, output_lens)
test_performance(client, prompts, output_lens)