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import argparse
import os
import sys
import warnings
from pathlib import Path
import datasets
import pandas as pd
import torch
from datasets import Dataset, DatasetDict
from transformers import (
AutoModelForSeq2SeqLM,
AutoTokenizer,
DataCollatorForSeq2Seq,
EarlyStoppingCallback,
Seq2SeqTrainer,
Seq2SeqTrainingArguments,
)
sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))
from utils import (
add_new_tokens,
filter_out,
get_accuracy_score,
preprocess_dataset,
seed_everything,
)
# Suppress warnings and disable progress bars
warnings.filterwarnings("ignore")
datasets.utils.logging.disable_progress_bar()
def parse_args():
"""Parse command line arguments."""
parser = argparse.ArgumentParser(
description="Training script for reaction prediction model."
)
parser.add_argument(
"--train_data_path", type=str, required=True, help="Path to training data CSV."
)
parser.add_argument(
"--valid_data_path",
type=str,
required=True,
help="Path to validation data CSV.",
)
parser.add_argument("--test_data_path", type=str, help="Path to test data CSV.")
parser.add_argument(
"--USPTO_test_data_path",
type=str,
help="The path to data used for USPTO testing. CSV file that contains ['REACTANT', 'PRODUCT'] columns is expected.",
)
parser.add_argument(
"--output_dir", type=str, default="t5", help="Path of the output directory."
)
parser.add_argument(
"--pretrained_model_name_or_path",
type=str,
required=True,
help="Pretrained model path or name.",
)
parser.add_argument(
"--debug", action="store_true", default=False, help="Enable debug mode."
)
parser.add_argument(
"--epochs",
type=int,
default=5,
help="Number of epochs.",
)
parser.add_argument("--lr", type=float, default=1e-3, help="Learning rate.")
parser.add_argument("--batch_size", type=int, default=16, help="Batch size.")
parser.add_argument(
"--input_max_length",
type=int,
default=400,
help="Max input token length.",
)
parser.add_argument(
"--target_max_length",
type=int,
default=150,
help="Max target token length.",
)
parser.add_argument(
"--eval_beams",
type=int,
default=5,
help="Number of beams used for beam search during evaluation.",
)
parser.add_argument(
"--target_column",
type=str,
default="REACTANT",
help="Target column name.",
)
parser.add_argument(
"--weight_decay",
type=float,
default=0.01,
help="Weight decay.",
)
parser.add_argument(
"--evaluation_strategy",
type=str,
default="epoch",
help="Evaluation strategy used during training. Select from 'no', 'steps', or 'epoch'. If you select 'steps', also give --eval_steps.",
)
parser.add_argument(
"--eval_steps",
type=int,
help="Evaluation steps.",
)
parser.add_argument(
"--save_strategy",
type=str,
default="epoch",
help="Save strategy used during training. Select from 'no', 'steps', or 'epoch'. If you select 'steps', also give --save_steps.",
)
parser.add_argument(
"--save_steps",
type=int,
default=500,
help="Save steps.",
)
parser.add_argument(
"--logging_strategy",
type=str,
default="epoch",
help="Logging strategy used during training. Select from 'no', 'steps', or 'epoch'. If you select 'steps', also give --logging_steps.",
)
parser.add_argument(
"--logging_steps",
type=int,
default=500,
help="Logging steps.",
)
parser.add_argument(
"--save_total_limit",
type=int,
default=2,
help="Limit of saved checkpoints.",
)
parser.add_argument(
"--fp16",
action="store_true",
default=False,
help="Enable fp16 training.",
)
parser.add_argument(
"--disable_tqdm",
action="store_true",
default=False,
help="Disable tqdm.",
)
parser.add_argument(
"--seed",
type=int,
default=42,
help="Random seed.",
)
return parser.parse_args()
def preprocess_df(df, drop_duplicates=True):
"""Preprocess the dataframe by filling NaNs, dropping duplicates, and formatting the input."""
for col in ["REACTANT", "PRODUCT", "CATALYST", "REAGENT", "SOLVENT"]:
if col not in df.columns:
df[col] = None
df[col] = df[col].fillna(" ")
if drop_duplicates:
df = (
df[["REACTANT", "PRODUCT", "CATALYST", "REAGENT", "SOLVENT"]]
.drop_duplicates()
.reset_index(drop=True)
)
df["input"] = df["PRODUCT"]
return df
def preprocess_USPTO(df):
df["REACTANT"] = df["REACTANT"].apply(lambda x: str(sorted(x.split("."))))
df["PRODUCT"] = df["PRODUCT"].apply(lambda x: str(sorted(x.split("."))))
df["pair"] = df["REACTANT"] + " - " + df["PRODUCT"].astype(str)
return df
if __name__ == "__main__":
CFG = parse_args()
CFG.disable_tqdm = True
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
seed_everything(seed=CFG.seed)
train = preprocess_df(
filter_out(pd.read_csv(CFG.train_data_path), ["REACTANT", "PRODUCT"])
)
valid = preprocess_df(
filter_out(pd.read_csv(CFG.valid_data_path), ["REACTANT", "PRODUCT"])
)
if CFG.USPTO_test_data_path:
train_copy = preprocess_USPTO(train.copy())
USPTO_test = preprocess_USPTO(pd.read_csv(CFG.USPTO_test_data_path))
train = train[~train_copy["pair"].isin(USPTO_test["pair"])].reset_index(
drop=True
)
train["pair"] = train["REACTANT"] + " - " + train["PRODUCT"]
valid["pair"] = valid["REACTANT"] + " - " + valid["PRODUCT"]
valid = valid[~valid["pair"].isin(train["pair"])].reset_index(drop=True)
train.to_csv("train.csv", index=False)
valid.to_csv("valid.csv", index=False)
if CFG.test_data_path:
test = preprocess_df(
filter_out(pd.read_csv(CFG.test_data_path), ["REACTANT", "PRODUCT"])
)
test["pair"] = test["REACTANT"] + " - " + test["PRODUCT"]
test = test[~test["pair"].isin(train["pair"])].reset_index(drop=True)
test = test.drop_duplicates(subset=["pair"]).reset_index(drop=True)
test.to_csv("test.csv", index=False)
dataset = DatasetDict(
{
"train": Dataset.from_pandas(train[["input", "REACTANT"]]),
"validation": Dataset.from_pandas(valid[["input", "REACTANT"]]),
}
)
# load tokenizer
tokenizer = AutoTokenizer.from_pretrained(
os.path.abspath(CFG.pretrained_model_name_or_path)
if os.path.exists(CFG.pretrained_model_name_or_path)
else CFG.pretrained_model_name_or_path,
return_tensors="pt",
)
tokenizer = add_new_tokens(
tokenizer,
Path(__file__).resolve().parent.parent / "data" / "additional_tokens.txt",
)
tokenizer.add_special_tokens(
{
"additional_special_tokens": tokenizer.additional_special_tokens
+ ["REACTANT:", "REAGENT:"]
}
)
CFG.tokenizer = tokenizer
model = AutoModelForSeq2SeqLM.from_pretrained(
os.path.abspath(CFG.pretrained_model_name_or_path) if os.path.exists(CFG.pretrained_model_name_or_path) else CFG.pretrained_model_name_or_path
)
model.resize_token_embeddings(len(tokenizer))
tokenized_datasets = dataset.map(
lambda examples: preprocess_dataset(examples, CFG),
batched=True,
remove_columns=dataset["train"].column_names,
load_from_cache_file=False,
)
data_collator = DataCollatorForSeq2Seq(tokenizer, model=model)
args = Seq2SeqTrainingArguments(
CFG.output_dir,
evaluation_strategy=CFG.evaluation_strategy,
eval_steps=CFG.eval_steps,
save_strategy=CFG.save_strategy,
save_steps=CFG.save_steps,
logging_strategy=CFG.logging_strategy,
logging_steps=CFG.logging_steps,
learning_rate=CFG.lr,
per_device_train_batch_size=CFG.batch_size,
per_device_eval_batch_size=CFG.batch_size,
weight_decay=CFG.weight_decay,
save_total_limit=CFG.save_total_limit,
num_train_epochs=CFG.epochs,
predict_with_generate=True,
fp16=CFG.fp16,
disable_tqdm=CFG.disable_tqdm,
push_to_hub=False,
load_best_model_at_end=True,
)
model.config.eval_beams = CFG.eval_beams
model.config.max_length = CFG.target_max_length
trainer = Seq2SeqTrainer(
model,
args,
train_dataset=tokenized_datasets["train"],
eval_dataset=tokenized_datasets["validation"],
data_collator=data_collator,
tokenizer=tokenizer,
compute_metrics=lambda eval_preds: get_accuracy_score(eval_preds, CFG),
callbacks=[EarlyStoppingCallback(early_stopping_patience=10)],
)
try:
trainer.train(resume_from_checkpoint=True)
except:
trainer.train(resume_from_checkpoint=None)
trainer.save_model("./best_model")
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