Merge branch 'vllm-project:main' into cpu-only

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

@@ -11,7 +11,7 @@ steps:
             - sh
             - .buildkite/nightly-benchmarks/scripts/wait-for-image.sh
   - wait
-  - label: "A100 Benchmark"
+  - label: "A100"
     agents:
       queue: A100
     plugins:
@@ -42,21 +42,20 @@ steps:
           - name: devshm
             emptyDir:
               medium: Memory
-  # - label: "H100: NVIDIA SMI"
-  #   agents:
-  #     queue: H100
-  #   plugins:
-  #   - docker#v5.11.0:
-  #       image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
-  #       command:
-  #       - bash
-  #       - .buildkite/nightly-benchmarks/run-benchmarks-suite.sh
-  #       mount-buildkite-agent: true
-  #       propagate-environment: true
-  #       propagate-uid-gid: false
-  #       ipc: host
-  #       gpus: all
-  #       environment:
-  #       - VLLM_USAGE_SOURCE
-  #       - HF_TOKEN
+  - label: "H100"
+    agents:
+      queue: H100
+    plugins:
+    - docker#v5.11.0:
+        image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
+        command:
+        - bash
+        - .buildkite/nightly-benchmarks/run-benchmarks-suite.sh
+        mount-buildkite-agent: true
+        propagate-environment: true
+        ipc: host
+        gpus: all
+        environment:
+        - VLLM_USAGE_SOURCE
+        - HF_TOKEN
 

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

@@ -54,7 +54,7 @@ wait_for_server() {
   # wait for vllm server to start
   # return 1 if vllm server crashes
   timeout 1200 bash -c '
-    until curl localhost:8000/v1/completions; do
+    until curl -X POST localhost:8000/v1/completions; do
       sleep 1
     done' && return 0 || return 1
 }
@@ -73,8 +73,17 @@ kill_gpu_processes() {
       echo "All GPU processes have been killed."
   fi
 
+  # Sometimes kill with pid doesn't work properly, we can also kill all process running python or python3
+  # since we are in container anyway
+  pkill -9 -f python
+  pkill -9 -f python3
+
   # waiting for GPU processes to be fully killed
-  sleep 10
+  # loop while nvidia-smi returns any processes
+  while [ -n "$(nvidia-smi --query-compute-apps=pid --format=csv,noheader)" ]; do
+    sleep 1
+    echo "Waiting for GPU processes to be killed"
+  done
 
   # remove vllm config file
   rm -rf ~/.config/vllm
@@ -90,12 +99,19 @@ upload_to_buildkite() {
   # upload the benchmarking results to buildkite
 
   # if the agent binary is not found, skip uploading the results, exit 0
-  if [ ! -f /workspace/buildkite-agent ]; then
+  # Check if buildkite-agent is available in the PATH or at /workspace/buildkite-agent
+  if command -v buildkite-agent >/dev/null 2>&1; then
+    BUILDKITE_AGENT_COMMAND="buildkite-agent"
+  elif [ -f /workspace/buildkite-agent ]; then
+    BUILDKITE_AGENT_COMMAND="/workspace/buildkite-agent"
+  else
     echo "buildkite-agent binary not found. Skip uploading the results."
     return 0
   fi
-  /workspace/buildkite-agent annotate --style "info" --context "benchmark-results" < $RESULTS_FOLDER/benchmark_results.md
-  /workspace/buildkite-agent artifact upload "$RESULTS_FOLDER/*"
+
+  # Use the determined command to annotate and upload artifacts
+  $BUILDKITE_AGENT_COMMAND annotate --style "info" --context "$BUILDKITE_LABEL-benchmark-results" < $RESULTS_FOLDER/benchmark_results.md
+  $BUILDKITE_AGENT_COMMAND artifact upload "$RESULTS_FOLDER/*"
 }
 
 run_latency_tests() {
@@ -269,6 +285,7 @@ run_serving_tests() {
     echo "Running test case $test_name"
     echo "Server command: $server_command"
     eval "$server_command" &
+    server_pid=$!
 
     # wait until the server is alive
     wait_for_server
@@ -318,6 +335,7 @@ run_serving_tests() {
     done
 
     # clean up
+    kill -9 $server_pid
     kill_gpu_processes
   done
 }

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

@@ -0,0 +1,77 @@
+#!/bin/bash
+
+set -euox pipefail
+
+if [[ $# -lt 3 ]]; then
+    echo "Please provide the number of nodes and GPU per node."
+    exit 1
+fi
+
+NUM_NODES=$1
+NUM_GPUS=$2
+DOCKER_IMAGE=$3
+
+shift 3
+COMMANDS=("$@")
+if [ ${#COMMANDS[@]} -ne $NUM_NODES ]; then
+    echo "The number of commands must be equal to the number of nodes."
+    echo "Number of nodes: $NUM_NODES"
+    echo "Number of commands: ${#COMMANDS[@]}"
+    exit 1
+fi
+
+echo "List of commands"
+for command in "${COMMANDS[@]}"; do
+    echo $command
+done
+
+start_network() {
+    docker network create --subnet=192.168.10.0/24 docker-net
+}
+
+start_nodes() {
+    for node in $(seq 0 $(($NUM_NODES-1))); do
+        GPU_DEVICES='"device='
+        for node_gpu in $(seq 0 $(($NUM_GPUS - 1))); do
+            DEVICE_NUM=$(($node * $NUM_GPUS + $node_gpu))
+            GPU_DEVICES+=$(($DEVICE_NUM))
+            if [ $node_gpu -lt $(($NUM_GPUS - 1)) ]; then
+                GPU_DEVICES+=','
+            fi
+        done
+        GPU_DEVICES+='"'
+        # echo "Starting node$node with GPU devices: $GPU_DEVICES"
+        docker run -d --gpus "$GPU_DEVICES" --name node$node --network docker-net --ip 192.168.10.$((10 + $node)) --rm $DOCKER_IMAGE tail -f /dev/null
+    done
+}
+
+run_nodes() {
+    for node in $(seq 0 $(($NUM_NODES-1))); do
+        GPU_DEVICES='"device='
+        for node_gpu in $(seq 0 $(($NUM_GPUS - 1))); do
+            DEVICE_NUM=$(($node * $NUM_GPUS + $node_gpu))
+            GPU_DEVICES+=$(($DEVICE_NUM))
+            if [ $node_gpu -lt $(($NUM_GPUS - 1)) ]; then
+                GPU_DEVICES+=','
+            fi
+        done
+        GPU_DEVICES+='"'
+        echo "Running node$node with GPU devices: $GPU_DEVICES"
+        if [ $node -lt $(($NUM_NODES - 1)) ]; then
+            docker exec -d node$node /bin/bash -c "${COMMANDS[$node]}"
+        else
+            docker exec node$node /bin/bash -c "${COMMANDS[$node]}"
+        fi
+    done
+}
+cleanup() {
+    for node in $(seq 0 $(($NUM_NODES-1))); do
+        docker stop node$node
+    done
+    docker network rm docker-net
+}
+trap cleanup EXIT
+start_network
+start_nodes
+run_nodes
+

.github/workflows/mypy.yaml

@@ -32,20 +32,22 @@ jobs:
         pip install types-setuptools
     - name: Mypy
       run: |
+        mypy tests --config-file pyproject.toml
+        mypy vllm/*.py --config-file pyproject.toml
         mypy vllm/attention --config-file pyproject.toml
         mypy vllm/core --config-file pyproject.toml
         mypy vllm/distributed --config-file pyproject.toml
+        mypy vllm/engine  --config-file pyproject.toml
         mypy vllm/entrypoints --config-file pyproject.toml
         mypy vllm/executor --config-file pyproject.toml
+        mypy vllm/inputs --config-file pyproject.toml
+        mypy vllm/logging --config-file pyproject.toml
+        mypy vllm/lora --config-file pyproject.toml
+        mypy vllm/model_executor  --config-file pyproject.toml
         mypy vllm/multimodal --config-file pyproject.toml
-        mypy vllm/usage --config-file pyproject.toml
-        mypy vllm/*.py --config-file pyproject.toml
+        mypy vllm/platforms --config-file pyproject.toml
+        mypy vllm/spec_decode --config-file pyproject.toml
         mypy vllm/transformers_utils --config-file pyproject.toml
-        mypy vllm/engine  --config-file pyproject.toml
+        mypy vllm/usage --config-file pyproject.toml
         mypy vllm/worker --config-file pyproject.toml
-        mypy vllm/spec_decode --config-file pyproject.toml
-        mypy vllm/model_executor  --config-file pyproject.toml
-        mypy vllm/lora --config-file pyproject.toml
-        mypy vllm/logging --config-file pyproject.toml
-        mypy tests --config-file pyproject.toml
 

README.md

@@ -56,10 +56,10 @@ vLLM is flexible and easy to use with:
 
 - Seamless integration with popular Hugging Face models
 - High-throughput serving with various decoding algorithms, including *parallel sampling*, *beam search*, and more
-- Tensor parallelism support for distributed inference
+- Tensor parallelism and pipieline parallelism support for distributed inference
 - Streaming outputs
 - OpenAI-compatible API server
-- Support NVIDIA GPUs, AMD GPUs, Intel CPUs and GPUs
+- Support NVIDIA GPUs, AMD CPUs and GPUs, Intel CPUs and GPUs, PowerPC CPUs
 - (Experimental) Prefix caching support
 - (Experimental) Multi-lora support
 

benchmarks/backend_request_func.py

@@ -390,17 +390,17 @@ def remove_prefix(text: str, prefix: str) -> str:
     return text
 
 
-def get_model(pretrained_model_name_or_path: str):
+def get_model(pretrained_model_name_or_path: str) -> str:
     if os.getenv('VLLM_USE_MODELSCOPE', 'False').lower() == 'true':
         from modelscope import snapshot_download
-    else:
-        from huggingface_hub import snapshot_download
-
-    model_path = snapshot_download(
-        model_id=pretrained_model_name_or_path,
-        local_files_only=huggingface_hub.constants.HF_HUB_OFFLINE,
-        ignore_file_pattern=[".*.pt", ".*.safetensors", ".*.bin"])
-    return model_path
+
+        model_path = snapshot_download(
+            model_id=pretrained_model_name_or_path,
+            local_files_only=huggingface_hub.constants.HF_HUB_OFFLINE,
+            ignore_file_pattern=[".*.pt", ".*.safetensors", ".*.bin"])
+
+        return model_path
+    return pretrained_model_name_or_path
 
 
 def get_tokenizer(

benchmarks/kernels/benchmark_marlin.py

@@ -5,14 +5,16 @@
 from benchmark_shapes import WEIGHT_SHAPES
 
 from vllm import _custom_ops as ops
-from vllm.model_executor.layers.quantization.gptq_marlin import (
-    GPTQ_MARLIN_MAX_PARALLEL, GPTQ_MARLIN_MIN_THREAD_N,
-    GPTQ_MARLIN_SUPPORTED_GROUP_SIZES, GPTQ_MARLIN_SUPPORTED_NUM_BITS)
 from vllm.model_executor.layers.quantization.gptq_marlin_24 import (
     GPTQ_MARLIN_24_MAX_PARALLEL, GPTQ_MARLIN_24_MIN_THREAD_N,
     GPTQ_MARLIN_24_SUPPORTED_GROUP_SIZES, GPTQ_MARLIN_24_SUPPORTED_NUM_BITS)
 from vllm.model_executor.layers.quantization.utils.marlin_utils import (
-    MarlinWorkspace, marlin_24_quantize, marlin_quantize)
+    GPTQ_MARLIN_MAX_PARALLEL, GPTQ_MARLIN_MIN_THREAD_N,
+    GPTQ_MARLIN_SUPPORTED_GROUP_SIZES, GPTQ_MARLIN_SUPPORTED_NUM_BITS)
+from vllm.model_executor.layers.quantization.utils.marlin_utils_test import (
+    MarlinWorkspace, marlin_quantize)
+from vllm.model_executor.layers.quantization.utils.marlin_utils_test_24 import (
+    marlin_24_quantize)
 from vllm.model_executor.layers.quantization.utils.quant_utils import (
     gptq_pack, quantize_weights, sort_weights)
 from vllm.utils import FlexibleArgumentParser

docs/source/_templates/sections/header.html

@@ -5,6 +5,7 @@
     justify-content: center;
     align-items: center;
     font-size: 16px;
+    padding: 0 6px 0 6px;
   }
   .notification-bar p {
     margin: 0;

docs/source/dev/multimodal/adding_multimodal_plugin.rst

@@ -0,0 +1,17 @@
+.. _adding_multimodal_plugin:
+
+Adding a Multimodal Plugin
+==========================
+
+This document teaches you how to add a new modality to vLLM.
+
+Each modality in vLLM is represented by a :class:`~vllm.multimodal.MultiModalPlugin` and registered to :data:`~vllm.multimodal.MULTIMODAL_REGISTRY`.
+For vLLM to recognize a new modality type, you have to create a new plugin and then pass it to :meth:`~vllm.multimodal.MultiModalRegistry.register_plugin`.
+
+The remainder of this document details how to define custom :class:`~vllm.multimodal.MultiModalPlugin` s.
+
+.. note::
+  This article is a work in progress.
+
+..
+  TODO: Add more instructions on how to add new plugins once embeddings is in.

docs/source/dev/multimodal/multimodal_index.rst

@@ -7,17 +7,21 @@ Multi-Modality
 
 vLLM provides experimental support for multi-modal models through the :mod:`vllm.multimodal` package.
 
-Multi-modal input can be passed alongside text and token prompts to :ref:`supported models <supported_vlms>`
+Multi-modal inputs can be passed alongside text and token prompts to :ref:`supported models <supported_vlms>`
 via the ``multi_modal_data`` field in :class:`vllm.inputs.PromptStrictInputs`.
 
-.. note::
-   ``multi_modal_data`` can accept keys and values beyond the builtin ones, as long as a customized plugin is registered through 
-   the :class:`~vllm.multimodal.MULTIMODAL_REGISTRY`.
+Currently, vLLM only has built-in support for image data. You can extend vLLM to process additional modalities
+by following :ref:`this guide <adding_multimodal_plugin>`.
 
-To implement a new multi-modal model in vLLM, please follow :ref:`this guide <enabling_multimodal_inputs>`.
+Looking to add your own multi-modal model? Please follow the instructions listed :ref:`here <enabling_multimodal_inputs>`.
 
-..
-  TODO: Add more instructions on how to add new plugins once embeddings is in.
+Guides
+++++++
+
+.. toctree::
+   :maxdepth: 1
+
+   adding_multimodal_plugin
 
 Module Contents
 +++++++++++++++
@@ -36,10 +40,14 @@ Registry
 Base Classes
 ------------
 
-.. autoclass:: vllm.multimodal.MultiModalDataDict
+.. autodata:: vllm.multimodal.BatchedTensors
+
+.. autoclass:: vllm.multimodal.MultiModalDataBuiltins
     :members:
     :show-inheritance:
 
+.. autodata:: vllm.multimodal.MultiModalDataDict
+
 .. autoclass:: vllm.multimodal.MultiModalInputs
     :members:
     :show-inheritance:

docs/source/getting_started/cpu-installation.rst

@@ -20,7 +20,7 @@ Requirements
 
 * OS: Linux
 * Compiler: gcc/g++>=12.3.0 (optional, recommended)
-* Instruction set architecture (ISA) requirement: AVX512 is required.
+* Instruction set architecture (ISA) requirement: AVX512 (optional, recommended)
 
 .. _cpu_backend_quick_start_dockerfile:
 

docs/source/index.rst

@@ -38,7 +38,7 @@ vLLM is flexible and easy to use with:
 
 * Seamless integration with popular HuggingFace models
 * High-throughput serving with various decoding algorithms, including *parallel sampling*, *beam search*, and more
-* Tensor parallelism support for distributed inference
+* Tensor parallelism and pipieline parallelism support for distributed inference
 * Streaming outputs
 * OpenAI-compatible API server
 * Support NVIDIA GPUs and AMD GPUs