Spaces:
Running
Running
File size: 5,605 Bytes
7dd9869 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 |
import os
import argparse
from transformers import set_seed
from src.scripts.mytokenizers import Tokenizer
from src.improved_diffusion import gaussian_diffusion as gd
from src.improved_diffusion.respace import SpacedDiffusion
from src.improved_diffusion import dist_util
from src.improved_diffusion.transformer_model import TransformerNetModel
from src.improved_diffusion.resample import create_named_schedule_sampler
from src.improved_diffusion.script_util import model_and_diffusion_defaults
from src.improved_diffusion.script_util import add_dict_to_argparser
from src.improved_diffusion.train_util import TrainLoop
import torch.distributed as dist
import wandb
from src.scripts.mydatasets import get_dataloader, Lang2molDataset_train
import warnings
import torch.multiprocessing as mp
def main_worker(rank, world_size):
args = create_argparser().parse_args()
set_seed(42)
wandb.login(key=args.wandb_token)
wandb.init(
project="ACL_Lang2Mol",
config=args.__dict__,
)
dist_util.setup_dist(rank, world_size)
tokenizer = Tokenizer()
model = TransformerNetModel(
in_channels=args.model_in_channels,
model_channels=args.model_model_channels,
dropout=args.model_dropout,
vocab_size=len(tokenizer),
hidden_size=args.model_hidden_size,
num_attention_heads=args.model_num_attention_heads,
num_hidden_layers=args.model_num_hidden_layers,
)
if args.model_path != "":
model.load_state_dict(
dist_util.load_state_dict(args.model_path, map_location="cpu")
)
model.train()
print("Total params:", sum(p.numel() for p in model.parameters()))
print(
"Total trainable params:",
sum(p.numel() for p in model.parameters() if p.requires_grad),
)
print("Tokenizer vocab length:", len(tokenizer))
diffusion = SpacedDiffusion(
use_timesteps=[i for i in range(args.diffusion_steps)],
betas=gd.get_named_beta_schedule("sqrt", args.diffusion_steps),
model_mean_type=(gd.ModelMeanType.START_X),
model_var_type=((gd.ModelVarType.FIXED_LARGE)),
loss_type=gd.LossType.E2E_MSE,
rescale_timesteps=True,
model_arch="transformer",
training_mode="e2e",
)
schedule_sampler = create_named_schedule_sampler("uniform", diffusion)
print("Loading data...")
train_dataset = Lang2molDataset_train(
dir=args.dataset_path,
tokenizer=tokenizer,
split="train",
corrupt_prob=0.0,
token_max_length=512,
dataset_name=args.dataset_name,
)
dataloader = get_dataloader(train_dataset, args.batch_size, rank, world_size)
print("Finish loading data")
TrainLoop(
model=model,
diffusion=diffusion,
data=dataloader,
batch_size=args.batch_size,
microbatch=args.microbatch,
lr=args.lr,
ema_rate=args.ema_rate,
log_interval=args.log_interval,
save_interval=args.save_interval,
resume_checkpoint=args.resume_checkpoint,
use_fp16=args.use_fp16,
fp16_scale_growth=args.fp16_scale_growth,
schedule_sampler=schedule_sampler,
weight_decay=args.weight_decay,
lr_anneal_steps=args.lr_anneal_steps,
checkpoint_path=args.checkpoint_path,
gradient_clipping=args.gradient_clipping,
eval_data=None,
eval_interval=args.eval_interval,
).run_loop()
dist.destroy_process_group()
def create_argparser():
defaults = dict()
text_defaults = dict(
wandb_token="",
batch_size=16,
cache_mode="no",
checkpoint_path="checkpoints",
class_cond=False,
config="ll",
config_name="QizhiPei/biot5-base-text2mol",
dataset_path="dataset",
diffusion_steps=2000,
dropout=0.01,
e2e_train="",
ema_rate="0.9999",
emb_scale_factor=1.0,
eval_interval=2000,
experiment="random",
experiment_mode="lm",
fp16_scale_growth=0.001,
gradient_clipping=2.4,
image_size=8,
in_channel=16,
learn_sigma=False,
log_interval=1000,
logits_mode=1,
lr=0.00005,
lr_anneal_steps=500000,
microbatch=-1,
modality="e2e-tgt",
model_arch="transformer",
noise_level=0.0,
noise_schedule="sqrt",
num_channels=128,
num_heads=4,
num_heads_upsample=-1,
num_res_blocks=2,
out_channel=16,
padding_mode="pad",
predict_xstart=True,
preprocessing_num_workers=1,
rescale_learned_sigmas=True,
rescale_timesteps=True,
resume_checkpoint="",
save_interval=50000,
schedule_sampler="uniform",
seed=42,
timestep_respacing="",
training_mode="e2e",
use_bert_tokenizer="no",
use_checkpoint=False,
use_fp16=False,
use_kl=False,
use_scale_shift_norm=True,
weight_decay=0.0,
model_in_channels=32,
model_model_channels=128,
model_dropout=0.01,
model_hidden_size=1024,
model_num_attention_heads=16,
model_num_hidden_layers=12,
dataset_name="",
model_path="",
)
defaults.update(model_and_diffusion_defaults())
defaults.update(text_defaults)
parser = argparse.ArgumentParser()
add_dict_to_argparser(parser, defaults)
return parser
if __name__ == "__main__":
world_size = 1
mp.spawn(main_worker, args=(world_size,), nprocs=world_size, join=True)
|