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hash verification for datasets used in vision transformer and neuralc…
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…hat finetuning workflow interface examples (#936)

* hash verification

Signed-off-by: kta-intel <[email protected]>

* seeding cell got removed, re-adding it

Signed-off-by: kta-intel <[email protected]>

* lint fix

Signed-off-by: kta-intel <[email protected]>

* sha384 and simplying hash computing

Signed-off-by: kta-intel <[email protected]>

* pep8 guideline fix

Signed-off-by: kta-intel <[email protected]>

---------

Signed-off-by: kta-intel <[email protected]>
Signed-off-by: manuelhsantana <[email protected]>
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kta-intel authored and manuelhsantana committed Jul 9, 2024
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63 changes: 26 additions & 37 deletions openfl-tutorials/experimental/LLM/neuralchat/Workflow_Interface_NeuralChat.ipynb
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Expand Up @@ -15,7 +15,7 @@
"id": "bd059520",
"metadata": {},
"source": [
"In this tutorial, we build on the ideas from the [first](https://github.com/intel/openfl/blob/develop/openfl-tutorials/experimental/Workflow_Interface_101_MNIST.ipynb) quick start notebook, and demonstrate how to fine-tune an LLM in a federated learning workflow. \n",
"In this tutorial, we build on the ideas from the [first](https://github.com/intel/openfl/blob/develop/openfl-tutorials/experimental/Workflow_Interface_101_MNIST.ipynb) quick start notebook, and demonstrate how to fine-tune a Large Language Model (LLM) in a federated learning workflow. \n",
"\n",
"We will fine-tune **Intel's [neural-chat-7b](https://huggingface.co/Intel/neural-chat-7b-v1)** model on the [MedQuAD](https://github.com/abachaa/MedQuAD) dataset, an open-source medical question-answer pair dataset collated from 12 NIH websites. To do this, we will leverage the **[Intel(R) Extension for Transformers](https://github.com/intel/intel-extension-for-transformers)**, which extends th [Hugging Face Transformers](https://github.com/huggingface/transformers) library with added features for optimal performance on Intel hardware.."
]
Expand All @@ -42,44 +42,30 @@
"metadata": {},
"source": [
"## Initial Setup\n",
"### Installing dependencies\n",
"Start by installing Intel(R) Extension for Transformers (for stability, we will use v1.2.2) and OpenFL"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "56f4628e-7a1b-4576-bf6e-637757b2726d",
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"!pip install intel-extension-for-transformers==1.2.2\n",
"### Installing Intel(R) Extension for Transformers*\n",
"- Start by installing Intel(R) Extension for Transformers* and the required dependencies for the Neural Chat framework. \n",
"For successful installation, please follow the steps outlined in the [Installation Guide](https://github.com/intel/intel-extension-for-transformers/tree/main/intel_extension_for_transformers/neural_chat#installation).\n",
"- For additional information, please refer to the [Official Documentation](https://intel.github.io/intel-extension-for-transformers/latest/docs/Welcome.html)\n",
"\n",
"# Requirements to run neuralchat on CPU\n",
"!wget https://raw.githubusercontent.com/intel/intel-extension-for-transformers/v1.2.2/intel_extension_for_transformers/neural_chat/requirements_cpu.txt\n",
"!pip install -r requirements_cpu.txt"
"*Note: This Jupyter Notebook has been tested and confirmed to work with `intel-extension-for-transformers==1.2.2`*"
]
},
{
"cell_type": "markdown",
"id": "124ae236-2e33-4349-9979-f506d796276d",
"metadata": {},
"source": [
"From here, we can install requirements needed to run OpenFL's workflow interface and Intel(R) Extension for Transformer's Neural Chat framework"
"### Installing OpenFL\n",
"- Lets now install OpenFL and the necessary dependencies for the workflow interface by running the cell below:"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "63207a15-e1e3-4b7a-8a85-53618f8ec8ef",
"metadata": {
"scrolled": true
},
"id": "c808dd12-6795-4203-9221-0f6b43fc785f",
"metadata": {},
"outputs": [],
"source": [
"# Requirements to run workflow interface\n",
"!pip install git+https://github.com/intel/openfl.git\n",
"!pip install -r ../../requirements_workflow_interface.txt\n",
"!pip install numpy --upgrade"
Expand All @@ -101,9 +87,18 @@
"metadata": {},
"outputs": [],
"source": [
"!rm -rf MedQuAD\n",
"!git clone https://github.com/abachaa/MedQuAD.git"
]
},
{
"cell_type": "markdown",
"id": "98014201-01b6-4726-b483-6d7101a3aa51",
"metadata": {},
"source": [
"From here, we provide a preprocessing code to verify the dataset and prepare it to be readily ingestible by the fine-tuning pipeline"
]
},
{
"cell_type": "code",
"execution_count": null,
Expand All @@ -115,17 +110,11 @@
"\n",
"# User input for folder paths\n",
"input_base_folder = \"./MedQuAD/\"\n",
"subfolders = [\"1_CancerGov_QA\", \"2_GARD_QA\", \"3_GHR_QA\", \"4_MPlus_Health_Topics_QA\",\n",
" \"5_NIDDK_QA\", \"6_NINDS_QA\", \"7_SeniorHealth_QA\", \"8_NHLBI_QA_XML\", \"9_CDC_QA\"]\n",
"output_folder = \"./\"\n",
"\n",
"xml_to_json(input_base_folder, output_folder)"
]
},
{
"cell_type": "markdown",
"id": "98014201-01b6-4726-b483-6d7101a3aa51",
"metadata": {},
"source": [
"From here, we provide a preprocessing code to prepare the dataset to be readily ingestible by the fine-tuning pipeline"
"xml_to_json(input_base_folder, subfolders, output_folder, verify_hash=1)"
]
},
{
Expand Down Expand Up @@ -170,7 +159,7 @@
{
"cell_type": "code",
"execution_count": null,
"id": "1d5b078f-599a-4264-b575-9d15e14afb7e",
"id": "c9aa89c7-76f7-49a1-a50b-b4a8cabe22d3",
"metadata": {},
"outputs": [],
"source": [
Expand Down Expand Up @@ -214,7 +203,7 @@
"\n",
"data_args = DataArguments(\n",
" train_file=\"medquad_alpaca_train.json\",\n",
" validation_split_percentage=20\n",
" validation_split_percentage=20,\n",
")\n",
"\n",
"training_args = TrainingArguments(\n",
Expand Down Expand Up @@ -660,9 +649,9 @@
],
"metadata": {
"kernelspec": {
"display_name": "NeuralChat Finetune",
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "neuralchat_finetune"
"name": "python3"
},
"language_info": {
"codemirror_mode": {
Expand Down
49 changes: 46 additions & 3 deletions openfl-tutorials/experimental/LLM/neuralchat/preprocess_dataset.py
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Expand Up @@ -5,9 +5,10 @@
import json
import os
import math
import hashlib


def xml_to_json(input_base_folder, output_folder):
def xml_to_json(input_base_folder, subfolders, output_folder, verify_hash=1):

if not os.path.exists(input_base_folder):
raise SystemExit(f"The folder '{input_base_folder}' does not exist.")
Expand All @@ -16,8 +17,11 @@ def xml_to_json(input_base_folder, output_folder):
test_data = []
train_count, test_count = 0, 0

subfolders = ["1_CancerGov_QA", "2_GARD_QA", "3_GHR_QA", "4_MPlus_Health_Topics_QA",
"5_NIDDK_QA", "6_NINDS_QA", "7_SeniorHealth_QA", "8_NHLBI_QA_XML", "9_CDC_QA"]
if verify_hash == 1:
expected_hash = ('9d645c469ba37eb9ec2e121ae6ac90fbebccfb91f2aff7f'
'faabc0531f2ede54ab4c91bea775922e5910b276340c040e8')
verify_aggregated_hashes(input_base_folder, subfolders,
expected_hash=expected_hash)

for subfolder in subfolders:
folder_path = os.path.join(input_base_folder, subfolder)
Expand All @@ -36,6 +40,8 @@ def xml_to_json(input_base_folder, output_folder):
new_data, count = process_xml_file(folder_path, xml_file)
test_data.extend(new_data)
test_count += count
else:
raise SystemError(f"{folder_path} does not exist")

# Save the data to JSON files
save_json(train_data, os.path.join(output_folder, 'medquad_alpaca_train.json'))
Expand All @@ -46,6 +52,8 @@ def xml_to_json(input_base_folder, output_folder):
f.write(f"Training data pairs: {train_count}\n")
f.write(f"Test data pairs: {test_count}\n")

print("Preprocessing complete")


def process_xml_file(folder, xml_file):
xml_path = os.path.join(folder, xml_file)
Expand Down Expand Up @@ -78,3 +86,38 @@ def process_xml_file(folder, xml_file):
def save_json(data, filename):
with open(filename, 'w', encoding='utf-8') as f:
json.dump(data, f, ensure_ascii=False, indent=4)


def compute_hash(file_path, hash_name='sha384'):
"""Compute the hash of a single file using SHA-384."""
hash_func = getattr(hashlib, hash_name)()
with open(file_path, 'rb') as f:
for chunk in iter(lambda: f.read(8192), b''):
hash_func.update(chunk)
return hash_func.hexdigest()


def verify_aggregated_hashes(input_base_folder, dir_list, expected_hash):
"""Verify the aggregated hash of all files against a single, hardcoded hash."""
aggregated_hash_func = hashlib.sha384()

for sub_directory in dir_list:
directory = os.path.join(input_base_folder, sub_directory)
if os.path.isdir(directory):
for root, _, files in os.walk(directory):
for file in files:
file_path = os.path.join(root, file)
file_hash = compute_hash(file_path)
aggregated_hash_func.update(file_hash.encode('utf-8'))
else:
raise SystemError(f"{directory} does not exist")

# Compute the aggregated hash
aggregated_hash = aggregated_hash_func.hexdigest()

# Compare the aggregated hash with the expected, hardcoded hash
if aggregated_hash != expected_hash:
raise SystemError(
"Verification failed. Downloaded hash doesn\'t match expected hash.")
else:
print("Verification passed")
46 changes: 39 additions & 7 deletions ...tutorials/experimental/Vision_Transformer/Workflow_Interface_102_Vision_Transformer.ipynb
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Expand Up @@ -50,9 +50,9 @@
"metadata": {},
"outputs": [],
"source": [
"# !pip install git+https://github.com/intel/openfl.git\n",
"# !pip install -r ../requirements_workflow_interface.txt\n",
"# !pip install -r requirements_vision_transformer.txt\n",
"!pip install git+https://github.com/intel/openfl.git\n",
"!pip install -r ../requirements_workflow_interface.txt\n",
"!pip install -r requirements_vision_transformer.txt\n",
"\n",
"# Uncomment this if running in Google Colab\n",
"#!pip install -r https://raw.githubusercontent.com/intel/openfl/develop/openfl-tutorials/experimental/requirements_workflow_interface.txt\n",
Expand Down Expand Up @@ -122,6 +122,38 @@
"DataClass = getattr(medmnist, info['python_class'])"
]
},
{
"cell_type": "markdown",
"id": "4b039af1-8806-4c90-839a-6919171ff181",
"metadata": {},
"source": [
"The cell below is download the PathMNIST dataset and perform hash verification. "
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6e642d59-ce00-4490-a4a5-e8f4cc4118fd",
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"from urllib.request import urlretrieve\n",
"from openfl.utilities import validate_file_hash\n",
"\n",
"def download_and_verify_data():\n",
" datapath = os.path.join(os.path.expanduser('~'), '.medmnist')\n",
" os.makedirs(datapath, exist_ok=True)\n",
" \n",
" _ = urlretrieve('https://zenodo.org/records/6496656/files/pathmnist.npz', os.path.join(datapath, 'pathmnist.npz'))\n",
" \n",
" validate_file_hash(os.path.join(datapath, 'pathmnist.npz'), \n",
" '3f281f2cb6673bb06799d5d31ddbf6d87203e418970f92366d4fce3310749595c7e3b09798b98e0c3c50cc9a63012333')\n",
" print('Verification passed')\n",
"\n",
"download_and_verify_data()"
]
},
{
"cell_type": "markdown",
"id": "2ed5bba7",
Expand Down Expand Up @@ -166,8 +198,8 @@
"\n",
"\n",
"# load the data\n",
"medmnist_train = DataClass(split='train', transform=train_transforms, download=True)\n",
"medmnist_test = DataClass(split='test', transform=test_transforms, download=True)\n",
"medmnist_train = DataClass(split='train', transform=train_transforms)\n",
"medmnist_test = DataClass(split='test', transform=test_transforms)\n",
"\n",
"# For demonstration purposes, we take a subset to reduce overall size and training time\n",
"##################\n",
Expand Down Expand Up @@ -668,9 +700,9 @@
],
"metadata": {
"kernelspec": {
"display_name": "openfl_ViT",
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "openfl_vit"
"name": "python3"
},
"language_info": {
"codemirror_mode": {
Expand Down

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