[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>

tests/utils.py

@@ -20,13 +20,143 @@
 import functools
 import os
 import signal
-from typing import Callable
+import subprocess
+import sys
+import time
+from typing import Callable, Optional
 
+import openai
+import requests
 from typing_extensions import ParamSpec
+from vllm.engine.arg_utils import AsyncEngineArgs
+from vllm.entrypoints.openai.cli_args import make_arg_parser
+from vllm.model_executor.model_loader import get_model_loader
+from vllm.utils import FlexibleArgumentParser, get_open_port
 
 _P = ParamSpec("_P")
 
 
+class RemoteOpenAIServer:
+    DUMMY_API_KEY = "token-abc123"  # vLLM's OpenAI server does not need API key
+
+    def __init__(self,
+                 model: str,
+                 vllm_serve_args: list[str],
+                 *,
+                 env_dict: Optional[dict[str, str]] = None,
+                 seed: Optional[int] = 0,
+                 auto_port: bool = True,
+                 max_wait_seconds: Optional[float] = None) -> None:
+        if auto_port:
+            if "-p" in vllm_serve_args or "--port" in vllm_serve_args:
+                raise ValueError("You have manually specified the port "
+                                 "when `auto_port=True`.")
+
+            # Don't mutate the input args
+            vllm_serve_args = vllm_serve_args + [
+                "--port", str(get_open_port())
+            ]
+        if seed is not None:
+            if "--seed" in vllm_serve_args:
+                raise ValueError("You have manually specified the seed "
+                                 f"when `seed={seed}`.")
+
+            vllm_serve_args = vllm_serve_args + ["--seed", str(seed)]
+
+        parser = FlexibleArgumentParser(
+            description="vLLM's remote OpenAI server.")
+        parser = make_arg_parser(parser)
+        args = parser.parse_args(["--model", model, *vllm_serve_args])
+        self.host = str(args.host or 'localhost')
+        self.port = int(args.port)
+
+        self.show_hidden_metrics = \
+            args.show_hidden_metrics_for_version is not None
+
+        # download the model before starting the server to avoid timeout
+        is_local = os.path.isdir(model)
+        if not is_local:
+            engine_args = AsyncEngineArgs.from_cli_args(args)
+            model_config = engine_args.create_model_config()
+            load_config = engine_args.create_load_config()
+
+            model_loader = get_model_loader(load_config)
+            model_loader.download_model(model_config)
+
+        env = os.environ.copy()
+        # the current process might initialize cuda,
+        # to be safe, we should use spawn method
+        env['VLLM_WORKER_MULTIPROC_METHOD'] = 'spawn'
+        if env_dict is not None:
+            env.update(env_dict)
+        self.proc = subprocess.Popen(
+            ["vllm", "serve", model, *vllm_serve_args],
+            env=env,
+            stdout=sys.stdout,
+            stderr=sys.stderr,
+        )
+        max_wait_seconds = max_wait_seconds or 240
+        self._wait_for_server(url=self.url_for("health"),
+                              timeout=max_wait_seconds)
+
+    def __enter__(self):
+        return self
+
+    def __exit__(self, exc_type, exc_value, traceback):
+        self.proc.terminate()
+        try:
+            self.proc.wait(8)
+        except subprocess.TimeoutExpired:
+            # force kill if needed
+            self.proc.kill()
+
+    def _wait_for_server(self, *, url: str, timeout: float):
+        # run health check
+        start = time.time()
+        while True:
+            try:
+                if requests.get(url).status_code == 200:
+                    break
+            except Exception:
+                # this exception can only be raised by requests.get,
+                # which means the server is not ready yet.
+                # the stack trace is not useful, so we suppress it
+                # by using `raise from None`.
+                result = self.proc.poll()
+                if result is not None and result != 0:
+                    raise RuntimeError("Server exited unexpectedly.") from None
+
+                time.sleep(0.5)
+                if time.time() - start > timeout:
+                    raise RuntimeError(
+                        "Server failed to start in time.") from None
+
+    @property
+    def url_root(self) -> str:
+        return f"http://{self.host}:{self.port}"
+
+    def url_for(self, *parts: str) -> str:
+        return self.url_root + "/" + "/".join(parts)
+
+    def get_client(self, **kwargs):
+        if "timeout" not in kwargs:
+            kwargs["timeout"] = 600
+        return openai.OpenAI(
+            base_url=self.url_for("v1"),
+            api_key=self.DUMMY_API_KEY,
+            max_retries=0,
+            **kwargs,
+        )
+
+    def get_async_client(self, **kwargs):
+        if "timeout" not in kwargs:
+            kwargs["timeout"] = 600
+        return openai.AsyncOpenAI(base_url=self.url_for("v1"),
+                                  api_key=self.DUMMY_API_KEY,
+                                  max_retries=0,
+                                  **kwargs)
+
+
 def fork_new_process_for_each_test(
         f: Callable[_P, None]) -> Callable[_P, None]:
     """Decorator to fork a new process for each test function.