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import tempfile
import gc
from pathlib import Path
import torch
from transformers import AutoModel, BertConfig, BertModel, BertTokenizerFast
try:
from probes.linear_probe import LinearProbe, LinearProbeConfig
from probes.packaged_probe_model import PackagedProbeConfig, PackagedProbeModel
from probes.transformer_probe import TransformerForSequenceClassification, TransformerProbeConfig
except ImportError:
from ..probes.linear_probe import LinearProbe, LinearProbeConfig
from ..probes.packaged_probe_model import PackagedProbeConfig, PackagedProbeModel
from ..probes.transformer_probe import TransformerForSequenceClassification, TransformerProbeConfig
def _copy_runtime_code(save_dir: Path) -> None:
repo_root = Path(__file__).resolve().parents[3]
src_package_dir = repo_root / "src" / "protify"
dst_package_dir = save_dir / "protify"
for src_file in src_package_dir.rglob("*.py"):
relative_path = src_file.relative_to(src_package_dir)
dst_file = dst_package_dir / relative_path
dst_file.parent.mkdir(parents=True, exist_ok=True)
shutil.copy2(src_file, dst_file)
packaged_model_file = repo_root / "src" / "protify" / "probes" / "packaged_probe_model.py"
shutil.copy2(packaged_model_file, save_dir / "packaged_probe_model.py")
def _create_tiny_backbone(backbone_dir: Path) -> tuple[BertModel, BertTokenizerFast]:
vocab_tokens = ["[PAD]", "[UNK]", "[CLS]", "[SEP]", "[MASK]", "A", "B", "C", "D"]
vocab_path = backbone_dir / "vocab.txt"
vocab_path.write_text("\n".join(vocab_tokens), encoding="utf-8")
tokenizer = BertTokenizerFast(vocab_file=str(vocab_path), do_lower_case=False)
config = BertConfig(
vocab_size=len(vocab_tokens),
hidden_size=16,
num_hidden_layers=2,
num_attention_heads=2,
intermediate_size=32,
)
model = BertModel(config).eval()
model.save_pretrained(str(backbone_dir))
tokenizer.save_pretrained(str(backbone_dir))
return model, tokenizer
def _save_and_load_with_automodel(
packaged_model: PackagedProbeModel,
tokenizer: BertTokenizerFast,
model_dir: Path,
) -> AutoModel:
packaged_model.config.auto_map = {
"AutoConfig": "packaged_probe_model.PackagedProbeConfig",
"AutoModel": "packaged_probe_model.PackagedProbeModel",
}
packaged_model.config.architectures = ["PackagedProbeModel"]
packaged_model.save_pretrained(str(model_dir), safe_serialization=True)
tokenizer.save_pretrained(str(model_dir))
_copy_runtime_code(model_dir)
return AutoModel.from_pretrained(str(model_dir), trust_remote_code=True)
def test_linear_packaged_roundtrip() -> None:
with tempfile.TemporaryDirectory(prefix="protify_linear_packaged_test_", ignore_cleanup_errors=True) as temp_dir:
temp_path = Path(temp_dir)
backbone_dir = temp_path / "backbone"
model_dir = temp_path / "linear_packaged_model"
backbone_dir.mkdir(parents=True, exist_ok=True)
model_dir.mkdir(parents=True, exist_ok=True)
backbone, tokenizer = _create_tiny_backbone(backbone_dir)
probe_config = LinearProbeConfig(
input_size=16,
hidden_size=32,
dropout=0.1,
num_labels=3,
n_layers=1,
task_type="singlelabel",
)
probe = LinearProbe(probe_config).eval()
packaged_config = PackagedProbeConfig(
base_model_name=str(backbone_dir),
probe_type="linear",
probe_config=probe.config.to_dict(),
tokenwise=False,
matrix_embed=False,
pooling_types=["mean"],
task_type="singlelabel",
num_labels=3,
ppi=False,
add_token_ids=False,
sep_token_id=tokenizer.sep_token_id,
)
packaged_model = PackagedProbeModel(config=packaged_config, base_model=backbone, probe=probe).eval()
loaded_model = _save_and_load_with_automodel(packaged_model, tokenizer, model_dir)
batch = tokenizer(["A B C A", "B C D A"], padding="longest", return_tensors="pt")
outputs = loaded_model(input_ids=batch["input_ids"], attention_mask=batch["attention_mask"])
assert outputs.logits.shape == (2, 3), f"Unexpected linear packaged logits shape: {outputs.logits.shape}"
del loaded_model
gc.collect()
def test_transformer_packaged_roundtrip() -> None:
with tempfile.TemporaryDirectory(prefix="protify_transformer_packaged_test_", ignore_cleanup_errors=True) as temp_dir:
temp_path = Path(temp_dir)
backbone_dir = temp_path / "backbone"
model_dir = temp_path / "transformer_packaged_model"
backbone_dir.mkdir(parents=True, exist_ok=True)
model_dir.mkdir(parents=True, exist_ok=True)
backbone, tokenizer = _create_tiny_backbone(backbone_dir)
probe_config = TransformerProbeConfig(
input_size=16,
hidden_size=16,
classifier_size=24,
transformer_dropout=0.1,
classifier_dropout=0.1,
num_labels=2,
n_layers=1,
token_attention=False,
n_heads=2,
task_type="singlelabel",
rotary=False,
pre_ln=True,
probe_pooling_types=["mean"],
use_bias=False,
add_token_ids=False,
)
probe = TransformerForSequenceClassification(probe_config).eval()
packaged_config = PackagedProbeConfig(
base_model_name=str(backbone_dir),
probe_type="transformer",
probe_config=probe.config.to_dict(),
tokenwise=False,
matrix_embed=True,
pooling_types=["mean"],
task_type="singlelabel",
num_labels=2,
ppi=False,
add_token_ids=False,
sep_token_id=tokenizer.sep_token_id,
)
packaged_model = PackagedProbeModel(config=packaged_config, base_model=backbone, probe=probe).eval()
loaded_model = _save_and_load_with_automodel(packaged_model, tokenizer, model_dir)
batch = tokenizer(["A B C D", "D C B A"], padding="longest", return_tensors="pt")
outputs = loaded_model(input_ids=batch["input_ids"], attention_mask=batch["attention_mask"])
assert outputs.logits.shape == (2, 2), f"Unexpected transformer packaged logits shape: {outputs.logits.shape}"
del loaded_model
gc.collect()
def test_ppi_packaged_inference_with_and_without_token_type_ids() -> None:
with tempfile.TemporaryDirectory(prefix="protify_ppi_packaged_test_", ignore_cleanup_errors=True) as temp_dir:
temp_path = Path(temp_dir)
backbone_dir = temp_path / "backbone"
model_dir = temp_path / "ppi_packaged_model"
backbone_dir.mkdir(parents=True, exist_ok=True)
model_dir.mkdir(parents=True, exist_ok=True)
backbone, tokenizer = _create_tiny_backbone(backbone_dir)
probe_config = LinearProbeConfig(
input_size=32,
hidden_size=24,
dropout=0.1,
num_labels=2,
n_layers=1,
task_type="singlelabel",
)
probe = LinearProbe(probe_config).eval()
packaged_config = PackagedProbeConfig(
base_model_name=str(backbone_dir),
probe_type="linear",
probe_config=probe.config.to_dict(),
tokenwise=False,
matrix_embed=False,
pooling_types=["mean"],
task_type="singlelabel",
num_labels=2,
ppi=True,
add_token_ids=False,
sep_token_id=tokenizer.sep_token_id,
)
packaged_model = PackagedProbeModel(config=packaged_config, base_model=backbone, probe=probe).eval()
loaded_model = _save_and_load_with_automodel(packaged_model, tokenizer, model_dir)
pair_batch = tokenizer(
["A B C", "B C D"],
["D C B", "A C B"],
padding="longest",
return_tensors="pt",
)
outputs_with_token_types = loaded_model(
input_ids=pair_batch["input_ids"],
attention_mask=pair_batch["attention_mask"],
token_type_ids=pair_batch["token_type_ids"],
)
assert outputs_with_token_types.logits.shape == (2, 2), "PPI logits shape mismatch with token_type_ids"
outputs_without_token_types = loaded_model(
input_ids=pair_batch["input_ids"],
attention_mask=pair_batch["attention_mask"],
)
assert outputs_without_token_types.logits.shape == (2, 2), "PPI logits shape mismatch without token_type_ids"
del loaded_model
gc.collect()
def main() -> None:
torch.manual_seed(0)
test_linear_packaged_roundtrip()
test_transformer_packaged_roundtrip()
test_ppi_packaged_inference_with_and_without_token_type_ids()
print("Packaged probe model smoke tests passed.")
if __name__ == "__main__":
main()
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