Adds method to read the pooling types from model's files

tests/engine/test_arg_utils.py

@@ -30,6 +30,13 @@ def test_limit_mm_per_prompt_parser(arg, expected):
     assert args.limit_mm_per_prompt == expected
 
 
+def test_valid_pooling_config():
+    parser = EngineArgs.add_cli_args(FlexibleArgumentParser())
+    args = parser.parse_args(["--pooling-type=MEAN"])
+    engine_args = EngineArgs.from_cli_args(args=args)
+    assert engine_args.pooling_type == 'MEAN'
+
+
 @pytest.mark.parametrize(
     ("arg"),
     [

tests/model_executor/test_model_load_with_params.py

@@ -0,0 +1,45 @@
+import os
+
+from vllm.model_executor.layers.pooler import PoolingType
+from vllm.model_executor.models.bert import BertEmbeddingModel
+
+MAX_MODEL_LEN = 128
+MODEL_NAME = os.environ.get("MODEL_NAME", "BAAI/bge-base-en-v1.5")
+REVISION = os.environ.get("REVISION", "main")
+
+
+def test_model_loading_with_params(vllm_runner):
+    """
+    Test parameter weight loading with tp>1.
+    """
+    with vllm_runner(model_name=MODEL_NAME,
+                     revision=REVISION,
+                     dtype="float16",
+                     max_model_len=MAX_MODEL_LEN) as model:
+        output = model.encode("Write a short story about a robot that"
+                              " dreams for the first time.\n")
+
+        model_config = model.model.llm_engine.model_config
+
+        model_tokenizer = model.model.llm_engine.tokenizer
+
+        # asserts on the bert model config file
+        assert model_config.bert_config["max_seq_length"] == 512
+        assert model_config.bert_config["do_lower_case"]
+
+        # asserts on the pooling config files
+        assert model_config.pooler_config.pooling_type == PoolingType.CLS.name
+        assert model_config.pooler_config.pooling_norm
+
+        # asserts on the tokenizer loaded
+        assert model_tokenizer.tokenizer_id == "BAAI/bge-base-en-v1.5"
+        assert model_tokenizer.tokenizer_config["do_lower_case"]
+        assert model_tokenizer.tokenizer.model_max_length == 512
+
+        model = model.model.llm_engine.model_executor\
+                     .driver_worker.model_runner.model
+        assert isinstance(model, BertEmbeddingModel)
+        assert model._pooler.pooling_type == PoolingType.CLS
+        assert model._pooler.normalize
+        # assert output
+        assert output

tests/test_config.py

@@ -1,6 +1,7 @@
 import pytest
 
 from vllm.config import ModelConfig
+from vllm.model_executor.layers.pooler import PoolingType
 
 
 @pytest.mark.parametrize(("model_id", "expected_task"), [
@@ -102,6 +103,70 @@ def test_get_sliding_window():
     assert mistral_model_config.get_sliding_window() == TEST_SLIDING_WINDOW
 
 
+def test_get_pooling_config():
+    model_id = "sentence-transformers/all-MiniLM-L12-v2"
+    minilm_model_config = ModelConfig(
+        model_id,
+        task="auto",
+        tokenizer=model_id,
+        tokenizer_mode="auto",
+        trust_remote_code=False,
+        seed=0,
+        dtype="float16",
+        revision=None,
+    )
+
+    minilm_pooling_config = minilm_model_config._init_pooler_config(
+        pooling_type=None,
+        pooling_norm=None,
+        pooling_returned_token_ids=None,
+        pooling_softmax=None,
+        pooling_step_tag_id=None)
+
+    assert minilm_pooling_config.pooling_norm
+    assert minilm_pooling_config.pooling_type == PoolingType.MEAN.name
+
+
+def test_get_pooling_config_from_args():
+    model_id = "sentence-transformers/all-MiniLM-L12-v2"
+    minilm_model_config = ModelConfig(model_id,
+                                      task="auto",
+                                      tokenizer=model_id,
+                                      tokenizer_mode="auto",
+                                      trust_remote_code=False,
+                                      seed=0,
+                                      dtype="float16",
+                                      revision=None)
+
+    minilm_pooling_config = minilm_model_config._init_pooler_config(
+        pooling_type='CLS',
+        pooling_norm=True,
+        pooling_returned_token_ids=None,
+        pooling_softmax=None,
+        pooling_step_tag_id=None)
+
+    assert minilm_pooling_config.pooling_norm
+    assert minilm_pooling_config.pooling_type == PoolingType.CLS.name
+
+
+def test_get_bert_tokenization_sentence_transformer_config():
+    bge_model_config = ModelConfig(
+        model="BAAI/bge-base-en-v1.5",
+        task="auto",
+        tokenizer="BAAI/bge-base-en-v1.5",
+        tokenizer_mode="auto",
+        trust_remote_code=False,
+        seed=0,
+        dtype="float16",
+        revision=None,
+    )
+
+    bert_bge_model_config = bge_model_config._get_bert_config()
+
+    assert bert_bge_model_config["max_seq_length"] == 512
+    assert bert_bge_model_config["do_lower_case"]
+
+
 def test_rope_customization():
     TEST_ROPE_SCALING = {"rope_type": "dynamic", "factor": 2.0}
     TEST_ROPE_THETA = 16_000_000.0

vllm/config.py

@@ -13,9 +13,10 @@
 from vllm.model_executor.models import ModelRegistry
 from vllm.platforms import current_platform
 from vllm.tracing import is_otel_available, otel_import_error_traceback
-from vllm.transformers_utils.config import (ConfigFormat, get_config,
-                                            get_hf_image_processor_config,
-                                            get_hf_text_config)
+from vllm.transformers_utils.config import (
+    ConfigFormat, get_config, get_hf_image_processor_config,
+    get_hf_text_config, get_pooling_config,
+    get_sentence_transformer_tokenizer_config)
 from vllm.utils import (GiB_bytes, cuda_device_count_stateless, get_cpu_memory,
                         print_warning_once)
 
@@ -186,6 +187,7 @@ def __init__(
                                     code_revision, rope_scaling, rope_theta,
                                     config_format)
         self.hf_text_config = get_hf_text_config(self.hf_config)
+        self.bert_config = self._get_bert_config()
         self.hf_image_processor_config = get_hf_image_processor_config(
             self.model, revision)
         self.dtype = _get_and_verify_dtype(self.hf_text_config, dtype)
@@ -218,7 +220,8 @@ def __init__(
             max_model_len=max_model_len,
             disable_sliding_window=self.disable_sliding_window,
             sliding_window_len=self.get_hf_config_sliding_window(),
-            spec_target_max_model_len=spec_target_max_model_len)
+            spec_target_max_model_len=spec_target_max_model_len,
+            bert_config=self.bert_config)
         self.served_model_name = get_served_model_name(model,
                                                        served_model_name)
         self.multimodal_config = self._init_multimodal_config(
@@ -262,6 +265,10 @@ def _init_multimodal_config(
 
         return None
 
+    def _get_bert_config(self):
+        return get_sentence_transformer_tokenizer_config(
+            self.model, self.revision)
+
     def _init_pooler_config(
         self,
         pooling_type: Optional[str] = None,
@@ -271,9 +278,20 @@ def _init_pooler_config(
         pooling_returned_token_ids: Optional[List[int]] = None
     ) -> Optional["PoolerConfig"]:
         if self.task == "embedding":
+            pooling_type_ = pooling_type
+            normalize_ = pooling_norm
+            pooling_config = get_pooling_config(self.model, self.revision)
+            if pooling_config is not None:
+                pooling_type_ = pooling_config["pooling_type"]
+                normalize_ = pooling_config["normalize"]
+                # override if user specifies pooling_type and/or pooling_norm
+                if pooling_type is not None:
+                    pooling_type_ = pooling_type
+                if pooling_norm is not None:
+                    normalize_ = pooling_norm
             return PoolerConfig(
-                pooling_type=pooling_type,
-                pooling_norm=pooling_norm,
+                pooling_type=pooling_type_,
+                pooling_norm=normalize_,
                 pooling_softmax=pooling_softmax,
                 pooling_step_tag_id=pooling_step_tag_id,
                 pooling_returned_token_ids=pooling_returned_token_ids)
@@ -1764,6 +1782,7 @@ def _get_and_verify_max_len(
     disable_sliding_window: bool,
     sliding_window_len: Optional[Union[int, List[Optional[int]]]],
     spec_target_max_model_len: Optional[int] = None,
+    bert_config: Optional[Any] = None,
 ) -> int:
     """Get and verify the model's maximum length."""
     derived_max_model_len = float("inf")
@@ -1846,6 +1865,9 @@ def _get_and_verify_max_len(
                     "original_max_position_embeddings"]
             derived_max_model_len *= scaling_factor
 
+    if bert_config and "max_seq_length" in bert_config:
+        derived_max_model_len = bert_config["max_seq_length"]
+
     # If the user specified a max length, make sure it is smaller than the
     # derived length from the HF model config.
     if max_model_len is None:

vllm/engine/arg_utils.py

@@ -15,6 +15,7 @@
                          SpeculativeConfig, TaskOption, TokenizerPoolConfig)
 from vllm.executor.executor_base import ExecutorBase
 from vllm.logger import init_logger
+from vllm.model_executor.layers.pooler import PoolingType
 from vllm.model_executor.layers.quantization import QUANTIZATION_METHODS
 from vllm.transformers_utils.config import (
     maybe_register_config_serialize_by_value)
@@ -850,7 +851,7 @@ def add_cli_args(parser: FlexibleArgumentParser) -> FlexibleArgumentParser:
 
         parser.add_argument(
             '--pooling-type',
-            choices=['LAST', 'ALL', 'CLS', 'STEP'],
+            choices=[pt.name for pt in PoolingType],
             default=None,
             help='Used to configure the pooling method in the embedding model.'
         )

vllm/model_executor/layers/pooler.py

@@ -16,6 +16,7 @@ class PoolingType(IntEnum):
     ALL = 1
     CLS = 2
     STEP = 3
+    MEAN = 4
 
 
 class Pooler(nn.Module):
@@ -27,7 +28,7 @@ class Pooler(nn.Module):
     3. Returns structured results as `PoolerOutput`.
 
     Attributes:
-        pooling_type: The type of pooling to use (LAST, ALL, CLS).
+        pooling_type: The type of pooling to use (LAST, ALL, CLS, MEAN).
         normalize: Whether to normalize the pooled data.
     """
 
@@ -97,6 +98,17 @@ def forward(
             for prompt_len in prompt_lens:
                 pooled_data.append(hidden_states[offset:offset + prompt_len])
                 offset += prompt_len
+        elif self.pooling_type == PoolingType.MEAN:
+            # Calculate mean pooling
+            cumsum = torch.cumsum(hidden_states, dim=0)
+            start_indices = torch.cat([
+                torch.tensor([0], device=hidden_states.device),
+                torch.cumsum(prompt_lens[:-1], dim=0)
+            ])
+            end_indices = torch.cumsum(prompt_lens, dim=0)
+            pooled_data = (
+                cumsum[end_indices - 1] - cumsum[start_indices] +
+                hidden_states[start_indices]) / prompt_lens.unsqueeze(1)
         elif self.pooling_type == PoolingType.STEP:
             if self.returned_token_ids is not None and len(
                     self.returned_token_ids) > 0: