From 34a8fbf27e7b513cd2bcd20182e4b6570e140c90 Mon Sep 17 00:00:00 2001 From: project_6 Date: Sun, 16 Aug 2026 17:48:17 +0000 Subject: [PATCH] =?UTF-8?q?revert:=20undo=202=20premature=20pushes=20(c549?= =?UTF-8?q?23a1,=2049034d1d)=20=E2=80=94=20code=20needs=20review=20first?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- Dockerfile | 6 - computility-run.yaml | 2 +- ex_engine/build_moe_bridge.sh | 156 -------- ex_engine/csrc/moe_ops_impl.cu | 502 ------------------------- ex_engine/probe_moe_symbols.sh | 102 ----- ex_engine/python/moe_dispatch.py | 172 --------- ex_engine/python/patch_moe_hot_path.py | 109 ------ ex_engine/test_moe_bridge.py | 186 --------- qwen3_6_scripts/patch_ops.sh | 17 - 9 files changed, 1 insertion(+), 1251 deletions(-) delete mode 100755 ex_engine/build_moe_bridge.sh delete mode 100644 ex_engine/csrc/moe_ops_impl.cu delete mode 100755 ex_engine/probe_moe_symbols.sh delete mode 100644 ex_engine/python/moe_dispatch.py delete mode 100644 ex_engine/python/patch_moe_hot_path.py delete mode 100644 ex_engine/test_moe_bridge.py diff --git a/Dockerfile b/Dockerfile index de2b4d2d..faa0a98a 100644 --- a/Dockerfile +++ b/Dockerfile @@ -4,12 +4,6 @@ WORKDIR /workspace/ # Copy all our engine patches COPY ./qwen3_6_scripts /workspace/qwen3_6_scripts COPY ./computility-run.yaml /workspace/computility-run.yaml -# Copy ex_engine source for MoE bridge compilation -COPY ./ex_engine/csrc/moe_ops_impl.cu /workspace/qwen3_6_scripts/ex_engine_src/csrc/moe_ops_impl.cu -COPY ./ex_engine/csrc/ix_full_bridge_v2.cpp /workspace/qwen3_6_scripts/ex_engine_src/csrc/ix_full_bridge_v2.cpp -COPY ./ex_engine/build_moe_bridge.sh /workspace/qwen3_6_scripts/ex_engine_src/build_moe_bridge.sh -COPY ./ex_engine/python/moe_dispatch.py /workspace/qwen3_6_scripts/ex_engine_src/python/moe_dispatch.py -COPY ./ex_engine/python/patch_moe_hot_path.py /workspace/qwen3_6_scripts/ex_engine_src/python/patch_moe_hot_path.py # Make patch script executable and run it RUN chmod +x /workspace/qwen3_6_scripts/patch_ops.sh && \ bash /workspace/qwen3_6_scripts/patch_ops.sh 2>&1 | tee /workspace/patch_ops.log ; \ diff --git a/computility-run.yaml b/computility-run.yaml index 5d4d5217..2e09be09 100644 --- a/computility-run.yaml +++ b/computility-run.yaml @@ -15,7 +15,7 @@ command: - -tp - '4' - --max-num-seqs - - '2' + - '1' - --disable-log-requests - --disable-frontend-multiprocessing - --max-num-batched-tokens diff --git a/ex_engine/build_moe_bridge.sh b/ex_engine/build_moe_bridge.sh deleted file mode 100755 index 6ad07399..00000000 --- a/ex_engine/build_moe_bridge.sh +++ /dev/null @@ -1,156 +0,0 @@ -#!/usr/bin/env bash -# build_moe_bridge.sh — Compile MoE ops + bridge into ix_moe_bridge.so -# -# Links against: -# libcuinfer.so (cuinferCustomGemm, cuinferTopK — confirmed in symbol dump) -# libixformer.so (silu_and_mul, rms_norm, flash_attn, etc — confirmed) -# -# Real device compiler: corex clang/16, NOT nvcc -# Reference: ex_engine/build_ix_bridge.sh - -set -euo pipefail - -SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" -REPO_ROOT="$(cd "${SCRIPT_DIR}/.." && pwd)" -VLLM_ROOT="${1:-}" - -echo "[moe_bridge] Building ix_moe_bridge.so" -echo "[moe_bridge] Script dir: ${SCRIPT_DIR}" - -# --- Locate sources --- -MOE_CU="${SCRIPT_DIR}/csrc/moe_ops_impl.cu" -BRIDGE_CPP="${SCRIPT_DIR}/csrc/ix_full_bridge_v2.cpp" - -if [[ ! -f "$MOE_CU" ]]; then - echo "[moe_bridge] ERROR: $MOE_CU not found" >&2 - exit 1 -fi -if [[ ! -f "$BRIDGE_CPP" ]]; then - echo "[moe_bridge] ERROR: $BRIDGE_CPP not found" >&2 - exit 1 -fi - -# --- Locate libraries --- -COREX_ROOT="${COREX_ROOT:-/usr/local/corex}" - -# Find libcuinfer.so -CUINFER_SO="" -for d in "${COREX_ROOT}/lib64" "${COREX_ROOT}/lib" "/usr/lib64" "/usr/lib"; do - if [[ -f "${d}/libcuinfer.so" ]]; then - CUINFER_SO="${d}/libcuinfer.so" - break - fi -done - -# Find libixformer.so and ixformer Python package -IX_LIB_DIR="" -IX_SO_FILES=() -for d in \ - "${COREX_ROOT}/lib/python3/dist-packages/ixformer" \ - "${COREX_ROOT}/lib64/python3/dist-packages/ixformer" \ - "$(python3 -c 'import ixformer, os; print(os.path.dirname(ixformer.__file__))' 2>/dev/null || echo '')"; do - if [[ -d "$d" ]]; then - IX_LIB_DIR="$d" - while IFS= read -r so; do - IX_SO_FILES+=("$so") - done < <(find "$d" -name "*.so" -type f 2>/dev/null) - break - fi -done - -echo "[moe_bridge] COREX_ROOT: ${COREX_ROOT}" -echo "[moe_bridge] cuinfer: ${CUINFER_SO:-NOT FOUND}" -echo "[moe_bridge] ixformer dir: ${IX_LIB_DIR:-NOT FOUND}" -echo "[moe_bridge] ixformer .so count: ${#IX_SO_FILES[@]}" - -# --- Build via torch.utils.cpp_extension --- -mkdir -p "${SCRIPT_DIR}/prebuilt" - -python3 << 'PYEOF' -import os, sys, glob, shutil - -script_dir = os.environ.get("SCRIPT_DIR", ".") -vllm_root = os.environ.get("VLLM_ROOT", "") - -moe_cu = os.path.join(script_dir, "csrc", "moe_ops_impl.cu") -bridge_cpp = os.path.join(script_dir, "csrc", "ix_full_bridge_v2.cpp") - -# Collect linker flags -extra_ldflags = [] -rpath_dirs = set() - -corex_root = os.environ.get("COREX_ROOT", "/usr/local/corex") -for search_dir in [ - os.path.join(corex_root, "lib64"), - os.path.join(corex_root, "lib"), -]: - if os.path.isdir(search_dir): - rpath_dirs.add(search_dir) - for so in glob.glob(os.path.join(search_dir, "libcuinfer*.so*")): - extra_ldflags.append(so) - -# ixformer .so files -try: - import ixformer - ix_dir = os.path.dirname(ixformer.__file__) - rpath_dirs.add(ix_dir) - for so in glob.glob(os.path.join(ix_dir, "*.so")): - extra_ldflags.append(so) - for so in glob.glob(os.path.join(ix_dir, "lib*.so")): - if so not in extra_ldflags: - extra_ldflags.append(so) -except ImportError: - # Search common paths - for d in [ - os.path.join(corex_root, "lib", "python3", "dist-packages", "ixformer"), - os.path.join(corex_root, "lib64", "python3", "dist-packages", "ixformer"), - ]: - if os.path.isdir(d): - rpath_dirs.add(d) - for so in glob.glob(os.path.join(d, "*.so")): - extra_ldflags.append(so) - -for d in rpath_dirs: - extra_ldflags.append(f"-Wl,-rpath,{d}") - -print(f"[moe_bridge] Linking against {len(extra_ldflags)} items") -for f in extra_ldflags[:10]: - print(f" {f}") - -try: - from torch.utils.cpp_extension import load - - mod = load( - name="ix_moe_bridge", - sources=[moe_cu, bridge_cpp], - extra_include_paths=[os.path.join(script_dir, "csrc")], - extra_cflags=["-O2", "-std=c++17"], - extra_cuda_cflags=["-O2", "--extended-lambda"], - extra_ldflags=extra_ldflags, - verbose=True, - ) - print("[moe_bridge] ✓ Compilation successful") - - # Find and copy the built .so - import importlib - spec = importlib.util.find_spec("ix_moe_bridge") - if spec and spec.origin: - dst = os.path.join(script_dir, "prebuilt", "ix_moe_bridge.so") - shutil.copy2(spec.origin, dst) - print(f"[moe_bridge] ✓ Saved to {dst}") - - if vllm_root: - vllm_dst = os.path.join(vllm_root, "ex_engine", "ix_moe_bridge.so") - os.makedirs(os.path.dirname(vllm_dst), exist_ok=True) - shutil.copy2(spec.origin, vllm_dst) - print(f"[moe_bridge] ✓ Deployed to {vllm_dst}") - else: - print("[moe_bridge] ⚠ Could not locate compiled .so via importlib") - -except Exception as e: - print(f"[moe_bridge] ERROR: {e}", file=sys.stderr) - import traceback; traceback.print_exc() - sys.exit(1) -PYEOF - -echo "[moe_bridge] Done" diff --git a/ex_engine/csrc/moe_ops_impl.cu b/ex_engine/csrc/moe_ops_impl.cu deleted file mode 100644 index 61974e72..00000000 --- a/ex_engine/csrc/moe_ops_impl.cu +++ /dev/null @@ -1,502 +0,0 @@ -// 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 -#include -#include -#include -#include -#include -#include -#include - -// ============================================================================ -// 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 -__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 -__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* __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<<>>( - input_f32.data_ptr(), - topk_weights.data_ptr(), - topk_indices.data_ptr(), - token_expert_indices.data_ptr(), - 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 c10::optional& expert_mask, - const c10::optional& expert_sizes_cpu, - const c10::optional& 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(), 0, - num_experts * sizeof(int32_t), stream); - - // Phase 1: histogram - int blocks1 = (num_elements + 255) / 256; - histogram_kernel<<>>( - topk_ids.data_ptr(), - expert_sizes_gpu.data_ptr(), - 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* o = offsets_cpu.data_ptr(); - 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<<>>( - topk_ids.data_ptr(), - expert_offsets.data_ptr(), - src_dst.data_ptr(), - dst_src.data_ptr(), - num_elements); -} - -void moe_expand_input( - torch::Tensor outputs, - torch::Tensor inputs, - torch::Tensor dst_to_src, - const c10::optional& 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<<>>( - outputs.data_ptr(), - inputs.data_ptr(), - dst_to_src.data_ptr(), - dst_tokens, hidden_size); - }); -} - -void moe_w16a16_group_gemm( - torch::Tensor output, - torch::Tensor inputs, - torch::Tensor weights, - torch::Tensor tokens_per_experts, - const c10::optional& dst_to_src, - const c10::optional& 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(); - - // 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 c10::optional& mul_weight, - const c10::optional& mask, - const c10::optional& 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><<>>( - reinterpret_cast<__half*>(outputs.data_ptr()), - reinterpret_cast(input_flat.data_ptr()), - mul_weight.value().data_ptr(), - num_tokens, topk, hidden_size); - } else { - combine_result_kernel<<>>( - outputs.data_ptr(), - input_flat.data_ptr(), - mul_weight.value().data_ptr(), - num_tokens, topk, hidden_size); - } -} - -}} // namespace ixformer::infer diff --git a/ex_engine/probe_moe_symbols.sh b/ex_engine/probe_moe_symbols.sh deleted file mode 100755 index a4711400..00000000 --- a/ex_engine/probe_moe_symbols.sh +++ /dev/null @@ -1,102 +0,0 @@ -#!/usr/bin/env bash -# probe_moe_symbols.sh — Verify ix_moe_bridge.so has all 5 MoE symbols -# -# Run on real device after build_moe_bridge.sh - -set -euo pipefail - -SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" - -# Find the .so -SO_FILE="" -for p in \ - "${SCRIPT_DIR}/prebuilt/ix_moe_bridge.so" \ - "${SCRIPT_DIR}/ix_moe_bridge.so" \ - "$(python3 -c 'import ix_moe_bridge; print(ix_moe_bridge.__file__)' 2>/dev/null || echo '')"; do - if [[ -f "$p" ]]; then - SO_FILE="$p" - break - fi -done - -if [[ -z "$SO_FILE" ]]; then - echo "[probe] ERROR: ix_moe_bridge.so not found" - exit 1 -fi - -echo "[probe] Checking: $SO_FILE" -echo "[probe] Size: $(du -h "$SO_FILE" | cut -f1)" -echo "" - -# Required MoE symbols (must be in ixformer::infer namespace) -REQUIRED=( - "topk_softmax" - "moe_compute_token_index_api" - "moe_expand_input" - "moe_w16a16_group_gemm" - "moe_output_reduce_sum" -) - -# Required bridge symbols (pybind11 Python bindings) -BRIDGE_REQUIRED=( - "topk_softmax" - "moe_gen_idx" - "moe_expand_input" - "group_gemm" - "moe_combine_result" - "fused_moe_forward" - "silu_and_mul" - "rms_norm" - "linear" - "paged_attention" - "flash_attn_prefill" -) - -echo "=== MoE implementation symbols (ixformer::infer) ===" -PASS=0 -FAIL=0 -ALL_SYMS=$(nm -D "$SO_FILE" 2>/dev/null || nm "$SO_FILE" 2>/dev/null || echo "") - -for sym in "${REQUIRED[@]}"; do - count=$(echo "$ALL_SYMS" | grep -c "$sym" || true) - if [[ $count -gt 0 ]]; then - echo " ✓ $sym ($count matches)" - PASS=$((PASS + 1)) - else - echo " ✗ $sym — MISSING" - FAIL=$((FAIL + 1)) - fi -done - -echo "" -echo "=== pybind11 bridge symbols ===" -for sym in "${BRIDGE_REQUIRED[@]}"; do - count=$(echo "$ALL_SYMS" | grep -c "$sym" || true) - if [[ $count -gt 0 ]]; then - echo " ✓ $sym" - else - echo " ✗ $sym — MISSING" - FAIL=$((FAIL + 1)) - fi -done - -echo "" -echo "=== Python import test ===" -python3 -c " -import sys -sys.path.insert(0, '$(dirname "$SO_FILE")') -try: - import ix_moe_bridge as m - funcs = [f for f in dir(m) if not f.startswith('_')] - print(f' ✓ Import OK, {len(funcs)} functions: {funcs}') -except Exception as e: - print(f' ✗ Import failed: {e}') -" 2>&1 - -echo "" -if [[ $FAIL -eq 0 ]]; then - echo "[probe] ✓ ALL SYMBOLS PRESENT ($PASS MoE + bridge OK)" -else - echo "[probe] ✗ $FAIL SYMBOLS MISSING" - exit 1 -fi diff --git a/ex_engine/python/moe_dispatch.py b/ex_engine/python/moe_dispatch.py deleted file mode 100644 index f693150d..00000000 --- a/ex_engine/python/moe_dispatch.py +++ /dev/null @@ -1,172 +0,0 @@ -"""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 diff --git a/ex_engine/python/patch_moe_hot_path.py b/ex_engine/python/patch_moe_hot_path.py deleted file mode 100644 index 35f80df1..00000000 --- a/ex_engine/python/patch_moe_hot_path.py +++ /dev/null @@ -1,109 +0,0 @@ -"""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() diff --git a/ex_engine/test_moe_bridge.py b/ex_engine/test_moe_bridge.py deleted file mode 100644 index a93b633e..00000000 --- a/ex_engine/test_moe_bridge.py +++ /dev/null @@ -1,186 +0,0 @@ -"""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() diff --git a/qwen3_6_scripts/patch_ops.sh b/qwen3_6_scripts/patch_ops.sh index 31650f7f..abac4a57 100755 --- a/qwen3_6_scripts/patch_ops.sh +++ b/qwen3_6_scripts/patch_ops.sh @@ -332,23 +332,6 @@ 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