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CANN: Support ROPE_VISION #12

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Empty file modified ggml/src/ggml-cann/CMakeLists.txt
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2,579 changes: 0 additions & 2,579 deletions ggml/src/ggml-cann/Doxyfile
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102 changes: 57 additions & 45 deletions ggml/src/ggml-cann/acl_tensor.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -48,31 +48,33 @@ aclDataType ggml_cann_type_mapping(ggml_type type) {
default:
return ACL_DT_UNDEFINED;
}
return ACL_DT_UNDEFINED;
}

aclTensor* ggml_cann_create_tensor(const ggml_tensor* tensor, int64_t* ne,
size_t* nb, int64_t dims, aclFormat format,
size_t offset) {
acl_tensor_ptr 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];

if (ne == nullptr) {
for (int i = 0; i < GGML_MAX_DIMS; i++) {
acl_ne[i] = tensor->ne[i];
acl_ne[i] = tensor->ne[i];
// The step size of acl is in elements.
acl_stride[i] = tensor->nb[i] / ggml_element_size(tensor);
}
} else {
// With bcast
for (int i = 0; i < dims; i++) {
acl_ne[i] = ne[i];
acl_ne[i] = ne[i];
acl_stride[i] = nb[i] / ggml_element_size(tensor);
}
}

int64_t final_dims = (dims == 0 ? GGML_MAX_DIMS : dims);
int64_t final_dims = (dims == 0 ? GGML_MAX_DIMS : dims);
int64_t acl_storage_len = 1;
for (int i = 0; i < final_dims; i++) {
acl_storage_len += (acl_ne[i] - 1) * acl_stride[i];
Expand All @@ -84,15 +86,23 @@ aclTensor* ggml_cann_create_tensor(const ggml_tensor* tensor, int64_t* ne,
std::reverse(acl_ne, acl_ne + final_dims);
std::reverse(acl_stride, acl_stride + final_dims);

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

return acl_tensor;
return acl_tensor_ptr(raw);
}

bool ggml_cann_need_bcast(const ggml_tensor* t0, const ggml_tensor* t1) {
acl_int_array_ptr ggml_cann_create_int_array(const int64_t * value, uint64_t size) {
aclIntArray * raw = aclCreateIntArray(value, size);
return acl_int_array_ptr(raw);
}

acl_scalar_ptr ggml_cann_create_scalar(void * value, aclDataType dataType) {
aclScalar * raw = aclCreateScalar(value, dataType);
return acl_scalar_ptr(raw);
}

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;
Expand All @@ -101,15 +111,16 @@ bool ggml_cann_need_bcast(const ggml_tensor* t0, const ggml_tensor* t1) {
return false;
}

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) {
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++) {
int64_t nr = src0->ne[i] / src1->ne[i];
int64_t nr = src0->ne[i] / src1->ne[i];
bcast_src0_ne[bcast_dim_cnt] = src0->ne[i] / nr;
bcast_src1_ne[bcast_dim_cnt] = src1->ne[i];
bcast_src0_nb[bcast_dim_cnt] = src0->nb[i];
Expand All @@ -119,21 +130,26 @@ int64_t ggml_cann_get_bcast_shape(const ggml_tensor* src0,
// Need to add an extra dim.
bcast_src0_ne[bcast_dim_cnt] = nr;
bcast_src1_ne[bcast_dim_cnt] = 1;
bcast_src0_nb[bcast_dim_cnt] = bcast_src0_nb[bcast_dim_cnt - 1] *
bcast_src0_ne[bcast_dim_cnt - 1];
bcast_src1_nb[bcast_dim_cnt] = bcast_src1_nb[bcast_dim_cnt - 1] *
bcast_src1_ne[bcast_dim_cnt - 1];
bcast_src0_nb[bcast_dim_cnt] = bcast_src0_nb[bcast_dim_cnt - 1] * bcast_src0_ne[bcast_dim_cnt - 1];
bcast_src1_nb[bcast_dim_cnt] = bcast_src1_nb[bcast_dim_cnt - 1] * bcast_src1_ne[bcast_dim_cnt - 1];
bcast_dim_cnt++;
}
}
return bcast_dim_cnt;
}

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) {
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]);
Expand All @@ -148,34 +164,30 @@ int64_t ggml_cann_get_mulmat_bcast_shape(
// Do not use bcast in the first two dimensions because we only support
// the bcast batch dimension. Just copy them.
if (i < 2 || nr == 1) {
bcast_input_ne[bcast_dim_cnt] = input_ne[i];
bcast_input_ne[bcast_dim_cnt] = input_ne[i];
bcast_weight_ne[bcast_dim_cnt] = weight_ne[i];
bcast_dst_ne[bcast_dim_cnt] = dst_ne[i];
bcast_dst_ne[bcast_dim_cnt] = dst_ne[i];

bcast_input_nb[bcast_dim_cnt] = input_nb[i];
bcast_input_nb[bcast_dim_cnt] = input_nb[i];
bcast_weight_nb[bcast_dim_cnt] = weight_nb[i];
bcast_dst_nb[bcast_dim_cnt] = dst_nb[i];
bcast_dst_nb[bcast_dim_cnt] = dst_nb[i];
bcast_dim_cnt++;
} else {
// Need to add an extra dim.
bcast_input_ne[bcast_dim_cnt] = nr;
bcast_dst_ne[bcast_dim_cnt] = nr;
bcast_input_ne[bcast_dim_cnt] = nr;
bcast_dst_ne[bcast_dim_cnt] = nr;
bcast_weight_ne[bcast_dim_cnt] = 1;
bcast_input_nb[bcast_dim_cnt] = input_nb[i];
bcast_dst_nb[bcast_dim_cnt] = dst_nb[i];
bcast_input_nb[bcast_dim_cnt] = input_nb[i];
bcast_dst_nb[bcast_dim_cnt] = dst_nb[i];
bcast_weight_nb[bcast_dim_cnt] = weight_nb[i];
bcast_dim_cnt++;

bcast_input_ne[bcast_dim_cnt] = input_ne[i] / nr;
bcast_dst_ne[bcast_dim_cnt] = dst_ne[i] / nr;
bcast_input_ne[bcast_dim_cnt] = input_ne[i] / nr;
bcast_dst_ne[bcast_dim_cnt] = dst_ne[i] / nr;
bcast_weight_ne[bcast_dim_cnt] = weight_ne[i];
bcast_input_nb[bcast_dim_cnt] = bcast_input_nb[bcast_dim_cnt - 1] *
bcast_input_ne[bcast_dim_cnt - 1];
bcast_dst_nb[bcast_dim_cnt] = bcast_dst_nb[bcast_dim_cnt - 1] *
bcast_dst_ne[bcast_dim_cnt - 1];
bcast_weight_nb[bcast_dim_cnt] =
bcast_weight_nb[bcast_dim_cnt - 1] *
bcast_weight_ne[bcast_dim_cnt - 1];
bcast_input_nb[bcast_dim_cnt] = bcast_input_nb[bcast_dim_cnt - 1] * bcast_input_ne[bcast_dim_cnt - 1];
bcast_dst_nb[bcast_dim_cnt] = bcast_dst_nb[bcast_dim_cnt - 1] * bcast_dst_ne[bcast_dim_cnt - 1];
bcast_weight_nb[bcast_dim_cnt] = bcast_weight_nb[bcast_dim_cnt - 1] * bcast_weight_ne[bcast_dim_cnt - 1];
bcast_dim_cnt++;
}
}
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