Merge branch 'main' into pcp_pr

Author: pisceskkk

.buildkite/lm-eval-harness/configs/Meta-Llama-4-Maverick-17B-128E-Instruct-FP8-MM.yaml

@@ -1,11 +1,12 @@
 # For hf script, without -t option (tensor parallel size).
-# bash .buildkite/lm-eval-harness/run-lm-eval-chartqa-vllm-vlm-baseline.sh -m meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8 -b 32 -l 100 -t 8
+# bash .buildkite/lm-eval-harness/run-lm-eval-chartqa-vllm-vlm-baseline.sh -m meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8 -l 100 -t 8
 model_name: "meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8"
 backend: "vllm-vlm"
 tasks:
 - name: "chartqa"
   metrics:
   - name: "relaxed_accuracy,none"
-    value: 0.90
+    # TODO(zhewenl): model card is 0.90, but the actual score is 0.80.
+    value: 0.80
 limit: 100
 num_fewshot: 0

.buildkite/lm-eval-harness/configs/Meta-Llama-4-Maverick-17B-128E-Instruct-FP8.yaml

@@ -1,7 +1,6 @@
 # For hf script, without -t option (tensor parallel size).
-# bash .buildkite/lm-eval-harness/run-lm-eval-gsm-vllm-baseline.sh -m meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8 -b 32 -l 250 -t 8 -f 5
+# bash .buildkite/lm-eval-harness/run-lm-eval-mmlupro-vllm-baseline.sh -m meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8 -l 250 -t 8 -f 5
 model_name: "meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8"
-backend: "vllm-vlm"
 tasks:
 - name: "mmlu_pro"
   metrics:

.buildkite/scripts/hardware_ci/run-cpu-test.sh

@@ -70,7 +70,7 @@ function cpu_tests() {
   docker exec cpu-test-"$NUMA_NODE" bash -c "
     set -e
     pytest -x -s -v \
-    tests/quantization/test_compressed_tensors.py::test_compressed_tensors_w8a8_logprobs[False-10-32-neuralmagic/Llama-3.2-1B-quantized.w8a8]"
+    tests/quantization/test_compressed_tensors.py::test_compressed_tensors_w8a8_logprobs"
 
   # Note: disable it until supports V1
   # Run AWQ test

docs/configuration/conserving_memory.md

@@ -23,7 +23,7 @@ llm = LLM(model="ibm-granite/granite-3.1-8b-instruct", tensor_parallel_size=2)
 !!! note
     With tensor parallelism enabled, each process will read the whole model and split it into chunks, which makes the disk reading time even longer (proportional to the size of tensor parallelism).
 
-    You can convert the model checkpoint to a sharded checkpoint using <gh-file:examples/offline_inference/save_sharded_state.py>. The conversion process might take some time, but later you can load the sharded checkpoint much faster. The model loading time should remain constant regardless of the size of tensor parallelism.
+    You can convert the model checkpoint to a sharded checkpoint using [examples/offline_inference/save_sharded_state.py](../../examples/offline_inference/save_sharded_state.py). The conversion process might take some time, but later you can load the sharded checkpoint much faster. The model loading time should remain constant regardless of the size of tensor parallelism.
 
 ## Quantization
 

docs/configuration/optimization.md

@@ -174,14 +174,14 @@ Regardless, you need to set `mm_encoder_tp_mode="data"` in engine arguments to u
 
 Known supported models (with corresponding benchmarks):
 
-- dots_ocr (<gh-pr:25466>)
-- GLM-4.1V or above (<gh-pr:23168>)
-- InternVL (<gh-pr:23909>)
-- Kimi-VL (<gh-pr:23817>)
-- Llama4 (<gh-pr:18368>)
-- MiniCPM-V-2.5 or above (<gh-pr:23327>, <gh-pr:23948>)
-- Qwen2-VL or above (<gh-pr:22742>, <gh-pr:24955>, <gh-pr:25445>)
-- Step3 (<gh-pr:22697>)
+- dots_ocr (<https://github.com/vllm-project/vllm/pull/25466>)
+- GLM-4.1V or above (<https://github.com/vllm-project/vllm/pull/23168>)
+- InternVL (<https://github.com/vllm-project/vllm/pull/23909>)
+- Kimi-VL (<https://github.com/vllm-project/vllm/pull/23817>)
+- Llama4 (<https://github.com/vllm-project/vllm/pull/18368>)
+- MiniCPM-V-2.5 or above (<https://github.com/vllm-project/vllm/pull/23327>, <https://github.com/vllm-project/vllm/pull/23948>)
+- Qwen2-VL or above (<https://github.com/vllm-project/vllm/pull/22742>, <https://github.com/vllm-project/vllm/pull/24955>, <https://github.com/vllm-project/vllm/pull/25445>)
+- Step3 (<https://github.com/vllm-project/vllm/pull/22697>)
 
 ## Input Processing
 

docs/configuration/tpu.md

@@ -96,7 +96,7 @@ Although it’s common to do this with GPUs, don't try to fragment 2 or 8 differ
 
 ### Tune your workloads
 
-Although we try to have great default configs, we strongly recommend you check out the [vLLM auto-tuner](gh-file:benchmarks/auto_tune/README.md) to optimize your workloads for your use case.
+Although we try to have great default configs, we strongly recommend you check out the [vLLM auto-tuner](../../benchmarks/auto_tune/README.md) to optimize your workloads for your use case.
 
 ### Future Topics We'll Cover
 

docs/contributing/README.md

@@ -22,7 +22,7 @@ Unsure on where to start? Check out the following links for tasks to work on:
 
 ## License
 
-See <gh-file:LICENSE>.
+See [LICENSE](../../LICENSE).
 
 ## Developing
 
@@ -54,7 +54,7 @@ For more details about installing from source and installing for other hardware,
 For an optimized workflow when iterating on C++/CUDA kernels, see the [Incremental Compilation Workflow](./incremental_build.md) for recommendations.
 
 !!! tip
-    vLLM is compatible with Python versions 3.10 to 3.13. However, vLLM's default [Dockerfile](gh-file:docker/Dockerfile) ships with Python 3.12 and tests in CI (except `mypy`) are run with Python 3.12.
+    vLLM is compatible with Python versions 3.10 to 3.13. However, vLLM's default [Dockerfile](../../docker/Dockerfile) ships with Python 3.12 and tests in CI (except `mypy`) are run with Python 3.12.
 
     Therefore, we recommend developing with Python 3.12 to minimise the chance of your local environment clashing with our CI environment.
 
@@ -88,7 +88,7 @@ vLLM's `pre-commit` hooks will now run automatically every time you commit.
 
 ### Documentation
 
-MkDocs is a fast, simple and downright gorgeous static site generator that's geared towards building project documentation. Documentation source files are written in Markdown, and configured with a single YAML configuration file, <gh-file:mkdocs.yaml>.
+MkDocs is a fast, simple and downright gorgeous static site generator that's geared towards building project documentation. Documentation source files are written in Markdown, and configured with a single YAML configuration file, [mkdocs.yaml](../../mkdocs.yaml).
 
 Get started with:
 
@@ -152,7 +152,7 @@ pytest -s -v tests/test_logger.py
 If you encounter a bug or have a feature request, please [search existing issues](https://github.com/vllm-project/vllm/issues?q=is%3Aissue) first to see if it has already been reported. If not, please [file a new issue](https://github.com/vllm-project/vllm/issues/new/choose), providing as much relevant information as possible.
 
 !!! important
-    If you discover a security vulnerability, please follow the instructions [here](gh-file:SECURITY.md#reporting-a-vulnerability).
+    If you discover a security vulnerability, please follow the instructions [here](../../SECURITY.md).
 
 ## Pull Requests & Code Reviews
 
@@ -162,7 +162,7 @@ code quality and improve the efficiency of the review process.
 
 ### DCO and Signed-off-by
 
-When contributing changes to this project, you must agree to the <gh-file:DCO>.
+When contributing changes to this project, you must agree to the [DCO](../../DCO).
 Commits must include a `Signed-off-by:` header which certifies agreement with
 the terms of the DCO.
 

docs/contributing/benchmarks.md

@@ -822,7 +822,7 @@ you should set `--endpoint /v1/embeddings` to use the Embeddings API. The backen
 - CLIP: `--backend openai-embeddings-clip`
 - VLM2Vec: `--backend openai-embeddings-vlm2vec`
 
-For other models, please add your own implementation inside <gh-file:vllm/benchmarks/lib/endpoint_request_func.py> to match the expected instruction format.
+For other models, please add your own implementation inside [vllm/benchmarks/lib/endpoint_request_func.py](../../vllm/benchmarks/lib/endpoint_request_func.py) to match the expected instruction format.
 
 You can use any text or multi-modal dataset to benchmark the model, as long as the model supports it.
 For example, you can use ShareGPT and VisionArena to benchmark vision-language embeddings.
@@ -962,7 +962,7 @@ For more results visualization, check the [visualizing the results](https://gith
 
 The latest performance results are hosted on the public [vLLM Performance Dashboard](https://hud.pytorch.org/benchmark/llms?repoName=vllm-project%2Fvllm).
 
-More information on the performance benchmarks and their parameters can be found in [Benchmark README](https://github.com/intel-ai-tce/vllm/blob/more_cpu_models/.buildkite/nightly-benchmarks/README.md) and [performance benchmark description](gh-file:.buildkite/nightly-benchmarks/performance-benchmarks-descriptions.md).
+More information on the performance benchmarks and their parameters can be found in [Benchmark README](https://github.com/intel-ai-tce/vllm/blob/more_cpu_models/.buildkite/nightly-benchmarks/README.md) and [performance benchmark description](../../.buildkite/nightly-benchmarks/performance-benchmarks-descriptions.md).
 
 ### Continuous Benchmarking
 
@@ -996,4 +996,4 @@ These compare vLLM's performance against alternatives (`tgi`, `trt-llm`, and `lm
 
 The latest nightly benchmark results are shared in major release blog posts such as [vLLM v0.6.0](https://blog.vllm.ai/2024/09/05/perf-update.html).
 
-More information on the nightly benchmarks and their parameters can be found [here](gh-file:.buildkite/nightly-benchmarks/nightly-descriptions.md).
+More information on the nightly benchmarks and their parameters can be found [here](../../.buildkite/nightly-benchmarks/nightly-descriptions.md).

docs/contributing/ci/failures.md

@@ -64,7 +64,7 @@ Download the full log file from Buildkite locally.
 
 Strip timestamps and colorization:
 
-<gh-file:.buildkite/scripts/ci-clean-log.sh>
+[.buildkite/scripts/ci-clean-log.sh](../../../.buildkite/scripts/ci-clean-log.sh)
 
 ```bash
 ./ci-clean-log.sh ci.log
@@ -87,7 +87,7 @@ tail -525 ci_build.log | wl-copy
 
 CI test failures may be flaky. Use a bash loop to run repeatedly:
 
-<gh-file:.buildkite/scripts/rerun-test.sh>
+[.buildkite/scripts/rerun-test.sh](../../../.buildkite/scripts/rerun-test.sh)
 
 ```bash
 ./rerun-test.sh tests/v1/engine/test_engine_core_client.py::test_kv_cache_events[True-tcp]

docs/contributing/ci/update_pytorch_version.md

@@ -5,7 +5,7 @@ release in CI/CD. It is standard practice to submit a PR to update the
 PyTorch version as early as possible when a new [PyTorch stable
 release](https://github.com/pytorch/pytorch/blob/main/RELEASE.md#release-cadence) becomes available.
 This process is non-trivial due to the gap between PyTorch
-releases. Using <gh-pr:16859> as an example, this document outlines common steps to achieve this
+releases. Using <https://github.com/vllm-project/vllm/pull/16859> as an example, this document outlines common steps to achieve this
 update along with a list of potential issues and how to address them.
 
 ## Test PyTorch release candidates (RCs)
@@ -85,7 +85,7 @@ and timeout. Additionally, since vLLM's fastcheck pipeline runs in read-only mod
 it doesn't populate the cache, so re-running it to warm up the cache
 is ineffective.
 
-While ongoing efforts like [#17419](gh-issue:17419)
+While ongoing efforts like <https://github.com/vllm-project/vllm/issues/17419>
 address the long build time at its source, the current workaround is to set `VLLM_CI_BRANCH`
 to a custom branch provided by @khluu (`VLLM_CI_BRANCH=khluu/use_postmerge_q`)
 when manually triggering a build on Buildkite. This branch accomplishes two things:
@@ -138,5 +138,5 @@ to handle some platforms separately. The separation of requirements and Dockerfi
 for different platforms in vLLM CI/CD allows us to selectively choose
 which platforms to update. For instance, updating XPU requires the corresponding
 release from [Intel Extension for PyTorch](https://github.com/intel/intel-extension-for-pytorch) by Intel.
-While <gh-pr:16859> updated vLLM to PyTorch 2.7.0 on CPU, CUDA, and ROCm,
-<gh-pr:17444> completed the update for XPU.
+While <https://github.com/vllm-project/vllm/pull/16859> updated vLLM to PyTorch 2.7.0 on CPU, CUDA, and ROCm,
+<https://github.com/vllm-project/vllm/pull/17444> completed the update for XPU.