Synopsis of what's going on with

bittensor/bittensor/_neuron/text/core_validator/__init__.py

Author's note because this file had probably one of the more complex scripts, with many more characters, I did my best to translate between myself and my AI assistant. Some words are mine, and I make edits, but I don't know what's going on down there. Validators are the core of the network, and if you've reached the ability to comprehend how to run a Validator, congratulations.

Proof of Work Solver

This script contains a set of classes and functions to perform a proof of work (PoW) computation for the registration process in a distributed network.

Bittensor Proof of Work Registration with CUDA

This document presents a Python module for creating a Proof of Work (PoW) registration for a given subtensor and wallet in a Bittensor network. The module leverages CUDA (Compute Unified Device Architecture) to speed up the registration process using parallel computing on NVIDIA GPUs.

The module contains:

  • _solve_for_difficulty_fast_cuda(): A function that solves the registration using CUDA. It takes several arguments like subtensor, wallet, netuid, output_in_place, update_interval, TPB, dev_id, n_samples, alpha_, and log_verbose. It calculates the hash rate as an exponentially weighted moving average for robustness.

  • _terminate_workers_and_wait_for_exit(): A function that terminates and waits for all worker processes to exit.

  • create_pow(): A function that creates a proof of work for the given subtensor and wallet. It takes arguments like subtensor, wallet, netuid, output_in_place, cuda, dev_id, tpb, num_processes, update_interval, and log_verbose. It returns the proof of work solution or None if the wallet is already registered or there's a different error.

Classes

  1. CUDAException: A custom exception raised when an error occurs in the CUDA environment.

  2. POWSolution: A data class representing a solution to the registration PoW problem.

    • is_stale(subtensor: 'bittensor.Subtensor') -> bool: Returns True if the PoW is stale (the block it is solved for is within 3 blocks of the current block).
  3. _SolverBase: An abstract base class for a multiprocessing process that solves the registration PoW problem.

  4. _Solver: A subclass of _SolverBase that implements the run method for solving the PoW problem.

Functions

  1. _hex_bytes_to_u8_list(hex_bytes: bytes): Converts a hexadecimal byte string to a list of unsigned 8-bit integers.

  2. _create_seal_hash(block_and_hotkey_hash_bytes: bytes, nonce:int) -> bytes: Creates a seal hash based on the given block and hotkey hash bytes and a nonce.

  3. _seal_meets_difficulty(seal: bytes, difficulty: int, limit: int): Checks if a seal meets the required difficulty.

  4. _SolverBase.create_shared_memory() -> Tuple[multiprocessing.Array, multiprocessing.Value, multiprocessing.Array]: Creates shared memory for the solver processes to use.

  5. _solve_for_nonce_block(nonce_start: int, nonce_end: int, block_and_hotkey_hash_bytes: bytes, block_difficulty: int, limit: int, block_number: int) -> Optional[POWSolution]: Attempts to solve the PoW problem for a range of nonces, returning a solution if successful.

Usage

The script is designed to perform PoW computation in a multiprocessing environment. The _Solver class can be instantiated with the necessary shared memory, queues, and events for parallel processing. The run method of the _Solver class will continuously attempt to solve the PoW problem and communicate with the main process to receive updates on the current block and difficulty.

By using this script, developers can efficiently perform PoW computations for the registration process in a distributed network.

CUDASolver Class

The _CUDASolver class is a subclass of _SolverBase and is used for solving the proof of work (POW) on a CUDA device. It takes the following parameters:

  • dev_id: ID of the CUDA device
  • TPB: Thread-per-block count

Methods

  • __init__(): Initializes the CUDASolver with the given parameters.
  • run(): Executes the solver on a CUDA device.

Functions

  • _solve_for_nonce_block_cuda(): Tries to solve the POW on a CUDA device for a block of nonces.
  • _solve_for_nonce_block(): Tries to solve the POW for a block of nonces.
  • _registration_diff_unpack(): Unpacks the packed two 32-bit integers into one 64-bit integer (little endian).
  • _registration_diff_pack(): Packs the difficulty into two 32-bit integers (little endian).
  • _hash_block_with_hotkey(): Hashes the block with the hotkey using Keccak-256 to get 32 bytes.
  • _update_curr_block(): Updates the current block.

Helper Functions

  • get_cpu_count(): Returns the CPU count.

Classes

  • RegistrationStatistics: Data class to store the statistics for a registration.
  • RegistrationStatisticsLogger: Logs statistics for a registration. Provides methods to start, stop, and update the statistics.

Methods

  • start(): Starts the status logger.
  • stop(): Stops the status logger.
  • get_status_message(): Returns a formatted status message.
  • update(): Updates the registration statistics.

Functions

_solve_for_difficulty_fast()

Solves the proof of work (POW) for registration using multiprocessing.

  • subtensor
  • wallet: Wallet to use for registration.
  • netuid: The netuid of the subnet to register to.
  • output_in_place: If true, prints the status in place. Otherwise, prints the status on a new line.
  • num_processes: Number of processes to use.
  • update_interval: Number of nonces to solve before updating block information.
  • n_samples: The number of samples of the hash_rate to keep for the EWMA.
  • alpha_: The alpha for the EWMA for the hash_rate calculation.
  • log_verbose: If true, prints more verbose logging of the registration metrics.

_get_block_with_retry()

Gets the current block number, difficulty, and block hash from the substrate node with retry logic.

  • subtensor: The subtensor object to use to get the block number, difficulty, and block hash.
  • netuid: The netuid of the network to get the block number, difficulty, and block hash from.

_UsingSpawnStartMethod

Context manager for switching the multiprocessing start method to 'spawn' temporarily.

  • force: If true, force the start method to be set to 'spawn'.

_check_for_newest_block_and_update()

Checks for a new block and updates the current block information if a new block is found.

  • subtensor: The subtensor object to use for getting the current block.
  • netuid: The netuid to use for retrieving the difficulty.
  • old_block_number: The old block number to check against.
  • hotkey_bytes: The bytes of the hotkey's pubkey.
  • curr_diff: The current difficulty as a multiprocessing array.
  • curr_block: Where the current block is stored as a multiprocessing array.
  • curr_block_num: Where the current block number is stored as a multiprocessing value.
  • update_curr_block: A function that updates the current block.
  • check_block: A multiprocessing lock that is used to check for a new block.
  • solvers: A list of solvers to update the current block for.
  • curr_stats: The current registration statistics to update.
  1. _solve_for_difficulty_fast_cuda:

    • This function solves the registration process quickly using CUDA (Compute Unified Device Architecture) which is a parallel computing platform and application programming interface (API) model created by NVIDIA.
    • It takes several parameters including subtensor, wallet, netuid, output_in_place, update_interval, TPB (Threads per block), dev_id (CUDA device ID), n_samples, alpha_ (alpha for EWMA), and log_verbose.
    • The function sets up multiprocessing with shared memory, creates a worker for each CUDA device, starts the solver processes, checks for new blocks, and updates the hash rate and registration statistics.
    • If a solution is found or the wallet is already registered, the function stops the solver processes, terminates the workers, and returns the solution.
  2. _terminate_workers_and_wait_for_exit:

    • This function takes a list of worker processes as input.
    • It terminates each worker process and waits for them to exit using the join() method.
  3. create_pow:

    • This function creates a proof of work for the given subtensor and wallet.
    • It takes several parameters including subtensor, wallet, netuid, output_in_place, cuda, dev_id, tpb (Threads per block), num_processes, update_interval, and log_verbose.
    • If the cuda parameter is True, the function uses the _solve_for_difficulty_fast_cuda function; otherwise, it uses the _solve_for_difficulty_fast function to find a solution.
    • The function returns the proof of work solution or None if the wallet is already registered or there is a different error.
    • It raises a ValueError if the subnet does not exist.