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import os
import random
import time
import pickle
import math
from argparse import ArgumentParser
import string
from collections import defaultdict
from tqdm import tqdm
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers import AutoTokenizer, AutoModelWithLMHead, pipeline, set_seed, GPT2Tokenizer, GPT2Model
from data import Dataset, load_rhyme_info
from model import Model
from util import save_checkpoint, ProgressMeter, AverageMeter, num_params
from constants import *
from poetry_util import get_rhymes, count_syllables
from predict_poetry import predict_couplet
def main(args):
with open(args.dataset_info, 'rb') as rf:
dataset_info = pickle.load(rf)
gpt_tokenizer = AutoTokenizer.from_pretrained(args.model_string)
gpt_tokenizer.add_special_tokens({'pad_token': PAD_TOKEN})
gpt_pad_id = gpt_tokenizer.encode(PAD_TOKEN)[0]
gpt_model = AutoModelWithLMHead.from_pretrained(args.model_string).to(args.device)
gpt_model.eval()
checkpoint = torch.load(args.iambic_ckpt, map_location=args.device)
model_args = checkpoint['args']
iambic_model = Model(model_args, gpt_pad_id, len(dataset_info.index2word)) # no need to get the glove embeddings when reloading since they're saved in model ckpt anyway
iambic_model.load_state_dict(checkpoint['state_dict'])
iambic_model = iambic_model.to(args.device)
iambic_model.eval()
if args.verbose:
print("=> loaded checkpoint '{}' (epoch {})"
.format(args.iambic_ckpt, checkpoint['epoch']))
print('iambic model num params', num_params(iambic_model))
with open(args.rhyme_info, 'rb') as rf:
rhyme_info = pickle.load(rf)
checkpoint = torch.load(args.rhyme_ckpt, map_location=args.device)
model_args = checkpoint['args']
rhyme_model = Model(model_args, gpt_pad_id, len(dataset_info.index2word), rhyme_group_size=len(rhyme_info.index2rhyme_group), verbose=args.verbose) # no need to get the glove embeddings when reloading since they're saved in model ckpt anyway
rhyme_model.load_state_dict(checkpoint['state_dict'])
rhyme_model = rhyme_model.to(args.device)
rhyme_model.eval()
if args.verbose:
print("=> loaded checkpoint '{}' (epoch {})"
.format(args.rhyme_ckpt, checkpoint['epoch']))
print('rhyme model num params', num_params(rhyme_model))
checkpoint = torch.load(args.newline_ckpt, map_location=args.device)
model_args = checkpoint['args']
newline_model = Model(model_args, gpt_pad_id, len(dataset_info.index2word)) # no need to get the glove embeddings when reloading since they're saved in model ckpt anyway
newline_model.load_state_dict(checkpoint['state_dict'])
newline_model = newline_model.to(args.device)
newline_model.eval()
if args.verbose:
print("=> loaded checkpoint '{}' (epoch {})"
.format(args.newline_ckpt, checkpoint['epoch']))
print('iambic model num params', num_params(newline_model))
with open(args.prefix_file, 'r') as rf:
lines = rf.readlines()
for line in tqdm(lines, total=len(lines)):
couplet = predict_couplet(gpt_model,
gpt_tokenizer,
iambic_model,
rhyme_model,
newline_model,
[line],
dataset_info,
rhyme_info,
args.precondition_topk,
args.topk,
condition_lambda=args.condition_lambda,
device=args.device)
assert len(couplet) == 2
print(couplet[1].strip().replace('\n', ''))
if __name__=='__main__':
parser = ArgumentParser()
# DATA
parser.add_argument('--iambic_ckpt', type=str, required=True)
parser.add_argument('--rhyme_ckpt', type=str, required=True)
parser.add_argument('--newline_ckpt', type=str, required=True)
parser.add_argument('--dataset_info', type=str, required=True, help='saved dataset info')
parser.add_argument('--rhyme_info', type=str, required=True, help='saved rhyme info')
parser.add_argument('--model_string', type=str, default='gpt2-medium')
parser.add_argument('--prefix_file', type=str, default=None, required=True, help='file of prefix lines for couplets')
parser.add_argument('--precondition_topk', type=int, default=200, help='consider top k outputs from gpt at each step before conditioning and re-pruning')
parser.add_argument('--topk', type=int, default=10, help='consider top k outputs from gpt at each step')
parser.add_argument('--condition_lambda', type=float, default=1.0, help='lambda weight on conditioning model')
parser.add_argument('--seed', type=int, default=1, help='random seed')
parser.add_argument('--device', type=str, default='cuda', choices=['cpu', 'cuda'])
parser.add_argument('--debug', action='store_true', default=False)
parser.add_argument('--verbose', action='store_true', default=False)
args = parser.parse_args()
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
main(args) |