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predict_poetry.py
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predict_poetry.py
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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
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()
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)) # 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()
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()
print("=> loaded checkpoint '{}' (epoch {})"
.format(args.newline_ckpt, checkpoint['epoch']))
print('iambic model num params', num_params(newline_model))
while True:
results = predict_couplet(gpt_model,
gpt_tokenizer,
iambic_model,
rhyme_model,
newline_model,
[args.input_text],
dataset_info,
rhyme_info,
args.precondition_topk,
args.topk,
condition_lambda=args.condition_lambda,
device=args.device)
for line in results:
print(line)
import pdb; pdb.set_trace()
def predict_couplet(gpt_model, gpt_tokenizer, iambic_model, rhyme_model, newline_model, input_text, dataset_info, rhyme_info, precondition_topk, postcondition_topk, condition_lambda=1.0, device='cuda'):
assert len(input_text) == 1 # only do one at a time for now
current_text = input_text[0]
current_line_text = ''
all_lines = [current_text]
ending_word = current_text.split()[-1].strip(string.punctuation)
word2rhyme_group = defaultdict(lambda: UNKNOWN_RHYME_GROUP, rhyme_info.word2rhyme_group)
rhyme_group = word2rhyme_group[ending_word]
line = predict_iambic_pentameter_line(gpt_model,
gpt_tokenizer,
iambic_model,
rhyme_model,
newline_model,
current_text,
current_line_text,
rhyme_group,
dataset_info,
rhyme_info,
precondition_topk,
postcondition_topk,
condition_lambda=condition_lambda,
device=device)
all_lines.append(line)
return all_lines
def predict_iambic_pentameter_line(gpt_model, gpt_tokenizer, iambic_model, rhyme_model, newline_model, current_text, current_line_text, rhyme_group, dataset_info, rhyme_info, precondition_topk, postcondition_topk, banned_tokens=POETRY_BANNED_TOKENS, condition_lambda=1.0, device='cuda', length_cutoff=30):
# TODO(poetry) delete banned tokens?
with torch.no_grad():
batch_size = 1
rhyme_group_index = rhyme_info.rhyme_group2index[rhyme_group]
future_words = torch.LongTensor([rhyme_group_index]).to(device) # 1
log_probs = torch.Tensor([math.log(rhyme_info.rhyme_group_counts[rhyme_group] / rhyme_info.total_rhyme_groups)]).to(device) # 1
# assumes initially all same length.
previous_encoded_text = [gpt_tokenizer.encode(it, return_tensors='pt').to(device) for it in [current_text]]
previous_enc_len = previous_encoded_text[0].shape[1]
encoded_input = [gpt_tokenizer.encode(it, return_tensors='pt').to(device) for it in [current_text + current_line_text]] # batch x seq
encoded_input = torch.cat(encoded_input, dim=0)
lengths = torch.LongTensor([encoded_input.shape[1]]).to(device)
line_syllable_count = count_syllables(current_line_text)
assert line_syllable_count < POETRY_LINE_SYLLABLES # assume we started with less than one full line
syllables_to_go = POETRY_LINE_SYLLABLES - line_syllable_count
for _ in range(length_cutoff): # really shouldn't have a line this long anyway
gpt_logits = gpt_model(encoded_input)[0][:, -1, :] # batch x vocab
gpt_logits[:, banned_tokens] = -1e8
top_logits, top_indices = gpt_logits.topk(precondition_topk, dim=1)
new_input_candidates = torch.cat([encoded_input.unsqueeze(1).expand(-1, precondition_topk, -1), top_indices.unsqueeze(2)], dim=2) # batch x topk x seq+1
expanded_lengths = (lengths + 1).unsqueeze(1).expand(batch_size, precondition_topk) # batch x topk
expanded_future_words = future_words.unsqueeze(0).unsqueeze(1).expand(batch_size, precondition_topk, -1) # batch x topk x N
candidate_syllables_to_go = []
for candidate in new_input_candidates[0]:
candidate_until_last_word_text = ' '.join(gpt_tokenizer.decode(candidate[previous_enc_len:]).split()[:-1])
candidate_syllables_to_go.append(10 - count_syllables(candidate_until_last_word_text))
# usually these are all the same, but run them all for correctness. could do more efficiently but it's not too slow anyway.
expanded_syllables_to_go = torch.LongTensor(candidate_syllables_to_go).to(device).view(1, precondition_topk)
if condition_lambda == 0:
iambic_logits = torch.zeros_like(expanded_lengths).float()
else:
# truncate prefix because we trained on single lines
iambic_logits = iambic_model(new_input_candidates[:, :, previous_enc_len:].flatten(0, 1), expanded_lengths.flatten(0, 1) - previous_enc_len, None, None, None)[:, -1] # batch*topk x seq+1 -> batch*topk
iambic_logits = iambic_logits.view(batch_size, precondition_topk)
iambic_logits = iambic_logits - torch.log(1 + torch.exp(iambic_logits))
if condition_lambda == 0:
rhyme_logits = torch.zeros_like(expanded_lengths).float()
else:
rhyme_logits = rhyme_model(new_input_candidates.flatten(0, 1), # batch*topk x seq+1
expanded_lengths.flatten(0, 1), # batch*topk
expanded_future_words.flatten(0, 1), # batch*topk x N
log_probs, # N
expanded_syllables_to_go.flatten(0, 1)) # batch*topk
rhyme_logits = rhyme_logits.view(batch_size, precondition_topk, -1) # batch x topk x N
rhyme_logits = rhyme_logits - torch.log(1 + torch.exp(rhyme_logits)) # batch x topk x N
rhyme_logits = rhyme_logits.squeeze(2) # batch x topk
if condition_lambda == 0:
newline_logits = torch.zeros_like(expanded_lengths).float()
else:
newline_logits = newline_model(new_input_candidates.flatten(0, 1), # batch*topk x seq+1
expanded_lengths.flatten(0, 1), # batch*topk
expanded_future_words.flatten(0, 1), # batch*topk x N
log_probs, # N
expanded_syllables_to_go.flatten(0, 1)) # batch*topk
newline_logits = newline_logits[:, -1].view(batch_size, precondition_topk, -1) # batch x topk x N
newline_logits = newline_logits - torch.log(1 + torch.exp(newline_logits)) # batch x topk x N
newline_logits = newline_logits.squeeze(2) # batch x topk
full_logits = top_logits + condition_lambda * iambic_logits + condition_lambda * rhyme_logits + condition_lambda * newline_logits
post_logits, post_indices = full_logits.topk(postcondition_topk, dim=1)
post_probs = F.softmax(post_logits, dim=1)
index_into_top_indices = post_indices[torch.arange(batch_size).to(post_indices.device), torch.multinomial(post_probs, 1).flatten()] # batch
next_indices = top_indices[torch.arange(batch_size).to(top_indices.device), index_into_top_indices] # batch
encoded_input = torch.cat([encoded_input, next_indices.unsqueeze(1)], dim=1) # batch x seq+1
lengths = lengths + 1
syllables_to_go = POETRY_LINE_SYLLABLES - count_syllables(gpt_tokenizer.decode(encoded_input[0][previous_enc_len:])) # if we get very unlucky with a partial word that the syllable counter doesn't recognize we might end early, but it's unlikely
if syllables_to_go <= 0 and [gpt_tokenizer.decode(s) for s in encoded_input][0][-1] in PHRASE_ENDS:
break
if syllables_to_go < 0:
# encoded_input = encoded_input[:, :-1]
break
return [gpt_tokenizer.decode(s) for s in encoded_input][0][len(current_text):]
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('--input_text', type=str, default=None, required=True, help='initial text')
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)
args = parser.parse_args()
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
main(args)