--- /dev/null
+#include <sycl/sycl.hpp>
+
+#include "common.hpp"
+
+template <u_int HEAD_SIZE>
+static void gated_linear_attn_f32_kernel(const dpct::queue_ptr stream, u_int B, u_int T, u_int C, u_int H, float scale,
+ const float * k, const float * v, const float * r, const float * td,
+ const float * s, float * dst) {
+ const u_int head_size = HEAD_SIZE;
+ const u_int state_size = C * head_size;
+ const u_int n_seq_tokens = T / B;
+ sycl::range<1> block_dims((C / H));
+ sycl::range<1> grid_dims((B * H));
+ stream->submit([&](sycl::handler & cgh) {
+ /* local memory accessors*/
+ auto _k = sycl::local_accessor<float, 1>(sycl::range<1>(head_size), cgh);
+ auto _r = sycl::local_accessor<float, 1>(sycl::range<1>(head_size), cgh);
+ auto _td = sycl::local_accessor<float, 1>(sycl::range<1>(head_size), cgh);
+
+ cgh.parallel_for(sycl::nd_range<1>(grid_dims * block_dims, block_dims), [=](sycl::nd_item<1> item) {
+ u_int tid = item.get_local_id(0);
+ u_int bid = item.get_group(0);
+
+ u_int batch_i = bid / H;
+ u_int head_i = bid % H;
+
+ float state[head_size];
+
+#pragma unroll
+ for (u_int i = 0; i < head_size; i++) {
+ state[i] = s[batch_i * state_size + head_i * head_size * head_size + i * head_size + tid];
+ }
+
+ for (u_int t = batch_i * n_seq_tokens * C + head_i * head_size + tid;
+ t < (batch_i + 1) * n_seq_tokens * C + head_i * head_size + tid; t += C) {
+
+ item.barrier(sycl::access::fence_space::local_space); //sync threads
+ _k[tid] = k[t];
+ _r[tid] = r[t];
+ _td[tid] = td[t];
+ item.barrier(sycl::access::fence_space::local_space); //sync threads
+
+ const float _v = v[t];
+ float y = 0;
+
+ for (u_int j = 0; j < head_size; j += 4) {
+ const sycl::float4 & k = (sycl::float4 &) (_k[j]);
+ const sycl::float4 & r = (sycl::float4 &) (_r[j]);
+ const sycl::float4 & td = (sycl::float4 &) (_td[j]);
+ sycl::float4 & s = (sycl::float4 &) (state[j]);
+ sycl::float4 kv;
+
+ kv.x() = k.x() * _v;
+ kv.y() = k.y() * _v;
+ kv.z() = k.z() * _v;
+ kv.w() = k.w() * _v;
+
+ s.x() = s.x() * td.x() + kv.x();
+ s.y() = s.y() * td.y() + kv.y();
+ s.z() = s.z() * td.z() + kv.z();
+ s.w() = s.w() * td.w() + kv.w();
+
+ y += r.x() * s.x();
+ y += r.y() * s.y();
+ y += r.z() * s.z();
+ y += r.w() * s.w();
+ }
+ dst[t] = y * scale;
+ }
+#pragma unroll
+ for (u_int i = 0; i < head_size; i++) {
+ dst[T * C + batch_i * state_size + head_i * head_size * head_size + i * head_size + tid] = state[i];
+ }
+ });
+ });
+}
+
+void ggml_sycl_op_gated_linear_attn(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
+ const float * k_d = static_cast<const float *>(dst->src[0]->data);
+ const float * v_d = static_cast<const float *>(dst->src[1]->data);
+ const float * r_d = static_cast<const float *>(dst->src[2]->data);
+ const float * td_d = static_cast<const float *>(dst->src[3]->data);
+ const float * s_d = static_cast<const float *>(dst->src[4]->data);
+
+ const int64_t B = dst->src[4]->ne[1];
+ const int64_t T = dst->src[0]->ne[2];
+ const int64_t C = dst->ne[0];
+ const int64_t H = dst->src[0]->ne[1];
+
+ dpct::queue_ptr stream = ctx.stream();
+ GGML_ASSERT(dst->src[4]->type == GGML_TYPE_F32);
+ GGML_ASSERT(C % H == 0);
+ GGML_ASSERT(C / H == 64 || C / H == 128);
+
+ float scale;
+ memcpy(&scale, dst->op_params, sizeof(float));
+
+ float * dst_d = (float *) dst->data;
+
+ if (C / H == 64) {
+ gated_linear_attn_f32_kernel<64>(stream, B, T, C, H, scale, k_d, v_d, r_d, td_d, s_d, dst_d);
+ } else {
+ gated_linear_attn_f32_kernel<128>(stream, B, T, C, H, scale, k_d, v_d, r_d, td_d, s_d, dst_d);
+ }
+}