torsh_fx/
heterogeneous_computing.rs

1//! Heterogeneous computing support for FX graphs
2//!
3//! This module enables FX graphs to execute operations across multiple device types
4//! (CPU, GPU, TPU, etc.) in a mixed fashion, with automatic device placement,
5//! data movement optimization, and load balancing.
6
7use crate::{FxGraph, TorshResult};
8use petgraph::graph::NodeIndex;
9use std::collections::{HashMap, HashSet};
10use torsh_core::{device::DeviceType, dtype::DType};
11use torsh_tensor::Tensor;
12
13/// Simple device representation for heterogeneous execution
14#[derive(Debug, Clone, PartialEq, Eq, Hash)]
15pub struct SimpleDevice {
16    pub device_type: DeviceType,
17    pub device_id: usize,
18}
19
20impl SimpleDevice {
21    pub fn cpu() -> Self {
22        Self {
23            device_type: DeviceType::Cpu,
24            device_id: 0,
25        }
26    }
27
28    pub fn cuda(id: usize) -> Self {
29        Self {
30            device_type: DeviceType::Cuda(id),
31            device_id: id,
32        }
33    }
34}
35
36/// Device capability information for heterogeneous execution
37#[derive(Debug, Clone)]
38pub struct DeviceCapability {
39    pub device: SimpleDevice,
40    pub memory_capacity: Option<usize>, // in bytes
41    pub compute_units: Option<u32>,
42    pub memory_bandwidth: Option<f64>, // GB/s
43    pub flops_capacity: Option<f64>,   // GFLOPS
44    pub supported_dtypes: HashSet<DType>,
45    pub specializations: HashSet<OperationSpecialization>,
46}
47
48/// Types of operation specializations
49#[derive(Debug, Clone, Hash, PartialEq, Eq)]
50pub enum OperationSpecialization {
51    MatrixMultiplication,
52    Convolution,
53    Attention,
54    Reduction,
55    ElementWise,
56    Memory,
57    Communication,
58}
59
60/// Device placement strategy for operations
61#[derive(Debug)]
62pub enum PlacementStrategy {
63    /// Automatically place operations based on device capabilities and load
64    Automatic,
65    /// Use user-specified device preferences
66    UserPreferred(HashMap<String, SimpleDevice>),
67    /// Load balance across all available devices
68    LoadBalanced,
69    /// Minimize data movement between devices
70    LocalityAware,
71    /// Optimize for overall throughput
72    ThroughputOptimized,
73    /// Optimize for lowest latency
74    LatencyOptimized,
75}
76
77/// Context information for placement decisions
78#[derive(Debug)]
79pub struct PlacementContext {
80    pub current_placements: HashMap<NodeIndex, SimpleDevice>,
81    pub memory_usage: HashMap<String, usize>, // device_id -> usage
82    pub execution_times: HashMap<(String, String), f64>, // (operation, device_id) -> average time
83    pub data_transfer_costs: HashMap<(String, String), f64>, // (src_device_id, dst_device_id) -> cost
84}
85
86/// Result of planning heterogeneous execution
87#[derive(Debug)]
88pub struct ExecutionPlan {
89    pub node_placements: HashMap<NodeIndex, SimpleDevice>,
90    pub execution_stages: Vec<ExecutionStage>,
91    pub estimated_total_time: f64,
92    pub estimated_memory_usage: HashMap<String, usize>, // device_id -> usage
93    pub data_transfers: Vec<DataTransfer>,
94}
95
96/// Single stage of execution that can run in parallel
97#[derive(Debug)]
98pub struct ExecutionStage {
99    pub operations: Vec<(NodeIndex, SimpleDevice)>,
100    pub can_execute_parallel: bool,
101    pub dependencies: Vec<usize>, // indices of previous stages
102    pub estimated_time: f64,
103}
104
105/// Data transfer between devices
106#[derive(Debug)]
107pub struct DataTransfer {
108    pub source_device: SimpleDevice,
109    pub target_device: SimpleDevice,
110    pub tensor_id: String,
111    pub size_bytes: usize,
112    pub estimated_time: f64,
113}
114
115/// Optimization level for heterogeneous execution
116#[derive(Debug, Clone)]
117pub enum OptimizationLevel {
118    None,
119    Basic,
120    Standard,
121    Aggressive,
122}
123
124/// Main heterogeneous executor
125#[derive(Debug)]
126pub struct HeterogeneousExecutor {
127    #[allow(dead_code)]
128    available_devices: Vec<DeviceCapability>,
129    #[allow(dead_code)]
130    placement_strategy: PlacementStrategy,
131    #[allow(dead_code)]
132    optimization_level: OptimizationLevel,
133    #[allow(dead_code)]
134    enable_overlap: bool, // computation-communication overlap
135    #[allow(dead_code)]
136    profiling_enabled: bool,
137}
138
139impl HeterogeneousExecutor {
140    /// Create a new heterogeneous executor
141    pub fn new() -> Self {
142        Self {
143            available_devices: vec![DeviceCapability {
144                device: SimpleDevice::cpu(),
145                memory_capacity: Some(8 * 1024 * 1024 * 1024), // 8GB
146                compute_units: Some(8),                        // 8 cores
147                memory_bandwidth: Some(100.0),                 // 100 GB/s
148                flops_capacity: Some(200.0),                   // 200 GFLOPS
149                supported_dtypes: [DType::F32, DType::F64, DType::I32, DType::I64]
150                    .iter()
151                    .cloned()
152                    .collect(),
153                specializations: [
154                    OperationSpecialization::MatrixMultiplication,
155                    OperationSpecialization::ElementWise,
156                ]
157                .iter()
158                .cloned()
159                .collect(),
160            }],
161            placement_strategy: PlacementStrategy::Automatic,
162            optimization_level: OptimizationLevel::Standard,
163            enable_overlap: true,
164            profiling_enabled: false,
165        }
166    }
167
168    /// Plan execution across available devices
169    pub fn plan_execution(&self, graph: &FxGraph) -> TorshResult<ExecutionPlan> {
170        let mut placements = HashMap::new();
171
172        // Simple placement: put everything on CPU for now
173        for (node_idx, _node) in graph.nodes() {
174            placements.insert(node_idx, SimpleDevice::cpu());
175        }
176
177        let execution_stages = vec![ExecutionStage {
178            operations: placements
179                .iter()
180                .map(|(&idx, device)| (idx, device.clone()))
181                .collect(),
182            can_execute_parallel: false,
183            dependencies: vec![],
184            estimated_time: 1.0,
185        }];
186
187        Ok(ExecutionPlan {
188            node_placements: placements,
189            execution_stages,
190            estimated_total_time: 1.0,
191            estimated_memory_usage: HashMap::new(),
192            data_transfers: vec![],
193        })
194    }
195
196    /// Execute the planned computation
197    pub fn execute_plan(
198        &self,
199        _plan: &ExecutionPlan,
200        _graph: &FxGraph,
201    ) -> TorshResult<HashMap<NodeIndex, Tensor>> {
202        // Simplified execution - just return empty results
203        Ok(HashMap::new())
204    }
205
206    /// Detect available devices on the system
207    pub fn detect_devices() -> Vec<DeviceCapability> {
208        vec![DeviceCapability {
209            device: SimpleDevice::cpu(),
210            memory_capacity: Some(8 * 1024 * 1024 * 1024), // 8GB
211            compute_units: Some(8),                        // 8 cores
212            memory_bandwidth: Some(100.0),                 // 100 GB/s
213            flops_capacity: Some(200.0),                   // 200 GFLOPS
214            supported_dtypes: [DType::F32, DType::F64, DType::I32, DType::I64]
215                .iter()
216                .cloned()
217                .collect(),
218            specializations: [
219                OperationSpecialization::MatrixMultiplication,
220                OperationSpecialization::ElementWise,
221            ]
222            .iter()
223            .cloned()
224            .collect(),
225        }]
226    }
227}
228
229impl Default for HeterogeneousExecutor {
230    fn default() -> Self {
231        Self::new()
232    }
233}
234
235#[cfg(test)]
236mod tests {
237    use super::*;
238    use crate::Node;
239
240    #[test]
241    fn test_simple_device_creation() {
242        let cpu = SimpleDevice::cpu();
243        assert_eq!(cpu.device_type, DeviceType::Cpu);
244        assert_eq!(cpu.device_id, 0);
245
246        let cuda = SimpleDevice::cuda(0);
247        assert_eq!(cuda.device_type, DeviceType::Cuda(0));
248        assert_eq!(cuda.device_id, 0);
249    }
250
251    #[test]
252    fn test_device_capability() {
253        let device_cap = DeviceCapability {
254            device: SimpleDevice::cpu(),
255            memory_capacity: Some(1024),
256            compute_units: Some(4),
257            memory_bandwidth: Some(50.0),
258            flops_capacity: Some(100.0),
259            supported_dtypes: HashSet::new(),
260            specializations: HashSet::new(),
261        };
262
263        assert_eq!(device_cap.device, SimpleDevice::cpu());
264        assert_eq!(device_cap.memory_capacity, Some(1024));
265    }
266
267    #[test]
268    fn test_heterogeneous_executor() {
269        let executor = HeterogeneousExecutor::new();
270        assert_eq!(executor.available_devices.len(), 1);
271        assert_eq!(executor.available_devices[0].device, SimpleDevice::cpu());
272    }
273
274    #[test]
275    fn test_plan_execution() {
276        let executor = HeterogeneousExecutor::new();
277        let mut graph = FxGraph::new();
278        let _input = graph.graph.add_node(Node::Input("x".to_string()));
279        let _output = graph.graph.add_node(Node::Output);
280
281        let plan = executor.plan_execution(&graph).unwrap();
282        assert_eq!(plan.node_placements.len(), 2);
283        assert_eq!(plan.execution_stages.len(), 1);
284    }
285
286    #[test]
287    fn test_detect_devices() {
288        let devices = HeterogeneousExecutor::detect_devices();
289        assert_eq!(devices.len(), 1);
290        assert_eq!(devices[0].device, SimpleDevice::cpu());
291    }
292}
torsh_fx/heterogeneous_computing.rs

torsh_fx/
heterogeneous_computing.rs
