[Model] Support `is_causal` HF config field for Qwen2 model (#10621)

Signed-off-by: DarkLight1337 <tlleungac@connect.ust.hk>

docs/source/models/supported_models.rst

@@ -342,7 +342,7 @@ Text Embedding
     - ✅︎
   * - :code:`Qwen2Model`, :code:`Qwen2ForCausalLM`
     - Qwen2-based
-    - :code:`ssmits/Qwen2-7B-Instruct-embed-base`, :code:`Alibaba-NLP/gte-Qwen2-1.5B-instruct`, etc.
+    - :code:`ssmits/Qwen2-7B-Instruct-embed-base`, :code:`Alibaba-NLP/gte-Qwen2-7B-instruct` (see note), etc.
     - ✅︎
     - ✅︎
   * - :code:`RobertaModel`, :code:`RobertaForMaskedLM`
@@ -363,6 +363,13 @@ Text Embedding
 .. tip::
   You can override the model's pooling method by passing :code:`--override-pooler-config`.
 
+.. note::
+  Unlike base Qwen2, :code:`Alibaba-NLP/gte-Qwen2-7B-instruct` uses bi-directional attention.
+  You can set `--hf-overrides '{"is_causal": false}'` to change the attention mask accordingly.
+
+  On the other hand, its 1.5B variant (:code:`Alibaba-NLP/gte-Qwen2-1.5B-instruct`) uses causal attention
+  despite being described otherwise on its model card.
+
 Reward Modeling
 ---------------
 
@@ -606,10 +613,10 @@ Text Generation
 | :sup:`+` Multiple items can be inputted per text prompt for this modality.
 
 .. note::
-  vLLM currently only supports adding LoRA to the language backbone of multimodal models.               
+  vLLM currently only supports adding LoRA to the language backbone of multimodal models.
 
 .. note::
-  For :code:`openbmb/MiniCPM-V-2`, the official repo doesn't work yet, so we need to use a fork (:code:`HwwwH/MiniCPM-V-2`) for now.
+  The official :code:`openbmb/MiniCPM-V-2` doesn't work yet, so we need to use a fork (:code:`HwwwH/MiniCPM-V-2`) for now.
   For more details, please see: https://github.com/vllm-project/vllm/pull/4087#issuecomment-2250397630
 
 Multimodal Embedding

tests/models/embedding/language/test_embedding.py

@@ -21,6 +21,7 @@ from ..utils import check_embeddings_close
                      marks=[pytest.mark.core_model]),
         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.mark.parametrize("dtype", ["half"])
@@ -31,6 +32,10 @@ def test_models(
     model,
     dtype: str,
 ) -> None:
+    vllm_extra_kwargs = {}
+    if model == "Alibaba-NLP/gte-Qwen2-7B-instruct":
+        vllm_extra_kwargs["hf_overrides"] = {"is_causal": False}
+
     # 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:
@@ -43,8 +48,11 @@ def test_models(
                    is_sentence_transformer=True) as hf_model:
         hf_outputs = hf_model.encode(example_prompts)
 
-    with vllm_runner(model, task="embedding", dtype=dtype,
-                     max_model_len=None) as vllm_model:
+    with vllm_runner(model,
+                     task="embedding",
+                     dtype=dtype,
+                     max_model_len=None,
+                     **vllm_extra_kwargs) as vllm_model:
         vllm_outputs = vllm_model.encode(example_prompts)
         # This test is for verifying whether the model's extra_repr
         # can be printed correctly.

tests/models/embedding/utils.py

@@ -24,7 +24,7 @@ def check_embeddings_close(
                                   dim=0)
 
         fail_msg = (f"Test{prompt_idx}:"
-                    f"\n{name_0}:\t{embeddings_0!r}"
-                    f"\n{name_1}:\t{embeddings_1!r}")
+                    f"\n{name_0}:\t{embeddings_0[:16]!r}"
+                    f"\n{name_1}:\t{embeddings_1[:16]!r}")
 
         assert sim >= 1 - tol, fail_msg

vllm/config.py

@@ -27,7 +27,7 @@ from vllm.transformers_utils.config import (
     get_hf_text_config, get_pooling_config,
     get_sentence_transformer_tokenizer_config, is_encoder_decoder, uses_mrope)
 from vllm.utils import (GiB_bytes, cuda_device_count_stateless, get_cpu_memory,
-                        identity, print_warning_once, resolve_obj_by_qualname)
+                        print_warning_once, resolve_obj_by_qualname)
 
 if TYPE_CHECKING:
     from ray.util.placement_group import PlacementGroup
@@ -183,7 +183,7 @@ class ModelConfig:
             hf_overrides_fn = hf_overrides
         else:
             hf_overrides_kw = hf_overrides
-            hf_overrides_fn = identity
+            hf_overrides_fn = None
 
         if rope_scaling is not None:
             hf_override: Dict[str, Any] = {"rope_scaling": rope_scaling}
@@ -212,8 +212,15 @@ class ModelConfig:
         self.skip_tokenizer_init = skip_tokenizer_init
 
         hf_config = get_config(self.model, trust_remote_code, revision,
-                               code_revision, config_format, **hf_overrides_kw)
-        hf_config = hf_overrides_fn(hf_config)
+                               code_revision, config_format)
+
+        if hf_overrides_kw:
+            logger.info("Overriding HF config with %s", hf_overrides_kw)
+            hf_config.update(hf_overrides_kw)
+        if hf_overrides_fn:
+            logger.info("Overriding HF config with %s", hf_overrides_fn)
+            hf_config = hf_overrides_fn(hf_config)
+
         self.hf_config = hf_config
 
         self.hf_text_config = get_hf_text_config(self.hf_config)

vllm/model_executor/models/qwen2.py

@@ -27,7 +27,7 @@ import torch
 from torch import nn
 from transformers import Qwen2Config
 
-from vllm.attention import Attention, AttentionMetadata
+from vllm.attention import Attention, AttentionMetadata, AttentionType
 from vllm.compilation.decorators import support_torch_compile
 from vllm.config import CacheConfig, VllmConfig
 from vllm.distributed import get_pp_group, get_tensor_model_parallel_world_size
@@ -164,11 +164,17 @@ class Qwen2Attention(nn.Module):
         hidden_states: torch.Tensor,
         kv_cache: torch.Tensor,
         attn_metadata: AttentionMetadata,
+        attn_type: str = AttentionType.DECODER,
     ) -> torch.Tensor:
         qkv, _ = self.qkv_proj(hidden_states)
         q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
         q, k = self.rotary_emb(positions, q, k)
-        attn_output = self.attn(q, k, v, kv_cache, attn_metadata)
+        attn_output = self.attn(q,
+                                k,
+                                v,
+                                kv_cache,
+                                attn_metadata,
+                                attn_type=attn_type)
         output, _ = self.o_proj(attn_output)
         return output
 
@@ -210,6 +216,15 @@ class Qwen2DecoderLayer(nn.Module):
         self.post_attention_layernorm = RMSNorm(config.hidden_size,
                                                 eps=config.rms_norm_eps)
 
+        # By default, Qwen2 uses causal attention as it is a decoder-only model.
+        # You can override the HF config with `is_causal=False` to enable
+        # bidirectional attention, which is used in some embedding models
+        # (e.g. Alibaba-NLP/gte-Qwen2-7B-instruct)
+        if getattr(config, "is_causal", True):
+            self._attn_type = AttentionType.DECODER
+        else:
+            self._attn_type = AttentionType.ENCODER_ONLY
+
     def forward(
         self,
         positions: torch.Tensor,
@@ -230,6 +245,7 @@ class Qwen2DecoderLayer(nn.Module):
             hidden_states=hidden_states,
             kv_cache=kv_cache,
             attn_metadata=attn_metadata,
+            attn_type=self._attn_type,
         )
 
         # Fully Connected