[New Model]: nomic-embed-text-v2-moe (#17785)

docs/source/models/supported_models.md

@@ -622,7 +622,7 @@ Specified using `--task embed`.
   * [PP](#distributed-serving)
 - * `BertModel`
   * BERT-based
-  * `BAAI/bge-base-en-v1.5`, etc.
+  * `BAAI/bge-base-en-v1.5`, `Snowflake/snowflake-arctic-embed-xs`, etc.
   *
   *
 - * `Gemma2Model`
@@ -635,6 +635,16 @@ Specified using `--task embed`.
   * `parasail-ai/GritLM-7B-vllm`.
   * ✅︎
   * ✅︎
+- * `GteModel`
+  * GteModel
+  * `Snowflake/snowflake-arctic-embed-m-v2.0`.
+  *
+  * ︎
+- * `NomicBertModel`
+  * NomicBertModel
+  * `nomic-ai/nomic-embed-text-v1`, `nomic-ai/nomic-embed-text-v2-moe`, `Snowflake/snowflake-arctic-embed-m-long`, etc.
+  * ︎
+  * ︎
 - * `LlamaModel`, `LlamaForCausalLM`, `MistralModel`, etc.
   * Llama-based
   * `intfloat/e5-mistral-7b-instruct`, etc.
@@ -647,12 +657,12 @@ Specified using `--task embed`.
   * ✅︎
 - * `RobertaModel`, `RobertaForMaskedLM`
   * RoBERTa-based
-  * `sentence-transformers/all-roberta-large-v1`, `sentence-transformers/all-roberta-large-v1`, etc.
+  * `sentence-transformers/all-roberta-large-v1`, etc.
   *
   *
 - * `XLMRobertaModel`
   * XLM-RoBERTa-based
-  * `intfloat/multilingual-e5-large`, `jinaai/jina-reranker-v2-base-multilingual`, etc.
+  * `intfloat/multilingual-e5-large`, `jinaai/jina-reranker-v2-base-multilingual`, `Snowflake/snowflake-arctic-embed-l-v2.0`, `jinaai/jina-embeddings-v3`(see note), etc.
   *
   *
 :::
@@ -670,6 +680,10 @@ For both the 1.5B and 7B variants, you also need to enable `--trust-remote-code`
 See [relevant issue on HF Transformers](https://github.com/huggingface/transformers/issues/34882).
 :::
 
+:::{note}
+`jinaai/jina-embeddings-v3` supports multiple tasks through lora, while vllm temporarily only supports text-matching tasks by merging lora weights.
+:::
+
 If your model is not in the above list, we will try to automatically convert the model using
 {func}`~vllm.model_executor.models.adapters.as_embedding_model`. By default, the embeddings
 of the whole prompt are extracted from the normalized hidden state corresponding to the last token.

tests/models/language/pooling/mteb_utils.py

@@ -0,0 +1,111 @@
+# SPDX-License-Identifier: Apache-2.0
+import math
+from collections.abc import Sequence
+
+import mteb
+import numpy as np
+import pytest
+
+from tests.models.utils import EmbedModelInfo
+
+# Most models on the STS12 task (See #17175):
+# - Model implementation and minor changes in tensor dtype
+#   results in differences less than 1e-4
+# - Different model results in differences more than 1e-3
+# 1e-4 is a good tolerance threshold
+MTEB_EMBED_TASKS = ["STS12"]
+MTEB_EMBED_TOL = 1e-4
+
+
+class VllmMtebEncoder(mteb.Encoder):
+
+    def __init__(self, vllm_model):
+        super().__init__()
+        self.model = vllm_model
+        self.rng = np.random.default_rng(seed=42)
+
+    def encode(
+        self,
+        sentences: Sequence[str],
+        *args,
+        **kwargs,
+    ) -> np.ndarray:
+        # Hoping to discover potential scheduling
+        # issues by randomizing the order.
+        r = self.rng.permutation(len(sentences))
+        sentences = [sentences[i] for i in r]
+        outputs = self.model.encode(sentences, use_tqdm=False)
+        embeds = np.array(outputs)
+        embeds = embeds[np.argsort(r)]
+        return embeds
+
+
+class OpenAIClientMtebEncoder(mteb.Encoder):
+
+    def __init__(self, model_name: str, client):
+        super().__init__()
+        self.model_name = model_name
+        self.client = client
+        self.rng = np.random.default_rng(seed=42)
+
+    def encode(self, sentences: Sequence[str], *args, **kwargs) -> np.ndarray:
+        # Hoping to discover potential scheduling
+        # issues by randomizing the order.
+        r = self.rng.permutation(len(sentences))
+        sentences = [sentences[i] for i in r]
+
+        embeddings = self.client.embeddings.create(model=self.model_name,
+                                                   input=sentences)
+        outputs = [d.embedding for d in embeddings.data]
+        embeds = np.array(outputs)
+        embeds = embeds[np.argsort(r)]
+        return embeds
+
+
+def run_mteb_embed_task(encoder, tasks):
+    tasks = mteb.get_tasks(tasks=tasks)
+    evaluation = mteb.MTEB(tasks=tasks)
+    results = evaluation.run(encoder, verbosity=0, output_folder=None)
+
+    main_score = results[0].scores["test"][0]["main_score"]
+    return main_score
+
+
+def run_mteb_embed_task_st(model_name, tasks):
+    from sentence_transformers import SentenceTransformer
+    model = SentenceTransformer(model_name)
+    return run_mteb_embed_task(model, tasks)
+
+
+def mteb_test_embed_models(hf_runner, vllm_runner, model_info: EmbedModelInfo):
+    if not model_info.enable_test:
+        # A model family has many models with the same architecture,
+        # and we don't need to test each one.
+        pytest.skip("Skipping test.")
+
+    with vllm_runner(model_info.name,
+                     task="embed",
+                     max_model_len=None,
+                     dtype=model_info.dtype) as vllm_model:
+
+        if model_info.architecture:
+            assert (model_info.architecture
+                    in vllm_model.model.llm_engine.model_config.architectures)
+
+        vllm_main_score = run_mteb_embed_task(VllmMtebEncoder(vllm_model),
+                                              MTEB_EMBED_TASKS)
+        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,
+                   is_sentence_transformer=True,
+                   dtype=model_dtype) as hf_model:
+        st_main_score = run_mteb_embed_task(hf_model, MTEB_EMBED_TASKS)
+
+    print("VLLM:", vllm_dtype, vllm_main_score)
+    print("SentenceTransformer:", model_dtype, st_main_score)
+    print("Difference:", st_main_score - vllm_main_score)
+
+    assert math.isclose(st_main_score, vllm_main_score, rel_tol=MTEB_EMBED_TOL)

tests/models/language/pooling/test_nomic.py

@@ -0,0 +1,47 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import pytest
+
+from ...utils import EmbedModelInfo, run_embedding_correctness_test
+
+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/nomic-embed-text-v2-moe",
+                   architecture="NomicBertModel",
+                   dtype="float32",
+                   enable_test=True)
+]
+
+
+@pytest.mark.parametrize("model_info", MODELS)
+def test_models_mteb(hf_runner, vllm_runner,
+                     model_info: EmbedModelInfo) -> None:
+    from .mteb_utils import mteb_test_embed_models
+    mteb_test_embed_models(hf_runner, vllm_runner, model_info)
+
+
+@pytest.mark.parametrize("model_info", MODELS)
+def test_models_correctness(hf_runner, vllm_runner, model_info: EmbedModelInfo,
+                            example_prompts) -> None:
+    if not model_info.enable_test:
+        pytest.skip("Skipping test.")
+
+    with vllm_runner(model_info.name,
+                     task="embed",
+                     dtype=model_info.dtype,
+                     max_model_len=None) as vllm_model:
+        vllm_outputs = vllm_model.encode(example_prompts)
+
+    with hf_runner(
+            model_info.name,
+            dtype=model_info.dtype,
+            is_sentence_transformer=True,
+    ) as hf_model:
+        run_embedding_correctness_test(hf_model, example_prompts, vllm_outputs)

tests/models/language/pooling/test_snowflake_arctic_embed.py

@@ -1,12 +1,8 @@
 # SPDX-License-Identifier: Apache-2.0
+
 import pytest
 
-from ...utils import EmbedModelInfo, check_embeddings_close
+from ...utils import EmbedModelInfo, run_embedding_correctness_test
-
-EMBEDDING_PROMPTS = [
-    'what is snowflake?', 'Where can I get the best tacos?', 'The Data Cloud!',
-    'Mexico City of Course!'
-]
 
 MODELS = [
     EmbedModelInfo("Snowflake/snowflake-arctic-embed-xs",
@@ -45,51 +41,34 @@ MODELS = [
 
 
 @pytest.mark.parametrize("model_info", MODELS)
-@pytest.mark.parametrize("dtype", ["half"])
+def test_models_mteb(
-def test_models(
     hf_runner,
     vllm_runner,
-    example_prompts,
     model_info: EmbedModelInfo,
-    dtype: str,
+) -> None:
-    monkeypatch,
+    from .mteb_utils import mteb_test_embed_models
+    mteb_test_embed_models(hf_runner, vllm_runner, model_info)
+
+
+@pytest.mark.parametrize("model_info", MODELS)
+def test_models_correctness(
+    hf_runner,
+    vllm_runner,
+    model_info: EmbedModelInfo,
+    example_prompts,
 ) -> None:
     if not model_info.enable_test:
-        # A model family has many models with the same architecture,
-        # and we don't need to test each one.
         pytest.skip("Skipping test.")
 
-    example_prompts = example_prompts + EMBEDDING_PROMPTS
-
-    vllm_extra_kwargs = {
-        "hf_overrides": {
-            "is_matryoshka": model_info.is_matryoshka
-        }
-    }
-
-    with hf_runner(model_info.name, dtype=dtype,
-                   is_sentence_transformer=True) as hf_model:
-        hf_outputs = hf_model.encode(example_prompts)
-
     with vllm_runner(model_info.name,
                      task="embed",
-                     dtype=dtype,
+                     dtype=model_info.dtype,
-                     max_model_len=None,
+                     max_model_len=None) as vllm_model:
-                     **vllm_extra_kwargs) as vllm_model:
-
-        assert (vllm_model.model.llm_engine.model_config.is_matryoshka ==
-                model_info.is_matryoshka)
-
-        if model_info.architecture:
-            assert (model_info.architecture
-                    in vllm_model.model.llm_engine.model_config.architectures)
-
         vllm_outputs = vllm_model.encode(example_prompts)
 
-    check_embeddings_close(
+    with hf_runner(
-        embeddings_0_lst=hf_outputs,
+            model_info.name,
-        embeddings_1_lst=vllm_outputs,
+            dtype=model_info.dtype,
-        name_0="hf",
+            is_sentence_transformer=True,
-        name_1="vllm",
+    ) as hf_model:
-        tol=1e-2,
+        run_embedding_correctness_test(hf_model, example_prompts, vllm_outputs)
-    )

tests/models/utils.py

@@ -332,9 +332,10 @@ def matryoshka_fy(tensor: torch.Tensor, dimensions: int):
 
 class EmbedModelInfo(NamedTuple):
     name: str
-    is_matryoshka: bool
+    is_matryoshka: bool = False
     matryoshka_dimensions: Optional[list[int]] = None
     architecture: str = ""
+    dtype: str = "auto"
     enable_test: bool = True
 
 

vllm/model_executor/models/bert.py

@@ -11,16 +11,13 @@ from vllm.compilation.decorators import support_torch_compile
 from vllm.config import CacheConfig, PoolerConfig, VllmConfig
 from vllm.distributed import get_tensor_model_parallel_world_size
 from vllm.forward_context import get_forward_context
-from vllm.model_executor.layers.activation import (get_act_and_mul_fn,
+from vllm.model_executor.layers.activation import get_act_fn
-                                                   get_act_fn)
 from vllm.model_executor.layers.linear import (ColumnParallelLinear,
-                                               MergedColumnParallelLinear,
                                                QKVParallelLinear,
                                                RowParallelLinear)
 from vllm.model_executor.layers.pooler import (CrossEncodingPooler, Pooler,
                                                PoolingType)
 from vllm.model_executor.layers.quantization import QuantizationConfig
-from vllm.model_executor.layers.rotary_embedding import get_rope
 from vllm.model_executor.layers.vocab_parallel_embedding import (
     VocabParallelEmbedding)
 from vllm.model_executor.model_loader.weight_utils import default_weight_loader
@@ -41,24 +38,19 @@ class BertEmbedding(nn.Module):
         self.size = config.hidden_size
         self.word_embeddings = VocabParallelEmbedding(config.vocab_size,
                                                       config.hidden_size)
-
+        self.position_embeddings = VocabParallelEmbedding(
+            config.max_position_embeddings, config.hidden_size)
         self.token_type_embeddings = VocabParallelEmbedding(
             config.type_vocab_size, config.hidden_size)
         self.LayerNorm = nn.LayerNorm(config.hidden_size,
                                       eps=config.layer_norm_eps)
+        self.position_ids = nn.Parameter(
+            torch.empty((1, config.max_position_embeddings)), )
 
         self.position_embedding_type = config.position_embedding_type
-        if self.position_embedding_type == "absolute":
+        if self.position_embedding_type != "absolute":
-            self.position_embeddings = VocabParallelEmbedding(
+            raise ValueError("Only 'absolute' position_embedding_type" +
-                config.max_position_embeddings, config.hidden_size)
+                             " is supported")
-            self.position_ids = nn.Parameter(
-                torch.empty((1, config.max_position_embeddings)), )
-        elif self.position_embedding_type == "rotary":
-            self.position_embeddings = None
-            self.position_ids = None
-        else:
-            raise ValueError("Only 'absolute' and 'rotary' " +
-                             "position_embedding_type is supported")
 
     def forward(
         self,
@@ -72,6 +64,9 @@ class BertEmbedding(nn.Module):
         # Input embeddings.
         inputs_embeds = self.word_embeddings(input_ids)
 
+        # Position embeddings.
+        position_embeddings = self.position_embeddings(position_ids)
+
         if token_type_ids is None:
             token_type_ids = torch.zeros(input_shape,
                                          dtype=torch.long,
@@ -79,12 +74,7 @@ class BertEmbedding(nn.Module):
 
         token_type_embeddings = self.token_type_embeddings(token_type_ids)
 
-        embeddings = inputs_embeds + token_type_embeddings
+        embeddings = inputs_embeds + token_type_embeddings + position_embeddings
-
-        if self.position_embedding_type == "absolute":
-            position_embeddings = self.position_embeddings(position_ids)
-            embeddings += position_embeddings
-
         embeddings = self.LayerNorm(embeddings)
         return embeddings
 
@@ -108,11 +98,7 @@ class BertPooler(nn.Module):
 @support_torch_compile
 class BertEncoder(nn.Module):
 
-    def __init__(self,
+    def __init__(self, vllm_config: VllmConfig, prefix: str = ""):
-                 vllm_config: VllmConfig,
-                 bias: bool = True,
-                 rotary_kwargs: Optional[dict] = None,
-                 prefix: str = ""):
         super().__init__()
         config = vllm_config.model_config.hf_config
         cache_config = vllm_config.cache_config
@@ -121,19 +107,16 @@ class BertEncoder(nn.Module):
             BertLayer(config=config,
                       cache_config=cache_config,
                       quant_config=quant_config,
-                      bias=bias,
-                      rotary_kwargs=rotary_kwargs,
                       prefix=f"{prefix}.layer.{layer_idx}")
             for layer_idx in range(config.num_hidden_layers)
         ])
 
     def forward(
         self,
-        positions: torch.Tensor,
         hidden_states: torch.Tensor,
     ) -> torch.Tensor:
         for layer in self.layer:
-            hidden_states = layer(positions, hidden_states)
+            hidden_states = layer(hidden_states)
         return hidden_states
 
 
@@ -143,8 +126,6 @@ class BertLayer(nn.Module):
                  config: BertConfig,
                  cache_config: Optional[CacheConfig] = None,
                  quant_config: Optional[QuantizationConfig] = None,
-                 bias: bool = True,
-                 rotary_kwargs: Optional[dict] = None,
                  prefix: str = ""):
         super().__init__()
 
@@ -154,36 +135,23 @@ class BertLayer(nn.Module):
             layer_norm_eps=config.layer_norm_eps,
             cache_config=cache_config,
             quant_config=quant_config,
-            bias=bias,
-            rotary_kwargs=rotary_kwargs,
             prefix=f"{prefix}.attention")
 
-        if config.hidden_act in ["silu", "gelu_and_mul"]:
+        self.intermediate = BertIntermediate(
-            self.intermediate = BertGatedIntermediate(
+            hidden_size=config.hidden_size,
-                hidden_size=config.hidden_size,
+            intermediate_size=config.intermediate_size,
-                intermediate_size=config.intermediate_size,
+            hidden_act=config.hidden_act,
-                hidden_act=config.hidden_act,
+            quant_config=quant_config,
-                bias=bias,
+            prefix=f"{prefix}.intermediate")
-                quant_config=quant_config,
-                prefix=f"{prefix}.intermediate")
-        else:
-            self.intermediate = BertIntermediate(
-                hidden_size=config.hidden_size,
-                intermediate_size=config.intermediate_size,
-                hidden_act=config.hidden_act,
-                bias=bias,
-                quant_config=quant_config,
-                prefix=f"{prefix}.intermediate")
 
         self.output = BertOutput(hidden_size=config.hidden_size,
                                  intermediate_size=config.intermediate_size,
                                  layer_norm_eps=config.layer_norm_eps,
-                                 bias=bias,
                                  quant_config=quant_config,
                                  prefix=f"{prefix}.output")
 
-    def forward(self, positions: torch.Tensor, hidden_states: torch.Tensor):
+    def forward(self, hidden_states: torch.Tensor):
-        attn_output = self.attention(positions, hidden_states)
+        attn_output = self.attention(hidden_states)
         intermediate_output = self.intermediate(attn_output)
         output = self.output(intermediate_output, attn_output)
         return output
@@ -198,8 +166,6 @@ class BertAttention(nn.Module):
         layer_norm_eps: float,
         cache_config: Optional[CacheConfig] = None,
         quant_config: Optional[QuantizationConfig] = None,
-        bias: bool = True,
-        rotary_kwargs: Optional[dict] = None,
         prefix: str = "",
     ):
         super().__init__()
@@ -208,22 +174,18 @@ class BertAttention(nn.Module):
                                       num_attention_heads=num_attention_heads,
                                       cache_config=cache_config,
                                       quant_config=quant_config,
-                                      bias=bias,
-                                      rotary_kwargs=rotary_kwargs,
                                       prefix=f"{prefix}.output")
 
         self.output = BertSelfOutput(hidden_size=hidden_size,
                                      layer_norm_eps=layer_norm_eps,
-                                     bias=bias,
                                      quant_config=quant_config,
                                      prefix=f"{prefix}.output")
 
     def forward(
         self,
-        positions: torch.Tensor,
         hidden_states: torch.Tensor,
     ) -> torch.Tensor:
-        self_output = self.self(positions, hidden_states)
+        self_output = self.self(hidden_states)
         return self.output(self_output, hidden_states)
 
 
@@ -235,8 +197,6 @@ class BertSelfAttention(nn.Module):
         num_attention_heads: int,
         cache_config: Optional[CacheConfig] = None,
         quant_config: Optional[QuantizationConfig] = None,
-        bias: bool = True,
-        rotary_kwargs: Optional[dict] = None,
         prefix: str = "",
     ):
         super().__init__()
@@ -261,15 +221,10 @@ class BertSelfAttention(nn.Module):
             head_size=self.head_dim,
             total_num_heads=self.total_num_heads,
             total_num_kv_heads=self.total_num_kv_heads,
-            bias=bias,
+            bias=True,
             quant_config=quant_config,
             prefix=f"{prefix}.qkv_proj")
 
-        if rotary_kwargs:
-            self.rotary_emb = get_rope(**rotary_kwargs)
-        else:
-            self.rotary_emb = None
-
         self.attn = Attention(num_heads=self.num_heads,
                               head_size=self.head_dim,
                               scale=self.scaling,
@@ -281,15 +236,10 @@ class BertSelfAttention(nn.Module):
 
     def forward(
         self,
-        positions: torch.Tensor,
         hidden_states: torch.Tensor,
     ) -> torch.Tensor:
         qkv, _ = self.qkv_proj(hidden_states)
         q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
-
-        if self.rotary_emb:
-            q, k = self.rotary_emb(positions, q, k)
-
         output = self.attn(q, k, v)
         return output
 
@@ -299,13 +249,12 @@ class BertSelfOutput(nn.Module):
     def __init__(self,
                  hidden_size: int,
                  layer_norm_eps: float,
-                 bias: bool = True,
                  quant_config: Optional[QuantizationConfig] = None,
                  prefix: str = ""):
         super().__init__()
         self.dense = RowParallelLinear(input_size=hidden_size,
                                        output_size=hidden_size,
-                                       bias=bias,
+                                       bias=True,
                                        quant_config=quant_config,
                                        prefix=f"{prefix}.dense")
         self.LayerNorm = nn.LayerNorm(hidden_size, eps=layer_norm_eps)
@@ -323,13 +272,12 @@ class BertIntermediate(nn.Module):
                  hidden_size: int,
                  intermediate_size: int,
                  hidden_act: str,
-                 bias: bool = True,
                  quant_config: Optional[QuantizationConfig] = None,
                  prefix: str = ""):
         super().__init__()
         self.dense = ColumnParallelLinear(input_size=hidden_size,
                                           output_size=intermediate_size,
-                                          bias=bias,
+                                          bias=True,
                                           quant_config=quant_config,
                                           prefix=f"{prefix}.dense")
         self.intermediate_act_fn = get_act_fn(hidden_act)
@@ -340,46 +288,19 @@ class BertIntermediate(nn.Module):
         return hidden_states
 
 
-class BertGatedIntermediate(nn.Module):
-    # for NomciBert and GteModel
-
-    def __init__(self,
-                 hidden_size: int,
-                 intermediate_size: int,
-                 hidden_act: str,
-                 bias: bool = True,
-                 quant_config: Optional[QuantizationConfig] = None,
-                 prefix: str = ""):
-        super().__init__()
-        self.act_fn = get_act_and_mul_fn(hidden_act)
-        self.gate_up_proj = MergedColumnParallelLinear(
-            hidden_size,
-            [intermediate_size] * 2,
-            bias=bias,
-            quant_config=quant_config,
-            prefix=f"{prefix}.gate_up_proj",
-        )
-
-    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
-        gate_up, _ = self.gate_up_proj(hidden_states)
-        hidden_states = self.act_fn(gate_up)
-        return hidden_states
-
-
 class BertOutput(nn.Module):
 
     def __init__(self,
                  hidden_size: int,
                  intermediate_size: int,
                  layer_norm_eps: float,
-                 bias: bool = True,
                  quant_config: Optional[QuantizationConfig] = None,
                  prefix: str = ""):
         super().__init__()
 
         self.dense = RowParallelLinear(input_size=intermediate_size,
                                        output_size=hidden_size,
-                                       bias=bias,
+                                       bias=True,
                                        quant_config=quant_config,
                                        prefix=f"{prefix}.dense")
 
@@ -393,33 +314,18 @@ class BertOutput(nn.Module):
 
 
 class BertModel(nn.Module, SupportsQuant):
-    packed_modules_mapping = {
+    packed_modules_mapping = {"qkv_proj": ["query", "key", "value"]}
-        "qkv_proj": ["query", "key", "value"],
-        "gate_up_proj": [
-            "gate_proj",
-            "up_proj",
-        ],
-    }
 
     def __init__(self,
                  *,
                  vllm_config: VllmConfig,
                  prefix: str = "",
                  embedding_class: type = BertEmbedding,
-                 bias: bool = True,
-                 rotary_kwargs: Optional[dict] = None,
                  add_pooling_layer: bool = False):
         super().__init__()
-        """
-        For BertModel, all linear layers have bias.
-        For NomicBertModel, all linear layers do not have bias.
-        """
-
         config = vllm_config.model_config.hf_config
         self.embeddings = embedding_class(config)
         self.encoder = BertEncoder(vllm_config=vllm_config,
-                                   bias=bias,
-                                   rotary_kwargs=rotary_kwargs,
                                    prefix=f"{prefix}.encoder")
         self.pooler = BertPooler(config) if add_pooling_layer else None
 
@@ -441,7 +347,7 @@ class BertModel(nn.Module, SupportsQuant):
                 seq_lens=attn_metadata.seq_lens_tensor,
                 position_ids=position_ids,
                 token_type_ids=token_type_ids)
-        return self.encoder(position_ids, hidden_states)
+        return self.encoder(hidden_states)
 
     def load_weights(self, weights: Iterable[Tuple[str,
                                                    torch.Tensor]]) -> Set[str]:
@@ -450,8 +356,6 @@ class BertModel(nn.Module, SupportsQuant):
             ("qkv_proj", "query", "q"),
             ("qkv_proj", "key", "k"),
             ("qkv_proj", "value", "v"),
-            ("gate_up_proj", "gate_proj", 0),
-            ("gate_up_proj", "up_proj", 1),
         ]
 
         params_dict = dict(self.named_parameters())
@@ -497,7 +401,6 @@ class BertEmbeddingModel(nn.Module, SupportsV0Only, SupportsQuant):
     def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
         super().__init__()
         pooler_config = vllm_config.model_config.pooler_config
-        self.config = vllm_config.model_config.hf_config
         self.model = self._build_model(vllm_config=vllm_config,
                                        prefix=maybe_prefix(prefix, "model"))
         self._pooler = self._build_pooler(pooler_config)
@@ -611,115 +514,3 @@ class BertForSequenceClassification(nn.Module, SupportsCrossEncoding,
                          inputs_embeds=inputs_embeds,
                          intermediate_tensors=intermediate_tensors,
                          token_type_ids=token_type_ids)
-
-
-class NomicBertEmbeddingModel(BertEmbeddingModel):
-
-    hf_to_vllm_mapper = WeightsMapper(
-        orig_to_new_substr={
-            "emb_ln": "embeddings.LayerNorm",
-            "layers": "layer",
-            "attn.Wqkv": "attention.self.qkv_proj",
-            "attn.out_proj": "attention.output.dense",
-            'norm1': "attention.output.LayerNorm",
-            'mlp.fc11': "intermediate.up_proj",
-            'mlp.fc12': "intermediate.gate_proj",
-            'mlp.fc2': "output.dense",
-            'norm2': "output.LayerNorm",
-        })
-
-    def _build_model(self,
-                     vllm_config: VllmConfig,
-                     prefix: str = "") -> BertModel:
-        config = vllm_config.model_config.hf_config
-
-        assert config.__class__.__name__ == "NomicBertConfig"
-        assert config.activation_function == "swiglu"
-
-        # Assume NomicBertModel all linear layers do not have bias
-        assert not config.mlp_fc1_bias
-        assert not config.mlp_fc2_bias
-        assert not config.qkv_proj_bias
-
-        config.layer_norm_eps = config.layer_norm_epsilon
-        config.position_embedding_type = "rotary"
-        config.intermediate_size = config.n_inner
-        config.hidden_act = "silu"
-        config.hidden_size = config.n_embd
-        config.num_hidden_layers = config.n_layer
-
-        head_dim = config.hidden_size // config.num_attention_heads
-        rotary_kwargs = {
-            "head_size": head_dim,
-            "rotary_dim": getattr(config, "rotary_emb_dim", head_dim),
-            "max_position": config.max_trained_positions,
-            "base": config.rotary_emb_base,
-            "rope_scaling": {
-                "rope_type": "dynamic",
-                "factor": config.rotary_scaling_factor
-            }
-        }
-
-        return BertModel(vllm_config=vllm_config,
-                         prefix=prefix,
-                         bias=False,
-                         rotary_kwargs=rotary_kwargs,
-                         embedding_class=BertEmbedding)
-
-
-class GteEmbeddingModel(BertEmbeddingModel):
-    hf_to_vllm_mapper = WeightsMapper(
-        orig_to_new_substr={
-            "attention.qkv_proj": "attention.self.qkv_proj",
-            "attention.o_proj": "attention.output.dense",
-            'attn_ln': "attention.output.LayerNorm",
-            'mlp.down_proj': "output.dense",
-            'mlp_ln': "output.LayerNorm",
-        })
-
-    def _build_model(self,
-                     vllm_config: VllmConfig,
-                     prefix: str = "") -> BertModel:
-        config = vllm_config.model_config.hf_config
-
-        assert config.__class__.__name__ == "GteConfig"
-        assert config.position_embedding_type == "rope"
-        assert config.hidden_act == "gelu"
-
-        config.position_embedding_type = "rotary"
-        config.hidden_act = "gelu_and_mul"
-
-        head_dim = config.hidden_size // config.num_attention_heads
-        rotary_kwargs = {
-            "head_size": head_dim,
-            "rotary_dim": getattr(config, "rotary_emb_dim", head_dim),
-            "max_position": config.max_position_embeddings,
-            "base": config.rope_theta,
-        }
-
-        model = BertModel(vllm_config=vllm_config,
-                          prefix=prefix,
-                          rotary_kwargs=rotary_kwargs,
-                          embedding_class=BertEmbedding)
-
-        # GteModel only gate_up_proj does not have bias.
-        # Hack method learned from vllm/model_executor/models/glm.py
-        for layer in model.encoder.layer:
-            layer.intermediate.gate_up_proj.bias = None
-            layer.intermediate.skip_bias_add = True
-        return model
-
-    def split_up_gate_proj(self, weights: Iterable[Tuple[str, torch.Tensor]]):
-        n = "mlp.up_gate_proj"
-        for name, weight in weights:
-            if n in name:
-                up, gate = weight.chunk(2, dim=0)
-                yield name.replace(n, "intermediate.up_proj"), up
-                yield name.replace(n, "intermediate.gate_proj"), gate
-            else:
-                yield name, weight
-
-    def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
-        weights = self.hf_to_vllm_mapper.apply(weights)
-        weights = self.split_up_gate_proj(weights)
-        self.model.load_weights(weights)

vllm/model_executor/models/bert_with_rope.py

@@ -0,0 +1,652 @@
+# SPDX-License-Identifier: Apache-2.0
+from typing import Iterable, Optional, Set, Tuple
+
+import torch
+from torch import nn
+from transformers import PretrainedConfig
+
+from vllm.attention import Attention, AttentionType
+from vllm.compilation.decorators import support_torch_compile
+from vllm.config import CacheConfig, VllmConfig
+from vllm.distributed import get_tensor_model_parallel_world_size
+from vllm.model_executor.layers.activation import (get_act_and_mul_fn,
+                                                   get_act_fn)
+from vllm.model_executor.layers.linear import (ColumnParallelLinear,
+                                               MergedColumnParallelLinear,
+                                               QKVParallelLinear,
+                                               ReplicatedLinear,
+                                               RowParallelLinear)
+from vllm.model_executor.layers.quantization import QuantizationConfig
+from vllm.model_executor.layers.rotary_embedding import get_rope
+from vllm.model_executor.layers.vocab_parallel_embedding import (
+    VocabParallelEmbedding)
+from vllm.model_executor.model_loader.weight_utils import default_weight_loader
+from vllm.model_executor.models import SupportsV0Only
+from vllm.model_executor.models.interfaces import SupportsQuant
+from vllm.model_executor.models.utils import WeightsMapper
+from vllm.sequence import IntermediateTensors
+
+
+class BertWithRopeEmbedding(nn.Module):
+
+    def __init__(self, config: PretrainedConfig):
+
+        super().__init__()
+        assert config.type_vocab_size > 0
+        self.word_embeddings = VocabParallelEmbedding(config.vocab_size,
+                                                      config.hidden_size)
+        self.token_type_embeddings = VocabParallelEmbedding(
+            config.type_vocab_size, config.hidden_size)
+        self.LayerNorm = nn.LayerNorm(config.hidden_size,
+                                      eps=config.layer_norm_eps)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        token_type_ids: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        input_shape = input_ids.size()
+        inputs_embeds = self.word_embeddings(input_ids)
+        if token_type_ids is None:
+            token_type_ids = torch.zeros(input_shape,
+                                         dtype=torch.long,
+                                         device=inputs_embeds.device)
+
+        token_type_embeddings = self.token_type_embeddings(token_type_ids)
+        embeddings = inputs_embeds + token_type_embeddings
+        embeddings = self.LayerNorm(embeddings)
+        return embeddings
+
+
+class BertWithRopeAttention(nn.Module):
+
+    def __init__(
+        self,
+        hidden_size: int,
+        num_attention_heads: int,
+        cache_config: Optional[CacheConfig] = None,
+        quant_config: Optional[QuantizationConfig] = None,
+        bias: bool = True,
+        rotary_kwargs: Optional[dict] = None,
+        prefix: str = "",
+    ):
+        super().__init__()
+
+        self.hidden_size = hidden_size
+        tp_size = get_tensor_model_parallel_world_size()
+
+        self.total_num_heads = num_attention_heads
+        assert self.total_num_heads % tp_size == 0
+
+        self.num_heads = self.total_num_heads // tp_size
+        self.total_num_kv_heads = self.total_num_heads
+        self.head_dim = self.hidden_size // self.total_num_heads
+        assert self.head_dim * self.total_num_heads == self.hidden_size
+
+        self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
+
+        self.q_size = self.num_heads * self.head_dim
+        self.kv_size = self.num_kv_heads * self.head_dim
+        self.scaling = self.head_dim**-0.5
+
+        self.qkv_proj = QKVParallelLinear(
+            hidden_size=self.hidden_size,
+            head_size=self.head_dim,
+            total_num_heads=self.total_num_heads,
+            total_num_kv_heads=self.total_num_kv_heads,
+            bias=bias,
+            quant_config=quant_config,
+            prefix=f"{prefix}.qkv_proj")
+
+        self.rotary_emb = get_rope(**rotary_kwargs)
+
+        self.attn = Attention(num_heads=self.num_heads,
+                              head_size=self.head_dim,
+                              scale=self.scaling,
+                              num_kv_heads=self.num_kv_heads,
+                              cache_config=cache_config,
+                              quant_config=quant_config,
+                              prefix=f"{prefix}.attn",
+                              attn_type=AttentionType.ENCODER_ONLY)
+
+        self.out_proj = RowParallelLinear(input_size=hidden_size,
+                                          output_size=hidden_size,
+                                          bias=bias,
+                                          quant_config=quant_config,
+                                          prefix=f"{prefix}.dense")
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+    ) -> 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)
+        output, _ = self.out_proj(attn_output)
+        return output
+
+
+class BertWithRopeGatedMLP(nn.Module):
+
+    def __init__(self,
+                 hidden_size: int,
+                 intermediate_size: int,
+                 hidden_act: str,
+                 bias: bool = True,
+                 quant_config: Optional[QuantizationConfig] = None,
+                 prefix: str = ""):
+        super().__init__()
+        self.act_fn = get_act_and_mul_fn(hidden_act)
+        self.gate_up_proj = MergedColumnParallelLinear(
+            hidden_size,
+            [intermediate_size] * 2,
+            bias=bias,
+            quant_config=quant_config,
+            prefix=f"{prefix}.gate_up_proj",
+        )
+        self.down_proj = RowParallelLinear(input_size=intermediate_size,
+                                           output_size=hidden_size,
+                                           bias=bias,
+                                           quant_config=quant_config,
+                                           prefix=f"{prefix}.down_proj")
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        gate_up, _ = self.gate_up_proj(hidden_states)
+        hidden_states = self.act_fn(gate_up)
+        hidden_states, _ = self.down_proj(hidden_states)
+        return hidden_states
+
+
+class BertWithRopeMLP(nn.Module):
+
+    def __init__(self,
+                 hidden_size: int,
+                 intermediate_size: int,
+                 hidden_act: str,
+                 bias: bool = True,
+                 quant_config: Optional[QuantizationConfig] = None,
+                 prefix: str = ""):
+        super().__init__()
+        self.act_fn = get_act_fn(hidden_act)
+        self.up_proj = ColumnParallelLinear(input_size=hidden_size,
+                                            output_size=intermediate_size,
+                                            bias=bias,
+                                            quant_config=quant_config,
+                                            prefix=f"{prefix}.up_proj")
+        self.down_proj = RowParallelLinear(input_size=intermediate_size,
+                                           output_size=hidden_size,
+                                           bias=bias,
+                                           quant_config=quant_config,
+                                           prefix=f"{prefix}.down_proj")
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        hidden_states, _ = self.up_proj(hidden_states)
+        hidden_states = self.act_fn(hidden_states)
+        hidden_states, _ = self.down_proj(hidden_states)
+        return hidden_states
+
+
+class NomicRouter(nn.Module):
+
+    def __init__(self, hidden_size: int, moe_num_experts: int, moe_top_k: int):
+        super().__init__()
+        self.moe_top_k = moe_top_k
+        self.layer = ReplicatedLinear(hidden_size, moe_num_experts, bias=False)
+
+    def forward(
+        self, x: torch.Tensor
+    ) -> Tuple[torch.Tensor, torch.Tensor, torch.LongTensor]:
+        weights = self.layer(x.view(-1, x.shape[-1]))[0].softmax(
+            dim=-1, dtype=torch.float32)
+        top_weights, top_experts = torch.topk(weights, self.moe_top_k, dim=-1)
+        weights = weights.to(x.dtype)
+        top_weights = top_weights.to(x.dtype)
+        return weights, top_weights, top_experts  # type: ignore
+
+
+class NomicExpertMLP(nn.Module):
+
+    def __init__(self, hidden_size: int, ffn_hidden_size: int,
+                 moe_num_experts: int, ffn_act_fn: str):
+        super().__init__()
+        self.hidden_size = hidden_size
+        self.ffn_hidden_size = ffn_hidden_size
+        self.moe_num_experts = moe_num_experts
+
+        self.w1 = nn.Parameter(
+            torch.empty(moe_num_experts * ffn_hidden_size, hidden_size))
+        self.w2 = nn.Parameter(
+            torch.empty(moe_num_experts * ffn_hidden_size, hidden_size))
+        self.activation_fn = get_act_fn(ffn_act_fn)
+
+    def forward(self, x: torch.Tensor, expert_idx: int) -> torch.Tensor:
+        expert_w1 = self.w1.view(self.moe_num_experts, self.ffn_hidden_size,
+                                 self.hidden_size)[expert_idx]
+        expert_w2 = self.w2.view(self.moe_num_experts, self.ffn_hidden_size,
+                                 self.hidden_size)[expert_idx]
+
+        x1 = x.matmul(expert_w1.t())
+        act_out = self.activation_fn(x1)
+        x2 = act_out.matmul(expert_w2)
+        return x2
+
+
+class NomicExperts(nn.Module):
+
+    def __init__(self, config, hidden_size: int, ffn_hidden_size: int,
+                 moe_num_experts: int):
+        super().__init__()
+        self.moe_num_experts = moe_num_experts
+
+        self.mlp = NomicExpertMLP(hidden_size=config.n_embd,
+                                  ffn_hidden_size=config.n_inner,
+                                  moe_num_experts=moe_num_experts,
+                                  ffn_act_fn=config.hidden_act)
+        self.bias = nn.Parameter(torch.zeros(config.n_embd))
+
+    def forward(self, x: torch.Tensor, weights: torch.Tensor,
+                top_weights: torch.Tensor,
+                top_experts: torch.LongTensor) -> torch.Tensor:
+        q_len, hidden_size = x.shape
+        x = x.view(-1, hidden_size)
+        out = torch.zeros_like(x)
+
+        expert_mask = nn.functional.one_hot(
+            top_experts, num_classes=self.moe_num_experts).permute(2, 1, 0)
+        for expert_idx in range(0, self.moe_num_experts):
+            topk_idx, token_idx = torch.where(expert_mask[expert_idx])
+            if token_idx.shape[0] == 0:
+                continue
+
+            token_list = token_idx.tolist()
+            topk_list = topk_idx.tolist()
+
+            expert_tokens = x[None, token_list].reshape(-1, hidden_size)
+            expert_out = self.mlp(
+                expert_tokens, expert_idx) * top_weights[token_list, topk_list,
+                                                         None]
+
+            out.index_add_(0, token_idx, expert_out)
+
+        out = out.reshape(q_len, hidden_size)
+        return out + self.bias
+
+
+class NomicMoELayer(nn.Module):
+
+    def __init__(self, config: PretrainedConfig):
+        super().__init__()
+
+        self.router = NomicRouter(
+            config.n_embd,
+            moe_num_experts=config.num_experts,
+            moe_top_k=config.moe_top_k,
+        )
+
+        self.experts = NomicExperts(
+            config,
+            hidden_size=config.n_embd,
+            ffn_hidden_size=config.n_inner,
+            moe_num_experts=config.num_experts,
+        )
+
+    def forward(self, x: torch.Tensor):
+        weights, top_weights, top_experts = self.router(x)
+        out = self.experts(x, weights, top_weights, top_experts)
+        return out
+
+
+class BertWithRopeBlock(nn.Module):
+
+    def __init__(self,
+                 config: PretrainedConfig,
+                 cache_config: Optional[CacheConfig] = None,
+                 quant_config: Optional[QuantizationConfig] = None,
+                 moe: bool = False,
+                 bias: bool = True,
+                 rotary_kwargs: Optional[dict] = None,
+                 prefix: str = ""):
+        super().__init__()
+        self.attn = BertWithRopeAttention(
+            hidden_size=config.hidden_size,
+            num_attention_heads=config.num_attention_heads,
+            cache_config=cache_config,
+            quant_config=quant_config,
+            bias=bias,
+            rotary_kwargs=rotary_kwargs,
+            prefix=f"{prefix}.attention")
+
+        if moe:
+            self.mlp = NomicMoELayer(config=config, )
+        else:
+            if config.hidden_act in ["silu", "gelu_and_mul"]:
+                self.mlp = BertWithRopeGatedMLP(
+                    hidden_size=config.hidden_size,
+                    intermediate_size=config.intermediate_size,
+                    hidden_act=config.hidden_act,
+                    bias=bias,
+                    quant_config=quant_config,
+                    prefix=f"{prefix}.mlp")
+            else:
+                self.mlp = BertWithRopeMLP(
+                    hidden_size=config.hidden_size,
+                    intermediate_size=config.intermediate_size,
+                    hidden_act=config.hidden_act,
+                    bias=bias,
+                    quant_config=quant_config,
+                    prefix=f"{prefix}.mlp")
+
+        self.attn_ln = nn.LayerNorm(config.hidden_size,
+                                    eps=config.layer_norm_eps)
+        self.mlp_ln = nn.LayerNorm(config.hidden_size,
+                                   eps=config.layer_norm_eps)
+
+    def forward(self, positions: torch.Tensor, hidden_states: torch.Tensor):
+        attn_output = self.attn(positions, hidden_states)
+        hidden_states = self.attn_ln(hidden_states + attn_output)
+        mlp_out = self.mlp(hidden_states)
+        hidden_states = self.mlp_ln(hidden_states + mlp_out)
+        return hidden_states
+
+
+@support_torch_compile
+class BertWithRopeEncoder(nn.Module):
+
+    def __init__(self,
+                 vllm_config: VllmConfig,
+                 bias: bool = True,
+                 rotary_kwargs: Optional[dict] = None,
+                 prefix: str = ""):
+        super().__init__()
+        config = vllm_config.model_config.hf_config
+        cache_config = vllm_config.cache_config
+        quant_config = vllm_config.quant_config
+        every_n = getattr(config, "moe_every_n_layers", 0)
+        self.layers = nn.ModuleList([
+            BertWithRopeBlock(config=config,
+                              cache_config=cache_config,
+                              quant_config=quant_config,
+                              bias=bias,
+                              moe=every_n > 0 and (layer_idx % every_n == 1),
+                              rotary_kwargs=rotary_kwargs,
+                              prefix=f"{prefix}.layer.{layer_idx}")
+            for layer_idx in range(config.num_hidden_layers)
+        ])
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+    ) -> torch.Tensor:
+        for layer in self.layers:
+            hidden_states = layer(positions, hidden_states)
+        return hidden_states
+
+
+class BertWithRope(nn.Module, SupportsV0Only, SupportsQuant):
+    hf_to_vllm_mapper = WeightsMapper(orig_to_new_prefix={"model.": ""})
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+        self.config = self.config_verify(vllm_config)
+        self.embeddings = BertWithRopeEmbedding(self.config)
+        self.encoder = BertWithRopeEncoder(
+            vllm_config=vllm_config,
+            bias=getattr(self.config, "bias", True),
+            rotary_kwargs=self.config.rotary_kwargs,
+            prefix=f"{prefix}.encoder")
+
+    def config_verify(self, vllm_config):
+        raise NotImplementedError
+
+    def forward(
+        self,
+        input_ids: Optional[torch.Tensor],
+        positions: torch.Tensor,
+        intermediate_tensors: Optional[IntermediateTensors] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        token_type_ids: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        if inputs_embeds is not None:
+            hidden_states = inputs_embeds
+        else:
+            hidden_states = self.embeddings(input_ids=input_ids,
+                                            token_type_ids=token_type_ids)
+        return self.encoder(positions, hidden_states)
+
+    def load_weights(self, weights: Iterable[Tuple[str,
+                                                   torch.Tensor]]) -> Set[str]:
+        weights = self.hf_to_vllm_mapper.apply(weights)
+
+        if self.config.hidden_act in ["silu", "gelu_and_mul"]:
+            stacked_params_mapping = [
+                # (param_name, shard_name, shard_id)
+                ("gate_up_proj", "gate_proj", 0),
+                ("gate_up_proj", "up_proj", 1),
+            ]
+        else:
+            stacked_params_mapping = []
+
+        params_dict = dict(self.named_parameters())
+        loaded_params: Set[str] = set()
+        for name, loaded_weight in weights:
+            if "pooler" in name:
+                continue
+            for (param_name, weight_name, shard_id) in stacked_params_mapping:
+                if weight_name not in name:
+                    continue
+                name = name.replace(weight_name, param_name)
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+                param = params_dict[name]
+                weight_loader = param.weight_loader
+                weight_loader(param, loaded_weight, shard_id)
+                break
+            else:
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+                param = params_dict[name]
+                weight_loader = getattr(param, "weight_loader",
+                                        default_weight_loader)
+                weight_loader(param, loaded_weight)
+            loaded_params.add(name)
+        return loaded_params
+
+
+class NomicBertModel(BertWithRope):
+    hf_to_vllm_mapper = WeightsMapper(
+        orig_to_new_substr={
+            "emb_ln": "embeddings.LayerNorm",
+            "attn.Wqkv": "attn.qkv_proj",
+            "norm1": "attn_ln",
+            "mlp.fc1.": "mlp.up_proj.",
+            "mlp.fc11": "mlp.up_proj",
+            "mlp.fc12": "mlp.gate_proj",
+            "mlp.fc2": "mlp.down_proj",
+            "norm2": "mlp_ln",
+        })
+
+    def config_verify(self, vllm_config):
+        config = vllm_config.model_config.hf_config
+
+        assert config.__class__.__name__ == "NomicBertConfig"
+        assert config.activation_function in ["swiglu", "gelu"]
+
+        if config.activation_function == "swiglu":
+            config.hidden_act = "silu"
+        else:
+            config.hidden_act = config.activation_function
+
+        assert (config.mlp_fc1_bias == config.mlp_fc2_bias ==
+                config.qkv_proj_bias)
+        config.bias = config.qkv_proj_bias
+
+        assert config.rotary_emb_scale_base is None
+        assert not config.rotary_emb_interleaved
+
+        config.layer_norm_eps = config.layer_norm_epsilon
+        config.intermediate_size = config.n_inner
+        config.hidden_size = config.n_embd
+        config.num_hidden_layers = config.n_layer
+
+        head_dim = config.hidden_size // config.num_attention_heads
+        rotary_emb_dim = head_dim * config.rotary_emb_fraction
+        config.rotary_kwargs = {
+            "head_size": head_dim,
+            "rotary_dim": rotary_emb_dim,
+            "max_position": config.max_trained_positions,
+            "base": getattr(config, "rope_theta", config.rotary_emb_base),
+            "rope_scaling": getattr(config, "rope_scaling", None)
+        }
+
+        # we ignore config.rotary_scaling_factor so that for datasets shorter
+        # than max_trained_positions 2048, the results are consistent
+        # with SentenceTransformer.
+        # The context extension uses vllm style rope_theta and rope_scaling.
+        # See #17785
+
+        return config
+
+
+class GteModel(BertWithRope):
+    hf_to_vllm_mapper = WeightsMapper(
+        orig_to_new_substr={
+            "layer": 'layers',
+            "attention.qkv_proj": "attn.qkv_proj",
+            "attention.o_proj": "attn.out_proj",
+        })
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__(vllm_config=vllm_config, prefix=prefix)
+
+        # GteModel only gate_up_proj does not have bias.
+        # Hack method learned from vllm/model_executor/models/glm.py
+        for layer in self.encoder.layers:
+            layer.mlp.gate_up_proj.bias = None
+            layer.mlp.gate_up_proj.skip_bias_add = True
+
+    def config_verify(self, vllm_config):
+        config = vllm_config.model_config.hf_config
+
+        assert config.__class__.__name__ == "GteConfig"
+        assert config.position_embedding_type == "rope"
+        assert config.hidden_act == "gelu"
+
+        config.position_embedding_type = "rotary"
+        config.hidden_act = "gelu_and_mul"
+
+        head_dim = config.hidden_size // config.num_attention_heads
+        config.rotary_kwargs = {
+            "head_size": head_dim,
+            "rotary_dim": getattr(config, "rotary_emb_dim", head_dim),
+            "max_position": config.max_position_embeddings,
+            "base": config.rope_theta,
+            "rope_scaling": getattr(config, "rope_scaling", None)
+        }
+        return config
+
+    def split_up_gate_proj(self, weights: Iterable[Tuple[str, torch.Tensor]]):
+        n = "mlp.up_gate_proj"
+        for name, weight in weights:
+            if n in name:
+                up, gate = weight.chunk(2, dim=0)
+                yield name.replace(n, "mlp.up_proj"), up
+                yield name.replace(n, "mlp.gate_proj"), gate
+            else:
+                yield name, weight
+
+    def load_weights(self, weights: Iterable[Tuple[str,
+                                                   torch.Tensor]]) -> Set[str]:
+        weights = self.split_up_gate_proj(weights)
+        return super().load_weights(weights)
+
+
+class JinaRobertaModel(BertWithRope):
+    hf_to_vllm_mapper = WeightsMapper(
+        orig_to_new_substr={
+            "emb_ln": "embeddings.LayerNorm",
+            "mixer.Wqkv": "attn.qkv_proj",
+            "mixer.out_proj": "attn.out_proj",
+            "norm1": "attn_ln",
+            "mlp.fc1.": "mlp.up_proj.",
+            "mlp.fc2": "mlp.down_proj",
+            "norm2": "mlp_ln",
+        })
+
+    def config_verify(self, vllm_config):
+        config = vllm_config.model_config.hf_config
+        head_dim = config.hidden_size // config.num_attention_heads
+        config.rotary_kwargs = {
+            "head_size": head_dim,
+            "rotary_dim": getattr(config, "rotary_emb_dim", head_dim),
+            "max_position": config.max_position_embeddings,
+            "base": getattr(config, "rope_theta", config.rotary_emb_base),
+            "rope_scaling": getattr(config, "rope_scaling", None)
+        }
+        return config
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        position_ids: torch.Tensor,
+        intermediate_tensors: Optional[IntermediateTensors] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        token_type_ids: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        return super().forward(input_ids=input_ids,
+                               positions=position_ids,
+                               intermediate_tensors=intermediate_tensors,
+                               inputs_embeds=inputs_embeds,
+                               token_type_ids=token_type_ids)
+
+    @torch.inference_mode()
+    def jina_merge_lora_weights(self, weights: Iterable[Tuple[str,
+                                                              torch.Tensor]]):
+        # use for jina-embeddings-v3
+        # Merge Lora weights into a single weight tensor.
+        # This is a temporary solution until we have a better way to handle
+
+        scaling = self.config.lora_alpha / self.config.lora_rank
+
+        weights = {name: weight for name, weight in weights}
+
+        o = ".original"
+        a = ".0.lora_A"
+        b = ".0.lora_B"
+
+        # text-matching
+        i = -1
+
+        for name in list(weights.keys()):
+            if o in name:
+                dtype = weights[name].dtype
+                shape = weights[name].shape
+                weight_name = name[:-len(o)]
+
+                if "embeddings" in weight_name:
+                    B = weights[weight_name + a][i].cuda().float()
+                    A = weights[weight_name + b][i].cuda().float()
+                else:
+                    B = weights[weight_name + b][i].cuda().float()
+                    A = weights[weight_name + a][i].cuda().float()
+
+                weight = (weights[weight_name + o].cuda() +
+                          torch.matmul(B, A).view(shape) * scaling)
+                weight = weight.cpu().to(dtype)
+
+                weights[weight_name.replace(".parametrizations", "")] = weight
+
+                del weights[weight_name + o], weights[weight_name +
+                                                      a], weights[weight_name +
+                                                                  b]
+
+        return [(name, weight) for name, weight in weights.items()]
+
+    def load_weights(self, weights: Iterable[Tuple[str,
+                                                   torch.Tensor]]) -> Set[str]:
+        weights = self.jina_merge_lora_weights(weights)
+        return super().load_weights(weights)

vllm/model_executor/models/registry.py

@@ -126,7 +126,7 @@ _EMBEDDING_MODELS = {
     "Gemma2Model": ("gemma2", "Gemma2ForCausalLM"),
     "GlmForCausalLM": ("glm", "GlmForCausalLM"),
     "GritLM": ("gritlm", "GritLM"),
-    "GteModel": ("bert", "GteEmbeddingModel"),
+    "GteModel": ("bert_with_rope", "GteModel"),
     "InternLM2ForRewardModel": ("internlm2", "InternLM2ForRewardModel"),
     "JambaForSequenceClassification": ("jamba", "JambaForSequenceClassification"),  # noqa: E501
     "LlamaModel": ("llama", "LlamaForCausalLM"),
@@ -136,7 +136,7 @@ _EMBEDDING_MODELS = {
         if arch == "LlamaForCausalLM"
     },
     "MistralModel": ("llama", "LlamaForCausalLM"),
-    "NomicBertModel": ("bert", "NomicBertEmbeddingModel"),
+    "NomicBertModel": ("bert_with_rope", "NomicBertModel"),
     "Phi3ForCausalLM": ("phi3", "Phi3ForCausalLM"),
     "Qwen2Model": ("qwen2", "Qwen2EmbeddingModel"),
     "Qwen2ForCausalLM": ("qwen2", "Qwen2ForCausalLM"),

vllm/model_executor/models/roberta.py

@@ -1,7 +1,7 @@
 # SPDX-License-Identifier: Apache-2.0
 
 import itertools
-from typing import Iterable, Optional, Tuple
+from typing import Iterable, Optional, Tuple, Union
 
 import torch
 from torch import nn
@@ -19,6 +19,7 @@ from vllm.sequence import IntermediateTensors, PoolerOutput
 from vllm.transformers_utils.config import (
     get_cross_encoder_activation_function)
 
+from .bert_with_rope import BertWithRope, JinaRobertaModel
 from .interfaces import SupportsCrossEncoding, SupportsV0Only
 
 
@@ -125,39 +126,20 @@ class RobertaEmbeddingModel(BertEmbeddingModel):
 
     def _build_model(self,
                      vllm_config: VllmConfig,
-                     prefix: str = "") -> BertModel:
+                     prefix: str = "") -> Union[BertModel, BertWithRope]:
         if (vllm_config.model_config.hf_config.position_embedding_type ==
                 "rotary"):
-            config = vllm_config.model_config.hf_config
+            return JinaRobertaModel(vllm_config=vllm_config, prefix=prefix)
-            head_dim = config.hidden_size // config.num_attention_heads
-
-            rotary_kwargs = {
-                "head_size": head_dim,
-                "rotary_dim": getattr(config, "rotary_emb_dim", head_dim),
-                "max_position": config.max_position_embeddings,
-                "base": config.rotary_emb_base,
-                "rope_scaling": getattr(config, "rope_scaling", None)
-            }
-
-            return BertModel(vllm_config=vllm_config,
-                             rotary_kwargs=rotary_kwargs,
-                             prefix=prefix)
         else:
             return BertModel(vllm_config=vllm_config,
                              prefix=prefix,
                              embedding_class=RobertaEmbedding)
 
     def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
-        if getattr(self.config, "lora_rank", 0) > 0:
-            scaling = self.config.lora_alpha / self.config.lora_rank
-            weights = jina_merge_lora_weights(weights, scaling)
-
         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)
-        bert_weights = jina_to_vllm_mapper.apply(bert_weights)
-
         loaded = self.model.load_weights(bert_weights)
         if not len(loaded):
             # Fix for models like `sentence-transformers/stsb-roberta-base-v2`
@@ -178,6 +160,18 @@ class RobertaForSequenceClassification(nn.Module, SupportsCrossEncoding,
        _pooler: An instance of Pooler used for pooling operations.
    """
 
+    jina_to_vllm_mapper = WeightsMapper(
+        orig_to_new_substr={
+            'emb_ln': "embeddings.LayerNorm",
+            'layers': "layer",
+            'mixer.Wqkv': "attention.self.qkv_proj",
+            'mixer.out_proj': "attention.output.dense",
+            'norm1': "attention.output.LayerNorm",
+            'mlp.fc1': "intermediate.dense",
+            'mlp.fc2': "output.dense",
+            'norm2': "output.LayerNorm",
+        })
+
     def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
         super().__init__()
         config = vllm_config.model_config.hf_config
@@ -195,7 +189,7 @@ class RobertaForSequenceClassification(nn.Module, SupportsCrossEncoding,
 
     def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
         bert_weights, task_weights = roberta_task_weights_filter(weights)
-        bert_weights = jina_to_vllm_mapper.apply(bert_weights)
+        bert_weights = self.jina_to_vllm_mapper.apply(bert_weights)
 
         self.roberta.load_weights(bert_weights)
 
@@ -276,57 +270,3 @@ def roberta_task_weights_filter(
 
     return encoder_decoder_weights(), ((n, w) for n, w in all_weights2
                                        if not n.startswith("roberta."))
-
-
-jina_to_vllm_mapper = WeightsMapper(
-    orig_to_new_substr={
-        'emb_ln': "embeddings.LayerNorm",
-        'layers': "layer",
-        'mixer.Wqkv': "attention.self.qkv_proj",
-        'mixer.out_proj': "attention.output.dense",
-        'norm1': "attention.output.LayerNorm",
-        'mlp.fc1': "intermediate.dense",
-        'mlp.fc2': "output.dense",
-        'norm2': "output.LayerNorm",
-    })
-
-
-@torch.inference_mode()
-def jina_merge_lora_weights(weights: Iterable[Tuple[str, torch.Tensor]],
-                            scaling: float = 1.0):
-    # use for jina-embeddings-v3
-    # Merge Lora weights into a single weight tensor.
-    # This is a temporary solution until we have a better way to handle
-
-    weights = {name: weight for name, weight in weights}
-
-    o = ".original"
-    a = ".0.lora_A"
-    b = ".0.lora_B"
-
-    # text-matching
-    i = -1
-
-    for name in list(weights.keys()):
-        if o in name:
-            dtype = weights[name].dtype
-            shape = weights[name].shape
-            weight_name = name[:-len(o)]
-
-            if "embeddings" in weight_name:
-                B = weights[weight_name + a][i].cuda().float()
-                A = weights[weight_name + b][i].cuda().float()
-            else:
-                B = weights[weight_name + b][i].cuda().float()
-                A = weights[weight_name + a][i].cuda().float()
-
-            weight = (weights[weight_name + o].cuda() +
-                      torch.matmul(B, A).view(shape) * scaling)
-            weight = weight.cpu().to(dtype)
-
-            weights[weight_name.replace(".parametrizations", "")] = weight
-
-            del weights[weight_name + o], weights[weight_name +
-                                                  a], weights[weight_name + b]
-
-    return [(name, weight) for name, weight in weights.items()]