[Quantization] Pool model support bitsandbytes

tests/quantization/test_bitsandbytes.py

@@ -8,9 +8,11 @@
 
 import pytest
 import torch
+from transformers import BitsAndBytesConfig
 
 from tests.quantization.utils import is_quant_method_supported
 
+from ..models.utils import check_embeddings_close
 from ..utils import compare_two_settings, create_new_process_for_each_test
 
 models_4bit_to_test = [
@@ -19,6 +21,10 @@
      "quantize inflight model with both HF and Mistral format weights")
 ]
 
+models_4bit_to_embedding_test = [
+    ("intfloat/e5-mistral-7b-instruct", "quantize embedding model inflight"),
+]
+
 models_pre_qaunt_4bit_to_test = [
     ('PrunaAI/Einstein-v6.1-Llama3-8B-bnb-4bit-smashed',
      'read pre-quantized 4-bit FP4 model'),
@@ -31,6 +37,12 @@
     ("yec019/fbopt-350m-8bit", "read pre-quantized 8-bit opt model"),
 ]
 
+models_pre_quant_8bit_to_test = [
+    ('meta-llama/Llama-Guard-3-8B-INT8',
+     'read pre-quantized llama 8-bit model'),
+    ("yec019/fbopt-350m-8bit", "read pre-quantized 8-bit opt model"),
+]
+
 
 @pytest.mark.skipif(not is_quant_method_supported("bitsandbytes"),
                     reason='bitsandbytes is not supported on this GPU type.')
@@ -39,7 +51,8 @@
 def test_load_4bit_bnb_model(hf_runner, vllm_runner, example_prompts,
                              model_name, description) -> None:
 
-    hf_model_kwargs = {"load_in_4bit": True}
+    hf_model_kwargs = dict(quantization_config=BitsAndBytesConfig(
+        load_in_4bit=True))
     validate_generated_texts(hf_runner, vllm_runner, example_prompts[:1],
                              model_name, False, hf_model_kwargs)
 
@@ -77,7 +90,8 @@ def test_load_8bit_bnb_model(hf_runner, vllm_runner, example_prompts,
 def test_load_tp_4bit_bnb_model(hf_runner, vllm_runner, example_prompts,
                                 model_name, description) -> None:
 
-    hf_model_kwargs = {"load_in_4bit": True}
+    hf_model_kwargs = dict(quantization_config=BitsAndBytesConfig(
+        load_in_4bit=True))
     validate_generated_texts(hf_runner,
                              vllm_runner,
                              example_prompts[:1],
@@ -113,6 +127,54 @@ def test_load_pp_4bit_bnb_model(model_name, description) -> None:
     compare_two_settings(model_name, common_args, pp_args)
 
 
+@pytest.mark.skipif(not is_quant_method_supported("bitsandbytes"),
+                    reason='bitsandbytes is not supported on this GPU type.')
+@pytest.mark.parametrize("model_name, description",
+                         models_4bit_to_embedding_test)
+@pytest.mark.parametrize("dtype", ["half"])
+@create_new_process_for_each_test()
+def test_4bit_bnb_embedding_model(
+    model_name,
+    description,
+    hf_runner,
+    vllm_runner,
+    example_prompts,
+    dtype: str,
+) -> None:
+
+    # The example_prompts has ending "\n", for example:
+    # "Write a short story about a robot that dreams for the first time.\n"
+    # sentence_transformers will strip the input texts, see:
+    # https://github.com/UKPLab/sentence-transformers/blob/v3.1.1/sentence_transformers/models/Transformer.py#L159
+    # This makes the input_ids different between hf_model and vllm_model.
+    # So we need to strip the input texts to avoid test failing.
+    example_prompts = [str(s).strip() for s in example_prompts]
+
+    # Inflight 4bit quantization
+    hf_model_kwargs = dict(quantization_config=BitsAndBytesConfig(
+        load_in_4bit=True))
+    with hf_runner(
+            model_name,
+            dtype=dtype,
+            model_kwargs=hf_model_kwargs,
+            is_sentence_transformer=True,
+    ) as hf_model:
+        hf_outputs = hf_model.encode(example_prompts)
+
+    with vllm_runner(model_name,
+                     task="embed",
+                     dtype=dtype,
+                     quantization="bitsandbytes") as vllm_model:
+        vllm_outputs = vllm_model.encode(example_prompts)
+    check_embeddings_close(
+        embeddings_0_lst=hf_outputs,
+        embeddings_1_lst=vllm_outputs,
+        name_0="hf",
+        name_1="vllm",
+        tol=5e-2,
+    )
+
+
 def log_generated_texts(prompts, outputs, runner_name):
     logged_texts = []
     for i, (_, generated_text) in enumerate(outputs):

vllm/model_executor/model_loader/bitsandbytes_loader.py

@@ -35,6 +35,7 @@
     download_safetensors_index_file_from_hf, download_weights_from_hf,
     filter_duplicate_safetensors_files, filter_files_not_needed_for_inference,
     pt_weights_iterator, safetensors_weights_iterator)
+from vllm.model_executor.models import is_pooling_model
 from vllm.model_executor.utils import set_weight_attrs
 from vllm.platforms import current_platform
 
@@ -133,6 +134,16 @@ def _prepare_weights(self, model_name_or_path: str,
         return hf_weights_files, use_safetensors
 
     def _hf_weight_iter(self, hf_weights_files, use_safetensors: bool):
+        def _maybe_pool_model(module_name:str):
+            # For pool model, we need to add the prefix `model.`
+            # for the weight name if possible.
+            if self.is_pool_model and self.target_modules[0]. \
+                startswith("model.") and not module_name.startswith(
+                    "model."):
+                return "model."+module_name
+
+            return module_name
+
         if use_safetensors:
             iterator = safetensors_weights_iterator(
                 hf_weights_files,
@@ -148,6 +159,9 @@ def _hf_weight_iter(self, hf_weights_files, use_safetensors: bool):
             # mapping weight names from transformers to vllm while preserving
             # original names.
             mapped_name = self.weight_mapper(org_name)
+            mapped_name=_maybe_pool_model(mapped_name)
+
+
             yield org_name, mapped_name, param
 
     def _get_quantized_weights_iterator(
@@ -405,7 +419,7 @@ def _load_weights(self, model_config: ModelConfig,
             raise AttributeError(
                 f"Model {type(model).__name__} does not support BitsAndBytes "
                 "quantization yet. No 'packed_modules_mapping' found.")
-
+        self.is_pool_model=is_pooling_model(model)
         self.modules_mapping = ParamMapping(
             copy.deepcopy(model.packed_modules_mapping))