trustformers_debug/profiler/
gpu.rs

1//! GPU profiling and kernel analysis
2// reason: debug/profiling scaffolding — structs are constructed and their fields/methods
3// are retained for the data model, serialization completeness, and future consumers that
4// do not yet read every member. Consolidated from many item-level #[allow(dead_code)].
5#![allow(dead_code)]
6
7use anyhow::Result;
8use serde::{Deserialize, Serialize};
9use std::collections::HashMap;
10use std::time::Duration;
11
12/// Enhanced GPU kernel profiling
13#[derive(Debug, Clone, Serialize, Deserialize)]
14pub struct GpuKernelProfile {
15    pub kernel_name: String,
16    pub grid_size: (u32, u32, u32),
17    pub block_size: (u32, u32, u32),
18    pub shared_memory_bytes: usize,
19    pub registers_per_thread: u32,
20    pub occupancy: f64,
21    pub execution_time: Duration,
22    pub memory_bandwidth_gb_s: f64,
23    pub compute_utilization: f64,
24    pub stream_id: i32,
25}
26
27/// GPU profiler for kernel analysis
28#[derive(Debug)]
29pub struct GpuProfiler {
30    device_count: i32,
31    pub(crate) active_streams: HashMap<i32, Vec<GpuKernelProfile>>,
32    memory_pools: HashMap<i32, GpuMemoryPool>,
33}
34
35#[derive(Debug)]
36pub struct GpuMemoryPool {
37    device_id: i32,
38    total_memory: usize,
39    free_memory: usize,
40    fragmentation_score: f64,
41}
42
43impl GpuProfiler {
44    pub fn new() -> Result<Self> {
45        // In practice, this would initialize CUDA/ROCm profiling
46        Ok(Self {
47            device_count: 1, // Simplified
48            active_streams: HashMap::new(),
49            memory_pools: HashMap::new(),
50        })
51    }
52
53    pub fn profile_kernel(&mut self, kernel_profile: GpuKernelProfile) {
54        self.active_streams
55            .entry(kernel_profile.stream_id)
56            .or_default()
57            .push(kernel_profile);
58    }
59
60    pub fn get_gpu_utilization(&self, device_id: i32) -> f64 {
61        // Simplified GPU utilization calculation
62        if let Some(kernels) = self.active_streams.get(&device_id) {
63            if kernels.is_empty() {
64                0.0
65            } else {
66                kernels.iter().map(|k| k.compute_utilization).sum::<f64>() / kernels.len() as f64
67            }
68        } else {
69            0.0
70        }
71    }
72}
73
74#[derive(Debug, Serialize, Deserialize)]
75pub struct GpuKernelSummary {
76    pub total_kernels: usize,
77    pub total_execution_time: Duration,
78    pub avg_occupancy: f64,
79    pub avg_compute_utilization: f64,
80    pub slowest_kernels: Vec<String>,
81}
82
83#[cfg(test)]
84mod tests {
85    use super::*;
86
87    #[test]
88    fn test_gpu_profiler_new() {
89        let profiler = GpuProfiler::new();
90        assert!(profiler.is_ok());
91    }
92
93    #[test]
94    fn test_gpu_profiler_utilization_empty() {
95        let profiler = GpuProfiler::new().expect("should create profiler");
96        assert!((profiler.get_gpu_utilization(0) - 0.0).abs() < 1e-9);
97    }
98
99    #[test]
100    fn test_gpu_profiler_profile_kernel() {
101        let mut profiler = GpuProfiler::new().expect("should create profiler");
102        let kernel = GpuKernelProfile {
103            kernel_name: "matmul".to_string(),
104            grid_size: (128, 1, 1),
105            block_size: (256, 1, 1),
106            shared_memory_bytes: 4096,
107            registers_per_thread: 32,
108            occupancy: 0.85,
109            execution_time: Duration::from_micros(500),
110            memory_bandwidth_gb_s: 300.0,
111            compute_utilization: 0.9,
112            stream_id: 0,
113        };
114        profiler.profile_kernel(kernel);
115        let util = profiler.get_gpu_utilization(0);
116        assert!((util - 0.9).abs() < 1e-9);
117    }
118
119    #[test]
120    fn test_gpu_profiler_multiple_kernels_avg() {
121        let mut profiler = GpuProfiler::new().expect("should create profiler");
122        for util in [0.8, 0.6] {
123            profiler.profile_kernel(GpuKernelProfile {
124                kernel_name: "kern".to_string(),
125                grid_size: (1, 1, 1),
126                block_size: (1, 1, 1),
127                shared_memory_bytes: 0,
128                registers_per_thread: 0,
129                occupancy: 0.5,
130                execution_time: Duration::from_micros(100),
131                memory_bandwidth_gb_s: 0.0,
132                compute_utilization: util,
133                stream_id: 0,
134            });
135        }
136        let avg = profiler.get_gpu_utilization(0);
137        assert!((avg - 0.7).abs() < 1e-9);
138    }
139}
trustformers_debug/profiler/gpu.rs

trustformers_debug/profiler/
gpu.rs
