Improve the output precision of embedding models (#19092)

tests/models/language/pooling/embed_utils.py

@@ -56,14 +56,10 @@ def correctness_test_embed_models(hf_runner,
                      max_model_len=None,
                      **vllm_extra_kwargs) as vllm_model:
         vllm_outputs = vllm_model.encode(example_prompts)
-        vllm_dtype = vllm_model.model.llm_engine.model_config.dtype
-        model_dtype = getattr(
-            vllm_model.model.llm_engine.model_config.hf_config, "torch_dtype",
-            vllm_dtype)
 
     with hf_runner(
             model_info.name,
-            dtype=model_dtype,
+            dtype="float32",
             is_sentence_transformer=True,
     ) as hf_model:
 

tests/models/language/pooling/mteb_utils.py

@@ -7,7 +7,6 @@ import numpy as np
 import pytest
 
 from tests.models.utils import EmbedModelInfo
-from vllm.model_executor.model_loader.utils import set_default_torch_dtype
 
 # Most models on the STS12 task (See #17175):
 # - Model implementation and minor changes in tensor dtype
@@ -104,17 +103,18 @@ def mteb_test_embed_models(hf_runner,
                                               MTEB_EMBED_TASKS)
         vllm_dtype = vllm_model.model.llm_engine.model_config.dtype
 
-    with set_default_torch_dtype(vllm_dtype) and hf_runner(
-            model_info.name, is_sentence_transformer=True,
-            dtype=vllm_dtype) as hf_model:
+    with hf_runner(model_info.name,
+                   is_sentence_transformer=True,
+                   dtype="float32") as hf_model:
 
         if hf_model_callback is not None:
             hf_model_callback(hf_model)
 
         st_main_score = run_mteb_embed_task(hf_model, MTEB_EMBED_TASKS)
+        st_dtype = next(hf_model.model.parameters()).dtype
 
-    print("VLLM:", vllm_main_score)
-    print("SentenceTransformers:", st_main_score)
+    print("VLLM:", vllm_dtype, vllm_main_score)
+    print("SentenceTransformers:", st_dtype, st_main_score)
     print("Difference:", st_main_score - vllm_main_score)
 
     assert st_main_score == pytest.approx(vllm_main_score, abs=MTEB_EMBED_TOL)

tests/models/language/pooling/test_gte.py

@@ -11,27 +11,21 @@ MODELS = [
     ########## BertModel
     EmbedModelInfo("thenlper/gte-large",
                    architecture="BertModel",
-                   dtype="float32",
                    enable_test=True),
     EmbedModelInfo("thenlper/gte-base",
                    architecture="BertModel",
-                   dtype="float32",
                    enable_test=False),
     EmbedModelInfo("thenlper/gte-small",
                    architecture="BertModel",
-                   dtype="float32",
                    enable_test=False),
     EmbedModelInfo("thenlper/gte-large-zh",
                    architecture="BertModel",
-                   dtype="float32",
                    enable_test=False),
     EmbedModelInfo("thenlper/gte-base-zh",
                    architecture="BertModel",
-                   dtype="float32",
                    enable_test=False),
     EmbedModelInfo("thenlper/gte-small-zh",
                    architecture="BertModel",
-                   dtype="float32",
                    enable_test=False),
     ########### NewModel
     EmbedModelInfo("Alibaba-NLP/gte-multilingual-base",
@@ -46,7 +40,6 @@ MODELS = [
     ########### Qwen2ForCausalLM
     EmbedModelInfo("Alibaba-NLP/gte-Qwen2-1.5B-instruct",
                    architecture="Qwen2ForCausalLM",
-                   dtype="float32",
                    enable_test=True),
     ########## ModernBertModel
     EmbedModelInfo("Alibaba-NLP/gte-modernbert-base",

tests/models/language/pooling/test_intfloat.py

+# SPDX-License-Identifier: Apache-2.0
+import pytest
+
+from ...utils import EmbedModelInfo
+from .embed_utils import correctness_test_embed_models
+from .mteb_utils import mteb_test_embed_models
+
+MODELS = [
+    ########## BertModel
+    EmbedModelInfo("intfloat/e5-small",
+                   architecture="BertModel",
+                   enable_test=True),
+    EmbedModelInfo("intfloat/e5-base",
+                   architecture="BertModel",
+                   enable_test=False),
+    EmbedModelInfo("intfloat/e5-large",
+                   architecture="BertModel",
+                   enable_test=False),
+    EmbedModelInfo("intfloat/multilingual-e5-small",
+                   architecture="BertModel",
+                   enable_test=False),
+    ########## XLMRobertaModel
+    EmbedModelInfo("intfloat/multilingual-e5-base",
+                   architecture="XLMRobertaModel",
+                   enable_test=True),
+    EmbedModelInfo("intfloat/multilingual-e5-large",
+                   architecture="XLMRobertaModel",
+                   enable_test=False),
+    EmbedModelInfo("intfloat/multilingual-e5-large-instruct",
+                   architecture="XLMRobertaModel",
+                   enable_test=False),
+]
+
+
+@pytest.mark.parametrize("model_info", MODELS)
+def test_embed_models_mteb(hf_runner, vllm_runner,
+                           model_info: EmbedModelInfo) -> None:
+    mteb_test_embed_models(hf_runner, vllm_runner, model_info)
+
+
+@pytest.mark.parametrize("model_info", MODELS)
+def test_embed_models_correctness(hf_runner, vllm_runner,
+                                  model_info: EmbedModelInfo,
+                                  example_prompts) -> None:
+    correctness_test_embed_models(hf_runner, vllm_runner, model_info,
+                                  example_prompts)

tests/models/language/pooling/test_jina.py

@@ -32,8 +32,7 @@ TEXTS_2 = [
 EMBEDDING_MODELS = [
     EmbedModelInfo("jinaai/jina-embeddings-v3",
                    architecture="XLMRobertaModel",
-                   is_matryoshka=True,
-                   dtype="float32")
+                   is_matryoshka=True)
 ]
 
 

tests/models/language/pooling/test_nomic.py

@@ -9,18 +9,15 @@ from .mteb_utils import mteb_test_embed_models
 MODELS = [
     EmbedModelInfo("nomic-ai/nomic-embed-text-v1",
                    architecture="NomicBertModel",
-                   dtype="float32",
                    enable_test=True),
     EmbedModelInfo("nomic-ai/nomic-embed-text-v1.5",
                    architecture="NomicBertModel",
-                   dtype="float32",
                    enable_test=False),
     EmbedModelInfo("nomic-ai/CodeRankEmbed",
                    architecture="NomicBertModel",
                    enable_test=False),
     EmbedModelInfo("nomic-ai/nomic-embed-text-v2-moe",
                    architecture="NomicBertModel",
-                   dtype="float32",
                    enable_test=True)
 ]
 

vllm/model_executor/models/bert.py

@@ -414,10 +414,15 @@ class BertEmbeddingModel(nn.Module, SupportsV0Only, SupportsQuant):
         intermediate_tensors: Optional[IntermediateTensors] = None,
         inputs_embeds: Optional[torch.Tensor] = None,
     ) -> torch.Tensor:
-        return self.model(input_ids=input_ids,
-                          position_ids=positions,
-                          inputs_embeds=inputs_embeds,
-                          intermediate_tensors=intermediate_tensors)
+        hidden_states = self.model(input_ids=input_ids,
+                                   position_ids=positions,
+                                   inputs_embeds=inputs_embeds,
+                                   intermediate_tensors=intermediate_tensors)
+
+        # convert the embedding output to float32,
+        # otherwise precision will be lost significantly
+        hidden_states = hidden_states.to(torch.float32)
+        return hidden_states
 
     def pooler(
         self,

vllm/model_executor/models/bert_with_rope.py

@@ -432,7 +432,12 @@ class BertWithRope(nn.Module, SupportsV0Only, SupportsQuant):
         else:
             hidden_states = self.embeddings(input_ids=input_ids,
                                             token_type_ids=token_type_ids)
-        return self.encoder(positions, hidden_states)
+        hidden_states = self.encoder(positions, hidden_states)
+
+        # convert the embedding output to float32,
+        # otherwise precision will be lost significantly
+        hidden_states = hidden_states.to(torch.float32)
+        return hidden_states
 
     def load_weights(self, weights: Iterable[tuple[str,
                                                    torch.Tensor]]) -> set[str]: