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71d248c | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 | #!/usr/bin/env python3
"""Generate FAISS embeddings from document chunks using ONNX BERT with sharding."""
import argparse
import json
import logging
import time
import numpy as np
import onnxruntime
from transformers import AutoTokenizer
logger = logging.getLogger(__name__)
def parse_args(argv=None):
parser = argparse.ArgumentParser(
description="Generate FAISS embeddings from document chunks using ONNX BERT"
)
parser.add_argument(
"--model-path", required=True, help="Path to ONNX quantized model"
)
parser.add_argument(
"--tokenizer-name",
default="nlpaueb/bert-base-uncased-eurlex",
help="Tokenizer name or path",
)
parser.add_argument(
"--chunks", required=True, help="Path to JSON file with chunk data"
)
parser.add_argument(
"--shard", type=int, default=0, help="Shard index for this process"
)
parser.add_argument(
"--total-shards", type=int, default=1, help="Total number of shards"
)
parser.add_argument(
"--output-dir", default="data/embeddings", help="Output directory"
)
parser.add_argument("--batch-size", type=int, default=32, help="Batch size")
parser.add_argument(
"--max-length", type=int, default=512, help="Maximum token length"
)
return parser.parse_args(argv)
def mean_pooling(token_embeds, attention_mask):
mask = attention_mask.astype(np.float32)
mask_expanded = np.expand_dims(mask, axis=-1)
mask_sum = np.sum(mask_expanded, axis=1)
mask_sum = np.maximum(mask_sum, 1e-9)
return np.sum(token_embeds * mask_expanded, axis=1) / mask_sum
def l2_normalize(embeddings):
norms = np.linalg.norm(embeddings, axis=1, keepdims=True)
norms = np.maximum(norms, 1e-9)
return embeddings / norms
def main(argv=None):
args = parse_args(argv)
logging.basicConfig(
level=logging.INFO,
format="%(asctime)s [%(levelname)s] %(message)s",
datefmt="%Y-%m-%d %H:%M:%S",
)
logger.info("Loading ONNX model from %s", args.model_path)
session = onnxruntime.InferenceSession(
args.model_path, providers=["CPUExecutionProvider"]
)
input_names = [inp.name for inp in session.get_inputs()]
logger.info("Model loaded. Input names: %s", input_names)
logger.info("Loading tokenizer: %s", args.tokenizer_name)
tokenizer = AutoTokenizer.from_pretrained(args.tokenizer_name)
logger.info("Loading chunks from %s", args.chunks)
with open(args.chunks) as f:
chunks = json.load(f)
shard = args.shard
total_shards = args.total_shards
my_chunks = chunks[shard::total_shards]
logger.info(
"Shard %d/%d: %d chunks out of %d total",
shard,
total_shards,
len(my_chunks),
len(chunks),
)
embed_dim = 768
all_embeddings = np.empty((0, embed_dim), dtype=np.float32)
celex_ids = []
chunk_indices = []
if len(my_chunks) == 0:
logger.warning("Empty shard — writing empty arrays")
else:
for batch_start in range(0, len(my_chunks), args.batch_size):
batch = my_chunks[batch_start : batch_start + args.batch_size]
texts = [chunk["text"] for chunk in batch]
encoded = tokenizer(
texts,
padding=True,
truncation=True,
max_length=args.max_length,
return_tensors="np",
)
input_feed = {name: encoded[name].astype(np.int64) for name in input_names}
outputs = session.run(None, input_feed)
last_hidden = outputs[0]
attention_mask = encoded.get(
"attention_mask",
np.ones((last_hidden.shape[0], last_hidden.shape[1]), dtype=np.int64),
)
pooled = mean_pooling(last_hidden, attention_mask)
normalized = l2_normalize(pooled)
all_embeddings = np.vstack([all_embeddings, normalized])
for chunk in batch:
celex_ids.append(chunk["celex"])
chunk_indices.append(chunk.get("article", ""))
if (batch_start // args.batch_size) % 50 == 0 and batch_start > 0:
done = min(batch_start + args.batch_size, len(my_chunks))
logger.info(
"Processed %d / %d chunks (shard %d)",
done,
len(my_chunks),
shard,
)
np.save(
f"{args.output_dir}/embeddings_shard_{shard}.npy",
all_embeddings.astype(np.float32),
)
metadata = {
"shard": shard,
"total_shards": total_shards,
"count": len(all_embeddings),
"celex_ids": celex_ids,
"chunk_indices": chunk_indices,
"model_name": args.model_path,
"embed_dim": embed_dim,
}
import os
os.makedirs(args.output_dir, exist_ok=True)
with open(f"{args.output_dir}/metadata_shard_{shard}.json", "w") as f:
json.dump(metadata, f, indent=2)
elapsed = time.time() - getattr(main, "_start_time", time.time())
logger.info(
"Shard %d complete: %d embeddings, dim=%d, shape=%s, time=%.2fs",
shard,
len(all_embeddings),
embed_dim,
all_embeddings.shape,
elapsed,
)
if __name__ == "__main__":
main._start_time = time.time()
main()
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