vbatts/llama.cpp
1
0
Fork 0
Code Issues Pull requests Projects Releases Packages Wiki Activity Actions

add helper function to get Qnn_TensorType_t from ggml_tensor

Browse source
This commit is contained in:
hongruichen 2024-07-05 19:34:56 +08:00
parent 0f2e68713c
commit 4b0f6b0cd6
4 changed files with 23 additions and 17 deletions
Download patch file Download diff file Expand all files Collapse all files

16
ggml/src/ggml-qnn.cpp
View file

@ -250,23 +250,15 @@ GGML_CALL static void ggml_backend_qnn_buffer_init_tensor(ggml_backend_buffer_t
static int idx = 0;
char tensor_name[GGML_MAX_NAME] = { 0 };
snprintf(tensor_name, GGML_MAX_NAME, "tensor_%04d", idx++);
uint32_t dimensions[] = { (uint32_t)tensor->ne[0], (uint32_t)tensor->ne[1], (uint32_t)tensor->ne[2],
(uint32_t)tensor->ne[3] };
Qnn_DataType_t qnn_data_type = qnn::datatype_from_ggml_datatype(tensor->type);
Qnn_TensorType_t qnn_tensor_type = QNN_TENSOR_TYPE_APP_WRITE;
if (tensor->flags & GGML_TENSOR_FLAG_INPUT) {
qnn_tensor_type = QNN_TENSOR_TYPE_APP_WRITE;
} else if (tensor->flags & GGML_TENSOR_FLAG_OUTPUT) {
qnn_tensor_type = QNN_TENSOR_TYPE_APP_READ;
}
Qnn_DataType_t qnn_data_type = qnn::device_datatype_from_ggml_datatype(tensor->type);
Qnn_TensorType_t qnn_tensor_type = qnn::device_tensortype_from_ggml_tensor(tensor);
Qnn_TensorMemType_t qnn_mem_type = QNN_TENSORMEMTYPE_RAW;
if (ctx->device == QNN_BACKEND_GPU) {
qnn_mem_type = QNN_TENSORMEMTYPE_MEMHANDLE;
}
uint32_t dimensions[] = { (uint32_t)tensor->ne[0], (uint32_t)tensor->ne[1], (uint32_t)tensor->ne[2],
(uint32_t)tensor->ne[3] };
Qnn_Tensor_t qnn_tensor;
qnn::device_tensor_init(qnn_tensor, qnn::get_ggml_tensor_rank(tensor), qnn_mem_type, tensor_name, qnn_tensor_type,
qnn_data_type, dimensions);

4
ggml/src/ggml-qnn/tensor.hpp
View file

@ -17,7 +17,7 @@ public:
ggml_backend_qnn_context *ctx) :
_tensor(tensor), _qnn_tensor(reinterpret_cast<Qnn_Tensor_t *>(tensor->extra)), _context(ctx) {
_old_dimensions = QNN_VER_PTR(*_qnn_tensor)->dimensions;
const auto qnn_data_type = datatype_from_ggml_datatype(tensor->type);
const auto qnn_data_type = device_datatype_from_ggml_datatype(tensor->type);
const bool is_npu = ctx->device == QNN_BACKEND_NPU;
QNN_VER_PTR(*_qnn_tensor)->type = _tensorType;
if (is_npu) {
@ -67,7 +67,7 @@ public:
ggml_backend_qnn_context *ctx) :
_tensor(tensor), _qnn_tensor(qnn_tensor), _context(ctx) {
_old_dimensions = QNN_VER_PTR(*_qnn_tensor)->dimensions;
const auto qnn_data_type = qnn::datatype_from_ggml_datatype(tensor->type);
const auto qnn_data_type = device_datatype_from_ggml_datatype(tensor->type);
const bool is_npu = ctx->device == QNN_BACKEND_NPU;
_dimensions[0] = (uint32_t)tensor->ne[0];

16
ggml/src/ggml-qnn/utils.cpp
View file

@ -23,8 +23,8 @@ namespace qnn {
// TODO: mapping more ggml data type to QNN data type
// ref:explanation of k-quants, https://github.com/ggerganov/llama.cpp/pull/1684
Qnn_DataType_t datatype_from_ggml_datatype(enum ggml_type ggmltype) {
switch (ggmltype) {
Qnn_DataType_t device_datatype_from_ggml_datatype(ggml_type ggml_type) {
switch (ggml_type) {
case GGML_TYPE_F16:
return QNN_DATATYPE_FLOAT_16;
case GGML_TYPE_F32:
@ -41,6 +41,18 @@ Qnn_DataType_t datatype_from_ggml_datatype(enum ggml_type ggmltype) {
return QNN_DATATYPE_UNDEFINED;
}
Qnn_TensorType_t device_tensortype_from_ggml_tensor(ggml_tensor *ggml_tensor) {
Qnn_TensorType_t qnn_tensor_type = QNN_TENSOR_TYPE_APP_WRITE;
if (ggml_tensor->flags & GGML_TENSOR_FLAG_INPUT) {
qnn_tensor_type = QNN_TENSOR_TYPE_APP_WRITE;
} else if (ggml_tensor->flags & GGML_TENSOR_FLAG_OUTPUT) {
qnn_tensor_type = QNN_TENSOR_TYPE_APP_READ;
}
return qnn_tensor_type;
}
uint32_t get_ggml_tensor_rank(const ggml_tensor *tensor) {
uint32_t rank = 0;
for (int i = 0; i < GGML_MAX_DIMS; i++) {

4
ggml/src/ggml-qnn/utils.hpp
View file

@ -15,7 +15,6 @@
namespace qnn {
Qnn_DataType_t datatype_from_ggml_datatype(enum ggml_type ggmltype);
uint32_t get_ggml_tensor_rank(const ggml_tensor *tensor);
const char *get_backend_name(int n_backend_type);
const char *get_chipset_desc(uint32_t chipset_id);
@ -169,6 +168,9 @@ inline void set_qnn_tensor_memhandle(Qnn_Tensor_t &tensor, Qnn_MemHandle_t handl
}
}
Qnn_DataType_t device_datatype_from_ggml_datatype(ggml_type ggml_type);
Qnn_TensorType_t device_tensortype_from_ggml_tensor(ggml_tensor *ggml_tensor);
void device_tensor_init(Qnn_Tensor_t &tensor, uint32_t rank, Qnn_TensorMemType_t mem_type, const char *tensor_name,
Qnn_TensorType_t qnn_tensor_type, Qnn_DataType_t qnn_data_type, uint32_t *dimensions);