Engage!

1 files changed, 4021 insertions, 0 deletions

diff --git a/llama.cpp/ggml/src/ggml-cann/aclnn_ops.cpp b/llama.cpp/ggml/src/ggml-cann/aclnn_ops.cpp
new file mode 100644
index 0000000..fc7c3e3
--- /dev/null
+++ b/llama.cpp/ggml/src/ggml-cann/aclnn_ops.cpp
@@ -0,0 +1,4021 @@
+/*
+ * Copyright (c) 2023-2026 The ggml authors
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in
+ * all copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
+ * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS
+ * IN THE SOFTWARE.
+ */
+
+#include "aclnn_ops.h"
+
+#include "ggml-impl.h"
+#include "ggml.h"
+
+#include <aclnnop/aclnn_add.h>
+#include <aclnnop/aclnn_add_rms_norm.h>
+#include <aclnnop/aclnn_addcdiv.h>
+#include <aclnnop/aclnn_argmax.h>
+#include <aclnnop/aclnn_avgpool2d.h>
+#include <aclnnop/aclnn_batch_matmul.h>
+#include <aclnnop/aclnn_cast.h>
+#include <aclnnop/aclnn_clamp.h>
+#include <aclnnop/aclnn_constant_pad_nd.h>
+#include <aclnnop/aclnn_convolution.h>
+#include <aclnnop/aclnn_copy.h>
+#include <aclnnop/aclnn_div.h>
+#include <aclnnop/aclnn_elu.h>
+#include <aclnnop/aclnn_embedding.h>
+#include <aclnnop/aclnn_eq_tensor.h>
+#include <aclnnop/aclnn_exp.h>
+#include <aclnnop/aclnn_fill_scalar.h>
+#include <aclnnop/aclnn_fused_infer_attention_score_v2.h>
+#include <aclnnop/aclnn_ger.h>
+#include <aclnnop/aclnn_group_norm.h>
+#include <aclnnop/aclnn_grouped_matmul_v3.h>
+#include <aclnnop/aclnn_gt_scalar.h>
+#include <aclnnop/aclnn_im2col.h>
+#include <aclnnop/aclnn_index_copy.h>
+#include <aclnnop/aclnn_index_fill_tensor.h>
+#include <aclnnop/aclnn_index_select.h>
+#include <aclnnop/aclnn_layer_norm.h>
+#include <aclnnop/aclnn_log.h>
+#include <aclnnop/aclnn_matmul.h>
+#include <aclnnop/aclnn_max_pool.h>
+#include <aclnnop/aclnn_mean.h>
+#include <aclnnop/aclnn_mm.h>
+#include <aclnnop/aclnn_mul.h>
+#include <aclnnop/aclnn_mv.h>
+#include <aclnnop/aclnn_permute.h>
+#include <aclnnop/aclnn_pow.h>
+#include <aclnnop/aclnn_pow_tensor_tensor.h>
+#include <aclnnop/aclnn_reduce_sum.h>
+#include <aclnnop/aclnn_reflection_pad1d.h>
+#include <aclnnop/aclnn_repeat.h>
+#include <aclnnop/aclnn_repeat_interleave.h>
+#include <aclnnop/aclnn_rms_norm.h>
+#include <aclnnop/aclnn_roll.h>
+#include <aclnnop/aclnn_softmax.h>
+#include <aclnnop/aclnn_sub.h>
+#include <aclnnop/aclnn_sum.h>
+#include <aclnnop/aclnn_threshold.h>
+#include <aclnnop/aclnn_tril.h>
+#include <aclnnop/aclnn_triu.h>
+#include <aclnnop/aclnn_upsample_nearest_2d.h>
+#include <aclnnop/aclnn_weight_quant_batch_matmul_v2.h>
+#include <aclnnop/aclnn_zero.h>
+#include <float.h>
+
+#include <cmath>
+#include <cstring>
+#include <exception>
+#include <vector>
+
+#define GGML_COMMON_DECL_C
+
+#include "../ggml-common.h"
+
+void bcast_shape(ggml_tensor *    src0,
+                 ggml_tensor *    src1,
+                 ggml_tensor *    dst,
+                 acl_tensor_ptr & acl_src0,
+                 acl_tensor_ptr & acl_src1,
+                 acl_tensor_ptr & acl_dst) {
+    GGML_ASSERT(ggml_are_same_shape(src0, dst) && ggml_can_repeat(src1, src0));
+    // Need bcast
+    if (!ggml_are_same_shape(src0, src1) && ggml_cann_need_bcast(src0, src1)) {
+        BCAST_SHAPE(src0, src1)
+        acl_src0 = ggml_cann_create_tensor(src0, BCAST_PARAM(src0));
+        acl_src1 = ggml_cann_create_tensor(src1, BCAST_PARAM(src1));
+        acl_dst  = ggml_cann_create_tensor(dst, BCAST_PARAM(src0));
+    } else {
+        acl_src0 = ggml_cann_create_tensor(src0);
+        acl_src1 = ggml_cann_create_tensor(src1);
+        acl_dst  = ggml_cann_create_tensor(dst);
+    }
+}
+
+void ggml_cann_op_unary(std::function<void(ggml_backend_cann_context &, aclTensor *, aclTensor *)> unary_op,
+                        ggml_backend_cann_context &                                                ctx,
+                        ggml_tensor *                                                              dst) {
+    ggml_tensor * src = dst->src[0];
+
+    acl_tensor_ptr acl_src = ggml_cann_create_tensor(src);
+    acl_tensor_ptr acl_dst = ggml_cann_create_tensor(dst);
+
+    unary_op(ctx, acl_src.get(), acl_dst.get());
+}
+
+void ggml_cann_op_unary_gated(std::function<void(ggml_backend_cann_context &, aclTensor *, aclTensor *)> unary_op,
+                              ggml_backend_cann_context &                                                ctx,
+                              ggml_tensor *                                                              dst) {
+    ggml_tensor * src0 = dst->src[0];
+    ggml_tensor * src1 = dst->src[1];
+
+    GGML_ASSERT(ggml_is_contiguous_1(src0));
+    GGML_ASSERT(ggml_is_contiguous_1(dst));
+    const int32_t swapped = ggml_get_op_params_i32(dst, 1);
+
+    acl_tensor_ptr acl_dst = ggml_cann_create_tensor(dst);
+    acl_tensor_ptr acl_src0, acl_src1;
+    if (src1) {
+        GGML_ASSERT(ggml_is_contiguous_1(src1));
+        GGML_ASSERT(src0->type == src1->type);
+
+        acl_src0 = ggml_cann_create_tensor(src0);
+        acl_src1 = ggml_cann_create_tensor(src1);
+    } else {
+        int64_t ne[] = { src0->ne[0] / 2, src0->ne[1], src0->ne[2], src0->ne[3] };
+        size_t  nb[] = { src0->nb[0], src0->nb[1], src0->nb[2], src0->nb[3] };
+        acl_src0     = ggml_cann_create_tensor(src0, ne, nb, GGML_MAX_DIMS, ACL_FORMAT_ND, 0);
+        acl_src1 = ggml_cann_create_tensor(src0, ne, nb, GGML_MAX_DIMS, ACL_FORMAT_ND, ne[0] * ggml_element_size(src0));
+        if (swapped) {
+            std::swap(acl_src0, acl_src1);
+        }
+    }
+
+    unary_op(ctx, acl_src0.get(), acl_dst.get());
+    GGML_CANN_CALL_ACLNN_OP(ctx, InplaceMul, acl_dst.get(), acl_src1.get());
+}
+
+/**
+ * @brief Repeats elements of a tensor along each dimension according to the
+ * specified repeat array.
+ *
+ * @param ctx The context for the CANN backend operations.
+ * @param acl_src The source tensor to be repeated.
+ * @param acl_dst The destination tensor after repeating.
+ * @param repeat_array The array specifying the number of repetitions along each
+ * dimension.
+ */
+static void aclnn_repeat(ggml_backend_cann_context & ctx,
+                         aclTensor *                 acl_src,
+                         aclTensor *                 acl_dst,
+                         int64_t *                   repeat_array) {
+    // repeat tensor along each dim with repeat_array
+    acl_int_array_ptr repeats = ggml_cann_create_int_array(repeat_array, GGML_MAX_DIMS);
+
+    GGML_CANN_CALL_ACLNN_OP(ctx, Repeat, acl_src, repeats.get(), acl_dst);
+}
+
+/**
+ * @brief Casts the data type of a source tensor to a destination tensor.
+ *
+ * This function casts the data type of the source tensor `acl_src` to the
+ * specified data type `cast_data_type` and stores the result in the destination
+ * tensor `acl_dst`.
+ *
+ * @param ctx The context for the CANN backend operations.
+ * @param acl_src The source tensor whose data type will be casted.
+ * @param acl_dst The destination tensor where the casted result will be stored.
+ * @param cast_data_type The target data type to which the source tensor will be
+ * casted.
+ */
+static void aclnn_cast(ggml_backend_cann_context & ctx,
+                       aclTensor *                 acl_src,
+                       aclTensor *                 acl_dst,
+                       aclDataType                 cast_data_type) {
+    GGML_CANN_CALL_ACLNN_OP(ctx, Cast, acl_src, cast_data_type, acl_dst);
+}
+
+void ggml_cann_repeat(ggml_backend_cann_context & ctx, ggml_tensor * dst) {
+    ggml_tensor * src = dst->src[0];
+    GGML_ASSERT(ggml_can_repeat(src, dst));
+
+    acl_tensor_ptr acl_src = ggml_cann_create_tensor(src);
+    acl_tensor_ptr acl_dst = ggml_cann_create_tensor(dst);
+
+    int64_t repeatsArray[] = { dst->ne[3] / src->ne[3], dst->ne[2] / src->ne[2], dst->ne[1] / src->ne[1],
+                               dst->ne[0] / src->ne[0] };
+
+    aclnn_repeat(ctx, acl_src.get(), acl_dst.get(), repeatsArray);
+}
+
+void aclnn_add(ggml_backend_cann_context & ctx, aclTensor * acl_src0, aclTensor * acl_src1, aclTensor * acl_dst) {
+    float          alphaValue = 1.0f;
+    acl_scalar_ptr alpha      = ggml_cann_create_scalar(&alphaValue, aclDataType::ACL_FLOAT);
+    if (acl_dst != nullptr) {
+        GGML_CANN_CALL_ACLNN_OP(ctx, Add, acl_src0, acl_src1, alpha.get(), acl_dst);
+    } else {
+        GGML_CANN_CALL_ACLNN_OP(ctx, InplaceAdd, acl_src0, acl_src1, alpha.get());
+    }
+}
+
+void aclnn_sub(ggml_backend_cann_context & ctx, aclTensor * acl_src0, aclTensor * acl_src1, aclTensor * acl_dst) {
+    float          alphaValue = 1.0f;
+    acl_scalar_ptr alpha      = ggml_cann_create_scalar(&alphaValue, aclDataType::ACL_FLOAT);
+    if (acl_dst != nullptr) {
+        GGML_CANN_CALL_ACLNN_OP(ctx, Sub, acl_src0, acl_src1, alpha.get(), acl_dst);
+    } else {
+        GGML_CANN_CALL_ACLNN_OP(ctx, InplaceSub, acl_src0, acl_src1, alpha.get());
+    }
+}
+
+void aclnn_mul(ggml_backend_cann_context & ctx, aclTensor * acl_src, aclTensor * acl_other, aclTensor * acl_dst) {
+    if (acl_dst != nullptr) {
+        GGML_CANN_CALL_ACLNN_OP(ctx, Mul, acl_src, acl_other, acl_dst);
+    } else {
+        GGML_CANN_CALL_ACLNN_OP(ctx, InplaceMul, acl_src, acl_other);
+    }
+}
+
+void aclnn_div(ggml_backend_cann_context & ctx, aclTensor * acl_src, aclTensor * acl_other, aclTensor * acl_dst) {
+    if (acl_dst != nullptr) {
+        GGML_CANN_CALL_ACLNN_OP(ctx, Div, acl_src, acl_other, acl_dst);
+    } else {
+        GGML_CANN_CALL_ACLNN_OP(ctx, InplaceDiv, acl_src, acl_other);
+    }
+}
+
+/**
+ * @brief Multiplies elements of a tensor by a scalar value, optionally
+ * in-place.
+ *
+ * This function multiplies each element of the source tensor `acl_src` by the
+ * scalar `scale` and stores the result in the destination tensor `acl_dst`. If
+ * `inplace` is true, `acl_dst` will not be used and the operation is performed
+ *  in-place on `acl_src`.
+ * The operation is defined as:
+ * \f[
+ *     \text {acl_dst }_i=\text {acl_src }_i \times \text {scale}
+ * \f]
+ *
+ * @param ctx The context for the CANN backend operations.
+ * @param acl_src The source tensor whose elements will be multiplied.
+ * @param scale The scalar value by which each element of `acl_src` will be
+ *  multiplied.
+ * @param acl_dst The destination tensor where the result will be stored if
+ * `inplace` is false.
+ * @param inplace Flag indicating whether to perform the operation in-place on
+ * `acl_src`.
+ */
+static void aclnn_muls(ggml_backend_cann_context & ctx,
+                       aclTensor *                 acl_src,
+                       float                       scale,
+                       aclTensor *                 acl_dst,
+                       bool                        inplace) {
+    acl_scalar_ptr acl_scale = ggml_cann_create_scalar(&scale, aclDataType::ACL_FLOAT);
+    if (inplace) {
+        GGML_CANN_CALL_ACLNN_OP(ctx, InplaceMuls, acl_src, acl_scale.get());
+    } else {
+        GGML_CANN_CALL_ACLNN_OP(ctx, Muls, acl_src, acl_scale.get(), acl_dst);
+    }
+}
+
+void ggml_cann_leaky_relu(ggml_backend_cann_context & ctx, ggml_tensor * dst) {
+    ggml_tensor * src = dst->src[0];
+
+    acl_tensor_ptr acl_src = ggml_cann_create_tensor(src);
+    acl_tensor_ptr acl_dst = ggml_cann_create_tensor(dst);
+
+    float negative_slope;
+    memcpy(&negative_slope, dst->op_params, sizeof(float));
+    acl_scalar_ptr acl_negative_slope = ggml_cann_create_scalar(&negative_slope, aclDataType::ACL_FLOAT);
+
+    GGML_CANN_CALL_ACLNN_OP(ctx, LeakyRelu, acl_src.get(), acl_negative_slope.get(), acl_dst.get());
+}
+
+/**
+ * @brief Concatenates a list of tensors along a specified dimension and stores
+ * the result in a destination tensor.
+ *
+ * @param ctx The context for the CANN backend operations.
+ * @param tensorList The list of tensors to be concatenated.
+ * @param acl_dst The destination tensor where the concatenated result will be
+ * stored.
+ * @param concat_dim The dimension along which the tensors will be concatenated.
+ */
+static void aclnn_concat(ggml_backend_cann_context & ctx,
+                         aclTensorList *             tensorList,
+                         aclTensor *                 acl_dst,
+                         int64_t                     concat_dim) {
+    GGML_CANN_CALL_ACLNN_OP(ctx, Cat, tensorList, concat_dim, acl_dst);
+}
+
+void ggml_cann_concat(ggml_backend_cann_context & ctx, ggml_tensor * dst) {
+    ggml_tensor *  src0     = dst->src[0];
+    ggml_tensor *  src1     = dst->src[1];
+    acl_tensor_ptr acl_src0 = ggml_cann_create_tensor(src0);
+    acl_tensor_ptr acl_src1 = ggml_cann_create_tensor(src1);
+    acl_tensor_ptr acl_dst  = ggml_cann_create_tensor(dst);
+
+    const int32_t dim = ggml_get_op_params_i32(dst, 0);
+
+    GGML_ASSERT(dim >= 0 && dim < 4);
+    int32_t acl_dim = 3 - dim;
+
+    acl_tensor_list_ptr tensor_list = ggml_cann_create_tensor_list(acl_src0, acl_src1);
+    aclnn_concat(ctx, tensor_list.get(), acl_dst.get(), acl_dim);
+}
+
+/**
+ * @brief Creates a tensor with values starting from `start`, incremented by
+ * `step`, and ending before `stop`.
+ *
+ * This function performs the operation:
+ * \f[
+ *    \text {out }_{i+1}=\text {out }_i+\text {step}
+ * \f]
+ * the range is [start, stop).
+ *
+ * @param ctx The context for the CANN backend operations.
+ * @param acl_dst The destination tensor where the values will be stored.
+ * @param start The starting value of the range.
+ * @param stop The ending value of the range (exclusive).
+ * @param step The step size between consecutive values.
+ * @param n_elements The number of elements in the destination tensor.
+ */
+static void aclnn_arange(ggml_backend_cann_context & ctx,
+                         aclTensor *                 acl_dst,
+                         float                       start,
+                         float                       stop,
+                         float                       step,
+                         int64_t                     n_elements) {
+    int64_t steps = (int64_t) std::ceil((stop - start) / step);
+    GGML_ASSERT(n_elements == steps);
+
+    acl_scalar_ptr acl_start = ggml_cann_create_scalar(&start, aclDataType::ACL_FLOAT);
+    acl_scalar_ptr acl_end   = ggml_cann_create_scalar(&stop, aclDataType::ACL_FLOAT);
+    acl_scalar_ptr acl_step  = ggml_cann_create_scalar(&step, aclDataType::ACL_FLOAT);
+
+    GGML_CANN_CALL_ACLNN_OP(ctx, Arange, acl_start.get(), acl_end.get(), acl_step.get(), acl_dst);
+}
+
+void ggml_cann_arange(ggml_backend_cann_context & ctx, ggml_tensor * dst) {
+    GGML_ASSERT(dst->type == GGML_TYPE_F32);
+
+    acl_tensor_ptr acl_dst = ggml_cann_create_tensor(dst);
+
+    int64_t n_elements = ggml_nelements(dst);
+    float   start;
+    float   stop;
+    float   step;
+    memcpy(&start, (float *) dst->op_params + 0, sizeof(float));
+    memcpy(&stop, (float *) dst->op_params + 1, sizeof(float));
+    memcpy(&step, (float *) dst->op_params + 2, sizeof(float));
+
+    aclnn_arange(ctx, acl_dst.get(), start, stop, step, n_elements);
+}
+
+void ggml_cann_clamp(ggml_backend_cann_context & ctx, ggml_tensor * dst) {
+    ggml_tensor * src = dst->src[0];
+
+    float min;
+    float max;
+    memcpy(&min, dst->op_params, sizeof(float));
+    memcpy(&max, (float *) dst->op_params + 1, sizeof(float));
+
+    acl_tensor_ptr acl_src = ggml_cann_create_tensor(src);
+    acl_tensor_ptr acl_dst = ggml_cann_create_tensor(dst);
+
+    acl_scalar_ptr acl_min = ggml_cann_create_scalar(&min, aclDataType::ACL_FLOAT);
+    acl_scalar_ptr acl_max = ggml_cann_create_scalar(&max, aclDataType::ACL_FLOAT);
+
+    GGML_CANN_CALL_ACLNN_OP(ctx, Clamp, acl_src.get(), acl_min.get(), acl_max.get(), acl_dst.get());
+}
+
+void ggml_cann_scale(ggml_backend_cann_context & ctx, ggml_tensor * dst) {
+    ggml_tensor * src = dst->src[0];
+
+    // scale factor
+    float v;
+    memcpy(&v, dst->op_params, sizeof(float));
+
+    acl_scalar_ptr scale   = ggml_cann_create_scalar(&v, aclDataType::ACL_FLOAT);
+    acl_tensor_ptr acl_src = ggml_cann_create_tensor(src);
+    acl_tensor_ptr acl_dst = ggml_cann_create_tensor(dst);
+
+    GGML_CANN_CALL_ACLNN_OP(ctx, Muls, acl_src.get(), scale.get(), acl_dst.get());
+}
+
+void ggml_cann_argsort(ggml_backend_cann_context & ctx, ggml_tensor * dst) {
+    ggml_tensor *        src   = dst->src[0];
+    enum ggml_sort_order order = (enum ggml_sort_order) dst->op_params[0];
+
+    acl_tensor_ptr       acl_src = ggml_cann_create_tensor(src);
+    acl_tensor_ptr       acl_dst = ggml_cann_create_tensor(dst);
+    ggml_cann_pool_alloc temp_buffer_allocator(ctx.pool(), ggml_nelements(dst) * sizeof(int64_t));
+    void *               buffer = temp_buffer_allocator.get();
+    acl_tensor_ptr       tmp_tensor =
+        ggml_cann_create_tensor(buffer, ACL_INT64, ggml_type_size(dst->type), dst->ne, dst->nb, GGML_MAX_DIMS);
+    GGML_CANN_CALL_ACLNN_OP(ctx, Argsort, acl_src.get(), -1, (order == GGML_SORT_ORDER_DESC ? true : false),
+                            tmp_tensor.get());
+    GGML_CANN_CALL_ACLNN_OP(ctx, Cast, tmp_tensor.get(), ggml_cann_type_mapping(dst->type), acl_dst.get());
+}
+
+void ggml_cann_norm(ggml_backend_cann_context & ctx, ggml_tensor * dst) {
+    ggml_tensor * src = dst->src[0];
+
+    acl_tensor_ptr acl_src = ggml_cann_create_tensor(src);
+    acl_tensor_ptr acl_dst = ggml_cann_create_tensor(dst);
+
+    float eps;
+    memcpy(&eps, dst->op_params, sizeof(float));
+
+    std::vector<int64_t> normData = { dst->ne[0] };
+    acl_int_array_ptr    norm     = ggml_cann_create_int_array(normData.data(), normData.size());
+    GGML_CANN_CALL_ACLNN_OP(ctx, LayerNorm, acl_src.get(), norm.get(), nullptr, nullptr, eps, acl_dst.get(), nullptr,
+                            nullptr);
+}
+
+void ggml_cann_l2_norm(ggml_backend_cann_context & ctx, ggml_tensor * dst) {
+    ggml_tensor * src = dst->src[0];
+
+    acl_tensor_ptr acl_src = ggml_cann_create_tensor(src);
+    acl_tensor_ptr acl_dst = ggml_cann_create_tensor(dst);
+
+    size_t               type_size = ggml_type_size(src->type);
+    int64_t              n_bytes   = src->ne[3] * src->ne[2] * src->ne[1] * type_size;
+    ggml_cann_pool_alloc temp_buffer_allocator(ctx.pool(), n_bytes);
+    void *               buffer = temp_buffer_allocator.get();
+
+    int64_t div_ne[] = { 1, src->ne[1], src->ne[2], src->ne[3] };
+    size_t  div_nb[GGML_MAX_DIMS];
+    div_nb[0] = sizeof(float);
+    for (int i = 1; i < GGML_MAX_DIMS; ++i) {
+        div_nb[i] = div_nb[i - 1] * div_ne[i - 1];
+    }
+    acl_tensor_ptr acl_div = ggml_cann_create_tensor(buffer, ACL_FLOAT, type_size, div_ne, div_nb, GGML_MAX_DIMS);
+
+    std::vector<int64_t> norm_dims  = { 3 };
+    acl_int_array_ptr    dims_array = ggml_cann_create_int_array(norm_dims.data(), norm_dims.size());
+
+    float          p_value  = 2.0f;
+    acl_scalar_ptr p_scalar = ggml_cann_create_scalar(&p_value, aclDataType::ACL_FLOAT);
+    GGML_CANN_CALL_ACLNN_OP(ctx, Norm, acl_src.get(), p_scalar.get(), dims_array.get(), true, acl_div.get());
+    GGML_CANN_CALL_ACLNN_OP(ctx, Div, acl_src.get(), acl_div.get(), acl_dst.get());
+}
+
+void ggml_cann_cross_entropy_loss(ggml_backend_cann_context & ctx, ggml_tensor * dst) {
+    ggml_tensor * src0 = dst->src[0];
+    ggml_tensor * src1 = dst->src[1];
+
+    const int64_t nc = src0->ne[0];
+    const int64_t nr = ggml_nrows(src0);
+
+    int64_t logits_ne[] = { nc, nr };
+    size_t  logits_nb[2];
+    logits_nb[0]              = ggml_type_size(src0->type);
+    logits_nb[1]              = logits_nb[0] * logits_ne[0];
+    acl_tensor_ptr acl_logits = ggml_cann_create_tensor(src0->data, ACL_FLOAT, sizeof(float), logits_ne, logits_nb, 2);
+
+    size_t               log_softmax_type_size = sizeof(float);
+    int64_t              log_softmax_n_bytes   = nr * nc * log_softmax_type_size;
+    ggml_cann_pool_alloc log_softmax_allocator(ctx.pool(), log_softmax_n_bytes);
+    void *               log_softmax_buffer = log_softmax_allocator.get();
+
+    int64_t log_softmax_ne[] = { nc, nr };
+    size_t  log_softmax_nb[2];
+    log_softmax_nb[0]              = log_softmax_type_size;
+    log_softmax_nb[1]              = log_softmax_nb[0] * log_softmax_ne[0];
+    acl_tensor_ptr acl_log_softmax = ggml_cann_create_tensor(log_softmax_buffer, ACL_FLOAT, log_softmax_type_size,
+                                                             log_softmax_ne, log_softmax_nb, 2);
+
+    GGML_CANN_CALL_ACLNN_OP(ctx, LogSoftmax, acl_logits.get(), 1, acl_log_softmax.get());
+
+    int64_t labels_ne[] = { nc, nr };
+    size_t  labels_nb[2];
+    labels_nb[0]              = ggml_type_size(src1->type);
+    labels_nb[1]              = labels_nb[0] * labels_ne[0];
+    acl_tensor_ptr acl_labels = ggml_cann_create_tensor(src1->data, ACL_FLOAT, sizeof(float), labels_ne, labels_nb, 2);
+
+    size_t               mul_type_size = sizeof(float);
+    int64_t              mul_n_bytes   = nr * nc * mul_type_size;
+    ggml_cann_pool_alloc mul_allocator(ctx.pool(), mul_n_bytes);
+    void *               mul_buffer = mul_allocator.get();
+
+    int64_t mul_ne[] = { nc, nr };
+    size_t  mul_nb[2];
+    mul_nb[0]                     = mul_type_size;
+    mul_nb[1]                     = mul_nb[0] * mul_ne[0];
+    acl_tensor_ptr acl_mul_result = ggml_cann_create_tensor(mul_buffer, ACL_FLOAT, mul_type_size, mul_ne, mul_nb, 2);
+
+    GGML_CANN_CALL_ACLNN_OP(ctx, Mul, acl_log_softmax.get(), acl_labels.get(), acl_mul_result.get());
+
+    size_t               sum_per_sample_type_size = sizeof(float);
+    int64_t              sum_per_sample_n_bytes   = nr * sum_per_sample_type_size;
+    ggml_cann_pool_alloc sum_per_sample_allocator(ctx.pool(), sum_per_sample_n_bytes);
+    void *               sum_per_sample_buffer = sum_per_sample_allocator.get();
+
+    int64_t sum_per_sample_ne[] = { nr };
+    size_t  sum_per_sample_nb[1];
+    sum_per_sample_nb[0]              = sum_per_sample_type_size;
+    acl_tensor_ptr acl_sum_per_sample = ggml_cann_create_tensor(
+        sum_per_sample_buffer, ACL_FLOAT, sum_per_sample_type_size, sum_per_sample_ne, sum_per_sample_nb, 1);
+
+    std::vector<int64_t> sum_dims   = { 1 };
+    acl_int_array_ptr    dims_array = ggml_cann_create_int_array(sum_dims.data(), sum_dims.size());
+    bool                 keep_dims  = false;
+
+    GGML_CANN_CALL_ACLNN_OP(ctx, ReduceSum, acl_mul_result.get(), dims_array.get(), keep_dims, ACL_FLOAT,
+                            acl_sum_per_sample.get());
+
+    size_t               total_sum_type_size = sizeof(float);
+    int64_t              total_sum_n_bytes   = 1 * total_sum_type_size;
+    ggml_cann_pool_alloc total_sum_allocator(ctx.pool(), total_sum_n_bytes);
+    void *               total_sum_buffer = total_sum_allocator.get();
+
+    int64_t total_sum_ne[] = { 1 };
+    size_t  total_sum_nb[1];
+    total_sum_nb[0] = total_sum_type_size;
+
+    acl_tensor_ptr acl_total_sum =
+        ggml_cann_create_tensor(total_sum_buffer, ACL_FLOAT, total_sum_type_size, total_sum_ne, total_sum_nb, 1);
+
+    std::vector<int64_t> total_sum_dims    = { 0 };
+    acl_int_array_ptr total_sum_dims_array = ggml_cann_create_int_array(total_sum_dims.data(), total_sum_dims.size());
+
+    GGML_CANN_CALL_ACLNN_OP(ctx, ReduceSum, acl_sum_per_sample.get(), total_sum_dims_array.get(), keep_dims, ACL_FLOAT,
+                            acl_total_sum.get());
+
+    float          value        = -1.0f / static_cast<float>(nr);
+    acl_scalar_ptr scale_factor = ggml_cann_create_scalar(&value, aclDataType::ACL_FLOAT);
+    acl_tensor_ptr acl_dst =
+        ggml_cann_create_tensor(dst->data, ACL_FLOAT, sizeof(float), total_sum_ne, total_sum_nb, 1);
+
+    GGML_CANN_CALL_ACLNN_OP(ctx, Muls, acl_total_sum.get(), scale_factor.get(), acl_dst.get());
+}
+
+void ggml_cann_group_norm(ggml_backend_cann_context & ctx, ggml_tensor * dst) {
+    ggml_tensor * src = dst->src[0];
+
+    acl_tensor_ptr acl_src = ggml_cann_create_tensor(src);
+    acl_tensor_ptr acl_dst = ggml_cann_create_tensor(dst);
+
+    int n_groups = dst->op_params[0];
+
+    float eps;
+    memcpy(&eps, dst->op_params + 1, sizeof(float));
+
+    int64_t N   = src->ne[3];
+    int64_t C   = src->ne[2];
+    int64_t HxW = src->ne[1] * src->ne[0];
+
+    size_t  type_size = ggml_type_size(src->type);
+    int64_t ne[]      = { n_groups, N };
+    size_t  nb[]      = { type_size, type_size * n_groups };
+    size_t  n_bytes   = N * n_groups;
+
+    ggml_cann_pool_alloc temp_buffer_allocator(ctx.pool(), n_bytes * 2);
+    void *               buffer       = temp_buffer_allocator.get();
+    acl_tensor_ptr       acl_mean_out = ggml_cann_create_tensor(buffer, ACL_FLOAT, type_size, ne, nb, ACL_FORMAT_ND);
+    acl_tensor_ptr       acl_rstd_out =
+        ggml_cann_create_tensor((char *) buffer + n_bytes, ACL_FLOAT, type_size, ne, nb, ACL_FORMAT_ND);
+
+    GGML_CANN_CALL_ACLNN_OP(ctx, GroupNorm, acl_src.get(), nullptr, nullptr, N, C, HxW, n_groups, eps, acl_dst.get(),
+                            acl_mean_out.get(), acl_rstd_out.get());
+}
+
+void ggml_cann_acc(ggml_backend_cann_context & ctx, ggml_tensor * dst) {
+    ggml_tensor * src0 = dst->src[0];
+    ggml_tensor * src1 = dst->src[1];
+
+    size_t nb1     = ((int32_t *) dst->op_params)[0];
+    size_t nb2     = ((int32_t *) dst->op_params)[1];
+    size_t nb3     = ((int32_t *) dst->op_params)[2];
+    size_t offset  = ((int32_t *) dst->op_params)[3];
+    bool   inplace = (bool) ((int32_t *) dst->op_params)[4];
+
+    size_t param_nb[] = { ggml_element_size(src0), nb1, nb2, nb3 };
+
+    acl_tensor_ptr acl_dst  = ggml_cann_create_tensor(dst, src1->ne, param_nb, GGML_MAX_DIMS, ACL_FORMAT_ND, offset);
+    acl_tensor_ptr acl_src1 = ggml_cann_create_tensor(src1);
+
+    acl_scalar_ptr alpha      = nullptr;
+    float          alphaValue = 1.0f;
+    alpha                     = ggml_cann_create_scalar(&alphaValue, aclDataType::ACL_FLOAT);
+
+    if (!inplace) {
+        size_t cpy_size = ggml_nbytes(dst);
+        ACL_CHECK(
+            aclrtMemcpyAsync(dst->data, cpy_size, src0->data, cpy_size, ACL_MEMCPY_DEVICE_TO_DEVICE, ctx.stream()));
+        acl_tensor_ptr acl_src0 =
+            ggml_cann_create_tensor(src0, src1->ne, src0->nb, GGML_MAX_DIMS, ACL_FORMAT_ND, offset);
+
+        GGML_CANN_CALL_ACLNN_OP(ctx, Add, acl_src0.get(), acl_src1.get(), alpha.get(), acl_dst.get());
+    } else {
+        GGML_CANN_CALL_ACLNN_OP(ctx, InplaceAdd, acl_dst.get(), acl_src1.get(), alpha.get());
+    }
+}
+
+/**
+ * @brief Performs sum reduction on a given tensor along specified dimensions.
+ *
+ * This function reduces the input tensor by summing along the specified dimensions.
+ *
+ * @param ctx The context for the CANN backend operations.
+ * @param dst The destination tensor where the reduced result will be stored.
+ * @param dim An array of dimension indices.
+ * @param dim_size The number of dimensions.
+ */
+static void aclnn_reduce_sum(ggml_backend_cann_context & ctx, ggml_tensor * dst, int64_t * dim, size_t dim_size) {
+    GGML_ASSERT(dst->ne[0] == 1);
+    ggml_tensor *     src         = dst->src[0];
+    acl_tensor_ptr    acl_src     = ggml_cann_create_tensor(src);
+    acl_tensor_ptr    acl_dst     = ggml_cann_create_tensor(dst);
+    acl_int_array_ptr reduce_dims = ggml_cann_create_int_array(dim, dim_size);
+
+    GGML_CANN_CALL_ACLNN_OP(ctx, ReduceSum, acl_src.get(), reduce_dims.get(), true, ggml_cann_type_mapping(dst->type),
+                            acl_dst.get());
+}
+
+void ggml_cann_sum_rows(ggml_backend_cann_context & ctx, ggml_tensor * dst) {
+    int64_t reduce_dims[] = { 3 };
+    aclnn_reduce_sum(ctx, dst, reduce_dims, 1);
+}
+
+void ggml_cann_sum(ggml_backend_cann_context & ctx, ggml_tensor * dst) {
+    int64_t reduce_dims[] = { 0, 1, 2, 3 };
+    aclnn_reduce_sum(ctx, dst, reduce_dims, 4);
+}
+
+void ggml_cann_upsample_nearest2d(ggml_backend_cann_context & ctx, ggml_tensor * dst) {
+    ggml_tensor *  src     = dst->src[0];
+    acl_tensor_ptr acl_src = ggml_cann_create_tensor(src, nullptr, nullptr, 0, ACL_FORMAT_NCHW);
+    acl_tensor_ptr acl_dst = ggml_cann_create_tensor(dst, nullptr, nullptr, 0, ACL_FORMAT_NCHW);
+
+    std::vector<int64_t> output_size{ dst->ne[1], dst->ne[0] };
+    acl_int_array_ptr    output_size_array = ggml_cann_create_int_array(output_size.data(), 2);
+
+    GGML_CANN_CALL_ACLNN_OP(ctx, UpsampleNearest2d, acl_src.get(), output_size_array.get(), acl_dst.get());
+}
+
+/**
+ * @brief Pads a tensor with a specified value along each dimension.
+ *
+ * This function performs padding of the source tensor `acl_src` and stores the
+ * result in the destination tensor `acl_dst`. The padding values for each
+ * dimension are specified in the `paddings` array.
+ *
+ * @param ctx The context for the CANN backend operations.
+ * @param acl_src The source tensor to be padded.
+ * @param acl_dst The destination tensor where the padded result will be stored.
+ * @param paddings An array specifying the padding values for each dimension.
+ * The size of the array should be twice the number of dimensions of the tensor.
+ * @param value The value to be used for padding. The default value is 0.0.
+ */
+static void aclnn_pad(ggml_backend_cann_context & ctx,
+                      aclTensor *                 acl_src,
+                      aclTensor *                 acl_dst,
+                      int64_t *                   paddings,
+                      float                       value = 0.0f) {
+    acl_int_array_ptr acl_pad   = ggml_cann_create_int_array(paddings, GGML_MAX_DIMS * 2);
+    acl_scalar_ptr    acl_value = ggml_cann_create_scalar(&value, aclDataType::ACL_FLOAT);
+
+    GGML_CANN_CALL_ACLNN_OP(ctx, ConstantPadNd, acl_src, acl_pad.get(), acl_value.get(), acl_dst);
+}
+
+void ggml_cann_pad(ggml_backend_cann_context & ctx, ggml_tensor * dst) {
+    ggml_tensor *  src     = dst->src[0];
+    acl_tensor_ptr acl_src = ggml_cann_create_tensor(src);
+    acl_tensor_ptr acl_dst = ggml_cann_create_tensor(dst);
+
+    // padding: value in the array means how much distance will be padding.
+    // the position of elements in the array means which dirction to padding,
+    // each position means: [dim0.front, dim0.behind, dim1.front, dim1.behind,
+    //                       dim2.front, dim2.behind, dim3.front, dim3.behind]
+    const int32_t lp0 = ggml_get_op_params_i32(dst, 0);
+    const int32_t rp0 = ggml_get_op_params_i32(dst, 1);
+    const int32_t lp1 = ggml_get_op_params_i32(dst, 2);
+    const int32_t rp1 = ggml_get_op_params_i32(dst, 3);
+    const int32_t lp2 = ggml_get_op_params_i32(dst, 4);
+    const int32_t rp2 = ggml_get_op_params_i32(dst, 5);
+    const int32_t lp3 = ggml_get_op_params_i32(dst, 6);
+    const int32_t rp3 = ggml_get_op_params_i32(dst, 7);
+
+    int64_t paddings[] = { lp0, rp0, lp1, rp1, lp2, rp2, lp3, rp3 };
+    aclnn_pad(ctx, acl_src.get(), acl_dst.get(), paddings);
+}
+
+/**
+ * @brief Performs 2D average pooling on the input tensor and stores the result
+ * in the destination tensor.
+ *
+ * This function performs average pooling on the source tensor and stores the
+ * result in the destination tensor. The pooling parameters (kernel size,
+ * strides, padding) are specified in the `op_params` of the destination tensor.
+ *
+ * @param ctx The context for the CANN backend operations.
+ * @param dst The destination tensor where the result will be stored. The source
+ * tensor is referenced by `dst->src[0]`.
+ */
+static void ggml_cann_avg_pool2d(ggml_backend_cann_context & ctx, ggml_tensor * dst) {
+    ggml_tensor * src = dst->src[0];
+    GGML_ASSERT(src->type == GGML_TYPE_F32);
+    GGML_ASSERT(dst->type == GGML_TYPE_F32);
+
+    acl_tensor_ptr acl_src = ggml_cann_create_tensor(src, nullptr, nullptr, 0, ACL_FORMAT_NCHW);
+    acl_tensor_ptr acl_dst = ggml_cann_create_tensor(dst, nullptr, nullptr, 0, ACL_FORMAT_NCHW);
+
+    const int32_t * opts = (const int32_t *) dst->op_params;
+    const int       k0   = opts[1];
+    const int       k1   = opts[2];
+    const int       s0   = opts[3];
+    const int       s1   = opts[4];
+    const int       p0   = opts[5];
+    const int       p1   = opts[6];
+
+    std::vector<int64_t> kernel_dims      = { k1, k0 };
+    std::vector<int64_t> stride_dims      = { s1, s0 };
+    std::vector<int64_t> padding_avg_dims = { p1, p0 };  // (padH, padW)
+
+    acl_int_array_ptr kernel_size  = ggml_cann_create_int_array(kernel_dims.data(), 2);
+    acl_int_array_ptr strides      = ggml_cann_create_int_array(stride_dims.data(), 2);
+    acl_int_array_ptr paddings_avg = ggml_cann_create_int_array(padding_avg_dims.data(), 2);
+
+    bool    ceil_mode         = false;
+    bool    count_include_pad = true;
+    int64_t divisor_override  = 0;
+    int8_t  cube_math_type    = 0;
+#ifdef ASCEND_310P
+    cube_math_type = 1;
+#endif
+
+    GGML_CANN_CALL_ACLNN_OP(ctx, AvgPool2d, acl_src.get(), kernel_size.get(), strides.get(), paddings_avg.get(),
+                            ceil_mode, count_include_pad, divisor_override, cube_math_type, acl_dst.get());
+}
+
+/**
+ * @brief Performs 2D max pooling on the input tensor and stores the result in
+ * the destination tensor.
+ *
+ * This function performs max pooling on the source tensor and stores the result
+ * in the destination tensor. The pooling parameters (kernel size, strides,
+ * padding) are specified in the `op_params` of the destination tensor.
+ *
+ * @param ctx The context for the CANN backend operations.
+ * @param dst The destination tensor where the result will be stored. The source
+ * tensor is referenced by `dst->src[0]`.
+ */
+static void ggml_cann_max_pool2d(ggml_backend_cann_context & ctx, ggml_tensor * dst) {
+    ggml_tensor * src = dst->src[0];
+    GGML_ASSERT(src->type == GGML_TYPE_F32);
+    GGML_ASSERT(dst->type == GGML_TYPE_F32);
+
+    acl_tensor_ptr acl_src = ggml_cann_create_tensor(src, nullptr, nullptr, 0, ACL_FORMAT_NCHW);
+    acl_tensor_ptr acl_dst = ggml_cann_create_tensor(dst, nullptr, nullptr, 0, ACL_FORMAT_NCHW);
+
+    const int32_t * opts = (const int32_t *) dst->op_params;
+    const int       k0   = opts[1];
+    const int       k1   = opts[2];
+    const int       s0   = opts[3];
+    const int       s1   = opts[4];
+    const int       p0   = opts[5];
+    const int       p1   = opts[6];
+
+    int64_t temp_ne[] = { src->ne[0] + p0 * 2, src->ne[1] + p1 * 2, src->ne[2], src->ne[3] };
+    size_t  temp_nb[GGML_MAX_DIMS];
+
+    temp_nb[0] = ggml_element_size(src);
+    for (int i = 1; i < GGML_MAX_DIMS; i++) {
+        temp_nb[i] = temp_nb[i - 1] * temp_ne[i - 1];
+    }
+
+    ggml_cann_pool_alloc temp_buffer_allocator(ctx.pool(), ggml_nbytes(src) + p0 * 2 + p1 * 2 * src->nb[1]);
+    void *               buffer = temp_buffer_allocator.get();
+    acl_tensor_ptr tmp_tensor   = ggml_cann_create_tensor(buffer, ACL_FLOAT, ggml_element_size(src), temp_ne, temp_nb,
+                                                          GGML_MAX_DIMS, ACL_FORMAT_NCHW);
+
+    // pad: see padding in ggml_cann_pad()
+    int64_t paddings[] = { p0, p0, p1, p1, 0, 0, 0, 0 };
+    float   value      = -FLT_MAX;
+    aclnn_pad(ctx, acl_src.get(), tmp_tensor.get(), paddings, value);
+
+    // max_pool
+    std::vector<int64_t> kernel_dims      = { k1, k0 };
+    std::vector<int64_t> stride_dims      = { s1, s0 };
+    // padding_max_dims: [dim0_start, dim0_end, dim1_start, dim1_end]
+    std::vector<int64_t> padding_max_dims = { 0, 0, 0, 0 };
+    std::vector<int64_t> dilation_size    = { 1, 1 };
+    acl_int_array_ptr    kernel_size      = ggml_cann_create_int_array(kernel_dims.data(), 2);
+    acl_int_array_ptr    strides          = ggml_cann_create_int_array(stride_dims.data(), 2);
+    acl_int_array_ptr    paddings_max     = ggml_cann_create_int_array(padding_max_dims.data(), 4);
+    acl_int_array_ptr    dilations        = ggml_cann_create_int_array(dilation_size.data(), 2);
+
+    bool    ceil_mode = false;
+    int64_t auto_pads = 0;
+    GGML_CANN_CALL_ACLNN_OP(ctx, MaxPool, tmp_tensor.get(), kernel_size.get(), strides.get(), auto_pads,
+                            paddings_max.get(), dilations.get(), ceil_mode, acl_dst.get());
+}
+
+void ggml_cann_pool2d(ggml_backend_cann_context & ctx, ggml_tensor * dst) {
+    const int32_t *   opts = (const int32_t *) dst->op_params;
+    enum ggml_op_pool op   = static_cast<ggml_op_pool>(opts[0]);
+    switch (op) {
+        case GGML_OP_POOL_AVG:
+            ggml_cann_avg_pool2d(ctx, dst);
+            break;
+        case GGML_OP_POOL_MAX:
+            ggml_cann_max_pool2d(ctx, dst);
+            break;
+        case GGML_OP_POOL_COUNT:
+            GGML_ABORT("fatal error");
+            break;
+    }
+}
+
+/**
+ * @brief Copies data from the source tensor to the destination tensor.
+ *
+ * This function copies data from the source tensor `acl_src` to the destination
+ * tensor `acl_dst`.
+ *
+ * @param ctx The context for the CANN backend operations.
+ * @param acl_src The source tensor from which data will be copied.
+ * @param acl_dst The destination tensor where the data will be copied to.
+ */
+static void cann_copy(ggml_backend_cann_context & ctx, aclTensor * acl_src, aclTensor * acl_dst) {
+    GGML_CANN_CALL_ACLNN_OP(ctx, InplaceCopy, acl_dst, acl_src);
+}
+
+void ggml_cann_dup(ggml_backend_cann_context & ctx, ggml_tensor * dst) {
+    ggml_tensor * src0 = dst->src[0];
+
+    if (ggml_are_same_shape(src0, dst)) {
+        acl_tensor_ptr acl_src = ggml_cann_create_tensor(src0);
+        acl_tensor_ptr acl_dst = ggml_cann_create_tensor(dst);
+        if (dst->type == src0->type) {
+            cann_copy(ctx, acl_src.get(), acl_dst.get());
+        } else {
+            aclnn_cast(ctx, acl_src.get(), acl_dst.get(), ggml_cann_type_mapping(dst->type));
+        }
+    } else {
+        void *               src_trans_buffer = src0->data;
+        ggml_cann_pool_alloc src_buffer_allocator;
+        if (!ggml_is_contiguous(src0)) {
+            acl_tensor_ptr acl_src = ggml_cann_create_tensor(src0);
+            src_buffer_allocator.alloc(ctx.pool(), ggml_nelements(src0) * ggml_type_size(src0->type));
+            src_trans_buffer = src_buffer_allocator.get();
+            size_t src_trans_nb[GGML_MAX_DIMS];
+            src_trans_nb[0] = ggml_type_size(src0->type);
+            for (int i = 1; i < GGML_MAX_DIMS; i++) {
+                src_trans_nb[i] = src_trans_nb[i - 1] * src0->ne[i - 1];
+            }
+            acl_tensor_ptr src_trans_tensor =
+                ggml_cann_create_tensor(src_trans_buffer, ggml_cann_type_mapping(src0->type),
+                                        ggml_type_size(src0->type), src0->ne, src_trans_nb, GGML_MAX_DIMS);
+            cann_copy(ctx, acl_src.get(), src_trans_tensor.get());
+        }
+
+        size_t src_reshape_nb[GGML_MAX_DIMS];
+        src_reshape_nb[0] = ggml_type_size(src0->type);
+        for (int i = 1; i < GGML_MAX_DIMS; i++) {
+            src_reshape_nb[i] = src_reshape_nb[i - 1] * dst->ne[i - 1];
+        }
+
+        acl_tensor_ptr trans_acl_src =
+            ggml_cann_create_tensor(src_trans_buffer, ggml_cann_type_mapping(src0->type), ggml_type_size(src0->type),
+                                    dst->ne, src_reshape_nb, GGML_MAX_DIMS, ACL_FORMAT_ND);
+        acl_tensor_ptr acl_dst = ggml_cann_create_tensor(dst);
+
+        if (dst->type == src0->type) {
+            cann_copy(ctx, trans_acl_src.get(), acl_dst.get());
+        } else {
+            aclnn_cast(ctx, trans_acl_src.get(), acl_dst.get(), ggml_cann_type_mapping(dst->type));
+        }
+    }
+}
+
+/**
+ * @brief Creates an ACL tensor initialized with zeros using a provided buffer.
+ *
+ * This function initializes a tensor with zeros using the specified buffer and
+ * tensor parameters.
+ *
+ * @param ctx The context for the CANN backend operations.
+ * @param buffer The buffer to be used for the tensor data.
+ * @param n_bytes The size of the buffer in bytes.
+ * @param ne An array specifying the extents (sizes) of each dimension of the
+ * tensor.
+ * @param dims The number of dimensions of the tensor.
+ * @param type The data type of the tensor.
+ * @param type_size The size of each element in the tensor data type.
+ * @return A tensor smart pointer initialized with zeros.
+ */
+static acl_tensor_ptr aclnn_zero(ggml_backend_cann_context & ctx,
+                                 void *                      buffer,
+                                 size_t                      n_bytes,
+                                 int64_t *                   ne,
+                                 int64_t                     dims,
+                                 aclDataType                 type,
+                                 size_t                      type_size) {
+    size_t nb[GGML_MAX_DIMS];
+    nb[0] = type_size;
+    for (int i = 1; i < dims; i++) {
+        nb[i] = nb[i - 1] * ne[i - 1];
+    }
+
+    acl_tensor_ptr zero = ggml_cann_create_tensor(buffer, type, type_size, ne, nb, dims);
+    GGML_CANN_CALL_ACLNN_OP(ctx, InplaceZero, zero.get());
+    return zero;
+    GGML_UNUSED(n_bytes);
+}
+
+/**
+ * @brief Creates an ACL tensor initialized with value using a provided buffer.
+ *
+ * This function initializes a tensor with value using the specified buffer and
+ * tensor parameters.
+ *
+ * @param ctx The context for the CANN backend operations.
+ * @param buffer The buffer to be used for the tensor data.
+ * @param n_bytes The size of the buffer in bytes.
+ * @param ne An array specifying the extents (sizes) of each dimension of the
+ * tensor.
+ * @param dims The number of dimensions of the tensor.
+ * @param type The data type of the tensor.
+ * @param type_size The size of each element in the tensor data type.
+ * @param value The value to be used for initializing the tensor (default
+ * is 1.0).
+ * @return A tensor smart pointer initialized with value.
+ */
+static acl_tensor_ptr aclnn_values(ggml_backend_cann_context & ctx,
+                                   void *                      buffer,
+                                   size_t                      n_bytes,
+                                   int64_t *                   ne,
+                                   int64_t                     dims,
+                                   aclDataType                 type,
+                                   size_t                      type_size,
+                                   float                       value = 1.0f) {
+    acl_tensor_ptr acl_tensor = aclnn_zero(ctx, buffer, n_bytes, ne, dims, type, type_size);
+    float          alpha_host = 1.0f;
+    acl_scalar_ptr alpha      = ggml_cann_create_scalar(&alpha_host, aclDataType::ACL_FLOAT);
+    acl_scalar_ptr other      = ggml_cann_create_scalar(&value, aclDataType::ACL_FLOAT);
+    GGML_CANN_CALL_ACLNN_OP(ctx, InplaceAdds, acl_tensor.get(), other.get(), alpha.get());
+    return acl_tensor;
+}
+
+/**
+ * @brief Fills a tensor with a scalar value.
+ *
+ * This function fills the destination tensor `acl_dst` with the scalar value
+ * `scalar`.
+ *
+ * @param ctx The context for the CANN backend operations.
+ * @param scalar The scalar value used to fill the tensor.
+ * @param acl_dst The destination tensor to be filled with the scalar value.
+ */
+static void aclnn_fill_scalar(ggml_backend_cann_context & ctx, float scalar, aclTensor * acl_dst) {
+    acl_scalar_ptr acl_scalar = ggml_cann_create_scalar(&scalar, aclDataType::ACL_FLOAT);
+    GGML_CANN_CALL_ACLNN_OP(ctx, InplaceFillScalar, acl_dst, acl_scalar.get());
+}
+
+/**
+ * @brief Get or expand a cached tensor filled with a scalar value.
+ *
+ * This function manages cached device memory for tensors. If the current
+ * cache size is insufficient for the requested tensor shape, the old memory will
+ * be released and new memory will be allocated. The allocated buffer is
+ * initialized  with the given scalar value using CANN operations.
+ * Finally, an aclTensor object is created from the cached memory and returned.
+ *
+ * @param ctx           The CANN backend context that manages device memory.
+ * @param buffer        A pointer to the cached device buffer (will be allocated
+ *                      or reallocated if necessary).
+ * @param cache_element The current number of cached elements. This will be
+ *                      updated when the cache is expanded.
+ * @param ne            The tensor shape array (number of elements in each dimension).
+ * @param nb            The stride size for each dimension.
+ * @param dtype         Data type of cached tensor.
+ * @param dims          The number of tensor dimensions.
+ * @param value         The scalar value used to fill the tensor (supports zero
+ *                      initialization via memset or arbitrary values via fill_scalar).
+ * @return              A tensor smart pointer created from the cached buffer.
+ */
+static acl_tensor_ptr get_cache_acl_tensor(ggml_backend_cann_context & ctx,
+                                           void **                     buffer,
+                                           int64_t &                   cache_element,
+                                           int64_t *                   ne,
+                                           size_t *                    nb,
+                                           ggml_type                   dtype,
+                                           int64_t                     dims,
+                                           float                       value) {
+    // Calculate total number of elements
+    int64_t n_element = 1;
+    for (int i = 0; i < dims; i++) {
+        n_element *= ne[i];
+    }
+    size_t size = n_element * ggml_type_size(dtype);
+
+    // Allocate or expand cache if needed
+    if (cache_element < n_element) {
+        if (*buffer != nullptr) {
+            aclrtFree(*buffer);
+            *buffer = nullptr;
+        }
+
+        ACL_CHECK(aclrtMalloc(buffer, size, ACL_MEM_MALLOC_HUGE_FIRST));
+        cache_element = n_element;
+
+        // Initialize cache
+        int64_t        pool_ne[1] = { n_element };
+        size_t         pool_nb[1] = { ggml_type_size(dtype) };
+        acl_tensor_ptr acl_value =
+            ggml_cann_create_tensor(*buffer, ggml_cann_type_mapping(dtype), ggml_type_size(dtype), pool_ne, pool_nb, 1);
+        aclnn_fill_scalar(ctx, value, acl_value.get());
+    }
+
+    return ggml_cann_create_tensor(*buffer, ggml_cann_type_mapping(dtype), ggml_type_size(dtype), ne, nb, dims);
+}
+
+void ggml_cann_rms_norm(ggml_backend_cann_context & ctx, ggml_tensor * dst) {
+    ggml_tensor * src = dst->src[0];
+
+    acl_tensor_ptr acl_src = ggml_cann_create_tensor(src);
+    acl_tensor_ptr acl_dst = ggml_cann_create_tensor(dst);
+
+    float eps;
+    memcpy(&eps, dst->op_params, sizeof(float));
+
+    // build gamma.
+    size_t acl_gamma_nb[GGML_MAX_DIMS];
+    // gamma's type is the same with dst.
+    acl_gamma_nb[0] = ggml_type_size(dst->type);
+    for (int i = 1; i < GGML_MAX_DIMS; i++) {
+        acl_gamma_nb[i] = acl_gamma_nb[i - 1] * src->ne[i - 1];
+    }
+    acl_tensor_ptr acl_gamma = get_cache_acl_tensor(
+        ctx, &ctx.rms_norm_one_tensor_cache.cache, ctx.rms_norm_one_tensor_cache.size, src->ne, acl_gamma_nb, dst->type,
+        1,    // dims
+        1.0f  // value
+    );
+
+    // build rstd.
+    int64_t acl_rstd_ne[] = { src->ne[1], src->ne[2], src->ne[3] };
+    size_t  acl_rstd_nb[GGML_MAX_DIMS - 1];
+    // rstd will always be F32.
+    acl_rstd_nb[0] = sizeof(float);
+    for (int i = 1; i < GGML_MAX_DIMS - 1; i++) {
+        acl_rstd_nb[i] = acl_rstd_nb[i - 1] * acl_rstd_ne[i - 1];
+    }
+    acl_tensor_ptr acl_rstd =
+        get_cache_acl_tensor(ctx, &ctx.rms_norm_zero_tensor_cache.cache, ctx.rms_norm_zero_tensor_cache.size,
+                             acl_rstd_ne, acl_rstd_nb, GGML_TYPE_F32, GGML_MAX_DIMS - 1,
+                             0.0f  // value
+        );
+
+    GGML_CANN_CALL_ACLNN_OP(ctx, RmsNorm, acl_src.get(), acl_gamma.get(), eps, acl_dst.get(), acl_rstd.get());
+}
+
+// TODO: performace is low.
+void ggml_cann_diag_mask(ggml_backend_cann_context & ctx, ggml_tensor * dst, float value) {
+    ggml_tensor * src = dst->src[0];
+
+    acl_tensor_ptr acl_src = ggml_cann_create_tensor(src);
+    acl_tensor_ptr acl_dst = ggml_cann_create_tensor(dst);
+
+    const int n_past = ((int32_t *) dst->op_params)[0];
+
+    ggml_cann_pool_alloc one_tensor_allocator(ctx.pool(), ggml_nbytes(src));
+    void *               buffer = one_tensor_allocator.get();
+
+    acl_tensor_ptr mask_tensor = ggml_cann_create_tensor(buffer, ggml_cann_type_mapping(src->type),
+                                                         ggml_type_size(src->type), src->ne, src->nb, GGML_MAX_DIMS);
+
+    aclnn_fill_scalar(ctx, value, mask_tensor.get());
+
+    float          alphaValue = 1.0f;
+    acl_scalar_ptr alpha      = ggml_cann_create_scalar(&alphaValue, aclDataType::ACL_FLOAT);
+
+    GGML_CANN_CALL_ACLNN_OP(ctx, InplaceTriu, mask_tensor.get(), n_past + 1);
+    GGML_CANN_CALL_ACLNN_OP(ctx, Tril, acl_src.get(), n_past + 1, acl_dst.get());
+    GGML_CANN_CALL_ACLNN_OP(ctx, InplaceAdd, acl_dst.get(), mask_tensor.get(), alpha.get());
+}
+
+/**
+ * @brief Permutes the dimensions of a tensor according to a specified order.
+ *
+ * This function permutes the dimensions of the source tensor `acl_src`
+ * according to the order specified in the `new_dim` array and stores the result
+ * in the destination tensor `acl_dst`.
+ *
+ * @param ctx The context for the CANN backend operations.
+ * @param acl_src The source tensor whose dimensions will be permuted.
+ * @param acl_dst The destination tensor where the permuted result will be
+ * stored.
+ * @param new_dim An array specifying the new order of dimensions for the
+ * tensor.
+ * @param dims The number of dimensions in the tensor.
+ */
+static void aclnn_permute(ggml_backend_cann_context & ctx,
+                          aclTensor *                 acl_src,
+                          aclTensor *                 acl_dst,
+                          int64_t *                   new_dim,
+                          uint64_t                    dims) {
+    acl_int_array_ptr acl_dims = ggml_cann_create_int_array(new_dim, dims);
+    GGML_CANN_CALL_ACLNN_OP(ctx, Permute, acl_src, acl_dims.get(), acl_dst);
+}
+
+static void ggml_cann_im2col_2d_post_process(ggml_backend_cann_context & ctx,
+                                             ggml_tensor *               dst,
+                                             ggml_tensor *               src1,
+                                             aclTensor *                 tmp_cast_tensor,
+                                             aclTensor *                 tmp_im2col_tensor) {
+    // Permute: [N, IC * KH * KW, OW * OH] -> [N, OW * OH, IC * KH * KW]
+    int64_t        dst_ne[] = { dst->ne[0], dst->ne[1] * dst->ne[2], dst->ne[3] };
+    size_t         dst_nb[] = { dst->nb[0], dst->nb[1], dst->nb[3] };
+    acl_tensor_ptr acl_dst  = ggml_cann_create_tensor(dst, dst_ne, dst_nb, GGML_MAX_DIMS - 1);
+
+    int64_t permute_dim[] = { 0, 2, 1 };
+    if (src1->type != dst->type) {
+        aclnn_permute(ctx, tmp_cast_tensor, acl_dst.get(), permute_dim, 3);
+    } else {
+        aclnn_permute(ctx, tmp_im2col_tensor, acl_dst.get(), permute_dim, 3);
+    }
+}
+
+static void ggml_cann_im2col_1d_post_process(ggml_backend_cann_context &  ctx,
+                                             ggml_tensor *                dst,
+                                             ggml_tensor *                src1,
+                                             aclTensor *                  tmp_cast_tensor,
+                                             aclTensor *                  tmp_im2col_tensor,
+                                             const std::vector<int64_t> & im2col_op_params) {
+    // get params
+    const int64_t KH             = im2col_op_params[0];
+    const int64_t KW             = im2col_op_params[1];
+    const int64_t IW             = im2col_op_params[2];
+    const int64_t IC             = im2col_op_params[3];
+    const int64_t N              = im2col_op_params[4];
+    const int64_t OH             = im2col_op_params[5];
+    const int64_t OW             = im2col_op_params[6];
+    const int64_t s0             = im2col_op_params[7];
+    const int64_t p0             = im2col_op_params[8];
+    const int64_t d0             = im2col_op_params[9];
+    const int64_t n_bytes_factor = im2col_op_params[10];
+
+    // Permute: [N, IC * KH * KW, OW * OH] ->
+    // [N, OW * OH * n_bytes_factor, IC * KH * KW]
+    ggml_cann_pool_alloc tmp_permute_allocator(ctx.pool());
+    tmp_permute_allocator.alloc(ggml_nbytes(dst) * n_bytes_factor);
+    void * tmp_permute_buffer = tmp_permute_allocator.get();
+
+    int64_t tmp_permute_ne[] = { IC * KH * KW, OW * OH * n_bytes_factor, N };
+    size_t  tmp_permute_nb[GGML_MAX_DIMS - 1];
+    tmp_permute_nb[0] = ggml_type_size(dst->type);
+    for (int i = 1; i < GGML_MAX_DIMS - 1; i++) {
+        tmp_permute_nb[i] = tmp_permute_nb[i - 1] * tmp_permute_ne[i - 1];
+    }
+
+    acl_tensor_ptr tmp_permute_tensor =
+        ggml_cann_create_tensor(tmp_permute_buffer, ggml_cann_type_mapping(dst->type), ggml_type_size(dst->type),
+                                tmp_permute_ne, tmp_permute_nb, GGML_MAX_DIMS - 1, ACL_FORMAT_ND);
+
+    int64_t permute_dim[] = { 0, 2, 1 };
+    if (src1->type != dst->type) {
+        aclnn_permute(ctx, tmp_cast_tensor, tmp_permute_tensor.get(), permute_dim, 3);
+    } else {
+        aclnn_permute(ctx, tmp_im2col_tensor, tmp_permute_tensor.get(), permute_dim, 3);
+    }
+
+    // number of times the kernel moves in W dimension
+    const int n_step_w = (IW + 2 * p0 - d0 * (KW - 1) - 1) / s0 + 1;
+    size_t    offset;
+    void *    cur_dst_buffer = dst->data, *cur_permute_buffer = tmp_permute_buffer;
+
+    // memory copy with offset to restore 1D im2col from 2d
+    if (IC > 1) {
+        offset          = IC * KH * KW * n_step_w * ggml_type_size(dst->type);
+        size_t cpy_size = KH * KW * ggml_type_size(dst->type);
+
+        for (int c = 0; c < IC; c++) {
+            cur_permute_buffer = (char *) tmp_permute_buffer + offset + KH * KW * c * ggml_type_size(dst->type);
+            cur_dst_buffer     = (char *) dst->data + c * KH * KW * n_step_w * ggml_type_size(dst->type);
+
+            for (int i = 0; i < n_step_w; i++) {
+                ACL_CHECK(aclrtMemcpyAsync(cur_dst_buffer, cpy_size, cur_permute_buffer, cpy_size,
+                                           ACL_MEMCPY_DEVICE_TO_DEVICE, ctx.stream()));
+                cur_dst_buffer     = (char *) cur_dst_buffer + KH * KW * ggml_type_size(dst->type);
+                cur_permute_buffer = (char *) cur_permute_buffer + KH * KW * IC * ggml_type_size(dst->type);
+            }
+        }
+    } else {
+        offset = KH * KW * n_step_w * ggml_type_size(dst->type);  // equal to ggml_nbytes(dst)
+        ACL_CHECK(aclrtMemcpyAsync(dst->data, offset, (char *) tmp_permute_buffer + offset, offset,
+                                   ACL_MEMCPY_DEVICE_TO_DEVICE, ctx.stream()));
+    }
+}
+
+void ggml_cann_im2col(ggml_backend_cann_context & ctx, ggml_tensor * dst) {
+    ggml_tensor * src0 = dst->src[0];  // kernel
+    ggml_tensor * src1 = dst->src[1];  // input
+
+    GGML_TENSOR_BINARY_OP_LOCALS;
+
+    // aclnnIm2col only works on 2D. set s1, p1, d1 to 1 to perform 2D
+    // im2col and do post-processing to restore it to 1D.
+    const bool    is_2D = ((const int32_t *) (dst->op_params))[6] == 1;
+    const int32_t s0    = ((const int32_t *) (dst->op_params))[0];
+    const int32_t s1    = is_2D ? ((const int32_t *) (dst->op_params))[1] : 1;
+    const int32_t p0    = ((const int32_t *) (dst->op_params))[2];
+    const int32_t p1    = is_2D ? ((const int32_t *) (dst->op_params))[3] : 1;
+    const int32_t d0    = ((const int32_t *) (dst->op_params))[4];
+    const int32_t d1    = is_2D ? ((const int32_t *) (dst->op_params))[5] : 1;
+
+    const int64_t N  = ne13;
+    const int64_t IC = ne12;
+    const int64_t KH = ne01;
+    const int64_t KW = ne00;
+    const int64_t IW = ne10;
+
+    const int64_t OH = is_2D ? ne2 : 1;
+    const int64_t OW = ne1;
+
+    // memory allocated increased to 3x when is_2D == false
+    const int64_t n_bytes_factor = is_2D ? 1 : 3;
+
+    // im2col: [N,C,H,W] -> [N, IC * KH * KW, OW * OH * n_bytes_factor]
+    acl_tensor_ptr acl_src1        = ggml_cann_create_tensor(src1);
+    int64_t        tmp_im2col_ne[] = { OW * OH * n_bytes_factor, IC * KH * KW, N };
+    size_t         tmp_im2col_nb[GGML_MAX_DIMS - 1];
+
+    tmp_im2col_nb[0] = ggml_type_size(src1->type);
+    for (int i = 1; i < GGML_MAX_DIMS - 1; i++) {
+        tmp_im2col_nb[i] = tmp_im2col_nb[i - 1] * tmp_im2col_ne[i - 1];
+    }
+
+    // Calculate im2col.
+    // If dst is f16, tmp_buffer is f32, we need alloc src.typesize *
+    // dst.elemcount.
+    ggml_cann_pool_alloc im2col_allocator(ctx.pool(), ggml_nelements(dst) * ggml_element_size(src1) * n_bytes_factor);
+    void *               tmp_im2col_buffer = im2col_allocator.get();
+
+    acl_tensor_ptr tmp_im2col_tensor =
+        ggml_cann_create_tensor(tmp_im2col_buffer, ggml_cann_type_mapping(src1->type), ggml_type_size(src1->type),
+                                tmp_im2col_ne, tmp_im2col_nb, GGML_MAX_DIMS - 1, ACL_FORMAT_ND);
+
+    std::vector<int64_t> kernel_dims   = { KH, KW };
+    std::vector<int64_t> dilation_size = { d1, d0 };
+    std::vector<int64_t> padding_dims  = { p1, p0 };
+    std::vector<int64_t> stride_dims   = { s1, s0 };
+    acl_int_array_ptr    kernel_size   = ggml_cann_create_int_array(kernel_dims.data(), 2);
+    acl_int_array_ptr    dilations     = ggml_cann_create_int_array(dilation_size.data(), 2);
+    acl_int_array_ptr    paddings      = ggml_cann_create_int_array(padding_dims.data(), 2);
+    acl_int_array_ptr    strides       = ggml_cann_create_int_array(stride_dims.data(), 2);
+    GGML_CANN_CALL_ACLNN_OP(ctx, Im2col, acl_src1.get(), kernel_size.get(), dilations.get(), paddings.get(),
+                            strides.get(), tmp_im2col_tensor.get());
+
+    // Cast if dst is f16.
+    acl_tensor_ptr       tmp_cast_tensor;
+    ggml_cann_pool_alloc tmp_cast_allocator(ctx.pool());
+    void *               tmp_cast_buffer = nullptr;
+    if (src1->type != dst->type) {
+        tmp_cast_allocator.alloc(ggml_nbytes(dst) * n_bytes_factor);
+        tmp_cast_buffer = tmp_cast_allocator.get();
+        size_t temp_cast_nb[GGML_MAX_DIMS - 1];
+        temp_cast_nb[0] = ggml_type_size(dst->type);
+        for (int i = 1; i < GGML_MAX_DIMS - 1; i++) {
+            temp_cast_nb[i] = temp_cast_nb[i - 1] * tmp_im2col_ne[i - 1];
+        }
+
+        tmp_cast_tensor =
+            ggml_cann_create_tensor(tmp_cast_buffer, ggml_cann_type_mapping(dst->type), ggml_type_size(dst->type),
+                                    tmp_im2col_ne, temp_cast_nb, GGML_MAX_DIMS - 1, ACL_FORMAT_ND);
+        aclnn_cast(ctx, tmp_im2col_tensor.get(), tmp_cast_tensor.get(), ggml_cann_type_mapping(dst->type));
+    }
+
+    // post-processing
+    if (is_2D) {
+        ggml_cann_im2col_2d_post_process(ctx, dst, src1, tmp_cast_tensor.get(), tmp_im2col_tensor.get());
+    } else {
+        std::vector<int64_t> im2col_op_params = { KH, KW, IW, IC, N, OH, OW, s0, p0, d0, n_bytes_factor };
+        ggml_cann_im2col_1d_post_process(ctx, dst, src1, tmp_cast_tensor.get(), tmp_im2col_tensor.get(),
+                                         im2col_op_params);
+    }
+}
+
+/**
+ * @brief Applies element-wise exponential function to the elements of a tensor.
+ *
+ * This function computes the exponential of each element in the source tensor
+ * `acl_src` and stores the result back into the same tensor.
+ * The operation is defined as:
+ * \f[
+ *     \text {acl_src }_i=e^{acl\_src_i}
+ * \f]
+ *
+ * @param ctx The context for the CANN backend operations.
+ * @param acl_src The tensor on which the exponential function will be applied.
+ */
+static void aclnn_exp(ggml_backend_cann_context & ctx, aclTensor * acl_src) {
+    GGML_CANN_CALL_ACLNN_OP(ctx, InplaceExp, acl_src);
+}
+
+void aclnn_cos(ggml_backend_cann_context & ctx, aclTensor * acl_src, aclTensor * acl_dst) {
+    if (acl_dst == nullptr) {
+        GGML_CANN_CALL_ACLNN_OP(ctx, InplaceCos, acl_src);
+    } else {
+        GGML_CANN_CALL_ACLNN_OP(ctx, Cos, acl_src, acl_dst);
+    }
+}
+
+void aclnn_sin(ggml_backend_cann_context & ctx, aclTensor * acl_src, aclTensor * acl_dst) {
+    if (acl_dst == nullptr) {
+        GGML_CANN_CALL_ACLNN_OP(ctx, InplaceSin, acl_src);
+    } else {
+        GGML_CANN_CALL_ACLNN_OP(ctx, Sin, acl_src, acl_dst);
+    }
+}
+
+void ggml_cann_timestep_embedding(ggml_backend_cann_context & ctx, ggml_tensor * dst) {
+    const ggml_tensor * src = dst->src[0];
+
+    GGML_ASSERT(src->type == GGML_TYPE_F32);
+    GGML_ASSERT(dst->type == GGML_TYPE_F32);
+
+    const int dim        = dst->op_params[0];
+    const int max_period = dst->op_params[1];
+    int       half       = dim / 2;
+
+    acl_tensor_ptr acl_src = ggml_cann_create_tensor(src);
+
+    // arange: [0, ..., half)
+    float   start             = 0;
+    float   stop              = half;
+    float   step              = 1;
+    int64_t n_elements_arange = half;
+    int64_t tmp_arange_ne[]   = { half };
+    size_t  tmp_arange_nb[]   = { sizeof(dst->type) };
+
+    ggml_cann_pool_alloc arange_allocator(ctx.pool(), half * sizeof(dst->type));
+    void *               tmp_arange_buffer = arange_allocator.get();
+    acl_tensor_ptr       tmp_arange_tensor =
+        ggml_cann_create_tensor(tmp_arange_buffer, ggml_cann_type_mapping(dst->type), ggml_type_size(dst->type),
+                                tmp_arange_ne, tmp_arange_nb, GGML_MAX_DIMS - 3, ACL_FORMAT_ND);
+
+    aclnn_arange(ctx, tmp_arange_tensor.get(), start, stop, step, n_elements_arange);
+
+    // freq
+    float freq_param = -logf(max_period) / half;
+    bool  inplace    = true;
+    aclnn_muls(ctx, tmp_arange_tensor.get(), freq_param, nullptr, inplace);
+    aclnn_exp(ctx, tmp_arange_tensor.get());
+
+    // permute: src [0,1,2,3]->[0,1,3,2]
+    int64_t tmp_permute_ne[] = { src->ne[1], src->ne[0], src->ne[2], src->ne[3] };
+    size_t  tmp_permute_nb[GGML_MAX_DIMS];
+    tmp_permute_nb[0] = ggml_type_size(src->type);
+    for (int i = 1; i < GGML_MAX_DIMS; i++) {
+        tmp_permute_nb[i] = tmp_permute_nb[i - 1] * tmp_permute_ne[i - 1];
+    }
+
+    ggml_cann_pool_alloc permute_allocator(ctx.pool(), ggml_nbytes(src));
+    void *               tmp_permute_buffer = permute_allocator.get();
+    acl_tensor_ptr       tmp_permute_tensor =
+        ggml_cann_create_tensor(tmp_permute_buffer, ggml_cann_type_mapping(src->type), ggml_type_size(src->type),
+                                tmp_permute_ne, tmp_permute_nb, GGML_MAX_DIMS, ACL_FORMAT_ND);
+    int64_t permute_dim[] = { 0, 1, 3, 2 };
+    int64_t num_dims      = 4;
+    aclnn_permute(ctx, acl_src.get(), tmp_permute_tensor.get(), permute_dim, num_dims);
+
+    // timestep * freq
+    int64_t tmp_mul_ne[] = { src->ne[1] * half, src->ne[0], src->ne[2], src->ne[3] };
+    size_t  tmp_mul_nb[GGML_MAX_DIMS];
+    tmp_mul_nb[0] = ggml_type_size(src->type);
+    for (int i = 1; i < GGML_MAX_DIMS; i++) {
+        tmp_mul_nb[i] = tmp_mul_nb[i - 1] * tmp_mul_ne[i - 1];
+    }
+
+    int mul_nelements = src->ne[1] * half * src->ne[0] * src->ne[2] * src->ne[3];
+
+    ggml_cann_pool_alloc mul_allocator(ctx.pool(), mul_nelements * ggml_type_size(src->type));
+    void *               tmp_mul_buffer = mul_allocator.get();
+    acl_tensor_ptr       tmp_mul_tensor =
+        ggml_cann_create_tensor(tmp_mul_buffer, ggml_cann_type_mapping(src->type), ggml_type_size(src->type),
+                                tmp_mul_ne, tmp_mul_nb, GGML_MAX_DIMS, ACL_FORMAT_ND);
+    aclnn_mul(ctx, tmp_permute_tensor.get(), tmp_arange_tensor.get(), tmp_mul_tensor.get());
+
+    // cos
+    ggml_cann_pool_alloc cos_allocator(ctx.pool(), mul_nelements * ggml_type_size(src->type));
+    void *               tmp_cos_buffer = cos_allocator.get();
+    acl_tensor_ptr       tmp_cos_tensor =
+        ggml_cann_create_tensor(tmp_cos_buffer, ggml_cann_type_mapping(dst->type), ggml_type_size(dst->type),
+                                tmp_mul_ne, tmp_mul_nb, GGML_MAX_DIMS, ACL_FORMAT_ND);
+
+    aclnn_cos(ctx, tmp_mul_tensor.get(), tmp_cos_tensor.get());
+
+    // sin
+    ggml_cann_pool_alloc sin_allocator(ctx.pool(), mul_nelements * ggml_type_size(src->type));
+    void *               tmp_sin_buffer = sin_allocator.get();
+    acl_tensor_ptr       tmp_sin_tensor =
+        ggml_cann_create_tensor(tmp_sin_buffer, ggml_cann_type_mapping(dst->type), ggml_type_size(dst->type),
+                                tmp_mul_ne, tmp_mul_nb, GGML_MAX_DIMS, ACL_FORMAT_ND);
+
+    aclnn_sin(ctx, tmp_mul_tensor.get(), tmp_sin_tensor.get());
+
+    // concat
+    int64_t             concat_dim  = 3;
+    acl_tensor_ptr      acl_dst     = ggml_cann_create_tensor(dst);
+    acl_tensor_list_ptr tensor_list = ggml_cann_create_tensor_list(tmp_cos_tensor, tmp_sin_tensor);
+    aclnn_concat(ctx, tensor_list.get(), acl_dst.get(), concat_dim);
+}
+
+/**
+ * @brief Raises each element of a tensor to the power of the corresponding
+ * element in another tensor.
+ *
+ * This function computes the element-wise power of the destination tensor
+ * `acl_dst` raised to the power of the exponent tensor `acl_exp`.
+ * The operation is defined as:
+ * \f[
+ *     \text {acl_dst }_i=acl\_dst_i^{\text {acl_exp }_i}
+ * \f]
+ *
+ * @param ctx The context for the CANN backend operations.
+ * @param acl_dst The destination tensor, which also serves as the base tensor.
+ * @param acl_exp The exponent tensor, each element of which is used to raise
+ * the corresponding element in the destination tensor.
+ */
+static void aclnn_pow_tensor_tensor(ggml_backend_cann_context & ctx, aclTensor * acl_dst, aclTensor * acl_exp) {
+    GGML_CANN_CALL_ACLNN_OP(ctx, InplacePowTensorTensor, acl_dst, acl_exp);
+}
+
+/**
+ * @brief Generate a range of values and apply a scalar base exponentiation.
+ *
+ * This function creates an evenly spaced sequence from `start` to `stop` (exclusive),
+ * with step size `step`, stores it in a temporary buffer, and then computes:
+ *
+ * @f[
+ * slope[i] = m^{\left( start + i \cdot step \right)}, \quad 0 \le i < size
+ * @f]
+ *
+ * The results are written to the provided @p slope_buffer.
+ *
+ * @param ctx           CANN backend context for memory allocation and operator execution.
+ * @param slope_buffer  Pointer to the output buffer (float array) for the computed slope values.
+ * @param m             Scalar base for the exponentiation.
+ * @param size          Number of elements in the generated sequence.
+ * @param start         Starting exponent offset.
+ * @param stop          Stopping exponent offset (exclusive).
+ * @param step          Step size for the exponent increment.
+ * @param dtype         Data type for slope tensor.
+ */
+static void aclnn_get_slope_inner(ggml_backend_cann_context & ctx,
+                                  void *                      slope_buffer,
+                                  float                       m,
+                                  int64_t                     size,
+                                  float                       start,
+                                  float                       stop,
+                                  float                       step,
+                                  ggml_type                   dtype) {
+    aclDataType acl_type  = ggml_cann_type_mapping(dtype);
+    size_t      type_size = ggml_type_size(dtype);
+
+    int64_t ne[] = { size };
+    size_t  nb[] = { type_size };
+
+    ggml_cann_pool_alloc arange_allocator(ctx.pool(), size * type_size);
+    void *               arange_buffer = arange_allocator.get();
+
+    acl_tensor_ptr arange_tensor = ggml_cann_create_tensor(arange_buffer, acl_type, type_size, ne, nb, 1);
+    aclnn_arange(ctx, arange_tensor.get(), start, stop, step, size);
+
+    acl_tensor_ptr slope_tensor = ggml_cann_create_tensor(slope_buffer, acl_type, type_size, ne, nb, 1);
+
+    acl_scalar_ptr sc = ggml_cann_create_scalar(&m, aclDataType::ACL_FLOAT);
+
+    GGML_CANN_CALL_ACLNN_OP(ctx, PowScalarTensor, sc.get(), arange_tensor.get(), slope_tensor.get());
+}
+
+/**
+ * @brief Compute slope values for multiple attention heads based on ALiBi bias parameters.
+ *
+ * This function generates slope values for each attention head according to the ALiBi
+ * (Attention with Linear Biases) method. It splits the computation into two ranges depending
+ * on whether the head index is less than @p n_head_log2 or not, and uses different base values
+ * (`m0` and `m1`) for the exponentiation.
+ *
+ * @f[
+ * slope[h] =
+ * \begin{cases}
+ * m_0^{(h + 1)}, & h < n\_head\_log2 \\
+ * m_1^{\left( 2 \cdot (h - n\_head\_log2) + 1 \right)}, & h \geq n\_head\_log2
+ * \end{cases}
+ * \quad , \quad \text{if } max\_bias > 0
+ * @f]
+ *
+ * If @p max_bias <= 0, all slope values are set to 1.0.
+ *
+ * @param ctx           CANN backend context for memory allocation and operator execution.
+ * @param n_head        Total number of attention heads.
+ * @param slope_buffer  Pointer to the output buffer (float array) for storing slopes.
+ * @param max_bias      Maximum bias value for slope computation.
+ * @param dtype         Data type for slope tensor.
+ *
+*/
+static void aclnn_get_slope(ggml_backend_cann_context & ctx,
+                            int64_t                     n_head,
+                            void *                      slope_buffer,
+                            float                       max_bias,
+                            ggml_type                   dtype) {
+    const int n_head_log2 = 1u << (uint32_t) floor(log2(n_head));
+
+    float m0 = powf(2.0f, -(max_bias) / n_head_log2);
+    float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2);
+
+    // const float slope = (max_bias > 0.0f) ?
+    //                          h < n_head_log2 ?
+    //                              powf(m0, h + 1) :
+    //                              powf(m1, 2*(h - n_head_log2) + 1) :
+    //                          1.0f;
+    // arange1
+    float start = 0 + 1;
+    float end   = (n_head_log2 - 1) + 1;
+    float step  = 1;
+    float count = n_head_log2;
+    // end needs to be +1 because aclnn uses a left-closed, right-open interval.
+    aclnn_get_slope_inner(ctx, slope_buffer, m0, count, start, end + 1, step, dtype);
+    if (n_head_log2 < n_head) {
+        // arange2
+        start = 2 * (n_head_log2 - n_head_log2) + 1;
+        end   = 2 * ((n_head - 1) - n_head_log2) + 1;
+        step  = 2;
+        count = n_head - n_head_log2;
+        aclnn_get_slope_inner(ctx, (char *) slope_buffer + n_head_log2 * sizeof(float), m1, count, start, end + 1, step,
+                              dtype);
+    }
+}
+
+/**
+ * @brief Add ALiBi (Attention with Linear Biases) positional biases to the attention mask.
+ *
+ * This function computes the ALiBi slopes for each attention head (if max_bias > 0),
+ * multiplies them with the attention mask to produce bias tensors, and adds these biases
+ * to the destination tensor (@p dst).
+ *
+ * The function performs necessary broadcasting of the mask and slope tensors to match
+ * the shape of the destination tensor, then applies element-wise multiplication and addition
+ * using CANN operators.
+ *
+ * @param ctx         CANN backend context for memory management and operator execution.
+ * @param mask        Input attention mask tensor, assumed to be contiguous.
+ * @param dst         Destination tensor to which ALiBi biases will be added.
+ * @param dst_ptr     Pointer to the memory of the destination tensor.
+ * @param max_bias    Maximum bias value controlling the slope scaling.
+ *
+ * @note
+ * - Write data into dst_ptr using only the shape information of the dst tensor.
+ * - `GGML_MAX_DIMS + 2` is used to extend tensor dimensions for broadcasting.
+ */
+static void aclnn_add_alibi(ggml_backend_cann_context & ctx,
+                            ggml_tensor *               mask,
+                            ggml_tensor *               dst,
+                            void *                      dst_ptr,
+                            float                       max_bias) {
+    void * slope_buffer = nullptr;
+    void * bias_buffer  = nullptr;
+
+    if (max_bias > 0.0f) {
+        int64_t              n_heads = dst->ne[2];
+        ggml_cann_pool_alloc slope_allocator(ctx.pool(), n_heads * sizeof(float));
+        slope_buffer = slope_allocator.get();
+        ggml_cann_pool_alloc bias_allocator(ctx.pool(), ggml_nelements(dst) * ggml_element_size(dst));
+        bias_buffer = bias_allocator.get();
+        aclnn_get_slope(ctx, n_heads, slope_buffer, max_bias, GGML_TYPE_F32);
+    }
+
+    // broadcast for mask, slop and dst;
+    int64_t nr2 = dst->ne[2] / mask->ne[2];
+    int64_t nr3 = dst->ne[3] / mask->ne[3];
+
+    // broadcast the mask across rows
+    int64_t mask_ne[] = { mask->ne[0], dst->ne[1], mask->ne[2], 1, mask->ne[3], 1 };
+    size_t  mask_nb[] = { mask_nb[0] = mask->nb[0], mask_nb[1] = mask->nb[1], mask_nb[2] = mask->nb[2],
+                          mask_nb[3] = mask->nb[2], mask_nb[4] = mask->nb[3], mask_nb[5] = mask->nb[3] };
+
+    int64_t dst_ne[] = { dst->ne[0], dst->ne[1], mask->ne[2], nr2, mask->ne[3], nr3 };
+    size_t  dst_nb[] = { dst_nb[0] = dst->nb[0], dst_nb[1] = dst->nb[1], dst_nb[2] = dst->nb[2],
+                         dst_nb[3] = dst->nb[2], dst_nb[4] = dst->nb[3], dst_nb[5] = dst->nb[3] };
+
+    // slope is a 1 dim tensor, slope.ne2 == dst.ne2
+    int64_t slope_ne[] = { 1, 1, mask->ne[2], nr2, 1, 1 };
+    size_t  slope_nb[GGML_MAX_DIMS + 2];
+    slope_nb[0] = sizeof(float);
+    for (int i = 1; i < GGML_MAX_DIMS + 2; i++) {
+        slope_nb[i] = slope_nb[i - 1] * slope_ne[i - 1];
+    }
+
+    acl_tensor_ptr acl_slope =
+        ggml_cann_create_tensor(slope_buffer, ACL_FLOAT, sizeof(float), slope_ne, slope_nb, GGML_MAX_DIMS + 2);
+    acl_tensor_ptr acl_mask = ggml_cann_create_tensor(mask, mask_ne, mask_nb, GGML_MAX_DIMS + 2);
+
+    // write data into dst_ptr using only the shape information of the dst tensor.
+    acl_tensor_ptr acl_dst = ggml_cann_create_tensor(dst_ptr, ggml_cann_type_mapping(dst->type),
+                                                     ggml_type_size(dst->type), dst_ne, dst_nb, GGML_MAX_DIMS + 2);
+
+    if (max_bias > 0.0f) {
+        int64_t bias_ne[] = { mask->ne[0], dst->ne[1], mask->ne[2], nr2, mask->ne[3], 1 };
+        size_t  bias_nb[GGML_MAX_DIMS + 2];
+        bias_nb[0] = sizeof(float);
+        for (int i = 1; i < GGML_MAX_DIMS + 2; i++) {
+            bias_nb[i] = bias_nb[i - 1] * bias_ne[i - 1];
+        }
+        acl_tensor_ptr bias_tensor =
+            ggml_cann_create_tensor(bias_buffer, ACL_FLOAT, sizeof(float), bias_ne, bias_nb, GGML_MAX_DIMS + 2);
+
+        aclnn_mul(ctx, acl_slope.get(), acl_mask.get(), bias_tensor.get());
+        aclnn_add(ctx, acl_dst.get(), bias_tensor.get());
+    } else {
+        aclnn_add(ctx, acl_dst.get(), acl_mask.get());
+    }
+}
+
+void ggml_cann_cpy(ggml_backend_cann_context & ctx, ggml_tensor * dst) {
+    ggml_cann_dup(ctx, dst);
+}
+
+/**
+ * @brief Applies the softmax function to a tensor along a specified dimension.
+ *
+ * This function computes the softmax of the source tensor `acl_src` along the
+ * specified dimension `dim` and stores the result in the destination tensor
+ * `acl_dst`.
+ *
+ * @param ctx The context for the CANN backend operations.
+ * @param acl_src The source tensor on which the softmax function will be
+ * applied.
+ * @param dim The dimension along which the softmax function will be computed.
+ * @param acl_dst The destination tensor where the softmax results will be
+ * stored.
+ */
+static void aclnn_softmax(ggml_backend_cann_context & ctx, aclTensor * acl_src, int64_t dim, aclTensor * acl_dst) {
+    GGML_CANN_CALL_ACLNN_OP(ctx, Softmax, acl_src, dim, acl_dst);
+}
+
+void ggml_cann_softmax(ggml_backend_cann_context & ctx, ggml_tensor * dst) {
+    ggml_tensor * src0 = dst->src[0];
+    ggml_tensor * src1 = dst->src[1];  // mask
+
+    acl_tensor_ptr acl_src0 = ggml_cann_create_tensor(src0);
+    acl_tensor_ptr acl_dst  = ggml_cann_create_tensor(dst);
+
+    float scale    = 1.0f;
+    float max_bias = 0.0f;
+
+    memcpy(&scale, (float *) dst->op_params + 0, sizeof(float));
+    memcpy(&max_bias, (float *) dst->op_params + 1, sizeof(float));
+
+    // input mul scale
+    acl_scalar_ptr       acl_scale = ggml_cann_create_scalar(&scale, aclDataType::ACL_FLOAT);
+    ggml_cann_pool_alloc src_tensor_allocator(ctx.pool(), ggml_nbytes(src0));
+    void *               src_tensor_buffer = src_tensor_allocator.get();
+    acl_tensor_ptr       softmax_tensor = ggml_cann_create_tensor(src_tensor_buffer, ggml_cann_type_mapping(src0->type),
+                                                                  ggml_element_size(src0), src0->ne, src0->nb, GGML_MAX_DIMS);
+
+    aclnn_muls(ctx, acl_src0.get(), scale, softmax_tensor.get(), false);
+
+    // mask
+    if (src1) {
+        aclnn_add_alibi(ctx, src1, src0, src_tensor_buffer, max_bias);
+    }
+    // softmax
+    aclnn_softmax(ctx, softmax_tensor.get(), 3, acl_dst.get());
+}
+
+/**
+ * @brief Performs index select operation on a 4D tensor using the CANN backend.
+ *
+ * This function applies the `IndexSelect` operation along a specific dimension
+ * of the source tensor (`src_buffer`) using the indices from the index tensor (`index`).
+ * It iterates over the last two dimensions of the source tensor, creates the corresponding
+ * CANN tensors for the source, index, and output slices, and executes the `IndexSelect`
+ * operation for each slice.
+ *
+ * @param ctx The context for CANN backend operations.
+ * @param src_buffer The source buffer containing the 4D input tensor data.
+ * @param src_ne The dimensions of the source tensor.
+ * @param src_nb The strides (byte offsets) of the source tensor.
+ * @param dst_buffer The destination buffer where the output tensor data will be written.
+ * @param dst_ne The dimensions of the destination tensor.
+ * @param dst_nb The strides (byte offsets) of the destination tensor.
+ * @param index The index tensor specifying the indices to select from the source tensor.
+ * @param type The data type of the source and destination tensors.
+ */
+static void aclnn_index_select_4d(ggml_backend_cann_context & ctx,
+                                  void *                      src_buffer,
+                                  int64_t *                   src_ne,
+                                  size_t *                    src_nb,
+                                  void *                      dst_buffer,
+                                  int64_t *                   dst_ne,
+                                  size_t *                    dst_nb,
+                                  ggml_tensor *               index,
+                                  ggml_type                   type) {
+    for (int64_t i = 0; i < src_ne[3]; i++) {
+        for (int64_t j = 0; j < src_ne[2]; j++) {
+            // src
+            acl_tensor_ptr acl_src_tensor =
+                ggml_cann_create_tensor((char *) src_buffer + i * src_nb[3] + j * src_nb[2],
+                                        ggml_cann_type_mapping(type), ggml_type_size(type), src_ne, src_nb, 2);
+
+            // index
+            acl_tensor_ptr acl_index = ggml_cann_create_tensor(
+                (char *) index->data + (i % index->ne[2]) * index->nb[2] + (j % index->ne[1]) * index->nb[1],
+                ggml_cann_type_mapping(index->type), ggml_element_size(index), index->ne, index->nb, 1);
+
+            // out
+            acl_tensor_ptr acl_out =
+                ggml_cann_create_tensor((char *) dst_buffer + i * dst_nb[3] + j * dst_nb[2],
+                                        ggml_cann_type_mapping(type), ggml_type_size(type), dst_ne, dst_nb, 2);
+            GGML_CANN_CALL_ACLNN_OP(ctx, IndexSelect, acl_src_tensor.get(), 0, acl_index.get(), acl_out.get());
+        }
+    }
+}
+
+/**
+ * @brief Performs inplace index copy operation on a 4D tensor using the CANN backend.
+ *
+ * This function applies the `IndexCopy` operation along a specific dimension of the
+ * destination tensor (`dst_buffer`) by copying elements from the source tensor (`src_buffer`)
+ * to positions specified by the index tensor (`index`).
+ * It iterates over the last two dimensions of the tensors, creates the corresponding
+ * CANN tensors for source, index, and destination slices, and performs the index copy
+ * operation for each slice.
+ *
+ * @param ctx The context for CANN backend operations.
+ * @param src_buffer The source buffer containing the 4D input tensor data to be copied.
+ * @param src_ne The dimensions of the source tensor.
+ * @param src_nb The strides (byte offsets) of the source tensor.
+ * @param dst_buffer The destination buffer where values will be copied to.
+ * @param dst_ne The dimensions of the destination tensor.
+ * @param dst_nb The strides (byte offsets) of the destination tensor.
+ * @param index The index tensor specifying target positions in the destination tensor.
+ * @param type The data type of the source and destination tensors.
+ */
+static void aclnn_index_copy_4d(ggml_backend_cann_context & ctx,
+                                void *                      src_buffer,
+                                int64_t *                   src_ne,
+                                size_t *                    src_nb,
+                                void *                      dst_buffer,
+                                int64_t *                   dst_ne,
+                                size_t *                    dst_nb,
+                                ggml_tensor *               index,
+                                ggml_type                   type) {
+    for (int64_t i = 0; i < src_ne[3]; i++) {
+        for (int64_t j = 0; j < src_ne[2]; j++) {
+            // src
+            acl_tensor_ptr acl_src_tensor =
+                ggml_cann_create_tensor((char *) src_buffer + i * src_nb[3] + j * src_nb[2],
+                                        ggml_cann_type_mapping(type), ggml_type_size(type), src_ne, src_nb, 2);
+
+            // index
+            acl_tensor_ptr acl_index = ggml_cann_create_tensor(
+                (char *) index->data + (i % index->ne[2]) * index->nb[2] + (j % index->ne[1]) * index->nb[1],
+                ggml_cann_type_mapping(index->type), ggml_element_size(index), index->ne, index->nb, 1);
+
+            // out
+            acl_tensor_ptr acl_out =
+                ggml_cann_create_tensor((char *) dst_buffer + i * dst_nb[3] + j * dst_nb[2],
+                                        ggml_cann_type_mapping(type), ggml_type_size(type), dst_ne, dst_nb, 2);
+            GGML_CANN_CALL_ACLNN_OP(ctx, InplaceIndexCopy, acl_out.get(), 0, acl_index.get(), acl_src_tensor.get());
+        }
+    }
+}
+
+void ggml_cann_get_rows(ggml_backend_cann_context & ctx, ggml_tensor * dst) {
+    ggml_tensor * src0 = dst->src[0];  // src
+    ggml_tensor * src1 = dst->src[1];  // index
+
+    GGML_ASSERT(dst->type == GGML_TYPE_F32 || dst->type == GGML_TYPE_F16);
+
+    switch (src0->type) {
+        case GGML_TYPE_F16:
+        case GGML_TYPE_F32:
+            if (src0->type == dst->type) {
+                aclnn_index_select_4d(ctx, src0->data, src0->ne, src0->nb, dst->data, dst->ne, dst->nb, src1,
+                                      dst->type);
+            } else {
+                acl_tensor_ptr       acl_src0 = ggml_cann_create_tensor(src0);
+                ggml_cann_pool_alloc src_buffer_allocator(ctx.pool(), ggml_nelements(src0) * ggml_element_size(dst));
+                void *               src_trans_buffer = src_buffer_allocator.get();
+                size_t               src_trans_nb[GGML_MAX_DIMS];
+                src_trans_nb[0] = dst->nb[0];
+                for (int i = 1; i < GGML_MAX_DIMS; i++) {
+                    src_trans_nb[i] = src_trans_nb[i - 1] * src0->ne[i - 1];
+                }
+                acl_tensor_ptr src_trans_tensor =
+                    ggml_cann_create_tensor(src_trans_buffer, ggml_cann_type_mapping(dst->type),
+                                            ggml_type_size(dst->type), src0->ne, src_trans_nb, GGML_MAX_DIMS);
+                aclnn_cast(ctx, acl_src0.get(), src_trans_tensor.get(), ggml_cann_type_mapping(dst->type));
+                aclnn_index_select_4d(ctx, src_trans_buffer, src0->ne, src_trans_nb, dst->data, dst->ne, dst->nb, src1,
+                                      dst->type);
+            }
+            break;
+        case GGML_TYPE_Q8_0:
+            {
+                // add 1 dim for bcast mul.
+                size_t  weight_nb[GGML_MAX_DIMS + 1], scale_nb[GGML_MAX_DIMS + 1], dequant_nb[GGML_MAX_DIMS + 1];
+                int64_t weight_ne[GGML_MAX_DIMS + 1], scale_ne[GGML_MAX_DIMS + 1], *dequant_ne;
+                int64_t scale_offset = 0;
+                // [3,4,5,64] -> [3,4,5,2,32]
+                weight_ne[0]         = QK8_0;
+                weight_ne[1]         = src0->ne[0] / QK8_0;
+                weight_nb[0]         = sizeof(int8_t);
+                weight_nb[1]         = weight_nb[0] * weight_ne[0];
+                for (int i = 2; i < GGML_MAX_DIMS + 1; i++) {
+                    weight_ne[i] = src0->ne[i - 1];
+                    weight_nb[i] = weight_nb[i - 1] * weight_ne[i - 1];
+                }
+                // [3,4,5,64] -> [3,4,5,2,1]
+                scale_ne[0] = 1;
+                scale_ne[1] = src0->ne[0] / QK8_0;
+                scale_nb[0] = sizeof(uint16_t);
+                scale_nb[1] = scale_nb[0] * scale_ne[0];
+                for (int i = 2; i < GGML_MAX_DIMS + 1; i++) {
+                    scale_ne[i] = src0->ne[i - 1];
+                    scale_nb[i] = scale_nb[i - 1] * scale_ne[i - 1];
+                }
+                // [3,4,5,64] -> [3,4,5,2,32]
+                dequant_ne    = weight_ne;
+                dequant_nb[0] = ggml_type_size(dst->type);
+                for (int i = 1; i < GGML_MAX_DIMS + 1; i++) {
+                    dequant_nb[i] = dequant_nb[i - 1] * dequant_ne[i - 1];
+                }
+                scale_offset = ggml_nelements(src0) * sizeof(int8_t);
+                ggml_cann_pool_alloc dequant_buffer_allocator(ctx.pool(),
+                                                              ggml_nelements(src0) * ggml_type_size(dst->type));
+                acl_tensor_ptr       acl_weight_tensor = ggml_cann_create_tensor(src0->data, ACL_INT8, sizeof(int8_t),
+                                                                                 weight_ne, weight_nb, GGML_MAX_DIMS + 1);
+                acl_tensor_ptr       acl_scale_tensor =
+                    ggml_cann_create_tensor(src0->data, ACL_FLOAT16, sizeof(uint16_t), scale_ne, scale_nb,
+                                            GGML_MAX_DIMS + 1, ACL_FORMAT_ND, scale_offset);
+                acl_tensor_ptr dequant_tensor =
+                    ggml_cann_create_tensor(dequant_buffer_allocator.get(), ggml_cann_type_mapping(dst->type),
+                                            ggml_type_size(dst->type), dequant_ne, dequant_nb, GGML_MAX_DIMS + 1);
+                aclnn_mul(ctx, acl_weight_tensor.get(), acl_scale_tensor.get(), dequant_tensor.get());
+                dequant_nb[0] = ggml_type_size(dst->type);
+                dequant_ne    = src0->ne;
+                for (int i = 1; i < GGML_MAX_DIMS; i++) {
+                    dequant_nb[i] = dequant_nb[i - 1] * src0->ne[i - 1];
+                }
+                aclnn_index_select_4d(ctx, dequant_buffer_allocator.get(), dequant_ne, dequant_nb, dst->data, dst->ne,
+                                      dst->nb, src1, dst->type);
+                break;
+            }
+        default:
+            GGML_ABORT("Unsupported tensor type for GGML_OP_GET_ROWS");
+            break;
+    }
+}
+
+void ggml_cann_set_rows(ggml_backend_cann_context & ctx, ggml_tensor * dst) {
+    ggml_tensor * src0 = dst->src[0];  // src
+    ggml_tensor * src1 = dst->src[1];  // index
+
+    switch (dst->type) {
+        case GGML_TYPE_F32:
+            {
+                aclnn_index_copy_4d(ctx, src0->data, src0->ne, src0->nb, dst->data, dst->ne, dst->nb, src1, dst->type);
+                break;
+            }
+        case GGML_TYPE_F16:
+            {
+                acl_tensor_ptr       acl_src0 = ggml_cann_create_tensor(src0);
+                ggml_cann_pool_alloc src_buffer_allocator(ctx.pool(), ggml_nelements(src0) * sizeof(uint16_t));
+                void *               src_trans_buffer = src_buffer_allocator.get();
+                size_t               src_trans_nb[GGML_MAX_DIMS];
+                src_trans_nb[0] = sizeof(uint16_t);
+                for (int i = 1; i < GGML_MAX_DIMS; i++) {
+                    src_trans_nb[i] = src_trans_nb[i - 1] * src0->ne[i - 1];
+                }
+                acl_tensor_ptr src_trans_tensor = ggml_cann_create_tensor(
+                    src_trans_buffer, ACL_FLOAT16, ggml_type_size(dst->type), src0->ne, src_trans_nb, GGML_MAX_DIMS);
+                aclnn_cast(ctx, acl_src0.get(), src_trans_tensor.get(), ggml_cann_type_mapping(dst->type));
+                aclnn_index_copy_4d(ctx, src_trans_buffer, src0->ne, src_trans_nb, dst->data, dst->ne, dst->nb, src1,
+                                    dst->type);
+                break;
+            }
+        default:
+            GGML_ABORT("Unsupported tensor type for GGML_OP_SET_ROWS");
+            break;
+    }
+}
+
+/**
+ * @brief Repeats elements of a tensor along a specified dimension.
+ *
+ * This function repeats each element of the source tensor `acl_src` a specified
+ * number of times (`repeats`) along the specified dimension `dim` and stores
+ * the result in the destination tensor `acl_dst`.
+ *
+ * @param ctx The context for the CANN backend operations.
+ * @param acl_src The source tensor whose elements will be repeated.
+ * @param acl_dst The destination tensor where the repeated elements will be
+ * stored.
+ * @param dim The dimension along which the elements will be repeated.
+ * @param repeats The number of times each element will be repeated.
+ * @param output_size The size of the output tensor.
+ */
+static void aclnn_repeat_interleave(ggml_backend_cann_context & ctx,
+                                    aclTensor *                 acl_src,
+                                    aclTensor *                 acl_dst,
+                                    int64_t                     dim,
+                                    int64_t                     repeats,
+                                    int64_t                     output_size) {
+    GGML_CANN_CALL_ACLNN_OP(ctx, RepeatInterleaveIntWithDim, acl_src, repeats, dim, output_size, acl_dst);
+}
+
+/**
+ * @brief Performs matrix multiplication with floating-point precision on
+ * tensors using the CANN backend.
+ *
+ * This function performs matrix multiplication of the input tensor and the
+ * weight tensor, handling broadcasting and transposing as needed, and stores
+ * the result in the destination tensor `dst`.
+ *
+ * @param ctx The context for the CANN backend operations.
+ * @param dst The destination tensor where the result of the matrix
+ * multiplication will be stored.
+ */
+static void ggml_cann_mat_mul_fp(ggml_backend_cann_context & ctx, ggml_tensor * dst) {
+    ggml_tensor * weight = dst->src[0];  // weight
+    ggml_tensor * input  = dst->src[1];  // input
+
+    // when weight ne2 or ne3 is 1, aclnnMatmulGetWorkspaceSize will auto
+    // broadcast, when weight ne2 or ne3 is not 1, weight need repeat.
+    BCAST_MUL_MAT_SHAPE(input, weight, dst);
+
+    int64_t n_dims = bcast_dims;
+    if (bcast_input_ne[3] == bcast_weight_ne[3] && bcast_input_ne[3] == 1) {
+        if (bcast_input_ne[2] == 1 && bcast_weight_ne[2] == 1) {
+            n_dims = 2;
+        } else if (bcast_input_ne[2] == 1) {
+            n_dims = 3;
+        }
+    }
+
+    acl_tensor_ptr acl_input_tensor = ggml_cann_create_tensor(input, bcast_input_ne, bcast_input_nb, n_dims);
+    int64_t        transpose_ne[]   = { bcast_weight_ne[1], bcast_weight_ne[0], bcast_weight_ne[2],
+                                        bcast_weight_ne[3], bcast_weight_ne[4], bcast_weight_ne[5] };
+    size_t         transpose_nb[]   = { bcast_weight_nb[1], bcast_weight_nb[0], bcast_weight_nb[2],
+                                        bcast_weight_nb[3], bcast_weight_nb[4], bcast_weight_nb[5] };
+    acl_tensor_ptr acl_weight_tensor;
+
+    // Only check env once.
+    static bool weight_to_nz = parse_bool(get_env_as_lowercase("GGML_CANN_WEIGHT_NZ").value_or("on"));
+    if (weight_to_nz && is_matmul_weight(weight)) {
+        acl_weight_tensor = ggml_cann_create_tensor(weight, transpose_ne, transpose_nb, n_dims, ACL_FORMAT_FRACTAL_NZ);
+    } else {
+        acl_weight_tensor = ggml_cann_create_tensor(weight, transpose_ne, transpose_nb, n_dims, ACL_FORMAT_ND);
+    }
+    acl_tensor_ptr acl_dst = ggml_cann_create_tensor(dst, bcast_dst_ne, bcast_dst_nb, n_dims);
+
+    switch (n_dims) {
+        case 2:
+            GGML_CANN_CALL_ACLNN_OP(ctx, Mm, acl_input_tensor.get(), acl_weight_tensor.get(), acl_dst.get(), 2);
+            break;
+        case 3:
+            GGML_CANN_CALL_ACLNN_OP(ctx, BatchMatMul, acl_input_tensor.get(), acl_weight_tensor.get(), acl_dst.get(),
+                                    2);
+            break;
+        default:
+            // ALLOW_FP32_DOWN_PRECISION, when input is
+            // fp32, atlas a2 will transpose it to HFLOAT32.
+            GGML_CANN_CALL_ACLNN_OP(ctx, Matmul, acl_input_tensor.get(), acl_weight_tensor.get(), acl_dst.get(), 1);
+            break;
+    }
+}
+
+/**
+ * @brief Performs matrix multiplication with quantized weights and
+ * floating-point inputs using the CANN backend.
+ *
+ * This function performs matrix multiplication of the input tensor `src1` and
+ * the weight tensor `src0`, handling broadcasting, transposing, and
+ * quantization as needed, and stores the result in the destination tensor
+ * `dst`.
+ *
+ * @param ctx The context for the CANN backend operations.
+ * @param dst The destination tensor where the result of the matrix
+ * multiplication will be stored.
+ */
+static void ggml_cann_mul_mat_quant(ggml_backend_cann_context & ctx, ggml_tensor * dst, const enum ggml_type type) {
+    ggml_tensor * src0 = dst->src[0];  // weight
+    ggml_tensor * src1 = dst->src[1];  // input
+
+    // The shape of the weight is NCHW.
+    // Matrix multiplication uses HW dims.
+    // HC is regarded as batch.
+    // weight need transpose.
+    float weight_elem_size;
+    if (type == GGML_TYPE_Q4_0) {
+        weight_elem_size = float(sizeof(uint8_t)) / 2;
+    } else if (type == GGML_TYPE_Q8_0) {
+        weight_elem_size = float(sizeof(uint8_t));
+    } else {
+        GGML_ABORT("Only support Q4_0 and Q8_0 MUL_MAT");
+    }
+    float  weight_nb[]   = { src0->ne[0] * weight_elem_size, weight_elem_size };
+    size_t weight_stride = src0->ne[1] * src0->ne[0] * weight_elem_size;
+    size_t weight_size   = weight_stride * src0->ne[2] * src0->ne[3];
+
+    // scale stored at the end of weight. Also need transpose.
+    size_t scale_elem_size = sizeof(uint16_t);
+    size_t scale_nb[]      = { src0->ne[0] / QK8_0 * scale_elem_size, scale_elem_size };
+    size_t scale_stride    = src0->ne[1] * src0->ne[0] / QK8_0 * scale_elem_size;
+    char * scale_offset    = (char *) src0->data + weight_size;
+
+    // input
+    size_t               input_elem_size = sizeof(uint16_t);
+    int64_t              input_ne[]      = { src1->ne[0], src1->ne[1] };
+    size_t               input_nb[]      = { input_elem_size, input_ne[0] * input_elem_size };
+    size_t               input_stride    = input_ne[0] * input_ne[1] * input_elem_size;
+    ggml_cann_pool_alloc input_alloctor(ctx.pool());
+    void *               input_buffer = src1->data;
+
+    // case in
+    if (src1->type != GGML_TYPE_F16) {
+        acl_tensor_ptr acl_src1_tensor = ggml_cann_create_tensor(src1);
+        input_buffer                   = input_alloctor.alloc(ggml_nelements(src1) * input_elem_size);
+
+        int64_t * input_cast_ne = src1->ne;
+        size_t    input_cast_nb[GGML_MAX_DIMS];
+        input_cast_nb[0] = sizeof(uint16_t);
+        for (int i = 1; i < GGML_MAX_DIMS; i++) {
+            input_cast_nb[i] = input_cast_nb[i - 1] * input_cast_ne[i - 1];
+        }
+
+        acl_tensor_ptr acl_input_tensor = ggml_cann_create_tensor(input_buffer, ACL_FLOAT16, input_elem_size,
+                                                                  input_cast_ne, input_cast_nb, GGML_MAX_DIMS);
+        aclnn_cast(ctx, acl_src1_tensor.get(), acl_input_tensor.get(), ACL_FLOAT16);
+    }
+
+    // output
+    size_t               output_elem_size = sizeof(uint16_t);
+    size_t               output_nb[]      = { output_elem_size, dst->ne[0] * output_elem_size };
+    ggml_cann_pool_alloc output_allocator(ctx.pool());
+    void *               output_buffer = output_allocator.alloc(ggml_nelements(dst) * output_elem_size);
+    size_t               output_stride = dst->ne[0] * dst->ne[1] * output_elem_size;
+
+    // aclnn
+    int64_t              max_elem_size = 65535;
+    int64_t              split_size    = (src0->ne[1] / max_elem_size) + 1;
+    ggml_cann_pool_alloc workspace_allocator(ctx.pool());
+    for (int64_t n1 = 0; n1 < src1->ne[3]; n1++) {
+        for (int64_t c1 = 0; c1 < src1->ne[2]; c1++) {
+            int64_t n0 = n1 / (src1->ne[3] / src0->ne[3]);
+            int64_t c0 = c1 / (src1->ne[2] / src0->ne[2]);
+
+            int64_t batch1 = (n1 * src1->ne[2]) + c1;
+            int64_t batch0 = (n0 * src0->ne[2]) + c0;
+
+            acl_tensor_ptr acl_input_tensor = ggml_cann_create_tensor(
+                (char *) input_buffer + batch1 * input_stride, ACL_FLOAT16, input_elem_size, input_ne, input_nb, 2);
+
+            // first split
+            int64_t weight_ne_offset = 0;
+            int64_t weight_ne[2]     = { max_elem_size > src0->ne[1] ? src0->ne[1] : max_elem_size, src0->ne[0] };
+            int64_t scale_ne_offset  = 0;
+            int64_t scale_ne[2]      = { weight_ne[0], weight_ne[1] / QK8_0 };
+            int64_t output_ne_offset = 0;
+            int64_t output_ne[2]     = { weight_ne[0], dst->ne[1] };
+
+            acl_tensor_ptr acl_weight_tensor =
+                ggml_cann_create_tensor((char *) src0->data + batch0 * weight_stride, ggml_cann_type_mapping(type),
+                                        weight_elem_size, weight_ne, weight_nb, 2, ACL_FORMAT_ND, weight_ne_offset);
+            acl_tensor_ptr acl_scale_tensor =
+                ggml_cann_create_tensor(scale_offset + batch0 * scale_stride, ACL_FLOAT16, scale_elem_size, scale_ne,
+                                        scale_nb, 2, ACL_FORMAT_ND, scale_ne_offset);
+            acl_tensor_ptr acl_output_tensor =
+                ggml_cann_create_tensor((char *) output_buffer + batch1 * output_stride, ACL_FLOAT16, output_elem_size,
+                                        output_ne, output_nb, 2, ACL_FORMAT_ND, output_ne_offset);
+            int64_t antiquantGroupSize = 0;
+            if (src0->ne[0] > QK8_0) {
+                antiquantGroupSize = QK8_0;
+            }
+            GGML_CANN_CALL_ACLNN_OP(ctx, WeightQuantBatchMatmulV2, acl_input_tensor.get(), acl_weight_tensor.get(),
+                                    acl_scale_tensor.get(), nullptr, nullptr, nullptr, nullptr, antiquantGroupSize,
+                                    acl_output_tensor.get());
+
+            // other splits
+            for (int64_t split = 1; split < split_size; split++) {
+                weight_ne_offset += weight_elem_size * weight_ne[0] * weight_ne[1];
+                weight_ne[0] =
+                    max_elem_size * (split + 1) > src0->ne[1] ? src0->ne[1] - (max_elem_size * split) : max_elem_size;
+                scale_ne_offset += scale_elem_size * scale_ne[0] * scale_ne[1];
+                scale_ne[0] = weight_ne[0];
+                output_ne_offset += output_elem_size * output_ne[0] * output_ne[1];
+                output_ne[0] = weight_ne[0];
+
+                acl_weight_tensor =
+                    ggml_cann_create_tensor((char *) src0->data + batch0 * weight_stride, ggml_cann_type_mapping(type),
+                                            weight_elem_size, weight_ne, weight_nb, 2, ACL_FORMAT_ND, weight_ne_offset);
+                acl_scale_tensor =
+                    ggml_cann_create_tensor(scale_offset + batch0 * scale_stride, ACL_FLOAT16, scale_elem_size,
+                                            scale_ne, scale_nb, 2, ACL_FORMAT_ND, scale_ne_offset);
+                acl_output_tensor =
+                    ggml_cann_create_tensor((char *) output_buffer + batch1 * output_stride, ACL_FLOAT16,
+                                            output_elem_size, output_ne, output_nb, 2, ACL_FORMAT_ND, output_ne_offset);
+                GGML_CANN_CALL_ACLNN_OP(ctx, WeightQuantBatchMatmulV2, acl_input_tensor.get(), acl_weight_tensor.get(),
+                                        acl_scale_tensor.get(), nullptr, nullptr, nullptr, nullptr, antiquantGroupSize,
+                                        acl_output_tensor.get());
+            }
+        }
+    }
+
+    // cast out
+    if (dst->type != GGML_TYPE_F16) {
+        int64_t * output_cast_ne = dst->ne;
+        size_t    output_cast_nb[GGML_MAX_DIMS];
+        output_cast_nb[0] = sizeof(uint16_t);
+        for (int i = 1; i < GGML_MAX_DIMS; i++) {
+            output_cast_nb[i] = output_cast_nb[i - 1] * output_cast_ne[i - 1];
+        }
+
+        acl_tensor_ptr acl_output_tensor = ggml_cann_create_tensor(output_buffer, ACL_FLOAT16, output_elem_size,
+                                                                   output_cast_ne, output_cast_nb, GGML_MAX_DIMS);
+        acl_tensor_ptr acl_dst_tensor    = ggml_cann_create_tensor(dst);
+        aclnn_cast(ctx, acl_output_tensor.get(), acl_dst_tensor.get(), ggml_cann_type_mapping(dst->type));
+    }
+}
+
+void ggml_cann_mul_mat(ggml_backend_cann_context & ctx, ggml_tensor * dst) {
+    const enum ggml_type type = dst->src[0]->type;
+    switch (type) {
+        case GGML_TYPE_F32:
+        case GGML_TYPE_F16:
+            ggml_cann_mat_mul_fp(ctx, dst);
+            break;
+        case GGML_TYPE_Q4_0:
+        case GGML_TYPE_Q8_0:
+            ggml_cann_mul_mat_quant(ctx, dst, type);
+            break;
+        default:
+            GGML_ABORT("Unsupported type for mul_mat");
+            break;
+    }
+}
+
+/**
+ * @brief Rolls the elements of a tensor along a specified dimension.
+ *
+ * This function rolls the elements of the source tensor `acl_src` by the
+ * specified shifts `shifts` along the specified dimensions `dims`, and stores
+ * the result in the destination tensor `acl_dst`.
+ *
+ * @param ctx The context for the CANN backend operations.
+ * @param acl_src The source tensor whose elements will be rolled.
+ * @param acl_dst The destination tensor where the rolled elements will be
+ * stored.
+ * @param shifts An array specifying the number of positions by which elements
+ * are shifted.
+ * @param dims An array specifying the dimensions along which elements are
+ * shifted.
+ */
+static void aclnn_roll(ggml_backend_cann_context & ctx,
+                       aclTensor *                 acl_src,
+                       aclTensor *                 acl_dst,
+                       int64_t *                   shifts,
+                       int64_t *                   dims) {
+    acl_int_array_ptr acl_shifts = ggml_cann_create_int_array(shifts, 1);
+    acl_int_array_ptr acl_dims   = ggml_cann_create_int_array(dims, 1);
+    GGML_CANN_CALL_ACLNN_OP(ctx, Roll, acl_src, acl_shifts.get(), acl_dims.get(), acl_dst);
+}
+
+/**
+ * @brief Fills specified positions of a tensor with a scalar value.
+ *
+ * This function fills the positions in the source tensor `acl_src` specified by
+ * `index` along the dimension `dim` with the scalar value `value`.
+ *
+ * @param ctx The context for the CANN backend operations.
+ * @param acl_src The source tensor where the positions will be filled.
+ * @param dim The dimension along which the positions are specified.
+ * @param index An array specifying the positions to be filled.
+ * @param index_num The number of positions specified in the index array.
+ * @param value The scalar value used to fill the specified positions.
+ */
+static void aclnn_index_fill_tensor(ggml_backend_cann_context & ctx,
+                                    aclTensor *                 acl_src,
+                                    int64_t                     dim,
+                                    int64_t *                   index,
+                                    int64_t                     index_num,
+                                    float                       value) {
+    acl_int_array_ptr acl_index = ggml_cann_create_int_array(index, index_num);
+    acl_scalar_ptr    acl_value = ggml_cann_create_scalar(&value, aclDataType::ACL_FLOAT);
+    GGML_CANN_CALL_ACLNN_OP(ctx, InplaceIndexFillTensor, acl_src, dim, acl_index.get(), acl_value.get());
+}
+
+/**
+ * @brief Initializes and caches all intermediate tensors required for RoPE
+ *        (Rotary Position Embedding), including support for Yarn, mRoPE,
+ *        i-mRoPE, Neox repeat strategy, independent sectors, frequency factors，
+ *        and multi-section rotary groups.
+ *
+ * This function computes and caches the per-dimension θ coefficients used for
+ * Q/K rotary embedding. The cache is shared across layers, and recomputed only
+ * when any dependent parameter changes.
+ *
+ * The function now supports:
+ *   - Yarn RoPE extrapolation (via @param corr_dims and @param ext_factor)
+ *   - Per-dimension independent sector exponent rules (indep_sects + sections[])
+ *   - Multi-section RoPE (mRoPE) index mapping (mrope_used + is_imrope)
+ *   - Frequency factor division (src2)
+ *   - Neox / normal repeat expansion modes
+ *
+ * @param ctx                CANN backend context, containing memory pool,
+ *                           cached buffers, and runtime stream.
+ * @param dst                Destination ggml_tensor whose computation
+ *                           depends on RoPE (typically Qcur or Kcur).
+ * @param corr_dims          [low, high] Yarn correction range.
+ * @param ext_factor         Yarn extrapolation strength. 0 = disabled.
+ * @param theta_scale        Base multiplier for per-dimension θ exponent.
+ * @param freq_scale         Global frequency scaling factor.
+ * @param attn_factor        Optional scaling applied to sin/cos (if needed).
+ * @param is_neox            Whether to use Neox-style dimension interleave.
+ * @param sections           4-way sector sizes for independent-section RoPE
+ *                           and multi-section mRoPE (t/h/w/e).
+ * @param mrope_used         Whether to enable multi-section rotary embedding.
+ * @param is_imrope          Whether to apply interleaved mRoPE rules.
+ * @param indep_sects        Whether each dimension runs independent exponent
+ *                           resets based on @p sections.
+ */
+static void aclnn_rope_cache_init(ggml_backend_cann_context & ctx,
+                                  ggml_tensor *               dst,
+                                  float *                     corr_dims,
+                                  float                       ext_factor,
+                                  float                       theta_scale,
+                                  float                       freq_scale,
+                                  float                       attn_factor,
+                                  bool                        is_neox,
+                                  int                         sections[4],
+                                  bool                        mrope_used,
+                                  bool                        is_imrope,
+                                  bool                        indep_sects,
+                                  int64_t                     rope_dims) {
+    ggml_tensor * src1 = dst->src[1];  // position
+    ggml_tensor * src2 = dst->src[2];  // freq_factors
+
+    int64_t theta_scale_length = rope_dims / 2;
+    int64_t position_length    = dst->ne[2];
+
+    // TODO: check theta_scale_length and position_length.
+    if (src2 == nullptr && ctx.rope_cache.cached &&
+        ctx.rope_cache.equal(theta_scale_length, position_length, ext_factor, theta_scale, freq_scale, attn_factor,
+                             is_neox, indep_sects, mrope_used, is_imrope, sections)) {
+        // use cache.
+        return;
+    }
+
+    // Step0: calculate tensor shape.
+    int64_t theta_scale_ne[] = { theta_scale_length, 1, 1, 1 };
+    size_t  theta_scale_nb[] = { sizeof(float), theta_scale_length * sizeof(float), theta_scale_length * sizeof(float),
+                                 theta_scale_length * sizeof(float) };
+
+    GGML_ASSERT(src1->type == GGML_TYPE_I32);
+    int64_t position_ne[] = { 1, 1, position_length, 1 };
+    size_t  position_nb[] = { sizeof(int32_t), sizeof(int32_t), sizeof(int32_t), sizeof(int32_t) * position_length };
+
+    int64_t cache_ne[] = { theta_scale_length, 1, position_length, 1 };
+    size_t  cache_nb[GGML_MAX_DIMS];
+    cache_nb[0] = sizeof(float);
+    for (int i = 1; i < GGML_MAX_DIMS; i++) {
+        cache_nb[i] = cache_nb[i - 1] * cache_ne[i - 1];
+    }
+
+    // Step1: Compute the coefficient of theta. During the cache_init process, aside from
+    // (1) multiplying by the position,
+    // (2) dividing by freq_factors,
+    // (3) computing the sine and cosine,
+    // the other parameters used in the computation generally do not change in most scenarios.
+    // Therefore, we can first compute this part of the result and then cache it.
+
+    // Step1.1: prepare theta_scale exponent. if this exponent updated, should update theta_scale_tensor.
+    acl_tensor_ptr acl_theta_scale_tensor;
+    bool           theta_scale_updated = false;
+    if (ctx.rope_cache.theta_scale_length != theta_scale_length || ctx.rope_cache.theta_scale != theta_scale ||
+        ctx.rope_cache.indep_sects != indep_sects) {
+        theta_scale_updated = true;
+        if (ctx.rope_cache.theta_scale_exp_host != nullptr) {
+            free(ctx.rope_cache.theta_scale_exp_host);
+        }
+        ctx.rope_cache.theta_scale_exp_host = (float *) malloc(theta_scale_length * sizeof(float));
+        GGML_ASSERT(ctx.rope_cache.theta_scale_exp_host != nullptr);
+        if (!indep_sects) {
+            ctx.rope_cache.theta_scale_exp_host[0] = 1;
+            for (int i = 1; i < theta_scale_length; i++) {
+                ctx.rope_cache.theta_scale_exp_host[i] = ctx.rope_cache.theta_scale_exp_host[i - 1] * theta_scale;
+            }
+        } else {
+            int sect_dims = sections[0] + sections[1] + sections[2] + sections[3];
+            int sec_w     = sections[1] + sections[0];
+            int sec_e     = sections[2] + sec_w;
+
+            ctx.rope_cache.theta_scale_exp_host[0] = 1;
+            for (int i = 1; i < theta_scale_length; i++) {
+                int sector = i % sect_dims;
+                if (sector == 0 || sector == sections[0] || sector == sec_w || sector == sec_e) {
+                    ctx.rope_cache.theta_scale_exp_host[i] = 1;
+                    continue;
+                }
+                ctx.rope_cache.theta_scale_exp_host[i] = ctx.rope_cache.theta_scale_exp_host[i - 1] * theta_scale;
+            }
+        }
+
+        if (ctx.rope_cache.theta_scale_cache != nullptr) {
+            ACL_CHECK(aclrtFree(ctx.rope_cache.theta_scale_cache));
+        }
+        ACL_CHECK(aclrtMalloc(&ctx.rope_cache.theta_scale_cache, theta_scale_length * sizeof(float),
+                              ACL_MEM_MALLOC_HUGE_FIRST));
+
+        ACL_CHECK(aclrtMemcpyAsync(ctx.rope_cache.theta_scale_cache, theta_scale_length * sizeof(float),
+                                   ctx.rope_cache.theta_scale_exp_host, theta_scale_length * sizeof(float),
+                                   ACL_MEMCPY_HOST_TO_DEVICE, ctx.stream()));
+    }
+    acl_theta_scale_tensor = ggml_cann_create_tensor(ctx.rope_cache.theta_scale_cache, ACL_FLOAT, sizeof(float),
+                                                     theta_scale_ne, theta_scale_nb, 1);
+
+    // Step1.2: prepare rope_yarn_ramp, if this part updated, should update theta_scale_tensor.
+    // TODO: acl_yarn_ramp_tensor use rope cache.
+    bool           yarn_ramp_tensor_updated = false;
+    acl_tensor_ptr acl_yarn_ramp_tensor;
+    if (ext_factor != 0 && (theta_scale_updated || ctx.rope_cache.theta_scale_length != theta_scale_length ||
+                            ctx.rope_cache.freq_scale != freq_scale)) {
+        yarn_ramp_tensor_updated = true;
+        if (ctx.rope_cache.yarn_ramp_cache != nullptr) {
+            ACL_CHECK(aclrtFree(ctx.rope_cache.yarn_ramp_cache));
+        }
+        ACL_CHECK(aclrtMalloc(&ctx.rope_cache.yarn_ramp_cache, theta_scale_length * sizeof(float),
+                              ACL_MEM_MALLOC_HUGE_FIRST));
+        // -rope_yarn_ramp
+        // const float y = (i0 / 2 - low) / MAX(0.001f, high - low);
+        // return MIN(1, MAX(0, y)) - 1;
+        acl_yarn_ramp_tensor      = ggml_cann_create_tensor(ctx.rope_cache.yarn_ramp_cache, ACL_FLOAT, sizeof(float),
+                                                            theta_scale_ne, theta_scale_nb, 1);
+        float          zero_value = 0, one_value = 1;
+        float          denom_safe_value = MAX(0.001f, corr_dims[1] - corr_dims[0]);
+        acl_scalar_ptr low              = ggml_cann_create_scalar(&corr_dims[0], aclDataType::ACL_FLOAT);
+        acl_scalar_ptr zero             = ggml_cann_create_scalar(&zero_value, aclDataType::ACL_FLOAT);
+        acl_scalar_ptr one              = ggml_cann_create_scalar(&one_value, aclDataType::ACL_FLOAT);
+        acl_scalar_ptr denom_safe       = ggml_cann_create_scalar(&denom_safe_value, aclDataType::ACL_FLOAT);
+        acl_scalar_ptr ext_factor_sc    = ggml_cann_create_scalar(&ext_factor, aclDataType::ACL_FLOAT);
+
+        aclnn_arange(ctx, acl_yarn_ramp_tensor.get(), 0, theta_scale_length, 1, theta_scale_length);
+        GGML_CANN_CALL_ACLNN_OP(ctx, InplaceSubs, acl_yarn_ramp_tensor.get(), low.get(), one.get());
+        GGML_CANN_CALL_ACLNN_OP(ctx, InplaceDivs, acl_yarn_ramp_tensor.get(), denom_safe.get());
+        GGML_CANN_CALL_ACLNN_OP(ctx, InplaceThreshold, acl_yarn_ramp_tensor.get(), zero.get(), zero.get());
+        GGML_CANN_CALL_ACLNN_OP(ctx, InplaceClampMax, acl_yarn_ramp_tensor.get(), one.get());
+        GGML_CANN_CALL_ACLNN_OP(ctx, InplaceSubs, acl_yarn_ramp_tensor.get(), one.get(), one.get());
+        GGML_CANN_CALL_ACLNN_OP(ctx, InplaceMuls, acl_yarn_ramp_tensor.get(), ext_factor_sc.get());
+
+        // theta_interp = freq_scale * theta_extrap;
+        // theta = theta_interp * (1 - ramp_mix) + theta_extrap * ramp_mix;
+        // theta = freq_scale * theta_extrap * (1 - ramp_mix) + theta_extrap * ramp_mix;
+        // theta = freq_scale * theta_extrap - freq_scale * theta_extrap * ramp_mix + theta_extrap * ramp_mix;
+        // theta = theta_extrap * (freq_scale - freq_scale * ramp_mix + ramp_mix);
+        //
+        // we cache (freq_scale - freq_scale * ramp_mix + ramp_mix), Considering that the rope_yarn_ramp here is the inverse
+        // cache freq_scale + (freq_scale - 1) * ramp_mix
+        float          freq_scale_1    = freq_scale - 1;
+        acl_scalar_ptr freq_scale_sc   = ggml_cann_create_scalar(&freq_scale, aclDataType::ACL_FLOAT);
+        acl_scalar_ptr freq_scale_1_sc = ggml_cann_create_scalar(&freq_scale_1, aclDataType::ACL_FLOAT);
+        GGML_CANN_CALL_ACLNN_OP(ctx, InplaceMuls, acl_yarn_ramp_tensor.get(), freq_scale_1_sc.get());
+        GGML_CANN_CALL_ACLNN_OP(ctx, InplaceAdds, acl_yarn_ramp_tensor.get(), freq_scale_sc.get(), one.get());
+    } else {
+        acl_yarn_ramp_tensor = ggml_cann_create_tensor(ctx.rope_cache.yarn_ramp_cache, ACL_FLOAT, sizeof(float),
+                                                       theta_scale_ne, theta_scale_nb, 1);
+    }
+    // Step 1.3: update theta_scale_tensor according to ext_factor or freq_scale.
+    if (ext_factor != 0) {
+        if (theta_scale_updated || yarn_ramp_tensor_updated) {
+            theta_scale_updated = true;
+            aclnn_mul(ctx, acl_theta_scale_tensor.get(), acl_yarn_ramp_tensor.get());
+        }
+    } else {
+        if (freq_scale != 1 && (ctx.rope_cache.freq_scale != freq_scale || theta_scale_updated)) {
+            theta_scale_updated = true;
+            aclnn_muls(ctx, acl_theta_scale_tensor.get(), freq_scale, nullptr, true);
+        }
+    }
+
+    // Nothing changed, use cache.
+    if (!theta_scale_updated) {
+        acl_theta_scale_tensor = ggml_cann_create_tensor(ctx.rope_cache.theta_scale_cache, ACL_FLOAT, sizeof(float),
+                                                         theta_scale_ne, theta_scale_nb, GGML_MAX_DIMS);
+    }
+
+    // Step 1.4: prepare select index if mrope
+    acl_tensor_ptr position_select_index_tensor;
+    if (mrope_used) {
+        if (ctx.rope_cache.sections[0] != sections[0] || ctx.rope_cache.sections[1] != sections[1] ||
+            ctx.rope_cache.sections[2] != sections[2] || ctx.rope_cache.sections[3] != sections[3] ||
+            ctx.rope_cache.theta_scale_length != theta_scale_length || ctx.rope_cache.is_imrope != is_imrope) {
+            if (ctx.rope_cache.position_select_index_host != nullptr) {
+                free(ctx.rope_cache.position_select_index_host);
+            }
+            ctx.rope_cache.position_select_index_host = (int *) malloc(theta_scale_length * sizeof(int));
+            GGML_ASSERT(ctx.rope_cache.position_select_index_host != nullptr);
+            int sect_dims = sections[0] + sections[1] + sections[2] + sections[3];
+            int sec_w     = sections[1] + sections[0];
+            int sec_e     = sections[2] + sec_w;
+            // t,h,w,e
+            for (int i = 0; i < theta_scale_length; i++) {
+                int sector = i % sect_dims;
+
+                if (is_imrope) {  // qwen3vl apply interleaved mrope
+                    if (sector % 3 == 1 && sector < 3 * sections[1]) {
+                        ctx.rope_cache.position_select_index_host[i] = 1;
+                    } else if (sector % 3 == 2 && sector < 3 * sections[2]) {
+                        ctx.rope_cache.position_select_index_host[i] = 2;
+                    } else if (sector % 3 == 0 && sector < 3 * sections[0]) {
+                        ctx.rope_cache.position_select_index_host[i] = 0;
+                    } else {
+                        ctx.rope_cache.position_select_index_host[i] = 3;
+                    }
+                } else {
+                    if (sector >= sections[0] && sector < sec_w) {
+                        ctx.rope_cache.position_select_index_host[i] = 1;
+                    } else if (sector >= sec_w && sector < sec_e) {
+                        ctx.rope_cache.position_select_index_host[i] = 2;
+                    } else if (sector >= sec_e) {
+                        ctx.rope_cache.position_select_index_host[i] = 3;
+                    } else {
+                        ctx.rope_cache.position_select_index_host[i] = 0;
+                    }
+                }
+            }
+
+            if (ctx.rope_cache.position_select_index != nullptr) {
+                ACL_CHECK(aclrtFree(ctx.rope_cache.position_select_index));
+            }
+            ACL_CHECK(aclrtMalloc(&ctx.rope_cache.position_select_index, theta_scale_length * sizeof(int),
+                                  ACL_MEM_MALLOC_HUGE_FIRST));
+
+            ACL_CHECK(aclrtMemcpyAsync(ctx.rope_cache.position_select_index, theta_scale_length * sizeof(int),
+                                       ctx.rope_cache.position_select_index_host, theta_scale_length * sizeof(int),
+                                       ACL_MEMCPY_HOST_TO_DEVICE, ctx.stream()));
+        }
+
+        position_select_index_tensor = ggml_cann_create_tensor(ctx.rope_cache.position_select_index, ACL_INT32,
+                                                               sizeof(int), theta_scale_ne, theta_scale_nb, 1);
+    }
+
+    // Step2: divide by freq_factors
+    ggml_cann_pool_alloc freq_fac_res_allocator(ctx.pool());
+    if (src2) {
+        freq_fac_res_allocator.alloc(theta_scale_length * sizeof(float));
+        void *         freq_fac_res_ptr = freq_fac_res_allocator.get();
+        acl_tensor_ptr acl_freq_factors_tensor =
+            ggml_cann_create_tensor(src2->data, ggml_cann_type_mapping(src2->type), ggml_type_size(src2->type),
+                                    theta_scale_ne, theta_scale_nb, GGML_MAX_DIMS);
+        acl_tensor_ptr acl_freq_fac_res_tensor = ggml_cann_create_tensor(freq_fac_res_ptr, ACL_FLOAT, sizeof(float),
+                                                                         theta_scale_ne, theta_scale_nb, GGML_MAX_DIMS);
+        aclnn_div(ctx, acl_theta_scale_tensor.get(), acl_freq_factors_tensor.get(), acl_freq_fac_res_tensor.get());
+        std::swap(acl_theta_scale_tensor, acl_freq_fac_res_tensor);
+    }
+
+    // Step3: prepare position_tensor
+    acl_tensor_ptr       acl_position_tensor;
+    ggml_cann_pool_alloc mrope_position_acllocator(ctx.pool());
+    if (mrope_used) {
+        // Step3.1: select current position;
+        // position :
+        // pos1: [[0, 1 ,2 ,3 ],
+        // pos2:  [4, 5 ,6 ,7 ],
+        // pos3:  [8, 9 ,10,11],
+        // pos4:  [12,13,14,15] ]
+        //
+        // select index = [0, 1, 2, 2, 1, 0]
+        //
+        // selected_tensor:
+        // [[0, 1 ,2 ,3 ],
+        //  [4, 5 ,6 ,7 ],
+        //  [8, 9 ,10,11],
+        //  [8, 9 ,10,11],
+        //  [4, 5 ,6 ,7 ],
+        //  [0, 1 ,2 ,3 ]]
+        //
+        // transpose, from [seq_len:dims] to [dims:seq_len]
+        // [0, 4, 8 ,8 ,4, 0],
+        // [1, 5, 9, 9, 5, 1],
+        // [2, 6, 10,10,6 ,2],
+        // [3, 7, 11,11,7 3 ]]
+        //
+        // multipy by theta_scale_tensor
+        // [theta_scale^0, theta_scale^1, ..., theta_scale ^ n]
+
+        int64_t        mrope_position_ne[] = { position_length, 4 };
+        size_t         mrope_position_nb[] = { sizeof(int), position_length * sizeof(int) };
+        acl_tensor_ptr mrope_position =
+            ggml_cann_create_tensor(src1->data, ggml_cann_type_mapping(src1->type), ggml_type_size(src1->type),
+                                    mrope_position_ne, mrope_position_nb, 2);
+
+        // selected position tensor's shape is a transpose of cache tensor.
+        int64_t selected_position_ne[] = { position_length, theta_scale_length };
+        size_t  selected_position_nb[] = { sizeof(float), position_length * sizeof(float) };
+        mrope_position_acllocator.alloc(theta_scale_length * position_length * sizeof(float));
+        void * mrope_position_buffer = mrope_position_acllocator.get();
+        acl_position_tensor =
+            ggml_cann_create_tensor(mrope_position_buffer, ggml_cann_type_mapping(src1->type),
+                                    ggml_type_size(src1->type), selected_position_ne, selected_position_nb, 2);
+        GGML_CANN_CALL_ACLNN_OP(ctx, IndexSelect, mrope_position.get(), 0, position_select_index_tensor.get(),
+                                acl_position_tensor.get());
+
+        // transpose
+        int64_t transposed_ne[] = { position_length, 1, theta_scale_length, 1 };
+        size_t  transposed_nb[GGML_MAX_DIMS];
+        transposed_nb[0] = sizeof(float);
+        for (int i = 1; i < GGML_MAX_DIMS; i++) {
+            transposed_nb[i] = transposed_nb[i - 1] * transposed_ne[i - 1];
+        }
+
+        std::swap(transposed_ne[0], transposed_ne[2]);
+        std::swap(transposed_nb[0], transposed_nb[2]);
+
+        acl_position_tensor =
+            ggml_cann_create_tensor(mrope_position_buffer, ggml_cann_type_mapping(src1->type),
+                                    ggml_type_size(src1->type), transposed_ne, transposed_nb, GGML_MAX_DIMS);
+
+    } else {
+        // auto bcast.
+        acl_position_tensor =
+            ggml_cann_create_tensor(src1->data, ggml_cann_type_mapping(src1->type), ggml_type_size(src1->type),
+                                    position_ne, position_nb, GGML_MAX_DIMS);
+    }
+
+    // Step4: multiply by the position
+    int64_t              theta_length = theta_scale_length * position_length;
+    ggml_cann_pool_alloc theta_allocator(ctx.pool(), theta_length * sizeof(float));
+    void *               theta_buffer = theta_allocator.get();
+
+    acl_tensor_ptr acl_theta_tensor =
+        ggml_cann_create_tensor(theta_buffer, ACL_FLOAT, sizeof(float), cache_ne, cache_nb, GGML_MAX_DIMS);
+    aclnn_mul(ctx, acl_position_tensor.get(), acl_theta_scale_tensor.get(), acl_theta_tensor.get());
+
+    // Step5: calculate sin cos.
+    // init sin_repeat && cos_repeat, only to accelerate first layer on each device
+    if (position_length > ctx.rope_cache.position_length) {
+        ctx.rope_cache.position_length = position_length;
+        if (ctx.rope_cache.sin_cache != nullptr) {
+            ACL_CHECK(aclrtFree(ctx.rope_cache.sin_cache));
+        }
+        if (ctx.rope_cache.cos_cache != nullptr) {
+            ACL_CHECK(aclrtFree(ctx.rope_cache.cos_cache));
+        }
+        int64_t repeat_theta_length = theta_scale_length * position_length * 2;
+        ACL_CHECK(
+            aclrtMalloc(&ctx.rope_cache.sin_cache, repeat_theta_length * sizeof(float), ACL_MEM_MALLOC_HUGE_FIRST));
+        ACL_CHECK(
+            aclrtMalloc(&ctx.rope_cache.cos_cache, repeat_theta_length * sizeof(float), ACL_MEM_MALLOC_HUGE_FIRST));
+    }
+
+    // sin/cos
+    ggml_cann_pool_alloc sin_allocator(ctx.pool(), theta_length * sizeof(float));
+    void *               sin_buffer = sin_allocator.get();
+    acl_tensor_ptr       acl_sin_tensor =
+        ggml_cann_create_tensor(sin_buffer, ACL_FLOAT, sizeof(float), cache_ne, cache_nb, GGML_MAX_DIMS, ACL_FORMAT_ND);
+    aclnn_sin(ctx, acl_theta_tensor.get(), acl_sin_tensor.get());
+
+    ggml_cann_pool_alloc cos_allocator(ctx.pool(), theta_length * sizeof(float));
+    void *               cos_buffer = cos_allocator.get();
+    acl_tensor_ptr       acl_cos_tensor =
+        ggml_cann_create_tensor(cos_buffer, ACL_FLOAT, sizeof(float), cache_ne, cache_nb, GGML_MAX_DIMS, ACL_FORMAT_ND);
+    aclnn_cos(ctx, acl_theta_tensor.get(), acl_cos_tensor.get());
+
+    if (ext_factor != 0) {
+        attn_factor *= 1.0f + 0.1f * logf(1.0f / freq_scale);
+    }
+
+    // Step 5: multiply by attn_factor
+    if (attn_factor != 1) {
+        aclnn_muls(ctx, acl_sin_tensor.get(), attn_factor, nullptr, true);
+        aclnn_muls(ctx, acl_cos_tensor.get(), attn_factor, nullptr, true);
+    }
+
+    int64_t sin_reshape_ne[4] = { rope_dims, 1, dst->ne[2], 1 };
+    size_t  sin_reshape_nb[GGML_MAX_DIMS];
+    sin_reshape_nb[0] = sizeof(float);
+    for (int i = 1; i < GGML_MAX_DIMS; i++) {
+        sin_reshape_nb[i] = sin_reshape_nb[i - 1] * sin_reshape_ne[i - 1];
+    }
+    acl_tensor_ptr acl_sin_repeat_tensor = ggml_cann_create_tensor(ctx.rope_cache.sin_cache, ACL_FLOAT, sizeof(float),
+                                                                   sin_reshape_ne, sin_reshape_nb, GGML_MAX_DIMS);
+    acl_tensor_ptr acl_cos_repeat_tensor = ggml_cann_create_tensor(ctx.rope_cache.cos_cache, ACL_FLOAT, sizeof(float),
+                                                                   sin_reshape_ne, sin_reshape_nb, GGML_MAX_DIMS);
+
+    // Step 6: repeat
+    if (is_neox) {
+        // [sinθ1, sinθ1, sinθ2, sinθ2, ..., sinθn, sinθn]
+        int64_t repeatsArray[] = { 1, 1, 1, 2 };
+        aclnn_repeat(ctx, acl_sin_tensor.get(), acl_sin_repeat_tensor.get(), repeatsArray);
+        aclnn_repeat(ctx, acl_cos_tensor.get(), acl_cos_repeat_tensor.get(), repeatsArray);
+    } else {
+        int64_t num_repeats = 2;
+        int64_t dim         = 3;
+        int64_t output_size = theta_scale_length * num_repeats;
+        // [sinθ1, sinθ2, ..., sinθn, sinθ1, sinθ2, ..., sinθn]
+        aclnn_repeat_interleave(ctx, acl_sin_tensor.get(), acl_sin_repeat_tensor.get(), dim, num_repeats, output_size);
+        aclnn_repeat_interleave(ctx, acl_cos_tensor.get(), acl_cos_repeat_tensor.get(), dim, num_repeats, output_size);
+    }
+
+    // Update cached value.
+    ctx.rope_cache.cached = true;
+    ctx.rope_cache.set(theta_scale_length, position_length, ext_factor, theta_scale, freq_scale, attn_factor, is_neox,
+                       indep_sects, mrope_used, is_imrope, sections);
+}
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+aclnnStatus aclnnRotaryPositionEmbeddingGetWorkspaceSize(const aclTensor * x,
+                                                         const aclTensor * cos,
+                                                         const aclTensor * sin,
+                                                         int64_t           mode,
+                                                         const aclTensor * yOut,
+                                                         uint64_t *        workspaceSize,
+                                                         aclOpExecutor **  executor);
+aclnnStatus aclnnRotaryPositionEmbedding(void *          workspace,
+                                         uint64_t        workspaceSize,
+                                         aclOpExecutor * executor,
+                                         aclrtStream     stream);
+#ifdef __cplusplus
+}
+#endif
+
+void ggml_cann_rope(ggml_backend_cann_context & ctx, ggml_tensor * dst) {
+    ggml_tensor * src0 = dst->src[0];  // input
+
+    // param
+    float     freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow;
+    int       sections[4];
+    // const int n_past     = ((int32_t *) dst->op_params)[0];
+    const int n_dims     = ((int32_t *) dst->op_params)[1];
+    const int mode       = ((int32_t *) dst->op_params)[2];
+    // const int n_ctx      = ((int32_t *) dst->op_params)[3];
+    const int n_ctx_orig = ((int32_t *) dst->op_params)[4];
+
+    GGML_TENSOR_UNARY_OP_LOCALS
+
+    memcpy(&freq_base, (int32_t *) dst->op_params + 5, sizeof(float));
+    memcpy(&freq_scale, (int32_t *) dst->op_params + 6, sizeof(float));
+    memcpy(&ext_factor, (int32_t *) dst->op_params + 7, sizeof(float));
+    memcpy(&attn_factor, (int32_t *) dst->op_params + 8, sizeof(float));
+    memcpy(&beta_fast, (int32_t *) dst->op_params + 9, sizeof(float));
+    memcpy(&beta_slow, (int32_t *) dst->op_params + 10, sizeof(float));
+    memcpy(&sections, (int32_t *) dst->op_params + 11, sizeof(int) * 4);
+
+    GGML_ASSERT(n_dims % 2 == 0);
+    GGML_ASSERT(n_dims <= ne00);
+
+    const float theta_scale = powf(freq_base, -2.0f / n_dims);
+
+    float corr_dims[2];
+    ggml_rope_yarn_corr_dims(n_dims, n_ctx_orig, freq_base, beta_fast, beta_slow, corr_dims);
+
+    bool       is_neox    = mode & GGML_ROPE_TYPE_NEOX;
+    const bool is_imrope  = mode == GGML_ROPE_TYPE_IMROPE;  // qwen3vl apply interleaved mrope
+    // mrope_used means the GGML_ROPE_TYPE_MROPE bit is set.
+    // Note: this bit is also set for imrope and some vision modes,
+    // so mrope_used does NOT exclusively indicate pure mrope.
+    const bool mrope_used = mode & GGML_ROPE_TYPE_MROPE;
+    const bool is_vision  = mode == GGML_ROPE_TYPE_VISION;
+
+    if (mrope_used) {
+        GGML_ASSERT(sections[0] > 0 || sections[1] > 0 || sections[2] > 0);
+    }
+
+    if (is_vision) {
+        GGML_ASSERT(n_dims == ne0 / 2);
+    }
+
+    if (is_imrope || mrope_used) {
+        is_neox = true;
+    }
+
+    int64_t rope_dims = n_dims;
+
+    //Our current RotaryPositionEmbedding does not support the VISION mode,
+    //but essentially it only modifies theta_base in mrope,
+    //then repeats it at the end in the same way as is_neox.
+    //In fact, RoPE is still applied across all dimensions.
+    if (is_vision) {
+        rope_dims = src0->ne[0];
+    }
+    int64_t tail_dims = ne00 - rope_dims;
+    bool    has_tail  = tail_dims > 0;
+
+    // init ctx.rope_cos/rope_sin cache
+    aclnn_rope_cache_init(ctx, dst, corr_dims, ext_factor, theta_scale, freq_scale, attn_factor, is_neox, sections,
+                          mrope_used, is_imrope, is_vision, rope_dims);
+
+    // Cache is generated with ne00 dimensions, so we use ne00 for reshape
+    int64_t sin_reshape_ne[4] = { rope_dims, 1, ne02, 1 };
+    size_t  sin_reshape_nb[GGML_MAX_DIMS];
+    sin_reshape_nb[0] = sizeof(float);
+    for (int i = 1; i < GGML_MAX_DIMS; i++) {
+        sin_reshape_nb[i] = sin_reshape_nb[i - 1] * sin_reshape_ne[i - 1];
+    }
+    acl_tensor_ptr acl_sin_reshape_tensor = ggml_cann_create_tensor(ctx.rope_cache.sin_cache, ACL_FLOAT, sizeof(float),
+                                                                    sin_reshape_ne, sin_reshape_nb, GGML_MAX_DIMS);
+    acl_tensor_ptr acl_cos_reshape_tensor = ggml_cann_create_tensor(ctx.rope_cache.cos_cache, ACL_FLOAT, sizeof(float),
+                                                                    sin_reshape_ne, sin_reshape_nb, GGML_MAX_DIMS);
+
+    acl_tensor_ptr acl_src = ggml_cann_create_tensor(src0);
+    acl_tensor_ptr acl_dst = ggml_cann_create_tensor(dst);
+#ifdef ASCEND_310P
+    // Special ROPE operation for 310P
+
+    // roll input
+    void *               input_roll_buffer;
+    acl_tensor_ptr       acl_minus_one_tensor;
+    void *               minus_one_scale_buffer = nullptr;
+    ggml_cann_pool_alloc roll_allocator(ctx.pool(), ggml_nbytes(src0));
+    ggml_cann_pool_alloc minus_one_scale_allocator(ctx.pool(), sizeof(float) * src0->ne[0]);
+    if (!is_neox) {
+        // roll input: [q0,q1,q2,q3,...] -> [q1,q0,q3,q2,...]
+        input_roll_buffer        = roll_allocator.get();
+        int64_t input_roll_ne[4] = { 2, src0->ne[1] * (src0->ne[0] / 2), src0->ne[2], src0->ne[3] };
+        size_t  input_roll_nb[GGML_MAX_DIMS];
+        input_roll_nb[0] = ggml_type_size(src0->type);
+        for (int i = 1; i < GGML_MAX_DIMS; i++) {
+            input_roll_nb[i] = input_roll_nb[i - 1] * input_roll_ne[i - 1];
+        }
+        acl_tensor_ptr acl_input_roll_tensor =
+            ggml_cann_create_tensor(input_roll_buffer, ggml_cann_type_mapping(src0->type), ggml_type_size(src0->type),
+                                    input_roll_ne, input_roll_nb, GGML_MAX_DIMS);
+        acl_tensor_ptr acl_input_tensor =
+            ggml_cann_create_tensor(src0->data, ggml_cann_type_mapping(src0->type), ggml_type_size(src0->type),
+                                    input_roll_ne, input_roll_nb, GGML_MAX_DIMS);
+
+        int64_t shifts[] = { 1 };
+        int64_t dims[]   = { 3 };
+        aclnn_roll(ctx, acl_input_tensor.get(), acl_input_roll_tensor.get(), shifts, dims);
+
+        // init [-1, 1, -1, 1, ...]
+        minus_one_scale_buffer = minus_one_scale_allocator.get();
+
+        int64_t minus_one_ne[4] = { src0->ne[0], 1, 1, 1 };
+        size_t  minus_one_nb[GGML_MAX_DIMS];
+        minus_one_nb[0] = sizeof(float);
+        for (int i = 1; i < GGML_MAX_DIMS; i++) {
+            minus_one_nb[i] = minus_one_nb[i - 1] * minus_one_ne[i - 1];
+        }
+        acl_minus_one_tensor = aclnn_values(ctx, minus_one_scale_buffer, sizeof(float) * src0->ne[0], minus_one_ne,
+                                            GGML_MAX_DIMS, ACL_FLOAT, sizeof(float), 1);
+        int64_t   dim        = 3;
+        int64_t * index      = new int64_t[src0->ne[0]];
+        for (int i = 0; i < src0->ne[0]; i++) {
+            index[i] = i / 2 * 2;
+        }
+        int64_t index_num = src0->ne[0];
+        float   value     = -1;
+        aclnn_index_fill_tensor(ctx, acl_minus_one_tensor.get(), dim, index, index_num, value);
+    } else {
+        // roll input: [q0,q1,q2,...] ->
+        // [q_half,q_half+1,...,q_end,q0,q1,...q_half-1]
+        input_roll_buffer = roll_allocator.get();
+        acl_tensor_ptr acl_input_roll_tensor =
+            ggml_cann_create_tensor(input_roll_buffer, ggml_cann_type_mapping(src0->type), ggml_type_size(src0->type),
+                                    src0->ne, src0->nb, GGML_MAX_DIMS);
+        acl_tensor_ptr acl_input_tensor = ggml_cann_create_tensor(src0);
+
+        int64_t shifts[] = { src0->ne[0] / 2 };
+        int64_t dims[]   = { 3 };
+        aclnn_roll(ctx, acl_input_tensor.get(), acl_input_roll_tensor.get(), shifts, dims);
+
+        // init [-1, -1, -1, 1, 1，1，...]
+        minus_one_scale_buffer  = minus_one_scale_allocator.get();
+        int64_t minus_one_ne[4] = { src0->ne[0], 1, 1, 1 };
+        size_t  minus_one_nb[GGML_MAX_DIMS];
+        minus_one_nb[0] = sizeof(float);
+        for (int i = 1; i < GGML_MAX_DIMS; i++) {
+            minus_one_nb[i] = minus_one_nb[i - 1] * minus_one_ne[i - 1];
+        }
+        acl_minus_one_tensor     = aclnn_values(ctx, minus_one_scale_buffer, sizeof(float) * src0->ne[0], minus_one_ne,
+                                                GGML_MAX_DIMS, ACL_FLOAT, sizeof(float), 1);
+        // -1 * first half
+        int64_t first_half_ne[4] = { src0->ne[0] / 2, 1, 1, 1 };
+        size_t  first_half_nb[GGML_MAX_DIMS];
+        first_half_nb[0] = sizeof(float);
+        for (int i = 1; i < GGML_MAX_DIMS; i++) {
+            first_half_nb[i] = first_half_nb[i - 1] * first_half_ne[i - 1];
+        }
+        acl_tensor_ptr acl_first_half_tensor = ggml_cann_create_tensor(minus_one_scale_buffer, ACL_FLOAT, sizeof(float),
+                                                                       first_half_ne, first_half_nb, GGML_MAX_DIMS);
+        bool           inplace               = true;
+        float          scale                 = -1;
+        aclnn_muls(ctx, acl_first_half_tensor.get(), scale, nullptr, inplace);
+    }
+
+    // TODO: n_dims < ne0
+    GGML_ASSERT(n_dims == src0->ne[0]);
+
+    // input * scale
+    ggml_cann_pool_alloc roll_mul_scale_allocator(ctx.pool(), ggml_nbytes(src0));
+    void *               input_roll_mul_scale_buffer = roll_mul_scale_allocator.get();
+    size_t               input_nb[GGML_MAX_DIMS];
+    input_nb[0] = ggml_type_size(src0->type);
+    for (int i = 1; i < GGML_MAX_DIMS; i++) {
+        input_nb[i] = input_nb[i - 1] * src0->ne[i - 1];
+    }
+    acl_tensor_ptr acl_input_roll_mul_scale_tensor =
+        ggml_cann_create_tensor(input_roll_mul_scale_buffer, ggml_cann_type_mapping(src0->type),
+                                ggml_type_size(src0->type), src0->ne, input_nb, GGML_MAX_DIMS);
+    acl_tensor_ptr acl_input_roll_reshape_tensor =
+        ggml_cann_create_tensor(input_roll_buffer, ggml_cann_type_mapping(src0->type), ggml_type_size(src0->type),
+                                src0->ne, input_nb, GGML_MAX_DIMS);
+
+    aclnn_mul(ctx, acl_input_roll_reshape_tensor.get(), acl_minus_one_tensor.get(),
+              acl_input_roll_mul_scale_tensor.get());
+
+    // output
+    void * output_fp32_buffer;
+    if (src0->type == GGML_TYPE_F32) {
+        aclnn_mul(ctx, acl_src.get(), acl_cos_reshape_tensor.get());
+        aclnn_mul(ctx, acl_input_roll_mul_scale_tensor.get(), acl_sin_reshape_tensor.get());
+        aclnn_add(ctx, acl_src.get(), acl_input_roll_mul_scale_tensor.get(), acl_dst.get());
+        // TODO: ne0 != n_dims in mode2
+    } else if (src0->type == GGML_TYPE_F16) {
+        size_t input_fp32_nb[GGML_MAX_DIMS];
+        input_fp32_nb[0] = sizeof(float);
+        for (int i = 1; i < GGML_MAX_DIMS; i++) {
+            input_fp32_nb[i] = input_fp32_nb[i - 1] * dst->ne[i - 1];
+        }
+        ggml_cann_pool_alloc fp32_allocator1(ctx.pool(), ggml_nelements(dst) * sizeof(float));
+        void *               input_fp32_buffer1 = fp32_allocator1.get();
+        acl_tensor_ptr       input_fp32_tensor1 = ggml_cann_create_tensor(input_fp32_buffer1, ACL_FLOAT, sizeof(float),
+                                                                          dst->ne, input_fp32_nb, GGML_MAX_DIMS);
+        ggml_cann_pool_alloc fp32_allocator2(ctx.pool(), ggml_nelements(dst) * sizeof(float));
+        void *               input_fp32_buffer2 = fp32_allocator2.get();
+        acl_tensor_ptr       input_fp32_tensor2 = ggml_cann_create_tensor(input_fp32_buffer2, ACL_FLOAT, sizeof(float),
+                                                                          dst->ne, input_fp32_nb, GGML_MAX_DIMS);
+
+        ggml_cann_pool_alloc fp32_allocator(ctx.pool(), ggml_nelements(dst) * sizeof(float));
+        output_fp32_buffer                = fp32_allocator.get();
+        acl_tensor_ptr output_fp32_tensor = ggml_cann_create_tensor(output_fp32_buffer, ACL_FLOAT, sizeof(float),
+                                                                    dst->ne, input_fp32_nb, GGML_MAX_DIMS);
+        aclnn_mul(ctx, acl_src.get(), acl_cos_reshape_tensor.get(), input_fp32_tensor1.get());
+        aclnn_mul(ctx, acl_input_roll_mul_scale_tensor.get(), acl_sin_reshape_tensor.get(), input_fp32_tensor2.get());
+        aclnn_add(ctx, input_fp32_tensor1.get(), input_fp32_tensor2.get(), output_fp32_tensor.get());
+        aclnn_cast(ctx, output_fp32_tensor.get(), acl_dst.get(), ACL_FLOAT16);
+    }
+    return;
+#endif
+    int64_t acl_mode = is_neox ? 0 : 1;
+
+    // Pre-define head and tail dimensions for reuse
+    int64_t head_ne[GGML_MAX_DIMS] = { rope_dims, ne01, ne02, ne03 };
+    int64_t tail_ne[GGML_MAX_DIMS] = { tail_dims, ne01, ne02, ne03 };
+
+    // Step 1: Prepare trans tensors for F16 type conversion to F32 if needed
+    bool                 src_dst_need_trans = false;
+    ggml_cann_pool_alloc src_trans_allocator(ctx.pool());
+    ggml_cann_pool_alloc dst_trans_allocator(ctx.pool());
+    acl_tensor_ptr       acl_src_trans_tensor;
+    acl_tensor_ptr       acl_dst_trans_tensor;
+    void *               src_trans_buffer = nullptr;
+    void *               dst_trans_buffer = nullptr;
+    size_t               src_dst_trans_nb[GGML_MAX_DIMS];
+    if (src0->type == GGML_TYPE_F16) {
+        src_dst_need_trans = true;
+        src_trans_buffer   = src_trans_allocator.alloc(ggml_nelements(src0) * sizeof(float));
+        dst_trans_buffer   = dst_trans_allocator.alloc(ggml_nelements(dst) * sizeof(float));
+
+        src_dst_trans_nb[0] = sizeof(float);
+        for (int i = 1; i < GGML_MAX_DIMS; i++) {
+            src_dst_trans_nb[i] = src_dst_trans_nb[i - 1] * src0->ne[i - 1];
+        }
+        acl_src_trans_tensor = ggml_cann_create_tensor(src_trans_buffer, ACL_FLOAT, sizeof(float), src0->ne,
+                                                       src_dst_trans_nb, GGML_MAX_DIMS);
+        acl_dst_trans_tensor = ggml_cann_create_tensor(dst_trans_buffer, ACL_FLOAT, sizeof(float), dst->ne,
+                                                       src_dst_trans_nb, GGML_MAX_DIMS);
+        aclnn_cast(ctx, acl_src.get(), acl_src_trans_tensor.get(), ACL_FLOAT);
+    }
+
+    // Step 2: Prepare head tensors for tail splitting if needed
+    acl_tensor_ptr acl_src_head;
+    acl_tensor_ptr acl_dst_head;
+    if (has_tail) {
+        // Create head views for RotaryPositionEmbedding (only first rope_dims dimensions)
+        // RotaryPositionEmbedding requires contiguous dst tensor, so we use a temporary buffer
+        if (src_dst_need_trans) {
+            // Use F32 trans tensor strides
+            acl_src_head = ggml_cann_create_tensor((char *) src_trans_buffer, ACL_FLOAT, sizeof(float), head_ne,
+                                                   src_dst_trans_nb, GGML_MAX_DIMS);
+        } else {
+            // Use original F32 tensor strides
+            acl_src_head = ggml_cann_create_tensor((char *) src0->data, ACL_FLOAT, sizeof(float), head_ne, src0->nb,
+                                                   GGML_MAX_DIMS);
+        }
+
+        int64_t              head_elements = rope_dims * ne01 * ne02 * ne03;
+        ggml_cann_pool_alloc dst_head_contiguous_allocator(ctx.pool(), head_elements * sizeof(float));
+        void *               dst_head_contiguous_buffer = dst_head_contiguous_allocator.get();
+
+        size_t head_contiguous_nb[GGML_MAX_DIMS];
+        head_contiguous_nb[0] = sizeof(float);
+        for (int i = 1; i < GGML_MAX_DIMS; i++) {
+            head_contiguous_nb[i] = head_contiguous_nb[i - 1] * head_ne[i - 1];
+        }
+        acl_dst_head = ggml_cann_create_tensor(dst_head_contiguous_buffer, ACL_FLOAT, sizeof(float), head_ne,
+                                               head_contiguous_nb, GGML_MAX_DIMS);
+    }
+
+    // Step 3: Execute RotaryPositionEmbedding
+    if (has_tail) {
+        // Rotate only the head portion (first rope_dims dimensions)
+        GGML_CANN_CALL_ACLNN_OP(ctx, RotaryPositionEmbedding, acl_src_head.get(), acl_cos_reshape_tensor.get(),
+                                acl_sin_reshape_tensor.get(), acl_mode, acl_dst_head.get());
+
+        // Copy head result from contiguous buffer back to destination tensor
+        if (src_dst_need_trans) {
+            acl_tensor_ptr acl_dst_head_target = ggml_cann_create_tensor(
+                (char *) dst_trans_buffer, ACL_FLOAT, sizeof(float), head_ne, src_dst_trans_nb, GGML_MAX_DIMS);
+            cann_copy(ctx, acl_dst_head.get(), acl_dst_head_target.get());
+        } else {
+            acl_tensor_ptr acl_dst_head_target =
+                ggml_cann_create_tensor((char *) dst->data, ACL_FLOAT, sizeof(float), head_ne, dst->nb, GGML_MAX_DIMS);
+            cann_copy(ctx, acl_dst_head.get(), acl_dst_head_target.get());
+        }
+    } else if (src_dst_need_trans) {
+        // Rotate full tensor (no tail), using trans tensors
+        GGML_CANN_CALL_ACLNN_OP(ctx, RotaryPositionEmbedding, acl_src_trans_tensor.get(), acl_cos_reshape_tensor.get(),
+                                acl_sin_reshape_tensor.get(), acl_mode, acl_dst_trans_tensor.get());
+    } else {
+        // Rotate full tensor (no tail), using original tensors
+        GGML_CANN_CALL_ACLNN_OP(ctx, RotaryPositionEmbedding, acl_src.get(), acl_cos_reshape_tensor.get(),
+                                acl_sin_reshape_tensor.get(), acl_mode, acl_dst.get());
+    }
+
+    // Step 4: Copy unrotated tail portion from source to destination
+    if (has_tail) {
+        size_t src_tail_offset;
+        size_t dst_tail_offset;
+
+        auto copy_tail_device = [&](void * src_ptr, void * dst_ptr, aclDataType dtype, size_t elem_size,
+                                    size_t * nb_src_arr, size_t * nb_dst_arr) {
+            acl_tensor_ptr acl_src_tail =
+                ggml_cann_create_tensor(src_ptr, dtype, elem_size, tail_ne, nb_src_arr, GGML_MAX_DIMS);
+            acl_tensor_ptr acl_dst_tail =
+                ggml_cann_create_tensor(dst_ptr, dtype, elem_size, tail_ne, nb_dst_arr, GGML_MAX_DIMS);
+            cann_copy(ctx, acl_src_tail.get(), acl_dst_tail.get());
+        };
+
+        if (src_dst_need_trans) {
+            // Use F32 trans tensor strides and offsets
+            src_tail_offset = rope_dims * src_dst_trans_nb[0];
+            dst_tail_offset = rope_dims * src_dst_trans_nb[0];
+            copy_tail_device((char *) src_trans_buffer + src_tail_offset, (char *) dst_trans_buffer + dst_tail_offset,
+                             ACL_FLOAT, sizeof(float), src_dst_trans_nb, src_dst_trans_nb);
+        } else {
+            // Use original tensor strides and offsets
+            src_tail_offset = rope_dims * nb00;
+            dst_tail_offset = rope_dims * nb0;
+            copy_tail_device((char *) src0->data + src_tail_offset, (char *) dst->data + dst_tail_offset,
+                             ggml_cann_type_mapping(dst->type), ggml_element_size(dst), src0->nb, dst->nb);
+        }
+    }
+
+    // Step 5: Cast back to F16 if needed
+    if (src_dst_need_trans) {
+        aclnn_cast(ctx, acl_dst_trans_tensor.get(), acl_dst.get(), ACL_FLOAT16);
+    }
+}
+
+void ggml_cann_argmax(ggml_backend_cann_context & ctx, ggml_tensor * dst) {
+    ggml_tensor * src0 = dst->src[0];
+
+    acl_tensor_ptr acl_src = ggml_cann_create_tensor(src0);
+    acl_tensor_ptr acl_dst = ggml_cann_create_tensor(dst, dst->ne, dst->nb, 3);
+
+    GGML_CANN_CALL_ACLNN_OP(ctx, ArgMax, acl_src.get(), 3, false, acl_dst.get());
+}
+
+void ggml_cann_conv_transpose_1d(ggml_backend_cann_context & ctx, ggml_tensor * dst) {
+    ggml_tensor * src0 = dst->src[0];
+    ggml_tensor * src1 = dst->src[1];
+
+    // stride
+    int64_t s0 = ((const int32_t *) (dst->op_params))[0];
+
+    acl_tensor_ptr acl_input  = ggml_cann_create_tensor(src1, src1->ne, src1->nb, 3, ACL_FORMAT_NCL);
+    acl_tensor_ptr acl_weight = ggml_cann_create_tensor(src0, src0->ne, src0->nb, 3, ACL_FORMAT_NCL);
+    acl_tensor_ptr acl_dst    = ggml_cann_create_tensor(dst, dst->ne, dst->nb, 3, ACL_FORMAT_NCL);
+
+    // get base information of input and kernel
+    int64_t input_len   = *(src1->ne);
+    int64_t dst_len     = *(dst->ne);
+    int64_t kernel_size = *(src0->ne);
+
+    // set the max kernel size for each conv
+    int64_t max_kernel_size = 255;
+
+    // compute the partition of kernel
+    int64_t part_num = 1;
+    part_num         = (kernel_size + max_kernel_size - 1) / max_kernel_size;
+
+    int64_t strideVal[1];
+    strideVal[0]                    = s0;
+    acl_int_array_ptr stride        = ggml_cann_create_int_array(strideVal, 1);
+    int64_t           paddingVal[]  = { 0 };
+    acl_int_array_ptr padding       = ggml_cann_create_int_array(paddingVal, 1);
+    int64_t           dilationVal[] = { 1 };
+    acl_int_array_ptr dilation      = ggml_cann_create_int_array(dilationVal, 1);
+    bool              transposed    = true;
+    int64_t           groups        = 1;
+    int8_t            cubeMathType  = 0;
+
+#ifdef ASCEND_310P
+    cubeMathType = 1;
+#endif
+
+    auto weight_type = ggml_cann_type_mapping(src0->type);
+    auto dst_type    = ggml_cann_type_mapping(dst->type);
+
+    // slice the kernel to make each conv available
+    int64_t slice_dim   = -1;
+    int64_t slice_start = 0;
+    int64_t slice_end   = max_kernel_size;
+    int64_t slice_step  = 1;
+    int64_t interval    = max_kernel_size;
+
+    int64_t left_pad_len  = dilationVal[0] * (max_kernel_size - 1) + 1 - 2 * paddingVal[0];
+    int64_t right_pad_len = 0;
+
+    acl_scalar_ptr alpha      = nullptr;
+    float          alphaValue = 1.0;
+    alpha                     = ggml_cann_create_scalar(&alphaValue, aclDataType::ACL_FLOAT);
+
+    // set zero to destination
+    GGML_CANN_CALL_ACLNN_OP(ctx, InplaceZero, acl_dst.get());
+
+    for (int k = 0; k < part_num; k++) {
+        // create part kernel tensor and slice from big kernel
+        slice_start = max_kernel_size * k;
+        if (k == part_num - 1) {
+            slice_end = kernel_size;
+            interval  = kernel_size - max_kernel_size * k;
+        } else {
+            slice_end = max_kernel_size * (k + 1);
+        }
+
+        int64_t part_ne[4];
+        for (int i = 0; i < 4; i++) {
+            part_ne[i] = *(src0->ne + i);
+        }
+        part_ne[0] = interval;
+
+        size_t part_nb[4];
+        part_nb[0] = sizeof(weight_type);
+        for (int i = 1; i < 4; i++) {
+            part_nb[i] = part_nb[i - 1] * part_ne[i - 1];
+        }
+
+        ggml_cann_pool_alloc part_kernel_allocator;
+        part_kernel_allocator.alloc(ctx.pool(), part_nb[3]);
+        void * part_kernel_buf = part_kernel_allocator.get();
+
+        acl_tensor_ptr part_kernel = ggml_cann_create_tensor(part_kernel_buf, weight_type, ggml_element_size(src0),
+                                                             part_ne, part_nb, 3, ACL_FORMAT_NCL);
+
+        GGML_CANN_CALL_ACLNN_OP(ctx, Slice, acl_weight.get(), slice_dim, slice_start, slice_end, slice_step,
+                                part_kernel.get());
+
+        // create the part conv result tensor
+        int64_t part_dst_ne[4];
+        for (int i = 0; i < 4; i++) {
+            part_dst_ne[i] = *(dst->ne + i);
+        }
+        part_dst_ne[0] = (input_len - 1) * strideVal[0] - 2 * paddingVal[0] + dilationVal[0] * (part_ne[0] - 1) + 1;
+
+        size_t part_dst_nb[4];
+        part_dst_nb[0] = sizeof(weight_type);
+        for (int i = 1; i < 4; i++) {
+            part_dst_nb[i] = part_dst_nb[i - 1] * part_dst_ne[i - 1];
+        }
+        ggml_cann_pool_alloc part_dst_allocator;
+        part_dst_allocator.alloc(ctx.pool(), part_dst_nb[3]);
+        void * part_dst_buf = part_dst_allocator.get();
+
+        acl_tensor_ptr acl_part_dst = ggml_cann_create_tensor(part_dst_buf, dst_type, ggml_element_size(dst),
+                                                              part_dst_ne, part_dst_nb, 3, ACL_FORMAT_NCL);
+        GGML_CANN_CALL_ACLNN_OP(ctx, InplaceZero, acl_part_dst.get());
+
+        // compute part conv transpose 1d
+        GGML_CANN_CALL_ACLNN_OP(ctx, Convolution, acl_input.get(), part_kernel.get(), nullptr, stride.get(),
+                                padding.get(), dilation.get(), transposed, padding.get(), groups, acl_part_dst.get(),
+                                cubeMathType);
+
+        // compute the position of part result in final result
+        int64_t global_start = slice_start;
+        int64_t global_end   = std::min((input_len - 1) * strideVal[0] + slice_end, dst_len);
+
+        left_pad_len  = global_start;
+        right_pad_len = dst_len - global_end;
+
+        std::vector<int64_t> padDataVal = { left_pad_len, right_pad_len };
+        acl_int_array_ptr    padData    = ggml_cann_create_int_array(padDataVal.data(), 2);
+
+        acl_scalar_ptr pad_value    = nullptr;
+        float          pad_valueVal = 0.0;
+        pad_value                   = ggml_cann_create_scalar(&pad_valueVal, aclDataType::ACL_FLOAT);
+
+        int64_t conv_result_ne[4];
+        for (int i = 0; i < 4; i++) {
+            conv_result_ne[i] = *(dst->ne + i);
+        }
+
+        size_t conv_result_nb[4];
+        conv_result_nb[0] = sizeof(weight_type);
+        for (int i = 1; i < 4; i++) {
+            conv_result_nb[i] = conv_result_nb[i - 1] * conv_result_ne[i - 1];
+        }
+
+        ggml_cann_pool_alloc conv_result_allocator;
+        conv_result_allocator.alloc(ctx.pool(), conv_result_nb[3]);
+        void * conv_result_buf = conv_result_allocator.get();
+
+        acl_tensor_ptr conv_result = ggml_cann_create_tensor(conv_result_buf, dst_type, ggml_element_size(dst),
+                                                             conv_result_ne, conv_result_nb, 3, ACL_FORMAT_NCL);
+
+        GGML_CANN_CALL_ACLNN_OP(ctx, InplaceZero, conv_result.get());
+        GGML_CANN_CALL_ACLNN_OP(ctx, ConstantPadNd, acl_part_dst.get(), padData.get(), pad_value.get(),
+                                conv_result.get());
+        GGML_CANN_CALL_ACLNN_OP(ctx, InplaceAdd, acl_dst.get(), conv_result.get(), alpha.get());
+    }
+}
+
+void ggml_cann_elu(ggml_backend_cann_context & ctx, ggml_tensor * dst) {
+    ggml_tensor * src0 = dst->src[0];
+
+    acl_tensor_ptr acl_input = ggml_cann_create_tensor(src0);
+    acl_tensor_ptr acl_dst   = ggml_cann_create_tensor(dst);
+
+    float          alphaValue = 1.0f;
+    acl_scalar_ptr alpha      = nullptr;
+    alpha                     = ggml_cann_create_scalar(&alphaValue, aclDataType::ACL_FLOAT);
+
+    GGML_CANN_CALL_ACLNN_OP(ctx, Elu, acl_input.get(), alpha.get(), alpha.get(), alpha.get(), acl_dst.get());
+}
+
+void ggml_cann_mean(ggml_backend_cann_context & ctx, ggml_tensor * dst) {
+    ggml_tensor * src0 = dst->src[0];
+
+    acl_tensor_ptr acl_src = ggml_cann_create_tensor(src0);
+    acl_tensor_ptr acl_dst = ggml_cann_create_tensor(dst);
+
+    int64_t           reduceDimValue[] = { 3 };
+    acl_int_array_ptr reduceDim        = ggml_cann_create_int_array(reduceDimValue, 1);
+    bool              keepDim          = true;
+
+    GGML_CANN_CALL_ACLNN_OP(ctx, Mean, acl_src.get(), reduceDim.get(), keepDim, ACL_FLOAT, acl_dst.get());
+}
+
+void ggml_cann_pad_reflect_1d(ggml_backend_cann_context & ctx, ggml_tensor * dst) {
+    ggml_tensor *     src0             = dst->src[0];
+    int32_t *         opts             = (int32_t *) dst->op_params;
+    int64_t           paddingsArray[2] = { opts[0], opts[1] };
+    acl_int_array_ptr paddings         = ggml_cann_create_int_array(paddingsArray, 2);
+
+    for (int64_t i = 0; i < src0->ne[3]; i++) {
+        acl_tensor_ptr acl_src =
+            ggml_cann_create_tensor((char *) src0->data + i * src0->ne[3], ggml_cann_type_mapping(src0->type),
+                                    ggml_element_size(src0), src0->ne, src0->nb, 3);
+
+        acl_tensor_ptr acl_dst =
+            ggml_cann_create_tensor((char *) dst->data + i * src0->ne[3], ggml_cann_type_mapping(dst->type),
+                                    ggml_element_size(dst), dst->ne, dst->nb, 3);
+
+        GGML_CANN_CALL_ACLNN_OP(ctx, ReflectionPad1d, acl_src.get(), paddings.get(), acl_dst.get());
+    }
+}
+
+void ggml_cann_count_equal(ggml_backend_cann_context & ctx, ggml_tensor * dst) {
+    ggml_tensor * src0 = dst->src[0];
+    ggml_tensor * src1 = dst->src[1];
+
+    acl_tensor_ptr acl_self  = ggml_cann_create_tensor(src0);
+    acl_tensor_ptr acl_other = ggml_cann_create_tensor(src1);
+
+    GGML_CANN_CALL_ACLNN_OP(ctx, InplaceEqTensor, acl_self.get(), acl_other.get());
+
+    ggml_cann_sum(ctx, dst);
+}
+
+void ggml_cann_step(ggml_backend_cann_context & ctx, ggml_tensor * dst) {
+    ggml_tensor * src0 = dst->src[0];
+
+    acl_tensor_ptr acl_src = ggml_cann_create_tensor(src0);
+    acl_tensor_ptr acl_dst = ggml_cann_create_tensor(dst);
+
+    float          alphaValue = 0.0f;
+    acl_scalar_ptr alpha      = nullptr;
+    alpha                     = ggml_cann_create_scalar(&alphaValue, aclDataType::ACL_FLOAT);
+
+    GGML_CANN_CALL_ACLNN_OP(ctx, GtScalar, acl_src.get(), alpha.get(), acl_dst.get());
+}
+
+/**
+ * @brief Performs expert-specific matrix multiplication (MoE) with
+ * floating-point precision using the CANN backend.
+ *
+ * This function executes a matrix multiplication operation tailored for
+ * Mixture of Experts (MoE) models, where the input tensor is multiplied
+ * with expert-specific weight matrices. It uses the CANN backend for
+ * efficient computation and stores the result in the destination tensor `dst`.
+ * The operation may leverage identity-based optimizations or routing masks
+ * as part of sparse expert selection.
+ *
+ * @param ctx The context for executing CANN backend operations.
+ * @param dst The destination tensor where the MoE multiplication result
+ * will be stored.
+ *
+ * @note This function assumes floating-point data types and is designed for
+ * MoE architectures, possibly involving sparse expert routing.
+ */
+static void ggml_cann_mul_mat_id_fp(ggml_backend_cann_context & ctx, ggml_tensor * dst) {
+    //dst   [M, K, N, 1]
+    ggml_tensor * src0 = dst->src[0];  //src0	[D, M, A, 1]  -> [D, M, K, 1]
+    ggml_tensor * src1 = dst->src[1];  //src1	[D, B, N, 1], B = K or B = 1 -> [D, 1, K, 1]
+    ggml_tensor * ids  = dst->src[2];  //ids	[K, N]
+
+    GGML_ASSERT(src0->ne[3] == 1);
+    GGML_ASSERT(src1->ne[3] == 1);
+    GGML_ASSERT(dst->ne[3] == 1);
+
+    int64_t batch = src1->ne[2];
+    GGML_ASSERT(batch == ids->ne[1]);
+
+    ggml_cann_pool_alloc export_allocator(ctx.pool(), src0->ne[0] * src0->ne[1] * ids->ne[0] * ggml_element_size(src0));
+    void *               export_ptr = export_allocator.get();
+    for (int64_t i = 0; i < batch; i++) {
+        acl_tensor_ptr select_index  = ggml_cann_create_tensor(ids, ids->ne, ids->nb, 1, ACL_FORMAT_ND, i * ids->nb[1]);
+        acl_tensor_ptr export_weight = ggml_cann_create_tensor(src0, src0->ne, src0->nb, 3);
+
+        int64_t select_export_ne[] = { src0->ne[0], src0->ne[1], ids->ne[0] };
+        size_t  select_export_nb[3];
+        select_export_nb[0] = src0->nb[0];
+        for (int k = 1; k < 3; k++) {
+            select_export_nb[k] = select_export_nb[k - 1] * select_export_ne[k - 1];
+        }
+
+        acl_tensor_ptr select_export =
+            ggml_cann_create_tensor(export_ptr, ggml_cann_type_mapping(src0->type), ggml_element_size(src0),
+                                    select_export_ne, select_export_nb, 3);
+        GGML_CANN_CALL_ACLNN_OP(ctx, IndexSelect, export_weight.get(), 0, select_index.get(), select_export.get());
+
+        int64_t        select_transpose_ne[] = { select_export_ne[1], select_export_ne[0], select_export_ne[2] };
+        size_t         select_transpose_nb[] = { select_export_nb[1], select_export_nb[0], select_export_nb[2] };
+        acl_tensor_ptr select_export_transpose =
+            ggml_cann_create_tensor(export_ptr, ggml_cann_type_mapping(src0->type), ggml_element_size(src0),
+                                    select_transpose_ne, select_transpose_nb, 3);
+
+        int64_t        active_tensor_ne[] = { src1->ne[0], 1, src1->ne[1] };
+        size_t         active_tensor_nb[] = { src1->nb[0], src1->nb[1], src1->nb[1] };
+        acl_tensor_ptr active_tensor =
+            ggml_cann_create_tensor(src1, active_tensor_ne, active_tensor_nb, 3, ACL_FORMAT_ND, i * src1->nb[2]);
+
+        int64_t        dst_ne[] = { dst->ne[0], 1, dst->ne[1] };
+        size_t         dst_nb[] = { dst->nb[0], dst->nb[1], dst->nb[1] };
+        acl_tensor_ptr acl_dst  = ggml_cann_create_tensor(dst, dst_ne, dst_nb, 3, ACL_FORMAT_ND, i * dst->nb[2]);
+
+        GGML_CANN_CALL_ACLNN_OP(ctx, BatchMatMul, active_tensor.get(), select_export_transpose.get(), acl_dst.get(), 2);
+    }
+}
+
+/**
+ * @brief Performs quantized matrix multiplication for Mixture of Experts (MoE)
+ * models using the CANN backend.
+ *
+ * This function implements MUL_MAT_ID operation for quantized weight matrices
+ * (Q4_0 and Q8_0 formats). It selects expert-specific weight matrices based on
+ * the provided expert indices, and computes matrix multiplication using CANN's
+ * WeightQuantBatchMatmulV2 operator.
+ *
+ * The function performs the following steps:
+ * 1. Converts input/output tensors to F16 format if necessary
+ * 2. Uses IndexSelect to extract expert-specific weights and scales based on indices
+ * 3. Performs quantized matrix multiplication for each expert using WeightQuantBatchMatmulV2
+ * 4. Converts output back to the target type if needed
+ *
+ * Tensor shapes:
+ * - dst:  [M, K, N, 1] - output tensor
+ * - src0: [D, M, A, 1] - quantized weight matrices (Q4_0 or Q8_0)
+ * - src1: [D, B, N, 1] - input activations (B = K for per-expert input, or B = 1 for broadcast)
+ * - ids:  [K, N] - expert indices for routing
+ *
+ * @param ctx The CANN backend context for operation execution.
+ * @param dst The destination tensor where the multiplication result will be stored.
+ *
+ * @note Only Q4_0 and Q8_0 quantization formats are supported.
+ * @note The function handles automatic type conversion to/from F16 as needed by the hardware.
+ */
+static void ggml_cann_mul_mat_id_quant(ggml_backend_cann_context & ctx, ggml_tensor * dst) {
+    // dst:  [M, K, N, 1]
+    // src0: [D, M, A, 1] - quantized weights
+    // src1: [D, B, N, 1] - input activations, B = K or B = 1
+    // ids:  [K, N] - expert indices
+    ggml_tensor * src0 = dst->src[0];
+    ggml_tensor * src1 = dst->src[1];
+    ggml_tensor * ids  = dst->src[2];
+
+    GGML_ASSERT(src0->ne[3] == 1);
+    GGML_ASSERT(src1->ne[3] == 1);
+    GGML_ASSERT(dst->ne[3] == 1);
+    GGML_ASSERT(src1->ne[2] == ids->ne[1]);
+
+    const int64_t        n_batches        = ids->ne[1];
+    const int64_t        n_select_experts = ids->ne[0];
+    const enum ggml_type type             = src0->type;
+
+    const int32_t group_size = QK8_0;  // Both Q4_0 and Q8_0 use group size of 32
+    GGML_ASSERT(group_size == QK4_0);
+
+    // Calculate element size for quantized weights
+    const float weight_elem_size =
+        (type == GGML_TYPE_Q4_0) ? 0.5f :
+        (type == GGML_TYPE_Q8_0) ? 1.0f :
+                                   (GGML_ABORT("MUL_MAT_ID only supports Q4_0 and Q8_0"), 0.0f);
+
+    // Calculate scale offset in memory
+    const size_t weight_size     = src0->ne[0] * src0->ne[1] * src0->ne[2] * weight_elem_size;
+    const size_t scale_elem_size = sizeof(uint16_t);
+    char *       scale_data      = (char *) src0->data + weight_size;
+
+    // Allocate buffers for selected expert weights and scales
+    const size_t         selected_weight_size = src0->ne[0] * src0->ne[1] * n_select_experts * weight_elem_size;
+    ggml_cann_pool_alloc selected_weight_alloc(ctx.pool(), selected_weight_size);
+    void *               selected_weight_buffer = selected_weight_alloc.get();
+
+    const size_t selected_scale_size = (src0->ne[0] / group_size) * src0->ne[1] * n_select_experts * scale_elem_size;
+    ggml_cann_pool_alloc selected_scale_alloc(ctx.pool(), selected_scale_size);
+    void *               selected_scale_buffer = selected_scale_alloc.get();
+
+    // Helper lambda to allocate and cast tensor to F16 if needed
+    constexpr size_t f16_elem_size      = sizeof(uint16_t);
+    auto             prepare_f16_buffer = [&](ggml_tensor * tensor, ggml_cann_pool_alloc & allocator,
+                                  bool need_cast = false) -> void * {
+        if (tensor->type == GGML_TYPE_F16) {
+            return tensor->data;
+        }
+
+        size_t total_size = f16_elem_size;
+        for (int i = 0; i < GGML_MAX_DIMS; i++) {
+            total_size *= tensor->ne[i];
+        }
+        void * buffer = allocator.alloc(total_size);
+
+        if (need_cast == false) {
+            return buffer;
+        }
+
+        int64_t ne[GGML_MAX_DIMS];
+        size_t  nb[GGML_MAX_DIMS] = { f16_elem_size };
+        for (int i = 0; i < GGML_MAX_DIMS; i++) {
+            ne[i] = tensor->ne[i];
+            if (i > 0) {
+                nb[i] = nb[i - 1] * ne[i - 1];
+            }
+        }
+
+        acl_tensor_ptr src_tensor = ggml_cann_create_tensor(tensor);
+        acl_tensor_ptr f16_tensor = ggml_cann_create_tensor(buffer, ACL_FLOAT16, f16_elem_size, ne, nb, GGML_MAX_DIMS);
+        aclnn_cast(ctx, src_tensor.get(), f16_tensor.get(), ACL_FLOAT16);
+
+        return buffer;
+    };
+
+    // Prepare input and output buffers
+    ggml_cann_pool_alloc input_alloc(ctx.pool());
+    void *               input_buffer = prepare_f16_buffer(src1, input_alloc, true);
+
+    ggml_cann_pool_alloc output_alloc(ctx.pool());
+    void *               output_buffer = prepare_f16_buffer(dst, output_alloc, false);
+
+    // Process each batch
+    for (int64_t batch_idx = 0; batch_idx < n_batches; batch_idx++) {
+        // Create index tensor for current batch
+        const size_t   index_offset  = batch_idx * ids->nb[1];
+        acl_tensor_ptr batch_indices = ggml_cann_create_tensor(ids, ids->ne, ids->nb, 1, ACL_FORMAT_ND, index_offset);
+
+        // Select quantized weights using expert indices
+        // Q4_0 stores 2 values per byte, Q8_0 stores 1 value per byte
+        const int64_t weight_d         = (type == GGML_TYPE_Q4_0) ? src0->ne[0] / 2 : src0->ne[0];
+        const int64_t weight_m         = src0->ne[1];
+        const int64_t weight_n_experts = src0->ne[2];
+
+        int64_t weight_ne[3] = { weight_d, weight_m, weight_n_experts };
+        size_t  weight_nb[3] = { sizeof(int8_t), weight_d * sizeof(int8_t), weight_d * weight_m * sizeof(int8_t) };
+
+        acl_tensor_ptr all_weights =
+            ggml_cann_create_tensor(src0->data, ACL_INT8, sizeof(int8_t), weight_ne, weight_nb, 3);
+
+        int64_t selected_weight_ne[3] = { weight_d, weight_m, n_select_experts };
+        size_t  selected_weight_nb[3] = { sizeof(int8_t), weight_d * sizeof(int8_t),
+                                          weight_d * weight_m * sizeof(int8_t) };
+
+        acl_tensor_ptr selected_weights = ggml_cann_create_tensor(selected_weight_buffer, ACL_INT8, sizeof(int8_t),
+                                                                  selected_weight_ne, selected_weight_nb, 3);
+
+        GGML_CANN_CALL_ACLNN_OP(ctx, IndexSelect, all_weights.get(), 0, batch_indices.get(), selected_weights.get());
+
+        // Select scales using the same expert indices
+        const int64_t scale_d     = src0->ne[0] / group_size;
+        int64_t       scale_ne[3] = { scale_d, weight_m, weight_n_experts };
+        size_t scale_nb[3] = { scale_elem_size, scale_d * scale_elem_size, scale_d * weight_m * scale_elem_size };
+
+        acl_tensor_ptr all_scales =
+            ggml_cann_create_tensor(scale_data, ACL_FLOAT16, scale_elem_size, scale_ne, scale_nb, 3);
+
+        int64_t selected_scale_ne[3] = { scale_d, weight_m, n_select_experts };
+        size_t  selected_scale_nb[3] = { scale_elem_size, scale_d * scale_elem_size,
+                                         scale_d * weight_m * scale_elem_size };
+
+        acl_tensor_ptr selected_scales = ggml_cann_create_tensor(selected_scale_buffer, ACL_FLOAT16, scale_elem_size,
+                                                                 selected_scale_ne, selected_scale_nb, 3);
+
+        GGML_CANN_CALL_ACLNN_OP(ctx, IndexSelect, all_scales.get(), 0, batch_indices.get(), selected_scales.get());
+
+        // Process each expert for current batch
+        // IndexSelect output layout: [D, M, K] in contiguous format
+        // WeightQuantBatchMatmulV2 expects: [M, D] with row-major stride
+        for (int64_t expert_idx = 0; expert_idx < n_select_experts; expert_idx++) {
+            // Determine input offset: broadcast if src1->ne[1]==1, otherwise use per-expert input
+            const size_t input_offset =
+                (batch_idx * src1->ne[1] + (src1->ne[1] == 1 ? 0 : expert_idx)) * src1->ne[0] * f16_elem_size;
+            const size_t output_offset = (batch_idx * dst->ne[1] + expert_idx) * dst->ne[0] * f16_elem_size;
+
+            // Create weight view for current expert: [D, M, K] -> [M, D]
+            int64_t      weight_view_ne[2]  = { weight_m, src0->ne[0] };
+            float        weight_view_nb[2]  = { src0->ne[0] * weight_elem_size, weight_elem_size };
+            const size_t weight_view_offset = expert_idx * selected_weight_nb[2];
+
+            acl_tensor_ptr weight_view =
+                ggml_cann_create_tensor(selected_weight_buffer, ggml_cann_type_mapping(type), weight_elem_size,
+                                        weight_view_ne, weight_view_nb, 2, ACL_FORMAT_ND, weight_view_offset);
+
+            // Create scale view for current expert: [D, M, K] -> [M, D]
+            int64_t      scale_view_ne[2]  = { weight_m, scale_d };
+            size_t       scale_view_nb[2]  = { selected_scale_nb[1], selected_scale_nb[0] };
+            const size_t scale_view_offset = expert_idx * selected_scale_nb[2];
+
+            acl_tensor_ptr scale_view =
+                ggml_cann_create_tensor(selected_scale_buffer, ACL_FLOAT16, scale_elem_size, scale_view_ne,
+                                        scale_view_nb, 2, ACL_FORMAT_ND, scale_view_offset);
+
+            // Create input activation tensor [D, 1]
+            int64_t input_ne[2] = { src1->ne[0], 1 };
+            size_t  input_nb[2] = { f16_elem_size, src1->ne[0] * f16_elem_size };
+
+            acl_tensor_ptr input_tensor = ggml_cann_create_tensor(input_buffer, ACL_FLOAT16, f16_elem_size, input_ne,
+                                                                  input_nb, 2, ACL_FORMAT_ND, input_offset);
+
+            // Create output tensor [M, 1]
+            int64_t output_ne[2] = { dst->ne[0], 1 };
+            size_t  output_nb[2] = { f16_elem_size, dst->ne[0] * f16_elem_size };
+
+            acl_tensor_ptr output_tensor = ggml_cann_create_tensor(output_buffer, ACL_FLOAT16, f16_elem_size, output_ne,
+                                                                   output_nb, 2, ACL_FORMAT_ND, output_offset);
+
+            // Perform quantized matrix multiplication
+            GGML_CANN_CALL_ACLNN_OP(ctx, WeightQuantBatchMatmulV2, input_tensor.get(), weight_view.get(),
+                                    scale_view.get(), nullptr, nullptr, nullptr, nullptr, group_size,
+                                    output_tensor.get());
+        }
+    }
+
+    // Cast output back to original type if we used a temporary F16 buffer
+    if (dst->type != GGML_TYPE_F16) {
+        int64_t ne[GGML_MAX_DIMS];
+        size_t  nb[GGML_MAX_DIMS] = { f16_elem_size };
+        for (int i = 0; i < GGML_MAX_DIMS; i++) {
+            ne[i] = dst->ne[i];
+            if (i > 0) {
+                nb[i] = nb[i - 1] * ne[i - 1];
+            }
+        }
+
+        acl_tensor_ptr f16_output =
+            ggml_cann_create_tensor(output_buffer, ACL_FLOAT16, f16_elem_size, ne, nb, GGML_MAX_DIMS);
+        acl_tensor_ptr dst_tensor = ggml_cann_create_tensor(dst);
+
+        aclnn_cast(ctx, f16_output.get(), dst_tensor.get(), ggml_cann_type_mapping(dst->type));
+    }
+}
+
+void ggml_cann_mul_mat_id(ggml_backend_cann_context & ctx, ggml_tensor * dst) {
+    const enum ggml_type type = dst->src[0]->type;
+    switch (type) {
+        case GGML_TYPE_F32:
+        case GGML_TYPE_F16:
+            ggml_cann_mul_mat_id_fp(ctx, dst);
+            break;
+        case GGML_TYPE_Q4_0:
+        case GGML_TYPE_Q8_0:
+            ggml_cann_mul_mat_id_quant(ctx, dst);
+            break;
+        default:
+            GGML_ABORT("Unsupported type for mul_mat_id");
+            break;
+    }
+}
+
+void ggml_cann_flash_attn_ext(ggml_backend_cann_context & ctx, ggml_tensor * dst) {
+    ggml_tensor * src0 = dst->src[0];  // q, fp32 | B, N, S, D (uncont) -> B, S, N, D (cont)
+    ggml_tensor * src1 = dst->src[1];  // k, fp16 | B, N, S, D (uncont) -> B, S, N, D (cont)
+    ggml_tensor * src2 = dst->src[2];  // v, fp16 | B, N, S, D (uncont) -> B, S, N, D (cont)
+    ggml_tensor * src3 = dst->src[3];  // mask, fp16
+
+    // B, N, S, D (uncont) -> B, S, N, D (cont)
+    int64_t src0_bsnd_ne[GGML_MAX_DIMS];
+    memcpy(src0_bsnd_ne, src0->ne, GGML_MAX_DIMS * sizeof(int64_t));
+    size_t src0_bsnd_nb[GGML_MAX_DIMS];
+    memcpy(src0_bsnd_nb, src0->nb, GGML_MAX_DIMS * sizeof(size_t));
+    int64_t src1_bsnd_ne[GGML_MAX_DIMS];
+    memcpy(src1_bsnd_ne, src1->ne, GGML_MAX_DIMS * sizeof(int64_t));
+    size_t src1_bsnd_nb[GGML_MAX_DIMS];
+    memcpy(src1_bsnd_nb, src1->nb, GGML_MAX_DIMS * sizeof(size_t));
+    int64_t src2_bsnd_ne[GGML_MAX_DIMS];
+    memcpy(src2_bsnd_ne, src2->ne, GGML_MAX_DIMS * sizeof(int64_t));
+    size_t src2_bsnd_nb[GGML_MAX_DIMS];
+    memcpy(src2_bsnd_nb, src2->nb, GGML_MAX_DIMS * sizeof(size_t));
+
+    auto transpose12 = [](int64_t * ne, size_t * nb) {
+        int64_t ne_tmp = ne[1];
+        size_t  nb_tmp = nb[1];
+        ne[1]          = ne[2];
+        nb[1]          = nb[2];
+        ne[2]          = ne_tmp;
+        nb[2]          = nb_tmp;
+    };
+
+    transpose12(src0_bsnd_ne, src0_bsnd_nb);
+    transpose12(src1_bsnd_ne, src1_bsnd_nb);
+    transpose12(src2_bsnd_ne, src2_bsnd_nb);
+
+    float maxBias      = 0.0f;
+    float scaleValue   = 1.0f;
+    float logitSoftcap = 0.0f;
+    memcpy(&scaleValue, (float *) dst->op_params + 0, sizeof(float));
+    memcpy(&maxBias, (float *) dst->op_params + 1, sizeof(float));
+    memcpy(&logitSoftcap, (float *) dst->op_params + 2, sizeof(float));
+
+    if (logitSoftcap == 0.0f) {
+        size_t faElemSize = sizeof(uint16_t);
+        auto   faDataType = ACL_FLOAT16;  //ACL_BF16;
+
+        acl_tensor_ptr acl_q_tensor = nullptr;
+        acl_tensor_ptr acl_k_tensor = nullptr;
+        acl_tensor_ptr acl_v_tensor = nullptr;
+
+        // Step 1: cast the src0 (Query) to fp16 if needed
+        ggml_cann_pool_alloc src0_f16_allocator(ctx.pool());
+        void *               src0_f16_buffer = nullptr;
+
+        if (ggml_cann_type_mapping(src0->type) != faDataType) {
+            acl_tensor_ptr acl_src0_f32_tensor =
+                ggml_cann_create_tensor(src0, src0_bsnd_ne, src0_bsnd_nb, GGML_MAX_DIMS);
+            src0_f16_buffer = src0_f16_allocator.alloc(ggml_nelements(src0) * faElemSize);
+
+            int64_t * src0_f16_ne = src0_bsnd_ne;
+            size_t    src0_f16_nb[GGML_MAX_DIMS];
+            src0_f16_nb[0] = sizeof(uint16_t);
+            for (int i = 1; i < GGML_MAX_DIMS; ++i) {
+                src0_f16_nb[i] = src0_f16_nb[i - 1] * src0_f16_ne[i - 1];
+            }
+
+            acl_q_tensor = ggml_cann_create_tensor(src0_f16_buffer, faDataType, faElemSize, src0_f16_ne, src0_f16_nb,
+                                                   GGML_MAX_DIMS);
+            aclnn_cast(ctx, acl_src0_f32_tensor.get(), acl_q_tensor.get(), faDataType);
+        } else {
+            acl_q_tensor = ggml_cann_create_tensor(src0, src0_bsnd_ne, src0_bsnd_nb, GGML_MAX_DIMS);
+        }
+
+        // Step 2: create the acl tensors for src1 (Key), src2 (Value),
+        //         and the direct output from FusedInferAttention
+
+        acl_k_tensor = ggml_cann_create_tensor(src1, src1_bsnd_ne, src1_bsnd_nb, GGML_MAX_DIMS);
+        acl_v_tensor = ggml_cann_create_tensor(src2, src2_bsnd_ne, src2_bsnd_nb, GGML_MAX_DIMS);
+
+        // Step 3: create the PSEShift tensor if needed
+        //         this tensor is considered as mask (f16) in the llama.cpp
+        acl_tensor_ptr       bcast_pse_tensor;
+        ggml_cann_pool_alloc bcast_pse_allocator(ctx.pool());
+        if (src3 != nullptr) {
+            // Construct the truncated pse tensor (common for prefill/decode)
+            int64_t trunc_pse_ne[GGML_MAX_DIMS] = {
+                src3->ne[0],  // D
+                src0->ne[1],  // S (number of Q tokens)
+                src3->ne[2],  // mask N
+                src3->ne[3]   // B
+            };
+            size_t * trunc_pse_nb = src3->nb;
+
+            acl_tensor_ptr acl_mask_f16_trunc_tensor = ggml_cann_create_tensor(
+                src3->data, ACL_FLOAT16, sizeof(uint16_t), trunc_pse_ne, trunc_pse_nb, GGML_MAX_DIMS);
+
+            int64_t bcast_pse_ne[GGML_MAX_DIMS];
+            size_t  bcast_pse_nb[GGML_MAX_DIMS];
+            bcast_pse_ne[0] = src3->ne[0];  // D
+            bcast_pse_ne[1] = src0->ne[1];  // S
+            bcast_pse_ne[2] = src0->ne[2];  // N (num_heads)
+            bcast_pse_ne[3] = src3->ne[3];  // B
+            if (maxBias == 0.0f) {
+                // When maxBias == 0.0f, use nb = 0 reduce once repeat (Qwen2)
+                // Construct the bcast tensor (simulate repeat on the head dimension using stride=0)
+                bcast_pse_nb[0] = sizeof(uint16_t);
+                bcast_pse_nb[1] = bcast_pse_nb[0] * bcast_pse_ne[0];
+                bcast_pse_nb[2] = 0;  // <---- the head dimension shares the same data
+                bcast_pse_nb[3] = src3->nb[3];
+
+                bcast_pse_tensor = ggml_cann_create_tensor(src3->data, ACL_FLOAT16, sizeof(uint16_t), bcast_pse_ne,
+                                                           bcast_pse_nb, GGML_MAX_DIMS);
+
+            } else {
+                bcast_pse_nb[0] = sizeof(uint16_t);
+                for (int i = 1; i < GGML_MAX_DIMS; i++) {
+                    bcast_pse_nb[i] = bcast_pse_nb[i - 1] * bcast_pse_ne[i - 1];
+                }
+
+                void * bcast_pse_buffer =
+                    bcast_pse_allocator.alloc(ggml_nelements(src3) * src0->ne[2] * sizeof(uint16_t));
+
+                bcast_pse_tensor = ggml_cann_create_tensor(bcast_pse_buffer, ACL_FLOAT16, sizeof(uint16_t),
+                                                           bcast_pse_ne, bcast_pse_nb, GGML_MAX_DIMS);
+
+                int64_t repeats[] = { 1, src0->ne[2], 1, 1 };
+                aclnn_repeat(ctx, acl_mask_f16_trunc_tensor.get(), bcast_pse_tensor.get(), repeats);
+
+                // alibi
+                // Compute the slope if needed. Derived from ggml_cann_softmax().
+                const int64_t        n_heads = src0->ne[2];
+                ggml_cann_pool_alloc slope_allocator(ctx.pool(), n_heads * sizeof(uint16_t));
+                void *               slope_buffer = slope_allocator.get();
+                aclnn_get_slope(ctx, n_heads, slope_buffer, maxBias, GGML_TYPE_F16);
+
+                int64_t slope_ne[] = { 1, 1, n_heads, 1 };
+                size_t  slope_nb[GGML_MAX_DIMS];
+                slope_nb[0] = sizeof(uint16_t);
+                for (int i = 1; i < GGML_MAX_DIMS; i++) {
+                    slope_nb[i] = slope_nb[i - 1] * slope_ne[0];
+                }
+
+                acl_tensor_ptr slope_tensor = ggml_cann_create_tensor(slope_buffer, ACL_FLOAT16, sizeof(uint16_t),
+                                                                      slope_ne, slope_nb, GGML_MAX_DIMS);
+                GGML_CANN_CALL_ACLNN_OP(ctx, InplaceMul, bcast_pse_tensor.get(), slope_tensor.get());
+            }
+        }
+
+        // Step 4: set the inputs for FusedInferAttention.
+        acl_tensor_list_ptr acl_k_tensor_list = ggml_cann_create_tensor_list(acl_k_tensor);
+        acl_tensor_list_ptr acl_v_tensor_list = ggml_cann_create_tensor_list(acl_v_tensor);
+
+        int64_t numHeads           = src0->ne[2];  // N
+        int64_t numKeyValueHeads   = src1->ne[2];
+        // double  scaleValue = 1 / sqrt(src0->ne[0]); // 1/sqrt(d)
+        int64_t preTokens          = 65535;
+        int64_t nextTokens         = 65535;
+        char    layout[5]          = { 'B', 'S', 'N', 'D', 0 };
+        int64_t sparseMode         = 0;
+        int64_t innerPrecise       = (src0->ne[1] == 1) ? 0 : 2;
+        int64_t blockSize          = 0;
+        int64_t antiquantMode      = 0;
+        bool    softmaxLseFlag     = false;
+        int64_t keyAntiquantMode   = 0;
+        int64_t valueAntiquantMode = 0;
+
+        GGML_ASSERT(dst->type == GGML_TYPE_F32 || dst->type == GGML_TYPE_F16);
+        acl_tensor_ptr       fa_dst_tensor;
+        acl_tensor_ptr       acl_dst_tensor;
+        ggml_cann_pool_alloc out_f16_allocator(ctx.pool());
+        if (dst->type == GGML_TYPE_F32) {
+            void * out_f16_buffer = out_f16_allocator.alloc(ggml_nelements(dst) * faElemSize);
+
+            int64_t * out_f16_ne = src0_bsnd_ne;
+            size_t    out_f16_nb[GGML_MAX_DIMS];
+            out_f16_nb[0] = faElemSize;
+            for (int i = 1; i < GGML_MAX_DIMS; ++i) {
+                out_f16_nb[i] = out_f16_nb[i - 1] * out_f16_ne[i - 1];
+            }
+
+            fa_dst_tensor =
+                ggml_cann_create_tensor(out_f16_buffer, faDataType, faElemSize, out_f16_ne, out_f16_nb, GGML_MAX_DIMS);
+        } else {
+            fa_dst_tensor = ggml_cann_create_tensor(dst);
+        }
+
+        GGML_CANN_CALL_ACLNN_OP(ctx, FusedInferAttentionScoreV2, acl_q_tensor.get(), acl_k_tensor_list.get(),
+                                acl_v_tensor_list.get(),               // q, k, v
+                                bcast_pse_tensor.get(), nullptr,       // pse, mask
+                                nullptr, nullptr,                      // actSeqLen, actSeqLenkv
+                                nullptr, nullptr,                      // deqScale1, quantScale1
+                                nullptr, nullptr, nullptr,             // deqScale2, quantScale2, quantOffset2
+                                nullptr, nullptr,                      // antiquantScale, antiquantOffset
+                                nullptr,                               // blockTable
+                                nullptr, nullptr,                      // qPadSize, kvPadSize
+                                nullptr, nullptr,                      // kAntiquantScale, kAntiQuantOffset
+                                nullptr, nullptr,                      // vAntiquantScale, vAntiQuantOffset
+                                nullptr, nullptr, nullptr,             // kSharedPrefix, vSharedPrefix, actSharedLen
+                                numHeads, scaleValue,                  // heads, scaleValue
+                                preTokens, nextTokens,                 // preTokens, nextTokens
+                                layout,                                // inputLayout
+                                numKeyValueHeads,                      // numKVHeads
+                                sparseMode, innerPrecise,              // sparseMode, innerPrecise
+                                blockSize, antiquantMode,              // blockSize, antiquantMode
+                                softmaxLseFlag,                        // softmaxLseFlag
+                                keyAntiquantMode, valueAntiquantMode,  // keyAntiqMode, valueAntiqMode
+                                fa_dst_tensor.get(),                   // attentionOut
+                                nullptr                                // softmaxLse
+        );
+
+        if (dst->type == GGML_TYPE_F32) {
+            // Step 6: post-processing, permute and cast to f32
+            acl_tensor_ptr acl_dst_tensor = ggml_cann_create_tensor(dst);
+            aclnn_cast(ctx, fa_dst_tensor.get(), acl_dst_tensor.get(), ggml_cann_type_mapping(dst->type));
+        }
+    } else {
+        GGML_ABORT("Function is not implemented.");
+    }
+}
+
+static void ggml_cann_out_prod_fp(ggml_backend_cann_context & ctx, ggml_tensor * dst) {
+    ggml_tensor * src0 = dst->src[0];  // weight
+    ggml_tensor * src1 = dst->src[1];  // input
+    GGML_TENSOR_BINARY_OP_LOCALS
+
+    acl_tensor_ptr acl_dst = ggml_cann_create_tensor(dst);
+    GGML_CANN_CALL_ACLNN_OP(ctx, InplaceZero, acl_dst.get());
+
+    const int64_t dps2 = ne2 / ne02;
+    const int64_t dps3 = ne3 / ne03;
+    for (int64_t i3 = 0; i3 < ne3; i3++) {
+        for (int64_t i2 = 0; i2 < ne2; i2++) {
+            const int64_t i02 = i2 / dps2;
+            const int64_t i03 = i3 / dps3;
+
+            const int64_t  i12 = i2;
+            const int64_t  i13 = i3;
+            acl_tensor_ptr accumulator =
+                ggml_cann_create_tensor((char *) dst->data + i2 * nb2 + i3 * nb3, ggml_cann_type_mapping(dst->type),
+                                        ggml_type_size(dst->type), dst->ne, dst->nb, 2);
+
+            // The outer product needs to be accumulated in this dimension.
+            for (int64_t i1 = 0; i1 < ne11; i1++) {
+                acl_tensor_ptr acl_input = ggml_cann_create_tensor(
+                    (char *) src1->data + i1 * nb11 + i12 * nb12 + i13 * nb13, ggml_cann_type_mapping(src0->type),
+                    ggml_type_size(src0->type), src1->ne, src1->nb, 1);
+
+                acl_tensor_ptr acl_weight = ggml_cann_create_tensor(
+                    (char *) src0->data + i1 * nb01 + i02 * nb02 + i03 * nb03, ggml_cann_type_mapping(src0->type),
+                    ggml_type_size(src0->type), src0->ne, src0->nb, 1);
+
+                ggml_cann_pool_alloc output_allocator(ctx.pool());
+                void *               output_buffer = output_allocator.alloc(ggml_nbytes(dst));
+                acl_tensor_ptr       acl_out = ggml_cann_create_tensor(output_buffer, ggml_cann_type_mapping(dst->type),
+                                                                       ggml_type_size(dst->type), dst->ne, dst->nb, 2);
+
+                GGML_CANN_CALL_ACLNN_OP(ctx, Ger, acl_input.get(), acl_weight.get(), acl_out.get());
+                float       alpha_value = 1.0f;
+                aclScalar * alpha       = aclCreateScalar(&alpha_value, ACL_FLOAT);
+                GGML_CANN_CALL_ACLNN_OP(ctx, InplaceAdd, accumulator.get(), acl_out.get(), alpha);
+            }
+        }
+    }
+}
+
+void ggml_cann_out_prod(ggml_backend_cann_context & ctx, ggml_tensor * dst) {
+    ggml_tensor * src0 = dst->src[0];
+
+    const enum ggml_type type = src0->type;
+
+    switch (type) {
+        case GGML_TYPE_F32:
+        case GGML_TYPE_F16:
+            ggml_cann_out_prod_fp(ctx, dst);
+            break;
+        default:
+            GGML_ABORT("Unsupport type for GGML_OP_OUT_PROD");
+            break;
+    }
+}
+
+void ggml_cann_ssm_conv(ggml_backend_cann_context & ctx, ggml_tensor * dst) {
+    ggml_tensor * src0 = dst->src[0];  // conv_x
+    ggml_tensor * src1 = dst->src[1];  // conv1d.weight
+
+    // This op is currently defined only for F32 in ggml_cpu
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT(src1->type == GGML_TYPE_F32);
+    GGML_ASSERT(dst->type == GGML_TYPE_F32);
+
+    // Shapes follow ggml_compute_forward_ssm_conv_f32
+    const int64_t nc  = src1->ne[0];   // d_conv
+    const int64_t ncs = src0->ne[0];   // d_conv - 1 + n_t
+    const int64_t nr  = src0->ne[1];   // d_inner
+    const int64_t n_s = src0->ne[2];   // n_seqs
+
+    const int64_t n_t = dst->ne[1];    // tokens per sequence
+
+    GGML_ASSERT(dst->ne[0] == nr);     // dst: {d_inner, n_t, n_s}
+    GGML_ASSERT(src1->ne[1] == nr);    // weight: {d_conv, d_inner}
+    GGML_ASSERT(ncs == nc - 1 + n_t);  // conv_x: {d_conv - 1 + n_t, d_inner, n_s}
+    GGML_ASSERT(src0->nb[0] == sizeof(float));
+    GGML_ASSERT(src1->nb[0] == sizeof(float));
+
+    // --- Build CANN tensors ---
+
+    // 1) Input: conv_x as NCL
+    //
+    // src0->ne = { ncs, nr, n_s, 1 }  // {L_in, C, N}
+    // Passing ACL_FORMAT_NCL here means:
+    //   reversed dims -> [N, C, L_in] = [n_s, nr, ncs]
+    acl_tensor_ptr acl_x = ggml_cann_create_tensor(src0, src0->ne, src0->nb, 3, ACL_FORMAT_NCL);
+
+    // 2) Weights: depthwise conv kernel, view src1 as {K, 1, C}
+    //
+    // src1 original:   ne = { nc, nr, 1, 1 }  // [K, C, 1, 1]
+    // we want a view:  ne_w = { nc, 1, nr }   // [K, 1, C]
+    // so that reversed dims -> [C, 1, K] which matches
+    //   [out_channels, in_channels/groups, kernel_size]
+    int64_t w_ne[GGML_MAX_DIMS] = { nc, 1, nr, 1 };  // [K, 1 input ch. per group, C groups]
+    // Layout: src1 data is [K, C] with
+    //   offset(k, c) = k*nb0 + c*nb1
+    // We want offset_w(k, 0, c) = k*nb0 + c*nb1,
+    // so we can reuse nb0 and nb1, and set nb2 = nb1.
+    size_t  w_nb[GGML_MAX_DIMS] = { src1->nb[0], src1->nb[1], src1->nb[1], src1->nb[3] };  // same as src1
+
+    acl_tensor_ptr acl_w = ggml_cann_create_tensor(src1->data, ggml_cann_type_mapping(src1->type),
+                                                   ggml_type_size(src1->type), w_ne, w_nb, 3, ACL_FORMAT_NCL);
+
+    // 3) Output: dst is { d_inner, n_t, n_s } (CLN)
+    //
+    // We need an NCL view of the same buffer:
+    //   desired NCL logical shape: { L_out = n_t, C = nr, N = n_s }
+    //
+    // Original CLN layout:
+    //   dst->ne = { nr, n_t, n_s }
+    //   dst->nb[0] = sizeof(float)
+    //   dst->nb[1] = nr * sizeof(float)
+    //   dst->nb[2] = nr * n_t * sizeof(float)
+    //
+    // We want offset_new(L, C, N) = offset_orig(C, L, N).
+    // Choose:
+    //   nb_y[0] = nr * sizeof(float);           // step in L
+    //   nb_y[1] = sizeof(float);                // step in C
+    //   nb_y[2] = nr * n_t * sizeof(float);     // step in N
+    int64_t y_ne[GGML_MAX_DIMS] = { n_t, nr, n_s, 1 };  // [L_out, C, N]
+    size_t  y_nb[GGML_MAX_DIMS] = { dst->ne[0] * sizeof(float), sizeof(float), dst->ne[0] * dst->ne[1] * sizeof(float),
+                                    dst->nb[3] };       // [nr, 1, nr * n_t]
+
+    acl_tensor_ptr acl_y = ggml_cann_create_tensor(dst->data, ggml_cann_type_mapping(dst->type),
+                                                   ggml_type_size(dst->type), y_ne, y_nb, 3, ACL_FORMAT_NCL);
+
+    // --- Conv1d parameters: depthwise, stride 1, no padding ("valid") ---
+    int64_t strideVal[1]   = { 1 };
+    int64_t paddingVal[1]  = { 0 };
+    int64_t dilationVal[1] = { 1 };
+
+    acl_int_array_ptr stride   = ggml_cann_create_int_array(strideVal, 1);
+    acl_int_array_ptr padding  = ggml_cann_create_int_array(paddingVal, 1);
+    acl_int_array_ptr dilation = ggml_cann_create_int_array(dilationVal, 1);
+
+    const bool    transposed   = false;
+    const int64_t groups       = nr;  // depthwise: one group per inner dim
+    int8_t        cubeMathType = 0;
+
+#ifdef ASCEND_310P
+    cubeMathType = 1;
+#endif
+
+    GGML_CANN_CALL_ACLNN_OP(ctx, Convolution,
+                            acl_x.get(),    // input:  N, C, L_in = ncs
+                            acl_w.get(),    // weight: [C, 1, K] with groups=nr
+                            nullptr,        // bias
+                            stride.get(), padding.get(), dilation.get(), transposed,
+                            padding.get(),  // output padding (unused for non-transposed)
+                            groups, acl_y.get(), cubeMathType);
+}
+
+void ggml_cann_op_add_rms_norm_fused(ggml_backend_cann_context & ctx,
+                                     ggml_tensor *               add_node,
+                                     ggml_tensor *               rms_norm_node) {
+    // Get the two input tensors for ADD operation
+    ggml_tensor * x1 = add_node->src[0];
+    ggml_tensor * x2 = add_node->src[1];
+
+    // Create ACL tensors for the two ADD inputs
+    acl_tensor_ptr acl_x1 = ggml_cann_create_tensor(x1);
+    acl_tensor_ptr acl_x2 = ggml_cann_create_tensor(x2);
+
+    // Get epsilon parameter from rms_norm_tensor
+    float eps;
+    memcpy(&eps, rms_norm_node->op_params, sizeof(float));
+
+    // Build gamma tensor (RMS normalization scaling factor)
+    // Gamma should match the normalized dimensions (last dimension of x1)
+    size_t acl_gamma_nb[GGML_MAX_DIMS];
+    acl_gamma_nb[0] = ggml_type_size(rms_norm_node->type);
+    for (int i = 1; i < GGML_MAX_DIMS; i++) {
+        acl_gamma_nb[i] = acl_gamma_nb[i - 1] * x1->ne[i - 1];
+    }
+    acl_tensor_ptr acl_gamma =
+        get_cache_acl_tensor(ctx, &ctx.rms_norm_one_tensor_cache.cache, ctx.rms_norm_one_tensor_cache.size, x1->ne,
+                             acl_gamma_nb, rms_norm_node->type,
+                             1,    // dims - only the last dimension
+                             1.0f  // value
+        );
+
+    // Build rstdOut tensor (output for normalized standard deviation)
+    // Shape should be the dimensions that are NOT normalized
+    int64_t acl_rstd_ne[] = { 1, x1->ne[1], x1->ne[2], x1->ne[3] };
+    size_t  acl_rstd_nb[GGML_MAX_DIMS - 1];
+    acl_rstd_nb[0] = sizeof(float);
+    for (int i = 1; i < GGML_MAX_DIMS - 1; i++) {
+        acl_rstd_nb[i] = acl_rstd_nb[i - 1] * acl_rstd_ne[i - 1];
+    }
+    acl_tensor_ptr acl_rstd =
+        get_cache_acl_tensor(ctx, &ctx.rms_norm_zero_tensor_cache.cache, ctx.rms_norm_zero_tensor_cache.size,
+                             acl_rstd_ne, acl_rstd_nb, GGML_TYPE_F32, GGML_MAX_DIMS,
+                             0.0f  // value
+        );
+
+    acl_tensor_ptr acl_xout = ggml_cann_create_tensor(add_node);
+
+    // Create yOut tensor (final output after RMS normalization)
+    acl_tensor_ptr acl_yout = ggml_cann_create_tensor(rms_norm_node);
+
+    // Call fused ADD + RMS_NORM operator
+    GGML_CANN_CALL_ACLNN_OP(ctx, AddRmsNorm, acl_x1.get(), acl_x2.get(), acl_gamma.get(),
+                            eps,  // double type
+                            acl_yout.get(), acl_rstd.get(), acl_xout.get());
+}
+
+void ggml_cann_gated_linear_attn(ggml_backend_cann_context & ctx, ggml_tensor * dst) {
+    ggml_tensor * k = dst->src[0];
+    ggml_tensor * v = dst->src[1];
+    ggml_tensor * q = dst->src[2];
+    ggml_tensor * g = dst->src[3];
+    ggml_tensor * s = dst->src[4];
+
+    int64_t B = dst->src[4]->ne[1];
+    int64_t T = dst->src[0]->ne[2];
+    int64_t H = dst->src[0]->ne[1];
+    int64_t C = dst->ne[0];
+    int64_t D = C / H;
+    int64_t L = T / B;
+
+    int64_t ne_qkg[2] = { 1, D };
+    int64_t ne_s[2]   = { D, D };
+    int64_t ne_st[2]  = { ne_s[1], ne_s[0] };
+    int64_t ne_vo[2]  = { D, 1 };
+    int64_t ne_q[1]   = { D };
+    size_t  nb_base   = ggml_type_size(k->type);
+    size_t  nb_qkg[2] = { nb_base, nb_base };
+    size_t  nb_s[2]   = { nb_base, D * nb_base };
+    size_t  nb_st[2]  = { nb_s[1], nb_s[0] };
+    size_t  nb_vo[2]  = { nb_base, D * nb_base };
+    size_t  nb_q[1]   = { nb_base };
+
+    const float scale = ggml_get_op_params_f32(dst, 0);
+
+    acl_tensor_ptr acl_s     = ggml_cann_create_tensor(s, s->ne, s->nb, 2, ACL_FORMAT_ND);
+    acl_tensor_ptr new_state = ggml_cann_create_tensor(dst, s->ne, s->nb, 2, ACL_FORMAT_ND, (B * L * H * D) * nb_base);
+    cann_copy(ctx, acl_s.get(), new_state.get());
+
+    for (int64_t b = 0; b < B; b++) {
+        for (int64_t h = 0; h < H; h++) {
+            size_t         s_offset = (b * (H * D * D) + h * (D * D)) * nb_base;
+            // D * D
+            acl_tensor_ptr acl_s_new =
+                ggml_cann_create_tensor(dst, ne_s, nb_s, 2, ACL_FORMAT_ND, (B * L * H * D) * nb_base + s_offset);
+            acl_tensor_ptr acl_s_new_t =
+                ggml_cann_create_tensor(dst, ne_st, nb_st, 2, ACL_FORMAT_ND, (B * L * H * D) * nb_base + s_offset);
+            for (int64_t l = 0; l < L; l++) {
+                size_t               qkvgo_offset = (b * (L * H * D) + l * (H * D) + h * (D)) * nb_base;
+                // D * 1
+                acl_tensor_ptr       acl_k = ggml_cann_create_tensor(k, ne_qkg, nb_qkg, 2, ACL_FORMAT_ND, qkvgo_offset);
+                acl_tensor_ptr       acl_g = ggml_cann_create_tensor(g, ne_qkg, nb_qkg, 2, ACL_FORMAT_ND, qkvgo_offset);
+                // D
+                acl_tensor_ptr       acl_q = ggml_cann_create_tensor(q, ne_q, nb_q, 1, ACL_FORMAT_ND, qkvgo_offset);
+                // 1 * D
+                acl_tensor_ptr       acl_v = ggml_cann_create_tensor(v, ne_vo, nb_vo, 2, ACL_FORMAT_ND, qkvgo_offset);
+                // D
+                acl_tensor_ptr       acl_o = ggml_cann_create_tensor(dst, ne_q, nb_q, 1, ACL_FORMAT_ND, qkvgo_offset);
+                // k ⊗ v
+                size_t               buf_size = D * D * nb_base;
+                ggml_cann_pool_alloc buffer_allocator(ctx.pool(), buf_size);
+                acl_tensor_ptr       tmp_tensor = ggml_cann_create_tensor(
+                    buffer_allocator.get(), ggml_cann_type_mapping(k->type), nb_base, ne_s, nb_s, 2);
+                aclnn_mul(ctx, acl_k.get(), acl_v.get(), tmp_tensor.get());
+                //s_new = g ⊗ s_old + k ⊗ v
+                aclnn_mul(ctx, acl_s_new.get(), acl_g.get(), nullptr);
+                aclnn_add(ctx, acl_s_new.get(), tmp_tensor.get(), nullptr);
+                // compute output
+                GGML_CANN_CALL_ACLNN_OP(ctx, Mv, acl_s_new_t.get(), acl_q.get(), acl_o.get(), 1);
+                aclnn_muls(ctx, acl_o.get(), scale, nullptr, true);
+            }
+        }
+    }
+}