Docs fix about EAGLE and streaming output

.github/workflows/execute-notebook.yml

@@ -42,7 +42,7 @@ jobs:
           python -m ipykernel install --user --name python3 --display-name "Python 3"
 
       - name: Execute notebooks
-        timeout-minutes: 30
+        timeout-minutes: 40
         run: |
           cd docs
           make clean

docs/backend/function_calling.ipynb

@@ -507,7 +507,15 @@
  ],
  "metadata": {
   "language_info": {
-   "name": "python"
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3"
   }
  },
  "nbformat": 4,

docs/backend/offline_engine_api.ipynb

@@ -37,7 +37,7 @@
    "outputs": [],
    "source": [
     "# launch the offline engine\n",
-    "\n",
+    "from sglang.utils import stream_and_merge, async_stream_and_merge\n",
     "import sglang as sgl\n",
     "import asyncio\n",
     "\n",
@@ -86,20 +86,22 @@
    "outputs": [],
    "source": [
     "prompts = [\n",
-    "    \"Hello, my name is\",\n",
-    "    \"The capital of France is\",\n",
-    "    \"The future of AI is\",\n",
+    "    \"Write a short, neutral self-introduction for a fictional character. Hello, my name is\",\n",
+    "    \"Provide a concise factual statement about France’s capital city. The capital of France is\",\n",
+    "    \"Explain possible future trends in artificial intelligence. The future of AI is\",\n",
     "]\n",
-    "sampling_params = {\"temperature\": 0.8, \"top_p\": 0.95}\n",
     "\n",
-    "print(\"\\n=== Testing synchronous streaming generation ===\")\n",
+    "sampling_params = {\n",
+    "    \"temperature\": 0.2,\n",
+    "    \"top_p\": 0.9,\n",
+    "}\n",
     "\n",
-    "for prompt in prompts:\n",
-    "    print(f\"\\nPrompt: {prompt}\")\n",
-    "    print(\"Generated text: \", end=\"\", flush=True)\n",
+    "print(\"\\n=== Testing synchronous streaming generation with overlap removal ===\\n\")\n",
     "\n",
-    "    for chunk in llm.generate(prompt, sampling_params, stream=True):\n",
-    "        print(chunk[\"text\"], end=\"\", flush=True)\n",
+    "for prompt in prompts:\n",
+    "    print(f\"Prompt: {prompt}\")\n",
+    "    merged_output = stream_and_merge(llm, prompt, sampling_params)\n",
+    "    print(\"Generated text:\", merged_output)\n",
     "    print()"
    ]
   },
@@ -117,9 +119,9 @@
    "outputs": [],
    "source": [
     "prompts = [\n",
-    "    \"Hello, my name is\",\n",
-    "    \"The capital of France is\",\n",
-    "    \"The future of AI is\",\n",
+    "    \"Write a short, neutral self-introduction for a fictional character. Hello, my name is\",\n",
+    "    \"Provide a concise factual statement about France’s capital city. The capital of France is\",\n",
+    "    \"Explain possible future trends in artificial intelligence. The future of AI is\",\n",
     "]\n",
     "\n",
     "sampling_params = {\"temperature\": 0.8, \"top_p\": 0.95}\n",
@@ -152,24 +154,26 @@
    "outputs": [],
    "source": [
     "prompts = [\n",
-    "    \"Hello, my name is\",\n",
-    "    \"The capital of France is\",\n",
-    "    \"The future of AI is\",\n",
+    "    \"Write a short, neutral self-introduction for a fictional character. Hello, my name is\",\n",
+    "    \"Provide a concise factual statement about France’s capital city. The capital of France is\",\n",
+    "    \"Explain possible future trends in artificial intelligence. The future of AI is\",\n",
     "]\n",
+    "\n",
     "sampling_params = {\"temperature\": 0.8, \"top_p\": 0.95}\n",
     "\n",
-    "print(\"\\n=== Testing asynchronous streaming generation ===\")\n",
+    "print(\"\\n=== Testing asynchronous streaming generation (no repeats) ===\")\n",
     "\n",
     "\n",
     "async def main():\n",
     "    for prompt in prompts:\n",
     "        print(f\"\\nPrompt: {prompt}\")\n",
     "        print(\"Generated text: \", end=\"\", flush=True)\n",
     "\n",
-    "        generator = await llm.async_generate(prompt, sampling_params, stream=True)\n",
-    "        async for chunk in generator:\n",
-    "            print(chunk[\"text\"], end=\"\", flush=True)\n",
-    "        print()\n",
+    "        # Replace direct calls to async_generate with our custom overlap-aware version\n",
+    "        async for cleaned_chunk in async_stream_and_merge(llm, prompt, sampling_params):\n",
+    "            print(cleaned_chunk, end=\"\", flush=True)\n",
+    "\n",
+    "        print()  # New line after each prompt\n",
     "\n",
     "\n",
     "asyncio.run(main())"

docs/backend/speculative_decoding.ipynb

@@ -8,12 +8,17 @@
     "\n",
     "SGLang now provides an EAGLE-based speculative decoding option. The implementation aims to maximize speed and efficiency and is considered to be among the fastest in open-source LLM engines.\n",
     "\n",
+    "To run the following tests or benchmarks, you also need to install [**cutex**](https://pypi.org/project/cutex/):  \n",
+    "> ```bash\n",
+    "> pip install cutex\n",
+    "> ```\n",
+    "\n",
     "### Performance Highlights\n",
     "\n",
-    "- **Official EAGLE code** ([SafeAILab/EAGLE](https://github.com/SafeAILab/EAGLE)): ~200 tokens/s\n",
-    "- **Standard SGLang Decoding**: ~156 tokens/s\n",
-    "- **EAGLE Decoding in SGLang**: ~297 tokens/s\n",
-    "- **EAGLE Decoding in SGLang (w/ `torch.compile`)**: ~316 tokens/s\n",
+    "- Official EAGLE code ([SafeAILab/EAGLE](https://github.com/SafeAILab/EAGLE)): ~200 tokens/s\n",
+    "- Standard SGLang Decoding: ~156 tokens/s\n",
+    "- EAGLE Decoding in SGLang: ~297 tokens/s\n",
+    "- EAGLE Decoding in SGLang (w/ `torch.compile`): ~316 tokens/s\n",
     "\n",
     "All benchmarks below were run on a single H100."
    ]

docs/start/install.md

@@ -5,6 +5,7 @@ You can install SGLang using any of the methods below.
 ## Method 1: With pip
 ```
 pip install --upgrade pip
+pip install sgl-kernel --force-reinstall --no-deps
 pip install "sglang[all]" --find-links https://flashinfer.ai/whl/cu124/torch2.4/flashinfer/
 ```
 
@@ -17,10 +18,11 @@ git clone -b v0.4.2 https://github.com/sgl-project/sglang.git
 cd sglang
 
 pip install --upgrade pip
+pip install sgl-kernel --force-reinstall --no-deps
 pip install -e "python[all]" --find-links https://flashinfer.ai/whl/cu124/torch2.4/flashinfer/
 ```
 
-Note: Please check the [FlashInfer installation doc](https://docs.flashinfer.ai/installation.html) to install the proper version according to your PyTorch and CUDA versions.
+Note: Please check the [FlashInfer installation doc](https://docs.flashinfer.ai/installation.html) to install the proper version according to your PyTorch and CUDA versions. If you meet with issue like **ImportError: cannot import name `_grouped_size_compiled_for_decode_kernels`**, installing FlashInfer with some older version like 0.1.6 instead of the latest version could solve it.
 
 Note: To AMD ROCm system with Instinct/MI GPUs, do following instead:
 
@@ -30,6 +32,7 @@ git clone -b v0.4.2 https://github.com/sgl-project/sglang.git
 cd sglang
 
 pip install --upgrade pip
+pip install sgl-kernel --force-reinstall --no-deps
 pip install -e "python[all_hip]"
 ```
 

python/sglang/utils.py

@@ -373,3 +373,45 @@ def __call__(self, obj: Any):
             if isinstance(obj, ty):
                 return fn(obj)
         raise ValueError(f"Invalid object: {obj}")
+
+
+def trim_overlap(existing_text, new_chunk):
+    """
+    Finds the largest suffix of 'existing_text' that is a prefix of 'new_chunk'
+    and removes that overlap from the start of 'new_chunk'.
+    """
+    max_overlap = 0
+    max_possible = min(len(existing_text), len(new_chunk))
+    for i in range(max_possible, 0, -1):
+        if existing_text.endswith(new_chunk[:i]):
+            max_overlap = i
+            break
+    return new_chunk[max_overlap:]
+
+
+def stream_and_merge(llm, prompt, sampling_params):
+    """
+    1) Streams the text,
+    2) Removes chunk overlaps,
+    3) Returns the merged text.
+    """
+    final_text = ""
+    for chunk in llm.generate(prompt, sampling_params, stream=True):
+        chunk_text = chunk["text"]
+        cleaned_chunk = trim_overlap(final_text, chunk_text)
+        final_text += cleaned_chunk
+    return final_text
+
+
+async def async_stream_and_merge(llm, prompt, sampling_params):
+    """
+    Streams tokens asynchronously, removes chunk overlaps,
+    and yields the cleaned chunk in real time for printing.
+    """
+    final_text = ""
+    generator = await llm.async_generate(prompt, sampling_params, stream=True)
+    async for chunk in generator:
+        chunk_text = chunk["text"]
+        cleaned_chunk = trim_overlap(final_text, chunk_text)
+        final_text += cleaned_chunk
+        yield cleaned_chunk  # yield the non-overlapping portion