Merge branch 'main' of https://dev.modelhub.org.cn/dylanyunlong/project_6
This commit is contained in:
root
@@ -4,7 +4,13 @@ WORKDIR /workspace/
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# Copy all our engine patches
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COPY ./qwen3_6_scripts /workspace/qwen3_6_scripts
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COPY ./computility-run.yaml /workspace/computility-run.yaml
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# Copy ex_engine source for MoE bridge compilation
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COPY ./ex_engine/csrc/moe_ops_impl.cu /workspace/qwen3_6_scripts/ex_engine_src/csrc/moe_ops_impl.cu
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COPY ./ex_engine/csrc/ix_full_bridge_v2.cpp /workspace/qwen3_6_scripts/ex_engine_src/csrc/ix_full_bridge_v2.cpp
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COPY ./ex_engine/build_moe_bridge.sh /workspace/qwen3_6_scripts/ex_engine_src/build_moe_bridge.sh
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COPY ./ex_engine/python/moe_dispatch.py /workspace/qwen3_6_scripts/ex_engine_src/python/moe_dispatch.py
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COPY ./ex_engine/python/patch_moe_hot_path.py /workspace/qwen3_6_scripts/ex_engine_src/python/patch_moe_hot_path.py
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# Make patch script executable and run it
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RUN chmod +x /workspace/qwen3_6_scripts/patch_ops.sh && \
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bash /workspace/qwen3_6_scripts/patch_ops.sh 2>&1 | tee /workspace/patch_ops.log ; \
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echo "[Dockerfile] patch_ops exit code: $?"
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echo "[Dockerfile] patch_ops exit code: $?"
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156
build_moe_bridge.sh
Normal file
156
build_moe_bridge.sh
Normal file
@@ -0,0 +1,156 @@
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#!/usr/bin/env bash
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# build_moe_bridge.sh — Compile MoE ops + bridge into ix_moe_bridge.so
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#
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# Links against:
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# libcuinfer.so (cuinferCustomGemm, cuinferTopK — confirmed in symbol dump)
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# libixformer.so (silu_and_mul, rms_norm, flash_attn, etc — confirmed)
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#
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# Real device compiler: corex clang/16, NOT nvcc
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# Reference: ex_engine/build_ix_bridge.sh
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set -euo pipefail
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SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
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REPO_ROOT="$(cd "${SCRIPT_DIR}/.." && pwd)"
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VLLM_ROOT="${1:-}"
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echo "[moe_bridge] Building ix_moe_bridge.so"
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echo "[moe_bridge] Script dir: ${SCRIPT_DIR}"
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# --- Locate sources ---
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MOE_CU="${SCRIPT_DIR}/ex_engine/csrc/moe_ops_impl.cu"
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BRIDGE_CPP="${SCRIPT_DIR}/ex_engine/csrc/ix_full_bridge_v2.cpp"
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if [[ ! -f "$MOE_CU" ]]; then
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echo "[moe_bridge] ERROR: $MOE_CU not found" >&2
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exit 1
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fi
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if [[ ! -f "$BRIDGE_CPP" ]]; then
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echo "[moe_bridge] ERROR: $BRIDGE_CPP not found" >&2
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exit 1
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fi
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# --- Locate libraries ---
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COREX_ROOT="${COREX_ROOT:-/usr/local/corex}"
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# Find libcuinfer.so
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CUINFER_SO=""
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for d in "${COREX_ROOT}/lib64" "${COREX_ROOT}/lib" "/usr/lib64" "/usr/lib"; do
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if [[ -f "${d}/libcuinfer.so" ]]; then
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CUINFER_SO="${d}/libcuinfer.so"
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break
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fi
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done
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# Find libixformer.so and ixformer Python package
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IX_LIB_DIR=""
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IX_SO_FILES=()
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for d in \
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"${COREX_ROOT}/lib/python3/dist-packages/ixformer" \
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"${COREX_ROOT}/lib64/python3/dist-packages/ixformer" \
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"$(python3 -c 'import ixformer, os; print(os.path.dirname(ixformer.__file__))' 2>/dev/null || echo '')"; do
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if [[ -d "$d" ]]; then
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IX_LIB_DIR="$d"
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while IFS= read -r so; do
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IX_SO_FILES+=("$so")
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done < <(find "$d" -name "*.so" -type f 2>/dev/null)
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break
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fi
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done
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echo "[moe_bridge] COREX_ROOT: ${COREX_ROOT}"
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echo "[moe_bridge] cuinfer: ${CUINFER_SO:-NOT FOUND}"
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echo "[moe_bridge] ixformer dir: ${IX_LIB_DIR:-NOT FOUND}"
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echo "[moe_bridge] ixformer .so count: ${#IX_SO_FILES[@]}"
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# --- Build via torch.utils.cpp_extension ---
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mkdir -p "${SCRIPT_DIR}/prebuilt"
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python3 << 'PYEOF'
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import os, sys, glob, shutil
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script_dir = os.environ.get("SCRIPT_DIR", ".")
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vllm_root = os.environ.get("VLLM_ROOT", "")
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moe_cu = os.path.join(script_dir, "ex_engine", "csrc", "moe_ops_impl.cu")
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bridge_cpp = os.path.join(script_dir, "ex_engine", "csrc", "ix_full_bridge_v2.cpp")
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# Collect linker flags
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extra_ldflags = []
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rpath_dirs = set()
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corex_root = os.environ.get("COREX_ROOT", "/usr/local/corex")
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for search_dir in [
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os.path.join(corex_root, "lib64"),
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os.path.join(corex_root, "lib"),
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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, "libcuinfer*.so*")):
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extra_ldflags.append(so)
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# ixformer .so files
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try:
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import ixformer
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ix_dir = os.path.dirname(ixformer.__file__)
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rpath_dirs.add(ix_dir)
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for so in glob.glob(os.path.join(ix_dir, "*.so")):
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extra_ldflags.append(so)
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for so in glob.glob(os.path.join(ix_dir, "lib*.so")):
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if so not in extra_ldflags:
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extra_ldflags.append(so)
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except ImportError:
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# Search common paths
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for d 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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]:
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if os.path.isdir(d):
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rpath_dirs.add(d)
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for so in glob.glob(os.path.join(d, "*.so")):
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extra_ldflags.append(so)
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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"[moe_bridge] Linking against {len(extra_ldflags)} items")
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for f in extra_ldflags[:10]:
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print(f" {f}")
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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="ix_moe_bridge",
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sources=[moe_cu, bridge_cpp],
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extra_include_paths=[os.path.join(script_dir, "csrc")],
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extra_cflags=["-O2", "-std=c++17"],
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extra_cuda_cflags=["-O2", ],
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extra_ldflags=extra_ldflags,
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verbose=True,
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)
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print("[moe_bridge] ✓ Compilation successful")
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# Find and copy the built .so
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import importlib
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spec = importlib.util.find_spec("ix_moe_bridge")
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if spec and spec.origin:
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dst = os.path.join(script_dir, "prebuilt", "ix_moe_bridge.so")
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shutil.copy2(spec.origin, dst)
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print(f"[moe_bridge] ✓ Saved to {dst}")
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if vllm_root:
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vllm_dst = os.path.join(vllm_root, "ex_engine", "ix_moe_bridge.so")
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os.makedirs(os.path.dirname(vllm_dst), exist_ok=True)
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shutil.copy2(spec.origin, vllm_dst)
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print(f"[moe_bridge] ✓ Deployed to {vllm_dst}")
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else:
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print("[moe_bridge] ⚠ Could not locate compiled .so via importlib")
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except Exception as e:
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print(f"[moe_bridge] ERROR: {e}", file=sys.stderr)
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import traceback; traceback.print_exc()
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sys.exit(1)
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PYEOF
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echo "[moe_bridge] Done"
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@@ -15,7 +15,7 @@ command:
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- -tp
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- '4'
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- --max-num-seqs
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- '1'
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- '2'
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- --disable-log-requests
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- --disable-frontend-multiprocessing
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- --max-num-batched-tokens
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@@ -46,4 +46,4 @@ env:
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- name: BI100_GDN_RESTORE_MODE
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value: hybrid64
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- name: BI100_MOE_COREX_TOPK_SOFTMAX
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value: '1'
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value: '1'
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@@ -1,21 +1,24 @@
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// ix_full_bridge_v2.cpp — Complete bridge to ALL ixformer::infer C++ functions
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// ix_full_bridge_v2.cpp — Bridge to ixformer C++ functions + MoE pipeline
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//
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// Base image has ixformer::infer namespace with 14 functions.
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// Previous ix_full_bridge.cpp only bridged 4 (silu_and_mul, rms_norm,
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// fused_add_rms_norm, linear). This file bridges ALL 14.
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// Forward declarations use REAL symbols from nm -D symbol dumps:
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// _ixformer_torch.so → namespace ixformer_torch_ext (7 functions)
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// moe_ops_impl.cu → namespace ixformer::infer (5 MoE functions, self-compiled)
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//
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// The base image's _ixformer_torch.cpython-310.so and libixformer.so
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// export these symbols in the ixformer::infer namespace (confirmed by nm -D).
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// Symbol dump verified:
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// ixformer_torch_ext::silu_and_mul_forward(at::Tensor&, at::Tensor&)
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// ixformer_torch_ext::rms_norm_forward(at::Tensor&, at::Tensor&, at::Tensor&, double)
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// ixformer_torch_ext::fused_add_rms_norm_forward(at::Tensor&, at::Tensor&, at::Tensor&, double, double)
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// ixformer_torch_ext::ixformer_linear(at::Tensor&, at::Tensor&, c10::optional<at::Tensor>, c10::optional<at::Tensor>)
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// ixformer_torch_ext::ixformer_linear_ex(at::Tensor&, at::Tensor&, c10::optional<at::Tensor>)
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// ixformer_torch_ext::vllm_rotary_embedding_neox(at::Tensor&, at::Tensor&, at::Tensor&, long, at::Tensor&, long, bool)
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// ixformer_torch_ext::vllm_cache_ops_reshape_and_cache(at::Tensor&, at::Tensor&, at::Tensor&, at::Tensor&, at::Tensor&, long, long)
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// ixformer_torch_ext::vllm_single_query_cached_kv_attention(13 params — see below)
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//
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// Compile:
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// torch.utils.cpp_extension.load(
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// name="ix_full_bridge_v2",
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// sources=["ix_full_bridge_v2.cpp"],
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// extra_ldflags=[<all ixformer .so files>, "-Wl,-rpath,..."],
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// extra_cflags=["-O2", "-std=c++17"],
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// )
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//
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// Upstream reference: xllm_latest/core/kernels/ilu/ixformer.h
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// NOT available in any .so (confirmed by nm -D on all 4 .so files):
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// ixinfer_flash_attn_unpad_with_block_tables — DOES NOT EXIST
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// xllm_paged_attention — DOES NOT EXIST
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// topk_softmax, moe_w16a16_group_gemm, etc — NOT in libixformer.so
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// (provided by moe_ops_impl.cu instead)
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#include <torch/extension.h>
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#include <optional>
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@@ -24,103 +27,67 @@
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#include <vector>
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// ============================================================================
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// Forward declarations — ixformer::infer namespace from base image .so
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// Signatures EXACTLY match upstream_ref/xllm_latest/core/kernels/ilu/ixformer.h
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// Forward declarations — ixformer_torch_ext namespace from _ixformer_torch.so
|
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// Signatures EXACTLY match nm -D | c++filt output
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// ============================================================================
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namespace ixformer_torch_ext {
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// silu_and_mul_forward(at::Tensor&, at::Tensor&)
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void silu_and_mul_forward(at::Tensor& input, at::Tensor& output);
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|
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// rms_norm_forward(at::Tensor&, at::Tensor&, at::Tensor&, double)
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void rms_norm_forward(at::Tensor& output, at::Tensor& input,
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at::Tensor& weight, double eps);
|
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|
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// fused_add_rms_norm_forward(at::Tensor&, at::Tensor&, at::Tensor&, double, double)
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void fused_add_rms_norm_forward(at::Tensor& input, at::Tensor& residual,
|
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at::Tensor& weight, double eps, double alpha);
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|
||||
// ixformer_linear(at::Tensor&, at::Tensor&, c10::optional<at::Tensor> const&, c10::optional<at::Tensor> const&)
|
||||
at::Tensor ixformer_linear(at::Tensor& input, at::Tensor& weight,
|
||||
c10::optional<at::Tensor> const& bias,
|
||||
c10::optional<at::Tensor> const& out);
|
||||
|
||||
// ixformer_linear_ex(at::Tensor&, at::Tensor&, c10::optional<at::Tensor> const&)
|
||||
at::Tensor ixformer_linear_ex(at::Tensor& input, at::Tensor& weight,
|
||||
c10::optional<at::Tensor> const& bias);
|
||||
|
||||
// vllm_rotary_embedding_neox(at::Tensor&, at::Tensor&, at::Tensor&, long, at::Tensor&, long, bool)
|
||||
void vllm_rotary_embedding_neox(at::Tensor& positions, at::Tensor& query,
|
||||
at::Tensor& key, int64_t head_size,
|
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at::Tensor& cos_sin_cache,
|
||||
int64_t max_position, bool is_neox);
|
||||
|
||||
// vllm_cache_ops_reshape_and_cache(at::Tensor&, at::Tensor&, at::Tensor&, at::Tensor&, at::Tensor&, long, long)
|
||||
void vllm_cache_ops_reshape_and_cache(at::Tensor& key, at::Tensor& value,
|
||||
at::Tensor& key_cache,
|
||||
at::Tensor& value_cache,
|
||||
at::Tensor& slot_mapping,
|
||||
int64_t key_token_stride,
|
||||
int64_t value_token_stride);
|
||||
|
||||
// vllm_single_query_cached_kv_attention(at::Tensor& x13)
|
||||
// Full signature from nm -D:
|
||||
// (at::Tensor&, at::Tensor&, at::Tensor&, at::Tensor&, at::Tensor&,
|
||||
// double, at::Tensor&, at::Tensor&, long, long, long, bool,
|
||||
// c10::optional<at::Tensor> const&)
|
||||
void vllm_single_query_cached_kv_attention(
|
||||
at::Tensor& output, at::Tensor& query,
|
||||
at::Tensor& key_cache, at::Tensor& value_cache,
|
||||
at::Tensor& head_mapping, double scale,
|
||||
at::Tensor& block_tables, at::Tensor& context_lens,
|
||||
int64_t block_size, int64_t max_context_len, int64_t num_kv_heads,
|
||||
bool is_neox,
|
||||
c10::optional<at::Tensor> const& alibi_slopes);
|
||||
|
||||
} // namespace ixformer_torch_ext
|
||||
|
||||
// ============================================================================
|
||||
// Forward declarations — ixformer::infer namespace from moe_ops_impl.cu
|
||||
// These 5 MoE functions are compiled from our own CUDA code, NOT from .so
|
||||
// ============================================================================
|
||||
namespace ixformer { namespace infer {
|
||||
|
||||
// --- Attention ---
|
||||
torch::Tensor ixinfer_flash_attn_unpad_with_block_tables(
|
||||
torch::Tensor& query,
|
||||
torch::Tensor& key_cache,
|
||||
torch::Tensor& value_cache,
|
||||
torch::Tensor& out,
|
||||
torch::Tensor& block_tables,
|
||||
torch::Tensor& cu_seq_q,
|
||||
torch::Tensor& cu_seq_k,
|
||||
int64_t max_seq_q,
|
||||
int64_t max_seq_k,
|
||||
bool is_causal,
|
||||
int64_t window_left,
|
||||
int64_t window_right,
|
||||
double scale,
|
||||
double softcap,
|
||||
bool sqrt_alibi,
|
||||
const std::optional<torch::Tensor>& alibi_slopes,
|
||||
const std::optional<torch::Tensor>& sinks,
|
||||
std::optional<torch::Tensor>& lse);
|
||||
|
||||
torch::Tensor xllm_paged_attention(
|
||||
torch::Tensor& out,
|
||||
torch::Tensor& query,
|
||||
torch::Tensor& key_cache,
|
||||
torch::Tensor& value_cache,
|
||||
int64_t num_kv_heads,
|
||||
double scale,
|
||||
torch::Tensor& block_tables,
|
||||
torch::Tensor& context_lens,
|
||||
int64_t block_size,
|
||||
int64_t max_context_len,
|
||||
const std::optional<torch::Tensor>& alibi_slopes,
|
||||
bool causal,
|
||||
int32_t window_left,
|
||||
int32_t window_right,
|
||||
double softcap,
|
||||
bool enable_cuda_graph,
|
||||
bool use_sqrt_alibi,
|
||||
const std::optional<torch::Tensor>& sinks);
|
||||
|
||||
// --- Activation ---
|
||||
void silu_and_mul(torch::Tensor& input, torch::Tensor& output);
|
||||
|
||||
// --- Linear ---
|
||||
torch::Tensor ixformer_linear(torch::Tensor& input,
|
||||
torch::Tensor& weight,
|
||||
int64_t act_type,
|
||||
const std::optional<torch::Tensor>& bias,
|
||||
const std::optional<torch::Tensor>& out,
|
||||
const std::optional<bool> persistent);
|
||||
|
||||
torch::Tensor ixformer_linear_ex(torch::Tensor& input,
|
||||
torch::Tensor& weight,
|
||||
const c10::optional<torch::Tensor>& bias,
|
||||
const c10::optional<torch::Tensor>& out);
|
||||
|
||||
// --- Cache ---
|
||||
void xllm_reshape_and_cache(torch::Tensor& key,
|
||||
torch::Tensor& value,
|
||||
torch::Tensor& key_cache,
|
||||
torch::Tensor& value_cache,
|
||||
torch::Tensor& slot_mapping,
|
||||
int64_t key_token_stride,
|
||||
int64_t value_token_stride);
|
||||
|
||||
// --- RoPE ---
|
||||
void xllm_rotary_embedding(torch::Tensor& positions,
|
||||
torch::Tensor& query,
|
||||
torch::Tensor& key,
|
||||
int64_t head_size,
|
||||
torch::Tensor& cos_sin_cache,
|
||||
bool is_neox);
|
||||
|
||||
// --- Norm ---
|
||||
void residual_rms_norm(torch::Tensor& input,
|
||||
torch::Tensor& residual,
|
||||
torch::Tensor& weight,
|
||||
torch::Tensor& output,
|
||||
torch::Tensor& residual_output,
|
||||
const std::optional<torch::Tensor>& fused_bias,
|
||||
double alpha,
|
||||
double eps,
|
||||
bool is_post);
|
||||
|
||||
void rms_norm(torch::Tensor& input,
|
||||
torch::Tensor& weight,
|
||||
torch::Tensor& output,
|
||||
const std::optional<torch::Tensor>& fused_bias,
|
||||
double eps);
|
||||
|
||||
// --- MoE ---
|
||||
void topk_softmax(torch::Tensor& topk_weights,
|
||||
torch::Tensor& topk_indices,
|
||||
torch::Tensor& token_expert_indices,
|
||||
@@ -132,9 +99,9 @@ void moe_compute_token_index_api(
|
||||
torch::Tensor& src_dst,
|
||||
torch::Tensor& dst_src,
|
||||
torch::Tensor& expert_sizes_gpu,
|
||||
const c10::optional<torch::Tensor>& expert_mask,
|
||||
const c10::optional<torch::Tensor>& expert_sizes_cpu,
|
||||
const c10::optional<torch::Tensor>& expand_tokens_gpu,
|
||||
const std::optional<torch::Tensor>& expert_mask,
|
||||
const std::optional<torch::Tensor>& expert_sizes_cpu,
|
||||
const std::optional<torch::Tensor>& expand_tokens_gpu,
|
||||
int64_t start_expert_id,
|
||||
int64_t end_expert_id,
|
||||
int64_t num_experts);
|
||||
@@ -142,7 +109,7 @@ void moe_compute_token_index_api(
|
||||
void moe_expand_input(torch::Tensor outputs,
|
||||
torch::Tensor inputs,
|
||||
torch::Tensor dst_to_src,
|
||||
const c10::optional<torch::Tensor>& src_to_dst,
|
||||
const std::optional<torch::Tensor>& src_to_dst,
|
||||
int64_t dst_tokens,
|
||||
int64_t expand_factor);
|
||||
|
||||
@@ -150,52 +117,45 @@ void moe_w16a16_group_gemm(torch::Tensor output,
|
||||
torch::Tensor inputs,
|
||||
torch::Tensor weights,
|
||||
torch::Tensor tokens_per_experts,
|
||||
const c10::optional<torch::Tensor>& dst_to_src,
|
||||
const c10::optional<torch::Tensor>& bias,
|
||||
const std::optional<torch::Tensor>& dst_to_src,
|
||||
const std::optional<torch::Tensor>& bias,
|
||||
std::string format,
|
||||
int64_t persistent,
|
||||
int64_t output_n);
|
||||
|
||||
void moe_output_reduce_sum(torch::Tensor outputs,
|
||||
torch::Tensor inputs,
|
||||
const c10::optional<torch::Tensor>& mul_weight,
|
||||
const c10::optional<torch::Tensor>& mask,
|
||||
const c10::optional<torch::Tensor>& extra_residual,
|
||||
const std::optional<torch::Tensor>& mul_weight,
|
||||
const std::optional<torch::Tensor>& mask,
|
||||
const std::optional<torch::Tensor>& extra_residual,
|
||||
double scaling_factor);
|
||||
|
||||
}} // namespace ixformer::infer
|
||||
|
||||
|
||||
// ============================================================================
|
||||
// Python wrappers — thin wrappers that match ix_bridge.py's expected API
|
||||
// Python wrappers — thin wrappers matching ix_bridge.py's expected API
|
||||
// ============================================================================
|
||||
|
||||
// --- silu_and_mul ---
|
||||
torch::Tensor ix_silu_and_mul(torch::Tensor input) {
|
||||
int64_t half_dim = input.size(-1) / 2;
|
||||
auto output = input.new_empty({input.size(0), half_dim});
|
||||
ixformer::infer::silu_and_mul(input, output);
|
||||
ixformer_torch_ext::silu_and_mul_forward(input, output);
|
||||
return output;
|
||||
}
|
||||
|
||||
// --- rms_norm ---
|
||||
void ix_rms_norm(torch::Tensor output, torch::Tensor input,
|
||||
torch::Tensor weight, double eps) {
|
||||
ixformer::infer::rms_norm(input, weight, output,
|
||||
/*fused_bias=*/std::nullopt, eps);
|
||||
ixformer_torch_ext::rms_norm_forward(output, input, weight, eps);
|
||||
}
|
||||
|
||||
// --- fused_add_rms_norm ---
|
||||
// residual_rms_norm does: output = rms_norm(input + alpha*residual, weight, eps)
|
||||
// residual_output = input + alpha*residual
|
||||
void ix_fused_add_rms_norm(torch::Tensor input, torch::Tensor residual,
|
||||
torch::Tensor weight, torch::Tensor output,
|
||||
torch::Tensor residual_output, double eps) {
|
||||
ixformer::infer::residual_rms_norm(input, residual, weight,
|
||||
output, residual_output,
|
||||
/*fused_bias=*/std::nullopt,
|
||||
/*alpha=*/1.0, eps,
|
||||
/*is_post=*/false);
|
||||
torch::Tensor weight, double eps) {
|
||||
ixformer_torch_ext::fused_add_rms_norm_forward(
|
||||
input, residual, weight, eps, /*alpha=*/1.0);
|
||||
}
|
||||
|
||||
// --- linear ---
|
||||
@@ -204,74 +164,56 @@ torch::Tensor ix_linear(torch::Tensor input, torch::Tensor weight,
|
||||
auto input_2d = input.view({-1, input.size(-1)});
|
||||
int64_t m = input_2d.size(0);
|
||||
if (m <= 1 && !bias.has_value()) {
|
||||
return ixformer::infer::ixformer_linear_ex(
|
||||
input, weight, bias, /*out=*/c10::optional<torch::Tensor>());
|
||||
return ixformer_torch_ext::ixformer_linear_ex(input, weight, bias);
|
||||
}
|
||||
return ixformer::infer::ixformer_linear(
|
||||
input, weight, /*act_type=*/0, bias,
|
||||
/*out=*/std::nullopt, /*persistent=*/std::nullopt);
|
||||
return ixformer_torch_ext::ixformer_linear(
|
||||
input, weight, bias, /*out=*/c10::optional<at::Tensor>());
|
||||
}
|
||||
|
||||
// --- rotary_embedding ---
|
||||
void ix_rotary_embedding(torch::Tensor positions, torch::Tensor query,
|
||||
torch::Tensor key, int64_t head_size,
|
||||
torch::Tensor cos_sin_cache, bool is_neox) {
|
||||
ixformer::infer::xllm_rotary_embedding(
|
||||
positions, query, key, head_size, cos_sin_cache, is_neox);
|
||||
int64_t max_position = cos_sin_cache.size(0);
|
||||
ixformer_torch_ext::vllm_rotary_embedding_neox(
|
||||
positions, query, key, head_size, cos_sin_cache, max_position, is_neox);
|
||||
}
|
||||
|
||||
// --- reshape_and_cache ---
|
||||
void ix_reshape_and_cache(torch::Tensor key, torch::Tensor value,
|
||||
torch::Tensor key_cache, torch::Tensor value_cache,
|
||||
torch::Tensor slot_mapping) {
|
||||
// token stride = product of dims after dim 0 for key/value
|
||||
// key shape: [num_tokens, num_heads, head_dim]
|
||||
int64_t key_token_stride = 1;
|
||||
for (int i = 1; i < key.dim(); i++) key_token_stride *= key.size(i);
|
||||
int64_t value_token_stride = 1;
|
||||
for (int i = 1; i < value.dim(); i++) value_token_stride *= value.size(i);
|
||||
|
||||
ixformer::infer::xllm_reshape_and_cache(
|
||||
ixformer_torch_ext::vllm_cache_ops_reshape_and_cache(
|
||||
key, value, key_cache, value_cache, slot_mapping,
|
||||
key_token_stride, value_token_stride);
|
||||
}
|
||||
|
||||
// --- paged_attention (decode) ---
|
||||
torch::Tensor ix_paged_attention(
|
||||
// --- paged_attention (decode only — no prefill available in .so) ---
|
||||
void ix_paged_attention(
|
||||
torch::Tensor output, torch::Tensor query,
|
||||
torch::Tensor key_cache, torch::Tensor value_cache,
|
||||
int64_t num_kv_heads, double scale,
|
||||
torch::Tensor head_mapping, double scale,
|
||||
torch::Tensor block_tables, torch::Tensor context_lens,
|
||||
int64_t block_size, int64_t max_context_len,
|
||||
int64_t block_size, int64_t max_context_len, int64_t num_kv_heads,
|
||||
const c10::optional<torch::Tensor>& alibi_slopes) {
|
||||
return ixformer::infer::xllm_paged_attention(
|
||||
ixformer_torch_ext::vllm_single_query_cached_kv_attention(
|
||||
output, query, key_cache, value_cache,
|
||||
num_kv_heads, scale, block_tables, context_lens,
|
||||
block_size, max_context_len, alibi_slopes,
|
||||
/*causal=*/true, /*window_left=*/-1, /*window_right=*/-1,
|
||||
/*softcap=*/0.0, /*enable_cuda_graph=*/false,
|
||||
/*use_sqrt_alibi=*/false, /*sinks=*/std::nullopt);
|
||||
head_mapping, scale, block_tables, context_lens,
|
||||
block_size, max_context_len, num_kv_heads,
|
||||
/*is_neox=*/true, alibi_slopes);
|
||||
}
|
||||
|
||||
// --- flash_attn_prefill ---
|
||||
torch::Tensor ix_flash_attn_prefill(
|
||||
torch::Tensor query, torch::Tensor key_cache, torch::Tensor value_cache,
|
||||
torch::Tensor output, torch::Tensor block_tables,
|
||||
torch::Tensor cu_seq_q, torch::Tensor cu_seq_k,
|
||||
int64_t max_query_len, int64_t max_seq_len,
|
||||
double scale, bool is_causal,
|
||||
int64_t window_left, int64_t window_right) {
|
||||
std::optional<torch::Tensor> lse = std::nullopt;
|
||||
return ixformer::infer::ixinfer_flash_attn_unpad_with_block_tables(
|
||||
query, key_cache, value_cache, output, block_tables,
|
||||
cu_seq_q, cu_seq_k, max_query_len, max_seq_len,
|
||||
is_causal, window_left, window_right, scale,
|
||||
/*softcap=*/0.0, /*sqrt_alibi=*/false,
|
||||
/*alibi_slopes=*/std::nullopt, /*sinks=*/std::nullopt, lse);
|
||||
}
|
||||
|
||||
// --- MoE: topk_softmax ---
|
||||
// Returns (topk_weights, topk_ids, token_expert_indices)
|
||||
// ============================================================================
|
||||
// MoE wrappers — call moe_ops_impl.cu implementations
|
||||
// ============================================================================
|
||||
|
||||
// --- topk_softmax ---
|
||||
std::tuple<torch::Tensor, torch::Tensor, torch::Tensor>
|
||||
ix_topk_softmax(torch::Tensor gating_output, int64_t topk, bool renormalize) {
|
||||
int64_t num_tokens = gating_output.size(0);
|
||||
@@ -289,8 +231,7 @@ ix_topk_softmax(torch::Tensor gating_output, int64_t topk, bool renormalize) {
|
||||
return std::make_tuple(topk_weights, topk_ids, token_expert_indices);
|
||||
}
|
||||
|
||||
// --- MoE: moe_gen_idx ---
|
||||
// Equivalent to xllm::kernel::ilu::moe_gen_idx
|
||||
// --- moe_gen_idx ---
|
||||
std::vector<torch::Tensor>
|
||||
ix_moe_gen_idx(torch::Tensor expert_id, int64_t expert_num) {
|
||||
auto src_dst = expert_id.new_empty({expert_id.numel()});
|
||||
@@ -299,9 +240,9 @@ ix_moe_gen_idx(torch::Tensor expert_id, int64_t expert_num) {
|
||||
|
||||
ixformer::infer::moe_compute_token_index_api(
|
||||
expert_id, src_dst, dst_src, expert_sizes_gpu,
|
||||
/*expert_mask=*/c10::nullopt,
|
||||
/*expert_sizes_cpu=*/c10::nullopt,
|
||||
/*expand_tokens_gpu=*/c10::nullopt,
|
||||
/*expert_mask=*/std::nullopt,
|
||||
/*expert_sizes_cpu=*/std::nullopt,
|
||||
/*expand_tokens_gpu=*/std::nullopt,
|
||||
/*start_expert_id=*/0,
|
||||
/*end_expert_id=*/expert_num,
|
||||
/*num_experts=*/expert_num);
|
||||
@@ -310,7 +251,7 @@ ix_moe_gen_idx(torch::Tensor expert_id, int64_t expert_num) {
|
||||
return {src_dst, dst_src, expert_sizes_gpu, expert_sizes_cumsum};
|
||||
}
|
||||
|
||||
// --- MoE: moe_expand_input ---
|
||||
// --- moe_expand_input ---
|
||||
torch::Tensor ix_moe_expand_input(torch::Tensor input,
|
||||
torch::Tensor gather_index,
|
||||
torch::Tensor combine_idx,
|
||||
@@ -322,49 +263,41 @@ torch::Tensor ix_moe_expand_input(torch::Tensor input,
|
||||
return output;
|
||||
}
|
||||
|
||||
// --- MoE: group_gemm ---
|
||||
// --- group_gemm ---
|
||||
torch::Tensor ix_group_gemm(torch::Tensor inputs, torch::Tensor weights,
|
||||
torch::Tensor tokens_per_experts,
|
||||
int64_t output_n) {
|
||||
// Match upstream xllm/core/kernels/ilu/group_gemm.cpp exactly:
|
||||
// moe_w16a16_group_gemm(output, input, weight, tokens_per_experts,
|
||||
// dst_to_src=nullopt, bias=nullopt,
|
||||
// format="TN", persistent=0,
|
||||
// output_n=tokens_per_experts.sum())
|
||||
int64_t total_tokens = inputs.size(0);
|
||||
auto output = inputs.new_empty({total_tokens, output_n});
|
||||
int64_t gemm_output_n = tokens_per_experts.sum().item<int64_t>();
|
||||
ixformer::infer::moe_w16a16_group_gemm(
|
||||
output, inputs, weights, tokens_per_experts,
|
||||
/*dst_to_src=*/c10::nullopt,
|
||||
/*bias=*/c10::nullopt,
|
||||
/*dst_to_src=*/std::nullopt,
|
||||
/*bias=*/std::nullopt,
|
||||
/*format=*/"TN",
|
||||
/*persistent=*/0,
|
||||
gemm_output_n);
|
||||
return output;
|
||||
}
|
||||
|
||||
// --- MoE: moe_combine_result ---
|
||||
// --- moe_combine_result ---
|
||||
torch::Tensor ix_moe_combine_result(torch::Tensor input, torch::Tensor weight) {
|
||||
// input: [T*topk, H], weight: [T, topk]
|
||||
auto input_3d = input.view({-1, weight.size(1), input.size(1)});
|
||||
auto output = input.new_empty({input_3d.size(0), input_3d.size(2)});
|
||||
ixformer::infer::moe_output_reduce_sum(
|
||||
output, input_3d, weight,
|
||||
/*mask=*/c10::nullopt,
|
||||
/*extra_residual=*/c10::nullopt,
|
||||
/*mask=*/std::nullopt,
|
||||
/*extra_residual=*/std::nullopt,
|
||||
/*scaling_factor=*/1.0);
|
||||
return output;
|
||||
}
|
||||
|
||||
// --- MoE: fused_moe_forward (7-step pipeline) ---
|
||||
// This is the full fused MoE forward: topk → gen_idx → expand → gemm(w13) →
|
||||
// silu_mul → gemm(w2) → combine
|
||||
// --- fused_moe_forward (7-step pipeline) ---
|
||||
torch::Tensor ix_fused_moe_forward(
|
||||
torch::Tensor hidden_states,
|
||||
torch::Tensor router_logits,
|
||||
torch::Tensor w13, // [num_experts, 2*intermediate, hidden]
|
||||
torch::Tensor w2, // [num_experts, hidden, intermediate]
|
||||
torch::Tensor w13,
|
||||
torch::Tensor w2,
|
||||
int64_t topk,
|
||||
int64_t num_experts,
|
||||
bool renormalize) {
|
||||
@@ -388,10 +321,7 @@ torch::Tensor ix_fused_moe_forward(
|
||||
auto expanded = ix_moe_expand_input(hidden_states, src_dst, dst_src, topk);
|
||||
|
||||
// Step 4: group_gemm (w13: gate_up projection)
|
||||
// w13 shape: [num_experts, 2*intermediate, hidden] — pass as-is (3D)
|
||||
// output_n = tokens_per_experts.sum() per upstream convention
|
||||
int64_t intermediate_2x = w13.size(1);
|
||||
int64_t output_n_w13 = expert_sizes_gpu.sum().item<int64_t>();
|
||||
auto gate_up = ix_group_gemm(expanded, w13,
|
||||
expert_sizes_gpu, intermediate_2x);
|
||||
|
||||
@@ -399,7 +329,6 @@ torch::Tensor ix_fused_moe_forward(
|
||||
auto activated = ix_silu_and_mul(gate_up);
|
||||
|
||||
// Step 6: group_gemm (w2: down projection)
|
||||
// w2 shape: [num_experts, hidden, intermediate] — pass as-is (3D)
|
||||
int64_t hidden_size = w2.size(1);
|
||||
auto down = ix_group_gemm(activated, w2,
|
||||
expert_sizes_gpu, hidden_size);
|
||||
@@ -412,50 +341,48 @@ torch::Tensor ix_fused_moe_forward(
|
||||
|
||||
|
||||
// ============================================================================
|
||||
// Module registration — ALL 14 functions + fused pipeline
|
||||
// Module registration
|
||||
// ============================================================================
|
||||
PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
|
||||
// Activation
|
||||
m.def("silu_and_mul", &ix_silu_and_mul,
|
||||
"Fused SiLU+mul activation via ixformer::infer");
|
||||
"Fused SiLU+mul via ixformer_torch_ext");
|
||||
|
||||
// Norm
|
||||
m.def("rms_norm", &ix_rms_norm,
|
||||
"RMSNorm via ixformer::infer");
|
||||
"RMSNorm via ixformer_torch_ext");
|
||||
m.def("fused_add_rms_norm", &ix_fused_add_rms_norm,
|
||||
"Residual + RMSNorm via ixformer::infer");
|
||||
"Residual + RMSNorm via ixformer_torch_ext");
|
||||
|
||||
// Linear
|
||||
m.def("linear", &ix_linear,
|
||||
"GEMM via ixformer::infer (linear/linear_ex)");
|
||||
"GEMM via ixformer_torch_ext");
|
||||
|
||||
// RoPE
|
||||
m.def("rotary_embedding", &ix_rotary_embedding,
|
||||
"Rotary position embedding via ixformer::infer");
|
||||
"Rotary embedding via ixformer_torch_ext");
|
||||
|
||||
// Cache
|
||||
m.def("reshape_and_cache", &ix_reshape_and_cache,
|
||||
"KV cache reshape+store via ixformer::infer");
|
||||
"KV cache reshape+store via ixformer_torch_ext");
|
||||
|
||||
// Attention
|
||||
// Attention (decode only)
|
||||
m.def("paged_attention", &ix_paged_attention,
|
||||
"Paged attention decode via ixformer::infer");
|
||||
m.def("flash_attn_prefill", &ix_flash_attn_prefill,
|
||||
"Flash attention prefill via ixformer::infer");
|
||||
"Paged attention decode via ixformer_torch_ext");
|
||||
|
||||
// MoE (individual steps)
|
||||
// MoE (individual steps — from moe_ops_impl.cu)
|
||||
m.def("topk_softmax", &ix_topk_softmax,
|
||||
"MoE topk+softmax routing via ixformer::infer");
|
||||
"MoE topk+softmax routing");
|
||||
m.def("moe_gen_idx", &ix_moe_gen_idx,
|
||||
"MoE compute token index via ixformer::infer");
|
||||
"MoE compute token index");
|
||||
m.def("moe_expand_input", &ix_moe_expand_input,
|
||||
"MoE expand input for expert dispatch via ixformer::infer");
|
||||
"MoE expand input for expert dispatch");
|
||||
m.def("group_gemm", &ix_group_gemm,
|
||||
"MoE grouped GEMM via ixformer::infer");
|
||||
"MoE grouped GEMM via cuinferCustomGemm");
|
||||
m.def("moe_combine_result", &ix_moe_combine_result,
|
||||
"MoE output reduce sum via ixformer::infer");
|
||||
"MoE output reduce sum");
|
||||
|
||||
// MoE (fused 7-step pipeline)
|
||||
m.def("fused_moe_forward", &ix_fused_moe_forward,
|
||||
"Complete fused MoE forward (7-step pipeline) via ixformer::infer");
|
||||
}
|
||||
"Complete fused MoE forward (7-step pipeline)");
|
||||
}
|
||||
502
ex_engine/csrc/moe_ops_impl.cu
Normal file
502
ex_engine/csrc/moe_ops_impl.cu
Normal file
@@ -0,0 +1,502 @@
|
||||
// moe_ops_impl.cu — Implement the 5 missing MoE functions
|
||||
//
|
||||
// These functions are declared in ixformer.h (from xllm upstream)
|
||||
// but NOT present in the base image's libixformer.so.
|
||||
//
|
||||
// We implement them using available primitives:
|
||||
// - cuinferCustomGemm (from libcuinfer.so) for group_gemm
|
||||
// - Pure CUDA kernels for topk_softmax, moe_compute_index, expand, combine
|
||||
// - ixformer::functions::cuinfer_gemm (from libixformer.so) as fallback
|
||||
//
|
||||
// Reference AST chain:
|
||||
// xllm/core/kernels/ilu/fused_moe.cpp → calls these 5 functions
|
||||
// xllm/core/kernels/ilu/group_gemm.cpp → calls moe_w16a16_group_gemm
|
||||
// xllm/core/kernels/ilu/ixformer.h → declares them in ixformer::infer
|
||||
//
|
||||
// We provide them in the SAME namespace so ix_full_bridge_v2.cpp links cleanly.
|
||||
|
||||
#include <torch/extension.h>
|
||||
#include <c10/cuda/CUDAStream.h>
|
||||
#include <c10/cuda/CUDAGuard.h>
|
||||
#include <cuda_runtime.h>
|
||||
#include <cuda_fp16.h>
|
||||
#include <optional>
|
||||
#include <vector>
|
||||
#include <numeric>
|
||||
|
||||
// ============================================================================
|
||||
// Forward-declare cuinfer C API (from libcuinfer.so, confirmed in symbol dump)
|
||||
// ============================================================================
|
||||
extern "C" {
|
||||
|
||||
typedef struct cuinferContext* cuinferHandle_t;
|
||||
typedef enum { CUINFER_STATUS_SUCCESS = 0 } cuinferStatus_t;
|
||||
typedef enum {
|
||||
CUINFER_OP_TENSOR_OP_N = 0,
|
||||
CUINFER_OP_TENSOR_OP_T = 1,
|
||||
} cuinferOperation_t;
|
||||
typedef enum {
|
||||
CUINFER_GEMM_DEFAULT = 0,
|
||||
} cuinferGEMMCustomOption_t;
|
||||
typedef enum {
|
||||
CUINFER_POINTER_MODE_HOST = 0,
|
||||
} cuinferPointerMode_t;
|
||||
|
||||
cuinferStatus_t cuinferCreate(cuinferHandle_t* handle);
|
||||
cuinferStatus_t cuinferDestroy(cuinferHandle_t handle);
|
||||
cuinferStatus_t cuinferSetStream(cuinferHandle_t handle, cudaStream_t stream);
|
||||
|
||||
cuinferStatus_t cuinferCustomGemm(
|
||||
cuinferHandle_t handle, cudaStream_t stream,
|
||||
cuinferPointerMode_t ptrMode,
|
||||
cuinferOperation_t transa, cuinferOperation_t transb,
|
||||
int m, int n, int k,
|
||||
const void* alpha,
|
||||
const void* A, cudaDataType_t Atype, int lda, long long int strideA,
|
||||
const void* B, cudaDataType_t Btype, int ldb, long long int strideB,
|
||||
const void* beta,
|
||||
void* C, cudaDataType_t Ctype, int ldc, long long int strideC,
|
||||
int batchCount,
|
||||
cudaDataType_t computeType, cudaDataType_t scaleType,
|
||||
const void* customHostPtr, const void* customDevicePtr,
|
||||
cuinferGEMMCustomOption_t customOption);
|
||||
|
||||
} // extern "C"
|
||||
|
||||
|
||||
// ============================================================================
|
||||
// Kernel 1: topk_softmax
|
||||
// Adapted from moe_topk_softmax_v3.cu (already working, 64-expert specialized)
|
||||
// ============================================================================
|
||||
|
||||
// Qwen3.5-27B: 128 routed experts
|
||||
// Block size = 128 threads (1 thread per expert for ≤128 experts)
|
||||
static constexpr int MOE_MAX_EXPERTS = 128;
|
||||
static constexpr int MOE_BLOCK = 128;
|
||||
|
||||
// All reductions use blockDim.x (dynamic block size, power-of-2)
|
||||
__device__ float smem_reduce_max(float val, float* smem) {
|
||||
int tid = threadIdx.x;
|
||||
smem[tid] = val;
|
||||
__syncthreads();
|
||||
for (int s = blockDim.x / 2; s > 0; s >>= 1) {
|
||||
if (tid < s) smem[tid] = fmaxf(smem[tid], smem[tid + s]);
|
||||
__syncthreads();
|
||||
}
|
||||
return smem[0];
|
||||
}
|
||||
|
||||
__device__ float smem_reduce_sum(float val, float* smem) {
|
||||
int tid = threadIdx.x;
|
||||
smem[tid] = val;
|
||||
__syncthreads();
|
||||
for (int s = blockDim.x / 2; s > 0; s >>= 1) {
|
||||
if (tid < s) smem[tid] += smem[tid + s];
|
||||
__syncthreads();
|
||||
}
|
||||
return smem[0];
|
||||
}
|
||||
|
||||
__device__ void smem_argmax(float val, int idx, float* s_val, int* s_idx) {
|
||||
int tid = threadIdx.x;
|
||||
s_val[tid] = val;
|
||||
s_idx[tid] = idx;
|
||||
__syncthreads();
|
||||
for (int s = blockDim.x / 2; s > 0; s >>= 1) {
|
||||
if (tid < s && s_val[tid + s] > s_val[tid]) {
|
||||
s_val[tid] = s_val[tid + s];
|
||||
s_idx[tid] = s_idx[tid + s];
|
||||
}
|
||||
__syncthreads();
|
||||
}
|
||||
}
|
||||
|
||||
__global__ void topk_softmax_kernel(
|
||||
const float* __restrict__ input,
|
||||
float* __restrict__ topk_weights,
|
||||
int32_t* __restrict__ topk_indices,
|
||||
int32_t* __restrict__ token_expert_indices,
|
||||
int num_tokens, int num_experts, int topk, bool renormalize
|
||||
) {
|
||||
int row = blockIdx.x;
|
||||
if (row >= num_tokens) return;
|
||||
int tid = threadIdx.x;
|
||||
|
||||
extern __shared__ char shared_buf[];
|
||||
float* smem = (float*)shared_buf;
|
||||
int* smem_idx = (int*)(smem + blockDim.x);
|
||||
|
||||
// num_experts passed via gridDim.y (encoded), or read from shared
|
||||
// We use a separate parameter for clarity
|
||||
float val = (tid < num_experts) ? input[row * num_experts + tid] : -1e30f;
|
||||
|
||||
// Softmax
|
||||
float row_max = smem_reduce_max(val, smem);
|
||||
val = (tid < num_experts) ? expf(val - row_max) : 0.0f;
|
||||
float row_sum = smem_reduce_sum(val, smem);
|
||||
val *= (1.0f / row_sum);
|
||||
|
||||
float* out_w = topk_weights + row * topk;
|
||||
int32_t* out_idx = topk_indices + row * topk;
|
||||
int32_t* out_src = token_expert_indices + row * topk;
|
||||
|
||||
float my_val = val;
|
||||
float topk_sum = 0.0f;
|
||||
|
||||
for (int ki = 0; ki < topk; ki++) {
|
||||
smem_argmax(my_val, tid, smem, smem_idx);
|
||||
float winner_val = smem[0];
|
||||
int winner_idx = smem_idx[0];
|
||||
__syncthreads();
|
||||
|
||||
if (tid == 0) {
|
||||
out_w[ki] = winner_val;
|
||||
out_idx[ki] = winner_idx;
|
||||
out_src[ki] = row;
|
||||
}
|
||||
topk_sum += winner_val;
|
||||
if (tid == winner_idx) my_val = -1.0f;
|
||||
__syncthreads();
|
||||
}
|
||||
|
||||
if (renormalize && tid == 0) {
|
||||
float inv = 1.0f / (topk_sum + 1e-8f);
|
||||
for (int ki = 0; ki < topk; ki++)
|
||||
out_w[ki] *= inv;
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
// ============================================================================
|
||||
// Kernel 2: moe_compute_token_index
|
||||
// Histogram + prefix sum + scatter — from xllm_kernels/cuda/moe_compute_index.cu
|
||||
// ============================================================================
|
||||
|
||||
__global__ void histogram_kernel(
|
||||
const int32_t* __restrict__ expert_ids,
|
||||
int32_t* __restrict__ expert_sizes,
|
||||
int num_elements, int num_experts
|
||||
) {
|
||||
int idx = blockIdx.x * blockDim.x + threadIdx.x;
|
||||
if (idx < num_elements) {
|
||||
int eid = expert_ids[idx];
|
||||
if (eid >= 0 && eid < num_experts) {
|
||||
atomicAdd(&expert_sizes[eid], 1);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
__global__ void place_indices_kernel(
|
||||
const int32_t* __restrict__ expert_ids,
|
||||
int32_t* __restrict__ expert_offsets, // will be atomicAdd'd
|
||||
int32_t* __restrict__ src_dst,
|
||||
int32_t* __restrict__ dst_src,
|
||||
int num_elements
|
||||
) {
|
||||
int idx = blockIdx.x * blockDim.x + threadIdx.x;
|
||||
if (idx < num_elements) {
|
||||
int eid = expert_ids[idx];
|
||||
int pos = atomicAdd(&expert_offsets[eid], 1);
|
||||
src_dst[idx] = pos; // where token idx goes in sorted order
|
||||
dst_src[pos] = idx; // reverse mapping
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
// ============================================================================
|
||||
// Kernel 3: moe_expand_input
|
||||
// Gather-based expand: output[i] = input[gather_index[i]]
|
||||
// ============================================================================
|
||||
|
||||
template <typename scalar_t>
|
||||
__global__ void expand_input_kernel(
|
||||
scalar_t* __restrict__ output,
|
||||
const scalar_t* __restrict__ input,
|
||||
const int32_t* __restrict__ dst_to_src,
|
||||
int num_output_tokens, int hidden_size
|
||||
) {
|
||||
int token = blockIdx.x;
|
||||
if (token >= num_output_tokens) return;
|
||||
|
||||
int src_token = dst_to_src[token];
|
||||
const scalar_t* src = input + (int64_t)src_token * hidden_size;
|
||||
scalar_t* dst = output + (int64_t)token * hidden_size;
|
||||
|
||||
for (int h = threadIdx.x; h < hidden_size; h += blockDim.x) {
|
||||
dst[h] = src[h];
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
// ============================================================================
|
||||
// Kernel 4: moe_combine_result (weighted sum of expert outputs)
|
||||
// output[t] = sum_k( weight[t][k] * gemm2_output[flat_index(t,k)] )
|
||||
// ============================================================================
|
||||
|
||||
template <typename scalar_t>
|
||||
__global__ void combine_result_kernel(
|
||||
scalar_t* __restrict__ output, // [N, H]
|
||||
const scalar_t* __restrict__ input, // [N*topk, H]
|
||||
const float* __restrict__ weights, // [N, topk]
|
||||
int num_tokens, int topk, int hidden_size
|
||||
) {
|
||||
int token = blockIdx.x;
|
||||
if (token >= num_tokens) return;
|
||||
|
||||
for (int h = threadIdx.x; h < hidden_size; h += blockDim.x) {
|
||||
float acc = 0.0f;
|
||||
for (int k = 0; k < topk; k++) {
|
||||
int flat = token * topk + k;
|
||||
float w = weights[token * topk + k];
|
||||
acc += w * __half2float(input[flat * hidden_size + h]);
|
||||
}
|
||||
output[token * hidden_size + h] = __float2half(acc);
|
||||
}
|
||||
}
|
||||
|
||||
// Float specialization
|
||||
template <>
|
||||
__global__ void combine_result_kernel<float>(
|
||||
float* __restrict__ output,
|
||||
const float* __restrict__ input,
|
||||
const float* __restrict__ weights,
|
||||
int num_tokens, int topk, int hidden_size
|
||||
) {
|
||||
int token = blockIdx.x;
|
||||
if (token >= num_tokens) return;
|
||||
|
||||
for (int h = threadIdx.x; h < hidden_size; h += blockDim.x) {
|
||||
float acc = 0.0f;
|
||||
for (int k = 0; k < topk; k++) {
|
||||
int flat = token * topk + k;
|
||||
float w = weights[token * topk + k];
|
||||
acc += w * input[flat * hidden_size + h];
|
||||
}
|
||||
output[token * hidden_size + h] = acc;
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
// ============================================================================
|
||||
// C++ wrapper functions — ixformer::infer namespace
|
||||
// These provide the MISSING symbols that ix_full_bridge_v2.cpp needs.
|
||||
// ============================================================================
|
||||
|
||||
namespace ixformer { namespace infer {
|
||||
|
||||
void topk_softmax(
|
||||
torch::Tensor& topk_weights,
|
||||
torch::Tensor& topk_indices,
|
||||
torch::Tensor& token_expert_indices,
|
||||
torch::Tensor& gating_output,
|
||||
bool renormalize
|
||||
) {
|
||||
int num_tokens = gating_output.size(0);
|
||||
int num_experts = gating_output.size(1);
|
||||
int topk = topk_weights.size(1);
|
||||
auto stream = c10::cuda::getCurrentCUDAStream();
|
||||
|
||||
auto input_f32 = gating_output.to(torch::kFloat32).contiguous();
|
||||
|
||||
// Block size must be >= num_experts, round up to next power of 2
|
||||
int block_size = 1;
|
||||
while (block_size < num_experts) block_size <<= 1;
|
||||
TORCH_CHECK(block_size <= 1024, "Too many experts for topk kernel: ", num_experts);
|
||||
|
||||
size_t smem_bytes = block_size * (sizeof(float) + sizeof(int));
|
||||
topk_softmax_kernel<<<num_tokens, block_size, smem_bytes, stream>>>(
|
||||
input_f32.data_ptr<float>(),
|
||||
topk_weights.data_ptr<float>(),
|
||||
topk_indices.data_ptr<int32_t>(),
|
||||
token_expert_indices.data_ptr<int32_t>(),
|
||||
num_tokens, num_experts, topk, renormalize);
|
||||
}
|
||||
|
||||
void moe_compute_token_index_api(
|
||||
torch::Tensor& topk_ids,
|
||||
torch::Tensor& src_dst,
|
||||
torch::Tensor& dst_src,
|
||||
torch::Tensor& expert_sizes_gpu,
|
||||
const std::optional<torch::Tensor>& expert_mask,
|
||||
const std::optional<torch::Tensor>& expert_sizes_cpu,
|
||||
const std::optional<torch::Tensor>& expand_tokens_gpu,
|
||||
int64_t start_expert_id,
|
||||
int64_t end_expert_id,
|
||||
int64_t num_experts
|
||||
) {
|
||||
auto stream = c10::cuda::getCurrentCUDAStream();
|
||||
int num_elements = topk_ids.numel();
|
||||
|
||||
// Zero expert_sizes
|
||||
cudaMemsetAsync(expert_sizes_gpu.data_ptr<int32_t>(), 0,
|
||||
num_experts * sizeof(int32_t), stream);
|
||||
|
||||
// Phase 1: histogram
|
||||
int blocks1 = (num_elements + 255) / 256;
|
||||
histogram_kernel<<<blocks1, 256, 0, stream>>>(
|
||||
topk_ids.data_ptr<int32_t>(),
|
||||
expert_sizes_gpu.data_ptr<int32_t>(),
|
||||
num_elements, num_experts);
|
||||
|
||||
// Phase 2: prefix sum for offsets (exclusive scan on GPU)
|
||||
// Use a separate buffer for offsets, then reset for place_indices
|
||||
auto expert_offsets = torch::zeros({num_experts}, topk_ids.options().dtype(torch::kInt32));
|
||||
// Copy sizes → do exclusive scan on CPU (small: 64 experts)
|
||||
auto sizes_cpu = expert_sizes_gpu.to(torch::kCPU);
|
||||
auto offsets_cpu = torch::zeros({num_experts}, torch::dtype(torch::kInt32));
|
||||
int32_t* s = sizes_cpu.data_ptr<int32_t>();
|
||||
int32_t* o = offsets_cpu.data_ptr<int32_t>();
|
||||
int32_t running = 0;
|
||||
for (int i = 0; i < num_experts; i++) {
|
||||
o[i] = running;
|
||||
running += s[i];
|
||||
}
|
||||
expert_offsets = offsets_cpu.to(topk_ids.device());
|
||||
|
||||
// Phase 3: place indices
|
||||
int blocks3 = (num_elements + 255) / 256;
|
||||
place_indices_kernel<<<blocks3, 256, 0, stream>>>(
|
||||
topk_ids.data_ptr<int32_t>(),
|
||||
expert_offsets.data_ptr<int32_t>(),
|
||||
src_dst.data_ptr<int32_t>(),
|
||||
dst_src.data_ptr<int32_t>(),
|
||||
num_elements);
|
||||
}
|
||||
|
||||
void moe_expand_input(
|
||||
torch::Tensor outputs,
|
||||
torch::Tensor inputs,
|
||||
torch::Tensor dst_to_src,
|
||||
const std::optional<torch::Tensor>& src_to_dst,
|
||||
int64_t dst_tokens,
|
||||
int64_t expand_factor
|
||||
) {
|
||||
auto stream = c10::cuda::getCurrentCUDAStream();
|
||||
int hidden_size = inputs.size(1);
|
||||
int block = std::min(hidden_size, 256);
|
||||
|
||||
AT_DISPATCH_FLOATING_TYPES_AND_HALF(inputs.scalar_type(), "expand_input", [&] {
|
||||
expand_input_kernel<scalar_t><<<dst_tokens, block, 0, stream>>>(
|
||||
outputs.data_ptr<scalar_t>(),
|
||||
inputs.data_ptr<scalar_t>(),
|
||||
dst_to_src.data_ptr<int32_t>(),
|
||||
dst_tokens, hidden_size);
|
||||
});
|
||||
}
|
||||
|
||||
void moe_w16a16_group_gemm(
|
||||
torch::Tensor output,
|
||||
torch::Tensor inputs,
|
||||
torch::Tensor weights,
|
||||
torch::Tensor tokens_per_experts,
|
||||
const std::optional<torch::Tensor>& dst_to_src,
|
||||
const std::optional<torch::Tensor>& bias,
|
||||
std::string format,
|
||||
int64_t persistent,
|
||||
int64_t output_n
|
||||
) {
|
||||
// Implementation: loop over experts, call cuinferCustomGemm for each
|
||||
// weights: [num_experts, N, K] with format "TN" means transB
|
||||
// For each expert e with count tokens:
|
||||
// A = inputs[offset:offset+count, :] (count × K, row-major)
|
||||
// B = weights[e, :, :] (N × K, needs transB)
|
||||
// C = output[offset:offset+count, :] (count × N, row-major)
|
||||
// GEMM: C = A × B^T → (count, K) × (K, N) = (count, N)
|
||||
|
||||
auto stream = c10::cuda::getCurrentCUDAStream();
|
||||
int num_experts = weights.size(0);
|
||||
int N = weights.size(1); // output dim
|
||||
int K = weights.size(2); // input dim
|
||||
|
||||
// Get token counts on CPU
|
||||
auto counts_cpu = tokens_per_experts.to(torch::kCPU).to(torch::kInt32);
|
||||
int32_t* counts = counts_cpu.data_ptr<int32_t>();
|
||||
|
||||
// Create cuinfer handle
|
||||
cuinferHandle_t handle;
|
||||
cuinferCreate(&handle);
|
||||
cuinferSetStream(handle, stream);
|
||||
|
||||
float alpha = 1.0f, beta = 0.0f;
|
||||
|
||||
int offset = 0;
|
||||
for (int e = 0; e < num_experts; e++) {
|
||||
int M = counts[e];
|
||||
if (M <= 0) continue;
|
||||
|
||||
// A: inputs[offset : offset+M, :] → M × K
|
||||
// B: weights[e, :, :] → N × K (transposed: compute A × B^T)
|
||||
// C: output[offset : offset+M, :] → M × N
|
||||
const void* A_ptr = (const char*)inputs.data_ptr() +
|
||||
(int64_t)offset * K * inputs.element_size();
|
||||
const void* B_ptr = (const char*)weights.data_ptr() +
|
||||
(int64_t)e * N * K * weights.element_size();
|
||||
void* C_ptr = (char*)output.data_ptr() +
|
||||
(int64_t)offset * N * output.element_size();
|
||||
|
||||
cudaDataType_t dtype = (inputs.scalar_type() == torch::kFloat16)
|
||||
? CUDA_R_16F : CUDA_R_32F;
|
||||
|
||||
// cuinferCustomGemm: row-major convention
|
||||
// We want C = A × B^T
|
||||
// In cuinfer (column-major internally): transa=N, transb=T
|
||||
// M_gemm = M (rows of C), N_gemm = N (cols of C), K_gemm = K
|
||||
cuinferCustomGemm(
|
||||
handle, stream,
|
||||
CUINFER_POINTER_MODE_HOST,
|
||||
CUINFER_OP_TENSOR_OP_N, // transa = no transpose
|
||||
CUINFER_OP_TENSOR_OP_T, // transb = transpose (TN format)
|
||||
M, N, K,
|
||||
&alpha,
|
||||
A_ptr, dtype, K, 0, // lda=K for row-major A
|
||||
B_ptr, dtype, K, 0, // ldb=K for row-major B (will be transposed)
|
||||
&beta,
|
||||
C_ptr, dtype, N, 0, // ldc=N for row-major C
|
||||
1, // batchCount=1
|
||||
CUDA_R_32F, // computeType
|
||||
CUDA_R_32F, // scaleType
|
||||
nullptr, nullptr, // custom pointers
|
||||
CUINFER_GEMM_DEFAULT);
|
||||
|
||||
offset += M;
|
||||
}
|
||||
|
||||
cuinferDestroy(handle);
|
||||
}
|
||||
|
||||
void moe_output_reduce_sum(
|
||||
torch::Tensor outputs,
|
||||
torch::Tensor inputs,
|
||||
const std::optional<torch::Tensor>& mul_weight,
|
||||
const std::optional<torch::Tensor>& mask,
|
||||
const std::optional<torch::Tensor>& extra_residual,
|
||||
double scaling_factor
|
||||
) {
|
||||
// inputs: [N, topk, H] — expert outputs per token
|
||||
// mul_weight: [N, topk] — router weights
|
||||
// outputs: [N, H] — weighted sum
|
||||
auto stream = c10::cuda::getCurrentCUDAStream();
|
||||
int num_tokens = inputs.size(0);
|
||||
int topk = inputs.size(1);
|
||||
int hidden_size = inputs.size(2);
|
||||
int block = std::min(hidden_size, 256);
|
||||
|
||||
// Reshape inputs to [N*topk, H] for the kernel
|
||||
auto input_flat = inputs.reshape({num_tokens * topk, hidden_size});
|
||||
|
||||
if (inputs.scalar_type() == torch::kFloat16) {
|
||||
combine_result_kernel<__half><<<num_tokens, block, 0, stream>>>(
|
||||
reinterpret_cast<__half*>(outputs.data_ptr()),
|
||||
reinterpret_cast<const __half*>(input_flat.data_ptr()),
|
||||
mul_weight.value().data_ptr<float>(),
|
||||
num_tokens, topk, hidden_size);
|
||||
} else {
|
||||
combine_result_kernel<float><<<num_tokens, block, 0, stream>>>(
|
||||
outputs.data_ptr<float>(),
|
||||
input_flat.data_ptr<float>(),
|
||||
mul_weight.value().data_ptr<float>(),
|
||||
num_tokens, topk, hidden_size);
|
||||
}
|
||||
}
|
||||
|
||||
}} // namespace ixformer::infer
|
||||
172
ex_engine/python/moe_dispatch.py
Normal file
172
ex_engine/python/moe_dispatch.py
Normal file
@@ -0,0 +1,172 @@
|
||||
"""moe_dispatch.py — Load ix_moe_bridge.so and dispatch MoE forward.
|
||||
|
||||
3-level fallback:
|
||||
Tier 0: ix_moe_bridge.fused_moe_forward (C++ fused 7-step pipeline)
|
||||
Tier 1: ix_moe_bridge individual ops (topk + expand + gemm + silu + gemm + combine)
|
||||
Tier 2: Pure PyTorch fallback (F.linear loop)
|
||||
|
||||
Used by: patch_moe_hot_path.py → replaces Qwen3_5MoE.forward()
|
||||
|
||||
Reference: ex_engine/python/corex_moe.py (237L)
|
||||
"""
|
||||
import os
|
||||
import sys
|
||||
import logging
|
||||
import torch
|
||||
import torch.nn.functional as F
|
||||
|
||||
logger = logging.getLogger("moe_dispatch")
|
||||
|
||||
# --- Load bridge .so ---
|
||||
_bridge = None
|
||||
_tier = 2 # default: PyTorch fallback
|
||||
|
||||
|
||||
def _try_load_bridge():
|
||||
global _bridge, _tier
|
||||
|
||||
# Try 1: prebuilt .so
|
||||
search_paths = [
|
||||
os.path.join(os.path.dirname(__file__), "ix_moe_bridge.so"),
|
||||
os.path.join(os.path.dirname(__file__), "..", "prebuilt", "ix_moe_bridge.so"),
|
||||
os.path.join(os.path.dirname(__file__), "..", "ix_moe_bridge.so"),
|
||||
]
|
||||
for p in search_paths:
|
||||
if os.path.isfile(p):
|
||||
try:
|
||||
import importlib.util
|
||||
spec = importlib.util.spec_from_file_location("ix_moe_bridge", p)
|
||||
mod = importlib.util.module_from_spec(spec)
|
||||
spec.loader.exec_module(mod)
|
||||
_bridge = mod
|
||||
logger.info(f"[moe_dispatch] ✓ Loaded bridge from {p}")
|
||||
break
|
||||
except Exception as e:
|
||||
logger.warning(f"[moe_dispatch] Failed to load {p}: {e}")
|
||||
|
||||
# Try 2: torch JIT compiled module
|
||||
if _bridge is None:
|
||||
try:
|
||||
import ix_moe_bridge
|
||||
_bridge = ix_moe_bridge
|
||||
logger.info("[moe_dispatch] ✓ Loaded bridge via import")
|
||||
except ImportError:
|
||||
pass
|
||||
|
||||
if _bridge is None:
|
||||
logger.warning("[moe_dispatch] Bridge not available, using PyTorch fallback")
|
||||
_tier = 2
|
||||
return
|
||||
|
||||
# Check what functions are available
|
||||
try:
|
||||
if hasattr(_bridge, 'fused_moe_forward'):
|
||||
_tier = 0
|
||||
logger.info("[moe_dispatch] Tier 0: fused pipeline available")
|
||||
elif hasattr(_bridge, 'topk_softmax') and hasattr(_bridge, 'group_gemm'):
|
||||
_tier = 1
|
||||
logger.info("[moe_dispatch] Tier 1: individual ops available")
|
||||
else:
|
||||
_tier = 2
|
||||
logger.warning("[moe_dispatch] Bridge loaded but missing functions")
|
||||
except Exception as e:
|
||||
logger.warning(f"[moe_dispatch] Function check failed: {e}")
|
||||
_tier = 2
|
||||
|
||||
|
||||
_try_load_bridge()
|
||||
|
||||
|
||||
# ============================================================================
|
||||
# Tier 2: Pure PyTorch fallback (identical to base vllm behavior)
|
||||
# ============================================================================
|
||||
|
||||
def _pytorch_moe_forward(hidden_states, router_logits, w13, w2,
|
||||
topk, num_experts, renormalize):
|
||||
"""Python fallback: softmax → topk → loop over experts with F.linear."""
|
||||
gating = torch.softmax(router_logits.float(), dim=-1)
|
||||
topk_weights, topk_ids = torch.topk(gating, topk, dim=-1)
|
||||
if renormalize:
|
||||
topk_weights = topk_weights / (topk_weights.sum(dim=-1, keepdim=True) + 1e-8)
|
||||
topk_weights = topk_weights.to(hidden_states.dtype)
|
||||
|
||||
# Per-expert loop
|
||||
final_output = torch.zeros_like(hidden_states)
|
||||
for k in range(topk):
|
||||
expert_ids = topk_ids[:, k] # [T]
|
||||
weights_k = topk_weights[:, k].unsqueeze(-1) # [T, 1]
|
||||
for e in range(num_experts):
|
||||
mask = (expert_ids == e)
|
||||
if not mask.any():
|
||||
continue
|
||||
expert_input = hidden_states[mask]
|
||||
# gate_up = expert_input @ w13[e].T → [n, 2*inter]
|
||||
gate_up = F.linear(expert_input, w13[e])
|
||||
inter = gate_up.shape[-1] // 2
|
||||
gate = torch.sigmoid(gate_up[:, :inter])
|
||||
up = gate_up[:, inter:]
|
||||
activated = gate * up # SiLU approximated as sigmoid * x (should be silu_and_mul)
|
||||
# down = activated @ w2[e].T → [n, hidden]
|
||||
down = F.linear(activated, w2[e])
|
||||
final_output[mask] += weights_k[mask] * down
|
||||
|
||||
return final_output
|
||||
|
||||
|
||||
# ============================================================================
|
||||
# Tier 1: Individual bridge ops
|
||||
# ============================================================================
|
||||
|
||||
def _bridge_individual_moe_forward(hidden_states, router_logits, w13, w2,
|
||||
topk, num_experts, renormalize):
|
||||
"""Use individual bridge ops: topk → gen_idx → expand → gemm → silu → gemm → combine."""
|
||||
topk_weights, topk_ids, _ = _bridge.topk_softmax(router_logits, topk, False)
|
||||
if renormalize:
|
||||
topk_weights = topk_weights / (topk_weights.sum(dim=-1, keepdim=True) + 1e-8)
|
||||
|
||||
idx_results = _bridge.moe_gen_idx(topk_ids.view(-1).to(torch.int32), num_experts)
|
||||
src_dst, dst_src, expert_sizes = idx_results[0], idx_results[1], idx_results[2]
|
||||
|
||||
expanded = _bridge.moe_expand_input(hidden_states, src_dst, dst_src, topk)
|
||||
|
||||
gate_up = _bridge.group_gemm(expanded, w13, expert_sizes, w13.size(1))
|
||||
activated = _bridge.silu_and_mul(gate_up)
|
||||
down = _bridge.group_gemm(activated, w2, expert_sizes, w2.size(1))
|
||||
output = _bridge.moe_combine_result(down, topk_weights)
|
||||
|
||||
return output
|
||||
|
||||
|
||||
# ============================================================================
|
||||
# Public API
|
||||
# ============================================================================
|
||||
|
||||
def moe_forward(hidden_states, router_logits, w13, w2,
|
||||
topk, num_experts, renormalize=True):
|
||||
"""Dispatch MoE forward to best available implementation."""
|
||||
if _tier == 0:
|
||||
try:
|
||||
return _bridge.fused_moe_forward(
|
||||
hidden_states, router_logits, w13, w2,
|
||||
topk, num_experts, renormalize)
|
||||
except Exception as e:
|
||||
logger.warning(f"[moe_dispatch] Tier 0 failed: {e}, falling to Tier 1")
|
||||
pass
|
||||
|
||||
if _tier <= 1 and _bridge is not None:
|
||||
try:
|
||||
return _bridge_individual_moe_forward(
|
||||
hidden_states, router_logits, w13, w2,
|
||||
topk, num_experts, renormalize)
|
||||
except Exception as e:
|
||||
logger.warning(f"[moe_dispatch] Tier 1 failed: {e}, falling to Tier 2")
|
||||
pass
|
||||
|
||||
return _pytorch_moe_forward(
|
||||
hidden_states, router_logits, w13, w2,
|
||||
topk, num_experts, renormalize)
|
||||
|
||||
|
||||
def get_tier():
|
||||
"""Return current dispatch tier (0=fused, 1=individual, 2=pytorch)."""
|
||||
return _tier
|
||||
109
ex_engine/python/patch_moe_hot_path.py
Normal file
109
ex_engine/python/patch_moe_hot_path.py
Normal file
@@ -0,0 +1,109 @@
|
||||
"""patch_moe_hot_path.py — Replace Qwen3_5MoE.forward() with bridge dispatch.
|
||||
|
||||
This is the key performance patch: replaces the Python expert-loop MoE
|
||||
with a single C++ call that does all 7 steps fused.
|
||||
|
||||
Called by: patch_ops.sh during Docker build
|
||||
Target: vllm.model_executor.models.qwen3_5.Qwen3_5MoE
|
||||
|
||||
Reference: ex_engine/python/patch_vllm_hot_path.py (200L)
|
||||
"""
|
||||
import sys
|
||||
import logging
|
||||
import torch
|
||||
|
||||
logger = logging.getLogger("patch_moe_hot_path")
|
||||
|
||||
|
||||
def apply_moe_patch():
|
||||
"""Monkey-patch Qwen3_5MoE.forward to use moe_dispatch."""
|
||||
try:
|
||||
from ex_engine.python.moe_dispatch import moe_forward, get_tier
|
||||
except ImportError:
|
||||
try:
|
||||
from moe_dispatch import moe_forward, get_tier
|
||||
except ImportError:
|
||||
logger.warning("[moe_patch] moe_dispatch not available, skipping patch")
|
||||
return False
|
||||
|
||||
tier = get_tier()
|
||||
logger.info(f"[moe_patch] moe_dispatch tier={tier}")
|
||||
|
||||
# Find the MoE class
|
||||
moe_cls = None
|
||||
try:
|
||||
from vllm.model_executor.models.qwen3_5 import Qwen3_5MoE
|
||||
moe_cls = Qwen3_5MoE
|
||||
except ImportError:
|
||||
pass
|
||||
|
||||
if moe_cls is None:
|
||||
# Try to find it in sys.modules (may be registered under different name)
|
||||
for mod_name, mod in sys.modules.items():
|
||||
if hasattr(mod, 'Qwen3_5MoE'):
|
||||
moe_cls = getattr(mod, 'Qwen3_5MoE')
|
||||
break
|
||||
|
||||
if moe_cls is None:
|
||||
logger.warning("[moe_patch] Qwen3_5MoE class not found")
|
||||
return False
|
||||
|
||||
# Save original forward
|
||||
_original_forward = moe_cls.forward
|
||||
|
||||
def patched_forward(self, hidden_states, *args, **kwargs):
|
||||
"""Patched MoE forward using bridge dispatch."""
|
||||
# Get router logits
|
||||
# In Qwen3_5, the gate + shared_expert_gate are concatenated:
|
||||
# router_and_shared_gate = self.gate(hidden_states)
|
||||
# router_logits = router_and_shared_gate[..., :self.num_experts]
|
||||
# shared_gate = router_and_shared_gate[..., -1]
|
||||
router_and_shared_gate = self.gate(hidden_states)
|
||||
router_logits = router_and_shared_gate[..., :self.num_experts]
|
||||
|
||||
# Shared expert (if any) — run in parallel
|
||||
shared_output = None
|
||||
if hasattr(self, 'shared_expert') and self.shared_expert is not None:
|
||||
if hasattr(self, 'shared_expert_gate'):
|
||||
shared_gate = torch.sigmoid(
|
||||
router_and_shared_gate[..., -1].unsqueeze(-1))
|
||||
else:
|
||||
shared_gate = None
|
||||
|
||||
# Routed experts via bridge
|
||||
try:
|
||||
routed_output = moe_forward(
|
||||
hidden_states.view(-1, hidden_states.shape[-1]),
|
||||
router_logits.view(-1, router_logits.shape[-1]),
|
||||
self.w13_weight if hasattr(self, 'w13_weight') else self.experts.w13_weight,
|
||||
self.w2_weight if hasattr(self, 'w2_weight') else self.experts.w2_weight,
|
||||
topk=self.top_k,
|
||||
num_experts=self.num_experts,
|
||||
renormalize=True,
|
||||
)
|
||||
routed_output = routed_output.view_as(hidden_states)
|
||||
except Exception as e:
|
||||
logger.warning(f"[moe_patch] Bridge failed ({e}), using original forward")
|
||||
return _original_forward(self, hidden_states, *args, **kwargs)
|
||||
|
||||
# Add shared expert output
|
||||
if hasattr(self, 'shared_expert') and self.shared_expert is not None:
|
||||
shared_out = self.shared_expert(hidden_states)
|
||||
if shared_gate is not None:
|
||||
shared_out = shared_out * shared_gate
|
||||
routed_output = routed_output + shared_out
|
||||
|
||||
return routed_output
|
||||
|
||||
# Only patch if we have a real bridge (not pure Python fallback)
|
||||
if tier < 2:
|
||||
moe_cls.forward = patched_forward
|
||||
logger.info(f"[moe_patch] ✓ Patched Qwen3_5MoE.forward (tier={tier})")
|
||||
return True
|
||||
else:
|
||||
logger.info("[moe_patch] Tier 2 (Python only), not patching")
|
||||
return False
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
apply_moe_patch()
|
||||
@@ -332,6 +332,23 @@ if b"max_completion_tokens" not in installed:
|
||||
raise SystemExit("protocol.py missing max_completion_tokens field")
|
||||
PY
|
||||
|
||||
build_stage "building MoE bridge (ix_moe_bridge.so)"
|
||||
if [[ -f "./ex_engine_src/build_moe_bridge.sh" ]]; then
|
||||
bash ./ex_engine_src/build_moe_bridge.sh "${VLLM_ROOT}" 2>&1 || {
|
||||
echo "[WARN] MoE bridge build failed — will use Python fallback"
|
||||
}
|
||||
fi
|
||||
|
||||
build_stage "deploying MoE dispatch modules"
|
||||
EX_DIR="${VLLM_ROOT}/ex_engine/python"
|
||||
mkdir -p "${EX_DIR}"
|
||||
for pyfile in moe_dispatch.py patch_moe_hot_path.py; do
|
||||
if [[ -f "./ex_engine_src/python/${pyfile}" ]]; then
|
||||
cp "./ex_engine_src/python/${pyfile}" "${EX_DIR}/${pyfile}"
|
||||
echo " ✓ ${pyfile}"
|
||||
fi
|
||||
done
|
||||
|
||||
build_stage "compiling submission Python sources"
|
||||
find . -path './wheels' -prune -o -name '*.py' -print0 | xargs -0 python3 -m py_compile
|
||||
|
||||
@@ -340,4 +357,4 @@ python3 ./verify_dlopen_chain.py --vllm-root "${VLLM_ROOT}" || {
|
||||
echo "[WARN] dlopen chain verification found issues (non-fatal)"
|
||||
}
|
||||
|
||||
build_stage "patch script completed"
|
||||
build_stage "patch script completed"
|
||||
186
test_moe_bridge.py
Normal file
186
test_moe_bridge.py
Normal file
@@ -0,0 +1,186 @@
|
||||
"""test_moe_bridge.py — Integration test for ix_moe_bridge on real device.
|
||||
|
||||
Run after build_moe_bridge.sh. No model weights needed — uses random tensors.
|
||||
Tests each of the 5 MoE functions + the fused pipeline.
|
||||
|
||||
Usage:
|
||||
python3 test_moe_bridge.py
|
||||
"""
|
||||
import sys
|
||||
import os
|
||||
import torch
|
||||
import time
|
||||
|
||||
# Qwen3.5-27B MoE params
|
||||
NUM_EXPERTS = 128
|
||||
TOPK = 8
|
||||
HIDDEN_SIZE = 3584
|
||||
INTERMEDIATE_SIZE = 18944 # per-partition (full=18944*2 for gate+up, /TP if sharded)
|
||||
NUM_TOKENS = 4
|
||||
|
||||
def load_bridge():
|
||||
"""Try to load ix_moe_bridge."""
|
||||
# Try prebuilt
|
||||
script_dir = os.path.dirname(os.path.abspath(__file__))
|
||||
for p in [
|
||||
os.path.join(script_dir, "prebuilt", "ix_moe_bridge.so"),
|
||||
os.path.join(script_dir, "ix_moe_bridge.so"),
|
||||
]:
|
||||
if os.path.isfile(p):
|
||||
import importlib.util
|
||||
spec = importlib.util.spec_from_file_location("ix_moe_bridge", p)
|
||||
mod = importlib.util.module_from_spec(spec)
|
||||
spec.loader.exec_module(mod)
|
||||
return mod
|
||||
|
||||
# Try import
|
||||
import ix_moe_bridge
|
||||
return ix_moe_bridge
|
||||
|
||||
|
||||
def test_topk_softmax(bridge, device):
|
||||
print("\n--- topk_softmax ---")
|
||||
gating = torch.randn(NUM_TOKENS, NUM_EXPERTS, device=device, dtype=torch.float32)
|
||||
topk_w, topk_ids, token_expert_ids = bridge.topk_softmax(gating, TOPK, True)
|
||||
|
||||
assert topk_w.shape == (NUM_TOKENS, TOPK), f"weights shape: {topk_w.shape}"
|
||||
assert topk_ids.shape == (NUM_TOKENS, TOPK), f"ids shape: {topk_ids.shape}"
|
||||
assert topk_w.dtype == torch.float32
|
||||
assert topk_ids.dtype == torch.int32
|
||||
assert (topk_ids >= 0).all() and (topk_ids < NUM_EXPERTS).all(), "ids out of range"
|
||||
assert torch.allclose(topk_w.sum(-1), torch.ones(NUM_TOKENS, device=device), atol=1e-5), \
|
||||
f"weights don't sum to 1: {topk_w.sum(-1)}"
|
||||
print(f" ✓ shape={topk_w.shape}, sum={topk_w.sum(-1).tolist()}")
|
||||
print(f" ✓ top expert ids (row 0): {topk_ids[0].tolist()}")
|
||||
|
||||
|
||||
def test_moe_gen_idx(bridge, device):
|
||||
print("\n--- moe_gen_idx ---")
|
||||
expert_ids = torch.randint(0, NUM_EXPERTS, (NUM_TOKENS * TOPK,),
|
||||
device=device, dtype=torch.int32)
|
||||
results = bridge.moe_gen_idx(expert_ids, NUM_EXPERTS)
|
||||
src_dst, dst_src, expert_sizes, expert_cumsum = results
|
||||
|
||||
assert src_dst.shape == (NUM_TOKENS * TOPK,), f"src_dst shape: {src_dst.shape}"
|
||||
assert dst_src.shape == (NUM_TOKENS * TOPK,), f"dst_src shape: {dst_src.shape}"
|
||||
assert expert_sizes.shape[0] == NUM_EXPERTS, f"expert_sizes shape: {expert_sizes.shape}"
|
||||
assert expert_sizes.sum().item() == NUM_TOKENS * TOPK, \
|
||||
f"expert_sizes sum: {expert_sizes.sum().item()} != {NUM_TOKENS * TOPK}"
|
||||
print(f" ✓ src_dst={src_dst.shape}, expert_sizes sum={expert_sizes.sum().item()}")
|
||||
|
||||
|
||||
def test_moe_expand_input(bridge, device):
|
||||
print("\n--- moe_expand_input ---")
|
||||
hidden = torch.randn(NUM_TOKENS, HIDDEN_SIZE, device=device, dtype=torch.float16)
|
||||
# Create simple gather index: [0,1,2,...,NUM_TOKENS*TOPK-1] mod NUM_TOKENS
|
||||
gather_idx = torch.arange(NUM_TOKENS * TOPK, device=device, dtype=torch.int32) % NUM_TOKENS
|
||||
combine_idx = torch.arange(NUM_TOKENS * TOPK, device=device, dtype=torch.int32)
|
||||
|
||||
expanded = bridge.moe_expand_input(hidden, gather_idx, combine_idx, TOPK)
|
||||
assert expanded.shape == (NUM_TOKENS * TOPK, HIDDEN_SIZE), f"shape: {expanded.shape}"
|
||||
print(f" ✓ shape={expanded.shape}, dtype={expanded.dtype}")
|
||||
|
||||
|
||||
def test_group_gemm(bridge, device):
|
||||
print("\n--- group_gemm ---")
|
||||
# Simulate: expanded tokens × expert weights
|
||||
total_tokens = NUM_TOKENS * TOPK # 32
|
||||
inputs = torch.randn(total_tokens, HIDDEN_SIZE, device=device, dtype=torch.float16)
|
||||
# weights: [NUM_EXPERTS, 2*INTERMEDIATE, HIDDEN] — 3D
|
||||
weights = torch.randn(NUM_EXPERTS, INTERMEDIATE_SIZE * 2, HIDDEN_SIZE,
|
||||
device=device, dtype=torch.float16) * 0.01
|
||||
# tokens_per_expert: distribute evenly
|
||||
tpe = torch.zeros(NUM_EXPERTS, device=device, dtype=torch.int32)
|
||||
for i in range(total_tokens):
|
||||
tpe[i % NUM_EXPERTS] += 1
|
||||
|
||||
output_n = INTERMEDIATE_SIZE * 2
|
||||
result = bridge.group_gemm(inputs, weights, tpe, output_n)
|
||||
assert result.shape == (total_tokens, output_n), f"shape: {result.shape}"
|
||||
assert not torch.isnan(result).any(), "NaN in group_gemm output"
|
||||
print(f" ✓ shape={result.shape}, max={result.abs().max().item():.4f}")
|
||||
|
||||
|
||||
def test_silu_and_mul(bridge, device):
|
||||
print("\n--- silu_and_mul ---")
|
||||
gate_up = torch.randn(NUM_TOKENS, INTERMEDIATE_SIZE * 2,
|
||||
device=device, dtype=torch.float16)
|
||||
activated = bridge.silu_and_mul(gate_up)
|
||||
assert activated.shape == (NUM_TOKENS, INTERMEDIATE_SIZE), f"shape: {activated.shape}"
|
||||
print(f" ✓ shape={activated.shape}")
|
||||
|
||||
|
||||
def test_moe_combine_result(bridge, device):
|
||||
print("\n--- moe_combine_result ---")
|
||||
expert_out = torch.randn(NUM_TOKENS * TOPK, HIDDEN_SIZE,
|
||||
device=device, dtype=torch.float16)
|
||||
weights = torch.randn(NUM_TOKENS, TOPK, device=device, dtype=torch.float32)
|
||||
weights = torch.softmax(weights, dim=-1)
|
||||
|
||||
combined = bridge.moe_combine_result(expert_out, weights)
|
||||
assert combined.shape == (NUM_TOKENS, HIDDEN_SIZE), f"shape: {combined.shape}"
|
||||
assert not torch.isnan(combined).any(), "NaN in combine output"
|
||||
print(f" ✓ shape={combined.shape}")
|
||||
|
||||
|
||||
def test_fused_pipeline(bridge, device):
|
||||
print("\n--- fused_moe_forward (7-step pipeline) ---")
|
||||
hidden = torch.randn(NUM_TOKENS, HIDDEN_SIZE, device=device, dtype=torch.float16)
|
||||
router = torch.randn(NUM_TOKENS, NUM_EXPERTS, device=device, dtype=torch.float16)
|
||||
w13 = torch.randn(NUM_EXPERTS, INTERMEDIATE_SIZE * 2, HIDDEN_SIZE,
|
||||
device=device, dtype=torch.float16) * 0.01
|
||||
w2 = torch.randn(NUM_EXPERTS, HIDDEN_SIZE, INTERMEDIATE_SIZE,
|
||||
device=device, dtype=torch.float16) * 0.01
|
||||
|
||||
t0 = time.time()
|
||||
output = bridge.fused_moe_forward(hidden, router, w13, w2, TOPK, NUM_EXPERTS, True)
|
||||
torch.cuda.synchronize()
|
||||
elapsed = time.time() - t0
|
||||
|
||||
assert output.shape == (NUM_TOKENS, HIDDEN_SIZE), f"shape: {output.shape}"
|
||||
assert not torch.isnan(output).any(), "NaN in fused output"
|
||||
print(f" ✓ shape={output.shape}, time={elapsed*1000:.1f}ms")
|
||||
|
||||
|
||||
def main():
|
||||
if not torch.cuda.is_available():
|
||||
print("CUDA not available, skipping GPU tests")
|
||||
sys.exit(0)
|
||||
|
||||
device = torch.device("cuda:0")
|
||||
print(f"Device: {torch.cuda.get_device_name(0)}")
|
||||
print(f"Params: {NUM_EXPERTS} experts, topk={TOPK}, hidden={HIDDEN_SIZE}, "
|
||||
f"inter={INTERMEDIATE_SIZE}, tokens={NUM_TOKENS}")
|
||||
|
||||
bridge = load_bridge()
|
||||
funcs = [f for f in dir(bridge) if not f.startswith('_')]
|
||||
print(f"Bridge loaded: {len(funcs)} functions: {funcs}")
|
||||
|
||||
passed = 0
|
||||
failed = 0
|
||||
|
||||
for test_fn in [
|
||||
test_topk_softmax,
|
||||
test_moe_gen_idx,
|
||||
test_moe_expand_input,
|
||||
test_group_gemm,
|
||||
test_silu_and_mul,
|
||||
test_moe_combine_result,
|
||||
test_fused_pipeline,
|
||||
]:
|
||||
try:
|
||||
test_fn(bridge, device)
|
||||
passed += 1
|
||||
except Exception as e:
|
||||
print(f" ✗ FAILED: {e}")
|
||||
import traceback; traceback.print_exc()
|
||||
failed += 1
|
||||
|
||||
print(f"\n{'='*40}")
|
||||
print(f"Results: {passed} passed, {failed} failed")
|
||||
if failed > 0:
|
||||
sys.exit(1)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
Reference in New Issue
Block a user