Update tch to 0.15 (#443)

* Update tch to 0.15

It allows to run in system with libtorch v2.2.0, upgrading from v2.1.0

* remove torch-sys dependency from benches

* Updated readmes

---------

Co-authored-by: Guillaume Becquin <guillaume.becquin@gmail.com>

CHANGELOG.md

@@ -3,6 +3,9 @@ All notable changes to this project will be documented in this file. The format
 
 ## [Unreleased]
 
+## Changed
+- (BREAKING) Upgraded to `torch` 2.2 (via `tch` 0.15.0).
+
 ## [0.22.0] - 2024-01-20
 ## Added
 - Addition of `new_with_tokenizer` constructor for `SentenceEmbeddingsModel` allowing passing custom tokenizers for sentence embeddings pipelines.
@@ -10,7 +13,7 @@ All notable changes to this project will be documented in this file. The format
 - (BREAKING) Most model configuration can now take an optional `kind` parameter to specify the model weight precision. If not provided, will default to full precision on CPU, or the serialized weights precision otherwise.
 
 ## Fixed
-- (BREAKING) Fixed the keyword extraction pipeline for n-gram sizes > 2. Add new configuration option `tokenizer_forbidden_ngram_chars` to specify characters that should be excluded from n-grams (allows filtering m-grams spanning multiple sentences).
+- (BREAKING) Fixed the keyword extraction pipeline for n-gram sizes > 2. Add new configuration option `tokenizer_forbidden_ngram_chars` to specify characters that should be excluded from n-grams (allows filtering n-grams spanning multiple sentences).
 - Improved MPS device compatibility setting the `sparse_grad` flag to false for `gather` operations
 - Updated ONNX runtime backend version to 1.15.x
 - Issue with incorrect results for QA models with a tokenizer not using segment ids
@@ -449,4 +452,4 @@ All notable changes to this project will be documented in this file. The format
 
 - Tensor conversion tools from Pytorch to Libtorch format
 - DistilBERT model architecture
-- Ready-to-use `SentimentClassifier` using a DistilBERT model fine-tuned on SST2
+- Ready-to-use `SentimentClassifier` using a DistilBERT model fine-tuned on SST2

Cargo.toml

@@ -76,7 +76,7 @@ features = ["doc-only"]
 
 [dependencies]
 rust_tokenizers = "8.1.1"
-tch = "0.14.0"
+tch = "0.15.0"
 serde_json = "1"
 serde = { version = "1", features = ["derive"] }
 ordered-float = "3"
@@ -97,7 +97,6 @@ anyhow = "1"
 csv = "1"
 criterion = "0.5"
 tokio = { version = "1.35", features = ["sync", "rt-multi-thread", "macros"] }
-torch-sys =  "0.14.0"
 tempfile = "3"
 itertools = "0.12"
 tracing-subscriber = { version = "0.3", default-features = false, features = [ "env-filter", "fmt" ] }

README.md

@@ -80,8 +80,8 @@ This cache location defaults to `~/.cache/.rustbert`, but can be changed by sett
 
 ### Manual installation (recommended)
 
-1. Download `libtorch` from https://pytorch.org/get-started/locally/. This package requires `v2.1`: if this version is no longer available on the "get started" page,
-the file should be accessible by modifying the target link, for example `https://download.pytorch.org/libtorch/cu118/libtorch-cxx11-abi-shared-with-deps-2.1.1%2Bcu118.zip` for a Linux version with CUDA11. **NOTE:** When using `rust-bert` as dependency from [crates.io](https://crates.io), please check the required `LIBTORCH` on the published package [readme](https://crates.io/crates/rust-bert) as it may differ from the version documented here (applying to the current repository version).
+1. Download `libtorch` from https://pytorch.org/get-started/locally/. This package requires `v2.2`: if this version is no longer available on the "get started" page,
+the file should be accessible by modifying the target link, for example `https://download.pytorch.org/libtorch/cu121/libtorch-cxx11-abi-shared-with-deps-2.2.0%2Bcu121.zip` for a Linux version with CUDA12. **NOTE:** When using `rust-bert` as dependency from [crates.io](https://crates.io), please check the required `LIBTORCH` on the published package [readme](https://crates.io/crates/rust-bert) as it may differ from the version documented here (applying to the current repository version).
 2. Extract the library to a location of your choice
 3. Set the following environment variables
 ##### Linux:

benches/generation_benchmark.rs

@@ -53,10 +53,6 @@ fn generation_forward_pass(iters: u64, model: &TextGenerationModel, data: &[&str
 }
 
 fn bench_generation(c: &mut Criterion) {
-    //    Set-up summarization model
-    unsafe {
-        torch_sys::dummy_cuda_dependency();
-    }
     let model = create_text_generation_model();
 
     //    Define input

benches/squad_benchmark.rs

@@ -73,9 +73,7 @@ fn qa_load_model(iters: u64) -> Duration {
 fn bench_squad(c: &mut Criterion) {
     //    Set-up QA model
     let model = create_qa_model();
-    unsafe {
-        torch_sys::dummy_cuda_dependency();
-    }
+
     //    Define input
     let mut squad_path = PathBuf::from(env::var("squad_dataset")
         .expect("Please set the \"squad_dataset\" environment variable pointing to the SQuAD dataset folder"));

benches/sst2_benchmark.rs

@@ -79,9 +79,7 @@ fn sst2_load_model(iters: u64) -> Duration {
 fn bench_sst2(c: &mut Criterion) {
     //    Set-up classifier
     let model = create_sentiment_model();
-    unsafe {
-        torch_sys::dummy_cuda_dependency();
-    }
+
     //    Define input
     let mut sst2_path = PathBuf::from(env::var("SST2_PATH").expect(
         "Please set the \"SST2_PATH\" environment variable pointing to the SST2 dataset folder",

benches/summarization_benchmark.rs

@@ -40,9 +40,6 @@ fn summarization_load_model(iters: u64) -> Duration {
 
 fn bench_squad(c: &mut Criterion) {
     //    Set-up summarization model
-    unsafe {
-        torch_sys::dummy_cuda_dependency();
-    }
     let model = create_summarization_model();
 
     //    Define input

benches/tensor_operations_benchmark.rs

@@ -17,10 +17,6 @@ fn matrix_multiply(iters: u64, input: &Tensor, weights: &Tensor) -> Duration {
 }
 
 fn bench_tensor_ops(c: &mut Criterion) {
-    //    Set-up summarization model
-    unsafe {
-        torch_sys::dummy_cuda_dependency();
-    }
     let input = Tensor::rand([32, 128, 512], (Kind::Float, Device::cuda_if_available()));
     let weights = Tensor::rand([512, 512], (Kind::Float, Device::cuda_if_available()));
 

benches/token_classification_benchmark.rs

@@ -14,9 +14,6 @@ fn create_model() -> TokenClassificationModel {
 
 fn bench_token_classification_predict(c: &mut Criterion) {
     //    Set-up model
-    unsafe {
-        torch_sys::dummy_cuda_dependency();
-    }
     let model = create_model();
 
     //    Define input

benches/translation_benchmark.rs

@@ -73,9 +73,6 @@ fn translation_load_model(iters: u64) -> Duration {
 
 fn bench_squad(c: &mut Criterion) {
     //    Set-up translation model
-    unsafe {
-        torch_sys::dummy_cuda_dependency();
-    }
     let model = create_translation_model();
 
     //    Define input

src/lib.rs

@@ -90,8 +90,8 @@
 //!
 //! ### Manual installation (recommended)
 //!
-//! 1. Download `libtorch` from <https://pytorch.org/get-started/locally/>. This package requires `v2.1`: if this version is no longer available on the "get started" page,
-//! the file should be accessible by modifying the target link, for example `https://download.pytorch.org/libtorch/cu118/libtorch-cxx11-abi-shared-with-deps-2.1.1%2Bcu118.zip` for a Linux version with CUDA11.
+//! 1. Download `libtorch` from <https://pytorch.org/get-started/locally/>. This package requires `v2.2`: if this version is no longer available on the "get started" page,
+//! the file should be accessible by modifying the target link, for example `https://download.pytorch.org/libtorch/cu121/libtorch-cxx11-abi-shared-with-deps-2.2.0%2Bcu121.zip` for a Linux version with CUDA12.
 //! 2. Extract the library to a location of your choice
 //! 3. Set the following environment variables
 //! ##### Linux: