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This commit is contained in:
hongruichen 2024-07-05 17:31:22 +08:00
parent 13dc3a02c3
commit 58cec14092
2 changed files with 203 additions and 252 deletions
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191
ggml/src/ggml-qnn.cpp
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@ -1,39 +1,39 @@
#include "ggml-qnn.h"
#include <stdatomic.h>
#include <stdio.h> #include <stdio.h>
#include <stdlib.h> #include <stdlib.h>
#include <stdatomic.h>
#include <string.h> #include <string.h>
#include <sys/stat.h>
#include <time.h> #include <time.h>
#include <unistd.h> #include <unistd.h>
#include <sys/stat.h>
#include <vector>
#include <thread>
#include <mutex>
#include <set>
#include <tuple>
#include <queue>
#include <fstream>
#include <iostream>
#include <sstream>
#include <chrono>
#include <memory>
#include <regex>
#include <random>
#include <functional>
#include <condition_variable>
#include <cassert> #include <cassert>
#include <chrono>
#include <condition_variable>
#include <fstream>
#include <functional>
#include <iostream>
#include <memory>
#include <mutex>
#include <queue>
#include <random>
#include <regex>
#include <set>
#include <sstream>
#include <thread>
#include <tuple>
#include <unordered_set> #include <unordered_set>
#include <utility> #include <utility>
#include <vector>
#include "ggml-qnn.h"
#include "ggml-backend-impl.h" #include "ggml-backend-impl.h"
#include "ggml-qnn/logger.hpp"
#include "ggml-qnn/utils.hpp"
#include "ggml-qnn/tensor.hpp"
#include "ggml-qnn/backend.hpp"
#include "ggml-qnn/backend-ops.hpp" #include "ggml-qnn/backend-ops.hpp"
#include "ggml-qnn/backend.hpp"
#include "ggml-qnn/logger.hpp"
#include "ggml-qnn/tensor.hpp"
#include "ggml-qnn/utils.hpp"
// ================================================================================================= // =================================================================================================
// //
@ -57,28 +57,16 @@ static int free_qnn_tensor(Qnn_Tensor_t & tensor);
static struct qnn::qcom_socinfo g_qnn_soc_info_table[] = { static struct qnn::qcom_socinfo g_qnn_soc_info_table[] = {
/* Qualcomm SnapDragon 8 Gen 1 */ /* Qualcomm SnapDragon 8 Gen 1 */
[qnn::SM8450] = { [qnn::SM8450] = { .soc_model = qnn::SM8450, .htp_arch = qnn::V69, .vtcm_size_in_mb = 8 },
.soc_model = qnn::SM8450,
.htp_arch = qnn::V69,
.vtcm_size_in_mb = 8},
/* Qualcomm SnapDragon 8 Gen 1+ */ /* Qualcomm SnapDragon 8 Gen 1+ */
[qnn::SM8475] = { [qnn::SM8475] = { .soc_model = qnn::SM8475, .htp_arch = qnn::V69, .vtcm_size_in_mb = 8 },
.soc_model = qnn::SM8475,
.htp_arch = qnn::V69,
.vtcm_size_in_mb = 8},
/* Qualcomm SnapDragon 8 Gen 2 */ /* Qualcomm SnapDragon 8 Gen 2 */
[qnn::SM8550] = { [qnn::SM8550] = { .soc_model = qnn::SM8550, .htp_arch = qnn::V73, .vtcm_size_in_mb = 8 },
.soc_model = qnn::SM8550,
.htp_arch = qnn::V73,
.vtcm_size_in_mb = 8},
/* Qualcomm SnapDragon 8 Gen 3 */ /* Qualcomm SnapDragon 8 Gen 3 */
[qnn::SM8650] = { [qnn::SM8650] = { .soc_model = qnn::SM8650, .htp_arch = qnn::V75, .vtcm_size_in_mb = 8 },
.soc_model = qnn::SM8650,
.htp_arch = qnn::V75,
.vtcm_size_in_mb = 8},
}; };
@ -128,9 +116,7 @@ static struct ggml_backend_qnn_context g_qnn_mgr[GGML_QNN_MAX_DEVICES] = {
}; };
struct ggml_backend_qnn_buffer_context { struct ggml_backend_qnn_buffer_context {
ggml_backend_qnn_buffer_context(size_t device) ggml_backend_qnn_buffer_context(size_t device) : device(device), name(QNN_BACKEND_NAME + std::to_string(device)) {}
: device(device)
, name(QNN_BACKEND_NAME + std::to_string(device)) {}
~ggml_backend_qnn_buffer_context() { ~ggml_backend_qnn_buffer_context() {
if (buffer) { if (buffer) {
@ -185,8 +171,7 @@ static int deep_copy_qnn_tensors(Qnn_Tensor_t & src, Qnn_Tensor_t & dst) {
VALIDATE_TENSOR_VERSION(src, err); VALIDATE_TENSOR_VERSION(src, err);
dst.version = src.version; dst.version = src.version;
QNN_TENSOR_SET_NAME( QNN_TENSOR_SET_NAME(dst, ::strndup(QNN_TENSOR_GET_NAME(src), std::string(QNN_TENSOR_GET_NAME(src)).size()));
dst, ::strndup(QNN_TENSOR_GET_NAME(src),std::string(QNN_TENSOR_GET_NAME(src)).size()));
if (nullptr == QNN_TENSOR_GET_NAME(dst)) { if (nullptr == QNN_TENSOR_GET_NAME(dst)) {
return 1; return 1;
} }
@ -213,9 +198,7 @@ static int deep_copy_qnn_tensors(Qnn_Tensor_t & src, Qnn_Tensor_t & dst) {
Qnn_ScaleOffset_t **scaleOffset = &axis_scale_offset.scaleOffset; Qnn_ScaleOffset_t **scaleOffset = &axis_scale_offset.scaleOffset;
size_t scaleOffsetSize = axis_scale_offset.numScaleOffsets * sizeof(Qnn_ScaleOffset_t); size_t scaleOffsetSize = axis_scale_offset.numScaleOffsets * sizeof(Qnn_ScaleOffset_t);
*scaleOffset = (Qnn_ScaleOffset_t *)malloc(scaleOffsetSize); *scaleOffset = (Qnn_ScaleOffset_t *)malloc(scaleOffsetSize);
memscpy(*scaleOffset, scaleOffsetSize, memscpy(*scaleOffset, scaleOffsetSize, src_qparam.axisScaleOffsetEncoding.scaleOffset, scaleOffsetSize);
src_qparam.axisScaleOffsetEncoding.scaleOffset,
scaleOffsetSize);
QNN_TENSOR_SET_QUANT_PARAMS(dst, src_qparam_cpy); QNN_TENSOR_SET_QUANT_PARAMS(dst, src_qparam_cpy);
} else if (encoding == QNN_QUANTIZATION_ENCODING_BW_AXIS_SCALE_OFFSET) { } else if (encoding == QNN_QUANTIZATION_ENCODING_BW_AXIS_SCALE_OFFSET) {
Qnn_QuantizeParams_t src_qparam_cpy = src_qparam; Qnn_QuantizeParams_t src_qparam_cpy = src_qparam;
@ -224,14 +207,12 @@ static int deep_copy_qnn_tensors(Qnn_Tensor_t & src, Qnn_Tensor_t & dst) {
float **scales = &bwaxis_scale_offset.scales; float **scales = &bwaxis_scale_offset.scales;
int32_t **offsets = &bwaxis_scale_offset.offsets; int32_t **offsets = &bwaxis_scale_offset.offsets;
*scales = (float *)malloc(scaleSize); *scales = (float *)malloc(scaleSize);
memscpy(*scales, scaleSize, src_qparam.bwAxisScaleOffsetEncoding.scales, memscpy(*scales, scaleSize, src_qparam.bwAxisScaleOffsetEncoding.scales, scaleSize);
scaleSize);
if (bwaxis_scale_offset.offsets != nullptr) { if (bwaxis_scale_offset.offsets != nullptr) {
size_t offsetSize = bwaxis_scale_offset.numElements * sizeof(int32_t); size_t offsetSize = bwaxis_scale_offset.numElements * sizeof(int32_t);
*offsets = (int32_t *)malloc(offsetSize); *offsets = (int32_t *)malloc(offsetSize);
memscpy(*offsets, offsetSize, memscpy(*offsets, offsetSize, src_qparam.bwAxisScaleOffsetEncoding.offsets, offsetSize);
src_qparam.bwAxisScaleOffsetEncoding.offsets, offsetSize);
} }
QNN_TENSOR_SET_QUANT_PARAMS(dst, src_qparam_cpy); QNN_TENSOR_SET_QUANT_PARAMS(dst, src_qparam_cpy);
} else { } else {
@ -243,7 +224,8 @@ static int deep_copy_qnn_tensors(Qnn_Tensor_t & src, Qnn_Tensor_t & dst) {
size_t dim_size = rank * sizeof(uint32_t); size_t dim_size = rank * sizeof(uint32_t);
uint32_t *dimensions = (uint32_t *)malloc(dim_size); uint32_t *dimensions = (uint32_t *)malloc(dim_size);
if (dimensions == nullptr) { if (dimensions == nullptr) {
QNN_LOG_WARN("deep_copy_qnn_tensors() allocation error while copying " QNN_LOG_WARN(
"deep_copy_qnn_tensors() allocation error while copying "
"tensor %s\n", "tensor %s\n",
QNN_TENSOR_GET_NAME(src)); QNN_TENSOR_GET_NAME(src));
return 1; return 1;
@ -269,8 +251,7 @@ static int free_qnn_tensor(Qnn_Tensor_t & tensor) {
// implementation of QNN backend for GGML // implementation of QNN backend for GGML
// //
// ================================================================================================= // =================================================================================================
static bool ggml_qnn_can_handle_op(ggml_backend_qnn_context * ctx, static bool ggml_qnn_can_handle_op(ggml_backend_qnn_context *ctx, const struct ggml_tensor *tensor,
const struct ggml_tensor * tensor,
bool b_dump_tensor_info) { bool b_dump_tensor_info) {
if (ggml_is_empty(tensor) || !qnn::ggml_qnn_op_array()[tensor->op]) { if (ggml_is_empty(tensor) || !qnn::ggml_qnn_op_array()[tensor->op]) {
return false; return false;
@ -353,8 +334,7 @@ GGML_CALL static void * ggml_backend_qnn_buffer_get_base(ggml_backend_buffer_t b
return ctx->buffer; return ctx->buffer;
} }
GGML_CALL static void ggml_backend_qnn_buffer_init_tensor(ggml_backend_buffer_t buffer, GGML_CALL static void ggml_backend_qnn_buffer_init_tensor(ggml_backend_buffer_t buffer, ggml_tensor *tensor) {
ggml_tensor * tensor) {
Qnn_ErrorHandle_t error = QNN_SUCCESS; Qnn_ErrorHandle_t error = QNN_SUCCESS;
ggml_backend_qnn_buffer_context *ctx = (ggml_backend_qnn_buffer_context *)buffer->context; ggml_backend_qnn_buffer_context *ctx = (ggml_backend_qnn_buffer_context *)buffer->context;
@ -362,11 +342,9 @@ GGML_CALL static void ggml_backend_qnn_buffer_init_tensor(ggml_backend_buffer_t
char tensor_name[GGML_MAX_NAME] = { 0 }; char tensor_name[GGML_MAX_NAME] = { 0 };
snprintf(tensor_name, GGML_MAX_NAME, "tensor_%04d", idx++); snprintf(tensor_name, GGML_MAX_NAME, "tensor_%04d", idx++);
uint32_t dimensions[] = {(uint32_t) tensor->ne[0], (uint32_t) tensor->ne[1], uint32_t dimensions[] = { (uint32_t)tensor->ne[0], (uint32_t)tensor->ne[1], (uint32_t)tensor->ne[2],
(uint32_t) tensor->ne[2],
(uint32_t)tensor->ne[3] }; (uint32_t)tensor->ne[3] };
Qnn_DataType_t qnn_data_type = Qnn_DataType_t qnn_data_type = qnn::datatype_from_ggml_datatype(tensor->type);
qnn::datatype_from_ggml_datatype(tensor->type);
Qnn_TensorType_t qnn_tensor_type = QNN_TENSOR_TYPE_APP_WRITE; Qnn_TensorType_t qnn_tensor_type = QNN_TENSOR_TYPE_APP_WRITE;
if (tensor->flags & GGML_TENSOR_FLAG_INPUT) { if (tensor->flags & GGML_TENSOR_FLAG_INPUT) {
@ -381,25 +359,22 @@ GGML_CALL static void ggml_backend_qnn_buffer_init_tensor(ggml_backend_buffer_t
qnn_mem_type = QNN_TENSORMEMTYPE_MEMHANDLE; qnn_mem_type = QNN_TENSORMEMTYPE_MEMHANDLE;
} }
qnn_tensor = { qnn_tensor = { .version = QNN_TENSOR_VERSION_1,
.version = QNN_TENSOR_VERSION_1, { .v1 = {
{.v1 = {.id = 0, .id = 0,
.name = tensor_name, .name = tensor_name,
.type = qnn_tensor_type, .type = qnn_tensor_type,
.dataFormat = QNN_TENSOR_DATA_FORMAT_FLAT_BUFFER, .dataFormat = QNN_TENSOR_DATA_FORMAT_FLAT_BUFFER,
.dataType = qnn_data_type, .dataType = qnn_data_type,
.quantizeParams = .quantizeParams = { QNN_DEFINITION_UNDEFINED,
{QNN_DEFINITION_UNDEFINED,
QNN_QUANTIZATION_ENCODING_UNDEFINED, QNN_QUANTIZATION_ENCODING_UNDEFINED,
{.scaleOffsetEncoding = {.scale = 0.0000000000000000f, { .scaleOffsetEncoding = { .scale = 0.0000000000000000f, .offset = 0 } } },
.offset = 0}}},
.rank = qnn::get_ggml_tensor_rank(tensor), .rank = qnn::get_ggml_tensor_rank(tensor),
.dimensions = dimensions, .dimensions = dimensions,
.memType = qnn_mem_type, .memType = qnn_mem_type,
{ .clientBuf = { .data = nullptr, .dataSize = 0 } } } } }; { .clientBuf = { .data = nullptr, .dataSize = 0 } } } } };
Qnn_Tensor_t * p_qnn_tensor = Qnn_Tensor_t *p_qnn_tensor = (Qnn_Tensor_t *)calloc(1, sizeof(Qnn_Tensor_t));
(Qnn_Tensor_t *)calloc(1, sizeof(Qnn_Tensor_t));
if (nullptr == p_qnn_tensor) { if (nullptr == p_qnn_tensor) {
QNN_LOG_WARN("calloc failed"); QNN_LOG_WARN("calloc failed");
return; return;
@ -414,23 +389,20 @@ GGML_CALL static void ggml_backend_qnn_buffer_init_tensor(ggml_backend_buffer_t
ctx->qnn_tensors.push_back(p_qnn_tensor); ctx->qnn_tensors.push_back(p_qnn_tensor);
} }
GGML_CALL static void ggml_backend_qnn_buffer_set_tensor(ggml_backend_buffer_t buffer, GGML_CALL static void ggml_backend_qnn_buffer_set_tensor(ggml_backend_buffer_t buffer, ggml_tensor *tensor,
ggml_tensor * tensor, const void * data, const void *data, size_t offset, size_t size) {
size_t offset, size_t size) {
GGML_UNUSED(buffer); GGML_UNUSED(buffer);
memcpy((char *)tensor->data + offset, data, size); memcpy((char *)tensor->data + offset, data, size);
} }
GGML_CALL static void ggml_backend_qnn_buffer_get_tensor(ggml_backend_buffer_t buffer, GGML_CALL static void ggml_backend_qnn_buffer_get_tensor(ggml_backend_buffer_t buffer, const ggml_tensor *tensor,
const ggml_tensor * tensor, void * data, void *data, size_t offset, size_t size) {
size_t offset, size_t size) {
GGML_UNUSED(buffer); GGML_UNUSED(buffer);
memcpy(data, (const char *)tensor->data + offset, size); memcpy(data, (const char *)tensor->data + offset, size);
} }
GGML_CALL static bool ggml_backend_qnn_buffer_cpy_tensor(ggml_backend_buffer_t buffer, GGML_CALL static bool ggml_backend_qnn_buffer_cpy_tensor(ggml_backend_buffer_t buffer, const struct ggml_tensor *src,
const struct ggml_tensor * src,
struct ggml_tensor *dst) { struct ggml_tensor *dst) {
GGML_UNUSED(buffer); GGML_UNUSED(buffer);
if (ggml_backend_buffer_is_host(src->buffer)) { if (ggml_backend_buffer_is_host(src->buffer)) {
@ -459,9 +431,7 @@ static ggml_backend_buffer_i ggml_backend_qnn_buffer_interface = {
/* .reset = */ nullptr, /* .reset = */ nullptr,
}; };
GGML_CALL static const char * ggml_backend_qnn_buffer_type_name(ggml_backend_buffer_type_t buft) { GGML_CALL static const char *ggml_backend_qnn_buffer_type_name(ggml_backend_buffer_type_t buft) { return "QNN"; }
return "QNN";
}
static void *ggml_qnn_host_malloc(size_t n) { static void *ggml_qnn_host_malloc(size_t n) {
void *data = nullptr; void *data = nullptr;
@ -474,8 +444,8 @@ static void * ggml_qnn_host_malloc(size_t n) {
return data; return data;
} }
GGML_CALL static ggml_backend_buffer_t ggml_backend_qnn_buffer_type_alloc_buffer( GGML_CALL static ggml_backend_buffer_t ggml_backend_qnn_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft,
ggml_backend_buffer_type_t buft, size_t size) { size_t size) {
ggml_backend_qnn_buffer_type_context *buft_ctx = (ggml_backend_qnn_buffer_type_context *)buft->context; ggml_backend_qnn_buffer_type_context *buft_ctx = (ggml_backend_qnn_buffer_type_context *)buft->context;
ggml_backend_qnn_buffer_context *ctx = new ggml_backend_qnn_buffer_context(buft_ctx->device); ggml_backend_qnn_buffer_context *ctx = new ggml_backend_qnn_buffer_context(buft_ctx->device);
@ -500,8 +470,7 @@ GGML_CALL static ggml_backend_buffer_t ggml_backend_qnn_buffer_type_alloc_buffer
return ggml_backend_buffer_init(buft, ggml_backend_qnn_buffer_interface, ctx, size); return ggml_backend_buffer_init(buft, ggml_backend_qnn_buffer_interface, ctx, size);
} }
GGML_CALL static size_t ggml_backend_qnn_buffer_type_get_alignment( GGML_CALL static size_t ggml_backend_qnn_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) {
ggml_backend_buffer_type_t buft) {
GGML_UNUSED(buft); GGML_UNUSED(buft);
return 32; return 32;
} }
@ -518,9 +487,7 @@ GGML_CALL static bool ggml_backend_qnn_buffer_is_host(ggml_backend_buffer_type_t
return true; return true;
} }
GGML_CALL static const char * ggml_backend_qnn_name(ggml_backend_t backend) { GGML_CALL static const char *ggml_backend_qnn_name(ggml_backend_t backend) { return "QNN"; }
return "QNN";
}
GGML_CALL static void ggml_backend_qnn_free(ggml_backend_t backend) { GGML_CALL static void ggml_backend_qnn_free(ggml_backend_t backend) {
QNN_LOG_INFO("enter %s", __func__); QNN_LOG_INFO("enter %s", __func__);
@ -560,9 +527,8 @@ GGML_CALL static ggml_status ggml_backend_qnn_graph_compute(ggml_backend_t backe
for (int i = 0; i < cgraph->n_nodes; i++) { for (int i = 0; i < cgraph->n_nodes; i++) {
ggml_tensor *node = cgraph->nodes[i]; ggml_tensor *node = cgraph->nodes[i];
if (ggml_is_empty(node) || node->op == GGML_OP_RESHAPE || if (ggml_is_empty(node) || node->op == GGML_OP_RESHAPE || node->op == GGML_OP_TRANSPOSE ||
node->op == GGML_OP_TRANSPOSE || node->op == GGML_OP_VIEW || node->op == GGML_OP_VIEW || node->op == GGML_OP_PERMUTE || node->op == GGML_OP_NONE) {
node->op == GGML_OP_PERMUTE || node->op == GGML_OP_NONE) {
continue; continue;
} }
bool ok = ggml_qnn_compute_forward(ctx, node); bool ok = ggml_qnn_compute_forward(ctx, node);
@ -574,8 +540,7 @@ GGML_CALL static ggml_status ggml_backend_qnn_graph_compute(ggml_backend_t backe
return result; return result;
} }
GGML_CALL static bool ggml_backend_qnn_supports_op(ggml_backend_t backend, GGML_CALL static bool ggml_backend_qnn_supports_op(ggml_backend_t backend, const ggml_tensor *op) {
const ggml_tensor * op) {
ggml_backend_qnn_context *ctx = (ggml_backend_qnn_context *)backend->context; ggml_backend_qnn_context *ctx = (ggml_backend_qnn_context *)backend->context;
return (ggml_qnn_can_handle_op(ctx, op, false)); return (ggml_qnn_can_handle_op(ctx, op, false));
@ -611,10 +576,8 @@ static ggml_backend_i ggml_backend_qnn_interface = {
}; };
static ggml_guid_t ggml_backend_qnn_guid() { static ggml_guid_t ggml_backend_qnn_guid() {
static ggml_guid guid = { static ggml_guid guid = { 0x1a, 0x2b, 0x3c, 0x4d, 0x5e, 0x6f, 0x70, 0x81,
0x1a, 0x2b, 0x3c, 0x4d, 0x5e, 0x6f, 0x70, 0x81, 0x92, 0xa3, 0xb4, 0xc5, 0xd6, 0xe7, 0xf8, 0x09 };
0x92, 0xa3, 0xb4, 0xc5, 0xd6, 0xe7, 0xf8, 0x09
};
return &guid; return &guid;
} }
@ -641,13 +604,9 @@ void ggml_backend_qnn_set_n_threads(ggml_backend_t backend, int n_threads) {
ctx->threads = n_threads; ctx->threads = n_threads;
} }
const char * ggml_backend_qnn_get_name(ggml_backend_t backend) { const char *ggml_backend_qnn_get_name(ggml_backend_t backend) { return backend->iface.get_name(backend); }
return backend->iface.get_name(backend);
}
int ggml_backend_qnn_get_device_count() { int ggml_backend_qnn_get_device_count() { return GGML_QNN_MAX_DEVICES; }
return GGML_QNN_MAX_DEVICES;
}
void ggml_backend_qnn_get_device_description(size_t dev_num, char *description, size_t description_size) { void ggml_backend_qnn_get_device_description(size_t dev_num, char *description, size_t description_size) {
if (nullptr == description || 0 == description_size) { if (nullptr == description || 0 == description_size) {
@ -665,7 +624,8 @@ void ggml_backend_qnn_get_device_description(size_t dev_num, char * description,
ggml_backend_buffer_type_t ggml_backend_qnn_buffer_type(size_t device) { ggml_backend_buffer_type_t ggml_backend_qnn_buffer_type(size_t device) {
if (device >= GGML_QNN_MAX_DEVICES) { if (device >= GGML_QNN_MAX_DEVICES) {
QNN_LOG_DEBUG("ggml_backend_qnn_buffer_type error: device_index:%d is " QNN_LOG_DEBUG(
"ggml_backend_qnn_buffer_type error: device_index:%d is "
"out of range [0, %d]\n", "out of range [0, %d]\n",
device, GGML_QNN_MAX_DEVICES - 1); device, GGML_QNN_MAX_DEVICES - 1);
return nullptr; return nullptr;
@ -679,14 +639,12 @@ ggml_backend_buffer_type_t ggml_backend_qnn_buffer_type(size_t device) {
auto &context = ggml_backend_qnn_buffer_type_contexts[i]; auto &context = ggml_backend_qnn_buffer_type_contexts[i];
context = { i, std::string(QNN_BACKEND_NAME) + std::to_string(i) }; context = { i, std::string(QNN_BACKEND_NAME) + std::to_string(i) };
ggml_backend_qnn_buffer_types[i] = { ggml_backend_qnn_buffer_types[i] = {
/* .iface = */ { /* .iface = */ { /* .get_name = */ ggml_backend_qnn_buffer_type_name,
/* .get_name = */ ggml_backend_qnn_buffer_type_name,
/* .alloc_buffer = */ ggml_backend_qnn_buffer_type_alloc_buffer, /* .alloc_buffer = */ ggml_backend_qnn_buffer_type_alloc_buffer,
/* .get_alignment = */ ggml_backend_qnn_buffer_type_get_alignment, /* .get_alignment = */ ggml_backend_qnn_buffer_type_get_alignment,
/* .get_max_size = */ ggml_backend_qnn_buffer_type_get_max_size, /* .get_max_size = */ ggml_backend_qnn_buffer_type_get_max_size,
/* .get_alloc_size = */ nullptr, // defaults to ggml_nbytes /* .get_alloc_size = */ nullptr, // defaults to ggml_nbytes
/* .is_host = */ ggml_backend_qnn_buffer_is_host /* .is_host = */ ggml_backend_qnn_buffer_is_host },
},
/* .context = */ &context, /* .context = */ &context,
}; };
} }
@ -729,8 +687,7 @@ ggml_backend_t ggml_backend_qnn_init(size_t device, const char * qnn_lib_path) {
QNN_LOG_ERROR("QNN NPU backend setenv failure"); QNN_LOG_ERROR("QNN NPU backend setenv failure");
} }
if (0 == setenv("ADSP_LIBRARY_PATH", if (0 == setenv("ADSP_LIBRARY_PATH",
(path + (path + ";/vendor/dsp/cdsp;/vendor/lib/rfsa/adsp;/system/lib/"
";/vendor/dsp/cdsp;/vendor/lib/rfsa/adsp;/system/lib/"
"rfsa/adsp;/vendor/dsp/dsp;/vendor/dsp/images;/dsp") "rfsa/adsp;/vendor/dsp/dsp;/vendor/dsp/images;/dsp")
.c_str(), .c_str(),
1)) { 1)) {
@ -740,20 +697,16 @@ ggml_backend_t ggml_backend_qnn_init(size_t device, const char * qnn_lib_path) {
} }
} else { } else {
if (0 == setenv("LD_LIBRARY_PATH", path.c_str(), 1)) { if (0 == setenv("LD_LIBRARY_PATH", path.c_str(), 1)) {
QNN_LOG_INFO("%s backend setenv successfully\n", QNN_LOG_INFO("%s backend setenv successfully\n", qnn::get_backend_name(device));
qnn::get_backend_name(device));
} else { } else {
QNN_LOG_ERROR("%s backend setenv failure\n", QNN_LOG_ERROR("%s backend setenv failure\n", qnn::get_backend_name(device));
qnn::get_backend_name(device));
} }
} }
auto *instance = new qnn::qnn_instance(qnn_lib_path, g_qnn_mgr[device].lib, ""); auto *instance = new qnn::qnn_instance(qnn_lib_path, g_qnn_mgr[device].lib, "");
result = instance->qnn_init(nullptr); result = instance->qnn_init(nullptr);
if (0 != result) { if (0 != result) {
QNN_LOG_WARN( QNN_LOG_WARN("init qnn subsystem failed with qnn backend %s, pls check why\n", qnn::get_backend_name(device));
"init qnn subsystem failed with qnn backend %s, pls check why\n",
qnn::get_backend_name(device));
delete instance; delete instance;
return nullptr; return nullptr;
} }
@ -771,8 +724,7 @@ ggml_backend_t ggml_backend_qnn_init(size_t device, const char * qnn_lib_path) {
g_qnn_mgr[device].raw_system_interface = instance->get_qnn_raw_system_interface(); g_qnn_mgr[device].raw_system_interface = instance->get_qnn_raw_system_interface();
g_qnn_mgr[device].socinfo = instance->get_soc_info(); g_qnn_mgr[device].socinfo = instance->get_soc_info();
ggml_backend_t qnn_backend = ggml_backend_t qnn_backend = new ggml_backend{ /* .guid = */ ggml_backend_qnn_guid(),
new ggml_backend{/* .guid = */ ggml_backend_qnn_guid(),
/* .iface = */ ggml_backend_qnn_interface, /* .iface = */ ggml_backend_qnn_interface,
/* .context = */ &g_qnn_mgr[device] }; /* .context = */ &g_qnn_mgr[device] };
g_qnn_mgr[device].backend = qnn_backend; g_qnn_mgr[device].backend = qnn_backend;
@ -786,8 +738,7 @@ GGML_CALL int ggml_backend_qnn_reg_devices() {
for (size_t idx = 0; idx < GGML_QNN_MAX_DEVICES; idx++) { for (size_t idx = 0; idx < GGML_QNN_MAX_DEVICES; idx++) {
char name[GGML_MAX_NAME]; char name[GGML_MAX_NAME];
ggml_backend_qnn_get_device_description(idx, name, GGML_MAX_NAME); ggml_backend_qnn_get_device_description(idx, name, GGML_MAX_NAME);
ggml_backend_register(name, ggml_backend_qnn_reg_init, ggml_backend_register(name, ggml_backend_qnn_reg_init, ggml_backend_qnn_buffer_type(idx),
ggml_backend_qnn_buffer_type(idx),
(void *)(intptr_t)idx); (void *)(intptr_t)idx);
} }

2
ggml/src/ggml-qnn/utils.hpp
View file

@ -30,7 +30,7 @@ Fn load_qnn_functionpointers(void *handle, const char *function_name) {
return reinterpret_cast<Fn>(dlsym(handle, function_name)); return reinterpret_cast<Fn>(dlsym(handle, function_name));
} }
inline int validate_tensor_version(Qnn_Tensor_t tensor) { inline int validate_tensor_version(const Qnn_Tensor_t &tensor) {
if (tensor.version != QNN_TENSOR_VERSION_1) { if (tensor.version != QNN_TENSOR_VERSION_1) {
QNN_LOG_WARN("validate_tensor_version() tensor %s, got unsupported version %d\n", tensor.v1.name, QNN_LOG_WARN("validate_tensor_version() tensor %s, got unsupported version %d\n", tensor.v1.name,
tensor.version); tensor.version);