pegaflow-llm-cu13 0.21.1

High-performance key-value storage engine with Python bindings

PegaFlow Python Package

High-performance key-value storage engine with Python bindings, built with Rust and PyO3.

Features

• PegaEngine: Fast Rust-based key-value storage with Python bindings
• PegaKVConnector: vLLM KV connector for distributed inference with KV cache transfer

Installation

From Source

# Install maturin if you haven't already
pip install maturin

# Build and install in development mode
cd python
maturin develop

# Or build a wheel
maturin build --release

From PyPI (coming soon)

pip install pegaflow

Usage

Basic KV Storage

from pegaflow import PegaEngine

# Create a new engine
engine = PegaEngine()

# Store key-value pairs
engine.put("name", "PegaFlow")
engine.put("version", "0.1.0")

# Retrieve values
name = engine.get("name")  # Returns "PegaFlow"
missing = engine.get("nonexistent")  # Returns None

# Remove keys
removed = engine.remove("name")  # Returns "PegaFlow"

Sglang Examples:

example 1:

python3 -m sglang.launch_server --model-path Qwen/Qwen3-0.6B --served-model-name Qwen/Qwen3-0.6B --trust-remote-code --enable-cache-report --page-size 256 --host "0.0.0.0" --port 8000 --mem-fraction-static 0.8 --max-running-requests 32 --enable-pegaflow

example 2:

python3 -m sglang.launch_server --model-loader-extra-config "{\"enable_multithread_load\": true, \"num_threads\": 64}"  --model-path deepseek-ai/DeepSeek-V3.2 --served-model-name deepseek-ai/DeepSeek-V3.2 --trust-remote-code --page-size "64" --reasoning-parser deepseek-v3 --tool-call-parser deepseekv32 --enable-cache-report --host "0.0.0.0" --port 8031 --mem-fraction-static 0.83 --max-running-requests 64 --tp-size "8" --enable-pegaflow

vLLM KV Connector

from vllm import LLM
from vllm.distributed.kv_transfer.kv_transfer_agent import KVTransferConfig

# Configure vLLM to use PegaKVConnector
kv_transfer_config = KVTransferConfig(
    kv_connector="PegaKVConnector",
    kv_role="kv_both",
    kv_connector_module_path="pegaflow.connector",
)

# Create LLM with KV transfer enabled
llm = LLM(
    model="gpt2",
    kv_transfer_config=kv_transfer_config,
)

Development

See the examples directory for more usage examples.

Testing

Running Unit Tests

The test suite includes integration tests that verify the EngineRpcClient can correctly communicate with a running pegaflow-server instance.

Prerequisites

1. Build the Rust extension:

   cd python
   maturin develop --release
   

2. Build the server binary:

   cd ..
   cargo build --release --bin pegaflow-server
   

3. Ensure CUDA is available (tests require GPU):

   python -c "import torch; assert torch.cuda.is_available()"
   

Running Tests

cd python

# Run all tests
pytest tests/ -v

# Run specific test file
pytest tests/test_engine_client.py -v

# Run with coverage
pytest tests/ --cov=pegaflow --cov-report=html

Test Structure

• tests/conftest.py: Contains pytest fixtures for:

  • pega_server: Automatically starts/stops pegaflow-server for integration tests
  • engine_client: Creates an EngineRpcClient connected to the test server
  • client_context: Provides a ClientContext representing a vLLM instance with GPU KV cache tensors
  • registered_instance: Provides a registered instance ID for query tests

• tests/test_engine_client.py: Integration tests for:

  • Server connectivity
  • Query operations with various inputs

Test Fixtures

The ClientContext class abstracts a vLLM instance and provides:

• register_kv_caches(): Register GPU KV cache tensors with the server
• query(block_hashes): Query available blocks
• unregister_context(): Unregister context from server

Example test usage:

def test_query(client_context):
    """Test query operation."""
    result = client_context.query([])
    assert result is not None

License

MIT