758 lines
22 KiB
Rust
758 lines
22 KiB
Rust
//! Integration layer for RTX Science with existing RTX ecosystem
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//!
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//! This module provides seamless integration with other RTX components,
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//! including tensor operations, autograd, distributed computing, and GPU acceleration.
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use crate::error::{Result, ScienceError};
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use crate::types::MemoryPool; // DistributedContext temporarily disabled
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use async_trait::async_trait;
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use rtx_autograd::Variable;
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use rtx_tensor::{DType, Device, Tensor};
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use std::collections::HashMap;
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/// RTX Device abstraction for scientific computing
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#[derive(Debug, Clone)]
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pub struct RTXDevice {
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/// Underlying RTX device
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pub device: Device,
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/// Memory pool for efficient allocation
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pub memory_pool: Option<MemoryPool>,
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/// Device capabilities
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pub capabilities: DeviceCapabilities,
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}
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/// Scientific tensor with enhanced functionality
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#[derive(Debug, Clone)]
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pub struct ScientificTensor {
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/// Underlying RTX tensor
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pub tensor: Tensor,
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/// Physical units (if applicable)
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pub units: Option<String>,
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/// Uncertainty/error bars
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pub uncertainty: Option<Tensor>,
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/// Metadata for scientific context
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pub metadata: HashMap<String, String>,
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}
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/// Automatic differentiation wrapper for scientific computing
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pub struct AutoDiff {
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/// Variable tracking for gradients
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pub variables: HashMap<String, Variable>,
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/// Computation graph context
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pub graph_context: Option<String>,
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}
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/// Gradient computation utilities
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pub struct GradientCompute {
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/// Device for computations
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device: Device,
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/// Precision settings
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precision: ComputePrecision,
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}
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/// Device capabilities for scientific computing
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#[derive(Debug, Clone)]
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pub struct DeviceCapabilities {
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/// Supports double precision
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pub double_precision: bool,
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/// Supports complex numbers
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pub complex_support: bool,
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/// CUDA compute capability (if applicable)
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pub cuda_compute: Option<String>,
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/// Memory bandwidth (GB/s)
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pub memory_bandwidth: Option<f64>,
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/// Peak FLOPS
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pub peak_flops: Option<f64>,
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}
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/// Computation precision settings
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#[derive(Debug, Clone)]
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pub enum ComputePrecision {
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/// Single precision (32-bit)
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Single,
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/// Double precision (64-bit)
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Double,
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/// Mixed precision
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Mixed,
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/// Adaptive precision
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Adaptive { tolerance: f64 },
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}
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/// Validation metrics for scientific models
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pub struct ValidationMetrics {
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/// R-squared coefficient
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pub r_squared: f64,
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/// Mean Absolute Error
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pub mae: f64,
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/// Root Mean Square Error
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pub rmse: f64,
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/// Mean Absolute Percentage Error
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pub mape: f64,
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/// Physics-specific metrics
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pub physics_metrics: HashMap<String, f64>,
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}
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/// Benchmark suite for scientific computing performance
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pub struct BenchmarkSuite {
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/// Available benchmarks
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benchmarks: Vec<Box<dyn ScientificBenchmark + Send + Sync>>,
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/// Results storage
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results: HashMap<String, BenchmarkResult>,
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}
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/// Scientific benchmark trait
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#[async_trait]
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pub trait ScientificBenchmark: Send + Sync {
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/// Run the benchmark
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async fn run(&self, device: &RTXDevice) -> Result<BenchmarkResult>;
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/// Benchmark name
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fn name(&self) -> &str;
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/// Benchmark description
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fn description(&self) -> &str;
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}
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/// Benchmark result
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#[derive(Debug, Clone)]
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pub struct BenchmarkResult {
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/// Benchmark name
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pub name: String,
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/// Execution time (seconds)
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pub execution_time: f64,
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/// Memory usage (bytes)
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pub memory_usage: u64,
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/// FLOPS achieved
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pub flops: Option<f64>,
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/// Accuracy metrics (if applicable)
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pub accuracy: Option<f64>,
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/// Additional metrics
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pub additional_metrics: HashMap<String, f64>,
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}
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/// Data loader for scientific datasets
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pub struct DataLoader {
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/// Batch size
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pub batch_size: usize,
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/// Device for data loading
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pub device: Device,
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/// Shuffle data
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pub shuffle: bool,
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/// Number of workers for parallel loading
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pub num_workers: usize,
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}
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impl RTXDevice {
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/// Create RTX device wrapper
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#[must_use]
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pub fn new(device: Device) -> Self {
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let capabilities = DeviceCapabilities::detect(&device);
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Self {
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device,
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memory_pool: None,
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capabilities,
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}
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}
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/// Initialize with memory pool
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#[must_use]
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pub fn with_memory_pool(mut self, pool: MemoryPool) -> Self {
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self.memory_pool = Some(pool);
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self
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}
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/// Check if device supports double precision
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#[must_use]
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pub fn supports_double_precision(&self) -> bool {
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self.capabilities.double_precision
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}
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/// Get optimal tensor dtype for computations
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#[must_use]
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pub fn optimal_dtype(&self, precision: &ComputePrecision) -> DType {
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match precision {
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ComputePrecision::Single => DType::F32,
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ComputePrecision::Double if self.supports_double_precision() => DType::F64,
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ComputePrecision::Double => DType::F32, // Fallback
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ComputePrecision::Mixed => DType::F16, // Use half precision for mixed
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ComputePrecision::Adaptive { .. } => DType::F32, // Default to single
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}
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}
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/// Allocate scientific tensor with metadata
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pub fn allocate_scientific_tensor(
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&self,
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shape: &[usize],
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_dtype: DType,
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units: Option<String>,
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) -> Result<ScientificTensor> {
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let tensor = Tensor::zeros(shape, &self.device)?;
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Ok(ScientificTensor {
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tensor,
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units,
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uncertainty: None,
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metadata: HashMap::new(),
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})
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}
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/// Create tensor from scientific data with validation
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pub fn tensor_from_data<T>(
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&self,
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data: &[T],
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shape: &[usize],
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units: Option<String>,
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) -> Result<ScientificTensor>
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where
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T: Clone + Into<f32>,
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{
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// Validate data integrity
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let expected_len: usize = shape.iter().product();
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if data.len() != expected_len {
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return Err(ScienceError::data_validation(
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"Data length mismatch",
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"data",
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format!("length {expected_len}"),
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format!("length {}", data.len()),
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));
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}
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// Check for invalid values (NaN, Inf)
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let float_data: Vec<f32> = data.iter().cloned().map(std::convert::Into::into).collect();
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for (i, &value) in float_data.iter().enumerate() {
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if !value.is_finite() {
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return Err(ScienceError::data_validation(
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format!("Invalid value at index {i}: {value}"),
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"data_validation",
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"finite numbers",
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"NaN or Inf",
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));
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}
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}
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let tensor = Tensor::from_slice(&float_data, shape, &self.device)?;
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Ok(ScientificTensor {
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tensor,
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units,
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uncertainty: None,
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metadata: HashMap::new(),
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})
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}
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}
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impl DeviceCapabilities {
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/// Detect device capabilities
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#[must_use]
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pub fn detect(device: &Device) -> Self {
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let double_precision = match device {
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Device::Cpu => true, // CPU supports double precision
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Device::Cuda(_) => true, // All CUDA GPUs support double precision
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Device::Rocm(_) | Device::Metal(_) => true, // Assume support
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};
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let complex_support = true; // RTX supports complex numbers
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// Detect CUDA compute capability if applicable
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let cuda_compute = match device {
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Device::Cpu => None,
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Device::Cuda(id) => Some(format!("8.{id}")), // Placeholder
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Device::Rocm(_) | Device::Metal(_) => None,
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};
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// Estimate memory bandwidth (placeholder values)
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let memory_bandwidth = match device {
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Device::Cpu => Some(100.0), // ~100 GB/s for modern CPU
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Device::Cuda(_) => Some(900.0), // ~900 GB/s for modern GPU
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Device::Rocm(_) => Some(800.0), // Estimate for ROCm
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Device::Metal(_) => Some(400.0), // Estimate for Metal
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};
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// Estimate peak FLOPS (placeholder values)
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let peak_flops = match device {
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Device::Cpu => Some(1e12), // ~1 TFLOPS for CPU
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Device::Cuda(_) => Some(50e12), // ~50 TFLOPS for modern GPU
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Device::Rocm(_) => Some(45e12), // Estimate for ROCm
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Device::Metal(_) => Some(10e12), // Estimate for Metal
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};
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Self {
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double_precision,
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complex_support,
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cuda_compute,
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memory_bandwidth,
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peak_flops,
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}
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}
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}
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impl ScientificTensor {
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/// Create scientific tensor from RTX tensor
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#[must_use]
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pub fn from_tensor(tensor: Tensor, units: Option<String>) -> Self {
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Self {
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tensor,
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units,
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uncertainty: None,
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metadata: HashMap::new(),
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}
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}
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/// Add uncertainty/error bars
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pub fn with_uncertainty(mut self, uncertainty: Tensor) -> Result<Self> {
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// Validate uncertainty tensor matches data tensor shape
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if uncertainty.shape() != self.tensor.shape() {
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return Err(ScienceError::data_validation(
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"Uncertainty tensor shape mismatch",
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"uncertainty",
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format!("{:?}", self.tensor.shape()),
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format!("{:?}", uncertainty.shape()),
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));
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}
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self.uncertainty = Some(uncertainty);
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Ok(self)
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}
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/// Add metadata
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#[must_use]
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pub fn with_metadata(mut self, key: String, value: String) -> Self {
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self.metadata.insert(key, value);
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self
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}
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/// Get tensor with units validation
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pub fn tensor_with_units(&self, expected_units: Option<&str>) -> Result<&Tensor> {
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if let Some(expected) = expected_units {
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if let Some(ref actual) = self.units {
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if actual != expected {
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return Err(ScienceError::data_validation(
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"Unit mismatch",
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"units",
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expected.to_string(),
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actual.clone(),
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));
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}
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} else {
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return Err(ScienceError::data_validation(
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"Missing units",
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"units",
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expected.to_string(),
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"none".to_string(),
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));
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}
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}
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Ok(&self.tensor)
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}
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/// Convert units (simplified implementation)
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pub fn convert_units(&mut self, target_units: &str) -> Result<()> {
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if let Some(ref current_units) = self.units {
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let conversion_factor = get_unit_conversion_factor(current_units, target_units)?;
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self.tensor = self.tensor.mul_scalar(conversion_factor as f32)?;
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if let Some(ref mut uncertainty) = self.uncertainty {
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*uncertainty = uncertainty.mul_scalar(conversion_factor as f32)?;
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}
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self.units = Some(target_units.to_string());
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}
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Ok(())
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}
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/// Statistical summary
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pub fn statistical_summary(&self) -> Result<StatisticalSummary> {
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let mean = f64::from(self.tensor.mean(&[], false)?.to_scalar::<f32>()?);
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let std_dev = f64::from(
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self.tensor
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.std(Some(&[]), false, true)?
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.to_scalar::<f32>()?,
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);
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// For now, use mean as a placeholder for min/max until reduction ops are available
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let min_val = mean - 2.0 * std_dev; // Approximate min
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let max_val = mean + 2.0 * std_dev; // Approximate max
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Ok(StatisticalSummary {
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mean,
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std_dev,
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min: min_val,
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max: max_val,
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units: self.units.clone(),
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sample_size: self.tensor.numel(),
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})
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}
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}
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/// Statistical summary of scientific data
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#[derive(Debug, Clone)]
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pub struct StatisticalSummary {
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/// Mean value
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pub mean: f64,
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/// Standard deviation
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pub std_dev: f64,
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/// Minimum value
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pub min: f64,
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/// Maximum value
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pub max: f64,
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/// Data units
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pub units: Option<String>,
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/// Number of data points
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pub sample_size: usize,
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}
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impl AutoDiff {
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/// Create new `AutoDiff` context
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#[must_use]
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pub fn new() -> Self {
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Self {
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variables: HashMap::new(),
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graph_context: None,
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}
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}
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/// Register variable for gradient tracking
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pub fn register_variable(&mut self, name: String, tensor: Tensor) -> Result<()> {
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let variable = Variable::new(tensor, true);
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self.variables.insert(name, variable);
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Ok(())
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}
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/// Get variable by name
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#[must_use]
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pub fn get_variable(&self, name: &str) -> Option<&Variable> {
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self.variables.get(name)
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}
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/// Compute gradients with respect to all variables
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///
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/// NOTE: This is a stub implementation. Real gradient computation requires
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/// the Autodiff<B> decorator pattern. Currently returns zero gradients.
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pub async fn compute_gradients(&self, loss: &Variable) -> Result<HashMap<String, Tensor>> {
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// Perform backward pass (stub returns empty HashMap)
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let _grad_map = loss.backward();
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// For now, return zero gradients for all variables
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// Real implementation would extract gradients from the autograd graph
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let mut gradients = HashMap::new();
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for (name, variable) in &self.variables {
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let zero_grad = Tensor::zeros(variable.shape().as_slice(), variable.device())?;
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gradients.insert(name.clone(), zero_grad);
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}
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Ok(gradients)
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}
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}
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impl GradientCompute {
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/// Create gradient computer
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#[must_use]
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pub fn new(device: Device, precision: ComputePrecision) -> Self {
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Self { device, precision }
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}
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/// Compute numerical gradient using finite differences
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pub async fn numerical_gradient(
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&self,
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f: &dyn Fn(&Tensor) -> Result<Tensor>,
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x: &Tensor,
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h: f64,
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) -> Result<Tensor> {
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let h_tensor = Tensor::full(x.shape().dims(), h as f32, &self.device)?;
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let f_plus = f(&x.add(&h_tensor)?)?;
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let f_minus = f(&x.subtract(&h_tensor)?)?;
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let gradient = f_plus.subtract(&f_minus)?.div(&h_tensor.mul_scalar(2.0)?)?;
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Ok(gradient)
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}
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/// Validate analytical vs numerical gradients
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pub async fn validate_gradients(
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&self,
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analytical: &Tensor,
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numerical: &Tensor,
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tolerance: f64,
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) -> Result<bool> {
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let diff = analytical.subtract(numerical)?;
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let max_diff = f64::from(diff.abs()?.max()?.to_scalar::<f32>()?);
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Ok(max_diff < tolerance)
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}
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}
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impl ValidationMetrics {
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/// Compute metrics from predictions and targets
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pub fn compute(predictions: &Tensor, targets: &Tensor) -> Result<Self> {
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let pred_vec = predictions.to_cpu()?;
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let target_vec = targets.to_cpu()?;
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if pred_vec.len() != target_vec.len() {
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return Err(ScienceError::data_validation(
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"Prediction and target lengths mismatch",
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"validation",
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format!("length {}", target_vec.len()),
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format!("length {}", pred_vec.len()),
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));
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}
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// Compute R-squared
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let target_mean = target_vec.iter().sum::<f32>() / target_vec.len() as f32;
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let ss_tot: f32 = target_vec.iter().map(|&y| (y - target_mean).powi(2)).sum();
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let ss_res: f32 = pred_vec
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.iter()
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.zip(target_vec.iter())
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.map(|(&pred, &target)| (target - pred).powi(2))
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.sum();
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let r_squared = 1.0 - (ss_res / ss_tot);
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// Compute MAE
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let mae = pred_vec
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.iter()
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.zip(target_vec.iter())
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.map(|(&pred, &target)| (pred - target).abs())
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.sum::<f32>()
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/ pred_vec.len() as f32;
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// Compute RMSE
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let mse = pred_vec
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.iter()
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.zip(target_vec.iter())
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.map(|(&pred, &target)| (pred - target).powi(2))
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.sum::<f32>()
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/ pred_vec.len() as f32;
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let rmse = mse.sqrt();
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// Compute MAPE
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let mape = pred_vec
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.iter()
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.zip(target_vec.iter())
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.map(|(&pred, &target)| {
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if target.abs() > 1e-8 {
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((pred - target) / target).abs()
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} else {
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0.0
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}
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})
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.sum::<f32>()
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* 100.0
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/ pred_vec.len() as f32;
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Ok(Self {
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r_squared: f64::from(r_squared),
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mae: f64::from(mae),
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rmse: f64::from(rmse),
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mape: f64::from(mape),
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physics_metrics: HashMap::new(),
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})
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}
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/// Add physics-specific metric
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#[must_use]
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pub fn add_physics_metric(mut self, name: String, value: f64) -> Self {
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self.physics_metrics.insert(name, value);
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self
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}
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}
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impl BenchmarkSuite {
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/// Create new benchmark suite
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|
#[must_use]
|
|
pub fn new() -> Self {
|
|
Self {
|
|
benchmarks: Vec::new(),
|
|
results: HashMap::new(),
|
|
}
|
|
}
|
|
|
|
/// Add benchmark
|
|
#[must_use]
|
|
pub fn add_benchmark(mut self, benchmark: Box<dyn ScientificBenchmark + Send + Sync>) -> Self {
|
|
self.benchmarks.push(benchmark);
|
|
self
|
|
}
|
|
|
|
/// Run all benchmarks
|
|
pub async fn run_all(&mut self, device: &RTXDevice) -> Result<()> {
|
|
for benchmark in &self.benchmarks {
|
|
let result = benchmark.run(device).await?;
|
|
self.results.insert(result.name.clone(), result);
|
|
}
|
|
Ok(())
|
|
}
|
|
|
|
/// Get results summary
|
|
#[must_use]
|
|
pub fn results_summary(&self) -> HashMap<String, String> {
|
|
let mut summary = HashMap::new();
|
|
|
|
for (name, result) in &self.results {
|
|
summary.insert(
|
|
format!("{name}_time"),
|
|
format!("{:.4} s", result.execution_time),
|
|
);
|
|
summary.insert(
|
|
format!("{name}_memory"),
|
|
format!("{:.2} MB", result.memory_usage as f64 / 1024.0 / 1024.0),
|
|
);
|
|
if let Some(flops) = result.flops {
|
|
summary.insert(format!("{name}_flops"), format!("{flops:.2e} FLOPS"));
|
|
}
|
|
}
|
|
|
|
summary
|
|
}
|
|
}
|
|
|
|
impl DataLoader {
|
|
/// Create new data loader
|
|
#[must_use]
|
|
pub fn new(batch_size: usize, device: Device) -> Self {
|
|
Self {
|
|
batch_size,
|
|
device,
|
|
shuffle: true,
|
|
num_workers: 1,
|
|
}
|
|
}
|
|
|
|
/// Load batch of scientific data
|
|
pub async fn load_batch(&self, data: &[ScientificTensor]) -> Result<Vec<ScientificTensor>> {
|
|
if data.len() < self.batch_size {
|
|
return Ok(data.to_vec());
|
|
}
|
|
|
|
// Simple batch loading (in practice would be more sophisticated)
|
|
let batch = data.iter().take(self.batch_size).cloned().collect();
|
|
Ok(batch)
|
|
}
|
|
|
|
/// Set number of parallel workers
|
|
#[must_use]
|
|
pub fn with_workers(mut self, num_workers: usize) -> Self {
|
|
self.num_workers = num_workers;
|
|
self
|
|
}
|
|
|
|
/// Enable/disable shuffling
|
|
#[must_use]
|
|
pub fn with_shuffle(mut self, shuffle: bool) -> Self {
|
|
self.shuffle = shuffle;
|
|
self
|
|
}
|
|
}
|
|
|
|
/// Simple unit conversion (placeholder implementation)
|
|
fn get_unit_conversion_factor(from_units: &str, to_units: &str) -> Result<f32> {
|
|
if from_units == to_units {
|
|
return Ok(1.0);
|
|
}
|
|
|
|
// Placeholder conversions
|
|
match (from_units, to_units) {
|
|
("m", "cm") => Ok(100.0),
|
|
("cm", "m") => Ok(0.01),
|
|
("kg", "g") => Ok(1000.0),
|
|
("g", "kg") => Ok(0.001),
|
|
("K", "C") => Ok(1.0), // Temperature difference conversion
|
|
("eV", "J") => Ok(1.602_176_6e-19),
|
|
("J", "eV") => Ok(6.241_509e18),
|
|
_ => Err(ScienceError::data_validation(
|
|
format!("Unknown unit conversion: {from_units} to {to_units}"),
|
|
"units",
|
|
to_units.to_string(),
|
|
from_units.to_string(),
|
|
)),
|
|
}
|
|
}
|
|
|
|
/// Matrix multiplication benchmark
|
|
pub struct MatMulBenchmark {
|
|
pub size: usize,
|
|
}
|
|
|
|
#[async_trait]
|
|
impl ScientificBenchmark for MatMulBenchmark {
|
|
async fn run(&self, device: &RTXDevice) -> Result<BenchmarkResult> {
|
|
let start = std::time::Instant::now();
|
|
|
|
let a = Tensor::randn(&[self.size, self.size], &device.device)?;
|
|
let b = Tensor::randn(&[self.size, self.size], &device.device)?;
|
|
let _c = a.matmul(&b)?;
|
|
|
|
let elapsed = start.elapsed().as_secs_f64();
|
|
let flops = 2.0 * (self.size as f64).powi(3); // 2 * n^3 operations
|
|
|
|
Ok(BenchmarkResult {
|
|
name: format!("MatMul{}x{}", self.size, self.size),
|
|
execution_time: elapsed,
|
|
memory_usage: (3 * self.size * self.size * 4) as u64, // 3 matrices * f32
|
|
flops: Some(flops / elapsed),
|
|
accuracy: None,
|
|
additional_metrics: HashMap::new(),
|
|
})
|
|
}
|
|
|
|
fn name(&self) -> &'static str {
|
|
"Matrix Multiplication"
|
|
}
|
|
|
|
fn description(&self) -> &'static str {
|
|
"Benchmark matrix multiplication performance"
|
|
}
|
|
}
|
|
|
|
#[cfg(test)]
|
|
mod tests {
|
|
use super::*;
|
|
|
|
#[test]
|
|
fn test_rtx_device_creation() {
|
|
let device = Device::cpu();
|
|
let rtx_device = RTXDevice::new(device);
|
|
assert!(rtx_device.supports_double_precision());
|
|
}
|
|
|
|
#[tokio::test]
|
|
async fn test_scientific_tensor() -> Result<()> {
|
|
let device = Device::cpu();
|
|
let rtx_device = RTXDevice::new(device);
|
|
|
|
let data = vec![1.0, 2.0, 3.0, 4.0];
|
|
let tensor = rtx_device.tensor_from_data(&data, &[2, 2], Some("m".to_string()))?;
|
|
|
|
assert_eq!(tensor.units, Some("m".to_string()));
|
|
Ok(())
|
|
}
|
|
|
|
#[test]
|
|
fn test_validation_metrics() -> Result<()> {
|
|
let device = Device::cpu();
|
|
let predictions = Tensor::from_slice(&[1.0, 2.0, 3.0], &[3], &device)?;
|
|
let targets = Tensor::from_slice(&[1.1, 1.9, 3.1], &[3], &device)?;
|
|
|
|
let metrics = ValidationMetrics::compute(&predictions, &targets)?;
|
|
assert!(metrics.r_squared > 0.0);
|
|
assert!(metrics.mae < 0.5);
|
|
|
|
Ok(())
|
|
}
|
|
|
|
#[tokio::test]
|
|
async fn test_benchmark_suite() -> Result<()> {
|
|
let device = RTXDevice::new(Device::cpu());
|
|
let mut suite =
|
|
BenchmarkSuite::new().add_benchmark(Box::new(MatMulBenchmark { size: 100 }));
|
|
|
|
suite.run_all(&device).await?;
|
|
let summary = suite.results_summary();
|
|
assert!(!summary.is_empty());
|
|
|
|
Ok(())
|
|
}
|
|
}
|