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import argparse
import json
from pathlib import Path
from .assets import (
default_assets_root,
discover_cached_embeddings,
discover_checkpoints,
discover_repo_checkpoints,
package_root,
resolve_cache_dir,
resolve_checkpoint_path,
)
from .beir_data import load_beir_source
from .caching import build_cache
from .checkpoints import default_checkpoint_name, load_model, save_checkpoint
from .data import ContrastiveCachedDataset, load_cached_split
from .encoders import encoder_storage_key, normalize_encoder_name, resolve_encoder_spec
from .evaluation import evaluate_model
from .model import IMRNN, ModelConfig
from .training import TrainingConfig, train_model
def _add_common_args(parser: argparse.ArgumentParser) -> None:
parser.add_argument("--assets-root", type=Path, default=default_assets_root())
parser.add_argument("--datasets-dir", type=Path)
parser.add_argument("--encoder")
parser.add_argument("--encoder-model-name")
parser.add_argument("--embedding-dim", type=int)
parser.add_argument("--query-prefix", default="")
parser.add_argument("--passage-prefix", default="")
parser.add_argument("--dataset", required=True)
parser.add_argument("--device", default="cuda")
def _resolve_encoder_spec(args: argparse.Namespace):
return resolve_encoder_spec(
encoder=args.encoder,
encoder_model_name=args.encoder_model_name,
embedding_dim=args.embedding_dim,
query_prefix=args.query_prefix,
passage_prefix=args.passage_prefix,
)
def _encoder_label(args: argparse.Namespace, encoder_spec) -> str:
if args.encoder:
return encoder_storage_key(args.encoder)
return encoder_storage_key(encoder_spec.key)
def _command_list_assets(args: argparse.Namespace) -> int:
payload = {
"assets_root": str(args.assets_root),
"repo_root": str(package_root()),
"cached_embeddings": [
{"encoder": item.encoder, "dataset": item.dataset, "path": str(item.path)}
for item in discover_cached_embeddings(args.assets_root)
],
"workspace_checkpoints": [
{"encoder": item.encoder, "dataset": item.dataset, "path": str(item.path)}
for item in discover_checkpoints(args.assets_root)
],
"repo_checkpoints": [
{"encoder": item.encoder, "dataset": item.dataset, "path": str(item.path)}
for item in discover_repo_checkpoints(package_root())
],
}
print(json.dumps(payload, indent=2))
return 0
def _load_training_inputs(args: argparse.Namespace):
encoder_spec = _resolve_encoder_spec(args)
encoder_label = _encoder_label(args, encoder_spec)
cache_dir = args.cache_dir or resolve_cache_dir(args.assets_root, encoder_label, args.dataset)
datasets_dir = args.datasets_dir or (args.assets_root / "datasets")
beir_source = load_beir_source(args.dataset, datasets_dir=datasets_dir, max_queries=args.max_queries)
train_split = load_cached_split(cache_dir, "train", beir_source, encoder_spec, args.device)
val_split = load_cached_split(cache_dir, "val", beir_source, encoder_spec, args.device)
test_split = load_cached_split(cache_dir, "test", beir_source, encoder_spec, args.device)
return encoder_spec, cache_dir, train_split, val_split, test_split
def _k_values(args: argparse.Namespace) -> list[int]:
return [args.k]
def _command_cache(args: argparse.Namespace) -> int:
encoder_spec = _resolve_encoder_spec(args)
encoder_label = _encoder_label(args, encoder_spec)
cache_dir = args.cache_dir or (args.assets_root / f"cache_{encoder_label}_{args.dataset}")
built = build_cache(
dataset_name=args.dataset,
encoder_spec=encoder_spec,
cache_dir=cache_dir,
datasets_dir=args.datasets_dir or (args.assets_root / "datasets"),
device=args.device,
batch_size=args.batch_size,
num_negatives=args.num_negatives,
negative_pool=args.negative_pool,
)
print(
json.dumps(
{
"cache_dir": str(built),
"encoder": encoder_label,
"encoder_model_name": encoder_spec.model_name,
"dataset": args.dataset,
},
indent=2,
)
)
return 0
def _command_train(args: argparse.Namespace) -> int:
encoder_spec, cache_dir, train_split, val_split, test_split = _load_training_inputs(args)
model = IMRNN(
ModelConfig(
input_dim=encoder_spec.embedding_dim,
output_dim=args.output_dim,
hidden_dim=args.hidden_dim,
dropout=args.dropout,
)
)
train_dataset = ContrastiveCachedDataset(train_split, args.num_negatives)
val_dataset = ContrastiveCachedDataset(val_split, args.num_negatives)
if len(train_dataset) == 0:
raise ValueError(
"No training examples were constructed from the cached split. "
"Increase --max-queries or verify that cached negatives/embeddings match the dataset split."
)
if len(val_dataset) == 0:
raise ValueError(
"No validation examples were constructed from the cached split. "
"Increase --max-queries or verify that cached negatives/embeddings match the dataset split."
)
metrics = train_model(
model=model,
train_dataset=train_dataset,
val_dataset=val_dataset,
config=TrainingConfig(
batch_size=args.batch_size,
epochs=args.epochs,
lr=args.lr,
weight_decay=args.weight_decay,
num_negatives=args.num_negatives,
),
device=args.device,
)
eval_metrics = evaluate_model(
model=model,
cached_split=test_split,
device=args.device,
feedback_k=args.feedback_k,
ranking_k=args.ranking_k,
k_values=_k_values(args),
)
output_dir = args.output_dir or args.assets_root
encoder_label = _encoder_label(args, encoder_spec)
checkpoint_path = output_dir / default_checkpoint_name(encoder_label, args.dataset)
metadata = {
"encoder": encoder_label,
"encoder_model_name": encoder_spec.model_name,
"dataset": args.dataset,
"cache_dir": str(cache_dir),
"model_config": {
"input_dim": encoder_spec.embedding_dim,
"output_dim": args.output_dim,
"hidden_dim": args.hidden_dim,
"dropout": args.dropout,
},
"training": metrics,
"evaluation": eval_metrics,
}
save_checkpoint(checkpoint_path, model, metadata)
print(json.dumps({"checkpoint": str(checkpoint_path), "training": metrics, "evaluation": eval_metrics}, indent=2))
return 0
def _command_evaluate(args: argparse.Namespace) -> int:
encoder_spec = _resolve_encoder_spec(args)
encoder_label = _encoder_label(args, encoder_spec)
cache_dir = args.cache_dir or resolve_cache_dir(args.assets_root, encoder_label, args.dataset)
checkpoint_path = args.checkpoint or resolve_checkpoint_path(args.assets_root, encoder_label, args.dataset)
if checkpoint_path is None:
raise FileNotFoundError(
f"No checkpoint found for encoder='{encoder_label}' dataset='{args.dataset}'. Provide --checkpoint."
)
datasets_dir = args.datasets_dir or (args.assets_root / "datasets")
beir_source = load_beir_source(args.dataset, datasets_dir=datasets_dir, max_queries=args.max_queries)
test_split = load_cached_split(cache_dir, "test", beir_source, encoder_spec, args.device)
model, metadata, missing, unexpected = load_model(
checkpoint_path=checkpoint_path,
model_config=ModelConfig(
input_dim=encoder_spec.embedding_dim,
output_dim=args.output_dim,
hidden_dim=args.hidden_dim,
dropout=args.dropout,
),
device=args.device,
)
metrics = evaluate_model(
model=model,
cached_split=test_split,
device=args.device,
feedback_k=args.feedback_k,
ranking_k=args.ranking_k,
k_values=_k_values(args),
)
print(
json.dumps(
{
"checkpoint": str(checkpoint_path),
"metrics": metrics,
"metadata": metadata,
"missing_keys": missing,
"unexpected_keys": unexpected,
},
indent=2,
)
)
return 0
def _command_run(args: argparse.Namespace) -> int:
encoder_spec = _resolve_encoder_spec(args)
encoder_label = _encoder_label(args, encoder_spec)
cache_dir = args.cache_dir or (args.assets_root / f"cache_{encoder_label}_{args.dataset}")
if not cache_dir.exists():
cache_args = argparse.Namespace(**vars(args))
cache_args.cache_dir = cache_dir
_command_cache(cache_args)
train_args = argparse.Namespace(**vars(args))
train_args.cache_dir = cache_dir
return _command_train(train_args)
def build_parser() -> argparse.ArgumentParser:
parser = argparse.ArgumentParser(description="Train and evaluate IMRNNs over cached BEIR embeddings.")
subparsers = parser.add_subparsers(dest="command", required=True)
list_assets = subparsers.add_parser("list-assets", help="List cached embeddings and checkpoints.")
list_assets.add_argument("--assets-root", type=Path, default=default_assets_root())
list_assets.set_defaults(func=_command_list_assets)
train = subparsers.add_parser("train", help="Train IMRNNs from cached embeddings.")
_add_common_args(train)
train.add_argument("--cache-dir", type=Path)
train.add_argument("--output-dir", type=Path)
train.add_argument("--max-queries", type=int)
train.add_argument("--batch-size", type=int, default=32)
train.add_argument("--epochs", type=int, default=10)
train.add_argument("--lr", type=float, default=1e-4)
train.add_argument("--weight-decay", type=float, default=1e-5)
train.add_argument("--num-negatives", type=int, default=20)
train.add_argument("--output-dim", type=int, default=256)
train.add_argument("--hidden-dim", type=int, default=128)
train.add_argument("--dropout", type=float, default=0.1)
train.add_argument("--feedback-k", type=int, default=100)
train.add_argument("--ranking-k", type=int, default=10)
train.add_argument("--k", type=int, default=10)
train.set_defaults(func=_command_train)
evaluate = subparsers.add_parser("evaluate", help="Evaluate an IMRNN checkpoint.")
_add_common_args(evaluate)
evaluate.add_argument("--cache-dir", type=Path)
evaluate.add_argument("--checkpoint", type=Path)
evaluate.add_argument("--max-queries", type=int)
evaluate.add_argument("--output-dim", type=int, default=256)
evaluate.add_argument("--hidden-dim", type=int, default=128)
evaluate.add_argument("--dropout", type=float, default=0.1)
evaluate.add_argument("--feedback-k", type=int, default=100)
evaluate.add_argument("--ranking-k", type=int, default=10)
evaluate.add_argument("--k", type=int, default=10)
evaluate.set_defaults(func=_command_evaluate)
cache = subparsers.add_parser("cache", help="Download a BEIR dataset and cache embeddings plus negatives.")
_add_common_args(cache)
cache.add_argument("--cache-dir", type=Path)
cache.add_argument("--batch-size", type=int, default=64)
cache.add_argument("--num-negatives", type=int, default=20)
cache.add_argument("--negative-pool", type=int, default=200)
cache.set_defaults(func=_command_cache)
run = subparsers.add_parser("run", help="Cache embeddings if needed, then train and evaluate IMRNNs end to end.")
_add_common_args(run)
run.add_argument("--cache-dir", type=Path)
run.add_argument("--output-dir", type=Path)
run.add_argument("--max-queries", type=int)
run.add_argument("--batch-size", type=int, default=32)
run.add_argument("--epochs", type=int, default=10)
run.add_argument("--lr", type=float, default=1e-4)
run.add_argument("--weight-decay", type=float, default=1e-5)
run.add_argument("--num-negatives", type=int, default=20)
run.add_argument("--negative-pool", type=int, default=200)
run.add_argument("--output-dim", type=int, default=256)
run.add_argument("--hidden-dim", type=int, default=128)
run.add_argument("--dropout", type=float, default=0.1)
run.add_argument("--feedback-k", type=int, default=100)
run.add_argument("--ranking-k", type=int, default=10)
run.add_argument("--k", type=int, default=10)
run.set_defaults(func=_command_run)
return parser
def main() -> int:
parser = build_parser()
args = parser.parse_args()
return args.func(args)
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