76cc798e3e
* Add timing instrumentation and multi-dataset support for multi-vector retrieval - Add timing measurements for search operations (load and core time) - Increase embedding batch size from 1 to 32 for better performance - Add explicit memory cleanup with del all_embeddings - Support loading and merging multiple datasets with different splits - Add CLI arguments for search method selection (ann/exact/exact-all) - Auto-detect image field names across different dataset structures - Print candidate doc counts for performance monitoring 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com> * update vidore * reproduce docvqa results * reproduce docvqa results and add debug file * fix: format colqwen_forward.py to pass pre-commit checks --------- Co-authored-by: Claude <noreply@anthropic.com>
400 lines
13 KiB
Python
400 lines
13 KiB
Python
#!/usr/bin/env python3
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"""
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Modular script to reproduce NDCG results for ViDoRe v1 benchmark.
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This script uses the interface from leann_multi_vector.py to:
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1. Download ViDoRe v1 datasets
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2. Build indexes (LEANN or Fast-Plaid)
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3. Perform retrieval
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4. Evaluate using NDCG metrics
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Usage:
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# Evaluate all ViDoRe v1 tasks
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python vidore_v1_benchmark.py --model colqwen2 --tasks all
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# Evaluate specific task
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python vidore_v1_benchmark.py --model colqwen2 --task VidoreArxivQARetrieval
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# Use Fast-Plaid index
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python vidore_v1_benchmark.py --model colqwen2 --use-fast-plaid --fast-plaid-index-path ./indexes/vidore_fastplaid
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# Rebuild index
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python vidore_v1_benchmark.py --model colqwen2 --rebuild-index
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"""
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import argparse
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import json
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import os
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from typing import Optional
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from datasets import load_dataset
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from leann_multi_vector import (
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ViDoReBenchmarkEvaluator,
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_ensure_repo_paths_importable,
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)
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_ensure_repo_paths_importable(__file__)
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# ViDoRe v1 task configurations
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# Prompts match MTEB task metadata prompts
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VIDORE_V1_TASKS = {
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"VidoreArxivQARetrieval": {
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"dataset_path": "vidore/arxivqa_test_subsampled_beir",
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"revision": "7d94d570960eac2408d3baa7a33f9de4822ae3e4",
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"prompt": {"query": "Find a screenshot that relevant to the user's question."},
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},
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"VidoreDocVQARetrieval": {
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"dataset_path": "vidore/docvqa_test_subsampled_beir",
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"revision": "162ba2fc1a8437eda8b6c37b240bc1c0f0deb092",
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"prompt": {"query": "Find a screenshot that relevant to the user's question."},
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},
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"VidoreInfoVQARetrieval": {
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"dataset_path": "vidore/infovqa_test_subsampled_beir",
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"revision": "b802cc5fd6c605df2d673a963667d74881d2c9a4",
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"prompt": {"query": "Find a screenshot that relevant to the user's question."},
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},
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"VidoreTabfquadRetrieval": {
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"dataset_path": "vidore/tabfquad_test_subsampled_beir",
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"revision": "61a2224bcd29b7b261a4892ff4c8bea353527a31",
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"prompt": {"query": "Find a screenshot that relevant to the user's question."},
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},
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"VidoreTatdqaRetrieval": {
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"dataset_path": "vidore/tatdqa_test_beir",
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"revision": "5feb5630fdff4d8d189ffedb2dba56862fdd45c0",
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"prompt": {"query": "Find a screenshot that relevant to the user's question."},
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},
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"VidoreShiftProjectRetrieval": {
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"dataset_path": "vidore/shiftproject_test_beir",
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"revision": "84a382e05c4473fed9cff2bbae95fe2379416117",
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"prompt": {"query": "Find a screenshot that relevant to the user's question."},
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},
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"VidoreSyntheticDocQAAIRetrieval": {
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"dataset_path": "vidore/syntheticDocQA_artificial_intelligence_test_beir",
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"revision": "2d9ebea5a1c6e9ef4a3b902a612f605dca11261c",
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"prompt": {"query": "Find a screenshot that relevant to the user's question."},
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},
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"VidoreSyntheticDocQAEnergyRetrieval": {
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"dataset_path": "vidore/syntheticDocQA_energy_test_beir",
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"revision": "9935aadbad5c8deec30910489db1b2c7133ae7a7",
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"prompt": {"query": "Find a screenshot that relevant to the user's question."},
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},
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"VidoreSyntheticDocQAGovernmentReportsRetrieval": {
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"dataset_path": "vidore/syntheticDocQA_government_reports_test_beir",
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"revision": "b4909afa930f81282fd20601e860668073ad02aa",
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"prompt": {"query": "Find a screenshot that relevant to the user's question."},
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},
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"VidoreSyntheticDocQAHealthcareIndustryRetrieval": {
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"dataset_path": "vidore/syntheticDocQA_healthcare_industry_test_beir",
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"revision": "f9e25d5b6e13e1ad9f5c3cce202565031b3ab164",
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"prompt": {"query": "Find a screenshot that relevant to the user's question."},
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},
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}
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def load_vidore_v1_data(
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dataset_path: str,
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revision: Optional[str] = None,
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split: str = "test",
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):
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"""
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Load ViDoRe v1 dataset.
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Returns:
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corpus: dict mapping corpus_id to PIL Image
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queries: dict mapping query_id to query text
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qrels: dict mapping query_id to dict of {corpus_id: relevance_score}
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"""
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print(f"Loading dataset: {dataset_path} (split={split})")
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# Load queries
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query_ds = load_dataset(dataset_path, "queries", split=split, revision=revision)
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queries = {}
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for row in query_ds:
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query_id = f"query-{split}-{row['query-id']}"
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queries[query_id] = row["query"]
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# Load corpus (images)
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corpus_ds = load_dataset(dataset_path, "corpus", split=split, revision=revision)
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corpus = {}
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for row in corpus_ds:
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corpus_id = f"corpus-{split}-{row['corpus-id']}"
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# Extract image from the dataset row
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if "image" in row:
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corpus[corpus_id] = row["image"]
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elif "page_image" in row:
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corpus[corpus_id] = row["page_image"]
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else:
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raise ValueError(
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f"No image field found in corpus. Available fields: {list(row.keys())}"
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)
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# Load qrels (relevance judgments)
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qrels_ds = load_dataset(dataset_path, "qrels", split=split, revision=revision)
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qrels = {}
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for row in qrels_ds:
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query_id = f"query-{split}-{row['query-id']}"
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corpus_id = f"corpus-{split}-{row['corpus-id']}"
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if query_id not in qrels:
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qrels[query_id] = {}
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qrels[query_id][corpus_id] = int(row["score"])
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print(
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f"Loaded {len(queries)} queries, {len(corpus)} corpus items, {len(qrels)} query-relevance mappings"
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)
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# Filter qrels to only include queries that exist
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qrels = {qid: rel_docs for qid, rel_docs in qrels.items() if qid in queries}
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# Filter out queries without any relevant documents (matching MTEB behavior)
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# This is important for correct NDCG calculation
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qrels_filtered = {qid: rel_docs for qid, rel_docs in qrels.items() if len(rel_docs) > 0}
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queries_filtered = {
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qid: query_text for qid, query_text in queries.items() if qid in qrels_filtered
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}
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print(
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f"After filtering queries without positives: {len(queries_filtered)} queries, {len(qrels_filtered)} query-relevance mappings"
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)
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return corpus, queries_filtered, qrels_filtered
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def evaluate_task(
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task_name: str,
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model_name: str,
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index_path: str,
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use_fast_plaid: bool = False,
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fast_plaid_index_path: Optional[str] = None,
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rebuild_index: bool = False,
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top_k: int = 1000,
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first_stage_k: int = 500,
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k_values: Optional[list[int]] = None,
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output_dir: Optional[str] = None,
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):
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"""
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Evaluate a single ViDoRe v1 task.
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"""
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print(f"\n{'=' * 80}")
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print(f"Evaluating task: {task_name}")
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print(f"{'=' * 80}")
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# Get task config
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if task_name not in VIDORE_V1_TASKS:
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raise ValueError(f"Unknown task: {task_name}. Available: {list(VIDORE_V1_TASKS.keys())}")
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task_config = VIDORE_V1_TASKS[task_name]
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dataset_path = task_config["dataset_path"]
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revision = task_config["revision"]
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# Load data
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corpus, queries, qrels = load_vidore_v1_data(
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dataset_path=dataset_path,
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revision=revision,
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split="test",
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)
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# Initialize k_values if not provided
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if k_values is None:
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k_values = [1, 3, 5, 10, 20, 100, 1000]
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# Check if we have any queries
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if len(queries) == 0:
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print(f"\nWarning: No queries found for task {task_name}. Skipping evaluation.")
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# Return zero scores
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scores = {}
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for k in k_values:
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scores[f"ndcg_at_{k}"] = 0.0
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scores[f"map_at_{k}"] = 0.0
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scores[f"recall_at_{k}"] = 0.0
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scores[f"precision_at_{k}"] = 0.0
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scores[f"mrr_at_{k}"] = 0.0
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return scores
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# Initialize evaluator
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evaluator = ViDoReBenchmarkEvaluator(
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model_name=model_name,
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use_fast_plaid=use_fast_plaid,
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top_k=top_k,
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first_stage_k=first_stage_k,
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k_values=k_values,
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)
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# Build or load index
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index_path_full = index_path if not use_fast_plaid else fast_plaid_index_path
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if index_path_full is None:
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index_path_full = f"./indexes/{task_name}_{model_name}"
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if use_fast_plaid:
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index_path_full = f"./indexes/{task_name}_{model_name}_fastplaid"
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index_or_retriever, corpus_ids_ordered = evaluator.build_index_from_corpus(
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corpus=corpus,
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index_path=index_path_full,
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rebuild=rebuild_index,
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)
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# Search queries
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task_prompt = task_config.get("prompt")
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results = evaluator.search_queries(
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queries=queries,
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corpus_ids=corpus_ids_ordered,
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index_or_retriever=index_or_retriever,
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fast_plaid_index_path=fast_plaid_index_path,
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task_prompt=task_prompt,
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)
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# Evaluate
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scores = evaluator.evaluate_results(results, qrels, k_values=k_values)
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# Print results
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print(f"\n{'=' * 80}")
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print(f"Results for {task_name}:")
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print(f"{'=' * 80}")
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for metric, value in scores.items():
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if isinstance(value, (int, float)):
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print(f" {metric}: {value:.5f}")
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# Save results
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if output_dir:
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os.makedirs(output_dir, exist_ok=True)
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results_file = os.path.join(output_dir, f"{task_name}_results.json")
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scores_file = os.path.join(output_dir, f"{task_name}_scores.json")
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with open(results_file, "w") as f:
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json.dump(results, f, indent=2)
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print(f"\nSaved results to: {results_file}")
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with open(scores_file, "w") as f:
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json.dump(scores, f, indent=2)
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print(f"Saved scores to: {scores_file}")
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return scores
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def main():
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parser = argparse.ArgumentParser(
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description="Evaluate ViDoRe v1 benchmark using LEANN/Fast-Plaid indexing"
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)
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parser.add_argument(
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"--model",
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type=str,
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default="colqwen2",
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choices=["colqwen2", "colpali"],
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help="Model to use",
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)
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parser.add_argument(
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"--task",
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type=str,
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default=None,
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help="Specific task to evaluate (or 'all' for all tasks)",
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)
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parser.add_argument(
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"--tasks",
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type=str,
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default="all",
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help="Tasks to evaluate: 'all' or comma-separated list",
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)
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parser.add_argument(
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"--index-path",
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type=str,
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default=None,
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help="Path to LEANN index (auto-generated if not provided)",
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)
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parser.add_argument(
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"--use-fast-plaid",
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action="store_true",
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help="Use Fast-Plaid instead of LEANN",
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)
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parser.add_argument(
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"--fast-plaid-index-path",
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type=str,
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default=None,
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help="Path to Fast-Plaid index (auto-generated if not provided)",
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)
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parser.add_argument(
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"--rebuild-index",
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action="store_true",
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help="Rebuild index even if it exists",
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)
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parser.add_argument(
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"--top-k",
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type=int,
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default=1000,
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help="Top-k results to retrieve (MTEB default is max(k_values)=1000)",
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)
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parser.add_argument(
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"--first-stage-k",
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type=int,
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default=500,
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help="First stage k for LEANN search",
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)
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parser.add_argument(
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"--k-values",
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type=str,
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default="1,3,5,10,20,100,1000",
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help="Comma-separated k values for evaluation (e.g., '1,3,5,10,100')",
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)
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parser.add_argument(
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"--output-dir",
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type=str,
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default="./vidore_v1_results",
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help="Output directory for results",
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)
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args = parser.parse_args()
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# Parse k_values
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k_values = [int(k.strip()) for k in args.k_values.split(",")]
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# Determine tasks to evaluate
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if args.task:
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tasks_to_eval = [args.task]
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elif args.tasks.lower() == "all":
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tasks_to_eval = list(VIDORE_V1_TASKS.keys())
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else:
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tasks_to_eval = [t.strip() for t in args.tasks.split(",")]
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print(f"Tasks to evaluate: {tasks_to_eval}")
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# Evaluate each task
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all_scores = {}
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for task_name in tasks_to_eval:
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try:
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scores = evaluate_task(
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task_name=task_name,
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model_name=args.model,
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index_path=args.index_path,
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use_fast_plaid=args.use_fast_plaid,
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fast_plaid_index_path=args.fast_plaid_index_path,
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rebuild_index=args.rebuild_index,
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top_k=args.top_k,
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first_stage_k=args.first_stage_k,
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k_values=k_values,
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output_dir=args.output_dir,
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)
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all_scores[task_name] = scores
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except Exception as e:
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print(f"\nError evaluating {task_name}: {e}")
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import traceback
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traceback.print_exc()
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continue
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# Print summary
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if all_scores:
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print(f"\n{'=' * 80}")
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print("SUMMARY")
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print(f"{'=' * 80}")
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for task_name, scores in all_scores.items():
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print(f"\n{task_name}:")
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# Print main metrics
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for metric in ["ndcg_at_5", "ndcg_at_10", "ndcg_at_100", "map_at_10", "recall_at_10"]:
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if metric in scores:
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print(f" {metric}: {scores[metric]:.5f}")
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if __name__ == "__main__":
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main()
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