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use qnn graph inside add and mul ops

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This commit is contained in:
hongruichen 2024-07-05 13:08:14 +08:00
parent a688ed324b
commit 13dc3a02c3
4 changed files with 88 additions and 252 deletions
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2
ggml/src/ggml-qnn.cpp
View file

@ -530,8 +530,6 @@ GGML_CALL static void ggml_backend_qnn_free(ggml_backend_t backend) {
auto *instance = g_qnn_mgr[ctx->device].instance;
if (instance != nullptr) {
for (const auto &graph_item: ctx->qnn_graph_map) {
Qnn_GraphHandle_t graph_handle = std::get<0>(graph_item.second);
GGML_UNUSED(graph_handle);
QNN_LOG_INFO("graph type:%s", graph_item.first.c_str());
}

323
ggml/src/ggml-qnn/backend-ops.cpp
View file

@ -1,12 +1,22 @@
#include "backend-ops.hpp"
#include <memory>
#include "graph.hpp"
#include "logger.hpp"
#include "tensor.hpp"
#include "utils.hpp"
static bool qnn_is_valid_params(ggml_backend_qnn_context *ctx, const ggml_tensor *src0, const ggml_tensor *src1,
namespace {
void print_ggml_tensor(const ggml_tensor *tensor) {
QNN_LOG_DEBUG("%15s: type = %i (%5s) ne = %5" PRIi64 " x %5" PRIi64 " x %5" PRIi64 ", nb = (%5zi, %5zi, %5zi)\n",
tensor->name, tensor->type, ggml_type_name(tensor->type), tensor->ne[0], tensor->ne[1], tensor->ne[2],
tensor->nb[0], tensor->nb[1], tensor->nb[2]);
}
bool qnn_is_valid_params(ggml_backend_qnn_context *ctx, const ggml_tensor *src0, const ggml_tensor *src1,
ggml_tensor *dst) {
if (!ctx || !src0 || !src1 || !dst) {
QNN_LOG_WARN("invalid params\n");
@ -25,6 +35,8 @@ static bool qnn_is_valid_params(ggml_backend_qnn_context *ctx, const ggml_tensor
return true;
}
} // namespace
#ifndef NDEBUG
#define CHECK_PARAMS(ctx, src0, src1, dst) \
do { \
@ -41,157 +53,65 @@ static bool qnn_is_valid_params(ggml_backend_qnn_context *ctx, const ggml_tensor
// keep it for illustrate how to implement a specified GGMPL OP using QNN API + QNN RPC
static void ggml_qnn_add(ggml_backend_qnn_context *ctx, const ggml_tensor *src0, const ggml_tensor *src1,
ggml_tensor *dst) {
Qnn_ErrorHandle_t error = QNN_SUCCESS;
bool graph_initialized = false;
qnn::qnn_instance *instance = nullptr;
std::string graph_name = "ggml_op_qnn_add";
Qnn_GraphHandle_t graph_handle = nullptr;
Qnn_Param_t qnn_params[] = {};
enum ggml_op ggmlop = GGML_OP_ADD;
CHECK_PARAMS(ctx, src0, src1, dst);
instance = ctx->instance;
auto qnn_raw_interface = ctx->raw_interface;
qnn::qnn_perf perf("ggml_qnn_add");
std::string graph_name = "ggml_op_qnn_add";
qnn::qnn_perf perf(graph_name);
perf.start();
std::string map_entry(ggml_op_name(ggmlop));
if (ctx->qnn_graph_map.find(map_entry) != ctx->qnn_graph_map.end()) {
graph_initialized = true;
auto &graph_item = ctx->qnn_graph_map[map_entry];
graph_handle = std::get<0>(graph_item);
}
if (!graph_initialized) {
bool succeed = false;
std::string graph_key(ggml_op_name(GGML_OP_ADD));
auto it = ctx->qnn_graph_map.find(graph_key);
if (it != ctx->qnn_graph_map.end()) {
const auto &graph_item = it->second;
qnn::ggml_qnn_tensor_input tensor_input0(src0, std::get<1>(graph_item), ctx);
qnn::ggml_qnn_tensor_input tensor_input1(src1, std::get<2>(graph_item), ctx);
qnn::ggml_qnn_tensor_output tensor_output(dst, std::get<3>(graph_item), ctx);
std::get<0>(graph_item)->execute();
} else {
graph_name = graph_name + "_" + std::to_string(ctx->threads) + "_" + src0->name + "_" + src1->name;
QNN_LOG_INFO("graph name %s", graph_name.c_str());
if (ctx->device == QNN_BACKEND_NPU) {
QnnHtpGraph_CustomConfig_t hvx_config;
hvx_config.option = QNN_HTP_GRAPH_CONFIG_OPTION_NUM_HVX_THREADS;
hvx_config.numHvxThreads = 8;
QnnGraph_Config_t graph_hvx_config;
graph_hvx_config.option = QNN_GRAPH_CONFIG_OPTION_CUSTOM;
graph_hvx_config.customConfig = &hvx_config;
auto graph = std::make_unique<qnn::ggml_qnn_graph_binary>(graph_name, (QNNBackend)(ctx->device),
ctx->instance->get_qnn_context_handle(),
ctx->raw_interface, ctx->socinfo.vtcm_size_in_mb);
QnnHtpGraph_CustomConfig_t dlbc_config;
dlbc_config.option = QNN_HTP_GRAPH_CONFIG_OPTION_OPTIMIZATION;
dlbc_config.optimizationOption.type = QNN_HTP_GRAPH_OPTIMIZATION_TYPE_ENABLE_DLBC;
dlbc_config.optimizationOption.floatValue = 1.0; // set to 0.0 to turn off DLBC
QnnGraph_Config_t graph_dlbc_config;
graph_dlbc_config.option = QNN_GRAPH_CONFIG_OPTION_CUSTOM;
graph_dlbc_config.customConfig = &dlbc_config;
QnnHtpGraph_CustomConfig_t opt_config;
opt_config.optimizationOption.type = QNN_HTP_GRAPH_OPTIMIZATION_TYPE_FINALIZE_OPTIMIZATION_FLAG;
opt_config.optimizationOption.floatValue = 1; // 1 / 3
QnnGraph_Config_t graph_opt_config;
graph_opt_config.option = QNN_GRAPH_CONFIG_OPTION_CUSTOM;
graph_opt_config.customConfig = &opt_config;
QnnHtpGraph_CustomConfig_t vtcm_config;
vtcm_config.option = QNN_HTP_GRAPH_CONFIG_OPTION_VTCM_SIZE;
vtcm_config.vtcmSizeInMB = ctx->socinfo.vtcm_size_in_mb;
QnnGraph_Config_t graph_vtcm_config;
graph_vtcm_config.option = QNN_GRAPH_CONFIG_OPTION_CUSTOM;
graph_vtcm_config.customConfig = &vtcm_config;
const QnnGraph_Config_t *p_graphconfig[] = { &graph_hvx_config, &graph_dlbc_config, &graph_vtcm_config,
&graph_opt_config, NULL };
error = qnn_raw_interface.graphCreate(instance->get_qnn_context_handle(), graph_name.c_str(), p_graphconfig,
&graph_handle);
} else {
error = qnn_raw_interface.graphCreate(instance->get_qnn_context_handle(), graph_name.c_str(), nullptr,
&graph_handle);
}
if (QNN_SUCCESS != error) {
QNN_LOG_INFO(
"can't create qnn graph handle with graph name %s, "
"error = %d\n",
graph_name.c_str(), error);
if (!graph->is_valid()) {
goto failure;
} else {
QNN_LOG_INFO("create qnn graph handle with graph name %s ok\n", graph_name.c_str());
}
qnn::ggml_qnn_tensor_input tensor_input0(src0, graph_handle, ctx);
qnn::ggml_qnn_tensor_input tensor_input0(src0, graph->get_graph_handler(), ctx);
if (!tensor_input0.is_valid()) {
goto failure;
}
qnn::ggml_qnn_tensor_input tensor_input1(src1, graph_handle, ctx);
qnn::ggml_qnn_tensor_input tensor_input1(src1, graph->get_graph_handler(), ctx);
if (!tensor_input1.is_valid()) {
QNN_LOG_INFO("error = %d\n", error);
goto failure;
}
qnn::ggml_qnn_tensor_output tensor_output(dst, graph_handle, ctx);
qnn::ggml_qnn_tensor_output tensor_output(dst, graph->get_graph_handler(), ctx);
if (!tensor_output.is_valid()) {
goto failure;
}
Qnn_Tensor_t tensor_inputs[] = { *tensor_input0.get_qnn_tensor(), *tensor_input1.get_qnn_tensor() };
Qnn_Tensor_t tensor_outputs[] = { *tensor_output.get_qnn_tensor() };
Qnn_OpConfig_t op_config = { QNN_OPCONFIG_VERSION_1,
.v1 = { "ggml_op_add", QNN_OP_PACKAGE_NAME_QTI_AISW, QNN_OP_ELEMENT_WISE_ADD, 0,
qnn_params, 2, tensor_inputs, 1, tensor_outputs } };
error = qnn_raw_interface.graphAddNode(graph_handle, op_config);
if (QNN_SUCCESS != error) {
QNN_LOG_INFO("error = %d\n", error);
goto failure;
}
error = qnn_raw_interface.graphFinalize(graph_handle, nullptr, nullptr);
if (QNN_SUCCESS != error) {
QNN_LOG_INFO("error = %d\n", error);
goto failure;
}
error = qnn_raw_interface.graphExecute(graph_handle, tensor_inputs, 2, tensor_outputs, 1, nullptr, nullptr);
if (ctx->device == QNN_BACKEND_NPU) {
if (QNN_COMMON_ERROR_SYSTEM_COMMUNICATION == error) {
QNN_LOG_WARN("NPU crashed. SSR detected. Caused QNN graph execute error\n");
}
}
if (QNN_SUCCESS != error) {
QNN_LOG_INFO("error = %d\n", error);
if (!graph->add_nodes(QNN_OP_ELEMENT_WISE_ADD,
{ *tensor_input0.get_qnn_tensor(), *tensor_input1.get_qnn_tensor() },
{ *tensor_output.get_qnn_tensor() })) {
goto failure;
}
auto graph_item = std::make_tuple(graph_handle, tensor_input0.get_qnn_tensor(), tensor_input1.get_qnn_tensor(),
tensor_output.get_qnn_tensor());
ctx->qnn_graph_map[map_entry] = graph_item;
} else {
auto &graph_item = ctx->qnn_graph_map[map_entry];
qnn::ggml_qnn_tensor_input tensor_input0(src0, std::get<1>(graph_item), ctx);
qnn::ggml_qnn_tensor_input tensor_input1(src1, std::get<2>(graph_item), ctx);
qnn::ggml_qnn_tensor_output tensor_output(dst, std::get<3>(graph_item), ctx);
Qnn_Tensor_t tensor_inputs[] = { *tensor_input0.get_qnn_tensor(), *tensor_input1.get_qnn_tensor() };
Qnn_Tensor_t tensor_outputs[] = { *tensor_output.get_qnn_tensor() };
error = qnn_raw_interface.graphExecute(graph_handle, tensor_inputs, 2, tensor_outputs, 1, nullptr, nullptr);
if (ctx->device == QNN_BACKEND_NPU) {
if (QNN_COMMON_ERROR_SYSTEM_COMMUNICATION == error) {
QNN_LOG_WARN("NPU crashed. SSR detected. Caused QNN graph execute error\n");
}
}
if (QNN_SUCCESS != error) {
QNN_LOG_INFO("error = %d\n", error);
if (!graph->execute()) {
goto failure;
}
ctx->qnn_graph_map[graph_key] = std::make_tuple(std::move(graph), tensor_input0.get_qnn_tensor(),
tensor_input1.get_qnn_tensor(), tensor_output.get_qnn_tensor());
}
succeed = true;
failure:
if (QNN_SUCCESS != error) {
QNN_LOG_DEBUG("%15s: type = %i (%5s) ne = %5" PRIi64 " x %5" PRIi64 " x %5" PRIi64
", nb = (%5zi, %5zi, %5zi)\n",
src0->name, src0->type, ggml_type_name(src0->type), src0->ne[0], src0->ne[1], src0->ne[2],
src0->nb[0], src0->nb[1], src0->nb[2]);
QNN_LOG_DEBUG("%15s: type = %i (%5s) ne = %5" PRIi64 " x %5" PRIi64 " x %5" PRIi64
", nb = (%5zi, %5zi, %5zi)\n",
src1->name, src1->type, ggml_type_name(src1->type), src1->ne[0], src1->ne[1], src1->ne[2],
src1->nb[0], src1->nb[1], src1->nb[2]);
QNN_LOG_DEBUG("%15s: type = %i (%5s) ne = %5" PRIi64 " x %5" PRIi64 " x %5" PRIi64
", nb = (%5zi, %5zi, %5zi)\n",
dst->name, dst->type, ggml_type_name(dst->type), dst->ne[0], dst->ne[1], dst->ne[2], dst->nb[0],
dst->nb[1], dst->nb[2]);
if (!succeed) {
print_ggml_tensor(src0);
print_ggml_tensor(src1);
print_ggml_tensor(dst);
}
perf.info();
@ -210,158 +130,69 @@ failure:
*/
static void ggml_qnn_mul_mat(ggml_backend_qnn_context *ctx, const ggml_tensor *src0, const ggml_tensor *src1,
ggml_tensor *dst) {
Qnn_ErrorHandle_t error = QNN_SUCCESS;
bool graph_initialized = false;
qnn::qnn_instance *instance = nullptr;
std::string graph_name = "ggml_op_qnn_mul_mat";
Qnn_GraphHandle_t graph_handle = nullptr;
Qnn_Param_t qnn_params[] = {};
enum ggml_op ggmlop = GGML_OP_MUL_MAT;
CHECK_PARAMS(ctx, src0, src1, dst);
instance = ctx->instance;
auto qnn_raw_interface = ctx->raw_interface;
qnn::qnn_perf perf("ggml_qnn_mul_mat");
std::string graph_name = "ggml_op_qnn_mul_mat";
qnn::qnn_perf perf(graph_name);
perf.start();
std::string map_entry = std::string(ggml_op_name(ggmlop));
if (ctx->qnn_graph_map.find(map_entry) != ctx->qnn_graph_map.end()) {
graph_initialized = true;
auto &graph_item = ctx->qnn_graph_map[map_entry];
graph_handle = std::get<0>(graph_item);
}
// TODO: for scenarios of quantized data in src0
// pass-1: dequantize src0 to FP32
// pass-2: dq-src0 * src1
// the performance gains is worth although there is performance loss in pass-1
if (!graph_initialized) {
graph_name = graph_name + "_" + std::to_string(ctx->threads) + "_" + src0->name + "_" + src1->name;
QNN_LOG_INFO("graph name %s", graph_name.c_str());
if (ctx->device == QNN_BACKEND_NPU) {
QnnHtpGraph_CustomConfig_t hvx_config;
hvx_config.option = QNN_HTP_GRAPH_CONFIG_OPTION_NUM_HVX_THREADS;
hvx_config.numHvxThreads = 8;
QnnGraph_Config_t graph_hvx_config;
graph_hvx_config.option = QNN_GRAPH_CONFIG_OPTION_CUSTOM;
graph_hvx_config.customConfig = &hvx_config;
QnnHtpGraph_CustomConfig_t dlbc_config;
dlbc_config.option = QNN_HTP_GRAPH_CONFIG_OPTION_OPTIMIZATION;
dlbc_config.optimizationOption.type = QNN_HTP_GRAPH_OPTIMIZATION_TYPE_ENABLE_DLBC;
dlbc_config.optimizationOption.floatValue = 1.0; // set to 0.0 to turn off DLBC
QnnGraph_Config_t graph_dlbc_config;
graph_dlbc_config.option = QNN_GRAPH_CONFIG_OPTION_CUSTOM;
graph_dlbc_config.customConfig = &dlbc_config;
QnnHtpGraph_CustomConfig_t opt_config;
opt_config.optimizationOption.type = QNN_HTP_GRAPH_OPTIMIZATION_TYPE_FINALIZE_OPTIMIZATION_FLAG;
opt_config.optimizationOption.floatValue = 1; // 1 / 3
QnnGraph_Config_t graph_opt_config;
graph_opt_config.option = QNN_GRAPH_CONFIG_OPTION_CUSTOM;
graph_opt_config.customConfig = &opt_config;
QnnHtpGraph_CustomConfig_t vtcm_config;
vtcm_config.option = QNN_HTP_GRAPH_CONFIG_OPTION_VTCM_SIZE;
vtcm_config.vtcmSizeInMB = ctx->socinfo.vtcm_size_in_mb;
QnnGraph_Config_t graph_vtcm_config;
graph_vtcm_config.option = QNN_GRAPH_CONFIG_OPTION_CUSTOM;
graph_vtcm_config.customConfig = &vtcm_config;
const QnnGraph_Config_t *p_graphconfig[] = { &graph_hvx_config, &graph_dlbc_config, &graph_vtcm_config,
&graph_opt_config, NULL };
error = qnn_raw_interface.graphCreate(instance->get_qnn_context_handle(), graph_name.c_str(), p_graphconfig,
&graph_handle);
bool succeed = false;
std::string graph_key(ggml_op_name(GGML_OP_MUL_MAT));
auto it = ctx->qnn_graph_map.find(graph_key);
if (it != ctx->qnn_graph_map.end()) {
const auto &graph_item = it->second;
qnn::ggml_qnn_tensor_input tensor_input0(src0, std::get<1>(graph_item), ctx);
qnn::ggml_qnn_tensor_input tensor_input1(src1, std::get<2>(graph_item), ctx);
qnn::ggml_qnn_tensor_output tensor_output(dst, std::get<3>(graph_item), ctx);
std::get<0>(graph_item)->execute();
} else {
error = qnn_raw_interface.graphCreate(instance->get_qnn_context_handle(), graph_name.c_str(), nullptr,
&graph_handle);
}
if (QNN_SUCCESS != error) {
QNN_LOG_INFO(
"can't create qnn graph handle with graph name %s, "
"error = %d\n",
graph_name.c_str(), error);
graph_name = graph_name + "_" + std::to_string(ctx->threads) + "_" + src0->name + "_" + src1->name;
auto graph = std::make_unique<qnn::ggml_qnn_graph_binary>(graph_name, (QNNBackend)(ctx->device),
ctx->instance->get_qnn_context_handle(),
ctx->raw_interface, ctx->socinfo.vtcm_size_in_mb);
if (!graph->is_valid()) {
goto failure;
}
qnn::ggml_qnn_tensor_input tensor_input0(src0, graph_handle, ctx);
qnn::ggml_qnn_tensor_input tensor_input0(src0, graph->get_graph_handler(), ctx);
if (!tensor_input0.is_valid()) {
goto failure;
}
qnn::ggml_qnn_tensor_input tensor_input1(src1, graph_handle, ctx);
qnn::ggml_qnn_tensor_input tensor_input1(src1, graph->get_graph_handler(), ctx);
if (!tensor_input1.is_valid()) {
goto failure;
}
qnn::ggml_qnn_tensor_output tensor_output(dst, graph_handle, ctx);
qnn::ggml_qnn_tensor_output tensor_output(dst, graph->get_graph_handler(), ctx);
if (!tensor_output.is_valid()) {
goto failure;
}
Qnn_Tensor_t tensor_inputs[] = { *tensor_input0.get_qnn_tensor(), *tensor_input1.get_qnn_tensor() };
Qnn_Tensor_t tensor_outputs[] = { *tensor_output.get_qnn_tensor() };
Qnn_OpConfig_t op_config = { QNN_OPCONFIG_VERSION_1,
.v1 = { "ggml_op_mul_mat", QNN_OP_PACKAGE_NAME_QTI_AISW, QNN_OP_MAT_MUL, 0,
qnn_params, 2, tensor_inputs, 1, tensor_outputs } };
error = qnn_raw_interface.graphAddNode(graph_handle, op_config);
if (QNN_SUCCESS != error) {
QNN_LOG_INFO("error = %d\n", error);
goto failure;
}
error = qnn_raw_interface.graphFinalize(graph_handle, nullptr, nullptr);
if (QNN_SUCCESS != error) {
QNN_LOG_INFO("error = %d\n", error);
goto failure;
}
error = qnn_raw_interface.graphExecute(graph_handle, tensor_inputs, 2, tensor_outputs, 1, nullptr, nullptr);
if (ctx->device == QNN_BACKEND_NPU) {
if (QNN_COMMON_ERROR_SYSTEM_COMMUNICATION == error) {
QNN_LOG_WARN("NPU crashed. SSR detected. Caused QNN graph execute error\n");
}
}
if (QNN_SUCCESS != error) {
QNN_LOG_INFO("error = %d\n", error);
if (!graph->add_nodes(QNN_OP_MAT_MUL, { *tensor_input0.get_qnn_tensor(), *tensor_input1.get_qnn_tensor() },
{ *tensor_output.get_qnn_tensor() })) {
goto failure;
}
auto graph_item = std::make_tuple(graph_handle, tensor_input0.get_qnn_tensor(), tensor_input1.get_qnn_tensor(),
tensor_output.get_qnn_tensor());
ctx->qnn_graph_map[map_entry] = graph_item;
} else {
auto &graph_item = ctx->qnn_graph_map[map_entry];
qnn::ggml_qnn_tensor_input tensor_input0(src0, std::get<1>(graph_item), ctx);
qnn::ggml_qnn_tensor_input tensor_input1(src1, std::get<2>(graph_item), ctx);
qnn::ggml_qnn_tensor_output tensor_output(dst, std::get<3>(graph_item), ctx);
Qnn_Tensor_t tensor_inputs[] = { *tensor_input0.get_qnn_tensor(), *tensor_input1.get_qnn_tensor() };
Qnn_Tensor_t tensor_outputs[] = { *tensor_output.get_qnn_tensor() };
error = qnn_raw_interface.graphExecute(graph_handle, tensor_inputs, 2, tensor_outputs, 1, nullptr, nullptr);
if (ctx->device == QNN_BACKEND_NPU) {
if (QNN_COMMON_ERROR_SYSTEM_COMMUNICATION == error) {
QNN_LOG_WARN("NPU crashed. SSR detected. Caused QNN graph execute error\n");
}
}
if (QNN_SUCCESS != error) {
QNN_LOG_INFO("error = %d\n", error);
if (!graph->execute()) {
goto failure;
}
ctx->qnn_graph_map[graph_key] = std::make_tuple(std::move(graph), tensor_input0.get_qnn_tensor(),
tensor_input1.get_qnn_tensor(), tensor_output.get_qnn_tensor());
}
succeed = true;
failure:
if (QNN_SUCCESS != error) {
QNN_LOG_DEBUG("%15s: type = %i (%5s) ne = %5" PRIi64 " x %5" PRIi64 " x %5" PRIi64
", nb = (%5zi, %5zi, %5zi)\n",
src0->name, src0->type, ggml_type_name(src0->type), src0->ne[0], src0->ne[1], src0->ne[2],
src0->nb[0], src0->nb[1], src0->nb[2]);
QNN_LOG_DEBUG("%15s: type = %i (%5s) ne = %5" PRIi64 " x %5" PRIi64 " x %5" PRIi64
", nb = (%5zi, %5zi, %5zi)\n",
src1->name, src1->type, ggml_type_name(src1->type), src1->ne[0], src1->ne[1], src1->ne[2],
src1->nb[0], src1->nb[1], src1->nb[2]);
QNN_LOG_DEBUG("%15s: type = %i (%5s) ne = %5" PRIi64 " x %5" PRIi64 " x %5" PRIi64
", nb = (%5zi, %5zi, %5zi)\n",
dst->name, dst->type, ggml_type_name(dst->type), dst->ne[0], dst->ne[1], dst->ne[2], dst->nb[0],
dst->nb[1], dst->nb[2]);
if (!succeed) {
print_ggml_tensor(src0);
print_ggml_tensor(src1);
print_ggml_tensor(dst);
}
perf.info();

6
ggml/src/ggml-qnn/backend.hpp
View file

@ -1,12 +1,14 @@
#pragma once
#include <memory>
#include <unordered_map>
#include "ggml.h"
#include "ggml-backend.h"
#include "graph.hpp"
#include "qnn.hpp"
struct ggml_backend_qnn_context {
@ -19,5 +21,7 @@ struct ggml_backend_qnn_context {
QNN_INTERFACE_VER_TYPE raw_interface;
QNN_SYSTEM_INTERFACE_VER_TYPE raw_system_interface;
qnn::qcom_socinfo socinfo;
std::unordered_map<std::string, std::tuple<Qnn_GraphHandle_t, Qnn_Tensor_t *, Qnn_Tensor_t *, Qnn_Tensor_t *>> qnn_graph_map;
std::unordered_map<std::string, std::tuple<std::unique_ptr<qnn::ggml_qnn_graph_binary>, Qnn_Tensor_t *,
Qnn_Tensor_t *, Qnn_Tensor_t *>>
qnn_graph_map;
};

9
ggml/src/ggml-qnn/graph.hpp
View file

@ -81,11 +81,14 @@ public:
return false;
}
_tensor_inputs = tensor_inputs;
_tensor_outputs = tensor_outputs;
Qnn_Param_t qnn_params[] = {};
Qnn_OpConfig_t op_config = { QNN_OPCONFIG_VERSION_1,
Qnn_OpConfig_t op_config = { .version = QNN_OPCONFIG_VERSION_1,
.v1 = { _graph_name.c_str(), QNN_OP_PACKAGE_NAME_QTI_AISW, op_name.c_str(), 0,
qnn_params, _tensor_inputs.size(), _tensor_inputs.data(),
_tensor_outputs.size(), _tensor_outputs.data() } };
qnn_params, (uint32_t)_tensor_inputs.size(), _tensor_inputs.data(),
(uint32_t)_tensor_outputs.size(), _tensor_outputs.data() } };
auto error = _qnn_interface.graphAddNode(_graph_handle, op_config);
if (error != QNN_SUCCESS) {
QNN_LOG_ERROR("graphAddNode.error = %d\n", error);