🐌 SNPE env and example fix

examples/snpe/inception_snpe_qualcomm_npu/README.md

@@ -10,11 +10,16 @@ Outputs a summary of the accuracy and latency metrics for each SNPE model.
 ### Download and unzip SNPE SDK
 Download the SNPE SDK zip following [instructions from Qualcomm](https://developer.qualcomm.com/software/qualcomm-neural-processing-sdk)
 
+We test it with SNPE v2.18.0.240101.
+
 Unzip the file and set the unzipped directory path as environment variable `SNPE_ROOT`
 
 ### Configure SNPE
-```
-python -m olive.snpe.configure
+```sh
+# in general, python 3.8 is recommended
+python -m olive.snpe.configure --py_version 3.8
+# only when the tensorflow 1.15.0 is needed, use python 3.6
+python -m olive.snpe.configure --py_version 3.6
 ```
 
 ### Pip requirements

examples/snpe/inception_snpe_qualcomm_npu/inception_config.json

@@ -1,5 +1,5 @@
 {
-    "input_model":{
+    "input_model": {
         "type": "TensorFlowModel",
         "config": {
             "model_path": "models/inception_v3.pb"
@@ -17,8 +17,17 @@
             },
             "params_config": {
                 "data_dir": "data",
-                "input_names": ["input"],
-                "input_shapes": [[1, 299, 299, 3]],
+                "input_names": [
+                    "input"
+                ],
+                "input_shapes": [
+                    [
+                        1,
+                        299,
+                        299,
+                        3
+                    ]
+                ],
                 "input_order_file": "input_order.txt",
                 "annotations_file": "labels.npy",
                 "batch_size": 7
@@ -27,14 +36,22 @@
     },
     "evaluators": {
         "common_evaluator": {
-            "metrics":[
+            "metrics": [
                 {
                     "name": "accuracy",
                     "type": "accuracy",
                     "sub_types": [
-                        {"name": "accuracy_score", "priority": 1}
+                        {
+                            "name": "accuracy_score",
+                            "priority": 1,
+                            "metric_config": {
+                                "task": "multiclass",
+                                "num_classes": "100",
+                                "top_k": 1
+                            }
+                        }
                     ],
-                    "user_config":{
+                    "user_config": {
                         "inference_settings": {
                             "snpe": {
                                 "return_numpy_results": true
@@ -47,7 +64,15 @@
                     "name": "latency",
                     "type": "latency",
                     "sub_types": [
-                        {"name": "avg", "priority": 2, "metric_config": {"warmup_num": 0, "repeat_test_num": 5, "sleep_num": 2}}
+                        {
+                            "name": "avg",
+                            "priority": 2,
+                            "metric_config": {
+                                "warmup_num": 0,
+                                "repeat_test_num": 5,
+                                "sleep_num": 2
+                            }
+                        }
                     ],
                     "user_config": {
                         "inference_settings": {
@@ -67,9 +92,20 @@
         "snpe_conversion": {
             "type": "SNPEConversion",
             "config": {
-                "input_names": ["input"],
-                "input_shapes": [[1, 299, 299, 3]],
-                "output_names": ["InceptionV3/Predictions/Reshape_1"]
+                "input_names": [
+                    "input"
+                ],
+                "input_shapes": [
+                    [
+                        1,
+                        299,
+                        299,
+                        3
+                    ]
+                ],
+                "output_names": [
+                    "InceptionV3/Predictions/Reshape_1"
+                ]
             }
         },
         "snpe_quantization": {
@@ -82,9 +118,10 @@
     },
     "engine": {
         "search_strategy": false,
+        "evaluate_input_model": false,
         "evaluator": "common_evaluator",
         "cache_dir": "cache",
-        "output_dir" : "outputs",
+        "output_dir": "outputs",
         "output_name": "snpe_quantized"
     }
 }

examples/snpe/vgg_snpe_qualcomm_npu/README.md

@@ -8,11 +8,16 @@ Performs optimization pipeline:
 ### Download and unzip SNPE SDK
 Download the SNPE SDK zip following [instructions from Qualcomm](https://developer.qualcomm.com/software/qualcomm-neural-processing-sdk)
 
+We test it with SNPE v2.18.0.240101.
+
 Unzip the file and set the unzipped directory path as environment variable `SNPE_ROOT`.
 
 ### Configure SNPE
-```
-python -m olive.snpe.configure
+```sh
+# in general, python 3.8 is recommended
+python -m olive.snpe.configure --py_version 3.8
+# only when the tensorflow 1.15.0 is needed, use python 3.6
+python -m olive.snpe.configure --py_version 3.6
 ```
 
 ### Pip requirements
@@ -32,3 +37,12 @@ Run the conversion and quantization locally. Only supports `x64-Linux`.
 ```
 python -m olive.workflows.run --config vgg_config.json
 ```
+
+## Issues
+
+1. "Module 'qti.aisw.converters' has no attribute 'onnx':
+    Refer to this: https://developer.qualcomm.com/comment/21810#comment-21810,
+    change the import statement in `{SNPE_ROOT}/lib/python/qti/aisw/converters/onnx/onnx_to_ir.py:L30` to:
+    ```python
+    from qti.aisw.converters.onnx import composable_custom_op_utils as ComposableCustomOp
+    ```

examples/snpe/vgg_snpe_qualcomm_npu/vgg_config.json

@@ -39,7 +39,9 @@
         }
     },
     "engine": {
+        "log_severity_level": 0,
         "search_strategy": false,
+        "clean_cache": true,
         "cache_dir": "cache",
         "output_dir" : "outputs"
     }

examples/stable_diffusion/openvino/stable_diffusion.py

@@ -92,7 +92,8 @@ def __init__(
                 [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
                 the clip-vit-large-patch14(https://huggingface.co/openai/clip-vit-large-patch14) variant.
             tokenizer (CLIPTokenizer):
-                Tokenizer of class CLIPTokenizer(https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
+                Tokenizer of class CLIPTokenizer
+                (https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
             unet (Model): Conditional U-Net architecture to denoise the encoded image latents.
             scheduler (SchedulerMixin):
                 A scheduler to be used in combination with unet to denoise the encoded image latents. Can be one of

olive/evaluator/olive_evaluator.py

@@ -936,8 +936,8 @@ def _inference(
                 raise ValueError("Post processing function is required for SNPE model")
             preds.extend(outputs.tolist())
             targets.extend(labels.tolist())
-            # TODO(trajep): verify if we need to return logits
-            logits.extend(result.tolist())
+            lg = result["results"].get("logits")
+            logits.extend(lg.to_list() if lg else [])
         return OliveModelOutput(preds=preds, logits=logits), targets
 
     def _evaluate_accuracy(

olive/snpe/configure.py

@@ -2,6 +2,7 @@
 # Copyright (c) Microsoft Corporation. All rights reserved.
 # Licensed under the MIT License.
 # --------------------------------------------------------------------------
+import argparse
 import logging
 import shutil
 from importlib import resources
@@ -13,16 +14,16 @@
 logger = logging.getLogger("olive.snpe.configure")
 
 
-def dev():
+def dev(args):
     snpe_arch = get_snpe_target_arch(False)
     if snpe_arch != "x64-Linux":
         return
 
     get_snpe_root()
 
-    logger.info(f"Configuring SNPE for {snpe_arch}...")
-    with resources.path("olive.snpe", "create_python36_env.sh") as create_python36_env_path:
-        cmd = f"bash {create_python36_env_path}"
+    logger.info(f"Configuring SNPE for {snpe_arch} with python{args.py_version}...")
+    with resources.path("olive.snpe", "create_python_env.sh") as create_python_env_path:
+        cmd = f"bash {create_python_env_path} -v {args.py_version}"
         return_code, stdout, stderr = run_subprocess(cmd)
         if return_code != 0:
             raise RuntimeError(f"Failed to create python36 environment. stdout: {stdout}, stderr: {stderr}")
@@ -66,5 +67,15 @@ def eval():  # noqa: A001  #pylint: disable=redefined-builtin
 
 
 if __name__ == "__main__":
-    dev()
+    # create args for py_version
+    parser = argparse.ArgumentParser("Olive SNPE: Configure")
+    parser.add_argument(
+        "--py_version",
+        type=str,
+        help="Python version, use 3.6 for tensorflow 1.15. Otherwise 3.8",
+        required=True,
+        choices=["3.6", "3.8"],
+    )
+    args = parser.parse_args()
+    dev(args)
     eval()

olive/snpe/create_python_env.sh

@@ -0,0 +1,61 @@
+#!/usr/bin/env bash
+# -------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License.
+# --------------------------------------------------------------------------
+set -eux
+
+# This script creates a python 3.6 environment in $SNPE_ROOT/olive-pyenv
+# and installs the required packages for SNPE-v2.18.0.240101
+
+# Usage: ./create_python_env.sh -v/--version <python_version>
+while [[ "$#" -gt 0 ]]; do
+    key="$1"
+    case $key in
+        -v|--version)
+            PY_VERSION="$2"
+            shift
+            shift
+            ;;
+        *)
+            echo "Unknown option: $key"
+            exit 1
+            ;;
+    esac
+done
+
+PY_ENV_NAME=olive-pyenv
+FILES_DIR=$SNPE_ROOT/python-env-setup
+rm -rf "$FILES_DIR"
+mkdir "$FILES_DIR"
+
+# Install conda if not already installed
+if ! command -v conda; then
+    # Install conda
+    curl -fsSL -o "$FILES_DIR"/install_conda.sh https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh
+    sh "$FILES_DIR"/install_conda.sh -b -p "$FILES_DIR"/miniconda
+    CONDA=$FILES_DIR/miniconda/bin/conda
+else
+    CONDA=conda
+fi
+
+# Create python environment
+$CONDA create -y -p "$FILES_DIR"/$PY_ENV_NAME python="$PY_VERSION"
+
+# Install snpe requirements
+"$FILES_DIR"/$PY_ENV_NAME/bin/python -m pip install --upgrade pip
+if [ "$PY_VERSION" == "3.6" ]; then
+    "$FILES_DIR"/$PY_ENV_NAME/bin/python -m pip install onnx==1.11.0 onnx-simplifier packaging tensorflow==1.15.0 pyyaml
+elif [ "$PY_VERSION" == "3.8" ]; then
+    "$FILES_DIR"/$PY_ENV_NAME/bin/python -m pip install onnx onnx-simplifier packaging tensorflow pyyaml
+else
+    echo "Unsupported python version: $PY_VERSION, only 3.6 and 3.8 are supported"
+    exit 1
+fi
+
+
+rm -rf "${SNPE_ROOT:?}"/$PY_ENV_NAME
+mv "$FILES_DIR"/$PY_ENV_NAME "$SNPE_ROOT"/$PY_ENV_NAME
+
+# Remove all unnecessary files
+rm -rf "$FILES_DIR"

olive/snpe/utils/local.py

@@ -90,20 +90,22 @@ def get_snpe_env(dev: bool = False) -> dict:
 
     bin_path = str(Path(f"{snpe_root}/bin/{target_arch_name}"))
     lib_path = str(Path(f"{snpe_root}/lib/{target_arch_name}"))
+    python_env_bin_path = str(Path(f"{snpe_root}/olive-pyenv/bin"))
+    python_env_lib_path = str(Path(f"{snpe_root}/olive-pyenv/lib"))
 
     env = {}
     delimiter = os.path.pathsep
     if platform.system() == "Linux":
-        env["LD_LIBRARY_PATH"] = lib_path
         if dev:
-            python36_env_path = str(Path(f"{snpe_root}/python36-env/bin"))
-            if not Path(python36_env_path).exists():
+            if not Path(python_env_bin_path).exists():
                 raise FileNotFoundError(
-                    f"Path {python36_env_path} does not exist. Please run 'python -m olive.snpe.configure' to add the"
-                    " missing file"
+                    f"Path {python_env_bin_path} does not exist."
+                    " Please run 'python -m olive.snpe.configure' to add the missing file"
                 )
-            bin_path += delimiter + python36_env_path
+            bin_path += delimiter + python_env_bin_path
+            lib_path += delimiter + python_env_lib_path
             env["PYTHONPATH"] = str(Path(f"{snpe_root}/lib/python"))
+        env["LD_LIBRARY_PATH"] = lib_path
         bin_path += delimiter + "/usr/bin"
     elif platform.system() == "Windows":
         if target_arch == "ARM64-Windows":

setup.py

@@ -86,7 +86,7 @@ def get_extra_deps(rel_path):
         "olive": ["extra_dependencies.json"],
         "olive.auto_optimizer": ["config_template/*.yaml"],
         "olive.engine.packaging": ["sample_code/*/*/*"],
-        "olive.snpe": ["create_python36_env.sh", "copy_libcdsprpc.ps1"],
+        "olive.snpe": ["create_python_env.sh", "copy_libcdsprpc.ps1"],
         "olive.systems.docker": ["Dockerfile*"],
     },
     data_files=[],