9e6613db1e
remove SNPE EP and add initial QNN EP docs. (more updates coming pending Qualcomm online docs)
3.4 KiB
| title | description | parent | nav_order | redirect_from |
|---|---|---|---|---|
| Qualcomm - QNN | Execute ONNX models with QNN Execution Provider | Execution Providers | 6 | /docs/reference/execution-providers/QNN-ExecutionProvider |
QNN Execution Provider
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The QNN Execution Provider for ONNX Runtime enables hardware accelerated execution on Qualcomm chipsets. It uses the Qualcomm AI Engine Direct SDK (QNN SDK) to construct a QNN graph from an ONNX model which can be executed by a supported accelerator backend library.
Contents
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Install Pre-requisites
Download the Qualcomm AI Engine Direct SDK (QNN SDK) from https://qpm.qualcomm.com/main/tools/details/qualcomm_ai_engine_direct
QNN Version Requirements
ONNX Runtime QNN Execution Provider has been built and tested with QNN 2.10.x and Qualcomm SC8280, SM8350 SOC's
Build
For build instructions, please see the BUILD page.
Configuration Options
The QNN Execution Provider supports a number of configuration options. The provider_option_keys, provider_options_values enable different options for the application. Each provider_options_keys accepts values as shown below:
provider_options_values for provider_options_keys = "backend_path" |
Description |
|---|---|
| 'libQnnCpu.so' or 'QnnCpu.dll' | Enable CPU backend. Useful for integration testing. CPU backend is a reference implementation of QNN operators |
| 'libQnnHtp.do' or 'QnnHtp.dll' | Enable Htp backend. Offloads compute to NPU. |
provider_options_values for provider_options_keys = "profiling_level" |
Description |
|---|---|
| 'off' | |
| 'basic' | |
| 'detailed' |
provider_options_values for provider_options_keys = "rpc_control_latency" |
Description |
|---|---|
| microseconds (string) | allows client to set up RPC control latency in microseconds |
provider_options_values for provider_options_keys = "htp_performance_mode" |
Description |
|---|---|
| 'burst' | |
| 'balanced' | |
| 'default' | |
| 'high_performance' | |
| 'high_power_saver' | |
| 'low_balanced' | |
| 'low_power_saver' | |
| 'power_saver' | |
| 'sustained_high_performance' |
provider_options_values for provider_options_keys = "qnn_context_cache_enable" |
Description |
|---|---|
| '0' | disabled (default) |
| '1' | enable qnn context cache. write out prepared Htp Context Binary to disk to save initialization costs. |
provider_options_values for provider_options_keys = "qnn_context_cache_path" |
Description |
|---|---|
| '/path/to/context/cache' | string path to context cache binary |
Usage
C++
C API details are here.
Ort::Env env = Ort::Env{ORT_LOGGING_LEVEL_ERROR, "Default"};
std::unordered_map<std::string, std::string> qnn_options;
qnn_options["backend_path"] = "QnnHtp.dll";
Ort::SessionOptions session_options;
session_options.AppendExecutionProvider("QNN", qnn_options);
Ort::Session session(env, model_path, session_options);
Python
import onnxruntime as ort
# Create a session with QNN EP using HTP (NPU) backend.
sess = ort.InferenceSession(model_path, providers=['QNNExecutionProvider'], provider_options=[{'backend_path':'QnnHtp.dll'}])`
Inference example
Image classification with SqueezeNet in CPP using QNN Execution Provider with QNN CPU Backend