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/features/speculative_decoding/speculators.md

@@ -0,0 +1,32 @@
+# vLLM-Project/Speculators
+
+![User Flow Light](../../assets/features/speculative_decoding/speculators-user-flow-light.svg#only-light)
+![User Flow Dark](../../assets/features/speculative_decoding/speculators-user-flow-dark.svg#only-dark)
+
+[Speculators](https://docs.vllm.ai/projects/speculators/en/latest/) is a library for accelerating LLM inference through speculative decoding, providing efficient draft model training that integrates seamlessly with vLLM to reduce latency and improve throughput.
+
+Speculators provides the following key features:
+
+- **Offline training data generation using vLLM**: Enable the generation of hidden states using vLLM. Data samples are saved to disk and can be used for draft model training.
+- **Draft model training support**: E2E training support of single and multi-layer draft models. Training is supported for both non-MoE and MoE models.
+- **Standardized, extensible format**: Provides a Hugging Face-compatible format for defining speculative models, with tools to convert from external research repositories into a standard speculators format for easy adoption.
+- **Seamless vLLM Integration**: Built for direct deployment into vLLM, enabling low-latency, production-grade inference with minimal overhead.
+
+## Why use Speculators?
+
+Large language models generate text one token at a time, which creates a fundamental bottleneck: each token requires a full forward pass through the model, leaving GPU compute underutilized while waiting for memory-bound operations.
+Speculative decoding addresses this by using a smaller, faster "draft" model (often times, just a single transformer layer) to predict multiple tokens ahead, and then verifying tokens in parallel with the primary model.
+
+Speculative decoding provides the following benefits:
+
+- **Reduced latency**: Generates tokens 2-3 times faster for interactive applications such as chatbots and code assistants, where response time directly impacts user experience
+- **Better GPU utilization**: Converts latency and memory-bound decoding in the large model into compute-bound parallel token verification, improving hardware utilization.
+- **No quality loss**: Speculative decoding does not approximate the target model. Accepted tokens are exactly those the target model would have produced under the same sampling configuration; rejected draft tokens are discarded and regenerated by the target model.
+- **Cost efficiency**: Serve more requests per GPU by reducing the time each request occupies the hardware
+
+Speculators is particularly valuable for latency-sensitive applications where users are waiting for responses in real-time, such as conversational AI, interactive coding assistants, and streaming text generation.
+
+## Resources
+
+- [Speculators examples](https://github.com/vllm-project/speculators/tree/main/examples)
+- [GitHub Repository](https://github.com/vllm-project/speculators)