Bump version to v0.6.0 (#8166)

Co-authored-by: Harsha Bikki <harbikh@amazon.com>

.buildkite/check-wheel-size.py

@@ -1,36 +1,43 @@
 import os
+import sys
 import zipfile
 
-MAX_SIZE_MB = 200
+# Read the VLLM_MAX_SIZE_MB environment variable, defaulting to 250 MB
+VLLM_MAX_SIZE_MB = int(os.environ.get('VLLM_MAX_SIZE_MB', 250))
 
 
 def print_top_10_largest_files(zip_file):
+    """Print the top 10 largest files in the given zip file."""
     with zipfile.ZipFile(zip_file, 'r') as z:
         file_sizes = [(f, z.getinfo(f).file_size) for f in z.namelist()]
         file_sizes.sort(key=lambda x: x[1], reverse=True)
         for f, size in file_sizes[:10]:
-            print(f"{f}: {size/(1024*1024)} MBs uncompressed.")
+            print(f"{f}: {size / (1024 * 1024):.2f} MBs uncompressed.")
 
 
 def check_wheel_size(directory):
+    """Check the size of .whl files in the given directory."""
     for root, _, files in os.walk(directory):
-        for f in files:
-            if f.endswith(".whl"):
-                wheel_path = os.path.join(root, f)
-                wheel_size = os.path.getsize(wheel_path)
-                wheel_size_mb = wheel_size / (1024 * 1024)
-                if wheel_size_mb > MAX_SIZE_MB:
-                    print(
-                        f"Wheel {wheel_path} is too large ({wheel_size_mb} MB) "
-                        f"compare to the allowed size ({MAX_SIZE_MB} MB).")
+        for file_name in files:
+            if file_name.endswith(".whl"):
+                wheel_path = os.path.join(root, file_name)
+                wheel_size_mb = os.path.getsize(wheel_path) / (1024 * 1024)
+                if wheel_size_mb > VLLM_MAX_SIZE_MB:
+                    print(f"Not allowed: Wheel {wheel_path} is larger "
+                          f"({wheel_size_mb:.2f} MB) than the limit "
+                          f"({VLLM_MAX_SIZE_MB} MB).")
                     print_top_10_largest_files(wheel_path)
                     return 1
                 else:
                     print(f"Wheel {wheel_path} is within the allowed size "
-                          f"({wheel_size_mb} MB).")
+                          f"({wheel_size_mb:.2f} MB).")
     return 0
 
 
 if __name__ == "__main__":
-    import sys
-    sys.exit(check_wheel_size(sys.argv[1]))
+    if len(sys.argv) < 2:
+        print("Usage: python check-wheel-size.py <directory>")
+        sys.exit(1)
+
+    directory = sys.argv[1]
+    sys.exit(check_wheel_size(directory))

.buildkite/download-images.sh

@@ -1,18 +0,0 @@
-#!/bin/bash
-
-set -ex
-set -o pipefail
-
-(which wget && which curl) || (apt-get update && apt-get install -y wget curl)
-
-# aws s3 sync s3://air-example-data-2/vllm_opensource_llava/ images/
-mkdir -p images
-cd images
-wget https://air-example-data-2.s3.us-west-2.amazonaws.com/vllm_opensource_llava/stop_sign_pixel_values.pt
-wget https://air-example-data-2.s3.us-west-2.amazonaws.com/vllm_opensource_llava/stop_sign_image_features.pt
-wget https://air-example-data-2.s3.us-west-2.amazonaws.com/vllm_opensource_llava/cherry_blossom_pixel_values.pt
-wget https://air-example-data-2.s3.us-west-2.amazonaws.com/vllm_opensource_llava/cherry_blossom_image_features.pt
-wget https://air-example-data-2.s3.us-west-2.amazonaws.com/vllm_opensource_llava/stop_sign.jpg
-wget https://air-example-data-2.s3.us-west-2.amazonaws.com/vllm_opensource_llava/cherry_blossom.jpg
-
-cd -

.buildkite/lm-eval-harness/configs/DeepSeek-V2-Lite-Chat.yaml

@@ -0,0 +1,12 @@
+# bash ./run-lm-eval-gsm-vllm-baseline.sh -m deepseek-ai/DeepSeek-V2-Lite-Chat -b "auto" -l 1000 -f 5 -t 2
+model_name: "deepseek-ai/DeepSeek-V2-Lite-Chat"
+tasks:
+- name: "gsm8k"
+  metrics:
+  - name: "exact_match,strict-match"
+    value: 0.671
+  - name: "exact_match,flexible-extract"
+    value: 0.664
+limit: 1000
+num_fewshot: 5
+trust_remote_code: True

.buildkite/lm-eval-harness/configs/Meta-Llama-3-70B-Instruct-FBGEMM-nonuniform.yaml

@@ -0,0 +1,11 @@
+# bash .buildkite/lm-eval-harness/run-lm-eval-gsm-hf-baseline.sh -m nm-testing/Meta-Llama-3-70B-Instruct-FBGEMM-nonuniform -b auto -l 1000 -f 5
+model_name: "nm-testing/Meta-Llama-3-70B-Instruct-FBGEMM-nonuniform"
+tasks:
+- name: "gsm8k"
+  metrics:
+  - name: "exact_match,strict-match"
+    value: 0.905
+  - name: "exact_match,flexible-extract"
+    value: 0.905
+limit: 1000
+num_fewshot: 5

.buildkite/lm-eval-harness/configs/Meta-Llama-3-70B-Instruct.yaml

@@ -0,0 +1,11 @@
+# bash .buildkite/lm-eval-harness/run-lm-eval-gsm-hf-baseline.sh -m meta-llama/Meta-Llama-3-70B-Instruct -b 32 -l 250 -f 5
+model_name: "meta-llama/Meta-Llama-3-70B-Instruct"
+tasks:
+- name: "gsm8k"
+  metrics:
+  - name: "exact_match,strict-match"
+    value: 0.892
+  - name: "exact_match,flexible-extract"
+    value: 0.892
+limit: 250
+num_fewshot: 5

.buildkite/lm-eval-harness/configs/Meta-Llama-3-8B-Instruct-Channelwise-compressed-tensors.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-W8A8-FP8-Channelwise-compressed-tensors -b auto -l 1000 -f 5 -t 1
+model_name: "nm-testing/Meta-Llama-3-8B-Instruct-W8A8-FP8-Channelwise-compressed-tensors"
+tasks:
+- name: "gsm8k"
+  metrics:
+  - name: "exact_match,strict-match"
+    value: 0.752
+  - name: "exact_match,flexible-extract"
+    value: 0.754
+limit: 1000
+num_fewshot: 5

.buildkite/lm-eval-harness/configs/Meta-Llama-3-8B-Instruct-FBGEMM-nonuniform.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-FBGEMM-nonuniform -b auto -l 1000 -f 5 -t 1
+model_name: "nm-testing/Meta-Llama-3-8B-Instruct-FBGEMM-nonuniform"
+tasks:
+- name: "gsm8k"
+  metrics:
+  - name: "exact_match,strict-match"
+    value: 0.753
+  - name: "exact_match,flexible-extract"
+    value: 0.753
+limit: 1000
+num_fewshot: 5

.buildkite/lm-eval-harness/configs/Meta-Llama-3-8B-Instruct-FP8-compressed-tensors.yaml

@@ -0,0 +1,11 @@
+# bash .buildkite/lm-eval-harness/run-lm-eval-gsm-vllm-baseline.sh -m nm-testing/Meta-Llama-3-8B-FP8-compressed-tensors-test -b 32 -l 1000 -f 5 -t 1
+model_name: "nm-testing/Meta-Llama-3-8B-FP8-compressed-tensors-test"
+tasks:
+- name: "gsm8k"
+  metrics:
+  - name: "exact_match,strict-match"
+    value: 0.755
+  - name: "exact_match,flexible-extract"
+    value: 0.755
+limit: 1000
+num_fewshot: 5

.buildkite/lm-eval-harness/configs/Meta-Llama-3-8B-Instruct-FP8.yaml

@@ -0,0 +1,11 @@
+# bash .buildkite/lm-eval-harness/run-lm-eval-gsm-vllm-baseline.sh -m neuralmagic/Meta-Llama-3-8B-Instruct-FP8 -b 32 -l 250 -f 5 -t 1
+model_name: "neuralmagic/Meta-Llama-3-8B-Instruct-FP8"
+tasks:
+- name: "gsm8k"
+  metrics:
+  - name: "exact_match,strict-match"
+    value: 0.753
+  - name: "exact_match,flexible-extract"
+    value: 0.753
+limit: 1000
+num_fewshot: 5

.buildkite/lm-eval-harness/configs/Meta-Llama-3-8B-Instruct-INT8-compressed-tensors.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-Per-Token-Test -b "auto" -l 250 -f 5 -t 1
+model_name: "nm-testing/Meta-Llama-3-8B-Instruct-W8-Channel-A8-Dynamic-Per-Token-Test"
+tasks:
+- name: "gsm8k"
+  metrics:
+  - name: "exact_match,strict-match"
+    value: 0.728
+  - name: "exact_match,flexible-extract"
+    value: 0.728
+limit: 250
+num_fewshot: 5

.buildkite/lm-eval-harness/configs/Meta-Llama-3-8B-Instruct-nonuniform-compressed-tensors.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-nonuniform-test -b auto -l 1000 -f 5 -t 1
+model_name: "nm-testing/Meta-Llama-3-8B-Instruct-nonuniform-test"
+tasks:
+- name: "gsm8k"
+  metrics:
+  - name: "exact_match,strict-match"
+    value: 0.758
+  - name: "exact_match,flexible-extract"
+    value: 0.759
+limit: 1000
+num_fewshot: 5

.buildkite/lm-eval-harness/configs/Meta-Llama-3-8B-Instruct.yaml

@@ -0,0 +1,11 @@
+# bash .buildkite/lm-eval-harness/run-lm-eval-gsm-hf-baseline.sh -m meta-llama/Meta-Llama-3-8B-Instruct -b 32 -l 250 -f 5 -t 1
+model_name: "meta-llama/Meta-Llama-3-8B-Instruct"
+tasks:
+- name: "gsm8k"
+  metrics:
+  - name: "exact_match,strict-match"
+    value: 0.756
+  - name: "exact_match,flexible-extract"
+    value: 0.752
+limit: 250
+num_fewshot: 5

.buildkite/lm-eval-harness/configs/Meta-Llama-3-8B-QQQ.yaml

@@ -0,0 +1,11 @@
+# bash .buildkite/lm-eval-harness/run-lm-eval-gsm-vllm-baseline.sh -m HandH1998/QQQ-Llama-3-8b-g128 -b 32 -l 1000 -f 5 -t 1
+model_name: "HandH1998/QQQ-Llama-3-8b-g128"
+tasks:
+- name: "gsm8k"
+  metrics:
+  - name: "exact_match,strict-match"
+    value: 0.419
+  - name: "exact_match,flexible-extract"
+    value: 0.416
+limit: 1000
+num_fewshot: 5

.buildkite/lm-eval-harness/configs/Minitron-4B-Base-FP8.yaml

@@ -0,0 +1,11 @@
+# bash .buildkite/lm-eval-harness/run-lm-eval-gsm-vllm-baseline.sh -m mgoin/Minitron-4B-Base-FP8 -b auto -l 1000 -f 5 -t 1
+model_name: "mgoin/Minitron-4B-Base-FP8"
+tasks:
+- name: "gsm8k"
+  metrics:
+  - name: "exact_match,strict-match"
+    value: 0.233
+  - name: "exact_match,flexible-extract"
+    value: 0.236
+limit: 1000
+num_fewshot: 5

.buildkite/lm-eval-harness/configs/Mixtral-8x22B-Instruct-v0.1-FP8-Dynamic.yaml

@@ -0,0 +1,11 @@
+# bash ./run-lm-eval-gsm-vllm-baseline.sh -m neuralmagic/Mixtral-8x22B-Instruct-v0.1-FP8-dynamic -b "auto" -l 250 -f 5 -t 8
+model_name: "neuralmagic/Mixtral-8x22B-Instruct-v0.1-FP8-dynamic"
+tasks:
+- name: "gsm8k"
+  metrics:
+  - name: "exact_match,strict-match"
+    value: 0.86
+  - name: "exact_match,flexible-extract"
+    value: 0.86
+limit: 250
+num_fewshot: 5

.buildkite/lm-eval-harness/configs/Mixtral-8x7B-Instruct-v0.1-FP8.yaml

@@ -0,0 +1,11 @@
+# bash ./run-lm-eval-gsm-vllm-baseline.sh -m neuralmagic/Mixtral-8x7B-Instruct-v0.1-FP8 -b "auto" -l 250 -f 5 -t 4
+model_name: "neuralmagic/Mixtral-8x7B-Instruct-v0.1-FP8"
+tasks:
+- name: "gsm8k"
+  metrics:
+  - name: "exact_match,strict-match"
+    value: 0.624
+  - name: "exact_match,flexible-extract"
+    value: 0.624
+limit: 250
+num_fewshot: 5

.buildkite/lm-eval-harness/configs/Mixtral-8x7B-Instruct-v0.1.yaml

@@ -0,0 +1,11 @@
+# bash .buildkite/lm-eval-harness/run-lm-eval-gsm-hf-baseline.sh -m neuralmagic/Mixtral-8x7B-Instruct-v0.1 -b 32 -l 250 -f 5 -t 4
+model_name: "mistralai/Mixtral-8x7B-Instruct-v0.1"
+tasks:
+- name: "gsm8k"
+  metrics:
+  - name: "exact_match,strict-match"
+    value: 0.616
+  - name: "exact_match,flexible-extract"
+    value: 0.632
+limit: 250
+num_fewshot: 5

.buildkite/lm-eval-harness/configs/Qwen2-1.5B-Instruct-FP8W8.yaml

@@ -0,0 +1,11 @@
+# bash .buildkite/lm-eval-harness/run-lm-eval-gsm-vllm-baseline.sh -m nm-testing/Qwen2-1.5B-Instruct-FP8W8 -b auto -l 1000 -f 5 -t 1
+model_name: "nm-testing/Qwen2-1.5B-Instruct-FP8W8"
+tasks:
+- name: "gsm8k"
+  metrics:
+  - name: "exact_match,strict-match"
+    value: 0.578
+  - name: "exact_match,flexible-extract"
+    value: 0.585
+limit: 1000
+num_fewshot: 5

.buildkite/lm-eval-harness/configs/Qwen2-1.5B-Instruct-INT8-compressed-tensors.yaml

@@ -0,0 +1,11 @@
+# bash .buildkite/lm-eval-harness/run-lm-eval-gsm-vllm-baseline.sh -m neuralmagic/Qwen2-1.5B-Instruct-quantized.w8a8 -b "auto" -l 1000 -f 5 -t 1
+model_name: "neuralmagic/Qwen2-1.5B-Instruct-quantized.w8a8"
+tasks:
+- name: "gsm8k"
+  metrics:
+  - name: "exact_match,strict-match"
+    value: 0.593
+  - name: "exact_match,flexible-extract"
+    value: 0.588
+limit: 1000
+num_fewshot: 5

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

@@ -0,0 +1,11 @@
+# bash .buildkite/lm-eval-harness/run-lm-eval-gsm-vllm-baseline.sh -m nm-testing/Qwen2-1.5B-Instruct-W8A16-Channelwise -b "auto" -l 1000 -f 5 -t 1
+model_name: "nm-testing/Qwen2-1.5B-Instruct-W8A16-Channelwise"
+tasks:
+- name: "gsm8k"
+  metrics:
+  - name: "exact_match,strict-match"
+    value: 0.595
+  - name: "exact_match,flexible-extract"
+    value: 0.582
+limit: 1000
+num_fewshot: 5

.buildkite/lm-eval-harness/configs/Qwen2-57B-A14-Instruct.yaml

@@ -0,0 +1,11 @@
+# bash ./run-lm-eval-gsm-vllm-baseline.sh -m Qwen/Qwen2-57B-A14B-Instruct -b "auto" -l 250 -f 5 -t 4
+model_name: "Qwen/Qwen2-57B-A14B-Instruct"
+tasks:
+- name: "gsm8k"
+  metrics:
+  - name: "exact_match,strict-match"
+    value: 0.792
+  - name: "exact_match,flexible-extract"
+    value: 0.824
+limit: 250
+num_fewshot: 5

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

@@ -0,0 +1,5 @@
+Meta-Llama-3-70B-Instruct-FBGEMM-nonuniform.yaml
+Meta-Llama-3-70B-Instruct.yaml
+Mixtral-8x7B-Instruct-v0.1.yaml
+Qwen2-57B-A14-Instruct.yaml
+DeepSeek-V2-Lite-Chat.yaml

.buildkite/lm-eval-harness/configs/models-small.txt

@@ -0,0 +1,9 @@
+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-3-8B-Instruct-nonuniform-compressed-tensors.yaml
+Meta-Llama-3-8B-Instruct-Channelwise-compressed-tensors.yaml
+Minitron-4B-Base-FP8.yaml
+Qwen2-1.5B-Instruct-INT8-compressed-tensors.yaml
+Qwen2-1.5B-Instruct-FP8W8.yaml
+Meta-Llama-3-8B-QQQ.yaml

.buildkite/lm-eval-harness/run-lm-eval-gsm-hf-baseline.sh

@@ -0,0 +1,46 @@
+#!/bin/bash
+# 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
+
+usage() {
+    echo``
+    echo "Runs lm eval harness on GSM8k using huggingface transformers."
+    echo "This pathway is intended to be used to create baselines for "
+    echo "our automated nm-test-accuracy workflow"
+    echo
+    echo "usage: ${0} <options>"
+    echo
+    echo "  -m    - huggingface stub or local directory of the model"
+    echo "  -b    - batch size to run the evaluation at"
+    echo "  -l    - limit number of samples to run"
+    echo "  -f    - number of fewshot samples to use"
+    echo
+}
+
+while getopts "m:b:l:f:" OPT; do
+  case ${OPT} in
+    m ) 
+        MODEL="$OPTARG"
+        ;;
+    b ) 
+        BATCH_SIZE="$OPTARG"
+        ;;
+    l ) 
+        LIMIT="$OPTARG"
+        ;;
+    f ) 
+        FEWSHOT="$OPTARG"
+        ;;
+    \? ) 
+        usage
+        exit 1
+        ;;
+  esac
+done
+
+lm_eval --model hf \
+  --model_args pretrained=$MODEL,parallelize=True \
+  --tasks gsm8k --num_fewshot $FEWSHOT --limit $LIMIT \
+  --batch_size $BATCH_SIZE

.buildkite/lm-eval-harness/run-lm-eval-gsm-vllm-baseline.sh

@@ -0,0 +1,51 @@
+#!/bin/bash
+# We can use this script to compute baseline accuracy on GSM for vllm.
+# 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
+
+usage() {
+    echo``
+    echo "Runs lm eval harness on GSM8k using huggingface transformers."
+    echo "This pathway is intended to be used to create baselines for "
+    echo "our automated nm-test-accuracy workflow"
+    echo
+    echo "usage: ${0} <options>"
+    echo
+    echo "  -m    - huggingface stub or local directory of the model"
+    echo "  -b    - batch size to run the evaluation at"
+    echo "  -l    - limit number of samples to run"
+    echo "  -f    - number of fewshot samples to use"
+    echo "  -t    - tensor parallel size to run at"
+    echo
+}
+
+while getopts "m:b:l:f:t:" OPT; do
+  case ${OPT} in
+    m ) 
+        MODEL="$OPTARG"
+        ;;
+    b ) 
+        BATCH_SIZE="$OPTARG"
+        ;;
+    l ) 
+        LIMIT="$OPTARG"
+        ;;
+    f ) 
+        FEWSHOT="$OPTARG"
+        ;;
+    t )
+        TP_SIZE="$OPTARG"
+        ;;
+    \? ) 
+        usage
+        exit 1
+        ;;
+  esac
+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 \
+  --tasks gsm8k --num_fewshot $FEWSHOT --limit $LIMIT \
+  --batch_size $BATCH_SIZE

.buildkite/lm-eval-harness/run-tests.sh

@@ -0,0 +1,59 @@
+#!/bin/bash
+
+usage() {
+    echo``
+    echo "Runs lm eval harness on GSM8k using vllm and compares to "
+    echo "precomputed baseline (measured by HF transformers.)"
+    echo
+    echo "usage: ${0} <options>"
+    echo
+    echo "  -c    - path to the test data config (e.g. configs/small-models.txt)"
+    echo "  -t    - tensor parallel size"
+    echo
+}
+
+SUCCESS=0
+
+while getopts "c:t:" OPT; do
+  case ${OPT} in
+    c ) 
+        CONFIG="$OPTARG"
+        ;;
+    t )
+        TP_SIZE="$OPTARG"
+        ;;
+    \? )
+        usage
+        exit 1
+        ;;
+  esac
+done
+
+# Parse list of configs.
+IFS=$'\n' read -d '' -r -a MODEL_CONFIGS < $CONFIG
+
+for MODEL_CONFIG in "${MODEL_CONFIGS[@]}"
+do
+    LOCAL_SUCCESS=0
+    
+    echo "=== RUNNING MODEL: $MODEL_CONFIG WITH TP SIZE: $TP_SIZE==="
+
+    export LM_EVAL_TEST_DATA_FILE=$PWD/configs/${MODEL_CONFIG}
+    export LM_EVAL_TP_SIZE=$TP_SIZE
+    pytest -s test_lm_eval_correctness.py || LOCAL_SUCCESS=$?
+
+    if [[ $LOCAL_SUCCESS == 0 ]]; then
+        echo "=== PASSED MODEL: ${MODEL_CONFIG} ==="
+    else
+        echo "=== FAILED MODEL: ${MODEL_CONFIG} ==="
+    fi
+
+    SUCCESS=$((SUCCESS + LOCAL_SUCCESS))
+
+done
+
+if [ "${SUCCESS}" -eq "0" ]; then
+    exit 0
+else
+    exit 1
+fi

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

@@ -0,0 +1,58 @@
+"""
+LM eval harness on model to compare vs HF baseline computed offline.
+Configs are found in configs/$MODEL.yaml
+
+* export LM_EVAL_TEST_DATA_FILE=configs/Meta-Llama-3-70B-Instruct.yaml
+* export LM_EVAL_TP_SIZE=4 
+* pytest -s test_lm_eval_correctness.py
+"""
+
+import os
+from pathlib import Path
+
+import lm_eval
+import numpy
+import yaml
+
+RTOL = 0.05
+TEST_DATA_FILE = os.environ.get(
+    "LM_EVAL_TEST_DATA_FILE",
+    ".buildkite/lm-eval-harness/configs/Meta-Llama-3-8B-Instruct.yaml")
+
+TP_SIZE = os.environ.get("LM_EVAL_TP_SIZE", 1)
+
+
+def launch_lm_eval(eval_config):
+    trust_remote_code = eval_config.get('trust_remote_code', False)
+
+    model_args = f"pretrained={eval_config['model_name']}," \
+                 f"tensor_parallel_size={TP_SIZE}," \
+                 f"add_bos_token=true," \
+                 f"trust_remote_code={trust_remote_code}"
+
+    results = lm_eval.simple_evaluate(
+        model="vllm",
+        model_args=model_args,
+        tasks=[task["name"] for task in eval_config["tasks"]],
+        num_fewshot=eval_config["num_fewshot"],
+        limit=eval_config["limit"],
+        batch_size="auto")
+
+    return results
+
+
+def test_lm_eval_correctness():
+    eval_config = yaml.safe_load(
+        Path(TEST_DATA_FILE).read_text(encoding="utf-8"))
+
+    # Launch eval requests.
+    results = launch_lm_eval(eval_config)
+
+    # Confirm scores match ground truth.
+    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)

.buildkite/nightly-benchmarks/README.md

@@ -0,0 +1,153 @@
+# vLLM benchmark suite
+
+
+## Introduction
+
+This directory contains two sets of benchmark for vllm.
+- Performance benchmark: benchmark vllm's performance under various workload, for **developers** to gain clarity on whether their PR improves/degrades vllm's performance
+- Nightly benchmark: compare vllm's performance against alternatives (tgi, trt-llm and lmdeploy), for **the public** to know when to choose vllm.
+
+
+See  [vLLM performance dashboard](https://perf.vllm.ai) for the latest performance benchmark results and [vLLM GitHub README](https://github.com/vllm-project/vllm/blob/main/README.md) for latest nightly benchmark results.
+
+
+## Performance benchmark quick overview
+
+**Benchmarking Coverage**: latency, throughput and fix-qps serving on A100 (the support for FP8 benchmark on H100 is coming!), with different models.
+
+**Benchmarking Duration**: about 1hr.
+
+**For benchmarking developers**: please try your best to constraint the duration of benchmarking to about 1 hr so that it won't take forever to run.
+
+
+## Nightly benchmark quick overview
+
+**Benchmarking Coverage**: Fix-qps serving on A100 (the support for FP8 benchmark on H100 is coming!) on Llama-3 8B, 70B and Mixtral 8x7B. 
+
+**Benchmarking engines**: vllm, TGI, trt-llm and lmdeploy.
+
+**Benchmarking Duration**: about 3.5hrs.
+
+
+
+## Trigger the benchmark
+
+Performance benchmark will be triggered when:
+- A PR being merged into vllm.
+- Every commit for those PRs with `perf-benchmarks` label AND `ready` label.
+
+Nightly benchmark will be triggered when:
+- Every commit for those PRs with `perf-benchmarks` label and `nightly-benchmarks` label.
+
+
+
+
+## Performance benchmark details
+
+
+See [performance-benchmarks-descriptions.md](performance-benchmarks-descriptions.md) for detailed descriptions, and use `tests/latency-tests.json`, `tests/throughput-tests.json`, `tests/serving-tests.json` to configure the test cases.
+
+
+#### Latency test
+
+Here is an example of one test inside `latency-tests.json`:
+
+```json
+[
+    {
+        "test_name": "latency_llama8B_tp1",
+        "parameters": {
+            "model": "meta-llama/Meta-Llama-3-8B",
+            "tensor_parallel_size": 1,
+            "load_format": "dummy",
+            "num_iters_warmup": 5,
+            "num_iters": 15
+        }
+    },
+]
+```
+
+In this example:
+-  The `test_name` attributes is a unique identifier for the test. In `latency-tests.json`, it must start with `latency_`.
+-  The `parameters` attribute control the command line arguments to be used for `benchmark_latency.py`. Note that please use underline `_` instead of the dash `-` when specifying the command line arguments, and `run-performance-benchmarks.sh` will convert the underline to dash when feeding the arguments to `benchmark_latency.py`. For example, the corresponding command line arguments for `benchmark_latency.py` will be `--model meta-llama/Meta-Llama-3-8B --tensor-parallel-size 1 --load-format dummy --num-iters-warmup 5 --num-iters 15`
+
+Note that the performance numbers are highly sensitive to the value of the parameters. Please make sure the parameters are set correctly.
+
+WARNING: The benchmarking script will save json results by itself, so please do not configure `--output-json` parameter in the json file.
+
+
+#### Throughput test
+The tests are specified in `throughput-tests.json`. The syntax is similar to `latency-tests.json`, except for that the parameters will be fed forward to `benchmark_throughput.py`.
+
+The number of this test is also stable -- a slight change on the value of this number might vary the performance numbers by a lot.
+
+#### Serving test
+We test the throughput by using `benchmark_serving.py` with request rate = inf to cover the online serving overhead. The corresponding parameters are in `serving-tests.json`, and here is an example:
+
+```
+[
+    {
+        "test_name": "serving_llama8B_tp1_sharegpt",
+        "qps_list": [1, 4, 16, "inf"],
+        "server_parameters": {
+            "model": "meta-llama/Meta-Llama-3-8B",
+            "tensor_parallel_size": 1,
+            "swap_space": 16,
+            "disable_log_stats": "",
+            "disable_log_requests": "",
+            "load_format": "dummy"
+        },
+        "client_parameters": {
+            "model": "meta-llama/Meta-Llama-3-8B",
+            "backend": "vllm",
+            "dataset_name": "sharegpt",
+            "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
+            "num_prompts": 200
+        }
+    },
+]
+```
+
+Inside this example:
+- The `test_name` attribute is also a unique identifier for the test. It must start with `serving_`.
+- The `server-parameters` includes the command line arguments for vLLM server.
+- The `client-parameters` includes the command line arguments for `benchmark_serving.py`.
+- The `qps_list` controls the list of qps for test. It will be used to configure the `--request-rate` parameter in `benchmark_serving.py`
+
+The number of this test is less stable compared to the delay and latency benchmarks (due to randomized sharegpt dataset sampling inside `benchmark_serving.py`), but a large change on this number (e.g. 5% change) still vary the output greatly.
+
+WARNING: The benchmarking script will save json results by itself, so please do not configure `--save-results` or other results-saving-related parameters in `serving-tests.json`.
+
+#### Visualizing the results
+The `convert-results-json-to-markdown.py` helps you put the benchmarking results inside a markdown table, by formatting [descriptions.md](tests/descriptions.md) with real benchmarking results.
+You can find the result presented as a table inside the `buildkite/performance-benchmark` job page.
+If you do not see the table, please wait till the benchmark finish running.
+The json version of the table (together with the json version of the benchmark) will be also attached to the markdown file.
+The raw benchmarking results (in the format of json files) are in the `Artifacts` tab of the benchmarking.
+
+
+
+## Nightly test details
+
+See [nightly-descriptions.md](nightly-descriptions.md) for the detailed description on test workload, models and docker containers of benchmarking other llm engines.
+
+
+#### Workflow
+
+- The [nightly-pipeline.yaml](nightly-pipeline.yaml) specifies the docker containers for different LLM serving engines. 
+- Inside each container, we run [run-nightly-suite.sh](run-nightly-suite.sh), which will probe the serving engine of the current container.
+- The `run-nightly-suite.sh` will redirect the request to `tests/run-[llm serving engine name]-nightly.sh`, which parses the workload described in [nightly-tests.json](tests/nightly-tests.json) and performs the benchmark.
+- At last, we run [scripts/plot-nightly-results.py](scripts/plot-nightly-results.py) to collect and plot the final benchmarking results, and update the results to buildkite.
+
+#### Nightly tests
+
+In [nightly-tests.json](tests/nightly-tests.json), we include the command line arguments for benchmarking commands, together with the benchmarking test cases. The format is highly similar to performance benchmark.
+
+#### Docker containers
+
+The docker containers for benchmarking are specified in `nightly-pipeline.yaml`.
+
+WARNING: the docker versions are HARD-CODED and SHOULD BE ALIGNED WITH `nightly-descriptions.md`. The docker versions need to be hard-coded as there are several version-specific bug fixes inside `tests/run-[llm serving engine name]-nightly.sh`.
+
+WARNING: populating `trt-llm` to latest version is not easy, as it requires updating several protobuf files in [tensorrt-demo](https://github.com/neuralmagic/tensorrt-demo.git).
+

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

@@ -0,0 +1,61 @@
+steps:
+  - label: "Wait for container to be ready"
+    agents:
+      queue: A100
+    plugins:
+    - kubernetes:
+        podSpec:
+          containers:
+          - image: badouralix/curl-jq
+            command:
+            - sh
+            - .buildkite/nightly-benchmarks/scripts/wait-for-image.sh
+  - wait
+  - label: "A100"
+    agents:
+      queue: A100
+    plugins:
+    - kubernetes:
+        podSpec:
+          priorityClassName: perf-benchmark
+          containers:
+          - image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
+            command:
+            - bash .buildkite/nightly-benchmarks/scripts/run-performance-benchmarks.sh
+            resources:
+              limits:
+                nvidia.com/gpu: 8
+            volumeMounts:
+            - name: devshm
+              mountPath: /dev/shm
+            env:
+            - name: VLLM_USAGE_SOURCE
+              value: ci-test
+            - name: HF_TOKEN
+              valueFrom:
+                secretKeyRef:
+                  name: hf-token-secret
+                  key: token
+          nodeSelector:
+            nvidia.com/gpu.product: NVIDIA-A100-SXM4-80GB
+          volumes:
+          - name: devshm
+            emptyDir:
+              medium: Memory
+  # - label: "H100"
+  #   agents:
+  #     queue: H100
+  #   plugins:
+  #   - docker#v5.11.0:
+  #       image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$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
+

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

@@ -0,0 +1,45 @@
+
+# Nightly benchmark
+
+The main goal of this benchmarking is two-fold:
+- Performance clarity: Provide clarity on which one (vllm, tensorrt-llm, lmdeploy and tgi) 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]().
+
+
+## Docker images
+
+We benchmark vllm, tensorrt-llm, lmdeploy and tgi using the following docker images:
+- vllm/vllm-openai:v0.5.0.post1
+- nvcr.io/nvidia/tritonserver:24.04-trtllm-python-py3
+- openmmlab/lmdeploy:v0.5.0
+- ghcr.io/huggingface/text-generation-inference:2.1
+
+<!-- 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. -->
+
+
+## Hardware
+
+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}

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

@@ -0,0 +1,120 @@
+common_pod_spec: &common_pod_spec
+  priorityClassName: perf-benchmark
+  nodeSelector:
+    nvidia.com/gpu.product: NVIDIA-A100-SXM4-80GB
+  volumes:
+    - name: devshm
+      emptyDir:
+        medium: Memory
+    - name: hf-cache
+      hostPath:
+        path: /root/.cache/huggingface
+        type: Directory
+
+common_container_settings: &common_container_settings
+  command:
+    - bash .buildkite/nightly-benchmarks/run-nightly-suite.sh
+  resources:
+    limits:
+      nvidia.com/gpu: 8
+  volumeMounts:
+    - name: devshm
+      mountPath: /dev/shm
+    - name: hf-cache
+      mountPath: /root/.cache/huggingface
+  env:
+    - name: VLLM_USAGE_SOURCE
+      value: ci-test
+    - name: HF_HOME
+      value: /root/.cache/huggingface
+    - name: VLLM_SOURCE_CODE_LOC
+      value: /workspace/build/buildkite/vllm/performance-benchmark
+    - name: HF_TOKEN
+      valueFrom:
+        secretKeyRef:
+          name: hf-token-secret
+          key: token
+
+steps:
+  - block: ":rocket: Ready for comparing vllm against alternatives? This will take 4 hours."
+  - label: "A100 trt benchmark"
+    priority: 100
+    agents:
+      queue: A100
+    plugins:
+      - kubernetes:
+          podSpec:
+            <<: *common_pod_spec
+            containers:
+              - image: nvcr.io/nvidia/tritonserver:24.04-trtllm-python-py3
+                <<: *common_container_settings
+
+  - label: "A100 lmdeploy benchmark"
+    priority: 100
+    agents:
+      queue: A100
+    plugins:
+      - kubernetes:
+          podSpec:
+            <<: *common_pod_spec
+            containers:
+              - image: openmmlab/lmdeploy:v0.5.0
+                <<: *common_container_settings
+  
+
+  - label: "A100 vllm benchmark"
+    priority: 100
+    agents:
+      queue: A100
+    plugins:
+      - kubernetes:
+          podSpec:
+            <<: *common_pod_spec
+            containers:
+              - image: vllm/vllm-openai:latest 
+                <<: *common_container_settings
+
+  - label: "A100 tgi benchmark"
+    priority: 100
+    agents:
+      queue: A100
+    plugins:
+      - kubernetes:
+          podSpec:
+            <<: *common_pod_spec
+            containers:
+              - image: ghcr.io/huggingface/text-generation-inference:2.1 
+                <<: *common_container_settings
+        
+  - wait
+
+  - label: "Plot"
+    priority: 100
+    agents:
+      queue: A100
+    plugins:
+      - kubernetes:
+          podSpec:
+            <<: *common_pod_spec
+            containers:
+            - image: vllm/vllm-openai:v0.5.0.post1
+              command:
+              - bash .buildkite/nightly-benchmarks/scripts/nightly-annotate.sh
+              resources:
+                limits:
+                  nvidia.com/gpu: 8
+              volumeMounts:
+              - name: devshm
+                mountPath: /dev/shm
+              env:
+              - name: VLLM_USAGE_SOURCE
+                value: ci-test
+              - name: VLLM_SOURCE_CODE_LOC
+                value: /workspace/build/buildkite/vllm/performance-benchmark
+              - name: HF_TOKEN
+                valueFrom:
+                  secretKeyRef:
+                    name: hf-token-secret
+                    key: token
+
+  - wait

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

@@ -0,0 +1,62 @@
+
+## Latency tests
+
+- Input length: 32 tokens.
+- Output length: 128 tokens.
+- Batch size: fixed (8).
+- Models: llama-3.1 8B, llama-3 70B, mixtral 8x7B.
+- Evaluation metrics: end-to-end latency (mean, median, p99).
+
+
+{latency_tests_markdown_table}
+
+
+## Throughput tests
+
+- Input length: randomly sample 200 prompts from ShareGPT dataset (with fixed random seed).
+- Output length: the corresponding output length of these 200 prompts.
+- Batch size: dynamically determined by vllm to achieve maximum throughput.
+- Models: llama-3.1 8B, llama-3 70B, mixtral 8x7B.
+- Evaluation metrics: throughput.
+
+
+{throughput_tests_markdown_table}
+
+
+## Serving tests
+
+- Input length: randomly sample 200 prompts from ShareGPT dataset (with fixed random seed).
+- Output length: the corresponding output length of these 200 prompts.
+- Batch size: dynamically determined by vllm and the arrival pattern of the requests.
+- **Average QPS (query per second)**: 1, 4, 16 and inf. QPS = inf means all requests come at once. For other QPS values, the arrival time of each query is determined using a random Poisson process (with fixed random seed).
+- Models: llama-3.1 8B, llama-3 70B, mixtral 8x7B.
+- We also added a speculative decoding test for llama-3 70B, under QPS 2
+- Evaluation metrics: throughput, TTFT (time to the first token, with mean, median and p99), ITL (inter-token latency, with mean, median and p99).
+
+
+{serving_tests_markdown_table}
+
+
+## json version of the benchmarking tables
+
+This section contains the data of the markdown tables above in JSON format. 
+You can load the benchmarking tables into pandas dataframes as follows:
+
+```python
+import json
+import pandas as pd
+
+benchmarking_results_json = """The json string"""
+benchmarking_results = json.loads(benchmarking_results_json)
+latency_results = pd.DataFrame.from_dict(benchmarking_results["latency"])
+throughput_results = pd.DataFrame.from_dict(benchmarking_results["throughput"])
+serving_results = pd.DataFrame.from_dict(benchmarking_results["serving"])
+```
+
+The json string for all benchmarking tables:
+```json
+{benchmarking_results_in_json_string}
+```
+
+You can also check the raw experiment data in the Artifact tab of the Buildkite page.
+

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

@@ -0,0 +1,76 @@
+#!/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 $VLLM_SOURCE_CODE_LOC/benchmarks
+    wget https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/ShareGPT_V3_unfiltered_cleaned_split.json
+    
+
+    # run lmdeploy
+    if which lmdeploy >/dev/null; then
+        echo "lmdeploy is available, redirect to run-lmdeploy-nightly.sh"
+        bash ../.buildkite/nightly-benchmarks/scripts/run-lmdeploy-nightly.sh
+        exit 0
+    fi
+
+    # run tgi
+    if [ -e /tgi-entrypoint.sh ]; then
+        echo "tgi is available, redirect to run-tgi-nightly.sh"
+        bash ../.buildkite/nightly-benchmarks/scripts/run-tgi-nightly.sh
+        exit 0
+    fi
+
+    # run trt
+    if which trtllm-build >/dev/null; then
+        echo "trtllm is available, redirect to run-trt-nightly.sh"
+        bash ../.buildkite/nightly-benchmarks/scripts/run-trt-nightly.sh
+        exit 0
+    fi
+
+    # run vllm
+    if [ -e /vllm-workspace ]; then
+        echo "vllm is available, redirect to run-vllm-nightly.sh"
+        bash ../.buildkite/nightly-benchmarks/scripts/run-vllm-nightly.sh
+        exit 0
+    fi
+
+}
+
+main "$@"

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

@@ -0,0 +1,192 @@
+import json
+import os
+from pathlib import Path
+
+import pandas as pd
+from tabulate import tabulate
+
+results_folder = Path("results/")
+
+# latency results and the keys that will be printed into markdown
+latency_results = []
+latency_column_mapping = {
+    "test_name": "Test name",
+    "gpu_type": "GPU",
+    "avg_latency": "Mean latency (ms)",
+    # "P10": "P10 (s)",
+    # "P25": "P25 (s)",
+    "P50": "Median latency (ms)",
+    # "P75": "P75 (s)",
+    # "P90": "P90 (s)",
+    "P99": "P99 latency (ms)",
+}
+
+# throughput tests and the keys that will be printed into markdown
+throughput_results = []
+throughput_results_column_mapping = {
+    "test_name": "Test name",
+    "gpu_type": "GPU",
+    # "num_requests": "# of req.",
+    # "total_num_tokens": "Total # of tokens",
+    # "elapsed_time": "Elapsed time (s)",
+    "requests_per_second": "Tput (req/s)",
+    # "tokens_per_second": "Tput (tok/s)",
+}
+
+# serving results and the keys that will be printed into markdown
+serving_results = []
+serving_column_mapping = {
+    "test_name": "Test name",
+    "gpu_type": "GPU",
+    # "completed": "# of req.",
+    "request_throughput": "Tput (req/s)",
+    # "input_throughput": "Input Tput (tok/s)",
+    # "output_throughput": "Output Tput (tok/s)",
+    "mean_ttft_ms": "Mean TTFT (ms)",
+    "median_ttft_ms": "Median TTFT (ms)",
+    "p99_ttft_ms": "P99 TTFT (ms)",
+    # "mean_tpot_ms": "Mean TPOT (ms)",
+    # "median_tpot_ms": "Median",
+    # "p99_tpot_ms": "P99",
+    "mean_itl_ms": "Mean ITL (ms)",
+    "median_itl_ms": "Median ITL (ms)",
+    "p99_itl_ms": "P99 ITL (ms)",
+}
+
+
+def read_markdown(file):
+    if os.path.exists(file):
+        with open(file, "r") as f:
+            return f.read() + "\n"
+    else:
+        return f"{file} not found.\n"
+
+
+def results_to_json(latency, throughput, serving):
+    return json.dumps({
+        'latency': latency.to_dict(),
+        'throughput': throughput.to_dict(),
+        'serving': serving.to_dict()
+    })
+
+
+if __name__ == "__main__":
+
+    # collect results
+    for test_file in results_folder.glob("*.json"):
+
+        with open(test_file, "r") 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:
+                command = json.loads(f.read())
+            raw_result.update(command)
+
+            # update the test name of this result
+            raw_result.update({"test_name": test_file.stem})
+
+            # add the result to raw_result
+            serving_results.append(raw_result)
+            continue
+
+        elif "latency" in f.name:
+            # 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:
+                command = json.loads(f.read())
+            raw_result.update(command)
+
+            # update the test name of this result
+            raw_result.update({"test_name": test_file.stem})
+
+            # get different percentiles
+            for perc in [10, 25, 50, 75, 90, 99]:
+                # Multiply 1000 to convert the time unit from s to ms
+                raw_result.update(
+                    {f"P{perc}": 1000 * raw_result["percentiles"][str(perc)]})
+            raw_result["avg_latency"] = raw_result["avg_latency"] * 1000
+
+            # add the result to raw_result
+            latency_results.append(raw_result)
+            continue
+
+        elif "throughput" in f.name:
+            # 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:
+                command = json.loads(f.read())
+            raw_result.update(command)
+
+            # update the test name of this result
+            raw_result.update({"test_name": test_file.stem})
+
+            # add the result to raw_result
+            throughput_results.append(raw_result)
+            continue
+
+        print(f"Skipping {test_file}")
+
+    latency_results = pd.DataFrame.from_dict(latency_results)
+    serving_results = pd.DataFrame.from_dict(serving_results)
+    throughput_results = pd.DataFrame.from_dict(throughput_results)
+
+    raw_results_json = results_to_json(latency_results, throughput_results,
+                                       serving_results)
+
+    # remapping the key, for visualization purpose
+    if not latency_results.empty:
+        latency_results = latency_results[list(
+            latency_column_mapping.keys())].rename(
+                columns=latency_column_mapping)
+    if not serving_results.empty:
+        serving_results = serving_results[list(
+            serving_column_mapping.keys())].rename(
+                columns=serving_column_mapping)
+    if not throughput_results.empty:
+        throughput_results = throughput_results[list(
+            throughput_results_column_mapping.keys())].rename(
+                columns=throughput_results_column_mapping)
+
+    processed_results_json = results_to_json(latency_results,
+                                             throughput_results,
+                                             serving_results)
+
+    # get markdown tables
+    latency_md_table = tabulate(latency_results,
+                                headers='keys',
+                                tablefmt='pipe',
+                                showindex=False)
+    serving_md_table = tabulate(serving_results,
+                                headers='keys',
+                                tablefmt='pipe',
+                                showindex=False)
+    throughput_md_table = tabulate(throughput_results,
+                                   headers='keys',
+                                   tablefmt='pipe',
+                                   showindex=False)
+
+    # document the result
+    with open(results_folder / "benchmark_results.md", "w") as f:
+
+        results = read_markdown("../.buildkite/nightly-benchmarks/" +
+                                "performance-benchmarks-descriptions.md")
+        results = results.format(
+            latency_tests_markdown_table=latency_md_table,
+            throughput_tests_markdown_table=throughput_md_table,
+            serving_tests_markdown_table=serving_md_table,
+            benchmarking_results_in_json_string=processed_results_json)
+        f.write(results)
+
+    # document benchmarking results in json
+    with open(results_folder / "benchmark_results.json", "w") as f:
+
+        results = latency_results.to_dict(
+            orient='records') + throughput_results.to_dict(
+                orient='records') + serving_results.to_dict(orient='records')
+        f.write(json.dumps(results))

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

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

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

@@ -0,0 +1,6 @@
+from lmdeploy.serve.openai.api_client import APIClient
+
+api_client = APIClient("http://localhost:8000")
+model_name = api_client.available_models[0]
+
+print(model_name)

.buildkite/nightly-benchmarks/scripts/launch-trt-server.sh

@@ -0,0 +1,102 @@
+#!/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 &

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

@@ -0,0 +1,40 @@
+#!/bin/bash
+
+set -ex
+set -o pipefail
+
+
+main() {
+
+    (which wget && which curl) || (apt-get update && apt-get install -y wget curl)
+    (which jq) || (apt-get update && apt-get -y install jq)
+
+    if [ ! -f /workspace/buildkite-agent ]; then
+        echo "buildkite-agent binary not found. Skip plotting the results."
+        exit 0
+    fi
+
+    # initial annotation
+    description="$VLLM_SOURCE_CODE_LOC/.buildkite/nightly-benchmarks/nightly-descriptions.md"
+
+    # download results
+    cd $VLLM_SOURCE_CODE_LOC/benchmarks
+    mkdir -p results/
+    /workspace/buildkite-agent artifact download 'results/*nightly_results.json' results/
+    ls
+    ls results/
+
+    # generate figures
+    python3 -m pip install tabulate pandas matplotlib
+    python3 $VLLM_SOURCE_CODE_LOC/.buildkite/nightly-benchmarks/scripts/plot-nightly-results.py \
+        --description $description \
+        --results-folder results/
+    
+    # upload results and figures
+    /workspace/buildkite-agent artifact upload "nightly_results.png"
+    /workspace/buildkite-agent artifact upload $VLLM_SOURCE_CODE_LOC/.buildkite/nightly-benchmarks/nightly-pipeline.yaml
+    /workspace/buildkite-agent artifact upload $VLLM_SOURCE_CODE_LOC/.buildkite/nightly-benchmarks/tests/nightly-tests.json
+    /workspace/buildkite-agent annotate --style "success" --context "nightly-benchmarks-results" --append < nightly_results.md
+}
+
+main "$@"

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

@@ -0,0 +1,135 @@
+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)

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

@@ -0,0 +1,218 @@
+#!/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 $BENCHMARK_ROOT/scripts/summary-nightly-results.py
+  upload_to_buildkite
+
+}
+
+main "$@"

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

@@ -0,0 +1,381 @@
+#!/bin/bash
+
+# This script should be run inside the CI process
+# This script assumes that we are already inside the vllm/ directory
+# Benchmarking results will be available inside vllm/benchmarks/results/
+
+# 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 -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 $gpu_type"
+}
+
+check_hf_token() {
+  # check if HF_TOKEN is available and valid
+  if [[ -z "$HF_TOKEN" ]]; then
+    echo "Error: HF_TOKEN is not set."
+    exit 1
+  elif [[ ! "$HF_TOKEN" =~ ^hf_ ]]; then
+    echo "Error: HF_TOKEN does not start with 'hf_'."
+    exit 1
+  else
+    echo "HF_TOKEN is set and valid."
+  fi
+}
+
+ensure_sharegpt_downloaded() {
+  local FILE=ShareGPT_V3_unfiltered_cleaned_split.json
+  if [ ! -f "$FILE" ]; then
+    wget https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/$FILE
+  else
+    echo "$FILE already exists."
+  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"
+}
+
+wait_for_server() {
+  # wait for vllm server to start
+  # return 1 if vllm server crashes
+  timeout 1200 bash -c '
+    until curl -X POST localhost:8000/v1/completions; do
+      sleep 1
+    done' && return 0 || return 1
+}
+
+kill_processes_launched_by_current_bash() {
+  # Kill all python processes launched from current bash script
+  current_shell_pid=$$
+  processes=$(ps -eo pid,ppid,command | awk -v ppid="$current_shell_pid" -v proc="$1" '$2 == ppid && $3 ~ proc {print $1}')
+  if [ -n "$processes" ]; then
+    echo "Killing the following processes matching '$1':"
+    echo "$processes"
+    echo "$processes" | xargs kill -9
+  else
+    echo "No processes found matching '$1'."
+  fi
+}
+
+kill_gpu_processes() {
+
+  ps -aux
+  lsof -t -i:8000 | xargs -r kill -9
+  pkill -f pt_main_thread
+  # 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
+    sleep 1
+  done
+
+  # remove vllm config file
+  rm -rf ~/.config/vllm
+
+}
+
+upload_to_buildkite() {
+  # upload the benchmarking results to buildkite
+
+  # if the agent binary is not found, skip uploading the results, exit 0
+  # Check if buildkite-agent is available in the PATH or at /workspace/buildkite-agent
+  if command -v buildkite-agent >/dev/null 2>&1; then
+    BUILDKITE_AGENT_COMMAND="buildkite-agent"
+  elif [ -f /workspace/buildkite-agent ]; then
+    BUILDKITE_AGENT_COMMAND="/workspace/buildkite-agent"
+  else
+    echo "buildkite-agent binary not found. Skip uploading the results."
+    return 0
+  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
+  $BUILDKITE_AGENT_COMMAND artifact upload "$RESULTS_FOLDER/*"
+}
+
+run_latency_tests() {
+  # run latency tests using `benchmark_latency.py`
+  # $1: a json file specifying latency test cases
+
+  local latency_test_file
+  latency_test_file=$1
+
+  # Iterate over latency tests
+  jq -c '.[]' "$latency_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 [[ ! "$test_name" =~ ^latency_ ]]; then
+      echo "In latency-test.json, test_name must start with \"latency_\"."
+      exit 1
+    fi
+
+    # if TEST_SELECTOR is set, only run the test cases that match the selector
+    if [[ -n "$TEST_SELECTOR" ]] && [[ ! "$test_name" =~ $TEST_SELECTOR ]]; then
+      echo "Skip test case $test_name."
+      continue
+    fi
+
+    # get arguments
+    latency_params=$(echo "$params" | jq -r '.parameters')
+    latency_args=$(json2args "$latency_params")
+
+    # check if there is enough GPU to run the test
+    tp=$(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."
+      continue
+    fi
+
+    latency_command="python3 benchmark_latency.py \
+      --output-json $RESULTS_FOLDER/${test_name}.json \
+      $latency_args"
+
+    echo "Running test case $test_name"
+    echo "Latency command: $latency_command"
+
+    # recoding benchmarking command ang GPU command
+    jq_output=$(jq -n \
+      --arg latency "$latency_command" \
+      --arg gpu "$gpu_type" \
+      '{
+        latency_command: $latency,
+        gpu_type: $gpu
+      }')
+    echo "$jq_output" >"$RESULTS_FOLDER/$test_name.commands"
+
+    # run the benchmark
+    eval "$latency_command"
+
+    kill_gpu_processes
+
+  done
+}
+
+run_throughput_tests() {
+  # run throughput tests using `benchmark_throughput.py`
+  # $1: a json file specifying throughput test cases
+
+  local throughput_test_file
+  throughput_test_file=$1
+
+  # Iterate over throughput tests
+  jq -c '.[]' "$throughput_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 [[ ! "$test_name" =~ ^throughput_ ]]; then
+      echo "In throughput-test.json, test_name must start with \"throughput_\"."
+      exit 1
+    fi
+
+    # if TEST_SELECTOR is set, only run the test cases that match the selector
+    if [[ -n "$TEST_SELECTOR" ]] && [[ ! "$test_name" =~ $TEST_SELECTOR ]]; then
+      echo "Skip test case $test_name."
+      continue
+    fi
+
+    # get arguments
+    throughput_params=$(echo "$params" | jq -r '.parameters')
+    throughput_args=$(json2args "$throughput_params")
+
+    # check if there is enough GPU to run the test
+    tp=$(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."
+      continue
+    fi
+
+    throughput_command="python3 benchmark_throughput.py \
+      --output-json $RESULTS_FOLDER/${test_name}.json \
+      $throughput_args"
+
+    echo "Running test case $test_name"
+    echo "Throughput command: $throughput_command"
+    # recoding benchmarking command ang GPU command
+    jq_output=$(jq -n \
+      --arg command "$throughput_command" \
+      --arg gpu "$gpu_type" \
+      '{
+        throughput_command: $command,
+        gpu_type: $gpu
+      }')
+    echo "$jq_output" >"$RESULTS_FOLDER/$test_name.commands"
+
+    # run the benchmark
+    eval "$throughput_command"
+
+    kill_gpu_processes
+
+  done
+}
+
+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 [[ ! "$test_name" =~ ^serving_ ]]; then
+      echo "In serving-test.json, test_name must start with \"serving_\"."
+      exit 1
+    fi
+
+    # if TEST_SELECTOR is set, only run the test cases that match the selector
+    if [[ -n "$TEST_SELECTOR" ]] && [[ ! "$test_name" =~ $TEST_SELECTOR ]]; then
+      echo "Skip test case $test_name."
+      continue
+    fi
+
+    # get client and server arguments
+    server_params=$(echo "$params" | jq -r '.server_parameters')
+    client_params=$(echo "$params" | jq -r '.client_parameters')
+    server_args=$(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 $qps_list"
+
+    # check if there is enough GPU to run the test
+    tp=$(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 $testname."
+      continue
+    fi
+
+    # check if server model and client model is aligned
+    server_model=$(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."
+      continue
+    fi
+
+    server_command="python3 \
+      -m vllm.entrypoints.openai.api_server \
+      $server_args"
+
+    # run the server
+    echo "Running test case $test_name"
+    echo "Server command: $server_command"
+    eval "$server_command" &
+    server_pid=$!
+
+    # 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."
+    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 \
+        --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" \
+        '{
+          server_command: $server,
+          client_command: $client,
+          gpu_type: $gpu
+        }')
+      echo "$jq_output" >"$RESULTS_FOLDER/${new_test_name}.commands"
+
+    done
+
+    # clean up
+    kill -9 $server_pid
+    kill_gpu_processes
+  done
+}
+
+main() {
+  check_gpus
+  check_hf_token
+
+  # dependencies
+  (which wget && which curl) || (apt-get update && apt-get install -y wget curl)
+  (which jq) || (apt-get update && apt-get -y install jq)
+  (which lsof) || (apt-get update && apt-get install -y lsof)
+
+  # get the current IP address, required by benchmark_serving.py
+  export VLLM_HOST_IP=$(hostname -I | awk '{print $1}')
+  # turn of the reporting of the status of each request, to clean up the terminal output
+  export VLLM_LOG_LEVEL="WARNING"
+
+  # prepare for benchmarking
+  cd benchmarks || exit 1
+  ensure_sharegpt_downloaded
+  declare -g RESULTS_FOLDER=results/
+  mkdir -p $RESULTS_FOLDER
+  QUICK_BENCHMARK_ROOT=../.buildkite/nightly-benchmarks/
+
+  # benchmarking
+  run_serving_tests $QUICK_BENCHMARK_ROOT/tests/serving-tests.json
+  run_latency_tests $QUICK_BENCHMARK_ROOT/tests/latency-tests.json
+  run_throughput_tests $QUICK_BENCHMARK_ROOT/tests/throughput-tests.json
+
+  # postprocess benchmarking results
+  pip install tabulate pandas
+  python3 $QUICK_BENCHMARK_ROOT/scripts/convert-results-json-to-markdown.py
+
+  upload_to_buildkite
+}
+
+main "$@"

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

@@ -0,0 +1,216 @@
+#!/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 $BENCHMARK_ROOT/scripts/summary-nightly-results.py
+  upload_to_buildkite
+
+}
+
+main "$@"

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

@@ -0,0 +1,214 @@
+#!/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 $BENCHMARK_ROOT/scripts/summary-nightly-results.py
+  upload_to_buildkite
+
+}
+
+main "$@"

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

@@ -0,0 +1,221 @@
+#!/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 $BENCHMARK_ROOT/scripts/summary-nightly-results.py
+  upload_to_buildkite
+
+}
+
+main "$@"

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

@@ -0,0 +1,76 @@
+import datetime
+import json
+import os
+from pathlib import Path
+
+import pandas as pd
+from tabulate import tabulate
+
+results_folder = Path("results/")
+
+# serving results and the keys that will be printed into markdown
+serving_results = []
+serving_column_mapping = {
+    "test_name": "Test name",
+    "gpu_type": "GPU",
+    "completed": "Successful req.",
+    "request_throughput": "Tput (req/s)",
+    "mean_ttft_ms": "Mean TTFT (ms)",
+    "std_ttft_ms": "Std TTFT (ms)",
+    "mean_itl_ms": "Mean ITL (ms)",
+    "std_itl_ms": "Std ITL (ms)",
+    "input_throughput": "Input Tput (tok/s)",
+    "output_throughput": "Output Tput (tok/s)",
+    "engine": "Engine",
+}
+
+if __name__ == "__main__":
+
+    # collect results
+    for test_file in results_folder.glob("*.json"):
+
+        with open(test_file, "r") 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:
+            command = json.loads(f.read())
+        raw_result.update(command)
+
+        # update the test name of this result
+        raw_result.update({"test_name": test_file.stem})
+
+        # add the result to raw_result
+        serving_results.append(raw_result)
+        continue
+
+    serving_results = pd.DataFrame.from_dict(serving_results)
+
+    if not serving_results.empty:
+        serving_results = serving_results[list(
+            serving_column_mapping.keys())].rename(
+                columns=serving_column_mapping)
+
+    serving_md_table_with_headers = tabulate(serving_results,
+                                             headers='keys',
+                                             tablefmt='pipe',
+                                             showindex=False)
+    # remove the first line of header
+    serving_md_table_lines = serving_md_table_with_headers.split('\n')
+    serving_md_table_without_header = '\n'.join(serving_md_table_lines[2:])
+
+    prefix = datetime.datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
+    prefix = prefix + "_" + os.environ.get("CURRENT_LLM_SERVING_ENGINE")
+
+    # document benchmarking results in markdown
+    with open(results_folder / f"{prefix}_nightly_results.md", "w") as f:
+        # document results with header.
+        # for those who wants to reproduce our benchmark.
+        f.write(serving_md_table_with_headers)
+        f.write('\n')
+
+    # document benchmarking results in json
+    with open(results_folder / f"{prefix}_nightly_results.json", "w") as f:
+
+        results = serving_results.to_dict(orient='records')
+        f.write(json.dumps(results))

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

@@ -0,0 +1,17 @@
+#!/bin/sh
+TOKEN=$(curl -s -L "https://public.ecr.aws/token?service=public.ecr.aws&scope=repository:q9t5s3a7/vllm-ci-test-repo:pull" | jq -r .token)
+URL="https://public.ecr.aws/v2/q9t5s3a7/vllm-ci-test-repo/manifests/$BUILDKITE_COMMIT"
+
+retries=0
+while [ $retries -lt 1000 ]; do
+    if [ $(curl -s -L -H "Authorization: Bearer $TOKEN" -o /dev/null -w "%{http_code}" $URL) -eq 200 ]; then
+        exit 0
+    fi
+
+    echo "Waiting for image to be available..."
+
+    retries=$((retries + 1))
+    sleep 5
+done
+
+exit 1

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

@@ -0,0 +1,32 @@
+[
+    {
+        "test_name": "latency_llama8B_tp1",
+        "parameters": {
+            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
+            "tensor_parallel_size": 1,
+            "load_format": "dummy",
+            "num_iters_warmup": 5,
+            "num_iters": 15
+        }
+    },
+    {
+        "test_name": "latency_llama70B_tp4",
+        "parameters": {
+            "model": "meta-llama/Meta-Llama-3.1-70B-Instruct",
+            "tensor_parallel_size": 4,
+            "load_format": "dummy",
+            "num-iters-warmup": 5,
+            "num-iters": 15
+        }
+    },
+    {
+        "test_name": "latency_mixtral8x7B_tp2",
+        "parameters": {
+            "model": "mistralai/Mixtral-8x7B-Instruct-v0.1",
+            "tensor_parallel_size": 2,
+            "load_format": "dummy",
+            "num-iters-warmup": 5,
+            "num-iters": 15
+        }
+    }
+]

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

@@ -0,0 +1,116 @@
+[
+    {
+        "test_name": "llama8B_tp1",
+        "qps_list": [4],
+        "common_parameters": {
+            "model": "meta-llama/Meta-Llama-3-8B",
+            "tp": 1,
+            "dataset_name": "sharegpt",
+            "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
+            "num_prompts": 500,
+            "port": 8000
+        },
+        "lmdeploy_server_parameters": {
+        },
+        "lmdeploy_client_parameters": {
+        },
+        "tgi_server_parameters": {
+        },
+        "tgi_client_parameters": {
+            "endpoint": "/generate_stream"
+        },
+        "trt_server_parameters": {
+            "model_type": "llama",
+            "model_dtype": "float16",
+            "max_batch_size": 256,
+            "max_input_len": 4096,
+            "max_output_len": 4096,
+            "trt_llm_version": "r24.04"
+        },
+        "trt_client_parameters": {
+            "endpoint": "/v2/models/ensemble/generate_stream"
+        },
+        "vllm_server_parameters": {
+            "disable_log_stats": "",
+            "disable_log_requests": ""
+        },
+        "vllm_client_parameters": {
+        }
+    },
+    {
+        "test_name": "llama70B_tp4",
+        "qps_list": [2],
+        "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
+        },
+        "lmdeploy_server_parameters": {
+        },
+        "lmdeploy_client_parameters": {
+        },
+        "tgi_server_parameters": {
+        },
+        "tgi_client_parameters": {
+            "endpoint": "/generate_stream"
+        },
+        "trt_server_parameters": {
+            "model_type": "llama",
+            "model_dtype": "float16",
+            "max_batch_size": 256,
+            "max_input_len": 4096,
+            "max_output_len": 4096,
+            "trt_llm_version": "r24.04"
+        },
+        "trt_client_parameters": {
+            "endpoint": "/v2/models/ensemble/generate_stream"
+        },
+        "vllm_server_parameters": {
+            "disable_log_stats": "",
+            "disable_log_requests": ""
+        },
+        "vllm_client_parameters": {
+        }
+    },
+    {
+        "test_name": "mixtral8x7B_tp2",
+        "qps_list": [2],
+        "common_parameters": {
+            "model": "mistralai/Mixtral-8x7B-Instruct-v0.1",
+            "tp": 2,
+            "dataset_name": "sharegpt",
+            "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
+            "num_prompts": 500,
+            "port": 8000
+        },
+        "lmdeploy_server_parameters": {
+        },
+        "lmdeploy_client_parameters": {
+        },
+        "tgi_server_parameters": {
+        },
+        "tgi_client_parameters": {
+            "endpoint": "/generate_stream"
+        },
+        "trt_server_parameters": {
+            "model_type": "llama",
+            "model_dtype": "float16",
+            "max_batch_size": 256,
+            "max_input_len": 4096,
+            "max_output_len": 4096,
+            "trt_llm_version": "r24.04"
+        },
+        "trt_client_parameters": {
+            "endpoint": "/v2/models/ensemble/generate_stream"
+        },
+        "vllm_server_parameters": {
+            "disable_log_stats": "",
+            "disable_log_requests": ""
+        },
+        "vllm_client_parameters": {
+        }
+    }
+]

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

@@ -0,0 +1,80 @@
+[
+    {
+        "test_name": "serving_llama8B_tp1_sharegpt",
+        "qps_list": [1, 4, 16, "inf"],
+        "server_parameters": {
+            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
+            "tensor_parallel_size": 1,
+            "swap_space": 16,
+            "disable_log_stats": "",
+            "disable_log_requests": "",
+            "load_format": "dummy"
+        },
+        "client_parameters": {
+            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
+            "backend": "vllm",
+            "dataset_name": "sharegpt",
+            "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
+            "num_prompts": 200
+        }
+    },
+    {
+        "test_name": "serving_llama70B_tp4_sharegpt",
+        "qps_list": [1, 4, 16, "inf"],
+        "server_parameters": {
+            "model": "meta-llama/Meta-Llama-3.1-70B-Instruct",
+            "tensor_parallel_size": 4,
+            "swap_space": 16,
+            "disable_log_stats": "",
+            "disable_log_requests": "",
+            "load_format": "dummy"
+        },
+        "client_parameters": {
+            "model": "meta-llama/Meta-Llama-3.1-70B-Instruct",
+            "backend": "vllm",
+            "dataset_name": "sharegpt",
+            "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
+            "num_prompts": 200
+        }
+    },
+    {
+        "test_name": "serving_mixtral8x7B_tp2_sharegpt",
+        "qps_list": [1, 4, 16, "inf"],
+        "server_parameters": {
+            "model": "mistralai/Mixtral-8x7B-Instruct-v0.1",
+            "tensor_parallel_size": 2,
+            "swap_space": 16,
+            "disable_log_stats": "",
+            "disable_log_requests": "",
+            "load_format": "dummy"
+        },
+        "client_parameters": {
+            "model": "mistralai/Mixtral-8x7B-Instruct-v0.1",
+            "backend": "vllm",
+            "dataset_name": "sharegpt",
+            "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
+            "num_prompts": 200
+        }
+    },
+    {
+        "test_name": "serving_llama70B_tp4_sharegpt_specdecode",
+        "qps_list": [2],
+        "server_parameters": {
+            "model": "meta-llama/Meta-Llama-3.1-70B-Instruct",
+            "disable_log_requests": "", 
+            "tensor_parallel_size": 4,
+            "swap_space": 16, 
+            "speculative_model": "turboderp/Qwama-0.5B-Instruct",
+            "num_speculative_tokens": 4,
+            "speculative_draft_tensor_parallel_size": 1,
+            "use_v2_block_manager": ""
+        },
+        "client_parameters": {
+            "model": "meta-llama/Meta-Llama-3.1-70B-Instruct",
+            "backend": "vllm",
+            "dataset_name": "sharegpt",
+            "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
+            "num_prompts": 200 
+        }
+    }
+]

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

@@ -0,0 +1,35 @@
+[
+    {
+        "test_name": "throughput_llama8B_tp1",
+        "parameters": {
+            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
+            "tensor_parallel_size": 1,
+            "load_format": "dummy",
+            "dataset": "./ShareGPT_V3_unfiltered_cleaned_split.json",
+            "num_prompts": 200,
+            "backend": "vllm"
+        }
+    },
+    {
+        "test_name": "throughput_llama70B_tp4",
+        "parameters": {
+            "model": "meta-llama/Meta-Llama-3.1-70B-Instruct",
+            "tensor_parallel_size": 4,
+            "load_format": "dummy",
+            "dataset": "./ShareGPT_V3_unfiltered_cleaned_split.json",
+            "num_prompts": 200,
+            "backend": "vllm"
+        }
+    },
+    {
+        "test_name": "throughput_mixtral8x7B_tp2",
+        "parameters": {
+            "model": "mistralai/Mixtral-8x7B-Instruct-v0.1",
+            "tensor_parallel_size": 2,
+            "load_format": "dummy",
+            "dataset": "./ShareGPT_V3_unfiltered_cleaned_split.json",
+            "num_prompts": 200,
+            "backend": "vllm"
+        }
+    }
+]

.buildkite/release-pipeline.yaml

@@ -0,0 +1,32 @@
+steps:
+  - label: "Build wheel - CUDA 12.1"
+    agents:
+      queue: cpu_queue
+    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 ."
+      - "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
+      - "for f in artifacts/dist/*.whl; do mv -- \"$$f\" \"$${f/linux/manylinux1}\"; done"
+      - "aws s3 cp --recursive artifacts/dist s3://vllm-wheels/$BUILDKITE_COMMIT/"
+      - "aws s3 cp --recursive artifacts/dist s3://vllm-wheels/nightly/"
+    env:
+      DOCKER_BUILDKIT: "1"
+
+  - block: "Build CUDA 11.8 wheel"
+    key: block-build-cu118-wheel
+  
+  - label: "Build wheel - CUDA 11.8"
+    depends_on: block-build-cu118-wheel
+    agents:
+      queue: cpu_queue
+    commands:
+      - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg buildkite_commit=$BUILDKITE_COMMIT --build-arg USE_SCCACHE=1 --build-arg CUDA_VERSION=11.8.0 --tag vllm-ci:build-image --target build --progress plain ."
+      - "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
+      - "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"

.buildkite/run-amd-test.sh

@@ -1,7 +1,16 @@
 # This script runs test inside the corresponding ROCm docker container.
-set -ex
+set -o pipefail
 
 # Print ROCm version
+echo "--- Confirming Clean Initial State"
+while true; do
+        sleep 3
+        if grep -q clean /opt/amdgpu/etc/gpu_state; then
+                echo "GPUs state is \"clean\""
+                break
+        fi
+done
+
 echo "--- ROCm info"
 rocminfo
 
@@ -45,15 +54,10 @@ while true; do
         fi
 done
 
-echo "--- Building container"
-sha=$(git rev-parse --short HEAD)
-image_name=rocm_${sha}
-container_name=rocm_${sha}_$(tr -dc A-Za-z0-9 < /dev/urandom | head -c 10; echo)
-docker build \
-        -t ${image_name} \
-        -f Dockerfile.rocm \
-        --progress plain \
-        .
+echo "--- Pulling container" 
+image_name="rocm/vllm-ci:${BUILDKITE_COMMIT}"
+container_name="rocm_${BUILDKITE_COMMIT}_$(tr -dc A-Za-z0-9 < /dev/urandom | head -c 10; echo)"
+docker pull ${image_name}
 
 remove_docker_container() {
    docker rm -f ${container_name} || docker image rm -f ${image_name} || true
@@ -62,12 +66,55 @@ trap remove_docker_container EXIT
 
 echo "--- Running container"
 
-docker run \
+HF_CACHE="$(realpath ~)/huggingface"
+mkdir -p ${HF_CACHE}
+HF_MOUNT="/root/.cache/huggingface"
+
+commands=$@
+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
+  for GPU in $(seq 0 $(($PARALLEL_JOB_COUNT-1))); do
+    #replace shard arguments
+    commands=${@//"--shard-id= "/"--shard-id=${GPU} "}
+    commands=${commands//"--num-shards= "/"--num-shards=${PARALLEL_JOB_COUNT} "}
+    docker run \
         --device /dev/kfd --device /dev/dri \
         --network host \
+        --shm-size=16gb \
         --rm \
+        -e HIP_VISIBLE_DEVICES=${GPU} \
         -e HF_TOKEN \
-        --name ${container_name} \
+        -v ${HF_CACHE}:${HF_MOUNT} \
+        -e HF_HOME=${HF_MOUNT} \
+        --name ${container_name}_${GPU}  \
         ${image_name} \
-        /bin/bash -c "${@}"
-
+        /bin/bash -c "${commands}" \
+        |& 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
+    wait ${pid}
+    STATUS+=($?)
+  done
+  for st in ${STATUS[@]}; do
+    if [[ ${st} -ne 0 ]]; then
+      echo "One of the processes failed with $st"
+      exit ${st}
+    fi
+  done
+else
+  docker run \
+          --device /dev/kfd --device /dev/dri \
+          --network host \
+          --shm-size=16gb \
+          --rm \
+          -e HIP_VISIBLE_DEVICES=0 \
+          -e HF_TOKEN \
+          -v ${HF_CACHE}:${HF_MOUNT} \
+          -e HF_HOME=${HF_MOUNT} \
+          --name ${container_name} \
+          ${image_name} \
+          /bin/bash -c "${commands}"
+fi

.buildkite/run-benchmarks.sh

@@ -50,16 +50,16 @@ echo "### Serving Benchmarks" >> benchmark_results.md
 sed -n '1p' benchmark_serving.txt >> benchmark_results.md # first line
 echo "" >> benchmark_results.md
 echo '```' >> benchmark_results.md
-tail -n 20 benchmark_serving.txt >> benchmark_results.md # last 20 lines
+tail -n 24 benchmark_serving.txt >> benchmark_results.md # last 24 lines
 echo '```' >> benchmark_results.md
 
 # if the agent binary is not found, skip uploading the results, exit 0
-if [ ! -f /workspace/buildkite-agent ]; then
+if [ ! -f /usr/bin/buildkite-agent ]; then
     exit 0
 fi
 
 # upload the results to buildkite
-/workspace/buildkite-agent annotate --style "info" --context "benchmark-results" < benchmark_results.md
+buildkite-agent annotate --style "info" --context "benchmark-results" < benchmark_results.md
 
 # exit with the exit code of the benchmarks
 if [ $bench_latency_exit_code -ne 0 ]; then
@@ -75,4 +75,4 @@ if [ $bench_serving_exit_code -ne 0 ]; then
 fi
 
 rm ShareGPT_V3_unfiltered_cleaned_split.json
-/workspace/buildkite-agent artifact upload "*.json"
+buildkite-agent artifact upload "*.json"

.buildkite/run-cpu-test.sh

@@ -3,12 +3,43 @@
 set -ex
 
 # Try building the docker image
-docker build -t cpu-test -f Dockerfile.cpu .
+numactl -C 48-95 -N 1 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 .
 
 # Setup cleanup
-remove_docker_container() { docker rm -f cpu-test || true; }
+remove_docker_container() { docker rm -f cpu-test cpu-test-avx2 || true; }
 trap remove_docker_container EXIT
 remove_docker_container
 
-# Run the image and launch offline inference
-docker run --network host --env VLLM_CPU_KVCACHE_SPACE=1 --name cpu-test cpu-test python3 vllm/examples/offline_inference.py
+# 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 \
+ --cpuset-mems=1 --privileged=true --network host -e HF_TOKEN --env VLLM_CPU_KVCACHE_SPACE=4 --shm-size=4g --name cpu-test cpu-test
+docker run -itd --entrypoint /bin/bash -v ~/.cache/huggingface:/root/.cache/huggingface --cpuset-cpus=48-95 \
+ --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
+
+# offline inference
+docker exec cpu-test-avx2 bash -c "python3 examples/offline_inference.py"
+
+# Run basic model test
+docker exec cpu-test bash -c "
+  pip install pytest matplotlib einops transformers_stream_generator
+  pytest -v -s tests/models -m \"not vlm\" --ignore=tests/models/test_embedding.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
+docker exec cpu-test bash -c "
+  export VLLM_CPU_KVCACHE_SPACE=10 
+  export VLLM_CPU_OMP_THREADS_BIND=48-92 
+  python3 -m vllm.entrypoints.openai.api_server --model facebook/opt-125m & 
+  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"

.buildkite/run-multi-node-test.sh

@@ -0,0 +1,105 @@
+#!/bin/bash
+
+set -euox pipefail
+
+if [[ $# -lt 4 ]]; then
+    echo "Usage: .buildkite/run-multi-node-test.sh WORKING_DIR NUM_NODES NUM_GPUS DOCKER_IMAGE COMMAND1 COMMAND2 ... COMMANDN"
+    exit 1
+fi
+
+WORKING_DIR=$1
+NUM_NODES=$2
+NUM_GPUS=$3
+DOCKER_IMAGE=$4
+
+shift 4
+COMMANDS=("$@")
+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: ${#COMMANDS[@]}"
+    exit 1
+fi
+
+echo "List of commands"
+for command in "${COMMANDS[@]}"; do
+    echo $command
+done
+
+start_network() {
+    docker network create --subnet=192.168.10.0/24 docker-net
+}
+
+start_nodes() {
+    for node in $(seq 0 $(($NUM_NODES-1))); do
+        GPU_DEVICES='"device='
+        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
+                GPU_DEVICES+=','
+            fi
+        done
+        GPU_DEVICES+='"'
+
+        # start the container in detached mode
+        # things to note:
+        # 1. --shm-size=10.24gb is required. don't use --ipc=host
+        # 2. pass HF_TOKEN to the container
+        # 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"
+
+        # organize containers into a ray cluster
+        if [ $node -eq 0 ]; then
+            # start the ray head node
+            docker exec -d node$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"
+        fi
+    done
+
+    # wait for the cluster to be ready
+    sleep 10
+
+    # print the cluster status
+    docker exec node0 /bin/bash -c "ray status"
+}
+
+run_nodes() {
+    # important: iterate in reverse order to start the head node last
+    # we start the worker nodes first, in detached mode, and then start the head node
+    # in the foreground, so that the output of the head node is visible in the buildkite logs
+    for node in $(seq $(($NUM_NODES - 1)) -1 0); do
+        GPU_DEVICES='"device='
+        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
+                GPU_DEVICES+=','
+            fi
+        done
+        GPU_DEVICES+='"'
+        echo "Running node$node with GPU devices: $GPU_DEVICES"
+        if [ $node -ne 0 ]; then
+            docker exec -d node$node /bin/bash -c "cd $WORKING_DIR ; ${COMMANDS[$node]}"
+        else
+            docker exec node$node /bin/bash -c "cd $WORKING_DIR ; ${COMMANDS[$node]}"
+        fi
+    done
+}
+cleanup() {
+    for node in $(seq 0 $(($NUM_NODES-1))); do
+        docker stop node$node
+    done
+    docker network rm docker-net
+}
+trap cleanup EXIT
+start_network
+start_nodes
+run_nodes
+

.buildkite/run-openvino-test.sh

@@ -0,0 +1,14 @@
+# 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
+
+# Try building the docker image
+docker build -t openvino-test -f Dockerfile.openvino .
+
+# Setup cleanup
+remove_docker_container() { docker rm -f openvino-test || true; }
+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

.buildkite/run-tpu-test.sh

@@ -0,0 +1,15 @@
+set -e
+
+# Build the docker image.
+docker build -f Dockerfile.tpu -t vllm-tpu .
+
+# Set up cleanup.
+remove_docker_container() { docker rm -f tpu-test || true; }
+trap remove_docker_container EXIT
+# Remove the container that might not be cleaned up in the previous run.
+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"

.buildkite/run-xpu-test.sh

@@ -0,0 +1,14 @@
+# 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 xpu-test -f Dockerfile.xpu .
+
+# Setup cleanup
+remove_docker_container() { docker rm -f xpu-test || true; }
+trap remove_docker_container EXIT
+remove_docker_container
+
+# Run the image and launch offline inference
+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

.buildkite/test-pipeline.yaml

@@ -1,166 +1,415 @@
 # In this file, you can add more tests to run either by adding a new step or
 # adding a new command to an existing step. See different options here for examples.
-# This script will be feed into Jinja template in `test-template.j2` to generate
-# the final pipeline yaml file.
+
+# This script will be feed into Jinja template in `test-template-aws.j2` at
+# https://github.com/vllm-project/buildkite-ci/blob/main/scripts/test-template-aws.j2 
+# to generate the final pipeline yaml file.
+
+# Documentation
+# 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
+# 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]
+# gpu(str): override the GPU selection for the test. default is on L4 GPUs. currently only supports a100
+# num_gpus(int): override the number of GPUs for the test. default to 1 GPU. currently support 2,4.
+# num_nodes(int): whether to simulate multi-node setup by launch multiple containers on one host, 
+#     in this case, commands must be specified. the first command runs on first host, the second
+#     command runs on the second host.
+# working_dir(str): specify the place where command should execute, default to /vllm-workspace/tests
+# source_file_dependencies(list): the list of prefix to opt-in the test for, if empty, the test will always run.
+
+# When adding a test
+# - If the test belong to an existing group, add it there
+# - If the test is short, add to any existing step
+# - If the test takes more than 10min, then it is okay to create a new step. 
+#   Note that all steps execute in parallel. 
 
 steps:
-- label: Regression Test
-  mirror_hardwares: [amd]
-  command: pytest -v -s test_regression.py
-  working_dir: "/vllm-workspace/tests" # optional
+##### fast check tests  #####
 
-- label: AsyncEngine Test
-  #mirror_hardwares: [amd]
-  command: pytest -v -s async_engine
-
-- label: Basic Correctness Test
-  mirror_hardwares: [amd]
-  commands:
-  - VLLM_ATTENTION_BACKEND=XFORMERS pytest -v -s basic_correctness/test_basic_correctness.py
-  - VLLM_ATTENTION_BACKEND=FLASH_ATTN pytest -v -s basic_correctness/test_basic_correctness.py
-  - 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
-  mirror_hardwares: [amd]
-  command: pytest -v -s core
-
-- label: Distributed Comm Ops Test
-  #mirror_hardwares: [amd]
-  command: pytest -v -s distributed/test_comm_ops.py
-  working_dir: "/vllm-workspace/tests"
-  num_gpus: 2
-
-- label: Distributed Tests
-  mirror_hardwares: [amd]
-  working_dir: "/vllm-workspace/tests"
-  num_gpus: 2
-  commands:
-  - TEST_DIST_MODEL=facebook/opt-125m DISTRIBUTED_EXECUTOR_BACKEND=ray pytest -v -s distributed/test_basic_distributed_correctness.py
-  - TEST_DIST_MODEL=meta-llama/Llama-2-7b-hf DISTRIBUTED_EXECUTOR_BACKEND=ray pytest -v -s distributed/test_basic_distributed_correctness.py
-  - TEST_DIST_MODEL=facebook/opt-125m DISTRIBUTED_EXECUTOR_BACKEND=ray pytest -v -s distributed/test_chunked_prefill_distributed.py
-  - TEST_DIST_MODEL=meta-llama/Llama-2-7b-hf DISTRIBUTED_EXECUTOR_BACKEND=ray pytest -v -s distributed/test_chunked_prefill_distributed.py
-  - TEST_DIST_MODEL=facebook/opt-125m DISTRIBUTED_EXECUTOR_BACKEND=mp pytest -v -s distributed/test_basic_distributed_correctness.py
-  - TEST_DIST_MODEL=meta-llama/Llama-2-7b-hf DISTRIBUTED_EXECUTOR_BACKEND=mp pytest -v -s distributed/test_basic_distributed_correctness.py
-  - TEST_DIST_MODEL=facebook/opt-125m DISTRIBUTED_EXECUTOR_BACKEND=mp pytest -v -s distributed/test_chunked_prefill_distributed.py
-  - TEST_DIST_MODEL=meta-llama/Llama-2-7b-hf DISTRIBUTED_EXECUTOR_BACKEND=mp pytest -v -s distributed/test_chunked_prefill_distributed.py
-  - pytest -v -s spec_decode/e2e/test_integration_dist.py 
-
-- label: Distributed Tests (Multiple Groups)
-  #mirror_hardwares: [amd]
-  working_dir: "/vllm-workspace/tests"
-  num_gpus: 4
-  commands:
-  - pytest -v -s distributed/test_pynccl.py
-
-- label: Engine Test
-  mirror_hardwares: [amd]
-  command: pytest -v -s engine tokenization test_sequence.py test_config.py test_logger.py
-
-- label: Entrypoints Test
-  mirror_hardwares: [amd]
-
-  commands:
-  - pytest -v -s test_inputs.py
-  - pytest -v -s entrypoints -m llm
-  - pytest -v -s entrypoints -m openai
-
-- label: Examples Test
-  working_dir: "/vllm-workspace/examples"
-  mirror_hardwares: [amd]
-  commands:
-    # install aws cli for llava_example.py
-    # install tensorizer for tensorize_vllm_model.py
-    - pip install awscli tensorizer
-    - python3 offline_inference.py
-    - python3 offline_inference_with_prefix.py
-    - python3 llm_engine_example.py
-    - python3 llava_example.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
-
-- label: Kernels Test %N
-  #mirror_hardwares: [amd]
-  command: pytest -v -s kernels --shard-id=$$BUILDKITE_PARALLEL_JOB --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT
-  parallelism: 4
-
-- label: Models Test
-  #mirror_hardwares: [amd]
-  commands:
-    - bash ../.buildkite/download-images.sh
-    - pytest -v -s models --ignore=models/test_llava.py
-
-- label: Llava Test
-  mirror_hardwares: [amd]
-  commands:
-    - bash ../.buildkite/download-images.sh
-    - pytest -v -s models/test_llava.py
-
-- label: Prefix Caching Test
-  mirror_hardwares: [amd]
-  commands:
-    - pytest -v -s prefix_caching
-
-- label: Samplers Test
-  #mirror_hardwares: [amd]
-  command: pytest -v -s samplers
-
-- label: LogitsProcessor Test
-  mirror_hardwares: [amd]
-  command: pytest -v -s test_logits_processor.py
-
-- label: Utils Test
-  command: pytest -v -s test_utils.py
-
-- label: Worker Test
-  mirror_hardwares: [amd]
-  command: pytest -v -s worker
-
-- label: Speculative decoding tests
-  #mirror_hardwares: [amd]
-  command: pytest -v -s spec_decode
-
-- label: LoRA Test %N
-  #mirror_hardwares: [amd]
-  command: pytest -v -s lora --shard-id=$$BUILDKITE_PARALLEL_JOB --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT --ignore=lora/test_long_context.py
-  parallelism: 4
-
-- label: LoRA Long Context (Distributed)
-  #mirror_hardwares: [amd]
-  num_gpus: 4
-  # This test runs llama 13B, so it is required to run on 4 GPUs.
-  commands:
-    # Temporarily run this way because we cannot clean up GPU mem usage
-    # for multi GPU tests.
-    # TODO(sang): Fix it.
-    - pytest -v -s lora/test_long_context.py::test_rotary_emb_replaced
-    - pytest -v -s lora/test_long_context.py::test_batched_rope_kernel
-    - pytest -v -s lora/test_long_context.py::test_self_consistency
-    - pytest -v -s lora/test_long_context.py::test_quality
-    - pytest -v -s lora/test_long_context.py::test_max_len
-
-- label: Tensorizer Test
-  #mirror_hardwares: [amd]
-  command: apt-get install curl libsodium23 && pytest -v -s tensorizer_loader
-
-- label: Metrics Test
-  mirror_hardwares: [amd]
-  command: pytest -v -s metrics
-
-- label: Quantization Test
-  #mirror_hardwares: [amd]
-  command: pytest -v -s quantization
-
-- label: Benchmarks
-  working_dir: "/vllm-workspace/.buildkite"
-  mirror_hardwares: [amd]
-  commands:
-  - pip install aiohttp
-  - bash run-benchmarks.sh
-
-- label: Documentation Build
+- label: Documentation Build # 2min
   working_dir: "/vllm-workspace/test_docs/docs"
+  fast_check: true
   no_gpu: True
   commands:
   - pip install -r requirements-docs.txt
   - SPHINXOPTS=\"-W\" make html
+  # 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
+  fast_check: true
+  source_file_dependencies:
+  - vllm/
+  - tests/async_engine
+  - tests/test_inputs
+  - tests/multimodal
+  - tests/test_utils
+  - tests/worker
+  commands:
+  - pytest -v -s async_engine # Async Engine
+  - pytest -v -s test_inputs.py
+  - pytest -v -s multimodal
+  - pytest -v -s test_utils.py # Utils
+  - pytest -v -s worker # Worker
+
+- label: Basic Correctness Test # 30min
+  #mirror_hardwares: [amd]
+  fast_check: true
+  source_file_dependencies:
+  - vllm/
+  - tests/basic_correctness
+  commands:
+  - pytest -v -s basic_correctness/test_basic_correctness.py
+  - pytest -v -s basic_correctness/test_cpu_offload.py
+  - 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
+  source_file_dependencies:
+  - vllm/core
+  - vllm/distributed
+  - tests/core
+  commands:
+  - pytest -v -s core
+
+- label: Entrypoints Test # 20min
+  working_dir: "/vllm-workspace/tests"
+  fast_check: true
+  #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
+  - pytest -v -s entrypoints/llm/test_lazy_outlines.py # it needs a clean process
+  - pytest -v -s entrypoints/openai
+  - pytest -v -s entrypoints/test_chat_utils.py
+
+
+- label: Distributed Tests (4 GPUs) # 10min
+  working_dir: "/vllm-workspace/tests"
+  num_gpus: 4
+  fast_check: true
+  source_file_dependencies:
+  - vllm/distributed/
+  - vllm/core/
+  - tests/distributed
+  - tests/spec_decode/e2e/test_integration_dist_tp4
+  commands:
+  - pytest -v -s distributed/test_pynccl.py
+  - pytest -v -s spec_decode/e2e/test_integration_dist_tp4.py
+
+- label: Metrics, Tracing Test # 10min
+  num_gpus: 2 
+  fast_check: true
+  source_file_dependencies:
+  - vllm/
+  - tests/metrics
+  - tests/tracing
+  commands:
+  - pytest -v -s metrics 
+  - "pip install \
+      'opentelemetry-sdk>=1.26.0,<1.27.0' \
+      'opentelemetry-api>=1.26.0,<1.27.0' \
+      'opentelemetry-exporter-otlp>=1.26.0,<1.27.0' \
+      'opentelemetry-semantic-conventions-ai>=0.4.1,<0.5.0'"
+  - pytest -v -s tracing
+
+##### fast check tests  #####
+#####  1 GPU test  #####
+
+- label: Regression Test # 5min
+  mirror_hardwares: [amd]
+  source_file_dependencies:
+  - vllm/
+  - tests/test_regression
+  command: pytest -v -s test_regression.py
+  working_dir: "/vllm-workspace/tests" # optional
+
+- label: Engine Test # 10min
+  mirror_hardwares: [amd]
+  source_file_dependencies:
+  - vllm/
+  - tests/engine
+  - tests/tokenization
+  commands:
+  - pytest -v -s engine test_sequence.py test_config.py test_logger.py
+  # OOM in the CI unless we run this separately
+  - pytest -v -s tokenization
+
+- label: Examples Test # 12min
+  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
+    - 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 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
+
+- label: Models Test # 1hr10min
+  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 -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
+  source_file_dependencies:
+  - vllm/model_executor/layers
+  - vllm/sampling_metadata.py
+  - tests/samplers
+  commands:
+    - pytest -v -s samplers
+    - VLLM_USE_FLASHINFER_SAMPLER=1 pytest -v -s samplers
+
+- label: LogitsProcessor Test # 5min
+  mirror_hardwares: [amd]
+  source_file_dependencies:
+  - vllm/model_executor/layers
+  - tests/test_logits_processor
+  command: pytest -v -s test_logits_processor.py
+
+- label: Speculative decoding tests # 22min
+  source_file_dependencies:
+  - vllm/spec_decode
+  - tests/spec_decode
+  commands:
+    # See https://github.com/vllm-project/vllm/issues/5152
+    - export VLLM_ATTENTION_BACKEND=XFORMERS
+    - pytest -v -s spec_decode
+
+- label: LoRA Test %N # 30min 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
+  parallelism: 4
+
+- label: Kernels Test %N # 30min each
+  source_file_dependencies:
+  - csrc/
+  - vllm/attention
+  - tests/kernels
+  commands:
+    - pytest -v -s kernels --shard-id=$$BUILDKITE_PARALLEL_JOB --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT
+  parallelism: 4
+
+- label: Tensorizer Test # 11min
+  mirror_hardwares: [amd]
+  soft_fail: true
+  source_file_dependencies:
+  - vllm/model_executor/model_loader
+  - tests/tensorizer_loader
+  commands:
+    - apt-get update && apt-get install -y curl libsodium23
+    - export VLLM_WORKER_MULTIPROC_METHOD=spawn
+    - pytest -v -s tensorizer_loader
+
+- label: Benchmarks # 9min
+  working_dir: "/vllm-workspace/.buildkite"
+  mirror_hardwares: [amd]
+  source_file_dependencies:
+  - benchmarks/
+  commands:
+  - pip install aiohttp
+  - bash run-benchmarks.sh
+
+- label: Quantization Test # 15min
+  source_file_dependencies:
+  - csrc/
+  - vllm/model_executor/layers/quantization
+  - tests/quantization
+  command: pytest -v -s quantization
+
+- label: LM Eval Small Models # 53min
+  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-small.txt -t 1
+
+- label: OpenAI-Compatible Tool Use # 20 min
+  fast_check: false
+  mirror_hardwares: [ amd ]
+  source_file_dependencies:
+    - vllm/
+    - tests/tool_use
+  commands:
+    - pytest -v -s tool_use
+
+#####  1 GPU test  #####
+#####  multi gpus test  #####
+
+- label: Distributed Comm Ops Test # 7min
+  working_dir: "/vllm-workspace/tests"
+  num_gpus: 2
+  source_file_dependencies:
+  - vllm/distributed
+  - tests/distributed
+  commands:
+  - pytest -v -s distributed/test_comm_ops.py
+  - pytest -v -s distributed/test_shm_broadcast.py
+
+- label: 2 Node Tests (4 GPUs in total) # 16min
+  working_dir: "/vllm-workspace/tests"
+  num_gpus: 2
+  num_nodes: 2
+  source_file_dependencies:
+  - vllm/distributed/
+  - vllm/engine/
+  - vllm/executor/
+  - vllm/model_executor/models/
+  - 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_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
+
+- label: Distributed Tests (2 GPUs) # 28min
+  #mirror_hardwares: [amd]
+  working_dir: "/vllm-workspace/tests"
+  num_gpus: 2
+  source_file_dependencies:
+  - vllm/distributed/
+  - vllm/engine/
+  - vllm/executor/
+  - vllm/model_executor/models/
+  - tests/distributed/
+  commands:
+  - VLLM_TEST_SAME_HOST=1 torchrun --nproc-per-node=4 distributed/test_same_node.py
+  - TARGET_TEST_SUITE=L4 pytest -v -s distributed/test_basic_distributed_correctness.py
+  - pytest -v -s distributed/test_basic_distributed_correctness_enc_dec.py
+  - pytest -v -s distributed/test_chunked_prefill_distributed.py
+  - pytest -v -s distributed/test_multimodal_broadcast.py
+  - 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
+
+- label: Multi-step Tests (4 GPUs) # 21min
+  working_dir: "/vllm-workspace/tests"
+  num_gpus: 4
+  source_file_dependencies:
+  - vllm/model_executor/layers/sampler.py
+  - vllm/sequence.py
+  - vllm/worker/worker_base.py
+  - vllm/worker/worker.py
+  - vllm/worker/multi_step_worker.py
+  - vllm/worker/model_runner_base.py
+  - vllm/worker/model_runner.py
+  - vllm/worker/multi_step_model_runner.py
+  - vllm/engine
+  - tests/multi_step
+  commands:
+  - pytest -v -s multi_step/test_correctness_async_llm.py
+  - pytest -v -s multi_step/test_correctness_llm.py
+
+- label: Pipeline Parallelism Test # 23min
+  working_dir: "/vllm-workspace/tests"
+  num_gpus: 4
+  source_file_dependencies:
+  - vllm/distributed/
+  - vllm/engine/
+  - vllm/executor/
+  - vllm/model_executor/models/
+  - tests/distributed/
+  commands:
+  - pytest -v -s distributed/test_pp_cudagraph.py
+  - pytest -v -s distributed/test_pipeline_parallel.py
+
+- label: LoRA Long Context (Distributed) # 11min
+  # 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
+  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
+    - pytest -v -s -x lora/test_long_context.py
+
+- label: Weight Loading Multiple GPU Test
+  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
+
+
+##### multi gpus test #####
+##### A100 test #####
+
+- label: Distributed Tests (A100) # optional
+  gpu: a100
+  num_gpus: 4
+  source_file_dependencies:
+  - vllm/
+  commands: 
+  # 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
+  - pytest -v -s -x lora/test_mixtral.py
+
+- label: LM Eval Large Models # optional
+  gpu: a100
+  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

.buildkite/test-template.j2

@@ -1,93 +0,0 @@
-{% set docker_image = "us-central1-docker.pkg.dev/vllm-405802/vllm-ci-test-repo/vllm-test:$BUILDKITE_COMMIT" %}
-{% set default_num_gpu = 1 %}
-{% set default_working_dir = "/vllm-workspace/tests" %}
-
-steps:
-  - label: ":docker: build image"
-    commands: 
-      - "docker build --build-arg max_jobs=16 --tag {{ docker_image }} --target test --progress plain ."
-      - "docker push {{ docker_image }}"
-    env:
-      DOCKER_BUILDKIT: "1"
-    retry:
-      automatic:
-        - exit_status: -1  # Agent was lost
-          limit: 5
-        - exit_status: -10  # Agent was lost
-          limit: 5
-  - wait
-
-  - group: "AMD Tests"
-    depends_on: ~
-    steps:
-    {% for step in steps %}
-    {% if step.mirror_hardwares and "amd" in step.mirror_hardwares %}
-      - label: "AMD: {{ step.label }}"
-        agents:
-          queue: amd
-        command: bash .buildkite/run-amd-test.sh "cd {{ (step.working_dir or default_working_dir) | safe  }} ; {{ step.command  or (step.commands | join(" ; ")) | safe }}"
-        env:
-          DOCKER_BUILDKIT: "1"
-    {% endif %}
-    {% endfor %}
-
-  - label: "Neuron Test"
-    depends_on: ~
-    agents:
-      queue: neuron
-    command: bash .buildkite/run-neuron-test.sh
-    soft_fail: true
-
-  - label: "Intel Test"
-    depends_on: ~
-    command: bash .buildkite/run-cpu-test.sh
-
-  {% for step in steps %}
-  - label: "{{ step.label }}"
-    agents:
-      queue: kubernetes
-    soft_fail: {{ step.soft_fail or false }}
-    {% if step.parallelism %}
-    parallelism: {{ step.parallelism }}
-    {% endif %}
-    retry:
-      automatic:
-        - exit_status: -1  # Agent was lost
-          limit: 5
-        - exit_status: -10  # Agent was lost
-          limit: 5
-    plugins:
-      - kubernetes:
-          podSpec:
-            {% if step.num_gpus %}
-            priorityClassName: gpu-priority-cls-{{ step.num_gpus }}
-            {% endif %}
-            volumes:
-              - name: dshm
-                emptyDir:
-                  medium: Memory
-            containers:
-              - image: "{{ docker_image }}"
-                command: ["bash"]
-                args:
-                - '-c'
-                - "'cd {{ (step.working_dir or default_working_dir) | safe  }} && {{ step.command  or (step.commands | join(' && ')) | safe }}'"
-                {% if not step.no_gpu %}
-                resources:
-                  requests:
-                    nvidia.com/gpu: "{{ step.num_gpus or default_num_gpu }}"
-                  limits:
-                    nvidia.com/gpu: "{{ step.num_gpus or default_num_gpu }}"
-                {% endif %}
-                env:
-                  - name: VLLM_USAGE_SOURCE
-                    value: ci-test
-                  - name: HF_TOKEN
-                    valueFrom:
-                      secretKeyRef:
-                        name: hf-token-secret
-                        key: token
-                volumeMounts:
-                  - mountPath: /dev/shm
-                    name: dshm
-  {% endfor %}

.dockerignore

@@ -1 +1,4 @@
 vllm/*.so
+/.venv
+/build
+dist

.github/FUNDING.yml

@@ -0,0 +1,2 @@
+github: [vllm-project]
+open_collective: [vllm]

.github/ISSUE_TEMPLATE/100-documentation.yml

@@ -20,3 +20,10 @@ body:
   attributes:
     value: >
       Thanks for contributing 🎉!
+- type: checkboxes
+  id: askllm
+  attributes:
+    label: Before submitting a new issue...
+    options:
+      - label: Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.
+        required: true

.github/ISSUE_TEMPLATE/200-installation.yml

@@ -38,3 +38,10 @@ body:
   attributes:
     value: >
       Thanks for contributing 🎉!
+- type: checkboxes
+  id: askllm
+  attributes:
+    label: Before submitting a new issue...
+    options:
+      - label: Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.
+        required: true

.github/ISSUE_TEMPLATE/300-usage.yml

@@ -36,3 +36,10 @@ body:
   attributes:
     value: >
       Thanks for contributing 🎉!
+- type: checkboxes
+  id: askllm
+  attributes:
+    label: Before submitting a new issue...
+    options:
+      - label: Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.
+        required: true

.github/ISSUE_TEMPLATE/400-bug report.yml

@@ -20,9 +20,14 @@ body:
       ```
       It is suggested to download and execute the latest script, as vllm might frequently update the diagnosis information needed for accurately and quickly responding to issues.
     value: |
+      <details>
+      <summary>The output of `python collect_env.py`</summary>
+
       ```text
-      The output of `python collect_env.py`
+      Your output of `python collect_env.py` here
       ```
+      
+      </details>
   validations:
     required: true
 - type: textarea
@@ -84,3 +89,10 @@ body:
       - If the error only appears in vllm, please provide the detailed script of how you run `transformers` and `vllm`, also highlight the difference and what you expect.
 
       Thanks for contributing 🎉!
+- type: checkboxes
+  id: askllm
+  attributes:
+    label: Before submitting a new issue...
+    options:
+      - label: Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.
+        required: true

.github/ISSUE_TEMPLATE/500-feature request.yml

@@ -29,3 +29,10 @@ body:
   attributes:
     value: >
       Thanks for contributing 🎉!
+- type: checkboxes
+  id: askllm
+  attributes:
+    label: Before submitting a new issue...
+    options:
+      - label: Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.
+        required: true

.github/ISSUE_TEMPLATE/600-new model.yml

@@ -31,3 +31,10 @@ body:
   attributes:
     value: >
       Thanks for contributing 🎉!
+- type: checkboxes
+  id: askllm
+  attributes:
+    label: Before submitting a new issue...
+    options:
+      - label: Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.
+        required: true

.github/ISSUE_TEMPLATE/700-performance discussion.yml

@@ -50,3 +50,10 @@ body:
   attributes:
     value: >
       Thanks for contributing 🎉!
+- type: checkboxes
+  id: askllm
+  attributes:
+    label: Before submitting a new issue...
+    options:
+      - label: Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.
+        required: true

.github/ISSUE_TEMPLATE/750-RFC.yml

@@ -47,3 +47,10 @@ body:
   attributes:
     value: >
       Thanks for contributing 🎉!
+- type: checkboxes
+  id: askllm
+  attributes:
+    label: Before submitting a new issue...
+    options:
+      - label: Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.
+        required: true

.github/ISSUE_TEMPLATE/800-misc discussion.yml

@@ -19,3 +19,10 @@ body:
   attributes:
     value: >
       Thanks for contributing 🎉!
+- type: checkboxes
+  id: askllm
+  attributes:
+    label: Before submitting a new issue...
+    options:
+      - label: Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.
+        required: true

.github/workflows/add_label_automerge.yml

@@ -0,0 +1,21 @@
+name: Add label on auto-merge enabled
+on:
+    pull_request_target:
+        types:
+            - auto_merge_enabled
+jobs:
+    add-label-on-auto-merge:
+        runs-on: ubuntu-latest
+        steps:
+            -   name: Add label
+                uses: actions/github-script@v5
+                with:
+                    script: |
+                        github.rest.issues.addLabels({
+                            owner: context.repo.owner,
+                            repo: context.repo.repo,
+                            issue_number: context.issue.number,
+                            labels: ['ready']
+                        })
+                env:
+                    GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}

.github/workflows/clang-format.yml

@@ -30,12 +30,11 @@ jobs:
       run: |
         EXCLUDES=(
             'csrc/moe/topk_softmax_kernels.cu'
-            'csrc/punica/bgmv/bgmv_bf16_bf16_bf16.cu'
-            'csrc/punica/bgmv/bgmv_config.h'
-            'csrc/punica/bgmv/bgmv_impl.cuh'
-            'csrc/punica/bgmv/vec_dtypes.cuh'
-            'csrc/punica/punica_ops.cu'
-            'csrc/punica/type_convert.h'
+            'csrc/quantization/gguf/ggml-common.h'
+            'csrc/quantization/gguf/dequantize.cuh'
+            'csrc/quantization/gguf/vecdotq.cuh'
+            'csrc/quantization/gguf/mmq.cuh'
+            'csrc/quantization/gguf/mmvq.cuh'
         )
         find csrc/ \( -name '*.h' -o -name '*.cpp' -o -name '*.cu' -o -name '*.cuh' \) -print \
             | grep -vFf <(printf "%s\n" "${EXCLUDES[@]}") \

.github/workflows/mypy.yaml

@@ -15,7 +15,7 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: ["3.8", "3.9", "3.10", "3.11"]
+        python-version: ["3.8", "3.9", "3.10", "3.11", "3.12"]
     steps:
     - uses: actions/checkout@v2
     - name: Set up Python ${{ matrix.python-version }}
@@ -25,26 +25,22 @@ jobs:
     - name: Install dependencies
       run: |
         python -m pip install --upgrade pip
-        pip install mypy==1.9.0
+        pip install mypy==1.11.1
         pip install types-setuptools
         pip install types-PyYAML
         pip install types-requests
         pip install types-setuptools
     - name: Mypy
       run: |
-        mypy vllm/attention --config-file pyproject.toml
-        mypy vllm/core --config-file pyproject.toml
-        mypy vllm/distributed --config-file pyproject.toml
-        mypy vllm/entrypoints --config-file pyproject.toml
-        mypy vllm/executor --config-file pyproject.toml
-        mypy vllm/usage --config-file pyproject.toml
-        mypy vllm/*.py --config-file pyproject.toml
-        mypy vllm/transformers_utils --config-file pyproject.toml
-        mypy vllm/engine  --config-file pyproject.toml
-        mypy vllm/worker --config-file pyproject.toml
-        mypy vllm/spec_decode --config-file pyproject.toml
-        mypy vllm/model_executor  --config-file pyproject.toml
-        mypy vllm/lora --config-file pyproject.toml
-        mypy vllm/logging --config-file pyproject.toml
-        mypy vllm/model_executor --config-file pyproject.toml
+        mypy
+        mypy tests --follow-imports skip
+        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
 

.github/workflows/publish.yml

@@ -48,8 +48,8 @@ jobs:
       fail-fast: false
       matrix:
           os: ['ubuntu-20.04']
-          python-version: ['3.8', '3.9', '3.10', '3.11']
-          pytorch-version: ['2.3.0']  # Must be the most recent version that meets requirements-cuda.txt.
+          python-version: ['3.8', '3.9', '3.10', '3.11', '3.12']
+          pytorch-version: ['2.4.0']  # Must be the most recent version that meets requirements-cuda.txt.
           cuda-version: ['11.8', '12.1']
 
     steps:

.github/workflows/reminder_comment.yml

@@ -0,0 +1,21 @@
+name: PR Reminder Comment Bot
+on:
+  pull_request_target:
+    types: [opened]
+
+jobs:
+  pr_reminder:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Remind to run full CI on PR
+        uses: actions/github-script@v6
+        with:
+          script: |
+            github.rest.issues.createComment({
+              owner: context.repo.owner,
+              repo: context.repo.repo,
+              issue_number: context.issue.number,
+              body: '👋 Hi! Thank you for contributing to the vLLM project.\n Just a reminder: PRs would not trigger full CI run by default. Instead, it would only run `fastcheck` CI which starts running only a small and essential subset of CI tests to quickly catch errors. You can run other CI tests on top of those by going to your `fastcheck` build on Buildkite UI (linked in the PR checks section) and unblock them. If you do not have permission to unblock, ping `simon-mo` or `khluu` to add you in our Buildkite org. \n\nOnce the PR is approved and ready to go, your PR reviewer(s) can run CI to test the changes comprehensively before merging.\n\n To run CI, PR reviewers can do one of these:\n- Add `ready` label to the PR\n- Enable auto-merge.\n\n🚀'
+            })
+        env:
+          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}

.github/workflows/ruff.yml

@@ -15,7 +15,7 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: ["3.8", "3.9", "3.10", "3.11"]
+        python-version: ["3.8", "3.9", "3.10", "3.11", "3.12"]
     steps:
     - uses: actions/checkout@v2
     - name: Set up Python ${{ matrix.python-version }}
@@ -25,7 +25,7 @@ jobs:
     - name: Install dependencies
       run: |
         python -m pip install --upgrade pip
-        pip install ruff==0.1.5 codespell==2.2.6 tomli==2.0.1 isort==5.13.2
+        pip install ruff==0.1.5 codespell==2.3.0 tomli==2.0.1 isort==5.13.2
     - name: Analysing the code with ruff
       run: |
         ruff .

.github/workflows/scripts/build.sh

@@ -13,8 +13,6 @@ $python_executable -m pip install -r requirements-cuda.txt
 
 # Limit the number of parallel jobs to avoid OOM
 export MAX_JOBS=1
-# Make sure punica is built for the release (for LoRA)
-export VLLM_INSTALL_PUNICA_KERNELS=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"
 # Build

.github/workflows/yapf.yml

@@ -14,7 +14,7 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: ["3.8", "3.9", "3.10", "3.11"]
+        python-version: ["3.8", "3.9", "3.10", "3.11", "3.12"]
     steps:
     - uses: actions/checkout@v2
     - name: Set up Python ${{ matrix.python-version }}

.gitignore

@@ -1,3 +1,6 @@
+# vllm commit id, generated by setup.py
+vllm/commit_id.py
+
 # Byte-compiled / optimized / DLL files
 __pycache__/
 *.py[cod]
@@ -84,6 +87,9 @@ target/
 profile_default/
 ipython_config.py
 
+# generated files
+**/generated/**
+
 # pyenv
 #   For a library or package, you might want to ignore these files since the code is
 #   intended to run in multiple environments; otherwise, check them in:
@@ -186,4 +192,4 @@ _build/
 hip_compat.h
 
 # Benchmark dataset
-*.json
+benchmarks/*.json

.readthedocs.yaml

@@ -10,6 +10,7 @@ build:
 
 sphinx:
    configuration: docs/source/conf.py
+   fail_on_warning: true
 
 # If using Sphinx, optionally build your docs in additional formats such as PDF
 formats:

CMakeLists.txt

@@ -1,19 +1,23 @@
-cmake_minimum_required(VERSION 3.21)
+cmake_minimum_required(VERSION 3.26)
 
 project(vllm_extensions LANGUAGES CXX)
 
-option(VLLM_TARGET_DEVICE "Target device backend for vLLM" "cuda")
+# CUDA by default, can be overridden by using -DVLLM_TARGET_DEVICE=... (used by setup.py)
+set(VLLM_TARGET_DEVICE "cuda" CACHE STRING "Target device backend for vLLM")
 
 message(STATUS "Build type: ${CMAKE_BUILD_TYPE}")
 message(STATUS "Target device: ${VLLM_TARGET_DEVICE}")
 
 include(${CMAKE_CURRENT_LIST_DIR}/cmake/utils.cmake)
 
+# Suppress potential warnings about unused manually-specified variables
+set(ignoreMe "${VLLM_PYTHON_PATH}")
+
 #
 # 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")
+set(PYTHON_SUPPORTED_VERSIONS "3.8" "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")
@@ -31,9 +35,8 @@ 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 "2.3.0")
-set(TORCH_SUPPORTED_VERSION_ROCM_5X "2.0.1")
-set(TORCH_SUPPORTED_VERSION_ROCM_6X "2.1.1")
+set(TORCH_SUPPORTED_VERSION_CUDA "2.4.0")
+set(TORCH_SUPPORTED_VERSION_ROCM "2.5.0")
 
 #
 # Try to find python package with an executable that exactly matches
@@ -67,17 +70,37 @@ endif()
 find_package(Torch REQUIRED)
 
 #
-# Normally `torch.utils.cpp_extension.CUDAExtension` would add
-# `libtorch_python.so` for linking against an extension. Torch's cmake
-# configuration does not include this library (presumably since the cmake
-# config is used for standalone C++ binaries that link against torch).
-# The `libtorch_python.so` library defines some of the glue code between
-# torch/python via pybind and is required by VLLM extensions for this
-# reason. So, add it by manually with `find_library` using torch's
-# installed library path.
+# 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.
 #
-find_library(torch_python_LIBRARY torch_python PATHS
-  "${TORCH_INSTALL_PREFIX}/lib")
+# 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.
@@ -87,7 +110,7 @@ if (NOT VLLM_TARGET_DEVICE STREQUAL "cuda" AND
     if (VLLM_TARGET_DEVICE STREQUAL "cpu")
         include(${CMAKE_CURRENT_LIST_DIR}/cmake/cpu_extension.cmake)
     else()
-        message(FATAL_ERROR "Unsupported vLLM target device: ${VLLM_TARGET_DEVICE}")
+        return()
     endif()
     return()
 endif()
@@ -111,18 +134,11 @@ elseif(HIP_FOUND)
   # .hip extension automatically, HIP must be enabled explicitly.
   enable_language(HIP)
 
-  # ROCm 5.x
-  if (ROCM_VERSION_DEV_MAJOR EQUAL 5 AND
-      NOT Torch_VERSION VERSION_EQUAL ${TORCH_SUPPORTED_VERSION_ROCM_5X})
-    message(WARNING "Pytorch version ${TORCH_SUPPORTED_VERSION_ROCM_5X} "
-      "expected for ROCMm 5.x build, saw ${Torch_VERSION} instead.")
-  endif()
-
-  # ROCm 6.x
-  if (ROCM_VERSION_DEV_MAJOR EQUAL 6 AND
-      NOT Torch_VERSION VERSION_EQUAL ${TORCH_SUPPORTED_VERSION_ROCM_6X})
-    message(WARNING "Pytorch version ${TORCH_SUPPORTED_VERSION_ROCM_6X} "
-      "expected for ROCMm 6.x build, saw ${Torch_VERSION} instead.")
+  # ROCm 5.X and 6.X
+  if (ROCM_VERSION_DEV_MAJOR GREATER_EQUAL 5 AND
+      NOT Torch_VERSION VERSION_EQUAL ${TORCH_SUPPORTED_VERSION_ROCM})
+    message(WARNING "Pytorch version >= ${TORCH_SUPPORTED_VERSION_ROCM} "
+      "expected for ROCm build, saw ${Torch_VERSION} instead.")
   endif()
 else()
   message(FATAL_ERROR "Can't find CUDA or HIP installation.")
@@ -152,7 +168,7 @@ if(NVCC_THREADS AND VLLM_GPU_LANG STREQUAL "CUDA")
 endif()
 
 #
-# Define extension targets
+# Define other extension targets
 #
 
 #
@@ -171,30 +187,38 @@ set(VLLM_EXT_SRC
   "csrc/quantization/fp8/common.cu"
   "csrc/cuda_utils_kernels.cu"
   "csrc/moe_align_block_size_kernels.cu"
-  "csrc/pybind.cpp")
+  "csrc/prepare_inputs/advance_step.cu"
+  "csrc/torch_bindings.cpp")
 
 if(VLLM_GPU_LANG STREQUAL "CUDA")
   include(FetchContent)
-  SET(CUTLASS_ENABLE_HEADERS_ONLY=ON)
+  SET(CUTLASS_ENABLE_HEADERS_ONLY ON CACHE BOOL "Enable only the header library")
   FetchContent_Declare(
         cutlass
         GIT_REPOSITORY https://github.com/nvidia/cutlass.git
-        # CUTLASS 3.5.0
-        GIT_TAG 7d49e6c7e2f8896c47f586706e67e1fb215529dc
+        # CUTLASS 3.5.1
+        GIT_TAG 06b21349bcf6ddf6a1686a47a137ad1446579db9 
+        GIT_PROGRESS TRUE
   )
   FetchContent_MakeAvailable(cutlass)
 
   list(APPEND VLLM_EXT_SRC
+    "csrc/mamba/mamba_ssm/selective_scan_fwd.cu"
+    "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/quantization/cutlass_w8a8/scaled_mm_dq_entry.cu"
-    "csrc/quantization/cutlass_w8a8/scaled_mm_dq_c2x.cu"
-    "csrc/quantization/cutlass_w8a8/scaled_mm_dq_c3x.cu")
+    "csrc/quantization/cutlass_w8a8/scaled_mm_entry.cu"
+    "csrc/quantization/cutlass_w8a8/scaled_mm_c2x.cu"
+    "csrc/quantization/cutlass_w8a8/scaled_mm_c3x.cu")
 
   #
   # The CUTLASS kernels for Hopper require sm90a to be enabled.
@@ -202,12 +226,57 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
   # That adds an extra 17MB to compiled binary, so instead we selectively enable it.
   if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER 12.0)
     set_source_files_properties(
-          "csrc/quantization/cutlass_w8a8/scaled_mm_dq_c3x.cu"
+          "csrc/quantization/cutlass_w8a8/scaled_mm_c3x.cu"
           PROPERTIES
           COMPILE_FLAGS
           "-gencode arch=compute_90a,code=sm_90a")
   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)
+    #
+    # For the Machete kernels we automatically generate sources for various 
+    # preselected input type pairs and schedules.
+    # Generate sources:
+    execute_process(
+      COMMAND ${CMAKE_COMMAND} -E env 
+      PYTHONPATH=${CMAKE_CURRENT_SOURCE_DIR}/csrc/cutlass_extensions/:${CUTLASS_DIR}/python/:${VLLM_PYTHON_PATH}:$PYTHONPATH 
+        ${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 ${CMAKE_CURRENT_BINARY_DIR}/machete_generation.log
+    )
+
+    if (NOT machete_generation_result EQUAL 0)
+      message(FATAL_ERROR "Machete generation failed."
+                          " Result: \"${machete_generation_result}\"" 
+                          "\nCheck the log for details: "
+                          "${CMAKE_CURRENT_BINARY_DIR}/machete_generation.log")
+    else()
+      message(STATUS "Machete generation completed successfully.")
+    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(
+          ${MACHETE_GEN_SOURCES}
+          PROPERTIES
+          COMPILE_FLAGS
+          "-gencode arch=compute_90a,code=sm_90a")
+  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()
 
 define_gpu_extension_target(
@@ -217,7 +286,8 @@ define_gpu_extension_target(
   SOURCES ${VLLM_EXT_SRC}
   COMPILE_FLAGS ${VLLM_GPU_FLAGS}
   ARCHITECTURES ${VLLM_GPU_ARCHES}
-  INCLUDE_DIRECTORIES ${CUTLASS_INCLUDE_DIR};${CUTLASS_TOOLS_UTIL_INCLUDE_DIR}
+  INCLUDE_DIRECTORIES ${CUTLASS_INCLUDE_DIR}
+  USE_SABI 3
   WITH_SOABI)
 
 #
@@ -225,9 +295,14 @@ define_gpu_extension_target(
 #
 
 set(VLLM_MOE_EXT_SRC
-  "csrc/moe/moe_ops.cpp"
+  "csrc/moe/torch_bindings.cpp"
   "csrc/moe/topk_softmax_kernels.cu")
 
+if(VLLM_GPU_LANG STREQUAL "CUDA")
+  list(APPEND VLLM_MOE_EXT_SRC
+      "csrc/moe/marlin_moe_ops.cu")
+endif()
+
 define_gpu_extension_target(
   _moe_C
   DESTINATION vllm
@@ -235,93 +310,16 @@ define_gpu_extension_target(
   SOURCES ${VLLM_MOE_EXT_SRC}
   COMPILE_FLAGS ${VLLM_GPU_FLAGS}
   ARCHITECTURES ${VLLM_GPU_ARCHES}
+  USE_SABI 3
   WITH_SOABI)
 
-#
-# _punica_C extension
-#
 
-set(VLLM_PUNICA_EXT_SRC
-  "csrc/punica/bgmv/bgmv_bf16_bf16_bf16.cu"
-  "csrc/punica/bgmv/bgmv_bf16_fp32_bf16.cu"
-  "csrc/punica/bgmv/bgmv_fp16_fp16_fp16.cu"
-  "csrc/punica/bgmv/bgmv_fp16_fp32_fp16.cu"
-  "csrc/punica/bgmv/bgmv_fp32_bf16_bf16.cu"
-  "csrc/punica/bgmv/bgmv_fp32_fp16_fp16.cu"
-  "csrc/punica/punica_ops.cu"
-  "csrc/punica/punica_pybind.cpp")
-
-#
-# Copy GPU compilation flags+update for punica
-#
-set(VLLM_PUNICA_GPU_FLAGS ${VLLM_GPU_FLAGS})
-list(REMOVE_ITEM VLLM_PUNICA_GPU_FLAGS
-  "-D__CUDA_NO_HALF_OPERATORS__"
-  "-D__CUDA_NO_HALF_CONVERSIONS__"
-  "-D__CUDA_NO_BFLOAT16_CONVERSIONS__"
-  "-D__CUDA_NO_HALF2_OPERATORS__")
-
-#
-# Filter out CUDA architectures < 8.0 for punica.
-#
-if (${VLLM_GPU_LANG} STREQUAL "CUDA")
-  set(VLLM_PUNICA_GPU_ARCHES)
-  foreach(ARCH ${VLLM_GPU_ARCHES})
-    string_to_ver(CODE_VER ${ARCH})
-    if (CODE_VER GREATER_EQUAL 8.0)
-      list(APPEND VLLM_PUNICA_GPU_ARCHES ${ARCH})
-    endif()
-  endforeach()
-  message(STATUS "Punica target arches: ${VLLM_PUNICA_GPU_ARCHES}")
-elseif(${VLLM_GPU_LANG} STREQUAL "HIP")
-  set(VLLM_PUNICA_GPU_ARCHES ${VLLM_GPU_ARCHES})
-  message(STATUS "Punica target arches: ${VLLM_PUNICA_GPU_ARCHES}")
-endif()
-
-if (VLLM_PUNICA_GPU_ARCHES)
-  define_gpu_extension_target(
-    _punica_C
-    DESTINATION vllm
-    LANGUAGE ${VLLM_GPU_LANG}
-    SOURCES ${VLLM_PUNICA_EXT_SRC}
-    COMPILE_FLAGS ${VLLM_PUNICA_GPU_FLAGS}
-    ARCHITECTURES ${VLLM_PUNICA_GPU_ARCHES}
-    WITH_SOABI)
-else()
-  message(WARNING "Unable to create _punica_C target because none of the "
-    "requested architectures (${VLLM_GPU_ARCHES}) are supported, i.e. >= 8.0")
-endif()
-
-#
-# 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)
 
 if(VLLM_GPU_LANG STREQUAL "CUDA" OR VLLM_GPU_LANG STREQUAL "HIP")
   message(STATUS "Enabling C extension.")
   add_dependencies(default _C)
 
-  # Enable punica if -DVLLM_INSTALL_PUNICA_KERNELS=ON or
-  # VLLM_INSTALL_PUNICA_KERNELS is set in the environment and
-  # there are supported target arches.
-  if (VLLM_PUNICA_GPU_ARCHES AND
-      (ENV{VLLM_INSTALL_PUNICA_KERNELS} OR VLLM_INSTALL_PUNICA_KERNELS))
-    message(STATUS "Enabling punica extension.")
-    add_dependencies(default _punica_C)
-  endif()
-endif()
-
-if(VLLM_GPU_LANG STREQUAL "CUDA")
   message(STATUS "Enabling moe extension.")
   add_dependencies(default _moe_C)
+
 endif()

Dockerfile

@@ -5,31 +5,43 @@
 # docs/source/dev/dockerfile/dockerfile.rst and
 # docs/source/assets/dev/dockerfile-stages-dependency.png
 
+ARG CUDA_VERSION=12.4.1
 #################### BASE BUILD IMAGE ####################
 # prepare basic build environment
-FROM nvidia/cuda:12.4.1-devel-ubuntu22.04 AS dev
+FROM nvidia/cuda:${CUDA_VERSION}-devel-ubuntu20.04 AS base
+ARG CUDA_VERSION=12.4.1
+ARG PYTHON_VERSION=3.10
+ENV DEBIAN_FRONTEND=noninteractive
 
-RUN apt-get update -y \
-    && apt-get install -y python3-pip git
+# Install Python and other dependencies
+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 \
+    && add-apt-repository ppa:deadsnakes/ppa \
+    && apt-get update -y \
+    && apt-get install -y python${PYTHON_VERSION} python${PYTHON_VERSION}-dev python${PYTHON_VERSION}-venv \
+    && update-alternatives --install /usr/bin/python3 python3 /usr/bin/python${PYTHON_VERSION} 1 \
+    && update-alternatives --set python3 /usr/bin/python${PYTHON_VERSION} \
+    && ln -sf /usr/bin/python${PYTHON_VERSION}-config /usr/bin/python3-config \
+    && curl -sS https://bootstrap.pypa.io/get-pip.py | python${PYTHON_VERSION} \
+    && python3 --version && python3 -m pip --version
 
 # 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
 # or future versions of triton.
-RUN ldconfig /usr/local/cuda-12.4/compat/
+RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
 
 WORKDIR /workspace
 
 # install build and runtime dependencies
 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 \
-    pip install -r requirements-cuda.txt
+    python3 -m pip install -r requirements-cuda.txt
 
-# install development dependencies
-COPY requirements-dev.txt requirements-dev.txt
-RUN --mount=type=cache,target=/root/.cache/pip \
-    pip install -r requirements-dev.txt
 
 # cuda arch list used by torch
 # can be useful for both `dev` and `test`
@@ -39,17 +51,14 @@ 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}
 #################### BASE BUILD IMAGE ####################
 
-
 #################### WHEEL BUILD IMAGE ####################
-FROM dev AS build
+FROM base AS build
 
 # install build dependencies
 COPY requirements-build.txt requirements-build.txt
-RUN --mount=type=cache,target=/root/.cache/pip \
-    pip install -r requirements-build.txt
 
-# install compiler cache to speed up compilation leveraging local or remote caching
-RUN apt-get update -y && apt-get install -y ccache
+RUN --mount=type=cache,target=/root/.cache/pip \
+    python3 -m pip install -r requirements-build.txt
 
 # files and directories related to build wheels
 COPY csrc csrc
@@ -57,6 +66,7 @@ COPY setup.py setup.py
 COPY cmake cmake
 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
@@ -67,38 +77,99 @@ ENV MAX_JOBS=${max_jobs}
 # number of threads used by nvcc
 ARG nvcc_threads=8
 ENV NVCC_THREADS=$nvcc_threads
-# make sure punica kernels are built (for LoRA)
-ENV VLLM_INSTALL_PUNICA_KERNELS=1
+
+ARG buildkite_commit
+ENV BUILDKITE_COMMIT=${buildkite_commit}
+
+ARG USE_SCCACHE
+ARG SCCACHE_BUCKET_NAME=vllm-build-sccache
+ARG SCCACHE_REGION_NAME=us-west-2
+# if USE_SCCACHE is set, use sccache to speed up compilation
+RUN --mount=type=cache,target=/root/.cache/pip \
+    if [ "$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 \
+        && tar -xzf sccache.tar.gz \
+        && sudo mv sccache-v0.8.1-x86_64-unknown-linux-musl/sccache /usr/bin/sccache \
+        && 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_IDLE_TIMEOUT=0 \
+        && export CMAKE_BUILD_TYPE=Release \
+        && sccache --show-stats \
+        && python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38 \
+        && sccache --show-stats; \
+    fi
 
 ENV CCACHE_DIR=/root/.cache/ccache
 RUN --mount=type=cache,target=/root/.cache/ccache \
     --mount=type=cache,target=/root/.cache/pip \
-    python3 setup.py bdist_wheel --dist-dir=dist
+    if [ "$USE_SCCACHE" != "1" ]; then \
+        python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38; \
+    fi
 
-# check the size of the wheel, we cannot upload wheels larger than 100MB
+# Check the size of the wheel if RUN_WHEEL_CHECK is true
 COPY .buildkite/check-wheel-size.py check-wheel-size.py
-RUN python3 check-wheel-size.py dist
-
+# Default max size of the wheel is 250MB
+ARG VLLM_MAX_SIZE_MB=250
+ENV VLLM_MAX_SIZE_MB=$VLLM_MAX_SIZE_MB
+ARG RUN_WHEEL_CHECK=true
+RUN if [ "$RUN_WHEEL_CHECK" = "true" ]; then \
+        python3 check-wheel-size.py dist; \
+    else \
+        echo "Skipping wheel size check."; \
+    fi
 #################### EXTENSION Build IMAGE ####################
 
+#################### DEV IMAGE ####################
+FROM base as dev
+
+COPY requirements-lint.txt requirements-lint.txt
+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:12.4.1-base-ubuntu22.04 AS vllm-base
+FROM nvidia/cuda:${CUDA_VERSION}-base-ubuntu20.04 AS vllm-base
+ARG CUDA_VERSION=12.4.1
+ARG PYTHON_VERSION=3.10
 WORKDIR /vllm-workspace
+ENV DEBIAN_FRONTEND=noninteractive
 
-RUN apt-get update -y \
-    && apt-get install -y python3-pip git vim
+RUN PYTHON_VERSION_STR=$(echo ${PYTHON_VERSION} | sed 's/\.//g') && \
+    echo "export PYTHON_VERSION_STR=${PYTHON_VERSION_STR}" >> /etc/environment
+
+# Install Python and other dependencies
+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 \
+    && add-apt-repository ppa:deadsnakes/ppa \
+    && apt-get update -y \
+    && apt-get install -y python${PYTHON_VERSION} python${PYTHON_VERSION}-dev python${PYTHON_VERSION}-venv libibverbs-dev \
+    && update-alternatives --install /usr/bin/python3 python3 /usr/bin/python${PYTHON_VERSION} 1 \
+    && update-alternatives --set python3 /usr/bin/python${PYTHON_VERSION} \
+    && ln -sf /usr/bin/python${PYTHON_VERSION}-config /usr/bin/python3-config \
+    && curl -sS https://bootstrap.pypa.io/get-pip.py | python${PYTHON_VERSION} \
+    && python3 --version && python3 -m pip --version
 
 # 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
 # or future versions of triton.
-RUN ldconfig /usr/local/cuda-12.4/compat/
+RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
 
 # 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 \
-    pip install dist/*.whl --verbose
+    python3 -m pip install dist/*.whl --verbose
+
+RUN --mount=type=cache,target=/root/.cache/pip \
+    . /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
 #################### vLLM installation IMAGE ####################
 
 
@@ -111,7 +182,7 @@ ADD . /vllm-workspace/
 
 # install development dependencies (for testing)
 RUN --mount=type=cache,target=/root/.cache/pip \
-    pip install -r requirements-dev.txt
+    python3 -m pip install -r requirements-dev.txt
 
 # doc requires source code
 # we hide them inside `test_docs/` , so that this source code
@@ -128,7 +199,7 @@ 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
+    pip install accelerate hf_transfer 'modelscope!=1.15.0'
 
 ENV VLLM_USAGE_SOURCE production-docker-image
 

Dockerfile.cpu

@@ -1,22 +1,53 @@
 # This vLLM Dockerfile is used to construct image that can build and run vLLM on x86 CPU platform.
 
-FROM ubuntu:22.04
+FROM ubuntu:22.04 AS cpu-test-1
 
-RUN apt-get update  -y \
-    && apt-get install -y git wget vim numactl gcc-12 g++-12 python3 python3-pip \
+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 \
     && update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-12 10 --slave /usr/bin/g++ g++ /usr/bin/g++-12
 
-RUN pip install --upgrade pip \
-    && pip install wheel packaging ninja setuptools>=49.4.0 numpy
+# 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
 
-COPY ./ /workspace/vllm
+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
+
+ENV PIP_EXTRA_INDEX_URL=https://download.pytorch.org/whl/cpu
+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-1 AS build
 
 WORKDIR /workspace/vllm
 
-RUN pip install -v -r requirements-cpu.txt --extra-index-url https://download.pytorch.org/whl/cpu
+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
 
-RUN VLLM_TARGET_DEVICE=cpu python3 setup.py install
+COPY ./ ./
+
+# 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 \
+    VLLM_TARGET_DEVICE=cpu python3 setup.py bdist_wheel && \
+    pip install dist/*.whl
 
 WORKDIR /workspace/
 
-CMD ["/bin/bash"]
+RUN ln -s /workspace/vllm/tests && ln -s /workspace/vllm/examples && ln -s /workspace/vllm/benchmarks
+
+ENTRYPOINT ["python3", "-m", "vllm.entrypoints.openai.api_server"]

Dockerfile.neuron

@@ -1,5 +1,5 @@
 # default base image
-ARG BASE_IMAGE="763104351884.dkr.ecr.us-west-2.amazonaws.com/pytorch-inference-neuronx:2.1.1-neuronx-py310-sdk2.17.0-ubuntu20.04"
+ARG BASE_IMAGE="public.ecr.aws/neuron/pytorch-inference-neuronx:2.1.2-neuronx-py310-sdk2.19.1-ubuntu20.04"
 
 FROM $BASE_IMAGE
 
@@ -28,7 +28,7 @@ COPY ./requirements-neuron.txt /app/vllm/requirements-neuron.txt
 RUN cd /app/vllm \
     && python3 -m pip install -U -r requirements-neuron.txt
 
-ENV VLLM_BUILD_WITH_NEURON 1
+ENV VLLM_TARGET_DEVICE neuron
 RUN cd /app/vllm \
     && pip install -e . \
     && cd ..

Dockerfile.openvino

@@ -0,0 +1,29 @@
+# The vLLM Dockerfile is used to construct vLLM image that can be directly used
+# to run the OpenAI compatible server.
+
+FROM ubuntu:22.04 AS dev
+
+RUN apt-get update -y && \
+    apt-get install -y python3-pip git
+WORKDIR /workspace
+
+# copy requirements
+COPY requirements-build.txt /workspace/vllm/
+COPY requirements-common.txt /workspace/vllm/
+COPY requirements-openvino.txt /workspace/vllm/
+
+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
+# 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/
+
+COPY examples/ /workspace/vllm/examples
+COPY benchmarks/ /workspace/vllm/benchmarks
+
+CMD ["/bin/bash"]

Dockerfile.ppc64le

@@ -0,0 +1,22 @@
+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
+
+# 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
+
+COPY ./ /workspace/vllm
+
+WORKDIR /workspace/vllm
+
+# 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 VLLM_TARGET_DEVICE=cpu python3 setup.py install
+
+WORKDIR /vllm-workspace
+ENTRYPOINT ["/opt/conda/bin/python3", "-m", "vllm.entrypoints.openai.api_server"]

Dockerfile.rocm

@@ -1,35 +1,33 @@
-# default base image
-ARG BASE_IMAGE="rocm/pytorch:rocm6.0_ubuntu20.04_py3.9_pytorch_2.1.1"
+# Default ROCm 6.1 base image
+ARG BASE_IMAGE="rocm/pytorch:rocm6.1.2_ubuntu20.04_py3.9_pytorch_staging"
 
-FROM $BASE_IMAGE
+# Default ROCm ARCHes to build vLLM for.
+ARG PYTORCH_ROCM_ARCH="gfx908;gfx90a;gfx942;gfx1100"
 
-ARG BASE_IMAGE="rocm/pytorch:rocm6.0_ubuntu20.04_py3.9_pytorch_2.1.1"
-
-RUN echo "Base image is $BASE_IMAGE"
-
-# BASE_IMAGE for ROCm_5.7: "rocm/pytorch:rocm5.7_ubuntu22.04_py3.10_pytorch_2.0.1"
-# BASE_IMAGE for ROCm_6.0: "rocm/pytorch:rocm6.0_ubuntu20.04_py3.9_pytorch_2.1.1"
-
-
-ARG FA_GFX_ARCHS="gfx90a;gfx942"
-RUN echo "FA_GFX_ARCHS is $FA_GFX_ARCHS"
-
-ARG FA_BRANCH="ae7928c"
-RUN echo "FA_BRANCH is $FA_BRANCH"
-
-# whether to build flash-attention
-# if 0, will not build flash attention
-# this is useful for gfx target where flash-attention is not supported
-# In that case, we need to use the python reference attention implementation in vllm
+# 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"
 
-# whether to build triton on rocm
+# Whether to build triton on rocm
 ARG BUILD_TRITON="1"
+ARG TRITON_BRANCH="e0fc12c"
+
+### Base image build stage
+FROM $BASE_IMAGE AS base
+
+# Import arg(s) defined before this build stage
+ARG PYTORCH_ROCM_ARCH
 
 # Install some basic utilities
 RUN apt-get update && apt-get install python3 python3-pip -y
-
-# Install some basic utilities
 RUN apt-get update && apt-get install -y \
     curl \
     ca-certificates \
@@ -40,75 +38,144 @@ RUN apt-get update && apt-get install -y \
     build-essential \
     wget \
     unzip \
-    nvidia-cuda-toolkit \
     tmux \
+    ccache \
  && rm -rf /var/lib/apt/lists/*
 
-### Mount Point ###
-# When launching the container, mount the code directory to /app
+# When launching the container, mount the code directory to /vllm-workspace
 ARG APP_MOUNT=/vllm-workspace
-VOLUME [ ${APP_MOUNT} ]
 WORKDIR ${APP_MOUNT}
 
 RUN python3 -m pip install --upgrade pip
-RUN python3 -m pip install --no-cache-dir fastapi ninja tokenizers pandas
+# 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 \
+        *"rocm-6.1"*) \
+            python3 -m pip uninstall -y torch torchvision \
+            && python3 -m pip install --no-cache-dir --pre \
+                torch==2.5.0.dev20240726 \
+                torchvision==0.20.0.dev20240726 \
+               --index-url https://download.pytorch.org/whl/nightly/rocm6.1;; \
+        *) ;; esac
 
 ENV LLVM_SYMBOLIZER_PATH=/opt/rocm/llvm/bin/llvm-symbolizer
 ENV PATH=$PATH:/opt/rocm/bin:/libtorch/bin:
 ENV LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/opt/rocm/lib/:/libtorch/lib:
 ENV CPLUS_INCLUDE_PATH=$CPLUS_INCLUDE_PATH:/libtorch/include:/libtorch/include/torch/csrc/api/include/:/opt/rocm/include/:
 
-# Install ROCm flash-attention
-RUN if [ "$BUILD_FA" = "1" ]; then \
-    mkdir libs \
-    && cd libs \
-    && git clone https://github.com/ROCm/flash-attention.git \
-    && cd flash-attention \
-    && git checkout ${FA_BRANCH} \
-    && git submodule update --init \
-    && export GPU_ARCHS=${FA_GFX_ARCHS} \
-    && if [ "$BASE_IMAGE" = "rocm/pytorch:rocm5.7_ubuntu22.04_py3.10_pytorch_2.0.1" ]; then \
-        patch /opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/utils/hipify/hipify_python.py hipify_patch.patch; fi \
-    && python3 setup.py install \
-    && cd ..; \
+ENV PYTORCH_ROCM_ARCH=${PYTORCH_ROCM_ARCH}
+ENV CCACHE_DIR=/root/.cache/ccache
+
+
+### AMD-SMI build stage
+FROM base AS build_amdsmi
+# Build amdsmi wheel always
+RUN cd /opt/rocm/share/amd_smi \
+    && python3 -m pip wheel . --wheel-dir=/install
+
+
+### 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 \
+            # If a suitable wheel exists, we download it instead of building FA
+            mkdir -p /install && wget -N "${FA_WHEEL_URL}" -P /install; \
+        else \
+            mkdir -p libs \
+            && cd libs \
+            && git clone https://github.com/ROCm/flash-attention.git \
+            && cd flash-attention \
+            && git checkout "${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
 
-# Error related to odd state for numpy 1.20.3 where there is no METADATA etc, but an extra LICENSES_bundled.txt.
-# Manually removed it so that later steps of numpy upgrade can continue
-RUN if [ "$BASE_IMAGE" = "rocm/pytorch:rocm6.0_ubuntu20.04_py3.9_pytorch_2.1.1" ]; then \
-    rm -rf /opt/conda/envs/py_3.9/lib/python3.9/site-packages/numpy-1.20.3.dist-info/; fi
 
-# build triton
-RUN if [ "$BUILD_TRITON" = "1" ]; then \
+### Triton wheel build stage
+FROM base AS build_triton
+ARG BUILD_TRITON
+ARG TRITON_BRANCH
+# Build triton wheel if `BUILD_TRITON = 1`
+RUN --mount=type=cache,target=${CCACHE_DIR} \
+    if [ "$BUILD_TRITON" = "1" ]; then \
     mkdir -p libs \
     && cd libs \
-    && pip uninstall -y triton \
-    && git clone https://github.com/ROCm/triton.git \
-    && cd triton/python \
-    && pip3 install . \
-    && cd ../..; \
+    && git clone https://github.com/OpenAI/triton.git \
+    && cd triton \
+    && git checkout "${TRITON_BRANCH}" \
+    && cd python \
+    && python3 setup.py bdist_wheel --dist-dir=/install; \
+    # Create an empty directory otherwise as later build stages expect one
+    else mkdir -p /install; \
     fi
 
-WORKDIR /vllm-workspace
+
+### Final vLLM build stage
+FROM base AS final
+# Import the vLLM development directory from the build context
 COPY . .
 
-#RUN python3 -m pip install pynvml # to be removed eventually
-RUN python3 -m pip install --upgrade pip numba
+# 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]
+
 
-# make sure punica kernels are built (for LoRA)
-ENV VLLM_INSTALL_PUNICA_KERNELS=1
 # Workaround for ray >= 2.10.0
 ENV RAY_EXPERIMENTAL_NOSET_ROCR_VISIBLE_DEVICES=1
+# Silences the HF Tokenizers warning
+ENV TOKENIZERS_PARALLELISM=false
 
-ENV VLLM_NCCL_SO_PATH=/opt/rocm/lib/librccl.so
+RUN --mount=type=cache,target=${CCACHE_DIR} \
+    --mount=type=cache,target=/root/.cache/pip \
+    python3 -m pip install -Ur requirements-rocm.txt \
+    && case "$(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
 
+# Copy amdsmi wheel into final image
+RUN --mount=type=bind,from=build_amdsmi,src=/install,target=/install \
+    mkdir -p libs \
+    && cp /install/*.whl libs \
+    # Preemptively uninstall to avoid same-version no-installs
+    && python3 -m pip uninstall -y amdsmi;
+
+# Copy triton wheel(s) into final image if they were built
+RUN --mount=type=bind,from=build_triton,src=/install,target=/install \
+    mkdir -p libs \
+    && if ls /install/*.whl; then \
+        cp /install/*.whl libs \
+        # Preemptively uninstall to avoid same-version no-installs
+        && python3 -m pip uninstall -y triton; fi
+
+# Copy flash-attn wheel(s) into final image if they were built
+RUN --mount=type=bind,from=build_fa,src=/install,target=/install \
+    mkdir -p libs \
+    && if ls /install/*.whl; then \
+        cp /install/*.whl libs \
+        # Preemptively uninstall to avoid same-version no-installs
+        && python3 -m pip uninstall -y flash-attn; fi
+
+# Install wheels that were built to the final image
 RUN --mount=type=cache,target=/root/.cache/pip \
-    pip install -U -r requirements-rocm.txt \
-    && patch /opt/rocm/include/hip/amd_detail/amd_hip_bf16.h ./rocm_patch/rocm_bf16.patch \
-    && python3 setup.py install \
-    && cp build/lib.linux-x86_64-cpython-39/vllm/_C.cpython-39-x86_64-linux-gnu.so vllm/ \
-    && cp build/lib.linux-x86_64-cpython-39/vllm/_punica_C.cpython-39-x86_64-linux-gnu.so vllm/ \
-    && cd ..
-
+    if ls libs/*.whl; then \
+    python3 -m pip install libs/*.whl; fi
 
 CMD ["/bin/bash"]

Dockerfile.tpu

@@ -0,0 +1,17 @@
+ARG NIGHTLY_DATE="20240828"
+ARG BASE_IMAGE="us-central1-docker.pkg.dev/tpu-pytorch-releases/docker/xla:nightly_3.10_tpuvm_$NIGHTLY_DATE"
+
+FROM $BASE_IMAGE
+WORKDIR /workspace
+
+# Install the TPU and Pallas dependencies.
+RUN python3 -m pip install torch_xla[tpu] -f https://storage.googleapis.com/libtpu-releases/index.html
+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
+
+# Build vLLM.
+COPY . /workspace/vllm
+ENV VLLM_TARGET_DEVICE="tpu"
+RUN cd /workspace/vllm && python3 -m pip install -r requirements-tpu.txt
+RUN cd /workspace/vllm && python3 setup.py develop
+
+CMD ["/bin/bash"]

Dockerfile.xpu

@@ -0,0 +1,22 @@
+FROM intel/oneapi-basekit:2024.1.0-devel-ubuntu20.04
+
+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 \
+&& apt-get install -y curl libicu70 lsb-release git wget vim numactl python3 python3-pip
+
+COPY ./ /workspace/vllm
+
+WORKDIR /workspace/vllm
+
+RUN pip install -v -r requirements-xpu.txt
+
+RUN VLLM_TARGET_DEVICE=xpu python3 setup.py install
+
+CMD ["/bin/bash"]

MANIFEST.in

@@ -1,4 +1,5 @@
 include LICENSE
+include requirements-adag.txt
 include requirements-common.txt
 include requirements-cuda.txt
 include requirements-rocm.txt

README.md

@@ -10,32 +10,29 @@ 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://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> |
 
 </p>
 
+
 ---
 
-**The Fourth vLLM Bay Area Meetup (June 11th 5:30pm-8pm PT)**
+**vLLM & NVIDIA Triton User Meetup (Monday, September 9, 5pm-9pm PT) at Fort Mason, San Francisco**
 
-We are thrilled to announce our fourth vLLM Meetup!
-The vLLM team will share recent updates and roadmap.
-We will also have vLLM collaborators from BentoML and Cloudflare coming up to the stage to discuss their experience in deploying LLMs with vLLM.
-Please register [here](https://lu.ma/agivllm) and join us!
+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* 🔥
+- [2024/07] 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).
 - [2024/04] We hosted [the third vLLM meetup](https://robloxandvllmmeetup2024.splashthat.com/) with Roblox! Please find the meetup slides [here](https://docs.google.com/presentation/d/1A--47JAK4BJ39t954HyTkvtfwn0fkqtsL8NGFuslReM/edit?usp=sharing).
-- [2024/01] We hosted [the second vLLM meetup](https://lu.ma/ygxbpzhl) in SF! Please find the meetup slides [here](https://docs.google.com/presentation/d/12mI2sKABnUw5RBWXDYY-HtHth4iMSNcEoQ10jDQbxgA/edit?usp=sharing).
-- [2024/01] Added ROCm 6.0 support to vLLM.
-- [2023/12] Added ROCm 5.7 support to vLLM.
-- [2023/10] We hosted [the first vLLM meetup](https://lu.ma/first-vllm-meetup) in SF! Please find the meetup slides [here](https://docs.google.com/presentation/d/1QL-XPFXiFpDBh86DbEegFXBXFXjix4v032GhShbKf3s/edit?usp=sharing).
-- [2023/09] We created our [Discord server](https://discord.gg/jz7wjKhh6g)! Join us to discuss vLLM and LLM serving! We will also post the latest announcements and updates there.
-- [2023/09] We released our [PagedAttention paper](https://arxiv.org/abs/2309.06180) on arXiv!
+- [2024/01] We hosted [the second vLLM meetup](https://lu.ma/ygxbpzhl) with IBM! Please find the meetup slides [here](https://docs.google.com/presentation/d/12mI2sKABnUw5RBWXDYY-HtHth4iMSNcEoQ10jDQbxgA/edit?usp=sharing).
+- [2023/10] We hosted [the first vLLM meetup](https://lu.ma/first-vllm-meetup) with a16z! Please find the meetup slides [here](https://docs.google.com/presentation/d/1QL-XPFXiFpDBh86DbEegFXBXFXjix4v032GhShbKf3s/edit?usp=sharing).
 - [2023/08] We would like to express our sincere gratitude to [Andreessen Horowitz](https://a16z.com/2023/08/30/supporting-the-open-source-ai-community/) (a16z) for providing a generous grant to support the open-source development and research of vLLM.
-- [2023/07] Added support for LLaMA-2! You can run and serve 7B/13B/70B LLaMA-2s on vLLM with a single command!
-- [2023/06] Serving vLLM On any Cloud with SkyPilot. Check out a 1-click [example](https://github.com/skypilot-org/skypilot/blob/master/llm/vllm) to start the vLLM demo, and the [blog post](https://blog.skypilot.co/serving-llm-24x-faster-on-the-cloud-with-vllm-and-skypilot/) for the story behind vLLM development on the clouds.
 - [2023/06] We officially released vLLM! FastChat-vLLM integration has powered [LMSYS Vicuna and Chatbot Arena](https://chat.lmsys.org) since mid-April. Check out our [blog post](https://vllm.ai).
 
 ---
@@ -48,30 +45,35 @@ vLLM is fast with:
 - Efficient management of attention key and value memory with **PagedAttention**
 - Continuous batching of incoming requests
 - Fast model execution with CUDA/HIP graph
-- Quantization: [GPTQ](https://arxiv.org/abs/2210.17323), [AWQ](https://arxiv.org/abs/2306.00978), [SqueezeLLM](https://arxiv.org/abs/2306.07629), FP8 KV Cache
-- Optimized CUDA kernels
+- Quantizations: [GPTQ](https://arxiv.org/abs/2210.17323), [AWQ](https://arxiv.org/abs/2306.00978), INT4, INT8, and FP8.
+- Optimized CUDA kernels, including integration with FlashAttention and FlashInfer.
+- 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)).
 
 vLLM is flexible and easy to use with:
 
 - Seamless integration with popular Hugging Face models
 - High-throughput serving with various decoding algorithms, including *parallel sampling*, *beam search*, and more
-- Tensor parallelism support for distributed inference
+- Tensor parallelism and pipeline parallelism support for distributed inference
 - Streaming outputs
 - OpenAI-compatible API server
-- Support NVIDIA GPUs and AMD GPUs
-- (Experimental) Prefix caching support
-- (Experimental) Multi-lora support
+- Support NVIDIA GPUs, AMD CPUs and GPUs, Intel CPUs and GPUs, PowerPC CPUs, TPU, and AWS Neuron.
+- Prefix caching support
+- Multi-lora support
 
 vLLM seamlessly supports most popular open-source models on HuggingFace, including:
 - Transformer-like LLMs (e.g., Llama)
 - Mixture-of-Expert LLMs (e.g., Mixtral)
+- Embedding Models (e.g. E5-Mistral)
 - Multi-modal LLMs (e.g., LLaVA)
 
 Find the full list of supported models [here](https://docs.vllm.ai/en/latest/models/supported_models.html).
 
 ## Getting Started
 
-Install vLLM with pip or [from source](https://vllm.readthedocs.io/en/latest/getting_started/installation.html#build-from-source):
+Install vLLM with `pip` or [from source](https://vllm.readthedocs.io/en/latest/getting_started/installation.html#build-from-source):
 
 ```bash
 pip install vllm
@@ -102,14 +104,18 @@ vLLM is a community project. Our compute resources for development and testing a
 - Databricks
 - DeepInfra
 - Dropbox
+- Google Cloud
 - Lambda Lab
 - NVIDIA
 - Replicate
 - Roblox
 - RunPod
+- Sequoia Capital
+- Skywork AI
 - Trainy
 - UC Berkeley
 - UC San Diego
+- ZhenFund
 
 We also have an official fundraising venue through [OpenCollective](https://opencollective.com/vllm). We plan to use the fund to support the development, maintenance, and adoption of vLLM.
 

benchmarks/backend_request_func.py

@@ -4,10 +4,13 @@ import sys
 import time
 import traceback
 from dataclasses import dataclass, field
-from typing import List, Optional
+from typing import List, Optional, Union
 
 import aiohttp
+import huggingface_hub.constants
 from tqdm.asyncio import tqdm
+from transformers import (AutoTokenizer, PreTrainedTokenizer,
+                          PreTrainedTokenizerFast)
 
 AIOHTTP_TIMEOUT = aiohttp.ClientTimeout(total=6 * 60 * 60)
 
@@ -68,9 +71,13 @@ async def async_request_tgi(
                         chunk_bytes = chunk_bytes.strip()
                         if not chunk_bytes:
                             continue
+                        chunk_bytes = chunk_bytes.decode("utf-8")
 
-                        chunk = remove_prefix(chunk_bytes.decode("utf-8"),
-                                              "data:")
+                        #NOTE: Sometimes TGI returns a ping response without
+                        # any data, we should skip it.
+                        if chunk_bytes.startswith(":"):
+                            continue
+                        chunk = remove_prefix(chunk_bytes, "data:")
 
                         data = json.loads(chunk)
                         timestamp = time.perf_counter()
@@ -218,8 +225,8 @@ async def async_request_openai_completions(
 ) -> RequestFuncOutput:
     api_url = request_func_input.api_url
     assert api_url.endswith(
-        "v1/completions"
-    ), "OpenAI Completions API URL must end with 'v1/completions'."
+        ("completions", "profile")
+    ), "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
@@ -258,6 +265,9 @@ async def async_request_openai_completions(
                         else:
                             data = json.loads(chunk)
 
+                            # NOTE: Some completion API might have a last
+                            # usage summary response without a token so we
+                            # want to check a token was generated
                             if data["choices"][0]["text"]:
                                 timestamp = time.perf_counter()
                                 # First token
@@ -266,10 +276,7 @@ async def async_request_openai_completions(
                                     output.ttft = ttft
 
                                 # Decoding phase
-                                # NOTE: Some completion API might have a last
-                                # usage summary response without a token so we
-                                # do not want to include as inter-token-latency
-                                elif data.get("usage", None) is None:
+                                else:
                                     output.itl.append(timestamp -
                                                       most_recent_timestamp)
 
@@ -298,8 +305,8 @@ async def async_request_openai_chat_completions(
 ) -> RequestFuncOutput:
     api_url = request_func_input.api_url
     assert api_url.endswith(
-        "v1/chat/completions"
-    ), "OpenAI Chat Completions API URL must end with 'v1/chat/completions'."
+        "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
@@ -384,6 +391,30 @@ def remove_prefix(text: str, prefix: str) -> str:
     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
+
+        model_path = snapshot_download(
+            model_id=pretrained_model_name_or_path,
+            local_files_only=huggingface_hub.constants.HF_HUB_OFFLINE,
+            ignore_file_pattern=[".*.pt", ".*.safetensors", ".*.bin"])
+
+        return model_path
+    return pretrained_model_name_or_path
+
+
+def get_tokenizer(
+    pretrained_model_name_or_path: str, trust_remote_code: bool
+) -> Union[PreTrainedTokenizer, PreTrainedTokenizerFast]:
+    if pretrained_model_name_or_path is not None and not os.path.exists(
+            pretrained_model_name_or_path):
+        pretrained_model_name_or_path = get_model(
+            pretrained_model_name_or_path)
+    return AutoTokenizer.from_pretrained(pretrained_model_name_or_path,
+                                         trust_remote_code=trust_remote_code)
+
+
 ASYNC_REQUEST_FUNCS = {
     "tgi": async_request_tgi,
     "vllm": async_request_openai_completions,
@@ -392,4 +423,5 @@ ASYNC_REQUEST_FUNCS = {
     "openai": async_request_openai_completions,
     "openai-chat": async_request_openai_chat_completions,
     "tensorrt-llm": async_request_trt_llm,
+    "scalellm": async_request_openai_completions,
 }

benchmarks/benchmark_latency.py

@@ -10,8 +10,10 @@ import torch
 from tqdm import tqdm
 
 from vllm import LLM, SamplingParams
-from vllm.inputs import PromptStrictInputs
+from vllm.engine.arg_utils import EngineArgs
+from vllm.inputs import PromptInputs
 from vllm.model_executor.layers.quantization import QUANTIZATION_METHODS
+from vllm.utils import FlexibleArgumentParser
 
 
 def main(args: argparse.Namespace):
@@ -19,24 +21,33 @@ def main(args: argparse.Namespace):
 
     # NOTE(woosuk): If the request cannot be processed in a single batch,
     # the engine will automatically process the request in multiple batches.
-    llm = LLM(model=args.model,
-              speculative_model=args.speculative_model,
-              num_speculative_tokens=args.num_speculative_tokens,
-              tokenizer=args.tokenizer,
-              quantization=args.quantization,
-              tensor_parallel_size=args.tensor_parallel_size,
-              trust_remote_code=args.trust_remote_code,
-              dtype=args.dtype,
-              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)
+    llm = LLM(
+        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,
@@ -50,7 +61,7 @@ def main(args: argparse.Namespace):
     dummy_prompt_token_ids = np.random.randint(10000,
                                                size=(args.batch_size,
                                                      args.input_len))
-    dummy_inputs: List[PromptStrictInputs] = [{
+    dummy_inputs: List[PromptInputs] = [{
         "prompt_token_ids": batch
     } for batch in dummy_prompt_token_ids.tolist()]
 
@@ -95,7 +106,7 @@ def main(args: argparse.Namespace):
     for _ in tqdm(range(args.num_iters), desc="Profiling iterations"):
         latencies.append(run_to_completion(profile_dir=None))
     latencies = np.array(latencies)
-    percentages = [10, 25, 50, 75, 90]
+    percentages = [10, 25, 50, 75, 90, 99]
     percentiles = np.percentile(latencies, percentages)
     print(f'Avg latency: {np.mean(latencies)} seconds')
     for percentage, percentile in zip(percentages, percentiles):
@@ -113,12 +124,16 @@ def main(args: argparse.Namespace):
 
 
 if __name__ == '__main__':
-    parser = argparse.ArgumentParser(
+    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',
@@ -144,6 +159,12 @@ if __name__ == '__main__':
     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,
@@ -187,9 +208,10 @@ if __name__ == '__main__':
     parser.add_argument(
         "--device",
         type=str,
-        default="cuda",
-        choices=["cuda", "cpu"],
-        help='device type for vLLM execution, supporting CUDA and CPU.')
+        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,
@@ -199,6 +221,9 @@ if __name__ == '__main__':
         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",
@@ -221,5 +246,40 @@ if __name__ == '__main__':
                         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)

benchmarks/benchmark_prefix_caching.py

@@ -1,7 +1,44 @@
-import argparse
+"""
+Benchmark the efficiency of prefix caching.
+
+This script allows you to benchmark the performance of
+a model with and without prefix caching using either fixed prompts
+or prompts sampled from the ShareGPT dataset.
+
+Fixed example usage:
+    python benchmark_prefix_caching.py \
+        --model meta-llama/Llama-2-7b-chat-hf \
+        --enable-prefix-caching \
+        --num-prompts 1 \
+        --repeat-count 100
+
+ShareGPT example usage:
+    # This command samples 20 prompts with input lengths
+    # between 128 and 256 tokens from the ShareGPT dataset,
+    # then replicates each prompt 5 times.
+    python benchmark_prefix_caching.py \
+        --model meta-llama/Llama-2-7b-chat-hf \
+        --dataset-path /path/to/ShareGPT_V3_unfiltered_cleaned_split.json \
+        --enable-prefix-caching \
+        --num-prompts 20 \
+        --repeat-count 5 \
+        --input-length-range 128:256
+"""
+
+import json
+import random
 import time
+from typing import List, Optional, Tuple
+
+from transformers import PreTrainedTokenizerBase
 
 from vllm import LLM, SamplingParams
+from vllm.utils import FlexibleArgumentParser
+
+try:
+    from vllm.transformers_utils.tokenizer import get_tokenizer
+except ImportError:
+    from backend_request_func import get_tokenizer
 
 PROMPT = "You are a helpful assistant in recognizes the content of tables in markdown format. Here is a table as fellows. You need to answer my question about the table.\n# Table\n|Opening|Opening|Sl. No.|Film|Cast|Director|Music Director|Notes|\n|----|----|----|----|----|----|----|----|\n|J A N|9|1|Agni Pushpam|Jayabharathi, Kamalahasan|Jeassy|M. K. Arjunan||\n|J A N|16|2|Priyamvada|Mohan Sharma, Lakshmi, KPAC Lalitha|K. S. Sethumadhavan|V. Dakshinamoorthy||\n|J A N|23|3|Yakshagaanam|Madhu, Sheela|Sheela|M. S. Viswanathan||\n|J A N|30|4|Paalkkadal|Sheela, Sharada|T. K. Prasad|A. T. Ummer||\n|F E B|5|5|Amma|Madhu, Srividya|M. Krishnan Nair|M. K. Arjunan||\n|F E B|13|6|Appooppan|Thikkurissi Sukumaran Nair, Kamal Haasan|P. Bhaskaran|M. S. Baburaj||\n|F E B|20|7|Srishti|Chowalloor Krishnankutty, Ravi Alummoodu|K. T. Muhammad|M. S. Baburaj||\n|F E B|20|8|Vanadevatha|Prem Nazir, Madhubala|Yusufali Kechery|G. Devarajan||\n|F E B|27|9|Samasya|Madhu, Kamalahaasan|K. Thankappan|Shyam||\n|F E B|27|10|Yudhabhoomi|K. P. Ummer, Vidhubala|Crossbelt Mani|R. K. Shekhar||\n|M A R|5|11|Seemantha Puthran|Prem Nazir, Jayabharathi|A. B. Raj|M. K. Arjunan||\n|M A R|12|12|Swapnadanam|Rani Chandra, Dr. Mohandas|K. G. George|Bhaskar Chandavarkar||\n|M A R|19|13|Thulavarsham|Prem Nazir, sreedevi, Sudheer|N. Sankaran Nair|V. Dakshinamoorthy||\n|M A R|20|14|Aruthu|Kaviyoor Ponnamma, Kamalahasan|Ravi|G. Devarajan||\n|M A R|26|15|Swimming Pool|Kamal Haasan, M. G. Soman|J. Sasikumar|M. K. Arjunan||\n\n# Question\nWhat' s the content in the (1,1) cells\n"  # noqa: E501
 
@@ -15,7 +52,83 @@ def test_prefix(llm=None, sampling_params=None, prompts=None):
     print(f"cost time {end_time - start_time}")
 
 
+def sample_requests(
+    dataset_path: str,
+    num_requests: int,
+    tokenizer: PreTrainedTokenizerBase,
+    input_length_range: Tuple[int, int],
+    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)
+
+    min_len, max_len = input_length_range
+
+    # 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 min_len <= prompt_len <= max_len:
+            filtered_dataset.append((prompt, prompt_len, output_len))
+
+    return filtered_dataset
+
+
+def repeat_and_sort_requests(requests: List[Tuple[str, int, int]],
+                             repeat_count: int,
+                             sort: bool = False) -> List[str]:
+    repeated_requests = requests * repeat_count
+    if sort:
+        repeated_requests.sort(key=lambda x: x[1])
+    else:
+        random.shuffle(repeated_requests)
+    return [req[0] 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(':')))
+
+    if args.dataset_path is not None:
+        print(f"Start to sample {args.num_prompts} prompts"
+              "from {args.dataset_path}")
+        filtered_datasets = sample_requests(
+            dataset_path=args.dataset_path,
+            num_requests=args.num_prompts,
+            tokenizer=tokenizer,
+            input_length_range=input_length_range,
+            fixed_output_len=args.output_len,
+        )
+    else:
+        prompt_len = len(tokenizer(PROMPT).input_ids)
+        filtered_datasets = [(PROMPT, prompt_len, args.output_len)
+                             ] * args.num_prompts
+
     llm = LLM(model=args.model,
               tokenizer_mode='auto',
               trust_remote_code=True,
@@ -24,10 +137,13 @@ def main(args):
               tensor_parallel_size=args.tensor_parallel_size,
               enable_prefix_caching=args.enable_prefix_caching)
 
-    num_prompts = 100
-    prompts = [PROMPT] * num_prompts
     sampling_params = SamplingParams(temperature=0, max_tokens=args.output_len)
 
+    print("Testing filtered datasets")
+    prompts = repeat_and_sort_requests(filtered_datasets,
+                                       repeat_count=args.repeat_count,
+                                       sort=args.sort)
+
     print("------warm up------")
     test_prefix(
         llm=llm,
@@ -44,12 +160,16 @@ def main(args):
 
 
 if __name__ == "__main__":
-    parser = argparse.ArgumentParser(
-        description='Benchmark the performance with or without automatic '
-        'prefix caching.')
+    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',
@@ -58,5 +178,21 @@ if __name__ == "__main__":
     parser.add_argument('--use-v2-block-manager',
                         action='store_true',
                         help='Use BlockSpaceMangerV2')
+    parser.add_argument('--num-prompts',
+                        type=int,
+                        default=1,
+                        help="Number of the prompts sampled from dataset")
+    parser.add_argument('--repeat-count',
+                        type=int,
+                        default=100,
+                        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',
+                        help='Range of input lengths for sampling prompts,'
+                        'specified as "min:max" (e.g., "128:256").')
     args = parser.parse_args()
     main(args)

benchmarks/benchmark_serving.py

@@ -2,8 +2,8 @@
 
 On the server side, run one of the following commands:
     vLLM OpenAI API server
-    python -m vllm.entrypoints.openai.api_server \
-        --model <your_model> --swap-space 16 \
+    vllm serve <your_model> \
+        --swap-space 16 \
         --disable-log-requests
 
     (TGI backend)
@@ -17,7 +17,7 @@ On the client side, run:
         --dataset-path <path to dataset> \
         --request-rate <request_rate> \ # By default <request_rate> is inf
         --num-prompts <num_prompts> # By default <num_prompts> is 1000
-        
+
     when using tgi backend, add
         --endpoint /generate_stream
     to the end of the command above.
@@ -31,7 +31,7 @@ import time
 import warnings
 from dataclasses import dataclass
 from datetime import datetime
-from typing import AsyncGenerator, List, Optional, Tuple
+from typing import Any, AsyncGenerator, Dict, List, Optional, Tuple
 
 import numpy as np
 from backend_request_func import (ASYNC_REQUEST_FUNCS, RequestFuncInput,
@@ -39,7 +39,15 @@ from backend_request_func import (ASYNC_REQUEST_FUNCS, RequestFuncInput,
 from tqdm.asyncio import tqdm
 from transformers import PreTrainedTokenizerBase
 
-from vllm.transformers_utils.tokenizer import get_tokenizer
+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
 
 
 @dataclass
@@ -48,14 +56,27 @@ class BenchmarkMetrics:
     total_input: int
     total_output: int
     request_throughput: float
-    input_throughput: float
     output_throughput: float
+    total_token_throughput: float
     mean_ttft_ms: float
     median_ttft_ms: float
-    p99_ttft_ms: float
+    std_ttft_ms: float
+    percentiles_ttft_ms: List[Tuple[float, float]]
     mean_tpot_ms: float
     median_tpot_ms: float
-    p99_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]]
 
 
 def sample_sharegpt_requests(
@@ -66,7 +87,6 @@ def sample_sharegpt_requests(
 ) -> 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)
@@ -174,6 +194,31 @@ def sample_sonnet_requests(
     return sampled_requests
 
 
+def sample_random_requests(
+        input_len: int, output_len: int, num_prompts: int, range_ratio: float,
+        tokenizer: PreTrainedTokenizerBase) -> List[Tuple[str, int, int]]:
+
+    input_lens = np.random.randint(
+        int(input_len * range_ratio),
+        input_len + 1,
+        size=num_prompts,
+    )
+    output_lens = np.random.randint(
+        int(output_len * range_ratio),
+        output_len + 1,
+        size=num_prompts,
+    )
+    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
+                                   for j in range(input_lens[i])])
+        input_requests.append(
+            (prompt, int(input_lens[i]), int(output_lens[i])))
+
+    return input_requests
+
+
 async def get_request(
     input_requests: List[Tuple[str, int, int]],
     request_rate: float,
@@ -185,6 +230,7 @@ async def get_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 exponential distribution.
         interval = np.random.exponential(1.0 / request_rate)
         # The next request will be sent after the interval.
@@ -196,21 +242,33 @@ def calculate_metrics(
     outputs: List[RequestFuncOutput],
     dur_s: float,
     tokenizer: PreTrainedTokenizerBase,
+    selected_percentile_metrics: List[str],
+    selected_percentiles: List[float],
 ) -> Tuple[BenchmarkMetrics, List[int]]:
-    actual_output_lens = []
+    actual_output_lens: List[int] = []
     total_input = 0
     completed = 0
-    tpots = []
-    ttfts = []
+    itls: List[float] = []
+    tpots: List[float] = []
+    ttfts: List[float] = []
+    e2els: List[float] = []
     for i in range(len(outputs)):
         if outputs[i].success:
-            output_len = len(tokenizer(outputs[i].generated_text).input_ids)
+            # 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][1]
             if output_len > 1:
                 tpots.append(
                     (outputs[i].latency - outputs[i].ttft) / (output_len - 1))
+            itls += outputs[i].itl
             ttfts.append(outputs[i].ttft)
+            e2els.append(outputs[i].latency)
             completed += 1
         else:
             actual_output_lens.append(0)
@@ -225,15 +283,29 @@ def calculate_metrics(
         total_input=total_input,
         total_output=sum(actual_output_lens),
         request_throughput=completed / dur_s,
-        input_throughput=total_input / 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,
-        p99_ttft_ms=np.percentile(ttfts or 0, 99) * 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,
-        p99_tpot_ms=np.percentile(tpots or 0, 99) * 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.median(e2els or 0) * 1000,
+        std_e2el_ms=np.std(e2els or 0) * 1000,
+        median_e2el_ms=np.mean(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
@@ -242,6 +314,7 @@ def calculate_metrics(
 async def benchmark(
     backend: str,
     api_url: str,
+    base_url: str,
     model_id: str,
     tokenizer: PreTrainedTokenizerBase,
     input_requests: List[Tuple[str, int, int]],
@@ -249,9 +322,12 @@ async def benchmark(
     use_beam_search: bool,
     request_rate: float,
     disable_tqdm: bool,
+    profile: bool,
+    selected_percentile_metrics: List[str],
+    selected_percentiles: List[str],
 ):
     if backend in ASYNC_REQUEST_FUNCS:
-        request_func = ASYNC_REQUEST_FUNCS.get(backend)
+        request_func = ASYNC_REQUEST_FUNCS[backend]
     else:
         raise ValueError(f"Unknown backend: {backend}")
 
@@ -273,12 +349,28 @@ async def benchmark(
             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_prompt,
+            api_url=base_url + "/start_profile",
+            prompt_len=test_prompt_len,
+            output_len=test_output_len,
+            best_of=best_of,
+            use_beam_search=use_beam_search,
+        )
+        profile_output = await request_func(request_func_input=profile_input)
+        if profile_output.success:
+            print("Profiler started")
+
     print(f"Traffic request rate: {request_rate}")
 
     pbar = None if disable_tqdm else tqdm(total=len(input_requests))
 
     benchmark_start_time = time.perf_counter()
-    tasks = []
+    tasks: List[asyncio.Task] = []
     async for request in get_request(input_requests, request_rate):
         prompt, prompt_len, output_len = request
         request_func_input = RequestFuncInput(
@@ -296,7 +388,22 @@ async def benchmark(
                              pbar=pbar)))
     outputs: List[RequestFuncOutput] = await asyncio.gather(*tasks)
 
-    if not disable_tqdm:
+    if profile:
+        print("Stopping profiler...")
+        profile_input = RequestFuncInput(
+            model=model_id,
+            prompt=test_prompt,
+            api_url=base_url + "/stop_profile",
+            prompt_len=test_prompt_len,
+            output_len=test_output_len,
+            best_of=best_of,
+            use_beam_search=use_beam_search,
+        )
+        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
@@ -306,6 +413,8 @@ async def benchmark(
         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='='))
@@ -317,23 +426,10 @@ async def benchmark(
                                  metrics.total_output))
     print("{:<40} {:<10.2f}".format("Request throughput (req/s):",
                                     metrics.request_throughput))
-    print("{:<40} {:<10.2f}".format("Input token throughput (tok/s):",
-                                    metrics.input_throughput))
     print("{:<40} {:<10.2f}".format("Output token throughput (tok/s):",
                                     metrics.output_throughput))
-    print("{s:{c}^{n}}".format(s='Time to First Token', n=50, c='-'))
-    print("{:<40} {:<10.2f}".format("Mean TTFT (ms):", metrics.mean_ttft_ms))
-    print("{:<40} {:<10.2f}".format("Median TTFT (ms):",
-                                    metrics.median_ttft_ms))
-    print("{:<40} {:<10.2f}".format("P99 TTFT (ms):", metrics.p99_ttft_ms))
-    print("{s:{c}^{n}}".format(s='Time per Output Token (excl. 1st token)',
-                               n=50,
-                               c='-'))
-    print("{:<40} {:<10.2f}".format("Mean TPOT (ms):", metrics.mean_tpot_ms))
-    print("{:<40} {:<10.2f}".format("Median TPOT (ms):",
-                                    metrics.median_tpot_ms))
-    print("{:<40} {:<10.2f}".format("P99 TPOT (ms):", metrics.p99_tpot_ms))
-    print("=" * 50)
+    print("{:<40} {:<10.2f}".format("Total Token throughput (tok/s):",
+                                    metrics.total_token_throughput))
 
     result = {
         "duration": benchmark_duration,
@@ -341,14 +437,8 @@ async def benchmark(
         "total_input_tokens": metrics.total_input,
         "total_output_tokens": metrics.total_output,
         "request_throughput": metrics.request_throughput,
-        "input_throughput": metrics.input_throughput,
         "output_throughput": metrics.output_throughput,
-        "mean_ttft_ms": metrics.mean_ttft_ms,
-        "median_ttft_ms": metrics.median_ttft_ms,
-        "p99_ttft_ms": metrics.p99_ttft_ms,
-        "mean_tpot_ms": metrics.mean_tpot_ms,
-        "median_tpot_ms": metrics.median_tpot_ms,
-        "p99_tpot_ms": metrics.p99_tpot_ms,
+        "total_token_throughput": metrics.total_token_throughput,
         "input_lens": [output.prompt_len for output in outputs],
         "output_lens": actual_output_lens,
         "ttfts": [output.ttft for output in outputs],
@@ -356,6 +446,47 @@ async def benchmark(
         "generated_texts": [output.generated_text for output in outputs],
         "errors": [output.error for output in outputs],
     }
+
+    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 print and add 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
 
 
@@ -370,8 +501,10 @@ def main(args: argparse.Namespace):
 
     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)
@@ -427,6 +560,15 @@ def main(args: argparse.Namespace):
                               for prompt, prompt_formatted, prompt_len,
                               output_len in input_requests]
 
+    elif args.dataset_name == "random":
+        input_requests = sample_random_requests(
+            input_len=args.random_input_len,
+            output_len=args.random_output_len,
+            num_prompts=args.num_prompts,
+            range_ratio=args.random_range_ratio,
+            tokenizer=tokenizer,
+        )
+
     else:
         raise ValueError(f"Unknown dataset: {args.dataset_name}")
 
@@ -434,6 +576,7 @@ def main(args: argparse.Namespace):
         benchmark(
             backend=backend,
             api_url=api_url,
+            base_url=base_url,
             model_id=model_id,
             tokenizer=tokenizer,
             input_requests=input_requests,
@@ -441,11 +584,16 @@ def main(args: argparse.Namespace):
             use_beam_search=args.use_beam_search,
             request_rate=args.request_rate,
             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(",")
+            ],
         ))
 
     # Save config and results to json
     if args.save_result:
-        result_json = {}
+        result_json: Dict[str, Any] = {}
 
         # Setup
         current_dt = datetime.now().strftime("%Y%m%d-%H%M%S")
@@ -478,6 +626,8 @@ def main(args: argparse.Namespace):
         # 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
+        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:
@@ -485,7 +635,7 @@ def main(args: argparse.Namespace):
 
 
 if __name__ == "__main__":
-    parser = argparse.ArgumentParser(
+    parser = FlexibleArgumentParser(
         description="Benchmark the online serving throughput.")
     parser.add_argument(
         "--backend",
@@ -518,7 +668,7 @@ if __name__ == "__main__":
         "--dataset-name",
         type=str,
         default="sharegpt",
-        choices=["sharegpt", "sonnet"],
+        choices=["sharegpt", "sonnet", "random"],
         help="Name of the dataset to benchmark on.",
     )
     parser.add_argument("--dataset-path",
@@ -535,7 +685,7 @@ if __name__ == "__main__":
         "--tokenizer",
         type=str,
         help=
-        "Name or path of the tokenizer, if not using the default tokenizer.",
+        "Name or path of the tokenizer, if not using the default tokenizer.",  # noqa: E501
     )
     parser.add_argument(
         "--best-of",
@@ -578,6 +728,27 @@ if __name__ == "__main__":
         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",
         type=float,
@@ -598,6 +769,12 @@ if __name__ == "__main__":
         action="store_true",
         help="Specify to disable tqdm progress bar.",
     )
+    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(
         "--save-result",
         action="store_true",
@@ -618,6 +795,32 @@ if __name__ == "__main__":
         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(
+        "--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.",
+    )
 
     args = parser.parse_args()
     main(args)

benchmarks/benchmark_throughput.py

@@ -6,11 +6,16 @@ import time
 from typing import List, Optional, Tuple
 
 import torch
+import uvloop
 from tqdm import tqdm
 from transformers import (AutoModelForCausalLM, 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.model_executor.layers.quantization import QUANTIZATION_METHODS
+from vllm.utils import FlexibleArgumentParser, merge_async_iterators
 
 
 def sample_requests(
@@ -78,8 +83,13 @@ def run_vllm(
     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(
@@ -100,11 +110,16 @@ def run_vllm(
         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 = []
-    sampling_params = []
+    prompts: List[str] = []
+    sampling_params: List[SamplingParams] = []
     for prompt, _, output_len in requests:
         prompts.append(prompt)
         sampling_params.append(
@@ -123,6 +138,93 @@ def run_vllm(
     return end - start
 
 
+async def run_vllm_async(
+    requests: List[Tuple[str, int, int]],
+    model: str,
+    tokenizer: str,
+    quantization: Optional[str],
+    tensor_parallel_size: int,
+    seed: int,
+    n: int,
+    use_beam_search: bool,
+    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] = []
+        sampling_params: List[SamplingParams] = []
+        for prompt, _, output_len in requests:
+            prompts.append(prompt)
+            sampling_params.append(
+                SamplingParams(
+                    n=n,
+                    temperature=0.0 if use_beam_search else 1.0,
+                    top_p=1.0,
+                    use_beam_search=use_beam_search,
+                    ignore_eos=True,
+                    max_tokens=output_len,
+                ))
+
+        generators = []
+        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)
+        all_gens = merge_async_iterators(*generators)
+        async for i, res in all_gens:
+            pass
+        end = time.perf_counter()
+        return end - start
+
+
 def run_hf(
     requests: List[Tuple[str, int, int]],
     model: str,
@@ -218,15 +320,24 @@ def main(args: argparse.Namespace):
                                    args.output_len)
 
     if args.backend == "vllm":
-        elapsed_time = run_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.gpu_memory_utilization,
-            args.download_dir)
+            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(run_vllm_async(*run_args))
+        else:
+            elapsed_time = run_vllm(*run_args)
     elif args.backend == "hf":
         assert args.tensor_parallel_size == 1
         elapsed_time = run_hf(requests, args.model, tokenizer, args.n,
@@ -256,7 +367,7 @@ def main(args: argparse.Namespace):
 
 
 if __name__ == "__main__":
-    parser = argparse.ArgumentParser(description="Benchmark the throughput.")
+    parser = FlexibleArgumentParser(description="Benchmark the throughput.")
     parser.add_argument("--backend",
                         type=str,
                         choices=["vllm", "hf", "mii"],
@@ -343,13 +454,22 @@ if __name__ == "__main__":
     parser.add_argument(
         "--device",
         type=str,
-        default="cuda",
-        choices=["cuda", "cpu"],
-        help='device type for vLLM execution, supporting CUDA and CPU.')
+        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.")
+        help="Enable automatic prefix caching for vLLM backend.")
     parser.add_argument("--enable-chunked-prefill",
                         action='store_true',
                         help="enable chunked prefill for vLLM backend.")
@@ -368,6 +488,49 @@ if __name__ == "__main__":
         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,
+                        help="Use vLLM async engine rather than LLM class.")
+    parser.add_argument("--disable-frontend-multiprocessing",
+                        action='store_true',
+                        default=False,
+                        help="Disable decoupled async engine frontend.")
     args = parser.parse_args()
     if args.tokenizer is None:
         args.tokenizer = args.model

benchmarks/cutlass_benchmarks/w8a8_benchmarks.py

@@ -0,0 +1,389 @@
+import argparse
+import copy
+import itertools
+import pickle as pkl
+import time
+from typing import Callable, Iterable, List, Tuple
+
+import torch
+import torch.utils.benchmark as TBenchmark
+from torch.utils.benchmark import Measurement as TMeasurement
+from weight_shapes import WEIGHT_SHAPES
+
+from vllm import _custom_ops as ops
+from vllm.utils import FlexibleArgumentParser
+
+DEFAULT_MODELS = list(WEIGHT_SHAPES.keys())
+DEFAULT_BATCH_SIZES = [1, 16, 32, 64, 128, 256, 512]
+DEFAULT_TP_SIZES = [1]
+
+# helpers
+
+
+def to_fp8(tensor: torch.Tensor) -> torch.Tensor:
+    finfo = torch.finfo(torch.float8_e4m3fn)
+    return torch.round(tensor.clamp(
+        min=finfo.min, max=finfo.max)).to(dtype=torch.float8_e4m3fn)
+
+
+def to_int8(tensor: torch.Tensor) -> torch.Tensor:
+    return torch.round(tensor.clamp(min=-128, max=127)).to(dtype=torch.int8)
+
+
+def make_rand_tensors(dtype: torch.dtype, m: int, n: int,
+                      k: int) -> Tuple[torch.Tensor, torch.Tensor]:
+    a = torch.randn((m, k), device='cuda') * 5
+    b = torch.randn((n, k), device='cuda').t() * 5
+
+    if dtype == torch.int8:
+        return to_int8(a), to_int8(b)
+    if dtype == torch.float8_e4m3fn:
+        return to_fp8(a), to_fp8(b)
+
+    raise ValueError("unsupported dtype")
+
+
+# bench
+def bench_fn(label: str, sub_label: str, description: str, fn: Callable, *args,
+             **kwargs) -> TMeasurement:
+    min_run_time = 1
+
+    globals = {
+        "args": args,
+        "kwargs": kwargs,
+        "fn": fn,
+    }
+    return TBenchmark.Timer(
+        stmt="fn(*args, **kwargs)",
+        globals=globals,
+        label=label,
+        sub_label=sub_label,
+        description=description,
+    ).blocked_autorange(min_run_time=min_run_time)
+
+
+def bench_int8(dtype: torch.dtype, m: int, k: int, n: int, label: str,
+               sub_label: str) -> Iterable[TMeasurement]:
+    assert dtype == torch.int8
+    a, b = make_rand_tensors(torch.int8, m, n, k)
+    scale_a = torch.tensor(1.0, device="cuda", dtype=torch.float32)
+    scale_b = torch.tensor(1.0, device="cuda", dtype=torch.float32)
+    bias = torch.zeros((n, ), device="cuda", dtype=torch.bfloat16)
+    azp = torch.zeros((m, ), device="cuda", dtype=torch.int32)
+    azp_adj = torch.zeros((n, ), device="cuda", dtype=torch.int32)
+
+    timers = []
+    # pytorch impl - bfloat16
+    timers.append(
+        bench_fn(label, sub_label, "pytorch_bf16_bf16_bf16_matmul-no-scales",
+                 torch.mm, a.to(dtype=torch.bfloat16),
+                 b.to(dtype=torch.bfloat16)))
+
+    # pytorch impl - float16
+    timers.append(
+        bench_fn(label, sub_label,
+                 "pytorch_fp16_fp16_fp16_matmul-no-scales", torch.mm,
+                 a.to(dtype=torch.float16), b.to(dtype=torch.float16)))
+
+    # cutlass impl
+    timers.append(
+        bench_fn(label, sub_label, "cutlass_i8_i8_bf16_scaled_mm",
+                 ops.cutlass_scaled_mm, a, b, scale_a, scale_b,
+                 torch.bfloat16))
+
+    # cutlass with bias
+    timers.append(
+        bench_fn(label, sub_label, "cutlass_i8_i8_bf16_scaled_mm_bias",
+                 ops.cutlass_scaled_mm, a, b, scale_a, scale_b, torch.bfloat16,
+                 bias))
+
+    # cutlass with azp per-tensor
+    timers.append(
+        bench_fn(label, sub_label, "cutlass_i8_i8_bf16_scaled_mm_azp",
+                 ops.cutlass_scaled_mm_azp, a, b, scale_a, scale_b,
+                 torch.bfloat16, azp_adj))
+
+    # cutlass with azp per-tensor + bias
+    timers.append(
+        bench_fn(label, sub_label, "cutlass_i8_i8_bf16_scaled_mm_azp_bias",
+                 ops.cutlass_scaled_mm_azp, a, b, scale_a, scale_b,
+                 torch.bfloat16, azp_adj, None, bias))
+
+    # cutlass with azp per-token
+    timers.append(
+        bench_fn(label, sub_label, "cutlass_i8_i8_bf16_scaled_mm_azp_pt",
+                 ops.cutlass_scaled_mm_azp, a, b, scale_a, scale_b,
+                 torch.bfloat16, azp_adj, azp))
+
+    # cutlass with azp per-token + bias
+    timers.append(
+        bench_fn(label, sub_label, "cutlass_i8_i8_bf16_scaled_mm_azp_pt_bias",
+                 ops.cutlass_scaled_mm_azp, a, b, scale_a, scale_b,
+                 torch.bfloat16, azp_adj, azp, bias))
+
+    return timers
+
+
+def bench_fp8(dtype: torch.dtype, m: int, k: int, n: int, label: str,
+              sub_label: str) -> Iterable[TMeasurement]:
+    assert dtype == torch.float8_e4m3fn
+    a, b = make_rand_tensors(torch.float8_e4m3fn, m, n, k)
+    scale_a = torch.tensor(1.0, device="cuda", dtype=torch.float32)
+    scale_b = torch.tensor(1.0, device="cuda", dtype=torch.float32)
+    bias = torch.zeros((n, ), device="cuda", dtype=torch.bfloat16)
+
+    timers = []
+
+    # pytorch impl w. bf16
+    timers.append(
+        bench_fn(label, sub_label, "pytorch_bf16_bf16_bf16_matmul-no-scales",
+                 torch.mm, a.to(dtype=torch.bfloat16, device="cuda"),
+                 b.to(dtype=torch.bfloat16, device="cuda")))
+
+    # pytorch impl: bf16 output, without fp8 fast accum
+    timers.append(
+        bench_fn(label,
+                 sub_label,
+                 "pytorch_fp8_fp8_bf16_scaled_mm",
+                 torch._scaled_mm,
+                 a,
+                 b,
+                 scale_a=scale_a,
+                 scale_b=scale_b,
+                 out_dtype=torch.bfloat16))
+
+    # pytorch impl: bf16 output, with fp8 fast accum
+    timers.append(
+        bench_fn(label,
+                 sub_label,
+                 "pytorch_fp8_fp8_bf16_scaled_mm_fast_accum",
+                 torch._scaled_mm,
+                 a,
+                 b,
+                 scale_a=scale_a,
+                 scale_b=scale_b,
+                 out_dtype=torch.bfloat16,
+                 use_fast_accum=True))
+
+    # pytorch impl: fp16 output, without fp8 fast accum
+    timers.append(
+        bench_fn(label,
+                 sub_label,
+                 "pytorch_fp8_fp8_fp16_scaled_mm",
+                 torch._scaled_mm,
+                 a,
+                 b,
+                 scale_a=scale_a,
+                 scale_b=scale_b,
+                 out_dtype=torch.float16))
+
+    # pytorch impl: fp16 output, with fp8 fast accum
+    timers.append(
+        bench_fn(label,
+                 sub_label,
+                 "pytorch_fp8_fp8_fp16_scaled_mm_fast_accum",
+                 torch._scaled_mm,
+                 a,
+                 b,
+                 scale_a=scale_a,
+                 scale_b=scale_b,
+                 out_dtype=torch.float16,
+                 use_fast_accum=True))
+
+    # cutlass impl: bf16 output
+    timers.append(
+        bench_fn(label, sub_label, "cutlass_fp8_fp8_bf16_scaled_mm",
+                 ops.cutlass_scaled_mm, a, b, scale_a, scale_b,
+                 torch.bfloat16))
+    # cutlass impl: fp16 output
+    timers.append(
+        bench_fn(label, sub_label, "cutlass_fp8_fp8_fp16_scaled_mm",
+                 ops.cutlass_scaled_mm, a, b, scale_a, scale_b, torch.float16))
+
+    # cutlass impl: bf16 output, with bias
+    timers.append(
+        bench_fn(label, sub_label, "cutlass_fp8_fp8_bf16_scaled_mm_bias",
+                 ops.cutlass_scaled_mm, a, b, scale_a, scale_b, torch.bfloat16,
+                 bias))
+
+    # cutlass impl: fp16 output, with bias
+    timers.append(
+        bench_fn(label, sub_label, "cutlass_fp8_fp8_fp16_scaled_mm_bias",
+                 ops.cutlass_scaled_mm, a, b, scale_a, scale_b, torch.float16,
+                 bias.to(dtype=torch.float16)))
+
+    return timers
+
+
+def bench(dtype: torch.dtype, m: int, k: int, n: int, label: str,
+          sub_label: str) -> Iterable[TMeasurement]:
+    if dtype == torch.int8:
+        return bench_int8(dtype, m, k, n, label, sub_label)
+    if dtype == torch.float8_e4m3fn:
+        return bench_fp8(dtype, m, k, n, label, sub_label)
+    raise ValueError("unsupported type")
+
+
+# runner
+def print_timers(timers: Iterable[TMeasurement]):
+    compare = TBenchmark.Compare(timers)
+    compare.print()
+
+
+def run(dtype: torch.dtype,
+        MKNs: Iterable[Tuple[int, int, int]]) -> Iterable[TMeasurement]:
+    results = []
+    for m, k, n in MKNs:
+        timers = bench(dtype, m, k, n, f"scaled-{dtype}-gemm",
+                       f"MKN=({m}x{k}x{n})")
+        print_timers(timers)
+        results.extend(timers)
+
+    return results
+
+
+# output makers
+def make_output(data: Iterable[TMeasurement],
+                MKNs: Iterable[Tuple[int, int, int]],
+                base_description: str,
+                timestamp=None):
+    print(f"== All Results {base_description} ====")
+    print_timers(data)
+
+    # pickle all the results
+    timestamp = int(time.time()) if timestamp is None else timestamp
+    with open(f"{base_description}-{timestamp}.pkl", "wb") as f:
+        pkl.dump(data, f)
+
+
+# argparse runners
+
+
+def run_square_bench(args):
+    dim_sizes = list(
+        range(args.dim_start, args.dim_end + 1, args.dim_increment))
+    MKNs = list(zip(dim_sizes, dim_sizes, dim_sizes))
+    data = run(args.dtype, MKNs)
+
+    make_output(data, MKNs, f"square_bench-{args.dtype}")
+
+
+def run_range_bench(args):
+    dim_sizes = list(range(args.dim_start, args.dim_end, args.dim_increment))
+    n = len(dim_sizes)
+    Ms = [args.m_constant] * n if args.m_constant is not None else dim_sizes
+    Ks = [args.k_constant] * n if args.k_constant is not None else dim_sizes
+    Ns = [args.n_constant] * n if args.n_constant is not None else dim_sizes
+    MKNs = list(zip(Ms, Ks, Ns))
+    data = run(args.dtype, MKNs)
+
+    make_output(data, MKNs, f"range_bench-{args.dtype}")
+
+
+def run_model_bench(args):
+    print("Benchmarking models:")
+    for i, model in enumerate(args.models):
+        print(f"[{i}]  {model}")
+
+    def model_shapes(model_name: str, tp_size: int) -> List[Tuple[int, int]]:
+        KNs = []
+        for KN, tp_split_dim in copy.deepcopy(WEIGHT_SHAPES[model_name]):
+            KN[tp_split_dim] = KN[tp_split_dim] // tp_size
+            KNs.append(KN)
+        return KNs
+
+    model_bench_data = []
+    models_tps = list(itertools.product(args.models, args.tp_sizes))
+    for model, tp_size in models_tps:
+        Ms = args.batch_sizes
+        KNs = model_shapes(model, tp_size)
+        MKNs = []
+        for m in Ms:
+            for k, n in KNs:
+                MKNs.append((m, k, n))
+
+        data = run(args.dtype, MKNs)
+        model_bench_data.append(data)
+
+    # Print all results
+    for data, model_tp in zip(model_bench_data, models_tps):
+        model, tp_size = model_tp
+        print(f"== Results {args.dtype} {model}-TP{tp_size} ====")
+        print_timers(data)
+
+    timestamp = int(time.time())
+
+    all_data = []
+    for d in model_bench_data:
+        all_data.extend(d)
+    # pickle all data
+    with open(f"model_bench-{args.dtype}-{timestamp}.pkl", "wb") as f:
+        pkl.dump(all_data, f)
+
+
+if __name__ == '__main__':
+
+    def to_torch_dtype(dt):
+        if dt == "int8":
+            return torch.int8
+        if dt == "fp8":
+            return torch.float8_e4m3fn
+        raise ValueError("unsupported dtype")
+
+    parser = FlexibleArgumentParser(
+        description="""
+Benchmark Cutlass GEMM.
+
+    To run square GEMMs:
+        python3 ./benchmarks/cutlass_benchmarks/w8a8_benchmarks.py --dtype fp8 square_bench --dim-start 128 --dim-end 512 --dim-increment 64
+    
+    To run constant N and K and sweep M:
+        python3 ./benchmarks/cutlass_benchmarks/w8a8_benchmarks.py --dtype fp8 range_bench --dim-start 128 --dim-end 512 --dim-increment 64 --n-constant 16384 --k-constant 16384
+    
+    To run dimensions from a model:
+        python3 ./benchmarks/cutlass_benchmarks/w8a8_benchmarks.py --dtype fp8 model_bench --models meta-llama/Llama-2-7b-hf --batch-sizes 16 --tp-sizes 1
+    
+    Output:
+        - a .pkl file, that is a list of raw torch.benchmark.utils.Measurements for the pytorch and cutlass implementations for the various GEMMs.
+            """,  # noqa: E501
+        formatter_class=argparse.RawTextHelpFormatter)
+
+    parser.add_argument("--dtype",
+                        type=to_torch_dtype,
+                        required=True,
+                        help="Available options are ['int8', 'fp8']")
+    subparsers = parser.add_subparsers(dest="cmd")
+
+    square_parser = subparsers.add_parser("square_bench")
+    square_parser.add_argument("--dim-start", type=int, required=True)
+    square_parser.add_argument("--dim-end", type=int, required=True)
+    square_parser.add_argument("--dim-increment", type=int, required=True)
+    square_parser.set_defaults(func=run_square_bench)
+
+    range_parser = subparsers.add_parser("range_bench")
+    range_parser.add_argument("--dim-start", type=int, required=True)
+    range_parser.add_argument("--dim-end", type=int, required=True)
+    range_parser.add_argument("--dim-increment", type=int, required=True)
+    range_parser.add_argument("--m-constant", type=int, default=None)
+    range_parser.add_argument("--n-constant", type=int, default=None)
+    range_parser.add_argument("--k-constant", type=int, default=None)
+    range_parser.set_defaults(func=run_range_bench)
+
+    model_parser = subparsers.add_parser("model_bench")
+    model_parser.add_argument("--models",
+                              nargs="+",
+                              type=str,
+                              default=DEFAULT_MODELS,
+                              choices=WEIGHT_SHAPES.keys())
+    model_parser.add_argument("--tp-sizes",
+                              nargs="+",
+                              type=int,
+                              default=DEFAULT_TP_SIZES)
+    model_parser.add_argument("--batch-sizes",
+                              nargs="+",
+                              type=int,
+                              default=DEFAULT_BATCH_SIZES)
+    model_parser.set_defaults(func=run_model_bench)
+
+    args = parser.parse_args()
+    args.func(args)

benchmarks/cutlass_benchmarks/weight_shapes.py

@@ -0,0 +1,43 @@
+# Weight Shapes are in the format
+# ([K, N], TP_SPLIT_DIM)
+# Example:
+#  A shape of ([14336, 4096], 0) indicates the following GEMM shape,
+#   - TP1 : K = 14336, N = 4096
+#   - TP2 : K = 7168, N = 4096
+#  A shape of ([4096, 6144], 1) indicates the following GEMM shape,
+#   - TP1 : K = 4096, N = 6144
+#   - TP4 : K = 4096, N = 1536
+
+# TP1 shapes
+WEIGHT_SHAPES = {
+    "mistralai/Mistral-7B-v0.1": [
+        ([4096, 6144], 1),
+        ([4096, 4096], 0),
+        ([4096, 28672], 1),
+        ([14336, 4096], 0),
+    ],
+    "meta-llama/Llama-2-7b-hf": [
+        ([4096, 12288], 1),
+        ([4096, 4096], 0),
+        ([4096, 22016], 1),
+        ([11008, 4096], 0),
+    ],
+    "meta-llama/Llama-3-8b": [
+        ([4096, 6144], 1),
+        ([4096, 4096], 0),
+        ([4096, 28672], 1),
+        ([14336, 4096], 0),
+    ],
+    "meta-llama/Llama-2-13b-hf": [
+        ([5120, 15360], 1),
+        ([5120, 5120], 0),
+        ([5120, 27648], 1),
+        ([13824, 5120], 0),
+    ],
+    "meta-llama/Llama-2-70b-hf": [
+        ([8192, 10240], 1),
+        ([8192, 8192], 0),
+        ([8192, 57344], 1),
+        ([28672, 8192], 0),
+    ],
+}

benchmarks/kernels/benchmark_aqlm.py

@@ -1,4 +1,3 @@
-import argparse
 import os
 import sys
 from typing import Optional
@@ -10,6 +9,7 @@ from vllm import _custom_ops as ops
 from vllm.model_executor.layers.quantization.aqlm import (
     dequantize_weight, generic_dequantize_gemm, get_int_dtype,
     optimized_dequantize_gemm)
+from vllm.utils import FlexibleArgumentParser
 
 os.environ['CUDA_VISIBLE_DEVICES'] = '0'
 
@@ -86,9 +86,9 @@ def dequant_no_scale(
 # Compare the optimized 1x16 and 2x8 cuda decompression/dequant kernels against
 # the generic pytorch version.
 # Just visual comparison.
-def dequant_test(k: int, parts: torch.tensor, nbooks: int, bits: int) -> None:
+def dequant_test(k: int, parts: torch.Tensor, nbooks: int, bits: int) -> None:
 
-    n = parts.sum().item()
+    n = int(parts.sum().item())
 
     device = torch.device('cuda:0')
 
@@ -137,7 +137,7 @@ def dequant_test(k: int, parts: torch.tensor, nbooks: int, bits: int) -> None:
 
 def main():
 
-    parser = argparse.ArgumentParser(description="Benchmark aqlm performance.")
+    parser = FlexibleArgumentParser(description="Benchmark aqlm performance.")
 
     # Add arguments
     parser.add_argument("--nbooks",
@@ -204,7 +204,7 @@ def main():
         sys.stdout = sys.__stdout__
 
 
-def run_grid(m: int, k: int, parts: torch.tensor, nbooks: int, bits: int,
+def run_grid(m: int, k: int, parts: torch.Tensor, nbooks: int, bits: int,
              methods):
 
     # I didn't see visible improvements from increasing these, but feel free :)
@@ -252,10 +252,10 @@ def run_grid(m: int, k: int, parts: torch.tensor, nbooks: int, bits: int,
     print('')
 
 
-def run_timing(num_calls: int, m: int, k: int, parts: torch.tensor,
+def run_timing(num_calls: int, m: int, k: int, parts: torch.Tensor,
                nbooks: int, bits: int, method) -> float:
 
-    n = parts.sum().item()
+    n = int(parts.sum().item())
 
     device = torch.device('cuda:0')