From bd12c3607edab55c13f49a0ff13c4f8a0b457d60 Mon Sep 17 00:00:00 2001
From: adrianlizarraga <adlizarraga@microsoft.com>
Date: Mon, 5 Feb 2024 16:42:52 -0800
Subject: [PATCH 01/14] Start adding more documentation for quantizing/running
 models on QNN EP

---
 .../QNN-ExecutionProvider.md                  | 111 ++++++++++++++++++
 images/qnn_ep_quant_workflow.png              | Bin 0 -> 115115 bytes
 2 files changed, 111 insertions(+)
 create mode 100644 images/qnn_ep_quant_workflow.png

diff --git a/docs/execution-providers/QNN-ExecutionProvider.md b/docs/execution-providers/QNN-ExecutionProvider.md
index 8adb87d9cbc51..9e2b7c3bfb498 100644
--- a/docs/execution-providers/QNN-ExecutionProvider.md
+++ b/docs/execution-providers/QNN-ExecutionProvider.md
@@ -78,6 +78,117 @@ The QNN Execution Provider supports a number of configuration options. The `prov
 |'2'|longer preparation time, more optimal graph.|
 |'3'|longest preparation time, most likely even more optimal graph.|
 
+## Running a model with QNN EP's HTP backend (Python)
+The QNN HTP backend, which offloads compute to the NPU, 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.
+
+<p align="center"><img width="50%" src="../../images/qnn_ep_quant_workflow.png" alt="Offline workflow for quantizing an ONNX model for use on QNN EP"/></p>
+
+### Quantizing a model
+The ONNX Runtime python package provides utilities for quantizing ONNX models via the `onnxruntime.quantization` import. Note that the quantization utilities are currently only supported on x86_64.
+Therefore, it is recommend to either use an x64 machine to quantize models or, alternatively, use a separate x64 python installation on Windows ARM64 machines.
+
+Install the ONNX Runtime x64 python package. We currently recommend installing the nightly version of ONNX Runtime to get the latest updates to the quantization utilities.
+```shell
+## Install nightly ORT built from main branch
+python -m pip install -i https://aiinfra.pkgs.visualstudio.com/PublicPackages/_packaging/ORT-Nightly/pypi/simple/ ort-nightly
+```
+
+Model quantization for QNN EP requires the use of calibration input data to compute quantization parameters for all activations and weights in the model. 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 `CalibrationDataReader` class that provides calibration data to the quantization utilies. Note, however, that the following example uses random inputs as the calibration dataset only for simplicity. Using random input data results in inaccurate models in practice.
+
+```Python3
+# data_reader.py
+
+import numpy as np
+import onnxruntime
+import os
+from onnxruntime.quantization import CalibrationDataReader
+
+
+NP_TYPE = {
+    "tensor(float)": np.float32,
+    "tensor(uint8)": np.uint8,
+    "tensor(int8)": np.int8,
+    "tensor(uint16)": np.uint16,
+    "tensor(int16)": np.int16,
+}
+
+def get_np_type(onnx_type):
+    if onnx_type in NP_TYPE:
+        return NP_TYPE[onnx_type]
+    else:
+        raise Exception("Unhandled onnx_type in np_type_from_onnx_type")
+
+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 inputs
+        # TODO: Load valid calibration input data
+        for _ in range(10):
+            input_data = {inp.name : np.random.random(inp.shape).astype(get_np_type(inp.type)) 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 using the above `CalibrationDataReader` class.
+
+```Python3
+# quantize_model.py
+
+import data_reader
+import numpy as np
+import onnx
+import onnxruntime
+from onnxruntime.quantization import QuantFormat, 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, prepoc_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 (`model.qdq.onnx`) that can be run on Windows ARM64 devices via ONNX Runtime's QNN EP.
+Refer to [quantization/execution_providers/qnn/preprocess.py](https://github.com/microsoft/onnxruntime/blob/23996bbbbe0406a5c8edbf6b7dbd71e5780d3f4b/onnxruntime/python/tools/quantization/execution_providers/qnn/preprocess.py#L16) and
+[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)
+for more information on available function parameters.
+
 
 ## 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.
diff --git a/images/qnn_ep_quant_workflow.png b/images/qnn_ep_quant_workflow.png
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HcmV?d00001


From 57be3c4221d52e5de039d5bb2061493299dc9fb9 Mon Sep 17 00:00:00 2001
From: adrianlizarraga <adlizarraga@microsoft.com>
Date: Mon, 5 Feb 2024 17:01:24 -0800
Subject: [PATCH 02/14] Update image size. Update python language tags

---
 docs/execution-providers/QNN-ExecutionProvider.md | 7 ++++---
 1 file changed, 4 insertions(+), 3 deletions(-)

diff --git a/docs/execution-providers/QNN-ExecutionProvider.md b/docs/execution-providers/QNN-ExecutionProvider.md
index 9e2b7c3bfb498..0266c5e6c37e4 100644
--- a/docs/execution-providers/QNN-ExecutionProvider.md
+++ b/docs/execution-providers/QNN-ExecutionProvider.md
@@ -80,9 +80,10 @@ The QNN Execution Provider supports a number of configuration options. The `prov
 
 ## Running a model with QNN EP's HTP backend (Python)
 The QNN HTP backend, which offloads compute to the NPU, 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.
 
-<p align="center"><img width="50%" src="../../images/qnn_ep_quant_workflow.png" alt="Offline workflow for quantizing an ONNX model for use on QNN EP"/></p>
+<p align="center"><img width="90%" src="../../images/qnn_ep_quant_workflow.png" alt="Offline workflow for quantizing an ONNX model for use on QNN EP"/></p>
 
 ### Quantizing a model
 The ONNX Runtime python package provides utilities for quantizing ONNX models via the `onnxruntime.quantization` import. Note that the quantization utilities are currently only supported on x86_64.
@@ -97,7 +98,7 @@ python -m pip install -i https://aiinfra.pkgs.visualstudio.com/PublicPackages/_p
 Model quantization for QNN EP requires the use of calibration input data to compute quantization parameters for all activations and weights in the model. 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 `CalibrationDataReader` class that provides calibration data to the quantization utilies. Note, however, that the following example uses random inputs as the calibration dataset only for simplicity. Using random input data results in inaccurate models in practice.
 
-```Python3
+```python
 # data_reader.py
 
 import numpy as np
@@ -153,7 +154,7 @@ class DataReader(CalibrationDataReader):
 
 The following snippet pre-processes the original model and then quantizes the pre-processed model using the above `CalibrationDataReader` class.
 
-```Python3
+```python
 # quantize_model.py
 
 import data_reader

From 9a297b421a453a1347d41621b6964a522daedf5f Mon Sep 17 00:00:00 2001
From: adrianlizarraga <adlizarraga@microsoft.com>
Date: Mon, 5 Feb 2024 17:05:02 -0800
Subject: [PATCH 03/14] Add list

---
 docs/execution-providers/QNN-ExecutionProvider.md | 7 ++++---
 1 file changed, 4 insertions(+), 3 deletions(-)

diff --git a/docs/execution-providers/QNN-ExecutionProvider.md b/docs/execution-providers/QNN-ExecutionProvider.md
index 0266c5e6c37e4..efd0bfe2e82e9 100644
--- a/docs/execution-providers/QNN-ExecutionProvider.md
+++ b/docs/execution-providers/QNN-ExecutionProvider.md
@@ -186,9 +186,10 @@ if __name__ == "__main__":
 ```
 
 Running `python quantize_model.py` will generate a quantized model (`model.qdq.onnx`) that can be run on Windows ARM64 devices via ONNX Runtime's QNN EP.
-Refer to [quantization/execution_providers/qnn/preprocess.py](https://github.com/microsoft/onnxruntime/blob/23996bbbbe0406a5c8edbf6b7dbd71e5780d3f4b/onnxruntime/python/tools/quantization/execution_providers/qnn/preprocess.py#L16) and
-[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)
-for more information on available function parameters.
+
+Refer to the following pages for more information on API usage:
+- [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)
 
 
 ## QNN context binary cache feature

From a5f6569d39ee457133f2249c0c4fd2f1b919e83d Mon Sep 17 00:00:00 2001
From: adrianlizarraga <adlizarraga@microsoft.com>
Date: Mon, 5 Feb 2024 17:33:32 -0800
Subject: [PATCH 04/14] Simplify imports

---
 .../QNN-ExecutionProvider.md                  | 54 +++++++++----------
 1 file changed, 25 insertions(+), 29 deletions(-)

diff --git a/docs/execution-providers/QNN-ExecutionProvider.md b/docs/execution-providers/QNN-ExecutionProvider.md
index efd0bfe2e82e9..e2e71c691af20 100644
--- a/docs/execution-providers/QNN-ExecutionProvider.md
+++ b/docs/execution-providers/QNN-ExecutionProvider.md
@@ -83,44 +83,29 @@ The QNN HTP backend, which offloads compute to the NPU, only supports quantized
 
 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.
 
-<p align="center"><img width="90%" src="../../images/qnn_ep_quant_workflow.png" alt="Offline workflow for quantizing an ONNX model for use on QNN EP"/></p>
+<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>
 
-### Quantizing a model
-The ONNX Runtime python package provides utilities for quantizing ONNX models via the `onnxruntime.quantization` import. Note that the quantization utilities are currently only supported on x86_64.
+### 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.
 Therefore, it is recommend to either use an x64 machine to quantize models or, alternatively, use a separate x64 python installation on Windows ARM64 machines.
 
-Install the ONNX Runtime x64 python package. We currently recommend installing the nightly version of ONNX Runtime to get the latest updates to the quantization utilities.
+Install the nightly ONNX Runtime x64 python package.
 ```shell
-## Install nightly ORT built from main branch
 python -m pip install -i https://aiinfra.pkgs.visualstudio.com/PublicPackages/_packaging/ORT-Nightly/pypi/simple/ ort-nightly
 ```
 
-Model quantization for QNN EP requires the use of calibration input data to compute quantization parameters for all activations and weights in the model. 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 `CalibrationDataReader` class that provides calibration data to the quantization utilies. Note, however, that the following example uses random inputs as the calibration dataset only for simplicity. Using random input data results in inaccurate models in practice.
+Model 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 `CalibrationDataReader` class that generates random float32 input data. Note, that using random input data will most likely produce an inaccurate quantized model.
 
 ```python
 # data_reader.py
 
 import numpy as np
 import onnxruntime
-import os
 from onnxruntime.quantization import CalibrationDataReader
 
 
-NP_TYPE = {
-    "tensor(float)": np.float32,
-    "tensor(uint8)": np.uint8,
-    "tensor(int8)": np.int8,
-    "tensor(uint16)": np.uint16,
-    "tensor(int16)": np.int16,
-}
-
-def get_np_type(onnx_type):
-    if onnx_type in NP_TYPE:
-        return NP_TYPE[onnx_type]
-    else:
-        raise Exception("Unhandled onnx_type in np_type_from_onnx_type")
-
 class DataReader(CalibrationDataReader):
     def __init__(self, model_path: str):
         self.enum_data = None
@@ -132,10 +117,10 @@ class DataReader(CalibrationDataReader):
 
         self.data_list = []
 
-        # Generate 10 random inputs
-        # TODO: Load valid calibration input data
+        # 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(get_np_type(inp.type)) for inp in inputs}
+            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)
@@ -152,7 +137,8 @@ class DataReader(CalibrationDataReader):
 
 ```
 
-The following snippet pre-processes the original model and then quantizes the pre-processed model using the above `CalibrationDataReader` class.
+The following snippet pre-processes the original model and then quantizes the pre-processed model to use `uint16` activations and `uint8` weights.
+QNN EP currently supports the `uint8` and `uint16` quantization data types.
 
 ```python
 # quantize_model.py
@@ -160,8 +146,7 @@ The following snippet pre-processes the original model and then quantizes the pr
 import data_reader
 import numpy as np
 import onnx
-import onnxruntime
-from onnxruntime.quantization import QuantFormat, QuantType, quantize
+from onnxruntime.quantization import QuantType, quantize
 from onnxruntime.quantization.execution_providers.qnn import get_qnn_qdq_config, qnn_preprocess_model
 
 if __name__ == "__main__":
@@ -185,12 +170,23 @@ if __name__ == "__main__":
     quantize(model_to_quantize, output_model_path, qnn_config)
 ```
 
-Running `python quantize_model.py` will generate a quantized model (`model.qdq.onnx`) that can be run on Windows ARM64 devices via ONNX Runtime's QNN EP.
+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 API usage:
 - [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 QNN HTP backend can execute quantized models on Windows ARM64 devices with Qualcomm chipsets.
+
+Install the nightly ONNX Runtime ARM64 python package with QNN EP.
+```shell
+python -m pip install -i https://aiinfra.pkgs.visualstudio.com/PublicPackages/_packaging/ORT-Nightly/pypi/simple/ ort-nightly-qnn
+```
+
+Download the Qualcomm AI Engine Direct SDK (QNN SDK) from https://qpm.qualcomm.com/main/tools/details/qualcomm_ai_engine_direct. ONNX Runtime's QNN EP is currently compatible with QNN SDK version 2.18.
+
+
 
 ## 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.

From 5b9195e7dd1ff14c29216621d6cc45ef044b8172 Mon Sep 17 00:00:00 2001
From: adrianlizarraga <adlizarraga@microsoft.com>
Date: Tue, 6 Feb 2024 00:35:57 -0800
Subject: [PATCH 05/14] Update provider options. Add snippet for running QDQ
 model on HTP backend.

---
 .../QNN-ExecutionProvider.md                  | 102 +++++++++++++++---
 1 file changed, 90 insertions(+), 12 deletions(-)

diff --git a/docs/execution-providers/QNN-ExecutionProvider.md b/docs/execution-providers/QNN-ExecutionProvider.md
index e2e71c691af20..d008743d12b15 100644
--- a/docs/execution-providers/QNN-ExecutionProvider.md
+++ b/docs/execution-providers/QNN-ExecutionProvider.md
@@ -33,24 +33,28 @@ For build instructions, please see the [BUILD page](../build/eps.md#qnn).
 [prebuilt NuGet package](https://www.nuget.org/packages/Microsoft.ML.OnnxRuntime.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'||
@@ -62,8 +66,12 @@ 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.|
@@ -71,13 +79,29 @@ The QNN Execution Provider supports a number of configuration options. The `prov
 |'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).|
+
 ## Running a model with QNN EP's HTP backend (Python)
 The QNN HTP backend, which offloads compute to the NPU, 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).
 
@@ -96,7 +120,7 @@ python -m pip install -i https://aiinfra.pkgs.visualstudio.com/PublicPackages/_p
 
 Model 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 `CalibrationDataReader` class that generates random float32 input data. Note, that using random input data will most likely produce an inaccurate quantized model.
+The following snippet defines a sample `CalibrationDataReader` class that generates random float32 input data. Note that using random input data will most likely produce an inaccurate quantized model.
 
 ```python
 # data_reader.py
@@ -177,16 +201,70 @@ Refer to the following pages for more information on API usage:
 - [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 QNN HTP backend can execute quantized models on Windows ARM64 devices with Qualcomm chipsets.
+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`.
+
+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 C:\Qualcomm\AIStack\QNN\2.18.0.240101\lib\hexagon-v73\unsigned\libQnnHtpV73Skel.so C:\Qualcomm\AIStack\QNN\2.18.0.240101\lib\aarch64-windows-msvc\
+```
 
-Install the nightly ONNX Runtime ARM64 python package with QNN EP.
+Add the `<QNN_SDK>\lib\aarch64-windows-msvc\` directory 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:
 ```shell
 python -m pip install -i https://aiinfra.pkgs.visualstudio.com/PublicPackages/_packaging/ORT-Nightly/pypi/simple/ ort-nightly-qnn
 ```
 
-Download the Qualcomm AI Engine Direct SDK (QNN SDK) from https://qpm.qualcomm.com/main/tools/details/qualcomm_ai_engine_direct. ONNX Runtime's QNN EP is currently compatible with QNN SDK version 2.18.
+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((1,2,1,4), dtype=np.float32)
+result = session.run(None, {"input0": input0})
+
+# Print output.
+print(result)
+```
+
+Running `python run_qdq_model.py` will execute the quantized 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 to check 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 EP context feature to dump a cached version of the model in order to decrease session creation time.
 
+Also, the above snippet only specifies the `backend_path` provider option, which denotes the path to the QNN SDK HTP dll. Refer to the [Configuration options section](./#configuration-options) for an list of all 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.

From 40b3a5533ce124a84e0d3da78f0d7add37c25d8f Mon Sep 17 00:00:00 2001
From: adrianlizarraga <adlizarraga@microsoft.com>
Date: Tue, 6 Feb 2024 00:59:08 -0800
Subject: [PATCH 06/14] More clean up

---
 .../QNN-ExecutionProvider.md                  | 35 ++++++++++---------
 1 file changed, 18 insertions(+), 17 deletions(-)

diff --git a/docs/execution-providers/QNN-ExecutionProvider.md b/docs/execution-providers/QNN-ExecutionProvider.md
index d008743d12b15..31535dd9ca0d1 100644
--- a/docs/execution-providers/QNN-ExecutionProvider.md
+++ b/docs/execution-providers/QNN-ExecutionProvider.md
@@ -103,24 +103,24 @@ The QNN Execution Provider supports a number of configuration options. These pro
 |Device ID (string)|The ID of the device to use when setting `htp_arch`. Defaults to "0" (for single device).|
 
 ## Running a model with QNN EP's HTP backend (Python)
-The QNN HTP backend, which offloads compute to the NPU, 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).
+<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>
 
-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.
+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).
 
-<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>
+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.
 
 ### 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.
-Therefore, it is recommend to either use an x64 machine to quantize models or, alternatively, use a separate x64 python installation on Windows ARM64 machines.
+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
 ```
 
-Model 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.
+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 `CalibrationDataReader` class that generates random float32 input data. Note that using random input data will most likely produce an inaccurate 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.
 
 ```python
 # data_reader.py
@@ -162,7 +162,7 @@ class DataReader(CalibrationDataReader):
 ```
 
 The following snippet pre-processes the original model and then quantizes the pre-processed model to use `uint16` activations and `uint8` weights.
-QNN EP currently supports the `uint8` and `uint16` quantization data types.
+QNN EP typically supports the `uint8`, and `uint16` quantization 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 for each QNN operator.
 
 ```python
 # quantize_model.py
@@ -196,30 +196,31 @@ if __name__ == "__main__":
 
 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 API usage:
+Refer to the following pages for more information on quantization utilities usage:
+- [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`.
+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
+- 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:
+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 C:\Qualcomm\AIStack\QNN\2.18.0.240101\lib\hexagon-v73\unsigned\libQnnHtpV73Skel.so C:\Qualcomm\AIStack\QNN\2.18.0.240101\lib\aarch64-windows-msvc\
+cp QNN_SDK\lib\hexagon-v73\unsigned\libQnnHtpV73Skel.so QNN_SDK\lib\aarch64-windows-msvc\
 ```
 
-Add the `<QNN_SDK>\lib\aarch64-windows-msvc\` directory your Windows PATH environment variable:
+Add the `QNN_SDK\lib\aarch64-windows-msvc\` directory 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\`
+- Add a new entry that points to `QNN_SDK\lib\aarch64-windows-msvc\`
 ```
 
 Install the nightly ONNX Runtime ARM64 python package for QNN EP:
@@ -257,14 +258,14 @@ result = session.run(None, {"input0": input0})
 print(result)
 ```
 
-Running `python run_qdq_model.py` will execute the quantized model on the QNN HTP backend.
+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 to check 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 EP context feature to dump a cached version of the model in order to decrease session creation time.
 
-Also, the above snippet only specifies the `backend_path` provider option, which denotes the path to the QNN SDK HTP dll. Refer to the [Configuration options section](./#configuration-options) for an list of all QNN EP provider options.
+Also, 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.

From d155202207aaf939ccdb72d75954a9f41bfd85c6 Mon Sep 17 00:00:00 2001
From: adrianlizarraga <adlizarraga@microsoft.com>
Date: Tue, 6 Feb 2024 01:24:21 -0800
Subject: [PATCH 07/14] Add python snippets for QNN context cache docs

---
 .../QNN-ExecutionProvider.md                  | 30 ++++++++++++++++---
 1 file changed, 26 insertions(+), 4 deletions(-)

diff --git a/docs/execution-providers/QNN-ExecutionProvider.md b/docs/execution-providers/QNN-ExecutionProvider.md
index 31535dd9ca0d1..3ba9e7ed4e948 100644
--- a/docs/execution-providers/QNN-ExecutionProvider.md
+++ b/docs/execution-providers/QNN-ExecutionProvider.md
@@ -30,7 +30,10 @@ 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)
+
+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): `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. These provider options are specified as key-value string pairs.
@@ -162,7 +165,8 @@ class DataReader(CalibrationDataReader):
 ```
 
 The following snippet pre-processes the original model and then quantizes the pre-processed model to use `uint16` activations and `uint8` weights.
-QNN EP typically supports the `uint8`, and `uint16` quantization 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 for each QNN operator.
+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
@@ -215,7 +219,7 @@ Copy the `.so` file `QNN_SDK\lib\hexagon-v<HTP_ARCH>\unsigned\libQnnHtpV<HTP_ARC
 cp QNN_SDK\lib\hexagon-v73\unsigned\libQnnHtpV73Skel.so QNN_SDK\lib\aarch64-windows-msvc\
 ```
 
-Add the `QNN_SDK\lib\aarch64-windows-msvc\` directory your Windows PATH environment variable:
+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`.
@@ -263,7 +267,7 @@ Running `python run_qdq_model.py` will execute the quantized `model.qdq.onnx` mo
 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 to check 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 EP context feature to dump a cached version of the model in order to decrease session creation time.
+- [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.
 
 Also, 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.
 
@@ -294,6 +298,14 @@ 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:
 
@@ -305,6 +317,11 @@ 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.
 
@@ -316,6 +333,11 @@ 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).

From d0dbddac160b3e0a938586a684eb1f86ac838d8c Mon Sep 17 00:00:00 2001
From: adrianlizarraga <adlizarraga@microsoft.com>
Date: Tue, 6 Feb 2024 01:33:49 -0800
Subject: [PATCH 08/14] More cleanup

---
 docs/execution-providers/QNN-ExecutionProvider.md | 7 ++++---
 1 file changed, 4 insertions(+), 3 deletions(-)

diff --git a/docs/execution-providers/QNN-ExecutionProvider.md b/docs/execution-providers/QNN-ExecutionProvider.md
index 3ba9e7ed4e948..f39a26a6b752a 100644
--- a/docs/execution-providers/QNN-ExecutionProvider.md
+++ b/docs/execution-providers/QNN-ExecutionProvider.md
@@ -124,6 +124,7 @@ python -m pip install -i https://aiinfra.pkgs.visualstudio.com/PublicPackages/_p
 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
@@ -200,7 +201,7 @@ if __name__ == "__main__":
 
 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 quantization utilities usage:
+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)
@@ -264,12 +265,12 @@ 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 to check that the quantized model is fully supported by QNN EP.
+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.
 
-Also, 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.
+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.

From b7318e9b1dac2120a31bfd455f871f4b76e8a092 Mon Sep 17 00:00:00 2001
From: adrianlizarraga <adlizarraga@microsoft.com>
Date: Tue, 6 Feb 2024 10:16:44 -0800
Subject: [PATCH 09/14] Add requirements for python arm64 package

---
 docs/execution-providers/QNN-ExecutionProvider.md | 12 +++++++++---
 1 file changed, 9 insertions(+), 3 deletions(-)

diff --git a/docs/execution-providers/QNN-ExecutionProvider.md b/docs/execution-providers/QNN-ExecutionProvider.md
index f39a26a6b752a..e5e27deb8ad50 100644
--- a/docs/execution-providers/QNN-ExecutionProvider.md
+++ b/docs/execution-providers/QNN-ExecutionProvider.md
@@ -31,9 +31,15 @@ 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).
 
+## 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): `python -m pip install -i https://aiinfra.pkgs.visualstudio.com/PublicPackages/_packaging/ORT-Nightly/pypi/simple/ ort-nightly-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. These provider options are specified as key-value string pairs.
@@ -113,7 +119,7 @@ The QNN HTP backend only supports quantized models. Models with 32-bit floating-
 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.
 
 ### 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.
+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 install 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.
@@ -228,7 +234,7 @@ Add the `QNN_SDK\lib\aarch64-windows-msvc\` directory to your Windows PATH envir
 - Add a new entry that points to `QNN_SDK\lib\aarch64-windows-msvc\`
 ```
 
-Install the nightly ONNX Runtime ARM64 python package for QNN EP:
+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
 ```

From 5ed9b17e13a97464ed38cdd91643b506035265a4 Mon Sep 17 00:00:00 2001
From: adrianlizarraga <adlizarraga@microsoft.com>
Date: Tue, 6 Feb 2024 10:20:50 -0800
Subject: [PATCH 10/14] Fix typo

---
 docs/execution-providers/QNN-ExecutionProvider.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/execution-providers/QNN-ExecutionProvider.md b/docs/execution-providers/QNN-ExecutionProvider.md
index e5e27deb8ad50..1a97e6ae22ecd 100644
--- a/docs/execution-providers/QNN-ExecutionProvider.md
+++ b/docs/execution-providers/QNN-ExecutionProvider.md
@@ -119,7 +119,7 @@ The QNN HTP backend only supports quantized models. Models with 32-bit floating-
 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.
 
 ### 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 install the `onnx` package on ARM64.
+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.

From f1b0a99cfd73e5df382a73273478f98ea3cc2a13 Mon Sep 17 00:00:00 2001
From: adrianlizarraga <adlizarraga@microsoft.com>
Date: Tue, 6 Feb 2024 11:30:53 -0800
Subject: [PATCH 11/14] Add list of supported operators; Add info about dynamic
 shapes

---
 .../QNN-ExecutionProvider.md                  | 94 ++++++++++++++++++-
 1 file changed, 91 insertions(+), 3 deletions(-)

diff --git a/docs/execution-providers/QNN-ExecutionProvider.md b/docs/execution-providers/QNN-ExecutionProvider.md
index 1a97e6ae22ecd..e46c03c4cbead 100644
--- a/docs/execution-providers/QNN-ExecutionProvider.md
+++ b/docs/execution-providers/QNN-ExecutionProvider.md
@@ -111,6 +111,90 @@ The QNN Execution Provider supports a number of configuration options. These pro
 |---|---|
 |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 support positive indices|
+|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: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>
 
@@ -118,6 +202,10 @@ The QNN HTP backend only supports quantized models. Models with 32-bit floating-
 
 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.
@@ -191,7 +279,7 @@ if __name__ == "__main__":
 
     # Pre-process the original float32 model.
     preproc_model_path = "model.preproc.onnx"
-    model_changed = qnn_preprocess_model(input_model_path, prepoc_model_path)
+    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.
@@ -262,8 +350,8 @@ session = onnxruntime.InferenceSession("model.qdq.onnx",
 
 # Run the model with your input.
 # TODO: Use numpy to load your actual input from a file or generate random input.
-input0 = np.ones((1,2,1,4), dtype=np.float32)
-result = session.run(None, {"input0": input0})
+input0 = np.ones((1,3,224,224), dtype=np.float32)
+result = session.run(None, {"input": input0})
 
 # Print output.
 print(result)

From 6804732650373103d82956deba38eba70dc472ad Mon Sep 17 00:00:00 2001
From: adrianlizarraga <adlizarraga@microsoft.com>
Date: Tue, 6 Feb 2024 11:36:26 -0800
Subject: [PATCH 12/14] Add new line

---
 docs/execution-providers/QNN-ExecutionProvider.md | 1 +
 1 file changed, 1 insertion(+)

diff --git a/docs/execution-providers/QNN-ExecutionProvider.md b/docs/execution-providers/QNN-ExecutionProvider.md
index e46c03c4cbead..ae0a4bb5d2d4c 100644
--- a/docs/execution-providers/QNN-ExecutionProvider.md
+++ b/docs/execution-providers/QNN-ExecutionProvider.md
@@ -112,6 +112,7 @@ The QNN Execution Provider supports a number of configuration options. These pro
 |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||

From 155923782dc4be53874e7e7445418ced3cddd2a4 Mon Sep 17 00:00:00 2001
From: adrianlizarraga <adlizarraga@microsoft.com>
Date: Tue, 6 Feb 2024 11:44:07 -0800
Subject: [PATCH 13/14] Typo

---
 docs/execution-providers/QNN-ExecutionProvider.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/execution-providers/QNN-ExecutionProvider.md b/docs/execution-providers/QNN-ExecutionProvider.md
index ae0a4bb5d2d4c..b2c4d7e3dde6a 100644
--- a/docs/execution-providers/QNN-ExecutionProvider.md
+++ b/docs/execution-providers/QNN-ExecutionProvider.md
@@ -139,7 +139,7 @@ The QNN Execution Provider supports a number of configuration options. These pro
 |ai.onnx:Expand||
 |ai.onnx:Flatten||
 |ai.onnx:Floor||
-|ai.onnx:Gather|Only support positive indices|
+|ai.onnx:Gather|Only supports positive indices|
 |ai.onnx:Gemm||
 |ai.onnx:GlobalAveragePool||
 |ai.onnx:Greater||

From 5af86cc86586abd0d629339a9f5dbb3fbcd3f5fb Mon Sep 17 00:00:00 2001
From: adrianlizarraga <adlizarraga@microsoft.com>
Date: Tue, 6 Feb 2024 11:47:32 -0800
Subject: [PATCH 14/14] Add Gelu

---
 docs/execution-providers/QNN-ExecutionProvider.md | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/docs/execution-providers/QNN-ExecutionProvider.md b/docs/execution-providers/QNN-ExecutionProvider.md
index b2c4d7e3dde6a..6ad125c231bef 100644
--- a/docs/execution-providers/QNN-ExecutionProvider.md
+++ b/docs/execution-providers/QNN-ExecutionProvider.md
@@ -140,6 +140,7 @@ The QNN Execution Provider supports a number of configuration options. These pro
 |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||
@@ -192,6 +193,7 @@ The QNN Execution Provider supports a number of configuration options. These pro
 |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.