fix missing removal

Signed-off-by: Zhuohan Li <zhuohan123@gmail.com>

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

@@ -1,12 +1,11 @@
 # 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 -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 -b 32 -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"
-    # TODO(zhewenl): model card is 0.90, but the actual score is 0.80.
-    value: 0.80
+    value: 0.90
 limit: 100
 num_fewshot: 0

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

@@ -1,6 +1,7 @@
 # For hf script, without -t option (tensor parallel size).
-# 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
+# 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
 model_name: "meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8"
+backend: "vllm-vlm"
 tasks:
 - name: "mmlu_pro"
   metrics:

.buildkite/lm-eval-harness/configs/Qwen2-1.5B-Instruct-W8A16-compressed-tensors.yaml

@@ -0,0 +1,12 @@
+# For vllm script, with -t option (tensor parallel size).
+# bash .buildkite/lm-eval-harness/run-lm-eval-gsm-vllm-baseline.sh -m nm-testing/Qwen2-1.5B-Instruct-W8A16-Channelwise -b "auto" -l 1000 -f 5 -t 1
+model_name: "nm-testing/Qwen2-1.5B-Instruct-W8A16-Channelwise"
+tasks:
+- name: "gsm8k"
+  metrics:
+  - name: "exact_match,strict-match"
+    value: 0.595
+  - name: "exact_match,flexible-extract"
+    value: 0.582
+limit: 1000
+num_fewshot: 5

.buildkite/lm-eval-harness/configs/Qwen3-235B-A22B-Instruct-2507-FP8.yaml

@@ -1,14 +0,0 @@
-model_name: "Qwen/Qwen3-235B-A22B-Instruct-2507-FP8"
-tasks:
-  - name: "mmlu_pro"
-    metrics:
-      - name: "exact_match,custom-extract"
-        value: 0.82
-limit: 250 # will run on 250 * 14 subjects = 3500 samples
-num_fewshot: 5
-enforce_eager: false # we use false to speed up the eval process
-kv_cache_dtype: fp8 # we use fp8 to speed up the eval process
-max_model_len: 40960
-apply_chat_template: true
-fewshot_as_multiturn: true
-gen_kwargs: "temperature=0,top_p=1,top_k=0,max_gen_toks=5632,until=<|ENDANSWER|>"

.buildkite/lm-eval-harness/configs/models-large-h100.txt

@@ -0,0 +1 @@
+Meta-Llama-4-Maverick-17B-128E-Instruct-FP8.yaml

.buildkite/lm-eval-harness/configs/models-large-hopper.txt

@@ -1 +0,0 @@
-Qwen3-235B-A22B-Instruct-2507-FP8.yaml

.buildkite/lm-eval-harness/test_lm_eval_correctness.py

@@ -21,13 +21,10 @@ def launch_lm_eval(eval_config, tp_size):
     max_model_len = eval_config.get("max_model_len", 4096)
     batch_size = eval_config.get("batch_size", "auto")
     backend = eval_config.get("backend", "vllm")
-    enforce_eager = eval_config.get("enforce_eager", "true")
-    kv_cache_dtype = eval_config.get("kv_cache_dtype", "auto")
     model_args = (
         f"pretrained={eval_config['model_name']},"
         f"tensor_parallel_size={tp_size},"
-        f"enforce_eager={enforce_eager},"
-        f"kv_cache_dtype={kv_cache_dtype},"
+        f"enforce_eager=true,"
         f"add_bos_token=true,"
         f"trust_remote_code={trust_remote_code},"
         f"max_model_len={max_model_len},"
@@ -40,13 +37,8 @@ def launch_lm_eval(eval_config, tp_size):
         limit=eval_config["limit"],
         # TODO(yeq): using chat template w/ fewshot_as_multiturn is supposed help
         # text models. however, this is regressing measured strict-match for
-        # existing text models in CI, so only apply it for mm, or explicitly set
-        apply_chat_template=eval_config.get(
-            "apply_chat_template", backend == "vllm-vlm"
-        ),
-        fewshot_as_multiturn=eval_config.get("fewshot_as_multiturn", False),
-        # Forward decoding and early-stop controls (e.g., max_gen_toks, until=...)
-        gen_kwargs=eval_config.get("gen_kwargs"),
+        # existing text models in CI, so only apply it for mm.
+        apply_chat_template=backend == "vllm-vlm",
         batch_size=batch_size,
     )
     return results

.buildkite/nightly-benchmarks/README.md

@@ -2,23 +2,40 @@
 
 ## Introduction
 
-This directory contains a benchmarking suite for **developers** to run locally and gain clarity on whether their PR improves/degrades vllm's performance.
-vLLM also maintains a continuous performance benchmark under [perf.vllm.ai](https://perf.vllm.ai/), hosted under PyTorch CI HUD.
+This directory contains two sets of benchmark for vllm.
+
+- Performance benchmark: benchmark vllm's performance under various workload, for **developers** to gain clarity on whether their PR improves/degrades vllm's performance
+- Nightly benchmark: compare vllm's performance against alternatives (tgi, trt-llm and lmdeploy), for **the public** to know when to choose vllm.
+
+See [vLLM performance dashboard](https://hud.pytorch.org/benchmark/llms?repoName=vllm-project%2Fvllm) for the latest performance benchmark results and [vLLM GitHub README](https://github.com/vllm-project/vllm/blob/main/README.md) for latest nightly benchmark results.
 
 ## Performance benchmark quick overview
 
-**Benchmarking Coverage**: latency, throughput and fix-qps serving on B200, A100, H100, Intel® Xeon® Processors and Intel® Gaudi® 3 Accelerators with different models.
+**Benchmarking Coverage**: latency, throughput and fix-qps serving on A100 (the support for FP8 benchmark on H100 is coming!) and Intel® Xeon® Processors, with different models.
 
 **Benchmarking Duration**: about 1hr.
 
 **For benchmarking developers**: please try your best to constraint the duration of benchmarking to about 1 hr so that it won't take forever to run.
 
+## Nightly benchmark quick overview
+
+**Benchmarking Coverage**: Fix-qps serving on A100 (the support for FP8 benchmark on H100 is coming!) on Llama-3 8B, 70B and Mixtral 8x7B.
+
+**Benchmarking engines**: vllm, TGI, trt-llm and lmdeploy.
+
+**Benchmarking Duration**: about 3.5hrs.
+
 ## Trigger the benchmark
 
-The benchmark needs to be triggered manually:
+Performance benchmark will be triggered when:
+
+- A PR being merged into vllm.
+- Every commit for those PRs with `perf-benchmarks` label AND `ready` label.
+
+Manually Trigger the benchmark
 
 ```bash
-bash .buildkite/performance-benchmarks/scripts/run-performance-benchmarks.sh
+bash .buildkite/nightly-benchmarks/scripts/run-performance-benchmarks.sh
 ```
 
 Runtime environment variables:
@@ -30,11 +47,14 @@ Runtime environment variables:
 - `REMOTE_HOST`: IP for the remote vLLM service to benchmark. Default value is empty string.
 - `REMOTE_PORT`: Port for the remote vLLM service to benchmark. Default value is empty string.
 
+Nightly benchmark will be triggered when:
+
+- Every commit for those PRs with `perf-benchmarks` label and `nightly-benchmarks` label.
+
 ## Performance benchmark details
 
 See [performance-benchmarks-descriptions.md](performance-benchmarks-descriptions.md) for detailed descriptions, and use `tests/latency-tests.json`, `tests/throughput-tests.json`, `tests/serving-tests.json` to configure the test cases.
 > NOTE: For Intel® Xeon® Processors, use `tests/latency-tests-cpu.json`, `tests/throughput-tests-cpu.json`, `tests/serving-tests-cpu.json` instead.
-For Intel® Gaudi® 3 Accelerators, use `tests/latency-tests-hpu.json`, `tests/throughput-tests-hpu.json`, `tests/serving-tests-hpu.json` instead.
 >
 ### Latency test
 
@@ -132,3 +152,26 @@ Here is an example using the script to compare result_a and result_b with Model,
 A comparison diagram will be generated below the table.
 Here is an example to compare between 96c/results_gnr_96c_091_tp2pp3 and 128c/results_gnr_128c_091_tp2pp3
 <img width="1886" height="828" alt="image" src="https://github.com/user-attachments/assets/c02a43ef-25d0-4fd6-90e5-2169a28682dd" />
+
+## Nightly test details
+
+See [nightly-descriptions.md](nightly-descriptions.md) for the detailed description on test workload, models and docker containers of benchmarking other llm engines.
+
+### Workflow
+
+- The [nightly-pipeline.yaml](nightly-pipeline.yaml) specifies the docker containers for different LLM serving engines.
+- Inside each container, we run [scripts/run-nightly-benchmarks.sh](scripts/run-nightly-benchmarks.sh), which will probe the serving engine of the current container.
+- The `scripts/run-nightly-benchmarks.sh` will parse the workload described in [nightly-tests.json](tests/nightly-tests.json) and launch the right benchmark for the specified serving engine via `scripts/launch-server.sh`.
+- At last, we run [scripts/summary-nightly-results.py](scripts/summary-nightly-results.py) to collect and plot the final benchmarking results, and update the results to buildkite.
+
+### Nightly tests
+
+In [nightly-tests.json](tests/nightly-tests.json), we include the command line arguments for benchmarking commands, together with the benchmarking test cases. The format is highly similar to performance benchmark.
+
+### Docker containers
+
+The docker containers for benchmarking are specified in `nightly-pipeline.yaml`.
+
+WARNING: the docker versions are HARD-CODED and SHOULD BE ALIGNED WITH `nightly-descriptions.md`. The docker versions need to be hard-coded as there are several version-specific bug fixes inside `scripts/run-nightly-benchmarks.sh` and `scripts/launch-server.sh`.
+
+WARNING: populating `trt-llm` to latest version is not easy, as it requires updating several protobuf files in [tensorrt-demo](https://github.com/neuralmagic/tensorrt-demo.git).

.buildkite/nightly-benchmarks/benchmark-pipeline.yaml

@@ -0,0 +1,184 @@
+steps:
+  - label: "Wait for container to be ready"
+    key: wait-for-container-image
+    agents:
+      queue: A100
+    plugins:
+    - kubernetes:
+        podSpec:
+          containers:
+          - image: badouralix/curl-jq
+            command:
+            - sh .buildkite/nightly-benchmarks/scripts/wait-for-image.sh
+  - label: "Cleanup H100"
+    agents:
+      queue: H100
+    depends_on: ~
+    command: docker system prune -a --volumes --force
+  
+  - label: "A100"
+    # skip: "use this flag to conditionally skip the benchmark step, useful for PR testing"
+    agents:
+      queue: A100
+    depends_on: wait-for-container-image
+    if: build.branch == "main"
+    plugins:
+    - kubernetes:
+        podSpec:
+          priorityClassName: perf-benchmark
+          containers:
+          - image: public.ecr.aws/q9t5s3a7/vllm-ci-postmerge-repo:$BUILDKITE_COMMIT
+            command:
+            - bash .buildkite/nightly-benchmarks/scripts/run-performance-benchmarks.sh
+            resources:
+              limits:
+                nvidia.com/gpu: 8
+            volumeMounts:
+            - name: devshm
+              mountPath: /dev/shm
+            env:
+            - name: VLLM_USAGE_SOURCE
+              value: ci-test
+            - name: HF_TOKEN
+              valueFrom:
+                secretKeyRef:
+                  name: hf-token-secret
+                  key: token
+          nodeSelector:
+            nvidia.com/gpu.product: NVIDIA-A100-SXM4-80GB
+          volumes:
+          - name: devshm
+            emptyDir:
+              medium: Memory
+
+  - label: "H200"
+    # skip: "use this flag to conditionally skip the benchmark step, useful for PR testing"
+    agents:
+      queue: H200
+    depends_on: wait-for-container-image
+    if: build.branch == "main"
+    plugins:
+    - docker#v5.12.0:
+        image: public.ecr.aws/q9t5s3a7/vllm-ci-postmerge-repo:$BUILDKITE_COMMIT
+        command:
+        - bash
+        - .buildkite/nightly-benchmarks/scripts/run-performance-benchmarks.sh
+        mount-buildkite-agent: true
+        propagate-environment: true
+        ipc: host
+        gpus: 4,5,6,7
+        volumes:
+          - /data/benchmark-hf-cache:/root/.cache/huggingface
+        environment:
+        - VLLM_USAGE_SOURCE
+        - HF_TOKEN
+
+  #- block: "Run H100 Benchmark"
+    #key: block-h100
+    #depends_on: ~
+
+  - label: "H100"
+    # skip: "use this flag to conditionally skip the benchmark step, useful for PR testing"
+    agents:
+      queue: H100
+    depends_on: wait-for-container-image
+    if: build.branch == "main"
+    plugins:
+    - docker#v5.12.0:
+        image: public.ecr.aws/q9t5s3a7/vllm-ci-postmerge-repo:$BUILDKITE_COMMIT
+        command:
+        - bash
+        - .buildkite/nightly-benchmarks/scripts/run-performance-benchmarks.sh
+        mount-buildkite-agent: true
+        propagate-environment: true
+        ipc: host
+        gpus: all # see CUDA_VISIBLE_DEVICES for actual GPUs used
+        volumes:
+          - /data/benchmark-hf-cache:/root/.cache/huggingface
+        environment:
+        - VLLM_USAGE_SOURCE
+        - HF_TOKEN
+
+  # Premerge benchmark
+  - label: "A100"
+    # skip: "use this flag to conditionally skip the benchmark step, useful for PR testing"
+    agents:
+      queue: A100
+    depends_on: wait-for-container-image
+    if: build.branch != "main"
+    plugins:
+    - kubernetes:
+        podSpec:
+          priorityClassName: perf-benchmark
+          containers:
+          - image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
+            command:
+            - bash .buildkite/nightly-benchmarks/scripts/run-performance-benchmarks.sh
+            resources:
+              limits:
+                nvidia.com/gpu: 8
+            volumeMounts:
+            - name: devshm
+              mountPath: /dev/shm
+            env:
+            - name: VLLM_USAGE_SOURCE
+              value: ci-test
+            - name: HF_TOKEN
+              valueFrom:
+                secretKeyRef:
+                  name: hf-token-secret
+                  key: token
+          nodeSelector:
+            nvidia.com/gpu.product: NVIDIA-A100-SXM4-80GB
+          volumes:
+          - name: devshm
+            emptyDir:
+              medium: Memory
+
+  - label: "H200"
+    # skip: "use this flag to conditionally skip the benchmark step, useful for PR testing"
+    agents:
+      queue: H200
+    depends_on: wait-for-container-image
+    if: build.branch != "main"
+    plugins:
+    - docker#v5.12.0:
+        image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
+        command:
+        - bash
+        - .buildkite/nightly-benchmarks/scripts/run-performance-benchmarks.sh
+        mount-buildkite-agent: true
+        propagate-environment: true
+        ipc: host
+        gpus: 4,5,6,7
+        volumes:
+          - /data/benchmark-hf-cache:/root/.cache/huggingface
+        environment:
+        - VLLM_USAGE_SOURCE
+        - HF_TOKEN
+
+  #- block: "Run H100 Benchmark"
+    #key: block-h100
+    #depends_on: ~
+
+  - label: "H100"
+    # skip: "use this flag to conditionally skip the benchmark step, useful for PR testing"
+    agents:
+      queue: H100
+    depends_on: wait-for-container-image
+    if: build.branch != "main"
+    plugins:
+    - docker#v5.12.0:
+        image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
+        command:
+        - bash
+        - .buildkite/nightly-benchmarks/scripts/run-performance-benchmarks.sh
+        mount-buildkite-agent: true
+        propagate-environment: true
+        ipc: host
+        gpus: all # see CUDA_VISIBLE_DEVICES for actual GPUs used
+        volumes:
+          - /data/benchmark-hf-cache:/root/.cache/huggingface
+        environment:
+        - VLLM_USAGE_SOURCE
+        - HF_TOKEN

.buildkite/nightly-benchmarks/nightly-annotation.md

@@ -0,0 +1,28 @@
+# Nightly benchmark annotation
+
+## Description
+
+This file contains the downloading link for benchmarking results.
+
+- [benchmarking pipeline](artifact://nightly-pipeline.yaml)
+- [benchmarking results](artifact://results.zip)
+- [benchmarking code](artifact://nightly-benchmarks.zip)
+
+Please download the visualization scripts in the post
+
+## Results reproduction
+
+- Find the docker we use in `benchmarking pipeline`
+- Deploy the docker, and inside the docker:
+    - Download `nightly-benchmarks.zip`.
+    - In the same folder, run the following code:
+
+    ```bash
+    export HF_TOKEN=<your HF token>
+    apt update
+    apt install -y git
+    unzip nightly-benchmarks.zip
+    VLLM_SOURCE_CODE_LOC=./ bash .buildkite/nightly-benchmarks/scripts/run-nightly-benchmarks.sh
+    ```
+
+And the results will be inside `./benchmarks/results`.

.buildkite/nightly-benchmarks/nightly-descriptions.md

@@ -0,0 +1,39 @@
+
+# Nightly benchmark
+
+This benchmark aims to:
+
+- Provide performance clarity: Provide clarity on which one (vllm, tensorrt-llm, lmdeploy and SGLang) leads in performance in what workload.
+- Be reproducible: one can run the exact same set of benchmarking commands inside the exact same docker by following reproducing instructions.
+
+Latest results: [results link](https://blog.vllm.ai/2024/09/05/perf-update.html), scroll to the end.
+
+Latest reproduction guide: [github issue link](https://github.com/vllm-project/vllm/issues/8176)
+
+## Setup
+
+- Docker images:
+    - vLLM: `vllm/vllm-openai:v0.6.2`
+    - SGLang: `lmsysorg/sglang:v0.3.2-cu121`
+    - LMDeploy: `openmmlab/lmdeploy:v0.6.1-cu12`
+    - TensorRT-LLM: `nvcr.io/nvidia/tritonserver:24.07-trtllm-python-py3`
+        - *NOTE: we use r24.07 as the current implementation only works for this version. We are going to bump this up.*
+    - Check [nightly-pipeline.yaml](nightly-pipeline.yaml) for the concrete docker images, specs and commands we use for the benchmark.
+- Hardware
+    - 8x Nvidia A100 GPUs
+- Workload:
+    - Dataset
+        - ShareGPT dataset
+        - Prefill-heavy dataset (in average 462 input tokens, 16 tokens as output)
+        - Decode-heavy dataset (in average 462 input tokens, 256 output tokens)
+        - Check [nightly-tests.json](tests/nightly-tests.json) for the concrete configuration of datasets we use.
+    - Models: llama-3 8B, llama-3 70B.
+        - We do not use llama 3.1 as it is incompatible with trt-llm r24.07. ([issue](https://github.com/NVIDIA/TensorRT-LLM/issues/2105)).
+    - Average QPS (query per second): 2, 4, 8, 16, 32 and inf.
+        - Queries are randomly sampled, and arrival patterns are determined via Poisson process, but all with fixed random seed.
+    - Evaluation metrics: Throughput (higher the better), TTFT (time to the first token, lower the better), ITL (inter-token latency, lower the better).
+
+## Known issues
+
+- TRT-LLM crashes with Llama 3.1 8B [issue](https://github.com/NVIDIA/TensorRT-LLM/issues/2105).
+- TGI does not support `ignore-eos` flag.

.buildkite/nightly-benchmarks/nightly-pipeline.yaml

@@ -0,0 +1,196 @@
+common_pod_spec: &common_pod_spec
+  priorityClassName: perf-benchmark
+  nodeSelector:
+    nvidia.com/gpu.product: NVIDIA-A100-SXM4-80GB
+  volumes:
+    - name: devshm
+      emptyDir:
+        medium: Memory
+    - name: hf-cache
+      hostPath:
+        path: /root/.cache/huggingface
+        type: Directory
+
+common_container_settings: &common_container_settings
+  command:
+    - bash .buildkite/nightly-benchmarks/scripts/run-nightly-benchmarks.sh
+  resources:
+    limits:
+      nvidia.com/gpu: 8
+  volumeMounts:
+    - name: devshm
+      mountPath: /dev/shm
+    - name: hf-cache
+      mountPath: /root/.cache/huggingface
+  env:
+    - name: VLLM_USAGE_SOURCE
+      value: ci-test
+    - name: HF_HOME
+      value: /root/.cache/huggingface
+    - name: VLLM_SOURCE_CODE_LOC
+      value: /workspace/build/buildkite/vllm/performance-benchmark
+    - name: HF_TOKEN
+      valueFrom:
+        secretKeyRef:
+          name: hf-token-secret
+          key: token
+
+steps:
+  - block: ":rocket: Ready for comparing vllm against alternatives? This will take 4 hours."
+
+
+
+  - label: "A100 vllm step 10"
+    priority: 100
+    agents:
+      queue: A100
+    plugins:
+      - kubernetes:
+          podSpec:
+            <<: *common_pod_spec
+            containers:
+              - image: vllm/vllm-openai:v0.6.2
+                <<: *common_container_settings
+
+
+
+  - label: "A100 sglang benchmark"
+    priority: 100
+    agents:
+      queue: A100
+    plugins:
+      - kubernetes:
+          podSpec:
+            <<: *common_pod_spec
+            containers:
+              - image: lmsysorg/sglang:v0.3.2-cu121
+                <<: *common_container_settings
+
+  - label: "A100 lmdeploy benchmark"
+    priority: 100
+    agents:
+      queue: A100
+    plugins:
+      - kubernetes:
+          podSpec:
+            <<: *common_pod_spec
+            containers:
+              - image: openmmlab/lmdeploy:v0.6.1-cu12
+                <<: *common_container_settings
+
+
+
+
+  - label: "A100 trt llama-8B"
+    priority: 100
+    agents:
+      queue: A100
+    plugins:
+      - kubernetes:
+          podSpec:
+            <<: *common_pod_spec
+            containers:
+              - image: nvcr.io/nvidia/tritonserver:24.07-trtllm-python-py3
+                <<: *common_container_settings
+                env:
+                  - name: VLLM_USAGE_SOURCE
+                    value: ci-test
+                  - name: HF_HOME
+                    value: /root/.cache/huggingface
+                  - name: VLLM_SOURCE_CODE_LOC
+                    value: /workspace/build/buildkite/vllm/performance-benchmark
+                  - name: HF_TOKEN
+                    valueFrom:
+                      secretKeyRef:
+                        name: hf-token-secret
+                        key: token
+                  - name: TEST_SELECTOR
+                    value: "llama8B"
+
+
+  - label: "A100 trt llama-70B"
+    priority: 100
+    agents:
+      queue: A100
+    plugins:
+      - kubernetes:
+          podSpec:
+            <<: *common_pod_spec
+            containers:
+              - image: nvcr.io/nvidia/tritonserver:24.07-trtllm-python-py3
+                <<: *common_container_settings
+                env:
+                  - name: VLLM_USAGE_SOURCE
+                    value: ci-test
+                  - name: HF_HOME
+                    value: /root/.cache/huggingface
+                  - name: VLLM_SOURCE_CODE_LOC
+                    value: /workspace/build/buildkite/vllm/performance-benchmark
+                  - name: HF_TOKEN
+                    valueFrom:
+                      secretKeyRef:
+                        name: hf-token-secret
+                        key: token
+                  - name: TEST_SELECTOR
+                    value: "llama70B"
+
+
+  # FIXME(Kuntai): uncomment this after NVIDIA gives us their test docker image 
+  # - label: "A100 trt benchmark"
+  #   priority: 100
+  #   agents:
+  #     queue: A100
+  #   plugins:
+  #     - kubernetes:
+  #         podSpec:
+  #           <<: *common_pod_spec
+  #           containers:
+  #             - image: nvcr.io/nvidia/tritonserver:24.07-trtllm-python-py3
+  #               <<: *common_container_settings
+
+
+  # FIXME(Kuntai): uncomment this after TGI supports `--ignore-eos`.
+  # - label: "A100 tgi benchmark"
+  #   priority: 100
+  #   agents:
+  #     queue: A100
+  #   plugins:
+  #     - kubernetes:
+  #         podSpec:
+  #           <<: *common_pod_spec
+  #           containers:
+  #             - image: ghcr.io/huggingface/text-generation-inference:2.2.0
+  #               <<: *common_container_settings
+        
+  - wait
+
+  - label: "Collect the results"
+    priority: 100
+    agents:
+      queue: A100
+    plugins:
+      - kubernetes:
+          podSpec:
+            <<: *common_pod_spec
+            containers:
+            - image: vllm/vllm-openai:v0.5.0.post1
+              command:
+              - bash .buildkite/nightly-benchmarks/scripts/nightly-annotate.sh
+              resources:
+                limits:
+                  nvidia.com/gpu: 8
+              volumeMounts:
+              - name: devshm
+                mountPath: /dev/shm
+              env:
+              - name: VLLM_USAGE_SOURCE
+                value: ci-test
+              - name: VLLM_SOURCE_CODE_LOC
+                value: /workspace/build/buildkite/vllm/performance-benchmark
+              - name: HF_TOKEN
+                valueFrom:
+                  secretKeyRef:
+                    name: hf-token-secret
+                    key: token
+
+  - block: ":rocket: check the results!"

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

@@ -5,7 +5,7 @@
 - Input length: 32 tokens.
 - Output length: 128 tokens.
 - Batch size: fixed (8).
-- GPU/HPU Models: llama-3.1 8B, llama-3 70B, mixtral 8x7B.
+- GPU 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).
 
@@ -16,7 +16,7 @@
 - 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.
+- GPU Models: llama-3.1 8B, llama-3 70B, mixtral 8x7B.
 - CPU Models: llama-3.1 8B.
 - Evaluation metrics: throughput.
 
@@ -28,7 +28,7 @@
 - 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.
+- GPU 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).

.buildkite/nightly-benchmarks/scripts/compare-json-results.py

@@ -7,7 +7,6 @@ from importlib import util
 
 import pandas as pd
 
-pd.options.display.float_format = "{:.2f}".format
 plotly_found = util.find_spec("plotly.express") is not None
 
 
@@ -110,10 +109,7 @@ def compare_data_columns(
         if len(compare_frames) >= 2:
             base = compare_frames[0]
             current = compare_frames[-1]
-            if "P99" in data_column or "Median" in data_column:
-                ratio = base / current  # for latency
-            else:
-                ratio = current / base
+            ratio = current / base
             ratio = ratio.mask(base == 0)  # avoid inf when baseline is 0
             ratio.name = f"Ratio 1 vs {len(compare_frames)}"
             frames.append(ratio)
@@ -203,71 +199,6 @@ def split_json_by_tp_pp(
     return saved_paths
 
 
-def _add_limit_line(fig, y_value, label):
-    # Visible dashed line + annotation
-    fig.add_hline(
-        y=y_value,
-        line_dash="dash",
-        line_color="red" if "ttft" in label.lower() else "blue",
-        annotation_text=f"{label}: {y_value} ms",
-        annotation_position="top left",
-    )
-    # Optional: add a legend item (as a transparent helper trace)
-    if plot and plotly_found:
-        import plotly.graph_objects as go
-
-        fig.add_trace(
-            go.Scatter(
-                x=[None],
-                y=[None],
-                mode="lines",
-                line=dict(
-                    dash="dash", color="red" if "ttft" in label.lower() else "blue"
-                ),
-                name=f"{label}",
-            )
-        )
-
-
-def _find_concurrency_col(df: pd.DataFrame) -> str:
-    for c in [
-        "# of max concurrency.",
-        "# of max concurrency",
-        "Max Concurrency",
-        "max_concurrency",
-        "Concurrency",
-    ]:
-        if c in df.columns:
-            return c
-    # Fallback: guess an integer-like column (harmless if unused)
-    for c in df.columns:
-        if df[c].dtype.kind in "iu" and df[c].nunique() > 1 and df[c].min() >= 1:
-            return c
-    return "# of max concurrency."
-
-
-def _highlight_threshold(
-    df: pd.DataFrame, threshold: float
-) -> "pd.io.formats.style.Styler":
-    """Highlight numeric per-configuration columns with value <= threshold."""
-    conc_col = _find_concurrency_col(df)
-    key_cols = [
-        c
-        for c in ["Model", "Dataset Name", "Input Len", "Output Len", conc_col]
-        if c in df.columns
-    ]
-    conf_cols = [
-        c for c in df.columns if c not in key_cols and not str(c).startswith("Ratio")
-    ]
-    conf_cols = [c for c in conf_cols if pd.api.types.is_numeric_dtype(df[c])]
-    return df.style.map(
-        lambda v: "background-color:#e6ffe6;font-weight:bold;"
-        if pd.notna(v) and v <= threshold
-        else "",
-        subset=conf_cols,
-    )
-
-
 if __name__ == "__main__":
     parser = argparse.ArgumentParser()
     parser.add_argument(
@@ -289,26 +220,6 @@ if __name__ == "__main__":
         default="# of max concurrency.",
         help="column name to use as X Axis in comparison graph",
     )
-    parser.add_argument(
-        "-l",
-        "--latency",
-        type=str,
-        default="p99",
-        help="take median|p99 for latency like TTFT/TPOT",
-    )
-    parser.add_argument(
-        "--ttft-max-ms",
-        type=float,
-        default=3000.0,
-        help="Reference limit for TTFT plots (ms)",
-    )
-    parser.add_argument(
-        "--tpot-max-ms",
-        type=float,
-        default=100.0,
-        help="Reference limit for TPOT plots (ms)",
-    )
-
     args = parser.parse_args()
 
     drop_column = "P99"
@@ -323,22 +234,12 @@ if __name__ == "__main__":
         "# of max concurrency.",
         "qps",
     ]
-
-    if "median" in args.latency:
-        data_cols_to_compare = ["Output Tput (tok/s)", "Median TTFT (ms)", "Median"]
-        html_msgs_for_data_cols = [
-            "Compare Output Tokens /n",
-            "Median TTFT /n",
-            "Median TPOT /n",
-        ]
-        drop_column = "P99"
-    elif "p99" in args.latency:
-        data_cols_to_compare = ["Output Tput (tok/s)", "P99 TTFT (ms)", "P99"]
-        html_msgs_for_data_cols = [
-            "Compare Output Tokens /n",
-            "P99 TTFT /n",
-            "P99 TPOT /n",
-        ]
+    data_cols_to_compare = ["Output Tput (tok/s)", "Median TTFT (ms)", "Median"]
+    html_msgs_for_data_cols = [
+        "Compare Output Tokens /n",
+        "Median TTFT /n",
+        "Median TPOT /n",
+    ]
 
     if len(args.file) == 1:
         files = split_json_by_tp_pp(args.file[0], output_root="splits")
@@ -374,83 +275,33 @@ if __name__ == "__main__":
                     f"Expected subset: {filtered_info_cols}, "
                     f"but DataFrame has: {list(output_df.columns)}"
                 )
-            # output_df_sorted = output_df.sort_values(by=existing_group_cols)
-            output_df_sorted = output_df.sort_values(by=args.xaxis)
+            output_df_sorted = output_df.sort_values(by=existing_group_cols)
             output_groups = output_df_sorted.groupby(existing_group_cols, dropna=False)
             for name, group in output_groups:
-                group_name = (
-                    ",".join(map(str, name)).replace(",", "_").replace("/", "-")
-                )
-                group_html_name = "perf_comparison_" + group_name + ".html"
-
-                metric_name = str(data_cols_to_compare[i]).lower()
-                if "tok/s" in metric_name:
-                    html = group.to_html()
-                elif "ttft" in metric_name:
-                    styler = _highlight_threshold(group, args.ttft_max_ms).format(
-                        {c: "{:.2f}" for c in group.select_dtypes("number").columns},
-                        na_rep="—",
-                    )
-                    html = styler.to_html(
-                        table_attributes='border="1" class="dataframe"'
-                    )
-                elif (
-                    "tpot" in metric_name
-                    or "median" in metric_name
-                    or "p99" in metric_name
-                ):
-                    styler = _highlight_threshold(group, args.tpot_max_ms).format(
-                        {c: "{:.2f}" for c in group.select_dtypes("number").columns},
-                        na_rep="—",
-                    )
-                    html = styler.to_html(
-                        table_attributes='border="1" class="dataframe"'
-                    )
-
+                html = group.to_html()
                 text_file.write(html_msgs_for_data_cols[i])
                 text_file.write(html)
-                with open(group_html_name, "a+") as sub_text_file:
-                    sub_text_file.write(html_msgs_for_data_cols[i])
-                    sub_text_file.write(html)
 
-                    if plot and plotly_found:
-                        import plotly.express as px
+                if plot and plotly_found:
+                    import plotly.express as px
 
-                        df = group[raw_data_cols]
-                        df_sorted = df.sort_values(by=info_cols[y_axis_index])
-                        # Melt DataFrame for plotting
-                        df_melted = df_sorted.melt(
-                            id_vars=info_cols[y_axis_index],
-                            var_name="Configuration",
-                            value_name=data_cols_to_compare[i],
-                        )
-                        title = (
-                            data_cols_to_compare[i] + " vs " + info_cols[y_axis_index]
-                        )
-                        # Create Plotly line chart
-                        fig = px.line(
-                            df_melted,
-                            x=info_cols[y_axis_index],
-                            y=data_cols_to_compare[i],
-                            color="Configuration",
-                            title=title,
-                            markers=True,
-                        )
-
-                        # ---- Add threshold lines based on metric name ----
-                        if "ttft" in metric_name:
-                            _add_limit_line(fig, args.ttft_max_ms, "TTFT limit")
-                        elif (
-                            "tpot" in metric_name
-                            or "median" in metric_name
-                            or "p99" in metric_name
-                        ):
-                            _add_limit_line(fig, args.tpot_max_ms, "TPOT limit")
-
-                        # Export to HTML
-                        text_file.write(
-                            fig.to_html(full_html=True, include_plotlyjs="cdn")
-                        )
-                        sub_text_file.write(
-                            fig.to_html(full_html=True, include_plotlyjs="cdn")
-                        )
+                    df = group[raw_data_cols]
+                    df_sorted = df.sort_values(by=info_cols[y_axis_index])
+                    # Melt DataFrame for plotting
+                    df_melted = df_sorted.melt(
+                        id_vars=info_cols[y_axis_index],
+                        var_name="Configuration",
+                        value_name=data_cols_to_compare[i],
+                    )
+                    title = data_cols_to_compare[i] + " vs " + info_cols[y_axis_index]
+                    # Create Plotly line chart
+                    fig = px.line(
+                        df_melted,
+                        x=info_cols[y_axis_index],
+                        y=data_cols_to_compare[i],
+                        color="Configuration",
+                        title=title,
+                        markers=True,
+                    )
+                    # Export to HTML
+                    text_file.write(fig.to_html(full_html=True, include_plotlyjs="cdn"))

.buildkite/nightly-benchmarks/scripts/convert-results-json-to-markdown.py

@@ -63,11 +63,9 @@ serving_column_mapping = {
     "mean_ttft_ms": "Mean TTFT (ms)",
     "median_ttft_ms": "Median TTFT (ms)",
     "p99_ttft_ms": "P99 TTFT (ms)",
-    "std_ttft_ms": "STD TTFT (ms)",
     "mean_tpot_ms": "Mean TPOT (ms)",
     "median_tpot_ms": "Median",
     "p99_tpot_ms": "P99",
-    "std_tpot_ms": "STD TPOT (ms)",
     "mean_itl_ms": "Mean ITL (ms)",
     "median_itl_ms": "Median ITL (ms)",
     "p99_itl_ms": "P99 ITL (ms)",
@@ -370,7 +368,7 @@ if __name__ == "__main__":
         # The GPUs sometimes come in format of "GPUTYPE\nGPUTYPE\n...",
         # we want to turn it into "8xGPUTYPE"
         df["GPU"] = df["GPU"].apply(
-            lambda x: "{}x{}".format(len(x.split("\n")), x.split("\n")[0])
+            lambda x: f"{len(x.splitlines())}x{x.splitlines()[0]}"
         )
 
     # get markdown tables
@@ -392,7 +390,7 @@ if __name__ == "__main__":
     json_file = "benchmark_results.json"
     with open(results_folder / md_file, "w") as f:
         results = read_markdown(
-            "../.buildkite/performance-benchmarks/"
+            "../.buildkite/nightly-benchmarks/"
             + "performance-benchmarks-descriptions.md"
         )
         results = results.format(

.buildkite/nightly-benchmarks/scripts/download-tokenizer.py

@@ -0,0 +1,26 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import argparse
+
+from transformers import AutoTokenizer
+
+
+def main(model, cachedir):
+    # Load the tokenizer and save it to the specified directory
+    tokenizer = AutoTokenizer.from_pretrained(model)
+    tokenizer.save_pretrained(cachedir)
+    print(f"Tokenizer saved to {cachedir}")
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(
+        description="Download and save Hugging Face tokenizer"
+    )
+    parser.add_argument("--model", type=str, required=True, help="Name of the model")
+    parser.add_argument(
+        "--cachedir", type=str, required=True, help="Directory to save the tokenizer"
+    )
+
+    args = parser.parse_args()
+    main(args.model, args.cachedir)

.buildkite/nightly-benchmarks/scripts/generate-nightly-markdown.py

@@ -0,0 +1,97 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import argparse
+import json
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+from tabulate import tabulate
+
+
+def parse_arguments():
+    parser = argparse.ArgumentParser(
+        description="Parse command line arguments for summary-nightly-results script."
+    )
+    parser.add_argument(
+        "--results-folder",
+        type=str,
+        required=True,
+        help="The folder where the results are stored.",
+    )
+    parser.add_argument(
+        "--description", type=str, required=True, help="Description of the results."
+    )
+
+    args = parser.parse_args()
+    return args
+
+
+def get_perf(df, method, model, metric):
+    means = []
+
+    for qps in [2, 4, 8, 16, "inf"]:
+        target = df["Test name"].str.contains(model)
+        target = target & df["Engine"].str.contains(method)
+        target = target & df["Test name"].str.contains("qps_" + str(qps))
+        filtered_df = df[target]
+
+        if filtered_df.empty:
+            means.append(0.0)
+        else:
+            means.append(filtered_df[metric].values[0])
+
+    return np.array(means)
+
+
+def get_perf_w_std(df, method, model, metric):
+    if metric in ["TTFT", "ITL"]:
+        mean = get_perf(df, method, model, "Mean " + metric + " (ms)")
+        mean = mean.tolist()
+        std = get_perf(df, method, model, "Std " + metric + " (ms)")
+        if std.mean() == 0:
+            std = None
+        success = get_perf(df, method, model, "Successful req.")
+        if std is not None:
+            std = std / np.sqrt(success)
+            std = std.tolist()
+
+    else:
+        assert metric == "Tput"
+        mean = get_perf(df, method, model, "Input Tput (tok/s)") + get_perf(
+            df, method, model, "Output Tput (tok/s)"
+        )
+        mean = mean.tolist()
+        std = None
+
+    return mean, std
+
+
+def main(args):
+    results_folder = Path(args.results_folder)
+
+    results = []
+
+    # collect results
+    for test_file in results_folder.glob("*_nightly_results.json"):
+        with open(test_file) as f:
+            results = results + json.loads(f.read())
+
+    # generate markdown table
+    df = pd.DataFrame.from_dict(results)
+
+    md_table = tabulate(df, headers="keys", tablefmt="pipe", showindex=False)
+
+    with open(args.description) as f:
+        description = f.read()
+
+    description = description.format(nightly_results_benchmarking_table=md_table)
+
+    with open("nightly_results.md", "w") as f:
+        f.write(description)
+
+
+if __name__ == "__main__":
+    args = parse_arguments()
+    main(args)

.buildkite/nightly-benchmarks/scripts/get-lmdeploy-modelname.py

@@ -0,0 +1,9 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from lmdeploy.serve.openai.api_client import APIClient
+
+api_client = APIClient("http://localhost:8000")
+model_name = api_client.available_models[0]
+
+print(model_name)

.buildkite/nightly-benchmarks/scripts/nightly-annotate.sh

@@ -0,0 +1,78 @@
+#!/bin/bash
+
+set -ex
+set -o pipefail
+
+
+main() {
+
+    (which wget && which curl) || (apt-get update && apt-get install -y wget curl)
+    (which jq) || (apt-get update && apt-get -y install jq)
+    (which zip) || (apt-get install -y zip)
+
+    if [ ! -f /workspace/buildkite-agent ]; then
+        echo "buildkite-agent binary not found. Skip plotting the results."
+        exit 0
+    fi
+
+    # initial annotation
+    #description="$VLLM_SOURCE_CODE_LOC/.buildkite/nightly-benchmarks/nightly-descriptions.md"
+
+    # download results
+    cd "$VLLM_SOURCE_CODE_LOC/benchmarks"
+    mkdir -p results/
+    /workspace/buildkite-agent artifact download 'results/*nightly_results.json' results/
+    ls
+    ls results/
+
+    # upload benchmark results
+    zip -r results.zip results/
+    /workspace/buildkite-agent artifact upload "results.zip"
+
+    # upload benchmarking scripts
+    cd "$VLLM_SOURCE_CODE_LOC/"
+    zip -r nightly-benchmarks.zip .buildkite/ benchmarks/
+    /workspace/buildkite-agent artifact upload "nightly-benchmarks.zip"
+
+    cd "$VLLM_SOURCE_CODE_LOC/.buildkite/nightly-benchmarks/"
+    # upload benchmarking pipeline
+    /workspace/buildkite-agent artifact upload "nightly-pipeline.yaml"
+
+    cd "$VLLM_SOURCE_CODE_LOC/.buildkite/nightly-benchmarks/"
+    /workspace/buildkite-agent annotate --style "success" --context "nightly-benchmarks-results" --append < nightly-annotation.md
+    
+
+
+    # The figures should be generated by a separate process outside the CI/CD pipeline
+
+    # # generate figures
+    # python3 -m pip install tabulate pandas matplotlib
+
+    # python3 $VLLM_SOURCE_CODE_LOC/.buildkite/nightly-benchmarks/scripts/generate-nightly-markdown.py \
+    #     --description $description \
+    #     --results-folder results/ 
+
+
+    # python3 $VLLM_SOURCE_CODE_LOC/.buildkite/nightly-benchmarks/scripts/plot-nightly-results.py \
+    #     --description $description \
+    #     --results-folder results/ \
+    #     --dataset sharegpt
+
+    # python3 $VLLM_SOURCE_CODE_LOC/.buildkite/nightly-benchmarks/scripts/plot-nightly-results.py \
+    #     --description $description \
+    #     --results-folder results/ \
+    #     --dataset sonnet_2048_128
+
+    # python3 $VLLM_SOURCE_CODE_LOC/.buildkite/nightly-benchmarks/scripts/plot-nightly-results.py \
+    #     --description $description \
+    #     --results-folder results/ \
+    #     --dataset sonnet_128_2048
+    
+    # # upload results and figures
+    # /workspace/buildkite-agent artifact upload "nightly_results*.png"
+    # /workspace/buildkite-agent artifact upload $VLLM_SOURCE_CODE_LOC/.buildkite/nightly-benchmarks/nightly-pipeline.yaml
+    # /workspace/buildkite-agent artifact upload $VLLM_SOURCE_CODE_LOC/.buildkite/nightly-benchmarks/tests/nightly-tests.json
+    # /workspace/buildkite-agent annotate --style "success" --context "nightly-benchmarks-results" --append < nightly_results.md
+}
+
+main "$@"

.buildkite/nightly-benchmarks/scripts/run-nightly-benchmarks.sh

@@ -0,0 +1,464 @@
+#!/bin/bash
+
+set -o pipefail
+set -x
+
+check_gpus() {
+  # check the number of GPUs and GPU type.
+  declare -g gpu_count=$(nvidia-smi --list-gpus | wc -l)
+  if [[ $gpu_count -gt 0 ]]; then
+    echo "GPU found."
+  else
+    echo "Need at least 1 GPU to run benchmarking."
+    exit 1
+  fi
+  declare -g gpu_type="$(nvidia-smi --query-gpu=name --format=csv,noheader | awk '{print $2}')"
+  echo "GPU type is $gpu_type"
+}
+
+check_hf_token() {
+  # check if HF_TOKEN is available and valid
+  if [[ -z "$HF_TOKEN" ]]; then
+    echo "Error: HF_TOKEN is not set."
+    exit 1
+  elif [[ ! "$HF_TOKEN" =~ ^hf_ ]]; then
+    echo "Error: HF_TOKEN does not start with 'hf_'."
+    exit 1
+  else
+    echo "HF_TOKEN is set and valid."
+  fi
+}
+
+
+upload_to_buildkite() {
+  # upload the benchmarking results to buildkite
+
+  # if the agent binary is not found, skip uploading the results, exit 0
+  if [ ! -f /workspace/buildkite-agent ]; then
+    echo "buildkite-agent binary not found. Skip uploading the results."
+    return 0
+  fi
+  # /workspace/buildkite-agent annotate --style "success" --context "benchmark-results" --append < $RESULTS_FOLDER/${CURRENT_LLM_SERVING_ENGINE}_nightly_results.md
+  /workspace/buildkite-agent artifact upload "$RESULTS_FOLDER/*"
+}
+
+
+get_current_llm_serving_engine() {
+
+  if which lmdeploy >/dev/null; then
+    echo "Container: lmdeploy"
+    export CURRENT_LLM_SERVING_ENGINE=lmdeploy
+    return
+  fi
+
+  if [ -e /tgi-entrypoint.sh ]; then
+    echo "Container: tgi"
+    export CURRENT_LLM_SERVING_ENGINE=tgi
+    return
+  fi
+
+  if which trtllm-build >/dev/null; then
+    echo "Container: tensorrt-llm"
+    export CURRENT_LLM_SERVING_ENGINE=trt
+    return
+  fi
+
+  if [ -e /sgl-workspace ]; then
+    echo "Container: sglang"
+    export CURRENT_LLM_SERVING_ENGINE=sglang
+    return
+  fi
+
+  if [ -e /vllm-workspace ]; then
+    echo "Container: vllm"
+    # move to a completely irrelevant directory, to avoid import vllm from current folder
+    export CURRENT_LLM_SERVING_ENGINE=vllm
+
+    return
+  fi
+}
+
+json2args() {
+  # transforms the JSON string to command line args, and '_' is replaced to '-'
+  # example:
+  # input: { "model": "meta-llama/Llama-2-7b-chat-hf", "tensor_parallel_size": 1 }
+  # output: --model meta-llama/Llama-2-7b-chat-hf --tensor-parallel-size 1
+  local json_string=$1
+  local args=$(
+    echo "$json_string" | jq -r '
+      to_entries |
+      map("--" + (.key | gsub("_"; "-")) + " " + (.value | tostring)) |
+      join(" ")
+    '
+  )
+  echo "$args"
+}
+
+kill_gpu_processes() {
+  pkill -f '[p]ython'
+  pkill -f '[p]ython3'
+  pkill -f '[t]ritonserver'
+  pkill -f '[p]t_main_thread'
+  pkill -f '[t]ext-generation'
+  pkill -f '[l]mdeploy'
+  # vLLM now names the process with VLLM prefix after https://github.com/vllm-project/vllm/pull/21445
+  pkill -f '[V]LLM'
+
+  while [ "$(nvidia-smi --query-gpu=memory.used --format=csv,noheader,nounits | head -n 1)" -ge 1000 ]; do
+    sleep 1
+  done
+}
+
+wait_for_server() {
+  # wait for vllm server to start
+  # return 1 if vllm server crashes
+  timeout 1200 bash -c '
+    until curl -s localhost:8000/v1/completions > /dev/null; do
+      sleep 1
+    done' && return 0 || return 1
+}
+
+ensure_installed() {
+  # Ensure that the given command is installed by apt-get
+  local cmd=$1
+  if ! which "$cmd" >/dev/null; then
+    apt-get update && apt-get install -y "$cmd"
+  fi
+}
+
+run_serving_tests() {
+  # run serving tests using `vllm bench serve` command
+  # $1: a json file specifying serving test cases
+
+  local serving_test_file
+  serving_test_file=$1
+
+  # Iterate over serving tests
+  jq -c '.[]' "$serving_test_file" | while read -r params; do
+    # get the test name, and append the GPU type back to it.
+    test_name=$(echo "$params" | jq -r '.test_name')
+
+    # if TEST_SELECTOR is set, only run the test cases that match the selector
+    if [[ -n "$TEST_SELECTOR" ]] && [[ ! "$test_name" =~ $TEST_SELECTOR ]]; then
+      echo "Skip test case $test_name."
+      continue
+    fi
+
+    # prepend the current serving engine to the test name
+    test_name=${CURRENT_LLM_SERVING_ENGINE}_${test_name}
+
+    # get common parameters
+    common_params=$(echo "$params" | jq -r '.common_parameters')
+    model=$(echo "$common_params" | jq -r '.model')
+    tp=$(echo "$common_params" | jq -r '.tp')
+    dataset_name=$(echo "$common_params" | jq -r '.dataset_name')
+    dataset_path=$(echo "$common_params" | jq -r '.dataset_path')
+    port=$(echo "$common_params" | jq -r '.port')
+    num_prompts=$(echo "$common_params" | jq -r '.num_prompts')
+    reuse_server=$(echo "$common_params" | jq -r '.reuse_server')
+
+    # get client and server arguments
+    server_params=$(echo "$params" | jq -r ".${CURRENT_LLM_SERVING_ENGINE}_server_parameters")
+    client_params=$(echo "$params" | jq -r ".${CURRENT_LLM_SERVING_ENGINE}_client_parameters")
+    client_args=$(json2args "$client_params")
+    qps_list=$(echo "$params" | jq -r '.qps_list')
+    qps_list=$(echo "$qps_list" | jq -r '.[] | @sh')
+    echo "Running over qps list $qps_list"
+
+    # check if there is enough GPU to run the test
+    if [[ $gpu_count -lt $tp ]]; then
+      echo "Required num-shard $tp but only $gpu_count GPU found. Skip testcase $test_name."
+      continue
+    fi
+
+    if [[ $reuse_server == "true" ]]; then
+      echo "Reuse previous server for test case $test_name"
+    else
+      kill_gpu_processes
+      bash "$VLLM_SOURCE_CODE_LOC/.buildkite/nightly-benchmarks/scripts/launch-server.sh" \
+        "$server_params" "$common_params"
+    fi
+
+    if wait_for_server; then
+      echo ""
+      echo "$CURRENT_LLM_SERVING_ENGINE server is up and running."
+    else
+      echo ""
+      echo "$CURRENT_LLM_SERVING_ENGINE failed to start within the timeout period."
+      break
+    fi
+
+    # prepare tokenizer
+    # this is required for lmdeploy.
+    cd "$VLLM_SOURCE_CODE_LOC/benchmarks"
+    rm -rf /tokenizer_cache
+    mkdir /tokenizer_cache
+    python3 ../.buildkite/nightly-benchmarks/scripts/download-tokenizer.py \
+      --model "$model" \
+      --cachedir /tokenizer_cache
+    cd "$VLLM_SOURCE_CODE_LOC/benchmarks"
+
+
+    # change model name for lmdeploy (it will not follow standard hf name)
+    if [[ "$CURRENT_LLM_SERVING_ENGINE" == "lmdeploy" ]]; then
+      model=$(python ../.buildkite/nightly-benchmarks/scripts/get-lmdeploy-modelname.py)
+    fi
+
+    # iterate over different QPS
+    for qps in $qps_list; do
+      # remove the surrounding single quote from qps
+      if [[ "$qps" == *"inf"* ]]; then
+        echo "qps was $qps"
+        qps="inf"
+        echo "now qps is $qps"
+      fi
+
+      new_test_name=$test_name"_qps_"$qps
+
+      backend=$CURRENT_LLM_SERVING_ENGINE
+
+      if [[ $backend = "trt" ]]; then
+        backend="tensorrt-llm"
+      fi
+
+      if [[ "$backend" == *"vllm"* ]]; then
+        backend="vllm"
+      fi
+
+      if [[ "$dataset_name" = "sharegpt" ]]; then
+
+        client_command="vllm bench serve \
+          --backend $backend \
+          --tokenizer /tokenizer_cache \
+          --model $model \
+          --dataset-name $dataset_name \
+          --dataset-path $dataset_path \
+          --num-prompts $num_prompts \
+          --port $port \
+          --save-result \
+          --result-dir $RESULTS_FOLDER \
+          --result-filename ${new_test_name}.json \
+          --request-rate $qps \
+          --ignore-eos \
+          $client_args"
+
+      elif [[ "$dataset_name" = "sonnet" ]]; then
+
+        sonnet_input_len=$(echo "$common_params" | jq -r '.sonnet_input_len')
+        sonnet_output_len=$(echo "$common_params" | jq -r '.sonnet_output_len')
+        sonnet_prefix_len=$(echo "$common_params" | jq -r '.sonnet_prefix_len')
+
+        client_command="vllm bench serve \
+          --backend $backend \
+          --tokenizer /tokenizer_cache \
+          --model $model \
+          --dataset-name $dataset_name \
+          --dataset-path $dataset_path \
+          --num-prompts $num_prompts \
+          --sonnet-input-len $sonnet_input_len \
+          --sonnet-output-len $sonnet_output_len \
+          --sonnet-prefix-len $sonnet_prefix_len \
+          --port $port \
+          --save-result \
+          --result-dir $RESULTS_FOLDER \
+          --result-filename ${new_test_name}.json \
+          --request-rate $qps \
+          --ignore-eos \
+          $client_args"
+
+      else
+
+        echo "The dataset name must be either 'sharegpt' or 'sonnet'. Got $dataset_name."
+        exit 1
+
+      fi
+
+
+
+      echo "Running test case $test_name with qps $qps"
+      echo "Client command: $client_command"
+
+      eval "$client_command"
+
+      server_command="None"
+
+      # record the benchmarking commands
+      jq_output=$(jq -n \
+        --arg server "$server_command" \
+        --arg client "$client_command" \
+        --arg gpu "$gpu_type" \
+        --arg engine "$CURRENT_LLM_SERVING_ENGINE" \
+        '{
+          server_command: $server,
+          client_command: $client,
+          gpu_type: $gpu,
+          engine: $engine
+        }')
+      echo "$jq_output" >"$RESULTS_FOLDER/${new_test_name}.commands"
+
+    done
+
+  done
+
+  kill_gpu_processes
+}
+
+run_genai_perf_tests() {
+  # run genai-perf tests
+
+  # $1: a json file specifying genai-perf test cases
+  local genai_perf_test_file
+  genai_perf_test_file=$1
+
+  # Iterate over genai-perf tests
+  jq -c '.[]' "$genai_perf_test_file" | while read -r params; do
+    # get the test name, and append the GPU type back to it.
+    test_name=$(echo "$params" | jq -r '.test_name')
+
+    # if TEST_SELECTOR is set, only run the test cases that match the selector
+    if [[ -n "$TEST_SELECTOR" ]] && [[ ! "$test_name" =~ $TEST_SELECTOR ]]; then
+      echo "Skip test case $test_name."
+      continue
+    fi
+
+    # prepend the current serving engine to the test name
+    test_name=${CURRENT_LLM_SERVING_ENGINE}_${test_name}
+
+    # get common parameters
+    common_params=$(echo "$params" | jq -r '.common_parameters')
+    model=$(echo "$common_params" | jq -r '.model')
+    tp=$(echo "$common_params" | jq -r '.tp')
+    dataset_name=$(echo "$common_params" | jq -r '.dataset_name')
+    dataset_path=$(echo "$common_params" | jq -r '.dataset_path')
+    port=$(echo "$common_params" | jq -r '.port')
+    num_prompts=$(echo "$common_params" | jq -r '.num_prompts')
+    reuse_server=$(echo "$common_params" | jq -r '.reuse_server')
+
+    # get client and server arguments
+    server_params=$(echo "$params" | jq -r ".${CURRENT_LLM_SERVING_ENGINE}_server_parameters")
+    qps_list=$(echo "$params" | jq -r '.qps_list')
+    qps_list=$(echo "$qps_list" | jq -r '.[] | @sh')
+    echo "Running over qps list $qps_list"
+
+    # check if there is enough GPU to run the test
+    if [[ $gpu_count -lt $tp ]]; then
+      echo "Required num-shard $tp but only $gpu_count GPU found. Skip testcase $test_name."
+      continue
+    fi
+
+    if [[ $reuse_server == "true" ]]; then
+      echo "Reuse previous server for test case $test_name"
+    else
+      kill_gpu_processes
+      bash "$VLLM_SOURCE_CODE_LOC/.buildkite/nightly-benchmarks/scripts/launch-server.sh" \
+        "$server_params" "$common_params"
+    fi
+
+    if wait_for_server; then
+      echo ""
+      echo "$CURRENT_LLM_SERVING_ENGINE server is up and running."
+    else
+      echo ""
+      echo "$CURRENT_LLM_SERVING_ENGINE failed to start within the timeout period."
+      break
+    fi
+
+    # iterate over different QPS
+    for qps in $qps_list; do
+      # remove the surrounding single quote from qps
+      if [[ "$qps" == *"inf"* ]]; then
+        echo "qps was $qps"
+        qps=$num_prompts
+        echo "now qps is $qps"
+      fi
+
+      new_test_name=$test_name"_qps_"$qps
+      backend=$CURRENT_LLM_SERVING_ENGINE
+
+      if [[ "$backend" == *"vllm"* ]]; then
+        backend="vllm"
+      fi
+      #TODO: add output dir.
+      client_command="genai-perf profile \
+        -m $model \
+        --service-kind openai \
+        --backend "$backend" \
+        --endpoint-type chat \
+        --streaming \
+        --url localhost:$port \
+        --request-rate $qps \
+        --num-prompts $num_prompts \
+      "
+
+    echo "Client command: $client_command"
+
+    eval "$client_command"
+
+    #TODO: process/record outputs
+    done
+  done
+
+  kill_gpu_processes
+
+}
+
+prepare_dataset() {
+
+  # download sharegpt dataset
+  cd "$VLLM_SOURCE_CODE_LOC/benchmarks"
+  wget https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/ShareGPT_V3_unfiltered_cleaned_split.json
+
+  # duplicate sonnet by 4x, to allow benchmarking with input length 2048
+  cd "$VLLM_SOURCE_CODE_LOC/benchmarks"
+  echo "" > sonnet_4x.txt
+  for _ in {1..4}
+  do
+    cat sonnet.txt >> sonnet_4x.txt
+  done
+
+}
+
+main() {
+
+  # check if the environment variable is successfully injected from yaml
+
+  check_gpus
+  check_hf_token
+  get_current_llm_serving_engine
+
+  pip install -U transformers
+
+  pip install -r requirements/dev.txt
+  which genai-perf
+
+  # check storage
+  df -h
+
+  ensure_installed wget
+  ensure_installed curl
+  ensure_installed jq
+  # genai-perf dependency
+  ensure_installed libb64-0d
+
+  prepare_dataset
+
+  cd "$VLLM_SOURCE_CODE_LOC/benchmarks"
+  declare -g RESULTS_FOLDER=results/
+  mkdir -p $RESULTS_FOLDER
+  BENCHMARK_ROOT="$VLLM_SOURCE_CODE_LOC/.buildkite/nightly-benchmarks/"
+
+  # run the test
+  run_serving_tests "$BENCHMARK_ROOT/tests/nightly-tests.json"
+
+  # run genai-perf tests
+  run_genai_perf_tests "$BENCHMARK_ROOT/tests/genai-perf-tests.json"
+  mv artifacts/ $RESULTS_FOLDER/
+
+  # upload benchmark results to buildkite
+  python3 -m pip install tabulate pandas
+  python3 "$BENCHMARK_ROOT/scripts/summary-nightly-results.py"
+  upload_to_buildkite
+
+}
+
+main "$@"

.buildkite/nightly-benchmarks/scripts/run-performance-benchmarks.sh

@@ -15,8 +15,6 @@ check_gpus() {
     declare -g gpu_count=$(nvidia-smi --list-gpus | wc -l)
   elif command -v amd-smi; then
     declare -g gpu_count=$(amd-smi list | grep 'GPU' | wc -l)
-  elif command -v hl-smi; then
-    declare -g gpu_count=$(hl-smi --list | grep -i "Module ID" | wc -l)
   fi
 
   if [[ $gpu_count -gt 0 ]]; then
@@ -25,16 +23,10 @@ check_gpus() {
     echo "Need at least 1 GPU to run benchmarking."
     exit 1
   fi
-  
-  declare -g arch_suffix=''
-  
   if command -v nvidia-smi; then
     declare -g gpu_type=$(nvidia-smi --query-gpu=name --format=csv,noheader | awk '{print $2}')
   elif command -v amd-smi; then
     declare -g gpu_type=$(amd-smi static -g 0 -a | grep 'MARKET_NAME' | awk '{print $2}')
-  elif command -v hl-smi; then
-    declare -g gpu_type=$(hl-smi -q | grep "Product Name" | head -n 1 | awk -F ':' '{print $2}' | sed 's/^ *//')
-    arch_suffix='-hpu'
   fi
   echo "GPU type is $gpu_type"
 }
@@ -146,10 +138,6 @@ kill_gpu_processes() {
     while [ "$(amd-smi metric -g 0 | grep 'USED_VRAM' | awk '{print $2}')" -ge 1000 ]; do
       sleep 1
     done
-  elif command -v hl-smi; then
-    while [ "$(hl-smi -q | grep "Used" | head -n 1 | awk '{print $3}')" -ge 1000 ]; do
-      sleep 1
-    done
   fi
 
   # remove vllm config file
@@ -463,7 +451,6 @@ main() {
      ARCH='-cpu'
   else
      check_gpus
-     ARCH="$arch_suffix"
   fi
   check_hf_token
 
@@ -482,12 +469,7 @@ main() {
   ensure_sharegpt_downloaded
   declare -g RESULTS_FOLDER=results/
   mkdir -p $RESULTS_FOLDER
-  QUICK_BENCHMARK_ROOT=../.buildkite/performance-benchmarks/
-
-  # dump vllm info via vllm collect-env
-  env_output=$(vllm collect-env)
-
-  echo "$env_output" >"$RESULTS_FOLDER/vllm_env.txt"
+  QUICK_BENCHMARK_ROOT=../.buildkite/nightly-benchmarks/
 
   # benchmarking
   run_serving_tests $QUICK_BENCHMARK_ROOT/tests/"${SERVING_JSON:-serving-tests$ARCH.json}"

.buildkite/nightly-benchmarks/scripts/summary-nightly-results.py

@@ -0,0 +1,82 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import datetime
+import json
+import os
+from pathlib import Path
+
+import pandas as pd
+from tabulate import tabulate
+
+results_folder = Path("results/")
+
+# serving results and the keys that will be printed into markdown
+serving_results = []
+serving_column_mapping = {
+    "test_name": "Test name",
+    "gpu_type": "GPU",
+    "completed": "Successful req.",
+    "request_throughput": "Tput (req/s)",
+    "mean_ttft_ms": "Mean TTFT (ms)",
+    "std_ttft_ms": "Std TTFT (ms)",
+    "median_ttft_ms": "Median TTFT (ms)",
+    "mean_itl_ms": "Mean ITL (ms)",
+    "std_itl_ms": "Std ITL (ms)",
+    "median_itl_ms": "Median ITL (ms)",
+    "mean_tpot_ms": "Mean TPOT (ms)",
+    "std_tpot_ms": "Std TPOT (ms)",
+    "median_tpot_ms": "Median TPOT (ms)",
+    "total_token_throughput": "Total Token Tput (tok/s)",
+    "output_throughput": "Output Tput (tok/s)",
+    "total_input_tokens": "Total input tokens",
+    "total_output_tokens": "Total output tokens",
+    "engine": "Engine",
+}
+
+if __name__ == "__main__":
+    # collect results
+    for test_file in results_folder.glob("*.json"):
+        with open(test_file) as f:
+            raw_result = json.loads(f.read())
+
+        # attach the benchmarking command to raw_result
+        with open(test_file.with_suffix(".commands")) as f:
+            command = json.loads(f.read())
+        raw_result.update(command)
+
+        # update the test name of this result
+        raw_result.update({"test_name": test_file.stem})
+
+        # add the result to raw_result
+        serving_results.append(raw_result)
+        continue
+
+    serving_results = pd.DataFrame.from_dict(serving_results)
+
+    if not serving_results.empty:
+        serving_results = serving_results[list(serving_column_mapping.keys())].rename(
+            columns=serving_column_mapping
+        )
+
+    serving_md_table_with_headers = tabulate(
+        serving_results, headers="keys", tablefmt="pipe", showindex=False
+    )
+    # remove the first line of header
+    serving_md_table_lines = serving_md_table_with_headers.split("\n")
+    serving_md_table_without_header = "\n".join(serving_md_table_lines[2:])
+
+    prefix = datetime.datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
+    prefix = prefix + "_" + os.environ.get("CURRENT_LLM_SERVING_ENGINE")
+
+    # document benchmarking results in markdown
+    with open(results_folder / f"{prefix}_nightly_results.md", "w") as f:
+        # document results with header.
+        # for those who wants to reproduce our benchmark.
+        f.write(serving_md_table_with_headers)
+        f.write("\n")
+
+    # document benchmarking results in json
+    with open(results_folder / f"{prefix}_nightly_results.json", "w") as f:
+        results = serving_results.to_dict(orient="records")
+        f.write(json.dumps(results))

.buildkite/nightly-benchmarks/scripts/wait-for-image.sh

@@ -0,0 +1,23 @@
+#!/bin/sh
+TOKEN=$(curl -s -L "https://public.ecr.aws/token?service=public.ecr.aws&scope=repository:q9t5s3a7/vllm-ci-postmerge-repo:pull" | jq -r .token)
+if [[ "$BUILDKITE_BRANCH" == "main" ]]; then
+    URL="https://public.ecr.aws/v2/q9t5s3a7/vllm-ci-postmerge-repo/manifests/$BUILDKITE_COMMIT"
+else
+    URL="https://public.ecr.aws/v2/q9t5s3a7/vllm-ci-test-repo/manifests/$BUILDKITE_COMMIT"
+fi
+
+TIMEOUT_SECONDS=10
+
+retries=0
+while [ $retries -lt 1000 ]; do
+    if [ "$(curl -s --max-time "$TIMEOUT_SECONDS" -L -H "Authorization: Bearer $TOKEN" -o /dev/null -w "%{http_code}" "$URL")" -eq 200 ]; then
+        exit 0
+    fi
+
+    echo "Waiting for image to be available..."
+
+    retries=$((retries + 1))
+    sleep 5
+done
+
+exit 1

.buildkite/nightly-benchmarks/tests/latency-tests-cpu.json

@@ -0,0 +1,30 @@
+[
+    {
+        "test_name": "latency_llama8B_tp1",
+        "environment_variables": {
+	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
+	    "VLLM_CPU_KVCACHE_SPACE": 40
+        },
+        "parameters": {
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
+            "tensor_parallel_size": 1,
+            "load_format": "dummy",
+            "num_iters_warmup": 5,
+            "num_iters": 15
+        }
+    },
+    {
+        "test_name": "latency_llama8B_tp4",
+        "environment_variables": {
+	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
+	    "VLLM_CPU_KVCACHE_SPACE": 40
+        },
+        "parameters": {
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
+            "tensor_parallel_size": 4,
+            "load_format": "dummy",
+            "num_iters_warmup": 5,
+            "num_iters": 15
+        }
+    }
+]

.buildkite/nightly-benchmarks/tests/serving-tests-cpu-snc3.json

@@ -95,38 +95,6 @@
             "num_prompts": 200
         }
     },
-    {
-        "test_name": "serving_llama8B_bf16_tp4_sharegpt",
-        "qps_list": ["inf"],
-        "max_concurrency_list": [12, 16, 24, 32, 64, 128, 200],
-        "server_environment_variables": {
-            "VLLM_RPC_TIMEOUT": 100000,
-	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
-	    "VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
-	    "VLLM_CPU_SGL_KERNEL": 1,
-	    "VLLM_CPU_KVCACHE_SPACE": 40
-        },
-        "server_parameters": {
-            "model": "meta-llama/Llama-3.1-8B-Instruct",
-            "tensor_parallel_size": 4,
-	    "dtype": "bfloat16",
-	    "distributed_executor_backend": "mp",
-	    "block_size": 128,
-	    "trust_remote_code": "",
-            "disable_log_stats": "",
-	    "enforce_eager": "",
-	    "max_num_batched_tokens": 2048,
-	    "max_num_seqs": 256,
-            "load_format": "dummy"
-        },
-        "client_parameters": {
-            "model": "meta-llama/Llama-3.1-8B-Instruct",
-            "backend": "vllm",
-            "dataset_name": "sharegpt",
-            "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
-            "num_prompts": 200
-        }
-    },
     {
         "test_name": "serving_llama8B_bf16_tp2pp3_sharegpt",
         "qps_list": ["inf"],
@@ -265,41 +233,6 @@
             "num_prompts": 1000
         }
     },
-    {
-        "test_name": "serving_llama8B_bf16_tp4_random_128_128",
-        "qps_list": ["inf"],
-        "max_concurrency_list": [12, 16, 24, 32, 64, 128, 200, 1000],
-        "server_environment_variables": {
-            "VLLM_RPC_TIMEOUT": 100000,
-	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
-	    "VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
-	    "VLLM_CPU_SGL_KERNEL": 1,
-	    "VLLM_CPU_KVCACHE_SPACE": 40
-        },
-        "server_parameters": {
-            "model": "meta-llama/Llama-3.1-8B-Instruct",
-            "tensor_parallel_size": 4,
-	    "dtype": "bfloat16",
-	    "distributed_executor_backend": "mp",
-	    "block_size": 128,
-	    "trust_remote_code": "",
-	    "enable_chunked_prefill": "",
-            "disable_log_stats": "",
-	    "enforce_eager": "",
-	    "max_num_batched_tokens": 2048,
-	    "max_num_seqs": 256,
-            "load_format": "dummy"
-        },
-        "client_parameters": {
-            "model": "meta-llama/Llama-3.1-8B-Instruct",
-            "backend": "vllm",
-            "dataset_name": "random",
-	    "random-input-len": 128,
-	    "random-output-len": 128,
-	    "ignore-eos": "",
-            "num_prompts": 1000
-        }
-    },
     {
         "test_name": "serving_llama8B_bf16_tp2pp3_random_128_128",
         "qps_list": ["inf"],
@@ -432,38 +365,6 @@
             "num_prompts": 200
         }
     },
-    {
-        "test_name": "serving_llama8B_int8_tp4_sharegpt",
-        "qps_list": ["inf"],
-        "max_concurrency_list": [12, 16, 24, 32, 64, 128, 200],
-        "server_environment_variables": {
-            "VLLM_RPC_TIMEOUT": 100000,
-	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
-	    "VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
-	    "VLLM_CPU_SGL_KERNEL": 1,
-	    "VLLM_CPU_KVCACHE_SPACE": 40
-        },
-        "server_parameters": {
-            "model": "RedHatAI/Meta-Llama-3.1-8B-Instruct-quantized.w8a8",
-            "tensor_parallel_size": 4,
-	    "dtype": "bfloat16",
-	    "distributed_executor_backend": "mp",
-	    "block_size": 128,
-	    "trust_remote_code": "",
-            "disable_log_stats": "",
-	    "enforce_eager": "",
-	    "max_num_batched_tokens": 2048,
-	    "max_num_seqs": 256,
-            "load_format": "dummy"
-        },
-        "client_parameters": {
-            "model": "RedHatAI/Meta-Llama-3.1-8B-Instruct-quantized.w8a8",
-            "backend": "vllm",
-            "dataset_name": "sharegpt",
-            "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
-            "num_prompts": 200
-        }
-    },
     {
         "test_name": "serving_llama8B_int8_tp2pp3_sharegpt",
         "qps_list": ["inf"],
@@ -602,41 +503,6 @@
             "num_prompts": 1000
         }
     },
-    {
-        "test_name": "serving_llama8B_int8_tp4_random_128_128",
-        "qps_list": ["inf"],
-        "max_concurrency_list": [12, 16, 24, 32, 64, 128, 200, 1000],
-        "server_environment_variables": {
-            "VLLM_RPC_TIMEOUT": 100000,
-	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
-	    "VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
-	    "VLLM_CPU_SGL_KERNEL": 1,
-	    "VLLM_CPU_KVCACHE_SPACE": 40
-        },
-        "server_parameters": {
-            "model": "RedHatAI/Meta-Llama-3.1-8B-Instruct-quantized.w8a8",
-            "tensor_parallel_size": 4,
-	    "dtype": "bfloat16",
-	    "distributed_executor_backend": "mp",
-	    "block_size": 128,
-	    "trust_remote_code": "",
-	    "enable_chunked_prefill": "",
-            "disable_log_stats": "",
-	    "enforce_eager": "",
-	    "max_num_batched_tokens": 2048,
-	    "max_num_seqs": 256,
-            "load_format": "dummy"
-        },
-        "client_parameters": {
-            "model": "RedHatAI/Meta-Llama-3.1-8B-Instruct-quantized.w8a8",
-            "backend": "vllm",
-            "dataset_name": "random",
-	    "random-input-len": 128,
-	    "random-output-len": 128,
-	    "ignore-eos": "",
-            "num_prompts": 1000
-        }
-    },
     {
         "test_name": "serving_llama8B_int8_tp2pp3_random_128_128",
         "qps_list": ["inf"],
@@ -772,39 +638,6 @@
             "num_prompts": 200
         }
     },
-    {
-        "test_name": "serving_llama8B_int4_tp4_sharegpt",
-        "qps_list": ["inf"],
-        "max_concurrency_list": [12, 16, 24, 32, 64, 128, 200],
-        "server_environment_variables": {
-            "VLLM_RPC_TIMEOUT": 100000,
-	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
-	    "VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
-	    "VLLM_CPU_SGL_KERNEL": 1,
-	    "VLLM_CPU_KVCACHE_SPACE": 40
-        },
-        "server_parameters": {
-            "model": "hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4",
-	    "quantization": "awq",
-            "tensor_parallel_size": 4,
-	    "dtype": "bfloat16",
-	    "distributed_executor_backend": "mp",
-	    "block_size": 128,
-	    "trust_remote_code": "",
-            "disable_log_stats": "",
-	    "enforce_eager": "",
-	    "max_num_batched_tokens": 2048,
-	    "max_num_seqs": 256,
-            "load_format": "dummy"
-        },
-        "client_parameters": {
-            "model": "hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4",
-            "backend": "vllm",
-            "dataset_name": "sharegpt",
-            "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
-            "num_prompts": 200
-        }
-    },
     {
         "test_name": "serving_llama8B_int4_tp2pp3_sharegpt",
         "qps_list": ["inf"],
@@ -947,42 +780,6 @@
             "num_prompts": 1000
         }
     },
-    {
-        "test_name": "serving_llama8B_int4_tp4_random_128_128",
-        "qps_list": ["inf"],
-        "max_concurrency_list": [12, 16, 24, 32, 64, 128, 200, 1000],
-        "server_environment_variables": {
-            "VLLM_RPC_TIMEOUT": 100000,
-	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
-	    "VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
-	    "VLLM_CPU_SGL_KERNEL": 1,
-	    "VLLM_CPU_KVCACHE_SPACE": 40
-        },
-        "server_parameters": {
-            "model": "hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4",
-	    "quantization": "awq",
-            "tensor_parallel_size": 4,
-	    "dtype": "bfloat16",
-	    "distributed_executor_backend": "mp",
-	    "block_size": 128,
-	    "trust_remote_code": "",
-	    "enable_chunked_prefill": "",
-            "disable_log_stats": "",
-	    "enforce_eager": "",
-	    "max_num_batched_tokens": 2048,
-	    "max_num_seqs": 256,
-            "load_format": "dummy"
-        },
-        "client_parameters": {
-            "model": "hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4",
-            "backend": "vllm",
-            "dataset_name": "random",
-	    "random-input-len": 128,
-	    "random-output-len": 128,
-	    "ignore-eos": "",
-            "num_prompts": 1000
-        }
-    },
     {
         "test_name": "serving_llama8B_int4_tp2pp3_random_128_128",
         "qps_list": ["inf"],

.buildkite/nightly-benchmarks/tests/serving-tests-cpu.json

@@ -2,7 +2,7 @@
     {
         "test_name": "serving_llama8B_tp1_sharegpt",
         "qps_list": [1, 4, 16, "inf"],
-        "max_concurrency_list": [32],
+        "max_concurrency_list": [12, 16, 24, 32, 64, 128, 200],
         "server_environment_variables": {
             "VLLM_RPC_TIMEOUT": 100000,
 	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
@@ -28,13 +28,13 @@
             "backend": "vllm",
             "dataset_name": "sharegpt",
             "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
-            "num_prompts": 32
+            "num_prompts": 200
         }
     },
     {
         "test_name": "serving_llama8B_tp2_sharegpt",
         "qps_list": [1, 4, 16, "inf"],
-        "max_concurrency_list": [32],
+        "max_concurrency_list": [12, 16, 24, 32, 64, 128, 200],
         "server_environment_variables": {
             "VLLM_RPC_TIMEOUT": 100000,
 	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
@@ -60,13 +60,13 @@
             "backend": "vllm",
             "dataset_name": "sharegpt",
             "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
-            "num_prompts": 32
+            "num_prompts": 200
         }
     },
     {
-        "test_name": "serving_llama8B_tp1_random_128_128",
+        "test_name": "serving_llama8B_tp4_sharegpt",
         "qps_list": [1, 4, 16, "inf"],
-        "max_concurrency_list": [32],
+        "max_concurrency_list": [12, 16, 24, 32, 64, 128, 200],
         "server_environment_variables": {
             "VLLM_RPC_TIMEOUT": 100000,
 	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
@@ -76,7 +76,39 @@
         },
         "server_parameters": {
             "model": "meta-llama/Llama-3.1-8B-Instruct",
-            "tensor_parallel_size": 1,
+            "tensor_parallel_size": 4,
+	    "dtype": "bfloat16",
+	    "distributed_executor_backend": "mp",
+	    "block_size": 128,
+	    "trust_remote_code": "",
+            "disable_log_stats": "",
+	    "enforce_eager": "",
+	    "max_num_batched_tokens": 2048,
+	    "max_num_seqs": 256,
+            "load_format": "dummy"
+        },
+        "client_parameters": {
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
+            "backend": "vllm",
+            "dataset_name": "sharegpt",
+            "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
+            "num_prompts": 200
+        }
+    },
+    {
+        "test_name": "serving_llama8B_tp4_random_1024_128",
+        "qps_list": [1, 4, 16, "inf"],
+        "max_concurrency_list": [12, 16, 24, 32, 64, 128, 200],
+        "server_environment_variables": {
+            "VLLM_RPC_TIMEOUT": 100000,
+	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
+	    "VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
+	    "VLLM_CPU_SGL_KERNEL": 1,
+	    "VLLM_CPU_KVCACHE_SPACE": 40
+        },
+        "server_parameters": {
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
+            "tensor_parallel_size": 4,
 	    "dtype": "bfloat16",
 	    "distributed_executor_backend": "mp",
 	    "block_size": 128,
@@ -92,16 +124,16 @@
             "model": "meta-llama/Llama-3.1-8B-Instruct",
             "backend": "vllm",
             "dataset_name": "random",
-	    "random-input-len": 128,
+	    "random-input-len": 1024,
 	    "random-output-len": 128,
 	    "ignore-eos": "",
-            "num_prompts": 32
+            "num_prompts": 100
         }
     },
     {
-        "test_name": "serving_llama8B_tp2_random_128_128",
+        "test_name": "serving_llama8B_pp6_random_1024_128",
         "qps_list": [1, 4, 16, "inf"],
-        "max_concurrency_list": [32],
+        "max_concurrency_list": [12, 16, 24, 32, 64, 128, 200],
         "server_environment_variables": {
             "VLLM_RPC_TIMEOUT": 100000,
 	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
@@ -111,7 +143,7 @@
         },
         "server_parameters": {
             "model": "meta-llama/Llama-3.1-8B-Instruct",
-            "tensor_parallel_size": 2,
+            "pipeline_parallel_size": 6,
 	    "dtype": "bfloat16",
 	    "distributed_executor_backend": "mp",
 	    "block_size": 128,
@@ -127,150 +159,10 @@
             "model": "meta-llama/Llama-3.1-8B-Instruct",
             "backend": "vllm",
             "dataset_name": "random",
-	    "random-input-len": 128,
+	    "random-input-len": 1024,
 	    "random-output-len": 128,
 	    "ignore-eos": "",
-            "num_prompts": 32
-        }
-    },
-    {
-        "test_name": "serving_llama8B_tp1_random_128_2048",
-        "qps_list": [1, 4, 16, "inf"],
-        "max_concurrency_list": [32],
-        "server_environment_variables": {
-            "VLLM_RPC_TIMEOUT": 100000,
-	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
-	    "VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
-	    "VLLM_CPU_SGL_KERNEL": 1,
-	    "VLLM_CPU_KVCACHE_SPACE": 40
-        },
-        "server_parameters": {
-            "model": "meta-llama/Llama-3.1-8B-Instruct",
-            "tensor_parallel_size": 1,
-	    "dtype": "bfloat16",
-	    "distributed_executor_backend": "mp",
-	    "block_size": 128,
-	    "trust_remote_code": "",
-	    "enable_chunked_prefill": "",
-            "disable_log_stats": "",
-	    "enforce_eager": "",
-	    "max_num_batched_tokens": 2048,
-	    "max_num_seqs": 256,
-            "load_format": "dummy"
-        },
-        "client_parameters": {
-            "model": "meta-llama/Llama-3.1-8B-Instruct",
-            "backend": "vllm",
-            "dataset_name": "random",
-	    "random-input-len": 128,
-	    "random-output-len": 2048,
-	    "ignore-eos": "",
-            "num_prompts": 32
-        }
-    },
-    {
-        "test_name": "serving_llama8B_tp2_random_128_2048",
-        "qps_list": [1, 4, 16, "inf"],
-        "max_concurrency_list": [32],
-        "server_environment_variables": {
-            "VLLM_RPC_TIMEOUT": 100000,
-	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
-	    "VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
-	    "VLLM_CPU_SGL_KERNEL": 1,
-	    "VLLM_CPU_KVCACHE_SPACE": 40
-        },
-        "server_parameters": {
-            "model": "meta-llama/Llama-3.1-8B-Instruct",
-            "tensor_parallel_size": 2,
-	    "dtype": "bfloat16",
-	    "distributed_executor_backend": "mp",
-	    "block_size": 128,
-	    "trust_remote_code": "",
-	    "enable_chunked_prefill": "",
-            "disable_log_stats": "",
-	    "enforce_eager": "",
-	    "max_num_batched_tokens": 2048,
-	    "max_num_seqs": 256,
-            "load_format": "dummy"
-        },
-        "client_parameters": {
-            "model": "meta-llama/Llama-3.1-8B-Instruct",
-            "backend": "vllm",
-            "dataset_name": "random",
-	    "random-input-len": 128,
-	    "random-output-len": 2048,
-	    "ignore-eos": "",
-            "num_prompts": 32
-        }
-    },
-    {
-        "test_name": "serving_llama8B_tp1_random_2048_128",
-        "qps_list": [1, 4, 16, "inf"],
-        "max_concurrency_list": [32],
-        "server_environment_variables": {
-            "VLLM_RPC_TIMEOUT": 100000,
-	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
-	    "VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
-	    "VLLM_CPU_SGL_KERNEL": 1,
-	    "VLLM_CPU_KVCACHE_SPACE": 40
-        },
-        "server_parameters": {
-            "model": "meta-llama/Llama-3.1-8B-Instruct",
-            "tensor_parallel_size": 1,
-	    "dtype": "bfloat16",
-	    "distributed_executor_backend": "mp",
-	    "block_size": 128,
-	    "trust_remote_code": "",
-	    "enable_chunked_prefill": "",
-            "disable_log_stats": "",
-	    "enforce_eager": "",
-	    "max_num_batched_tokens": 2048,
-	    "max_num_seqs": 256,
-            "load_format": "dummy"
-        },
-        "client_parameters": {
-            "model": "meta-llama/Llama-3.1-8B-Instruct",
-            "backend": "vllm",
-            "dataset_name": "random",
-	    "random-input-len": 2048,
-	    "random-output-len": 128,
-	    "ignore-eos": "",
-            "num_prompts": 32
-        }
-    },
-    {
-        "test_name": "serving_llama8B_tp2_random_2048_128",
-        "qps_list": [1, 4, 16, "inf"],
-        "max_concurrency_list": [32],
-        "server_environment_variables": {
-            "VLLM_RPC_TIMEOUT": 100000,
-	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
-	    "VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
-	    "VLLM_CPU_SGL_KERNEL": 1,
-	    "VLLM_CPU_KVCACHE_SPACE": 40
-        },
-        "server_parameters": {
-            "model": "meta-llama/Llama-3.1-8B-Instruct",
-            "tensor_parallel_size": 2,
-	    "dtype": "bfloat16",
-	    "distributed_executor_backend": "mp",
-	    "block_size": 128,
-	    "trust_remote_code": "",
-	    "enable_chunked_prefill": "",
-            "disable_log_stats": "",
-	    "enforce_eager": "",
-	    "max_num_batched_tokens": 2048,
-	    "max_num_seqs": 256,
-            "load_format": "dummy"
-        },
-        "client_parameters": {
-            "model": "meta-llama/Llama-3.1-8B-Instruct",
-            "backend": "vllm",
-            "dataset_name": "random",
-	    "random-input-len": 2048,
-	    "random-output-len": 128,
-	    "ignore-eos": "",
-            "num_prompts": 32
+            "num_prompts": 100
         }
     }
 ]

.buildkite/nightly-benchmarks/tests/throughput-tests-cpu.json

@@ -0,0 +1,32 @@
+[
+    {
+        "test_name": "throughput_llama8B_tp1",
+        "environment_variables": {
+	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
+	    "VLLM_CPU_KVCACHE_SPACE": 40
+        },
+        "parameters": {
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
+            "tensor_parallel_size": 1,
+            "load_format": "dummy",
+            "dataset": "./ShareGPT_V3_unfiltered_cleaned_split.json",
+            "num_prompts": 200,
+            "backend": "vllm"
+        }
+    },
+    {
+        "test_name": "throughput_llama8B_tp4",
+        "environment_variables": {
+	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
+	    "VLLM_CPU_KVCACHE_SPACE": 40
+        },
+        "parameters": {
+            "model": "meta-llama/Llama-3.1-8B-Instruct",
+            "tensor_parallel_size": 4,
+            "load_format": "dummy",
+            "dataset": "./ShareGPT_V3_unfiltered_cleaned_split.json",
+            "num_prompts": 200,
+            "backend": "vllm"
+        }
+    }
+]

.buildkite/performance-benchmarks/tests/latency-tests-cpu.json

@@ -1,26 +0,0 @@
-[
-    {
-        "test_name": "latency_llama8B_tp2",
-        "environment_variables": {
-            "VLLM_RPC_TIMEOUT": 100000,
-	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
-	    "VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
-	    "VLLM_CPU_SGL_KERNEL": 1,
-	    "VLLM_CPU_KVCACHE_SPACE": 40
-        },
-        "parameters": {
-            "model": "meta-llama/Llama-3.1-8B-Instruct",
-            "tensor_parallel_size": 2,
-	    "dtype": "bfloat16",
-	    "distributed_executor_backend": "mp",
-	    "block_size": 128,
-	    "trust_remote_code": "",
-            "disable_log_stats": "",
-	    "enforce_eager": "",
-	    "max_num_batched_tokens": 2048,
-	    "max_num_seqs": 256,
-            "num_iters_warmup": 5,
-            "num_iters": 15
-        }
-    }
-]

.buildkite/performance-benchmarks/tests/latency-tests-hpu.json

@@ -1,55 +0,0 @@
-[
-    {
-        "test_name": "latency_llama8B_tp1",
-        "environment_variables": {
-            "PT_HPU_LAZY_MODE": 1,
-            "VLLM_CONTIGUOUS_PA": 1,
-            "VLLM_DEFRAG": 1
-        },
-        "parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
-            "tensor_parallel_size": 1,
-            "load_format": "dummy",
-            "num-iters-warmup": 5,
-            "num-iters": 15,
-            "max-model-len": 256,
-            "async-scheduling": ""
-        }
-    },
-    {
-        "test_name": "latency_llama70B_tp4",
-        "environment_variables": {
-            "PT_HPU_LAZY_MODE": 1,
-            "PT_HPU_ENABLE_LAZY_COLLECTIVES": 1,
-            "VLLM_CONTIGUOUS_PA": 1,
-            "VLLM_DEFRAG": 1
-        },
-        "parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-70B-Instruct",
-            "tensor_parallel_size": 4,
-            "load_format": "dummy",
-            "num-iters-warmup": 5,
-            "num-iters": 15,
-            "max-model-len": 256,
-            "async-scheduling": ""
-        }
-    },
-    {
-        "test_name": "latency_mixtral8x7B_tp2",
-        "environment_variables": {
-            "PT_HPU_LAZY_MODE": 1,
-            "PT_HPU_ENABLE_LAZY_COLLECTIVES": 1,
-            "VLLM_CONTIGUOUS_PA": 1,
-            "VLLM_DEFRAG": 1
-        },
-        "parameters": {
-            "model": "mistralai/Mixtral-8x7B-Instruct-v0.1",
-            "tensor_parallel_size": 2,
-            "load_format": "dummy",
-            "num-iters-warmup": 5,
-            "num-iters": 15,
-            "max-model-len": 256,
-            "async-scheduling": ""
-        }
-    }
-]

.buildkite/performance-benchmarks/tests/serving-tests-hpu.json

@@ -1,82 +0,0 @@
-[
-    {
-        "test_name": "serving_llama8B_tp1_sharegpt",
-        "qps_list": [1, 4, 16, "inf"],
-        "server_environment_variables": {
-            "PT_HPU_LAZY_MODE": 1,
-            "VLLM_CONTIGUOUS_PA": 1,
-            "VLLM_DEFRAG": 1
-        },
-        "server_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
-            "tensor_parallel_size": 1,
-            "swap_space": 16,
-            "disable_log_stats": "",
-            "load_format": "dummy",
-            "max-model-len": 2048,
-            "max-num-seqs": 256,
-            "async-scheduling": ""
-        },
-        "client_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
-            "backend": "vllm",
-            "dataset_name": "sharegpt",
-            "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
-            "num_prompts": 200
-        }
-    },
-    {
-        "test_name": "serving_llama70B_tp4_sharegpt",
-        "qps_list": [1, 4, 16, "inf"],
-        "server_environment_variables": {
-            "PT_HPU_LAZY_MODE": 1,
-            "PT_HPU_ENABLE_LAZY_COLLECTIVES": 1,
-            "VLLM_CONTIGUOUS_PA": 1,
-            "VLLM_DEFRAG": 1
-        },
-        "server_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-70B-Instruct",
-            "tensor_parallel_size": 4,
-            "swap_space": 16,
-            "disable_log_stats": "",
-            "load_format": "dummy",
-            "max-model-len": 2048,
-            "max-num-seqs": 256,
-            "async-scheduling": ""
-        },
-        "client_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-70B-Instruct",
-            "backend": "vllm",
-            "dataset_name": "sharegpt",
-            "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
-            "num_prompts": 200
-        }
-    },
-    {
-        "test_name": "serving_mixtral8x7B_tp2_sharegpt",
-        "qps_list": [1, 4, 16, "inf"],
-        "server_environment_variables": {
-            "PT_HPU_LAZY_MODE": 1,
-            "PT_HPU_ENABLE_LAZY_COLLECTIVES": 1,
-            "VLLM_CONTIGUOUS_PA": 1,
-            "VLLM_DEFRAG": 1
-        },
-        "server_parameters": {
-            "model": "mistralai/Mixtral-8x7B-Instruct-v0.1",
-            "tensor_parallel_size": 2,
-            "swap_space": 16,
-            "disable_log_stats": "",
-            "load_format": "dummy",
-            "max-model-len": 2048,
-            "max-num-seqs": 256,
-            "async-scheduling": ""
-        },
-        "client_parameters": {
-            "model": "mistralai/Mixtral-8x7B-Instruct-v0.1",
-            "backend": "vllm",
-            "dataset_name": "sharegpt",
-            "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
-            "num_prompts": 200
-        }
-    }
-]

.buildkite/performance-benchmarks/tests/throughput-tests-cpu.json

@@ -1,27 +0,0 @@
-[
-    {
-        "test_name": "throughput_llama8B_tp2",
-        "environment_variables": {
-            "VLLM_RPC_TIMEOUT": 100000,
-	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
-	    "VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
-	    "VLLM_CPU_SGL_KERNEL": 1,
-	    "VLLM_CPU_KVCACHE_SPACE": 40
-        },
-        "parameters": {
-            "model": "meta-llama/Llama-3.1-8B-Instruct",
-            "tensor_parallel_size": 2,
-	    "dtype": "bfloat16",
-	    "distributed_executor_backend": "mp",
-	    "block_size": 128,
-	    "trust_remote_code": "",
-            "disable_log_stats": "",
-	    "enforce_eager": "",
-	    "max_num_batched_tokens": 2048,
-	    "max_num_seqs": 256,
-            "dataset": "./ShareGPT_V3_unfiltered_cleaned_split.json",
-            "num_prompts": 200,
-            "backend": "vllm"
-        }
-    }
-]

.buildkite/performance-benchmarks/tests/throughput-tests-hpu.json

@@ -1,61 +0,0 @@
-[
-    {
-        "test_name": "throughput_llama8B_tp1",
-        "environment_variables": {
-            "PT_HPU_LAZY_MODE": 1,
-            "VLLM_CONTIGUOUS_PA": 1,
-            "VLLM_DEFRAG": 1
-        },
-        "parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
-            "tensor_parallel_size": 1,
-            "load_format": "dummy",
-            "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
-            "num_prompts": 1000,
-            "backend": "vllm",
-            "max-model-len": 2048,
-            "max-num-seqs": 512,
-            "async-scheduling": ""
-        }
-    },
-    {
-        "test_name": "throughput_llama70B_tp4",
-        "environment_variables": {
-            "PT_HPU_LAZY_MODE": 1,
-            "PT_HPU_ENABLE_LAZY_COLLECTIVES": 1,
-            "VLLM_CONTIGUOUS_PA": 1,
-            "VLLM_DEFRAG": 1
-        },
-        "parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-70B-Instruct",
-            "tensor_parallel_size": 4,
-            "load_format": "dummy",
-            "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
-            "num_prompts": 1000,
-            "backend": "vllm",
-            "max-model-len": 2048,
-            "max-num-seqs": 512,
-            "async-scheduling": ""
-        }
-    },
-    {
-        "test_name": "throughput_mixtral8x7B_tp2",
-        "environment_variables": {
-            "PT_HPU_LAZY_MODE": 1,
-            "PT_HPU_ENABLE_LAZY_COLLECTIVES": 1,
-            "VLLM_CONTIGUOUS_PA": 1,
-            "VLLM_DEFRAG": 1
-        },
-        "parameters": {
-            "model": "mistralai/Mixtral-8x7B-Instruct-v0.1",
-            "tensor_parallel_size": 2,
-            "load_format": "dummy",
-            "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
-            "num_prompts": 1000,
-            "backend": "vllm",
-            "max-model-len": 2048,
-            "max-num-seqs": 512,
-            "async-scheduling": ""
-        }
-    }
-]

.buildkite/release-pipeline.yaml

@@ -1,5 +1,5 @@
 steps:
-  # aarch64 + CUDA builds
+  # aarch64 + CUDA builds. PyTorch 2.8 aarch64 + CUDA wheel is only available on CUDA 12.9
   - label: "Build arm64 wheel - CUDA 12.9"
     depends_on: ~
     id: build-wheel-arm64-cuda-12-9
@@ -15,21 +15,6 @@ steps:
     env:
       DOCKER_BUILDKIT: "1"
 
-  # aarch64 build
-  - label: "Build arm64 CPU wheel"
-    depends_on: ~
-    id: build-wheel-arm64-cpu
-    agents:
-      queue: arm64_cpu_queue_postmerge
-    commands:
-      - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg GIT_REPO_CHECK=1 --build-arg VLLM_BUILD_ACL=ON --tag vllm-ci:build-image --target vllm-build --progress plain -f docker/Dockerfile.cpu ."
-      - "mkdir artifacts"
-      - "docker run --rm -v $(pwd)/artifacts:/artifacts_host vllm-ci:build-image bash -c 'cp -r dist /artifacts_host && chmod -R a+rw /artifacts_host'"
-      - "bash .buildkite/scripts/upload-wheels.sh"
-    env:
-      DOCKER_BUILDKIT: "1"
-
-  # x86 + CUDA builds
   - label: "Build wheel - CUDA 12.8"
     depends_on: ~
     id: build-wheel-cuda-12-8
@@ -43,6 +28,20 @@ steps:
     env:
       DOCKER_BUILDKIT: "1"
 
+  - label: "Build wheel - CUDA 12.6"
+    depends_on: ~
+    id: build-wheel-cuda-12-6
+    agents:
+      queue: cpu_queue_postmerge
+    commands:
+      - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=12.6.3 --build-arg torch_cuda_arch_list='7.0 7.5 8.0 8.9 9.0+PTX' --tag vllm-ci:build-image --target build --progress plain -f docker/Dockerfile ."
+      - "mkdir artifacts"
+      - "docker run --rm -v $(pwd)/artifacts:/artifacts_host vllm-ci:build-image bash -c 'cp -r dist /artifacts_host && chmod -R a+rw /artifacts_host'"
+      - "bash .buildkite/scripts/upload-wheels.sh"
+    env:
+      DOCKER_BUILDKIT: "1"
+
+  # x86 + CUDA builds
   - label: "Build wheel - CUDA 12.9"
     depends_on: ~
     id: build-wheel-cuda-12-9
@@ -56,20 +55,6 @@ steps:
     env:
       DOCKER_BUILDKIT: "1"
 
-  - label: "Build wheel - CUDA 13.0"
-    depends_on: ~
-    id: build-wheel-cuda-13-0
-    agents:
-      queue: cpu_queue_postmerge
-    commands:
-      - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=13.0.1 --build-arg BUILD_BASE_IMAGE=nvidia/cuda:13.0.1-devel-ubuntu22.04 --tag vllm-ci:build-image --target build --progress plain -f docker/Dockerfile ."
-      - "mkdir artifacts"
-      - "docker run --rm -v $(pwd)/artifacts:/artifacts_host vllm-ci:build-image bash -c 'cp -r dist /artifacts_host && chmod -R a+rw /artifacts_host'"
-      - "bash .buildkite/scripts/upload-wheels.sh"
-    env:
-      DOCKER_BUILDKIT: "1"
-
-  # Build release images (12.9)
   - label: "Build release image (x86)"
     depends_on: ~
     id: build-release-image-x86
@@ -77,12 +62,13 @@ steps:
       queue: cpu_queue_postmerge
     commands:
       - "aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7"
-      - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=12.9.1 --build-arg FLASHINFER_AOT_COMPILE=true --build-arg INSTALL_KV_CONNECTORS=true --tag public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m) --target vllm-openai --progress plain -f docker/Dockerfile ."
+      - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=12.8.1 --build-arg FLASHINFER_AOT_COMPILE=true --build-arg INSTALL_KV_CONNECTORS=true --tag public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m) --target vllm-openai --progress plain -f docker/Dockerfile ."
       - "docker push public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m)"
       # re-tag to default image tag and push, just in case arm64 build fails
       - "docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m) public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT"
       - "docker push public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT"
 
+  # PyTorch 2.8 aarch64 + CUDA wheel is only available on CUDA 12.9
   - label: "Build release image (arm64)"
     depends_on: ~
     id: build-release-image-arm64
@@ -116,6 +102,24 @@ steps:
     commands:
       - "bash .buildkite/scripts/annotate-release.sh"
 
+  - label: "Build and publish TPU release image"
+    depends_on: ~
+    if: build.env("NIGHTLY") == "1"
+    agents:
+      queue: tpu_queue_postmerge
+    commands:
+      - "yes | docker system prune -a"
+      - "git fetch --all"
+      - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --tag vllm/vllm-tpu:nightly --tag vllm/vllm-tpu:$BUILDKITE_COMMIT --progress plain -f docker/Dockerfile.tpu ."
+      - "docker push vllm/vllm-tpu:nightly"
+      - "docker push vllm/vllm-tpu:$BUILDKITE_COMMIT"
+    plugins:
+      - docker-login#v3.0.0:
+          username: vllmbot
+          password-env: DOCKERHUB_TOKEN
+    env:
+      DOCKER_BUILDKIT: "1"
+
   - input: "Provide Release version here"
     id: input-release-version
     fields:
@@ -138,22 +142,6 @@ steps:
     env:
       DOCKER_BUILDKIT: "1"
 
-  - block: "Build arm64 CPU release image"
-    key: block-arm64-cpu-release-image-build
-    depends_on: ~
-
-  - label: "Build and publish arm64 CPU release image"
-    depends_on: block-arm64-cpu-release-image-build
-    agents:
-      queue: arm64_cpu_queue_postmerge
-    commands:
-      - "aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7"
-      - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg GIT_REPO_CHECK=1 --tag public.ecr.aws/q9t5s3a7/vllm-arm64-cpu-release-repo:$(buildkite-agent meta-data get release-version) --tag public.ecr.aws/q9t5s3a7/vllm-arm64-cpu-release-repo:latest --progress plain --target vllm-openai -f docker/Dockerfile.cpu ."
-      - "docker push public.ecr.aws/q9t5s3a7/vllm-arm64-cpu-release-repo:latest"
-      - "docker push public.ecr.aws/q9t5s3a7/vllm-arm64-cpu-release-repo:$(buildkite-agent meta-data get release-version)"
-    env:
-      DOCKER_BUILDKIT: "1"
-
   - label: "Build and publish nightly multi-arch image to DockerHub"
     depends_on:
       - create-multi-arch-manifest

.buildkite/scripts/annotate-release.sh

@@ -2,23 +2,16 @@
 
 set -ex
 
-# Get release version, default to 1.0.0.dev for nightly/per-commit builds
-RELEASE_VERSION=$(buildkite-agent meta-data get release-version 2>/dev/null | sed 's/^v//')
-if [ -z "${RELEASE_VERSION}" ]; then
-  RELEASE_VERSION="1.0.0.dev"
+# Get release version and strip leading 'v' if present
+RELEASE_VERSION=$(buildkite-agent meta-data get release-version | sed 's/^v//')
+
+if [ -z "$RELEASE_VERSION" ]; then
+  echo "Error: RELEASE_VERSION is empty. 'release-version' metadata might not be set or is invalid."
+  exit 1
 fi
 
 buildkite-agent annotate --style 'info' --context 'release-workflow' << EOF
-To download the wheel (by commit):
-\`\`\`
-aws s3 cp s3://vllm-wheels/${BUILDKITE_COMMIT}/vllm-${RELEASE_VERSION}-cp38-abi3-manylinux1_x86_64.whl .
-aws s3 cp s3://vllm-wheels/${BUILDKITE_COMMIT}/vllm-${RELEASE_VERSION}-cp38-abi3-manylinux2014_aarch64.whl .
-
-aws s3 cp s3://vllm-wheels/${BUILDKITE_COMMIT}/vllm-${RELEASE_VERSION}+cu129-cp38-abi3-manylinux1_x86_64.whl .
-aws s3 cp s3://vllm-wheels/${BUILDKITE_COMMIT}/vllm-${RELEASE_VERSION}+cu129-cp38-abi3-manylinux1_x86_64.whl .
-\`\`\`
-
-To download the wheel (by version):
+To download the wheel:
 \`\`\`
 aws s3 cp s3://vllm-wheels/${RELEASE_VERSION}/vllm-${RELEASE_VERSION}-cp38-abi3-manylinux1_x86_64.whl .
 aws s3 cp s3://vllm-wheels/${RELEASE_VERSION}/vllm-${RELEASE_VERSION}-cp38-abi3-manylinux2014_aarch64.whl .

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

@@ -173,14 +173,6 @@ fi
 PARALLEL_JOB_COUNT=8
 MYPYTHONPATH=".."
 
-# Test that we're launching on the machine that has
-# proper access to GPUs
-render_gid=$(getent group render | cut -d: -f3)
-if [[ -z "$render_gid" ]]; then
-  echo "Error: 'render' group not found. This is required for GPU access." >&2
-  exit 1
-fi
-
 # check if the command contains shard flag, we will run all shards in parallel because the host have 8 GPUs. 
 if [[ $commands == *"--shard-id="* ]]; then
   # assign job count as the number of shards used   
@@ -194,7 +186,6 @@ if [[ $commands == *"--shard-id="* ]]; then
         --device /dev/kfd $BUILDKITE_AGENT_META_DATA_RENDER_DEVICES \
         --network=host \
         --shm-size=16gb \
-        --group-add "$render_gid" \
         --rm \
         -e HIP_VISIBLE_DEVICES="${GPU}" \
         -e HF_TOKEN \
@@ -226,8 +217,8 @@ else
           --device /dev/kfd $BUILDKITE_AGENT_META_DATA_RENDER_DEVICES \
           --network=host \
           --shm-size=16gb \
-          --group-add "$render_gid" \
           --rm \
+          -e HIP_VISIBLE_DEVICES=0 \
           -e HF_TOKEN \
           -e AWS_ACCESS_KEY_ID \
           -e AWS_SECRET_ACCESS_KEY \

.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"
+    tests/quantization/test_compressed_tensors.py::test_compressed_tensors_w8a8_logprobs[False-10-32-neuralmagic/Llama-3.2-1B-quantized.w8a8]"
 
   # Note: disable it until supports V1
   # Run AWQ test

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

@@ -20,10 +20,7 @@ trap remove_docker_container EXIT
 
 # Run the image and test offline inference/tensor parallel
 docker run \
-    --device /dev/dri:/dev/dri \
-    --net=host \
-    --ipc=host \
-    --privileged \
+    --device /dev/dri \
     -v /dev/dri/by-path:/dev/dri/by-path \
     --entrypoint="" \
     -e "HF_TOKEN=${HF_TOKEN}" \
@@ -45,7 +42,7 @@ docker run \
     pytest -v -s v1/sample --ignore=v1/sample/test_logprobs.py --ignore=v1/sample/test_logprobs_e2e.py
     pytest -v -s v1/worker --ignore=v1/worker/test_gpu_model_runner.py
     pytest -v -s v1/structured_output
-    pytest -v -s v1/spec_decode --ignore=v1/spec_decode/test_max_len.py --ignore=v1/spec_decode/test_tree_attention.py --ignore=v1/spec_decode/test_speculators_eagle3.py
+    pytest -v -s v1/spec_decode --ignore=v1/spec_decode/test_max_len.py --ignore=v1/spec_decode/test_tree_attention.py
     pytest -v -s v1/kv_connector/unit --ignore=v1/kv_connector/unit/test_multi_connector.py --ignore=v1/kv_connector/unit/test_nixl_connector.py --ignore=v1/kv_connector/unit/test_shared_storage_connector.py
     pytest -v -s v1/test_serial_utils.py
 '

.buildkite/scripts/scheduled_integration_test/deepseek_v2_lite_ep_eplb.sh

@@ -1,62 +0,0 @@
-#!/usr/bin/env bash
-set -euxo pipefail
-
-# args: [THRESHOLD] [NUM_QUESTIONS] [START_PORT]
-THRESHOLD=${1:-0.25}
-NUM_Q=${2:-1319}
-PORT=${3:-8010}
-OUT_DIR=${OUT_DIR:-/tmp/vllm-scheduled}
-mkdir -p "${OUT_DIR}"
-
-wait_for_server() {
-  local port=$1
-  timeout 600 bash -c '
-    until curl -sf "http://127.0.0.1:'"$port"'/health" > /dev/null; do
-      sleep 1
-    done'
-}
-
-MODEL="deepseek-ai/DeepSeek-V2-lite"
-BACKENDS=("deepep_high_throughput" "deepep_low_latency")
-
-cleanup() {
-  if [[ -n "${SERVER_PID:-}" ]] && kill -0 "${SERVER_PID}" 2>/dev/null; then
-    kill "${SERVER_PID}" 2>/dev/null || true
-    for _ in {1..20}; do
-      kill -0 "${SERVER_PID}" 2>/dev/null || break
-      sleep 0.5
-    done
-    kill -9 "${SERVER_PID}" 2>/dev/null || true
-  fi
-}
-trap cleanup EXIT
-
-for BACK in "${BACKENDS[@]}"; do
-  VLLM_DEEP_GEMM_WARMUP=skip \
-  VLLM_ALL2ALL_BACKEND=$BACK \
-  vllm serve "$MODEL" \
-    --enforce-eager \
-    --tensor-parallel-size 2 \
-    --data-parallel-size 2 \
-    --enable-expert-parallel \
-    --enable-eplb \
-    --trust-remote-code \
-    --max-model-len 2048 \
-    --port $PORT &
-  SERVER_PID=$!
-  wait_for_server $PORT
-
-  TAG=$(echo "$MODEL" | tr '/: \\n' '_____')
-  OUT="${OUT_DIR}/${TAG}_${BACK}.json"
-  python3 tests/evals/gsm8k/gsm8k_eval.py --host http://127.0.0.1 --port $PORT --num-questions ${NUM_Q} --save-results ${OUT}
-  python3 - <<PY
-import json; acc=json.load(open('${OUT}'))['accuracy']
-print(f"${MODEL} ${BACK}: accuracy {acc:.3f}")
-assert acc >= ${THRESHOLD}, f"${MODEL} ${BACK} accuracy {acc}"
-PY
-
-  cleanup
-  SERVER_PID=
-  sleep 1
-  PORT=$((PORT+1))
-done

.buildkite/scripts/scheduled_integration_test/qwen30b_a3b_fp8_block_ep.sh

@@ -1,61 +0,0 @@
-#!/usr/bin/env bash
-set -euxo pipefail
-
-# args: [THRESHOLD] [NUM_QUESTIONS] [START_PORT]
-THRESHOLD=${1:-0.8}
-NUM_Q=${2:-1319}
-PORT=${3:-8020}
-OUT_DIR=${OUT_DIR:-/tmp/vllm-scheduled}
-mkdir -p "${OUT_DIR}"
-
-wait_for_server() {
-  local port=$1
-  timeout 600 bash -c '
-    until curl -sf "http://127.0.0.1:'"$port"'/health" > /dev/null; do
-      sleep 1
-    done'
-}
-
-MODEL="QWen/Qwen3-30B-A3B-FP8"
-BACKENDS=("deepep_high_throughput" "deepep_low_latency")
-
-cleanup() {
-  if [[ -n "${SERVER_PID:-}" ]] && kill -0 "${SERVER_PID}" 2>/dev/null; then
-    kill "${SERVER_PID}" 2>/dev/null || true
-    for _ in {1..20}; do
-      kill -0 "${SERVER_PID}" 2>/dev/null || break
-      sleep 0.5
-    done
-    kill -9 "${SERVER_PID}" 2>/dev/null || true
-  fi
-}
-trap cleanup EXIT
-
-for BACK in "${BACKENDS[@]}"; do
-  VLLM_DEEP_GEMM_WARMUP=skip \
-  VLLM_ALL2ALL_BACKEND=$BACK \
-  vllm serve "$MODEL" \
-    --enforce-eager \
-    --tensor-parallel-size 2 \
-    --data-parallel-size 2 \
-    --enable-expert-parallel \
-    --trust-remote-code \
-    --max-model-len 2048 \
-    --port $PORT &
-  SERVER_PID=$!
-  wait_for_server $PORT
-
-  TAG=$(echo "$MODEL" | tr '/: \\n' '_____')
-  OUT="${OUT_DIR}/${TAG}_${BACK}.json"
-  python3 tests/evals/gsm8k/gsm8k_eval.py --host http://127.0.0.1 --port $PORT --num-questions ${NUM_Q} --save-results ${OUT}
-  python3 - <<PY
-import json; acc=json.load(open('${OUT}'))['accuracy']
-print(f"${MODEL} ${BACK}: accuracy {acc:.3f}")
-assert acc >= ${THRESHOLD}, f"${MODEL} ${BACK} accuracy {acc}"
-PY
-
-  cleanup
-  SERVER_PID=
-  sleep 1
-  PORT=$((PORT+1))
-done

.buildkite/scripts/upload-wheels.sh

@@ -58,25 +58,33 @@ python3 .buildkite/generate_index.py --wheel "$normal_wheel"
 aws s3 cp "$wheel" "s3://vllm-wheels/$BUILDKITE_COMMIT/"
 aws s3 cp "$normal_wheel" "s3://vllm-wheels/$BUILDKITE_COMMIT/"
 
-if [[ $normal_wheel == *"cu129"* ]]; then
+if [[ $normal_wheel == *"cu126"* ]]; then
+    # if $normal_wheel matches cu126, do not upload the index.html
+    echo "Skipping index files for cu126 wheels"
+elif [[ $normal_wheel == *"cu128"* ]]; then
+    # if $normal_wheel matches cu128, do not upload the index.html
+    echo "Skipping index files for cu128 wheels"
+else
     # only upload index.html for cu129 wheels (default wheels) as it
     # is available on both x86 and arm64
     aws s3 cp index.html "s3://vllm-wheels/$BUILDKITE_COMMIT/vllm/index.html"
     aws s3 cp "s3://vllm-wheels/nightly/index.html" "s3://vllm-wheels/$BUILDKITE_COMMIT/index.html"
-else
-    echo "Skipping index files for non-cu129 wheels"
 fi
 
 # generate index for nightly
 aws s3 cp "$wheel" "s3://vllm-wheels/nightly/"
 aws s3 cp "$normal_wheel" "s3://vllm-wheels/nightly/"
 
-if [[ $normal_wheel == *"cu129"* ]]; then
+if [[ $normal_wheel == *"cu126"* ]]; then
+    # if $normal_wheel matches cu126, do not upload the index.html
+    echo "Skipping index files for cu126 wheels"
+elif [[ $normal_wheel == *"cu128"* ]]; then
+    # if $normal_wheel matches cu128, do not upload the index.html
+    echo "Skipping index files for cu128 wheels"
+else
     # only upload index.html for cu129 wheels (default wheels) as it
     # is available on both x86 and arm64
     aws s3 cp index.html "s3://vllm-wheels/nightly/vllm/index.html"
-else
-    echo "Skipping index files for non-cu129 wheels"
 fi
 
 aws s3 cp "$wheel" "s3://vllm-wheels/$version/"

.buildkite/test-amd.yaml

@@ -38,7 +38,7 @@ steps:
 - label: Pytorch Nightly Dependency Override Check # 2min
   # if this test fails, it means the nightly torch version is not compatible with some
   # of the dependencies. Please check the error message and add the package to whitelist
-  # in /vllm/tools/pre_commit/generate_nightly_torch_test.py
+  # in /vllm/tools/generate_nightly_torch_test.py
   mirror_hardwares: [amdexperimental]
   agent_pool: mi325_1
   # grade: Blocking
@@ -48,9 +48,9 @@ steps:
   commands:
   - bash standalone_tests/pytorch_nightly_dependency.sh
 
-- label: Async Engine, Inputs, Utils, Worker Test # 10min
-  timeout_in_minutes: 15
-  mirror_hardwares: [amdexperimental, amdproduction]
+- label: Async Engine, Inputs, Utils, Worker Test # 36min
+  timeout_in_minutes: 50
+  mirror_hardwares: [amdexperimental]
   agent_pool: mi325_1
   # grade: Blocking
   source_file_dependencies:
@@ -286,7 +286,7 @@ steps:
 
 - label: Engine Test # 25min
   timeout_in_minutes: 40
-  mirror_hardwares: [amdexperimental, amdproduction]
+  mirror_hardwares: [amdexperimental]
   agent_pool: mi325_1
   #grade: Blocking
   source_file_dependencies:
@@ -318,7 +318,7 @@ steps:
 
 - label: V1 Test entrypoints # 35min
   timeout_in_minutes: 50
-  mirror_hardwares: [amdexperimental, amdproduction]
+  mirror_hardwares: [amdexperimental]
   agent_pool: mi325_1
   # grade: Blocking
   source_file_dependencies:
@@ -344,7 +344,7 @@ steps:
     - pytest -v -s v1/logits_processors
     - pytest -v -s v1/worker
     - pytest -v -s v1/spec_decode
-    - pytest -v -s -m 'not cpu_test' v1/kv_connector/unit --ignore=v1/kv_connector/unit/test_lmcache_integration.py
+    - pytest -v -s -m 'not cpu_test' v1/kv_connector/unit
     - pytest -v -s -m 'not cpu_test' v1/metrics
     - pytest -v -s v1/test_oracle.py
     - pytest -v -s v1/test_request.py
@@ -395,9 +395,7 @@ steps:
     - python3 offline_inference/basic/embed.py
     - python3 offline_inference/basic/score.py
     - python3 offline_inference/spec_decode.py --test --method eagle --num_spec_tokens 3 --dataset-name hf --dataset-path philschmid/mt-bench --num-prompts 80 --temp 0 --top-p 1.0 --top-k -1 --tp 1 --enable-chunked-prefill --max-model-len 2048
-    # https://github.com/vllm-project/vllm/pull/26682 uses slightly more memory in PyTorch 2.9+ causing this test to OOM in 1xL4 GPU
-    - python3 offline_inference/spec_decode.py --test --method eagle3 --num_spec_tokens 3 --dataset-name hf --dataset-path philschmid/mt-bench --num-prompts 80 --temp 0 --top-p 1.0 --top-k -1 --tp 1 --enable-chunked-prefill --max-model-len 1536
-    #- python3 offline_inference/spec_decode.py --test --method eagle3 --num_spec_tokens 3 --dataset-name hf --dataset-path philschmid/mt-bench --num-prompts 80 --temp 0 --top-p 1.0 --top-k -1 --tp 1 --enable-chunked-prefill --max-model-len 2048
+    - python3 offline_inference/spec_decode.py --test --method eagle3 --num_spec_tokens 3 --dataset-name hf --dataset-path philschmid/mt-bench --num-prompts 80 --temp 0 --top-p 1.0 --top-k -1 --tp 1 --enable-chunked-prefill --max-model-len 2048
 
 - label: Platform Tests (CUDA) # 4min
   timeout_in_minutes: 15
@@ -438,11 +436,7 @@ steps:
       --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT \
       --ignore=lora/test_chatglm3_tp.py \
       --ignore=lora/test_llama_tp.py \
-      --ignore=lora/test_llm_with_multi_loras.py \
-      --ignore=lora/test_olmoe_tp.py \
-      --ignore=lora/test_deepseekv2_tp.py \
-      --ignore=lora/test_gptoss_tp.py \
-      --ignore=lora/test_qwen3moe_tp.py
+      --ignore=lora/test_llm_with_multi_loras.py
   parallelism: 4
 
 - label: PyTorch Compilation Unit Tests # 15min
@@ -460,8 +454,8 @@ steps:
     - pytest -v -s compile/test_fusion_attn.py
     - pytest -v -s compile/test_functionalization.py
     - pytest -v -s compile/test_silu_mul_quant_fusion.py
-  #  - pytest -v -s compile/test_sequence_parallelism.py
-  #  - pytest -v -s compile/test_async_tp.py
+    - pytest -v -s compile/test_sequence_parallelism.py
+    - pytest -v -s compile/test_async_tp.py
     - pytest -v -s compile/test_fusion_all_reduce.py
     - pytest -v -s compile/test_decorator.py
     - pytest -v -s compile/test_noop_elimination.py
@@ -480,8 +474,8 @@ steps:
   - pytest -v -s compile/test_basic_correctness.py
   - pytest -v -s compile/piecewise/
 
-- label: PyTorch Fullgraph Test # 22min
-  timeout_in_minutes: 35
+- label: PyTorch Fullgraph Test # 20min
+  timeout_in_minutes: 30
   mirror_hardwares: [amdexperimental, amdproduction]
   agent_pool: mi325_1
   # grade: Blocking
@@ -491,7 +485,6 @@ steps:
   - tests/compile
   commands:
   - pytest -v -s compile/test_full_graph.py
-  - pytest -v -s compile/test_fusions_e2e.py
 
 - label: Kernels Core Operation Test # 48min
   timeout_in_minutes: 75
@@ -501,7 +494,6 @@ steps:
   source_file_dependencies:
   - csrc/
   - tests/kernels/core
-  - tests/kernels/test_top_k_per_row.py
   commands:
     - pytest -v -s kernels/core kernels/test_top_k_per_row.py
 
@@ -561,7 +553,7 @@ steps:
 
 - label: Model Executor Test # 23min
   timeout_in_minutes: 35
-  mirror_hardwares: [amdexperimental, amdproduction]
+  mirror_hardwares: [amdexperimental]
   agent_pool: mi325_1
   # grade: Blocking
   source_file_dependencies:
@@ -614,11 +606,11 @@ steps:
   # we can only upgrade after this is resolved
   # TODO(jerryzh168): resolve the above comment
   - uv pip install --system torchao==0.13.0
-  - VLLM_TEST_FORCE_LOAD_FORMAT=auto pytest -v -s quantization/ --ignore quantization/test_blackwell_moe.py
+  - VLLM_TEST_FORCE_LOAD_FORMAT=auto pytest -v -s quantization/
 
-- label: LM Eval Small Models # 15min
-  timeout_in_minutes: 20
-  mirror_hardwares: [amdexperimental, amdproduction]
+- label: LM Eval Small Models # 53min
+  timeout_in_minutes: 75
+  mirror_hardwares: [amdexperimental]
   agent_pool: mi325_1
   # grade: Blocking
   source_file_dependencies:
@@ -627,18 +619,17 @@ steps:
   commands:
   - pytest -s -v evals/gsm8k/test_gsm8k_correctness.py --config-list-file=configs/models-small.txt --tp-size=1
 
-- label: OpenAI API correctness # 10min
-  timeout_in_minutes: 15
-  mirror_hardwares: [amdexperimental, amdproduction]
+- label: OpenAI API correctness # 22min
+  timeout_in_minutes: 30
+  mirror_hardwares: [amdexperimental]
   agent_pool: mi325_1
   # grade: Blocking
   source_file_dependencies:
   - csrc/
   - vllm/entrypoints/openai/
   - vllm/model_executor/models/whisper.py
-  commands: # LMEval
-  # Transcription WER check is skipped because encoder-decoder models are not supported on ROCm, see https://github.com/vllm-project/vllm/issues/27442
-  - pytest -s entrypoints/openai/correctness/  --ignore entrypoints/openai/correctness/test_transcription_api_correctness.py
+  commands: # LMEval+Transcription WER check
+  - pytest -s entrypoints/openai/correctness/
 
 - label: OpenAI-Compatible Tool Use # 23 min
   timeout_in_minutes: 35
@@ -790,10 +781,8 @@ steps:
   - vllm/
   - tests/models/language/generation
   commands:
-    # Install fast path packages for testing against transformers
-    # Note: also needed to run plamo2 model in vLLM
-    - uv pip install --system --no-build-isolation 'git+https://github.com/state-spaces/mamba@v2.2.5'
-    - uv pip install --system --no-build-isolation 'git+https://github.com/Dao-AILab/causal-conv1d@v1.5.2'
+    # Install causal-conv1d for plamo2 models here, as it is not compatible with pip-compile.
+    - pip install 'git+https://github.com/Dao-AILab/causal-conv1d@v1.5.0.post8'
     - pytest -v -s models/language/generation -m '(not core_model) and (not hybrid_model)'
 
 - label: Language Models Test (PPL)
@@ -859,18 +848,6 @@ steps:
     - pytest -v -s models/multimodal -m core_model --ignore models/multimodal/generation/test_whisper.py --ignore models/multimodal/processing
     - cd .. && VLLM_WORKER_MULTIPROC_METHOD=spawn pytest -v -s tests/models/multimodal/generation/test_whisper.py -m core_model  # Otherwise, mp_method="spawn" doesn't work
 
-- label: Multi-Modal Accuracy Eval (Small Models) # 10min
-  mirror_hardwares: [amdexperimental, amdproduction]
-  agent_pool: mi325_1
-  timeout_in_minutes: 15
-  working_dir: "/vllm-workspace/.buildkite/lm-eval-harness"
-  source_file_dependencies:
-  - vllm/multimodal/
-  - vllm/inputs/
-  - vllm/v1/core/
-  commands:
-  - pytest -s -v test_lm_eval_correctness.py --config-list-file=configs/models-mm-small.txt --tp-size=1
-
 - label: Multi-Modal Models Test (Extended) 1
   mirror_hardwares: [amdexperimental]
   agent_pool: mi325_1
@@ -909,7 +886,7 @@ steps:
 
 - label: Quantized Models Test # 45 min
   timeout_in_minutes: 60
-  mirror_hardwares: [amdexperimental, amdproduction]
+  mirror_hardwares: [amdexperimental]
   agent_pool: mi325_1
   # grade: Blocking
   source_file_dependencies:
@@ -946,8 +923,8 @@ steps:
     # Whisper needs spawn method to avoid deadlock
     - VLLM_WORKER_MULTIPROC_METHOD=spawn python3 examples/offline_inference/audio_language.py --model-type whisper
 
-- label: Blackwell Test # 21 min
-  timeout_in_minutes: 30
+- label: Blackwell Test # 38 min
+  timeout_in_minutes: 60
   working_dir: "/vllm-workspace/"
   gpu: b200
   # optional: true
@@ -960,6 +937,8 @@ steps:
   - vllm/model_executor/layers/fused_moe/flashinfer_cutlass_prepare_finalize.py
   - vllm/model_executor/layers/quantization/utils/flashinfer_utils.py
   - vllm/v1/attention/backends/flashinfer.py
+  - vllm/compilation/fusion.py
+  - vllm/compilation/fusion_attn.py
   commands:
     - nvidia-smi
     - python3 examples/offline_inference/basic/chat.py
@@ -976,32 +955,13 @@ steps:
     - pytest -v -s tests/kernels/quantization/test_nvfp4_scaled_mm.py
     - pytest -v -s tests/kernels/quantization/test_flashinfer_scaled_mm.py
     - pytest -v -s tests/kernels/quantization/test_flashinfer_nvfp4_scaled_mm.py
-    - pytest -v -s tests/kernels/quantization/test_nvfp4_qutlass.py
-    - pytest -v -s tests/kernels/quantization/test_mxfp4_qutlass.py
     - pytest -v -s tests/kernels/moe/test_nvfp4_moe.py
     - pytest -v -s tests/kernels/moe/test_ocp_mx_moe.py
-    - pytest -v -s tests/kernels/moe/test_flashinfer.py
-
-- label: Blackwell Fusion Tests # 30 min
-  timeout_in_minutes: 40
-  working_dir: "/vllm-workspace/"
-  gpu: b200
-  source_file_dependencies:
-  - csrc/quantization/fp4/
-  - vllm/model_executor/layers/quantization/utils/flashinfer_utils.py
-  - vllm/v1/attention/backends/flashinfer.py
-  - vllm/compilation/
-  # can affect pattern matching
-  - vllm/model_executor/layers/layernorm.py
-  - vllm/model_executor/layers/activation.py
-  - vllm/model_executor/layers/quantization/input_quant_fp8.py
-  commands:
-    - nvidia-smi
-    - pytest -v -s tests/compile/test_fusion_attn.py
-    - pytest -v -s tests/compile/test_silu_mul_quant_fusion.py
-    # this runner has 2 GPUs available even though num_gpus=2 is not set
+    # Fusion
     - pytest -v -s tests/compile/test_fusion_all_reduce.py
-    - pytest -v -s tests/compile/test_fusions_e2e.py
+    - pytest -v -s tests/compile/test_fusion_attn.py::test_attention_quant_pattern
+    - pytest -v -s tests/kernels/moe/test_flashinfer.py
+    - pytest -v -s tests/compile/test_silu_mul_quant_fusion.py
 
 - label: Blackwell GPT-OSS Eval
   timeout_in_minutes: 60
@@ -1121,7 +1081,6 @@ steps:
   - pytest -v -s ./compile/test_basic_correctness.py
   - pytest -v -s ./compile/test_wrapper.py
   - VLLM_TEST_SAME_HOST=1 torchrun --nproc-per-node=4 distributed/test_same_node.py | grep 'Same node test passed'
-  - VLLM_TEST_SAME_HOST=1 VLLM_TEST_WITH_DEFAULT_DEVICE_SET=1 torchrun --nproc-per-node=4 distributed/test_same_node.py | grep 'Same node test passed'
   - pytest -v -s distributed/test_sequence_parallel.py
   - CUDA_VISIBLE_DEVICES=0,1 pytest -v -s v1/shutdown
   - pytest -v -s v1/worker/test_worker_memory_snapshot.py
@@ -1169,11 +1128,6 @@ steps:
   - pytest -v -s plugins_tests/test_io_processor_plugins.py
   - pip uninstall prithvi_io_processor_plugin -y
   # end io_processor plugins test
-  # begin stat_logger plugins test
-  - pip install -e ./plugins/vllm_add_dummy_stat_logger
-  - pytest -v -s plugins_tests/test_stats_logger_plugins.py
-  - pip uninstall dummy_stat_logger -y
-  # end stat_logger plugins test
   # other tests continue here:
   - pytest -v -s plugins_tests/test_scheduler_plugins.py
   - pip install -e ./plugins/vllm_add_dummy_model
@@ -1217,8 +1171,6 @@ steps:
     - pytest -v -s -x lora/test_chatglm3_tp.py
     - pytest -v -s -x lora/test_llama_tp.py
     - pytest -v -s -x lora/test_llm_with_multi_loras.py
-    - pytest -v -s -x lora/test_olmoe_tp.py
-    - pytest -v -s -x lora/test_gptoss_tp.py
 
 
 - label: Weight Loading Multiple GPU Test  # 33min
@@ -1249,18 +1201,6 @@ steps:
   commands:
     - bash weight_loading/run_model_weight_loading_test.sh -c weight_loading/models-large.txt
 
-- label: NixlConnector PD accuracy tests (Distributed) # 30min
-  mirror_hardwares: [amdexperimental]
-  agent_pool: mi325_4
-  timeout_in_minutes: 30
-  working_dir: "/vllm-workspace/tests"
-  num_gpus: 4
-  source_file_dependencies:
-    - vllm/distributed/kv_transfer/kv_connector/v1/nixl_connector.py
-    - tests/v1/kv_connector/nixl_integration/
-  commands:
-    - uv pip install --system -r /vllm-workspace/requirements/kv_connectors.txt
-    - bash v1/kv_connector/nixl_integration/tp_config_sweep_accuracy_test.sh
 
 ##### multi gpus test #####
 ##### A100 test #####
@@ -1292,16 +1232,12 @@ steps:
   - pytest -s -v test_lm_eval_correctness.py --config-list-file=configs/models-large.txt --tp-size=4
 
 ##### H200 test #####
-- label: Distributed Tests (H200) # optional
+- label: Distrubted Tests (H200) # optional
   gpu: h200
   optional: true
   working_dir: "/vllm-workspace/"
   num_gpus: 2
   commands:
-    - pytest -v -s tests/compile/test_async_tp.py
-    - pytest -v -s tests/compile/test_sequence_parallelism.py
-    - pytest -v -s tests/compile/test_fusion_all_reduce.py
-    - pytest -v -s tests/compile/test_fusions_e2e.py::test_tp2_attn_quant_allreduce_rmsnorm
     - pytest -v -s tests/distributed/test_context_parallel.py
     - CUDA_VISIBLE_DEVICES=1,2 VLLM_ALL2ALL_BACKEND=deepep_high_throughput VLLM_USE_DEEP_GEMM=1 VLLM_LOGGING_LEVEL=DEBUG python3 examples/offline_inference/data_parallel.py --model Qwen/Qwen1.5-MoE-A2.7B --tp-size=1  --dp-size=2 --max-model-len 2048
 

.buildkite/test-pipeline.yaml

@@ -38,7 +38,7 @@ steps:
 - label: Pytorch Nightly Dependency Override Check # 2min
   # if this test fails, it means the nightly torch version is not compatible with some
   # of the dependencies. Please check the error message and add the package to whitelist
-  # in /vllm/tools/pre_commit/generate_nightly_torch_test.py
+  # in /vllm/tools/generate_nightly_torch_test.py
   soft_fail: true
   source_file_dependencies:
   - requirements/nightly_torch_test.txt
@@ -172,8 +172,6 @@ steps:
   - tests/v1/engine/test_engine_core_client.py
   - tests/distributed/test_symm_mem_allreduce.py
   commands:
-  # https://github.com/NVIDIA/nccl/issues/1838
-  - export NCCL_CUMEM_HOST_ENABLE=0
   # test with torchrun tp=2 and external_dp=2
   - torchrun --nproc-per-node=4 distributed/test_torchrun_example.py
   # test with torchrun tp=2 and pp=2
@@ -205,24 +203,6 @@ steps:
   - VLLM_ALLOW_INSECURE_SERIALIZATION=1 RAY_DEDUP_LOGS=0 python3 rlhf_colocate.py
   - popd
 
-- label: Distributed Tests (8 GPUs) # 4min
-  timeout_in_minutes: 10
-  gpu: h100
-  num_gpus: 8
-  working_dir: "/vllm-workspace/tests"
-  source_file_dependencies:
-  - examples/offline_inference/torchrun_dp_example.py
-  - vllm/config/parallel.py
-  - vllm/distributed/
-  - vllm/v1/engine/llm_engine.py
-  - vllm/v1/executor/uniproc_executor.py
-  - vllm/v1/worker/gpu_worker.py
-  commands:
-  # https://github.com/NVIDIA/nccl/issues/1838
-  - export NCCL_CUMEM_HOST_ENABLE=0
-  # test with torchrun tp=2 and dp=4 with ep
-  - torchrun --nproc-per-node=8 ../examples/offline_inference/torchrun_dp_example.py --tp-size=2 --pp-size=1 --dp-size=4 --enable-ep
-
 - label: EPLB Algorithm Test # 5min
   timeout_in_minutes: 15
   working_dir: "/vllm-workspace/tests"
@@ -232,8 +212,8 @@ steps:
   commands:
   - pytest -v -s distributed/test_eplb_algo.py
 
-- label: EPLB Execution Test # 10min
-  timeout_in_minutes: 20
+- label: EPLB Execution Test # 5min
+  timeout_in_minutes: 15
   working_dir: "/vllm-workspace/tests"
   num_gpus: 4
   source_file_dependencies:
@@ -241,7 +221,6 @@ steps:
   - tests/distributed/test_eplb_execute.py
   commands:
   - pytest -v -s distributed/test_eplb_execute.py
-  - pytest -v -s distributed/test_eplb_spec_decode.py
 
 - label: Metrics, Tracing Test # 12min
   timeout_in_minutes: 20
@@ -316,7 +295,6 @@ steps:
     - vllm/
     - tests/v1
   commands:
-    - uv pip install --system -r /vllm-workspace/requirements/kv_connectors.txt
     # split the test to avoid interference
     - pytest -v -s -m 'not cpu_test' v1/core
     - pytest -v -s v1/executor
@@ -333,24 +311,6 @@ steps:
     - pip install -U git+https://github.com/robertgshaw2-redhat/lm-evaluation-harness.git@streaming-api
     - pytest -v -s entrypoints/openai/correctness/test_lmeval.py::test_lm_eval_accuracy_v1_engine
 
-- label: V1 Test attention (H100) # 10min
-  timeout_in_minutes: 30
-  gpu: h100
-  source_file_dependencies:
-    - vllm/v1/attention
-    - tests/v1/attention
-  commands:
-    - pytest -v -s v1/attention
-
-- label: V1 Test attention (B200) # 10min
-  timeout_in_minutes: 30
-  gpu: b200
-  source_file_dependencies:
-    - vllm/v1/attention
-    - tests/v1/attention
-  commands:
-    - VLLM_DISABLE_FLASHINFER_PREFILL=1 pytest -v -s v1/attention # TODO: FI prefill is bugged and causes incorrectness, fix this
-
 - label: V1 Test others (CPU) # 5 mins
   source_file_dependencies:
     - vllm/
@@ -389,8 +349,7 @@ steps:
     - python3 offline_inference/basic/embed.py
     - python3 offline_inference/basic/score.py
     - python3 offline_inference/spec_decode.py --test --method eagle --num_spec_tokens 3 --dataset-name hf --dataset-path philschmid/mt-bench --num-prompts 80 --temp 0 --top-p 1.0 --top-k -1 --tp 1 --enable-chunked-prefill --max-model-len 2048
-    # https://github.com/vllm-project/vllm/pull/26682 uses slightly more memory in PyTorch 2.9+ causing this test to OOM in 1xL4 GPU
-    - python3 offline_inference/spec_decode.py --test --method eagle3 --num_spec_tokens 3 --dataset-name hf --dataset-path philschmid/mt-bench --num-prompts 80 --temp 0 --top-p 1.0 --top-k -1 --tp 1 --enable-chunked-prefill --max-model-len 1536
+    - python3 offline_inference/spec_decode.py --test --method eagle3 --num_spec_tokens 3 --dataset-name hf --dataset-path philschmid/mt-bench --num-prompts 80 --temp 0 --top-p 1.0 --top-k -1 --tp 1 --enable-chunked-prefill --max-model-len 2048
 
 - label: Platform Tests (CUDA) # 4min
   timeout_in_minutes: 15
@@ -425,12 +384,7 @@ steps:
       --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT \
       --ignore=lora/test_chatglm3_tp.py \
       --ignore=lora/test_llama_tp.py \
-      --ignore=lora/test_llm_with_multi_loras.py \
-      --ignore=lora/test_olmoe_tp.py \
-      --ignore=lora/test_deepseekv2_tp.py \
-      --ignore=lora/test_gptoss_tp.py \
-      --ignore=lora/test_qwen3moe_tp.py
-
+      --ignore=lora/test_llm_with_multi_loras.py
   parallelism: 4
 
 - label: PyTorch Compilation Unit Tests # 15min
@@ -441,7 +395,6 @@ steps:
     - vllm/
     - tests/compile
   commands:
-    - pytest -v -s compile/test_config.py
     - pytest -v -s compile/test_pass_manager.py
     - pytest -v -s compile/test_fusion.py
     - pytest -v -s compile/test_fusion_attn.py
@@ -461,34 +414,17 @@ steps:
   - tests/compile
   commands:
   - pytest -v -s compile/test_basic_correctness.py
-  - pytest -v -s compile/test_multimodal_compile.py
   - pytest -v -s compile/piecewise/
 
-- label: PyTorch Fullgraph Test # 22min
-  timeout_in_minutes: 35
+- label: PyTorch Fullgraph Test # 20min
+  timeout_in_minutes: 30
   mirror_hardwares: [amdexperimental]
   torch_nightly: true
   source_file_dependencies:
   - vllm/
   - tests/compile
   commands:
-    # fp8 kv scales not supported on sm89, tested on Blackwell instead
-  - pytest -v -s compile/test_full_graph.py -k 'not test_fp8_kv_scale_compile'
-    # Limit to no custom ops to reduce running time
-    # Wrap with quotes to escape yaml and avoid starting -k string with a -
-  - "pytest -v -s compile/test_fusions_e2e.py -k 'TRITON and not +quant_fp8 and not Llama-4'"
-
-- label: Cudagraph test
-  timeout_in_minutes: 20
-  mirror_hardwares: [amdexperimental]
-  source_file_dependencies:
-  - tests/v1/cudagraph
-  - vllm/v1/cudagraph_dispatcher.py
-  - vllm/config/compilation.py
-  - vllm/compilation
-  commands:
-    - pytest -v -s v1/cudagraph/test_cudagraph_dispatch.py
-    - pytest -v -s v1/cudagraph/test_cudagraph_mode.py
+  - pytest -v -s compile/test_full_graph.py
 
 - label: Kernels Core Operation Test # 48min
   timeout_in_minutes: 75
@@ -532,8 +468,6 @@ steps:
   - tests/kernels/moe
   - vllm/model_executor/layers/fused_moe/
   - vllm/distributed/device_communicators/
-  - vllm/envs.py
-  - vllm/config
   commands:
     - pytest -v -s kernels/moe --shard-id=$$BUILDKITE_PARALLEL_JOB --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT
   parallelism: 2
@@ -550,11 +484,8 @@ steps:
 
 - label: Model Executor Test # 23min
   timeout_in_minutes: 35
-  torch_nightly: true
   mirror_hardwares: [amdexperimental]
   source_file_dependencies:
-  - vllm/engine/arg_utils.py
-  - vllm/config/model.py
   - vllm/model_executor
   - tests/model_executor
   - tests/entrypoints/openai/test_tensorizer_entrypoint.py
@@ -597,7 +528,7 @@ steps:
   # https://github.com/pytorch/ao/issues/2919, we'll have to skip new torchao tests for now
   # we can only upgrade after this is resolved
   # TODO(jerryzh168): resolve the above comment
-  - uv pip install --system torchao==0.13.0 --index-url https://download.pytorch.org/whl/cu129
+  - uv pip install --system torchao==0.13.0
   - VLLM_TEST_FORCE_LOAD_FORMAT=auto pytest -v -s quantization/ --ignore quantization/test_blackwell_moe.py
 
 - label: LM Eval Small Models # 53min
@@ -747,10 +678,8 @@ steps:
   - vllm/
   - tests/models/language/generation
   commands:
-    # Install fast path packages for testing against transformers
-    # Note: also needed to run plamo2 model in vLLM
-    - uv pip install --system --no-build-isolation 'git+https://github.com/state-spaces/mamba@v2.2.5'
-    - uv pip install --system --no-build-isolation 'git+https://github.com/Dao-AILab/causal-conv1d@v1.5.2'
+    # Install causal-conv1d for plamo2 models here, as it is not compatible with pip-compile.
+    - pip install 'git+https://github.com/Dao-AILab/causal-conv1d@v1.5.0.post8'
     - pytest -v -s models/language/generation -m '(not core_model) and (not hybrid_model)'
 
 - label: Language Models Test (PPL)
@@ -869,17 +798,17 @@ steps:
   optional: true
   commands:
     - pip install --upgrade git+https://github.com/huggingface/transformers
-    - pytest -v -s tests/models/test_initialization.py -k 'not (Gemma3 or ModernBert or Qwen2_5_VL or Qwen2_5vl or Qwen2VL or TransformersMultiModalEmbeddingModel or TransformersMultiModalForSequenceClassification or Ultravox or Phi4Multimodal or LlavaNextVideo or MiniCPMO or Lfm2Moe or PaliGemma or RobertaForSequenceClassification or Ovis2_5 or Fuyu or DeepseekOCR or KimiVL)'
+    - pytest -v -s tests/models/test_initialization.py
     - pytest -v -s tests/models/test_transformers.py
-    # - pytest -v -s tests/models/multimodal/processing/
-    - pytest -v -s tests/models/multimodal/test_mapping.py -k 'not (Gemma3 or Qwen2VL or Qwen2_5_VL)'
+    - pytest -v -s tests/models/multimodal/processing/
+    - pytest -v -s tests/models/multimodal/test_mapping.py
     - python3 examples/offline_inference/basic/chat.py
-    # - python3 examples/offline_inference/vision_language.py --model-type qwen2_5_vl
+    - python3 examples/offline_inference/vision_language.py --model-type qwen2_5_vl
     # Whisper needs spawn method to avoid deadlock
     - VLLM_WORKER_MULTIPROC_METHOD=spawn python3 examples/offline_inference/audio_language.py --model-type whisper
 
-- label: Blackwell Test # 21 min
-  timeout_in_minutes: 30
+- label: Blackwell Test # 38 min
+  timeout_in_minutes: 60
   working_dir: "/vllm-workspace/"
   gpu: b200
   # optional: true
@@ -892,6 +821,8 @@ steps:
   - vllm/model_executor/layers/fused_moe/flashinfer_cutlass_prepare_finalize.py
   - vllm/model_executor/layers/quantization/utils/flashinfer_utils.py
   - vllm/v1/attention/backends/flashinfer.py
+  - vllm/compilation/fusion.py
+  - vllm/compilation/fusion_attn.py
   commands:
     - nvidia-smi
     - python3 examples/offline_inference/basic/chat.py
@@ -908,58 +839,15 @@ steps:
     - pytest -v -s tests/kernels/quantization/test_nvfp4_scaled_mm.py
     - pytest -v -s tests/kernels/quantization/test_flashinfer_scaled_mm.py
     - pytest -v -s tests/kernels/quantization/test_flashinfer_nvfp4_scaled_mm.py
-    - pytest -v -s tests/kernels/quantization/test_nvfp4_qutlass.py
-    - pytest -v -s tests/kernels/quantization/test_mxfp4_qutlass.py
     - pytest -v -s tests/kernels/moe/test_nvfp4_moe.py
     - pytest -v -s tests/kernels/moe/test_ocp_mx_moe.py
-    - pytest -v -s tests/kernels/moe/test_flashinfer.py
-
-- label: Blackwell Fusion & Compile Tests # 30 min
-  timeout_in_minutes: 40
-  working_dir: "/vllm-workspace/"
-  gpu: b200
-  source_file_dependencies:
-  - csrc/quantization/fp4/
-  - vllm/model_executor/layers/quantization/utils/flashinfer_utils.py
-  - vllm/v1/attention/backends/flashinfer.py
-  - vllm/compilation/
-  # can affect pattern matching
-  - vllm/model_executor/layers/layernorm.py
-  - vllm/model_executor/layers/activation.py
-  - vllm/model_executor/layers/quantization/input_quant_fp8.py
-  commands:
-    - nvidia-smi
-    - pytest -v -s tests/compile/test_fusion_attn.py
-    - pytest -v -s tests/compile/test_silu_mul_quant_fusion.py
-    # this runner has 2 GPUs available even though num_gpus=2 is not set
+    # Fusion
     - pytest -v -s tests/compile/test_fusion_all_reduce.py
-    # Limit to Inductor partition, no custom ops, and allreduce & attn fusion to reduce running time
-    # Wrap with quotes to escape yaml
-    - "pytest -v -s tests/compile/test_fusions_e2e.py::test_tp2_attn_quant_allreduce_rmsnorm -k 'True and not +quant_fp8 and not +rms_norm'"
-    # test_fp8_kv_scale_compile requires FlashAttention (not supported on default L4/L40)
-    - pytest -v -s tests/compile/test_full_graph.py::test_fp8_kv_scale_compile
-
-- label: Blackwell Fusion E2E Tests # 30 min
-  timeout_in_minutes: 40
-  working_dir: "/vllm-workspace/"
-  gpu: b200
-  optional: true
-  num_gpus: 2
-  source_file_dependencies:
-  - csrc/quantization/fp4/
-  - vllm/model_executor/layers/quantization/utils/flashinfer_utils.py
-  - vllm/v1/attention/backends/flashinfer.py
-  - vllm/compilation/
-  # can affect pattern matching
-  - vllm/model_executor/layers/layernorm.py
-  - vllm/model_executor/layers/activation.py
-  - vllm/model_executor/layers/quantization/input_quant_fp8.py
-  - tests/compile/test_fusions_e2e.py
-  - tests/compile/test_full_graph.py
-  commands:
-    - nvidia-smi
-    # Run all e2e fusion tests
-    - pytest -v -s tests/compile/test_fusions_e2e.py
+    - pytest -v -s tests/compile/test_fusion_attn.py::test_attention_quant_pattern
+    - pytest -v -s tests/kernels/moe/test_flashinfer.py
+    - pytest -v -s tests/compile/test_silu_mul_quant_fusion.py
+    - pytest -v -s tests/kernels/quantization/test_nvfp4_qutlass.py
+    - pytest -v -s tests/kernels/quantization/test_mxfp4_qutlass.py
 
 - label: Blackwell GPT-OSS Eval
   timeout_in_minutes: 60
@@ -1066,8 +954,6 @@ steps:
   - tests/v1/shutdown
   - tests/v1/worker/test_worker_memory_snapshot.py
   commands:
-  # https://github.com/NVIDIA/nccl/issues/1838
-  - export NCCL_CUMEM_HOST_ENABLE=0
   - TP_SIZE=1 DP_SIZE=2 pytest -v -s v1/distributed/test_async_llm_dp.py
   - TP_SIZE=1 DP_SIZE=2 pytest -v -s v1/distributed/test_external_lb_dp.py
   - DP_SIZE=2 pytest -v -s v1/entrypoints/openai/test_multi_api_servers.py
@@ -1075,7 +961,6 @@ steps:
   - pytest -v -s ./compile/test_basic_correctness.py
   - pytest -v -s ./compile/test_wrapper.py
   - VLLM_TEST_SAME_HOST=1 torchrun --nproc-per-node=4 distributed/test_same_node.py | grep 'Same node test passed'
-  - VLLM_TEST_SAME_HOST=1 VLLM_TEST_WITH_DEFAULT_DEVICE_SET=1 torchrun --nproc-per-node=4 distributed/test_same_node.py | grep 'Same node test passed'
   - pytest -v -s distributed/test_sequence_parallel.py
   - CUDA_VISIBLE_DEVICES=0,1 pytest -v -s v1/shutdown
   - pytest -v -s v1/worker/test_worker_memory_snapshot.py
@@ -1119,11 +1004,6 @@ steps:
   - pytest -v -s plugins_tests/test_io_processor_plugins.py
   - pip uninstall prithvi_io_processor_plugin -y
   # end io_processor plugins test
-  # begin stat_logger plugins test
-  - pip install -e ./plugins/vllm_add_dummy_stat_logger
-  - pytest -v -s plugins_tests/test_stats_logger_plugins.py
-  - pip uninstall dummy_stat_logger -y
-  # end stat_logger plugins test
   # other tests continue here:
   - pytest -v -s plugins_tests/test_scheduler_plugins.py
   - pip install -e ./plugins/vllm_add_dummy_model
@@ -1163,8 +1043,6 @@ steps:
     - pytest -v -s -x lora/test_chatglm3_tp.py
     - pytest -v -s -x lora/test_llama_tp.py
     - pytest -v -s -x lora/test_llm_with_multi_loras.py
-    - pytest -v -s -x lora/test_olmoe_tp.py
-    - pytest -v -s -x lora/test_gptoss_tp.py
 
 
 - label: Weight Loading Multiple GPU Test  # 33min
@@ -1191,17 +1069,6 @@ steps:
   commands:
     - bash weight_loading/run_model_weight_loading_test.sh -c weight_loading/models-large.txt
 
-- label: NixlConnector PD accuracy tests (Distributed) # 30min
-  timeout_in_minutes: 30
-  working_dir: "/vllm-workspace/tests"
-  num_gpus: 4
-  source_file_dependencies:
-    - vllm/distributed/kv_transfer/kv_connector/v1/nixl_connector.py
-    - tests/v1/kv_connector/nixl_integration/
-  commands:
-    - uv pip install --system -r /vllm-workspace/requirements/kv_connectors.txt
-    - bash v1/kv_connector/nixl_integration/tp_config_sweep_accuracy_test.sh
-
 
 ##### multi gpus test #####
 ##### A100 test #####
@@ -1232,21 +1099,8 @@ steps:
   - export VLLM_WORKER_MULTIPROC_METHOD=spawn
   - pytest -s -v test_lm_eval_correctness.py --config-list-file=configs/models-large.txt --tp-size=4
 
-##### H100 test #####
-- label: LM Eval Large Models (H100) # optional
-  gpu: h100
-  optional: true
-  num_gpus: 4
-  working_dir: "/vllm-workspace/.buildkite/lm-eval-harness"
-  source_file_dependencies:
-  - csrc/
-  - vllm/model_executor/layers/quantization
-  commands:
-    - export VLLM_USE_DEEP_GEMM=0  # We found Triton is faster than DeepGEMM for H100
-    - pytest -s -v test_lm_eval_correctness.py --config-list-file=configs/models-large-hopper.txt --tp-size=4
-
 ##### H200 test #####
-- label: Distributed Tests (H200) # optional
+- label: Distrubted Tests (H200) # optional
   gpu: h200
   optional: true
   working_dir: "/vllm-workspace/"
@@ -1254,12 +1108,8 @@ steps:
   commands:
     - pytest -v -s tests/compile/test_async_tp.py
     - pytest -v -s tests/compile/test_sequence_parallelism.py
-    - pytest -v -s tests/compile/test_fusion_all_reduce.py
-    - "pytest -v -s tests/compile/test_fusions_e2e.py -k 'not Llama-4'"
-    - pytest -v -s tests/distributed/test_sequence_parallel.py
     - pytest -v -s tests/distributed/test_context_parallel.py
     - CUDA_VISIBLE_DEVICES=1,2 VLLM_ALL2ALL_BACKEND=deepep_high_throughput VLLM_USE_DEEP_GEMM=1 VLLM_LOGGING_LEVEL=DEBUG python3 examples/offline_inference/data_parallel.py --model Qwen/Qwen1.5-MoE-A2.7B --tp-size=1  --dp-size=2 --max-model-len 2048
-    - pytest -v -s tests/v1/distributed/test_dbo.py
 
 ##### B200 test #####
 - label: Distributed Tests (B200) # optional
@@ -1270,7 +1120,6 @@ steps:
   commands:
     - pytest -v -s tests/distributed/test_context_parallel.py
     - pytest -v -s tests/distributed/test_nccl_symm_mem_allreduce.py
-    - pytest -v -s tests/v1/distributed/test_dbo.py
 
 ##### RL Integration Tests #####
 - label: Prime-RL Integration Test # 15min
@@ -1283,21 +1132,3 @@ steps:
   - .buildkite/scripts/run-prime-rl-test.sh
   commands:
     - bash .buildkite/scripts/run-prime-rl-test.sh
-
-- label: DeepSeek V2-Lite Accuracy
-  timeout_in_minutes: 60
-  gpu: h100
-  optional: true
-  num_gpus: 4
-  working_dir: "/vllm-workspace"
-  commands:
-  - bash .buildkite/scripts/scheduled_integration_test/deepseek_v2_lite_ep_eplb.sh 0.25 200 8010
-
-- label: Qwen3-30B-A3B-FP8-block Accuracy
-  timeout_in_minutes: 60
-  gpu: h100
-  optional: true
-  num_gpus: 4
-  working_dir: "/vllm-workspace"
-  commands:
-  - bash .buildkite/scripts/scheduled_integration_test/qwen30b_a3b_fp8_block_ep.sh 0.8 200 8020

.github/CODEOWNERS

@@ -5,11 +5,11 @@
 /vllm/attention @LucasWilkinson
 /vllm/attention/backends/abstract.py @WoosukKwon @zhuohan123 @youkaichao @alexm-redhat @comaniac @njhill
 /vllm/executor/executor_base.py @zhuohan123 @youkaichao @alexm-redhat @comaniac @njhill @22quinn
-/vllm/model_executor/layers/fused_moe @mgoin @pavanimajety
-/vllm/model_executor/layers/quantization @mgoin @robertgshaw2-redhat @tlrmchlsmth @yewentao256 @pavanimajety
+/vllm/model_executor/layers/fused_moe @mgoin
+/vllm/model_executor/layers/quantization @mgoin @robertgshaw2-redhat @tlrmchlsmth @yewentao256
 /vllm/model_executor/layers/mamba @tdoublep
 /vllm/model_executor/model_loader @22quinn
-/vllm/multimodal @DarkLight1337 @ywang96 @NickLucche @tjtanaa
+/vllm/multimodal @DarkLight1337 @ywang96 @NickLucche
 /vllm/vllm_flash_attn @LucasWilkinson
 /vllm/lora @jeejeelee
 /vllm/reasoning @aarnphm @chaunceyjiang
@@ -25,8 +25,7 @@ CMakeLists.txt @tlrmchlsmth @LucasWilkinson
 
 # vLLM V1
 /vllm/v1/attention @LucasWilkinson
-/vllm/v1/attention/backends/mla @pavanimajety
-/vllm/v1/attention/backends/flashinfer.py @mgoin @pavanimajety
+/vllm/v1/attention/backends/flashinfer.py @mgoin
 /vllm/v1/attention/backends/triton_attn.py @tdoublep
 /vllm/v1/core @WoosukKwon @robertgshaw2-redhat @njhill @ywang96 @comaniac @alexm-redhat @heheda12345 @ApostaC
 /vllm/v1/sample @22quinn @houseroad @njhill
@@ -45,7 +44,7 @@ CMakeLists.txt @tlrmchlsmth @LucasWilkinson
 /tests/kernels @mgoin @tlrmchlsmth @WoosukKwon @yewentao256
 /tests/models @DarkLight1337 @ywang96
 /tests/multimodal @DarkLight1337 @ywang96 @NickLucche
-/tests/quantization @mgoin @robertgshaw2-redhat @yewentao256 @pavanimajety
+/tests/quantization @mgoin @robertgshaw2-redhat @yewentao256
 /tests/test_inputs.py @DarkLight1337 @ywang96
 /tests/v1/entrypoints/llm/test_struct_output_generate.py @mgoin @russellb @aarnphm
 /tests/v1/structured_output @mgoin @russellb @aarnphm
@@ -105,21 +104,11 @@ mkdocs.yaml @hmellor
 /vllm/attention/ops/triton_unified_attention.py @tdoublep
 
 # ROCm related: specify owner with write access to notify AMD folks for careful code review
-/vllm/**/*rocm* @tjtanaa
-/docker/Dockerfile.rocm* @gshtras @tjtanaa
-/vllm/v1/attention/backends/rocm*.py @gshtras @tjtanaa
-/vllm/v1/attention/backends/mla/rocm*.py @gshtras @tjtanaa
-/vllm/attention/ops/rocm*.py @gshtras @tjtanaa
-/vllm/model_executor/layers/fused_moe/rocm*.py @gshtras @tjtanaa
-/csrc/rocm @gshtras @tjtanaa
-/requirements/*rocm* @tjtanaa
-/tests/**/*rocm* @tjtanaa
-/docs/**/*rocm* @tjtanaa
-/vllm/**/*quark* @tjtanaa
-/tests/**/*quark* @tjtanaa
-/docs/**/*quark* @tjtanaa
-/vllm/**/*aiter* @tjtanaa
-/tests/**/*aiter* @tjtanaa
+/docker/Dockerfile.rocm* @gshtras
+/vllm/v1/attention/backends/rocm*.py @gshtras
+/vllm/v1/attention/backends/mla/rocm*.py @gshtras
+/vllm/attention/ops/rocm*.py @gshtras
+/vllm/model_executor/layers/fused_moe/rocm*.py @gshtras
 
 # TPU
 /vllm/v1/worker/tpu* @NickLucche
@@ -137,8 +126,3 @@ mkdocs.yaml @hmellor
 /vllm/config/pooler.py @noooop
 /vllm/pooling_params.py @noooop
 /vllm/model_executor/layers/pooler.py @noooop
-
-# Security guide and policies
-/docs/usage/security.md @russellb
-/SECURITY.md @russellb
-/docs/contributing/vulnerability_management.md @russellb

.github/mergify.yml

@@ -108,7 +108,7 @@ pull_request_rules:
       - files~=^benchmarks/
       - files~=^vllm/benchmarks/
       - files~=^tests/benchmarks/
-      - files~=^\.buildkite/performance-benchmarks/
+      - files~=^\.buildkite/nightly-benchmarks/
   actions:
     label:
       add:

.gitignore

@@ -94,9 +94,6 @@ ipython_config.py
 # generated files
 **/generated/**
 
-# uv
-uv.lock
-
 # pyenv
 #   For a library or package, you might want to ignore these files since the code is
 #   intended to run in multiple environments; otherwise, check them in:
@@ -221,6 +218,3 @@ csrc/moe/marlin_moe_wna16/kernel_*
 
 # Ignore ep_kernels_workspace folder
 ep_kernels_workspace/
-
-# Allow tracked library source folders under submodules (e.g., benchmarks/lib)
-!vllm/benchmarks/lib/

.markdownlint.yaml

@@ -4,6 +4,7 @@ MD013: false
 MD024:
   siblings_only: true
 MD033: false
+MD042: false
 MD045: false
 MD046: false
 MD051: false

.pre-commit-config.yaml

@@ -38,18 +38,18 @@ repos:
   rev: 0.9.1
   hooks:
     - id: pip-compile
-      args: [requirements/test.in, -o, requirements/test.txt, --index-strategy, unsafe-best-match, --torch-backend, cu129, --python-platform, x86_64-manylinux_2_28, --python-version, "3.12"]
+      args: [requirements/test.in, -o, requirements/test.txt, --index-strategy, unsafe-best-match, --torch-backend, cu128, --python-platform, x86_64-manylinux_2_28]
       files: ^requirements/test\.(in|txt)$
 - repo: local
   hooks:
   - id: format-torch-nightly-test
     name: reformat nightly_torch_test.txt to be in sync with test.in
     language: python
-    entry: python tools/pre_commit/generate_nightly_torch_test.py
+    entry: python tools/generate_nightly_torch_test.py
     files: ^requirements/test\.(in|txt)$
   - id: mypy-local
-    name: Run mypy locally for lowest supported Python version
-    entry: python tools/pre_commit/mypy.py 0 "3.10"
+    name: Run mypy for local Python installation
+    entry: python tools/pre_commit/mypy.py 0 "local"
     stages: [pre-commit] # Don't run in CI
     <<: &mypy_common
       language: python
@@ -78,12 +78,12 @@ repos:
     stages: [manual] # Only run in CI
   - id: shellcheck
     name: Lint shell scripts
-    entry: tools/pre_commit/shellcheck.sh
+    entry: tools/shellcheck.sh
     language: script
     types: [shell]
   - id: png-lint
     name: Lint PNG exports from excalidraw
-    entry: tools/pre_commit/png-lint.sh
+    entry: tools/png-lint.sh
     language: script
     types: [png]
   - id: signoff-commit
@@ -100,12 +100,12 @@ repos:
     stages: [commit-msg]
   - id: check-spdx-header
     name: Check SPDX headers
-    entry: python tools/pre_commit/check_spdx_header.py
+    entry: python tools/check_spdx_header.py
     language: python
     types: [python]
   - id: check-root-lazy-imports
     name: Check root lazy imports
-    entry: python tools/pre_commit/check_init_lazy_imports.py
+    entry: python tools/check_init_lazy_imports.py
     language: python
     types: [python]
   - id: check-filenames
@@ -119,11 +119,11 @@ repos:
     pass_filenames: false
   - id: update-dockerfile-graph
     name: Update Dockerfile dependency graph
-    entry: tools/pre_commit/update-dockerfile-graph.sh
+    entry: tools/update-dockerfile-graph.sh
     language: script
   - id: enforce-import-regex-instead-of-re
     name: Enforce import regex as re
-    entry: python tools/pre_commit/enforce_regex_import.py
+    entry: python tools/enforce_regex_import.py
     language: python
     types: [python]
     pass_filenames: false
@@ -131,7 +131,7 @@ repos:
   # forbid directly import triton
   - id: forbid-direct-triton-import
     name: "Forbid direct 'import triton'"
-    entry: python tools/pre_commit/check_triton_import.py
+    entry: python tools/check_triton_import.py
     language: python
     types: [python]
     pass_filenames: false
@@ -144,7 +144,7 @@ repos:
     additional_dependencies: [regex]
   - id: validate-config
     name: Validate configuration has default values and that each field has a docstring
-    entry: python tools/pre_commit/validate_config.py
+    entry: python tools/validate_config.py
     language: python
     additional_dependencies: [regex]
   # Keep `suggestion` last

CMakeLists.txt

@@ -49,8 +49,8 @@ set(HIP_SUPPORTED_ARCHS "gfx906;gfx908;gfx90a;gfx942;gfx950;gfx1030;gfx1100;gfx1
 # requirements.txt files and should be kept consistent.  The ROCm torch
 # versions are derived from docker/Dockerfile.rocm
 #
-set(TORCH_SUPPORTED_VERSION_CUDA "2.9.0")
-set(TORCH_SUPPORTED_VERSION_ROCM "2.9.0")
+set(TORCH_SUPPORTED_VERSION_CUDA "2.8.0")
+set(TORCH_SUPPORTED_VERSION_ROCM "2.8.0")
 
 #
 # Try to find python package with an executable that exactly matches
@@ -241,7 +241,7 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
   message(STATUS "Enabling cumem allocator extension.")
   # link against cuda driver library
   list(APPEND CUMEM_LIBS CUDA::cuda_driver)
-  define_extension_target(
+  define_gpu_extension_target(
     cumem_allocator
     DESTINATION vllm
     LANGUAGE CXX
@@ -858,7 +858,7 @@ if (VLLM_GPU_LANG STREQUAL "HIP")
 endif()
 
 message(STATUS "Enabling C extension.")
-define_extension_target(
+define_gpu_extension_target(
   _C
   DESTINATION vllm
   LANGUAGE ${VLLM_GPU_LANG}
@@ -883,7 +883,6 @@ target_compile_definitions(_C PRIVATE CUTLASS_ENABLE_DIRECT_CUDA_DRIVER_CALL=1)
 set(VLLM_MOE_EXT_SRC
   "csrc/moe/torch_bindings.cpp"
   "csrc/moe/moe_align_sum_kernels.cu"
-  "csrc/moe/moe_lora_align_sum_kernels.cu"
   "csrc/moe/topk_softmax_kernels.cu")
 
 if(VLLM_GPU_LANG STREQUAL "CUDA")
@@ -973,7 +972,7 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
 endif()
 
 message(STATUS "Enabling moe extension.")
-define_extension_target(
+define_gpu_extension_target(
   _moe_C
   DESTINATION vllm
   LANGUAGE ${VLLM_GPU_LANG}
@@ -994,7 +993,7 @@ if(VLLM_GPU_LANG STREQUAL "HIP")
     "csrc/rocm/skinny_gemms.cu"
     "csrc/rocm/attention.cu")
 
-  define_extension_target(
+  define_gpu_extension_target(
     _rocm_C
     DESTINATION vllm
     LANGUAGE ${VLLM_GPU_LANG}

README.md

@@ -21,8 +21,6 @@ Join us at the [PyTorch Conference, October 22-23](https://events.linuxfoundatio
 
 *Latest News* 🔥
 
-- [2025/11] We hosted [vLLM Beijing Meetup](https://mp.weixin.qq.com/s/xSrYXjNgr1HbCP4ExYNG1w) focusing on distributed inference and diverse accelerator support with vLLM! Please find the meetup slides [here](https://drive.google.com/drive/folders/1nQJ8ZkLSjKxvu36sSHaceVXtttbLvvu-?usp=drive_link).
-- [2025/10] We hosted [vLLM Shanghai Meetup](https://mp.weixin.qq.com/s/__xb4OyOsImz-9eAVrdlcg) focused on hands-on vLLM inference optimization! Please find the meetup slides [here](https://drive.google.com/drive/folders/1KqwjsFJLfEsC8wlDugnrR61zsWHt94Q6).
 - [2025/09] We hosted [vLLM Toronto Meetup](https://luma.com/e80e0ymm) focused on tackling inference at scale and speculative decoding with speakers from NVIDIA and Red Hat! Please find the meetup slides [here](https://docs.google.com/presentation/d/1IYJYmJcu9fLpID5N5RbW_vO0XLo0CGOR14IXOjB61V8/edit?usp=sharing).
 - [2025/08] We hosted [vLLM Shenzhen Meetup](https://mp.weixin.qq.com/s/k8ZBO1u2_2odgiKWH_GVTQ) focusing on the ecosystem around vLLM! Please find the meetup slides [here](https://drive.google.com/drive/folders/1Ua2SVKVSu-wp5vou_6ElraDt2bnKhiEA).
 - [2025/08] We hosted [vLLM Singapore Meetup](https://www.sginnovate.com/event/vllm-sg-meet). We shared V1 updates, disaggregated serving and MLLM speedups with speakers from Embedded LLM, AMD, WekaIO, and A*STAR. Please find the meetup slides [here](https://drive.google.com/drive/folders/1ncf3GyqLdqFaB6IeB834E5TZJPLAOiXZ?usp=sharing).
@@ -84,7 +82,7 @@ vLLM is flexible and easy to use with:
 - Tensor, pipeline, data and expert parallelism support for distributed inference
 - Streaming outputs
 - OpenAI-compatible API server
-- Support for NVIDIA GPUs, AMD CPUs and GPUs, Intel CPUs and GPUs, PowerPC CPUs, Arm CPUs, and TPU. Additionally, support for diverse hardware plugins such as Intel Gaudi, IBM Spyre and Huawei Ascend.
+- Support for NVIDIA GPUs, AMD CPUs and GPUs, Intel CPUs and GPUs, PowerPC CPUs, and TPU. Additionally, support for diverse hardware plugins such as Intel Gaudi, IBM Spyre and Huawei Ascend.
 - Prefix caching support
 - Multi-LoRA support
 

benchmarks/benchmark_block_pool.py

@@ -5,7 +5,7 @@ import gc
 from benchmark_utils import TimeCollector
 from tabulate import tabulate
 
-from vllm.utils.argparse_utils import FlexibleArgumentParser
+from vllm.utils import FlexibleArgumentParser
 from vllm.v1.core.block_pool import BlockPool
 
 

benchmarks/benchmark_long_document_qa_throughput.py

@@ -46,7 +46,7 @@ import time
 
 from vllm import LLM, SamplingParams
 from vllm.engine.arg_utils import EngineArgs
-from vllm.utils.argparse_utils import FlexibleArgumentParser
+from vllm.utils import FlexibleArgumentParser
 
 
 def test_long_document_qa(llm=None, sampling_params=None, prompts=None):

benchmarks/benchmark_ngram_proposer.py

@@ -19,7 +19,7 @@ from vllm.config import (
     VllmConfig,
 )
 from vllm.platforms import current_platform
-from vllm.utils.argparse_utils import FlexibleArgumentParser
+from vllm.utils import FlexibleArgumentParser
 from vllm.v1.spec_decode.ngram_proposer import NgramProposer
 from vllm.v1.worker.gpu_input_batch import InputBatch
 from vllm.v1.worker.gpu_model_runner import GPUModelRunner

benchmarks/benchmark_prefix_caching.py

@@ -37,7 +37,7 @@ from transformers import PreTrainedTokenizerBase
 
 from vllm import LLM, SamplingParams
 from vllm.engine.arg_utils import EngineArgs
-from vllm.utils.argparse_utils import FlexibleArgumentParser
+from vllm.utils import FlexibleArgumentParser
 
 try:
     from vllm.transformers_utils.tokenizer import get_tokenizer

benchmarks/benchmark_prioritization.py

@@ -11,7 +11,7 @@ import time
 from transformers import AutoTokenizer, PreTrainedTokenizerBase
 
 from vllm.engine.arg_utils import EngineArgs
-from vllm.utils.argparse_utils import FlexibleArgumentParser
+from vllm.utils import FlexibleArgumentParser
 
 
 # Select a equi-probable random priority

benchmarks/benchmark_serving_structured_output.py

@@ -51,7 +51,7 @@ except ImportError:
     from backend_request_func import get_tokenizer
 
 try:
-    from vllm.utils.argparse_utils import FlexibleArgumentParser
+    from vllm.utils import FlexibleArgumentParser
 except ImportError:
     from argparse import ArgumentParser as FlexibleArgumentParser
 

benchmarks/cutlass_benchmarks/sparse_benchmarks.py

@@ -15,7 +15,7 @@ from utils import make_rand_sparse_tensors
 from weight_shapes import WEIGHT_SHAPES
 
 from vllm import _custom_ops as ops
-from vllm.utils.argparse_utils import FlexibleArgumentParser
+from vllm.utils import FlexibleArgumentParser
 
 DEFAULT_MODELS = list(WEIGHT_SHAPES.keys())
 DEFAULT_BATCH_SIZES = [1, 16, 32, 64, 128, 256, 512]

benchmarks/cutlass_benchmarks/w8a8_benchmarks.py

@@ -18,8 +18,7 @@ from vllm import _custom_ops as ops
 from vllm.model_executor.layers.quantization.utils.fp8_utils import (
     w8a8_triton_block_scaled_mm,
 )
-from vllm.utils.argparse_utils import FlexibleArgumentParser
-from vllm.utils.math_utils import cdiv
+from vllm.utils import FlexibleArgumentParser, cdiv
 
 DEFAULT_MODELS = list(WEIGHT_SHAPES.keys())
 DEFAULT_BATCH_SIZES = [1, 16, 32, 64, 128, 256, 512]

benchmarks/kernels/bench_per_token_quant_fp8.py

@@ -10,8 +10,7 @@ import torch
 from vllm.model_executor.layers.quantization.input_quant_fp8 import QuantFP8
 from vllm.model_executor.layers.quantization.utils.quant_utils import GroupShape
 from vllm.triton_utils import triton
-from vllm.utils.argparse_utils import FlexibleArgumentParser
-from vllm.utils.torch_utils import STR_DTYPE_TO_TORCH_DTYPE
+from vllm.utils import STR_DTYPE_TO_TORCH_DTYPE, FlexibleArgumentParser
 
 
 def with_triton_mode(fn):

benchmarks/kernels/benchmark_activation.py

@@ -10,8 +10,7 @@ import vllm.model_executor.layers.activation  # noqa F401
 from vllm.model_executor.custom_op import CustomOp
 from vllm.platforms import current_platform
 from vllm.triton_utils import triton
-from vllm.utils.argparse_utils import FlexibleArgumentParser
-from vllm.utils.torch_utils import STR_DTYPE_TO_TORCH_DTYPE
+from vllm.utils import STR_DTYPE_TO_TORCH_DTYPE, FlexibleArgumentParser
 
 batch_size_range = [1, 16, 32, 64, 128]
 seq_len_range = [1, 16, 64, 128, 256, 512, 1024, 2048, 4096]

benchmarks/kernels/benchmark_bitblas.py

@@ -28,7 +28,7 @@ except ImportError as e:
 
 from bitblas import Matmul, MatmulConfig, auto_detect_nvidia_target
 
-from vllm.utils.argparse_utils import FlexibleArgumentParser
+from vllm.utils import FlexibleArgumentParser
 
 parser = FlexibleArgumentParser(
     description="Benchmark BitBLAS int4 on a specific target."

benchmarks/kernels/benchmark_cutlass_fp4_moe.py

@@ -20,7 +20,7 @@ from vllm.model_executor.layers.fused_moe.config import (
 from vllm.model_executor.layers.fused_moe.cutlass_moe import cutlass_moe_fp4
 from vllm.model_executor.layers.fused_moe.fused_moe import fused_experts, fused_topk
 from vllm.scalar_type import scalar_types
-from vllm.utils.argparse_utils import FlexibleArgumentParser
+from vllm.utils import FlexibleArgumentParser
 
 WEIGHT_SHAPES_MOE = {
     "nvidia/DeepSeek-R1-FP4": [

benchmarks/kernels/benchmark_cutlass_moe_fp8.py

@@ -14,7 +14,7 @@ from vllm.model_executor.layers.fused_moe.config import fp8_w8a8_moe_quant_confi
 from vllm.model_executor.layers.fused_moe.cutlass_moe import cutlass_moe_fp8
 from vllm.model_executor.layers.fused_moe.fused_moe import fused_experts, fused_topk
 from vllm.platforms import current_platform
-from vllm.utils.argparse_utils import FlexibleArgumentParser
+from vllm.utils import FlexibleArgumentParser
 
 # Weight shapes for different models: [num_experts, topk, hidden_size,
 # intermediate_size]

benchmarks/kernels/benchmark_device_communicators.py

@@ -39,7 +39,7 @@ from vllm.distributed.device_communicators.pynccl_allocator import (
 )
 from vllm.distributed.device_communicators.symm_mem import SymmMemCommunicator
 from vllm.logger import init_logger
-from vllm.utils.argparse_utils import FlexibleArgumentParser
+from vllm.utils import FlexibleArgumentParser
 
 logger = init_logger(__name__)
 

benchmarks/kernels/benchmark_grouped_gemm_cutlass.py

@@ -13,11 +13,11 @@ from vllm.model_executor.layers.fused_moe.fused_moe import (
     fused_experts,
     fused_topk,
 )
-from vllm.utils.argparse_utils import FlexibleArgumentParser
+from vllm.utils import FlexibleArgumentParser
 
 DEFAULT_MODELS = [
-    "mistralai/Mixtral-8x7B-Instruct-v0.1",
-    "deepseek-ai/DeepSeek-V2-Lite",
+    "nm-testing/Mixtral-8x7B-Instruct-v0.1",
+    "nm-testing/deepseekv2-lite",
     "ibm-granite/granite-3.0-1b-a400m",
     "ibm-granite/granite-3.0-3b-a800m",
 ]

benchmarks/kernels/benchmark_layernorm.py

@@ -7,8 +7,7 @@ import torch
 
 from vllm.model_executor.layers.layernorm import RMSNorm
 from vllm.platforms import current_platform
-from vllm.utils.argparse_utils import FlexibleArgumentParser
-from vllm.utils.torch_utils import STR_DTYPE_TO_TORCH_DTYPE
+from vllm.utils import STR_DTYPE_TO_TORCH_DTYPE, FlexibleArgumentParser
 
 
 @torch.inference_mode()

benchmarks/kernels/benchmark_lora.py

@@ -19,24 +19,13 @@ from torch.utils.benchmark import Measurement as TMeasurement
 from utils import ArgPool, Bench, CudaGraphBenchParams
 from weight_shapes import WEIGHT_SHAPES
 
-from vllm.lora.ops.triton_ops.utils import get_lora_op_configs
-from vllm.triton_utils import HAS_TRITON, triton
+from vllm.triton_utils import HAS_TRITON
 
 if HAS_TRITON:
-    from vllm.lora.ops.triton_ops import (  ## added fused_moe_lora
-        LoRAKernelMeta,
-        fused_moe_lora_expand,
-        fused_moe_lora_shrink,
-        lora_expand,
-        lora_shrink,
-    )
-    from vllm.lora.ops.triton_ops.fused_moe_lora_op import (
-        _LORA_PTR_DICT,  ## added _LORA_PTR_DICT for fused_moe_lora
-    )
+    from vllm.lora.ops.triton_ops import LoRAKernelMeta, lora_expand, lora_shrink
     from vllm.lora.ops.triton_ops.utils import _LORA_A_PTR_DICT, _LORA_B_PTR_DICT
-from vllm import _custom_ops as ops
-from vllm.utils.argparse_utils import FlexibleArgumentParser
-from vllm.utils.math_utils import round_up
+
+from vllm.utils import FlexibleArgumentParser
 
 DEFAULT_MODELS = list(WEIGHT_SHAPES.keys())
 DEFAULT_TP_SIZES = [1]
@@ -70,8 +59,6 @@ DEFAULT_NUM_LORAS = [1, 2, 3, 4]
 DEFAULT_SORT_BY_LORA_IDS = [False, True]
 DEFAULT_SEQ_LENGTHS = [1]
 DEFAULT_EXPAND_FN_ADD_INPUTS = [True, False]
-DEFAULT_TOP_K_NUMS = [1]  # Added for MoE LoRA top_k
-DEFAULT_NUM_EXPERTS = [8]  # Added for MoE LoRA num_experts
 
 
 # Utilities
@@ -204,11 +191,6 @@ class OpType(Enum):
 
     LORA_SHRINK = auto()
     LORA_EXPAND = auto()
-    ## Adding support for fused moe lora
-    FUSED_MOE_LORA_GATE_UP_SHRINK = auto()  ## Gate/Up projection variant with shrink
-    FUSED_MOE_LORA_GATE_UP_EXPAND = auto()  ## Gate/Up projection variant with expand
-    FUSED_MOE_LORA_DOWN_SHRINK = auto()  ## Down projection variant with shrink
-    FUSED_MOE_LORA_DOWN_EXPAND = auto()  ## Down projection variant with expand
 
     @staticmethod
     def from_str(s: str) -> "OpType":
@@ -216,15 +198,6 @@ class OpType(Enum):
             return OpType.LORA_SHRINK
         if s.lower() == "lora_expand":
             return OpType.LORA_EXPAND
-        # Adding support for fused moe lora, both in gate_up and down
-        if s.lower() == "fused_moe_lora_gate_up_shrink":  ## Gate/Up variant with shrink
-            return OpType.FUSED_MOE_LORA_GATE_UP_SHRINK
-        if s.lower() == "fused_moe_lora_gate_up_expand":  ## Gate/Up variant with expand
-            return OpType.FUSED_MOE_LORA_GATE_UP_EXPAND
-        if s.lower() == "fused_moe_lora_down_shrink":  ## Down variant with shrink
-            return OpType.FUSED_MOE_LORA_DOWN_SHRINK
-        if s.lower() == "fused_moe_lora_down_expand":  ## Down variant with expand
-            return OpType.FUSED_MOE_LORA_DOWN_EXPAND
         raise ValueError(f"Unrecognized str {s} to convert to OpType")
 
     def is_shrink_fn(self) -> bool:
@@ -233,56 +206,19 @@ class OpType(Enum):
     def is_expand_fn(self) -> bool:
         return self in [OpType.LORA_EXPAND]
 
-    def is_fused_moe_lora_fn(self) -> bool:  ## adding for fused MoE LoRA
-        return self in [
-            OpType.FUSED_MOE_LORA_GATE_UP_SHRINK,
-            OpType.FUSED_MOE_LORA_DOWN_SHRINK,
-            OpType.FUSED_MOE_LORA_GATE_UP_EXPAND,
-            OpType.FUSED_MOE_LORA_DOWN_EXPAND,
-        ]
-
-    def is_fused_moe_lora_gate_up_fn(
-        self,
-    ) -> bool:  ## adding for fused MoE LoRA Gate/Up
-        return self in [
-            OpType.FUSED_MOE_LORA_GATE_UP_SHRINK,
-            OpType.FUSED_MOE_LORA_GATE_UP_EXPAND,
-        ]
-
-    def is_fused_moe_lora_down_fn(self) -> bool:  ## adding for fused MoE LoRA Down
-        return self in [
-            OpType.FUSED_MOE_LORA_DOWN_SHRINK,
-            OpType.FUSED_MOE_LORA_DOWN_EXPAND,
-        ]
-
-    def is_fused_moe_lora_shrink_fn(self) -> bool:
-        return self in [
-            OpType.FUSED_MOE_LORA_GATE_UP_SHRINK,
-            OpType.FUSED_MOE_LORA_DOWN_SHRINK,
-        ]
-
-    def is_fused_moe_lora_expand_fn(self) -> bool:
-        return self in [
-            OpType.FUSED_MOE_LORA_GATE_UP_EXPAND,
-            OpType.FUSED_MOE_LORA_DOWN_EXPAND,
-        ]
-
     def num_slices(self) -> list[int]:
-        if self.is_fused_moe_lora_gate_up_fn():
-            return [2]
-        elif self.is_fused_moe_lora_down_fn():
-            return [1]
         return [1, 2, 3]
 
     def mkn(
         self, batch_size: int, seq_length: int, hidden_size: int, lora_rank: int
     ) -> tuple[int, int, int]:
         num_tokens = batch_size * seq_length
-        if self.is_shrink_fn() or self.is_fused_moe_lora_fn():
+        if self.is_shrink_fn():
             m = num_tokens
             k = hidden_size
             n = lora_rank
-        elif self.is_expand_fn():
+        else:
+            assert self.is_expand_fn()
             m = num_tokens
             k = lora_rank
             n = hidden_size
@@ -296,36 +232,9 @@ class OpType(Enum):
         """
         if self.is_shrink_fn():
             return op_dtype, op_dtype, torch.float32
-        elif self.is_expand_fn():
+        else:
+            assert self.is_expand_fn()
             return torch.float32, op_dtype, op_dtype
-        else:
-            assert self.is_fused_moe_lora_fn()
-            return op_dtype, op_dtype, op_dtype
-
-    def matmul_shapes_fused_moe_lora(
-        self,
-        m: int,
-        n: int,
-        k: int,
-        num_loras: int,
-        num_slices: int,
-        top_k_num: int,
-        num_experts: int,
-    ) -> tuple[tuple[int], tuple[int], tuple[int], tuple[int]]:
-        if self.is_fused_moe_lora_shrink_fn():
-            input_shape = (
-                (m * top_k_num, n)
-                if self in [OpType.FUSED_MOE_LORA_DOWN_SHRINK]
-                else (m, n)
-            )
-            output_shape = (num_slices, m, top_k_num, k)
-            weight_shape = (num_loras, num_experts, k, n)
-        else:
-            assert self.is_fused_moe_lora_expand_fn()
-            input_shape = (num_slices, m, top_k_num, k)
-            output_shape = (m, top_k_num, n * num_slices)
-            weight_shape = (num_loras, num_experts, n, k)
-        return (input_shape, weight_shape, output_shape)
 
     def matmul_shapes(
         self,
@@ -335,8 +244,6 @@ class OpType(Enum):
         lora_rank: int,
         num_loras: int,
         num_slices: int,
-        top_k_num: int | None = None,
-        num_experts: int | None = None,
     ) -> tuple[tuple[int, ...], tuple[int, ...], tuple[int, ...]]:
         """
         Given num_slices, return the shapes of the A, B, and C matrices
@@ -351,16 +258,6 @@ class OpType(Enum):
         if self in [OpType.LORA_EXPAND]:
             # LoRA expand kernels support num_slices inherently in the kernel
             return ((num_slices, m, k), b_shape, (m, n * num_slices))
-        if self.is_fused_moe_lora_fn():
-            return self.matmul_shapes_fused_moe_lora(
-                m,
-                k,
-                n,
-                num_loras,
-                num_slices,
-                top_k_num,
-                num_experts,
-            )
         raise ValueError(f"Unrecognized op_type {self}")
 
     def bench_fn(self) -> Callable:
@@ -368,16 +265,6 @@ class OpType(Enum):
             return lora_shrink
         if self == OpType.LORA_EXPAND:
             return lora_expand
-        if self in [
-            OpType.FUSED_MOE_LORA_GATE_UP_SHRINK,
-            OpType.FUSED_MOE_LORA_DOWN_SHRINK,
-        ]:
-            return fused_moe_lora_shrink
-        if self in [
-            OpType.FUSED_MOE_LORA_GATE_UP_EXPAND,
-            OpType.FUSED_MOE_LORA_DOWN_EXPAND,
-        ]:
-            return fused_moe_lora_expand
 
         raise ValueError(f"Unrecognized optype {self}")
 
@@ -431,8 +318,6 @@ class BenchmarkContext:
     sort_by_lora_id: bool
     dtype: torch.dtype
     seq_length: int | None = None
-    num_experts: int | None = None  # num_experts for MoE based ops
-    top_k_num: int | None = None  # top_k for MoE based ops
     num_slices: int | None = None  # num_slices for slice based ops
 
     def with_seq_length(self, seq_length: int) -> "BenchmarkContext":
@@ -488,11 +373,6 @@ class BenchmarkTensors:
             f"{dtype_to_str(self.output.dtype)}"
         )
 
-    def get_num_tokens(self, size: int, top_k_num: int, op_type: OpType):
-        return (
-            size * top_k_num if op_type in [OpType.FUSED_MOE_LORA_DOWN_SHRINK] else size
-        )
-
     @staticmethod
     def make(
         ctx: BenchmarkContext, op_type: OpType, device: str = "cuda"
@@ -505,8 +385,6 @@ class BenchmarkTensors:
             ctx.lora_rank,
             ctx.num_loras,
             ctx.num_slices,
-            ctx.top_k_num,
-            ctx.num_experts,
         )
         a_type, b_type, c_type = op_type.matmul_dtypes(ctx.dtype)
         input_tensor, lora_weights, output_tensor = make_rand_tensors(
@@ -554,27 +432,17 @@ class BenchmarkTensors:
             prompt_lora_indices_tensor,
         )
 
-    def sanity_check(self, ctx: BenchmarkContext, op_type: OpType) -> None:
+    def sanity_check(self) -> None:
         """
         Fails asserts when non-conformality is detected.
         """
-        num_tokens = (
-            self.input.shape[1]
-            if op_type.is_fused_moe_lora_expand_fn()
-            else self.input.shape[-2]
-        )
+        num_tokens = self.input.shape[-2]
         # check metadata tensors
-        ## In down shrink case, each token is repeated top_k_num times
-        assert num_tokens == self.get_num_tokens(
-            torch.sum(self.seq_lens), ctx.top_k_num, op_type
-        ), f"Expected {num_tokens} tokens, but got {torch.sum(self.seq_lens)}"
+        assert torch.sum(self.seq_lens) == num_tokens
         num_seqs = self.seq_lens.shape[0]
         # assert self.seq_start_loc.shape[0] == num_seqs
-        ## In down shrink case, each prompt corresponds to top_k_num sequences
         assert self.prompt_lora_mapping.shape[0] == num_seqs
-        assert self.get_num_tokens(
-            self.lora_kernel_meta.token_lora_mapping.shape[0], ctx.top_k_num, op_type
-        )
+        assert self.lora_kernel_meta.token_lora_mapping.shape[0] == num_tokens
 
     def to_device(self, device: str):
         """
@@ -603,111 +471,21 @@ class BenchmarkTensors:
                 to_device(field) if field_name != "no_lora_flag_cpu" else field,
             )
 
-    def metadata(self, ctx: BenchmarkContext, op_type: OpType) -> tuple[int, int, int]:
+    def metadata(self) -> tuple[int, int, int]:
         """
         Return num_seqs, num_tokens and max_seq_len
         """
         num_seqs = self.seq_lens.shape[0]
-        num_tokens = self.get_num_tokens(
-            self.lora_kernel_meta.token_lora_mapping.shape[0], ctx.top_k_num, op_type
-        )
+        num_tokens = self.lora_kernel_meta.token_lora_mapping.shape[0]
         max_seq_len = torch.max(self.seq_lens).item()
         num_slices = len(self.lora_weights_lst)
         return num_seqs, num_tokens, max_seq_len, num_slices
 
-    def fused_moe_lora_data_prepare(
-        self,
-        block_size: int,
-        token_lora_mapping: torch.Tensor,
-        ctx: BenchmarkContext,
-    ):
-        def moe_lora_align_block_size(
-            topk_ids: torch.Tensor,
-            token_lora_mapping: torch.Tensor,
-            block_size: int,
-            num_experts: int,
-            max_loras: int,
-            expert_map: torch.Tensor | None = None,
-            pad_sorted_ids: bool = False,
-        ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
-            """
-            Aligns tokens and experts into block-sized chunks for LoRA-based
-            mixture-of-experts (MoE) execution.
-            """
-            max_num_tokens_padded = topk_ids.numel() + num_experts * (block_size - 1)
-            if pad_sorted_ids:
-                max_num_tokens_padded = round_up(max_num_tokens_padded, block_size)
-            sorted_ids = torch.empty(
-                (max_loras * max_num_tokens_padded,),
-                dtype=torch.int32,
-                device=topk_ids.device,
-            )
-            max_num_m_blocks = triton.cdiv(max_num_tokens_padded, block_size)
-            # Expert ids must be set default to -1 to prevent a blank block
-            expert_ids = torch.empty(
-                (max_loras * max_num_m_blocks,),
-                dtype=torch.int32,
-                device=topk_ids.device,
-            )
-            num_tokens_post_pad = torch.empty(
-                (max_loras), dtype=torch.int32, device=topk_ids.device
-            )
-
-            ops.moe_lora_align_block_size(
-                topk_ids,
-                token_lora_mapping,
-                num_experts,
-                block_size,
-                max_loras,
-                max_num_tokens_padded,
-                max_num_m_blocks,
-                sorted_ids,
-                expert_ids,
-                num_tokens_post_pad,
-            )
-            if expert_map is not None:
-                expert_ids = expert_map[expert_ids]
-
-            return sorted_ids, expert_ids, num_tokens_post_pad
-
-        num_tokens = ctx.batch_size
-        curr_topk_ids = torch.randint(
-            0,
-            ctx.num_experts,
-            (num_tokens, ctx.top_k_num),
-            device="cuda",
-            dtype=torch.int32,
-        )
-        topk_weights = torch.randint(
-            0,
-            ctx.num_experts,
-            (num_tokens, ctx.top_k_num),
-            device="cuda",
-            dtype=torch.int32,
-        )
-
-        (sorted_token_ids_lora, expert_ids_lora, num_tokens_post_padded_lora) = (
-            moe_lora_align_block_size(
-                topk_ids=curr_topk_ids,
-                token_lora_mapping=token_lora_mapping,
-                block_size=block_size,
-                num_experts=ctx.num_experts,
-                max_loras=ctx.num_loras,
-            )
-        )
-
-        sorted_token_ids = sorted_token_ids_lora.view(ctx.num_loras, -1)
-        expert_ids = expert_ids_lora.view(ctx.num_loras, -1)
-        num_tokens_post_padded = num_tokens_post_padded_lora
-        return (topk_weights, sorted_token_ids, expert_ids, num_tokens_post_padded)
-
-    def as_lora_shrink_kwargs(
-        self, ctx: BenchmarkContext, op_type: OpType
-    ) -> dict[str, Any]:
-        self.sanity_check(ctx, op_type)
+    def as_lora_shrink_kwargs(self) -> dict[str, Any]:
+        self.sanity_check()
         self.to_device(self.input.device)
 
-        _, num_tokens, _, num_slices = self.metadata(ctx, op_type)
+        _, num_tokens, _, num_slices = self.metadata()
 
         # Sanity check matrix shapes.
         i_shape, lw_shape, o_shape = (
@@ -742,13 +520,11 @@ class BenchmarkTensors:
             "no_lora_flag_cpu": self.lora_kernel_meta.no_lora_flag_cpu,
         }
 
-    def as_lora_expand_kwargs(
-        self, ctx: BenchmarkContext, op_type: OpType, add_inputs: bool
-    ) -> dict[str, Any]:
-        self.sanity_check(ctx, op_type)
+    def as_lora_expand_kwargs(self, add_inputs: bool) -> dict[str, Any]:
+        self.sanity_check()
         self.to_device(self.input.device)
 
-        _, num_tokens, _, num_slices = self.metadata(ctx, op_type)
+        _, num_tokens, _, num_slices = self.metadata()
 
         # Sanity check matrix shapes.
         i_shape, lw_shape, o_shape = (
@@ -785,173 +561,18 @@ class BenchmarkTensors:
             "no_lora_flag_cpu": self.lora_kernel_meta.no_lora_flag_cpu,
         }
 
-    def as_fused_moe_lora_shrink_kwargs(
-        self, ctx: BenchmarkContext, op_type: OpType
-    ) -> dict[str, Any]:
-        self.sanity_check(ctx, op_type)
-        self.to_device(self.input.device)
-
-        _, num_tokens, _, num_slices = self.metadata(ctx, op_type)
-
-        # Sanity check matrix shapes.
-        i_shape, lw_shape, o_shape = (
-            self.input.shape,
-            self.lora_weights_lst[0].shape,
-            self.output.shape,
-        )
-        # Expected input shape : [num_tokens, hidden_size] for gate_up
-        # Expected input shape : [top_k_num * num_tokens, hidden_size] for down
-        assert len(i_shape) == 2
-        assert i_shape[0] == num_tokens
-        hidden_size = i_shape[1]
-        # Expected lora weight shape [max_lora, num_experts, lora_rank, hidden_size]
-        assert len(lw_shape) == 4
-        assert lw_shape[-1] == hidden_size
-        lora_rank = lw_shape[-2]
-        # Expected output shape : [num_slices, num_tokens, top_k_num, lora_rank]
-        assert len(o_shape) == 4
-        assert (
-            o_shape
-            == (num_slices, num_tokens // ctx.top_k_num, ctx.top_k_num, lora_rank)
-            if op_type in [OpType.FUSED_MOE_LORA_DOWN_SHRINK]
-            else o_shape == (num_slices, num_tokens, ctx.top_k_num, lora_rank)
-        )
-        kernel_config = get_lora_op_configs(
-            op_type.name.lower(),
-            max_loras=lw_shape[0],
-            batch=num_tokens,
-            hidden_size=hidden_size,
-            rank=lora_rank,
-            num_slices=num_slices,
-            add_inputs=False,
-        )
-
-        (topk_weights, sorted_token_ids, expert_ids, num_tokens_post_padded) = (
-            self.fused_moe_lora_data_prepare(
-                block_size=kernel_config["BLOCK_SIZE_M"],
-                token_lora_mapping=self.lora_kernel_meta.token_lora_mapping,
-                ctx=ctx,
-            )
-        )
-
-        return {
-            "qcurr_hidden_states": self.input,
-            "lora_a_stacked": self.lora_weights_lst,
-            "a_intermediate_cache1": self.output,
-            "topk_weights": topk_weights,
-            "sorted_token_ids": sorted_token_ids,
-            "expert_ids": expert_ids,
-            "num_tokens_post_padded": num_tokens_post_padded,
-            "top_k_num": ctx.top_k_num,
-            "device": self.input.device,
-            "N": lora_rank,
-            "M": topk_weights.shape[0],
-            "EM": sorted_token_ids.shape[1],
-            "K": self.input.shape[1],
-            "num_tokens": num_tokens,
-            "num_experts": ctx.num_experts,
-            "num_slices": num_slices,
-            "shrink_block_size_m": kernel_config["BLOCK_SIZE_M"],
-            "shrink_block_size_n": kernel_config["BLOCK_SIZE_N"],
-            "shrink_block_size_k": kernel_config["BLOCK_SIZE_K"],
-            "shrink_group_size_m": kernel_config["GROUP_SIZE_M"],
-            "shrink_num_warps": kernel_config["NUM_WARPS"],
-            "shrink_num_stages": kernel_config["NUM_STAGES"],
-            "shrink_split_k": kernel_config.get("SPLIT_K", 1),
-            "mul_routed_weight": op_type.is_fused_moe_lora_down_fn(),
-        }
-
-    def as_fused_moe_lora_expand_kwargs(
-        self, ctx: BenchmarkContext, op_type: OpType
-    ) -> dict[str, Any]:
-        self.sanity_check(ctx, op_type)
-        self.to_device(self.input.device)
-
-        _, num_tokens, _, num_slices = self.metadata(ctx, op_type)
-
-        # Sanity check matrix shapes.
-        i_shape, lw_shape, o_shape = (
-            self.input.shape,
-            self.lora_weights_lst[0].shape,
-            self.output.shape,
-        )
-
-        # Expected input shape : [num_slices, num_tokens, top_k_num, lora_rank]
-        assert len(i_shape) == 4
-        assert i_shape[0] == num_slices
-        assert i_shape[1] == num_tokens
-        lora_rank = i_shape[-1]
-        # Expected lora weight shape : [num_loras, num_experts, hidden_size, lora_rank]
-        assert len(lw_shape) == 4
-        assert lw_shape[-1] == lora_rank
-        hidden_size = lw_shape[-2]
-        # Expected output shape : [num_tokens, top_k_num, hidden_size * num_slices]
-        assert len(o_shape) == 3
-        assert o_shape == (num_tokens, ctx.top_k_num, hidden_size * num_slices)
-
-        kernel_config = get_lora_op_configs(
-            op_type.name.lower(),
-            max_loras=lw_shape[0],
-            batch=num_tokens,
-            hidden_size=hidden_size,
-            rank=lora_rank,
-            num_slices=num_slices,
-            add_inputs=False,
-        )
-
-        (topk_weights, sorted_token_ids, expert_ids, num_tokens_post_padded) = (
-            self.fused_moe_lora_data_prepare(
-                block_size=kernel_config["BLOCK_SIZE_M"],
-                token_lora_mapping=self.lora_kernel_meta.token_lora_mapping,
-                ctx=ctx,
-            )
-        )
-
-        return {
-            "a_intermediate_cache1": self.input,
-            "lora_b_stacked": self.lora_weights_lst,
-            "output": self.output,
-            "topk_weights": topk_weights,
-            "sorted_token_ids": sorted_token_ids,
-            "expert_ids": expert_ids,
-            "num_tokens_post_padded": num_tokens_post_padded,
-            "top_k_num": ctx.top_k_num,
-            "device": self.input.device,
-            "N": lora_rank,
-            "M": topk_weights.shape[0],
-            "EM": sorted_token_ids.shape[1],
-            "K": self.input.shape[1],
-            "num_tokens": num_tokens,
-            "num_experts": ctx.num_experts,
-            "num_slices": num_slices,
-            "max_lora_rank": lora_rank,
-            "w1_output_dim_size": lw_shape[2],
-            "expand_block_size_m": kernel_config["BLOCK_SIZE_M"],
-            "expand_block_size_n": kernel_config["BLOCK_SIZE_N"],
-            "expand_block_size_k": kernel_config["BLOCK_SIZE_K"],
-            "expand_group_size_m": kernel_config["GROUP_SIZE_M"],
-            "expand_num_warps": kernel_config["NUM_WARPS"],
-            "expand_num_stages": kernel_config["NUM_STAGES"],
-            "expand_split_k": kernel_config.get("SPLIT_K", 1),
-            "mul_routed_weight": op_type.is_fused_moe_lora_down_fn(),
-        }
-
     def bench_fn_kwargs(
-        self, ctx: BenchmarkContext, op_type: OpType, add_inputs: bool | None = None
+        self, op_type: OpType, add_inputs: bool | None = None
     ) -> dict[str, Any]:
-        if op_type.is_shrink_fn() or op_type.is_fused_moe_lora_fn():
+        if op_type.is_shrink_fn():
             assert add_inputs is None
         else:
             assert add_inputs is not None
 
         if op_type == OpType.LORA_SHRINK:
-            return self.as_lora_shrink_kwargs(ctx, op_type)
+            return self.as_lora_shrink_kwargs()
         if op_type == OpType.LORA_EXPAND:
-            return self.as_lora_expand_kwargs(ctx, op_type, add_inputs)
-        if op_type.is_fused_moe_lora_shrink_fn():
-            return self.as_fused_moe_lora_shrink_kwargs(ctx, op_type)
-        if op_type.is_fused_moe_lora_expand_fn():
-            return self.as_fused_moe_lora_expand_kwargs(ctx, op_type)
+            return self.as_lora_expand_kwargs(add_inputs)
         raise ValueError(f"Unrecognized optype {self}")
 
     def test_correctness(
@@ -996,7 +617,7 @@ def bench_optype(
     test_correctness: bool = False,
 ) -> TMeasurement:
     assert arg_pool_size >= 1
-    if op_type.is_shrink_fn() or op_type.is_fused_moe_lora_fn():
+    if op_type.is_shrink_fn():
         assert expand_fn_add_inputs is None
     else:
         assert expand_fn_add_inputs is not None
@@ -1006,30 +627,23 @@ def bench_optype(
         BenchmarkTensors.make(ctx, op_type) for _ in range(arg_pool_size)
     ]
     for bt in bench_tensors:
-        bt.sanity_check(ctx, op_type)
+        bt.sanity_check()
 
     # Test correctness of our implementation.
     if test_correctness:
-        assert op_type in [OpType.LORA_SHRINK, OpType.LORA_EXPAND], (
-            f"Correctness testing is not supported for {op_type.name}."
-        )
         assert all(
-            [
-                bt.test_correctness(ctx, op_type, expand_fn_add_inputs)
-                for bt in bench_tensors
-            ]
+            [bt.test_correctness(op_type, expand_fn_add_inputs) for bt in bench_tensors]
         )
 
     # BenchmarkTensors -> dict (kwargs)
     kwargs_list = [
-        bt.bench_fn_kwargs(ctx, op_type, add_inputs=expand_fn_add_inputs)
+        bt.bench_fn_kwargs(op_type, add_inputs=expand_fn_add_inputs)
         for bt in bench_tensors
     ]
 
     # Clear LoRA optimization hash-maps.
     _LORA_A_PTR_DICT.clear()
     _LORA_B_PTR_DICT.clear()
-    _LORA_PTR_DICT.clear()
     # Run bench function so that _LORA_A_PTR_DICT and _LORA_B_PTR_DICT are set up
     for kwargs in kwargs_list:
         op_type.bench_fn()(**kwargs)
@@ -1179,9 +793,7 @@ def run(args: argparse.Namespace, bench_ctxs: list[BenchmarkContext]):
 
                     # Benchmark bench_op
                     expand_fn_add_inputs = (
-                        [None]
-                        if bench_op.is_shrink_fn() or bench_op.is_fused_moe_lora_fn()
-                        else args.expand_fn_add_inputs
+                        [None] if bench_op.is_shrink_fn() else args.expand_fn_add_inputs
                     )
                     for add_input_arg in expand_fn_add_inputs:
                         seq_len_timers.append(
@@ -1219,22 +831,12 @@ def as_benchmark_contexts(
     hidden_sizes: list[int], lora_ranks: list[int], args: argparse.Namespace
 ) -> list[BenchmarkContext]:
     ctxs: list[BenchmarkContext] = []
-    for (
-        batch_size,
-        hidden_size,
-        lora_rank,
-        num_loras,
-        sort_by_lora_id,
-        top_k_num,
-        num_experts,
-    ) in product(  # noqa
+    for batch_size, hidden_size, lora_rank, num_loras, sort_by_lora_id in product(  # noqa
         args.batch_sizes,
         list(hidden_sizes),
         lora_ranks,
         args.num_loras,
         args.sort_by_lora_id,
-        args.top_k_nums,
-        args.num_experts,
     ):
         ctxs.append(
             BenchmarkContext(
@@ -1249,8 +851,6 @@ def as_benchmark_contexts(
                 seq_length=None,
                 sort_by_lora_id=sort_by_lora_id,
                 dtype=args.dtype,
-                top_k_num=top_k_num,
-                num_experts=num_experts,
                 # To be filled based on the OpType to benchmark
                 num_slices=None,
             )
@@ -1412,22 +1012,6 @@ if __name__ == "__main__":
             ),
         )
 
-        p.add_argument(
-            "--top-k-nums",
-            nargs="+",
-            type=int,
-            default=DEFAULT_TOP_K_NUMS,
-            help="Top-K values for MoE LoRA operations",
-        )
-
-        p.add_argument(
-            "--num-experts",
-            nargs="+",
-            type=int,
-            default=DEFAULT_NUM_EXPERTS,
-            help="Number of experts for MoE LoRA operations",
-        )
-
     parser = FlexibleArgumentParser(
         description=f"""
 Benchmark LoRA kernels:

benchmarks/kernels/benchmark_machete.py

@@ -33,7 +33,7 @@ from vllm.model_executor.layers.quantization.utils.quant_utils import (
     quantize_weights,
 )
 from vllm.scalar_type import ScalarType, scalar_types
-from vllm.utils.argparse_utils import FlexibleArgumentParser
+from vllm.utils import FlexibleArgumentParser
 
 DEFAULT_MODELS = ["meta-llama/Llama-3-8b", "meta-llama/Llama-2-70b-hf"]
 DEFAULT_BATCH_SIZES = [1, 16, 32, 64, 128, 256, 512, 1024]

benchmarks/kernels/benchmark_marlin.py

@@ -44,7 +44,7 @@ from vllm.model_executor.layers.quantization.utils.quant_utils import (
     sort_weights,
 )
 from vllm.scalar_type import ScalarType, scalar_types
-from vllm.utils.argparse_utils import FlexibleArgumentParser
+from vllm.utils import FlexibleArgumentParser
 
 DEFAULT_MODELS = ["meta-llama/Llama-2-7b-hf/TP1"]
 DEFAULT_BATCH_SIZES = [1, 16, 32, 64, 128, 256, 512, 1024, 2048, 4096, 8192]

benchmarks/kernels/benchmark_moe.py

@@ -22,7 +22,7 @@ from vllm.model_executor.layers.fused_moe.fused_moe import *
 from vllm.platforms import current_platform
 from vllm.transformers_utils.config import get_config
 from vllm.triton_utils import triton
-from vllm.utils.argparse_utils import FlexibleArgumentParser
+from vllm.utils import FlexibleArgumentParser
 
 FP8_DTYPE = current_platform.fp8_dtype()
 
@@ -211,7 +211,7 @@ def get_rocm_tuning_space(use_fp16):
     num_warps_range = [1, 2, 4, 8]
     group_m_range = [1, 4, 8, 16, 32]
     num_stage_range = [2]
-    waves_per_eu_range = [0, 1, 2, 4]
+    waves_per_eu_range = [0]
     matrix_instr_nonkdim_range = [16, 32] if use_fp16 else []
     kpack_range = [1, 2] if use_fp16 else []
 
@@ -590,7 +590,6 @@ def main(args: argparse.Namespace):
         "DeepseekV3ForCausalLM",
         "DeepseekV32ForCausalLM",
         "Glm4MoeForCausalLM",
-        "NemotronHForCausalLM",
     ):
         E = config.n_routed_experts
         topk = config.num_experts_per_tok
@@ -616,11 +615,6 @@ def main(args: argparse.Namespace):
         topk = config.moe_topk[0]
         intermediate_size = config.moe_intermediate_size[0]
         hidden_size = config.hidden_size
-    elif config.architectures[0] in ["Qwen3OmniMoeForConditionalGeneration"]:
-        E = config.thinker_config.text_config.num_experts
-        topk = config.thinker_config.text_config.num_experts_per_tok
-        intermediate_size = config.thinker_config.text_config.moe_intermediate_size
-        hidden_size = config.thinker_config.text_config.hidden_size
     else:
         # Support for llama4
         config = config.get_text_config()

benchmarks/kernels/benchmark_moe_permute_unpermute.py

@@ -17,7 +17,7 @@ from vllm.model_executor.layers.fused_moe.moe_permute_unpermute import (
 )
 from vllm.model_executor.layers.fused_moe.utils import _fp8_quantize
 from vllm.platforms import current_platform
-from vllm.utils.argparse_utils import FlexibleArgumentParser
+from vllm.utils import FlexibleArgumentParser
 
 FP8_DTYPE = current_platform.fp8_dtype()
 

benchmarks/kernels/benchmark_mrope.py

@@ -39,7 +39,7 @@ import torch
 from vllm.model_executor.layers.rotary_embedding import get_rope
 from vllm.platforms import current_platform
 from vllm.transformers_utils.config import get_config
-from vllm.utils.argparse_utils import FlexibleArgumentParser
+from vllm.utils import FlexibleArgumentParser
 
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 

benchmarks/kernels/benchmark_paged_attention.py

@@ -9,9 +9,9 @@ import torch
 from vllm import _custom_ops as ops
 from vllm.logger import init_logger
 from vllm.platforms import current_platform
-from vllm.utils.argparse_utils import FlexibleArgumentParser
-from vllm.utils.torch_utils import (
+from vllm.utils import (
     STR_DTYPE_TO_TORCH_DTYPE,
+    FlexibleArgumentParser,
     create_kv_caches_with_random,
 )
 

benchmarks/kernels/benchmark_polynorm.py

@@ -0,0 +1,155 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import itertools
+
+import torch
+
+from vllm import _custom_ops as vllm_ops
+from vllm.triton_utils import triton
+
+
+def polynorm_naive(
+    x: torch.Tensor,
+    weight: torch.Tensor,
+    bias: torch.Tensor,
+    eps: float = 1e-6,
+):
+    orig_shape = x.shape
+    x = x.view(-1, x.shape[-1])
+
+    def norm(x, eps: float):
+        return x / torch.sqrt(x.pow(2).mean(-1, keepdim=True) + eps)
+
+    x = x.float()
+    return (
+        (
+            weight[0] * norm(x**3, eps)
+            + weight[1] * norm(x**2, eps)
+            + weight[2] * norm(x, eps)
+            + bias
+        )
+        .to(weight.dtype)
+        .view(orig_shape)
+    )
+
+
+def polynorm_vllm(
+    x: torch.Tensor,
+    weight: torch.Tensor,
+    bias: torch.Tensor,
+    eps: float = 1e-6,
+):
+    orig_shape = x.shape
+    x = x.view(-1, x.shape[-1])
+
+    out = torch.empty_like(x)
+    vllm_ops.poly_norm(out, x, weight, bias, eps)
+    output = out
+
+    output = output.view(orig_shape)
+    return output
+
+
+def calculate_diff(batch_size, seq_len, hidden_dim):
+    dtype = torch.bfloat16
+    x = torch.randn(batch_size, seq_len, hidden_dim, dtype=dtype, device="cuda")
+    weight = torch.ones(3, dtype=dtype, device="cuda")
+    bias = torch.ones(1, dtype=dtype, device="cuda")
+
+    output_naive = polynorm_naive(x, weight, bias)
+    output_vllm = polynorm_vllm(x, weight, bias)
+
+    if torch.allclose(output_naive, output_vllm, atol=1e-2, rtol=1e-2):
+        print("✅ All implementations match")
+    else:
+        print("❌ Implementations differ")
+
+
+batch_size_range = [2**i for i in range(0, 7, 2)]
+seq_length_range = [2**i for i in range(6, 11, 1)]
+dim_range = [2048, 4096]
+configs = list(itertools.product(dim_range, batch_size_range, seq_length_range))
+
+
+def get_benchmark():
+    @triton.testing.perf_report(
+        triton.testing.Benchmark(
+            x_names=["dim", "batch_size", "seq_len"],
+            x_vals=[list(_) for _ in configs],
+            line_arg="provider",
+            line_vals=["naive", "vllm"],
+            line_names=["Naive", "vLLM"],
+            styles=[("blue", "-"), ("red", "-")],
+            ylabel="us",
+            plot_name="polynorm-perf",
+            args={},
+        )
+    )
+    def benchmark(dim, batch_size, seq_len, provider):
+        dtype = torch.bfloat16
+        hidden_dim = dim * 4
+
+        x = torch.randn(batch_size, seq_len, hidden_dim, dtype=dtype, device="cuda")
+        weight = torch.ones(3, dtype=dtype, device="cuda")
+        bias = torch.ones(1, dtype=dtype, device="cuda")
+
+        quantiles = [0.5, 0.2, 0.8]
+
+        if provider == "naive":
+            ms, min_ms, max_ms = triton.testing.do_bench(
+                lambda: polynorm_naive(x, weight, bias),
+                quantiles=quantiles,
+            )
+        else:
+            ms, min_ms, max_ms = triton.testing.do_bench(
+                lambda: polynorm_vllm(x, weight, bias),
+                quantiles=quantiles,
+            )
+
+        return 1000 * ms, 1000 * max_ms, 1000 * min_ms
+
+    return benchmark
+
+
+if __name__ == "__main__":
+    import argparse
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--batch-size",
+        type=int,
+        default=4,
+        help="Batch size",
+    )
+    parser.add_argument(
+        "--seq-len",
+        type=int,
+        default=128,
+        help="Sequence length",
+    )
+    parser.add_argument(
+        "--hidden-dim",
+        type=int,
+        default=8192,
+        help="Intermediate size of MLP",
+    )
+    parser.add_argument(
+        "--save-path",
+        type=str,
+        default="./configs/polnorm/",
+        help="Path to save polnorm benchmark results",
+    )
+
+    args = parser.parse_args()
+
+    # Run correctness test
+    calculate_diff(
+        batch_size=args.batch_size,
+        seq_len=args.seq_len,
+        hidden_dim=args.hidden_dim,
+    )
+
+    benchmark = get_benchmark()
+    # Run performance benchmark
+    benchmark.run(print_data=True, save_path=args.save_path)

benchmarks/kernels/benchmark_quant.py

@@ -7,8 +7,7 @@ import torch
 
 from vllm import _custom_ops as ops
 from vllm.platforms import current_platform
-from vllm.utils.argparse_utils import FlexibleArgumentParser
-from vllm.utils.torch_utils import STR_DTYPE_TO_TORCH_DTYPE
+from vllm.utils import STR_DTYPE_TO_TORCH_DTYPE, FlexibleArgumentParser
 
 
 @torch.inference_mode()

benchmarks/kernels/benchmark_reshape_and_cache.py

@@ -9,9 +9,9 @@ from tabulate import tabulate
 from vllm import _custom_ops as ops
 from vllm.logger import init_logger
 from vllm.platforms import current_platform
-from vllm.utils.argparse_utils import FlexibleArgumentParser
-from vllm.utils.torch_utils import (
+from vllm.utils import (
     STR_DTYPE_TO_TORCH_DTYPE,
+    FlexibleArgumentParser,
     create_kv_caches_with_random,
 )
 

benchmarks/kernels/benchmark_reshape_and_cache_flash.py

@@ -12,9 +12,9 @@ from vllm.attention.ops.triton_reshape_and_cache_flash import (
 )
 from vllm.logger import init_logger
 from vllm.platforms import current_platform
-from vllm.utils.argparse_utils import FlexibleArgumentParser
-from vllm.utils.torch_utils import (
+from vllm.utils import (
     STR_DTYPE_TO_TORCH_DTYPE,
+    FlexibleArgumentParser,
     create_kv_caches_with_random_flash,
 )
 

benchmarks/kernels/benchmark_rope.py

@@ -8,7 +8,7 @@ import torch
 
 from vllm.model_executor.layers.rotary_embedding import RotaryEmbedding, get_rope
 from vllm.platforms import current_platform
-from vllm.utils.argparse_utils import FlexibleArgumentParser
+from vllm.utils import FlexibleArgumentParser
 
 
 def benchmark_rope_kernels_multi_lora(

benchmarks/kernels/benchmark_shapes.py

@@ -78,11 +78,11 @@ WEIGHT_SHAPES = {
 }
 
 WEIGHT_SHAPES_MOE = {
-    "mistralai/Mixtral-8x7B-Instruct-v0.1": [
+    "nm-testing/Mixtral-8x7B-Instruct-v0.1": [
         [8, 2, 4096, 28672],
         [8, 2, 14336, 4096],
     ],
-    "deepseek-ai/DeepSeek-V2-Lite": [
+    "nm-testing/deepseekv2-lite": [
         [64, 6, 2048, 1408],
     ],
     "ibm-granite/granite-3.0-1b-a400m": [

benchmarks/kernels/benchmark_trtllm_decode_attention.py

@@ -8,7 +8,7 @@ from datetime import datetime
 import flashinfer
 import torch
 
-from vllm.utils.math_utils import round_up
+from vllm.utils import round_up
 
 FLOAT32_BYTES = torch.finfo(torch.float).bits // 8
 FP8_DTYPE = torch.float8_e4m3fn

benchmarks/kernels/benchmark_trtllm_prefill_attention.py

@@ -8,7 +8,7 @@ from datetime import datetime
 import flashinfer
 import torch
 
-from vllm.utils.math_utils import round_up
+from vllm.utils import round_up
 
 FLOAT32_BYTES = torch.finfo(torch.float).bits // 8
 FP8_DTYPE = torch.float8_e4m3fn

benchmarks/kernels/benchmark_w8a8_block_fp8.py

@@ -18,7 +18,7 @@ from vllm.model_executor.layers.quantization.utils.fp8_utils import (
 )
 from vllm.platforms import current_platform
 from vllm.triton_utils import triton
-from vllm.utils.argparse_utils import FlexibleArgumentParser
+from vllm.utils import FlexibleArgumentParser
 
 mp.set_start_method("spawn", force=True)
 

benchmarks/kernels/graph_machete_bench.py

@@ -11,7 +11,7 @@ import regex as re
 import seaborn as sns
 from torch.utils.benchmark import Measurement as TMeasurement
 
-from vllm.utils.argparse_utils import FlexibleArgumentParser
+from vllm.utils import FlexibleArgumentParser
 
 if __name__ == "__main__":
     parser = FlexibleArgumentParser(

benchmarks/multi_turn/benchmark_serving_multi_turn.py

@@ -1251,7 +1251,7 @@ async def main() -> None:
         default=None,
         help="The model name used in the API. "
         "If not specified, the model name will be the "
-        "same as the `--model` argument. ",
+        "same as the ``--model`` argument. ",
     )
 
     parser.add_argument(
@@ -1429,6 +1429,8 @@ async def main() -> None:
     random.seed(args.seed)
     np.random.seed(args.seed)
 
+    if not os.path.exists(args.model):
+        raise OSError(f"Path does not exist: {args.model}")
     logger.info("Loading tokenizer")
     tokenizer = AutoTokenizer.from_pretrained(args.model)
 

benchmarks/overheads/benchmark_hashing.py

@@ -5,7 +5,7 @@ import cProfile
 import pstats
 
 from vllm import LLM, SamplingParams
-from vllm.utils.argparse_utils import FlexibleArgumentParser
+from vllm.utils import FlexibleArgumentParser
 
 # A very long prompt, total number of tokens is about 15k.
 LONG_PROMPT = ["You are an expert in large language models, aren't you?"] * 1000

cmake/cpu_extension.cmake

@@ -188,60 +188,16 @@ else()
     message(FATAL_ERROR "vLLM CPU backend requires AVX512, AVX2, Power9+ ISA, S390X ISA, ARMv8 or RISC-V support.")
 endif()
 
+#
+# Build oneDNN for W8A8 GEMM kernels (only for x86-AVX512 /ARM platforms)
+# Flag to enable ACL kernels for AARCH64 platforms
+if (VLLM_BUILD_ACL STREQUAL "ON")
+    set(USE_ACL ON)
+else()
+    set(USE_ACL OFF)
+endif()
 
-# Build oneDNN for GEMM kernels (only for x86-AVX512 /ARM platforms)
 if ((AVX512_FOUND AND NOT AVX512_DISABLED) OR (ASIMD_FOUND AND NOT APPLE_SILICON_FOUND) OR POWER9_FOUND OR POWER10_FOUND OR POWER11_FOUND)
-    # Fetch and build Arm Compute Library (ACL) as oneDNN's backend for AArch64
-    # TODO [fadara01]: remove this once ACL can be fetched and built automatically as a dependency of oneDNN
-    if(ASIMD_FOUND)
-        if(DEFINED ENV{ACL_ROOT_DIR} AND IS_DIRECTORY "$ENV{ACL_ROOT_DIR}")
-            message(STATUS "Using ACL from specified source directory: $ENV{ACL_ROOT_DIR}")
-        else()
-            message(STATUS "Downloading Arm Compute Library (ACL) from GitHub")
-            FetchContent_Populate(arm_compute
-                SUBBUILD_DIR "${FETCHCONTENT_BASE_DIR}/arm_compute-subbuild"
-                SOURCE_DIR   "${FETCHCONTENT_BASE_DIR}/arm_compute-src"
-                GIT_REPOSITORY https://github.com/ARM-software/ComputeLibrary.git
-                GIT_TAG        v52.2.0
-                GIT_SHALLOW    TRUE
-                GIT_PROGRESS   TRUE
-            )
-            set(ENV{ACL_ROOT_DIR} "${arm_compute_SOURCE_DIR}")
-        endif()
-
-        # Build ACL with scons
-        include(ProcessorCount)
-        ProcessorCount(_NPROC)
-        set(_scons_cmd
-        scons -j${_NPROC}
-            Werror=0 debug=0 neon=1 examples=0 embed_kernels=0 os=linux
-            arch=armv8.2-a build=native benchmark_examples=0 fixed_format_kernels=1
-            multi_isa=1 openmp=1 cppthreads=0
-        )
-
-        # locate PyTorch's libgomp (e.g. site-packages/torch.libs/libgomp-947d5fa1.so.1.0.0)
-        # and create a local shim dir with it
-        include("${CMAKE_CURRENT_LIST_DIR}/utils.cmake")
-        vllm_prepare_torch_gomp_shim(VLLM_TORCH_GOMP_SHIM_DIR)
-
-        if(NOT VLLM_TORCH_GOMP_SHIM_DIR STREQUAL "")
-            list(APPEND _scons_cmd extra_link_flags=-L${VLLM_TORCH_GOMP_SHIM_DIR})
-        endif()
-
-        execute_process(
-            COMMAND ${_scons_cmd}
-            WORKING_DIRECTORY "$ENV{ACL_ROOT_DIR}"
-            RESULT_VARIABLE _acl_rc
-        )
-        if(NOT _acl_rc EQUAL 0)
-            message(FATAL_ERROR "ACL SCons build failed (exit ${_acl_rc}).")
-        endif()
-
-        set(ONEDNN_AARCH64_USE_ACL "ON")
-        set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wl,-rpath,$ENV{ACL_ROOT_DIR}/build/")
-        add_compile_definitions(VLLM_USE_ACL)
-    endif()
-
     set(FETCHCONTENT_SOURCE_DIR_ONEDNN "$ENV{FETCHCONTENT_SOURCE_DIR_ONEDNN}" CACHE PATH "Path to a local oneDNN source directory.")
 
     if(FETCHCONTENT_SOURCE_DIR_ONEDNN)
@@ -261,6 +217,16 @@ if ((AVX512_FOUND AND NOT AVX512_DISABLED) OR (ASIMD_FOUND AND NOT APPLE_SILICON
         )
     endif()
 
+    if(USE_ACL)
+        find_library(ARM_COMPUTE_LIBRARY NAMES arm_compute PATHS $ENV{ACL_ROOT_DIR}/build/)
+        if(NOT ARM_COMPUTE_LIBRARY)
+            message(FATAL_ERROR "Could not find ARM Compute Library: please set ACL_ROOT_DIR")
+        endif()
+        set(ONEDNN_AARCH64_USE_ACL "ON")
+        set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wl,-rpath,$ENV{ACL_ROOT_DIR}/build/")
+        add_compile_definitions(VLLM_USE_ACL)
+    endif()
+
     set(ONEDNN_LIBRARY_TYPE "STATIC")
     set(ONEDNN_BUILD_DOC "OFF")
     set(ONEDNN_BUILD_EXAMPLES "OFF")
@@ -343,7 +309,7 @@ message(STATUS "CPU extension source files: ${VLLM_EXT_SRC}")
 # Define extension targets
 #
 
-define_extension_target(
+define_gpu_extension_target(
     _C
     DESTINATION vllm
     LANGUAGE CXX
@@ -354,4 +320,4 @@ define_extension_target(
     WITH_SOABI
 )
 
-message(STATUS "Enabling C extension.")
+message(STATUS "Enabling C extension.")

cmake/external_projects/flashmla.cmake

@@ -19,7 +19,7 @@ else()
   FetchContent_Declare(
         flashmla
         GIT_REPOSITORY https://github.com/vllm-project/FlashMLA
-        GIT_TAG 46d64a8ebef03fa50b4ae74937276a5c940e3f95
+        GIT_TAG 5f65b85703c7ed75fda01e06495077caad207c3f
         GIT_PROGRESS TRUE
         CONFIGURE_COMMAND ""
         BUILD_COMMAND ""
@@ -66,7 +66,6 @@ if(FLASH_MLA_ARCHS)
         ${flashmla_SOURCE_DIR}/csrc/extension/torch_api.cpp
         ${flashmla_SOURCE_DIR}/csrc/extension/sm90/dense_fp8/pybind.cpp
         ${flashmla_SOURCE_DIR}/csrc/extension/sm90/dense_fp8/flash_fwd_mla_fp8_sm90.cu
-        ${flashmla_SOURCE_DIR}/csrc/extension/sm90/dense_fp8/flash_fwd_mla_metadata.cu
     )
 
     set(FlashMLA_INCLUDES
@@ -92,7 +91,7 @@ if(FLASH_MLA_ARCHS)
         SRCS "${FlashMLA_Extension_SOURCES}"
         CUDA_ARCHS "${FLASH_MLA_ARCHS}")
 
-    define_extension_target(
+    define_gpu_extension_target(
         _flashmla_C
         DESTINATION vllm
         LANGUAGE ${VLLM_GPU_LANG}
@@ -109,7 +108,7 @@ if(FLASH_MLA_ARCHS)
         $<$<COMPILE_LANGUAGE:CUDA>:-UPy_LIMITED_API>
         $<$<COMPILE_LANGUAGE:CXX>:-UPy_LIMITED_API>)
 
-    define_extension_target(
+    define_gpu_extension_target(
         _flashmla_extension_C
         DESTINATION vllm
         LANGUAGE ${VLLM_GPU_LANG}

cmake/external_projects/vllm_flash_attn.cmake

@@ -38,7 +38,7 @@ else()
   FetchContent_Declare(
           vllm-flash-attn
           GIT_REPOSITORY https://github.com/vllm-project/flash-attention.git
-          GIT_TAG 8e1b01d56210dc72030a2d0d41c2d8d266ba6309
+          GIT_TAG 8f468e7da54a8e2f98abfa7c38636aac91c0cba1
           GIT_PROGRESS TRUE
           # Don't share the vllm-flash-attn build between build types
           BINARY_DIR ${CMAKE_BINARY_DIR}/vllm-flash-attn

cmake/utils.cmake

@@ -129,44 +129,6 @@ function (get_torch_gpu_compiler_flags OUT_GPU_FLAGS GPU_LANG)
   set(${OUT_GPU_FLAGS} ${GPU_FLAGS} PARENT_SCOPE)
 endfunction()
 
-# Find libgomp that gets shipped with PyTorch wheel and create a shim dir with:
-#   libgomp.so    -> libgomp-<hash>.so...
-#   libgomp.so.1  -> libgomp-<hash>.so...
-# OUTPUT: TORCH_GOMP_SHIM_DIR  ("" if not found)
-function(vllm_prepare_torch_gomp_shim TORCH_GOMP_SHIM_DIR)
-  set(${TORCH_GOMP_SHIM_DIR} "" PARENT_SCOPE)
-
-  # Use run_python to locate vendored libgomp; never throw on failure.
-  run_python(_VLLM_TORCH_GOMP_PATH
-    "
-import os, glob
-try:
-  import torch
-  torch_pkg = os.path.dirname(torch.__file__)
-  site_root = os.path.dirname(torch_pkg)
-  torch_libs = os.path.join(site_root, 'torch.libs')
-  print(glob.glob(os.path.join(torch_libs, 'libgomp-*.so*'))[0])
-except:
-  print('')
-"
-    "failed to probe torch.libs for libgomp")
-
-  if(_VLLM_TORCH_GOMP_PATH STREQUAL "" OR NOT EXISTS "${_VLLM_TORCH_GOMP_PATH}")
-    return()
-  endif()
-
-  # Create shim under the build tree
-  set(_shim "${CMAKE_BINARY_DIR}/gomp_shim")
-  file(MAKE_DIRECTORY "${_shim}")
-
-  execute_process(COMMAND ${CMAKE_COMMAND} -E rm -f "${_shim}/libgomp.so")
-  execute_process(COMMAND ${CMAKE_COMMAND} -E rm -f "${_shim}/libgomp.so.1")
-  execute_process(COMMAND ${CMAKE_COMMAND} -E create_symlink "${_VLLM_TORCH_GOMP_PATH}" "${_shim}/libgomp.so")
-  execute_process(COMMAND ${CMAKE_COMMAND} -E create_symlink "${_VLLM_TORCH_GOMP_PATH}" "${_shim}/libgomp.so.1")
-
-  set(${TORCH_GOMP_SHIM_DIR} "${_shim}" PARENT_SCOPE)
-endfunction()
-
 # Macro for converting a `gencode` version number to a cmake version number.
 macro(string_to_ver OUT_VER IN_STR)
   string(REGEX REPLACE "\([0-9]+\)\([0-9]\)" "\\1.\\2" ${OUT_VER} ${IN_STR})
@@ -453,20 +415,21 @@ macro(override_gpu_arches GPU_ARCHES GPU_LANG GPU_SUPPORTED_ARCHES)
 endmacro()
 
 #
-# Define a target named `MOD_NAME` for a single extension. The
+# Define a target named `GPU_MOD_NAME` for a single extension. The
 # arguments are:
 #
 # DESTINATION <dest>         - Module destination directory.
-# LANGUAGE <lang>            - The language for this module, e.g. CUDA, HIP,
-#                              CXX, etc.
+# LANGUAGE <lang>            - The GPU language for this module, e.g CUDA, HIP,
+#                              etc.
 # SOURCES <sources>          - List of source files relative to CMakeLists.txt
 #                              directory.
 #
 # Optional arguments:
 #
-# ARCHITECTURES <arches>     - A list of target architectures in cmake format.
-#                              For GPU, refer to CMAKE_CUDA_ARCHITECTURES and
-#                              CMAKE_HIP_ARCHITECTURES for more info.
+# ARCHITECTURES <arches>     - A list of target GPU architectures in cmake
+#                              format.
+#                              Refer `CMAKE_CUDA_ARCHITECTURES` documentation
+#                              and `CMAKE_HIP_ARCHITECTURES` for more info.
 #                              ARCHITECTURES will use cmake's defaults if
 #                              not provided.
 # COMPILE_FLAGS <flags>      - Extra compiler flags passed to NVCC/hip.
@@ -477,61 +440,63 @@ endmacro()
 #
 # Note: optimization level/debug info is set via cmake build type.
 #
-function (define_extension_target MOD_NAME)
+function (define_gpu_extension_target GPU_MOD_NAME)
   cmake_parse_arguments(PARSE_ARGV 1
-    ARG
+    GPU
     "WITH_SOABI"
     "DESTINATION;LANGUAGE;USE_SABI"
     "SOURCES;ARCHITECTURES;COMPILE_FLAGS;INCLUDE_DIRECTORIES;LIBRARIES")
 
   # Add hipify preprocessing step when building with HIP/ROCm.
-  if (ARG_LANGUAGE STREQUAL "HIP")
-    hipify_sources_target(ARG_SOURCES ${MOD_NAME} "${ARG_SOURCES}")
+  if (GPU_LANGUAGE STREQUAL "HIP")
+    hipify_sources_target(GPU_SOURCES ${GPU_MOD_NAME} "${GPU_SOURCES}")
   endif()
 
-  if (ARG_WITH_SOABI)
-    set(SOABI_KEYWORD WITH_SOABI)
+  if (GPU_WITH_SOABI)
+    set(GPU_WITH_SOABI WITH_SOABI)
   else()
-    set(SOABI_KEYWORD "")
+    set(GPU_WITH_SOABI)
   endif()
 
-  if (ARG_USE_SABI)
-    Python_add_library(${MOD_NAME} MODULE USE_SABI ${ARG_USE_SABI} ${SOABI_KEYWORD} "${ARG_SOURCES}")
+  if (GPU_USE_SABI)
+    Python_add_library(${GPU_MOD_NAME} MODULE USE_SABI ${GPU_USE_SABI} ${GPU_WITH_SOABI} "${GPU_SOURCES}")
   else()
-    Python_add_library(${MOD_NAME} MODULE ${SOABI_KEYWORD} "${ARG_SOURCES}")
+    Python_add_library(${GPU_MOD_NAME} MODULE ${GPU_WITH_SOABI} "${GPU_SOURCES}")
   endif()
 
-  if (ARG_LANGUAGE STREQUAL "HIP")
+  if (GPU_LANGUAGE STREQUAL "HIP")
     # Make this target dependent on the hipify preprocessor step.
-    add_dependencies(${MOD_NAME} hipify${MOD_NAME})
+    add_dependencies(${GPU_MOD_NAME} hipify${GPU_MOD_NAME})
     # Make sure we include the hipified versions of the headers, and avoid conflicts with the ones in the original source folder
-    target_include_directories(${MOD_NAME} PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/csrc
-      ${ARG_INCLUDE_DIRECTORIES})
+    target_include_directories(${GPU_MOD_NAME} PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/csrc
+      ${GPU_INCLUDE_DIRECTORIES})
   else()
-    target_include_directories(${MOD_NAME} PRIVATE csrc
-      ${ARG_INCLUDE_DIRECTORIES})
+    target_include_directories(${GPU_MOD_NAME} PRIVATE csrc
+      ${GPU_INCLUDE_DIRECTORIES})
   endif()
 
-  if (ARG_ARCHITECTURES)
-    set_target_properties(${MOD_NAME} PROPERTIES
-      ${ARG_LANGUAGE}_ARCHITECTURES "${ARG_ARCHITECTURES}")
+  if (GPU_ARCHITECTURES)
+    set_target_properties(${GPU_MOD_NAME} PROPERTIES
+      ${GPU_LANGUAGE}_ARCHITECTURES "${GPU_ARCHITECTURES}")
   endif()
 
-  target_compile_options(${MOD_NAME} PRIVATE
-    $<$<COMPILE_LANGUAGE:${ARG_LANGUAGE}>:${ARG_COMPILE_FLAGS}>)
 
-  target_compile_definitions(${MOD_NAME} PRIVATE
-    "-DTORCH_EXTENSION_NAME=${MOD_NAME}")
+  target_compile_options(${GPU_MOD_NAME} PRIVATE
+    $<$<COMPILE_LANGUAGE:${GPU_LANGUAGE}>:${GPU_COMPILE_FLAGS}>)
 
-  target_link_libraries(${MOD_NAME} PRIVATE torch ${ARG_LIBRARIES})
+  target_compile_definitions(${GPU_MOD_NAME} PRIVATE
+    "-DTORCH_EXTENSION_NAME=${GPU_MOD_NAME}")
+
+
+  target_link_libraries(${GPU_MOD_NAME} PRIVATE torch ${GPU_LIBRARIES})
 
   # Don't use `TORCH_LIBRARIES` for CUDA since it pulls in a bunch of
   # dependencies that are not necessary and may not be installed.
-  if (ARG_LANGUAGE STREQUAL "CUDA")
-    target_link_libraries(${MOD_NAME} PRIVATE torch CUDA::cudart CUDA::cuda_driver ${ARG_LIBRARIES})
+  if (GPU_LANGUAGE STREQUAL "CUDA")
+    target_link_libraries(${GPU_MOD_NAME} PRIVATE CUDA::cudart CUDA::cuda_driver)
   else()
-    target_link_libraries(${MOD_NAME} PRIVATE torch ${TORCH_LIBRARIES} ${ARG_LIBRARIES})
+    target_link_libraries(${GPU_MOD_NAME} PRIVATE ${TORCH_LIBRARIES})
   endif()
 
-  install(TARGETS ${MOD_NAME} LIBRARY DESTINATION ${ARG_DESTINATION} COMPONENT ${MOD_NAME})
+  install(TARGETS ${GPU_MOD_NAME} LIBRARY DESTINATION ${GPU_DESTINATION} COMPONENT ${GPU_MOD_NAME})
 endfunction()

csrc/attention/merge_attn_states.cu

@@ -46,32 +46,6 @@ __global__ void merge_attn_states_kernel(
   s_lse = std::isinf(s_lse) ? -std::numeric_limits<float>::infinity() : s_lse;
 
   const float max_lse = fmaxf(p_lse, s_lse);
-
-  /* In certain edge cases, MLA can produce p_lse = s_lse = -inf;
-     continuing the pipeline then yields NaN. Root cause: with chunked prefill
-     a batch may be split into two chunks; if a request in that batch has no
-     prefix hit, every LSE entry for that request’s position is -inf, and at
-     this moment we merge cross-attention at first. For now we simply emit
-     prefix_output (expected to be all zeros) and prefix_lse (-inf) to fix
-     this problem.
-  */
-  if (std::isinf(max_lse)) {
-    if (pack_offset < head_size) {
-      // Pack 128b load
-      pack_128b_t p_out_pack = reinterpret_cast<const pack_128b_t*>(
-          prefix_head_ptr)[pack_offset / pack_size];
-
-      // Pack 128b storage
-      reinterpret_cast<pack_128b_t*>(output_head_ptr)[pack_offset / pack_size] =
-          p_out_pack;
-    }
-    // We only need to write to output_lse once per head.
-    if (output_lse != nullptr && pack_idx == 0) {
-      output_lse[head_idx * num_tokens + token_idx] = max_lse;
-    }
-    return;
-  }
-
   p_lse = p_lse - max_lse;
   s_lse = s_lse - max_lse;
   const float p_se = expf(p_lse);

csrc/cpu/dnnl_helper.cpp

@@ -187,8 +187,7 @@ template <>
 struct hash<MatMulPrimitiveHandler::ClassMatmulCacheKey> {
   size_t operator()(
       const MatMulPrimitiveHandler::ClassMatmulCacheKey& val) const {
-    return hash<dnnl_dim_t>()(val.b_n_size) ^ hash<dnnl_dim_t>()(val.b_k_size) ^
-           hash<int>()(static_cast<int>(val.b_type));
+    return hash<dnnl_dim_t>()(val.b_n_size) ^ hash<dnnl_dim_t>()(val.b_k_size);
   }
 };
 
@@ -217,8 +216,7 @@ bool operator==(const W8A8MatMulPrimitiveHandler::MSizeCacheKey& l,
 
 bool operator==(const MatMulPrimitiveHandler::ClassMatmulCacheKey& l,
                 const MatMulPrimitiveHandler::ClassMatmulCacheKey& r) {
-  return l.b_n_size == r.b_n_size && l.b_k_size == r.b_k_size &&
-         l.b_type == r.b_type;
+  return l.b_n_size == r.b_n_size && l.b_k_size == r.b_k_size;
 }
 
 bool operator==(const MatMulPrimitiveHandler::MSizeCacheKey& l,
@@ -495,10 +493,8 @@ void MatMulPrimitiveHandler::execute(ExecArgs& args) {
 dnnl::matmul MatMulPrimitiveHandler::get_matmul_cache(
     const MSizeCacheKey& key) {
   if (m_size_cache_.get() == nullptr) {
-    ClassMatmulCacheKey class_key = {
-        .b_n_size = b_n_size_, .b_k_size = b_k_size_, .b_type = b_type_};
-    m_size_cache_ =
-        get_matul_class_primitive_cache(class_key, primitive_cache_size_);
+    ClassMatmulCacheKey key = {.b_n_size = b_n_size_, .b_k_size = b_k_size_};
+    m_size_cache_ = get_matul_class_primitive_cache(key, primitive_cache_size_);
   }
   return m_size_cache_->get_or_create(key, [&]() {
     dnnl::matmul::primitive_desc desc = this->create_primitive_desc(key, false);

csrc/cpu/dnnl_helper.h

@@ -199,7 +199,6 @@ class MatMulPrimitiveHandler : public DNNLMatMulPrimitiveHandler {
   struct ClassMatmulCacheKey {
     dnnl_dim_t b_n_size;
     dnnl_dim_t b_k_size;
-    dnnl::memory::data_type b_type;
 
     friend bool operator==(const ClassMatmulCacheKey& l,
                            const ClassMatmulCacheKey& r);

csrc/layernorm_kernels.cu

@@ -148,6 +148,211 @@ fused_add_rms_norm_kernel(
   }
 }
 
+/* Function specialization in the case of FP16/BF16 tensors.
+   Additional optimizations we can make in this case are
+   packed and vectorized operations, which help with the
+   memory latency bottleneck.
+
+   _f16VecPN struct extends _f16Vec to add operations specifically required for
+   polynomial normalization (poly norm).
+   The original _f16Vec does not include the sum-of-powers computation or
+   in-place polynomial normalization logic. */
+template <typename scalar_t, int width>
+struct alignas(16) _f16VecPN : _f16Vec<scalar_t, width> {
+  using Base = _f16Vec<scalar_t, width>;
+  using Converter = typename Base::Converter;
+  using T1 = typename Base::T1;
+  using T2 = typename Base::T2;
+  using Base::data;
+
+  __device__ auto sum_pows() const {
+    float s2 = 0.0f, s4 = 0.0f, s6 = 0.0f;
+
+#pragma unroll
+    for (int i = 0; i < width; i += 2) {
+      float2 z = Converter::convert(T2{data[i], data[i + 1]});
+      float x2 = z.x * z.x;
+      float x4 = x2 * x2;
+      float x6 = x4 * x2;
+
+      float y2 = z.y * z.y;
+      float y4 = y2 * y2;
+      float y6 = y4 * y2;
+
+      s2 += x2 + y2;
+      s4 += x4 + y4;
+      s6 += x6 + y6;
+    }
+    return std::make_tuple(s2, s4, s6);
+  }
+
+  __device__ void poly_norm_inplace(const float w2_inv_std,
+                                    const float w1_inv_std2,
+                                    const float w0_inv_std3, const float bias) {
+#pragma unroll
+    for (int i = 0; i < width; i += 2) {
+      float2 z = Converter::convert(T2{data[i], data[i + 1]});
+
+      float x2 = z.x * z.x;
+      float x3 = x2 * z.x;
+      z.x = w2_inv_std * z.x + w1_inv_std2 * x2 + w0_inv_std3 * x3 + bias;
+
+      float y2 = z.y * z.y;
+      float y3 = y2 * z.y;
+      z.y = w2_inv_std * z.y + w1_inv_std2 * y2 + w0_inv_std3 * y3 + bias;
+
+      auto out = Converter::convert(z);
+      data[i] = out.x;
+      data[i + 1] = out.y;
+    }
+  }
+};
+
+template <typename scalar_t, int width>
+__global__ std::enable_if_t<(width > 0) && _typeConvert<scalar_t>::exists>
+poly_norm_kernel(scalar_t* __restrict__ out,           // [..., hidden_size]
+                 const scalar_t* __restrict__ input,   // [..., hidden_size]
+                 const scalar_t* __restrict__ weight,  // [3]
+                 const scalar_t* __restrict__ bias,    // [1]
+                 const float epsilon, const int hidden_size) {
+  // Sanity checks on our vector struct and type-punned pointer arithmetic
+  static_assert(std::is_pod_v<_f16VecPN<scalar_t, width>>);
+  static_assert(sizeof(_f16VecPN<scalar_t, width>) == sizeof(scalar_t) * width);
+
+  /* These and the argument pointers are all declared `restrict` as they are
+     not aliased in practice. Argument pointers should not be dereferenced
+     in this kernel as that would be undefined behavior */
+  auto* __restrict__ input_v =
+      reinterpret_cast<const _f16VecPN<scalar_t, width>*>(input);
+  const int vec_hidden_size = hidden_size / width;
+  float variance = 0.0f;
+  float variance2 = 0.0f;
+  float variance3 = 0.0f;
+
+  for (int idx = threadIdx.x; idx < vec_hidden_size; idx += blockDim.x) {
+    int id = blockIdx.x * vec_hidden_size + idx;
+    _f16VecPN<scalar_t, width> temp = input_v[id];
+    auto [x2, x4, x6] = temp.sum_pows();
+
+    variance += x2;
+    variance2 += x4;
+    variance3 += x6;
+  }
+
+  float3 thread_variances = make_float3(variance, variance2, variance3);
+
+  struct SumOp {
+    __device__ float3 operator()(const float3& a, const float3& b) const {
+      return make_float3(a.x + b.x, a.y + b.y, a.z + b.z);
+    }
+  };
+
+  using BlockReduce = cub::BlockReduce<float3, 1024>;
+  __shared__ typename BlockReduce::TempStorage reduceStore;
+  float3 block_variances =
+      BlockReduce(reduceStore).Reduce(thread_variances, SumOp{}, blockDim.x);
+
+  variance = block_variances.x;
+  variance2 = block_variances.y;
+  variance3 = block_variances.z;
+
+  __shared__ float s_w2_inv_std;
+  __shared__ float s_w1_inv_std2;
+  __shared__ float s_w0_inv_std3;
+  __shared__ float s_bias;
+
+  if (threadIdx.x == 0) {
+    float w0 = (float)weight[0];
+    float w1 = (float)weight[1];
+    float w2 = (float)weight[2];
+    s_bias = (float)bias[0];
+
+    s_w2_inv_std = w2 * rsqrtf(variance / hidden_size + epsilon);
+    s_w1_inv_std2 = w1 * rsqrtf(variance2 / hidden_size + epsilon);
+    s_w0_inv_std3 = w0 * rsqrtf(variance3 / hidden_size + epsilon);
+  }
+  __syncthreads();
+
+  auto* __restrict__ out_v = reinterpret_cast<_f16VecPN<scalar_t, width>*>(out);
+
+  for (int idx = threadIdx.x; idx < vec_hidden_size; idx += blockDim.x) {
+    int id = blockIdx.x * vec_hidden_size + idx;
+    _f16VecPN<scalar_t, width> temp = input_v[id];
+    temp.poly_norm_inplace(s_w2_inv_std, s_w1_inv_std2, s_w0_inv_std3, s_bias);
+    out_v[id] = temp;
+  }
+}
+
+/* Generic poly_norm_kernel
+   The width field is not used here but necessary for other specializations.
+ */
+template <typename scalar_t, int width>
+__global__ std::enable_if_t<(width == 0) || !_typeConvert<scalar_t>::exists>
+poly_norm_kernel(scalar_t* __restrict__ out,           // [..., hidden_size]
+                 const scalar_t* __restrict__ input,   // [..., hidden_size]
+                 const scalar_t* __restrict__ weight,  // [3]
+                 const scalar_t* __restrict__ bias,    // [1]
+                 const float epsilon, const int hidden_size) {
+  float variance = 0.0f;
+  float variance2 = 0.0f;
+  float variance3 = 0.0f;
+
+  for (int idx = threadIdx.x; idx < hidden_size; idx += blockDim.x) {
+    float x = (float)input[blockIdx.x * hidden_size + idx];
+    float x2 = x * x;
+    float x4 = x2 * x2;
+    float x6 = x4 * x2;
+
+    variance += x2;
+    variance2 += x4;
+    variance3 += x6;
+  }
+
+  float3 thread_variances = make_float3(variance, variance2, variance3);
+
+  struct SumOp {
+    __device__ float3 operator()(const float3& a, const float3& b) const {
+      return make_float3(a.x + b.x, a.y + b.y, a.z + b.z);
+    }
+  };
+
+  using BlockReduce = cub::BlockReduce<float3, 1024>;
+  __shared__ typename BlockReduce::TempStorage reduceStore;
+  float3 block_variances =
+      BlockReduce(reduceStore).Reduce(thread_variances, SumOp{}, blockDim.x);
+
+  variance = block_variances.x;
+  variance2 = block_variances.y;
+  variance3 = block_variances.z;
+
+  __shared__ float s_w2_inv_std;
+  __shared__ float s_w1_inv_std2;
+  __shared__ float s_w0_inv_std3;
+  __shared__ float s_bias;
+
+  if (threadIdx.x == 0) {
+    float w0 = (float)weight[0];
+    float w1 = (float)weight[1];
+    float w2 = (float)weight[2];
+    s_bias = (float)bias[0];
+
+    s_w2_inv_std = w2 * rsqrtf(variance / hidden_size + epsilon);
+    s_w1_inv_std2 = w1 * rsqrtf(variance2 / hidden_size + epsilon);
+    s_w0_inv_std3 = w0 * rsqrtf(variance3 / hidden_size + epsilon);
+  }
+  __syncthreads();
+
+  for (int idx = threadIdx.x; idx < hidden_size; idx += blockDim.x) {
+    float x = (float)input[blockIdx.x * hidden_size + idx];
+    float x2 = x * x;
+    float x3 = x2 * x;
+
+    out[blockIdx.x * hidden_size + idx] =
+        (scalar_t)(x * s_w2_inv_std + x2 * s_w1_inv_std2 + x3 * s_w0_inv_std3 +
+                   s_bias);
+  }
+}
+
 }  // namespace vllm
 
 void rms_norm(torch::Tensor& out,     // [..., hidden_size]
@@ -159,26 +364,18 @@ void rms_norm(torch::Tensor& out,     // [..., hidden_size]
   TORCH_CHECK(weight.is_contiguous());
 
   int hidden_size = input.size(-1);
-
-  // We cannot just use `input.stride(-2)` if the tensor is not row-major.
-  // Instead, we use a 2d view to get the second-innermost stride.
-  // That way the dimensions (except the last one) can be arbitrarily permuted.
-  torch::Tensor input_view = input.view({-1, hidden_size});
-
-  int num_tokens = input_view.numel() / hidden_size;
-  int64_t input_stride = input_view.stride(-2);
+  int num_tokens = input.numel() / hidden_size;
+  int64_t input_stride = input.stride(-2);
 
   dim3 grid(num_tokens);
   dim3 block(std::min(hidden_size, 1024));
-  const at::cuda::OptionalCUDAGuard device_guard(device_of(input_view));
+  const at::cuda::OptionalCUDAGuard device_guard(device_of(input));
   const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
-  VLLM_DISPATCH_FLOATING_TYPES(
-      input_view.scalar_type(), "rms_norm_kernel", [&] {
-        vllm::rms_norm_kernel<scalar_t><<<grid, block, 0, stream>>>(
-            out.data_ptr<scalar_t>(), input_view.data_ptr<scalar_t>(),
-            input_stride, weight.data_ptr<scalar_t>(), epsilon, num_tokens,
-            hidden_size);
-      });
+  VLLM_DISPATCH_FLOATING_TYPES(input.scalar_type(), "rms_norm_kernel", [&] {
+    vllm::rms_norm_kernel<scalar_t><<<grid, block, 0, stream>>>(
+        out.data_ptr<scalar_t>(), input.data_ptr<scalar_t>(), input_stride,
+        weight.data_ptr<scalar_t>(), epsilon, num_tokens, hidden_size);
+  });
 }
 
 #define LAUNCH_FUSED_ADD_RMS_NORM(width)                                    \
@@ -195,8 +392,6 @@ void fused_add_rms_norm(torch::Tensor& input,     // [..., hidden_size]
                         torch::Tensor& residual,  // [..., hidden_size]
                         torch::Tensor& weight,    // [hidden_size]
                         double epsilon) {
-  TORCH_CHECK(weight.scalar_type() == input.scalar_type());
-  TORCH_CHECK(input.scalar_type() == residual.scalar_type());
   TORCH_CHECK(residual.is_contiguous());
   TORCH_CHECK(weight.is_contiguous());
   int hidden_size = input.size(-1);
@@ -239,3 +434,50 @@ void fused_add_rms_norm(torch::Tensor& input,     // [..., hidden_size]
     LAUNCH_FUSED_ADD_RMS_NORM(0);
   }
 }
+
+#define LAUNCH_FUSED_POLY_NORM(width)                                         \
+  VLLM_DISPATCH_FLOATING_TYPES(input.scalar_type(), "poly_norm_kernel", [&] { \
+    vllm::poly_norm_kernel<scalar_t, width><<<grid, block, 0, stream>>>(      \
+        out.data_ptr<scalar_t>(), input.data_ptr<scalar_t>(),                 \
+        weight.data_ptr<scalar_t>(), bias.data_ptr<scalar_t>(), epsilon,      \
+        hidden_size);                                                         \
+  });
+
+void poly_norm(torch::Tensor& out,     // [..., hidden_size]
+               torch::Tensor& input,   // [..., hidden_size]
+               torch::Tensor& weight,  // [3]
+               torch::Tensor& bias,    // [1]
+               double epsilon) {
+  TORCH_CHECK(out.is_contiguous());
+  TORCH_CHECK(input.is_contiguous());
+  TORCH_CHECK(out.data_ptr() != input.data_ptr());
+
+  int hidden_size = input.size(-1);
+  int num_tokens = input.numel() / hidden_size;
+
+  dim3 grid(num_tokens);
+  /* This kernel is memory-latency bound in many scenarios.
+     When num_tokens is large, a smaller block size allows
+     for increased block occupancy on CUs and better latency
+     hiding on global mem ops. */
+  const int max_block_size = (num_tokens < 256) ? 1024 : 256;
+  dim3 block(std::min(hidden_size, max_block_size));
+  const at::cuda::OptionalCUDAGuard device_guard(device_of(input));
+  const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
+  /*If the tensor types are FP16/BF16, try to use the optimized kernel
+    with packed + vectorized ops.
+    Max optimization is achieved with a width-8 vector of FP16/BF16s
+    since we can load at most 128 bits at once in a global memory op.
+    However, this requires each tensor's data to be aligned to 16
+    bytes.
+   */
+  auto inp_ptr = reinterpret_cast<std::uintptr_t>(input.data_ptr());
+  auto out_ptr = reinterpret_cast<std::uintptr_t>(out.data_ptr());
+  bool ptrs_are_aligned = inp_ptr % 16 == 0 && out_ptr % 16 == 0;
+  bool batch_invariant_launch = vllm::vllm_is_batch_invariant();
+  if (ptrs_are_aligned && hidden_size % 8 == 0 && !batch_invariant_launch) {
+    LAUNCH_FUSED_POLY_NORM(8);
+  } else {
+    LAUNCH_FUSED_POLY_NORM(0);
+  }
+}

csrc/layernorm_quant_kernels.cu

@@ -229,8 +229,6 @@ void fused_add_rms_norm_static_fp8_quant(
     double epsilon) {
   TORCH_CHECK(out.is_contiguous());
   TORCH_CHECK(residual.is_contiguous());
-  TORCH_CHECK(residual.scalar_type() == input.scalar_type());
-  TORCH_CHECK(weight.scalar_type() == input.scalar_type());
   int hidden_size = input.size(-1);
   int input_stride = input.stride(-2);
   int num_tokens = input.numel() / hidden_size;