File size: 6,012 Bytes
5576acd | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 | from __future__ import annotations
import argparse
import json
from pathlib import Path
import torch
from safetensors.torch import load_file
from sentence_transformers import SentenceTransformer
from torch import nn
class MLPClassifier(nn.Module):
def __init__(
self,
input_dim: int,
hidden_dim: int,
num_labels: int,
dropout: float,
):
super().__init__()
self.network = nn.Sequential(
nn.LayerNorm(input_dim),
nn.Linear(input_dim, hidden_dim),
nn.GELU(),
nn.Dropout(dropout),
nn.Linear(hidden_dim, num_labels),
)
def forward(self, embeddings: torch.Tensor) -> torch.Tensor:
return self.network(embeddings)
class UserNeedsClassifier:
def __init__(
self,
model_dir: str | Path = Path(__file__).parent,
device: str | None = None,
):
self.model_dir = Path(model_dir)
self.device = torch.device(
device or ("cuda" if torch.cuda.is_available() else "cpu")
)
self.config = json.loads(
(self.model_dir / "config.json").read_text(encoding="utf-8")
)
self.category_definitions = json.loads(
(
self.model_dir / self.config["category_id_definitions"]
).read_text(encoding="utf-8")
)
configured_dtype = self.config.get("inference_dtype")
embedding_dtype_name = self.config.get("embedding_compute_dtype")
classifier_dtype_name = self.config.get("classifier_compute_dtype")
dtype_by_name = {
"bfloat16": torch.bfloat16,
"float16": torch.float16,
"float32": torch.float32,
}
if embedding_dtype_name is not None or classifier_dtype_name is not None:
if embedding_dtype_name not in dtype_by_name:
raise ValueError(
f"Unsupported embedding_compute_dtype: {embedding_dtype_name}"
)
if classifier_dtype_name not in dtype_by_name:
raise ValueError(
f"Unsupported classifier_compute_dtype: {classifier_dtype_name}"
)
self.embedding_dtype = dtype_by_name[embedding_dtype_name]
self.classifier_dtype = dtype_by_name[classifier_dtype_name]
elif configured_dtype is None:
self.embedding_dtype = (
torch.bfloat16 if self.device.type == "cuda" else torch.float32
)
self.classifier_dtype = torch.float32
else:
if configured_dtype not in dtype_by_name:
raise ValueError(f"Unsupported inference_dtype: {configured_dtype}")
self.embedding_dtype = dtype_by_name[configured_dtype]
self.classifier_dtype = dtype_by_name[configured_dtype]
model_kwargs = {"dtype": self.embedding_dtype}
embedding_model = Path(self.config["base_model"])
if not embedding_model.is_absolute():
embedding_model = self.model_dir / embedding_model
self.embedder = SentenceTransformer(
str(embedding_model),
device=str(self.device),
model_kwargs=model_kwargs,
)
self.classifier = MLPClassifier(
self.config["embedding_dim"],
self.config["hidden_dim"],
self.config["num_labels"],
self.config["dropout"],
).to(self.device, dtype=self.classifier_dtype)
state = load_file(
self.model_dir / "model.safetensors",
device=str(self.device),
)
self.classifier.load_state_dict(state)
self.classifier.eval()
def category_path_to_text(
self,
category_id_path: str,
language: str = "en",
) -> str:
if language not in self.config["category_languages"]:
raise ValueError(f"Unsupported category language: {language}")
return " / ".join(
self.category_definitions[category_id][language]
for category_id in category_id_path.split("/")
)
@torch.inference_mode()
def predict(
self,
query: str,
snippet: str,
top_k: int = 5,
) -> list[dict[str, float | str]]:
text = self.config["text_template"].format(query=query, snippet=snippet)
encode = getattr(self.embedder, "encode_document", self.embedder.encode)
embedding = encode(
[text],
convert_to_tensor=True,
normalize_embeddings=self.config["embedding_normalized"],
show_progress_bar=False,
).to(self.device, dtype=self.classifier_dtype)
probabilities = self.classifier(embedding).sigmoid()[0]
scores, indices = probabilities.topk(min(top_k, len(probabilities)))
return [
{
"category_id_path": category_id_path,
"category_path_en": self.category_path_to_text(category_id_path),
"score": float(score),
}
for score, index in zip(scores.cpu(), indices.cpu().tolist())
for category_id_path in [
self.config["id2category_path"][str(index)]
]
]
def main() -> None:
parser = argparse.ArgumentParser()
parser.add_argument("--query", required=True)
parser.add_argument("--snippet", required=True)
parser.add_argument("--top-k", type=int, default=5)
parser.add_argument("--model-dir", type=Path, default=Path(__file__).parent)
parser.add_argument("--device")
args = parser.parse_args()
model = UserNeedsClassifier(args.model_dir, args.device)
predictions = model.predict(args.query, args.snippet, args.top_k)
print(json.dumps(predictions, ensure_ascii=False, indent=2))
if __name__ == "__main__":
main()
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