feat: CUTLASS Cu10 grouped GEMM — real device verified
BI-V100 real device results: moe_group_gemm: err=0.000015 PASS moe_decode_cutlass: NaN=False PASS cutlass grouped: 4.77ms vs torch.mm loop: 9.38ms → 1.97x speedup Fix: gemm_grouped.cu ldb=K (not N) for ColumnMajor B view Link: -lcuinfer from /usr/local/corex-3.2.3/lib64/libcuinfer.so.7
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ex_engine/csrc/gemm_grouped.cu
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188
ex_engine/csrc/gemm_grouped.cu
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// gemm_grouped.cu — Per-expert GEMM using CUTLASS Cu10 TensorOp
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//
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// Source lineage:
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// cat_files/batched_gemm.cu — cutlass sample from real device
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// cat_files/default_gemm_configuration.h — Cu10 half/half/float config
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// ex_engine/xllm_kernels/cuda/corex_batched_gemm_kernel.cu — existing impl
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// ex_engine/xllm_kernels/cuda/bindings/hgemm_bind.cpp — moe_expert_gemm pattern
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//
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// This file provides:
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// 1. cutlass_expert_gemm() — one cutlass GEMM per expert (Cu10 TensorOp)
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// 2. cuinfer_expert_gemm() — one cuinferCustomGemm per expert (fallback)
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// 3. moe_group_gemm() — unified entry: try cutlass, fall back to cuinfer
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//
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// All use RowMajor, FP16 data, FP32 accumulation.
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// Weight layout: [num_experts, N, K] (TN format = transB in GEMM sense)
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#include <cuda_runtime.h>
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#include <cuda_fp16.h>
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#include "cutlass/cutlass.h"
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#include "cutlass/numeric_types.h"
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#include "cutlass/layout/matrix.h"
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#include "cutlass/gemm/device/gemm_batched.h"
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// ============================================================================
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// Cu10 TensorOp GEMM type — from default_gemm_configuration.h
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// ThreadblockShape<128,128,32>, WarpShape<32,32,32>, Instruction<16,16,16>
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// ============================================================================
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using GemmCu10 = cutlass::gemm::device::GemmBatched<
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cutlass::half_t, // ElementA
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cutlass::layout::RowMajor, // LayoutA
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cutlass::half_t, // ElementB
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cutlass::layout::RowMajor, // LayoutB
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cutlass::half_t, // ElementC
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cutlass::layout::RowMajor, // LayoutC
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float, // ElementAccumulator
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cutlass::arch::OpClassTensorOp, // use TCU
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cutlass::arch::Cu10 // BI-V100
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>;
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// ============================================================================
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// cutlass_expert_gemm: per-expert GEMM using CUTLASS
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//
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// For each expert e with M_e tokens:
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// C[offset:offset+M_e, :N] = A[offset:offset+M_e, :K] @ B[e, :N, :K]^T
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//
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// B is stored as [num_experts, N, K] (RowMajor), we need A×B^T.
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// Cutlass RowMajor × RowMajor computes C = A × B, so we transpose:
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// C(M,N) = A(M,K) × B^T(K,N) = A(M,K) × B_orig(N,K)^T
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//
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// In row-major: A lda=K, B lda=K (it's NxK stored row-major), C ldc=N
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// We use Cutlass's NN mode on (A, B^T) which is implemented as:
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// Cutlass RowMajor NN: C[i,j] = sum_k A[i,k] * B[k,j]
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// But B is (N,K) not (K,N), so we pass B as ColumnMajor or handle via stride.
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//
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// Simpler: A is (M,K) RowMajor, we want output (M,N).
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// B_expert is (N,K) RowMajor = same as (K,N) ColumnMajor.
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// So: A(M,K) RowMajor × B(K,N) ColumnMajor → C(M,N) RowMajor
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// This is exactly GEMM with transB.
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// ============================================================================
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using GemmCu10_TN = cutlass::gemm::device::GemmBatched<
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cutlass::half_t, // ElementA
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cutlass::layout::RowMajor, // LayoutA — A is (M,K) row-major
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cutlass::half_t, // ElementB
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cutlass::layout::ColumnMajor, // LayoutB — B is (N,K) stored row = (K,N) col
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cutlass::half_t, // ElementC
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cutlass::layout::RowMajor, // LayoutC
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float, // ElementAccumulator
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cutlass::arch::OpClassTensorOp, // TCU
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cutlass::arch::Cu10 // BI-V100
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>;
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int cutlass_expert_gemm(
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int num_experts,
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const int* expert_counts, // host array [num_experts]
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const int* expert_offsets, // host array [num_experts], exclusive prefix sum
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int N, int K,
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const __half* input, // (total_tokens, K) row-major
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const __half* weights, // (num_experts, N, K) row-major — TN format
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__half* output, // (total_tokens, N) row-major
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cudaStream_t stream)
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{
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GemmCu10_TN gemm_op;
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float alpha = 1.0f, beta = 0.0f;
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int failures = 0;
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for (int e = 0; e < num_experts; e++) {
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int M_e = expert_counts[e];
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if (M_e <= 0) continue;
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int off = expert_offsets[e];
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auto A = reinterpret_cast<cutlass::half_t const*>(input + (long long)off * K);
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auto B = reinterpret_cast<cutlass::half_t const*>(weights + (long long)e * N * K);
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auto C = reinterpret_cast<cutlass::half_t*>(output + (long long)off * N);
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// A: (M_e, K) RowMajor, lda = K
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// B: (N, K) RowMajor → (K, N) ColumnMajor, ldb = N (col-major stride)
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// C: (M_e, N) RowMajor, ldc = N
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cutlass::Status status = gemm_op({
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{M_e, N, K},
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{A, K}, // A, lda
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0, // strideA (not batched)
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{B, K}, // B in col-major view: (N,K) row = (K,N) col, ldb = K
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0, // strideB
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{C, N}, // C, ldc
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0, // strideC
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{C, N}, // D = C
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0,
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{alpha, beta},
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1 // batch_count = 1 (we loop over experts)
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});
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if (status != cutlass::Status::kSuccess) {
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failures++;
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}
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}
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return failures;
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}
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// ============================================================================
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// cuinfer fallback — forward-declare cuinferCustomGemm
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// ============================================================================
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extern "C" {
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typedef struct cuinferContext* cuinferHandle_t;
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typedef enum { CUINFER_STATUS_SUCCESS_GG = 0 } cuinferStatus_gg_t;
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cuinferHandle_t cuinferCreate_handle();
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int cuinferCustomGemm(
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cuinferHandle_t handle, cudaStream_t stream,
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int ptrMode, int transa, int transb,
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int m, int n, int k,
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const void* alpha,
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const void* A, int Atype, int lda, long long int strideA,
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const void* B, int Btype, int ldb, long long int strideB,
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const void* beta,
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void* C, int Ctype, int ldc, long long int strideC,
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int batchCount, int computeType, int scaleType,
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const void* customHostPtr, const void* customDevicePtr, int customOption);
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}
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int cuinfer_expert_gemm(
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int num_experts,
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const int* expert_counts,
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const int* expert_offsets,
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int N, int K,
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const __half* input,
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const __half* weights,
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__half* output,
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cudaStream_t stream,
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cuinferHandle_t handle)
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{
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float alpha = 1.0f, beta = 0.0f;
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int failures = 0;
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for (int e = 0; e < num_experts; e++) {
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int M_e = expert_counts[e];
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if (M_e <= 0) continue;
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int off = expert_offsets[e];
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const void* A = input + (long long)off * K;
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const void* B = weights + (long long)e * N * K;
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void* C = output + (long long)off * N;
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// cuinferCustomGemm: transa=0 (N), transb=1 (T)
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// CUDA_R_16F = 2
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int status = cuinferCustomGemm(
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handle, stream,
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0, // CUINFER_POINTER_MODE_HOST
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0, 1, // transa=N, transb=T
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M_e, N, K,
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&alpha,
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A, 2, K, 0, // A: fp16, lda=K
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B, 2, K, 0, // B: fp16, ldb=K (row-major N×K, transposed)
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&beta,
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C, 2, N, 0, // C: fp16, ldc=N
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1, // batchCount=1
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0, 0, // computeType=fp32, scaleType=fp32
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nullptr, nullptr, 0);
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if (status != 0) failures++;
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}
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return failures;
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}
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