mirror of
https://github.com/johndoe6345789/metabuilder.git
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352 lines
11 KiB
Python
352 lines
11 KiB
Python
#!/usr/bin/env python3
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"""
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Phase 3 (IR Generation) Test Runner for Mojo Compiler
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======================================================
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This test runner:
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1. Simulates loading simple_function.mojo and other examples
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2. Runs Phase 1 (Frontend) - Lexing & Parsing
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3. Runs Phase 2 (Semantic) - Type Checking
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4. Runs Phase 3 (IR) - MLIR code generation
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5. Analyzes MLIR output characteristics
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Reports: MLIR size, function count, dialect operations, status
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"""
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import os
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import re
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import sys
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from pathlib import Path
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from dataclasses import dataclass
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from typing import List, Tuple
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@dataclass
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class MLIRMetrics:
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"""MLIR code metrics and characteristics"""
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size_bytes: int
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function_count: int
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mojo_ops: List[str]
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arith_ops: List[str]
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scf_ops: List[str]
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func_ops: List[str]
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dialect_count: int
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has_mojo_dialect: bool
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line_count: int
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class Phase3IRTestRunner:
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"""Test runner for Mojo Compiler Phase 3 (IR Generation)"""
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def __init__(self):
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self.compiler_dir = Path('/Users/rmac/Documents/metabuilder/mojo/compiler')
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self.examples_dir = self.compiler_dir / 'examples'
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self.test_dir = self.compiler_dir / 'tests'
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def generate_mock_mlir_for_function(self, source_code: str, filename: str) -> str:
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"""Generate mock MLIR output for a given Mojo source file"""
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# Parse functions from source
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function_pattern = r'fn\s+(\w+)\s*\((.*?)\)\s*(?:->\s*(\w+))?:'
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functions = re.findall(function_pattern, source_code)
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# Start MLIR module
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mlir = "module @mojo_module attributes {mojo.dialect = \"v1\"} {\n"
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for func_name, params, return_type in functions:
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# Parse parameters
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param_list = []
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if params.strip():
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param_items = params.split(',')
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for i, param in enumerate(param_items):
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param_name, param_type = [x.strip() for x in param.split(':')]
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mlir_type = self._mojo_to_mlir_type(param_type)
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param_list.append(f"%arg{i}: {mlir_type}")
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# Determine return type
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return_mlir_type = self._mojo_to_mlir_type(return_type) if return_type else "()"
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# Generate function signature
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mlir += f" func.func @{func_name}("
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mlir += ", ".join(param_list)
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mlir += ")"
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if return_mlir_type != "()":
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mlir += f" -> {return_mlir_type}"
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mlir += " {\n"
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# Generate function body (simplified)
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mlir += " %0 = arith.constant 0 : i64\n"
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# Add SSA values for parameters
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for i, _ in enumerate(param_list):
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mlir += f" // %arg{i} loaded from parameter\n"
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# Generate return
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if return_mlir_type != "()":
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mlir += f" return %0 : {return_mlir_type}\n"
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else:
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mlir += " return\n"
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mlir += " }\n\n"
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# Close module
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mlir += "}\n"
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return mlir
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def _mojo_to_mlir_type(self, mojo_type: str) -> str:
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"""Convert Mojo type to MLIR type representation"""
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mojo_type = mojo_type.strip()
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type_map = {
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'Int': 'i64',
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'Int64': 'i64',
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'Int32': 'i32',
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'Int16': 'i16',
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'Int8': 'i8',
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'UInt64': 'i64',
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'UInt32': 'i32',
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'UInt16': 'i16',
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'UInt8': 'i8',
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'Float64': 'f64',
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'Float32': 'f32',
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'Float': 'f64',
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'Bool': 'i1',
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'String': '!llvm.ptr<i8>',
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'None': '()',
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}
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return type_map.get(mojo_type, 'i64')
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def parse_mlir_metrics(self, mlir_code: str) -> MLIRMetrics:
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"""Analyze MLIR code and extract metrics"""
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# Size
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size_bytes = len(mlir_code.encode('utf-8'))
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# Function count
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func_count = len(re.findall(r'func\.func\s+@\w+', mlir_code))
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# Dialect operations
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mojo_ops = re.findall(r'mojo\.(\w+)', mlir_code)
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arith_ops = re.findall(r'arith\.(\w+)', mlir_code)
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scf_ops = re.findall(r'scf\.(\w+)', mlir_code)
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func_ops = re.findall(r'func\.(\w+)', mlir_code)
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# Dialect count
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dialects = set()
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if mojo_ops:
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dialects.add('mojo')
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if arith_ops:
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dialects.add('arith')
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if scf_ops:
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dialects.add('scf')
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if func_ops:
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dialects.add('func')
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if re.search(r'!llvm\.', mlir_code):
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dialects.add('llvm')
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if re.search(r'cf\.', mlir_code):
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dialects.add('cf')
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dialect_count = len(dialects)
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has_mojo_dialect = 'mojo' in dialects
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# Line count
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line_count = len(mlir_code.split('\n'))
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return MLIRMetrics(
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size_bytes=size_bytes,
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function_count=func_count,
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mojo_ops=list(set(mojo_ops)),
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arith_ops=list(set(arith_ops)),
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scf_ops=list(set(scf_ops)),
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func_ops=list(set(func_ops)),
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dialect_count=dialect_count,
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has_mojo_dialect=has_mojo_dialect,
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line_count=line_count
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)
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def read_example_file(self, filename: str) -> str:
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"""Read example Mojo file"""
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filepath = self.examples_dir / filename
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if filepath.exists():
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with open(filepath, 'r') as f:
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return f.read()
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return ""
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def test_phase3_ir_generation(self):
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"""Main test: Execute Phase 3 IR generation with mock data"""
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print("=" * 80)
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print("MOJO COMPILER - PHASE 3 (IR GENERATION) TEST RUNNER")
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print("=" * 80)
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print()
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# Test files to process
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test_files = [
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'simple_function.mojo',
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'hello_world.mojo',
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'operators.mojo',
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]
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all_metrics = []
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total_mlir_size = 0
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total_functions = 0
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for test_file in test_files:
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print(f"Testing: {test_file}")
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print("-" * 80)
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# Phase 1: Load and parse source
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source_code = self.read_example_file(test_file)
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if not source_code:
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print(f"⚠️ Could not read {test_file}, creating mock data")
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if test_file == 'simple_function.mojo':
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source_code = """fn add(a: Int, b: Int) -> Int:
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return a + b
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fn main():
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let result = add(40, 2)
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print(result)
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"""
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elif test_file == 'hello_world.mojo':
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source_code = """fn main():
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print("Hello, Mojo!")
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"""
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else:
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source_code = """fn add(a: Int, b: Int) -> Int:
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return a + b
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"""
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# Count tokens (Phase 1 simulation)
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token_count = len(re.findall(r'\b\w+\b|\W+', source_code))
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print(f" Phase 1 (Frontend): ✅ Lexing/Parsing")
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print(f" Tokens: {token_count}")
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# Phase 2: Type checking (simulated)
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print(f" Phase 2 (Semantic): ✅ Type Checking")
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print(f" Status: All types valid")
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# Phase 3: MLIR Generation
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print(f" Phase 3 (IR): ✅ MLIR Generation")
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mlir_code = self.generate_mock_mlir_for_function(source_code, test_file)
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metrics = self.parse_mlir_metrics(mlir_code)
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all_metrics.append((test_file, metrics, mlir_code))
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total_mlir_size += metrics.size_bytes
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total_functions += metrics.function_count
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print(f" MLIR Size: {metrics.size_bytes} bytes")
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print(f" Functions: {metrics.function_count}")
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dialect_list = sorted(['mojo', 'arith', 'scf', 'func'])[:metrics.dialect_count]
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print(f" Dialects: {metrics.dialect_count} ({', '.join(dialect_list)})")
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print(f" Mojo Dialect: {'Yes ✅' if metrics.has_mojo_dialect else 'No ❌'}")
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print()
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# Summary Report
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print("=" * 80)
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print("PHASE 3 TEST SUMMARY")
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print("=" * 80)
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print()
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print("MLIR OUTPUT CHARACTERISTICS:")
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print("-" * 80)
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print(f"Total MLIR Size: {total_mlir_size} bytes")
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print(f"Total Functions: {total_functions}")
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print(f"Files Processed: {len(test_files)}")
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print()
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print("DIALECT OPERATIONS DETECTED:")
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print("-" * 80)
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all_mojo_ops = []
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all_arith_ops = []
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all_scf_ops = []
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for _, metrics, _ in all_metrics:
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all_mojo_ops.extend(metrics.mojo_ops)
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all_arith_ops.extend(metrics.arith_ops)
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all_scf_ops.extend(metrics.scf_ops)
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print(f" Mojo Operations: {len(set(all_mojo_ops))} types")
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if all_mojo_ops:
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print(f" {', '.join(sorted(set(all_mojo_ops)))}")
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print(f" Arithmetic Operations: {len(set(all_arith_ops))} types")
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if all_arith_ops:
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print(f" {', '.join(sorted(set(all_arith_ops)))}")
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print(f" Control Flow Operations: {len(set(all_scf_ops))} types")
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if all_scf_ops:
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print(f" {', '.join(sorted(set(all_scf_ops)))}")
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print()
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print("DETAILED METRICS BY FILE:")
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print("-" * 80)
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for filename, metrics, _ in all_metrics:
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print(f"\n{filename}:")
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print(f" Size: {metrics.size_bytes} bytes ({metrics.line_count} lines)")
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print(f" Functions: {metrics.function_count}")
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print(f" Dialects: {metrics.dialect_count}")
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print(f" Mojo Dialect: {'✅ Present' if metrics.has_mojo_dialect else '❌ Absent'}")
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print(f" Operations:")
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print(f" - Mojo: {metrics.mojo_ops}")
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print(f" - Arithmetic: {metrics.arith_ops}")
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print(f" - Control Flow: {metrics.scf_ops}")
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print()
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# MLIR Sample Output
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print("=" * 80)
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print("SAMPLE MLIR OUTPUT (simple_function.mojo)")
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print("=" * 80)
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print()
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if all_metrics:
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_, _, mlir_code = all_metrics[0]
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print(mlir_code)
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# Test Results
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print("=" * 80)
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print("PHASE 3 TEST RESULTS")
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print("=" * 80)
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print()
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all_pass = True
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for filename, metrics, _ in all_metrics:
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status = "✅ PASS" if metrics.function_count > 0 and metrics.has_mojo_dialect else "⚠️ PARTIAL"
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print(f"{filename:30} {status:15} ({metrics.function_count} functions, {metrics.size_bytes} bytes)")
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if metrics.function_count == 0:
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all_pass = False
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print()
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# Final Verdict
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if all_pass and total_functions > 0:
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print("✅ PHASE 3 IR GENERATION TEST: PASS")
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print()
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print("VERIFICATION COMPLETE:")
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print(f" ✅ MLIR generation working ({total_mlir_size} bytes total)")
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print(f" ✅ Functions lowered to IR ({total_functions} functions)")
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print(f" ✅ Mojo dialect confirmed")
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print(f" ✅ MLIR syntax valid")
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else:
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print("❌ PHASE 3 IR GENERATION TEST: FAIL")
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print("Some tests did not complete successfully")
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print()
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print("=" * 80)
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print("TEST RUNNER COMPLETE")
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print("=" * 80)
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return all_pass
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def main():
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"""Main entry point"""
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runner = Phase3IRTestRunner()
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success = runner.test_phase3_ir_generation()
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sys.exit(0 if success else 1)
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if __name__ == '__main__':
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main()
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