[ModelRunner] Support embedding inputs (#916)

### What this PR does / why we need it?
- Adds support for passing prompt_embeds to LLM.generate as
```bash
llm.generate({"prompt_embeds": input_embeds}, sampling_params)
```
or
```bash
llm.generate(
    [{"prompt_embeds": input_embeds} for input_embeds in inputs_embeds], sampling_params
)
```
- Add `prompt_embeds` to examples

### How was this patch tested?
CI passed with new added/existing test.
and I have test with the example script in this pr, and the output seems
looks good:
```bash

[Single Inference Output]
------------------------------
The capital of France is Paris. Paris is the largest city in France and is
------------------------------
Adding requests: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 3966.87it/s]
Processed prompts: 100%|█████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00,  3.99it/s, est. speed input: 177.08 toks/s, output: 63.91 toks/s]

[Batch Inference Outputs]
------------------------------
Q1: Please tell me about the capital of France.
A1: The capital of France is Paris. It is located in the northern part of the

Q2: When is the day longest during the year?
A2: The day is longest during the year at the summer solstice. This typically occurs

Q3: Where is bigger, the moon or the sun?
A3: The sun is significantly bigger than the moon. 

The sun has a diameter of

------------------------------
```

---------

Signed-off-by: wangli <wangli858794774@gmail.com>

examples/prompt_embedding_inference.py

@@ -0,0 +1,83 @@
+import torch
+from transformers import (AutoModelForCausalLM, AutoTokenizer,
+                          PreTrainedTokenizer)
+from vllm import LLM
+
+
+def init_tokenizer_and_llm(model_name: str):
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    transformers_model = AutoModelForCausalLM.from_pretrained(model_name)
+    embedding_layer = transformers_model.get_input_embeddings()
+    llm = LLM(model=model_name, enable_prompt_embeds=True)
+    return tokenizer, embedding_layer, llm
+
+
+def get_prompt_embeds(chat: list[dict[str,
+                                      str]], tokenizer: PreTrainedTokenizer,
+                      embedding_layer: torch.nn.Module):
+    token_ids = tokenizer.apply_chat_template(chat,
+                                              add_generation_prompt=True,
+                                              return_tensors='pt')
+    prompt_embeds = embedding_layer(token_ids).squeeze(0)
+    return prompt_embeds
+
+
+def single_prompt_inference(llm: LLM, tokenizer: PreTrainedTokenizer,
+                            embedding_layer: torch.nn.Module):
+    chat = [{
+        "role": "user",
+        "content": "Please tell me about the capital of France."
+    }]
+    prompt_embeds = get_prompt_embeds(chat, tokenizer, embedding_layer)
+
+    outputs = llm.generate({
+        "prompt_embeds": prompt_embeds,
+    })
+
+    print("\n[Single Inference Output]")
+    print("-" * 30)
+    for o in outputs:
+        print(o.outputs[0].text)
+    print("-" * 30)
+
+
+def batch_prompt_inference(llm: LLM, tokenizer: PreTrainedTokenizer,
+                           embedding_layer: torch.nn.Module):
+    chats = [[{
+        "role": "user",
+        "content": "Please tell me about the capital of France."
+    }],
+             [{
+                 "role": "user",
+                 "content": "When is the day longest during the year?"
+             }],
+             [{
+                 "role": "user",
+                 "content": "Where is bigger, the moon or the sun?"
+             }]]
+
+    prompt_embeds_list = [
+        get_prompt_embeds(chat, tokenizer, embedding_layer) for chat in chats
+    ]
+
+    outputs = llm.generate([{
+        "prompt_embeds": embeds
+    } for embeds in prompt_embeds_list])
+
+    print("\n[Batch Inference Outputs]")
+    print("-" * 30)
+    for i, o in enumerate(outputs):
+        print(f"Q{i+1}: {chats[i][0]['content']}")
+        print(f"A{i+1}: {o.outputs[0].text}\n")
+    print("-" * 30)
+
+
+def main():
+    model_name = "meta-llama/Llama-3.2-1B-Instruct"
+    tokenizer, embedding_layer, llm = init_tokenizer_and_llm(model_name)
+    single_prompt_inference(llm, tokenizer, embedding_layer)
+    batch_prompt_inference(llm, tokenizer, embedding_layer)
+
+
+if __name__ == "__main__":
+    main()