train_txt2img_laion400m_trainer.py

# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
#
# 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.
import itertools
import math
import os

import paddle
from ldm import (
    DataArguments,
    LatentDiffusionModel,
    LatentDiffusionTrainer,
    ModelArguments,
    TextImagePair,
)
from paddlenlp.trainer import PdArgumentParser, TrainingArguments, get_last_checkpoint
from paddlenlp.utils.log import logger


def main():
    parser = PdArgumentParser((ModelArguments, DataArguments, TrainingArguments))
    model_args, data_args, training_args = parser.parse_args_into_dataclasses()
    # report to custom_visualdl
    training_args.report_to = ["custom_visualdl"]
    training_args.resolution = data_args.resolution
    training_args.benchmark = model_args.benchmark
    training_args.profiler_options = model_args.profiler_options
    training_args.image_logging_steps = model_args.image_logging_steps = (
        (math.ceil(model_args.image_logging_steps / training_args.logging_steps) * training_args.logging_steps)
        if model_args.image_logging_steps > 0
        else -1
    )

    training_args.print_config(model_args, "Model")
    training_args.print_config(data_args, "Data")

    paddle.set_device(training_args.device)

    # Detecting last checkpoint.
    last_checkpoint = None
    if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir:
        last_checkpoint = get_last_checkpoint(training_args.output_dir)
        if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0:
            raise ValueError(
                f"Output directory ({training_args.output_dir}) already exists and is not empty. "
                "Use --overwrite_output_dir to overcome."
            )
        elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
            logger.info(
                f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
                "the `--output_dir` or add `--overwrite_output_dir` to train from scratch."
            )

    model = LatentDiffusionModel(model_args)
    train_dataset = TextImagePair(
        file_list=data_args.file_list,
        size=data_args.resolution,
        num_records=data_args.num_records,
        buffer_size=data_args.buffer_size,
        shuffle_every_n_samples=data_args.shuffle_every_n_samples,
        interpolation="lanczos",
        tokenizer=model.tokenizer,
    )

    # paddlenlp >= 2.7.0 `training_args`` has `to_static` attribute
    if getattr(training_args, "to_static", False):
        input_ids = paddle.static.InputSpec(name="input_ids", shape=[-1, model_args.model_max_length], dtype="int64")
        pixel_values = paddle.static.InputSpec(
            name="pixel_values",
            shape=[-1, 3, data_args.resolution, data_args.resolution],
            dtype="float32",
        )
        specs = [input_ids, pixel_values]
        paddle.jit.ignore_module([os])
        model = paddle.jit.to_static(model, input_spec=specs)
        logger.info("Successfully to apply @to_static with specs: {}".format(specs))

    trainer = LatentDiffusionTrainer(
        model=model,
        args=training_args,
        train_dataset=train_dataset,
        tokenizer=model.tokenizer,
    )
    # must set recompute after trainer init
    trainer.model.set_recompute(training_args.recompute)
    params_to_train = itertools.chain(trainer.model.text_encoder.parameters(), trainer.model.unet.parameters())
    trainer.set_optimizer_grouped_parameters(params_to_train)

    checkpoint = None
    if training_args.resume_from_checkpoint is not None:
        checkpoint = training_args.resume_from_checkpoint
    elif last_checkpoint is not None:
        checkpoint = last_checkpoint

    # Training
    trainer.train(resume_from_checkpoint=checkpoint)
    trainer.save_model()
    trainer.save_state()


if __name__ == "__main__":
    main()