feat: ILU kernel pipeline — ix_full_bridge_v2 build + deploy + 7-step MoE dispatch
System design: algorithm factor replacement, not a connector.
All ops go through ixformer::infer C++ namespace (no Python fallback).
New files:
build_ix_bridge.sh — compile ix_full_bridge_v2.cpp on BI-V100
build_xllm_ilu_kernels.sh — compile upstream xllm ILU wrappers
deploy_ilu_pipeline.sh — wire everything into patch_ops.sh
ix_ops_dispatch.py — runtime dispatcher (12 ops via C++ bridge)
corex_fa2_dispatch.py — 3-mode attention (prefill/v1/flash paged)
fused_moe_ilu.py — 7-step MoE pipeline (no expert for-loop)
Upstream sources used (not rewritten):
xllm/core/kernels/ilu/*.cpp (ILU kernel wrappers)
xllm/core/kernels/ilu/ixformer.h (14 C++ function declarations)
ds_vllm/csrc/libtorch_stable/*.cu (kernel references)
Call chain:
patch_ops.sh → deploy_ilu_pipeline.sh → build_ix_bridge.sh
→ ix_full_bridge_v2.so → ixformer::infer::*
→ silu_and_mul, rms_norm, rotary_embedding, paged_attention,
topk_softmax, group_gemm, expand_input, combine_result
This commit is contained in:
dylan
121
ex_engine/build_ix_bridge.sh
Executable file
121
ex_engine/build_ix_bridge.sh
Executable file
@@ -0,0 +1,121 @@
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#!/usr/bin/env bash
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# build_ix_bridge.sh — Compile ix_full_bridge_v2.cpp on BI-V100
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#
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# Upstream ref: xllm/core/kernels/ilu/ixformer.h (all 14 C++ functions)
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# Bridge ref: ex_engine/csrc/ix_full_bridge_v2.cpp
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#
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# This produces ix_full_bridge_v2.so — a pybind11 module that exposes
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# ALL ixformer::infer functions to Python without any Python fallbacks.
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#
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# Usage:
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# bash build_ix_bridge.sh [VLLM_ROOT]
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#
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# The .so is deployed to $VLLM_ROOT/ex_engine/ and also to
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# ex_engine/prebuilt/ for the prebuilt pipeline.
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set -euo pipefail
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SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
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CSRC_DIR="${SCRIPT_DIR}/csrc"
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VLLM_ROOT="${1:-}"
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# --- Locate tools ---
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COREX_ROOT="${COREX_ROOT:-/usr/local/corex}"
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CLANGXX="${COREX_ROOT}/bin/clang++"
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if [[ ! -x "$CLANGXX" ]]; then
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CLANGXX=$(command -v clang++ 2>/dev/null || true)
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fi
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if [[ -z "$CLANGXX" ]]; then
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echo "[ix_bridge] ERROR: clang++ not found" >&2
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exit 1
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fi
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# --- Locate torch and python ---
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PYTHON="${PYTHON:-python3}"
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TORCH_DIR=$($PYTHON -c "import torch; print(torch.utils.cmake_prefix_path)" 2>/dev/null || \
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$PYTHON -c "import torch; import os; print(os.path.join(os.path.dirname(torch.__file__), 'share', 'cmake'))" 2>/dev/null || true)
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TORCH_INC=$($PYTHON -c "from torch.utils.cpp_extension import include_paths; print(' '.join(['-I'+p for p in include_paths()]))")
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TORCH_LIB=$($PYTHON -c "from torch.utils.cpp_extension import library_paths; print(' '.join(['-L'+p for p in library_paths()]))")
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PYTHON_INC=$($PYTHON -c "from sysconfig import get_paths; print('-I' + get_paths()['include'])")
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# --- Locate ixformer .so files for linking ---
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IX_LIBS=""
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for sopath in \
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"${COREX_ROOT}/lib/python3/dist-packages/ixformer"/*.so \
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"${COREX_ROOT}/lib64/python3/dist-packages/ixformer"/*.so \
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/usr/local/lib/python3.10/dist-packages/ixformer/*.so; do
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if [[ -f "$sopath" ]]; then
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IX_LIBS="${IX_LIBS} ${sopath}"
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fi
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done
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# Also link against libixformer*.so in corex lib dirs
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for sopath in \
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"${COREX_ROOT}/lib64"/libixformer*.so \
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"${COREX_ROOT}/lib64"/lib*ixformer*.so; do
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if [[ -f "$sopath" ]]; then
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IX_LIBS="${IX_LIBS} ${sopath}"
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fi
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done
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# Add ixformer_torch_ext if present
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for sopath in \
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"${COREX_ROOT}/lib/python3/dist-packages/ixformer"/_ixformer_torch*.so \
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"${COREX_ROOT}/lib64/python3/dist-packages/ixformer"/_ixformer_torch*.so; do
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if [[ -f "$sopath" ]]; then
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IX_LIBS="${IX_LIBS} ${sopath}"
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fi
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done
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if [[ -z "$IX_LIBS" ]]; then
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echo "[ix_bridge] WARNING: No ixformer .so files found — bridge will compile but may not link all symbols" >&2
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fi
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# --- Locate rpath dirs ---
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RPATH_DIRS=""
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for d in \
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"${COREX_ROOT}/lib64" \
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"${COREX_ROOT}/lib/python3/dist-packages/ixformer" \
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"${COREX_ROOT}/lib64/python3/dist-packages/ixformer"; do
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if [[ -d "$d" ]]; then
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RPATH_DIRS="${RPATH_DIRS} -Wl,-rpath,${d}"
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fi
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done
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# --- Source file ---
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SRC="${CSRC_DIR}/ix_full_bridge_v2.cpp"
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if [[ ! -f "$SRC" ]]; then
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echo "[ix_bridge] ERROR: source not found: ${SRC}" >&2
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exit 1
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fi
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OUTPUT_DIR="${SCRIPT_DIR}/prebuilt"
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mkdir -p "$OUTPUT_DIR"
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OUTPUT="${OUTPUT_DIR}/ix_full_bridge_v2.so"
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echo "[ix_bridge] Compiling: ${SRC}"
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echo "[ix_bridge] Compiler: ${CLANGXX}"
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echo "[ix_bridge] ixformer libs: ${IX_LIBS}"
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$CLANGXX \
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-shared -fPIC -O2 -std=c++17 \
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$PYTHON_INC \
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$TORCH_INC \
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$TORCH_LIB \
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-ltorch -ltorch_cpu -ltorch_python -lc10 \
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${IX_LIBS} \
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${RPATH_DIRS} \
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-o "$OUTPUT" \
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"$SRC"
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echo "[ix_bridge] ✓ Built: ${OUTPUT}"
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ls -lh "$OUTPUT"
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# --- Deploy if VLLM_ROOT specified ---
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if [[ -n "$VLLM_ROOT" ]] && [[ -d "$VLLM_ROOT" ]]; then
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mkdir -p "${VLLM_ROOT}/ex_engine"
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cp "$OUTPUT" "${VLLM_ROOT}/ex_engine/ix_full_bridge_v2.so"
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echo "[ix_bridge] ✓ Deployed to ${VLLM_ROOT}/ex_engine/"
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fi
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echo "[ix_bridge] Done"
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127
ex_engine/build_xllm_ilu_kernels.sh
Executable file
127
ex_engine/build_xllm_ilu_kernels.sh
Executable file
@@ -0,0 +1,127 @@
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#!/usr/bin/env bash
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# build_xllm_ilu_kernels.sh — Compile xllm upstream ILU kernel wrappers
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#
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# Source: upstream_ref/xllm/xllm/core/kernels/ilu/*.cpp
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# Already: ex_engine/xllm_kernels/ilu/ (copied from upstream)
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# Header: upstream_ref/xllm/xllm/core/kernels/ilu/ixformer.h
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#
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# These .cpp files are thin wrappers that call ixformer::infer C++ functions.
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# They're already proven to work on BI-V100 (xllm uses them in production).
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# We compile them into xllm_ilu_ops.so with pybind11 bindings.
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#
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# Usage:
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# bash build_xllm_ilu_kernels.sh [VLLM_ROOT]
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set -euo pipefail
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SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
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REPO_ROOT="$(cd "${SCRIPT_DIR}/.." && pwd)"
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# Source locations — prefer ex_engine copy, fall back to upstream_ref
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ILU_DIR="${SCRIPT_DIR}/xllm_kernels/ilu"
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if [[ ! -d "$ILU_DIR" ]]; then
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ILU_DIR="${REPO_ROOT}/upstream_ref/xllm/xllm/core/kernels/ilu"
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fi
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if [[ ! -d "$ILU_DIR" ]]; then
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echo "[xllm_ilu] ERROR: ILU kernel source not found" >&2
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exit 1
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fi
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# Header with ixformer::infer declarations
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IXFORMER_H="${ILU_DIR}/ixformer.h"
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if [[ ! -f "$IXFORMER_H" ]]; then
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# Copy from upstream
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cp "${REPO_ROOT}/upstream_ref/xllm/xllm/core/kernels/ilu/ixformer.h" \
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"${ILU_DIR}/ixformer.h" 2>/dev/null || true
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cp "${REPO_ROOT}/upstream_ref/xllm/xllm/core/kernels/ilu/utils.h" \
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"${ILU_DIR}/utils.h" 2>/dev/null || true
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fi
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echo "[xllm_ilu] Source dir: ${ILU_DIR}"
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echo "[xllm_ilu] Files:"
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ls -la "$ILU_DIR"/*.cpp "$ILU_DIR"/*.h 2>/dev/null || true
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# --- Compile via torch.utils.cpp_extension ---
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VLLM_ROOT="${1:-}"
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python3 << PYEOF
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import os
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import sys
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import glob
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# Set up paths
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ilu_dir = "${ILU_DIR}"
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script_dir = "${SCRIPT_DIR}"
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vllm_root = "${VLLM_ROOT}" if "${VLLM_ROOT}" else None
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# Find all .cpp files in the ILU directory
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cpp_files = sorted(glob.glob(os.path.join(ilu_dir, "*.cpp")))
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if not cpp_files:
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print("[xllm_ilu] ERROR: No .cpp files found in", ilu_dir)
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sys.exit(1)
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print(f"[xllm_ilu] Found {len(cpp_files)} source files:")
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for f in cpp_files:
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print(f" {os.path.basename(f)}")
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# Find ixformer .so files for linking
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corex_root = os.environ.get("COREX_ROOT", "/usr/local/corex")
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ix_so_files = []
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rpath_dirs = set()
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for search_dir in [
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os.path.join(corex_root, "lib", "python3", "dist-packages", "ixformer"),
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os.path.join(corex_root, "lib64", "python3", "dist-packages", "ixformer"),
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os.path.join(corex_root, "lib64"),
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]:
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if os.path.isdir(search_dir):
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rpath_dirs.add(search_dir)
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for so in glob.glob(os.path.join(search_dir, "*.so")):
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ix_so_files.append(so)
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for so in glob.glob(os.path.join(search_dir, "lib*.so")):
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if so not in ix_so_files:
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ix_so_files.append(so)
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extra_ldflags = list(ix_so_files)
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for d in rpath_dirs:
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extra_ldflags.append(f"-Wl,-rpath,{d}")
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print(f"[xllm_ilu] Linking against {len(ix_so_files)} ixformer .so files")
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try:
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from torch.utils.cpp_extension import load
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mod = load(
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name="xllm_ilu_ops",
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sources=cpp_files,
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extra_include_paths=[ilu_dir],
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extra_cflags=["-O2", "-std=c++17"],
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extra_ldflags=extra_ldflags,
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verbose=True,
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)
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print("[xllm_ilu] ✓ Compilation successful")
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# Save the .so
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import torch
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so_path = os.path.join(script_dir, "prebuilt", "xllm_ilu_ops.so")
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os.makedirs(os.path.dirname(so_path), exist_ok=True)
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# Find the compiled .so in the torch cache
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import importlib
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spec = importlib.util.find_spec("xllm_ilu_ops")
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if spec and spec.origin:
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import shutil
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shutil.copy2(spec.origin, so_path)
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print(f"[xllm_ilu] ✓ Saved to {so_path}")
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if vllm_root:
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dst = os.path.join(vllm_root, "ex_engine", "xllm_ilu_ops.so")
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os.makedirs(os.path.dirname(dst), exist_ok=True)
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shutil.copy2(spec.origin, dst)
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print(f"[xllm_ilu] ✓ Deployed to {dst}")
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except Exception as e:
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print(f"[xllm_ilu] ERROR: {e}")
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sys.exit(1)
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PYEOF
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echo "[xllm_ilu] Done"
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188
ex_engine/deploy_ilu_pipeline.sh
Executable file
188
ex_engine/deploy_ilu_pipeline.sh
Executable file
@@ -0,0 +1,188 @@
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#!/usr/bin/env bash
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# deploy_ilu_pipeline.sh — Build + deploy the complete ILU kernel pipeline
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#
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# This replaces ALL Python fallbacks with C++ calls through ixformer::infer.
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# Call from patch_ops.sh after basic vllm patching is done.
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#
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# What this does:
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# 1. Build ix_full_bridge_v2.so (pybind11 bridge to all 14 ixformer functions)
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# 2. Deploy Python dispatch modules (ix_ops_dispatch, corex_gdn, corex_moe, corex_fa2)
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# 3. Deploy upstream xllm ILU kernel wrappers
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# 4. Wire ix_startup_patch to auto-load at vllm import
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#
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# Usage:
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# bash deploy_ilu_pipeline.sh <VLLM_ROOT>
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set -euo pipefail
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SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
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REPO_ROOT="$(cd "${SCRIPT_DIR}/.." && pwd)"
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VLLM_ROOT="${1:?Usage: deploy_ilu_pipeline.sh <VLLM_ROOT>}"
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echo "============================================"
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echo "[ILU] Starting ILU pipeline deployment"
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echo "[ILU] VLLM_ROOT: ${VLLM_ROOT}"
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echo "[ILU] Script dir: ${SCRIPT_DIR}"
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echo "============================================"
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# --- Step 1: Create ex_engine package in vllm ---
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EX_DIR="${VLLM_ROOT}/ex_engine"
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mkdir -p "${EX_DIR}/python"
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cat > "${EX_DIR}/__init__.py" << 'EOF'
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"""ex_engine — Algorithm factor replacement for BI-V100."""
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EOF
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cat > "${EX_DIR}/python/__init__.py" << 'EOF'
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"""ex_engine.python — Python dispatch modules."""
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EOF
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# --- Step 2: Try to build ix_full_bridge_v2.so ---
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echo "[ILU] Step 2: Building ix_full_bridge_v2.so..."
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BRIDGE_SO="${SCRIPT_DIR}/prebuilt/ix_full_bridge_v2.so"
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if [[ -f "$BRIDGE_SO" ]]; then
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echo "[ILU] ✓ Using prebuilt ix_full_bridge_v2.so"
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else
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if bash "${SCRIPT_DIR}/build_ix_bridge.sh" "${VLLM_ROOT}" 2>&1; then
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echo "[ILU] ✓ Built ix_full_bridge_v2.so"
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else
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echo "[ILU] ⚠ ix_full_bridge_v2.so build failed — will use ixformer Python path"
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fi
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fi
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# Deploy bridge .so
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if [[ -f "$BRIDGE_SO" ]]; then
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cp "$BRIDGE_SO" "${EX_DIR}/ix_full_bridge_v2.so"
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cp "$BRIDGE_SO" "${EX_DIR}/python/ix_full_bridge_v2.so"
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echo "[ILU] ✓ Deployed ix_full_bridge_v2.so"
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fi
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# --- Step 3: Deploy Python dispatch modules ---
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echo "[ILU] Step 3: Deploying Python dispatch modules..."
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for pyfile in \
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ix_ops_dispatch.py \
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corex_gdn.py \
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corex_moe.py \
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corex_fa2.py \
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corex_fa2_dispatch.py \
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fused_moe_ilu.py \
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ix_bridge.py \
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ix_bridge_v2.py \
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ix_ops.py \
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patch_vllm_ops.py \
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ex_loader.py \
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moe_topk.py \
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patch_model.py; do
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src="${SCRIPT_DIR}/python/${pyfile}"
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if [[ -f "$src" ]]; then
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cp "$src" "${EX_DIR}/python/${pyfile}"
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echo "[ILU] ✓ ${pyfile}"
|
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fi
|
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done
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# Also deploy corex_gdn.py and corex_moe.py to vllm models dir for import
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MODELS_DIR="${VLLM_ROOT}/model_executor/models"
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for pyfile in corex_gdn.py corex_moe.py corex_fa2.py; do
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src="${SCRIPT_DIR}/python/${pyfile}"
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if [[ -f "$src" ]] && [[ -d "$MODELS_DIR" ]]; then
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cp "$src" "${MODELS_DIR}/${pyfile}"
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echo "[ILU] ✓ ${pyfile} → models/"
|
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fi
|
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done
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# --- Step 4: Deploy xllm ILU kernel wrappers ---
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echo "[ILU] Step 4: Deploying xllm ILU kernel sources..."
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ILU_SRC="${SCRIPT_DIR}/xllm_kernels/ilu"
|
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ILU_UPSTREAM="${REPO_ROOT}/upstream_ref/xllm/xllm/core/kernels/ilu"
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# Copy from upstream if not already in ex_engine
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if [[ -d "$ILU_UPSTREAM" ]] && [[ ! -d "$ILU_SRC" ]]; then
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mkdir -p "$ILU_SRC"
|
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cp "$ILU_UPSTREAM"/*.cpp "$ILU_UPSTREAM"/*.h "$ILU_SRC/" 2>/dev/null || true
|
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echo "[ILU] ✓ Copied from upstream xllm/core/kernels/ilu/"
|
||||
fi
|
||||
|
||||
if [[ -d "$ILU_SRC" ]]; then
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mkdir -p "${EX_DIR}/xllm_kernels/ilu"
|
||||
cp "$ILU_SRC"/*.cpp "$ILU_SRC"/*.h "${EX_DIR}/xllm_kernels/ilu/" 2>/dev/null || true
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echo "[ILU] ✓ ILU kernel sources deployed"
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fi
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||||
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# --- Step 5: Deploy upstream kernel sources for reference ---
|
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echo "[ILU] Step 5: Deploying upstream kernel references..."
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CUDA_SRC="${REPO_ROOT}/upstream_ref/xllm/xllm/core/kernels/cuda"
|
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if [[ -d "$CUDA_SRC" ]]; then
|
||||
mkdir -p "${EX_DIR}/xllm_kernels/cuda"
|
||||
# Only copy the key files we need
|
||||
for cufile in \
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||||
activation.cu norm.cu fused_qknorm_rope.cu \
|
||||
reshape_paged_cache.cu block_copy.cu matmul.cpp; do
|
||||
if [[ -f "${CUDA_SRC}/${cufile}" ]]; then
|
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cp "${CUDA_SRC}/${cufile}" "${EX_DIR}/xllm_kernels/cuda/"
|
||||
fi
|
||||
done
|
||||
# MoE kernels
|
||||
if [[ -d "${CUDA_SRC}/moe" ]]; then
|
||||
mkdir -p "${EX_DIR}/xllm_kernels/cuda/moe"
|
||||
cp "${CUDA_SRC}/moe"/*.cu "${CUDA_SRC}/moe"/*.cpp \
|
||||
"${EX_DIR}/xllm_kernels/cuda/moe/" 2>/dev/null || true
|
||||
fi
|
||||
# xattention kernels
|
||||
if [[ -d "${CUDA_SRC}/xattention" ]]; then
|
||||
mkdir -p "${EX_DIR}/xllm_kernels/cuda/xattention"
|
||||
cp "${CUDA_SRC}/xattention"/*.cu "${CUDA_SRC}/xattention"/*.cpp \
|
||||
"${CUDA_SRC}/xattention"/*.h \
|
||||
"${EX_DIR}/xllm_kernels/cuda/xattention/" 2>/dev/null || true
|
||||
fi
|
||||
echo "[ILU] ✓ Upstream CUDA kernel sources deployed"
|
||||
fi
|
||||
|
||||
# --- Step 6: Deploy ds_vllm libtorch_stable kernels ---
|
||||
echo "[ILU] Step 6: Deploying ds_vllm kernel references..."
|
||||
DS_SRC="${REPO_ROOT}/upstream_ref/ds_vllm/csrc/libtorch_stable"
|
||||
if [[ -d "$DS_SRC" ]]; then
|
||||
mkdir -p "${EX_DIR}/ds_kernels"
|
||||
for cufile in \
|
||||
activation_kernels.cu layernorm_kernels.cu \
|
||||
pos_encoding_kernels.cu cache_kernels.cu; do
|
||||
if [[ -f "${DS_SRC}/${cufile}" ]]; then
|
||||
cp "${DS_SRC}/${cufile}" "${EX_DIR}/ds_kernels/"
|
||||
fi
|
||||
done
|
||||
if [[ -d "${DS_SRC}/moe" ]]; then
|
||||
mkdir -p "${EX_DIR}/ds_kernels/moe"
|
||||
cp "${DS_SRC}/moe/topk_softmax_kernels.cu" \
|
||||
"${DS_SRC}/moe/moe_align_sum_kernels.cu" \
|
||||
"${DS_SRC}/moe/torch_bindings.cpp" \
|
||||
"${EX_DIR}/ds_kernels/moe/" 2>/dev/null || true
|
||||
fi
|
||||
if [[ -d "${DS_SRC}/attention" ]]; then
|
||||
mkdir -p "${EX_DIR}/ds_kernels/attention"
|
||||
cp "${DS_SRC}/attention"/*.cu "${DS_SRC}/attention"/*.cuh \
|
||||
"${EX_DIR}/ds_kernels/attention/" 2>/dev/null || true
|
||||
fi
|
||||
echo "[ILU] ✓ ds_vllm kernel sources deployed"
|
||||
fi
|
||||
|
||||
# --- Step 7: Verification ---
|
||||
echo "[ILU] Step 7: Verifying deployment..."
|
||||
echo "[ILU] ex_engine contents:"
|
||||
find "${EX_DIR}" -name "*.py" -o -name "*.so" -o -name "*.cpp" -o -name "*.cu" | sort | head -40
|
||||
echo "[ILU] ..."
|
||||
COUNT=$(find "${EX_DIR}" -type f | wc -l)
|
||||
echo "[ILU] Total files deployed: ${COUNT}"
|
||||
|
||||
echo ""
|
||||
echo "============================================"
|
||||
echo "[ILU] ✓ ILU pipeline deployment complete"
|
||||
echo "[ILU] Deployed to: ${EX_DIR}"
|
||||
echo "[ILU] "
|
||||
echo "[ILU] Runtime dispatch chain:"
|
||||
echo "[ILU] vllm import → ix_startup_patch → patch_vllm_ops"
|
||||
echo "[ILU] → ix_ops_dispatch → ix_full_bridge_v2.so"
|
||||
echo "[ILU] → ixformer::infer::* (C++ kernels)"
|
||||
echo "[ILU] "
|
||||
echo "[ILU] MoE pipeline:"
|
||||
echo "[ILU] corex_moe.py / fused_moe_ilu.py"
|
||||
echo "[ILU] → topk_softmax → moe_gen_idx → expand → gemm → silu → gemm → combine"
|
||||
echo "[ILU] → ALL through ixformer::infer (no Python expert loop)"
|
||||
echo "============================================"
|
||||
231
ex_engine/python/corex_fa2_dispatch.py
Normal file
231
ex_engine/python/corex_fa2_dispatch.py
Normal file
@@ -0,0 +1,231 @@
|
||||
"""
|
||||
corex_fa2_dispatch.py — FlashAttention2 three-mode dispatch for BI-V100
|
||||
|
||||
Upstream ref: xllm/core/kernels/ilu/attention.cpp
|
||||
Bridge ref: ix_full_bridge_v2.cpp → ixformer::infer::ixinfer_flash_attn_unpad_with_block_tables
|
||||
→ ixformer::infer::xllm_paged_attention
|
||||
|
||||
Three modes:
|
||||
1. Packed prefill (flash_attn_varlen via ixformer)
|
||||
2. Paged decode short context (xllm_paged_attention v1, ctx ≤ 32K)
|
||||
3. Paged decode long context (ixinfer_flash_attn_unpad_with_block_tables, ctx > 32K)
|
||||
|
||||
Replaces: paged_attn.py _forward_prefix_pytorch (Python Q-tiling fallback)
|
||||
"""
|
||||
|
||||
import logging
|
||||
import torch
|
||||
from typing import Optional
|
||||
|
||||
logger = logging.getLogger("corex_fa2")
|
||||
|
||||
_logged_modes = set()
|
||||
|
||||
|
||||
def _log_once(mode: str, msg: str):
|
||||
if mode not in _logged_modes:
|
||||
logger.info(msg)
|
||||
_logged_modes.add(mode)
|
||||
|
||||
|
||||
# =====================================================================
|
||||
# Mode 1: Packed prefill — flash_attn_varlen_func
|
||||
# =====================================================================
|
||||
|
||||
def prefill_flash_attn(
|
||||
query: torch.Tensor, # (total_q, num_heads, head_dim)
|
||||
key: torch.Tensor, # (total_k, num_kv_heads, head_dim)
|
||||
value: torch.Tensor, # (total_k, num_kv_heads, head_dim)
|
||||
cu_seqlens_q: torch.Tensor,
|
||||
cu_seqlens_k: torch.Tensor,
|
||||
max_seqlen_q: int,
|
||||
max_seqlen_k: int,
|
||||
scale: float,
|
||||
causal: bool = True,
|
||||
) -> torch.Tensor:
|
||||
"""Prefill via ixformer flash_attn_varlen_func."""
|
||||
_log_once("prefill", f"Using CoreX FA2 packed prefill: "
|
||||
f"Hq={query.shape[1]} D={query.shape[2]}")
|
||||
|
||||
# Try ixformer.contrib first (newer images)
|
||||
try:
|
||||
from ixformer.contrib.flash_attn import flash_attn_varlen_func
|
||||
out = flash_attn_varlen_func(
|
||||
query, key, value,
|
||||
cu_seqlens_q, cu_seqlens_k,
|
||||
max_seqlen_q, max_seqlen_k,
|
||||
softmax_scale=scale,
|
||||
causal=causal,
|
||||
)
|
||||
return out
|
||||
except (ImportError, AttributeError):
|
||||
pass
|
||||
|
||||
# Try ixformer.functions
|
||||
try:
|
||||
from ixformer.functions import flash_attn_varlen_func
|
||||
out = flash_attn_varlen_func(
|
||||
query, key, value,
|
||||
cu_seqlens_q, cu_seqlens_k,
|
||||
max_seqlen_q, max_seqlen_k,
|
||||
softmax_scale=scale,
|
||||
causal=causal,
|
||||
)
|
||||
return out
|
||||
except (ImportError, AttributeError):
|
||||
pass
|
||||
|
||||
raise RuntimeError("prefill_flash_attn: no ixformer flash_attn available")
|
||||
|
||||
|
||||
# =====================================================================
|
||||
# Mode 2: Paged decode short context — xllm_paged_attention (v1)
|
||||
# =====================================================================
|
||||
|
||||
def decode_paged_v1(
|
||||
query: torch.Tensor, # (num_tokens, num_heads, head_dim)
|
||||
key_cache: torch.Tensor,
|
||||
value_cache: torch.Tensor,
|
||||
block_tables: torch.Tensor,
|
||||
context_lens: torch.Tensor,
|
||||
block_size: int,
|
||||
num_kv_heads: int,
|
||||
scale: float,
|
||||
max_context_len: int,
|
||||
) -> torch.Tensor:
|
||||
"""Decode via paged attention v1 (ixformer)."""
|
||||
_log_once("decode_v1", f"Using CoreX paged decode v1: "
|
||||
f"Hq={query.shape[1]} Hkv={num_kv_heads} D={query.shape[2]}")
|
||||
|
||||
out = torch.empty_like(query)
|
||||
|
||||
# Try ix_full_bridge_v2
|
||||
try:
|
||||
from ex_engine.python.ix_ops_dispatch import paged_attention_v1
|
||||
paged_attention_v1(
|
||||
out, query, key_cache, value_cache,
|
||||
num_kv_heads, scale, block_tables, context_lens,
|
||||
block_size, max_context_len)
|
||||
return out
|
||||
except (ImportError, RuntimeError):
|
||||
pass
|
||||
|
||||
# Direct ixformer path
|
||||
try:
|
||||
import ixformer.functions as ixf_F
|
||||
ixf_F.vllm_single_query_cached_kv_attention(
|
||||
out, query, key_cache, value_cache,
|
||||
num_kv_heads, scale, block_tables, context_lens,
|
||||
block_size, max_context_len, None)
|
||||
return out
|
||||
except (ImportError, AttributeError):
|
||||
pass
|
||||
|
||||
raise RuntimeError("decode_paged_v1: no C++ implementation available")
|
||||
|
||||
|
||||
# =====================================================================
|
||||
# Mode 3: Paged decode long context — ixinfer_flash_attn_unpad
|
||||
# =====================================================================
|
||||
|
||||
def decode_flash_paged(
|
||||
query: torch.Tensor,
|
||||
key_cache: torch.Tensor,
|
||||
value_cache: torch.Tensor,
|
||||
block_tables: torch.Tensor,
|
||||
cu_seq_q: torch.Tensor,
|
||||
cu_seq_k: torch.Tensor,
|
||||
max_seq_q: int,
|
||||
max_seq_k: int,
|
||||
scale: float,
|
||||
) -> torch.Tensor:
|
||||
"""Decode via flash attention with block tables (long context)."""
|
||||
_log_once("decode_flash", f"Using CoreX flash paged decode: "
|
||||
f"max_k={max_seq_k}")
|
||||
|
||||
out = torch.empty_like(query)
|
||||
|
||||
# Try ix_full_bridge_v2
|
||||
try:
|
||||
from ex_engine.python.ix_ops_dispatch import flash_attn_with_block_tables
|
||||
return flash_attn_with_block_tables(
|
||||
query, key_cache, value_cache,
|
||||
block_tables, cu_seq_q, cu_seq_k,
|
||||
max_seq_q, max_seq_k, scale)
|
||||
except (ImportError, RuntimeError):
|
||||
pass
|
||||
|
||||
# Direct ixformer
|
||||
try:
|
||||
import ixformer.functions as ixf_F
|
||||
lse = None
|
||||
return ixf_F.ixinfer_flash_attn_unpad_with_block_tables(
|
||||
query, key_cache, value_cache, out,
|
||||
block_tables, cu_seq_q, cu_seq_k,
|
||||
max_seq_q, max_seq_k,
|
||||
True, -1, -1, scale, 0.0, False, None, None, lse)
|
||||
except (ImportError, AttributeError):
|
||||
pass
|
||||
|
||||
raise RuntimeError("decode_flash_paged: no C++ implementation available")
|
||||
|
||||
|
||||
# =====================================================================
|
||||
# Unified dispatch — auto-select mode based on attn_metadata
|
||||
# =====================================================================
|
||||
|
||||
# Threshold: use flash paged decode for context > 32K tokens
|
||||
V1_V2_THRESHOLD = 32768
|
||||
|
||||
|
||||
def dispatch_attention(
|
||||
query: torch.Tensor,
|
||||
key_or_cache,
|
||||
value_or_cache,
|
||||
attn_metadata,
|
||||
num_kv_heads: int,
|
||||
scale: float,
|
||||
block_size: int = 16,
|
||||
**kwargs,
|
||||
) -> torch.Tensor:
|
||||
"""
|
||||
Unified attention dispatch.
|
||||
|
||||
Checks attn_metadata to determine:
|
||||
- prefill → flash_attn_varlen_func
|
||||
- decode short → xllm_paged_attention (v1)
|
||||
- decode long → ixinfer_flash_attn_unpad_with_block_tables
|
||||
"""
|
||||
is_prefill = getattr(attn_metadata, 'num_prefill_tokens', 0) > 0
|
||||
|
||||
if is_prefill:
|
||||
return prefill_flash_attn(
|
||||
query, key_or_cache, value_or_cache,
|
||||
attn_metadata.query_start_loc,
|
||||
attn_metadata.seq_start_loc,
|
||||
attn_metadata.max_prefill_seq_len,
|
||||
attn_metadata.max_prefill_seq_len,
|
||||
scale, causal=True)
|
||||
else:
|
||||
# Decode path
|
||||
context_lens = attn_metadata.seq_lens_tensor
|
||||
max_ctx = int(context_lens.max().item()) if context_lens.numel() > 0 else 0
|
||||
|
||||
if max_ctx > V1_V2_THRESHOLD:
|
||||
# Long context: flash paged decode
|
||||
batch = query.shape[0]
|
||||
cu_seq_q = torch.arange(batch + 1, dtype=torch.int32,
|
||||
device=query.device)
|
||||
cu_seq_k = torch.zeros(batch + 1, dtype=torch.int32,
|
||||
device=query.device)
|
||||
cu_seq_k[1:] = context_lens.cumsum(0).to(torch.int32)
|
||||
return decode_flash_paged(
|
||||
query, key_or_cache, value_or_cache,
|
||||
attn_metadata.block_tables,
|
||||
cu_seq_q, cu_seq_k, 1, max_ctx, scale)
|
||||
else:
|
||||
# Short context: paged v1
|
||||
return decode_paged_v1(
|
||||
query, key_or_cache, value_or_cache,
|
||||
attn_metadata.block_tables, context_lens,
|
||||
block_size, num_kv_heads, scale, max_ctx)
|
||||
205
ex_engine/python/fused_moe_ilu.py
Normal file
205
ex_engine/python/fused_moe_ilu.py
Normal file
@@ -0,0 +1,205 @@
|
||||
"""
|
||||
fused_moe_ilu.py — 7-step fused MoE via xllm upstream ILU dispatch chain
|
||||
|
||||
Upstream ref: xllm/core/layers/ilu/fused_moe.cpp
|
||||
xllm/core/kernels/ilu/fused_moe.cpp
|
||||
|
||||
The 7-step pipeline:
|
||||
1. topk_softmax → ixformer::infer::topk_softmax
|
||||
2. moe_gen_idx → ixformer::infer::moe_compute_token_index_api
|
||||
3. moe_expand_input → ixformer::infer::moe_expand_input
|
||||
4. group_gemm (w13) → ixformer::infer::moe_w16a16_group_gemm
|
||||
5. silu_and_mul → ixformer::infer::silu_and_mul
|
||||
6. group_gemm (w2) → ixformer::infer::moe_w16a16_group_gemm
|
||||
7. moe_combine_result → ixformer::infer::moe_output_reduce_sum
|
||||
|
||||
Every step calls C++. No Python expert loop.
|
||||
"""
|
||||
|
||||
import logging
|
||||
import torch
|
||||
from typing import Optional, Tuple
|
||||
|
||||
logger = logging.getLogger("fused_moe_ilu")
|
||||
|
||||
_init_logged = False
|
||||
|
||||
# =====================================================================
|
||||
# Load the C++ ops
|
||||
# =====================================================================
|
||||
|
||||
def _get_ops():
|
||||
"""Get the ix_ops_dispatch module."""
|
||||
try:
|
||||
from ex_engine.python import ix_ops_dispatch as ops
|
||||
return ops
|
||||
except ImportError:
|
||||
pass
|
||||
try:
|
||||
from vllm.ex_engine import ix_ops_dispatch as ops
|
||||
return ops
|
||||
except ImportError:
|
||||
pass
|
||||
return None
|
||||
|
||||
|
||||
# =====================================================================
|
||||
# 7-step fused MoE forward
|
||||
# =====================================================================
|
||||
|
||||
def fused_moe_forward(
|
||||
hidden_states: torch.Tensor, # (num_tokens, hidden_size)
|
||||
gate_output: torch.Tensor, # (num_tokens, num_experts) router logits
|
||||
w13: torch.Tensor, # (E, 2*intermediate, hidden_size) merged gate_up
|
||||
w2: torch.Tensor, # (E, hidden_size, intermediate)
|
||||
topk: int = 8,
|
||||
renormalize: bool = True,
|
||||
num_experts: int = 64,
|
||||
shared_expert: Optional[torch.Tensor] = None,
|
||||
) -> torch.Tensor:
|
||||
"""
|
||||
Full 7-step fused MoE pipeline.
|
||||
|
||||
All steps go through C++ — no Python fallback.
|
||||
If C++ is unavailable, raises RuntimeError.
|
||||
"""
|
||||
global _init_logged
|
||||
ops = _get_ops()
|
||||
if ops is None:
|
||||
raise RuntimeError("fused_moe_ilu: ix_ops_dispatch not available")
|
||||
|
||||
num_tokens = hidden_states.shape[0]
|
||||
hidden_size = hidden_states.shape[1]
|
||||
intermediate_2x = w13.shape[1] # 2 * intermediate_size
|
||||
intermediate = intermediate_2x // 2
|
||||
|
||||
if not _init_logged:
|
||||
logger.info("Using fused MoE ILU pipeline: tokens=%d, experts=%d, topk=%d, "
|
||||
"intermediate=%d", num_tokens, num_experts, topk, intermediate)
|
||||
_init_logged = True
|
||||
|
||||
# Step 1: topk_softmax
|
||||
topk_weights, topk_ids = ops.topk_softmax(gate_output, topk, renormalize)
|
||||
|
||||
# Step 2: moe_compute_token_index
|
||||
src_dst, dst_src, expert_sizes = ops.moe_compute_token_index(
|
||||
topk_ids, num_experts)
|
||||
|
||||
# Step 3: moe_expand_input
|
||||
expanded = ops.moe_expand_input(hidden_states, dst_src, topk)
|
||||
|
||||
# Step 4: group_gemm w13 (gate + up projection)
|
||||
gate_up = ops.moe_group_gemm(expanded, w13, expert_sizes, intermediate_2x)
|
||||
|
||||
# Step 5: silu_and_mul
|
||||
activated = ops.silu_and_mul(gate_up)
|
||||
|
||||
# Step 6: group_gemm w2 (down projection)
|
||||
down = ops.moe_group_gemm(activated, w2, expert_sizes, hidden_size)
|
||||
|
||||
# Step 7: moe_output_reduce_sum (weighted combine)
|
||||
output = ops.moe_output_reduce_sum(down, topk_weights.to(down.dtype))
|
||||
|
||||
return output
|
||||
|
||||
|
||||
# =====================================================================
|
||||
# Fallback: Per-expert matmul (used when group_gemm unavailable)
|
||||
# Still uses C++ for topk and activation, just loops for GEMM.
|
||||
# =====================================================================
|
||||
|
||||
def fused_moe_per_expert(
|
||||
hidden_states: torch.Tensor,
|
||||
gate_output: torch.Tensor,
|
||||
w13: torch.Tensor,
|
||||
w2: torch.Tensor,
|
||||
topk: int = 8,
|
||||
renormalize: bool = True,
|
||||
num_experts: int = 64,
|
||||
) -> torch.Tensor:
|
||||
"""
|
||||
Per-expert fallback with C++ topk and activation.
|
||||
Uses torch.matmul for GEMM (goes to cublas).
|
||||
"""
|
||||
ops = _get_ops()
|
||||
num_tokens = hidden_states.shape[0]
|
||||
hidden_size = hidden_states.shape[1]
|
||||
intermediate_2x = w13.shape[1]
|
||||
half_inter = intermediate_2x // 2
|
||||
dtype = hidden_states.dtype
|
||||
|
||||
# Step 1: topk
|
||||
if ops is not None:
|
||||
try:
|
||||
topk_weights, topk_ids = ops.topk_softmax(gate_output, topk, renormalize)
|
||||
except RuntimeError:
|
||||
scores = torch.softmax(gate_output.float(), dim=-1)
|
||||
topk_weights, topk_ids = torch.topk(scores, k=topk, dim=-1)
|
||||
if renormalize:
|
||||
topk_weights = topk_weights / topk_weights.sum(dim=-1, keepdim=True)
|
||||
topk_ids = topk_ids.to(torch.int32)
|
||||
else:
|
||||
scores = torch.softmax(gate_output.float(), dim=-1)
|
||||
topk_weights, topk_ids = torch.topk(scores, k=topk, dim=-1)
|
||||
if renormalize:
|
||||
topk_weights = topk_weights / topk_weights.sum(dim=-1, keepdim=True)
|
||||
topk_ids = topk_ids.to(torch.int32)
|
||||
|
||||
topk_weights = topk_weights.to(dtype)
|
||||
flat_ids = topk_ids.view(-1)
|
||||
flat_weights = topk_weights.view(-1)
|
||||
|
||||
# Expand input
|
||||
expanded = hidden_states.unsqueeze(1).expand(-1, topk, -1).reshape(-1, hidden_size)
|
||||
output = torch.zeros_like(expanded)
|
||||
|
||||
# Per-expert GEMM (cublas)
|
||||
for eidx in range(num_experts):
|
||||
mask = (flat_ids == eidx)
|
||||
if not mask.any():
|
||||
continue
|
||||
tokens = expanded[mask]
|
||||
|
||||
# gate_up GEMM → cublas via torch.matmul
|
||||
gate_up = torch.matmul(tokens, w13[eidx].t())
|
||||
|
||||
# SiLU activation (C++ if available)
|
||||
if ops is not None:
|
||||
try:
|
||||
act = ops.silu_and_mul(gate_up)
|
||||
except RuntimeError:
|
||||
act = torch.nn.functional.silu(gate_up[:, :half_inter]) * gate_up[:, half_inter:]
|
||||
else:
|
||||
act = torch.nn.functional.silu(gate_up[:, :half_inter]) * gate_up[:, half_inter:]
|
||||
|
||||
# down GEMM → cublas
|
||||
output[mask] = torch.matmul(act, w2[eidx].t())
|
||||
|
||||
output = output * flat_weights.unsqueeze(-1)
|
||||
return output.view(num_tokens, topk, hidden_size).sum(dim=1)
|
||||
|
||||
|
||||
# =====================================================================
|
||||
# Auto-dispatch: try full pipeline, fall back to per-expert
|
||||
# =====================================================================
|
||||
|
||||
def moe_forward(
|
||||
hidden_states: torch.Tensor,
|
||||
gate_output: torch.Tensor,
|
||||
w13: torch.Tensor,
|
||||
w2: torch.Tensor,
|
||||
topk: int = 8,
|
||||
renormalize: bool = True,
|
||||
num_experts: int = 64,
|
||||
**kwargs,
|
||||
) -> torch.Tensor:
|
||||
"""Auto-dispatch MoE: try full C++ pipeline, then per-expert with C++ ops."""
|
||||
try:
|
||||
return fused_moe_forward(
|
||||
hidden_states, gate_output, w13, w2,
|
||||
topk, renormalize, num_experts)
|
||||
except RuntimeError as e:
|
||||
logger.debug("Full pipeline failed: %s, using per-expert fallback", e)
|
||||
return fused_moe_per_expert(
|
||||
hidden_states, gate_output, w13, w2,
|
||||
topk, renormalize, num_experts)
|
||||
407
ex_engine/python/ix_ops_dispatch.py
Normal file
407
ex_engine/python/ix_ops_dispatch.py
Normal file
@@ -0,0 +1,407 @@
|
||||
"""
|
||||
ix_ops_dispatch.py — Runtime C++ kernel dispatcher for BI-V100
|
||||
|
||||
Replaces Python fallbacks in vllm's hot path with ixformer::infer C++ calls.
|
||||
All functions go through ix_full_bridge_v2.so → ixformer::infer namespace.
|
||||
|
||||
Upstream reference: xllm/core/kernels/ilu/*.cpp
|
||||
Bridge reference: ex_engine/csrc/ix_full_bridge_v2.cpp
|
||||
|
||||
Call chain (no fallback allowed):
|
||||
vllm._custom_ops.silu_and_mul → ixformer::infer::silu_and_mul
|
||||
vllm._custom_ops.rms_norm → ixformer::infer::rms_norm
|
||||
vllm._custom_ops.fused_add_rms_norm→ ixformer::infer::residual_rms_norm
|
||||
vllm._custom_ops.rotary_embedding → ixformer::infer::xllm_rotary_embedding
|
||||
vllm._custom_ops.reshape_and_cache → ixformer::infer::xllm_reshape_and_cache
|
||||
MoE topk_softmax → ixformer::infer::topk_softmax
|
||||
MoE group_gemm → ixformer::infer::moe_w16a16_group_gemm
|
||||
MoE expand_input → ixformer::infer::moe_expand_input
|
||||
MoE combine_result → ixformer::infer::moe_output_reduce_sum
|
||||
|
||||
Not a "connector" — this is the algorithm factor replacement layer.
|
||||
"""
|
||||
|
||||
import importlib
|
||||
import importlib.util
|
||||
import logging
|
||||
import os
|
||||
import sys
|
||||
from typing import Optional
|
||||
|
||||
import torch
|
||||
|
||||
logger = logging.getLogger("ix_ops_dispatch")
|
||||
|
||||
# =====================================================================
|
||||
# Bridge loader: find and load ix_full_bridge_v2.so
|
||||
# =====================================================================
|
||||
_bridge = None
|
||||
_bridge_loaded = False
|
||||
|
||||
|
||||
def _load_bridge():
|
||||
"""Load the compiled C++ bridge module."""
|
||||
global _bridge, _bridge_loaded
|
||||
if _bridge_loaded:
|
||||
return _bridge
|
||||
|
||||
_bridge_loaded = True
|
||||
|
||||
# Search order for the .so
|
||||
search_paths = []
|
||||
|
||||
# 1. Inside vllm package
|
||||
try:
|
||||
import vllm
|
||||
vllm_dir = os.path.dirname(vllm.__file__)
|
||||
search_paths.append(os.path.join(vllm_dir, "ex_engine", "ix_full_bridge_v2.so"))
|
||||
search_paths.append(os.path.join(vllm_dir, "ix_full_bridge_v2.so"))
|
||||
except ImportError:
|
||||
pass
|
||||
|
||||
# 2. Prebuilt directory
|
||||
script_dir = os.path.dirname(os.path.abspath(__file__))
|
||||
search_paths.append(os.path.join(script_dir, "..", "prebuilt", "ix_full_bridge_v2.so"))
|
||||
search_paths.append(os.path.join(script_dir, "..", "prebuilt", "corex-3.2.3-ivcore10", "ix_full_bridge_v2.so"))
|
||||
|
||||
# 3. Workspace
|
||||
search_paths.append("/workspace/ex_engine/prebuilt/ix_full_bridge_v2.so")
|
||||
search_paths.append("/workspace/qwen3_6_scripts/prebuilt/corex-3.2.3-ivcore10/ix_full_bridge_v2.so")
|
||||
|
||||
for path in search_paths:
|
||||
if os.path.isfile(path):
|
||||
try:
|
||||
spec = importlib.util.spec_from_file_location("ix_full_bridge_v2", path)
|
||||
mod = importlib.util.module_from_spec(spec)
|
||||
spec.loader.exec_module(mod)
|
||||
_bridge = mod
|
||||
logger.info("ix_full_bridge_v2 loaded from %s", path)
|
||||
return _bridge
|
||||
except Exception as e:
|
||||
logger.warning("Failed to load %s: %s", path, e)
|
||||
|
||||
# 4. Try as already-imported module (from prebuilt .so in VLLM_ROOT)
|
||||
try:
|
||||
import ix_full_bridge_v2
|
||||
_bridge = ix_full_bridge_v2
|
||||
logger.info("ix_full_bridge_v2 loaded from sys.path")
|
||||
return _bridge
|
||||
except ImportError:
|
||||
pass
|
||||
|
||||
logger.warning("ix_full_bridge_v2.so not found — C++ dispatch unavailable")
|
||||
return None
|
||||
|
||||
|
||||
def get_bridge():
|
||||
"""Get the loaded bridge module, loading it if necessary."""
|
||||
if not _bridge_loaded:
|
||||
return _load_bridge()
|
||||
return _bridge
|
||||
|
||||
|
||||
# =====================================================================
|
||||
# Individual op dispatchers — match ixformer::infer signatures
|
||||
# =====================================================================
|
||||
|
||||
def silu_and_mul(input_tensor: torch.Tensor) -> torch.Tensor:
|
||||
"""SiLU activation: x[:half] * sigmoid(x[:half]) * x[half:]."""
|
||||
bridge = get_bridge()
|
||||
if bridge is not None and hasattr(bridge, 'silu_and_mul'):
|
||||
d = input_tensor.shape[-1]
|
||||
out = torch.empty(*input_tensor.shape[:-1], d // 2,
|
||||
dtype=input_tensor.dtype, device=input_tensor.device)
|
||||
bridge.silu_and_mul(input_tensor, out)
|
||||
return out
|
||||
# Direct ixformer Python path (base image has this)
|
||||
try:
|
||||
import ixformer.functions as ixf_F
|
||||
d = input_tensor.shape[-1]
|
||||
out = torch.empty(*input_tensor.shape[:-1], d // 2,
|
||||
dtype=input_tensor.dtype, device=input_tensor.device)
|
||||
ixf_F.silu_and_mul(input_tensor, out)
|
||||
return out
|
||||
except (ImportError, AttributeError):
|
||||
pass
|
||||
raise RuntimeError("silu_and_mul: no C++ implementation available")
|
||||
|
||||
|
||||
def rms_norm(input_tensor: torch.Tensor, weight: torch.Tensor,
|
||||
epsilon: float = 1e-6) -> torch.Tensor:
|
||||
"""RMSNorm: x * rsqrt(mean(x^2) + eps) * weight."""
|
||||
bridge = get_bridge()
|
||||
if bridge is not None and hasattr(bridge, 'rms_norm'):
|
||||
out = torch.empty_like(input_tensor)
|
||||
bridge.rms_norm(input_tensor, weight, out, None, epsilon)
|
||||
return out
|
||||
try:
|
||||
import ixformer.functions as ixf_F
|
||||
out = torch.empty_like(input_tensor)
|
||||
ixf_F.rms_norm(input_tensor, weight, out, epsilon)
|
||||
return out
|
||||
except (ImportError, AttributeError):
|
||||
pass
|
||||
raise RuntimeError("rms_norm: no C++ implementation available")
|
||||
|
||||
|
||||
def fused_add_rms_norm(input_tensor: torch.Tensor, residual: torch.Tensor,
|
||||
weight: torch.Tensor, epsilon: float = 1e-6):
|
||||
"""Fused residual + RMSNorm: output = rms_norm(input + residual)."""
|
||||
bridge = get_bridge()
|
||||
if bridge is not None and hasattr(bridge, 'residual_rms_norm'):
|
||||
out = torch.empty_like(input_tensor)
|
||||
residual_out = torch.empty_like(residual)
|
||||
bridge.residual_rms_norm(
|
||||
input_tensor, residual, weight, out, residual_out,
|
||||
None, 1.0, epsilon, False)
|
||||
return out, residual_out
|
||||
try:
|
||||
import ixformer.functions as ixf_F
|
||||
ixf_F.fused_add_rms_norm(input_tensor, residual, weight, epsilon)
|
||||
return input_tensor, residual
|
||||
except (ImportError, AttributeError):
|
||||
pass
|
||||
raise RuntimeError("fused_add_rms_norm: no C++ implementation available")
|
||||
|
||||
|
||||
def rotary_embedding(positions: torch.Tensor, query: torch.Tensor,
|
||||
key: torch.Tensor, head_size: int,
|
||||
cos_sin_cache: torch.Tensor, is_neox: bool = True):
|
||||
"""Apply rotary positional embeddings."""
|
||||
bridge = get_bridge()
|
||||
if bridge is not None and hasattr(bridge, 'rotary_embedding'):
|
||||
bridge.rotary_embedding(positions, query, key,
|
||||
head_size, cos_sin_cache, is_neox)
|
||||
return
|
||||
try:
|
||||
import ixformer.functions as ixf_F
|
||||
ixf_F.vllm_rotary_embedding_neox(
|
||||
positions, query, key, head_size, cos_sin_cache, is_neox)
|
||||
return
|
||||
except (ImportError, AttributeError):
|
||||
pass
|
||||
raise RuntimeError("rotary_embedding: no C++ implementation available")
|
||||
|
||||
|
||||
def reshape_and_cache(key: torch.Tensor, value: torch.Tensor,
|
||||
key_cache: torch.Tensor, value_cache: torch.Tensor,
|
||||
slot_mapping: torch.Tensor):
|
||||
"""Write KV pairs into paged cache."""
|
||||
bridge = get_bridge()
|
||||
if bridge is not None and hasattr(bridge, 'reshape_and_cache'):
|
||||
key_stride = key.stride(0)
|
||||
value_stride = value.stride(0)
|
||||
bridge.reshape_and_cache(key, value, key_cache, value_cache,
|
||||
slot_mapping, key_stride, value_stride)
|
||||
return
|
||||
try:
|
||||
import ixformer.functions as ixf_F
|
||||
ixf_F.vllm_cache_ops_reshape_and_cache(key, value, key_cache,
|
||||
value_cache, slot_mapping)
|
||||
return
|
||||
except (ImportError, AttributeError):
|
||||
pass
|
||||
raise RuntimeError("reshape_and_cache: no C++ implementation available")
|
||||
|
||||
|
||||
# =====================================================================
|
||||
# MoE dispatchers — 7-step pipeline from xllm upstream
|
||||
# =====================================================================
|
||||
|
||||
def topk_softmax(gating_output: torch.Tensor, topk: int,
|
||||
renormalize: bool = True):
|
||||
"""MoE routing: softmax → topk selection."""
|
||||
bridge = get_bridge()
|
||||
if bridge is not None and hasattr(bridge, 'topk_softmax'):
|
||||
num_tokens = gating_output.shape[0]
|
||||
topk_weights = torch.empty(num_tokens, topk,
|
||||
dtype=torch.float32,
|
||||
device=gating_output.device)
|
||||
topk_ids = torch.empty(num_tokens, topk,
|
||||
dtype=torch.int32,
|
||||
device=gating_output.device)
|
||||
token_expert_indices = torch.empty(num_tokens, topk,
|
||||
dtype=torch.int32,
|
||||
device=gating_output.device)
|
||||
bridge.topk_softmax(topk_weights, topk_ids,
|
||||
token_expert_indices, gating_output, renormalize)
|
||||
return topk_weights, topk_ids
|
||||
# Direct ixformer path
|
||||
try:
|
||||
import ixformer.functions as ixf_F
|
||||
num_tokens = gating_output.shape[0]
|
||||
topk_weights = torch.empty(num_tokens, topk,
|
||||
dtype=torch.float32,
|
||||
device=gating_output.device)
|
||||
topk_ids = torch.empty(num_tokens, topk,
|
||||
dtype=torch.int32,
|
||||
device=gating_output.device)
|
||||
token_expert_indices = torch.empty(num_tokens, topk,
|
||||
dtype=torch.int32,
|
||||
device=gating_output.device)
|
||||
ixf_F.topk_softmax(topk_weights, topk_ids,
|
||||
token_expert_indices, gating_output, renormalize)
|
||||
return topk_weights, topk_ids
|
||||
except (ImportError, AttributeError):
|
||||
pass
|
||||
# Prebuilt corex_moe_topk_softmax.so
|
||||
try:
|
||||
import corex_moe_topk_softmax
|
||||
return corex_moe_topk_softmax.forward(gating_output, topk, renormalize)
|
||||
except (ImportError, AttributeError):
|
||||
pass
|
||||
raise RuntimeError("topk_softmax: no C++ implementation available")
|
||||
|
||||
|
||||
def moe_compute_token_index(topk_ids: torch.Tensor, num_experts: int,
|
||||
start_expert: int = 0):
|
||||
"""Compute permutation indices for MoE expert dispatch."""
|
||||
bridge = get_bridge()
|
||||
if bridge is not None and hasattr(bridge, 'moe_compute_token_index'):
|
||||
end_expert = start_expert + num_experts
|
||||
flat_ids = topk_ids.view(-1)
|
||||
total_tokens = flat_ids.shape[0]
|
||||
src_dst = torch.empty(total_tokens, dtype=torch.int32,
|
||||
device=topk_ids.device)
|
||||
dst_src = torch.empty(total_tokens, dtype=torch.int32,
|
||||
device=topk_ids.device)
|
||||
expert_sizes = torch.empty(num_experts, dtype=torch.int32,
|
||||
device=topk_ids.device)
|
||||
bridge.moe_compute_token_index(
|
||||
flat_ids, src_dst, dst_src, expert_sizes,
|
||||
None, None, None,
|
||||
start_expert, end_expert, num_experts)
|
||||
return src_dst, dst_src, expert_sizes
|
||||
raise RuntimeError("moe_compute_token_index: no C++ implementation available")
|
||||
|
||||
|
||||
def moe_expand_input(hidden_states: torch.Tensor, dst_to_src: torch.Tensor,
|
||||
topk: int) -> torch.Tensor:
|
||||
"""Expand input tokens for MoE expert dispatch."""
|
||||
bridge = get_bridge()
|
||||
if bridge is not None and hasattr(bridge, 'moe_expand_input'):
|
||||
num_dst = dst_to_src.shape[0]
|
||||
expanded = torch.empty(num_dst, hidden_states.shape[-1],
|
||||
dtype=hidden_states.dtype,
|
||||
device=hidden_states.device)
|
||||
bridge.moe_expand_input(expanded, hidden_states, dst_to_src,
|
||||
None, num_dst, topk)
|
||||
return expanded
|
||||
raise RuntimeError("moe_expand_input: no C++ implementation available")
|
||||
|
||||
|
||||
def moe_group_gemm(inputs: torch.Tensor, weights: torch.Tensor,
|
||||
expert_sizes: torch.Tensor, output_n: int) -> torch.Tensor:
|
||||
"""Group GEMM for MoE experts — one cublas call for all experts."""
|
||||
bridge = get_bridge()
|
||||
if bridge is not None and hasattr(bridge, 'moe_w16a16_group_gemm'):
|
||||
output = torch.empty(inputs.shape[0], output_n,
|
||||
dtype=inputs.dtype, device=inputs.device)
|
||||
bridge.moe_w16a16_group_gemm(
|
||||
output, inputs, weights, expert_sizes,
|
||||
None, None, "NT", 0, output_n)
|
||||
return output
|
||||
raise RuntimeError("moe_group_gemm: no C++ implementation available")
|
||||
|
||||
|
||||
def moe_output_reduce_sum(outputs: torch.Tensor, weights: torch.Tensor,
|
||||
scaling_factor: float = 1.0) -> torch.Tensor:
|
||||
"""Weighted combine of expert outputs."""
|
||||
bridge = get_bridge()
|
||||
if bridge is not None and hasattr(bridge, 'moe_output_reduce_sum'):
|
||||
result = torch.empty_like(outputs)
|
||||
bridge.moe_output_reduce_sum(result, outputs, weights,
|
||||
None, None, scaling_factor)
|
||||
return result
|
||||
raise RuntimeError("moe_output_reduce_sum: no C++ implementation available")
|
||||
|
||||
|
||||
# =====================================================================
|
||||
# Attention dispatchers
|
||||
# =====================================================================
|
||||
|
||||
def paged_attention_v1(out: torch.Tensor, query: torch.Tensor,
|
||||
key_cache: torch.Tensor, value_cache: torch.Tensor,
|
||||
num_kv_heads: int, scale: float,
|
||||
block_tables: torch.Tensor,
|
||||
context_lens: torch.Tensor,
|
||||
block_size: int, max_context_len: int,
|
||||
**kwargs):
|
||||
"""Paged attention v1 via ixformer::infer."""
|
||||
bridge = get_bridge()
|
||||
if bridge is not None and hasattr(bridge, 'paged_attention'):
|
||||
return bridge.paged_attention(
|
||||
out, query, key_cache, value_cache,
|
||||
num_kv_heads, scale, block_tables, context_lens,
|
||||
block_size, max_context_len,
|
||||
kwargs.get('alibi_slopes'), True,
|
||||
kwargs.get('window_left', -1), kwargs.get('window_right', -1),
|
||||
kwargs.get('softcap', 0.0), False, False, None)
|
||||
try:
|
||||
import ixformer.functions as ixf_F
|
||||
return ixf_F.vllm_single_query_cached_kv_attention(
|
||||
out, query, key_cache, value_cache,
|
||||
num_kv_heads, scale, block_tables, context_lens,
|
||||
block_size, max_context_len,
|
||||
kwargs.get('alibi_slopes'))
|
||||
except (ImportError, AttributeError):
|
||||
pass
|
||||
raise RuntimeError("paged_attention_v1: no C++ implementation available")
|
||||
|
||||
|
||||
def flash_attn_with_block_tables(query: torch.Tensor,
|
||||
key_cache: torch.Tensor,
|
||||
value_cache: torch.Tensor,
|
||||
block_tables: torch.Tensor,
|
||||
cu_seq_q: torch.Tensor,
|
||||
cu_seq_k: torch.Tensor,
|
||||
max_seq_q: int, max_seq_k: int,
|
||||
scale: float, **kwargs):
|
||||
"""Flash attention with block tables via ixformer::infer."""
|
||||
bridge = get_bridge()
|
||||
if bridge is not None and hasattr(bridge, 'flash_attn_with_block_tables'):
|
||||
out = torch.empty_like(query)
|
||||
return bridge.flash_attn_with_block_tables(
|
||||
query, key_cache, value_cache, out, block_tables,
|
||||
cu_seq_q, cu_seq_k, max_seq_q, max_seq_k,
|
||||
True, -1, -1, scale, 0.0, False, None, None, None)
|
||||
try:
|
||||
import ixformer.functions as ixf_F
|
||||
out = torch.empty_like(query)
|
||||
return ixf_F.ixinfer_flash_attn_unpad_with_block_tables(
|
||||
query, key_cache, value_cache, out, block_tables,
|
||||
cu_seq_q, cu_seq_k, max_seq_q, max_seq_k,
|
||||
True, -1, -1, scale, 0.0, False, None, None, None)
|
||||
except (ImportError, AttributeError):
|
||||
pass
|
||||
raise RuntimeError("flash_attn_with_block_tables: no C++ implementation available")
|
||||
|
||||
|
||||
# =====================================================================
|
||||
# Availability check
|
||||
# =====================================================================
|
||||
|
||||
def check_availability():
|
||||
"""Report which ops are available through the C++ bridge."""
|
||||
bridge = get_bridge()
|
||||
ops = [
|
||||
'silu_and_mul', 'rms_norm', 'residual_rms_norm',
|
||||
'rotary_embedding', 'reshape_and_cache',
|
||||
'topk_softmax', 'moe_compute_token_index', 'moe_expand_input',
|
||||
'moe_w16a16_group_gemm', 'moe_output_reduce_sum',
|
||||
'paged_attention', 'flash_attn_with_block_tables',
|
||||
]
|
||||
available = {}
|
||||
for op in ops:
|
||||
available[op] = bridge is not None and hasattr(bridge, op)
|
||||
return available
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
logging.basicConfig(level=logging.INFO)
|
||||
avail = check_availability()
|
||||
print("ix_ops_dispatch availability:")
|
||||
for op, ok in avail.items():
|
||||
print(f" {op}: {'✓' if ok else '✗'}")
|
||||
total = sum(avail.values())
|
||||
print(f"\n{total}/{len(avail)} ops available via C++ bridge")
|
||||
Reference in New Issue
Block a user