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6 files changed

+390
-365
lines changed

6 files changed

+390
-365
lines changed

‎ggml/src/ggml-cann/acl_tensor.cpp‎

Lines changed: 9 additions & 9 deletions
Original file line numberDiff line numberDiff line change
@@ -51,11 +51,11 @@ aclDataType ggml_cann_type_mapping(ggml_type type) {
5151
}
5252

5353
acl_tensor_ptr ggml_cann_create_tensor(const ggml_tensor * tensor,
54-
int64_t * ne,
55-
size_t * nb,
56-
int64_t dims,
57-
aclFormat format,
58-
size_t offset) {
54+
int64_t * ne,
55+
size_t * nb,
56+
int64_t dims,
57+
aclFormat format,
58+
size_t offset) {
5959
// If tensor is bcasted, Up to GGML_MAX_DIMS additional dimensions will be
6060
// added.
6161
int64_t acl_ne[GGML_MAX_DIMS * 2], acl_stride[GGML_MAX_DIMS * 2];
@@ -86,18 +86,18 @@ acl_tensor_ptr ggml_cann_create_tensor(const ggml_tensor * tensor,
8686
std::reverse(acl_ne, acl_ne + final_dims);
8787
std::reverse(acl_stride, acl_stride + final_dims);
8888

89-
aclTensor * raw = aclCreateTensor(acl_ne, final_dims, ggml_cann_type_mapping(tensor->type), acl_stride,
90-
elem_offset, format, &acl_storage_len, 1, tensor->data);
89+
aclTensor * raw = aclCreateTensor(acl_ne, final_dims, ggml_cann_type_mapping(tensor->type), acl_stride, elem_offset,
90+
format, &acl_storage_len, 1, tensor->data);
9191

9292
return acl_tensor_ptr(raw);
9393
}
9494

95-
acl_int_array_ptr ggml_cann_create_int_array(const int64_t *value, uint64_t size) {
95+
acl_int_array_ptr ggml_cann_create_int_array(const int64_t * value, uint64_t size) {
9696
aclIntArray * raw = aclCreateIntArray(value, size);
9797
return acl_int_array_ptr(raw);
9898
}
9999

100-
acl_scalar_ptr ggml_cann_create_scalar(void *value, aclDataType dataType) {
100+
acl_scalar_ptr ggml_cann_create_scalar(void * value, aclDataType dataType) {
101101
aclScalar * raw = aclCreateScalar(value, dataType);
102102
return acl_scalar_ptr(raw);
103103
}

‎ggml/src/ggml-cann/acl_tensor.h‎

Lines changed: 26 additions & 27 deletions
Original file line numberDiff line numberDiff line change
@@ -23,11 +23,12 @@
2323
#ifndef CANN_ACL_TENSOR_H
2424
#define CANN_ACL_TENSOR_H
2525

26-
#include <algorithm>
27-
#include <cstring>
26+
#include "common.h"
2827

2928
#include <aclnn/aclnn_base.h>
30-
#include "common.h"
29+
30+
#include <algorithm>
31+
#include <cstring>
3132

3233
/**
3334
* @brief Maps a ggml_type to its corresponding aclDataType.
@@ -44,7 +45,7 @@
4445
aclDataType ggml_cann_type_mapping(ggml_type type);
4546

4647
struct acl_tensor_deleter {
47-
void operator()(aclTensor* ptr) const noexcept {
48+
void operator()(aclTensor * ptr) const noexcept {
4849
if (ptr != nullptr) {
4950
ACL_CHECK(aclDestroyTensor(ptr));
5051
}
@@ -54,7 +55,7 @@ struct acl_tensor_deleter {
5455
using acl_tensor_ptr = std::unique_ptr<aclTensor, acl_tensor_deleter>;
5556

5657
struct acl_int_array_deleter {
57-
void operator()(aclIntArray* ptr) const noexcept {
58+
void operator()(aclIntArray * ptr) const noexcept {
5859
if (ptr != nullptr) {
5960
ACL_CHECK(aclDestroyIntArray(ptr));
6061
}
@@ -64,7 +65,7 @@ struct acl_int_array_deleter {
6465
using acl_int_array_ptr = std::unique_ptr<aclIntArray, acl_int_array_deleter>;
6566

6667
struct acl_scalar_deleter {
67-
void operator()(aclScalar* ptr) const noexcept {
68+
void operator()(aclScalar * ptr) const noexcept {
6869
if (ptr != nullptr) {
6970
ACL_CHECK(aclDestroyScalar(ptr));
7071
}
@@ -74,7 +75,7 @@ struct acl_scalar_deleter {
7475
using acl_scalar_ptr = std::unique_ptr<aclScalar, acl_scalar_deleter>;
7576

7677
struct acl_tensor_list_deleter {
77-
void operator()(aclTensorList* ptr) const noexcept {
78+
void operator()(aclTensorList * ptr) const noexcept {
7879
if (ptr != nullptr) {
7980
ACL_CHECK(aclDestroyTensorList(ptr));
8081
}
@@ -103,11 +104,11 @@ using acl_tensor_list_ptr = std::unique_ptr<aclTensorList, acl_tensor_list_delet
103104
* @return Pointer to the created ACL tensor.
104105
*/
105106
acl_tensor_ptr ggml_cann_create_tensor(const ggml_tensor * tensor,
106-
int64_t * ne = nullptr,
107-
size_t * nb = nullptr,
108-
int64_t dims = 0,
109-
aclFormat format = ACL_FORMAT_ND,
110-
size_t offset = 0);
107+
int64_t * ne = nullptr,
108+
size_t * nb = nullptr,
109+
int64_t dims = 0,
110+
aclFormat format = ACL_FORMAT_ND,
111+
size_t offset = 0);
111112

112113
/**
113114
* @brief Template for creating an ACL tensor from provided parameters. typename TYPE
@@ -131,13 +132,13 @@ acl_tensor_ptr ggml_cann_create_tensor(const ggml_tensor * tensor,
131132
*/
132133
template <typename TYPE>
133134
acl_tensor_ptr ggml_cann_create_tensor(void * data_ptr,
134-
aclDataType dtype,
135-
TYPE type_size,
136-
int64_t * ne,
137-
TYPE * nb,
138-
int64_t dims,
139-
aclFormat format = ACL_FORMAT_ND,
140-
size_t offset = 0) {
135+
aclDataType dtype,
136+
TYPE type_size,
137+
int64_t * ne,
138+
TYPE * nb,
139+
int64_t dims,
140+
aclFormat format = ACL_FORMAT_ND,
141+
size_t offset = 0) {
141142
int64_t tmp_ne[GGML_MAX_DIMS * 2];
142143
int64_t tmp_stride[GGML_MAX_DIMS * 2];
143144

@@ -160,21 +161,19 @@ acl_tensor_ptr ggml_cann_create_tensor(void * data_ptr,
160161
return acl_tensor_ptr(raw);
161162
}
162163

163-
acl_int_array_ptr ggml_cann_create_int_array(const int64_t *value, uint64_t size);
164-
acl_scalar_ptr ggml_cann_create_scalar(void *value, aclDataType dataType);
164+
acl_int_array_ptr ggml_cann_create_int_array(const int64_t * value, uint64_t size);
165+
acl_scalar_ptr ggml_cann_create_scalar(void * value, aclDataType dataType);
165166

166-
template <typename... acl_tensor_ptr>
167-
acl_tensor_list_ptr ggml_cann_create_tensor_list(acl_tensor_ptr&&... tensors) {
168-
aclTensor* raw_tensors[] = { tensors.get()... };
169-
aclTensorList * raw = aclCreateTensorList(raw_tensors, sizeof...(tensors));
167+
template <typename... acl_tensor_ptr> acl_tensor_list_ptr ggml_cann_create_tensor_list(acl_tensor_ptr &&... tensors) {
168+
aclTensor * raw_tensors[] = { tensors.get()... };
169+
aclTensorList * raw = aclCreateTensorList(raw_tensors, sizeof...(tensors));
170170
// aclTensor will release by aclTensorList, so release ownership without
171171
// destroying the tensor
172-
int dummy[] = { (tensors.release(), 0)... };
172+
int dummy[] = { (tensors.release(), 0)... };
173173
GGML_UNUSED(dummy);
174174
return acl_tensor_list_ptr(raw);
175175
}
176176

177-
178177
/**
179178
* @brief Checks if tensors require broadcasting based on their shapes.
180179
*

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