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# Copyright 2024 Llamole Team
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING, List, Optional
from ...data import get_dataset, split_dataset, DataCollatorForSeqGraph
from ...extras.constants import IGNORE_INDEX
from ...extras.misc import get_logits_processor
from ...extras.ploting import plot_loss
from ...model import load_language_model, load_tokenizer
from ...model import GraphLLMForCausalMLM
from .metric import ComputeMetrics, compute_accuracy, eval_logit_processor
from .trainer import CustomSeq2SeqTrainer
from torch.utils.data import DataLoader
if TYPE_CHECKING:
from transformers import Seq2SeqTrainingArguments, TrainerCallback
from ...hparams import (
DataArguments,
FinetuningArguments,
GeneratingArguments,
ModelArguments,
)
def run_mmsft(
model_args: "ModelArguments",
data_args: "DataArguments",
training_args: "Seq2SeqTrainingArguments",
finetuning_args: "FinetuningArguments",
generating_args: "GeneratingArguments",
callbacks: Optional[List["TrainerCallback"]] = None,
):
tokenizer_module = load_tokenizer(model_args)
tokenizer = tokenizer_module["tokenizer"]
mol_id_to_pyg, dataset = get_dataset(
model_args, data_args, training_args, tokenizer=tokenizer
)
data_collator = DataCollatorForSeqGraph(
tokenizer=tokenizer,
mol_id_to_pyg=mol_id_to_pyg,
pad_to_multiple_of=(
8 if tokenizer.padding_side == "right" else None
), # for shift short attention
label_pad_token_id=(
IGNORE_INDEX
if data_args.ignore_pad_token_for_loss
else tokenizer.pad_token_id
),
)
model = GraphLLMForCausalMLM.from_pretrained(
tokenizer, model_args, data_args, training_args, finetuning_args
)
# Override the decoding parameters of Seq2SeqTrainer
training_args.generation_max_length = (
training_args.generation_max_length or data_args.cutoff_len
)
training_args.generation_num_beams = (
data_args.eval_num_beams or training_args.generation_num_beams
)
training_args.remove_unused_columns = False
trainer = CustomSeq2SeqTrainer(
model=model,
args=training_args,
finetuning_args=finetuning_args,
data_collator=data_collator,
callbacks=callbacks,
compute_metrics=(
ComputeMetrics(tokenizer)
if training_args.predict_with_generate
else compute_accuracy
),
preprocess_logits_for_metrics=(
None if training_args.predict_with_generate else eval_logit_processor
),
**tokenizer_module,
**split_dataset(dataset, data_args, training_args),
)
# Keyword arguments for `model.generate`
gen_kwargs = generating_args.to_dict()
gen_kwargs["eos_token_id"] = [
tokenizer.eos_token_id
] + tokenizer.additional_special_tokens_ids
gen_kwargs["pad_token_id"] = tokenizer.pad_token_id
gen_kwargs["logits_processor"] = get_logits_processor()
# Training
if training_args.do_train:
train_result = trainer.train(
resume_from_checkpoint=training_args.resume_from_checkpoint
)
trainer.save_model()
trainer.log_metrics("train", train_result.metrics)
trainer.save_metrics("train", train_result.metrics)
trainer.save_state()
if trainer.is_world_process_zero() and finetuning_args.plot_loss:
plot_loss(
training_args.output_dir, keys=["loss", "eval_loss", "eval_accuracy"]
) |