DialoGPT是什么

本篇內(nèi)容介紹了“DialoGPT是什么”的有關(guān)知識(shí)，在實(shí)際案例的操作過(guò)程中，不少人都會(huì)遇到這樣的困境，接下來(lái)就讓小編帶領(lǐng)大家學(xué)習(xí)一下如何處理這些情況吧！希望大家仔細(xì)閱讀，能夠?qū)W有所成！

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引言

Large-scale pretraining for dialogue

DialoGPT是基于GPT-2的對(duì)話生成預(yù)訓(xùn)練模型，在reddit數(shù)據(jù)集上訓(xùn)練

假定已經(jīng)設(shè)置好環(huán)境，

在eval_util.py中增加 inference函數(shù)

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("?") 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函數(shù)

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

自定義inference_LSP.py 文件

文件內(nèi)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:鄭州婦科醫(yī)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

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

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