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本篇内容介绍了“DialoGPT是什么”的有关知识，在实际案例的操作过程中，不少人都会遇到这样的困境，接下来就让小编带领大家学习一下如何处理这些情况吧！希望大家仔细阅读，能够学有所成！
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引言
Large-scale pretraining for dialogue
DialoGPT是基于GPT-2的对话生成预训练模型，在reddit数据集上训练
假定已经设置好环境，
在eval_util.py中增加 inference函数
def inference_model_results(model, tokenizer, inference_dataloader, args):
# use the same signature with eval_model_generation
logger.info('compute eval model loss, using eval mode, '
'please change it back to train after calling this function')
model.eval()
tot_sample = []
with torch.no_grad():
for step, batch in enumerate(inference_dataloader):
batch = tuple(t.to(args.device) for t in batch)
input_ids, position_ids, token_ids, label_ids, src_len, _ = batch
if args.no_token_id:
token_ids = None
n_sample = input_ids.shape[0]
logits = model.inference(input_ids, position_ids, token_ids)
def decode(batch_data, tokenizer, input_flag):
results = []
batch_data = batch_data.cpu().data.numpy()
for one_logits in batch_data: # [sentence_len, vocabulary_size]
if not input_flag:
word_ids = np.argmax(one_logits, axis=1)
else:
word_ids = one_logits
words = []
for id in word_ids:
if tokenizer.decoder[id] != "<|endoftext|>":
words.append(tokenizer.decoder[id])
else:
break
output_words = []
for word in words:
output_words.append(word[1:]) if word.startswith("Ġ") else output_words.append(word)
results.append(" ".join(output_words))
return results
posts = decode(input_ids, tokenizer, True)
inferences = decode(logits, tokenizer, False)
tot_sample.append(n_sample)
logger.info("model inference results")
for index in range(len(posts)):
print("post: ", posts[index])
print("inference: ", inferences[index])
# print(inferences)
break
# todo
return None
在modeling_gpt2.py中class GPT2LMHeadModel(GPT2PreTrainedModel)中增加inference函数
def inference(self, input_ids, position_ids=None, token_type_ids=None, past=None):
hidden_states, presents = self.transformer(input_ids, position_ids, token_type_ids, past)
lm_logits = self.lm_head(hidden_states)
return lm_logits
自定义inference_LSP.py 文件
文件内容
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
'''
* @Desc: train GPT2 from scratch/ fine tuning.
Modified based on Huggingface GPT-2 implementation
'''
import json
import os
import sys
import argparse
import logging
import time
import tqdm
import datetime
import torch
import numpy as np
from os.path import join
from torch.distributed import get_rank, get_world_size
from lsp_model import GPT2LMHeadModel, GPT2Tokenizer, GPT2Config, Adam
from gpt2_training.train_utils import load_model, boolean_string, set_lr, get_eval_list_same_length
from gpt2_training.eval_utils import eval_model_loss, inference_model_results
from data_loader import BucketingDataLoader, DynamicBatchingLoader, DistributedBucketingDataLoader
from gpt2_training.distributed import all_reduce_and_rescale_tensors, all_gather_list
os.environ['CUDA_VISIBLE_DEVICES'] = "0"
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(name)s - %(message)s',
datefmt='%m/%d/%Y %H:%M:%S', level=logging.INFO)
logger = logging.getLogger(__name__)
INF = 100000000
CACHE_EMPTY_STEP = 10000
EVAL_STEP = 10000
#########################################################################
# Prepare Parser
##########################################################################
parser = argparse.ArgumentParser()
parser.add_argument('--model_name_or_path', type=str, required=True,
help='pretrained model name or path to local checkpoint')
parser.add_argument("--seed", type=int, default=42)
parser.add_argument("--max_seq_length", type=int, default=128)
parser.add_argument("--init_checkpoint", type=str, required=True)
parser.add_argument("--inference_input_file", type=str, required=True)
parser.add_argument("--inference_batch_size", type=int, default=8)
parser.add_argument("--num_optim_steps", type=int, default=1000000,
help="new API specifies num update steps")
parser.add_argument("--fp16", type=boolean_string, default=True)
parser.add_argument("--normalize_data", type=boolean_string, default=True)
parser.add_argument("--loss_scale", type=float, default=0)
parser.add_argument("--no_token_id", type=boolean_string, default=True)
parser.add_argument("--log_dir", type=str, required=True)
# distributed
parser.add_argument('--local_rank', type=int, default=-1,
help='for torch.distributed')
parser.add_argument('--config', help='JSON config file')
# do normal parsing
args = parser.parse_args()
if args.config is not None:
# override argparse defaults by config JSON
opts = json.load(open(args.config))
for k, v in opts.items():
if isinstance(v, str):
# PHILLY ENV special cases
if 'PHILLY_JOB_DIRECTORY' in v:
v = v.replace('PHILLY_JOB_DIRECTORY',
os.environ['PHILLY_JOB_DIRECTORY'])
elif 'PHILLY_LOG_DIRECTORY' in v:
v = v.replace('PHILLY_LOG_DIRECTORY',
os.environ['PHILLY_LOG_DIRECTORY'])
setattr(args, k, v)
# command line should override config JSON
argv = sys.argv[1:]
overrides, _ = parser.parse_known_args(argv)
for k, v in vars(overrides).items():
if f'--{k}' in argv:
setattr(args, k, v)
setattr(args, 'local_rank', overrides.local_rank)
if args.local_rank == -1:
logger.info('CUDA available? {}'.format(str(torch.cuda.is_available())))
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
n_gpu = torch.cuda.device_count()
args.device, args.n_gpu = device, n_gpu
else:郑州妇科医院哪家好 http://www.120zzzy.com/
# distributed training
torch.cuda.set_device(args.local_rank)
device = torch.device("cuda", args.local_rank)
# Initializes the distributed backend which will take care of
# sychronizing nodes/GPUs
torch.distributed.init_process_group(backend='nccl')
n_gpu = torch.distributed.get_world_size()
args.device, args.n_gpu = device, 1
logger.info("device: {} n_gpu: {}, distributed training: {}, "
"16-bits training: {}".format(
device, n_gpu, bool(args.local_rank != -1), args.fp16))
timestamp = datetime.datetime.now().strftime('%Y-%m-%d%H%M%S')
log_dir = args.log_dir
logger.info('Input Argument Information')
args_dict = vars(args)
for a in args_dict:
logger.info('%-28s %s' % (a, args_dict[a]))
#########################################################################
# Prepare Data Set
##########################################################################
print("Prepare Data")
enc = GPT2Tokenizer.from_pretrained(args.model_name_or_path)
config = GPT2Config.from_json_file(
join(args.model_name_or_path, 'config.json'))
inference_dataloader_loss = DynamicBatchingLoader(
args.inference_input_file, enc, args.normalize_data,
args.inference_batch_size, args.max_seq_length)
inference_dataloader_gen = get_eval_list_same_length(
args.inference_input_file, enc, args.inference_batch_size, True)
# eval_dataloader_loss = DynamicBatchingLoader(
# args.eval_input_file, enc, args.normalize_data,
# args.eval_batch_size, args.max_seq_length)
#
# eval_dataloader_gen = get_eval_list_same_length(
# args.eval_input_file, enc, args.eval_batch_size, True)
#########################################################################
# Prepare Model
##########################################################################
print("Prepare Model")
logger.info("Prepare Model")
model = load_model(GPT2LMHeadModel(config), args.init_checkpoint,
args, verbose=True)
if args.local_rank != -1:
# when from scratch make sure initial models are the same
params = [p.data for p in model.parameters()]
all_reduce_and_rescale_tensors(params, float(torch.distributed.get_world_size()))
no_decay = ['bias', 'ln'] # no decay for bias and LayerNorm (ln)
#########################################################################
# Inference !
##########################################################################
print("Model inference")
logger.info("Model inference")
inference_logger = open(join(log_dir, 'inference_log.txt'), 'a+', buffering=1)
epoch = 0
if args.local_rank != -1:
n_gpu = 1
# todo modify loss out.
results = inference_model_results(model, enc, inference_dataloader_loss, args)
# todo output format
# print('{},{},{},{},{}'.format(epoch + 1, global_step + 1, step + 1, eval_loss, eval_ppl), file=inference_logger)
logger.info("inference_final_results:")
if results is None:
logger.info("current results are None")
else:
logger.info(results)
inference_logger.close()
python inference_LSP.py --model_name_or_path ./models/medium/ --init_checkpoint ./12_5_self_output/GPT2.1e-05.8.3gpu.2019-12-04225327/GP2-pretrain-step-50000.pkl --inference_input_file ./selfdata/attack_chatbot.tsv --log_dir inference_logs_dir/
Inference
python inference_LSP.py --model_name_or_path ./models/medium/ --init_checkpoint ./12_5_self_output/GPT2.1e-05.8.3gpu.2019-12-04225327/GP2-pretrain-step-50000.pkl --inference_input_file ./selfdata/attack_chatbot.tsv --log_dir inference_logs_dir/
validset.tsv:
–model_name_or_path ./models/medium/ --init_checkpoint ./12_5_self_output/GPT2.1e-05.8.3gpu.2019-12-04225327/GP2-pretrain-step-50000.pkl --inference_input_file ./selfdata/validset.tsv --log_dir inference_logs_dir/
./models/medium/medium_ft.pkl
“DialoGPT是什么”的内容就介绍到这里了，感谢大家的阅读。如果想了解更多行业相关的知识可以关注创新互联网站，小编将为大家输出更多高质量的实用文章！
