Add VLM support when passing raw token IDs to vLLM client

tests/test_vllm_client_server.py

@@ -18,7 +18,7 @@
 
 import pytest
 from packaging.version import Version
-from transformers import AutoModelForCausalLM, AutoTokenizer
+from transformers import AutoModelForCausalLM, AutoProcessor, AutoTokenizer
 from transformers.testing_utils import torch_device
 
 from trl.generation.vllm_client import VLLMClient
@@ -31,6 +31,7 @@
     kill_process,
     require_3_accelerators,
     require_torch_multi_accelerator,
+    require_vision,
     require_vllm,
 )
 
@@ -207,6 +208,31 @@ def multiply(a: int, b: int) -> int:
         decoded_prompt = tokenizer.decode(outputs["prompt_ids"][0])
         assert "Multiplies two integers." in decoded_prompt
 
+    def test_generate_with_token_ids(self):
+        tokenizer = AutoTokenizer.from_pretrained(self.model_id)
+        prompts = ["Hello, AI!", "Tell me a joke"]
+        prompt_token_ids = tokenizer(prompts)["input_ids"]
+        outputs = self.client.generate(prompt_token_ids)
+        prompt_ids = outputs["prompt_ids"]
+        completion_ids = outputs["completion_ids"]
+
+        # Check that the outputs are lists
+        assert isinstance(prompt_ids, list)
+        assert isinstance(completion_ids, list)
+
+        # Check that the number of sequences are equal to the number of prompts
+        assert len(prompt_ids) == len(prompts)
+        assert len(completion_ids) == len(prompts)
+
+        # Check that prompt_ids match the input token IDs
+        assert prompt_ids == prompt_token_ids
+
+        # Check that the sequences are lists of integers
+        for seq in prompt_ids:
+            assert all(isinstance(tok, int) for tok in seq)
+        for seq in completion_ids:
+            assert all(isinstance(tok, int) for tok in seq)
+
     def test_generate_with_params(self):
         prompts = ["Hello, AI!", "Tell me a joke"]
         completion_ids = self.client.generate(prompts, n=2, repetition_penalty=0.9, temperature=0.8, max_tokens=32)[
@@ -411,6 +437,31 @@ def multiply(a: int, b: int) -> int:
         decoded_prompt = tokenizer.decode(outputs["prompt_ids"][0])
         assert "Multiplies two integers." in decoded_prompt
 
+    def test_generate_with_token_ids(self):
+        tokenizer = AutoTokenizer.from_pretrained(self.model_id)
+        prompts = ["Hello, AI!", "Tell me a joke"]
+        prompt_token_ids = tokenizer(prompts)["input_ids"]
+        outputs = self.client.generate(prompt_token_ids)
+        prompt_ids = outputs["prompt_ids"]
+        completion_ids = outputs["completion_ids"]
+
+        # Check that the outputs are lists
+        assert isinstance(prompt_ids, list)
+        assert isinstance(completion_ids, list)
+
+        # Check that the number of sequences are equal to the number of prompts
+        assert len(prompt_ids) == len(prompts)
+        assert len(completion_ids) == len(prompts)
+
+        # Check that prompt_ids match the input token IDs
+        assert prompt_ids == prompt_token_ids
+
+        # Check that the sequences are lists of integers
+        for seq in prompt_ids:
+            assert all(isinstance(tok, int) for tok in seq)
+        for seq in completion_ids:
+            assert all(isinstance(tok, int) for tok in seq)
+
     def test_generate_with_params(self):
         prompts = ["Hello, AI!", "Tell me a joke"]
         completion_ids = self.client.generate(prompts, n=2, repetition_penalty=0.9, temperature=0.8, max_tokens=32)[
@@ -536,6 +587,31 @@ def multiply(a: int, b: int) -> int:
         decoded_prompt = tokenizer.decode(outputs["prompt_ids"][0])
         assert "Multiplies two integers." in decoded_prompt
 
+    def test_generate_with_token_ids(self):
+        tokenizer = AutoTokenizer.from_pretrained(self.model_id)
+        prompts = ["Hello, AI!", "Tell me a joke"]
+        prompt_token_ids = tokenizer(prompts)["input_ids"]
+        outputs = self.client.generate(prompt_token_ids)
+        prompt_ids = outputs["prompt_ids"]
+        completion_ids = outputs["completion_ids"]
+
+        # Check that the outputs are lists
+        assert isinstance(prompt_ids, list)
+        assert isinstance(completion_ids, list)
+
+        # Check that the number of sequences are equal to the number of prompts
+        assert len(prompt_ids) == len(prompts)
+        assert len(completion_ids) == len(prompts)
+
+        # Check that prompt_ids match the input token IDs
+        assert prompt_ids == prompt_token_ids
+
+        # Check that the sequences are lists of integers
+        for seq in prompt_ids:
+            assert all(isinstance(tok, int) for tok in seq)
+        for seq in completion_ids:
+            assert all(isinstance(tok, int) for tok in seq)
+
     def test_generate_with_params(self):
         prompts = ["Hello, AI!", "Tell me a joke"]
         completion_ids = self.client.generate(prompts, n=2, repetition_penalty=0.9, temperature=0.8, max_tokens=32)[
@@ -665,6 +741,31 @@ def multiply(a: int, b: int) -> int:
         decoded_prompt = tokenizer.decode(outputs["prompt_ids"][0])
         assert "Multiplies two integers." in decoded_prompt
 
+    def test_generate_with_token_ids(self):
+        tokenizer = AutoTokenizer.from_pretrained(self.model_id)
+        prompts = ["Hello, AI!", "Tell me a joke"]
+        prompt_token_ids = tokenizer(prompts)["input_ids"]
+        outputs = self.client.generate(prompt_token_ids)
+        prompt_ids = outputs["prompt_ids"]
+        completion_ids = outputs["completion_ids"]
+
+        # Check that the outputs are lists
+        assert isinstance(prompt_ids, list)
+        assert isinstance(completion_ids, list)
+
+        # Check that the number of sequences are equal to the number of prompts
+        assert len(prompt_ids) == len(prompts)
+        assert len(completion_ids) == len(prompts)
+
+        # Check that prompt_ids match the input token IDs
+        assert prompt_ids == prompt_token_ids
+
+        # Check that the sequences are lists of integers
+        for seq in prompt_ids:
+            assert all(isinstance(tok, int) for tok in seq)
+        for seq in completion_ids:
+            assert all(isinstance(tok, int) for tok in seq)
+
     def test_generate_with_params(self):
         prompts = ["Hello, AI!", "Tell me a joke"]
         completion_ids = self.client.generate(prompts, n=2, repetition_penalty=0.9, temperature=0.8, max_tokens=32)[
@@ -774,3 +875,88 @@ def teardown_class(cls):
         # vLLM x pytest (or Popen) seems not to handle process termination well. To avoid zombie processes, we need to
         # kill the server process and its children explicitly.
         kill_process(cls.server_process)
+
+
+@pytest.mark.slow
+@require_vllm
+@require_vision
+class TestVLLMClientServerVLM(TrlTestCase):
+    model_id = "Qwen/Qwen2.5-VL-3B-Instruct"
+
+    @classmethod
+    def setup_class(cls):
+        # Start the server process
+        cls.server_process = subprocess.Popen(
+            ["trl", "vllm-serve", "--model", cls.model_id], stdout=subprocess.PIPE, stderr=subprocess.PIPE
+        )
+
+        # Initialize the client (no communicator needed for generation-only tests)
+        cls.client = VLLMClient(connection_timeout=240, host="localhost")
+
+    def test_generate_with_token_ids_and_image(self):
+        from PIL import Image
+
+        processor = AutoProcessor.from_pretrained(self.model_id)
+        image1 = Image.new("RGB", (64, 64), color="red")
+        image2 = Image.new("RGB", (64, 64), color="blue")
+        image3 = Image.new("RGB", (64, 64), color="green")
+        messages = [
+            [
+                {
+                    "role": "user",
+                    "content": [
+                        {"type": "image", "image": image1},
+                        {"type": "image", "image": image2},
+                        {"type": "text", "text": "What are the differences between these two images?"},
+                    ],
+                }
+            ],
+            [
+                {
+                    "role": "user",
+                    "content": [
+                        {"type": "image", "image": image3},
+                        {"type": "text", "text": "What is the color of this image?"},
+                    ],
+                }
+            ],
+        ]
+        prompt_token_ids = processor.apply_chat_template(
+            conversation=messages, tokenize=True, add_generation_prompt=True
+        )
+        outputs = self.client.generate(prompt_token_ids, images=[[image1, image2], [image3]], max_tokens=64)
+        prompt_ids = outputs["prompt_ids"]
+        completion_ids = outputs["completion_ids"]
+
+        assert len(prompt_ids) == 2
+        assert len(completion_ids) == 2
+        assert all(isinstance(tok, int) for tok in prompt_ids[0])
+        assert all(isinstance(tok, int) for tok in completion_ids[0])
+
+    def test_generate_with_token_ids_mixed_images(self):
+        """Test a batch where one prompt has an image and the other does not."""
+        from PIL import Image
+
+        processor = AutoProcessor.from_pretrained(self.model_id)
+        image = Image.new("RGB", (64, 64), color="red")
+        messages = [
+            [{"role": "user", "content": [{"type": "image", "image": image}, {"type": "text", "text": "Describe this image."}]}],
+            [{"role": "user", "content": [{"type": "text", "text": "What is 1+1?"}]}],
+        ]
+        prompt_token_ids = processor.apply_chat_template(
+            conversation=messages, tokenize=True, add_generation_prompt=True
+        )
+        outputs = self.client.generate(prompt_token_ids, images=[[image], None], max_tokens=64)
+        prompt_ids = outputs["prompt_ids"]
+        completion_ids = outputs["completion_ids"]
+
+        assert len(prompt_ids) == 2
+        assert len(completion_ids) == 2
+        assert all(isinstance(tok, int) for tok in prompt_ids[0])
+        assert all(isinstance(tok, int) for tok in prompt_ids[1])
+        assert all(isinstance(tok, int) for tok in completion_ids[0])
+        assert all(isinstance(tok, int) for tok in completion_ids[1])
+
+    @classmethod
+    def teardown_class(cls):
+        kill_process(cls.server_process)

trl/generation/vllm_client.py

@@ -201,7 +201,7 @@ def check_server(self, total_timeout: float = 0.0, retry_interval: float = 2.0):
 
     def generate(
         self,
-        prompts: list[str],
+        prompts: list[str] | list[list[int]],
         images: list | None = None,
         n: int = 1,
         repetition_penalty: float = 1.0,
@@ -219,10 +219,11 @@ def generate(
         Generates model completions for the provided prompts.
 
         Args:
-            prompts (`list[str]`):
-                List of text prompts for which the model will generate completions.
-            images (`list[PIL.Image]`, *optional*):
-                List of PIL Images to send along with the prompts.
+            prompts (`list[str]` or `list[list[int]]`):
+                List of text prompts or list of token ID lists for which the model will generate completions.
+            images (`list[list[PIL.Image] | None]`, *optional*):
+                List of image lists for VLM support. Each element is a list of PIL images for the corresponding prompt,
+                or `None` if no images for that prompt.
             n (`int`, *optional*, defaults to `1`):
                 Number of completions to generate for each prompt.
             repetition_penalty (`float`, *optional*, defaults to `1.0`):
@@ -265,8 +266,12 @@ def generate(
         """
         url = f"{self.base_url}/generate/"
 
-        # Convert PIL images to base64 strings
-        images = [pil_to_base64(img) for img in images] if images else None
+        # Convert PIL images to base64 strings. Each element is a list of images for the corresponding prompt,
+        # or None if no images for that prompt.
+        if images:
+            images = [
+                [pil_to_base64(img) for img in img_list] if img_list is not None else None for img_list in images
+            ]
 
         response = self.session.post(
             url,

trl/generation/vllm_generation.py

@@ -627,7 +627,8 @@ def generate(self, prompts: list, num_generations: int, profiler: ProfilingConte
                             chat_template=chat_template,
                         )
                     else:
-                        output = self.vllm_client.generate(prompts=ordered_set_of_prompts, **sampling_params)
+                        ordered_set_of_prompt_ids = self.processing_class(text=ordered_set_of_prompts)["input_ids"]
+                        output = self.vllm_client.generate(prompts=ordered_set_of_prompt_ids, **sampling_params)
                     # Extract required fields and collect any extra fields for reward functions
                     required_keys = {"prompt_ids", "completion_ids", "logprobs", "logprob_token_ids"}
                     extra_fields = {k: v for k, v in output.items() if k not in required_keys}

trl/scripts/vllm_serve.py

@@ -495,8 +495,8 @@ async def get_world_size():
         return {"world_size": script_args.tensor_parallel_size * script_args.data_parallel_size}
 
     class GenerateRequest(BaseModel):
-        prompts: list[str]
-        images: list[str] | None = None
+        prompts: list[str] | list[list[int]]
+        images: list[list[str] | None] | None = None
         n: int = 1
         repetition_penalty: float = 1.0
         temperature: float = 1.0
@@ -522,9 +522,10 @@ async def generate(request: GenerateRequest):
 
         Args:
             request (`GenerateRequest`):
-                - `prompts` (list of `str`): A list of prompts (text strings) for the model to generate completions.
-                - `images` (list of `str`, *optional*, default to `None`): A list of base64 encoded images to process
-                  along with prompts.
+                - `prompts` (list of `str` or list of list of `int`): A list of prompts. It accepts either text strings
+                  or pre-tokenized token ID lists. When text strings are provided, `images` can optionally be included.
+                - `images` (list of list of `str` or `None`, *optional*): A list of image lists. Each element is a list
+                  of base64-encoded images for the corresponding prompt, or `None` if no images for that prompt.
                 - `n` (`int`, *optional*, defaults to `1`): Number of completions to generate for each prompt.
                 - `repetition_penalty` (`float`, *optional*, defaults to `1.0`): Repetition penalty to apply during
                   generation.
@@ -558,28 +559,35 @@ async def generate(request: GenerateRequest):
                 - `logprob_token_ids` (list of list of list of `int`): Token IDs corresponding to each logprob, same
                   shape as `logprobs`.
 
-        Example request:
+        Example request (text prompts):
         ```json
         {"prompts": ["Hello world", "What is AI?"]}
         ```
 
+        Example request (token IDs):
+        ```json
+        {"prompts": [[101, 102], [201, 202]]}
+        ```
+
         Example response:
         ```json
         {
-          "prompt_ids": [[101, 102], [201, 202]],
-          "completion_ids": [[103, 104, 105], [203, 204, 205]],
-          "logprobs": [[[-0.1], [-0.2], [-0.3]], [[-0.4], [-0.5], [-0.6]]],
-          "logprob_token_ids": [[[103], [104], [105]], [[203], [204], [205]]]
+          "prompt_ids": [[101, 102], [201, 202]], "completion_ids": [[103, 104, 105], [203, 204, 205]], "logprobs":
+          [[[-0.1], [-0.2], [-0.3]], [[-0.4], [-0.5], [-0.6]]], "logprob_token_ids": [[[103], [104], [105]], [[203],
+          [204], [205]]]
         }
         ```
         """
+        # Build vLLM-compatible prompt inputs
+        is_token_ids = request.prompts and isinstance(request.prompts[0], list)
         request.images = request.images or [None] * len(request.prompts)
 
         prompts = []
-        for prompt, image in zip(request.prompts, request.images, strict=True):
-            row = {"prompt": prompt}
-            if image is not None:
-                row["multi_modal_data"] = {"image": Image.open(BytesIO(base64.b64decode(image)))}
+        for prompt, image_list in zip(request.prompts, request.images, strict=True):
+            row = {"prompt_token_ids": prompt} if is_token_ids else {"prompt": prompt}
+            if image_list is not None:
+                decoded_images = [Image.open(BytesIO(base64.b64decode(img))) for img in image_list]
+                row["multi_modal_data"] = {"image": decoded_images if len(decoded_images) > 1 else decoded_images[0]}
             prompts.append(row)
 
         generation_kwargs = {