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Add evaluators to the recipes
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Lines changed: 775 additions & 72 deletions

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‎google-bert-bert-base-multilingual-cased/QNN/README.md‎

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### QNN-GPU:
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Running QNN-GPU configs requires features and fixes that are not available in the released Olive version 0.9.3.
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To ensure compatibility, please install Olive directly from the source at the required commit:
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Please install Olive directly using:
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```bash
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pip install git+https://github.com/microsoft/Olive.git@da24463e14ed976503dc5871572b285bc5ddc4b2
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```
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If you previously installed Olive via PyPI or pinned it to version 0.9.3, please uninstall it first and then use the above
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commit to install:
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```bash
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pip uninstall olive-ai
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pip install olive-ai
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```
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To run the config:

‎google-bert-bert-base-multilingual-cased/QNN/config_gpu_fp32.json‎

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]
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}
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},
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"data_configs": [
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{
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"name": "evaluation_data_config",
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"type": "HuggingfaceContainer",
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"load_dataset_config": {
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"data_name": "facebook/xnli",
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"subset": "en",
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"split": "validation"
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},
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"pre_process_data_config": {
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"input_cols": [
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"premise"
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],
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"padding": "max_length",
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"max_length": 128,
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"max_samples": 10
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},
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"dataloader_config": {
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"batch_size": 1
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}
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}
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],
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"evaluators": {
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"common_evaluator": {
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"metrics": [
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{
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"name": "latency",
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"type": "latency",
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"data_config": "evaluation_data_config",
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"sub_types": [
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{
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"name": "avg",
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"priority": 1,
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"goal": {
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"type": "percent-min-improvement",
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"value": 0.1
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}
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},
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{
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"name": "max"
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},
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{
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"name": "min"
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}
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]
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},
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{
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"name": "throughput",
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"type": "throughput",
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"data_config": "evaluation_data_config",
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"sub_types": [
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{
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"name": "avg"
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},
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{
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"name": "max"
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},
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{
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"name": "min"
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}
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]
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}
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]
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}
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},
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"passes": {
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"conversion": {
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"type": "OnnxConversion",
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}
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},
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"target": "target_system",
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"evaluator": "common_evaluator",
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"cache_dir": "cache",
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"output_dir": "output",
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"evaluate_input_model": false,
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"clean_cache": true
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}
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}

‎google-vit-base-patch16-224/QNN/README.md‎

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2020
### QNN-GPU:
2121

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Running QNN-GPU configs requires features and fixes that are not available in the released Olive version 0.9.3.
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To ensure compatibility, please install Olive directly from the source at the required commit:
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Please install Olive directly using:
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```bash
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pip install git+https://github.com/microsoft/Olive.git@da24463e14ed976503dc5871572b285bc5ddc4b2
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```
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If you previously installed Olive via PyPI or pinned it to version 0.9.3, please uninstall it first and then use the above
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commit to install:
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```bash
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pip uninstall olive-ai
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pip install olive-ai
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```
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To run the config:

‎google-vit-base-patch16-224/QNN/config_gpu_fp32.json‎

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]
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}
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},
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"data_configs": [
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{
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"name": "evaluation_data_config",
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"type": "HuggingfaceContainer",
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"user_script": "vit-base-patch16-224.py",
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"load_dataset_config": {
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"data_name": "timm/mini-imagenet",
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"split": "validation",
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"streaming": true,
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"trust_remote_code": true
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},
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"pre_process_data_config": {
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"type": "dataset_pre_process",
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"size": 1000,
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"cache_key": "imagedata_evaluation"
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},
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"post_process_data_config": {
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"type": "dataset_post_process"
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}
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}
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],
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"evaluators": {
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"common_evaluator": {
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"metrics": [
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{
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"name": "accuracy",
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"type": "accuracy",
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"data_config": "evaluation_data_config",
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"sub_types": [
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{
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"name": "accuracy_score",
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"priority": 1,
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"metric_config": {
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"task": "multiclass",
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"num_classes": 1000
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}
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}
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]
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},
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{
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"name": "latency",
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"type": "latency",
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"data_config": "evaluation_data_config",
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"sub_types": [
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{
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"name": "avg",
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"priority": 2
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}
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]
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}
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]
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}
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},
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"passes": {
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"conversion": {
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"type": "OnnxConversion",
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}
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},
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"target": "target_system",
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"evaluator": "common_evaluator",
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"cache_dir": "cache",
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"output_dir": "output",
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"evaluate_input_model": false,

‎google-vit-base-patch16-224/QNN/vit-base-patch16-224.py‎

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# -------------------------------------------------------------------------
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# Copyright (c) Microsoft Corporation. All rights reserved.
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# Licensed under the MIT License.
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# --------------------------------------------------------------------------
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from logging import getLogger
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from pathlib import Path
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import numpy as np
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import torchvision.transforms as transforms
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import transformers
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from torch import from_numpy
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from torch.utils.data import Dataset
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from olive.data.registry import Registry
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logger = getLogger(__name__)
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def get_imagenet_label_map():
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import json
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cache_file = Path(f"./cache/data/imagenet_class_index.json")
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if not cache_file.exists():
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import requests
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imagenet_class_index_url = (
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"https://raw.githubusercontent.com/pytorch/vision/main/gallery/assets/imagenet_class_index.json"
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)
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response = requests.get(imagenet_class_index_url)
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response.raise_for_status() # Ensure the request was successful
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content = response.json()
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cache_file.parent.resolve().mkdir(parents=True, exist_ok=True)
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with open(cache_file, "w") as f:
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json.dump(content, f)
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else:
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with open(cache_file) as f:
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content = json.loads(f.read())
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return {v[0]: int(k) for k, v in content.items()}
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def adapt_label_for_mini_imagenet(labels: list, label_names: list):
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label_map = get_imagenet_label_map()
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return [label_map[label_names[x]] for x in labels]
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class ImagenetDataset(Dataset):
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def __init__(self, data):
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self.images = from_numpy(data["images"])
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self.labels = from_numpy(data["labels"])
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def __len__(self):
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return min(len(self.images), len(self.labels))
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def __getitem__(self, idx):
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return {"pixel_values": self.images[idx]}, self.labels[idx]
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@Registry.register_post_process()
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def dataset_post_process(output):
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return (
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output.logits.argmax(axis=1)
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if isinstance(output, transformers.modeling_outputs.ModelOutput)
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else output.argmax(axis=1)
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)
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from transformers import AutoImageProcessor
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processor = AutoImageProcessor.from_pretrained("google/vit-base-patch16-224", use_fast=True)
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@Registry.register_pre_process()
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def dataset_pre_process(output_data, **kwargs):
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shuffle = kwargs.get("shuffle", True)
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if shuffle:
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seed = kwargs.get("seed", 42)
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output_data = output_data.shuffle(seed=seed)
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cache_key = kwargs.get("cache_key")
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size = kwargs.get("size", 256)
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cache_file = None
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if cache_key:
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cache_file = Path(f"./cache/data/{cache_key}_{output_data.info.dataset_name}_{size}.npz")
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if cache_file.exists():
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with np.load(Path(cache_file)) as data:
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return ImagenetDataset(data)
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labels = []
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images = []
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for i, sample in enumerate(output_data):
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if i >= size:
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break
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image = sample["image"]
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label = sample["label"]
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image = image.convert("RGB")
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image = processor(image)["pixel_values"][0]
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images.append(image)
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labels.append(label)
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if(output_data.info.dataset_name == "mini-imagenet"):
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labels = adapt_label_for_mini_imagenet(labels, output_data.features["label"].names)
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result_data = ImagenetDataset({"images": np.array(images), "labels": np.array(labels)})
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if cache_file:
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cache_file.parent.resolve().mkdir(parents=True, exist_ok=True)
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np.savez(cache_file, images=np.array(images), labels=np.array(labels))
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return result_data

‎intel-bert-base-uncased-mrpc/QNN/README.md‎

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### QNN-GPU:
44

5-
Running QNN-GPU configs requires features and fixes that are not available in the released Olive version 0.9.3.
6-
To ensure compatibility, please install Olive directly from the source at the required commit:
5+
Please install Olive directly using:
76

87
```bash
9-
pip install git+https://github.com/microsoft/Olive.git@da24463e14ed976503dc5871572b285bc5ddc4b2
10-
```
11-
12-
If you previously installed Olive via PyPI or pinned it to version 0.9.3, please uninstall it first and then use the above
13-
commit to install:
14-
15-
```bash
16-
pip uninstall olive-ai
8+
pip install olive-ai
179
```
1810

1911
To run the config:

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