diff --git a/.envrc b/.envrc index 38cef425f..3550a30f2 100644 --- a/.envrc +++ b/.envrc @@ -1,8 +1 @@ use flake - -# creates .venv if doesn't exist and loads its environment -export VIRTUAL_ENV=".venv" -if ! [ -d "./$VIRTUAL_ENV" ]; then - uv venv -fi -layout python diff --git a/flake.nix b/flake.nix index 95d69c2e4..eaff877da 100644 --- a/flake.nix +++ b/flake.nix @@ -146,7 +146,7 @@ config.treefmt.build.wrapper # PYTHON - self'.packages.editableVenv + self'.packages.exo.passthru.evenv uv # RUST diff --git a/justfile b/justfile index 96e26c84b..49711b017 100644 --- a/justfile +++ b/justfile @@ -40,6 +40,18 @@ build-app: rust-rebuild sync-clean package xcodebuild build -project app/EXO/EXO.xcodeproj -scheme EXO -configuration Debug -derivedDataPath app/EXO/build @echo "\nBuild complete. Run with:\n open {{justfile_directory()}}/app/EXO/build/Build/Products/Debug/EXO.app" +sync-cuda: + #!/usr/bin/env bash + uv sync --extra vllm-cuda13 --extra mlx-cuda13 --no-install-package vllm + dest=".venv/lib/python3.13/site-packages" + [[ -d $dest/vllm ]] || { + nix build .#exo-cuda-13.passthru.evenv + # will also grab vllm-0.19.1-distinfo + cp -aL result/lib/python3.13/site-packages/vllm* .venv/lib/python3.13/site-packages + chmod -R u+rwX .venv/lib/python3.13/site-packages/vllm* + rm result + } + clean: rm -rf **/__pycache__ rm -rf target/ diff --git a/nix/vllm-setuppy-cmake.patch b/nix/vllm-setuppy-cmake.patch new file mode 100644 index 000000000..39a0bb7a7 --- /dev/null +++ b/nix/vllm-setuppy-cmake.patch @@ -0,0 +1,26 @@ +diff --git a/setup.py b/setup.py +index 6dc2ed028..bdcc6354a 100644 +--- a/setup.py ++++ b/setup.py +@@ -18,6 +18,13 @@ from setuptools import Extension, setup + from setuptools.command.build_ext import build_ext + + ++if "NIX_ATTRS_JSON_FILE" in os.environ: ++ with open(os.environ["NIX_ATTRS_JSON_FILE"], "r") as f: ++ NIX_ATTRS = json.load(f) ++else: ++ NIX_ATTRS = { "cmakeFlags": os.environ.get("cmakeFlags", "").split() } ++ ++ + def load_module_from_path(module_name, path): + spec = importlib.util.spec_from_file_location(module_name, path) + module = importlib.util.module_from_spec(spec) +@@ -184,6 +191,7 @@ class cmake_build_ext(build_ext): + cmake_args = [ + "-DCMAKE_BUILD_TYPE={}".format(cfg), + "-DVLLM_TARGET_DEVICE={}".format(VLLM_TARGET_DEVICE), ++ *NIX_ATTRS["cmakeFlags"], + ] + + verbose = envs.VERBOSE diff --git a/pyproject.toml b/pyproject.toml index c6a0f37f4..f9802a92a 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -48,27 +48,27 @@ dev = [ [project.optional-dependencies] build = ["nanobind"] -cpu = [ - "mlx==0.31.1; sys_platform == 'linux'", - "mlx-cpu==0.31.1; sys_platform == 'linux'", +mlx-none = ["anyio"] +mlx-cpu = [ + "mlx==0.31.2; sys_platform == 'linux'", + "mlx-cpu==0.31.2; sys_platform == 'linux'", "mlx-lm; sys_platform == 'linux'", "mlx-vlm>=0.3.11; sys_platform== 'linux'", - "torch>=2.10.0; sys_platform == 'linux'", ] -cuda12 = [ +mlx-cuda12 = [ "mlx==0.31.1; sys_platform == 'linux'", "mlx-cuda-12==0.31.1; sys_platform == 'linux'", "mlx-lm; sys_platform == 'linux'", "mlx-vlm>=0.3.11; sys_platform== 'linux'", - "torch>=2.10.0; sys_platform == 'linux'", ] -cuda13 = [ +mlx-cuda13 = [ "mlx==0.31.1; sys_platform == 'linux'", "mlx-cuda-13==0.31.1; sys_platform == 'linux'", "mlx-lm; sys_platform == 'linux'", "mlx-vlm>=0.3.11; sys_platform== 'linux'", - "torch>=2.10.0; sys_platform == 'linux'", ] +vllm-none = ["anyio"] +vllm-cuda13 = ["vllm[cuda13, fastsafetensors]; sys_platform == 'linux'"] ### # workspace configuration @@ -82,11 +82,12 @@ exo-pyo3-bindings = { workspace = true } mlx = { git = "https://github.com/rltakashige/mlx-jaccl-fix-small-recv.git", branch = "address-rdma-gpu-locks", marker = "sys_platform == 'darwin'" } mlx-lm = { git = "https://github.com/rltakashige/mlx-lm", branch = "leo/deepseek-v4" } torch = [ - { index = "pytorch-cu130", marker = "sys_platform == 'linux' and extra == 'cuda13' and extra != 'cpu' and extra != 'cuda12'" }, - { index = "pytorch-cu120", marker = "sys_platform == 'linux' and extra == 'cuda12' and extra != 'cpu' and extra != 'cuda13'" }, - { index = "pytorch-cpu", marker = "(extra != 'cuda12' and extra != 'cuda13' and sys_platform == 'linux') or sys_platform == 'darwin'" }, + { index = "pytorch-cu130", marker = "(extra == 'mlx-cuda13' or extra == 'vllm-cuda13') and sys_platform == 'linux'" }, + { index = "pytorch-cu120", marker = "(extra == 'mlx-cuda12' and extra != 'mlx-cuda13' and extra != 'vllm-cuda13') and sys_platform == 'linux'" }, + { index = "pytorch-cpu", marker = "(extra != 'mlx-cuda12' and extra != 'mlx-cuda13' and extra != 'vllm-cuda13') and sys_platform == 'linux'" }, + { index = "pytorch-cpu", marker = "sys_platform == 'darwin'" }, ] -vllm = { git = "https://github.com/hmellor/vllm.git", branch = "transformers-v5" } +vllm = { git = "http://github.com/evanev7/vllm", branch = "exo" } [[tool.uv.index]] name = "pytorch-cu130" @@ -156,11 +157,19 @@ root = "src" required-version = ">=0.8.6" prerelease = "allow" environments = ["sys_platform == 'darwin'", "sys_platform == 'linux'"] -conflicts = [[{ extra = "cuda12" }, { extra = "cuda13" }, { extra = "cpu" }]] -constraint-dependencies = ["transformers>=5.6.2"] -override-dependencies = [ - "mlx==0.31.1; sys_platform=='linux'", - "mlx; sys_platform=='darwin'", +override-dependencies = ["mlx", "opencv-python; python_version < '0'"] +conflicts = [ + [ + { extra = "mlx-cuda13" }, + { extra = "mlx-cuda12" }, + { extra = "mlx-cpu" }, + { extra = "mlx-none" }, + ], + [ + { extra = "vllm-cuda13" }, + { extra = "mlx-cuda12" }, + { extra = "vllm-none" }, + ], ] [tool.uv.extra-build-dependencies] diff --git a/python/parts.nix b/python/parts.nix index ed5eb0b94..99704b240 100644 --- a/python/parts.nix +++ b/python/parts.nix @@ -10,8 +10,10 @@ let inherit (pkgs.stdenv.hostPlatform) isLinux isDarwin isx86_64; inherit (pkgs.config) cudaSupport; inherit (pkgs) cudaPackages; - cuda13Support = cudaSupport && cudaPackages.cudaMajorVersion == "13"; - libmlx_source = if cuda13Support then "mlx-cuda-13" else if cudaSupport then "mlx-cuda-12" else "mlx-cpu"; + libmlx_source = + if (builtins.elem "mlx-cuda13" members.exo or [ ]) then "mlx-cuda-13" + else if (builtins.elem "mlx-cuda12" members.exo or [ ]) then "mlx-cuda-12" + else "mlx-cpu"; python = pkgs.python313; cudaLibs = with cudaPackages; [ cuda_cudart @@ -113,37 +115,213 @@ let }); } // lib.optionalAttrs isLinux { mlx = prev.mlx.overrideAttrs (old: { + nativeBuildInputs = old.nativeBuildInputs ++ lib.optionals cudaSupport [ pkgs.autoAddDriverRunpath ]; buildInputs = old.buildInputs ++ lib.optionals cudaSupport cudaLibs; - autoPatchelfIgnoreMissingDeps = lib.optionals cudaSupport [ "libcuda.so.1" ]; postInstall = '' cp -r "${final.${libmlx_source}}/${final.python.sitePackages}/mlx" "$out/${final.python.sitePackages}/mlx/" ''; + autoPatchelfIgnoreMissingDeps = [ "libcuda.so.1" ]; }); } // lib.optionalAttrs cudaSupport { "${libmlx_source}" = prev."${libmlx_source}".overrideAttrs (old: { + nativeBuildInputs = old.nativeBuildInputs ++ [ pkgs.autoAddDriverRunpath ]; buildInputs = old.buildInputs ++ cudaLibs; autoPatchelfIgnoreMissingDeps = [ "libcuda.so.1" ]; }); nvidia-cufile = prev.nvidia-cufile.overrideAttrs (old: { + nativeBuildInputs = old.nativeBuildInputs ++ [ pkgs.autoAddDriverRunpath ]; buildInputs = old.buildInputs ++ [ pkgs.rdma-core ]; - autoPatchelfIgnoreMissingDeps = [ "libcuda.so.1" ]; }); nvidia-cusolver = prev.nvidia-cusolver.overrideAttrs (old: { + nativeBuildInputs = old.nativeBuildInputs ++ [ pkgs.autoAddDriverRunpath ]; buildInputs = old.buildInputs ++ cudaLibs; - autoPatchelfIgnoreMissingDeps = [ "libcuda.so.1" ]; }); nvidia-nvshmem-cu13 = prev.nvidia-nvshmem-cu13.overrideAttrs (old: { + nativeBuildInputs = old.nativeBuildInputs ++ [ pkgs.autoAddDriverRunpath ]; buildInputs = old.buildInputs ++ [ pkgs.rdma-core pkgs.pmix pkgs.libfabric pkgs.ucx pkgs.openmpi ]; - autoPatchelfIgnoreMissingDeps = [ "libcuda.so.1" ]; }); nvidia-cusparse = prev.nvidia-cusparse.overrideAttrs (old: { - buildInputs = old.buildInputs ++ [ cudaLibs ]; - autoPatchelfIgnoreMissingDeps = [ "libcuda.so.1" ]; + nativeBuildInputs = old.nativeBuildInputs ++ [ pkgs.autoAddDriverRunpath ]; + buildInputs = old.buildInputs ++ cudaLibs; }); torch = prev.torch.overrideAttrs (old: { + nativeBuildInputs = old.nativeBuildInputs ++ [ pkgs.autoAddDriverRunpath ]; buildInputs = old.buildInputs ++ cudaLibs; autoPatchelfIgnoreMissingDeps = [ "libcuda.so.1" ]; }); + torchaudio = prev.torchaudio.overrideAttrs (old: { + nativeBuildInputs = old.nativeBuildInputs ++ [ pkgs.autoAddDriverRunpath ]; + buildInputs = old.buildInputs ++ [ cudaPackages.cuda_cudart ]; + preFixup = "addAutoPatchelfSearchPath '${final.torch}'"; + }); + torchvision = prev.torchvision.overrideAttrs (old: { + nativeBuildInputs = old.nativeBuildInputs ++ [ pkgs.autoAddDriverRunpath ]; + preFixup = "addAutoPatchelfSearchPath '${final.torch}'"; + }); + + torch-c-dlpack-ext = prev.torch-c-dlpack-ext.overrideAttrs (old: { + buildInputs = old.buildInputs ++ cudaLibs; + autoPatchelfIgnoreMissingDeps = [ "libcuda.so.1" ]; + preFixup = "addAutoPatchelfSearchPath '${final.torch}'"; + }); + # Currently treating vllm as a cuda dep. it obviously exists as a non cuda dep + vllm = prev.vllm.overrideAttrs (old: + let + cuda_cccl_compat = pkgs.runCommand "cuda-cccl-compat" { } '' + mkdir -p $out/include + ln -s ${cudaPackages.cuda_cccl}/include $out/include/cccl + ''; + + cudaRoot = pkgs.symlinkJoin { + name = "cuda-merged-exo"; + paths = builtins.concatMap (p: [ (lib.getBin p) (lib.getLib p) (lib.getDev p) ]) (cudaLibs ++ [ cudaPackages.cuda_nvcc cuda_cccl_compat ]); + }; + + cutlass = pkgs.fetchFromGitHub { + name = "cutlass-source"; + owner = "NVIDIA"; + repo = "cutlass"; + tag = "v4.2.1"; + hash = "sha256-iP560D5Vwuj6wX1otJhwbvqe/X4mYVeKTpK533Wr5gY="; + }; + triton-kernels = pkgs.fetchFromGitHub { + owner = "triton-lang"; + repo = "triton"; + tag = "v3.6.0"; + hash = "sha256-JFSpQn+WsNnh7CAPlcpOcUp0nyKXNbJEANdXqmkt4Tc="; + }; + + cutlass-flashmla = pkgs.fetchFromGitHub { + owner = "NVIDIA"; + repo = "cutlass"; + rev = "147f5673d0c1c3dcf66f78d677fd647e4a020219"; + hash = "sha256-dHQto08IwTDOIuFUp9jwm1MWkFi8v2YJ/UESrLuG71g="; + }; + + flashmla = pkgs.stdenv.mkDerivation { + pname = "flashmla"; + version = "1.0.0"; + + src = pkgs.fetchFromGitHub { + name = "FlashMLA-source"; + owner = "vllm-project"; + repo = "FlashMLA"; + rev = "c2afa9cb93e674d5a9120a170a6da57b89267208"; + hash = "sha256-pKlwxV6G9iHag/jbu3bAyvYvnu5TbrQwUMFV0AlGC3s="; + }; + + dontConfigure = true; + + buildPhase = '' + rm -rf csrc/cutlass + ln -sf ${cutlass-flashmla} csrc/cutlass + ''; + + installPhase = '' + cp -rva . $out + ''; + }; + qutlass = pkgs.fetchFromGitHub { + name = "qutlass-source"; + owner = "IST-DASLab"; + repo = "qutlass"; + rev = "830d2c4537c7396e14a02a46fbddd18b5d107c65"; + hash = "sha256-aG4qd0vlwP+8gudfvHwhtXCFmBOJKQQTvcwahpEqC84="; + }; + vllm-flash-attn = pkgs.stdenv.mkDerivation { + pname = "vllm-flash-attn"; + version = "2.7.2.post1"; + + src = pkgs.fetchFromGitHub { + name = "flash-attention-source"; + owner = "vllm-project"; + repo = "flash-attention"; + rev = "188be16520ceefdc625fdf71365585d2ee348fe2"; + hash = "sha256-Osec+/IF3+UDtbIhDMBXzUeWJ7hDJNb5FpaVaziPSgM="; + }; + + patches = [ + (pkgs.fetchpatch { + url = "https://github.com/Dao-AILab/flash-attention/commit/dad67c88d4b6122c69d0bed1cebded0cded71cea.patch"; + hash = "sha256-JSgXWItOp5KRpFbTQj/cZk+Tqez+4mEz5kmH5EUeQN4="; + }) + (pkgs.fetchpatch { + url = "https://github.com/Dao-AILab/flash-attention/commit/e26dd28e487117ee3e6bc4908682f41f31e6f83a.patch"; + hash = "sha256-NkCEowXSi+tiWu74Qt+VPKKavx0H9JeteovSJKToK9A="; + }) + ]; + + dontConfigure = true; + + buildPhase = '' + rm -rf csrc/cutlass + ln -sf ${cutlass} csrc/cutlass + ''; + + installPhase = '' + cp -rva . $out + ''; + }; + in + { + patches = (old.patches or [ ]) ++ [ ../nix/vllm-setuppy-cmake.patch ]; + nativeBuildInputs = (old.nativeBuildInputs or [ ]) ++ [ + pkgs.cmake + pkgs.ninja + pkgs.autoAddDriverRunpath + ] ++ lib.optionals cudaSupport [ + cudaPackages.cuda_nvcc + ]; + # TODO: vllm rocm/cpu + VLLM_TARGET_DEVICE = "empty"; + preConfigure = '' + export MAX_JOBS="$NIX_BUILD_CORES" + ''; + + # TODO: vllm non cuda13 support, more arch's, etc. + } // lib.optionalAttrs cudaSupport { + buildInputs = cudaLibs ++ [ cudaRoot ]; + + VLLM_CUDA_VERSION = cudaPackages.cudaMajorMinorVersion; + CUDA_HOME = "${cudaRoot}"; + CUDAToolkit_ROOT = "${cudaRoot}"; + CUDACXX = "${cudaRoot}/bin/nvcc"; + VLLM_CUTLASS_SRC_DIR = "${lib.getDev cutlass}"; + VLLM_TARGET_DEVICE = "cuda"; + TORCH_CUDA_ARCH_LIST = "12.0;12.1"; + TRITON_KERNELS_SRC_DIR = "${lib.getDev triton-kernels}/python/triton_kernels/triton_kernels"; + FLASH_MLA_SRC_DIR = "${lib.getDev flashmla}"; + QUTLASS_SRC_DIR = "${lib.getDev qutlass}"; + VLLM_FLASH_ATTN_SRC_DIR = "${lib.getDev vllm-flash-attn}"; + CAFFE2_USE_CUDNN = "ON"; + CAFFE2_USE_CUFILE = "ON"; + CUTLASS_ENABLE_CUBLAS = "ON"; + CUTLASS_NVCC_ARCHS_ENABLED = "12.0;12.1"; + + cmakeFlags = [ + (lib.cmakeBool "CMAKE_SKIP_INSTALL_RPATH" true) + (lib.cmakeBool "CMAKE_BUILD_WITH_INSTALL_RPATH" true) + (lib.cmakeFeature "CUDA_HOME" "${cudaRoot}") + (lib.cmakeFeature "CUDAToolkit_ROOT" "${cudaRoot}") + (lib.cmakeFeature "CMAKE_CUDA_COMPILER" "${cudaRoot}/bin/nvcc") + (lib.cmakeFeature "CMAKE_PREFIX_PATH" "${cudaRoot}") + (lib.cmakeFeature "FETCHCONTENT_SOURCE_DIR_CUTLASS" "${lib.getDev cutlass}") + (lib.cmakeFeature "FLASH_MLA_SRC_DIR" "${lib.getDev flashmla}") + (lib.cmakeFeature "VLLM_FLASH_ATTN_SRC_DIR" "${lib.getDev vllm-flash-attn}") + (lib.cmakeFeature "QUTLASS_SRC_DIR" "${lib.getDev qutlass}") + (lib.cmakeFeature "TORCH_CUDA_ARCH_LIST" "12.0;12.1") + (lib.cmakeFeature "CUTLASS_NVCC_ARCHS_ENABLED" "${cudaPackages.flags.cmakeCudaArchitecturesString}") + (lib.cmakeFeature "CUDA_TOOLKIT_ROOT_DIR" "${cudaRoot}") + (lib.cmakeFeature "CAFFE2_USE_CUDNN" "ON") + (lib.cmakeFeature "CAFFE2_USE_CUFILE" "ON") + (lib.cmakeFeature "CUTLASS_ENABLE_CUBLAS" "ON") + ]; + }); + + } // lib.optionalAttrs (cudaSupport && isx86_64) { + numba = prev.numba.overrideAttrs (old: { + buildInputs = (old.buildInputs or [ ]) ++ [ pkgs.tbb ]; + }); }; pyprojectOverlay = workspace.mkPyprojectOverlay { sourcePreference = "wheel"; @@ -164,24 +342,28 @@ let buildSystemsOverlay ] ); - venv = name: (pythonSet.mkVirtualEnv "${name}-env" members).overrideAttrs (_: { venvSkip = [ "lib/python${python.pythonVersion}/site-packages/mlx/share/cmake/*" ]; }); - mkApp = cmd: name: pkgs.writeShellApplication { + # mlx and mlx-cuda ship clashing cmake files - we dont need them at runtime anyway + venv = name: (pythonSet.mkVirtualEnv "${name}-venv" members).overrideAttrs (_: { venvSkip = [ "lib/python${python.pythonVersion}/site-packages/mlx/share/cmake/*" "lib/python${python.pythonVersion}/site-packages/build_backend.py" ]; }); + mkApp = text: name: pkgs.writeShellApplication { inherit name; + text = "exec " + lib.optionalString cudaSupport "nixglhost " + text; runtimeEnv = { EXO_DASHBOARD_DIR = self'.packages.dashboard; EXO_RESOURCES_DIR = inputs.self + /resources; }; runtimeInputs = [ - # mlx and mlx-cuda ship clashing cmake files - we dont need them at runtime anyway (venv name) + pkgs.nix-gl-host ] ++ lib.optionals isDarwin [ pkgs.macmon ]; - text = "exec " + lib.optionalString cudaSupport "${lib.getExe pkgs.nix-gl-host} " + cmd; + passthru = { + venv = venv name; + evenv = ((pythonSet.overrideScope editableOverlay).mkVirtualEnv "${name}-evenv" (members // { exo = (members.exo or [ ]) ++ [ "dev" ]; })).overrideAttrs (_: { venvSkip = [ "lib/python${python.pythonVersion}/site-packages/mlx/share/cmake/*" "lib/python${python.pythonVersion}/site-packages/build_backend.py" ]; }); + }; }; in { inherit venv; - editablePythonSet = pythonSet.overrideScope editableOverlay; mkPythonScript = path: mkApp ''python ${path} "$@"''; mkExo = mkApp ''exo "$@"''; }; @@ -191,18 +373,18 @@ in { self', pkgs, unfreePkgs, lib, ... }: let inherit (pkgs.stdenv.hostPlatform) isLinux; - inherit (mkPythonSet { inherit self' pkgs lib; members = { exo = [ "cpu" ]; }; }) editablePythonSet mkExo; + inherit (mkPythonSet { inherit self' pkgs lib; members = { exo = [ "mlx-cpu" "vllm-none" ]; }; }) mkExo; # Virtual environment with dev dependencies for testing testVenv = (mkPythonSet { inherit self' pkgs lib; members = { - exo = [ "dev" "cpu" ]; # Include pytest, pytest-asyncio, pytest-env + exo = [ "dev" "mlx-cpu" "vllm-none" ]; # Include pytest, pytest-asyncio, pytest-env }; }).venv "exo-test"; mkBenchScript = (mkPythonSet { inherit self' pkgs lib; members = { - exo = [ "cpu" ]; + exo = [ "mlx-cpu" "vllm-none" ]; exo-bench = [ ]; # Include pytest, pytest-asyncio, pytest-env }; }).mkPythonScript; @@ -212,12 +394,12 @@ in runtimeInputs = [ pkgs.python313 ]; text = ''exec python ${path} "$@"''; }; - + cuda12Set = mkPythonSet { inherit self' lib; inherit (unfreePkgs.pkgsCuda.cudaPackages_12) pkgs; members = { exo = [ "mlx-cuda12" "vllm-none" ]; }; }; + cuda13Set = mkPythonSet { inherit self' lib; inherit (unfreePkgs.pkgsCuda.cudaPackages_13) pkgs; members = { exo = [ "mlx-cpu" "vllm-cuda13" ]; }; }; in { packages = { exo = mkExo "exo"; - editableVenv = editablePythonSet.mkVirtualEnv "exo-dev-env" { exo = [ "dev" ]; }; # for running tests in ci exo-test-env = testVenv; exo-bench = mkBenchScript "exo-bench" (inputs.self + /bench/exo_bench.py); @@ -226,8 +408,8 @@ in # used by ./tests/run_exo_on.sh exo-get-all-models-on-cluster = mkSimplePythonScript "exo-get-all-models-on-cluster" (inputs.self + /tests/get_all_models_on_cluster.py); } // lib.optionalAttrs isLinux { - exo-cuda-12 = (mkPythonSet { inherit self' lib; inherit (unfreePkgs.pkgsCuda.cudaPackages_12) pkgs; members = { exo = [ "cuda12" ]; }; }).mkExo "exo-cuda-12"; - exo-cuda-13 = (mkPythonSet { inherit self' lib; inherit (unfreePkgs.pkgsCuda.cudaPackages_13) pkgs; members = { exo = [ "cuda13" ]; }; }).mkExo "exo-cuda-13"; + exo-cuda-12 = cuda12Set.mkExo "exo-cuda-12"; + exo-cuda-13 = cuda13Set.mkExo "exo-cuda-13"; }; checks = { diff --git a/src/exo/master/placement.py b/src/exo/master/placement.py index 715939514..654292d5e 100644 --- a/src/exo/master/placement.py +++ b/src/exo/master/placement.py @@ -43,6 +43,7 @@ from exo.shared.types.worker.instances import ( InstanceMeta, MlxJacclInstance, MlxRingInstance, + VllmInstance, ) from exo.shared.types.worker.shards import Sharding from exo.utils.ports import random_ephemeral_port @@ -202,7 +203,7 @@ def place_instance( ) # Single-node: force Pipeline/Ring (Tensor and Jaccl require multi-node) - if len(selected_cycle) == 1: + if len(selected_cycle) == 1 and command.instance_meta != InstanceMeta.Vllm: command = command.model_copy( update={ "instance_meta": InstanceMeta.MlxRing, @@ -266,6 +267,11 @@ def place_instance( hosts_by_node=hosts_by_node, ephemeral_port=ephemeral_port, ) + case InstanceMeta.Vllm: + target_instances[instance_id] = VllmInstance( + instance_id=instance_id, + shard_assignments=shard_assignments, + ) return target_instances diff --git a/src/exo/shared/types/worker/instances.py b/src/exo/shared/types/worker/instances.py index 16233f3f0..f081c5135 100644 --- a/src/exo/shared/types/worker/instances.py +++ b/src/exo/shared/types/worker/instances.py @@ -15,6 +15,7 @@ class InstanceId(Id): class InstanceMeta(str, Enum): MlxRing = "MlxRing" MlxJaccl = "MlxJaccl" + Vllm = "Vllm" class BaseInstance(TaggedModel): @@ -35,8 +36,12 @@ class MlxJacclInstance(BaseInstance): jaccl_coordinators: dict[NodeId, str] +class VllmInstance(BaseInstance): + pass + + # TODO: Single node instance -Instance = MlxRingInstance | MlxJacclInstance +Instance = MlxRingInstance | MlxJacclInstance | VllmInstance class BoundInstance(FrozenModel): diff --git a/src/exo/worker/engines/mlx/builder.py b/src/exo/worker/engines/mlx/builder.py index af7c75bb9..ae4a0201e 100644 --- a/src/exo/worker/engines/mlx/builder.py +++ b/src/exo/worker/engines/mlx/builder.py @@ -34,7 +34,7 @@ class MlxBuilder(Builder): model_id: ModelId event_sender: MpSender[Event] cancel_receiver: MpReceiver[TaskId] - inference_model: Model | None = None + model: Model | None = None tokenizer: TokenizerWrapper | None = None group: mx.distributed.Group | None = None vision_processor: VisionProcessor | None = None @@ -44,7 +44,7 @@ class MlxBuilder(Builder): def load(self, bound_instance: BoundInstance) -> Generator[ModelLoadingResponse]: ( - self.inference_model, + self.model, self.tokenizer, self.vision_processor, ) = yield from load_mlx_items(bound_instance, self.group) @@ -60,7 +60,7 @@ class MlxBuilder(Builder): def build( self, ) -> Engine: - assert self.inference_model + assert self.model assert self.tokenizer vision_processor = self.vision_processor @@ -86,7 +86,7 @@ class MlxBuilder(Builder): if os.environ.get("EXO_NO_BATCH"): logger.info("using SequentialGenerator (batching disabled)") return SequentialGenerator( - model=self.inference_model, + model=self.model, tokenizer=self.tokenizer, group=self.group, tool_parser=tool_parser, @@ -100,7 +100,7 @@ class MlxBuilder(Builder): else: logger.info("using BatchGenerator") return BatchGenerator( - model=self.inference_model, + model=self.model, tokenizer=self.tokenizer, group=self.group, tool_parser=tool_parser, diff --git a/src/exo/worker/engines/mlx/utils_mlx.py b/src/exo/worker/engines/mlx/utils_mlx.py index 7021204ac..1422c18ef 100644 --- a/src/exo/worker/engines/mlx/utils_mlx.py +++ b/src/exo/worker/engines/mlx/utils_mlx.py @@ -50,6 +50,7 @@ from exo.shared.types.worker.instances import ( BoundInstance, MlxJacclInstance, MlxRingInstance, + VllmInstance, ) from exo.shared.types.worker.runner_response import ModelLoadingResponse from exo.shared.types.worker.shards import ( @@ -140,6 +141,8 @@ def mlx_distributed_init( os.environ["MLX_RANK"] = str(rank) os.environ["MLX_JACCL_COORDINATOR"] = jaccl_coordinator group = mx.distributed.init(backend="jaccl", strict=True) + case VllmInstance(): + raise ValueError("loaded VllmInstance in MLX engine") logger.info(f"Rank {rank} mlx distributed initialization complete") diff --git a/src/exo/worker/engines/vllm/__init__.py b/src/exo/worker/engines/vllm/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/src/exo/worker/engines/vllm/builder.py b/src/exo/worker/engines/vllm/builder.py new file mode 100644 index 000000000..149ec207c --- /dev/null +++ b/src/exo/worker/engines/vllm/builder.py @@ -0,0 +1,77 @@ +import contextlib +import os +from collections.abc import Generator +from dataclasses import dataclass + +from exo.shared.constants import EXO_MAX_CONCURRENT_REQUESTS +from exo.shared.types.common import ModelId +from exo.shared.types.events import Event +from exo.shared.types.tasks import TaskId +from exo.shared.types.worker.instances import BoundInstance +from exo.shared.types.worker.runner_response import ModelLoadingResponse +from exo.utils.channels import MpReceiver, MpSender +from exo.worker.engines.base import Builder, Engine +from exo.worker.engines.vllm.engine import VllmEngine +from exo.worker.engines.vllm.generator import VllmBatchEngine, load_vllm_engine +from exo.worker.runner.bootstrap import logger + + +@dataclass +class VllmBuilder(Builder): + model_id: ModelId + event_sender: MpSender[Event] + cancel_receiver: MpReceiver[TaskId] + + def connect(self, bound_instance: BoundInstance) -> None: + raise NotImplementedError( + "Multiple node VLLM instances are not supported at the moment!" + ) + + def load( + self, + bound_instance: BoundInstance, + ) -> Generator[ModelLoadingResponse]: + kv_connector_cls: type[object] | None = None + # overlapping = not os.environ.get("EXO_NO_OVERLAPPING_PREFILL_SENDS") + + def on_layer_loaded(loaded: int, total: int) -> None: + pass + + self._bound_runner_id = bound_instance.bound_runner_id + self._engine, self._tool_parser = load_vllm_engine( + model_id=self.model_id, + trust_remote_code=bound_instance.bound_shard.model_card.trust_remote_code, + n_layers=bound_instance.bound_shard.model_card.n_layers, + on_layer_loaded=on_layer_loaded, + kv_connector_cls=kv_connector_cls, + ) + return + yield + + def build(self) -> Engine: + gen = VllmBatchEngine( + engine=self._engine, + model_id=self.model_id, + ) + try: + max_concurrent = ( + 1 + if bool(os.getenv("EXO_NO_BATCH", False)) + else EXO_MAX_CONCURRENT_REQUESTS + ) + except Exception: + max_concurrent = EXO_MAX_CONCURRENT_REQUESTS + + logger.info(f"using VllmEngine (max_concurrent={max_concurrent})") + return VllmEngine( + tool_parser=self._tool_parser, + model_id=self.model_id, + cancel_receiver=self.cancel_receiver, + event_sender=self.event_sender, + _gen=gen, + max_concurrent_requests=max_concurrent, + ) + + def close(self) -> None: + with contextlib.suppress(NameError, AttributeError): + del self._engine, self._tool_parser diff --git a/src/exo/worker/engines/vllm/engine.py b/src/exo/worker/engines/vllm/engine.py new file mode 100644 index 000000000..0c7e54475 --- /dev/null +++ b/src/exo/worker/engines/vllm/engine.py @@ -0,0 +1,285 @@ +import itertools +import time +from collections import deque +from collections.abc import Generator, Iterator +from dataclasses import dataclass, field +from typing import BinaryIO + +from mlx_lm.tokenizer_utils import TokenizerWrapper + +from exo.shared.constants import EXO_MAX_CONCURRENT_REQUESTS +from exo.shared.types.chunks import ErrorChunk, GenerationChunk, PrefillProgressChunk +from exo.shared.types.common import ModelId +from exo.shared.types.events import ChunkGenerated, Event +from exo.shared.types.tasks import ( + CANCEL_ALL_TASKS, + GenerationTask, + TaskId, + TextGeneration, +) +from exo.shared.types.text_generation import TextGenerationTaskParams +from exo.shared.types.worker.runner_response import ( + CancelledResponse, + FinishedResponse, + GenerationResponse, +) +from exo.utils.channels import MpReceiver, MpSender +from exo.worker.disaggregated.server import PrefillJob +from exo.worker.engines.base import Engine +from exo.worker.engines.vllm.prompt_format import format_vllm_prompt +from exo.worker.engines.vllm.vllm_generator import VllmBatchEngine +from exo.worker.runner.bootstrap import logger +from exo.worker.runner.llm_inference.model_output_parsers import ( + apply_all_parsers, + map_responses_to_chunks, +) +from exo.worker.runner.llm_inference.tool_parsers import ToolParser + + +class GeneratorQueue[T]: + def __init__(self) -> None: + self._q = deque[T]() + + def push(self, t: T) -> None: + self._q.append(t) + + def gen(self) -> Generator[T | None]: + while True: + if len(self._q) == 0: + yield None + else: + yield self._q.popleft() + + +EXO_RUNNER_MUST_FAIL = "EXO RUNNER MUST FAIL" +EXO_RUNNER_MUST_TIMEOUT = "EXO RUNNER MUST TIMEOUT" + + +def _check_for_debug_prompts(task_params: TextGenerationTaskParams) -> None: + """Keep the cheap debug prompt hooks without importing the MLX engine.""" + if len(task_params.input) == 0: + return + prompt = task_params.input[0].content + if not prompt: + return + if EXO_RUNNER_MUST_FAIL in prompt: + raise Exception("Artificial runner exception - for testing purposes only.") + if EXO_RUNNER_MUST_TIMEOUT in prompt: + time.sleep(100) + + +@dataclass(eq=False) +class VllmEngine(Engine): + """Single-node vLLM implementation of the exo Engine interface. + + This intentionally duplicates the local orchestration from the MLX + BatchGenerator instead of trying to share a batch abstraction too early. + The vLLM-specific tokenization/sampling/stepping remains inside + VllmBatchEngine. + """ + + tool_parser: ToolParser | None + model_id: ModelId + cancel_receiver: MpReceiver[TaskId] + event_sender: MpSender[Event] + _gen: VllmBatchEngine + max_concurrent_requests: int = EXO_MAX_CONCURRENT_REQUESTS + check_for_cancel_every: int = 50 + + _cancelled_tasks: set[TaskId] = field(default_factory=set, init=False) + _all_tasks: dict[TaskId, TextGeneration] = field(default_factory=dict, init=False) + _queue: deque[TextGeneration] = field(default_factory=deque, init=False) + _active_tasks: dict[ + TaskId, + tuple[ + TextGeneration, + GeneratorQueue[GenerationResponse], + Iterator[GenerationChunk | None], + ], + ] = field(default_factory=dict, init=False) + + def warmup(self) -> None: + self.check_for_cancel_every = self._gen.warmup() + + def submit(self, task: GenerationTask) -> None: + assert isinstance(task, TextGeneration) + self._cancelled_tasks.discard(CANCEL_ALL_TASKS) + self._all_tasks[task.task_id] = task + self._queue.append(task) + + def step( + self, + ) -> Iterator[ + tuple[TaskId, GenerationChunk | CancelledResponse | FinishedResponse] + ]: + self._collect_cancellations() + output: list[ + tuple[TaskId, GenerationChunk | CancelledResponse | FinishedResponse] + ] = list(self._apply_cancellations()) + + while self._queue and len(self._active_tasks) < self.max_concurrent_requests: + task = self._queue.popleft() + if self.should_cancel(task.task_id): + output.append((task.task_id, CancelledResponse())) + self._all_tasks.pop(task.task_id, None) + continue + + try: + task_id, queue, output_generator = self._start_task(task) + except Exception as e: + self._send_error(task, e) + self._all_tasks.pop(task.task_id, None) + raise + + self._active_tasks[task_id] = (task, queue, output_generator) + + if not self._gen.has_work: + return iter(output) + + results = self._gen.step() + for task_id, response in results: + if task_id not in self._active_tasks: + logger.warning(f"{task_id=} not found in active vLLM tasks") + continue + + task, queue, output_generator = self._active_tasks[task_id] + queue.push(response) + while (parsed := next(output_generator, None)) is not None: + output.append((task.task_id, parsed)) + + if response.finish_reason is not None: + output.append((task.task_id, FinishedResponse())) + del self._active_tasks[task_id] + self._all_tasks.pop(task.task_id, None) + + return itertools.chain(output, self._apply_cancellations()) + + def close(self) -> None: + self._gen.close() + + def serve_prefill(self, job: PrefillJob, wfile: BinaryIO) -> None: + raise NotImplementedError("vLLM serve_prefill is not supported yet") + + def _start_task( + self, task: TextGeneration + ) -> tuple[ + TaskId, + GeneratorQueue[GenerationResponse], + Iterator[GenerationChunk | None], + ]: + _check_for_debug_prompts(task.task_params) + + token_ids, prompt_text, _ = format_vllm_prompt( + self._gen.engine, task.task_params + ) + + queue = GeneratorQueue[GenerationResponse]() + if task.task_params.bench: + output_generator: Iterator[GenerationChunk | None] = map( + lambda r: map_responses_to_chunks(r, self.model_id), queue.gen() + ) + else: + output_generator = apply_all_parsers( + queue.gen(), + prompt_text, + self.tool_parser, + TokenizerWrapper(self._gen.engine.get_tokenizer()), + self.model_id, + task.task_params.tools, + ) + + check_for_cancel_every = max(self.check_for_cancel_every, 1) + tokens_since_cancel_check = check_for_cancel_every + + def on_prefill_progress(processed: int, total: int) -> None: + self._collect_cancellations() + if self.should_cancel(task.task_id): + self._cancelled_tasks.add(task.task_id) + self.event_sender.send( + ChunkGenerated( + command_id=task.command_id, + chunk=PrefillProgressChunk( + model=self.model_id, + processed_tokens=processed, + total_tokens=total, + ), + ) + ) + + def on_generation_token() -> None: + nonlocal tokens_since_cancel_check + tokens_since_cancel_check += 1 + if tokens_since_cancel_check >= check_for_cancel_every: + tokens_since_cancel_check = 0 + self._collect_cancellations() + if self.should_cancel(task.task_id): + self._cancelled_tasks.add(task.task_id) + + task_id = self._gen.submit( + task_id=task.task_id, + task_params=task.task_params, + on_prefill_progress=on_prefill_progress, + on_generation_token=on_generation_token, + token_ids=token_ids, + ) + return task_id, queue, output_generator + + def _collect_cancellations(self) -> None: + for task_id in self.cancel_receiver.collect(): + if task_id == CANCEL_ALL_TASKS: + self._cancelled_tasks.add(CANCEL_ALL_TASKS) + elif task_id in self._all_tasks: + self._cancelled_tasks.add(task_id) + + def _apply_cancellations(self) -> Iterator[tuple[TaskId, CancelledResponse]]: + if not self._cancelled_tasks: + return iter([]) + + cancel_all = CANCEL_ALL_TASKS in self._cancelled_tasks + results: list[tuple[TaskId, CancelledResponse]] = [] + task_ids_to_abort: list[TaskId] = [] + + for task_id, (task, _, _) in list(self._active_tasks.items()): + if cancel_all or task.task_id in self._cancelled_tasks: + task_ids_to_abort.append(task_id) + results.append((task.task_id, CancelledResponse())) + del self._active_tasks[task_id] + self._all_tasks.pop(task.task_id, None) + + if self._queue: + kept_queue: deque[TextGeneration] = deque() + for task in self._queue: + if cancel_all or task.task_id in self._cancelled_tasks: + results.append((task.task_id, CancelledResponse())) + self._all_tasks.pop(task.task_id, None) + else: + kept_queue.append(task) + self._queue = kept_queue + + if task_ids_to_abort: + self._gen.cancel(task_ids_to_abort) + + already_cancelled = {task_id for task_id, _ in results} + for task_id in self._cancelled_tasks: + if ( + task_id != CANCEL_ALL_TASKS + and task_id in self._all_tasks + and task_id not in already_cancelled + ): + results.append((task_id, CancelledResponse())) + self._all_tasks.pop(task_id, None) + + self._cancelled_tasks.clear() + return iter(results) + + def _send_error(self, task: TextGeneration, e: Exception) -> None: + self.event_sender.send( + ChunkGenerated( + command_id=task.command_id, + chunk=ErrorChunk( + model=self.model_id, + finish_reason="error", + error_message=str(e), + ), + ) + ) diff --git a/src/exo/worker/engines/vllm/generator.py b/src/exo/worker/engines/vllm/generator.py new file mode 100644 index 000000000..898a4cbdb --- /dev/null +++ b/src/exo/worker/engines/vllm/generator.py @@ -0,0 +1,477 @@ +import gc +import json +import math +import re +import sys +import time +from collections.abc import Callable, Generator +from dataclasses import dataclass, field +from typing import cast + +import torch +from vllm.config import CompilationConfig +from vllm.config.compilation import CUDAGraphMode +from vllm.config.kv_transfer import KVTransferConfig +from vllm.engine.arg_utils import EngineArgs +from vllm.entrypoints.chat_utils import ( + ChatCompletionMessageParam, + CustomChatCompletionMessageParam, +) +from vllm.outputs import RequestOutput +from vllm.sampling_params import SamplingParams +from vllm.tokenizers import TokenizerLike +from vllm.v1.attention.backends.registry import AttentionBackendEnum +from vllm.v1.engine.llm_engine import LLMEngine + +from exo.api.types import ( + CompletionTokensDetails, + GenerationStats, + PromptTokensDetails, + Usage, +) +from exo.download.download_utils import build_model_path +from exo.shared.types.common import ModelId +from exo.shared.types.memory import Memory +from exo.shared.types.tasks import TaskId +from exo.shared.types.text_generation import TextGenerationTaskParams +from exo.shared.types.worker.runner_response import GenerationResponse + +# todo; move to different file +from exo.worker.engines.mlx.utils_mlx import get_eos_token_ids_for_model +from exo.worker.engines.vllm.prompt_format import ( + make_vllm_sampling_params, +) +from exo.worker.runner.bootstrap import logger +from exo.worker.runner.llm_inference.tool_parsers import ToolParser, infer_tool_parser + + +@dataclass +class _EngineRequest: + request_id: str + prompt_token_count: int + prefill_done: bool = False + prefill_steps: int = 0 + prev_text: str = "" + prev_token_count: int = 0 + start_time: float = field(default_factory=time.perf_counter) + first_token_time: float | None = None + on_generation_token: Callable[[], None] | None = None + on_prefill_progress: Callable[[int, int], None] | None = None + + +def _stop_token_ids(tokenizer: TokenizerLike, model_id: ModelId) -> set[int]: + ids: set[int] = set() + eos_id = getattr(tokenizer, "eos_token_id", None) + if eos_id is not None: + ids.add(eos_id) # pyright: ignore[reportAny] + extra = get_eos_token_ids_for_model(model_id) + if extra: + ids.update(extra) + return ids + + +def _build_generation_response( + tokenizer: TokenizerLike, + token_id: int, + finish_reason: str | None, + prompt_token_count: int, + completion_tokens: int, + start_time: float, + first_token_time: float | None, + suppress_text: bool = False, +) -> GenerationResponse: + token_text: str = "" if suppress_text else tokenizer.decode([token_id]) + finish_usage: Usage | None = None + finish_stats: GenerationStats | None = None + mapped_finish_reason: str | None = None + if finish_reason: + now = time.perf_counter() + prefill_elapsed = (first_token_time or now) - start_time + decode_elapsed = now - (first_token_time or now) + finish_usage = Usage( + prompt_tokens=prompt_token_count, + completion_tokens=completion_tokens, + total_tokens=prompt_token_count + completion_tokens, + prompt_tokens_details=PromptTokensDetails(), + completion_tokens_details=CompletionTokensDetails(), + ) + finish_stats = GenerationStats( + prompt_tps=prompt_token_count / prefill_elapsed + if prefill_elapsed > 0 + else 0.0, + generation_tps=completion_tokens / decode_elapsed + if decode_elapsed > 0 + else 0.0, + prompt_tokens=prompt_token_count, + generation_tokens=completion_tokens, + peak_memory_usage=Memory.from_bytes(torch.cuda.max_memory_allocated()), + ) + mapped_finish_reason = ( + finish_reason + if finish_reason in ("stop", "length", "content_filter") + else "stop" + ) + return GenerationResponse( + text=token_text, + token=token_id, + finish_reason=mapped_finish_reason, + usage=finish_usage, + stats=finish_stats, + ) + + +def warmup_vllm_engine(engine: LLMEngine) -> int: + tokenizer = engine.get_tokenizer() + messages = [ + cast( + ChatCompletionMessageParam, + CustomChatCompletionMessageParam( + role="user", + content="Prompt to warm up the inference engine. Repeat this.", + ), + ) + ] + prompt_text: str | list[int] = tokenizer.apply_chat_template( # pyright: ignore[reportUnknownMemberType] + messages, tokenize=False, add_generation_prompt=True + ) + if isinstance(prompt_text, list): + token_ids = prompt_text + else: + token_ids: list[int] = tokenizer.encode(prompt_text, add_special_tokens=False) + + params = SamplingParams(max_tokens=50, detokenize=False) + engine.add_request("warmup", {"prompt_token_ids": token_ids}, params) + t = time.monotonic() + tokens_generated = 0 + while engine.has_unfinished_requests(): + engine.step() + tokens_generated += 1 + elapsed = max(time.monotonic() - t, 0.001) + check_for_cancel_every = min(math.ceil(tokens_generated / elapsed), 100) + logger.info( + f"vLLM warmup complete, check_for_cancel_every={check_for_cancel_every}" + ) + return check_for_cancel_every + + +@dataclass(eq=False) +class VllmBatchEngine: + engine: LLMEngine + model_id: ModelId + + _active: dict[TaskId, _EngineRequest] = field(default_factory=dict, init=False) + + def warmup(self) -> int: + return warmup_vllm_engine(self.engine) + + @property + def has_work(self) -> bool: + return bool(self._active) or self.engine.has_unfinished_requests() + + def submit( + self, + task_id: TaskId, + task_params: TextGenerationTaskParams, + token_ids: list[int], + on_prefill_progress: Callable[[int, int], None] | None = None, + on_generation_token: Callable[[], None] | None = None, + ) -> TaskId: + sampling_params = make_vllm_sampling_params( + self.engine, task_params, self.model_id + ) + self.engine.add_request( + task_id, {"prompt_token_ids": token_ids}, sampling_params + ) + self._active[task_id] = _EngineRequest( + request_id=task_id, + prompt_token_count=len(token_ids), + on_generation_token=on_generation_token, + on_prefill_progress=on_prefill_progress, + ) + return task_id + + def step(self) -> list[tuple[TaskId, GenerationResponse]]: + if not self.has_work: + return [] + + outputs = self.engine.step() + tokenizer = self.engine.get_tokenizer() + stop_ids = _stop_token_ids(tokenizer, self.model_id) + max_batch_tokens: int = ( + getattr(self.engine.model_config, "max_num_batched_tokens", 2048) or 2048 + ) + results: list[tuple[TaskId, GenerationResponse]] = [] + + for output in outputs: + # todo: PoolingRequestOutputs + assert isinstance(output, RequestOutput) + task_id = TaskId(output.request_id) + if task_id not in self._active: + continue + req = self._active[task_id] + completion = output.outputs[0] + new_token_count = len(completion.token_ids) + new_tokens = completion.token_ids[req.prev_token_count :] + finish_reason = completion.finish_reason + req.prev_token_count = new_token_count + + if not req.prefill_done and not new_tokens: + req.prefill_steps += 1 + if req.on_prefill_progress: + req.on_prefill_progress( + min( + req.prefill_steps * max_batch_tokens, req.prompt_token_count + ), + req.prompt_token_count, + ) + continue + + if not req.prefill_done and new_tokens: + req.first_token_time = time.perf_counter() + req.prefill_done = True + + for i, token_id in enumerate(new_tokens): + is_last = i == len(new_tokens) - 1 + is_final_stop = is_last and finish_reason and token_id in stop_ids + if req.on_generation_token: + req.on_generation_token() + results.append( + ( + task_id, + _build_generation_response( + tokenizer, + token_id, + finish_reason if is_last and finish_reason else None, + req.prompt_token_count, + new_token_count, + req.start_time, + req.first_token_time, + suppress_text=bool(is_final_stop), + ), + ) + ) + + if finish_reason: + del self._active[task_id] + + for req in self._active.values(): + if not req.prefill_done: + req.prefill_steps += 1 + if req.on_prefill_progress: + req.on_prefill_progress( + min( + req.prefill_steps * max_batch_tokens, req.prompt_token_count + ), + req.prompt_token_count, + ) + + return results + + def cancel(self, task_ids: list[TaskId]) -> None: + to_abort = [str(tid) for tid in task_ids if tid in self._active] + if to_abort: + self.engine.abort_request(to_abort) + for tid in task_ids: + self._active.pop(tid, None) + + def close(self) -> None: + if not hasattr(self, "engine"): + return + rids = [req.request_id for req in self._active.values()] + if rids: + self.engine.abort_request(rids) + self._active.clear() + del self.engine + gc.collect() + torch.cuda.empty_cache() + if torch.distributed.is_initialized(): + torch.distributed.destroy_process_group() + + +_weight_loading_callback: Callable[[int, int], None] | None = None +_weight_loading_patched = False + + +def get_weight_loading_callback() -> Callable[[int, int], None] | None: + return _weight_loading_callback + + +def set_weight_loading_callback(cb: Callable[[int, int], None] | None) -> None: + global _weight_loading_callback + _weight_loading_callback = cb + + +_LAYER_INDEX_PATTERN = re.compile(r"\.layers\.(\d+)\.") +_n_layers: int = 1 + + +def get_n_layers() -> int: + return _n_layers + + +def set_n_layers(n: int) -> None: + global _n_layers + _n_layers = n + + +def _wrap_weights_iterator( + original: Callable[..., Generator[tuple[str, "torch.Tensor"], None, None]], +) -> Callable[..., Generator[tuple[str, "torch.Tensor"], None, None]]: + def patched( + hf_weights_files: list[str], *args: object, **kwargs: object + ) -> Generator[tuple[str, "torch.Tensor"], None, None]: + callback = get_weight_loading_callback() + if callback is not None and hf_weights_files: + total_layers = get_n_layers() + seen_layers: set[int] = set() + last_reported = 0 + for name, tensor in original(hf_weights_files, *args, **kwargs): + yield name, tensor + match = _LAYER_INDEX_PATTERN.search(name) + if match: + seen_layers.add(int(match.group(1))) + current = len(seen_layers) + if current > last_reported: + callback(current, total_layers) + last_reported = current + callback(total_layers, total_layers) + else: + yield from original(hf_weights_files, *args, **kwargs) + + return patched + + +def _monkey_patch_iterator(weight_utils: object, attr_name: str) -> None: + original = getattr(weight_utils, attr_name, None) + if original is None: + return + patched = _wrap_weights_iterator(original) # pyright: ignore[reportAny] + setattr(weight_utils, attr_name, patched) + for mod in list(sys.modules.values()): + if mod is weight_utils: + continue + for name in list(vars(mod)): + if vars(mod)[name] is original: + setattr(mod, name, patched) + + +def _patch_weight_loading_progress() -> None: + global _weight_loading_patched + if _weight_loading_patched: + return + _weight_loading_patched = True + + from vllm.model_executor.model_loader import ( + weight_utils, + ) + + _monkey_patch_iterator(weight_utils, "safetensors_weights_iterator") + _monkey_patch_iterator(weight_utils, "fastsafetensors_weights_iterator") + + import huggingface_hub + + def _noop_metadata(*_a: object, **_kw: object) -> None: + pass + + original_metadata = huggingface_hub.get_safetensors_metadata + huggingface_hub.get_safetensors_metadata = _noop_metadata + for mod in list(sys.modules.values()): + if mod is huggingface_hub: + continue + for attr in list(vars(mod)): + if vars(mod)[attr] is original_metadata: + setattr(mod, attr, _noop_metadata) + + +def load_vllm_engine( + model_id: ModelId, + trust_remote_code: bool, + n_layers: int = 1, + on_layer_loaded: Callable[[int, int], None] | None = None, + kv_connector_cls: type[object] | None = None, +) -> tuple[LLMEngine, ToolParser | None]: + model_path = build_model_path(model_id) + _patch_weight_loading_progress() + + # todo + # if kv_connector_cls is not None: + # from exo.disaggregated.prefill_server import _patch_vllm_for_connector + + # _patch_vllm_for_connector(kv_connector_cls) + + set_n_layers(n_layers) + + kv_transfer_config: KVTransferConfig | None = None + if kv_connector_cls is not None: + kv_transfer_config = KVTransferConfig( + kv_connector=kv_connector_cls.__name__, + kv_connector_module_path=kv_connector_cls.__module__, + kv_role="kv_both", + kv_load_failure_policy="recompute", + ) + + has_mamba = False + try: + with open(model_path / "config.json") as f: + model_config = json.load(f) # pyright: ignore[reportAny] + text_config = model_config.get("text_config", model_config) # pyright: ignore[reportAny] + has_mamba = "mamba_ssm_dtype" in text_config or "linear_attention" in ( + text_config.get("layer_types") or [] # pyright: ignore[reportAny] + ) + except Exception: + pass + if has_mamba: + backends = [AttentionBackendEnum.FLASH_ATTN, AttentionBackendEnum.TRITON_ATTN] + else: + backends = [ + AttentionBackendEnum.FLASHINFER, + AttentionBackendEnum.FLASH_ATTN, + AttentionBackendEnum.TRITON_ATTN, + ] + + engine: LLMEngine | None = None + for backend in backends: + try: + engine_args = EngineArgs( + model=str(model_path.expanduser().resolve()), + served_model_name=str(model_id), + gpu_memory_utilization=0.05, + trust_remote_code=trust_remote_code, + load_format="fastsafetensors", + enable_prefix_caching=True, + attention_backend=backend, + compilation_config=CompilationConfig(cudagraph_mode=CUDAGraphMode.NONE), + disable_log_stats=True, + max_num_batched_tokens=4096, + kv_transfer_config=kv_transfer_config, + disable_hybrid_kv_cache_manager=False, + ) + + set_weight_loading_callback(on_layer_loaded) + engine = LLMEngine.from_engine_args(engine_args) + logger.info(f"vLLM engine using attention backend: {backend}") + break + except (ValueError, RuntimeError, NotImplementedError) as e: + logger.warning(f"Attention backend {backend} failed: {e}, trying next") + engine = None + + gc.collect() + torch.cuda.empty_cache() + continue + + if engine is None: + raise RuntimeError(f"No attention backend worked for {model_id}") + + tool_parser: ToolParser | None = None + tokenizer = engine.get_tokenizer() + chat_template = getattr(tokenizer, "chat_template", None) + if isinstance(chat_template, str): + tool_parser = infer_tool_parser(chat_template) + if tool_parser: + logger.info( + f"inferred tool parser: {tool_parser.start_parsing} / {tool_parser.end_parsing}" + ) + + logger.info(f"vLLM engine loaded for {model_id}") + + return engine, tool_parser diff --git a/src/exo/worker/engines/vllm/growable_cache.py b/src/exo/worker/engines/vllm/growable_cache.py new file mode 100644 index 000000000..1973420f9 --- /dev/null +++ b/src/exo/worker/engines/vllm/growable_cache.py @@ -0,0 +1,565 @@ +# pyright: reportPrivateUsage=false, reportAttributeAccessIssue=false +from typing import TYPE_CHECKING, Any, cast + +import torch +from vllm.v1.core.block_pool import BlockPool +from vllm.v1.core.kv_cache_metrics import KVCacheMetricsCollector +from vllm.v1.kv_cache_interface import KVCacheConfig +from vllm.v1.request import Request +from vllm.v1.worker.gpu_model_runner import GPUModelRunner + +from exo.shared.logging import logger +from exo.worker.engines.mlx.cache import KVPrefixCache + +INITIAL_FRACTION = 0.05 +GROWTH_HEADROOM_BYTES = 512 * 1024 * 1024 +MIN_GROWTH_BLOCKS = 16 + +if TYPE_CHECKING: + from vllm.v1.core.kv_cache_manager import KVCacheManager + + +_patched = False +_prefix_cache: KVPrefixCache | None = None +_model_runner: GPUModelRunner | None = None + + +def get_prefix_cache() -> KVPrefixCache | None: + return _prefix_cache + + +def set_prefix_cache(cache: KVPrefixCache | None) -> None: + global _prefix_cache + _prefix_cache = cache + + +def get_model_runner() -> GPUModelRunner | None: + return _model_runner + + +def set_model_runner(runner: GPUModelRunner | None) -> None: + global _model_runner + _model_runner = runner + + +def patch_vllm() -> None: + global _patched + if _patched: + return + _patched = True + + _patch_nogds() + _patch_determine_available_memory() + _patch_check_enough_kv_cache_memory() + _patch_initialize_kv_cache_tensors() + _patch_initialize_from_config() + _patch_kv_cache_manager_init() + _patch_allocate_slots() + _patch_get_computed_blocks() + _patch_moe_sum() + _patch_marlin_w2_thread_config() + logger.info("vLLM growable KV cache patch applied") + + +def _patch_nogds() -> None: + from vllm.model_executor.model_loader import weight_utils + + original = weight_utils._init_fastsafetensors_loader + + def patched( # pyright: ignore[reportUnknownParameterType] + pg: "torch.distributed.ProcessGroup", + device: "torch.device", + f_list: list[str], + *, + nogds: bool = False, + ): + return original(pg, device, f_list, nogds=True) # pyright: ignore[reportUnknownVariableType] + + weight_utils._init_fastsafetensors_loader = patched + + +def _patch_determine_available_memory() -> None: + from vllm.v1.worker.gpu_worker import Worker + + # original = Worker.determine_available_memory + + @torch.inference_mode() + def patched(self: "Worker") -> int: + import pathlib + import shutil + + compile_cache = pathlib.Path.home() / ".cache" / "vllm" / "torch_compile_cache" + if compile_cache.exists(): + shutil.rmtree(compile_cache, ignore_errors=True) + + free_bytes, _ = torch.cuda.mem_get_info() + initial = max(int(free_bytes * INITIAL_FRACTION), 1) + self._growable_max_kv_bytes = free_bytes + self.available_kv_cache_memory_bytes = initial + logger.info( + f"Growable KV cache: initial {initial / (1024**3):.2f} GiB " + f"(max {free_bytes / (1024**3):.2f} GiB)" + ) + return initial + + Worker.determine_available_memory = patched + + +def _patch_check_enough_kv_cache_memory() -> None: + from vllm.v1.core import kv_cache_utils + + def noop(*_args: "object", **_kwargs: "object") -> None: + pass + + kv_cache_utils._check_enough_kv_cache_memory = noop + + +def _patch_initialize_kv_cache_tensors() -> None: + from vllm.v1.worker.gpu_model_runner import GPUModelRunner + + original_alloc = GPUModelRunner._allocate_kv_cache_tensors + + def patched_alloc( + self: GPUModelRunner, kv_cache_config: KVCacheConfig + ) -> dict[str, torch.Tensor]: + raw_tensors = original_alloc(self, kv_cache_config) + self._growable_raw_tensors = {name: t for name, t in raw_tensors.items()} + return raw_tensors + + GPUModelRunner._allocate_kv_cache_tensors = patched_alloc + + original_init_tensors = GPUModelRunner.initialize_kv_cache_tensors + + def patched_init_tensors( + self: GPUModelRunner, + kv_cache_config: KVCacheConfig, + kernel_block_sizes: list[int], + ) -> dict[str, torch.Tensor]: + self._growable_kv_cache_config = kv_cache_config + self._growable_kernel_block_sizes = kernel_block_sizes + return original_init_tensors(self, kv_cache_config, kernel_block_sizes) + + GPUModelRunner.initialize_kv_cache_tensors = patched_init_tensors + + +def _patch_initialize_from_config() -> None: + from vllm.v1.worker.gpu_model_runner import GPUModelRunner + from vllm.v1.worker.gpu_worker import Worker + + original_init_attn = GPUModelRunner.initialize_attn_backend + + def clear_and_reinit_attn( + self: GPUModelRunner, + kv_cache_config: KVCacheConfig, + ) -> None: + self.attn_groups.clear() + original_init_attn(self, kv_cache_config) + + GPUModelRunner.initialize_attn_backend = clear_and_reinit_attn + + original = Worker.initialize_from_config + + def patched(self: Worker, kv_cache_config: KVCacheConfig) -> None: + original(self, kv_cache_config) + set_model_runner(self.model_runner) + + Worker.initialize_from_config = patched + + +def _patch_kv_cache_manager_init() -> None: + from vllm.v1.core.kv_cache_manager import KVCacheManager + + original_init = KVCacheManager.__init__ + + def patched_init( + self: KVCacheManager, + kv_cache_config: KVCacheConfig, + max_model_len: int, + hash_block_size: int, + enable_caching: bool = True, + use_eagle: bool = False, + log_stats: bool = False, + enable_kv_cache_events: bool = False, + dcp_world_size: int = 1, + pcp_world_size: int = 1, + metrics_collector: KVCacheMetricsCollector | None = None, + ) -> None: + original_init( + self, + kv_cache_config, + max_model_len, + hash_block_size, + enable_caching, + use_eagle, + log_stats, + enable_kv_cache_events, + dcp_world_size, + pcp_world_size, + metrics_collector, + ) + self._growable_model_runner = get_model_runner() + + KVCacheManager.__init__ = patched_init + + +def _patch_allocate_slots() -> None: + from vllm.v1.core.kv_cache_manager import KVCacheBlocks, KVCacheManager + + original = KVCacheManager.allocate_slots + + def patched( + self: KVCacheManager, + request: Request, + num_new_tokens: int, + num_new_computed_tokens: int = 0, + new_computed_blocks: KVCacheBlocks | None = None, + num_lookahead_tokens: int = 0, + num_external_computed_tokens: int = 0, + delay_cache_blocks: bool = False, + num_encoder_tokens: int = 0, + ) -> KVCacheBlocks | None: + result = original( + self, + request, + num_new_tokens, + num_new_computed_tokens, + new_computed_blocks, + num_lookahead_tokens, + num_external_computed_tokens, + delay_cache_blocks, + num_encoder_tokens, + ) + while result is None and _try_grow_cache(self): + result = original( + self, + request, + num_new_tokens, + num_new_computed_tokens, + new_computed_blocks, + num_lookahead_tokens, + num_external_computed_tokens, + delay_cache_blocks, + num_encoder_tokens, + ) + + return result + + KVCacheManager.allocate_slots = patched + + if hasattr(KVCacheManager, "can_fit_full_sequence"): + original_can_fit = KVCacheManager.can_fit_full_sequence + + def patched_can_fit( + self: KVCacheManager, + request: Request, + num_new_computed_tokens: int = 0, + new_computed_blocks: KVCacheBlocks | None = None, + num_external_computed_tokens: int = 0, + num_encoder_tokens: int = 0, + ) -> bool: + result = original_can_fit( + self, + request, + num_new_computed_tokens, + new_computed_blocks, + num_external_computed_tokens, + num_encoder_tokens, + ) + while not result and _try_grow_cache(self): + result = original_can_fit( + self, + request, + num_new_computed_tokens, + new_computed_blocks, + num_external_computed_tokens, + num_encoder_tokens, + ) + return result + + KVCacheManager.can_fit_full_sequence = patched_can_fit + + +def _try_grow_cache(kv_cache_manager: "KVCacheManager") -> bool: + block_pool = kv_cache_manager.block_pool + model_runner = cast(GPUModelRunner | None, kv_cache_manager._growable_model_runner) + + if model_runner is None: + return False + + free_bytes, _ = torch.cuda.mem_get_info() + if free_bytes < GROWTH_HEADROOM_BYTES: + return False + + kv_cache_config = cast(KVCacheConfig, model_runner._growable_kv_cache_config) + old_num_blocks: int = kv_cache_config.num_blocks + + total_tensor_bytes = sum(t.size for t in kv_cache_config.kv_cache_tensors) + per_block_bytes = total_tensor_bytes // old_num_blocks + + usable_bytes = int(free_bytes * 0.8) + growth_blocks = min(usable_bytes // per_block_bytes, old_num_blocks) + + if growth_blocks < MIN_GROWTH_BLOCKS: + return False + + new_num_blocks = old_num_blocks + growth_blocks + + logger.info( + f"Growing KV cache: {old_num_blocks} → {new_num_blocks} blocks " + f"(+{growth_blocks * per_block_bytes / (1024**3):.2f} GiB)" + ) + + try: + kv_cache_config.num_blocks = new_num_blocks + for tensor_spec in kv_cache_config.kv_cache_tensors: + tensor_spec.size = int(tensor_spec.size * new_num_blocks / old_num_blocks) + _grow_tensors(model_runner, kv_cache_config, old_num_blocks, new_num_blocks) + _grow_block_pool(block_pool, old_num_blocks, new_num_blocks) + logger.info(f"KV cache grown successfully to {new_num_blocks} blocks") + return True + except Exception: + logger.opt(exception=True).error("Failed to grow KV cache") + return False + + +def _grow_tensors( + model_runner: GPUModelRunner, + kv_cache_config: KVCacheConfig, + old_num_blocks: int, + new_num_blocks: int, +) -> None: + raw_tensors: dict[str, torch.Tensor] = cast( + dict[str, torch.Tensor], model_runner._growable_raw_tensors + ) + ratio = new_num_blocks / old_num_blocks + + already_grown: dict[int, torch.Tensor] = {} + new_raw_tensors: dict[str, torch.Tensor] = {} + + for layer_name, old_raw in raw_tensors.items(): + storage_id = old_raw.data_ptr() + if storage_id in already_grown: + new_raw_tensors[layer_name] = already_grown[storage_id] + continue + + old_size = old_raw.numel() + new_size = int(old_size * ratio) + new_raw = torch.zeros(new_size, dtype=torch.int8, device=old_raw.device) + new_raw[:old_size] = old_raw + already_grown[storage_id] = new_raw + new_raw_tensors[layer_name] = new_raw + + model_runner._growable_raw_tensors = new_raw_tensors + + kernel_block_sizes: list[int] = cast( + list[int], model_runner._growable_kernel_block_sizes + ) + new_kv_caches: dict[str, torch.Tensor] = model_runner._reshape_kv_cache_tensors( + kv_cache_config, + new_raw_tensors, + kernel_block_sizes, + ) + + forward_context: dict[str, Any] = ( + model_runner.compilation_config.static_forward_context + ) + runner_kv_caches: list[torch.Tensor] = model_runner.kv_caches + + from collections import defaultdict + + from vllm.model_executor.models.utils import extract_layer_index + + num_attn_module = 1 + hf_config = getattr(getattr(model_runner, "model_config", None), "hf_config", None) + if getattr(hf_config, "model_type", "") == "longcat_flash": + num_attn_module = 2 + + index2name: dict[int, list[str]] = defaultdict(list) + for ln in new_kv_caches: + index2name[extract_layer_index(ln, num_attn_module)].append(ln) + + new_ordered: list[torch.Tensor] = [] + for layer_index in sorted(index2name.keys()): + for ln in index2name[layer_index]: + new_ordered.append(new_kv_caches[ln]) + + for i, new_kv in enumerate(new_ordered): + if i < len(runner_kv_caches): + runner_kv_caches[i] = new_kv + else: + runner_kv_caches.append(new_kv) + + for layer_name, new_kv in new_kv_caches.items(): + old_kv_list = forward_context[layer_name].kv_cache + if old_kv_list is not None and ( + not isinstance(old_kv_list, torch.Tensor) or old_kv_list.numel() > 0 + ): + old_entry = old_kv_list[0] + if isinstance(old_entry, list) and isinstance(new_kv, list): + for old_t, new_t in zip(old_entry, new_kv): + old_t.set_( + new_t.storage(), + new_t.storage_offset(), + new_t.shape, + new_t.stride(), + ) + elif isinstance(old_entry, torch.Tensor) and isinstance( + new_kv, torch.Tensor + ): + old_entry.set_( + new_kv.storage(), + new_kv.storage_offset(), + new_kv.shape, + new_kv.stride(), + ) + else: + forward_context[layer_name].kv_cache = [new_kv] + else: + forward_context[layer_name].kv_cache = [new_kv] + + +def _grow_block_pool( + block_pool: BlockPool, old_num_blocks: int, new_num_blocks: int +) -> None: + from vllm.v1.core.kv_cache_utils import KVCacheBlock + + new_blocks: list[KVCacheBlock] = [] + for idx in range(old_num_blocks, new_num_blocks): + block = KVCacheBlock(idx) + block_pool.blocks.append(block) + new_blocks.append(block) + + block_pool.free_block_queue.append_n(new_blocks) + block_pool.num_gpu_blocks = new_num_blocks + + +def _patch_moe_sum() -> None: + import vllm._custom_ops as ops + + def moe_sum_f32(x: "torch.Tensor", output: "torch.Tensor") -> None: + output[:] = x.to(torch.float32).sum(dim=1).to(output.dtype) + + ops.moe_sum = moe_sum_f32 + + +def _patch_marlin_w2_thread_config() -> None: + try: + import vllm._custom_ops as ops + except ImportError: + return + + original_gemm = ops.moe_wna16_marlin_gemm + + def patched_gemm(*args: "object", **kwargs: "object") -> "object": + kwargs["thread_k"] = 64 + kwargs["thread_n"] = 128 + return original_gemm(*args, **kwargs) + + ops.moe_wna16_marlin_gemm = patched_gemm + + +def _patch_get_computed_blocks() -> None: + from vllm.v1.core.kv_cache_manager import KVCacheBlocks, KVCacheManager + from vllm.v1.core.kv_cache_utils import KVCacheBlock + from vllm.v1.request import Request + + original = KVCacheManager.get_computed_blocks + + def patched( + self: KVCacheManager, + request: Request, + ) -> tuple[KVCacheBlocks, int]: + prefix_cache = get_prefix_cache() + if prefix_cache is None or request.prompt_token_ids is None: + return original(self, request) + + from exo.worker.engines.vllm.kv_cache import ( + TorchKVCache as _TorchKVCache, # noqa: F811 + ) + + try: + torch_cache, num_matched, _ = prefix_cache.lookup( + list(request.prompt_token_ids) + ) + except Exception: + return original(self, request) + + if ( + torch_cache is None + or not isinstance(torch_cache, _TorchKVCache) + or num_matched == 0 + ): + return original(self, request) + + from vllm.utils.math_utils import cdiv + + from exo.worker.engines.vllm.generator import _build_layer_groups + + num_groups = len(self.kv_cache_config.kv_cache_groups) + null_block = self.block_pool.null_block + save_offsets = torch_cache.token_offset_per_group or [0] * num_groups + + for gi in range(num_groups): + save_off = save_offsets[gi] if gi < len(save_offsets) else 0 + if save_off > 0: + spec = self.kv_cache_config.kv_cache_groups[gi].kv_cache_spec + window = getattr(spec, "sliding_window", 0) or 0 + if window > 0 and num_matched < save_off + window: + return original(self, request) + + real_block_counts: list[int] = [] + skipped_block_counts: list[int] = [] + total_needed = 0 + for gi in range(num_groups): + mgr = self.coordinator.single_type_managers[gi] + block_size: int = self.kv_cache_config.kv_cache_groups[ + gi + ].kv_cache_spec.block_size + num_skipped: int = mgr.get_num_skipped_tokens(num_matched) + num_skipped_blocks = num_skipped // block_size + num_real = cdiv(num_matched, block_size) - num_skipped_blocks + real_block_counts.append(num_real) + skipped_block_counts.append(num_skipped_blocks) + total_needed += num_real + + if self.block_pool.get_num_free_blocks() < total_needed: + return original(self, request) + + blocks_per_group: list[list[KVCacheBlock]] = [] + token_offset_per_group: list[int] = [] + for gi in range(num_groups): + mgr = self.coordinator.single_type_managers[gi] + block_size = self.kv_cache_config.kv_cache_groups[ + gi + ].kv_cache_spec.block_size + real_blocks: list[KVCacheBlock] = self.block_pool.get_new_blocks( + real_block_counts[gi] + ) + blocks_per_group.append(real_blocks) + + full_block_list = [null_block] * skipped_block_counts[gi] + list( + real_blocks + ) + req_blocks = mgr.req_to_blocks[request.request_id] + req_blocks.extend(full_block_list) + + token_offset_per_group.append(skipped_block_counts[gi] * block_size) + + block_ids_per_group = [[b.block_id for b in grp] for grp in blocks_per_group] + layer_to_group = _build_layer_groups(self.kv_cache_config) + model_runner = self._growable_model_runner # type: ignore[reportAttributeAccessIssue] + if model_runner is not None: + torch_cache.write_to_vllm_blocks( + model_runner.kv_caches, + block_ids_per_group, + layer_to_group, # type: ignore + token_offset_per_group, + ) + + total_blocks = sum(len(g) for g in blocks_per_group) + logger.info( + f"Prefix cache hit: {num_matched} tokens, {total_blocks} blocks ({num_groups} groups)" + ) + return self.empty_kv_cache_blocks, num_matched + + KVCacheManager.get_computed_blocks = patched diff --git a/src/exo/worker/engines/vllm/kv_cache.py b/src/exo/worker/engines/vllm/kv_cache.py new file mode 100644 index 000000000..f0d482545 --- /dev/null +++ b/src/exo/worker/engines/vllm/kv_cache.py @@ -0,0 +1,346 @@ +from collections.abc import Iterator, Sequence +from copy import deepcopy +from dataclasses import dataclass + +import mlx.core as mx +import numpy as np +import torch +from mlx_lm.models.cache import ( + ArraysCache, + CacheList, + KVCache, + QuantizedKVCache, + RotatingKVCache, +) + + +@dataclass +class KVLayerState: + keys: torch.Tensor # [seq_len, n_heads, head_dim] + values: torch.Tensor # [seq_len, n_heads, head_dim] + + +@dataclass +class RotatingKVLayerState: + keys: torch.Tensor # [buffer_len, n_heads, head_dim] + values: torch.Tensor # [buffer_len, n_heads, head_dim] + keep: int + max_size: int + offset: int + idx: int + + +@dataclass +class ArraysLayerState: + arrays: list[torch.Tensor | None] + + +LayerState = KVLayerState | RotatingKVLayerState | ArraysLayerState + + +def _mx_to_torch(arr: mx.array) -> torch.Tensor: + mx.eval(arr) + if arr.dtype == mx.bfloat16: + return torch.from_numpy(np.array(arr.astype(mx.float32))).to(torch.bfloat16) + return torch.from_numpy(np.array(arr)) + + +def _torch_to_mx(t: torch.Tensor) -> mx.array: + t = t.detach().cpu() + if t.dtype == torch.bfloat16: + return mx.array(t.float().numpy()).astype(mx.bfloat16) # pyright: ignore[reportAny] + return mx.array(t.numpy()) # pyright: ignore[reportAny] + + +def _split_kv( + kv: torch.Tensor | list[torch.Tensor], +) -> tuple[torch.Tensor, torch.Tensor]: + if isinstance(kv, list): + return kv[0], kv[1] + if kv.shape[0] == 2 and kv.shape[1] != 2: + return kv[0], kv[1] + return kv[:, 0], kv[:, 1] + + +def _kv_to_nhd(k: mx.array, v: mx.array) -> tuple[torch.Tensor, torch.Tensor]: + """Convert MLX BHSD [1, H, S, D] to NHD [S, H, D] torch tensors.""" + kt = _mx_to_torch(k).squeeze(0).permute(1, 0, 2) # [H,S,D] -> [S,H,D] + vt = _mx_to_torch(v).squeeze(0).permute(1, 0, 2) + return kt, vt + + +def _nhd_to_bhsd(kt: torch.Tensor, vt: torch.Tensor) -> tuple[mx.array, mx.array]: + """Convert NHD [S, H, D] torch tensors to MLX BHSD [1, H, S, D].""" + k_mx = _torch_to_mx(kt.permute(1, 0, 2).unsqueeze(0)) # [S,H,D] -> [1,H,S,D] + v_mx = _torch_to_mx(vt.permute(1, 0, 2).unsqueeze(0)) + return k_mx, v_mx + + +class TorchKVCache: + def __init__( + self, layers: list[LayerState], token_offset_per_group: list[int] | None = None + ): + self.layers = layers + self.token_offset_per_group = token_offset_per_group or [] + self._num_tokens: int | None = None + + @property + def num_layers(self) -> int: + return len(self.layers) + + def layer(self, idx: int) -> LayerState: + return self.layers[idx] + + def kv_layers(self) -> list[tuple[int, KVLayerState | RotatingKVLayerState]]: + return [ + (i, layer) + for i, layer in enumerate(self.layers) + if isinstance(layer, (KVLayerState, RotatingKVLayerState)) + ] + + def detach_cpu(self) -> "TorchKVCache": + layers: list[LayerState] = [] + for layer in self.layers: + if isinstance(layer, KVLayerState): + if not layer.keys.is_cuda: + layers.append(layer) + else: + layers.append( + KVLayerState( + keys=layer.keys.detach().to("cpu", non_blocking=True), + values=layer.values.detach().to("cpu", non_blocking=True), + ) + ) + elif isinstance(layer, RotatingKVLayerState): + layers.append( + RotatingKVLayerState( + keys=layer.keys.detach().to("cpu", non_blocking=True), + values=layer.values.detach().to("cpu", non_blocking=True), + keep=layer.keep, + max_size=layer.max_size, + offset=layer.offset, + idx=layer.idx, + ) + ) + else: + layers.append(deepcopy(layer)) + if any( + layer.keys.is_cuda + for layer in self.layers + if isinstance(layer, (KVLayerState, RotatingKVLayerState)) + ): + torch.cuda.synchronize() + return TorchKVCache(layers, list(self.token_offset_per_group)) + + def trim_to(self, num_tokens: int) -> "TorchKVCache": + trimmed = TorchKVCache(list(self.layers), list(self.token_offset_per_group)) + trimmed._num_tokens = num_tokens + return trimmed + + @property + def num_tokens(self) -> int | None: + return getattr(self, "_num_tokens", None) + + @classmethod + def from_mlx_cache( + cls, + cache: Sequence[ + KVCache | RotatingKVCache | QuantizedKVCache | ArraysCache | CacheList + ], + ) -> "TorchKVCache": + layers: list[LayerState] = [] + for c in cache: + if isinstance(c, RotatingKVCache): + if c.keys is None: + layers.append( + RotatingKVLayerState( + keys=torch.empty(0), + values=torch.empty(0), + keep=c.keep, + max_size=c.max_size, + offset=c.offset, + idx=c._idx, + ) + ) + else: + k, v = c.state + kt, vt = _kv_to_nhd(k, v) # pyright: ignore[reportArgumentType] + keep, max_size, offset, idx = (int(x) for x in c.meta_state) # pyright: ignore[reportUnknownMemberType, reportUnknownVariableType, reportUnknownArgumentType] + layers.append( + RotatingKVLayerState( + keys=kt, + values=vt, + keep=keep, + max_size=max_size, + offset=offset, + idx=idx, + ) + ) + elif isinstance(c, ArraysCache): + arrays: list[torch.Tensor | None] = [] + for arr in c.state: + arrays.append(_mx_to_torch(arr) if arr is not None else None) + layers.append(ArraysLayerState(arrays=arrays)) + else: + if c.keys is None: # pyright: ignore[reportUnnecessaryComparison] + layers.append( + KVLayerState(keys=torch.empty(0), values=torch.empty(0)) + ) + else: + k, v = c.state + kt, vt = _kv_to_nhd(k, v) # pyright: ignore[reportArgumentType] + layers.append(KVLayerState(keys=kt, values=vt)) + return cls(layers) + + def to_mlx_cache(self) -> list[KVCache | RotatingKVCache | ArraysCache]: + result: list[KVCache | RotatingKVCache | ArraysCache] = [] + for layer in self.layers: + if isinstance(layer, RotatingKVLayerState): + c = RotatingKVCache(max_size=layer.max_size, keep=layer.keep) + if layer.keys.numel() > 0: + k_mx, v_mx = _nhd_to_bhsd(layer.keys, layer.values) + c.state = (k_mx, v_mx) + c.meta_state = tuple( + str(x) + for x in (layer.keep, layer.max_size, layer.offset, layer.idx) + ) + result.append(c) + elif isinstance(layer, ArraysLayerState): + c = ArraysCache(size=len(layer.arrays)) + c.state = [ + _torch_to_mx(arr) if arr is not None else None + for arr in layer.arrays + ] + result.append(c) + else: + c = KVCache() + if layer.keys.numel() > 0: + k_mx, v_mx = _nhd_to_bhsd(layer.keys, layer.values) + c.state = (k_mx, v_mx) + result.append(c) + return result + + @classmethod + def from_vllm_cache( + cls, + kv_caches: list[torch.Tensor | list[torch.Tensor]], + block_ids_per_group: list[list[int]], + layer_to_group: list[int], + num_tokens: int, + token_offset_per_group: list[int] | None = None, + block_sizes_per_group: list[int] | None = None, + ) -> "TorchKVCache": + block_tables = [ + torch.tensor(ids, dtype=torch.long) for ids in block_ids_per_group + ] + if token_offset_per_group is None: + token_offset_per_group = [0] * len(block_ids_per_group) + + layers: list[LayerState] = [] + for layer_idx, kv in enumerate(kv_caches): + gi = layer_to_group[layer_idx] + bt = block_tables[gi] + k_all, v_all = _split_kv(kv) + + if len(bt) == 0: + layers.append(KVLayerState(keys=torch.empty(0), values=torch.empty(0))) + continue + + if k_all.dim() >= 4 and len(bt) > 0 and block_sizes_per_group is not None: + page_size = k_all.shape[1] + sched_block_size = block_sizes_per_group[gi] + pages_per_block = sched_block_size // page_size + if pages_per_block > 1: + expanded = [] + for b in bt.tolist(): + start_page = b * pages_per_block + end_page = min(start_page + pages_per_block, k_all.shape[0]) + expanded.extend(range(start_page, end_page)) + bt = torch.tensor(expanded, dtype=torch.long) + + keys = k_all[bt].to("cpu", non_blocking=True) + values = v_all[bt].to("cpu", non_blocking=True) + torch.cuda.synchronize() + layers.append(KVLayerState(keys=keys, values=values)) + return cls(layers, list(token_offset_per_group)) + + def write_to_vllm_blocks( + self, + kv_caches: list[torch.Tensor | list[torch.Tensor]], + block_ids_per_group: list[list[int]], + layer_to_group: list[int], + token_offset_per_group: list[int] | None = None, + ) -> None: + block_tables = [ + torch.tensor(ids, dtype=torch.long) for ids in block_ids_per_group + ] + + first = kv_caches[0] + device = first[0].device if isinstance(first, list) else first.device + for layer_idx, layer in enumerate(self.layers): + if isinstance(layer, ArraysLayerState): + gi = layer_to_group[layer_idx] + bt = block_tables[gi] + kv = kv_caches[layer_idx] + if isinstance(kv, list): + for ti, (stored, target) in enumerate(zip(layer.arrays, kv)): + if stored is not None and target is not None: + n = min(len(bt), stored.shape[0]) + if n > 0: + target[bt[:n]] = stored[:n].to( + device, non_blocking=True + ) + continue + if not isinstance(layer, KVLayerState): + continue + gi = layer_to_group[layer_idx] + bt = block_tables[gi] + kv = kv_caches[layer_idx] + k_all, v_all = _split_kv(kv) + + keys = layer.keys + values = layer.values + block_size = k_all.shape[-3] if k_all.dim() >= 3 else k_all.shape[1] + needs_reshape = keys.dim() == 3 and keys.shape[1:] != k_all.shape[1:] + if needs_reshape: + offset = token_offset_per_group[gi] if token_offset_per_group else 0 + if offset > 0: + keys = keys[offset:] + values = values[offset:] + s, h, d = keys.shape + pad = (block_size - s % block_size) % block_size + if pad > 0: + keys = torch.nn.functional.pad(keys, (0, 0, 0, 0, 0, pad)) + values = torch.nn.functional.pad(values, (0, 0, 0, 0, 0, pad)) + keys = keys.reshape(-1, block_size, h, d) + values = values.reshape(-1, block_size, h, d) + + n_blocks = min(len(bt), keys.shape[0]) + if n_blocks > 0: + k_all[bt[:n_blocks]] = keys[:n_blocks].to(device, non_blocking=True) + v_all[bt[:n_blocks]] = values[:n_blocks].to(device, non_blocking=True) + torch.cuda.synchronize() + + def __iter__(self) -> Iterator[LayerState]: + return iter(self.layers) + + def __len__(self) -> int: + return len(self.layers) + + def __repr__(self) -> str: + parts: list[str] = [f"TorchKVCache({self.num_layers} layers)"] + for i, layer in enumerate(self.layers): + if isinstance(layer, KVLayerState): + parts.append( + f" [{i}] KV: keys={list(layer.keys.shape)} values={list(layer.values.shape)} {layer.keys.dtype}" + ) + elif isinstance(layer, RotatingKVLayerState): + parts.append( + f" [{i}] RotatingKV: keys={list(layer.keys.shape)} keep={layer.keep} max_size={layer.max_size} offset={layer.offset} idx={layer.idx}" + ) + else: + shapes = [ + list(a.shape) if a is not None else None for a in layer.arrays + ] + parts.append(f" [{i}] Arrays: {shapes}") + return "\n".join(parts) diff --git a/src/exo/worker/engines/vllm/prompt_format.py b/src/exo/worker/engines/vllm/prompt_format.py new file mode 100644 index 000000000..78e64793b --- /dev/null +++ b/src/exo/worker/engines/vllm/prompt_format.py @@ -0,0 +1,61 @@ +from mlx_lm.tokenizer_utils import TokenizerWrapper +from vllm.sampling_params import SamplingParams +from vllm.v1.engine.llm_engine import LLMEngine + +from exo.shared.types.common import ModelId +from exo.shared.types.text_generation import TextGenerationTaskParams +from exo.worker.engines.mlx.utils_mlx import ( + apply_chat_template, + get_eos_token_ids_for_model, +) + + +def format_vllm_prompt( + engine: LLMEngine, params: TextGenerationTaskParams +) -> tuple[list[int], str, int]: + # we should have our own wrapper + # (instead of abusing mlx's TokenizerWrapper, use tokenizers Tokenizer) + tokenizer = TokenizerWrapper(engine.get_tokenizer()) + prompt_text = apply_chat_template(tokenizer, params) + token_ids: list[int] = tokenizer.encode(prompt_text, add_special_tokens=False) + return token_ids, prompt_text, len(token_ids) + + +def make_vllm_sampling_params( + engine: LLMEngine, + params: TextGenerationTaskParams, + model_id: ModelId | None = None, +) -> SamplingParams: + kwargs: SamplingParams = SamplingParams() + + if params.max_output_tokens is not None: + kwargs.max_tokens = params.max_output_tokens + else: + kwargs.max_tokens = min(engine.model_config.max_model_len, 32168) + if params.temperature is not None: + kwargs.temperature = params.temperature + if params.top_p is not None: + kwargs.top_p = params.top_p + if params.top_k is not None: + kwargs.top_k = params.top_k + if params.min_p is not None: + kwargs.min_p = params.min_p + if params.stop is not None: + kwargs.stop = params.stop + if params.seed is not None: + kwargs.seed = params.seed + if params.repetition_penalty is not None: + kwargs.repetition_penalty = params.repetition_penalty + if params.logprobs: + kwargs.logprobs = params.top_logprobs or 1 + + if model_id is not None: + extra_stop = get_eos_token_ids_for_model(model_id) + if extra_stop: + kwargs.stop_token_ids = extra_stop + + if params.bench: + kwargs.ignore_eos = True + kwargs.min_tokens = kwargs.max_tokens + + return kwargs diff --git a/src/exo/worker/runner/bootstrap.py b/src/exo/worker/runner/bootstrap.py index e90411802..c7fc19004 100644 --- a/src/exo/worker/runner/bootstrap.py +++ b/src/exo/worker/runner/bootstrap.py @@ -5,7 +5,7 @@ import loguru from exo.shared.types.events import Event, RunnerStatusUpdated from exo.shared.types.tasks import Task, TaskId -from exo.shared.types.worker.instances import BoundInstance +from exo.shared.types.worker.instances import BoundInstance, VllmInstance from exo.shared.types.worker.runners import RunnerFailed from exo.utils.channels import ClosedResourceError, MpReceiver, MpSender from exo.worker.engines.base import Builder @@ -46,6 +46,21 @@ def entrypoint( builder = MfluxBuilder( event_sender, cancel_receiver, bound_instance.bound_shard ) + elif isinstance(bound_instance.instance, VllmInstance): + from exo.worker.engines.vllm.builder import VllmBuilder + from exo.worker.engines.vllm.growable_cache import patch_vllm + + os.environ["VLLM_ENABLE_V1_MULTIPROCESSING"] = "0" + os.environ["VLLM_KV_CACHE_LAYOUT"] = "NHD" + os.environ["VLLM_BATCH_INVARIANT"] = "1" + os.environ.setdefault("FASTSAFETENSORS_NOGDS", "1") + + patch_vllm() + builder = VllmBuilder( + bound_instance.bound_shard.model_card.model_id, + event_sender, + cancel_receiver, + ) else: from exo.worker.engines.mlx.patches import apply_mlx_patches diff --git a/src/exo/worker/runner/llm_inference/batch_generator.py b/src/exo/worker/runner/llm_inference/batch_generator.py index c049161f4..60658acd0 100644 --- a/src/exo/worker/runner/llm_inference/batch_generator.py +++ b/src/exo/worker/runner/llm_inference/batch_generator.py @@ -221,7 +221,6 @@ class SequentialGenerator(Engine): apply_chat_template(self.tokenizer, task.task_params), self.tool_parser, self.tokenizer, - type(self.model), self.model_id, task.task_params.tools, ) @@ -418,7 +417,6 @@ class BatchGenerator(Engine): apply_chat_template(self.tokenizer, task.task_params), self.tool_parser, self.tokenizer, - type(self.model), self.model_id, task.task_params.tools, ) diff --git a/src/exo/worker/runner/llm_inference/model_output_parsers.py b/src/exo/worker/runner/llm_inference/model_output_parsers.py index 4952688dc..478a1ce40 100644 --- a/src/exo/worker/runner/llm_inference/model_output_parsers.py +++ b/src/exo/worker/runner/llm_inference/model_output_parsers.py @@ -2,9 +2,6 @@ from collections.abc import Callable, Generator, Iterator from functools import cache from typing import Any -from mlx_lm.models.deepseek_v4 import Model as DeepseekV4Model -from mlx_lm.models.deepseek_v32 import Model as DeepseekV32Model -from mlx_lm.models.gpt_oss import Model as GptOssModel from mlx_lm.tokenizer_utils import TokenizerWrapper from openai_harmony import ( # pyright: ignore[reportMissingTypeStubs] HarmonyEncodingName, @@ -23,7 +20,6 @@ from exo.shared.types.chunks import ( ) from exo.shared.types.common import ModelId from exo.shared.types.worker.runner_response import GenerationResponse, ToolCallResponse -from exo.worker.engines.mlx.types import Model from exo.worker.engines.mlx.utils_mlx import ( detect_thinking_prompt_suffix, ) @@ -69,16 +65,15 @@ def apply_all_parsers( prompt: str, tool_parser: ToolParser | None, tokenizer: TokenizerWrapper, - model_type: type[Model], model_id: ModelId, tools: list[dict[str, Any]] | None, ) -> Iterator[GenerationChunk | None]: generator = receiver - normalized_id = model_id.normalize().lower() - if issubclass(model_type, GptOssModel): + normalized_id = model_id.short().lower() + if "gpt-oss" in normalized_id: generator = parse_gpt_oss(generator) - elif issubclass(model_type, DeepseekV32Model) and "deepseek" in normalized_id: + elif "deepseek-v3.2" in normalized_id: if tokenizer.has_thinking: generator = parse_thinking_models( generator, @@ -87,7 +82,7 @@ def apply_all_parsers( starts_in_thinking=detect_thinking_prompt_suffix(prompt, tokenizer), ) generator = parse_deepseek_v32(generator) - elif issubclass(model_type, DeepseekV4Model) and "deepseek-v4" in normalized_id: + elif "deepseek-v4" in normalized_id: if tokenizer.has_thinking: generator = parse_thinking_models( generator, diff --git a/uv.lock b/uv.lock index 9a13fcdcd..80759a652 100644 --- a/uv.lock +++ b/uv.lock @@ -10,9 +10,14 @@ supported-markers = [ "sys_platform == 'linux'", ] conflicts = [[ - { package = "exo", extra = "cpu" }, - { package = "exo", extra = "cuda12" }, - { package = "exo", extra = "cuda13" }, + { package = "exo", extra = "mlx-cpu" }, + { package = "exo", extra = "mlx-cuda12" }, + { package = "exo", extra = "mlx-cuda13" }, + { package = "exo", extra = "mlx-none" }, +], [ + { package = "exo", extra = "mlx-cuda12" }, + { package = "exo", extra = "vllm-cuda13" }, + { package = "exo", extra = "vllm-none" }, ]] [options] @@ -24,10 +29,10 @@ members = [ "exo-bench", "exo-pyo3-bindings", ] -constraints = [{ name = "transformers", specifier = ">=5.6.2" }] overrides = [ + { name = "mlx", marker = "sys_platform != 'darwin'" }, { name = "mlx", marker = "sys_platform == 'darwin'", git = "https://github.com/rltakashige/mlx-jaccl-fix-small-recv.git?branch=address-rdma-gpu-locks" }, - { name = "mlx", marker = "sys_platform == 'linux'", specifier = "==0.31.1" }, + { name = "opencv-python", marker = "python_full_version < '0'" }, ] [[package]] @@ -62,13 +67,13 @@ name = "aiohttp" version = "3.13.3" source = { registry = "https://pypi.org/simple" } dependencies = [ - { name = "aiohappyeyeballs", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or (extra == 'extra-3-exo-cpu' and extra == 'extra-3-exo-cuda12') or (extra == 'extra-3-exo-cpu' and extra == 'extra-3-exo-cuda13') or (extra == 'extra-3-exo-cuda12' and extra == 'extra-3-exo-cuda13')" }, - { name = "aiosignal", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or (extra == 'extra-3-exo-cpu' and extra == 'extra-3-exo-cuda12') or (extra == 'extra-3-exo-cpu' and extra == 'extra-3-exo-cuda13') or (extra == 'extra-3-exo-cuda12' and extra == 'extra-3-exo-cuda13')" }, - { name = "attrs", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or (extra == 'extra-3-exo-cpu' and extra == 'extra-3-exo-cuda12') or (extra == 'extra-3-exo-cpu' and extra == 'extra-3-exo-cuda13') or (extra == 'extra-3-exo-cuda12' and extra == 'extra-3-exo-cuda13')" }, - { name = "frozenlist", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or (extra == 'extra-3-exo-cpu' and extra == 'extra-3-exo-cuda12') or (extra == 'extra-3-exo-cpu' and extra == 'extra-3-exo-cuda13') or (extra == 'extra-3-exo-cuda12' and extra == 'extra-3-exo-cuda13')" }, - { name = "multidict", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or (extra == 'extra-3-exo-cpu' and extra == 'extra-3-exo-cuda12') or (extra == 'extra-3-exo-cpu' and extra == 'extra-3-exo-cuda13') or (extra == 'extra-3-exo-cuda12' and extra == 'extra-3-exo-cuda13')" }, - { name = "propcache", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or (extra == 'extra-3-exo-cpu' and extra == 'extra-3-exo-cuda12') or (extra == 'extra-3-exo-cpu' and extra == 'extra-3-exo-cuda13') or (extra == 'extra-3-exo-cuda12' and 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