375 lines
15 KiB
Python
375 lines
15 KiB
Python
#!/usr/bin/env python3
|
|
"""
|
|
🚀 智慧拓撲排序算法演示 (獨立版本)
|
|
|
|
不依賴外部模組，純粹展示拓撲排序算法的核心功能
|
|
"""
|
|
|
|
import json
|
|
from typing import List, Dict, Any, Tuple
|
|
from collections import deque
|
|
|
|
class TopologyDemo:
|
|
"""演示拓撲排序算法的類別"""
|
|
|
|
def __init__(self):
|
|
self.stage_order = []
|
|
|
|
def analyze_pipeline(self, pipeline_data: Dict[str, Any]):
|
|
"""分析pipeline並執行拓撲排序"""
|
|
print("🔍 Starting intelligent pipeline topology analysis...")
|
|
|
|
# 提取模型節點
|
|
model_nodes = [node for node in pipeline_data.get('nodes', [])
|
|
if 'model' in node.get('type', '').lower()]
|
|
connections = pipeline_data.get('connections', [])
|
|
|
|
if not model_nodes:
|
|
print(" ⚠️ No model nodes found!")
|
|
return []
|
|
|
|
# 建立依賴圖
|
|
dependency_graph = self._build_dependency_graph(model_nodes, connections)
|
|
|
|
# 檢測循環
|
|
cycles = self._detect_cycles(dependency_graph)
|
|
if cycles:
|
|
print(f" ⚠️ Found {len(cycles)} cycles!")
|
|
dependency_graph = self._resolve_cycles(dependency_graph, cycles)
|
|
|
|
# 執行拓撲排序
|
|
sorted_stages = self._topological_sort_with_optimization(dependency_graph, model_nodes)
|
|
|
|
# 計算指標
|
|
metrics = self._calculate_pipeline_metrics(sorted_stages, dependency_graph)
|
|
self._display_pipeline_analysis(sorted_stages, metrics)
|
|
|
|
return sorted_stages
|
|
|
|
def _build_dependency_graph(self, model_nodes: List[Dict], connections: List[Dict]) -> Dict[str, Dict]:
|
|
"""建立依賴圖"""
|
|
print(" 📊 Building dependency graph...")
|
|
|
|
graph = {}
|
|
for node in model_nodes:
|
|
graph[node['id']] = {
|
|
'node': node,
|
|
'dependencies': set(),
|
|
'dependents': set(),
|
|
'depth': 0
|
|
}
|
|
|
|
# 分析連接
|
|
for conn in connections:
|
|
output_node_id = conn.get('output_node')
|
|
input_node_id = conn.get('input_node')
|
|
|
|
if output_node_id in graph and input_node_id in graph:
|
|
graph[input_node_id]['dependencies'].add(output_node_id)
|
|
graph[output_node_id]['dependents'].add(input_node_id)
|
|
|
|
dep_count = sum(len(data['dependencies']) for data in graph.values())
|
|
print(f" ✅ Graph built: {len(graph)} nodes, {dep_count} dependencies")
|
|
return graph
|
|
|
|
def _detect_cycles(self, graph: Dict[str, Dict]) -> List[List[str]]:
|
|
"""檢測循環"""
|
|
print(" 🔍 Checking for dependency cycles...")
|
|
|
|
cycles = []
|
|
visited = set()
|
|
rec_stack = set()
|
|
|
|
def dfs_cycle_detect(node_id, path):
|
|
if node_id in rec_stack:
|
|
cycle_start = path.index(node_id)
|
|
cycle = path[cycle_start:] + [node_id]
|
|
cycles.append(cycle)
|
|
return True
|
|
|
|
if node_id in visited:
|
|
return False
|
|
|
|
visited.add(node_id)
|
|
rec_stack.add(node_id)
|
|
path.append(node_id)
|
|
|
|
for dependent in graph[node_id]['dependents']:
|
|
if dfs_cycle_detect(dependent, path):
|
|
return True
|
|
|
|
path.pop()
|
|
rec_stack.remove(node_id)
|
|
return False
|
|
|
|
for node_id in graph:
|
|
if node_id not in visited:
|
|
dfs_cycle_detect(node_id, [])
|
|
|
|
if cycles:
|
|
print(f" ⚠️ Found {len(cycles)} cycles")
|
|
else:
|
|
print(" ✅ No cycles detected")
|
|
|
|
return cycles
|
|
|
|
def _resolve_cycles(self, graph: Dict[str, Dict], cycles: List[List[str]]) -> Dict[str, Dict]:
|
|
"""解決循環"""
|
|
print(" 🔧 Resolving dependency cycles...")
|
|
|
|
for cycle in cycles:
|
|
node_names = [graph[nid]['node']['name'] for nid in cycle]
|
|
print(f" Breaking cycle: {' → '.join(node_names)}")
|
|
|
|
if len(cycle) >= 2:
|
|
node_to_break = cycle[-2]
|
|
dependent_to_break = cycle[-1]
|
|
|
|
graph[dependent_to_break]['dependencies'].discard(node_to_break)
|
|
graph[node_to_break]['dependents'].discard(dependent_to_break)
|
|
|
|
print(f" 🔗 Broke dependency: {graph[node_to_break]['node']['name']} → {graph[dependent_to_break]['node']['name']}")
|
|
|
|
return graph
|
|
|
|
def _topological_sort_with_optimization(self, graph: Dict[str, Dict], model_nodes: List[Dict]) -> List[Dict]:
|
|
"""執行優化的拓撲排序"""
|
|
print(" 🎯 Performing optimized topological sort...")
|
|
|
|
# 計算深度層級
|
|
self._calculate_depth_levels(graph)
|
|
|
|
# 按深度分組
|
|
depth_groups = self._group_by_depth(graph)
|
|
|
|
# 排序
|
|
sorted_nodes = []
|
|
for depth in sorted(depth_groups.keys()):
|
|
group_nodes = depth_groups[depth]
|
|
|
|
group_nodes.sort(key=lambda nid: (
|
|
len(graph[nid]['dependencies']),
|
|
-len(graph[nid]['dependents']),
|
|
graph[nid]['node']['name']
|
|
))
|
|
|
|
for node_id in group_nodes:
|
|
sorted_nodes.append(graph[node_id]['node'])
|
|
|
|
print(f" ✅ Sorted {len(sorted_nodes)} stages into {len(depth_groups)} execution levels")
|
|
return sorted_nodes
|
|
|
|
def _calculate_depth_levels(self, graph: Dict[str, Dict]):
|
|
"""計算深度層級"""
|
|
print(" 📏 Calculating execution depth levels...")
|
|
|
|
no_deps = [nid for nid, data in graph.items() if not data['dependencies']]
|
|
queue = deque([(nid, 0) for nid in no_deps])
|
|
|
|
while queue:
|
|
node_id, depth = queue.popleft()
|
|
|
|
if graph[node_id]['depth'] < depth:
|
|
graph[node_id]['depth'] = depth
|
|
|
|
for dependent in graph[node_id]['dependents']:
|
|
queue.append((dependent, depth + 1))
|
|
|
|
def _group_by_depth(self, graph: Dict[str, Dict]) -> Dict[int, List[str]]:
|
|
"""按深度分組"""
|
|
depth_groups = {}
|
|
|
|
for node_id, data in graph.items():
|
|
depth = data['depth']
|
|
if depth not in depth_groups:
|
|
depth_groups[depth] = []
|
|
depth_groups[depth].append(node_id)
|
|
|
|
return depth_groups
|
|
|
|
def _calculate_pipeline_metrics(self, sorted_stages: List[Dict], graph: Dict[str, Dict]) -> Dict[str, Any]:
|
|
"""計算指標"""
|
|
print(" 📈 Calculating pipeline metrics...")
|
|
|
|
total_stages = len(sorted_stages)
|
|
max_depth = max([data['depth'] for data in graph.values()]) + 1 if graph else 1
|
|
|
|
depth_distribution = {}
|
|
for data in graph.values():
|
|
depth = data['depth']
|
|
depth_distribution[depth] = depth_distribution.get(depth, 0) + 1
|
|
|
|
max_parallel = max(depth_distribution.values()) if depth_distribution else 1
|
|
critical_path = self._find_critical_path(graph)
|
|
|
|
return {
|
|
'total_stages': total_stages,
|
|
'pipeline_depth': max_depth,
|
|
'max_parallel_stages': max_parallel,
|
|
'parallelization_efficiency': (total_stages / max_depth) if max_depth > 0 else 1.0,
|
|
'critical_path_length': len(critical_path),
|
|
'critical_path': critical_path
|
|
}
|
|
|
|
def _find_critical_path(self, graph: Dict[str, Dict]) -> List[str]:
|
|
"""找出關鍵路徑"""
|
|
longest_path = []
|
|
|
|
def dfs_longest_path(node_id, current_path):
|
|
nonlocal longest_path
|
|
|
|
current_path.append(node_id)
|
|
|
|
if not graph[node_id]['dependents']:
|
|
if len(current_path) > len(longest_path):
|
|
longest_path = current_path.copy()
|
|
else:
|
|
for dependent in graph[node_id]['dependents']:
|
|
dfs_longest_path(dependent, current_path)
|
|
|
|
current_path.pop()
|
|
|
|
for node_id, data in graph.items():
|
|
if not data['dependencies']:
|
|
dfs_longest_path(node_id, [])
|
|
|
|
return longest_path
|
|
|
|
def _display_pipeline_analysis(self, sorted_stages: List[Dict], metrics: Dict[str, Any]):
|
|
"""顯示分析結果"""
|
|
print("\n" + "="*60)
|
|
print("🚀 INTELLIGENT PIPELINE TOPOLOGY ANALYSIS COMPLETE")
|
|
print("="*60)
|
|
|
|
print(f"📊 Pipeline Metrics:")
|
|
print(f" • Total Stages: {metrics['total_stages']}")
|
|
print(f" • Pipeline Depth: {metrics['pipeline_depth']} levels")
|
|
print(f" • Max Parallel Stages: {metrics['max_parallel_stages']}")
|
|
print(f" • Parallelization Efficiency: {metrics['parallelization_efficiency']:.1%}")
|
|
|
|
print(f"\n🎯 Optimized Execution Order:")
|
|
for i, stage in enumerate(sorted_stages, 1):
|
|
print(f" {i:2d}. {stage['name']} (ID: {stage['id'][:8]}...)")
|
|
|
|
if metrics['critical_path']:
|
|
print(f"\n⚡ Critical Path ({metrics['critical_path_length']} stages):")
|
|
critical_names = []
|
|
for node_id in metrics['critical_path']:
|
|
node_name = next((stage['name'] for stage in sorted_stages if stage['id'] == node_id), 'Unknown')
|
|
critical_names.append(node_name)
|
|
print(f" {' → '.join(critical_names)}")
|
|
|
|
print(f"\n💡 Performance Insights:")
|
|
if metrics['parallelization_efficiency'] > 0.8:
|
|
print(" ✅ Excellent parallelization potential!")
|
|
elif metrics['parallelization_efficiency'] > 0.6:
|
|
print(" ✨ Good parallelization opportunities available")
|
|
else:
|
|
print(" ⚠️ Limited parallelization - consider pipeline redesign")
|
|
|
|
if metrics['pipeline_depth'] <= 3:
|
|
print(" ⚡ Low latency pipeline - great for real-time applications")
|
|
elif metrics['pipeline_depth'] <= 6:
|
|
print(" ⚖️ Balanced pipeline depth - good throughput/latency trade-off")
|
|
else:
|
|
print(" 🎯 Deep pipeline - optimized for maximum throughput")
|
|
|
|
print("="*60 + "\n")
|
|
|
|
def create_demo_pipelines():
|
|
"""創建演示用的pipeline"""
|
|
|
|
# Demo 1: 簡單線性pipeline
|
|
simple_pipeline = {
|
|
"project_name": "Simple Linear Pipeline",
|
|
"nodes": [
|
|
{"id": "model_001", "name": "Object Detection", "type": "ExactModelNode"},
|
|
{"id": "model_002", "name": "Fire Classification", "type": "ExactModelNode"},
|
|
{"id": "model_003", "name": "Result Verification", "type": "ExactModelNode"}
|
|
],
|
|
"connections": [
|
|
{"output_node": "model_001", "input_node": "model_002"},
|
|
{"output_node": "model_002", "input_node": "model_003"}
|
|
]
|
|
}
|
|
|
|
# Demo 2: 並行pipeline
|
|
parallel_pipeline = {
|
|
"project_name": "Parallel Processing Pipeline",
|
|
"nodes": [
|
|
{"id": "model_001", "name": "RGB Processor", "type": "ExactModelNode"},
|
|
{"id": "model_002", "name": "IR Processor", "type": "ExactModelNode"},
|
|
{"id": "model_003", "name": "Depth Processor", "type": "ExactModelNode"},
|
|
{"id": "model_004", "name": "Fusion Engine", "type": "ExactModelNode"}
|
|
],
|
|
"connections": [
|
|
{"output_node": "model_001", "input_node": "model_004"},
|
|
{"output_node": "model_002", "input_node": "model_004"},
|
|
{"output_node": "model_003", "input_node": "model_004"}
|
|
]
|
|
}
|
|
|
|
# Demo 3: 複雜多層pipeline
|
|
complex_pipeline = {
|
|
"project_name": "Advanced Multi-Stage Fire Detection Pipeline",
|
|
"nodes": [
|
|
{"id": "model_rgb_001", "name": "RGB Feature Extractor", "type": "ExactModelNode"},
|
|
{"id": "model_edge_002", "name": "Edge Feature Extractor", "type": "ExactModelNode"},
|
|
{"id": "model_thermal_003", "name": "Thermal Feature Extractor", "type": "ExactModelNode"},
|
|
{"id": "model_fusion_004", "name": "Feature Fusion", "type": "ExactModelNode"},
|
|
{"id": "model_attention_005", "name": "Attention Mechanism", "type": "ExactModelNode"},
|
|
{"id": "model_classifier_006", "name": "Fire Classifier", "type": "ExactModelNode"}
|
|
],
|
|
"connections": [
|
|
{"output_node": "model_rgb_001", "input_node": "model_fusion_004"},
|
|
{"output_node": "model_edge_002", "input_node": "model_fusion_004"},
|
|
{"output_node": "model_thermal_003", "input_node": "model_attention_005"},
|
|
{"output_node": "model_fusion_004", "input_node": "model_classifier_006"},
|
|
{"output_node": "model_attention_005", "input_node": "model_classifier_006"}
|
|
]
|
|
}
|
|
|
|
# Demo 4: 有循環的pipeline (測試循環檢測)
|
|
cycle_pipeline = {
|
|
"project_name": "Pipeline with Cycles (Testing)",
|
|
"nodes": [
|
|
{"id": "model_A", "name": "Model A", "type": "ExactModelNode"},
|
|
{"id": "model_B", "name": "Model B", "type": "ExactModelNode"},
|
|
{"id": "model_C", "name": "Model C", "type": "ExactModelNode"}
|
|
],
|
|
"connections": [
|
|
{"output_node": "model_A", "input_node": "model_B"},
|
|
{"output_node": "model_B", "input_node": "model_C"},
|
|
{"output_node": "model_C", "input_node": "model_A"} # 創建循環!
|
|
]
|
|
}
|
|
|
|
return [simple_pipeline, parallel_pipeline, complex_pipeline, cycle_pipeline]
|
|
|
|
def main():
|
|
"""主演示函數"""
|
|
print("🚀 INTELLIGENT PIPELINE TOPOLOGY SORTING DEMONSTRATION")
|
|
print("="*60)
|
|
print("This demo showcases our advanced pipeline analysis capabilities:")
|
|
print("• Automatic dependency resolution")
|
|
print("• Parallel execution optimization")
|
|
print("• Cycle detection and prevention")
|
|
print("• Critical path analysis")
|
|
print("• Performance metrics calculation")
|
|
print("="*60 + "\n")
|
|
|
|
demo = TopologyDemo()
|
|
pipelines = create_demo_pipelines()
|
|
demo_names = ["Simple Linear", "Parallel Processing", "Complex Multi-Stage", "Cycle Detection"]
|
|
|
|
for i, (pipeline, name) in enumerate(zip(pipelines, demo_names), 1):
|
|
print(f"🎯 DEMO {i}: {name} Pipeline")
|
|
print("="*50)
|
|
demo.analyze_pipeline(pipeline)
|
|
print("\n")
|
|
|
|
print("🎉 ALL DEMONSTRATIONS COMPLETED SUCCESSFULLY!")
|
|
print("Ready for production deployment and progress reporting! 🚀")
|
|
|
|
if __name__ == "__main__":
|
|
main() |