Your Name
1 month ago
parent
e6d616b2c3
commit
c2cd677575
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/* Further customization as needed */
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/* * Further customization as needed */ */
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.md-typeset__table {
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min-width: 100%;
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}
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.md-typeset table:not([class]) {
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display: table;
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}
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/*
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:root {
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--md-primary-fg-color: #EE0F0F;
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--md-primary-fg-color--light: #ECB7B7;
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--md-primary-fg-color--dark: #90030C;
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} */
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min-width: 100%;
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}
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.md-typeset table:not([class]) {
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display: table;
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}
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/* Dark mode */
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[data-md-color-scheme="slate"] {
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--md-default-bg-color: black;
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}
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.header__ellipsis {
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color: black;
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}
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/*
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:root {
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--md-primary-fg-color: #EE0F0F;
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--md-primary-fg-color--light: #ECB7B7;
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--md-primary-fg-color--dark: #90030C;
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} */
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@ -0,0 +1,308 @@
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import os
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from swarms import Agent
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from swarm_models import OpenAIChat
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from web3 import Web3
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from typing import Dict, Optional, Any
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from datetime import datetime
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import asyncio
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from loguru import logger
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from dotenv import load_dotenv
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import csv
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import requests
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import time
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BLOCKCHAIN_AGENT_PROMPT = """
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You are an expert blockchain and cryptocurrency analyst with deep knowledge of Ethereum markets and DeFi ecosystems.
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You have access to real-time ETH price data and transaction information.
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For each transaction, analyze:
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1. MARKET CONTEXT
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- Current ETH price and what this transaction means in USD terms
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- How this movement compares to typical market volumes
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- Whether this could impact ETH price
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2. BEHAVIORAL ANALYSIS
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- Whether this appears to be institutional, whale, or protocol movement
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- If this fits any known wallet patterns or behaviors
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- Signs of smart contract interaction or DeFi activity
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3. RISK & IMPLICATIONS
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- Potential market impact or price influence
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- Signs of potential market manipulation or unusual activity
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- Protocol or DeFi risks if applicable
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4. STRATEGIC INSIGHTS
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- What traders should know about this movement
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- Potential chain reactions or follow-up effects
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- Market opportunities or risks created
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Write naturally but precisely. Focus on actionable insights and important patterns.
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Your analysis helps traders and researchers understand significant market movements in real-time."""
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class EthereumAnalyzer:
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def __init__(self, min_value_eth: float = 100.0):
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load_dotenv()
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logger.add(
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"eth_analysis.log",
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rotation="500 MB",
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retention="10 days",
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level="INFO",
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format="{time:YYYY-MM-DD at HH:mm:ss} | {level} | {message}",
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)
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self.w3 = Web3(
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Web3.HTTPProvider(
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"https://mainnet.infura.io/v3/9aa3d95b3bc440fa88ea12eaa4456161"
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)
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)
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if not self.w3.is_connected():
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raise ConnectionError(
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"Failed to connect to Ethereum network"
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)
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self.min_value_eth = min_value_eth
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self.last_processed_block = self.w3.eth.block_number
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self.eth_price = self.get_eth_price()
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self.last_price_update = time.time()
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# Initialize AI agent
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api_key = os.getenv("OPENAI_API_KEY")
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if not api_key:
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raise ValueError(
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"OpenAI API key not found in environment variables"
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)
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model = OpenAIChat(
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openai_api_key=api_key,
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model_name="gpt-4",
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temperature=0.1,
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)
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self.agent = Agent(
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agent_name="Ethereum-Analysis-Agent",
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system_prompt=BLOCKCHAIN_AGENT_PROMPT,
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llm=model,
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max_loops=1,
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autosave=True,
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dashboard=False,
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verbose=True,
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dynamic_temperature_enabled=True,
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saved_state_path="eth_agent.json",
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user_name="eth_analyzer",
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retry_attempts=1,
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context_length=200000,
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output_type="string",
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streaming_on=False,
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)
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self.csv_filename = "ethereum_analysis.csv"
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self.initialize_csv()
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def get_eth_price(self) -> float:
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"""Get current ETH price from CoinGecko API."""
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try:
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response = requests.get(
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"https://api.coingecko.com/api/v3/simple/price",
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params={"ids": "ethereum", "vs_currencies": "usd"},
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)
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return float(response.json()["ethereum"]["usd"])
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except Exception as e:
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logger.error(f"Error fetching ETH price: {str(e)}")
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return 0.0
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def update_eth_price(self):
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"""Update ETH price if more than 5 minutes have passed."""
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if time.time() - self.last_price_update > 300: # 5 minutes
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self.eth_price = self.get_eth_price()
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self.last_price_update = time.time()
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logger.info(f"Updated ETH price: ${self.eth_price:,.2f}")
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def initialize_csv(self):
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"""Initialize CSV file with headers."""
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headers = [
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"timestamp",
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"transaction_hash",
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"from_address",
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"to_address",
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"value_eth",
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"value_usd",
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"eth_price",
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"gas_used",
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"gas_price_gwei",
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"block_number",
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"analysis",
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]
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if not os.path.exists(self.csv_filename):
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with open(self.csv_filename, "w", newline="") as f:
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writer = csv.writer(f)
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writer.writerow(headers)
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async def analyze_transaction(
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self, tx_hash: str
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) -> Optional[Dict[str, Any]]:
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"""Analyze a single transaction."""
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try:
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tx = self.w3.eth.get_transaction(tx_hash)
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receipt = self.w3.eth.get_transaction_receipt(tx_hash)
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value_eth = float(self.w3.from_wei(tx.value, "ether"))
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if value_eth < self.min_value_eth:
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return None
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block = self.w3.eth.get_block(tx.blockNumber)
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# Update ETH price if needed
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self.update_eth_price()
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value_usd = value_eth * self.eth_price
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analysis = {
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"timestamp": datetime.fromtimestamp(
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block.timestamp
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).isoformat(),
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"transaction_hash": tx_hash.hex(),
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"from_address": tx["from"],
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"to_address": tx.to if tx.to else "Contract Creation",
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"value_eth": value_eth,
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"value_usd": value_usd,
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"eth_price": self.eth_price,
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"gas_used": receipt.gasUsed,
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"gas_price_gwei": float(
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self.w3.from_wei(tx.gasPrice, "gwei")
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),
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"block_number": tx.blockNumber,
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}
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# Check if it's a contract
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if tx.to:
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code = self.w3.eth.get_code(tx.to)
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analysis["is_contract"] = len(code) > 0
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# Get contract events
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if analysis["is_contract"]:
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analysis["events"] = receipt.logs
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return analysis
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except Exception as e:
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logger.error(
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f"Error analyzing transaction {tx_hash}: {str(e)}"
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)
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return None
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def prepare_analysis_prompt(self, tx_data: Dict[str, Any]) -> str:
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"""Prepare detailed analysis prompt including price context."""
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value_usd = tx_data["value_usd"]
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eth_price = tx_data["eth_price"]
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prompt = f"""Analyze this Ethereum transaction in current market context:
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Transaction Details:
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- Value: {tx_data['value_eth']:.2f} ETH (${value_usd:,.2f} at current price)
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- Current ETH Price: ${eth_price:,.2f}
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- From: {tx_data['from_address']}
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- To: {tx_data['to_address']}
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- Contract Interaction: {tx_data.get('is_contract', False)}
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- Gas Used: {tx_data['gas_used']:,} units
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- Gas Price: {tx_data['gas_price_gwei']:.2f} Gwei
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- Block: {tx_data['block_number']}
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- Timestamp: {tx_data['timestamp']}
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{f"Event Count: {len(tx_data['events'])} events" if tx_data.get('events') else "No contract events"}
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Consider the transaction's significance given the current ETH price of ${eth_price:,.2f} and total USD value of ${value_usd:,.2f}.
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Analyze market impact, patterns, risks, and strategic implications."""
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return prompt
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def save_to_csv(self, tx_data: Dict[str, Any], ai_analysis: str):
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"""Save transaction data and analysis to CSV."""
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row = [
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tx_data["timestamp"],
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tx_data["transaction_hash"],
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tx_data["from_address"],
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tx_data["to_address"],
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tx_data["value_eth"],
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tx_data["value_usd"],
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tx_data["eth_price"],
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tx_data["gas_used"],
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tx_data["gas_price_gwei"],
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tx_data["block_number"],
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ai_analysis.replace("\n", " "),
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]
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with open(self.csv_filename, "a", newline="") as f:
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writer = csv.writer(f)
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writer.writerow(row)
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async def monitor_transactions(self):
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"""Monitor and analyze transactions one at a time."""
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logger.info(
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f"Starting transaction monitor (minimum value: {self.min_value_eth} ETH)"
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)
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while True:
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try:
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current_block = self.w3.eth.block_number
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block = self.w3.eth.get_block(
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current_block, full_transactions=True
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)
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for tx in block.transactions:
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tx_analysis = await self.analyze_transaction(
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tx.hash
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)
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if tx_analysis:
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# Get AI analysis
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analysis_prompt = (
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self.prepare_analysis_prompt(tx_analysis)
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)
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ai_analysis = self.agent.run(analysis_prompt)
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print(ai_analysis)
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# Save to CSV
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self.save_to_csv(tx_analysis, ai_analysis)
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# Print analysis
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print("\n" + "=" * 50)
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print("New Transaction Analysis")
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print(
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f"Hash: {tx_analysis['transaction_hash']}"
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)
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print(
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f"Value: {tx_analysis['value_eth']:.2f} ETH (${tx_analysis['value_usd']:,.2f})"
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)
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print(
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f"Current ETH Price: ${self.eth_price:,.2f}"
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)
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print("=" * 50)
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print(ai_analysis)
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print("=" * 50 + "\n")
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await asyncio.sleep(1) # Wait for next block
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except Exception as e:
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logger.error(f"Error in monitoring loop: {str(e)}")
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await asyncio.sleep(1)
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async def main():
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"""Entry point for the analysis system."""
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analyzer = EthereumAnalyzer(min_value_eth=100.0)
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await analyzer.monitor_transactions()
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if __name__ == "__main__":
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print("Starting Ethereum Transaction Analyzer...")
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print("Saving results to ethereum_analysis.csv")
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print("Press Ctrl+C to stop")
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try:
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asyncio.run(main())
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except KeyboardInterrupt:
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print("\nStopping analyzer...")
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Can't render this file because it has a wrong number of fields in line 4.
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@ -0,0 +1,292 @@
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import torch
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import torch.nn as nn
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import torch.distributed as dist
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from dataclasses import dataclass
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from typing import Optional, Tuple, Union
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from loguru import logger
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import math
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@dataclass
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class StarAttentionConfig:
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"""Configuration for StarAttention module.
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Attributes:
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hidden_size: Dimension of the model's hidden states
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num_attention_heads: Number of attention heads
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num_hosts: Number of hosts in the distributed system
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block_size: Size of each context block
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anchor_size: Size of the anchor block
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dropout_prob: Dropout probability (default: 0.1)
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layer_norm_eps: Layer normalization epsilon (default: 1e-12)
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"""
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hidden_size: int
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num_attention_heads: int
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num_hosts: int
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block_size: int
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anchor_size: int
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dropout_prob: float = 0.1
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layer_norm_eps: float = 1e-12
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class StarAttention(nn.Module):
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"""
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Implementation of Star Attention mechanism for distributed inference.
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The module implements a two-phase attention mechanism:
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1. Local Context Encoding with Anchor Blocks
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2. Query Encoding and Output Generation with Global Attention
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"""
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def __init__(self, config: StarAttentionConfig):
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super().__init__()
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if config.hidden_size % config.num_attention_heads != 0:
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raise ValueError(
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f"Hidden size {config.hidden_size} not divisible by number of attention "
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f"heads {config.num_attention_heads}"
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)
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self.config = config
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self.head_dim = (
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config.hidden_size // config.num_attention_heads
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)
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# Initialize components
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self.query = nn.Linear(config.hidden_size, config.hidden_size)
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self.key = nn.Linear(config.hidden_size, config.hidden_size)
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self.value = nn.Linear(config.hidden_size, config.hidden_size)
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self.dropout = nn.Dropout(config.dropout_prob)
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self.layer_norm = nn.LayerNorm(
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config.hidden_size, eps=config.layer_norm_eps
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)
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# KV cache for storing computed key/value pairs
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self.kv_cache = {}
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logger.info(
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f"Initialized StarAttention with config: {config}"
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)
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def _split_heads(
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self, tensor: torch.Tensor, num_heads: int
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) -> torch.Tensor:
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"""Split the last dimension into (num_heads, head_dim)."""
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batch_size, seq_len, _ = tensor.size()
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tensor = tensor.view(
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batch_size, seq_len, num_heads, self.head_dim
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)
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# Transpose to (batch_size, num_heads, seq_len, head_dim)
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return tensor.transpose(1, 2)
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def _merge_heads(self, tensor: torch.Tensor) -> torch.Tensor:
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"""Merge the head dimension back into hidden_size."""
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batch_size, _, seq_len, _ = tensor.size()
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tensor = tensor.transpose(1, 2)
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return tensor.reshape(
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batch_size, seq_len, self.config.hidden_size
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)
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def _compute_attention_scores(
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self,
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query: torch.Tensor,
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key: torch.Tensor,
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value: torch.Tensor,
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mask: Optional[torch.Tensor] = None,
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) -> Tuple[torch.Tensor, torch.Tensor]:
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"""Compute attention scores and weighted values."""
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# Scale dot-product attention
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scores = torch.matmul(
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query, key.transpose(-2, -1)
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) / math.sqrt(self.head_dim)
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if mask is not None:
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scores = scores.masked_fill(mask == 0, float("-inf"))
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# Online softmax computation
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attention_probs = torch.nn.functional.softmax(scores, dim=-1)
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attention_probs = self.dropout(attention_probs)
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context = torch.matmul(attention_probs, value)
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return context, attention_probs
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def phase1_local_context_encoding(
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self,
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input_ids: torch.Tensor,
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host_id: int,
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device: Union[str, torch.device] = "cuda",
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) -> None:
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"""
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Phase 1: Local Context Encoding with Anchor Blocks
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Args:
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input_ids: Input tensor of shape (batch_size, seq_len)
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host_id: ID of the current host
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device: Device to run computations on
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"""
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logger.debug(f"Starting Phase 1 on host {host_id}")
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# Calculate block assignments
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block_start = host_id * self.config.block_size
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block_end = block_start + self.config.block_size
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# Get local block
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local_block = input_ids[:, block_start:block_end].to(device)
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# Get anchor block (first block)
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anchor_block = input_ids[:, : self.config.anchor_size].to(
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device
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)
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# Compute KV pairs for local block
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local_hidden = self.layer_norm(local_block)
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local_key = self._split_heads(
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self.key(local_hidden), self.config.num_attention_heads
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)
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local_value = self._split_heads(
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self.value(local_hidden), self.config.num_attention_heads
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)
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# Store in KV cache
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self.kv_cache[host_id] = {
|
||||
"key": local_key,
|
||||
"value": local_value,
|
||||
"anchor_key": (
|
||||
None
|
||||
if host_id == 0
|
||||
else self._split_heads(
|
||||
self.key(self.layer_norm(anchor_block)),
|
||||
self.config.num_attention_heads,
|
||||
)
|
||||
),
|
||||
}
|
||||
|
||||
logger.debug(
|
||||
f"Phase 1 complete on host {host_id}. KV cache shapes - "
|
||||
f"key: {local_key.shape}, value: {local_value.shape}"
|
||||
)
|
||||
|
||||
def phase2_query_encoding(
|
||||
self,
|
||||
query_input: torch.Tensor,
|
||||
host_id: int,
|
||||
is_query_host: bool,
|
||||
device: Union[str, torch.device] = "cuda",
|
||||
) -> Optional[torch.Tensor]:
|
||||
"""
|
||||
Phase 2: Query Encoding and Output Generation
|
||||
|
||||
Args:
|
||||
query_input: Query tensor of shape (batch_size, seq_len, hidden_size)
|
||||
host_id: ID of the current host
|
||||
is_query_host: Whether this host is the query host
|
||||
device: Device to run computations on
|
||||
|
||||
Returns:
|
||||
Output tensor if this is the query host, None otherwise
|
||||
"""
|
||||
logger.debug(f"Starting Phase 2 on host {host_id}")
|
||||
|
||||
# Transform query
|
||||
query_hidden = self.layer_norm(query_input)
|
||||
query = self._split_heads(
|
||||
self.query(query_hidden), self.config.num_attention_heads
|
||||
)
|
||||
|
||||
# Compute local attention scores
|
||||
local_context, local_probs = self._compute_attention_scores(
|
||||
query,
|
||||
self.kv_cache[host_id]["key"],
|
||||
self.kv_cache[host_id]["value"],
|
||||
)
|
||||
|
||||
if not is_query_host:
|
||||
# Non-query hosts send their local attention statistics
|
||||
dist.send(local_probs, dst=self.config.num_hosts - 1)
|
||||
return None
|
||||
|
||||
# Query host aggregates attention from all hosts
|
||||
all_attention_probs = [local_probs]
|
||||
for src_rank in range(self.config.num_hosts - 1):
|
||||
probs = torch.empty_like(local_probs)
|
||||
dist.recv(probs, src=src_rank)
|
||||
all_attention_probs.append(probs)
|
||||
|
||||
# Compute global attention
|
||||
torch.mean(torch.stack(all_attention_probs), dim=0)
|
||||
|
||||
# Final output computation
|
||||
output = self._merge_heads(local_context)
|
||||
output = self.dropout(output)
|
||||
|
||||
logger.debug(
|
||||
f"Phase 2 complete on host {host_id}. Output shape: {output.shape}"
|
||||
)
|
||||
|
||||
return output
|
||||
|
||||
def forward(
|
||||
self,
|
||||
input_ids: torch.Tensor,
|
||||
query_input: torch.Tensor,
|
||||
host_id: int,
|
||||
is_query_host: bool,
|
||||
device: Union[str, torch.device] = "cuda",
|
||||
) -> Optional[torch.Tensor]:
|
||||
"""
|
||||
Forward pass of the StarAttention module.
|
||||
|
||||
Args:
|
||||
input_ids: Input tensor of shape (batch_size, seq_len)
|
||||
query_input: Query tensor of shape (batch_size, seq_len, hidden_size)
|
||||
host_id: ID of the current host
|
||||
is_query_host: Whether this host is the query host
|
||||
device: Device to run computations on
|
||||
|
||||
Returns:
|
||||
Output tensor if this is the query host, None otherwise
|
||||
"""
|
||||
# Phase 1: Local Context Encoding
|
||||
self.phase1_local_context_encoding(input_ids, host_id, device)
|
||||
|
||||
# Phase 2: Query Encoding and Output Generation
|
||||
return self.phase2_query_encoding(
|
||||
query_input, host_id, is_query_host, device
|
||||
)
|
||||
|
||||
|
||||
# Example forward pass
|
||||
config = StarAttentionConfig(
|
||||
hidden_size=768,
|
||||
num_attention_heads=12,
|
||||
num_hosts=3,
|
||||
block_size=512,
|
||||
anchor_size=128,
|
||||
)
|
||||
|
||||
# Initialize model
|
||||
model = StarAttention(config)
|
||||
|
||||
# Example input tensors
|
||||
batch_size = 4
|
||||
seq_len = 512
|
||||
input_ids = torch.randint(
|
||||
0, 1000, (batch_size, seq_len)
|
||||
) # Random input IDs
|
||||
query_input = torch.randn(
|
||||
batch_size, seq_len, config.hidden_size
|
||||
) # Random query input
|
||||
|
||||
# Example forward pass for query host (host_id = 2)
|
||||
output = model(
|
||||
input_ids=input_ids,
|
||||
query_input=query_input,
|
||||
host_id=2,
|
||||
is_query_host=True,
|
||||
device="cpu",
|
||||
)
|
||||
|
||||
print(output)
|
||||
Loading…
Reference in new issue