diff --git a/ggml/src/ggml-sycl/common.hpp b/ggml/src/ggml-sycl/common.hpp
index 298fddc103..9f0efb6535 100644
--- a/ggml/src/ggml-sycl/common.hpp
+++ b/ggml/src/ggml-sycl/common.hpp
@@ -874,4 +874,95 @@ static bool fast_fp16_available(const int cc) {
     return true;   //Intel GPUs always support FP16.
 }
 
+enum class block_reduce_method {
+    MAX,
+    SUM,
+};
+
+template<block_reduce_method method_t, typename T, int warp_size>
+struct block_reduce_policy;
+
+template <typename T, typename... Ts>
+inline constexpr bool is_any = (std::is_same_v<T, Ts> || ...);
+
+template<typename...>
+inline constexpr bool ggml_sycl_dependent_false_v = false;
+
+#define WARP_32_SIZE 32
+
+template <typename T, int warp_size> struct block_reduce_policy<block_reduce_method::SUM, T, warp_size> {
+    static T reduce(T val) {
+        if constexpr (is_any<T, float, sycl::float2, sycl::half2, int>) {
+            return warp_reduce_sum<warp_size>(val);
+        } else {
+            static_assert(ggml_sycl_dependent_false_v<T>, "Unsupported type for block reduce sum");
+        }
+    }
+
+    static T sentinel() {
+        if constexpr (std::is_same_v<T, float>) {
+            return 0.0f;
+        } else if constexpr (std::is_same_v<T, sycl::float2>) {
+            return sycl::float2(0.0f, 0.0f);
+        } else if constexpr (std::is_same_v<T, sycl::half2>) {
+            return sycl::half2(0.0f, 0.0f);
+        } else if constexpr (std::is_same_v<T, int>) {
+            return 0;
+        } else {
+            static_assert(ggml_sycl_dependent_false_v<T>, "Unsupported type for block reduce sum");
+        }
+    }
+};
+
+template <typename T, int warp_size> struct block_reduce_policy<block_reduce_method::MAX, T, warp_size> {
+    static T reduce(T val) {
+        if constexpr (is_any<T, float, sycl::half2>) {
+            return warp_reduce_max<warp_size>(val);
+        } else {
+            static_assert(ggml_sycl_dependent_false_v<T>, "Unsupported type for block reduce max");
+        }
+    }
+
+    static T sentinel() {
+        if constexpr (std::is_same_v<T, float>) {
+            return -INFINITY;
+        } else if constexpr (std::is_same_v<T, sycl::half2>) {
+            return sycl::half2(-INFINITY, -INFINITY);
+        } else {
+            static_assert(ggml_sycl_dependent_false_v<T>, "Unsupported type for block reduce max");
+        }
+    }
+};
+
+
+template <block_reduce_method reduce_method_t, int warp_size, typename T>
+static T block_reduce(T val, T * shared_vals, int block_size_template) {
+    auto item_ct1                 = sycl::ext::oneapi::this_work_item::get_nd_item<3>();
+    val                           = block_reduce_policy<reduce_method_t, T,warp_size>::reduce(val);
+    const int block_size = block_size_template == 0 ? item_ct1.get_local_range(2) : block_size_template;
+    const int nthreads = item_ct1.get_local_range(2);
+    const int nwarps = nthreads / WARP_SIZE;
+
+    if (block_size > warp_size) {
+        assert((block_size <= 1024) && (block_size % warp_size) == 0);
+        const int warp_id = item_ct1.get_local_id(2) / warp_size;
+        const int lane_id = item_ct1.get_local_id(2) % warp_size;
+        if (lane_id == 0) {
+            shared_vals[warp_id] = val;
+        }
+        item_ct1.barrier(sycl::access::fence_space::local_space);
+
+        size_t nreduce = nwarps / WARP_SIZE;
+        float tmp = 0.f;
+        if (lane_id < (static_cast<int>(block_size) / warp_size)) {
+            for (size_t i = 0; i < nreduce; i += 1)
+            {
+                tmp += shared_vals[lane_id + i * WARP_SIZE];
+            }
+        }
+        return block_reduce_policy<reduce_method_t, T, warp_size>::reduce(tmp);
+    }
+    return val;
+}
+
 #endif // GGML_SYCL_COMMON_HPP
diff --git a/ggml/src/ggml-sycl/element_wise.cpp b/ggml/src/ggml-sycl/element_wise.cpp
index 00d54b83f8..acd51bf45b 100644
--- a/ggml/src/ggml-sycl/element_wise.cpp
+++ b/ggml/src/ggml-sycl/element_wise.cpp
@@ -9,23 +9,32 @@
 #define SYCL_LOCAL_ID_CALC(ITEM, IDX) \
     (ITEM.get_local_range(IDX) * ITEM.get_group(IDX) + ITEM.get_local_id(IDX))
 
+static void acc_f32(const float * x, const float * y, float * dst, const int64_t ne,
+        const int64_t ne10, const int64_t ne11, const int64_t ne12, const int64_t ne13,
+        const int64_t s11, const int64_t s12, const int64_t s13, const int64_t offset) {
+    auto item_ct1 = sycl::ext::oneapi::this_work_item::get_nd_item<3>();
+    const int64_t i = SYCL_LOCAL_ID_CALC(item_ct1, 2);
 
-static void acc_f32(const float * x, const float * y, float * dst, const int ne,
-    const int ne10, const int ne11, const int ne12,
-    const int nb1, const int nb2, int offset, const sycl::nd_item<1> &item_ct1) {
-    const int i = SYCL_LOCAL_ID_CALC(item_ct1, 0);
     if (i >= ne) {
         return;
     }
-    int src1_idx = i - offset;
-    int oz = src1_idx / nb2;
-    int oy = (src1_idx - (oz * nb2)) / nb1;
-    int ox = src1_idx % nb1;
-    if (src1_idx >= 0 && ox < ne10 && oy < ne11 && oz < ne12) {
-        dst[i] = x[i] + y[ox + oy * ne10 + oz * ne10 * ne11];
-    } else {
-        dst[i] = x[i];
+
+    int64_t src1_idx = i - offset;
+
+    int64_t tmp = src1_idx;
+    const int64_t i13 = tmp / s13;
+    tmp -= i13 * s13;
+    const int64_t i12 = tmp / s12;
+    tmp -= i12 * s12;
+    const int64_t i11 = tmp / s11;
+    tmp -= i11 * s11;
+    const int64_t i10 = tmp;
+
+    float val = x[i];
+    if (src1_idx >= 0 && i10 < ne10 && i11 < ne11 && i12 < ne12 && i13 < ne13) {
+        val += y[((i13*ne12 + i12) * ne11 + i11) * ne10 + i10];
     }
+    dst[i] = val;
 }
 
 /* Unary OP funcs */
@@ -364,18 +373,15 @@ static void gated_op_fused_geglu_quick(const T * x, const T * g, T * dst, const
 
 namespace ggml_sycl_detail {
 static void acc_f32_sycl(const float *x, const float *y, float *dst,
-                         const int n_elements, const int ne10, const int ne11,
-                         const int ne12, const int nb1, const int nb2,
-                         const int offset, queue_ptr stream) {
-    int num_blocks = ceil_div(n_elements, SYCL_ACC_BLOCK_SIZE);
-    stream->parallel_for(
-        sycl::nd_range<1>(sycl::range<1>(num_blocks) *
-                              sycl::range<1>(SYCL_ACC_BLOCK_SIZE),
-                          sycl::range<1>(SYCL_ACC_BLOCK_SIZE)),
-        [=](sycl::nd_item<1> item_ct1) {
-            acc_f32(x, y, dst, n_elements, ne10, ne11, ne12, nb1, nb2, offset,
-                    item_ct1);
-        });
+                         const int64_t n_elements, const int64_t ne10, const int64_t ne11,
+                         const int64_t ne12, const int64_t ne13, const int64_t s1, const int64_t s2, const int64_t s3,
+                         const int64_t offset, queue_ptr stream) {
+    const int num_blocks = (n_elements + SYCL_ACC_BLOCK_SIZE - 1) / SYCL_ACC_BLOCK_SIZE;
+    stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_ACC_BLOCK_SIZE),
+                                           sycl::range<3>(1, 1, SYCL_ACC_BLOCK_SIZE)),
+                         [=](sycl::nd_item<3> item_ct1) {
+                             acc_f32(x, y, dst, n_elements, ne10, ne11, ne12, ne13, s1, s2, s3, offset);
+                         });
 }
 
 template<typename T>
@@ -402,25 +408,19 @@ static void upscale_sycl(const T *x, T *dst, const int nb00, const int nb01,
 
 template<typename KernelInvoker, typename... Args>
 static inline void dispatch_ggml_sycl_op_unary(ggml_backend_sycl_context & ctx, ggml_tensor * dst, KernelInvoker kernel_invoker, Args&&... args) {
-#if defined (GGML_SYCL_F16)
     GGML_ASSERT(dst->src[0]->type == GGML_TYPE_F32 || dst->src[0]->type == GGML_TYPE_F16);
     GGML_ASSERT(dst->type == GGML_TYPE_F32 || dst->type == GGML_TYPE_F16);
-#else
-    GGML_ASSERT(dst->src[0]->type == GGML_TYPE_F32);
-    GGML_ASSERT(dst->type == GGML_TYPE_F32);
-#endif
     GGML_ASSERT(dst->src[0]->type == dst->type);
+
     dpct::queue_ptr main_stream = ctx.stream();
     SYCL_CHECK(ggml_sycl_set_device(ctx.device));
     switch (dst->type) {
-#if defined (GGML_SYCL_F16)
         case GGML_TYPE_F16:
             {
                 auto data_pts = cast_data<sycl::half>(dst);
                 kernel_invoker(data_pts.src, data_pts.dst, (int)ggml_nelements(dst->src[0]), main_stream, std::forward<Args>(args)...);
                 break;
             }
-#endif
         case GGML_TYPE_F32:
             {
                 auto data_pts = cast_data<float>(dst);
@@ -434,14 +434,10 @@ static inline void dispatch_ggml_sycl_op_unary(ggml_backend_sycl_context & ctx,
 
 template<typename KernelInvoker, typename... Args>
 static inline void dispatch_ggml_sycl_op_fused_glu(ggml_backend_sycl_context & ctx, ggml_tensor * dst, KernelInvoker kernel_invoker, Args&&... args) {
-#if defined (GGML_SYCL_F16)
     GGML_ASSERT(dst->src[0]->type == GGML_TYPE_F32 || dst->src[0]->type == GGML_TYPE_F16);
     GGML_ASSERT(dst->type == GGML_TYPE_F32 || dst->type == GGML_TYPE_F16);
-#else
-    GGML_ASSERT(dst->src[0]->type == GGML_TYPE_F32);
-    GGML_ASSERT(dst->type == GGML_TYPE_F32);
-#endif
     GGML_ASSERT(dst->src[0]->type == dst->type);
+
     dpct::queue_ptr main_stream = ctx.stream();
     SYCL_CHECK(ggml_sycl_set_device(ctx.device));
     const ggml_tensor * src0 = dst->src[0];
@@ -463,7 +459,6 @@ static inline void dispatch_ggml_sycl_op_fused_glu(ggml_backend_sycl_context & c
         GGML_ASSERT(src0->type == src1->type);
     }
     switch (dst->type) {
-#if defined (GGML_SYCL_F16)
         case GGML_TYPE_F16:
             {
                 sycl::half * src0_p = (sycl::half *) src0_d;
@@ -484,7 +479,6 @@ static inline void dispatch_ggml_sycl_op_fused_glu(ggml_backend_sycl_context & c
                                std::forward<Args>(args)...);
                 break;
             }
-#endif
         case GGML_TYPE_F32:
             {
                 float * src0_p = (float *) src0_d;
@@ -513,13 +507,9 @@ static inline void dispatch_ggml_sycl_op_fused_glu(ggml_backend_sycl_context & c
 
 template<typename KernelInvoker, typename... Args>
 static inline void dispatch_ggml_sycl_op_upscale(ggml_backend_sycl_context & ctx, ggml_tensor * dst, KernelInvoker kernel_invoker, Args&&... args) {
-#if defined (GGML_SYCL_F16)
     GGML_ASSERT(dst->src[0]->type == GGML_TYPE_F32 || dst->src[0]->type == GGML_TYPE_F16);
     GGML_ASSERT(dst->type == GGML_TYPE_F32 || dst->type == GGML_TYPE_F16);
-#else
-    GGML_ASSERT(dst->src[0]->type == GGML_TYPE_F32);
-    GGML_ASSERT(dst->type == GGML_TYPE_F32);
-#endif
+
     GGML_ASSERT(dst->src[0]->type == dst->type);
 
     dpct::queue_ptr main_stream = ctx.stream();
@@ -530,7 +520,6 @@ static inline void dispatch_ggml_sycl_op_upscale(ggml_backend_sycl_context & ctx
     const float sf2 = (float) dst->ne[2] / dst->src[0]->ne[2];
     const float sf3 = (float) dst->ne[3] / dst->src[0]->ne[3];
     switch (dst->type) {
-#if defined (GGML_SYCL_F16)
         case GGML_TYPE_F16:
             {
                 auto data_pts = cast_data<sycl::half>(dst);
@@ -539,7 +528,6 @@ static inline void dispatch_ggml_sycl_op_upscale(ggml_backend_sycl_context & ctx
                                main_stream, std::forward<Args>(args)...);
                 break;
             }
-#endif
         case GGML_TYPE_F32:
             {
                 auto data_pts = cast_data<float>(dst);
@@ -868,22 +856,31 @@ static inline void ggml_sycl_op_trunc(ggml_backend_sycl_context & ctx, ggml_tens
 }
 
 static inline void ggml_sycl_op_acc(ggml_backend_sycl_context & ctx, ggml_tensor *dst) {
-    GGML_ASSERT(dst->src[0]->type == GGML_TYPE_F32);
-    GGML_ASSERT(dst->src[1]->type == GGML_TYPE_F32);
+    const ggml_tensor * src0 = dst->src[0];
+    const ggml_tensor * src1 = dst->src[1];
+
+    const float * src0_d = (const float *) src0->data;
+    const float * src1_d = (const float *) src1->data;
+    float       * dst_d  = (float       *)  dst->data;
+
+    dpct::queue_ptr stream = ctx.stream();
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT(src1->type == GGML_TYPE_F32);
     GGML_ASSERT( dst->type == GGML_TYPE_F32);
-    GGML_ASSERT(dst->ne[3] == 1); // just 3D tensors supported
-    dpct::queue_ptr main_stream = ctx.stream();
-    SYCL_CHECK(ggml_sycl_set_device(ctx.device));
-    const float * src0_dd = static_cast<const float *>(dst->src[0]->data);
-    const float * src1_dd = static_cast<const float*>(dst->src[1]->data);
-    float *       dst_dd  = static_cast<float *>(dst->data);
 
-    int nb1 = dst->op_params[0] / 4; // 4 bytes of float32
-    int nb2 = dst->op_params[1] / 4; // 4 bytes of float32
-    // int nb3 = dst->op_params[2] / 4; // 4 bytes of float32 - unused
-    int offset = dst->op_params[3] / 4; // offset in bytes
+    GGML_ASSERT(ggml_is_contiguous(src1));
+    GGML_ASSERT(dst->nb[0] == ggml_element_size(dst));
+    GGML_ASSERT(ggml_is_contiguously_allocated(dst));
 
-    ggml_sycl_detail::acc_f32_sycl(src0_dd, src1_dd, dst_dd, (int)ggml_nelements(dst), (int)dst->src[1]->ne[0], (int)dst->src[1]->ne[1], (int)dst->src[1]->ne[2], nb1, nb2, offset, main_stream);
+    const int64_t s1     = dst->op_params[0] / sizeof(float);
+    const int64_t s2     = dst->op_params[1] / sizeof(float);
+    const int64_t s3     = dst->op_params[2] / sizeof(float);
+    const int64_t offset = dst->op_params[3] / sizeof(float);
+
+    ggml_sycl_detail::acc_f32_sycl(src0_d, src1_d, dst_d, ggml_nelements(dst),
+        src1->ne[0], src1->ne[1], src1->ne[2], src1->ne[3],
+        s1, s2, s3, offset, stream);
 }
 
 static inline void ggml_sycl_op_geglu(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
diff --git a/ggml/src/ggml-sycl/ggml-sycl.cpp b/ggml/src/ggml-sycl/ggml-sycl.cpp
index dfacde0af3..66dfc4532c 100644
--- a/ggml/src/ggml-sycl/ggml-sycl.cpp
+++ b/ggml/src/ggml-sycl/ggml-sycl.cpp
@@ -4872,8 +4872,9 @@ static bool ggml_backend_sycl_device_supports_op(ggml_backend_dev_t dev, const g
                 k > 0 && k <= 32;
         }
         case GGML_OP_POOL_2D:
-        case GGML_OP_ACC:
             return true;
+        case GGML_OP_ACC:
+            return ggml_is_contiguous(op->src[0]) && ggml_is_contiguous(op->src[1]);
         case GGML_OP_PAD:
             // TODO: add circular padding support for syscl, see https://github.com/ggml-org/llama.cpp/pull/16985
             if (ggml_get_op_params_i32(op, 8) != 0) {
diff --git a/ggml/src/ggml-sycl/norm.cpp b/ggml/src/ggml-sycl/norm.cpp
index 00702b5d09..09fce1280a 100644
--- a/ggml/src/ggml-sycl/norm.cpp
+++ b/ggml/src/ggml-sycl/norm.cpp
@@ -202,47 +202,34 @@ static void rms_norm_f32(const float* x, float* dst, const int ncols, const int6
     }
 }
 
-static void l2_norm_f32(const float* x, float* dst, const int ncols, const float eps,
-    const sycl::nd_item<3>& item_ct1, float* s_sum, int block_size) {
-    const int row = item_ct1.get_group(2) * item_ct1.get_local_range(1) +
-        item_ct1.get_local_id(1);
-    const int tid = item_ct1.get_local_id(2);
-    const int nthreads = item_ct1.get_local_range(2);
-    const int nwarps = nthreads / WARP_SIZE;
+template<int warp_size>
+static void l2_norm_f32(const float * x, float * dst, const int ncols,
+    const int64_t stride_row, const int64_t stride_channel,
+    const int64_t stride_sample, const float eps,
+    const sycl::nd_item<3>& item_ct1, float* s_sum, const int block_size) {
+    const int nrows     = item_ct1.get_group_range(2);
+    const int nchannels = item_ct1.get_group_range(1);
+
+    const int row     = item_ct1.get_group(2);
+    const int channel = item_ct1.get_group(1);
+    const int sample  = item_ct1.get_group(0);
+    const int tid     = item_ct1.get_local_id(2);
+
+    x   += sample*stride_sample + channel*stride_channel + row*stride_row;
+    dst += ((sample*nchannels + channel)*nrows + row)*ncols;
+
     float tmp = 0.0f; // partial sum for thread in warp
 
     for (int col = tid; col < ncols; col += block_size) {
-        const float xi = x[row * ncols + col];
+        const float xi = x[col];
         tmp += xi * xi;
     }
 
-    // sum up partial sums
-    tmp = warp_reduce_sum(tmp, item_ct1);
-    if (block_size > WARP_SIZE) {
-
-        int warp_id = item_ct1.get_local_id(2) / WARP_SIZE;
-        int lane_id = item_ct1.get_local_id(2) % WARP_SIZE;
-        if (lane_id == 0) {
-            s_sum[warp_id] = tmp;
-        }
-        /*
-        DPCT1118:3: SYCL group functions and algorithms must be encountered in
-        converged control flow. You may need to adjust the code.
-        */
-        item_ct1.barrier(sycl::access::fence_space::local_space);
-        size_t nreduce = nwarps / WARP_SIZE;
-        tmp = 0.f;
-        for (size_t i = 0; i < nreduce; i += 1)
-        {
-            tmp += s_sum[lane_id + i * WARP_SIZE];
-        }
-        tmp = warp_reduce_sum(tmp, item_ct1);
-    }
-
-    const float scale = sycl::rsqrt(sycl::max(tmp, eps * eps));
+    tmp = block_reduce<block_reduce_method::SUM, warp_size>(tmp, s_sum, block_size);
+    const float scale = sycl::rsqrt(sycl::fmax(tmp, eps * eps));
 
     for (int col = tid; col < ncols; col += block_size) {
-        dst[row * ncols + col] = scale * x[row * ncols + col];
+        dst[col] = scale * x[col];
     }
 }
 
@@ -369,42 +356,50 @@ static void rms_norm_f32_sycl(const float* x, float* dst, const int ncols, const
     }
 }
 
-static void l2_norm_f32_sycl(const float* x, float* dst, const int ncols,
-    const int nrows, const float eps,
-    queue_ptr stream, int device) {
-    // printf("%s ncols=%d, nrows=%d, WARP_SIZE=%d\n", __func__, ncols, nrows, WARP_SIZE);
+template<int warp_size>
+static void l2_norm_f32_sycl(const float *   x,
+                             float *         dst,
+                             const int       ncols,
+                             const int       nrows,
+                             const int       nchannels,
+                             const int       nsamples,
+                             const int64_t   stride_row,
+                             const int64_t   stride_channel,
+                             const int64_t   stride_sample,
+                             const float     eps,
+                             queue_ptr       stream,
+                             int             device) {
+    const dpct::dim3 blocks_num(nrows, nchannels, nsamples);
+
     if (ncols < 1024) {
-        const sycl::range<3> block_dims(1, 1, WARP_SIZE);
+        const dpct::dim3 block_dims(warp_size, 1, 1);
         stream->submit([&](sycl::handler& cgh) {
             cgh.parallel_for(
-                sycl::nd_range<3>(sycl::range<3>(1, 1, nrows) * block_dims,
+                sycl::nd_range<3>(blocks_num * block_dims,
                     block_dims),
                 [=](sycl::nd_item<3> item_ct1)
-                [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
-                    l2_norm_f32(x, dst, ncols, eps, item_ct1,
-                        nullptr, WARP_SIZE);
+                [[sycl::reqd_sub_group_size(warp_size)]] {
+                    l2_norm_f32<warp_size>(x, dst, ncols, stride_row, stride_channel, stride_sample, eps, item_ct1,
+                        nullptr, warp_size);
                 });
             });
     }
     else {
         const int work_group_size = ggml_sycl_info().max_work_group_sizes[device];
-        assert(work_group_size % (WARP_SIZE * WARP_SIZE) == 0);
+        assert(work_group_size % (warp_size * warp_size) == 0);
         const sycl::range<3> block_dims(1, 1, work_group_size);
-        /*
-        DPCT1049:19: The work-group size passed to the SYCL kernel may exceed
-        the limit. To get the device limit, query
-        info::device::max_work_group_size. Adjust the work-group size if needed.
-        */
+        int lsm_size =  block_dims[2] > warp_size ? work_group_size / warp_size * sizeof(float): 0;
         stream->submit([&](sycl::handler& cgh) {
-            sycl::local_accessor<float, 1> s_sum_acc_ct1(sycl::range<1>(work_group_size / WARP_SIZE),
+            sycl::local_accessor<float, 1> s_sum_acc_ct1(sycl::range<1>(lsm_size),
                 cgh);
+
             cgh.parallel_for(
-                sycl::nd_range<3>(sycl::range<3>(1, 1, nrows) * block_dims,
+                sycl::nd_range<3>(blocks_num * block_dims,
                     block_dims),
                 [=](sycl::nd_item<3> item_ct1)
-                [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
-                    l2_norm_f32(x, dst, ncols, eps, item_ct1,
-                        get_pointer(s_sum_acc_ct1), work_group_size);
+                [[sycl::reqd_sub_group_size(warp_size)]] {
+                    l2_norm_f32<warp_size>(x, dst, ncols, stride_row, stride_channel, stride_sample,
+                        eps, item_ct1, get_pointer(s_sum_acc_ct1), work_group_size);
                 });
             });
     }
@@ -634,21 +629,28 @@ void ggml_sycl_op_rms_norm_back(ggml_backend_sycl_context & ctx, ggml_tensor * d
 }
 
 void ggml_sycl_op_l2_norm(ggml_backend_sycl_context& ctx, ggml_tensor* dst) {
+    const ggml_tensor * src0 = dst->src[0];
+    const float * src0_d = (const float *) src0->data;
+    float * dst_d = (float *) dst->data;
+    dpct::queue_ptr     stream = ctx.stream();
 
-    GGML_ASSERT(dst->src[0]->type == GGML_TYPE_F32);
-    GGML_ASSERT(dst->type == GGML_TYPE_F32);
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT( dst->type == GGML_TYPE_F32);
 
-    dpct::queue_ptr main_stream = ctx.stream();
-    SYCL_CHECK(ggml_sycl_set_device(ctx.device));
-
-    const int64_t ne00 = dst->src[0]->ne[0];
-    const int64_t nrows = ggml_nrows(dst->src[0]);
-    const float * src0_dd = static_cast<const float *>(dst->src[0]->data);
-    float * dst_dd = static_cast<float *>(dst->data);
+    GGML_TENSOR_UNARY_OP_LOCALS;
 
     float eps;
     memcpy(&eps, dst->op_params, sizeof(float));
+    GGML_ASSERT(eps >= 0.0f);
 
-    l2_norm_f32_sycl(src0_dd, dst_dd, ne00, nrows, eps, main_stream, ctx.device);
+    const size_t ts0 = ggml_type_size(src0->type);
+    GGML_ASSERT(nb00 == ts0);
+    const int64_t s01 = nb01 / ts0;
+    const int64_t s02 = nb02 / ts0;
+    const int64_t s03 = nb03 / ts0;
 
+    /*support both WARP_SIZE or WARP_32_SIZE in code
+      choose by hardware for better performance
+    */
+    l2_norm_f32_sycl<WARP_SIZE>(src0_d, dst_d, ne00, ne01, ne02, ne03, s01, s02, s03, eps, stream, ctx.device);
 }