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synced 2024-10-05 16:47:24 +03:00
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>
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@ -3,6 +3,9 @@ All notable changes to this project will be documented in this file. The format
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## [Unreleased]
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## Changed
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- (BREAKING) Upgraded to `torch` 2.2 (via `tch` 0.15.0).
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## [0.22.0] - 2024-01-20
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## Added
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- Addition of `new_with_tokenizer` constructor for `SentenceEmbeddingsModel` allowing passing custom tokenizers for sentence embeddings pipelines.
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@ -10,7 +13,7 @@ All notable changes to this project will be documented in this file. The format
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- (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.
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## Fixed
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- (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).
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- (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).
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- Improved MPS device compatibility setting the `sparse_grad` flag to false for `gather` operations
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- Updated ONNX runtime backend version to 1.15.x
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- Issue with incorrect results for QA models with a tokenizer not using segment ids
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@ -76,7 +76,7 @@ features = ["doc-only"]
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[dependencies]
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rust_tokenizers = "8.1.1"
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tch = "0.14.0"
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tch = "0.15.0"
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serde_json = "1"
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serde = { version = "1", features = ["derive"] }
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ordered-float = "3"
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@ -97,7 +97,6 @@ anyhow = "1"
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csv = "1"
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criterion = "0.5"
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tokio = { version = "1.35", features = ["sync", "rt-multi-thread", "macros"] }
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torch-sys = "0.14.0"
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tempfile = "3"
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itertools = "0.12"
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tracing-subscriber = { version = "0.3", default-features = false, features = [ "env-filter", "fmt" ] }
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@ -80,8 +80,8 @@ This cache location defaults to `~/.cache/.rustbert`, but can be changed by sett
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### Manual installation (recommended)
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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,
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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).
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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,
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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).
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2. Extract the library to a location of your choice
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3. Set the following environment variables
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##### Linux:
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@ -53,10 +53,6 @@ fn generation_forward_pass(iters: u64, model: &TextGenerationModel, data: &[&str
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}
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fn bench_generation(c: &mut Criterion) {
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// Set-up summarization model
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unsafe {
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torch_sys::dummy_cuda_dependency();
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}
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let model = create_text_generation_model();
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// Define input
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@ -73,9 +73,7 @@ fn qa_load_model(iters: u64) -> Duration {
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fn bench_squad(c: &mut Criterion) {
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// Set-up QA model
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let model = create_qa_model();
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unsafe {
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torch_sys::dummy_cuda_dependency();
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}
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// Define input
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let mut squad_path = PathBuf::from(env::var("squad_dataset")
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.expect("Please set the \"squad_dataset\" environment variable pointing to the SQuAD dataset folder"));
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@ -79,9 +79,7 @@ fn sst2_load_model(iters: u64) -> Duration {
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fn bench_sst2(c: &mut Criterion) {
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// Set-up classifier
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let model = create_sentiment_model();
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unsafe {
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torch_sys::dummy_cuda_dependency();
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}
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// Define input
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let mut sst2_path = PathBuf::from(env::var("SST2_PATH").expect(
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"Please set the \"SST2_PATH\" environment variable pointing to the SST2 dataset folder",
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@ -40,9 +40,6 @@ fn summarization_load_model(iters: u64) -> Duration {
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fn bench_squad(c: &mut Criterion) {
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// Set-up summarization model
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unsafe {
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torch_sys::dummy_cuda_dependency();
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}
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let model = create_summarization_model();
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// Define input
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@ -17,10 +17,6 @@ fn matrix_multiply(iters: u64, input: &Tensor, weights: &Tensor) -> Duration {
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}
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fn bench_tensor_ops(c: &mut Criterion) {
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// Set-up summarization model
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unsafe {
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torch_sys::dummy_cuda_dependency();
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}
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let input = Tensor::rand([32, 128, 512], (Kind::Float, Device::cuda_if_available()));
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let weights = Tensor::rand([512, 512], (Kind::Float, Device::cuda_if_available()));
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@ -14,9 +14,6 @@ fn create_model() -> TokenClassificationModel {
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fn bench_token_classification_predict(c: &mut Criterion) {
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// Set-up model
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unsafe {
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torch_sys::dummy_cuda_dependency();
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}
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let model = create_model();
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// Define input
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@ -73,9 +73,6 @@ fn translation_load_model(iters: u64) -> Duration {
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fn bench_squad(c: &mut Criterion) {
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// Set-up translation model
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unsafe {
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torch_sys::dummy_cuda_dependency();
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}
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let model = create_translation_model();
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// Define input
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@ -90,8 +90,8 @@
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//!
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//! ### Manual installation (recommended)
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//!
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//! 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,
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//! 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.
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//! 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,
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//! 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.
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//! 2. Extract the library to a location of your choice
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//! 3. Set the following environment variables
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//! ##### Linux:
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