NLP Model Warmup Reworked (#748)

backend/danswer/indexing/embedder.py

@@ -13,7 +13,7 @@ from danswer.utils.timing import log_function_time
 
 
 @log_function_time()
-def encode_chunks(
+def embed_chunks(
     chunks: list[DocAwareChunk],
     embedding_model: SentenceTransformer | None = None,
     batch_size: int = BATCH_SIZE_ENCODE_CHUNKS,
@@ -67,4 +67,4 @@ def encode_chunks(
 
 class DefaultEmbedder(Embedder):
     def embed(self, chunks: list[DocAwareChunk]) -> list[IndexChunk]:
-        return encode_chunks(chunks)
+        return embed_chunks(chunks)

backend/danswer/main.py

@@ -200,14 +200,14 @@ def get_application() -> FastAPI:
             )
         else:
             logger.info("Warming up local NLP models.")
+            warm_up_models()
+
             if torch.cuda.is_available():
                 logger.info("GPU is available")
             else:
                 logger.info("GPU is not available")
             logger.info(f"Torch Threads: {torch.get_num_threads()}")
 
-            warm_up_models()
-
         # This is for the LLM, most LLMs will not need warming up
         get_default_llm().log_model_configs()
         get_default_qa_model().warm_up_model()

backend/danswer/search/search_nlp_models.py

@@ -46,7 +46,8 @@ def get_local_embedding_model(
     max_context_length: int = DOC_EMBEDDING_CONTEXT_SIZE,
 ) -> SentenceTransformer:
     global _EMBED_MODEL
-    if _EMBED_MODEL is None:
+    if _EMBED_MODEL is None or max_context_length != _EMBED_MODEL.max_seq_length:
+        logger.info(f"Loading {model_name}")
         _EMBED_MODEL = SentenceTransformer(model_name)
         _EMBED_MODEL.max_seq_length = max_context_length
     return _EMBED_MODEL
@@ -57,10 +58,13 @@ def get_local_reranking_model_ensemble(
     max_context_length: int = CROSS_EMBED_CONTEXT_SIZE,
 ) -> list[CrossEncoder]:
     global _RERANK_MODELS
-    if _RERANK_MODELS is None:
-        _RERANK_MODELS = [CrossEncoder(model_name) for model_name in model_names]
-        for model in _RERANK_MODELS:
+    if _RERANK_MODELS is None or max_context_length != _RERANK_MODELS[0].max_length:
+        _RERANK_MODELS = []
+        for model_name in model_names:
+            logger.info(f"Loading {model_name}")
+            model = CrossEncoder(model_name)
             model.max_length = max_context_length
+            _RERANK_MODELS.append(model)
     return _RERANK_MODELS
 
 
@@ -76,7 +80,7 @@ def get_local_intent_model(
     max_context_length: int = QUERY_MAX_CONTEXT_SIZE,
 ) -> TFDistilBertForSequenceClassification:
     global _INTENT_MODEL
-    if _INTENT_MODEL is None:
+    if _INTENT_MODEL is None or max_context_length != _INTENT_MODEL.max_seq_length:
         _INTENT_MODEL = TFDistilBertForSequenceClassification.from_pretrained(
             model_name
         )
@@ -84,30 +88,6 @@ def get_local_intent_model(
     return _INTENT_MODEL
 
 
-def warm_up_models(
-    indexer_only: bool = False, skip_cross_encoders: bool = SKIP_RERANKING
-) -> None:
-    warm_up_str = "Danswer is amazing"
-    get_default_tokenizer()(warm_up_str)
-    get_local_embedding_model().encode(warm_up_str)
-
-    if indexer_only:
-        return
-
-    if not skip_cross_encoders:
-        cross_encoders = get_local_reranking_model_ensemble()
-        [
-            cross_encoder.predict((warm_up_str, warm_up_str))
-            for cross_encoder in cross_encoders
-        ]
-
-    intent_tokenizer = get_intent_model_tokenizer()
-    inputs = intent_tokenizer(
-        warm_up_str, return_tensors="tf", truncation=True, padding=True
-    )
-    get_local_intent_model()(inputs)
-
-
 class EmbeddingModel:
     def __init__(
         self,
@@ -269,3 +249,27 @@ class IntentModel:
         class_percentages = np.round(probabilities.numpy() * 100, 2)
 
         return list(class_percentages.tolist()[0])
+
+
+def warm_up_models(
+    indexer_only: bool = False, skip_cross_encoders: bool = SKIP_RERANKING
+) -> None:
+    warm_up_str = (
+        "Danswer is amazing! Check out our easy deployment guide at "
+        "https://docs.danswer.dev/quickstart"
+    )
+    get_default_tokenizer()(warm_up_str)
+
+    EmbeddingModel().encode(texts=[warm_up_str])
+
+    if indexer_only:
+        return
+
+    if not skip_cross_encoders:
+        CrossEncoderEnsembleModel().predict(query=warm_up_str, passages=[warm_up_str])
+
+    intent_tokenizer = get_intent_model_tokenizer()
+    inputs = intent_tokenizer(
+        warm_up_str, return_tensors="tf", truncation=True, padding=True
+    )
+    get_local_intent_model()(inputs)

backend/danswer/search/search_runner.py

@@ -7,7 +7,6 @@ import numpy
 from nltk.corpus import stopwords  # type:ignore
 from nltk.stem import WordNetLemmatizer  # type:ignore
 from nltk.tokenize import word_tokenize  # type:ignore
-from sentence_transformers import SentenceTransformer  # type: ignore
 from sqlalchemy.orm import Session
 
 from danswer.configs.app_configs import DISABLE_LLM_CHUNK_FILTER
@@ -17,7 +16,6 @@ from danswer.configs.app_configs import NUM_RERANKED_RESULTS
 from danswer.configs.model_configs import ASYM_QUERY_PREFIX
 from danswer.configs.model_configs import CROSS_ENCODER_RANGE_MAX
 from danswer.configs.model_configs import CROSS_ENCODER_RANGE_MIN
-from danswer.configs.model_configs import NORMALIZE_EMBEDDINGS
 from danswer.configs.model_configs import SIM_SCORE_RANGE_HIGH
 from danswer.configs.model_configs import SIM_SCORE_RANGE_LOW
 from danswer.db.feedback import create_query_event
@@ -75,17 +73,10 @@ def query_processing(
 
 def embed_query(
     query: str,
-    embedding_model: SentenceTransformer | None = None,
     prefix: str = ASYM_QUERY_PREFIX,
-    normalize_embeddings: bool = NORMALIZE_EMBEDDINGS,
 ) -> list[float]:
-    model = embedding_model or EmbeddingModel()
     prefixed_query = prefix + query
-    query_embedding = model.encode(
-        [prefixed_query], normalize_embeddings=normalize_embeddings
-    )[0]
-
-    return query_embedding
+    return EmbeddingModel().encode([prefixed_query])[0]
 
 
 def chunks_to_search_docs(chunks: list[InferenceChunk] | None) -> list[SearchDoc]: