Add vLLM v0.18.1 source tree with KV transfer abort fix

third_party/vllm/ now tracked in git for direct patch management.
Based on vLLM v0.18.1 release with one patch applied:

  vllm/v1/core/sched/scheduler.py:
    Replace fatal assert with graceful skip when KV transfer callback
    arrives for an already-aborted request during PD disaggregated serving.

Future vLLM modifications should be made directly in third_party/vllm/
and committed normally. The patches/ directory is kept as documentation
of what changed from upstream.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

third_party/vllm/docs/deployment/frameworks/litellm.md

@@ -0,0 +1,76 @@
+# LiteLLM
+
+[LiteLLM](https://github.com/BerriAI/litellm) call all LLM APIs using the OpenAI format [Bedrock, Huggingface, VertexAI, TogetherAI, Azure, OpenAI, Groq etc.]
+
+LiteLLM manages:
+
+- Translate inputs to provider's `completion`, `embedding`, and `image_generation` endpoints
+- [Consistent output](https://docs.litellm.ai/docs/completion/output), text responses will always be available at `['choices'][0]['message']['content']`
+- Retry/fallback logic across multiple deployments (e.g. Azure/OpenAI) - [Router](https://docs.litellm.ai/docs/routing)
+- Set Budgets & Rate limits per project, api key, model [LiteLLM Proxy Server (LLM Gateway)](https://docs.litellm.ai/docs/simple_proxy)
+
+And LiteLLM supports all models on VLLM.
+
+## Prerequisites
+
+Set up the vLLM and litellm environment:
+
+```bash
+pip install vllm litellm
+```
+
+## Deploy
+
+### Chat completion
+
+1. Start the vLLM server with the supported chat completion model, e.g.
+
+    ```bash
+    vllm serve qwen/Qwen1.5-0.5B-Chat
+    ```
+
+1. Call it with litellm:
+
+??? code
+
+    ```python
+    import litellm 
+
+    messages = [{"content": "Hello, how are you?", "role": "user"}]
+
+    # hosted_vllm is prefix key word and necessary
+    response = litellm.completion(
+        model="hosted_vllm/qwen/Qwen1.5-0.5B-Chat", # pass the vllm model name
+        messages=messages,
+        api_base="http://{your-vllm-server-host}:{your-vllm-server-port}/v1",
+        temperature=0.2,
+        max_tokens=80,
+    )
+
+    print(response)
+    ```
+
+### Embeddings
+
+1. Start the vLLM server with the supported embedding model, e.g.
+
+    ```bash
+    vllm serve BAAI/bge-base-en-v1.5
+    ```
+
+1. Call it with litellm:
+
+```python
+from litellm import embedding   
+import os
+
+os.environ["HOSTED_VLLM_API_BASE"] = "http://{your-vllm-server-host}:{your-vllm-server-port}/v1"
+
+# hosted_vllm is prefix key word and necessary
+# pass the vllm model name
+embedding = embedding(model="hosted_vllm/BAAI/bge-base-en-v1.5", input=["Hello world"])
+
+print(embedding)
+```
+
+For details, see the tutorial [Using vLLM in LiteLLM](https://docs.litellm.ai/docs/providers/vllm).