add reranker model support for python backend (#386)

Signed-off-by: kaixuanliu <kaixuan.liu@intel.com>

Author: kaixuanliu

Dockerfile-intel

@@ -77,7 +77,7 @@ RUN python -m pip install torch==2.4.0 torchvision torchaudio==2.4.0 --index-url
 RUN cd backends/python/server && \
     make install
 
-FROM vault.habana.ai/gaudi-docker/1.17.1/ubuntu22.04/habanalabs/pytorch-installer-2.3.1:latest AS hpu
+FROM vault.habana.ai/gaudi-docker/1.19.0/ubuntu22.04/habanalabs/pytorch-installer-2.5.1:latest AS hpu
 ENV HUGGINGFACE_HUB_CACHE=/data \
     PORT=80
 

backends/grpc-client/src/client.rs

@@ -64,4 +64,25 @@ impl Client {
         let response = self.stub.embed(request).await?.into_inner();
         Ok(response.embeddings)
     }
+
+    #[instrument(skip_all)]
+    pub async fn predict(
+        &mut self,
+        input_ids: Vec<u32>,
+        token_type_ids: Vec<u32>,
+        position_ids: Vec<u32>,
+        cu_seq_lengths: Vec<u32>,
+        max_length: u32,
+    ) -> Result<Vec<Score>> {
+        let request = tonic::Request::new(EmbedRequest {
+            input_ids,
+            token_type_ids,
+            position_ids,
+            max_length,
+            cu_seq_lengths,
+        })
+        .inject_context();
+        let response = self.stub.predict(request).await?.into_inner();
+        Ok(response.scores)
+    }
 }

backends/proto/embed.proto

@@ -7,6 +7,8 @@ service EmbeddingService {
     rpc Embed (EmbedRequest) returns (EmbedResponse);
     /// Health check
     rpc Health (HealthRequest) returns (HealthResponse);
+    /// Predict
+    rpc Predict (EmbedRequest) returns (PredictResponse);
 }
 
 message HealthRequest {}
@@ -28,3 +30,11 @@ message Embedding {
 message EmbedResponse {
     repeated Embedding embeddings = 1;
 }
+
+message Score {
+    repeated float values = 1;
+}
+
+message PredictResponse {
+    repeated Score scores = 1;
+}

backends/python/server/requirements-hpu.txt

@@ -1,3 +1,4 @@
+accelerate==0.33.0 ; python_version >= "3.9" and python_version < "3.13"
 backoff==2.2.1 ; python_version >= "3.9" and python_version < "3.13"
 certifi==2023.7.22 ; python_version >= "3.9" and python_version < "3.13"
 charset-normalizer==3.2.0 ; python_version >= "3.9" and python_version < "3.13"
@@ -31,8 +32,8 @@ opentelemetry-instrumentation==0.36b0 ; python_version >= "3.9" and python_versi
 opentelemetry-proto==1.15.0 ; python_version >= "3.9" and python_version < "3.13"
 opentelemetry-sdk==1.15.0 ; python_version >= "3.9" and python_version < "3.13"
 opentelemetry-semantic-conventions==0.36b0 ; python_version >= "3.9" and python_version < "3.13"
-optimum-habana==1.13.1 ; python_version >= "3.9" and python_version < "3.13"
-optimum==1.21.4 ; python_version >= "3.9" and python_version < "3.13"
+optimum-habana==1.15.0 ; python_version >= "3.9" and python_version < "3.13"
+optimum==1.23.3 ; python_version >= "3.9" and python_version < "3.13"
 packaging==23.1 ; python_version >= "3.9" and python_version < "3.13"
 pandas==2.2.2 ; python_version >= "3.9" and python_version < "3.13"
 pillow==10.3.0 ; python_version >= "3.9" and python_version < "3.13"
@@ -46,8 +47,8 @@ six==1.16.0 ; python_version >= "3.9" and python_version < "3.13"
 sympy==1.12 ; python_version >= "3.9" and python_version < "3.13"
 tokenizers==0.19.1 ; python_version >= "3.9" and python_version < "3.13"
 tqdm==4.66.2 ; python_version >= "3.9" and python_version < "3.13"
-transformers==4.43.4 ; python_version >= "3.9" and python_version < "3.13"
-transformers[sentencepiece]==4.43.4 ; python_version >= "3.9" and python_version < "3.13"
+transformers==4.45.2 ; python_version >= "3.9" and python_version < "3.13"
+transformers[sentencepiece]==4.45.2 ; python_version >= "3.9" and python_version < "3.13"
 typer==0.6.1 ; python_version >= "3.9" and python_version < "3.13"
 typing-extensions==4.8.0 ; python_version >= "3.9" and python_version < "3.13"
 tzdata==2024.1 ; python_version >= "3.9" and python_version < "3.13"

backends/python/server/text_embeddings_server/models/__init__.py

@@ -8,6 +8,7 @@
 
 from text_embeddings_server.models.model import Model
 from text_embeddings_server.models.default_model import DefaultModel
+from text_embeddings_server.models.classification_model import ClassificationModel
 from text_embeddings_server.utils.device import get_device, use_ipex
 
 __all__ = ["Model"]
@@ -43,18 +44,19 @@ def get_model(model_path: Path, dtype: Optional[str], pool: str):
     if config.model_type == "bert":
         config: BertConfig
         if (
-            device.type == "cuda"
+            use_ipex()
+            or device.type in ["cuda", "hpu"]
             and config.position_embedding_type == "absolute"
             and datatype in [torch.float16, torch.bfloat16]
             and FLASH_ATTENTION
         ):
             if pool != "cls":
-                raise ValueError("FlashBert only supports cls pooling")
-            return FlashBert(model_path, device, datatype)  # type: ignore
-        if use_ipex() or device.type == "hpu":
-            return FlashBert(model_path, device, datatype)  # type: ignore
-
-        return DefaultModel(model_path, device, datatype)
+                return DefaultModel(model_path, device, datatype, pool)
+            return FlashBert(model_path, device, datatype)
+        if config.architectures[0].endswith("Classification"):
+            return ClassificationModel(model_path, device, datatype)
+        else:
+            return DefaultModel(model_path, device, datatype, pool)
     else:
         if device.type == "hpu":
             from habana_frameworks.torch.hpu import wrap_in_hpu_graph
@@ -63,7 +65,14 @@ def get_model(model_path: Path, dtype: Optional[str], pool: str):
             )
 
             adapt_transformers_to_gaudi()
-            model_handle = DefaultModel(model_path, device, datatype)
+            if config.architectures[0].endswith("Classification"):
+                model_handle = ClassificationModel(model_path, device, datatype)
+            else:
+                model_handle = DefaultModel(model_path, device, datatype, pool)
             model_handle.model = wrap_in_hpu_graph(model_handle.model)
             return model_handle
-        return DefaultModel(model_path, device, datatype)
+        elif use_ipex():
+            if config.architectures[0].endswith("Classification"):
+                return ClassificationModel(model_path, device, datatype)
+            else:
+                return DefaultModel(model_path, device, datatype, pool)

backends/python/server/text_embeddings_server/models/classification_model.py

@@ -0,0 +1,52 @@
+import inspect
+import torch
+
+from pathlib import Path
+from typing import Type, List
+from transformers import AutoModelForSequenceClassification
+from opentelemetry import trace
+
+from text_embeddings_server.models import Model
+from text_embeddings_server.models.types import PaddedBatch, Embedding, Score
+
+tracer = trace.get_tracer(__name__)
+
+
+class ClassificationModel(Model):
+    def __init__(self, model_path: Path, device: torch.device, dtype: torch.dtype):
+        model = AutoModelForSequenceClassification.from_pretrained(model_path)
+        model = model.to(dtype).to(device)
+
+        self.hidden_size = model.config.hidden_size
+        self.has_position_ids = (
+            inspect.signature(model.forward).parameters.get("position_ids", None)
+            is not None
+        )
+        self.has_token_type_ids = (
+            inspect.signature(model.forward).parameters.get("token_type_ids", None)
+            is not None
+        )
+
+        super(ClassificationModel, self).__init__(
+            model=model, dtype=dtype, device=device
+        )
+
+    @property
+    def batch_type(self) -> Type[PaddedBatch]:
+        return PaddedBatch
+
+    @tracer.start_as_current_span("embed")
+    def embed(self, batch: PaddedBatch) -> List[Embedding]:
+        pass
+
+    @tracer.start_as_current_span("predict")
+    def predict(self, batch: PaddedBatch) -> List[Score]:
+        kwargs = {"input_ids": batch.input_ids, "attention_mask": batch.attention_mask}
+        if self.has_token_type_ids:
+            kwargs["token_type_ids"] = batch.token_type_ids
+        if self.has_position_ids:
+            kwargs["position_ids"] = batch.position_ids
+
+        output = self.model(**kwargs, return_dict=True)
+        all_scores = output.logits.tolist()
+        return [Score(values=scores) for scores in all_scores]

backends/python/server/text_embeddings_server/models/default_model.py

@@ -8,7 +8,7 @@
 from sentence_transformers.models import Pooling
 
 from text_embeddings_server.models import Model
-from text_embeddings_server.models.types import PaddedBatch, Embedding
+from text_embeddings_server.models.types import PaddedBatch, Embedding, Score
 
 tracer = trace.get_tracer(__name__)
 
@@ -59,3 +59,7 @@ def embed(self, batch: PaddedBatch) -> List[Embedding]:
             )
             for i in range(len(batch))
         ]
+
+    @tracer.start_as_current_span("predict")
+    def predict(self, batch: PaddedBatch) -> List[Score]:
+        pass

backends/python/server/text_embeddings_server/models/types.py

@@ -5,7 +5,7 @@
 from opentelemetry import trace
 
 from text_embeddings_server.pb import embed_pb2
-from text_embeddings_server.pb.embed_pb2 import Embedding
+from text_embeddings_server.pb.embed_pb2 import Embedding, Score
 
 tracer = trace.get_tracer(__name__)
 

backends/python/server/text_embeddings_server/server.py

@@ -31,6 +31,13 @@ async def Embed(self, request, context):
 
         return embed_pb2.EmbedResponse(embeddings=embeddings)
 
+    async def Predict(self, request, context):
+        batch = self.model.batch_type.from_pb(request, self.model.device)
+
+        scores = self.model.predict(batch)
+
+        return embed_pb2.PredictResponse(scores=scores)
+
 
 def serve(
     model_path: Path,

backends/python/src/lib.rs

@@ -5,7 +5,7 @@ use backend_grpc_client::Client;
 use nohash_hasher::BuildNoHashHasher;
 use std::collections::HashMap;
 use text_embeddings_backend_core::{
-    Backend, BackendError, Batch, Embedding, Embeddings, ModelType, Predictions,
+    Backend, BackendError, Batch, Embedding, Embeddings, ModelType, Pool, Predictions,
 };
 use tokio::runtime::Runtime;
 
@@ -25,11 +25,7 @@ impl PythonBackend {
         otlp_service_name: String,
     ) -> Result<Self, BackendError> {
         let pool = match model_type {
-            ModelType::Classifier => {
-                return Err(BackendError::Start(
-                    "`classifier` model type is not supported".to_string(),
-                ))
-            }
+            ModelType::Classifier => Pool::Cls,
             ModelType::Embedding(pool) => pool,
         };
 
@@ -105,9 +101,32 @@ impl Backend for PythonBackend {
         Ok(embeddings)
     }
 
-    fn predict(&self, _batch: Batch) -> Result<Predictions, BackendError> {
-        Err(BackendError::Inference(
-            "`predict` is not implemented".to_string(),
-        ))
+    fn predict(&self, batch: Batch) -> Result<Predictions, BackendError> {
+        if !batch.raw_indices.is_empty() {
+            return Err(BackendError::Inference(
+                "raw embeddings are not supported for the Python backend.".to_string(),
+            ));
+        }
+        let batch_size = batch.len();
+        let results = self
+            .tokio_runtime
+            .block_on(self.backend_client.clone().predict(
+                batch.input_ids,
+                batch.token_type_ids,
+                batch.position_ids,
+                batch.cumulative_seq_lengths,
+                batch.max_length,
+            ))
+            .map_err(|err| BackendError::Inference(err.to_string()))?;
+        let raw_results: Vec<Vec<f32>> = results.into_iter().map(|r| r.values).collect();
+
+        let mut predictions =
+            HashMap::with_capacity_and_hasher(batch_size, BuildNoHashHasher::default());
+
+        for (i, r) in raw_results.into_iter().enumerate() {
+            predictions.insert(i, r);
+        }
+
+        Ok(predictions)
     }
 }