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Genie_Generate.py
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Genie_Generate.py
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import os
from util import logger
from train_util import dist_util
from util.util import (
create_model_and_diffusion,
args_to_dict,
)
# from transformers import set_seed
import torch
import collections
import argparse
from transformers import AutoTokenizer
import numpy as np
from functools import partial
from data_util.s2s_data_util import load_s2s_data
import torch.distributed as dist
from torch.nn.parallel.distributed import DistributedDataParallel as DDP
from torch.utils.data import DataLoader
from data_util.s2s_data_util import S2S_dataset, QG_dataset_Diff
from torch.serialization import default_restore_location
from transformers import (
BertModel,
BertConfig,
AutoTokenizer,
)
from data_util.text_data_util import load_data_text
from tqdm import tqdm
import random
def get_arguments():
parser = argparse.ArgumentParser()
# out path
parser.add_argument('--generate_path', type=str, default='', help='output path')
parser.add_argument('--eval_model_path', type=str, default='', help='model path')
parser.add_argument('--num_samples', type=int, default=50, help='sample query')
parser.add_argument('--interval_step', type=int, default=1, help='inference t interval step')
# load model
parser.add_argument('--model_arch', type=str, default='transformer', help='Core architecture of diffusion model')
parser.add_argument('--model_channels', type=int, default=768,
help='Try to set it to the same size as the model hidden')
parser.add_argument('--in_channel', type=int, default=768,
help='The input chanel size here must be the same as the word embedding size')
parser.add_argument('--out_channel', type=int, default=768,
help='The dimension size of the output is recommended to be the same as that of word embedding for easy reasoning')
parser.add_argument('--dropout', type=float, default=0.1, help='')
parser.add_argument("--learn_sigma", default=False, action="store_true", help="Whether to learning variance")
parser.add_argument('--logits_mode', type=int, default=1, help='final logits mode of Diffusion model')
parser.add_argument('--vocab_size', type=int, default=30522, help='vocab size')
parser.add_argument('--config_name', type=str, default='bert-base-uncased', help='')
parser.add_argument('--token_emb_type', type=str, default='random', help='token embedding type')
parser.add_argument("--init_pretrained", default=False, action="store_true",
help="Whether to using pretrain BERT encoder")
# load diffusion
# parser.add_argument('--model_arch', type=str, default='transformer', help='Core architecture of diffusion model')
parser.add_argument('--diffusion_steps', type=int, default=2000, help='Diffusion model maximum T')
# parser.add_argument("--learn_sigma", default=False, action="store_true", help="Whether to learning variance")
parser.add_argument('--use_kl', default=False, action="store_true",
help="Whether to using kl loss in Diffsion loss")
parser.add_argument('--training_mode', type=str, default='e2e', help='using e2e simple loss or e2e loss')
parser.add_argument('--noise_schedule', type=str, default='sqrt',
help='How to plan the noise change of Gaussian distribution')
parser.add_argument('--predict_xstart', default=False, action="store_true",
help="Model prediction target, if True, predict xstart, if False, predict EPSILON")
parser.add_argument("--sigma_small", default=False, action="store_true", help="about learning variance")
parser.add_argument("--rescale_learned_sigmas", default=True, action="store_false", help="about learning variance")
parser.add_argument("--rescale_timesteps", default=True, action="store_false", help="about time rescale")
# data args
parser.add_argument('--data_path', type=str, default='', help='data path')
parser.add_argument('--data_name', type=str, default='', help='data name')
# for seq2seq
parser.add_argument('--src_max_len', type=int, default=144, help='src max len')
parser.add_argument('--tgt_max_len', type=int, default=32, help='tgt max len')
parser.add_argument('--answer_max_len', type=int, default=10, help='tgt max len')
# gen args
parser.add_argument('--batch_size', type=int, default=64, help='')
# seed
parser.add_argument('--seed', type=int, default=101, help='')
#
# muti-gpu
parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank")
parser.add_argument("--server_ip", type=str, default="", help="For distant debugging.")
parser.add_argument("--server_port", type=str, default="", help="For distant debugging.")
args = parser.parse_args()
return args
CheckpointState = collections.namedtuple("CheckpointState",
['model_dict', 'optimizer_dict', 'scheduler_dict', 'offset'])
def load_states_from_checkpoint(model_file: str) -> CheckpointState:
logger.info('Reading saved model from %s', model_file)
state_dict = torch.load(model_file, map_location=lambda s, l: default_restore_location(s, 'cpu'))
logger.info('model_state_dict keys %s', state_dict.keys())
return CheckpointState(**state_dict)
'''
rounding
'''
def denoised_fn_round(args, model, text_emb, t):
# thresh_t = 50
# # print(thresh_t)
# if thresh_t is not None and t[0] > thresh_t:
# return text_emb
if args.model_arch == '1d-unet':
text_emb = text_emb.permute(0, 2, 1)
# return text_emb
# print(t.float().mean(), t[0])
# assert t.float().mean() == t[0].float()
# print(text_emb.shape) # bsz, seqlen, dim
down_proj_emb = model.weight # input_embs
# print(t)
old_shape = text_emb.shape
old_device = text_emb.device
def get_efficient_knn(down_proj_emb, text_emb, dist='l2'):
if dist == 'l2':
emb_norm = (down_proj_emb ** 2).sum(-1).view(-1, 1) # vocab
text_emb_t = torch.transpose(text_emb.view(-1, text_emb.size(-1)), 0, 1) # d, bsz*seqlen
arr_norm = (text_emb ** 2).sum(-1).view(-1, 1) # bsz*seqlen, 1
# print(emb_norm.shape, arr_norm.shape)
dist = emb_norm + arr_norm.transpose(0, 1) - 2.0 * torch.mm(down_proj_emb,
text_emb_t) # (vocab, d) x (d, bsz*seqlen)
dist = torch.clamp(dist, 0.0, np.inf)
# print(dist.shape)
topk_out = torch.topk(-dist, k=1, dim=0)
# adjacency = down_proj_emb.unsqueeze(1).expand(-1, text_emb.size(0), -1) - text_emb.unsqueeze(0).expand(
# down_proj_emb.size(0), -1, -1)
# adjacency = -th.norm(adjacency, dim=-1)
# topk_out = th.topk(adjacency, k=1, dim=0)
# print(topk_out1.indices == topk_out.indices)
# assert th.all(topk_out1.indices == topk_out.indices)
return topk_out.values, topk_out.indices
def get_knn(down_proj_emb, text_emb, dist='l2'):
if dist == 'l2':
adjacency = down_proj_emb.unsqueeze(1).expand(-1, text_emb.size(0), -1) - text_emb.unsqueeze(0).expand(
down_proj_emb.size(0), -1, -1)
adjacency = -torch.norm(adjacency, dim=-1)
topk_out = torch.topk(adjacency, k=1, dim=0)
return topk_out.values, topk_out.indices
dist = 'l2'
if len(text_emb.shape) > 2:
text_emb = text_emb.reshape(-1, text_emb.size(-1))
else:
text_emb = text_emb
# val, indices = get_knn(down_proj_emb,
# text_emb.to(down_proj_emb.device), dist=dist)
val, indices = get_efficient_knn(down_proj_emb,
text_emb.to(down_proj_emb.device), dist=dist)
rounded_tokens = indices[0]
# print(rounded_tokens.shape)
new_embeds = model(rounded_tokens).view(old_shape).to(old_device)
if args.model_arch == '1d-unet':
new_embeds = new_embeds.permute(0, 2, 1)
return new_embeds
def setup_seed(seed):
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
np.random.seed(seed)
random.seed(seed)
def setup_env(args):
if args.local_rank == -1:
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
args.n_gpu = torch.cuda.device_count()
else: # Initializes the distributed backend which will take care of sychronizing nodes/GPUs
torch.cuda.set_device(args.local_rank)
device = torch.device("cuda", args.local_rank)
torch.distributed.init_process_group(backend="nccl")
args.n_gpu = 1
args.device = device
# store args
if args.local_rank != -1:
args.world_size = torch.distributed.get_world_size()
args.rank = dist.get_rank()
def main():
# env setting
args = get_arguments()
# setup_seed(args.seed)
setup_env(args)
if dist.get_rank() == 0:
if not os.path.exists(args.generate_path):
os.makedirs(args.generate_path)
log_path = os.path.join(args.generate_path, 'log')
logger.configure(dir=log_path)
args.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# define model and diffusion
model, diffusion = create_model_and_diffusion(
args
)
model.to(args.device)
model.eval()
# load trained model
model_saved_state = load_states_from_checkpoint(args.eval_model_path)
model.load_state_dict(model_saved_state.model_dict)
pytorch_total_params = sum(p.numel() for p in model.parameters())
logger.log(f'the parameter count is {pytorch_total_params}')
if dist.get_world_size() > 1:
model = DDP(
model, device_ids=[dist.get_rank()], output_device=dist.get_rank(), find_unused_parameters=False,
)
logger.log("sampling text from random noise...")
print("sample num is :", args.num_samples)
print("sample interval step is :", args.interval_step)
print("total inverse diffusion step is :", 2000 // args.interval_step)
sample_fn = (
diffusion.p_sample_loop
)
if dist.get_world_size() > 1:
emb_model = model.module.word_embedding
else:
emb_model = model.word_embedding
if args.model_arch == 'transformer':
sample_shape = (args.num_samples, args.text_max_len, args.in_channel)
sample = sample_fn(
model,
sample_shape,
clip_denoised=False,
denoised_fn=partial(denoised_fn_round, args, emb_model.cuda()),
model_kwargs=None,
top_p=-1.0,
)
print("sample result shape: ", sample.shape)
print('decoding for e2e... ')
logits = model.get_logits(sample)
cands = torch.topk(logits, k=1, dim=-1)
sample_id_list = cands.indices
print("decode id list example :", type(sample_id_list[0]), " ", sample_id_list[0])
logger.log("creating tokenizer...")
tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')
for sample_id in sample_id_list:
sentence = tokenizer.decode(sample_id.squeeze())
print(sentence)
elif args.model_arch == 's2s_CAT':
# bert tokenizer
tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')
print("-------------------------------------------------------------")
print("start generate query from dev dataset, for every passage, we generate ", args.num_samples, " querys...")
print("-------------------------------------------------------------")
print("***** load " + args.data_name + " test src dataset*****")
src = []
test_src_path = os.path.join(args.data_path, args.data_name + "/org_data/test.src")
with open(test_src_path, "r", encoding="utf-8") as ifile:
for line in tqdm(ifile):
line = line.strip()
text = line
src.append(text)
print("***** load " + args.data_name + " dev tgt dataset*****")
tgt = []
test_tgt_path = os.path.join(args.data_path, args.data_name + "/org_data/test.tgt")
with open(test_tgt_path, "r", encoding="utf-8") as ifile:
for line in tqdm(ifile):
line = line.strip()
text = line
tgt.append(text)
shard_size = len(src) // args.world_size
start_idx = args.local_rank * shard_size
end_idx = start_idx + shard_size
if args.local_rank == args.world_size - 1:
end_idx = len(src)
scr_data_piece = src[start_idx:end_idx]
tgt_data_piece = tgt[start_idx:end_idx]
print('generation for ', len(scr_data_piece), " src text from idx ", start_idx, " to ", end_idx)
if args.data_name == "squadqg_data":
test_dataset = QG_dataset_Diff(scr_data_piece, tgt_data_piece, tokenizer, src_maxlength=args.src_max_len,
answer_maxlength=args.answer_max_len, tgt_maxlength=args.tgt_max_len)
test_dataloader = DataLoader(test_dataset, batch_size=args.batch_size, drop_last=False,
num_workers=20, collate_fn=QG_dataset_Diff.get_collate_fn())
else:
test_dataset = S2S_dataset(scr_data_piece, tgt_data_piece, tokenizer, src_maxlength=args.src_max_len,
tgt_maxlength=args.tgt_max_len)
test_dataloader = DataLoader(test_dataset, batch_size=args.batch_size, drop_last=False,
num_workers=20, collate_fn=S2S_dataset.get_collate_fn())
if args.generate_path is not None:
model_gen_files = []
if os.path.exists(args.generate_path):
for item in os.scandir(args.generate_path):
if item.is_file():
if "gen_seed" in item.path:
model_gen_files.append(item.path)
if len(model_gen_files) != 0 :
model_gen_files.sort(key=lambda f: int((f.split('_epoch')[-1]).split('.txt')[0]), reverse=True)
epoch_num = int((model_gen_files[0].split('_epoch')[-1]).split('.txt')[0])
logger.info("***** load " + model_gen_files[0] + " *****")
else:
epoch_num = 0
else:
logger.info("generate_path is None")
exit(0)
for epoch in range(args.num_samples - epoch_num):
each_sample_list = []
print("-------------------------------------------------------------")
print("start sample ", epoch+1+epoch_num, " epoch...")
print("-------------------------------------------------------------")
for index, batch in enumerate(tqdm(test_dataloader)):
'''
for s2s
'''
input_shape = (batch['src_input_ids'].shape[0], args.tgt_max_len, args.in_channel)
src_input_ids = batch['src_input_ids']
tgt_input_ids = batch['tgt_input_ids']
# print(p_input_ids.shape)
src_attention_mask = batch['src_attention_mask']
model_kwargs = {'src_input_ids' : src_input_ids, 'src_attention_mask': src_attention_mask}
sample = sample_fn(
model,
input_shape,
clip_denoised=False,
denoised_fn=partial(denoised_fn_round, args, emb_model.cuda()),
model_kwargs=model_kwargs,
top_p=-1.0,
interval_step=args.interval_step,
)
print("sample result shape: ", sample.shape)
print('decoding for e2e... ')
logits = model.module.get_logits(sample)
cands = torch.topk(logits, k=1, dim=-1)
sample_id_list = cands.indices
#print("decode id list example :", type(sample_id_list[0]), " ", sample_id_list[0])
'''
for s2s
'''
# print("src text: ", tokenizer.decode(src_input_ids.squeeze()))
# print("tgt text: ", tokenizer.decode(tgt_input_ids.squeeze()))
print("sample control generate query: ")
for sample_id in sample_id_list:
sentence = tokenizer.decode(sample_id.squeeze())
each_sample_list.append(clean(sentence))
# print(sentence)
# total_sample_list.append(each_sample_list)
out_path = os.path.join(args.generate_path, "rank" + str(dist.get_rank()) + "_gen_seed_101" +
"_num" + str(args.num_samples) + "_epoch" + str(epoch + 1 + epoch_num) + ".txt")
with open(out_path, 'w') as f:
for sentence in each_sample_list:
f.write(sentence + '\n')
else:
return NotImplementedError
def clean(sentence):
sentence = sentence.replace('[CLS]', '')
sentence = sentence.replace('[SEP]', '')
sentence = sentence.replace('[PAD]', '')
sentence = sentence.replace('[UNK]', 'unk')
return sentence.strip()
if __name__ == "__main__":
main()