Fix logprob_start_len for multi modal models (#2597)

Co-authored-by: libra <lihu723@gmail.com>
Co-authored-by: fzyzcjy <ch271828n@outlook.com>
Co-authored-by: Wang, Haoyu <haoyu.wang@intel.com>

benchmark/deepseek_v3/README.md

@@ -9,12 +9,16 @@ Special thanks to Meituan's Search & Recommend Platform Team and Baseten's Model
 ## Hardware Recommendation
 - 8 x NVIDIA H200 GPUs
 
+If you do not have GPUs with large enough memory, please try multi-node tensor parallelism ([help 1](https://github.com/sgl-project/sglang/blob/637de9e8ce91fd3e92755eb2a842860925954ab1/docs/backend/backend.md?plain=1#L88-L95) [help 2](https://github.com/sgl-project/sglang/blob/637de9e8ce91fd3e92755eb2a842860925954ab1/docs/backend/backend.md?plain=1#L152-L168)).
+
 ## Installation & Launch
 
+If you see errors when launching the server, please check if it has finished downloading the weights. It is recommended to download the weights before launching, or to launch multiple times until all the weights have been downloaded.
+
 ### Using Docker (Recommended)
 ```bash
 docker run --gpus all --shm-size 32g -p 30000:30000 -v ~/.cache/huggingface:/root/.cache/huggingface --ipc=host lmsysorg/sglang:latest \
-    python3 -m sglang.launch_server --model deepseek-ai/DeepSeek-V3-Base --enable-dp-attention --tp 8 --trust-remote-code --port 30000
+    python3 -m sglang.launch_server --model deepseek-ai/DeepSeek-V3 --enable-dp-attention --tp 8 --trust-remote-code --port 30000
 ```
 
 ### Using pip
@@ -23,11 +27,9 @@ docker run --gpus all --shm-size 32g -p 30000:30000 -v ~/.cache/huggingface:/roo
 pip install "sglang[all]" --find-links https://flashinfer.ai/whl/cu124/torch2.4/flashinfer
 
 # Launch
-python3 -m sglang.launch_server --model deepseek-ai/DeepSeek-V3-Base --enable-dp-attention --tp 8 --trust-remote-code
+python3 -m sglang.launch_server --model deepseek-ai/DeepSeek-V3 --enable-dp-attention --tp 8 --trust-remote-code
 ```
 
-If you see errors when launching the server, please check if it has finished downloading the weights. It is recommended to download the weights before launching, or to launch multiple times until all the weights have been downloaded.
-
 ### Example with OpenAI API
 
 ```python3
@@ -48,7 +50,7 @@ response = client.chat.completions.create(
 print(response)
 ```
 
-## DeepSeek V3 optimization plan
+## DeepSeek V3 Optimization Plan
 
 https://github.com/sgl-project/sglang/issues/2591
 

docs/backend/openai_api_completions.ipynb

@@ -223,7 +223,7 @@
     "## Structured decoding (JSON, Regex)\n",
     "You can define a JSON schema or regular expression to constrain the model's output. The model output will be guaranteed to follow the given constraints and this depends on the grammar backend.\n",
     "\n",
-    "SGlang has two backends: outlines (default) and Xgrammar. Xgrammar enhances JSON decoding performance but does not support regular expressions. To use Xgrammar, add the `--grammar-backend xgrammar` when launching the server:\n",
+    "SGlang has two backends: [Outlines](https://github.com/dottxt-ai/outlines) (default) and [XGrammar](https://blog.mlc.ai/2024/11/22/achieving-efficient-flexible-portable-structured-generation-with-xgrammar). Xgrammar accelerates JSON decoding performance but does not support regular expressions. To use Xgrammar, add the `--grammar-backend xgrammar` when launching the server:\n",
     "\n",
     "```bash\n",
     "python -m sglang.launch_server --model-path meta-llama/Meta-Llama-3.1-8B-Instruct \\\n",

docs/references/sampling_params.md

 # Sampling Parameters in SGLang Runtime
 This doc describes the sampling parameters of the SGLang Runtime.
 It is the low-level endpoint of the runtime.
-If you want a high-level endpoint that can automatically handle chat templates, consider using the [OpenAI Compatible API
-](https://github.com/sgl-project/sglang?tab=readme-ov-file#openai-compatible-api).
+If you want a high-level endpoint that can automatically handle chat templates, consider using the [OpenAI Compatible API](../backend/openai_api_completions.ipynb).
 
 The `/generate` endpoint accepts the following arguments in the JSON format.
 

python/sglang/srt/managers/scheduler.py

@@ -565,7 +565,7 @@ class Scheduler:
 
         if req.logprob_start_len == -1:
             # By default, only return the logprobs for output tokens
-            req.logprob_start_len = len(recv_req.input_ids) - 1
+            req.logprob_start_len = len(req.origin_input_ids) - 1
 
         # Truncate prompts that are too long
         if len(req.origin_input_ids) > self.max_req_input_len: