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# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved.
#
# 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 argparse
import sys
import typing
import numpy as np
from pytriton.client import DecoupledModelClient, ModelClient
def get_args(argv):
parser = argparse.ArgumentParser(
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
description=f"Sends a single query to an LLM hosted on a Triton server.",
)
parser.add_argument("-u", "--url", default="0.0.0.0", type=str, help="url for the triton server")
parser.add_argument("-mn", "--model_name", required=True, type=str, help="Name of the triton model")
prompt_group = parser.add_mutually_exclusive_group(required=True)
prompt_group.add_argument("-p", "--prompt", required=False, type=str, help="Prompt")
prompt_group.add_argument("-pf", "--prompt_file", required=False, type=str, help="File to read the prompt from")
parser.add_argument("-swl", "--stop_words_list", type=str, help="Stop words list")
parser.add_argument("-bwl", "--bad_words_list", type=str, help="Bad words list")
parser.add_argument("-nrns", "--no_repeat_ngram_size", type=int, help="No repeat ngram size")
parser.add_argument("-mol", "--max_output_len", default=128, type=int, help="Max output token length")
parser.add_argument("-tk", "--top_k", default=1, type=int, help="top_k")
parser.add_argument("-tpp", "--top_p", default=0.0, type=float, help="top_p")
parser.add_argument("-t", "--temperature", default=1.0, type=float, help="temperature")
parser.add_argument("-ti", "--task_id", type=str, help="Task id for the prompt embedding tables")
parser.add_argument(
"-lt",
"--lora_task_uids",
default=None,
type=str,
nargs="+",
help="The list of LoRA task uids; use -1 to disable the LoRA module",
)
parser.add_argument(
"-es", '--enable_streaming', default=False, action='store_true', help="Enables streaming sentences."
)
parser.add_argument("-it", "--init_timeout", default=60.0, type=float, help="init timeout for the triton server")
args = parser.parse_args(argv)
return args
def str_list2numpy(str_list: typing.List[str]) -> np.ndarray:
str_ndarray = np.array(str_list)[..., np.newaxis]
return np.char.encode(str_ndarray, "utf-8")
def query_llm(
url,
model_name,
prompts,
stop_words_list=None,
bad_words_list=None,
no_repeat_ngram_size=None,
max_output_len=128,
top_k=1,
top_p=0.0,
temperature=1.0,
random_seed=None,
task_id=None,
lora_uids=None,
init_timeout=60.0,
):
prompts = str_list2numpy(prompts)
inputs = {"prompts": prompts}
if max_output_len is not None:
inputs["max_output_len"] = np.full(prompts.shape, max_output_len, dtype=np.int_)
if top_k is not None:
inputs["top_k"] = np.full(prompts.shape, top_k, dtype=np.int_)
if top_p is not None:
inputs["top_p"] = np.full(prompts.shape, top_p, dtype=np.single)
if temperature is not None:
inputs["temperature"] = np.full(prompts.shape, temperature, dtype=np.single)
if random_seed is not None:
inputs["random_seed"] = np.full(prompts.shape, random_seed, dtype=np.single)
if stop_words_list is not None:
stop_words_list = np.char.encode(stop_words_list, "utf-8")
inputs["stop_words_list"] = np.full((prompts.shape[0], len(stop_words_list)), stop_words_list)
if bad_words_list is not None:
bad_words_list = np.char.encode(bad_words_list, "utf-8")
inputs["bad_words_list"] = np.full((prompts.shape[0], len(bad_words_list)), bad_words_list)
if no_repeat_ngram_size is not None:
inputs["no_repeat_ngram_size"] = np.full(prompts.shape, no_repeat_ngram_size, dtype=np.single)
if task_id is not None:
task_id = np.char.encode(task_id, "utf-8")
inputs["task_id"] = np.full((prompts.shape[0], len([task_id])), task_id)
if lora_uids is not None:
lora_uids = np.char.encode(lora_uids, "utf-8")
inputs["lora_uids"] = np.full((prompts.shape[0], len(lora_uids)), lora_uids)
with ModelClient(url, model_name, init_timeout_s=init_timeout) as client:
result_dict = client.infer_batch(**inputs)
output_type = client.model_config.outputs[0].dtype
if output_type == np.bytes_:
sentences = np.char.decode(result_dict["outputs"].astype("bytes"), "utf-8")
return sentences
else:
return result_dict["outputs"]
def query_llm_streaming(
url,
model_name,
prompts,
stop_words_list=None,
bad_words_list=None,
no_repeat_ngram_size=None,
max_output_len=512,
top_k=1,
top_p=0.0,
temperature=1.0,
random_seed=None,
task_id=None,
lora_uids=None,
init_timeout=60.0,
):
prompts = str_list2numpy(prompts)
inputs = {"prompts": prompts}
if max_output_len is not None:
inputs["max_output_len"] = np.full(prompts.shape, max_output_len, dtype=np.int_)
if top_k is not None:
inputs["top_k"] = np.full(prompts.shape, top_k, dtype=np.int_)
if top_p is not None:
inputs["top_p"] = np.full(prompts.shape, top_p, dtype=np.single)
if temperature is not None:
inputs["temperature"] = np.full(prompts.shape, temperature, dtype=np.single)
if random_seed is not None:
inputs["random_seed"] = np.full(prompts.shape, random_seed, dtype=np.int_)
if stop_words_list is not None:
stop_words_list = np.char.encode(stop_words_list, "utf-8")
inputs["stop_words_list"] = np.full((prompts.shape[0], len(stop_words_list)), stop_words_list)
if bad_words_list is not None:
bad_words_list = np.char.encode(bad_words_list, "utf-8")
inputs["bad_words_list"] = np.full((prompts.shape[0], len(bad_words_list)), bad_words_list)
if no_repeat_ngram_size is not None:
inputs["no_repeat_ngram_size"] = np.full(prompts.shape, no_repeat_ngram_size, dtype=np.single)
if task_id is not None:
task_id = np.char.encode(task_id, "utf-8")
inputs["task_id"] = np.full((prompts.shape[0], len([task_id])), task_id)
if lora_uids is not None:
lora_uids = np.char.encode(lora_uids, "utf-8")
inputs["lora_uids"] = np.full((prompts.shape[0], len(lora_uids)), lora_uids)
with DecoupledModelClient(url, model_name, init_timeout_s=init_timeout) as client:
for partial_result_dict in client.infer_batch(**inputs):
output_type = client.model_config.outputs[0].dtype
if output_type == np.bytes_:
sentences = np.char.decode(partial_result_dict["outputs"].astype("bytes"), "utf-8")
yield sentences
else:
yield partial_result_dict["outputs"]
def query(argv):
args = get_args(argv)
if args.prompt_file is not None:
with open(args.prompt_file, "r") as f:
args.prompt = f.read()
if args.enable_streaming:
output_generator = query_llm_streaming(
url=args.url,
model_name=args.model_name,
prompts=[args.prompt],
stop_words_list=None if args.stop_words_list is None else [args.stop_words_list],
bad_words_list=None if args.bad_words_list is None else [args.bad_words_list],
no_repeat_ngram_size=args.no_repeat_ngram_size,
max_output_len=args.max_output_len,
top_k=args.top_k,
top_p=args.top_p,
temperature=args.temperature,
task_id=args.task_id,
lora_uids=args.lora_task_uids,
init_timeout=args.init_timeout,
)
# The query returns a generator that yields one array per model step,
# with the partial generated text in the last dimension. Print that partial text
# incrementally and compare it with all the text generated so far.
prev_output = ''
for output in output_generator:
cur_output = output[0][0]
if prev_output == '' or cur_output.startswith(prev_output):
print(cur_output[len(prev_output) :], end='', flush=True)
else:
print("WARN: Partial output mismatch, restarting output...")
print(cur_output, end='', flush=True)
prev_output = cur_output
print()
else:
outputs = query_llm(
url=args.url,
model_name=args.model_name,
prompts=[args.prompt],
stop_words_list=None if args.stop_words_list is None else [args.stop_words_list],
bad_words_list=None if args.bad_words_list is None else [args.bad_words_list],
no_repeat_ngram_size=args.no_repeat_ngram_size,
max_output_len=args.max_output_len,
top_k=args.top_k,
top_p=args.top_p,
temperature=args.temperature,
task_id=args.task_id,
lora_uids=args.lora_task_uids,
init_timeout=args.init_timeout,
)
print(outputs[0][0])
if __name__ == '__main__':
query(sys.argv[1:])
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