text: # Copyright (c) 2021 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 sys
  import os
  import numpy as np
  from attrdict import AttrDict
  import argparse
  import time
  
  import paddle
  
  import yaml
  from pprint import pprint
  
  from paddlenlp.ops import FasterTransformer
  
  from paddlenlp.utils.log import logger
  
  paddle.seed(2)
  np.random.seed(2)
  
  
  def parse_args():
      parser = argparse.ArgumentParser()
      parser.add_argument(
          "--config",
          default="./sample/config/decoding.sample.yaml",
          type=str,
          help="Path of the config file. ")
      parser.add_argument(
          "--decoding_lib",
          default="./build/lib/libdecoding_op.so",
          type=str,
          help="Path of libdecoding_op.so. ")
      parser.add_argument(
          "--use_fp16_decoding",
          action="store_true",
          help="Whether to use fp16 decoding to predict. ")
      args = parser.parse_args()
      return args
  
  
  def do_predict(args):
      place = "gpu"
      place = paddle.set_device(place)
  
      # Define model
      transformer = FasterTransformer(
          src_vocab_size=args.src_vocab_size,
          trg_vocab_size=args.trg_vocab_size,
          max_length=args.max_length + 1,
          num_encoder_layers=args.n_layer,
          num_decoder_layers=args.n_layer,
          n_head=args.n_head,
          d_model=args.d_model,
          d_inner_hid=args.d_inner_hid,
          dropout=args.dropout,
          weight_sharing=args.weight_sharing,
          bos_id=args.bos_idx,
          eos_id=args.eos_idx,
          decoding_strategy=args.decoding_strategy,
          beam_size=args.beam_size,
          topk=args.topk,
          topp=args.topp,
          max_out_len=args.max_out_len,
          decoding_lib=args.decoding_lib,
          use_fp16_decoding=args.use_fp16_decoding)
  
      # Set evaluate mode
      transformer.eval()
  
      enc_output = paddle.randn(
          [args.infer_batch_size, args.max_length, args.d_model])
      if args.use_fp16_decoding:
          enc_output = paddle.cast(enc_output, "float16")
      mem_seq_len = paddle.randint(
          1, args.max_length + 1, shape=[args.infer_batch_size], dtype="int32")
      with paddle.no_grad():
          for i in range(100):
              # For warmup. 
              if 50 == i:
                  start = time.time()
              transformer.decoding(
                  enc_output=enc_output, memory_seq_lens=mem_seq_len)
          logger.info("Average test time for decoding is %f ms" % (
              (time.time() - start) / 50 * 1000))
  
  
  if __name__ == "__main__":
      ARGS = parse_args()
      yaml_file = ARGS.config
      with open(yaml_file, 'rt') as f:
          args = AttrDict(yaml.safe_load(f))
          pprint(args)
      args.decoding_lib = ARGS.decoding_lib
      args.use_fp16_decoding = ARGS.use_fp16_decoding
  
      do_predict(args)
  