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from pathlib import Path
from scgpt.tokenizer import GeneVocab, random_mask_value
import sys
from datasets import Dataset, load_dataset
import os
sys.path.insert(0, "../")
parser = argparse.ArgumentParser()
parser.add_argument(
"-d",
"--data-source",
type=str,
required=True,
help='The name of the data source (currently support "scvi" datasets), or the '
"path to the data file.",
)
parser.add_argument(
"-s",
"--save-dir",
type=str,
required=True,
help="The directory to save the trained model and the results.",
)
parser.add_argument(
"--load-model",
type=str,
default=None,
help="The directory containing the model and configs to load and continue training.",
)
# settings for data
parser.add_argument(
"--n-hvg",
type=int,
default=None,
help="The number of highly variable genes. If set to 0, will use all genes. "
"Default is None, which will determine the n_hvg automatically.",
)
parser.add_argument(
"--valid-size-or-ratio",
type=float,
default=0.1,
help="The ratio or size of the validation set size if split the dataset. "
"If value is between 0 and 1, will be parsed as the ratio. If value is "
"greater than 1 and be an integer, will be parsed as the size. If value "
"is 0, will not split the dataset.",
)
parser.add_argument(
"--grad-accu-steps",
type=int,
default=1,
help="The number of gradient accumulation steps. Default is 1.",
)
# settings for tokenizer
parser.add_argument(
"--pad-token",
type=str,
default="<pad>",
help="The token to use for padding. Default is <pad>.",
)
parser.add_argument(
"--input-style",
type=str,
choices=["normed_raw", "log1p", "binned"],
default="binned",
help="The style of the input data. Default is binned.",
)
parser.add_argument(
"--input-emb-style",
type=str,
choices=["category", "continuous", "scaling"],
default="continuous",
help="The style of the input embedding. Default is continuous.",
)
parser.add_argument(
"--n-bins",
type=int,
default=51,
help="The number of bins to use for the binned input style. Default is 51.",
)
parser.add_argument(
"--max-seq-len",
type=int,
default=1536,
help="The maximum length of the sequence. Default is 1000. The actual used "
"max length would be the minimum of this value and the length of the longest "
"sequence in the data.",
)
# omit the args for MLM and MVC, will always use them by default
parser.add_argument(
"--training-tasks", # choices of "mlm", "gen", "both"
type=str,
default="both",
choices=["pcpt", "gen", "both"],
help="The tasks to use for training. pcpt: perception training with maked token "
"learning. gen: generation. Default is both.",
)
parser.add_argument(
"--mask-ratio",
type=float,
default=0.40,
help="The ratio of masked values in the training data. Default is 0.40. This"
"value will be ignored if --training-tasks is set to gen or both.",
)
parser.add_argument(
"--trunc-by-sample",
action="store_true",
help="Whether to truncate the input by sampling rather than cutting off if "
"sequence length > max_seq_length. Default is False.",
)
parser.add_argument(
"--vocab-path",
type=str,
help="Path to the vocabulary file.",
)
# settings for training
parser.add_argument(
"--local-rank",
type=int,
default=-1,
help="The local rank of the process for using the torch.distributed.launch "
"utility. Will be -1 if not running in distributed model.",
)
parser.add_argument(
"--batch-size",
type=int,
default=32,
help="The batch size for training. Default is 32.",
)
parser.add_argument(
"--eval-batch-size",
type=int,
default=32,
help="The batch size for evaluation. Default is 32.",
)
parser.add_argument(
"--epochs",
type=int,
default=10,
help="The number of epochs for training.",
)
parser.add_argument(
"--lr",
type=float,
default=1e-3,
help="The learning rate for training. Default is 1e-3.",
)
parser.add_argument(
"--scheduler-interval",
type=int,
default=100,
help="The interval iterations for updating the learning rate. Default is 100. "
"This will only be used when warmup-ratio is 0.",
)
parser.add_argument(
"--scheduler-factor",
type=float,
default=0.99,
help="The factor for updating the learning rate. Default is 0.99. "
"This will only be used when warmup-ratio is 0.",
)
parser.add_argument(
"--warmup-ratio-or-step",
type=float,
default=0.1,
help="The ratio of warmup steps out of the total training steps. Default is 0.1. "
"If warmup-ratio is above 0, will use a cosine scheduler with warmup. If "
"the value is above 1, will use it as the number of warmup steps.",
)
parser.add_argument(
"--no-cls",
action="store_true",
help="Whether to deactivate the classification loss. Default is False.",
)
parser.add_argument(
"--no-cce",
action="store_true",
help="Whether to deactivate the contrastive cell embedding objective. "
"Default is False.",
)
parser.add_argument(
"--fp16",
action="store_true",
help="Whether to train in automatic mixed precision. Default is False.",
)
parser.add_argument(
"--fast-transformer",
type=bool,
default=True,
help="Whether to use the fast transformer. Default is True.",
)
# settings for model
parser.add_argument(
"--nlayers",
type=int,
default=4,
help="The number of layers for the transformer. Default is 4.",
)
parser.add_argument(
"--nheads",
type=int,
default=4,
help="The number of heads for the transformer. Default is 4.",
)
parser.add_argument(
"--embsize",
type=int,
default=64,
help="The embedding size for the transformer. Default is 64.",
)
parser.add_argument(
"--d-hid",
type=int,
default=64,
help="dimension of the feedforward network model in the transformer. "
"Default is 64.",
)
parser.add_argument(
"--dropout",
type=float,
default=0.2,
help="The dropout rate. Default is 0.2.",
)
parser.add_argument(
"--n-layers-cls",
type=int,
default=3,
help="The number of layers for the classification network, including the "
"output layer. Default is 3.",
)
# settings for logging
parser.add_argument(
"--log-interval",
type=int,
default=100,
help="The interval for logging. Default is 100.",
)
parser.add_argument(
"--save-interval",
type=int,
default=1000,
help="The interval for saving the model. Default is 1000.",
)
args = parser.parse_args()
# args.pad_value = -2
if args.input_style == "binned":
if args.input_emb_style == "scaling":
raise ValueError("input_emb_style `scaling` is not supported for binned input.")
elif args.input_style == "log1p" or args.input_style == "normed_raw":
if args.input_emb_style == "category":
raise ValueError(
"input_emb_style `category` is not supported for log1p or normed_raw input."
)
if args.input_emb_style == "category":
args.mask_value = args.n_bins + 1
args.pad_value = args.n_bins # for padding gene expr values
n_input_bins = args.n_bins + 2
else:
args.mask_value = -1
args.pad_value = -2
n_input_bins = args.n_bins
def _map_append_cls(dataset: Dataset) -> Dataset:
dataset = dataset.map(
lambda example: {
"genes": [vocab["<cls>"]] + example["genes"],
"expressions": [args.pad_value] + example["expressions"],
},
# batched=True, # not using since then the map func needs to loop
num_proc=len(os.sched_getaffinity(0)),
)
return dataset
special_tokens = [args.pad_token, "<cls>", "<eoc>"]
parquet_files = [str(f) for f in Path(args.data_source).glob("*.parquet")]
cache_dir = Path(args.data_source).parent / "cache"
vocab = GeneVocab.from_file(Path(args.vocab_path))
for s in special_tokens:
if s not in vocab:
vocab.append_token(s)
# load or make the dataset w/ <cls> appended at the beginning
cls_prefix_datatable = Path(args.data_source) / "cls_prefix_data.parquet"
if not cls_prefix_datatable.exists():
print("preparing cls prefix dataset")
raw_dataset = load_dataset(
"parquet",
data_files=parquet_files,
split="train",
cache_dir=str(cache_dir),
)
raw_dataset = _map_append_cls(raw_dataset)
raw_dataset.to_parquet(str(cls_prefix_datatable))
raw_dataset = load_dataset(
"parquet",
data_files=str(cls_prefix_datatable),
split="train",
cache_dir=str(cache_dir),
)
# others, pancreas, lung, kidney, heart, blood
|