[Model][Misc] Add e5-mistral-7b-instruct and Embedding API (#3734)

vllm/entrypoints/llm.py

@@ -6,13 +6,17 @@ from transformers import PreTrainedTokenizer, PreTrainedTokenizerFast
 
 from vllm.engine.arg_utils import EngineArgs
 from vllm.engine.llm_engine import LLMEngine
+from vllm.logger import init_logger
 from vllm.lora.request import LoRARequest
-from vllm.outputs import RequestOutput
+from vllm.outputs import EmbeddingRequestOutput, RequestOutput
+from vllm.pooling_params import PoolingParams
 from vllm.sampling_params import SamplingParams
 from vllm.sequence import MultiModalData
 from vllm.usage.usage_lib import UsageContext
 from vllm.utils import Counter
 
+logger = init_logger(__name__)
+
 
 class LLM:
     """An LLM for generating texts from given prompts and sampling parameters.
@@ -164,8 +168,89 @@ class LLM:
             multi_modal_data: Multi modal data.
 
         Returns:
-            A list of `RequestOutput` objects containing the generated
-            completions in the same order as the input prompts.
+            A list of `RequestOutput` objects containing the
+            generated completions in the same order as the input prompts.
+        """
+        if sampling_params is None:
+            # Use default sampling params.
+            sampling_params = SamplingParams()
+
+        requests_data = self._validate_and_prepare_requests(
+            prompts,
+            sampling_params,
+            prompt_token_ids,
+            lora_request,
+            multi_modal_data,
+        )
+
+        # Add requests to the engine and run the engine
+        for request_data in requests_data:
+            self._add_request(**request_data)
+
+        return self._run_engine(use_tqdm)
+
+    def encode(
+        self,
+        prompts: Optional[Union[str, List[str]]] = None,
+        pooling_params: Optional[Union[PoolingParams,
+                                       List[PoolingParams]]] = None,
+        prompt_token_ids: Optional[List[List[int]]] = None,
+        use_tqdm: bool = True,
+        lora_request: Optional[LoRARequest] = None,
+        multi_modal_data: Optional[MultiModalData] = None,
+    ) -> List[EmbeddingRequestOutput]:
+        """Generates the completions for the input prompts.
+
+        NOTE: This class automatically batches the given prompts, considering
+        the memory constraint. For the best performance, put all of your prompts
+        into a single list and pass it to this method.
+
+        Args:
+            prompts: A list of prompts to generate completions for.
+            pooling_params: The pooling parameters for pooling. If None, we
+                use the default pooling parameters.
+            prompt_token_ids: A list of token IDs for the prompts. If None, we
+                use the tokenizer to convert the prompts to token IDs.
+            use_tqdm: Whether to use tqdm to display the progress bar.
+            lora_request: LoRA request to use for generation, if any.
+            multi_modal_data: Multi modal data.
+
+        Returns:
+            A list of `EmbeddingRequestOutput` objects containing the
+            generated embeddings in the same order as the input prompts.
+        """
+        if pooling_params is None:
+            # Use default pooling params.
+            pooling_params = PoolingParams()
+
+        requests_data = self._validate_and_prepare_requests(
+            prompts,
+            pooling_params,
+            prompt_token_ids,
+            lora_request,
+            multi_modal_data,
+        )
+
+        # Add requests to the engine and run the engine
+        for request_data in requests_data:
+            self._add_request(**request_data)
+
+        return self._run_engine(use_tqdm)
+
+    def _validate_and_prepare_requests(
+        self,
+        prompts: Optional[Union[str, List[str]]],
+        params: Union[Union[SamplingParams, PoolingParams],
+                      List[Union[SamplingParams,
+                                 PoolingParams]]],  # Unified parameter
+        prompt_token_ids: Optional[List[List[int]]] = None,
+        lora_request: Optional[LoRARequest] = None,
+        multi_modal_data: Optional[MultiModalData] = None,
+    ) -> List[dict]:
+        """Validates and prepares request data for adding to the engine.
+
+        Ensures prompts and token IDs are consistent, and returns a list of
+        dictionaries with request data for further processing.
         """
         if prompts is None and prompt_token_ids is None:
             raise ValueError("Either prompts or prompt_token_ids must be "
@@ -188,40 +273,43 @@ class LLM:
             assert prompt_token_ids is not None
             num_requests = len(prompt_token_ids)
 
-        if sampling_params is None:
-            # Use default sampling params.
-            sampling_params = SamplingParams()
-
-        elif isinstance(sampling_params,
-                        list) and len(sampling_params) != num_requests:
-            raise ValueError("The lengths of prompts and sampling_params "
+        if isinstance(params, list) and len(params) != num_requests:
+            raise ValueError("The lengths of prompts and params "
                              "must be the same.")
         if multi_modal_data:
             multi_modal_data.data = multi_modal_data.data.to(torch.float16)
 
         # Add requests to the engine.
+        requests_data = []
         for i in range(num_requests):
             prompt = prompts[i] if prompts is not None else None
             token_ids = None if prompt_token_ids is None else prompt_token_ids[
                 i]
-            self._add_request(
+
+            multi_modal_item = MultiModalData(
+                type=multi_modal_data.type,
+                data=multi_modal_data.data[i].unsqueeze(0),
+            ) if multi_modal_data else None
+
+            requests_data.append({
+                "prompt":
                 prompt,
-                sampling_params[i]
-                if isinstance(sampling_params, list) else sampling_params,
+                "params":
+                params[i] if isinstance(params, list) else params,
+                "prompt_token_ids":
                 token_ids,
-                lora_request=lora_request,
-                # Get ith image while maintaining the batch dim.
-                multi_modal_data=MultiModalData(
-                    type=multi_modal_data.type,
-                    data=multi_modal_data.data[i].unsqueeze(0))
-                if multi_modal_data else None,
-            )
-        return self._run_engine(use_tqdm)
+                "lora_request":
+                lora_request,
+                "multi_modal_data":
+                multi_modal_item,
+            })
+
+        return requests_data
 
     def _add_request(
         self,
         prompt: Optional[str],
-        sampling_params: SamplingParams,
+        params: Union[SamplingParams, PoolingParams],
         prompt_token_ids: Optional[List[int]],
         lora_request: Optional[LoRARequest] = None,
         multi_modal_data: Optional[MultiModalData] = None,
@@ -229,12 +317,14 @@ class LLM:
         request_id = str(next(self.request_counter))
         self.llm_engine.add_request(request_id,
                                     prompt,
-                                    sampling_params,
+                                    params,
                                     prompt_token_ids,
                                     lora_request=lora_request,
                                     multi_modal_data=multi_modal_data)
 
-    def _run_engine(self, use_tqdm: bool) -> List[RequestOutput]:
+    def _run_engine(
+            self, use_tqdm: bool
+    ) -> List[Union[RequestOutput, EmbeddingRequestOutput]]:
         # Initialize tqdm.
         if use_tqdm:
             num_requests = self.llm_engine.get_num_unfinished_requests()
@@ -245,7 +335,7 @@ class LLM:
                 postfix=f"Generation Speed: {0:.2f} toks/s",
             )
         # Run the engine.
-        outputs: List[RequestOutput] = []
+        outputs: List[Union[RequestOutput, EmbeddingRequestOutput]] = []
         total_toks = 0
         while self.llm_engine.has_unfinished_requests():
             step_outputs = self.llm_engine.step()
@@ -253,10 +343,12 @@ class LLM:
                 if output.finished:
                     outputs.append(output)
                     if use_tqdm:
-                        total_toks += (sum(
-                            len(stp.token_ids) for stp in output.outputs))
-                        spd = total_toks / pbar.format_dict["elapsed"]
-                        pbar.postfix = f"Generation Speed: {spd:.2f} toks/s"
+                        if isinstance(output, RequestOutput):
+                            # Calculate tokens only for RequestOutput
+                            total_toks += sum(
+                                len(stp.token_ids) for stp in output.outputs)
+                            spd = total_toks / pbar.format_dict["elapsed"]
+                            pbar.postfix = f"Generation Speed: {spd:.2f} toks/s"
                         pbar.update(1)
         if use_tqdm:
             pbar.close()

vllm/entrypoints/openai/api_server.py

@@ -22,9 +22,11 @@ from vllm.engine.async_llm_engine import AsyncLLMEngine
 from vllm.entrypoints.openai.cli_args import make_arg_parser
 from vllm.entrypoints.openai.protocol import (ChatCompletionRequest,
                                               ChatCompletionResponse,
-                                              CompletionRequest, ErrorResponse)
+                                              CompletionRequest,
+                                              EmbeddingRequest, ErrorResponse)
 from vllm.entrypoints.openai.serving_chat import OpenAIServingChat
 from vllm.entrypoints.openai.serving_completion import OpenAIServingCompletion
+from vllm.entrypoints.openai.serving_embedding import OpenAIServingEmbedding
 from vllm.logger import init_logger
 from vllm.usage.usage_lib import UsageContext
 
@@ -32,6 +34,8 @@ TIMEOUT_KEEP_ALIVE = 5  # seconds
 
 openai_serving_chat: OpenAIServingChat
 openai_serving_completion: OpenAIServingCompletion
+openai_serving_embedding: OpenAIServingEmbedding
+
 logger = init_logger(__name__)
 
 _running_tasks: Set[asyncio.Task] = set()
@@ -123,6 +127,17 @@ async def create_completion(request: CompletionRequest, raw_request: Request):
         return JSONResponse(content=generator.model_dump())
 
 
+@app.post("/v1/embeddings")
+async def create_embedding(request: EmbeddingRequest, raw_request: Request):
+    generator = await openai_serving_embedding.create_embedding(
+        request, raw_request)
+    if isinstance(generator, ErrorResponse):
+        return JSONResponse(content=generator.model_dump(),
+                            status_code=generator.code)
+    else:
+        return JSONResponse(content=generator.model_dump())
+
+
 if __name__ == "__main__":
     args = parse_args()
 
@@ -190,7 +205,8 @@ if __name__ == "__main__":
                                             args.chat_template)
     openai_serving_completion = OpenAIServingCompletion(
         engine, model_config, served_model_names, args.lora_modules)
-
+    openai_serving_embedding = OpenAIServingEmbedding(engine, model_config,
+                                                      served_model_names)
     app.root_path = args.root_path
     uvicorn.run(app,
                 host=args.host,

vllm/entrypoints/openai/protocol.py

 # Adapted from
 # https://github.com/lm-sys/FastChat/blob/168ccc29d3f7edc50823016105c024fe2282732a/fastchat/protocol/openai_api_protocol.py
 import time
-from typing import Dict, List, Literal, Optional, Union
+from typing import Any, Dict, List, Literal, Optional, Union
 
 import torch
 from openai.types.chat import ChatCompletionMessageParam
 from pydantic import BaseModel, ConfigDict, Field, model_validator
 from typing_extensions import Annotated
 
+from vllm.pooling_params import PoolingParams
 from vllm.sampling_params import SamplingParams
 from vllm.utils import random_uuid
 
@@ -363,6 +364,24 @@ class CompletionRequest(OpenAIBaseModel):
         return data
 
 
+class EmbeddingRequest(BaseModel):
+    # Ordered by official OpenAI API documentation
+    # https://platform.openai.com/docs/api-reference/embeddings
+    model: str
+    input: Union[List[int], List[List[int]], str, List[str]]
+    encoding_format: Optional[str] = Field('float', pattern='^(float|base64)$')
+    dimensions: Optional[int] = None
+    user: Optional[str] = None
+
+    # doc: begin-embedding-pooling-params
+    additional_data: Optional[Any] = None
+
+    # doc: end-embedding-pooling-params
+
+    def to_pooling_params(self):
+        return PoolingParams(additional_data=self.additional_data)
+
+
 class LogProbs(OpenAIBaseModel):
     text_offset: List[int] = Field(default_factory=list)
     token_logprobs: List[Optional[float]] = Field(default_factory=list)
@@ -416,6 +435,21 @@ class CompletionStreamResponse(OpenAIBaseModel):
     usage: Optional[UsageInfo] = Field(default=None)
 
 
+class EmbeddingResponseData(BaseModel):
+    index: int
+    object: str = "embedding"
+    embedding: List[float]
+
+
+class EmbeddingResponse(BaseModel):
+    id: str = Field(default_factory=lambda: f"cmpl-{random_uuid()}")
+    object: str = "list"
+    created: int = Field(default_factory=lambda: int(time.time()))
+    model: str
+    data: List[EmbeddingResponseData]
+    usage: UsageInfo
+
+
 class ChatMessage(OpenAIBaseModel):
     role: str
     content: str

vllm/entrypoints/openai/serving_embedding.py

+import time
+from typing import AsyncIterator, List, Tuple
+
+from fastapi import Request
+
+from vllm.config import ModelConfig
+from vllm.engine.async_llm_engine import AsyncLLMEngine
+from vllm.entrypoints.openai.protocol import (EmbeddingRequest,
+                                              EmbeddingResponse,
+                                              EmbeddingResponseData, UsageInfo)
+from vllm.entrypoints.openai.serving_completion import parse_prompt_format
+from vllm.entrypoints.openai.serving_engine import OpenAIServing
+from vllm.logger import init_logger
+from vllm.outputs import EmbeddingRequestOutput
+from vllm.utils import merge_async_iterators, random_uuid
+
+logger = init_logger(__name__)
+
+TypeTokenIDs = List[int]
+
+
+def request_output_to_embedding_response(
+    final_res_batch: List[EmbeddingRequestOutput],
+    request_id: str,
+    created_time: int,
+    model_name: str,
+) -> EmbeddingResponse:
+    data = []
+    num_prompt_tokens = 0
+    for idx, final_res in enumerate(final_res_batch):
+        assert final_res is not None
+        prompt_token_ids = final_res.prompt_token_ids
+
+        embedding_data = EmbeddingResponseData(
+            index=idx, embedding=final_res.outputs.embedding)
+        data.append(embedding_data)
+
+        num_prompt_tokens += len(prompt_token_ids)
+
+    usage = UsageInfo(
+        prompt_tokens=num_prompt_tokens,
+        total_tokens=num_prompt_tokens,
+    )
+
+    return EmbeddingResponse(
+        id=request_id,
+        created=created_time,
+        model=model_name,
+        data=data,
+        usage=usage,
+    )
+
+
+class OpenAIServingEmbedding(OpenAIServing):
+
+    def __init__(self, engine: AsyncLLMEngine, model_config: ModelConfig,
+                 served_model_names: List[str]):
+        super().__init__(engine=engine,
+                         model_config=model_config,
+                         served_model_names=served_model_names,
+                         lora_modules=None)
+        self._check_embedding_mode(model_config.embedding_mode)
+
+    async def create_embedding(self, request: EmbeddingRequest,
+                               raw_request: Request):
+        """Completion API similar to OpenAI's API.
+
+        See https://platform.openai.com/docs/api-reference/embeddings/create
+        for the API specification. This API mimics the OpenAI Embedding API.
+        """
+        error_check_ret = await self._check_model(request)
+        if error_check_ret is not None:
+            return error_check_ret
+
+        # Return error for unsupported features.
+        if request.encoding_format == "base64":
+            return self.create_error_response(
+                "base64 encoding is not currently supported")
+        if request.dimensions is not None:
+            return self.create_error_response(
+                "dimensions is currently not supported")
+
+        model_name = request.model
+        request_id = f"cmpl-{random_uuid()}"
+        created_time = int(time.monotonic())
+
+        # Schedule the request and get the result generator.
+        generators = []
+        try:
+            prompt_is_tokens, prompts = parse_prompt_format(request.input)
+            pooling_params = request.to_pooling_params()
+
+            for i, prompt in enumerate(prompts):
+                if prompt_is_tokens:
+                    prompt_formats = self._validate_prompt_and_tokenize(
+                        request, prompt_ids=prompt)
+                else:
+                    prompt_formats = self._validate_prompt_and_tokenize(
+                        request, prompt=prompt)
+
+                prompt_ids, prompt_text = prompt_formats
+
+                generators.append(
+                    self.engine.generate(prompt_text,
+                                         pooling_params,
+                                         f"{request_id}-{i}",
+                                         prompt_token_ids=prompt_ids))
+        except ValueError as e:
+            # TODO: Use a vllm-specific Validation Error
+            return self.create_error_response(str(e))
+
+        result_generator: AsyncIterator[Tuple[
+            int, EmbeddingRequestOutput]] = merge_async_iterators(*generators)
+
+        # Non-streaming response
+        final_res_batch: EmbeddingRequestOutput = [None] * len(prompts)
+        async for i, res in result_generator:
+            if await raw_request.is_disconnected():
+                # Abort the request if the client disconnects.
+                await self.engine.abort(f"{request_id}-{i}")
+                # TODO: Use a vllm-specific Validation Error
+                return self.create_error_response("Client disconnected")
+            final_res_batch[i] = res
+        response = request_output_to_embedding_response(
+            final_res_batch, request_id, created_time, model_name)
+
+        return response
+
+    def _check_embedding_mode(self, embedding_mode: bool):
+        if not embedding_mode:
+            logger.warning(
+                "embedding_mode is False. Embedding API will not work.")
+        else:
+            logger.info("Activating the server engine with embedding enabled.")

vllm/entrypoints/openai/serving_engine.py

@@ -9,7 +9,8 @@ from typing_extensions import Annotated
 from vllm.config import ModelConfig
 from vllm.engine.async_llm_engine import AsyncLLMEngine
 from vllm.entrypoints.openai.protocol import (ChatCompletionRequest,
-                                              CompletionRequest, ErrorResponse,
+                                              CompletionRequest,
+                                              EmbeddingRequest, ErrorResponse,
                                               LogProbs, ModelCard, ModelList,
                                               ModelPermission)
 from vllm.logger import init_logger
@@ -165,7 +166,8 @@ class OpenAIServing:
 
     def _validate_prompt_and_tokenize(
         self,
-        request: Union[ChatCompletionRequest, CompletionRequest],
+        request: Union[ChatCompletionRequest, CompletionRequest,
+                       EmbeddingRequest],
         prompt: Optional[str] = None,
         prompt_ids: Optional[List[int]] = None,
         truncate_prompt_tokens: Optional[Annotated[int, Field(ge=1)]] = None
@@ -191,6 +193,16 @@ class OpenAIServing:
             prompt_ids)
         token_num = len(input_ids)
 
+        # Note: EmbeddingRequest doesn't have max_tokens
+        if isinstance(request, EmbeddingRequest):
+            if token_num > self.max_model_len:
+                raise ValueError(
+                    f"This model's maximum context length is "
+                    f"{self.max_model_len} tokens. However, you requested "
+                    f"{token_num} tokens in the input for embedding "
+                    f"generation. Please reduce the length of the input.", )
+            return input_ids, input_text
+
         if request.max_tokens is None:
             if token_num >= self.max_model_len:
                 raise ValueError(

vllm/executor/gpu_executor.py

-from typing import Any, Dict, List, Optional, Set, Tuple
+from typing import Any, Dict, List, Optional, Set, Tuple, Union
 
 from vllm.executor.executor_base import ExecutorAsyncBase, ExecutorBase
 from vllm.logger import init_logger
 from vllm.lora.request import LoRARequest
-from vllm.sequence import ExecuteModelRequest, SamplerOutput
+from vllm.sequence import ExecuteModelRequest, PoolerOutput, SamplerOutput
 from vllm.utils import (get_distributed_init_method, get_ip, get_open_port,
                         make_async)
 from vllm.worker.worker_base import WorkerWrapperBase
@@ -123,8 +123,8 @@ class GPUExecutor(ExecutorBase):
         self.driver_worker.initialize_cache(num_gpu_blocks, num_cpu_blocks)
 
     def execute_model(
-            self,
-            execute_model_req: ExecuteModelRequest) -> List[SamplerOutput]:
+        self, execute_model_req: ExecuteModelRequest
+    ) -> List[Union[SamplerOutput, PoolerOutput]]:
         output = self.driver_worker.execute_model(execute_model_req)
         return output
 
@@ -150,7 +150,7 @@ class GPUExecutorAsync(GPUExecutor, ExecutorAsyncBase):
     async def execute_model_async(
         self,
         execute_model_req: ExecuteModelRequest,
-    ) -> List[SamplerOutput]:
+    ) -> List[Union[SamplerOutput, PoolerOutput]]:
         output = await make_async(self.driver_worker.execute_model
                                   )(execute_model_req=execute_model_req, )
         return output

vllm/model_executor/layers/pooler.py

+from enum import IntEnum
+
+import torch
+import torch.nn as nn
+
+from vllm.model_executor.pooling_metadata import (PoolingMetadata,
+                                                  PoolingTensors)
+from vllm.sequence import EmbeddingSequenceGroupOutput, PoolerOutput
+
+
+class PoolingType(IntEnum):
+    """Enumeration for different types of pooling methods."""
+    LAST = 0
+
+
+class Pooler(nn.Module):
+    """A layer that pools specific information from hidden states.
+
+    This layer does the following:
+    1. Extracts specific tokens or aggregates data based on pooling method.
+    2. Normalizes output if specified.
+    3. Returns structured results as `PoolerOutput`.
+
+    Attributes:
+        pooling_type: The type of pooling to use (LAST, AVERAGE, MAX).
+        normalize: Whether to normalize the pooled data.
+    """
+
+    def __init__(self, pooling_type: PoolingType, normalize: bool):
+        super().__init__()
+        self.pooling_type = pooling_type
+        self.normalize = normalize
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        pooling_metadata: PoolingMetadata,
+    ) -> PoolerOutput:
+        """Pools specific information from hidden states based on metadata."""
+        prompt_lens = PoolingTensors.from_pooling_metadata(
+            pooling_metadata, hidden_states.device).prompt_lens
+
+        if self.pooling_type == PoolingType.LAST:
+            last_token_flat_indices = torch.cumsum(prompt_lens, dim=0) - 1
+            pooled_data = hidden_states[last_token_flat_indices]
+        else:
+            raise ValueError(f"Invalid pooling type: {self.pooling_type}")
+
+        if self.normalize:
+            pooled_data = nn.functional.normalize(pooled_data, p=2, dim=1)
+
+        pooled_outputs = [
+            EmbeddingSequenceGroupOutput(data.tolist()) for data in pooled_data
+        ]
+
+        return PoolerOutput(outputs=pooled_outputs)

vllm/model_executor/layers/sampler.py

@@ -10,8 +10,9 @@ from vllm.model_executor.sampling_metadata import (SamplingMetadata,
                                                    SamplingTensors,
                                                    SequenceGroupToSample)
 from vllm.sampling_params import SamplingType
-from vllm.sequence import (Logprob, PromptLogprobs, SampleLogprobs,
-                           SamplerOutput, SequenceGroupOutput, SequenceOutput)
+from vllm.sequence import (CompletionSequenceGroupOutput, Logprob,
+                           PromptLogprobs, SampleLogprobs, SamplerOutput,
+                           SequenceOutput)
 
 # (num_token_ids, num_parent_ids) per sequence group.
 SampleResultType = List[Tuple[List[int], List[int]]]
@@ -1019,7 +1020,7 @@ def _build_sampler_output(
             seq_outputs.append(
                 SequenceOutput(seq_ids[parent_id], next_token_id, logprobs))
         sampler_output.append(
-            SequenceGroupOutput(seq_outputs, group_prompt_logprobs))
+            CompletionSequenceGroupOutput(seq_outputs, group_prompt_logprobs))
 
     # If not specified, store None values in SamplerOutput.
     if on_device_tensors is not None:

vllm/model_executor/models/__init__.py

@@ -9,7 +9,7 @@ from vllm.utils import is_hip
 logger = init_logger(__name__)
 
 # Architecture -> (module, class).
-_MODELS = {
+_GENERATION_MODELS = {
     "AquilaModel": ("llama", "LlamaForCausalLM"),
     "AquilaForCausalLM": ("llama", "LlamaForCausalLM"),  # AquilaChat2
     "BaiChuanForCausalLM": ("baichuan", "BaiChuanForCausalLM"),  # baichuan-7b
@@ -58,6 +58,12 @@ _MODELS = {
     "XverseForCausalLM": ("xverse", "XverseForCausalLM"),
 }
 
+_EMBEDDING_MODELS = {
+    "MistralModel": ("llama_embedding", "LlamaEmbeddingModel"),
+}
+
+_MODELS = {**_GENERATION_MODELS, **_EMBEDDING_MODELS}
+
 # Architecture -> type.
 # out of tree models
 _OOT_MODELS: Dict[str, Type[nn.Module]] = {}
@@ -114,6 +120,10 @@ class ModelRegistry:
         global _OOT_MODELS
         _OOT_MODELS[model_arch] = model_cls
 
+    @staticmethod
+    def is_embedding_model(model_arch: str) -> bool:
+        return model_arch in _EMBEDDING_MODELS
+
 
 __all__ = [
     "ModelRegistry",

vllm/model_executor/models/llama_embedding.py

+from typing import Iterable, List, Optional, Tuple
+
+import torch
+from torch import nn
+
+from vllm.attention import AttentionMetadata
+from vllm.model_executor.layers.pooler import Pooler, PoolingType
+from vllm.model_executor.model_loader.weight_utils import default_weight_loader
+from vllm.model_executor.models.llama import LlamaModel
+from vllm.model_executor.pooling_metadata import PoolingMetadata
+from vllm.sequence import PoolerOutput
+
+
+class LlamaEmbeddingModel(nn.Module):
+    """A model that uses Llama with additional embedding functionalities.
+
+   This class encapsulates the LlamaModel and provides an interface for
+   embedding operations and customized pooling functions.
+
+   Attributes:
+       model: An instance of LlamaModel used for forward operations.
+       _pooler: An instance of Pooler used for pooling operations.
+   """
+
+    def __init__(
+        self,
+        **kwargs,
+    ) -> None:
+        super().__init__()
+        self.model = LlamaModel(**kwargs)
+        self._pooler = Pooler(pooling_type=PoolingType.LAST, normalize=True)
+
+    def forward(
+        self,
+        input_ids: Optional[torch.Tensor],
+        positions: torch.Tensor,
+        kv_caches: List[torch.Tensor],
+        attn_metadata: AttentionMetadata,
+        inputs_embeds: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        return self.model.forward(input_ids, positions, kv_caches,
+                                  attn_metadata, inputs_embeds)
+
+    def pooler(
+        self,
+        hidden_states: torch.Tensor,
+        pooling_metadata: PoolingMetadata,
+    ) -> Optional[PoolerOutput]:
+        return self._pooler(hidden_states, pooling_metadata)
+
+    def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
+        stacked_params_mapping = [
+            # (param_name, shard_name, shard_id)
+            ("qkv_proj", "q_proj", "q"),
+            ("qkv_proj", "k_proj", "k"),
+            ("qkv_proj", "v_proj", "v"),
+            ("gate_up_proj", "gate_proj", 0),
+            ("gate_up_proj", "up_proj", 1),
+        ]
+        params_dict = dict(self.model.named_parameters())
+        for name, loaded_weight in weights:
+            if "rotary_emb.inv_freq" in name:
+                continue
+            if ("rotary_emb.cos_cached" in name
+                    or "rotary_emb.sin_cached" in name):
+                # Models trained using ColossalAI may include these tensors in
+                # the checkpoint. Skip them.
+                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)

vllm/model_executor/pooling_metadata.py

+from dataclasses import dataclass
+from typing import Any, Dict, List, Tuple
+
+import torch
+
+from vllm.pooling_params import PoolingParams
+from vllm.utils import is_pin_memory_available
+
+
+class PoolingMetadata:
+    """Metadata for pooling operations in the Pooler layer.
+
+    This class holds the necessary information for pooling operations,
+    providing context for how to perform pooling and other related operations.
+
+    Attributes:
+        seq_groups: List of (seq_ids, pooling_params).
+        seq_data: A mapping of sequence ID to additional sequence data.
+        prompt_lens: List of the lengths of each prompt.
+    """
+
+    def __init__(
+        self,
+        seq_groups: List[Tuple[List[int], PoolingParams]],
+        seq_data: Dict[int, Any],  # Specific data related to sequences
+        prompt_lens: List[int],
+    ) -> None:
+        self.seq_groups = seq_groups
+        self.seq_data = seq_data
+        self.prompt_lens = prompt_lens
+
+    def __repr__(self) -> str:
+        return ("PoolingMetadata("
+                f"seq_groups={self.seq_groups}, "
+                f"seq_data={self.seq_data}, "
+                f"prompt_lens={self.prompt_lens})")
+
+
+@dataclass
+class PoolingTensors:
+    """Tensors for pooling."""
+
+    prompt_lens: torch.Tensor
+
+    @classmethod
+    def from_pooling_metadata(
+        cls,
+        pooling_metadata: "PoolingMetadata",
+        device: torch.device,
+    ) -> "PoolingTensors":
+        """
+        Create PoolingTensors from PoolingMetadata.
+
+        Args:
+            pooling_metadata: PoolingMetadata instance to convert.
+            device: Device to store the tensors.
+        """
+        # Convert prompt lengths to tensor
+        pin_memory = is_pin_memory_available()
+
+        prompt_lens_t = torch.tensor(
+            pooling_metadata.prompt_lens,
+            device="cpu",
+            dtype=torch.long,
+            pin_memory=pin_memory,
+        )
+
+        return cls(prompt_lens=prompt_lens_t.to(device=device,
+                                                non_blocking=True), )

vllm/outputs.py

@@ -57,8 +57,27 @@ class CompletionOutput:
                 f"stop_reason={self.stop_reason})")
 
 
+class EmbeddingOutput:
+    """The output data of one completion output of a request.
+
+    Args:
+        embedding: The embedding vector, which is a list of floats. The
+        length of vector depends on the model as listed in the embedding guide.
+    """
+
+    def __init__(
+        self,
+        embedding: List[float],
+    ) -> None:
+        self.embedding = embedding
+
+    def __repr__(self) -> str:
+        return (f"EmbeddingOutput("
+                f"embedding={len(self.embedding)}")
+
+
 class RequestOutput:
-    """The output data of a request to the LLM.
+    """The output data of a completion request to the LLM.
 
     Args:
         request_id: The unique ID of the request.
@@ -93,6 +112,9 @@ class RequestOutput:
 
     @classmethod
     def from_seq_group(cls, seq_group: SequenceGroup) -> "RequestOutput":
+        if seq_group.sampling_params is None:
+            raise ValueError(
+                "Sampling parameters are missing for a CompletionRequest.")
         seqs = seq_group.get_seqs()
         if len(seqs) == 1:
             top_n_seqs = seqs
@@ -148,3 +170,61 @@ class RequestOutput:
                 f"finished={self.finished}, "
                 f"metrics={self.metrics}, "
                 f"lora_request={self.lora_request})")
+
+
+class EmbeddingRequestOutput:
+    """
+    The output data of an embedding request to the LLM.
+
+    Args:
+        request_id (str): A unique identifier for the embedding request.
+        outputs (EmbeddingOutput): The embedding results for the given input.
+        prompt_token_ids (List[int]): A list of token IDs used in the prompt.
+        finished (bool): A flag indicating whether the embedding is completed.
+    """
+
+    def __init__(self, request_id: str, outputs: 'EmbeddingOutput',
+                 prompt_token_ids: List[int], finished: bool):
+        self.request_id = request_id
+        self.prompt_token_ids = prompt_token_ids
+        self.finished = finished
+        self.outputs = outputs
+
+    @classmethod
+    def from_seq_group(cls,
+                       seq_group: 'SequenceGroup') -> "EmbeddingRequestOutput":
+        if seq_group.embeddings is None:
+            raise ValueError(
+                "Embeddings are missing in seq_group for EmbeddingRequest.")
+        output = EmbeddingOutput(seq_group.embeddings)
+        prompt_token_ids = seq_group.prompt_token_ids
+        finished = seq_group.is_finished()
+
+        return cls(seq_group.request_id, output, prompt_token_ids, finished)
+
+    def __repr__(self):
+        """
+        Returns a string representation of an EmbeddingRequestOutput instance.
+
+        The representation includes the request_id and the number of outputs,
+        providing a quick overview of the embedding request's results.
+
+        Returns:
+            str: A string representation of the EmbeddingRequestOutput instance.
+        """
+        return (f"EmbeddingRequestOutput(request_id='{self.request_id}', "
+                f"outputs={repr(self.outputs)}, "
+                f"prompt_token_ids={self.prompt_token_ids}, "
+                f"finished={self.finished})")
+
+
+class RequestOutputFactory:
+
+    @staticmethod
+    def create(seq_group):
+        # Determine the type based on a condition, for example:
+        if hasattr(seq_group,
+                   'embeddings') and seq_group.embeddings is not None:
+            return EmbeddingRequestOutput.from_seq_group(seq_group)
+        else:
+            return RequestOutput.from_seq_group(seq_group)

vllm/pooling_params.py

+from typing import Any, Optional
+
+
+class PoolingParams:
+    """Pooling parameters for pooling.
+
+    Attributes:
+        additional_data: Any additional data needed for pooling.
+    """
+
+    def __init__(self, additional_data: Optional[Any] = None):
+        self.additional_data = additional_data
+
+    def clone(self) -> "PoolingParams":
+        """Returns a deep copy of the PoolingParams instance."""
+        return PoolingParams(additional_data=self.additional_data, )
+
+    def __repr__(self) -> str:
+        return (f"PoolingParams("
+                f"additional_metadata={self.additional_data})")

vllm/sequence.py

 """Sequence and its related classes."""
 import copy
 import enum
+from abc import ABC, abstractmethod
 from dataclasses import dataclass, field
 from typing import TYPE_CHECKING, Dict, List, Optional, Tuple, Union
 
 from vllm.block import LogicalTokenBlock
 from vllm.lora.request import LoRARequest
+from vllm.pooling_params import PoolingParams
 from vllm.sampling_params import SamplingParams
 
 if TYPE_CHECKING:
@@ -375,12 +377,12 @@ class SequenceGroupState:
 
 class MultiModalData:
     """Multi modal request.
-    
+
     Args:
         type: The data type.
         data: The actual data.
         The required shape and semantic meaning of it depends on the vision
-        language config of the hosted model. 
+        language config of the hosted model.
         See `VisionLanguageConfig` in `config.py`.
     """
 
@@ -402,16 +404,22 @@ class SequenceGroup:
         arrival_time: The arrival time of the request.
         lora_request: LoRA request.
         multi_modal_data: Multi modal data associated with the request.
+        embeddings: The embeddings vectors of the prompt of the sequence group
+            for an embedding model.
+        pooling_params: The pooling parameters used to generate the pooling
+            for an embedding model.
     """
 
     def __init__(
         self,
         request_id: str,
         seqs: List[Sequence],
-        sampling_params: SamplingParams,
         arrival_time: float,
+        sampling_params: Optional[SamplingParams] = None,
         lora_request: Optional[LoRARequest] = None,
         multi_modal_data: Optional[MultiModalData] = None,
+        embeddings: Optional[List[float]] = None,
+        pooling_params: Optional[PoolingParams] = None,
     ) -> None:
         self.request_id = request_id
         self.seqs_dict = {seq.seq_id: seq for seq in seqs}
@@ -425,6 +433,8 @@ class SequenceGroup:
         self.prompt_logprobs: Optional[PromptLogprobs] = None
         self.state = SequenceGroupState()
         self.multi_modal_data = multi_modal_data
+        self.embeddings = embeddings
+        self.pooling_params = pooling_params
 
     @property
     def prompt(self) -> str:
@@ -479,12 +489,13 @@ class SequenceGroup:
     def get_max_num_running_seqs(self) -> int:
         """The maximum number of sequences running in parallel in the remaining
         lifetime of the request."""
-        if self.sampling_params.use_beam_search:
+        if self.sampling_params and self.sampling_params.use_beam_search:
             # For beam search, maximally there will always be `best_of` beam
             # candidates running in the future.
             return self.sampling_params.best_of
         else:
-            if self.sampling_params.best_of > self.num_seqs():
+            if (self.sampling_params
+                    and self.sampling_params.best_of > self.num_seqs()):
                 # At prompt stage, the sequence group is not yet filled up
                 # and only have one sequence running. However, in the
                 # generation stage, we will have `best_of` sequences running.
@@ -555,7 +566,7 @@ class SequenceGroup:
         return all(seq.is_finished() for seq in self.get_seqs())
 
     def is_prefill(self) -> bool:
-        # Every sequences should be in the same stage.
+        # Every sequence should be in the same stage.
         return self.get_seqs()[0].is_prefill()
 
     def __repr__(self) -> str:
@@ -594,6 +605,7 @@ class SequenceGroupMetadata:
         sampling_params: SamplingParams,
         block_tables: Dict[int, List[int]],
         do_sample: bool = True,
+        pooling_params: Optional[PoolingParams] = None,
         token_chunk_size: Optional[int] = None,
         lora_request: Optional[LoRARequest] = None,
         computed_block_nums: Optional[List[int]] = None,
@@ -605,6 +617,7 @@ class SequenceGroupMetadata:
         self.seq_data = seq_data
         self.sampling_params = sampling_params
         self.block_tables = block_tables
+        self.pooling_params = pooling_params
         self.lora_request = lora_request
         self.computed_block_nums = computed_block_nums
         self.multi_modal_data = multi_modal_data
@@ -669,8 +682,20 @@ class SequenceOutput:
         return equal and log_probs_equal
 
 
-class SequenceGroupOutput:
-    """The model output associated with a sequence group."""
+class SequenceGroupOutput(ABC):
+    """The base class for model outputs associated with a sequence group."""
+
+    @abstractmethod
+    def __repr__(self) -> str:
+        pass
+
+    @abstractmethod
+    def __eq__(self, other: object) -> bool:
+        pass
+
+
+class CompletionSequenceGroupOutput(SequenceGroupOutput):
+    """The model output associated with a completion sequence group."""
 
     def __init__(
         self,
@@ -682,26 +707,45 @@ class SequenceGroupOutput:
         self.prompt_logprobs = prompt_logprobs
 
     def __repr__(self) -> str:
-        return (f"SequenceGroupOutput(samples={self.samples}, "
+        return (f"CompletionSequenceGroupOutput(samples={self.samples}, "
                 f"prompt_logprobs={self.prompt_logprobs})")
 
     def __eq__(self, other: object) -> bool:
-        if not isinstance(other, SequenceGroupOutput):
+        if not isinstance(other, CompletionSequenceGroupOutput):
             raise NotImplementedError()
         return (self.samples == other.samples
                 and self.prompt_logprobs == other.prompt_logprobs)
 
 
+class EmbeddingSequenceGroupOutput(SequenceGroupOutput):
+    """The model output associated with an embedding sequence group."""
+
+    def __init__(
+        self,
+        embeddings: List[float],
+    ) -> None:
+        self.embeddings = embeddings
+
+    def __repr__(self) -> str:
+        return (f"EmbeddingSequenceGroupOutput("
+                f"embeddings_shape={len(self.embeddings)})")
+
+    def __eq__(self, other: object) -> bool:
+        if not isinstance(other, EmbeddingSequenceGroupOutput):
+            raise NotImplementedError()
+        return self.embeddings == other.embeddings
+
+
 @dataclass
 class SamplerOutput:
     """For each sequence group, we generate a list of SequenceOutput object,
     each of which contains one possible candidate for the next token.
 
-    This datastructure implements methods so it can be used like a list, but
+    This data structure implements methods, so it can be used like a list, but
     also has optional fields for device tensors.
     """
 
-    outputs: List[SequenceGroupOutput]
+    outputs: List[CompletionSequenceGroupOutput]
 
     # On-device tensor containing probabilities of each token.
     sampled_token_probs: Optional["torch.Tensor"] = None
@@ -742,6 +786,27 @@ class SamplerOutput:
             f"spec_decode_worker_metrics={self.spec_decode_worker_metrics})")
 
 
+@dataclass
+class PoolerOutput:
+    """The output from a pooling operation in the embedding model."""
+    outputs: List[EmbeddingSequenceGroupOutput]
+
+    spec_decode_worker_metrics: Optional["SpecDecodeWorkerMetrics"] = None
+
+    def __getitem__(self, idx: int):
+        return self.outputs[idx]
+
+    def __setitem__(self, idx: int, value):
+        self.outputs[idx] = value
+
+    def __len__(self):
+        return len(self.outputs)
+
+    def __eq__(self, other: object):
+        return isinstance(other,
+                          self.__class__) and self.outputs == other.outputs
+
+
 @dataclass
 class ExecuteModelRequest:
     """The model execution request."""

vllm/spec_decode/util.py

@@ -4,7 +4,8 @@ from typing import Dict, List, Tuple
 
 import torch
 
-from vllm.sequence import (Logprob, SamplerOutput, SequenceGroupMetadata,
+from vllm.sequence import (CompletionSequenceGroupOutput, Logprob,
+                           SamplerOutput, SequenceGroupMetadata,
                            SequenceGroupOutput, SequenceOutput)
 
 SeqId = int
@@ -94,7 +95,7 @@ def create_sequence_group_output(
         for topk_logprob_index, _ in enumerate(topk_token_ids)
     })
 
-    return SequenceGroupOutput(
+    return CompletionSequenceGroupOutput(
         samples=[
             SequenceOutput(parent_seq_id=seq_id,
                            output_token=token_id,

vllm/worker/embedding_model_runner.py

+from typing import Dict, List, Optional, Set, Tuple
+
+import torch
+
+from vllm.attention import AttentionMetadata
+from vllm.config import (CacheConfig, DeviceConfig, LoadConfig, LoRAConfig,
+                         ModelConfig, ParallelConfig, SchedulerConfig,
+                         VisionLanguageConfig)
+from vllm.distributed import broadcast_tensor_dict
+from vllm.logger import init_logger
+from vllm.lora.layers import LoRAMapping
+from vllm.lora.request import LoRARequest
+from vllm.model_executor.pooling_metadata import PoolingMetadata
+from vllm.pooling_params import PoolingParams
+from vllm.sequence import PoolerOutput, SequenceData, SequenceGroupMetadata
+from vllm.worker.model_runner import BatchType, ModelRunner
+
+logger = init_logger(__name__)
+
+
+class EmbeddingModelRunner(ModelRunner):
+
+    def __init__(
+        self,
+        model_config: ModelConfig,
+        parallel_config: ParallelConfig,
+        scheduler_config: SchedulerConfig,
+        device_config: DeviceConfig,
+        cache_config: CacheConfig,
+        load_config: LoadConfig,
+        lora_config: Optional[LoRAConfig],
+        kv_cache_dtype: Optional[str] = "auto",
+        is_driver_worker: bool = False,
+        vision_language_config: Optional[VisionLanguageConfig] = None,
+    ):
+        super().__init__(model_config,
+                         parallel_config,
+                         scheduler_config,
+                         device_config,
+                         cache_config,
+                         load_config,
+                         lora_config=lora_config,
+                         kv_cache_dtype=kv_cache_dtype,
+                         is_driver_worker=is_driver_worker,
+                         vision_language_config=vision_language_config)
+
+    @torch.inference_mode()
+    def execute_model(
+        self,
+        seq_group_metadata_list: List[SequenceGroupMetadata],
+        kv_caches: List[torch.Tensor],
+    ) -> Optional[PoolerOutput]:
+        (input_tokens, input_positions, attn_metadata, pooling_metadata,
+         lora_requests, lora_mapping, multi_modal_input
+         ) = self.prepare_input_tensors(seq_group_metadata_list)
+
+        if self.lora_config:
+            self.set_active_loras(lora_requests, lora_mapping)
+
+        # Currently cuda graph is only supported by the decode phase.
+        prefill_meta = attn_metadata.prefill_metadata
+        decode_meta = attn_metadata.decode_metadata
+        if prefill_meta is None and decode_meta.use_cuda_graph:
+            graph_batch_size = input_tokens.shape[0]
+            model_executable = self.graph_runners[graph_batch_size]
+        else:
+            model_executable = self.model
+
+        num_layers = self.model_config.get_num_layers(self.parallel_config)
+        kv_caches = [None] * num_layers
+
+        execute_model_kwargs = {
+            "input_ids": input_tokens,
+            "positions": input_positions,
+            "kv_caches": kv_caches,
+            "attn_metadata": attn_metadata,
+        }
+        if self.vision_language_config:
+            execute_model_kwargs.update({"image_input": multi_modal_input})
+        hidden_states = model_executable(**execute_model_kwargs)
+
+        return self.model.pooler(hidden_states=hidden_states,
+                                 pooling_metadata=pooling_metadata)
+
+    def prepare_input_tensors(
+        self,
+        seq_group_metadata_list: List[SequenceGroupMetadata],
+    ) -> Tuple[torch.Tensor, torch.Tensor, AttentionMetadata, PoolingMetadata,
+               Set[LoRARequest], LoRAMapping, torch.Tensor]:
+        if self.is_driver_worker:
+            prefill_reqs = []
+            decode_reqs = []
+            for seq_group_meta in seq_group_metadata_list:
+                if seq_group_meta.is_prompt:
+                    prefill_reqs.append(seq_group_meta)
+                else:
+                    decode_reqs.append(seq_group_meta)
+
+            # Prepare input tensors.
+            (
+                input_tokens,
+                input_positions,
+                prefill_attn_metadata,
+                prompt_lens,
+                subquery_lens,
+                lora_index_mapping,
+                lora_prompt_mapping,
+                lora_requests,
+                multi_modal_input,
+                slot_mapping,
+            ) = self._prepare_prompt(prefill_reqs)
+            (
+                decode_input_tokens,
+                decode_input_positions,
+                decode_attn_metadata,
+                decode_lora_index_mapping,
+                decode_lora_prompt_mapping,
+                decode_lora_requests,
+                decode_slot_mapping,
+            ) = self._prepare_decode(decode_reqs)
+
+            # Prepare PoolingMetadata
+            pooling_metadata = self._prepare_pooling(seq_group_metadata_list,
+                                                     prompt_lens)
+
+            if not self.scheduler_config.chunked_prefill_enabled:
+                assert (len(prefill_reqs) and len(decode_reqs)) == 0
+
+            num_prefills = len(prompt_lens)
+            num_prefill_tokens = len(input_tokens)
+            num_decode_tokens = len(decode_input_tokens)
+
+            # Coalesce tensors. Note that attn_metadata is currently not
+            # coalesced for simplicity.
+            input_tokens.extend(decode_input_tokens)
+            input_positions.extend(decode_input_positions)
+            slot_mapping.extend(decode_slot_mapping)
+            lora_index_mapping.extend(decode_lora_index_mapping)
+            lora_prompt_mapping.extend(decode_lora_prompt_mapping)
+            lora_requests.update(decode_lora_requests)
+
+            input_tokens = torch.tensor(input_tokens,
+                                        dtype=torch.long,
+                                        device=self.device)
+            input_positions = torch.tensor(input_positions,
+                                           dtype=torch.long,
+                                           device=self.device)
+            slot_mapping = torch.tensor(slot_mapping,
+                                        dtype=torch.long,
+                                        device=self.device)
+
+            if self.lora_config:
+                lora_mapping = LoRAMapping(
+                    lora_index_mapping,
+                    lora_prompt_mapping,
+                )
+            else:
+                lora_mapping = None
+
+            # Broadcast the metadata.
+            # If batch contains both prefill and decode, it sends 2 broadcasts.
+            # If it only contains 1 type, it triggers a single broadcast.
+            if (prefill_attn_metadata is not None
+                    and decode_attn_metadata is not None):
+                batch_type = BatchType.MIXED
+            elif prefill_attn_metadata is not None:
+                batch_type = BatchType.PREFILL
+            else:
+                batch_type = BatchType.DECODE
+
+            metadata_dict = {
+                "input_tokens": input_tokens,
+                "input_positions": input_positions,
+                "lora_requests": lora_requests,
+                "lora_mapping": lora_mapping,
+                "multi_modal_input": multi_modal_input,
+                "num_prefill_tokens": num_prefill_tokens,
+                "num_decode_tokens": num_decode_tokens,
+                "slot_mapping": slot_mapping,
+                "num_prefills": num_prefills,
+                "batch_type": batch_type,
+            }
+            if prefill_attn_metadata is not None:
+                metadata_dict.update(prefill_attn_metadata.asdict_zerocopy())
+            else:
+                assert decode_attn_metadata is not None
+                metadata_dict.update(decode_attn_metadata.asdict_zerocopy())
+            broadcast_tensor_dict(metadata_dict, src=0)
+
+            # Broadcast decode attn metadata for mixed batch type.
+            # The additional broadcast costs 300us overhead on 4 A10 GPUs.
+            # We can potentially reduce the overhead by coelescing tensors.
+            if batch_type == BatchType.MIXED:
+                assert decode_attn_metadata is not None
+                metadata_dict = decode_attn_metadata.asdict_zerocopy()
+                broadcast_tensor_dict(metadata_dict, src=0)
+        else:
+            metadata_dict = broadcast_tensor_dict(src=0)
+            input_tokens = metadata_dict.pop("input_tokens")
+            input_positions = metadata_dict.pop("input_positions")
+            slot_mapping = metadata_dict.pop("slot_mapping")
+            num_prefills = metadata_dict.pop("num_prefills")
+            lora_mapping = metadata_dict.pop("lora_mapping")
+            lora_requests = metadata_dict.pop("lora_requests")
+            multi_modal_input = metadata_dict.pop("multi_modal_input")
+            num_prefill_tokens = metadata_dict.pop("num_prefill_tokens")
+            num_decode_tokens = metadata_dict.pop("num_decode_tokens")
+            batch_type = metadata_dict.pop("batch_type")
+
+            # Create an attention metadata.
+            prefill_attn_metadata = None
+            decode_attn_metadata = None
+            if batch_type == BatchType.PREFILL or batch_type == BatchType.MIXED:
+                prefill_attn_metadata = self.attn_backend.make_metadata(
+                    **metadata_dict)
+            else:
+                decode_attn_metadata = self.attn_backend.make_metadata(
+                    **metadata_dict)
+
+            pooling_metadata = PoolingMetadata(seq_groups=None,
+                                               seq_data=None,
+                                               prompt_lens=None)
+
+            # if it is a mixed batch, decode attn_metadata is broadcasted
+            # separately.
+            if batch_type == BatchType.MIXED:
+                metadata_dict = broadcast_tensor_dict(src=0)
+                decode_attn_metadata = self.attn_backend.make_metadata(
+                    **metadata_dict)
+
+        attn_metadata = AttentionMetadata(
+            num_prefills=num_prefills,
+            slot_mapping=slot_mapping,
+            num_prefill_tokens=num_prefill_tokens,
+            num_decode_tokens=num_decode_tokens,
+            prefill_metadata=prefill_attn_metadata,
+            decode_metadata=decode_attn_metadata,
+            kv_cache_dtype=self.kv_cache_dtype,
+        )
+
+        return (input_tokens, input_positions, attn_metadata, pooling_metadata,
+                lora_requests, lora_mapping, multi_modal_input)
+
+    def _prepare_pooling(
+        self,
+        seq_group_metadata_list: List[SequenceGroupMetadata],
+        prompt_lens: List[int],
+    ) -> PoolingMetadata:
+        """Prepare PoolingMetadata for the sequence group metadata list."""
+        seq_groups: List[Tuple[List[int], PoolingParams]] = []
+        for i, seq_group_metadata in enumerate(seq_group_metadata_list):
+            seq_ids = list(seq_group_metadata.seq_data.keys())
+            pooling_params = seq_group_metadata.pooling_params
+            seq_groups.append((seq_ids, pooling_params))
+
+        seq_data: Dict[int, SequenceData] = {}
+        for seq_group_metadata in seq_group_metadata_list:
+            seq_data.update(seq_group_metadata.seq_data)
+
+        pooling_metadata = PoolingMetadata(
+            seq_groups=seq_groups,
+            seq_data=seq_data,
+            prompt_lens=prompt_lens,
+        )
+
+        return pooling_metadata

vllm/worker/model_runner.py

 import time
 from enum import IntEnum
-from typing import Dict, List, NamedTuple, Optional, Set, Tuple
+from typing import Dict, List, NamedTuple, Optional, Set, Tuple, Union
 
 import numpy as np
 import torch
@@ -287,18 +287,18 @@ class ModelRunner:
                 lora_requests.add(seq_group_metadata.lora_request)
 
             lora_index_mapping += [lora_id] * (seq_len - context_len)
-            lora_prompt_mapping.extend(
-                [lora_id] *
-                (seq_len - context_len
-                 if seq_group_metadata.sampling_params.prompt_logprobs else 1))
+            lora_prompt_mapping.extend([lora_id] * (
+                seq_len - context_len if seq_group_metadata.sampling_params
+                and seq_group_metadata.sampling_params.prompt_logprobs else 1))
 
             if seq_group_metadata.multi_modal_data:
                 multi_modal_input_list.append(
                     seq_group_metadata.multi_modal_data.data)
 
-            if seq_group_metadata.block_tables is None:
+            if _is_block_tables_empty(seq_group_metadata.block_tables):
                 # During memory profiling, the block tables are not initialized
                 # yet. In this case, we just use a dummy slot mapping.
+                # In embeddings, the block tables are {seq_id: None}.
                 slot_mapping.extend([_PAD_SLOT_ID] * seq_len)
                 continue
 
@@ -813,7 +813,6 @@ class ModelRunner:
         sampling_params = SamplingParams(top_p=0.99, top_k=self.vocab_size - 1)
         max_num_batched_tokens = self.scheduler_config.max_num_batched_tokens
         max_num_seqs = self.scheduler_config.max_num_seqs
-
         # This represents the maximum number of different requests
         # that will have unique loras, an therefore the max amount of memory
         # consumption create dummy lora request copies from the lora request
@@ -1139,3 +1138,15 @@ def _prepare_fake_inputs(
         prompt_tokens = [0] * seq_len
         fake_image_input = None
     return SequenceData(prompt_tokens), fake_image_input
+
+
+def _is_block_tables_empty(block_tables: Union[None, Dict]):
+    """
+    Check if block_tables is None or a dictionary with all None values.
+    """
+    if block_tables is None:
+        return True
+    if isinstance(block_tables, dict) and all(
+            value is None for value in block_tables.values()):
+        return True
+    return False

vllm/worker/worker.py

 """A GPU worker class."""
 import gc
 import os
-from typing import Any, Dict, List, Optional, Set, Tuple
+from typing import Any, Dict, List, Optional, Set, Tuple, Union
 
 import torch
 import torch.distributed
@@ -16,8 +16,9 @@ from vllm.distributed.device_communicators.custom_all_reduce import (
     init_custom_ar)
 from vllm.lora.request import LoRARequest
 from vllm.model_executor import set_random_seed
-from vllm.sequence import ExecuteModelRequest, SamplerOutput
+from vllm.sequence import ExecuteModelRequest, PoolerOutput, SamplerOutput
 from vllm.worker.cache_engine import CacheEngine
+from vllm.worker.embedding_model_runner import EmbeddingModelRunner
 from vllm.worker.model_runner import ModelRunner
 from vllm.worker.worker_base import WorkerBase
 
@@ -68,7 +69,9 @@ class Worker(WorkerBase):
             assert not self.lora_config, (
                 "To be tested: vision language model with LoRA settings.")
 
-        self.model_runner = ModelRunner(
+        ModelRunnerClass = (EmbeddingModelRunner if
+                            self.model_config.embedding_mode else ModelRunner)
+        self.model_runner = ModelRunnerClass(
             model_config,
             parallel_config,
             scheduler_config,
@@ -83,7 +86,8 @@ class Worker(WorkerBase):
         # Uninitialized cache engine. Will be initialized by
         # initialize_cache.
         self.cache_engine: CacheEngine
-        self.gpu_cache: List[torch.Tensor]
+        # Initialize gpu_cache as embedding models don't initialize kv_caches
+        self.gpu_cache: Optional[List[torch.tensor]] = None
 
     def init_device(self) -> None:
         if self.device_config.device.type == "cuda":
@@ -209,7 +213,7 @@ class Worker(WorkerBase):
     def execute_model(
         self,
         execute_model_req: Optional[ExecuteModelRequest] = None
-    ) -> List[SamplerOutput]:
+    ) -> List[Union[SamplerOutput, PoolerOutput]]:
 
         if execute_model_req is None:
             seq_group_metadata_list = None