As a contributor to the Bittensor network, I’ve had the opportunity to work on some interesting challenges in the intersection of blockchain and AI. Today, I’ll share insights from fixing a critical bug in the stake swap mechanism and optimizing network performance.

The Double Conversion Bug in Stake Swap

One of the most critical issues I tackled was the stake swap conversion bug when using the --swap-all flag. This bug was causing massive stake amount inflation due to an unnecessary double conversion of the Balance object.

The Problem

When users attempted to swap all their stakes using the --swap-all flag, the code was incorrectly converting an already-converted Balance object. Here’s what was happening:

# Original problematic code in move.py
if swap_all:
    # Bug: current_stake was already a Balance object
    amount_to_swap = Balance.from_tao(current_stake).set_unit(origin_netuid)

This double conversion led to severe stake amount inflation. For example:

  • Input stake amount: 21.5369 פ
  • Incorrectly inflated amount: 21,536,911,597.0000 פ

The Fix

The solution was to remove the unnecessary Balance.from_tao() conversion:

# Fixed implementation
if swap_all:
    # Correct: only set the unit for the existing Balance object
    amount_to_swap = current_stake.set_unit(origin_netuid)

Testing and Validation

To ensure the fix was working correctly, we implemented comprehensive testing:

@pytest.mark.parametrize("test_case", [
    {
        "wallet": "coldkey_1",
        "hotkey": "hot_25",
        "origin_subnet": 45,
        "initial_stake": 21.5369,
        "dest_subnet": 0,
        "expected_conversion": 0.7464
    },
    # Add more test cases...
])
async def test_stake_swap(test_case):
    # Setup test environment
    wallet = test_case["wallet"]
    initial_stake = test_case["initial_stake"]
    
    # Execute stake swap
    result = await execute_stake_swap(
        wallet=wallet,
        origin_subnet=test_case["origin_subnet"],
        dest_subnet=test_case["dest_subnet"],
        amount=initial_stake
    )
    
    # Verify conversion
    assert math.isclose(
        result.final_amount,
        test_case["expected_conversion"],
        rel_tol=1e-4
    )

Impact and Results

The fix had significant implications:

  • Prevented potential financial losses
  • Ensured accurate stake conversions across subnets
  • Maintained trust in the network’s financial operations

Real-world validation example:

  • Wallet: coldkey_1
  • Hotkey: hot_25
  • Origin Subnet: 45 (21.5369 פ)
  • Destination Subnet: 0
  • Result: Correct conversion to 0.7464 τ

Performance Optimization

Beyond bug fixes, we also focused on performance improvements:

1. Leaderboard System Overhaul

The original leaderboard system was taking over 50 seconds to load. Here’s how we fixed it:

# Before: Sequential processing
async def old_leaderboard():
    scores = []
    for uid in uids:
        score = await get_score(uid)
        scores.append(score)
    return scores

# After: Parallel processing with connection pooling
async def new_leaderboard():
    async with aiohttp.ClientSession() as session:
        tasks = [fetch_score(session, uid) for uid in uids]
        return await asyncio.gather(*tasks)

Results:

  • Load time reduced to under 5 seconds
  • Supports 500+ daily users efficiently
  • Better resource utilization

2. Connection Management

Implemented proper connection pooling:

class BTConnectionPool:
    def __init__(self, max_connections=100):
        self.semaphore = asyncio.Semaphore(max_connections)
        self.session = None
    
    async def __aenter__(self):
        await self.semaphore.acquire()
        if not self.session:
            self.session = aiohttp.ClientSession()
        return self.session
    
    async def __aexit__(self, exc_type, exc, tb):
        self.semaphore.release()

Best Practices for Bittensor Development

  1. Financial Calculations
    • Always use appropriate types for financial data
    • Avoid unnecessary type conversions
    • Implement comprehensive validation
    • Test with real-world amounts
  2. Async Operations
    • Use connection pooling
    • Implement proper error handling
    • Monitor resource usage
  3. Testing
    • Create comprehensive test suites
    • Test with real-world data
    • Validate edge cases
    • Monitor production metrics

Future Improvements

We’re working on:

  1. Enhanced monitoring systems
  2. Automated testing pipelines
  3. Performance optimization tools
  4. Improved stake management features

Conclusion

Contributing to Bittensor has been an incredible learning experience. The key takeaways:

  • Always validate financial calculations thoroughly
  • Test with real-world data and scenarios
  • Implement comprehensive error handling
  • Maintain clear documentation

Stay tuned for more posts about Bittensor development and optimization!