[Bugfix] Fix RobertaModel loading

tests/model_executor/test_model_load_with_params.py

@@ -2,7 +2,7 @@
 
 import pytest
 
-from vllm.model_executor.layers.pooler import PoolingType
+from vllm.model_executor.layers.pooler import CLSPool, PoolingType
 from vllm.model_executor.models.bert import BertEmbeddingModel
 from vllm.model_executor.models.roberta import RobertaEmbeddingModel
 from vllm.platforms import current_platform
@@ -92,3 +92,28 @@ def test_roberta_model_loading_with_params(vllm_runner):
 
         # assert output
         assert output
+
+
+@pytest.mark.skipif(current_platform.is_rocm(),
+                    reason="Xformers backend is not supported on ROCm.")
+def test_facebook_roberta_model_loading_with_params(vllm_runner):
+    """
+    Test loading roberta-base model with no lm_head.
+    """
+    model_name = "FacebookAI/roberta-base"
+    with vllm_runner(model_name=model_name,
+                     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_tokenizer = model.model.llm_engine.tokenizer
+        assert model_tokenizer.tokenizer_id == model_name
+
+        model = model.model.llm_engine.model_executor\
+                     .driver_worker.model_runner.model
+        assert not hasattr(model, "lm_head")
+        assert isinstance(model, RobertaEmbeddingModel)
+        assert isinstance(model._pooler, CLSPool)
+
+        assert output

tests/models/embedding/language/test_embedding.py

@@ -25,6 +25,7 @@
         pytest.param("ssmits/Qwen2-7B-Instruct-embed-base"),
         pytest.param("Alibaba-NLP/gte-Qwen2-1.5B-instruct"),
         pytest.param("Alibaba-NLP/gte-Qwen2-7B-instruct"),
+        pytest.param("sentence-transformers/stsb-roberta-base-v2"),
     ],
 )
 @pytest.mark.parametrize("dtype", ["half"])

vllm/model_executor/models/roberta.py

@@ -1,3 +1,4 @@
+import itertools
 from typing import Iterable, List, Optional, Tuple
 
 import torch
@@ -20,6 +21,30 @@
 from .interfaces import SupportsCrossEncoding
 
 
+def roberta_task_weights_filter(
+    all_weights: Iterable[Tuple[str, torch.Tensor]]
+) -> Tuple[Iterable[Tuple[str, torch.Tensor]], Iterable[Tuple[str,
+                                                              torch.Tensor]]]:
+    """
+    Separate task-specific weights that are applied on top
+    of the encoder-decoder bert base.
+    To do so, return two generators over the original iterator.
+    Also, remove the "roberta." prefix to make it loadable
+    from vanilla BertModel.
+    """
+    # Copy of a lazy iterator without in-memory overhead so both
+    # iterators can be iterated upon independently.
+    all_weights1, all_weights2 = itertools.tee(all_weights)
+
+    def encoder_decoder_weights():
+        for name, weight in all_weights1:
+            if name.startswith("roberta."):
+                yield (name[len("roberta."):], weight)
+
+    return encoder_decoder_weights(), ((n, w) for n, w in all_weights2
+                                       if not n.startswith("roberta."))
+
+
 class RobertaEmbedding(nn.Module):
 
     def __init__(self, config: RobertaConfig):
@@ -152,6 +177,18 @@ def _build_model(self,
                          prefix=prefix,
                          embedding_class=RobertaEmbedding)
 
+    def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
+        weights = self.hf_to_vllm_mapper.apply(weights)
+        # Separate weights in "roberta"-prefixed and all else (not in memory).
+        # For use with models like FacebookAI/roberta-base.
+        bert_weights, task_weights = roberta_task_weights_filter(weights)
+        loaded = self.model.load_weights(bert_weights)
+        if not len(loaded):
+            # Fix for models like `sentence-transformers/stsb-roberta-base-v2`
+            # which use the same architecture, but have no "roberta" prefix.
+            loaded = self.model.load_weights(task_weights)
+        assert len(loaded), "Unable to load RobertaEmbeddingModel"
+
 
 class RobertaForSequenceClassification(nn.Module, SupportsCrossEncoding):
     """A model that uses Roberta to provide embedding functionalities.
@@ -181,20 +218,12 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
 
     def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
 
-        self_weights = []
-
-        def weight_filter():
-            for name, weight in weights:
-                if name.startswith("roberta."):
-                    yield (name[len("roberta."):], weight)
-                else:
-                    self_weights.append((name, weight))
-
-        self.roberta.load_weights(weight_filter())
+        bert_weights, task_weights = roberta_task_weights_filter(weights)
+        self.roberta.load_weights(bert_weights)
 
         params_dict = dict(self.named_parameters())
 
-        for name, loaded_weight in self_weights:
+        for name, loaded_weight in task_weights:
             if name.startswith("classifier"):
                 param = params_dict[name]
                 weight_loader = getattr(param, "weight_loader",