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### Description
Preview:
https://adrianlizarraga.github.io/onnxruntime/docs/execution-providers/QNN-ExecutionProvider.html#running-a-model-with-qnn-eps-htp-backend-python

- Adds QNN EP documentation:
  - Create a quantized QDQ model using python quantization utilities
  - Run quantized QDQ model on Windows ARM64 using python ORT APIs.
- Updates QNN EP provider options
- Adds list of supported ONNX operators
- Provides instructions for using the new arm64 python wheel


### Motivation and Context
The aim is to make it easier for users to try QNN EP.
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Expand Up @@ -30,27 +30,40 @@ ONNX Runtime QNN Execution Provider has been built and tested with QNN 2.18.x an

## Build
For build instructions, please see the [BUILD page](../build/eps.md#qnn).
[prebuilt NuGet package](https://www.nuget.org/packages/Microsoft.ML.OnnxRuntime.QNN)

## Pre-built Packages
Alternatively, ONNX Runtime with QNN EP can be installed from:
- [NuGet package](https://www.nuget.org/packages/Microsoft.ML.OnnxRuntime.QNN)
- Nightly Python package (Windows ARM64):
- Requirements:
- Windows ARM64
- Python 3.11.x
- Numpy 1.25.2 or >= 1.26.4
- Install: `python -m pip install -i https://aiinfra.pkgs.visualstudio.com/PublicPackages/_packaging/ORT-Nightly/pypi/simple/ ort-nightly-qnn`

## 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:
The QNN Execution Provider supports a number of configuration options. These provider options are specified as key-value string pairs.

|`provider_options_values` for `provider_options_keys = "backend_path"`|Description|
|`"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.so' or 'QnnHtp.dll'|Enable Htp backend. Offloads compute to NPU.|
|'libQnnHtp.so' or 'QnnHtp.dll'|Enable HTP backend. Offloads compute to NPU.|

|`provider_options_values` for `provider_options_keys = "profiling_level"`|Description|
|`"profiling_level"`|Description|
|---|---|
|'off'||
|'basic'||
|'detailed'||

|`provider_options_values` for `provider_options_keys = "rpc_control_latency"`|Description|
|`"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|
|`"vtcm_mb"`|Description|
|---|---|
|size in MB (string)|QNN VTCM size in MB, defaults to 0 (not set)|

|`"htp_performance_mode"`|Description|
|---|---|
|'burst'||
|'balanced'||
Expand All @@ -62,22 +75,299 @@ The QNN Execution Provider supports a number of configuration options. The `prov
|'power_saver'||
|'sustained_high_performance'||

|`"qnn_saver_path"`|Description|
|---|---|
|filpath to 'QnnSaver.dll' or 'libQnnSaver.so'|File path to the QNN Saver backend library. Dumps QNN API calls to disk for replay/debugging.|


|`provider_options_values` for `provider_options_keys = "qnn_context_priority"`|[Description](https://docs.qualcomm.com/bundle/publicresource/topics/80-63442-50/htp_yielding.html)|
|`"qnn_context_priority"`|[Description](https://docs.qualcomm.com/bundle/publicresource/topics/80-63442-50/htp_yielding.html)|
|---|---|
|'low'||
|'normal'|default.|
|'normal_high'||
|'high'||


|`provider_options_values` for `provider_options_keys = "htp_graph_finalization_optimization_mode"`|Description|
|`"htp_graph_finalization_optimization_mode"`|Description|
|---|---|
|'0'|default.|
|'1'|faster preparation time, less optimal graph.|
|'2'|longer preparation time, more optimal graph.|
|'3'|longest preparation time, most likely even more optimal graph.|

|`"soc_model"`|Description|
|---|---|
|Model number (string)|The SoC model number. Refer to the QNN SDK documentation for valid values. Defaults to "0" (unknown).|

|`"htp_arch"`|Description|
|---|---|
|"0"|Default (none)|
|"68"||
|"69"||
|"73"||
|"75"||

|`"device_id"`|Description|
|---|---|
|Device ID (string)|The ID of the device to use when setting `htp_arch`. Defaults to "0" (for single device).|

## Supported ONNX operators

|Operator|Notes|
|---|---|
|ai.onnx:Abs||
|ai.onnx:Add||
|ai.onnx:And||
|ai.onnx:ArgMax||
|ai.onnx:ArgMin||
|ai.onnx:Asin||
|ai.onnx:Atan||
|ai.onnx:AveragePool||
|ai.onnx:BatchNormalization||
|ai.onnx:Cast||
|ai.onnx:Clip||
|ai.onnx:Concat||
|ai.onnx:Conv||
|ai.onnx:ConvTranspose||
|ai.onnx:Cos||
|ai.onnx:DepthToSpace||
|ai.onnx:DequantizeLinear||
|ai.onnx:Div||
|ai.onnx:Elu||
|ai.onnx:Equal||
|ai.onnx:Exp||
|ai.onnx:Expand||
|ai.onnx:Flatten||
|ai.onnx:Floor||
|ai.onnx:Gather|Only supports positive indices|
|ai.onnx:Gelu||
|ai.onnx:Gemm||
|ai.onnx:GlobalAveragePool||
|ai.onnx:Greater||
|ai.onnx:GreaterOrEqual||
|ai.onnx:GridSample||
|ai.onnx:HardSwish||
|ai.onnx:InstanceNormalization||
|ai.onnx:LRN||
|ai.onnx:LayerNormalization||
|ai.onnx:LeakyRelu||
|ai.onnx:Less||
|ai.onnx:LessOrEqual||
|ai.onnx:Log||
|ai.onnx:LogSoftmax||
|ai.onnx:LpNormalization|p == 2|
|ai.onnx:MatMul|Supported input data types on HTP backend: (uint8, uint8), (uint8, uint16), (uint16, uint8)|
|ai.onnx:Max||
|ai.onnx:MaxPool||
|ai.onnx:Min||
|ai.onnx:Mul||
|ai.onnx:Neg||
|ai.onnx:Not||
|ai.onnx:Or||
|ai.onnx:Prelu||
|ai.onnx:Pad||
|ai.onnx:Pow||
|ai.onnx:QuantizeLinear||
|ai.onnx:ReduceMax||
|ai.onnx:ReduceMean||
|ai.onnx:ReduceMin||
|ai.onnx:ReduceProd||
|ai.onnx:ReduceSum||
|ai.onnx:Relu||
|ai.onnx:Resize||
|ai.onnx:Round||
|ai.onnx:Sigmoid||
|ai.onnx:Sign||
|ai.onnx:Sin||
|ai.onnx:Slice||
|ai.onnx:Softmax||
|ai.onnx:SpaceToDepth||
|ai.onnx:Split||
|ai.onnx:Sqrt||
|ai.onnx:Squeeze||
|ai.onnx:Sub||
|ai.onnx:Tanh||
|ai.onnx:Tile||
|ai.onnx:TopK||
|ai.onnx:Transpose||
|ai.onnx:Unsqueeze||
|ai.onnx:Where||
|com.microsoft:DequantizeLinear|Provides 16-bit integer dequantization support|
|com.microsoft:Gelu||
|com.microsoft:QuantizeLinear|Provides 16-bit integer quantization support|

Supported data types vary by operator and QNN backend. Refer to the [QNN SDK documentation](https://docs.qualcomm.com/bundle/publicresource/topics/80-63442-50/operations.html) for more information.

## Running a model with QNN EP's HTP backend (Python)
<p align="center"><img width="100%" src="../../images/qnn_ep_quant_workflow.png" alt="Offline workflow for quantizing an ONNX model for use on QNN EP"/></p>

The QNN HTP backend only supports quantized models. Models with 32-bit floating-point activations and weights must first be quantized to use a lower integer precision (e.g., 8-bit or 16-bit integers).

This section provides instructions for quantizing a model and then running the quantized model on QNN EP's HTP backend using Python APIs. Please refer to the [quantization page](../performance/model-optimizations/quantization.md) for a broader overview of quantization concepts.

### Model requirements
QNN EP does not support models with dynamic shapes (e.g., a dynamic batch size). Dynamic shapes must be fixed to a specific value. Refer to the documentation for [making dynamic input shapes fixed](../tutorials/mobile/helpers/make-dynamic-shape-fixed.md) for more information.

Additionally, QNN EP supports a subset of ONNX operators (e.g., Loops and Ifs are not supported). Refer to the [list of supported ONNX operators](./QNN-ExecutionProvider.md#supported-onnx-operators).
### Generating a quantized model (x64)
The ONNX Runtime python package provides utilities for quantizing ONNX models via the `onnxruntime.quantization` import. The quantization utilities are currently only supported on x86_64 due to issues installing the `onnx` package on ARM64.
Therefore, it is recommended to either use an x64 machine to quantize models or, alternatively, use a separate x64 python installation on Windows ARM64 machines.

Install the nightly ONNX Runtime x64 python package.
```shell
python -m pip install -i https://aiinfra.pkgs.visualstudio.com/PublicPackages/_packaging/ORT-Nightly/pypi/simple/ ort-nightly
```

Quantization for QNN EP requires the use of calibration input data. Using a calibration dataset that is representative of typical model inputs is crucial in generating an accurate quantized model.

The following snippet defines a sample `DataReader` class that generates random float32 input data. Note that using random input data will most likely produce an inaccurate quantized model.
Refer to the [implementation of a Resnet data reader](https://github.com/microsoft/onnxruntime-inference-examples/blob/main/quantization/image_classification/cpu/resnet50_data_reader.py) for one example of how to create a `CalibrationDataReader` that provides input from image files on disk.

```python
# data_reader.py

import numpy as np
import onnxruntime
from onnxruntime.quantization import CalibrationDataReader


class DataReader(CalibrationDataReader):
def __init__(self, model_path: str):
self.enum_data = None

# Use inference session to get input shape.
session = onnxruntime.InferenceSession(model_path, providers=['CPUExecutionProvider'])

inputs = session.get_inputs()

self.data_list = []

# Generate 10 random float32 inputs
# TODO: Load valid calibration input data for your model
for _ in range(10):
input_data = {inp.name : np.random.random(inp.shape).astype(np.float32) for inp in inputs}
self.data_list.append(input_data)

self.datasize = len(self.data_list)

def get_next(self):
if self.enum_data is None:
self.enum_data = iter(
self.data_list
)
return next(self.enum_data, None)

def rewind(self):
self.enum_data = None

```

The following snippet pre-processes the original model and then quantizes the pre-processed model to use `uint16` activations and `uint8` weights.
Although the quantization utilities expose the `uint8`, `int8`, `uint16`, and `int16` quantization data types, QNN operators typically support the `uint8` and `uint16` data types.
Refer to the [QNN SDK operator documentation](https://docs.qualcomm.com/bundle/publicresource/topics/80-63442-50/HtpOpDefSupplement.html) for the data type requirements of each QNN operator.

```python
# quantize_model.py

import data_reader
import numpy as np
import onnx
from onnxruntime.quantization import QuantType, quantize
from onnxruntime.quantization.execution_providers.qnn import get_qnn_qdq_config, qnn_preprocess_model

if __name__ == "__main__":
input_model_path = "model.onnx" # TODO: Replace with your actual model
output_model_path = "model.qdq.onnx" # Name of final quantized model
my_data_reader = data_reader.DataReader(input_model_path)

# Pre-process the original float32 model.
preproc_model_path = "model.preproc.onnx"
model_changed = qnn_preprocess_model(input_model_path, preproc_model_path)
model_to_quantize = preproc_model_path if model_changed else input_model_path

# Generate a suitable quantization configuration for this model.
# Note that we're choosing to use uint16 activations and uint8 weights.
qnn_config = get_qnn_qdq_config(model_to_quantize,
my_data_reader,
activation_type=QuantType.QUInt16, # uint16 activations
weight_type=QuantType.QUInt8) # uint8 weights

# Quantize the model.
quantize(model_to_quantize, output_model_path, qnn_config)
```

Running `python quantize_model.py` will generate a quantized model called `model.qdq.onnx` that can be run on Windows ARM64 devices via ONNX Runtime's QNN EP.

Refer to the following pages for more information on usage of the quantization utilities:
- [Quantization example for mobilenet on CPU EP](https://github.com/microsoft/onnxruntime-inference-examples/tree/main/quantization/image_classification/cpu)
- [quantization/execution_providers/qnn/preprocess.py](https://github.com/microsoft/onnxruntime/blob/23996bbbbe0406a5c8edbf6b7dbd71e5780d3f4b/onnxruntime/python/tools/quantization/execution_providers/qnn/preprocess.py#L16)
- [quantization/execution_providers/qnn/quant_config.py](https://github.com/microsoft/onnxruntime/blob/23996bbbbe0406a5c8edbf6b7dbd71e5780d3f4b/onnxruntime/python/tools/quantization/execution_providers/qnn/quant_config.py#L20-L27)

### Running a quantized model on Windows ARM64
The following assumes that the [Qualcomm AI Engine SDK (QNN SDK)](https://qpm.qualcomm.com/main/tools/details/qualcomm_ai_engine_direct) has already been downloaded and installed to a location such as `C:\Qualcomm\AIStack\QNN\2.18.0.240101`, hereafter referred to as `QNN_SDK`.

First, determine the HTP architecture version for your device by referring to the QNN SDK documentation:
- QNN_SDK\docs\QNN\general\htp\htp_backend.html#qnn-htp-backend-api
- QNN_SDK\docs\QNN\general\overview.html#supported-snapdragon-devices

For example, Snapdragon 8cx Gen 3 (SC8280X) devices have an HTP architecture value of 68, and Snapdragon 8cx Gen 4 (SC8380XP) have an HTP architecture value of 73. In the following, replace `<HTP_ARCH>` with your device's HTP architecture value.

Copy the `.so` file `QNN_SDK\lib\hexagon-v<HTP_ARCH>\unsigned\libQnnHtpV<HTP_ARCH>Skel.so` to the folder `QNN_SDK\lib\aarch64-windows-msvc\`. For example, the following terminal command copies the `libQnnHtpV73Skel.so` file:
```
cp QNN_SDK\lib\hexagon-v73\unsigned\libQnnHtpV73Skel.so QNN_SDK\lib\aarch64-windows-msvc\
```

Add the `QNN_SDK\lib\aarch64-windows-msvc\` directory to your Windows PATH environment variable:
```
- Open the `Edit the system environment variables` Control Panel.
- Click on `Environment variables`.
- Highlight the `Path` entry under `User variables for ..` and click `Edit`.
- Add a new entry that points to `QNN_SDK\lib\aarch64-windows-msvc\`
```

Install the nightly ONNX Runtime ARM64 python package for QNN EP (requires Python 3.11.x and Numpy 1.25.2 or >= 1.26.4):
```shell
python -m pip install -i https://aiinfra.pkgs.visualstudio.com/PublicPackages/_packaging/ORT-Nightly/pypi/simple/ ort-nightly-qnn
```

The following Python snippet creates an ONNX Runtime session with QNN EP and runs the quantized model `model.qdq.onnx` on the HTP backend.

```python
# run_qdq_model.py

import onnxruntime
import numpy as np

options = onnxruntime.SessionOptions()

# (Optional) Enable configuration that raises an exception if the model can't be
# run entirely on the QNN HTP backend.
options.add_session_config_entry("session.disable_cpu_ep_fallback", "1")

# Create an ONNX Runtime session.
# TODO: Provide the path to your ONNX model
session = onnxruntime.InferenceSession("model.qdq.onnx",
sess_options=options,
providers=["QNNExecutionProvider"],
provider_options=[{"backend_path": "QnnHtp.dll"}]) # Provide path to Htp dll in QNN SDK

# Run the model with your input.
# TODO: Use numpy to load your actual input from a file or generate random input.
input0 = np.ones((1,3,224,224), dtype=np.float32)
result = session.run(None, {"input": input0})

# Print output.
print(result)
```

Running `python run_qdq_model.py` will execute the quantized `model.qdq.onnx` model on the QNN HTP backend.

Notice that the session has been optionally configured to raise an exception if the entire model cannot be executed on the QNN HTP backend. This is useful for verifying that the quantized model is fully supported by QNN EP.
Available session configurations include:
- [session.disable_cpu_ep_fallback](https://github.com/microsoft/onnxruntime/blob/a4cfdc1c28ac95ec6fd0667e856b6a6b8dd1020c/include/onnxruntime/core/session/onnxruntime_session_options_config_keys.h#L229): Disables fallback of unsupported operators to the CPU EP.
- [ep.context_enable](https://github.com/microsoft/onnxruntime/blob/a4cfdc1c28ac95ec6fd0667e856b6a6b8dd1020c/include/onnxruntime/core/session/onnxruntime_session_options_config_keys.h#L243): [Enable QNN context cache](./QNN-ExecutionProvider.md#qnn-context-binary-cache-feature) feature to dump a cached version of the model in order to decrease session creation time.

The above snippet only specifies the `backend_path` provider option. Refer to the [Configuration options section](./QNN-ExecutionProvider.md#configuration-options) for a list of all available QNN EP provider options.

## QNN context binary cache feature
There's a QNN context which contains QNN graphs after converting, compiling, filnalizing the model. QNN can serialize the context into binary file, so that user can use it for futher inference direclty (without the QDQ model) to improve the model loading cost.
Expand Down Expand Up @@ -106,6 +396,14 @@ CheckStatus(g_ort, g_ort->CreateSessionOptions(&session_options));
g_ort->AddSessionConfigEntry(session_options, kOrtSessionOptionEpContextEnable, "1");
```

```python
# Python
import onnxruntime

options = onnxruntime.SessionOptions()
options.add_session_config_entry("ep.context_enable", "1")
```

### Configure the context binary file path
The generated Onnx model with QNN context binary is default to [input_QDQ_model_path]_ctx.onnx in case user does not specify the path. User can to set the path in the session option with the key "ep.context_file_path". Example code below:

Expand All @@ -117,6 +415,11 @@ so.AddConfigEntry(kOrtSessionOptionEpContextFilePath, "./model_a_ctx.onnx");
g_ort->AddSessionConfigEntry(session_options, kOrtSessionOptionEpContextFilePath, "./model_a_ctx.onnx");
```

```python
# Python
options.add_session_config_entry("ep.context_file_path", "./model_a_ctx.onnx")
```

### Disable the embed mode
The QNN context binary content is embeded in the generated Onnx model by default. User can to disable it by setting "ep.context_embed_mode" to "0". In that case, a bin file will be generated separately. The file name looks like [ctx.onnx]_QNNExecutionProvider_QNN_[hash_id]_x_x.bin. The name is provided by Ort and tracked in the generated Onnx model. It will cause problems if any changes to the bin file. This bin file needs to sit together with the generated Onnx file.

Expand All @@ -128,6 +431,11 @@ so.AddConfigEntry(kOrtSessionOptionEpContextEmbedMode, "0");
g_ort->AddSessionConfigEntry(session_options, kOrtSessionOptionEpContextEmbedMode, "0");
```

```python
# Python
options.add_session_config_entry("ep.context_embed_mode", "0")
```

## Usage
### C++
C API details are [here](../get-started/with-c.md).
Expand Down
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