test

Signed-off-by: Roger Wang <hey@rogerw.me>

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

@@ -1,3 +1,4 @@
+# For vllm script, with -t option (tensor parallel size).
 # 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:

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

@@ -1,3 +1,4 @@
+# For hf script, without -t option (tensor parallel size).
 # 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:

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

@@ -1,3 +1,4 @@
+# For hf script, without -t option (tensor parallel size).
 # 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:

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

@@ -1,3 +1,4 @@
+# For vllm script, with -t option (tensor parallel size).
 # 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:

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

@@ -1,3 +1,4 @@
+# For vllm script, with -t option (tensor parallel size).
 # 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:

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

@@ -1,3 +1,4 @@
+# For vllm script, with -t option (tensor parallel size).
 # 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:

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

@@ -1,3 +1,4 @@
+# For vllm script, with -t option (tensor parallel size).
 # 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:

.buildkite/lm-eval-harness/configs/Meta-Llama-3-8B-Instruct-INT8-compressed-tensors-asym.yaml

@@ -1,3 +1,4 @@
+# For vllm script, with -t option (tensor parallel size).
 # bash .buildkite/lm-eval-harness/run-lm-eval-gsm-vllm-baseline.sh -m nm-testing/Meta-Llama-3-8B-Instruct-W8-Channel-A8-Dynamic-Asym-Per-Token-Test -b "auto" -l 250 -f 5 -t 1
 model_name: "nm-testing/Meta-Llama-3-8B-Instruct-W8-Channel-A8-Dynamic-Asym-Per-Token-Test"
 tasks:

.buildkite/lm-eval-harness/configs/Meta-Llama-3-8B-Instruct-INT8-compressed-tensors.yaml

@@ -1,3 +1,4 @@
+# For vllm script, with -t option (tensor parallel size).
 # 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:

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

@@ -1,3 +1,4 @@
+# For vllm script, with -t option (tensor parallel size).
 # 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:

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

@@ -1,4 +1,5 @@
-# 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
+# For hf script, without -t option (tensor parallel size).
+# 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
 model_name: "meta-llama/Meta-Llama-3-8B-Instruct"
 tasks:
 - name: "gsm8k"

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

@@ -1,3 +1,4 @@
+# For vllm script, with -t option (tensor parallel size).
 # 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:

.buildkite/lm-eval-harness/configs/Meta-Llama-3.2-1B-Instruct-FP8-compressed-tensors.yaml

@@ -0,0 +1,11 @@
+# bash .buildkite/lm-eval-harness/run-lm-eval-gsm-vllm-baseline.sh -m RedHatAI/Llama-3.2-1B-Instruct-FP8 -b "auto" -l 1319 -f 5 -t 1
+model_name: "RedHatAI/Llama-3.2-1B-Instruct-FP8"
+tasks:
+- name: "gsm8k"
+  metrics:
+  - name: "exact_match,strict-match"
+    value: 0.335
+  - name: "exact_match,flexible-extract"
+    value: 0.323
+limit: 1319
+num_fewshot: 5

.buildkite/lm-eval-harness/configs/Meta-Llama-3.2-1B-Instruct-INT8-compressed-tensors.yaml

@@ -1,3 +1,4 @@
+# For vllm script, with -t option (tensor parallel size).
 # bash .buildkite/lm-eval-harness/run-lm-eval-gsm-vllm-baseline.sh -m neuralmagic/Llama-3.2-1B-Instruct-quantized.w8a8 -b "auto" -l 1000 -f 5 -t 1
 model_name: "neuralmagic/Llama-3.2-1B-Instruct-quantized.w8a8"
 tasks:

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

@@ -1,3 +1,4 @@
+# For vllm script, with -t option (tensor parallel size).
 # 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:

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

@@ -1,3 +1,4 @@
+# For vllm script, with -t option (tensor parallel size).
 # 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:

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

@@ -1,3 +1,4 @@
+# For vllm script, with -t option (tensor parallel size).
 # 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:

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

@@ -1,4 +1,5 @@
-# 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
+# For hf script, without -t option (tensor parallel size).
+# 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
 model_name: "mistralai/Mixtral-8x7B-Instruct-v0.1"
 tasks:
 - name: "gsm8k"

.buildkite/lm-eval-harness/configs/Qwen1.5-MoE-W4A16-compressed-tensors.yaml

@@ -0,0 +1,12 @@
+# For vllm script, with -t option (tensor parallel size).
+# bash .buildkite/lm-eval-harness/run-lm-eval-gsm-vllm-baseline.sh -m nm-testing/Qwen1.5-MoE-A2.7B-Chat-quantized.w4a16 -b auto -l 1319 -f 5 -t 1
+model_name: "nm-testing/Qwen1.5-MoE-A2.7B-Chat-quantized.w4a16"
+tasks:
+- name: "gsm8k"
+  metrics:
+  - name: "exact_match,strict-match"
+    value: 0.30
+  - name: "exact_match,flexible-extract"
+    value: 0.465
+limit: 1319
+num_fewshot: 5

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

@@ -1,3 +1,4 @@
+# For vllm script, with -t option (tensor parallel size).
 # bash .buildkite/lm-eval-harness/run-lm-eval-gsm-vllm-baseline.sh -m nm-testing/Qwen2-1.5B-Instruct-FP8W8 -b auto -l 1000 -f 5 -t 1
 model_name: "nm-testing/Qwen2-1.5B-Instruct-FP8W8"
 tasks:

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

@@ -1,3 +1,4 @@
+# For vllm script, with -t option (tensor parallel size).
 # 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:

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

@@ -1,3 +1,4 @@
+# For vllm script, with -t option (tensor parallel size).
 # bash .buildkite/lm-eval-harness/run-lm-eval-gsm-vllm-baseline.sh -m nm-testing/Qwen2-1.5B-Instruct-W8A16-Channelwise -b "auto" -l 1000 -f 5 -t 1
 model_name: "nm-testing/Qwen2-1.5B-Instruct-W8A16-Channelwise"
 tasks:

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

@@ -1,3 +1,4 @@
+# For vllm script, with -t option (tensor parallel size).
 # 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:

.buildkite/lm-eval-harness/configs/Qwen2.5-1.5B-Instruct.yaml

@@ -0,0 +1,11 @@
+# bash .buildkite/lm-eval-harness/run-lm-eval-gsm-vllm-baseline.sh -m Qwen/Qwen2.5-1.5B-Instruct -b auto -l 1319 -f 5 -t 1
+model_name: "Qwen/Qwen2.5-1.5B-Instruct"
+tasks:
+- name: "gsm8k"
+  metrics:
+  - name: "exact_match,strict-match"
+    value: 0.54
+  - name: "exact_match,flexible-extract"
+    value: 0.59
+limit: 1319
+num_fewshot: 5

.buildkite/lm-eval-harness/configs/Qwen2.5-VL-3B-Instruct-FP8-dynamic.yaml

@@ -0,0 +1,11 @@
+# bash .buildkite/lm-eval-harness/run-lm-eval-gsm-vllm-baseline.sh -m RedHatAI/Qwen2.5-VL-3B-Instruct-FP8-Dynamic -b auto -l 1319 -f 5 -t 1
+model_name: "RedHatAI/Qwen2.5-VL-3B-Instruct-FP8-Dynamic"
+tasks:
+- name: "gsm8k"
+  metrics:
+  - name: "exact_match,strict-match"
+    value: 0.47
+  - name: "exact_match,flexible-extract"
+    value: 0.64
+limit: 1319
+num_fewshot: 5

.buildkite/lm-eval-harness/configs/SparseLlama3.1_2of4_fp8_compressed.yaml

@@ -1,3 +1,4 @@
+# For vllm script, with -t option (tensor parallel size).
 # bash ./run-lm-eval-gsm-vllm-baseline.sh -m nm-testing/SparseLlama-3.1-8B-gsm8k-pruned.2of4-chnl_wts_per_tok_dyn_act_fp8-BitM -b "auto" -t 2
 model_name: "nm-testing/SparseLlama-3.1-8B-gsm8k-pruned.2of4-chnl_wts_per_tok_dyn_act_fp8-BitM"
 tasks:

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

@@ -3,3 +3,4 @@ Meta-Llama-3-70B-Instruct.yaml
 Mixtral-8x7B-Instruct-v0.1.yaml
 Qwen2-57B-A14-Instruct.yaml
 DeepSeek-V2-Lite-Chat.yaml
+Meta-Llama-3-8B-QQQ.yaml

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

@@ -1,10 +1,6 @@
-Meta-Llama-3-8B-Instruct.yaml
-Meta-Llama-3-8B-Instruct-FP8-compressed-tensors.yaml
+Qwen2.5-1.5B-Instruct.yaml
 Meta-Llama-3.2-1B-Instruct-INT8-compressed-tensors.yaml
 Meta-Llama-3-8B-Instruct-INT8-compressed-tensors-asym.yaml
 Meta-Llama-3-8B-Instruct-nonuniform-compressed-tensors.yaml
-Meta-Llama-3-8B-Instruct-Channelwise-compressed-tensors.yaml
-Minitron-4B-Base-FP8.yaml
-Qwen2-1.5B-Instruct-INT8-compressed-tensors.yaml
-Qwen2-1.5B-Instruct-FP8W8.yaml
-Meta-Llama-3-8B-QQQ.yaml
+Qwen2.5-VL-3B-Instruct-FP8-dynamic.yaml
+Qwen1.5-MoE-W4A16-compressed-tensors.yaml

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

@@ -0,0 +1,39 @@
+# SPDX-License-Identifier: Apache-2.0
+from pathlib import Path
+
+import pytest
+
+
+def pytest_addoption(parser):
+    parser.addoption(
+        "--config-list-file",
+        action="store",
+        help="Path to the file listing model config YAMLs (one per line)")
+    parser.addoption("--tp-size",
+                     action="store",
+                     default="1",
+                     help="Tensor parallel size to use for evaluation")
+
+
+@pytest.fixture(scope="session")
+def config_list_file(pytestconfig, config_dir):
+    rel_path = pytestconfig.getoption("--config-list-file")
+    return config_dir / rel_path
+
+
+@pytest.fixture(scope="session")
+def tp_size(pytestconfig):
+    return pytestconfig.getoption("--tp-size")
+
+
+def pytest_generate_tests(metafunc):
+    if "config_filename" in metafunc.fixturenames:
+        rel_path = metafunc.config.getoption("--config-list-file")
+        config_list_file = Path(rel_path).resolve()
+        config_dir = config_list_file.parent
+        with open(config_list_file, encoding="utf-8") as f:
+            configs = [
+                config_dir / line.strip() for line in f
+                if line.strip() and not line.startswith("#")
+            ]
+        metafunc.parametrize("config_filename", configs)

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

@@ -1,59 +0,0 @@
-#!/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

@@ -3,35 +3,25 @@
 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
+pytest -s -v test_lm_eval_correctness.py \
+    --config-list-file=configs/models-small.txt \
+    --tp-size=1
 """
 
-import os
-from pathlib import Path
-
 import lm_eval
-import numpy
-import pytest
+import numpy as np
 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)
+RTOL = 0.08
 
 
-def launch_lm_eval(eval_config):
+def launch_lm_eval(eval_config, tp_size):
     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"tensor_parallel_size={tp_size}," \
+                 f"enforce_eager=true," \
                  f"add_bos_token=true," \
                  f"trust_remote_code={trust_remote_code}"
-
     results = lm_eval.simple_evaluate(
         model="vllm",
         model_args=model_args,
@@ -39,22 +29,14 @@ def launch_lm_eval(eval_config):
         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"))
+def test_lm_eval_correctness_param(config_filename, tp_size):
+    eval_config = yaml.safe_load(config_filename.read_text(encoding="utf-8"))
 
-    if eval_config[
-            "model_name"] == "nm-testing/Meta-Llama-3-70B-Instruct-FBGEMM-nonuniform":  #noqa: E501
-        pytest.skip("FBGEMM is currently failing on main.")
+    results = launch_lm_eval(eval_config, tp_size)
 
-    # Launch eval requests.
-    results = launch_lm_eval(eval_config)
-
-    # Confirm scores match ground truth.
     success = True
     for task in eval_config["tasks"]:
         for metric in task["metrics"]:
@@ -62,8 +44,7 @@ def test_lm_eval_correctness():
             measured_value = results["results"][task["name"]][metric["name"]]
             print(f'{task["name"]} | {metric["name"]}: '
                   f'ground_truth={ground_truth} | measured={measured_value}')
-            success = success and numpy.isclose(
+            success = success and np.isclose(
                 ground_truth, measured_value, rtol=RTOL)
 
-    # Assert at the end, print all scores even on failure for debugging.
     assert success

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

@@ -10,15 +10,24 @@ set -x
 set -o pipefail
 
 check_gpus() {
-  # check the number of GPUs and GPU type.
-  declare -g gpu_count=$(nvidia-smi --list-gpus | wc -l)
+  if command -v nvidia-smi; then
+    # check the number of GPUs and GPU type.
+    declare -g gpu_count=$(nvidia-smi --list-gpus | wc -l)
+  elif command -v amd-smi; then
+    declare -g gpu_count=$(amd-smi list | grep 'GPU' | wc -l)
+  fi
+
   if [[ $gpu_count -gt 0 ]]; then
     echo "GPU found."
   else
     echo "Need at least 1 GPU to run benchmarking."
     exit 1
   fi
-  declare -g gpu_type=$(nvidia-smi --query-gpu=name --format=csv,noheader | awk '{print $2}')
+  if command -v nvidia-smi; then
+    declare -g gpu_type=$(nvidia-smi --query-gpu=name --format=csv,noheader | awk '{print $2}')
+  elif command -v amd-smi; then
+    declare -g gpu_type=$(amd-smi static -g 0 -a | grep 'MARKET_NAME' | awk '{print $2}')
+  fi
   echo "GPU type is $gpu_type"
 }
 
@@ -90,9 +99,15 @@ kill_gpu_processes() {
 
 
   # wait until GPU memory usage smaller than 1GB
-  while [ "$(nvidia-smi --query-gpu=memory.used --format=csv,noheader,nounits | head -n 1)" -ge 1000 ]; do
-    sleep 1
-  done
+  if command -v nvidia-smi; then
+    while [ "$(nvidia-smi --query-gpu=memory.used --format=csv,noheader,nounits | head -n 1)" -ge 1000 ]; do
+      sleep 1
+    done
+  elif command -v amd-smi; then
+    while [ "$(amd-smi metric -g 0 | grep 'USED_VRAM' | awk '{print $2}')" -ge 1000 ]; do
+      sleep 1
+    done
+  fi
 
   # remove vllm config file
   rm -rf ~/.config/vllm
@@ -361,7 +376,7 @@ main() {
   # 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"
+  export VLLM_LOGGING_LEVEL="WARNING"
 
   # prepare for benchmarking
   cd benchmarks || exit 1

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

@@ -63,10 +63,12 @@
             "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
+            "swap_space": 16,
+            "speculative_config": {
+                "model": "turboderp/Qwama-0.5B-Instruct",
+                "num_speculative_tokens": 4,
+                "draft_tensor_parallel_size": 1
+            }
         },
         "client_parameters": {
             "model": "meta-llama/Meta-Llama-3.1-70B-Instruct",

.buildkite/release-pipeline.yaml

@@ -1,23 +1,23 @@
 steps:
-  - label: "Build wheel - CUDA 12.4"
+  - label: "Build wheel - CUDA 12.8"
     agents:
       queue: cpu_queue_postmerge
     commands:
-      - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=12.4.0 --tag vllm-ci:build-image --target build --progress plain ."
+      - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=12.8.1 --tag vllm-ci:build-image --target build --progress plain -f docker/Dockerfile ."
       - "mkdir artifacts"
       - "docker run --rm -v $(pwd)/artifacts:/artifacts_host vllm-ci:build-image bash -c 'cp -r dist /artifacts_host && chmod -R a+rw /artifacts_host'"
-      - "bash .buildkite/upload-wheels.sh"
+      - "bash .buildkite/scripts/upload-wheels.sh"
     env:
       DOCKER_BUILDKIT: "1"
 
-  - label: "Build wheel - CUDA 12.1"
+  - label: "Build wheel - CUDA 12.6"
     agents:
       queue: cpu_queue_postmerge
     commands:
-      - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=12.1.0 --tag vllm-ci:build-image --target build --progress plain ."
+      - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=12.6.3 --tag vllm-ci:build-image --target build --progress plain -f docker/Dockerfile ."
       - "mkdir artifacts"
       - "docker run --rm -v $(pwd)/artifacts:/artifacts_host vllm-ci:build-image bash -c 'cp -r dist /artifacts_host && chmod -R a+rw /artifacts_host'"
-      - "bash .buildkite/upload-wheels.sh"
+      - "bash .buildkite/scripts/upload-wheels.sh"
     env:
       DOCKER_BUILDKIT: "1"
 
@@ -31,10 +31,10 @@ steps:
     agents:
       queue: cpu_queue_postmerge
     commands:
-      - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=11.8.0 --tag vllm-ci:build-image --target build --progress plain ."
+      - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=11.8.0 --tag vllm-ci:build-image --target build --progress plain -f docker/Dockerfile ."
       - "mkdir artifacts"
       - "docker run --rm -v $(pwd)/artifacts:/artifacts_host vllm-ci:build-image bash -c 'cp -r dist /artifacts_host && chmod -R a+rw /artifacts_host'"
-      - "bash .buildkite/upload-wheels.sh"
+      - "bash .buildkite/scripts/upload-wheels.sh"
     env:
       DOCKER_BUILDKIT: "1"
 
@@ -48,7 +48,7 @@ steps:
       queue: cpu_queue_postmerge
     commands:
       - "aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7"
-      - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=12.4.0 --tag public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT --target vllm-openai --progress plain ."
+      - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=12.8.1 --tag public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT --target vllm-openai --progress plain -f docker/Dockerfile ."
       - "docker push public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT"
 
   - label: "Build and publish TPU release image"
@@ -57,7 +57,9 @@ steps:
     agents:
       queue: tpu_queue_postmerge
     commands:
-      - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --tag vllm/vllm-tpu:nightly --tag vllm/vllm-tpu:$BUILDKITE_COMMIT --progress plain -f Dockerfile.tpu ."
+      - "yes | docker system prune -a"
+      - "git fetch --all"
+      - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --tag vllm/vllm-tpu:nightly --tag vllm/vllm-tpu:$BUILDKITE_COMMIT --progress plain -f docker/Dockerfile.tpu ."
       - "docker push vllm/vllm-tpu:nightly"
       - "docker push vllm/vllm-tpu:$BUILDKITE_COMMIT"
     plugins:
@@ -82,7 +84,22 @@ steps:
       queue: cpu_queue_postmerge
     commands:
       - "aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7"
-      - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg GIT_REPO_CHECK=1 --tag public.ecr.aws/q9t5s3a7/vllm-cpu-release-repo:$(buildkite-agent meta-data get release-version) --progress plain -f Dockerfile.cpu ."
+      - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg GIT_REPO_CHECK=1 --tag public.ecr.aws/q9t5s3a7/vllm-cpu-release-repo:$(buildkite-agent meta-data get release-version) --tag public.ecr.aws/q9t5s3a7/vllm-cpu-release-repo:latest --progress plain --target vllm-openai -f docker/Dockerfile.cpu ."
       - "docker push public.ecr.aws/q9t5s3a7/vllm-cpu-release-repo:$(buildkite-agent meta-data get release-version)"
     env:
       DOCKER_BUILDKIT: "1"
+
+  - block: "Build Neuron release image"
+    key: block-neuron-release-image-build
+    depends_on: ~
+
+  - label: "Build and publish Neuron release image"
+    depends_on: block-neuron-release-image-build
+    agents:
+      queue: neuron-postmerge
+    commands:
+      - "aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7"
+      - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg GIT_REPO_CHECK=1 --tag public.ecr.aws/q9t5s3a7/vllm-neuron-release-repo:$(buildkite-agent meta-data get release-version) --tag public.ecr.aws/q9t5s3a7/vllm-neuron-release-repo:latest --progress plain -f docker/Dockerfile.neuron ."
+      - "docker push public.ecr.aws/q9t5s3a7/vllm-neuron-release-repo:$(buildkite-agent meta-data get release-version)"
+    env:
+      DOCKER_BUILDKIT: "1"

.buildkite/run-openvino-test.sh

@@ -1,16 +0,0 @@
-#!/bin/bash
-
-# This script build the OpenVINO docker image and run the offline inference inside the container.
-# It serves a sanity check for compilation and basic model usage.
-set -ex
-
-# 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/examples/offline_inference/basic/generate.py --model facebook/opt-125m

.buildkite/run-tpu-test.sh

@@ -1,25 +0,0 @@
-#!/bin/bash
-
-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 \
-    && python3 -m pip install lm_eval[api]==0.4.4 \
-    && pytest -v -s /workspace/vllm/tests/tpu/test_custom_dispatcher.py \
-    && python3 /workspace/vllm/tests/tpu/test_compilation.py \
-    && python3 /workspace/vllm/tests/tpu/test_quantization_accuracy.py \
-    && python3 /workspace/vllm/examples/offline_inference/tpu.py"

.buildkite/run-tpu-v1-test.sh

@@ -1,27 +0,0 @@
-#!/bin/bash
-
-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" -e "VLLM_USE_V1=1" --name tpu-test \
-    vllm-tpu /bin/bash -c "python3 -m pip install git+https://github.com/thuml/depyf.git \
-    && python3 -m pip install pytest \
-    && python3 -m pip install lm_eval[api]==0.4.4 \
-    && pytest -v -s /workspace/vllm/tests/tpu/test_custom_dispatcher.py \
-    && pytest -v -s /workspace/vllm/tests/v1/tpu/test_basic.py \
-    && pytest -v -s /workspace/vllm/tests/entrypoints/llm/test_accuracy.py::test_lm_eval_accuracy_v1_engine \
-    && python3 /workspace/vllm/tests/tpu/test_compilation.py \
-    && python3 /workspace/vllm/tests/tpu/test_quantization_accuracy.py \
-    && python3 /workspace/vllm/examples/offline_inference/tpu.py"

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

@@ -75,49 +75,84 @@ HF_MOUNT="/root/.cache/huggingface"
 commands=$@
 echo "Commands:$commands"
 #ignore certain kernels tests
-if [[ $commands == *" kernels "* ]]; then
+if [[ $commands == *" kernels/core"* ]]; then
   commands="${commands} \
-  --ignore=kernels/test_attention_selector.py \
-  --ignore=kernels/test_blocksparse_attention.py \
-  --ignore=kernels/test_causal_conv1d.py \
-  --ignore=kernels/test_cutlass.py \
-  --ignore=kernels/test_encoder_decoder_attn.py \
-  --ignore=kernels/test_flash_attn.py \
-  --ignore=kernels/test_flashinfer.py \
-  --ignore=kernels/test_int8_quant.py \
-  --ignore=kernels/test_machete_gemm.py \
-  --ignore=kernels/test_mamba_ssm.py \
-  --ignore=kernels/test_marlin_gemm.py \
-  --ignore=kernels/test_moe.py \
-  --ignore=kernels/test_prefix_prefill.py \
-  --ignore=kernels/test_rand.py \
-  --ignore=kernels/test_sampler.py \
-  --ignore=kernels/test_cascade_flash_attn.py \
-  --ignore=kernels/test_mamba_mixer2.py \
-  --ignore=kernels/test_aqlm.py \
-  --ignore=kernels/test_machete_mm.py \
-  --ignore=kernels/test_mha_attn.py \
-  --ignore=kernels/test_block_fp8.py \
-  --ignore=kernels/test_permute_cols.py"
+  --ignore=kernels/core/test_fused_quant_layernorm.py \
+  --ignore=kernels/core/test_permute_cols.py"
+fi
+
+if [[ $commands == *" kernels/attention"* ]]; then
+  commands="${commands} \
+  --ignore=kernels/attention/stest_attention_selector.py \
+  --ignore=kernels/attention/test_blocksparse_attention.py \
+  --ignore=kernels/attention/test_encoder_decoder_attn.py \
+  --ignore=kernels/attention/test_attention_selector.py \
+  --ignore=kernels/attention/test_flash_attn.py \
+  --ignore=kernels/attention/test_flashinfer.py \
+  --ignore=kernels/attention/test_prefix_prefill.py \
+  --ignore=kernels/attention/test_cascade_flash_attn.py \
+  --ignore=kernels/attention/test_mha_attn.py \
+  --ignore=kernels/attention/test_lightning_attn.py \
+  --ignore=kernels/attention/test_attention.py"
+fi
+
+if [[ $commands == *" kernels/quantization"* ]]; then
+  commands="${commands} \
+  --ignore=kernels/quantization/test_int8_quant.py \
+  --ignore=kernels/quantization/test_aqlm.py \
+  --ignore=kernels/quantization/test_machete_mm.py \
+  --ignore=kernels/quantization/test_block_fp8.py \
+  --ignore=kernels/quantization/test_block_int8.py \
+  --ignore=kernels/quantization/test_marlin_gemm.py \
+  --ignore=kernels/quantization/test_cutlass_scaled_mm.py \
+  --ignore=kernels/quantization/test_int8_kernel.py"
+fi
+
+if [[ $commands == *" kernels/mamba"* ]]; then
+  commands="${commands} \
+  --ignore=kernels/mamba/test_mamba_mixer2.py \
+  --ignore=kernels/mamba/test_causal_conv1d.py \
+  --ignore=kernels/mamba/test_mamba_ssm_ssd.py"
+fi
+
+if [[ $commands == *" kernels/moe"* ]]; then
+  commands="${commands} \
+  --ignore=kernels/moe/test_moe.py \
+  --ignore=kernels/moe/test_cutlass_moe.py \
+  --ignore=kernels/moe/test_triton_moe_ptpc_fp8.py"
 fi
 
 #ignore certain Entrypoints/openai tests
 if [[ $commands == *" entrypoints/openai "* ]]; then
   commands=${commands//" entrypoints/openai "/" entrypoints/openai \
   --ignore=entrypoints/openai/test_audio.py \
-  --ignore=entrypoints/openai/test_chat.py \
   --ignore=entrypoints/openai/test_shutdown.py \
   --ignore=entrypoints/openai/test_completion.py \
   --ignore=entrypoints/openai/test_sleep.py \
   --ignore=entrypoints/openai/test_models.py \
+  --ignore=entrypoints/openai/test_lora_adapters.py \
+  --ignore=entrypoints/openai/test_return_tokens_as_ids.py \
+  --ignore=entrypoints/openai/test_root_path.py \
+  --ignore=entrypoints/openai/test_tokenization.py \
   --ignore=entrypoints/openai/test_prompt_validation.py "}
 fi
 
 #ignore certain Entrypoints/llm tests
-if [[ $commands == *" && pytest -v -s entrypoints/llm/test_guided_generate.py"* ]]; then
-  commands=${commands//" && pytest -v -s entrypoints/llm/test_guided_generate.py"/" "}
+if [[ $commands == *" entrypoints/llm "* ]]; then
+  commands=${commands//" entrypoints/llm "/" entrypoints/llm \
+  --ignore=entrypoints/llm/test_chat.py \
+  --ignore=entrypoints/llm/test_accuracy.py \
+  --ignore=entrypoints/llm/test_init.py \
+  --ignore=entrypoints/llm/test_generate_multiple_loras.py \
+  --ignore=entrypoints/llm/test_prompt_validation.py "}
 fi
 
+#Obsolete currently
+##ignore certain Entrypoints/llm tests
+#if [[ $commands == *" && pytest -v -s entrypoints/llm/test_guided_generate.py"* ]]; then
+#  commands=${commands//" && pytest -v -s entrypoints/llm/test_guided_generate.py"/" "}
+#fi
+
 # --ignore=entrypoints/openai/test_encoder_decoder.py \
 # --ignore=entrypoints/openai/test_embedding.py \
 # --ignore=entrypoints/openai/test_oot_registration.py
@@ -134,9 +169,10 @@ if [[ $commands == *"--shard-id="* ]]; then
     # assign shard-id for each shard
     commands_gpu=${commands//"--shard-id= "/"--shard-id=${GPU} "}
     echo "Shard ${GPU} commands:$commands_gpu"
+    echo "Render devices: $BUILDKITE_AGENT_META_DATA_RENDER_DEVICES"
     docker run \
-        --device /dev/kfd --device /dev/dri \
-        --network host \
+        --device /dev/kfd $BUILDKITE_AGENT_META_DATA_RENDER_DEVICES \
+        --network=host \
         --shm-size=16gb \
         --rm \
         -e HIP_VISIBLE_DEVICES="${GPU}" \
@@ -163,9 +199,10 @@ if [[ $commands == *"--shard-id="* ]]; then
     fi
   done
 else
+  echo "Render devices: $BUILDKITE_AGENT_META_DATA_RENDER_DEVICES"
   docker run \
-          --device /dev/kfd --device /dev/dri \
-          --network host \
+          --device /dev/kfd $BUILDKITE_AGENT_META_DATA_RENDER_DEVICES \
+          --network=host \
           --shm-size=16gb \
           --rm \
           -e HIP_VISIBLE_DEVICES=0 \

.buildkite/scripts/hardware_ci/run-cpu-test-ppc64le.sh

@@ -0,0 +1,45 @@
+#!/bin/bash
+
+# This script build the CPU docker image and run the offline inference inside the container.
+# It serves a sanity check for compilation and basic model usage.
+set -ex
+
+# Setup cleanup
+remove_docker_container() {
+  if [[ -n "$container_id" ]]; then
+      podman rm -f "$container_id" || true
+  fi
+  podman system prune -f
+}
+trap remove_docker_container EXIT
+remove_docker_container
+
+# Try building the docker image
+podman build -t cpu-test-ubi9-ppc -f docker/Dockerfile.ppc64le .
+
+# Run the image
+container_id=$(podman run -itd --entrypoint /bin/bash -v /tmp/:/root/.cache/huggingface --privileged=true --network host -e HF_TOKEN cpu-test-ubi9-ppc)
+
+function cpu_tests() {
+
+  # offline inference
+  podman exec -it "$container_id" bash -c "
+    set -e
+    python3 examples/offline_inference/basic/generate.py --model facebook/opt-125m"
+
+  # Run basic model test
+  podman exec -it "$container_id" bash -c "
+    set -e
+    pip install pytest pytest-asyncio einops peft Pillow soundfile transformers_stream_generator matplotlib
+    pip install sentence-transformers datamodel_code_generator
+    pytest -v -s tests/models/embedding/language/test_cls_models.py::test_classification_models[float-jason9693/Qwen2.5-1.5B-apeach]
+    pytest -v -s tests/models/embedding/language/test_embedding.py::test_models[half-BAAI/bge-base-en-v1.5]
+    pytest -v -s tests/models/encoder_decoder/language -m cpu_model"
+}
+
+# All of CPU tests are expected to be finished less than 40 mins.
+
+export container_id
+export -f cpu_tests
+timeout 40m bash -c cpu_tests
+

.buildkite/scripts/hardware_ci/run-cpu-test-s390x.sh

@@ -10,5 +10,4 @@ trap remove_docker_container EXIT
 remove_docker_container
 
 # Try building the docker image
-docker build -t cpu-test -f Dockerfile.ppc64le .
-
+docker build -t cpu-test -f docker/Dockerfile.s390x .

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

@@ -8,15 +8,19 @@ set -ex
 CORE_RANGE=${CORE_RANGE:-48-95}
 NUMA_NODE=${NUMA_NODE:-1}
 
-# Try building the docker image
-numactl -C "$CORE_RANGE" -N "$NUMA_NODE" docker build -t cpu-test-"$BUILDKITE_BUILD_NUMBER" -f Dockerfile.cpu .
-numactl -C "$CORE_RANGE" -N "$NUMA_NODE" docker build --build-arg VLLM_CPU_DISABLE_AVX512="true" -t cpu-test-"$BUILDKITE_BUILD_NUMBER"-avx2 -f Dockerfile.cpu .
-
 # Setup cleanup
-remove_docker_container() { set -e; docker rm -f cpu-test-"$BUILDKITE_BUILD_NUMBER"-"$NUMA_NODE" cpu-test-"$BUILDKITE_BUILD_NUMBER"-avx2-"$NUMA_NODE" || true; }
+remove_docker_container() { 
+    set -e; 
+    docker rm -f cpu-test-"$BUILDKITE_BUILD_NUMBER"-"$NUMA_NODE" cpu-test-"$BUILDKITE_BUILD_NUMBER"-avx2-"$NUMA_NODE" || true; 
+    docker image rm cpu-test-"$BUILDKITE_BUILD_NUMBER" cpu-test-"$BUILDKITE_BUILD_NUMBER"-avx2 || true; 
+}
 trap remove_docker_container EXIT
 remove_docker_container
 
+# Try building the docker image
+numactl -C "$CORE_RANGE" -N "$NUMA_NODE" docker build --tag cpu-test-"$BUILDKITE_BUILD_NUMBER" --target vllm-test -f docker/Dockerfile.cpu .
+numactl -C "$CORE_RANGE" -N "$NUMA_NODE" docker build --build-arg VLLM_CPU_DISABLE_AVX512="true" --tag cpu-test-"$BUILDKITE_BUILD_NUMBER"-avx2 --target vllm-test -f docker/Dockerfile.cpu .
+
 # Run the image, setting --shm-size=4g for tensor parallel.
 docker run -itd --entrypoint /bin/bash -v ~/.cache/huggingface:/root/.cache/huggingface --cpuset-cpus="$CORE_RANGE"  \
  --cpuset-mems="$NUMA_NODE" --privileged=true -e HF_TOKEN --env VLLM_CPU_KVCACHE_SPACE=4 --shm-size=4g --name cpu-test-"$BUILDKITE_BUILD_NUMBER"-"$NUMA_NODE" cpu-test-"$BUILDKITE_BUILD_NUMBER"
@@ -36,8 +40,8 @@ function cpu_tests() {
   # Run basic model test
   docker exec cpu-test-"$BUILDKITE_BUILD_NUMBER"-"$NUMA_NODE" bash -c "
     set -e
-    pip install -r vllm/requirements/test.txt
-    pip install -r vllm/requirements/cpu.txt
+    pytest -v -s tests/kernels/test_cache.py -m cpu_model
+    pytest -v -s tests/kernels/test_mla_decode_cpu.py -m cpu_model
     pytest -v -s tests/models/decoder_only/language -m cpu_model
     pytest -v -s tests/models/embedding/language -m cpu_model
     pytest -v -s tests/models/encoder_decoder/language -m cpu_model

.buildkite/scripts/hardware_ci/run-gh200-test.sh

@@ -9,11 +9,13 @@ python3 use_existing_torch.py
 
 # Try building the docker image
 DOCKER_BUILDKIT=1 docker build . \
+  --file docker/Dockerfile \
   --target vllm-openai \
   --platform "linux/arm64" \
   -t gh200-test \
   --build-arg max_jobs=66 \
   --build-arg nvcc_threads=2 \
+  --build-arg RUN_WHEEL_CHECK=false \
   --build-arg torch_cuda_arch_list="9.0+PTX" \
   --build-arg vllm_fa_cmake_gpu_arches="90-real"
 
@@ -23,6 +25,6 @@ trap remove_docker_container EXIT
 remove_docker_container
 
 # Run the image and test offline inference
-docker run -e HF_TOKEN -v /root/.cache/huggingface:/root/.cache/huggingface --name gh200-test --gpus=all --entrypoint="" gh200-test bash -c '
+docker run -e HF_TOKEN -e VLLM_WORKER_MULTIPROC_METHOD=spawn -v /root/.cache/huggingface:/root/.cache/huggingface --name gh200-test --gpus=all --entrypoint="" gh200-test bash -c '
     python3 examples/offline_inference/basic/generate.py --model meta-llama/Llama-3.2-1B
 '

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

@@ -5,7 +5,7 @@
 set -ex
 
 # Try building the docker image
-docker build -t hpu-test-env -f Dockerfile.hpu .
+docker build -t hpu-test-env -f docker/Dockerfile.hpu .
 
 # Setup cleanup
 # certain versions of HPU software stack have a bug that can

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

@@ -35,7 +35,7 @@ else
     date "+%s" > /tmp/neuron-docker-build-timestamp
 fi
 
-docker build -t "${image_name}" -f Dockerfile.neuron .
+docker build -t "${image_name}" -f docker/Dockerfile.neuron .
 
 # Setup cleanup
 remove_docker_container() {

.buildkite/scripts/hardware_ci/run-tpu-v1-test.sh

@@ -0,0 +1,103 @@
+#!/bin/bash
+
+set -xu
+
+# Build the docker image.
+docker build -f docker/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-asyncio tpu-info \
+    && python3 -m pip install lm_eval[api]==0.4.4 \
+    && export VLLM_XLA_CACHE_PATH= \
+    && export VLLM_USE_V1=1 \
+    && export VLLM_XLA_CHECK_RECOMPILATION=1 \
+    && echo HARDWARE \
+    && tpu-info \
+    && { \
+        echo TEST_0: Running test_perf.py; \
+        pytest -s -v /workspace/vllm/tests/tpu/test_perf.py; \
+        echo TEST_0_EXIT_CODE: \$?; \
+    } & \
+    && { \
+        echo TEST_1: Running test_compilation.py; \
+        pytest -s -v /workspace/vllm/tests/tpu/test_compilation.py; \
+        echo TEST_1_EXIT_CODE: \$?; \
+    } & \
+    { \
+        echo TEST_2: Running test_basic.py; \
+        pytest -s -v /workspace/vllm/tests/v1/tpu/test_basic.py; \
+        echo TEST_2_EXIT_CODE: \$?; \
+    } & \
+    { \
+        echo TEST_3: Running test_accuracy.py::test_lm_eval_accuracy_v1_engine; \
+        pytest -s -v /workspace/vllm/tests/entrypoints/llm/test_accuracy.py::test_lm_eval_accuracy_v1_engine; \
+        echo TEST_3_EXIT_CODE: \$?; \
+    } & \
+    { \
+        echo TEST_4: Running test_quantization_accuracy.py; \
+        pytest -s -v /workspace/vllm/tests/tpu/test_quantization_accuracy.py; \
+        echo TEST_4_EXIT_CODE: \$?; \
+    } & \
+    { \
+        echo TEST_5: Running examples/offline_inference/tpu.py; \
+        python3 /workspace/vllm/examples/offline_inference/tpu.py; \
+        echo TEST_5_EXIT_CODE: \$?; \
+    } & \
+    { \
+        echo TEST_6: Running test_tpu_model_runner.py; \
+        pytest -s -v /workspace/vllm/tests/tpu/worker/test_tpu_model_runner.py; \
+        echo TEST_6_EXIT_CODE: \$?; \
+    } & \
+    && { \
+        echo TEST_7: Running test_sampler.py; \
+        pytest -s -v /workspace/vllm/tests/v1/tpu/test_sampler.py; \
+        echo TEST_7_EXIT_CODE: \$?; \
+    } & \
+    && { \
+        echo TEST_8: Running test_topk_topp_sampler.py; \
+        pytest -s -v /workspace/vllm/tests/v1/tpu/test_topk_topp_sampler.py; \
+        echo TEST_8_EXIT_CODE: \$?; \
+    } & \
+    && { \
+        echo TEST_9: Running test_multimodal.py; \
+        pytest -s -v /workspace/vllm/tests/v1/tpu/test_multimodal.py; \
+        echo TEST_9_EXIT_CODE: \$?; \
+    } & \
+    && { \
+        echo TEST_10: Running test_pallas.py; \
+        pytest -s -v /workspace/vllm/tests/v1/tpu/test_pallas.py; \
+        echo TEST_10_EXIT_CODE: \$?; \
+    } & \
+    && { \
+        echo TEST_11: Running test_struct_output_generate.py; \
+        pytest -s -v /workspace/vllm/tests/v1/entrypoints/llm/test_struct_output_generate.py; \
+        echo TEST_11_EXIT_CODE: \$?; \
+    } & \
+    && { \
+        echo TEST_12: Running test_moe_pallas.py; \
+        pytest -s -v /workspace/vllm/tests/tpu/test_moe_pallas.py; \
+        echo TEST_12_EXIT_CODE: \$?; \
+    } & \
+    # Disable the TPU LoRA tests until the feature is activated
+    # && { \
+    #     echo TEST_13: Running test_moe_pallas.py; \
+    #     pytest -s -v /workspace/vllm/tests/tpu/lora/; \
+    #     echo TEST_13_EXIT_CODE: \$?; \
+    # } & \
+    wait \
+    && echo 'All tests have attempted to run. Check logs for individual test statuses and exit codes.' \
+"
+
+# TODO: This test fails because it uses RANDOM_SEED sampling
+# && VLLM_USE_V1=1 pytest -v -s /workspace/vllm/tests/tpu/test_custom_dispatcher.py \

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

@@ -8,14 +8,15 @@ image_name="xpu/vllm-ci:${BUILDKITE_COMMIT}"
 container_name="xpu_${BUILDKITE_COMMIT}_$(tr -dc A-Za-z0-9 < /dev/urandom | head -c 10; echo)"
 
 # Try building the docker image
-docker build -t ${image_name} -f Dockerfile.xpu .
+docker build -t ${image_name} -f docker/Dockerfile.xpu .
 
 # Setup cleanup
 remove_docker_container() { 
-  docker rm -f "${container_name}" || docker image rm -f "${image_name}" || true;
+  docker rm -f "${container_name}" || true; 
+  docker image rm -f "${image_name}" || true;
+  docker system prune -f || true;
 }
 trap remove_docker_container EXIT
-remove_docker_container
 
 # Run the image and test offline inference/tensor parallel
 docker run \
@@ -25,6 +26,6 @@ docker run \
     --name "${container_name}" \
     "${image_name}" \
     sh -c '
-    python3 examples/offline_inference/basic/generate.py --model facebook/opt-125m
-    python3 examples/offline_inference/basic/generate.py --model facebook/opt-125m -tp 2
+    VLLM_USE_V1=0 python3 examples/offline_inference/basic/generate.py --model facebook/opt-125m
+    VLLM_USE_V1=0 python3 examples/offline_inference/basic/generate.py --model facebook/opt-125m -tp 2
 '

.buildkite/scripts/run-benchmarks.sh

@@ -5,8 +5,8 @@
 set -ex
 set -o pipefail
 
-# cd into parent directory of this file
-cd "$(dirname "${BASH_SOURCE[0]}")/.."
+# cd 2 levels into the working directory
+cd "$(dirname "${BASH_SOURCE[0]}")/../.."
 
 (which wget && which curl) || (apt-get update && apt-get install -y wget curl)
 

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

@@ -3,7 +3,7 @@
 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"
+    echo "Usage: .buildkite/scripts/run-multi-node-test.sh WORKING_DIR NUM_NODES NUM_GPUS DOCKER_IMAGE COMMAND1 COMMAND2 ... COMMANDN"
     exit 1
 fi
 

.buildkite/scripts/upload-wheels.sh

@@ -50,11 +50,11 @@ aws s3 cp "$normal_wheel" "s3://vllm-wheels/$BUILDKITE_COMMIT/"
 if [[ $normal_wheel == *"cu118"* ]]; then
     # if $normal_wheel matches cu118, do not upload the index.html
     echo "Skipping index files for cu118 wheels"
-elif [[ $normal_wheel == *"cu121"* ]]; then
-    # if $normal_wheel matches cu121, do not upload the index.html
-    echo "Skipping index files for cu121 wheels"
+elif [[ $normal_wheel == *"cu126"* ]]; then
+    # if $normal_wheel matches cu126, do not upload the index.html
+    echo "Skipping index files for cu126 wheels"
 else
-    # only upload index.html for cu124 wheels (default wheels)
+    # only upload index.html for cu128 wheels (default wheels)
     aws s3 cp index.html "s3://vllm-wheels/$BUILDKITE_COMMIT/vllm/index.html"
     aws s3 cp "s3://vllm-wheels/nightly/index.html" "s3://vllm-wheels/$BUILDKITE_COMMIT/index.html"
 fi
@@ -66,12 +66,12 @@ aws s3 cp "$normal_wheel" "s3://vllm-wheels/nightly/"
 if [[ $normal_wheel == *"cu118"* ]]; then
     # if $normal_wheel matches cu118, do not upload the index.html
     echo "Skipping index files for cu118 wheels"
-elif [[ $normal_wheel == *"cu121"* ]]; then
-    # if $normal_wheel matches cu121, do not upload the index.html
-    echo "Skipping index files for cu121 wheels"
+elif [[ $normal_wheel == *"cu126"* ]]; then
+    # if $normal_wheel matches cu126, do not upload the index.html
+    echo "Skipping index files for cu126 wheels"
 else
-    # only upload index.html for cu124 wheels (default wheels)
+    # only upload index.html for cu128 wheels (default wheels)
     aws s3 cp index.html "s3://vllm-wheels/nightly/vllm/index.html"
 fi
 
-aws s3 cp "$wheel" "s3://vllm-wheels/$version/"
+aws s3 cp "$wheel" "s3://vllm-wheels/$version/"

.buildkite/test-pipeline.yaml

@@ -8,6 +8,7 @@
 # Documentation
 # label(str): the name of the test. emoji allowed.
 # fast_check(bool): whether to run this on each commit on fastcheck pipeline.
+# torch_nightly(bool): whether to run this on vllm against torch nightly pipeline.
 # fast_check_only(bool): run this test on fastcheck pipeline only
 # optional(bool): never run this test by default (i.e. need to unblock manually) unless it's scheduled nightly run.
 # command(str): the single command to run for tests. incompatible with commands.
@@ -38,7 +39,7 @@ steps:
   - 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/api/inference_params.html
+  - grep \"sig sig-object py\" build/html/api/vllm/vllm.sampling_params.html
 
 - label: Async Engine, Inputs, Utils, Worker Test # 24min
   source_file_dependencies:
@@ -70,6 +71,7 @@ steps:
 - label: Basic Correctness Test # 30min
   #mirror_hardwares: [amd]
   fast_check: true
+  torch_nightly: true
   source_file_dependencies:
   - vllm/
   - tests/basic_correctness/test_basic_correctness
@@ -104,7 +106,8 @@ steps:
 - label: Entrypoints Test # 40min
   working_dir: "/vllm-workspace/tests"
   fast_check: true
-  mirror_hardwares: [amd]
+  torch_nightly: true
+  #mirror_hardwares: [amd]
   source_file_dependencies:
   - vllm/
   - tests/entrypoints/llm
@@ -118,7 +121,7 @@ steps:
   - pytest -v -s entrypoints/llm/test_generate.py # it needs a clean process
   - pytest -v -s entrypoints/llm/test_generate_multiple_loras.py # it needs a clean process
   - VLLM_USE_V1=0 pytest -v -s entrypoints/llm/test_guided_generate.py # it needs a clean process
-  - pytest -v -s entrypoints/openai --ignore=entrypoints/openai/test_oot_registration.py --ignore=entrypoints/openai/correctness/
+  - pytest -v -s entrypoints/openai --ignore=entrypoints/openai/test_oot_registration.py  --ignore=entrypoints/openai/test_chat_with_tool_reasoning.py --ignore=entrypoints/openai/correctness/ --ignore=entrypoints/openai/test_openai_schema.py
   - pytest -v -s entrypoints/test_chat_utils.py
   - VLLM_USE_V1=0 pytest -v -s entrypoints/offline_mode # Needs to avoid interference with other tests
 
@@ -135,8 +138,14 @@ steps:
   - examples/offline_inference/rlhf.py
   - examples/offline_inference/rlhf_colocate.py
   - tests/examples/offline_inference/data_parallel.py
+  - tests/v1/test_async_llm_dp.py
   commands:
+  # test with tp=2 and external_dp=2
+  - VLLM_USE_V1=0 torchrun --nproc-per-node=4 distributed/test_torchrun_example.py
+  - torchrun --nproc-per-node=4 distributed/test_torchrun_example.py
+  # test with internal dp
   - python3 ../examples/offline_inference/data_parallel.py
+  - TP_SIZE=2 DP_SIZE=2 pytest -v -s v1/test_async_llm_dp.py
   - pytest -v -s distributed/test_utils.py
   - pytest -v -s compile/test_basic_correctness.py
   - pytest -v -s distributed/test_pynccl.py
@@ -149,6 +158,7 @@ steps:
   - popd
 
 - label: Metrics, Tracing Test # 10min
+  mirror_hardwares: [amd]
   num_gpus: 2
   source_file_dependencies:
   - vllm/
@@ -156,18 +166,13 @@ steps:
   - 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]
+  #mirror_hardwares: [amd]
   source_file_dependencies:
   - vllm/
   - tests/test_regression
@@ -198,12 +203,13 @@ steps:
   commands:
     # split the test to avoid interference
     - pytest -v -s v1/core
-    - pytest -v -s v1/entrypoints
     - pytest -v -s v1/engine
     - pytest -v -s v1/entrypoints
     - pytest -v -s v1/sample
     - pytest -v -s v1/worker
     - pytest -v -s v1/structured_output
+    - pytest -v -s v1/spec_decode
+    - pytest -v -s v1/test_serial_utils.py
     - pytest -v -s v1/test_stats.py
     - pytest -v -s v1/test_utils.py
     - pytest -v -s v1/test_oracle.py
@@ -279,14 +285,25 @@ steps:
     - pytest -v -s spec_decode/e2e/test_eagle_correctness.py
 
 - label: LoRA Test %N # 15min each
-  mirror_hardwares: [amd]
+  #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 --ignore=lora/test_chatglm3_tp.py --ignore=lora/test_llama_tp.py --ignore=lora/test_minicpmv_tp.py  --ignore=lora/test_transfomers_model.py
+  command: pytest -v -s lora --shard-id=$$BUILDKITE_PARALLEL_JOB --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT --ignore=lora/test_chatglm3_tp.py --ignore=lora/test_llama_tp.py
   parallelism: 4
 
+- label: PyTorch Compilation Unit Tests
+  torch_nightly: true
+  source_file_dependencies:
+    - vllm/
+    - tests/compile
+  commands:
+    - pytest -v -s compile/test_pass_manager.py
+    - pytest -v -s compile/test_fusion.py
+    - pytest -v -s compile/test_sequence_parallelism.py
+
 - label: PyTorch Fullgraph Smoke Test # 9min
+  torch_nightly: true
   source_file_dependencies:
   - vllm/
   - tests/compile
@@ -297,24 +314,61 @@ steps:
   - pytest -v -s compile/piecewise/test_toy_llama.py
 
 - label: PyTorch Fullgraph Test # 18min
+  torch_nightly: true
   source_file_dependencies:
   - vllm/
   - tests/compile
   commands:
   - pytest -v -s compile/test_full_graph.py
 
-- label: Kernels Test %N # 1h each
+- label: Kernels Core Operation Test
   mirror_hardwares: [amd]
   source_file_dependencies:
   - csrc/
-  - vllm/attention
-  - tests/kernels
+  - tests/kernels/core
   commands:
-    - pytest -v -s kernels --shard-id=$$BUILDKITE_PARALLEL_JOB --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT
-  parallelism: 4
+    - pytest -v -s kernels/core
+
+- label: Kernels Attention Test %N
+  mirror_hardwares: [amd]
+  source_file_dependencies:
+  - csrc/attention/
+  - vllm/attention
+  - vllm/v1/attention
+  - tests/kernels/attention
+  commands:
+    - pytest -v -s kernels/attention --shard-id=$$BUILDKITE_PARALLEL_JOB --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT
+  parallelism: 2
+
+- label: Kernels Quantization Test %N
+  mirror_hardwares: [amd]
+  source_file_dependencies:
+  - csrc/quantization/
+  - vllm/model_executor/layers/quantization
+  - tests/kernels/quantization
+  commands:
+    - pytest -v -s kernels/quantization  --shard-id=$$BUILDKITE_PARALLEL_JOB --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT
+  parallelism: 2
+
+- label: Kernels MoE Test
+  #mirror_hardwares: [amd]
+  source_file_dependencies:
+  - csrc/moe/
+  - tests/kernels/moe
+  - vllm/model_executor/layers/fused_moe/
+  commands:
+    - pytest -v -s kernels/moe
+
+- label: Kernels Mamba Test
+  #mirror_hardwares: [amd]
+  source_file_dependencies:
+  - csrc/mamba/
+  - tests/kernels/mamba
+  commands:
+    - pytest -v -s kernels/mamba
 
 - label: Tensorizer Test # 11min
-  mirror_hardwares: [amd]
+  # mirror_hardwares: [amd]
   soft_fail: true
   source_file_dependencies:
   - vllm/model_executor/model_loader
@@ -330,14 +384,22 @@ steps:
   source_file_dependencies:
   - benchmarks/
   commands:
-  - bash run-benchmarks.sh
+  - bash scripts/run-benchmarks.sh
 
-- label: Quantization Test # 33min
+- label: Benchmarks CLI Test # 10min
+  source_file_dependencies:
+  - vllm/
+  - tests/benchmarks/
+  commands:
+  - pytest -v -s benchmarks/
+
+- label: Quantization Test
   source_file_dependencies:
   - csrc/
   - vllm/model_executor/layers/quantization
   - tests/quantization
-  command: VLLM_TEST_FORCE_LOAD_FORMAT=auto pytest -v -s quantization
+  commands:
+  - VLLM_TEST_FORCE_LOAD_FORMAT=auto pytest -v -s quantization
 
 - label: LM Eval Small Models # 53min
   working_dir: "/vllm-workspace/.buildkite/lm-eval-harness"
@@ -346,7 +408,7 @@ steps:
   - vllm/model_executor/layers/quantization
   commands:
   - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-  - bash ./run-tests.sh -c configs/models-small.txt -t 1
+  - pytest -s -v test_lm_eval_correctness.py --config-list-file=configs/models-small.txt --tp-size=1
 
 - label: OpenAI API correctness
   source_file_dependencies:
@@ -365,97 +427,101 @@ steps:
 
 - label: OpenAI-Compatible Tool Use # 20 min
   fast_check: false
-  mirror_hardwares: [ amd ]
+  #mirror_hardwares: [ amd ]
   source_file_dependencies:
     - vllm/
     - tests/tool_use
+    - tests/mistral_tool_use
   commands:
     - pytest -v -s tool_use
+    - pytest -v -s mistral_tool_use
 
 #####  models test  #####
 
 - label: Basic Models Test # 24min
+  torch_nightly: true
   source_file_dependencies:
   - vllm/
   - tests/models
   commands:
     - pytest -v -s models/test_transformers.py
     - pytest -v -s models/test_registry.py
+    - pytest -v -s models/test_utils.py
+    - pytest -v -s models/test_vision.py
     # V1 Test: https://github.com/vllm-project/vllm/issues/14531
-    - VLLM_USE_V1=0 pytest -v -s models/test_initialization.py
+    - VLLM_USE_V1=0 pytest -v -s models/test_initialization.py -k 'not llama4 and not plamo2'
+    - VLLM_USE_V1=0 pytest -v -s models/test_initialization.py -k 'llama4'
+    - VLLM_USE_V1=0 pytest -v -s models/test_initialization.py -k 'plamo2'
 
-- label: Language Models Test (Standard) # 32min
+- label: Language Models Test (Standard)
   #mirror_hardwares: [amd]
   source_file_dependencies:
   - vllm/
-  - tests/models/decoder_only/language
-  - tests/models/embedding/language
-  - tests/models/encoder_decoder/language
+  - tests/models/language
   commands:
-    - pytest -v -s models/decoder_only/language -m 'core_model or quant_model'
-    - pytest -v -s models/embedding/language -m core_model
+    # Install causal-conv1d for plamo2 models here, as it is not compatible with pip-compile.
+    - pip install 'git+https://github.com/Dao-AILab/causal-conv1d@v1.5.0.post8'
+    - pytest -v -s models/language -m core_model
 
-- label: Language Models Test (Extended) # 1h10min
+- label: Language Models Test (Extended)
   optional: true
   source_file_dependencies:
   - vllm/
-  - tests/models/decoder_only/language
-  - tests/models/embedding/language
-  - tests/models/encoder_decoder/language
+  - tests/models/language
   commands:
-    - pytest -v -s models/decoder_only/language -m 'not core_model and not quant_model'
-    - pytest -v -s models/embedding/language -m 'not core_model'
+    # Install causal-conv1d for plamo2 models here, as it is not compatible with pip-compile.
+    - pip install 'git+https://github.com/Dao-AILab/causal-conv1d@v1.5.0.post8'
+    - pytest -v -s models/language -m 'not core_model'
 
-- label: Multi-Modal Models Test (Standard) # 40min
+- label: Multi-Modal Models Test (Standard)
   #mirror_hardwares: [amd]
   source_file_dependencies:
   - vllm/
-  - tests/models/decoder_only/audio_language
-  - tests/models/decoder_only/vision_language
-  - tests/models/embedding/vision_language
-  - tests/models/encoder_decoder/audio_language
-  - tests/models/encoder_decoder/vision_language
+  - tests/models/multimodal
   commands:
     - pip install git+https://github.com/TIGER-AI-Lab/Mantis.git
-    - pytest -v -s models/multimodal
-    - pytest -v -s models/decoder_only/audio_language -m 'core_model or quant_model'
-    - pytest -v -s --ignore models/decoder_only/vision_language/test_phi3v.py models/decoder_only/vision_language -m 'core_model or quant_model'
-    - pytest -v -s models/embedding/vision_language -m core_model
-    - pytest -v -s models/encoder_decoder/audio_language -m core_model
-    - pytest -v -s models/encoder_decoder/language -m core_model
-    - pytest -v -s models/encoder_decoder/vision_language -m core_model
+    - pytest -v -s models/multimodal/processing
+    - pytest -v -s --ignore models/multimodal/generation/test_whisper.py models/multimodal -m core_model
+    - cd .. && pytest -v -s tests/models/multimodal/generation/test_whisper.py -m core_model  # Otherwise, mp_method="spawn" doesn't work
 
-- label: Multi-Modal Models Test (Extended) 1 # 48m
+- label: Multi-Modal Models Test (Extended) 1
   optional: true
   source_file_dependencies:
   - vllm/
-  - tests/models/decoder_only/audio_language
-  - tests/models/decoder_only/vision_language
-  - tests/models/embedding/vision_language
-  - tests/models/encoder_decoder/vision_language
+  - tests/models/multimodal
   commands:
     - pip install git+https://github.com/TIGER-AI-Lab/Mantis.git
-    - pytest -v -s models/decoder_only/audio_language -m 'not core_model and not quant_model'
-    - pytest -v -s models/decoder_only/vision_language/test_models.py -m 'split(group=0) and not core_model and not quant_model'
-    # HACK - run phi3v tests separately to sidestep this transformers bug
-    # https://github.com/huggingface/transformers/issues/34307
-    - pytest -v -s models/decoder_only/vision_language/test_phi3v.py
-    - pytest -v -s --ignore models/decoder_only/vision_language/test_models.py --ignore models/decoder_only/vision_language/test_phi3v.py models/decoder_only/vision_language -m 'not core_model and not quant_model'
-    - pytest -v -s models/embedding/vision_language -m 'not core_model'
-    - pytest -v -s models/encoder_decoder/language -m 'not core_model'
-    - pytest -v -s models/encoder_decoder/vision_language -m 'not core_model'
+    - pytest -v -s --ignore models/multimodal/generation/test_common.py --ignore models/multimodal/processing models/multimodal -m 'not core_model'
 
-- label: Multi-Modal Models Test (Extended) 2 # 38m
+- label: Multi-Modal Models Test (Extended) 2
   optional: true
   source_file_dependencies:
   - vllm/
-  - tests/models/decoder_only/vision_language
+  - tests/models/multimodal
   commands:
     - pip install git+https://github.com/TIGER-AI-Lab/Mantis.git
-    - pytest -v -s models/decoder_only/vision_language/test_models.py -m 'split(group=1) and not core_model and not quant_model'
+    - pytest -v -s models/multimodal/generation/test_common.py -m 'split(group=0) and not core_model'
+
+- label: Multi-Modal Models Test (Extended) 3
+  optional: true
+  source_file_dependencies:
+  - vllm/
+  - tests/models/multimodal
+  commands:
+    - pip install git+https://github.com/TIGER-AI-Lab/Mantis.git
+    - pytest -v -s models/multimodal/generation/test_common.py -m 'split(group=1) and not core_model'
+
+- label: Quantized Models Test
+  #mirror_hardwares: [amd]
+  source_file_dependencies:
+  - vllm/model_executor/layers/quantization
+  - tests/models/quantization
+  commands:
+    - pytest -v -s models/quantization
 
 # This test is used only in PR development phase to test individual models and should never run on main
 - label: Custom Models Test
+  mirror_hardwares: [amd]
   optional: true
   commands:
     - echo 'Testing custom models...'
@@ -467,6 +533,7 @@ steps:
 #####  multi gpus test  #####
 
 - label: Distributed Comm Ops Test # 7min
+  mirror_hardwares: [amd]
   working_dir: "/vllm-workspace/tests"
   num_gpus: 2
   source_file_dependencies:
@@ -509,24 +576,27 @@ steps:
   - vllm/worker/worker.py
   - vllm/worker/model_runner.py
   - entrypoints/llm/test_collective_rpc.py
+  - tests/v1/test_async_llm_dp.py
+  - vllm/v1/engine/
   commands:
+  - TP_SIZE=1 DP_SIZE=2 pytest -v -s v1/test_async_llm_dp.py
   - pytest -v -s entrypoints/llm/test_collective_rpc.py
-  - VLLM_USE_V1=1 torchrun --nproc-per-node=2 distributed/test_torchrun_example.py
-  - torchrun --nproc-per-node=2 distributed/test_torchrun_example.py
   - pytest -v -s ./compile/test_basic_correctness.py
   - pytest -v -s ./compile/test_wrapper.py
   - VLLM_TEST_SAME_HOST=1 torchrun --nproc-per-node=4 distributed/test_same_node.py | grep 'Same node test passed'
   - TARGET_TEST_SUITE=L4 pytest basic_correctness/ -v -s -m 'distributed(num_gpus=2)'
   # Avoid importing model tests that cause CUDA reinitialization error
   - pytest models/test_transformers.py -v -s -m 'distributed(num_gpus=2)'
-  - pytest models/encoder_decoder/language/test_bart.py -v -s -m 'distributed(num_gpus=2)'
-  - pytest models/encoder_decoder/vision_language/test_broadcast.py -v -s -m 'distributed(num_gpus=2)'
-  - pytest models/decoder_only/vision_language/test_models.py -v -s -m 'distributed(num_gpus=2)'
+  - pytest models/language -v -s -m 'distributed(num_gpus=2)'
+  - pytest models/multimodal -v -s -m 'distributed(num_gpus=2)'
+  # test sequence parallel
+  - pytest -v -s distributed/test_sequence_parallel.py
   # this test fails consistently.
   # TODO: investigate and fix
   # - pytest -v -s spec_decode/e2e/test_integration_dist_tp2.py
   - VLLM_USE_V1=0 CUDA_VISIBLE_DEVICES=0,1 pytest -v -s test_sharded_state_loader.py
   - VLLM_USE_V1=0 CUDA_VISIBLE_DEVICES=0,1 pytest -v -s kv_transfer/test_disagg.py
+  - CUDA_VISIBLE_DEVICES=0,1 pytest -v -s v1/shutdown
 
 - label: Plugin Tests (2 GPUs) # 40min
   working_dir: "/vllm-workspace/tests"
@@ -589,14 +659,10 @@ steps:
     # FIXIT: find out which code initialize cuda before running the test
     # before the fix, we need to use spawn to test it
     - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-    # This test runs llama 13B, so it is required to run on 4 GPUs.
-    - pytest -v -s -x lora/test_long_context.py
     # There is some Tensor Parallelism related processing logic in LoRA that
     # requires multi-GPU testing for validation.
     - pytest -v -s -x lora/test_chatglm3_tp.py
     - pytest -v -s -x lora/test_llama_tp.py
-    - pytest -v -s -x lora/test_minicpmv_tp.py
-    - pytest -v -s -x lora/test_transfomers_model.py
 
 
 - label: Weight Loading Multiple GPU Test  # 33min
@@ -647,4 +713,4 @@ steps:
   - vllm/model_executor/layers/quantization
   commands:
   - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-  - bash ./run-tests.sh -c configs/models-large.txt -t 4
+  - pytest -s -v test_lm_eval_correctness.py --config-list-file=configs/models-large.txt --tp-size=4

.github/CODEOWNERS

@@ -12,6 +12,7 @@
 /vllm/model_executor/layers/quantization @mgoin @robertgshaw2-redhat @tlrmchlsmth
 /vllm/model_executor/guided_decoding @mgoin @russellb
 /vllm/multimodal @DarkLight1337 @ywang96
+/vllm/vllm_flash_attn @LucasWilkinson
 CMakeLists.txt @tlrmchlsmth
 
 # vLLM V1

.github/ISSUE_TEMPLATE/200-installation.yml

@@ -14,7 +14,7 @@ body:
     description: |
       Please run the following and paste the output below.
       ```sh
-      wget https://raw.githubusercontent.com/vllm-project/vllm/main/collect_env.py
+      wget https://raw.githubusercontent.com/vllm-project/vllm/main/vllm/collect_env.py
       # For security purposes, please feel free to check the contents of collect_env.py before running it.
       python collect_env.py
       ```

.github/ISSUE_TEMPLATE/300-usage.yml

@@ -14,7 +14,7 @@ body:
     description: |
       Please run the following and paste the output below.
       ```sh
-      wget https://raw.githubusercontent.com/vllm-project/vllm/main/collect_env.py
+      wget https://raw.githubusercontent.com/vllm-project/vllm/main/vllm/collect_env.py
       # For security purposes, please feel free to check the contents of collect_env.py before running it.
       python collect_env.py
       ```

.github/ISSUE_TEMPLATE/400-bug-report.yml

@@ -14,19 +14,19 @@ body:
     description: |
       Please run the following and paste the output below.
       ```sh
-      wget https://raw.githubusercontent.com/vllm-project/vllm/main/collect_env.py
+      wget https://raw.githubusercontent.com/vllm-project/vllm/main/vllm/collect_env.py
       # For security purposes, please feel free to check the contents of collect_env.py before running it.
       python collect_env.py
       ```
       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>
+      <summary>The output of <code>python collect_env.py</code></summary>
 
       ```text
       Your output of `python collect_env.py` here
       ```
-      
+
       </details>
   validations:
     required: true
@@ -75,7 +75,7 @@ body:
       ```
 
       ```
-      The error message you got, with the full traceback.
+      The error message you got, with the full traceback and the error logs with [dump_input.py:##] if present.
       ```
   validations:
     required: true

.github/ISSUE_TEMPLATE/600-new-model.yml

@@ -9,7 +9,7 @@ body:
     value: >
       #### Before submitting an issue, please make sure the issue hasn't been already addressed by searching through [the existing and past issues](https://github.com/vllm-project/vllm/issues?q=is%3Aissue+sort%3Acreated-desc+).
 
-      #### We also highly recommend you read https://docs.vllm.ai/en/latest/contributing/model/adding_model.html first to understand how to add a new model.
+      #### We also highly recommend you read https://docs.vllm.ai/en/latest/contributing/model/index.html first to understand how to add a new model.
 - type: textarea
   attributes:
     label: The model to consider.

.github/ISSUE_TEMPLATE/700-performance-discussion.yml

@@ -35,7 +35,7 @@ body:
     description: |
       Please run the following and paste the output below.
       ```sh
-      wget https://raw.githubusercontent.com/vllm-project/vllm/main/collect_env.py
+      wget https://raw.githubusercontent.com/vllm-project/vllm/main/vllm/collect_env.py
       # For security purposes, please feel free to check the contents of collect_env.py before running it.
       python collect_env.py
       ```

.github/ISSUE_TEMPLATE/800-misc-discussion.yml

@@ -1,28 +0,0 @@
-name: 🎲 Misc/random discussions that do not fit into the above categories.
-description: Submit a discussion as you like. Note that developers are heavily overloaded and we mainly rely on community users to answer these issues.
-title: "[Misc]: "
-labels: ["misc"]
-
-body:
-- type: markdown
-  attributes:
-    value: >
-      #### Before submitting an issue, please make sure the issue hasn't been already addressed by searching through [the existing and past issues](https://github.com/vllm-project/vllm/issues?q=is%3Aissue+sort%3Acreated-desc+).
-- type: textarea
-  attributes:
-    label: Anything you want to discuss about vllm.
-    description: >
-      Anything you want to discuss about vllm.
-  validations:
-    required: true
-- type: markdown
-  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/config.yml

@@ -1 +1,5 @@
 blank_issues_enabled: false
+contact_links:
+  - name: Questions
+    url: https://discuss.vllm.ai
+    about: Ask questions and discuss with other vLLM community members

.github/PULL_REQUEST_TEMPLATE.md

@@ -3,4 +3,4 @@ FILL IN THE PR DESCRIPTION HERE
 FIX #xxxx (*link existing issues this PR will resolve*)
 
 <!--- pyml disable-next-line no-emphasis-as-heading -->
-**BEFORE SUBMITTING, PLEASE READ <https://docs.vllm.ai/en/latest/contributing/overview.html>**
+**BEFORE SUBMITTING, PLEASE READ <https://docs.vllm.ai/en/latest/contributing/overview.html>** (anything written below this line will be removed by GitHub Actions)

.github/mergify.yml

@@ -19,7 +19,7 @@ pull_request_rules:
       - files~=\.buildkite/
       - files~=^cmake/
       - files=CMakeLists.txt
-      - files~=^Dockerfile
+      - files~=^docker/Dockerfile
       - files~=^requirements.*\.txt
       - files=setup.py
   actions:
@@ -55,11 +55,19 @@ pull_request_rules:
   description: Automatically apply structured-output label
   conditions:
     - or:
+      - files~=^benchmarks/structured_schemas/
+      - files=benchmarks/benchmark_serving_structured_output.py
+      - files=benchmarks/run_structured_output_benchmark.sh
+      - files=docs/source/features/structured_outputs.md
+      - files=examples/offline_inference/structured_outputs.py
+      - files=examples/online_serving/openai_chat_completion_structured_outputs.py
+      - files=examples/online_serving/openai_chat_completion_structured_outputs_with_reasoning.py
       - files~=^vllm/model_executor/guided_decoding/
       - files=tests/model_executor/test_guided_processors.py
       - files=tests/entrypoints/llm/test_guided_generate.py
-      - files=benchmarks/benchmark_serving_guided.py
-      - files=benchmarks/benchmark_guided.py
+      - files~=^tests/v1/structured_output/
+      - files=tests/v1/entrypoints/llm/test_guided_generate.py
+      - files~=^vllm/v1/structured_output/
   actions:
     label:
       add:
@@ -88,6 +96,58 @@ pull_request_rules:
       add:
         - v1
 
+- name: label-tpu
+  description: Automatically apply tpu label
+  # Keep this list in sync with `label-tpu-remove` conditions
+  conditions:
+    - or:
+      - files~=tpu.py
+      - files~=_tpu
+      - files~=tpu_
+      - files~=/tpu/
+      - files~=pallas
+  actions:
+    label:
+      add:
+        - tpu
+
+- name: label-tpu-remove
+  description: Automatically remove tpu label
+  # Keep this list in sync with `label-tpu` conditions
+  conditions:
+    - and:
+      - -files~=tpu.py
+      - -files~=_tpu
+      - -files~=tpu_
+      - -files~=/tpu/
+      - -files~=pallas
+  actions:
+    label:
+      remove:
+        - tpu
+
+- name: label-tool-calling
+  description: Automatically add tool-calling label
+  conditions:
+    - or:
+      - files~=^tests/tool_use/
+      - files~=^tests/mistral_tool_use/
+      - files~=^tests/entrypoints/openai/tool_parsers/
+      - files=tests/entrypoints/openai/test_chat_with_tool_reasoning.py
+      - files~=^vllm/entrypoints/openai/tool_parsers/
+      - files=docs/source/features/tool_calling.md
+      - files=docs/source/getting_started/examples/openai_chat_completion_client_with_tools.md
+      - files=docs/source/getting_started/examples/chat_with_tools.md
+      - files~=^examples/tool_chat_*
+      - files=examples/offline_inference/chat_with_tools.py
+      - files=examples/online_serving/openai_chat_completion_client_with_tools_required.py
+      - files=examples/online_serving/openai_chat_completion_tool_calls_with_reasoning.py
+      - files=examples/online_serving/openai_chat_completion_client_with_tools.py
+  actions:
+    label:
+      add:
+        - tool-calling
+
 - name: ping author on conflicts and add 'needs-rebase' label
   conditions:
       - conflict

.github/workflows/lint-and-deploy.yaml

@@ -50,7 +50,7 @@ jobs:
         uses: helm/kind-action@a1b0e391336a6ee6713a0583f8c6240d70863de3 # v1.12.0
 
       - name: Build the Docker image vllm cpu
-        run: docker buildx build -f Dockerfile.cpu -t vllm-cpu-env .
+        run: docker buildx build -f docker/Dockerfile.cpu -t vllm-cpu-env .
 
       - name: Configuration of docker images, network and namespace for the kind cluster
         run: |
@@ -66,7 +66,7 @@ jobs:
           export AWS_SECRET_ACCESS_KEY=minioadmin
           sleep 30 && kubectl -n ns-vllm logs -f "$(kubectl -n ns-vllm get pods | awk '/deployment/ {print $1;exit}')" &
           helm install --wait --wait-for-jobs --timeout 5m0s --debug --create-namespace --namespace=ns-vllm test-vllm examples/online_serving/chart-helm -f examples/online_serving/chart-helm/values.yaml --set secrets.s3endpoint=http://minio:9000 --set secrets.s3bucketname=testbucket --set secrets.s3accesskeyid=$AWS_ACCESS_KEY_ID --set secrets.s3accesskey=$AWS_SECRET_ACCESS_KEY --set resources.requests.cpu=1 --set resources.requests.memory=4Gi --set resources.limits.cpu=2 --set resources.limits.memory=5Gi --set image.env[0].name=VLLM_CPU_KVCACHE_SPACE --set image.env[1].name=VLLM_LOGGING_LEVEL --set-string image.env[0].value="1" --set-string image.env[1].value="DEBUG" --set-string extraInit.s3modelpath="opt-125m/" --set-string 'resources.limits.nvidia\.com/gpu=0' --set-string 'resources.requests.nvidia\.com/gpu=0' --set-string image.repository="vllm-cpu-env"
-    
+
       - name: curl test
         run: |
           kubectl -n ns-vllm port-forward service/test-vllm-service 8001:80 &
@@ -79,4 +79,4 @@ jobs:
                           "max_tokens": 7,
                           "temperature": 0
                   }'):$CODE"
-          echo "$CODE"
+          echo "$CODE"

.gitignore

@@ -2,7 +2,7 @@
 /vllm/_version.py
 
 # vllm-flash-attn built from source
-vllm/vllm_flash_attn/
+vllm/vllm_flash_attn/*
 
 # Byte-compiled / optimized / DLL files
 __pycache__/
@@ -80,6 +80,7 @@ instance/
 # Sphinx documentation
 docs/_build/
 docs/source/getting_started/examples/
+docs/source/api/vllm
 
 # PyBuilder
 .pybuilder/
@@ -202,3 +203,6 @@ benchmarks/**/*.json
 # Linting
 actionlint
 shellcheck*/
+
+# Ingore moe/marlin_moe gen code
+csrc/moe/marlin_moe_wna16/kernel_*

.pre-commit-config.yaml

@@ -1,3 +1,6 @@
+default_install_hook_types:
+  - pre-commit
+  - commit-msg
 default_stages:
   - pre-commit # Run locally
   - manual # Run in CI
@@ -8,31 +11,30 @@ repos:
   hooks:
   - id: yapf
     args: [--in-place, --verbose]
-    additional_dependencies: [toml] # TODO: Remove when yapf is upgraded
 - repo: https://github.com/astral-sh/ruff-pre-commit
-  rev: v0.9.3
+  rev: v0.11.7
   hooks:
   - id: ruff
     args: [--output-format, github, --fix]
 - repo: https://github.com/codespell-project/codespell
-  rev: v2.4.0
+  rev: v2.4.1
   hooks:
   - id: codespell
     additional_dependencies: ['tomli']
     args: ['--toml', 'pyproject.toml']
 - repo: https://github.com/PyCQA/isort
-  rev: 0a0b7a830386ba6a31c2ec8316849ae4d1b8240d # 6.0.0
+  rev: 6.0.1
   hooks:
   - id: isort
 - repo: https://github.com/pre-commit/mirrors-clang-format
-  rev: v19.1.7
+  rev: v20.1.3
   hooks:
   - id: clang-format
     exclude: 'csrc/(moe/topk_softmax_kernels.cu|quantization/gguf/(ggml-common.h|dequantize.cuh|vecdotq.cuh|mmq.cuh|mmvq.cuh))|vllm/third_party/.*'
     types_or: [c++, cuda]
     args: [--style=file, --verbose]
 - repo: https://github.com/jackdewinter/pymarkdown
-  rev: v0.9.27
+  rev: v0.9.29
   hooks:
   - id: pymarkdown
     args: [fix]
@@ -41,10 +43,10 @@ repos:
   hooks:
   - id: actionlint
 - repo: https://github.com/astral-sh/uv-pre-commit
-  rev: 0.6.2
+  rev: 0.6.17
   hooks:
     - id: pip-compile
-      args: [requirements/test.in, -o, requirements/test.txt]
+      args: [requirements/test.in, -o, requirements/test.txt, --index-strategy, unsafe-best-match, --torch-backend, cu128]
       files: ^requirements/test\.(in|txt)$
 - repo: local
   hooks:
@@ -99,8 +101,8 @@ repos:
     args:
       - -c
       - |
-        if ! grep -q "^Signed-off-by: $(git config user.name) <$(git config user.email)>" .git/COMMIT_EDITMSG; then
-          printf "\nSigned-off-by: $(git config user.name) <$(git config user.email)>\n" >> .git/COMMIT_EDITMSG
+        if ! grep -q "^Signed-off-by: $(git config user.name) <$(git config user.email)>" "$(git rev-parse --git-path COMMIT_EDITMSG)"; then
+          printf "\nSigned-off-by: $(git config user.name) <$(git config user.email)>\n" >> "$(git rev-parse --git-path COMMIT_EDITMSG)"
         fi
     language: system
     verbose: true
@@ -119,6 +121,10 @@ repos:
     language: system
     always_run: true
     pass_filenames: false
+  - id: update-dockerfile-graph
+    name: Update Dockerfile dependency graph
+    entry: tools/update-dockerfile-graph.sh
+    language: script
   # Keep `suggestion` last
   - id: suggestion
     name: Suggestion

CMakeLists.txt

@@ -15,7 +15,6 @@ project(vllm_extensions LANGUAGES CXX)
 
 # 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}")
 
@@ -34,7 +33,7 @@ set(PYTHON_SUPPORTED_VERSIONS "3.9" "3.10" "3.11" "3.12")
 set(CUDA_SUPPORTED_ARCHS "7.0;7.2;7.5;8.0;8.6;8.7;8.9;9.0;10.0;10.1;12.0")
 
 # Supported AMD GPU architectures.
-set(HIP_SUPPORTED_ARCHS "gfx906;gfx908;gfx90a;gfx942;gfx1030;gfx1100;gfx1101")
+set(HIP_SUPPORTED_ARCHS "gfx906;gfx908;gfx90a;gfx942;gfx950;gfx1030;gfx1100;gfx1101;gfx1200;gfx1201")
 
 #
 # Supported/expected torch versions for CUDA/ROCm.
@@ -44,10 +43,10 @@ set(HIP_SUPPORTED_ARCHS "gfx906;gfx908;gfx90a;gfx942;gfx1030;gfx1100;gfx1101")
 #
 # Note: the CUDA torch version is derived from pyproject.toml and various
 # requirements.txt files and should be kept consistent.  The ROCm torch
-# versions are derived from Dockerfile.rocm
+# versions are derived from docker/Dockerfile.rocm
 #
-set(TORCH_SUPPORTED_VERSION_CUDA "2.6.0")
-set(TORCH_SUPPORTED_VERSION_ROCM "2.6.0")
+set(TORCH_SUPPORTED_VERSION_CUDA "2.7.0")
+set(TORCH_SUPPORTED_VERSION_ROCM "2.7.0")
 
 #
 # Try to find python package with an executable that exactly matches
@@ -230,25 +229,28 @@ set(VLLM_EXT_SRC
   "csrc/cache_kernels.cu"
   "csrc/attention/paged_attention_v1.cu"
   "csrc/attention/paged_attention_v2.cu"
+  "csrc/attention/merge_attn_states.cu"
   "csrc/pos_encoding_kernels.cu"
   "csrc/activation_kernels.cu"
   "csrc/layernorm_kernels.cu"
   "csrc/layernorm_quant_kernels.cu"
+  "csrc/cuda_view.cu"
   "csrc/quantization/gptq/q_gemm.cu"
   "csrc/quantization/compressed_tensors/int8_quant_kernels.cu"
   "csrc/quantization/fp8/common.cu"
   "csrc/quantization/fused_kernels/fused_layernorm_dynamic_per_token_quant.cu"
   "csrc/quantization/gguf/gguf_kernel.cu"
+  "csrc/quantization/activation_kernels.cu"
   "csrc/cuda_utils_kernels.cu"
   "csrc/prepare_inputs/advance_step.cu"
+  "csrc/custom_all_reduce.cu"
   "csrc/torch_bindings.cpp")
 
 if(VLLM_GPU_LANG STREQUAL "CUDA")
   SET(CUTLASS_ENABLE_HEADERS_ONLY ON CACHE BOOL "Enable only the header library")
 
-  # Set CUTLASS_REVISION manually -- its revision detection doesn't work in this case.
-  # Please keep this in sync with FetchContent_Declare line below.
-  set(CUTLASS_REVISION "v3.8.0" CACHE STRING "CUTLASS revision to use")
+  # Set CUTLASS_REVISION. Used for FetchContent. Also fixes some bogus messages when building.
+  set(CUTLASS_REVISION "v3.9.2" CACHE STRING "CUTLASS revision to use")
 
   # Use the specified CUTLASS source directory for compilation if VLLM_CUTLASS_SRC_DIR is provided
   if (DEFINED ENV{VLLM_CUTLASS_SRC_DIR})
@@ -266,7 +268,7 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
         cutlass
         GIT_REPOSITORY https://github.com/nvidia/cutlass.git
         # Please keep this in sync with CUTLASS_REVISION line above.
-        GIT_TAG v3.8.0
+        GIT_TAG ${CUTLASS_REVISION}
         GIT_PROGRESS TRUE
 
         # Speed up CUTLASS download by retrieving only the specified GIT_TAG instead of the history.
@@ -282,13 +284,13 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
     "csrc/mamba/causal_conv1d/causal_conv1d.cu"
     "csrc/quantization/aqlm/gemm_kernels.cu"
     "csrc/quantization/awq/gemm_kernels.cu"
-    "csrc/custom_all_reduce.cu"
     "csrc/permute_cols.cu"
     "csrc/quantization/cutlass_w8a8/scaled_mm_entry.cu"
     "csrc/quantization/fp4/nvfp4_quant_entry.cu"
     "csrc/quantization/fp4/nvfp4_scaled_mm_entry.cu"
     "csrc/sparse/cutlass/sparse_scaled_mm_entry.cu"
-    "csrc/cutlass_extensions/common.cpp")
+    "csrc/cutlass_extensions/common.cpp"
+    "csrc/attention/mla/cutlass_mla_entry.cu")
 
   set_gencode_flags_for_srcs(
     SRCS "${VLLM_EXT_SRC}"
@@ -299,8 +301,52 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
   # are not supported by Machete yet.
   cuda_archs_loose_intersection(MARLIN_ARCHS "8.0;8.6;8.7;8.9;9.0;10.0;10.1;12.0" "${CUDA_ARCHS}")
   if (MARLIN_ARCHS)
+
+    #
+    # For the Marlin kernels we automatically generate sources for various
+    # preselected input type pairs and schedules.
+    # Generate sources:
+    set(MARLIN_GEN_SCRIPT
+      ${CMAKE_CURRENT_SOURCE_DIR}/csrc/quantization/gptq_marlin/generate_kernels.py)
+    file(MD5 ${MARLIN_GEN_SCRIPT} MARLIN_GEN_SCRIPT_HASH)
+
+    message(STATUS "Marlin generation script hash: ${MARLIN_GEN_SCRIPT_HASH}")
+    message(STATUS "Last run Marlin generate script hash: $CACHE{MARLIN_GEN_SCRIPT_HASH}")
+
+    if (NOT DEFINED CACHE{MARLIN_GEN_SCRIPT_HASH}
+        OR NOT $CACHE{MARLIN_GEN_SCRIPT_HASH} STREQUAL ${MARLIN_GEN_SCRIPT_HASH})
+      execute_process(
+        COMMAND ${CMAKE_COMMAND} -E env
+        PYTHONPATH=$PYTHONPATH
+          ${Python_EXECUTABLE} ${MARLIN_GEN_SCRIPT}
+        RESULT_VARIABLE marlin_generation_result
+        OUTPUT_VARIABLE marlin_generation_result
+        OUTPUT_FILE ${CMAKE_CURRENT_BINARY_DIR}/marlin_generation.log
+        ERROR_FILE ${CMAKE_CURRENT_BINARY_DIR}/marlin_generation.log
+      )
+
+      if (NOT marlin_generation_result EQUAL 0)
+        message(FATAL_ERROR "Marlin generation failed."
+                            " Result: \"${marlin_generation_result}\""
+                            "\nCheck the log for details: "
+                            "${CMAKE_CURRENT_BINARY_DIR}/marlin_generation.log")
+      else()
+        set(MARLIN_GEN_SCRIPT_HASH ${MARLIN_GEN_SCRIPT_HASH}
+            CACHE STRING "Last run Marlin generate script hash" FORCE)
+        message(STATUS "Marlin generation completed successfully.")
+      endif()
+    else()
+      message(STATUS "Marlin generation script has not changed, skipping generation.")
+    endif()
+
+    file(GLOB MARLIN_TEMPLATE_KERNEL_SRC "csrc/quantization/gptq_marlin/kernel_*.cu")
+    set_gencode_flags_for_srcs(
+      SRCS "${MARLIN_TEMPLATE_KERNEL_SRC}"
+      CUDA_ARCHS "${MARLIN_ARCHS}")
+
+    list(APPEND VLLM_EXT_SRC ${MARLIN_TEMPLATE_KERNEL_SRC})
+
     set(MARLIN_SRCS
-       "csrc/quantization/fp8/fp8_marlin.cu"
        "csrc/quantization/marlin/dense/marlin_cuda_kernel.cu"
        "csrc/quantization/marlin/sparse/marlin_24_cuda_kernel.cu"
        "csrc/quantization/marlin/qqq/marlin_qqq_gemm_kernel.cu"
@@ -372,6 +418,7 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
     set(SRCS
       "csrc/quantization/cutlass_w8a8/scaled_mm_c3x_sm100.cu"
       "csrc/quantization/cutlass_w8a8/c3x/scaled_mm_sm100_fp8.cu"
+      "csrc/quantization/cutlass_w8a8/c3x/scaled_mm_blockwise_sm100_fp8.cu"
     )
     set_gencode_flags_for_srcs(
       SRCS "${SRCS}"
@@ -461,6 +508,52 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
     set(FP4_ARCHS)
   endif()
 
+  # CUTLASS MLA Archs and flags
+  cuda_archs_loose_intersection(MLA_ARCHS "10.0a" "${CUDA_ARCHS}")
+  if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER 12.8 AND MLA_ARCHS)
+    set(SRCS
+      "csrc/attention/mla/cutlass_mla_kernels.cu")
+    set_gencode_flags_for_srcs(
+      SRCS "${SRCS}"
+      CUDA_ARCHS "${MLA_ARCHS}")
+    list(APPEND VLLM_EXT_SRC "${SRCS}")
+    list(APPEND VLLM_GPU_FLAGS "-DENABLE_CUTLASS_MLA=1")
+    # Add MLA-specific include directories only to MLA source files
+    set_source_files_properties(${SRCS}
+      PROPERTIES INCLUDE_DIRECTORIES "${CUTLASS_DIR}/examples/77_blackwell_fmha;${CUTLASS_DIR}/examples/common")
+    message(STATUS "Building CUTLASS MLA for archs: ${MLA_ARCHS}")
+  else()
+    message(STATUS "Not building CUTLASS MLA as no compatible archs were found.")
+    # clear MLA_ARCHS
+    set(MLA_ARCHS)
+  endif()
+
+  # CUTLASS MoE kernels
+
+  # The MoE kernel cutlass_moe_mm requires CUDA 12.3 or later (and only works
+  # on Hopper). get_cutlass_moe_mm_data should only be compiled if it's possible
+  # to compile MoE kernels that use its output.
+  cuda_archs_loose_intersection(SCALED_MM_ARCHS "9.0a;" "${CUDA_ARCHS}")
+  if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.3 AND SCALED_MM_ARCHS)
+    set(SRCS "csrc/quantization/cutlass_w8a8/moe/grouped_mm_c3x.cu"
+             "csrc/quantization/cutlass_w8a8/moe/moe_data.cu")
+    set_gencode_flags_for_srcs(
+      SRCS "${SRCS}"
+      CUDA_ARCHS "${SCALED_MM_ARCHS}")
+    list(APPEND VLLM_EXT_SRC "${SRCS}")
+    list(APPEND VLLM_GPU_FLAGS "-DENABLE_CUTLASS_MOE_SM90=1")
+    message(STATUS "Building grouped_mm_c3x for archs: ${SCALED_MM_ARCHS}")
+  else()
+    if (NOT ${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.3 AND SCALED_MM_ARCHS)
+      message(STATUS "Not building grouped_mm_c3x kernels as CUDA Compiler version is "
+                     "not >= 12.3, we recommend upgrading to CUDA 12.3 or later "
+                     "if you intend on running FP8 quantized MoE models on Hopper.")
+    else()
+      message(STATUS "Not building grouped_mm_c3x as no compatible archs found "
+                     "in CUDA target architectures")
+    endif()
+  endif()
+
   #
   # Machete kernels
 
@@ -580,21 +673,51 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
   list(APPEND VLLM_MOE_EXT_SRC "${VLLM_MOE_WNA16_SRC}")
   cuda_archs_loose_intersection(MARLIN_MOE_ARCHS "8.0;8.6;8.7;8.9;9.0;10.0;10.1;12.0" "${CUDA_ARCHS}")
   if (MARLIN_MOE_ARCHS)
-    set(MARLIN_MOE_SRC
-        "csrc/moe/marlin_kernels/marlin_moe_kernel.h"
-        "csrc/moe/marlin_kernels/marlin_moe_kernel_ku4b8.h"
-        "csrc/moe/marlin_kernels/marlin_moe_kernel_ku4b8.cu"
-        "csrc/moe/marlin_kernels/marlin_moe_kernel_ku8b128.h"
-        "csrc/moe/marlin_kernels/marlin_moe_kernel_ku8b128.cu"
-        "csrc/moe/marlin_kernels/marlin_moe_kernel_ku4.h"
-        "csrc/moe/marlin_kernels/marlin_moe_kernel_ku4.cu"
-        "csrc/moe/marlin_moe_ops.cu")
 
+    #
+    # For the Marlin MOE kernels we automatically generate sources for various
+    # preselected input type pairs and schedules.
+    # Generate sources:
+    set(MOE_MARLIN_GEN_SCRIPT
+      ${CMAKE_CURRENT_SOURCE_DIR}/csrc/moe/marlin_moe_wna16/generate_kernels.py)
+    file(MD5 ${MOE_MARLIN_GEN_SCRIPT} MOE_MARLIN_GEN_SCRIPT_HASH)
+
+    message(STATUS "Marlin MOE generation script hash: ${MOE_MARLIN_GEN_SCRIPT_HASH}")
+    message(STATUS "Last run Marlin MOE generate script hash: $CACHE{MOE_MARLIN_GEN_SCRIPT_HASH}")
+
+    if (NOT DEFINED CACHE{MOE_MARLIN_GEN_SCRIPT_HASH}
+        OR NOT $CACHE{MOE_MARLIN_GEN_SCRIPT_HASH} STREQUAL ${MOE_MARLIN_GEN_SCRIPT_HASH})
+      execute_process(
+        COMMAND ${CMAKE_COMMAND} -E env
+        PYTHONPATH=$PYTHONPATH
+          ${Python_EXECUTABLE} ${MOE_MARLIN_GEN_SCRIPT}
+        RESULT_VARIABLE moe_marlin_generation_result
+        OUTPUT_VARIABLE moe_marlin_generation_output
+        OUTPUT_FILE ${CMAKE_CURRENT_BINARY_DIR}/moe_marlin_generation.log
+        ERROR_FILE ${CMAKE_CURRENT_BINARY_DIR}/moe_marlin_generation.log
+      )
+
+      if (NOT moe_marlin_generation_result EQUAL 0)
+        message(FATAL_ERROR "Marlin MOE generation failed."
+                            " Result: \"${moe_marlin_generation_result}\""
+                            "\nCheck the log for details: "
+                            "${CMAKE_CURRENT_BINARY_DIR}/moe_marlin_generation.log")
+      else()
+        set(MOE_MARLIN_GEN_SCRIPT_HASH ${MOE_MARLIN_GEN_SCRIPT_HASH}
+            CACHE STRING "Last run Marlin MOE generate script hash" FORCE)
+        message(STATUS "Marlin MOE generation completed successfully.")
+      endif()
+    else()
+      message(STATUS "Marlin MOE generation script has not changed, skipping generation.")
+    endif()
+
+    file(GLOB MOE_WNAA16_MARLIN_SRC "csrc/moe/marlin_moe_wna16/*.cu")
     set_gencode_flags_for_srcs(
-      SRCS "${MARLIN_MOE_SRC}"
+      SRCS "${MOE_WNAA16_MARLIN_SRC}"
       CUDA_ARCHS "${MARLIN_MOE_ARCHS}")
 
-    list(APPEND VLLM_MOE_EXT_SRC "${MARLIN_MOE_SRC}")
+    list(APPEND VLLM_MOE_EXT_SRC ${MOE_WNAA16_MARLIN_SRC})
+
     message(STATUS "Building Marlin MOE kernels for archs: ${MARLIN_MOE_ARCHS}")
   else()
     message(STATUS "Not building Marlin MOE kernels as no compatible archs found"
@@ -602,6 +725,17 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
   endif()
 endif()
 
+if(VLLM_GPU_LANG STREQUAL "CUDA")
+  set(MOE_PERMUTE_SRC
+      "csrc/moe/permute_unpermute_kernels/moe_permute_unpermute_kernel.cu"
+      "csrc/moe/moe_permute_unpermute_op.cu")
+
+  set_gencode_flags_for_srcs(
+    SRCS "${MARLIN_PERMUTE_SRC}"
+    CUDA_ARCHS "${MOE_PERMUTE_ARCHS}")
+
+  list(APPEND VLLM_MOE_EXT_SRC "${MOE_PERMUTE_SRC}")
+endif()
 message(STATUS "Enabling moe extension.")
 define_gpu_extension_target(
   _moe_C
@@ -610,6 +744,8 @@ define_gpu_extension_target(
   SOURCES ${VLLM_MOE_EXT_SRC}
   COMPILE_FLAGS ${VLLM_GPU_FLAGS}
   ARCHITECTURES ${VLLM_GPU_ARCHES}
+  INCLUDE_DIRECTORIES ${CUTLASS_INCLUDE_DIR}
+  INCLUDE_DIRECTORIES ${CUTLASS_TOOLS_UTIL_INCLUDE_DIR}
   USE_SABI 3
   WITH_SOABI)
 
@@ -619,6 +755,7 @@ if(VLLM_GPU_LANG STREQUAL "HIP")
   #
   set(VLLM_ROCM_EXT_SRC
     "csrc/rocm/torch_bindings.cpp"
+    "csrc/rocm/skinny_gemms.cu"
     "csrc/rocm/attention.cu")
 
   define_gpu_extension_target(

Dockerfile.cpu

@@ -1,69 +0,0 @@
-# This vLLM Dockerfile is used to construct image that can build and run vLLM on x86 CPU platform.
-
-FROM ubuntu:22.04 AS cpu-test-1
-
-ENV CCACHE_DIR=/root/.cache/ccache
-
-ENV CMAKE_CXX_COMPILER_LAUNCHER=ccache
-
-RUN --mount=type=cache,target=/var/cache/apt \
-    apt-get update -y \
-    && apt-get install -y curl ccache git wget vim numactl gcc-12 g++-12 python3 python3-pip libtcmalloc-minimal4 libnuma-dev \
-    && apt-get install -y ffmpeg libsm6 libxext6 libgl1 \
-    && update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-12 10 --slave /usr/bin/g++ g++ /usr/bin/g++-12
-
-# https://intel.github.io/intel-extension-for-pytorch/cpu/latest/tutorials/performance_tuning/tuning_guide.html
-# intel-openmp provides additional performance improvement vs. openmp
-# tcmalloc provides better memory allocation efficiency, e.g, holding memory in caches to speed up access of commonly-used objects.
-RUN --mount=type=cache,target=/root/.cache/pip \
-    pip install intel-openmp==2025.0.1
-
-ENV LD_PRELOAD="/usr/lib/x86_64-linux-gnu/libtcmalloc_minimal.so.4:/usr/local/lib/libiomp5.so"
-
-RUN echo 'ulimit -c 0' >> ~/.bashrc
-
-RUN pip install intel_extension_for_pytorch==2.6.0
-
-WORKDIR /workspace
-
-ARG PIP_EXTRA_INDEX_URL="https://download.pytorch.org/whl/cpu"
-ENV PIP_EXTRA_INDEX_URL=${PIP_EXTRA_INDEX_URL}
-RUN --mount=type=cache,target=/root/.cache/pip \
-    --mount=type=bind,src=requirements/build.txt,target=requirements/build.txt \
-    pip install --upgrade pip && \
-    pip install -r requirements/build.txt
-
-FROM cpu-test-1 AS build
-
-WORKDIR /workspace/vllm
-
-RUN --mount=type=cache,target=/root/.cache/pip \
-    --mount=type=bind,src=requirements/common.txt,target=requirements/common.txt \
-    --mount=type=bind,src=requirements/cpu.txt,target=requirements/cpu.txt \
-    pip install -v -r requirements/cpu.txt
-
-COPY . .
-ARG GIT_REPO_CHECK=0
-RUN --mount=type=bind,source=.git,target=.git \
-    if [ "$GIT_REPO_CHECK" != 0 ]; then bash tools/check_repo.sh ; fi
-
-# 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}
-
-RUN --mount=type=cache,target=/root/.cache/pip \
-    --mount=type=cache,target=/root/.cache/ccache \
-    --mount=type=bind,source=.git,target=.git \
-    VLLM_TARGET_DEVICE=cpu python3 setup.py bdist_wheel && \
-    pip install dist/*.whl && \
-    rm -rf dist
-
-WORKDIR /workspace/
-
-RUN ln -s /workspace/vllm/tests && ln -s /workspace/vllm/examples && ln -s /workspace/vllm/benchmarks
-
-# install development dependencies (for testing)
-RUN --mount=type=cache,target=/root/.cache/pip \
-    pip install -e tests/vllm_test_utils
-
-ENTRYPOINT ["python3", "-m", "vllm.entrypoints.openai.api_server"]

Dockerfile.openvino

@@ -1,29 +0,0 @@
-# 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 \
-        git python3-pip \
-        ffmpeg libsm6 libxext6 libgl1
-WORKDIR /workspace
-
-COPY . .
-ARG GIT_REPO_CHECK=0
-RUN --mount=type=bind,source=.git,target=.git \
-    if [ "$GIT_REPO_CHECK" != 0 ]; then bash tools/check_repo.sh ; fi
-
-RUN python3 -m pip install -U pip
-# install build requirements
-RUN PIP_EXTRA_INDEX_URL="https://download.pytorch.org/whl/cpu" python3 -m pip install -r /workspace/requirements/build.txt
-# build vLLM with OpenVINO backend
-RUN PIP_EXTRA_INDEX_URL="https://download.pytorch.org/whl/cpu" VLLM_TARGET_DEVICE="openvino" python3 -m pip install /workspace
-
-COPY examples/ /workspace/examples
-COPY benchmarks/ /workspace/benchmarks
-
-# install development dependencies (for testing)
-RUN python3 -m pip install -e tests/vllm_test_utils
-
-CMD ["/bin/bash"]

Dockerfile.ppc64le

@@ -1,37 +0,0 @@
-FROM mambaorg/micromamba
-ARG MAMBA_DOCKERFILE_ACTIVATE=1
-USER root
-
-ENV PATH="/usr/local/cargo/bin:$PATH:/opt/conda/bin/"
-
-RUN apt-get update -y && apt-get install -y git wget kmod curl vim libnuma-dev libsndfile-dev libprotobuf-dev build-essential ffmpeg libsm6 libxext6 libgl1 libssl-dev 
-
-# Some packages in requirements/cpu are installed here
-# IBM provides optimized packages for ppc64le processors in the open-ce project for mamba
-# Currently these may not be available for venv or pip directly
-RUN micromamba install -y -n base -c https://ftp.osuosl.org/pub/open-ce/1.11.0-p10/ -c defaults python=3.10 rust && micromamba clean --all --yes
-
-COPY ./ /workspace/vllm
-
-WORKDIR /workspace/vllm
-ARG GIT_REPO_CHECK=0
-RUN --mount=type=bind,source=.git,target=.git \
-    if [ "$GIT_REPO_CHECK" != 0 ]; then bash tools/check_repo.sh; fi
-
-RUN --mount=type=cache,target=/root/.cache/pip  \
-    RUSTFLAGS='-L /opt/conda/lib' pip install -v --prefer-binary --extra-index-url https://repo.fury.io/mgiessing \
-        'cmake>=3.26' ninja packaging 'setuptools-scm>=8' wheel jinja2 \
-        -r requirements/cpu.txt \
-        xformers uvloop==0.20.0
-
-RUN --mount=type=bind,source=.git,target=.git \
-    VLLM_TARGET_DEVICE=cpu python3 setup.py install
-
-# install development dependencies (for testing)
-RUN python3 -m pip install -e tests/vllm_test_utils
-
-WORKDIR /workspace/
-
-RUN ln -s /workspace/vllm/tests && ln -s /workspace/vllm/examples && ln -s /workspace/vllm/benchmarks
-
-ENTRYPOINT ["/opt/conda/bin/python3", "-m", "vllm.entrypoints.openai.api_server"]

README.md

@@ -10,15 +10,24 @@ 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://x.com/vllm_project"><b>Twitter/X</b></a> | <a href="https://slack.vllm.ai"><b>Developer Slack</b></a> |
+| <a href="https://docs.vllm.ai"><b>Documentation</b></a> | <a href="https://blog.vllm.ai/"><b>Blog</b></a> | <a href="https://arxiv.org/abs/2309.06180"><b>Paper</b></a> | <a href="https://x.com/vllm_project"><b>Twitter/X</b></a> | <a href="https://discuss.vllm.ai"><b>User Forum</b></a> | <a href="https://slack.vllm.ai"><b>Developer Slack</b></a> |
 </p>
 
-*Latest News* 🔥
+---
 
-- [2025/03] We hosted [the first vLLM China Meetup](https://mp.weixin.qq.com/s/n77GibL2corAtQHtVEAzfg)! Please find the meetup slides from vLLM team [here](https://docs.google.com/presentation/d/1REHvfQMKGnvz6p3Fd23HhSO4c8j5WPGZV0bKYLwnHyQ/edit#slide=id.g33fb1ff286e_0_29).
+*Latest News* 🔥
+- [2025/05] We hosted [NYC vLLM Meetup](https://lu.ma/c1rqyf1f)! Please find the meetup slides [here](https://docs.google.com/presentation/d/1_q_aW_ioMJWUImf1s1YM-ZhjXz8cUeL0IJvaquOYBeA/edit?usp=sharing).
+- [2025/05] vLLM is now a hosted project under PyTorch Foundation! Please find the announcement [here](https://pytorch.org/blog/pytorch-foundation-welcomes-vllm/).
+- [2025/04] We hosted [Asia Developer Day](https://www.sginnovate.com/event/limited-availability-morning-evening-slots-remaining-inaugural-vllm-asia-developer-day)! Please find the meetup slides from the vLLM team [here](https://docs.google.com/presentation/d/19cp6Qu8u48ihB91A064XfaXruNYiBOUKrBxAmDOllOo/edit?usp=sharing).
+- [2025/01] We are excited to announce the alpha release of vLLM V1: A major architectural upgrade with 1.7x speedup! Clean code, optimized execution loop, zero-overhead prefix caching, enhanced multimodal support, and more. Please check out our blog post [here](https://blog.vllm.ai/2025/01/27/v1-alpha-release.html).
+
+<details>
+<summary>Previous News</summary>
+
+- [2025/03] We hosted [vLLM x Ollama Inference Night](https://lu.ma/vllm-ollama)! Please find the meetup slides from the vLLM team [here](https://docs.google.com/presentation/d/16T2PDD1YwRnZ4Tu8Q5r6n53c5Lr5c73UV9Vd2_eBo4U/edit?usp=sharing).
+- [2025/03] We hosted [the first vLLM China Meetup](https://mp.weixin.qq.com/s/n77GibL2corAtQHtVEAzfg)! Please find the meetup slides from vLLM team [here](https://docs.google.com/presentation/d/1REHvfQMKGnvz6p3Fd23HhSO4c8j5WPGZV0bKYLwnHyQ/edit?usp=sharing).
 - [2025/03] We hosted [the East Coast vLLM Meetup](https://lu.ma/7mu4k4xx)! Please find the meetup slides [here](https://docs.google.com/presentation/d/1NHiv8EUFF1NLd3fEYODm56nDmL26lEeXCaDgyDlTsRs/edit#slide=id.g31441846c39_0_0).
 - [2025/02] We hosted [the ninth vLLM meetup](https://lu.ma/h7g3kuj9) with Meta! Please find the meetup slides from vLLM team [here](https://docs.google.com/presentation/d/1jzC_PZVXrVNSFVCW-V4cFXb6pn7zZ2CyP_Flwo05aqg/edit?usp=sharing) and AMD [here](https://drive.google.com/file/d/1Zk5qEJIkTmlQ2eQcXQZlljAx3m9s7nwn/view?usp=sharing). The slides from Meta will not be posted.
-- [2025/01] We are excited to announce the alpha release of vLLM V1: A major architectural upgrade with 1.7x speedup! Clean code, optimized execution loop, zero-overhead prefix caching, enhanced multimodal support, and more. Please check out our blog post [here](https://blog.vllm.ai/2025/01/27/v1-alpha-release.html).
 - [2025/01] We hosted [the eighth vLLM meetup](https://lu.ma/zep56hui) with Google Cloud! Please find the meetup slides from vLLM team [here](https://docs.google.com/presentation/d/1epVkt4Zu8Jz_S5OhEHPc798emsYh2BwYfRuDDVEF7u4/edit?usp=sharing), and Google Cloud team [here](https://drive.google.com/file/d/1h24pHewANyRL11xy5dXUbvRC9F9Kkjix/view?usp=sharing).
 - [2024/12] vLLM joins [pytorch ecosystem](https://pytorch.org/blog/vllm-joins-pytorch)! Easy, Fast, and Cheap LLM Serving for Everyone!
 - [2024/11] We hosted [the seventh vLLM meetup](https://lu.ma/h0qvrajz) with Snowflake! Please find the meetup slides from vLLM team [here](https://docs.google.com/presentation/d/1e3CxQBV3JsfGp30SwyvS3eM_tW-ghOhJ9PAJGK6KR54/edit?usp=sharing), and Snowflake team [here](https://docs.google.com/presentation/d/1qF3RkDAbOULwz9WK5TOltt2fE9t6uIc_hVNLFAaQX6A/edit?usp=sharing).
@@ -34,8 +43,9 @@ Easy, fast, and cheap LLM serving for everyone
 - [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.
 - [2023/06] 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).
 
----
+</details>
 
+---
 ## About
 
 vLLM is a fast and easy-to-use library for LLM inference and serving.
@@ -90,7 +100,7 @@ Visit our [documentation](https://docs.vllm.ai/en/latest/) to learn more.
 ## Contributing
 
 We welcome and value any contributions and collaborations.
-Please check out [CONTRIBUTING.md](./CONTRIBUTING.md) for how to get involved.
+Please check out [Contributing to vLLM](https://docs.vllm.ai/en/stable/contributing/overview.html) for how to get involved.
 
 ## Sponsors
 
@@ -113,6 +123,7 @@ Compute Resources:
 - Databricks
 - DeepInfra
 - Google Cloud
+- Intel
 - Lambda Lab
 - Nebius
 - Novita AI
@@ -143,10 +154,11 @@ If you use vLLM for your research, please cite our [paper](https://arxiv.org/abs
 
 ## Contact Us
 
-- For technical questions and feature requests, please use GitHub issues or discussions.
-- For discussing with fellow users and coordinating contributions and development, please use Slack.
-- For security disclosures, please use GitHub's security advisory feature.
-- For collaborations and partnerships, please contact us at vllm-questions AT lists.berkeley.edu.
+- For technical questions and feature requests, please use GitHub [Issues](https://github.com/vllm-project/vllm/issues) or [Discussions](https://github.com/vllm-project/vllm/discussions)
+- For discussing with fellow users, please use the [vLLM Forum](https://discuss.vllm.ai)
+- coordinating contributions and development, please use [Slack](https://slack.vllm.ai)
+- For security disclosures, please use GitHub's [Security Advisories](https://github.com/vllm-project/vllm/security/advisories) feature
+- For collaborations and partnerships, please contact us at [vllm-questions@lists.berkeley.edu](mailto:vllm-questions@lists.berkeley.edu)
 
 ## Media Kit
 

benchmarks/README.md

@@ -41,29 +41,39 @@ become available.
       <td><code>synthetic</code></td>
     </tr>
     <tr>
-      <td><strong>HuggingFace</strong></td>
+      <td><strong>HuggingFace-VisionArena</strong></td>
       <td style="text-align: center;">✅</td>
-      <td style="text-align: center;">🟡</td>
-      <td>Specify your dataset path on HuggingFace</td>
+      <td style="text-align: center;">✅</td>
+      <td><code>lmarena-ai/VisionArena-Chat</code></td>
     </tr>
     <tr>
-      <td><strong>VisionArena</strong></td>
+      <td><strong>HuggingFace-InstructCoder</strong></td>
       <td style="text-align: center;">✅</td>
       <td style="text-align: center;">✅</td>
-      <td><code>lmarena-ai/vision-arena-bench-v0.1</code> (a HuggingFace dataset)</td>
+      <td><code>likaixin/InstructCoder</code></td>
+    </tr>
+      <tr>
+      <td><strong>HuggingFace-AIMO</strong></td>
+      <td style="text-align: center;">✅</td>
+      <td style="text-align: center;">✅</td>
+      <td><code>AI-MO/aimo-validation-aime</code> , <code>AI-MO/NuminaMath-1.5</code>, <code>AI-MO/NuminaMath-CoT</code></td>
+    </tr>
+    <tr>
+      <td><strong>HuggingFace-Other</strong></td>
+      <td style="text-align: center;">✅</td>
+      <td style="text-align: center;">✅</td>
+      <td><code>lmms-lab/LLaVA-OneVision-Data</code>, <code>Aeala/ShareGPT_Vicuna_unfiltered</code></td>
     </tr>
   </tbody>
 </table>
 
 ✅: supported
 
+🟡: Partial support
+
 🚧: to be supported
 
-🟡: Partial support. Currently, HuggingFaceDataset only supports dataset formats
-similar to `lmms-lab/LLaVA-OneVision-Data`. If you need support for other dataset
-formats, please consider contributing.
-
-**Note**: VisionArena’s `dataset-name` should be set to `hf`
+**Note**: HuggingFace dataset's `dataset-name` should be set to `hf`
 
 ---
 ## Example - Online Benchmark
@@ -71,8 +81,7 @@ formats, please consider contributing.
 First start serving your model
 
 ```bash
-MODEL_NAME="NousResearch/Hermes-3-Llama-3.1-8B"
-vllm serve ${MODEL_NAME} --disable-log-requests
+vllm serve NousResearch/Hermes-3-Llama-3.1-8B --disable-log-requests
 ```
 
 Then run the benchmarking script
@@ -80,12 +89,13 @@ Then run the benchmarking script
 ```bash
 # download dataset
 # wget https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/ShareGPT_V3_unfiltered_cleaned_split.json
-MODEL_NAME="NousResearch/Hermes-3-Llama-3.1-8B"
-NUM_PROMPTS=10
-BACKEND="vllm"
-DATASET_NAME="sharegpt"
-DATASET_PATH="<your data path>/ShareGPT_V3_unfiltered_cleaned_split.json"
-python3 vllm/benchmarks/benchmark_serving.py --backend ${BACKEND} --model ${MODEL_NAME} --endpoint /v1/completions --dataset-name ${DATASET_NAME} --dataset-path ${DATASET_PATH} --num-prompts ${NUM_PROMPTS}
+python3 vllm/benchmarks/benchmark_serving.py \
+  --backend vllm \
+  --model NousResearch/Hermes-3-Llama-3.1-8B \
+  --endpoint /v1/completions \
+  --dataset-name sharegpt \
+  --dataset-path <your data path>/ShareGPT_V3_unfiltered_cleaned_split.json \
+  --num-prompts 10
 ```
 
 If successful, you will see the following output
@@ -122,37 +132,105 @@ vllm serve Qwen/Qwen2-VL-7B-Instruct --disable-log-requests
 ```
 
 ```bash
-MODEL_NAME="Qwen/Qwen2-VL-7B-Instruct"
-NUM_PROMPTS=10
-BACKEND="openai-chat"
-DATASET_NAME="hf"
-DATASET_PATH="lmarena-ai/vision-arena-bench-v0.1"
-DATASET_SPLIT='train'
-
 python3 vllm/benchmarks/benchmark_serving.py \
-  --backend "${BACKEND}" \
-  --model "${MODEL_NAME}" \
-  --endpoint "/v1/chat/completions" \
-  --dataset-name "${DATASET_NAME}" \
-  --dataset-path "${DATASET_PATH}" \
-  --hf-split "${DATASET_SPLIT}" \
-  --num-prompts "${NUM_PROMPTS}"
+  --backend openai-chat \
+  --model Qwen/Qwen2-VL-7B-Instruct \
+  --endpoint /v1/chat/completions \
+  --dataset-name hf \
+  --dataset-path lmarena-ai/VisionArena-Chat \
+  --hf-split train \
+  --num-prompts 1000
+```
+
+### InstructCoder Benchmark with Speculative Decoding
+
+``` bash
+VLLM_USE_V1=1 vllm serve meta-llama/Meta-Llama-3-8B-Instruct \
+    --speculative-model "[ngram]" \
+    --ngram_prompt_lookup_min 2 \
+    --ngram-prompt-lookup-max 5 \
+    --num_speculative_tokens 5
+```
+
+``` bash
+python3 benchmarks/benchmark_serving.py \
+    --model meta-llama/Meta-Llama-3-8B-Instruct \
+    --dataset-name hf \
+    --dataset-path likaixin/InstructCoder \
+    --num-prompts 2048
+```
+
+### Other HuggingFaceDataset Examples
+
+```bash
+vllm serve Qwen/Qwen2-VL-7B-Instruct --disable-log-requests
+```
+
+**`lmms-lab/LLaVA-OneVision-Data`**
+
+```bash
+python3 vllm/benchmarks/benchmark_serving.py \
+  --backend openai-chat \
+  --model Qwen/Qwen2-VL-7B-Instruct \
+  --endpoint /v1/chat/completions \
+  --dataset-name hf \
+  --dataset-path lmms-lab/LLaVA-OneVision-Data \
+  --hf-split train \
+  --hf-subset "chart2text(cauldron)" \
+  --num-prompts 10
+```
+
+**`Aeala/ShareGPT_Vicuna_unfiltered`**
+
+```bash
+python3 vllm/benchmarks/benchmark_serving.py \
+  --backend openai-chat \
+  --model Qwen/Qwen2-VL-7B-Instruct \
+  --endpoint /v1/chat/completions \
+  --dataset-name hf \
+  --dataset-path Aeala/ShareGPT_Vicuna_unfiltered \
+  --hf-split train \
+  --num-prompts 10
+```
+
+**`AI-MO/aimo-validation-aime`**
+
+``` bash
+python3 vllm/benchmarks/benchmark_serving.py \
+    --model Qwen/QwQ-32B \
+    --dataset-name hf \
+    --dataset-path AI-MO/aimo-validation-aime \
+    --num-prompts 10 \
+    --seed 42
+```
+
+### Running With Sampling Parameters
+
+When using OpenAI-compatible backends such as `vllm`, optional sampling
+parameters can be specified. Example client command:
+
+```bash
+python3 vllm/benchmarks/benchmark_serving.py \
+  --backend vllm \
+  --model NousResearch/Hermes-3-Llama-3.1-8B \
+  --endpoint /v1/completions \
+  --dataset-name sharegpt \
+  --dataset-path <your data path>/ShareGPT_V3_unfiltered_cleaned_split.json \
+  --top-k 10 \
+  --top-p 0.9 \
+  --temperature 0.5 \
+  --num-prompts 10
 ```
 
 ---
 ## Example - Offline Throughput Benchmark
 
 ```bash
-MODEL_NAME="NousResearch/Hermes-3-Llama-3.1-8B"
-NUM_PROMPTS=10
-DATASET_NAME="sonnet"
-DATASET_PATH="vllm/benchmarks/sonnet.txt"
-
 python3 vllm/benchmarks/benchmark_throughput.py \
-  --model "${MODEL_NAME}" \
-  --dataset-name "${DATASET_NAME}" \
-  --dataset-path "${DATASET_PATH}" \
-  --num-prompts "${NUM_PROMPTS}"
+  --model NousResearch/Hermes-3-Llama-3.1-8B \
+  --dataset-name sonnet \
+  --dataset-path vllm/benchmarks/sonnet.txt \
+  --num-prompts 10
 ```
 
 If successful, you will see the following output
@@ -166,19 +244,13 @@ Total num output tokens:  1500
 ### VisionArena Benchmark for Vision Language Models
 
 ``` bash
-MODEL_NAME="Qwen/Qwen2-VL-7B-Instruct"
-NUM_PROMPTS=10
-DATASET_NAME="hf"
-DATASET_PATH="lmarena-ai/vision-arena-bench-v0.1"
-DATASET_SPLIT="train"
-
 python3 vllm/benchmarks/benchmark_throughput.py \
-  --model "${MODEL_NAME}" \
-  --backend "vllm-chat" \
-  --dataset-name "${DATASET_NAME}" \
-  --dataset-path "${DATASET_PATH}" \
-  --num-prompts "${NUM_PROMPTS}" \
-  --hf-split "${DATASET_SPLIT}"
+  --model Qwen/Qwen2-VL-7B-Instruct \
+  --backend vllm-chat \
+  --dataset-name hf \
+  --dataset-path lmarena-ai/VisionArena-Chat \
+  --num-prompts 1000 \
+  --hf-split train
 ```
 
 The `num prompt tokens` now includes image token counts
@@ -189,29 +261,83 @@ Total num prompt tokens:  14527
 Total num output tokens:  1280
 ```
 
+### InstructCoder Benchmark with Speculative Decoding
+
+``` bash
+VLLM_WORKER_MULTIPROC_METHOD=spawn \
+VLLM_USE_V1=1 \
+python3 vllm/benchmarks/benchmark_throughput.py \
+    --dataset-name=hf \
+    --dataset-path=likaixin/InstructCoder \
+    --model=meta-llama/Meta-Llama-3-8B-Instruct \
+    --input-len=1000 \
+    --output-len=100 \
+    --num-prompts=2048 \
+    --async-engine \
+    --speculative-model="[ngram]" \
+    --ngram_prompt_lookup_min=2 \
+    --ngram-prompt-lookup-max=5 \
+    --num_speculative_tokens=5
+```
+
+```
+Throughput: 104.77 requests/s, 23836.22 total tokens/s, 10477.10 output tokens/s
+Total num prompt tokens:  261136
+Total num output tokens:  204800
+```
+
+### Other HuggingFaceDataset Examples
+
+**`lmms-lab/LLaVA-OneVision-Data`**
+
+```bash
+python3 vllm/benchmarks/benchmark_throughput.py \
+  --model Qwen/Qwen2-VL-7B-Instruct \
+  --backend vllm-chat \
+  --dataset-name hf \
+  --dataset-path lmms-lab/LLaVA-OneVision-Data \
+  --hf-split train \
+  --hf-subset "chart2text(cauldron)" \
+  --num-prompts 10
+```
+
+**`Aeala/ShareGPT_Vicuna_unfiltered`**
+
+```bash
+python3 vllm/benchmarks/benchmark_throughput.py \
+  --model Qwen/Qwen2-VL-7B-Instruct \
+  --backend vllm-chat \
+  --dataset-name hf \
+  --dataset-path Aeala/ShareGPT_Vicuna_unfiltered \
+  --hf-split train \
+  --num-prompts 10
+```
+
+**`AI-MO/aimo-validation-aime`**
+
+```bash
+python3 benchmarks/benchmark_throughput.py \
+  --model Qwen/QwQ-32B \
+  --backend vllm \
+  --dataset-name hf \
+  --dataset-path AI-MO/aimo-validation-aime \
+  --hf-split train \
+  --num-prompts 10
+```
+
 ### Benchmark with LoRA Adapters
 
 ``` bash
 # download dataset
 # wget https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/ShareGPT_V3_unfiltered_cleaned_split.json
-MODEL_NAME="meta-llama/Llama-2-7b-hf"
-BACKEND="vllm"
-DATASET_NAME="sharegpt"
-DATASET_PATH="<your data path>/ShareGPT_V3_unfiltered_cleaned_split.json"
-NUM_PROMPTS=10
-MAX_LORAS=2
-MAX_LORA_RANK=8
-ENABLE_LORA="--enable-lora"
-LORA_PATH="yard1/llama-2-7b-sql-lora-test"
-
 python3 vllm/benchmarks/benchmark_throughput.py \
-  --model "${MODEL_NAME}" \
-  --backend "${BACKEND}" \
-  --dataset_path "${DATASET_PATH}" \
-  --dataset_name "${DATASET_NAME}" \
-  --num-prompts "${NUM_PROMPTS}" \
-  --max-loras "${MAX_LORAS}" \
-  --max-lora-rank "${MAX_LORA_RANK}" \
-  ${ENABLE_LORA} \
-  --lora-path "${LORA_PATH}"
+  --model meta-llama/Llama-2-7b-hf \
+  --backend vllm \
+  --dataset_path <your data path>/ShareGPT_V3_unfiltered_cleaned_split.json \
+  --dataset_name sharegpt \
+  --num-prompts 10 \
+  --max-loras 2 \
+  --max-lora-rank 8 \
+  --enable-lora \
+  --lora-path yard1/llama-2-7b-sql-lora-test
   ```

benchmarks/auto_tune.sh

@@ -0,0 +1,212 @@
+#!/bin/bash
+
+# This script aims to tune the best server parameter combinations to maximize throughput for given requirement. 
+# The current server parameter combination is  max_num_seqs and max_num_batched_tokens
+# It also supports additional requirement: e2e latency and prefix cache. 
+
+# Pre-requisite:
+# 1. Checkout to your branch, install/ update the correct running env. For TPU, activate conda env and install the corresponding torch, xla version. 
+# 2. If the model is customized, replace the MODEL's config with the customized config.
+# 3. Set variables (ALL REQUIRED)
+#   BASE: your directory for vllm repo
+#   MODEL: the model served by vllm
+#   DOWNLOAD_DIR: directory to download and load model weights.
+#   INPUT_LEN: request input len
+#   OUTPUT_LEN: request output len
+#   MIN_CACHE_HIT_PCT: prefix cache rate
+#   MAX_LATENCY_ALLOWED_MS: (e2e) latency requirement. If there's no latency requirement, set it to a large number like 1000000000
+# 4. Run the script, it might take a long time, you can use tmux to avoid the script stop if disconnection happens.
+# 5. The final result will be saved in RESULT file. 
+
+
+# Example use cases 
+# 1. Given input_len=1800, output_len=20, what's the best max_num_seqs and max_num_batched_tokens to get highest throughput?
+# Use INPUT_LEN=1800,  OUTPUT_LEN=20, MIN_CACHE_HIT_PCT=0, MAX_LATENCY_ALLOWED_MS=100000000000
+# 2. If we have latency requirement to be lower than 500ms, what's the best server parameter?
+# Use INPUT_LEN=1800,  OUTPUT_LEN=20, MIN_CACHE_HIT_PCT=0, MAX_LATENCY_ALLOWED_MS=500
+# 3. If we want to reach 60% prefix cache, what's the best server parameter? 
+# Use INPUT_LEN=1800,  OUTPUT_LEN=20, MIN_CACHE_HIT_PCT=60, MAX_LATENCY_ALLOWED_MS=500
+
+TAG=$(date +"%Y_%m_%d_%H_%M")
+BASE=""
+MODEL="meta-llama/Llama-3.1-8B-Instruct"
+DOWNLOAD_DIR=""
+INPUT_LEN=4000
+OUTPUT_LEN=16
+MIN_CACHE_HIT_PCT_PCT=0
+MAX_LATENCY_ALLOWED_MS=100000000000
+
+LOG_FOLDER="$BASE/auto-benchmark/$TAG"
+RESULT="$LOG_FOLDER/result.txt"
+
+echo "result file$ $RESULT"
+echo "model: $MODEL"
+echo
+
+rm -rf $LOG_FOLDER
+mkdir -p $LOG_FOLDER
+
+cd "$BASE/vllm"
+# create sonnet-4x.txt so that we can sample 2048 tokens for input
+echo "" > benchmarks/sonnet_4x.txt
+for _ in {1..4}
+do
+cat benchmarks/sonnet.txt >> benchmarks/sonnet_4x.txt
+done
+
+pip install datasets
+
+current_hash=$(git rev-parse HEAD)
+echo "hash:$current_hash" >> "$RESULT"
+echo "current_hash: $current_hash"
+
+best_throughput=0
+best_max_num_seqs=0
+best_num_batched_tokens=0
+best_goodput=0
+run_benchmark() {
+    local max_num_seqs=$1
+    local max_num_batched_tokens=$2
+    echo "max_num_seq: $max_num_seqs, max_num_batched_tokens: $max_num_batched_tokens"
+    local vllm_log="$LOG_FOLDER/vllm_log_${max_num_seqs}_${max_num_batched_tokens}.txt"
+    echo "vllm_log: $vllm_log"
+    echo
+    rm -f $vllm_log
+
+    # start the server
+    VLLM_USE_V1=1 VLLM_SERVER_DEV_MODE=1 vllm serve $MODEL \
+        --disable-log-requests \
+        --port 8004 \
+        --gpu-memory-utilization 0.98 \
+        --max-num-seqs $max_num_seqs \
+        --max-num-batched-tokens $max_num_batched_tokens \
+        --tensor-parallel-size 1 \
+        --enable-prefix-caching \
+        --load-format dummy \
+        --download-dir $DOWNLOAD_DIR \
+        --max-model-len $(( INPUT_LEN+OUTPUT_LEN )) > "$vllm_log" 2>&1 &
+    echo "wait for 10 minutes.."
+    echo
+    # wait for 10 minutes...
+    server_started=0
+    for i in {1..60}; do        
+        if grep -Fq "Application startup complete" "$vllm_log"; then
+            echo "Application started"
+            server_started=1
+            break
+        else
+            # echo "wait for 10 seconds..."
+            sleep 10
+        fi
+    done
+ 
+    if (( ! server_started )); then
+        echo "server did not start within 10 minutes, terminate the benchmarking. Please check server log at $vllm_log"
+        echo "pkill -f vllm"
+        echo
+        pkill vllm
+        sleep 10
+        return 1
+    fi
+    
+    echo "run benchmark test..."
+    echo
+    meet_latency_requirement=0
+    # get a basic qps by using request-rate inf
+    bm_log="$LOG_FOLDER/bm_log_${max_num_seqs}_${max_num_batched_tokens}_requestrate_inf.txt"
+    prefix_len=$(( INPUT_LEN * MIN_CACHE_HIT_PCT / 100 ))
+    python benchmarks/benchmark_serving.py \
+        --backend vllm \
+        --model $MODEL  \
+        --dataset-name sonnet \
+        --dataset-path benchmarks/sonnet_4x.txt \
+        --sonnet-input-len $INPUT_LEN \
+        --sonnet-output-len $OUTPUT_LEN \
+        --ignore-eos \
+        --disable-tqdm \
+        --request-rate inf \
+        --percentile-metrics ttft,tpot,itl,e2el \
+        --goodput e2el:$MAX_LATENCY_ALLOWED_MS \
+        --num-prompts 100 \
+        --sonnet-prefix-len $prefix_len \
+        --port 8004 > "$bm_log"
+    through_put=$(grep "Request throughput (req/s):" "$bm_log" | sed 's/[^0-9.]//g')
+    e2el=$(grep "P99 E2EL (ms):" "$bm_log" | awk '{print $NF}')
+    goodput=$(grep "Request goodput (req/s):" "$bm_log" | sed 's/[^0-9.]//g')
+
+    if (( $(echo "$e2el <= $MAX_LATENCY_ALLOWED_MS" | bc -l) )); then
+        meet_latency_requirement=1
+    fi
+
+    if (( ! meet_latency_requirement )); then
+    # start from request-rate as int(through_put) + 1
+        request_rate=$((${through_put%.*} + 1))
+        while ((request_rate > 0)); do
+            # clear prefix cache
+            curl -X POST http://0.0.0.0:8004/reset_prefix_cache
+            sleep 5
+            bm_log="$LOG_FOLDER/bm_log_${max_num_seqs}_${max_num_batched_tokens}_requestrate_${request_rate}.txt"
+            python benchmarks/benchmark_serving.py \
+                --backend vllm \
+                --model $MODEL  \
+                --dataset-name sonnet \
+                --dataset-path benchmarks/sonnet_4x.txt \
+                --sonnet-input-len $INPUT_LEN \
+                --sonnet-output-len $OUTPUT_LEN \
+                --ignore_eos \
+                --disable-tqdm \
+                --request-rate $request_rate \
+                --percentile-metrics ttft,tpot,itl,e2el \
+                --goodput e2el:$MAX_LATENCY_ALLOWED_MS \
+                --num-prompts 100 \
+                --sonnet-prefix-len $prefix_len \
+                --port 8004 > "$bm_log"
+            through_put=$(grep "Request throughput (req/s):" "$bm_log" | sed 's/[^0-9.]//g')
+            e2el=$(grep "P99 E2EL (ms):" "$bm_log" | awk '{print $NF}')
+            goodput=$(grep "Request goodput (req/s):" "$bm_log" | sed 's/[^0-9.]//g')
+            if (( $(echo "$e2el <= $MAX_LATENCY_ALLOWED_MS" | bc -l) )); then
+                meet_latency_requirement=1
+                break
+            fi
+            request_rate=$((request_rate-1))
+        done
+    fi
+    # write the results and update the best result.
+    if ((meet_latency_requirement)); then
+        echo "max_num_seqs: $max_num_seqs, max_num_batched_tokens: $max_num_batched_tokens, request_rate: $request_rate, e2el: $e2el, through put: $through_put, goodput: $goodput"
+        echo "max_num_seqs: $max_num_seqs, max_num_batched_tokens: $max_num_batched_tokens, request_rate: $request_rate, e2el: $e2el, through put: $through_put, goodput: $goodput" >> "$RESULT"
+        if (( $(echo "$through_put > $best_throughput" | bc -l) )); then
+            best_throughput=$through_put
+            best_max_num_seqs=$max_num_seqs
+            best_num_batched_tokens=$max_num_batched_tokens
+            best_goodput=$goodput
+        fi
+    else
+        echo "max_num_seqs: $max_num_seqs, max_num_batched_tokens: $max_num_batched_tokens does not meet latency requirement ${MAX_LATENCY_ALLOWED_MS}"
+        echo "max_num_seqs: $max_num_seqs, max_num_batched_tokens: $max_num_batched_tokens does not meet latency requirement ${MAX_LATENCY_ALLOWED_MS}" >> "$RESULT"
+    fi
+
+    echo "best_max_num_seqs: $best_max_num_seqs, best_num_batched_tokens: $best_num_batched_tokens, best_throughput: $best_throughput"
+
+    echo "pkill -f vllm"
+    echo
+    pkill vllm
+    sleep 10
+    rm -f $vllm_log
+    printf '=%.0s' $(seq 1 20)
+    return 0
+}
+
+
+num_seqs_list="128 256"
+num_batched_tokens_list="512 1024 2048 4096"
+for num_seqs in $num_seqs_list; do
+    for num_batched_tokens in $num_batched_tokens_list; do
+        run_benchmark $num_seqs $num_batched_tokens
+        exit 0
+    done
+done
+echo "finish permutations"
+echo "best_max_num_seqs: $best_max_num_seqs, best_num_batched_tokens: $best_num_batched_tokens, best_throughput: $best_throughput"
+echo "best_max_num_seqs: $best_max_num_seqs, best_num_batched_tokens: $best_num_batched_tokens, best_throughput: $best_throughput" >> "$RESULT"
+

benchmarks/backend_request_func.py

@@ -1,5 +1,6 @@
 # SPDX-License-Identifier: Apache-2.0
 
+import io
 import json
 import os
 import sys
@@ -32,6 +33,7 @@ class RequestFuncInput:
     extra_body: Optional[dict] = None
     multi_modal_content: Optional[dict] = None
     ignore_eos: bool = False
+    language: Optional[str] = None
 
 
 @dataclass
@@ -63,7 +65,7 @@ async def async_request_tgi(
             "temperature": 0.01,  # TGI does not accept 0.0 temperature.
             "top_p": 0.99,  # TGI does not accept 1.0 top_p.
             "truncate": request_func_input.prompt_len,
-            # TGI does not accept ignore_eos flag.
+            "ignore_eos_token": request_func_input.ignore_eos,
         }
         payload = {
             "inputs": request_func_input.prompt,
@@ -71,6 +73,10 @@ async def async_request_tgi(
         }
         output = RequestFuncOutput()
         output.prompt_len = request_func_input.prompt_len
+        if request_func_input.ignore_eos:
+            output.output_tokens = request_func_input.output_len
+        else:
+            output.output_tokens = None
 
         ttft = 0.0
         st = time.perf_counter()
@@ -195,6 +201,7 @@ async def async_request_deepspeed_mii(
                                      timeout=AIOHTTP_TIMEOUT) as session:
 
         payload = {
+            "model": request_func_input.model,
             "prompt": request_func_input.prompt,
             "max_tokens": request_func_input.output_len,
             "temperature": 0.01,  # deepspeed-mii does not accept 0.0 temp.
@@ -215,7 +222,15 @@ async def async_request_deepspeed_mii(
                 if response.status == 200:
                     parsed_resp = await response.json()
                     output.latency = time.perf_counter() - st
-                    output.generated_text = parsed_resp["text"][0]
+                    if "choices" in parsed_resp:
+                        output.generated_text = parsed_resp["choices"][0][
+                            "text"]
+                    elif "text" in parsed_resp:
+                        output.generated_text = parsed_resp["text"][0]
+                    else:
+                        output.error = ("Unexpected response format: "
+                                        "neither 'choices' nor 'text' found")
+                        output.success = False
                     output.success = True
                 else:
                     output.error = response.reason or ""
@@ -246,6 +261,7 @@ async def async_request_openai_completions(
                 if request_func_input.model_name else request_func_input.model,
             "prompt": request_func_input.prompt,
             "temperature": 0.0,
+            "repetition_penalty": 1.0,
             "max_tokens": request_func_input.output_len,
             "logprobs": request_func_input.logprobs,
             "stream": True,
@@ -424,6 +440,110 @@ async def async_request_openai_chat_completions(
     return output
 
 
+async def async_request_openai_audio(
+    request_func_input: RequestFuncInput,
+    pbar: Optional[tqdm] = None,
+) -> RequestFuncOutput:
+    # Lazy import without PlaceholderModule to avoid vllm dep.
+    import soundfile
+    api_url = request_func_input.api_url
+    assert api_url.endswith(
+        ("transcriptions", "translations"
+         )), "OpenAI Chat Completions API URL must end with 'transcriptions' "
+    "or `translations`."
+
+    async with aiohttp.ClientSession(trust_env=True,
+                                     timeout=AIOHTTP_TIMEOUT) as session:
+        content = [{"type": "text", "text": request_func_input.prompt}]
+        payload = {
+            "model": request_func_input.model_name \
+                if request_func_input.model_name else request_func_input.model,
+            "temperature": 0.0,
+            "max_completion_tokens": request_func_input.output_len,
+            "stream": True,
+            "language": "en",
+            # Flattened due to multipart/form-data
+            "stream_include_usage": True,
+            "stream_continuous_usage_stats": True
+        }
+        if request_func_input.extra_body:
+            payload.update(request_func_input.extra_body)
+        headers = {
+            "Authorization": f"Bearer {os.environ.get('OPENAI_API_KEY')}",
+        }
+
+        # Send audio file
+        def to_bytes(y, sr):
+            buffer = io.BytesIO()
+            soundfile.write(buffer, y, sr, format="WAV")
+            buffer.seek(0)
+            return buffer
+
+        with to_bytes(*request_func_input.multi_modal_content['audio']) as f:
+            form = aiohttp.FormData()
+            form.add_field('file', f, content_type='audio/wav')
+            for key, value in payload.items():
+                form.add_field(key, str(value))
+
+            output = RequestFuncOutput()
+            output.prompt_len = request_func_input.prompt_len
+
+            generated_text = ""
+            ttft = 0.0
+            st = time.perf_counter()
+            most_recent_timestamp = st
+            try:
+                async with session.post(url=api_url,
+                                        data=form,
+                                        headers=headers) as response:
+                    if response.status == 200:
+                        async for chunk_bytes in response.content:
+                            chunk_bytes = chunk_bytes.strip()
+                            if not chunk_bytes:
+                                continue
+
+                            chunk = chunk_bytes.decode("utf-8").removeprefix(
+                                "data: ")
+                            if chunk != "[DONE]":
+                                timestamp = time.perf_counter()
+                                data = json.loads(chunk)
+
+                                if choices := data.get("choices"):
+                                    content = choices[0]["delta"].get(
+                                        "content")
+                                    # First token
+                                    if ttft == 0.0:
+                                        ttft = timestamp - st
+                                        output.ttft = ttft
+
+                                    # Decoding phase
+                                    else:
+                                        output.itl.append(
+                                            timestamp - most_recent_timestamp)
+
+                                    generated_text += content or ""
+                                elif usage := data.get("usage"):
+                                    output.output_tokens = usage.get(
+                                        "completion_tokens")
+
+                                most_recent_timestamp = timestamp
+
+                        output.generated_text = generated_text
+                        output.success = True
+                        output.latency = most_recent_timestamp - st
+                    else:
+                        output.error = response.reason or ""
+                        output.success = False
+            except Exception:
+                output.success = False
+                exc_info = sys.exc_info()
+                output.error = "".join(traceback.format_exception(*exc_info))
+
+        if pbar:
+            pbar.update(1)
+        return output
+
+
 def get_model(pretrained_model_name_or_path: str) -> str:
     if os.getenv('VLLM_USE_MODELSCOPE', 'False').lower() == 'true':
         from modelscope import snapshot_download
@@ -481,7 +601,14 @@ ASYNC_REQUEST_FUNCS = {
     "deepspeed-mii": async_request_deepspeed_mii,
     "openai": async_request_openai_completions,
     "openai-chat": async_request_openai_chat_completions,
+    "openai-audio": async_request_openai_audio,
     "tensorrt-llm": async_request_trt_llm,
     "scalellm": async_request_openai_completions,
     "sglang": async_request_openai_completions,
 }
+
+OPENAI_COMPATIBLE_BACKENDS = [
+    k for k, v in ASYNC_REQUEST_FUNCS.items()
+    if v in (async_request_openai_completions,
+             async_request_openai_chat_completions)
+]

benchmarks/benchmark_dataset.py

@@ -17,12 +17,14 @@ SampleRequest instances, similar to the approach used in ShareGPT.
 import base64
 import io
 import json
+import logging
 import random
 from abc import ABC, abstractmethod
 from collections.abc import Mapping
 from dataclasses import dataclass
 from functools import cache
-from typing import Any, Optional, Union
+from io import BytesIO
+from typing import Any, Callable, Optional, Union
 
 import numpy as np
 import pandas as pd
@@ -35,6 +37,8 @@ from vllm.lora.utils import get_adapter_absolute_path
 from vllm.multimodal import MultiModalDataDict
 from vllm.transformers_utils.tokenizer import AnyTokenizer, get_lora_tokenizer
 
+logger = logging.getLogger(__name__)
+
 # -----------------------------------------------------------------------------
 # Data Classes
 # -----------------------------------------------------------------------------
@@ -60,9 +64,7 @@ class SampleRequest:
 
 class BenchmarkDataset(ABC):
     DEFAULT_SEED = 0
-
-    # num_requests has default 1000 in both the benchmark_serving.py and
-    # benchmark_throughput.py
+    IS_MULTIMODAL = False
 
     def __init__(
         self,
@@ -90,8 +92,8 @@ class BenchmarkDataset(ABC):
             mm_content: Optional[MultiModalDataDict] = None) -> list[dict]:
         """
         Transform a prompt and optional multimodal content into a chat format.
-        This method is used for chat models that expect a specific 
-        conversation format.
+        This method is used for chat models that expect a specific conversation
+        format.
         """
         content = [{"text": prompt, "type": "text"}]
         if mm_content is not None:
@@ -101,10 +103,10 @@ class BenchmarkDataset(ABC):
     def load_data(self) -> None:
         """
         Load data from the dataset path into self.data.
-        
+
         This method must be overridden by subclasses since the method to load
         data will vary depending on the dataset format and source.
-        
+
         Raises:
             NotImplementedError: If a subclass does not implement this method.
         """
@@ -121,18 +123,18 @@ class BenchmarkDataset(ABC):
         """
         Optionally select a random LoRA request and return its associated
         tokenizer.
-        
+
         This method is used when LoRA parameters are provided.  It randomly
         selects a LoRA based on max_loras and retrieves a cached tokenizer for
         that LoRA if available. Otherwise, it returns the base tokenizer.
-        
+
         Args:
             tokenizer (PreTrainedTokenizerBase): The base tokenizer to use if no
             LoRA is selected.  max_loras (Optional[int]): The maximum number of
             LoRAs available. If None, LoRA is not used.  lora_path
             (Optional[str]): Path to the LoRA parameters on disk. If None, LoRA
             is not used.
-        
+
         Returns:
             tuple[Optional[LoRARequest], AnyTokenizer]: A tuple where the first
             element is a LoRARequest (or None if not applicable) and the second
@@ -160,21 +162,39 @@ class BenchmarkDataset(ABC):
                num_requests: int) -> list[SampleRequest]:
         """
         Abstract method to generate sample requests from the dataset.
-        
+
         Subclasses must override this method to implement dataset-specific logic
         for generating a list of SampleRequest objects.
-        
+
         Args:
             tokenizer (PreTrainedTokenizerBase): The tokenizer to be used
              for processing the dataset's text.
             num_requests (int): The number of sample requests to generate.
-        
+
         Returns:
             list[SampleRequest]: A list of sample requests generated from the
             dataset.
         """
         raise NotImplementedError("sample must be implemented in subclasses.")
 
+    def maybe_oversample_requests(self, requests: list[SampleRequest],
+                                  num_requests: int) -> None:
+        """
+        Oversamples the list of requests if its size is less than the desired
+        number.
+
+        Args:
+            requests (List[SampleRequest]): The current list of sampled
+            requests.  num_requests (int): The target number of requests.
+        """
+        if len(requests) < num_requests:
+            random.seed(self.random_seed)
+            additional = random.choices(requests,
+                                        k=num_requests - len(requests))
+            requests.extend(additional)
+            logger.info("Oversampled requests to reach %d total samples.",
+                        num_requests)
+
 
 # -----------------------------------------------------------------------------
 # Utility Functions and Global Caches
@@ -221,21 +241,24 @@ def process_image(image: Any) -> Mapping[str, Any]:
     """
     Process a single image input and return a multimedia content dictionary.
 
-    For a PIL.Image.Image input:
-      - Converts the image to RGB.
-      - Saves the image as a JPEG in-memory.
-      - Encodes the JPEG data as a base64 string.
-      - Returns a dictionary with the image as a base64 data URL.
+    Supports three input types:
 
-    For a string input:
-      - Treats the string as a URL or file path.
-      - Prepends "file://" if the string doesn't start with "http://" or
-        "file://".
-      - Returns a dictionary with the image URL.
+    1. Dictionary with raw image bytes: - Expects a dict with a 'bytes' key
+       containing raw image data.  - Loads the bytes as a PIL.Image.Image.
+
+    2. PIL.Image.Image input: - Converts the image to RGB.  - Saves the image as
+       a JPEG in memory.  - Encodes the JPEG data as a base64 string.  - Returns
+       a dictionary with the image as a base64 data URL.
+
+    3. String input: - Treats the string as a URL or local file path.  -
+       Prepends "file://" if the string doesn't start with "http://" or
+       "file://".  - Returns a dictionary with the image URL.
 
     Raises:
-      ValueError: If the input is neither a PIL.Image.Image nor a string.
+        ValueError: If the input is not a supported type.
     """
+    if isinstance(image, dict) and 'bytes' in image:
+        image = Image.open(BytesIO(image['bytes']))
     if isinstance(image, Image.Image):
         image = image.convert("RGB")
         with io.BytesIO() as image_data:
@@ -254,8 +277,8 @@ def process_image(image: Any) -> Mapping[str, Any]:
             ("http://", "file://")) else f"file://{image}")
         return {"type": "image_url", "image_url": {"url": image_url}}
 
-    raise ValueError(
-        f"Invalid image input {image}. Must be a PIL.Image.Image or str.")
+    raise ValueError(f"Invalid image input {image}. Must be a PIL.Image.Image"
+                     " or str or dictionary with raw image bytes.")
 
 
 # -----------------------------------------------------------------------------
@@ -266,7 +289,7 @@ def process_image(image: Any) -> Mapping[str, Any]:
 class RandomDataset(BenchmarkDataset):
     # Default values copied from benchmark_serving.py for the random dataset.
     DEFAULT_PREFIX_LEN = 0
-    DEFAULT_RANGE_RATIO = 1.0
+    DEFAULT_RANGE_RATIO = 0.0
     DEFAULT_INPUT_LEN = 1024
     DEFAULT_OUTPUT_LEN = 128
 
@@ -276,28 +299,44 @@ class RandomDataset(BenchmarkDataset):
     ) -> None:
         super().__init__(**kwargs)
 
-    def sample(self,
-               tokenizer: PreTrainedTokenizerBase,
-               num_requests: int,
-               prefix_len: int = DEFAULT_PREFIX_LEN,
-               range_ratio: float = DEFAULT_RANGE_RATIO,
-               input_len: int = DEFAULT_INPUT_LEN,
-               output_len: int = DEFAULT_OUTPUT_LEN,
-               **kwargs) -> list[SampleRequest]:
+    def sample(
+        self,
+        tokenizer: PreTrainedTokenizerBase,
+        num_requests: int,
+        prefix_len: int = DEFAULT_PREFIX_LEN,
+        range_ratio: float = DEFAULT_RANGE_RATIO,
+        input_len: int = DEFAULT_INPUT_LEN,
+        output_len: int = DEFAULT_OUTPUT_LEN,
+        **kwargs,
+    ) -> list[SampleRequest]:
+        # Enforce range_ratio < 1
+        assert range_ratio < 1.0, (
+            "random_range_ratio must be < 1.0 to ensure a valid sampling range"
+        )
 
         vocab_size = tokenizer.vocab_size
+        num_special_tokens = tokenizer.num_special_tokens_to_add()
+        real_input_len = input_len - num_special_tokens
 
         prefix_token_ids = (np.random.randint(
             0, vocab_size, size=prefix_len).tolist() if prefix_len > 0 else [])
 
-        input_low = int(input_len * range_ratio)
-        output_low = int(output_len * range_ratio)
+        # New sampling logic: [X * (1 - b), X * (1 + b)]
+        input_low = int(real_input_len * (1 - range_ratio))
+        input_high = int(real_input_len * (1 + range_ratio))
+        output_low = int(output_len * (1 - range_ratio))
+        output_high = int(output_len * (1 + range_ratio))
+
+        # Add logging for debugging
+        logger.info("Sampling input_len from [%s, %s]", input_low, input_high)
+        logger.info("Sampling output_len from [%s, %s]", output_low,
+                    output_high)
 
         input_lens = np.random.randint(input_low,
-                                       input_len + 1,
+                                       input_high + 1,
                                        size=num_requests)
         output_lens = np.random.randint(output_low,
-                                        output_len + 1,
+                                        output_high + 1,
                                         size=num_requests)
         offsets = np.random.randint(0, vocab_size, size=num_requests)
 
@@ -307,6 +346,17 @@ class RandomDataset(BenchmarkDataset):
                          vocab_size).tolist()
             token_sequence = prefix_token_ids + inner_seq
             prompt = tokenizer.decode(token_sequence)
+            # After decoding the prompt we have to encode and decode it again.
+            # This is done because in some cases N consecutive tokens
+            # give a string tokenized into != N number of tokens.
+            # For example for GPT2Tokenizer:
+            # [6880, 6881] -> ['Ġcalls', 'here'] ->
+            # [1650, 939, 486] -> ['Ġcall', 'sh', 'ere']
+            # To avoid uncontrolled change of the prompt length,
+            # the encoded sequence is truncated before being decode again.
+            re_encoded_sequence = tokenizer.encode(
+                prompt, add_special_tokens=False)[:input_lens[i]]
+            prompt = tokenizer.decode(re_encoded_sequence)
             total_input_len = prefix_len + int(input_lens[i])
             requests.append(
                 SampleRequest(
@@ -346,20 +396,24 @@ class ShareGPTDataset(BenchmarkDataset):
         random.seed(self.random_seed)
         random.shuffle(self.data)
 
-    def sample(self,
-               tokenizer: PreTrainedTokenizerBase,
-               num_requests: int,
-               lora_path: Optional[str] = None,
-               max_loras: Optional[int] = None,
-               output_len: Optional[int] = None,
-               enable_multimodal_chat: bool = False,
-               **kwargs) -> list:
+    def sample(
+        self,
+        tokenizer: PreTrainedTokenizerBase,
+        num_requests: int,
+        lora_path: Optional[str] = None,
+        max_loras: Optional[int] = None,
+        output_len: Optional[int] = None,
+        enable_multimodal_chat: bool = False,
+        **kwargs,
+    ) -> list:
         samples: list = []
         for entry in self.data:
             if len(samples) >= num_requests:
                 break
-            prompt, completion = entry["conversations"][0]["value"],\
-                entry["conversations"][1]["value"]
+            prompt, completion = (
+                entry["conversations"][0]["value"],
+                entry["conversations"][1]["value"],
+            )
 
             lora_request, tokenizer = self.get_random_lora_request(
                 tokenizer=tokenizer, max_loras=max_loras, lora_path=lora_path)
@@ -383,6 +437,7 @@ class ShareGPTDataset(BenchmarkDataset):
                     expected_output_len=new_output_len,
                     lora_request=lora_request,
                 ))
+        self.maybe_oversample_requests(samples, num_requests)
         return samples
 
 
@@ -415,19 +470,20 @@ class SonnetDataset(BenchmarkDataset):
         with open(self.dataset_path, encoding="utf-8") as f:
             self.data = f.readlines()
 
-    def sample(self,
-               tokenizer,
-               num_requests: int,
-               prefix_len: int = DEFAULT_PREFIX_LEN,
-               input_len: int = DEFAULT_INPUT_LEN,
-               output_len: int = DEFAULT_OUTPUT_LEN,
-               return_prompt_formatted: bool = False,
-               **kwargs) -> list:
+    def sample(
+        self,
+        tokenizer,
+        num_requests: int,
+        prefix_len: int = DEFAULT_PREFIX_LEN,
+        input_len: int = DEFAULT_INPUT_LEN,
+        output_len: int = DEFAULT_OUTPUT_LEN,
+        return_prompt_formatted: bool = False,
+        **kwargs,
+    ) -> list:
         # Calculate average token length for a poem line.
         tokenized_lines = [tokenizer(line).input_ids for line in self.data]
         avg_len = sum(len(tokens)
-                      for tokens in \
-                        tokenized_lines) / len(tokenized_lines)
+                      for tokens in tokenized_lines) / len(tokenized_lines)
 
         # Build the base prompt.
         base_prompt = "Pick as many lines as you can from these poem lines:\n"
@@ -443,11 +499,11 @@ class SonnetDataset(BenchmarkDataset):
 
         # Determine how many poem lines to use.
         num_input_lines = round((input_len - base_offset) / avg_len)
-        num_prefix_lines = round((prefix_len - base_offset) / avg_len)
+        num_prefix_lines = max(round((prefix_len - base_offset) / avg_len), 0)
         prefix_lines = self.data[:num_prefix_lines]
 
         samples = []
-        for _ in range(num_requests):
+        while len(samples) < num_requests:
             extra_lines = random.choices(self.data,
                                          k=num_input_lines - num_prefix_lines)
             prompt = f"{base_prompt}{''.join(prefix_lines + extra_lines)}"
@@ -455,13 +511,14 @@ class SonnetDataset(BenchmarkDataset):
             prompt_formatted = tokenizer.apply_chat_template(
                 msg, add_generation_prompt=True, tokenize=False)
             prompt_len = len(tokenizer(prompt_formatted).input_ids)
-            samples.append(
-                SampleRequest(
-                    prompt=prompt_formatted
-                    if return_prompt_formatted else prompt,
-                    prompt_len=prompt_len,
-                    expected_output_len=output_len,
-                ))
+            if prompt_len <= input_len:
+                samples.append(
+                    SampleRequest(
+                        prompt=prompt_formatted
+                        if return_prompt_formatted else prompt,
+                        prompt_len=prompt_len,
+                        expected_output_len=output_len,
+                    ))
         return samples
 
 
@@ -506,12 +563,14 @@ class BurstGPTDataset(BenchmarkDataset):
         # Convert the dataframe to a list of lists.
         return data.values.tolist()
 
-    def sample(self,
-               tokenizer: PreTrainedTokenizerBase,
-               num_requests: int,
-               max_loras: Optional[int] = None,
-               lora_path: Optional[str] = None,
-               **kwargs) -> list[SampleRequest]:
+    def sample(
+        self,
+        tokenizer: PreTrainedTokenizerBase,
+        num_requests: int,
+        max_loras: Optional[int] = None,
+        lora_path: Optional[str] = None,
+        **kwargs,
+    ) -> list[SampleRequest]:
         samples = []
         data = self._sample_loaded_data(num_requests=num_requests)
         for i in range(num_requests):
@@ -535,49 +594,48 @@ class BurstGPTDataset(BenchmarkDataset):
 
 
 # -----------------------------------------------------------------------------
-# HuggingFace Dataset Implementation
+# HuggingFace Dataset Base Implementation
 # -----------------------------------------------------------------------------
-
-
 class HuggingFaceDataset(BenchmarkDataset):
-    """
-    Dataset class for processing a HuggingFace dataset with conversation data
-    and optional images.
-    """
-    DEFAULT_NUM_REQUESTS = 1000
+    """Base class for datasets hosted on HuggingFace."""
+
+    SUPPORTED_DATASET_PATHS: Union[set[str], dict[str, Callable]] = set()
 
     def __init__(
         self,
+        dataset_path: str,
         dataset_split: str,
         dataset_subset: Optional[str] = None,
         **kwargs,
     ) -> None:
-        super().__init__(**kwargs)
+        super().__init__(dataset_path=dataset_path, **kwargs)
+
         self.dataset_split = dataset_split
         self.dataset_subset = dataset_subset
-
         self.load_data()
 
     def load_data(self) -> None:
-        if not self.dataset_path:
-            raise ValueError("dataset_path must be provided for loading data.")
-
+        """Load data from HuggingFace datasets."""
         self.data = load_dataset(
             self.dataset_path,
             name=self.dataset_subset,
             split=self.dataset_split,
             streaming=True,
         )
-        if self.data.features is None or "conversations" \
-            not in self.data.features:
-            raise ValueError(
-                "HuggingFaceDataset currently only supports datasets with "
-                "a 'conversations' column like lmms-lab/LLaVA-OneVision-Data. "
-                "Please consider contributing if you would like to add "
-                "support for additional dataset formats.")
-        # Shuffle and filter examples with at least 2 conversations.
-        self.data = self.data.shuffle(seed=self.random_seed).filter(
-            lambda x: len(x["conversations"]) >= 2)
+        self.data = self.data.shuffle(seed=self.random_seed)
+
+
+# -----------------------------------------------------------------------------
+# Conversation Dataset Implementation
+# -----------------------------------------------------------------------------
+
+
+class ConversationDataset(HuggingFaceDataset):
+    """Dataset for conversation data with multimodal support."""
+    SUPPORTED_DATASET_PATHS = {
+        'lmms-lab/LLaVA-OneVision-Data', 'Aeala/ShareGPT_Vicuna_unfiltered'
+    }
+    IS_MULTIMODAL = True
 
     def sample(self,
                tokenizer: PreTrainedTokenizerBase,
@@ -585,10 +643,13 @@ class HuggingFaceDataset(BenchmarkDataset):
                output_len: Optional[int] = None,
                enable_multimodal_chat: bool = False,
                **kwargs) -> list:
+        # Filter examples with at least 2 conversations
+        filtered_data = self.data.filter(
+            lambda x: len(x["conversations"]) >= 2)
         sampled_requests = []
         dynamic_output = output_len is None
 
-        for item in self.data:
+        for item in filtered_data:
             if len(sampled_requests) >= num_requests:
                 break
             conv = item["conversations"]
@@ -618,6 +679,7 @@ class HuggingFaceDataset(BenchmarkDataset):
                     expected_output_len=output_len,
                     multi_modal_data=mm_content,
                 ))
+        self.maybe_oversample_requests(sampled_requests, num_requests)
         return sampled_requests
 
 
@@ -632,44 +694,33 @@ class VisionArenaDataset(HuggingFaceDataset):
     """
 
     DEFAULT_OUTPUT_LEN = 128
-    DEFAULT_NUM_REQUESTS = 1000
-    VISION_ARENA_DATASET_PATH = "lmarena-ai/vision-arena-bench-v0.1"
+    SUPPORTED_DATASET_PATHS = {
+        "lmarena-ai/VisionArena-Chat":
+        lambda x: x["conversation"][0][0]["content"],
+        "lmarena-ai/vision-arena-bench-v0.1":
+        lambda x: x["turns"][0][0]["content"]
+    }
+    IS_MULTIMODAL = True
 
-    def __init__(
+    def sample(
         self,
+        tokenizer: PreTrainedTokenizerBase,
+        num_requests: int,
+        output_len: Optional[int] = None,
+        enable_multimodal_chat: bool = False,
         **kwargs,
-    ) -> None:
-        super().__init__(**kwargs)
-        if self.dataset_path != self.VISION_ARENA_DATASET_PATH:
-            raise ValueError(f"Only support Vision Arena dataset.\
-                    This data path {self.dataset_path} is not valid.")
-        if self.dataset_subset is None and self.dataset_split != "train":
-            raise ValueError("Dataset split must be 'train'.")
-
-        self.load_data()
-
-    def load_data(self) -> None:
-        dataset = load_dataset(
-            self.dataset_path,
-            name=self.dataset_subset,
-            split=self.dataset_split,
-            streaming=True,
-        )
-        self.data = dataset.shuffle(seed=self.random_seed)
-
-    def sample(self,
-               tokenizer: PreTrainedTokenizerBase,
-               num_requests: int,
-               output_len: Optional[int] = None,
-               enable_multimodal_chat: bool = False,
-               **kwargs) -> list:
+    ) -> list:
         output_len = (output_len
                       if output_len is not None else self.DEFAULT_OUTPUT_LEN)
         sampled_requests = []
         for item in self.data:
             if len(sampled_requests) >= num_requests:
                 break
-            prompt = item["turns"][0][0]["content"]
+            parser_fn = self.SUPPORTED_DATASET_PATHS.get(self.dataset_path)
+            if parser_fn is None:
+                raise ValueError(
+                    f"Unsupported dataset path: {self.dataset_path}")
+            prompt = parser_fn(item)
             mm_content = process_image(item["images"][0])
             prompt_len = len(tokenizer(prompt).input_ids)
             if enable_multimodal_chat:
@@ -685,4 +736,317 @@ class VisionArenaDataset(HuggingFaceDataset):
                     expected_output_len=output_len,
                     multi_modal_data=mm_content,
                 ))
+        self.maybe_oversample_requests(sampled_requests, num_requests)
+        return sampled_requests
+
+
+# -----------------------------------------------------------------------------
+# Instruct Coder Dataset Implementation
+# -----------------------------------------------------------------------------
+
+
+class InstructCoderDataset(HuggingFaceDataset):
+    """
+    InstructCoder Dataset.
+    https://huggingface.co/datasets/likaixin/InstructCoder
+
+    InstructCoder is the dataset designed for general code editing.  It consists
+    of 114,239 instruction-input-output triplets, and covers multiple distinct
+    code editing scenario.
+    """
+
+    DEFAULT_OUTPUT_LEN = 200  # this is the average default output length
+    SUPPORTED_DATASET_PATHS = {
+        "likaixin/InstructCoder",
+    }
+
+    def sample(self,
+               tokenizer: PreTrainedTokenizerBase,
+               num_requests: int,
+               output_len: Optional[int] = None,
+               enable_multimodal_chat: bool = False,
+               **kwargs) -> list:
+        output_len = (output_len
+                      if output_len is not None else self.DEFAULT_OUTPUT_LEN)
+        sampled_requests = []
+        for item in self.data:
+            if len(sampled_requests) >= num_requests:
+                break
+            prompt = f"{item['instruction']}:\n{item['input']}"
+            prompt_len = len(tokenizer(prompt).input_ids)
+            sampled_requests.append(
+                SampleRequest(
+                    prompt=prompt,
+                    prompt_len=prompt_len,
+                    expected_output_len=output_len,
+                ))
+        self.maybe_oversample_requests(sampled_requests, num_requests)
+        return sampled_requests
+
+
+# -----------------------------------------------------------------------------
+# MT-Bench Dataset Implementation
+# -----------------------------------------------------------------------------
+
+
+class MTBenchDataset(HuggingFaceDataset):
+    """
+    MT-Bench Dataset.
+    https://huggingface.co/datasets/philschmid/mt-bench
+
+    We create a single turn dataset for MT-Bench. 
+    This is similar to Spec decoding benchmark setup in vLLM
+    https://github.com/vllm-project/vllm/blob/9d98ab5ec/examples/offline_inference/eagle.py#L14-L18
+    """ # noqa: E501
+
+    DEFAULT_OUTPUT_LEN = 256  # avg len used in SD bench in vLLM
+    SUPPORTED_DATASET_PATHS = {
+        "philschmid/mt-bench",
+    }
+
+    def sample(self,
+               tokenizer: PreTrainedTokenizerBase,
+               num_requests: int,
+               output_len: Optional[int] = None,
+               enable_multimodal_chat: bool = False,
+               **kwargs) -> list:
+        output_len = (output_len
+                      if output_len is not None else self.DEFAULT_OUTPUT_LEN)
+        sampled_requests = []
+
+        for item in self.data:
+            if len(sampled_requests) >= num_requests:
+                break
+            prompt = item['turns'][0]
+
+            # apply template
+            prompt = tokenizer.apply_chat_template([{
+                "role": "user",
+                "content": prompt
+            }],
+                                                   add_generation_prompt=True,
+                                                   tokenize=False)
+
+            prompt_len = len(tokenizer(prompt).input_ids)
+            sampled_requests.append(
+                SampleRequest(
+                    prompt=prompt,
+                    prompt_len=prompt_len,
+                    expected_output_len=output_len,
+                ))
+        self.maybe_oversample_requests(sampled_requests, num_requests)
+        return sampled_requests
+
+
+# -----------------------------------------------------------------------------
+# AIMO Dataset Implementation
+# -----------------------------------------------------------------------------
+
+
+class AIMODataset(HuggingFaceDataset):
+    """
+    Dataset class for processing a AIMO dataset with reasoning questions.
+    """
+    SUPPORTED_DATASET_PATHS = {
+        "AI-MO/aimo-validation-aime", "AI-MO/NuminaMath-1.5",
+        "AI-MO/NuminaMath-CoT"
+    }
+
+    def sample(self,
+               tokenizer: PreTrainedTokenizerBase,
+               num_requests: int,
+               output_len: Optional[int] = None,
+               **kwargs) -> list:
+        sampled_requests = []
+        dynamic_output = output_len is None
+
+        for item in self.data:
+            if len(sampled_requests) >= num_requests:
+                break
+            prompt, completion = item['problem'], item["solution"]
+
+            prompt_ids = tokenizer(prompt).input_ids
+            completion_ids = tokenizer(completion).input_ids
+            prompt_len = len(prompt_ids)
+            completion_len = len(completion_ids)
+            output_len = completion_len if dynamic_output else output_len
+            assert isinstance(output_len, int) and output_len > 0
+            if dynamic_output and not is_valid_sequence(prompt_len,
+                                                        completion_len,
+                                                        max_prompt_len=2048,
+                                                        max_total_len=32000):
+                continue
+            sampled_requests.append(
+                SampleRequest(
+                    prompt=prompt,
+                    prompt_len=prompt_len,
+                    expected_output_len=output_len,
+                    multi_modal_data=None,
+                ))
+        self.maybe_oversample_requests(sampled_requests, num_requests)
+        return sampled_requests
+
+
+# -----------------------------------------------------------------------------
+# Next Edit Prediction Dataset Implementation
+# -----------------------------------------------------------------------------
+
+
+zeta_prompt = """### Instruction:
+You are a code completion assistant and your task is to analyze user edits and then rewrite an excerpt that the user provides, suggesting the appropriate edits within the excerpt, taking into account the cursor location.
+
+### User Edits:
+
+{}
+
+### User Excerpt:
+
+{}
+
+### Response:
+
+""" # noqa: E501
+
+
+def _format_zeta_prompt(
+        sample: dict,
+        original_start_marker: str = "<|editable_region_start|>") -> dict:
+    """Format the zeta prompt for the Next Edit Prediction (NEP) dataset.
+    
+    This function formats examples from the NEP dataset 
+    into prompts and expected outputs. It could be 
+    further extended to support more NEP datasets.
+    
+    Args:
+        sample: The dataset sample containing events, 
+            inputs, and outputs.
+        original_start_marker: The marker indicating the 
+            start of the editable region. Defaults to 
+            "<|editable_region_start|>".
+            
+    Returns:
+        A dictionary with the formatted prompts and expected outputs.
+    """
+    events = sample["events"]
+    input = sample["input"]
+    output = sample["output"]
+    prompt = zeta_prompt.format(events, input)
+
+    # following the original implementation, extract the focused region
+    # from the raw output
+    output_start_index = output.find(original_start_marker)
+    output_focused_region = output[output_start_index:]
+    expected_output = output_focused_region
+
+    return {"prompt": prompt, "expected_output": expected_output}
+
+
+class NextEditPredictionDataset(HuggingFaceDataset):
+    """
+    Dataset class for processing a Next Edit Prediction dataset.
+    """
+
+    SUPPORTED_DATASET_PATHS = {
+        "zed-industries/zeta",
+    }
+    MAPPING_PROMPT_FUNCS = {
+        "zed-industries/zeta": _format_zeta_prompt,
+    }
+
+    def sample(self, tokenizer: PreTrainedTokenizerBase, num_requests: int,
+               **kwargs):
+        formatting_prompt_func = self.MAPPING_PROMPT_FUNCS.get(
+            self.dataset_path)
+        if formatting_prompt_func is None:
+            raise ValueError(f"Unsupported dataset path: {self.dataset_path}")
+        samples = []
+        for sample in self.data:
+            sample = formatting_prompt_func(sample)
+            samples.append(
+                SampleRequest(
+                    prompt=sample["prompt"],
+                    prompt_len=len(tokenizer(sample["prompt"]).input_ids),
+                    expected_output_len=len(
+                        tokenizer(sample["expected_output"]).input_ids),
+                ))
+            if len(samples) >= num_requests:
+                break
+        self.maybe_oversample_requests(samples, num_requests)
+        return samples
+
+
+# -----------------------------------------------------------------------------
+# ASR Dataset Implementation
+# -----------------------------------------------------------------------------
+
+
+class ASRDataset(HuggingFaceDataset):
+    """
+    Dataset class for processing a ASR dataset for transcription.
+    Tested on the following set:
+
+    +----------------+----------------------------------------+--------------------------+-----------------------------+
+    | Dataset        | Domain                                 | Speaking Style           | hf-subset                   |
+    +----------------+----------------------------------------+--------------------------+-----------------------------+
+    | TED-LIUM       | TED talks                              | Oratory                  | release1, release2, release3|
+    |                |                                        |                          | release3-speaker-adaptation |
+    | VoxPopuli      | European Parliament                    | Oratory                  | en, de, it, fr,  ...        |
+    | LibriSpeech    | Audiobook                              | Narrated                 | "LIUM/tedlium"              |
+    | GigaSpeech     | Audiobook, podcast, YouTube            | Narrated, spontaneous    | xs, s, m, l, xl, dev, test  |
+    | SPGISpeech     | Financial meetings                     | Oratory, spontaneous     | S, M, L, dev, test          |
+    | AMI            | Meetings                               | Spontaneous              | ihm, sdm                    |
+    +----------------+----------------------------------------+--------------------------+-----------------------------+
+
+    """ # noqa: E501
+    SUPPORTED_DATASET_PATHS = {
+        "openslr/librispeech_asr", "facebook/voxpopuli", "LIUM/tedlium",
+        "edinburghcstr/ami", "speechcolab/gigaspeech", "kensho/spgispeech"
+    }
+
+    DEFAULT_OUTPUT_LEN = 128
+    IS_MULTIMODAL = True
+
+    # TODO Whisper-specific. Abstract interface when more models are supported.
+    TRANSCRIPTION_PREAMBLE = "<|startoftranscript|><|en|><|transcribe|>"\
+                              "<|notimestamps|>"
+    skip_long_audios: bool = True
+
+    def sample(
+        self,
+        tokenizer: PreTrainedTokenizerBase,
+        num_requests: int,
+        output_len: Optional[int] = None,
+        **kwargs,
+    ) -> list:
+        import librosa
+        output_len = (output_len
+                      if output_len is not None else self.DEFAULT_OUTPUT_LEN)
+        prompt = ASRDataset.TRANSCRIPTION_PREAMBLE
+        prompt_len = len(tokenizer(prompt).input_ids)
+        sampled_requests = []
+        skipped = 0
+        for item in self.data:
+            if len(sampled_requests) >= num_requests:
+                break
+            audio = item["audio"]
+            y, sr = audio["array"], audio["sampling_rate"]
+            duration_s = librosa.get_duration(y=y, sr=sr)
+            # Whisper max supported duration
+            if self.skip_long_audios and duration_s > 30:
+                skipped += 1
+                continue
+
+            mm_content = {"audio": (y, sr)}
+            sampled_requests.append(
+                SampleRequest(
+                    prompt=prompt,
+                    prompt_len=prompt_len,
+                    expected_output_len=output_len,
+                    multi_modal_data=mm_content,
+                ))
+        if skipped:
+            logger.warning("%d samples discarded from dataset due to" \
+                           " their length being greater than" \
+                           " what Whisper supports.", skipped)
+        self.maybe_oversample_requests(sampled_requests, num_requests)
         return sampled_requests

benchmarks/benchmark_prefix_caching.py

@@ -63,14 +63,16 @@ class Request:
     output_len: int
 
 
-def sample_tokens(tokenizer: PreTrainedTokenizerBase, length: int) -> str:
+def sample_tokens(tokenizer: PreTrainedTokenizerBase,
+                  length: int) -> list[int]:
     vocab = tokenizer.get_vocab()
+    all_special_ids = set(tokenizer.all_special_ids)
+
     # Remove the special tokens.
-    vocab = {
-        k: v
-        for k, v in vocab.items() if k not in tokenizer.all_special_ids
-    }
-    return random.choices(list(vocab.values()), k=length)
+    return random.choices(
+        [v for k, v in vocab.items() if k not in all_special_ids],
+        k=length,
+    )
 
 
 def sample_requests_from_dataset(

benchmarks/benchmark_serving.py

@@ -7,9 +7,6 @@ On the server side, run one of the following commands:
         --swap-space 16 \
         --disable-log-requests
 
-    (TGI backend)
-    ./launch_tgi_server.sh <your_model> <max_batch_total_tokens>
-
 On the client side, run:
     python benchmarks/benchmark_serving.py \
         --backend <backend> \
@@ -37,7 +34,8 @@ from datetime import datetime
 from typing import Any, Optional
 
 import numpy as np
-from backend_request_func import (ASYNC_REQUEST_FUNCS, RequestFuncInput,
+from backend_request_func import (ASYNC_REQUEST_FUNCS,
+                                  OPENAI_COMPATIBLE_BACKENDS, RequestFuncInput,
                                   RequestFuncOutput)
 from tqdm.asyncio import tqdm
 from transformers import PreTrainedTokenizerBase
@@ -52,9 +50,12 @@ try:
 except ImportError:
     from argparse import ArgumentParser as FlexibleArgumentParser
 
-from benchmark_dataset import (BurstGPTDataset, HuggingFaceDataset,
-                               RandomDataset, SampleRequest, ShareGPTDataset,
-                               SonnetDataset, VisionArenaDataset)
+from benchmark_dataset import (AIMODataset, ASRDataset, BurstGPTDataset,
+                               ConversationDataset, HuggingFaceDataset,
+                               InstructCoderDataset, MTBenchDataset,
+                               NextEditPredictionDataset, RandomDataset,
+                               SampleRequest, ShareGPTDataset, SonnetDataset,
+                               VisionArenaDataset)
 from benchmark_utils import convert_to_pytorch_benchmark_format, write_to_json
 
 MILLISECONDS_TO_SECONDS_CONVERSION = 1000
@@ -156,7 +157,7 @@ def calculate_metrics(
         if outputs[i].success:
             output_len = outputs[i].output_tokens
 
-            if output_len is None:
+            if not output_len:
                 # We use the tokenizer to count the number of output tokens
                 # for some serving backends instead of looking at
                 # len(outputs[i].itl) since multiple output tokens may be
@@ -261,6 +262,7 @@ async def benchmark(
     goodput_config_dict: dict[str, float],
     max_concurrency: Optional[int],
     lora_modules: Optional[Iterable[str]],
+    extra_body: Optional[dict],
 ):
     if backend in ASYNC_REQUEST_FUNCS:
         request_func = ASYNC_REQUEST_FUNCS[backend]
@@ -273,10 +275,6 @@ async def benchmark(
         input_requests[0].expected_output_len, \
             input_requests[0].multi_modal_data
 
-    if backend != "openai-chat" and test_mm_content is not None:
-        # multi-modal benchmark is only available on OpenAI Chat backend.
-        raise ValueError(
-            "Multi-modal content is only supported on 'openai-chat' backend.")
     assert test_mm_content is None or isinstance(test_mm_content, dict)
     test_input = RequestFuncInput(
         model=model_id,
@@ -288,6 +286,7 @@ async def benchmark(
         logprobs=logprobs,
         multi_modal_content=test_mm_content,
         ignore_eos=ignore_eos,
+        extra_body=extra_body,
     )
 
     test_output = await request_func(request_func_input=test_input)
@@ -314,7 +313,8 @@ async def benchmark(
                                          output_len=test_output_len,
                                          logprobs=logprobs,
                                          multi_modal_content=test_mm_content,
-                                         ignore_eos=ignore_eos)
+                                         ignore_eos=ignore_eos,
+                                         extra_body=extra_body)
         profile_output = await request_func(request_func_input=profile_input)
         if profile_output.success:
             print("Profiler started")
@@ -364,7 +364,8 @@ async def benchmark(
                                               output_len=output_len,
                                               logprobs=logprobs,
                                               multi_modal_content=mm_content,
-                                              ignore_eos=ignore_eos)
+                                              ignore_eos=ignore_eos,
+                                              extra_body=extra_body)
         tasks.append(
             asyncio.create_task(
                 limited_request_func(request_func_input=request_func_input,
@@ -586,19 +587,55 @@ def main(args: argparse.Namespace):
                                             return_prompt_formatted=True)
 
     elif args.dataset_name == "hf":
-        # Choose between VisionArenaDataset
-        # and HuggingFaceDataset based on provided parameters.
-        dataset_class = (VisionArenaDataset if args.dataset_path
-                         == VisionArenaDataset.VISION_ARENA_DATASET_PATH
-                         and args.hf_subset is None else HuggingFaceDataset)
+        # all following datasets are implemented from the
+        # HuggingFaceDataset base class
+        if args.dataset_path in VisionArenaDataset.SUPPORTED_DATASET_PATHS:
+            dataset_class = VisionArenaDataset
+            args.hf_split = "train"
+            args.hf_subset = None
+        elif args.dataset_path in InstructCoderDataset.SUPPORTED_DATASET_PATHS:
+            dataset_class = InstructCoderDataset
+            args.hf_split = "train"
+        elif args.dataset_path in MTBenchDataset.SUPPORTED_DATASET_PATHS:
+            dataset_class = MTBenchDataset
+            args.hf_split = "train"
+        elif args.dataset_path in ConversationDataset.SUPPORTED_DATASET_PATHS:
+            dataset_class = ConversationDataset
+        elif args.dataset_path in AIMODataset.SUPPORTED_DATASET_PATHS:
+            dataset_class = AIMODataset
+            args.hf_split = "train"
+        elif args.dataset_path in NextEditPredictionDataset.SUPPORTED_DATASET_PATHS:  # noqa: E501
+            dataset_class = NextEditPredictionDataset
+            args.hf_split = "train"
+        elif args.dataset_path in ASRDataset.SUPPORTED_DATASET_PATHS:
+            dataset_class = ASRDataset
+            args.hf_split = "train"
+        else:
+            supported_datasets = set([
+                dataset_name for cls in HuggingFaceDataset.__subclasses__()
+                for dataset_name in cls.SUPPORTED_DATASET_PATHS
+            ])
+            raise ValueError(
+                f"Unsupported dataset path: {args.dataset_path}. "
+                "Huggingface dataset only supports dataset_path"
+                f" from one of following: {supported_datasets}. "
+                "Please consider contributing if you would "
+                "like to add support for additional dataset formats.")
+
+        if (dataset_class.IS_MULTIMODAL and backend not in \
+            ["openai-chat", "openai-audio"]):
+            # multi-modal benchmark is only available on OpenAI Chat backend.
+            raise ValueError(
+                "Multi-modal content is only supported on 'openai-chat' and " \
+                "'openai-audio' backend.")
         input_requests = dataset_class(
             dataset_path=args.dataset_path,
             dataset_subset=args.hf_subset,
             dataset_split=args.hf_split,
+            random_seed=args.seed,
         ).sample(
             num_requests=args.num_prompts,
             tokenizer=tokenizer,
-            random_seed=args.seed,
             output_len=args.hf_output_len,
         )
 
@@ -633,6 +670,26 @@ def main(args: argparse.Namespace):
             raise ValueError(f"Unknown dataset: {args.dataset_name}") from err
     goodput_config_dict = check_goodput_args(args)
 
+    # Collect the sampling parameters.
+    sampling_params = {
+        k: v
+        for k, v in {
+            "top_p": args.top_p,
+            "top_k": args.top_k,
+            "min_p": args.min_p,
+            "temperature": args.temperature
+        }.items() if v is not None
+    }
+
+    # Sampling parameters are only supported by openai-compatible backend.
+    if sampling_params and args.backend not in OPENAI_COMPATIBLE_BACKENDS:
+        raise ValueError(
+            "Sampling parameters are only supported by openai-compatible "
+            "backends.")
+
+    if "temperature" not in sampling_params:
+        sampling_params["temperature"] = 0.0  # Default to greedy decoding.
+
     # Avoid GC processing "static" data - reduce pause times.
     gc.collect()
     gc.freeze()
@@ -659,10 +716,11 @@ def main(args: argparse.Namespace):
             goodput_config_dict=goodput_config_dict,
             max_concurrency=args.max_concurrency,
             lora_modules=args.lora_modules,
+            extra_body=sampling_params,
         ))
 
     # Save config and results to json
-    if args.save_result:
+    if args.save_result or args.append_result:
         result_json: dict[str, Any] = {}
 
         # Setup
@@ -683,6 +741,14 @@ def main(args: argparse.Namespace):
                     raise ValueError(
                         "Invalid metadata format. Please use KEY=VALUE format."
                     )
+        # Traffic
+        result_json["request_rate"] = (args.request_rate if args.request_rate
+                                       < float("inf") else "inf")
+        result_json["burstiness"] = args.burstiness
+        result_json["max_concurrency"] = args.max_concurrency
+
+        # Merge with benchmark result
+        result_json = {**result_json, **benchmark_result}
 
         if not args.save_detailed:
             # Remove fields with too many data points
@@ -693,15 +759,6 @@ def main(args: argparse.Namespace):
                 if field in result_json:
                     del result_json[field]
 
-        # Traffic
-        result_json["request_rate"] = (args.request_rate if args.request_rate
-                                       < float("inf") else "inf")
-        result_json["burstiness"] = args.burstiness
-        result_json["max_concurrency"] = args.max_concurrency
-
-        # Merge with benchmark result
-        result_json = {**result_json, **benchmark_result}
-
         # Save to file
         base_model_id = model_id.split("/")[-1]
         max_concurrency_str = (f"-concurrency{args.max_concurrency}"
@@ -711,7 +768,12 @@ def main(args: argparse.Namespace):
             file_name = args.result_filename
         if args.result_dir:
             file_name = os.path.join(args.result_dir, file_name)
-        with open(file_name, "w", encoding='utf-8') as outfile:
+        with open(file_name,
+                  mode="a+" if args.append_result else "w",
+                  encoding='utf-8') as outfile:
+            # Append a newline.
+            if args.append_result and outfile.tell() != 0:
+                outfile.write("\n")
             json.dump(result_json, outfile)
         save_to_pytorch_benchmark_format(args, result_json, file_name)
 
@@ -843,6 +905,11 @@ if __name__ == "__main__":
         help="When saving the results, whether to include per request "
         "information such as response, error, ttfs, tpots, etc.",
     )
+    parser.add_argument(
+        "--append-result",
+        action="store_true",
+        help="Append the benchmark result to the existing json file.",
+    )
     parser.add_argument(
         "--metadata",
         metavar="KEY=VALUE",
@@ -876,7 +943,7 @@ if __name__ == "__main__":
         "--percentile-metrics",
         type=str,
         default="ttft,tpot,itl",
-        help="Comma-seperated list of selected metrics to report percentils. "
+        help="Comma-separated 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\".")
@@ -884,7 +951,7 @@ if __name__ == "__main__":
         "--metric-percentiles",
         type=str,
         default="99",
-        help="Comma-seperated list of percentiles for selected metrics. "
+        help="Comma-separated 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.",
@@ -951,18 +1018,23 @@ if __name__ == "__main__":
     random_group.add_argument(
         "--random-range-ratio",
         type=float,
-        default=1.0,
-        help="Range of sampled ratio of input/output length, "
-        "used only for random sampling.",
+        default=0.0,
+        help="Range ratio for sampling input/output length, "
+        "used only for random sampling. Must be in the range [0, 1) to define "
+        "a symmetric sampling range"
+        "[length * (1 - range_ratio), length * (1 + range_ratio)].",
     )
     random_group.add_argument(
         "--random-prefix-len",
         type=int,
         default=0,
-        help="Number of fixed prefix tokens before random "
-        " context. The length range of context in a random "
-        " request is [random-prefix-len, "
-        " random-prefix-len + random-prefix-len * random-range-ratio).")
+        help=("Number of fixed prefix tokens before the random context "
+              "in a request. "
+              "The total input length is the sum of `random-prefix-len` and "
+              "a random "
+              "context length sampled from [input_len * (1 - range_ratio), "
+              "input_len * (1 + range_ratio)]."),
+    )
 
     hf_group = parser.add_argument_group("hf dataset options")
     hf_group.add_argument("--hf-subset",
@@ -981,6 +1053,33 @@ if __name__ == "__main__":
         "from the sampled HF dataset.",
     )
 
+    sampling_group = parser.add_argument_group("sampling parameters")
+    sampling_group.add_argument(
+        "--top-p",
+        type=float,
+        default=None,
+        help="Top-p sampling parameter. Only has effect on openai-compatible "
+        "backends.")
+    sampling_group.add_argument(
+        "--top-k",
+        type=int,
+        default=None,
+        help="Top-k sampling parameter. Only has effect on openai-compatible "
+        "backends.")
+    sampling_group.add_argument(
+        "--min-p",
+        type=float,
+        default=None,
+        help="Min-p sampling parameter. Only has effect on openai-compatible "
+        "backends.")
+    sampling_group.add_argument(
+        "--temperature",
+        type=float,
+        default=None,
+        help="Temperature sampling parameter. Only has effect on "
+        "openai-compatible backends. If not specified, default to greedy "
+        "decoding (i.e. temperature==0.0).")
+
     parser.add_argument(
         '--tokenizer-mode',
         type=str,

benchmarks/benchmark_serving_structured_output.py

@@ -5,16 +5,13 @@ On the server side, run one of the following commands:
     (vLLM OpenAI API server)
     vllm serve <your_model> --disable-log-requests
 
-    (TGI backend)
-    ./launch_tgi_server.sh <your_model> <max_batch_total_tokens>
-
 On the client side, run:
     python benchmarks/benchmark_serving_structured_output.py \
         --backend <backend> \
         --model <your_model> \
         --dataset json \
         --structured-output-ratio 1.0 \
-        --structured-output-backend xgrammar \
+        --structured-output-backend auto \
         --request-rate 10 \
         --num-prompts 1000
 
@@ -54,7 +51,7 @@ try:
 except ImportError:
     from argparse import ArgumentParser as FlexibleArgumentParser
 
-from vllm.v1.structured_output.utils import (
+from vllm.v1.structured_output.backend_xgrammar import (
     has_xgrammar_unsupported_json_features)
 
 MILLISECONDS_TO_SECONDS_CONVERSION = 1000
@@ -126,6 +123,8 @@ def sample_requests(tokenizer: PreTrainedTokenizerBase,
                 copy.deepcopy(schema) for _ in range(args.num_prompts)
             ]
             for i in range(len(json_schemas)):
+                if "properties" not in json_schemas[i]:
+                    json_schemas[i]["properties"] = {}
                 json_schemas[i]["properties"][
                     f"__optional_field_{uuid.uuid4()}"] = {
                         "type":
@@ -133,10 +132,11 @@ def sample_requests(tokenizer: PreTrainedTokenizerBase,
                         "description":
                         "An unique optional field to avoid cached schemas"
                     }
+        else:
+            json_schemas = [schema] * args.num_prompts
 
         def gen_prompt(index: int):
-            schema = json_schemas[index % len(json_schemas)]
-            return f"Generate an example of a user profile given the following schema: {json.dumps(schema)}"  # noqa: E501
+            return f"Generate an example of a brief user profile given the following schema: {json.dumps(get_schema(index))}"  # noqa: E501
 
         def get_schema(index: int):
             return json_schemas[index % len(json_schemas)]
@@ -152,17 +152,17 @@ def sample_requests(tokenizer: PreTrainedTokenizerBase,
 
     elif args.dataset == "grammar":
         schema = """
-            ?start: select_statement
+        root ::= select_statement
 
-            ?select_statement: "SELECT " column_list " FROM " table_name
+        select_statement ::= "SELECT " column " from " table " where " condition
 
-            ?column_list: column_name ("," column_name)*
+        column ::= "col_1 " | "col_2 "
 
-            ?table_name: identifier
+        table ::= "table_1 " | "table_2 "
 
-            ?column_name: identifier
+        condition ::= column "= " number
 
-            ?identifier: /[a-zA-Z_][a-zA-Z0-9_]*/
+        number ::= "1 " | "2 "
         """
         prompt = "Generate an SQL query to show the 'username' \
             and 'email' from the 'users' table."
@@ -233,7 +233,8 @@ def sample_requests(tokenizer: PreTrainedTokenizerBase,
                 idx -= len_dataset
             schema = dataset["schema"][idx]
             prompt = tokenizer.apply_chat_template(dataset["prompt"][idx],
-                                                   tokenize=False)
+                                                   tokenize=False,
+                                                   add_generation_prompt=True)
             input_len = len(tokenizer(prompt).input_ids)
             completion = dataset["completion"][idx]
 
@@ -413,7 +414,6 @@ async def benchmark(
     ignore_eos: bool,
     max_concurrency: Optional[int],
     structured_output_ratio: float,
-    structured_output_backend: str,
     goodput_config_dict: Optional[dict[str, float]] = None,
 ):
     if backend in ASYNC_REQUEST_FUNCS:
@@ -425,8 +425,6 @@ async def benchmark(
         extra_body = {}
         # Add the schema to the extra_body
         extra_body[request.structure_type] = request.schema
-        # Add the specific structured_output_backend
-        extra_body["guided_decoding_backend"] = structured_output_backend
         return extra_body
 
     print("Starting initial single prompt test run...")
@@ -732,8 +730,11 @@ def main(args: argparse.Namespace):
         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)
+    tokenizer = get_tokenizer(
+        tokenizer_id,
+        trust_remote_code=args.trust_remote_code,
+        tokenizer_mode=args.tokenizer_mode,
+    )
 
     if args.dataset == 'grammar':
         args.structure_type = 'guided_grammar'
@@ -781,7 +782,6 @@ def main(args: argparse.Namespace):
             ignore_eos=args.ignore_eos,
             max_concurrency=args.max_concurrency,
             structured_output_ratio=args.structured_output_ratio,
-            structured_output_backend=args.structured_output_backend,
             goodput_config_dict=goodput_config_dict,
         ))
 
@@ -848,7 +848,7 @@ if __name__ == "__main__":
                             'json', 'json-unique', 'grammar', 'regex',
                             'choice', 'xgrammar_bench'
                         ])
-    parser.add_argument("--json_schema_path",
+    parser.add_argument("--json-schema-path",
                         type=str,
                         default=None,
                         help="Path to json schema.")
@@ -876,6 +876,13 @@ if __name__ == "__main__":
         help=
         "Name or path of the tokenizer, if not using the default tokenizer.",  # noqa: E501
     )
+    parser.add_argument(
+        "--tokenizer-mode",
+        type=str,
+        default="auto",
+        help=
+        "Name or path of the tokenizer, if not using the default tokenizer.",  # noqa: E501
+    )
     parser.add_argument(
         "--num-prompts",
         type=int,
@@ -956,7 +963,7 @@ if __name__ == "__main__":
         "--percentile-metrics",
         type=str,
         default="ttft,tpot,itl",
-        help="Comma-seperated list of selected metrics to report percentils. "
+        help="Comma-separated 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\".")
@@ -964,7 +971,7 @@ if __name__ == "__main__":
         "--metric-percentiles",
         type=str,
         default="99",
-        help="Comma-seperated list of percentiles for selected metrics. "
+        help="Comma-separated 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.",
@@ -989,11 +996,6 @@ if __name__ == "__main__":
                         type=float,
                         default=1.0,
                         help="Ratio of Structured Outputs requests")
-    parser.add_argument("--structured-output-backend",
-                        type=str,
-                        choices=["outlines", "lm-format-enforcer", "xgrammar"],
-                        default="xgrammar",
-                        help="Backend to use for structured outputs")
 
     args = parser.parse_args()
     main(args)

benchmarks/benchmark_throughput.py

@@ -11,7 +11,8 @@ from typing import Any, Optional, Union
 
 import torch
 import uvloop
-from benchmark_dataset import (BurstGPTDataset, HuggingFaceDataset,
+from benchmark_dataset import (AIMODataset, BurstGPTDataset,
+                               ConversationDataset, InstructCoderDataset,
                                RandomDataset, SampleRequest, ShareGPTDataset,
                                SonnetDataset, VisionArenaDataset)
 from benchmark_utils import convert_to_pytorch_benchmark_format, write_to_json
@@ -212,14 +213,17 @@ def run_hf(
     max_prompt_len = 0
     max_output_len = 0
     for i in range(len(requests)):
-        prompt, prompt_len, output_len = requests[i]
+        prompt = requests[i].prompt
+        prompt_len = requests[i].prompt_len
+        output_len = requests[i].expected_output_len
         # Add the prompt to the batch.
         batch.append(prompt)
         max_prompt_len = max(max_prompt_len, prompt_len)
         max_output_len = max(max_output_len, output_len)
         if len(batch) < max_batch_size and i != len(requests) - 1:
             # Check if we can add more requests to the batch.
-            _, next_prompt_len, next_output_len = requests[i + 1]
+            next_prompt_len = requests[i + 1].prompt_len
+            next_output_len = requests[i + 1].expected_output_len
             if (max(max_prompt_len, next_prompt_len) +
                     max(max_output_len, next_output_len)) <= 2048:
                 # We can add more requests to the batch.
@@ -300,6 +304,7 @@ def get_requests(args, tokenizer):
         "input_len": args.input_len,
         "output_len": args.output_len,
     }
+
     if args.dataset_path is None or args.dataset_name == "random":
         sample_kwargs["range_ratio"] = args.random_range_ratio
         sample_kwargs["prefix_len"] = args.prefix_len
@@ -317,18 +322,23 @@ def get_requests(args, tokenizer):
     elif args.dataset_name == "burstgpt":
         dataset_cls = BurstGPTDataset
     elif args.dataset_name == "hf":
-        if args.backend != "vllm-chat":
-            raise ValueError(
-                "hf datasets only are supported by vllm-chat backend")
-        # Choose between VisionArenaDataset and HuggingFaceDataset based on
-        # provided parameters.
-        dataset_cls = (VisionArenaDataset if args.dataset_path
-                       == VisionArenaDataset.VISION_ARENA_DATASET_PATH
-                       and args.hf_subset is None else HuggingFaceDataset)
-        common_kwargs['dataset_subset'] = args.hf_subset
-        common_kwargs['dataset_split'] = args.hf_split
-        sample_kwargs["enable_multimodal_chat"] = True
-
+        if args.dataset_path in VisionArenaDataset.SUPPORTED_DATASET_PATHS:
+            dataset_cls = VisionArenaDataset
+            common_kwargs['dataset_subset'] = None
+            common_kwargs['dataset_split'] = "train"
+            sample_kwargs["enable_multimodal_chat"] = True
+        elif args.dataset_path in InstructCoderDataset.SUPPORTED_DATASET_PATHS:
+            dataset_cls = InstructCoderDataset
+            common_kwargs['dataset_split'] = "train"
+        elif args.dataset_path in ConversationDataset.SUPPORTED_DATASET_PATHS:
+            dataset_cls = ConversationDataset
+            common_kwargs['dataset_subset'] = args.hf_subset
+            common_kwargs['dataset_split'] = args.hf_split
+            sample_kwargs["enable_multimodal_chat"] = True
+        elif args.dataset_path in AIMODataset.SUPPORTED_DATASET_PATHS:
+            dataset_cls = AIMODataset
+            common_kwargs['dataset_subset'] = None
+            common_kwargs['dataset_split'] = "train"
     else:
         raise ValueError(f"Unknown dataset name: {args.dataset_name}")
     # Remove None values
@@ -462,9 +472,17 @@ def validate_args(args):
         warnings.warn("--hf-subset and --hf-split will be ignored \
                 since --dataset-name is not 'hf'.",
                       stacklevel=2)
-    elif args.dataset_name == "hf" and args.backend != "vllm-chat":
-        raise ValueError(
-            "When --dataset-name is 'hf', backend must be 'vllm-chat'")
+    elif args.dataset_name == "hf":
+        if args.dataset_path in (
+                VisionArenaDataset.SUPPORTED_DATASET_PATHS.keys()
+                | ConversationDataset.SUPPORTED_DATASET_PATHS):
+            assert args.backend == "vllm-chat", f"{args.dataset_path} needs to use vllm-chat as the backend."  #noqa: E501
+        elif args.dataset_path in (InstructCoderDataset.SUPPORTED_DATASET_PATHS
+                                   | AIMODataset.SUPPORTED_DATASET_PATHS):
+            assert args.backend == "vllm", f"{args.dataset_path} needs to use vllm as the backend."  #noqa: E501
+        else:
+            raise ValueError(
+                f"{args.dataset_path} is not supported by hf dataset.")
 
     # --random-range-ratio: only used when dataset_name is 'random'
     if args.dataset_name != 'random' and args.random_range_ratio is not None:
@@ -505,6 +523,13 @@ def validate_args(args):
         raise ValueError(
             "Tokenizer must be the same as the model for MII backend.")
 
+    # --data-parallel is not supported currently.
+    # https://github.com/vllm-project/vllm/issues/16222
+    if args.data_parallel_size > 1:
+        raise ValueError(
+            "Data parallel is not supported in offline benchmark, \
+            please use benchmark serving instead")
+
 
 if __name__ == "__main__":
     parser = FlexibleArgumentParser(description="Benchmark the throughput.")
@@ -576,18 +601,30 @@ if __name__ == "__main__":
         default=None,
         help="Path to the lora adapters to use. This can be an absolute path, "
         "a relative path, or a Hugging Face model identifier.")
-    parser.add_argument("--prefix-len",
-                        type=int,
-                        default=None,
-                        help="Number of prefix tokens per request."
-                        "This is for the RandomDataset and SonnetDataset")
+    parser.add_argument(
+        "--prefix-len",
+        type=int,
+        default=None,
+        help=f"Number of prefix tokens to be used in RandomDataset "
+        "and SonnetDataset. For RandomDataset, the total input "
+        "length is the sum of prefix-len (default: "
+        f"{RandomDataset.DEFAULT_PREFIX_LEN}) and a random context length "
+        "sampled from [input_len * (1 - range_ratio), "
+        "input_len * (1 + range_ratio)]. For SonnetDataset, "
+        f"prefix_len (default: {SonnetDataset.DEFAULT_PREFIX_LEN}) "
+        "controls how much of the input is fixed lines versus "
+        "random lines, but the total input length remains approximately "
+        "input_len tokens.")
     # random dataset
     parser.add_argument(
         "--random-range-ratio",
         type=float,
         default=None,
-        help="Range of sampled ratio of input/output length, "
-        "used only for RandomDataSet.",
+        help=f"Range ratio (default : {RandomDataset.DEFAULT_RANGE_RATIO}) "
+        "for sampling input/output length, "
+        "used only for RandomDataset. Must be in the range [0, 1) to "
+        "define a symmetric sampling range "
+        "[length * (1 - range_ratio), length * (1 + range_ratio)].",
     )
 
     # hf dtaset

benchmarks/kernels/benchmark_bitblas.py

@@ -0,0 +1,236 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT License.
+
+from vllm.model_executor.layers.quantization.utils.bitblas_utils import (
+    MINIMUM_BITBLAS_VERSION)
+
+try:
+    import bitblas
+    if bitblas.__version__ < MINIMUM_BITBLAS_VERSION:
+        raise ImportError("bitblas version is wrong. Please "
+                          f"install bitblas>={MINIMUM_BITBLAS_VERSION}")
+except ImportError as e:
+    bitblas_import_exception = e
+    raise ValueError("Trying to use the bitblas backend, but could not import"
+                     f"with the following error: {bitblas_import_exception}. "
+                     "Please install bitblas through the following command: "
+                     f"`pip install bitblas>={MINIMUM_BITBLAS_VERSION}`"
+                     ) from bitblas_import_exception
+
+from bitblas import Matmul, MatmulConfig, auto_detect_nvidia_target
+
+from vllm.utils import FlexibleArgumentParser
+
+parser = FlexibleArgumentParser(
+    description="Benchmark BitBLAS int4 on a specific target.")
+
+# Add arguments to the parser
+parser.add_argument(
+    "--target",
+    type=str,
+    default=auto_detect_nvidia_target(),
+    help="Specify the target device for benchmarking.",
+)
+parser.add_argument("--group_size",
+                    type=int,
+                    default=None,
+                    help="Group size for grouped quantization.")
+parser.add_argument(
+    "--A_dtype",
+    type=str,
+    default="float16",
+    choices=["float16", "float32", "float64", "int32", "int8"],
+    help="Data type of activation A.",
+)
+parser.add_argument(
+    "--W_dtype",
+    type=str,
+    default="int4",
+    choices=[
+        "float16",
+        "float32",
+        "float64",
+        "int32",
+        "int8",
+        "int4",
+        "int2",
+        "int1",
+        "nf4",
+        "fp4_e2m1",
+    ],
+    help="Data type of weight W.",
+)
+parser.add_argument(
+    "--accum_dtype",
+    type=str,
+    default="float16",
+    choices=["float16", "int32"],
+    help="Data type for accumulation.",
+)
+parser.add_argument(
+    "--out_dtype",
+    type=str,
+    default="float16",
+    choices=["float16", "float32", "int32", "int8"],
+    help="Data type for output.",
+)
+parser.add_argument(
+    "--layout",
+    type=str,
+    default="nt",
+    choices=["nt", "nn"],
+    help="Matrix layout, 'nt' for non-transpose A and transpose W.",
+)
+parser.add_argument("--with_bias",
+                    action="store_true",
+                    help="Include bias in the benchmark.")
+parser.add_argument(
+    "--with_scaling",
+    action="store_true",
+    help="Include scaling factor in the quantization.",
+)
+parser.add_argument("--with_zeros",
+                    action="store_true",
+                    help="Include zeros in the quantization.")
+parser.add_argument(
+    "--zeros_mode",
+    type=str,
+    default=None,
+    choices=["original", "rescale", "quantized"],
+    help="Specify the mode for calculating zeros.",
+)
+
+# Parse the arguments
+args = parser.parse_args()
+
+# Assign arguments to variables
+target = args.target
+A_dtype = args.A_dtype
+W_dtype = args.W_dtype
+accum_dtype = args.accum_dtype
+out_dtype = args.out_dtype
+layout = args.layout
+with_bias = args.with_bias
+group_size = args.group_size
+with_scaling = args.with_scaling
+with_zeros = args.with_zeros
+zeros_mode = args.zeros_mode
+
+# Define a list of shared arguments that repeat in every config
+shared_args = [
+    A_dtype,
+    W_dtype,
+    out_dtype,
+    accum_dtype,
+    layout,
+    with_bias,
+    group_size,
+    with_scaling,
+    with_zeros,
+    zeros_mode,
+]
+
+# Define just the (M, K, N) shapes in a more compact list
+shapes = [
+    # square test
+    (1, 16384, 16384),
+    # BLOOM-176B
+    (1, 43008, 14336),
+    (1, 14336, 14336),
+    (1, 57344, 14336),
+    (1, 14336, 57344),
+    # OPT-65B
+    (1, 9216, 9216),
+    (1, 36864, 9216),
+    (1, 9216, 36864),
+    (1, 22016, 8192),
+    # LLAMA-70B/65B
+    (1, 8192, 22016),
+    (1, 8192, 8192),
+    (1, 28672, 8192),
+    (1, 8192, 28672),
+    # square test
+    (16384, 16384, 16384),
+    # BLOOM-176B
+    (8192, 43008, 14336),
+    (8192, 14336, 14336),
+    (8192, 57344, 14336),
+    (8192, 14336, 57344),
+    # OPT-65B
+    (8192, 9216, 9216),
+    (8192, 36864, 9216),
+    (8192, 9216, 36864),
+    (8192, 22016, 8192),
+    # LLAMA-70B/65B
+    (8192, 8192, 22016),
+    (8192, 8192, 8192),
+    (8192, 28672, 8192),
+    (8192, 8192, 28672),
+]
+
+# Build test shapes with all the shared arguments
+test_shapes = [(MatmulConfig, Matmul, (*shape, *shared_args))
+               for shape in shapes]
+
+benchmark_sets = []
+benchmark_sets.extend(test_shapes)
+
+benchmark_results = {}
+for config_class, operator, input_args in benchmark_sets:
+    config = config_class(*input_args)
+    matmul = operator(config, target=target, enable_tuning=True)
+    kernel_latency = matmul.profile_latency()
+
+    print("Time cost is: {:.3f} ms".format(kernel_latency))
+
+    profile_config = {
+        f"{operator.__name__}-{'-'.join([str(i) for i in input_args])}": {
+            "BitBLAS_top20_latency": kernel_latency,
+        }
+    }
+
+    benchmark_results.update(profile_config)
+
+# Define headers for the table
+headers = [
+    "PrimFunc",
+    "Input Arguments",
+    "BitBLAS Top20 Latency",
+]
+
+# Calculate column widths for pretty printing
+col_widths = [0, 0, 0]
+for config_key, values in benchmark_results.items():
+    args_split = config_key.split("-")
+    func_name = args_split[0]
+    input_args_str = "-".join(args_split[1:])
+    col_widths[0] = max(col_widths[0], len(func_name) + 2, len(headers[0]) + 2)
+    col_widths[1] = max(col_widths[1],
+                        len(input_args_str) + 2,
+                        len(headers[1]) + 2)
+    col_widths[2] = max(col_widths[2],
+                        len(f"{values['BitBLAS_top20_latency']:.3f} ms") + 2,
+                        len(headers[2]) + 2)
+    # break only if you want to measure widths from a single example;
+    # otherwise, let it loop over all items.
+
+# Print header
+for i, header in enumerate(headers):
+    headers[i] = header.ljust(col_widths[i])
+print("".join(headers))
+print("-" * sum(col_widths))
+
+# Print rows
+for config_key, values in benchmark_results.items():
+    args_split = config_key.split("-")
+    func_name = args_split[0]
+    input_args_str = "-".join(args_split[1:])
+    row = [
+        func_name,
+        input_args_str,
+        f"{values['BitBLAS_top20_latency']:.3f} ms",
+    ]
+    row_str = "".join(
+        [str(cell).ljust(col_widths[idx]) for idx, cell in enumerate(row)])
+    print(row_str)

benchmarks/kernels/benchmark_grouped_gemm_cutlass.py

@@ -0,0 +1,341 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import torch
+import torch.utils.benchmark as benchmark
+from benchmark_shapes import WEIGHT_SHAPES_MOE
+
+from vllm import _custom_ops as ops
+from vllm.config import ParallelConfig, VllmConfig, set_current_vllm_config
+from vllm.model_executor.layers.fused_moe.fused_moe import (cutlass_moe_fp8,
+                                                            fused_experts,
+                                                            fused_topk)
+from vllm.utils import FlexibleArgumentParser
+
+DEFAULT_MODELS = [
+    "nm-testing/Mixtral-8x7B-Instruct-v0.1", "nm-testing/deepseekv2-lite",
+    "ibm-granite/granite-3.0-1b-a400m", "ibm-granite/granite-3.0-3b-a800m"
+]
+DEFAULT_BATCH_SIZES = [1, 4, 8, 16, 32, 64, 128, 256, 512]
+DEFAULT_TP_SIZES = [1]
+
+PER_ACT_TOKEN_OPTS = [False]
+PER_OUT_CH_OPTS = [False]
+
+
+def to_fp8(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 bench_run(results: list[benchmark.Measurement], model: str,
+              num_experts: int, topk: int, per_act_token: bool,
+              per_out_ch: bool, mkn: tuple[int, int, int]):
+    label = "Quant Matmul"
+
+    sub_label = (
+        "{}, num_experts={}, topk={}, per_act_token={} per_out_ch={}, "
+        "MKN=({})".format(model, num_experts, topk, per_act_token, per_out_ch,
+                          mkn))
+
+    print(f"Testing: {sub_label}")
+
+    (m, k, n) = mkn
+
+    dtype = torch.half
+
+    a = torch.randn((m, k), device="cuda", dtype=dtype) / 10
+    w1 = torch.randn((num_experts, 2 * n, k), device="cuda", dtype=dtype) / 10
+    w2 = torch.randn((num_experts, k, n), device="cuda", dtype=dtype) / 10
+
+    _, a_scale = ops.scaled_fp8_quant(a)
+
+    w1_q = torch.empty((num_experts, 2 * n, k),
+                       device="cuda",
+                       dtype=torch.float8_e4m3fn)
+    w2_q = torch.empty((num_experts, k, n),
+                       device="cuda",
+                       dtype=torch.float8_e4m3fn)
+    w1_scale = torch.empty((num_experts, 1, 1),
+                           device="cuda",
+                           dtype=torch.float32)
+    w2_scale = torch.empty((num_experts, 1, 1),
+                           device="cuda",
+                           dtype=torch.float32)
+
+    ab_strides1 = torch.full((num_experts, ),
+                             k,
+                             device="cuda",
+                             dtype=torch.int64)
+    c_strides1 = torch.full((num_experts, ),
+                            2 * n,
+                            device="cuda",
+                            dtype=torch.int64)
+    ab_strides2 = torch.full((num_experts, ),
+                             n,
+                             device="cuda",
+                             dtype=torch.int64)
+    c_strides2 = torch.full((num_experts, ),
+                            k,
+                            device="cuda",
+                            dtype=torch.int64)
+
+    for expert in range(num_experts):
+        w1_q[expert], w1_scale[expert] = ops.scaled_fp8_quant(w1[expert])
+        w2_q[expert], w2_scale[expert] = ops.scaled_fp8_quant(w2[expert])
+    w1_q_notransp = w1_q.clone()
+    w2_q_notransp = w2_q.clone()
+    w1_q = w1_q.transpose(1, 2)
+    w2_q = w2_q.transpose(1, 2)
+
+    score = torch.randn((m, num_experts), device="cuda", dtype=dtype)
+
+    topk_weights, topk_ids, token_expert_indices = fused_topk(
+        a, score, topk, renormalize=False)
+
+    def run_triton_moe(a: torch.Tensor, w1: torch.Tensor, w2: torch.Tensor,
+                       topk_weights: torch.Tensor, topk_ids: torch.Tensor,
+                       w1_scale: torch.Tensor, w2_scale: torch.Tensor,
+                       a_scale: torch.Tensor, num_repeats: int):
+        for _ in range(num_repeats):
+            fused_experts(a,
+                          w1,
+                          w2,
+                          topk_weights,
+                          topk_ids,
+                          use_fp8_w8a8=True,
+                          w1_scale=w1_scale,
+                          w2_scale=w2_scale,
+                          a1_scale=a_scale)
+
+    def run_cutlass_moe(a: torch.Tensor, a_scale: torch.Tensor,
+                        w1: torch.Tensor, w2: torch.Tensor,
+                        w1_scale: torch.Tensor, w2_scale: torch.Tensor,
+                        topk_weights: torch.Tensor, topk_ids: torch.Tensor,
+                        ab_strides1: torch.Tensor, c_strides1: torch.Tensor,
+                        ab_strides2: torch.Tensor, c_strides2: torch.Tensor,
+                        num_repeats: int):
+        for _ in range(num_repeats):
+            cutlass_moe_fp8(a,
+                            w1,
+                            w2,
+                            w1_scale,
+                            w2_scale,
+                            topk_weights,
+                            topk_ids,
+                            ab_strides1,
+                            c_strides1,
+                            ab_strides2,
+                            c_strides2,
+                            a1_scale=a_scale)
+
+    def run_cutlass_from_graph(
+            a: torch.Tensor, a_scale: torch.Tensor, w1_q: torch.Tensor,
+            w2_q: torch.Tensor, w1_scale: torch.Tensor, w2_scale: torch.Tensor,
+            topk_weights: torch.Tensor, topk_ids: torch.Tensor,
+            ab_strides1: torch.Tensor, c_strides1: torch.Tensor,
+            ab_strides2: torch.Tensor, c_strides2: torch.Tensor):
+        with set_current_vllm_config(
+                VllmConfig(parallel_config=ParallelConfig(
+                    pipeline_parallel_size=1))):
+            return cutlass_moe_fp8(a,
+                                   w1_q,
+                                   w2_q,
+                                   w1_scale,
+                                   w2_scale,
+                                   topk_weights,
+                                   topk_ids,
+                                   ab_strides1,
+                                   c_strides1,
+                                   ab_strides2,
+                                   c_strides2,
+                                   a1_scale=a_scale)
+
+    def run_triton_from_graph(a: torch.Tensor, w1: torch.Tensor,
+                              w2: torch.Tensor, topk_weights: torch.Tensor,
+                              topk_ids: torch.Tensor, w1_scale: torch.Tensor,
+                              w2_scale: torch.Tensor, a_scale: torch.Tensor):
+        with set_current_vllm_config(
+                VllmConfig(parallel_config=ParallelConfig(
+                    pipeline_parallel_size=1))):
+            return fused_experts(a,
+                                 w1,
+                                 w2,
+                                 topk_weights,
+                                 topk_ids,
+                                 use_fp8_w8a8=True,
+                                 w1_scale=w1_scale,
+                                 w2_scale=w2_scale,
+                                 a1_scale=a_scale)
+
+    def replay_graph(graph, num_repeats):
+        for _ in range(num_repeats):
+            graph.replay()
+        torch.cuda.synchronize()
+
+    cutlass_stream = torch.cuda.Stream()
+    cutlass_graph = torch.cuda.CUDAGraph()
+    with torch.cuda.graph(cutlass_graph, stream=cutlass_stream):
+        run_cutlass_from_graph(a, a_scale, w1_q, w2_q, w1_scale, w2_scale,
+                               topk_weights, topk_ids, ab_strides1, c_strides1,
+                               ab_strides2, c_strides2)
+    torch.cuda.synchronize()
+
+    triton_stream = torch.cuda.Stream()
+    triton_graph = torch.cuda.CUDAGraph()
+    with torch.cuda.graph(triton_graph, stream=triton_stream):
+        run_triton_from_graph(a, w1_q_notransp, w2_q_notransp, topk_weights,
+                              topk_ids, w1_scale, w2_scale, a_scale)
+    torch.cuda.synchronize()
+
+    min_run_time = 5
+    num_warmup = 5
+    num_runs = 25
+
+    globals = {
+        # Baseline params
+        "w1": w1,
+        "w2": w2,
+        "score": score,
+        "topk": topk,
+        "w1_q_notransp": w1_q_notransp,
+        "w2_q_notransp": w2_q_notransp,
+        # Cutlass params
+        "a_scale": a_scale,
+        "w1_q": w1_q,
+        "w2_q": w2_q,
+        "w1_scale": w1_scale,
+        "w2_scale": w2_scale,
+        "ab_strides1": ab_strides1,
+        "c_strides1": c_strides1,
+        "ab_strides2": ab_strides2,
+        "c_strides2": c_strides2,
+        # cuda graph params
+        "cutlass_graph": cutlass_graph,
+        "triton_graph": triton_graph,
+        # Gen params
+        "a": a,
+        "topk_weights": topk_weights,
+        "topk_ids": topk_ids,
+        "num_runs": num_runs,
+        # Kernels
+        "run_triton_moe": run_triton_moe,
+        "run_cutlass_moe": run_cutlass_moe,
+        "replay_graph": replay_graph,
+    }
+
+    # Warmup
+    run_triton_moe(a, w1_q_notransp, w2_q_notransp, topk_weights, topk_ids,
+                   w1_scale, w2_scale, a_scale, num_warmup)
+
+    results.append(
+        benchmark.Timer(
+            stmt=
+            "run_triton_moe(a, w1_q_notransp, w2_q_notransp, topk_weights, topk_ids, w1_scale, w2_scale, a_scale, num_runs)",  # noqa: E501
+            globals=globals,
+            label=label,
+            sub_label=sub_label,
+            description="triton_moe",
+        ).blocked_autorange(min_run_time=min_run_time))
+
+    # Warmup
+    replay_graph(triton_graph, num_warmup)
+
+    results.append(
+        benchmark.Timer(
+            stmt="replay_graph(triton_graph, num_runs)",
+            globals=globals,
+            label=label,
+            sub_label=sub_label,
+            description="triton_moe_cuda_graphs",
+        ).blocked_autorange(min_run_time=min_run_time))
+
+    # Warmup
+    run_cutlass_moe(a, a_scale, w1_q, w2_q, w1_scale, w2_scale, topk_weights,
+                    topk_ids, ab_strides1, c_strides1, ab_strides2, c_strides2,
+                    num_warmup)
+
+    results.append(
+        benchmark.Timer(
+            stmt=
+            "run_cutlass_moe(a, a_scale, w1_q, w2_q, w1_scale, w2_scale, topk_weights, topk_ids, ab_strides1, c_strides1, ab_strides2, c_strides2, num_runs)",  # noqa: E501
+            globals=globals,
+            label=label,
+            sub_label=sub_label,
+            description="grouped_gemm_moe",
+        ).blocked_autorange(min_run_time=min_run_time))
+
+    # Warmup
+    replay_graph(cutlass_graph, num_warmup)
+
+    results.append(
+        benchmark.Timer(
+            stmt="replay_graph(cutlass_graph, num_runs)",
+            globals=globals,
+            label=label,
+            sub_label=sub_label,
+            description="grouped_gemm_moe_cuda_graphs",
+        ).blocked_autorange(min_run_time=min_run_time))
+
+
+def main(args):
+    print("Benchmarking models:")
+    for i, model in enumerate(args.models):
+        print(f"[{i}]  {model}")
+
+    results: list[benchmark.Measurement] = []
+
+    for model in args.models:
+        for tp in args.tp_sizes:
+            for layer in WEIGHT_SHAPES_MOE[model]:
+                num_experts = layer[0]
+                topk = layer[1]
+                size_k = layer[2]
+                size_n = layer[3] // tp
+
+                if len(args.limit_k) > 0 and size_k not in args.limit_k:
+                    continue
+
+                if len(args.limit_n) > 0 and size_n not in args.limit_n:
+                    continue
+
+                for per_act_token in PER_ACT_TOKEN_OPTS:
+                    for per_out_ch in PER_OUT_CH_OPTS:
+                        for size_m in DEFAULT_BATCH_SIZES:
+                            mkn = (size_m, size_k, size_n)
+                            bench_run(results, model, num_experts, topk,
+                                      per_act_token, per_out_ch, mkn)
+
+    compare = benchmark.Compare(results)
+    compare.print()
+
+
+if __name__ == "__main__":
+    parser = FlexibleArgumentParser(
+        description="Benchmark Marlin across specified models/shapes/batches")
+    parser.add_argument(
+        "--models",
+        nargs="+",
+        type=str,
+        default=DEFAULT_MODELS,
+        choices=WEIGHT_SHAPES_MOE.keys(),
+    )
+    parser.add_argument("--tp-sizes",
+                        nargs="+",
+                        type=int,
+                        default=DEFAULT_TP_SIZES)
+    parser.add_argument("--batch-sizes",
+                        nargs="+",
+                        type=int,
+                        default=DEFAULT_BATCH_SIZES)
+    parser.add_argument("--limit-k", nargs="+", type=int, default=[])
+    parser.add_argument("--limit-n", nargs="+", type=int, default=[])
+    parser.add_argument("--limit-num-groups", nargs="+", type=int, default=[])
+    parser.add_argument("--limit-per-act-token",
+                        nargs="+",
+                        type=int,
+                        default=[])
+    parser.add_argument("--limit-per-out-ch", nargs="+", type=int, default=[])
+
+    args = parser.parse_args()
+    main(args)

benchmarks/kernels/benchmark_lora.py

@@ -17,13 +17,14 @@ from torch.utils.benchmark import Measurement as TMeasurement
 from utils import ArgPool, Bench, CudaGraphBenchParams
 from weight_shapes import WEIGHT_SHAPES
 
-from vllm.lora.ops.triton_ops.bgmv_expand import bgmv_expand
-from vllm.lora.ops.triton_ops.bgmv_expand_slice import bgmv_expand_slice
-from vllm.lora.ops.triton_ops.bgmv_shrink import bgmv_shrink
-from vllm.lora.ops.triton_ops.sgmv_expand import sgmv_expand
-from vllm.lora.ops.triton_ops.sgmv_shrink import sgmv_shrink
-from vllm.lora.ops.triton_ops.utils import _LORA_A_PTR_DICT, _LORA_B_PTR_DICT
-from vllm.lora.ops.triton_ops.v1 import V1KernelMeta, v1_expand, v1_shrink
+from vllm.triton_utils import HAS_TRITON
+
+if HAS_TRITON:
+    from vllm.lora.ops.triton_ops import (LoRAKernelMeta, lora_expand,
+                                          lora_shrink)
+    from vllm.lora.ops.triton_ops.utils import (_LORA_A_PTR_DICT,
+                                                _LORA_B_PTR_DICT)
+
 from vllm.utils import FlexibleArgumentParser
 
 DEFAULT_MODELS = list(WEIGHT_SHAPES.keys())
@@ -167,69 +168,25 @@ class OpType(Enum):
     """
     LoRA Ops to benchmark and its properties.
     """
-    SGMV_SHRINK = auto()
-    BGMV_SHRINK = auto()
-    SGMV_EXPAND = auto()
-    BGMV_EXPAND = auto()
-    BGMV_EXPAND_SLICE = auto()
-    V1_SHRINK = auto()
-    V1_EXPAND = auto()
+    LORA_SHRINK = auto()
+    LORA_EXPAND = auto()
 
     @staticmethod
     def from_str(s: str) -> "OpType":
-        if s.lower() == 'sgmv_shrink':
-            return OpType.SGMV_SHRINK
-        if s.lower() == 'sgmv_expand':
-            return OpType.SGMV_EXPAND
-        if s.lower() == 'bgmv_shrink':
-            return OpType.BGMV_SHRINK
-        if s.lower() == 'bgmv_expand':
-            return OpType.BGMV_EXPAND
-        if s.lower() == "bgmv_expand_slice":
-            return OpType.BGMV_EXPAND_SLICE
-        if s.lower() == "v1_shrink":
-            return OpType.V1_SHRINK
-        if s.lower() == "v1_expand":
-            return OpType.V1_EXPAND
+        if s.lower() == "lora_shrink":
+            return OpType.LORA_SHRINK
+        if s.lower() == "lora_expand":
+            return OpType.LORA_EXPAND
         raise ValueError(f"Unrecognized str {s} to convert to OpType")
 
     def is_shrink_fn(self) -> bool:
-        return self in [
-            OpType.SGMV_SHRINK, OpType.BGMV_SHRINK, OpType.V1_SHRINK
-        ]
+        return self in [OpType.LORA_SHRINK]
 
     def is_expand_fn(self) -> bool:
-        return self in [
-            OpType.SGMV_EXPAND, OpType.BGMV_EXPAND, OpType.V1_EXPAND
-        ]
-
-    def is_prefill_op(self) -> bool:
-        return self in [
-            OpType.SGMV_SHRINK, OpType.SGMV_EXPAND, OpType.V1_SHRINK,
-            OpType.V1_EXPAND
-        ]
-
-    def is_decode_op(self) -> bool:
-        return self in [
-            OpType.BGMV_SHRINK, OpType.BGMV_EXPAND, OpType.BGMV_EXPAND_SLICE,
-            OpType.V1_SHRINK, OpType.V1_EXPAND
-        ]
-
-    def is_expand_slice_fn(self) -> bool:
-        return self in [OpType.BGMV_EXPAND_SLICE]
+        return self in [OpType.LORA_EXPAND]
 
     def num_slices(self) -> list[int]:
-        if self in [
-                OpType.SGMV_EXPAND, OpType.SGMV_SHRINK, OpType.V1_SHRINK,
-                OpType.V1_EXPAND
-        ]:
-            # SGMV kernels and v1 kernels supports slices
-            return [1, 2, 3]
-        if self in [OpType.BGMV_SHRINK, OpType.BGMV_EXPAND]:
-            return [1]
-        if self in [OpType.BGMV_EXPAND_SLICE]:
-            return [2, 3]
-        raise ValueError(f"Unrecognized OpType {self}")
+        return [1, 2, 3]
 
     def mkn(self, batch_size: int, seq_length: int, hidden_size: int,
             lora_rank: int) -> tuple[int, int, int]:
@@ -239,7 +196,7 @@ class OpType(Enum):
             k = hidden_size
             n = lora_rank
         else:
-            assert self.is_expand_fn() or self.is_expand_slice_fn()
+            assert self.is_expand_fn()
             m = num_tokens
             k = lora_rank
             n = hidden_size
@@ -254,7 +211,7 @@ class OpType(Enum):
         if self.is_shrink_fn():
             return op_dtype, op_dtype, torch.float32
         else:
-            assert self.is_expand_fn() or self.is_expand_slice_fn()
+            assert self.is_expand_fn()
             return torch.float32, op_dtype, op_dtype
 
     def matmul_shapes(
@@ -268,43 +225,19 @@ class OpType(Enum):
         m, k, n = self.mkn(batch_size, seq_length, hidden_size, lora_rank)
 
         b_shape = (num_loras, n, k)  # col-major
-        if self in [OpType.SGMV_SHRINK, OpType.V1_SHRINK]:
-            # SGMV shrink and V1 shrink kernels support num_slices inherently
-            # in the kernel.
+        if self in [OpType.LORA_SHRINK]:
+            # LoRA shrink kernels support num_slices inherently in the kernel.
             return ((m, k), b_shape, (num_slices, m, n))
-        if self in [OpType.SGMV_EXPAND, OpType.V1_EXPAND]:
-            # SGMV expand and V1 expand kernels support num_slices inherently
-            # in the kernel
+        if self in [OpType.LORA_EXPAND]:
+            # LoRA expand kernels support num_slices inherently in the kernel
             return ((num_slices, m, k), b_shape, (m, n * num_slices))
-        if self == OpType.BGMV_SHRINK:
-            return ((m, k), b_shape, (m, n))
-        if self == OpType.BGMV_EXPAND:
-            return ((m, k), b_shape, (m, n))
-        if self == OpType.BGMV_EXPAND_SLICE:
-            return ((num_slices, m, k), b_shape, (m, n * num_slices))
-
         raise ValueError(f"Unrecognized op_type {self}")
 
     def bench_fn(self) -> Callable:
-
-        def emulate_bgmv_expand_slice(kwargs_list: list[dict[str, Any]]):
-            for x in kwargs_list:
-                bgmv_expand_slice(**x)
-
-        if self == OpType.SGMV_SHRINK:
-            return sgmv_shrink
-        if self == OpType.SGMV_EXPAND:
-            return sgmv_expand
-        if self == OpType.BGMV_SHRINK:
-            return bgmv_shrink
-        if self == OpType.BGMV_EXPAND:
-            return bgmv_expand
-        if self == OpType.BGMV_EXPAND_SLICE:
-            return emulate_bgmv_expand_slice
-        if self == OpType.V1_SHRINK:
-            return v1_shrink
-        if self == OpType.V1_EXPAND:
-            return v1_expand
+        if self == OpType.LORA_SHRINK:
+            return lora_shrink
+        if self == OpType.LORA_EXPAND:
+            return lora_expand
 
         raise ValueError(f"Unrecognized optype {self}")
 
@@ -318,34 +251,13 @@ class OpType(Enum):
         """
         w_dtype = lora_weights[0].dtype
         num_slices = len(lora_weights)
-        if self in [OpType.SGMV_SHRINK, OpType.V1_SHRINK]:
+        if self in [OpType.LORA_SHRINK]:
             for slice_idx in range(num_slices):
                 ref_group_gemm(ref_out=output[slice_idx, :],
                                input=input,
                                lora_weights=lora_weights[slice_idx],
                                **kwargs)
-        elif self in [OpType.SGMV_EXPAND, OpType.V1_EXPAND]:
-            hidden_size = lora_weights[0].shape[1]
-            for slice_idx in range(num_slices):
-                slice_offset = slice_idx * hidden_size
-                ref_group_gemm(
-                    ref_out=output[:, slice_offset:slice_offset + hidden_size],
-                    input=input[slice_idx].clone().to(dtype=w_dtype),
-                    lora_weights=lora_weights[slice_idx],
-                    **kwargs)
-        elif self == OpType.BGMV_SHRINK:
-            assert num_slices == 1
-            ref_group_gemm(ref_out=output,
-                           input=input,
-                           lora_weights=lora_weights[0],
-                           **kwargs)
-        elif self == OpType.BGMV_EXPAND:
-            assert num_slices == 1
-            ref_group_gemm(ref_out=output,
-                           input=input.clone().to(dtype=w_dtype),
-                           lora_weights=lora_weights[0],
-                           **kwargs)
-        elif self == OpType.BGMV_EXPAND_SLICE:
+        elif self in [OpType.LORA_EXPAND]:
             hidden_size = lora_weights[0].shape[1]
             for slice_idx in range(num_slices):
                 slice_offset = slice_idx * hidden_size
@@ -411,13 +323,11 @@ class BenchmarkTensors:
     input: torch.Tensor
     lora_weights_lst: list[torch.Tensor]
     output: torch.Tensor
-    # metadata tensors
+    # LoRA kernel metadata
+    lora_kernel_meta: LoRAKernelMeta
+    # Metadata tensors used in testing correctness
     seq_lens: torch.Tensor
-    seq_start_loc: torch.Tensor
     prompt_lora_mapping: torch.Tensor
-    token_lora_mapping: torch.Tensor
-    # v1 kernel metadata
-    v1_kernel_meta: Optional[V1KernelMeta] = None
 
     def io_types(self) -> str:
         return (f"{dtype_to_str(self.input.dtype)}x"
@@ -444,35 +354,29 @@ class BenchmarkTensors:
         assert ctx.num_active_loras <= ctx.num_loras
         total_tokens = ctx.batch_size * ctx.seq_length
 
+        # Make metadata tensors involved in correctness testing.
         # Prepare seq lens tensor
         seq_len_tensor = torch.randint(ctx.seq_length, ctx.seq_length + 1,
                                        (ctx.batch_size, ))
-        # Prepare seq_start_loc tensor
-        seq_start_loc_tensor = torch.cumsum(torch.tensor(
-            [0] + seq_len_tensor[:-1].tolist(), dtype=torch.long),
-                                            dim=0)
         assert total_tokens == seq_len_tensor.sum()
         # Prepare prompt lora indices tensor
         prompt_lora_indices_tensor = make_prompt_lora_mapping(
             ctx.batch_size, ctx.num_active_loras, ctx.sort_by_lora_id, "cpu")
-        # Prepare token lora indices tensor
+
+        # Make LoRAKernelMeta
         token_lora_indices_tensor = make_token_lora_mapping(
             total_tokens, ctx.batch_size, prompt_lora_indices_tensor,
             seq_len_tensor, "cpu")
-
-        v1_kernel_meta = None
-        if op_type in [OpType.V1_SHRINK, OpType.V1_EXPAND]:
-            v1_kernel_meta = V1KernelMeta.make(
-                max_loras=ctx.num_loras,
-                max_num_tokens=token_lora_indices_tensor.size(0),
-                device="cpu")
-            v1_kernel_meta.prepare_tensors(
-                token_lora_mapping=token_lora_indices_tensor)
+        lora_kernel_meta = LoRAKernelMeta.make(
+            max_loras=ctx.num_loras,
+            max_num_tokens=token_lora_indices_tensor.size(0),
+            device="cpu")
+        lora_kernel_meta.prepare_tensors(
+            token_lora_mapping=token_lora_indices_tensor)
 
         return BenchmarkTensors(input_tensor, lora_weights, output_tensor,
-                                seq_len_tensor, seq_start_loc_tensor,
-                                prompt_lora_indices_tensor,
-                                token_lora_indices_tensor, v1_kernel_meta)
+                                lora_kernel_meta, seq_len_tensor,
+                                prompt_lora_indices_tensor)
 
     def sanity_check(self) -> None:
         """
@@ -482,9 +386,9 @@ class BenchmarkTensors:
         # check metadata tensors
         assert torch.sum(self.seq_lens) == num_tokens
         num_seqs = self.seq_lens.shape[0]
-        assert self.seq_start_loc.shape[0] == num_seqs
+        #assert self.seq_start_loc.shape[0] == num_seqs
         assert self.prompt_lora_mapping.shape[0] == num_seqs
-        assert self.token_lora_mapping.shape[0] == num_tokens
+        assert self.lora_kernel_meta.token_lora_mapping.shape[0] == num_tokens
 
     def to_device(self, device: str):
         """
@@ -499,220 +403,27 @@ class BenchmarkTensors:
         self.input = to_device(self.input)
         self.output = to_device(self.output)
         self.seq_lens = to_device(self.seq_lens)
-        self.seq_start_loc = to_device(self.seq_start_loc)
         self.prompt_lora_mapping = to_device(self.prompt_lora_mapping)
-        self.token_lora_mapping = to_device(self.token_lora_mapping)
         for i in range(len(self.lora_weights_lst)):
             self.lora_weights_lst[i] = to_device(self.lora_weights_lst[i])
 
-        # v1 meta
-        if self.v1_kernel_meta:
-            for field_name in V1KernelMeta.__dataclass_fields__:
-                field = getattr(self.v1_kernel_meta, field_name)
-                assert isinstance(field, torch.Tensor)
-                setattr(self.v1_kernel_meta, field_name, to_device(field))
+        # LoRA meta
+        for field_name in LoRAKernelMeta.__dataclass_fields__:
+            field = getattr(self.lora_kernel_meta, field_name)
+            assert isinstance(field, torch.Tensor)
+            setattr(self.lora_kernel_meta, field_name, to_device(field))
 
     def metadata(self) -> tuple[int, int, int]:
         """
         Return num_seqs, num_tokens and max_seq_len
         """
         num_seqs = self.seq_lens.shape[0]
-        num_tokens = self.token_lora_mapping.shape[0]
+        num_tokens = self.lora_kernel_meta.token_lora_mapping.shape[0]
         max_seq_len = torch.max(self.seq_lens).item()
         num_slices = len(self.lora_weights_lst)
         return num_seqs, num_tokens, max_seq_len, num_slices
 
-    def convert_to_sgmv_benchmark_tensors(self):
-        """
-        For sgmv punica kernels, when consecutive sequences have the
-        same LoRA ID, we just merge them together.
-        This happens in punica.py::compute_metadata
-        """
-
-        # Collapse seq_lens and seq_start_loc
-        _, seq_lens = torch.unique_consecutive(self.token_lora_mapping,
-                                               return_counts=True)
-        cum_result = torch.cumsum(seq_lens, dim=0)
-        seq_start_loc = torch.zeros_like(seq_lens)
-        seq_start_loc[1:].copy_(cum_result[:-1])
-
-        # Collapse prompt mapping
-        prompt_lora_mapping = torch.unique_consecutive(
-            self.prompt_lora_mapping)
-
-        assert torch.sum(seq_lens) == torch.sum(self.seq_lens), \
-         f"dont match - new {torch.sum(seq_lens)} vs {torch.sum(self.seq_lens)}"
-
-        self.prompt_lora_mapping = prompt_lora_mapping.to(
-            dtype=self.prompt_lora_mapping.dtype)
-        self.seq_lens = seq_lens.to(dtype=self.seq_lens.dtype)
-        self.seq_start_loc = seq_start_loc.to(dtype=self.seq_start_loc.dtype)
-
-    def as_sgmv_shrink_kwargs(self) -> dict[str, Any]:
-        self.convert_to_sgmv_benchmark_tensors()
-        self.sanity_check()
-        self.to_device(self.input.device)
-
-        num_seqs, num_tokens, max_seq_len, num_slices = self.metadata()
-
-        # Sanity check matrix shapes.
-        i_shape, lw_shape, o_shape = self.input.shape, self.lora_weights_lst[
-            0].shape, self.output.shape
-        # Expected input shape [num_tokens, hidden_size]
-        assert len(i_shape) == 2
-        assert i_shape[0] == num_tokens
-        hidden_size = i_shape[1]
-        # Expected lora weight shape [num_loras, lora_rank, hidden_size]
-        assert len(lw_shape) == 3
-        assert lw_shape[2] == hidden_size
-        lora_rank = lw_shape[1]
-        # Expected output shape [num_slices, num_tokens, lora_rank]
-        assert len(o_shape) == 3
-        assert o_shape == (num_slices, num_tokens, lora_rank)
-
-        return {
-            'inputs': self.input,
-            'lora_a_weights': self.lora_weights_lst,
-            'output_tensor': self.output,
-            'b_seq_start_loc': self.seq_start_loc,
-            'seq_len_tensor': self.seq_lens,
-            'lora_indices_tensor': self.prompt_lora_mapping,
-            'batches': num_seqs,
-            'max_seq_length': max_seq_len,
-            'token_nums': num_tokens,
-            'scaling': 1.0,
-        }
-
-    def as_sgmv_expand_kwargs(self, add_inputs: bool) -> dict[str, Any]:
-
-        self.convert_to_sgmv_benchmark_tensors()
-        self.sanity_check()
-        self.to_device(self.input.device)
-
-        num_seqs, num_tokens, max_seq_len, num_slices = self.metadata()
-
-        # Sanity check matrix shapes.
-        i_shape, lw_shape, o_shape = self.input.shape, self.lora_weights_lst[
-            0].shape, self.output.shape
-        # Expected input shape : [num_slices, num_tokens, lora_rank]
-        assert len(i_shape) == 3
-        assert i_shape[0] == num_slices
-        assert i_shape[1] == num_tokens
-        lora_rank = i_shape[2]
-        # Expected lora weight shape : [num_lora, hidden_size, lora_rank]
-        assert len(lw_shape) == 3
-        assert lw_shape[2] == lora_rank
-        hidden_size = lw_shape[1]
-        # Expected output shape : [num_tokens, hidden_size * num_slices]
-        assert len(o_shape) == 2
-        assert o_shape == (num_tokens, hidden_size * num_slices)
-
-        return {
-            'inputs': self.input,
-            'lora_b_weights': self.lora_weights_lst,
-            'output_tensor': self.output,
-            'b_seq_start_loc': self.seq_start_loc,
-            'seq_len_tensor': self.seq_lens,
-            'lora_indices_tensor': self.prompt_lora_mapping,
-            'batches': num_seqs,
-            'max_seq_length': max_seq_len,
-            'token_nums': num_tokens,
-            'offset_start': 0,
-            'add_inputs': add_inputs,
-        }
-
-    def as_bgmv_shrink_kwargs(self) -> dict[str, Any]:
-        assert len(self.lora_weights_lst) == 1
-        self.to_device(self.input.device)
-
-        _, num_tokens, _, _ = self.metadata()
-        # Sanity check shapes
-        i_shape, lw_shape, o_shape = self.input.shape, self.lora_weights_lst[
-            0].shape, self.output.shape
-        # Expected input shape [num_tokens, hidden_size]
-        assert len(i_shape) == 2
-        assert i_shape[0] == num_tokens
-        hidden_size = i_shape[1]
-        # Expected lora weight shape [num_loras, lora_rank, hidden_size]
-        assert len(lw_shape) == 3
-        assert lw_shape[2] == hidden_size
-        lora_rank = lw_shape[1]
-        # Expected output shape [num_tokens, lora_rank]
-        assert len(o_shape) == 2
-        assert o_shape == (num_tokens, lora_rank)
-
-        return {
-            'inputs': self.input,
-            'lora_a_weights': self.lora_weights_lst[0],
-            'output_tensor': self.output,
-            'lora_indices_tensor': self.token_lora_mapping,
-            'scaling': 1.0
-        }
-
-    def as_bgmv_expand_kwargs(self, add_inputs: bool):
-        assert len(self.lora_weights_lst) == 1
-        self.to_device(self.input.device)
-
-        _, num_tokens, _, _ = self.metadata()
-        # Sanity check shapes
-        i_shape, lw_shape, o_shape = self.input.shape, self.lora_weights_lst[
-            0].shape, self.output.shape
-        # Expected input shape [num_tokens, lora_rank]
-        assert len(i_shape) == 2
-        assert i_shape[0] == num_tokens
-        lora_rank = i_shape[1]
-        # Expected lora weight shape [num_loras, hidden_size, lora_rank]
-        assert len(lw_shape) == 3
-        assert lw_shape[2] == lora_rank
-        hidden_size = lw_shape[1]
-        # Expected output shape [num_tokens, hidden_size]
-        assert len(o_shape) == 2
-        assert o_shape == (num_tokens, hidden_size)
-
-        return {
-            'inputs': self.input,
-            'lora_b_weights': self.lora_weights_lst[0],
-            'output_tensor': self.output,
-            'lora_indices_tensor': self.token_lora_mapping,
-            'add_inputs': add_inputs
-        }
-
-    def as_bgmv_expand_slice_kwargs(self, add_inputs: bool) -> dict[str, Any]:
-
-        _, num_tokens, _, num_slices = self.metadata()
-        # Sanity check shapes
-        i_shape, lw_shape, o_shape = self.input.shape, self.lora_weights_lst[
-            0].shape, self.output.shape
-        # Expected input shape [num_slices, num_tokens, lora_rank]
-        assert len(i_shape) == 3
-        assert i_shape[0] == num_slices
-        assert i_shape[1] == num_tokens
-        lora_rank = i_shape[2]
-        # Expected lora weight shape [num_loras, hidden_size, lora_rank]
-        assert len(lw_shape) == 3
-        assert lw_shape[2] == lora_rank
-        hidden_size = lw_shape[1]
-        # Expected output shape [num_tokens, hidden_size * num_slices]
-        assert len(o_shape) == 2
-        assert o_shape == (num_tokens, hidden_size * num_slices)
-
-        self.to_device(self.input.device)
-
-        kwargs_list = []
-        for i in range(num_slices):
-            kwargs_list.append({
-                'inputs': self.input[i],
-                'lora_b_weights': self.lora_weights_lst[i],
-                'output_tensor': self.output,
-                'lora_indices_tensor': self.token_lora_mapping,
-                'slice_offset': i * hidden_size,
-                'slice_size': hidden_size,
-                'add_inputs': add_inputs,
-            })
-        return {'kwargs_list': kwargs_list}
-
-    def as_v1_shrink_kwargs(self) -> dict[str, Any]:
-        assert self.v1_kernel_meta is not None
+    def as_lora_shrink_kwargs(self) -> dict[str, Any]:
         self.sanity_check()
         self.to_device(self.input.device)
 
@@ -737,17 +448,16 @@ class BenchmarkTensors:
             'inputs': self.input,
             'lora_a_weights': self.lora_weights_lst,
             'output_tensor': self.output,
-            'token_lora_mapping': self.v1_kernel_meta.token_lora_mapping,
+            'token_lora_mapping': self.lora_kernel_meta.token_lora_mapping,
             'token_indices_sorted_by_lora_ids':
-            self.v1_kernel_meta.token_indices_sorted_by_lora_ids,
-            'num_tokens_per_lora': self.v1_kernel_meta.num_tokens_per_lora,
-            'lora_token_start_loc': self.v1_kernel_meta.lora_token_start_loc,
-            'lora_ids': self.v1_kernel_meta.active_lora_ids,
+            self.lora_kernel_meta.token_indices_sorted_by_lora_ids,
+            'num_tokens_per_lora': self.lora_kernel_meta.num_tokens_per_lora,
+            'lora_token_start_loc': self.lora_kernel_meta.lora_token_start_loc,
+            'lora_ids': self.lora_kernel_meta.active_lora_ids,
             'scaling': 1.0,
         }
 
-    def as_v1_expand_kwargs(self, add_inputs: bool) -> dict[str, Any]:
-        assert self.v1_kernel_meta is not None
+    def as_lora_expand_kwargs(self, add_inputs: bool) -> dict[str, Any]:
         self.sanity_check()
         self.to_device(self.input.device)
 
@@ -773,12 +483,12 @@ class BenchmarkTensors:
             'inputs': self.input,
             'lora_b_weights': self.lora_weights_lst,
             'output_tensor': self.output,
-            'token_lora_mapping': self.v1_kernel_meta.token_lora_mapping,
+            'token_lora_mapping': self.lora_kernel_meta.token_lora_mapping,
             'token_indices_sorted_by_lora_ids':
-            self.v1_kernel_meta.token_indices_sorted_by_lora_ids,
-            'num_tokens_per_lora': self.v1_kernel_meta.num_tokens_per_lora,
-            'lora_token_start_loc': self.v1_kernel_meta.lora_token_start_loc,
-            'lora_ids': self.v1_kernel_meta.active_lora_ids,
+            self.lora_kernel_meta.token_indices_sorted_by_lora_ids,
+            'num_tokens_per_lora': self.lora_kernel_meta.num_tokens_per_lora,
+            'lora_token_start_loc': self.lora_kernel_meta.lora_token_start_loc,
+            'lora_ids': self.lora_kernel_meta.active_lora_ids,
             'offset_start': 0,
             'add_inputs': add_inputs,
         }
@@ -791,20 +501,10 @@ class BenchmarkTensors:
         else:
             assert add_inputs is not None
 
-        if op_type == OpType.SGMV_SHRINK:
-            return self.as_sgmv_shrink_kwargs()
-        if op_type == OpType.SGMV_EXPAND:
-            return self.as_sgmv_expand_kwargs(add_inputs)
-        if op_type == OpType.BGMV_SHRINK:
-            return self.as_bgmv_shrink_kwargs()
-        if op_type == OpType.BGMV_EXPAND:
-            return self.as_bgmv_expand_kwargs(add_inputs)
-        if op_type == OpType.BGMV_EXPAND_SLICE:
-            return self.as_bgmv_expand_slice_kwargs(add_inputs)
-        if op_type == OpType.V1_SHRINK:
-            return self.as_v1_shrink_kwargs()
-        if op_type == OpType.V1_EXPAND:
-            return self.as_v1_expand_kwargs(add_inputs)
+        if op_type == OpType.LORA_SHRINK:
+            return self.as_lora_shrink_kwargs()
+        if op_type == OpType.LORA_EXPAND:
+            return self.as_lora_expand_kwargs(add_inputs)
         raise ValueError(f"Unrecognized optype {self}")
 
     def test_correctness(self, op_type: OpType,
@@ -993,10 +693,6 @@ def run(args: argparse.Namespace, bench_ctxs: list[BenchmarkContext]):
     for bench_ctx in bench_ctxs:
         for seq_len in args.seq_lengths:
             bench_ops: list[OpType] = args.op_types
-            if seq_len > 1:
-                # bench only prefill ops
-                bench_ops = [op for op in args.op_types if op.is_prefill_op()]
-
             seq_len_timers = []
             for bench_op in bench_ops:
                 for num_slices in bench_op.num_slices():
@@ -1206,13 +902,13 @@ Benchmark LoRA kernels:
     {use_cuda_graph_recommendation()}
 
     list_bench example:
-        python3 benchmarks/kernels/benchmark_lora.py list_bench --arg-pool-size 32 --batch-sizes 1 16 32 --dtype torch.float16 --hidden-sizes 2048 --lora-ranks 16 --num-loras 1 4 --op-types bgmv_shrink bgmv_expand sgmv_shrink sgmv_expand bgmv_expand_slice --seq-lengths 1 16 --sort-by-lora-id 1 --cuda-graph-nops 32
+        python3 benchmarks/kernels/benchmark_lora.py list_bench --arg-pool-size 32 --batch-sizes 1 16 32 --dtype torch.float16 --hidden-sizes 2048 --lora-ranks 16 --num-loras 1 4 --op-types lora_shrink lora_expand --seq-lengths 1 16 --sort-by-lora-id 1 --cuda-graph-nops 32
 
     model_bench example:
-        python3 benchmarks/kernels/benchmark_lora.py model_bench --models meta-llama/Llama-3-8b  --arg-pool-size 32 --batch-sizes 1 16 32 --dtype torch.float16  --lora-ranks 16 --num-loras 1 4 --op-types bgmv_shrink bgmv_expand sgmv_shrink sgmv_expand bgmv_expand_slice --seq-lengths 1 16 --sort-by-lora-id 1 --cuda-graph-nops 32 
+        python3 benchmarks/kernels/benchmark_lora.py model_bench --models meta-llama/Llama-3-8b  --arg-pool-size 32 --batch-sizes 1 16 32 --dtype torch.float16  --lora-ranks 16 --num-loras 1 4 --op-types lora_shrink lora_expand --seq-lengths 1 16 --sort-by-lora-id 1 --cuda-graph-nops 32 
 
     range_bench example:
-        python3 benchmarks/kernels/benchmark_lora.py range_bench  --arg-pool-size 32 --batch-sizes 1 16 32 --dtype torch.float16   --num-loras 1 4 --op-types bgmv_shrink bgmv_expand sgmv_shrink sgmv_expand bgmv_expand_slice --seq-lengths 1 16 --sort-by-lora-id 1 --cuda-graph-nops 32 --hidden-sizes-start 1024 --hidden-sizes-end 4096 --hidden-sizes-increment 1024 --lora-ranks-start 8 --lora-ranks-end 24 --lora-ranks-increment 8 
+        python3 benchmarks/kernels/benchmark_lora.py range_bench  --arg-pool-size 32 --batch-sizes 1 16 32 --dtype torch.float16   --num-loras 1 4 --op-types lora_shrink lora_expand --seq-lengths 1 16 --sort-by-lora-id 1 --cuda-graph-nops 32 --hidden-sizes-start 1024 --hidden-sizes-end 4096 --hidden-sizes-increment 1024 --lora-ranks-start 8 --lora-ranks-end 24 --lora-ranks-increment 8 
             """,  # noqa: E501
         formatter_class=argparse.RawTextHelpFormatter)
 

benchmarks/kernels/benchmark_moe.py

@@ -6,16 +6,17 @@ import time
 from contextlib import nullcontext
 from datetime import datetime
 from itertools import product
+from types import SimpleNamespace
 from typing import Any, TypedDict
 
 import ray
 import torch
-import triton
 from ray.experimental.tqdm_ray import tqdm
-from transformers import AutoConfig
 
 from vllm.model_executor.layers.fused_moe.fused_moe import *
 from vllm.platforms import current_platform
+from vllm.transformers_utils.config import get_config
+from vllm.triton_utils import triton
 from vllm.utils import FlexibleArgumentParser
 
 FP8_DTYPE = current_platform.fp8_dtype()
@@ -30,19 +31,18 @@ class BenchmarkConfig(TypedDict):
     num_stages: int
 
 
-def benchmark_config(
-    config: BenchmarkConfig,
-    num_tokens: int,
-    num_experts: int,
-    shard_intermediate_size: int,
-    hidden_size: int,
-    topk: int,
-    dtype: torch.dtype,
-    use_fp8_w8a8: bool,
-    use_int8_w8a16: bool,
-    num_iters: int = 100,
-    block_quant_shape: List[int] = None,
-) -> float:
+def benchmark_config(config: BenchmarkConfig,
+                     num_tokens: int,
+                     num_experts: int,
+                     shard_intermediate_size: int,
+                     hidden_size: int,
+                     topk: int,
+                     dtype: torch.dtype,
+                     use_fp8_w8a8: bool,
+                     use_int8_w8a16: bool,
+                     num_iters: int = 100,
+                     block_quant_shape: List[int] = None,
+                     use_deep_gemm: bool = False) -> float:
     init_dtype = torch.float16 if use_fp8_w8a8 else dtype
     x = torch.randn(num_tokens, hidden_size, dtype=dtype)
     if use_int8_w8a16:
@@ -115,22 +115,41 @@ def benchmark_config(
     def run():
         from vllm.model_executor.layers.fused_moe import override_config
         with override_config(config):
-            fused_moe(
-                x,
-                w1,
-                w2,
-                input_gating,
-                topk,
-                renormalize=True,
-                inplace=True,
-                use_fp8_w8a8=use_fp8_w8a8,
-                use_int8_w8a16=use_int8_w8a16,
-                w1_scale=w1_scale,
-                w2_scale=w2_scale,
-                a1_scale=a1_scale,
-                a2_scale=a2_scale,
-                block_shape=block_quant_shape,
-            )
+            if use_deep_gemm:
+                topk_weights, topk_ids, token_expert_indices = fused_topk(
+                    x, input_gating, topk, False)
+                return fused_experts(
+                    x,
+                    w1,
+                    w2,
+                    topk_weights,
+                    topk_ids,
+                    inplace=True,
+                    use_fp8_w8a8=use_fp8_w8a8,
+                    w1_scale=w1_scale,
+                    w2_scale=w2_scale,
+                    a1_scale=a1_scale,
+                    a2_scale=a2_scale,
+                    block_shape=block_quant_shape,
+                    allow_deep_gemm=True,
+                )
+            else:
+                fused_moe(
+                    x,
+                    w1,
+                    w2,
+                    input_gating,
+                    topk,
+                    renormalize=True,
+                    inplace=True,
+                    use_fp8_w8a8=use_fp8_w8a8,
+                    use_int8_w8a16=use_int8_w8a16,
+                    w1_scale=w1_scale,
+                    w2_scale=w2_scale,
+                    a1_scale=a1_scale,
+                    a2_scale=a2_scale,
+                    block_shape=block_quant_shape,
+                )
 
     # JIT compilation & warmup
     run()
@@ -366,6 +385,7 @@ class BenchmarkWorker:
         use_fp8_w8a8: bool,
         use_int8_w8a16: bool,
         block_quant_shape: List[int] = None,
+        use_deep_gemm: bool = False,
     ) -> tuple[dict[str, int], float]:
         current_platform.seed_everything(self.seed)
         dtype_str = get_config_dtype_str(dtype,
@@ -396,7 +416,8 @@ class BenchmarkWorker:
                                        use_fp8_w8a8,
                                        use_int8_w8a16,
                                        num_iters=100,
-                                       block_quant_shape=block_quant_shape)
+                                       block_quant_shape=block_quant_shape,
+                                       use_deep_gemm=use_deep_gemm)
         return config, kernel_time
 
     def tune(
@@ -411,6 +432,7 @@ class BenchmarkWorker:
         use_int8_w8a16: bool,
         search_space: list[dict[str, int]],
         block_quant_shape: list[int],
+        use_deep_gemm: bool,
     ) -> dict[str, int]:
         best_config = None
         best_time = float("inf")
@@ -421,8 +443,14 @@ class BenchmarkWorker:
                                                    hidden_size, search_space,
                                                    is_fp16, topk)
 
-        with torch.cuda.device(self.device_id) if current_platform.is_rocm(
-        ) else nullcontext():
+        need_device_guard = False
+        if current_platform.is_rocm():
+            visible_device = os.environ.get("ROCR_VISIBLE_DEVICES", None)
+            if visible_device != f"{self.device_id}":
+                need_device_guard = True
+
+        with torch.cuda.device(
+                self.device_id) if need_device_guard else nullcontext():
             for config in tqdm(search_space):
                 try:
                     kernel_time = benchmark_config(
@@ -436,7 +464,8 @@ class BenchmarkWorker:
                         use_fp8_w8a8,
                         use_int8_w8a16,
                         num_iters=20,
-                        block_quant_shape=block_quant_shape)
+                        block_quant_shape=block_quant_shape,
+                        use_deep_gemm=use_deep_gemm)
                 except triton.runtime.autotuner.OutOfResources:
                     # Some configurations may be invalid and fail to compile.
                     continue
@@ -505,9 +534,13 @@ def get_weight_block_size_safety(config, default_value=None):
 
 def main(args: argparse.Namespace):
     print(args)
-    block_quant_shape = None
-    config = AutoConfig.from_pretrained(
-        args.model, trust_remote_code=args.trust_remote_code)
+
+    config = get_config(model=args.model,
+                        trust_remote_code=args.trust_remote_code)
+    if args.model_prefix:
+        config = getattr(config, args.model_prefix)
+    config = SimpleNamespace(**config)
+
     if config.architectures[0] == "DbrxForCausalLM":
         E = config.ffn_config.moe_num_experts
         topk = config.ffn_config.moe_top_k
@@ -518,19 +551,21 @@ def main(args: argparse.Namespace):
         topk = config.num_experts_per_tok
         intermediate_size = config.intermediate_size
         shard_intermediate_size = 2 * intermediate_size // args.tp_size
-    elif (config.architectures[0] == "DeepseekV3ForCausalLM"
-          or config.architectures[0] == "DeepseekV2ForCausalLM"):
+    elif (config.architectures[0]
+          in ("DeepseekV3ForCausalLM", "DeepseekV2ForCausalLM")):
         E = config.n_routed_experts
         topk = config.num_experts_per_tok
         intermediate_size = config.moe_intermediate_size
         shard_intermediate_size = 2 * intermediate_size // args.tp_size
-        block_quant_shape = get_weight_block_size_safety(config)
-    elif config.architectures[0] == "Qwen2MoeForCausalLM":
+    elif config.architectures[0] in ("Qwen2MoeForCausalLM",
+                                     "Qwen3MoeForCausalLM"):
         E = config.num_experts
         topk = config.num_experts_per_tok
         intermediate_size = config.moe_intermediate_size
         shard_intermediate_size = 2 * intermediate_size // args.tp_size
     else:
+        # Support for llama4
+        config = config.get_text_config()
         # Default: Mixtral.
         E = config.num_local_experts
         topk = config.num_experts_per_tok
@@ -538,9 +573,11 @@ def main(args: argparse.Namespace):
         shard_intermediate_size = 2 * intermediate_size // args.tp_size
 
     hidden_size = config.hidden_size
-    dtype = torch.float16 if current_platform.is_rocm() else config.torch_dtype
+    dtype = torch.float16 if current_platform.is_rocm() else getattr(
+        torch, config.torch_dtype)
     use_fp8_w8a8 = args.dtype == "fp8_w8a8"
     use_int8_w8a16 = args.dtype == "int8_w8a16"
+    block_quant_shape = get_weight_block_size_safety(config)
 
     if args.batch_size is None:
         batch_sizes = [
@@ -550,6 +587,17 @@ def main(args: argparse.Namespace):
     else:
         batch_sizes = [args.batch_size]
 
+    use_deep_gemm = bool(args.use_deep_gemm)
+
+    if current_platform.is_rocm() and "HIP_VISIBLE_DEVICES" in os.environ:
+        # Ray will set ROCR_VISIBLE_DEVICES for device visibility
+        logger.warning(
+            "Ray uses ROCR_VISIBLE_DEVICES to control device accessibility."
+            "Replacing HIP_VISIBLE_DEVICES with ROCR_VISIBLE_DEVICES.")
+        val = os.environ["HIP_VISIBLE_DEVICES"]
+        os.environ["ROCR_VISIBLE_DEVICES"] = val
+        del os.environ["HIP_VISIBLE_DEVICES"]
+
     ray.init()
     num_gpus = int(ray.available_resources()["GPU"])
     workers = [BenchmarkWorker.remote(args.seed) for _ in range(num_gpus)]
@@ -572,10 +620,10 @@ def main(args: argparse.Namespace):
 
         start = time.time()
         configs = _distribute(
-            "tune",
-            [(batch_size, E, shard_intermediate_size, hidden_size, topk, dtype,
-              use_fp8_w8a8, use_int8_w8a16, search_space, block_quant_shape)
-             for batch_size in batch_sizes])
+            "tune", [(batch_size, E, shard_intermediate_size, hidden_size,
+                      topk, dtype, use_fp8_w8a8, use_int8_w8a16, search_space,
+                      block_quant_shape, use_deep_gemm)
+                     for batch_size in batch_sizes])
         best_configs = {
             M: sort_config(config)
             for M, config in zip(batch_sizes, configs)
@@ -589,7 +637,7 @@ def main(args: argparse.Namespace):
         outputs = _distribute(
             "benchmark",
             [(batch_size, E, shard_intermediate_size, hidden_size, topk, dtype,
-              use_fp8_w8a8, use_int8_w8a16, block_quant_shape)
+              use_fp8_w8a8, use_int8_w8a16, block_quant_shape, use_deep_gemm)
              for batch_size in batch_sizes])
 
         for batch_size, (config, kernel_time) in zip(batch_sizes, outputs):
@@ -611,10 +659,12 @@ if __name__ == "__main__":
                         type=str,
                         choices=["auto", "fp8_w8a8", "int8_w8a16"],
                         default="auto")
+    parser.add_argument("--use-deep-gemm", action="store_true")
     parser.add_argument("--seed", type=int, default=0)
     parser.add_argument("--batch-size", type=int, required=False)
     parser.add_argument("--tune", action="store_true")
     parser.add_argument("--trust-remote-code", action="store_true")
+    parser.add_argument("--model-prefix", type=str, required=False)
     args = parser.parse_args()
 
     main(args)

benchmarks/kernels/benchmark_moe_permute_unpermute.py

@@ -0,0 +1,349 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import argparse
+from typing import Any, TypedDict
+
+import ray
+import torch
+from transformers import AutoConfig
+
+from vllm.model_executor.layers.fused_moe.deep_gemm_moe import (
+    _moe_permute, _moe_unpermute_and_reduce)
+from vllm.model_executor.layers.fused_moe.fused_moe import *
+from vllm.model_executor.layers.fused_moe.moe_permute_unpermute import *
+from vllm.model_executor.layers.fused_moe.utils import _fp8_quantize
+from vllm.platforms import current_platform
+from vllm.utils import FlexibleArgumentParser
+
+FP8_DTYPE = current_platform.fp8_dtype()
+
+
+class BenchmarkConfig(TypedDict):
+    BLOCK_SIZE_M: int
+    BLOCK_SIZE_N: int
+    BLOCK_SIZE_K: int
+    GROUP_SIZE_M: int
+    num_warps: int
+    num_stages: int
+
+
+def benchmark_permute(num_tokens: int,
+                      num_experts: int,
+                      hidden_size: int,
+                      topk: int,
+                      dtype: torch.dtype,
+                      use_fp8_w8a8: bool,
+                      use_int8_w8a16: bool,
+                      num_iters: int = 100,
+                      use_customized_permute: bool = False) -> float:
+    # init_dtype = torch.float16 if use_fp8_w8a8 else dtype
+    hidden_states = torch.randn(num_tokens, hidden_size, dtype=dtype)
+    # output_hidden_states = torch.empty_like(hidden_states)
+    if use_fp8_w8a8:
+        align_block_size = 128  # deepgemm needs 128 m aligned block
+        qhidden_states, scale = _fp8_quantize(hidden_states, None, None)
+    else:
+        align_block_size = None
+        qhidden_states = hidden_states
+
+    gating_output = torch.randn(num_iters,
+                                num_tokens,
+                                num_experts,
+                                dtype=torch.float32)
+
+    input_gating = torch.randn(num_tokens, num_experts, dtype=torch.float32)
+    topk_weights, topk_ids, token_expert_indices = fused_topk(
+        qhidden_states, input_gating, topk, False)
+
+    def prepare(i: int):
+        input_gating.copy_(gating_output[i])
+
+    def run():
+        if use_customized_permute:
+            (permuted_hidden_states, first_token_off, inv_perm_idx,
+             m_indices) = moe_permute(
+                 qhidden_states,
+                 topk_weights=topk_weights,
+                 topk_ids=topk_ids,
+                 token_expert_indices=token_expert_indices,
+                 topk=topk,
+                 n_expert=num_experts,
+                 n_local_expert=num_experts,
+                 expert_map=None,
+                 align_block_size=align_block_size,
+             )
+        else:
+            (permuted_hidden_states, a1q_scale, sorted_token_ids, expert_ids,
+             inv_perm) = _moe_permute(qhidden_states, None, topk_ids,
+                                      num_experts, None, align_block_size)
+
+    # JIT compilation & warmup
+    run()
+    torch.cuda.synchronize()
+
+    # Capture 10 invocations with CUDA graph
+    graph = torch.cuda.CUDAGraph()
+    with torch.cuda.graph(graph):
+        for _ in range(10):
+            run()
+    torch.cuda.synchronize()
+
+    # Warmup
+    for _ in range(5):
+        graph.replay()
+    torch.cuda.synchronize()
+
+    start_event = torch.cuda.Event(enable_timing=True)
+    end_event = torch.cuda.Event(enable_timing=True)
+
+    latencies: list[float] = []
+    for i in range(num_iters):
+        prepare(i)
+        torch.cuda.synchronize()
+
+        start_event.record()
+        graph.replay()
+        end_event.record()
+        end_event.synchronize()
+        latencies.append(start_event.elapsed_time(end_event))
+    avg = sum(latencies) / (num_iters * 10) * 1000  # us
+    graph.reset()
+    return avg
+
+
+def benchmark_unpermute(num_tokens: int,
+                        num_experts: int,
+                        hidden_size: int,
+                        topk: int,
+                        dtype: torch.dtype,
+                        use_fp8_w8a8: bool,
+                        use_int8_w8a16: bool,
+                        num_iters: int = 100,
+                        use_customized_permute: bool = False) -> float:
+    # init_dtype = torch.float16 if use_fp8_w8a8 else dtype
+    hidden_states = torch.randn(num_tokens, hidden_size, dtype=dtype)
+    output_hidden_states = torch.empty_like(hidden_states)
+    if use_fp8_w8a8:
+        align_block_size = 128  # deepgemm needs 128 m aligned block
+        qhidden_states, scale = _fp8_quantize(hidden_states, None, None)
+    else:
+        align_block_size = None
+        qhidden_states = hidden_states
+
+    input_gating = torch.randn(num_tokens, num_experts, dtype=torch.float32)
+
+    topk_weights, topk_ids, token_expert_indices = fused_topk(
+        qhidden_states, input_gating, topk, False)
+
+    def prepare():
+        if use_customized_permute:
+            (permuted_hidden_states, first_token_off, inv_perm_idx,
+             m_indices) = moe_permute(
+                 qhidden_states,
+                 topk_weights=topk_weights,
+                 topk_ids=topk_ids,
+                 token_expert_indices=token_expert_indices,
+                 topk=topk,
+                 n_expert=num_experts,
+                 n_local_expert=num_experts,
+                 expert_map=None,
+                 align_block_size=align_block_size,
+             )
+            # convert to fp16/bf16 as gemm output
+            return (permuted_hidden_states.to(dtype), first_token_off,
+                    inv_perm_idx, m_indices)
+        else:
+            (permuted_qhidden_states, a1q_scale, sorted_token_ids, expert_ids,
+             inv_perm) = _moe_permute(qhidden_states, None, topk_ids,
+                                      num_experts, None, align_block_size)
+            # convert to fp16/bf16 as gemm output
+            return (permuted_qhidden_states.to(dtype), a1q_scale,
+                    sorted_token_ids, expert_ids, inv_perm)
+
+    def run(input: tuple):
+        if use_customized_permute:
+            (permuted_hidden_states, first_token_off, inv_perm_idx,
+             m_indices) = input
+            moe_unpermute(permuted_hidden_states, topk_weights, topk_ids,
+                          inv_perm_idx, first_token_off, topk, num_experts,
+                          num_experts)
+        else:
+            (permuted_hidden_states, a1q_scale, sorted_token_ids, expert_ids,
+             inv_perm) = input
+            _moe_unpermute_and_reduce(output_hidden_states,
+                                      permuted_hidden_states, inv_perm,
+                                      topk_weights)
+
+    # JIT compilation & warmup
+    input = prepare()
+    run(input)
+    torch.cuda.synchronize()
+
+    # Capture 10 invocations with CUDA graph
+    graph = torch.cuda.CUDAGraph()
+    with torch.cuda.graph(graph):
+        for _ in range(10):
+            run(input)
+    torch.cuda.synchronize()
+
+    # Warmup
+    for _ in range(5):
+        graph.replay()
+    torch.cuda.synchronize()
+
+    start_event = torch.cuda.Event(enable_timing=True)
+    end_event = torch.cuda.Event(enable_timing=True)
+
+    latencies: list[float] = []
+    for i in range(num_iters):
+        torch.cuda.synchronize()
+        start_event.record()
+        graph.replay()
+        end_event.record()
+        end_event.synchronize()
+        latencies.append(start_event.elapsed_time(end_event))
+    avg = sum(latencies) / (num_iters * 10) * 1000  # us
+    graph.reset()
+    return avg
+
+
+@ray.remote(num_gpus=1)
+class BenchmarkWorker:
+
+    def __init__(self, seed: int) -> None:
+        torch.set_default_device("cuda")
+        current_platform.seed_everything(seed)
+        self.seed = seed
+        # Get the device ID to allocate tensors and kernels
+        # on the respective GPU. This is required for Ray to work
+        # correctly with multi-GPU tuning on the ROCm platform.
+        self.device_id = int(ray.get_gpu_ids()[0])
+
+    def benchmark(
+        self,
+        num_tokens: int,
+        num_experts: int,
+        hidden_size: int,
+        topk: int,
+        dtype: torch.dtype,
+        use_fp8_w8a8: bool,
+        use_int8_w8a16: bool,
+        use_customized_permute: bool = False,
+    ) -> tuple[dict[str, int], float]:
+        current_platform.seed_everything(self.seed)
+
+        permute_time = benchmark_permute(
+            num_tokens,
+            num_experts,
+            hidden_size,
+            topk,
+            dtype,
+            use_fp8_w8a8,
+            use_int8_w8a16,
+            num_iters=100,
+            use_customized_permute=use_customized_permute)
+        unpermute_time = benchmark_unpermute(
+            num_tokens,
+            num_experts,
+            hidden_size,
+            topk,
+            dtype,
+            use_fp8_w8a8,
+            use_int8_w8a16,
+            num_iters=100,
+            use_customized_permute=use_customized_permute)
+        return permute_time, unpermute_time
+
+
+def get_weight_block_size_safety(config, default_value=None):
+
+    quantization_config = getattr(config, 'quantization_config', {})
+    if isinstance(quantization_config, dict):
+        return quantization_config.get('weight_block_size', default_value)
+    return default_value
+
+
+def main(args: argparse.Namespace):
+    print(args)
+
+    config = AutoConfig.from_pretrained(
+        args.model, trust_remote_code=args.trust_remote_code)
+    if config.architectures[0] == "DbrxForCausalLM":
+        E = config.ffn_config.moe_num_experts
+        topk = config.ffn_config.moe_top_k
+    elif config.architectures[0] == "JambaForCausalLM":
+        E = config.num_experts
+        topk = config.num_experts_per_tok
+    elif (config.architectures[0] == "DeepseekV3ForCausalLM"
+          or config.architectures[0] == "DeepseekV2ForCausalLM"):
+        E = config.n_routed_experts
+        topk = config.num_experts_per_tok
+    elif config.architectures[0] in [
+            "Qwen2MoeForCausalLM", "Qwen3MoeForCausalLM"
+    ]:
+        E = config.num_experts
+        topk = config.num_experts_per_tok
+
+    else:
+        # Support for llama4
+        config = config.get_text_config()
+        # Default: Mixtral.
+        E = config.num_local_experts
+        topk = config.num_experts_per_tok
+
+    hidden_size = config.hidden_size
+    dtype = torch.float16 if current_platform.is_rocm() else config.torch_dtype
+    use_fp8_w8a8 = args.dtype == "fp8_w8a8"
+    use_int8_w8a16 = args.dtype == "int8_w8a16"
+    use_customized_permute = args.use_customized_permute
+
+    if args.batch_size is None:
+        batch_sizes = [
+            1, 2, 4, 8, 16, 24, 32, 48, 64, 96, 128, 256, 512, 1024, 1536,
+            2048, 3072, 4096
+        ]
+    else:
+        batch_sizes = [args.batch_size]
+
+    ray.init()
+    num_gpus = int(ray.available_resources()["GPU"])
+    workers = [BenchmarkWorker.remote(args.seed) for _ in range(num_gpus)]
+
+    def _distribute(method: str, inputs: list[Any]) -> list[Any]:
+        outputs = []
+        worker_idx = 0
+        for input_args in inputs:
+            worker = workers[worker_idx]
+            worker_method = getattr(worker, method)
+            output = worker_method.remote(*input_args)
+            outputs.append(output)
+            worker_idx = (worker_idx + 1) % num_gpus
+        return ray.get(outputs)
+
+    outputs = _distribute(
+        "benchmark", [(batch_size, E, hidden_size, topk, dtype, use_fp8_w8a8,
+                       use_int8_w8a16, use_customized_permute)
+                      for batch_size in batch_sizes])
+
+    for batch_size, (permute, unpermute) in zip(batch_sizes, outputs):
+        print(f"Batch size: {batch_size}")
+        print(f"Permute time: {permute:.2f} us")
+        print(f"Unpermute time: {unpermute:.2f} us")
+
+
+if __name__ == "__main__":
+    parser = FlexibleArgumentParser()
+    parser.add_argument("--model",
+                        type=str,
+                        default="mistralai/Mixtral-8x7B-Instruct-v0.1")
+    parser.add_argument("--dtype",
+                        type=str,
+                        choices=["auto", "fp8_w8a8", "int8_w8a16"],
+                        default="auto")
+    parser.add_argument("--use-customized-permute", action="store_true")
+    parser.add_argument("--seed", type=int, default=0)
+    parser.add_argument("--batch-size", type=int, required=False)
+    parser.add_argument("--trust-remote-code", action="store_true")
+    args = parser.parse_args()
+
+    main(args)

benchmarks/kernels/benchmark_paged_attention.py

@@ -7,10 +7,13 @@ from typing import Optional
 import torch
 
 from vllm import _custom_ops as ops
+from vllm.logger import init_logger
 from vllm.platforms import current_platform
 from vllm.utils import (STR_DTYPE_TO_TORCH_DTYPE, FlexibleArgumentParser,
                         create_kv_caches_with_random)
 
+logger = init_logger(__name__)
+
 NUM_BLOCKS = 128 * 1024
 PARTITION_SIZE = 512
 PARTITION_SIZE_ROCM = 256
@@ -193,6 +196,9 @@ def main(
 
 
 if __name__ == '__main__':
+    logger.warning("This script benchmarks the paged attention kernel. "
+                   "By default this is no longer used in vLLM inference.")
+
     parser = FlexibleArgumentParser(
         description="Benchmark the paged attention kernel.")
     parser.add_argument("--version",

benchmarks/kernels/benchmark_rmsnorm.py

@@ -4,11 +4,11 @@ import itertools
 from typing import Optional, Union
 
 import torch
-import triton
 from flashinfer.norm import fused_add_rmsnorm, rmsnorm
 from torch import nn
 
 from vllm import _custom_ops as vllm_ops
+from vllm.triton_utils import triton
 
 
 class HuggingFaceRMSNorm(nn.Module):

benchmarks/kernels/benchmark_shapes.py

@@ -75,3 +75,19 @@ WEIGHT_SHAPES = {
         [7168, 8192],
     ],
 }
+
+WEIGHT_SHAPES_MOE = {
+    "nm-testing/Mixtral-8x7B-Instruct-v0.1": [
+        [8, 2, 4096, 28672],
+        [8, 2, 14336, 4096],
+    ],
+    "nm-testing/deepseekv2-lite": [
+        [64, 6, 2048, 1408],
+    ],
+    "ibm-granite/granite-3.0-1b-a400m": [
+        [32, 8, 1024, 1024],
+    ],
+    "ibm-granite/granite-3.0-3b-a800m": [
+        [40, 8, 1024, 1536],
+    ],
+}

benchmarks/kernels/benchmark_w8a8_block_fp8.py

@@ -0,0 +1,420 @@
+# SPDX-License-Identifier: Apache-2.0
+# Adapted from sglang quantization/tuning_block_wise_kernel.py
+
+import argparse
+import json
+import multiprocessing as mp
+import os
+import time
+from datetime import datetime
+from typing import Any
+
+import torch
+import tqdm
+import triton
+
+from vllm.model_executor.layers.quantization.utils.fp8_utils import (
+    _w8a8_block_fp8_matmul)
+from vllm.platforms import current_platform
+from vllm.utils import FlexibleArgumentParser
+
+mp.set_start_method("spawn", force=True)
+
+assert current_platform.is_cuda(
+), "Only support tune w8a8 block fp8 kernel on CUDA device."
+
+DTYPE_MAP = {
+    "float32": torch.float32,
+    "float16": torch.float16,
+    "half": torch.half,
+    "bfloat16": torch.bfloat16,
+}
+
+
+def w8a8_block_matmul(
+    A: torch.Tensor,
+    B: torch.Tensor,
+    As: torch.Tensor,
+    Bs: torch.Tensor,
+    block_size: list[int],
+    config: dict[str, Any],
+    output_dtype: torch.dtype = torch.float16,
+) -> torch.Tensor:
+    """This function performs matrix multiplication with 
+    block-wise quantization.
+
+    It takes two input tensors `A` and `B` with scales `As` and `Bs`.
+    The output is returned in the specified `output_dtype`.
+
+    Args:
+        A: The input tensor, e.g., activation.
+        B: The input tensor, e.g., weight.
+        As: The per-token-group quantization scale for `A`.
+        Bs: The per-block quantization scale for `B`.
+        block_size: The block size for per-block quantization. 
+                    It should be 2-dim, e.g., [128, 128].
+        output_dytpe: The dtype of the returned tensor.
+
+    Returns:
+        torch.Tensor: The result of matmul.
+    """
+    assert len(block_size) == 2
+    block_n, block_k = block_size[0], block_size[1]
+
+    assert A.shape[-1] == B.shape[-1]
+    assert A.shape[:-1] == As.shape[:-1] and A.is_contiguous()
+    assert triton.cdiv(A.shape[-1], block_k) == As.shape[-1]
+    M = A.numel() // A.shape[-1]
+
+    assert B.ndim == 2 and B.is_contiguous() and Bs.ndim == 2
+    N, K = B.shape
+    assert triton.cdiv(N, block_n) == Bs.shape[0]
+    assert triton.cdiv(K, block_k) == Bs.shape[1]
+
+    C_shape = A.shape[:-1] + (N, )
+    C = A.new_empty(C_shape, dtype=output_dtype)
+
+    def grid(META):
+        return (triton.cdiv(M, META["BLOCK_SIZE_M"]) *
+                triton.cdiv(N, META["BLOCK_SIZE_N"]), )
+
+    if A.dtype == torch.float8_e4m3fn:
+        kernel = _w8a8_block_fp8_matmul
+    else:
+        raise RuntimeError(
+            "Currently, only support tune w8a8 block fp8 kernel.")
+
+    kernel[grid](
+        A,
+        B,
+        C,
+        As,
+        Bs,
+        M,
+        N,
+        K,
+        block_n,
+        block_k,
+        A.stride(-2),
+        A.stride(-1),
+        B.stride(1),
+        B.stride(0),
+        C.stride(-2),
+        C.stride(-1),
+        As.stride(-2),
+        As.stride(-1),
+        Bs.stride(1),
+        Bs.stride(0),
+        **config,
+    )
+
+    return C
+
+
+def get_configs_compute_bound():
+    configs = []
+    for num_stages in [2, 3, 4, 5]:
+        for block_m in [16, 32, 64, 128, 256]:
+            for block_k in [64, 128]:
+                for block_n in [32, 64, 128, 256]:
+                    for num_warps in [4, 8]:
+                        for group_size in [1, 16, 32, 64]:
+                            configs.append({
+                                "BLOCK_SIZE_M": block_m,
+                                "BLOCK_SIZE_N": block_n,
+                                "BLOCK_SIZE_K": block_k,
+                                "GROUP_SIZE_M": group_size,
+                                "num_warps": num_warps,
+                                "num_stages": num_stages,
+                            })
+    return configs
+
+
+def get_weight_shapes(tp_size):
+    # NOTE(HandH1998): The weight shapes only works for DeepSeek-V3.
+    # Modify them, if you tune for another different model.
+    # cannot TP
+    total = [
+        (512 + 64, 7168),
+        ((128 + 64) * 128, 7168),
+        (128 * (128 + 128), 512),
+        (7168, 16384),
+        (7168, 18432),
+    ]
+    # N can TP
+    n_tp = [
+        (18432 * 2, 7168),
+        ((128 + 64) * 128, 7168),
+        (128 * (128 + 128), 512),
+        (24576, 1536),
+        (12288, 7168),
+        (4096, 7168),
+    ]
+    # K can TP
+    k_tp = [(7168, 18432), (7168, 16384), (7168, 2048)]
+
+    weight_shapes = []
+    for t in total:
+        weight_shapes.append(t)
+    for n_t in n_tp:
+        new_t = (n_t[0] // tp_size, n_t[1])
+        weight_shapes.append(new_t)
+    for k_t in k_tp:
+        new_t = (k_t[0], k_t[1] // tp_size)
+        weight_shapes.append(new_t)
+    return weight_shapes
+
+
+def benchmark_config(A,
+                     B,
+                     As,
+                     Bs,
+                     block_size,
+                     config,
+                     out_dtype=torch.float16,
+                     num_iters=10):
+
+    def run():
+        w8a8_block_matmul(A, B, As, Bs, block_size, config, out_dtype)
+
+    torch.cuda.synchronize()
+    # JIT complication & warmup
+    for _ in range(5):
+        run()
+    torch.cuda.synchronize()
+
+    start_event = torch.cuda.Event(enable_timing=True)
+    end_event = torch.cuda.Event(enable_timing=True)
+
+    latencies: list[float] = []
+    for i in range(num_iters):
+        torch.cuda.synchronize()
+        start_event.record()
+        run()
+        end_event.record()
+        end_event.synchronize()
+        latencies.append(start_event.elapsed_time(end_event))
+    avg = sum(latencies) / (num_iters * 10) * 1000  # us
+    return avg
+
+
+def tune(M, N, K, block_size, out_dtype, search_space, input_type):
+    factor_for_scale = 1e-2
+
+    if input_type == "fp8":
+        fp8_info = torch.finfo(torch.float8_e4m3fn)
+        fp8_max, fp8_min = fp8_info.max, fp8_info.min
+
+        A_fp32 = (
+            (torch.rand(M, K, dtype=torch.float32, device="cuda") - 0.5) * 2 *
+            fp8_max)
+        A = A_fp32.clamp(min=fp8_min, max=fp8_max).to(torch.float8_e4m3fn)
+
+        B_fp32 = (
+            (torch.rand(N, K, dtype=torch.float32, device="cuda") - 0.5) * 2 *
+            fp8_max)
+        B = B_fp32.clamp(min=fp8_min, max=fp8_max).to(torch.float8_e4m3fn)
+    else:
+        raise RuntimeError(
+            "Currently, only support tune w8a8 block fp8 kernel.")
+
+    block_n, block_k = block_size[0], block_size[1]
+    n_tiles = (N + block_n - 1) // block_n
+    k_tiles = (K + block_k - 1) // block_k
+
+    As = torch.rand(M, k_tiles, dtype=torch.float32,
+                    device="cuda") * factor_for_scale
+    Bs = (torch.rand(n_tiles, k_tiles, dtype=torch.float32, device="cuda") *
+          factor_for_scale)
+
+    best_config = None
+    best_time = float("inf")
+    for config in tqdm(search_space):
+        try:
+            kernel_time = benchmark_config(
+                A,
+                B,
+                As,
+                Bs,
+                block_size,
+                config,
+                out_dtype,
+                num_iters=10,
+            )
+        except triton.runtime.autotuner.OutOfResources:
+            # Some configurations may be invalid and fail to compile.
+            continue
+
+        if kernel_time < best_time:
+            best_time = kernel_time
+            best_config = config
+    now = datetime.now()
+    print(f"{now.ctime()}] Completed tuning for batch_size={M}")
+    assert best_config is not None
+    return best_config
+
+
+def save_configs(
+    N,
+    K,
+    block_n,
+    block_k,
+    configs,
+    save_path,
+    input_type="fp8",
+) -> None:
+    os.makedirs(save_path, exist_ok=True)
+    device_name = current_platform.get_device_name().replace(" ", "_")
+    json_file_name = (
+        f"N={N},K={K},device_name={device_name},dtype={input_type}_w8a8,"
+        f"block_shape=[{block_n},{block_k}].json")
+
+    config_file_path = os.path.join(save_path, json_file_name)
+    print(f"Writing best config to {config_file_path}...")
+
+    with open(config_file_path, "w") as f:
+        json.dump(configs, f, indent=4)
+        f.write("\n")
+
+
+def tune_on_gpu(args_dict):
+    """Run tuning on a specific GPU."""
+    gpu_id = args_dict["gpu_id"]
+    batch_sizes = args_dict["batch_sizes"]
+    weight_shapes = args_dict["weight_shapes"]
+    args = args_dict["args"]
+
+    torch.cuda.set_device(gpu_id)
+    print(f"Starting tuning on GPU {gpu_id} with batch sizes {batch_sizes}")
+
+    block_n = args.block_n
+    block_k = args.block_k
+    out_dtype = DTYPE_MAP[args.out_dtype]
+    save_path = args.save_path
+    input_type = args.input_type
+
+    search_space = get_configs_compute_bound()
+    search_space = [
+        config for config in search_space
+        if block_k % config["BLOCK_SIZE_K"] == 0
+    ]
+
+    start = time.time()
+    for shape in tqdm(weight_shapes, desc=f"GPU {gpu_id} - Shapes"):
+        N, K = shape[0], shape[1]
+        print(f"[GPU {gpu_id}] Tune for weight shape of `N: {N}, K: {K}`")
+        benchmark_results = [
+            tune(
+                batch_size,
+                N,
+                K,
+                [block_n, block_k],
+                out_dtype,
+                search_space,
+                input_type,
+            ) for batch_size in tqdm(batch_sizes,
+                                     desc=f"GPU {gpu_id} - Batch sizes")
+        ]
+        best_configs = {
+            M: config
+            for M, config in zip(batch_sizes, benchmark_results)
+        }
+        save_configs(N, K, block_n, block_k, best_configs, save_path,
+                     input_type)
+
+    end = time.time()
+    print(f"Tuning on GPU {gpu_id} took {end - start:.2f} seconds")
+
+
+def distribute_batch_sizes(batch_sizes, num_gpus):
+    """Distribute batch sizes across available GPUs."""
+    batches_per_gpu = []
+    for i in range(num_gpus):
+        start_idx = i * len(batch_sizes) // num_gpus
+        end_idx = (i + 1) * len(batch_sizes) // num_gpus
+        batches_per_gpu.append(batch_sizes[start_idx:end_idx])
+    return batches_per_gpu
+
+
+def main(args):
+    print(args)
+    num_gpus = torch.cuda.device_count()
+    if num_gpus == 0:
+        raise RuntimeError("No GPU available for tuning")
+    print(f"Found {num_gpus} GPUs for parallel tuning")
+
+    torch.cuda.init()
+
+    if args.batch_size is None:
+        batch_sizes = [
+            1,
+            2,
+            4,
+            8,
+            16,
+            24,
+            32,
+            48,
+            64,
+            96,
+            128,
+            256,
+            512,
+            1024,
+            1536,
+            2048,
+            3072,
+            4096,
+        ]
+    else:
+        batch_sizes = [args.batch_size]
+        num_gpus = 1  # If only one batch size, use only one GPU
+
+    weight_shapes = get_weight_shapes(args.tp_size)
+
+    batches_per_gpu = distribute_batch_sizes(batch_sizes, num_gpus)
+
+    process_args = []
+    for gpu_id in range(num_gpus):
+        process_args.append({
+            "gpu_id": gpu_id,
+            "batch_sizes": batches_per_gpu[gpu_id],
+            "weight_shapes":
+            weight_shapes,  # Each GPU processes all weight shapes
+            "args": args,
+        })
+
+    ctx = mp.get_context("spawn")
+    with ctx.Pool(num_gpus) as pool:
+        pool.map(tune_on_gpu, process_args)
+
+    print("Multi-GPU tuning completed")
+
+
+if __name__ == "__main__":
+    parser = FlexibleArgumentParser(
+        description="""
+Tune triton w8a8 block fp8 for DeepSeek-V3/DeepSeek-R1:
+    python3 benchmark_w8a8_block_fp8.py --tp-size 8 --input-type fp8
+Then copy to model_executor/layers/quantization/utils/configs
+        """,
+        formatter_class=argparse.RawTextHelpFormatter)
+
+    parser.add_argument("--tp-size", "-tp", type=int, default=8)
+    parser.add_argument("--input-type",
+                        type=str,
+                        choices=["fp8"],
+                        default="fp8")
+    parser.add_argument(
+        "--out-dtype",
+        type=str,
+        choices=["float32", "float16", "bfloat16", "half"],
+        default="float16",
+    )
+    parser.add_argument("--block-n", type=int, default=128)
+    parser.add_argument("--block-k", type=int, default=128)
+    parser.add_argument("--batch-size", type=int, required=False)
+    parser.add_argument("--save-path", type=str, default="./")
+    args = parser.parse_args()
+
+    main(args)

benchmarks/kernels/deepgemm/benchmark_fp8_block_dense_gemm.py

@@ -6,13 +6,13 @@ import time
 # Import DeepGEMM functions
 import deep_gemm
 import torch
-import triton
 from deep_gemm import calc_diff, ceil_div, get_col_major_tma_aligned_tensor
 
 # Import vLLM functions
 from vllm import _custom_ops as ops
 from vllm.model_executor.layers.quantization.utils.fp8_utils import (
     per_token_group_quant_fp8, w8a8_block_fp8_matmul)
+from vllm.triton_utils import triton
 
 
 # Copied from

benchmarks/launch_tgi_server.sh

@@ -1,16 +0,0 @@
-#!/bin/bash
-
-PORT=8000
-MODEL=$1
-TOKENS=$2
-
-docker run -e "HF_TOKEN=$HF_TOKEN" --gpus all --shm-size 1g -p $PORT:80 \
-           -v "$PWD/data:/data" \
-           ghcr.io/huggingface/text-generation-inference:2.2.0 \
-           --model-id "$MODEL" \
-           --sharded false  \
-           --max-input-length 1024 \
-           --max-total-tokens 2048 \
-           --max-best-of 5 \
-           --max-concurrent-requests 5000 \
-           --max-batch-total-tokens "$TOKENS"

benchmarks/run_structured_output_benchmark.sh

@@ -9,13 +9,10 @@ BACKEND=${2:-"vllm"}
 # Define the dataset to use
 DATASET=${3:-"xgrammar_bench"}
 
-# Define the guided decoding backend
-GUIDED_BACKEND=${4:-"xgrammar"}
-
 SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
-OUTPUT_DIR=${5:-"$SCRIPT_DIR/structured_output_benchmark_results"}
+OUTPUT_DIR=${4:-"$SCRIPT_DIR/structured_output_benchmark_results"}
 
-GUIDED_RATIO=${6:-0.5}
+GUIDED_RATIO=${5:-0.5}
 
 # Create output directory if it doesn't exist
 mkdir -p "$OUTPUT_DIR"
@@ -27,7 +24,6 @@ QPS_VALUES=(70 60 50 25 20 15 10)
 COMMON_PARAMS="--backend $BACKEND \
                --model $MODEL \
                --dataset $DATASET \
-               --structured-output-backend $GUIDED_BACKEND \
                --structured-output-ratio $GUIDED_RATIO \
                --save-results \
                --result-dir $OUTPUT_DIR"
@@ -35,7 +31,6 @@ COMMON_PARAMS="--backend $BACKEND \
 echo "Starting structured output benchmark with model: $MODEL"
 echo "Backend: $BACKEND"
 echo "Dataset: $DATASET"
-echo "Structured output backend: $GUIDED_BACKEND"
 echo "Results will be saved to: $OUTPUT_DIR"
 echo "----------------------------------------"
 
@@ -48,12 +43,13 @@ for qps in "${QPS_VALUES[@]}"; do
   GIT_BRANCH=$(git rev-parse --abbrev-ref HEAD 2>/dev/null || echo "unknown")
 
   # Construct filename for this run
-  FILENAME="${GUIDED_BACKEND}_${BACKEND}_${qps}qps_$(basename $MODEL)_${DATASET}_${GIT_HASH}.json"
+  FILENAME="${BACKEND}_${qps}qps_$(basename $MODEL)_${DATASET}_${GIT_HASH}.json"
 
   # Run the benchmark
   python "$SCRIPT_DIR/benchmark_serving_structured_output.py" $COMMON_PARAMS \
     --request-rate $qps \
     --result-filename "$FILENAME" \
+    --tokenizer-mode ${TOKENIZER_MODE:-"auto"} \
     --port ${PORT:-8000}
 
   echo "Completed benchmark with QPS: $qps"

cmake/cpu_extension.cmake

@@ -33,8 +33,6 @@ endif()
 
 if(MACOSX_FOUND)
     list(APPEND CXX_COMPILE_FLAGS
-        "-Xpreprocessor"
-        "-fopenmp"
         "-DVLLM_CPU_EXTENSION")
 else()
     list(APPEND CXX_COMPILE_FLAGS
@@ -169,6 +167,33 @@ if (AVX512_FOUND AND NOT AVX512_DISABLED)
 
     FetchContent_MakeAvailable(oneDNN)
     
+    list(APPEND LIBS dnnl)
+elseif(POWER10_FOUND)
+    FetchContent_Declare(
+        oneDNN
+        GIT_REPOSITORY https://github.com/oneapi-src/oneDNN.git
+        GIT_TAG v3.7.2
+        GIT_PROGRESS TRUE
+        GIT_SHALLOW TRUE
+    )
+
+    set(ONEDNN_LIBRARY_TYPE "STATIC")
+    set(ONEDNN_BUILD_DOC "OFF")
+    set(ONEDNN_BUILD_EXAMPLES "OFF")
+    set(ONEDNN_BUILD_TESTS "OFF")
+    set(ONEDNN_ENABLE_WORKLOAD "INFERENCE")
+    set(ONEDNN_ENABLE_PRIMITIVE "MATMUL;REORDER")
+    set(ONEDNN_BUILD_GRAPH "OFF")
+    set(ONEDNN_ENABLE_JIT_PROFILING "OFF")
+    set(ONEDNN_ENABLE_ITT_TASKS "OFF")
+    set(ONEDNN_ENABLE_MAX_CPU_ISA "OFF")
+    set(ONEDNN_ENABLE_CPU_ISA_HINTS "OFF")
+    set(CMAKE_POLICY_DEFAULT_CMP0077 NEW)
+
+    set(DNNL_CPU_RUNTIME "OMP")
+
+    FetchContent_MakeAvailable(oneDNN)
+
     list(APPEND LIBS dnnl)
 endif()
 
@@ -190,10 +215,16 @@ set(VLLM_EXT_SRC
     "csrc/cpu/cache.cpp"
     "csrc/cpu/utils.cpp"
     "csrc/cpu/layernorm.cpp"
+    "csrc/cpu/mla_decode.cpp"
     "csrc/cpu/pos_encoding.cpp"
     "csrc/cpu/torch_bindings.cpp")
 
 if (AVX512_FOUND AND NOT AVX512_DISABLED)
+    set(VLLM_EXT_SRC
+        "csrc/cpu/quant.cpp"
+        "csrc/cpu/shm.cpp"
+        ${VLLM_EXT_SRC})
+elseif(POWER10_FOUND)
     set(VLLM_EXT_SRC
         "csrc/cpu/quant.cpp"
         ${VLLM_EXT_SRC})
@@ -214,4 +245,4 @@ define_gpu_extension_target(
     WITH_SOABI
 )
 
-message(STATUS "Enabling C extension.")
+message(STATUS "Enabling C extension.")

cmake/external_projects/vllm_flash_attn.cmake

@@ -38,7 +38,7 @@ else()
   FetchContent_Declare(
           vllm-flash-attn
           GIT_REPOSITORY https://github.com/vllm-project/flash-attention.git
-          GIT_TAG 9bfa9869829d8c593527eb34c5271d0090f7ccc9 
+          GIT_TAG 8798f27777fb57f447070301bf33a9f9c607f491
           GIT_PROGRESS TRUE
           # Don't share the vllm-flash-attn build between build types
           BINARY_DIR ${CMAKE_BINARY_DIR}/vllm-flash-attn

csrc/attention/merge_attn_states.cu

@@ -0,0 +1,178 @@
+#include <optional>
+#include <torch/all.h>
+#include <ATen/cuda/CUDAContext.h>
+#include <c10/cuda/CUDAGuard.h>
+#include <algorithm>
+
+#include "attention_dtypes.h"
+#include "attention_utils.cuh"
+
+namespace vllm {
+
+// Implements section 2.2 of https://www.arxiv.org/pdf/2501.01005
+// can be used to combine partial attention results (in the split-KV case)
+template <typename scalar_t, const uint NUM_THREADS>
+__global__ void merge_attn_states_kernel(
+    scalar_t* output, float* output_lse, const scalar_t* prefix_output,
+    const float* prefix_lse, const scalar_t* suffix_output,
+    const float* suffix_lse, const uint num_tokens, const uint num_heads,
+    const uint head_size) {
+  using pack_128b_t = uint4;
+  const uint pack_size = 16 / sizeof(scalar_t);
+  const uint threads_per_head = head_size / pack_size;
+
+  const uint global_idx = blockIdx.x * NUM_THREADS + threadIdx.x;
+  const uint token_head_threads = num_tokens * num_heads * threads_per_head;
+
+  if (global_idx >= token_head_threads) return;
+
+  // global_idx -> token_idx + head_idx + pack_idx
+  const uint token_head_idx = global_idx / threads_per_head;
+  const uint pack_idx = global_idx % threads_per_head;
+
+  const uint token_idx = token_head_idx / num_heads;
+  const uint head_idx = token_head_idx % num_heads;
+
+  const uint pack_offset = pack_idx * pack_size;  // (0~15)*8, etc.
+  const uint head_offset =
+      token_idx * num_heads * head_size + head_idx * head_size;
+  const scalar_t* prefix_head_ptr = prefix_output + head_offset;
+  const scalar_t* suffix_head_ptr = suffix_output + head_offset;
+  scalar_t* output_head_ptr = output + head_offset;
+
+  float p_lse = prefix_lse[head_idx * num_tokens + token_idx];
+  float s_lse = suffix_lse[head_idx * num_tokens + token_idx];
+  p_lse = std::isinf(p_lse) ? -std::numeric_limits<float>::infinity() : p_lse;
+  s_lse = std::isinf(s_lse) ? -std::numeric_limits<float>::infinity() : s_lse;
+
+  const float max_lse = fmaxf(p_lse, s_lse);
+  p_lse = p_lse - max_lse;
+  s_lse = s_lse - max_lse;
+  const float p_se = expf(p_lse);
+  const float s_se = expf(s_lse);
+  const float out_se = p_se + s_se;
+  const float p_scale = p_se / out_se;
+  const float s_scale = s_se / out_se;
+
+  if (pack_offset < head_size) {
+    // Pack 128b load
+    pack_128b_t p_out_pack = reinterpret_cast<const pack_128b_t*>(
+        prefix_head_ptr)[pack_offset / pack_size];
+    pack_128b_t s_out_pack = reinterpret_cast<const pack_128b_t*>(
+        suffix_head_ptr)[pack_offset / pack_size];
+    pack_128b_t o_out_pack;
+
+#pragma unroll
+    for (uint i = 0; i < pack_size; ++i) {
+      // Always use float for FMA to keep high precision.
+      // half(uint16_t), bfloat16, float -> float.
+      const float p_out_f =
+          vllm::to_float(reinterpret_cast<const scalar_t*>(&p_out_pack)[i]);
+      const float s_out_f =
+          vllm::to_float(reinterpret_cast<const scalar_t*>(&s_out_pack)[i]);
+      // fma: a * b + c = p_out_f * p_scale + (s_out_f * s_scale)
+      const float o_out_f = p_out_f * p_scale + (s_out_f * s_scale);
+      // float -> half(uint16_t), bfloat16, float.
+      vllm::from_float(reinterpret_cast<scalar_t*>(&o_out_pack)[i], o_out_f);
+    }
+
+    // Pack 128b storage
+    reinterpret_cast<pack_128b_t*>(output_head_ptr)[pack_offset / pack_size] =
+        o_out_pack;
+  }
+  // We only need to write to output_lse once per head.
+  if (output_lse != nullptr && pack_idx == 0) {
+    float out_lse = logf(out_se) + max_lse;
+    output_lse[head_idx * num_tokens + token_idx] = out_lse;
+  }
+}
+
+}  // namespace vllm
+
+// The following macro is used to dispatch the conversion function based on
+// the output data type. The FN is a macro that calls a function with
+// template<typename scalar_t>.
+#define DISPATCH_BY_SCALAR_DTYPE(scalar_dtype, fn)                      \
+  {                                                                     \
+    if (scalar_dtype == at::ScalarType::Float) {                        \
+      fn(float);                                                        \
+    } else if (scalar_dtype == at::ScalarType::Half) {                  \
+      fn(uint16_t);                                                     \
+    } else if (scalar_dtype == at::ScalarType::BFloat16) {              \
+      fn(__nv_bfloat16);                                                \
+    } else {                                                            \
+      TORCH_CHECK(false, "Unsupported data type of O: ", scalar_dtype); \
+    }                                                                   \
+  }
+
+#define LAUNCH_MERGE_ATTN_STATES(scalar_t, NUM_THREADS)                     \
+  {                                                                         \
+    vllm::merge_attn_states_kernel<scalar_t, NUM_THREADS>                   \
+        <<<grid, block, 0, stream>>>(                                       \
+            reinterpret_cast<scalar_t*>(output.data_ptr()), output_lse_ptr, \
+            reinterpret_cast<scalar_t*>(prefix_output.data_ptr()),          \
+            reinterpret_cast<float*>(prefix_lse.data_ptr()),                \
+            reinterpret_cast<scalar_t*>(suffix_output.data_ptr()),          \
+            reinterpret_cast<float*>(suffix_lse.data_ptr()), num_tokens,    \
+            num_heads, head_size);                                          \
+  }
+
+/*@brief Merges the attention states from prefix and suffix
+ * into the output tensor. NUM_TOKENS: n, NUM_HEADS: h, HEAD_SIZE: d
+ *
+ * @param output [n,h,d] The output tensor to store the merged attention states.
+ * @param output_lse [h,d] Optional tensor to store the log-sum-exp values.
+ * @param prefix_output [n,h,d] The prefix attention states.
+ * @param prefix_lse [h,n] The log-sum-exp values for the prefix attention
+ * states.
+ * @param suffix_output [n,h,d] The suffix attention states.
+ * @param suffix_lse [h,n] The log-sum-exp values for the suffix attention
+ * states.
+ */
+template <typename scalar_t>
+void merge_attn_states_launcher(torch::Tensor& output,
+                                std::optional<torch::Tensor> output_lse,
+                                const torch::Tensor& prefix_output,
+                                const torch::Tensor& prefix_lse,
+                                const torch::Tensor& suffix_output,
+                                const torch::Tensor& suffix_lse) {
+  constexpr uint NUM_THREADS = 128;
+  const uint num_tokens = output.size(0);
+  const uint num_heads = output.size(1);
+  const uint head_size = output.size(2);
+  const uint pack_size = 16 / sizeof(scalar_t);
+  TORCH_CHECK(head_size % pack_size == 0,
+              "headsize must be multiple of pack_size:", pack_size);
+  float* output_lse_ptr = nullptr;
+  if (output_lse.has_value()) {
+    output_lse_ptr = output_lse.value().data_ptr<float>();
+  }
+  // Process one pack elements per thread. for float, the
+  // pack_size is 4 for half/bf16, the pack_size is 8.
+  const uint threads_per_head = head_size / pack_size;
+  const uint total_threads = num_tokens * num_heads * threads_per_head;
+
+  dim3 block(NUM_THREADS);
+  dim3 grid((total_threads + NUM_THREADS - 1) / NUM_THREADS);
+
+  const c10::cuda::OptionalCUDAGuard device_guard(prefix_output.device());
+  auto stream = at::cuda::getCurrentCUDAStream();
+
+  LAUNCH_MERGE_ATTN_STATES(scalar_t, NUM_THREADS);
+}
+
+#define CALL_MERGE_ATTN_STATES_LAUNCHER(scalar_t)                           \
+  {                                                                         \
+    merge_attn_states_launcher<scalar_t>(output, output_lse, prefix_output, \
+                                         prefix_lse, suffix_output,         \
+                                         suffix_lse);                       \
+  }
+
+void merge_attn_states(torch::Tensor& output,
+                       std::optional<torch::Tensor> output_lse,
+                       const torch::Tensor& prefix_output,
+                       const torch::Tensor& prefix_lse,
+                       const torch::Tensor& suffix_output,
+                       const torch::Tensor& suffix_lse) {
+  DISPATCH_BY_SCALAR_DTYPE(output.dtype(), CALL_MERGE_ATTN_STATES_LAUNCHER);
+}

csrc/attention/mla/cutlass_mla_entry.cu

@@ -0,0 +1,38 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.  All rights reserved.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <torch/all.h>
+
+#if defined ENABLE_CUTLASS_MLA && ENABLE_CUTLASS_MLA
+void cutlass_mla_decode_sm100a(torch::Tensor const& out,
+                               torch::Tensor const& q_nope,
+                               torch::Tensor const& q_pe,
+                               torch::Tensor const& kv_c_and_k_pe_cache,
+                               torch::Tensor const& seq_lens,
+                               torch::Tensor const& page_table, double scale);
+#endif
+
+void cutlass_mla_decode(torch::Tensor const& out, torch::Tensor const& q_nope,
+                        torch::Tensor const& q_pe,
+                        torch::Tensor const& kv_c_and_k_pe_cache,
+                        torch::Tensor const& seq_lens,
+                        torch::Tensor const& page_table, double scale) {
+#if defined ENABLE_CUTLASS_MLA && ENABLE_CUTLASS_MLA
+  return cutlass_mla_decode_sm100a(out, q_nope, q_pe, kv_c_and_k_pe_cache,
+                                   seq_lens, page_table, scale);
+#endif
+  TORCH_CHECK_NOT_IMPLEMENTED(false, "No compiled cutlass MLA");
+}

csrc/attention/mla/cutlass_mla_kernels.cu

@@ -0,0 +1,225 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.  All rights reserved.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <torch/all.h>
+
+#include <ATen/cuda/CUDAContext.h>
+#include <c10/cuda/CUDAGuard.h>
+
+#include "cute/tensor.hpp"
+
+#include "cutlass/cutlass.h"
+#include "cutlass/kernel_hardware_info.h"
+
+#include "cutlass_extensions/common.hpp"
+
+#include "device/sm100_mla.hpp"
+#include "kernel/sm100_mla_tile_scheduler.hpp"
+
+using namespace cute;
+using namespace cutlass::fmha::kernel;
+
+template <typename T, bool PersistenceOption = true>
+struct MlaSm100 {
+  using Element = T;
+  using ElementAcc = float;
+  using ElementOut = T;
+
+  using TileShape = Shape<_128, _128, Shape<_512, _64>>;
+  using TileShapeH = cute::tuple_element_t<0, TileShape>;
+  using TileShapeD = cute::tuple_element_t<2, TileShape>;
+
+  // H K (D_latent D_rope) B
+  using ProblemShape = cute::tuple<TileShapeH, int, TileShapeD, int>;
+
+  using StrideQ = cute::tuple<int64_t, _1, int64_t>;  // H D B
+  using StrideK = cute::tuple<int64_t, _1, int64_t>;  // K D B
+  using StrideO = StrideK;                            // H D B
+  using StrideLSE = cute::tuple<_1, int>;             // H B
+
+  using TileScheduler =
+      std::conditional_t<PersistenceOption, Sm100MlaPersistentTileScheduler,
+                         Sm100MlaIndividualTileScheduler>;
+
+  using FmhaKernel =
+      cutlass::fmha::kernel::Sm100FmhaMlaKernelTmaWarpspecialized<
+          TileShape, Element, ElementAcc, ElementOut, ElementAcc, TileScheduler,
+          /*kIsCpAsync=*/true>;
+  using Fmha = cutlass::fmha::device::MLA<FmhaKernel>;
+};
+
+template <typename T>
+typename T::Fmha::Arguments args_from_options(
+    at::Tensor const& out, at::Tensor const& q_nope, at::Tensor const& q_pe,
+    at::Tensor const& kv_c_and_k_pe_cache, at::Tensor const& seq_lens,
+    at::Tensor const& page_table, double scale) {
+  cutlass::KernelHardwareInfo hw_info;
+  hw_info.device_id = q_nope.device().index();
+  hw_info.sm_count =
+      cutlass::KernelHardwareInfo::query_device_multiprocessor_count(
+          hw_info.device_id);
+
+  int batches = q_nope.sizes()[0];
+  int page_count_per_seq = page_table.sizes()[1];
+  int page_count_total = kv_c_and_k_pe_cache.sizes()[0];
+  int page_size = kv_c_and_k_pe_cache.sizes()[1];
+  int max_seq_len = page_size * page_count_per_seq;
+  using TileShapeH = typename T::TileShapeH;
+  using TileShapeD = typename T::TileShapeD;
+  auto problem_shape =
+      cute::make_tuple(TileShapeH{}, max_seq_len, TileShapeD{}, batches);
+
+  auto [H, K, D, B] = problem_shape;
+  auto [D_latent, D_rope] = D;
+
+  using StrideQ = typename T::StrideQ;
+  using StrideK = typename T::StrideK;
+  using StrideO = typename T::StrideO;
+  using StrideLSE = typename T::StrideLSE;
+
+  StrideQ stride_Q_latent = cute::make_tuple(
+      static_cast<int64_t>(D_latent), _1{}, static_cast<int64_t>(H * D_latent));
+  StrideQ stride_Q_rope = cute::make_tuple(static_cast<int64_t>(D_rope), _1{},
+                                           static_cast<int64_t>(H * D_rope));
+  StrideK stride_C =
+      cute::make_tuple(static_cast<int64_t>(D_latent + D_rope), _1{},
+                       static_cast<int64_t>(page_size * (D_latent + D_rope)));
+  StrideLSE stride_PT = cute::make_stride(_1{}, page_count_per_seq);
+  StrideLSE stride_LSE = cute::make_tuple(_1{}, static_cast<int>(H));
+  StrideO stride_O = cute::make_tuple(static_cast<int64_t>(D_latent), _1{},
+                                      static_cast<int64_t>(H * D_latent));
+
+  using Element = typename T::Element;
+  using ElementOut = typename T::ElementOut;
+  using ElementAcc = typename T::ElementAcc;
+  auto Q_latent_ptr = static_cast<Element*>(q_nope.data_ptr());
+  auto Q_rope_ptr = static_cast<Element*>(q_pe.data_ptr());
+  auto C_ptr = static_cast<Element*>(kv_c_and_k_pe_cache.data_ptr());
+  auto scale_f = static_cast<float>(scale);
+  typename T::Fmha::Arguments arguments{
+      problem_shape,
+      {scale_f, Q_latent_ptr, stride_Q_latent, Q_rope_ptr, stride_Q_rope, C_ptr,
+       stride_C, C_ptr + D_latent, stride_C,
+       static_cast<int*>(seq_lens.data_ptr()),
+       static_cast<int*>(page_table.data_ptr()), stride_PT, page_count_total,
+       page_size},
+      {static_cast<ElementOut*>(out.data_ptr()), stride_O,
+       static_cast<ElementAcc*>(nullptr), stride_LSE},
+      hw_info,
+      -1,       // split_kv
+      nullptr,  // is_var_split_kv
+  };
+  // TODO(kaixih@nvidia): When split_kv=-1 and is_var_split_kv=false, we compute
+  // split_kv automatically based on batch size and sequence length to balance
+  // workload across available SMs. Consider using var_split_kv for manual
+  // control if needed.
+  T::Fmha::set_split_kv(arguments);
+  return arguments;
+}
+
+template <typename Element>
+void runMla(at::Tensor const& out, at::Tensor const& q_nope,
+            at::Tensor const& q_pe, at::Tensor const& kv_c_and_k_pe_cache,
+            at::Tensor const& seq_lens, at::Tensor const& page_table,
+            float scale, cudaStream_t stream) {
+  using MlaSm100Type = MlaSm100<Element>;
+  typename MlaSm100Type::Fmha fmha;
+  auto arguments = args_from_options<MlaSm100Type>(
+      out, q_nope, q_pe, kv_c_and_k_pe_cache, seq_lens, page_table, scale);
+  size_t workspace_size = MlaSm100Type::Fmha::get_workspace_size(arguments);
+  auto const workspace_options =
+      torch::TensorOptions().dtype(torch::kUInt8).device(q_nope.device());
+  auto workspace = torch::empty(workspace_size, workspace_options);
+
+  CUTLASS_CHECK(fmha.can_implement(arguments));
+
+  CUTLASS_CHECK(fmha.initialize(arguments, workspace.data_ptr(), stream));
+
+  CUTLASS_CHECK(fmha.run(arguments, workspace.data_ptr(), stream));
+}
+
+void cutlass_mla_decode_sm100a(torch::Tensor const& out,
+                               torch::Tensor const& q_nope,
+                               torch::Tensor const& q_pe,
+                               torch::Tensor const& kv_c_and_k_pe_cache,
+                               torch::Tensor const& seq_lens,
+                               torch::Tensor const& page_table, double scale) {
+  TORCH_CHECK(q_nope.device().is_cuda(), "q_nope must be on CUDA");
+  TORCH_CHECK(q_nope.dim() == 3, "q_nope must be a 3D tensor");
+  TORCH_CHECK(q_pe.dim() == 3, "q_pe must be a 3D tensor");
+  TORCH_CHECK(kv_c_and_k_pe_cache.dim() == 3,
+              "kv_c_and_k_pe_cache must be a 3D tensor");
+  TORCH_CHECK(seq_lens.dim() == 1, "seq_lens must be a 1D tensor");
+  TORCH_CHECK(page_table.dim() == 2, "page_table must be a 2D tensor");
+  TORCH_CHECK(out.dim() == 3, "out must be a 3D tensor");
+
+  auto B_q_nope = q_nope.size(0);
+  auto H_q_nope = q_nope.size(1);
+  auto D_q_nope = q_nope.size(2);
+  auto B_q_pe = q_pe.size(0);
+  auto H_q_pe = q_pe.size(1);
+  auto D_q_pe = q_pe.size(2);
+  auto B_pt = page_table.size(0);
+  auto PAGE_NUM = page_table.size(1);
+  auto PAGE_SIZE = kv_c_and_k_pe_cache.size(1);
+  auto D_ckv = kv_c_and_k_pe_cache.size(2);
+  auto B_o = out.size(0);
+  auto H_o = out.size(1);
+  auto D_o = out.size(2);
+
+  TORCH_CHECK(D_q_nope == 512, "D_q_nope must be equal to 512");
+  TORCH_CHECK(D_q_pe == 64, "D_q_pe must be equal to 64");
+  TORCH_CHECK(D_ckv == 576, "D_ckv must be equal to 576");
+  TORCH_CHECK(H_q_nope == H_q_pe && H_q_nope == H_o && H_o == 128,
+              "H_q_nope, H_q_pe, and H_o must be equal to 128");
+  TORCH_CHECK(PAGE_SIZE > 0 && (PAGE_SIZE & (PAGE_SIZE - 1)) == 0,
+              "PAGE_SIZE must be a power of 2");
+  TORCH_CHECK(
+      B_q_nope == B_q_pe && B_q_nope == B_pt && B_q_nope == B_o,
+      "Batch dims must be same for page_table, q_nope and q_pe, and out");
+  TORCH_CHECK(PAGE_NUM % (128 / PAGE_SIZE) == 0,
+              "PAGE_NUM must be divisible by 128 / PAGE_SIZE");
+  TORCH_CHECK(D_o == 512, "D_o must be equal to 512");
+
+  TORCH_CHECK(q_nope.dtype() == at::ScalarType::Half ||
+                  q_nope.dtype() == at::ScalarType::BFloat16 ||
+                  q_nope.dtype() == at::ScalarType::Float8_e4m3fn,
+              "q_nope must be a half, bfloat16, or float8_e4m3fn tensor");
+  TORCH_CHECK(kv_c_and_k_pe_cache.dtype() == q_nope.dtype() &&
+                  q_nope.dtype() == q_pe.dtype(),
+              "kv_c_and_k_pe_cache, q_nope, and q_pe must be the same type");
+  TORCH_CHECK(seq_lens.dtype() == torch::kInt32,
+              "seq_lens must be a 32-bit integer tensor");
+  TORCH_CHECK(page_table.dtype() == torch::kInt32,
+              "page_table must be a 32-bit integer tensor");
+
+  auto in_dtype = q_nope.dtype();
+  at::cuda::CUDAGuard device_guard{(char)q_nope.get_device()};
+  const cudaStream_t stream =
+      at::cuda::getCurrentCUDAStream(q_nope.get_device());
+  if (in_dtype == at::ScalarType::Half) {
+    runMla<cutlass::half_t>(out, q_nope, q_pe, kv_c_and_k_pe_cache, seq_lens,
+                            page_table, scale, stream);
+  } else if (in_dtype == at::ScalarType::BFloat16) {
+    runMla<cutlass::bfloat16_t>(out, q_nope, q_pe, kv_c_and_k_pe_cache,
+                                seq_lens, page_table, scale, stream);
+  } else if (in_dtype == at::ScalarType::Float8_e4m3fn) {
+    runMla<cutlass::float_e4m3_t>(out, q_nope, q_pe, kv_c_and_k_pe_cache,
+                                  seq_lens, page_table, scale, stream);
+  } else {
+    TORCH_CHECK(false, "Unsupported input data type of MLA");
+  }
+}

csrc/cache_kernels.cu

@@ -270,9 +270,10 @@ __global__ void reshape_and_cache_flash_kernel(
     cache_t* __restrict__ value_cache,   // [num_blocks, block_size, num_heads,
                                          // head_size]
     const int64_t* __restrict__ slot_mapping,  // [num_tokens]
-    const int block_stride, const int key_stride, const int value_stride,
-    const int num_heads, const int head_size, const int block_size,
-    const float* k_scale, const float* v_scale) {
+    const int64_t block_stride, const int64_t page_stride,
+    const int64_t head_stride, const int64_t key_stride,
+    const int64_t value_stride, const int num_heads, const int head_size,
+    const int block_size, const float* k_scale, const float* v_scale) {
   const int64_t token_idx = blockIdx.x;
   const int64_t slot_idx = slot_mapping[token_idx];
   // NOTE: slot_idx can be -1 if the token is padded
@@ -288,8 +289,8 @@ __global__ void reshape_and_cache_flash_kernel(
     const int head_idx = i / head_size;
     const int head_offset = i % head_size;
     const int64_t tgt_key_value_idx = block_idx * block_stride +
-                                      block_offset * num_heads * head_size +
-                                      head_idx * head_size + head_offset;
+                                      block_offset * page_stride +
+                                      head_idx * head_stride + head_offset;
     scalar_t tgt_key = key[src_key_idx];
     scalar_t tgt_value = value[src_value_idx];
     if constexpr (kv_dt == Fp8KVCacheDataType::kAuto) {
@@ -350,8 +351,8 @@ __global__ void concat_and_cache_mla_kernel(
 
 }  // namespace vllm
 
-// KV_T is the stored data type of kv-cache.
-// CACHE_T is the data type of key and value tensors.
+// KV_T is the data type of key and value tensors.
+// CACHE_T is the stored data type of kv-cache.
 // KV_DTYPE is the real data type of kv-cache.
 #define CALL_RESHAPE_AND_CACHE(KV_T, CACHE_T, KV_DTYPE)               \
   vllm::reshape_and_cache_kernel<KV_T, CACHE_T, KV_DTYPE>             \
@@ -393,19 +394,19 @@ void reshape_and_cache(
                              CALL_RESHAPE_AND_CACHE)
 }
 
-// KV_T is the stored data type of kv-cache.
-// CACHE_T is the data type of key and value tensors.
+// KV_T is the data type of key and value tensors.
+// CACHE_T is the stored data type of kv-cache.
 // KV_DTYPE is the real data type of kv-cache.
-#define CALL_RESHAPE_AND_CACHE_FLASH(KV_T, CACHE_T, KV_DTYPE)         \
-  vllm::reshape_and_cache_flash_kernel<KV_T, CACHE_T, KV_DTYPE>       \
-      <<<grid, block, 0, stream>>>(                                   \
-          reinterpret_cast<KV_T*>(key.data_ptr()),                    \
-          reinterpret_cast<KV_T*>(value.data_ptr()),                  \
-          reinterpret_cast<CACHE_T*>(key_cache.data_ptr()),           \
-          reinterpret_cast<CACHE_T*>(value_cache.data_ptr()),         \
-          slot_mapping.data_ptr<int64_t>(), block_stride, key_stride, \
-          value_stride, num_heads, head_size, block_size,             \
-          reinterpret_cast<const float*>(k_scale.data_ptr()),         \
+#define CALL_RESHAPE_AND_CACHE_FLASH(KV_T, CACHE_T, KV_DTYPE)             \
+  vllm::reshape_and_cache_flash_kernel<KV_T, CACHE_T, KV_DTYPE>           \
+      <<<grid, block, 0, stream>>>(                                       \
+          reinterpret_cast<KV_T*>(key.data_ptr()),                        \
+          reinterpret_cast<KV_T*>(value.data_ptr()),                      \
+          reinterpret_cast<CACHE_T*>(key_cache.data_ptr()),               \
+          reinterpret_cast<CACHE_T*>(value_cache.data_ptr()),             \
+          slot_mapping.data_ptr<int64_t>(), block_stride, page_stride,    \
+          head_stride, key_stride, value_stride, num_heads, head_size,    \
+          block_size, reinterpret_cast<const float*>(k_scale.data_ptr()), \
           reinterpret_cast<const float*>(v_scale.data_ptr()));
 
 void reshape_and_cache_flash(
@@ -432,9 +433,11 @@ void reshape_and_cache_flash(
   int head_size = key.size(2);
   int block_size = key_cache.size(1);
 
-  int key_stride = key.stride(0);
-  int value_stride = value.stride(0);
-  int block_stride = key_cache.stride(0);
+  int64_t key_stride = key.stride(0);
+  int64_t value_stride = value.stride(0);
+  int64_t block_stride = key_cache.stride(0);
+  int64_t page_stride = key_cache.stride(1);
+  int64_t head_stride = key_cache.stride(2);
   TORCH_CHECK(key_cache.stride(0) == value_cache.stride(0));
 
   dim3 grid(num_tokens);
@@ -446,8 +449,8 @@ void reshape_and_cache_flash(
                              CALL_RESHAPE_AND_CACHE_FLASH);
 }
 
-// KV_T is the stored data type of kv-cache.
-// CACHE_T is the data type of key and value tensors.
+// KV_T is the data type of key and value tensors.
+// CACHE_T is the stored data type of kv-cache.
 // KV_DTYPE is the real data type of kv-cache.
 #define CALL_CONCAT_AND_CACHE_MLA(KV_T, CACHE_T, KV_DTYPE)              \
   vllm::concat_and_cache_mla_kernel<KV_T, CACHE_T, KV_DTYPE>            \

csrc/core/math.hpp

@@ -7,3 +7,22 @@ inline constexpr uint32_t next_pow_2(uint32_t const num) {
   if (num <= 1) return num;
   return 1 << (CHAR_BIT * sizeof(num) - __builtin_clz(num - 1));
 }
+
+template <typename A, typename B>
+static inline constexpr auto div_ceil(A a, B b) {
+  return (a + b - 1) / b;
+}
+
+// Round a down to the next multiple of b. The caller is responsible for making
+// sure that b is non-zero
+template <typename T>
+inline constexpr T round_to_previous_multiple_of(T a, T b) {
+  return a % b == 0 ? a : (a / b) * b;
+}
+
+// Round a up to the next multiple of b. The caller is responsible for making
+// sure that b is non-zero
+template <typename T>
+inline constexpr T round_to_next_multiple_of(T a, T b) {
+  return a % b == 0 ? a : ((a / b) + 1) * b;
+}

csrc/cpu/cache.cpp

@@ -88,6 +88,48 @@ void reshape_and_cache_cpu_impl(
 }
 };  // namespace
 
+template <typename scalar_t>
+void concat_and_cache_mla_cpu_impl(
+    const scalar_t* __restrict__ kv_c,  // [num_tokens, kv_lora_rank]
+    const scalar_t* __restrict__ k_pe,  // [num_tokens, pe_dim]
+    scalar_t* __restrict__ kv_cache,  // [num_blocks, block_size, (kv_lora_rank
+                                      // + pe_dim)]
+    const int64_t* __restrict__ slot_mapping,  // [num_tokens]
+    const int num_tokens,                      //
+    const int block_stride,                    //
+    const int entry_stride,                    //
+    const int kv_c_stride,                     //
+    const int k_pe_stride,                     //
+    const int kv_lora_rank,                    //
+    const int pe_dim,                          //
+    const int block_size                       //
+) {
+#pragma omp parallel for
+  for (int token_idx = 0; token_idx < num_tokens; ++token_idx) {
+    const int64_t slot_idx = slot_mapping[token_idx];
+    // NOTE: slot_idx can be -1 if the token is padded
+    if (slot_idx < 0) {
+      continue;
+    }
+    const int64_t block_idx = slot_idx / block_size;
+    const int64_t block_offset = slot_idx % block_size;
+
+    auto copy = [&](const scalar_t* __restrict__ src,
+                    scalar_t* __restrict__ dst, int src_stride, int dst_stride,
+                    int size, int offset) {
+      for (int i = 0; i < size; i++) {
+        const int64_t src_idx = token_idx * src_stride + i;
+        const int64_t dst_idx =
+            block_idx * block_stride + block_offset * entry_stride + i + offset;
+        dst[dst_idx] = src[src_idx];
+      }
+    };
+
+    copy(kv_c, kv_cache, kv_c_stride, block_stride, kv_lora_rank, 0);
+    copy(k_pe, kv_cache, k_pe_stride, block_stride, pe_dim, kv_lora_rank);
+  }
+}
+
 // Note: the key_caches and value_caches vectors are constant but
 // not the Tensors they contain. The vectors need to be const refs
 // in order to satisfy pytorch's C++ operator registration code.
@@ -134,6 +176,38 @@ void reshape_and_cache(torch::Tensor& key, torch::Tensor& value,
   });
 }
 
+void concat_and_cache_mla(
+    torch::Tensor& kv_c,          // [num_tokens, kv_lora_rank]
+    torch::Tensor& k_pe,          // [num_tokens, pe_dim]
+    torch::Tensor& kv_cache,      // [num_blocks, block_size, (kv_lora_rank +
+                                  // pe_dim)]
+    torch::Tensor& slot_mapping,  // [num_tokens] or [num_actual_tokens]
+    const std::string& kv_cache_dtype, torch::Tensor& scale) {
+  int num_tokens = slot_mapping.size(0);
+  int kv_lora_rank = kv_c.size(1);
+  int pe_dim = k_pe.size(1);
+  int block_size = kv_cache.size(1);
+
+  TORCH_CHECK(kv_cache.size(2) == kv_lora_rank + pe_dim);
+  TORCH_CHECK(kv_cache_dtype != "fp8");
+
+  int kv_c_stride = kv_c.stride(0);
+  int k_pe_stride = k_pe.stride(0);
+  int block_stride = kv_cache.stride(0);
+  int entry_stride = kv_cache.stride(1);
+
+  VLLM_DISPATCH_FLOATING_TYPES(
+      kv_c.scalar_type(), "concat_and_cache_mla_cpu_impl", [&] {
+        CPU_KERNEL_GUARD_IN(concat_and_cache_mla_cpu_impl)
+        concat_and_cache_mla_cpu_impl<scalar_t>(
+            kv_c.data_ptr<scalar_t>(), k_pe.data_ptr<scalar_t>(),
+            kv_cache.data_ptr<scalar_t>(), slot_mapping.data_ptr<int64_t>(),
+            num_tokens, block_stride, entry_stride, kv_c_stride, k_pe_stride,
+            kv_lora_rank, pe_dim, block_size);
+        CPU_KERNEL_GUARD_OUT(concat_and_cache_mla_cpu_impl)
+      });
+}
+
 void swap_blocks(torch::Tensor& src, torch::Tensor& dst,
                  const torch::Tensor& block_mapping) {
   TORCH_CHECK(false, "swap_blocks is unsupported on CPU.")

csrc/cpu/cpu_types_vsx.hpp

@@ -4,6 +4,7 @@
 
 #include <altivec.h>
 #include <cmath>
+#include <algorithm>
 #include <torch/all.h>
 
 namespace vec_op {
@@ -62,6 +63,10 @@ typedef struct f32x4x4_t {
   __vector float val[4];
 } f32x4x4_t;
 
+typedef struct i32x4x4_t {
+  __vector int32_t val[4];
+} i32x4x4_t;
+
 struct FP32Vec8;
 struct FP32Vec16;
 
@@ -98,6 +103,28 @@ struct BF16Vec16 : public Vec<BF16Vec16> {
     vec_xst(reg.val[0], 0, (signed short*)ptr);
     vec_xst(reg.val[1], 16, (signed short*)ptr);
   }
+
+  void save(void* ptr, const int elem_num) const {
+    const int clamped_elem = std::max(0, std::min(elem_num, 16));
+
+    // Calculate elements to store in each 128-bit part (8 elements each)
+    const int elements_val0 = std::min(clamped_elem, 8);
+    const int elements_val1 = std::max(clamped_elem - 8, 0);
+
+    // Convert elements to bytes (2 bytes per element)
+    const size_t bytes_val0 = elements_val0 * sizeof(signed short);
+    const size_t bytes_val1 = elements_val1 * sizeof(signed short);
+
+    signed short* dest = static_cast<signed short*>(ptr);
+    // Store the first part using vec_xst_len
+    if (bytes_val0 > 0) {
+      vec_xst_len(reg.val[0], dest, bytes_val0);
+    }
+    // Store the second part if needed
+    if (bytes_val1 > 0) {
+      vec_xst_len(reg.val[1], dest + elements_val0, bytes_val1);
+    }
+  }
 };
 
 const static __vector signed short zero = vec_splats((signed short)0);
@@ -257,6 +284,64 @@ struct FP32Vec8 : public Vec<FP32Vec8> {
   }
 };
 
+struct INT32Vec16 : public Vec<INT32Vec16> {
+  constexpr static int VEC_ELEM_NUM = 16;
+  union AliasReg {
+    i32x4x4_t reg;
+    int32_t values[VEC_ELEM_NUM];
+  };
+
+  i32x4x4_t reg;
+
+  explicit INT32Vec16(const void* data_ptr) {
+    reg.val[0] = vec_xl(0, reinterpret_cast<const __vector int32_t*>(data_ptr));
+    reg.val[1] =
+        vec_xl(16, reinterpret_cast<const __vector int32_t*>(data_ptr));
+    reg.val[2] =
+        vec_xl(32, reinterpret_cast<const __vector int32_t*>(data_ptr));
+    reg.val[3] =
+        vec_xl(48, reinterpret_cast<const __vector int32_t*>(data_ptr));
+  }
+
+  void save(int32_t* ptr) const {
+    vec_xst(reg.val[0], 0, reinterpret_cast<__vector int32_t*>(ptr));
+    vec_xst(reg.val[1], 16, reinterpret_cast<__vector int32_t*>(ptr));
+    vec_xst(reg.val[2], 32, reinterpret_cast<__vector int32_t*>(ptr));
+    vec_xst(reg.val[3], 48, reinterpret_cast<__vector int32_t*>(ptr));
+  }
+
+  void save(int32_t* ptr, const int elem_num) const {
+    const int elements_in_chunk1 =
+        (elem_num >= 0) ? ((elem_num >= 4) ? 4 : elem_num) : 0;
+    const int elements_in_chunk2 =
+        (elem_num > 4) ? ((elem_num >= 8) ? 4 : elem_num - 4) : 0;
+    const int elements_in_chunk3 =
+        (elem_num > 8) ? ((elem_num >= 12) ? 4 : elem_num - 8) : 0;
+    const int elements_in_chunk4 =
+        (elem_num > 12) ? ((elem_num >= 16) ? 4 : elem_num - 12) : 0;
+
+    const size_t bytes_chunk1 =
+        static_cast<size_t>(elements_in_chunk1 * sizeof(int32_t));
+    const size_t bytes_chunk2 =
+        static_cast<size_t>(elements_in_chunk2 * sizeof(int32_t));
+    const size_t bytes_chunk3 =
+        static_cast<size_t>(elements_in_chunk3 * sizeof(int32_t));
+    const size_t bytes_chunk4 =
+        static_cast<size_t>(elements_in_chunk4 * sizeof(int32_t));
+
+    vec_xst_len(reg.val[0], reinterpret_cast<int32_t*>(ptr), bytes_chunk1);
+    vec_xst_len(reg.val[1],
+                reinterpret_cast<int32_t*>(reinterpret_cast<char*>(ptr) + 16),
+                bytes_chunk2);
+    vec_xst_len(reg.val[2],
+                reinterpret_cast<int32_t*>(reinterpret_cast<char*>(ptr) + 32),
+                bytes_chunk3);
+    vec_xst_len(reg.val[3],
+                reinterpret_cast<int32_t*>(reinterpret_cast<char*>(ptr) + 48),
+                bytes_chunk4);
+  }
+};
+
 struct FP32Vec16 : public Vec<FP32Vec16> {
   constexpr static int VEC_ELEM_NUM = 16;
   union AliasReg {
@@ -319,6 +404,13 @@ struct FP32Vec16 : public Vec<FP32Vec16> {
 
   explicit FP32Vec16(const BF16Vec8& v) : FP32Vec16(FP32Vec8(v)) {}
 
+  explicit FP32Vec16(const INT32Vec16& v) {
+    reg.val[0] = vec_ctf(v.reg.val[0], 0);
+    reg.val[1] = vec_ctf(v.reg.val[1], 0);
+    reg.val[2] = vec_ctf(v.reg.val[2], 0);
+    reg.val[3] = vec_ctf(v.reg.val[3], 0);
+  }
+
   FP32Vec16 operator*(const FP32Vec16& b) const {
     return FP32Vec16(f32x4x4_t({vec_mul(reg.val[0], b.reg.val[0]),
                                 vec_mul(reg.val[1], b.reg.val[1]),
@@ -347,6 +439,117 @@ struct FP32Vec16 : public Vec<FP32Vec16> {
                                 vec_div(reg.val[3], b.reg.val[3])}));
   }
 
+  FP32Vec16 clamp(const FP32Vec16& min, const FP32Vec16& max) const {
+    return FP32Vec16(f32x4x4_t(
+        {vec_min(max.reg.val[0], vec_max(min.reg.val[0], reg.val[0])),
+         vec_min(max.reg.val[1], vec_max(min.reg.val[1], reg.val[1])),
+         vec_min(max.reg.val[2], vec_max(min.reg.val[2], reg.val[2])),
+         vec_min(max.reg.val[3], vec_max(min.reg.val[3], reg.val[3]))}));
+  }
+
+  FP32Vec16 max(const FP32Vec16& b) const {
+    return FP32Vec16(f32x4x4_t({vec_max(reg.val[0], b.reg.val[0]),
+                                vec_max(reg.val[1], b.reg.val[1]),
+                                vec_max(reg.val[2], b.reg.val[2]),
+                                vec_max(reg.val[3], b.reg.val[3])}));
+  }
+
+  FP32Vec16 max(const FP32Vec16& b, int elem_num) const {
+    FP32Vec16 result;
+
+    // Create a vector of element indices for each chunk
+    __vector unsigned int indices = {0, 1, 2, 3};
+    __vector unsigned int elem_num_vec =
+        vec_splats(static_cast<unsigned int>(elem_num));
+
+    // Compute masks for each chunk
+    __vector unsigned int chunk_offset0 = {0, 0, 0,
+                                           0};  // Chunk 0: Elements 0-3
+    __vector unsigned int chunk_offset1 = {4, 4, 4,
+                                           4};  // Chunk 1: Elements 4-7
+    __vector unsigned int chunk_offset2 = {8, 8, 8,
+                                           8};  // Chunk 2: Elements 8-11
+    __vector unsigned int chunk_offset3 = {12, 12, 12,
+                                           12};  // Chunk 3: Elements 12-15
+
+    // Compute masks for each chunk
+    __vector bool int mask0 = vec_cmplt(indices + chunk_offset0, elem_num_vec);
+    __vector bool int mask1 = vec_cmplt(indices + chunk_offset1, elem_num_vec);
+    __vector bool int mask2 = vec_cmplt(indices + chunk_offset2, elem_num_vec);
+    __vector bool int mask3 = vec_cmplt(indices + chunk_offset3, elem_num_vec);
+
+    // Apply masks to compute the result for each chunk
+    result.reg.val[0] = vec_sel(this->reg.val[0],
+                                vec_max(this->reg.val[0], b.reg.val[0]), mask0);
+    result.reg.val[1] = vec_sel(this->reg.val[1],
+                                vec_max(this->reg.val[1], b.reg.val[1]), mask1);
+    result.reg.val[2] = vec_sel(this->reg.val[2],
+                                vec_max(this->reg.val[2], b.reg.val[2]), mask2);
+    result.reg.val[3] = vec_sel(this->reg.val[3],
+                                vec_max(this->reg.val[3], b.reg.val[3]), mask3);
+
+    return FP32Vec16(result.reg);
+  }
+
+  FP32Vec16 min(const FP32Vec16& b) const {
+    return FP32Vec16(f32x4x4_t({vec_min(reg.val[0], b.reg.val[0]),
+                                vec_min(reg.val[1], b.reg.val[1]),
+                                vec_min(reg.val[2], b.reg.val[2]),
+                                vec_min(reg.val[3], b.reg.val[3])}));
+  }
+
+  FP32Vec16 min(const FP32Vec16& b, int elem_num) const {
+    FP32Vec16 result;
+
+    vector unsigned int indices = {0, 1, 2, 3};
+    vector unsigned int elem_num_vec =
+        vec_splats(static_cast<unsigned int>(elem_num));
+
+    vector unsigned int chunk_offset0 = {0, 0, 0, 0};
+    vector unsigned int chunk_offset1 = {4, 4, 4, 4};
+    vector unsigned int chunk_offset2 = {8, 8, 8, 8};
+    vector unsigned int chunk_offset3 = {12, 12, 12, 12};
+
+    vector bool int mask0 = vec_cmplt(indices + chunk_offset0, elem_num_vec);
+    vector bool int mask1 = vec_cmplt(indices + chunk_offset1, elem_num_vec);
+    vector bool int mask2 = vec_cmplt(indices + chunk_offset2, elem_num_vec);
+    vector bool int mask3 = vec_cmplt(indices + chunk_offset3, elem_num_vec);
+
+    result.reg.val[0] = vec_sel(this->reg.val[0],
+                                vec_min(this->reg.val[0], b.reg.val[0]), mask0);
+    result.reg.val[1] = vec_sel(this->reg.val[1],
+                                vec_min(this->reg.val[1], b.reg.val[1]), mask1);
+    result.reg.val[2] = vec_sel(this->reg.val[2],
+                                vec_min(this->reg.val[2], b.reg.val[2]), mask2);
+    result.reg.val[3] = vec_sel(this->reg.val[3],
+                                vec_min(this->reg.val[3], b.reg.val[3]), mask3);
+
+    return FP32Vec16(result.reg);
+  }
+
+  FP32Vec16 abs() const {
+    return FP32Vec16(f32x4x4_t({vec_abs(reg.val[0]), vec_abs(reg.val[1]),
+                                vec_abs(reg.val[2]), vec_abs(reg.val[3])}));
+  }
+
+  float reduce_max() {
+    __vector float max01 = vec_max(reg.val[0], reg.val[1]);
+    __vector float max23 = vec_max(reg.val[2], reg.val[3]);
+    __vector float max_all = vec_max(max01, max23);
+    __vector float temp = vec_max(max_all, vec_sld(max_all, max_all, 8));
+    temp = vec_max(temp, vec_sld(temp, temp, 4));
+    return vec_extract(temp, 0);
+  }
+
+  float reduce_min() {
+    __vector float min01 = vec_min(reg.val[0], reg.val[1]);
+    __vector float min23 = vec_min(reg.val[2], reg.val[3]);
+    __vector float min_all = vec_min(min01, min23);
+    __vector float temp = vec_min(min_all, vec_sld(min_all, min_all, 8));
+    temp = vec_min(temp, vec_sld(temp, temp, 4));
+    return vec_extract(temp, 0);
+  }
+
   float reduce_sum() const {
     AliasReg ar;
     ar.reg = reg;
@@ -377,6 +580,68 @@ struct FP32Vec16 : public Vec<FP32Vec16> {
     vec_xst(reg.val[2], 32, ptr);
     vec_xst(reg.val[3], 48, ptr);
   }
+
+  void save(float* ptr, const int elem_num) const {
+    const int elements_in_chunk1 =
+        (elem_num >= 0) ? ((elem_num >= 4) ? 4 : elem_num) : 0;
+    const int elements_in_chunk2 =
+        (elem_num > 4) ? ((elem_num >= 8) ? 4 : elem_num - 4) : 0;
+    const int elements_in_chunk3 =
+        (elem_num > 8) ? ((elem_num >= 12) ? 4 : elem_num - 8) : 0;
+    const int elements_in_chunk4 =
+        (elem_num > 12) ? ((elem_num >= 16) ? 4 : elem_num - 12) : 0;
+
+    const size_t bytes_chunk1 =
+        static_cast<size_t>(elements_in_chunk1 * sizeof(float));
+    const size_t bytes_chunk2 =
+        static_cast<size_t>(elements_in_chunk2 * sizeof(float));
+    const size_t bytes_chunk3 =
+        static_cast<size_t>(elements_in_chunk3 * sizeof(float));
+    const size_t bytes_chunk4 =
+        static_cast<size_t>(elements_in_chunk4 * sizeof(float));
+
+    vec_xst_len(reg.val[0], ptr, bytes_chunk1);
+    vec_xst_len(reg.val[1],
+                reinterpret_cast<float*>(reinterpret_cast<char*>(ptr) + 16),
+                bytes_chunk2);
+    vec_xst_len(reg.val[2],
+                reinterpret_cast<float*>(reinterpret_cast<char*>(ptr) + 32),
+                bytes_chunk3);
+    vec_xst_len(reg.val[3],
+                reinterpret_cast<float*>(reinterpret_cast<char*>(ptr) + 48),
+                bytes_chunk4);
+  }
+};
+
+struct INT8Vec16 : public Vec<INT8Vec16> {
+  constexpr static int VEC_NUM_ELEM = 16;  // 128 bits / 8 bits = 16
+
+  union AliasReg {
+    __vector signed char reg;
+    int8_t values[VEC_NUM_ELEM];
+  };
+
+  __vector signed char reg;
+
+  explicit INT8Vec16(const FP32Vec16& vec) {
+    __vector signed int ret[4];
+    ret[0] = vec_cts(vec.reg.val[0], 0);
+    ret[1] = vec_cts(vec.reg.val[1], 0);
+    ret[2] = vec_cts(vec.reg.val[2], 0);
+    ret[3] = vec_cts(vec.reg.val[3], 0);
+
+    __vector signed short packed1 = vec_packs(ret[0], ret[1]);
+    __vector signed short packed2 = vec_packs(ret[2], ret[3]);
+
+    reg = vec_packs(packed1, packed2);
+  }
+
+  void save(void* ptr) const {
+    *reinterpret_cast<__vector signed char*>(ptr) = reg;
+  }
+  void save(signed char* ptr, const int elem_num) {
+    vec_xst_len(reg, ptr, static_cast<size_t>(elem_num));
+  }
 };
 
 template <typename T>

csrc/cpu/cpu_types_x86.hpp

@@ -78,9 +78,14 @@ struct FP16Vec16 : public Vec<FP16Vec16> {
 
   __m256i reg;
 
+  // normal load
   explicit FP16Vec16(const void* ptr)
       : reg((__m256i)_mm256_loadu_si256((__m256i*)ptr)) {}
 
+  // non-temproal load
+  explicit FP16Vec16(bool, void* ptr)
+      : reg(_mm256_stream_load_si256((__m256i*)ptr)) {}
+
   explicit FP16Vec16(const FP32Vec16&);
 
   void save(void* ptr) const { *reinterpret_cast<__m256i*>(ptr) = reg; }
@@ -110,9 +115,14 @@ struct BF16Vec16 : public Vec<BF16Vec16> {
 
   __m256i reg;
 
+  // normal load
   explicit BF16Vec16(const void* ptr)
       : reg((__m256i)_mm256_loadu_si256((__m256i*)ptr)) {}
 
+  // non-temproal load
+  explicit BF16Vec16(bool, void* ptr)
+      : reg(_mm256_stream_load_si256((__m256i*)ptr)) {}
+
   explicit BF16Vec16(const FP32Vec16&);
 
   void save(void* ptr) const { *reinterpret_cast<__m256i*>(ptr) = reg; }
@@ -130,6 +140,8 @@ struct BF16Vec32 : public Vec<BF16Vec32> {
 
   __m512i reg;
 
+  explicit BF16Vec32() : reg(_mm512_setzero_si512()) {}
+
   explicit BF16Vec32(const void* ptr) : reg((__m512i)_mm512_loadu_si512(ptr)) {}
 
   explicit BF16Vec32(__m512i data) : reg(data) {}
@@ -311,8 +323,13 @@ struct FP32Vec16 : public Vec<FP32Vec16> {
 
   explicit FP32Vec16() : reg(_mm512_set1_ps(0.0)) {}
 
+  // normal load
   explicit FP32Vec16(const float* ptr) : reg(_mm512_loadu_ps(ptr)) {}
 
+  // non-temproal load
+  explicit FP32Vec16(bool, void* ptr)
+      : reg((__m512)_mm512_stream_load_si512(ptr)) {}
+
   explicit FP32Vec16(__m512 data) : reg(data) {}
 
   explicit FP32Vec16(const FP32Vec4& data)
@@ -545,6 +562,33 @@ struct INT8Vec16 : public Vec<INT8Vec16> {
     _mm_mask_storeu_epi8(ptr, mask, reg);
   }
 };
+
+struct INT8Vec64 : public Vec<INT8Vec64> {
+  constexpr static int VEC_ELEM_NUM = 64;
+  union AliasReg {
+    __m512i reg;
+    int8_t values[VEC_ELEM_NUM];
+  };
+
+  __m512i reg;
+
+  // normal load
+  explicit INT8Vec64(void* ptr) : reg(_mm512_loadu_epi8(ptr)) {}
+
+  // non-temproal load
+  explicit INT8Vec64(bool, void* ptr) : reg(_mm512_stream_load_si512(ptr)) {}
+
+  void save(void* ptr) const { _mm512_storeu_epi8(ptr, reg); }
+
+  void save(int8_t* ptr, const int elem_num) const {
+    constexpr uint64_t M = 0xFFFFFFFFFFFFFFFF;
+    __mmask64 mask = _cvtu64_mask64(M >> (64 - elem_num));
+    _mm512_mask_storeu_epi8(ptr, mask, reg);
+  }
+
+  // non-temproal save
+  void nt_save(int8_t* ptr) { _mm512_stream_si512((__m512i*)ptr, reg); }
+};
 #endif
 
 template <typename T>
@@ -655,6 +699,22 @@ inline BF16Vec16::BF16Vec16(const FP32Vec16& v) {
 
 inline void prefetch(const void* addr) { _mm_prefetch(addr, _MM_HINT_T1); }
 
+#ifdef __AVX512F__
+inline void non_temporal_save(FP16Vec16& vec, void* ptr) {
+  _mm256_stream_si256((__m256i*)ptr, vec.reg);
+}
+inline void non_temporal_save(BF16Vec32& vec, void* ptr) {
+  _mm512_stream_si512((__m512i*)ptr, vec.reg);
+}
+inline void non_temporal_save(BF16Vec16& vec, void* ptr) {
+  _mm256_stream_si256((__m256i*)ptr, vec.reg);
+}
+inline void non_temporal_save(FP32Vec16& vec, void* ptr) {
+  _mm512_stream_ps((float*)ptr, vec.reg);
+}
+#endif
+
+inline void mem_barrier() { _mm_mfence(); }
 };  // namespace vec_op
 
 #endif

csrc/cpu/mla_decode.cpp

@@ -0,0 +1,393 @@
+#include "cpu_types.hpp"
+#include <float.h>
+
+namespace {
+template <typename scalar_t>
+struct KernelVecType {
+  using qk_load_vec_type = void;
+  using qk_vec_type = void;
+  using v_load_vec_type = void;
+};
+
+template <>
+struct KernelVecType<float> {
+  using qk_load_vec_type = vec_op::FP32Vec16;
+  using qk_vec_type = vec_op::FP32Vec16;
+  using v_load_vec_type = vec_op::FP32Vec16;
+};
+
+template <>
+struct KernelVecType<c10::Half> {
+#if defined(__powerpc64__) || defined(__s390x__)
+  // Power and s390x architecture-specific vector types
+  using qk_load_vec_type = vec_op::FP32Vec16;
+  using qk_vec_type = vec_op::FP32Vec16;
+  using v_load_vec_type = vec_op::FP32Vec16;
+#else
+  // Fallback for other architectures, including x86
+  using qk_load_vec_type = vec_op::FP16Vec16;
+  using qk_vec_type = vec_op::FP32Vec16;
+  using v_load_vec_type = vec_op::FP16Vec16;
+#endif
+};
+
+#ifdef __AVX512BF16__
+template <>
+struct KernelVecType<c10::BFloat16> {
+  using qk_load_vec_type = vec_op::BF16Vec32;
+  using qk_vec_type = vec_op::BF16Vec32;
+  using v_load_vec_type = vec_op::BF16Vec16;
+};
+#elif defined(__aarch64__) && !defined(ARM_BF16_SUPPORT)
+// pass
+#else
+template <>
+struct KernelVecType<c10::BFloat16> {
+  using qk_load_vec_type = vec_op::BF16Vec16;
+  using qk_vec_type = vec_op::FP32Vec16;
+  using v_load_vec_type = vec_op::BF16Vec16;
+};
+#endif
+
+template <int HEAD_DIM, int V_HEAD_DIM, int BLOCK_SIZE, int HEAD_UNROLL,
+          typename qk_vec_type>
+void mla_decode_block_head(
+    const qk_vec_type* __restrict__ q_vecs,          // [HEAD_UNROLL, head_dim]
+    const qk_vec_type* __restrict__ k_vecs,          // [block_size, head_dim]
+    const vec_op::FP32Vec16* __restrict v_vecs_f32,  // [block_size, v_head_dim]
+    float* __restrict__ acc_out,  // [HEAD_UNROLL, v_head_dim]
+    float* __restrict__ acc_lse,  // [HEAD_UNROLL]
+    const float scale, const int num_tokens) {
+  using f32_vec_type = vec_op::FP32Vec16;
+  constexpr int QK_NUM_ELEM = qk_vec_type::VEC_ELEM_NUM;
+  constexpr int V_NUM_ELEM = f32_vec_type::VEC_ELEM_NUM;
+
+  float logits[BLOCK_SIZE][HEAD_UNROLL] = {};  // initialize to zeros
+  float max_val[HEAD_UNROLL];
+  std::fill(max_val, max_val + HEAD_UNROLL, -FLT_MAX);
+
+  f32_vec_type acc_vec[BLOCK_SIZE][HEAD_UNROLL];
+  for (int i = 0; i < HEAD_DIM; i += QK_NUM_ELEM) {
+    // load to registers
+    qk_vec_type q_vec[HEAD_UNROLL];
+
+#pragma unroll
+    for (int unroll = 0; unroll < HEAD_UNROLL; ++unroll)
+      q_vec[unroll] =
+          qk_vec_type{q_vecs[(i + unroll * HEAD_DIM) / QK_NUM_ELEM]};
+
+    for (int block_offset = 0; block_offset < num_tokens; ++block_offset) {
+      qk_vec_type k_vec(k_vecs[(block_offset * HEAD_DIM + i) / QK_NUM_ELEM]);
+
+#pragma unroll
+      for (int unroll = 0; unroll < HEAD_UNROLL; ++unroll)
+        vec_op::fma(acc_vec[block_offset][unroll], q_vec[unroll], k_vec);
+    }
+  }
+
+  for (int block_offset = 0; block_offset < num_tokens; ++block_offset) {
+#pragma unroll
+    for (int unroll = 0; unroll < HEAD_UNROLL; ++unroll) {
+      const float acc = acc_vec[block_offset][unroll].reduce_sum() * scale;
+      logits[block_offset][unroll] = acc;
+      max_val[unroll] = std::max(max_val[unroll], acc);
+    }
+  }
+
+  float sum_exp[HEAD_UNROLL] = {};
+  for (int block_offset = 0; block_offset < num_tokens; ++block_offset) {
+#pragma unroll
+    for (int unroll = 0; unroll < HEAD_UNROLL; ++unroll) {
+      const float val =
+          std::exp(logits[block_offset][unroll] - max_val[unroll]);
+      logits[block_offset][unroll] = val;
+      sum_exp[unroll] += val;
+    }
+  }
+
+  f32_vec_type this_out[V_HEAD_DIM / V_NUM_ELEM][HEAD_UNROLL];
+
+  for (int block_offset = 0; block_offset < num_tokens; ++block_offset) {
+    // load to registers
+    f32_vec_type scale_[HEAD_UNROLL];
+
+#pragma unroll
+    for (int unroll = 0; unroll < HEAD_UNROLL; ++unroll)
+      scale_[unroll] =
+          f32_vec_type{logits[block_offset][unroll] / sum_exp[unroll]};
+
+    for (int i = 0; i < V_HEAD_DIM; i += V_NUM_ELEM) {
+      f32_vec_type v_vec(
+          v_vecs_f32[(block_offset * HEAD_DIM + i) / V_NUM_ELEM]);
+
+#pragma unroll
+      for (int unroll = 0; unroll < HEAD_UNROLL; ++unroll)
+        vec_op::fma(this_out[i / V_NUM_ELEM][unroll], v_vec, scale_[unroll]);
+    }
+  }
+
+  // merge attention state
+  // section 2.2 in https://arxiv.org/pdf/2501.01005
+  f32_vec_type prev_scale[HEAD_UNROLL];
+  f32_vec_type curr_scale[HEAD_UNROLL];
+
+#pragma unroll
+  for (int unroll = 0; unroll < HEAD_UNROLL; ++unroll) {
+    const float prev_lse = acc_lse[unroll];
+    const float curr_lse = std::log(sum_exp[unroll]) +
+                           max_val[unroll];  // add back max_val to get true lse
+    // softmax trick
+    const float max_lse = std::max(prev_lse, curr_lse);
+    const float prev_sum_exp = std::exp(prev_lse - max_lse);
+    const float curr_sum_exp = std::exp(curr_lse - max_lse);
+
+    const float new_sum_exp = prev_sum_exp + curr_sum_exp;
+    acc_lse[unroll] = std::log(new_sum_exp) + max_lse;
+
+    prev_scale[unroll] = f32_vec_type{prev_sum_exp / new_sum_exp};
+    curr_scale[unroll] = f32_vec_type{curr_sum_exp / new_sum_exp};
+  }
+
+  for (int i = 0; i < V_HEAD_DIM; i += V_NUM_ELEM) {
+#pragma unroll
+    for (int unroll = 0; unroll < HEAD_UNROLL; ++unroll) {
+      f32_vec_type o_vec(acc_out + i + V_HEAD_DIM * unroll);
+      o_vec = o_vec * prev_scale[unroll] +
+              this_out[i / V_NUM_ELEM][unroll] * curr_scale[unroll];
+      o_vec.save(acc_out + i + V_HEAD_DIM * unroll);
+    }
+  }
+
+  q_vecs += HEAD_DIM / QK_NUM_ELEM * HEAD_UNROLL;
+  acc_out += V_HEAD_DIM * HEAD_UNROLL;
+}
+
+template <typename scalar_t, int HEAD_DIM, int V_HEAD_DIM, int BLOCK_SIZE,
+          typename qk_vec_type>
+void mla_decode_block(
+    const qk_vec_type* __restrict__ q_vecs,  // [num_heads, head_dim]
+    const scalar_t* __restrict__ kv_cache,   // [block_size, head_dim]
+    float* __restrict__ acc_out,             // [num_heads, v_head_dim]
+    float* __restrict__ acc_lse,             // [num_heads]
+    const int num_heads, const float scale, const int num_tokens) {
+  using qk_load_vec_type = typename KernelVecType<scalar_t>::qk_load_vec_type;
+  static_assert(
+      std::is_same<qk_vec_type,
+                   typename KernelVecType<scalar_t>::qk_vec_type>::value);
+  using v_load_vec_type = typename KernelVecType<scalar_t>::v_load_vec_type;
+  using f32_vec_type = vec_op::FP32Vec16;
+  static_assert(qk_load_vec_type::VEC_ELEM_NUM == qk_vec_type::VEC_ELEM_NUM);
+  static_assert(v_load_vec_type::VEC_ELEM_NUM == f32_vec_type::VEC_ELEM_NUM);
+  constexpr int QK_NUM_ELEM = qk_vec_type::VEC_ELEM_NUM;
+  constexpr int V_NUM_ELEM = v_load_vec_type::VEC_ELEM_NUM;
+
+  const qk_vec_type* k_vecs;
+  const f32_vec_type* v_vecs_f32;
+  float* kv_cache_f32 = nullptr;
+
+  if constexpr (!std::is_same<scalar_t, float>::value) {
+    // convert KV cache block to FP32 to reuse it across query heads and
+    // attn @ V computation, since FP16/BF16->FP32 is expensive.
+    // TODO: move malloc outside of this fn to reuse across iterations.
+    const int nbytes = BLOCK_SIZE * HEAD_DIM * sizeof(float);
+    kv_cache_f32 = static_cast<float*>(std::aligned_alloc(64, nbytes));
+
+    for (int block_offset = 0; block_offset < num_tokens; ++block_offset)
+      for (int i = 0; i < HEAD_DIM; i += V_NUM_ELEM) {
+        v_load_vec_type kv_load_vec(kv_cache + block_offset * HEAD_DIM + i);
+        f32_vec_type kv_vec_f32(kv_load_vec);
+        kv_vec_f32.save(kv_cache_f32 + block_offset * HEAD_DIM + i);
+      }
+
+    if constexpr (std::is_same<qk_load_vec_type, qk_vec_type>::value) {
+      // for AVX512_BF16, Q @ K.T uses BF16 for K (no conversion)
+      // NOTE: in this case, we only need to convert the V section to FP32.
+      // But for simplicity, we will convert the whole KV block to FP32.
+      k_vecs = reinterpret_cast<const qk_vec_type*>(kv_cache);
+    } else {
+      k_vecs = reinterpret_cast<const qk_vec_type*>(kv_cache_f32);
+    }
+
+    // attn @ V always use FP32 for V, since attn is FP32.
+    v_vecs_f32 = reinterpret_cast<const f32_vec_type*>(kv_cache_f32);
+
+  } else {
+    // KV cache is FP32. don't need to do anything.
+    k_vecs = reinterpret_cast<const qk_vec_type*>(kv_cache);
+    v_vecs_f32 = reinterpret_cast<const f32_vec_type*>(kv_cache);
+  }
+
+  // compute 2 heads at the same time to improve ILP and
+  // take advantage of register cache for K and V.
+  constexpr int HEAD_UNROLL = 2;
+  for (int iter = 0; iter < num_heads / HEAD_UNROLL; ++iter) {
+    mla_decode_block_head<HEAD_DIM, V_HEAD_DIM, BLOCK_SIZE, HEAD_UNROLL>(
+        q_vecs, k_vecs, v_vecs_f32, acc_out, acc_lse, scale, num_tokens);
+
+    q_vecs += HEAD_UNROLL * HEAD_DIM / QK_NUM_ELEM;
+    acc_out += HEAD_UNROLL * V_HEAD_DIM;
+    acc_lse += HEAD_UNROLL;
+  }
+
+  // take care of the remaining heads
+  for (int iter = 0; iter < num_heads % HEAD_UNROLL; ++iter) {
+    mla_decode_block_head<HEAD_DIM, V_HEAD_DIM, BLOCK_SIZE, 1>(
+        q_vecs, k_vecs, v_vecs_f32, acc_out, acc_lse, scale, num_tokens);
+
+    q_vecs += HEAD_DIM / QK_NUM_ELEM;
+    acc_out += V_HEAD_DIM;
+    acc_lse += 1;
+  }
+
+  if (kv_cache_f32 != nullptr) {
+    std::free(kv_cache_f32);
+  }
+}
+}  // namespace
+
+template <typename scalar_t, int HEAD_DIM, int V_HEAD_DIM, int BLOCK_SIZE>
+void mla_decode_kvcache_cpu_impl(
+    scalar_t* __restrict__ out,             // [num_seqs, num_heads, v_head_dim]
+    const scalar_t* __restrict__ q,         // [num_seqs, num_heads, head_dim]
+    const scalar_t* __restrict__ kv_cache,  // [num_blocks, block_size,
+                                            // head_dim]
+    const int num_heads, const float scale,
+    const int* __restrict__ block_tables,  // [num_seqs, max_num_blocks_per_seq]
+    const int* __restrict__ seq_lens,      // [num_seqs]
+    const int max_num_blocks_per_seq, const int o_stride, const int q_stride,
+    const int kv_stride, const int num_seqs) {
+  using qk_load_vec_type = typename KernelVecType<scalar_t>::qk_load_vec_type;
+  using qk_vec_type = typename KernelVecType<scalar_t>::qk_vec_type;
+  constexpr int QK_NUM_ELEM = qk_vec_type::VEC_ELEM_NUM;
+
+  // shared across threads
+  const int max_threads = omp_get_max_threads();
+  const int acc_out_nbytes =
+      max_threads * num_heads * V_HEAD_DIM * sizeof(float);
+  float* acc_out = static_cast<float*>(std::aligned_alloc(64, acc_out_nbytes));
+  std::vector<float> acc_lse(max_threads * num_heads);
+
+  // allocate memory to pre-convert query to FP32 later
+  float* q_f32;
+  constexpr bool PRE_CONVERT_QUERY =
+      !std::is_same<scalar_t, float>::value &&
+      std::is_same<qk_vec_type, vec_op::FP32Vec16>::value;
+  if constexpr (PRE_CONVERT_QUERY) {
+    const int q_f32_nbytes = num_heads * HEAD_DIM * sizeof(float);
+    q_f32 = static_cast<float*>(std::aligned_alloc(64, q_f32_nbytes));
+  }
+
+#pragma omp parallel
+  {
+    const int num_threads = omp_get_num_threads();
+    const int thread_id = omp_get_thread_num();
+    float* __restrict__ acc_out_thread =
+        acc_out + thread_id * num_heads * V_HEAD_DIM;
+    float* __restrict__ acc_lse_thread = acc_lse.data() + thread_id * num_heads;
+
+    for (int seq_idx = 0; seq_idx < num_seqs; ++seq_idx) {
+      // reset accumulator
+      std::fill(acc_out_thread, acc_out_thread + num_heads * V_HEAD_DIM, 0.0f);
+      std::fill(acc_lse_thread, acc_lse_thread + num_heads, -FLT_MAX);
+
+      const int seq_len = seq_lens[seq_idx];
+      const int block_num = (seq_len + BLOCK_SIZE - 1) / BLOCK_SIZE;
+      const int last_block_size = seq_len - (block_num - 1) * BLOCK_SIZE;
+
+      const qk_vec_type* q_vecs;
+      if constexpr (PRE_CONVERT_QUERY) {
+// pre-convert query to FP32 since FP16/BF16->FP32 is slow.
+#pragma omp for
+        for (int i = 0; i < num_heads * HEAD_DIM; i += QK_NUM_ELEM) {
+          qk_load_vec_type q_load_vec(q + seq_idx * q_stride + i);
+          qk_vec_type q_vec(q_load_vec);
+          q_vec.save(q_f32 + i);
+        }
+        q_vecs = reinterpret_cast<const qk_vec_type*>(q_f32);
+      } else {
+        q_vecs = reinterpret_cast<const qk_vec_type*>(q + seq_idx * q_stride);
+      }
+
+#pragma omp for
+      for (int block_idx = 0; block_idx < block_num; ++block_idx) {
+        const int physical_block_idx =
+            block_tables[seq_idx * max_num_blocks_per_seq + block_idx];
+        const int num_tokens =
+            block_idx < block_num - 1 ? BLOCK_SIZE : last_block_size;
+
+        mla_decode_block<scalar_t, HEAD_DIM, V_HEAD_DIM, BLOCK_SIZE>(
+            q_vecs, kv_cache + physical_block_idx * kv_stride, acc_out_thread,
+            acc_lse_thread, num_heads, scale, num_tokens);
+      }
+
+// merge attention states across threads
+// section 2.2 in https://arxiv.org/pdf/2501.01005
+// each thread is responsible for 1 head
+#pragma omp for
+      for (int head_idx = 0; head_idx < num_heads; ++head_idx) {
+        float* acc_lse_head = acc_lse.data() + head_idx;
+        float* acc_out_head = acc_out + head_idx * V_HEAD_DIM;
+
+        float max_val = -FLT_MAX;
+        for (int thread_id_ = 0; thread_id_ < num_threads; ++thread_id_) {
+          max_val = std::max(max_val, acc_lse_head[thread_id_ * num_heads]);
+        }
+
+        float sum_exp = 0.0f;
+        for (int thread_id_ = 0; thread_id_ < num_threads; ++thread_id_) {
+          float val = std::exp(acc_lse_head[thread_id_ * num_heads] - max_val);
+          acc_lse_head[thread_id_ * num_heads] = val;
+          sum_exp += val;
+        }
+
+        float inv_sum = 1.0f / sum_exp;
+        float out_head[V_HEAD_DIM] = {};
+        for (int thread_id_ = 0; thread_id_ < num_threads; ++thread_id_) {
+          float scale_ = acc_lse_head[thread_id_ * num_heads] * inv_sum;
+          for (int i = 0; i < V_HEAD_DIM; ++i) {
+            out_head[i] +=
+                acc_out_head[thread_id_ * num_heads * V_HEAD_DIM + i] * scale_;
+          }
+        }
+
+        for (int i = 0; i < V_HEAD_DIM; ++i) {
+          vec_op::storeFP32(out_head[i], out + seq_idx * o_stride +
+                                             head_idx * V_HEAD_DIM + i);
+        }
+      }
+    }
+  }
+  if (PRE_CONVERT_QUERY) {
+    std::free(q_f32);
+  }
+  std::free(acc_out);
+}
+
+void mla_decode_kvcache(torch::Tensor& out, torch::Tensor& query,
+                        torch::Tensor& kv_cache, double scale,
+                        torch::Tensor& block_tables, torch::Tensor& seq_lens) {
+  const int num_seqs = query.size(0);
+  const int num_heads = query.size(1);
+  const int head_dim = query.size(2);
+  const int block_size = kv_cache.size(1);
+  const int v_head_dim = out.size(2);
+
+  const int max_num_blocks_per_seq = block_tables.size(1);
+  const int o_stride = out.stride(0);
+  const int q_stride = query.stride(0);
+  const int kv_stride = kv_cache.stride(0);
+
+  VLLM_DISPATCH_FLOATING_TYPES(
+      query.scalar_type(), "mla_decode_kvcache_cpu_impl", [&] {
+        CPU_KERNEL_GUARD_IN(mla_decode_kvcache_cpu_impl)
+        if (head_dim == 576 && v_head_dim == 512 && block_size == 16)
+          mla_decode_kvcache_cpu_impl<scalar_t, 576, 512, 16>(
+              out.data_ptr<scalar_t>(), query.data_ptr<scalar_t>(),
+              kv_cache.data_ptr<scalar_t>(), num_heads, scale,
+              block_tables.data_ptr<int>(), seq_lens.data_ptr<int>(),
+              max_num_blocks_per_seq, o_stride, q_stride, kv_stride, num_seqs);
+        else
+          TORCH_CHECK(false, "Unsupported block size: ", block_size);
+        CPU_KERNEL_GUARD_OUT(mla_decode_kvcache_cpu_impl)
+      });
+}

csrc/cpu/pos_encoding.cpp

@@ -9,7 +9,8 @@ void rotary_embedding_impl(
     scalar_t* __restrict__ query,           /// [batch_size, seq_len, num_heads,
                                    /// head_size] or [num_tokens, num_heads,
                                    /// head_size]
-    scalar_t* __restrict__ key,  // [batch_size, seq_len, num_kv_heads,
+    scalar_t* __restrict__ key,  // nullptr (optional) or
+                                 // [batch_size, seq_len, num_kv_heads,
                                  // head_size] or [num_tokens, num_kv_heads,
                                  // head_size]
     const scalar_t* __restrict__ cos_sin_cache,  // [max_position, 2, rot_dim //
@@ -85,10 +86,13 @@ void rotary_embedding_impl(
       compute_loop(token_head, cache_ptr, query);
     }
 
-    for (int i = 0; i < num_kv_heads; ++i) {
-      const int head_idx = i;
-      const int64_t token_head = token_idx * key_stride + head_idx * head_size;
-      compute_loop(token_head, cache_ptr, key);
+    if (key != nullptr) {
+      for (int i = 0; i < num_kv_heads; ++i) {
+        const int head_idx = i;
+        const int64_t token_head =
+            token_idx * key_stride + head_idx * head_size;
+        compute_loop(token_head, cache_ptr, key);
+      }
     }
   }
 }
@@ -100,7 +104,8 @@ void rotary_embedding_gptj_impl(
     scalar_t* __restrict__ query,           /// [batch_size, seq_len, num_heads,
                                    /// head_size] or [num_tokens, num_heads,
                                    /// head_size]
-    scalar_t* __restrict__ key,  // [batch_size, seq_len, num_kv_heads,
+    scalar_t* __restrict__ key,  // nullptr (optional) or
+                                 // [batch_size, seq_len, num_kv_heads,
                                  // head_size] or [num_tokens, num_kv_heads,
                                  // head_size]
     const scalar_t* __restrict__ cos_sin_cache,  // [max_position, 2, rot_dim //
@@ -138,6 +143,10 @@ void rotary_embedding_gptj_impl(
     }
   }
 
+  if (key == nullptr) {
+    return;
+  }
+
 #pragma omp parallel for collapse(2)
   for (int token_idx = 0; token_idx < num_tokens; ++token_idx) {
     for (int i = 0; i < num_kv_heads; ++i) {
@@ -168,13 +177,13 @@ void rotary_embedding_gptj_impl(
 };  // namespace
 
 void rotary_embedding(torch::Tensor& positions, torch::Tensor& query,
-                      torch::Tensor& key, int64_t head_size,
+                      std::optional<torch::Tensor> key, int64_t head_size,
                       torch::Tensor& cos_sin_cache, bool is_neox) {
   int num_tokens = positions.numel();
   int rot_dim = cos_sin_cache.size(1);
   int num_heads = query.size(-1) / head_size;
-  int num_kv_heads = key.size(-1) / head_size;
-  int64_t key_stride = key.stride(-2);
+  int num_kv_heads = key.has_value() ? key->size(-1) / head_size : num_heads;
+  int64_t key_stride = key.has_value() ? key->stride(-2) : 0;
   int64_t query_stride = query.stride(-2);
 
   VLLM_DISPATCH_FLOATING_TYPES(
@@ -183,15 +192,15 @@ void rotary_embedding(torch::Tensor& positions, torch::Tensor& query,
         if (is_neox) {
           rotary_embedding_impl(
               positions.data_ptr<int64_t>(), query.data_ptr<scalar_t>(),
-              key.data_ptr<scalar_t>(), cos_sin_cache.data_ptr<scalar_t>(),
-              rot_dim, query_stride, key_stride, num_heads, num_kv_heads,
-              head_size, num_tokens);
+              key.has_value() ? key->data_ptr<scalar_t>() : nullptr,
+              cos_sin_cache.data_ptr<scalar_t>(), rot_dim, query_stride,
+              key_stride, num_heads, num_kv_heads, head_size, num_tokens);
         } else {
           rotary_embedding_gptj_impl(
               positions.data_ptr<int64_t>(), query.data_ptr<scalar_t>(),
-              key.data_ptr<scalar_t>(), cos_sin_cache.data_ptr<scalar_t>(),
-              rot_dim, query_stride, key_stride, num_heads, num_kv_heads,
-              head_size, num_tokens);
+              key.has_value() ? key->data_ptr<scalar_t>() : nullptr,
+              cos_sin_cache.data_ptr<scalar_t>(), rot_dim, query_stride,
+              key_stride, num_heads, num_kv_heads, head_size, num_tokens);
         }
 
         CPU_KERNEL_GUARD_OUT(rotary_embedding_impl)