Merge branch 'main' into feature/gte-classifier

Dockerfile

@@ -28,9 +28,22 @@ ARG ACTIONS_CACHE_URL
 ARG ACTIONS_RUNTIME_TOKEN
 ARG SCCACHE_GHA_ENABLED
 
+RUN wget -O- https://apt.repos.intel.com/intel-gpg-keys/GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB \
+| gpg --dearmor | tee /usr/share/keyrings/oneapi-archive-keyring.gpg > /dev/null && \
+  echo "deb [signed-by=/usr/share/keyrings/oneapi-archive-keyring.gpg] https://apt.repos.intel.com/oneapi all main" | \
+  tee /etc/apt/sources.list.d/oneAPI.list
+
+RUN apt-get update && DEBIAN_FRONTEND=noninteractive apt-get install -y --no-install-recommends \
+    intel-oneapi-mkl-devel=2024.0.0-49656 \
+    build-essential \
+    && rm -rf /var/lib/apt/lists/*
+
+RUN echo "int mkl_serv_intel_cpu_true() {return 1;}" > fakeintel.c && \
+    gcc -shared -fPIC -o libfakeintel.so fakeintel.c
+
 COPY --from=planner /usr/src/recipe.json recipe.json
 
-RUN cargo chef cook --release --features ort --no-default-features --recipe-path recipe.json && sccache -s
+RUN cargo chef cook --release --features ort --features candle --features mkl-dynamic --no-default-features --recipe-path recipe.json && sccache -s
 
 COPY backends backends
 COPY core core
@@ -40,7 +53,7 @@ COPY Cargo.lock ./
 
 FROM builder AS http-builder
 
-RUN cargo build --release --bin text-embeddings-router -F ort -F http --no-default-features && sccache -s
+RUN cargo build --release --bin text-embeddings-router -F ort -F candle -F mkl-dynamic -F http --no-default-features && sccache -s
 
 FROM builder AS grpc-builder
 
@@ -52,19 +65,35 @@ RUN PROTOC_ZIP=protoc-21.12-linux-x86_64.zip && \
 
 COPY proto proto
 
-RUN cargo build --release --bin text-embeddings-router -F grpc -F ort --no-default-features && sccache -s
+RUN cargo build --release --bin text-embeddings-router -F grpc -F ort -F candle -F mkl-dynamic --no-default-features && sccache -s
 
 FROM debian:bookworm-slim AS base
 
 ENV HUGGINGFACE_HUB_CACHE=/data \
-    PORT=80
+    PORT=80 \
+    MKL_ENABLE_INSTRUCTIONS=AVX512_E4 \
+    RAYON_NUM_THREADS=8 \
+    LD_PRELOAD=/usr/local/libfakeintel.so \
+    LD_LIBRARY_PATH=/usr/local/lib
 
 RUN apt-get update && DEBIAN_FRONTEND=noninteractive apt-get install -y --no-install-recommends \
+    libomp-dev \
     ca-certificates \
     libssl-dev \
     curl \
     && rm -rf /var/lib/apt/lists/*
 
+# Copy a lot of the Intel shared objects because of the mkl_serv_intel_cpu_true patch...
+COPY --from=builder /opt/intel/oneapi/mkl/latest/lib/intel64/libmkl_intel_lp64.so.2 /usr/local/lib/libmkl_intel_lp64.so.2
+COPY --from=builder /opt/intel/oneapi/mkl/latest/lib/intel64/libmkl_intel_thread.so.2 /usr/local/lib/libmkl_intel_thread.so.2
+COPY --from=builder /opt/intel/oneapi/mkl/latest/lib/intel64/libmkl_core.so.2 /usr/local/lib/libmkl_core.so.2
+COPY --from=builder /opt/intel/oneapi/mkl/latest/lib/intel64/libmkl_vml_def.so.2 /usr/local/lib/libmkl_vml_def.so.2
+COPY --from=builder /opt/intel/oneapi/mkl/latest/lib/intel64/libmkl_def.so.2 /usr/local/lib/libmkl_def.so.2
+COPY --from=builder /opt/intel/oneapi/mkl/latest/lib/intel64/libmkl_vml_avx2.so.2 /usr/local/lib/libmkl_vml_avx2.so.2
+COPY --from=builder /opt/intel/oneapi/mkl/latest/lib/intel64/libmkl_vml_avx512.so.2 /usr/local/lib/libmkl_vml_avx512.so.2
+COPY --from=builder /opt/intel/oneapi/mkl/latest/lib/intel64/libmkl_avx2.so.2 /usr/local/lib/libmkl_avx2.so.2
+COPY --from=builder /opt/intel/oneapi/mkl/latest/lib/intel64/libmkl_avx512.so.2 /usr/local/lib/libmkl_avx512.so.2
+COPY --from=builder /usr/src/libfakeintel.so /usr/local/libfakeintel.so
 
 FROM base AS grpc
 

backends/candle/src/lib.rs

@@ -25,7 +25,7 @@ use candle_nn::VarBuilder;
 use nohash_hasher::BuildNoHashHasher;
 use serde::Deserialize;
 use std::collections::HashMap;
-use std::path::PathBuf;
+use std::path::Path;
 use text_embeddings_backend_core::{
     Backend, BackendError, Batch, Embedding, Embeddings, ModelType, Predictions,
 };
@@ -69,7 +69,7 @@ pub struct CandleBackend {
 
 impl CandleBackend {
     pub fn new(
-        model_path: PathBuf,
+        model_path: &Path,
         dtype: String,
         model_type: ModelType,
     ) -> Result<Self, BackendError> {

backends/core/src/lib.rs

@@ -88,4 +88,6 @@ pub enum BackendError {
     Inference(String),
     #[error("Backend is unhealthy")]
     Unhealthy,
+    #[error("Weights not found: {0}")]
+    WeightsNotFound(String),
 }

backends/ort/src/lib.rs

@@ -1,9 +1,9 @@
 use ndarray::{s, Axis};
 use nohash_hasher::BuildNoHashHasher;
-use ort::{GraphOptimizationLevel, Session};
+use ort::session::{builder::GraphOptimizationLevel, Session};
 use std::collections::HashMap;
 use std::ops::{Div, Mul};
-use std::path::PathBuf;
+use std::path::Path;
 use text_embeddings_backend_core::{
     Backend, BackendError, Batch, Embedding, Embeddings, ModelType, Pool, Predictions,
 };
@@ -16,12 +16,12 @@ pub struct OrtBackend {
 
 impl OrtBackend {
     pub fn new(
-        model_path: PathBuf,
+        model_path: &Path,
         dtype: String,
         model_type: ModelType,
     ) -> Result<Self, BackendError> {
         // Check dtype
-        if &dtype == "float32" {
+        if dtype == "float32" {
         } else {
             return Err(BackendError::Start(format!(
                 "DType {dtype} is not supported"
@@ -246,7 +246,8 @@ impl Backend for OrtBackend {
         if has_raw_requests {
             // Reshape outputs
             let s = outputs.shape().to_vec();
-            let outputs = outputs.into_shape_with_order((s[0] * s[1], s[2])).e()?;
+            #[allow(deprecated)]
+            let outputs = outputs.into_shape((s[0] * s[1], s[2])).e()?;
 
             // We need to remove the padding tokens only if batch_size > 1 and there are some
             // member of the batch that require pooling

backends/src/lib.rs

@@ -37,8 +37,9 @@ pub struct Backend {
 }
 
 impl Backend {
-    pub fn new(
+    pub async fn new(
         model_path: PathBuf,
+        api_repo: Option<ApiRepo>,
         dtype: DType,
         model_type: ModelType,
         uds_path: String,
@@ -49,12 +50,14 @@ impl Backend {
 
         let backend = init_backend(
             model_path,
+            api_repo,
             dtype,
             model_type.clone(),
             uds_path,
             otlp_endpoint,
             otlp_service_name,
-        )?;
+        )
+        .await?;
         let padded_model = backend.is_padded();
         let max_batch_size = backend.max_batch_size();
 
@@ -193,48 +196,102 @@ impl Backend {
 }
 
 #[allow(unused)]
-fn init_backend(
+async fn init_backend(
     model_path: PathBuf,
+    api_repo: Option<ApiRepo>,
     dtype: DType,
     model_type: ModelType,
     uds_path: String,
     otlp_endpoint: Option<String>,
     otlp_service_name: String,
 ) -> Result<Box<dyn CoreBackend + Send>, BackendError> {
+    let mut backend_start_failed = false;
+
+    if cfg!(feature = "ort") {
+        #[cfg(feature = "ort")]
+        {
+            if let Some(api_repo) = api_repo.as_ref() {
+                let start = std::time::Instant::now();
+                download_onnx(api_repo)
+                    .await
+                    .map_err(|err| BackendError::WeightsNotFound(err.to_string()));
+                tracing::info!("Model ONNX weights downloaded in {:?}", start.elapsed());
+            }
+
+            let backend = OrtBackend::new(&model_path, dtype.to_string(), model_type.clone());
+            match backend {
+                Ok(b) => return Ok(Box::new(b)),
+                Err(err) => {
+                    tracing::error!("Could not start ORT backend: {err}");
+                    backend_start_failed = true;
+                }
+            }
+        }
+    }
+
+    if let Some(api_repo) = api_repo.as_ref() {
+        if cfg!(feature = "python") || cfg!(feature = "candle") {
+            let start = std::time::Instant::now();
+            if download_safetensors(api_repo).await.is_err() {
+                tracing::warn!("safetensors weights not found. Using `pytorch_model.bin` instead. Model loading will be significantly slower.");
+                tracing::info!("Downloading `pytorch_model.bin`");
+                api_repo
+                    .get("pytorch_model.bin")
+                    .await
+                    .map_err(|err| BackendError::WeightsNotFound(err.to_string()))?;
+            }
+
+            tracing::info!("Model weights downloaded in {:?}", start.elapsed());
+        }
+    }
+
     if cfg!(feature = "candle") {
         #[cfg(feature = "candle")]
-        return Ok(Box::new(CandleBackend::new(
-            model_path,
-            dtype.to_string(),
-            model_type,
-        )?));
-    } else if cfg!(feature = "python") {
+        {
+            let backend = CandleBackend::new(&model_path, dtype.to_string(), model_type.clone());
+            match backend {
+                Ok(b) => return Ok(Box::new(b)),
+                Err(err) => {
+                    tracing::error!("Could not start Candle backend: {err}");
+                    backend_start_failed = true;
+                }
+            }
+        }
+    }
+
+    if cfg!(feature = "python") {
         #[cfg(feature = "python")]
         {
-            return Ok(Box::new(
-                std::thread::spawn(move || {
-                    PythonBackend::new(
-                        model_path.to_str().unwrap().to_string(),
-                        dtype.to_string(),
-                        model_type,
-                        uds_path,
-                        otlp_endpoint,
-                        otlp_service_name,
-                    )
-                })
-                .join()
-                .expect("Python Backend management thread failed")?,
-            ));
+            let backend = std::thread::spawn(move || {
+                PythonBackend::new(
+                    model_path.to_str().unwrap().to_string(),
+                    dtype.to_string(),
+                    model_type,
+                    uds_path,
+                    otlp_endpoint,
+                    otlp_service_name,
+                )
+            })
+            .join()
+            .expect("Python Backend management thread failed");
+
+            match backend {
+                Ok(b) => return Ok(Box::new(b)),
+                Err(err) => {
+                    tracing::error!("Could not start Python backend: {err}");
+                    backend_start_failed = true;
+                }
+            }
         }
-    } else if cfg!(feature = "ort") {
-        #[cfg(feature = "ort")]
-        return Ok(Box::new(OrtBackend::new(
-            model_path,
-            dtype.to_string(),
-            model_type,
-        )?));
     }
-    Err(BackendError::NoBackend)
+
+    if backend_start_failed {
+        Err(BackendError::Start(
+            "Could not start a suitable backend".to_string(),
+        ))
+    } else {
+        Err(BackendError::NoBackend)
+    }
 }
 
 #[derive(Debug)]
@@ -298,31 +355,6 @@ enum BackendCommand {
     ),
 }
 
-pub async fn download_weights(api: &ApiRepo) -> Result<Vec<PathBuf>, ApiError> {
-    let model_files = if cfg!(feature = "python") || cfg!(feature = "candle") {
-        match download_safetensors(api).await {
-            Ok(p) => p,
-            Err(_) => {
-                tracing::warn!("safetensors weights not found. Using `pytorch_model.bin` instead. Model loading will be significantly slower.");
-                tracing::info!("Downloading `pytorch_model.bin`");
-                let p = api.get("pytorch_model.bin").await?;
-                vec![p]
-            }
-        }
-    } else if cfg!(feature = "ort") {
-        match download_onnx(api).await {
-            Ok(p) => p,
-            Err(err) => {
-                panic!("failed to download `model.onnx` or `model.onnx_data`. Check the onnx file exists in the repository. {err}");
-            }
-        }
-    } else {
-        unreachable!()
-    };
-
-    Ok(model_files)
-}
-
 async fn download_safetensors(api: &ApiRepo) -> Result<Vec<PathBuf>, ApiError> {
     // Single file
     tracing::info!("Downloading `model.safetensors`");
@@ -362,6 +394,7 @@ async fn download_safetensors(api: &ApiRepo) -> Result<Vec<PathBuf>, ApiError> {
     Ok(safetensors_files)
 }
 
+#[cfg(feature = "ort")]
 async fn download_onnx(api: &ApiRepo) -> Result<Vec<PathBuf>, ApiError> {
     let mut model_files: Vec<PathBuf> = Vec::new();
 

core/src/download.rs

@@ -1,6 +1,5 @@
 use hf_hub::api::tokio::{ApiError, ApiRepo};
 use std::path::PathBuf;
-use text_embeddings_backend::download_weights;
 use tracing::instrument;
 
 // Old classes used other config names than 'sentence_bert_config.json'
@@ -15,20 +14,36 @@ pub const ST_CONFIG_NAMES: [&str; 7] = [
 ];
 
 #[instrument(skip_all)]
-pub async fn download_artifacts(api: &ApiRepo) -> Result<PathBuf, ApiError> {
+pub async fn download_artifacts(api: &ApiRepo, pool_config: bool) -> Result<PathBuf, ApiError> {
     let start = std::time::Instant::now();
 
     tracing::info!("Starting download");
 
+    // Optionally download the pooling config.
+    if pool_config {
+        // If a pooling config exist, download it
+        let _ = download_pool_config(api).await.map_err(|err| {
+            tracing::warn!("Download failed: {err}");
+            err
+        });
+    }
+
+    // Download legacy sentence transformers config
+    // We don't warn on failure as it is a legacy file
+    let _ = download_st_config(api).await;
+    // Download new sentence transformers config
+    let _ = download_new_st_config(api).await.map_err(|err| {
+        tracing::warn!("Download failed: {err}");
+        err
+    });
+
     tracing::info!("Downloading `config.json`");
     api.get("config.json").await?;
 
     tracing::info!("Downloading `tokenizer.json`");
-    api.get("tokenizer.json").await?;
-
-    let model_files = download_weights(api).await?;
-    let model_root = model_files[0].parent().unwrap().to_path_buf();
+    let tokenizer_path = api.get("tokenizer.json").await?;
 
+    let model_root = tokenizer_path.parent().unwrap().to_path_buf();
     tracing::info!("Model artifacts downloaded in {:?}", start.elapsed());
     Ok(model_root)
 }