From 4b6df9c7d15f9c9edb768a0e01cacffaeb672ecb Mon Sep 17 00:00:00 2001 From: Pringled Date: Wed, 10 Sep 2025 11:16:43 +0200 Subject: [PATCH 1/6] Added vocabulary quantization support and tests --- src/model.rs | 101 ++++++++++++++++-- tests/common.rs | 12 +++ .../fixtures/embeddings_vocab_quantized.json | 1 + .../test-model-vocab-quantized/README.md | 100 +++++++++++++++++ .../test-model-vocab-quantized/config.json | 13 +++ .../model.safetensors | Bin 0 -> 387288 bytes .../test-model-vocab-quantized/modules.json | 14 +++ .../test-model-vocab-quantized/tokenizer.json | 1 + tests/test_load.rs | 2 + tests/test_model.rs | 46 ++++++++ 10 files changed, 284 insertions(+), 6 deletions(-) create mode 100644 tests/fixtures/embeddings_vocab_quantized.json create mode 100644 tests/fixtures/test-model-vocab-quantized/README.md create mode 100644 tests/fixtures/test-model-vocab-quantized/config.json create mode 100644 tests/fixtures/test-model-vocab-quantized/model.safetensors create mode 100644 tests/fixtures/test-model-vocab-quantized/modules.json create mode 100644 tests/fixtures/test-model-vocab-quantized/tokenizer.json diff --git a/src/model.rs b/src/model.rs index 455c54c..8a6e9fe 100644 --- a/src/model.rs +++ b/src/model.rs @@ -11,6 +11,8 @@ use tokenizers::Tokenizer; pub struct StaticModel { tokenizer: Tokenizer, embeddings: Array2, + weights: Option>, + token_mapping: Option>, normalize: bool, median_token_length: usize, unk_token_id: Option, @@ -114,9 +116,69 @@ impl StaticModel { }; let embeddings = Array2::from_shape_vec((rows, cols), floats).context("failed to build embeddings array")?; + // Load optional weights + let weights = match safet.tensor("weights") { + Ok(t) => { + if t.shape().len() != 1 { + return Err(anyhow!("weights tensor is not 1-D")); + } + let raw = t.data(); + let v = match t.dtype() { + Dtype::F32 => raw + .chunks_exact(4) + .map(|b| f32::from_le_bytes(b.try_into().unwrap())) + .collect(), + Dtype::F64 => raw + .chunks_exact(8) + .map(|b| f64::from_le_bytes(b.try_into().unwrap()) as f32) + .collect(), + Dtype::F16 => raw + .chunks_exact(2) + .map(|b| f16::from_le_bytes(b.try_into().unwrap()).to_f32()) + .collect(), + other => return Err(anyhow!("unsupported weights dtype: {other:?}")), + }; + Some(v) + } + Err(_) => None, + }; + + // Load optional token_mapping + let token_mapping = match safet.tensor("mapping") { + Ok(t) => { + if t.shape().len() != 1 { + return Err(anyhow!("mapping tensor is not 1-D")); + } + let raw = t.data(); + let v = match t.dtype() { + Dtype::I64 => raw + .chunks_exact(8) + .map(|b| i64::from_le_bytes(b.try_into().unwrap()) as usize) + .collect(), + Dtype::I32 => raw + .chunks_exact(4) + .map(|b| i32::from_le_bytes(b.try_into().unwrap()) as usize) + .collect(), + Dtype::U64 => raw + .chunks_exact(8) + .map(|b| u64::from_le_bytes(b.try_into().unwrap()) as usize) + .collect(), + Dtype::U32 => raw + .chunks_exact(4) + .map(|b| u32::from_le_bytes(b.try_into().unwrap()) as usize) + .collect(), + other => return Err(anyhow!("unsupported mapping dtype: {other:?}")), + }; + Some(v) + } + Err(_) => None, + }; + Ok(Self { tokenizer, embeddings, + weights, + token_mapping, normalize, median_token_length, unk_token_id: Some(unk_token_id), @@ -201,19 +263,46 @@ impl StaticModel { /// Mean-pool a single token-ID list into a vector fn pool_ids(&self, ids: Vec) -> Vec { - let mut sum = vec![0.0; self.embeddings.ncols()]; + let dim = self.embeddings.ncols(); + let mut sum = vec![0.0; dim]; + let mut cnt = 0usize; + for &id in &ids { - let row = self.embeddings.row(id as usize); + let tok = id as usize; + + // Remap: row = token_mapping[id] or id + let row_idx = if let Some(m) = &self.token_mapping { + *m.get(tok).unwrap_or(&0usize) + } else { + tok + }; + + // Scale by per-token weight if present + let scale = if let Some(w) = &self.weights { + *w.get(tok).unwrap_or(&1.0) + } else { + 1.0 + }; + + let row = self.embeddings.row(row_idx); for (i, &v) in row.iter().enumerate() { - sum[i] += v; + sum[i] += v * scale; } + cnt += 1; } - let cnt = ids.len().max(1) as f32; - sum.iter_mut().for_each(|x| *x /= cnt); + + let denom = (cnt.max(1)) as f32; + for x in &mut sum { + *x /= denom; + } + if self.normalize { let norm = sum.iter().map(|&v| v * v).sum::().sqrt().max(1e-12); - sum.iter_mut().for_each(|x| *x /= norm); + for x in &mut sum { + *x /= norm; + } } sum } + } diff --git a/tests/common.rs b/tests/common.rs index 1626cdb..9b88e70 100644 --- a/tests/common.rs +++ b/tests/common.rs @@ -10,3 +10,15 @@ pub fn load_test_model() -> StaticModel { ) .expect("Failed to load test model") } + +/// Load the vocab quantized test model from fixtures +pub fn load_test_model_vocab_quantized() -> StaticModel { + StaticModel::from_pretrained( + "tests/fixtures/test-model-vocab-quantized", + None, // token + None, // normalize + None, // subfolder + ) + .expect("Failed to load test model") +} + diff --git a/tests/fixtures/embeddings_vocab_quantized.json b/tests/fixtures/embeddings_vocab_quantized.json new file mode 100644 index 0000000..9f10f67 --- /dev/null +++ b/tests/fixtures/embeddings_vocab_quantized.json @@ -0,0 +1 @@ +[[-0.363402567303566, -0.17280622671667234, 0.39940570500107386, -0.16813011070789555, -0.15618451933846883, -0.14994650725286524, 0.006071081245258223, -0.16688048919255372, 0.22185219763399205, -0.02887293996697886, -0.17287425174176316, -0.01464316366136059, 0.16364637740934518, 0.15876414737891392, 0.06036445094799073, 0.15604592511625437, -0.0671308839546444, -0.016413190151500695, 0.016156947144592284, -0.04046410877612431, 0.08342219180115457, -0.06072982382315607, -0.15155935894530448, -0.27756653365043565, 0.04183386122272067, -0.02478048814648766, 0.048693007647196467, 0.15564136567656622, 0.03729875053535759, -0.06892603188806953, 0.08513432033392887, 0.0036654278831274112, -0.017677908666363845, 0.062035159999304555, -0.1394435606629564, 0.05264278960819571, -0.10000422994390393, 0.162456462739632, 0.0026303158188036926, -0.010224468015697916, -0.12629957405039433, -0.08506841545219175, -0.06720500777509077, -0.04443293593977252, 0.01816271214883152, 0.11269895366859049, 0.15572718186207016, -0.12838458617894438, 0.020126459971623472, -0.16689367919078762, 0.1038076137507656, 0.005876202291780198, 0.11467950137199819, -0.06360069640738063, 0.12898717602987858, 0.06665970239323335, 0.1263998072107107, 0.054322006298590964, 0.02275680905863399, -0.09242075684142392, 0.0003214892909238989, 0.06269664923701938, 0.007532826486481935, 0.006629162182434642]] \ No newline at end of file diff --git a/tests/fixtures/test-model-vocab-quantized/README.md b/tests/fixtures/test-model-vocab-quantized/README.md new file mode 100644 index 0000000..a3b8ef1 --- /dev/null +++ b/tests/fixtures/test-model-vocab-quantized/README.md @@ -0,0 +1,100 @@ +--- +base_model: BAAI/bge-base-en-v1.5 +language: +- en +library_name: model2vec +license: mit +model_name: test-model-vocab-quantized +tags: +- embeddings +- static-embeddings +- sentence-transformers +--- + +# test-model-vocab-quantized Model Card + +This [Model2Vec](https://github.com/MinishLab/model2vec) model is a distilled version of the BAAI/bge-base-en-v1.5(https://huggingface.co/BAAI/bge-base-en-v1.5) Sentence Transformer. It uses static embeddings, allowing text embeddings to be computed orders of magnitude faster on both GPU and CPU. It is designed for applications where computational resources are limited or where real-time performance is critical. Model2Vec models are the smallest, fastest, and most performant static embedders available. The distilled models are up to 50 times smaller and 500 times faster than traditional Sentence Transformers. + + +## Installation + +Install model2vec using pip: +``` +pip install model2vec +``` + +## Usage + +### Using Model2Vec + +The [Model2Vec library](https://github.com/MinishLab/model2vec) is the fastest and most lightweight way to run Model2Vec models. + +Load this model using the `from_pretrained` method: +```python +from model2vec import StaticModel + +# Load a pretrained Model2Vec model +model = StaticModel.from_pretrained("test-model-vocab-quantized") + +# Compute text embeddings +embeddings = model.encode(["Example sentence"]) +``` + +### Using Sentence Transformers + +You can also use the [Sentence Transformers library](https://github.com/UKPLab/sentence-transformers) to load and use the model: + +```python +from sentence_transformers import SentenceTransformer + +# Load a pretrained Sentence Transformer model +model = SentenceTransformer("test-model-vocab-quantized") + +# Compute text embeddings +embeddings = model.encode(["Example sentence"]) +``` + +### Distilling a Model2Vec model + +You can distill a Model2Vec model from a Sentence Transformer model using the `distill` method. First, install the `distill` extra with `pip install model2vec[distill]`. Then, run the following code: + +```python +from model2vec.distill import distill + +# Distill a Sentence Transformer model, in this case the BAAI/bge-base-en-v1.5 model +m2v_model = distill(model_name="BAAI/bge-base-en-v1.5", pca_dims=256) + +# Save the model +m2v_model.save_pretrained("m2v_model") +``` + +## How it works + +Model2vec creates a small, fast, and powerful model that outperforms other static embedding models by a large margin on all tasks we could find, while being much faster to create than traditional static embedding models such as GloVe. Best of all, you don't need any data to distill a model using Model2Vec. + +It works by passing a vocabulary through a sentence transformer model, then reducing the dimensionality of the resulting embeddings using PCA, and finally weighting the embeddings using [SIF weighting](https://openreview.net/pdf?id=SyK00v5xx). During inference, we simply take the mean of all token embeddings occurring in a sentence. + +## Additional Resources + +- [Model2Vec Repo](https://github.com/MinishLab/model2vec) +- [Model2Vec Base Models](https://huggingface.co/collections/minishlab/model2vec-base-models-66fd9dd9b7c3b3c0f25ca90e) +- [Model2Vec Results](https://github.com/MinishLab/model2vec/tree/main/results) +- [Model2Vec Tutorials](https://github.com/MinishLab/model2vec/tree/main/tutorials) +- [Website](https://minishlab.github.io/) + + +## Library Authors + +Model2Vec was developed by the [Minish Lab](https://github.com/MinishLab) team consisting of [Stephan Tulkens](https://github.com/stephantul) and [Thomas van Dongen](https://github.com/Pringled). + +## Citation + +Please cite the [Model2Vec repository](https://github.com/MinishLab/model2vec) if you use this model in your work. +``` +@article{minishlab2024model2vec, + author = {Tulkens, Stephan and {van Dongen}, Thomas}, + title = {Model2Vec: Fast State-of-the-Art Static Embeddings}, + year = {2024}, + url = {https://github.com/MinishLab/model2vec} +} +``` \ No newline at end of file diff --git a/tests/fixtures/test-model-vocab-quantized/config.json b/tests/fixtures/test-model-vocab-quantized/config.json new file mode 100644 index 0000000..ec185a7 --- /dev/null +++ b/tests/fixtures/test-model-vocab-quantized/config.json @@ -0,0 +1,13 @@ +{ + "model_type": "model2vec", + "architectures": [ + "StaticModel" + ], + "tokenizer_name": "BAAI/bge-base-en-v1.5", + "apply_pca": 64, + "apply_zipf": null, + "sif_coefficient": 0.0001, + "hidden_dim": 64, + "seq_length": 1000000, + "normalize": true +} \ No newline at end of file diff --git a/tests/fixtures/test-model-vocab-quantized/model.safetensors b/tests/fixtures/test-model-vocab-quantized/model.safetensors new file mode 100644 index 0000000000000000000000000000000000000000..97570747d7c34a56638316466cda12a0df922968 GIT binary patch literal 387288 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\ No newline at end of file diff --git a/tests/test_load.rs b/tests/test_load.rs index ec8c358..54d412c 100644 --- a/tests/test_load.rs +++ b/tests/test_load.rs @@ -28,3 +28,5 @@ fn quantized_models_match_float32() { } } } + + diff --git a/tests/test_model.rs b/tests/test_model.rs index ac247ad..8622176 100644 --- a/tests/test_model.rs +++ b/tests/test_model.rs @@ -1,9 +1,12 @@ mod common; use approx::assert_relative_eq; use common::load_test_model; +use common::load_test_model_vocab_quantized; use model2vec_rs::model::StaticModel; use std::fs; + + #[test] fn test_encode_matches_python_model2vec() { // Load the test model @@ -47,6 +50,49 @@ fn test_encode_matches_python_model2vec() { } } + + +#[test] +fn test_encode_matches_python_model2vec_vocab_quantized() { + // Load the test model + let model = load_test_model_vocab_quantized(); + + // Define the short and long text inputs + let long_text = vec!["hello"; 1000].join(" "); + let cases = vec![ + ("tests/fixtures/embeddings_vocab_quantized.json", vec![long_text]), + ]; + + for (fixture_path, inputs) in cases { + // Read and parse the Python‐generated embedding fixture + let fixture = + fs::read_to_string(fixture_path).unwrap_or_else(|_| panic!("Fixture not found: {}", fixture_path)); + let expected: Vec> = serde_json::from_str(&fixture).expect("Failed to parse fixture"); + + // Encode with the Rust model + let output = model.encode(&inputs); + + // Sanity checks + assert_eq!( + output.len(), + expected.len(), + "number of sentences mismatch for {}", + fixture_path + ); + assert_eq!( + output[0].len(), + expected[0].len(), + "vector dimensionality mismatch for {}", + fixture_path + ); + + // Element‐wise comparison + for (o, e) in output[0].iter().zip(&expected[0]) { + assert_relative_eq!(o, e, max_relative = 1e-5); + } + } +} + /// Test that encoding an empty input slice yields an empty output #[test] fn test_encode_empty_input() { From 0f0f52730313c2a285a1766973e4e71c5dcb4305 Mon Sep 17 00:00:00 2001 From: Pringled Date: Thu, 11 Sep 2025 11:48:19 +0200 Subject: [PATCH 2/6] Simplified code --- src/model.rs | 54 ++++++++++++++-------------------------------------- 1 file changed, 14 insertions(+), 40 deletions(-) diff --git a/src/model.rs b/src/model.rs index 8a6e9fe..a90f240 100644 --- a/src/model.rs +++ b/src/model.rs @@ -116,59 +116,33 @@ impl StaticModel { }; let embeddings = Array2::from_shape_vec((rows, cols), floats).context("failed to build embeddings array")?; - // Load optional weights + // Load optional weights for vocabulary quantization let weights = match safet.tensor("weights") { Ok(t) => { - if t.shape().len() != 1 { - return Err(anyhow!("weights tensor is not 1-D")); + if t.shape().len() != 1 || t.dtype() != Dtype::F64 { + return Err(anyhow!("weights must be 1-D F64")); } let raw = t.data(); - let v = match t.dtype() { - Dtype::F32 => raw - .chunks_exact(4) - .map(|b| f32::from_le_bytes(b.try_into().unwrap())) - .collect(), - Dtype::F64 => raw - .chunks_exact(8) - .map(|b| f64::from_le_bytes(b.try_into().unwrap()) as f32) - .collect(), - Dtype::F16 => raw - .chunks_exact(2) - .map(|b| f16::from_le_bytes(b.try_into().unwrap()).to_f32()) - .collect(), - other => return Err(anyhow!("unsupported weights dtype: {other:?}")), - }; + let v: Vec = raw + .chunks_exact(8) + .map(|b| f64::from_le_bytes(b.try_into().unwrap()) as f32) + .collect(); Some(v) } Err(_) => None, }; - // Load optional token_mapping + // Load optional token mapping for vocabulary quantization let token_mapping = match safet.tensor("mapping") { Ok(t) => { - if t.shape().len() != 1 { - return Err(anyhow!("mapping tensor is not 1-D")); + if t.shape().len() != 1 || t.dtype() != Dtype::I32 { + return Err(anyhow!("mapping must be 1-D I32")); } let raw = t.data(); - let v = match t.dtype() { - Dtype::I64 => raw - .chunks_exact(8) - .map(|b| i64::from_le_bytes(b.try_into().unwrap()) as usize) - .collect(), - Dtype::I32 => raw - .chunks_exact(4) - .map(|b| i32::from_le_bytes(b.try_into().unwrap()) as usize) - .collect(), - Dtype::U64 => raw - .chunks_exact(8) - .map(|b| u64::from_le_bytes(b.try_into().unwrap()) as usize) - .collect(), - Dtype::U32 => raw - .chunks_exact(4) - .map(|b| u32::from_le_bytes(b.try_into().unwrap()) as usize) - .collect(), - other => return Err(anyhow!("unsupported mapping dtype: {other:?}")), - }; + let v: Vec = raw + .chunks_exact(4) + .map(|b| i32::from_le_bytes(b.try_into().unwrap()) as usize) + .collect(); Some(v) } Err(_) => None, From d77741611a0b1e9a470d222494425a851d8d051c Mon Sep 17 00:00:00 2001 From: Pringled Date: Thu, 11 Sep 2025 12:08:37 +0200 Subject: [PATCH 3/6] Cleaned up tests --- src/model.rs | 2 + tests/common.rs | 10 +++ tests/test_model.rs | 88 ------------------- tests/{test_load.rs => test_quantization.rs} | 11 +-- tests/test_rust_matches_python.rs | 91 ++++++++++++++++++++ 5 files changed, 105 insertions(+), 97 deletions(-) rename tests/{test_load.rs => test_quantization.rs} (57%) create mode 100644 tests/test_rust_matches_python.rs diff --git a/src/model.rs b/src/model.rs index a90f240..4f41325 100644 --- a/src/model.rs +++ b/src/model.rs @@ -265,11 +265,13 @@ impl StaticModel { cnt += 1; } + // Mean pool the embeddings let denom = (cnt.max(1)) as f32; for x in &mut sum { *x /= denom; } + // Normalize the embeddings if required if self.normalize { let norm = sum.iter().map(|&v| v * v).sum::().sqrt().max(1e-12); for x in &mut sum { diff --git a/tests/common.rs b/tests/common.rs index 9b88e70..51c8bf2 100644 --- a/tests/common.rs +++ b/tests/common.rs @@ -1,3 +1,4 @@ +#![allow(dead_code)] use model2vec_rs::model::StaticModel; /// Load the small float32 test model from fixtures @@ -22,3 +23,12 @@ pub fn load_test_model_vocab_quantized() -> StaticModel { .expect("Failed to load test model") } +pub fn encode_with_model(path: &str) -> Vec { + // Helper function to load the model and encode "hello world" + let model = StaticModel::from_pretrained(path, None, None, None) + .unwrap_or_else(|e| panic!("Failed to load model at {path}: {e}")); + + let out = model.encode(&["hello world".to_string()]); + assert_eq!(out.len(), 1); + out.into_iter().next().unwrap() +} \ No newline at end of file diff --git a/tests/test_model.rs b/tests/test_model.rs index 8622176..afe7717 100644 --- a/tests/test_model.rs +++ b/tests/test_model.rs @@ -1,97 +1,9 @@ mod common; -use approx::assert_relative_eq; use common::load_test_model; -use common::load_test_model_vocab_quantized; use model2vec_rs::model::StaticModel; -use std::fs; -#[test] -fn test_encode_matches_python_model2vec() { - // Load the test model - let model = load_test_model(); - - // Define the short and long text inputs - let long_text = vec!["hello"; 1000].join(" "); - let short_text = "hello world".to_string(); - let cases = vec![ - ("tests/fixtures/embeddings_short.json", vec![short_text]), - ("tests/fixtures/embeddings_long.json", vec![long_text]), - ]; - - for (fixture_path, inputs) in cases { - // Read and parse the Python‐generated embedding fixture - let fixture = - fs::read_to_string(fixture_path).unwrap_or_else(|_| panic!("Fixture not found: {}", fixture_path)); - let expected: Vec> = serde_json::from_str(&fixture).expect("Failed to parse fixture"); - - // Encode with the Rust model - let output = model.encode(&inputs); - - // Sanity checks - assert_eq!( - output.len(), - expected.len(), - "number of sentences mismatch for {}", - fixture_path - ); - assert_eq!( - output[0].len(), - expected[0].len(), - "vector dimensionality mismatch for {}", - fixture_path - ); - - // Element‐wise comparison - for (o, e) in output[0].iter().zip(&expected[0]) { - assert_relative_eq!(o, e, max_relative = 1e-5); - } - } -} - - - -#[test] -fn test_encode_matches_python_model2vec_vocab_quantized() { - // Load the test model - let model = load_test_model_vocab_quantized(); - - // Define the short and long text inputs - let long_text = vec!["hello"; 1000].join(" "); - let cases = vec![ - ("tests/fixtures/embeddings_vocab_quantized.json", vec![long_text]), - ]; - - for (fixture_path, inputs) in cases { - // Read and parse the Python‐generated embedding fixture - let fixture = - fs::read_to_string(fixture_path).unwrap_or_else(|_| panic!("Fixture not found: {}", fixture_path)); - let expected: Vec> = serde_json::from_str(&fixture).expect("Failed to parse fixture"); - - // Encode with the Rust model - let output = model.encode(&inputs); - - // Sanity checks - assert_eq!( - output.len(), - expected.len(), - "number of sentences mismatch for {}", - fixture_path - ); - assert_eq!( - output[0].len(), - expected[0].len(), - "vector dimensionality mismatch for {}", - fixture_path - ); - - // Element‐wise comparison - for (o, e) in output[0].iter().zip(&expected[0]) { - assert_relative_eq!(o, e, max_relative = 1e-5); - } - } -} /// Test that encoding an empty input slice yields an empty output #[test] diff --git a/tests/test_load.rs b/tests/test_quantization.rs similarity index 57% rename from tests/test_load.rs rename to tests/test_quantization.rs index 54d412c..23adec9 100644 --- a/tests/test_load.rs +++ b/tests/test_quantization.rs @@ -1,15 +1,8 @@ +mod common; use approx::assert_relative_eq; -use model2vec_rs::model::StaticModel; +use common::encode_with_model; -fn encode_with_model(path: &str) -> Vec { - // Helper function to load the model and encode "hello world" - let model = StaticModel::from_pretrained(path, None, None, None) - .unwrap_or_else(|e| panic!("Failed to load model at {path}: {e}")); - let out = model.encode(&["hello world".to_string()]); - assert_eq!(out.len(), 1); - out.into_iter().next().unwrap() -} #[test] fn quantized_models_match_float32() { diff --git a/tests/test_rust_matches_python.rs b/tests/test_rust_matches_python.rs new file mode 100644 index 0000000..1ed4730 --- /dev/null +++ b/tests/test_rust_matches_python.rs @@ -0,0 +1,91 @@ +mod common; +use approx::assert_relative_eq; +use common::load_test_model; +use common::load_test_model_vocab_quantized; +use std::fs; + +#[test] +fn test_encode_matches_python_model2vec() { + // Load the test model + let model = load_test_model(); + + // Define the short and long text inputs + let long_text = vec!["hello"; 1000].join(" "); + let short_text = "hello world".to_string(); + let cases = vec![ + ("tests/fixtures/embeddings_short.json", vec![short_text]), + ("tests/fixtures/embeddings_long.json", vec![long_text]), + ]; + + for (fixture_path, inputs) in cases { + // Read and parse the Python‐generated embedding fixture + let fixture = + fs::read_to_string(fixture_path).unwrap_or_else(|_| panic!("Fixture not found: {}", fixture_path)); + let expected: Vec> = serde_json::from_str(&fixture).expect("Failed to parse fixture"); + + // Encode with the Rust model + let output = model.encode(&inputs); + + // Sanity checks + assert_eq!( + output.len(), + expected.len(), + "number of sentences mismatch for {}", + fixture_path + ); + assert_eq!( + output[0].len(), + expected[0].len(), + "vector dimensionality mismatch for {}", + fixture_path + ); + + // Element‐wise comparison + for (o, e) in output[0].iter().zip(&expected[0]) { + assert_relative_eq!(o, e, max_relative = 1e-5); + } + } +} + + + +#[test] +fn test_encode_matches_python_model2vec_vocab_quantized() { + // Load the test model + let model = load_test_model_vocab_quantized(); + + // Define the short and long text inputs + let long_text = vec!["hello"; 1000].join(" "); + let cases = vec![ + ("tests/fixtures/embeddings_vocab_quantized.json", vec![long_text]), + ]; + + for (fixture_path, inputs) in cases { + // Read and parse the Python‐generated embedding fixture + let fixture = + fs::read_to_string(fixture_path).unwrap_or_else(|_| panic!("Fixture not found: {}", fixture_path)); + let expected: Vec> = serde_json::from_str(&fixture).expect("Failed to parse fixture"); + + // Encode with the Rust model + let output = model.encode(&inputs); + + // Sanity checks + assert_eq!( + output.len(), + expected.len(), + "number of sentences mismatch for {}", + fixture_path + ); + assert_eq!( + output[0].len(), + expected[0].len(), + "vector dimensionality mismatch for {}", + fixture_path + ); + + // Element‐wise comparison + for (o, e) in output[0].iter().zip(&expected[0]) { + assert_relative_eq!(o, e, max_relative = 1e-5); + } + } +} From 99da964579883bafca8f4292768bd05a18f3427f Mon Sep 17 00:00:00 2001 From: Pringled Date: Thu, 11 Sep 2025 12:09:52 +0200 Subject: [PATCH 4/6] Formatting --- src/model.rs | 1 - tests/common.rs | 2 +- tests/test_model.rs | 3 --- tests/test_quantization.rs | 4 ---- tests/test_rust_matches_python.rs | 6 +----- 5 files changed, 2 insertions(+), 14 deletions(-) diff --git a/src/model.rs b/src/model.rs index 4f41325..57dd0e7 100644 --- a/src/model.rs +++ b/src/model.rs @@ -280,5 +280,4 @@ impl StaticModel { } sum } - } diff --git a/tests/common.rs b/tests/common.rs index 51c8bf2..38c1983 100644 --- a/tests/common.rs +++ b/tests/common.rs @@ -31,4 +31,4 @@ pub fn encode_with_model(path: &str) -> Vec { let out = model.encode(&["hello world".to_string()]); assert_eq!(out.len(), 1); out.into_iter().next().unwrap() -} \ No newline at end of file +} diff --git a/tests/test_model.rs b/tests/test_model.rs index afe7717..f09b8c2 100644 --- a/tests/test_model.rs +++ b/tests/test_model.rs @@ -2,9 +2,6 @@ mod common; use common::load_test_model; use model2vec_rs::model::StaticModel; - - - /// Test that encoding an empty input slice yields an empty output #[test] fn test_encode_empty_input() { diff --git a/tests/test_quantization.rs b/tests/test_quantization.rs index 23adec9..86e491f 100644 --- a/tests/test_quantization.rs +++ b/tests/test_quantization.rs @@ -2,8 +2,6 @@ mod common; use approx::assert_relative_eq; use common::encode_with_model; - - #[test] fn quantized_models_match_float32() { // Compare quantized models against the float32 model @@ -21,5 +19,3 @@ fn quantized_models_match_float32() { } } } - - diff --git a/tests/test_rust_matches_python.rs b/tests/test_rust_matches_python.rs index 1ed4730..fd175d6 100644 --- a/tests/test_rust_matches_python.rs +++ b/tests/test_rust_matches_python.rs @@ -47,8 +47,6 @@ fn test_encode_matches_python_model2vec() { } } - - #[test] fn test_encode_matches_python_model2vec_vocab_quantized() { // Load the test model @@ -56,9 +54,7 @@ fn test_encode_matches_python_model2vec_vocab_quantized() { // Define the short and long text inputs let long_text = vec!["hello"; 1000].join(" "); - let cases = vec![ - ("tests/fixtures/embeddings_vocab_quantized.json", vec![long_text]), - ]; + let cases = vec![("tests/fixtures/embeddings_vocab_quantized.json", vec![long_text])]; for (fixture_path, inputs) in cases { // Read and parse the Python‐generated embedding fixture From dee12abbf79d086b0d4229b153e320354566caa0 Mon Sep 17 00:00:00 2001 From: Pringled Date: Mon, 29 Sep 2025 16:55:30 +0200 Subject: [PATCH 5/6] Made weight dtype checks more flexible --- src/model.rs | 27 ++++++++++++++------------- 1 file changed, 14 insertions(+), 13 deletions(-) diff --git a/src/model.rs b/src/model.rs index 57dd0e7..8d8f7b1 100644 --- a/src/model.rs +++ b/src/model.rs @@ -119,14 +119,19 @@ impl StaticModel { // Load optional weights for vocabulary quantization let weights = match safet.tensor("weights") { Ok(t) => { - if t.shape().len() != 1 || t.dtype() != Dtype::F64 { - return Err(anyhow!("weights must be 1-D F64")); - } let raw = t.data(); - let v: Vec = raw - .chunks_exact(8) - .map(|b| f64::from_le_bytes(b.try_into().unwrap()) as f32) - .collect(); + let v: Vec = match t.dtype() { + Dtype::F64 => raw.chunks_exact(8) + .map(|b| f64::from_le_bytes(b.try_into().unwrap()) as f32) + .collect(), + Dtype::F32 => raw.chunks_exact(4) + .map(|b| f32::from_le_bytes(b.try_into().unwrap())) + .collect(), + Dtype::F16 => raw.chunks_exact(2) + .map(|b| half::f16::from_le_bytes(b.try_into().unwrap()).to_f32()) + .collect(), + other => return Err(anyhow!("unsupported weights dtype: {:?}", other)), + }; Some(v) } Err(_) => None, @@ -135,12 +140,8 @@ impl StaticModel { // Load optional token mapping for vocabulary quantization let token_mapping = match safet.tensor("mapping") { Ok(t) => { - if t.shape().len() != 1 || t.dtype() != Dtype::I32 { - return Err(anyhow!("mapping must be 1-D I32")); - } let raw = t.data(); - let v: Vec = raw - .chunks_exact(4) + let v: Vec = raw.chunks_exact(4) .map(|b| i32::from_le_bytes(b.try_into().unwrap()) as usize) .collect(); Some(v) @@ -246,7 +247,7 @@ impl StaticModel { // Remap: row = token_mapping[id] or id let row_idx = if let Some(m) = &self.token_mapping { - *m.get(tok).unwrap_or(&0usize) + *m.get(tok).unwrap_or(&tok) } else { tok }; From 9650d1e7dfbded241a69c82cb61f90db9dbea97c Mon Sep 17 00:00:00 2001 From: Pringled Date: Mon, 29 Sep 2025 16:57:22 +0200 Subject: [PATCH 6/6] Formatting --- src/model.rs | 12 ++++++++---- 1 file changed, 8 insertions(+), 4 deletions(-) diff --git a/src/model.rs b/src/model.rs index 8d8f7b1..4641a11 100644 --- a/src/model.rs +++ b/src/model.rs @@ -121,13 +121,16 @@ impl StaticModel { Ok(t) => { let raw = t.data(); let v: Vec = match t.dtype() { - Dtype::F64 => raw.chunks_exact(8) + Dtype::F64 => raw + .chunks_exact(8) .map(|b| f64::from_le_bytes(b.try_into().unwrap()) as f32) .collect(), - Dtype::F32 => raw.chunks_exact(4) + Dtype::F32 => raw + .chunks_exact(4) .map(|b| f32::from_le_bytes(b.try_into().unwrap())) .collect(), - Dtype::F16 => raw.chunks_exact(2) + Dtype::F16 => raw + .chunks_exact(2) .map(|b| half::f16::from_le_bytes(b.try_into().unwrap()).to_f32()) .collect(), other => return Err(anyhow!("unsupported weights dtype: {:?}", other)), @@ -141,7 +144,8 @@ impl StaticModel { let token_mapping = match safet.tensor("mapping") { Ok(t) => { let raw = t.data(); - let v: Vec = raw.chunks_exact(4) + let v: Vec = raw + .chunks_exact(4) .map(|b| i32::from_le_bytes(b.try_into().unwrap()) as usize) .collect(); Some(v)