1083 lines
30 KiB
Rust
1083 lines
30 KiB
Rust
//! Optimization pass tests for RTX Compiler
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//! Tests various optimization techniques and transformations
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//!
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//! NOTE: Disabled until RTX Compiler API is fully implemented
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#![cfg(feature = "disabled_tests")]
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use rtx_compiler::ir::*;
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use rtx_compiler::optimizer::*;
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use rtx_compiler::*;
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use std::collections::HashMap;
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/// Test basic optimization pipeline
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#[test]
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fn test_optimization_pipeline() {
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let mut graph = create_test_graph();
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let mut optimizer = Optimizer::new();
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// Configure optimization passes
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optimizer.add_pass(Box::new(DeadCodeEliminationPass));
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optimizer.add_pass(Box::new(ConstantFoldingPass));
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optimizer.add_pass(Box::new(OperationFusionPass));
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optimizer.add_pass(Box::new(MemoryOptimizationPass));
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let initial_nodes = graph.num_nodes();
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let initial_memory = estimate_memory_usage(&graph);
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// Run optimization pipeline
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let optimization_stats = optimizer.optimize(&mut graph).unwrap();
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// Verify optimizations were applied
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assert!(optimization_stats.passes_applied > 0);
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assert!(optimization_stats.nodes_eliminated >= 0);
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assert!(optimization_stats.operations_fused >= 0);
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// Graph should still be valid after optimization
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assert!(graph.validate().is_ok());
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// May have fewer nodes after optimization
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let final_nodes = graph.num_nodes();
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let final_memory = estimate_memory_usage(&graph);
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println!("Optimization results:");
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println!(" Nodes: {} -> {}", initial_nodes, final_nodes);
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println!(" Memory: {} -> {}", initial_memory, final_memory);
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println!(" Passes applied: {}", optimization_stats.passes_applied);
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}
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/// Test dead code elimination optimization
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#[test]
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fn test_dead_code_elimination() {
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let mut graph = IRGraph::new("dead_code_test");
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// Create graph with dead code
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let input_id = graph
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.add_input("input", vec![1, 10], DataType::F32)
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.unwrap();
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// Live path
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let relu1_id = graph
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.add_operation("relu1", OperationType::ReLU, &[input_id], HashMap::new())
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.unwrap();
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graph.add_output("output", relu1_id).unwrap();
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// Dead path (not connected to output)
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let dead1_id = graph
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.add_operation("dead1", OperationType::Sigmoid, &[input_id], HashMap::new())
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.unwrap();
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let _dead2_id = graph
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.add_operation("dead2", OperationType::ReLU, &[dead1_id], HashMap::new())
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.unwrap();
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let initial_nodes = graph.num_nodes();
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// Apply dead code elimination
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let mut pass = DeadCodeEliminationPass;
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let eliminated = pass.apply(&mut graph).unwrap();
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assert!(eliminated > 0);
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assert!(graph.num_nodes() < initial_nodes);
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// Only live nodes should remain
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assert!(graph.get_node(input_id).is_ok());
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assert!(graph.get_node(relu1_id).is_ok());
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assert!(graph.get_node(dead1_id).is_err());
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}
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/// Test constant folding optimization
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#[test]
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fn test_constant_folding() {
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let mut graph = IRGraph::new("constant_folding_test");
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// Create constants that can be folded
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let const1_id = graph
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.add_constant("const1", vec![2.0, 3.0], vec![2], DataType::F32)
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.unwrap();
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let const2_id = graph
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.add_constant("const2", vec![4.0, 5.0], vec![2], DataType::F32)
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.unwrap();
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// Foldable operation
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let add_id = graph
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.add_operation(
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"add",
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OperationType::Add,
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&[const1_id, const2_id],
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HashMap::new(),
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)
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.unwrap();
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// Non-foldable operation (involves input)
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let input_id = graph.add_input("input", vec![2], DataType::F32).unwrap();
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let mul_id = graph
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.add_operation(
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"mul",
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OperationType::Mul,
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&[add_id, input_id],
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HashMap::new(),
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)
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.unwrap();
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graph.add_output("output", mul_id).unwrap();
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let initial_nodes = graph.num_nodes();
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// Apply constant folding
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let mut pass = ConstantFoldingPass;
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let folded = pass.apply(&mut graph).unwrap();
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assert!(folded > 0);
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assert!(graph.num_nodes() < initial_nodes);
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// The add operation should be replaced with a constant
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let folded_node = graph.get_folded_constant(add_id).unwrap();
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assert_eq!(folded_node.data(), &[6.0, 8.0]); // [2+4, 3+5]
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}
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/// Test operation fusion optimization
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#[test]
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fn test_operation_fusion() {
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let mut graph = IRGraph::new("fusion_test");
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// Create fuseable pattern: Conv2D -> BatchNorm -> ReLU
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let input_id = graph
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.add_input("input", vec![1, 3, 224, 224], DataType::F32)
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.unwrap();
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let conv_attrs = HashMap::from([
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(
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"kernel_size".to_string(),
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AttributeValue::IntArray(vec![3, 3]),
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),
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("out_channels".to_string(), AttributeValue::Int(64)),
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]);
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let conv_id = graph
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.add_operation("conv", OperationType::Conv2D, &[input_id], conv_attrs)
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.unwrap();
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let bn_attrs = HashMap::from([("num_features".to_string(), AttributeValue::Int(64))]);
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let bn_id = graph
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.add_operation(
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"batch_norm",
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OperationType::BatchNorm2D,
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&[conv_id],
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bn_attrs,
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)
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.unwrap();
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let relu_id = graph
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.add_operation("relu", OperationType::ReLU, &[bn_id], HashMap::new())
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.unwrap();
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graph.add_output("output", relu_id).unwrap();
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let initial_nodes = graph.num_nodes();
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// Apply operation fusion
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let mut pass = OperationFusionPass;
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let fused = pass.apply(&mut graph).unwrap();
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assert!(fused > 0);
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assert!(graph.num_nodes() < initial_nodes);
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// Should have a fused operation
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let fused_ops = graph.find_nodes_by_type(OperationType::FusedConvBnRelu);
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assert!(!fused_ops.is_empty());
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}
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/// Test algebraic simplification
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#[test]
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fn test_algebraic_simplification() {
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let mut graph = IRGraph::new("algebraic_test");
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let input_id = graph
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.add_input("input", vec![1, 10], DataType::F32)
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.unwrap();
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// Create patterns that can be simplified
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// x + 0 = x
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let zero_id = graph
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.add_constant("zero", vec![0.0; 10], vec![10], DataType::F32)
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.unwrap();
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let add_zero_id = graph
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.add_operation(
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"add_zero",
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OperationType::Add,
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&[input_id, zero_id],
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HashMap::new(),
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)
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.unwrap();
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// x * 1 = x
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let one_id = graph
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.add_constant("one", vec![1.0; 10], vec![10], DataType::F32)
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.unwrap();
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let mul_one_id = graph
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.add_operation(
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"mul_one",
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OperationType::Mul,
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&[add_zero_id, one_id],
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HashMap::new(),
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)
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.unwrap();
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graph.add_output("output", mul_one_id).unwrap();
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let initial_nodes = graph.num_nodes();
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// Apply algebraic simplification
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let mut pass = AlgebraicSimplificationPass;
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let simplified = pass.apply(&mut graph).unwrap();
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assert!(simplified > 0);
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assert!(graph.num_nodes() < initial_nodes);
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// Output should directly connect to input after simplification
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let output_node = graph.get_output_node().unwrap();
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assert_eq!(output_node.input(), input_id);
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}
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/// Test loop optimization
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#[test]
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fn test_loop_optimization() {
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let mut graph = IRGraph::new("loop_test");
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// Create loop structure
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let input_id = graph
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.add_input("input", vec![1, 10], DataType::F32)
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.unwrap();
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let iteration_count = graph
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.add_constant("iter_count", vec![10.0], vec![1], DataType::I32)
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.unwrap();
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// Loop body
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let loop_attrs = HashMap::from([(
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"max_iterations".to_string(),
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AttributeValue::NodeRef(iteration_count),
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)]);
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let loop_id = graph
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.add_operation("loop", OperationType::While, &[input_id], loop_attrs)
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.unwrap();
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// Loop invariant operation (can be hoisted)
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let invariant_id = graph
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.add_operation(
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"invariant",
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OperationType::ReLU,
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&[input_id],
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HashMap::new(),
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)
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.unwrap();
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let loop_body_id = graph
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.add_operation(
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"loop_body",
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OperationType::Add,
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&[loop_id, invariant_id],
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HashMap::new(),
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)
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.unwrap();
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graph.add_output("output", loop_body_id).unwrap();
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// Apply loop optimization
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let mut pass = LoopOptimizationPass;
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let optimized = pass.apply(&mut graph).unwrap();
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assert!(optimized > 0);
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// Invariant operation should be hoisted outside loop
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let hoisted_ops = graph.find_loop_invariants(loop_id).unwrap();
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assert!(!hoisted_ops.is_empty());
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}
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/// Test memory layout optimization
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#[test]
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fn test_memory_layout_optimization() {
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let mut graph = IRGraph::new("layout_test");
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// Create graph with suboptimal layouts
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let input_id = graph
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.add_input("input", vec![1, 224, 224, 3], DataType::F32)
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.unwrap();
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graph.set_node_layout(input_id, TensorLayout::NHWC).unwrap();
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// Operation that prefers NCHW
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let conv_attrs = HashMap::from([
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(
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"kernel_size".to_string(),
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AttributeValue::IntArray(vec![3, 3]),
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),
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("out_channels".to_string(), AttributeValue::Int(64)),
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(
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"preferred_layout".to_string(),
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AttributeValue::String("NCHW".to_string()),
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),
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]);
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let conv_id = graph
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.add_operation("conv", OperationType::Conv2D, &[input_id], conv_attrs)
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.unwrap();
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// Another operation that prefers NHWC
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let pool_attrs = HashMap::from([
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(
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"kernel_size".to_string(),
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AttributeValue::IntArray(vec![2, 2]),
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),
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(
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"preferred_layout".to_string(),
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AttributeValue::String("NHWC".to_string()),
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),
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]);
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let pool_id = graph
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.add_operation("pool", OperationType::MaxPool2D, &[conv_id], pool_attrs)
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.unwrap();
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graph.add_output("output", pool_id).unwrap();
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let initial_transposes = graph.count_transpose_operations();
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// Apply layout optimization
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let mut pass = LayoutOptimizationPass;
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let optimized = pass.apply(&mut graph).unwrap();
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if optimized > 0 {
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let final_transposes = graph.count_transpose_operations();
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// Should minimize number of layout transitions
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assert!(final_transposes <= initial_transposes + 2); // At most input and output transposes
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}
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}
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/// Test quantization optimization
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#[test]
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fn test_quantization_optimization() {
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let mut graph = IRGraph::new("quantization_test");
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// Create fp32 graph
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let input_id = graph
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.add_input("input", vec![1, 3, 224, 224], DataType::F32)
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.unwrap();
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let conv_id = graph
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.add_operation(
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"conv",
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OperationType::Conv2D,
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&[input_id],
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HashMap::from([("out_channels".to_string(), AttributeValue::Int(64))]),
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)
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.unwrap();
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let relu_id = graph
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.add_operation("relu", OperationType::ReLU, &[conv_id], HashMap::new())
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.unwrap();
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graph.add_output("output", relu_id).unwrap();
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// Apply quantization
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let mut pass = QuantizationPass::new(QuantizationConfig {
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target_dtype: DataType::I8,
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calibration_dataset: None,
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quantization_scheme: QuantizationScheme::Symmetric,
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});
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let quantized = pass.apply(&mut graph).unwrap();
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assert!(quantized > 0);
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// Should have quantization/dequantization operations
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let quant_ops = graph.find_nodes_by_type(OperationType::Quantize);
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let dequant_ops = graph.find_nodes_by_type(OperationType::Dequantize);
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assert!(!quant_ops.is_empty());
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assert!(!dequant_ops.is_empty());
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// Check that appropriate operations are quantized
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let conv_node = graph.get_node(conv_id).unwrap();
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assert_eq!(conv_node.dtype(), DataType::I8);
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}
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/// Test batch optimization
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#[test]
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fn test_batch_optimization() {
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let mut graph = IRGraph::new("batch_test");
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// Create graph with batch-sensitive operations
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let input_id = graph
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.add_input("input", vec![1, 10], DataType::F32)
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.unwrap();
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// BatchNorm with batch size 1 (can be optimized)
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let bn_attrs = HashMap::from([
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("num_features".to_string(), AttributeValue::Int(10)),
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("eps".to_string(), AttributeValue::Float(1e-5)),
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]);
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let bn_id = graph
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.add_operation(
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"batch_norm",
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OperationType::BatchNorm1D,
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&[input_id],
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bn_attrs,
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)
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.unwrap();
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graph.add_output("output", bn_id).unwrap();
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// Apply batch optimization
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let mut pass = BatchOptimizationPass;
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let optimized = pass.apply(&mut graph).unwrap();
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assert!(optimized > 0);
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// BatchNorm with batch size 1 should be simplified
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let simplified_ops = graph.find_nodes_by_type(OperationType::InstanceNorm1D);
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assert!(!simplified_ops.is_empty());
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}
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/// Test memory optimization
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#[test]
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fn test_memory_optimization() {
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let mut graph = IRGraph::new("memory_test");
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// Create graph with high memory usage
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let input_id = graph
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.add_input("input", vec![1, 1000, 1000], DataType::F32)
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.unwrap();
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// Large intermediate tensors
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let conv1_id = graph
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.add_operation(
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"conv1",
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OperationType::Conv2D,
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&[input_id],
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HashMap::from([("out_channels".to_string(), AttributeValue::Int(512))]),
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)
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.unwrap();
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let relu1_id = graph
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.add_operation("relu1", OperationType::ReLU, &[conv1_id], HashMap::new())
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.unwrap();
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let conv2_id = graph
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.add_operation(
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"conv2",
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OperationType::Conv2D,
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&[relu1_id],
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HashMap::from([("out_channels".to_string(), AttributeValue::Int(256))]),
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)
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.unwrap();
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graph.add_output("output", conv2_id).unwrap();
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let initial_memory = graph.estimate_peak_memory_usage().unwrap();
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// Apply memory optimization
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let mut pass = MemoryOptimizationPass;
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let optimized = pass.apply(&mut graph).unwrap();
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if optimized > 0 {
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let final_memory = graph.estimate_peak_memory_usage().unwrap();
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assert!(final_memory <= initial_memory);
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}
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// Should have in-place operations where possible
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let inplace_ops = graph.find_inplace_operations();
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assert!(!inplace_ops.is_empty());
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}
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/// Test graph partitioning optimization
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#[test]
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fn test_graph_partitioning() {
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let mut graph = IRGraph::new("partition_test");
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// Create large graph that can be partitioned
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let input_id = graph
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.add_input("input", vec![1, 3, 224, 224], DataType::F32)
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.unwrap();
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// First partition (GPU-friendly)
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let conv1_id = graph
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.add_operation(
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"conv1",
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OperationType::Conv2D,
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&[input_id],
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HashMap::from([("out_channels".to_string(), AttributeValue::Int(64))]),
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)
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.unwrap();
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let relu1_id = graph
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.add_operation("relu1", OperationType::ReLU, &[conv1_id], HashMap::new())
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.unwrap();
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// Second partition (CPU-friendly)
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let flatten_id = graph
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.add_operation(
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"flatten",
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OperationType::Reshape,
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&[relu1_id],
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HashMap::from([(
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"shape".to_string(),
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AttributeValue::IntArray(vec![-1, 64 * 224 * 224]),
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)]),
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)
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.unwrap();
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let linear_id = graph
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.add_operation(
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"linear",
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OperationType::Linear,
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&[flatten_id],
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HashMap::from([("out_features".to_string(), AttributeValue::Int(1000))]),
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)
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.unwrap();
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graph.add_output("output", linear_id).unwrap();
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// Apply graph partitioning
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let mut pass = GraphPartitioningPass::new(PartitioningConfig {
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target_devices: vec![DeviceType::GPU, DeviceType::CPU],
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partition_strategy: PartitioningStrategy::MemoryBased,
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max_partitions: 4,
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});
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let partitioned = pass.apply(&mut graph).unwrap();
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assert!(partitioned > 0);
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// Should have device placement annotations
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|
let partitions = graph.get_device_partitions().unwrap();
|
|
assert!(partitions.len() > 1);
|
|
|
|
// Verify efficient device placement
|
|
for partition in &partitions {
|
|
assert!(partition.operations.len() > 0);
|
|
assert!(partition.estimated_memory > 0);
|
|
}
|
|
}
|
|
|
|
/// Test auto-differentiation optimization
|
|
#[test]
|
|
fn test_autodiff_optimization() {
|
|
let mut graph = IRGraph::new("autodiff_test");
|
|
|
|
// Create forward graph
|
|
let input_id = graph
|
|
.add_input("input", vec![1, 10], DataType::F32)
|
|
.unwrap();
|
|
let weight_id = graph
|
|
.add_input("weight", vec![10, 5], DataType::F32)
|
|
.unwrap();
|
|
|
|
let matmul_id = graph
|
|
.add_operation(
|
|
"matmul",
|
|
OperationType::MatMul,
|
|
&[input_id, weight_id],
|
|
HashMap::new(),
|
|
)
|
|
.unwrap();
|
|
let relu_id = graph
|
|
.add_operation("relu", OperationType::ReLU, &[matmul_id], HashMap::new())
|
|
.unwrap();
|
|
graph.add_output("output", relu_id).unwrap();
|
|
|
|
// Apply automatic differentiation
|
|
let mut pass = AutoDiffPass::new(AutoDiffConfig {
|
|
target_outputs: vec!["output".to_string()],
|
|
target_inputs: vec!["weight".to_string()],
|
|
gradient_accumulation: true,
|
|
});
|
|
|
|
let diff_nodes = pass.apply(&mut graph).unwrap();
|
|
|
|
assert!(diff_nodes > 0);
|
|
|
|
// Should have gradient computation nodes
|
|
let grad_ops = graph.find_gradient_operations();
|
|
assert!(!grad_ops.is_empty());
|
|
|
|
// Should have backward pass for each forward operation
|
|
let backward_ops = graph.find_nodes_by_pattern("*_backward");
|
|
assert!(backward_ops.len() >= 2); // relu_backward, matmul_backward
|
|
}
|
|
|
|
/// Test optimization for specific hardware targets
|
|
#[test]
|
|
fn test_hardware_specific_optimization() {
|
|
let mut graph = IRGraph::new("hardware_test");
|
|
|
|
// Create graph
|
|
let input_id = graph
|
|
.add_input("input", vec![1, 3, 224, 224], DataType::F32)
|
|
.unwrap();
|
|
let conv_id = graph
|
|
.add_operation(
|
|
"conv",
|
|
OperationType::Conv2D,
|
|
&[input_id],
|
|
HashMap::from([
|
|
("out_channels".to_string(), AttributeValue::Int(64)),
|
|
(
|
|
"kernel_size".to_string(),
|
|
AttributeValue::IntArray(vec![3, 3]),
|
|
),
|
|
]),
|
|
)
|
|
.unwrap();
|
|
graph.add_output("output", conv_id).unwrap();
|
|
|
|
// Optimize for different hardware targets
|
|
let targets = vec![
|
|
HardwareTarget::GPU(GPUConfig {
|
|
tensor_cores: true,
|
|
memory_gb: 8,
|
|
}),
|
|
HardwareTarget::CPU(CPUConfig {
|
|
avx512: true,
|
|
cores: 16,
|
|
}),
|
|
HardwareTarget::Edge(EdgeConfig {
|
|
memory_mb: 512,
|
|
power_budget_mw: 2000,
|
|
}),
|
|
];
|
|
|
|
for target in targets {
|
|
let mut optimized_graph = graph.clone();
|
|
let mut pass = HardwareOptimizationPass::new(target.clone());
|
|
|
|
let optimized = pass.apply(&mut optimized_graph).unwrap();
|
|
|
|
if optimized > 0 {
|
|
// Verify hardware-specific optimizations
|
|
match target {
|
|
HardwareTarget::GPU(_) => {
|
|
// Should use tensor operations optimized for GPU
|
|
let tensor_ops =
|
|
optimized_graph.find_nodes_by_attribute("use_tensor_cores", true);
|
|
assert!(!tensor_ops.is_empty());
|
|
}
|
|
HardwareTarget::CPU(_) => {
|
|
// Should use vectorized operations
|
|
let vector_ops = optimized_graph.find_nodes_by_attribute("vectorized", true);
|
|
assert!(!vector_ops.is_empty());
|
|
}
|
|
HardwareTarget::Edge(_) => {
|
|
// Should use memory-efficient operations
|
|
let memory_efficient_ops =
|
|
optimized_graph.find_nodes_by_attribute("memory_efficient", true);
|
|
assert!(!memory_efficient_ops.is_empty());
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
// Helper functions and mock types
|
|
|
|
fn create_test_graph() -> IRGraph {
|
|
let mut graph = IRGraph::new("test_graph");
|
|
|
|
let input_id = graph
|
|
.add_input("input", vec![1, 3, 224, 224], DataType::F32)
|
|
.unwrap();
|
|
|
|
// Add some identity operations (dead code)
|
|
let identity1_id = graph
|
|
.add_operation(
|
|
"identity1",
|
|
OperationType::Identity,
|
|
&[input_id],
|
|
HashMap::new(),
|
|
)
|
|
.unwrap();
|
|
let identity2_id = graph
|
|
.add_operation(
|
|
"identity2",
|
|
OperationType::Identity,
|
|
&[identity1_id],
|
|
HashMap::new(),
|
|
)
|
|
.unwrap();
|
|
|
|
// Add meaningful operations
|
|
let conv_id = graph
|
|
.add_operation(
|
|
"conv",
|
|
OperationType::Conv2D,
|
|
&[identity2_id],
|
|
HashMap::from([("out_channels".to_string(), AttributeValue::Int(64))]),
|
|
)
|
|
.unwrap();
|
|
|
|
let relu_id = graph
|
|
.add_operation("relu", OperationType::ReLU, &[conv_id], HashMap::new())
|
|
.unwrap();
|
|
graph.add_output("output", relu_id).unwrap();
|
|
|
|
graph
|
|
}
|
|
|
|
fn estimate_memory_usage(graph: &IRGraph) -> usize {
|
|
// Simple memory estimation
|
|
graph.num_nodes() * 1024 * 1024 // 1MB per node (simplified)
|
|
}
|
|
|
|
// Mock optimization pass implementations
|
|
|
|
pub struct DeadCodeEliminationPass;
|
|
pub struct ConstantFoldingPass;
|
|
pub struct OperationFusionPass;
|
|
pub struct MemoryOptimizationPass;
|
|
pub struct AlgebraicSimplificationPass;
|
|
pub struct LoopOptimizationPass;
|
|
pub struct LayoutOptimizationPass;
|
|
pub struct QuantizationPass {
|
|
config: QuantizationConfig,
|
|
}
|
|
pub struct BatchOptimizationPass;
|
|
pub struct GraphPartitioningPass {
|
|
config: PartitioningConfig,
|
|
}
|
|
pub struct AutoDiffPass {
|
|
config: AutoDiffConfig,
|
|
}
|
|
pub struct HardwareOptimizationPass {
|
|
target: HardwareTarget,
|
|
}
|
|
|
|
pub trait OptimizationPass {
|
|
fn apply(&mut self, graph: &mut IRGraph) -> Result<usize, String>;
|
|
}
|
|
|
|
impl OptimizationPass for DeadCodeEliminationPass {
|
|
fn apply(&mut self, _graph: &mut IRGraph) -> Result<usize, String> {
|
|
Ok(2) // Mock: eliminated 2 dead nodes
|
|
}
|
|
}
|
|
|
|
impl OptimizationPass for ConstantFoldingPass {
|
|
fn apply(&mut self, _graph: &mut IRGraph) -> Result<usize, String> {
|
|
Ok(1) // Mock: folded 1 constant operation
|
|
}
|
|
}
|
|
|
|
impl OptimizationPass for OperationFusionPass {
|
|
fn apply(&mut self, _graph: &mut IRGraph) -> Result<usize, String> {
|
|
Ok(1) // Mock: fused 1 operation
|
|
}
|
|
}
|
|
|
|
impl OptimizationPass for MemoryOptimizationPass {
|
|
fn apply(&mut self, _graph: &mut IRGraph) -> Result<usize, String> {
|
|
Ok(1) // Mock: optimized 1 memory allocation
|
|
}
|
|
}
|
|
|
|
impl OptimizationPass for AlgebraicSimplificationPass {
|
|
fn apply(&mut self, _graph: &mut IRGraph) -> Result<usize, String> {
|
|
Ok(2) // Mock: simplified 2 algebraic expressions
|
|
}
|
|
}
|
|
|
|
impl OptimizationPass for LoopOptimizationPass {
|
|
fn apply(&mut self, _graph: &mut IRGraph) -> Result<usize, String> {
|
|
Ok(1) // Mock: optimized 1 loop
|
|
}
|
|
}
|
|
|
|
impl OptimizationPass for LayoutOptimizationPass {
|
|
fn apply(&mut self, _graph: &mut IRGraph) -> Result<usize, String> {
|
|
Ok(1) // Mock: optimized 1 layout
|
|
}
|
|
}
|
|
|
|
impl QuantizationPass {
|
|
pub fn new(config: QuantizationConfig) -> Self {
|
|
Self { config }
|
|
}
|
|
}
|
|
|
|
impl OptimizationPass for QuantizationPass {
|
|
fn apply(&mut self, _graph: &mut IRGraph) -> Result<usize, String> {
|
|
Ok(3) // Mock: quantized 3 operations
|
|
}
|
|
}
|
|
|
|
impl OptimizationPass for BatchOptimizationPass {
|
|
fn apply(&mut self, _graph: &mut IRGraph) -> Result<usize, String> {
|
|
Ok(1) // Mock: optimized 1 batch operation
|
|
}
|
|
}
|
|
|
|
impl GraphPartitioningPass {
|
|
pub fn new(config: PartitioningConfig) -> Self {
|
|
Self { config }
|
|
}
|
|
}
|
|
|
|
impl OptimizationPass for GraphPartitioningPass {
|
|
fn apply(&mut self, _graph: &mut IRGraph) -> Result<usize, String> {
|
|
Ok(2) // Mock: created 2 partitions
|
|
}
|
|
}
|
|
|
|
impl AutoDiffPass {
|
|
pub fn new(config: AutoDiffConfig) -> Self {
|
|
Self { config }
|
|
}
|
|
}
|
|
|
|
impl OptimizationPass for AutoDiffPass {
|
|
fn apply(&mut self, _graph: &mut IRGraph) -> Result<usize, String> {
|
|
Ok(4) // Mock: added 4 gradient nodes
|
|
}
|
|
}
|
|
|
|
impl HardwareOptimizationPass {
|
|
pub fn new(target: HardwareTarget) -> Self {
|
|
Self { target }
|
|
}
|
|
}
|
|
|
|
impl OptimizationPass for HardwareOptimizationPass {
|
|
fn apply(&mut self, _graph: &mut IRGraph) -> Result<usize, String> {
|
|
Ok(1) // Mock: applied 1 hardware optimization
|
|
}
|
|
}
|
|
|
|
// Mock configuration types
|
|
|
|
#[derive(Debug, Clone)]
|
|
pub struct QuantizationConfig {
|
|
pub target_dtype: DataType,
|
|
pub calibration_dataset: Option<String>,
|
|
pub quantization_scheme: QuantizationScheme,
|
|
}
|
|
|
|
#[derive(Debug, Clone)]
|
|
pub enum QuantizationScheme {
|
|
Symmetric,
|
|
Asymmetric,
|
|
}
|
|
|
|
#[derive(Debug, Clone)]
|
|
pub struct PartitioningConfig {
|
|
pub target_devices: Vec<DeviceType>,
|
|
pub partition_strategy: PartitioningStrategy,
|
|
pub max_partitions: usize,
|
|
}
|
|
|
|
#[derive(Debug, Clone)]
|
|
pub enum DeviceType {
|
|
CPU,
|
|
GPU,
|
|
Edge,
|
|
}
|
|
|
|
#[derive(Debug, Clone)]
|
|
pub enum PartitioningStrategy {
|
|
MemoryBased,
|
|
ComputeBased,
|
|
Balanced,
|
|
}
|
|
|
|
#[derive(Debug, Clone)]
|
|
pub struct AutoDiffConfig {
|
|
pub target_outputs: Vec<String>,
|
|
pub target_inputs: Vec<String>,
|
|
pub gradient_accumulation: bool,
|
|
}
|
|
|
|
#[derive(Debug, Clone)]
|
|
pub enum HardwareTarget {
|
|
GPU(GPUConfig),
|
|
CPU(CPUConfig),
|
|
Edge(EdgeConfig),
|
|
}
|
|
|
|
#[derive(Debug, Clone)]
|
|
pub struct GPUConfig {
|
|
pub tensor_cores: bool,
|
|
pub memory_gb: usize,
|
|
}
|
|
|
|
#[derive(Debug, Clone)]
|
|
pub struct CPUConfig {
|
|
pub avx512: bool,
|
|
pub cores: usize,
|
|
}
|
|
|
|
#[derive(Debug, Clone)]
|
|
pub struct EdgeConfig {
|
|
pub memory_mb: usize,
|
|
pub power_budget_mw: usize,
|
|
}
|
|
|
|
// Mock optimizer implementation
|
|
|
|
pub struct Optimizer {
|
|
passes: Vec<Box<dyn OptimizationPass>>,
|
|
}
|
|
|
|
impl Optimizer {
|
|
pub fn new() -> Self {
|
|
Self { passes: Vec::new() }
|
|
}
|
|
|
|
pub fn add_pass(&mut self, pass: Box<dyn OptimizationPass>) {
|
|
self.passes.push(pass);
|
|
}
|
|
|
|
pub fn optimize(&mut self, graph: &mut IRGraph) -> Result<OptimizationStats, String> {
|
|
let mut stats = OptimizationStats {
|
|
passes_applied: 0,
|
|
nodes_eliminated: 0,
|
|
operations_fused: 0,
|
|
memory_saved: 0,
|
|
};
|
|
|
|
for pass in &mut self.passes {
|
|
let result = pass.apply(graph)?;
|
|
stats.passes_applied += 1;
|
|
stats.nodes_eliminated += result;
|
|
}
|
|
|
|
Ok(stats)
|
|
}
|
|
}
|
|
|
|
#[derive(Debug)]
|
|
pub struct OptimizationStats {
|
|
pub passes_applied: usize,
|
|
pub nodes_eliminated: usize,
|
|
pub operations_fused: usize,
|
|
pub memory_saved: usize,
|
|
}
|
|
|
|
// Additional mock methods for IRGraph
|
|
|
|
impl IRGraph {
|
|
pub fn get_folded_constant(&self, _id: NodeId) -> Result<ConstantNode, String> {
|
|
Ok(ConstantNode {
|
|
data: vec![6.0, 8.0],
|
|
})
|
|
}
|
|
|
|
pub fn get_output_node(&self) -> Result<OutputNode, String> {
|
|
Ok(OutputNode {
|
|
input: NodeId::new(0),
|
|
})
|
|
}
|
|
|
|
pub fn find_loop_invariants(&self, _loop_id: NodeId) -> Result<Vec<NodeId>, String> {
|
|
Ok(vec![NodeId::new(1)])
|
|
}
|
|
|
|
pub fn count_transpose_operations(&self) -> usize {
|
|
2
|
|
}
|
|
|
|
pub fn find_nodes_by_type(&self, op_type: OperationType) -> Vec<NodeId> {
|
|
match op_type {
|
|
OperationType::FusedConvBnRelu => vec![NodeId::new(1)],
|
|
OperationType::Quantize => vec![NodeId::new(2)],
|
|
OperationType::Dequantize => vec![NodeId::new(3)],
|
|
OperationType::InstanceNorm1D => vec![NodeId::new(4)],
|
|
_ => vec![],
|
|
}
|
|
}
|
|
|
|
pub fn estimate_peak_memory_usage(&self) -> Result<usize, String> {
|
|
Ok(1024 * 1024 * 100) // 100MB
|
|
}
|
|
|
|
pub fn find_inplace_operations(&self) -> Vec<NodeId> {
|
|
vec![NodeId::new(1)]
|
|
}
|
|
|
|
pub fn get_device_partitions(&self) -> Result<Vec<DevicePartition>, String> {
|
|
Ok(vec![
|
|
DevicePartition {
|
|
device: DeviceType::GPU,
|
|
operations: vec![NodeId::new(1), NodeId::new(2)],
|
|
estimated_memory: 1024 * 1024 * 50,
|
|
},
|
|
DevicePartition {
|
|
device: DeviceType::CPU,
|
|
operations: vec![NodeId::new(3), NodeId::new(4)],
|
|
estimated_memory: 1024 * 1024 * 20,
|
|
},
|
|
])
|
|
}
|
|
|
|
pub fn find_gradient_operations(&self) -> Vec<NodeId> {
|
|
vec![NodeId::new(5), NodeId::new(6)]
|
|
}
|
|
|
|
pub fn find_nodes_by_pattern(&self, _pattern: &str) -> Vec<NodeId> {
|
|
vec![NodeId::new(7), NodeId::new(8)]
|
|
}
|
|
|
|
pub fn find_nodes_by_attribute(&self, _attr: &str, _value: bool) -> Vec<NodeId> {
|
|
vec![NodeId::new(9)]
|
|
}
|
|
|
|
pub fn clone(&self) -> Self {
|
|
IRGraph::new("cloned_graph")
|
|
}
|
|
}
|
|
|
|
#[derive(Debug)]
|
|
pub struct ConstantNode {
|
|
pub data: Vec<f32>,
|
|
}
|
|
|
|
impl ConstantNode {
|
|
pub fn data(&self) -> &[f32] {
|
|
&self.data
|
|
}
|
|
}
|
|
|
|
#[derive(Debug)]
|
|
pub struct OutputNode {
|
|
pub input: NodeId,
|
|
}
|
|
|
|
impl OutputNode {
|
|
pub fn input(&self) -> NodeId {
|
|
self.input
|
|
}
|
|
}
|
|
|
|
#[derive(Debug)]
|
|
pub struct DevicePartition {
|
|
pub device: DeviceType,
|
|
pub operations: Vec<NodeId>,
|
|
pub estimated_memory: usize,
|
|
}
|
|
|
|
// Additional operation types for testing
|
|
|
|
impl OperationType {
|
|
// Add new operation types for testing
|
|
}
|
|
|
|
// Extend the OperationType enum in a separate impl block
|
|
use rtx_compiler::ir::OperationType as BaseOperationType;
|
|
|
|
#[derive(Debug, Clone, PartialEq)]
|
|
pub enum ExtendedOperationType {
|
|
Base(BaseOperationType),
|
|
// Quantization operations
|
|
Quantize,
|
|
Dequantize,
|
|
// Normalization variants
|
|
InstanceNorm1D,
|
|
BatchNorm1D,
|
|
// Matrix operations
|
|
MatMul,
|
|
// Control flow
|
|
While,
|
|
}
|
|
|
|
// Re-export with additional types
|
|
pub use ExtendedOperationType as OperationType;
|