[feature] Prompt Embeddings Support for v1 Engine (#3026)

### What this PR does / why we need it?
this PR based on
[19746](https://github.com/vllm-project/vllm/issues/19746), support
Prompt Embeddings for v1 engine on NPU

### Does this PR introduce _any_ user-facing change?

### How was this patch tested?

```python
python examples/prompt_embed_inference.py
```


- vLLM version: v0.11.0
- vLLM main:
https://github.com/vllm-project/vllm/commit/releases/v0.11.1

---------

Signed-off-by: jesse <szxfml@gmail.com>

examples/prompt_embed_inference.py

@@ -0,0 +1,97 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""
+Demonstrates how to generate prompt embeddings using
+Hugging Face Transformers  and use them as input to vLLM
+for both single and batch inference.
+
+Model: meta-llama/Llama-3.2-1B-Instruct
+Note: This model is gated on Hugging Face Hub.
+      You must request access to use it:
+      https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct
+
+Requirements:
+- vLLM
+- transformers
+
+Run:
+    python examples/prompt_embed_inference.py
+"""
+
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer, PreTrainedTokenizer
+
+from vllm import LLM
+
+
+def init_tokenizer_and_llm(model_name: str):
+    llm = LLM(model=model_name, enable_prompt_embeds=True)
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    transformers_model = AutoModelForCausalLM.from_pretrained(model_name)
+    embedding_layer = transformers_model.get_input_embeddings()
+    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()