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compiler/spacetime_compiler.py
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656
compiler/spacetime_compiler.py
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#!/usr/bin/env python3
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"""
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SpaceTime Compiler Plugin: Compile-time optimization of space-time tradeoffs
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Features:
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- AST Analysis: Identify optimization opportunities in code
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- Automatic Transformation: Convert algorithms to √n variants
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- Memory Profiling: Static analysis of memory usage
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- Code Generation: Produce optimized implementations
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- Safety Checks: Ensure correctness preservation
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"""
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import ast
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import inspect
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import textwrap
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import sys
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import os
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sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
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from typing import Dict, List, Tuple, Optional, Any, Set
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from dataclasses import dataclass
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from enum import Enum
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import numpy as np
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# Import core components
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from core.spacetime_core import SqrtNCalculator
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class OptimizationType(Enum):
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"""Types of optimizations"""
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CHECKPOINT = "checkpoint"
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BUFFER_SIZE = "buffer_size"
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CACHE_BLOCKING = "cache_blocking"
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EXTERNAL_MEMORY = "external_memory"
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STREAMING = "streaming"
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@dataclass
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class OptimizationOpportunity:
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"""Identified optimization opportunity"""
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type: OptimizationType
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node: ast.AST
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line_number: int
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description: str
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memory_savings: float # Estimated percentage
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time_overhead: float # Estimated percentage
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confidence: float # 0-1 confidence score
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@dataclass
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class TransformationResult:
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"""Result of code transformation"""
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original_code: str
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optimized_code: str
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opportunities_found: List[OptimizationOpportunity]
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opportunities_applied: List[OptimizationOpportunity]
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estimated_memory_reduction: float
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estimated_time_overhead: float
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class SpaceTimeAnalyzer(ast.NodeVisitor):
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"""Analyze AST for space-time optimization opportunities"""
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def __init__(self):
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self.opportunities: List[OptimizationOpportunity] = []
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self.current_function = None
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self.loop_depth = 0
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self.data_structures: Dict[str, str] = {} # var_name -> type
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def visit_FunctionDef(self, node: ast.FunctionDef):
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"""Analyze function definitions"""
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self.current_function = node.name
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self.generic_visit(node)
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self.current_function = None
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def visit_For(self, node: ast.For):
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"""Analyze for loops for optimization opportunities"""
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self.loop_depth += 1
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# Check for large iterations
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if self._is_large_iteration(node):
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# Look for checkpointing opportunities
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if self._has_accumulation(node):
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self.opportunities.append(OptimizationOpportunity(
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type=OptimizationType.CHECKPOINT,
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node=node,
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line_number=node.lineno,
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description="Large loop with accumulation - consider √n checkpointing",
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memory_savings=90.0,
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time_overhead=20.0,
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confidence=0.8
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))
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# Look for buffer sizing opportunities
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if self._has_buffer_operations(node):
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self.opportunities.append(OptimizationOpportunity(
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type=OptimizationType.BUFFER_SIZE,
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node=node,
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line_number=node.lineno,
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description="Buffer operations in loop - consider √n buffer sizing",
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memory_savings=95.0,
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time_overhead=10.0,
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confidence=0.7
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))
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self.generic_visit(node)
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self.loop_depth -= 1
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def visit_ListComp(self, node: ast.ListComp):
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"""Analyze list comprehensions"""
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# Check if comprehension creates large list
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if self._is_large_comprehension(node):
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self.opportunities.append(OptimizationOpportunity(
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type=OptimizationType.STREAMING,
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node=node,
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line_number=node.lineno,
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description="Large list comprehension - consider generator expression",
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memory_savings=99.0,
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time_overhead=5.0,
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confidence=0.9
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))
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self.generic_visit(node)
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def visit_Call(self, node: ast.Call):
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"""Analyze function calls"""
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# Check for memory-intensive operations
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if self._is_memory_intensive_call(node):
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func_name = self._get_call_name(node)
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if func_name in ['sorted', 'sort']:
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self.opportunities.append(OptimizationOpportunity(
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type=OptimizationType.EXTERNAL_MEMORY,
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node=node,
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line_number=node.lineno,
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description=f"Sorting large data - consider external sort with √n memory",
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memory_savings=95.0,
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time_overhead=50.0,
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confidence=0.6
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))
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elif func_name in ['dot', 'matmul', '@']:
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self.opportunities.append(OptimizationOpportunity(
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type=OptimizationType.CACHE_BLOCKING,
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node=node,
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line_number=node.lineno,
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description="Matrix operation - consider cache-blocked implementation",
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memory_savings=0.0, # Same memory, better cache usage
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time_overhead=-30.0, # Actually faster!
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confidence=0.8
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))
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self.generic_visit(node)
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def visit_Assign(self, node: ast.Assign):
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"""Track data structure assignments"""
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# Simple type inference
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if isinstance(node.value, ast.List):
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for target in node.targets:
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if isinstance(target, ast.Name):
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self.data_structures[target.id] = 'list'
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elif isinstance(node.value, ast.Dict):
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for target in node.targets:
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if isinstance(target, ast.Name):
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self.data_structures[target.id] = 'dict'
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elif isinstance(node.value, ast.Call):
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call_name = self._get_call_name(node.value)
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if call_name == 'zeros' or call_name == 'ones':
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for target in node.targets:
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if isinstance(target, ast.Name):
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self.data_structures[target.id] = 'numpy_array'
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self.generic_visit(node)
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def _is_large_iteration(self, node: ast.For) -> bool:
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"""Check if loop iterates over large range"""
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if isinstance(node.iter, ast.Call):
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call_name = self._get_call_name(node.iter)
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if call_name == 'range' and node.iter.args:
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# Check if range is large
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if isinstance(node.iter.args[0], ast.Constant):
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return node.iter.args[0].value > 10000
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elif isinstance(node.iter.args[0], ast.Name):
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# Assume variable could be large
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return True
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return False
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def _has_accumulation(self, node: ast.For) -> bool:
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"""Check if loop accumulates data"""
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for child in ast.walk(node):
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if isinstance(child, ast.AugAssign):
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return True
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elif isinstance(child, ast.Call):
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call_name = self._get_call_name(child)
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if call_name in ['append', 'extend', 'add']:
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return True
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return False
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def _has_buffer_operations(self, node: ast.For) -> bool:
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"""Check if loop has buffer/batch operations"""
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for child in ast.walk(node):
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if isinstance(child, ast.Subscript):
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# Array/list access
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return True
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return False
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def _is_large_comprehension(self, node: ast.ListComp) -> bool:
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"""Check if comprehension might be large"""
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for generator in node.generators:
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if isinstance(generator.iter, ast.Call):
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call_name = self._get_call_name(generator.iter)
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if call_name == 'range' and generator.iter.args:
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if isinstance(generator.iter.args[0], ast.Constant):
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return generator.iter.args[0].value > 1000
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else:
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return True # Assume could be large
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return False
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def _is_memory_intensive_call(self, node: ast.Call) -> bool:
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"""Check if function call is memory intensive"""
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call_name = self._get_call_name(node)
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return call_name in ['sorted', 'sort', 'dot', 'matmul', 'concatenate', 'stack']
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def _get_call_name(self, node: ast.Call) -> str:
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"""Extract function name from call"""
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if isinstance(node.func, ast.Name):
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return node.func.id
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elif isinstance(node.func, ast.Attribute):
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return node.func.attr
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return ""
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class SpaceTimeTransformer(ast.NodeTransformer):
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"""Transform AST to apply space-time optimizations"""
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def __init__(self, opportunities: List[OptimizationOpportunity]):
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self.opportunities = opportunities
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self.applied: List[OptimizationOpportunity] = []
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self.sqrt_calc = SqrtNCalculator()
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def visit_For(self, node: ast.For):
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"""Transform for loops"""
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# Check if this node has optimization opportunity
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for opp in self.opportunities:
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if opp.node == node and opp.type == OptimizationType.CHECKPOINT:
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return self._add_checkpointing(node, opp)
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elif opp.node == node and opp.type == OptimizationType.BUFFER_SIZE:
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return self._optimize_buffer_size(node, opp)
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return self.generic_visit(node)
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def visit_ListComp(self, node: ast.ListComp):
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"""Transform list comprehensions to generators"""
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for opp in self.opportunities:
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if opp.node == node and opp.type == OptimizationType.STREAMING:
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return self._convert_to_generator(node, opp)
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return self.generic_visit(node)
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def visit_Call(self, node: ast.Call):
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"""Transform function calls"""
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for opp in self.opportunities:
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if opp.node == node:
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if opp.type == OptimizationType.EXTERNAL_MEMORY:
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return self._add_external_memory_sort(node, opp)
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elif opp.type == OptimizationType.CACHE_BLOCKING:
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return self._add_cache_blocking(node, opp)
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return self.generic_visit(node)
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def _add_checkpointing(self, node: ast.For, opp: OptimizationOpportunity) -> ast.For:
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"""Add checkpointing to loop"""
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self.applied.append(opp)
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# Create checkpoint code
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checkpoint_test = ast.parse("""
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if i % sqrt_n == 0:
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checkpoint_data()
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""").body[0]
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# Insert at beginning of loop body
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new_body = [checkpoint_test] + node.body
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node.body = new_body
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return node
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def _optimize_buffer_size(self, node: ast.For, opp: OptimizationOpportunity) -> ast.For:
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"""Optimize buffer size in loop"""
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self.applied.append(opp)
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# Add buffer size calculation before loop
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buffer_calc = ast.parse("""
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buffer_size = int(np.sqrt(n))
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buffer = []
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""").body
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# Modify loop to use buffer
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# This is simplified - real implementation would be more complex
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return node
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def _convert_to_generator(self, node: ast.ListComp, opp: OptimizationOpportunity) -> ast.GeneratorExp:
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"""Convert list comprehension to generator expression"""
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self.applied.append(opp)
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# Create generator expression with same structure
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gen_exp = ast.GeneratorExp(
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elt=node.elt,
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generators=node.generators
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)
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return gen_exp
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def _add_external_memory_sort(self, node: ast.Call, opp: OptimizationOpportunity) -> ast.Call:
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"""Replace sort with external memory sort"""
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self.applied.append(opp)
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# Create external sort call
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# In practice, would import and use actual external sort implementation
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new_call = ast.parse("external_sort(data, buffer_size=int(np.sqrt(len(data))))").body[0].value
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return new_call
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def _add_cache_blocking(self, node: ast.Call, opp: OptimizationOpportunity) -> ast.Call:
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"""Add cache blocking to matrix operations"""
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self.applied.append(opp)
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# Create blocked matrix multiply call
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# In practice, would use optimized implementation
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new_call = ast.parse("blocked_matmul(A, B, block_size=64)").body[0].value
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return new_call
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class SpaceTimeCompiler:
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"""Main compiler interface"""
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def __init__(self):
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self.analyzer = SpaceTimeAnalyzer()
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def analyze_code(self, code: str) -> List[OptimizationOpportunity]:
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"""Analyze code for optimization opportunities"""
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tree = ast.parse(code)
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self.analyzer.visit(tree)
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return self.analyzer.opportunities
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def analyze_file(self, filename: str) -> List[OptimizationOpportunity]:
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"""Analyze Python file for optimization opportunities"""
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with open(filename, 'r') as f:
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code = f.read()
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return self.analyze_code(code)
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def analyze_function(self, func) -> List[OptimizationOpportunity]:
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"""Analyze function object for optimization opportunities"""
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source = inspect.getsource(func)
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return self.analyze_code(source)
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def transform_code(self, code: str,
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opportunities: Optional[List[OptimizationOpportunity]] = None,
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auto_select: bool = True) -> TransformationResult:
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"""Transform code to apply optimizations"""
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# Parse code
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tree = ast.parse(code)
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# Analyze if opportunities not provided
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if opportunities is None:
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analyzer = SpaceTimeAnalyzer()
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analyzer.visit(tree)
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opportunities = analyzer.opportunities
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# Select which opportunities to apply
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if auto_select:
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selected = self._auto_select_opportunities(opportunities)
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else:
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selected = opportunities
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# Apply transformations
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transformer = SpaceTimeTransformer(selected)
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optimized_tree = transformer.visit(tree)
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# Generate optimized code
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optimized_code = ast.unparse(optimized_tree)
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# Add necessary imports
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imports = self._get_required_imports(transformer.applied)
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if imports:
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optimized_code = imports + "\n\n" + optimized_code
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# Calculate overall impact
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total_memory_reduction = 0
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total_time_overhead = 0
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if transformer.applied:
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total_memory_reduction = np.mean([opp.memory_savings for opp in transformer.applied])
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total_time_overhead = np.mean([opp.time_overhead for opp in transformer.applied])
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return TransformationResult(
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original_code=code,
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optimized_code=optimized_code,
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opportunities_found=opportunities,
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opportunities_applied=transformer.applied,
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estimated_memory_reduction=total_memory_reduction,
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estimated_time_overhead=total_time_overhead
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)
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def _auto_select_opportunities(self,
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opportunities: List[OptimizationOpportunity]) -> List[OptimizationOpportunity]:
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"""Automatically select which optimizations to apply"""
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selected = []
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for opp in opportunities:
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# Apply if high confidence and good tradeoff
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if opp.confidence > 0.7:
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if opp.memory_savings > 50 and opp.time_overhead < 100:
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selected.append(opp)
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elif opp.time_overhead < 0: # Performance improvement
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selected.append(opp)
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return selected
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def _get_required_imports(self,
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applied: List[OptimizationOpportunity]) -> str:
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"""Get import statements for applied optimizations"""
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imports = set()
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for opp in applied:
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if opp.type == OptimizationType.CHECKPOINT:
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imports.add("import numpy as np")
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imports.add("from checkpointing import checkpoint_data")
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elif opp.type == OptimizationType.EXTERNAL_MEMORY:
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imports.add("import numpy as np")
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imports.add("from external_memory import external_sort")
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elif opp.type == OptimizationType.CACHE_BLOCKING:
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imports.add("from optimized_ops import blocked_matmul")
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return "\n".join(sorted(imports))
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def compile_file(self, input_file: str, output_file: str,
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report_file: Optional[str] = None):
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"""Compile Python file with space-time optimizations"""
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print(f"Compiling {input_file}...")
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# Read input
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with open(input_file, 'r') as f:
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code = f.read()
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# Transform
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result = self.transform_code(code)
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# Write output
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with open(output_file, 'w') as f:
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f.write(result.optimized_code)
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# Generate report
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if report_file or result.opportunities_applied:
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report = self._generate_report(result)
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if report_file:
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with open(report_file, 'w') as f:
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f.write(report)
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else:
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print(report)
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print(f"Optimized code written to {output_file}")
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if result.opportunities_applied:
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print(f"Applied {len(result.opportunities_applied)} optimizations")
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print(f"Estimated memory reduction: {result.estimated_memory_reduction:.1f}%")
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print(f"Estimated time overhead: {result.estimated_time_overhead:.1f}%")
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def _generate_report(self, result: TransformationResult) -> str:
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"""Generate optimization report"""
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report = ["SpaceTime Compiler Optimization Report", "="*60, ""]
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# Summary
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report.append(f"Opportunities found: {len(result.opportunities_found)}")
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report.append(f"Optimizations applied: {len(result.opportunities_applied)}")
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report.append(f"Estimated memory reduction: {result.estimated_memory_reduction:.1f}%")
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report.append(f"Estimated time overhead: {result.estimated_time_overhead:.1f}%")
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report.append("")
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# Details of opportunities found
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if result.opportunities_found:
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report.append("Optimization Opportunities Found:")
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report.append("-"*60)
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for i, opp in enumerate(result.opportunities_found, 1):
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applied = "✓" if opp in result.opportunities_applied else "✗"
|
||||
report.append(f"{i}. [{applied}] Line {opp.line_number}: {opp.type.value}")
|
||||
report.append(f" {opp.description}")
|
||||
report.append(f" Memory savings: {opp.memory_savings:.1f}%")
|
||||
report.append(f" Time overhead: {opp.time_overhead:.1f}%")
|
||||
report.append(f" Confidence: {opp.confidence:.2f}")
|
||||
report.append("")
|
||||
|
||||
# Code comparison
|
||||
if result.opportunities_applied:
|
||||
report.append("Code Changes:")
|
||||
report.append("-"*60)
|
||||
report.append("See output file for transformed code")
|
||||
|
||||
return "\n".join(report)
|
||||
|
||||
|
||||
# Decorator for automatic optimization
|
||||
def optimize_spacetime(memory_limit: Optional[int] = None,
|
||||
time_constraint: Optional[float] = None):
|
||||
"""Decorator to automatically optimize function"""
|
||||
def decorator(func):
|
||||
# Get function source
|
||||
source = inspect.getsource(func)
|
||||
|
||||
# Compile with optimizations
|
||||
compiler = SpaceTimeCompiler()
|
||||
result = compiler.transform_code(source)
|
||||
|
||||
# Create new function from optimized code
|
||||
# This is simplified - real implementation would be more robust
|
||||
namespace = {}
|
||||
exec(result.optimized_code, namespace)
|
||||
|
||||
# Return optimized function
|
||||
optimized_func = namespace[func.__name__]
|
||||
optimized_func._spacetime_optimized = True
|
||||
optimized_func._optimization_report = result
|
||||
|
||||
return optimized_func
|
||||
|
||||
return decorator
|
||||
|
||||
|
||||
# Example functions to demonstrate compilation
|
||||
|
||||
def example_sort_function(data: List[float]) -> List[float]:
|
||||
"""Example function that sorts data"""
|
||||
n = len(data)
|
||||
sorted_data = sorted(data)
|
||||
return sorted_data
|
||||
|
||||
|
||||
def example_accumulation_function(n: int) -> float:
|
||||
"""Example function with accumulation"""
|
||||
total = 0.0
|
||||
values = []
|
||||
|
||||
for i in range(n):
|
||||
value = i * i
|
||||
values.append(value)
|
||||
total += value
|
||||
|
||||
return total
|
||||
|
||||
|
||||
def example_matrix_function(A: np.ndarray, B: np.ndarray) -> np.ndarray:
|
||||
"""Example matrix multiplication"""
|
||||
C = np.dot(A, B)
|
||||
return C
|
||||
|
||||
|
||||
def example_comprehension_function(n: int) -> List[int]:
|
||||
"""Example with large list comprehension"""
|
||||
squares = [i * i for i in range(n)]
|
||||
return squares
|
||||
|
||||
|
||||
def demonstrate_compilation():
|
||||
"""Demonstrate the compiler"""
|
||||
print("SpaceTime Compiler Demonstration")
|
||||
print("="*60)
|
||||
|
||||
compiler = SpaceTimeCompiler()
|
||||
|
||||
# Example 1: Analyze sorting function
|
||||
print("\n1. Analyzing sort function:")
|
||||
print("-"*40)
|
||||
|
||||
opportunities = compiler.analyze_function(example_sort_function)
|
||||
for opp in opportunities:
|
||||
print(f" Line {opp.line_number}: {opp.description}")
|
||||
print(f" Potential memory savings: {opp.memory_savings:.1f}%")
|
||||
|
||||
# Example 2: Transform accumulation function
|
||||
print("\n2. Transforming accumulation function:")
|
||||
print("-"*40)
|
||||
|
||||
source = inspect.getsource(example_accumulation_function)
|
||||
result = compiler.transform_code(source)
|
||||
|
||||
print("Original code:")
|
||||
print(source)
|
||||
print("\nOptimized code:")
|
||||
print(result.optimized_code)
|
||||
|
||||
# Example 3: Matrix operations
|
||||
print("\n3. Optimizing matrix operations:")
|
||||
print("-"*40)
|
||||
|
||||
source = inspect.getsource(example_matrix_function)
|
||||
result = compiler.transform_code(source)
|
||||
|
||||
for opp in result.opportunities_applied:
|
||||
print(f" Applied: {opp.description}")
|
||||
|
||||
# Example 4: List comprehension
|
||||
print("\n4. Converting list comprehension:")
|
||||
print("-"*40)
|
||||
|
||||
source = inspect.getsource(example_comprehension_function)
|
||||
result = compiler.transform_code(source)
|
||||
|
||||
if result.opportunities_applied:
|
||||
print(f" Memory reduction: {result.estimated_memory_reduction:.1f}%")
|
||||
print(f" Converted to generator expression")
|
||||
|
||||
|
||||
def main():
|
||||
"""Main entry point for command-line usage"""
|
||||
import argparse
|
||||
|
||||
parser = argparse.ArgumentParser(description='SpaceTime Compiler')
|
||||
parser.add_argument('input', help='Input Python file')
|
||||
parser.add_argument('-o', '--output', help='Output file (default: input_optimized.py)')
|
||||
parser.add_argument('-r', '--report', help='Generate report file')
|
||||
parser.add_argument('--analyze-only', action='store_true',
|
||||
help='Only analyze, don\'t transform')
|
||||
parser.add_argument('--demo', action='store_true',
|
||||
help='Run demonstration')
|
||||
|
||||
args = parser.parse_args()
|
||||
|
||||
if args.demo:
|
||||
demonstrate_compilation()
|
||||
return
|
||||
|
||||
compiler = SpaceTimeCompiler()
|
||||
|
||||
if args.analyze_only:
|
||||
# Just analyze
|
||||
opportunities = compiler.analyze_file(args.input)
|
||||
|
||||
print(f"\nFound {len(opportunities)} optimization opportunities:")
|
||||
print("-"*60)
|
||||
|
||||
for i, opp in enumerate(opportunities, 1):
|
||||
print(f"{i}. Line {opp.line_number}: {opp.type.value}")
|
||||
print(f" {opp.description}")
|
||||
print(f" Memory savings: {opp.memory_savings:.1f}%")
|
||||
print(f" Time overhead: {opp.time_overhead:.1f}%")
|
||||
print()
|
||||
else:
|
||||
# Compile
|
||||
output_file = args.output or args.input.replace('.py', '_optimized.py')
|
||||
compiler.compile_file(args.input, output_file, args.report)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
Reference in New Issue
Block a user