hybrid_model.py

from dataclasses import dataclass
from typing import Optional
import logging
import torch
from cpufeature import CPUFeature
from petals.constants import PUBLIC_INITIAL_PEERS

# Configure logging
logging.basicConfig(
level=logging.DEBUG,
format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
handlers=[
logging.FileHandler("app.log"),
logging.StreamHandler()
]
)
logger = logging.getLogger(__name__)

@dataclass
class ModelInfo:
repo: str
adapter: Optional[str] = None

MODELS = [
ModelInfo(repo="meta-llama/Llama-2-70b-chat-hf"),
ModelInfo(repo="stabilityai/StableBeluga2"),
ModelInfo(repo="enoch/llama-65b-hf"),
ModelInfo(repo="enoch/llama-65b-hf", adapter="timdettmers/guanaco-65b"),
ModelInfo(repo="bigscience/bloomz"),
# Add more models here
ModelInfo(repo="roda-1"),
ModelInfo(repo="kubu-hai.model.h5-2", adapter="kubu-hai.model.mat-2"),
]
DEFAULT_MODEL_NAME = "enoch/llama-65b-hf"

INITIAL_PEERS = PUBLIC_INITIAL_PEERS
# Set this to a list of multiaddrs to connect to a private swarm instead of the public one, for example:
# INITIAL_PEERS = ['/ip4/10.1.2.3/tcp/31234/p2p/QmcXhze98AcgGQDDYna23s4Jho96n8wkwLJv78vxtFNq44']

DEVICE = "cuda" if torch.cuda.is_available() else "cpu"

if DEVICE == "cuda":
TORCH_DTYPE = "auto"
elif CPUFeature["AVX512f"] and CPUFeature["OS_AVX512"]:
TORCH_DTYPE = torch.bfloat16
else:
TORCH_DTYPE = torch.float32  # You can use bfloat16 in this case too, but it will be slow

STEP_TIMEOUT = 10 * 60  # Changed from 5 minutes to 10 minutes
MAX_SESSIONS = 50  # Has effect only for API v1 (HTTP-based)

logger.info("Configuration setup complete.")

# Example preprocess and postprocess functions
def preprocess(data):
logger.debug("Preprocessing data")
# Add your preprocessing steps here
return data

def postprocess(data):
logger.debug("Postprocessing data")
# Add your postprocessing steps here
return data

# Example model class
class MyModel(torch.nn.Module):
def __init__(self):
super(MyModel, self).__init__()
# Define your model layers here

def forward(self, x):
# Define the forward pass
return x

# Initialize model
model = MyModel().to(DEVICE)

# Example hybrid function
def hybrid_function(data):
# Preprocessing on CPU
data_cpu = data.to("cpu")
preprocessed_data = preprocess(data_cpu)

# Move data to GPU for inference if available
preprocessed_data = preprocessed_data.to(DEVICE)
output = model(preprocessed_data)

# Postprocessing on CPU
output_cpu = output.to("cpu")
result = postprocess(output_cpu)

return result

# Example usage
data = torch.randn(100, 10).to(DEVICE)  # Example data
result = hybrid_function(data)
logger.info("Processing complete.")