example: add vlm to token in & out example (#3941)

Co-authored-by: zhaochenyang20 <zhaochen20@outlook.com>

docs/backend/server_arguments.md

@@ -52,7 +52,7 @@ Please consult the documentation below to learn more about the parameters you ma
 * `chat_template`: The chat template to use. Deviating from the default might lead to unexpected responses. For multi-modal chat templates, refer to [here](https://docs.sglang.ai/backend/openai_api_vision.ipynb#Chat-Template).
 * `is_embedding`: Set to true to perform [embedding](./openai_api_embeddings.ipynb) / [encode](https://docs.sglang.ai/backend/native_api#Encode-(embedding-model)) and [reward](https://docs.sglang.ai/backend/native_api#Classify-(reward-model)) tasks.
 * `revision`: Adjust if a specific version of the model should be used.
-* `skip_tokenizer_init`: Set to true to provide the tokens to the engine and get the output tokens directly, typically used in RLHF. Please see this [example for reference](https://github.com/sgl-project/sglang/blob/main/examples/runtime/engine/token_in_token_out_llm.py).
+* `skip_tokenizer_init`: Set to true to provide the tokens to the engine and get the output tokens directly, typically used in RLHF. Please see this [example for reference](https://github.com/sgl-project/sglang/blob/main/examples/runtime/engine/token_in_token_out/).
 * `json_model_override_args`: Override model config with the provided JSON.
 * `delete_ckpt_after_loading`: Delete the model checkpoint after loading the model.
 

examples/runtime/engine/readme.md

@@ -9,15 +9,15 @@ SGLang provides a direct inference engine without the need for an HTTP server. T
 
 ## Examples
 
-### 1. [Offline Batch Inference](./offline_batch_inference.py)
+### [Offline Batch Inference](./offline_batch_inference.py)
 
 In this example, we launch an SGLang engine and feed a batch of inputs for inference. If you provide a very large batch, the engine will intelligently schedule the requests to process efficiently and prevent OOM (Out of Memory) errors.
 
-### 2. [Embedding Generation](./embedding.py)
+### [Embedding Generation](./embedding.py)
 
 In this example, we launch an SGLang engine and feed a batch of inputs for embedding generation.
 
-### 3. [Custom Server](./custom_server.py)
+### [Custom Server](./custom_server.py)
 
 This example demonstrates how to create a custom server on top of the SGLang Engine. We use [Sanic](https://sanic.dev/en/) as an example. The server supports both non-streaming and streaming endpoints.
 
@@ -43,3 +43,7 @@ curl -X POST http://localhost:8000/generate_stream  -H "Content-Type: applicatio
 ```
 
 This will send both non-streaming and streaming requests to the server.
+
+### [Token-In-Token-Out for RLHF](./token_in_token_out)
+
+In this example, we launch an SGLang engine, feed tokens as input and generate tokens as output.

examples/runtime/engine/token_in_token_out/token_in_token_out_llm.py

@@ -30,7 +30,7 @@ def main():
     # Print the outputs.
     for prompt, output in zip(prompts, outputs):
         print("===============================")
-        print(f"Prompt: {prompt}\nGenerated token ids: {output['token_ids']}")
+        print(f"Prompt: {prompt}\nGenerated token ids: {output['output_ids']}")
 
 
 # The __main__ condition is necessary here because we use "spawn" to create subprocesses

examples/runtime/engine/token_in_token_out/token_in_token_out_vlm.py

@@ -0,0 +1,75 @@
+import argparse
+import dataclasses
+from io import BytesIO
+from typing import Tuple
+
+import requests
+from PIL import Image
+from transformers import AutoProcessor
+
+from sglang import Engine
+from sglang.lang.chat_template import get_chat_template_by_model_path
+from sglang.srt.configs.model_config import ModelConfig
+from sglang.srt.server_args import ServerArgs
+from sglang.test.test_utils import DEFAULT_IMAGE_URL
+
+
+def get_input_ids(
+    server_args: ServerArgs, model_config: ModelConfig
+) -> Tuple[list[int], list]:
+    chat_template = get_chat_template_by_model_path(model_config.model_path)
+    text = f"{chat_template.image_token}What is in this picture?"
+    images = [Image.open(BytesIO(requests.get(DEFAULT_IMAGE_URL).content))]
+    image_data = [DEFAULT_IMAGE_URL]
+
+    processor = AutoProcessor.from_pretrained(
+        model_config.model_path, trust_remote_code=server_args.trust_remote_code
+    )
+
+    inputs = processor(
+        text=[text],
+        images=images,
+        return_tensors="pt",
+    )
+
+    return inputs.input_ids[0].tolist(), image_data
+
+
+def token_in_out_example(
+    server_args: ServerArgs,
+):
+    input_ids, image_data = get_input_ids(
+        server_args,
+        ModelConfig(
+            server_args.model_path,
+            trust_remote_code=server_args.trust_remote_code,
+            model_override_args=server_args.json_model_override_args,
+        ),
+    )
+    backend = Engine(**dataclasses.asdict(server_args))
+
+    output = backend.generate(
+        input_ids=input_ids,
+        image_data=image_data,
+        sampling_params={
+            "temperature": 0.8,
+            "max_new_tokens": 32,
+        },
+    )
+
+    print("===============================")
+    print(f"Output token ids: ", output["output_ids"])
+
+    backend.shutdown()
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    ServerArgs.add_cli_args(parser)
+    args = [
+        "--model-path=Qwen/Qwen2-VL-2B",
+    ]
+    args = parser.parse_args(args=args)
+    server_args = ServerArgs.from_cli_args(args)
+    server_args.skip_tokenizer_init = True
+    token_in_out_example(server_args)

python/sglang/srt/configs/model_config.py

@@ -40,7 +40,7 @@ class ModelConfig:
         trust_remote_code: bool = True,
         revision: Optional[str] = None,
         context_length: Optional[int] = None,
-        model_override_args: Optional[dict] = None,
+        model_override_args: Optional[str] = None,
         is_embedding: Optional[bool] = None,
         dtype: str = "auto",
         quantization: Optional[str] = None,

python/sglang/srt/managers/scheduler.py

@@ -42,7 +42,6 @@ from sglang.srt.layers.logits_processor import LogitsProcessorOutput
 from sglang.srt.managers.io_struct import (
     AbortReq,
     BatchEmbeddingOut,
-    BatchMultimodalDecodeReq,
     BatchTokenIDOut,
     CloseSessionReqInput,
     FlushCacheReq,
@@ -104,7 +103,6 @@ from sglang.srt.utils import (
     crash_on_warnings,
     get_bool_env_var,
     get_zmq_socket,
-    kill_itself_when_parent_died,
     pyspy_dump_schedulers,
     set_gpu_proc_affinity,
     set_random_seed,
@@ -1199,7 +1197,6 @@ class Scheduler:
                 self.spec_num_total_forward_ct += batch.batch_size()
                 self.num_generated_tokens += num_accepted_tokens
             batch.output_ids = next_token_ids
-
             # These 2 values are needed for processing the output, but the values can be
             # modified by overlap schedule. So we have to copy them here so that
             # we can use the correct values in output processing.
@@ -1480,7 +1477,6 @@ class Scheduler:
             batch.next_batch_sampling_info.update_regex_vocab_mask()
             self.current_stream.synchronize()
             batch.next_batch_sampling_info.sampling_info_done.set()
-
         self.stream_output(batch.reqs, batch.return_logprob)
 
         self.token_to_kv_pool.free_group_end()
@@ -1580,11 +1576,11 @@ class Scheduler:
             if req.top_logprobs_num > 0:
                 req.input_top_logprobs_val = [None]
                 req.input_top_logprobs_idx = [None]
-
+                assert len(req.temp_input_token_ids_logprobs_val) == len(
+                    req.temp_input_token_ids_logprobs_idx
+                )
                 for val, idx in zip(
-                    req.temp_input_top_logprobs_val,
+                    req.temp_input_top_logprobs_val, req.temp_input_top_logprobs_idx
-                    req.temp_input_top_logprobs_idx,
-                    strict=True,
                 ):
                     req.input_top_logprobs_val.extend(val)
                     req.input_top_logprobs_idx.extend(idx)
@@ -1779,7 +1775,6 @@ class Scheduler:
             if rids:
                 if self.model_config.is_multimodal_gen:
                     raise NotImplementedError()
-
                 self.send_to_detokenizer.send_pyobj(
                     BatchTokenIDOut(
                         rids,

python/sglang/srt/model_loader/loader.py

@@ -11,7 +11,7 @@ import math
 import os
 from abc import ABC, abstractmethod
 from contextlib import contextmanager
-from typing import Any, Dict, Generator, Iterable, List, Optional, Tuple, Type, cast
+from typing import Any, Dict, Generator, Iterable, List, Optional, Tuple, cast
 
 import gguf
 import huggingface_hub
@@ -19,7 +19,7 @@ import numpy as np
 import torch
 from huggingface_hub import HfApi, hf_hub_download
 from torch import nn
-from transformers import AutoModelForCausalLM, PretrainedConfig
+from transformers import AutoModelForCausalLM
 from transformers.utils import SAFE_WEIGHTS_INDEX_NAME
 
 from sglang.srt.configs.device_config import DeviceConfig
@@ -197,7 +197,7 @@ class DefaultModelLoader(BaseModelLoader):
 
         Returns the path to the downloaded model, or None if the model is not
         downloaded from ModelScope."""
-        if "SGLANG_USE_MODELSCOPE" in os.environ:
+        if os.environ.get("SGLANG_USE_MODELSCOPE", None) == "True":
             # download model from ModelScope hub,
             # lazy import so that modelscope is not required for normal use.
             # pylint: disable=C.

python/sglang/test/test_utils.py

@@ -43,10 +43,15 @@ DEFAULT_MODEL_NAME_FOR_NIGHTLY_EVAL_FP8_TP1 = "neuralmagic/Meta-Llama-3.1-8B-Ins
 DEFAULT_MODEL_NAME_FOR_NIGHTLY_EVAL_FP8_TP2 = "neuralmagic/Meta-Llama-3.1-70B-Instruct-FP8,neuralmagic/Mixtral-8x7B-Instruct-v0.1-FP8,neuralmagic/Qwen2-72B-Instruct-FP8,neuralmagic/Qwen2-57B-A14B-Instruct-FP8,neuralmagic/DeepSeek-Coder-V2-Lite-Instruct-FP8"
 DEFAULT_MODEL_NAME_FOR_NIGHTLY_EVAL_QUANT_TP1 = "hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4,hugging-quants/Meta-Llama-3.1-8B-Instruct-GPTQ-INT4"
 DEFAULT_SMALL_MODEL_NAME_FOR_TEST_QWEN = "Qwen/Qwen2.5-1.5B-Instruct"
+DEFAULT_SMALL_VLM_MODEL_NAME = "Qwen/Qwen2-VL-2B"
+
 
 DEFAULT_EAGLE_TARGET_MODEL_FOR_TEST = "meta-llama/Llama-2-7b-chat-hf"
 DEFAULT_EAGLE_DRAFT_MODEL_FOR_TEST = "lmsys/sglang-EAGLE-llama2-chat-7B"
 
+DEFAULT_IMAGE_URL = "https://github.com/sgl-project/sglang/blob/main/test/lang/example_image.png?raw=true"
+DEFAULT_VIDEO_URL = "https://raw.githubusercontent.com/EvolvingLMMs-Lab/sglang/dev/onevision_local/assets/jobs.mp4"
+
 
 def is_in_ci():
     """Return whether it is in CI runner."""

test/srt/test_skip_tokenizer_init.py

@@ -5,13 +5,18 @@ python3 -m unittest test_skip_tokenizer_init.TestSkipTokenizerInit.run_decode_st
 
 import json
 import unittest
+from io import BytesIO
 
 import requests
-from transformers import AutoTokenizer
+from PIL import Image
+from transformers import AutoProcessor, AutoTokenizer
 
+from sglang.lang.chat_template import get_chat_template_by_model_path
 from sglang.srt.utils import kill_process_tree
 from sglang.test.test_utils import (
+    DEFAULT_IMAGE_URL,
     DEFAULT_SMALL_MODEL_NAME_FOR_TEST,
+    DEFAULT_SMALL_VLM_MODEL_NAME,
     DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
     DEFAULT_URL_FOR_TEST,
     popen_launch_server,
@@ -29,6 +34,7 @@ class TestSkipTokenizerInit(unittest.TestCase):
             timeout=DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
             other_args=["--skip-tokenizer-init", "--stream-output"],
         )
+        cls.eos_token_id = [119690]
         cls.tokenizer = AutoTokenizer.from_pretrained(
             DEFAULT_SMALL_MODEL_NAME_FOR_TEST, use_fast=False
         )
@@ -45,9 +51,7 @@ class TestSkipTokenizerInit(unittest.TestCase):
         top_logprobs_num=0,
         n=1,
     ):
-        input_ids = self.tokenizer(prompt_text, return_tensors="pt")["input_ids"][
+        input_ids = self.get_input_ids(prompt_text)
-            0
-        ].tolist()
 
         response = requests.post(
             self.base_url + "/generate",
@@ -104,7 +108,7 @@ class TestSkipTokenizerInit(unittest.TestCase):
 
     def run_decode_stream(self, return_logprob=False, top_logprobs_num=0, n=1):
         max_new_tokens = 32
-        input_ids = [128000, 791, 6864, 315, 9822, 374]  # The capital of France is
+        input_ids = self.get_input_ids("The capital of France is")
         requests.post(self.base_url + "/flush_cache")
         response = requests.post(
             self.base_url + "/generate",
@@ -114,7 +118,7 @@ class TestSkipTokenizerInit(unittest.TestCase):
                     "temperature": 0 if n == 1 else 0.5,
                     "max_new_tokens": max_new_tokens,
                     "n": n,
-                    "stop_token_ids": [119690],
+                    "stop_token_ids": self.eos_token_id,
                 },
                 "stream": False,
                 "return_logprob": return_logprob,
@@ -125,6 +129,9 @@ class TestSkipTokenizerInit(unittest.TestCase):
         ret = response.json()
         print(json.dumps(ret))
         output_ids = ret["output_ids"]
+        print("output from non-streaming request:")
+        print(output_ids)
+        print(self.tokenizer.decode(output_ids, skip_special_tokens=True))
 
         requests.post(self.base_url + "/flush_cache")
         response_stream = requests.post(
@@ -135,7 +142,7 @@ class TestSkipTokenizerInit(unittest.TestCase):
                     "temperature": 0 if n == 1 else 0.5,
                     "max_new_tokens": max_new_tokens,
                     "n": n,
-                    "stop_token_ids": [119690],
+                    "stop_token_ids": self.eos_token_id,
                 },
                 "stream": True,
                 "return_logprob": return_logprob,
@@ -143,13 +150,10 @@ class TestSkipTokenizerInit(unittest.TestCase):
                 "logprob_start_len": 0,
             },
         )
-        ret = response.json()
-        output_ids = ret["output_ids"]
-        print("output from non-streaming request:")
-        print(output_ids)
 
         response_stream_json = []
         for line in response_stream.iter_lines():
+            print(line)
             if line.startswith(b"data: ") and line[6:] != b"[DONE]":
                 response_stream_json.append(json.loads(line[6:]))
         out_stream_ids = []
@@ -157,6 +161,8 @@ class TestSkipTokenizerInit(unittest.TestCase):
             out_stream_ids += x["output_ids"]
         print("output from streaming request:")
         print(out_stream_ids)
+        print(self.tokenizer.decode(out_stream_ids, skip_special_tokens=True))
+
         assert output_ids == out_stream_ids
 
     def test_simple_decode(self):
@@ -175,6 +181,46 @@ class TestSkipTokenizerInit(unittest.TestCase):
     def test_simple_decode_stream(self):
         self.run_decode_stream()
 
+    def get_input_ids(self, prompt_text) -> list[int]:
+        input_ids = self.tokenizer(prompt_text, return_tensors="pt")["input_ids"][
+            0
+        ].tolist()
+        return input_ids
+
+
+class TestSkipTokenizerInitVLM(TestSkipTokenizerInit):
+    @classmethod
+    def setUpClass(cls):
+        cls.image_url = DEFAULT_IMAGE_URL
+        response = requests.get(cls.image_url)
+        cls.image = Image.open(BytesIO(response.content))
+        cls.model = DEFAULT_SMALL_VLM_MODEL_NAME
+        cls.tokenizer = AutoTokenizer.from_pretrained(cls.model, use_fast=False)
+        cls.processor = AutoProcessor.from_pretrained(cls.model, trust_remote_code=True)
+        cls.base_url = DEFAULT_URL_FOR_TEST
+        cls.process = popen_launch_server(
+            cls.model,
+            cls.base_url,
+            timeout=DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
+            other_args=["--skip-tokenizer-init"],
+        )
+        cls.eos_token_id = [cls.tokenizer.eos_token_id]
+
+    def get_input_ids(self, _prompt_text) -> list[int]:
+        chat_template = get_chat_template_by_model_path(self.model)
+        text = f"{chat_template.image_token}What is in this picture?"
+        inputs = self.processor(
+            text=[text],
+            images=[self.image],
+            return_tensors="pt",
+        )
+
+        return inputs.input_ids[0].tolist()
+
+    def test_simple_decode_stream(self):
+        # TODO mick
+        pass
+
 
 if __name__ == "__main__":
     unittest.main()