wehub-resource-sync
115 lines
4.3 KiB
Python
115 lines
4.3 KiB
Python
# Copyright (c) 2022 PaddlePaddle Authors. 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 os
|
|
from pprint import pprint
|
|
|
|
import paddle
|
|
|
|
from paddlenlp.ops import FasterUNIMOText
|
|
from paddlenlp.transformers import UNIMOLMHeadModel, UNIMOTokenizer
|
|
from paddlenlp.utils.log import logger
|
|
|
|
|
|
def parse_args():
|
|
parser = argparse.ArgumentParser()
|
|
parser.add_argument(
|
|
"--model_name_or_path",
|
|
default="unimo-text-1.0-summary",
|
|
type=str,
|
|
help="The model name to specify the UNIMOText to use. ",
|
|
)
|
|
parser.add_argument(
|
|
"--inference_model_dir",
|
|
default="./inference_model",
|
|
type=str,
|
|
help="Path to save inference model of UNIMOText. ",
|
|
)
|
|
parser.add_argument("--topk", default=4, type=int, help="The number of candidate to procedure top_k sampling. ")
|
|
parser.add_argument(
|
|
"--topp", default=1.0, type=float, help="The probability threshold to procedure top_p sampling. "
|
|
)
|
|
parser.add_argument("--max_out_len", default=64, type=int, help="Maximum output length. ")
|
|
parser.add_argument("--min_out_len", default=1, type=int, help="Minimum output length. ")
|
|
parser.add_argument("--num_return_sequence", default=1, type=int, help="The number of returned sequence. ")
|
|
parser.add_argument("--temperature", default=1.0, type=float, help="The temperature to set. ")
|
|
parser.add_argument("--num_return_sequences", default=1, type=int, help="The number of returned sequences. ")
|
|
parser.add_argument("--use_fp16_decoding", action="store_true", help="Whether to use fp16 decoding to predict. ")
|
|
parser.add_argument(
|
|
"--decoding_strategy",
|
|
default="beam_search",
|
|
choices=["sampling", "beam_search"],
|
|
type=str,
|
|
help="The main strategy to decode. ",
|
|
)
|
|
parser.add_argument("--num_beams", default=4, type=int, help="The number of candidate to procedure beam search. ")
|
|
parser.add_argument(
|
|
"--diversity_rate", default=0.0, type=float, help="The diversity rate to procedure beam search. "
|
|
)
|
|
|
|
args = parser.parse_args()
|
|
return args
|
|
|
|
|
|
def do_predict(args):
|
|
place = "gpu"
|
|
place = paddle.set_device(place)
|
|
|
|
model_name_or_path = args.model_name_or_path
|
|
model = UNIMOLMHeadModel.from_pretrained(model_name_or_path)
|
|
tokenizer = UNIMOTokenizer.from_pretrained(model_name_or_path)
|
|
|
|
unimo_text = FasterUNIMOText(model=model, use_fp16_decoding=args.use_fp16_decoding, trans_out=True)
|
|
|
|
# Set evaluate mode
|
|
unimo_text.eval()
|
|
|
|
# Convert dygraph model to static graph model
|
|
unimo_text = paddle.jit.to_static(
|
|
unimo_text,
|
|
input_spec=[
|
|
# input_ids
|
|
paddle.static.InputSpec(shape=[None, None], dtype="int64"),
|
|
# token_type_ids
|
|
paddle.static.InputSpec(shape=[None, None], dtype="int64"),
|
|
# attention_mask
|
|
paddle.static.InputSpec(shape=[None, 1, None, None], dtype="float32"),
|
|
# seq_len
|
|
paddle.static.InputSpec(shape=[None], dtype="int64"),
|
|
args.max_out_len,
|
|
args.min_out_len,
|
|
args.topk,
|
|
args.topp,
|
|
args.num_beams, # num_beams. Used for beam_search.
|
|
args.decoding_strategy,
|
|
tokenizer.cls_token_id, # cls/bos
|
|
tokenizer.mask_token_id, # mask/eos
|
|
tokenizer.pad_token_id, # pad
|
|
args.diversity_rate, # diversity rate. Used for beam search.
|
|
args.temperature,
|
|
args.num_return_sequences,
|
|
],
|
|
)
|
|
|
|
# Save converted static graph model
|
|
paddle.jit.save(unimo_text, os.path.join(args.inference_model_dir, "unimo_text"))
|
|
logger.info("UNIMOText has been saved to {}.".format(args.inference_model_dir))
|
|
|
|
|
|
if __name__ == "__main__":
|
|
args = parse_args()
|
|
pprint(args)
|
|
|
|
do_predict(args)
|