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Add MS MARCO v1 passage regressions for BGE w/ ONNX (castorini#2350)
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docs/regressions/regressions-dl19-passage-bge-base-en-v1.5-hnsw-int8-onnx.md
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| # Anserini Regressions: TREC 2019 Deep Learning Track (Passage) | ||
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| **Model**: [BGE-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) with HNSW indexes (using ONNX for on-the-fly query encoding) | ||
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| This page describes regression experiments, integrated into Anserini's regression testing framework, using the [BGE-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) model on the [TREC 2019 Deep Learning Track passage ranking task](https://trec.nist.gov/data/deep2019.html), as described in the following paper: | ||
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| > Shitao Xiao, Zheng Liu, Peitian Zhang, and Niklas Muennighoff. [C-Pack: Packaged Resources To Advance General Chinese Embedding.](https://arxiv.org/abs/2309.07597) _arXiv:2309.07597_, 2023. | ||
| In these experiments, we are performing query inference "on-the-fly" with ONNX. | ||
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| Note that the NIST relevance judgments provide far more relevant passages per topic, unlike the "sparse" judgments provided by Microsoft (these are sometimes called "dense" judgments to emphasize this contrast). | ||
| For additional instructions on working with MS MARCO passage collection, refer to [this page](experiments-msmarco-passage.md). | ||
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| The exact configurations for these regressions are stored in [this YAML file](../../src/main/resources/regression/dl19-passage-bge-base-en-v1.5-hnsw-int8-onnx.yaml). | ||
| Note that this page is automatically generated from [this template](../../src/main/resources/docgen/templates/dl19-passage-bge-base-en-v1.5-hnsw-int8-onnx.template) as part of Anserini's regression pipeline, so do not modify this page directly; modify the template instead and then run `bin/build.sh` to rebuild the documentation. | ||
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| From one of our Waterloo servers (e.g., `orca`), the following command will perform the complete regression, end to end: | ||
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| ```bash | ||
| python src/main/python/run_regression.py --index --verify --search --regression dl19-passage-bge-base-en-v1.5-hnsw-int8-onnx | ||
| ``` | ||
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| We make available a version of the MS MARCO Passage Corpus that has already been encoded with cosDPR-distil. | ||
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| From any machine, the following command will download the corpus and perform the complete regression, end to end: | ||
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| ```bash | ||
| python src/main/python/run_regression.py --download --index --verify --search --regression dl19-passage-bge-base-en-v1.5-hnsw-int8-onnx | ||
| ``` | ||
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| The `run_regression.py` script automates the following steps, but if you want to perform each step manually, simply copy/paste from the commands below and you'll obtain the same regression results. | ||
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| ## Corpus Download | ||
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| Download the corpus and unpack into `collections/`: | ||
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| ```bash | ||
| wget https://rgw.cs.uwaterloo.ca/pyserini/data/msmarco-passage-bge-base-en-v1.5.tar -P collections/ | ||
| tar xvf collections/msmarco-passage-bge-base-en-v1.5.tar -C collections/ | ||
| ``` | ||
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| To confirm, `msmarco-passage-bge-base-en-v1.5.tar` is 59 GB and has MD5 checksum `353d2c9e72e858897ad479cca4ea0db1`. | ||
| With the corpus downloaded, the following command will perform the remaining steps below: | ||
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| ```bash | ||
| python src/main/python/run_regression.py --index --verify --search --regression dl19-passage-bge-base-en-v1.5-hnsw-int8-onnx \ | ||
| --corpus-path collections/msmarco-passage-bge-base-en-v1.5 | ||
| ``` | ||
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| ## Indexing | ||
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| Sample indexing command, building HNSW indexes: | ||
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| ```bash | ||
| target/appassembler/bin/IndexHnswDenseVectors \ | ||
| -collection JsonDenseVectorCollection \ | ||
| -input /path/to/msmarco-passage-bge-base-en-v1.5 \ | ||
| -generator HnswDenseVectorDocumentGenerator \ | ||
| -index indexes/lucene-hnsw.msmarco-passage-bge-base-en-v1.5-int8/ \ | ||
| -threads 16 -M 16 -efC 100 -memoryBuffer 65536 -noMerge -quantize.int8 \ | ||
| >& logs/log.msmarco-passage-bge-base-en-v1.5 & | ||
| ``` | ||
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| The path `/path/to/msmarco-passage-bge-base-en-v1.5/` should point to the corpus downloaded above. | ||
| Upon completion, we should have an index with 8,841,823 documents. | ||
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| Note that here we are explicitly using Lucene's `NoMergePolicy` merge policy, which suppresses any merging of index segments. | ||
| This is because merging index segments is a costly operation and not worthwhile given our query set. | ||
| Furthermore, we are using Lucene's [Automatic Byte Quantization](https://www.elastic.co/search-labs/blog/articles/scalar-quantization-in-lucene) feature, which increase the on-disk footprint of the indexes since we're storing both the int8 quantized vectors and the float32 vectors, but only the int8 quantized vectors need to be loaded into memory. | ||
| See [issue #2292](https://github.com/castorini/anserini/issues/2292) for some experiments reporting the performance impact. | ||
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| ## Retrieval | ||
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| Topics and qrels are stored [here](https://github.com/castorini/anserini-tools/tree/master/topics-and-qrels), which is linked to the Anserini repo as a submodule. | ||
| The regression experiments here evaluate on the 43 topics for which NIST has provided judgments as part of the TREC 2019 Deep Learning Track. | ||
| The original data can be found [here](https://trec.nist.gov/data/deep2019.html). | ||
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| After indexing has completed, you should be able to perform retrieval as follows: | ||
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| ```bash | ||
| target/appassembler/bin/SearchHnswDenseVectors \ | ||
| -index indexes/lucene-hnsw.msmarco-passage-bge-base-en-v1.5-int8/ \ | ||
| -topics tools/topics-and-qrels/topics.dl19-passage.txt \ | ||
| -topicReader TsvInt \ | ||
| -output runs/run.msmarco-passage-bge-base-en-v1.5.bge-hnsw.topics.dl19-passage.txt \ | ||
| -generator VectorQueryGenerator -topicField title -threads 16 -hits 1000 -efSearch 1000 -encoder BgeBaseEn15 & | ||
| ``` | ||
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| Evaluation can be performed using `trec_eval`: | ||
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| ```bash | ||
| target/appassembler/bin/trec_eval -m map -c -l 2 tools/topics-and-qrels/qrels.dl19-passage.txt runs/run.msmarco-passage-bge-base-en-v1.5.bge-hnsw.topics.dl19-passage.txt | ||
| target/appassembler/bin/trec_eval -m ndcg_cut.10 -c tools/topics-and-qrels/qrels.dl19-passage.txt runs/run.msmarco-passage-bge-base-en-v1.5.bge-hnsw.topics.dl19-passage.txt | ||
| target/appassembler/bin/trec_eval -m recall.100 -c -l 2 tools/topics-and-qrels/qrels.dl19-passage.txt runs/run.msmarco-passage-bge-base-en-v1.5.bge-hnsw.topics.dl19-passage.txt | ||
| target/appassembler/bin/trec_eval -m recall.1000 -c -l 2 tools/topics-and-qrels/qrels.dl19-passage.txt runs/run.msmarco-passage-bge-base-en-v1.5.bge-hnsw.topics.dl19-passage.txt | ||
| ``` | ||
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| ## Effectiveness | ||
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| With the above commands, you should be able to reproduce the following results: | ||
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| | **AP@1000** | **BGE-base-en-v1.5**| | ||
| |:-------------------------------------------------------------------------------------------------------------|-----------| | ||
| | [DL19 (Passage)](https://trec.nist.gov/data/deep2020.html) | 0.444 | | ||
| | **nDCG@10** | **BGE-base-en-v1.5**| | ||
| | [DL19 (Passage)](https://trec.nist.gov/data/deep2020.html) | 0.702 | | ||
| | **R@100** | **BGE-base-en-v1.5**| | ||
| | [DL19 (Passage)](https://trec.nist.gov/data/deep2020.html) | 0.609 | | ||
| | **R@1000** | **BGE-base-en-v1.5**| | ||
| | [DL19 (Passage)](https://trec.nist.gov/data/deep2020.html) | 0.836 | | ||
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| Note that due to the non-deterministic nature of HNSW indexing, results may differ slightly between each experimental run. | ||
| Nevertheless, scores are generally within 0.005 of the reference values recorded in [our YAML configuration file](../../src/main/resources/regression/dl19-passage-bge-base-en-v1.5-hnsw-int8-onnx.yaml). | ||
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| Also note that retrieval metrics are computed to depth 1000 hits per query (as opposed to 100 hits per query for document ranking). | ||
| Also, for computing nDCG, remember that we keep qrels of _all_ relevance grades, whereas for other metrics (e.g., AP), relevance grade 1 is considered not relevant (i.e., use the `-l 2` option in `trec_eval`). | ||
| The experimental results reported here are directly comparable to the results reported in the [track overview paper](https://arxiv.org/abs/2003.07820). | ||
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| ## Reproduction Log[*](reproducibility.md) | ||
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| To add to this reproduction log, modify [this template](../../src/main/resources/docgen/templates/dl19-passage-bge-base-en-v1.5-hnsw-int8-onnx.template) and run `bin/build.sh` to rebuild the documentation. |
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