#include "ggml-sycl/backend.hpp"
#include "ggml-sycl/common.hpp"
#include "ggml-sycl/element_wise.hpp"
+#include "ggml-sycl/norm.hpp"
#include "ggml-sycl/presets.hpp"
#include "ggml-sycl/gemm.hpp"
#include "ggml-sycl/set_rows.hpp"
ggml_sycl_op_rms_norm(ctx, dst);
}
+static void ggml_sycl_rms_norm_back(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
+ scope_op_debug_print scope_dbg_print(__func__, dst, /*num_src=*/2);
+ ggml_sycl_op_rms_norm_back(ctx, dst);
+}
+
static void ggml_sycl_l2_norm(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
scope_op_debug_print scope_dbg_print(__func__, dst, /*num_src=*/1);
ggml_sycl_op_l2_norm(ctx, dst);
case GGML_OP_LEAKY_RELU:
ggml_sycl_leaky_relu(ctx, dst);
break;
+ case GGML_OP_RMS_NORM_BACK:
+ ggml_sycl_rms_norm_back(ctx, dst);
+ break;
case GGML_OP_RMS_NORM:
ggml_sycl_rms_norm(ctx, dst);
break;
return ggml_is_contiguous(op->src[0]);
case GGML_OP_RMS_NORM:
return ((op->src[0]->ne[0] % WARP_SIZE) == 0);
+ case GGML_OP_RMS_NORM_BACK:
+ return ((op->src[0]->ne[0] % WARP_SIZE) == 0);
case GGML_OP_SCALE:
return true;
case GGML_OP_CONT:
rms_norm_f32_sycl(src0_dd, dst_dd, ne00, ne01, ne02, ne03, s01, s02, s03, eps, main_stream, ctx.device);
}
+void ggml_sycl_op_rms_norm_back(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
+ scope_op_debug_print scope_dbg_print(__func__, dst, /*num_src=*/2);
+
+ GGML_ASSERT(dst->src[0]->type == GGML_TYPE_F32); // dz
+ GGML_ASSERT(dst->src[1]->type == GGML_TYPE_F32); // x
+ GGML_ASSERT(dst->type == GGML_TYPE_F32);
+
+ float eps = 1e-5f;
+ std::memcpy(&eps, dst->op_params, sizeof(float));
+ if (!(eps > 0.0f) || !std::isfinite(eps)) eps = 1e-5f;
+
+ const float * g_base = static_cast<const float *>(dst->src[0]->data); // dz
+ const float * x_base = static_cast<const float *>(dst->src[1]->data); // x
+ float * dx_base = static_cast< float *>(dst->data);
+
+ const int64_t D = dst->ne[0];
+ const int64_t n1 = dst->ne[1], n2 = dst->ne[2], n3 = dst->ne[3]; (void) n3;
+ const int64_t N = ggml_nrows(dst);
+ if (D == 0 || N == 0) return;
+
+ const ggml_tensor *G = dst->src[0];
+ const ggml_tensor *X = dst->src[1];
+ const int ts = (int) ggml_type_size(X->type);
+ GGML_ASSERT((size_t) X->nb[0] == (size_t) ts);
+ GGML_ASSERT((size_t) G->nb[0] == (size_t) ts);
+ GGML_ASSERT((size_t) dst->nb[0] == (size_t) ts);
+
+ const int64_t xs1 = X->nb[1] / ts, xs2 = X->nb[2] / ts, xs3 = X->nb[3] / ts;
+ const int64_t gs1 = G->nb[1] / ts, gs2 = G->nb[2] / ts, gs3 = G->nb[3] / ts;
+ const int64_t ds1 = dst->nb[1] / ts, ds2 = dst->nb[2] / ts, ds3 = dst->nb[3] / ts;
+
+ dpct::queue_ptr q = ctx.stream();
+
+ // work-group size: multiple of WARP_SIZE, capped by device and 256, and not larger than D
+ const int device_max_wg = ggml_sycl_info().max_work_group_sizes[ctx.device];
+ auto roundup = [](int v, int m) { return ((v + m - 1) / m) * m; };
+ int wg_cap = 256;
+ if (device_max_wg > 0) wg_cap = std::min(wg_cap, device_max_wg);
+ int WG = std::max(WARP_SIZE, std::min(roundup((int)std::min<int64_t>(D, wg_cap), WARP_SIZE), wg_cap));
+
+ // FP32 path: per-thread compensated accumulation + hierarchical reduction
+ q->submit([&](sycl::handler &cgh) {
+ const int nwarps_loc = std::max(1, WG / WARP_SIZE);
+ // store one partial value per warp (xx and xg) for cross-warp reduction
+ auto l_xx = sycl::local_accessor<sycl::float2, 1>(sycl::range<1>(nwarps_loc), cgh);
+ auto l_xg = sycl::local_accessor<sycl::float2, 1>(sycl::range<1>(nwarps_loc), cgh);
+
+ cgh.parallel_for(
+ sycl::nd_range<3>(sycl::range<3>(1, 1, N) * sycl::range<3>(1, 1, WG),
+ sycl::range<3>(1, 1, WG)),
+ [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
+ const int row = item_ct1.get_group(2);
+ const int tid = item_ct1.get_local_id(2);
+
+ const int64_t i1 = row % n1;
+ const int64_t i2 = (row / n1) % n2;
+ const int64_t i3 = row / (n1 * n2);
+
+ const float *__restrict x_row = x_base + i3 * xs3 + i2 * xs2 + i1 * xs1;
+ const float *__restrict g_row = g_base + i3 * gs3 + i2 * gs2 + i1 * gs1;
+ float *__restrict d_row = dx_base + i3 * ds3 + i2 * ds2 + i1 * ds1;
+
+ // per-thread accumulation (compensated by default)
+ float sum_xx = 0.f, sum_xg = 0.f;
+#ifndef GGML_SYCL_RMS_BACK_FAST
+ float c_xx = 0.f, c_xg = 0.f;
+#endif
+ for (int64_t col = tid; col < D; col += WG) {
+ const float xv = x_row[col];
+ const float gv = g_row[col];
+#ifdef GGML_SYCL_RMS_BACK_FAST
+ sum_xx += xv * xv;
+ sum_xg += xv * gv;
+#else
+ float y1 = xv * xv - c_xx;
+ float t1 = sum_xx + y1;
+ c_xx = (t1 - sum_xx) - y1;
+ sum_xx = t1;
+
+ float y2 = xv * gv - c_xg;
+ float t2 = sum_xg + y2;
+ c_xg = (t2 - sum_xg) - y2;
+ sum_xg = t2;
+#endif
+ }
+
+ // warp-level reduction
+ sycl::float2 xx = sycl::float2(sum_xx,
+#ifndef GGML_SYCL_RMS_BACK_FAST
+ c_xx
+#else
+ 0.f
+#endif
+ );
+ sycl::float2 xg = sycl::float2(sum_xg,
+#ifndef GGML_SYCL_RMS_BACK_FAST
+ c_xg
+#else
+ 0.f
+#endif
+ );
+ xx = warp_reduce_sum(xx, item_ct1);
+ xg = warp_reduce_sum(xg, item_ct1);
+
+ // cross-warp reduction using local memory (single barrier)
+ const auto sub_group = item_ct1.get_sub_group();
+ const auto sg_id = sub_group.get_group_linear_id();
+ const auto wi_in_sg = sub_group.get_local_linear_id();
+ const int nthreads = item_ct1.get_local_range(2);
+ const int nwarps = nthreads / WARP_SIZE;
+
+ sycl::float2 xx_total = xx;
+ sycl::float2 xg_total = xg;
+ if (nwarps > 1) {
+ if (wi_in_sg == 0) {
+ l_xx[sg_id] = xx;
+ l_xg[sg_id] = xg;
+ }
+ item_ct1.barrier(sycl::access::fence_space::local_space);
+
+ if (sg_id == 0) {
+ const unsigned wi_u = wi_in_sg;
+ sycl::float2 xx_first = (wi_u < static_cast<unsigned>(nwarps)) ? l_xx[wi_u] : sycl::float2(0.f, 0.f);
+ sycl::float2 xg_first = (wi_u < static_cast<unsigned>(nwarps)) ? l_xg[wi_u] : sycl::float2(0.f, 0.f);
+ xx_total = warp_reduce_sum(xx_first, item_ct1);
+ xg_total = warp_reduce_sum(xg_first, item_ct1);
+ } else {
+ // other subgroups keep their local totals; they'll be ignored
+ xx_total = xx;
+ xg_total = xg;
+ }
+ // ensure all threads see the first-subgroup result via broadcast below
+ }
+
+ // compute inv_r and coeff once per row and broadcast to the whole work-group
+ float inv_r = 0.f;
+ float coeff = 0.f;
+ if (tid == 0) {
+ const float sum_xx_f = xx_total.x() + xx_total.y();
+ const float sum_xdz_f = xg_total.x() + xg_total.y();
+ const float mean_eps = sum_xx_f / (float) D + eps;
+ const float sum_eps = sum_xx_f + eps * (float) D;
+ inv_r = sycl::rsqrt(mean_eps);
+ coeff = -sum_xdz_f / sum_eps;
+ }
+ inv_r = sycl::group_broadcast(item_ct1.get_group(), inv_r);
+ coeff = sycl::group_broadcast(item_ct1.get_group(), coeff);
+
+ for (int64_t col = tid; col < D; col += WG) {
+ d_row[col] = (g_row[col] + coeff * x_row[col]) * inv_r;
+ }
+ });
+ });
+
+}
+
void ggml_sycl_op_l2_norm(ggml_backend_sycl_context& ctx, ggml_tensor* dst) {
GGML_ASSERT(dst->src[0]->type == GGML_TYPE_F32);
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