trustformers_models/comprehensive_testing/
performance.rs

1//! Performance profiling for models
2
3use anyhow::Result;
4use std::collections::HashMap;
5use std::time::{Duration, Instant};
6use trustformers_core::tensor::Tensor;
7use trustformers_core::traits::Model;
8
9use super::config::{TestDataType, TestInputConfig, ValidationConfig};
10use super::types::{
11    LayerPerformance, MemoryAnalysis, OverallPerformance, PerformanceResults,
12    ThroughputMeasurements,
13};
14
15/// Performance profiler for models
16pub struct PerformanceProfiler {
17    config: ValidationConfig,
18}
19
20impl PerformanceProfiler {
21    /// Create a new performance profiler
22    pub fn new() -> Self {
23        Self {
24            config: ValidationConfig::default(),
25        }
26    }
27
28    /// Create profiler with custom configuration
29    pub fn with_config(config: ValidationConfig) -> Self {
30        Self { config }
31    }
32
33    /// Profile model performance
34    pub fn profile_model<M: Model<Input = Tensor, Output = Tensor>>(
35        &self,
36        model: &M,
37    ) -> Result<PerformanceResults> {
38        let mut layer_performance = Vec::new();
39        let mut total_time = Duration::ZERO;
40        let total_memory = 0.0;
41
42        // Profile each test input
43        for test_input in &self.config.test_inputs {
44            let input = self.create_test_input(test_input)?;
45
46            let start_time = Instant::now();
47            let _output = model.forward(input)?;
48            let inference_time = start_time.elapsed();
49
50            total_time += inference_time;
51
52            // For now, create placeholder layer performance data
53            // In a real implementation, this would hook into the model's layers
54            layer_performance.push(LayerPerformance {
55                layer_name: format!("layer_{}", layer_performance.len()),
56                layer_type: "transformer".to_string(),
57                forward_time: inference_time / 10, // Rough estimate
58                memory_usage_mb: 100.0,            // Placeholder
59                flops: None,
60                utilization_percent: None,
61            });
62        }
63
64        let overall_performance = OverallPerformance {
65            total_inference_time: total_time,
66            tokens_per_second: 1000.0, // Placeholder calculation
67            total_flops: None,
68            peak_memory_mb: total_memory,
69            average_memory_mb: total_memory / self.config.test_inputs.len() as f64,
70        };
71
72        let memory_analysis = MemoryAnalysis {
73            by_layer_type: HashMap::new(),
74            by_tensor_type: HashMap::new(),
75            efficiency_score: 75.0,     // Placeholder
76            fragmentation_percent: 5.0, // Placeholder
77        };
78
79        let throughput = ThroughputMeasurements {
80            batch_size: 1,
81            sequence_length: 128,
82            tokens_per_second: 1000.0,
83            samples_per_second: 10.0,
84            latency_per_token_ms: 1.0,
85        };
86
87        Ok(PerformanceResults {
88            layer_performance,
89            overall_performance,
90            memory_analysis,
91            throughput,
92        })
93    }
94
95    /// Create test input (helper method)
96    fn create_test_input(&self, config: &TestInputConfig) -> Result<Tensor> {
97        match config.data_type {
98            TestDataType::I32 => {
99                // Create token IDs for language models
100                let mut input_ids = Vec::new();
101                for i in 0..config.dimensions.iter().product::<usize>() {
102                    input_ids.push(((i % 1000 + 1) as i32) as f32); // Keep in reasonable token range
103                }
104                Ok(Tensor::from_vec(input_ids, &config.dimensions)?)
105            },
106            TestDataType::F32 => {
107                // Create floating point input
108                Ok(Tensor::randn(&config.dimensions)?)
109            },
110            TestDataType::F16 => {
111                // Create half precision input (placeholder)
112                Ok(Tensor::randn(&config.dimensions)?)
113            },
114            TestDataType::I64 => {
115                // Create 64-bit integer input
116                let mut input_ids = Vec::new();
117                for i in 0..config.dimensions.iter().product::<usize>() {
118                    input_ids.push(((i % 1000 + 1) as i64) as f32);
119                }
120                Ok(Tensor::from_vec(input_ids, &config.dimensions)?)
121            },
122        }
123    }
124
125    /// Get the model name being profiled
126    pub fn get_model_name(&self) -> &str {
127        "Unknown"
128    }
129}
130
131impl Default for PerformanceProfiler {
132    fn default() -> Self {
133        Self::new()
134    }
135}
trustformers_models/comprehensive_testing/performance.rs

trustformers_models/comprehensive_testing/
performance.rs
