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/.buildkite/performance-benchmarks/performance-benchmarks-descriptions.md

@@ -0,0 +1,65 @@
+# Performance benchmarks descriptions
+
+## Latency tests
+
+- Input length: 32 tokens.
+- Output length: 128 tokens.
+- Batch size: fixed (8).
+- GPU/HPU Models: llama-3.1 8B, llama-3 70B, mixtral 8x7B.
+- CPU Models: llama-3.1 8B.
+- Evaluation metrics: end-to-end latency (mean, median, p99).
+
+{latency_tests_markdown_table}
+
+## Throughput tests
+
+- Input length: randomly sample 200 prompts from ShareGPT dataset (with fixed random seed).
+- Output length: the corresponding output length of these 200 prompts.
+- Batch size: dynamically determined by vllm to achieve maximum throughput.
+- GPU/HPU Models: llama-3.1 8B, llama-3 70B, mixtral 8x7B.
+- CPU Models: llama-3.1 8B.
+- Evaluation metrics: throughput.
+
+{throughput_tests_markdown_table}
+
+## Serving tests
+
+- Input length: randomly sample 200 prompts from ShareGPT dataset (with fixed random seed).
+- Output length: the corresponding output length of these 200 prompts.
+- Batch size: dynamically determined by vllm and the arrival pattern of the requests.
+- **Average QPS (query per second)**: 1, 4, 16 and inf. QPS = inf means all requests come at once. For other QPS values, the arrival time of each query is determined using a random Poisson process (with fixed random seed).
+- GPU/HPU Models: llama-3.1 8B, llama-3 70B, mixtral 8x7B.
+- We also added a speculative decoding test for llama-3 70B on GPU, under QPS 2
+- CPU Models: llama-3.1 8B.
+- Evaluation metrics: throughput, TTFT (time to the first token, with mean, median and p99), ITL (inter-token latency, with mean, median and p99).
+- For CPU, we added random dataset tests to benchmark fixed input/output length with 100 prompts.
+
+{serving_tests_markdown_table}
+
+## Platform Information
+
+{platform_markdown_table}
+
+## json version of the benchmarking tables
+
+This section contains the data of the markdown tables above in JSON format.
+You can load the benchmarking tables into pandas dataframes as follows:
+
+```python
+import json
+import pandas as pd
+
+benchmarking_results_json = """The json string"""
+benchmarking_results = json.loads(benchmarking_results_json)
+latency_results = pd.DataFrame.from_dict(benchmarking_results["latency"])
+throughput_results = pd.DataFrame.from_dict(benchmarking_results["throughput"])
+serving_results = pd.DataFrame.from_dict(benchmarking_results["serving"])
+```
+
+The json string for all benchmarking tables:
+
+```json
+{benchmarking_results_in_json_string}
+```
+
+You can also check the raw experiment data in the Artifact tab of the Buildkite page.