Deepseek v3 (#11502 )

Signed-off-by: mgoin <michael@neuralmagic.com> Co-authored-by: mgoin <michael@neuralmagic.com> Co-authored-by: robertgshaw2-neuralmagic <rshaw@neuralmagic.com>
[2/N] API Server: Avoid ulimit footgun (#11530 )
2024-12-26 16:09:44 -08:00 · 2024-12-26 23:43:05 +00:00 · 2024-12-26 15:33:30 -08:00 · 2024-12-26 23:03:43 +00:00 · 2024-12-26 17:31:29 +00:00 · 2024-12-26 13:12:51 +00:00
1324 changed files with 146344 additions and 45786 deletions
--- a/.buildkite/generate_index.py
+++ b/.buildkite/generate_index.py
@@ -0,0 +1,24 @@
 import argparse
 import os
 template = """<!DOCTYPE html>
 <html>
    <body>
    <h1>Links for vLLM</h1/>
        <a href="../{wheel_html_escaped}">{wheel}</a><br/>
    </body>
 </html>
 """
 parser = argparse.ArgumentParser()
 parser.add_argument("--wheel", help="The wheel path.", required=True)
 args = parser.parse_args()
 filename = os.path.basename(args.wheel)
 with open("index.html", "w") as f:
    print(f"Generated index.html for {args.wheel}")
    # cloudfront requires escaping the '+' character
    f.write(
        template.format(wheel=filename,
                        wheel_html_escaped=filename.replace("+", "%2B")))
--- a/.buildkite/lm-eval-harness/configs/Meta-Llama-3-8B-Instruct-INT8-compressed-tensors-asym.yaml
+++ b/.buildkite/lm-eval-harness/configs/Meta-Llama-3-8B-Instruct-INT8-compressed-tensors-asym.yaml
@@ -0,0 +1,11 @@
 # bash .buildkite/lm-eval-harness/run-lm-eval-gsm-vllm-baseline.sh -m nm-testing/Meta-Llama-3-8B-Instruct-W8-Channel-A8-Dynamic-Asym-Per-Token-Test -b "auto" -l 250 -f 5 -t 1
 model_name: "nm-testing/Meta-Llama-3-8B-Instruct-W8-Channel-A8-Dynamic-Asym-Per-Token-Test"
 tasks:
 - name: "gsm8k"
  metrics:
  - name: "exact_match,strict-match"
    value: 0.764
  - name: "exact_match,flexible-extract"
    value: 0.764
 limit: 250
 num_fewshot: 5
--- a/.buildkite/lm-eval-harness/configs/Meta-Llama-3.2-1B-Instruct-INT8-compressed-tensors.yaml
+++ b/.buildkite/lm-eval-harness/configs/Meta-Llama-3.2-1B-Instruct-INT8-compressed-tensors.yaml
@@ -0,0 +1,11 @@
 # bash .buildkite/lm-eval-harness/run-lm-eval-gsm-vllm-baseline.sh -m neuralmagic/Llama-3.2-1B-Instruct-quantized.w8a8 -b "auto" -l 1000 -f 5 -t 1
 model_name: "neuralmagic/Llama-3.2-1B-Instruct-quantized.w8a8"
 tasks:
 - name: "gsm8k"
  metrics:
  - name: "exact_match,strict-match"
    value: 0.356
  - name: "exact_match,flexible-extract"
    value: 0.358
 limit: 1000
 num_fewshot: 5
--- a/.buildkite/lm-eval-harness/configs/models-small.txt
+++ b/.buildkite/lm-eval-harness/configs/models-small.txt
@@ -1,6 +1,7 @@
 Meta-Llama-3-8B-Instruct.yaml
 Meta-Llama-3-8B-Instruct-FP8-compressed-tensors.yaml
-Meta-Llama-3-8B-Instruct-INT8-compressed-tensors.yaml
+Meta-Llama-3.2-1B-Instruct-INT8-compressed-tensors.yaml
 Meta-Llama-3-8B-Instruct-INT8-compressed-tensors-asym.yaml
 Meta-Llama-3-8B-Instruct-nonuniform-compressed-tensors.yaml
 Meta-Llama-3-8B-Instruct-Channelwise-compressed-tensors.yaml
 Minitron-4B-Base-FP8.yaml
--- a/.buildkite/lm-eval-harness/run-lm-eval-gsm-hf-baseline.sh
+++ b/.buildkite/lm-eval-harness/run-lm-eval-gsm-hf-baseline.sh
@@ -2,7 +2,7 @@
 # We can use this script to compute baseline accuracy on GSM for transformers.
 #
 # Make sure you have lm-eval-harness installed:
-#   pip install git+https://github.com/EleutherAI/lm-evaluation-harness.git@9516087b81a61d0e220b22cc1b75be76de23bc10
+#   pip install lm-eval==0.4.4
 usage() {
    echo``
@@ -41,6 +41,6 @@ while getopts "m:b:l:f:" OPT; do
 done
 lm_eval --model hf \
-  --model_args pretrained=$MODEL,parallelize=True \
+  --model_args "pretrained=$MODEL,parallelize=True" \
-  --tasks gsm8k --num_fewshot $FEWSHOT --limit $LIMIT \
+  --tasks gsm8k --num_fewshot "$FEWSHOT" --limit "$LIMIT" \
-  --batch_size $BATCH_SIZE
+  --batch_size "$BATCH_SIZE"
--- a/.buildkite/lm-eval-harness/run-lm-eval-gsm-vllm-baseline.sh
+++ b/.buildkite/lm-eval-harness/run-lm-eval-gsm-vllm-baseline.sh
@@ -3,7 +3,7 @@
 # We use this for fp8, which HF does not support.
 #
 # Make sure you have lm-eval-harness installed:
-#   pip install lm-eval==0.4.3
+#   pip install lm-eval==0.4.4
 usage() {
    echo``
@@ -46,6 +46,6 @@ while getopts "m:b:l:f:t:" OPT; do
 done
 lm_eval --model vllm \
-  --model_args pretrained=$MODEL,tensor_parallel_size=$TP_SIZE,distributed_executor_backend="ray",trust_remote_code=true,max_model_len=4096 \
+  --model_args "pretrained=$MODEL,tensor_parallel_size=$TP_SIZE,distributed_executor_backend=ray,trust_remote_code=true,max_model_len=4096" \
-  --tasks gsm8k --num_fewshot $FEWSHOT --limit $LIMIT \
+  --tasks gsm8k --num_fewshot "$FEWSHOT" --limit "$LIMIT" \
-  --batch_size $BATCH_SIZE
+  --batch_size "$BATCH_SIZE"
--- a/.buildkite/lm-eval-harness/run-tests.sh
+++ b/.buildkite/lm-eval-harness/run-tests.sh
@@ -30,7 +30,7 @@ while getopts "c:t:" OPT; do
 done
 # Parse list of configs.
-IFS=$'\n' read -d '' -r -a MODEL_CONFIGS < $CONFIG
+IFS=$'\n' read -d '' -r -a MODEL_CONFIGS < "$CONFIG"
 for MODEL_CONFIG in "${MODEL_CONFIGS[@]}"
 do
--- a/.buildkite/lm-eval-harness/test_lm_eval_correctness.py
+++ b/.buildkite/lm-eval-harness/test_lm_eval_correctness.py
@@ -49,10 +49,15 @@ def test_lm_eval_correctness():
    results = launch_lm_eval(eval_config)
    # Confirm scores match ground truth.
    success = True
    for task in eval_config["tasks"]:
        for metric in task["metrics"]:
            ground_truth = metric["value"]
            measured_value = results["results"][task["name"]][metric["name"]]
            print(f'{task["name"]} | {metric["name"]}: '
                  f'ground_truth={ground_truth} | measured={measured_value}')
-            assert numpy.isclose(ground_truth, measured_value, rtol=RTOL)
+            success = success and numpy.isclose(
                ground_truth, measured_value, rtol=RTOL)
    # Assert at the end, print all scores even on failure for debugging.
    assert success
--- a/.buildkite/nightly-benchmarks/benchmark-pipeline.yaml
+++ b/.buildkite/nightly-benchmarks/benchmark-pipeline.yaml
@@ -8,10 +8,12 @@ steps:
          containers:
          - image: badouralix/curl-jq
            command:
-            - sh
+            - sh .buildkite/nightly-benchmarks/scripts/wait-for-image.sh
-            - .buildkite/nightly-benchmarks/scripts/wait-for-image.sh
+
  - wait
  - label: "A100"
    # skip: "use this flag to conditionally skip the benchmark step, useful for PR testing"
    agents:
      queue: A100
    plugins:
@@ -19,7 +21,7 @@ steps:
        podSpec:
          priorityClassName: perf-benchmark
          containers:
-          - image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
+          - image: public.ecr.aws/q9t5s3a7/vllm-ci-postmerge-repo:$BUILDKITE_COMMIT
            command:
            - bash .buildkite/nightly-benchmarks/scripts/run-performance-benchmarks.sh
            resources:
@@ -42,20 +44,48 @@ steps:
          - name: devshm
            emptyDir:
              medium: Memory
  # - label: "H100"
  #   agents:
  #     queue: H100
  #   plugins:
  #   - docker#v5.11.0:
  #       image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
  #       command:
  #       - bash
  #       - .buildkite/nightly-benchmarks/run-benchmarks-suite.sh
  #       mount-buildkite-agent: true
  #       propagate-environment: true
  #       ipc: host
  #       gpus: all
  #       environment:
  #       - VLLM_USAGE_SOURCE
  #       - HF_TOKEN
  - label: "H200"
    # skip: "use this flag to conditionally skip the benchmark step, useful for PR testing"
    agents:
      queue: H200
    plugins:
    - docker#v5.12.0:
        image: public.ecr.aws/q9t5s3a7/vllm-ci-postmerge-repo:$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: block-h100
    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
--- a/.buildkite/nightly-benchmarks/nightly-annotation.md
+++ b/.buildkite/nightly-benchmarks/nightly-annotation.md
@@ -0,0 +1,28 @@
 ## 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
 ```
 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`.
--- a/.buildkite/nightly-benchmarks/nightly-descriptions.md
+++ b/.buildkite/nightly-benchmarks/nightly-descriptions.md
@@ -1,45 +1,39 @@
 # Nightly benchmark
-The main goal of this benchmarking is two-fold:
+This benchmark aims to:
- Performance clarity: Provide clarity on which one (vllm, tensorrt-llm, lmdeploy and tgi) leads in performance in what workload.
+- Provide performance clarity: Provide clarity on which one (vllm, tensorrt-llm, lmdeploy and SGLang) leads in performance in what workload.
- Reproducible: one can run the exact same set of benchmarking commands inside the exact same docker by following reproducing instructions in [reproduce.md]().
+- 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 guilde: [github issue link](https://github.com/vllm-project/vllm/issues/8176)
-## Docker images
+## Setup
-We benchmark vllm, tensorrt-llm, lmdeploy and tgi using the following docker images:
+- Docker images:
- vllm/vllm-openai:v0.5.0.post1
+  - vLLM: `vllm/vllm-openai:v0.6.2`
- nvcr.io/nvidia/tritonserver:24.04-trtllm-python-py3
+  - SGLang: `lmsysorg/sglang:v0.3.2-cu121`
- openmmlab/lmdeploy:v0.5.0
+  - LMDeploy: `openmmlab/lmdeploy:v0.6.1-cu12`
- ghcr.io/huggingface/text-generation-inference:2.1
+  - TensorRT-LLM: `nvcr.io/nvidia/tritonserver:24.07-trtllm-python-py3`
    - *NOTE: we uses 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).
-<!-- Please check <a href="artifact://workspace/build/buildkite/vllm/performance-benchmark/.buildkite/nightly-benchmarks/nightly-pipeline.yaml">nightly-pipeline.yaml</a> artifact for more details on how we deploy the docker images. -->
+# Known issues
-
+- TRT-LLM crashes with Llama 3.1 8B [issue](https://github.com/NVIDIA/TensorRT-LLM/issues/2105).
-## Hardware
+- TGI does not support `ignore-eos` flag.
 One AWS node with 8x NVIDIA A100 GPUs.
 ## Workload description
 We benchmark vllm, tensorrt-llm, lmdeploy and tgi using the following workload:
 - Input length: randomly sample 500 prompts from ShareGPT dataset (with fixed random seed).
 - Output length: the corresponding output length of these 500 prompts.
 - Models: llama-3 8B, llama-3 70B, mixtral 8x7B.
 - Average QPS (query per second): 4 for the small model (llama-3 8B) and 2 for other two models. For each QPS, the arrival time of each query is determined using a random Poisson process (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).
 <!-- Check <a href="artifact://workspace/build/buildkite/vllm/performance-benchmark/.buildkite/nightly-benchmarks/tests/nightly-tests.json">nightly-tests.json</a> artifact for more details. -->
 ## Plots
 In the following plots, the dot shows the mean and the error bar shows the standard error of the mean. Value 0 means that the corresponding benchmark crashed.
 <img src="artifact://nightly_results.png" alt="Benchmarking results" height=250 >
 ## Results
 {nightly_results_benchmarking_table}
--- a/.buildkite/nightly-benchmarks/nightly-pipeline.yaml
+++ b/.buildkite/nightly-benchmarks/nightly-pipeline.yaml
@@ -13,7 +13,7 @@ common_pod_spec: &common_pod_spec
 common_container_settings: &common_container_settings
  command:
-    - bash .buildkite/nightly-benchmarks/run-nightly-suite.sh
+    - bash .buildkite/nightly-benchmarks/scripts/run-nightly-benchmarks.sh
  resources:
    limits:
      nvidia.com/gpu: 8
@@ -37,7 +37,10 @@ common_container_settings: &common_container_settings
 steps:
  - block: ":rocket: Ready for comparing vllm against alternatives? This will take 4 hours."
-  - label: "A100 trt benchmark"
+
  - label: "A100 vllm step 10"
    priority: 100
    agents:
      queue: A100
@@ -46,7 +49,21 @@ steps:
          podSpec:
            <<: *common_pod_spec
            containers:
-              - image: nvcr.io/nvidia/tritonserver:24.04-trtllm-python-py3
+              - 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"
@@ -58,11 +75,13 @@ steps:
          podSpec:
            <<: *common_pod_spec
            containers:
-              - image: openmmlab/lmdeploy:v0.5.0
+              - image: openmmlab/lmdeploy:v0.6.1-cu12
                <<: *common_container_settings
-  - label: "A100 vllm benchmark"
+
  - label: "A100 trt llama-8B"
    priority: 100
    agents:
      queue: A100
@@ -71,10 +90,25 @@ steps:
          podSpec:
            <<: *common_pod_spec
            containers:
-              - image: vllm/vllm-openai:latest 
+              - 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 tgi benchmark"
+
  - label: "A100 trt llama-70B"
    priority: 100
    agents:
      queue: A100
@@ -83,12 +117,54 @@ steps:
          podSpec:
            <<: *common_pod_spec
            containers:
-              - image: ghcr.io/huggingface/text-generation-inference:2.1 
+              - 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: "Plot"
+  - label: "Collect the results"
    priority: 100
    agents:
      queue: A100
@@ -117,4 +193,4 @@ steps:
                    name: hf-token-secret
                    key: token
-  - wait
+  - block: ":rocket: check the results!"
--- a/.buildkite/nightly-benchmarks/run-nightly-suite.sh
+++ b/.buildkite/nightly-benchmarks/run-nightly-suite.sh
@@ -1,76 +0,0 @@
 #!/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 [[ $gpu_count -gt 0 ]]; then
        echo "GPU found."
    else
        echo "Need at least 1 GPU to run benchmarking."
        exit 1
    fi
    declare -g gpu_type=$(echo $(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
 }
 main() {
    check_gpus
    check_hf_token
    df -h
    (which wget && which curl) || (apt-get update && apt-get install -y wget curl)
    (which jq) || (apt-get update && apt-get -y install jq)
    cd $VLLM_SOURCE_CODE_LOC/benchmarks
    wget https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/ShareGPT_V3_unfiltered_cleaned_split.json
    # run lmdeploy
    if which lmdeploy >/dev/null; then
        echo "lmdeploy is available, redirect to run-lmdeploy-nightly.sh"
        bash ../.buildkite/nightly-benchmarks/scripts/run-lmdeploy-nightly.sh
        exit 0
    fi
    # run tgi
    if [ -e /tgi-entrypoint.sh ]; then
        echo "tgi is available, redirect to run-tgi-nightly.sh"
        bash ../.buildkite/nightly-benchmarks/scripts/run-tgi-nightly.sh
        exit 0
    fi
    # run trt
    if which trtllm-build >/dev/null; then
        echo "trtllm is available, redirect to run-trt-nightly.sh"
        bash ../.buildkite/nightly-benchmarks/scripts/run-trt-nightly.sh
        exit 0
    fi
    # run vllm
    if [ -e /vllm-workspace ]; then
        echo "vllm is available, redirect to run-vllm-nightly.sh"
        bash ../.buildkite/nightly-benchmarks/scripts/run-vllm-nightly.sh
        exit 0
    fi
 }
 main "$@"
--- a/.buildkite/nightly-benchmarks/scripts/convert-results-json-to-markdown.py
+++ b/.buildkite/nightly-benchmarks/scripts/convert-results-json-to-markdown.py
@@ -56,7 +56,7 @@ serving_column_mapping = {
 def read_markdown(file):
    if os.path.exists(file):
-        with open(file, "r") as f:
+        with open(file) as f:
            return f.read() + "\n"
    else:
        return f"{file} not found.\n"
@@ -75,14 +75,14 @@ if __name__ == "__main__":
    # collect results
    for test_file in results_folder.glob("*.json"):
-        with open(test_file, "r") as f:
+        with open(test_file) as f:
            raw_result = json.loads(f.read())
        if "serving" in str(test_file):
            # this result is generated via `benchmark_serving.py`
            # attach the benchmarking command to raw_result
-            with open(test_file.with_suffix(".commands"), "r") as f:
+            with open(test_file.with_suffix(".commands")) as f:
                command = json.loads(f.read())
            raw_result.update(command)
@@ -97,7 +97,7 @@ if __name__ == "__main__":
            # this result is generated via `benchmark_latency.py`
            # attach the benchmarking command to raw_result
-            with open(test_file.with_suffix(".commands"), "r") as f:
+            with open(test_file.with_suffix(".commands")) as f:
                command = json.loads(f.read())
            raw_result.update(command)
@@ -119,7 +119,7 @@ if __name__ == "__main__":
            # this result is generated via `benchmark_throughput.py`
            # attach the benchmarking command to raw_result
-            with open(test_file.with_suffix(".commands"), "r") as f:
+            with open(test_file.with_suffix(".commands")) as f:
                command = json.loads(f.read())
            raw_result.update(command)
@@ -157,6 +157,18 @@ if __name__ == "__main__":
                                             throughput_results,
                                             serving_results)
    for df in [latency_results, serving_results, throughput_results]:
        if df.empty:
            continue
        # Sort all dataframes by their respective "Test name" columns
        df.sort_values(by="Test name", inplace=True)
        # The GPUs sometimes come in format of "GPUTYPE\nGPUTYPE\n...",
        # we want to turn it into "8xGPUTYPE"
        df["GPU"] = df["GPU"].apply(
            lambda x: f"{len(x.split('\n'))}x{x.split('\n')[0]}")
    # get markdown tables
    latency_md_table = tabulate(latency_results,
                                headers='keys',
--- a/.buildkite/nightly-benchmarks/scripts/generate-nightly-markdown.py
+++ b/.buildkite/nightly-benchmarks/scripts/generate-nightly-markdown.py
@@ -0,0 +1,95 @@
 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.)
        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)
--- a/.buildkite/nightly-benchmarks/scripts/launch-server.sh
+++ b/.buildkite/nightly-benchmarks/scripts/launch-server.sh
@@ -0,0 +1,228 @@
 #!/bin/bash
 # Currently FP8 benchmark is NOT enabled.
 set -x
 server_params=$1
 common_params=$2
 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"
 }
 launch_trt_server() {
  model_path=$(echo "$common_params" | jq -r '.model')
  model_name="${model_path#*/}"
  model_type=$(echo "$server_params" | jq -r '.model_type')
  model_dtype=$(echo "$server_params" | jq -r '.model_dtype')
  model_tp_size=$(echo "$common_params" | jq -r '.tp')
  max_batch_size=$(echo "$server_params" | jq -r '.max_batch_size')
  max_input_len=$(echo "$server_params" | jq -r '.max_input_len')
  max_seq_len=$(echo "$server_params" | jq -r '.max_seq_len')
  max_num_tokens=$(echo "$server_params" | jq -r '.max_num_tokens')
  trt_llm_version=$(echo "$server_params" | jq -r '.trt_llm_version')
  # create model caching directory
  cd ~
  rm -rf models
  mkdir -p models
  cd models
  models_dir=$(pwd)
  trt_model_path=${models_dir}/${model_name}-trt-ckpt
  trt_engine_path=${models_dir}/${model_name}-trt-engine
  # clone tensorrt backend
  cd /
  rm -rf tensorrtllm_backend
  git clone https://github.com/triton-inference-server/tensorrtllm_backend.git
  git lfs install
  cd tensorrtllm_backend
  git checkout "$trt_llm_version"
  git submodule update --init --recursive
  # build trtllm engine
  cd /tensorrtllm_backend
  cd "./tensorrt_llm/examples/${model_type}"
  python3 convert_checkpoint.py \
    --model_dir "${model_path}" \
    --dtype "${model_dtype}" \
    --tp_size "${model_tp_size}" \
    --output_dir "${trt_model_path}"
  trtllm-build \
    --checkpoint_dir "${trt_model_path}" \
    --use_fused_mlp \
    --reduce_fusion disable \
    --workers 8 \
    --gpt_attention_plugin "${model_dtype}" \
    --gemm_plugin "${model_dtype}" \
    --tp_size "${model_tp_size}" \
    --max_batch_size "${max_batch_size}" \
    --max_input_len "${max_input_len}" \
    --max_seq_len "${max_seq_len}" \
    --max_num_tokens "${max_num_tokens}" \
    --output_dir "${trt_engine_path}"
  # handle triton protobuf files and launch triton server
  cd /tensorrtllm_backend
  mkdir triton_model_repo
  cp -r all_models/inflight_batcher_llm/* triton_model_repo/
  cd triton_model_repo
  rm -rf ./tensorrt_llm/1/*
  cp -r "${trt_engine_path}"/* ./tensorrt_llm/1
  python3 ../tools/fill_template.py -i tensorrt_llm/config.pbtxt triton_backend:tensorrtllm,engine_dir:/tensorrtllm_backend/triton_model_repo/tensorrt_llm/1,decoupled_mode:true,batching_strategy:inflight_fused_batching,batch_scheduler_policy:guaranteed_no_evict,exclude_input_in_output:true,triton_max_batch_size:2048,max_queue_delay_microseconds:0,max_beam_width:1,max_queue_size:2048,enable_kv_cache_reuse:false
  python3 ../tools/fill_template.py -i preprocessing/config.pbtxt "triton_max_batch_size:2048,tokenizer_dir:$model_path,preprocessing_instance_count:5"
  python3 ../tools/fill_template.py -i postprocessing/config.pbtxt "triton_max_batch_size:2048,tokenizer_dir:$model_path,postprocessing_instance_count:5,skip_special_tokens:false"
  python3 ../tools/fill_template.py -i ensemble/config.pbtxt triton_max_batch_size:"$max_batch_size"
  python3 ../tools/fill_template.py -i tensorrt_llm_bls/config.pbtxt "triton_max_batch_size:$max_batch_size,decoupled_mode:true,accumulate_tokens:False,bls_instance_count:1"
  cd /tensorrtllm_backend
  python3 scripts/launch_triton_server.py \
    --world_size="${model_tp_size}" \
    --model_repo=/tensorrtllm_backend/triton_model_repo &
 }
 launch_tgi_server() {
  model=$(echo "$common_params" | jq -r '.model')
  tp=$(echo "$common_params" | jq -r '.tp')
  port=$(echo "$common_params" | jq -r '.port')
  server_args=$(json2args "$server_params")
  if echo "$common_params" | jq -e 'has("fp8")' >/dev/null; then
    echo "Key 'fp8' exists in common params."
    server_command="/tgi-entrypoint.sh \
                --model-id $model \
                --num-shard $tp \
                --port $port \
                --quantize fp8 \
                $server_args"
  else
    echo "Key 'fp8' does not exist in common params."
    server_command="/tgi-entrypoint.sh \
                --model-id $model \
                --num-shard $tp \
                --port $port \
                $server_args"
  fi
  echo "Server command: $server_command"
  eval "$server_command" &
 }
 launch_lmdeploy_server() {
  model=$(echo "$common_params" | jq -r '.model')
  tp=$(echo "$common_params" | jq -r '.tp')
  port=$(echo "$common_params" | jq -r '.port')
  server_args=$(json2args "$server_params")
  server_command="lmdeploy serve api_server $model \
    --tp $tp \
    --server-port $port \
    $server_args"
  # run the server
  echo "Server command: $server_command"
  bash -c "$server_command" &
 }
 launch_sglang_server() {
  model=$(echo "$common_params" | jq -r '.model')
  tp=$(echo "$common_params" | jq -r '.tp')
  port=$(echo "$common_params" | jq -r '.port')
  server_args=$(json2args "$server_params")
  if echo "$common_params" | jq -e 'has("fp8")' >/dev/null; then
    echo "Key 'fp8' exists in common params. Use neuralmagic fp8 model for convenience."
    model=$(echo "$common_params" | jq -r '.neuralmagic_quantized_model')
    server_command="python3 \
        -m sglang.launch_server \
        --tp $tp \
        --model-path $model \
        --port $port \
        $server_args"
  else
    echo "Key 'fp8' does not exist in common params."
    server_command="python3 \
        -m sglang.launch_server \
        --tp $tp \
        --model-path $model \
        --port $port \
        $server_args"
  fi
  # run the server
  echo "Server command: $server_command"
  eval "$server_command" &
 }
 launch_vllm_server() {
  export VLLM_HOST_IP=$(hostname -I | awk '{print $1}')
  model=$(echo "$common_params" | jq -r '.model')
  tp=$(echo "$common_params" | jq -r '.tp')
  port=$(echo "$common_params" | jq -r '.port')
  server_args=$(json2args "$server_params")
  if echo "$common_params" | jq -e 'has("fp8")' >/dev/null; then
    echo "Key 'fp8' exists in common params. Use neuralmagic fp8 model for convenience."
    model=$(echo "$common_params" | jq -r '.neuralmagic_quantized_model')
    server_command="python3 \
        -m vllm.entrypoints.openai.api_server \
        -tp $tp \
        --model $model \
        --port $port \
        $server_args"
  else
    echo "Key 'fp8' does not exist in common params."
    server_command="python3 \
        -m vllm.entrypoints.openai.api_server \
        -tp $tp \
        --model $model \
        --port $port \
        $server_args"
  fi
  # run the server
  echo "Server command: $server_command"
  eval "$server_command" &
 }
 main() {
  if [[ "$CURRENT_LLM_SERVING_ENGINE" == "trt" ]]; then
    launch_trt_server
  fi
  if [[ "$CURRENT_LLM_SERVING_ENGINE" == "tgi" ]]; then
    launch_tgi_server
  fi
  if [[ "$CURRENT_LLM_SERVING_ENGINE" == "lmdeploy" ]]; then
    launch_lmdeploy_server
  fi
  if [[ "$CURRENT_LLM_SERVING_ENGINE" == "sglang" ]]; then
    launch_sglang_server
  fi
  if [[ "$CURRENT_LLM_SERVING_ENGINE" == *"vllm"* ]]; then
    launch_vllm_server
  fi
 }
 main
--- a/.buildkite/nightly-benchmarks/scripts/launch-trt-server.sh
+++ b/.buildkite/nightly-benchmarks/scripts/launch-trt-server.sh
@@ -1,102 +0,0 @@
 #!/bin/bash
 server_params=$1
 common_params=$2
 model_path=$(echo "$common_params" | jq -r '.model')
 model_name="${model_path#*/}"
 model_type=$(echo "$server_params" | jq -r '.model_type')
 model_dtype=$(echo "$server_params" | jq -r '.model_dtype')
 model_tp_size=$(echo "$common_params" | jq -r '.tp')
 max_batch_size=$(echo "$server_params" | jq -r '.max_batch_size')
 max_input_len=$(echo "$server_params" | jq -r '.max_input_len')
 max_output_len=$(echo "$server_params" | jq -r '.max_output_len')
 trt_llm_version=$(echo "$server_params" | jq -r '.trt_llm_version')
 cd ~
 rm -rf models
 mkdir -p models
 cd models
 models_dir=$(pwd)
 trt_model_path=${models_dir}/${model_name}-trt-ckpt
 trt_engine_path=${models_dir}/${model_name}-trt-engine
 cd ~
 rm -rf tensorrt-demo
 git clone https://github.com/neuralmagic/tensorrt-demo.git
 cd tensorrt-demo
 tensorrt_demo_dir=$(pwd)
 # make sure the parameter inside tensorrt_demo is consistent to envvar
 sed -i.bak "/key: \"tokenizer_dir\"/,/string_value:/s|string_value: \".*\"|string_value: \"$model_path\"|" ./triton_model_repo/postprocessing/config.pbtxt
 sed -i.bak "/key: \"tokenizer_dir\"/,/string_value:/s|string_value: \".*\"|string_value: \"$model_path\"|" ./triton_model_repo/preprocessing/config.pbtxt
 sed -i.bak "s|\(max_batch_size:\s*\)[0-9]*|\1$max_batch_size|g" ./triton_model_repo/ensemble/config.pbtxt
 sed -i.bak "s|\(max_batch_size:\s*\)[0-9]*|\1$max_batch_size|g" ./triton_model_repo/preprocessing/config.pbtxt
 sed -i.bak "s|\(max_batch_size:\s*\)[0-9]*|\1$max_batch_size|g" ./triton_model_repo/postprocessing/config.pbtxt
 sed -i.bak "s|\(max_batch_size:\s*\)[0-9]*|\1$max_batch_size|g" ./triton_model_repo/tensorrt_llm_bls/config.pbtxt
 cd /
 rm -rf tensorrtllm_backend
 git clone https://github.com/triton-inference-server/tensorrtllm_backend.git
 git lfs install
 cd tensorrtllm_backend
 git checkout $trt_llm_version
 tensorrtllm_backend_dir=$(pwd)
 git submodule update --init --recursive
 cp -r ${tensorrt_demo_dir}/triton_model_repo ${tensorrtllm_backend_dir}/
 cd /tensorrtllm_backend
 cd ./tensorrt_llm/examples/${model_type}
 if echo "$common_params" | jq -e 'has("fp8")' > /dev/null; then
    echo "Key 'fp8' exists in common params. Use quantize.py instead of convert_checkpoint.py"
    echo "Reference: https://github.com/NVIDIA/TensorRT-LLM/blob/main/examples/llama/README.md"
    python ../quantization/quantize.py \
        --model_dir ${model_path} \
        --dtype ${model_dtype} \
        --tp_size ${model_tp_size} \
        --output_dir ${trt_model_path} \
        --qformat fp8 \
        --kv_cache_dtype fp8 \
        --calib_size 2
 else
    echo "Key 'fp8' does not exist in common params. Use convert_checkpoint.py"
    python3 convert_checkpoint.py \
        --model_dir ${model_path} \
        --dtype ${model_dtype} \
        --tp_size ${model_tp_size} \
        --output_dir ${trt_model_path}
 fi
 trtllm-build \
 --checkpoint_dir=${trt_model_path} \
 --gpt_attention_plugin=${model_dtype} \
 --gemm_plugin=${model_dtype} \
 --remove_input_padding=enable \
 --paged_kv_cache=enable \
 --tp_size=${model_tp_size} \
 --max_batch_size=${max_batch_size} \
 --max_input_len=${max_input_len} \
 --max_output_len=${max_output_len} \
 --max_num_tokens=${max_output_len} \
 --opt_num_tokens=${max_output_len} \
 --output_dir=${trt_engine_path} 
 cd /tensorrtllm_backend/triton_model_repo
 rm -rf ./tensorrt_llm/1/*
 cp -r ${trt_engine_path}/* ./tensorrt_llm/1
 cd /tensorrtllm_backend
 python3 scripts/launch_triton_server.py \
 --world_size=${model_tp_size} \
 --model_repo=/tensorrtllm_backend/triton_model_repo &
--- a/.buildkite/nightly-benchmarks/scripts/nightly-annotate.sh
+++ b/.buildkite/nightly-benchmarks/scripts/nightly-annotate.sh
@@ -8,6 +8,7 @@ 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."
@@ -15,26 +16,63 @@ main() {
    fi
    # initial annotation
-    description="$VLLM_SOURCE_CODE_LOC/.buildkite/nightly-benchmarks/nightly-descriptions.md"
+    #description="$VLLM_SOURCE_CODE_LOC/.buildkite/nightly-benchmarks/nightly-descriptions.md"
    # download results
-    cd $VLLM_SOURCE_CODE_LOC/benchmarks
+    cd "$VLLM_SOURCE_CODE_LOC/benchmarks"
    mkdir -p results/
    /workspace/buildkite-agent artifact download 'results/*nightly_results.json' results/
    ls
    ls results/
-    # generate figures
+    # upload benchmark results
-    python3 -m pip install tabulate pandas matplotlib
+    zip -r results.zip results/
-    python3 $VLLM_SOURCE_CODE_LOC/.buildkite/nightly-benchmarks/scripts/plot-nightly-results.py \
+    /workspace/buildkite-agent artifact upload "results.zip"
-        --description $description \
+
-        --results-folder results/
+    # upload benchmarking scripts
    cd "$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
-    # 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
+    # The figures should be genereated by a separate process outside the CI/CD pipeline
-    /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
+    # # 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 $VLLM_SOURCE_CODE_LOC/.buildkite/nightly-benchmarks/tests/nightly-tests.json
    # /workspace/buildkite-agent annotate --style "success" --context "nightly-benchmarks-results" --append < nightly_results.md
 }
-main "$@"
+main "$@"
--- a/.buildkite/nightly-benchmarks/scripts/plot-nightly-results.py
+++ b/.buildkite/nightly-benchmarks/scripts/plot-nightly-results.py
@@ -1,135 +0,0 @@
 import argparse
 import json
 import math
 from pathlib import Path
 import matplotlib.pyplot as plt
 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 main(args):
    bar_colors = ['#56B4E9', '#009E73', '#D55E00', '#E69F00']
    results_folder = Path(args.results_folder)
    results = []
    # collect results
    for test_file in results_folder.glob("*_nightly_results.json"):
        with open(test_file, "r") 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, "r") 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)
    plt.rcParams.update({'font.size': 20})
    # plot results
    fig, axes = plt.subplots(3, 3, figsize=(16, 14))
    fig.subplots_adjust(hspace=1)
    methods = ["vllm", "trt", "lmdeploy", "tgi"]
    for i, model in enumerate(["llama8B", "llama70B", "mixtral8x7B"]):
        for j, metric in enumerate(["TTFT", "ITL"]):
            means, stds = [], []
            for method in methods:
                target = df['Test name'].str.contains(model)
                target = target & df['Engine'].str.contains(method)
                filtered_df = df[target]
                if filtered_df.empty:
                    means.append(0.)
                    stds.append(0.)
                else:
                    means.append(filtered_df[f"Mean {metric} (ms)"].values[0])
                    std = filtered_df[f"Std {metric} (ms)"].values[0]
                    success = filtered_df["Successful req."].values[0]
                    stds.append(std / math.sqrt(success))
            print(model, metric)
            print(means, stds)
            ax = axes[i, j + 1]
            bars = ax.bar(
                ["vllm", "trt", "lmdeploy", "tgi"],
                means,
                yerr=stds,
                capsize=10,
            )
            for idx, bar in enumerate(bars):
                bar.set_color(bar_colors[idx])
            ax.set_ylim(bottom=0)
            ax.set_ylabel(f"{metric} (ms)")
            ax.set_title(f"{model} {metric}")
            ax.grid(axis='y')
        metric = "Tput"
        j = 0
        if True:
            tputs = []
            for method in methods:
                target = df['Test name'].str.contains(model)
                target = target & df['Engine'].str.contains(method)
                filtered_df = df[target]
                if filtered_df.empty:
                    tputs.append(0.)
                else:
                    input_tput = filtered_df["Input Tput (tok/s)"].values[0]
                    output_tput = filtered_df["Output Tput (tok/s)"].values[0]
                    tputs.append(input_tput + output_tput)
            print(model, metric)
            print(tputs)
            ax = axes[i, j]
            bars = ax.bar(
                ["vllm", "trt", "lmdeploy", "tgi"],
                tputs,
            )
            for idx, bar in enumerate(bars):
                bar.set_color(bar_colors[idx])
            ax.set_ylim(bottom=0)
            ax.set_ylabel("Tput (token/s)")
            ax.set_title(f"{model} {metric}")
            ax.grid(axis='y')
    fig.tight_layout()
    fig.savefig("nightly_results.png", bbox_inches='tight', dpi=400)
 if __name__ == '__main__':
    args = parse_arguments()
    main(args)
--- a/.buildkite/nightly-benchmarks/scripts/run-lmdeploy-nightly.sh
+++ b/.buildkite/nightly-benchmarks/scripts/run-lmdeploy-nightly.sh
@@ -1,218 +0,0 @@
 #!/bin/bash
 set -o pipefail
 check_gpus() {
  # check the number of GPUs and GPU type.
  declare -g gpu_count=$(nvidia-smi --list-gpus | wc -l)
  if [[ $gpu_count -gt 0 ]]; then
    echo "GPU found."
  else
    echo "Need at least 1 GPU to run benchmarking."
    exit 1
  fi
  declare -g gpu_type=$(echo $(nvidia-smi --query-gpu=name --format=csv,noheader) | awk '{print $2}')
  echo "GPU type is $gpu_type"
 }
 kill_gpu_processes() {
  pkill lmdeploy || true
  # waiting for GPU processes to be fully killed
  sleep 10
  # Print the GPU memory usage
  # so that we know if all GPU processes are killed.
  gpu_memory_usage=$(nvidia-smi --query-gpu=memory.used --format=csv,noheader,nounits -i 0)
  # The memory usage should be 0 MB.
  echo "GPU 0 Memory Usage: $gpu_memory_usage MB"
 }
 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"
 }
 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
 }
 run_serving_tests() {
  # run serving tests using `benchmark_serving.py`
  # $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 $test_name."
      continue
    fi
    # append lmdeploy to the test name
    test_name=lmdeploy_$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')
    # get client and server arguments
    server_params=$(echo "$params" | jq -r '.lmdeploy_server_parameters')
    client_params=$(echo "$params" | jq -r '.lmdeploy_client_parameters')
    server_args=$(json2args "$server_params")
    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 tensor-parallel-size $tp but only $gpu_count GPU found. Skip testcase $test_name."
      continue
    fi
    # prepare tokenizer
    rm -rf /tokenizer_cache
    mkdir /tokenizer_cache
    python ../.buildkite/nightly-benchmarks/scripts/download-tokenizer.py \
      --model "$model" \
      --cachedir /tokenizer_cache
    server_command="lmdeploy serve api_server $model \
      --tp $tp \
      --server-port $port \
      $server_args"
    # run the server
    echo "Running test case $test_name"
    echo "Server command: $server_command"
    bash -c "$server_command" &
    # wait until the server is alive
    wait_for_server
    if [ $? -eq 0 ]; then
      echo ""
      echo "lmdeploy server is up and running."
    else
      echo ""
      echo "lmdeploy failed to start within the timeout period."
      break
    fi
    # get model name
    model_name=$(python ../.buildkite/nightly-benchmarks/scripts/get-lmdeploy-modelname.py)
    # 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
      client_command="python3 benchmark_serving.py \
        --backend lmdeploy \
        --tokenizer /tokenizer_cache \
        --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 $qps \
        --model \"$model_name\" \
        $client_args"
      echo "Running test case $test_name with qps $qps"
      echo "Client command: $client_command"
      eval "$client_command"
      # record the benchmarking commands
      jq_output=$(jq -n \
        --arg server "$server_command" \
        --arg client "$client_command" \
        --arg gpu "$gpu_type" \
        --arg engine "lmdeploy" \
        '{
          server_command: $server,
          client_command: $client,
          gpu_type: $gpu,
          engine: $engine
        }')
      echo "$jq_output" >"$RESULTS_FOLDER/${new_test_name}.commands"
    done
    # clean up
    kill_gpu_processes
    rm -rf /root/.cache/huggingface/*
  done
 }
 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/*"
 }
 main() {
  check_gpus
  # enter vllm directory
  cd $VLLM_SOURCE_CODE_LOC/benchmarks
  declare -g RESULTS_FOLDER=results/
  mkdir -p $RESULTS_FOLDER
  BENCHMARK_ROOT=../.buildkite/nightly-benchmarks/
  python -m pip install transformers==4.41.2
  export CURRENT_LLM_SERVING_ENGINE=lmdeploy
  run_serving_tests $BENCHMARK_ROOT/tests/nightly-tests.json
  python -m pip install tabulate pandas
  python $BENCHMARK_ROOT/scripts/summary-nightly-results.py
  upload_to_buildkite
 }
 main "$@"
--- a/.buildkite/nightly-benchmarks/scripts/run-nightly-benchmarks.sh
+++ b/.buildkite/nightly-benchmarks/scripts/run-nightly-benchmarks.sh
@@ -0,0 +1,355 @@
 #!/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 python
  pkill -f python3
  pkill -f tritonserver
  pkill -f pt_main_thread
  pkill -f text-generation
  pkill -f lmdeploy
  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 `benchmark_serving.py`
  # $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="python3 benchmark_serving.py \
          --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="python3 benchmark_serving.py \
          --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
 }
 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
  # check storage
  df -h
  ensure_installed wget
  ensure_installed curl
  ensure_installed jq
  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 "$BENCHMARK_ROOT/tests/nightly-tests.json"
  # upload benchmark results to buildkite
  python3 -m pip install tabulate pandas
  python3 "$BENCHMARK_ROOT/scripts/summary-nightly-results.py"
  upload_to_buildkite
 }
 main "$@"
--- a/.buildkite/nightly-benchmarks/scripts/run-performance-benchmarks.sh
+++ b/.buildkite/nightly-benchmarks/scripts/run-performance-benchmarks.sh
@@ -6,6 +6,7 @@
 # Do not set -e, as the mixtral 8x22B model tends to crash occasionally
 # and we still want to see other benchmarking results even when mixtral crashes.
 set -x
 set -o pipefail
 check_gpus() {
@@ -17,7 +18,7 @@ check_gpus() {
    echo "Need at least 1 GPU to run benchmarking."
    exit 1
  fi
-  declare -g gpu_type=$(echo $(nvidia-smi --query-gpu=name --format=csv,noheader) | awk '{print $2}')
+  declare -g gpu_type=$(nvidia-smi --query-gpu=name --format=csv,noheader | awk '{print $2}')
  echo "GPU type is $gpu_type"
 }
@@ -85,15 +86,11 @@ kill_gpu_processes() {
  ps -aux
  lsof -t -i:8000 | xargs -r kill -9
-  pkill -f pt_main_thread
+  pgrep python3 | xargs -r kill -9
  # this line doesn't work now
  # ps aux | grep python | grep openai | awk '{print $2}' | xargs -r kill -9
  pkill -f python3
  pkill -f /usr/bin/python3
  # wait until GPU memory usage smaller than 1GB
-  while [ $(nvidia-smi --query-gpu=memory.used --format=csv,noheader,nounits | head -n 1) -ge 1000 ]; do
+  while [ "$(nvidia-smi --query-gpu=memory.used --format=csv,noheader,nounits | head -n 1)" -ge 1000 ]; do
    sleep 1
  done
@@ -117,7 +114,7 @@ upload_to_buildkite() {
  fi
  # Use the determined command to annotate and upload artifacts
-  $BUILDKITE_AGENT_COMMAND annotate --style "info" --context "$BUILDKITE_LABEL-benchmark-results" <$RESULTS_FOLDER/benchmark_results.md
+  $BUILDKITE_AGENT_COMMAND annotate --style "info" --context "$BUILDKITE_LABEL-benchmark-results" < "$RESULTS_FOLDER/benchmark_results.md"
  $BUILDKITE_AGENT_COMMAND artifact upload "$RESULTS_FOLDER/*"
 }
@@ -150,7 +147,7 @@ run_latency_tests() {
    # check if there is enough GPU to run the test
    tp=$(echo "$latency_params" | jq -r '.tensor_parallel_size')
    if [[ $gpu_count -lt $tp ]]; then
-      echo "Required tensor-parallel-size $tp but only $gpu_count GPU found. Skip testcase $testname."
+      echo "Required tensor-parallel-size $tp but only $gpu_count GPU found. Skip testcase $test_name."
      continue
    fi
@@ -206,9 +203,9 @@ run_throughput_tests() {
    throughput_args=$(json2args "$throughput_params")
    # check if there is enough GPU to run the test
-    tp=$(echo $throughput_params | jq -r '.tensor_parallel_size')
+    tp=$(echo "$throughput_params" | jq -r '.tensor_parallel_size')
    if [[ $gpu_count -lt $tp ]]; then
-      echo "Required tensor-parallel-size $tp but only $gpu_count GPU found. Skip testcase $testname."
+      echo "Required tensor-parallel-size $tp but only $gpu_count GPU found. Skip testcase $test_name."
      continue
    fi
@@ -270,7 +267,7 @@ run_serving_tests() {
    # check if there is enough GPU to run the test
    tp=$(echo "$server_params" | jq -r '.tensor_parallel_size')
    if [[ $gpu_count -lt $tp ]]; then
-      echo "Required tensor-parallel-size $tp but only $gpu_count GPU found. Skip testcase $testname."
+      echo "Required tensor-parallel-size $tp but only $gpu_count GPU found. Skip testcase $test_name."
      continue
    fi
@@ -278,7 +275,7 @@ run_serving_tests() {
    server_model=$(echo "$server_params" | jq -r '.model')
    client_model=$(echo "$client_params" | jq -r '.model')
    if [[ $server_model != "$client_model" ]]; then
-      echo "Server model and client model must be the same. Skip testcase $testname."
+      echo "Server model and client model must be the same. Skip testcase $test_name."
      continue
    fi
@@ -289,12 +286,11 @@ run_serving_tests() {
    # run the server
    echo "Running test case $test_name"
    echo "Server command: $server_command"
-    eval "$server_command" &
+    bash -c "$server_command" &
    server_pid=$!
    # wait until the server is alive
-    wait_for_server
+    if wait_for_server; then
    if [ $? -eq 0 ]; then
      echo ""
      echo "vllm server is up and running."
    else
@@ -323,7 +319,7 @@ run_serving_tests() {
      echo "Running test case $test_name with qps $qps"
      echo "Client command: $client_command"
-      eval "$client_command"
+      bash -c "$client_command"
      # record the benchmarking commands
      jq_output=$(jq -n \
--- a/.buildkite/nightly-benchmarks/scripts/run-tgi-nightly.sh
+++ b/.buildkite/nightly-benchmarks/scripts/run-tgi-nightly.sh
@@ -1,216 +0,0 @@
 #!/bin/bash
 set -o pipefail
 check_gpus() {
  # check the number of GPUs and GPU type.
  declare -g gpu_count=$(nvidia-smi --list-gpus | wc -l)
  if [[ $gpu_count -gt 0 ]]; then
    echo "GPU found."
  else
    echo "Need at least 1 GPU to run benchmarking."
    exit 1
  fi
  declare -g gpu_type=$(echo $(nvidia-smi --query-gpu=name --format=csv,noheader) | awk '{print $2}')
  echo "GPU type is $gpu_type"
 }
 kill_gpu_processes() {
  pkill text-generation || true
  # waiting for GPU processes to be fully killed
  sleep 10
  # Print the GPU memory usage
  # so that we know if all GPU processes are killed.
  gpu_memory_usage=$(nvidia-smi --query-gpu=memory.used --format=csv,noheader,nounits -i 0)
  # The memory usage should be 0 MB.
  echo "GPU 0 Memory Usage: $gpu_memory_usage MB"
 }
 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"
 }
 wait_for_server() {
  timeout 1200 bash -c '
    until curl -s localhost:8000/generate_stream > /dev/null; do
      sleep 1
    done' && return 0 || return 1
 }
 run_serving_tests() {
  # run serving tests using `benchmark_serving.py`
  # $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 $test_name."
      continue
    fi
    # append tgi to the test name
    test_name=tgi_$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')
    # get client and server arguments
    server_params=$(echo "$params" | jq -r '.tgi_server_parameters')
    client_params=$(echo "$params" | jq -r '.tgi_client_parameters')
    server_args=$(json2args "$server_params")
    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 $test_name."
      continue
    fi
    if echo "$common_params" | jq -e 'has("fp8")' > /dev/null; then
      echo "Key 'fp8' exists in common params."
      server_command="/tgi-entrypoint.sh \
        --model-id $model \
        --num-shard $tp \
        --port $port \
        --quantize fp8 \
        $server_args"
    else
      echo "Key 'fp8' does not exist in common params."
      server_command="/tgi-entrypoint.sh \
        --model-id $model \
        --num-shard $tp \
        --port $port \
        $server_args"
    fi
    # run the server
    echo "Running test case $test_name"
    echo "Server command: $server_command"
    eval "$server_command" &
    # wait until the server is alive
    wait_for_server
    if [ $? -eq 0 ]; then
      echo ""
      echo "tgi server is up and running."
    else
      echo ""
      echo "tgi 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="inf"
        echo "now qps is $qps"
      fi
      new_test_name=$test_name"_qps_"$qps
      client_command="python3 benchmark_serving.py \
        --backend tgi \
        --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 \
        $client_args"
      echo "Running test case $test_name with qps $qps"
      echo "Client command: $client_command"
      eval "$client_command"
      # record the benchmarking commands
      jq_output=$(jq -n \
        --arg server "$server_command" \
        --arg client "$client_command" \
        --arg gpu "$gpu_type" \
        --arg engine "tgi" \
        '{
          server_command: $server,
          client_command: $client,
          gpu_type: $gpu,
          engine: $engine
        }')
      echo "$jq_output" >"$RESULTS_FOLDER/${new_test_name}.commands"
    done
    # clean up
    kill_gpu_processes
    rm -rf /root/.cache/huggingface/*
  done
 }
 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/*"
 }
 main() {
  check_gpus
  # enter vllm directory
  cd $VLLM_SOURCE_CODE_LOC/benchmarks
  declare -g RESULTS_FOLDER=results/
  mkdir -p $RESULTS_FOLDER
  BENCHMARK_ROOT=../.buildkite/nightly-benchmarks/
  export CURRENT_LLM_SERVING_ENGINE=tgi
  run_serving_tests $BENCHMARK_ROOT/tests/nightly-tests.json
  python -m pip install tabulate pandas
  python $BENCHMARK_ROOT/scripts/summary-nightly-results.py
  upload_to_buildkite
 }
 main "$@"
--- a/.buildkite/nightly-benchmarks/scripts/run-trt-nightly.sh
+++ b/.buildkite/nightly-benchmarks/scripts/run-trt-nightly.sh
@@ -1,214 +0,0 @@
 #!/bin/bash
 set -o pipefail
 check_gpus() {
  # check the number of GPUs and GPU type.
  declare -g gpu_count=$(nvidia-smi --list-gpus | wc -l)
  if [[ $gpu_count -gt 0 ]]; then
    echo "GPU found."
  else
    echo "Need at least 1 GPU to run benchmarking."
    exit 1
  fi
  declare -g gpu_type=$(echo $(nvidia-smi --query-gpu=name --format=csv,noheader) | awk '{print $2}')
  echo "GPU type is $gpu_type"
 }
 kill_gpu_processes() {
  pkill tritonserver || true
  # waiting for GPU processes to be fully killed
  sleep 20
  # Print the GPU memory usage
  # so that we know if all GPU processes are killed.
  gpu_memory_usage=$(nvidia-smi --query-gpu=memory.used --format=csv,noheader,nounits -i 0)
  # The memory usage should be 0 MB.
  echo "GPU 0 Memory Usage: $gpu_memory_usage MB"
 }
 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"
 }
 wait_for_server() {
  timeout 1200 bash -c '
    until curl -s localhost:8000/generate_stream > /dev/null; do
      sleep 1
    done' && return 0 || return 1
 }
 run_serving_tests() {
  # run serving tests using `benchmark_serving.py`
  # $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 $test_name."
      continue
    fi
    # append trt to the test name
    test_name=trt_$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')
    # get client and server arguments
    server_params=$(echo "$params" | jq -r '.trt_server_parameters')
    client_params=$(echo "$params" | jq -r '.trt_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 model_tp_size $tp but only $gpu_count GPU found. Skip testcase $test_name."
      continue
    fi
    cd $VLLM_SOURCE_CODE_LOC/benchmarks
    echo "Running test case $test_name"
    bash ../.buildkite/nightly-benchmarks/scripts/launch-trt-server.sh "$server_params" "$common_params"
    # wait until the server is alive
    wait_for_server
    if [ $? -eq 0 ]; then
      echo ""
      echo "trt server is up and running."
    else
      echo ""
      echo "trt failed to start within the timeout period."
      break
    fi
    # prepare tokenizer
    cd $VLLM_SOURCE_CODE_LOC/benchmarks
    rm -rf /tokenizer_cache
    mkdir /tokenizer_cache
    python ../.buildkite/nightly-benchmarks/scripts/download-tokenizer.py \
      --model "$model" \
      --cachedir /tokenizer_cache
    cd $VLLM_SOURCE_CODE_LOC/benchmarks
    # 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
      client_command="python3 benchmark_serving.py \
        --backend tensorrt-llm \
        --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 \
        $client_args"
      echo "Running test case $test_name with qps $qps"
      echo "Client command: $client_command"
      eval "$client_command"
      server_command=""
      # record the benchmarking commands
      jq_output=$(jq -n \
        --arg server "$server_command" \
        --arg client "$client_command" \
        --arg gpu "$gpu_type" \
        --arg engine "trt" \
        '{
          server_command: $server,
          client_command: $client,
          gpu_type: $gpu,
          engine: $engine
        }')
      echo "$jq_output" >"$RESULTS_FOLDER/${new_test_name}.commands"
    done
    # clean up
    kill_gpu_processes
    rm -rf /root/.cache/huggingface/*
  done
 }
 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/*"
 }
 main() {
  check_gpus
  # enter vllm directory
  cd $VLLM_SOURCE_CODE_LOC/benchmarks
  declare -g RESULTS_FOLDER=results/
  mkdir -p $RESULTS_FOLDER
  BENCHMARK_ROOT=../.buildkite/nightly-benchmarks/
  # update transformers package, to make sure mixtral tokenizer is available
  python -m pip install transformers -U
  export CURRENT_LLM_SERVING_ENGINE=trt
  run_serving_tests $BENCHMARK_ROOT/tests/nightly-tests.json
  python -m pip install tabulate pandas
  python $BENCHMARK_ROOT/scripts/summary-nightly-results.py
  upload_to_buildkite
 }
 main "$@"
--- a/.buildkite/nightly-benchmarks/scripts/run-vllm-nightly.sh
+++ b/.buildkite/nightly-benchmarks/scripts/run-vllm-nightly.sh
@@ -1,221 +0,0 @@
 #!/bin/bash
 set -o pipefail
 check_gpus() {
  # check the number of GPUs and GPU type.
  declare -g gpu_count=$(nvidia-smi --list-gpus | wc -l)
  if [[ $gpu_count -gt 0 ]]; then
    echo "GPU found."
  else
    echo "Need at least 1 GPU to run benchmarking."
    exit 1
  fi
  declare -g gpu_type=$(echo $(nvidia-smi --query-gpu=name --format=csv,noheader) | awk '{print $2}')
  echo "GPU type is $gpu_type"
 }
 kill_gpu_processes() {
  # kill all processes on GPU.
  pkill pt_main_thread
  sleep 10
  # remove vllm config file
  rm -rf ~/.config/vllm
  # Print the GPU memory usage
  # so that we know if all GPU processes are killed.
  gpu_memory_usage=$(nvidia-smi --query-gpu=memory.used --format=csv,noheader,nounits -i 0)
  # The memory usage should be 0 MB.
  echo "GPU 0 Memory Usage: $gpu_memory_usage MB"
 }
 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"
 }
 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
 }
 run_serving_tests() {
  # run serving tests using `benchmark_serving.py`
  # $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 $test_name."
      continue
    fi
    # append vllm to the test name
    test_name=vllm_$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')
    # get client and server arguments
    server_params=$(echo "$params" | jq -r '.vllm_server_parameters')
    client_params=$(echo "$params" | jq -r '.vllm_client_parameters')
    server_args=$(json2args "$server_params")
    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 tensor-parallel-size $tp but only $gpu_count GPU found. Skip testcase $test_name."
      continue
    fi
    if echo "$common_params" | jq -e 'has("fp8")' > /dev/null; then
      echo "Key 'fp8' exists in common params. Use neuralmagic fp8 model for convenience."
      model=$(echo "$common_params" | jq -r '.neuralmagic_quantized_model')
      server_command="python3 \
        -m vllm.entrypoints.openai.api_server \
        -tp $tp \
        --model $model \
        --port $port \
        $server_args"
    else
      echo "Key 'fp8' does not exist in common params."
      server_command="python3 \
        -m vllm.entrypoints.openai.api_server \
        -tp $tp \
        --model $model \
        --port $port \
        $server_args"
    fi
    # run the server
    echo "Running test case $test_name"
    echo "Server command: $server_command"
    eval "$server_command" &
    # wait until the server is alive
    wait_for_server
    if [ $? -eq 0 ]; then
      echo ""
      echo "vllm server is up and running."
    else
      echo ""
      echo "vllm 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="inf"
        echo "now qps is $qps"
      fi
      new_test_name=$test_name"_qps_"$qps
      client_command="python3 benchmark_serving.py \
        --backend vllm \
        --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 \
        $client_args"
      echo "Running test case $test_name with qps $qps"
      echo "Client command: $client_command"
      eval "$client_command"
      # record the benchmarking commands
      jq_output=$(jq -n \
        --arg server "$server_command" \
        --arg client "$client_command" \
        --arg gpu "$gpu_type" \
        --arg engine "vllm" \
        '{
          server_command: $server,
          client_command: $client,
          gpu_type: $gpu,
          engine: $engine
        }')
      echo "$jq_output" >"$RESULTS_FOLDER/${new_test_name}.commands"
    done
    # clean up
    kill_gpu_processes
    rm -rf /root/.cache/huggingface/*
  done
 }
 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/*"
 }
 main() {
  check_gpus
  # enter vllm directory
  cd $VLLM_SOURCE_CODE_LOC/benchmarks
  declare -g RESULTS_FOLDER=results/
  mkdir -p $RESULTS_FOLDER
  BENCHMARK_ROOT=../.buildkite/nightly-benchmarks/
  export CURRENT_LLM_SERVING_ENGINE=vllm
  run_serving_tests $BENCHMARK_ROOT/tests/nightly-tests.json
  python3 -m pip install tabulate pandas
  python3 $BENCHMARK_ROOT/scripts/summary-nightly-results.py
  upload_to_buildkite
 }
 main "$@"
--- a/.buildkite/nightly-benchmarks/scripts/summary-nightly-results.py
+++ b/.buildkite/nightly-benchmarks/scripts/summary-nightly-results.py
@@ -17,10 +17,17 @@ serving_column_mapping = {
    "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)",
-    "input_throughput": "Input Tput (tok/s)",
+    "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",
 }
@@ -29,11 +36,11 @@ if __name__ == "__main__":
    # collect results
    for test_file in results_folder.glob("*.json"):
-        with open(test_file, "r") as f:
+        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"), "r") as f:
+        with open(test_file.with_suffix(".commands")) as f:
            command = json.loads(f.read())
        raw_result.update(command)
--- a/.buildkite/nightly-benchmarks/scripts/wait-for-image.sh
+++ b/.buildkite/nightly-benchmarks/scripts/wait-for-image.sh
@@ -1,10 +1,12 @@
 #!/bin/sh
-TOKEN=$(curl -s -L "https://public.ecr.aws/token?service=public.ecr.aws&scope=repository:q9t5s3a7/vllm-ci-test-repo:pull" | jq -r .token)
+TOKEN=$(curl -s -L "https://public.ecr.aws/token?service=public.ecr.aws&scope=repository:q9t5s3a7/vllm-ci-postmerge-repo:pull" | jq -r .token)
-URL="https://public.ecr.aws/v2/q9t5s3a7/vllm-ci-test-repo/manifests/$BUILDKITE_COMMIT"
+URL="https://public.ecr.aws/v2/q9t5s3a7/vllm-ci-postmerge-repo/manifests/$BUILDKITE_COMMIT"
 TIMEOUT_SECONDS=10
 retries=0
 while [ $retries -lt 1000 ]; do
-    if [ $(curl -s -L -H "Authorization: Bearer $TOKEN" -o /dev/null -w "%{http_code}" $URL) -eq 200 ]; then
+    if [ "$(curl -s --max-time "$TIMEOUT_SECONDS" -L -H "Authorization: Bearer $TOKEN" -o /dev/null -w "%{http_code}" "$URL")" -eq 200 ]; then
        exit 0
    fi
@@ -14,4 +16,4 @@ while [ $retries -lt 1000 ]; do
    sleep 5
 done
-exit 1
+exit 1
--- a/.buildkite/nightly-benchmarks/tests/nightly-tests.json
+++ b/.buildkite/nightly-benchmarks/tests/nightly-tests.json
@@ -1,16 +1,18 @@
 [
    {
-        "test_name": "llama8B_tp1",
+        "test_name": "llama8B_tp1_sharegpt",
-        "qps_list": [4],
+        "qps_list": [4,8,16,32,"inf"],
        "common_parameters": {
-            "model": "meta-llama/Meta-Llama-3-8B",
+            "model": "meta-llama/Meta-Llama-3-8B-Instruct",
            "tp": 1,
            "dataset_name": "sharegpt",
            "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
            "num_prompts": 500,
-            "port": 8000
+            "port": 8000,
            "reuse_server": false
        },
        "lmdeploy_server_parameters": {
            "dtype": "bfloat16"
        },
        "lmdeploy_client_parameters": {
        },
@@ -21,34 +23,158 @@
        },
        "trt_server_parameters": {
            "model_type": "llama",
-            "model_dtype": "float16",
+            "model_dtype": "bfloat16",
-            "max_batch_size": 256,
+            "max_batch_size": 2048,
            "max_input_len": 4096,
-            "max_output_len": 4096,
+            "max_seq_len": 6144,
-            "trt_llm_version": "r24.04"
+            "max_num_tokens": 16384,
            "trt_llm_version": "v0.11.0"
        },
        "trt_client_parameters": {
            "endpoint": "/v2/models/ensemble/generate_stream"
-        },
+        }, 
        "vllm_server_parameters": {
            "disable_log_stats": "",
-            "disable_log_requests": ""
+            "disable_log_requests": "",
            "gpu_memory_utilization": 0.9,
            "num_scheduler_steps": 10,
            "max_num_seqs": 512,
            "dtype": "bfloat16"
        },
        "vllm_client_parameters": {
        },
        "sglang_server_parameters": {
            "disable_radix_cache": "",
            "enable_torch_compile": "",
            "dtype": "bfloat16"
        },
        "sglang_client_parameters": {
        }
    },
    {
-        "test_name": "llama70B_tp4",
+        "test_name": "llama8B_tp1_sonnet_512_16",
-        "qps_list": [2],
+        "qps_list": [4,8,16,32,"inf"],
        "common_parameters": {
            "model": "meta-llama/Meta-Llama-3-8B-Instruct",
            "tp": 1,
            "dataset_name": "sonnet",
            "dataset_path": "./sonnet_4x.txt",
            "num_prompts": 500,
            "port": 8000,
            "sonnet_input_len": 512,
            "sonnet_output_len": 16,
            "sonnet_prefix_len": 50,
            "reuse_server": true
        },
        "lmdeploy_server_parameters": {
            "dtype": "bfloat16"
        },
        "lmdeploy_client_parameters": {
        },
        "tgi_server_parameters": {
        },
        "tgi_client_parameters": {
            "endpoint": "/generate_stream"
        },
        "trt_server_parameters": {
            "model_type": "llama",
            "model_dtype": "bfloat16",
            "max_batch_size": 2048,
            "max_input_len": 4096,
            "max_seq_len": 6144,
            "max_num_tokens": 16384,
            "trt_llm_version": "v0.11.0"
        },
        "trt_client_parameters": {
            "endpoint": "/v2/models/ensemble/generate_stream"
        }, 
        "vllm_server_parameters": {
            "disable_log_stats": "",
            "disable_log_requests": "",
            "gpu_memory_utilization": 0.9,
            "num_scheduler_steps": 10,
            "max_num_seqs": 512,
            "dtype": "bfloat16"
        },
        "vllm_client_parameters": {
        },
        "sglang_server_parameters": {
            "disable_radix_cache": "",
            "enable_torch_compile": "",
            "dtype": "bfloat16"
        },
        "sglang_client_parameters": {
        }
    },
    {
        "test_name": "llama8B_tp1_sonnet_512_256",
        "qps_list": [4,8,16,32,"inf"],
        "common_parameters": {
            "model": "meta-llama/Meta-Llama-3-8B-Instruct",
            "tp": 1,
            "dataset_name": "sonnet",
            "dataset_path": "./sonnet_4x.txt",
            "num_prompts": 500,
            "port": 8000,
            "sonnet_input_len": 512,
            "sonnet_output_len": 256,
            "sonnet_prefix_len": 50,
            "reuse_server": true
        },
        "lmdeploy_server_parameters": {
            "dtype": "bfloat16"
        },
        "lmdeploy_client_parameters": {
        },
        "tgi_server_parameters": {
        },
        "tgi_client_parameters": {
            "endpoint": "/generate_stream"
        },
        "trt_server_parameters": {
            "model_type": "llama",
            "model_dtype": "bfloat16",
            "max_batch_size": 2048,
            "max_input_len": 4096,
            "max_seq_len": 6144,
            "max_num_tokens": 16384,
            "trt_llm_version": "v0.11.0"
        },
        "trt_client_parameters": {
            "endpoint": "/v2/models/ensemble/generate_stream"
        }, 
        "vllm_server_parameters": {
            "disable_log_stats": "",
            "disable_log_requests": "",
            "gpu_memory_utilization": 0.9,
            "num_scheduler_steps": 10,
            "max_num_seqs": 512,
            "dtype": "bfloat16"
        },
        "vllm_client_parameters": {
        },
        "sglang_server_parameters": {
            "disable_radix_cache": "",
            "enable_torch_compile": "",
            "dtype": "bfloat16"
        },
        "sglang_client_parameters": {
        }
    },
    {
        "test_name": "llama70B_tp4_sharegpt",
        "qps_list": [4,8,16,32,"inf"],
        "common_parameters": {
            "model": "meta-llama/Meta-Llama-3-70B-Instruct",
            "tp": 4,
            "dataset_name": "sharegpt",
            "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
            "num_prompts": 500,
-            "port": 8000
+            "port": 8000,
            "reuse_server": false
        },
        "lmdeploy_server_parameters": {
            "dtype": "bfloat16"
        },
        "lmdeploy_client_parameters": {
        },
@@ -59,34 +185,50 @@
        },
        "trt_server_parameters": {
            "model_type": "llama",
-            "model_dtype": "float16",
+            "model_dtype": "bfloat16",
-            "max_batch_size": 256,
+            "max_batch_size": 2048,
            "max_input_len": 4096,
-            "max_output_len": 4096,
+            "max_seq_len": 6144,
-            "trt_llm_version": "r24.04"
+            "max_num_tokens": 16384,
            "trt_llm_version": "v0.11.0"
        },
        "trt_client_parameters": {
            "endpoint": "/v2/models/ensemble/generate_stream"
-        },
+        }, 
        "vllm_server_parameters": {
            "disable_log_stats": "",
-            "disable_log_requests": ""
+            "disable_log_requests": "",
            "gpu_memory_utilization": 0.9,
            "num_scheduler_steps": 10,
            "max_num_seqs": 512,
            "dtype": "bfloat16"
        },
        "vllm_client_parameters": {
        },
        "sglang_server_parameters": {
            "disable_radix_cache": "",
            "dtype": "bfloat16"
        },
        "sglang_client_parameters": {
        }
    },
    {
-        "test_name": "mixtral8x7B_tp2",
+        "test_name": "llama70B_tp4_sonnet_512_16",
-        "qps_list": [2],
+        "qps_list": [4,8,16,32,"inf"],
        "common_parameters": {
-            "model": "mistralai/Mixtral-8x7B-Instruct-v0.1",
+            "model": "meta-llama/Meta-Llama-3-70B-Instruct",
-            "tp": 2,
+            "tp": 4,
-            "dataset_name": "sharegpt",
+            "dataset_name": "sonnet",
-            "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
+            "dataset_path": "./sonnet_4x.txt",
            "num_prompts": 500,
-            "port": 8000
+            "port": 8000,
            "sonnet_input_len": 512,
            "sonnet_output_len": 16,
            "sonnet_prefix_len": 50,
            "reuse_server": true
        },
        "lmdeploy_server_parameters": {
            "dtype": "bfloat16"
        },
        "lmdeploy_client_parameters": {
        },
@@ -97,20 +239,85 @@
        },
        "trt_server_parameters": {
            "model_type": "llama",
-            "model_dtype": "float16",
+            "model_dtype": "bfloat16",
-            "max_batch_size": 256,
+            "max_batch_size": 2048,
            "max_input_len": 4096,
-            "max_output_len": 4096,
+            "max_seq_len": 6144,
-            "trt_llm_version": "r24.04"
+            "max_num_tokens": 16384,
            "trt_llm_version": "v0.11.0"
        },
        "trt_client_parameters": {
            "endpoint": "/v2/models/ensemble/generate_stream"
-        },
+        }, 
        "vllm_server_parameters": {
            "disable_log_stats": "",
-            "disable_log_requests": ""
+            "disable_log_requests": "",
            "gpu_memory_utilization": 0.9,
            "num_scheduler_steps": 10,
            "max_num_seqs": 512,
            "dtype": "bfloat16"
        },
        "vllm_client_parameters": {
        },
        "sglang_server_parameters": {
            "disable_radix_cache": "",
            "dtype": "bfloat16"
        },
        "sglang_client_parameters": {
        }
    },
    {
        "test_name": "llama70B_tp4_sonnet_512_256",
        "qps_list": [4,8,16,32,"inf"],
        "common_parameters": {
            "model": "meta-llama/Meta-Llama-3-70B-Instruct",
            "tp": 4,
            "dataset_name": "sonnet",
            "dataset_path": "./sonnet_4x.txt",
            "num_prompts": 500,
            "port": 8000,
            "sonnet_input_len": 512,
            "sonnet_output_len": 256,
            "sonnet_prefix_len": 50,
            "reuse_server": true
        },
        "lmdeploy_server_parameters": {
            "dtype": "bfloat16"
        },
        "lmdeploy_client_parameters": {
        },
        "tgi_server_parameters": {
        },
        "tgi_client_parameters": {
            "endpoint": "/generate_stream"
        },
        "trt_server_parameters": {
            "model_type": "llama",
            "model_dtype": "bfloat16",
            "max_batch_size": 2048,
            "max_input_len": 4096,
            "max_seq_len": 6144,
            "max_num_tokens": 16384,
            "trt_llm_version": "v0.11.0"
        },
        "trt_client_parameters": {
            "endpoint": "/v2/models/ensemble/generate_stream"
        }, 
        "vllm_server_parameters": {
            "disable_log_stats": "",
            "disable_log_requests": "",
            "gpu_memory_utilization": 0.9,
            "num_scheduler_steps": 10,
            "max_num_seqs": 512,
            "dtype": "bfloat16"
        },
        "vllm_client_parameters": {
        },
        "sglang_server_parameters": {
            "disable_radix_cache": "",
            "dtype": "bfloat16"
        },
        "sglang_client_parameters": {
        }
    }
 ]
--- a/.buildkite/release-pipeline.yaml
+++ b/.buildkite/release-pipeline.yaml
@@ -1,32 +1,72 @@
 steps:
  - label: "Build wheel - CUDA 12.1"
    agents:
-      queue: cpu_queue
+      queue: cpu_queue_postmerge
    commands:
-      - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg buildkite_commit=$BUILDKITE_COMMIT --build-arg USE_SCCACHE=1 --build-arg CUDA_VERSION=12.1.0 --tag vllm-ci:build-image --target build --progress plain ."
+      - "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=12.1.0 --tag vllm-ci:build-image --target build --progress plain ."
      - "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'"
-      # rename the files to change linux -> manylinux1
+      - "bash .buildkite/upload-wheels.sh"
      - "for f in artifacts/dist/*.whl; do mv -- \"$$f\" \"$${f/linux/manylinux1}\"; done"
      - "aws s3 cp --recursive artifacts/dist s3://vllm-wheels/$BUILDKITE_COMMIT/"
      - "aws s3 cp --recursive artifacts/dist s3://vllm-wheels/nightly/"
    env:
      DOCKER_BUILDKIT: "1"
-  - block: "Build CUDA 11.8 wheel"
+  # Note(simon): We can always build CUDA 11.8 wheel to ensure the build is working.
-    key: block-build-cu118-wheel
+  # However, this block can be uncommented to save some compute hours.
-  
+  # - block: "Build CUDA 11.8 wheel"
  #   key: block-build-cu118-wheel
  - label: "Build wheel - CUDA 11.8"
-    depends_on: block-build-cu118-wheel
+    # depends_on: block-build-cu118-wheel
    agents:
-      queue: cpu_queue
+      queue: cpu_queue_postmerge
    commands:
-      - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg buildkite_commit=$BUILDKITE_COMMIT --build-arg USE_SCCACHE=1 --build-arg CUDA_VERSION=11.8.0 --tag vllm-ci:build-image --target build --progress plain ."
+      - "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=11.8.0 --tag vllm-ci:build-image --target build --progress plain ."
      - "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'"
-      # rename the files to change linux -> manylinux1
+      - "bash .buildkite/upload-wheels.sh"
-      - "for f in artifacts/dist/*.whl; do mv -- \"$$f\" \"$${f/linux/manylinux1}\"; done"
+    env:
-      - "aws s3 cp --recursive artifacts/dist s3://vllm-wheels/$BUILDKITE_COMMIT/"
+      DOCKER_BUILDKIT: "1"
-      - "aws s3 cp --recursive artifacts/dist s3://vllm-wheels/nightly/"
+
  - block: "Build release image"
    depends_on: ~
    key: block-release-image-build
  - label: "Build release image"
    depends_on: block-release-image-build
    agents:
      queue: cpu_queue_postmerge
    commands:
      - "aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7"
      - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=12.1.0 --tag public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT --target vllm-openai --progress plain ."
      - "docker push public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT"
  - label: "Build and publish TPU release image"
    depends_on: ~
    if: build.env("NIGHTLY") == "1"
    agents:
      queue: tpu_queue_postmerge
    commands:
      - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --tag vllm/vllm-tpu:nightly --tag vllm/vllm-tpu:$BUILDKITE_COMMIT --progress plain -f Dockerfile.tpu ."
      - "docker push vllm/vllm-tpu:nightly"
      - "docker push vllm/vllm-tpu:$BUILDKITE_COMMIT"
    plugins:
      - docker-login#v3.0.0:
          username: vllm
          password-env: DOCKERHUB_TOKEN
    env:
      DOCKER_BUILDKIT: "1"
  - block: "Build CPU release image"
    key: block-cpu-release-image-build
    depends_on: ~
  - label: "Build and publish CPU release image"
    depends_on: block-cpu-release-image-build
    agents:
      queue: cpu_queue_postmerge
    commands:
      - "aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7"
      - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg GIT_REPO_CHECK=1 --tag public.ecr.aws/q9t5s3a7/vllm-cpu-release-repo:$RELEASE_VERSION --progress plain -f Dockerfile.cpu ."
      - "docker push public.ecr.aws/q9t5s3a7/vllm-cpu-release-repo:$RELEASE_VERSION"
    env:
      DOCKER_BUILDKIT: "1"
--- a/.buildkite/run-amd-test.sh
+++ b/.buildkite/run-amd-test.sh
@@ -1,3 +1,5 @@
 #!/bin/bash
 # This script runs test inside the corresponding ROCm docker container.
 set -o pipefail
@@ -31,8 +33,8 @@ cleanup_docker() {
    echo "Disk usage is above $threshold%. Cleaning up Docker images and volumes..."
    # Remove dangling images (those that are not tagged and not used by any container)
    docker image prune -f
-    # Remove unused volumes
+    # Remove unused volumes / force the system prune for old images as well.
-    docker volume prune -f
+    docker volume prune -f && docker system prune --force --filter "until=72h" --all
    echo "Docker images and volumes cleanup completed."
  else
    echo "Disk usage is below $threshold%. No cleanup needed."
@@ -57,51 +59,85 @@ done
 echo "--- Pulling container" 
 image_name="rocm/vllm-ci:${BUILDKITE_COMMIT}"
 container_name="rocm_${BUILDKITE_COMMIT}_$(tr -dc A-Za-z0-9 < /dev/urandom | head -c 10; echo)"
-docker pull ${image_name}
+docker pull "${image_name}"
 remove_docker_container() {
-   docker rm -f ${container_name} || docker image rm -f ${image_name} || true
+   docker rm -f "${container_name}" || docker image rm -f "${image_name}" || true
 }
 trap remove_docker_container EXIT
 echo "--- Running container"
 HF_CACHE="$(realpath ~)/huggingface"
-mkdir -p ${HF_CACHE}
+mkdir -p "${HF_CACHE}"
 HF_MOUNT="/root/.cache/huggingface"
 commands=$@
 echo "Commands:$commands"
 #ignore certain kernels tests
 if [[ $commands == *" kernels "* ]]; then
  commands="${commands} \
  --ignore=kernels/test_attention.py \
  --ignore=kernels/test_attention_selector.py \
  --ignore=kernels/test_blocksparse_attention.py \
  --ignore=kernels/test_causal_conv1d.py \
  --ignore=kernels/test_cutlass.py \
  --ignore=kernels/test_encoder_decoder_attn.py \
  --ignore=kernels/test_flash_attn.py \
  --ignore=kernels/test_flashinfer.py \
  --ignore=kernels/test_int8_quant.py \
  --ignore=kernels/test_machete_gemm.py \
  --ignore=kernels/test_mamba_ssm.py \
  --ignore=kernels/test_marlin_gemm.py \
  --ignore=kernels/test_moe.py \
  --ignore=kernels/test_prefix_prefill.py \
  --ignore=kernels/test_rand.py \
  --ignore=kernels/test_sampler.py"
 fi
 #ignore certain Entrypoints tests
 if [[ $commands == *" entrypoints/openai "* ]]; then
  commands=${commands//" entrypoints/openai "/" entrypoints/openai \
  --ignore=entrypoints/openai/test_accuracy.py \
  --ignore=entrypoints/openai/test_audio.py \
  --ignore=entrypoints/openai/test_encoder_decoder.py \
  --ignore=entrypoints/openai/test_embedding.py \
  --ignore=entrypoints/openai/test_oot_registration.py "}
 fi
 PARALLEL_JOB_COUNT=8
 # check if the command contains shard flag, we will run all shards in parallel because the host have 8 GPUs. 
 if [[ $commands == *"--shard-id="* ]]; then
  # assign job count as the number of shards used   
  commands=${commands//"--num-shards= "/"--num-shards=${PARALLEL_JOB_COUNT} "}
  for GPU in $(seq 0 $(($PARALLEL_JOB_COUNT-1))); do
-    #replace shard arguments
+    # assign shard-id for each shard
-    commands=${@//"--shard-id= "/"--shard-id=${GPU} "}
+    commands_gpu=${commands//"--shard-id= "/"--shard-id=${GPU} "}
-    commands=${commands//"--num-shards= "/"--num-shards=${PARALLEL_JOB_COUNT} "}
+    echo "Shard ${GPU} commands:$commands_gpu"
    docker run \
        --device /dev/kfd --device /dev/dri \
        --network host \
        --shm-size=16gb \
        --rm \
-        -e HIP_VISIBLE_DEVICES=${GPU} \
+        -e HIP_VISIBLE_DEVICES="${GPU}" \
        -e HF_TOKEN \
-        -v ${HF_CACHE}:${HF_MOUNT} \
+        -v "${HF_CACHE}:${HF_MOUNT}" \
-        -e HF_HOME=${HF_MOUNT} \
+        -e "HF_HOME=${HF_MOUNT}" \
-        --name ${container_name}_${GPU}  \
+        --name "${container_name}_${GPU}" \
-        ${image_name} \
+        "${image_name}" \
-        /bin/bash -c "${commands}" \
+        /bin/bash -c "${commands_gpu}" \
        |& while read -r line; do echo ">>Shard $GPU: $line"; done &
    PIDS+=($!)
  done
  #wait for all processes to finish and collect exit codes
-  for pid in ${PIDS[@]}; do
+  for pid in "${PIDS[@]}"; do
-    wait ${pid}
+    wait "${pid}"
    STATUS+=($?)
  done
-  for st in ${STATUS[@]}; do
+  for st in "${STATUS[@]}"; do
    if [[ ${st} -ne 0 ]]; then
      echo "One of the processes failed with $st"
-      exit ${st}
+      exit "${st}"
    fi
  done
 else
@@ -112,9 +148,9 @@ else
          --rm \
          -e HIP_VISIBLE_DEVICES=0 \
          -e HF_TOKEN \
-          -v ${HF_CACHE}:${HF_MOUNT} \
+          -v "${HF_CACHE}:${HF_MOUNT}" \
-          -e HF_HOME=${HF_MOUNT} \
+          -e "HF_HOME=${HF_MOUNT}" \
-          --name ${container_name} \
+          --name "${container_name}" \
-          ${image_name} \
+          "${image_name}" \
          /bin/bash -c "${commands}"
 fi
--- a/.buildkite/run-benchmarks.sh
+++ b/.buildkite/run-benchmarks.sh
@@ -1,3 +1,5 @@
 #!/bin/bash
 # This script is run by buildkite to run the benchmarks and upload the results to buildkite
 set -ex
--- a/.buildkite/run-cpu-test-ppc64le.sh
+++ b/.buildkite/run-cpu-test-ppc64le.sh
@@ -0,0 +1,14 @@
 #!/bin/bash
 # This script build the CPU docker image and run the offline inference inside the container.
 # It serves a sanity check for compilation and basic model usage.
 set -ex
 # Setup cleanup
 remove_docker_container() { docker rm -f cpu-test || true; docker system prune -f; }
 trap remove_docker_container EXIT
 remove_docker_container
 # Try building the docker image
 docker build -t cpu-test -f Dockerfile.ppc64le .
--- a/.buildkite/run-cpu-test.sh
+++ b/.buildkite/run-cpu-test.sh
@@ -1,45 +1,85 @@
 #!/bin/bash
 # This script build the CPU docker image and run the offline inference inside the container.
 # It serves a sanity check for compilation and basic model usage.
 set -ex
 # allow to bind to different cores
 CORE_RANGE=${CORE_RANGE:-48-95}
 NUMA_NODE=${NUMA_NODE:-1}
 # Try building the docker image
-numactl -C 48-95 -N 1 docker build -t cpu-test -f Dockerfile.cpu .
+numactl -C "$CORE_RANGE" -N "$NUMA_NODE" docker build -t cpu-test -f Dockerfile.cpu .
-numactl -C 48-95 -N 1 docker build --build-arg VLLM_CPU_DISABLE_AVX512="true" -t cpu-test-avx2 -f Dockerfile.cpu .
+numactl -C "$CORE_RANGE" -N "$NUMA_NODE" docker build --build-arg VLLM_CPU_DISABLE_AVX512="true" -t cpu-test-avx2 -f Dockerfile.cpu .
 # Setup cleanup
-remove_docker_container() { docker rm -f cpu-test cpu-test-avx2 || true; }
+remove_docker_container() { docker rm -f cpu-test-"$NUMA_NODE" cpu-test-avx2-"$NUMA_NODE" || true; }
 trap remove_docker_container EXIT
 remove_docker_container
 # Run the image, setting --shm-size=4g for tensor parallel.
-docker run -itd --entrypoint /bin/bash -v ~/.cache/huggingface:/root/.cache/huggingface --cpuset-cpus=48-95 \
+docker run -itd --entrypoint /bin/bash -v ~/.cache/huggingface:/root/.cache/huggingface --cpuset-cpus="$CORE_RANGE"  \
- --cpuset-mems=1 --privileged=true --network host -e HF_TOKEN --env VLLM_CPU_KVCACHE_SPACE=4 --shm-size=4g --name cpu-test cpu-test
+ --cpuset-mems="$NUMA_NODE" --privileged=true --network host -e HF_TOKEN --env VLLM_CPU_KVCACHE_SPACE=4 --shm-size=4g --name cpu-test-"$NUMA_NODE" cpu-test
-docker run -itd --entrypoint /bin/bash -v ~/.cache/huggingface:/root/.cache/huggingface --cpuset-cpus=48-95 \
+docker run -itd --entrypoint /bin/bash -v ~/.cache/huggingface:/root/.cache/huggingface --cpuset-cpus="$CORE_RANGE" \
- --cpuset-mems=1 --privileged=true --network host -e HF_TOKEN --env VLLM_CPU_KVCACHE_SPACE=4 --shm-size=4g --name cpu-test-avx2 cpu-test-avx2
+ --cpuset-mems="$NUMA_NODE" --privileged=true --network host -e HF_TOKEN --env VLLM_CPU_KVCACHE_SPACE=4 --shm-size=4g --name cpu-test-avx2-"$NUMA_NODE" cpu-test-avx2
-# offline inference
+function cpu_tests() {
-docker exec cpu-test-avx2 bash -c "python3 examples/offline_inference.py"
+  set -e
  export NUMA_NODE=$2
-# Run basic model test
+  # offline inference
-docker exec cpu-test bash -c "
+  docker exec cpu-test-avx2-"$NUMA_NODE" bash -c "
-  pip install pytest matplotlib einops transformers_stream_generator
+    set -e
-  pytest -v -s tests/models -m \"not vlm\" --ignore=tests/models/test_embedding.py \
+    python3 examples/offline_inference.py"
      --ignore=tests/models/test_oot_registration.py \
      --ignore=tests/models/test_registry.py \
      --ignore=tests/models/test_fp8.py \
      --ignore=tests/models/test_jamba.py \
      --ignore=tests/models/test_danube3_4b.py" # Mamba and Danube3-4B on CPU is not supported
-# online inference
+  # Run basic model test
-docker exec cpu-test bash -c "
+  docker exec cpu-test-"$NUMA_NODE" bash -c "
-  export VLLM_CPU_KVCACHE_SPACE=10 
+    set -e
-  export VLLM_CPU_OMP_THREADS_BIND=48-92 
+    pip install pytest pytest-asyncio \
-  python3 -m vllm.entrypoints.openai.api_server --model facebook/opt-125m & 
+      decord einops librosa peft Pillow sentence-transformers soundfile \
-  timeout 600 bash -c 'until curl localhost:8000/v1/models; do sleep 1; done' || exit 1
+      transformers_stream_generator matplotlib datamodel_code_generator
-  python3 benchmarks/benchmark_serving.py \
+    pip install torchvision --index-url https://download.pytorch.org/whl/cpu
-    --backend vllm \
+    pytest -v -s tests/models/decoder_only/language -m cpu_model
-    --dataset-name random \
+    pytest -v -s tests/models/embedding/language -m cpu_model
-    --model facebook/opt-125m \
+    pytest -v -s tests/models/encoder_decoder/language -m cpu_model
-    --num-prompts 20 \
+    pytest -v -s tests/models/decoder_only/audio_language -m cpu_model
-    --endpoint /v1/completions \
+    pytest -v -s tests/models/decoder_only/vision_language -m cpu_model"
-    --tokenizer facebook/opt-125m"
+
  # Run compressed-tensor test
  docker exec cpu-test-"$NUMA_NODE" bash -c "
    set -e
    pytest -s -v \
    tests/quantization/test_compressed_tensors.py::test_compressed_tensors_w8a8_static_setup \
    tests/quantization/test_compressed_tensors.py::test_compressed_tensors_w8a8_dynamic_per_token"
  # Run AWQ test
  docker exec cpu-test-"$NUMA_NODE" bash -c "
    set -e
    pytest -s -v \
    tests/quantization/test_ipex_quant.py"
  # Run chunked-prefill and prefix-cache test
  docker exec cpu-test-"$NUMA_NODE" bash -c "
    set -e
    pytest -s -v -k cpu_model \
    tests/basic_correctness/test_chunked_prefill.py"  
  # online inference
  docker exec cpu-test-"$NUMA_NODE" bash -c "
    set -e
    export VLLM_CPU_KVCACHE_SPACE=10 
    export VLLM_CPU_OMP_THREADS_BIND=$1
    python3 -m vllm.entrypoints.openai.api_server --model facebook/opt-125m --dtype half & 
    timeout 600 bash -c 'until curl localhost:8000/v1/models; do sleep 1; done' || exit 1
    python3 benchmarks/benchmark_serving.py \
      --backend vllm \
      --dataset-name random \
      --model facebook/opt-125m \
      --num-prompts 20 \
      --endpoint /v1/completions \
      --tokenizer facebook/opt-125m"
 }
 # All of CPU tests are expected to be finished less than 25 mins.
 export -f cpu_tests
 timeout 30m bash -c "cpu_tests $CORE_RANGE $NUMA_NODE"
--- a/.buildkite/run-gh200-test.sh
+++ b/.buildkite/run-gh200-test.sh
@@ -0,0 +1,28 @@
 #!/bin/bash
 # This script build the GH200 docker image and run the offline inference inside the container.
 # It serves a sanity check for compilation and basic model usage.
 set -ex
 # Skip the new torch installation during build since we are using the specified version for arm64 in the Dockerfile
 python3 use_existing_torch.py
 # Try building the docker image
 DOCKER_BUILDKIT=1 docker build . \
  --target vllm-openai \
  --platform "linux/arm64" \
  -t gh200-test \
  --build-arg max_jobs=66 \
  --build-arg nvcc_threads=2 \
  --build-arg torch_cuda_arch_list="9.0+PTX" \
  --build-arg vllm_fa_cmake_gpu_arches="90-real"
 # Setup cleanup
 remove_docker_container() { docker rm -f gh200-test || true; }
 trap remove_docker_container EXIT
 remove_docker_container
 # Run the image and test offline inference
 docker run --name gh200-test --gpus=all --entrypoint="" gh200-test bash -c '
    python3 examples/offline_inference.py
 '
--- a/.buildkite/run-hpu-test.sh
+++ b/.buildkite/run-hpu-test.sh
@@ -0,0 +1,16 @@
 #!/bin/bash
 # This script build the CPU docker image and run the offline inference inside the container.
 # It serves a sanity check for compilation and basic model usage.
 set -ex
 # Try building the docker image
 docker build -t hpu-test-env -f Dockerfile.hpu .
 # Setup cleanup
 remove_docker_container() { docker rm -f hpu-test || true; }
 trap remove_docker_container EXIT
 remove_docker_container
 # Run the image and launch offline inference
 docker run --runtime=habana --name=hpu-test --network=host -e HABANA_VISIBLE_DEVICES=all -e VLLM_SKIP_WARMUP=true --entrypoint="" hpu-test-env python3 examples/offline_inference.py
--- a/.buildkite/run-multi-node-test.sh
+++ b/.buildkite/run-multi-node-test.sh
@@ -14,7 +14,7 @@ DOCKER_IMAGE=$4
 shift 4
 COMMANDS=("$@")
-if [ ${#COMMANDS[@]} -ne $NUM_NODES ]; then
+if [ ${#COMMANDS[@]} -ne "$NUM_NODES" ]; then
    echo "The number of commands must be equal to the number of nodes."
    echo "Number of nodes: $NUM_NODES"
    echo "Number of commands: ${#COMMANDS[@]}"
@@ -23,7 +23,7 @@ fi
 echo "List of commands"
 for command in "${COMMANDS[@]}"; do
-    echo $command
+    echo "$command"
 done
 start_network() {
@@ -36,7 +36,7 @@ start_nodes() {
        for node_gpu in $(seq 0 $(($NUM_GPUS - 1))); do
            DEVICE_NUM=$(($node * $NUM_GPUS + $node_gpu))
            GPU_DEVICES+=$(($DEVICE_NUM))
-            if [ $node_gpu -lt $(($NUM_GPUS - 1)) ]; then
+            if [ "$node_gpu" -lt $(($NUM_GPUS - 1)) ]; then
                GPU_DEVICES+=','
            fi
        done
@@ -49,17 +49,20 @@ start_nodes() {
        # 3. map the huggingface cache directory to the container
        # 3. assign ip addresses to the containers (head node: 192.168.10.10, worker nodes:
        #    starting from 192.168.10.11)
-        docker run -d --gpus "$GPU_DEVICES" --shm-size=10.24gb -e HF_TOKEN -v ~/.cache/huggingface:/root/.cache/huggingface --name node$node --network docker-net --ip 192.168.10.$((10 + $node)) --rm $DOCKER_IMAGE /bin/bash -c "tail -f /dev/null"
+        docker run -d --gpus "$GPU_DEVICES" --shm-size=10.24gb -e HF_TOKEN \
            -v ~/.cache/huggingface:/root/.cache/huggingface --name "node$node" \
            --network docker-net --ip 192.168.10.$((10 + $node)) --rm "$DOCKER_IMAGE" \
            /bin/bash -c "tail -f /dev/null"
        # organize containers into a ray cluster
-        if [ $node -eq 0 ]; then
+        if [ "$node" -eq 0 ]; then
            # start the ray head node
-            docker exec -d node$node /bin/bash -c "ray start --head --port=6379 --block"
+            docker exec -d "node$node" /bin/bash -c "ray start --head --port=6379 --block"
            # wait for the head node to be ready
            sleep 10
        else
            # start the ray worker nodes, and connect them to the head node
-            docker exec -d node$node /bin/bash -c "ray start --address=192.168.10.10:6379 --block"
+            docker exec -d "node$node" /bin/bash -c "ray start --address=192.168.10.10:6379 --block"
        fi
    done
@@ -79,22 +82,22 @@ run_nodes() {
        for node_gpu in $(seq 0 $(($NUM_GPUS - 1))); do
            DEVICE_NUM=$(($node * $NUM_GPUS + $node_gpu))
            GPU_DEVICES+=$(($DEVICE_NUM))
-            if [ $node_gpu -lt $(($NUM_GPUS - 1)) ]; then
+            if [ "$node_gpu" -lt $(($NUM_GPUS - 1)) ]; then
                GPU_DEVICES+=','
            fi
        done
        GPU_DEVICES+='"'
        echo "Running node$node with GPU devices: $GPU_DEVICES"
-        if [ $node -ne 0 ]; then
+        if [ "$node" -ne 0 ]; then
-            docker exec -d node$node /bin/bash -c "cd $WORKING_DIR ; ${COMMANDS[$node]}"
+            docker exec -d "node$node" /bin/bash -c "cd $WORKING_DIR ; ${COMMANDS[$node]}"
        else
-            docker exec node$node /bin/bash -c "cd $WORKING_DIR ; ${COMMANDS[$node]}"
+            docker exec "node$node" /bin/bash -c "cd $WORKING_DIR ; ${COMMANDS[$node]}"
        fi
    done
 }
 cleanup() {
    for node in $(seq 0 $(($NUM_NODES-1))); do
-        docker stop node$node
+        docker stop "node$node"
    done
    docker network rm docker-net
 }
--- a/.buildkite/run-neuron-test.sh
+++ b/.buildkite/run-neuron-test.sh
@@ -1,3 +1,5 @@
 #!/bin/bash
 # This script build the Neuron docker image and run the API server inside the container.
 # It serves a sanity check for compilation and basic model usage.
 set -e
@@ -12,10 +14,10 @@ if [ -f /tmp/neuron-docker-build-timestamp ]; then
    current_time=$(date +%s)
    if [ $((current_time - last_build)) -gt 86400 ]; then
        docker system prune -f
-        echo $current_time > /tmp/neuron-docker-build-timestamp
+        echo "$current_time" > /tmp/neuron-docker-build-timestamp
    fi
 else
-    echo $(date +%s) > /tmp/neuron-docker-build-timestamp
+    date "+%s" > /tmp/neuron-docker-build-timestamp
 fi
 docker build -t neuron -f Dockerfile.neuron .
@@ -34,7 +36,7 @@ wait_for_server_to_start() {
    timeout=300
    counter=0
-    while [ "$(curl -s -o /dev/null -w ''%{http_code}'' localhost:8000/health)" != "200" ]; do
+    while [ "$(curl -s -o /dev/null -w '%{http_code}' localhost:8000/health)" != "200" ]; do
        sleep 1
        counter=$((counter + 1))
        if [ $counter -ge $timeout ]; then
--- a/.buildkite/run-openvino-test.sh
+++ b/.buildkite/run-openvino-test.sh
@@ -1,3 +1,5 @@
 #!/bin/bash
 # This script build the OpenVINO docker image and run the offline inference inside the container.
 # It serves a sanity check for compilation and basic model usage.
 set -ex
@@ -11,4 +13,4 @@ trap remove_docker_container EXIT
 remove_docker_container
 # Run the image and launch offline inference
-docker run --network host --env VLLM_OPENVINO_KVCACHE_SPACE=1 --name openvino-test openvino-test python3 /workspace/vllm/examples/offline_inference.py
+docker run --network host --env VLLM_OPENVINO_KVCACHE_SPACE=1 --name openvino-test openvino-test python3 /workspace/examples/offline_inference.py
--- a/.buildkite/run-tpu-test.sh
+++ b/.buildkite/run-tpu-test.sh
@@ -1,3 +1,5 @@
 #!/bin/bash
 set -e
 # Build the docker image.
@@ -12,4 +14,4 @@ remove_docker_container
 # For HF_TOKEN.
 source /etc/environment
 # Run a simple end-to-end example.
-docker run --privileged --net host --shm-size=16G -it -e HF_TOKEN=$HF_TOKEN --name tpu-test vllm-tpu /bin/bash -c "python3 -m pip install git+https://github.com/thuml/depyf.git && python3 -m pip install pytest  && pytest -v -s /workspace/vllm/tests/tpu/test_custom_dispatcher.py && python3 /workspace/vllm/tests/tpu/test_compilation.py && python3 /workspace/vllm/examples/offline_inference_tpu.py"
+docker run --privileged --net host --shm-size=16G -it -e "HF_TOKEN=$HF_TOKEN" --name tpu-test vllm-tpu /bin/bash -c "python3 -m pip install git+https://github.com/thuml/depyf.git && python3 -m pip install pytest && python3 -m pip install lm_eval[api]==0.4.4 && pytest -v -s /workspace/vllm/tests/entrypoints/openai/test_accuracy.py && pytest -v -s /workspace/vllm/tests/tpu/test_custom_dispatcher.py && python3 /workspace/vllm/tests/tpu/test_compilation.py && python3 /workspace/vllm/examples/offline_inference_tpu.py"
--- a/.buildkite/run-xpu-test.sh
+++ b/.buildkite/run-xpu-test.sh
@@ -1,3 +1,5 @@
 #!/bin/bash
 # This script build the CPU docker image and run the offline inference inside the container.
 # It serves a sanity check for compilation and basic model usage.
 set -ex
@@ -10,5 +12,8 @@ remove_docker_container() { docker rm -f xpu-test || true; }
 trap remove_docker_container EXIT
 remove_docker_container
-# Run the image and launch offline inference
+# Run the image and test offline inference/tensor parallel
-docker run --network host --name xpu-test --device /dev/dri -v /dev/dri/by-path:/dev/dri/by-path xpu-test python3 examples/offline_inference.py
+docker run --name xpu-test --device /dev/dri -v /dev/dri/by-path:/dev/dri/by-path --entrypoint="" xpu-test sh -c '
    python3 examples/offline_inference.py
    python3 examples/offline_inference_cli.py -tp 2
 '
--- a/.buildkite/test-pipeline.yaml
+++ b/.buildkite/test-pipeline.yaml
@@ -9,6 +9,7 @@
 # label(str): the name of the test. emoji allowed.
 # fast_check(bool): whether to run this on each commit on fastcheck pipeline.
 # fast_check_only(bool): run this test on fastcheck pipeline only
 # optional(bool): never run this test by default (i.e. need to unblock manually) unless it's scheduled nightly run.
 # command(str): the single command to run for tests. incompatible with commands.
 # commands(list): the list of commands to run for test. incompatbile with command.
 # mirror_hardwares(list): the list of hardwares to run the test on as well. currently only supports [amd]
@@ -39,35 +40,55 @@ steps:
  # Check API reference (if it fails, you may have missing mock imports)
  - grep \"sig sig-object py\" build/html/dev/sampling_params.html
- label: Async Engine, Inputs, Utils, Worker Test # 15min
+- label: Async Engine, Inputs, Utils, Worker Test # 24min
  fast_check: true
  source_file_dependencies:
  - vllm/
  - tests/mq_llm_engine
  - tests/async_engine
  - tests/test_inputs
  - tests/multimodal
  - tests/test_utils
  - tests/worker
  - tests/standalone_tests/lazy_torch_compile.py
  commands:
-  - pytest -v -s async_engine # Async Engine
+  - python3 standalone_tests/lazy_torch_compile.py
  - pytest -v -s mq_llm_engine # MQLLMEngine
  - pytest -v -s async_engine # AsyncLLMEngine
  - NUM_SCHEDULER_STEPS=4 pytest -v -s async_engine/test_async_llm_engine.py
  - pytest -v -s test_inputs.py
  - pytest -v -s multimodal
  - pytest -v -s test_utils.py # Utils
  - pytest -v -s worker # Worker
 - label: Python-only Installation Test
  source_file_dependencies:
  - tests/standalone_tests/python_only_compile.sh
  - setup.py
  commands:
  - bash standalone_tests/python_only_compile.sh
 - label: Basic Correctness Test # 30min
  #mirror_hardwares: [amd]
  fast_check: true
  source_file_dependencies:
  - vllm/
-  - tests/basic_correctness
+  - tests/basic_correctness/test_basic_correctness
  - tests/basic_correctness/test_cpu_offload
  - tests/basic_correctness/test_preemption
  commands:
  - pytest -v -s basic_correctness/test_basic_correctness.py
  - pytest -v -s basic_correctness/test_cpu_offload.py
  - VLLM_TEST_ENABLE_ARTIFICIAL_PREEMPT=1 pytest -v -s basic_correctness/test_preemption.py
 - label: Chunked Prefill Test
  source_file_dependencies:
  - vllm/
  - tests/basic_correctness/test_chunked_prefill
  commands:
  - VLLM_ATTENTION_BACKEND=XFORMERS pytest -v -s basic_correctness/test_chunked_prefill.py
  - VLLM_ATTENTION_BACKEND=FLASH_ATTN pytest -v -s basic_correctness/test_chunked_prefill.py
-  - VLLM_TEST_ENABLE_ARTIFICIAL_PREEMPT=1 pytest -v -s basic_correctness/test_preemption.py
+
 - label: Core Test # 10min
  mirror_hardwares: [amd]
  fast_check: true
@@ -78,20 +99,23 @@ steps:
  commands:
  - pytest -v -s core
- label: Entrypoints Test # 20min
+- label: Entrypoints Test # 40min
  working_dir: "/vllm-workspace/tests"
  fast_check: true
-  #mirror_hardwares: [amd]
+  mirror_hardwares: [amd]
  source_file_dependencies:
  - vllm/
  commands:
  - pip install -e ./plugins/vllm_add_dummy_model
-  - pip install git+https://github.com/EleutherAI/lm-evaluation-harness.git@a4987bba6e9e9b3f22bd3a6c1ecf0abd04fd5622#egg=lm_eval[api]
+  - pytest -v -s entrypoints/llm --ignore=entrypoints/llm/test_lazy_outlines.py --ignore=entrypoints/llm/test_generate.py --ignore=entrypoints/llm/test_generate_multiple_loras.py --ignore=entrypoints/llm/test_guided_generate.py
  - pytest -v -s entrypoints/llm --ignore=entrypoints/llm/test_lazy_outlines.py
  - pytest -v -s entrypoints/llm/test_lazy_outlines.py # it needs a clean process
-  - pytest -v -s entrypoints/openai
+  - pytest -v -s entrypoints/llm/test_generate.py # it needs a clean process
  - pytest -v -s entrypoints/llm/test_generate_multiple_loras.py # it needs a clean process
  - pytest -v -s entrypoints/llm/test_guided_generate.py # it needs a clean process
  - pytest -v -s entrypoints/openai --ignore=entrypoints/openai/test_oot_registration.py
  - pytest -v -s entrypoints/openai/test_oot_registration.py # it needs a clean process
  - pytest -v -s entrypoints/test_chat_utils.py
-
+  - pytest -v -s entrypoints/offline_mode # Needs to avoid interference with other tests
 - label: Distributed Tests (4 GPUs) # 10min
  working_dir: "/vllm-workspace/tests"
@@ -102,7 +126,10 @@ steps:
  - vllm/core/
  - tests/distributed
  - tests/spec_decode/e2e/test_integration_dist_tp4
  - tests/compile
  commands:
  - pytest -v -s distributed/test_utils.py
  - pytest -v -s compile/test_basic_correctness.py
  - pytest -v -s distributed/test_pynccl.py
  - pytest -v -s spec_decode/e2e/test_integration_dist_tp4.py
@@ -130,7 +157,9 @@ steps:
  source_file_dependencies:
  - vllm/
  - tests/test_regression
-  command: pytest -v -s test_regression.py
+  commands:
  - pip install modelscope
  - pytest -v -s test_regression.py
  working_dir: "/vllm-workspace/tests" # optional
 - label: Engine Test # 10min
@@ -144,56 +173,45 @@ steps:
  # OOM in the CI unless we run this separately
  - pytest -v -s tokenization
- label: Examples Test # 12min
+- label: V1 Test
  #mirror_hardwares: [amd]
  source_file_dependencies:
    - vllm/
    - tests/v1
  commands:
    - VLLM_USE_V1=1 pytest -v -s v1
 - label: Examples Test # 25min
  working_dir: "/vllm-workspace/examples"
  #mirror_hardwares: [amd]
  source_file_dependencies:
  - vllm/entrypoints
  - examples/
  commands:
-    - pip install awscli tensorizer # for llava example and tensorizer test
+    - pip install tensorizer # for tensorizer test
    - python3 offline_inference.py
    - python3 cpu_offload.py
    - python3 offline_inference_chat.py
    - python3 offline_inference_with_prefix.py
    - python3 llm_engine_example.py
    - python3 offline_inference_vision_language.py
    - python3 offline_inference_vision_language_multi_image.py
    - python3 tensorize_vllm_model.py --model facebook/opt-125m serialize --serialized-directory /tmp/ --suffix v1 && python3 tensorize_vllm_model.py --model facebook/opt-125m deserialize --path-to-tensors /tmp/vllm/facebook/opt-125m/v1/model.tensors
    - python3 offline_inference_encoder_decoder.py
    - python3 offline_inference_classification.py
    - python3 offline_inference_embedding.py
    - python3 offline_inference_scoring.py
    - python3 offline_profile.py --model facebook/opt-125m run_num_steps --num-steps 2
- label: Models Test # 1hr10min
+- label: Prefix Caching Test # 9min
-  source_file_dependencies:
+  mirror_hardwares: [amd]
  - vllm/
  - tests/models
  commands:
    - pip install -e ./plugins/vllm_add_dummy_model
    - pytest -v -s models/test_oot_registration.py # it needs a clean process
    - pytest -v -s models -m \"not vlm\" --ignore=models/test_oot_registration.py
 - label: torch compile integration test
  source_file_dependencies:
  - vllm/
  commands:
    - pytest -v -s ./compile/test_full_graph.py
    - pytest -v -s ./compile/test_wrapper.py
 - label: Vision Language Models Test # 42min
  #mirror_hardwares: [amd]
  source_file_dependencies:
  - vllm/
  commands:
    - pytest -v -s models -m vlm
 - label: Prefix Caching Test # 7min
  #mirror_hardwares: [amd]
  source_file_dependencies:
  - vllm/
  - tests/prefix_caching
  commands:
    - pytest -v -s prefix_caching
- label: Samplers Test # 18min
+- label: Samplers Test # 36min
  source_file_dependencies:
  - vllm/model_executor/layers
  - vllm/sampling_metadata.py
@@ -206,27 +224,49 @@ steps:
  mirror_hardwares: [amd]
  source_file_dependencies:
  - vllm/model_executor/layers
  - vllm/model_executor/guided_decoding
  - tests/test_logits_processor
-  command: pytest -v -s test_logits_processor.py
+  - tests/model_executor/test_guided_processors
  commands: 
    - pytest -v -s test_logits_processor.py
    - pytest -v -s model_executor/test_guided_processors.py
- label: Speculative decoding tests # 22min
+- label: Speculative decoding tests # 30min
  source_file_dependencies:
  - vllm/spec_decode
  - tests/spec_decode
  commands:
-    # See https://github.com/vllm-project/vllm/issues/5152
+    - pytest -v -s spec_decode/e2e/test_multistep_correctness.py
-    - export VLLM_ATTENTION_BACKEND=XFORMERS
+    - VLLM_ATTENTION_BACKEND=FLASH_ATTN pytest -v -s spec_decode --ignore=spec_decode/e2e/test_multistep_correctness.py
    - pytest -v -s spec_decode
- label: LoRA Test %N # 30min each
+- label: LoRA Test %N # 15min each
  mirror_hardwares: [amd]
  source_file_dependencies:
  - vllm/lora
  - tests/lora
-  command: pytest -v -s lora --shard-id=$$BUILDKITE_PARALLEL_JOB --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT --ignore=lora/test_long_context.py
+  command: pytest -v -s lora --shard-id=$$BUILDKITE_PARALLEL_JOB --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT --ignore=lora/test_long_context.py --ignore=lora/test_chatglm3_tp.py --ignore=lora/test_llama_tp.py
  parallelism: 4
- label: Kernels Test %N # 30min each
+- label: "PyTorch Fullgraph Smoke Test" # 9min
  fast_check: true
  source_file_dependencies:
  - vllm/
  - tests/compile
  commands:
  - pytest -v -s compile/test_basic_correctness.py
  # these tests need to be separated, cannot combine
  - pytest -v -s compile/piecewise/test_simple.py
  - pytest -v -s compile/piecewise/test_toy_llama.py
 - label: "PyTorch Fullgraph Test" # 18min
  source_file_dependencies:
  - vllm/
  - tests/compile
  commands:
  - pytest -v -s compile/test_full_graph.py
 - label: Kernels Test %N # 1h each
  mirror_hardwares: [amd]
  source_file_dependencies:
  - csrc/
  - vllm/attention
@@ -252,15 +292,14 @@ steps:
  source_file_dependencies:
  - benchmarks/
  commands:
  - pip install aiohttp
  - bash run-benchmarks.sh
- label: Quantization Test # 15min
+- label: Quantization Test # 33min
  source_file_dependencies:
  - csrc/
  - vllm/model_executor/layers/quantization
  - tests/quantization
-  command: pytest -v -s quantization
+  command: VLLM_TEST_FORCE_LOAD_FORMAT=auto pytest -v -s quantization
 - label: LM Eval Small Models # 53min
  working_dir: "/vllm-workspace/.buildkite/lm-eval-harness"
@@ -268,10 +307,16 @@ steps:
  - csrc/
  - vllm/model_executor/layers/quantization
  commands:
  - pip install lm-eval
  - export VLLM_WORKER_MULTIPROC_METHOD=spawn
  - bash ./run-tests.sh -c configs/models-small.txt -t 1
 - label: Encoder Decoder tests # 5min
  source_file_dependencies:
  - vllm/
  - tests/encoder_decoder
  commands:
    - pytest -v -s encoder_decoder
 - label: OpenAI-Compatible Tool Use # 20 min
  fast_check: false
  mirror_hardwares: [ amd ]
@@ -281,6 +326,94 @@ steps:
  commands:
    - pytest -v -s tool_use
 #####  models test  #####
 - label: Basic Models Test # 24min
  source_file_dependencies:
  - vllm/
  - tests/models
  commands:
    - pip install -e ./plugins/vllm_add_dummy_model
    - pytest -v -s models/test_oot_registration.py # it needs a clean process
    - pytest -v -s models/test_registry.py
    - pytest -v -s models/test_initialization.py
 - label: Language Models Test (Standard) # 32min
  #mirror_hardwares: [amd]
  source_file_dependencies:
  - vllm/
  - tests/models/decoder_only/language
  - tests/models/embedding/language
  - tests/models/encoder_decoder/language
  commands:
    - pytest -v -s models/decoder_only/language -m 'core_model or quant_model'
    - pytest -v -s models/embedding/language -m core_model
 - label: Language Models Test (Extended) # 1h10min
  optional: true
  source_file_dependencies:
  - vllm/
  - tests/models/decoder_only/language
  - tests/models/embedding/language
  - tests/models/encoder_decoder/language
  commands:
    - pytest -v -s models/decoder_only/language -m 'not core_model and not quant_model'
    - pytest -v -s models/embedding/language -m 'not core_model'
 - label: Multi-Modal Models Test (Standard) # 28min
  #mirror_hardwares: [amd]
  source_file_dependencies:
  - vllm/
  - tests/models/decoder_only/audio_language
  - tests/models/decoder_only/vision_language
  - tests/models/embedding/vision_language
  - tests/models/encoder_decoder/vision_language
  commands:
    - pip install git+https://github.com/TIGER-AI-Lab/Mantis.git
    - pytest -v -s models/decoder_only/audio_language -m 'core_model or quant_model'
    - pytest -v -s --ignore models/decoder_only/vision_language/test_phi3v.py models/decoder_only/vision_language -m 'core_model or quant_model'
    - pytest -v -s models/embedding/vision_language -m core_model
    - pytest -v -s models/encoder_decoder/language -m core_model
    - pytest -v -s models/encoder_decoder/vision_language -m core_model
 - label: Multi-Modal Models Test (Extended) 1 # 1h16m
  optional: true
  source_file_dependencies:
  - vllm/
  - tests/models/decoder_only/audio_language
  - tests/models/decoder_only/vision_language
  - tests/models/embedding/vision_language
  - tests/models/encoder_decoder/vision_language
  commands:
    - pip install git+https://github.com/TIGER-AI-Lab/Mantis.git
    - pytest -v -s models/decoder_only/audio_language -m 'not core_model and not quant_model'
    - pytest -v -s models/decoder_only/vision_language/test_models.py -m 'split(group=0) and not core_model and not quant_model'
    # HACK - run phi3v tests separately to sidestep this transformers bug
    # https://github.com/huggingface/transformers/issues/34307
    - pytest -v -s models/decoder_only/vision_language/test_phi3v.py
    - pytest -v -s --ignore models/decoder_only/vision_language/test_models.py --ignore models/decoder_only/vision_language/test_phi3v.py models/decoder_only/vision_language -m 'not core_model and not quant_model'
    - pytest -v -s models/embedding/vision_language -m 'not core_model'
    - pytest -v -s models/encoder_decoder/language -m 'not core_model'
    - pytest -v -s models/encoder_decoder/vision_language -m 'not core_model'
 - label: Multi-Modal Models Test (Extended) 2 # 38m
  optional: true
  source_file_dependencies:
  - vllm/
  - tests/models/decoder_only/vision_language
  commands:
    - pip install git+https://github.com/TIGER-AI-Lab/Mantis.git
    - pytest -v -s models/decoder_only/vision_language/test_models.py -m 'split(group=1) and not core_model and not quant_model'
 # This test is used only in PR development phase to test individual models and should never run on main
 - label: Custom Models Test
  optional: true
  commands:
    - echo 'Testing custom models...'
    # PR authors can temporarily add commands below to test individual models
    # e.g. pytest -v -s models/encoder_decoder/vision_language/test_mllama.py
    # *To avoid merge conflicts, remember to REMOVE (not just comment out) them before merging the PR*
 #####  1 GPU test  #####
 #####  multi gpus test  #####
@@ -306,13 +439,13 @@ steps:
  - tests/distributed/
  commands:
  - # the following commands are for the first node, with ip 192.168.10.10 (ray environment already set up)
-    - VLLM_TEST_SAME_HOST=0 torchrun --nnodes 2 --nproc-per-node=2 --rdzv_backend=c10d --rdzv_endpoint=192.168.10.10 distributed/test_same_node.py
+    - VLLM_TEST_SAME_HOST=0 torchrun --nnodes 2 --nproc-per-node=2 --rdzv_backend=c10d --rdzv_endpoint=192.168.10.10 distributed/test_same_node.py | grep 'Same node test passed'
    - VLLM_MULTI_NODE=1 pytest -v -s distributed/test_multi_node_assignment.py
    - VLLM_MULTI_NODE=1 pytest -v -s distributed/test_pipeline_parallel.py
  - # the following commands are for the second node, with ip 192.168.10.11 (ray environment already set up)
-    - VLLM_TEST_SAME_HOST=0 torchrun --nnodes 2 --nproc-per-node=2 --rdzv_backend=c10d --rdzv_endpoint=192.168.10.10 distributed/test_same_node.py
+    - VLLM_TEST_SAME_HOST=0 torchrun --nnodes 2 --nproc-per-node=2 --rdzv_backend=c10d --rdzv_endpoint=192.168.10.10 distributed/test_same_node.py | grep 'Same node test passed'
- label: Distributed Tests (2 GPUs) # 28min
+- label: Distributed Tests (2 GPUs) # 40min
  #mirror_hardwares: [amd]
  working_dir: "/vllm-workspace/tests"
  num_gpus: 2
@@ -322,19 +455,26 @@ steps:
  - vllm/executor/
  - vllm/model_executor/models/
  - tests/distributed/
  - vllm/compilation
  - vllm/worker/worker_base.py
  - vllm/worker/worker.py
  - vllm/worker/model_runner.py
  commands:
-  - VLLM_TEST_SAME_HOST=1 torchrun --nproc-per-node=4 distributed/test_same_node.py
+  - pytest -v -s ./compile/test_basic_correctness.py
-  - TARGET_TEST_SUITE=L4 pytest -v -s distributed/test_basic_distributed_correctness.py
+  - pytest -v -s ./compile/test_wrapper.py
-  - pytest -v -s distributed/test_basic_distributed_correctness_enc_dec.py
+  - VLLM_TEST_SAME_HOST=1 torchrun --nproc-per-node=4 distributed/test_same_node.py | grep 'Same node test passed'
-  - pytest -v -s distributed/test_chunked_prefill_distributed.py
+  - TARGET_TEST_SUITE=L4 pytest basic_correctness/ -v -s -m 'distributed(num_gpus=2)'
-  - pytest -v -s distributed/test_multimodal_broadcast.py
+  # Avoid importing model tests that cause CUDA reinitialization error
  - pytest models/encoder_decoder/language/test_bart.py -v -s -m 'distributed(num_gpus=2)'
  - pytest models/encoder_decoder/vision_language/test_broadcast.py -v -s -m 'distributed(num_gpus=2)'
  - pytest models/decoder_only/vision_language/test_models.py -v -s -m 'distributed(num_gpus=2)'
  - pytest -v -s spec_decode/e2e/test_integration_dist_tp2.py
  - pip install -e ./plugins/vllm_add_dummy_model
  - pytest -v -s distributed/test_distributed_oot.py
  - CUDA_VISIBLE_DEVICES=0,1 pytest -v -s test_sharded_state_loader.py
-  - CUDA_VISIBLE_DEVICES=0,1 pytest -v -s distributed/test_utils.py
+  - CUDA_VISIBLE_DEVICES=0,1 pytest -v -s kv_transfer/disagg_test.py
- label: Multi-step Tests (4 GPUs) # 21min
+- label: Multi-step Tests (4 GPUs) # 36min
  working_dir: "/vllm-workspace/tests"
  num_gpus: 4
  source_file_dependencies:
@@ -352,7 +492,7 @@ steps:
  - pytest -v -s multi_step/test_correctness_async_llm.py
  - pytest -v -s multi_step/test_correctness_llm.py
- label: Pipeline Parallelism Test # 23min
+- label: Pipeline Parallelism Test # 45min
  working_dir: "/vllm-workspace/tests"
  num_gpus: 4
  source_file_dependencies:
@@ -365,26 +505,42 @@ steps:
  - pytest -v -s distributed/test_pp_cudagraph.py
  - pytest -v -s distributed/test_pipeline_parallel.py
- label: LoRA Long Context (Distributed) # 11min
+- label: LoRA TP Test (Distributed)
  # This test runs llama 13B, so it is required to run on 4 GPUs.
  num_gpus: 4
  source_file_dependencies:
  - vllm/lora
-  - tests/lora/test_long_context
+  - tests/lora
  commands:
    # FIXIT: find out which code initialize cuda before running the test
    # before the fix, we need to use spawn to test it
    - export VLLM_WORKER_MULTIPROC_METHOD=spawn
    # This test runs llama 13B, so it is required to run on 4 GPUs.
    - pytest -v -s -x lora/test_long_context.py
    # There is some Tensor Parallelism related processing logic in LoRA that 
    # requires multi-GPU testing for validation.
    - pytest -v -s -x lora/test_chatglm3_tp.py
    - pytest -v -s -x lora/test_llama_tp.py
- label: Weight Loading Multiple GPU Test
+
 - label: Weight Loading Multiple GPU Test  # 33min
  working_dir: "/vllm-workspace/tests"
  num_gpus: 2
  source_file_dependencies:
  - vllm/
  - tests/weight_loading
  commands:
-    - bash weight_loading/run_model_weight_loading_test.sh
+    - bash weight_loading/run_model_weight_loading_test.sh -c weight_loading/models.txt
 - label: Weight Loading Multiple GPU Test - Large Models # optional
  working_dir: "/vllm-workspace/tests"
  num_gpus: 2
  gpu: a100
  optional: true
  source_file_dependencies:
  - vllm/
  - tests/weight_loading
  commands:
    - bash weight_loading/run_model_weight_loading_test.sh -c weight_loading/models-large.txt 
 ##### multi gpus test #####
@@ -392,6 +548,7 @@ steps:
 - label: Distributed Tests (A100) # optional
  gpu: a100
  optional: true
  num_gpus: 4
  source_file_dependencies:
  - vllm/
@@ -399,17 +556,18 @@ steps:
  # NOTE: don't test llama model here, it seems hf implementation is buggy
  # see https://github.com/vllm-project/vllm/pull/5689 for details
  - pytest -v -s distributed/test_custom_all_reduce.py
-  - TARGET_TEST_SUITE=A100 pytest -v -s distributed/test_basic_distributed_correctness.py
+  - torchrun --nproc_per_node=2 distributed/test_ca_buffer_sharing.py
  - TARGET_TEST_SUITE=A100 pytest basic_correctness/ -v -s -m 'distributed(num_gpus=2)'
  - pytest -v -s -x lora/test_mixtral.py
 - label: LM Eval Large Models # optional
  gpu: a100
  optional: true
  num_gpus: 4
  working_dir: "/vllm-workspace/.buildkite/lm-eval-harness"
  source_file_dependencies:
  - csrc/
  - vllm/model_executor/layers/quantization
  commands:
  - pip install lm-eval
  - export VLLM_WORKER_MULTIPROC_METHOD=spawn
  - bash ./run-tests.sh -c configs/models-large.txt -t 4
--- a/.buildkite/upload-wheels.sh
+++ b/.buildkite/upload-wheels.sh
@@ -0,0 +1,71 @@
 #!/usr/bin/env bash
 set -ex
 # Assume wheels are in artifacts/dist/*.whl
 wheel_files=(artifacts/dist/*.whl)
 # Check that exactly one wheel is found
 if [[ ${#wheel_files[@]} -ne 1 ]]; then
  echo "Error: Expected exactly one wheel file in artifacts/dist/, but found ${#wheel_files[@]}"
  exit 1
 fi
 # Get the single wheel file
 wheel="${wheel_files[0]}"
 # Rename 'linux' to 'manylinux1' in the wheel filename
 new_wheel="${wheel/linux/manylinux1}"
 mv -- "$wheel" "$new_wheel"
 wheel="$new_wheel"
 # Extract the version from the wheel
 version=$(unzip -p "$wheel" '**/METADATA' | grep '^Version: ' | cut -d' ' -f2)
 echo "Version: $version"
 normal_wheel="$wheel" # Save the original wheel filename
 # If the version contains "dev", rename it to v1.0.0.dev for consistency
 if [[ $version == *dev* ]]; then
    suffix="${version##*.}"
    if [[ $suffix == cu* ]]; then
        new_version="1.0.0.dev+${suffix}"
    else
        new_version="1.0.0.dev"
    fi
    new_wheel="${wheel/$version/$new_version}"
    # use cp to keep both files in the artifacts directory
    cp -- "$wheel" "$new_wheel"
    wheel="$new_wheel"
    version="$new_version"
 fi
 # Upload the wheel to S3
 python3 .buildkite/generate_index.py --wheel "$normal_wheel"
 # generate index for this commit
 aws s3 cp "$wheel" "s3://vllm-wheels/$BUILDKITE_COMMIT/"
 aws s3 cp "$normal_wheel" "s3://vllm-wheels/$BUILDKITE_COMMIT/"
 if [[ $normal_wheel == *"cu118"* ]]; then
    # if $normal_wheel matches cu118, do not upload the index.html
    echo "Skipping index files for cu118 wheels"
 else
    # only upload index.html for cu12 wheels (default wheels)
    aws s3 cp index.html "s3://vllm-wheels/$BUILDKITE_COMMIT/vllm/index.html"
    aws s3 cp "s3://vllm-wheels/nightly/index.html" "s3://vllm-wheels/$BUILDKITE_COMMIT/index.html"
 fi
 # generate index for nightly
 aws s3 cp "$wheel" "s3://vllm-wheels/nightly/"
 aws s3 cp "$normal_wheel" "s3://vllm-wheels/nightly/"
 if [[ $normal_wheel == *"cu118"* ]]; then
    # if $normal_wheel matches cu118, do not upload the index.html
    echo "Skipping index files for cu118 wheels"
 else
    # only upload index.html for cu12 wheels (default wheels)
    aws s3 cp index.html "s3://vllm-wheels/nightly/vllm/index.html"
 fi
 aws s3 cp "$wheel" "s3://vllm-wheels/$version/"
--- a/.dockerignore
+++ b/.dockerignore
@@ -1,4 +1,33 @@
 vllm/*.so
 /.venv
 /build
 dist
 vllm/*.so
 # Byte-compiled / optimized / DLL files
 __pycache__/
 *.py[cod]
 *$py.class
 .mypy_cache
 # Distribution / packaging
 .Python
 /build/
 cmake-build-*/
 CMakeUserPresets.json
 develop-eggs/
 /dist/
 downloads/
 eggs/
 .eggs/
 lib/
 lib64/
 parts/
 sdist/
 var/
 wheels/
 share/python-wheels/
 *.egg-info/
 .installed.cfg
 *.egg
 MANIFEST
--- a/.github/CODEOWNERS
+++ b/.github/CODEOWNERS
@@ -0,0 +1,33 @@
 # See https://help.github.com/articles/about-codeowners/
 # for more info about CODEOWNERS file
 # This lists cover the "core" components of vLLM that require careful review
 /vllm/attention/backends/abstract.py @WoosukKwon @zhuohan123 @youkaichao @alexm-neuralmagic @comaniac @njhill
 /vllm/core @zhuohan123 @youkaichao @alexm-neuralmagic @comaniac @njhill
 /vllm/engine/llm_engine.py @zhuohan123 @youkaichao @alexm-neuralmagic @comaniac @njhill
 /vllm/executor/executor_base.py @zhuohan123 @youkaichao @alexm-neuralmagic @comaniac @njhill
 /vllm/worker/worker_base.py @zhuohan123 @youkaichao @alexm-neuralmagic @comaniac @njhill
 /vllm/worker/worker.py @zhuohan123 @youkaichao @alexm-neuralmagic @comaniac @njhill
 /vllm/model_executor/layers/sampler.py @zhuohan123 @youkaichao @alexm-neuralmagic @comaniac @njhill
 CMakeLists.txt @tlrmchlsmth
 # vLLM V1
 /vllm/v1 @WoosukKwon @robertgshaw2-neuralmagic @njhill @ywang96 @comaniac @alexm-neuralmagic
 # Test ownership
 /tests/async_engine @njhill @robertgshaw2-neuralmagic @simon-mo
 /tests/test_inputs.py @DarkLight1337 @ywang96
 /tests/entrypoints @DarkLight1337 @robertgshaw2-neuralmagic @simon-mo
 /tests/models @DarkLight1337 @ywang96
 /tests/multimodal @DarkLight1337 @ywang96
 /tests/prefix_caching @comaniac @KuntaiDu
 /tests/spec_decode @njhill @LiuXiaoxuanPKU
 /tests/kernels @tlrmchlsmth @WoosukKwon
 /tests/quantization @mgoin @robertgshaw2-neuralmagic
 /.buildkite/lm-eval-harness @mgoin @simon-mo
 /tests/distributed/test_multi_node_assignment.py @youkaichao
 /tests/distributed/test_pipeline_parallel.py @youkaichao
 /tests/distributed/test_same_node.py @youkaichao
 /tests/multi_step @alexm-neuralmagic @comaniac
 /tests/weight_loading @mgoin @youkaichao
 /tests/basic_correctness/test_chunked_prefill @rkooo567 @comaniac
--- a/.github/FUNDING.yml
+++ b/.github/FUNDING.yml
@@ -1,2 +1,2 @@
 github: [vllm-project]
-open_collective: [vllm]
+open_collective: vllm
--- a/.github/ISSUE_TEMPLATE/400-bug
+++ b/.github/ISSUE_TEMPLATE/400-bug
@@ -30,6 +30,15 @@ body:
      </details>
  validations:
    required: true
 - type: textarea
  attributes:
    label: Model Input Dumps
    description: |
      If you are facing crashing due to illegal memory access or other issues with model execution, vLLM may dump the problematic input of the model. In this case, you will see the message `Error in model execution (input dumped to /tmp/err_xxx.pkl)`. If you see this message, please zip the file (because GitHub doesn't support .pkl file format) and upload it here. This will help us to reproduce the issue and facilitate the debugging process.
    placeholder: |
      Upload the dumped input file.
  validations:
    required: false
 - type: textarea
  attributes:
    label: 🐛 Describe the bug
--- a/.github/PULL_REQUEST_TEMPLATE.md
+++ b/.github/PULL_REQUEST_TEMPLATE.md
@@ -2,63 +2,4 @@ FILL IN THE PR DESCRIPTION HERE
 FIX #xxxx (*link existing issues this PR will resolve*)
-**BEFORE SUBMITTING, PLEASE READ THE CHECKLIST BELOW AND FILL IN THE DESCRIPTION ABOVE**
+**BEFORE SUBMITTING, PLEASE READ https://docs.vllm.ai/en/latest/contributing/overview.html **
 ---
 <details>
 <!-- inside this <details> section, markdown rendering does not work, so we use raw html here. -->
 <summary><b> PR Checklist (Click to Expand) </b></summary>
 <p>Thank you for your contribution to vLLM! Before submitting the pull request, please ensure the PR meets the following criteria. This helps vLLM maintain the code quality and improve the efficiency of the review process.</p>
 <h3>PR Title and Classification</h3>
 <p>Only specific types of PRs will be reviewed. The PR title is prefixed appropriately to indicate the type of change. Please use one of the following:</p>
 <ul>
    <li><code>[Bugfix]</code> for bug fixes.</li>
    <li><code>[CI/Build]</code> for build or continuous integration improvements.</li>
    <li><code>[Doc]</code> for documentation fixes and improvements.</li>
    <li><code>[Model]</code> for adding a new model or improving an existing model. Model name should appear in the title.</li>
    <li><code>[Frontend]</code> For changes on the vLLM frontend (e.g., OpenAI API server, <code>LLM</code> class, etc.) </li>
    <li><code>[Kernel]</code> for changes affecting CUDA kernels or other compute kernels.</li>
    <li><code>[Core]</code> for changes in the core vLLM logic (e.g., <code>LLMEngine</code>, <code>AsyncLLMEngine</code>, <code>Scheduler</code>, etc.)</li>
    <li><code>[Hardware][Vendor]</code> for hardware-specific changes. Vendor name should appear in the prefix (e.g., <code>[Hardware][AMD]</code>).</li>
    <li><code>[Misc]</code> for PRs that do not fit the above categories. Please use this sparingly.</li>
 </ul>
 <p><strong>Note:</strong> If the PR spans more than one category, please include all relevant prefixes.</p>
 <h3>Code Quality</h3>
 <p>The PR need to meet the following code quality standards:</p>
 <ul>
    <li>We adhere to <a href="https://google.github.io/styleguide/pyguide.html">Google Python style guide</a> and <a href="https://google.github.io/styleguide/cppguide.html">Google C++ style guide</a>.</li>
    <li>Pass all linter checks. Please use <a href="https://github.com/vllm-project/vllm/blob/main/format.sh"><code>format.sh</code></a> to format your code.</li>
    <li>The code need to be well-documented to ensure future contributors can easily understand the code.</li>
    <li>Include sufficient tests to ensure the project to stay correct and robust. This includes both unit tests and integration tests.</li>
    <li>Please add documentation to <code>docs/source/</code> if the PR modifies the user-facing behaviors of vLLM. It helps vLLM user understand and utilize the new features or changes.</li>
 </ul>
 <h3>Notes for Large Changes</h3>
 <p>Please keep the changes as concise as possible. For major architectural changes (>500 LOC excluding kernel/data/config/test), we would expect a GitHub issue (RFC) discussing the technical design and justification. Otherwise, we will tag it with <code>rfc-required</code> and might not go through the PR.</p>
 <h3>What to Expect for the Reviews</h3>
 <p>The goal of the vLLM team is to be a <i>transparent reviewing machine</i>. We would like to make the review process transparent and efficient and make sure no contributor feel confused or frustrated. However, the vLLM team is small, so we need to prioritize some PRs over others. Here is what you can expect from the review process: </p>
 <ul>
    <li> After the PR is submitted, the PR will be assigned to a reviewer. Every reviewer will pick up the PRs based on their expertise and availability.</li>
    <li> After the PR is assigned, the reviewer will provide status update every 2-3 days. If the PR is not reviewed within 7 days, please feel free to ping the reviewer or the vLLM team.</li>
    <li> After the review, the reviewer will put an <code> action-required</code> label on the PR if there are changes required. The contributor should address the comments and ping the reviewer to re-review the PR.</li>
    <li> Please respond to all comments within a reasonable time frame. If a comment isn't clear or you disagree with a suggestion, feel free to ask for clarification or discuss the suggestion.
 </li>
 </ul>
 <h3>Thank You</h3>
 <p> Finally, thank you for taking the time to read these guidelines and for your interest in contributing to vLLM. Your contributions make vLLM a great tool for everyone! </p>
 </details>
--- a/.github/dependabot.yml
+++ b/.github/dependabot.yml
@@ -0,0 +1,31 @@
 version: 2
 updates:
  # Maintain dependencies for GitHub Actions
  - package-ecosystem: "github-actions"
    directory: "/"
    schedule:
      interval: "weekly"
  - package-ecosystem: "pip"
    directory: "/"
    schedule:
      interval: "weekly"
    labels: ["dependencies"]
    open-pull-requests-limit: 5
    reviewers: ["khluu", "simon-mo"]
    allow:
      - dependency-type: "all"
    ignore:
      - dependency-name: "*"
        update-types: ["version-update:semver-patch"]
      - dependency-name: "torch"
      - dependency-name: "torchvision"
      - dependency-name: "xformers"
      - dependency-name: "lm-format-enforcer"
      - dependency-name: "gguf"
      - dependency-name: "compressed-tensors"
      - dependency-name: "ray[adag]"
      - dependency-name: "lm-eval"
    groups:
      minor-update:
        applies-to: version-updates
        update-types: ["minor"]
--- a/.github/mergify.yml
+++ b/.github/mergify.yml
@@ -0,0 +1,60 @@
 pull_request_rules:
 - name: label-documentation
  description: Automatically apply documentation label
  conditions:
    - or:
      - files~=^[^/]+\.md$
      - files~=^docs/
  actions:
    label:
      add:
        - documentation
 - name: label-ci-build
  description: Automatically apply ci/build label
  conditions:
    - or:
      - files~=^\.github/
      - files~=\.buildkite/
      - files~=^cmake/
      - files=CMakeLists.txt
      - files~=^Dockerfile
      - files~=^requirements.*\.txt
      - files=setup.py
  actions:
    label:
      add:
        - ci/build
 - name: label-frontend
  description: Automatically apply frontend label
  conditions:
    - files~=^vllm/entrypoints/
  actions:
    label:
      add:
        - frontend
 - name: ping author on conflicts and add 'needs-rebase' label
  conditions:
      - conflict
      - -closed
  actions:
    label:
      add:
        - needs-rebase
    comment:
      message: |
       This pull request has merge conflicts that must be resolved before it can be
       merged. Please rebase the PR, @{{author}}.
       https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/syncing-a-fork
 - name: remove 'needs-rebase' label when conflict is resolved
  conditions:
      - -conflict
      - -closed
  actions:
    label:
      remove:
        - needs-rebase
--- a/.github/scripts/cleanup_pr_body.sh
+++ b/.github/scripts/cleanup_pr_body.sh
@@ -0,0 +1,50 @@
 #!/bin/bash
 set -eu
 # ensure 1 argument is passed
 if [ "$#" -ne 1 ]; then
    echo "Usage: $0 <pr_number>"
    exit 1
 fi
 PR_NUMBER=$1
 OLD=/tmp/orig_pr_body.txt
 NEW=/tmp/new_pr_body.txt
 gh pr view --json body --template "{{.body}}" "${PR_NUMBER}" > "${OLD}"
 cp "${OLD}" "${NEW}"
 # Remove "FIX #xxxx (*link existing issues this PR will resolve*)"
 sed -i '/FIX #xxxx.*$/d' "${NEW}"
 # Remove "FILL IN THE PR DESCRIPTION HERE"
 sed -i '/FILL IN THE PR DESCRIPTION HERE/d' "${NEW}"
 # Remove all lines after and including "**BEFORE SUBMITTING, PLEASE READ THE CHECKLIST BELOW AND FILL IN THE DESCRIPTION ABOVE**"
 sed -i '/\*\*BEFORE SUBMITTING, PLEASE READ.*\*\*/,$d' "${NEW}"
 # Remove HTML <details> section that includes <summary> text of "PR Checklist (Click to Expand)"
 python3 - <<EOF
 import re
 with open("${NEW}", "r") as file:
    content = file.read()
 pattern = re.compile(r'(---\n\n)?<details>.*?<summary>.*?PR Checklist \(Click to Expand\).*?</summary>.*?</details>', re.DOTALL)
 content = re.sub(pattern, '', content)
 with open("${NEW}", "w") as file:
    file.write(content)
 EOF
 # Run this only if ${NEW} is different than ${OLD}
 if ! cmp -s "${OLD}" "${NEW}"; then
    gh pr edit --body-file "${NEW}" "${PR_NUMBER}"
    echo
    echo "Updated PR body:"
    echo
    cat "${NEW}"
 else
    echo "No changes needed"
 fi
--- a/.github/workflows/actionlint.yml
+++ b/.github/workflows/actionlint.yml
@@ -0,0 +1,40 @@
 name: Lint GitHub Actions workflows
 on:
  push:
    branches:
      - "main"
    paths:
      - '.github/workflows/*.ya?ml'
      - '.github/workflows/actionlint.*'
      - '.github/workflows/matchers/actionlint.json'
  pull_request:
    branches:
      - "main"
    paths:
      - '.github/workflows/*.ya?ml'
      - '.github/workflows/actionlint.*'
      - '.github/workflows/matchers/actionlint.json'
 env:
  LC_ALL: en_US.UTF-8
 defaults:
  run:
    shell: bash
 permissions:
  contents: read
 jobs:
  actionlint:
    runs-on: ubuntu-latest
    steps:
      - name: "Checkout"
        uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
        with:
          fetch-depth: 0
      - name: "Run actionlint"
        run: |
          echo "::add-matcher::.github/workflows/matchers/actionlint.json"
          tools/actionlint.sh -color
--- a/.github/workflows/add_label_automerge.yml
+++ b/.github/workflows/add_label_automerge.yml
@@ -8,7 +8,7 @@ jobs:
        runs-on: ubuntu-latest
        steps:
            -   name: Add label
-                uses: actions/github-script@v5
+                uses: actions/github-script@60a0d83039c74a4aee543508d2ffcb1c3799cdea # v7.0.1
                with:
                    script: |
                        github.rest.issues.addLabels({
--- a/.github/workflows/clang-format.yml
+++ b/.github/workflows/clang-format.yml
@@ -6,9 +6,21 @@ on:
  push:
    branches:
      - main
    paths:
      - '**/*.h'
      - '**/*.cpp'
      - '**/*.cu'
      - '**/*.cuh'
      - '.github/workflows/clang-format.yml'
  pull_request:
    branches:
      - main
    paths:
      - '**/*.h'
      - '**/*.cpp'
      - '**/*.cu'
      - '**/*.cuh'
      - '.github/workflows/clang-format.yml'
 jobs:
  clang-format:
@@ -17,9 +29,9 @@ jobs:
      matrix:
        python-version: ["3.11"]
    steps:
-    - uses: actions/checkout@v2
+    - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
    - name: Set up Python ${{ matrix.python-version }}
-      uses: actions/setup-python@v2
+      uses: actions/setup-python@0b93645e9fea7318ecaed2b359559ac225c90a2b # v5.3.0
      with:
        python-version: ${{ matrix.python-version }}
    - name: Install dependencies
@@ -38,4 +50,4 @@ jobs:
        )
        find csrc/ \( -name '*.h' -o -name '*.cpp' -o -name '*.cu' -o -name '*.cuh' \) -print \
            | grep -vFf <(printf "%s\n" "${EXCLUDES[@]}") \
-            | xargs clang-format --dry-run --Werror
+            | xargs clang-format --dry-run --Werror
--- a/.github/workflows/cleanup_pr_body.yml
+++ b/.github/workflows/cleanup_pr_body.yml
@@ -0,0 +1,26 @@
 name: Cleanup PR Body
 on:
  pull_request_target:
    types: [opened, reopened, edited]
 permissions:
  pull-requests: write
 jobs:
  update-description:
    runs-on: ubuntu-latest
    steps:
      - name: Checkout repository
        uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
      - name: Set up Python
        uses: actions/setup-python@0b93645e9fea7318ecaed2b359559ac225c90a2b # v5.3.0
        with:
          python-version: '3.12'
      - name: Update PR description
        env:
          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
        run: .github/scripts/cleanup_pr_body.sh "${{ github.event.number }}"
--- a/.github/workflows/codespell.yml
+++ b/.github/workflows/codespell.yml
@@ -0,0 +1,45 @@
 name: codespell
 on:
  # Trigger the workflow on push or pull request,
  # but only for the main branch
  push:
    branches:
      - main
    paths:
      - "**/*.py"
      - "**/*.md"
      - "**/*.rst"
      - pyproject.toml
      - requirements-lint.txt
      - .github/workflows/codespell.yml
  pull_request:
    branches:
      - main
    paths:
      - "**/*.py"
      - "**/*.md"
      - "**/*.rst"
      - pyproject.toml
      - requirements-lint.txt
      - .github/workflows/codespell.yml
 jobs:
  codespell:
    runs-on: ubuntu-latest
    strategy:
      matrix:
        python-version: ["3.12"]
    steps:
    - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
    - name: Set up Python ${{ matrix.python-version }}
      uses: actions/setup-python@0b93645e9fea7318ecaed2b359559ac225c90a2b # v5.3.0
      with:
        python-version: ${{ matrix.python-version }}
    - name: Install dependencies
      run: |
        python -m pip install --upgrade pip
        pip install -r requirements-lint.txt
    - name: Spelling check with codespell
      run: |
        codespell --toml pyproject.toml
--- a/.github/workflows/lint-and-deploy.yaml
+++ b/.github/workflows/lint-and-deploy.yaml
@@ -0,0 +1,81 @@
 name: Lint and Deploy Charts
 on: pull_request
 jobs:
  lint-and-deploy:
    runs-on: ubuntu-latest
    steps:
      - name: Checkout
        uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
        with:
          fetch-depth: 0
      - name: Set up Helm
        uses: azure/setup-helm@fe7b79cd5ee1e45176fcad797de68ecaf3ca4814 # v4.2.0
        with:
          version: v3.14.4
       #Python is required because ct lint runs Yamale and yamllint which require Python.
      - uses: actions/setup-python@0b93645e9fea7318ecaed2b359559ac225c90a2b # v5.3.0
        with:
          python-version: '3.13'
      - name: Set up chart-testing
        uses: helm/chart-testing-action@e6669bcd63d7cb57cb4380c33043eebe5d111992 # v2.6.1
        with:
          version: v3.10.1
      - name: Run chart-testing (lint)
        run: ct lint --target-branch ${{ github.event.repository.default_branch }} --chart-dirs examples/chart-helm --charts examples/chart-helm
      - name: Setup minio
        run: |
          docker network create vllm-net
          docker run -d -p 9000:9000 --name minio --net vllm-net \
                     -e "MINIO_ACCESS_KEY=minioadmin" \
                     -e "MINIO_SECRET_KEY=minioadmin" \
                     -v /tmp/data:/data \
                     -v /tmp/config:/root/.minio \
                     minio/minio server /data
          export AWS_ACCESS_KEY_ID=minioadmin
          export AWS_SECRET_ACCESS_KEY=minioadmin
          export AWS_EC2_METADATA_DISABLED=true
          mkdir opt-125m
          cd opt-125m && curl -O -Ls "https://huggingface.co/facebook/opt-125m/resolve/main/{pytorch_model.bin,config.json,generation_config.json,merges.txt,special_tokens_map.json,tokenizer_config.json,vocab.json}" && cd ..
          aws --endpoint-url http://127.0.0.1:9000/ s3 mb s3://testbucket
          aws --endpoint-url http://127.0.0.1:9000/ s3 cp opt-125m/ s3://testbucket/opt-125m --recursive
      - name: Create kind cluster
        uses: helm/kind-action@0025e74a8c7512023d06dc019c617aa3cf561fde # v1.10.0
      - name: Build the Docker image vllm cpu
        run: docker buildx build -f Dockerfile.cpu -t vllm-cpu-env .
      - name: Configuration of docker images, network and namespace for the kind cluster
        run: |
          docker pull amazon/aws-cli:2.6.4
          kind load docker-image  amazon/aws-cli:2.6.4 --name chart-testing
          kind load docker-image vllm-cpu-env:latest --name chart-testing
          docker network connect vllm-net "$(docker ps -aqf "name=chart-testing-control-plane")"
          kubectl create ns ns-vllm
      - name: Run chart-testing (install)
        run: |
          export AWS_ACCESS_KEY_ID=minioadmin
          export AWS_SECRET_ACCESS_KEY=minioadmin
          helm install --wait --wait-for-jobs --timeout 5m0s --debug --create-namespace --namespace=ns-vllm test-vllm examples/chart-helm -f examples/chart-helm/values.yaml --set secrets.s3endpoint=http://minio:9000 --set secrets.s3bucketname=testbucket --set secrets.s3accesskeyid=$AWS_ACCESS_KEY_ID --set secrets.s3accesskey=$AWS_SECRET_ACCESS_KEY --set resources.requests.cpu=1 --set resources.requests.memory=4Gi --set resources.limits.cpu=2 --set resources.limits.memory=5Gi --set image.env[0].name=VLLM_CPU_KVCACHE_SPACE --set image.env[1].name=VLLM_LOGGING_LEVEL --set-string image.env[0].value="1" --set-string image.env[1].value="DEBUG" --set-string extraInit.s3modelpath="opt-125m/" --set-string 'resources.limits.nvidia\.com/gpu=0' --set-string 'resources.requests.nvidia\.com/gpu=0' --set-string image.repository="vllm-cpu-env"
      - name: curl test
        run: |
          kubectl -n ns-vllm port-forward service/test-vllm-service 8001:80 &
          sleep 10
          CODE="$(curl -v -f --location http://localhost:8001/v1/completions \
                  --header "Content-Type: application/json" \
                  --data '{
                          "model": "opt-125m",
                          "prompt": "San Francisco is a",
                          "max_tokens": 7,
                          "temperature": 0
                  }'):$CODE"
          echo "$CODE"
--- a/.github/workflows/matchers/actionlint.json
+++ b/.github/workflows/matchers/actionlint.json
@@ -0,0 +1,17 @@
 {
  "problemMatcher": [
    {
      "owner": "actionlint",
      "pattern": [
        {
          "regexp": "^(?:\\x1b\\[\\d+m)?(.+?)(?:\\x1b\\[\\d+m)*:(?:\\x1b\\[\\d+m)*(\\d+)(?:\\x1b\\[\\d+m)*:(?:\\x1b\\[\\d+m)*(\\d+)(?:\\x1b\\[\\d+m)*: (?:\\x1b\\[\\d+m)*(.+?)(?:\\x1b\\[\\d+m)* \\[(.+?)\\]$",
          "file": 1,
          "line": 2,
          "column": 3,
          "message": 4,
          "code": 5
        }
      ]
    }
  ]
 }
--- a/.github/workflows/matchers/mypy.json
+++ b/.github/workflows/matchers/mypy.json
@@ -0,0 +1,16 @@
 {
  "problemMatcher": [
    {
      "owner": "mypy",
      "pattern": [
        {
          "regexp": "^(.+):(\\d+):\\s(error|warning):\\s(.+)$",
          "file": 1,
          "line": 2,
          "severity": 3,
          "message": 4
        }
      ]
    }
  ]
 }
--- a/.github/workflows/matchers/ruff.json
+++ b/.github/workflows/matchers/ruff.json
@@ -0,0 +1,17 @@
 {
    "problemMatcher": [
      {
        "owner": "ruff",
        "pattern": [
          {
            "regexp": "^(.+?):(\\d+):(\\d+): (\\w+): (.+)$",
            "file": 1,
            "line": 2,
            "column": 3,
            "code": 4,
            "message": 5
          }
        ]
      }
    ]
  }
--- a/.github/workflows/mypy.yaml
+++ b/.github/workflows/mypy.yaml
@@ -6,20 +6,35 @@ on:
  push:
    branches:
      - main
    paths:
      - '**/*.py'
      - '.github/workflows/mypy.yaml'
      - 'tools/mypy.sh'
      - 'pyproject.toml'
  pull_request:
    branches:
      - main
    # This workflow is only relevant when one of the following files changes.
    # However, we have github configured to expect and require this workflow
    # to run and pass before github with auto-merge a pull request. Until github
    # allows more flexible auto-merge policy, we can just run this on every PR.
    # It doesn't take that long to run, anyway.
    #paths:
    #  - '**/*.py'
    #  - '.github/workflows/mypy.yaml'
    #  - 'tools/mypy.sh'
    #  - 'pyproject.toml'
 jobs:
-  ruff:
+  mypy:
    runs-on: ubuntu-latest
    strategy:
      matrix:
-        python-version: ["3.8", "3.9", "3.10", "3.11", "3.12"]
+        python-version: ["3.9", "3.10", "3.11", "3.12"]
    steps:
-    - uses: actions/checkout@v2
+    - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
    - name: Set up Python ${{ matrix.python-version }}
-      uses: actions/setup-python@v2
+      uses: actions/setup-python@0b93645e9fea7318ecaed2b359559ac225c90a2b # v5.3.0
      with:
        python-version: ${{ matrix.python-version }}
    - name: Install dependencies
@@ -32,15 +47,5 @@ jobs:
        pip install types-setuptools
    - name: Mypy
      run: |
-        mypy
+        echo "::add-matcher::.github/workflows/matchers/mypy.json"
-        mypy tests --follow-imports skip
+        tools/mypy.sh 1 ${{ matrix.python-version }}
        mypy vllm/attention --follow-imports skip
        mypy vllm/distributed --follow-imports skip
        mypy vllm/engine  --follow-imports skip
        mypy vllm/executor --follow-imports skip
        mypy vllm/lora --follow-imports skip
        mypy vllm/model_executor  --follow-imports skip
        mypy vllm/prompt_adapter --follow-imports skip
        mypy vllm/spec_decode --follow-imports skip
        mypy vllm/worker --follow-imports skip
--- a/.github/workflows/png-lint.yml
+++ b/.github/workflows/png-lint.yml
@@ -0,0 +1,37 @@
 name: Lint PNG exports from excalidraw
 on:
  push:
    branches:
      - "main"
    paths:
      - '*.excalidraw.png'
      - '.github/workflows/png-lint.yml'
  pull_request:
    branches:
      - "main"
    paths:
      - '*.excalidraw.png'
      - '.github/workflows/png-lint.yml'
 env:
  LC_ALL: en_US.UTF-8
 defaults:
  run:
    shell: bash
 permissions:
  contents: read
 jobs:
  actionlint:
    runs-on: ubuntu-latest
    steps:
      - name: "Checkout"
        uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
        with:
          fetch-depth: 0
      - name: "Run png-lint.sh to check excalidraw exported images"
        run: |
          tools/png-lint.sh
--- a/.github/workflows/publish.yml
+++ b/.github/workflows/publish.yml
@@ -21,16 +21,16 @@ jobs:
      upload_url: ${{ steps.create_release.outputs.upload_url }}
    steps:
      - name: Checkout
-        uses: actions/checkout@v3
+        uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
      - name: Extract branch info
        shell: bash
        run: |
-          echo "release_tag=${GITHUB_REF#refs/*/}" >> $GITHUB_ENV
+          echo "release_tag=${GITHUB_REF#refs/*/}" >> "$GITHUB_ENV"
      - name: Create Release
        id: create_release
-        uses: "actions/github-script@v6"
+        uses: actions/github-script@60a0d83039c74a4aee543508d2ffcb1c3799cdea # v7.0.1
        env:
          RELEASE_TAG: ${{ env.release_tag }}
        with:
@@ -39,67 +39,68 @@ jobs:
            const script = require('.github/workflows/scripts/create_release.js')
            await script(github, context, core)
-  wheel:
+  # NOTE(simon): No longer build wheel using Github Actions. See buildkite's release workflow. 
-    name: Build Wheel
+  # wheel:
-    runs-on: ${{ matrix.os }}
+  #   name: Build Wheel
-    needs: release
+  #   runs-on: ${{ matrix.os }}
  #   needs: release
-    strategy:
+  #   strategy:
-      fail-fast: false
+  #     fail-fast: false
-      matrix:
+  #     matrix:
-          os: ['ubuntu-20.04']
+  #         os: ['ubuntu-20.04']
-          python-version: ['3.8', '3.9', '3.10', '3.11', '3.12']
+  #         python-version: ['3.9', '3.10', '3.11', '3.12']
-          pytorch-version: ['2.4.0']  # Must be the most recent version that meets requirements-cuda.txt.
+  #         pytorch-version: ['2.4.0']  # Must be the most recent version that meets requirements-cuda.txt.
-          cuda-version: ['11.8', '12.1']
+  #         cuda-version: ['11.8', '12.1']
-    steps:
+  #   steps:
-      - name: Checkout
+  #     - name: Checkout
-        uses: actions/checkout@v3
+  #       uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
-      - name: Setup ccache
+  #     - name: Setup ccache
-        uses: hendrikmuhs/ccache-action@v1.2
+  #       uses: hendrikmuhs/ccache-action@ed74d11c0b343532753ecead8a951bb09bb34bc9 # v1.2.14
-        with:
+  #       with:
-          create-symlink: true
+  #         create-symlink: true
-          key: ${{ github.job }}-${{ matrix.python-version }}-${{ matrix.cuda-version }}
+  #         key: ${{ github.job }}-${{ matrix.python-version }}-${{ matrix.cuda-version }}
-      - name: Set up Linux Env
+  #     - name: Set up Linux Env
-        if: ${{ runner.os == 'Linux' }}
+  #       if: ${{ runner.os == 'Linux' }}
-        run: |
+  #       run: |
-          bash -x .github/workflows/scripts/env.sh
+  #         bash -x .github/workflows/scripts/env.sh
-      - name: Set up Python
+  #     - name: Set up Python
-        uses: actions/setup-python@v4
+  #       uses: actions/setup-python@0b93645e9fea7318ecaed2b359559ac225c90a2b # v5.3.0
-        with:
+  #       with:
-            python-version: ${{ matrix.python-version }}
+  #           python-version: ${{ matrix.python-version }}
-      - name: Install CUDA ${{ matrix.cuda-version }}
+  #     - name: Install CUDA ${{ matrix.cuda-version }}
-        run: |
+  #       run: |
-          bash -x .github/workflows/scripts/cuda-install.sh ${{ matrix.cuda-version }} ${{ matrix.os }}
+  #         bash -x .github/workflows/scripts/cuda-install.sh ${{ matrix.cuda-version }} ${{ matrix.os }}
-      - name: Install PyTorch ${{ matrix.pytorch-version }} with CUDA ${{ matrix.cuda-version }}
+  #     - name: Install PyTorch ${{ matrix.pytorch-version }} with CUDA ${{ matrix.cuda-version }}
-        run: |
+  #       run: |
-          bash -x .github/workflows/scripts/pytorch-install.sh ${{ matrix.python-version }} ${{ matrix.pytorch-version }} ${{ matrix.cuda-version }}
+  #         bash -x .github/workflows/scripts/pytorch-install.sh ${{ matrix.python-version }} ${{ matrix.pytorch-version }} ${{ matrix.cuda-version }}
-      - name: Build wheel
+  #     - name: Build wheel
-        shell: bash
+  #       shell: bash
-        env:
+  #       env:
-          CMAKE_BUILD_TYPE: Release # do not compile with debug symbol to reduce wheel size
+  #         CMAKE_BUILD_TYPE: Release # do not compile with debug symbol to reduce wheel size
-        run: |
+  #       run: |
-          bash -x .github/workflows/scripts/build.sh ${{ matrix.python-version }} ${{ matrix.cuda-version }}
+  #         bash -x .github/workflows/scripts/build.sh ${{ matrix.python-version }} ${{ matrix.cuda-version }}
-          wheel_name=$(ls dist/*whl | xargs -n 1 basename)
+  #         wheel_name=$(find dist -name "*whl" -print0 | xargs -0 -n 1 basename)
-          asset_name=${wheel_name//"linux"/"manylinux1"}
+  #         asset_name=${wheel_name//"linux"/"manylinux1"}
-          echo "wheel_name=${wheel_name}" >> $GITHUB_ENV
+  #         echo "wheel_name=${wheel_name}" >> "$GITHUB_ENV"
-          echo "asset_name=${asset_name}" >> $GITHUB_ENV
+  #         echo "asset_name=${asset_name}" >> "$GITHUB_ENV"
-      - name: Upload Release Asset
+  #     - name: Upload Release Asset
-        uses: actions/upload-release-asset@v1
+  #       uses: actions/upload-release-asset@e8f9f06c4b078e705bd2ea027f0926603fc9b4d5 # v1.0.2
-        env:
+  #       env:
-          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+  #         GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-        with:
+  #       with:
-          upload_url: ${{ needs.release.outputs.upload_url }}
+  #         upload_url: ${{ needs.release.outputs.upload_url }}
-          asset_path: ./dist/${{ env.wheel_name }}
+  #         asset_path: ./dist/${{ env.wheel_name }}
-          asset_name: ${{ env.asset_name }}
+  #         asset_name: ${{ env.asset_name }}
-          asset_content_type: application/*
+  #         asset_content_type: application/*
      # (Danielkinz): This last step will publish the .whl to pypi. Warning: untested
      # - name: Publish package
--- a/.github/workflows/reminder_comment.yml
+++ b/.github/workflows/reminder_comment.yml
@@ -8,7 +8,7 @@ jobs:
    runs-on: ubuntu-latest
    steps:
      - name: Remind to run full CI on PR
-        uses: actions/github-script@v6
+        uses: actions/github-script@60a0d83039c74a4aee543508d2ffcb1c3799cdea # v7.0.1
        with:
          script: |
            github.rest.issues.createComment({
--- a/.github/workflows/ruff.yml
+++ b/.github/workflows/ruff.yml
@@ -6,32 +6,47 @@ on:
  push:
    branches:
      - main
    paths:
      - "**/*.py"
      - pyproject.toml
      - requirements-lint.txt
      - .github/workflows/matchers/ruff.json
      - .github/workflows/ruff.yml
  pull_request:
    branches:
      - main
    # This workflow is only relevant when one of the following files changes.
    # However, we have github configured to expect and require this workflow
    # to run and pass before github with auto-merge a pull request. Until github
    # allows more flexible auto-merge policy, we can just run this on every PR.
    # It doesn't take that long to run, anyway.
    #paths:
    #  - "**/*.py"
    #  - pyproject.toml
    #  - requirements-lint.txt
    #  - .github/workflows/matchers/ruff.json
    #  - .github/workflows/ruff.yml
 jobs:
  ruff:
    runs-on: ubuntu-latest
    strategy:
      matrix:
-        python-version: ["3.8", "3.9", "3.10", "3.11", "3.12"]
+        python-version: ["3.12"]
    steps:
-    - uses: actions/checkout@v2
+      - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
-    - name: Set up Python ${{ matrix.python-version }}
+      - name: Set up Python ${{ matrix.python-version }}
-      uses: actions/setup-python@v2
+        uses: actions/setup-python@0b93645e9fea7318ecaed2b359559ac225c90a2b # v5.3.0
-      with:
+        with:
-        python-version: ${{ matrix.python-version }}
+          python-version: ${{ matrix.python-version }}
-    - name: Install dependencies
+      - name: Install dependencies
-      run: |
+        run: |
-        python -m pip install --upgrade pip
+          python -m pip install --upgrade pip
-        pip install ruff==0.1.5 codespell==2.3.0 tomli==2.0.1 isort==5.13.2
+          pip install -r requirements-lint.txt
-    - name: Analysing the code with ruff
+      - name: Analysing the code with ruff
-      run: |
+        run: |
-        ruff .
+          echo "::add-matcher::.github/workflows/matchers/ruff.json"
-    - name: Spelling check with codespell
+          ruff check --output-format github .
-      run: |
+      - name: Run isort
-        codespell --toml pyproject.toml
+        run: |
-    - name: Run isort
+          isort . --check-only
      run: |
        isort . --check-only
--- a/.github/workflows/scripts/build.sh
+++ b/.github/workflows/scripts/build.sh
@@ -1,4 +1,5 @@
 #!/bin/bash
 set -eux
 python_executable=python$1
 cuda_home=/usr/local/cuda-$2
@@ -8,12 +9,15 @@ PATH=${cuda_home}/bin:$PATH
 LD_LIBRARY_PATH=${cuda_home}/lib64:$LD_LIBRARY_PATH
 # Install requirements
-$python_executable -m pip install wheel packaging
+$python_executable -m pip install -r requirements-build.txt -r requirements-cuda.txt
 $python_executable -m pip install -r requirements-cuda.txt
 # Limit the number of parallel jobs to avoid OOM
 export MAX_JOBS=1
 # Make sure release wheels are built for the following architectures
 export TORCH_CUDA_ARCH_LIST="7.0 7.5 8.0 8.6 8.9 9.0+PTX"
 export VLLM_FA_CMAKE_GPU_ARCHES="80-real;90-real"
 bash tools/check_repo.sh
 # Build
 $python_executable setup.py bdist_wheel --dist-dir=dist
--- a/.github/workflows/scripts/cuda-install.sh
+++ b/.github/workflows/scripts/cuda-install.sh
@@ -1,16 +1,16 @@
 #!/bin/bash
 # Replace '.' with '-' ex: 11.8 -> 11-8
-cuda_version=$(echo $1 | tr "." "-")
+cuda_version=$(echo "$1" | tr "." "-")
 # Removes '-' and '.' ex: ubuntu-20.04 -> ubuntu2004
-OS=$(echo $2 | tr -d ".\-")
+OS=$(echo "$2" | tr -d ".\-")
 # Installs CUDA
-wget -nv https://developer.download.nvidia.com/compute/cuda/repos/${OS}/x86_64/cuda-keyring_1.1-1_all.deb
+wget -nv "https://developer.download.nvidia.com/compute/cuda/repos/${OS}/x86_64/cuda-keyring_1.1-1_all.deb"
 sudo dpkg -i cuda-keyring_1.1-1_all.deb
 rm cuda-keyring_1.1-1_all.deb
 sudo apt -qq update
-sudo apt -y install cuda-${cuda_version} cuda-nvcc-${cuda_version} cuda-libraries-dev-${cuda_version}
+sudo apt -y install "cuda-${cuda_version}" "cuda-nvcc-${cuda_version}" "cuda-libraries-dev-${cuda_version}"
 sudo apt clean
 # Test nvcc
--- a/.github/workflows/scripts/pytorch-install.sh
+++ b/.github/workflows/scripts/pytorch-install.sh
@@ -6,7 +6,7 @@ cuda_version=$3
 # Install torch
 $python_executable -m pip install numpy pyyaml scipy ipython mkl mkl-include ninja cython typing pandas typing-extensions dataclasses setuptools && conda clean -ya
-$python_executable -m pip install torch==${pytorch_version}+cu${cuda_version//./} --extra-index-url https://download.pytorch.org/whl/cu${cuda_version//./}
+$python_executable -m pip install torch=="${pytorch_version}+cu${cuda_version//./}" --extra-index-url "https://download.pytorch.org/whl/cu${cuda_version//./}"
 # Print version information
 $python_executable --version
--- a/.github/workflows/shellcheck.yml
+++ b/.github/workflows/shellcheck.yml
@@ -0,0 +1,37 @@
 name: Lint shell scripts
 on:
  push:
    branches:
      - "main"
    paths:
      - '**/*.sh'
      - '.github/workflows/shellcheck.yml'
  pull_request:
    branches:
      - "main"
    paths:
      - '**/*.sh'
      - '.github/workflows/shellcheck.yml'
 env:
  LC_ALL: en_US.UTF-8
 defaults:
  run:
    shell: bash
 permissions:
  contents: read
 jobs:
  shellcheck:
    runs-on: ubuntu-latest
    steps:
      - name: "Checkout"
        uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
        with:
          fetch-depth: 0
      - name: "Check shell scripts"
        run: |
          tools/shellcheck.sh
--- a/.github/workflows/sphinx-lint.yml
+++ b/.github/workflows/sphinx-lint.yml
@@ -0,0 +1,32 @@
 name: Lint documentation
 on:
  push:
    branches:
      - main
    paths:
      - "docs/**"
  pull_request:
    branches:
      - main
    paths:
      - "docs/**"
 jobs:
  sphinx-lint:
    runs-on: ubuntu-latest
    strategy:
      matrix:
        python-version: ["3.12"]
    steps:
      - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
      - name: Set up Python ${{ matrix.python-version }}
        uses: actions/setup-python@0b93645e9fea7318ecaed2b359559ac225c90a2b # v5.3.0
        with:
          python-version: ${{ matrix.python-version }}
      - name: Install dependencies
        run: |
          python -m pip install --upgrade pip
          pip install -r requirements-lint.txt
      - name: Linting docs
        run: tools/sphinx-lint.sh
--- a/.github/workflows/stale.yml
+++ b/.github/workflows/stale.yml
@@ -0,0 +1,52 @@
 name: 'Close inactive issues and PRs'
 on:
  schedule:
    # Daily at 1:30 AM UTC
    - cron: '30 1 * * *'
 jobs:
  close-issues-and-pull-requests:
    permissions:
      issues: write
      pull-requests: write
      actions: write
    runs-on: ubuntu-latest
    steps:
      - uses: actions/stale@28ca1036281a5e5922ead5184a1bbf96e5fc984e # v9.0.0
        with:
          # Increasing this value ensures that changes to this workflow
          # propagate to all issues and PRs in days rather than months
          operations-per-run: 1000
          exempt-draft-pr: true
          exempt-issue-labels: 'keep-open'
          exempt-pr-labels: 'keep-open'
          labels-to-add-when-unstale: 'unstale'
          labels-to-remove-when-stale: 'unstale'
          days-before-issue-stale: 90
          days-before-issue-close: 30
          stale-issue-label: 'stale'
          stale-issue-message: >
            This issue has been automatically marked as stale because it has not
            had any activity within 90 days. It will be automatically closed if no
            further activity occurs within 30 days. Leave a comment if
            you feel this issue should remain open. Thank you!
          close-issue-message: >
            This issue has been automatically closed due to inactivity. Please
            feel free to reopen if you feel it is still relevant. Thank you!
          days-before-pr-stale: 90
          days-before-pr-close: 30
          stale-pr-label: 'stale'
          stale-pr-message: >
            This pull request has been automatically marked as stale because it
            has not had any activity within 90 days. It will be automatically
            closed if no further activity occurs within 30 days. Leave a comment
            if you feel this pull request should remain open. Thank you!
          close-pr-message: >
            This pull request has been automatically closed due to inactivity.
            Please feel free to reopen if you intend to continue working on it.
            Thank you!
--- a/.github/workflows/yapf.yml
+++ b/.github/workflows/yapf.yml
@@ -6,26 +6,33 @@ on:
  push:
    branches:
      - main
    paths:
      - "**/*.py"
      - .github/workflows/yapf.yml
  pull_request:
    branches:
      - main
    paths:
      - "**/*.py"
      - .github/workflows/yapf.yml
 jobs:
  yapf:
    runs-on: ubuntu-latest
    strategy:
      matrix:
-        python-version: ["3.8", "3.9", "3.10", "3.11", "3.12"]
+        python-version: ["3.12"]
    steps:
-    - uses: actions/checkout@v2
+      - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
-    - name: Set up Python ${{ matrix.python-version }}
+      - name: Set up Python ${{ matrix.python-version }}
-      uses: actions/setup-python@v2
+        uses: actions/setup-python@0b93645e9fea7318ecaed2b359559ac225c90a2b # v5.3.0
-      with:
+        with:
-        python-version: ${{ matrix.python-version }}
+          python-version: ${{ matrix.python-version }}
-    - name: Install dependencies
+      - name: Install dependencies
-      run: |
+        run: |
-        python -m pip install --upgrade pip
+          python -m pip install --upgrade pip
-        pip install yapf==0.32.0
+          pip install yapf==0.32.0
-        pip install toml==0.10.2
+          pip install toml==0.10.2
-    - name: Running yapf
+      - name: Running yapf
-      run: |
+        run: |
-        yapf --diff --recursive .
+          yapf --diff --recursive .
--- a/.gitignore
+++ b/.gitignore
@@ -1,5 +1,8 @@
-# vllm commit id, generated by setup.py
+# version file generated by setuptools-scm
-vllm/commit_id.py
+/vllm/_version.py
 # vllm-flash-attn built from source
 vllm/vllm_flash_attn/
 # Byte-compiled / optimized / DLL files
 __pycache__/
@@ -12,6 +15,8 @@ __pycache__/
 # Distribution / packaging
 .Python
 build/
 cmake-build-*/
 CMakeUserPresets.json
 develop-eggs/
 dist/
 downloads/
@@ -28,6 +33,7 @@ share/python-wheels/
 .installed.cfg
 *.egg
 MANIFEST
 /.deps/
 # PyInstaller
 #  Usually these files are written by a python script from a template
@@ -75,6 +81,8 @@ instance/
 docs/_build/
 docs/source/getting_started/examples/*.rst
 !**/*.template.rst
 docs/source/getting_started/examples/*.md
 !**/*.template.md
 # PyBuilder
 .pybuilder/
@@ -193,3 +201,7 @@ hip_compat.h
 # Benchmark dataset
 benchmarks/*.json
 # Linting
 actionlint
 shellcheck*/
--- a/.readthedocs.yaml
+++ b/.readthedocs.yaml
@@ -6,17 +6,16 @@ version: 2
 build:
  os: ubuntu-22.04
  tools:
-    python: "3.8"
+    python: "3.12"
 sphinx:
-   configuration: docs/source/conf.py
+  configuration: docs/source/conf.py
-   fail_on_warning: true
+  fail_on_warning: true
 # If using Sphinx, optionally build your docs in additional formats such as PDF
-formats:
+formats: []
   - pdf
 # Optionally declare the Python requirements required to build your docs
 python:
-   install:
+  install:
-   - requirements: docs/requirements-docs.txt
+    - requirements: docs/requirements-docs.txt
--- a/.shellcheckrc
+++ b/.shellcheckrc
@@ -0,0 +1,9 @@
 # rules currently disabled:
 #
 #   SC1091 (info): Not following: <sourced file> was not specified as input (see shellcheck -x)
 #   SC2004 (style): $/${} is unnecessary on arithmetic variables.
 #   SC2129 (style): Consider using { cmd1; cmd2; } >> file instead of individual redirects.
 #   SC2155 (warning): Declare and assign separately to avoid masking return values.
 #   SC2164 (warning): Use 'cd ... || exit' or 'cd ... || return' in case cd fails.
 #
 disable=SC1091,SC2004,SC2129,SC2155,SC2164
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -1,5 +1,16 @@
 cmake_minimum_required(VERSION 3.26)
 # When building directly using CMake, make sure you run the install step
 # (it places the .so files in the correct location).
 #
 # Example:
 # mkdir build && cd build
 # cmake -G Ninja -DVLLM_PYTHON_EXECUTABLE=`which python3` -DCMAKE_INSTALL_PREFIX=.. ..
 # cmake --build . --target install
 #
 # If you want to only build one target, make sure to install it manually:
 # cmake --build . --target _C
 # cmake --install . --component _C
 project(vllm_extensions LANGUAGES CXX)
 # CUDA by default, can be overridden by using -DVLLM_TARGET_DEVICE=... (used by setup.py)
@@ -13,17 +24,20 @@ include(${CMAKE_CURRENT_LIST_DIR}/cmake/utils.cmake)
 # Suppress potential warnings about unused manually-specified variables
 set(ignoreMe "${VLLM_PYTHON_PATH}")
 # Prevent installation of dependencies (cutlass) by default.
 install(CODE "set(CMAKE_INSTALL_LOCAL_ONLY TRUE)" ALL_COMPONENTS)
 #
 # Supported python versions.  These versions will be searched in order, the
 # first match will be selected.  These should be kept in sync with setup.py.
 #
-set(PYTHON_SUPPORTED_VERSIONS "3.8" "3.9" "3.10" "3.11" "3.12")
+set(PYTHON_SUPPORTED_VERSIONS "3.9" "3.10" "3.11" "3.12")
 # Supported NVIDIA architectures.
-set(CUDA_SUPPORTED_ARCHS "7.0;7.5;8.0;8.6;8.9;9.0")
+set(CUDA_SUPPORTED_ARCHS "7.0;7.2;7.5;8.0;8.6;8.7;8.9;9.0")
 # Supported AMD GPU architectures.
-set(HIP_SUPPORTED_ARCHS "gfx906;gfx908;gfx90a;gfx940;gfx941;gfx942;gfx1030;gfx1100")
+set(HIP_SUPPORTED_ARCHS "gfx906;gfx908;gfx90a;gfx940;gfx941;gfx942;gfx1030;gfx1100;gfx1101")
 #
 # Supported/expected torch versions for CUDA/ROCm.
@@ -35,8 +49,8 @@ set(HIP_SUPPORTED_ARCHS "gfx906;gfx908;gfx90a;gfx940;gfx941;gfx942;gfx1030;gfx11
 # requirements.txt files and should be kept consistent.  The ROCm torch
 # versions are derived from Dockerfile.rocm
 #
-set(TORCH_SUPPORTED_VERSION_CUDA "2.4.0")
+set(TORCH_SUPPORTED_VERSION_CUDA "2.5.1")
-set(TORCH_SUPPORTED_VERSION_ROCM "2.5.0")
+set(TORCH_SUPPORTED_VERSION_ROCM "2.5.1")
 #
 # Try to find python package with an executable that exactly matches
@@ -69,39 +83,6 @@ endif()
 #
 find_package(Torch REQUIRED)
 #
 # Add the `default` target which detects which extensions should be
 # built based on platform/architecture.  This is the same logic that
 # setup.py uses to select which extensions should be built and should
 # be kept in sync.
 #
 # The `default` target makes direct use of cmake easier since knowledge
 # of which extensions are supported has been factored in, e.g.
 #
 # mkdir build && cd build
 # cmake -G Ninja -DVLLM_PYTHON_EXECUTABLE=`which python3` -DCMAKE_LIBRARY_OUTPUT_DIRECTORY=../vllm ..
 # cmake --build . --target default
 #
 add_custom_target(default)
 message(STATUS "Enabling core extension.")
 # Define _core_C extension
 #  built for (almost) every target platform, (excludes TPU and Neuron)
 set(VLLM_EXT_SRC
  "csrc/core/torch_bindings.cpp")
 define_gpu_extension_target(
  _core_C
  DESTINATION vllm
  LANGUAGE CXX
  SOURCES ${VLLM_EXT_SRC}
  COMPILE_FLAGS ${CXX_COMPILE_FLAGS}
  USE_SABI 3
  WITH_SOABI)
 add_dependencies(default _core_C)
 #
 # Forward the non-CUDA device extensions to external CMake scripts.
 #
@@ -144,14 +125,32 @@ else()
  message(FATAL_ERROR "Can't find CUDA or HIP installation.")
 endif()
-#
+
-# Override the GPU architectures detected by cmake/torch and filter them by
+if(VLLM_GPU_LANG STREQUAL "CUDA")
-# the supported versions for the current language.
+  #
-# The final set of arches is stored in `VLLM_GPU_ARCHES`.
+  # For cuda we want to be able to control which architectures we compile for on
-#
+  # a per-file basis in order to cut down on compile time. So here we extract
-override_gpu_arches(VLLM_GPU_ARCHES
+  # the set of architectures we want to compile for and remove the from the
-  ${VLLM_GPU_LANG}
+  # CMAKE_CUDA_FLAGS so that they are not applied globally.
-  "${${VLLM_GPU_LANG}_SUPPORTED_ARCHS}")
+  #
  clear_cuda_arches(CUDA_ARCH_FLAGS)
  extract_unique_cuda_archs_ascending(CUDA_ARCHS "${CUDA_ARCH_FLAGS}")
  message(STATUS "CUDA target architectures: ${CUDA_ARCHS}")
  # Filter the target architectures by the supported supported archs
  # since for some files we will build for all CUDA_ARCHS.
  cuda_archs_loose_intersection(CUDA_ARCHS
    "${CUDA_SUPPORTED_ARCHS}" "${CUDA_ARCHS}")
  message(STATUS "CUDA supported target architectures: ${CUDA_ARCHS}")
 else()
  #
  # For other GPU targets override the GPU architectures detected by cmake/torch
  # and filter them by the supported versions for the current language.
  # The final set of arches is stored in `VLLM_GPU_ARCHES`.
  #
  override_gpu_arches(VLLM_GPU_ARCHES
    ${VLLM_GPU_LANG}
    "${${VLLM_GPU_LANG}_SUPPORTED_ARCHS}")
 endif()
 #
 # Query torch for additional GPU compilation flags for the given
@@ -167,6 +166,17 @@ if(NVCC_THREADS AND VLLM_GPU_LANG STREQUAL "CUDA")
  list(APPEND VLLM_GPU_FLAGS "--threads=${NVCC_THREADS}")
 endif()
 #
 # Use FetchContent for C++ dependencies that are compiled as part of vLLM's build process.
 # setup.py will override FETCHCONTENT_BASE_DIR to play nicely with sccache.
 # Each dependency that produces build artifacts should override its BINARY_DIR to avoid
 # conflicts between build types. It should instead be set to ${CMAKE_BINARY_DIR}/<dependency>.
 #
 include(FetchContent)
 file(MAKE_DIRECTORY ${FETCHCONTENT_BASE_DIR}) # Ensure the directory exists
 message(STATUS "FetchContent base directory: ${FETCHCONTENT_BASE_DIR}")
 #
 # Define other extension targets
 #
@@ -177,29 +187,51 @@ endif()
 set(VLLM_EXT_SRC
  "csrc/cache_kernels.cu"
-  "csrc/attention/attention_kernels.cu"
+  "csrc/attention/paged_attention_v1.cu"
  "csrc/attention/paged_attention_v2.cu"
  "csrc/pos_encoding_kernels.cu"
  "csrc/activation_kernels.cu"
  "csrc/layernorm_kernels.cu"
-  "csrc/quantization/squeezellm/quant_cuda_kernel.cu"
+  "csrc/layernorm_quant_kernels.cu"
  "csrc/quantization/gptq/q_gemm.cu"
  "csrc/quantization/compressed_tensors/int8_quant_kernels.cu"
  "csrc/quantization/fp8/common.cu"
  "csrc/quantization/fused_kernels/fused_layernorm_dynamic_per_token_quant.cu"
  "csrc/quantization/gguf/gguf_kernel.cu"
  "csrc/cuda_utils_kernels.cu"
  "csrc/moe_align_block_size_kernels.cu"
  "csrc/prepare_inputs/advance_step.cu"
  "csrc/torch_bindings.cpp")
 if(VLLM_GPU_LANG STREQUAL "CUDA")
  include(FetchContent)
  SET(CUTLASS_ENABLE_HEADERS_ONLY ON CACHE BOOL "Enable only the header library")
-  FetchContent_Declare(
+
  # Set CUTLASS_REVISION manually -- its revision detection doesn't work in this case.
  set(CUTLASS_REVISION "v3.6.0" CACHE STRING "CUTLASS revision to use")
  # Use the specified CUTLASS source directory for compilation if VLLM_CUTLASS_SRC_DIR is provided
  if (DEFINED ENV{VLLM_CUTLASS_SRC_DIR})
    set(VLLM_CUTLASS_SRC_DIR $ENV{VLLM_CUTLASS_SRC_DIR})
  endif()
  if(VLLM_CUTLASS_SRC_DIR)
    if(NOT IS_ABSOLUTE VLLM_CUTLASS_SRC_DIR)
      get_filename_component(VLLM_CUTLASS_SRC_DIR "${VLLM_CUTLASS_SRC_DIR}" ABSOLUTE)
    endif()
    message(STATUS "The VLLM_CUTLASS_SRC_DIR is set, using ${VLLM_CUTLASS_SRC_DIR} for compilation")
    FetchContent_Declare(cutlass SOURCE_DIR ${VLLM_CUTLASS_SRC_DIR})
  else()
    FetchContent_Declare(
        cutlass
        GIT_REPOSITORY https://github.com/nvidia/cutlass.git
-        # CUTLASS 3.5.1
+        GIT_TAG 8aa95dbb888be6d81c6fbf7169718c5244b53227
        GIT_TAG 06b21349bcf6ddf6a1686a47a137ad1446579db9 
        GIT_PROGRESS TRUE
-  )
+
        # Speed up CUTLASS download by retrieving only the specified GIT_TAG instead of the history.
        # Important: If GIT_SHALLOW is enabled then GIT_TAG works only with branch names and tags.
        # So if the GIT_TAG above is updated to a commit hash, GIT_SHALLOW must be set to FALSE
        GIT_SHALLOW FALSE
    )
  endif()
  FetchContent_MakeAvailable(cutlass)
  list(APPEND VLLM_EXT_SRC
@@ -207,78 +239,191 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
    "csrc/mamba/causal_conv1d/causal_conv1d.cu"
    "csrc/quantization/aqlm/gemm_kernels.cu"
    "csrc/quantization/awq/gemm_kernels.cu"
    "csrc/quantization/marlin/dense/marlin_cuda_kernel.cu"
    "csrc/quantization/marlin/sparse/marlin_24_cuda_kernel.cu"
    "csrc/quantization/marlin/qqq/marlin_qqq_gemm_kernel.cu"
    "csrc/quantization/gptq_marlin/gptq_marlin.cu"
    "csrc/quantization/gptq_marlin/gptq_marlin_repack.cu"
    "csrc/quantization/gptq_marlin/awq_marlin_repack.cu"
    "csrc/quantization/gguf/gguf_kernel.cu"
    "csrc/quantization/fp8/fp8_marlin.cu"
    "csrc/custom_all_reduce.cu"
    "csrc/permute_cols.cu"
    "csrc/quantization/cutlass_w8a8/scaled_mm_entry.cu"
-    "csrc/quantization/cutlass_w8a8/scaled_mm_c2x.cu"
+    "csrc/sparse/cutlass/sparse_scaled_mm_entry.cu"
-    "csrc/quantization/cutlass_w8a8/scaled_mm_c3x.cu")
+    "csrc/sparse/cutlass/sparse_compressor_entry.cu"
    "csrc/cutlass_extensions/common.cpp")
  set_gencode_flags_for_srcs(
    SRCS "${VLLM_EXT_SRC}"
    CUDA_ARCHS "${CUDA_ARCHS}")
  # Only build Marlin kernels if we are building for at least some compatible archs.
  # Keep building Marlin for 9.0 as there are some group sizes and shapes that
  # are not supported by Machete yet.
  cuda_archs_loose_intersection(MARLIN_ARCHS "8.0;8.6;8.7;8.9;9.0" ${CUDA_ARCHS})
  if (MARLIN_ARCHS)
    set(MARLIN_SRCS
       "csrc/quantization/fp8/fp8_marlin.cu"
       "csrc/quantization/marlin/dense/marlin_cuda_kernel.cu"
       "csrc/quantization/marlin/sparse/marlin_24_cuda_kernel.cu"
       "csrc/quantization/marlin/qqq/marlin_qqq_gemm_kernel.cu"
       "csrc/quantization/gptq_marlin/gptq_marlin.cu"
       "csrc/quantization/gptq_marlin/gptq_marlin_repack.cu"
       "csrc/quantization/gptq_marlin/awq_marlin_repack.cu")
    set_gencode_flags_for_srcs(
      SRCS "${MARLIN_SRCS}"
      CUDA_ARCHS "${MARLIN_ARCHS}")
    list(APPEND VLLM_EXT_SRC "${MARLIN_SRCS}")
    message(STATUS "Building Marlin kernels for archs: ${MARLIN_ARCHS}")
  else()
    message(STATUS "Not building Marlin kernels as no compatible archs found"
                   " in CUDA target architectures")
  endif()
  # The cutlass_scaled_mm kernels for Hopper (c3x, i.e. CUTLASS 3.x) require
  # CUDA 12.0 or later (and only work on Hopper, 9.0/9.0a for now).
  cuda_archs_loose_intersection(SCALED_MM_3X_ARCHS "9.0;9.0a" "${CUDA_ARCHS}")
  if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER 12.0 AND SCALED_MM_3X_ARCHS)
    set(SRCS "csrc/quantization/cutlass_w8a8/scaled_mm_c3x.cu")
    set_gencode_flags_for_srcs(
      SRCS "${SRCS}"
      CUDA_ARCHS "${SCALED_MM_3X_ARCHS}")
    list(APPEND VLLM_EXT_SRC "${SRCS}")
    list(APPEND VLLM_GPU_FLAGS "-DENABLE_SCALED_MM_C3X=1")
    message(STATUS "Building scaled_mm_c3x for archs: ${SCALED_MM_3X_ARCHS}")
  else()
    if (NOT ${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER 12.0 AND SCALED_MM_3X_ARCHS)
      message(STATUS "Not building scaled_mm_c3x as CUDA Compiler version is "
                     "not >= 12.0, we recommend upgrading to CUDA 12.0 or "
                     "later if you intend on running FP8 quantized models on "
                     "Hopper.")
    else()
      message(STATUS "Not building scaled_mm_c3x as no compatible archs found "
                     "in CUDA target architectures")
    endif()
    # clear SCALED_MM_3X_ARCHS so the scaled_mm_c2x kernels know we didn't
    # build any 3x kernels
    set(SCALED_MM_3X_ARCHS)
  endif()
  #
-  # The CUTLASS kernels for Hopper require sm90a to be enabled.
+  # For the cutlass_scaled_mm kernels we want to build the c2x (CUTLASS 2.x)
-  # This is done via the below gencode option, BUT that creates kernels for both sm90 and sm90a.
+  # kernels for the remaining archs that are not already built for 3x.
-  # That adds an extra 17MB to compiled binary, so instead we selectively enable it.
+  cuda_archs_loose_intersection(SCALED_MM_2X_ARCHS
-  if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER 12.0)
+    "7.5;8.0;8.6;8.7;8.9;9.0" "${CUDA_ARCHS}")
-    set_source_files_properties(
+  # subtract out the archs that are already built for 3x
-          "csrc/quantization/cutlass_w8a8/scaled_mm_c3x.cu"
+  list(REMOVE_ITEM SCALED_MM_2X_ARCHS ${SCALED_MM_3X_ARCHS})
-          PROPERTIES
+  if (SCALED_MM_2X_ARCHS)
-          COMPILE_FLAGS
+    set(SRCS "csrc/quantization/cutlass_w8a8/scaled_mm_c2x.cu")
-          "-gencode arch=compute_90a,code=sm_90a")
+    set_gencode_flags_for_srcs(
      SRCS "${SRCS}"
      CUDA_ARCHS "${SCALED_MM_2X_ARCHS}")
    list(APPEND VLLM_EXT_SRC "${SRCS}")
    list(APPEND VLLM_GPU_FLAGS "-DENABLE_SCALED_MM_C2X=1")
    message(STATUS "Building scaled_mm_c2x for archs: ${SCALED_MM_2X_ARCHS}")
  else()
    if (SCALED_MM_3X_ARCHS)
      message(STATUS "Not building scaled_mm_c2x as all archs are already built"
                     " for and covered by scaled_mm_c3x")
    else()
      message(STATUS "Not building scaled_mm_c2x as no compatible archs found "
                    "in CUDA target architectures")
    endif()
  endif()
  #
  # 2:4 Sparse Kernels
  # The 2:4 sparse kernels cutlass_scaled_sparse_mm and cutlass_compressor
  # require CUDA 12.2 or later (and only work on Hopper, 9.0/9.0a for now).
  if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER 12.2 AND SCALED_MM_3X_ARCHS)
    set(SRCS "csrc/sparse/cutlass/sparse_compressor_c3x.cu"
             "csrc/sparse/cutlass/sparse_scaled_mm_c3x.cu")
    set_gencode_flags_for_srcs(
      SRCS "${SRCS}"
      CUDA_ARCHS "${SCALED_MM_3X_ARCHS}")
    list(APPEND VLLM_EXT_SRC "${SRCS}")
    list(APPEND VLLM_GPU_FLAGS "-DENABLE_SPARSE_SCALED_MM_C3X=1")
    message(STATUS "Building sparse_scaled_mm_c3x for archs: ${SCALED_MM_3X_ARCHS}")
  else()
    if (NOT ${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER 12.2 AND SCALED_MM_3X_ARCHS)
      message(STATUS "Not building sparse_scaled_mm_c3x kernels as CUDA Compiler version is "
                     "not >= 12.2, we recommend upgrading to CUDA 12.2 or later "
                     "if you intend on running FP8 sparse quantized models on Hopper.")
    else()
      message(STATUS "Not building sparse_scaled_mm_c3x as no compatible archs found "
                     "in CUDA target architectures")
    endif()
  endif()
  #
  # Machete kernels
  # The machete kernels only work on hopper and require CUDA 12.0 or later.
-  if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER 12.0)
+  # Only build Machete kernels if we are building for something compatible with sm90a
  cuda_archs_loose_intersection(MACHETE_ARCHS "9.0a" "${CUDA_ARCHS}")
  if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER 12.0 AND MACHETE_ARCHS)
    #
-    # For the Machete kernels we automatically generate sources for various 
+    # For the Machete kernels we automatically generate sources for various
    # preselected input type pairs and schedules.
    # Generate sources:
-    execute_process(
+    set(MACHETE_GEN_SCRIPT
-      COMMAND ${CMAKE_COMMAND} -E env 
+      ${CMAKE_CURRENT_SOURCE_DIR}/csrc/quantization/machete/generate.py)
-      PYTHONPATH=${CMAKE_CURRENT_SOURCE_DIR}/csrc/cutlass_extensions/:${CUTLASS_DIR}/python/:${VLLM_PYTHON_PATH}:$PYTHONPATH 
+    file(MD5 ${MACHETE_GEN_SCRIPT} MACHETE_GEN_SCRIPT_HASH)
        ${Python_EXECUTABLE} ${CMAKE_CURRENT_SOURCE_DIR}/csrc/quantization/machete/generate.py
      RESULT_VARIABLE machete_generation_result
      OUTPUT_VARIABLE machete_generation_output
      OUTPUT_FILE ${CMAKE_CURRENT_BINARY_DIR}/machete_generation.log
      ERROR_FILE ${CMAKE_CURRENT_BINARY_DIR}/machete_generation.log
    )
-    if (NOT machete_generation_result EQUAL 0)
+    message(STATUS "Machete generation script hash: ${MACHETE_GEN_SCRIPT_HASH}")
-      message(FATAL_ERROR "Machete generation failed."
+    message(STATUS "Last run machete generate script hash: $CACHE{MACHETE_GEN_SCRIPT_HASH}")
-                          " Result: \"${machete_generation_result}\"" 
+
-                          "\nCheck the log for details: "
+    if (NOT DEFINED CACHE{MACHETE_GEN_SCRIPT_HASH}
-                          "${CMAKE_CURRENT_BINARY_DIR}/machete_generation.log")
+        OR NOT $CACHE{MACHETE_GEN_SCRIPT_HASH} STREQUAL ${MACHETE_GEN_SCRIPT_HASH})
      execute_process(
        COMMAND ${CMAKE_COMMAND} -E env
        PYTHONPATH=${CMAKE_CURRENT_SOURCE_DIR}/csrc/cutlass_extensions/:${CUTLASS_DIR}/python/:${VLLM_PYTHON_PATH}:$PYTHONPATH
          ${Python_EXECUTABLE} ${MACHETE_GEN_SCRIPT}
        RESULT_VARIABLE machete_generation_result
        OUTPUT_VARIABLE machete_generation_output
        OUTPUT_FILE ${CMAKE_CURRENT_BINARY_DIR}/machete_generation.log
        ERROR_FILE ${CMAKE_CURRENT_BINARY_DIR}/machete_generation.log
      )
      if (NOT machete_generation_result EQUAL 0)
        message(FATAL_ERROR "Machete generation failed."
                            " Result: \"${machete_generation_result}\""
                            "\nCheck the log for details: "
                            "${CMAKE_CURRENT_BINARY_DIR}/machete_generation.log")
      else()
        set(MACHETE_GEN_SCRIPT_HASH ${MACHETE_GEN_SCRIPT_HASH}
            CACHE STRING "Last run machete generate script hash" FORCE)
        message(STATUS "Machete generation completed successfully.")
      endif()
    else()
-      message(STATUS "Machete generation completed successfully.")
+      message(STATUS "Machete generation script has not changed, skipping generation.")
    endif()
    # Add machete generated sources
    file(GLOB MACHETE_GEN_SOURCES "csrc/quantization/machete/generated/*.cu")
    list(APPEND VLLM_EXT_SRC ${MACHETE_GEN_SOURCES})
    message(STATUS "Machete generated sources: ${MACHETE_GEN_SOURCES}")
-    set_source_files_properties(
+    # forward compatible
-          ${MACHETE_GEN_SOURCES}
+    set_gencode_flags_for_srcs(
-          PROPERTIES
+      SRCS "${MACHETE_GEN_SOURCES}"
-          COMPILE_FLAGS
+      CUDA_ARCHS "${MACHETE_ARCHS}")
-          "-gencode arch=compute_90a,code=sm_90a")
+
    list(APPEND VLLM_EXT_SRC
      csrc/quantization/machete/machete_pytorch.cu)
    message(STATUS "Building Machete kernels for archs: ${MACHETE_ARCHS}")
  else()
    if (NOT ${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER 12.0
        AND MACHETE_ARCHS)
      message(STATUS "Not building Machete kernels as CUDA Compiler version is "
                     "not >= 12.0, we recommend upgrading to CUDA 12.0 or "
                     "later if you intend on running w4a16 quantized models on "
                     "Hopper.")
    else()
      message(STATUS "Not building Machete kernels as no compatible archs "
                     "found in CUDA target architectures")
    endif()
  endif()
-
+# if CUDA endif
  # Add pytorch binding for machete (add on even CUDA < 12.0 so that we can
  #  raise an error if the user that this was built with an incompatible 
  #  CUDA version)
  list(APPEND VLLM_EXT_SRC
    csrc/quantization/machete/machete_pytorch.cu)
 endif()
 message(STATUS "Enabling C extension.")
 define_gpu_extension_target(
  _C
  DESTINATION vllm
@@ -286,23 +431,55 @@ define_gpu_extension_target(
  SOURCES ${VLLM_EXT_SRC}
  COMPILE_FLAGS ${VLLM_GPU_FLAGS}
  ARCHITECTURES ${VLLM_GPU_ARCHES}
-  INCLUDE_DIRECTORIES ${CUTLASS_INCLUDE_DIR}
+  INCLUDE_DIRECTORIES ${CUTLASS_INCLUDE_DIR};${CUTLASS_TOOLS_UTIL_INCLUDE_DIR}
  USE_SABI 3
  WITH_SOABI)
 # If CUTLASS is compiled on NVCC >= 12.5, it by default uses
 # cudaGetDriverEntryPointByVersion as a wrapper to avoid directly calling the
 # driver API. This causes problems when linking with earlier versions of CUDA.
 # Setting this variable sidesteps the issue by calling the driver directly.
 target_compile_definitions(_C PRIVATE CUTLASS_ENABLE_DIRECT_CUDA_DRIVER_CALL=1)
 #
 # _moe_C extension
 #
 set(VLLM_MOE_EXT_SRC
  "csrc/moe/torch_bindings.cpp"
  "csrc/moe/moe_align_sum_kernels.cu"
  "csrc/moe/topk_softmax_kernels.cu")
 set_gencode_flags_for_srcs(
  SRCS "${VLLM_MOE_EXT_SRC}"
  CUDA_ARCHS "${CUDA_ARCHS}")
 if(VLLM_GPU_LANG STREQUAL "CUDA")
-  list(APPEND VLLM_MOE_EXT_SRC
+  cuda_archs_loose_intersection(MARLIN_MOE_ARCHS "8.0;8.6;8.7;8.9;9.0" "${CUDA_ARCHS}")
-      "csrc/moe/marlin_moe_ops.cu")
+  if (MARLIN_MOE_ARCHS)
    set(MARLIN_MOE_SRC
        "csrc/moe/marlin_kernels/marlin_moe_kernel.h"
        "csrc/moe/marlin_kernels/marlin_moe_kernel_ku4b8.h"
        "csrc/moe/marlin_kernels/marlin_moe_kernel_ku4b8.cu"
        "csrc/moe/marlin_kernels/marlin_moe_kernel_ku8b128.h"
        "csrc/moe/marlin_kernels/marlin_moe_kernel_ku8b128.cu"
        "csrc/moe/marlin_kernels/marlin_moe_kernel_ku4.h"
        "csrc/moe/marlin_kernels/marlin_moe_kernel_ku4.cu"
        "csrc/moe/marlin_moe_ops.cu")
    set_gencode_flags_for_srcs(
      SRCS "${MARLIN_MOE_SRC}"
      CUDA_ARCHS "${MARLIN_MOE_ARCHS}")
    list(APPEND VLLM_MOE_EXT_SRC "${MARLIN_MOE_SRC}")
    message(STATUS "Building Marlin MOE kernels for archs: ${MARLIN_MOE_ARCHS}")
  else()
    message(STATUS "Not building Marlin MOE kernels as no compatible archs found"
                   " in CUDA target architectures")
  endif()
 endif()
 message(STATUS "Enabling moe extension.")
 define_gpu_extension_target(
  _moe_C
  DESTINATION vllm
@@ -313,13 +490,98 @@ define_gpu_extension_target(
  USE_SABI 3
  WITH_SOABI)
 if(VLLM_GPU_LANG STREQUAL "HIP")
  #
  # _rocm_C extension
  #
  set(VLLM_ROCM_EXT_SRC
    "csrc/rocm/torch_bindings.cpp"
    "csrc/rocm/attention.cu")
-
+  define_gpu_extension_target(
-if(VLLM_GPU_LANG STREQUAL "CUDA" OR VLLM_GPU_LANG STREQUAL "HIP")
+    _rocm_C
-  message(STATUS "Enabling C extension.")
+    DESTINATION vllm
-  add_dependencies(default _C)
+    LANGUAGE ${VLLM_GPU_LANG}
-
+    SOURCES ${VLLM_ROCM_EXT_SRC}
-  message(STATUS "Enabling moe extension.")
+    COMPILE_FLAGS ${VLLM_GPU_FLAGS}
-  add_dependencies(default _moe_C)
+    ARCHITECTURES ${VLLM_GPU_ARCHES}
-
+    USE_SABI 3
    WITH_SOABI)
 endif()
 # vllm-flash-attn currently only supported on CUDA
 if (NOT VLLM_TARGET_DEVICE STREQUAL "cuda")
  return()
 endif ()
 # vLLM flash attention requires VLLM_GPU_ARCHES to contain the set of target
 # arches in the CMake syntax (75-real, 89-virtual, etc), since we clear the
 # arches in the CUDA case (and instead set the gencodes on a per file basis)
 # we need to manually set VLLM_GPU_ARCHES here.
 if(VLLM_GPU_LANG STREQUAL "CUDA")
  foreach(_ARCH ${CUDA_ARCHS})
    string(REPLACE "." "" _ARCH "${_ARCH}")
    list(APPEND VLLM_GPU_ARCHES "${_ARCH}-real")
  endforeach()
 endif()
 #
 # Build vLLM flash attention from source
 #
 # IMPORTANT: This has to be the last thing we do, because vllm-flash-attn uses the same macros/functions as vLLM.
 # Because functions all belong to the global scope, vllm-flash-attn's functions overwrite vLLMs.
 # They should be identical but if they aren't, this is a massive footgun.
 #
 # The vllm-flash-attn install rules are nested under vllm to make sure the library gets installed in the correct place.
 # To only install vllm-flash-attn, use --component vllm_flash_attn_c.
 # If no component is specified, vllm-flash-attn is still installed.
 # If VLLM_FLASH_ATTN_SRC_DIR is set, vllm-flash-attn is installed from that directory instead of downloading.
 # This is to enable local development of vllm-flash-attn within vLLM.
 # It can be set as an environment variable or passed as a cmake argument.
 # The environment variable takes precedence.
 if (DEFINED ENV{VLLM_FLASH_ATTN_SRC_DIR})
  set(VLLM_FLASH_ATTN_SRC_DIR $ENV{VLLM_FLASH_ATTN_SRC_DIR})
 endif()
 if(VLLM_FLASH_ATTN_SRC_DIR)
  FetchContent_Declare(vllm-flash-attn SOURCE_DIR ${VLLM_FLASH_ATTN_SRC_DIR})
 else()
  FetchContent_Declare(
          vllm-flash-attn
          GIT_REPOSITORY https://github.com/vllm-project/flash-attention.git
          GIT_TAG 04325b6798bcc326c86fb35af62d05a9c8c8eceb
          GIT_PROGRESS TRUE
          # Don't share the vllm-flash-attn build between build types
          BINARY_DIR ${CMAKE_BINARY_DIR}/vllm-flash-attn
  )
 endif()
 # Set the parent build flag so that the vllm-flash-attn library does not redo compile flag and arch initialization.
 set(VLLM_PARENT_BUILD ON)
 # Ensure the vllm/vllm_flash_attn directory exists before installation
 install(CODE "file(MAKE_DIRECTORY \"\${CMAKE_INSTALL_PREFIX}/vllm/vllm_flash_attn\")" COMPONENT vllm_flash_attn_c)
 # Make sure vllm-flash-attn install rules are nested under vllm/
 install(CODE "set(CMAKE_INSTALL_LOCAL_ONLY FALSE)" COMPONENT vllm_flash_attn_c)
 install(CODE "set(OLD_CMAKE_INSTALL_PREFIX \"\${CMAKE_INSTALL_PREFIX}\")" COMPONENT vllm_flash_attn_c)
 install(CODE "set(CMAKE_INSTALL_PREFIX \"\${CMAKE_INSTALL_PREFIX}/vllm/\")" COMPONENT vllm_flash_attn_c)
 # Fetch the vllm-flash-attn library
 FetchContent_MakeAvailable(vllm-flash-attn)
 message(STATUS "vllm-flash-attn is available at ${vllm-flash-attn_SOURCE_DIR}")
 # Restore the install prefix
 install(CODE "set(CMAKE_INSTALL_PREFIX \"\${OLD_CMAKE_INSTALL_PREFIX}\")" COMPONENT vllm_flash_attn_c)
 install(CODE "set(CMAKE_INSTALL_LOCAL_ONLY TRUE)" COMPONENT vllm_flash_attn_c)
 # Copy over the vllm-flash-attn python files
 install(
        DIRECTORY ${vllm-flash-attn_SOURCE_DIR}/vllm_flash_attn/
        DESTINATION vllm/vllm_flash_attn
        COMPONENT vllm_flash_attn_c
        FILES_MATCHING PATTERN "*.py"
 )
 # Nothing after vllm-flash-attn, see comment about macros above
--- a/CODE_OF_CONDUCT.md
+++ b/CODE_OF_CONDUCT.md
@@ -0,0 +1,128 @@
 # vLLM Code of Conduct
 ## Our Pledge
 We as members, contributors, and leaders pledge to make participation in our
 community a harassment-free experience for everyone, regardless of age, body
 size, visible or invisible disability, ethnicity, sex characteristics, gender
 identity and expression, level of experience, education, socioeconomic status,
 nationality, personal appearance, race, caste, color, religion, or sexual
 identity and orientation.
 We pledge to act and interact in ways that contribute to an open, welcoming,
 diverse, inclusive, and healthy community.
 ## Our Standards
 Examples of behavior that contributes to a positive environment for our
 community include:
 * Demonstrating empathy and kindness toward other people
 * Being respectful of differing opinions, viewpoints, and experiences
 * Giving and gracefully accepting constructive feedback
 * Accepting responsibility and apologizing to those affected by our mistakes,
  and learning from the experience
 * Focusing on what is best not just for us as individuals, but for the overall
  community
 Examples of unacceptable behavior include:
 * The use of sexualized language or imagery, and sexual attention or advances of
  any kind
 * Trolling, insulting or derogatory comments, and personal or political attacks
 * Public or private harassment
 * Publishing others' private information, such as a physical or email address,
  without their explicit permission
 * Other conduct which could reasonably be considered inappropriate in a
  professional setting
 ## Enforcement Responsibilities
 Community leaders are responsible for clarifying and enforcing our standards of
 acceptable behavior and will take appropriate and fair corrective action in
 response to any behavior that they deem inappropriate, threatening, offensive,
 or harmful.
 Community leaders have the right and responsibility to remove, edit, or reject
 comments, commits, code, wiki edits, issues, and other contributions that are
 not aligned to this Code of Conduct, and will communicate reasons for moderation
 decisions when appropriate.
 ## Scope
 This Code of Conduct applies within all community spaces, and also applies when
 an individual is officially representing the community in public spaces.
 Examples of representing our community include using an official email address,
 posting via an official social media account, or acting as an appointed
 representative at an online or offline/IRL event.
 ## Enforcement
 Instances of abusive, harassing, or otherwise unacceptable behavior may be
 reported to the community leaders responsible for enforcement in the #code-of-conduct
 channel in the [vLLM Discord](https://discord.com/invite/jz7wjKhh6g).
 All complaints will be reviewed and investigated promptly and fairly.
 All community leaders are obligated to respect the privacy and security of the
 reporter of any incident.
 ## Enforcement Guidelines
 Community leaders will follow these Community Impact Guidelines in determining
 the consequences for any action they deem in violation of this Code of Conduct:
 ### 1. Correction
 **Community Impact**: Use of inappropriate language or other behavior deemed
 unprofessional or unwelcome in the community.
 **Consequence**: A private, written warning from community leaders, providing
 clarity around the nature of the violation and an explanation of why the
 behavior was inappropriate. A public apology may be requested.
 ### 2. Warning
 **Community Impact**: A violation through a single incident or series of
 actions.
 **Consequence**: A warning with consequences for continued behavior. No
 interaction with the people involved, including unsolicited interaction with
 those enforcing the Code of Conduct, for a specified period of time. This
 includes avoiding interactions in community spaces as well as external channels
 like social media. Violating these terms may lead to a temporary or permanent
 ban.
 ### 3. Temporary Ban
 **Community Impact**: A serious violation of community standards, including
 sustained inappropriate behavior.
 **Consequence**: A temporary ban from any sort of interaction or public
 communication with the community for a specified period of time. No public or
 private interaction with the people involved, including unsolicited interaction
 with those enforcing the Code of Conduct, is allowed during this period.
 Violating these terms may lead to a permanent ban.
 ### 4. Permanent Ban
 **Community Impact**: Demonstrating a pattern of violation of community
 standards, including sustained inappropriate behavior, harassment of an
 individual, or aggression toward or disparagement of classes of individuals.
 **Consequence**: A permanent ban from any sort of public interaction within the
 community.
 ## Attribution
 This Code of Conduct is adapted from the [Contributor Covenant](https://www.contributor-covenant.org/),
 version 2.1, available at
 [v2.1](https://www.contributor-covenant.org/version/2/1/code_of_conduct.html).
 Community Impact Guidelines were inspired by
 [Mozilla's code of conduct enforcement ladder](https://github.com/mozilla/inclusion).
 For answers to common questions about this code of conduct, see the
 [Contributor Covenant FAQ](https://www.contributor-covenant.org/faq). Translations are available at
 [Contributor Covenant translations](https://www.contributor-covenant.org/translations).
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
@@ -1,56 +1,3 @@
 # Contributing to vLLM
-Thank you for your interest in contributing to vLLM!
+You may find information about contributing to vLLM on [docs.vllm.ai](https://docs.vllm.ai/en/latest/contributing/overview.html).
 Our community is open to everyone and welcomes all kinds of contributions, no matter how small or large.
 There are several ways you can contribute to the project:
 - Identify and report any issues or bugs.
 - Request or add a new model.
 - Suggest or implement new features.
 However, remember that contributions aren't just about code.
 We believe in the power of community support; thus, answering queries, assisting others, and enhancing the documentation are highly regarded and beneficial contributions.
 Finally, one of the most impactful ways to support us is by raising awareness about vLLM.
 Talk about it in your blog posts, highlighting how it's driving your incredible projects.
 Express your support on Twitter if vLLM aids you, or simply offer your appreciation by starring our repository.
 ## Setup for development
 ### Build from source
 ```bash
 pip install -e .  # This may take several minutes.
 ```
 ### Testing
 ```bash
 pip install -r requirements-dev.txt
 # linting and formatting
 bash format.sh
 # Static type checking
 mypy
 # Unit tests
 pytest tests/
 ```
 **Note:** Currently, the repository does not pass the mypy tests.
 ## Contributing Guidelines
 ### Issue Reporting
 If you encounter a bug or have a feature request, please check our issues page first to see if someone else has already reported it.
 If not, please file a new issue, providing as much relevant information as possible.
 ### Pull Requests & Code Reviews
 Please check the PR checklist in the [PR template](.github/PULL_REQUEST_TEMPLATE.md) for detailed guide for contribution.
 ### Thank You
 Finally, thank you for taking the time to read these guidelines and for your interest in contributing to vLLM.
 Your contributions make vLLM a great tool for everyone!
--- a/34
+++ b/34
@@ -0,0 +1,34 @@
 Developer Certificate of Origin
 Version 1.1
 Copyright (C) 2004, 2006 The Linux Foundation and its contributors.
 Everyone is permitted to copy and distribute verbatim copies of this
 license document, but changing it is not allowed.
 Developer's Certificate of Origin 1.1
 By making a contribution to this project, I certify that:
 (a) The contribution was created in whole or in part by me and I
    have the right to submit it under the open source license
    indicated in the file; or
 (b) The contribution is based upon previous work that, to the best
    of my knowledge, is covered under an appropriate open source
    license and I have the right under that license to submit that
    work with modifications, whether created in whole or in part
    by me, under the same open source license (unless I am
    permitted to submit under a different license), as indicated
    in the file; or
 (c) The contribution was provided directly to me by some other
    person who certified (a), (b) or (c) and I have not modified
    it.
 (d) I understand and agree that this project and the contribution
    are public and that a record of the contribution (including all
    personal information I submit with it, including my sign-off) is
    maintained indefinitely and may be redistributed consistent with
    this project or the open source license(s) involved.
--- a/98
+++ b/98
@@ -2,7 +2,7 @@
 # to run the OpenAI compatible server.
 # Please update any changes made here to
-# docs/source/dev/dockerfile/dockerfile.rst and
+# docs/source/dev/dockerfile/dockerfile.md and
 # docs/source/assets/dev/dockerfile-stages-dependency.png
 ARG CUDA_VERSION=12.4.1
@@ -10,7 +10,8 @@ ARG CUDA_VERSION=12.4.1
 # prepare basic build environment
 FROM nvidia/cuda:${CUDA_VERSION}-devel-ubuntu20.04 AS base
 ARG CUDA_VERSION=12.4.1
-ARG PYTHON_VERSION=3.10
+ARG PYTHON_VERSION=3.12
 ARG TARGETPLATFORM
 ENV DEBIAN_FRONTEND=noninteractive
 # Install Python and other dependencies
@@ -27,6 +28,14 @@ RUN echo 'tzdata tzdata/Areas select America' | debconf-set-selections \
    && curl -sS https://bootstrap.pypa.io/get-pip.py | python${PYTHON_VERSION} \
    && python3 --version && python3 -m pip --version
 # Upgrade to GCC 10 to avoid https://gcc.gnu.org/bugzilla/show_bug.cgi?id=92519
 # as it was causing spam when compiling the CUTLASS kernels
 RUN apt-get install -y gcc-10 g++-10
 RUN update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-10 110 --slave /usr/bin/g++ g++ /usr/bin/g++-10
 RUN <<EOF
 gcc --version
 EOF
 # Workaround for https://github.com/openai/triton/issues/2507 and
 # https://github.com/pytorch/pytorch/issues/107960 -- hopefully
 # this won't be needed for future versions of this docker image
@@ -36,23 +45,35 @@ RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
 WORKDIR /workspace
 # install build and runtime dependencies
 # arm64 (GH200) build follows the practice of "use existing pytorch" build,
 # we need to install torch and torchvision from the nightly builds first,
 # pytorch will not appear as a vLLM dependency in all of the following steps
 # after this step
 RUN --mount=type=cache,target=/root/.cache/pip \
    if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
        python3 -m pip install --index-url https://download.pytorch.org/whl/nightly/cu124 "torch==2.6.0.dev20241210+cu124" "torchvision==0.22.0.dev20241215";  \
    fi
 COPY requirements-common.txt requirements-common.txt
 COPY requirements-adag.txt requirements-adag.txt
 COPY requirements-cuda.txt requirements-cuda.txt
 RUN --mount=type=cache,target=/root/.cache/pip \
    python3 -m pip install -r requirements-cuda.txt
 # cuda arch list used by torch
 # can be useful for both `dev` and `test`
 # explicitly set the list to avoid issues with torch 2.2
 # see https://github.com/pytorch/pytorch/pull/123243
 ARG torch_cuda_arch_list='7.0 7.5 8.0 8.6 8.9 9.0+PTX'
 ENV TORCH_CUDA_ARCH_LIST=${torch_cuda_arch_list}
 # Override the arch list for flash-attn to reduce the binary size
 ARG vllm_fa_cmake_gpu_arches='80-real;90-real'
 ENV VLLM_FA_CMAKE_GPU_ARCHES=${vllm_fa_cmake_gpu_arches}
 #################### BASE BUILD IMAGE ####################
 #################### WHEEL BUILD IMAGE ####################
 FROM base AS build
 ARG TARGETPLATFORM
 # install build dependencies
 COPY requirements-build.txt requirements-build.txt
@@ -60,16 +81,10 @@ COPY requirements-build.txt requirements-build.txt
 RUN --mount=type=cache,target=/root/.cache/pip \
    python3 -m pip install -r requirements-build.txt
-# files and directories related to build wheels
+COPY . .
-COPY csrc csrc
+ARG GIT_REPO_CHECK=0
-COPY setup.py setup.py
+RUN --mount=type=bind,source=.git,target=.git \
-COPY cmake cmake
+    if [ "$GIT_REPO_CHECK" != 0 ]; then bash tools/check_repo.sh ; fi
 COPY CMakeLists.txt CMakeLists.txt
 COPY requirements-common.txt requirements-common.txt
 COPY requirements-adag.txt requirements-adag.txt
 COPY requirements-cuda.txt requirements-cuda.txt
 COPY pyproject.toml pyproject.toml
 COPY vllm vllm
 # max jobs used by Ninja to build extensions
 ARG max_jobs=2
@@ -78,14 +93,13 @@ ENV MAX_JOBS=${max_jobs}
 ARG nvcc_threads=8
 ENV NVCC_THREADS=$nvcc_threads
 ARG buildkite_commit
 ENV BUILDKITE_COMMIT=${buildkite_commit}
 ARG USE_SCCACHE
 ARG SCCACHE_BUCKET_NAME=vllm-build-sccache
 ARG SCCACHE_REGION_NAME=us-west-2
 ARG SCCACHE_S3_NO_CREDENTIALS=0
 # if USE_SCCACHE is set, use sccache to speed up compilation
 RUN --mount=type=cache,target=/root/.cache/pip \
    --mount=type=bind,source=.git,target=.git \
    if [ "$USE_SCCACHE" = "1" ]; then \
        echo "Installing sccache..." \
        && curl -L -o sccache.tar.gz https://github.com/mozilla/sccache/releases/download/v0.8.1/sccache-v0.8.1-x86_64-unknown-linux-musl.tar.gz \
@@ -94,6 +108,7 @@ RUN --mount=type=cache,target=/root/.cache/pip \
        && rm -rf sccache.tar.gz sccache-v0.8.1-x86_64-unknown-linux-musl \
        && export SCCACHE_BUCKET=${SCCACHE_BUCKET_NAME} \
        && export SCCACHE_REGION=${SCCACHE_REGION_NAME} \
        && export SCCACHE_S3_NO_CREDENTIALS=${SCCACHE_S3_NO_CREDENTIALS} \
        && export SCCACHE_IDLE_TIMEOUT=0 \
        && export CMAKE_BUILD_TYPE=Release \
        && sccache --show-stats \
@@ -104,6 +119,7 @@ RUN --mount=type=cache,target=/root/.cache/pip \
 ENV CCACHE_DIR=/root/.cache/ccache
 RUN --mount=type=cache,target=/root/.cache/ccache \
    --mount=type=cache,target=/root/.cache/pip \
    --mount=type=bind,source=.git,target=.git  \
    if [ "$USE_SCCACHE" != "1" ]; then \
        python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38; \
    fi
@@ -129,15 +145,16 @@ COPY requirements-test.txt requirements-test.txt
 COPY requirements-dev.txt requirements-dev.txt
 RUN --mount=type=cache,target=/root/.cache/pip \
    python3 -m pip install -r requirements-dev.txt
 #################### DEV IMAGE ####################
 #################### vLLM installation IMAGE ####################
 # image with vLLM installed
-FROM nvidia/cuda:${CUDA_VERSION}-base-ubuntu20.04 AS vllm-base
+FROM nvidia/cuda:${CUDA_VERSION}-base-ubuntu22.04 AS vllm-base
 ARG CUDA_VERSION=12.4.1
-ARG PYTHON_VERSION=3.10
+ARG PYTHON_VERSION=3.12
 WORKDIR /vllm-workspace
 ENV DEBIAN_FRONTEND=noninteractive
 ARG TARGETPLATFORM
 RUN PYTHON_VERSION_STR=$(echo ${PYTHON_VERSION} | sed 's/\.//g') && \
    echo "export PYTHON_VERSION_STR=${PYTHON_VERSION_STR}" >> /etc/environment
@@ -146,7 +163,8 @@ RUN PYTHON_VERSION_STR=$(echo ${PYTHON_VERSION} | sed 's/\.//g') && \
 RUN echo 'tzdata tzdata/Areas select America' | debconf-set-selections \
    && echo 'tzdata tzdata/Zones/America select Los_Angeles' | debconf-set-selections \
    && apt-get update -y \
-    && apt-get install -y ccache software-properties-common git curl sudo vim python3-pip \
+    && apt-get install -y ccache software-properties-common git curl wget sudo vim python3-pip \
    && apt-get install -y ffmpeg libsm6 libxext6 libgl1 \
    && add-apt-repository ppa:deadsnakes/ppa \
    && apt-get update -y \
    && apt-get install -y python${PYTHON_VERSION} python${PYTHON_VERSION}-dev python${PYTHON_VERSION}-venv libibverbs-dev \
@@ -162,17 +180,28 @@ RUN echo 'tzdata tzdata/Areas select America' | debconf-set-selections \
 # or future versions of triton.
 RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
-# install vllm wheel first, so that torch etc will be installed
+# arm64 (GH200) build follows the practice of "use existing pytorch" build,
 # we need to install torch and torchvision from the nightly builds first,
 # pytorch will not appear as a vLLM dependency in all of the following steps
 # after this step
 RUN --mount=type=cache,target=/root/.cache/pip \
    if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
        python3 -m pip install --index-url https://download.pytorch.org/whl/nightly/cu124 "torch==2.6.0.dev20241210+cu124" "torchvision==0.22.0.dev20241215";  \
    fi
 # Install vllm wheel first, so that torch etc will be installed.
 RUN --mount=type=bind,from=build,src=/workspace/dist,target=/vllm-workspace/dist \
    --mount=type=cache,target=/root/.cache/pip \
    python3 -m pip install dist/*.whl --verbose
 RUN --mount=type=cache,target=/root/.cache/pip \
-    . /etc/environment && \
+. /etc/environment && \
-    python3 -m pip install https://github.com/flashinfer-ai/flashinfer/releases/download/v0.1.6/flashinfer-0.1.6+cu121torch2.4-cp${PYTHON_VERSION_STR}-cp${PYTHON_VERSION_STR}-linux_x86_64.whl
+if [ "$TARGETPLATFORM" != "linux/arm64" ]; then \
    python3 -m pip install https://github.com/flashinfer-ai/flashinfer/releases/download/v0.1.6/flashinfer-0.1.6+cu121torch2.4-cp${PYTHON_VERSION_STR}-cp${PYTHON_VERSION_STR}-linux_x86_64.whl; \
 fi
 COPY examples examples
 #################### vLLM installation IMAGE ####################
 #################### TEST IMAGE ####################
 # image to run unit testing suite
 # note that this uses vllm installed by `pip`
@@ -184,13 +213,24 @@ ADD . /vllm-workspace/
 RUN --mount=type=cache,target=/root/.cache/pip \
    python3 -m pip install -r requirements-dev.txt
 # install development dependencies (for testing)
 RUN --mount=type=cache,target=/root/.cache/pip \
    python3 -m pip install -e tests/vllm_test_utils
 # enable fast downloads from hf (for testing)
 RUN --mount=type=cache,target=/root/.cache/pip \
    python3 -m pip install hf_transfer
 ENV HF_HUB_ENABLE_HF_TRANSFER 1
 # Copy in the v1 package for testing (it isn't distributed yet)
 COPY vllm/v1 /usr/local/lib/python3.12/dist-packages/vllm/v1
 # doc requires source code
 # we hide them inside `test_docs/` , so that this source code
 # will not be imported by other tests
 RUN mkdir test_docs
 RUN mv docs test_docs/
 RUN mv vllm test_docs/
 #################### TEST IMAGE ####################
 #################### OPENAI API SERVER ####################
@@ -199,7 +239,11 @@ FROM vllm-base AS vllm-openai
 # install additional dependencies for openai api server
 RUN --mount=type=cache,target=/root/.cache/pip \
-    pip install accelerate hf_transfer 'modelscope!=1.15.0'
+    if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
        pip install accelerate hf_transfer 'modelscope!=1.15.0' 'bitsandbytes>=0.42.0' 'timm==0.9.10' boto3 runai-model-streamer runai-model-streamer[s3]; \
    else \
        pip install accelerate hf_transfer 'modelscope!=1.15.0' 'bitsandbytes>=0.45.0' 'timm==0.9.10' boto3 runai-model-streamer runai-model-streamer[s3]; \
    fi
 ENV VLLM_USAGE_SOURCE production-docker-image
--- a/Dockerfile.arm
+++ b/Dockerfile.arm
@@ -0,0 +1,62 @@
 # This vLLM Dockerfile is used to construct an image that can build and run vLLM on ARM CPU platform.
 FROM ubuntu:22.04 AS cpu-test-arm
 ENV CCACHE_DIR=/root/.cache/ccache
 ENV CMAKE_CXX_COMPILER_LAUNCHER=ccache
 RUN --mount=type=cache,target=/var/cache/apt \
    apt-get update -y \
    && apt-get install -y curl ccache git wget vim numactl gcc-12 g++-12 python3 python3-pip libtcmalloc-minimal4 libnuma-dev \
    && apt-get install -y ffmpeg libsm6 libxext6 libgl1 \
    && update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-12 10 --slave /usr/bin/g++ g++ /usr/bin/g++-12
 # tcmalloc provides better memory allocation efficiency, e.g., holding memory in caches to speed up access of commonly-used objects.
 RUN --mount=type=cache,target=/root/.cache/pip \
    pip install py-cpuinfo  # Use this to gather CPU info and optimize based on ARM Neoverse cores
 # Set LD_PRELOAD for tcmalloc on ARM
 ENV LD_PRELOAD="/usr/lib/aarch64-linux-gnu/libtcmalloc_minimal.so.4"
 RUN echo 'ulimit -c 0' >> ~/.bashrc
 WORKDIR /workspace
 ARG PIP_EXTRA_INDEX_URL="https://download.pytorch.org/whl/cpu"
 ENV PIP_EXTRA_INDEX_URL=${PIP_EXTRA_INDEX_URL}
 RUN --mount=type=cache,target=/root/.cache/pip \
    --mount=type=bind,src=requirements-build.txt,target=requirements-build.txt \
    pip install --upgrade pip && \
    pip install -r requirements-build.txt
 FROM cpu-test-arm AS build
 WORKDIR /workspace/vllm
 RUN --mount=type=cache,target=/root/.cache/pip \
    --mount=type=bind,src=requirements-common.txt,target=requirements-common.txt \
    --mount=type=bind,src=requirements-cpu.txt,target=requirements-cpu.txt \
    pip install -v -r requirements-cpu.txt
 COPY . .
 ARG GIT_REPO_CHECK=0
 RUN --mount=type=bind,source=.git,target=.git \
    if [ "$GIT_REPO_CHECK" != 0 ]; then bash tools/check_repo.sh ; fi
 # Disabling AVX512 specific optimizations for ARM
 ARG VLLM_CPU_DISABLE_AVX512="true"
 ENV VLLM_CPU_DISABLE_AVX512=${VLLM_CPU_DISABLE_AVX512}
 RUN --mount=type=cache,target=/root/.cache/pip \
    --mount=type=cache,target=/root/.cache/ccache \
    --mount=type=bind,source=.git,target=.git \
    VLLM_TARGET_DEVICE=cpu python3 setup.py bdist_wheel && \
    pip install dist/*.whl && \
    rm -rf dist
 WORKDIR /workspace/
 RUN ln -s /workspace/vllm/tests && ln -s /workspace/vllm/examples && ln -s /workspace/vllm/benchmarks
 ENTRYPOINT ["python3", "-m", "vllm.entrypoints.openai.api_server"]
--- a/Dockerfile.cpu
+++ b/Dockerfile.cpu
@@ -2,26 +2,34 @@
 FROM ubuntu:22.04 AS cpu-test-1
 ENV CCACHE_DIR=/root/.cache/ccache
 ENV CMAKE_CXX_COMPILER_LAUNCHER=ccache
 RUN --mount=type=cache,target=/var/cache/apt \
    apt-get update -y \
    && apt-get install -y curl ccache git wget vim numactl gcc-12 g++-12 python3 python3-pip libtcmalloc-minimal4 libnuma-dev \
    && apt-get install -y ffmpeg libsm6 libxext6 libgl1 \
    && update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-12 10 --slave /usr/bin/g++ g++ /usr/bin/g++-12
 # https://intel.github.io/intel-extension-for-pytorch/cpu/latest/tutorials/performance_tuning/tuning_guide.html
 # intel-openmp provides additional performance improvement vs. openmp
 # tcmalloc provides better memory allocation efficiency, e.g, holding memory in caches to speed up access of commonly-used objects.
 RUN --mount=type=cache,target=/root/.cache/pip \
-    pip install intel-openmp
+    pip install intel-openmp==2025.0.1
 ENV LD_PRELOAD="/usr/lib/x86_64-linux-gnu/libtcmalloc_minimal.so.4:/usr/local/lib/libiomp5.so"
 RUN echo 'ulimit -c 0' >> ~/.bashrc
-RUN pip install https://intel-extension-for-pytorch.s3.amazonaws.com/ipex_dev/cpu/intel_extension_for_pytorch-2.4.0%2Bgitfbaa4bc-cp310-cp310-linux_x86_64.whl
+RUN pip install intel_extension_for_pytorch==2.5.0
-ENV PIP_EXTRA_INDEX_URL=https://download.pytorch.org/whl/cpu
+WORKDIR /workspace
 COPY requirements-build.txt requirements-build.txt
 ARG PIP_EXTRA_INDEX_URL="https://download.pytorch.org/whl/cpu"
 ENV PIP_EXTRA_INDEX_URL=${PIP_EXTRA_INDEX_URL}
 RUN --mount=type=cache,target=/root/.cache/pip \
    --mount=type=bind,src=requirements-build.txt,target=requirements-build.txt \
    pip install --upgrade pip && \
    pip install -r requirements-build.txt
@@ -29,25 +37,33 @@ FROM cpu-test-1 AS build
 WORKDIR /workspace/vllm
 COPY requirements-common.txt requirements-common.txt
 COPY requirements-cpu.txt requirements-cpu.txt
 RUN --mount=type=cache,target=/root/.cache/pip \
    --mount=type=bind,src=requirements-common.txt,target=requirements-common.txt \
    --mount=type=bind,src=requirements-cpu.txt,target=requirements-cpu.txt \
    pip install -v -r requirements-cpu.txt
-COPY ./ ./
+COPY . .
 ARG GIT_REPO_CHECK=0
 RUN --mount=type=bind,source=.git,target=.git \
    if [ "$GIT_REPO_CHECK" != 0 ]; then bash tools/check_repo.sh ; fi
 # Support for building with non-AVX512 vLLM: docker build --build-arg VLLM_CPU_DISABLE_AVX512="true" ...
 ARG VLLM_CPU_DISABLE_AVX512
 ENV VLLM_CPU_DISABLE_AVX512=${VLLM_CPU_DISABLE_AVX512}
 ENV CCACHE_DIR=/root/.cache/ccache
 RUN --mount=type=cache,target=/root/.cache/pip \
    --mount=type=cache,target=/root/.cache/ccache \
    --mount=type=bind,source=.git,target=.git \
    VLLM_TARGET_DEVICE=cpu python3 setup.py bdist_wheel && \
-    pip install dist/*.whl
+    pip install dist/*.whl && \
    rm -rf dist
 WORKDIR /workspace/
 RUN ln -s /workspace/vllm/tests && ln -s /workspace/vllm/examples && ln -s /workspace/vllm/benchmarks
 # install development dependencies (for testing)
 RUN --mount=type=cache,target=/root/.cache/pip \
    pip install -e tests/vllm_test_utils
 ENTRYPOINT ["python3", "-m", "vllm.entrypoints.openai.api_server"]
--- a/Dockerfile.hpu
+++ b/Dockerfile.hpu
@@ -0,0 +1,21 @@
 FROM vault.habana.ai/gaudi-docker/1.18.0/ubuntu22.04/habanalabs/pytorch-installer-2.4.0:latest
 COPY ./ /workspace/vllm
 WORKDIR /workspace/vllm
 RUN pip install -v -r requirements-hpu.txt
 ENV no_proxy=localhost,127.0.0.1
 ENV PT_HPU_ENABLE_LAZY_COLLECTIVES=true
 RUN VLLM_TARGET_DEVICE=hpu python3 setup.py install
 # install development dependencies (for testing)
 RUN python3 -m pip install -e tests/vllm_test_utils
 WORKDIR /workspace/
 RUN ln -s /workspace/vllm/tests && ln -s /workspace/vllm/examples && ln -s /workspace/vllm/benchmarks
 ENTRYPOINT ["python3", "-m", "vllm.entrypoints.openai.api_server"]
--- a/Dockerfile.neuron
+++ b/Dockerfile.neuron
@@ -1,36 +1,45 @@
 # default base image
-ARG BASE_IMAGE="public.ecr.aws/neuron/pytorch-inference-neuronx:2.1.2-neuronx-py310-sdk2.19.1-ubuntu20.04"
+# https://gallery.ecr.aws/neuron/pytorch-inference-neuronx
 ARG BASE_IMAGE="public.ecr.aws/neuron/pytorch-inference-neuronx:2.1.2-neuronx-py310-sdk2.20.2-ubuntu20.04"
 FROM $BASE_IMAGE
 RUN echo "Base image is $BASE_IMAGE"
 # Install some basic utilities
-RUN apt-get update && apt-get install python3 python3-pip -y
+RUN apt-get update && \
    apt-get install -y \
        git \
        python3 \
        python3-pip \
        ffmpeg libsm6 libxext6 libgl1
 ### Mount Point ###
 # When launching the container, mount the code directory to /app
 ARG APP_MOUNT=/app
 VOLUME [ ${APP_MOUNT} ]
-WORKDIR ${APP_MOUNT}
+WORKDIR ${APP_MOUNT}/vllm
 RUN python3 -m pip install --upgrade pip
 RUN python3 -m pip install --no-cache-dir fastapi ninja tokenizers pandas
 RUN python3 -m pip install sentencepiece transformers==4.36.2 -U
 RUN python3 -m pip install transformers-neuronx --extra-index-url=https://pip.repos.neuron.amazonaws.com -U
-RUN python3 -m pip install --pre neuronx-cc==2.12.* --extra-index-url=https://pip.repos.neuron.amazonaws.com -U
+RUN python3 -m pip install --pre neuronx-cc==2.15.* --extra-index-url=https://pip.repos.neuron.amazonaws.com -U
-COPY ./vllm /app/vllm/vllm
+COPY . .
-COPY ./setup.py /app/vllm/setup.py
+ARG GIT_REPO_CHECK=0
-COPY ./requirements-common.txt /app/vllm/requirements-common.txt
+RUN --mount=type=bind,source=.git,target=.git \
-COPY ./requirements-neuron.txt /app/vllm/requirements-neuron.txt
+    if [ "$GIT_REPO_CHECK" != 0 ]; then bash tools/check_repo.sh ; fi
-RUN cd /app/vllm \
+RUN python3 -m pip install -U \
-    && python3 -m pip install -U -r requirements-neuron.txt
+        'cmake>=3.26' ninja packaging 'setuptools-scm>=8' wheel jinja2 \
        -r requirements-neuron.txt
 ENV VLLM_TARGET_DEVICE neuron
-RUN cd /app/vllm \
+RUN --mount=type=bind,source=.git,target=.git \
-    && pip install -e . \
+    pip install --no-build-isolation -v -e .
-    && cd ..
+
 # install development dependencies (for testing)
 RUN python3 -m pip install -e tests/vllm_test_utils
 CMD ["/bin/bash"]
--- a/Dockerfile.openvino
+++ b/Dockerfile.openvino
@@ -4,26 +4,25 @@
 FROM ubuntu:22.04 AS dev
 RUN apt-get update -y && \
-    apt-get install -y python3-pip git
+    apt-get install -y \
        git python3-pip \
        ffmpeg libsm6 libxext6 libgl1
 WORKDIR /workspace
-# copy requirements
+COPY . .
-COPY requirements-build.txt /workspace/vllm/
+ARG GIT_REPO_CHECK=0
-COPY requirements-common.txt /workspace/vllm/
+RUN --mount=type=bind,source=.git,target=.git \
-COPY requirements-openvino.txt /workspace/vllm/
+    if [ "$GIT_REPO_CHECK" != 0 ]; then bash tools/check_repo.sh ; fi
 COPY vllm/ /workspace/vllm/vllm
 COPY csrc/core /workspace/vllm/csrc/core
 COPY cmake/utils.cmake /workspace/vllm/cmake/
 COPY CMakeLists.txt /workspace/vllm/
 COPY setup.py /workspace/vllm/
 # install build requirements
-RUN PIP_EXTRA_INDEX_URL="https://download.pytorch.org/whl/cpu" python3 -m pip install -r /workspace/vllm/requirements-build.txt
+RUN PIP_EXTRA_INDEX_URL="https://download.pytorch.org/whl/cpu" python3 -m pip install -r /workspace/requirements-build.txt
 # build vLLM with OpenVINO backend
-RUN PIP_EXTRA_INDEX_URL="https://download.pytorch.org/whl/cpu" VLLM_TARGET_DEVICE="openvino" python3 -m pip install /workspace/vllm/
+RUN PIP_EXTRA_INDEX_URL="https://download.pytorch.org/whl/cpu" VLLM_TARGET_DEVICE="openvino" python3 -m pip install /workspace
-COPY examples/ /workspace/vllm/examples
+COPY examples/ /workspace/examples
-COPY benchmarks/ /workspace/vllm/benchmarks
+COPY benchmarks/ /workspace/benchmarks
 # install development dependencies (for testing)
 RUN python3 -m pip install -e tests/vllm_test_utils
 CMD ["/bin/bash"]
--- a/Dockerfile.ppc64le
+++ b/Dockerfile.ppc64le
@@ -2,21 +2,38 @@ FROM mambaorg/micromamba
 ARG MAMBA_DOCKERFILE_ACTIVATE=1
 USER root
-RUN apt-get update  -y     && apt-get install -y git wget vim numactl gcc-12 g++-12 protobuf-compiler libprotobuf-dev     && update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-12 10 --slave /usr/bin/g++ g++ /usr/bin/g++-12
+ENV PATH="/usr/local/cargo/bin:$PATH:/opt/conda/bin/"
 RUN apt-get update -y && apt-get install -y git wget curl vim libnuma-dev libsndfile-dev libprotobuf-dev build-essential ffmpeg libsm6 libxext6 libgl1 
 # Some packages in requirements-cpu are installed here
 # IBM provides optimized packages for ppc64le processors in the open-ce project for mamba
 # Currently these may not be available for venv or pip directly
-RUN micromamba install -y -n base -c https://ftp.osuosl.org/pub/open-ce/1.11.0-p10/ -c defaults     python=3.10     pytorch-cpu=2.1.2     torchvision-cpu=0.16.2    &&     micromamba clean --all --yes
+RUN micromamba install -y -n base -c https://ftp.osuosl.org/pub/open-ce/1.11.0-p10/ -c defaults python=3.10 torchvision-cpu=0.16.2 rust && micromamba clean --all --yes
 COPY ./ /workspace/vllm
 WORKDIR /workspace/vllm
 ARG GIT_REPO_CHECK=0
 RUN --mount=type=bind,source=.git,target=.git \
    if [ "$GIT_REPO_CHECK" != 0 ]; then bash tools/check_repo.sh; fi
 # These packages will be in rocketce eventually
-RUN pip install -v -r requirements-cpu.txt --prefer-binary --extra-index-url https://repo.fury.io/mgiessing
+RUN --mount=type=cache,target=/root/.cache/pip  \
    pip install -v --prefer-binary --extra-index-url https://repo.fury.io/mgiessing \
        'cmake>=3.26' ninja packaging 'setuptools-scm>=8' wheel jinja2 \
        torch==2.3.1 \
        -r requirements-cpu.txt \
        xformers uvloop==0.20.0
-RUN VLLM_TARGET_DEVICE=cpu python3 setup.py install
+RUN --mount=type=bind,source=.git,target=.git \
    VLLM_TARGET_DEVICE=cpu python3 setup.py install
 # install development dependencies (for testing)
 RUN python3 -m pip install -e tests/vllm_test_utils
 WORKDIR /workspace/
 RUN ln -s /workspace/vllm/tests && ln -s /workspace/vllm/examples && ln -s /workspace/vllm/benchmarks
 WORKDIR /vllm-workspace
 ENTRYPOINT ["/opt/conda/bin/python3", "-m", "vllm.entrypoints.openai.api_server"]
--- a/Dockerfile.rocm
+++ b/Dockerfile.rocm
@@ -1,5 +1,5 @@
-# Default ROCm 6.1 base image
+# Default ROCm 6.2 base image
-ARG BASE_IMAGE="rocm/pytorch:rocm6.1.2_ubuntu20.04_py3.9_pytorch_staging"
+ARG BASE_IMAGE="rocm/pytorch:rocm6.2_ubuntu20.04_py3.9_pytorch_release_2.3.0"
 # Default ROCm ARCHes to build vLLM for.
 ARG PYTORCH_ROCM_ARCH="gfx908;gfx90a;gfx942;gfx1100"
@@ -7,18 +7,12 @@ ARG PYTORCH_ROCM_ARCH="gfx908;gfx90a;gfx942;gfx1100"
 # Whether to install CK-based flash-attention
 # If 0, will not install flash-attention
 ARG BUILD_FA="1"
 # If `TRY_FA_WHEEL=1`, we will try installing flash-attention from `FA_WHEEL_URL`
 # If this succeeds, we use the downloaded wheel and skip building flash-attention.
 # Otherwise, ROCm flash-attention from `FA_BRANCH` will be built for the
 # architectures specified in `FA_GFX_ARCHS`
 ARG TRY_FA_WHEEL="1"
 ARG FA_WHEEL_URL="https://github.com/ROCm/flash-attention/releases/download/v2.5.9post1-cktile-vllm/flash_attn-2.5.9.post1-cp39-cp39-linux_x86_64.whl"
 ARG FA_GFX_ARCHS="gfx90a;gfx942"
-ARG FA_BRANCH="23a2b1c2"
+ARG FA_BRANCH="3cea2fb"
 # Whether to build triton on rocm
 ARG BUILD_TRITON="1"
-ARG TRITON_BRANCH="e0fc12c"
+ARG TRITON_BRANCH="e192dba"
 ### Base image build stage
 FROM $BASE_IMAGE AS base
@@ -50,14 +44,17 @@ RUN python3 -m pip install --upgrade pip
 # Remove sccache so it doesn't interfere with ccache
 # TODO: implement sccache support across components
 RUN apt-get purge -y sccache; python3 -m pip uninstall -y sccache; rm -f "$(which sccache)"
-# Install torch == 2.5.0 on ROCm
+
-RUN case "$(ls /opt | grep -Po 'rocm-[0-9]\.[0-9]')" in \
+# Install torch == 2.6.0 on ROCm
-        *"rocm-6.1"*) \
+RUN --mount=type=cache,target=/root/.cache/pip \
    case "$(ls /opt | grep -Po 'rocm-[0-9]\.[0-9]')" in \
        *"rocm-6.2"*) \
            python3 -m pip uninstall -y torch torchvision \
-            && python3 -m pip install --no-cache-dir --pre \
+            && python3 -m pip install --pre \
-                torch==2.5.0.dev20240726 \
+                torch==2.6.0.dev20241113+rocm6.2 \
-                torchvision==0.20.0.dev20240726 \
+                'setuptools-scm>=8' \
-               --index-url https://download.pytorch.org/whl/nightly/rocm6.1;; \
+                torchvision==0.20.0.dev20241113+rocm6.2 \
                --extra-index-url https://download.pytorch.org/whl/nightly/rocm6.2;; \
        *) ;; esac
 ENV LLVM_SYMBOLIZER_PATH=/opt/rocm/llvm/bin/llvm-symbolizer
@@ -79,25 +76,18 @@ RUN cd /opt/rocm/share/amd_smi \
 ### Flash-Attention wheel build stage
 FROM base AS build_fa
 ARG BUILD_FA
 ARG TRY_FA_WHEEL
 ARG FA_WHEEL_URL
 ARG FA_GFX_ARCHS
 ARG FA_BRANCH
 # Build ROCm flash-attention wheel if `BUILD_FA = 1`
 RUN --mount=type=cache,target=${CCACHE_DIR} \
    if [ "$BUILD_FA" = "1" ]; then \
-        if [ "${TRY_FA_WHEEL}" = "1" ] && python3 -m pip install "${FA_WHEEL_URL}"; then \
+        mkdir -p libs \
-            # If a suitable wheel exists, we download it instead of building FA
+        && cd libs \
-            mkdir -p /install && wget -N "${FA_WHEEL_URL}" -P /install; \
+        && git clone https://github.com/ROCm/flash-attention.git \
-        else \
+        && cd flash-attention \
-            mkdir -p libs \
+        && git checkout "${FA_BRANCH}" \
-            && cd libs \
+        && git submodule update --init \
-            && git clone https://github.com/ROCm/flash-attention.git \
+        && GPU_ARCHS="${FA_GFX_ARCHS}" python3 setup.py bdist_wheel --dist-dir=/install; \
            && cd flash-attention \
            && git checkout "${FA_BRANCH}" \
            && git submodule update --init \
            && GPU_ARCHS="${FA_GFX_ARCHS}" python3 setup.py bdist_wheel --dist-dir=/install; \
        fi; \
    # Create an empty directory otherwise as later build stages expect one
    else mkdir -p /install; \
    fi
@@ -112,6 +102,7 @@ RUN --mount=type=cache,target=${CCACHE_DIR} \
    if [ "$BUILD_TRITON" = "1" ]; then \
    mkdir -p libs \
    && cd libs \
    && python3 -m pip install ninja cmake wheel pybind11 \
    && git clone https://github.com/OpenAI/triton.git \
    && cd triton \
    && git checkout "${TRITON_BRANCH}" \
@@ -126,10 +117,15 @@ RUN --mount=type=cache,target=${CCACHE_DIR} \
 FROM base AS final
 # Import the vLLM development directory from the build context
 COPY . .
 ARG GIT_REPO_CHECK=0
 RUN --mount=type=bind,source=.git,target=.git \
    if [ "$GIT_REPO_CHECK" != 0 ]; then bash tools/check_repo.sh ; fi
 RUN python3 -m pip install --upgrade pip
 # Package upgrades for useful functionality or to avoid dependency issues
 RUN --mount=type=cache,target=/root/.cache/pip \
-    python3 -m pip install --upgrade numba scipy huggingface-hub[cli]
+    python3 -m pip install --upgrade numba scipy huggingface-hub[cli] pytest-shard
 # Workaround for ray >= 2.10.0
@@ -138,15 +134,9 @@ ENV RAY_EXPERIMENTAL_NOSET_ROCR_VISIBLE_DEVICES=1
 ENV TOKENIZERS_PARALLELISM=false
 RUN --mount=type=cache,target=${CCACHE_DIR} \
    --mount=type=bind,source=.git,target=.git \
    --mount=type=cache,target=/root/.cache/pip \
    python3 -m pip install -Ur requirements-rocm.txt \
    && case "$(ls /opt | grep -Po 'rocm-[0-9]\.[0-9]')" in \
        *"rocm-6.1"*) \
            # Bring in upgrades to HIP graph earlier than ROCm 6.2 for vLLM
            wget -N https://github.com/ROCm/vllm/raw/fa78403/rocm_patch/libamdhip64.so.6 -P /opt/rocm/lib \
            # Prevent interference if torch bundles its own HIP runtime
            && rm -f "$(python3 -c 'import torch; print(torch.__path__[0])')"/lib/libamdhip64.so* || true;; \
        *) ;; esac \
    && python3 setup.py clean --all \
    && python3 setup.py develop
@@ -178,4 +168,7 @@ RUN --mount=type=cache,target=/root/.cache/pip \
    if ls libs/*.whl; then \
    python3 -m pip install libs/*.whl; fi
 # install development dependencies (for testing)
 RUN python3 -m pip install -e tests/vllm_test_utils
 CMD ["/bin/bash"]
--- a/Dockerfile.tpu
+++ b/Dockerfile.tpu
@@ -1,17 +1,28 @@
-ARG NIGHTLY_DATE="20240828"
+ARG NIGHTLY_DATE="20241017"
 ARG BASE_IMAGE="us-central1-docker.pkg.dev/tpu-pytorch-releases/docker/xla:nightly_3.10_tpuvm_$NIGHTLY_DATE"
 FROM $BASE_IMAGE
-WORKDIR /workspace
+WORKDIR /workspace/vllm
-# Install the TPU and Pallas dependencies.
+# Install some basic utilities
-RUN python3 -m pip install torch_xla[tpu] -f https://storage.googleapis.com/libtpu-releases/index.html
+RUN apt-get update && apt-get install -y \
-RUN python3 -m pip install torch_xla[pallas] -f https://storage.googleapis.com/jax-releases/jax_nightly_releases.html -f https://storage.googleapis.com/jax-releases/jaxlib_nightly_releases.html
+    git \
    ffmpeg libsm6 libxext6 libgl1
 # Build vLLM.
-COPY . /workspace/vllm
+COPY . .
 ARG GIT_REPO_CHECK=0
 RUN --mount=type=bind,source=.git,target=.git \
    if [ "$GIT_REPO_CHECK" != 0 ]; then bash tools/check_repo.sh; fi
 ENV VLLM_TARGET_DEVICE="tpu"
-RUN cd /workspace/vllm && python3 -m pip install -r requirements-tpu.txt
+RUN --mount=type=cache,target=/root/.cache/pip \
-RUN cd /workspace/vllm && python3 setup.py develop
+    --mount=type=bind,source=.git,target=.git \
    python3 -m pip install \
        -r requirements-tpu.txt
 RUN python3 setup.py develop
 # install development dependencies (for testing)
 RUN python3 -m pip install -e tests/vllm_test_utils
 CMD ["/bin/bash"]
--- a/Dockerfile.xpu
+++ b/Dockerfile.xpu
@@ -1,22 +1,69 @@
-FROM intel/oneapi-basekit:2024.1.0-devel-ubuntu20.04
+FROM intel/oneapi-basekit:2024.2.1-0-devel-ubuntu22.04 AS vllm-base
 RUN wget -O- https://apt.repos.intel.com/intel-gpg-keys/GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB | gpg --dearmor | tee /usr/share/keyrings/intel-oneapi-archive-keyring.gpg > /dev/null && \
    echo "deb [signed-by=/usr/share/keyrings/intel-oneapi-archive-keyring.gpg] https://apt.repos.intel.com/oneapi all main " | tee /etc/apt/sources.list.d/oneAPI.list && \
    chmod 644 /usr/share/keyrings/intel-oneapi-archive-keyring.gpg && \
    rm /etc/apt/sources.list.d/intel-graphics.list && \
    wget -O- https://repositories.intel.com/graphics/intel-graphics.key | gpg --dearmor | tee /usr/share/keyrings/intel-graphics.gpg > /dev/null && \
    echo "deb [arch=amd64,i386 signed-by=/usr/share/keyrings/intel-graphics.gpg] https://repositories.intel.com/graphics/ubuntu jammy arc" | tee /etc/apt/sources.list.d/intel.gpu.jammy.list && \
    chmod 644 /usr/share/keyrings/intel-graphics.gpg
-RUN apt-get update  -y \
+RUN apt-get update -y && \
-&& apt-get install -y curl libicu70 lsb-release git wget vim numactl python3 python3-pip
+    apt-get install -y --no-install-recommends --fix-missing \
-
+    curl \
-COPY ./ /workspace/vllm
+    ffmpeg \
    git \
    libsndfile1 \
    libsm6 \
    libxext6 \
    libgl1 \
    lsb-release \
    numactl \
    python3 \
    python3-dev \
    python3-pip \
    # vim \
    wget
 WORKDIR /workspace/vllm
 COPY requirements-xpu.txt /workspace/vllm/requirements-xpu.txt
 COPY requirements-common.txt /workspace/vllm/requirements-common.txt
-RUN pip install -v -r requirements-xpu.txt
+RUN --mount=type=cache,target=/root/.cache/pip \
    pip install --no-cache-dir \
    -r requirements-xpu.txt
-RUN VLLM_TARGET_DEVICE=xpu python3 setup.py install
+RUN git clone https://github.com/intel/pti-gpu && \
    cd pti-gpu/sdk && \
    git checkout 6c491f07a777ed872c2654ca9942f1d0dde0a082 && \
    mkdir build && \
    cd build && \
    cmake -DCMAKE_BUILD_TYPE=Release -DCMAKE_TOOLCHAIN_FILE=../cmake/toolchains/icpx_toolchain.cmake -DBUILD_TESTING=OFF .. && \
    make -j && \
    cmake --install . --config Release --prefix "/usr/local"
 ENV LD_LIBRARY_PATH="$LD_LIBRARY_PATH:/usr/local/lib/"
 COPY . .
 ARG GIT_REPO_CHECK
 RUN --mount=type=bind,source=.git,target=.git \
    if [ "$GIT_REPO_CHECK" != 0 ]; then bash tools/check_repo.sh; fi
 ENV VLLM_TARGET_DEVICE=xpu
 RUN --mount=type=cache,target=/root/.cache/pip \
    --mount=type=bind,source=.git,target=.git \
    python3 setup.py install
 CMD ["/bin/bash"]
 FROM vllm-base AS vllm-openai
 # install additional dependencies for openai api server
 RUN --mount=type=cache,target=/root/.cache/pip \
    pip install accelerate hf_transfer 'modelscope!=1.15.0'
 ENV VLLM_USAGE_SOURCE production-docker-image \
    TRITON_XPU_PROFILE 1
 # install development dependencies (for testing)
 RUN python3 -m pip install -e tests/vllm_test_utils
 ENTRYPOINT ["python3", "-m", "vllm.entrypoints.openai.api_server"]
--- a/MANIFEST.in
+++ b/MANIFEST.in
@@ -1,5 +1,4 @@
 include LICENSE
 include requirements-adag.txt
 include requirements-common.txt
 include requirements-cuda.txt
 include requirements-rocm.txt
--- a/README.md
+++ b/README.md
@@ -10,22 +10,17 @@ Easy, fast, and cheap LLM serving for everyone
 </h3>
 <p align="center">
-| <a href="https://docs.vllm.ai"><b>Documentation</b></a> | <a href="https://vllm.ai"><b>Blog</b></a> | <a href="https://arxiv.org/abs/2309.06180"><b>Paper</b></a> | <a href="https://discord.gg/jz7wjKhh6g"><b>Discord</b></a> | <a href="https://x.com/vllm_project"><b>Twitter/X</b></a> |
+| <a href="https://docs.vllm.ai"><b>Documentation</b></a> | <a href="https://vllm.ai"><b>Blog</b></a> | <a href="https://arxiv.org/abs/2309.06180"><b>Paper</b></a> | <a href="https://discord.gg/jz7wjKhh6g"><b>Discord</b></a> | <a href="https://x.com/vllm_project"><b>Twitter/X</b></a> | <a href="https://slack.vllm.ai"><b>Developer Slack</b></a> |
 </p>
 ---
 **vLLM & NVIDIA Triton User Meetup (Monday, September 9, 5pm-9pm PT) at Fort Mason, San Francisco**
 We are excited to announce our sixth vLLM Meetup, in collaboration with NVIDIA Triton Team.
 Join us to hear the vLLM's recent update about performance.
 Register now [here](https://lu.ma/87q3nvnh) and be part of the event!
 ---
 *Latest News* 🔥
 - [2024/12] vLLM joins [pytorch ecosystem](https://pytorch.org/blog/vllm-joins-pytorch)! Easy, Fast, and Cheap LLM Serving for Everyone!
 - [2024/11] We hosted [the seventh vLLM meetup](https://lu.ma/h0qvrajz) with Snowflake! Please find the meetup slides from vLLM team [here](https://docs.google.com/presentation/d/1e3CxQBV3JsfGp30SwyvS3eM_tW-ghOhJ9PAJGK6KR54/edit?usp=sharing), and Snowflake team [here](https://docs.google.com/presentation/d/1qF3RkDAbOULwz9WK5TOltt2fE9t6uIc_hVNLFAaQX6A/edit?usp=sharing).
 - [2024/10] We have just created a developer slack ([slack.vllm.ai](https://slack.vllm.ai)) focusing on coordinating contributions and discussing features. Please feel free to join us there!
 - [2024/10] Ray Summit 2024 held a special track for vLLM! Please find the opening talk slides from the vLLM team [here](https://docs.google.com/presentation/d/1B_KQxpHBTRa_mDF-tR6i8rWdOU5QoTZNcEg2MKZxEHM/edit?usp=sharing). Learn more from the [talks](https://www.youtube.com/playlist?list=PLzTswPQNepXl6AQwifuwUImLPFRVpksjR) from other vLLM contributors and users!
 - [2024/09] We hosted [the sixth vLLM meetup](https://lu.ma/87q3nvnh) with NVIDIA! Please find the meetup slides [here](https://docs.google.com/presentation/d/1wrLGwytQfaOTd5wCGSPNhoaW3nq0E-9wqyP7ny93xRs/edit?usp=sharing).
 - [2024/07] We hosted [the fifth vLLM meetup](https://lu.ma/lp0gyjqr) with AWS! Please find the meetup slides [here](https://docs.google.com/presentation/d/1RgUD8aCfcHocghoP3zmXzck9vX3RCI9yfUAB2Bbcl4Y/edit?usp=sharing).
 - [2024/07] In partnership with Meta, vLLM officially supports Llama 3.1 with FP8 quantization and pipeline parallelism! Please check out our blog post [here](https://blog.vllm.ai/2024/07/23/llama31.html).
 - [2024/06] We hosted [the fourth vLLM meetup](https://lu.ma/agivllm) with Cloudflare and BentoML! Please find the meetup slides [here](https://docs.google.com/presentation/d/1iJ8o7V2bQEi0BFEljLTwc5G1S10_Rhv3beed5oB0NJ4/edit?usp=sharing).
@@ -50,7 +45,7 @@ vLLM is fast with:
 - Speculative decoding
 - Chunked prefill
-**Performance benchmark**: We include a [performance benchmark](https://buildkite.com/vllm/performance-benchmark/builds/4068) that compares the performance of vLLM against other LLM serving engines ([TensorRT-LLM](https://github.com/NVIDIA/TensorRT-LLM), [text-generation-inference](https://github.com/huggingface/text-generation-inference) and [lmdeploy](https://github.com/InternLM/lmdeploy)).
+**Performance benchmark**: We include a performance benchmark at the end of [our blog post](https://blog.vllm.ai/2024/09/05/perf-update.html). It compares the performance of vLLM against other LLM serving engines ([TensorRT-LLM](https://github.com/NVIDIA/TensorRT-LLM), [SGLang](https://github.com/sgl-project/sglang) and [LMDeploy](https://github.com/InternLM/lmdeploy)). The implementation is under [nightly-benchmarks folder](.buildkite/nightly-benchmarks/) and you can [reproduce](https://github.com/vllm-project/vllm/issues/8176) this benchmark using our one-click runnable script.
 vLLM is flexible and easy to use with:
@@ -106,6 +101,7 @@ vLLM is a community project. Our compute resources for development and testing a
 - Dropbox
 - Google Cloud
 - Lambda Lab
 - Nebius
 - NVIDIA
 - Replicate
 - Roblox
@@ -130,3 +126,15 @@ If you use vLLM for your research, please cite our [paper](https://arxiv.org/abs
  year={2023}
 }
 ```
 ## Contact Us
 * For technical questions and feature requests, please use Github issues or discussions.
 * For discussing with fellow users, please use Discord.
 * For coordinating contributions and development, please use Slack.
 * For security disclosures, please use Github's security advisory feature.
 * For collaborations and partnerships, please contact us at vllm-questions AT lists.berkeley.edu.
 ## Media Kit
 * If you wish to use vLLM's logo, please refer to [our media kit repo](https://github.com/vllm-project/media-kit).
--- a/SECURITY.md
+++ b/SECURITY.md
@@ -0,0 +1,11 @@
 # Security Policy
 ## Reporting a Vulnerability
 If you believe you have found a security vulnerability in vLLM, we encourage you to let us know right away. We will investigate all legitimate reports and do our best to quickly fix the problem.
 Please report security issues privately using [the vulnerability submission form](https://github.com/vllm-project/vllm/security/advisories/new).
 ---
 Please see [PyTorch's Security Policy](https://github.com/pytorch/pytorch/blob/main/SECURITY.md) for more information and recommendations on how to securely interact with models.
--- a/benchmarks/README.md
+++ b/benchmarks/README.md
@@ -6,3 +6,14 @@ You can download the dataset by running:
 ```bash
 wget https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/ShareGPT_V3_unfiltered_cleaned_split.json
 ```
 ## Downloading the ShareGPT4V dataset
 The json file refers to several image datasets (coco, llava, etc.). The benchmark scripts
 will ignore a datapoint if the referred image is missing.
 ```bash
 wget https://huggingface.co/datasets/Lin-Chen/ShareGPT4V/resolve/main/sharegpt4v_instruct_gpt4-vision_cap100k.json
 mkdir coco -p
 wget http://images.cocodataset.org/zips/train2017.zip -O coco/train2017.zip
 unzip coco/train2017.zip -d coco/
 ```
--- a/benchmarks/backend_request_func.py
+++ b/benchmarks/backend_request_func.py
@@ -23,7 +23,10 @@ class RequestFuncInput:
    output_len: int
    model: str
    best_of: int = 1
-    use_beam_search: bool = False
+    logprobs: Optional[int] = None
    extra_body: Optional[dict] = None
    multi_modal_content: Optional[dict] = None
    ignore_eos: bool = False
@dataclass
@@ -34,6 +37,7 @@ class RequestFuncOutput:
    ttft: float = 0.0  # Time to first token
    itl: List[float] = field(
        default_factory=list)  # List of inter-token latencies
    tpot: float = 0.0  # avg next-token latencies
    prompt_len: int = 0
    error: str = ""
@@ -46,13 +50,14 @@ async def async_request_tgi(
    assert api_url.endswith("generate_stream")
    async with aiohttp.ClientSession(timeout=AIOHTTP_TIMEOUT) as session:
        assert not request_func_input.use_beam_search
        params = {
            "best_of": request_func_input.best_of,
            "max_new_tokens": request_func_input.output_len,
            "do_sample": True,
            "temperature": 0.01,  # TGI does not accept 0.0 temperature.
            "top_p": 0.99,  # TGI does not accept 1.0 top_p.
            "truncate": request_func_input.prompt_len,
            # TGI does not accept ignore_eos flag.
        }
        payload = {
            "inputs": request_func_input.prompt,
@@ -77,7 +82,7 @@ async def async_request_tgi(
                        # any data, we should skip it.
                        if chunk_bytes.startswith(":"):
                            continue
-                        chunk = remove_prefix(chunk_bytes, "data:")
+                        chunk = chunk_bytes.removeprefix("data:")
                        data = json.loads(chunk)
                        timestamp = time.perf_counter()
@@ -117,7 +122,6 @@ async def async_request_trt_llm(
    assert api_url.endswith("generate_stream")
    async with aiohttp.ClientSession(timeout=AIOHTTP_TIMEOUT) as session:
        assert not request_func_input.use_beam_search
        assert request_func_input.best_of == 1
        payload = {
            "accumulate_tokens": True,
@@ -127,6 +131,8 @@ async def async_request_trt_llm(
            "max_tokens": request_func_input.output_len,
            "stream": True,
        }
        if request_func_input.ignore_eos:
            payload["min_length"] = request_func_input.output_len
        output = RequestFuncOutput()
        output.prompt_len = request_func_input.prompt_len
@@ -141,8 +147,8 @@ async def async_request_trt_llm(
                        if not chunk_bytes:
                            continue
-                        chunk = remove_prefix(chunk_bytes.decode("utf-8"),
+                        chunk = chunk_bytes.decode("utf-8").removeprefix(
-                                              "data:")
+                            "data:")
                        data = json.loads(chunk)
                        output.generated_text += data["text_output"]
@@ -181,7 +187,6 @@ async def async_request_deepspeed_mii(
 ) -> RequestFuncOutput:
    async with aiohttp.ClientSession(timeout=AIOHTTP_TIMEOUT) as session:
        assert request_func_input.best_of == 1
        assert not request_func_input.use_beam_search
        payload = {
            "prompt": request_func_input.prompt,
@@ -229,15 +234,18 @@ async def async_request_openai_completions(
    ), "OpenAI Completions API URL must end with 'completions' or 'profile'."
    async with aiohttp.ClientSession(timeout=AIOHTTP_TIMEOUT) as session:
        assert not request_func_input.use_beam_search
        payload = {
            "model": request_func_input.model,
            "prompt": request_func_input.prompt,
            "temperature": 0.0,
            "best_of": request_func_input.best_of,
            "max_tokens": request_func_input.output_len,
            "logprobs": request_func_input.logprobs,
            "stream": True,
            "ignore_eos": request_func_input.ignore_eos,
        }
        if request_func_input.extra_body:
            payload.update(request_func_input.extra_body)
        headers = {
            "Authorization": f"Bearer {os.environ.get('OPENAI_API_KEY')}"
        }
@@ -253,13 +261,14 @@ async def async_request_openai_completions(
            async with session.post(url=api_url, json=payload,
                                    headers=headers) as response:
                if response.status == 200:
                    first_chunk_received = False
                    async for chunk_bytes in response.content:
                        chunk_bytes = chunk_bytes.strip()
                        if not chunk_bytes:
                            continue
-                        chunk = remove_prefix(chunk_bytes.decode("utf-8"),
+                        chunk = chunk_bytes.decode("utf-8").removeprefix(
-                                              "data: ")
+                            "data: ")
                        if chunk == "[DONE]":
                            latency = time.perf_counter() - st
                        else:
@@ -271,7 +280,8 @@ async def async_request_openai_completions(
                            if data["choices"][0]["text"]:
                                timestamp = time.perf_counter()
                                # First token
-                                if ttft == 0.0:
+                                if not first_chunk_received:
                                    first_chunk_received = True
                                    ttft = time.perf_counter() - st
                                    output.ttft = ttft
@@ -282,9 +292,14 @@ async def async_request_openai_completions(
                                most_recent_timestamp = timestamp
                                generated_text += data["choices"][0]["text"]
-
+                    if first_chunk_received:
                        output.success = True
                    else:
                        output.success = False
                        output.error = (
                            "Never received a valid chunk to calculate TTFT."
                            "This response will be marked as failed!")
                    output.generated_text = generated_text
                    output.success = True
                    output.latency = latency
                else:
                    output.error = response.reason or ""
@@ -309,19 +324,24 @@ async def async_request_openai_chat_completions(
    ), "OpenAI Chat Completions API URL must end with 'chat/completions'."
    async with aiohttp.ClientSession(timeout=AIOHTTP_TIMEOUT) as session:
-        assert not request_func_input.use_beam_search
+        content = [{"type": "text", "text": request_func_input.prompt}]
        if request_func_input.multi_modal_content:
            content.append(request_func_input.multi_modal_content)
        payload = {
            "model": request_func_input.model,
            "messages": [
                {
                    "role": "user",
-                    "content": request_func_input.prompt,
+                    "content": content
                },
            ],
            "temperature": 0.0,
-            "max_tokens": request_func_input.output_len,
+            "max_completion_tokens": request_func_input.output_len,
            "stream": True,
            "ignore_eos": request_func_input.ignore_eos,
        }
        if request_func_input.extra_body:
            payload.update(request_func_input.extra_body)
        headers = {
            "Content-Type": "application/json",
            "Authorization": f"Bearer {os.environ.get('OPENAI_API_KEY')}",
@@ -343,8 +363,8 @@ async def async_request_openai_chat_completions(
                        if not chunk_bytes:
                            continue
-                        chunk = remove_prefix(chunk_bytes.decode("utf-8"),
+                        chunk = chunk_bytes.decode("utf-8").removeprefix(
-                                              "data: ")
+                            "data: ")
                        if chunk == "[DONE]":
                            latency = time.perf_counter() - st
                        else:
@@ -383,14 +403,6 @@ async def async_request_openai_chat_completions(
    return output
 # Since vllm must support Python 3.8, we can't use str.removeprefix(prefix)
 # introduced in Python 3.9
 def remove_prefix(text: str, prefix: str) -> str:
    if text.startswith(prefix):
        return text[len(prefix):]
    return text
 def get_model(pretrained_model_name_or_path: str) -> str:
    if os.getenv('VLLM_USE_MODELSCOPE', 'False').lower() == 'true':
        from modelscope import snapshot_download
@@ -424,4 +436,5 @@ ASYNC_REQUEST_FUNCS = {
    "openai-chat": async_request_openai_chat_completions,
    "tensorrt-llm": async_request_trt_llm,
    "scalellm": async_request_openai_completions,
    "sglang": async_request_openai_completions,
 }
--- a/benchmarks/benchmark_guided.py
+++ b/benchmarks/benchmark_guided.py
@@ -0,0 +1,494 @@
 """Benchmark guided decoding throughput."""
 import argparse
 import dataclasses
 import json
 import os
 import random
 import time
 from typing import List
 import datasets
 import pandas as pd
 import uvloop
 from transformers import AutoTokenizer, PreTrainedTokenizerBase
 from vllm.engine.arg_utils import AsyncEngineArgs, EngineArgs
 from vllm.entrypoints.openai.api_server import (
    build_async_engine_client_from_engine_args)
 from vllm.sampling_params import GuidedDecodingParams
 from vllm.utils import FlexibleArgumentParser, merge_async_iterators
@dataclasses.dataclass
 class SampleRequest:
    """A class representing a single inference request for benchmarking.
    Attributes:
        prompt: The input text prompt for the model.
        multi_modal_data: Optional dictionary containing multi-modal data (e.g.
            images).
        prompt_len: The length of the prompt in tokens.
        expected_output_len: The expected length of the output in tokens.
    """
    prompt: str
    prompt_len: int
    expected_output_len: int
    schema: dict
    structure_type: str = 'json'
    completion: str = None
 def run_vllm(requests: List[SampleRequest],
             engine_args: EngineArgs,
             n: int,
             guided_decoding_rate: float = 1.0,
             warmup: bool = False) -> float:
    from vllm import LLM, SamplingParams
    llm = LLM(**vars(engine_args))
    # Add the requests to the engine.
    prompts: List[str] = []
    sampling_params: List[SamplingParams] = []
    # create a list containing random selected true or false
    guided_decoding_req_idx = random.sample(
        range(len(requests)), int(len(requests) * guided_decoding_rate))
    if warmup:
        print(">>>>> Running warmup prompt, for the first 5")
        # We setup the first 5 requests to warmup FSM
        # if using xgrammar dataset, we will skip warmup
        warmup_requests = requests[:5]
        for i, request in enumerate(warmup_requests):
            prompts.append(request.prompt)
            sampling_params.append(
                SamplingParams(
                    n=n,
                    temperature=1.0,
                    top_p=1.0,
                    ignore_eos=True,
                    max_tokens=request.expected_output_len,
                    guided_decoding=GuidedDecodingParams(json=request.schema)
                    if guided_decoding_rate > 0 else None,
                ))
        llm.generate(prompts, sampling_params, use_tqdm=False)
    print(">>>>> Benchmark started...")
    prompts = []
    sampling_params = []
    for i, request in enumerate(requests):
        prompts.append(request.prompt)
        sampling_params.append(
            SamplingParams(
                n=n,
                temperature=1.0,
                top_p=1.0,
                ignore_eos=True,
                max_tokens=request.expected_output_len,
                guided_decoding=GuidedDecodingParams(
                    **{request.structure_type: request.schema})
                if i in guided_decoding_req_idx else None,
            ))
    start = time.perf_counter()
    outputs = llm.generate(prompts, sampling_params, use_tqdm=False)
    ret = []
    for output, request in zip(outputs, requests):
        generated_text = output.outputs[0].text
        ret.append({
            "generated": generated_text,
            "expected": request.completion
        })
    end = time.perf_counter()
    return end - start, ret
 async def run_vllm_async(
        requests: List[SampleRequest],
        engine_args: AsyncEngineArgs,
        n: int,
        guided_decoding_rate: float = 1.0,
        warmup: bool = False,
        disable_frontend_multiprocessing: bool = False) -> float:
    from vllm import SamplingParams
    async with build_async_engine_client_from_engine_args(
            engine_args, disable_frontend_multiprocessing) as llm:
        # Add the requests to the engine.
        prompts: List[str] = []
        sampling_params: List[SamplingParams] = []
        guided_decoding_req_idx = random.sample(
            range(len(requests)), int(len(requests) * guided_decoding_rate))
        if warmup:
            print(">>>>>> Running warmup prompt, for the first 5")
            # We setup the first 5 requests to warmup FSM
            # if using xgrammar dataset, we will skip warmup
            warmup_requests = requests[:5]
            for i, request in enumerate(warmup_requests):
                prompts.append(request.prompt)
                sampling_params.append(
                    SamplingParams(
                        n=n,
                        temperature=1.0,
                        top_p=1.0,
                        ignore_eos=True,
                        max_tokens=request.expected_output_len,
                        guided_decoding=GuidedDecodingParams(
                            json=request.schema)
                        if guided_decoding_rate > 0 else None,
                    ))
            generators = []
            for i, (prompt, sp) in enumerate(zip(prompts, sampling_params)):
                generator = llm.generate(prompt, sp, request_id=f"test{i}")
                generators.append(generator)
            all_gens = merge_async_iterators(*generators)
            async for i, res in all_gens:
                pass
        print(">>>>> Benchmark started...")
        prompts = []
        sampling_params = []
        for i, request in enumerate(requests):
            prompts.append(request.prompt)
            sampling_params.append(
                SamplingParams(
                    n=n,
                    temperature=1.0,
                    top_p=1.0,
                    ignore_eos=True,
                    max_tokens=request.expected_output_len,
                    guided_decoding=GuidedDecodingParams(json=request.schema)
                    if i in guided_decoding_req_idx else None,
                ))
        generators = []
        start_time = []
        latencies = []
        start = time.perf_counter()
        for i, (prompt, sp) in enumerate(zip(prompts, sampling_params)):
            generator = llm.generate(prompt, sp, request_id=f"test{i}")
            generators.append(generator)
            start_time.append(time.perf_counter())
            latencies.append([])
        all_gens = merge_async_iterators(*generators)
        generated_texts = [''] * len(requests)
        async for i, res in all_gens:
            generated_texts[i] = res.outputs[0].text
            lat = time.perf_counter() - start_time[i]
            latencies[i].append(lat)
        ret = [{
            'generated': gt,
            'expected': req.completion
        } for gt, req in zip(generated_texts, requests)]
        end = time.perf_counter()
        first_latency = pd.Series([lat[0] * 1000 for lat in latencies])
        next_latency = pd.Series([(lat[-1] - lat[0]) / len(lat[1:]) * 1000
                                  for lat in latencies])
        return end - start, ret, (first_latency, next_latency)
 def sample_requests(tokenizer: PreTrainedTokenizerBase,
                    args: argparse.Namespace) -> List[SampleRequest]:
    if args.dataset == 'json':
        if args.json_schema_path is None:
            dir_path = os.path.dirname(os.path.realpath(__file__))
            args.json_schema_path = os.path.join(dir_path,
                                                 "structured_schemas",
                                                 "structured_schema_1.json")
        with open(args.json_schema_path) as f:
            schema = json.load(f)
        prompt = f"Generate an example of a user profile given the following schema: {json.dumps(schema)}"  # noqa: E501
        input_len = len(tokenizer(prompt).input_ids)
        print(f"Input length of the prompt: {input_len} tokens")
        requests = [
            SampleRequest(prompt=prompt,
                          prompt_len=input_len,
                          expected_output_len=args.output_len,
                          schema=schema,
                          structure_type=args.structure_type)
            for _ in range(args.num_prompts)
        ]
    elif args.dataset == "grammar":
        schema = """
            ?start: select_statement
            ?select_statement: "SELECT " column_list " FROM " table_name
            ?column_list: column_name ("," column_name)*
            ?table_name: identifier
            ?column_name: identifier
            ?identifier: /[a-zA-Z_][a-zA-Z0-9_]*/
        """
        prompt = "Generate an SQL query to show the 'username' \
            and 'email' from the 'users' table."
        input_len = len(tokenizer(prompt).input_ids)
        print(f"Input length of the prompt: {input_len} tokens")
        requests = [
            SampleRequest(prompt=prompt,
                          prompt_len=input_len,
                          expected_output_len=args.output_len,
                          schema=schema,
                          structure_type=args.structure_type)
            for _ in range(args.num_prompts)
        ]
    elif args.dataset == "regex":
        regex = r"\w+@\w+\.com\n"
        args.regex = regex
        prompt = "Generate an email address for Alan Turing, \
            who works in Enigma. End in .com and new line. \
                Example result: alan.turing@enigma.com\n"
        input_len = len(tokenizer(prompt).input_ids)
        print(f"Input length of the prompt: {input_len} tokens")
        requests = [
            SampleRequest(prompt=prompt,
                          prompt_len=input_len,
                          expected_output_len=args.output_len,
                          schema=regex,
                          structure_type=args.structure_type)
            for _ in range(args.num_prompts)
        ]
    elif args.dataset == "choice":
        choice = ["Positive", "Negative"]
        args.choice = choice
        prompt = "Classify this sentiment: vLLM is wonderful!"
        input_len = len(tokenizer(prompt).input_ids)
        print(f"Input length of the prompt: {input_len} tokens")
        requests = [
            SampleRequest(prompt=prompt,
                          prompt_len=input_len,
                          expected_output_len=args.output_len,
                          schema=choice,
                          structure_type=args.structure_type)
            for _ in range(args.num_prompts)
        ]
    elif args.dataset == "xgrammar_bench":
        args.warmup = False
        requests: List[SampleRequest] = []
        dataset = datasets.load_dataset("NousResearch/json-mode-eval",
                                        split="train")
        print(f"dataset has {len(dataset)} entries")
        len_dataset = len(dataset)
        for data_point_idx in range(args.num_prompts):
            idx = data_point_idx
            while idx >= len_dataset:
                idx -= len_dataset
            schema = dataset["schema"][idx]
            prompt = tokenizer.apply_chat_template(dataset["prompt"][idx],
                                                   tokenize=False)
            input_len = len(tokenizer(prompt).input_ids)
            completion = dataset["completion"][idx]
            requests.append(
                SampleRequest(prompt=prompt,
                              prompt_len=input_len,
                              expected_output_len=args.output_len,
                              schema=schema,
                              completion=completion))
    return requests
 def evaluate(ret, args):
    def _eval_correctness_json(expected, actual):
        # extract json string from string using regex
        import re
        actual = actual.replace('\n', '').replace(' ', '').strip()
        try:
            actual = re.search(r'\{.*\}', actual).group()
            actual = json.loads(actual)
        except Exception:
            return False
        return True
    def _eval_correctness_choice(expected, actual):
        return actual in args.choice
    def _eval_correctness_regex(expected, actual):
        import re
        return re.match(args.regex, actual) is not None
    def _eval_correctness(expected, actual):
        if args.structure_type == 'json':
            return _eval_correctness_json(expected, actual)
        elif args.structure_type == 'regex':
            return _eval_correctness_regex(expected, actual)
        elif args.structure_type == 'choice':
            return _eval_correctness_choice(expected, actual)
        else:
            return None
    scores = []
    for res in ret:
        score = _eval_correctness(res['expected'], res['generated'])
        res['correctness'] = score
        scores.append(score)
    not_none_scores = [score for score in scores if score is not None]
    return (sum(not_none_scores) / len(not_none_scores) *
            100) if len(not_none_scores) > 0 else None
 def main(args: argparse.Namespace):
    print(args)
    random.seed(args.seed)
    # async engine is working for 'regex', 'choice' and 'grammar'
    if args.dataset == 'grammar':
        args.structure_type = 'grammar'
        args.async_engine = False
    elif args.dataset == 'regex':
        args.structure_type = 'regex'
        args.async_engine = False
    elif args.dataset == 'choice':
        args.structure_type = 'choice'
        args.async_engine = False
    else:
        args.structure_type = 'json'
    if args.no_guided_decoding:
        args.guided_decoding_ratio = 0
    if args.save_results:
        result_file_name = f'{args.guided_decoding_ratio}guided'
        result_file_name += f"_{args.model.split('/')[-1]}"
        result_file_name += f"_{args.dataset}"
        result_file_name += f"_{args.num_prompts}"
        result_file_name += f"_out{args.output_len}"
        result_file_name += f"_async{args.async_engine}"
        result_file_name += f"_warmup{args.warmup}"
        result_file_name += f"_chunkedprefill{args.enable_chunked_prefill}"
        result_file_name += ".txt"
    else:
        result_file_name = None
    # Synthesize a prompt with the given input length.
    tokenizer = AutoTokenizer.from_pretrained(
        args.tokenizer, trust_remote_code=args.trust_remote_code)
    requests = sample_requests(tokenizer, args)
    if args.async_engine:
        engine_args = AsyncEngineArgs.from_cli_args(args)
        elapsed_time, ret, (first_latency, next_latency) = uvloop.run(
            run_vllm_async(requests, engine_args, args.n,
                           args.guided_decoding_ratio, args.warmup,
                           args.disable_frontend_multiprocessing))
    else:
        engine_args = EngineArgs.from_cli_args(args)
        elapsed_time, ret = run_vllm(requests, engine_args, args.n,
                                     args.guided_decoding_ratio, args.warmup)
        first_latency, next_latency = None, None
    score = evaluate(ret, args)
    total_num_tokens = sum(request.prompt_len + request.expected_output_len
                           for request in requests)
    total_output_tokens = sum(request.expected_output_len
                              for request in requests)
    if first_latency is not None:
        latency_breakdown = "\nFirst token latency(msecs):\n"
        latency_breakdown += f"{first_latency.describe()}"
        latency_breakdown += "\nNext token latency(msecs):\n"
        latency_breakdown += f"{next_latency.describe()}"
    print(
        f"Throughput: {len(requests) / elapsed_time:.2f} requests/s, "
        f"{total_num_tokens / elapsed_time:.2f} total tokens/s, "
        f"{total_output_tokens / elapsed_time:.2f} output tokens/s",
        f"Correct rate is {score} %",
        f"{latency_breakdown if first_latency is not None else ''}")
    # Output JSON results if specified
    if args.output_json or result_file_name:
        results = {
            "elapsed_time": elapsed_time,
            "num_requests": len(requests),
            "total_num_tokens": total_num_tokens,
            "total_output_tokens": total_output_tokens,
            "requests_per_second": len(requests) / elapsed_time,
            "tokens_per_second": f"{total_num_tokens / elapsed_time:.2f}",
            "output_tokens_per_second":
            f"{total_output_tokens / elapsed_time:.2f}",
            "correct_rate(%)": score
        }
        results = {"outputs": ret, **results}
        if first_latency is not None:
            results["first_token_latency(msecs)"] = first_latency.describe(
            ).to_dict()
            results["next_token_latency(msecs)"] = next_latency.describe(
            ).to_dict()
        if args.output_json:
            with open(args.output_json, "w") as f:
                json.dump(results, f, indent=4)
        elif result_file_name:
            with open(result_file_name, "w") as f:
                json.dump(results, f, indent=4)
 if __name__ == "__main__":
    parser = FlexibleArgumentParser(description="Benchmark guided decoding.")
    parser = AsyncEngineArgs.add_cli_args(parser)
    parser.add_argument("--output-len",
                        type=int,
                        default=512,
                        help="Output length for each request. Overrides the "
                        "output length from the dataset.")
    parser.add_argument(
        "--dataset",
        default='json',
        choices=['json', 'grammar', 'regex', 'choice', 'xgrammar_bench'])
    parser.add_argument("--json_schema_path",
                        type=str,
                        default=None,
                        help="Path to json schema.")
    parser.add_argument("--n",
                        type=int,
                        default=1,
                        help="Number of generated sequences per prompt.")
    parser.add_argument("--num-prompts",
                        type=int,
                        default=10,
                        help="Number of prompts to process.")
    parser.add_argument(
        '--output-json',
        type=str,
        default=None,
        help='Path to save the throughput results in JSON format.')
    parser.add_argument("--async-engine",
                        action='store_true',
                        default=False,
                        help="Use vLLM async engine rather than LLM class.")
    parser.add_argument("--no-guided-decoding",
                        action='store_true',
                        default=False,
                        help="Whether to disable JSON decoding or not.")
    parser.add_argument("--guided-decoding-ratio",
                        type=float,
                        default=1.0,
                        help="Ratio of Guided Decoding requests")
    parser.add_argument("--disable-frontend-multiprocessing",
                        action='store_true',
                        default=False,
                        help="Disable decoupled async engine frontend.")
    parser.add_argument("--warmup",
                        action="store_true",
                        default=False,
                        help="Run warmup prompts before benchmark.")
    parser.add_argument("--save-results",
                        action="store_true",
                        default=False,
                        help="save output results.")
    args = parser.parse_args()
    if args.tokenizer is None:
        args.tokenizer = args.model
    main(args)
--- a/benchmarks/benchmark_latency.py
+++ b/benchmarks/benchmark_latency.py
@@ -1,5 +1,6 @@
 """Benchmark the latency of processing a single batch of requests."""
 import argparse
 import dataclasses
 import json
 import time
 from pathlib import Path
@@ -11,49 +12,23 @@ from tqdm import tqdm
 from vllm import LLM, SamplingParams
 from vllm.engine.arg_utils import EngineArgs
-from vllm.inputs import PromptInputs
+from vllm.inputs import PromptType
 from vllm.model_executor.layers.quantization import QUANTIZATION_METHODS
 from vllm.utils import FlexibleArgumentParser
 def main(args: argparse.Namespace):
    print(args)
    engine_args = EngineArgs.from_cli_args(args)
    # NOTE(woosuk): If the request cannot be processed in a single batch,
    # the engine will automatically process the request in multiple batches.
-    llm = LLM(
+    llm = LLM(**dataclasses.asdict(engine_args))
        model=args.model,
        speculative_model=args.speculative_model,
        num_speculative_tokens=args.num_speculative_tokens,
        speculative_draft_tensor_parallel_size=\
            args.speculative_draft_tensor_parallel_size,
        tokenizer=args.tokenizer,
        quantization=args.quantization,
        tensor_parallel_size=args.tensor_parallel_size,
        trust_remote_code=args.trust_remote_code,
        dtype=args.dtype,
        max_model_len=args.max_model_len,
        enforce_eager=args.enforce_eager,
        kv_cache_dtype=args.kv_cache_dtype,
        quantization_param_path=args.quantization_param_path,
        device=args.device,
        ray_workers_use_nsight=args.ray_workers_use_nsight,
        use_v2_block_manager=args.use_v2_block_manager,
        enable_chunked_prefill=args.enable_chunked_prefill,
        download_dir=args.download_dir,
        block_size=args.block_size,
        gpu_memory_utilization=args.gpu_memory_utilization,
        load_format=args.load_format,
        distributed_executor_backend=args.distributed_executor_backend,
        otlp_traces_endpoint=args.otlp_traces_endpoint,
        enable_prefix_caching=args.enable_prefix_caching,
    )
    sampling_params = SamplingParams(
        n=args.n,
-        temperature=0.0 if args.use_beam_search else 1.0,
+        temperature=1.0,
        top_p=1.0,
        use_beam_search=args.use_beam_search,
        ignore_eos=True,
        max_tokens=args.output_len,
    )
@@ -61,7 +36,7 @@ def main(args: argparse.Namespace):
    dummy_prompt_token_ids = np.random.randint(10000,
                                               size=(args.batch_size,
                                                     args.input_len))
-    dummy_inputs: List[PromptInputs] = [{
+    dummy_prompts: List[PromptType] = [{
        "prompt_token_ids": batch
    } for batch in dummy_prompt_token_ids.tolist()]
@@ -74,13 +49,13 @@ def main(args: argparse.Namespace):
                    ],
                    on_trace_ready=torch.profiler.tensorboard_trace_handler(
                        str(profile_dir))) as p:
-                llm.generate(dummy_inputs,
+                llm.generate(dummy_prompts,
                             sampling_params=sampling_params,
                             use_tqdm=False)
            print(p.key_averages())
        else:
            start_time = time.perf_counter()
-            llm.generate(dummy_inputs,
+            llm.generate(dummy_prompts,
                         sampling_params=sampling_params,
                         use_tqdm=False)
            end_time = time.perf_counter()
@@ -127,19 +102,6 @@ if __name__ == '__main__':
    parser = FlexibleArgumentParser(
        description='Benchmark the latency of processing a single batch of '
        'requests till completion.')
    parser.add_argument('--model', type=str, default='facebook/opt-125m')
    parser.add_argument('--speculative-model', type=str, default=None)
    parser.add_argument('--num-speculative-tokens', type=int, default=None)
    parser.add_argument('--speculative-draft-tensor-parallel-size',
                        '-spec-draft-tp',
                        type=int,
                        default=None)
    parser.add_argument('--tokenizer', type=str, default=None)
    parser.add_argument('--quantization',
                        '-q',
                        choices=[*QUANTIZATION_METHODS, None],
                        default=None)
    parser.add_argument('--tensor-parallel-size', '-tp', type=int, default=1)
    parser.add_argument('--input-len', type=int, default=32)
    parser.add_argument('--output-len', type=int, default=128)
    parser.add_argument('--batch-size', type=int, default=8)
@@ -156,45 +118,6 @@ if __name__ == '__main__':
                        type=int,
                        default=30,
                        help='Number of iterations to run.')
    parser.add_argument('--trust-remote-code',
                        action='store_true',
                        help='trust remote code from huggingface')
    parser.add_argument(
        '--max-model-len',
        type=int,
        default=None,
        help='Maximum length of a sequence (including prompt and output). '
        'If None, will be derived from the model.')
    parser.add_argument(
        '--dtype',
        type=str,
        default='auto',
        choices=['auto', 'half', 'float16', 'bfloat16', 'float', 'float32'],
        help='data type for model weights and activations. '
        'The "auto" option will use FP16 precision '
        'for FP32 and FP16 models, and BF16 precision '
        'for BF16 models.')
    parser.add_argument('--enforce-eager',
                        action='store_true',
                        help='enforce eager mode and disable CUDA graph')
    parser.add_argument(
        '--kv-cache-dtype',
        type=str,
        choices=['auto', 'fp8', 'fp8_e5m2', 'fp8_e4m3'],
        default="auto",
        help='Data type for kv cache storage. If "auto", will use model '
        'data type. CUDA 11.8+ supports fp8 (=fp8_e4m3) and fp8_e5m2. '
        'ROCm (AMD GPU) supports fp8 (=fp8_e4m3)')
    parser.add_argument(
        '--quantization-param-path',
        type=str,
        default=None,
        help='Path to the JSON file containing the KV cache scaling factors. '
        'This should generally be supplied, when KV cache dtype is FP8. '
        'Otherwise, KV cache scaling factors default to 1.0, which may cause '
        'accuracy issues. FP8_E5M2 (without scaling) is only supported on '
        'cuda version greater than 11.8. On ROCm (AMD GPU), FP8_E4M3 is '
        'instead supported for common inference criteria.')
    parser.add_argument(
        '--profile',
        action='store_true',
@@ -205,81 +128,12 @@ if __name__ == '__main__':
        default=None,
        help=('path to save the pytorch profiler output. Can be visualized '
              'with ui.perfetto.dev or Tensorboard.'))
    parser.add_argument(
        "--device",
        type=str,
        default="auto",
        choices=["auto", "cuda", "cpu", "openvino", "tpu", "xpu"],
        help='device type for vLLM execution, supporting CUDA, OpenVINO and '
        'CPU.')
    parser.add_argument('--block-size',
                        type=int,
                        default=16,
                        help='block size of key/value cache')
    parser.add_argument(
        '--enable-chunked-prefill',
        action='store_true',
        help='If True, the prefill requests can be chunked based on the '
        'max_num_batched_tokens')
    parser.add_argument("--enable-prefix-caching",
                        action='store_true',
                        help="Enable automatic prefix caching")
    parser.add_argument('--use-v2-block-manager', action='store_true')
    parser.add_argument(
        "--ray-workers-use-nsight",
        action='store_true',
        help="If specified, use nsight to profile ray workers",
    )
    parser.add_argument('--download-dir',
                        type=str,
                        default=None,
                        help='directory to download and load the weights, '
                        'default to the default cache dir of huggingface')
    parser.add_argument(
        '--output-json',
        type=str,
        default=None,
        help='Path to save the latency results in JSON format.')
-    parser.add_argument('--gpu-memory-utilization',
+
-                        type=float,
+    parser = EngineArgs.add_cli_args(parser)
                        default=0.9,
                        help='the fraction of GPU memory to be used for '
                        'the model executor, which can range from 0 to 1.'
                        'If unspecified, will use the default value of 0.9.')
    parser.add_argument(
        '--load-format',
        type=str,
        default=EngineArgs.load_format,
        choices=[
            'auto', 'pt', 'safetensors', 'npcache', 'dummy', 'tensorizer',
            'bitsandbytes'
        ],
        help='The format of the model weights to load.\n\n'
        '* "auto" will try to load the weights in the safetensors format '
        'and fall back to the pytorch bin format if safetensors format '
        'is not available.\n'
        '* "pt" will load the weights in the pytorch bin format.\n'
        '* "safetensors" will load the weights in the safetensors format.\n'
        '* "npcache" will load the weights in pytorch format and store '
        'a numpy cache to speed up the loading.\n'
        '* "dummy" will initialize the weights with random values, '
        'which is mainly for profiling.\n'
        '* "tensorizer" will load the weights using tensorizer from '
        'CoreWeave. See the Tensorize vLLM Model script in the Examples'
        'section for more information.\n'
        '* "bitsandbytes" will load the weights using bitsandbytes '
        'quantization.\n')
    parser.add_argument(
        '--distributed-executor-backend',
        choices=['ray', 'mp'],
        default=None,
        help='Backend to use for distributed serving. When more than 1 GPU '
        'is used, will be automatically set to "ray" if installed '
        'or "mp" (multiprocessing) otherwise.')
    parser.add_argument(
        '--otlp-traces-endpoint',
        type=str,
        default=None,
        help='Target URL to which OpenTelemetry traces will be sent.')
    args = parser.parse_args()
    main(args)
--- a/benchmarks/benchmark_prefix_caching.py
+++ b/benchmarks/benchmark_prefix_caching.py
@@ -25,6 +25,7 @@ ShareGPT example usage:
        --input-length-range 128:256
 """
 import dataclasses
 import json
 import random
 import time
@@ -33,6 +34,7 @@ from typing import List, Optional, Tuple
 from transformers import PreTrainedTokenizerBase
 from vllm import LLM, SamplingParams
 from vllm.engine.arg_utils import EngineArgs
 from vllm.utils import FlexibleArgumentParser
 try:
@@ -52,13 +54,30 @@ def test_prefix(llm=None, sampling_params=None, prompts=None):
    print(f"cost time {end_time - start_time}")
-def sample_requests(
+@dataclasses.dataclass
 class Request:
    prompt: str
    prompt_len: int
    output_len: int
 def sample_tokens(tokenizer: PreTrainedTokenizerBase, length: int) -> str:
    vocab = tokenizer.get_vocab()
    # Remove the special tokens.
    vocab = {
        k: v
        for k, v in vocab.items() if k not in tokenizer.all_special_ids
    }
    return random.choices(list(vocab.values()), k=length)
 def sample_requests_from_dataset(
    dataset_path: str,
    num_requests: int,
    tokenizer: PreTrainedTokenizerBase,
    input_length_range: Tuple[int, int],
    fixed_output_len: Optional[int],
-) -> List[Tuple[str, int, int]]:
+) -> List[Request]:
    if fixed_output_len is not None and fixed_output_len < 4:
        raise ValueError("output_len too small")
@@ -75,31 +94,55 @@ def sample_requests(
    random.shuffle(dataset)
    min_len, max_len = input_length_range
    assert min_len >= 0 and max_len >= min_len, "input_length_range too small"
    # Filter out sequences that are too long or too short
-    filtered_dataset: List[Tuple[str, int, int]] = []
+    filtered_requests: List[Request] = []
    for i in range(len(dataset)):
-        if len(filtered_dataset) == num_requests:
+        if len(filtered_requests) == num_requests:
            break
        # Tokenize the prompts and completions.
-        prompt = dataset[i][0]
+        prompt_token_ids = tokenizer(dataset[i][0]).input_ids
-        prompt_token_ids = tokenizer(prompt).input_ids
+        prompt = tokenizer.decode(prompt_token_ids)
        completion = dataset[i][1]
        completion_token_ids = tokenizer(completion).input_ids
        prompt_len = len(prompt_token_ids)
-        output_len = len(completion_token_ids
+        output_len = (len(completion_token_ids)
-                         ) if fixed_output_len is None else fixed_output_len
+                      if fixed_output_len is None else fixed_output_len)
        if prompt_len < 4 or output_len < 4:
            # Prune too short sequences.
            continue
        if min_len <= prompt_len <= max_len:
-            filtered_dataset.append((prompt, prompt_len, output_len))
+            filtered_requests.append(Request(prompt, prompt_len, output_len))
-    return filtered_dataset
+    return filtered_requests
-def repeat_and_sort_requests(requests: List[Tuple[str, int, int]],
+def sample_requests_from_random(
    num_requests: int,
    tokenizer: PreTrainedTokenizerBase,
    input_length_range: Tuple[int, int],
    fixed_output_len: Optional[int],
    prefix_len: int,
 ) -> List[Request]:
    requests = []
    prefix_token_ids = sample_tokens(tokenizer, prefix_len)
    min_len, max_len = input_length_range
    for i in range(num_requests):
        unique_part_token_ids = sample_tokens(
            tokenizer,
            random.randint(min_len - prefix_len, max_len - prefix_len))
        prompt_token_ids = prefix_token_ids + unique_part_token_ids
        prompt = tokenizer.decode(prompt_token_ids)
        prompt_len = len(prompt_token_ids)
        assert (min_len <= prompt_len <= max_len
                ), f"prompt_len {prompt_len} out of range {min_len}:{max_len}"
        requests.append(Request(prompt, prompt_len, fixed_output_len))
    return requests
 def repeat_and_sort_requests(requests: List[Request],
                             repeat_count: int,
                             sort: bool = False) -> List[str]:
    repeated_requests = requests * repeat_count
@@ -107,17 +150,20 @@ def repeat_and_sort_requests(requests: List[Tuple[str, int, int]],
        repeated_requests.sort(key=lambda x: x[1])
    else:
        random.shuffle(repeated_requests)
-    return [req[0] for req in repeated_requests]
+    return [req.prompt for req in repeated_requests]
 def main(args):
    tokenizer = get_tokenizer(args.model, trust_remote_code=True)
    input_length_range = tuple(map(int, args.input_length_range.split(':')))
-
+    random.seed(args.seed)
    if args.dataset_path is not None:
-        print(f"Start to sample {args.num_prompts} prompts"
+        if args.prefix_len > 0:
-              "from {args.dataset_path}")
+            raise ValueError("prefix-len is not supported when "
-        filtered_datasets = sample_requests(
+                             "dataset-path is provided.")
        print(f"Start to sample {args.num_prompts} prompts "
              f"from {args.dataset_path}")
        filtered_requests = sample_requests_from_dataset(
            dataset_path=args.dataset_path,
            num_requests=args.num_prompts,
            tokenizer=tokenizer,
@@ -125,32 +171,34 @@ def main(args):
            fixed_output_len=args.output_len,
        )
    else:
-        prompt_len = len(tokenizer(PROMPT).input_ids)
+        print(f"Start to sample {args.num_prompts} prompts from random")
-        filtered_datasets = [(PROMPT, prompt_len, args.output_len)
+        filtered_requests = sample_requests_from_random(
-                             ] * args.num_prompts
+            num_requests=args.num_prompts,
            tokenizer=tokenizer,
            input_length_range=input_length_range,
            fixed_output_len=args.output_len,
            prefix_len=args.prefix_len,
        )
-    llm = LLM(model=args.model,
+    # Print some helpful stats of the requests.
-              tokenizer_mode='auto',
+    print(f"Sampled {len(filtered_requests)} requests.")
-              trust_remote_code=True,
+    prompt_lens = [req.prompt_len for req in filtered_requests]
-              enforce_eager=True,
+    print(f"Average input length: {sum(prompt_lens) / len(prompt_lens)}")
-              use_v2_block_manager=args.use_v2_block_manager,
+    print(f"P50 input length: {sorted(prompt_lens)[len(prompt_lens) // 2]}")
-              tensor_parallel_size=args.tensor_parallel_size,
+    print(f"Min Prompt Length: {min(prompt_lens)}")
-              enable_prefix_caching=args.enable_prefix_caching)
+    print(f"Max Prompt Length: {max(prompt_lens)}")
    engine_args = EngineArgs.from_cli_args(args)
    llm = LLM(**dataclasses.asdict(engine_args))
    sampling_params = SamplingParams(temperature=0, max_tokens=args.output_len)
-    print("Testing filtered datasets")
+    print("Testing filtered requests")
-    prompts = repeat_and_sort_requests(filtered_datasets,
+    prompts = repeat_and_sort_requests(filtered_requests,
                                       repeat_count=args.repeat_count,
                                       sort=args.sort)
    print("------warm up------")
    test_prefix(
        llm=llm,
        prompts=prompts,
        sampling_params=sampling_params,
    )
    print("------start generating------")
    test_prefix(
        llm=llm,
@@ -163,36 +211,37 @@ if __name__ == "__main__":
    parser = FlexibleArgumentParser(
        description=
        'Benchmark the performance with or without automatic prefix caching.')
    parser.add_argument('--model',
                        type=str,
                        default='baichuan-inc/Baichuan2-13B-Chat')
    parser.add_argument("--dataset-path",
                        type=str,
                        default=None,
                        help="Path to the dataset.")
    parser.add_argument('--tensor-parallel-size', '-tp', type=int, default=1)
    parser.add_argument('--output-len', type=int, default=10)
    parser.add_argument('--enable-prefix-caching',
                        action='store_true',
                        help='enable prefix caching')
    parser.add_argument('--use-v2-block-manager',
                        action='store_true',
                        help='Use BlockSpaceMangerV2')
    parser.add_argument('--num-prompts',
                        type=int,
-                        default=1,
+                        required=True,
                        help="Number of the prompts sampled from dataset")
    parser.add_argument('--repeat-count',
                        type=int,
-                        default=100,
+                        default=1,
                        help='Number of times to repeat each prompt')
    parser.add_argument('--sort',
                        action='store_true',
                        help='Sort prompts by input length')
    parser.add_argument('--input-length-range',
                        type=str,
-                        default='128:256',
+                        required=True,
                        help='Range of input lengths for sampling prompts,'
                        'specified as "min:max" (e.g., "128:256").')
    parser.add_argument(
        "--prefix-len",
        type=int,
        default=0,
        help="Specifies the length of a common prefix to be "
        "added to the input prompt. The input-length-range will "
        "subtract this length when filtering prompts. Only used "
        "when dataset-path is not provided.",
    )
    parser = EngineArgs.add_cli_args(parser)
    args = parser.parse_args()
    main(args)
--- a/benchmarks/benchmark_prioritization.py
+++ b/benchmarks/benchmark_prioritization.py
@@ -0,0 +1,177 @@
 """Benchmark offline prioritization."""
 import argparse
 import dataclasses
 import json
 import random
 import time
 from typing import List, Optional, Tuple
 from transformers import AutoTokenizer, PreTrainedTokenizerBase
 from vllm.engine.arg_utils import EngineArgs
 from vllm.utils import FlexibleArgumentParser
 def sample_requests(
    dataset_path: str,
    num_requests: int,
    tokenizer: PreTrainedTokenizerBase,
    fixed_output_len: Optional[int],
 ) -> List[Tuple[str, int, int]]:
    if fixed_output_len is not None and fixed_output_len < 4:
        raise ValueError("output_len too small")
    # Load the dataset.
    with open(dataset_path) as f:
        dataset = json.load(f)
    # Filter out the conversations with less than 2 turns.
    dataset = [data for data in dataset if len(data["conversations"]) >= 2]
    # Only keep the first two turns of each conversation.
    dataset = [(data["conversations"][0]["value"],
                data["conversations"][1]["value"]) for data in dataset]
    # Shuffle the dataset.
    random.shuffle(dataset)
    # Filter out sequences that are too long or too short
    filtered_dataset: List[Tuple[str, int, int]] = []
    for i in range(len(dataset)):
        if len(filtered_dataset) == num_requests:
            break
        # Tokenize the prompts and completions.
        prompt = dataset[i][0]
        prompt_token_ids = tokenizer(prompt).input_ids
        completion = dataset[i][1]
        completion_token_ids = tokenizer(completion).input_ids
        prompt_len = len(prompt_token_ids)
        output_len = len(completion_token_ids
                         ) if fixed_output_len is None else fixed_output_len
        if prompt_len < 4 or output_len < 4:
            # Prune too short sequences.
            continue
        if prompt_len > 1024 or prompt_len + output_len > 2048:
            # Prune too long sequences.
            continue
        #Select a equi-probable random priority
        priority = 0 if random.random() < 0.5 else 1
        filtered_dataset.append((prompt, prompt_len, output_len, priority))
    return filtered_dataset
 def run_vllm(
    requests: List[Tuple[str, int, int]],
    n: int,
    engine_args: EngineArgs,
 ) -> float:
    from vllm import LLM, SamplingParams
    llm = LLM(**dataclasses.asdict(engine_args))
    # Add the requests to the engine.
    prompts = []
    sampling_params = []
    priority = []
    for prompt, _, output_len, _priority in requests:
        prompts.append(prompt)
        priority.append(_priority)
        sampling_params.append(
            SamplingParams(
                n=n,
                temperature=1.0,
                top_p=1.0,
                ignore_eos=True,
                max_tokens=output_len,
            ))
    start = time.perf_counter()
    llm.generate(prompts, sampling_params, priority=priority, use_tqdm=True)
    end = time.perf_counter()
    return end - start
 def main(args: argparse.Namespace):
    print(args)
    random.seed(args.seed)
    # Sample the requests.
    tokenizer = AutoTokenizer.from_pretrained(
        args.tokenizer, trust_remote_code=args.trust_remote_code)
    if args.dataset is None:
        # Synthesize a prompt with the given input length.
        prompt = "hi" * (args.input_len - 1)
        requests = [(prompt, args.input_len, args.output_len)
                    for _ in range(args.num_prompts)]
    else:
        requests = sample_requests(args.dataset, args.num_prompts, tokenizer,
                                   args.output_len)
    if args.backend == "vllm":
        elapsed_time = run_vllm(requests, args.n,
                                EngineArgs.from_cli_args(args))
    else:
        raise ValueError(f"Unknown backend: {args.backend}")
    total_num_tokens = sum(prompt_len + output_len
                           for _, prompt_len, output_len, priority in requests)
    print(f"Throughput: {len(requests) / elapsed_time:.2f} requests/s, "
          f"{total_num_tokens / elapsed_time:.2f} tokens/s")
    # Output JSON results if specified
    if args.output_json:
        results = {
            "elapsed_time": elapsed_time,
            "num_requests": len(requests),
            "total_num_tokens": total_num_tokens,
            "requests_per_second": len(requests) / elapsed_time,
            "tokens_per_second": total_num_tokens / elapsed_time,
        }
        with open(args.output_json, "w") as f:
            json.dump(results, f, indent=4)
 if __name__ == "__main__":
    parser = FlexibleArgumentParser(description="Benchmark the throughput.")
    parser.add_argument("--backend",
                        type=str,
                        choices=["vllm", "hf", "mii"],
                        default="vllm")
    parser.add_argument("--dataset",
                        type=str,
                        default=None,
                        help="Path to the dataset.")
    parser.add_argument("--input-len",
                        type=int,
                        default=None,
                        help="Input prompt length for each request")
    parser.add_argument("--output-len",
                        type=int,
                        default=None,
                        help="Output length for each request. Overrides the "
                        "output length from the dataset.")
    parser.add_argument("--n",
                        type=int,
                        default=1,
                        help="Number of generated sequences per prompt.")
    parser.add_argument("--num-prompts",
                        type=int,
                        default=200,
                        help="Number of prompts to process.")
    parser.add_argument(
        '--output-json',
        type=str,
        default=None,
        help='Path to save the throughput results in JSON format.')
    parser = EngineArgs.add_cli_args(parser)
    args = parser.parse_args()
    if args.tokenizer is None:
        args.tokenizer = args.model
    if args.dataset is None:
        assert args.input_len is not None
        assert args.output_len is not None
    else:
        assert args.input_len is None
    main(args)
--- a/benchmarks/benchmark_serving.py
+++ b/benchmarks/benchmark_serving.py
@@ -1,4 +1,4 @@
-"""Benchmark online serving throughput.
+r"""Benchmark online serving throughput.
 On the server side, run one of the following commands:
    vLLM OpenAI API server
@@ -24,6 +24,8 @@ On the client side, run:
 """
 import argparse
 import asyncio
 import base64
 import io
 import json
 import os
 import random
@@ -31,11 +33,13 @@ import time
 import warnings
 from dataclasses import dataclass
 from datetime import datetime
-from typing import Any, AsyncGenerator, Dict, List, Optional, Tuple
+from typing import Any, AsyncGenerator, Collection, Dict, List, Optional, Tuple
 import numpy as np
 from backend_request_func import (ASYNC_REQUEST_FUNCS, RequestFuncInput,
                                  RequestFuncOutput)
 from datasets import load_dataset
 from PIL.Image import Image
 from tqdm.asyncio import tqdm
 from transformers import PreTrainedTokenizerBase
@@ -49,6 +53,8 @@ try:
 except ImportError:
    from argparse import ArgumentParser as FlexibleArgumentParser
 MILLISECONDS_TO_SECONDS_CONVERSION = 1000
@dataclass
 class BenchmarkMetrics:
@@ -56,6 +62,7 @@ class BenchmarkMetrics:
    total_input: int
    total_output: int
    request_throughput: float
    request_goodput: float
    output_throughput: float
    total_token_throughput: float
    mean_ttft_ms: float
@@ -84,11 +91,9 @@ def sample_sharegpt_requests(
    num_requests: int,
    tokenizer: PreTrainedTokenizerBase,
    fixed_output_len: Optional[int] = None,
-) -> List[Tuple[str, int, int]]:
+) -> List[Tuple[str, int, int, None]]:
    if fixed_output_len is not None and fixed_output_len < 4:
        raise ValueError("output_len too small")
    # Load the dataset.
-    with open(dataset_path) as f:
+    with open(dataset_path, encoding='utf-8') as f:
        dataset = json.load(f)
    # Filter out the conversations with less than 2 turns.
    dataset = [data for data in dataset if len(data["conversations"]) >= 2]
@@ -113,13 +118,13 @@ def sample_sharegpt_requests(
        prompt_len = len(prompt_token_ids)
        output_len = len(completion_token_ids
                         ) if fixed_output_len is None else fixed_output_len
-        if prompt_len < 4 or output_len < 4:
+        if prompt_len < 4 or (fixed_output_len is None and output_len < 4):
            # Prune too short sequences.
            continue
        if prompt_len > 1024 or prompt_len + output_len > 2048:
            # Prune too long sequences.
            continue
-        filtered_dataset.append((prompt, prompt_len, output_len))
+        filtered_dataset.append((prompt, prompt_len, output_len, None))
    return filtered_dataset
@@ -131,13 +136,13 @@ def sample_sonnet_requests(
    output_len: int,
    prefix_len: int,
    tokenizer: PreTrainedTokenizerBase,
-) -> List[Tuple[str, str, int, int]]:
+) -> List[Tuple[str, str, int, int, None]]:
    assert (
        input_len > prefix_len
    ), "'args.sonnet-input-len' must be greater than 'args.prefix-input-len'."
    # Load the dataset.
-    with open(dataset_path) as f:
+    with open(dataset_path, encoding='utf-8') as f:
        poem_lines = f.readlines()
    # Tokenize the poem lines.
@@ -174,9 +179,9 @@ def sample_sonnet_requests(
    # Sample the rest of lines per request.
    sampled_requests: List[Tuple[str, int, int]] = []
    for _ in range(num_requests):
-        sampled_lines = "".join(
+        num_lines_needed = num_input_lines - num_prefix_lines
-            prefix_lines +
+        sampled_lines = "".join(prefix_lines +
-            random.sample(poem_lines, num_input_lines - num_prefix_lines))
+                                random.choices(poem_lines, k=num_lines_needed))
        prompt = f"{base_prompt}{sampled_lines}"
        message = [
@@ -189,14 +194,160 @@ def sample_sonnet_requests(
            message, add_generation_prompt=True, tokenize=False)
        prompt_len = len(tokenizer(prompt_formatted).input_ids)
        sampled_requests.append(
-            (prompt, prompt_formatted, prompt_len, output_len))
+            (prompt, prompt_formatted, prompt_len, output_len, None))
    return sampled_requests
 def sample_mmmu_pro_vision_requests(
    dataset,
    num_requests: int,
    tokenizer: PreTrainedTokenizerBase,
    fixed_output_len: Optional[int] = None,
 ) -> List[Tuple[str, str, int, Optional[Dict[str, Collection[str]]]]]:
    sampled_requests: List[Tuple[str, int, int, Dict[str,
                                                     Collection[str]]]] = []
    for data in dataset:
        if len(sampled_requests) == num_requests:
            break
        # MMMU-Pro vision direct prompt
        # Ref: https://github.com/MMMU-Benchmark/MMMU/blob/6ce42f4d8f70c1841c67867152648974415b5cac/mmmu-pro/prompts.yaml#L5
        prompt = (
            "Answer with the option letter from the given choices directly. "
            "The last line of your response should be of the following "
            "format: 'Answer: $LETTER' (without quotes) where LETTER is one of "
            "options.")
        prompt_token_ids = tokenizer(prompt).input_ids
        if fixed_output_len is None:
            # Default max output len is set to 128
            print("--hf-output-len is not provided. Using default value 128.")
            fixed_output_len = 128
        prompt_len = len(prompt_token_ids)
        output_len = fixed_output_len
        assert isinstance(
            data["image"],
            Image), ("Input image format must be `PIL.Image.Image`, "
                     f"given {type(data['image'])}.")
        image: Image = data["image"]
        image = image.convert("RGB")
        image_data = io.BytesIO()
        image.save(image_data, format='JPEG')
        image_base64 = base64.b64encode(image_data.getvalue()).decode("utf-8")
        mm_content = {
            "type": "image_url",
            "image_url": {
                "url": f"data:image/jpeg;base64,{image_base64}"
            },
        }
        sampled_requests.append((prompt, prompt_len, output_len, mm_content))
    return sampled_requests
 def sample_hf_requests(
    dataset_path: str,
    dataset_subset: str,
    dataset_split: str,
    num_requests: int,
    tokenizer: PreTrainedTokenizerBase,
    random_seed: int,
    fixed_output_len: Optional[int] = None,
 ) -> List[Tuple[str, str, int, Optional[Dict[str, Collection[str]]]]]:
    # Special case for MMMU-Pro vision dataset
    if dataset_path == 'MMMU/MMMU_Pro' and dataset_subset == 'vision':
        assert dataset_split == "test"
        dataset = load_dataset(dataset_path,
                               name=dataset_subset,
                               split=dataset_split,
                               streaming=True)
        assert "image" in dataset.features, (
            "MMMU/MMMU_Pro vision dataset must have 'image' column.")
        filter_func = lambda x: isinstance(x["image"], Image)
        dataset = dataset.shuffle(seed=random_seed).filter(filter_func)
        return sample_mmmu_pro_vision_requests(dataset, num_requests,
                                               tokenizer, fixed_output_len)
    dataset = load_dataset(dataset_path,
                           name=dataset_subset,
                           split=dataset_split,
                           streaming=True)
    assert "conversations" in dataset.features, (
        "HF Dataset must have 'conversations' column.")
    filter_func = lambda x: len(x["conversations"]) >= 2
    filtered_dataset = dataset.shuffle(seed=random_seed).filter(filter_func)
    sampled_requests: List[Tuple[str, int, int, Dict[str,
                                                     Collection[str]]]] = []
    for data in filtered_dataset:
        if len(sampled_requests) == num_requests:
            break
        # Tokenize the prompts and completions.
        prompt = data["conversations"][0]["value"]
        prompt_token_ids = tokenizer(prompt).input_ids
        completion = data["conversations"][1]["value"]
        completion_token_ids = tokenizer(completion).input_ids
        prompt_len = len(prompt_token_ids)
        output_len = len(completion_token_ids
                         ) if fixed_output_len is None else fixed_output_len
        if fixed_output_len is None and (prompt_len < 4 or output_len < 4):
            # Prune too short sequences.
            continue
        if fixed_output_len is None and \
            (prompt_len > 1024 or prompt_len + output_len > 2048):
            # Prune too long sequences.
            continue
        if "image" in data and isinstance(data["image"], Image):
            image: Image = data["image"]
            image = image.convert("RGB")
            image_data = io.BytesIO()
            image.save(image_data, format='JPEG')
            image_base64 = base64.b64encode(
                image_data.getvalue()).decode("utf-8")
            mm_content = {
                "type": "image_url",
                "image_url": {
                    "url": f"data:image/jpeg;base64,{image_base64}"
                },
            }
        elif "image" in data and isinstance(data["image"], str):
            if (data["image"].startswith("http://") or \
                data["image"].startswith("file://")):
                image_url = data["image"]
            else:
                image_url = f"file://{data['image']}"
            mm_content = {
                "type": "image_url",
                "image_url": {
                    "url": image_url
                },
            }
        else:
            mm_content = None
        sampled_requests.append((prompt, prompt_len, output_len, mm_content))
    return sampled_requests
 def sample_random_requests(
-        input_len: int, output_len: int, num_prompts: int, range_ratio: float,
+    prefix_len: int,
-        tokenizer: PreTrainedTokenizerBase) -> List[Tuple[str, int, int]]:
+    input_len: int,
    output_len: int,
    num_prompts: int,
    range_ratio: float,
    tokenizer: PreTrainedTokenizerBase,
 ) -> List[Tuple[str, int, int]]:
    prefix_token_ids = np.random.randint(0,
                                         tokenizer.vocab_size,
                                         size=prefix_len).tolist()
    input_lens = np.random.randint(
        int(input_len * range_ratio),
@@ -211,10 +362,12 @@ def sample_random_requests(
    offsets = np.random.randint(0, tokenizer.vocab_size, size=num_prompts)
    input_requests = []
    for i in range(num_prompts):
-        prompt = tokenizer.decode([(offsets[i] + i + j) % tokenizer.vocab_size
+        prompt = tokenizer.decode(prefix_token_ids +
                                  [(offsets[i] + i + j) % tokenizer.vocab_size
                                   for j in range(input_lens[i])])
-        input_requests.append(
+
-            (prompt, int(input_lens[i]), int(output_lens[i])))
+        input_requests.append((prompt, int(prefix_len + input_lens[i]),
                               int(output_lens[i]), None))
    return input_requests
@@ -222,8 +375,33 @@ def sample_random_requests(
 async def get_request(
    input_requests: List[Tuple[str, int, int]],
    request_rate: float,
    burstiness: float = 1.0,
 ) -> AsyncGenerator[Tuple[str, int, int], None]:
    """
    Asynchronously generates requests at a specified rate 
    with OPTIONAL burstiness.
    Args:
        input_requests: 
            A list of input requests, each represented as a tuple.
        request_rate: 
            The rate at which requests are generated (requests/s).
        burstiness (optional): 
            The burstiness factor of the request generation. 
            Only takes effect when request_rate is not inf.
            Default value is 1, which follows a Poisson process.
            Otherwise, the request intervals follow a gamma distribution.
            A lower burstiness value (0 < burstiness < 1) results 
            in more bursty requests, while a higher burstiness value 
            (burstiness > 1) results in a more uniform arrival of requests.
    """
    input_requests = iter(input_requests)
    # Calculate scale parameter theta to maintain the desired request_rate.
    assert burstiness > 0, (
        f"A positive burstiness factor is expected, but given {burstiness}.")
    theta = 1.0 / (request_rate * burstiness)
    for request in input_requests:
        yield request
@@ -231,8 +409,9 @@ async def get_request(
            # If the request rate is infinity, then we don't need to wait.
            continue
-        # Sample the request interval from the exponential distribution.
+        # Sample the request interval from the gamma distribution.
-        interval = np.random.exponential(1.0 / request_rate)
+        # If burstiness is 1, it follows exponential distribution.
        interval = np.random.gamma(shape=burstiness, scale=theta)
        # The next request will be sent after the interval.
        await asyncio.sleep(interval)
@@ -244,12 +423,15 @@ def calculate_metrics(
    tokenizer: PreTrainedTokenizerBase,
    selected_percentile_metrics: List[str],
    selected_percentiles: List[float],
    gootput_config_dict: Dict[str, float],
 ) -> Tuple[BenchmarkMetrics, List[int]]:
    actual_output_lens: List[int] = []
    total_input = 0
    completed = 0
    good_completed = 0
    itls: List[float] = []
    tpots: List[float] = []
    all_tpots: List[float] = []
    ttfts: List[float] = []
    e2els: List[float] = []
    for i in range(len(outputs)):
@@ -263,9 +445,13 @@ def calculate_metrics(
                          add_special_tokens=False).input_ids)
            actual_output_lens.append(output_len)
            total_input += input_requests[i][1]
            tpot = 0
            if output_len > 1:
-                tpots.append(
+                tpot = (outputs[i].latency - outputs[i].ttft) / (output_len -
-                    (outputs[i].latency - outputs[i].ttft) / (output_len - 1))
+                                                                 1)
                tpots.append(tpot)
            # Note: if output_len <= 1, we regard tpot as 0 for goodput
            all_tpots.append(tpot)
            itls += outputs[i].itl
            ttfts.append(outputs[i].ttft)
            e2els.append(outputs[i].latency)
@@ -273,6 +459,28 @@ def calculate_metrics(
        else:
            actual_output_lens.append(0)
    if gootput_config_dict:
        valid_metrics = []
        slo_values = []
        if "ttft" in gootput_config_dict:
            valid_metrics.append(ttfts)
            slo_values.append(gootput_config_dict["ttft"] /
                              MILLISECONDS_TO_SECONDS_CONVERSION)
        if "tpot" in gootput_config_dict:
            valid_metrics.append(all_tpots)
            slo_values.append(gootput_config_dict["tpot"] /
                              MILLISECONDS_TO_SECONDS_CONVERSION)
        if "e2el" in gootput_config_dict:
            valid_metrics.append(e2els)
            slo_values.append(gootput_config_dict["e2el"] /
                              MILLISECONDS_TO_SECONDS_CONVERSION)
        for req_metric in zip(*valid_metrics):
            is_good_req = all([s >= r for s, r in zip(slo_values, req_metric)])
            if is_good_req:
                good_completed += 1
    if completed == 0:
        warnings.warn(
            "All requests failed. This is likely due to a misconfiguration "
@@ -283,6 +491,7 @@ def calculate_metrics(
        total_input=total_input,
        total_output=sum(actual_output_lens),
        request_throughput=completed / dur_s,
        request_goodput=good_completed / dur_s,
        output_throughput=sum(actual_output_lens) / dur_s,
        total_token_throughput=(total_input + sum(actual_output_lens)) / dur_s,
        mean_ttft_ms=np.mean(ttfts or 0) *
@@ -301,9 +510,9 @@ def calculate_metrics(
        median_itl_ms=np.median(itls or 0) * 1000,
        percentiles_itl_ms=[(p, np.percentile(itls or 0, p) * 1000)
                            for p in selected_percentiles],
-        mean_e2el_ms=np.median(e2els or 0) * 1000,
+        mean_e2el_ms=np.mean(e2els or 0) * 1000,
        std_e2el_ms=np.std(e2els or 0) * 1000,
-        median_e2el_ms=np.mean(e2els or 0) * 1000,
+        median_e2el_ms=np.median(e2els or 0) * 1000,
        percentiles_e2el_ms=[(p, np.percentile(e2els or 0, p) * 1000)
                             for p in selected_percentiles],
    )
@@ -318,13 +527,17 @@ async def benchmark(
    model_id: str,
    tokenizer: PreTrainedTokenizerBase,
    input_requests: List[Tuple[str, int, int]],
    logprobs: Optional[int],
    best_of: int,
    use_beam_search: bool,
    request_rate: float,
    burstiness: float,
    disable_tqdm: bool,
    profile: bool,
    selected_percentile_metrics: List[str],
    selected_percentiles: List[str],
    ignore_eos: bool,
    gootput_config_dict: Dict[str, float],
    max_concurrency: Optional[int],
 ):
    if backend in ASYNC_REQUEST_FUNCS:
        request_func = ASYNC_REQUEST_FUNCS[backend]
@@ -332,15 +545,22 @@ async def benchmark(
        raise ValueError(f"Unknown backend: {backend}")
    print("Starting initial single prompt test run...")
-    test_prompt, test_prompt_len, test_output_len = input_requests[0]
+    test_prompt, test_prompt_len, test_output_len, test_mm_content = (
        input_requests[0])
    if backend != "openai-chat" and test_mm_content is not None:
        # multi-modal benchmark is only available on OpenAI Chat backend.
        raise ValueError(
            "Multi-modal content is only supported on 'openai-chat' backend.")
    test_input = RequestFuncInput(
        model=model_id,
        prompt=test_prompt,
        api_url=api_url,
        prompt_len=test_prompt_len,
        output_len=test_output_len,
        logprobs=logprobs,
        best_of=best_of,
-        use_beam_search=use_beam_search,
+        multi_modal_content=test_mm_content,
        ignore_eos=ignore_eos,
    )
    test_output = await request_func(request_func_input=test_input)
    if not test_output.success:
@@ -352,40 +572,62 @@ async def benchmark(
    if profile:
        print("Starting profiler...")
-        profile_input = RequestFuncInput(
+        profile_input = RequestFuncInput(model=model_id,
-            model=model_id,
+                                         prompt=test_prompt,
-            prompt=test_prompt,
+                                         api_url=base_url + "/start_profile",
-            api_url=base_url + "/start_profile",
+                                         prompt_len=test_prompt_len,
-            prompt_len=test_prompt_len,
+                                         output_len=test_output_len,
-            output_len=test_output_len,
+                                         logprobs=logprobs,
-            best_of=best_of,
+                                         best_of=best_of,
-            use_beam_search=use_beam_search,
+                                         multi_modal_content=test_mm_content,
-        )
+                                         ignore_eos=ignore_eos)
        profile_output = await request_func(request_func_input=profile_input)
        if profile_output.success:
            print("Profiler started")
    if burstiness == 1.0:
        distribution = "Poisson process"
    else:
        distribution = "Gamma distribution"
    print(f"Traffic request rate: {request_rate}")
    print(f"Burstiness factor: {burstiness} ({distribution})")
    print(f"Maximum request concurrency: {max_concurrency}")
    pbar = None if disable_tqdm else tqdm(total=len(input_requests))
    # This can be used once the minimum Python version is 3.10 or higher,
    # and it will simplify the code in limited_request_func.
    #    semaphore = (asyncio.Semaphore(max_concurrency)
    #                 if max_concurrency else contextlib.nullcontext())
    semaphore = (asyncio.Semaphore(max_concurrency)
                 if max_concurrency else None)
    async def limited_request_func(request_func_input, pbar):
        if semaphore is None:
            return await request_func(request_func_input=request_func_input,
                                      pbar=pbar)
        async with semaphore:
            return await request_func(request_func_input=request_func_input,
                                      pbar=pbar)
    benchmark_start_time = time.perf_counter()
    tasks: List[asyncio.Task] = []
-    async for request in get_request(input_requests, request_rate):
+    async for request in get_request(input_requests, request_rate, burstiness):
-        prompt, prompt_len, output_len = request
+        prompt, prompt_len, output_len, mm_content = request
-        request_func_input = RequestFuncInput(
+        request_func_input = RequestFuncInput(model=model_id,
-            model=model_id,
+                                              prompt=prompt,
-            prompt=prompt,
+                                              api_url=api_url,
-            api_url=api_url,
+                                              prompt_len=prompt_len,
-            prompt_len=prompt_len,
+                                              output_len=output_len,
-            output_len=output_len,
+                                              logprobs=logprobs,
-            best_of=best_of,
+                                              best_of=best_of,
-            use_beam_search=use_beam_search,
+                                              multi_modal_content=mm_content,
-        )
+                                              ignore_eos=ignore_eos)
        tasks.append(
            asyncio.create_task(
-                request_func(request_func_input=request_func_input,
+                limited_request_func(request_func_input=request_func_input,
-                             pbar=pbar)))
+                                     pbar=pbar)))
    outputs: List[RequestFuncOutput] = await asyncio.gather(*tasks)
    if profile:
@@ -396,8 +638,8 @@ async def benchmark(
            api_url=base_url + "/stop_profile",
            prompt_len=test_prompt_len,
            output_len=test_output_len,
            logprobs=logprobs,
            best_of=best_of,
            use_beam_search=use_beam_search,
        )
        profile_output = await request_func(request_func_input=profile_input)
        if profile_output.success:
@@ -415,6 +657,7 @@ async def benchmark(
        tokenizer=tokenizer,
        selected_percentile_metrics=selected_percentile_metrics,
        selected_percentiles=selected_percentiles,
        gootput_config_dict=gootput_config_dict,
    )
    print("{s:{c}^{n}}".format(s=' Serving Benchmark Result ', n=50, c='='))
@@ -426,6 +669,9 @@ async def benchmark(
                                 metrics.total_output))
    print("{:<40} {:<10.2f}".format("Request throughput (req/s):",
                                    metrics.request_throughput))
    if gootput_config_dict:
        print("{:<40} {:<10.2f}".format("Request goodput (req/s):",
                                        metrics.request_goodput))
    print("{:<40} {:<10.2f}".format("Output token throughput (tok/s):",
                                    metrics.output_throughput))
    print("{:<40} {:<10.2f}".format("Total Token throughput (tok/s):",
@@ -437,6 +683,8 @@ async def benchmark(
        "total_input_tokens": metrics.total_input,
        "total_output_tokens": metrics.total_output,
        "request_throughput": metrics.request_throughput,
        "request_goodput:":
        metrics.request_goodput if gootput_config_dict else None,
        "output_throughput": metrics.output_throughput,
        "total_token_throughput": metrics.total_token_throughput,
        "input_lens": [output.prompt_len for output in outputs],
@@ -455,7 +703,7 @@ async def benchmark(
        # E.g., "Time to First Token"
        metric_header: str,
    ):
-        # This function print and add statistics of the specified
+        # This function prints and adds statistics of the specified
        # metric.
        if metric_attribute_name not in selected_percentile_metrics:
            return
@@ -490,6 +738,41 @@ async def benchmark(
    return result
 def check_goodput_args(args):
    # Check and parse goodput arguments
    gootput_config_dict = {}
    VALID_NAMES = ["ttft", "tpot", "e2el"]
    if args.goodput:
        gootput_config_dict = parse_goodput(args.goodput)
        for slo_name, slo_val in gootput_config_dict.items():
            if slo_name not in VALID_NAMES:
                raise ValueError(
                    f"Invalid metric name found, {slo_name}: {slo_val}. "
                    "The service level objective name should be one of "
                    f"{str(VALID_NAMES)}. ")
            if slo_val < 0:
                raise ValueError(
                    f"Invalid value found, {slo_name}: {slo_val}. "
                    "The service level objective value should be "
                    "non-negative.")
    return gootput_config_dict
 def parse_goodput(slo_pairs):
    gootput_config_dict = {}
    try:
        for slo_pair in slo_pairs:
            slo_name, slo_val = slo_pair.split(":")
            gootput_config_dict[slo_name] = float(slo_val)
    except ValueError as err:
        raise argparse.ArgumentTypeError(
            "Invalid format found for service level objectives. "
            "Specify service level objectives for goodput as \"KEY:VALUE\" "
            "pairs, where the key is a metric name, and the value is a "
            "number in milliseconds.") from err
    return gootput_config_dict
 def main(args: argparse.Namespace):
    print(args)
    random.seed(args.seed)
@@ -498,6 +781,7 @@ def main(args: argparse.Namespace):
    backend = args.backend
    model_id = args.model
    tokenizer_id = args.tokenizer if args.tokenizer is not None else args.model
    tokenizer_mode = args.tokenizer_mode
    if args.base_url is not None:
        api_url = f"{args.base_url}{args.endpoint}"
@@ -507,6 +791,7 @@ def main(args: argparse.Namespace):
        base_url = f"http://{args.host}:{args.port}"
    tokenizer = get_tokenizer(tokenizer_id,
                              tokenizer_mode=tokenizer_mode,
                              trust_remote_code=args.trust_remote_code)
    if args.dataset is not None:
@@ -541,9 +826,9 @@ def main(args: argparse.Namespace):
                prefix_len=args.sonnet_prefix_len,
                tokenizer=tokenizer,
            )
-            input_requests = [(prompt, prompt_len, output_len)
+            input_requests = [(prompt, prompt_len, output_len, None)
                              for prompt, prompt_formatted, prompt_len,
-                              output_len in input_requests]
+                              output_len, _ in input_requests]
        else:
            assert (
                tokenizer.chat_template or tokenizer.default_chat_template
@@ -556,12 +841,24 @@ def main(args: argparse.Namespace):
                prefix_len=args.sonnet_prefix_len,
                tokenizer=tokenizer,
            )
-            input_requests = [(prompt_formatted, prompt_len, output_len)
+            input_requests = [(prompt_formatted, prompt_len, output_len, None)
                              for prompt, prompt_formatted, prompt_len,
-                              output_len in input_requests]
+                              output_len, _ in input_requests]
    elif args.dataset_name == "hf":
        input_requests = sample_hf_requests(
            dataset_path=args.dataset_path,
            dataset_subset=args.hf_subset,
            dataset_split=args.hf_split,
            num_requests=args.num_prompts,
            tokenizer=tokenizer,
            random_seed=args.seed,
            fixed_output_len=args.hf_output_len,
        )
    elif args.dataset_name == "random":
        input_requests = sample_random_requests(
            prefix_len=args.random_prefix_len,
            input_len=args.random_input_len,
            output_len=args.random_output_len,
            num_prompts=args.num_prompts,
@@ -572,6 +869,8 @@ def main(args: argparse.Namespace):
    else:
        raise ValueError(f"Unknown dataset: {args.dataset_name}")
    gootput_config_dict = check_goodput_args(args)
    benchmark_result = asyncio.run(
        benchmark(
            backend=backend,
@@ -580,15 +879,19 @@ def main(args: argparse.Namespace):
            model_id=model_id,
            tokenizer=tokenizer,
            input_requests=input_requests,
            logprobs=args.logprobs,
            best_of=args.best_of,
            use_beam_search=args.use_beam_search,
            request_rate=args.request_rate,
            burstiness=args.burstiness,
            disable_tqdm=args.disable_tqdm,
            profile=args.profile,
            selected_percentile_metrics=args.percentile_metrics.split(","),
            selected_percentiles=[
                float(p) for p in args.metric_percentiles.split(",")
            ],
            ignore_eos=args.ignore_eos,
            gootput_config_dict=gootput_config_dict,
            max_concurrency=args.max_concurrency,
        ))
    # Save config and results to json
@@ -602,7 +905,6 @@ def main(args: argparse.Namespace):
        result_json["model_id"] = model_id
        result_json["tokenizer_id"] = tokenizer_id
        result_json["best_of"] = args.best_of
        result_json["use_beam_search"] = args.use_beam_search
        result_json["num_prompts"] = args.num_prompts
        # Metadata
@@ -619,18 +921,22 @@ def main(args: argparse.Namespace):
        # Traffic
        result_json["request_rate"] = (
            args.request_rate if args.request_rate < float("inf") else "inf")
        result_json["burstiness"] = args.burstiness
        result_json["max_concurrency"] = args.max_concurrency
        # Merge with benchmark result
        result_json = {**result_json, **benchmark_result}
        # Save to file
        base_model_id = model_id.split("/")[-1]
-        file_name = f"{backend}-{args.request_rate}qps-{base_model_id}-{current_dt}.json"  #noqa
+        max_concurrency_str = (f"-concurrency{args.max_concurrency}"
                               if args.max_concurrency is not None else "")
        file_name = f"{backend}-{args.request_rate}qps{max_concurrency_str}-{base_model_id}-{current_dt}.json"  #noqa
        if args.result_filename:
            file_name = args.result_filename
        if args.result_dir:
            file_name = os.path.join(args.result_dir, file_name)
-        with open(file_name, "w") as outfile:
+        with open(file_name, "w", encoding='utf-8') as outfile:
            json.dump(result_json, outfile)
@@ -668,13 +974,27 @@ if __name__ == "__main__":
        "--dataset-name",
        type=str,
        default="sharegpt",
-        choices=["sharegpt", "sonnet", "random"],
+        choices=["sharegpt", "sonnet", "random", "hf"],
        help="Name of the dataset to benchmark on.",
    )
    parser.add_argument("--dataset-path",
                        type=str,
                        default=None,
-                        help="Path to the dataset.")
+                        help="Path to the sharegpt/sonnet dataset. "
                        "Or the huggingface dataset ID if using HF dataset.")
    parser.add_argument(
        "--max-concurrency",
        type=int,
        default=None,
        help="Maximum number of concurrent requests. This can be used "
        "to help simulate an environment where a higher level component "
        "is enforcing a maximum number of concurrent requests. While the "
        "--request-rate argument controls the rate at which requests are "
        "initiated, this argument will control how many are actually allowed "
        "to execute at a time. This means that when used in combination, the "
        "actual request rate may be lower than specified with --request-rate, "
        "if the server is not processing requests fast enough to keep up.")
    parser.add_argument(
        "--model",
        type=str,
@@ -702,52 +1022,14 @@ if __name__ == "__main__":
        help="Number of prompts to process.",
    )
    parser.add_argument(
-        "--sharegpt-output-len",
+        "--logprobs",
        type=int,
        default=None,
-        help="Output length for each request. Overrides the output length "
+        help=("Number of logprobs-per-token to compute & return as part of "
-        "from the ShareGPT dataset.")
+              "the request. If unspecified, then either (1) if beam search "
-    parser.add_argument(
+              "is disabled, no logprobs are computed & a single dummy "
-        "--sonnet-input-len",
+              "logprob is returned for each token; or (2) if beam search "
-        type=int,
+              "is enabled 1 logprob per token is computed"),
        default=550,
        help=
        "Number of input tokens per request, used only for sonnet dataset.",
    )
    parser.add_argument(
        "--sonnet-output-len",
        type=int,
        default=150,
        help=
        "Number of output tokens per request, used only for sonnet dataset.",
    )
    parser.add_argument(
        "--sonnet-prefix-len",
        type=int,
        default=200,
        help=
        "Number of prefix tokens per request, used only for sonnet dataset.",
    )
    parser.add_argument(
        "--random-input-len",
        type=int,
        default=1024,
        help=
        "Number of input tokens per request, used only for random sampling.",
    )
    parser.add_argument(
        "--random-output-len",
        type=int,
        default=128,
        help=
        "Number of output tokens per request, used only for random sampling.",
    )
    parser.add_argument(
        "--random-range-ratio",
        type=float,
        default=1.0,
        help="Range of sampled ratio of input/output length, "
        "used only for random sampling.",
    )
    parser.add_argument(
        "--request-rate",
@@ -755,8 +1037,20 @@ if __name__ == "__main__":
        default=float("inf"),
        help="Number of requests per second. If this is inf, "
        "then all the requests are sent at time 0. "
-        "Otherwise, we use Poisson process to synthesize "
+        "Otherwise, we use Poisson process or gamma distribution "
-        "the request arrival times.",
+        "to synthesize the request arrival times.",
    )
    parser.add_argument(
        "--burstiness",
        type=float,
        default=1.0,
        help="Burstiness factor of the request generation. "
        "Only take effect when request_rate is not inf. "
        "Default value is 1, which follows Poisson process. "
        "Otherwise, the request intervals follow a gamma distribution. "
        "A lower burstiness value (0 < burstiness < 1) results in more "
        "bursty requests. A higher burstiness value (burstiness > 1) "
        "results in a more uniform arrival of requests.",
    )
    parser.add_argument("--seed", type=int, default=0)
    parser.add_argument(
@@ -804,6 +1098,11 @@ if __name__ == "__main__":
        "{backend}-{args.request_rate}qps-{base_model_id}-{current_dt}.json"
        " format.",
    )
    parser.add_argument(
        "--ignore-eos",
        action="store_true",
        help="Set ignore_eos flag when sending the benchmark request."
        "Warning: ignore_eos is not supported in deepspeed_mii and tgi.")
    parser.add_argument(
        "--percentile-metrics",
        type=str,
@@ -821,6 +1120,107 @@ if __name__ == "__main__":
        "Default value is \"99\". "
        "Use \"--percentile-metrics\" to select metrics.",
    )
    parser.add_argument(
        "--goodput",
        nargs="+",
        required=False,
        help="Specify service level objectives for goodput as \"KEY:VALUE\" "
        "pairs, where the key is a metric name, and the value is in "
        "milliseconds. Multiple \"KEY:VALUE\" pairs can be provided, "
        "separated by spaces. Allowed request level metric names are "
        "\"ttft\", \"tpot\", \"e2el\". For more context on the definition of "
        "goodput, refer to DistServe paper: https://arxiv.org/pdf/2401.09670 "
        "and the blog: https://hao-ai-lab.github.io/blogs/distserve")
    # group for dataset specific arguments
    sonnet_group = parser.add_argument_group("sonnet dataset options")
    sonnet_group.add_argument(
        "--sonnet-input-len",
        type=int,
        default=550,
        help=
        "Number of input tokens per request, used only for sonnet dataset.",
    )
    sonnet_group.add_argument(
        "--sonnet-output-len",
        type=int,
        default=150,
        help=
        "Number of output tokens per request, used only for sonnet dataset.",
    )
    sonnet_group.add_argument(
        "--sonnet-prefix-len",
        type=int,
        default=200,
        help=
        "Number of prefix tokens per request, used only for sonnet dataset.",
    )
    sharegpt_group = parser.add_argument_group("sharegpt dataset options")
    sharegpt_group.add_argument(
        "--sharegpt-output-len",
        type=int,
        default=None,
        help="Output length for each request. Overrides the output length "
        "from the ShareGPT dataset.")
    random_group = parser.add_argument_group("random dataset options")
    random_group.add_argument(
        "--random-input-len",
        type=int,
        default=1024,
        help=
        "Number of input tokens per request, used only for random sampling.",
    )
    random_group.add_argument(
        "--random-output-len",
        type=int,
        default=128,
        help=
        "Number of output tokens per request, used only for random sampling.",
    )
    random_group.add_argument(
        "--random-range-ratio",
        type=float,
        default=1.0,
        help="Range of sampled ratio of input/output length, "
        "used only for random sampling.",
    )
    random_group.add_argument(
        "--random-prefix-len",
        type=int,
        default=0,
        help="Number of fixed prefix tokens before random "
        " context. The length range of context in a random "
        " request is [random-prefix-len, "
        " random-prefix-len + random-prefix-len * random-range-ratio).")
    hf_group = parser.add_argument_group("hf dataset options")
    hf_group.add_argument("--hf-subset",
                          type=str,
                          default=None,
                          help="Subset of the HF dataset.")
    hf_group.add_argument("--hf-split",
                          type=str,
                          default=None,
                          help="Split of the HF dataset.")
    hf_group.add_argument(
        "--hf-output-len",
        type=int,
        default=None,
        help="Output length for each request. Overrides the output lengths "
        "from the sampled HF dataset.",
    )
    parser.add_argument(
        '--tokenizer-mode',
        type=str,
        default="auto",
        choices=['auto', 'slow', 'mistral'],
        help='The tokenizer mode.\n\n* "auto" will use the '
        'fast tokenizer if available.\n* "slow" will '
        'always use the slow tokenizer. \n* '
        '"mistral" will always use the `mistral_common` tokenizer.')
    args = parser.parse_args()
    main(args)
--- a/benchmarks/benchmark_serving_guided.py
+++ b/benchmarks/benchmark_serving_guided.py
@@ -0,0 +1,881 @@
 r"""Benchmark online serving throughput with guided decoding.
 On the server side, run one of the following commands:
    (vLLM OpenAI API server)
    vllm serve <your_model> --disable-log-requests
    (TGI backend)
    ./launch_tgi_server.sh <your_model> <max_batch_total_tokens>
 On the client side, run:
    python benchmarks/benchmark_serving.py \
        --backend <backend> \
        --model <your_model> \
        --dataset json \
        --guided-decoding-ratio 1.0 \
        --guided-decoding-backend xgrammar \
        --request-rate 10 \
        --num-prompts 1000
    when using tgi backend, add
        --endpoint /generate_stream
    to the end of the command above.
 """
 import argparse
 import asyncio
 import dataclasses
 import json
 import os
 import random
 import time
 import warnings
 from dataclasses import dataclass
 from typing import AsyncGenerator, List, Optional, Tuple
 import datasets
 import numpy as np
 import pandas as pd
 from backend_request_func import (ASYNC_REQUEST_FUNCS, RequestFuncInput,
                                  RequestFuncOutput)
 from tqdm.asyncio import tqdm
 from transformers import PreTrainedTokenizerBase
 try:
    from vllm.transformers_utils.tokenizer import get_tokenizer
 except ImportError:
    from backend_request_func import get_tokenizer
 try:
    from vllm.utils import FlexibleArgumentParser
 except ImportError:
    from argparse import ArgumentParser as FlexibleArgumentParser
 MILLISECONDS_TO_SECONDS_CONVERSION = 1000
@dataclass
 class BenchmarkMetrics:
    completed: int
    total_input: int
    total_output: int
    request_throughput: float
    request_goodput: float
    output_throughput: float
    total_token_throughput: float
    mean_ttft_ms: float
    median_ttft_ms: float
    std_ttft_ms: float
    percentiles_ttft_ms: List[Tuple[float, float]]
    mean_tpot_ms: float
    median_tpot_ms: float
    std_tpot_ms: float
    percentiles_tpot_ms: List[Tuple[float, float]]
    mean_itl_ms: float
    median_itl_ms: float
    std_itl_ms: float
    percentiles_itl_ms: List[Tuple[float, float]]
    # E2EL stands for end-to-end latency per request.
    # It is the time taken on the client side from sending
    # a request to receiving a complete response.
    mean_e2el_ms: float
    median_e2el_ms: float
    std_e2el_ms: float
    percentiles_e2el_ms: List[Tuple[float, float]]
@dataclasses.dataclass
 class SampleRequest:
    """A class representing a single inference request for benchmarking.
    Attributes:
        prompt: The input text prompt for the model.
        multi_modal_data: Optional dictionary containing multi-modal data (e.g.
            images).
        prompt_len: The length of the prompt in tokens.
        expected_output_len: The expected length of the output in tokens.
    """
    prompt: str
    prompt_len: int
    expected_output_len: int
    schema: dict
    structure_type: str
    completion: str = None
 def sample_requests(tokenizer: PreTrainedTokenizerBase,
                    args: argparse.Namespace) -> List[SampleRequest]:
    if args.dataset == 'json':
        if args.json_schema_path is None:
            dir_path = os.path.dirname(os.path.realpath(__file__))
            args.json_schema_path = os.path.join(dir_path,
                                                 "structured_schemas",
                                                 "structured_schema_1.json")
        with open(args.json_schema_path) as f:
            schema = json.load(f)
        prompt = f"Generate an example of a user profile given the following schema: {json.dumps(schema)}"  # noqa: E501
        input_len = len(tokenizer(prompt).input_ids)
        print(f"Input length of the prompt: {input_len} tokens")
        requests = [
            SampleRequest(prompt=prompt,
                          prompt_len=input_len,
                          expected_output_len=args.output_len,
                          schema=schema,
                          structure_type=args.structure_type)
            for _ in range(args.num_prompts)
        ]
    elif args.dataset == "grammar":
        schema = """
            ?start: select_statement
            ?select_statement: "SELECT " column_list " FROM " table_name
            ?column_list: column_name ("," column_name)*
            ?table_name: identifier
            ?column_name: identifier
            ?identifier: /[a-zA-Z_][a-zA-Z0-9_]*/
        """
        prompt = "Generate an SQL query to show the 'username' \
            and 'email' from the 'users' table."
        input_len = len(tokenizer(prompt).input_ids)
        print(f"Input length of the prompt: {input_len} tokens")
        requests = [
            SampleRequest(prompt=prompt,
                          prompt_len=input_len,
                          expected_output_len=args.output_len,
                          schema=schema,
                          structure_type=args.structure_type)
            for _ in range(args.num_prompts)
        ]
    elif args.dataset == "regex":
        regex = r"\w+@\w+\.com\n"
        args.regex = regex
        prompt = "Generate an email address for Alan Turing, \
            who works in Enigma. End in .com and new line. \
                Example result: alan.turing@enigma.com\n"
        input_len = len(tokenizer(prompt).input_ids)
        print(f"Input length of the prompt: {input_len} tokens")
        requests = [
            SampleRequest(prompt=prompt,
                          prompt_len=input_len,
                          expected_output_len=args.output_len,
                          schema=regex,
                          structure_type=args.structure_type)
            for _ in range(args.num_prompts)
        ]
    elif args.dataset == "choice":
        choice = ["Positive", "Negative"]
        args.choice = choice
        prompt = "Classify this sentiment: vLLM is wonderful!"
        input_len = len(tokenizer(prompt).input_ids)
        print(f"Input length of the prompt: {input_len} tokens")
        requests = [
            SampleRequest(prompt=prompt,
                          prompt_len=input_len,
                          expected_output_len=args.output_len,
                          schema=choice,
                          structure_type=args.structure_type)
            for _ in range(args.num_prompts)
        ]
    elif args.dataset == "xgrammar_bench":
        requests: List[SampleRequest] = []
        dataset = datasets.load_dataset("NousResearch/json-mode-eval",
                                        split="train")
        print(f"dataset has {len(dataset)} entries")
        len_dataset = len(dataset)
        for data_point_idx in range(args.num_prompts):
            idx = data_point_idx
            while idx >= len_dataset:
                idx -= len_dataset
            schema = dataset["schema"][idx]
            prompt = tokenizer.apply_chat_template(dataset["prompt"][idx],
                                                   tokenize=False)
            input_len = len(tokenizer(prompt).input_ids)
            completion = dataset["completion"][idx]
            requests.append(
                SampleRequest(prompt=prompt,
                              prompt_len=input_len,
                              expected_output_len=args.output_len,
                              schema=schema,
                              structure_type=args.structure_type,
                              completion=completion))
    return requests
 async def get_request(
    input_requests: List[SampleRequest],
    request_rate: float,
    burstiness: float = 1.0,
 ) -> AsyncGenerator[Tuple[int, SampleRequest], None]:
    """
    Asynchronously generates requests at a specified rate 
    with OPTIONAL burstiness.
    Args:
        input_requests: 
            A list of input requests, each represented as a tuple.
        request_rate: 
            The rate at which requests are generated (requests/s).
        burstiness (optional): 
            The burstiness factor of the request generation. 
            Only takes effect when request_rate is not inf.
            Default value is 1, which follows a Poisson process.
            Otherwise, the request intervals follow a gamma distribution.
            A lower burstiness value (0 < burstiness < 1) results 
            in more bursty requests, while a higher burstiness value 
            (burstiness > 1) results in a more uniform arrival of requests.
    """
    input_requests = iter(input_requests)
    # Calculate scale parameter theta to maintain the desired request_rate.
    assert burstiness > 0, (
        f"A positive burstiness factor is expected, but given {burstiness}.")
    theta = 1.0 / (request_rate * burstiness)
    for i, request in enumerate(input_requests):
        yield i, request
        if request_rate == float("inf"):
            # If the request rate is infinity, then we don't need to wait.
            continue
        # Sample the request interval from the gamma distribution.
        # If burstiness is 1, it follows exponential distribution.
        interval = np.random.gamma(shape=burstiness, scale=theta)
        # The next request will be sent after the interval.
        await asyncio.sleep(interval)
 def calculate_metrics(
    input_requests: List[Tuple[str, int, int]],
    outputs: List[RequestFuncOutput],
    dur_s: float,
    tokenizer: PreTrainedTokenizerBase,
    selected_percentile_metrics: List[str],
    selected_percentiles: List[float],
 ) -> Tuple[BenchmarkMetrics, List[int]]:
    actual_output_lens: List[int] = []
    total_input = 0
    completed = 0
    good_completed = 0
    itls: List[float] = []
    tpots: List[float] = []
    all_tpots: List[float] = []
    ttfts: List[float] = []
    e2els: List[float] = []
    for i in range(len(outputs)):
        if outputs[i].success:
            # We use the tokenizer to count the number of output tokens for all
            # serving backends instead of looking at len(outputs[i].itl) since
            # multiple output tokens may be bundled together
            # Note : this may inflate the output token count slightly
            output_len = len(
                tokenizer(outputs[i].generated_text,
                          add_special_tokens=False).input_ids)
            actual_output_lens.append(output_len)
            total_input += input_requests[i].prompt_len
            tpot = 0
            if output_len > 1:
                tpot = (outputs[i].latency - outputs[i].ttft) / (output_len -
                                                                 1)
                tpots.append(tpot)
            outputs[i].tpot = sum(tpots) / len(tpots) if len(tpots) else 0
            # Note: if output_len <= 1, we regard tpot as 0 for goodput
            all_tpots.append(tpot)
            itls += outputs[i].itl
            ttfts.append(outputs[i].ttft)
            e2els.append(outputs[i].latency)
            completed += 1
        else:
            actual_output_lens.append(0)
    if completed == 0:
        warnings.warn(
            "All requests failed. This is likely due to a misconfiguration "
            "on the benchmark arguments.",
            stacklevel=2)
    metrics = BenchmarkMetrics(
        completed=completed,
        total_input=total_input,
        total_output=sum(actual_output_lens),
        request_throughput=completed / dur_s,
        request_goodput=good_completed / dur_s,
        output_throughput=sum(actual_output_lens) / dur_s,
        total_token_throughput=(total_input + sum(actual_output_lens)) / dur_s,
        mean_ttft_ms=np.mean(ttfts or 0) *
        1000,  # ttfts is empty if streaming is not supported by backend
        std_ttft_ms=np.std(ttfts or 0) * 1000,
        median_ttft_ms=np.median(ttfts or 0) * 1000,
        percentiles_ttft_ms=[(p, np.percentile(ttfts or 0, p) * 1000)
                             for p in selected_percentiles],
        mean_tpot_ms=np.mean(tpots or 0) * 1000,
        std_tpot_ms=np.std(tpots or 0) * 1000,
        median_tpot_ms=np.median(tpots or 0) * 1000,
        percentiles_tpot_ms=[(p, np.percentile(tpots or 0, p) * 1000)
                             for p in selected_percentiles],
        mean_itl_ms=np.mean(itls or 0) * 1000,
        std_itl_ms=np.std(itls or 0) * 1000,
        median_itl_ms=np.median(itls or 0) * 1000,
        percentiles_itl_ms=[(p, np.percentile(itls or 0, p) * 1000)
                            for p in selected_percentiles],
        mean_e2el_ms=np.mean(e2els or 0) * 1000,
        std_e2el_ms=np.std(e2els or 0) * 1000,
        median_e2el_ms=np.median(e2els or 0) * 1000,
        percentiles_e2el_ms=[(p, np.percentile(e2els or 0, p) * 1000)
                             for p in selected_percentiles],
    )
    return metrics, actual_output_lens
 async def benchmark(
    backend: str,
    api_url: str,
    base_url: str,
    model_id: str,
    tokenizer: PreTrainedTokenizerBase,
    input_requests: List[SampleRequest],
    request_rate: float,
    burstiness: float,
    disable_tqdm: bool,
    profile: bool,
    selected_percentile_metrics: List[str],
    selected_percentiles: List[str],
    ignore_eos: bool,
    max_concurrency: Optional[int],
    guided_decoding_ratio: float,
    guided_decoding_backend: str,
 ):
    if backend in ASYNC_REQUEST_FUNCS:
        request_func = ASYNC_REQUEST_FUNCS[backend]
    else:
        raise ValueError(f"Unknown backend: {backend}")
    def prepare_extra_body(request) -> dict:
        extra_body = {}
        # Add the schema to the extra_body
        extra_body[request.structure_type] = request.schema
        # Add the specific guided_decoding_backend
        extra_body["guided_decoding_backend"] = guided_decoding_backend
        return extra_body
    print("Starting initial single prompt test run...")
    guided_decoding_req_idx = random.sample(
        range(len(input_requests)),
        int(len(input_requests) * guided_decoding_ratio))
    test_request = input_requests[0]
    test_input = RequestFuncInput(
        model=model_id,
        prompt=test_request.prompt,
        api_url=api_url,
        prompt_len=test_request.prompt_len,
        output_len=test_request.expected_output_len,
        ignore_eos=ignore_eos,
        extra_body=prepare_extra_body(test_request),
    )
    test_output = await request_func(request_func_input=test_input)
    if not test_output.success:
        raise ValueError(
            "Initial test run failed - Please make sure benchmark arguments "
            f"are correctly specified. Error: {test_output.error}")
    else:
        print("Initial test run completed. Starting main benchmark run...")
    if profile:
        print("Starting profiler...")
        profile_input = RequestFuncInput(
            model=model_id,
            prompt=test_request.prompt,
            api_url=base_url + "/start_profile",
            prompt_len=test_request.prompt_len,
            output_len=test_request.expected_output_len,
            ignore_eos=ignore_eos,
            extra_body=prepare_extra_body(test_request),
        )
        profile_output = await request_func(request_func_input=profile_input)
        if profile_output.success:
            print("Profiler started")
    if burstiness == 1.0:
        distribution = "Poisson process"
    else:
        distribution = "Gamma distribution"
    print(f"Traffic request rate: {request_rate}")
    print(f"Burstiness factor: {burstiness} ({distribution})")
    print(f"Maximum request concurrency: {max_concurrency}")
    pbar = None if disable_tqdm else tqdm(total=len(input_requests))
    # This can be used once the minimum Python version is 3.10 or higher,
    # and it will simplify the code in limited_request_func.
    #    semaphore = (asyncio.Semaphore(max_concurrency)
    #                 if max_concurrency else contextlib.nullcontext())
    semaphore = (asyncio.Semaphore(max_concurrency)
                 if max_concurrency else None)
    async def limited_request_func(request_func_input, pbar):
        if semaphore is None:
            return await request_func(request_func_input=request_func_input,
                                      pbar=pbar)
        async with semaphore:
            return await request_func(request_func_input=request_func_input,
                                      pbar=pbar)
    benchmark_start_time = time.perf_counter()
    tasks: List[asyncio.Task] = []
    expected: List[str] = []
    async for i, request in get_request(input_requests, request_rate,
                                        burstiness):
        extra_body = prepare_extra_body(
            request) if i in guided_decoding_req_idx else None
        request_func_input = RequestFuncInput(
            model=model_id,
            prompt=request.prompt,
            api_url=api_url,
            prompt_len=request.prompt_len,
            output_len=request.expected_output_len,
            ignore_eos=ignore_eos,
            extra_body=extra_body,
        )
        expected.append(request.completion)
        tasks.append(
            asyncio.create_task(
                limited_request_func(request_func_input=request_func_input,
                                     pbar=pbar)))
    outputs: List[RequestFuncOutput] = await asyncio.gather(*tasks)
    if profile:
        print("Stopping profiler...")
        profile_input = RequestFuncInput(
            model=model_id,
            prompt=test_request.prompt,
            api_url=base_url + "/stop_profile",
            prompt_len=test_request.prompt_len,
            output_len=test_request.expected_output_len,
            extra_body={test_request.structure_type: test_request.schema},
        )
        profile_output = await request_func(request_func_input=profile_input)
        if profile_output.success:
            print("Profiler stopped")
    if pbar is not None:
        pbar.close()
    benchmark_duration = time.perf_counter() - benchmark_start_time
    metrics, actual_output_lens = calculate_metrics(
        input_requests=input_requests,
        outputs=outputs,
        dur_s=benchmark_duration,
        tokenizer=tokenizer,
        selected_percentile_metrics=selected_percentile_metrics,
        selected_percentiles=selected_percentiles,
    )
    print("{s:{c}^{n}}".format(s=' Serving Benchmark Result ', n=50, c='='))
    print("{:<40} {:<10}".format("Successful requests:", metrics.completed))
    print("{:<40} {:<10.2f}".format("Benchmark duration (s):",
                                    benchmark_duration))
    print("{:<40} {:<10}".format("Total input tokens:", metrics.total_input))
    print("{:<40} {:<10}".format("Total generated tokens:",
                                 metrics.total_output))
    print("{:<40} {:<10.2f}".format("Request throughput (req/s):",
                                    metrics.request_throughput))
    print("{:<40} {:<10.2f}".format("Output token throughput (tok/s):",
                                    metrics.output_throughput))
    print("{:<40} {:<10.2f}".format("Total Token throughput (tok/s):",
                                    metrics.total_token_throughput))
    result = {
        "duration":
        benchmark_duration,
        "completed":
        metrics.completed,
        "total_input_tokens":
        metrics.total_input,
        "total_output_tokens":
        metrics.total_output,
        "request_throughput":
        metrics.request_throughput,
        "output_throughput":
        metrics.output_throughput,
        "total_token_throughput":
        metrics.total_token_throughput,
        "ttft_description":
        pd.Series([output.ttft for output in outputs]).describe().to_dict(),
        "tpot_description":
        pd.Series([output.tpot for output in outputs]).describe().to_dict(),
        "input_lens": [output.prompt_len for output in outputs],
        "output_lens":
        actual_output_lens,
        "ttfts": [output.ttft for output in outputs],
        "itls": [output.itl for output in outputs],
        "errors": [output.error for output in outputs],
    }
    ret = [{
        'generated': output.generated_text,
        'expected': gt
    } for output, gt in zip(outputs, expected)]
    def process_one_metric(
        # E.g., "ttft"
        metric_attribute_name: str,
        # E.g., "TTFT"
        metric_name: str,
        # E.g., "Time to First Token"
        metric_header: str,
    ):
        # This function prints and adds statistics of the specified
        # metric.
        if metric_attribute_name not in selected_percentile_metrics:
            return
        print("{s:{c}^{n}}".format(s=metric_header, n=50, c='-'))
        print("{:<40} {:<10.2f}".format(
            f"Mean {metric_name} (ms):",
            getattr(metrics, f"mean_{metric_attribute_name}_ms")))
        print("{:<40} {:<10.2f}".format(
            f"Median {metric_name} (ms):",
            getattr(metrics, f"median_{metric_attribute_name}_ms")))
        result[f"mean_{metric_attribute_name}_ms"] = getattr(
            metrics, f"mean_{metric_attribute_name}_ms")
        result[f"median_{metric_attribute_name}_ms"] = getattr(
            metrics, f"median_{metric_attribute_name}_ms")
        result[f"std_{metric_attribute_name}_ms"] = getattr(
            metrics, f"std_{metric_attribute_name}_ms")
        for p, value in getattr(metrics,
                                f"percentiles_{metric_attribute_name}_ms"):
            p_word = str(int(p)) if int(p) == p else str(p)
            print("{:<40} {:<10.2f}".format(f"P{p_word} {metric_name} (ms):",
                                            value))
            result[f"p{p_word}_{metric_attribute_name}_ms"] = value
    process_one_metric("ttft", "TTFT", "Time to First Token")
    process_one_metric("tpot", "TPOT",
                       "Time per Output Token (excl. 1st token)")
    process_one_metric("itl", "ITL", "Inter-token Latency")
    process_one_metric("e2el", "E2EL", "End-to-end Latency")
    print("=" * 50)
    return result, ret
 def evaluate(ret, args):
    def _eval_correctness_json(expected, actual):
        # extract json string from string using regex
        import re
        actual = actual.replace('\n', '').replace(' ', '').strip()
        try:
            actual = re.search(r'\{.*\}', actual).group()
            actual = json.loads(actual)
        except Exception:
            return False
        return True
    def _eval_correctness_choice(expected, actual):
        return actual in args.choice
    def _eval_correctness_regex(expected, actual):
        import re
        return re.match(args.regex, actual) is not None
    def _eval_correctness(expected, actual):
        if args.structure_type == 'guided_json':
            return _eval_correctness_json(expected, actual)
        elif args.structure_type == 'guided_regex':
            return _eval_correctness_regex(expected, actual)
        elif args.structure_type == 'guided_choice':
            return _eval_correctness_choice(expected, actual)
        else:
            return None
    scores = []
    for res in ret:
        score = _eval_correctness(res['expected'], res['generated'])
        res['correctness'] = score
        scores.append(score)
    not_none_scores = [score for score in scores if score is not None]
    return (sum(not_none_scores) / len(not_none_scores) *
            100) if len(not_none_scores) > 0 else None
 def main(args: argparse.Namespace):
    print(args)
    random.seed(args.seed)
    np.random.seed(args.seed)
    backend = args.backend
    model_id = args.model
    tokenizer_id = args.tokenizer if args.tokenizer is not None else args.model
    if args.base_url is not None:
        api_url = f"{args.base_url}{args.endpoint}"
        base_url = f"{args.base_url}"
    else:
        api_url = f"http://{args.host}:{args.port}{args.endpoint}"
        base_url = f"http://{args.host}:{args.port}"
    tokenizer = get_tokenizer(tokenizer_id,
                              trust_remote_code=args.trust_remote_code)
    if args.dataset == 'grammar':
        args.structure_type = 'guided_grammar'
    elif args.dataset == 'regex':
        args.structure_type = 'guided_regex'
    elif args.dataset == 'choice':
        args.structure_type = 'guided_choice'
    else:
        args.structure_type = 'guided_json'
    if args.no_guided_decoding:
        args.guided_decoding_ratio = 0
    if args.save_results:
        result_file_name = f'{args.guided_decoding_ratio}guided'
        result_file_name += f"_{backend}"
        result_file_name += f"_{args.request_rate}qps"
        result_file_name += f"_{args.model.split('/')[-1]}"
        result_file_name += f"_{args.dataset}"
        result_file_name += f"_{args.num_prompts}"
        result_file_name += f"_out{args.output_len}"
        result_file_name += ".txt"
    else:
        result_file_name = None
    input_requests = sample_requests(tokenizer, args)
    benchmark_result, ret = asyncio.run(
        benchmark(
            backend=backend,
            api_url=api_url,
            base_url=base_url,
            model_id=model_id,
            tokenizer=tokenizer,
            input_requests=input_requests,
            request_rate=args.request_rate,
            burstiness=args.burstiness,
            disable_tqdm=args.disable_tqdm,
            profile=args.profile,
            selected_percentile_metrics=args.percentile_metrics.split(","),
            selected_percentiles=[
                float(p) for p in args.metric_percentiles.split(",")
            ],
            ignore_eos=args.ignore_eos,
            max_concurrency=args.max_concurrency,
            guided_decoding_ratio=args.guided_decoding_ratio,
            guided_decoding_backend=args.guided_decoding_backend,
        ))
    # Save config and results to json
    score = evaluate(ret, args)
    print("correct_rate(%)", score, '\n')
    if args.save_results:
        results = {
            "backend":
            backend,
            "model_id":
            model_id,
            "tokenizer_id":
            tokenizer_id,
            "num_prompts":
            args.num_prompts,
            "request_rate":
            args.request_rate if args.request_rate < float("inf") else "inf",
            "burstiness":
            args.burstiness,
            "max_concurrency":
            args.max_concurrency,
            "correct_rate(%)":
            score
        }
        results = {"outputs": ret, **results, **benchmark_result}
        # Save to file
        if args.result_filename:
            result_file_name = args.result_filename
        if args.result_dir:
            result_file_name = os.path.join(args.result_dir, result_file_name)
        with open(result_file_name, "w", encoding='utf-8') as outfile:
            json.dump(results, outfile, indent=4)
 if __name__ == "__main__":
    parser = FlexibleArgumentParser(
        description="Benchmark the online serving throughput.")
    parser.add_argument(
        "--backend",
        type=str,
        default="vllm",
        choices=list(ASYNC_REQUEST_FUNCS.keys()),
    )
    parser.add_argument(
        "--base-url",
        type=str,
        default=None,
        help="Server or API base url if not using http host and port.",
    )
    parser.add_argument("--host", type=str, default="localhost")
    parser.add_argument("--port", type=int, default=8000)
    parser.add_argument(
        "--endpoint",
        type=str,
        default="/v1/completions",
        help="API endpoint.",
    )
    parser.add_argument(
        "--dataset",
        default='json',
        choices=['json', 'grammar', 'regex', 'choice', 'xgrammar_bench'])
    parser.add_argument("--json_schema_path",
                        type=str,
                        default=None,
                        help="Path to json schema.")
    parser.add_argument(
        "--max-concurrency",
        type=int,
        default=None,
        help="Maximum number of concurrent requests. This can be used "
        "to help simulate an environment where a higher level component "
        "is enforcing a maximum number of concurrent requests. While the "
        "--request-rate argument controls the rate at which requests are "
        "initiated, this argument will control how many are actually allowed "
        "to execute at a time. This means that when used in combination, the "
        "actual request rate may be lower than specified with --request-rate, "
        "if the server is not processing requests fast enough to keep up.")
    parser.add_argument(
        "--model",
        type=str,
        required=True,
        help="Name of the model.",
    )
    parser.add_argument(
        "--tokenizer",
        type=str,
        help=
        "Name or path of the tokenizer, if not using the default tokenizer.",  # noqa: E501
    )
    parser.add_argument(
        "--num-prompts",
        type=int,
        default=1000,
        help="Number of prompts to process.",
    )
    parser.add_argument(
        "--output-len",
        type=int,
        default=128,
        help="Number of output tokens.",
    )
    parser.add_argument(
        "--request-rate",
        type=float,
        default=float("inf"),
        help="Number of requests per second. If this is inf, "
        "then all the requests are sent at time 0. "
        "Otherwise, we use Poisson process or gamma distribution "
        "to synthesize the request arrival times.",
    )
    parser.add_argument(
        "--burstiness",
        type=float,
        default=1.0,
        help="Burstiness factor of the request generation. "
        "Only take effect when request_rate is not inf. "
        "Default value is 1, which follows Poisson process. "
        "Otherwise, the request intervals follow a gamma distribution. "
        "A lower burstiness value (0 < burstiness < 1) results in more "
        "bursty requests. A higher burstiness value (burstiness > 1) "
        "results in a more uniform arrival of requests.",
    )
    parser.add_argument("--seed", type=int, default=0)
    parser.add_argument(
        "--trust-remote-code",
        action="store_true",
        help="Trust remote code from huggingface",
    )
    parser.add_argument(
        "--disable-tqdm",
        action="store_true",
        help="Specify to disable tqdm progress bar.",
    )
    parser.add_argument(
        "--save-results",
        action="store_true",
        help="Specify to save benchmark results to a json file",
    )
    parser.add_argument(
        "--profile",
        action="store_true",
        help="Use Torch Profiler. The endpoint must be launched with "
        "VLLM_TORCH_PROFILER_DIR to enable profiler.",
    )
    parser.add_argument(
        "--result-dir",
        type=str,
        default=None,
        help="Specify directory to save benchmark json results."
        "If not specified, results are saved in the current directory.",
    )
    parser.add_argument(
        "--result-filename",
        type=str,
        default=None,
        help="Specify the filename to save benchmark json results."
        "If not specified, results will be saved in "
        "{backend}-{args.request_rate}qps-{base_model_id}-{current_dt}.json"
        " format.",
    )
    parser.add_argument(
        "--ignore-eos",
        action="store_true",
        help="Set ignore_eos flag when sending the benchmark request."
        "Warning: ignore_eos is not supported in deepspeed_mii and tgi.")
    parser.add_argument(
        "--percentile-metrics",
        type=str,
        default="ttft,tpot,itl",
        help="Comma-seperated list of selected metrics to report percentils. "
        "This argument specifies the metrics to report percentiles. "
        "Allowed metric names are \"ttft\", \"tpot\", \"itl\", \"e2el\". "
        "Default value is \"ttft,tpot,itl\".")
    parser.add_argument(
        "--metric-percentiles",
        type=str,
        default="99",
        help="Comma-seperated list of percentiles for selected metrics. "
        "To report 25-th, 50-th, and 75-th percentiles, use \"25,50,75\". "
        "Default value is \"99\". "
        "Use \"--percentile-metrics\" to select metrics.",
    )
    parser.add_argument("--no-guided-decoding",
                        action='store_true',
                        default=False,
                        help="Whether to disable JSON decoding or not.")
    parser.add_argument("--guided-decoding-ratio",
                        type=float,
                        default=1.0,
                        help="Ratio of Guided Decoding requests")
    parser.add_argument("--guided-decoding-backend",
                        type=str,
                        choices=["outlines", "lm-format-enforcer", "xgrammar"],
                        default="xgrammar",
                        help="Backend to use for guided decoding")
    args = parser.parse_args()
    main(args)
--- a/benchmarks/benchmark_throughput.py
+++ b/benchmarks/benchmark_throughput.py
@@ -1,12 +1,15 @@
 """Benchmark offline inference throughput."""
 import argparse
 import dataclasses
 import json
 import random
 import time
-from typing import List, Optional, Tuple
+from functools import cache
 from typing import Dict, List, Optional, Tuple
 import torch
 import uvloop
 from PIL import Image
 from tqdm import tqdm
 from transformers import (AutoModelForCausalLM, AutoTokenizer,
                          PreTrainedTokenizerBase)
@@ -14,16 +17,83 @@ from transformers import (AutoModelForCausalLM, AutoTokenizer,
 from vllm.engine.arg_utils import AsyncEngineArgs, EngineArgs
 from vllm.entrypoints.openai.api_server import (
    build_async_engine_client_from_engine_args)
-from vllm.model_executor.layers.quantization import QUANTIZATION_METHODS
+from vllm.inputs import TextPrompt
 from vllm.lora.request import LoRARequest
 from vllm.lora.utils import get_adapter_absolute_path
 from vllm.multimodal import MultiModalDataDict
 from vllm.sampling_params import BeamSearchParams
 from vllm.transformers_utils.tokenizer import AnyTokenizer, get_lora_tokenizer
 from vllm.utils import FlexibleArgumentParser, merge_async_iterators
-def sample_requests(
+@dataclasses.dataclass
-    dataset_path: str,
+class SampleRequest:
-    num_requests: int,
+    """A class representing a single inference request for benchmarking.
-    tokenizer: PreTrainedTokenizerBase,
+
-    fixed_output_len: Optional[int],
+    Attributes:
-) -> List[Tuple[str, int, int]]:
+        prompt: The input text prompt for the model.
        prompt_len: The length of the prompt in tokens.
        expected_output_len: The expected length of the output in tokens.
        multi_modal_data: Optional dictionary containing multi-modal data (e.g.
            images).
        lora_request: Optional LoRARequest specifying the LoRA to use. 
    """
    prompt: str
    prompt_len: int
    expected_output_len: int
    multi_modal_data: Optional[MultiModalDataDict] = None
    lora_request: Optional[LoRARequest] = None
 def _get_prompt_for_image_model(question: str, *, model: str) -> str:
    """Prepend and append special tokens around the question to form a prompt.
    Args:
        question: The input question text to wrap with special tokens
        model: The name of the model being used, to determine which special
            tokens to add
    Returns:
        The formatted prompt string with appropriate special tokens for the
            model
    Raises:
        ValueError: If an unsupported model name is provided
    """
    model = model.lower()
    if "pixtral" in model:
        return f"<s>[INST]{question}\n[IMG][/INST]"
    raise ValueError(f"Unsupported model {model}")
@cache
 def lora_path_on_disk(lora_path: str) -> str:
    return get_adapter_absolute_path(lora_path)
 lora_tokenizer_cache: Dict[int, AnyTokenizer] = {}
 def get_random_lora_request(
        args: argparse.Namespace
 ) -> Tuple[LoRARequest, Optional[AnyTokenizer]]:
    global lora_tokenizer_cache
    lora_id = random.randint(1, args.max_loras)
    lora_request = LoRARequest(lora_name=str(lora_id),
                               lora_int_id=lora_id,
                               lora_path=lora_path_on_disk(args.lora_path))
    if lora_id not in lora_tokenizer_cache:
        lora_tokenizer_cache[lora_id] = get_lora_tokenizer(lora_request)
    return lora_request, lora_tokenizer_cache[lora_id]
 def sample_requests(tokenizer: PreTrainedTokenizerBase,
                    args: argparse.Namespace) -> List[SampleRequest]:
    dataset_path: str = args.dataset
    num_requests: int = args.num_prompts
    fixed_output_len: Optional[int] = args.output_len
    model: str = args.model
    if fixed_output_len is not None and fixed_output_len < 4:
        raise ValueError("output_len too small")
@@ -32,24 +102,46 @@ def sample_requests(
        dataset = json.load(f)
    # Filter out the conversations with less than 2 turns.
    dataset = [data for data in dataset if len(data["conversations"]) >= 2]
    # Only keep the first two turns of each conversation.
    dataset = [(data["conversations"][0]["value"],
                data["conversations"][1]["value"]) for data in dataset]
    # Shuffle the dataset.
    random.shuffle(dataset)
    # Filter out sequences that are too long or too short
-    filtered_dataset: List[Tuple[str, int, int]] = []
+    filtered_dataset: List[SampleRequest] = []
-    for i in range(len(dataset)):
+    for data in tqdm(dataset,
                     total=len(filtered_dataset),
                     desc="sampling requests"):
        if len(filtered_dataset) == num_requests:
            break
        # Only keep the first two turns of each conversation.
        prompt = data["conversations"][0]["value"]
        completion = data["conversations"][1]["value"]
        multi_modal_data: Optional[MultiModalDataDict] = None
        if "image" in data:
            multi_modal_data = multi_modal_data or {}
            image_path = data["image"]
            # TODO(vllm-project/vllm/issues/9778): Support multiple images.
            assert isinstance(image_path,
                              str), "Only support single image input"
            try:
                multi_modal_data["image"] = Image.open(image_path).convert(
                    "RGB")
            except FileNotFoundError:
                # Ignore datapoint where asset is missing
                continue
            prompt = _get_prompt_for_image_model(question=prompt, model=model)
        request_tokenizer = tokenizer
        lora_request: Optional[LoRARequest] = None
        if args.enable_lora:
            lora_request, lora_tokenizer = get_random_lora_request(args)
            if lora_tokenizer:
                request_tokenizer = lora_tokenizer
        # Tokenize the prompts and completions.
-        prompt = dataset[i][0]
+        prompt_token_ids = request_tokenizer(prompt).input_ids
-        prompt_token_ids = tokenizer(prompt).input_ids
+        completion_token_ids = request_tokenizer(completion).input_ids
        completion = dataset[i][1]
        completion_token_ids = tokenizer(completion).input_ids
        prompt_len = len(prompt_token_ids)
        output_len = len(completion_token_ids
                         ) if fixed_output_len is None else fixed_output_len
@@ -59,164 +151,108 @@ def sample_requests(
        if prompt_len > 1024 or prompt_len + output_len > 2048:
            # Prune too long sequences.
            continue
-        filtered_dataset.append((prompt, prompt_len, output_len))
+        filtered_dataset.append(
            SampleRequest(prompt=prompt,
                          prompt_len=prompt_len,
                          expected_output_len=output_len,
                          multi_modal_data=multi_modal_data,
                          lora_request=lora_request))
    return filtered_dataset
 def run_vllm(
-    requests: List[Tuple[str, int, int]],
+    requests: List[SampleRequest],
    model: str,
    tokenizer: str,
    quantization: Optional[str],
    tensor_parallel_size: int,
    seed: int,
    n: int,
-    use_beam_search: bool,
+    engine_args: EngineArgs,
    trust_remote_code: bool,
    dtype: str,
    max_model_len: Optional[int],
    enforce_eager: bool,
    kv_cache_dtype: str,
    quantization_param_path: Optional[str],
    device: str,
    enable_prefix_caching: bool,
    enable_chunked_prefill: bool,
    max_num_batched_tokens: int,
    distributed_executor_backend: Optional[str],
    gpu_memory_utilization: float = 0.9,
    num_scheduler_steps: int = 1,
    use_v2_block_manager: bool = False,
    download_dir: Optional[str] = None,
    load_format: str = EngineArgs.load_format,
    disable_async_output_proc: bool = False,
 ) -> float:
    from vllm import LLM, SamplingParams
-    llm = LLM(
+    llm = LLM(**dataclasses.asdict(engine_args))
        model=model,
        tokenizer=tokenizer,
        quantization=quantization,
        tensor_parallel_size=tensor_parallel_size,
        seed=seed,
        trust_remote_code=trust_remote_code,
        dtype=dtype,
        max_model_len=max_model_len,
        gpu_memory_utilization=gpu_memory_utilization,
        enforce_eager=enforce_eager,
        kv_cache_dtype=kv_cache_dtype,
        quantization_param_path=quantization_param_path,
        device=device,
        enable_prefix_caching=enable_prefix_caching,
        download_dir=download_dir,
        enable_chunked_prefill=enable_chunked_prefill,
        max_num_batched_tokens=max_num_batched_tokens,
        distributed_executor_backend=distributed_executor_backend,
        load_format=load_format,
        num_scheduler_steps=num_scheduler_steps,
        use_v2_block_manager=use_v2_block_manager,
        disable_async_output_proc=disable_async_output_proc,
    )
    # Add the requests to the engine.
-    prompts: List[str] = []
+    prompts: List[TextPrompt] = []
    sampling_params: List[SamplingParams] = []
-    for prompt, _, output_len in requests:
+    for request in requests:
-        prompts.append(prompt)
+        prompts.append(
            TextPrompt(prompt=request.prompt,
                       multi_modal_data=request.multi_modal_data))
        sampling_params.append(
            SamplingParams(
                n=n,
-                temperature=0.0 if use_beam_search else 1.0,
+                temperature=1.0,
                top_p=1.0,
                use_beam_search=use_beam_search,
                ignore_eos=True,
-                max_tokens=output_len,
+                max_tokens=request.expected_output_len,
            ))
    lora_requests: Optional[List[LoRARequest]] = None
    if engine_args.enable_lora:
        lora_requests = [request.lora_request for request in requests]
-    start = time.perf_counter()
+    use_beam_search = False
-    llm.generate(prompts, sampling_params, use_tqdm=True)
+
-    end = time.perf_counter()
+    if not use_beam_search:
        start = time.perf_counter()
        llm.generate(prompts,
                     sampling_params,
                     lora_request=lora_requests,
                     use_tqdm=True)
        end = time.perf_counter()
    else:
        assert lora_requests is None, "BeamSearch API does not support LoRA"
        prompts = [request.prompt for request in requests]
        # output_len should be the same for all requests.
        output_len = requests[0][2]
        for request in requests:
            assert request.expected_output_len == output_len
        start = time.perf_counter()
        llm.beam_search(
            prompts,
            BeamSearchParams(
                beam_width=n,
                max_tokens=output_len,
                ignore_eos=True,
            ))
        end = time.perf_counter()
    return end - start
 async def run_vllm_async(
-    requests: List[Tuple[str, int, int]],
+    requests: List[SampleRequest],
    model: str,
    tokenizer: str,
    quantization: Optional[str],
    tensor_parallel_size: int,
    seed: int,
    n: int,
-    use_beam_search: bool,
+    engine_args: AsyncEngineArgs,
    trust_remote_code: bool,
    dtype: str,
    max_model_len: Optional[int],
    enforce_eager: bool,
    kv_cache_dtype: str,
    quantization_param_path: Optional[str],
    device: str,
    enable_prefix_caching: bool,
    enable_chunked_prefill: bool,
    max_num_batched_tokens: int,
    distributed_executor_backend: Optional[str],
    gpu_memory_utilization: float = 0.9,
    num_scheduler_steps: int = 1,
    use_v2_block_manager: bool = False,
    download_dir: Optional[str] = None,
    load_format: str = EngineArgs.load_format,
    disable_async_output_proc: bool = False,
    disable_frontend_multiprocessing: bool = False,
 ) -> float:
    from vllm import SamplingParams
    engine_args = AsyncEngineArgs(
        model=model,
        tokenizer=tokenizer,
        quantization=quantization,
        tensor_parallel_size=tensor_parallel_size,
        seed=seed,
        trust_remote_code=trust_remote_code,
        dtype=dtype,
        max_model_len=max_model_len,
        gpu_memory_utilization=gpu_memory_utilization,
        enforce_eager=enforce_eager,
        kv_cache_dtype=kv_cache_dtype,
        quantization_param_path=quantization_param_path,
        device=device,
        enable_prefix_caching=enable_prefix_caching,
        download_dir=download_dir,
        enable_chunked_prefill=enable_chunked_prefill,
        max_num_batched_tokens=max_num_batched_tokens,
        distributed_executor_backend=distributed_executor_backend,
        load_format=load_format,
        num_scheduler_steps=num_scheduler_steps,
        use_v2_block_manager=use_v2_block_manager,
        disable_async_output_proc=disable_async_output_proc,
        worker_use_ray=False,
        engine_use_ray=False,
        disable_log_requests=True,
    )
    async with build_async_engine_client_from_engine_args(
            engine_args, disable_frontend_multiprocessing) as llm:
        # Add the requests to the engine.
-        prompts: List[str] = []
+        prompts: List[TextPrompt] = []
        sampling_params: List[SamplingParams] = []
-        for prompt, _, output_len in requests:
+        lora_requests: List[Optional[LoRARequest]] = []
-            prompts.append(prompt)
+        for request in requests:
            prompts.append(
                TextPrompt(prompt=request.prompt,
                           multi_modal_data=request.multi_modal_data))
            sampling_params.append(
                SamplingParams(
                    n=n,
-                    temperature=0.0 if use_beam_search else 1.0,
+                    temperature=1.0,
                    top_p=1.0,
                    use_beam_search=use_beam_search,
                    ignore_eos=True,
-                    max_tokens=output_len,
+                    max_tokens=request.expected_output_len,
                ))
            lora_requests.append(request.lora_request)
        generators = []
        start = time.perf_counter()
-        for i, (prompt, sp) in enumerate(zip(prompts, sampling_params)):
+        for i, (prompt, sp,
-            generator = llm.generate(prompt, sp, request_id=f"test{i}")
+                lr) in enumerate(zip(prompts, sampling_params, lora_requests)):
            generator = llm.generate(prompt,
                                     sp,
                                     lora_request=lr,
                                     request_id=f"test{i}")
            generators.append(generator)
        all_gens = merge_async_iterators(*generators)
        async for i, res in all_gens:
@@ -226,15 +262,13 @@ async def run_vllm_async(
 def run_hf(
-    requests: List[Tuple[str, int, int]],
+    requests: List[SampleRequest],
    model: str,
    tokenizer: PreTrainedTokenizerBase,
    n: int,
    use_beam_search: bool,
    max_batch_size: int,
    trust_remote_code: bool,
 ) -> float:
    assert not use_beam_search
    llm = AutoModelForCausalLM.from_pretrained(
        model, torch_dtype=torch.float16, trust_remote_code=trust_remote_code)
    if llm.config.model_type == "llama":
@@ -266,7 +300,7 @@ def run_hf(
                              padding=True).input_ids
        llm_outputs = llm.generate(
            input_ids=input_ids.cuda(),
-            do_sample=not use_beam_search,
+            do_sample=True,
            num_return_sequences=n,
            temperature=1.0,
            top_p=1.0,
@@ -286,14 +320,14 @@ def run_hf(
 def run_mii(
-    requests: List[Tuple[str, int, int]],
+    requests: List[SampleRequest],
    model: str,
    tensor_parallel_size: int,
    output_len: int,
 ) -> float:
    from mii import client, serve
    llm = serve(model, tensor_parallel=tensor_parallel_size)
-    prompts = [prompt for prompt, _, _ in requests]
+    prompts = [request.prompt for request in requests]
    start = time.perf_counter()
    llm.generate(prompts, max_new_tokens=output_len)
@@ -311,47 +345,83 @@ def main(args: argparse.Namespace):
    tokenizer = AutoTokenizer.from_pretrained(
        args.tokenizer, trust_remote_code=args.trust_remote_code)
    if args.dataset is None:
-        # Synthesize a prompt with the given input length.
+        vocab_size = tokenizer.vocab_size
-        prompt = "hi" * (args.input_len - 1)
+        requests = []
-        requests = [(prompt, args.input_len, args.output_len)
+        for _ in range(args.num_prompts):
-                    for _ in range(args.num_prompts)]
+
            request_tokenizer = tokenizer
            lora_request: Optional[LoRARequest] = None
            if args.enable_lora:
                lora_request, lora_tokenizer = get_random_lora_request(args)
                if lora_tokenizer:
                    request_tokenizer = lora_tokenizer
            # Synthesize a prompt with the given input length.
            candidate_ids = [
                random.randint(0, vocab_size - 1)
                for _ in range(args.input_len)
            ]
            # As tokenizer may add additional tokens like BOS, we need to try
            # different lengths to get the desired input length.
            for _ in range(5):  # Max attempts to correct
                candidate_prompt = request_tokenizer.decode(candidate_ids)
                tokenized_len = len(request_tokenizer.encode(candidate_prompt))
                if tokenized_len == args.input_len:
                    break
                # Adjust length based on difference
                diff = args.input_len - tokenized_len
                if diff > 0:
                    candidate_ids.extend([
                        random.randint(100, vocab_size - 100)
                        for _ in range(diff)
                    ])
                else:
                    candidate_ids = candidate_ids[:diff]
            requests.append(
                SampleRequest(prompt=candidate_prompt,
                              prompt_len=args.input_len,
                              expected_output_len=args.output_len,
                              lora_request=lora_request))
    else:
-        requests = sample_requests(args.dataset, args.num_prompts, tokenizer,
+        requests = sample_requests(tokenizer, args)
                                   args.output_len)
    is_multi_modal = any(request.multi_modal_data is not None
                         for request in requests)
    if args.backend == "vllm":
        run_args = [
            requests, args.model, args.tokenizer, args.quantization,
            args.tensor_parallel_size, args.seed, args.n, args.use_beam_search,
            args.trust_remote_code, args.dtype, args.max_model_len,
            args.enforce_eager, args.kv_cache_dtype,
            args.quantization_param_path, args.device,
            args.enable_prefix_caching, args.enable_chunked_prefill,
            args.max_num_batched_tokens, args.distributed_executor_backend,
            args.gpu_memory_utilization, args.num_scheduler_steps,
            args.use_v2_block_manager, args.download_dir, args.load_format,
            args.disable_async_output_proc
        ]
        if args.async_engine:
-            run_args.append(args.disable_frontend_multiprocessing)
+            elapsed_time = uvloop.run(
-            elapsed_time = uvloop.run(run_vllm_async(*run_args))
+                run_vllm_async(
                    requests,
                    args.n,
                    AsyncEngineArgs.from_cli_args(args),
                    args.disable_frontend_multiprocessing,
                ))
        else:
-            elapsed_time = run_vllm(*run_args)
+            elapsed_time = run_vllm(requests, args.n,
                                    EngineArgs.from_cli_args(args))
    elif args.backend == "hf":
        assert args.tensor_parallel_size == 1
        elapsed_time = run_hf(requests, args.model, tokenizer, args.n,
-                              args.use_beam_search, args.hf_max_batch_size,
+                              args.hf_max_batch_size, args.trust_remote_code)
                              args.trust_remote_code)
    elif args.backend == "mii":
        elapsed_time = run_mii(requests, args.model, args.tensor_parallel_size,
                               args.output_len)
    else:
        raise ValueError(f"Unknown backend: {args.backend}")
-    total_num_tokens = sum(prompt_len + output_len
+    total_num_tokens = sum(request.prompt_len + request.expected_output_len
-                           for _, prompt_len, output_len in requests)
+                           for request in requests)
    total_output_tokens = sum(request.expected_output_len
                              for request in requests)
    if is_multi_modal:
        print("\033[91mWARNING\033[0m: Multi-modal request detected. The "
              "following metrics are not accurate because image tokens are not"
              " counted. See vllm-project/vllm/issues/9778 for details.")
        # TODO(vllm-project/vllm/issues/9778): Count molti-modal token length.
    print(f"Throughput: {len(requests) / elapsed_time:.2f} requests/s, "
-          f"{total_num_tokens / elapsed_time:.2f} tokens/s")
+          f"{total_num_tokens / elapsed_time:.2f} total tokens/s, "
          f"{total_output_tokens / elapsed_time:.2f} output tokens/s")
    # Output JSON results if specified
    if args.output_json:
@@ -375,7 +445,9 @@ if __name__ == "__main__":
    parser.add_argument("--dataset",
                        type=str,
                        default=None,
-                        help="Path to the dataset.")
+                        help="Path to the dataset. The dataset is expected to "
                        "be a json in form of List[Dict[..., conversations: "
                        "List[Dict[..., value: <prompt_or_response>]]]]")
    parser.add_argument("--input-len",
                        type=int,
                        default=None,
@@ -385,144 +457,23 @@ if __name__ == "__main__":
                        default=None,
                        help="Output length for each request. Overrides the "
                        "output length from the dataset.")
    parser.add_argument("--model", type=str, default="facebook/opt-125m")
    parser.add_argument("--tokenizer", type=str, default=None)
    parser.add_argument('--quantization',
                        '-q',
                        choices=[*QUANTIZATION_METHODS, None],
                        default=None)
    parser.add_argument("--tensor-parallel-size", "-tp", type=int, default=1)
    parser.add_argument("--n",
                        type=int,
                        default=1,
                        help="Number of generated sequences per prompt.")
    parser.add_argument("--use-beam-search", action="store_true")
    parser.add_argument("--num-prompts",
                        type=int,
                        default=1000,
                        help="Number of prompts to process.")
    parser.add_argument("--seed", type=int, default=0)
    parser.add_argument("--hf-max-batch-size",
                        type=int,
                        default=None,
                        help="Maximum batch size for HF backend.")
    parser.add_argument('--trust-remote-code',
                        action='store_true',
                        help='trust remote code from huggingface')
    parser.add_argument(
        '--max-model-len',
        type=int,
        default=None,
        help='Maximum length of a sequence (including prompt and output). '
        'If None, will be derived from the model.')
    parser.add_argument(
        '--dtype',
        type=str,
        default='auto',
        choices=['auto', 'half', 'float16', 'bfloat16', 'float', 'float32'],
        help='data type for model weights and activations. '
        'The "auto" option will use FP16 precision '
        'for FP32 and FP16 models, and BF16 precision '
        'for BF16 models.')
    parser.add_argument('--gpu-memory-utilization',
                        type=float,
                        default=0.9,
                        help='the fraction of GPU memory to be used for '
                        'the model executor, which can range from 0 to 1.'
                        'If unspecified, will use the default value of 0.9.')
    parser.add_argument("--enforce-eager",
                        action="store_true",
                        help="enforce eager execution")
    parser.add_argument(
        '--kv-cache-dtype',
        type=str,
        choices=['auto', 'fp8', 'fp8_e5m2', 'fp8_e4m3'],
        default="auto",
        help='Data type for kv cache storage. If "auto", will use model '
        'data type. CUDA 11.8+ supports fp8 (=fp8_e4m3) and fp8_e5m2. '
        'ROCm (AMD GPU) supports fp8 (=fp8_e4m3)')
    parser.add_argument(
        '--quantization-param-path',
        type=str,
        default=None,
        help='Path to the JSON file containing the KV cache scaling factors. '
        'This should generally be supplied, when KV cache dtype is FP8. '
        'Otherwise, KV cache scaling factors default to 1.0, which may cause '
        'accuracy issues. FP8_E5M2 (without scaling) is only supported on '
        'cuda version greater than 11.8. On ROCm (AMD GPU), FP8_E4M3 is '
        'instead supported for common inference criteria.')
    parser.add_argument(
        "--device",
        type=str,
        default="auto",
        choices=["auto", "cuda", "cpu", "openvino", "tpu", "xpu"],
        help='device type for vLLM execution, supporting CUDA, OpenVINO and '
        'CPU.')
    parser.add_argument(
        "--num-scheduler-steps",
        type=int,
        default=1,
        help="Maximum number of forward steps per scheduler call.")
    parser.add_argument("--use-v2-block-manager",
                        action='store_true',
                        help="Enable block manager v2.")
    parser.add_argument(
        "--enable-prefix-caching",
        action='store_true',
        help="Enable automatic prefix caching for vLLM backend.")
    parser.add_argument("--enable-chunked-prefill",
                        action='store_true',
                        help="enable chunked prefill for vLLM backend.")
    parser.add_argument('--max-num-batched-tokens',
                        type=int,
                        default=None,
                        help='maximum number of batched tokens per '
                        'iteration')
    parser.add_argument('--download-dir',
                        type=str,
                        default=None,
                        help='directory to download and load the weights, '
                        'default to the default cache dir of huggingface')
    parser.add_argument(
        '--output-json',
        type=str,
        default=None,
        help='Path to save the throughput results in JSON format.')
    parser.add_argument(
        '--distributed-executor-backend',
        choices=['ray', 'mp'],
        default=None,
        help='Backend to use for distributed serving. When more than 1 GPU '
        'is used, will be automatically set to "ray" if installed '
        'or "mp" (multiprocessing) otherwise.')
    parser.add_argument(
        '--load-format',
        type=str,
        default=EngineArgs.load_format,
        choices=[
            'auto', 'pt', 'safetensors', 'npcache', 'dummy', 'tensorizer',
            'bitsandbytes'
        ],
        help='The format of the model weights to load.\n\n'
        '* "auto" will try to load the weights in the safetensors format '
        'and fall back to the pytorch bin format if safetensors format '
        'is not available.\n'
        '* "pt" will load the weights in the pytorch bin format.\n'
        '* "safetensors" will load the weights in the safetensors format.\n'
        '* "npcache" will load the weights in pytorch format and store '
        'a numpy cache to speed up the loading.\n'
        '* "dummy" will initialize the weights with random values, '
        'which is mainly for profiling.\n'
        '* "tensorizer" will load the weights using tensorizer from '
        'CoreWeave. See the Tensorize vLLM Model script in the Examples'
        'section for more information.\n'
        '* "bitsandbytes" will load the weights using bitsandbytes '
        'quantization.\n')
    parser.add_argument(
        "--disable-async-output-proc",
        action='store_true',
        default=False,
        help="Disable async output processor for vLLM backend.")
    parser.add_argument("--async-engine",
                        action='store_true',
                        default=False,
@@ -531,6 +482,15 @@ if __name__ == "__main__":
                        action='store_true',
                        default=False,
                        help="Disable decoupled async engine frontend.")
    # LoRA
    parser.add_argument(
        "--lora-path",
        type=str,
        default=None,
        help="Path to the lora adapters to use. This can be an absolute path, "
        "a relative path, or a Hugging Face model identifier.")
    parser = AsyncEngineArgs.add_cli_args(parser)
    args = parser.parse_args()
    if args.tokenizer is None:
        args.tokenizer = args.model
@@ -539,6 +499,8 @@ if __name__ == "__main__":
        assert args.output_len is not None
    else:
        assert args.input_len is None
    if args.enable_lora:
        assert args.lora_path is not None
    if args.backend == "vllm":
        if args.hf_max_batch_size is not None:
@@ -548,13 +510,14 @@ if __name__ == "__main__":
            raise ValueError("HF max batch size is required for HF backend.")
        if args.quantization is not None:
            raise ValueError("Quantization is only for vLLM backend.")
        if args.enable_lora is not None:
            raise ValueError("LoRA benchmarking is only supported for vLLM"
                             " backend")
    elif args.backend == "mii":
        if args.dtype != "auto":
            raise ValueError("dtype must be auto for MII backend.")
        if args.n != 1:
            raise ValueError("n must be 1 for MII backend.")
        if args.use_beam_search:
            raise ValueError("Beam search is not supported for MII backend.")
        if args.quantization is not None:
            raise ValueError("Quantization is only for vLLM backend.")
        if args.hf_max_batch_size is not None:
@@ -562,4 +525,7 @@ if __name__ == "__main__":
        if args.tokenizer != args.model:
            raise ValueError("Tokenizer must be the same as the model for MII "
                             "backend.")
        if args.enable_lora is not None:
            raise ValueError("LoRA benchmarking is only supported for vLLM"
                             " backend")
    main(args)
--- a/Show More
+++ b/Show More
`@@ -1,2 +1,2 @@`
	`github: [vllm-project]`	`github: [vllm-project]`
	`open_collective: [vllm]`	`open_collective: vllm`