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import sys
import os
import argparse
import torch
import torch.nn.functional as F
import torch.distributed as dist
import deepspeed
tencentpretrain_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))
sys.path.insert(0, tencentpretrain_dir)
from tencentpretrain.opts import deepspeed_opts
from scripts.generate_seq2seq import *
if __name__ == '__main__':
parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
infer_opts(parser)
parser.add_argument("--top_k", type=int, default=70)
parser.add_argument("--top_p", type=float, default=0)
parser.add_argument("--temperature", type=float, default=1.0)
parser.add_argument("--tgt_vocab_path", type=str,
help="Path of the vocabulary file.")
tokenizer_opts(parser)
parser.add_argument("--tgt_tokenizer", choices=[None, "bert", "char", "space", "xlmroberta"], default=None,
help="Specify the tokenizer for target side.")
parser.add_argument("--tgt_seq_length", type=int, default=128,
help="Sequence length.")
deepspeed_opts(parser)
parser.add_argument("--mp_size", type=int, default=1, help="Model parallel size.")
args = parser.parse_args()
args.batch_size = 1
args = load_hyperparam(args)
args.tokenizer = str2tokenizer[args.tokenizer](args)
if args.tgt_tokenizer == None:
args.tgt_tokenizer = args.tokenizer
else:
args.vocab_path = args.tgt_vocab_path
args.tgt_tokenizer = str2tokenizer[args.tgt_tokenizer](args)
args.tgt_vocab = args.tgt_tokenizer.vocab
model = GenerateSeq2seq(args)
model = load_model(model, args.load_model_path)
deepspeed.init_distributed()
model = deepspeed.init_inference(model=model, mp_size=args.mp_size, replace_method=None)
rank = dist.get_rank()
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
if rank == 0:
model.eval()
with open(args.test_path, mode="r", encoding="utf-8") as f:
line = f.readline().strip()
src = args.tokenizer.convert_tokens_to_ids([CLS_TOKEN] + args.tokenizer.tokenize(line) + [SEP_TOKEN])
seg = [1] * len(src)
tgt = args.tokenizer.convert_tokens_to_ids([CLS_TOKEN])
beginning_length = len(src)
if len(src) > args.seq_length:
src = src[:args.seq_length]
seg = seg[:args.seq_length]
src_tensor, seg_tensor, tgt_tensor = torch.LongTensor([src]).to(device), torch.LongTensor([seg]).to(device), torch.LongTensor([tgt]).to(device)
with open(args.prediction_path, mode="w", encoding="utf-8") as f:
for i in range(args.tgt_seq_length-1):
output = model(src_tensor, seg_tensor, tgt_tensor)
next_token_logits = output[0][-1] / args.temperature
filtered_logits = top_k_top_p_filtering(next_token_logits, args.top_k, args.top_p)
next_token = torch.multinomial(F.softmax(filtered_logits, dim=-1), num_samples=1)
tgt_tensor = torch.cat([tgt_tensor, next_token.view(1, 1).to(device)], dim=1)
f.write(line + "\n")
generated_sentence = "".join(
args.tgt_tokenizer.convert_ids_to_tokens([token_id.item() for token_id in tgt_tensor[0]])
)
f.write(generated_sentence)
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