mirror of
https://github.com/luxfhe/examples.git
synced 2026-10-30 18:13:15 +00:00
zeekay
9546c92611
ML examples: - credit_scoring: Credit risk prediction with HMEQ data - disease_prediction: Healthcare ML on encrypted records - cifar: Encrypted image classification - federated_learning: Distributed FHE training - hybrid_model: Mixed FHE/plaintext inference Deployment: - breast_cancer: Cancer detection API - sentiment_analysis: Encrypted NLP - server: FHE inference server Blockchain: - hardhat-template: Solidity + FHE - react-template: Full dApp with FHE
91 lines
2.8 KiB
Python
91 lines
2.8 KiB
Python
"""Showcase for the hybrid model converter."""
|
|
|
|
import json
|
|
import os
|
|
import time
|
|
from pathlib import Path
|
|
|
|
import torch
|
|
from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
|
|
|
|
from concrete.ml.torch.hybrid_model import HybridFHEMode, HybridFHEModel
|
|
|
|
os.environ["TF_ENABLE_ONEDNN_OPTS"] = "0"
|
|
os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"
|
|
import tensorflow
|
|
|
|
tensorflow.compat.v1.logging.set_verbosity(tensorflow.compat.v1.logging.ERROR)
|
|
|
|
if __name__ == "__main__":
|
|
# Load configuration dumped by compilation
|
|
with open("configuration.json", "r") as file:
|
|
configuration = json.load(file)
|
|
|
|
module_names = configuration["module_names"]
|
|
model_name = configuration["model_name"]
|
|
model_name_no_special_char = configuration["model_name_no_special_char"]
|
|
|
|
device = "cpu"
|
|
if torch.cuda.is_available():
|
|
device = "cuda"
|
|
print(f"Using device: {device}")
|
|
|
|
# Get model from Hugging Face using model_name from configuration
|
|
tokenizer = AutoTokenizer.from_pretrained(model_name)
|
|
model = AutoModelForCausalLM.from_pretrained(
|
|
model_name,
|
|
device_map=device,
|
|
trust_remote_code=True,
|
|
)
|
|
|
|
# Modify model to use remote FHE server instead of local weights
|
|
hybrid_model = HybridFHEModel(
|
|
model,
|
|
module_names,
|
|
server_remote_address="http://0.0.0.0:8000",
|
|
model_name=model_name_no_special_char,
|
|
verbose=False,
|
|
)
|
|
path_to_clients = Path(__file__).parent / "clients"
|
|
hybrid_model.init_client(path_to_clients=path_to_clients)
|
|
hybrid_model.set_fhe_mode(HybridFHEMode.REMOTE)
|
|
|
|
# Run example
|
|
while True:
|
|
# Take inputs
|
|
num_tokens = input("Number of tokens:\n").strip()
|
|
if not num_tokens:
|
|
num_tokens = 5
|
|
else:
|
|
num_tokens = int(num_tokens)
|
|
prompt = input("Prompt:\n")
|
|
if not prompt:
|
|
prompt = "Computations on encrypted data can help"
|
|
|
|
# Encode and send to device
|
|
input_ids = tokenizer.encode(prompt, return_tensors="pt")
|
|
assert isinstance(input_ids, torch.Tensor)
|
|
input_ids = input_ids.to(device=device)
|
|
|
|
print("*" * 10)
|
|
print("*" * 10)
|
|
print(f"{input_ids.shape[1]} tokens in '{prompt}'")
|
|
print("*" * 10)
|
|
print("*" * 10)
|
|
|
|
# Print words as they are generated
|
|
streamer = TextStreamer(tokenizer=tokenizer)
|
|
start = time.time()
|
|
output_ids = model.generate(
|
|
input_ids, max_new_tokens=num_tokens, use_cache=True, streamer=streamer
|
|
)
|
|
end = time.time()
|
|
generated = tokenizer.decode(output_ids[0])
|
|
|
|
print(f"{end - start} seconds to generate")
|
|
print("*" * 10)
|
|
print("*" * 10)
|
|
print(generated)
|
|
print("*" * 10)
|
|
print("*" * 10)
|