Use gpu_1_queue

Signed-off-by: mgoin <michael@neuralmagic.com>

.buildkite/nightly-benchmarks/README.md

@@ -74,7 +74,7 @@ Here is an example of one test inside `latency-tests.json`:
 In this example:
 
 - The `test_name` attributes is a unique identifier for the test. In `latency-tests.json`, it must start with `latency_`.
-- The `parameters` attribute control the command line arguments to be used for `benchmark_latency.py`. Note that please use underline `_` instead of the dash `-` when specifying the command line arguments, and `run-performance-benchmarks.sh` will convert the underline to dash when feeding the arguments to `benchmark_latency.py`. For example, the corresponding command line arguments for `benchmark_latency.py` will be `--model meta-llama/Meta-Llama-3-8B --tensor-parallel-size 1 --load-format dummy --num-iters-warmup 5 --num-iters 15`
+- The `parameters` attribute control the command line arguments to be used for `vllm bench latency`. Note that please use underline `_` instead of the dash `-` when specifying the command line arguments, and `run-performance-benchmarks.sh` will convert the underline to dash when feeding the arguments to `vllm bench latency`. For example, the corresponding command line arguments for `vllm bench latency` will be `--model meta-llama/Meta-Llama-3-8B --tensor-parallel-size 1 --load-format dummy --num-iters-warmup 5 --num-iters 15`
 
 Note that the performance numbers are highly sensitive to the value of the parameters. Please make sure the parameters are set correctly.
 
@@ -82,13 +82,13 @@ WARNING: The benchmarking script will save json results by itself, so please do
 
 ### Throughput test
 
-The tests are specified in `throughput-tests.json`. The syntax is similar to `latency-tests.json`, except for that the parameters will be fed forward to `benchmark_throughput.py`.
+The tests are specified in `throughput-tests.json`. The syntax is similar to `latency-tests.json`, except for that the parameters will be fed forward to `vllm bench throughput`.
 
 The number of this test is also stable -- a slight change on the value of this number might vary the performance numbers by a lot.
 
 ### Serving test
 
-We test the throughput by using `benchmark_serving.py` with request rate = inf to cover the online serving overhead. The corresponding parameters are in `serving-tests.json`, and here is an example:
+We test the throughput by using `vllm bench serve` with request rate = inf to cover the online serving overhead. The corresponding parameters are in `serving-tests.json`, and here is an example:
 
 ```json
 [
@@ -118,8 +118,8 @@ Inside this example:
 
 - The `test_name` attribute is also a unique identifier for the test. It must start with `serving_`.
 - The `server-parameters` includes the command line arguments for vLLM server.
-- The `client-parameters` includes the command line arguments for `benchmark_serving.py`.
-- The `qps_list` controls the list of qps for test. It will be used to configure the `--request-rate` parameter in `benchmark_serving.py`
+- The `client-parameters` includes the command line arguments for `vllm bench serve`.
+- The `qps_list` controls the list of qps for test. It will be used to configure the `--request-rate` parameter in `vllm bench serve`
 
 The number of this test is less stable compared to the delay and latency benchmarks (due to randomized sharegpt dataset sampling inside `benchmark_serving.py`), but a large change on this number (e.g. 5% change) still vary the output greatly.
 

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

@@ -100,7 +100,7 @@ if __name__ == "__main__":
             raw_result = json.loads(f.read())
 
         if "serving" in str(test_file):
-            # this result is generated via `benchmark_serving.py`
+            # this result is generated via `vllm bench serve` command
 
             # attach the benchmarking command to raw_result
             try:
@@ -120,7 +120,7 @@ if __name__ == "__main__":
             continue
 
         elif "latency" in f.name:
-            # this result is generated via `benchmark_latency.py`
+            # this result is generated via `vllm bench latency` command
 
             # attach the benchmarking command to raw_result
             try:
@@ -148,7 +148,7 @@ if __name__ == "__main__":
             continue
 
         elif "throughput" in f.name:
-            # this result is generated via `benchmark_throughput.py`
+            # this result is generated via `vllm bench throughput` command
 
             # attach the benchmarking command to raw_result
             try:

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

@@ -73,7 +73,7 @@ get_current_llm_serving_engine() {
     echo "Container: vllm"
     # move to a completely irrelevant directory, to avoid import vllm from current folder
     export CURRENT_LLM_SERVING_ENGINE=vllm
-    
+
     return
   fi
 }
@@ -95,12 +95,14 @@ json2args() {
 }
 
 kill_gpu_processes() {
-  pkill -f python
-  pkill -f python3
-  pkill -f tritonserver
-  pkill -f pt_main_thread
-  pkill -f text-generation
-  pkill -f lmdeploy
+  pkill -f '[p]ython'
+  pkill -f '[p]ython3'
+  pkill -f '[t]ritonserver'
+  pkill -f '[p]t_main_thread'
+  pkill -f '[t]ext-generation'
+  pkill -f '[l]mdeploy'
+  # vLLM now names the process with VLLM prefix after https://github.com/vllm-project/vllm/pull/21445
+  pkill -f '[V]LLM'
 
   while [ "$(nvidia-smi --query-gpu=memory.used --format=csv,noheader,nounits | head -n 1)" -ge 1000 ]; do
     sleep 1
@@ -125,7 +127,7 @@ ensure_installed() {
 }
 
 run_serving_tests() {
-  # run serving tests using `benchmark_serving.py`
+  # run serving tests using `vllm bench serve` command
   # $1: a json file specifying serving test cases
 
   local serving_test_file
@@ -225,7 +227,7 @@ run_serving_tests() {
 
       if [[ "$dataset_name" = "sharegpt" ]]; then
 
-        client_command="python3 benchmark_serving.py \
+        client_command="vllm bench serve \
           --backend $backend \
           --tokenizer /tokenizer_cache \
           --model $model \
@@ -246,7 +248,7 @@ run_serving_tests() {
         sonnet_output_len=$(echo "$common_params" | jq -r '.sonnet_output_len')
         sonnet_prefix_len=$(echo "$common_params" | jq -r '.sonnet_prefix_len')
 
-        client_command="python3 benchmark_serving.py \
+        client_command="vllm bench serve \
           --backend $backend \
           --tokenizer /tokenizer_cache \
           --model $model \
@@ -265,13 +267,13 @@ run_serving_tests() {
           $client_args"
 
       else
-  
+
         echo "The dataset name must be either 'sharegpt' or 'sonnet'. Got $dataset_name."
         exit 1
 
       fi
 
-        
+
 
       echo "Running test case $test_name with qps $qps"
       echo "Client command: $client_command"
@@ -302,7 +304,7 @@ run_serving_tests() {
 }
 
 run_genai_perf_tests() {
-  # run genai-perf tests 
+  # run genai-perf tests
 
   # $1: a json file specifying genai-perf test cases
   local genai_perf_test_file
@@ -311,14 +313,14 @@ run_genai_perf_tests() {
   # Iterate over genai-perf tests
   jq -c '.[]' "$genai_perf_test_file" | while read -r params; do
     # get the test name, and append the GPU type back to it.
-    test_name=$(echo "$params" | jq -r '.test_name')    
-    
+    test_name=$(echo "$params" | jq -r '.test_name')
+
     # if TEST_SELECTOR is set, only run the test cases that match the selector
     if [[ -n "$TEST_SELECTOR" ]] && [[ ! "$test_name" =~ $TEST_SELECTOR ]]; then
       echo "Skip test case $test_name."
       continue
     fi
-    
+
     # prepend the current serving engine to the test name
     test_name=${CURRENT_LLM_SERVING_ENGINE}_${test_name}
 
@@ -369,10 +371,10 @@ run_genai_perf_tests() {
         qps=$num_prompts
         echo "now qps is $qps"
       fi
-    
+
       new_test_name=$test_name"_qps_"$qps
       backend=$CURRENT_LLM_SERVING_ENGINE
-      
+
       if [[ "$backend" == *"vllm"* ]]; then
         backend="vllm"
       fi
@@ -413,7 +415,7 @@ prepare_dataset() {
   do
     cat sonnet.txt >> sonnet_4x.txt
   done
-  
+
 }
 
 main() {

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

@@ -126,7 +126,8 @@ kill_gpu_processes() {
   ps -aux
   lsof -t -i:8000 | xargs -r kill -9
   pgrep python3 | xargs -r kill -9
-
+  # vLLM now names the process with VLLM prefix after https://github.com/vllm-project/vllm/pull/21445
+  pgrep VLLM | xargs -r kill -9
 
   # wait until GPU memory usage smaller than 1GB
   if command -v nvidia-smi; then
@@ -164,7 +165,7 @@ upload_to_buildkite() {
 }
 
 run_latency_tests() {
-  # run latency tests using `benchmark_latency.py`
+  # run latency tests using `vllm bench latency` command
   # $1: a json file specifying latency test cases
 
   local latency_test_file
@@ -205,7 +206,7 @@ run_latency_tests() {
       fi
     fi
 
-    latency_command=" $latency_envs python3 benchmark_latency.py \
+    latency_command=" $latency_envs vllm bench latency \
       --output-json $RESULTS_FOLDER/${test_name}.json \
       $latency_args"
 
@@ -231,7 +232,7 @@ run_latency_tests() {
 }
 
 run_throughput_tests() {
-  # run throughput tests using `benchmark_throughput.py`
+  # run throughput tests using `vllm bench throughput`
   # $1: a json file specifying throughput test cases
 
   local throughput_test_file
@@ -272,7 +273,7 @@ run_throughput_tests() {
       fi
     fi
 
-    throughput_command=" $throughput_envs python3 benchmark_throughput.py \
+    throughput_command=" $throughput_envs vllm bench throughput \
       --output-json $RESULTS_FOLDER/${test_name}.json \
       $throughput_args"
 
@@ -297,7 +298,7 @@ run_throughput_tests() {
 }
 
 run_serving_tests() {
-  # run serving tests using `benchmark_serving.py`
+  # run serving tests using `vllm bench serve` command
   # $1: a json file specifying serving test cases
 
   local serving_test_file
@@ -393,7 +394,7 @@ run_serving_tests() {
 
       # pass the tensor parallel size to the client so that it can be displayed
       # on the benchmark dashboard
-      client_command="python3 benchmark_serving.py \
+      client_command="vllm bench serve \
         --save-result \
         --result-dir $RESULTS_FOLDER \
         --result-filename ${new_test_name}.json \
@@ -447,7 +448,7 @@ main() {
   (which jq) || (apt-get update && apt-get -y install jq)
   (which lsof) || (apt-get update && apt-get install -y lsof)
 
-  # get the current IP address, required by benchmark_serving.py
+  # get the current IP address, required by `vllm bench serve` command
   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_LOGGING_LEVEL="WARNING"

.buildkite/release-pipeline.yaml

@@ -41,6 +41,20 @@ steps:
     env:
       DOCKER_BUILDKIT: "1"
 
+  - block: "Build FlashInfer wheel"
+    key: block-build-flashinfer-wheel
+    depends_on: ~
+
+  - label: "Build and upload FlashInfer wheel - CUDA 12.8"
+    depends_on: block-build-flashinfer-wheel
+    id: build-upload-flashinfer-wheel
+    agents:
+      queue: gpu_1_queue
+    commands:
+      - "bash .buildkite/scripts/build-upload-flashinfer-wheel.sh 12.8.1"
+    env:
+      DOCKER_BUILDKIT: "1"
+
   - block: "Build release image"
     depends_on: ~
     key: block-release-image-build

.buildkite/scripts/build-upload-flashinfer-wheel.sh

@@ -0,0 +1,57 @@
+#!/usr/bin/env bash
+
+set -ex
+
+CUDA_VERSION="${1:-12.8.1}"
+# FlashInfer version controlled in tools/flashinfer-build.sh
+
+echo "Building FlashInfer wheel for CUDA ${CUDA_VERSION} using vLLM Dockerfile"
+
+# Build the FlashInfer wheel using the existing Dockerfile stage
+DOCKER_BUILDKIT=1 docker build \
+  --build-arg max_jobs=16 \
+  --build-arg USE_SCCACHE=1 \
+  --build-arg CUDA_VERSION="${CUDA_VERSION}" \
+  --tag flashinfer-wheel-builder:${CUDA_VERSION} \
+  --target flashinfer-wheel-builder \
+  --progress plain \
+  -f docker/Dockerfile .
+
+# Extract the wheel
+mkdir -p artifacts/dist
+docker run --rm -v $(pwd)/artifacts:/output_host flashinfer-wheel-builder:${CUDA_VERSION} \
+  bash -c 'cp /output/*.whl /output_host/dist/ && chmod -R a+rw /output_host'
+
+# Upload the wheel to S3
+echo "Uploading FlashInfer wheel to S3..."
+wheel_files=(artifacts/dist/*.whl)
+
+# Check that exactly one wheel is found
+if [[ ${#wheel_files[@]} -ne 1 ]]; then
+  echo "Error: Expected exactly one wheel file in artifacts/dist/, but found ${#wheel_files[@]}"
+  exit 1
+fi
+
+# Get the single wheel file
+wheel="${wheel_files[0]}"
+echo "Processing FlashInfer wheel: $wheel"
+
+# Rename 'linux' to 'manylinux1' in the wheel filename for compatibility
+new_wheel="${wheel/linux/manylinux1}"
+if [[ "$wheel" != "$new_wheel" ]]; then
+  mv -- "$wheel" "$new_wheel"
+  wheel="$new_wheel"
+  echo "Renamed wheel to: $wheel"
+fi
+
+# Extract the version from the wheel
+version=$(unzip -p "$wheel" '**/METADATA' | grep '^Version: ' | cut -d' ' -f2)
+wheel_name=$(basename "$wheel")
+echo "FlashInfer version: $version"
+
+# Upload the wheel to S3 under flashinfer-python directory
+aws s3 cp "$wheel" "s3://vllm-wheels/flashinfer-python/"
+
+echo "✅ FlashInfer wheel built and uploaded successfully for CUDA ${CUDA_VERSION}"
+echo "📦 Wheel: $wheel_name (version $version)"
+ls -la artifacts/dist/

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

@@ -108,7 +108,6 @@ fi
 if [[ $commands == *" kernels/attention"* ]]; then
   commands="${commands} \
   --ignore=kernels/attention/test_attention_selector.py \
-  --ignore=kernels/attention/test_blocksparse_attention.py \
   --ignore=kernels/attention/test_encoder_decoder_attn.py \
   --ignore=kernels/attention/test_flash_attn.py \
   --ignore=kernels/attention/test_flashinfer.py \

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

@@ -6,15 +6,16 @@ set -ex
 
 # allow to bind to different cores
 CORE_RANGE=${CORE_RANGE:-48-95}
+# used for TP/PP E2E test
 OMP_CORE_RANGE=${OMP_CORE_RANGE:-48-95}
 NUMA_NODE=${NUMA_NODE:-1}
 
 export CMAKE_BUILD_PARALLEL_LEVEL=32
 
 # Setup cleanup
-remove_docker_container() { 
-    set -e; 
-    docker rm -f cpu-test-"$NUMA_NODE" cpu-test-"$NUMA_NODE"-avx2 || true; 
+remove_docker_container() {
+    set -e;
+    docker rm -f cpu-test-"$NUMA_NODE" cpu-test-"$NUMA_NODE"-avx2 || true;
 }
 trap remove_docker_container EXIT
 remove_docker_container
@@ -24,8 +25,8 @@ numactl -C "$CORE_RANGE" -N "$NUMA_NODE" docker build --tag cpu-test-"$NUMA_NODE
 numactl -C "$CORE_RANGE" -N "$NUMA_NODE" docker build --build-arg VLLM_CPU_DISABLE_AVX512="true" --tag cpu-test-"$NUMA_NODE"-avx2 --target vllm-test -f docker/Dockerfile.cpu .
 
 # Run the image, setting --shm-size=4g for tensor parallel.
-docker run -itd --cpuset-cpus="$CORE_RANGE" --cpuset-mems="$NUMA_NODE" --entrypoint /bin/bash -v ~/.cache/huggingface:/root/.cache/huggingface --privileged=true -e HF_TOKEN --env VLLM_CPU_KVCACHE_SPACE=4 --env VLLM_CPU_OMP_THREADS_BIND="$OMP_CORE_RANGE" --env VLLM_CPU_CI_ENV=1 --shm-size=4g --name cpu-test-"$NUMA_NODE" cpu-test-"$NUMA_NODE"
-docker run -itd --cpuset-cpus="$CORE_RANGE" --cpuset-mems="$NUMA_NODE" --entrypoint /bin/bash -v ~/.cache/huggingface:/root/.cache/huggingface --privileged=true -e HF_TOKEN --env VLLM_CPU_KVCACHE_SPACE=4 --env VLLM_CPU_OMP_THREADS_BIND="$OMP_CORE_RANGE" --env VLLM_CPU_CI_ENV=1 --shm-size=4g --name cpu-test-"$NUMA_NODE"-avx2 cpu-test-"$NUMA_NODE"-avx2
+docker run -itd --cpuset-cpus="$CORE_RANGE" --cpuset-mems="$NUMA_NODE" --entrypoint /bin/bash -v ~/.cache/huggingface:/root/.cache/huggingface --privileged=true -e HF_TOKEN --env VLLM_CPU_KVCACHE_SPACE=4 --env VLLM_CPU_CI_ENV=1 -e E2E_OMP_THREADS="$OMP_CORE_RANGE" --shm-size=4g --name cpu-test-"$NUMA_NODE" cpu-test-"$NUMA_NODE"
+docker run -itd --cpuset-cpus="$CORE_RANGE" --cpuset-mems="$NUMA_NODE" --entrypoint /bin/bash -v ~/.cache/huggingface:/root/.cache/huggingface --privileged=true -e HF_TOKEN --env VLLM_CPU_KVCACHE_SPACE=4 --env VLLM_CPU_CI_ENV=1 -e E2E_OMP_THREADS="$OMP_CORE_RANGE" --shm-size=4g --name cpu-test-"$NUMA_NODE"-avx2 cpu-test-"$NUMA_NODE"-avx2
 
 function cpu_tests() {
   set -e
@@ -68,7 +69,7 @@ function cpu_tests() {
   docker exec cpu-test-"$NUMA_NODE" bash -c "
     set -e
     pytest -s -v \
-    tests/quantization/test_compressed_tensors.py::test_compressed_tensors_w8a8_logprobs[False-10-32-neuralmagic/Llama-3.2-1B-quantized.w8a8]" 
+    tests/quantization/test_compressed_tensors.py::test_compressed_tensors_w8a8_logprobs[False-10-32-neuralmagic/Llama-3.2-1B-quantized.w8a8]"
 
   # Note: disable it until supports V1
   # Run AWQ test
@@ -77,24 +78,23 @@ function cpu_tests() {
   #   VLLM_USE_V1=0 pytest -s -v \
   #   tests/quantization/test_ipex_quant.py"
 
-  # online serving
-  docker exec cpu-test-"$NUMA_NODE" bash -c "
-    set -e
-    python3 -m vllm.entrypoints.openai.api_server --model facebook/opt-125m --dtype half & 
-    timeout 600 bash -c 'until curl localhost:8000/v1/models; do sleep 1; done' || exit 1
-    VLLM_CPU_CI_ENV=0 python3 benchmarks/benchmark_serving.py \
-      --backend vllm \
-      --dataset-name random \
-      --model facebook/opt-125m \
-      --num-prompts 20 \
-      --endpoint /v1/completions \
-      --tokenizer facebook/opt-125m"
-
   # Run multi-lora tests
   docker exec cpu-test-"$NUMA_NODE" bash -c "
     set -e
     pytest -s -v \
     tests/lora/test_qwen2vl.py"
+
+  # online serving
+  docker exec cpu-test-"$NUMA_NODE" bash -c '
+    set -e
+    VLLM_CPU_OMP_THREADS_BIND=$E2E_OMP_THREADS VLLM_CPU_SGL_KERNEL=1 vllm serve meta-llama/Llama-3.2-3B-Instruct -tp=2 -pp=2 &
+    timeout 600 bash -c "until curl localhost:8000/v1/models; do sleep 1; done" || exit 1
+    vllm bench serve \
+      --backend vllm \
+      --dataset-name random \
+      --model meta-llama/Llama-3.2-3B-Instruct \
+      --num-prompts 20 \
+      --endpoint /v1/completions'
 }
 
 # All of CPU tests are expected to be finished less than 40 mins.

.buildkite/scripts/hardware_ci/run-tpu-v1-test-part2.sh

@@ -0,0 +1,166 @@
+#!/bin/bash
+
+set -xu
+
+
+remove_docker_container() { 
+    docker rm -f tpu-test || true; 
+    docker rm -f vllm-tpu || true;
+}
+
+trap remove_docker_container EXIT
+
+# Remove the container that might not be cleaned up in the previous run.
+remove_docker_container
+
+# Build the docker image.
+docker build -f docker/Dockerfile.tpu -t vllm-tpu .
+
+# Set up cleanup.
+cleanup_docker() {
+  # Get Docker's root directory
+  docker_root=$(docker info -f '{{.DockerRootDir}}')
+  if [ -z "$docker_root" ]; then
+    echo "Failed to determine Docker root directory."
+    exit 1
+  fi
+  echo "Docker root directory: $docker_root"
+  # Check disk usage of the filesystem where Docker's root directory is located
+  disk_usage=$(df "$docker_root" | tail -1 | awk '{print $5}' | sed 's/%//')
+  # Define the threshold
+  threshold=70
+  if [ "$disk_usage" -gt "$threshold" ]; then
+    echo "Disk usage is above $threshold%. Cleaning up Docker images and volumes..."
+    # Remove dangling images (those that are not tagged and not used by any container)
+    docker image prune -f
+    # Remove unused volumes / force the system prune for old images as well.
+    docker volume prune -f && docker system prune --force --filter "until=72h" --all
+    echo "Docker images and volumes cleanup completed."
+  else
+    echo "Disk usage is below $threshold%. No cleanup needed."
+  fi
+}
+cleanup_docker
+
+# For HF_TOKEN.
+source /etc/environment
+
+docker run --privileged --net host --shm-size=16G -it \
+    -e "HF_TOKEN=$HF_TOKEN" --name tpu-test \
+    vllm-tpu /bin/bash -c '
+set -e # Exit immediately if a command exits with a non-zero status.
+set -u # Treat unset variables as an error.
+
+echo "--- Starting script inside Docker container ---"
+
+# Create results directory
+RESULTS_DIR=$(mktemp -d)
+# If mktemp fails, set -e will cause the script to exit.
+echo "Results will be stored in: $RESULTS_DIR"
+
+# Install dependencies
+echo "--- Installing Python dependencies ---"
+python3 -m pip install --progress-bar off git+https://github.com/thuml/depyf.git \
+    && python3 -m pip install --progress-bar off pytest pytest-asyncio tpu-info \
+    && python3 -m pip install --progress-bar off lm_eval[api]==0.4.4 \
+    && python3 -m pip install --progress-bar off hf-transfer
+echo "--- Python dependencies installed ---"
+export VLLM_USE_V1=1
+export VLLM_XLA_CHECK_RECOMPILATION=1
+export VLLM_XLA_CACHE_PATH=
+echo "Using VLLM V1"
+
+echo "--- Hardware Information ---"
+# tpu-info
+echo "--- Starting Tests ---"
+set +e
+overall_script_exit_code=0
+
+# --- Test Definitions ---
+# If a test fails, this function will print logs and will not cause the main script to exit.
+run_test() {
+    local test_num=$1
+    local test_name=$2
+    local test_command=$3
+    local log_file="$RESULTS_DIR/test_${test_num}.log"
+    local actual_exit_code
+
+    echo "--- TEST_$test_num: Running $test_name ---"
+    
+    # Execute the test command.
+    eval "$test_command" > >(tee -a "$log_file") 2> >(tee -a "$log_file" >&2)
+    actual_exit_code=$?
+
+    echo "TEST_${test_num}_COMMAND_EXIT_CODE: $actual_exit_code" # This goes to main log
+    echo "TEST_${test_num}_COMMAND_EXIT_CODE: $actual_exit_code" >> "$log_file" # Also to per-test log
+
+    if [ "$actual_exit_code" -ne 0 ]; then
+        echo "TEST_$test_num ($test_name) FAILED with exit code $actual_exit_code." >&2
+        echo "--- Log for failed TEST_$test_num ($test_name) ---" >&2
+        if [ -f "$log_file" ]; then
+            cat "$log_file" >&2
+        else
+            echo "Log file $log_file not found for TEST_$test_num ($test_name)." >&2
+        fi
+        echo "--- End of log for TEST_$test_num ($test_name) ---" >&2
+        return "$actual_exit_code" # Return the failure code
+    else
+        echo "TEST_$test_num ($test_name) PASSED."
+        return 0 # Return success
+    fi
+}
+
+# Helper function to call run_test and update the overall script exit code
+run_and_track_test() {
+    local test_num_arg="$1"
+    local test_name_arg="$2"
+    local test_command_arg="$3"
+
+    # Run the test
+    run_test "$test_num_arg" "$test_name_arg" "$test_command_arg"
+    local test_specific_exit_code=$?
+
+    # If the test failed, set the overall script exit code to 1
+    if [ "$test_specific_exit_code" -ne 0 ]; then
+        # No need for extra echo here, run_test already logged the failure.
+        overall_script_exit_code=1
+    fi
+}
+
+# --- Actual Test Execution ---
+run_and_track_test 1 "test_struct_output_generate.py" \
+    "HF_HUB_DISABLE_XET=1 python3 -m pytest -s -v /workspace/vllm/tests/v1/entrypoints/llm/test_struct_output_generate.py -k \"not test_structured_output_with_reasoning_matrices\""
+run_and_track_test 2 "test_moe_pallas.py" \
+    "python3 -m pytest -s -v /workspace/vllm/tests/tpu/test_moe_pallas.py"
+run_and_track_test 3 "test_lora.py" \
+    "VLLM_XLA_CHECK_RECOMPILATION=0 python3 -m pytest -s -v /workspace/vllm/tests/tpu/lora/test_lora.py"
+run_and_track_test 4 "test_tpu_qkv_linear.py" \
+    "python3 -m pytest -s -v /workspace/vllm/tests/v1/tpu/test_tpu_qkv_linear.py"
+run_and_track_test 5 "test_spmd_model_weight_loading.py" \
+    "python3 -m pytest -s -v /workspace/vllm/tests/v1/tpu/test_spmd_model_weight_loading.py"
+run_and_track_test 6 "test_kv_cache_update_kernel.py" \
+    "python3 -m pytest -s -v /workspace/vllm/tests/v1/tpu/test_kv_cache_update_kernel.py"
+
+# After all tests have been attempted, exit with the overall status.
+if [ "$overall_script_exit_code" -ne 0 ]; then
+    echo "--- One or more tests FAILED. Overall script exiting with failure code 1. ---"
+else
+    echo "--- All tests have completed and PASSED. Overall script exiting with success code 0. ---"
+fi
+exit "$overall_script_exit_code"
+' # IMPORTANT: This is the closing single quote for the bash -c "..." command. Ensure it is present and correct.
+
+# Capture the exit code of the docker run command
+DOCKER_RUN_EXIT_CODE=$?
+
+# The trap will run for cleanup.
+# Exit the main script with the Docker run command's exit code.
+if [ "$DOCKER_RUN_EXIT_CODE" -ne 0 ]; then
+    echo "Docker run command failed with exit code $DOCKER_RUN_EXIT_CODE."
+    exit "$DOCKER_RUN_EXIT_CODE"
+else
+    echo "Docker run command completed successfully."
+    exit 0
+fi
+# TODO: This test fails because it uses RANDOM_SEED sampling
+# pytest -v -s /workspace/vllm/tests/tpu/test_custom_dispatcher.py \

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

@@ -62,7 +62,8 @@ echo "Results will be stored in: $RESULTS_DIR"
 echo "--- Installing Python dependencies ---"
 python3 -m pip install --progress-bar off git+https://github.com/thuml/depyf.git \
     && python3 -m pip install --progress-bar off pytest pytest-asyncio tpu-info \
-    && python3 -m pip install --progress-bar off lm_eval[api]==0.4.4
+    && python3 -m pip install --progress-bar off lm_eval[api]==0.4.4 \
+    && python3 -m pip install --progress-bar off hf-transfer
 echo "--- Python dependencies installed ---"
 export VLLM_USE_V1=1
 export VLLM_XLA_CHECK_RECOMPILATION=1
@@ -70,7 +71,7 @@ export VLLM_XLA_CACHE_PATH=
 echo "Using VLLM V1"
 
 echo "--- Hardware Information ---"
-tpu-info
+# tpu-info
 echo "--- Starting Tests ---"
 set +e
 overall_script_exit_code=0
@@ -134,7 +135,7 @@ run_and_track_test 1 "test_compilation.py" \
 run_and_track_test 2 "test_basic.py" \
     "python3 -m pytest -s -v /workspace/vllm/tests/v1/tpu/test_basic.py"
 run_and_track_test 3 "test_accuracy.py::test_lm_eval_accuracy_v1_engine" \
-    "python3 -m pytest -s -v /workspace/vllm/tests/entrypoints/llm/test_accuracy.py::test_lm_eval_accuracy_v1_engine"
+    "HF_HUB_DISABLE_XET=1 python3 -m pytest -s -v /workspace/vllm/tests/entrypoints/llm/test_accuracy.py::test_lm_eval_accuracy_v1_engine"
 run_and_track_test 4 "test_quantization_accuracy.py" \
     "python3 -m pytest -s -v /workspace/vllm/tests/tpu/test_quantization_accuracy.py"
 run_and_track_test 5 "examples/offline_inference/tpu.py" \
@@ -149,18 +150,6 @@ run_and_track_test 9 "test_multimodal.py" \
     "python3 -m pytest -s -v /workspace/vllm/tests/v1/tpu/test_multimodal.py"
 run_and_track_test 10 "test_pallas.py" \
     "python3 -m pytest -s -v /workspace/vllm/tests/v1/tpu/test_pallas.py"
-run_and_track_test 11 "test_struct_output_generate.py" \
-    "python3 -m pytest -s -v /workspace/vllm/tests/v1/entrypoints/llm/test_struct_output_generate.py -k \"not test_structured_output_with_reasoning_matrices\""
-run_and_track_test 12 "test_moe_pallas.py" \
-    "python3 -m pytest -s -v /workspace/vllm/tests/tpu/test_moe_pallas.py"
-run_and_track_test 13 "test_lora.py" \
-    "VLLM_XLA_CHECK_RECOMPILATION=0 python3 -m pytest -s -v /workspace/vllm/tests/tpu/lora/test_lora.py"
-run_and_track_test 14 "test_tpu_qkv_linear.py" \
-    "python3 -m pytest -s -v /workspace/vllm/tests/v1/tpu/test_tpu_qkv_linear.py"
-run_and_track_test 15 "test_spmd_model_weight_loading.py" \
-    "python3 -m pytest -s -v /workspace/vllm/tests/v1/tpu/test_spmd_model_weight_loading.py"
-run_and_track_test 16 "test_kv_cache_update_kernel.py" \
-    "python3 -m pytest -s -v /workspace/vllm/tests/v1/tpu/test_kv_cache_update_kernel.py"
 
 # After all tests have been attempted, exit with the overall status.
 if [ "$overall_script_exit_code" -ne 0 ]; then

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

@@ -31,4 +31,13 @@ docker run \
     VLLM_USE_V1=1 python3 examples/offline_inference/basic/generate.py --model facebook/opt-125m --block-size 64 --enforce-eager -tp 2 --distributed-executor-backend mp
     cd tests
     pytest -v -s v1/core
+    pytest -v -s v1/engine
+    pytest -v -s v1/sample --ignore=v1/sample/test_logprobs.py --ignore=v1/sample/test_logprobs_e2e.py
+    pytest -v -s v1/worker --ignore=v1/worker/test_gpu_model_runner.py
+    pytest -v -s v1/structured_output
+    pytest -v -s v1/spec_decode --ignore=v1/spec_decode/test_max_len.py --ignore=v1/spec_decode/test_eagle.py
+    pytest -v -s v1/kv_connector/unit --ignore=v1/kv_connector/unit/test_multi_connector.py --ignore=v1/kv_connector/unit/test_nixl_connector.py
+    pytest -v -s v1/test_serial_utils.py
+    pytest -v -s v1/test_utils.py
+    pytest -v -s v1/test_metrics_reader.py
 '

.buildkite/scripts/run-benchmarks.sh

@@ -11,10 +11,10 @@ cd "$(dirname "${BASH_SOURCE[0]}")/../.."
 (which wget && which curl) || (apt-get update && apt-get install -y wget curl)
 
 # run python-based benchmarks and upload the result to buildkite
-python3 benchmarks/benchmark_latency.py --output-json latency_results.json 2>&1 | tee benchmark_latency.txt
+vllm bench latency --output-json latency_results.json 2>&1 | tee benchmark_latency.txt
 bench_latency_exit_code=$?
 
-python3 benchmarks/benchmark_throughput.py --input-len 256 --output-len 256 --output-json throughput_results.json 2>&1 | tee benchmark_throughput.txt
+vllm bench throughput --input-len 256 --output-len 256 --output-json throughput_results.json 2>&1 | tee benchmark_throughput.txt
 bench_throughput_exit_code=$?
 
 # run server-based benchmarks and upload the result to buildkite
@@ -24,7 +24,7 @@ wget https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/r
 
 # wait for server to start, timeout after 600 seconds
 timeout 600 bash -c 'until curl localhost:8000/v1/models; do sleep 1; done' || exit 1
-python3 benchmarks/benchmark_serving.py \
+vllm bench serve \
     --backend vllm \
     --dataset-name sharegpt \
     --dataset-path ./ShareGPT_V3_unfiltered_cleaned_split.json \

.buildkite/scripts/tpu/run_bm.sh

@@ -77,7 +77,7 @@ done
 echo "run benchmark test..."
 echo "logging to $BM_LOG"
 echo
-python benchmarks/benchmark_serving.py \
+vllm bench serve \
     --backend vllm \
     --model $MODEL  \
     --dataset-name sonnet \

.buildkite/test-pipeline.yaml

@@ -128,11 +128,10 @@ steps:
   - tests/entrypoints/offline_mode
   commands:
   - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-  - pytest -v -s entrypoints/llm --ignore=entrypoints/llm/test_lazy_outlines.py --ignore=entrypoints/llm/test_generate.py --ignore=entrypoints/llm/test_generate_multiple_loras.py --ignore=entrypoints/llm/test_guided_generate.py --ignore=entrypoints/llm/test_collective_rpc.py
+  - pytest -v -s entrypoints/llm --ignore=entrypoints/llm/test_lazy_outlines.py --ignore=entrypoints/llm/test_generate.py --ignore=entrypoints/llm/test_generate_multiple_loras.py --ignore=entrypoints/llm/test_collective_rpc.py
   - pytest -v -s entrypoints/llm/test_lazy_outlines.py # it needs a clean process
   - 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
   - VLLM_USE_V1=0 pytest -v -s entrypoints/offline_mode # Needs to avoid interference with other tests
 
 - label: Entrypoints Test (API Server) # 40min
@@ -159,13 +158,14 @@ steps:
   - tests/distributed/test_utils
   - tests/distributed/test_pynccl
   - tests/distributed/test_events
-  - tests/spec_decode/e2e/test_integration_dist_tp4
   - tests/compile/test_basic_correctness
   - 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
   - tests/v1/test_external_lb_dp.py
+  - tests/v1/test_internal_lb_dp.py
+  - tests/v1/test_hybrid_lb_dp.py
   - tests/v1/engine/test_engine_core_client.py
   commands:
   # test with tp=2 and external_dp=2
@@ -177,12 +177,13 @@ steps:
   - python3 ../examples/offline_inference/data_parallel.py --enforce-eager
   - TP_SIZE=2 DP_SIZE=2 pytest -v -s v1/test_async_llm_dp.py
   - TP_SIZE=2 DP_SIZE=2 pytest -v -s v1/test_external_lb_dp.py
+  - TP_SIZE=1 DP_SIZE=4 pytest -v -s v1/test_internal_lb_dp.py
+  - TP_SIZE=1 DP_SIZE=4 pytest -v -s v1/test_hybrid_lb_dp.py
   - pytest -v -s v1/engine/test_engine_core_client.py::test_kv_cache_events_dp
   - pytest -v -s distributed/test_utils.py
   - pytest -v -s compile/test_basic_correctness.py
   - pytest -v -s distributed/test_pynccl.py
   - pytest -v -s distributed/test_events.py
-  - pytest -v -s spec_decode/e2e/test_integration_dist_tp4.py
   # TODO: create a dedicated test section for multi-GPU example tests
   # when we have multiple distributed example tests
   - pushd ../examples/offline_inference
@@ -227,7 +228,7 @@ steps:
 #####  1 GPU test  #####
 
 - label: Regression Test # 5min
-  mirror_hardwares: [amdexperimental]
+  mirror_hardwares: [amdexperimental, amdproduction]
   source_file_dependencies:
   - vllm/
   - tests/test_regression
@@ -266,6 +267,7 @@ steps:
     - pytest -v -s v1/structured_output
     - pytest -v -s v1/spec_decode
     - pytest -v -s v1/kv_connector/unit
+    - pytest -v -s v1/metrics
     - pytest -v -s v1/test_serial_utils.py
     - pytest -v -s v1/test_utils.py
     - pytest -v -s v1/test_oracle.py
@@ -274,11 +276,11 @@ steps:
     # VLLM_USE_FLASHINFER_SAMPLER or not on H100.
     - pytest -v -s v1/e2e
     # Integration test for streaming correctness (requires special branch).
-    - pip install -U git+https://github.com/robertgshaw2-neuralmagic/lm-evaluation-harness.git@streaming-api
+    - pip install -U git+https://github.com/robertgshaw2-redhat/lm-evaluation-harness.git@streaming-api
     - pytest -v -s entrypoints/openai/correctness/test_lmeval.py::test_lm_eval_accuracy_v1_engine
 
 - label: Examples Test # 25min
-  mirror_hardwares: [amdexperimental]
+  mirror_hardwares: [amdexperimental, amdproduction]
   working_dir: "/vllm-workspace/examples"
   source_file_dependencies:
   - vllm/entrypoints
@@ -312,7 +314,7 @@ steps:
 
 
 - label: Platform Tests (CUDA)
-  mirror_hardwares: [amdexperimental]
+  mirror_hardwares: [amdexperimental, amdproduction]
   source_file_dependencies:
   - vllm/
   - tests/cuda
@@ -330,19 +332,8 @@ steps:
     - pytest -v -s samplers
     - VLLM_USE_FLASHINFER_SAMPLER=1 pytest -v -s samplers
 
-- label: Speculative decoding tests # 40min
-  mirror_hardwares: [amdexperimental]
-  source_file_dependencies:
-  - vllm/spec_decode
-  - tests/spec_decode
-  - vllm/model_executor/models/eagle.py
-  commands:
-    - pytest -v -s spec_decode/e2e/test_multistep_correctness.py
-    - VLLM_ATTENTION_BACKEND=FLASH_ATTN pytest -v -s spec_decode --ignore=spec_decode/e2e/test_multistep_correctness.py --ignore=spec_decode/e2e/test_mtp_correctness.py
-    - pytest -v -s spec_decode/e2e/test_eagle_correctness.py
-
 - label: LoRA Test %N # 15min each
-  mirror_hardwares: [amdexperimental, amdproduction]
+  mirror_hardwares: [amdexperimental]
   source_file_dependencies:
   - vllm/lora
   - tests/lora
@@ -394,7 +385,7 @@ steps:
     - pytest -v -s kernels/core
 
 - label: Kernels Attention Test %N
-  mirror_hardwares: [amdexperimental, amdproduction]
+  mirror_hardwares: [amdexperimental]
   source_file_dependencies:
   - csrc/attention/
   - vllm/attention
@@ -405,26 +396,27 @@ steps:
   parallelism: 2
 
 - label: Kernels Quantization Test %N
-  mirror_hardwares: [amdexperimental, amdproduction]
+  mirror_hardwares: [amdexperimental]
   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
+    - pytest -v -s kernels/quantization --shard-id=$$BUILDKITE_PARALLEL_JOB --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT
   parallelism: 2
 
-- label: Kernels MoE Test
+- label: Kernels MoE Test %N
   mirror_hardwares: [amdexperimental]
   source_file_dependencies:
   - csrc/moe/
   - tests/kernels/moe
   - vllm/model_executor/layers/fused_moe/
   commands:
-    - pytest -v -s kernels/moe
+    - pytest -v -s kernels/moe --shard-id=$$BUILDKITE_PARALLEL_JOB --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT
+  parallelism: 2
 
 - label: Kernels Mamba Test
-  mirror_hardwares: [amdexperimental]
+  mirror_hardwares: [amdexperimental, amdproduction]
   source_file_dependencies:
   - csrc/mamba/
   - tests/kernels/mamba
@@ -432,7 +424,7 @@ steps:
     - pytest -v -s kernels/mamba
 
 - label: Tensorizer Test # 11min
-  mirror_hardwares: [amdexperimental]
+  mirror_hardwares: [amdexperimental, amdproduction]
   soft_fail: true
   source_file_dependencies:
   - vllm/model_executor/model_loader
@@ -446,7 +438,6 @@ steps:
 
 - label: Model Executor Test
   mirror_hardwares: [amdexperimental, amdproduction]
-  soft_fail: true
   source_file_dependencies:
   - vllm/model_executor
   - tests/model_executor
@@ -503,7 +494,7 @@ steps:
   - pytest -s entrypoints/openai/correctness/
 
 - label: Encoder Decoder tests # 5min
-  mirror_hardwares: [amdexperimental]
+  mirror_hardwares: [amdexperimental, amdproduction]
   source_file_dependencies:
   - vllm/
   - tests/encoder_decoder
@@ -511,7 +502,7 @@ steps:
     - pytest -v -s encoder_decoder
 
 - label: OpenAI-Compatible Tool Use # 20 min
-  mirror_hardwares: [amdexperimental]
+  mirror_hardwares: [amdexperimental, amdproduction]
   fast_check: false
   source_file_dependencies:
     - vllm/
@@ -623,7 +614,7 @@ steps:
     - pytest -v -s models/multimodal/generation/test_common.py -m 'split(group=1) and not core_model'
 
 - label: Quantized Models Test
-  mirror_hardwares: [amdexperimental, amdproduction]
+  mirror_hardwares: [amdexperimental]
   source_file_dependencies:
   - vllm/model_executor/layers/quantization
   - tests/models/quantization
@@ -726,10 +717,10 @@ steps:
   - 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
+  - pytest -v -s models/multimodal/generation/test_maverick.py
 
 - label: Plugin Tests (2 GPUs) # 40min
   mirror_hardwares: [amdexperimental]

.github/CODEOWNERS

@@ -10,13 +10,12 @@
 /vllm/worker/worker.py @zhuohan123 @youkaichao @alexm-redhat @comaniac @njhill
 /vllm/model_executor/layers/sampler.py @zhuohan123 @youkaichao @alexm-redhat @comaniac @njhill
 /vllm/model_executor/layers/quantization @mgoin @robertgshaw2-redhat @tlrmchlsmth
-/vllm/model_executor/guided_decoding @mgoin @russellb @aarnphm
 /vllm/multimodal @DarkLight1337 @ywang96
 /vllm/vllm_flash_attn @LucasWilkinson
 /vllm/lora @jeejeelee
 /vllm/reasoning @aarnphm
 /vllm/entrypoints @aarnphm
-/vllm/compilation @zou3519 @youkaichao
+/vllm/compilation @zou3519 @youkaichao @ProExpertProg
 CMakeLists.txt @tlrmchlsmth @LucasWilkinson
 
 # Any change to the VllmConfig changes can have a large user-facing impact,
@@ -35,15 +34,12 @@ CMakeLists.txt @tlrmchlsmth @LucasWilkinson
 /tests/distributed/test_pipeline_parallel.py @youkaichao
 /tests/distributed/test_same_node.py @youkaichao
 /tests/entrypoints @DarkLight1337 @robertgshaw2-redhat @simon-mo @aarnphm
-/tests/entrypoints/llm/test_guided_generate.py @mgoin @russellb @aarnphm
 /tests/kernels @tlrmchlsmth @WoosukKwon
-/tests/model_executor/test_guided_processors.py @mgoin @russellb
 /tests/models @DarkLight1337 @ywang96
 /tests/multi_step @alexm-redhat @comaniac
 /tests/multimodal @DarkLight1337 @ywang96
 /tests/prefix_caching @comaniac @KuntaiDu
 /tests/quantization @mgoin @robertgshaw2-redhat
-/tests/spec_decode @njhill @LiuXiaoxuanPKU
 /tests/test_inputs.py @DarkLight1337 @ywang96
 /tests/v1/entrypoints/llm/test_struct_output_generate.py @mgoin @russellb @aarnphm
 /tests/v1/structured_output @mgoin @russellb @aarnphm
@@ -53,3 +49,15 @@ CMakeLists.txt @tlrmchlsmth @LucasWilkinson
 # Docs
 /docs @hmellor
 mkdocs.yaml @hmellor
+
+# CPU
+/vllm/v1/worker/^cpu @bigPYJ1151
+/csrc/cpu @bigPYJ1151
+/vllm/platforms/cpu.py @bigPYJ1151
+/cmake/cpu_extension.cmake @bigPYJ1151
+/docker/Dockerfile.cpu @bigPYJ1151
+
+# Intel GPU
+/vllm/v1/worker/^xpu @jikunshang
+/vllm/platforms/xpu.py @jikunshang
+/docker/Dockerfile.xpu @jikunshang

.github/ISSUE_TEMPLATE/750-RFC.yml

@@ -46,7 +46,7 @@ body:
 - type: markdown
   attributes:
     value: >
-      Thanks for contributing 🎉!
+      Thanks for contributing 🎉! The vLLM core team hosts a biweekly RFC review session at 9:30AM Pacific Time, while most RFCs can be discussed online, you can optionally sign up for a slot to discuss your RFC online [here](https://docs.google.com/document/d/1CiLVBZeIVfR7_PNAKVSusxpceywkoOOB78qoWqHvSZc/edit).
 - type: checkboxes
   id: askllm
   attributes:

.github/mergify.yml

@@ -149,9 +149,6 @@ pull_request_rules:
       - 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~=^tests/v1/structured_output/
       - files=tests/v1/entrypoints/llm/test_guided_generate.py
       - files~=^vllm/v1/structured_output/
@@ -164,10 +161,7 @@ pull_request_rules:
   description: Automatically apply speculative-decoding label
   conditions:
     - or:
-      - files~=^vllm/spec_decode/
       - files~=^vllm/v1/spec_decode/
-      - files=vllm/model_executor/layers/spec_decode_base_sampler.py
-      - files~=^tests/spec_decode/
       - files~=^tests/v1/spec_decode/
       - files~=^examples/.*(spec_decode|mlpspeculator|eagle|speculation).*\.py
       - files~=^vllm/model_executor/models/.*eagle.*\.py

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

@@ -7,7 +7,7 @@ permissions:
 
 jobs:
   lint-and-deploy:
-    runs-on: ubuntu-latest
+    runs-on: ubuntu-24.04-arm
     steps:
       - name: Checkout
         uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2

.readthedocs.yaml

@@ -7,6 +7,9 @@ build:
   os: ubuntu-22.04
   tools:
     python: "3.12"
+  jobs:
+    post_checkout:
+      - git fetch --unshallow || true
 
 mkdocs:
   configuration: mkdocs.yaml

CMakeLists.txt

@@ -45,7 +45,7 @@ set(HIP_SUPPORTED_ARCHS "gfx906;gfx908;gfx90a;gfx942;gfx950;gfx1030;gfx1100;gfx1
 # requirements.txt files and should be kept consistent.  The ROCm torch
 # versions are derived from docker/Dockerfile.rocm
 #
-set(TORCH_SUPPORTED_VERSION_CUDA "2.7.0")
+set(TORCH_SUPPORTED_VERSION_CUDA "2.7.1")
 set(TORCH_SUPPORTED_VERSION_ROCM "2.7.0")
 
 #
@@ -296,7 +296,8 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
     "csrc/quantization/fp4/nvfp4_blockwise_moe_kernel.cu"
     "csrc/sparse/cutlass/sparse_scaled_mm_entry.cu"
     "csrc/cutlass_extensions/common.cpp"
-    "csrc/attention/mla/cutlass_mla_entry.cu")
+    "csrc/attention/mla/cutlass_mla_entry.cu"
+    "csrc/quantization/fp8/per_token_group_quant.cu")
 
   set_gencode_flags_for_srcs(
     SRCS "${VLLM_EXT_SRC}"
@@ -577,7 +578,7 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
   # if it's possible to compile MoE kernels that use its output.
   cuda_archs_loose_intersection(SCALED_MM_ARCHS "9.0a" "${CUDA_ARCHS}")
   if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.3 AND SCALED_MM_ARCHS)
-    set(SRCS "csrc/quantization/cutlass_w8a8/moe/grouped_mm_c3x.cu")
+    set(SRCS "csrc/quantization/cutlass_w8a8/moe/grouped_mm_c3x_sm90.cu")
     set_gencode_flags_for_srcs(
       SRCS "${SRCS}"
       CUDA_ARCHS "${SCALED_MM_ARCHS}")
@@ -595,6 +596,26 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
     endif()
   endif()
 
+  cuda_archs_loose_intersection(SCALED_MM_ARCHS "10.0a" "${CUDA_ARCHS}")
+  if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.8 AND SCALED_MM_ARCHS)
+    set(SRCS "csrc/quantization/cutlass_w8a8/moe/grouped_mm_c3x_sm100.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_SM100=1")
+    message(STATUS "Building grouped_mm_c3x for archs: ${SCALED_MM_ARCHS}")
+  else()
+    if (NOT ${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.8 AND SCALED_MM_ARCHS)
+      message(STATUS "Not building grouped_mm_c3x kernels as CUDA Compiler version is "
+                     "not >= 12.8, we recommend upgrading to CUDA 12.8 or later "
+                     "if you intend on running FP8 quantized MoE models on Blackwell.")
+    else()
+      message(STATUS "Not building grouped_mm_c3x as no compatible archs found "
+                     "in CUDA target architectures.")
+    endif()
+  endif()
+
   # moe_data.cu is used by all CUTLASS MoE kernels.
   cuda_archs_loose_intersection(CUTLASS_MOE_DATA_ARCHS "9.0a;10.0a" "${CUDA_ARCHS}")
   if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.3 AND CUTLASS_MOE_DATA_ARCHS)
@@ -614,7 +635,7 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
                      "in CUDA target architectures.")
     endif()
   endif()
-  
+
   cuda_archs_loose_intersection(SCALED_MM_ARCHS "10.0a" "${CUDA_ARCHS}")
   if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.8 AND SCALED_MM_ARCHS)
     set(SRCS "csrc/quantization/cutlass_w8a8/moe/blockwise_scaled_group_mm_sm100.cu")
@@ -747,6 +768,14 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
   list(APPEND VLLM_MOE_EXT_SRC "csrc/moe/moe_wna16.cu")
 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")
+
+  list(APPEND VLLM_MOE_EXT_SRC "${MOE_PERMUTE_SRC}")
+endif()
+
 set_gencode_flags_for_srcs(
   SRCS "${VLLM_MOE_EXT_SRC}"
   CUDA_ARCHS "${CUDA_ARCHS}")
@@ -815,17 +844,6 @@ 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

RELEASE.md

@@ -52,3 +52,36 @@ After branch cut, we approach finalizing the release branch with clear criteria
 * Release branch specific changes (e.g. change version identifiers or CI fixes)
 
 Please note: **No feature work allowed for cherry picks**. All PRs that are considered for cherry-picks need to be merged on trunk, the only exception are Release branch specific changes.
+
+## Manual validations
+
+### E2E Performance Validation
+
+Before each release, we perform end-to-end performance validation to ensure no regressions are introduced. This validation uses the [vllm-benchmark workflow](https://github.com/pytorch/pytorch-integration-testing/actions/workflows/vllm-benchmark.yml) on PyTorch CI.
+
+**Current Coverage:**
+* Models: Llama3, Llama4, and Mixtral
+* Hardware: NVIDIA H100 and AMD MI300x
+* *Note: Coverage may change based on new model releases and hardware availability*
+
+**Performance Validation Process:**
+
+**Step 1: Get Access**
+Request write access to the [pytorch/pytorch-integration-testing](https://github.com/pytorch/pytorch-integration-testing) repository to run the benchmark workflow.
+
+**Step 2: Review Benchmark Setup**
+Familiarize yourself with the benchmark configurations:
+* [CUDA setup](https://github.com/pytorch/pytorch-integration-testing/tree/main/vllm-benchmarks/benchmarks/cuda)
+* [ROCm setup](https://github.com/pytorch/pytorch-integration-testing/tree/main/vllm-benchmarks/benchmarks/rocm)
+
+**Step 3: Run the Benchmark**
+Navigate to the [vllm-benchmark workflow](https://github.com/pytorch/pytorch-integration-testing/actions/workflows/vllm-benchmark.yml) and configure:
+* **vLLM branch**: Set to the release branch (e.g., `releases/v0.9.2`)
+* **vLLM commit**: Set to the RC commit hash
+
+**Step 4: Review Results**
+Once the workflow completes, benchmark results will be available on the [vLLM benchmark dashboard](https://hud.pytorch.org/benchmark/llms?repoName=vllm-project%2Fvllm) under the corresponding branch and commit.
+
+**Step 5: Performance Comparison**
+Compare the current results against the previous release to verify no performance regressions have occurred. Here is an
+example of [v0.9.1 vs v0.9.2](https://hud.pytorch.org/benchmark/llms?startTime=Thu%2C%2017%20Apr%202025%2021%3A43%3A50%20GMT&stopTime=Wed%2C%2016%20Jul%202025%2021%3A43%3A50%20GMT&granularity=week&lBranch=releases/v0.9.1&lCommit=b6553be1bc75f046b00046a4ad7576364d03c835&rBranch=releases/v0.9.2&rCommit=a5dd03c1ebc5e4f56f3c9d3dc0436e9c582c978f&repoName=vllm-project%2Fvllm&benchmarkName=&modelName=All%20Models&backendName=All%20Backends&modeName=All%20Modes&dtypeName=All%20DType&deviceName=All%20Devices&archName=All%20Platforms).

benchmarks/README.md

@@ -98,7 +98,7 @@ 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
-python3 vllm/benchmarks/benchmark_serving.py \
+vllm bench serve \
   --backend vllm \
   --model NousResearch/Hermes-3-Llama-3.1-8B \
   --endpoint /v1/completions \
@@ -111,25 +111,25 @@ If successful, you will see the following output
 
 ```
 ============ Serving Benchmark Result ============
-Successful requests:                     10        
-Benchmark duration (s):                  5.78      
-Total input tokens:                      1369      
-Total generated tokens:                  2212      
-Request throughput (req/s):              1.73      
-Output token throughput (tok/s):         382.89    
-Total Token throughput (tok/s):          619.85    
+Successful requests:                     10
+Benchmark duration (s):                  5.78
+Total input tokens:                      1369
+Total generated tokens:                  2212
+Request throughput (req/s):              1.73
+Output token throughput (tok/s):         382.89
+Total Token throughput (tok/s):          619.85
 ---------------Time to First Token----------------
-Mean TTFT (ms):                          71.54     
-Median TTFT (ms):                        73.88     
-P99 TTFT (ms):                           79.49     
+Mean TTFT (ms):                          71.54
+Median TTFT (ms):                        73.88
+P99 TTFT (ms):                           79.49
 -----Time per Output Token (excl. 1st token)------
-Mean TPOT (ms):                          7.91      
-Median TPOT (ms):                        7.96      
-P99 TPOT (ms):                           8.03      
+Mean TPOT (ms):                          7.91
+Median TPOT (ms):                        7.96
+P99 TPOT (ms):                           8.03
 ---------------Inter-token Latency----------------
-Mean ITL (ms):                           7.74      
-Median ITL (ms):                         7.70      
-P99 ITL (ms):                            8.39      
+Mean ITL (ms):                           7.74
+Median ITL (ms):                         7.70
+P99 ITL (ms):                            8.39
 ==================================================
 ```
 
@@ -141,7 +141,7 @@ If the dataset you want to benchmark is not supported yet in vLLM, even then you
 {"prompt": "What is the capital of India?"}
 {"prompt": "What is the capital of Iran?"}
 {"prompt": "What is the capital of China?"}
-``` 
+```
 
 ```bash
 # start server
@@ -150,7 +150,7 @@ VLLM_USE_V1=1 vllm serve meta-llama/Llama-3.1-8B-Instruct --disable-log-requests
 
 ```bash
 # run benchmarking script
-python3 benchmarks/benchmark_serving.py --port 9001 --save-result --save-detailed \
+vllm bench serve --port 9001 --save-result --save-detailed \
   --backend vllm \
   --model meta-llama/Llama-3.1-8B-Instruct \
   --endpoint /v1/completions \
@@ -174,7 +174,7 @@ vllm serve Qwen/Qwen2-VL-7B-Instruct --disable-log-requests
 ```
 
 ```bash
-python3 vllm/benchmarks/benchmark_serving.py \
+vllm bench serve \
   --backend openai-chat \
   --model Qwen/Qwen2-VL-7B-Instruct \
   --endpoint /v1/chat/completions \
@@ -194,7 +194,7 @@ VLLM_USE_V1=1 vllm serve meta-llama/Meta-Llama-3-8B-Instruct \
 ```
 
 ``` bash
-python3 benchmarks/benchmark_serving.py \
+vllm bench serve \
     --model meta-llama/Meta-Llama-3-8B-Instruct \
     --dataset-name hf \
     --dataset-path likaixin/InstructCoder \
@@ -210,7 +210,7 @@ vllm serve Qwen/Qwen2-VL-7B-Instruct --disable-log-requests
 **`lmms-lab/LLaVA-OneVision-Data`**
 
 ```bash
-python3 vllm/benchmarks/benchmark_serving.py \
+vllm bench serve \
   --backend openai-chat \
   --model Qwen/Qwen2-VL-7B-Instruct \
   --endpoint /v1/chat/completions \
@@ -224,7 +224,7 @@ python3 vllm/benchmarks/benchmark_serving.py \
 **`Aeala/ShareGPT_Vicuna_unfiltered`**
 
 ```bash
-python3 vllm/benchmarks/benchmark_serving.py \
+vllm bench serve \
   --backend openai-chat \
   --model Qwen/Qwen2-VL-7B-Instruct \
   --endpoint /v1/chat/completions \
@@ -237,7 +237,7 @@ python3 vllm/benchmarks/benchmark_serving.py \
 **`AI-MO/aimo-validation-aime`**
 
 ``` bash
-python3 vllm/benchmarks/benchmark_serving.py \
+vllm bench serve \
     --model Qwen/QwQ-32B \
     --dataset-name hf \
     --dataset-path AI-MO/aimo-validation-aime \
@@ -248,7 +248,7 @@ python3 vllm/benchmarks/benchmark_serving.py \
 **`philschmid/mt-bench`**
 
 ``` bash
-python3 vllm/benchmarks/benchmark_serving.py \
+vllm bench serve \
     --model Qwen/QwQ-32B \
     --dataset-name hf \
     --dataset-path philschmid/mt-bench \
@@ -261,7 +261,7 @@ When using OpenAI-compatible backends such as `vllm`, optional sampling
 parameters can be specified. Example client command:
 
 ```bash
-python3 vllm/benchmarks/benchmark_serving.py \
+vllm bench serve \
   --backend vllm \
   --model NousResearch/Hermes-3-Llama-3.1-8B \
   --endpoint /v1/completions \
@@ -296,7 +296,7 @@ The following arguments can be used to control the ramp-up:
 <br/>
 
 ```bash
-python3 vllm/benchmarks/benchmark_throughput.py \
+vllm bench throughput \
   --model NousResearch/Hermes-3-Llama-3.1-8B \
   --dataset-name sonnet \
   --dataset-path vllm/benchmarks/sonnet.txt \
@@ -314,7 +314,7 @@ Total num output tokens:  1500
 **VisionArena Benchmark for Vision Language Models**
 
 ``` bash
-python3 vllm/benchmarks/benchmark_throughput.py \
+vllm bench throughput \
   --model Qwen/Qwen2-VL-7B-Instruct \
   --backend vllm-chat \
   --dataset-name hf \
@@ -336,7 +336,7 @@ Total num output tokens:  1280
 ``` bash
 VLLM_WORKER_MULTIPROC_METHOD=spawn \
 VLLM_USE_V1=1 \
-python3 vllm/benchmarks/benchmark_throughput.py \
+vllm bench throughput \
     --dataset-name=hf \
     --dataset-path=likaixin/InstructCoder \
     --model=meta-llama/Meta-Llama-3-8B-Instruct \
@@ -360,7 +360,7 @@ Total num output tokens:  204800
 **`lmms-lab/LLaVA-OneVision-Data`**
 
 ```bash
-python3 vllm/benchmarks/benchmark_throughput.py \
+vllm bench throughput \
   --model Qwen/Qwen2-VL-7B-Instruct \
   --backend vllm-chat \
   --dataset-name hf \
@@ -373,7 +373,7 @@ python3 vllm/benchmarks/benchmark_throughput.py \
 **`Aeala/ShareGPT_Vicuna_unfiltered`**
 
 ```bash
-python3 vllm/benchmarks/benchmark_throughput.py \
+vllm bench throughput \
   --model Qwen/Qwen2-VL-7B-Instruct \
   --backend vllm-chat \
   --dataset-name hf \
@@ -385,7 +385,7 @@ python3 vllm/benchmarks/benchmark_throughput.py \
 **`AI-MO/aimo-validation-aime`**
 
 ```bash
-python3 benchmarks/benchmark_throughput.py \
+vllm bench throughput \
   --model Qwen/QwQ-32B \
   --backend vllm \
   --dataset-name hf \
@@ -399,7 +399,7 @@ python3 benchmarks/benchmark_throughput.py \
 ``` bash
 # download dataset
 # wget https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/ShareGPT_V3_unfiltered_cleaned_split.json
-python3 vllm/benchmarks/benchmark_throughput.py \
+vllm bench throughput \
   --model meta-llama/Llama-2-7b-hf \
   --backend vllm \
   --dataset_path <your data path>/ShareGPT_V3_unfiltered_cleaned_split.json \

benchmarks/auto_tune/README.md

@@ -0,0 +1,141 @@
+# Automated vLLM Server Parameter Tuning
+
+This script automates the process of finding the optimal server parameter combination (`max-num-seqs` and `max-num-batched-tokens`) to maximize throughput for a vLLM server. It also supports additional constraints such as E2E latency and prefix cache hit rate.
+
+## Table of Contents
+- [Prerequisites](#prerequisites)
+- [Configuration](#configuration)
+- [How to Run](#how-to-run)
+- [Example Use Cases](#example-use-cases)
+- [Output](#output)
+- [How It Works](#how-it-works)
+
+## Prerequisites
+
+Before running the script, please ensure the following steps are completed:
+
+1. **Clone vLLM & Set Up Branch**: Clone the vLLM repository and check out to your desired branch.
+
+```bash
+git clone https://github.com/vllm-project/vllm.git
+cd vllm
+# git checkout <your-branch>
+```
+
+1. **Install Environment**: Install or update the correct running environment. For TPU usage, activate your `conda` environment and install the corresponding `torch` and `torch_xla` versions.
+
+2. **Model Configuration**: If you are using a customized model, ensure its configuration files are correctly placed and accessible.
+
+## Configuration
+
+You must set the following variables at the top of the script before execution.
+
+| Variable | Description | Example Value |
+| --- | --- | --- |
+| `BASE` | **Required.** The absolute path to the parent directory of your vLLM repository directory. | `"$HOME"` |
+| `MODEL` | **Required.** The Hugging Face model identifier to be served by vllm. | `"meta-llama/Llama-3.1-8B-Instruct"` |
+| `SYSTEM`| **Required.** The hardware you are running on. Choices: `TPU` or `GPU`. (For other systems, it might not support saving profiles) | `"TPU"` |
+| `TP` | **Required.** The tensor-parallelism size. | `1` |
+| `DOWNLOAD_DIR` | **Required.** Directory to download and load model weights from. | `""` (default download path) |
+| `INPUT_LEN` | **Required.** Request input length. | `4000` |
+| `OUTPUT_LEN` | **Required.** Request output length. | `16` |
+| `MAX_MODEL_LEN` | **Required.** Max model length. | `4096` |
+| `MIN_CACHE_HIT_PCT` | Prefix cache hit rate in percentage (0-100). Set to `0` to disable. | `60` |
+| `MAX_LATENCY_ALLOWED_MS` | The maximum allowed P99 end-to-end latency in milliseconds. Set to a very large number (e.g., `100000000000`) to effectively ignore the latency constraint. | `500` |
+| `NUM_SEQS_LIST` | A space-separated string of `max-num-seqs` values to test. | `"128 256"` |
+| `NUM_BATCHED_TOKENS_LIST` | A space-separated string of `max-num-batched-tokens` values to test. | `"1024 2048 4096"` |
+
+**Note**: The default `NUM_SEQS_LIST` and `NUM_BATCHED_TOKENS_LIST` are set for medium-sized inputs/outputs. For very short contexts (e.g., 20 input, 20 output tokens), you may need to test larger values for `max-num-seqs`.
+
+## How to Run
+
+1. **Configure**: Edit the script and set the variables in the [Configuration](#configuration) section.
+2. **Execute**: Run the script. Since the process can take a long time, it is highly recommended to use a terminal multiplexer like `tmux` or `screen` to prevent the script from stopping if your connection is lost.
+
+```
+cd <FOLDER_OF_THIS_SCRIPT>
+bash auto_tune.sh
+```
+
+    Please note that the `bash auto_tune.sh` command cannot contain full or partial path with keyword `vllm`, otherwise `pkill -f vllm` command will also kill this script itself.
+
+## Example Use Cases
+
+Here are a few examples of how to configure the script for different goals:
+
+### 1. Maximize Throughput (No Latency Constraint)
+- **Goal**: Find the best `max-num-seqs` and `max-num-batched-tokens` to get the highest possible throughput for 1800 input tokens and 20 output tokens.
+- **Configuration**:
+
+```bash
+INPUT_LEN=1800
+OUTPUT_LEN=20
+MAX_MODEL_LEN=2048
+MIN_CACHE_HIT_PCT=0
+MAX_LATENCY_ALLOWED_MS=100000000000 # A very large number
+```
+
+#### 2. Maximize Throughput with a Latency Requirement
+- **Goal**: Find the best server parameters when P99 end-to-end latency must be below 500ms.
+- **Configuration**:
+
+```bash
+INPUT_LEN=1800
+OUTPUT_LEN=20
+MAX_MODEL_LEN=2048
+MIN_CACHE_HIT_PCT=0
+MAX_LATENCY_ALLOWED_MS=500
+```
+
+#### 3. Maximize Throughput with Prefix Caching and Latency Requirements
+- **Goal**: Find the best server parameters assuming a 60% prefix cache hit rate and a latency requirement of 500ms.
+- **Configuration**:
+
+```bash
+INPUT_LEN=1800
+OUTPUT_LEN=20
+MAX_MODEL_LEN=2048
+MIN_CACHE_HIT_PCT=60
+MAX_LATENCY_ALLOWED_MS=500
+```
+
+## Output
+
+After the script finishes, you will find the results in a new, timestamped directory created inside `$BASE/auto-benchmark/`.
+
+- **Log Files**: The directory (`$BASE/auto-benchmark/YYYY_MM_DD_HH_MM/`) contains detailed logs for each run:
+    - `vllm_log_...txt`: The log output from the vLLM server for each parameter combination.
+    - `bm_log_...txt`: The log output from the `vllm bench serve` command for each benchmark run.
+
+- **Final Result Summary**: A file named `result.txt` is created in the log directory. It contains a summary of each tested combination and concludes with the overall best parameters found.
+
+```
+# Example result.txt content
+hash:a1b2c3d4...
+max_num_seqs: 128, max_num_batched_tokens: 2048, request_rate: 10.0, e2el: 450.5, throughput: 9.8, goodput: 9.8
+max_num_seqs: 128, max_num_batched_tokens: 4096 does not meet latency requirement 500
+...
+best_max_num_seqs: 256, best_num_batched_tokens: 2048, best_throughput: 12.5, profile saved in: /home/user/vllm/auto-benchmark/2024_08_01_10_30/profile
+```
+
+  If it cannot find the best parameters, the final row will be `best_max_num_seqs: 0, best_num_batched_tokens: 0, best_throughput: 0`. This can be due to either the server not starting properly, or the latency requirement being too strict.
+
+- **Profiler Trace**: A directory named `profile` is created inside the log directory. It contains the profiler trace file (e.g., `.xplane.pb` for TPU or a `.json` trace for GPU) from the single best-performing run.
+
+## How It Works
+
+The script follows a systematic process to find the optimal parameters:
+
+1. **Find Max GPU Memory Utilization**: The script first determines the highest safe `gpu-memory-utilization` (starting from 0.98 and decreasing) that does not cause an Out-Of-Memory (OOM) error when launching the server. This ensures the benchmark runs use the maximum available memory without crashing.
+
+2. **Iterate and Benchmark**: It then enters a nested loop, iterating through every combination of `max-num-seqs` and `max-num-batched-tokens` provided in the configuration lists.
+
+3. **Latency-Aware Throughput Search**: For each parameter combination:
+    - The vLLM server is started.
+    - A benchmark is first run with an infinite request rate (`--request-rate inf`).
+    - If the resulting P99 E2E latency is within the `MAX_LATENCY_ALLOWED_MS` limit, this throughput is considered the maximum for this configuration.
+    - If the latency is too high, the script performs a search by iteratively decreasing the request rate until the latency constraint is met. This finds the highest sustainable throughput for the given parameters and latency requirement.
+
+4. **Track Best Result**: Throughout the process, the script tracks the parameter combination that has yielded the highest valid throughput so far.
+
+5. **Profile Collection**: For the best-performing run, the script saves the vLLM profiler output, which can be used for deep-dive performance analysis with tools like TensorBoard.

benchmarks/auto_tune/auto_tune.sh

@@ -1,45 +1,18 @@
 #!/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
-#   SYSTEM: the hardware, choice TPU or GPU, for other systems, "get best profile" might not support.
-#   TP: ways of tensor parallelism
-#   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
-#   NUM_SEQS_LIST: a list of `max-num-seqs` you want to loop with.
-#   NUM_BATCHED_TOKENS_LIST: a list of `max-num-batched-tokens` you want to loop with.
-#   Note that the default NUM_SEQS_LIST and NUM_BATCHED_TOKENS_LIST are set for medium size input/output len, for extra short context (such as 20:20), you might need to include larger numbers in NUM_SEQS_LIST.
-# 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
+# This script aims to tune the best server parameter combinations to maximize throughput for given requirement.
+# See details in README (benchmarks/auto_tune/README.md).
 
 TAG=$(date +"%Y_%m_%d_%H_%M")
-BASE=""
+SCRIPT_DIR=$( cd -- "$( dirname -- "${BASH_SOURCE[0]}" )" &> /dev/null && pwd )
+BASE="$SCRIPT_DIR/../../.."
 MODEL="meta-llama/Llama-3.1-8B-Instruct"
 SYSTEM="TPU"
 TP=1
 DOWNLOAD_DIR=""
 INPUT_LEN=4000
 OUTPUT_LEN=16
+MAX_MODEL_LEN=4096
 MIN_CACHE_HIT_PCT=0
 MAX_LATENCY_ALLOWED_MS=100000000000
 NUM_SEQS_LIST="128 256"
@@ -65,6 +38,13 @@ current_hash=$(git rev-parse HEAD)
 echo "hash:$current_hash" >> "$RESULT"
 echo "current_hash: $current_hash"
 
+TOTAL_LEN=$((INPUT_LEN + OUTPUT_LEN))
+RED='\033[0;31m'
+if (( TOTAL_LEN > MAX_MODEL_LEN )); then
+    echo -e "${RED}FAILED: INPUT_LEN($INPUT_LEN) + OUTPUT_LEN($OUTPUT_LEN) = $TOTAL_LEN, which is > MAX_MODEL_LEN = $MAX_MODEL_LEN.\033[0m" >&2
+    exit 1
+fi
+
 best_throughput=0
 best_max_num_seqs=0
 best_num_batched_tokens=0
@@ -76,7 +56,7 @@ start_server() {
     local max_num_batched_tokens=$3
     local vllm_log=$4
     local profile_dir=$5
-    
+
     pkill -f vllm
 
     VLLM_USE_V1=1 VLLM_SERVER_DEV_MODE=1 VLLM_TORCH_PROFILER_DIR=$profile_dir vllm serve $MODEL \
@@ -89,13 +69,13 @@ start_server() {
         --enable-prefix-caching \
         --load-format dummy \
         --download-dir "$DOWNLOAD_DIR" \
-        --max-model-len $(( INPUT_LEN+OUTPUT_LEN )) > "$vllm_log" 2>&1 &
+        --max-model-len $MAX_MODEL_LEN > "$vllm_log" 2>&1 &
 
     # wait for 10 minutes...
     server_started=0
-    for i in {1..60}; do  
+    for i in {1..60}; do
         RESPONSE=$(curl -s -X GET "http://0.0.0.0:8004/health" -w "%{http_code}" -o /dev/stdout)
-        STATUS_CODE=$(echo "$RESPONSE" | tail -n 1) 
+        STATUS_CODE=$(echo "$RESPONSE" | tail -n 1)
         if [[ "$STATUS_CODE" -eq 200 ]]; then
             server_started=1
             break
@@ -118,10 +98,10 @@ update_best_profile() {
     selected_profile_file=
     if [[ "$SYSTEM" == "TPU" ]]; then
         selected_profile_file="${sorted_paths[$profile_index]}/*.xplane.pb"
-    fi 
+    fi
     if [[ "$SYSTEM" == "GPU" ]]; then
         selected_profile_file="${sorted_paths[$profile_index]}"
-    fi 
+    fi
     rm -f $PROFILE_PATH/*
     cp $selected_profile_file $PROFILE_PATH
 }
@@ -149,17 +129,18 @@ run_benchmark() {
         echo "server started."
     fi
     echo
-    
+
     echo "run benchmark test..."
     meet_latency_requirement=0
     # get a basic qps by using request-rate inf
     bm_log="$LOG_FOLDER/bm_log_${max_num_seqs}_${max_num_batched_tokens}_requestrate_inf.txt"
     prefix_len=$(( INPUT_LEN * MIN_CACHE_HIT_PCT / 100 ))
-    python benchmarks/benchmark_serving.py \
+adjusted_input_len=$(( INPUT_LEN - prefix_len ))
+    vllm bench serve \
         --backend vllm \
         --model $MODEL  \
         --dataset-name random \
-        --random-input-len $INPUT_LEN \
+        --random-input-len $adjusted_input_len \
         --random-output-len $OUTPUT_LEN \
         --ignore-eos \
         --disable-tqdm \
@@ -188,11 +169,11 @@ run_benchmark() {
             curl -X POST http://0.0.0.0:8004/reset_prefix_cache
             sleep 5
             bm_log="$LOG_FOLDER/bm_log_${max_num_seqs}_${max_num_batched_tokens}_requestrate_${request_rate}.txt"
-            python benchmarks/benchmark_serving.py \
+            vllm bench serve \
                 --backend vllm \
                 --model $MODEL  \
                 --dataset-name random \
-                --random-input-len $INPUT_LEN \
+                --random-input-len $adjusted_input_len \
                 --random-output-len $OUTPUT_LEN \
                 --ignore-eos \
                 --disable-tqdm \
@@ -273,4 +254,3 @@ 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, profile saved in: $PROFILE_PATH"
 echo "best_max_num_seqs: $best_max_num_seqs, best_num_batched_tokens: $best_num_batched_tokens, best_throughput: $best_throughput, profile saved in: $PROFILE_PATH" >> "$RESULT"
-

benchmarks/benchmark_latency.py

@@ -11,6 +11,7 @@ from typing import Any, Optional
 
 import numpy as np
 from tqdm import tqdm
+from typing_extensions import deprecated
 
 import vllm.envs as envs
 from benchmark_utils import convert_to_pytorch_benchmark_format, write_to_json
@@ -34,6 +35,10 @@ def save_to_pytorch_benchmark_format(
         write_to_json(pt_file, pt_records)
 
 
+@deprecated(
+    "benchmark_latency.py is deprecated and will be removed in a "
+    "future version. Please use 'vllm bench latency' instead.",
+)
 def main(args: argparse.Namespace):
     print(args)
 

benchmarks/benchmark_serving.py

@@ -30,7 +30,7 @@ import os
 import random
 import time
 import warnings
-from collections.abc import AsyncGenerator, Iterable
+from collections.abc import Iterable
 from dataclasses import dataclass
 from datetime import datetime
 from typing import Any, Literal, Optional
@@ -38,6 +38,7 @@ from typing import Any, Literal, Optional
 import numpy as np
 from tqdm.asyncio import tqdm
 from transformers import PreTrainedTokenizerBase
+from typing_extensions import deprecated
 
 from backend_request_func import (
     ASYNC_REQUEST_FUNCS,
@@ -73,6 +74,7 @@ from benchmark_dataset import (
     VisionArenaDataset,
 )
 from benchmark_utils import convert_to_pytorch_benchmark_format, write_to_json
+from vllm.benchmarks.serve import get_request
 
 MILLISECONDS_TO_SECONDS_CONVERSION = 1000
 
@@ -107,101 +109,6 @@ class BenchmarkMetrics:
     percentiles_e2el_ms: list[tuple[float, float]]
 
 
-def _get_current_request_rate(
-    ramp_up_strategy: Optional[Literal["linear", "exponential"]],
-    ramp_up_start_rps: Optional[int],
-    ramp_up_end_rps: Optional[int],
-    request_index: int,
-    total_requests: int,
-    request_rate: float,
-) -> float:
-    if (
-        ramp_up_strategy
-        and ramp_up_start_rps is not None
-        and ramp_up_end_rps is not None
-    ):
-        progress = request_index / max(total_requests - 1, 1)
-        if ramp_up_strategy == "linear":
-            increase = (ramp_up_end_rps - ramp_up_start_rps) * progress
-            return ramp_up_start_rps + increase
-        elif ramp_up_strategy == "exponential":
-            ratio = ramp_up_end_rps / ramp_up_start_rps
-            return ramp_up_start_rps * (ratio**progress)
-        else:
-            raise ValueError(f"Unknown ramp-up strategy: {ramp_up_strategy}")
-    return request_rate
-
-
-async def get_request(
-    input_requests: list[SampleRequest],
-    request_rate: float,
-    burstiness: float = 1.0,
-    ramp_up_strategy: Optional[Literal["linear", "exponential"]] = None,
-    ramp_up_start_rps: Optional[int] = None,
-    ramp_up_end_rps: Optional[int] = None,
-) -> AsyncGenerator[tuple[SampleRequest, float], None]:
-    """
-    Asynchronously generates requests at a specified rate
-    with OPTIONAL burstiness and OPTIONAL ramp-up strategy.
-
-    Args:
-        input_requests:
-            A list of input requests, each represented as a SampleRequest.
-        request_rate:
-            The rate at which requests are generated (requests/s).
-        burstiness (optional):
-            The burstiness factor of the request generation.
-            Only takes effect when request_rate is not inf.
-            Default value is 1, which follows a Poisson process.
-            Otherwise, the request intervals follow a gamma distribution.
-            A lower burstiness value (0 < burstiness < 1) results
-            in more bursty requests, while a higher burstiness value
-            (burstiness > 1) results in a more uniform arrival of requests.
-         ramp_up_strategy (optional):
-            The ramp-up strategy. Can be "linear" or "exponential".
-            If None, uses constant request rate (specified by request_rate).
-        ramp_up_start_rps (optional):
-            The starting request rate for ramp-up.
-        ramp_up_end_rps (optional):
-            The ending request rate for ramp-up.
-    """
-    assert burstiness > 0, (
-        f"A positive burstiness factor is expected, but given {burstiness}."
-    )
-    # Convert to list to get length for ramp-up calculations
-    if isinstance(input_requests, Iterable) and not isinstance(input_requests, list):
-        input_requests = list(input_requests)
-
-    total_requests = len(input_requests)
-    request_index = 0
-
-    for request in input_requests:
-        current_request_rate = _get_current_request_rate(
-            ramp_up_strategy,
-            ramp_up_start_rps,
-            ramp_up_end_rps,
-            request_index,
-            total_requests,
-            request_rate,
-        )
-
-        yield request, current_request_rate
-
-        request_index += 1
-
-        if current_request_rate == float("inf"):
-            # If the request rate is infinity, then we don't need to wait.
-            continue
-
-        theta = 1.0 / (current_request_rate * burstiness)
-
-        # Sample the request interval from the gamma distribution.
-        # If burstiness is 1, it follows exponential distribution.
-        interval = np.random.gamma(shape=burstiness, scale=theta)
-        # The next request will be sent after the interval.
-        await asyncio.sleep(interval)
-
-
 def calculate_metrics(
     input_requests: list[SampleRequest],
     outputs: list[RequestFuncOutput],
@@ -489,20 +396,6 @@ async def benchmark(
         tasks.append(asyncio.create_task(task))
     outputs: list[RequestFuncOutput] = await asyncio.gather(*tasks)
 
-    if profile:
-        print("Stopping profiler...")
-        profile_input = RequestFuncInput(
-            model=model_id,
-            prompt=test_prompt,
-            api_url=base_url + "/stop_profile",
-            prompt_len=test_prompt_len,
-            output_len=test_output_len,
-            logprobs=logprobs,
-        )
-        profile_output = await request_func(request_func_input=profile_input)
-        if profile_output.success:
-            print("Profiler stopped")
-
     if pbar is not None:
         pbar.close()
 
@@ -611,6 +504,20 @@ async def benchmark(
 
     print("=" * 50)
 
+    if profile:
+        print("Stopping profiler...")
+        profile_input = RequestFuncInput(
+            model=model_id,
+            prompt=test_prompt,
+            api_url=base_url + "/stop_profile",
+            prompt_len=test_prompt_len,
+            output_len=test_output_len,
+            logprobs=logprobs,
+        )
+        profile_output = await request_func(request_func_input=profile_input)
+        if profile_output.success:
+            print("Profiler stopped")
+
     return result
 
 
@@ -687,6 +594,10 @@ def save_to_pytorch_benchmark_format(
         write_to_json(pt_file, pt_records)
 
 
+@deprecated(
+    "benchmark_serving.py is deprecated and will be removed in a future "
+    "version. Please use 'vllm bench serve' instead.",
+)
 def main(args: argparse.Namespace):
     print(args)
     random.seed(args.seed)

benchmarks/benchmark_serving_structured_output.py

@@ -538,20 +538,6 @@ async def benchmark(
         )
     outputs: list[RequestFuncOutput] = await asyncio.gather(*tasks)
 
-    if profile:
-        print("Stopping profiler...")
-        profile_input = RequestFuncInput(
-            model=model_id,
-            prompt=test_request.prompt,
-            api_url=base_url + "/stop_profile",
-            prompt_len=test_request.prompt_len,
-            output_len=test_request.expected_output_len,
-            extra_body={test_request.structure_type: test_request.schema},
-        )
-        profile_output = await request_func(request_func_input=profile_input)
-        if profile_output.success:
-            print("Profiler stopped")
-
     if pbar is not None:
         pbar.close()
 
@@ -666,6 +652,20 @@ async def benchmark(
 
     print("=" * 50)
 
+    if profile:
+        print("Stopping profiler...")
+        profile_input = RequestFuncInput(
+            model=model_id,
+            prompt=test_request.prompt,
+            api_url=base_url + "/stop_profile",
+            prompt_len=test_request.prompt_len,
+            output_len=test_request.expected_output_len,
+            extra_body={test_request.structure_type: test_request.schema},
+        )
+        profile_output = await request_func(request_func_input=profile_input)
+        if profile_output.success:
+            print("Profiler stopped")
+
     return result, ret
 
 

benchmarks/benchmark_throughput.py

@@ -15,6 +15,7 @@ import torch
 import uvloop
 from tqdm import tqdm
 from transformers import AutoModelForCausalLM, AutoTokenizer, PreTrainedTokenizerBase
+from typing_extensions import deprecated
 
 from benchmark_dataset import (
     AIMODataset,
@@ -167,7 +168,8 @@ async def run_vllm_async(
     from vllm import SamplingParams
 
     async with build_async_engine_client_from_engine_args(
-        engine_args, disable_frontend_multiprocessing
+        engine_args,
+        disable_frontend_multiprocessing=disable_frontend_multiprocessing,
     ) as llm:
         model_config = await llm.get_model_config()
         assert all(
@@ -381,6 +383,10 @@ def get_requests(args, tokenizer):
     return dataset_cls(**common_kwargs).sample(**sample_kwargs)
 
 
+@deprecated(
+    "benchmark_throughput.py is deprecated and will be removed in a "
+    "future version. Please use 'vllm bench throughput' instead.",
+)
 def main(args: argparse.Namespace):
     if args.seed is None:
         args.seed = 0

benchmarks/disagg_benchmarks/disagg_overhead_benchmark.sh

@@ -3,7 +3,7 @@
 # benchmark the overhead of disaggregated prefill.
 # methodology:
 # - send all request to prefill vLLM instance. It will buffer KV cache.
-# - then send all request to decode instance. 
+# - then send all request to decode instance.
 # - The TTFT of decode instance is the overhead.
 
 set -ex
@@ -12,6 +12,8 @@ kill_gpu_processes() {
   # kill all processes on GPU.
   pgrep pt_main_thread | xargs -r kill -9
   pgrep python3 | xargs -r kill -9
+  # vLLM now names the process with VLLM prefix after https://github.com/vllm-project/vllm/pull/21445
+  pgrep VLLM | xargs -r kill -9
   sleep 10
 
   # remove vllm config file
@@ -61,7 +63,7 @@ benchmark() {
     --gpu-memory-utilization 0.6 \
     --kv-transfer-config \
     '{"kv_connector":"PyNcclConnector","kv_role":"kv_producer","kv_rank":0,"kv_parallel_size":2,"kv_buffer_size":5e9}' &
-    
+
 
   CUDA_VISIBLE_DEVICES=1 python3 \
     -m vllm.entrypoints.openai.api_server \
@@ -76,38 +78,38 @@ benchmark() {
   wait_for_server 8200
 
   # let the prefill instance finish prefill
-  python3 ../benchmark_serving.py \
-          --backend vllm \
-          --model $model \
-          --dataset-name $dataset_name \
-          --dataset-path $dataset_path \
-          --sonnet-input-len $input_len \
-          --sonnet-output-len "$output_len" \
-          --sonnet-prefix-len $prefix_len \
-          --num-prompts $num_prompts \
-          --port 8100 \
-          --save-result \
-          --result-dir $results_folder \
-          --result-filename disagg_prefill_tp1.json \
-          --request-rate "inf"
+  vllm bench serve \
+    --backend vllm \
+    --model $model \
+    --dataset-name $dataset_name \
+    --dataset-path $dataset_path \
+    --sonnet-input-len $input_len \
+    --sonnet-output-len "$output_len" \
+    --sonnet-prefix-len $prefix_len \
+    --num-prompts $num_prompts \
+    --port 8100 \
+    --save-result \
+    --result-dir $results_folder \
+    --result-filename disagg_prefill_tp1.json \
+    --request-rate "inf"
 
 
   # send the request to decode.
   # The TTFT of this command will be the overhead of disagg prefill impl.
-  python3 ../benchmark_serving.py \
-          --backend vllm \
-          --model $model \
-          --dataset-name $dataset_name \
-          --dataset-path $dataset_path \
-          --sonnet-input-len $input_len \
-          --sonnet-output-len "$output_len" \
-          --sonnet-prefix-len $prefix_len \
-          --num-prompts $num_prompts \
-          --port 8200 \
-          --save-result \
-          --result-dir $results_folder \
-          --result-filename disagg_prefill_tp1_overhead.json \
-          --request-rate "$qps"
+  vllm bench serve \
+    --backend vllm \
+    --model $model \
+    --dataset-name $dataset_name \
+    --dataset-path $dataset_path \
+    --sonnet-input-len $input_len \
+    --sonnet-output-len "$output_len" \
+    --sonnet-prefix-len $prefix_len \
+    --num-prompts $num_prompts \
+    --port 8200 \
+    --save-result \
+    --result-dir $results_folder \
+    --result-filename disagg_prefill_tp1_overhead.json \
+    --request-rate "$qps"
   kill_gpu_processes
 
 }

benchmarks/disagg_benchmarks/disagg_performance_benchmark.sh

@@ -18,6 +18,8 @@ kill_gpu_processes() {
   # kill all processes on GPU.
   pgrep pt_main_thread | xargs -r kill -9
   pgrep python3 | xargs -r kill -9
+  # vLLM now names the process with VLLM prefix after https://github.com/vllm-project/vllm/pull/21445
+  pgrep VLLM | xargs -r kill -9
   for port in 8000 8100 8200; do lsof -t -i:$port | xargs -r kill -9; done
   sleep 1
 }
@@ -58,7 +60,7 @@ launch_chunked_prefill() {
 
 
 launch_disagg_prefill() {
-  model="meta-llama/Meta-Llama-3.1-8B-Instruct" 
+  model="meta-llama/Meta-Llama-3.1-8B-Instruct"
   # disagg prefill
   CUDA_VISIBLE_DEVICES=0 python3 \
     -m vllm.entrypoints.openai.api_server \
@@ -97,20 +99,20 @@ benchmark() {
   output_len=$2
   tag=$3
 
-  python3 ../benchmark_serving.py \
-          --backend vllm \
-          --model $model \
-          --dataset-name $dataset_name \
-          --dataset-path $dataset_path \
-          --sonnet-input-len $input_len \
-          --sonnet-output-len "$output_len" \
-          --sonnet-prefix-len $prefix_len \
-          --num-prompts $num_prompts \
-          --port 8000 \
-          --save-result \
-          --result-dir $results_folder \
-          --result-filename "$tag"-qps-"$qps".json \
-          --request-rate "$qps"
+  vllm bench serve \
+    --backend vllm \
+    --model $model \
+    --dataset-name $dataset_name \
+    --dataset-path $dataset_path \
+    --sonnet-input-len $input_len \
+    --sonnet-output-len "$output_len" \
+    --sonnet-prefix-len $prefix_len \
+    --num-prompts $num_prompts \
+    --port 8000 \
+    --save-result \
+    --result-dir $results_folder \
+    --result-filename "$tag"-qps-"$qps".json \
+    --request-rate "$qps"
 
   sleep 2
 }

benchmarks/kernels/benchmark_moe.py

@@ -576,7 +576,11 @@ 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] in ("DeepseekV3ForCausalLM", "DeepseekV2ForCausalLM"):
+    elif config.architectures[0] in (
+        "DeepseekV3ForCausalLM",
+        "DeepseekV2ForCausalLM",
+        "Glm4MoeForCausalLM",
+    ):
         E = config.n_routed_experts
         topk = config.num_experts_per_tok
         intermediate_size = config.moe_intermediate_size
@@ -586,6 +590,11 @@ def main(args: argparse.Namespace):
         topk = config.num_experts_per_tok
         intermediate_size = config.moe_intermediate_size
         shard_intermediate_size = 2 * intermediate_size // args.tp_size
+    elif config.architectures[0] in ("HunYuanMoEV1ForCausalLM"):
+        E = config.num_experts
+        topk = config.moe_topk[0]
+        intermediate_size = config.moe_intermediate_size[0]
+        shard_intermediate_size = 2 * intermediate_size // args.tp_size
     else:
         # Support for llama4
         config = config.get_text_config()

benchmarks/kernels/benchmark_moe_align_block_size.py

@@ -5,9 +5,8 @@ import itertools
 
 import torch
 
-from vllm import _custom_ops as ops
 from vllm.model_executor.layers.fused_moe.moe_align_block_size import (
-    moe_align_block_size_triton,
+    moe_align_block_size,
 )
 from vllm.triton_utils import triton
 
@@ -21,62 +20,6 @@ def get_topk_ids(num_tokens: int, num_experts: int, topk: int) -> torch.Tensor:
     )
 
 
-def check_correctness(num_tokens, num_experts=256, block_size=256, topk=8):
-    """
-    Verifies vllm vs. Triton
-    """
-    topk_ids = get_topk_ids(num_tokens, num_experts, topk)
-
-    # 1. malloc space for triton and vllm
-    # malloc enough space (max_num_tokens_padded) for the sorted ids
-    max_num_tokens_padded = topk_ids.numel() + num_experts * (block_size - 1)
-    sorted_ids_triton = torch.empty(
-        (max_num_tokens_padded,), dtype=torch.int32, device="cuda"
-    )
-    sorted_ids_triton.fill_(topk_ids.numel())  # fill with sentinel value
-    expert_ids_triton = torch.zeros(
-        (max_num_tokens_padded // block_size,), dtype=torch.int32, device="cuda"
-    )
-    num_tokens_post_pad_triton = torch.empty((1,), dtype=torch.int32, device="cuda")
-
-    sorted_ids_vllm = torch.empty_like(sorted_ids_triton)
-    sorted_ids_vllm.fill_(topk_ids.numel())
-    expert_ids_vllm = torch.zeros_like(expert_ids_triton)
-    num_tokens_post_pad_vllm = torch.empty_like(num_tokens_post_pad_triton)
-
-    # 2. run implementations
-    moe_align_block_size_triton(
-        topk_ids,
-        num_experts,
-        block_size,
-        sorted_ids_triton,
-        expert_ids_triton,
-        num_tokens_post_pad_triton,
-    )
-
-    ops.moe_align_block_size(
-        topk_ids,
-        num_experts,
-        block_size,
-        sorted_ids_vllm,
-        expert_ids_vllm,
-        num_tokens_post_pad_vllm,
-    )
-    print(f"✅ VLLM implementation works with {num_experts} experts!")
-
-    # 3. compare results
-    if torch.allclose(expert_ids_triton, expert_ids_vllm) and torch.allclose(
-        num_tokens_post_pad_triton, num_tokens_post_pad_vllm
-    ):
-        print("✅ Triton and VLLM implementations match.")
-    else:
-        print("❌ Triton and VLLM implementations DO NOT match.")
-        print("Triton expert_ids:", expert_ids_triton)
-        print("VLLM expert_ids:", expert_ids_vllm)
-        print("Triton num_tokens_post_pad:", num_tokens_post_pad_triton)
-        print("VLLM num_tokens_post_pad:", num_tokens_post_pad_vllm)
-
-
 # test configurations
 num_tokens_range = [1, 16, 256, 4096]
 num_experts_range = [16, 64, 224, 256, 280, 512]
@@ -89,8 +32,8 @@ configs = list(itertools.product(num_tokens_range, num_experts_range, topk_range
         x_names=["num_tokens", "num_experts", "topk"],
         x_vals=configs,
         line_arg="provider",
-        line_vals=["vllm", "triton"],  # "triton"
-        line_names=["VLLM", "Triton"],  # "Triton"
+        line_vals=["vllm"],
+        line_names=["vLLM"],
         plot_name="moe-align-block-size-performance",
         args={},
     )
@@ -100,37 +43,11 @@ def benchmark(num_tokens, num_experts, topk, provider):
     block_size = 256
     topk_ids = get_topk_ids(num_tokens, num_experts, topk)
 
-    max_num_tokens_padded = topk_ids.numel() + num_experts * (block_size - 1)
-    sorted_ids = torch.empty((max_num_tokens_padded,), dtype=torch.int32, device="cuda")
-    sorted_ids.fill_(topk_ids.numel())
-    max_num_m_blocks = max_num_tokens_padded // block_size
-    expert_ids = torch.empty((max_num_m_blocks,), dtype=torch.int32, device="cuda")
-    num_tokens_post_pad = torch.empty((1,), dtype=torch.int32, device="cuda")
-
     quantiles = [0.5, 0.2, 0.8]
 
     if provider == "vllm":
         ms, min_ms, max_ms = triton.testing.do_bench(
-            lambda: ops.moe_align_block_size(
-                topk_ids,
-                num_experts,
-                block_size,
-                sorted_ids.clone(),
-                expert_ids.clone(),
-                num_tokens_post_pad.clone(),
-            ),
-            quantiles=quantiles,
-        )
-    elif provider == "triton":
-        ms, min_ms, max_ms = triton.testing.do_bench(
-            lambda: moe_align_block_size_triton(
-                topk_ids,
-                num_experts,
-                block_size,
-                sorted_ids.clone(),
-                expert_ids.clone(),
-                num_tokens_post_pad.clone(),
-            ),
+            lambda: moe_align_block_size(topk_ids, block_size, num_experts),
             quantiles=quantiles,
         )
 
@@ -154,6 +71,4 @@ if __name__ == "__main__":
     )
     args = parser.parse_args()
 
-    print("Running correctness check...")
-    check_correctness(num_tokens=1024, num_experts=args.num_experts, topk=args.topk)
     benchmark.run(print_data=True, show_plots=True)

benchmarks/kernels/benchmark_moe_permute_unpermute.py

@@ -8,12 +8,13 @@ import ray
 import torch
 from transformers import AutoConfig
 
-from vllm.model_executor.layers.fused_moe.deep_gemm_moe import (
+from vllm.model_executor.layers.fused_moe.fused_moe import *
+from vllm.model_executor.layers.fused_moe.moe_permute_unpermute import (
     _moe_permute,
     _moe_unpermute_and_reduce,
+    moe_permute,
+    moe_unpermute,
 )
-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
@@ -63,18 +64,19 @@ def benchmark_permute(
 
     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,
-                )
+            (
+                permuted_hidden_states,
+                a1q_scale,
+                first_token_off,
+                inv_perm_idx,
+                m_indices,
+            ) = moe_permute(
+                qhidden_states,
+                a1q_scale=None,
+                topk_ids=topk_ids,
+                n_expert=num_experts,
+                expert_map=None,
+                align_block_size=align_block_size,
             )
         else:
             (
@@ -150,18 +152,19 @@ def benchmark_unpermute(
 
     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,
-                )
+            (
+                permuted_hidden_states,
+                a1q_scale,
+                first_token_off,
+                inv_perm_idx,
+                m_indices,
+            ) = moe_permute(
+                qhidden_states,
+                a1q_scale=None,
+                topk_ids=topk_ids,
+                n_expert=num_experts,
+                expert_map=None,
+                align_block_size=align_block_size,
             )
             # convert to fp16/bf16 as gemm output
             return (
@@ -191,16 +194,19 @@ def benchmark_unpermute(
 
     def run(input: tuple):
         if use_customized_permute:
-            (permuted_hidden_states, first_token_off, inv_perm_idx, m_indices) = input
+            (
+                permuted_hidden_states,
+                first_token_off,
+                inv_perm_idx,
+                m_indices,
+            ) = input
+            output = torch.empty_like(hidden_states)
             moe_unpermute(
+                output,
                 permuted_hidden_states,
                 topk_weights,
-                topk_ids,
                 inv_perm_idx,
                 first_token_off,
-                topk,
-                num_experts,
-                num_experts,
             )
         else:
             (
@@ -211,7 +217,11 @@ def benchmark_unpermute(
                 inv_perm,
             ) = input
             _moe_unpermute_and_reduce(
-                output_hidden_states, permuted_hidden_states, inv_perm, topk_weights
+                output_hidden_states,
+                permuted_hidden_states,
+                inv_perm,
+                topk_weights,
+                True,
             )
 
     # JIT compilation & warmup
@@ -318,6 +328,7 @@ def main(args: argparse.Namespace):
     elif (
         config.architectures[0] == "DeepseekV3ForCausalLM"
         or config.architectures[0] == "DeepseekV2ForCausalLM"
+        or config.architectures[0] == "Glm4MoeForCausalLM"
     ):
         E = config.n_routed_experts
         topk = config.num_experts_per_tok

benchmarks/kernels/benchmark_trtllm_attention.py

@@ -71,22 +71,20 @@ def benchmark_decode(
     if kv_cache_dtype.startswith("fp8"):
         kv_cache, _ = to_float8(kv_cache)
 
+    output_trtllm = torch.empty(q.shape, dtype=dtype)
+
     # Benchmark TRT decode
     def trt_decode():
         return flashinfer.decode.trtllm_batch_decode_with_kv_cache(
             q,
             kv_cache,
             workspace_buffer,
-            num_qo_heads,
-            num_kv_heads,
-            sm_scale,
             block_tables,
             kv_lens_tensor,
-            page_size,
             max_kv_len,
-            kv_cache_dtype,
-            k_scale,
-            v_scale,
+            bmm1_scale=k_scale * sm_scale,
+            bmm2_scale=v_scale,
+            out=output_trtllm,
         )
 
     def time_fn(fn, warmup=10, trials=20):
@@ -125,6 +123,8 @@ def benchmark_decode(
     kv_indices = torch.tensor(kv_indices, dtype=torch.int32)
     kv_last_page_lens = torch.tensor(kv_last_page_lens, dtype=torch.int32)
 
+    output_baseline = torch.empty(q.shape, dtype=dtype)
+
     wrapper = flashinfer.BatchDecodeWithPagedKVCacheWrapper(
         workspace_buffer,
         kv_layout,
@@ -145,7 +145,7 @@ def benchmark_decode(
     )
 
     def baseline_decode():
-        return wrapper.run(q, kv_cache, sm_scale, k_scale, v_scale)
+        return wrapper.run(q, kv_cache, sm_scale, k_scale, v_scale, output_baseline)
 
     baseline_mean, baseline_std = time_fn(baseline_decode)
 
@@ -214,25 +214,39 @@ if __name__ == "__main__":
     max_seq_lens = [1024, 2048, 4096, 8192, 16384, 32768, 65536, 131072]
     all_results = []
 
-    print("Running benchmark for kv_cache_dtype: bfloat16")
     print(
-        "\tnum_seqs\tmax_seq_len\ttrt_mean\ttrt_std\tbaseline_mean\tbaseline_std\tspeedup_percent"
+        "Running benchmark for q_dtype = bfloat16, kv_cache_dtype: bfloat16, "
+        "output_dtype: bfloat16"
+    )
+    print(
+        "\tnum_seqs\tmax_seq_len\ttrt_mean\ttrt_std\tbaseline_mean\t"
+        "baseline_std\tspeedup_percent"
     )
     for max_seq_len in max_seq_lens:
         for bs in num_seqs:
             result = benchmark_decode(
-                bs, max_seq_len, dtype=torch.bfloat16, kv_cache_dtype="auto"
+                bs,
+                max_seq_len,
+                dtype=torch.bfloat16,
+                kv_cache_dtype="auto",
             )
             all_results.append(result)
 
-    print("Running benchmark for q_dtype = bfloat16, kv_cache_dtype: fp8")
     print(
-        "\tnum_seqs\tmax_seq_len\ttrt_mean\ttrt_std\tbaseline_mean\tbaseline_std\tspeedup_percent"
+        "Running benchmark for q_dtype = bfloat16, kv_cache_dtype: fp8, "
+        "output_dtype: bfloat16"
+    )
+    print(
+        "\tnum_seqs\tmax_seq_len\ttrt_mean\ttrt_std\tbaseline_mean\t"
+        "baseline_std\tspeedup_percent"
     )
     for max_seq_len in max_seq_lens:
         for bs in num_seqs:
             result = benchmark_decode(
-                bs, max_seq_len, dtype=torch.bfloat16, kv_cache_dtype="fp8"
+                bs,
+                max_seq_len,
+                dtype=torch.bfloat16,
+                kv_cache_dtype="fp8",
             )
             all_results.append(result)
 

benchmarks/kv_cache/benchmark_block_pool.py

@@ -0,0 +1,108 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+import gc
+import time
+from typing import Optional
+
+from tabulate import tabulate
+
+from vllm.utils import FlexibleArgumentParser
+from vllm.v1.core.block_pool import BlockPool
+
+
+class Metric:
+    def __init__(self) -> None:
+        self.cnt: int = 0
+        self.sum_v: int = 0
+        self.max_v: Optional[int] = None
+
+    def update(self, v: int) -> None:
+        self.cnt += 1
+        self.sum_v += v
+        if self.max_v is None:
+            self.max_v = v
+        else:
+            self.max_v = max(self.max_v, v)
+
+    def avg_v(self) -> float:
+        return self.sum_v * 1.0 / self.cnt
+
+
+def main(args):
+    rows = []
+    for allocate_block in args.allocate_blocks:
+        # Enforce a GC collect ahead to minimize the impact among runs
+        gc.collect()
+        block_pool = BlockPool(num_gpu_blocks=args.num_gpu_blocks, enable_caching=True)
+
+        get_blocks_metric: Metric = Metric()
+        free_blocks_metric: Metric = Metric()
+        for _ in range(args.num_iteration):
+            t1 = time.monotonic_ns()
+            blocks = block_pool.get_new_blocks(allocate_block)
+            t2 = time.monotonic_ns()
+            block_pool.free_blocks(blocks)
+            t3 = time.monotonic_ns()
+            get_blocks_metric.update(t2 - t1)
+            free_blocks_metric.update(t3 - t2)
+
+        if get_blocks_metric.max_v is not None and free_blocks_metric.max_v is not None:
+            rows.append(
+                [
+                    get_blocks_metric.cnt,
+                    args.num_gpu_blocks,
+                    allocate_block,
+                    get_blocks_metric.avg_v() / 1000000,
+                    get_blocks_metric.max_v / 1000000.0,
+                    free_blocks_metric.avg_v() / 1000000,
+                    free_blocks_metric.max_v / 1000000.0,
+                ]
+            )
+        else:
+            print(
+                "No valid metrics found."
+                f" {get_blocks_metric.max_v=} {free_blocks_metric.max_v=}"
+            )
+
+    print(
+        tabulate(
+            rows,
+            headers=[
+                "Iterations",
+                "Total\nBlocks",
+                "Allocated\nBlocks",
+                "Get Blocks\nAvg (ms)",
+                "Get Blocks\nMax (ms)",
+                "Free Blocks\nAvg (ms)",
+                "Free Blocks\nMax (ms)",
+            ],
+            tablefmt="grid",
+            floatfmt=".6f",
+        )
+    )
+
+
+def invoke_main() -> None:
+    parser = FlexibleArgumentParser(
+        description="Benchmark the performance of BlockPool for KV Cache."
+    )
+    parser.add_argument("--num-gpu-blocks", type=int, default=100000)
+    parser.add_argument(
+        "--num-iteration",
+        type=int,
+        default=1000,
+        help="Number of iterations to run to stablize final data readings",
+    )
+    parser.add_argument(
+        "--allocate-blocks",
+        type=int,
+        nargs="*",
+        default=[10, 50, 100, 500, 1000],
+        help="Number of blocks to allocate",
+    )
+    args = parser.parse_args()
+    main(args)
+
+
+if __name__ == "__main__":
+    invoke_main()  # pragma: no cover

cmake/cpu_extension.cmake

@@ -58,6 +58,22 @@ function (find_isa CPUINFO TARGET OUT)
     endif()
 endfunction()
 
+
+function(check_sysctl TARGET OUT)
+    execute_process(COMMAND sysctl -n "${TARGET}"
+                    RESULT_VARIABLE SYSCTL_RET
+                    OUTPUT_VARIABLE SYSCTL_INFO
+                    ERROR_QUIET
+                    OUTPUT_STRIP_TRAILING_WHITESPACE)
+    if(SYSCTL_RET EQUAL 0 AND
+      (SYSCTL_INFO STREQUAL "1" OR SYSCTL_INFO GREATER 0))
+        set(${OUT} ON PARENT_SCOPE)
+    else()
+        set(${OUT} OFF PARENT_SCOPE)
+    endif()
+endfunction()
+
+
 function (is_avx512_disabled OUT)
     set(DISABLE_AVX512 $ENV{VLLM_CPU_DISABLE_AVX512})
     if(DISABLE_AVX512 AND DISABLE_AVX512 STREQUAL "true")
@@ -70,7 +86,10 @@ endfunction()
 is_avx512_disabled(AVX512_DISABLED)
 
 if (MACOSX_FOUND AND CMAKE_SYSTEM_PROCESSOR STREQUAL "arm64")
-    set(APPLE_SILICON_FOUND TRUE)
+    message(STATUS "Apple Silicon Detected")
+    set(ENABLE_NUMA OFF)
+    check_sysctl(hw.optional.neon ASIMD_FOUND)
+    check_sysctl(hw.optional.arm.FEAT_BF16 ARM_BF16_FOUND)
 else()
     find_isa(${CPUINFO} "avx2" AVX2_FOUND)
     find_isa(${CPUINFO} "avx512f" AVX512_FOUND)
@@ -82,7 +101,6 @@ else()
     find_isa(${CPUINFO} "S390" S390_FOUND)
 endif()
 
-
 if (AVX512_FOUND AND NOT AVX512_DISABLED)
     list(APPEND CXX_COMPILE_FLAGS
         "-mavx512f"
@@ -149,9 +167,6 @@ elseif (ASIMD_FOUND)
         set(MARCH_FLAGS "-march=armv8.2-a+dotprod+fp16")  
     endif()
     list(APPEND CXX_COMPILE_FLAGS ${MARCH_FLAGS})     
-elseif(APPLE_SILICON_FOUND)
-    message(STATUS "Apple Silicon Detected")
-    set(ENABLE_NUMA OFF)
 elseif (S390_FOUND)
     message(STATUS "S390 detected")
     # Check for S390 VXE support

csrc/attention/attention_kernels.cuh

@@ -24,7 +24,7 @@
 
 #include "attention_dtypes.h"
 #include "attention_utils.cuh"
-#include "cuda_compat.h"
+#include "../cuda_compat.h"
 
 #ifdef USE_ROCM
   #include <hip/hip_bf16.h>

csrc/attention/paged_attention_v1.cu

@@ -16,9 +16,8 @@
  * See the License for the specific language governing permissions and
  * limitations under the License.
  */
-
 #include "attention_kernels.cuh"
-#include "cuda_compat.h"
+#include "../cuda_compat.h"
 
 #define MAX(a, b) ((a) > (b) ? (a) : (b))
 #define MIN(a, b) ((a) < (b) ? (a) : (b))
@@ -75,7 +74,7 @@ void paged_attention_v1_launcher(
   const float* k_scale_ptr = reinterpret_cast<const float*>(k_scale.data_ptr());
   const float* v_scale_ptr = reinterpret_cast<const float*>(v_scale.data_ptr());
 
-  constexpr int NUM_WARPS = NUM_THREADS / WARP_SIZE;
+  const int NUM_WARPS = NUM_THREADS / WARP_SIZE;
   int padded_max_seq_len =
       DIVIDE_ROUND_UP(max_seq_len, BLOCK_SIZE) * BLOCK_SIZE;
   int logits_size = padded_max_seq_len * sizeof(float);

csrc/attention/paged_attention_v2.cu

@@ -16,9 +16,8 @@
  * See the License for the specific language governing permissions and
  * limitations under the License.
  */
-
 #include "attention_kernels.cuh"
-#include "cuda_compat.h"
+#include "../cuda_compat.h"
 
 #define MAX(a, b) ((a) > (b) ? (a) : (b))
 #define MIN(a, b) ((a) < (b) ? (a) : (b))
@@ -79,7 +78,7 @@ void paged_attention_v2_launcher(
   const float* k_scale_ptr = reinterpret_cast<const float*>(k_scale.data_ptr());
   const float* v_scale_ptr = reinterpret_cast<const float*>(v_scale.data_ptr());
 
-  constexpr int NUM_WARPS = NUM_THREADS / WARP_SIZE;
+  const int NUM_WARPS = NUM_THREADS / WARP_SIZE;
   int max_num_partitions = DIVIDE_ROUND_UP(max_seq_len, PARTITION_SIZE);
   int logits_size = PARTITION_SIZE * sizeof(float);
   int outputs_size = (NUM_WARPS / 2) * head_size * sizeof(float);

csrc/cpu/shm.cpp

@@ -7,7 +7,7 @@
 
 namespace {
 #define MAX_SHM_RANK_NUM 8
-#define PER_THREAD_SHM_BUFFER_BYTES (2 * 1024 * 1024)
+#define PER_THREAD_SHM_BUFFER_BYTES (4 * 1024 * 1024)
 static_assert(PER_THREAD_SHM_BUFFER_BYTES % 2 == 0);
 #define PER_THREAD_SHM_BUFFER_OFFSET (PER_THREAD_SHM_BUFFER_BYTES >> 1)
 #define MIN_THREAD_PROCESS_SIZE (256)
@@ -34,9 +34,10 @@ struct KernelVecType<c10::Half> {
 };
 
 struct ThreadSHMContext {
-  volatile char _curr_thread_stamp;
-  volatile char _ready_thread_stamp;
-  char _padding1[6];
+  volatile char _curr_thread_stamp[2];
+  volatile char _ready_thread_stamp[2];
+  int local_stamp_buffer_idx;
+  int remote_stamp_buffer_idx;
   int thread_id;
   int thread_num;
   int rank;
@@ -45,23 +46,28 @@ struct ThreadSHMContext {
   int swizzled_ranks[MAX_SHM_RANK_NUM];
   void* thread_shm_ptrs[MAX_SHM_RANK_NUM];
   ThreadSHMContext* shm_contexts[MAX_SHM_RANK_NUM];
-  size_t _thread_buffer_mask;
-  char _padding2[56];
+  size_t _thread_buffer_mask[2];
+  char _padding2[40];
 
   ThreadSHMContext(const int thread_id, const int thread_num, const int rank,
                    const int group_size, void* thread_shm_ptr)
-      : _curr_thread_stamp(1),
-        _ready_thread_stamp(0),
+      : local_stamp_buffer_idx(0),
+        remote_stamp_buffer_idx(0),
         thread_id(thread_id),
         thread_num(thread_num),
         rank(rank),
         group_size(group_size),
-        _spinning_count(0),
-        _thread_buffer_mask(0) {
+        _spinning_count(0) {
     static_assert(sizeof(ThreadSHMContext) % 64 == 0);
     TORCH_CHECK(group_size <= MAX_SHM_RANK_NUM);
     TORCH_CHECK((size_t)this % 64 == 0);
     TORCH_CHECK((size_t)thread_shm_ptr % 64 == 0);
+    _curr_thread_stamp[0] = 1;
+    _curr_thread_stamp[1] = 1;
+    _ready_thread_stamp[0] = 0;
+    _ready_thread_stamp[1] = 0;
+    _thread_buffer_mask[0] = 0;
+    _thread_buffer_mask[1] = 0;
     for (int i = 0; i < MAX_SHM_RANK_NUM; ++i) {
       shm_contexts[i] = nullptr;
       thread_shm_ptrs[i] = nullptr;
@@ -70,6 +76,11 @@ struct ThreadSHMContext {
     set_context(rank, this, thread_shm_ptr);
   }
 
+  void set_stamp_buffer_idx(int local, int remote) {
+    local_stamp_buffer_idx = local;
+    remote_stamp_buffer_idx = remote;
+  }
+
   void set_context(int rank, ThreadSHMContext* ptr, void* thread_shm_ptr) {
     TORCH_CHECK(rank < MAX_SHM_RANK_NUM);
     TORCH_CHECK(ptr);
@@ -84,23 +95,27 @@ struct ThreadSHMContext {
   T* get_thread_shm_ptr(int rank) {
     return reinterpret_cast<T*>(
         reinterpret_cast<int8_t*>(thread_shm_ptrs[rank]) +
-        (PER_THREAD_SHM_BUFFER_OFFSET & _thread_buffer_mask));
+        (PER_THREAD_SHM_BUFFER_OFFSET &
+         _thread_buffer_mask[local_stamp_buffer_idx]));
   }
 
-  void next_buffer() { _thread_buffer_mask ^= 0xFFFFFFFFFFFFFFFF; }
+  void next_buffer() {
+    _thread_buffer_mask[local_stamp_buffer_idx] ^= 0xFFFFFFFFFFFFFFFF;
+  }
 
-  char get_curr_stamp() const { return _curr_thread_stamp; }
+  char get_curr_stamp(int idx) const { return _curr_thread_stamp[idx]; }
 
-  char get_ready_stamp() const { return _ready_thread_stamp; }
+  char get_ready_stamp(int idx) const { return _ready_thread_stamp[idx]; }
 
   void next_stamp() {
     _mm_mfence();
-    _curr_thread_stamp += 1;
+    _curr_thread_stamp[local_stamp_buffer_idx] += 1;
   }
 
   void commit_ready_stamp() {
     _mm_mfence();
-    _ready_thread_stamp = _curr_thread_stamp;
+    _ready_thread_stamp[local_stamp_buffer_idx] =
+        _curr_thread_stamp[local_stamp_buffer_idx];
   }
 
   int get_swizzled_rank(int idx) { return swizzled_ranks[idx]; }
@@ -117,10 +132,11 @@ struct ThreadSHMContext {
   void wait_for_one(int rank, Cond&& cond) {
     ThreadSHMContext* rank_ctx = shm_contexts[rank];
     for (;;) {
-      char local_curr_stamp = get_curr_stamp();
-      char local_ready_stamp = get_ready_stamp();
-      char rank_curr_stamp = rank_ctx->get_curr_stamp();
-      char rank_ready_stamp = rank_ctx->get_ready_stamp();
+      char local_curr_stamp = get_curr_stamp(local_stamp_buffer_idx);
+      char local_ready_stamp = get_ready_stamp(local_stamp_buffer_idx);
+      char rank_curr_stamp = rank_ctx->get_curr_stamp(remote_stamp_buffer_idx);
+      char rank_ready_stamp =
+          rank_ctx->get_ready_stamp(remote_stamp_buffer_idx);
       if (cond(local_curr_stamp, local_ready_stamp, rank_curr_stamp,
                rank_ready_stamp)) {
         break;
@@ -361,6 +377,15 @@ void shm_cc_loop(ThreadSHMContext* ctx, int64_t elem_num, F&& inner_func) {
     }
   }
 }
+
+void reset_threads_stamp_buffer_idx(ThreadSHMContext* ctx, int local,
+                                    int remote) {
+  int thread_num = ctx->thread_num;
+  for (int i = 0; i < thread_num; ++i) {
+    ThreadSHMContext* thread_ctx = ctx + i;
+    thread_ctx->set_stamp_buffer_idx(local, remote);
+  }
+}
 };  // namespace shm_cc_ops
 
 namespace shm_cc_ops {
@@ -632,6 +657,7 @@ void shm_send_tensor_list_impl(ThreadSHMContext* ctx, int64_t dst,
   TensorListMeta* metadata = new (metadata_tensor.data_ptr()) TensorListMeta();
   metadata->bind_tensor_list(tensor_list_with_metadata);
 
+  shm_cc_ops::reset_threads_stamp_buffer_idx(ctx, 0, 1);
   shm_cc_ops::shm_cc_loop<int8_t>(
       ctx, metadata->total_bytes,
       [&](ThreadSHMContext* thread_ctx, int64_t data_offset,
@@ -659,6 +685,7 @@ std::vector<torch::Tensor> shm_recv_tensor_list_impl(ThreadSHMContext* ctx,
   torch::Tensor metadata_tensor =
       torch::empty({sizeof(TensorListMeta)}, options);
 
+  shm_cc_ops::reset_threads_stamp_buffer_idx(ctx, 1, 0);
   ctx->wait_for_one(src, ThreadSHMContext::check_stamp_ready);
   shm_cc_ops::memcpy(metadata_tensor.data_ptr(),
                      ctx->get_thread_shm_ptr<void>(src),
@@ -677,7 +704,7 @@ std::vector<torch::Tensor> shm_recv_tensor_list_impl(ThreadSHMContext* ctx,
       ctx, metadata.total_bytes,
       [&](ThreadSHMContext* thread_ctx, int64_t data_offset,
           int64_t data_elem_num, bool fast_mode) {
-        ctx->wait_for_one(src, ThreadSHMContext::check_stamp_ready);
+        thread_ctx->wait_for_one(src, ThreadSHMContext::check_stamp_ready);
         int64_t curr_shm_offset = 0;
         while (curr_shm_offset < data_elem_num) {
           MemPiece frag = metadata.get_data(data_offset + curr_shm_offset);

csrc/cpu/torch_bindings.cpp

@@ -151,7 +151,7 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, ops) {
   ops.impl("rotary_embedding", torch::kCPU, &rotary_embedding);
 
   // Quantization
-#if defined(__AVX512F__) || defined(__aarch64__)
+#if defined(__AVX512F__) || (defined(__aarch64__) && !defined(__APPLE__))
   at::Tag stride_tag = at::Tag::needs_fixed_stride_order;
 
   // Compute int8 quantized tensor for given scaling factor.

csrc/cuda_compat.h

@@ -4,8 +4,35 @@
   #include <hip/hip_runtime.h>
 #endif
 
-#if defined(USE_ROCM) && defined(__GFX9__)
-  #define WARP_SIZE 64
+#ifdef USE_ROCM
+struct Utils {
+  static __host__ int get_warp_size() {
+    static bool is_cached = false;
+    static int result;
+
+    if (!is_cached) {
+      int device_id;
+      cudaDeviceProp deviceProp;
+      cudaGetDevice(&device_id);
+      cudaGetDeviceProperties(&deviceProp, device_id);
+
+      result = deviceProp.warpSize;
+      is_cached = true;
+    }
+
+    return result;
+  }
+
+  static __device__ constexpr int get_warp_size() {
+  #ifdef __GFX9__
+    return 64;
+  #else
+    return 32;
+  #endif
+  }
+};
+
+  #define WARP_SIZE Utils::get_warp_size()
 #else
   #define WARP_SIZE 32
 #endif

csrc/layernorm_kernels.cu

@@ -15,15 +15,16 @@ namespace vllm {
 // TODO(woosuk): Further optimize this kernel.
 template <typename scalar_t>
 __global__ void rms_norm_kernel(
-    scalar_t* __restrict__ out,           // [..., hidden_size]
-    const scalar_t* __restrict__ input,   // [..., hidden_size]
+    scalar_t* __restrict__ out,          // [..., hidden_size]
+    const scalar_t* __restrict__ input,  // [..., hidden_size]
+    const int64_t input_stride,
     const scalar_t* __restrict__ weight,  // [hidden_size]
     const float epsilon, const int num_tokens, const int hidden_size) {
   __shared__ float s_variance;
   float variance = 0.0f;
 
   for (int idx = threadIdx.x; idx < hidden_size; idx += blockDim.x) {
-    const float x = (float)input[blockIdx.x * hidden_size + idx];
+    const float x = (float)input[blockIdx.x * input_stride + idx];
     variance += x * x;
   }
 
@@ -37,7 +38,7 @@ __global__ void rms_norm_kernel(
   __syncthreads();
 
   for (int idx = threadIdx.x; idx < hidden_size; idx += blockDim.x) {
-    float x = (float)input[blockIdx.x * hidden_size + idx];
+    float x = (float)input[blockIdx.x * input_stride + idx];
     out[blockIdx.x * hidden_size + idx] =
         ((scalar_t)(x * s_variance)) * weight[idx];
   }
@@ -50,7 +51,8 @@ __global__ void rms_norm_kernel(
 template <typename scalar_t, int width>
 __global__ std::enable_if_t<(width > 0) && _typeConvert<scalar_t>::exists>
 fused_add_rms_norm_kernel(
-    scalar_t* __restrict__ input,         // [..., hidden_size]
+    scalar_t* __restrict__ input,  // [..., hidden_size]
+    const int64_t input_stride,
     scalar_t* __restrict__ residual,      // [..., hidden_size]
     const scalar_t* __restrict__ weight,  // [hidden_size]
     const float epsilon, const int num_tokens, const int hidden_size) {
@@ -59,6 +61,7 @@ fused_add_rms_norm_kernel(
   static_assert(sizeof(_f16Vec<scalar_t, width>) == sizeof(scalar_t) * width);
 
   const int vec_hidden_size = hidden_size / width;
+  const int64_t vec_input_stride = input_stride / width;
   __shared__ float s_variance;
   float variance = 0.0f;
   /* These and the argument pointers are all declared `restrict` as they are
@@ -73,7 +76,8 @@ fused_add_rms_norm_kernel(
 
   for (int idx = threadIdx.x; idx < vec_hidden_size; idx += blockDim.x) {
     int id = blockIdx.x * vec_hidden_size + idx;
-    _f16Vec<scalar_t, width> temp = input_v[id];
+    int64_t strided_id = blockIdx.x * vec_input_stride + idx;
+    _f16Vec<scalar_t, width> temp = input_v[strided_id];
     temp += residual_v[id];
     variance += temp.sum_squares();
     residual_v[id] = temp;
@@ -90,10 +94,11 @@ fused_add_rms_norm_kernel(
 
   for (int idx = threadIdx.x; idx < vec_hidden_size; idx += blockDim.x) {
     int id = blockIdx.x * vec_hidden_size + idx;
+    int64_t strided_id = blockIdx.x * vec_input_stride + idx;
     _f16Vec<scalar_t, width> temp = residual_v[id];
     temp *= s_variance;
     temp *= weight_v[idx];
-    input_v[id] = temp;
+    input_v[strided_id] = temp;
   }
 }
 
@@ -103,7 +108,8 @@ fused_add_rms_norm_kernel(
 template <typename scalar_t, int width>
 __global__ std::enable_if_t<(width == 0) || !_typeConvert<scalar_t>::exists>
 fused_add_rms_norm_kernel(
-    scalar_t* __restrict__ input,         // [..., hidden_size]
+    scalar_t* __restrict__ input,  // [..., hidden_size]
+    const int64_t input_stride,
     scalar_t* __restrict__ residual,      // [..., hidden_size]
     const scalar_t* __restrict__ weight,  // [hidden_size]
     const float epsilon, const int num_tokens, const int hidden_size) {
@@ -111,7 +117,7 @@ fused_add_rms_norm_kernel(
   float variance = 0.0f;
 
   for (int idx = threadIdx.x; idx < hidden_size; idx += blockDim.x) {
-    scalar_t z = input[blockIdx.x * hidden_size + idx];
+    scalar_t z = input[blockIdx.x * input_stride + idx];
     z += residual[blockIdx.x * hidden_size + idx];
     float x = (float)z;
     variance += x * x;
@@ -129,7 +135,7 @@ fused_add_rms_norm_kernel(
 
   for (int idx = threadIdx.x; idx < hidden_size; idx += blockDim.x) {
     float x = (float)residual[blockIdx.x * hidden_size + idx];
-    input[blockIdx.x * hidden_size + idx] =
+    input[blockIdx.x * input_stride + idx] =
         ((scalar_t)(x * s_variance)) * weight[idx];
   }
 }
@@ -141,11 +147,12 @@ void rms_norm(torch::Tensor& out,     // [..., hidden_size]
               torch::Tensor& weight,  // [hidden_size]
               double epsilon) {
   TORCH_CHECK(out.is_contiguous());
-  TORCH_CHECK(input.is_contiguous());
+  TORCH_CHECK(input.stride(-1) == 1);
   TORCH_CHECK(weight.is_contiguous());
 
   int hidden_size = input.size(-1);
   int num_tokens = input.numel() / hidden_size;
+  int64_t input_stride = input.stride(-2);
 
   dim3 grid(num_tokens);
   dim3 block(std::min(hidden_size, 1024));
@@ -153,26 +160,29 @@ void rms_norm(torch::Tensor& out,     // [..., hidden_size]
   const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
   VLLM_DISPATCH_FLOATING_TYPES(input.scalar_type(), "rms_norm_kernel", [&] {
     vllm::rms_norm_kernel<scalar_t><<<grid, block, 0, stream>>>(
-        out.data_ptr<scalar_t>(), input.data_ptr<scalar_t>(),
+        out.data_ptr<scalar_t>(), input.data_ptr<scalar_t>(), input_stride,
         weight.data_ptr<scalar_t>(), epsilon, num_tokens, hidden_size);
   });
 }
 
-#define LAUNCH_FUSED_ADD_RMS_NORM(width)                                       \
-  VLLM_DISPATCH_FLOATING_TYPES(                                                \
-      input.scalar_type(), "fused_add_rms_norm_kernel", [&] {                  \
-        vllm::fused_add_rms_norm_kernel<scalar_t, width>                       \
-            <<<grid, block, 0, stream>>>(input.data_ptr<scalar_t>(),           \
-                                         residual.data_ptr<scalar_t>(),        \
-                                         weight.data_ptr<scalar_t>(), epsilon, \
-                                         num_tokens, hidden_size);             \
+#define LAUNCH_FUSED_ADD_RMS_NORM(width)                                    \
+  VLLM_DISPATCH_FLOATING_TYPES(                                             \
+      input.scalar_type(), "fused_add_rms_norm_kernel", [&] {               \
+        vllm::fused_add_rms_norm_kernel<scalar_t, width>                    \
+            <<<grid, block, 0, stream>>>(                                   \
+                input.data_ptr<scalar_t>(), input_stride,                   \
+                residual.data_ptr<scalar_t>(), weight.data_ptr<scalar_t>(), \
+                epsilon, num_tokens, hidden_size);                          \
       });
 
 void fused_add_rms_norm(torch::Tensor& input,     // [..., hidden_size]
                         torch::Tensor& residual,  // [..., hidden_size]
                         torch::Tensor& weight,    // [hidden_size]
                         double epsilon) {
+  TORCH_CHECK(residual.is_contiguous());
+  TORCH_CHECK(weight.is_contiguous());
   int hidden_size = input.size(-1);
+  int64_t input_stride = input.stride(-2);
   int num_tokens = input.numel() / hidden_size;
 
   dim3 grid(num_tokens);
@@ -194,9 +204,16 @@ void fused_add_rms_norm(torch::Tensor& input,     // [..., hidden_size]
   auto inp_ptr = reinterpret_cast<std::uintptr_t>(input.data_ptr());
   auto res_ptr = reinterpret_cast<std::uintptr_t>(residual.data_ptr());
   auto wt_ptr = reinterpret_cast<std::uintptr_t>(weight.data_ptr());
-  bool ptrs_are_aligned =
-      inp_ptr % 16 == 0 && res_ptr % 16 == 0 && wt_ptr % 16 == 0;
-  if (ptrs_are_aligned && hidden_size % 8 == 0) {
+  constexpr int vector_width = 8;
+  constexpr int req_alignment_bytes =
+      vector_width * 2;  // vector_width * sizeof(bfloat16 or float16) (float32
+                         // falls back to non-vectorized version anyway)
+  bool ptrs_are_aligned = inp_ptr % req_alignment_bytes == 0 &&
+                          res_ptr % req_alignment_bytes == 0 &&
+                          wt_ptr % req_alignment_bytes == 0;
+  bool offsets_are_multiple_of_vector_width =
+      hidden_size % vector_width == 0 && input_stride % vector_width == 0;
+  if (ptrs_are_aligned && offsets_are_multiple_of_vector_width) {
     LAUNCH_FUSED_ADD_RMS_NORM(8);
   } else {
     LAUNCH_FUSED_ADD_RMS_NORM(0);

csrc/layernorm_quant_kernels.cu

@@ -23,8 +23,9 @@ namespace vllm {
 // TODO(woosuk): Further optimize this kernel.
 template <typename scalar_t, typename fp8_type>
 __global__ void rms_norm_static_fp8_quant_kernel(
-    fp8_type* __restrict__ out,           // [..., hidden_size]
-    const scalar_t* __restrict__ input,   // [..., hidden_size]
+    fp8_type* __restrict__ out,          // [..., hidden_size]
+    const scalar_t* __restrict__ input,  // [..., hidden_size]
+    const int input_stride,
     const scalar_t* __restrict__ weight,  // [hidden_size]
     const float* __restrict__ scale,      // [1]
     const float epsilon, const int num_tokens, const int hidden_size) {
@@ -32,7 +33,7 @@ __global__ void rms_norm_static_fp8_quant_kernel(
   float variance = 0.0f;
 
   for (int idx = threadIdx.x; idx < hidden_size; idx += blockDim.x) {
-    const float x = (float)input[blockIdx.x * hidden_size + idx];
+    const float x = (float)input[blockIdx.x * input_stride + idx];
     variance += x * x;
   }
 
@@ -49,7 +50,7 @@ __global__ void rms_norm_static_fp8_quant_kernel(
   float const scale_inv = 1.0f / *scale;
 
   for (int idx = threadIdx.x; idx < hidden_size; idx += blockDim.x) {
-    float x = (float)input[blockIdx.x * hidden_size + idx];
+    float x = (float)input[blockIdx.x * input_stride + idx];
     float const out_norm = ((scalar_t)(x * s_variance)) * weight[idx];
     out[blockIdx.x * hidden_size + idx] =
         scaled_fp8_conversion<true, fp8_type>(out_norm, scale_inv);
@@ -63,8 +64,9 @@ __global__ void rms_norm_static_fp8_quant_kernel(
 template <typename scalar_t, int width, typename fp8_type>
 __global__ std::enable_if_t<(width > 0) && _typeConvert<scalar_t>::exists>
 fused_add_rms_norm_static_fp8_quant_kernel(
-    fp8_type* __restrict__ out,           // [..., hidden_size]
-    scalar_t* __restrict__ input,         // [..., hidden_size]
+    fp8_type* __restrict__ out,    // [..., hidden_size]
+    scalar_t* __restrict__ input,  // [..., hidden_size]
+    const int input_stride,
     scalar_t* __restrict__ residual,      // [..., hidden_size]
     const scalar_t* __restrict__ weight,  // [hidden_size]
     const float* __restrict__ scale,      // [1]
@@ -74,6 +76,7 @@ fused_add_rms_norm_static_fp8_quant_kernel(
   static_assert(sizeof(_f16Vec<scalar_t, width>) == sizeof(scalar_t) * width);
 
   const int vec_hidden_size = hidden_size / width;
+  const int vec_input_stride = input_stride / width;
   __shared__ float s_variance;
   float variance = 0.0f;
   /* These and the argument pointers are all declared `restrict` as they are
@@ -87,8 +90,9 @@ fused_add_rms_norm_static_fp8_quant_kernel(
       reinterpret_cast<const _f16Vec<scalar_t, width>*>(weight);
 
   for (int idx = threadIdx.x; idx < vec_hidden_size; idx += blockDim.x) {
+    int stride_id = blockIdx.x * vec_input_stride + idx;
     int id = blockIdx.x * vec_hidden_size + idx;
-    _f16Vec<scalar_t, width> temp = input_v[id];
+    _f16Vec<scalar_t, width> temp = input_v[stride_id];
     temp += residual_v[id];
     variance += temp.sum_squares();
     residual_v[id] = temp;
@@ -125,8 +129,9 @@ fused_add_rms_norm_static_fp8_quant_kernel(
 template <typename scalar_t, int width, typename fp8_type>
 __global__ std::enable_if_t<(width == 0) || !_typeConvert<scalar_t>::exists>
 fused_add_rms_norm_static_fp8_quant_kernel(
-    fp8_type* __restrict__ out,           // [..., hidden_size]
-    scalar_t* __restrict__ input,         // [..., hidden_size]
+    fp8_type* __restrict__ out,    // [..., hidden_size]
+    scalar_t* __restrict__ input,  // [..., hidden_size]
+    const int input_stride,
     scalar_t* __restrict__ residual,      // [..., hidden_size]
     const scalar_t* __restrict__ weight,  // [hidden_size]
     const float* __restrict__ scale,      // [1]
@@ -135,7 +140,7 @@ fused_add_rms_norm_static_fp8_quant_kernel(
   float variance = 0.0f;
 
   for (int idx = threadIdx.x; idx < hidden_size; idx += blockDim.x) {
-    scalar_t z = input[blockIdx.x * hidden_size + idx];
+    scalar_t z = input[blockIdx.x * input_stride + idx];
     z += residual[blockIdx.x * hidden_size + idx];
     float x = (float)z;
     variance += x * x;
@@ -169,7 +174,9 @@ void rms_norm_static_fp8_quant(torch::Tensor& out,     // [..., hidden_size]
                                torch::Tensor& weight,  // [hidden_size]
                                torch::Tensor& scale,   // [1]
                                double epsilon) {
+  TORCH_CHECK(out.is_contiguous());
   int hidden_size = input.size(-1);
+  int input_stride = input.stride(-2);
   int num_tokens = input.numel() / hidden_size;
 
   dim3 grid(num_tokens);
@@ -183,8 +190,9 @@ void rms_norm_static_fp8_quant(torch::Tensor& out,     // [..., hidden_size]
               vllm::rms_norm_static_fp8_quant_kernel<scalar_t, fp8_t>
                   <<<grid, block, 0, stream>>>(
                       out.data_ptr<fp8_t>(), input.data_ptr<scalar_t>(),
-                      weight.data_ptr<scalar_t>(), scale.data_ptr<float>(),
-                      epsilon, num_tokens, hidden_size);
+                      input_stride, weight.data_ptr<scalar_t>(),
+                      scale.data_ptr<float>(), epsilon, num_tokens,
+                      hidden_size);
             });
       });
 }
@@ -198,7 +206,7 @@ void rms_norm_static_fp8_quant(torch::Tensor& out,     // [..., hidden_size]
                                                                width, fp8_t> \
                   <<<grid, block, 0, stream>>>(                              \
                       out.data_ptr<fp8_t>(), input.data_ptr<scalar_t>(),     \
-                      residual.data_ptr<scalar_t>(),                         \
+                      input_stride, residual.data_ptr<scalar_t>(),           \
                       weight.data_ptr<scalar_t>(), scale.data_ptr<float>(),  \
                       epsilon, num_tokens, hidden_size);                     \
             });                                                              \
@@ -210,7 +218,10 @@ void fused_add_rms_norm_static_fp8_quant(
     torch::Tensor& weight,    // [hidden_size]
     torch::Tensor& scale,     // [1]
     double epsilon) {
+  TORCH_CHECK(out.is_contiguous());
+  TORCH_CHECK(residual.is_contiguous());
   int hidden_size = input.size(-1);
+  int input_stride = input.stride(-2);
   int num_tokens = input.numel() / hidden_size;
 
   dim3 grid(num_tokens);
@@ -234,7 +245,7 @@ void fused_add_rms_norm_static_fp8_quant(
   auto wt_ptr = reinterpret_cast<std::uintptr_t>(weight.data_ptr());
   bool ptrs_are_aligned =
       inp_ptr % 16 == 0 && res_ptr % 16 == 0 && wt_ptr % 16 == 0;
-  if (ptrs_are_aligned && hidden_size % 8 == 0) {
+  if (ptrs_are_aligned && hidden_size % 8 == 0 && input_stride % 8 == 0) {
     LAUNCH_FUSED_ADD_RMS_NORM(8);
   } else {
     LAUNCH_FUSED_ADD_RMS_NORM(0);

csrc/moe/moe_align_sum_kernels.cu

@@ -1,6 +1,7 @@
 #include <torch/all.h>
 #include <ATen/cuda/CUDAContext.h>
 #include <c10/cuda/CUDAGuard.h>
+#include <cub/cub.cuh>
 
 #include <ATen/ATen.h>
 #include <ATen/cuda/Atomic.cuh>
@@ -19,9 +20,14 @@ __global__ void moe_align_block_size_kernel(
     int32_t* __restrict__ sorted_token_ids, int32_t* __restrict__ expert_ids,
     int32_t* __restrict__ total_tokens_post_pad, int32_t num_experts,
     int32_t padded_num_experts, int32_t experts_per_warp, int32_t block_size,
-    size_t numel, int32_t* __restrict__ cumsum) {
+    size_t numel, int32_t* __restrict__ cumsum, int32_t max_num_tokens_padded) {
   extern __shared__ int32_t shared_counts[];
 
+  // Initialize sorted_token_ids with numel
+  for (size_t it = threadIdx.x; it < max_num_tokens_padded; it += blockDim.x) {
+    sorted_token_ids[it] = numel;
+  }
+
   const int warp_id = threadIdx.x / WARP_SIZE;
   const int my_expert_start = warp_id * experts_per_warp;
 
@@ -45,18 +51,27 @@ __global__ void moe_align_block_size_kernel(
 
   __syncthreads();
 
-  if (threadIdx.x == 0) {
-    cumsum[0] = 0;
-    for (int i = 1; i <= num_experts; ++i) {
-      int expert_count = 0;
-      int warp_idx = (i - 1) / experts_per_warp;
-      int expert_offset = (i - 1) % experts_per_warp;
-      expert_count = shared_counts[warp_idx * experts_per_warp + expert_offset];
+  // Compute prefix sum over token counts per expert
+  using BlockScan = cub::BlockScan<int32_t, 1024>;
+  __shared__ typename BlockScan::TempStorage temp_storage;
 
-      cumsum[i] =
-          cumsum[i - 1] + CEILDIV(expert_count, block_size) * block_size;
-    }
-    *total_tokens_post_pad = cumsum[num_experts];
+  int expert_count = 0;
+  int expert_id = threadIdx.x;
+  if (expert_id < num_experts) {
+    int warp_idx = expert_id / experts_per_warp;
+    int expert_offset = expert_id % experts_per_warp;
+    expert_count = shared_counts[warp_idx * experts_per_warp + expert_offset];
+    expert_count = CEILDIV(expert_count, block_size) * block_size;
+  }
+
+  int cumsum_val;
+  BlockScan(temp_storage).ExclusiveSum(expert_count, cumsum_val);
+  if (expert_id <= num_experts) {
+    cumsum[expert_id] = cumsum_val;
+  }
+
+  if (expert_id == num_experts) {
+    *total_tokens_post_pad = cumsum_val;
   }
 
   __syncthreads();
@@ -67,6 +82,13 @@ __global__ void moe_align_block_size_kernel(
       expert_ids[i / block_size] = threadIdx.x;
     }
   }
+
+  // Fill remaining expert_ids with 0
+  const size_t fill_start_idx = cumsum[num_experts] / block_size + threadIdx.x;
+  const size_t expert_ids_size = CEILDIV(max_num_tokens_padded, block_size);
+  for (size_t i = fill_start_idx; i < expert_ids_size; i += blockDim.x) {
+    expert_ids[i] = 0;
+  }
 }
 
 template <typename scalar_t>
@@ -105,7 +127,12 @@ __global__ void moe_align_block_size_small_batch_expert_kernel(
     const scalar_t* __restrict__ topk_ids,
     int32_t* __restrict__ sorted_token_ids, int32_t* __restrict__ expert_ids,
     int32_t* __restrict__ total_tokens_post_pad, int32_t num_experts,
-    int32_t block_size, size_t numel) {
+    int32_t block_size, size_t numel, int32_t max_num_tokens_padded) {
+  // Initialize sorted_token_ids with numel
+  for (size_t it = threadIdx.x; it < max_num_tokens_padded; it += blockDim.x) {
+    sorted_token_ids[it] = numel;
+  }
+
   const size_t tid = threadIdx.x;
   const size_t stride = blockDim.x;
 
@@ -153,6 +180,13 @@ __global__ void moe_align_block_size_small_batch_expert_kernel(
     }
   }
 
+  // Fill remaining expert_ids with 0
+  const size_t fill_start_idx = cumsum[num_experts] / block_size + threadIdx.x;
+  const size_t expert_ids_size = CEILDIV(max_num_tokens_padded, block_size);
+  for (size_t i = fill_start_idx; i < expert_ids_size; i += blockDim.x) {
+    expert_ids[i] = 0;
+  }
+
   for (size_t i = tid; i < numel; i += stride) {
     int32_t expert_id = topk_ids[i];
     int32_t rank_post_pad =
@@ -179,13 +213,17 @@ void moe_align_block_size(torch::Tensor topk_ids, int64_t num_experts,
   int threads = 1024;
   threads = ((threads + WARP_SIZE - 1) / WARP_SIZE) * WARP_SIZE;
 
+  // BlockScan uses 1024 threads and assigns one thread per expert.
+  TORCH_CHECK(padded_num_experts < 1024,
+              "padded_num_experts must be less than 1024");
+
   VLLM_DISPATCH_INTEGRAL_AND_UNSIGNED_TYPES(
       topk_ids.scalar_type(), "moe_align_block_size_kernel", [&] {
         // calc needed amount of shared mem for `cumsum` tensors
         auto options_int =
             torch::TensorOptions().dtype(torch::kInt).device(topk_ids.device());
         torch::Tensor cumsum_buffer =
-            torch::zeros({num_experts + 1}, options_int);
+            torch::empty({num_experts + 1}, options_int);
         bool small_batch_expert_mode =
             (topk_ids.numel() < 1024) && (num_experts <= 64);
 
@@ -203,7 +241,7 @@ void moe_align_block_size(torch::Tensor topk_ids, int64_t num_experts,
               sorted_token_ids.data_ptr<int32_t>(),
               experts_ids.data_ptr<int32_t>(),
               num_tokens_post_pad.data_ptr<int32_t>(), num_experts, block_size,
-              topk_ids.numel());
+              topk_ids.numel(), sorted_token_ids.size(0));
         } else {
           auto align_kernel = vllm::moe::moe_align_block_size_kernel<scalar_t>;
 
@@ -217,7 +255,8 @@ void moe_align_block_size(torch::Tensor topk_ids, int64_t num_experts,
               experts_ids.data_ptr<int32_t>(),
               num_tokens_post_pad.data_ptr<int32_t>(), num_experts,
               padded_num_experts, experts_per_warp, block_size,
-              topk_ids.numel(), cumsum_buffer.data_ptr<int32_t>());
+              topk_ids.numel(), cumsum_buffer.data_ptr<int32_t>(),
+              sorted_token_ids.size(0));
 
           const int block_threads = std::min(256, (int)threads);
           const int num_blocks =

csrc/moe/moe_permute_unpermute_op.cu

@@ -10,32 +10,28 @@
 
 void moe_permute(
     const torch::Tensor& input,                      // [n_token, hidden]
-    const torch::Tensor& topk_weights,               //[n_token, topk]
-    torch::Tensor& topk_ids,                         // [n_token, topk]
+    const torch::Tensor& topk_ids,                   // [n_token, topk]
     const torch::Tensor& token_expert_indices,       // [n_token, topk]
     const std::optional<torch::Tensor>& expert_map,  // [n_expert]
     int64_t n_expert, int64_t n_local_expert, int64_t topk,
     const std::optional<int64_t>& align_block_size,
-    torch::Tensor&
-        permuted_input,  // [topk * n_token/align_block_size_m, hidden]
+    torch::Tensor& permuted_input,             // [permuted_size, hidden]
     torch::Tensor& expert_first_token_offset,  // [n_local_expert + 1]
-    torch::Tensor& src_row_id2dst_row_id_map,  // [n_token, topk]
+    torch::Tensor& inv_permuted_idx,           // [n_token, topk]
+    torch::Tensor& permuted_idx,               // [permute_size]
     torch::Tensor& m_indices) {                // [align_expand_m]
-  TORCH_CHECK(topk_weights.scalar_type() == at::ScalarType::Float,
-              "topk_weights must be float32");
   TORCH_CHECK(expert_first_token_offset.scalar_type() == at::ScalarType::Long,
               "expert_first_token_offset must be int64");
   TORCH_CHECK(topk_ids.scalar_type() == at::ScalarType::Int,
               "topk_ids must be int32");
   TORCH_CHECK(token_expert_indices.scalar_type() == at::ScalarType::Int,
               "token_expert_indices must be int32");
-  TORCH_CHECK(src_row_id2dst_row_id_map.scalar_type() == at::ScalarType::Int,
-              "src_row_id2dst_row_id_map must be int32");
+  TORCH_CHECK(inv_permuted_idx.scalar_type() == at::ScalarType::Int,
+              "inv_permuted_idx must be int32");
   TORCH_CHECK(expert_first_token_offset.size(0) == n_local_expert + 1,
               "expert_first_token_offset shape != n_local_expert+1")
-  TORCH_CHECK(
-      src_row_id2dst_row_id_map.sizes() == token_expert_indices.sizes(),
-      "token_expert_indices shape must be same as src_row_id2dst_row_id_map");
+  TORCH_CHECK(inv_permuted_idx.sizes() == token_expert_indices.sizes(),
+              "token_expert_indices shape must be same as inv_permuted_idx");
   auto n_token = input.sizes()[0];
   auto n_hidden = input.sizes()[1];
   auto align_block_size_value =
@@ -46,8 +42,9 @@ void moe_permute(
   auto sort_workspace = torch::empty(
       {sorter_size},
       torch::dtype(torch::kInt8).device(torch::kCUDA).requires_grad(false));
+  auto copy_topk_ids = topk_ids.clone();  // copy topk_ids for preprocess
   auto permuted_experts_id = torch::empty_like(topk_ids);
-  auto dst_row_id2src_row_id_map = torch::empty_like(src_row_id2dst_row_id_map);
+  auto sorted_row_idx = torch::empty_like(inv_permuted_idx);
   auto align_expert_first_token_offset =
       torch::zeros_like(expert_first_token_offset);
 
@@ -67,24 +64,22 @@ void moe_permute(
     const int* expert_map_ptr = get_ptr<int>(expert_map.value());
     valid_num_ptr =
         get_ptr<int64_t>(expert_first_token_offset) + n_local_expert;
-    preprocessTopkIdLauncher(get_ptr<int>(topk_ids), n_token * topk,
+    preprocessTopkIdLauncher(get_ptr<int>(copy_topk_ids), n_token * topk,
                              expert_map_ptr, n_expert, stream);
   }
   // expert sort topk expert id and scan expert id get expert_first_token_offset
-  sortAndScanExpert(get_ptr<int>(topk_ids), get_ptr<int>(token_expert_indices),
-                    get_ptr<int>(permuted_experts_id),
-                    get_ptr<int>(dst_row_id2src_row_id_map),
-                    get_ptr<int64_t>(expert_first_token_offset), n_token,
-                    n_expert, n_local_expert, topk, sorter,
-                    get_ptr<int>(sort_workspace), stream);
+  sortAndScanExpert(
+      get_ptr<int>(copy_topk_ids), get_ptr<int>(token_expert_indices),
+      get_ptr<int>(permuted_experts_id), get_ptr<int>(sorted_row_idx),
+      get_ptr<int64_t>(expert_first_token_offset), n_token, n_expert,
+      n_local_expert, topk, sorter, get_ptr<int>(sort_workspace), stream);
 
   // dispatch expandInputRowsKernelLauncher
   MOE_DISPATCH(input.scalar_type(), [&] {
     expandInputRowsKernelLauncher<scalar_t>(
         get_ptr<scalar_t>(input), get_ptr<scalar_t>(permuted_input),
-        get_ptr<float>(topk_weights), get_ptr<int>(permuted_experts_id),
-        get_ptr<int>(dst_row_id2src_row_id_map),
-        get_ptr<int>(src_row_id2dst_row_id_map),
+        get_ptr<int>(permuted_experts_id), get_ptr<int>(sorted_row_idx),
+        get_ptr<int>(inv_permuted_idx), get_ptr<int>(permuted_idx),
         get_ptr<int64_t>(expert_first_token_offset), n_token, valid_num_ptr,
         n_hidden, topk, n_local_expert, align_block_size_value, stream);
   });
@@ -101,32 +96,34 @@ void moe_permute(
 }
 
 void moe_unpermute(
-    const torch::Tensor& permuted_hidden_states,     // [n_token * topk, hidden]
-    const torch::Tensor& topk_weights,               //[n_token, topk]
-    const torch::Tensor& topk_ids,                   // [n_token, topk]
-    const torch::Tensor& src_row_id2dst_row_id_map,  // [n_token, topk]
-    const torch::Tensor& expert_first_token_offset,  // [n_local_expert+1]
-    int64_t n_expert, int64_t n_local_expert, int64_t topk,
+    const torch::Tensor& permuted_hidden_states,  // [n_token * topk, hidden]
+    const torch::Tensor& topk_weights,            // [n_token, topk]
+    const torch::Tensor& inv_permuted_idx,        // [n_token, topk]
+    const std::optional<torch::Tensor>&
+        expert_first_token_offset,  // [n_local_expert+1]
+    int64_t topk,
     torch::Tensor& hidden_states  // [n_token, hidden]
 ) {
-  TORCH_CHECK(src_row_id2dst_row_id_map.sizes() == topk_ids.sizes(),
-              "topk_ids shape must be same as src_row_id2dst_row_id_map");
-  TORCH_CHECK(topk_ids.scalar_type() == at::ScalarType::Int,
-              "topk_ids must be int32");
   TORCH_CHECK(
       permuted_hidden_states.scalar_type() == hidden_states.scalar_type(),
-      "topk_ids dtype must be same as src_row_id2dst_row_id_map");
+      "permuted_hidden_states dtype must be same as hidden_states");
   auto n_token = hidden_states.size(0);
   auto n_hidden = hidden_states.size(1);
   auto stream = at::cuda::getCurrentCUDAStream().stream();
-  const int64_t* valid_ptr =
-      get_ptr<int64_t>(expert_first_token_offset) + n_local_expert;
+
+  int64_t const* valid_ptr = nullptr;
+  if (expert_first_token_offset.has_value()) {
+    int n_local_expert = expert_first_token_offset.value().size(0) - 1;
+    valid_ptr =
+        get_ptr<int64_t>(expert_first_token_offset.value()) + n_local_expert;
+  }
+
   MOE_DISPATCH(hidden_states.scalar_type(), [&] {
     finalizeMoeRoutingKernelLauncher<scalar_t, scalar_t>(
         get_ptr<scalar_t>(permuted_hidden_states),
         get_ptr<scalar_t>(hidden_states), get_ptr<float>(topk_weights),
-        get_ptr<int>(src_row_id2dst_row_id_map), get_ptr<int>(topk_ids),
-        n_token, n_hidden, topk, valid_ptr, stream);
+        get_ptr<int>(inv_permuted_idx), n_token, n_hidden, topk, valid_ptr,
+        stream);
   });
 }
 

csrc/moe/permute_unpermute_kernels/moe_permute_unpermute_kernel.cu

@@ -177,7 +177,7 @@ __global__ void getMIndicesKernel(int64_t* expert_first_token_offset,
   int tidx = threadIdx.x;
   extern __shared__ int64_t smem_expert_first_token_offset[];
   for (int i = tidx; i <= num_local_expert; i += blockDim.x) {
-    smem_expert_first_token_offset[tidx] = __ldg(expert_first_token_offset + i);
+    smem_expert_first_token_offset[i] = __ldg(expert_first_token_offset + i);
   }
   __syncthreads();
   auto last_token_offset = smem_expert_first_token_offset[eidx + 1];

csrc/moe/permute_unpermute_kernels/moe_permute_unpermute_kernel.h

@@ -57,31 +57,19 @@ void sortAndScanExpert(int* expert_for_source_row, const int* source_rows,
 
 template <typename T>
 void expandInputRowsKernelLauncher(
-    T const* unpermuted_input, T* permuted_output,
-    const float* unpermuted_scales, int* sorted_experts,
+    T const* unpermuted_input, T* permuted_output, int* sorted_experts,
     int const* expanded_dest_row_to_expanded_source_row,
-    int* expanded_source_row_to_expanded_dest_row,
+    int* expanded_source_row_to_expanded_dest_row, int* permuted_idx,
     int64_t* expert_first_token_offset, int64_t const num_rows,
     int64_t const* num_valid_tokens_ptr, int64_t const cols, int const k,
     int num_local_experts, const int& align_block_size, cudaStream_t stream);
 
-// Final kernel to unpermute and scale
-// This kernel unpermutes the original data, does the k-way reduction and
-// performs the final skip connection.
-template <typename T, typename OutputType, bool CHECK_SKIPPED>
-__global__ void finalizeMoeRoutingKernel(
-    T const* expanded_permuted_rows, OutputType* reduced_unpermuted_output,
-    float const* scales, int const* expanded_source_row_to_expanded_dest_row,
-    int const* expert_for_source_row, int64_t const orig_cols, int64_t const k,
-    int64_t const* num_valid_ptr);
-
 template <class T, class OutputType>
 void finalizeMoeRoutingKernelLauncher(
     T const* expanded_permuted_rows, OutputType* reduced_unpermuted_output,
     float const* scales, int const* expanded_source_row_to_expanded_dest_row,
-    int const* expert_for_source_row, int64_t const num_rows,
-    int64_t const cols, int64_t const k, int64_t const* num_valid_ptr,
-    cudaStream_t stream);
+    int64_t const num_rows, int64_t const cols, int64_t const k,
+    int64_t const* num_valid_ptr, cudaStream_t stream);
 
 void preprocessTopkIdLauncher(int* topk_id_ptr, int size,
                               const int* expert_map_ptr, int num_experts,

csrc/moe/permute_unpermute_kernels/moe_permute_unpermute_kernel.inl

@@ -2,10 +2,9 @@
 
 template <typename T, bool CHECK_SKIPPED, bool ALIGN_BLOCK_SIZE>
 __global__ void expandInputRowsKernel(
-    T const* unpermuted_input, T* permuted_output,
-    const float* unpermuted_scales, int* sorted_experts,
+    T const* unpermuted_input, T* permuted_output, int* sorted_experts,
     int const* expanded_dest_row_to_expanded_source_row,
-    int* expanded_source_row_to_expanded_dest_row,
+    int* expanded_source_row_to_expanded_dest_row, int* permuted_idx,
     int64_t* expert_first_token_offset, int64_t const num_rows,
     int64_t const* num_dest_rows, int64_t const cols, int64_t k,
     int num_local_experts, int align_block_size) {
@@ -54,6 +53,10 @@ __global__ void expandInputRowsKernel(
     assert(expanded_dest_row <= INT32_MAX);
     expanded_source_row_to_expanded_dest_row[expanded_source_row] =
         static_cast<int>(expanded_dest_row);
+    // skip non local expert token
+    if (!CHECK_SKIPPED || blockIdx.x < *num_dest_rows) {
+      permuted_idx[expanded_dest_row] = expanded_source_row;
+    }
   }
 
   if (!CHECK_SKIPPED || blockIdx.x < *num_dest_rows) {
@@ -62,7 +65,7 @@ __global__ void expandInputRowsKernel(
     using DataElem = cutlass::Array<T, ELEM_PER_THREAD>;
 
     // Duplicate and permute rows
-    int64_t const source_row = expanded_source_row % num_rows;
+    int64_t const source_row = expanded_source_row / k;
 
     auto const* source_row_ptr =
         reinterpret_cast<DataElem const*>(unpermuted_input + source_row * cols);
@@ -82,10 +85,9 @@ __global__ void expandInputRowsKernel(
 
 template <typename T>
 void expandInputRowsKernelLauncher(
-    T const* unpermuted_input, T* permuted_output,
-    const float* unpermuted_scales, int* sorted_experts,
+    T const* unpermuted_input, T* permuted_output, int* sorted_experts,
     int const* expanded_dest_row_to_expanded_source_row,
-    int* expanded_source_row_to_expanded_dest_row,
+    int* expanded_source_row_to_expanded_dest_row, int* permuted_idx,
     int64_t* expert_first_token_offset, int64_t const num_rows,
     int64_t const* num_valid_tokens_ptr, int64_t const cols, int const k,
     int num_local_experts, const int& align_block_size, cudaStream_t stream) {
@@ -105,11 +107,11 @@ void expandInputRowsKernelLauncher(
   int64_t smem_size = sizeof(int64_t) * (num_local_experts + 1);
 
   func<<<blocks, threads, smem_size, stream>>>(
-      unpermuted_input, permuted_output, unpermuted_scales, sorted_experts,
+      unpermuted_input, permuted_output, sorted_experts,
       expanded_dest_row_to_expanded_source_row,
-      expanded_source_row_to_expanded_dest_row, expert_first_token_offset,
-      num_rows, num_valid_tokens_ptr, cols, k, num_local_experts,
-      align_block_size);
+      expanded_source_row_to_expanded_dest_row, permuted_idx,
+      expert_first_token_offset, num_rows, num_valid_tokens_ptr, cols, k,
+      num_local_experts, align_block_size);
 }
 
 template <class T, class U>
@@ -128,11 +130,9 @@ template <typename T, typename OutputType, bool CHECK_SKIPPED>
 __global__ void finalizeMoeRoutingKernel(
     T const* expanded_permuted_rows, OutputType* reduced_unpermuted_output,
     float const* scales, int const* expanded_source_row_to_expanded_dest_row,
-    int const* expert_for_source_row, int64_t const orig_cols, int64_t const k,
-    int64_t const* num_valid_ptr) {
+    int64_t const orig_cols, int64_t const k, int64_t const* num_valid_ptr) {
   assert(orig_cols % 4 == 0);
   int64_t const original_row = blockIdx.x;
-  int64_t const num_rows = gridDim.x;
   auto const offset = original_row * orig_cols;
   OutputType* reduced_row_ptr = reduced_unpermuted_output + offset;
   int64_t const num_valid = *num_valid_ptr;
@@ -159,14 +159,13 @@ __global__ void finalizeMoeRoutingKernel(
     ComputeElem thread_output;
     thread_output.fill(0);
     for (int k_idx = 0; k_idx < k; ++k_idx) {
-      int64_t const expanded_original_row = original_row + k_idx * num_rows;
+      int64_t const expanded_original_row = original_row * k + k_idx;
       int64_t const expanded_permuted_row =
           expanded_source_row_to_expanded_dest_row[expanded_original_row];
 
       int64_t const k_offset = original_row * k + k_idx;
       float const row_scale = scales[k_offset];
 
-      // Check after row_rescale has accumulated
       if (CHECK_SKIPPED && expanded_permuted_row >= num_valid) {
         continue;
       }
@@ -189,9 +188,8 @@ template <class T, class OutputType>
 void finalizeMoeRoutingKernelLauncher(
     T const* expanded_permuted_rows, OutputType* reduced_unpermuted_output,
     float const* scales, int const* expanded_source_row_to_expanded_dest_row,
-    int const* expert_for_source_row, int64_t const num_rows,
-    int64_t const cols, int64_t const k, int64_t const* num_valid_ptr,
-    cudaStream_t stream) {
+    int64_t const num_rows, int64_t const cols, int64_t const k,
+    int64_t const* num_valid_ptr, cudaStream_t stream) {
   int64_t const blocks = num_rows;
   int64_t const threads = 256;
   bool const check_finished = num_valid_ptr != nullptr;
@@ -201,6 +199,5 @@ void finalizeMoeRoutingKernelLauncher(
   auto* const kernel = func_map[check_finished];
   kernel<<<blocks, threads, 0, stream>>>(
       expanded_permuted_rows, reduced_unpermuted_output, scales,
-      expanded_source_row_to_expanded_dest_row, expert_for_source_row, cols, k,
-      num_valid_ptr);
+      expanded_source_row_to_expanded_dest_row, cols, k, num_valid_ptr);
 }

csrc/moe/topk_softmax_kernels.cu

@@ -190,8 +190,8 @@ __launch_bounds__(TPB) __global__ void moeTopK(
   2) This implementation assumes k is small, but will work for any k.
 */
 
-template <int VPT, int NUM_EXPERTS, int WARPS_PER_CTA, int BYTES_PER_LDG, typename IndType>
-__launch_bounds__(WARPS_PER_CTA* WARP_SIZE) __global__
+template <int VPT, int NUM_EXPERTS, int WARPS_PER_CTA, int BYTES_PER_LDG, int WARP_SIZE_PARAM, typename IndType>
+__launch_bounds__(WARPS_PER_CTA* WARP_SIZE_PARAM) __global__
     void topkGatingSoftmax(const float* input, const bool* finished, float* output, const int num_rows, IndType* indices,
         int* source_rows, const int k, const int start_expert, const int end_expert)
 {
@@ -209,12 +209,12 @@ __launch_bounds__(WARPS_PER_CTA* WARP_SIZE) __global__
 
     // Restrictions based on previous section.
     static_assert(VPT % ELTS_PER_LDG == 0, "The elements per thread must be a multiple of the elements per ldg");
-    static_assert(WARP_SIZE % THREADS_PER_ROW == 0, "The threads per row must cleanly divide the threads per warp");
+    static_assert(WARP_SIZE_PARAM % THREADS_PER_ROW == 0, "The threads per row must cleanly divide the threads per warp");
     static_assert(THREADS_PER_ROW == (THREADS_PER_ROW & -THREADS_PER_ROW), "THREADS_PER_ROW must be power of 2");
-    static_assert(THREADS_PER_ROW <= WARP_SIZE, "THREADS_PER_ROW can be at most warp size");
+    static_assert(THREADS_PER_ROW <= WARP_SIZE_PARAM, "THREADS_PER_ROW can be at most warp size");
 
     // We have NUM_EXPERTS elements per row. We specialize for small #experts
-    static constexpr int ELTS_PER_WARP = WARP_SIZE * VPT;
+    static constexpr int ELTS_PER_WARP = WARP_SIZE_PARAM * VPT;
     static constexpr int ROWS_PER_WARP = ELTS_PER_WARP / ELTS_PER_ROW;
     static constexpr int ROWS_PER_CTA = WARPS_PER_CTA * ROWS_PER_WARP;
 
@@ -393,41 +393,51 @@ __launch_bounds__(WARPS_PER_CTA* WARP_SIZE) __global__
 namespace detail
 {
 // Constructs some constants needed to partition the work across threads at compile time.
-template <int EXPERTS, int BYTES_PER_LDG>
+template <int EXPERTS, int BYTES_PER_LDG, int WARP_SIZE_PARAM>
 struct TopkConstants
 {
     static constexpr int ELTS_PER_LDG = BYTES_PER_LDG / sizeof(float);
-    static_assert(EXPERTS / (ELTS_PER_LDG * WARP_SIZE) == 0 || EXPERTS % (ELTS_PER_LDG * WARP_SIZE) == 0, "");
-    static constexpr int VECs_PER_THREAD = MAX(1, EXPERTS / (ELTS_PER_LDG * WARP_SIZE));
+    static_assert(EXPERTS / (ELTS_PER_LDG * WARP_SIZE_PARAM) == 0 || EXPERTS % (ELTS_PER_LDG * WARP_SIZE_PARAM) == 0, "");
+    static constexpr int VECs_PER_THREAD = MAX(1, EXPERTS / (ELTS_PER_LDG * WARP_SIZE_PARAM));
     static constexpr int VPT = VECs_PER_THREAD * ELTS_PER_LDG;
     static constexpr int THREADS_PER_ROW = EXPERTS / VPT;
-    static constexpr int ROWS_PER_WARP = WARP_SIZE / THREADS_PER_ROW;
+    static const int ROWS_PER_WARP = WARP_SIZE_PARAM / THREADS_PER_ROW;
 };
 } // namespace detail
 
-template <int EXPERTS, int WARPS_PER_TB, typename IndType>
+template <int EXPERTS, int WARPS_PER_TB, int WARP_SIZE_PARAM, typename IndType>
 void topkGatingSoftmaxLauncherHelper(const float* input, const bool* finished, float* output, IndType* indices,
     int* source_row, const int num_rows, const int k, const int start_expert, const int end_expert, cudaStream_t stream)
 {
     static constexpr std::size_t MAX_BYTES_PER_LDG = 16;
 
     static constexpr int BYTES_PER_LDG = MIN(MAX_BYTES_PER_LDG, sizeof(float) * EXPERTS);
-    using Constants = detail::TopkConstants<EXPERTS, BYTES_PER_LDG>;
+    using Constants = detail::TopkConstants<EXPERTS, BYTES_PER_LDG, WARP_SIZE_PARAM>;
     static constexpr int VPT = Constants::VPT;
     static constexpr int ROWS_PER_WARP = Constants::ROWS_PER_WARP;
     const int num_warps = (num_rows + ROWS_PER_WARP - 1) / ROWS_PER_WARP;
     const int num_blocks = (num_warps + WARPS_PER_TB - 1) / WARPS_PER_TB;
 
-    dim3 block_dim(WARP_SIZE, WARPS_PER_TB);
-    topkGatingSoftmax<VPT, EXPERTS, WARPS_PER_TB, BYTES_PER_LDG><<<num_blocks, block_dim, 0, stream>>>(
+    dim3 block_dim(WARP_SIZE_PARAM, WARPS_PER_TB);
+    topkGatingSoftmax<VPT, EXPERTS, WARPS_PER_TB, BYTES_PER_LDG, WARP_SIZE_PARAM><<<num_blocks, block_dim, 0, stream>>>(
         input, finished, output, num_rows, indices, source_row, k, start_expert, end_expert);
 }
 
-#define LAUNCH_SOFTMAX(NUM_EXPERTS, WARPS_PER_TB)                       \
-    topkGatingSoftmaxLauncherHelper<NUM_EXPERTS, WARPS_PER_TB>(         \
-        gating_output, nullptr, topk_weights, topk_indices,            \
-        token_expert_indices, num_tokens, topk, 0, num_experts,         \
-        stream);
+#define LAUNCH_SOFTMAX(NUM_EXPERTS, WARPS_PER_TB)                                \
+    switch (warpSize) {                                                          \
+        case 32:                                                                 \
+            topkGatingSoftmaxLauncherHelper<NUM_EXPERTS, WARPS_PER_TB, 32>(      \
+                gating_output, nullptr, topk_weights, topk_indices,              \
+                token_expert_indices, num_tokens, topk, 0, num_experts, stream); \
+            break;                                                               \
+        case 64:                                                                 \
+            topkGatingSoftmaxLauncherHelper<NUM_EXPERTS, WARPS_PER_TB, 64>(      \
+                gating_output, nullptr, topk_weights, topk_indices,              \
+                token_expert_indices, num_tokens, topk, 0, num_experts, stream); \
+            break;                                                               \
+        default:                                                                 \
+            TORCH_CHECK(false, "Unsupported warp size: ", warpSize);             \
+    }
 
 template <typename IndType>
 void topkGatingSoftmaxKernelLauncher(
@@ -441,6 +451,7 @@ void topkGatingSoftmaxKernelLauncher(
     const int topk,
     cudaStream_t stream) {
     static constexpr int WARPS_PER_TB = 4;
+    auto warpSize = WARP_SIZE;
     switch (num_experts) {
         case 1:
             LAUNCH_SOFTMAX(1, WARPS_PER_TB);

csrc/moe/torch_bindings.cpp

@@ -56,18 +56,17 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, m) {
       " -> Tensor");
 
   m.def(
-      "moe_permute(Tensor input, Tensor topk_weight, Tensor! topk_ids,"
+      "moe_permute(Tensor input, Tensor topk_ids,"
       "Tensor token_expert_indices, Tensor? expert_map, int n_expert,"
       "int n_local_expert,"
       "int topk, int? align_block_size,Tensor! permuted_input, Tensor! "
-      "expert_first_token_offset, Tensor! src_row_id2dst_row_id_map, Tensor! "
-      "m_indices)->()");
+      "expert_first_token_offset, Tensor! inv_permuted_idx, Tensor! "
+      "permuted_idx, Tensor! m_indices)->()");
 
   m.def(
       "moe_unpermute(Tensor permuted_hidden_states, Tensor topk_weights,"
-      "Tensor topk_ids,Tensor src_row_id2dst_row_id_map, Tensor "
-      "expert_first_token_offset, int n_expert, int n_local_expert,int "
-      "topk, Tensor! hidden_states)->()");
+      "Tensor inv_permuted_idx, Tensor? expert_first_token_offset, "
+      "int topk, Tensor! hidden_states)->()");
 
   m.def("moe_permute_unpermute_supported() -> bool");
   m.impl("moe_permute_unpermute_supported", &moe_permute_unpermute_supported);

csrc/ops.h

@@ -287,6 +287,16 @@ void scaled_fp4_experts_quant(
     torch::Tensor const& input, torch::Tensor const& input_global_scale,
     torch::Tensor const& input_offset_by_experts,
     torch::Tensor const& output_scale_offset_by_experts);
+
+void per_token_group_quant_fp8(const torch::Tensor& input,
+                               torch::Tensor& output_q, torch::Tensor& output_s,
+                               int64_t group_size, double eps, double fp8_min,
+                               double fp8_max, bool scale_ue8m0);
+
+void per_token_group_quant_int8(const torch::Tensor& input,
+                                torch::Tensor& output_q,
+                                torch::Tensor& output_s, int64_t group_size,
+                                double eps, double int8_min, double int8_max);
 #endif
 
 void static_scaled_int8_quant(torch::Tensor& out, torch::Tensor const& input,

csrc/quantization/activation_kernels.cu

@@ -4,7 +4,7 @@
 
 #include <cmath>
 #include "core/math.hpp"
-#include "cuda_compat.h"
+#include "../cuda_compat.h"
 #include "dispatch_utils.h"
 
 #include "quantization/fp8/common.cuh"

csrc/quantization/compressed_tensors/int8_quant_kernels.cu

@@ -1,6 +1,10 @@
 #include <ATen/cuda/CUDAContext.h>
 #include <torch/all.h>
 
+#ifndef USE_ROCM
+  #include "../per_token_group_quant_8bit.h"
+#endif
+
 #include <cmath>
 
 #include "../../dispatch_utils.h"
@@ -336,3 +340,13 @@ void dynamic_scaled_int8_quant(
         }
       });
 }
+
+#ifndef USE_ROCM
+void per_token_group_quant_int8(const torch::Tensor& input,
+                                torch::Tensor& output_q,
+                                torch::Tensor& output_s, int64_t group_size,
+                                double eps, double int8_min, double int8_max) {
+  per_token_group_quant_8bit(input, output_q, output_s, group_size, eps,
+                             int8_min, int8_max);
+}
+#endif

csrc/quantization/cutlass_w8a8/c3x/scaled_mm_sm90_fp8.cu

@@ -1,6 +1,5 @@
 #include "scaled_mm_kernels.hpp"
 #include "scaled_mm_sm90_fp8_dispatch.cuh"
-#include "cutlass_extensions/epilogue/scaled_mm_epilogues_c3x.hpp"
 
 namespace vllm {
 
@@ -13,11 +12,11 @@ void cutlass_scaled_mm_sm90_fp8(torch::Tensor& out, torch::Tensor const& a,
   if (bias) {
     TORCH_CHECK(bias->dtype() == out.dtype(),
                 "currently bias dtype must match output dtype ", out.dtype());
-    return cutlass_scaled_mm_sm90_fp8_epilogue<c3x::ScaledEpilogueBias>(
-        out, a, b, a_scales, b_scales, *bias);
+    return cutlass_scaled_mm_sm90_fp8_epilogue<true>(out, a, b, a_scales,
+                                                     b_scales, *bias);
   } else {
-    return cutlass_scaled_mm_sm90_fp8_epilogue<c3x::ScaledEpilogue>(
-        out, a, b, a_scales, b_scales);
+    return cutlass_scaled_mm_sm90_fp8_epilogue<false>(out, a, b, a_scales,
+                                                      b_scales);
   }
 }
 

csrc/quantization/cutlass_w8a8/c3x/scaled_mm_sm90_fp8_dispatch.cuh

@@ -2,6 +2,7 @@
 
 #include "scaled_mm.cuh"
 #include "cutlass_gemm_caller.cuh"
+#include "cutlass_extensions/epilogue/scaled_mm_epilogues_c3x.hpp"
 
 /**
  * This file defines Gemm kernel configurations for SM90 (fp8) based on the Gemm
@@ -12,8 +13,91 @@ namespace vllm {
 
 using c3x::cutlass_gemm_caller;
 
-template <typename InType, typename OutType,
-          template <typename, typename, typename> typename Epilogue>
+template <typename ElementAB_, typename ElementD_,
+          template <typename, typename, typename> typename Epilogue_,
+          typename TileShape, typename ClusterShape, typename KernelSchedule,
+          typename EpilogueSchedule, bool swap_ab_ = false>
+struct cutlass_3x_gemm_sm90_fp8 {
+  using ElementAB = ElementAB_;
+  using ElementC = ElementD_;
+  using ElementD = ElementD_;
+  using ElementAcc =
+      typename std::conditional<std::is_same_v<ElementAB, int8_t>, int32_t,
+                                float>::type;
+
+  using Epilogue = Epilogue_<ElementAcc, ElementD, TileShape>;
+
+  using EVTCompute = typename Epilogue::EVTCompute;
+
+  static constexpr int AlignmentAB =
+      128 / cutlass::sizeof_bits<ElementAB>::value;
+  static constexpr int AlignmentCD =
+      128 / cutlass::sizeof_bits<ElementD>::value;
+
+  // Compile-time swap_ab flag
+  static constexpr bool swap_ab = swap_ab_;
+
+  // -----------------------------------------------------------
+  // Layout definitions
+  // -----------------------------------------------------------
+  using LayoutA = cutlass::layout::RowMajor;
+  using LayoutA_T = typename cutlass::layout::LayoutTranspose<LayoutA>::type;
+
+  using LayoutB = cutlass::layout::ColumnMajor;
+  using LayoutB_T = typename cutlass::layout::LayoutTranspose<LayoutB>::type;
+
+  using LayoutD = cutlass::layout::RowMajor;
+  using LayoutD_Transpose =
+      typename cutlass::layout::LayoutTranspose<LayoutD>::type;
+
+  using LayoutC = LayoutD;
+  using LayoutC_Transpose = LayoutD_Transpose;
+
+  // -----------------------------------------------------------
+  // Collective epilogue (conditionally swap operands and layouts)
+  // -----------------------------------------------------------
+  using CollectiveEpilogue =
+      typename cutlass::epilogue::collective::CollectiveBuilder<
+          cutlass::arch::Sm90, cutlass::arch::OpClassTensorOp, TileShape,
+          ClusterShape, cutlass::epilogue::collective::EpilogueTileAuto,
+          ElementAcc, float, ElementC,
+          conditional_t<swap_ab, LayoutC_Transpose, LayoutC>, AlignmentCD,
+          ElementD, conditional_t<swap_ab, LayoutD_Transpose, LayoutD>,
+          AlignmentCD, EpilogueSchedule, EVTCompute>::CollectiveOp;
+
+  static constexpr size_t CEStorageSize =
+      sizeof(typename CollectiveEpilogue::SharedStorage);
+
+  using Stages = typename cutlass::gemm::collective::StageCountAutoCarveout<
+      static_cast<int>(CEStorageSize)>;
+
+  // -----------------------------------------------------------
+  // Collective mainloop (conditionally swap operands and layouts)
+  // -----------------------------------------------------------
+  using CollectiveMainloop = conditional_t<
+      swap_ab,
+      typename cutlass::gemm::collective::CollectiveBuilder<
+          cutlass::arch::Sm90, cutlass::arch::OpClassTensorOp, ElementAB,
+          LayoutB_T, AlignmentAB,             // Swapped B (as A)
+          ElementAB, LayoutA_T, AlignmentAB,  // Swapped A (as B)
+          ElementAcc, TileShape, ClusterShape, Stages,
+          KernelSchedule>::CollectiveOp,
+      typename cutlass::gemm::collective::CollectiveBuilder<
+          cutlass::arch::Sm90, cutlass::arch::OpClassTensorOp, ElementAB,
+          LayoutA, AlignmentAB, ElementAB, LayoutB, AlignmentAB, ElementAcc,
+          TileShape, ClusterShape, Stages, KernelSchedule>::CollectiveOp>;
+
+  // -----------------------------------------------------------
+  // Kernel definition
+  // -----------------------------------------------------------
+  using KernelType = enable_sm90_or_later<cutlass::gemm::kernel::GemmUniversal<
+      cute::Shape<int, int, int, int>, CollectiveMainloop, CollectiveEpilogue,
+      cutlass::gemm::PersistentScheduler>>;
+
+  struct GemmKernel : public KernelType {};
+};
+
+template <typename InType, typename OutType, bool EnableBias>
 struct sm90_fp8_config_default {
   // M in (128, inf)
   static_assert(std::is_same<InType, cutlass::float_e4m3_t>());
@@ -22,13 +106,17 @@ struct sm90_fp8_config_default {
   using EpilogueSchedule = typename cutlass::epilogue::TmaWarpSpecialized;
   using TileShape = Shape<_128, _128, _128>;
   using ClusterShape = Shape<_2, _1, _1>;
-  using Cutlass3xGemm =
-      cutlass_3x_gemm<InType, OutType, Epilogue, TileShape, ClusterShape,
-                      KernelSchedule, EpilogueSchedule>;
+
+  using Cutlass3xGemm = conditional_t<
+      EnableBias,
+      cutlass_3x_gemm_sm90_fp8<InType, OutType, c3x::ScaledEpilogueBias,
+                               TileShape, ClusterShape, KernelSchedule,
+                               EpilogueSchedule>,
+      cutlass_3x_gemm_sm90_fp8<InType, OutType, c3x::ScaledEpilogue, TileShape,
+                               ClusterShape, KernelSchedule, EpilogueSchedule>>;
 };
 
-template <typename InType, typename OutType,
-          template <typename, typename, typename> typename Epilogue>
+template <typename InType, typename OutType, bool EnableBias>
 struct sm90_fp8_config_M128 {
   // M in (64, 128]
   static_assert(std::is_same<InType, cutlass::float_e4m3_t>());
@@ -37,33 +125,146 @@ struct sm90_fp8_config_M128 {
   using EpilogueSchedule = typename cutlass::epilogue::TmaWarpSpecialized;
   using TileShape = Shape<_64, _128, _128>;
   using ClusterShape = Shape<_2, _1, _1>;
-  using Cutlass3xGemm =
-      cutlass_3x_gemm<InType, OutType, Epilogue, TileShape, ClusterShape,
-                      KernelSchedule, EpilogueSchedule>;
+  using Cutlass3xGemm = conditional_t<
+      EnableBias,
+      cutlass_3x_gemm_sm90_fp8<InType, OutType, c3x::ScaledEpilogueBias,
+                               TileShape, ClusterShape, KernelSchedule,
+                               EpilogueSchedule>,
+      cutlass_3x_gemm_sm90_fp8<InType, OutType, c3x::ScaledEpilogue, TileShape,
+                               ClusterShape, KernelSchedule, EpilogueSchedule>>;
 };
 
-template <typename InType, typename OutType,
-          template <typename, typename, typename> typename Epilogue>
-struct sm90_fp8_config_M64 {
-  // M in [1, 64]
+template <typename InType, typename OutType, bool EnableBias>
+struct sm90_fp8_config_M64_N1280 {
+  // M in (16, 64], N in [1 1280]
   static_assert(std::is_same<InType, cutlass::float_e4m3_t>());
-  using KernelSchedule =
-      cutlass::gemm::KernelTmaWarpSpecializedPingpongFP8FastAccum;
+  using KernelSchedule = cutlass::gemm::KernelTmaWarpSpecializedFP8FastAccum;
   using EpilogueSchedule = typename cutlass::epilogue::TmaWarpSpecialized;
-  using TileShape = Shape<_64, _64, _128>;
-  using ClusterShape = Shape<_1, _8, _1>;
+  using TileShape = Shape<_64, _16, _256>;
+  using ClusterShape = Shape<_1, _4, _1>;
 
-  using Cutlass3xGemm =
-      cutlass_3x_gemm<InType, OutType, Epilogue, TileShape, ClusterShape,
-                      KernelSchedule, EpilogueSchedule>;
+  // enable swap AB for M < 64
+  using Cutlass3xGemm = conditional_t<
+      EnableBias,
+      cutlass_3x_gemm_sm90_fp8<InType, OutType, c3x::ScaledEpilogueColumnBias,
+                               TileShape, ClusterShape, KernelSchedule,
+                               EpilogueSchedule, true>,
+      cutlass_3x_gemm_sm90_fp8<InType, OutType, c3x::ScaledEpilogue, TileShape,
+                               ClusterShape, KernelSchedule, EpilogueSchedule,
+                               true>>;
 };
 
-template <typename InType, typename OutType,
-          template <typename, typename, typename> typename Epilogue,
+template <typename InType, typename OutType, bool EnableBias>
+struct sm90_fp8_config_M64_N8192 {
+  // M in (16, 64], N > 1280
+  static_assert(std::is_same<InType, cutlass::float_e4m3_t>());
+  using KernelSchedule = cutlass::gemm::KernelTmaWarpSpecializedFP8FastAccum;
+  using EpilogueSchedule = typename cutlass::epilogue::TmaWarpSpecialized;
+  using TileShape = Shape<_64, _64, _256>;
+  using ClusterShape = Shape<_1, _1, _1>;
+
+  // enable swap AB for M < 64
+  using Cutlass3xGemm = conditional_t<
+      EnableBias,
+      cutlass_3x_gemm_sm90_fp8<InType, OutType, c3x::ScaledEpilogueColumnBias,
+                               TileShape, ClusterShape, KernelSchedule,
+                               EpilogueSchedule, true>,
+      cutlass_3x_gemm_sm90_fp8<InType, OutType, c3x::ScaledEpilogue, TileShape,
+                               ClusterShape, KernelSchedule, EpilogueSchedule,
+                               true>>;
+};
+
+template <typename InType, typename OutType, bool EnableBias>
+struct sm90_fp8_config_M16_N1280 {
+  // M in [1, 16], N in [1, 1280]
+  static_assert(std::is_same<InType, cutlass::float_e4m3_t>());
+  using KernelSchedule = cutlass::gemm::KernelTmaWarpSpecializedFP8FastAccum;
+  using EpilogueSchedule = typename cutlass::epilogue::TmaWarpSpecialized;
+  using TileShape = Shape<_64, _16, _256>;
+  using ClusterShape = Shape<_1, _2, _1>;
+
+  // enable swap AB for M < 64
+  using Cutlass3xGemm = conditional_t<
+      EnableBias,
+      cutlass_3x_gemm_sm90_fp8<InType, OutType, c3x::ScaledEpilogueColumnBias,
+                               TileShape, ClusterShape, KernelSchedule,
+                               EpilogueSchedule, true>,
+      cutlass_3x_gemm_sm90_fp8<InType, OutType, c3x::ScaledEpilogue, TileShape,
+                               ClusterShape, KernelSchedule, EpilogueSchedule,
+                               true>>;
+};
+
+template <typename InType, typename OutType, bool EnableBias>
+struct sm90_fp8_config_M16_N8192 {
+  // M in [1, 16], N > 1280
+  static_assert(std::is_same<InType, cutlass::float_e4m3_t>());
+  using KernelSchedule = cutlass::gemm::KernelTmaWarpSpecializedFP8FastAccum;
+  using EpilogueSchedule = typename cutlass::epilogue::TmaWarpSpecialized;
+  using TileShape = Shape<_64, _16, _256>;
+  using ClusterShape = Shape<_1, _1, _1>;
+
+  // enable swap AB for M < 64
+  using Cutlass3xGemm = conditional_t<
+      EnableBias,
+      cutlass_3x_gemm_sm90_fp8<InType, OutType, c3x::ScaledEpilogueColumnBias,
+                               TileShape, ClusterShape, KernelSchedule,
+                               EpilogueSchedule, true>,
+      cutlass_3x_gemm_sm90_fp8<InType, OutType, c3x::ScaledEpilogue, TileShape,
+                               ClusterShape, KernelSchedule, EpilogueSchedule,
+                               true>>;
+};
+
+template <typename Gemm, typename... EpilogueArgs>
+void cutlass_gemm_caller_sm90_fp8(torch::Tensor& out, torch::Tensor const& a,
+                                  torch::Tensor const& b,
+                                  EpilogueArgs&&... epilogue_params) {
+  static constexpr bool swap_ab = Gemm::swap_ab;
+  using ElementAB = typename Gemm::ElementAB;
+  using ElementD = typename Gemm::ElementD;
+  using GemmKernel = typename Gemm::GemmKernel;
+
+  using StrideA = typename Gemm::GemmKernel::StrideA;
+  using StrideB = typename Gemm::GemmKernel::StrideB;
+  using StrideC = typename Gemm::GemmKernel::StrideC;
+
+  int32_t m = a.size(0), n = b.size(1), k = a.size(1);
+  auto prob_shape =
+      swap_ab ? cute::make_shape(n, m, k, 1) : cute::make_shape(m, n, k, 1);
+
+  StrideA a_stride =
+      cutlass::make_cute_packed_stride(StrideA{}, cute::make_shape(m, k, 1));
+  StrideB b_stride =
+      cutlass::make_cute_packed_stride(StrideB{}, cute::make_shape(n, k, 1));
+  StrideC c_stride = cutlass::make_cute_packed_stride(
+      StrideC{},
+      swap_ab ? cute::make_shape(n, m, 1) : cute::make_shape(m, n, 1));
+
+  auto a_ptr = static_cast<ElementAB*>(a.data_ptr());
+  auto b_ptr = static_cast<ElementAB*>(b.data_ptr());
+  auto c_ptr = static_cast<ElementD*>(out.data_ptr());
+
+  typename GemmKernel::MainloopArguments mainloop_args =
+      swap_ab ? typename GemmKernel::MainloopArguments{b_ptr, b_stride, a_ptr,
+                                                       a_stride}
+              : typename GemmKernel::MainloopArguments{a_ptr, a_stride, b_ptr,
+                                                       b_stride};
+
+  typename GemmKernel::EpilogueArguments epilogue_args{
+      Gemm::Epilogue::prepare_args(
+          std::forward<EpilogueArgs>(epilogue_params)...),
+      c_ptr, c_stride, c_ptr, c_stride};
+
+  c3x::cutlass_gemm_caller<GemmKernel>(a.device(), prob_shape, mainloop_args,
+                                       epilogue_args);
+}
+
+template <typename InType, typename OutType, bool EnableBias,
           typename... EpilogueArgs>
 inline void cutlass_gemm_sm90_fp8_dispatch(torch::Tensor& out,
                                            torch::Tensor const& a,
                                            torch::Tensor const& b,
+                                           torch::Tensor const& a_scales,
+                                           torch::Tensor const& b_scales,
                                            EpilogueArgs&&... args) {
   static_assert(std::is_same<InType, cutlass::float_e4m3_t>());
   TORCH_CHECK(a.dtype() == torch::kFloat8_e4m3fn);
@@ -71,50 +272,75 @@ inline void cutlass_gemm_sm90_fp8_dispatch(torch::Tensor& out,
 
   using Cutlass3xGemmDefault =
       typename sm90_fp8_config_default<InType, OutType,
-                                       Epilogue>::Cutlass3xGemm;
-  using Cutlass3xGemmM64 =
-      typename sm90_fp8_config_M64<InType, OutType, Epilogue>::Cutlass3xGemm;
+                                       EnableBias>::Cutlass3xGemm;
   using Cutlass3xGemmM128 =
-      typename sm90_fp8_config_M128<InType, OutType, Epilogue>::Cutlass3xGemm;
+      typename sm90_fp8_config_M128<InType, OutType, EnableBias>::Cutlass3xGemm;
+
+  using Cutlass3xGemmM64_N1280 =
+      typename sm90_fp8_config_M64_N1280<InType, OutType,
+                                         EnableBias>::Cutlass3xGemm;
+  using Cutlass3xGemmM64_N8192 =
+      typename sm90_fp8_config_M64_N8192<InType, OutType,
+                                         EnableBias>::Cutlass3xGemm;
+  using Cutlass3xGemmM16_N1280 =
+      typename sm90_fp8_config_M16_N1280<InType, OutType,
+                                         EnableBias>::Cutlass3xGemm;
+  using Cutlass3xGemmM16_N8192 =
+      typename sm90_fp8_config_M16_N8192<InType, OutType,
+                                         EnableBias>::Cutlass3xGemm;
 
   uint32_t const m = a.size(0);
-  uint32_t const mp2 =
-      std::max(static_cast<uint32_t>(64), next_pow_2(m));  // next power of 2
+  uint32_t const n = b.size(1);
 
-  if (mp2 <= 64) {
-    // m in [1, 64]
-    return cutlass_gemm_caller<Cutlass3xGemmM64>(
-        out, a, b, std::forward<EpilogueArgs>(args)...);
-  } else if (mp2 <= 128) {
+  if (m <= 16) {
+    // m in [1, 16]
+    if (n <= 1280) {
+      return cutlass_gemm_caller_sm90_fp8<Cutlass3xGemmM16_N1280>(
+          out, a, b, b_scales, a_scales, std::forward<EpilogueArgs>(args)...);
+    }
+    return cutlass_gemm_caller_sm90_fp8<Cutlass3xGemmM16_N8192>(
+        out, a, b, b_scales, a_scales, std::forward<EpilogueArgs>(args)...);
+  } else if (m <= 64) {
+    // m in (16, 64]
+    if (n <= 1280) {
+      return cutlass_gemm_caller_sm90_fp8<Cutlass3xGemmM64_N1280>(
+          out, a, b, b_scales, a_scales, std::forward<EpilogueArgs>(args)...);
+    }
+    return cutlass_gemm_caller_sm90_fp8<Cutlass3xGemmM64_N8192>(
+        out, a, b, b_scales, a_scales, std::forward<EpilogueArgs>(args)...);
+  } else if (m <= 128) {
     // m in (64, 128]
-    return cutlass_gemm_caller<Cutlass3xGemmM128>(
-        out, a, b, std::forward<EpilogueArgs>(args)...);
+    return cutlass_gemm_caller_sm90_fp8<Cutlass3xGemmM128>(
+        out, a, b, a_scales, b_scales, std::forward<EpilogueArgs>(args)...);
   } else {
     // m in (128, inf)
-    return cutlass_gemm_caller<Cutlass3xGemmDefault>(
-        out, a, b, std::forward<EpilogueArgs>(args)...);
+    return cutlass_gemm_caller_sm90_fp8<Cutlass3xGemmDefault>(
+        out, a, b, a_scales, b_scales, std::forward<EpilogueArgs>(args)...);
   }
 }
 
-template <template <typename, typename, typename> typename Epilogue,
-          typename... EpilogueArgs>
+template <bool EnableBias, typename... EpilogueArgs>
 void cutlass_scaled_mm_sm90_fp8_epilogue(torch::Tensor& out,
                                          torch::Tensor const& a,
                                          torch::Tensor const& b,
+                                         torch::Tensor const& a_scales,
+                                         torch::Tensor const& b_scales,
                                          EpilogueArgs&&... epilogue_args) {
   TORCH_CHECK(a.dtype() == torch::kFloat8_e4m3fn);
   TORCH_CHECK(b.dtype() == torch::kFloat8_e4m3fn);
 
   if (out.dtype() == torch::kBFloat16) {
     return cutlass_gemm_sm90_fp8_dispatch<cutlass::float_e4m3_t,
-                                          cutlass::bfloat16_t, Epilogue>(
-        out, a, b, std::forward<EpilogueArgs>(epilogue_args)...);
+                                          cutlass::bfloat16_t, EnableBias>(
+        out, a, b, a_scales, b_scales,
+        std::forward<EpilogueArgs>(epilogue_args)...);
   } else {
     TORCH_CHECK(out.dtype() == torch::kFloat16);
     return cutlass_gemm_sm90_fp8_dispatch<cutlass::float_e4m3_t,
-                                          cutlass::half_t, Epilogue>(
-        out, a, b, std::forward<EpilogueArgs>(epilogue_args)...);
+                                          cutlass::half_t, EnableBias>(
+        out, a, b, a_scales, b_scales,
+        std::forward<EpilogueArgs>(epilogue_args)...);
   }
 }
 
-}  // namespace vllm
+}  // namespace vllm

csrc/quantization/cutlass_w8a8/moe/grouped_mm_c3x.cuh

@@ -18,28 +18,34 @@ using ProblemShape =
     cutlass::gemm::GroupProblemShape<cute::Shape<int, int, int>>;
 
 using ElementAccumulator = float;
-using ArchTag = cutlass::arch::Sm90;
 using OperatorClass = cutlass::arch::OpClassTensorOp;
 
 using LayoutA = cutlass::layout::RowMajor;
+using LayoutA_Transpose =
+    typename cutlass::layout::LayoutTranspose<LayoutA>::type;
 using LayoutB = cutlass::layout::ColumnMajor;
-using LayoutC = cutlass::layout::RowMajor;
+using LayoutB_Transpose =
+    typename cutlass::layout::LayoutTranspose<LayoutB>::type;
+using LayoutD = cutlass::layout::RowMajor;
+using LayoutD_Transpose =
+    typename cutlass::layout::LayoutTranspose<LayoutD>::type;
+using LayoutC = LayoutD;
+using LayoutC_Transpose = LayoutD_Transpose;
 
-template <typename ElementAB_, typename ElementC_,
+template <typename ElementAB_, typename ElementC_, typename ArchTag_,
           template <typename, typename, typename> typename Epilogue_,
           typename TileShape, typename ClusterShape, typename KernelSchedule,
-          typename EpilogueSchedule>
+          typename EpilogueSchedule, bool swap_ab_ = false>
 struct cutlass_3x_group_gemm {
+  static constexpr bool swap_ab = swap_ab_;
   using ElementAB = ElementAB_;
   using ElementC = void;
   using ElementD = ElementC_;
   using ElementAccumulator = float;
+  using ArchTag = ArchTag_;
 
   using Epilogue = Epilogue_<ElementAccumulator, ElementD, TileShape>;
 
-  using StrideC =
-      cute::remove_pointer_t<cute::Stride<int64_t, cute::Int<1>, cute::Int<0>>>;
-
   static constexpr int AlignmentAB =
       128 / cutlass::sizeof_bits<ElementAB>::value;
   static constexpr int AlignmentC = 128 / cutlass::sizeof_bits<ElementD>::value;
@@ -50,21 +56,28 @@ struct cutlass_3x_group_gemm {
       typename cutlass::epilogue::collective::CollectiveBuilder<
           ArchTag, OperatorClass, TileShape, ClusterShape,
           cutlass::epilogue::collective::EpilogueTileAuto, ElementAccumulator,
-          ElementAccumulator, ElementC, LayoutC*, AlignmentC, ElementD,
-          LayoutC*, AlignmentC, EpilogueSchedule, EVTCompute>::CollectiveOp;
+          ElementAccumulator, ElementC,
+          conditional_t<swap_ab, LayoutC_Transpose*, LayoutC*>, AlignmentC,
+          ElementD, conditional_t<swap_ab, LayoutD_Transpose*, LayoutD*>,
+          AlignmentC, EpilogueSchedule, EVTCompute>::CollectiveOp;
 
   static constexpr size_t CEStorageSize =
       sizeof(typename CollectiveEpilogue::SharedStorage);
   using Stages = typename cutlass::gemm::collective::StageCountAutoCarveout<
       static_cast<int>(CEStorageSize)>;
 
-  using CollectiveMainloop =
+  using CollectiveMainloop = conditional_t<
+      swap_ab,
+      typename cutlass::gemm::collective::CollectiveBuilder<
+          ArchTag, OperatorClass, ElementAB, LayoutB_Transpose*, AlignmentAB,
+          ElementAB, LayoutA_Transpose*, AlignmentAB, ElementAccumulator,
+          TileShape, ClusterShape, Stages, KernelSchedule>::CollectiveOp,
       typename cutlass::gemm::collective::CollectiveBuilder<
           ArchTag, OperatorClass, ElementAB, LayoutA*, AlignmentAB, ElementAB,
           LayoutB*, AlignmentAB, ElementAccumulator, TileShape, ClusterShape,
-          Stages, KernelSchedule>::CollectiveOp;
+          Stages, KernelSchedule>::CollectiveOp>;
 
-  using KernelType = enable_sm90_only<cutlass::gemm::kernel::GemmUniversal<
+  using KernelType = enable_sm90_or_later<cutlass::gemm::kernel::GemmUniversal<
       ProblemShape, CollectiveMainloop, CollectiveEpilogue>>;
 
   struct GemmKernel : public KernelType {};
@@ -78,12 +91,12 @@ void cutlass_group_gemm_caller(
     torch::Tensor const& problem_sizes, torch::Tensor const& a_strides,
     torch::Tensor const& b_strides, torch::Tensor const& c_strides,
     bool per_act_token, bool per_out_ch) {
+  static constexpr bool swap_ab = Gemm::swap_ab;
+
   using ElementAB = typename Gemm::ElementAB;
   using ElementD = typename Gemm::ElementD;
 
   int num_experts = static_cast<int>(expert_offsets.size(0));
-  int k_size = a_tensors.size(1);
-  int n_size = out_tensors.size(1);
 
   auto stream = at::cuda::getCurrentCUDAStream(a_tensors.device().index());
 
@@ -110,26 +123,47 @@ void cutlass_group_gemm_caller(
           problem_sizes.data_ptr());
   ProblemShape prob_shape{num_experts, problem_sizes_as_shapes, nullptr};
 
-  typename GemmKernel::MainloopArguments mainloop_args{
-      static_cast<const ElementAB**>(a_ptrs.data_ptr()),
-      static_cast<StrideA*>(a_strides.data_ptr()),
-      static_cast<const ElementAB**>(b_ptrs.data_ptr()),
-      static_cast<StrideB*>(b_strides.data_ptr())};
+  typename GemmKernel::MainloopArguments mainloop_args;
+  if constexpr (swap_ab) {
+    mainloop_args = typename GemmKernel::MainloopArguments{
+        static_cast<const ElementAB**>(b_ptrs.data_ptr()),
+        static_cast<StrideB*>(b_strides.data_ptr()),
+        static_cast<const ElementAB**>(a_ptrs.data_ptr()),
+        static_cast<StrideA*>(a_strides.data_ptr())};
+  } else {
+    mainloop_args = typename GemmKernel::MainloopArguments{
+        static_cast<const ElementAB**>(a_ptrs.data_ptr()),
+        static_cast<StrideA*>(a_strides.data_ptr()),
+        static_cast<const ElementAB**>(b_ptrs.data_ptr()),
+        static_cast<StrideB*>(b_strides.data_ptr())};
+  }
 
   // Currently, we are only able to do broadcast on either all or none a_scales
   // and on either all or none b_scales
   typename GemmKernel::EpilogueArguments epilogue_args{
       Gemm::Epilogue::prepare_args(
-          static_cast<const ElementAccumulator**>(a_scales_ptrs.data_ptr()),
-          static_cast<const ElementAccumulator**>(b_scales_ptrs.data_ptr()),
-          per_act_token, per_out_ch),
+          swap_ab ? static_cast<const ElementAccumulator**>(
+                        b_scales_ptrs.data_ptr())
+                  : static_cast<const ElementAccumulator**>(
+                        a_scales_ptrs.data_ptr()),
+          swap_ab ? static_cast<const ElementAccumulator**>(
+                        a_scales_ptrs.data_ptr())
+                  : static_cast<const ElementAccumulator**>(
+                        b_scales_ptrs.data_ptr()),
+          swap_ab ? per_out_ch : per_act_token,
+          swap_ab ? per_act_token : per_out_ch),
       nullptr, static_cast<StrideC*>(c_strides.data_ptr()),
       static_cast<ElementD**>(out_ptrs.data_ptr()),
       static_cast<StrideC*>(c_strides.data_ptr())};
 
+  int device_id = a_tensors.device().index();
+  static const cutlass::KernelHardwareInfo hw_info{
+      device_id, cutlass::KernelHardwareInfo::query_device_multiprocessor_count(
+                     device_id)};
+
   typename GemmKernel::Arguments args{
       cutlass::gemm::GemmUniversalMode::kGrouped, prob_shape, mainloop_args,
-      epilogue_args};
+      epilogue_args, hw_info};
 
   using GemmOp = cutlass::gemm::device::GemmUniversalAdapter<GemmKernel>;
   GemmOp gemm_op;

csrc/quantization/cutlass_w8a8/moe/grouped_mm_c3x_sm100.cu

@@ -0,0 +1,140 @@
+#include <cudaTypedefs.h>
+
+#include <c10/cuda/CUDAGuard.h>
+#include <torch/all.h>
+
+#include "cutlass/cutlass.h"
+#include "grouped_mm_c3x.cuh"
+
+using namespace cute;
+
+namespace {
+
+template <typename InType, typename OutType,
+          template <typename, typename, typename> typename Epilogue>
+struct sm100_fp8_config_default {
+  static_assert(std::is_same<InType, cutlass::float_e4m3_t>());
+  using KernelSchedule =
+      cutlass::gemm::KernelPtrArrayTmaWarpSpecialized1SmSm100;
+  using EpilogueSchedule = cutlass::epilogue::PtrArrayTmaWarpSpecialized1Sm;
+  using TileShape = cute::Shape<cute::_128, cute::_256, cute::_128>;
+  using ClusterShape = cute::Shape<cute::_1, cute::_1, cute::_1>;
+  using ArchTag = cutlass::arch::Sm100;
+
+  using Cutlass3xGemm =
+      cutlass_3x_group_gemm<InType, OutType, ArchTag, Epilogue, TileShape,
+                            ClusterShape, KernelSchedule, EpilogueSchedule>;
+};
+
+template <typename InType, typename OutType,
+          template <typename, typename, typename> typename Epilogue>
+struct sm100_fp8_config_M64 {
+  // M in [1,64]
+  static_assert(std::is_same<InType, cutlass::float_e4m3_t>());
+  using KernelSchedule =
+      cutlass::gemm::KernelPtrArrayTmaWarpSpecialized1SmSm100;
+  using EpilogueSchedule = cutlass::epilogue::PtrArrayTmaWarpSpecialized1Sm;
+  using TileShape = cute::Shape<cute::_128, cute::_16, cute::_128>;
+  using ClusterShape = cute::Shape<cute::_1, cute::_1, cute::_1>;
+  using ArchTag = cutlass::arch::Sm100;
+
+  using Cutlass3xGemm =
+      cutlass_3x_group_gemm<InType, OutType, ArchTag, Epilogue, TileShape,
+                            ClusterShape, KernelSchedule, EpilogueSchedule,
+                            true>;
+};
+
+template <typename InType, typename OutType,
+          template <typename, typename, typename> typename Epilogue>
+struct sm100_fp8_config_N8192 {
+  // N in [8192, inf)
+  static_assert(std::is_same<InType, cutlass::float_e4m3_t>());
+  using KernelSchedule =
+      cutlass::gemm::KernelPtrArrayTmaWarpSpecialized2SmSm100;
+  using EpilogueSchedule = cutlass::epilogue::PtrArrayTmaWarpSpecialized2Sm;
+  using TileShape = cute::Shape<cute::_128, cute::_256, cute::_128>;
+  using ClusterShape = cute::Shape<cute::_2, cute::_1, cute::_1>;
+  using ArchTag = cutlass::arch::Sm100;
+
+  using Cutlass3xGemm =
+      cutlass_3x_group_gemm<InType, OutType, ArchTag, Epilogue, TileShape,
+                            ClusterShape, KernelSchedule, EpilogueSchedule>;
+};
+
+template <typename InType, typename OutType>
+void run_cutlass_moe_mm_sm100(
+    torch::Tensor& out_tensors, torch::Tensor const& a_tensors,
+    torch::Tensor const& b_tensors, torch::Tensor const& a_scales,
+    torch::Tensor const& b_scales, torch::Tensor const& expert_offsets,
+    torch::Tensor const& problem_sizes, torch::Tensor const& a_strides,
+    torch::Tensor const& b_strides, torch::Tensor const& c_strides,
+    bool per_act_token, bool per_out_ch) {
+  TORCH_CHECK(a_tensors.size(0) > 0, "No input A tensors provided.");
+  TORCH_CHECK(b_tensors.size(0) > 0, "No input B tensors provided.");
+  TORCH_CHECK(out_tensors.size(0) > 0, "No output tensors provided.");
+
+  TORCH_CHECK(a_tensors.dtype() == torch::kFloat8_e4m3fn,
+              "A tensors must be of type float8_e4m3fn.");
+  TORCH_CHECK(b_tensors.dtype() == torch::kFloat8_e4m3fn,
+              "B tensors must be of type float8_e4m3fn.");
+
+  using Cutlass3xGemmDefault = typename sm100_fp8_config_default<
+      InType, OutType, vllm::c3x::ScaledEpilogueArray>::Cutlass3xGemm;
+  using Cutlass3xGemmN8192 = typename sm100_fp8_config_N8192<
+      InType, OutType, vllm::c3x::ScaledEpilogueArray>::Cutlass3xGemm;
+  using Cutlass3xGemmM64 = typename sm100_fp8_config_M64<
+      InType, OutType, vllm::c3x::ScaledEpilogueArray>::Cutlass3xGemm;
+
+  uint32_t const m = a_tensors.size(0);
+  uint32_t const n = out_tensors.size(1);
+
+  if (m <= 64) {
+    cutlass_group_gemm_caller<Cutlass3xGemmM64>(
+        out_tensors, a_tensors, b_tensors, a_scales, b_scales, expert_offsets,
+        problem_sizes, a_strides, b_strides, c_strides, per_act_token,
+        per_out_ch);
+  } else if (n >= 8192) {
+    cutlass_group_gemm_caller<Cutlass3xGemmN8192>(
+        out_tensors, a_tensors, b_tensors, a_scales, b_scales, expert_offsets,
+        problem_sizes, a_strides, b_strides, c_strides, per_act_token,
+        per_out_ch);
+  } else {
+    cutlass_group_gemm_caller<Cutlass3xGemmDefault>(
+        out_tensors, a_tensors, b_tensors, a_scales, b_scales, expert_offsets,
+        problem_sizes, a_strides, b_strides, c_strides, per_act_token,
+        per_out_ch);
+  }
+}
+}  // namespace
+
+void dispatch_moe_mm_sm100(
+    torch::Tensor& out_tensors, torch::Tensor const& a_tensors,
+    torch::Tensor const& b_tensors, torch::Tensor const& a_scales,
+    torch::Tensor const& b_scales, torch::Tensor const& expert_offsets,
+    torch::Tensor const& problem_sizes, torch::Tensor const& a_strides,
+    torch::Tensor const& b_strides, torch::Tensor const& c_strides,
+    bool per_act_token, bool per_out_ch) {
+  if (out_tensors.dtype() == torch::kBFloat16) {
+    run_cutlass_moe_mm_sm100<cutlass::float_e4m3_t, cutlass::bfloat16_t>(
+        out_tensors, a_tensors, b_tensors, a_scales, b_scales, expert_offsets,
+        problem_sizes, a_strides, b_strides, c_strides, per_act_token,
+        per_out_ch);
+  } else {
+    run_cutlass_moe_mm_sm100<cutlass::float_e4m3_t, cutlass::half_t>(
+        out_tensors, a_tensors, b_tensors, a_scales, b_scales, expert_offsets,
+        problem_sizes, a_strides, b_strides, c_strides, per_act_token,
+        per_out_ch);
+  }
+}
+
+void cutlass_moe_mm_sm100(
+    torch::Tensor& out_tensors, torch::Tensor const& a_tensors,
+    torch::Tensor const& b_tensors, torch::Tensor const& a_scales,
+    torch::Tensor const& b_scales, torch::Tensor const& expert_offsets,
+    torch::Tensor const& problem_sizes, torch::Tensor const& a_strides,
+    torch::Tensor const& b_strides, torch::Tensor const& c_strides,
+    bool per_act_token, bool per_out_ch) {
+  dispatch_moe_mm_sm100(out_tensors, a_tensors, b_tensors, a_scales, b_scales,
+                        expert_offsets, problem_sizes, a_strides, b_strides,
+                        c_strides, per_act_token, per_out_ch);
+}

csrc/quantization/cutlass_w8a8/moe/grouped_mm_c3x_sm90.cu

@@ -21,27 +21,49 @@ struct sm90_fp8_config_default {
       cutlass::epilogue::PtrArrayTmaWarpSpecializedPingpong;
   using TileShape = cute::Shape<cute::_64, cute::_256, cute::_128>;
   using ClusterShape = cute::Shape<cute::_1, cute::_2, cute::_1>;
+  using ArchTag = cutlass::arch::Sm90;
 
   using Cutlass3xGemm =
-      cutlass_3x_group_gemm<InType, OutType, Epilogue, TileShape, ClusterShape,
-                            KernelSchedule, EpilogueSchedule>;
+      cutlass_3x_group_gemm<InType, OutType, ArchTag, Epilogue, TileShape,
+                            ClusterShape, KernelSchedule, EpilogueSchedule>;
 };
 
 template <typename InType, typename OutType,
           template <typename, typename, typename> typename Epilogue>
-struct sm90_fp8_config_M16 {
-  // M in [1, 16]
+struct sm90_fp8_config_M4 {
+  // M in [1, 4]
   static_assert(std::is_same<InType, cutlass::float_e4m3_t>());
   using KernelSchedule =
       cutlass::gemm::KernelPtrArrayTmaWarpSpecializedPingpongFP8FastAccum;
   using EpilogueSchedule =
       cutlass::epilogue::PtrArrayTmaWarpSpecializedPingpong;
-  using TileShape = cute::Shape<cute::_64, cute::_64, cute::_128>;
-  using ClusterShape = cute::Shape<cute::_1, cute::_4, cute::_1>;
+  using TileShape = cute::Shape<cute::_128, cute::_16, cute::_128>;
+  using ClusterShape = cute::Shape<cute::_1, cute::_1, cute::_1>;
+  using ArchTag = cutlass::arch::Sm90;
 
   using Cutlass3xGemm =
-      cutlass_3x_group_gemm<InType, OutType, Epilogue, TileShape, ClusterShape,
-                            KernelSchedule, EpilogueSchedule>;
+      cutlass_3x_group_gemm<InType, OutType, ArchTag, Epilogue, TileShape,
+                            ClusterShape, KernelSchedule, EpilogueSchedule,
+                            true>;
+};
+
+template <typename InType, typename OutType,
+          template <typename, typename, typename> typename Epilogue>
+struct sm90_fp8_config_M64 {
+  // M in (4, 64]
+  static_assert(std::is_same<InType, cutlass::float_e4m3_t>());
+  using KernelSchedule =
+      cutlass::gemm::KernelPtrArrayTmaWarpSpecializedPingpongFP8FastAccum;
+  using EpilogueSchedule =
+      cutlass::epilogue::PtrArrayTmaWarpSpecializedPingpong;
+  using TileShape = cute::Shape<cute::_128, cute::_16, cute::_256>;
+  using ClusterShape = cute::Shape<cute::_2, cute::_1, cute::_1>;
+  using ArchTag = cutlass::arch::Sm90;
+
+  using Cutlass3xGemm =
+      cutlass_3x_group_gemm<InType, OutType, ArchTag, Epilogue, TileShape,
+                            ClusterShape, KernelSchedule, EpilogueSchedule,
+                            true>;
 };
 
 template <typename InType, typename OutType,
@@ -55,10 +77,11 @@ struct sm90_fp8_config_K8192 {
       cutlass::epilogue::PtrArrayTmaWarpSpecializedPingpong;
   using TileShape = cute::Shape<cute::_128, cute::_128, cute::_128>;
   using ClusterShape = cute::Shape<cute::_1, cute::_8, cute::_1>;
+  using ArchTag = cutlass::arch::Sm90;
 
   using Cutlass3xGemm =
-      cutlass_3x_group_gemm<InType, OutType, Epilogue, TileShape, ClusterShape,
-                            KernelSchedule, EpilogueSchedule>;
+      cutlass_3x_group_gemm<InType, OutType, ArchTag, Epilogue, TileShape,
+                            ClusterShape, KernelSchedule, EpilogueSchedule>;
 };
 
 template <typename InType, typename OutType,
@@ -72,10 +95,11 @@ struct sm90_fp8_config_N8192 {
       cutlass::epilogue::PtrArrayTmaWarpSpecializedPingpong;
   using TileShape = cute::Shape<cute::_64, cute::_128, cute::_256>;
   using ClusterShape = cute::Shape<cute::_1, cute::_8, cute::_1>;
+  using ArchTag = cutlass::arch::Sm90;
 
   using Cutlass3xGemm =
-      cutlass_3x_group_gemm<InType, OutType, Epilogue, TileShape, ClusterShape,
-                            KernelSchedule, EpilogueSchedule>;
+      cutlass_3x_group_gemm<InType, OutType, ArchTag, Epilogue, TileShape,
+                            ClusterShape, KernelSchedule, EpilogueSchedule>;
 };
 
 template <typename InType, typename OutType>
@@ -95,14 +119,13 @@ void run_cutlass_moe_mm_sm90(
   TORCH_CHECK(b_tensors.dtype() == torch::kFloat8_e4m3fn,
               "B tensors must be of type float8_e4m3fn.");
 
-  TORCH_CHECK(a_tensors.dtype() == torch::kFloat8_e4m3fn);
-  TORCH_CHECK(b_tensors.dtype() == torch::kFloat8_e4m3fn);
-
   using Cutlass3xGemmN8192 = typename sm90_fp8_config_N8192<
       InType, OutType, vllm::c3x::ScaledEpilogueArray>::Cutlass3xGemm;
   using Cutlass3xGemmK8192 = typename sm90_fp8_config_K8192<
       InType, OutType, vllm::c3x::ScaledEpilogueArray>::Cutlass3xGemm;
-  using Cutlass3xGemmM16 = typename sm90_fp8_config_M16<
+  using Cutlass3xGemmM4 = typename sm90_fp8_config_M4<
+      InType, OutType, vllm::c3x::ScaledEpilogueArray>::Cutlass3xGemm;
+  using Cutlass3xGemmM64 = typename sm90_fp8_config_M64<
       InType, OutType, vllm::c3x::ScaledEpilogueArray>::Cutlass3xGemm;
   using Cutlass3xGemmDefault = typename sm90_fp8_config_default<
       InType, OutType, vllm::c3x::ScaledEpilogueArray>::Cutlass3xGemm;
@@ -111,7 +134,18 @@ void run_cutlass_moe_mm_sm90(
   uint32_t const n = out_tensors.size(1);
   uint32_t const k = a_tensors.size(1);
 
-  if (n >= 8192) {
+  // Use swap_ab for M <= 64 by default to reduce padding
+  if (m <= 4) {
+    cutlass_group_gemm_caller<Cutlass3xGemmM4>(
+        out_tensors, a_tensors, b_tensors, a_scales, b_scales, expert_offsets,
+        problem_sizes, a_strides, b_strides, c_strides, per_act_token,
+        per_out_ch);
+  } else if (m <= 64) {
+    cutlass_group_gemm_caller<Cutlass3xGemmM64>(
+        out_tensors, a_tensors, b_tensors, a_scales, b_scales, expert_offsets,
+        problem_sizes, a_strides, b_strides, c_strides, per_act_token,
+        per_out_ch);
+  } else if (n >= 8192) {
     cutlass_group_gemm_caller<Cutlass3xGemmN8192>(
         out_tensors, a_tensors, b_tensors, a_scales, b_scales, expert_offsets,
         problem_sizes, a_strides, b_strides, c_strides, per_act_token,
@@ -121,11 +155,6 @@ void run_cutlass_moe_mm_sm90(
         out_tensors, a_tensors, b_tensors, a_scales, b_scales, expert_offsets,
         problem_sizes, a_strides, b_strides, c_strides, per_act_token,
         per_out_ch);
-  } else if (m <= 16) {
-    cutlass_group_gemm_caller<Cutlass3xGemmM16>(
-        out_tensors, a_tensors, b_tensors, a_scales, b_scales, expert_offsets,
-        problem_sizes, a_strides, b_strides, c_strides, per_act_token,
-        per_out_ch);
   } else {
     cutlass_group_gemm_caller<Cutlass3xGemmDefault>(
         out_tensors, a_tensors, b_tensors, a_scales, b_scales, expert_offsets,

csrc/quantization/cutlass_w8a8/moe/moe_data.cu

@@ -6,7 +6,10 @@
 #include <iostream>
 
 constexpr uint64_t THREADS_PER_EXPERT = 512;
+// threshold must match the dispatch logic in run_cutlass_moe_mm_sm90()
+constexpr int SWAP_AB_THRESHOLD = 64;
 
+template <bool SWAP_AB>
 __global__ void compute_problem_sizes(const int32_t* __restrict__ topk_ids,
                                       int32_t* problem_sizes1,
                                       int32_t* problem_sizes2,
@@ -24,40 +27,51 @@ __global__ void compute_problem_sizes(const int32_t* __restrict__ topk_ids,
 
   if (threadIdx.x == 0) {
     int final_occurrences = atomic_buffer[expert_id];
-    problem_sizes1[expert_id * 3] = final_occurrences;
-    problem_sizes1[expert_id * 3 + 1] = 2 * n;
-    problem_sizes1[expert_id * 3 + 2] = k;
-    problem_sizes2[expert_id * 3] = final_occurrences;
-    problem_sizes2[expert_id * 3 + 1] = k;
-    problem_sizes2[expert_id * 3 + 2] = n;
+    if constexpr (!SWAP_AB) {
+      problem_sizes1[expert_id * 3] = final_occurrences;
+      problem_sizes1[expert_id * 3 + 1] = 2 * n;
+      problem_sizes1[expert_id * 3 + 2] = k;
+      problem_sizes2[expert_id * 3] = final_occurrences;
+      problem_sizes2[expert_id * 3 + 1] = k;
+      problem_sizes2[expert_id * 3 + 2] = n;
+    } else {
+      problem_sizes1[expert_id * 3] = 2 * n;
+      problem_sizes1[expert_id * 3 + 1] = final_occurrences;
+      problem_sizes1[expert_id * 3 + 2] = k;
+      problem_sizes2[expert_id * 3] = k;
+      problem_sizes2[expert_id * 3 + 1] = final_occurrences;
+      problem_sizes2[expert_id * 3 + 2] = n;
+    }
   }
 }
 
 __global__ void compute_expert_offsets(
     const int32_t* __restrict__ problem_sizes1, int32_t* expert_offsets,
-    int32_t* atomic_buffer, const int num_experts) {
+    int32_t* atomic_buffer, const int num_experts, const bool swap_ab) {
   int32_t tot_offset = 0;
   expert_offsets[0] = 0;
   for (int i = 0; i < num_experts; ++i) {
     atomic_buffer[i] = tot_offset;
-    tot_offset += problem_sizes1[i * 3];
+    tot_offset += swap_ab ? problem_sizes1[i * 3 + 1] : problem_sizes1[i * 3];
     expert_offsets[i + 1] = tot_offset;
   }
 }
 
 __global__ void compute_expert_blockscale_offsets(
     const int32_t* __restrict__ problem_sizes1, int32_t* expert_offsets,
-    int32_t* blockscale_offsets, int32_t* atomic_buffer,
-    const int num_experts) {
+    int32_t* blockscale_offsets, int32_t* atomic_buffer, const int num_experts,
+    const bool swap_ab) {
   int32_t tot_offset = 0;
   int32_t tot_offset_round = 0;
   expert_offsets[0] = 0;
   blockscale_offsets[0] = 0;
   for (int i = 0; i < num_experts; ++i) {
+    int32_t cur_offset =
+        swap_ab ? problem_sizes1[i * 3 + 1] : problem_sizes1[i * 3];
     atomic_buffer[i] = tot_offset;
-    tot_offset += problem_sizes1[i * 3];
+    tot_offset += cur_offset;
     expert_offsets[i + 1] = tot_offset;
-    tot_offset_round += (problem_sizes1[i * 3] + (128 - 1)) / 128 * 128;
+    tot_offset_round += (cur_offset + (128 - 1)) / 128 * 128;
     blockscale_offsets[i + 1] = tot_offset_round;
   }
 }
@@ -102,22 +116,41 @@ void get_cutlass_moe_mm_data_caller(
   torch::Tensor atomic_buffer = torch::zeros(num_experts, options_int32);
 
   int num_threads = min(THREADS_PER_EXPERT, topk_ids.numel());
-  compute_problem_sizes<<<num_experts, num_threads, 0, stream>>>(
-      static_cast<const int32_t*>(topk_ids.data_ptr()),
-      static_cast<int32_t*>(problem_sizes1.data_ptr()),
-      static_cast<int32_t*>(problem_sizes2.data_ptr()),
-      static_cast<int32_t*>(atomic_buffer.data_ptr()), topk_ids.numel(), n, k);
+
+  // Swap-AB should be disabled for FP4 path
+  bool may_swap_ab = (!blockscale_offsets.has_value()) &&
+                     (topk_ids.numel() <= SWAP_AB_THRESHOLD);
+
+  if (may_swap_ab) {
+    compute_problem_sizes<true><<<num_experts, num_threads, 0, stream>>>(
+        static_cast<const int32_t*>(topk_ids.data_ptr()),
+        static_cast<int32_t*>(problem_sizes1.data_ptr()),
+        static_cast<int32_t*>(problem_sizes2.data_ptr()),
+        static_cast<int32_t*>(atomic_buffer.data_ptr()), topk_ids.numel(), n,
+        k);
+  } else {
+    compute_problem_sizes<false><<<num_experts, num_threads, 0, stream>>>(
+        static_cast<const int32_t*>(topk_ids.data_ptr()),
+        static_cast<int32_t*>(problem_sizes1.data_ptr()),
+        static_cast<int32_t*>(problem_sizes2.data_ptr()),
+        static_cast<int32_t*>(atomic_buffer.data_ptr()), topk_ids.numel(), n,
+        k);
+  }
+
   if (blockscale_offsets.has_value()) {
+    // fp4 path
     compute_expert_blockscale_offsets<<<1, 1, 0, stream>>>(
         static_cast<const int32_t*>(problem_sizes1.data_ptr()),
         static_cast<int32_t*>(expert_offsets.data_ptr()),
         static_cast<int32_t*>(blockscale_offsets.value().data_ptr()),
-        static_cast<int32_t*>(atomic_buffer.data_ptr()), num_experts);
+        static_cast<int32_t*>(atomic_buffer.data_ptr()), num_experts,
+        may_swap_ab);
   } else {
     compute_expert_offsets<<<1, 1, 0, stream>>>(
         static_cast<const int32_t*>(problem_sizes1.data_ptr()),
         static_cast<int32_t*>(expert_offsets.data_ptr()),
-        static_cast<int32_t*>(atomic_buffer.data_ptr()), num_experts);
+        static_cast<int32_t*>(atomic_buffer.data_ptr()), num_experts,
+        may_swap_ab);
   }
   compute_arg_sorts<<<num_experts, num_threads, 0, stream>>>(
       static_cast<const int32_t*>(topk_ids.data_ptr()),
@@ -160,4 +193,4 @@ void get_cutlass_pplx_moe_mm_data_caller(torch::Tensor& expert_offsets,
       static_cast<int32_t*>(problem_sizes2.data_ptr()),
       static_cast<const int32_t*>(expert_num_tokens.data_ptr()), padded_m, n,
       k);
-}
+}

csrc/quantization/cutlass_w8a8/scaled_mm_entry.cu

@@ -41,6 +41,16 @@ void cutlass_moe_mm_sm90(
 
 #endif
 
+#if defined ENABLE_CUTLASS_MOE_SM100 && ENABLE_CUTLASS_MOE_SM100
+void cutlass_moe_mm_sm100(
+    torch::Tensor& out_tensors, torch::Tensor const& a_tensors,
+    torch::Tensor const& b_tensors, torch::Tensor const& a_scales,
+    torch::Tensor const& b_scales, torch::Tensor const& expert_offsets,
+    torch::Tensor const& problem_sizes, torch::Tensor const& a_strides,
+    torch::Tensor const& b_strides, torch::Tensor const& c_strides,
+    bool per_act_token, bool per_out_ch);
+#endif
+
 #if defined ENABLE_SCALED_MM_SM120 && ENABLE_SCALED_MM_SM120
 void cutlass_scaled_mm_sm120(torch::Tensor& c, torch::Tensor const& a,
                              torch::Tensor const& b,
@@ -130,10 +140,10 @@ bool cutlass_scaled_mm_supports_block_fp8(int64_t cuda_device_capability) {
   // and at least SM90 (Hopper)
 
 #if defined CUDA_VERSION
-  if (cuda_device_capability >= 90 && cuda_device_capability < 100) {
-    return CUDA_VERSION >= 12000;
-  } else if (cuda_device_capability >= 100) {
+  if (cuda_device_capability >= 100) {
     return CUDA_VERSION >= 12080;
+  } else if (cuda_device_capability >= 90) {
+    return CUDA_VERSION >= 12000;
   }
 #endif
 
@@ -141,11 +151,14 @@ bool cutlass_scaled_mm_supports_block_fp8(int64_t cuda_device_capability) {
 }
 
 bool cutlass_group_gemm_supported(int64_t cuda_device_capability) {
-  // CUTLASS grouped FP8 kernels need at least CUDA 12.3
-  // and SM90 (Hopper)
+  // CUTLASS grouped FP8 kernels need at least CUDA 12.3 and SM90 (Hopper)
+  // or CUDA 12.8 and SM100 (Blackwell)
 
 #if defined CUDA_VERSION
-  if (cuda_device_capability == 90) {
+  if (cuda_device_capability >= 100) {
+    return CUDA_VERSION >= 12080;
+  }
+  if (cuda_device_capability >= 90) {
     return CUDA_VERSION >= 12030;
   }
 #endif
@@ -234,16 +247,26 @@ void cutlass_moe_mm(
     torch::Tensor const& b_strides, torch::Tensor const& c_strides,
     bool per_act_token, bool per_out_ch) {
   int32_t version_num = get_sm_version_num();
+#if defined ENABLE_CUTLASS_MOE_SM100 && ENABLE_CUTLASS_MOE_SM100
+  if (version_num >= 100) {
+    cutlass_moe_mm_sm100(out_tensors, a_tensors, b_tensors, a_scales, b_scales,
+                         expert_offsets, problem_sizes, a_strides, b_strides,
+                         c_strides, per_act_token, per_out_ch);
+    return;
+  }
+#endif
 #if defined ENABLE_CUTLASS_MOE_SM90 && ENABLE_CUTLASS_MOE_SM90
-  cutlass_moe_mm_sm90(out_tensors, a_tensors, b_tensors, a_scales, b_scales,
-                      expert_offsets, problem_sizes, a_strides, b_strides,
-                      c_strides, per_act_token, per_out_ch);
-  return;
+  if (version_num >= 90) {
+    cutlass_moe_mm_sm90(out_tensors, a_tensors, b_tensors, a_scales, b_scales,
+                        expert_offsets, problem_sizes, a_strides, b_strides,
+                        c_strides, per_act_token, per_out_ch);
+    return;
+  }
 #endif
   TORCH_CHECK_NOT_IMPLEMENTED(
       false,
       "No compiled cutlass_scaled_mm for CUDA device capability: ", version_num,
-      ". Required capability: 90");
+      ". Required capability: 90 or 100");
 }
 
 void get_cutlass_moe_mm_data(

csrc/quantization/fp8/common.cu

@@ -88,6 +88,8 @@ void static_scaled_fp8_quant(torch::Tensor& out,          // [..., d]
                              torch::Tensor const& input,  // [..., d]
                              torch::Tensor const& scale)  // [1]
 {
+  TORCH_CHECK(input.is_contiguous());
+  TORCH_CHECK(out.is_contiguous());
   int const block_size = 256;
   int const num_tokens = input.numel() / input.size(-1);
   int const num_elems = input.numel();
@@ -111,6 +113,8 @@ void dynamic_scaled_fp8_quant(torch::Tensor& out,          // [..., d]
                               torch::Tensor const& input,  // [..., d]
                               torch::Tensor& scale)        // [1]
 {
+  TORCH_CHECK(input.is_contiguous());
+  TORCH_CHECK(out.is_contiguous());
   int const block_size = 256;
   int const num_tokens = input.numel() / input.size(-1);
   int const num_elems = input.numel();

csrc/quantization/fp8/per_token_group_quant.cu

@@ -0,0 +1,217 @@
+#include <ATen/cuda/CUDAContext.h>
+#include <c10/util/Float8_e4m3fn.h>
+
+#include "../per_token_group_quant_8bit.h"
+
+#include <cmath>
+
+#include <cuda_fp16.h>
+#include <cuda_bf16.h>
+
+#include <torch/all.h>
+
+#include "../vectorization.cuh"
+#include "../vectorization_utils.cuh"
+#include "../../dispatch_utils.h"
+
+__device__ __forceinline__ float GroupReduceMax(float val, const int tid) {
+  unsigned mask = 0xffff;
+
+  val = fmaxf(val, __shfl_xor_sync(mask, val, 8));
+  val = fmaxf(val, __shfl_xor_sync(mask, val, 4));
+  val = fmaxf(val, __shfl_xor_sync(mask, val, 2));
+  val = fmaxf(val, __shfl_xor_sync(mask, val, 1));
+  return val;
+}
+
+template <typename T, typename DST_DTYPE, bool IS_COLUMN_MAJOR = false,
+          bool SCALE_UE8M0 = false, typename scale_packed_t = float>
+__global__ void per_token_group_quant_8bit_kernel(
+    const T* __restrict__ input, void* __restrict__ output_q,
+    scale_packed_t* __restrict__ output_s, const int group_size,
+    const int num_groups, const int groups_per_block, const float eps,
+    const float min_8bit, const float max_8bit, const int scale_num_rows = 0,
+    const int scale_stride = 0) {
+  const int threads_per_group = 16;
+  const int64_t local_group_id = threadIdx.x / threads_per_group;
+  const int lane_id = threadIdx.x % threads_per_group;
+
+  const int64_t block_group_id = blockIdx.x * groups_per_block;
+  const int64_t global_group_id = block_group_id + local_group_id;
+  const int64_t block_group_offset = global_group_id * group_size;
+
+  float local_absmax = eps;
+
+  using scale_element_t = float;
+  static_assert(sizeof(scale_packed_t) % sizeof(scale_element_t) == 0);
+
+  const T* group_input = input + block_group_offset;
+  DST_DTYPE* group_output =
+      static_cast<DST_DTYPE*>(output_q) + block_group_offset;
+  scale_element_t* scale_output;
+
+  if constexpr (IS_COLUMN_MAJOR) {
+    const int num_elems_per_pack =
+        static_cast<int>(sizeof(scale_packed_t) / sizeof(scale_element_t));
+    const int scale_num_rows_element = scale_num_rows * num_elems_per_pack;
+    const int row_idx = global_group_id / scale_num_rows_element;
+    const int col_idx_raw = global_group_id % scale_num_rows_element;
+    const int col_idx = col_idx_raw / num_elems_per_pack;
+    const int pack_idx = col_idx_raw % num_elems_per_pack;
+    scale_output = reinterpret_cast<scale_element_t*>(output_s) +
+                   (col_idx * scale_stride * num_elems_per_pack +
+                    row_idx * num_elems_per_pack + pack_idx);
+  } else {
+    scale_output = output_s + global_group_id;
+  }
+
+  // shared memory to cache each group's data to avoid double DRAM reads.
+  extern __shared__ __align__(16) char smem_raw[];
+  T* smem = reinterpret_cast<T*>(smem_raw);
+  T* smem_group = smem + local_group_id * group_size;
+
+  constexpr int vec_size = 16 / sizeof(T);
+  using vec_t = vllm::vec_n_t<T, vec_size>;
+
+  // copy global -> shared & compute absmax
+  auto scalar_op_cache = [&] __device__(T & dst, const T& src) {
+    float abs_v = fabsf(static_cast<float>(src));
+    local_absmax = fmaxf(local_absmax, abs_v);
+    dst = src;
+  };
+
+  vllm::vectorize_with_alignment<vec_size>(
+      group_input,        // in
+      smem_group,         // out (shared)
+      group_size,         // elements per group
+      lane_id,            // thread id
+      threads_per_group,  // stride in group
+      scalar_op_cache);   // scalar handler
+
+  local_absmax = GroupReduceMax(local_absmax, lane_id);
+
+  float y_s = local_absmax / max_8bit;
+  if constexpr (SCALE_UE8M0) {
+    y_s = exp2f(ceilf(log2f(fmaxf(fabsf(y_s), 1e-10f))));
+  }
+
+  scale_element_t y_s_quant = y_s;
+
+  if (lane_id == 0) {
+    *scale_output = y_s_quant;
+  }
+
+  __syncthreads();
+
+  // quantize shared -> global 8-bit
+  auto scalar_op_quant = [&] __device__(DST_DTYPE & dst, const T& src) {
+    float q = fminf(fmaxf(static_cast<float>(src) / y_s, min_8bit), max_8bit);
+    dst = DST_DTYPE(q);
+  };
+
+  vllm::vectorize_with_alignment<vec_size>(
+      smem_group,         // in (shared)
+      group_output,       // out (global quant tensor)
+      group_size,         // elements
+      lane_id,            // tid
+      threads_per_group,  // stride
+      scalar_op_quant);   // scalar handler
+}
+
+void per_token_group_quant_8bit(const torch::Tensor& input,
+                                torch::Tensor& output_q,
+                                torch::Tensor& output_s, int64_t group_size,
+                                double eps, double min_8bit, double max_8bit,
+                                bool scale_ue8m0) {
+  TORCH_CHECK(input.is_contiguous());
+  TORCH_CHECK(output_q.is_contiguous());
+
+  const int num_groups = input.numel() / group_size;
+
+  TORCH_CHECK(input.numel() % group_size == 0);
+  TORCH_CHECK(output_s.dim() == 2);
+
+  cudaStream_t stream = at::cuda::getCurrentCUDAStream();
+
+  constexpr int THREADS_PER_GROUP = 16;
+
+  int groups_per_block = 1;
+
+  if (num_groups % 16 == 0) {
+    groups_per_block = 16;
+  } else if (num_groups % 8 == 0) {
+    groups_per_block = 8;
+  } else if (num_groups % 4 == 0) {
+    groups_per_block = 4;
+  } else if (num_groups % 2 == 0) {
+    groups_per_block = 2;
+  }
+
+  auto dst_type = output_q.scalar_type();
+  const int num_blocks = num_groups / groups_per_block;
+  const int num_threads = groups_per_block * THREADS_PER_GROUP;
+
+  const bool is_column_major = output_s.stride(0) < output_s.stride(1);
+  const int scale_num_rows = output_s.size(1);
+  const int scale_stride = output_s.stride(1);
+
+#define LAUNCH_KERNEL(T, DST_DTYPE)                                        \
+  do {                                                                     \
+    dim3 grid(num_blocks);                                                 \
+    dim3 block(num_threads);                                               \
+    size_t smem_bytes =                                                    \
+        static_cast<size_t>(groups_per_block) * group_size * sizeof(T);    \
+    if (is_column_major) {                                                 \
+      if (scale_ue8m0) {                                                   \
+        per_token_group_quant_8bit_kernel<T, DST_DTYPE, true, true>        \
+            <<<grid, block, smem_bytes, stream>>>(                         \
+                static_cast<T*>(input.data_ptr()), output_q.data_ptr(),    \
+                static_cast<float*>(output_s.data_ptr()), group_size,      \
+                num_groups, groups_per_block, (float)eps, (float)min_8bit, \
+                (float)max_8bit, scale_num_rows, scale_stride);            \
+      } else {                                                             \
+        per_token_group_quant_8bit_kernel<T, DST_DTYPE, true, false>       \
+            <<<grid, block, smem_bytes, stream>>>(                         \
+                static_cast<T*>(input.data_ptr()), output_q.data_ptr(),    \
+                static_cast<float*>(output_s.data_ptr()), group_size,      \
+                num_groups, groups_per_block, (float)eps, (float)min_8bit, \
+                (float)max_8bit, scale_num_rows, scale_stride);            \
+      }                                                                    \
+    } else {                                                               \
+      if (scale_ue8m0) {                                                   \
+        per_token_group_quant_8bit_kernel<T, DST_DTYPE, false, true>       \
+            <<<grid, block, smem_bytes, stream>>>(                         \
+                static_cast<T*>(input.data_ptr()), output_q.data_ptr(),    \
+                static_cast<float*>(output_s.data_ptr()), group_size,      \
+                num_groups, groups_per_block, (float)eps, (float)min_8bit, \
+                (float)max_8bit);                                          \
+      } else {                                                             \
+        per_token_group_quant_8bit_kernel<T, DST_DTYPE, false, false>      \
+            <<<grid, block, smem_bytes, stream>>>(                         \
+                static_cast<T*>(input.data_ptr()), output_q.data_ptr(),    \
+                static_cast<float*>(output_s.data_ptr()), group_size,      \
+                num_groups, groups_per_block, (float)eps, (float)min_8bit, \
+                (float)max_8bit);                                          \
+      }                                                                    \
+    }                                                                      \
+  } while (0)
+
+  VLLM_DISPATCH_FLOATING_TYPES(
+      input.scalar_type(), "per_token_group_quant_8bit", ([&] {
+        if (dst_type == at::ScalarType::Float8_e4m3fn) {
+          LAUNCH_KERNEL(scalar_t, c10::Float8_e4m3fn);
+        } else if (dst_type == at::ScalarType::Char) {
+          LAUNCH_KERNEL(scalar_t, int8_t);
+        }
+      }));
+
+#undef LAUNCH_KERNEL
+}
+
+void per_token_group_quant_fp8(const torch::Tensor& input,
+                               torch::Tensor& output_q, torch::Tensor& output_s,
+                               int64_t group_size, double eps, double fp8_min,
+                               double fp8_max, bool scale_ue8m0) {
+  per_token_group_quant_8bit(input, output_q, output_s, group_size, eps,
+                             fp8_min, fp8_max, scale_ue8m0);
+}

csrc/quantization/gguf/gguf_kernel.cu

@@ -4,7 +4,7 @@
 #include <torch/all.h>
 #include <c10/cuda/CUDAGuard.h>
 
-#include "cuda_compat.h"
+#include "../../cuda_compat.h"
 #include "dispatch_utils.h"
 
 #include "ggml-common.h"

csrc/quantization/machete/machete_prepacked_layout.cuh

@@ -187,8 +187,12 @@ struct PrepackedLayoutBTemplate {
   CUTE_HOST_DEVICE static constexpr auto TVbNbKL_to_offset_copy(
       Shape_NKL shape_mkl) {
     auto layout = TVbNbKL_to_offset(shape_mkl);
-    return make_layout(coalesce(get<0>(layout)), get<1>(layout),
-                       get<2>(layout));
+    // for 4-bit elements, having >= 64 values per column
+    // allows TMA to load full 32-byte sectors
+    auto inner_layout =
+        make_layout(make_shape(_256{}, size<0>(layout) / _256{}));
+
+    return make_layout(inner_layout, get<1>(layout), get<2>(layout));
   }
 
   // ((BlockN, BlockK), (BlocksN, BlocksK), L) -> (storage_idx)

csrc/quantization/per_token_group_quant_8bit.h

@@ -0,0 +1,10 @@
+#pragma once
+#include <torch/all.h>
+
+// TODO(wentao): refactor the folder to 8bit, then includes fp8 and int8 folders
+// 8-bit per-token-group quantization helper used by both FP8 and INT8
+void per_token_group_quant_8bit(const torch::Tensor& input,
+                                torch::Tensor& output_q,
+                                torch::Tensor& output_s, int64_t group_size,
+                                double eps, double min_8bit, double max_8bit,
+                                bool scale_ue8m0 = false);

csrc/rocm/attention.cu

@@ -19,7 +19,7 @@
 #include <c10/cuda/CUDAGuard.h>
 #include <hip/hip_fp8.h>
 #include <hip/hip_bf16.h>
-#include "cuda_compat.h"
+#include "../cuda_compat.h"
 
 #include <algorithm>
 #include "../attention/dtype_fp8.cuh"

csrc/rocm/skinny_gemms.cu

@@ -9,7 +9,7 @@
 #include <stdexcept>
 #include <algorithm>
 
-#include "cuda_compat.h"
+#include "../cuda_compat.h"
 #include "dispatch_utils.h"
 #include "quantization/fp8/common.cuh"
 

csrc/torch_bindings.cpp

@@ -20,13 +20,17 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, ops) {
   // vLLM custom ops
   //
 
-  // The default behavior in PyTorch 2.6 is "requires_contiguous", so we need
+  // The default behavior in PyTorch 2.6 was changed to "requires_contiguous",
+  // so we need
   // to override this for many GEMMs with the following tag. Otherwise,
   // torch.compile will force all input tensors to be contiguous(), which
   // will break many custom ops that require column-major weight matrices.
-  // TODO: remove this for PyTorch 2.8, when the default is planned to switch
-  // to match exact eager-mode strides.
-  at::Tag stride_tag = at::Tag::needs_fixed_stride_order;
+  // This was a bug and PyTorch 2.7 has since fixed this.
+#if TORCH_VERSION_MAJOR == 2 && TORCH_VERSION_MINOR == 6
+  #define stride_tag at::Tag::needs_fixed_stride_order
+#else
+  #define stride_tag
+#endif
 
   ops.def("weak_ref_tensor(Tensor input) -> Tensor");
   ops.impl("weak_ref_tensor", torch::kCUDA, &weak_ref_tensor);
@@ -611,6 +615,23 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, ops) {
   ops.impl("selective_scan_fwd", torch::kCUDA, &selective_scan_fwd);
 
 #ifndef USE_ROCM
+  // Compute per-token-group FP8 quantized tensor and scaling factor.
+  ops.def(
+      "per_token_group_fp8_quant(Tensor input, Tensor! output_q, Tensor! "
+      "output_s, "
+      "int group_size, float eps, float fp8_min, float fp8_max, bool "
+      "scale_ue8m0) -> ()");
+  ops.impl("per_token_group_fp8_quant", torch::kCUDA,
+           &per_token_group_quant_fp8);
+
+  // Compute per-token-group INT8 quantized tensor and scaling factor.
+  ops.def(
+      "per_token_group_quant_int8(Tensor input, Tensor! output_q, Tensor! "
+      "output_s, int group_size, float eps, float int8_min, float int8_max) -> "
+      "()");
+  ops.impl("per_token_group_quant_int8", torch::kCUDA,
+           &per_token_group_quant_int8);
+
   // reorder weight for AllSpark Ampere W8A16 Fused Gemm kernel
   ops.def(
       "rearrange_kn_weight_as_n32k16_order(Tensor b_qweight, Tensor b_scales, "

docker/Dockerfile

@@ -209,16 +209,7 @@ ARG SCCACHE_REGION_NAME=us-west-2
 ARG SCCACHE_S3_NO_CREDENTIALS=0
 
 # Flag to control whether to use pre-built vLLM wheels
-ARG VLLM_USE_PRECOMPILED
-# TODO: in setup.py VLLM_USE_PRECOMPILED is sensitive to truthiness, it will take =0 as "true", this should be fixed
-ENV VLLM_USE_PRECOMPILED=""
-RUN if [ "${VLLM_USE_PRECOMPILED}" = "1" ]; then \
-        export VLLM_USE_PRECOMPILED=1 && \
-        echo "Using precompiled wheels"; \
-    else \
-        unset VLLM_USE_PRECOMPILED && \
-        echo "Leaving VLLM_USE_PRECOMPILED unset to build wheels from source"; \
-    fi
+ARG VLLM_USE_PRECOMPILED=""
 
 # if USE_SCCACHE is set, use sccache to speed up compilation
 RUN --mount=type=cache,target=/root/.cache/uv \
@@ -235,6 +226,8 @@ RUN --mount=type=cache,target=/root/.cache/uv \
         && export SCCACHE_S3_NO_CREDENTIALS=${SCCACHE_S3_NO_CREDENTIALS} \
         && export SCCACHE_IDLE_TIMEOUT=0 \
         && export CMAKE_BUILD_TYPE=Release \
+        && export VLLM_USE_PRECOMPILED="${VLLM_USE_PRECOMPILED}" \
+        && export VLLM_DOCKER_BUILD_CONTEXT=1 \
         && sccache --show-stats \
         && python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38 \
         && sccache --show-stats; \
@@ -248,9 +241,22 @@ RUN --mount=type=cache,target=/root/.cache/ccache \
         # Clean any existing CMake artifacts
         rm -rf .deps && \
         mkdir -p .deps && \
+        export VLLM_USE_PRECOMPILED="${VLLM_USE_PRECOMPILED}" && \
+        export VLLM_DOCKER_BUILD_CONTEXT=1 && \
         python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38; \
     fi
 
+# When using precompiled wheels, keep only the newest manylinux1 wheel and delete others
+RUN if [ "$VLLM_USE_PRECOMPILED" = "1" ]; then \
+        echo "Cleaning up extra wheels in dist/..." && \
+        # Identify the most recent manylinux1_x86_64 wheel
+        KEEP_WHEEL=$(ls -t dist/*manylinux1_x86_64.whl 2>/dev/null | head -n1) && \
+        if [ -n "$KEEP_WHEEL" ]; then \
+            echo "Keeping wheel: $KEEP_WHEEL"; \
+            find dist/ -type f -name "*.whl" ! -path "${KEEP_WHEEL}" -delete; \
+        fi; \
+    fi
+
 # Check the size of the wheel if RUN_WHEEL_CHECK is true
 COPY .buildkite/check-wheel-size.py check-wheel-size.py
 # sync the default value with .buildkite/check-wheel-size.py
@@ -262,10 +268,19 @@ RUN if [ "$RUN_WHEEL_CHECK" = "true" ]; then \
     else \
         echo "Skipping wheel size check."; \
     fi
+#################### FLASHINFER WHEEL BUILD IMAGE ####################
+FROM base AS flashinfer-wheel-builder
+ARG CUDA_VERSION
+
+COPY tools/flashinfer-build.sh /tmp/flashinfer-build.sh
+RUN --mount=type=cache,target=/root/.cache/uv \
+    . /etc/environment && \
+    BUILD_WHEEL=true /tmp/flashinfer-build.sh
+
 #################### EXTENSION Build IMAGE ####################
 
 #################### DEV IMAGE ####################
-FROM base as dev
+FROM base AS dev
 
 ARG PIP_INDEX_URL UV_INDEX_URL
 ARG PIP_EXTRA_INDEX_URL UV_EXTRA_INDEX_URL
@@ -276,10 +291,6 @@ ARG PYTORCH_CUDA_INDEX_BASE_URL
 ENV UV_HTTP_TIMEOUT=500
 ENV UV_INDEX_STRATEGY="unsafe-best-match"
 
-# Workaround for #17068
-RUN --mount=type=cache,target=/root/.cache/uv \
-    uv pip install --system --no-build-isolation "git+https://github.com/state-spaces/mamba@v2.2.4"
-
 COPY requirements/lint.txt requirements/lint.txt
 COPY requirements/test.txt requirements/test.txt
 COPY requirements/dev.txt requirements/dev.txt
@@ -388,49 +399,12 @@ RUN --mount=type=bind,from=build,src=/workspace/dist,target=/vllm-workspace/dist
 # -rw-rw-r-- 1 mgoin mgoin 205M Jun  9 18:03 flashinfer_python-0.2.6.post1-cp39-abi3-linux_x86_64.whl
 # $ # upload the wheel to a public location, e.g. https://wheels.vllm.ai/flashinfer/v0.2.6.post1/flashinfer_python-0.2.6.post1-cp39-abi3-linux_x86_64.whl
 
-# Allow specifying a version, Git revision or local .whl file
-ARG FLASHINFER_CUDA128_INDEX_URL="https://download.pytorch.org/whl/cu128/flashinfer"
-ARG FLASHINFER_CUDA128_WHEEL="flashinfer_python-0.2.6.post1%2Bcu128torch2.7-cp39-abi3-linux_x86_64.whl"
-ARG FLASHINFER_GIT_REPO="https://github.com/flashinfer-ai/flashinfer.git"
-ARG FLASHINFER_GIT_REF="v0.2.8rc1"
-# Flag to control whether to use pre-built FlashInfer wheels (set to false to force build from source)
-# TODO: Currently disabled because the pre-built wheels are not available for FLASHINFER_GIT_REF
-ARG USE_FLASHINFER_PREBUILT_WHEEL=false
-RUN --mount=type=cache,target=/root/.cache/uv bash - <<'BASH'
-  . /etc/environment
-  if [ "$TARGETPLATFORM" != "linux/arm64" ]; then
-      # FlashInfer already has a wheel for PyTorch 2.7.0 and CUDA 12.8. This is enough for CI use
-      if [[ "$CUDA_VERSION" == 12.8* ]] && [[ "$USE_FLASHINFER_PREBUILT_WHEEL" == "true" ]]; then
-          uv pip install --system ${FLASHINFER_CUDA128_INDEX_URL}/${FLASHINFER_CUDA128_WHEEL}
-      else
-          # Exclude CUDA arches for older versions (11.x and 12.0-12.7)
-          # TODO: Update this to allow setting TORCH_CUDA_ARCH_LIST as a build arg.
-          if [[ "${CUDA_VERSION}" == 11.* ]]; then
-              FI_TORCH_CUDA_ARCH_LIST="7.5 8.0 8.9"
-          elif [[ "${CUDA_VERSION}" == 12.[0-7]* ]]; then
-              FI_TORCH_CUDA_ARCH_LIST="7.5 8.0 8.9 9.0a"
-          else
-              # CUDA 12.8+ supports 10.0a and 12.0
-              FI_TORCH_CUDA_ARCH_LIST="7.5 8.0 8.9 9.0a 10.0a 12.0"
-          fi
-          echo "🏗️  Building FlashInfer for arches: ${FI_TORCH_CUDA_ARCH_LIST}"
-
-          git clone --depth 1 --recursive --shallow-submodules \
-            --branch ${FLASHINFER_GIT_REF} \
-            ${FLASHINFER_GIT_REPO} flashinfer
-
-          # Needed to build AOT kernels
-          pushd flashinfer
-            TORCH_CUDA_ARCH_LIST="${FI_TORCH_CUDA_ARCH_LIST}" \
-              python3 -m flashinfer.aot
-            TORCH_CUDA_ARCH_LIST="${FI_TORCH_CUDA_ARCH_LIST}" \
-              uv pip install --system --no-build-isolation .
-          popd
-
-          rm -rf flashinfer
-      fi \
-  fi
-BASH
+# Install FlashInfer from source
+# Version controlled in tools/flashinfer-build.sh - keep in sync with requirements/cuda.txt
+COPY tools/flashinfer-build.sh /tmp/flashinfer-build.sh
+RUN --mount=type=cache,target=/root/.cache/uv \
+    . /etc/environment && \
+    /tmp/flashinfer-build.sh
 COPY examples examples
 COPY benchmarks benchmarks
 COPY ./vllm/collect_env.py .
@@ -467,10 +441,6 @@ ARG PIP_EXTRA_INDEX_URL UV_EXTRA_INDEX_URL
 ENV UV_HTTP_TIMEOUT=500
 ENV UV_INDEX_STRATEGY="unsafe-best-match"
 
-# Workaround for #17068
-RUN --mount=type=cache,target=/root/.cache/uv \
-    uv pip install --system --no-build-isolation "git+https://github.com/state-spaces/mamba@v2.2.4"
-
 # install development dependencies (for testing)
 RUN --mount=type=cache,target=/root/.cache/uv \
     CUDA_MAJOR="${CUDA_VERSION%%.*}"; \
@@ -521,10 +491,11 @@ RUN --mount=type=cache,target=/root/.cache/uv \
         uv pip install --system -r requirements/kv_connectors.txt; \
     fi; \
     if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
-        uv pip install --system accelerate hf_transfer 'modelscope!=1.15.0' 'bitsandbytes>=0.42.0' 'timm==0.9.10' boto3 runai-model-streamer runai-model-streamer[s3]; \
+        BITSANDBYTES_VERSION="0.42.0"; \
     else \
-        uv pip install --system accelerate hf_transfer 'modelscope!=1.15.0' 'bitsandbytes>=0.46.1' 'timm==0.9.10' boto3 runai-model-streamer runai-model-streamer[s3]; \
-    fi
+        BITSANDBYTES_VERSION="0.46.1"; \
+    fi; \
+    uv pip install --system accelerate hf_transfer modelscope "bitsandbytes>=${BITSANDBYTES_VERSION}" 'timm==0.9.10' boto3 runai-model-streamer runai-model-streamer[s3]
 
 ENV VLLM_USAGE_SOURCE production-docker-image
 

docker/Dockerfile.arm

@@ -1,62 +0,0 @@
-# This vLLM Dockerfile is used to construct an image that can build and run vLLM on ARM CPU platform.
-
-FROM ubuntu:22.04 AS cpu-test-arm
-
-ENV CCACHE_DIR=/root/.cache/ccache
-
-ENV CMAKE_CXX_COMPILER_LAUNCHER=ccache
-
-RUN --mount=type=cache,target=/var/cache/apt \
-    apt-get update -y \
-    && apt-get install -y curl ccache git wget vim numactl gcc-12 g++-12 python3 python3-pip libtcmalloc-minimal4 libnuma-dev \
-    && apt-get install -y ffmpeg libsm6 libxext6 libgl1 \
-    && update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-12 10 --slave /usr/bin/g++ g++ /usr/bin/g++-12
-
-# tcmalloc provides better memory allocation efficiency, e.g., holding memory in caches to speed up access of commonly-used objects.
-RUN --mount=type=cache,target=/root/.cache/pip \
-    pip install py-cpuinfo  # Use this to gather CPU info and optimize based on ARM Neoverse cores
-
-# Set LD_PRELOAD for tcmalloc on ARM
-ENV LD_PRELOAD="/usr/lib/aarch64-linux-gnu/libtcmalloc_minimal.so.4"
-
-RUN echo 'ulimit -c 0' >> ~/.bashrc
-
-WORKDIR /workspace
-
-ARG PIP_EXTRA_INDEX_URL="https://download.pytorch.org/whl/cpu"
-ENV PIP_EXTRA_INDEX_URL=${PIP_EXTRA_INDEX_URL}
-RUN --mount=type=cache,target=/root/.cache/pip \
-    --mount=type=bind,src=requirements/build.txt,target=requirements/build.txt \
-    pip install --upgrade pip && \
-    pip install -r requirements/build.txt
-
-FROM cpu-test-arm AS build
-
-WORKDIR /workspace/vllm
-
-RUN --mount=type=cache,target=/root/.cache/pip \
-    --mount=type=bind,src=requirements/common.txt,target=requirements/common.txt \
-    --mount=type=bind,src=requirements/cpu.txt,target=requirements/cpu.txt \
-    pip install -v -r requirements/cpu.txt
-
-COPY . .
-ARG GIT_REPO_CHECK=0
-RUN --mount=type=bind,source=.git,target=.git \
-    if [ "$GIT_REPO_CHECK" != 0 ]; then bash tools/check_repo.sh ; fi
-
-# Disabling AVX512 specific optimizations for ARM
-ARG VLLM_CPU_DISABLE_AVX512="true"
-ENV VLLM_CPU_DISABLE_AVX512=${VLLM_CPU_DISABLE_AVX512}
-
-RUN --mount=type=cache,target=/root/.cache/pip \
-    --mount=type=cache,target=/root/.cache/ccache \
-    --mount=type=bind,source=.git,target=.git \
-    VLLM_TARGET_DEVICE=cpu python3 setup.py bdist_wheel && \
-    pip install dist/*.whl && \
-    rm -rf dist
-
-WORKDIR /workspace/
-
-RUN ln -s /workspace/vllm/tests && ln -s /workspace/vllm/examples && ln -s /workspace/vllm/benchmarks
-
-ENTRYPOINT ["python3", "-m", "vllm.entrypoints.openai.api_server"]

docker/Dockerfile.cpu

@@ -1,4 +1,11 @@
-# This vLLM Dockerfile is used to construct image that can build and run vLLM on x86 CPU platform.
+# This vLLM Dockerfile is used to build images that can run vLLM on both x86_64 and arm64 CPU platforms.
+#
+# Supported platforms:
+#   - linux/amd64 (x86_64)
+#   - linux/arm64 (aarch64)
+#
+# Use the `--platform` option with `docker buildx build` to specify the target architecture, e.g.:
+#   docker buildx build --platform=linux/arm64 -f docker/Dockerfile.cpu .
 #
 # Build targets:
 #   vllm-openai (default): used for serving deployment
@@ -12,16 +19,14 @@
 #   VLLM_CPU_AVX512VNNI=false (default)|true
 #
 
-######################### BASE IMAGE #########################
-FROM ubuntu:22.04 AS base
+######################### COMMON BASE IMAGE #########################
+FROM ubuntu:22.04 AS base-common
 
 WORKDIR /workspace/
 
 ARG PYTHON_VERSION=3.12
 ARG PIP_EXTRA_INDEX_URL="https://download.pytorch.org/whl/cpu"
 
-ENV LD_PRELOAD=""
-
 # Install minimal dependencies and uv
 RUN --mount=type=cache,target=/var/cache/apt,sharing=locked \
     --mount=type=cache,target=/var/lib/apt,sharing=locked \
@@ -53,7 +58,21 @@ RUN --mount=type=cache,target=/root/.cache/uv \
     uv pip install --upgrade pip && \
     uv pip install -r requirements/cpu.txt
 
-ENV LD_PRELOAD="/usr/lib/x86_64-linux-gnu/libtcmalloc_minimal.so.4:/opt/venv/lib/libiomp5.so:$LD_PRELOAD"
+ARG TARGETARCH
+ENV TARGETARCH=${TARGETARCH}
+
+######################### x86_64 BASE IMAGE #########################
+FROM base-common AS base-amd64
+
+ENV LD_PRELOAD="/usr/lib/x86_64-linux-gnu/libtcmalloc_minimal.so.4:/opt/venv/lib/libiomp5.so"
+
+######################### arm64 BASE IMAGE #########################
+FROM base-common AS base-arm64
+
+ENV LD_PRELOAD="/usr/lib/aarch64-linux-gnu/libtcmalloc_minimal.so.4"
+
+######################### BASE IMAGE #########################
+FROM base-${TARGETARCH} AS base
 
 RUN echo 'ulimit -c 0' >> ~/.bashrc
 
@@ -95,7 +114,7 @@ WORKDIR /workspace/vllm
 RUN --mount=type=bind,src=requirements/test.in,target=requirements/test.in \
     cp requirements/test.in requirements/cpu-test.in && \
     sed -i '/mamba_ssm/d' requirements/cpu-test.in && \
-    sed -i 's/torch==.*/torch==2.6.0/g' requirements/cpu-test.in && \
+    sed -i 's/^torch==.*/torch==2.6.0/g' requirements/cpu-test.in && \
     sed -i 's/torchaudio.*/torchaudio/g' requirements/cpu-test.in && \
     sed -i 's/torchvision.*/torchvision/g' requirements/cpu-test.in && \
     uv pip compile requirements/cpu-test.in -o requirements/cpu-test.txt --index-strategy unsafe-best-match --torch-backend cpu

docker/Dockerfile.hpu

@@ -1,21 +0,0 @@
-FROM vault.habana.ai/gaudi-docker/1.20.1/ubuntu22.04/habanalabs/pytorch-installer-2.6.0:latest
-
-COPY ./ /workspace/vllm
-
-WORKDIR /workspace/vllm
-
-RUN pip install -v -r requirements/hpu.txt
-
-ENV no_proxy=localhost,127.0.0.1
-ENV PT_HPU_ENABLE_LAZY_COLLECTIVES=true
-
-RUN VLLM_TARGET_DEVICE=hpu python3 setup.py install
-
-# install development dependencies (for testing)
-RUN python3 -m pip install -e tests/vllm_test_utils
-
-WORKDIR /workspace/
-
-RUN ln -s /workspace/vllm/tests && ln -s /workspace/vllm/examples && ln -s /workspace/vllm/benchmarks
-
-ENTRYPOINT ["python3", "-m", "vllm.entrypoints.openai.api_server"]

docker/Dockerfile.tpu

@@ -1,5 +1,5 @@
-ARG NIGHTLY_DATE="20250124"
-ARG BASE_IMAGE="us-central1-docker.pkg.dev/tpu-pytorch-releases/docker/xla:nightly_3.10_tpuvm_$NIGHTLY_DATE"
+ARG NIGHTLY_DATE="20250724"
+ARG BASE_IMAGE="us-central1-docker.pkg.dev/tpu-pytorch-releases/docker/xla:nightly_3.12_tpuvm_$NIGHTLY_DATE"
 
 FROM $BASE_IMAGE
 WORKDIR /workspace/vllm

docker/Dockerfile.xpu

@@ -47,7 +47,7 @@ FROM vllm-base AS vllm-openai
 
 # install additional dependencies for openai api server
 RUN --mount=type=cache,target=/root/.cache/pip \
-    pip install accelerate hf_transfer pytest 'modelscope!=1.15.0'
+    pip install accelerate hf_transfer pytest pytest_asyncio lm_eval[api] modelscope
 
 ENV VLLM_USAGE_SOURCE production-docker-image \
     TRITON_XPU_PROFILE 1

docs/.nav.yml

@@ -56,9 +56,7 @@ nav:
       - contributing/model/tests.md
       - contributing/model/multimodal.md
     - CI: contributing/ci
-    - Design Documents:
-      - V0: design
-      - V1: design/v1
+    - Design Documents: design
   - API Reference:
     - Summary: api/README.md
     - Contents:

docs/api/README.md

@@ -14,7 +14,6 @@ API documentation for vLLM's configuration classes.
 - [vllm.config.DeviceConfig][]
 - [vllm.config.SpeculativeConfig][]
 - [vllm.config.LoRAConfig][]
-- [vllm.config.PromptAdapterConfig][]
 - [vllm.config.MultiModalConfig][]
 - [vllm.config.PoolerConfig][]
 - [vllm.config.DecodingConfig][]

docs/configuration/model_resolution.md

@@ -14,7 +14,7 @@ For example:
 ```python
 from vllm import LLM
 
-model = LLM(
+llm = LLM(
     model="cerebras/Cerebras-GPT-1.3B",
     hf_overrides={"architectures": ["GPT2LMHeadModel"]},  # GPT-2
 )

docs/configuration/tpu.md

@@ -0,0 +1,104 @@
+# TPU Optimization Tips
+
+This doc serves as a collection of handy tips for optimizing your vLLM on TPU workload.
+
+## Get started
+
+Looking for setup and installation instructions? Find them [here](../getting_started/installation/google_tpu.md).
+
+### TPU workload sizing
+
+When selecting the ideal number of chips for a single serving instance, it's important to account for both the model size and the average request context length. Adequate HBM for the KV cache is essential to ensure a sufficient number of concurrent requests can be processed.
+
+The following colab [calculator](https://colab.research.google.com/github/ericehanley/rightsize-vllm/blob/main/HBM_Calculator.ipynb) will tell you:
+
+- KV cache size requirement per token and per request
+- TPU/GPU memory consumed by the model weights
+- TPU/GPU memory allocated for the KV cache
+- Maximum \# of requests you can approximately set (--max-num-seqs)
+
+This approach serves as a general rule of thumb.
+
+#### Latency-throughput tradeoff
+
+As with rightsizing the number of chips for your workload, consider adjusting `--max-num-seqs` to fine-tune the latency-throughput balance. Decreasing `--max-num-seqs` and/or increasing the number of chips can help reduce latency.
+
+`--max-num-seqs` defines the number of concurrent decode slots, effectively limiting the number of requests the server can process tokens for simultaneously. Increasing this value allows the server to pre-allocate more HBM to handle a higher number of concurrent requests, which can maximize overall throughput. However, this often increases the end-to-end (e2e) latency per request.
+
+Therefore, carefully tuning `--max-num-seqs` is crucial to achieving the desired balance between latency and throughput for your specific workload.
+
+In a similar way, `--max-num-batch-tokens` can be adjusted down to improve latency, or adjusted up to improve throughput.
+
+#### Compilation and Caching
+
+Coming from a GPU background, one of the key differences you'll notice with TPUs is an initial compilation step. TPUs are specialized accelerators (ASICs) that achieve maximum performance by executing pre-compiled, static computation graphs via the XLA compiler. Unlike GPUs, which can handle dynamic input shapes more flexibly, TPUs require a specific compiled graph for each tensor shape (e.g., batch size and sequence length) they process.
+
+To manage this, vLLM performs a one-time "warmup" process when you first launch the server. During this phase, it pre-compiles the model for various common input shapes and saves these compiled graphs to a cache on disk or remote storage (located at `~/.cache/vllm/xla_cache` by default). This process can range significantly, anywhere from a few minutes to an hour depending on the size of the model and context length used.
+
+Although the first compilation can take some time, for all subsequent server launches, vLLM can load these graphs directly from the cache, eliminating the compilation time for future runs.
+
+Use `VLLM_XLA_CACHE_PATH` environment variable to write to shareable storage for future deployed nodes (like when using autoscaling).
+
+#### Reducing compilation time
+This initial compilation time ranges significantly and is impacted by many of the arguments discussed in this optimization doc. Factors that influence the length of time to compile are things like model size and `--max-num-batch-tokens`. Other arguments you can tune are things like `VLLM_TPU_MOST_MODEL_LEN`.
+
+### Optimize based on your data
+
+#### max model len vs. most model len
+
+![most_model_len](../assets/design/v1/tpu/most_model_len.png)
+
+If most of your requests are shorter than the maximum model length but you still need to accommodate occasional longer requests, setting a high maximum model length can negatively impact performance. In these cases, you can try introducing most model len by specifying the `VLLM_TPU_MOST_MODEL_LEN` environment variable.
+
+For example, 1% requests are 32k length and 99% requests are 2k length. You can pass 32k into `--max-model-len 32768` and use `VLLM_TPU_MOST_MODEL_LEN=2048`.
+
+The requests get subdivided into max-model-len and most-model-len categories, for the latter category, we can gain better performance since the server can process more requests at a time.
+
+#### Padding
+
+For online serving with latency requirements, consider switching to bucket padding by setting the `VLLM_TPU_BUCKET_PADDING_GAP` environment variable. Because of the layout of the TPU, try using increments of 128: 128, 256, etc.
+
+The server pads the requests into fixed lengths before sending them to the model to avoid recompilation. To read more about tpu padding, see [here](https://cloud.google.com/tpu/docs/performance-guide#xla-efficiencies). Currently, there are 2 ways to pad the requests:
+
+1) the default exponential padding (pad to the nearest power of 2)
+2) bucket padding (pad to the nearest linearly increasing bucket).
+
+When using bucket padding, the buckets start from 16, end at max_model_len, and increment by `VLLM_TPU_BUCKET_PADDING_GAP`.
+
+For example, max_model_len=512, padding_gap=64, the buckets will be [16, 32, 64, 128, 192, 256, 320, 384, 448, 512].
+
+The fewer tokens we pad, the less unnecessary computation TPU does, the better performance we can get. For example, if num_tokens=300, with exponential padding, we pad to 512, with the bucket_padding above, we pad to 320.
+
+However, you need to be careful to choose the padding gap. If the gap is too small, it means the number of buckets is large, leading to increased warmup (precompile) time and higher memory to store the compiled graph. Too many compilaed graphs may lead to HBM OOM. Conversely, an overly large gap yields no performance improvement compared to the default exponential padding.
+
+**If possible, use the precision that matches the chip’s hardware acceleration**
+
+- v5e has int4/int8 hardware acceleration in the MXU
+- v6e has int4/int8 hardware acceleration in the MXU
+
+Supported quantized formats and features in vLLM on TPU [Jul '25]
+- INT8 W8A8
+- INT8 W8A16
+- FP8 KV cache
+- [WIP] FP8 W8A8
+- [WIP] AWQ
+- [WIP] FP4 W4A8
+
+**Don't set TP to be less than the number of chips on a single-host deployment**
+
+Although it’s common to do this with GPUs, don't try to fragment 2 or 8 different workloads across 8 chips on a single host. If you need 1 or 4 chips, just create an instance with 1 or 4 chips (these are partial-host machine types).
+
+### Tune your workloads!
+
+Although we try to have great default configs, we strongly recommend you check out the [vLLM auto-tuner](../../benchmarks/auto_tune/README.md) to optimize your workloads for your use case.
+
+### Future Topics We'll Cover
+
+#### Profiling
+
+The auto-tuner provides a profile of optimized configurations as its final step. However, interpreting this profile can be challenging for new users. We plan to expand this section in the future with more detailed guidance. In the meantime, you can learn how to collect a TPU profile using vLLM's native profiling tools [here](../examples/offline_inference/profiling_tpu.md). This profile can provide valuable insights into your workload's performance.
+
+#### SPMD
+More details to come.
+
+**Want us to cover something that isn't listed here? Open up an issue please and cite this doc. We'd love to hear your questions or tips.**

docs/contributing/README.md

@@ -26,6 +26,8 @@ See <gh-file:LICENSE>.
 
 ## Developing
 
+--8<-- "docs/getting_started/installation/python_env_setup.inc.md"
+
 Depending on the kind of development you'd like to do (e.g. Python, CUDA), you can choose to build vLLM with or without compilation.
 Check out the [building from source][build-from-source] documentation for details.
 
@@ -42,7 +44,7 @@ For an optimized workflow when iterating on C++/CUDA kernels, see the [Increment
 Install MkDocs along with the [plugins](https://github.com/vllm-project/vllm/blob/main/mkdocs.yaml) used in the vLLM documentation, as well as required dependencies:
 
 ```bash
-pip install -r requirements/docs.txt
+uv pip install -r requirements/docs.txt
 ```
 
 !!! note
@@ -98,13 +100,14 @@ For additional features and advanced configurations, refer to the official [MkDo
 ??? console "Commands"
 
     ```bash
-    pip install -r requirements/dev.txt
+    # These commands are only for Nvidia CUDA platforms.
+    uv pip install -r requirements/common.txt -r requirements/dev.txt --torch-backend=auto
 
     # Linting, formatting and static type checking
-    pre-commit install --hook-type pre-commit --hook-type commit-msg
+    pre-commit install
 
     # You can manually run pre-commit with
-    pre-commit run --all-files
+    pre-commit run --all-files --show-diff-on-failure
 
     # To manually run something from CI that does not run
     # locally by default, you can run:
@@ -122,6 +125,10 @@ For additional features and advanced configurations, refer to the official [MkDo
 
     Therefore, we recommend developing with Python 3.12 to minimise the chance of your local environment clashing with our CI environment.
 
+!!! note "Install python3-dev if Python.h is missing"
+    If any of the above commands fails with `Python.h: No such file or directory`, install
+    `python3-dev` with `sudo apt install python3-dev`.
+
 !!! note
     Currently, the repository is not fully checked by `mypy`.
 
@@ -153,7 +160,7 @@ Using `-s` with `git commit` will automatically add this header.
 
 !!! tip
     You can enable automatic sign-off via your IDE:
-  
+
     - **PyCharm**: Click on the `Show Commit Options` icon to the right of the `Commit and Push...` button in the `Commit` window.
       It will bring up a `git` window where you can modify the `Author` and enable `Sign-off commit`.
     - **VSCode**: Open the [Settings editor](https://code.visualstudio.com/docs/configure/settings)

docs/contributing/ci/update_pytorch_version.md

@@ -57,8 +57,7 @@ cc the PyTorch release team to initiate discussion on how to address them.
 
 ## Update CUDA version
 
-The PyTorch release matrix includes both stable and experimental [CUDA versions](https://github.com/pytorch/pytorch/blob/main/RELEASE.md#release-compatibility-matrix). Due to limitations, only the latest stable CUDA version (for example,
-`torch2.7.0+cu12.6`) is uploaded to PyPI. However, vLLM may require a different CUDA version,
+The PyTorch release matrix includes both stable and experimental [CUDA versions](https://github.com/pytorch/pytorch/blob/main/RELEASE.md#release-compatibility-matrix). Due to limitations, only the latest stable CUDA version (for example, torch `2.7.1+cu126`) is uploaded to PyPI. However, vLLM may require a different CUDA version,
 such as 12.8 for Blackwell support.
 This complicates the process as we cannot use the out-of-the-box
 `pip install torch torchvision torchaudio` command. The solution is to use
@@ -134,7 +133,7 @@ MAX_JOBS=16 uv pip install --system \
 
 ```bash
 uv pip install --system \
-    --no-build-isolation "git+https://github.com/state-spaces/mamba@v2.2.4"
+    --no-build-isolation "git+https://github.com/state-spaces/mamba@v2.2.5"
 ```
 
 ### causal-conv1d

docs/contributing/profiling.md

@@ -9,10 +9,13 @@ We support tracing vLLM workers using the `torch.profiler` module. You can enabl
 
 The OpenAI server also needs to be started with the `VLLM_TORCH_PROFILER_DIR` environment variable set.
 
-When using `benchmarks/benchmark_serving.py`, you can enable profiling by passing the `--profile` flag.
+When using `vllm bench serve`, you can enable profiling by passing the `--profile` flag.
 
 Traces can be visualized using <https://ui.perfetto.dev/>.
 
+!!! tip
+You can directly call bench module without installing vllm using `python -m vllm.entrypoints.cli.main bench`.
+
 !!! tip
     Only send a few requests through vLLM when profiling, as the traces can get quite large. Also, no need to untar the traces, they can be viewed directly.
 
@@ -35,10 +38,10 @@ VLLM_TORCH_PROFILER_DIR=./vllm_profile \
     --model meta-llama/Meta-Llama-3-70B
 ```
 
-benchmark_serving.py:
+vllm bench command:
 
 ```bash
-python benchmarks/benchmark_serving.py \
+vllm bench serve \
     --backend vllm \
     --model meta-llama/Meta-Llama-3-70B \
     --dataset-name sharegpt \
@@ -69,13 +72,13 @@ apt install nsight-systems-cli
 
 For basic usage, you can just append `nsys profile -o report.nsys-rep --trace-fork-before-exec=true --cuda-graph-trace=node` before any existing script you would run for offline inference.
 
-The following is an example using the `benchmarks/benchmark_latency.py` script:
+The following is an example using the `vllm bench latency` script:
 
 ```bash
 nsys profile -o report.nsys-rep \
     --trace-fork-before-exec=true \
     --cuda-graph-trace=node \
-    python benchmarks/benchmark_latency.py \
+vllm bench latency \
     --model meta-llama/Llama-3.1-8B-Instruct \
     --num-iters-warmup 5 \
     --num-iters 1 \
@@ -98,7 +101,7 @@ nsys profile -o report.nsys-rep \
     vllm serve meta-llama/Llama-3.1-8B-Instruct
 
 # client
-python benchmarks/benchmark_serving.py \
+vllm bench serve \
     --backend vllm \
     --model meta-llama/Llama-3.1-8B-Instruct \
     --num-prompts 1 \
@@ -132,7 +135,7 @@ You can view these profiles either as summaries in the CLI, using `nsys stats [p
     ...
     ** CUDA GPU Kernel Summary (cuda_gpu_kern_sum):
 
-    Time (%)  Total Time (ns)  Instances   Avg (ns)     Med (ns)    Min (ns)  Max (ns)   StdDev (ns)                                                  Name                                                
+    Time (%)  Total Time (ns)  Instances   Avg (ns)     Med (ns)    Min (ns)  Max (ns)   StdDev (ns)                                                  Name
     --------  ---------------  ---------  -----------  -----------  --------  ---------  -----------  ----------------------------------------------------------------------------------------------------
         46.3   10,327,352,338     17,505    589,965.9    144,383.0    27,040  3,126,460    944,263.8  sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_of…
         14.8    3,305,114,764      5,152    641,520.7    293,408.0   287,296  2,822,716    867,124.9  sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize256x128x64_warpgroupsize2x1x1_execute_segment_k_of…
@@ -143,7 +146,7 @@ You can view these profiles either as summaries in the CLI, using `nsys stats [p
         2.6      587,283,113     37,824     15,526.7      3,008.0     2,719  2,517,756    139,091.1  std::enable_if<T2>(int)0&&vllm::_typeConvert<T1>::exists, void>::type vllm::fused_add_rms_norm_kern…
         1.9      418,362,605     18,912     22,121.5      3,871.0     3,328  2,523,870    175,248.2  void vllm::rotary_embedding_kernel<c10::BFloat16, (bool)1>(const long *, T1 *, T1 *, const T1 *, in…
         0.7      167,083,069     18,880      8,849.7      2,240.0     1,471  2,499,996    101,436.1  void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0…
-    ... 
+    ...
     ```
 
 GUI example:

docs/design/automatic_prefix_caching.md

@@ -1,40 +0,0 @@
-# Automatic Prefix Caching
-
-The core idea of [PagedAttention](https://blog.vllm.ai/2023/06/20/vllm.html) is to partition the KV cache of each request into KV Blocks. Each block contains the attention keys and values for a fixed number of tokens. The PagedAttention algorithm allows these blocks to be stored in non-contiguous physical memory so that we can eliminate memory fragmentation by allocating the memory on demand.
-
-To automatically cache the KV cache, we utilize the following key observation: Each KV block can be uniquely identified by the tokens within the block and the tokens in the prefix before the block.
-
-```text
-                    Block 1                  Block 2                  Block 3
-         [A gentle breeze stirred] [the leaves as children] [laughed in the distance]
-Block 1: |<--- block tokens ---->|
-Block 2: |<------- prefix ------>| |<--- block tokens --->|
-Block 3: |<------------------ prefix -------------------->| |<--- block tokens ---->|
-```
-
-In the example above, the KV cache in the first block can be uniquely identified with the tokens “A gentle breeze stirred”. The third block can be uniquely identified with the tokens in the block “laughed in the distance”, along with the prefix tokens “A gentle breeze stirred the leaves as children”. Therefore, we can build the following one-to-one mapping:
-
-```text
-hash(prefix tokens + block tokens) <--> KV Block
-```
-
-With this mapping, we can add another indirection in vLLM’s KV cache management. Previously, each sequence in vLLM maintained a mapping from their logical KV blocks to physical blocks. To achieve automatic caching of KV blocks, we map the logical KV blocks to their hash value and maintain a global hash table of all the physical blocks. In this way, all the KV blocks sharing the same hash value (e.g., shared prefix blocks across two requests) can be mapped to the same physical block and share the memory space.
-
-This design achieves automatic prefix caching without the need of maintaining a tree structure among the KV blocks. More specifically, all of the blocks are independent of each other and can be allocated and freed by itself, which enables us to manages the KV cache as ordinary caches in operating system.
-
-## Generalized Caching Policy
-
-Keeping all the KV blocks in a hash table enables vLLM to cache KV blocks from earlier requests to save memory and accelerate the computation of future requests. For example, if a new request shares the system prompt with the previous request, the KV cache of the shared prompt can directly be used for the new request without recomputation. However, the total KV cache space is limited and we have to decide which KV blocks to keep or evict when the cache is full.
-
-Managing KV cache with a hash table allows us to implement flexible caching policies. As an example, in current vLLM, we implement the following eviction policy:
-
-* When there are no free blocks left, we will evict a KV block with reference count (i.e., number of current requests using the block) equals 0.
-* If there are multiple blocks with reference count equals to 0, we prioritize to evict the least recently used block (LRU).
-* If there are multiple blocks whose last access time are the same, we prioritize the eviction of the block that is at the end of the longest prefix (i.e., has the maximum number of blocks before it).
-
-Note that this eviction policy effectively implements the exact policy as in [RadixAttention](https://lmsys.org/blog/2024-01-17-sglang/) when applied to models with full attention, which prioritizes to evict reference count zero and least recent used leaf nodes in the prefix tree.
-
-However, the hash-based KV cache management gives us the flexibility to handle more complicated serving scenarios and implement more complicated eviction policies beyond the policy above:
-
-* Multi-LoRA serving. When serving requests for multiple LoRA adapters, we can simply let the hash of each KV block to also include the LoRA ID the request is querying for to enable caching for all adapters. In this way, we can jointly manage the KV blocks for different adapters, which simplifies the system implementation and improves the global cache hit rate and efficiency.
-* Multi-modal models. When the user input includes more than just discrete tokens, we can use different hashing methods to handle the caching of inputs of different modalities. For example, perceptual hashing for images to cache similar input images.

docs/design/fused_moe_modular_kernel.md

@@ -0,0 +1,236 @@
+# Fused MoE Modular Kernel
+
+## Introduction
+FusedMoEModularKernel is implemented [here](gh-file:/vllm/model_executor/layers/fused_moe/modular_kernel.py)
+
+Based on the format of the input activations, FusedMoE implementations are broadly classified into 2 types.
+
+* Contiguous / Standard / Non-Batched, and
+* Batched
+
+!!! note
+    The terms Contiguous, Standard, and Non-Batched are used interchangeably throughout the document.
+
+The input activation format completely depends on the All2All Dispatch being used.
+
+* In the Contiguous variant, the All2All Dispatch returns the activations as a contiguous tensor of shape (M, K) along with TopK Ids and TopK weights of shape (M, num_topk). Look at `DeepEPHTPrepareAndFinalize` for an example.
+* In the Batched variant, the All2All Dispatch returns the activations as a tensor of shape (num_experts, max_tokens, K). Here, the activations/tokens that subscribe to the same expert are batched together. Note that not all entries of the tensor are valid. The activations tensor is typically accompanied by an `expert_num_tokens` tensor of size `num_experts`, where `expert_num_tokens[i]` indicates the number of valid tokens that subscribe to the ith expert. Look at `PplxPrepareAndFinalize` or `DeepEPLLPrepareAndFinalize` for an example.
+
+The FusedMoE operation is generally made of multiple operations, in both the Contiguous and Batched variants, as described in the diagrams below
+
+![](../assets/design/fused_moe_modular_kernel/fused_moe_non_batched.png "FusedMoE Non-Batched")
+
+![](../assets/design/fused_moe_modular_kernel/fused_moe_batched.png "FusedMoE Batched")
+
+!!! note
+    The main difference, in terms of operations, between the Batched and Non-Batched cases is the Permute / Unpermute operations. All other operations remain.
+
+## Motivation
+
+As can be seen from the diagrams, there are a lot of operations and there can be a variety of implementations for each operation. The set of ways the operations can be put together to make a valid FusedMoE implementation quickly becomes intractable. The Modular Kernel framework addresses this issue,  by grouping the operations into logical components. This broad categorization makes the combinations manageable and prevents code-duplication. This also decouples the All2All Dispatch & Combine implementations from the FusedMoE implementations and allows for their independent development and testing. Furthermore, the Modular Kernel framework introduces Abstract classes for the different components thus providing a well-defined skeleton for future implementations.
+
+The rest of the document will focus on the Contiguous / Non-Batched case. Extrapolating to the Batched case should be straight-forward.
+
+## ModularKernel Components:
+FusedMoEModularKernel splits the FusedMoE operation into 3 parts,
+
+1. TopKWeightAndReduce
+2. FusedMoEPrepareAndFinalize
+3. FusedMoEPermuteExpertsUnpermute
+
+### TopKWeightAndReduce
+The TopK Weight Application and Reduction components happen right after the Unpermute operation and before the All2All Combine. Note that the `FusedMoEPermuteExpertsUnpermute` is responsible for the Unpermute and `FusedMoEPrepareAndFinalize` is responsible for the All2All Combine. There is value in doing the TopK Weight Application and Reduction in the `FusedMoEPermuteExpertsUnpermute`. But some implementations choose to do it `FusedMoEPrepareAndFinalize`. In order to enable this flexibility, we have a TopKWeightAndReduce abstract class.
+
+Please find the implementations of TopKWeightAndReduce [here](gh-file:vllm/model_executor/layers/fused_moe/topk_weight_and_reduce.py).
+
+`FusedMoEPrepareAndFinalize::finalize()` method accepts a `TopKWeightAndReduce` argument that is invoked inside the method.
+The `FusedMoEModularKernel` acts as a bridge between the `FusedMoEPermuteExpertsUnpermute` and `FusedMoEPerpareAndFinalize` implementations to determine where the TopK Weight Application and Reduction happens.
+
+* `FusedMoEPermuteExpertsUnpermute::finalize_weight_and_reduce_impl` method returns `TopKWeightAndReduceNoOp` if the `FusedMoEPermuteExpertsUnpermute` implementation does the weight application and reduction itself.
+* `FusedMoEPermuteExpertsUnpermute::finalize_weight_and_reduce_impl` method returns `TopKWeightAndReduceContiguous` / `TopKWeightAndReduceNaiveBatched` / `TopKWeightAndReduceDelegate` if the `FusedMoEPermuteExpertsUnpermute` implementation needs the `FusedMoEPrepareAndFinalize::finalize()` to do the weight application and reduction.
+
+### FusedMoEPrepareAndFinalize
+The `FusedMoEPrepareAndFinalize` abstract class exposes `prepare` and `finalize` functions.
+The `prepare` function is responsible for input activation Quantization and All2All Dispatch. The `finalize` function is responsible for invoking the All2All Combine. Additionally the `finalize` function may or may not do the TopK weight application and reduction (Please refer to the TopKWeightAndReduce section)
+
+![](../assets/design/fused_moe_modular_kernel/prepare_and_finalize_blocks.png "FusedMoEPrepareAndFinalize Blocks")
+
+### FusedMoEPermuteExpertsUnpermute
+The `FusedMoEPermuteExpertsUnpermute` class is where the crux of the MoE operations happen. The `FusedMoEPermuteExpertsUnpermute` abstract class exposes a few important functions,
+
+* apply()
+* workspace_shapes()
+* finalize_weight_and_reduce_impl()
+
+#### apply()
+The `apply` method is where the implementations perform
+
+* Permute
+* Matmul with weight W1
+* Act + Mul
+* Quantization
+* Matmul with weight W2
+* Unpermute
+* Maybe TopK Weight Application + Reduction
+
+#### workspace_shapes()
+The core FusedMoE implementation performs a series of operations. It would be inefficient to create output memory for each of these operations separately. To that effect, implementations are required to declare 2 workspace shapes, the workspace datatype and the FusedMoE output shape as outputs of the workspace_shapes() method. This information is used to allocate the workspace tensors and the output tensor in `FusedMoEModularKernel::forward()` and passed on to the `FusedMoEPermuteExpertsUnpermute::apply()` method. The workspaces could then be used as intermediate buffers in the FusedMoE implementation.
+
+#### finalize_weight_and_reduce_impl()
+It is sometimes efficient to perform TopK weight application and Reduction inside the `FusedMoEPermuteExpertsUnpermute::apply()`. Find an example [here](https://github.com/vllm-project/vllm/pull/20228). We have a `TopKWeightAndReduce` abstract class to facilitate such implementations. Please refer to the TopKWeightAndReduce section.
+`FusedMoEPermuteExpertsUnpermute::finalize_weight_and_reduce_impl()` returns the `TopKWeightAndReduce` object that the implementation wants the `FusedMoEPrepareAndFinalize::finalize()` to use.
+
+![](../assets/design/fused_moe_modular_kernel/fused_experts_blocks.png "FusedMoEPermuteExpertsUnpermute Blocks")
+
+### FusedMoEModularKernel
+`FusedMoEModularKernel` is composed of the `FusedMoEPrepareAndFinalize` and `FusedMoEPermuteExpertsUnpermute` objects.
+`FusedMoEModularKernel` pseudocode/sketch,
+
+```
+FusedMoEModularKernel::__init__(self,
+            prepare_finalize: FusedMoEPrepareAndFinalize,
+            fused_experts: FusedMoEPermuteExpertsUnpermute):
+
+    self.prepare_finalize = prepare_finalize
+    self.fused_experts = fused_experts
+
+FusedMoEModularKernel::forward(self, DP_A):
+
+    Aq, A_scale, _, _, _ = self.prepare_finalize.prepare(DP_A, ...)
+
+    workspace13_shape, workspace2_shape, _, _ = self.fused_experts.workspace_shapes(...)
+
+    # allocate workspaces
+    workspace_13 = torch.empty(workspace13_shape, ...)
+    workspace_2 = torch.empty(workspace2_shape, ...)
+
+    # execute fused_experts
+    fe_out = self.fused_experts.apply(Aq, A_scale, workspace13, workspace2, ...)
+
+    # war_impl is an object of type TopKWeightAndReduceNoOp if the fused_experts implementations performs the TopK Weight Application and Reduction.
+    war_impl = self.fused_experts.finalize_weight_and_reduce_impl()
+
+    output = self.prepare_finalize.finalize(fe_out, war_impl,...)
+                            
+    return output
+```
+
+## How-To
+
+### How To Add a FusedMoEPrepareAndFinalize Type
+Typically a FusedMoEPrepareAndFinalize type is backed by an All2All Dispatch & Combine implementation / kernel. For example,
+
+* PplxPrepareAndFinalize type is backed by Pplx All2All kernels,
+* DeepEPHTPrepareAndFinalize type is backed by DeepEP High-Throughtput All2All kernels, and
+* DeepEPLLPrepareAndFinalize type is backed by DeepEP Low-Latency All2All kernels.
+
+#### Step 1: Add an All2All manager
+The purpose of the All2All Manager is to setup the All2All kernel implementations. The `FusedMoEPrepareAndFinalize` implementations typically fetch a kernel-implementation "handle" from the All2All Manager to invoke the Dispatch and Combine functions. Please look at the All2All Manager implementations [here](gh-file:vllm/distributed/device_communicators/all2all.py).
+
+#### Step 2: Add a FusedMoEPrepareAndFinalize Type
+This section describes the significance of the various functions exposed by the `FusedMoEPrepareAndFinalize` abstract class.
+
+`FusedMoEPrepareAndFinalize::prepare()`: The prepare method implements the Quantization and All2All Dispatch. Typically the Dispatch function from the relevant All2All Manager is invoked.
+
+`FusedMoEPrepareAndFinalize::finalize()`: Maybe perform TopK Weight Application and Reduction and All2All Combine. Typically the Combine function from the relevant All2AllManager is invoked.
+
+`FusedMoEPrepareAndFinalize::activation_format()`: Return `FusedMoEActivationFormat.BatchedExperts` if the output of the prepare method (i.e. the All2All dispatch) is Batched. Return `FusedMoEActivationFormat.Standard` otherwise.
+
+`FusedMoEPrepareAndFinalize::topk_indices_dtype()`: Data type of the TopK ids. Some All2All kernels have strict requirements pertaining to the data type of the TopK ids. This requirement is passed on to the `FusedMoe::select_experts` function so it could be respected. If there are no strict requirements return None.
+
+`FusedMoEPrepareAndFinalize::max_num_tokens_per_rank()`: This is the maximum number of tokens that would be submitted to the All2All Dispatch at once.
+
+`FusedMoEPrepareAndFinalize::num_dispatchers()`: Total number of dispatching units. This value determines the size of the Dispatch output. The Dispatch output is of shape (num_local_experts, max_num_tokens, K). Here max_num_tokens = num_dispatchers() * max_num_tokens_per_rank().
+
+We suggest picking an already existing `FusedMoEPrepareAndFinalize` implementation that matches your All2All implementation closely and using it as a reference.
+
+### How To Add a FusedMoEPermuteExpertsUnpermute Type
+FusedMoEPermuteExpertsUnpermute performs the core of the FusedMoE operations. The various functions exposed by the abstract class and their significance is as follows,
+
+`FusedMoEPermuteExpertsUnpermute::activation_formats()`: Return the supported Input and Output activation formats. i.e. Contiguous / Batched format.
+
+`FusedMoEPermuteExpertsUnpermute::supports_chunking()`: Return True if the implementation supports chunking. Typically
+implementations that input `FusedMoEActivationFormat.Standard` support chunking and `FusedMoEActivationFormat.BatchedExperts` do not.
+
+`FusedMoEPermuteExpertsUnpermute::supports_expert_map()`: Return True if the implementation supports expert map.
+
+`FusedMoEPermuteExpertsUnpermute::workspace_shapes()` /
+`FusedMoEPermuteExpertsUnpermute::finalize_weight_and_reduce_impl` /
+`FusedMoEPermuteExpertsUnpermute::apply`: Refer to `FusedMoEPermuteExpertsUnpermute` section above.
+
+### FusedMoEModularKernel Initialization
+`FusedMoEMethodBase` class has 2 methods that are collectively responsible in creating the `FusedMoEModularKernel` object. They are,
+
+* select_gemm_impl, and
+* init_prepare_finalize
+
+#### select_gemm_impl
+The `select_gemm_impl` method is undefined in the base class. It is the responsibility of the derived class to implement a method that constructs a valid/appropriate `FusedMoEPermuteExpertsUnpermute` object.
+Please refer to the implementations in,
+
+* `UnquantizedFusedMoEMethod`
+* `CompressedTensorsW8A8Fp8MoEMethod`
+* `CompressedTensorsW8A8Fp8MoECutlassMethod`
+* `Fp8MoEMethod`
+* `ModelOptNvFp4FusedMoE`
+dervied classes.
+
+#### init_prepare_finalize
+Based on the input and env settings, the `init_prepare_finalize` method creates the appropriate `FusedMoEPrepareAndFinalize` object. The method then queries `select_gemm_impl` for the appropriate `FusedMoEPermuteExpertsUnpermute` object and builds the `FusedMoEModularKernel` object
+
+Please take a look at [init_prepare_finalize](https://github.com/vllm-project/vllm/blob/1cbf951ba272c230823b947631065b826409fa62/vllm/model_executor/layers/fused_moe/layer.py#L188).
+**Important**: The `FusedMoEMethodBase` derived classes use the `FusedMoEMethodBase::fused_experts` object in their `apply` methods. When settings permit the construction of a valid `FusedMoEModularKernel` object, we override `FusedMoEMethodBase::fused_experts` with it. This essentially makes the derived classes agnostic to what FusedMoE implementation is used.
+
+### How To Unit Test
+We have `FusedMoEModularKernel` unit tests at [test_modular_kernel_combinations.py](gh-file:tests/kernels/moe/test_modular_kernel_combinations.py).
+
+The unit test iterates through all combinations of `FusedMoEPrepareAndFinalize` and `FusedMoEPremuteExpertsUnpermute` types and if they are
+compatible, runs some correctness tests.
+If you are adding some `FusedMoEPrepareAndFinalize` / `FusedMoEPermuteExpertsUnpermute` implementations,
+
+1. Add the implementation type to `MK_ALL_PREPARE_FINALIZE_TYPES` and `MK_FUSED_EXPERT_TYPES` in [mk_objects.py](gh-file:tests/kernels/moe/modular_kernel_tools/mk_objects.py) respectively.
+2. Update `Config::is_batched_prepare_finalize()`, `Config::is_batched_fused_experts()`, `Config::is_standard_fused_experts()`,
+`Config::is_fe_16bit_supported()`,  `Config::is_fe_fp8_supported()`, `Config::is_fe_block_fp8_supported()`,
+`Config::is_fe_supports_chunking()` methods in [/tests/kernels/moe/modular_kernel_tools/common.py](gh-file:tests/kernels/moe/modular_kernel_tools/common.py)
+
+Doing this will add the new implementation to the test suite.
+
+### How To Check `FusedMoEPrepareAndFinalize` & `FusedMoEPermuteExpertsUnpermute` Compatibility
+The unit test file [test_modular_kernel_combinations.py](gh-file:tests/kernels/moe/test_modular_kernel_combinations.py) can also be executed as a standalone script.
+Example: `python3 -m tests.kernels.moe.test_modular_kernel_combinations --pf-type PplxPrepareAndFinalize --experts-type BatchedTritonExperts`
+As a side-effect, this script can be used to test `FusedMoEPrepareAndFinalize` & `FusedMoEPermuteExpertsUnpermute` compatibility. When invoked
+with incompatible types, the script will error.
+
+### How To Profile
+Please take a look at [profile_modular_kernel.py](gh-file:tests/kernels/moe/modular_kernel_tools/profile_modular_kernel.py)
+The script can be used to generate Torch traces for a single `FusedMoEModularKernel::forward()` call for any compatible
+`FusedMoEPrepareAndFinalize` and `FusedMoEPermuteExpertsUnpermute` types.
+Example: `python3 -m tests.kernels.moe.modular_kernel_tools.profile_modular_kernel --pf-type PplxPrepareAndFinalize --experts-type BatchedTritonExperts`
+
+## FusedMoEPrepareAndFinalize Implementations
+The following table lists the `FusedMoEPrepareAndFinalize` implementations at the time of writing,
+
+| Implementation | Type | Comments |
+| :--- | :--- | :--- |
+| DeepEPHTPrepareAndFinalize | Contiguous / Non-Batched | Uses the DeepEP High-Throughput all2all kernels. |
+| DeepEPLLPrepareAndFinalize | Batched | Uses the DeepEP Low-Latency all2all kernels. |
+| PplxPrepareAndFinalize | Batched | Uses the Perplexity all2all kernels. |
+| FlashInferCutlassMoEPrepareAndFinalize | Contiguous | |
+| MoEPrepareAndFinalizeNoEP | Contiguous | This implementation is used when there is no EP. i.e. no all2all kernels are invoked. |
+| BatchedPrepareAndFinalize | Batched | A reference prepare/finalize class that reorganizes the tokens into expert batched format, i.e. E x max_num_tokens x K. (Doesn’t use any all2all kernels. This is primarily used in unit testing) |
+
+## FusedMoEPermuteExpertsUnpermute
+The following table lists the `FusedMoEPermuteExpertsUnpermute` implementations at the time of writing,
+
+| Implementation | Type | Comment |
+| :--- | :--- | :--- |
+| BatchedDeepGemmExperts | Batched | Uses the DeepGemm’s Masked Grouped Gemm kernels for the fused_moe operation. |
+| BatchedTritonExperts | Batched | Uses a Triton Kernel for the Batched matmuls. |
+| BatchedTritonOrDeepGemmExperts | Batched | Chooses either the `BatchedDeepGemmExperts` or `BatchedTritonExperts` based on environment settings. |
+| DeepGemmExperts | Contiguous / Non-Batched | Uses DeepGemm’s Grouped Gemm kernels for fused_moe operation. |
+| TritonExperts | Contiguous / Non-Batched | Uses a Triton Kernel for fused_moe matmuls. |
+| TritonOrDeepGemmExperts | Contiguous / Non-Batched | Chooses either the `DeepGemmExperts` or `TritonExperts` based on fused_moe inputs. |
+| CutlassExpertsFP8 | Supports both Batched and Contiguous formats | Uses Cutlass Grouped Gemm implementations for the fp8 matmuls. |
+| CutlassExpertsFP4 | Supports both Batched and Contiguous formats | Uses Cutlass Grouped Gemm implementations for the fp4 matmuls. |
+| FlashInferExperts | Contiguous | Uses fused_moe operation from FlashInfer |
+| NaiveBatchedExperts | Batched | Reference Batched Experts implementation. Primarily used in unit tests. |

docs/design/huggingface_integration.md

@@ -1,4 +1,4 @@
-# Integration with HuggingFace
+# Integration with Hugging Face
 
 This document describes how vLLM integrates with HuggingFace libraries. We will explain step by step what happens under the hood when we run `vllm serve`.
 

docs/design/metrics.md

@@ -5,17 +5,17 @@ Ensure the v1 LLM Engine exposes a superset of the metrics available in v0.
 ## Objectives
 
 - Achieve parity of metrics between v0 and v1.
-- The priority use case is accessing these metrics via Prometheus as this is what we expect to be used in production environments.
-- Logging support - i.e. printing metrics to the info log - is provided for more ad-hoc testing, debugging, development, and exploratory use cases.
+- The priority use case is accessing these metrics via Prometheus, as this is what we expect to be used in production environments.
+- Logging support (i.e. printing metrics to the info log) is provided for more ad-hoc testing, debugging, development, and exploratory use cases.
 
 ## Background
 
 Metrics in vLLM can be categorized as follows:
 
-1. Server-level metrics: these are global metrics that track the state and performance of the LLM engine. These are typically exposed as Gauges or Counters in Prometheus.
-2. Request-level metrics: these are metrics that track the characteristics - e.g. size and timing - of individual requests. These are typically exposed as Histograms in Prometheus, and are often the SLO that an SRE monitoring vLLM will be tracking.
+1. Server-level metrics: Global metrics that track the state and performance of the LLM engine. These are typically exposed as Gauges or Counters in Prometheus.
+2. Request-level metrics: Metrics that track the characteristics (e.g. size and timing) of individual requests. These are typically exposed as Histograms in Prometheus and are often the SLOs that an SRE monitoring vLLM will be tracking.
 
-The mental model is that the "Server-level Metrics" explain why the "Request-level Metrics" are what they are.
+The mental model is that server-level metrics help explain the values of request-level metrics.
 
 ### v0 Metrics
 
@@ -61,24 +61,24 @@ These are documented under [Inferencing and Serving -> Production Metrics](../..
 
 ### Grafana Dashboard
 
-vLLM also provides [a reference example](https://docs.vllm.ai/en/latest/examples/prometheus_grafana.html) for how to collect and store these metrics using Prometheus and visualize them using a Grafana dashboard.
+vLLM also provides [a reference example](../../examples/online_serving/prometheus_grafana.md) for how to collect and store these metrics using Prometheus and visualize them using a Grafana dashboard.
 
 The subset of metrics exposed in the Grafana dashboard gives us an indication of which metrics are especially important:
 
-- `vllm:e2e_request_latency_seconds_bucket` - End to end request latency measured in seconds
-- `vllm:prompt_tokens_total` - Prompt Tokens
-- `vllm:generation_tokens_total` - Generation Tokens
-- `vllm:time_per_output_token_seconds` - Inter token latency (Time Per Output Token, TPOT) in second.
+- `vllm:e2e_request_latency_seconds_bucket` - End to end request latency measured in seconds.
+- `vllm:prompt_tokens_total` - Prompt tokens.
+- `vllm:generation_tokens_total` - Generation tokens.
+- `vllm:time_per_output_token_seconds` - Inter-token latency (Time Per Output Token, TPOT) in seconds.
 - `vllm:time_to_first_token_seconds` - Time to First Token (TTFT) latency in seconds.
-- `vllm:num_requests_running` (also, `_swapped` and `_waiting`) - Number of requests in RUNNING, WAITING, and SWAPPED state
+- `vllm:num_requests_running` (also, `_swapped` and `_waiting`) - Number of requests in the RUNNING, WAITING, and SWAPPED states.
 - `vllm:gpu_cache_usage_perc` - Percentage of used cache blocks by vLLM.
-- `vllm:request_prompt_tokens` - Request prompt length
-- `vllm:request_generation_tokens` - request generation length
-- `vllm:request_success_total` - Number of finished requests by their finish reason: either an EOS token was generated or the max sequence length was reached
-- `vllm:request_queue_time_seconds` - Queue Time
-- `vllm:request_prefill_time_seconds` - Requests Prefill Time
-- `vllm:request_decode_time_seconds` - Requests Decode Time
-- `vllm:request_max_num_generation_tokens` - Max Generation Token in Sequence Group
+- `vllm:request_prompt_tokens` - Request prompt length.
+- `vllm:request_generation_tokens` - Request generation length.
+- `vllm:request_success_total` - Number of finished requests by their finish reason: either an EOS token was generated or the max sequence length was reached.
+- `vllm:request_queue_time_seconds` - Queue time.
+- `vllm:request_prefill_time_seconds` - Requests prefill time.
+- `vllm:request_decode_time_seconds` - Requests decode time.
+- `vllm:request_max_num_generation_tokens` - Max generation tokens in a sequence group.
 
 See [the PR which added this Dashboard](gh-pr:2316) for interesting and useful background on the choices made here.
 
@@ -103,7 +103,7 @@ In v0, metrics are collected in the engine core process and we use multi-process
 
 ### Built in Python/Process Metrics
 
-The following metrics are supported by default by `prometheus_client`, but the are not exposed with multiprocess mode is used:
+The following metrics are supported by default by `prometheus_client`, but they are not exposed when multi-process mode is used:
 
 - `python_gc_objects_collected_total`
 - `python_gc_objects_uncollectable_total`
@@ -158,6 +158,7 @@ In v1, we wish to move computation and overhead out of the engine core
 process to minimize the time between each forward pass.
 
 The overall idea of V1 EngineCore design is:
+
 - EngineCore is the inner loop. Performance is most critical here
 - AsyncLLM is the outer loop. This is overlapped with GPU execution
   (ideally), so this is where any "overheads" should be if
@@ -178,7 +179,7 @@ time" (`time.time()`) to calculate intervals as the former is
 unaffected by system clock changes (e.g. from NTP).
 
 It's also important to note that monotonic clocks differ between
-processes - each process has its own reference. point. So it is
+processes - each process has its own reference point. So it is
 meaningless to compare monotonic timestamps from different processes.
 
 Therefore, in order to calculate an interval, we must compare two
@@ -343,14 +344,15 @@ vllm:time_to_first_token_seconds_bucket{le="0.1",model_name="meta-llama/Llama-3.
 vllm:time_to_first_token_seconds_count{model_name="meta-llama/Llama-3.1-8B-Instruct"} 140.0
 ```
 
-Note - the choice of histogram buckets to be most useful to users
-across a broad set of use cases is not straightforward and will
-require refinement over time.
+!!! note
+    The choice of histogram buckets to be most useful to users
+    across a broad set of use cases is not straightforward and will
+    require refinement over time.
 
 ### Cache Config Info
 
-`prometheus_client` has support for [Info
-metrics](https://prometheus.github.io/client_python/instrumenting/info/)
+`prometheus_client` has support for
+[Info metrics](https://prometheus.github.io/client_python/instrumenting/info/)
 which are equivalent to a `Gauge` whose value is permanently set to 1,
 but exposes interesting key/value pair information via labels. This is
 used for information about an instance that does not change - so it
@@ -363,14 +365,11 @@ We use this concept for the `vllm:cache_config_info` metric:
 # HELP vllm:cache_config_info Information of the LLMEngine CacheConfig
 # TYPE vllm:cache_config_info gauge
 vllm:cache_config_info{block_size="16",cache_dtype="auto",calculate_kv_scales="False",cpu_offload_gb="0",enable_prefix_caching="False",gpu_memory_utilization="0.9",...} 1.0
-
 ```
 
-However, `prometheus_client` has [never supported Info metrics in
-multiprocessing
-mode](https://github.com/prometheus/client_python/pull/300) - for
-[unclear
-reasons](gh-pr:7279#discussion_r1710417152). We
+However, `prometheus_client` has
+[never supported Info metrics in multiprocessing mode](https://github.com/prometheus/client_python/pull/300) -
+for [unclear reasons](gh-pr:7279#discussion_r1710417152). We
 simply use a `Gauge` metric set to 1 and
 `multiprocess_mode="mostrecent"` instead.
 
@@ -395,11 +394,9 @@ distinguish between per-adapter counts. This should be revisited.
 Note that `multiprocess_mode="livemostrecent"` is used - the most
 recent metric is used, but only from currently running processes.
 
-This was added in
-<gh-pr:9477> and there is
-[at least one known
-user](https://github.com/kubernetes-sigs/gateway-api-inference-extension/pull/54). If
-we revisit this design and deprecate the old metric, we should reduce
+This was added in <gh-pr:9477> and there is
+[at least one known user](https://github.com/kubernetes-sigs/gateway-api-inference-extension/pull/54).
+If we revisit this design and deprecate the old metric, we should reduce
 the need for a significant deprecation period by making the change in
 v0 also and asking this project to move to the new metric.
 
@@ -442,23 +439,20 @@ suddenly (from their perspective) when it is removed, even if there is
 an equivalent metric for them to use.
 
 As an example, see how `vllm:avg_prompt_throughput_toks_per_s` was
-[deprecated](gh-pr:2764) (with a
-comment in the code),
-[removed](gh-pr:12383), and then
-[noticed by a
-user](gh-issue:13218).
+[deprecated](gh-pr:2764) (with a comment in the code),
+[removed](gh-pr:12383), and then [noticed by a user](gh-issue:13218).
 
 In general:
 
-1) We should be cautious about deprecating metrics, especially since
+1. We should be cautious about deprecating metrics, especially since
    it can be hard to predict the user impact.
-2) We should include a prominent deprecation notice in the help string
+2. We should include a prominent deprecation notice in the help string
    that is included in the `/metrics' output.
-3) We should list deprecated metrics in user-facing documentation and
+3. We should list deprecated metrics in user-facing documentation and
    release notes.
-4) We should consider hiding deprecated metrics behind a CLI argument
-   in order to give administrators [an escape
-   hatch](https://kubernetes.io/docs/concepts/cluster-administration/system-metrics/#show-hidden-metrics)
+4. We should consider hiding deprecated metrics behind a CLI argument
+   in order to give administrators
+   [an escape hatch](https://kubernetes.io/docs/concepts/cluster-administration/system-metrics/#show-hidden-metrics)
    for some time before deleting them.
 
 See the [deprecation policy](../../contributing/deprecation_policy.md) for
@@ -474,7 +468,7 @@ removed.
 The `vllm:time_in_queue_requests` Histogram metric was added by
 <gh-pr:9659> and its calculation is:
 
-```
+```python
     self.metrics.first_scheduled_time = now
     self.metrics.time_in_queue = now - self.metrics.arrival_time
 ```
@@ -482,7 +476,7 @@ The `vllm:time_in_queue_requests` Histogram metric was added by
 Two weeks later, <gh-pr:4464> added `vllm:request_queue_time_seconds` leaving
 us with:
 
-```
+```python
 if seq_group.is_finished():
     if (seq_group.metrics.first_scheduled_time is not None and
             seq_group.metrics.first_token_time is not None):
@@ -517,8 +511,7 @@ cache to complete other requests), we swap kv cache blocks out to CPU
 memory. This is also known as "KV cache offloading" and is configured
 with `--swap-space` and `--preemption-mode`.
 
-In v0, [vLLM has long supported beam
-search](gh-issue:6226). The
+In v0, [vLLM has long supported beam search](gh-issue:6226). The
 SequenceGroup encapsulated the idea of N Sequences which
 all shared the same prompt kv blocks. This enabled KV cache block
 sharing between requests, and copy-on-write to do branching. CPU
@@ -530,9 +523,8 @@ option than CPU swapping since blocks can be evicted slowly on demand
 and the part of the prompt that was evicted can be recomputed.
 
 SequenceGroup was removed in V1, although a replacement will be
-required for "parallel sampling" (`n>1`). [Beam search was moved out of
-the core (in
-V0)](gh-issue:8306). There was a
+required for "parallel sampling" (`n>1`).
+[Beam search was moved out of the core (in V0)](gh-issue:8306). There was a
 lot of complex code for a very uncommon feature.
 
 In V1, with prefix caching being better (zero over head) and therefore
@@ -547,18 +539,18 @@ Some v0 metrics are only relevant in the context of "parallel
 sampling". This is where the `n` parameter in a request is used to
 request multiple completions from the same prompt.
 
-As part of adding parallel sampling support in <gh-pr:10980> we should
+As part of adding parallel sampling support in <gh-pr:10980>, we should
 also add these metrics.
 
 - `vllm:request_params_n` (Histogram)
 
-Observes the value of the 'n' parameter of every finished request.
+  Observes the value of the 'n' parameter of every finished request.
 
 - `vllm:request_max_num_generation_tokens` (Histogram)
 
-Observes the maximum output length of all sequences in every finished
-sequence group. In the absence of parallel sampling, this is
-equivalent to `vllm:request_generation_tokens`.
+  Observes the maximum output length of all sequences in every finished
+  sequence group. In the absence of parallel sampling, this is
+  equivalent to `vllm:request_generation_tokens`.
 
 ### Speculative Decoding
 
@@ -576,26 +568,23 @@ There is a PR under review (<gh-pr:12193>) to add "prompt lookup (ngram)"
 seculative decoding to v1. Other techniques will follow. We should
 revisit the v0 metrics in this context.
 
-Note - we should probably expose acceptance rate as separate accepted
-and draft counters, like we do for prefix caching hit rate. Efficiency
-likely also needs similar treatment.
+!!! note
+    We should probably expose acceptance rate as separate accepted
+    and draft counters, like we do for prefix caching hit rate. Efficiency
+    likely also needs similar treatment.
 
 ### Autoscaling and Load-balancing
 
 A common use case for our metrics is to support automated scaling of
 vLLM instances.
 
-For related discussion from the [Kubernetes Serving Working
-Group](https://github.com/kubernetes/community/tree/master/wg-serving),
+For related discussion from the
+[Kubernetes Serving Working Group](https://github.com/kubernetes/community/tree/master/wg-serving),
 see:
 
-- [Standardizing Large Model Server Metrics in
-  Kubernetes](https://docs.google.com/document/d/1SpSp1E6moa4HSrJnS4x3NpLuj88sMXr2tbofKlzTZpk)
-- [Benchmarking LLM Workloads for Performance Evaluation and
-  Autoscaling in
-  Kubernetes](https://docs.google.com/document/d/1k4Q4X14hW4vftElIuYGDu5KDe2LtV1XammoG-Xi3bbQ)
-- [Inference
-  Perf](https://github.com/kubernetes-sigs/wg-serving/tree/main/proposals/013-inference-perf)
+- [Standardizing Large Model Server Metrics in Kubernetes](https://docs.google.com/document/d/1SpSp1E6moa4HSrJnS4x3NpLuj88sMXr2tbofKlzTZpk)
+- [Benchmarking LLM Workloads for Performance Evaluation and Autoscaling in Kubernetes](https://docs.google.com/document/d/1k4Q4X14hW4vftElIuYGDu5KDe2LtV1XammoG-Xi3bbQ)
+- [Inference Perf](https://github.com/kubernetes-sigs/wg-serving/tree/main/proposals/013-inference-perf)
 - <gh-issue:5041> and <gh-pr:12726>.
   
 This is a non-trivial topic. Consider this comment from Rob:
@@ -619,19 +608,16 @@ should judge an instance as approaching saturation:
 
 Our approach to naming metrics probably deserves to be revisited:
 
-1. The use of colons in metric names seems contrary to ["colons are
-   reserved for user defined recording
-   rules"](https://prometheus.io/docs/concepts/data_model/#metric-names-and-labels)
+1. The use of colons in metric names seems contrary to
+   ["colons are reserved for user defined recording rules"](https://prometheus.io/docs/concepts/data_model/#metric-names-and-labels).
 2. Most of our metrics follow the convention of ending with units, but
    not all do.
 3. Some of our metric names end with `_total`:
 
-```
-If there is a suffix of `_total` on the metric name, it will be removed. When
-exposing the time series for counter, a `_total` suffix will be added. This is
-for compatibility between OpenMetrics and the Prometheus text format, as OpenMetrics
-requires the `_total` suffix.
-```
+    If there is a suffix of `_total` on the metric name, it will be removed. When
+    exposing the time series for counter, a `_total` suffix will be added. This is
+    for compatibility between OpenMetrics and the Prometheus text format, as OpenMetrics
+    requires the `_total` suffix.
 
 ### Adding More Metrics
 
@@ -642,8 +628,7 @@ There is no shortage of ideas for new metrics:
 - Proposals arising from specific use cases, like the Kubernetes
   auto-scaling topic above
 - Proposals that might arise out of standardisation efforts like
-  [OpenTelemetry Semantic Conventions for Gen
-  AI](https://github.com/open-telemetry/semantic-conventions/tree/main/docs/gen-ai).
+  [OpenTelemetry Semantic Conventions for Gen AI](https://github.com/open-telemetry/semantic-conventions/tree/main/docs/gen-ai).
 
 We should be cautious in our approach to adding new metrics. While
 metrics are often relatively straightforward to add:
@@ -668,19 +653,14 @@ fall under the more general heading of "Observability".
 v0 has support for OpenTelemetry tracing:
 
 - Added by <gh-pr:4687>
-- Configured with `--oltp-traces-endpoint` and
-  `--collect-detailed-traces`
-- [OpenTelemetry blog
-  post](https://opentelemetry.io/blog/2024/llm-observability/)
-- [User-facing
-  docs](https://docs.vllm.ai/en/latest/examples/opentelemetry.html)
-- [Blog
-  post](https://medium.com/@ronen.schaffer/follow-the-trail-supercharging-vllm-with-opentelemetry-distributed-tracing-aa655229b46f)
-- [IBM product
-  docs](https://www.ibm.com/docs/en/instana-observability/current?topic=mgaa-monitoring-large-language-models-llms-vllm-public-preview)
+- Configured with `--oltp-traces-endpoint` and `--collect-detailed-traces`
+- [OpenTelemetry blog post](https://opentelemetry.io/blog/2024/llm-observability/)
+- [User-facing docs](../../examples/online_serving/opentelemetry.md)
+- [Blog post](https://medium.com/@ronen.schaffer/follow-the-trail-supercharging-vllm-with-opentelemetry-distributed-tracing-aa655229b46f)
+- [IBM product docs](https://www.ibm.com/docs/en/instana-observability/current?topic=mgaa-monitoring-large-language-models-llms-vllm-public-preview)
   
-OpenTelemetry has a [Gen AI Working
-Group](https://github.com/open-telemetry/community/blob/main/projects/gen-ai.md).
+OpenTelemetry has a
+[Gen AI Working Group](https://github.com/open-telemetry/community/blob/main/projects/gen-ai.md).
 
 Since metrics is a big enough topic on its own, we are going to tackle
 the topic of tracing in v1 separately.
@@ -699,7 +679,7 @@ These metrics are only enabled when OpenTelemetry tracing is enabled
 and if `--collect-detailed-traces=all/model/worker` is used. The
 documentation for this option states:
 
-> collect detailed traces for the specified "modules. This involves
+> collect detailed traces for the specified modules. This involves
 > use of possibly costly and or blocking operations and hence might
 > have a performance impact.
 

docs/design/p2p_nccl_connector.md

@@ -1,21 +1,23 @@
+# P2P NCCL Connector
+
 An implementation of xPyD with dynamic scaling based on point-to-point communication, partly inspired by Dynamo.
 
-# Detailed Design
+## Detailed Design
 
-## Overall Process
-As shown in Figure 1, the overall process of this **PD disaggregation** solution is described through a request flow:  
+### Overall Process
+As shown in Figure 1, the overall process of this **PD disaggregation** solution is described through a request flow:
 
-1. The client sends an HTTP request to the Proxy/Router's `/v1/completions` interface.  
-2. The Proxy/Router selects a **1P1D (1 Prefill instance + 1 Decode instance)** through either through round-robin or random selection, generates a `request_id` (rules to be introduced later), modifies the `max_tokens` in the HTTP request message to **1**, and then forwards the request to the **P instance**.  
-3. Immediately afterward, the Proxy/Router forwards the **original HTTP request** to the **D instance**.  
-4. The **P instance** performs **Prefill** and then **actively sends the generated KV cache** to the D instance (using **PUT_ASYNC** mode). The D instance's `zmq_addr` can be resolved through the `request_id`.  
-5. The **D instance** has a **dedicated thread** for receiving the KV cache (to avoid blocking the main process). The received KV cache is saved into the **GPU memory buffer**, the size of which is determined by the vLLM startup parameter `kv_buffer_size`. When the GPU buffer is full, the KV cache is stored in the **local Tensor memory pool**.  
-6. During the **Decode**, the D instance's main process retrieves the KV cache (transmitted by the P instance) from either the **GPU buffer** or the **memory pool**, thereby **skipping Prefill**.  
+1. The client sends an HTTP request to the Proxy/Router's `/v1/completions` interface.
+2. The Proxy/Router selects a **1P1D (1 Prefill instance + 1 Decode instance)** through either through round-robin or random selection, generates a `request_id` (rules to be introduced later), modifies the `max_tokens` in the HTTP request message to **1**, and then forwards the request to the **P instance**.
+3. Immediately afterward, the Proxy/Router forwards the **original HTTP request** to the **D instance**.
+4. The **P instance** performs **Prefill** and then **actively sends the generated KV cache** to the D instance (using **PUT_ASYNC** mode). The D instance's `zmq_addr` can be resolved through the `request_id`.
+5. The **D instance** has a **dedicated thread** for receiving the KV cache (to avoid blocking the main process). The received KV cache is saved into the **GPU memory buffer**, the size of which is determined by the vLLM startup parameter `kv_buffer_size`. When the GPU buffer is full, the KV cache is stored in the **local Tensor memory pool**.
+6. During the **Decode**, the D instance's main process retrieves the KV cache (transmitted by the P instance) from either the **GPU buffer** or the **memory pool**, thereby **skipping Prefill**.
 7. After completing **Decode**, the D instance returns the result to the **Proxy/Router**, which then forwards it to the **client**.
 
 ![image1](https://github.com/user-attachments/assets/fb01bde6-755b-49f7-ad45-48a94b1e10a7)
 
-## Proxy/Router (Demo)
+### Proxy/Router (Demo)
 
 A simple HTTP service acts as the entry point for client requests and starts a background thread to listen for P/D instances reporting their HTTP IP and PORT, as well as ZMQ IP and PORT. It maintains a dictionary of `http_addr -> zmq_addr`. The `http_addr` is the IP:PORT for the vLLM instance's request, while the `zmq_addr` is the address for KV cache handshake and metadata reception.
 
@@ -29,89 +31,73 @@ Currently, to quickly verify whether xPyD can work, a round-robin selection of 1
 
 Each P/D instance periodically sends a heartbeat packet to the Proxy/Router (currently every 3 seconds) to register (i.e., report `http_addr -> zmq_addr`) and keep the connection alive. If an instance crashes and fails to send a ping for a certain period of time, the Proxy/Router will remove the timed-out instance (this feature has not yet been developed).
 
-## KV Cache Transfer Methods
+### KV Cache Transfer Methods
 
-There are three methods for KVcache transfer: PUT, GET, and PUT_ASYNC. These methods can be specified using the `--kv-transfer-config` and `kv_connector_extra_config` parameters, specifically through the `send_type` field. Both PUT and PUT_ASYNC involve the P instance actively sending KVcache to the D instance. The difference is that PUT is a synchronous transfer method that blocks the main process, while PUT_ASYNC is an asynchronous transfer method. PUT_ASYNC uses a dedicated thread for sending KVcache, which means it does not block the main process. In contrast, the GET method involves the P instance saving the KVcache to the memory buffer after computing the prefill. The D instance then actively retrieves the computed KVcache from the P instance once it has allocated space for the KVcache.
+There are three methods for KVCache transfer: PUT, GET, and PUT_ASYNC. These methods can be specified using the `--kv-transfer-config` and `kv_connector_extra_config` parameters, specifically through the `send_type` field. Both PUT and PUT_ASYNC involve the P instance actively sending KVCache to the D instance. The difference is that PUT is a synchronous transfer method that blocks the main process, while PUT_ASYNC is an asynchronous transfer method. PUT_ASYNC uses a dedicated thread for sending KVCache, which means it does not block the main process. In contrast, the GET method involves the P instance saving the KVCache to the memory buffer after computing the prefill. The D instance then actively retrieves the computed KVCache from the P instance once it has allocated space for the KVCache.
 
 Experimental results have shown that the performance of these methods, from highest to lowest, is as follows: PUT_ASYNC → GET → PUT.
 
-## P2P Communication via ZMQ & NCCL
+### P2P Communication via ZMQ & NCCL
 
 As long as the address of the counterpart is known, point-to-point KV cache transfer (using NCCL) can be performed, without being constrained by rank and world size. To support dynamic scaling (expansion and contraction) of instances with PD disaggregation. This means that adding or removing P/D instances does not require a full system restart.
 
-Each P/D instance only needs to create a single `P2pNcclEngine` instance. This instance maintains a ZMQ Server, which runs a dedicated thread to listen on the `zmq_addr` address and receive control flow requests from other instances. These requests include requests to establish an NCCL connection and requests to send KVcache metadata (such as tensor shapes and data types). However, it does not actually transmit the KVcache data itself.
+Each P/D instance only needs to create a single `P2pNcclEngine` instance. This instance maintains a ZMQ Server, which runs a dedicated thread to listen on the `zmq_addr` address and receive control flow requests from other instances. These requests include requests to establish an NCCL connection and requests to send KVCache metadata (such as tensor shapes and data types). However, it does not actually transmit the KVCache data itself.
 
-When a P instance and a D instance transmit KVcache for the first time, they need to establish a ZMQ connection and an NCCL group. For subsequent KVcache transmissions, this ZMQ connection and NCCL group are reused. The NCCL group consists of only two ranks, meaning the world size is equal to 2. This design is intended to support dynamic scaling, which means that adding or removing P/D instances does not require a full system restart. As long as the address of the counterpart is known, point-to-point KVcache transmission can be performed, without being restricted by rank or world size.
+When a P instance and a D instance transmit KVCache for the first time, they need to establish a ZMQ connection and an NCCL group. For subsequent KVCache transmissions, this ZMQ connection and NCCL group are reused. The NCCL group consists of only two ranks, meaning the world size is equal to 2. This design is intended to support dynamic scaling, which means that adding or removing P/D instances does not require a full system restart. As long as the address of the counterpart is known, point-to-point KVCache transmission can be performed, without being restricted by rank or world size.
 
-## NCCL Group Topology
+### NCCL Group Topology
 
-Currently, only symmetric TP (Tensor Parallelism) methods are supported for KVcache transmission. Asymmetric TP and PP (Pipeline Parallelism) methods will be supported in the future. Figure 2 illustrates the 1P2D setup, where each instance has a TP (Tensor Parallelism) degree of 2. There are a total of 7 NCCL groups: three vLLM instances each have one NCCL group with TP=2. Additionally, the 0th GPU card of the P instance establishes an NCCL group with the 0th GPU card of each D instance. Similarly, the 1st GPU card of the P instance establishes an NCCL group with the 1st GPU card of each D instance.
+Currently, only symmetric TP (Tensor Parallelism) methods are supported for KVCache transmission. Asymmetric TP and PP (Pipeline Parallelism) methods will be supported in the future. Figure 2 illustrates the 1P2D setup, where each instance has a TP (Tensor Parallelism) degree of 2. There are a total of 7 NCCL groups: three vLLM instances each have one NCCL group with TP=2. Additionally, the 0th GPU card of the P instance establishes an NCCL group with the 0th GPU card of each D instance. Similarly, the 1st GPU card of the P instance establishes an NCCL group with the 1st GPU card of each D instance.
 
 ![image2](https://github.com/user-attachments/assets/837e61d6-365e-4cbf-8640-6dd7ab295b36)
 
 Each NCCL group occupies a certain amount of GPU memory buffer for communication, the size of which is primarily influenced by the `NCCL_MAX_NCHANNELS` environment variable. When `NCCL_MAX_NCHANNELS=16`, an NCCL group typically occupies 100MB, while when `NCCL_MAX_NCHANNELS=8`, it usually takes up 52MB. For large-scale xPyD configurations—such as DeepSeek's 96P144D—this implementation is currently not feasible. Moving forward, we are considering using RDMA for point-to-point communication and are also keeping an eye on UCCL.
 
-## GPU Memory Buffer and Tensor Memory Pool
+### GPU Memory Buffer and Tensor Memory Pool
 
-The trade-off in the size of the memory buffer is as follows: For P instances, the memory buffer is not required in PUT and PUT_ASYNC modes, but it is necessary in GET mode. For D instances, a memory buffer is needed in all three modes. The memory buffer for D instances should not be too large. Similarly, for P instances in GET mode, the memory buffer should also not be too large. The memory buffer of D instances is used to temporarily store KVcache sent by P instances. If it is too large, it will reduce the KVcache space available for normal inference by D instances, thereby decreasing the inference batch size and ultimately leading to a reduction in output throughput. The size of the memory buffer is configured by the parameter `kv_buffer_size`, measured in bytes, and is typically set to 5%～10% of the memory size.
+The trade-off in the size of the memory buffer is as follows: For P instances, the memory buffer is not required in PUT and PUT_ASYNC modes, but it is necessary in GET mode. For D instances, a memory buffer is needed in all three modes. The memory buffer for D instances should not be too large. Similarly, for P instances in GET mode, the memory buffer should also not be too large. The memory buffer of D instances is used to temporarily store KVCache sent by P instances. If it is too large, it will reduce the KVCache space available for normal inference by D instances, thereby decreasing the inference batch size and ultimately leading to a reduction in output throughput. The size of the memory buffer is configured by the parameter `kv_buffer_size`, measured in bytes, and is typically set to 5%～10% of the memory size.
 
-If the `--max-num-seqs` parameter for P instances is set to a large value, due to the large batch size, P instances will generate a large amount of KVcache simultaneously. This may exceed the capacity of the memory buffer of D instances, resulting in KVcache loss. Once KVcache is lost, D instances need to recompute Prefill, which is equivalent to performing Prefill twice. Consequently, the time-to-first-token (TTFT) will significantly increase, leading to degraded performance.
+If the `--max-num-seqs` parameter for P instances is set to a large value, due to the large batch size, P instances will generate a large amount of KVCache simultaneously. This may exceed the capacity of the memory buffer of D instances, resulting in KVCache loss. Once KVCache is lost, D instances need to recompute Prefill, which is equivalent to performing Prefill twice. Consequently, the time-to-first-token (TTFT) will significantly increase, leading to degraded performance.
 
-To address the above issues, I have designed and developed a local Tensor memory pool for storing KVcache, inspired by the buddy system used in Linux memory modules. Since the memory is sufficiently large, typically in the TB range on servers, there is no need to consider prefix caching or using block-based designs to reuse memory, thereby saving space. When the memory buffer is insufficient, KVcache can be directly stored in the Tensor memory pool, and D instances can subsequently retrieve KVcache from it. The read and write speed is that of PCIe, with PCIe 4.0 having a speed of approximately 21 GB/s, which is usually faster than the Prefill speed. Otherwise, solutions like Mooncake and lmcache would not be necessary. The Tensor memory pool acts as a flood diversion area, typically unused except during sudden traffic surges. In the worst-case scenario, my solution performs no worse than the normal situation with a Cache store.
+To address the above issues, I have designed and developed a local Tensor memory pool for storing KVCache, inspired by the buddy system used in Linux memory modules. Since the memory is sufficiently large, typically in the TB range on servers, there is no need to consider prefix caching or using block-based designs to reuse memory, thereby saving space. When the memory buffer is insufficient, KVCache can be directly stored in the Tensor memory pool, and D instances can subsequently retrieve KVCache from it. The read and write speed is that of PCIe, with PCIe 4.0 having a speed of approximately 21 GB/s, which is usually faster than the Prefill speed. Otherwise, solutions like Mooncake and lmcache would not be necessary. The Tensor memory pool acts as a flood diversion area, typically unused except during sudden traffic surges. In the worst-case scenario, my solution performs no worse than the normal situation with a Cache store.
 
-# Install vLLM
+## Install vLLM
 
-??? console "Commands"
+```shell
+pip install "vllm>=0.9.2"
+```
 
-    ```shell
-    # Enter the home directory or your working directory.
-    cd /home
+## Run xPyD
 
-    # Download the installation package, and I will update the commit-id in time. You can directly copy the command.
-    wget https://vllm-wheels.s3.us-west-2.amazonaws.com/9112b443a042d8d815880b8780633882ad32b183/vllm-1.0.0.dev-cp38-abi3-manylinux1_x86_64.whl
-
-    # Download the code repository.
-    git clone -b xpyd-v1 https://github.com/Abatom/vllm.git
-    cd vllm
-
-    # Set the installation package path.
-    export VLLM_PRECOMPILED_WHEEL_LOCATION=/home/vllm-1.0.0.dev-cp38-abi3-manylinux1_x86_64.whl
-
-    # installation
-    pip install -e . -v
-    ```
-
-# Run xPyD
-
-## Instructions
+### Instructions
 - The following examples are run on an A800 (80GB) device, using the Meta-Llama-3.1-8B-Instruct model.
 - Pay attention to the setting of the `kv_buffer_size` (in bytes). The empirical value is 10% of the GPU memory size. This is related to the kvcache size. If it is too small, the GPU memory buffer for temporarily storing the received kvcache will overflow, causing the kvcache to be stored in the tensor memory pool, which increases latency. If it is too large, the kvcache available for inference will be reduced, leading to a smaller batch size and decreased throughput.
 - For Prefill instances, when using non-GET mode, the `kv_buffer_size` can be set to 1, as Prefill currently does not need to receive kvcache. However, when using GET mode, a larger `kv_buffer_size` is required because it needs to store the kvcache sent to the D instance.
 - You may need to modify the `kv_buffer_size` and `port` in the following commands (if there is a conflict).
 - `PUT_ASYNC` offers the best performance and should be prioritized.
 - The `--port` must be consistent with the `http_port` in the `--kv-transfer-config`.
-- The `disagg_prefill_proxy_xpyd.py` script will use port 10001 (for receiving client requests) and port 30001 (for receiving service discovery from P and D instances).
+- The `disagg_proxy_p2p_nccl_xpyd.py` script will use port 10001 (for receiving client requests) and port 30001 (for receiving service discovery from P and D instances).
 - The node running the proxy must have `quart` installed.
 - Supports multiple nodes; you just need to modify the `proxy_ip` and `proxy_port` in `--kv-transfer-config`.
 - In the following examples, it is assumed that **the proxy's IP is 10.0.1.1**.
 
-## Run 1P3D
+### Run 1P3D
 
-### Proxy (e.g. 10.0.1.1)
+#### Proxy (e.g. 10.0.1.1)
 
 ```shell
-cd {your vllm directory}/examples/online_serving/disagg_xpyd/
-python3 disagg_prefill_proxy_xpyd.py &
+cd {your vllm directory}/examples/online_serving/disaggregated_serving_p2p_nccl_xpyd/
+python3 disagg_proxy_p2p_nccl_xpyd.py &
 ```
 
-### Prefill1 (e.g. 10.0.1.2 or 10.0.1.1)
+#### Prefill1 (e.g. 10.0.1.2 or 10.0.1.1)
 
 ??? console "Command"
 
     ```shell
     VLLM_USE_V1=1 CUDA_VISIBLE_DEVICES=0 vllm serve {your model directory} \
         --host 0.0.0.0 \
-        --port 20005 \
+        --port 20001 \
         --tensor-parallel-size 1 \
         --seed 1024 \
         --served-model-name base_model \
@@ -123,17 +109,17 @@ python3 disagg_prefill_proxy_xpyd.py &
         --gpu-memory-utilization 0.9 \
         --disable-log-request \
         --kv-transfer-config \
-        '{"kv_connector":"P2pNcclConnector","kv_role":"kv_producer","kv_buffer_size":"1e1","kv_port":"21001","kv_connector_extra_config":{"proxy_ip":"10.0.1.1","proxy_port":"30001","http_port":"20005","send_type":"PUT_ASYNC","nccl_num_channels":"16"}}' > /var/vllm.log 2>&1 &
+        '{"kv_connector":"P2pNcclConnector","kv_role":"kv_producer","kv_buffer_size":"1e1","kv_port":"21001","kv_connector_extra_config":{"proxy_ip":"10.0.1.1","proxy_port":"30001","http_port":"20001"}}' > /var/vllm.log 2>&1 &
     ```
 
-### Decode1 (e.g. 10.0.1.3 or 10.0.1.1)
+#### Decode1 (e.g. 10.0.1.3 or 10.0.1.1)
 
 ??? console "Command"
 
     ```shell
     VLLM_USE_V1=1 CUDA_VISIBLE_DEVICES=1 vllm serve {your model directory} \
         --host 0.0.0.0 \
-        --port 20009 \
+        --port 20002 \
         --tensor-parallel-size 1 \
         --seed 1024 \
         --served-model-name base_model \
@@ -145,10 +131,10 @@ python3 disagg_prefill_proxy_xpyd.py &
         --gpu-memory-utilization 0.7 \
         --disable-log-request \
         --kv-transfer-config \
-        '{"kv_connector":"P2pNcclConnector","kv_role":"kv_consumer","kv_buffer_size":"8e9","kv_port":"22001","kv_connector_extra_config":{"proxy_ip":"10.0.1.1","proxy_port":"30001","http_port":"20009","send_type":"PUT_ASYNC","nccl_num_channels":"16"}}' > /var/vllm.log 2>&1 &
+        '{"kv_connector":"P2pNcclConnector","kv_role":"kv_consumer","kv_buffer_size":"8e9","kv_port":"22001","kv_connector_extra_config":{"proxy_ip":"10.0.1.1","proxy_port":"30001","http_port":"20002"}}' > /var/vllm.log 2>&1 &
     ```
 
-### Decode2 (e.g. 10.0.1.4 or 10.0.1.1)
+#### Decode2 (e.g. 10.0.1.4 or 10.0.1.1)
 
 ??? console "Command"
 
@@ -167,17 +153,17 @@ python3 disagg_prefill_proxy_xpyd.py &
         --gpu-memory-utilization 0.7 \
         --disable-log-request \
         --kv-transfer-config \
-        '{"kv_connector":"P2pNcclConnector","kv_role":"kv_consumer","kv_buffer_size":"8e9","kv_port":"23001","kv_connector_extra_config":{"proxy_ip":"10.0.1.1","proxy_port":"30001","http_port":"20003","send_type":"PUT_ASYNC","nccl_num_channels":"16"}}' > /var/vllm.log 2>&1 &
+        '{"kv_connector":"P2pNcclConnector","kv_role":"kv_consumer","kv_buffer_size":"8e9","kv_port":"23001","kv_connector_extra_config":{"proxy_ip":"10.0.1.1","proxy_port":"30001","http_port":"20003"}}' > /var/vllm.log 2>&1 &
     ```
 
-### Decode3 (e.g. 10.0.1.5 or 10.0.1.1)
+#### Decode3 (e.g. 10.0.1.5 or 10.0.1.1)
 
 ??? console "Command"
 
     ```shell
     VLLM_USE_V1=1 CUDA_VISIBLE_DEVICES=3 vllm serve {your model directory} \
         --host 0.0.0.0 \
-        --port 20008 \
+        --port 20004 \
         --tensor-parallel-size 1 \
         --seed 1024 \
         --served-model-name base_model \
@@ -189,26 +175,26 @@ python3 disagg_prefill_proxy_xpyd.py &
         --gpu-memory-utilization 0.7 \
         --disable-log-request \
         --kv-transfer-config \
-        '{"kv_connector":"P2pNcclConnector","kv_role":"kv_consumer","kv_buffer_size":"8e9","kv_port":"24001","kv_connector_extra_config":{"proxy_ip":"10.0.1.1","proxy_port":"30001","http_port":"20008","send_type":"PUT_ASYNC","nccl_num_channels":"16"}}' > /var/vllm.log 2>&1 &
+        '{"kv_connector":"P2pNcclConnector","kv_role":"kv_consumer","kv_buffer_size":"8e9","kv_port":"24001","kv_connector_extra_config":{"proxy_ip":"10.0.1.1","proxy_port":"30001","http_port":"20004"}}' > /var/vllm.log 2>&1 &
     ```
 
-## Run 3P1D
+### Run 3P1D
 
-### Proxy (e.g. 10.0.1.1)
+#### Proxy (e.g. 10.0.1.1)
 
 ```shell
-cd {your vllm directory}/examples/online_serving/disagg_xpyd/
-python3 disagg_prefill_proxy_xpyd.py &
+cd {your vllm directory}/examples/online_serving/disaggregated_serving_p2p_nccl_xpyd/
+python3 disagg_proxy_p2p_nccl_xpyd.py &
 ```
 
-### Prefill1 (e.g. 10.0.1.2 or 10.0.1.1)
+#### Prefill1 (e.g. 10.0.1.2 or 10.0.1.1)
 
 ??? console "Command"
 
     ```shell
     VLLM_USE_V1=1 CUDA_VISIBLE_DEVICES=0 vllm serve {your model directory} \
         --host 0.0.0.0 \
-        --port 20005 \
+        --port 20001 \
         --tensor-parallel-size 1 \
         --seed 1024 \
         --served-model-name base_model \
@@ -220,17 +206,17 @@ python3 disagg_prefill_proxy_xpyd.py &
         --gpu-memory-utilization 0.9 \
         --disable-log-request \
         --kv-transfer-config \
-        '{"kv_connector":"P2pNcclConnector","kv_role":"kv_producer","kv_buffer_size":"1e1","kv_port":"21001","kv_connector_extra_config":{"proxy_ip":"10.0.1.1","proxy_port":"30001","http_port":"20005","send_type":"PUT_ASYNC","nccl_num_channels":"16"}}' > /var/vllm.log 2>&1 &
+        '{"kv_connector":"P2pNcclConnector","kv_role":"kv_producer","kv_buffer_size":"1e1","kv_port":"21001","kv_connector_extra_config":{"proxy_ip":"10.0.1.1","proxy_port":"30001","http_port":"20001"}}' > /var/vllm.log 2>&1 &
     ```
 
-### Prefill2 (e.g. 10.0.1.3 or 10.0.1.1)
+#### Prefill2 (e.g. 10.0.1.3 or 10.0.1.1)
 
 ??? console "Command"
 
     ```shell
     VLLM_USE_V1=1 CUDA_VISIBLE_DEVICES=1 vllm serve {your model directory} \
         --host 0.0.0.0 \
-        --port 20009 \
+        --port 20002 \
         --tensor-parallel-size 1 \
         --seed 1024 \
         --served-model-name base_model \
@@ -242,10 +228,10 @@ python3 disagg_prefill_proxy_xpyd.py &
         --gpu-memory-utilization 0.9 \
         --disable-log-request \
         --kv-transfer-config \
-        '{"kv_connector":"P2pNcclConnector","kv_role":"kv_producer","kv_buffer_size":"1e1","kv_port":"22001","kv_connector_extra_config":{"proxy_ip":"10.0.1.1","proxy_port":"30001","http_port":"20009","send_type":"PUT_ASYNC","nccl_num_channels":"16"}}' > /var/vllm.log 2>&1 &
+        '{"kv_connector":"P2pNcclConnector","kv_role":"kv_producer","kv_buffer_size":"1e1","kv_port":"22001","kv_connector_extra_config":{"proxy_ip":"10.0.1.1","proxy_port":"30001","http_port":"20002"}}' > /var/vllm.log 2>&1 &
     ```
 
-### Prefill3 (e.g. 10.0.1.4 or 10.0.1.1)
+#### Prefill3 (e.g. 10.0.1.4 or 10.0.1.1)
 
 ??? console "Command"
 
@@ -264,17 +250,17 @@ python3 disagg_prefill_proxy_xpyd.py &
         --gpu-memory-utilization 0.9 \
         --disable-log-request \
         --kv-transfer-config \
-        '{"kv_connector":"P2pNcclConnector","kv_role":"kv_producer","kv_buffer_size":"1e1","kv_port":"23001","kv_connector_extra_config":{"proxy_ip":"10.0.1.1","proxy_port":"30001","http_port":"20003","send_type":"PUT_ASYNC","nccl_num_channels":"16"}}' > /var/vllm.log 2>&1 &
+        '{"kv_connector":"P2pNcclConnector","kv_role":"kv_producer","kv_buffer_size":"1e1","kv_port":"23001","kv_connector_extra_config":{"proxy_ip":"10.0.1.1","proxy_port":"30001","http_port":"20003"}}' > /var/vllm.log 2>&1 &
     ```
 
-### Decode1 (e.g. 10.0.1.5 or 10.0.1.1)
+#### Decode1 (e.g. 10.0.1.5 or 10.0.1.1)
 
 ??? console "Command"
 
     ```shell
     VLLM_USE_V1=1 CUDA_VISIBLE_DEVICES=3 vllm serve {your model directory} \
         --host 0.0.0.0 \
-        --port 20008 \
+        --port 20004 \
         --tensor-parallel-size 1 \
         --seed 1024 \
         --served-model-name base_model \
@@ -286,10 +272,10 @@ python3 disagg_prefill_proxy_xpyd.py &
         --gpu-memory-utilization 0.7 \
         --disable-log-request \
         --kv-transfer-config \
-        '{"kv_connector":"P2pNcclConnector","kv_role":"kv_consumer","kv_buffer_size":"8e9","kv_port":"24001","kv_connector_extra_config":{"proxy_ip":"10.0.1.1","proxy_port":"30001","http_port":"20008","send_type":"PUT_ASYNC","nccl_num_channels":"16"}}' > /var/vllm.log 2>&1 &
+        '{"kv_connector":"P2pNcclConnector","kv_role":"kv_consumer","kv_buffer_size":"8e9","kv_port":"24001","kv_connector_extra_config":{"proxy_ip":"10.0.1.1","proxy_port":"30001","http_port":"20004"}}' > /var/vllm.log 2>&1 &
     ```
 
-# Single request
+## Single request
 
 ```shell
 curl -X POST -s http://10.0.1.1:10001/v1/completions \
@@ -302,12 +288,12 @@ curl -X POST -s http://10.0.1.1:10001/v1/completions \
 }'
 ```
 
-# Benchmark
+## Benchmark
 
 ??? console "Command"
 
     ```shell
-    python3 benchmark_serving.py \
+    vllm bench serve \
         --backend vllm \
         --model base_model \
         --tokenizer meta-llama/Llama-3.1-8B-Instruct \
@@ -326,32 +312,14 @@ curl -X POST -s http://10.0.1.1:10001/v1/completions \
         --num-prompts 1000
     ```
 
-# Shut down
+## Shut down
 
 ```shell
 pgrep python | xargs kill -9 && pkill -f python
 ```
 
-# Test data
+## Test data
 
-## **Scenario 1**: 1K input & 1K output tokens, E2E P99 latency ~20s
-- **1P5D (6×A800) vs vLLM (1×A800)**:
-  - Throughput ↑7.2% (1085 → 6979/6)
-  - ITL (P99) ↓81.3% (120ms → 22.9ms)
-  - TTFT (P99) ↑26.8% (175ms → 222ms)
-  - TPOT: No change
+### **Scenario**: 1K input & 200 output tokens, E2E P99 latency ~2s
 
-- **1P6D (7×A800) vs vLLM (1×A800)**:
-  - Throughput ↑9.6% (1085 → 8329/7)
-  - ITL (P99) ↓81.0% (120ms → 22.7ms)
-  - TTFT (P99) ↑210% (175ms →543ms)
-  - TPOT: No change
-
-## **Scenario 2**: 1K input & 200 output tokens, E2E P99 latency ~4s
-- **1P1D (2×A800) vs vLLM (1×A800)**:
-  - Throughput ↑37.4% (537 → 1476/2)
-  - ITL (P99) ↓81.8% (127ms → 23.1ms)
-  - TTFT (P99) ↑41.8% (160ms → 227ms)
-  - TPOT: No change
-
-![testdata](https://github.com/user-attachments/assets/f791bfc7-9f3d-4e5c-9171-a42f9f4da627)
+![testdata](https://github.com/user-attachments/assets/cef0953b-4567-4bf9-b940-405b92a28eb1)

docs/design/paged_attention.md

@@ -1,5 +1,9 @@
 # vLLM Paged Attention
 
+!!! warning
+    This document is being kept in the vLLM documentation for historical purposes.
+    It no longer describes the code used in vLLM today.
+
 Currently, vLLM utilizes its own implementation of a multi-head query
 attention kernel (`csrc/attention/attention_kernels.cu`).
 This kernel is designed to be compatible with

docs/features/compatibility_matrix.md

@@ -34,23 +34,24 @@ th:not(:first-child) {
 }
 </style>
 
-| Feature | [CP][chunked-prefill] | [APC](automatic_prefix_caching.md) | [LoRA](lora.md) | <abbr title="Prompt Adapter">prmpt adptr</abbr> | [SD](spec_decode.md) | CUDA graph | <abbr title="Pooling Models">pooling</abbr> | <abbr title="Encoder-Decoder Models">enc-dec</abbr> | <abbr title="Logprobs">logP</abbr> | <abbr title="Prompt Logprobs">prmpt logP</abbr> | <abbr title="Async Output Processing">async output</abbr> | multi-step | <abbr title="Multimodal Inputs">mm</abbr> | best-of | beam-search |
-|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
+| Feature | [CP][chunked-prefill] | [APC](automatic_prefix_caching.md) | [LoRA](lora.md) | [SD](spec_decode.md) | CUDA graph | [pooling](../models/pooling_models.md) | <abbr title="Encoder-Decoder Models">enc-dec</abbr> | <abbr title="Logprobs">logP</abbr> | <abbr title="Prompt Logprobs">prmpt logP</abbr> | <abbr title="Async Output Processing">async output</abbr> | multi-step | <abbr title="Multimodal Inputs">mm</abbr> | best-of | beam-search |
+|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
 | [CP][chunked-prefill] | ✅ | | | | | | | | | | | | | | |
 | [APC](automatic_prefix_caching.md) | ✅ | ✅ | | | | | | | | | | | | | |
 | [LoRA](lora.md) | ✅ | ✅ | ✅ | | | | | | | | | | | | |
-| <abbr title="Prompt Adapter">prmpt adptr</abbr> | ✅ | ✅ | ✅ | ✅ | | | | | | | | | | | |
-| [SD](spec_decode.md) | ✅ | ✅ | ❌ | ✅ | ✅ | | | | | | | | | | |
-| CUDA graph | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | | | | | | | | | |
-| <abbr title="Pooling Models">pooling</abbr> | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ✅ | | | | | | | | |
-| <abbr title="Encoder-Decoder Models">enc-dec</abbr> | ❌ | [❌](gh-issue:7366) | ❌ | ❌ | [❌](gh-issue:7366) | ✅ | ✅ | ✅ | | | | | | | |
-| <abbr title="Logprobs">logP</abbr> | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | | | | | | |
-| <abbr title="Prompt Logprobs">prmpt logP</abbr> | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ✅ | | | | | |
-| <abbr title="Async Output Processing">async output</abbr> | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ❌ | ❌ | ✅ | ✅ | ✅ | | | | |
-| multi-step | ❌ | ✅ | ❌ | ✅ | ❌ | ✅ | ❌ | ❌ | ✅ | ✅ | ✅ | ✅ | | | |
-| <abbr title="Multimodal Inputs">mm</abbr> | ✅ | [🟠](gh-pr:8348) | [🟠](gh-pr:4194) | ❔ | ❔ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❔ | ✅ | | |
-| best-of | ✅ | ✅ | ✅ | ✅ | [❌](gh-issue:6137) | ✅ | ❌ | ✅ | ✅ | ✅ | ❔ | [❌](gh-issue:7968) | ✅ | ✅ | |
-| beam-search | ✅ | ✅ | ✅ | ✅ | [❌](gh-issue:6137) | ✅ | ❌ | ✅ | ✅ | ✅ | ❔ | [❌](gh-issue:7968) | ❔ | ✅ | ✅ |
+| [SD](spec_decode.md) | ✅ | ✅ | ❌ | ✅ | | | | | | | | | | |
+| CUDA graph | ✅ | ✅ | ✅ | ✅ | ✅ | | | | | | | | | |
+| [pooling](../models/pooling_models.md) | ✅\* | ✅\* | ✅ | ❌ | ✅ | ✅ | | | | | | | | |
+| <abbr title="Encoder-Decoder Models">enc-dec</abbr> | ❌ | [❌](gh-issue:7366) | ❌ | [❌](gh-issue:7366) | ✅ | ✅ | ✅ | | | | | | | |
+| <abbr title="Logprobs">logP</abbr> | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | | | | | | |
+| <abbr title="Prompt Logprobs">prmpt logP</abbr> | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ✅ | | | | | |
+| <abbr title="Async Output Processing">async output</abbr> | ✅ | ✅ | ✅ | ❌ | ✅ | ❌ | ❌ | ✅ | ✅ | ✅ | | | | |
+| multi-step | ❌ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | ✅ | ✅ | ✅ | ✅ | | | |
+| [mm](multimodal_inputs.md) | ✅ | ✅ | [🟠](gh-pr:4194) | ❔ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❔ | ✅ | | |
+| best-of | ✅ | ✅ | ✅ | [❌](gh-issue:6137) | ✅ | ❌ | ✅ | ✅ | ✅ | ❔ | [❌](gh-issue:7968) | ✅ | ✅ | |
+| beam-search | ✅ | ✅ | ✅ | [❌](gh-issue:6137) | ✅ | ❌ | ✅ | ✅ | ✅ | ❔ | [❌](gh-issue:7968) | ❔ | ✅ | ✅ |
+
+\* Chunked prefill and prefix caching are only applicable to last-token pooling.
 
 [](){ #feature-x-hardware }
 
@@ -59,14 +60,13 @@ th:not(:first-child) {
 | Feature                                                   | Volta               | Turing    | Ampere    | Ada    | Hopper     | CPU                | AMD    | TPU |
 |-----------------------------------------------------------|---------------------|-----------|-----------|--------|------------|--------------------|--------|-----|
 | [CP][chunked-prefill]                                     | [❌](gh-issue:2729) | ✅        | ✅        | ✅     | ✅        | ✅                  | ✅     | ✅ |
-| [APC](automatic_prefix_caching.md)                           | [❌](gh-issue:3687) | ✅        | ✅        | ✅     | ✅        | ✅                  | ✅     | ✅ |
-| [LoRA](lora.md)                                      | ✅                  | ✅        | ✅        | ✅     | ✅        | ✅                  | ✅     | ✅ |
-| <abbr title="Prompt Adapter">prmpt adptr</abbr>           | ✅                  | ✅        | ✅        | ✅     | ✅        | [❌](gh-issue:8475) | ✅     | ❌ |
-| [SD](spec_decode.md)                                         | ✅                  | ✅        | ✅        | ✅     | ✅        | ✅                  | ✅     | ❌ |
+| [APC](automatic_prefix_caching.md)                        | [❌](gh-issue:3687) | ✅        | ✅        | ✅     | ✅        | ✅                  | ✅     | ✅ |
+| [LoRA](lora.md)                                           | ✅                  | ✅        | ✅        | ✅     | ✅        | ✅                  | ✅     | ✅ |
+| [SD](spec_decode.md)                                      | ✅                  | ✅        | ✅        | ✅     | ✅        | ✅                  | ✅     | ❌ |
 | CUDA graph                                                | ✅                  | ✅        | ✅        | ✅     | ✅        | ❌                  | ✅     | ❌ |
-| <abbr title="Pooling Models">pooling</abbr>               | ✅                  | ✅        | ✅        | ✅     | ✅        | ✅                  | ❔     | ❌ |
+| [pooling](../models/pooling_models.md)                    | ✅                  | ✅        | ✅        | ✅     | ✅        | ✅                  | ✅     | ❌ |
 | <abbr title="Encoder-Decoder Models">enc-dec</abbr>       | ✅                  | ✅        | ✅        | ✅     | ✅        | ✅                  | ❌     | ❌ |
-| <abbr title="Multimodal Inputs">mm</abbr>                 | ✅                  | ✅        | ✅        | ✅     | ✅        | ✅                  | ✅     | ❌ |
+| [mm](multimodal_inputs.md)                                | ✅                  | ✅        | ✅        | ✅     | ✅        | ✅                  | ✅     | ❌ |
 | <abbr title="Logprobs">logP</abbr>                        | ✅                  | ✅        | ✅        | ✅     | ✅        | ✅                  | ✅     | ❌ |
 | <abbr title="Prompt Logprobs">prmpt logP</abbr>           | ✅                  | ✅        | ✅        | ✅     | ✅        | ✅                  | ✅     | ❌ |
 | <abbr title="Async Output Processing">async output</abbr> | ✅                  | ✅        | ✅        | ✅     | ✅        | ❌                  | ❌     | ❌ |

docs/features/lora.md

@@ -302,7 +302,7 @@ To this end, we allow registration of default multimodal LoRAs to handle this au
         return tokenizer.apply_chat_template(chat, tokenize=False)
 
 
-    model = LLM(
+    llm = LLM(
         model=model_id,
         enable_lora=True,
         max_lora_rank=64,
@@ -329,7 +329,7 @@ To this end, we allow registration of default multimodal LoRAs to handle this au
     }
 
 
-    outputs = model.generate(
+    outputs = llm.generate(
         inputs,
         sampling_params=SamplingParams(
             temperature=0.2,

docs/features/multimodal_inputs.md

@@ -98,7 +98,7 @@ To substitute multiple images inside the same text prompt, you can pass in a lis
 
 Full example: <gh-file:examples/offline_inference/vision_language_multi_image.py>
 
-If using the [LLM.chat](https://docs.vllm.ai/en/stable/models/generative_models.html#llmchat) method, you can pass images directly in the message content using various formats: image URLs, PIL Image objects, or pre-computed embeddings:
+If using the [LLM.chat](../models/generative_models.md#llmchat) method, you can pass images directly in the message content using various formats: image URLs, PIL Image objects, or pre-computed embeddings:
 
 ```python
 from vllm import LLM
@@ -177,6 +177,70 @@ Multi-image input can be extended to perform video captioning. We show this with
 You can pass a list of NumPy arrays directly to the `'video'` field of the multi-modal dictionary
 instead of using multi-image input.
 
+Instead of NumPy arrays, you can also pass `'torch.Tensor'` instances, as shown in this example using Qwen2.5-VL:
+
+??? code
+
+    ```python
+    from transformers import AutoProcessor
+    from vllm import LLM, SamplingParams
+    from qwen_vl_utils import process_vision_info
+
+    model_path = "Qwen/Qwen2.5-VL-3B-Instruct/"
+    video_path = "https://content.pexels.com/videos/free-videos.mp4"
+
+    llm = LLM(
+        model=model_path,
+        gpu_memory_utilization=0.8,
+        enforce_eager=True,
+        limit_mm_per_prompt={"video": 1},
+    )
+
+    sampling_params = SamplingParams(
+        max_tokens=1024,
+    )
+
+    video_messages = [
+        {"role": "system", "content": "You are a helpful assistant."},
+        {"role": "user", "content": [
+                {"type": "text", "text": "describe this video."},
+                {
+                    "type": "video",
+                    "video": video_path,
+                    "total_pixels": 20480 * 28 * 28,
+                    "min_pixels": 16 * 28 * 28
+                }
+            ]
+        },
+    ]
+
+    messages = video_messages
+    processor = AutoProcessor.from_pretrained(model_path)
+    prompt = processor.apply_chat_template(
+        messages,
+        tokenize=False,
+        add_generation_prompt=True,
+    )
+
+    image_inputs, video_inputs = process_vision_info(messages)
+    mm_data = {}
+    if video_inputs is not None:
+        mm_data["video"] = video_inputs
+
+    llm_inputs = {
+        "prompt": prompt,
+        "multi_modal_data": mm_data,
+    }
+
+    outputs = llm.generate([llm_inputs], sampling_params=sampling_params)
+    for o in outputs:
+        generated_text = o.outputs[0].text
+        print(generated_text)
+    ```
+
+    !!! note
+        'process_vision_info' is only applicable to Qwen2.5-VL and similar models.
+
 Full example: <gh-file:examples/offline_inference/vision_language.py>
 
 ### Audio Inputs
@@ -279,7 +343,7 @@ Here is a simple example using Phi-3.5-Vision.
 First, launch the OpenAI-compatible server:
 
 ```bash
-vllm serve microsoft/Phi-3.5-vision-instruct --task generate \
+vllm serve microsoft/Phi-3.5-vision-instruct --runner generate \
   --trust-remote-code --max-model-len 4096 --limit-mm-per-prompt '{"image":2}'
 ```
 
@@ -358,7 +422,7 @@ Instead of `image_url`, you can pass a video file via `video_url`. Here is a sim
 First, launch the OpenAI-compatible server:
 
 ```bash
-vllm serve llava-hf/llava-onevision-qwen2-0.5b-ov-hf --task generate --max-model-len 8192
+vllm serve llava-hf/llava-onevision-qwen2-0.5b-ov-hf --runner generate --max-model-len 8192
 ```
 
 Then, you can use the OpenAI client as follows: