chore: import upstream snapshot with attribution
This commit is contained in:
wehub-resource-sync
@@ -0,0 +1,381 @@
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// ============================================================
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// Step 1: Naive INT8 TensorOps GEMM with Threadgroup Memory Staging
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// - BM=128, BN=128, BK=128, SK=32
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// - Only large tile (WM=4, WN=4, 512 threads)
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// - Threadgroup memory staging: load A/B tiles to TG memory first
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// - Swizzle decode included (required by C++ dispatch)
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// ============================================================
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#include <MetalPerformancePrimitives/MetalPerformancePrimitives.h>
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#include <metal_stdlib>
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using namespace metal;
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constant constexpr short kElemsPerFrag = 8;
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constant constexpr short kElemCols = 4;
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constant constexpr short kElemRowsJump = 8;
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inline short2 nax_get_coord(ushort lid) {
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short qid = short(lid >> 2);
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short fm = ((qid & 4) | ((short(lid) >> 1) & 3));
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short fn = ((qid & 2) | (short(lid) & 1)) * 4;
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return short2{fn, fm};
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}
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// Swizzle decode: convert swizzled grid coords to real tile coords
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inline void swizzle_decode(uint2 tgid, uint swizzle_log, uint tiles_n,
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thread uint &tid_y, thread uint &tid_x) {
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uint tile = 1u << swizzle_log;
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uint tgid_y_raw = tgid.y * tile + (tgid.x % tile);
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uint tgid_x_raw = tgid.x / tile;
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tid_y = tgid_y_raw;
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tid_x = tgid_x_raw;
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}
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template <typename T>
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inline void nax_frag_load_tg(thread T *dst, const threadgroup T *src, int ld,
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short2 sc, short off_m = 0, short off_n = 0) {
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src += (sc.y + off_m) * ld + (sc.x + off_n);
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for (short i = 0; i < 2; i++)
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for (short j = 0; j < kElemCols; j++)
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dst[i * kElemCols + j] = src[(i * kElemRowsJump) * ld + j];
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}
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inline void nax_frag_store_int32(const thread int32_t *src, device int32_t *dst,
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int ld, short2 sc, short off_m, short off_n,
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uint M, uint N, uint m_base, uint n_base) {
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for (short i = 0; i < 2; i++)
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for (short j = 0; j < kElemCols; j++) {
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uint mi = m_base + sc.y + off_m + i * kElemRowsJump;
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uint ni = n_base + sc.x + off_n + j;
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if (mi < M && ni < N)
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dst[(sc.y + off_m + i * kElemRowsJump) * ld + (sc.x + off_n + j)] =
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src[i * kElemCols + j];
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}
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}
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inline void nax_frag_store_dequant(const thread int32_t *src, device half *dst,
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int ld, short2 sc, short off_m, short off_n,
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uint M, uint N, uint m_base, uint n_base,
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const device float *scale_a,
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const device float *scale_w) {
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for (short i = 0; i < 2; i++)
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for (short j = 0; j < kElemCols; j++) {
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uint mi = m_base + sc.y + off_m + i * kElemRowsJump;
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uint ni = n_base + sc.x + off_n + j;
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if (mi < M && ni < N) {
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float val = float(src[i * kElemCols + j]) * scale_a[mi] * scale_w[ni];
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dst[(sc.y + off_m + i * kElemRowsJump) * ld + (sc.x + off_n + j)] =
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half(val);
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}
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}
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}
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// Step 1: TG-staged GEMM, BM=128 only
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template <int BM, int BN, int BK, int SK, int WM, int WN>
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void step1_gemm_int32_impl(
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const device int8_t *A, const device int8_t *B,
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device int32_t *C, uint M, uint N, uint K,
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uint swizzle_log, uint tiles_m, uint tiles_n,
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threadgroup int8_t *tg_A,
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threadgroup int8_t *tg_B,
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uint2 tgid, uint sgid, uint lid, uint tid_in_tg) {
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constexpr int SM = BM / WM; // 32
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constexpr int SN = BN / WN; // 32
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constexpr short TM = SM / 16; // 2
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constexpr short TN = SN / 16; // 2
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constexpr short TK = SK / 16; // 2
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constexpr int NUM_THREADS = WM * WN * 32; // 512
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uint tid_y, tid_x;
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swizzle_decode(tgid, swizzle_log, tiles_n, tid_y, tid_x);
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if (tid_x >= tiles_n || tid_y >= tiles_m) return;
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short2 sc = nax_get_coord(ushort(lid));
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uint sg_row = sgid / WN;
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uint sg_col = sgid % WN;
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uint m_base = tid_y * BM + sg_row * SM;
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uint n_base = tid_x * BN + sg_col * SN;
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constexpr auto desc = mpp::tensor_ops::matmul2d_descriptor(
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16, 32, 16, false, false, true,
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mpp::tensor_ops::matmul2d_descriptor::mode::multiply_accumulate);
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mpp::tensor_ops::matmul2d<desc, metal::execution_simdgroup> gemm_op;
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auto ct_a = gemm_op.get_left_input_cooperative_tensor<int8_t, int8_t, int32_t>();
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auto ct_b = gemm_op.get_right_input_cooperative_tensor<int8_t, int8_t, int32_t>();
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auto ct_c = gemm_op.get_destination_cooperative_tensor<decltype(ct_a), decltype(ct_b), int32_t>();
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int32_t c_frags[TM * TN][kElemsPerFrag];
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for (int f = 0; f < TM * TN; f++)
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for (int i = 0; i < kElemsPerFrag; i++)
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c_frags[f][i] = 0;
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for (uint k_base = 0; k_base < K; k_base += BK) {
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// Cooperative load A[BM, BK] → threadgroup
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uint a_elems = BM * BK;
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for (uint idx = tid_in_tg; idx < a_elems; idx += NUM_THREADS) {
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uint row = idx / BK;
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uint col = idx % BK;
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uint gm = tid_y * BM + row;
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uint gk = k_base + col;
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tg_A[row * BK + col] = (gm < M && gk < K) ? A[gm * K + gk] : int8_t(0);
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}
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// Cooperative load B[BK, BN] → threadgroup
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uint b_elems = BK * BN;
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for (uint idx = tid_in_tg; idx < b_elems; idx += NUM_THREADS) {
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uint row = idx / BN;
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uint col = idx % BN;
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uint gk = k_base + row;
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uint gn = tid_x * BN + col;
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tg_B[row * BN + col] = (gk < K && gn < N) ? B[gk * N + gn] : int8_t(0);
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}
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threadgroup_barrier(mem_flags::mem_threadgroup);
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for (int kk1 = 0; kk1 < BK; kk1 += SK) {
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int8_t a_frags[TM][TK][kElemsPerFrag];
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int8_t b_frags[TK][TN][kElemsPerFrag];
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for (short mm = 0; mm < TM; mm++)
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for (short kk = 0; kk < TK; kk++)
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nax_frag_load_tg(a_frags[mm][kk],
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tg_A + (sg_row * SM) * BK + kk1,
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BK, sc, short(mm * 16), short(kk * 16));
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for (short kk = 0; kk < TK; kk++)
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for (short nn = 0; nn < TN; nn++)
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nax_frag_load_tg(b_frags[kk][nn],
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tg_B + kk1 * BN + (sg_col * SN),
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BN, sc, short(kk * 16), short(nn * 16));
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for (short mm = 0; mm < TM; mm++) {
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for (short nn = 0; nn < TN; nn += 2) {
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for (short kk = 0; kk < TK; kk++) {
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for (short i = 0; i < kElemsPerFrag; i++) ct_a[i] = a_frags[mm][kk][i];
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for (short i = 0; i < kElemsPerFrag; i++) {
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ct_b[i] = b_frags[kk][nn][i];
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ct_b[kElemsPerFrag + i] = b_frags[kk][nn + 1][i];
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}
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short c0 = mm * TN + nn, c1 = c0 + 1;
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for (short i = 0; i < kElemsPerFrag; i++) {
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ct_c[i] = c_frags[c0][i];
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ct_c[kElemsPerFrag + i] = c_frags[c1][i];
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}
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gemm_op.run(ct_a, ct_b, ct_c);
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for (short i = 0; i < kElemsPerFrag; i++) {
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c_frags[c0][i] = ct_c[i];
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c_frags[c1][i] = ct_c[kElemsPerFrag + i];
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}
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}
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}
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}
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}
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threadgroup_barrier(mem_flags::mem_threadgroup);
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}
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device int32_t *D = C + m_base * N + n_base;
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for (short mm = 0; mm < TM; mm++)
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for (short nn = 0; nn < TN; nn++)
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nax_frag_store_int32(c_frags[mm * TN + nn], D, int(N), sc,
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short(mm * 16), short(nn * 16), M, N, m_base, n_base);
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}
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// Dequant version
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template <int BM, int BN, int BK, int SK, int WM, int WN>
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void step1_gemm_dequant_impl(
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const device int8_t *A, const device int8_t *B,
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device half *C, uint M, uint N, uint K,
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const device float *scale_a, const device float *scale_w,
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uint swizzle_log, uint tiles_m, uint tiles_n,
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threadgroup int8_t *tg_A,
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threadgroup int8_t *tg_B,
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uint2 tgid, uint sgid, uint lid, uint tid_in_tg) {
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constexpr int SM = BM / WM;
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constexpr int SN = BN / WN;
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constexpr short TM = SM / 16;
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constexpr short TN = SN / 16;
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constexpr short TK = SK / 16;
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constexpr int NUM_THREADS = WM * WN * 32;
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uint tid_y, tid_x;
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swizzle_decode(tgid, swizzle_log, tiles_n, tid_y, tid_x);
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if (tid_x >= tiles_n || tid_y >= tiles_m) return;
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short2 sc = nax_get_coord(ushort(lid));
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uint sg_row = sgid / WN;
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uint sg_col = sgid % WN;
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uint m_base = tid_y * BM + sg_row * SM;
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uint n_base = tid_x * BN + sg_col * SN;
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constexpr auto desc = mpp::tensor_ops::matmul2d_descriptor(
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16, 32, 16, false, false, true,
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mpp::tensor_ops::matmul2d_descriptor::mode::multiply_accumulate);
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mpp::tensor_ops::matmul2d<desc, metal::execution_simdgroup> gemm_op;
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auto ct_a = gemm_op.get_left_input_cooperative_tensor<int8_t, int8_t, int32_t>();
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auto ct_b = gemm_op.get_right_input_cooperative_tensor<int8_t, int8_t, int32_t>();
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auto ct_c = gemm_op.get_destination_cooperative_tensor<decltype(ct_a), decltype(ct_b), int32_t>();
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int32_t c_frags[TM * TN][kElemsPerFrag];
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for (int f = 0; f < TM * TN; f++)
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for (int i = 0; i < kElemsPerFrag; i++)
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c_frags[f][i] = 0;
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for (uint k_base = 0; k_base < K; k_base += BK) {
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uint a_elems = BM * BK;
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for (uint idx = tid_in_tg; idx < a_elems; idx += NUM_THREADS) {
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uint row = idx / BK;
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uint col = idx % BK;
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uint gm = tid_y * BM + row;
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uint gk = k_base + col;
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tg_A[row * BK + col] = (gm < M && gk < K) ? A[gm * K + gk] : int8_t(0);
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}
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uint b_elems = BK * BN;
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for (uint idx = tid_in_tg; idx < b_elems; idx += NUM_THREADS) {
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uint row = idx / BN;
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uint col = idx % BN;
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uint gk = k_base + row;
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uint gn = tid_x * BN + col;
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tg_B[row * BN + col] = (gk < K && gn < N) ? B[gk * N + gn] : int8_t(0);
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}
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threadgroup_barrier(mem_flags::mem_threadgroup);
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for (int kk1 = 0; kk1 < BK; kk1 += SK) {
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int8_t a_frags[TM][TK][kElemsPerFrag];
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int8_t b_frags[TK][TN][kElemsPerFrag];
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for (short mm = 0; mm < TM; mm++)
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for (short kk = 0; kk < TK; kk++)
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nax_frag_load_tg(a_frags[mm][kk],
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tg_A + (sg_row * SM) * BK + kk1,
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BK, sc, short(mm * 16), short(kk * 16));
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for (short kk = 0; kk < TK; kk++)
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for (short nn = 0; nn < TN; nn++)
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nax_frag_load_tg(b_frags[kk][nn],
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tg_B + kk1 * BN + (sg_col * SN),
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BN, sc, short(kk * 16), short(nn * 16));
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for (short mm = 0; mm < TM; mm++) {
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for (short nn = 0; nn < TN; nn += 2) {
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for (short kk = 0; kk < TK; kk++) {
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for (short i = 0; i < kElemsPerFrag; i++) ct_a[i] = a_frags[mm][kk][i];
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for (short i = 0; i < kElemsPerFrag; i++) {
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ct_b[i] = b_frags[kk][nn][i];
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ct_b[kElemsPerFrag + i] = b_frags[kk][nn + 1][i];
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}
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short c0 = mm * TN + nn, c1 = c0 + 1;
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for (short i = 0; i < kElemsPerFrag; i++) {
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ct_c[i] = c_frags[c0][i];
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ct_c[kElemsPerFrag + i] = c_frags[c1][i];
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}
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gemm_op.run(ct_a, ct_b, ct_c);
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for (short i = 0; i < kElemsPerFrag; i++) {
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c_frags[c0][i] = ct_c[i];
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c_frags[c1][i] = ct_c[kElemsPerFrag + i];
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}
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}
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}
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}
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}
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threadgroup_barrier(mem_flags::mem_threadgroup);
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}
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device half *D = C + m_base * N + n_base;
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for (short mm = 0; mm < TM; mm++)
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for (short nn = 0; nn < TN; nn++)
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nax_frag_store_dequant(c_frags[mm * TN + nn], D, int(N), sc,
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short(mm * 16), short(nn * 16), M, N, m_base, n_base,
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scale_a, scale_w);
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}
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// ============================================================
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// Kernel entry points
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// ============================================================
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kernel void w8a8_matmul_fused_dequant(
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const device int8_t *A [[buffer(0)]],
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const device int8_t *B [[buffer(1)]],
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device half *C [[buffer(2)]],
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constant uint &M [[buffer(3)]],
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constant uint &N [[buffer(4)]],
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constant uint &K [[buffer(5)]],
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const device float *scale_a [[buffer(6)]],
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const device float *scale_w [[buffer(7)]],
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constant uint &swizzle_log [[buffer(8)]],
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constant uint &tiles_m [[buffer(9)]],
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constant uint &tiles_n [[buffer(10)]],
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uint2 tgid [[threadgroup_position_in_grid]],
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uint sgid [[simdgroup_index_in_threadgroup]],
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uint lid [[thread_index_in_simdgroup]],
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uint tid_in_tg [[thread_index_in_threadgroup]]) {
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threadgroup int8_t tg_A[128 * 128];
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threadgroup int8_t tg_B[128 * 128];
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step1_gemm_dequant_impl<128, 128, 128, 32, 4, 4>(
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A, B, C, M, N, K, scale_a, scale_w, swizzle_log, tiles_m, tiles_n,
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tg_A, tg_B, tgid, sgid, lid, tid_in_tg);
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}
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kernel void w8a8_matmul_fused_dequant_small(
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const device int8_t *A [[buffer(0)]],
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const device int8_t *B [[buffer(1)]],
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device half *C [[buffer(2)]],
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constant uint &M [[buffer(3)]],
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constant uint &N [[buffer(4)]],
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constant uint &K [[buffer(5)]],
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const device float *scale_a [[buffer(6)]],
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const device float *scale_w [[buffer(7)]],
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constant uint &swizzle_log [[buffer(8)]],
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constant uint &tiles_m [[buffer(9)]],
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constant uint &tiles_n [[buffer(10)]],
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uint2 tgid [[threadgroup_position_in_grid]],
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uint sgid [[simdgroup_index_in_threadgroup]],
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uint lid [[thread_index_in_simdgroup]],
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uint tid_in_tg [[thread_index_in_threadgroup]]) {
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// Step 1 has no small tile — fallback to large tile
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threadgroup int8_t tg_A[128 * 128];
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threadgroup int8_t tg_B[128 * 128];
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step1_gemm_dequant_impl<128, 128, 128, 32, 4, 4>(
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A, B, C, M, N, K, scale_a, scale_w, swizzle_log, tiles_m, tiles_n,
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tg_A, tg_B, tgid, sgid, lid, tid_in_tg);
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}
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kernel void int8_matmul_int32(
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const device int8_t *A [[buffer(0)]],
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const device int8_t *B [[buffer(1)]],
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device int32_t *C [[buffer(2)]],
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constant uint &M [[buffer(3)]],
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constant uint &N [[buffer(4)]],
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constant uint &K [[buffer(5)]],
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constant uint &swizzle_log [[buffer(6)]],
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constant uint &tiles_m [[buffer(7)]],
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constant uint &tiles_n [[buffer(8)]],
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uint2 tgid [[threadgroup_position_in_grid]],
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uint sgid [[simdgroup_index_in_threadgroup]],
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uint lid [[thread_index_in_simdgroup]],
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uint tid_in_tg [[thread_index_in_threadgroup]]) {
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threadgroup int8_t tg_A[128 * 128];
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threadgroup int8_t tg_B[128 * 128];
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step1_gemm_int32_impl<128, 128, 128, 32, 4, 4>(
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A, B, C, M, N, K, swizzle_log, tiles_m, tiles_n,
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tg_A, tg_B, tgid, sgid, lid, tid_in_tg);
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}
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kernel void int8_matmul_int32_small(
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const device int8_t *A [[buffer(0)]],
|
||||
const device int8_t *B [[buffer(1)]],
|
||||
device int32_t *C [[buffer(2)]],
|
||||
constant uint &M [[buffer(3)]],
|
||||
constant uint &N [[buffer(4)]],
|
||||
constant uint &K [[buffer(5)]],
|
||||
constant uint &swizzle_log [[buffer(6)]],
|
||||
constant uint &tiles_m [[buffer(7)]],
|
||||
constant uint &tiles_n [[buffer(8)]],
|
||||
uint2 tgid [[threadgroup_position_in_grid]],
|
||||
uint sgid [[simdgroup_index_in_threadgroup]],
|
||||
uint lid [[thread_index_in_simdgroup]],
|
||||
uint tid_in_tg [[thread_index_in_threadgroup]]) {
|
||||
// Step 1 has no small tile — fallback to large tile
|
||||
threadgroup int8_t tg_A[128 * 128];
|
||||
threadgroup int8_t tg_B[128 * 128];
|
||||
step1_gemm_int32_impl<128, 128, 128, 32, 4, 4>(
|
||||
A, B, C, M, N, K, swizzle_log, tiles_m, tiles_n,
|
||||
tg_A, tg_B, tgid, sgid, lid, tid_in_tg);
|
||||
}
|
||||
@@ -0,0 +1,87 @@
|
||||
// ============================================================
|
||||
// Per-token quantization: FP16 → INT8 + float32 scale
|
||||
// Target: Apple M5, Metal 4
|
||||
//
|
||||
// Each threadgroup handles one row (one token).
|
||||
// Threads cooperate to find absmax via simdgroup reduce,
|
||||
// then quantize in parallel.
|
||||
//
|
||||
// Host dispatch:
|
||||
// threadgroup = (min(256, ceil(K/32)*32), 1, 1)
|
||||
// grid = (M, 1, 1)
|
||||
// ============================================================
|
||||
|
||||
#include <metal_stdlib>
|
||||
using namespace metal;
|
||||
|
||||
kernel void
|
||||
quantize_per_token(const device half *X [[buffer(0)]], // [M, K] FP16 input
|
||||
device int8_t *A [[buffer(1)]], // [M, K] INT8 output
|
||||
device float *scale [[buffer(2)]], // [M] float32 scale
|
||||
constant uint &M [[buffer(3)]],
|
||||
constant uint &K [[buffer(4)]],
|
||||
uint gid [[threadgroup_position_in_grid]], // row index
|
||||
uint lid [[thread_index_in_threadgroup]],
|
||||
uint tg_size [[threads_per_threadgroup]]) {
|
||||
if (gid >= M) {
|
||||
return;
|
||||
}
|
||||
|
||||
const device half *row_in = X + gid * K;
|
||||
device int8_t *row_out = A + gid * K;
|
||||
|
||||
// Step 1: Find local absmax
|
||||
float local_max = 0.0f;
|
||||
for (uint i = lid; i < K; i += tg_size) {
|
||||
float v = abs(float(row_in[i]));
|
||||
local_max = max(local_max, v);
|
||||
}
|
||||
|
||||
// Step 2: Simdgroup reduce max
|
||||
float sg_max = simd_max(local_max);
|
||||
|
||||
// Step 3: Threadgroup reduce across simdgroups via shared memory
|
||||
threadgroup float sg_maxes[8]; // up to 8 simdgroups (256/32)
|
||||
threadgroup float shared_scale;
|
||||
threadgroup float shared_inv_scale;
|
||||
uint sg_id = lid / 32;
|
||||
uint sg_lid = lid % 32;
|
||||
if (sg_lid == 0) {
|
||||
sg_maxes[sg_id] = sg_max;
|
||||
}
|
||||
threadgroup_barrier(mem_flags::mem_threadgroup);
|
||||
|
||||
// Final reduce (first simdgroup only)
|
||||
if (sg_id == 0) {
|
||||
float row_max = 0.0f;
|
||||
uint num_sgs = (tg_size + 31) / 32;
|
||||
if (sg_lid < num_sgs) {
|
||||
row_max = sg_maxes[sg_lid];
|
||||
}
|
||||
row_max = simd_max(row_max);
|
||||
|
||||
// Compute and broadcast scale
|
||||
float s = row_max / 255.0f;
|
||||
if (s == 0.0f) {
|
||||
s = 1.0f;
|
||||
}
|
||||
|
||||
if (sg_lid == 0) {
|
||||
shared_scale = s;
|
||||
shared_inv_scale = 1.0f / s;
|
||||
// Store scale to output
|
||||
scale[gid] = s;
|
||||
}
|
||||
}
|
||||
threadgroup_barrier(mem_flags::mem_threadgroup);
|
||||
|
||||
// Step 4: All threads read broadcasted scale
|
||||
float inv_s = shared_inv_scale;
|
||||
|
||||
// Step 5: Quantize
|
||||
for (uint i = lid; i < K; i += tg_size) {
|
||||
float v = float(row_in[i]) * inv_s;
|
||||
v = clamp(round(v), -128.0f, 127.0f);
|
||||
row_out[i] = int8_t(v);
|
||||
}
|
||||
}
|
||||
Reference in New Issue
Block a user