diff --git a/ggml/src/ggml-cann/acl_tensor.cpp b/ggml/src/ggml-cann/acl_tensor.cpp index 17d1f56836eee..960ce9a0368d7 100644 --- a/ggml/src/ggml-cann/acl_tensor.cpp +++ b/ggml/src/ggml-cann/acl_tensor.cpp @@ -25,7 +25,7 @@ #include #include -aclDataType type_mapping(ggml_type type) { +aclDataType ggml_cann_type_mapping(ggml_type type) { switch (type) { case GGML_TYPE_F32: return ACL_FLOAT; @@ -43,8 +43,9 @@ aclDataType type_mapping(ggml_type type) { return ACL_DT_UNDEFINED; } -aclTensor* create_acl_tensor(const ggml_tensor* tensor, int64_t* ne, size_t* nb, - int64_t dims, aclFormat format, size_t offset) { +aclTensor* ggml_cann_create_tensor(const ggml_tensor* tensor, int64_t* ne, + size_t* nb, int64_t dims, aclFormat format, + size_t offset) { // If tensor is bcasted, Up to GGML_MAX_DIMS additional dimensions will be // added. int64_t acl_ne[GGML_MAX_DIMS * 2], acl_stride[GGML_MAX_DIMS * 2]; @@ -71,15 +72,15 @@ aclTensor* create_acl_tensor(const ggml_tensor* tensor, int64_t* ne, size_t* nb, std::reverse(acl_ne, acl_ne + final_dims); std::reverse(acl_stride, acl_stride + final_dims); - aclTensor* acl_tensor = - aclCreateTensor(acl_ne, final_dims, type_mapping(tensor->type), - acl_stride, offset / ggml_element_size(tensor), format, - &acl_storage_len, 1, tensor->data); + aclTensor* acl_tensor = aclCreateTensor( + acl_ne, final_dims, ggml_cann_type_mapping(tensor->type), acl_stride, + offset / ggml_element_size(tensor), format, &acl_storage_len, 1, + tensor->data); return acl_tensor; } -bool need_bcast(const ggml_tensor* t0, const ggml_tensor* t1) { +bool ggml_cann_need_bcast(const ggml_tensor* t0, const ggml_tensor* t1) { for (int i = 0; i < GGML_MAX_DIMS; i++) { if (t1->ne[i] != t0->ne[i] && t1->ne[i] != 1) { return true; @@ -88,9 +89,10 @@ bool need_bcast(const ggml_tensor* t0, const ggml_tensor* t1) { return false; } -aclTensor* create_acl_tensor(void* data_ptr, aclDataType dtype, - size_t type_size, int64_t* ne, size_t* nb, - int64_t dims, aclFormat format, size_t offset) { +aclTensor* ggml_cann_create_tensor(void* data_ptr, aclDataType dtype, + size_t type_size, int64_t* ne, size_t* nb, + int64_t dims, aclFormat format, + size_t offset) { int64_t tmp_ne[GGML_MAX_DIMS * 2]; int64_t tmp_stride[GGML_MAX_DIMS * 2]; @@ -114,9 +116,11 @@ aclTensor* create_acl_tensor(void* data_ptr, aclDataType dtype, return acl_tensor; } -int64_t get_bcast_shape(const ggml_tensor* src0, const ggml_tensor* src1, - int64_t* bcast_src0_ne, int64_t* bcast_src1_ne, - size_t* bcast_src0_nb, size_t* bcast_src1_nb) { +int64_t ggml_cann_get_bcast_shape(const ggml_tensor* src0, + const ggml_tensor* src1, + int64_t* bcast_src0_ne, + int64_t* bcast_src1_ne, size_t* bcast_src0_nb, + size_t* bcast_src1_nb) { GGML_ASSERT(ggml_can_repeat(src1, src0)); int bcast_dim_cnt = 0; for (int i = 0; i < GGML_MAX_DIMS; i++) { @@ -140,13 +144,11 @@ int64_t get_bcast_shape(const ggml_tensor* src0, const ggml_tensor* src1, return bcast_dim_cnt; } -int64_t get_mul_mat_bcast_shape(const int64_t* input_ne, - const int64_t* weight_ne, const int64_t* dst_ne, - const size_t* input_nb, const size_t* weight_nb, - const size_t* dst_nb, int64_t* bcast_input_ne, - int64_t* bcast_weight_ne, int64_t* bcast_dst_ne, - size_t* bcast_input_nb, size_t* bcast_weight_nb, - size_t* bcast_dst_nb) { +int64_t ggml_cann_get_mulmat_bcast_shape( + const int64_t* input_ne, const int64_t* weight_ne, const int64_t* dst_ne, + const size_t* input_nb, const size_t* weight_nb, const size_t* dst_nb, + int64_t* bcast_input_ne, int64_t* bcast_weight_ne, int64_t* bcast_dst_ne, + size_t* bcast_input_nb, size_t* bcast_weight_nb, size_t* bcast_dst_nb) { // input and dst shoule in same shape, except first two dims. GGML_ASSERT(input_ne[2] == dst_ne[2]); GGML_ASSERT(input_ne[3] == dst_ne[3]); diff --git a/ggml/src/ggml-cann/acl_tensor.h b/ggml/src/ggml-cann/acl_tensor.h index d08659356da6d..f3e1c4858d2c0 100644 --- a/ggml/src/ggml-cann/acl_tensor.h +++ b/ggml/src/ggml-cann/acl_tensor.h @@ -38,7 +38,7 @@ * @return The corresponding aclDataType. If the input type is not recognized, * ACL_DT_UNDEFINED is returned. */ -aclDataType type_mapping(ggml_type type); +aclDataType ggml_cann_type_mapping(ggml_type type); /** * @brief Creates an ACL tensor from a ggml_tensor with optional shape. @@ -59,7 +59,7 @@ aclDataType type_mapping(ggml_type type); * @param offset Offset in bytes for the ACL tensor data. Defaults to 0. * @return Pointer to the created ACL tensor. */ -aclTensor* create_acl_tensor(const ggml_tensor* tensor, int64_t* ne = nullptr, +aclTensor* ggml_cann_create_tensor(const ggml_tensor* tensor, int64_t* ne = nullptr, size_t* nb = nullptr, int64_t dims = 0, aclFormat format = ACL_FORMAT_ND, size_t offset = 0); @@ -83,7 +83,7 @@ aclTensor* create_acl_tensor(const ggml_tensor* tensor, int64_t* ne = nullptr, * @param offset Offset in bytes for the ACL tensor data. Defaults to 0. * @return Pointer to the created ACL tensor. */ -aclTensor* create_acl_tensor(void* data_ptr, aclDataType dtype, +aclTensor* ggml_cann_create_tensor(void* data_ptr, aclDataType dtype, size_t type_size, int64_t* ne, size_t* nb, int64_t dims, aclFormat format = ACL_FORMAT_ND, size_t offset = 0); @@ -104,7 +104,7 @@ aclTensor* create_acl_tensor(void* data_ptr, aclDataType dtype, * to 1. If such a dimension is found, broadcasting is required to align t1 * with t0 for element-wise operations. */ -bool need_bcast(const ggml_tensor* t0, const ggml_tensor* t1); +bool ggml_cann_need_bcast(const ggml_tensor* t0, const ggml_tensor* t1); /** * @brief Computes broadcast shapes and strides for two ggml_tensors. @@ -159,19 +159,19 @@ bool need_bcast(const ggml_tensor* t0, const ggml_tensor* t1); * dim1 in a inserted dim, should add nb for dim1, * and all other nb moves to next in order. */ -int64_t get_bcast_shape(const ggml_tensor* src0, const ggml_tensor* src1, +int64_t ggml_cann_get_bcast_shape(const ggml_tensor* src0, const ggml_tensor* src1, int64_t* bcast_ne_src0, int64_t* bcast_ne_src1, size_t* bcast_nb_src0, size_t* bcast_nb_src1); // Bcast macro to avoid duplicate code. -#define BCAST_SHAPE(src0, src1) \ - int64_t bcast_##src0##_ne[GGML_MAX_DIMS * 2]; \ - int64_t bcast_##src1##_ne[GGML_MAX_DIMS * 2]; \ - size_t bcast_##src0##_nb[GGML_MAX_DIMS * 2]; \ - size_t bcast_##src1##_nb[GGML_MAX_DIMS * 2]; \ - int64_t bcast_dims = \ - get_bcast_shape(src0, src1, bcast_##src0##_ne, bcast_##src1##_ne, \ - bcast_##src0##_nb, bcast_##src1##_nb); +#define BCAST_SHAPE(src0, src1) \ + int64_t bcast_##src0##_ne[GGML_MAX_DIMS * 2]; \ + int64_t bcast_##src1##_ne[GGML_MAX_DIMS * 2]; \ + size_t bcast_##src0##_nb[GGML_MAX_DIMS * 2]; \ + size_t bcast_##src1##_nb[GGML_MAX_DIMS * 2]; \ + int64_t bcast_dims = ggml_cann_get_bcast_shape( \ + src0, src1, bcast_##src0##_ne, bcast_##src1##_ne, bcast_##src0##_nb, \ + bcast_##src1##_nb); #define BCAST_PARAM(tensor) bcast_##tensor##_ne, bcast_##tensor##_nb, bcast_dims @@ -201,17 +201,15 @@ int64_t get_bcast_shape(const ggml_tensor* src0, const ggml_tensor* src1, * shapes needed for matrix multiplication. It ensures that dimensions where * weight tensor requires expansion are appropriately handled to conform with * broadcasting rules. - * @note compare with get_bcast_shape，mul_mat broadcast need add this new dim before - * cast dim. - * @sa get_bcast_shape + * @note compare with ggml_cann_get_bcast_shape，mul_mat broadcast need add this new dim + * before cast dim. + * @sa ggml_cann_get_bcast_shape */ -int64_t get_mul_mat_bcast_shape(const int64_t* input_ne, - const int64_t* weight_ne, const int64_t* dst_ne, - const size_t* input_nb, const size_t* weight_nb, - const size_t* dst_nb, int64_t* bcast_input_ne, - int64_t* bcast_weight_ne, int64_t* bcast_dst_ne, - size_t* bcast_input_nb, size_t* bcast_weight_nb, - size_t* bcast_dst_nb); +int64_t ggml_cann_get_mulmat_bcast_shape( + const int64_t* input_ne, const int64_t* weight_ne, const int64_t* dst_ne, + const size_t* input_nb, const size_t* weight_nb, const size_t* dst_nb, + int64_t* bcast_input_ne, int64_t* bcast_weight_ne, int64_t* bcast_dst_ne, + size_t* bcast_input_nb, size_t* bcast_weight_nb, size_t* bcast_dst_nb); // Bcast macro to avoid duplicate code. #define BCAST_MUL_MAT_SHAPE(input, weight, dst) \ @@ -221,7 +219,7 @@ int64_t get_mul_mat_bcast_shape(const int64_t* input_ne, size_t bcast_##input##_nb[GGML_MAX_DIMS * 2]; \ size_t bcast_##weight##_nb[GGML_MAX_DIMS * 2]; \ size_t bcast_##dst##_nb[GGML_MAX_DIMS * 2]; \ - int64_t bcast_dims = get_mul_mat_bcast_shape( \ + int64_t bcast_dims = ggml_cann_get_mulmat_bcast_shape( \ input->ne, weight->ne, dst->ne, input->nb, weight->nb, dst->nb, \ bcast_##input##_ne, bcast_##weight##_ne, bcast_##dst##_ne, \ bcast_##input##_nb, bcast_##weight##_nb, bcast_##dst##_nb); diff --git a/ggml/src/ggml-cann/aclnn_ops.cpp b/ggml/src/ggml-cann/aclnn_ops.cpp index 91f17d185fd6e..a02efc8281adc 100644 --- a/ggml/src/ggml-cann/aclnn_ops.cpp +++ b/ggml/src/ggml-cann/aclnn_ops.cpp @@ -98,8 +98,8 @@ 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)); - aclTensor* acl_src = create_acl_tensor(src); - aclTensor* acl_dst = create_acl_tensor(dst); + aclTensor* acl_src = ggml_cann_create_tensor(src); + aclTensor* 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]}; @@ -156,15 +156,15 @@ void ggml_cann_add(ggml_backend_cann_context& ctx, ggml_tensor* dst) { aclTensor* acl_dst; // Need bcast - if (!ggml_are_same_shape(src0, src1) && need_bcast(src0, src1)) { + if (!ggml_are_same_shape(src0, src1) && ggml_cann_need_bcast(src0, src1)) { BCAST_SHAPE(src0, src1) - acl_src0 = create_acl_tensor(src0, BCAST_PARAM(src0)); - acl_src1 = create_acl_tensor(src1, BCAST_PARAM(src1)); - acl_dst = create_acl_tensor(dst, BCAST_PARAM(src0)); + 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 = create_acl_tensor(src0); - acl_src1 = create_acl_tensor(src1); - acl_dst = create_acl_tensor(dst); + acl_src0 = ggml_cann_create_tensor(src0); + acl_src1 = ggml_cann_create_tensor(src1); + acl_dst = ggml_cann_create_tensor(dst); } aclnn_add(ctx, acl_src0, acl_src1, acl_dst); @@ -180,8 +180,8 @@ void ggml_cann_leaky_relu(ggml_backend_cann_context& ctx, ggml_tensor* dst) { GGML_ASSERT(src->type == GGML_TYPE_F32); GGML_ASSERT(dst->type == GGML_TYPE_F32); - aclTensor* acl_src = create_acl_tensor(src); - aclTensor* acl_dst = create_acl_tensor(dst); + aclTensor* acl_src = ggml_cann_create_tensor(src); + aclTensor* acl_dst = ggml_cann_create_tensor(dst); float negative_slope; memcpy(&negative_slope, dst->op_params, sizeof(float)); @@ -237,9 +237,9 @@ static void aclnn_concat(ggml_backend_cann_context& ctx, void ggml_cann_concat(ggml_backend_cann_context& ctx, ggml_tensor* dst) { ggml_tensor* src0 = dst->src[0]; ggml_tensor* src1 = dst->src[1]; - aclTensor* acl_src0 = create_acl_tensor(src0); - aclTensor* acl_src1 = create_acl_tensor(src1); - aclTensor* acl_dst = create_acl_tensor(dst); + aclTensor* acl_src0 = ggml_cann_create_tensor(src0); + aclTensor* acl_src1 = ggml_cann_create_tensor(src1); + aclTensor* acl_dst = ggml_cann_create_tensor(dst); int64_t concat_dim = 1; aclTensor* tensors[] = {acl_src0, acl_src1}; @@ -299,7 +299,7 @@ static void aclnn_arange(ggml_backend_cann_context& ctx, aclTensor* acl_dst, void ggml_cann_arange(ggml_backend_cann_context& ctx, ggml_tensor* dst) { GGML_ASSERT(dst->type == GGML_TYPE_F32); - aclTensor* acl_dst = create_acl_tensor(dst); + aclTensor* acl_dst = ggml_cann_create_tensor(dst); int64_t n_elements = ggml_nelements(dst); float start; @@ -328,8 +328,8 @@ void ggml_cann_clamp(ggml_backend_cann_context& ctx, ggml_tensor* dst) { memcpy(&min, dst->op_params, sizeof(float)); memcpy(&max, (float*)dst->op_params + 1, sizeof(float)); - aclTensor* acl_src = create_acl_tensor(src); - aclTensor* acl_dst = create_acl_tensor(dst); + aclTensor* acl_src = ggml_cann_create_tensor(src); + aclTensor* acl_dst = ggml_cann_create_tensor(dst); aclScalar* acl_min = aclCreateScalar(&min, aclDataType::ACL_FLOAT); aclScalar* acl_max = aclCreateScalar(&max, aclDataType::ACL_FLOAT); @@ -361,8 +361,8 @@ void ggml_cann_scale(ggml_backend_cann_context& ctx, ggml_tensor* dst) { memcpy(&v, dst->op_params, sizeof(float)); aclScalar* scale = aclCreateScalar(&v, aclDataType::ACL_FLOAT); - aclTensor* acl_src = create_acl_tensor(src); - aclTensor* acl_dst = create_acl_tensor(dst); + aclTensor* acl_src = ggml_cann_create_tensor(src); + aclTensor* acl_dst = ggml_cann_create_tensor(dst); uint64_t workspaceSize = 0; aclOpExecutor* executor; @@ -386,14 +386,14 @@ 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]; - aclTensor* acl_src = create_acl_tensor(src); - aclTensor* acl_dst = create_acl_tensor(dst); + aclTensor* acl_src = ggml_cann_create_tensor(src); + aclTensor* 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(); aclTensor* tmp_tensor = - create_acl_tensor(buffer, ACL_INT64, ggml_type_size(dst->type), dst->ne, - dst->nb, GGML_MAX_DIMS); + ggml_cann_create_tensor(buffer, ACL_INT64, ggml_type_size(dst->type), + dst->ne, dst->nb, GGML_MAX_DIMS); uint64_t workspaceSize = 0; aclOpExecutor* executor; @@ -411,7 +411,8 @@ void ggml_cann_argsort(ggml_backend_cann_context& ctx, ggml_tensor* dst) { aclnnArgsort(workspaceAddr, workspaceSize, executor, ctx.stream())); workspaceSize = 0; - ACL_CHECK(aclnnCastGetWorkspaceSize(tmp_tensor, type_mapping(dst->type), + ACL_CHECK(aclnnCastGetWorkspaceSize(tmp_tensor, + ggml_cann_type_mapping(dst->type), acl_dst, &workspaceSize, &executor)); if (workspaceSize > 0) { ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize); @@ -428,8 +429,8 @@ void ggml_cann_argsort(ggml_backend_cann_context& ctx, ggml_tensor* dst) { void ggml_cann_norm(ggml_backend_cann_context& ctx, ggml_tensor* dst) { ggml_tensor* src = dst->src[0]; - aclTensor* acl_src = create_acl_tensor(src); - aclTensor* acl_dst = create_acl_tensor(dst); + aclTensor* acl_src = ggml_cann_create_tensor(src); + aclTensor* acl_dst = ggml_cann_create_tensor(dst); float eps; memcpy(&eps, dst->op_params, sizeof(float)); @@ -460,8 +461,8 @@ void ggml_cann_norm(ggml_backend_cann_context& ctx, ggml_tensor* dst) { void ggml_cann_group_norm(ggml_backend_cann_context& ctx, ggml_tensor* dst) { ggml_tensor* src = dst->src[0]; - aclTensor* acl_src = create_acl_tensor(src); - aclTensor* acl_dst = create_acl_tensor(dst); + aclTensor* acl_src = ggml_cann_create_tensor(src); + aclTensor* acl_dst = ggml_cann_create_tensor(dst); const float eps = 1e-6f; // TODO: make this a parameter int n_groups = dst->op_params[0]; @@ -481,9 +482,9 @@ void ggml_cann_group_norm(ggml_backend_cann_context& ctx, ggml_tensor* dst) { ggml_cann_pool_alloc temp_buffer_allocator(ctx.pool(), n_bytes * 2); void* buffer = temp_buffer_allocator.get(); - aclTensor* acl_mean_out = - create_acl_tensor(buffer, ACL_FLOAT, type_size, ne, nb, ACL_FORMAT_ND); - aclTensor* acl_rstd_out = create_acl_tensor( + aclTensor* acl_mean_out = ggml_cann_create_tensor( + buffer, ACL_FLOAT, type_size, ne, nb, ACL_FORMAT_ND); + aclTensor* acl_rstd_out = ggml_cann_create_tensor( (char*)buffer + n_bytes, ACL_FLOAT, type_size, ne, nb, ACL_FORMAT_ND); ACL_CHECK(aclnnGroupNormGetWorkspaceSize( @@ -516,9 +517,9 @@ void ggml_cann_acc(ggml_backend_cann_context& ctx, ggml_tensor* dst) { size_t param_nb[] = {ggml_element_size(src0), nb1, nb2, nb3}; - aclTensor* acl_dst = create_acl_tensor( + aclTensor* acl_dst = ggml_cann_create_tensor( dst, src1->ne, param_nb, GGML_MAX_DIMS, ACL_FORMAT_ND, offset); - aclTensor* acl_src1 = create_acl_tensor(src1); + aclTensor* acl_src1 = ggml_cann_create_tensor(src1); aclScalar* alpha = nullptr; float alphaValue = 1.0f; @@ -532,7 +533,7 @@ void ggml_cann_acc(ggml_backend_cann_context& ctx, ggml_tensor* dst) { 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())); - aclTensor* acl_src0 = create_acl_tensor( + aclTensor* acl_src0 = ggml_cann_create_tensor( src0, src1->ne, src0->nb, GGML_MAX_DIMS, ACL_FORMAT_ND, offset); ACL_CHECK(aclnnAddGetWorkspaceSize(acl_src0, acl_src1, alpha, acl_dst, &workspaceSize, &executor)); @@ -561,10 +562,10 @@ void ggml_cann_acc(ggml_backend_cann_context& ctx, ggml_tensor* dst) { void ggml_cann_sum_rows(ggml_backend_cann_context& ctx, ggml_tensor* dst) { ggml_tensor* src = dst->src[0]; - aclTensor* acl_src = create_acl_tensor(src); + aclTensor* acl_src = ggml_cann_create_tensor(src); GGML_ASSERT(dst->ne[0] == 1); - aclTensor* acl_dst = create_acl_tensor(dst); + aclTensor* acl_dst = ggml_cann_create_tensor(dst); int64_t reduce_dims_host[] = {3}; aclIntArray* reduce_dims = aclCreateIntArray(reduce_dims_host, 1); @@ -573,9 +574,9 @@ void ggml_cann_sum_rows(ggml_backend_cann_context& ctx, ggml_tensor* dst) { aclOpExecutor* executor; void* workspaceAddr = nullptr; - ACL_CHECK(aclnnReduceSumGetWorkspaceSize(acl_src, reduce_dims, true, - type_mapping(src->type), acl_dst, - &workspaceSize, &executor)); + ACL_CHECK(aclnnReduceSumGetWorkspaceSize( + acl_src, reduce_dims, true, ggml_cann_type_mapping(src->type), acl_dst, + &workspaceSize, &executor)); if (workspaceSize > 0) { ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize); workspaceAddr = workspace_allocator.get(); @@ -592,9 +593,9 @@ void ggml_cann_upsample_nearest2d(ggml_backend_cann_context& ctx, ggml_tensor* dst) { ggml_tensor* src = dst->src[0]; aclTensor* acl_src = - create_acl_tensor(src, nullptr, nullptr, 0, ACL_FORMAT_NCHW); + ggml_cann_create_tensor(src, nullptr, nullptr, 0, ACL_FORMAT_NCHW); aclTensor* acl_dst = - create_acl_tensor(dst, nullptr, nullptr, 0, ACL_FORMAT_NCHW); + ggml_cann_create_tensor(dst, nullptr, nullptr, 0, ACL_FORMAT_NCHW); std::vector output_size{dst->ne[1], dst->ne[0]}; auto output_size_array = aclCreateIntArray(output_size.data(), 2); @@ -659,8 +660,8 @@ static void aclnn_pad(ggml_backend_cann_context& ctx, aclTensor* acl_src, void ggml_cann_pad(ggml_backend_cann_context& ctx, ggml_tensor* dst) { ggml_tensor* src = dst->src[0]; - aclTensor* acl_src = create_acl_tensor(src); - aclTensor* acl_dst = create_acl_tensor(dst); + aclTensor* acl_src = ggml_cann_create_tensor(src); + aclTensor* 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, @@ -694,9 +695,9 @@ static void ggml_cann_avg_pool2d(ggml_backend_cann_context& ctx, GGML_ASSERT(dst->type == GGML_TYPE_F32); aclTensor* acl_src = - create_acl_tensor(src, nullptr, nullptr, 0, ACL_FORMAT_NCHW); + ggml_cann_create_tensor(src, nullptr, nullptr, 0, ACL_FORMAT_NCHW); aclTensor* acl_dst = - create_acl_tensor(dst, nullptr, nullptr, 0, ACL_FORMAT_NCHW); + 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]; @@ -732,7 +733,8 @@ static void ggml_cann_avg_pool2d(ggml_backend_cann_context& ctx, ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize); workspaceAddr = workspace_allocator.get(); } - ACL_CHECK(aclnnAvgPool2d(workspaceAddr, workspaceSize, executor, ctx.stream())); + ACL_CHECK( + aclnnAvgPool2d(workspaceAddr, workspaceSize, executor, ctx.stream())); ACL_CHECK(aclDestroyTensor(acl_src)); ACL_CHECK(aclDestroyTensor(acl_dst)); @@ -760,9 +762,9 @@ static void ggml_cann_max_pool2d(ggml_backend_cann_context& ctx, GGML_ASSERT(dst->type == GGML_TYPE_F32); aclTensor* acl_src = - create_acl_tensor(src, nullptr, nullptr, 0, ACL_FORMAT_NCHW); + ggml_cann_create_tensor(src, nullptr, nullptr, 0, ACL_FORMAT_NCHW); aclTensor* acl_dst = - create_acl_tensor(dst, nullptr, nullptr, 0, ACL_FORMAT_NCHW); + 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]; @@ -784,9 +786,9 @@ static void ggml_cann_max_pool2d(ggml_backend_cann_context& ctx, 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(); - aclTensor* tmp_tensor = - create_acl_tensor(buffer, ACL_FLOAT, ggml_element_size(src), temp_ne, - temp_nb, GGML_MAX_DIMS, ACL_FORMAT_NCHW); + aclTensor* 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}; @@ -819,7 +821,8 @@ static void ggml_cann_max_pool2d(ggml_backend_cann_context& ctx, workspaceAddr = workspace_allocator.get(); } - ACL_CHECK(aclnnMaxPool(workspaceAddr, workspaceSize, executor, ctx.stream())); + ACL_CHECK( + aclnnMaxPool(workspaceAddr, workspaceSize, executor, ctx.stream())); ACL_CHECK(aclDestroyTensor(acl_src)); ACL_CHECK(aclDestroyTensor(acl_dst)); @@ -870,14 +873,15 @@ static void cann_copy(ggml_backend_cann_context& ctx, aclTensor* acl_src, workspaceAddr = workspace_allocator.get(); } - ACL_CHECK(aclnnInplaceCopy(workspaceAddr, workspaceSize, executor, ctx.stream())); + ACL_CHECK( + aclnnInplaceCopy(workspaceAddr, workspaceSize, executor, ctx.stream())); } void ggml_cann_dup(ggml_backend_cann_context& ctx, ggml_tensor* dst) { ggml_tensor* src = dst->src[0]; - aclTensor* acl_src = create_acl_tensor(src); - aclTensor* acl_dst = create_acl_tensor(dst); + aclTensor* acl_src = ggml_cann_create_tensor(src); + aclTensor* acl_dst = ggml_cann_create_tensor(dst); ggml_cann_pool_alloc src_extra_allocator(ctx.pool(), sizeof(ggml_tensor)); ggml_cann_pool_alloc dst_extra_allocator(ctx.pool(), sizeof(ggml_tensor)); @@ -891,7 +895,7 @@ void ggml_cann_dup(ggml_backend_cann_context& ctx, ggml_tensor* dst) { ctx.stream())); if ((dst->type == GGML_TYPE_F16 || dst->type == GGML_TYPE_F32) && - ggml_are_same_shape(src, dst)) { + ggml_are_same_shape(src, dst)) { cann_copy(ctx, acl_src, acl_dst); ACL_CHECK(aclDestroyTensor(acl_src)); ACL_CHECK(aclDestroyTensor(acl_dst)); @@ -1070,7 +1074,8 @@ static aclTensor* aclnn_zero(ggml_backend_cann_context& ctx, void* buffer, } ACL_CHECK(aclrtMemsetAsync(buffer, n_bytes, 0, n_bytes, ctx.stream())); - aclTensor* zero = create_acl_tensor(buffer, type, type_size, ne, nb, dims); + aclTensor* zero = + ggml_cann_create_tensor(buffer, type, type_size, ne, nb, dims); return zero; } @@ -1122,8 +1127,8 @@ static aclTensor* aclnn_ones(ggml_backend_cann_context& ctx, void* buffer, void ggml_cann_rms_norm(ggml_backend_cann_context& ctx, ggml_tensor* dst) { ggml_tensor* src = dst->src[0]; - aclTensor* acl_src = create_acl_tensor(src); - aclTensor* acl_dst = create_acl_tensor(dst); + aclTensor* acl_src = ggml_cann_create_tensor(src); + aclTensor* acl_dst = ggml_cann_create_tensor(dst); float eps; memcpy(&eps, dst->op_params, sizeof(float)); @@ -1137,16 +1142,17 @@ void ggml_cann_rms_norm(ggml_backend_cann_context& ctx, ggml_tensor* dst) { size_t one_tensor_n_bytes = src->ne[0] * ggml_element_size(src); ggml_cann_pool_alloc one_tensor_allocator(ctx.pool(), one_tensor_n_bytes); - aclTensor* acl_gamma = - aclnn_ones(ctx, one_tensor_allocator.get(), one_tensor_n_bytes, src->ne, - 1, type_mapping(src->type), ggml_element_size(src)); + aclTensor* acl_gamma = aclnn_ones( + ctx, one_tensor_allocator.get(), one_tensor_n_bytes, src->ne, 1, + ggml_cann_type_mapping(src->type), ggml_element_size(src)); size_t zero_tensor_n_bytes = src->ne[1] * src->ne[2] * src->ne[3] * ggml_element_size(src); ggml_cann_pool_alloc zero_tensor_allocator(ctx.pool(), zero_tensor_n_bytes); - aclTensor* acl_rstd = aclnn_zero( - ctx, zero_tensor_allocator.get(), zero_tensor_n_bytes, src->ne, - GGML_MAX_DIMS, type_mapping(src->type), ggml_element_size(src)); + aclTensor* acl_rstd = + aclnn_zero(ctx, zero_tensor_allocator.get(), zero_tensor_n_bytes, + src->ne, GGML_MAX_DIMS, ggml_cann_type_mapping(src->type), + ggml_element_size(src)); ACL_CHECK(aclnnRmsNormGetWorkspaceSize( acl_src, acl_gamma, eps, acl_dst, acl_rstd, &workspaceSize, &executor)); @@ -1170,8 +1176,8 @@ void ggml_cann_diag_mask(ggml_backend_cann_context& ctx, ggml_tensor* dst, float value) { ggml_tensor* src = dst->src[0]; - aclTensor* acl_src = create_acl_tensor(src); - aclTensor* acl_dst = create_acl_tensor(dst); + aclTensor* acl_src = ggml_cann_create_tensor(src); + aclTensor* acl_dst = ggml_cann_create_tensor(dst); const int n_past = ((int32_t*)dst->op_params)[0]; @@ -1179,9 +1185,10 @@ void ggml_cann_diag_mask(ggml_backend_cann_context& ctx, ggml_tensor* dst, src->ne[3] * ggml_element_size(src); ggml_cann_pool_alloc one_tensor_allocator(ctx.pool(), one_tensor_n_bytes); - aclTensor* mask_tensor = aclnn_ones( - ctx, one_tensor_allocator.get(), one_tensor_n_bytes, src->ne, - GGML_MAX_DIMS, type_mapping(src->type), ggml_element_size(src), value); + aclTensor* mask_tensor = + aclnn_ones(ctx, one_tensor_allocator.get(), one_tensor_n_bytes, src->ne, + GGML_MAX_DIMS, ggml_cann_type_mapping(src->type), + ggml_element_size(src), value); uint64_t workspaceSize = 0; aclOpExecutor* executor; @@ -1336,7 +1343,7 @@ void ggml_cann_im2col(ggml_backend_cann_context& ctx, ggml_tensor* dst) { GGML_ASSERT(nb10 == sizeof(float)); // im2col: [N,C,H,W] -> [N, IC * KH * KW, OW * OH] - aclTensor* acl_src1 = create_acl_tensor(src1); + aclTensor* acl_src1 = ggml_cann_create_tensor(src1); int64_t tmp_im2col_ne[] = {OW * OH, IC * KH * KW, N}; size_t tmp_im2col_nb[GGML_MAX_DIMS - 1]; @@ -1351,9 +1358,10 @@ void ggml_cann_im2col(ggml_backend_cann_context& ctx, ggml_tensor* dst) { ggml_cann_pool_alloc im2col_allocator( ctx.pool(), ggml_nelements(dst) * ggml_element_size(src1)); void* tmp_im2col_buffer = im2col_allocator.get(); - aclTensor* tmp_im2col_tensor = create_acl_tensor( - tmp_im2col_buffer, type_mapping(src1->type), ggml_type_size(src1->type), - tmp_im2col_ne, tmp_im2col_nb, GGML_MAX_DIMS - 1, ACL_FORMAT_ND); + aclTensor* 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 kernel_dims = {KH, KW}; std::vector dilation_size = {d1, d0}; @@ -1377,7 +1385,8 @@ void ggml_cann_im2col(ggml_backend_cann_context& ctx, ggml_tensor* dst) { workspaceAddr = workspace_allocator.get(); } - ACL_CHECK(aclnnIm2col(workspaceAddr, workspaceSize, executor, ctx.stream())); + ACL_CHECK( + aclnnIm2col(workspaceAddr, workspaceSize, executor, ctx.stream())); // Cast if dst is f16. aclTensor* tmp_cast_tensor = nullptr; @@ -1391,18 +1400,19 @@ void ggml_cann_im2col(ggml_backend_cann_context& ctx, ggml_tensor* dst) { temp_cast_nb[i] = temp_cast_nb[i - 1] * tmp_im2col_ne[i - 1]; } - tmp_cast_tensor = create_acl_tensor( - tmp_cast_buffer, type_mapping(dst->type), ggml_type_size(dst->type), - tmp_im2col_ne, temp_cast_nb, GGML_MAX_DIMS - 1, ACL_FORMAT_ND); + 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, tmp_cast_tensor, - type_mapping(dst->type)); + ggml_cann_type_mapping(dst->type)); } // 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]}; aclTensor* acl_dst = - create_acl_tensor(dst, dst_ne, dst_nb, GGML_MAX_DIMS - 1); + 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) { @@ -1517,7 +1527,8 @@ static void aclnn_muls(ggml_backend_cann_context& ctx, aclTensor* acl_src, * \f] * * @param ctx The context for the CANN backend operations. - * @param acl_src The source tensor where the multiplication result will be stored. + * @param acl_src The source tensor where the multiplication result will be + * stored. * @param acl_other The tensor whose elements will be multiplied with `acl_src`. */ static void aclnn_inplace_mul(ggml_backend_cann_context& ctx, @@ -1553,9 +1564,8 @@ static void aclnn_inplace_mul(ggml_backend_cann_context& ctx, * @param acl_other The second tensor for element-wise multiplication. * @param acl_dst The destination tensor where the result will be stored. */ -static void aclnn_mul(ggml_backend_cann_context& ctx, - aclTensor* acl_src, aclTensor* acl_other, - aclTensor* acl_dst) { +static void aclnn_mul(ggml_backend_cann_context& ctx, aclTensor* acl_src, + aclTensor* acl_other, aclTensor* acl_dst) { uint64_t workspaceSize = 0; aclOpExecutor* executor; void* workspaceAddr = nullptr; @@ -1573,16 +1583,16 @@ static void aclnn_mul(ggml_backend_cann_context& ctx, /** * @brief Applies element-wise cosine function to the elements of a tensor. * - * This function computes the cosine of each element in the source tensor `acl_src` - * and stores the result in the destination tensor `acl_dst`. - * The operation is defined as: - * \f[ - * \text {acl_dst }_i=\cos \left(\text {acl_src }_i\right) - * \f] + * This function computes the cosine of each element in the source tensor + * `acl_src` and stores the result in the destination tensor `acl_dst`. The + * operation is defined as: \f[ \text {acl_dst }_i=\cos \left(\text {acl_src + * }_i\right) \f] * * @param ctx The context for the CANN backend operations. - * @param acl_src The source tensor on which the cosine function will be applied. - * @param acl_dst The destination tensor where the cosine results will be stored. + * @param acl_src The source tensor on which the cosine function will be + * applied. + * @param acl_dst The destination tensor where the cosine results will be + * stored. */ static void aclnn_cos(ggml_backend_cann_context& ctx, aclTensor* acl_src, aclTensor* acl_dst) { @@ -1603,7 +1613,8 @@ static void aclnn_cos(ggml_backend_cann_context& ctx, aclTensor* acl_src, /** * @brief Applies element-wise sine function to the elements of a tensor. * - * This function computes the sine of each element in the source tensor `acl_src` + * This function computes the sine of each element in the source tensor + `acl_src` * and stores the result in the destination tensor `acl_dst`. * The operation is defined as: * \f[ @@ -1641,7 +1652,7 @@ void ggml_cann_timestep_embedding(ggml_backend_cann_context& ctx, const int max_period = dst->op_params[1]; int half = dim / 2; - aclTensor* acl_src = create_acl_tensor(src); + aclTensor* acl_src = ggml_cann_create_tensor(src); // arange: [0, ..., half) float start = 0; @@ -1653,9 +1664,10 @@ void ggml_cann_timestep_embedding(ggml_backend_cann_context& ctx, ggml_cann_pool_alloc arange_allocator(ctx.pool(), half * sizeof(dst->type)); void* tmp_arange_buffer = arange_allocator.get(); - aclTensor* tmp_arange_tensor = create_acl_tensor( - tmp_arange_buffer, type_mapping(dst->type), ggml_type_size(dst->type), - tmp_arange_ne, tmp_arange_nb, GGML_MAX_DIMS - 3, ACL_FORMAT_ND); + aclTensor* 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, start, stop, step, n_elements_arange); @@ -1675,9 +1687,10 @@ void ggml_cann_timestep_embedding(ggml_backend_cann_context& ctx, ggml_cann_pool_alloc permute_allocator(ctx.pool(), ggml_nbytes(src)); void* tmp_permute_buffer = permute_allocator.get(); - aclTensor* tmp_permute_tenosr = create_acl_tensor( - tmp_permute_buffer, type_mapping(src->type), ggml_type_size(src->type), - tmp_permute_ne, tmp_permute_nb, GGML_MAX_DIMS, ACL_FORMAT_ND); + aclTensor* tmp_permute_tenosr = 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, tmp_permute_tenosr, permute_dim, num_dims); @@ -1697,19 +1710,20 @@ void ggml_cann_timestep_embedding(ggml_backend_cann_context& ctx, ggml_cann_pool_alloc mul_allocator( ctx.pool(), mul_nelements * ggml_type_size(src->type)); void* tmp_mul_buffer = mul_allocator.get(); - aclTensor* tmp_mul_tensor = create_acl_tensor( - tmp_mul_buffer, 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_tenosr, tmp_arange_tensor, - tmp_mul_tensor); + aclTensor* 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_tenosr, tmp_arange_tensor, tmp_mul_tensor); // cos ggml_cann_pool_alloc cos_allocator( ctx.pool(), mul_nelements * ggml_type_size(src->type)); void* tmp_cos_buffer = cos_allocator.get(); - aclTensor* tmp_cos_tensor = create_acl_tensor( - tmp_cos_buffer, type_mapping(dst->type), ggml_type_size(dst->type), - tmp_mul_ne, tmp_mul_nb, GGML_MAX_DIMS, ACL_FORMAT_ND); + aclTensor* 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, tmp_cos_tensor); @@ -1717,15 +1731,16 @@ void ggml_cann_timestep_embedding(ggml_backend_cann_context& ctx, ggml_cann_pool_alloc sin_allocator( ctx.pool(), mul_nelements * ggml_type_size(src->type)); void* tmp_sin_buffer = sin_allocator.get(); - aclTensor* tmp_sin_tensor = create_acl_tensor( - tmp_sin_buffer, type_mapping(dst->type), ggml_type_size(dst->type), - tmp_mul_ne, tmp_mul_nb, GGML_MAX_DIMS, ACL_FORMAT_ND); + aclTensor* 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, tmp_sin_tensor); // concat int64_t concat_dim = 3; - aclTensor* acl_dst = create_acl_tensor(dst); + aclTensor* acl_dst = ggml_cann_create_tensor(dst); aclTensor* tensors[] = {tmp_cos_tensor, tmp_sin_tensor}; aclTensorList* tensorList = aclCreateTensorList(tensors, 2); aclnn_concat(ctx, tensorList, acl_dst, concat_dim); @@ -1816,7 +1831,8 @@ static void aclnn_pow_tensor_tensor(ggml_backend_cann_context& ctx, * @param acl_dst The destination tensor where the result will be stored. * @param n_head The number of attention heads. * @param src_ne The dimensions of the source tensor. - * @param src_nb0 The byte size of the first dimension of the source tensor. + * @param src_nb0 The byte size of the first dimension of the source + tensor. * @param max_bias The maximum bias value used in the Alibi mechanism. * @param dst The destination tensor object for additional metadata. * @@ -1858,9 +1874,10 @@ static void aclnn_alibi(ggml_backend_cann_context& ctx, aclTensor* acl_src, int64_t tmp_arange1_ne[] = {n_heads_log2_floor}; size_t tmp_arange1_nb[] = {sizeof(dst->type)}; - aclTensor* tmp_arange1_tensor = create_acl_tensor( - tmp_arange_buffer, type_mapping(dst->type), ggml_type_size(dst->type), - tmp_arange1_ne, tmp_arange1_nb, GGML_MAX_DIMS - 3, ACL_FORMAT_ND); + aclTensor* tmp_arange1_tensor = ggml_cann_create_tensor( + tmp_arange_buffer, ggml_cann_type_mapping(dst->type), + ggml_type_size(dst->type), tmp_arange1_ne, tmp_arange1_nb, + GGML_MAX_DIMS - 3, ACL_FORMAT_ND); aclnn_arange(ctx, tmp_arange1_tensor, start, stop, step, n_elements_arange); @@ -1874,11 +1891,11 @@ static void aclnn_alibi(ggml_backend_cann_context& ctx, aclTensor* acl_src, int64_t tmp_arange2_ne[] = {ne2_ne3 - n_heads_log2_floor}; size_t tmp_arange2_nb[] = {sizeof(dst->type)}; - aclTensor* tmp_arange2_tensor = create_acl_tensor( + aclTensor* tmp_arange2_tensor = ggml_cann_create_tensor( (char*)tmp_arange_buffer + n_heads_log2_floor * ggml_type_size(dst->type), - type_mapping(dst->type), ggml_type_size(dst->type), tmp_arange2_ne, - tmp_arange2_nb, GGML_MAX_DIMS - 3, ACL_FORMAT_ND); + ggml_cann_type_mapping(dst->type), ggml_type_size(dst->type), + tmp_arange2_ne, tmp_arange2_nb, GGML_MAX_DIMS - 3, ACL_FORMAT_ND); aclnn_arange(ctx, tmp_arange2_tensor, start, stop, step, n_elements_arange); } @@ -1889,9 +1906,10 @@ static void aclnn_alibi(ggml_backend_cann_context& ctx, aclTensor* acl_src, void* tmp_mk_base_buffer = mk_base_allocator.get(); int64_t tmp_mk_base1_ne[] = {n_heads_log2_floor}; size_t tmp_mk_base1_nb[] = {sizeof(dst->type)}; - aclTensor* tmp_mk_base1_tensor = create_acl_tensor( - tmp_mk_base_buffer, type_mapping(dst->type), ggml_type_size(dst->type), - tmp_mk_base1_ne, tmp_mk_base1_nb, GGML_MAX_DIMS - 3, ACL_FORMAT_ND); + aclTensor* tmp_mk_base1_tensor = ggml_cann_create_tensor( + tmp_mk_base_buffer, ggml_cann_type_mapping(dst->type), + ggml_type_size(dst->type), tmp_mk_base1_ne, tmp_mk_base1_nb, + GGML_MAX_DIMS - 3, ACL_FORMAT_ND); aclnn_fill_scalar(ctx, m0, tmp_mk_base1_tensor); @@ -1899,23 +1917,25 @@ static void aclnn_alibi(ggml_backend_cann_context& ctx, aclTensor* acl_src, if (n_heads_log2_floor < ne2_ne3) { int64_t tmp_mk_base2_ne[] = {ne2_ne3 - n_heads_log2_floor}; size_t tmp_mk_base2_nb[] = {sizeof(dst->type)}; - aclTensor* tmp_mk_base2_tensor = create_acl_tensor( + aclTensor* tmp_mk_base2_tensor = ggml_cann_create_tensor( (char*)tmp_mk_base_buffer + n_heads_log2_floor * ggml_type_size(dst->type), - type_mapping(dst->type), ggml_type_size(dst->type), tmp_mk_base2_ne, - tmp_mk_base2_nb, GGML_MAX_DIMS - 3, ACL_FORMAT_ND); + ggml_cann_type_mapping(dst->type), ggml_type_size(dst->type), + tmp_mk_base2_ne, tmp_mk_base2_nb, GGML_MAX_DIMS - 3, ACL_FORMAT_ND); aclnn_fill_scalar(ctx, m1, tmp_mk_base2_tensor); } // init mk int64_t tmp_mk_base_ne[] = {ne2_ne3}; size_t tmp_mk_base_nb[] = {sizeof(dst->type)}; - aclTensor* tmp_mk_base_tensor = create_acl_tensor( - tmp_mk_base_buffer, type_mapping(dst->type), ggml_type_size(dst->type), - tmp_mk_base_ne, tmp_mk_base_nb, GGML_MAX_DIMS - 3, ACL_FORMAT_ND); - aclTensor* tmp_arange_tensor = create_acl_tensor( - tmp_arange_buffer, type_mapping(dst->type), ggml_type_size(dst->type), - tmp_mk_base_ne, tmp_mk_base_nb, GGML_MAX_DIMS - 3, ACL_FORMAT_ND); + aclTensor* tmp_mk_base_tensor = ggml_cann_create_tensor( + tmp_mk_base_buffer, ggml_cann_type_mapping(dst->type), + ggml_type_size(dst->type), tmp_mk_base_ne, tmp_mk_base_nb, + GGML_MAX_DIMS - 3, ACL_FORMAT_ND); + aclTensor* tmp_arange_tensor = ggml_cann_create_tensor( + tmp_arange_buffer, ggml_cann_type_mapping(dst->type), + ggml_type_size(dst->type), tmp_mk_base_ne, tmp_mk_base_nb, + GGML_MAX_DIMS - 3, ACL_FORMAT_ND); aclnn_pow_tensor_tensor(ctx, tmp_mk_base_tensor, tmp_arange_tensor); // reshape mk @@ -1925,9 +1945,10 @@ static void aclnn_alibi(ggml_backend_cann_context& ctx, aclTensor* acl_src, for (int i = 1; i < GGML_MAX_DIMS; i++) { tmp_mk_nb[i] = tmp_mk_nb[i - 1] * tmp_mk_ne[i - 1]; } - aclTensor* tmp_mk_tensor = create_acl_tensor( - tmp_mk_base_buffer, type_mapping(dst->type), ggml_type_size(dst->type), - tmp_mk_ne, tmp_mk_nb, GGML_MAX_DIMS, ACL_FORMAT_ND); + aclTensor* tmp_mk_tensor = ggml_cann_create_tensor( + tmp_mk_base_buffer, ggml_cann_type_mapping(dst->type), + ggml_type_size(dst->type), tmp_mk_ne, tmp_mk_nb, GGML_MAX_DIMS, + ACL_FORMAT_ND); // acl_position * mk int64_t tmp_output_ne[] = {src_ne[0], src_ne[1], src_ne[2], src_ne[3]}; @@ -1938,9 +1959,10 @@ static void aclnn_alibi(ggml_backend_cann_context& ctx, aclTensor* acl_src, } ggml_cann_pool_alloc output_allocator(ctx.pool(), ggml_nbytes(dst)); void* tmp_output_buffer = output_allocator.get(); - aclTensor* tmp_output_tensor = create_acl_tensor( - tmp_output_buffer, type_mapping(dst->type), ggml_type_size(dst->type), - tmp_output_ne, tmp_output_nb, GGML_MAX_DIMS, ACL_FORMAT_ND); + aclTensor* tmp_output_tensor = ggml_cann_create_tensor( + tmp_output_buffer, ggml_cann_type_mapping(dst->type), + ggml_type_size(dst->type), tmp_output_ne, tmp_output_nb, GGML_MAX_DIMS, + ACL_FORMAT_ND); aclnn_mul(ctx, acl_position, tmp_mk_tensor, tmp_output_tensor); // add @@ -2031,8 +2053,8 @@ 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 - aclTensor* acl_src0 = create_acl_tensor(src0); - aclTensor* acl_dst = create_acl_tensor(dst); + aclTensor* acl_src0 = ggml_cann_create_tensor(src0); + aclTensor* acl_dst = ggml_cann_create_tensor(dst); float scale = 1.0f; float max_bias = 0.0f; @@ -2046,7 +2068,7 @@ void ggml_cann_softmax(ggml_backend_cann_context& ctx, ggml_tensor* dst) { size_t n_bytes = ggml_nbytes(src0); ggml_cann_pool_alloc mul_scale_allocator(ctx.pool(), n_bytes); void* input_mul_scale_buffer = mul_scale_allocator.get(); - aclTensor* acl_input_mul_scale_tensor = create_acl_tensor( + aclTensor* acl_input_mul_scale_tensor = ggml_cann_create_tensor( input_mul_scale_buffer, ACL_FLOAT, ggml_type_size(src0->type), src0->ne, src0->nb, GGML_MAX_DIMS); @@ -2069,18 +2091,15 @@ void ggml_cann_softmax(ggml_backend_cann_context& ctx, ggml_tensor* dst) { } src1_fp32_allocator.alloc(n_bytes); void* src1_fp32_buffer = src1_fp32_allocator.get(); - acl_src1_fp32_tensor = create_acl_tensor(src1_fp32_buffer, - ACL_FLOAT, - sizeof(float), - src1->ne, - src1_fp32_nb, - GGML_MAX_DIMS); - aclTensor* acl_src1 = create_acl_tensor(src1); + acl_src1_fp32_tensor = ggml_cann_create_tensor( + src1_fp32_buffer, ACL_FLOAT, sizeof(float), src1->ne, + src1_fp32_nb, GGML_MAX_DIMS); + aclTensor* acl_src1 = ggml_cann_create_tensor(src1); aclnn_cast(ctx, acl_src1, acl_src1_fp32_tensor, ACL_FLOAT); ACL_CHECK(aclDestroyTensor(acl_src1)); } else { - acl_src1_fp32_tensor = create_acl_tensor(src1); + acl_src1_fp32_tensor = ggml_cann_create_tensor(src1); } // broadcast the mask across rows, only use ne11 of ne01 in mask @@ -2092,7 +2111,7 @@ void ggml_cann_softmax(ggml_backend_cann_context& ctx, ggml_tensor* dst) { for (int i = 1; i < GGML_MAX_DIMS; i++) { tmp_mask_nb[i] = tmp_mask_nb[i - 1] * tmp_mask_ne[i - 1]; } - tmp_mask_tensor = create_acl_tensor( + tmp_mask_tensor = ggml_cann_create_tensor( src1->data, ACL_FLOAT, sizeof(float), tmp_mask_ne, tmp_mask_nb, GGML_MAX_DIMS, ACL_FORMAT_ND); } @@ -2104,7 +2123,7 @@ void ggml_cann_softmax(ggml_backend_cann_context& ctx, ggml_tensor* dst) { n_bytes = ggml_nbytes(dst); ggml_cann_pool_alloc output_allocator(ctx.pool(), n_bytes); void* output_buffer = output_allocator.get(); - aclTensor* alibi_output_tensor = create_acl_tensor( + aclTensor* alibi_output_tensor = ggml_cann_create_tensor( output_buffer, ACL_FLOAT, ggml_type_size(dst->type), dst->ne, dst->nb, GGML_MAX_DIMS); if (max_bias <= 0.0f) { @@ -2116,18 +2135,16 @@ void ggml_cann_softmax(ggml_backend_cann_context& ctx, ggml_tensor* dst) { aclnn_add(ctx, acl_src1_fp32_tensor, acl_input_mul_scale_tensor, alibi_output_tensor); } - } - else { + } else { // slope != 1.0 if (tmp_mask_tensor) { aclnn_alibi(ctx, acl_input_mul_scale_tensor, tmp_mask_tensor, - alibi_output_tensor, n_head, src0->ne, src_nb0, max_bias, - dst); - } - else { - aclnn_alibi(ctx, acl_input_mul_scale_tensor, acl_src1_fp32_tensor, - alibi_output_tensor, n_head, src0->ne, src_nb0, max_bias, - dst); + alibi_output_tensor, n_head, src0->ne, src_nb0, + max_bias, dst); + } else { + aclnn_alibi(ctx, acl_input_mul_scale_tensor, + acl_src1_fp32_tensor, alibi_output_tensor, n_head, + src0->ne, src_nb0, max_bias, dst); } } @@ -2277,7 +2294,8 @@ static void aclnn_mat_mul(ggml_backend_cann_context& ctx, aclTensor* acl_input, workspaceAddr = workspace_allocator.get(); } - ACL_CHECK(aclnnMatmul(workspaceAddr, workspaceSize, executor, ctx.stream())); + ACL_CHECK( + aclnnMatmul(workspaceAddr, workspaceSize, executor, ctx.stream())); } /** @@ -2310,10 +2328,10 @@ static void ggml_cann_mat_mul_fp(ggml_backend_cann_context& ctx, bcast_weight_nb[4], bcast_weight_nb[5]}; aclTensor* acl_weight_tensor = - create_acl_tensor(weight, transpose_ne, transpose_nb, bcast_dims); + ggml_cann_create_tensor(weight, transpose_ne, transpose_nb, bcast_dims); aclTensor* acl_input_tensor = - create_acl_tensor(input, BCAST_MUL_MAT_PARAM(input)); - aclTensor* acl_dst = create_acl_tensor(dst, BCAST_MUL_MAT_PARAM(dst)); + ggml_cann_create_tensor(input, BCAST_MUL_MAT_PARAM(input)); + aclTensor* acl_dst = ggml_cann_create_tensor(dst, BCAST_MUL_MAT_PARAM(dst)); aclnn_mat_mul(ctx, acl_input_tensor, acl_weight_tensor, acl_dst); ACL_CHECK(aclDestroyTensor(acl_weight_tensor)); @@ -2364,7 +2382,7 @@ static void ggml_cann_mul_mat_q8_0(ggml_backend_cann_context& ctx, size_t input_stride = input_elem_size * src1->ne[0] * src1->ne[1]; if (src1->type != GGML_TYPE_F16) { - aclTensor* acl_src1_tensor = create_acl_tensor(src1); + aclTensor* acl_src1_tensor = ggml_cann_create_tensor(src1); ggml_cann_pool_alloc input_alloctor( ctx.pool(), ggml_nelements(src1) * input_elem_size); input_buffer = input_alloctor.get(); @@ -2376,9 +2394,9 @@ static void ggml_cann_mul_mat_q8_0(ggml_backend_cann_context& ctx, input_cast_nb[i] = input_cast_nb[i - 1] * input_cast_ne[i - 1]; } - aclTensor* acl_input_tensor = - create_acl_tensor(input_buffer, ACL_FLOAT16, input_elem_size, - input_cast_ne, input_cast_nb, GGML_MAX_DIMS); + aclTensor* 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, acl_input_tensor, ACL_FLOAT16); ACL_CHECK(aclDestroyTensor(acl_input_tensor)); ACL_CHECK(aclDestroyTensor(acl_src1_tensor)); @@ -2408,16 +2426,16 @@ static void ggml_cann_mul_mat_q8_0(ggml_backend_cann_context& ctx, int64_t batch1 = n1 * src1->ne[2] + c1; int64_t batch0 = n0 * src0->ne[2] + c0; - aclTensor* acl_input_tensor = create_acl_tensor( + aclTensor* acl_input_tensor = ggml_cann_create_tensor( (char*)input_buffer + batch1 * input_stride, ACL_FLOAT16, input_elem_size, input_ne, input_nb, 2); - aclTensor* acl_weight_tensor = create_acl_tensor( + aclTensor* acl_weight_tensor = ggml_cann_create_tensor( (char*)src0->data + batch0 * weight_stride, ACL_INT8, weight_elem_size, weight_ne, weight_nb, 2); - aclTensor* acl_scale_tensor = create_acl_tensor( + aclTensor* acl_scale_tensor = ggml_cann_create_tensor( scale_offset + batch0 * scale_stride, ACL_FLOAT16, scale_elem_size, scale_ne, scale_nb, 2); - aclTensor* acl_output_tensor = create_acl_tensor( + aclTensor* acl_output_tensor = ggml_cann_create_tensor( (char*)output_buffer + batch1 * output_stride, ACL_FLOAT16, output_elem_size, output_ne, output_nb, 2); @@ -2451,9 +2469,9 @@ static void ggml_cann_mul_mat_q8_0(ggml_backend_cann_context& ctx, } aclTensor* acl_output_tensor = - create_acl_tensor(output_buffer, ACL_FLOAT16, output_elem_size, - output_cast_ne, output_cast_nb, GGML_MAX_DIMS); - aclTensor* acl_dst_tensor = create_acl_tensor(dst); + ggml_cann_create_tensor(output_buffer, ACL_FLOAT16, output_elem_size, + output_cast_ne, output_cast_nb, GGML_MAX_DIMS); + aclTensor* acl_dst_tensor = ggml_cann_create_tensor(dst); aclnn_cast(ctx, acl_output_tensor, acl_dst_tensor, ACL_FLOAT); ACL_CHECK(aclDestroyTensor(acl_output_tensor)); @@ -2575,8 +2593,8 @@ static void aclnn_cache_init(ggml_backend_cann_context& ctx, ggml_tensor* dst, arange_length * sizeof(float_t)}; aclTensor* acl_arange_tensor = - create_acl_tensor(arange_buffer, ACL_FLOAT, sizeof(float_t), arange_ne, - arange_nb, GGML_MAX_DIMS); + ggml_cann_create_tensor(arange_buffer, ACL_FLOAT, sizeof(float_t), + arange_ne, arange_nb, GGML_MAX_DIMS); float start = 0; float step = 1; float stop = src0->ne[0] / 2; @@ -2604,9 +2622,9 @@ static void aclnn_cache_init(ggml_backend_cann_context& ctx, ggml_tensor* dst, size_t position_nb[] = {sizeof(int32_t), sizeof(int32_t), sizeof(int32_t) * position_length, sizeof(int32_t) * position_length}; - aclTensor* acl_position_tensor = create_acl_tensor( - src1->data, type_mapping(src1->type), ggml_type_size(src1->type), - position_ne, position_nb, GGML_MAX_DIMS); + aclTensor* 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); // power * position int64_t theta_length = arange_length * position_length; @@ -2620,10 +2638,10 @@ static void aclnn_cache_init(ggml_backend_cann_context& ctx, ggml_tensor* dst, theta_nb[i] = theta_nb[i - 1] * theta_ne[i - 1]; } aclTensor* acl_theta_tensor = - create_acl_tensor(theta_buffer, ACL_FLOAT, sizeof(float_t), theta_ne, - theta_nb, GGML_MAX_DIMS); + ggml_cann_create_tensor(theta_buffer, ACL_FLOAT, sizeof(float_t), + theta_ne, theta_nb, GGML_MAX_DIMS); aclnn_mul(ctx, acl_position_tensor, acl_theta_scale_tensor, - acl_theta_tensor); + acl_theta_tensor); // permute: [0,1,2,3]->[0,2,1,3] int64_t permute_ne[] = {arange_length, 1, position_length, 1}; @@ -2635,9 +2653,9 @@ static void aclnn_cache_init(ggml_backend_cann_context& ctx, ggml_tensor* dst, ggml_cann_pool_alloc permute_allocator(ctx.pool(), theta_length * sizeof(float_t)); void* permute_buffer = permute_allocator.get(); - aclTensor* acl_permute_tensor = - create_acl_tensor(permute_buffer, ACL_FLOAT, sizeof(float_t), - permute_ne, permute_nb, GGML_MAX_DIMS, ACL_FORMAT_ND); + aclTensor* acl_permute_tensor = ggml_cann_create_tensor( + permute_buffer, ACL_FLOAT, sizeof(float_t), permute_ne, permute_nb, + GGML_MAX_DIMS, ACL_FORMAT_ND); int64_t permute_dim[] = {0, 2, 1, 3}; int64_t num_dims = 4; aclnn_permute(ctx, acl_theta_tensor, acl_permute_tensor, permute_dim, @@ -2647,17 +2665,17 @@ static void aclnn_cache_init(ggml_backend_cann_context& ctx, ggml_tensor* dst, ggml_cann_pool_alloc sin_allocator(ctx.pool(), theta_length * sizeof(float_t)); void* sin_buffer = sin_allocator.get(); - aclTensor* acl_sin_tensor = - create_acl_tensor(sin_buffer, ACL_FLOAT, sizeof(float_t), permute_ne, - permute_nb, GGML_MAX_DIMS, ACL_FORMAT_ND); + aclTensor* acl_sin_tensor = ggml_cann_create_tensor( + sin_buffer, ACL_FLOAT, sizeof(float_t), permute_ne, permute_nb, + GGML_MAX_DIMS, ACL_FORMAT_ND); aclnn_sin(ctx, acl_permute_tensor, acl_sin_tensor); ggml_cann_pool_alloc cos_allocator(ctx.pool(), theta_length * sizeof(float_t)); void* cos_buffer = cos_allocator.get(); - aclTensor* acl_cos_tensor = - create_acl_tensor(cos_buffer, ACL_FLOAT, sizeof(float_t), permute_ne, - permute_nb, GGML_MAX_DIMS, ACL_FORMAT_ND); + aclTensor* acl_cos_tensor = ggml_cann_create_tensor( + cos_buffer, ACL_FLOAT, sizeof(float_t), permute_ne, permute_nb, + GGML_MAX_DIMS, ACL_FORMAT_ND); aclnn_cos(ctx, acl_permute_tensor, acl_cos_tensor); // repeat @@ -2742,11 +2760,11 @@ void ggml_cann_rope(ggml_backend_cann_context& ctx, ggml_tensor* dst) { sin_reshape_nb[i] = sin_reshape_nb[i - 1] * sin_reshape_ne[i - 1]; } aclTensor* acl_sin_reshape_tensor = - create_acl_tensor(sin_buffer, ACL_FLOAT, sizeof(float_t), - sin_reshape_ne, sin_reshape_nb, GGML_MAX_DIMS); + ggml_cann_create_tensor(sin_buffer, ACL_FLOAT, sizeof(float_t), + sin_reshape_ne, sin_reshape_nb, GGML_MAX_DIMS); aclTensor* acl_cos_reshape_tensor = - create_acl_tensor(cos_buffer, ACL_FLOAT, sizeof(float_t), - sin_reshape_ne, sin_reshape_nb, GGML_MAX_DIMS); + ggml_cann_create_tensor(cos_buffer, ACL_FLOAT, sizeof(float_t), + sin_reshape_ne, sin_reshape_nb, GGML_MAX_DIMS); aclnn_cache_init(ctx, dst, acl_cos_reshape_tensor, acl_sin_reshape_tensor, theta_scale, is_neox); @@ -2767,13 +2785,14 @@ void ggml_cann_rope(ggml_backend_cann_context& ctx, ggml_tensor* dst) { for (int i = 1; i < GGML_MAX_DIMS; i++) { input_roll_nb[i] = input_roll_nb[i - 1] * input_roll_ne[i - 1]; } - aclTensor* acl_input_roll_tensor = - create_acl_tensor(input_roll_buffer, type_mapping(src0->type), - ggml_type_size(src0->type), input_roll_ne, - input_roll_nb, GGML_MAX_DIMS); - aclTensor* acl_input_tensor = create_acl_tensor( - src0->data, type_mapping(src0->type), ggml_type_size(src0->type), - input_roll_ne, input_roll_nb, GGML_MAX_DIMS); + aclTensor* 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); + aclTensor* 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}; @@ -2806,10 +2825,10 @@ void ggml_cann_rope(ggml_backend_cann_context& ctx, ggml_tensor* dst) { // roll input: [q0,q1,q2,...] -> // [q_half,q_half+1,...,q_end,q0,q1,...q_half-1] input_roll_buffer = roll_allocator.get(); - aclTensor* acl_input_roll_tensor = create_acl_tensor( - input_roll_buffer, type_mapping(src0->type), + aclTensor* 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); - aclTensor* acl_input_tensor = create_acl_tensor(src0); + aclTensor* acl_input_tensor = ggml_cann_create_tensor(src0); int64_t shifts[] = {src0->ne[0] / 2}; int64_t dims[] = {3}; @@ -2837,7 +2856,7 @@ void ggml_cann_rope(ggml_backend_cann_context& ctx, ggml_tensor* dst) { for (int i = 1; i < GGML_MAX_DIMS; i++) { first_half_nb[i] = first_half_nb[i - 1] * first_half_ne[i - 1]; } - aclTensor* acl_first_half_tensor = create_acl_tensor( + aclTensor* acl_first_half_tensor = ggml_cann_create_tensor( minus_one_scale_buffer, ACL_FLOAT, sizeof(float_t), first_half_ne, first_half_nb, GGML_MAX_DIMS); bool inplace = true; @@ -2858,19 +2877,19 @@ void ggml_cann_rope(ggml_backend_cann_context& ctx, ggml_tensor* dst) { for (int i = 1; i < GGML_MAX_DIMS; i++) { input_nb[i] = input_nb[i - 1] * src0->ne[i - 1]; } - aclTensor* acl_input_roll_mul_scale_tensor = create_acl_tensor( - input_roll_mul_scale_buffer, type_mapping(src0->type), + aclTensor* 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); + aclTensor* 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); - aclTensor* acl_input_roll_reshape_tensor = create_acl_tensor( - input_roll_buffer, type_mapping(src0->type), ggml_type_size(src0->type), - src0->ne, input_nb, GGML_MAX_DIMS); - aclnn_mul(ctx, acl_input_roll_reshape_tensor, - acl_minus_one_tensor, acl_input_roll_mul_scale_tensor); + aclnn_mul(ctx, acl_input_roll_reshape_tensor, acl_minus_one_tensor, + acl_input_roll_mul_scale_tensor); // output - aclTensor* acl_src0 = create_acl_tensor(src0); - aclTensor* acl_dst = create_acl_tensor(dst); + aclTensor* acl_src0 = ggml_cann_create_tensor(src0); + aclTensor* acl_dst = ggml_cann_create_tensor(dst); void* output_fp32_buffer; if (src0->type == GGML_TYPE_F32) { aclnn_inplace_mul(ctx, acl_src0, acl_cos_reshape_tensor); @@ -2887,26 +2906,25 @@ void ggml_cann_rope(ggml_backend_cann_context& ctx, ggml_tensor* dst) { ggml_cann_pool_alloc fp32_allocator1( ctx.pool(), ggml_nelements(dst) * sizeof(float_t)); void* input_fp32_buffer1 = fp32_allocator1.get(); - aclTensor* input_fp32_tensor1 = - create_acl_tensor(input_fp32_buffer1, ACL_FLOAT, sizeof(float_t), - dst->ne, input_fp32_nb, GGML_MAX_DIMS); + aclTensor* input_fp32_tensor1 = ggml_cann_create_tensor( + input_fp32_buffer1, ACL_FLOAT, sizeof(float_t), dst->ne, + input_fp32_nb, GGML_MAX_DIMS); ggml_cann_pool_alloc fp32_allocator2( ctx.pool(), ggml_nelements(dst) * sizeof(float_t)); void* input_fp32_buffer2 = fp32_allocator2.get(); - aclTensor* input_fp32_tensor2 = - create_acl_tensor(input_fp32_buffer2, ACL_FLOAT, sizeof(float_t), - dst->ne, input_fp32_nb, GGML_MAX_DIMS); + aclTensor* input_fp32_tensor2 = ggml_cann_create_tensor( + input_fp32_buffer2, ACL_FLOAT, sizeof(float_t), dst->ne, + input_fp32_nb, GGML_MAX_DIMS); ggml_cann_pool_alloc fp32_allocator( ctx.pool(), ggml_nelements(dst) * sizeof(float_t)); output_fp32_buffer = fp32_allocator.get(); - aclTensor* output_fp32_tensor = - create_acl_tensor(output_fp32_buffer, ACL_FLOAT, sizeof(float_t), - dst->ne, input_fp32_nb, GGML_MAX_DIMS); - aclnn_mul(ctx, acl_src0, acl_cos_reshape_tensor, - input_fp32_tensor1); - aclnn_mul(ctx, acl_input_roll_mul_scale_tensor, - acl_sin_reshape_tensor, input_fp32_tensor2); + aclTensor* output_fp32_tensor = ggml_cann_create_tensor( + output_fp32_buffer, ACL_FLOAT, sizeof(float_t), dst->ne, + input_fp32_nb, GGML_MAX_DIMS); + aclnn_mul(ctx, acl_src0, acl_cos_reshape_tensor, input_fp32_tensor1); + aclnn_mul(ctx, acl_input_roll_mul_scale_tensor, acl_sin_reshape_tensor, + input_fp32_tensor2); aclnn_add(ctx, input_fp32_tensor1, input_fp32_tensor2, output_fp32_tensor); aclnn_cast(ctx, output_fp32_tensor, acl_dst, ACL_FLOAT16); diff --git a/ggml/src/ggml-cann/aclnn_ops.h b/ggml/src/ggml-cann/aclnn_ops.h index 2a0c001c95edb..680129c76de68 100644 --- a/ggml/src/ggml-cann/aclnn_ops.h +++ b/ggml/src/ggml-cann/aclnn_ops.h @@ -497,15 +497,15 @@ void ggml_cann_mul_div(ggml_backend_cann_context& ctx, ggml_tensor* dst) { aclTensor* acl_dst; // Need bcast - if (!ggml_are_same_shape(src0, src1) && need_bcast(src0, src1)) { + if (!ggml_are_same_shape(src0, src1) && ggml_cann_need_bcast(src0, src1)) { BCAST_SHAPE(src0, src1) - acl_src0 = create_acl_tensor(src0, BCAST_PARAM(src0)); - acl_src1 = create_acl_tensor(src1, BCAST_PARAM(src1)); - acl_dst = create_acl_tensor(dst, BCAST_PARAM(src0)); + 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 = create_acl_tensor(src0); - acl_src1 = create_acl_tensor(src1); - acl_dst = create_acl_tensor(dst); + acl_src0 = ggml_cann_create_tensor(src0); + acl_src1 = ggml_cann_create_tensor(src1); + acl_dst = ggml_cann_create_tensor(dst); } uint64_t workspaceSize = 0; @@ -538,8 +538,8 @@ void ggml_cann_activation(ggml_backend_cann_context& ctx, ggml_tensor* dst) { GGML_ASSERT(src->type == GGML_TYPE_F32); GGML_ASSERT(dst->type == GGML_TYPE_F32); - aclTensor* acl_src = create_acl_tensor(src); - aclTensor* acl_dst = create_acl_tensor(dst); + aclTensor* acl_src = ggml_cann_create_tensor(src); + aclTensor* acl_dst = ggml_cann_create_tensor(dst); uint64_t workspaceSize = 0; aclOpExecutor* executor; @@ -569,8 +569,8 @@ void ggml_cann_activation(ggml_backend_cann_context& ctx, ggml_tensor* dst) { GGML_ASSERT(src->type == GGML_TYPE_F32); GGML_ASSERT(dst->type == GGML_TYPE_F32); - aclTensor* acl_src = create_acl_tensor(src); - aclTensor* acl_dst = create_acl_tensor(dst); + aclTensor* acl_src = ggml_cann_create_tensor(src); + aclTensor* acl_dst = ggml_cann_create_tensor(dst); uint64_t workspaceSize = 0; aclOpExecutor* executor;