Infer geneformer

geneformer_infer_entrypoint()

Entrypoint for running inference on a geneformer checkpoint and data.

Source code in bionemo/geneformer/scripts/infer_geneformer.py

def geneformer_infer_entrypoint():
    """Entrypoint for running inference on a geneformer checkpoint and data."""
    # 1. get arguments
    parser = get_parser()
    args = parser.parse_args()
    # 2. Call infer with args
    infer_model(
        data_path=args.data_dir,
        checkpoint_path=args.checkpoint_path,
        results_path=args.results_path,
        include_hiddens=args.include_hiddens,
        micro_batch_size=args.micro_batch_size,
        include_embeddings=not args.no_embeddings,
        include_logits=args.include_logits,
        include_input_ids=args.include_input_ids,
        seq_length=args.seq_length,
        precision=args.precision,
        devices=args.num_gpus,
        num_nodes=args.num_nodes,
        num_dataset_workers=args.num_dataset_workers,
        config_class=args.config_class,
        include_unrecognized_vocab_in_dataset=args.include_unrecognized_vocab_in_dataset,
    )

get_parser()

Return the cli parser for this tool.

Source code in bionemo/geneformer/scripts/infer_geneformer.py

def get_parser():
    """Return the cli parser for this tool."""
    parser = argparse.ArgumentParser(
        description="Infer processed single cell data in SCDL memmap format with Geneformer from a checkpoint."
    )
    parser.add_argument(
        "--data-dir",
        type=Path,
        required=True,
        help="Path to the data directory, for example this might be "
        "/workspace/bionemo2/data/cellxgene_2023-12-15_small/processed_train",
    )
    parser.add_argument(
        "--checkpoint-path",
        type=Path,
        required=False,
        default=None,
        help="Path to the checkpoint directory to restore from.",
    )
    parser.add_argument(
        "--precision",
        type=str,
        choices=get_args(PrecisionTypes),
        required=False,
        default="bf16-mixed",
        help="Precision type to use for training.",
    )
    parser.add_argument("--include-hiddens", action="store_true", default=False, help="Include hiddens in output.")
    parser.add_argument("--no-embeddings", action="store_true", default=False, help="Do not output embeddings.")
    parser.add_argument(
        "--include-logits", action="store_true", default=False, help="Include per-token logits in output."
    )
    parser.add_argument(
        "--include-input-ids",
        action="store_true",
        default=False,
        help="Include input_ids in output of inference",
    )
    parser.add_argument("--results-path", type=Path, required=True, help="Path to the results directory.")
    parser.add_argument(
        "--num-gpus",
        type=int,
        required=False,
        default=1,
        help="Number of GPUs to use for training. Default is 1.",
    )
    parser.add_argument(
        "--num-nodes",
        type=int,
        required=False,
        default=1,
        help="Number of nodes to use for training. Default is 1.",
    )
    parser.add_argument(
        "--prediction-interval",
        type=str,
        required=False,
        choices=get_args(IntervalT),
        default="epoch",
        help="Intervals to write DDP predictions into disk",
    )
    parser.add_argument(
        "--num-dataset-workers",
        type=int,
        required=False,
        default=0,
        help="Number of steps to use for training. Default is 0.",
    )
    parser.add_argument(
        "--seq-length",
        type=int,
        required=False,
        default=2048,
        help="Sequence length of cell. Default is 2048.",
    )
    parser.add_argument(
        "--micro-batch-size",
        type=int,
        required=False,
        default=32,
        help="Micro-batch size. Global batch size is inferred from this.",
    )

    parser.add_argument(
        "--include-unrecognized-vocab-in-dataset",
        action="store_true",
        help="If set to True, a hard-check is performed to verify all gene identifers are in the user supplied tokenizer vocab. Defaults to False which means any gene identifier not in the user supplied tokenizer vocab will be excluded.",
    )

    # TODO consider whether nemo.run or some other method can simplify this config class lookup.
    config_class_options: Dict[str, Type[BioBertConfig]] = {
        "GeneformerConfig": GeneformerConfig,
        "FineTuneSeqLenBioBertConfig": FineTuneSeqLenBioBertConfig,
    }

    def config_class_type(desc: str) -> Type[BioBertConfig]:
        try:
            return config_class_options[desc]
        except KeyError:
            raise argparse.ArgumentTypeError(
                f"Do not recognize key {desc}, valid options are: {config_class_options.keys()}"
            )

    parser.add_argument(
        "--config-class",
        type=config_class_type,
        default="GeneformerConfig",
        help="Model configs link model classes with losses, and handle model initialization (including from a prior "
        "checkpoint). This is how you can fine-tune a model. First train with one config class that points to one model "
        "class and loss, then implement and provide an alternative config class that points to a variant of that model "
        "and alternative loss. In the future this script should also provide similar support for picking different data "
        f"modules for fine-tuning with different data types. Choices: {config_class_options.keys()}",
    )
    return parser

infer_model(data_path, checkpoint_path, results_path, include_hiddens=False, include_embeddings=False, include_logits=False, include_input_ids=False, seq_length=2048, micro_batch_size=64, precision='bf16-mixed', tensor_model_parallel_size=1, pipeline_model_parallel_size=1, devices=1, num_nodes=1, num_dataset_workers=0, prediction_interval='epoch', config_class=GeneformerConfig, include_unrecognized_vocab_in_dataset=False)

Inference function (requires DDP and only training data that fits in memory).

Source code in bionemo/geneformer/scripts/infer_geneformer.py

def infer_model(
    data_path: Path,
    checkpoint_path: Path,
    results_path: Path,
    include_hiddens: bool = False,
    include_embeddings: bool = False,
    include_logits: bool = False,
    include_input_ids: bool = False,
    seq_length: int = 2048,
    micro_batch_size: int = 64,
    precision: PrecisionTypes = "bf16-mixed",
    tensor_model_parallel_size: int = 1,
    pipeline_model_parallel_size: int = 1,
    devices: int = 1,
    num_nodes: int = 1,
    num_dataset_workers: int = 0,
    prediction_interval: IntervalT = "epoch",
    config_class: Type[BioBertConfig] = GeneformerConfig,
    include_unrecognized_vocab_in_dataset: bool = False,
) -> None:
    """Inference function (requires DDP and only training data that fits in memory)."""
    # create the directory to save the inference results
    os.makedirs(results_path, exist_ok=True)

    # This is just used to get the tokenizer :(
    train_data_path: Path = (
        load("single_cell/testdata-20241203") / "cellxgene_2023-12-15_small_processed_scdl" / "train"
    )

    # Setup the strategy and trainer
    pipeline_model_parallel_size = 1
    tensor_model_parallel_size = 1
    accumulate_grad_batches = 1
    global_batch_size = infer_global_batch_size(
        micro_batch_size=micro_batch_size,
        num_nodes=num_nodes,
        devices=devices,
        accumulate_grad_batches=accumulate_grad_batches,
        tensor_model_parallel_size=tensor_model_parallel_size,
        pipeline_model_parallel_size=pipeline_model_parallel_size,
    )

    preprocessor = GeneformerPreprocess(
        download_directory=train_data_path,
        medians_file_path=train_data_path / "medians.json",
        tokenizer_vocab_path=train_data_path / "geneformer.vocab",
    )
    match preprocessor.preprocess():
        case {"tokenizer": tokenizer, "median_dict": median_dict}:
            logging.info("*************** Preprocessing Finished ************")
        case _:
            logging.error("Preprocessing failed.")

    strategy = nl.MegatronStrategy(
        tensor_model_parallel_size=tensor_model_parallel_size,
        pipeline_model_parallel_size=pipeline_model_parallel_size,
        ddp="megatron",
        find_unused_parameters=True,
        ckpt_include_optimizer=True,
        progress_interval=1,
    )

    prediction_writer = PredictionWriter(output_dir=results_path, write_interval=prediction_interval)

    trainer = nl.Trainer(
        devices=devices,
        accelerator="gpu",
        strategy=strategy,
        num_nodes=num_nodes,
        callbacks=[prediction_writer],
        plugins=nl.MegatronMixedPrecision(precision=precision),
    )
    # Configure the data module and model
    datamodule = SingleCellDataModule(
        seq_length=seq_length,
        tokenizer=tokenizer,
        train_dataset_path=None,
        val_dataset_path=None,
        test_dataset_path=None,
        predict_dataset_path=data_path,
        mask_prob=0,
        mask_token_prob=0,
        random_token_prob=0,  # changed to represent the incorrect setting we originally used.
        median_dict=median_dict,
        micro_batch_size=micro_batch_size,
        global_batch_size=global_batch_size,
        # persistent workers is supported when num_dataset_workers > 0
        persistent_workers=num_dataset_workers > 0,
        pin_memory=False,
        num_workers=num_dataset_workers,
        include_unrecognized_vocab_in_dataset=include_unrecognized_vocab_in_dataset,
    )
    config = config_class(
        seq_length=seq_length,
        params_dtype=get_autocast_dtype(precision),
        pipeline_dtype=get_autocast_dtype(precision),
        autocast_dtype=get_autocast_dtype(precision),  # setting this speeds things up a lot
        # handle checkpoint resumption here rather than auto-resume so this supports fine-tuning capabilities
        initial_ckpt_path=str(checkpoint_path) if checkpoint_path is not None else None,
        include_embeddings=include_embeddings,
        include_hiddens=include_hiddens,
        include_input_ids=include_input_ids,
        skip_logits=not include_logits,
        initial_ckpt_skip_keys_with_these_prefixes=[],  # load everything from the checkpoint.
    )
    # The lightning class owns a copy of the actual model, and a loss function, both of which are configured
    #  and lazily returned by the `config` object defined above.
    module = biobert_lightning_module(config=config, tokenizer=tokenizer)

    trainer.predict(module, datamodule=datamodule)  # return_predictions=False failing due to a lightning bug