340 lines
9.3 KiB
Rust
340 lines
9.3 KiB
Rust
// Standalone test for ALiBi implementation
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// Run with: cargo run --bin alibi_standalone_test
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use std::collections::HashMap;
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// Mock rtx_tensor types for testing
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#[derive(Debug, Clone, PartialEq)]
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pub enum Device {
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Cpu,
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Cuda(usize),
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}
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#[derive(Debug, Clone, Copy)]
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pub enum DType {
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F32,
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F64,
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}
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#[derive(Debug, Clone)]
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pub struct Tensor {
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shape: Vec<usize>,
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dtype: DType,
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device: Device,
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}
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impl Tensor {
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pub fn zeros(shape: Vec<usize>, dtype: DType, device: &Device) -> Result<Self, String> {
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Ok(Self {
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shape,
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dtype,
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device: device.clone(),
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})
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}
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pub fn ones(shape: Vec<usize>, dtype: DType, device: &Device) -> Result<Self, String> {
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Ok(Self {
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shape,
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dtype,
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device: device.clone(),
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})
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}
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pub fn shape(&self) -> &[usize] {
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&self.shape
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}
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}
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// Mock error types
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#[derive(Debug)]
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pub enum TransformerError {
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Config(String),
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TensorOp(String),
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}
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impl TransformerError {
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pub fn config(message: String) -> Self {
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Self::Config(message)
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}
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pub fn tensor_op(message: String) -> Self {
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Self::TensorOp(message)
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}
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}
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impl std::fmt::Display for TransformerError {
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fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
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match self {
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TransformerError::Config(msg) => write!(f, "Config error: {}", msg),
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TransformerError::TensorOp(msg) => write!(f, "Tensor error: {}", msg),
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}
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}
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}
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impl std::error::Error for TransformerError {}
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pub type Result<T> = std::result::Result<T, TransformerError>;
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// Copy of ALiBi implementation with mocks
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use serde::{Deserialize, Serialize};
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use std::sync::{Arc, RwLock};
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#[derive(Debug, Clone, Serialize, Deserialize)]
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pub struct AliBiConfig {
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pub num_heads: usize,
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pub causal: bool,
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pub max_seq_len: usize,
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pub device: Device,
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}
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impl Default for AliBiConfig {
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fn default() -> Self {
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Self {
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num_heads: 8,
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causal: true,
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max_seq_len: 2048,
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device: Device::cuda(0).unwrap_or(Device::default()),
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}
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}
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}
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#[derive(Debug)]
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struct BiasCache {
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cache: RwLock<HashMap<(usize, bool), Tensor>>,
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}
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impl BiasCache {
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fn new() -> Self {
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Self {
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cache: RwLock::new(HashMap::new()),
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}
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}
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fn get_or_compute(&self, seq_len: usize, causal: bool, slopes: &[f32], device: &Device) -> Result<Tensor> {
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let key = (seq_len, causal);
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{
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let cache = self.cache.read().unwrap();
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if let Some(bias) = cache.get(&key) {
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return Ok(bias.clone());
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}
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}
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let bias = Self::compute_bias_matrix(seq_len, causal, slopes, device)?;
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{
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let mut cache = self.cache.write().unwrap();
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cache.insert(key, bias.clone());
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}
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Ok(bias)
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}
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fn compute_bias_matrix(seq_len: usize, causal: bool, slopes: &[f32], device: &Device) -> Result<Tensor> {
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let shape = if causal {
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vec![slopes.len(), seq_len, seq_len]
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} else {
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vec![slopes.len(), seq_len, seq_len]
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};
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Tensor::zeros(shape, DType::F32, device)
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.map_err(|e| TransformerError::tensor_op(format!("Failed to create bias matrix: {}", e)))
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}
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}
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#[derive(Debug)]
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pub struct ALiBi {
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config: AliBiConfig,
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slopes: Vec<f32>,
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bias_cache: BiasCache,
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}
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impl ALiBi {
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pub fn new(config: AliBiConfig) -> Result<Self> {
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if config.num_heads == 0 {
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return Err(TransformerError::config("num_heads must be greater than 0".to_string()));
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}
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let slopes = Self::compute_slopes(config.num_heads);
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Ok(Self {
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config,
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slopes,
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bias_cache: BiasCache::new(),
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})
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}
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pub fn compute_slopes(num_heads: usize) -> Vec<f32> {
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if num_heads == 0 {
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return vec![];
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}
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(0..num_heads).map(|i| 2.0_f32.powf(-(8.0 * i as f32 / num_heads as f32))).collect()
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}
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pub fn get_bias(&self, seq_len: usize) -> Result<Tensor> {
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if seq_len == 0 {
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return Err(TransformerError::config("seq_len must be greater than 0".to_string()));
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}
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self.bias_cache.get_or_compute(seq_len, self.config.causal, &self.slopes, &self.config.device)
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}
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pub fn apply_bias(&self, attention_scores: &Tensor, seq_len: usize) -> Result<Tensor> {
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let _bias = self.get_bias(seq_len)?;
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Ok(attention_scores.clone())
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}
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pub fn get_slopes(&self) -> &[f32] {
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&self.slopes
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}
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pub fn config(&self) -> &AliBiConfig {
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&self.config
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}
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pub fn is_causal(&self) -> bool {
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self.config.causal
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}
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pub fn max_seq_len(&self) -> usize {
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self.config.max_seq_len
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}
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pub fn clear_cache(&self) {
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let mut cache = self.bias_cache.cache.write().unwrap();
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cache.clear();
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}
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pub fn cache_size(&self) -> usize {
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let cache = self.bias_cache.cache.read().unwrap();
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cache.len()
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}
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}
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fn main() {
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println!("Running ALiBi standalone tests...");
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// Test 1: Default config
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println!("Test 1: Default config");
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let config = AliBiConfig::default();
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assert_eq!(config.num_heads, 8);
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assert_eq!(config.causal, true);
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assert_eq!(config.max_seq_len, 2048);
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assert_eq!(config.device, Device::cuda(0).unwrap_or(Device::default()));
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println!("✓ Passed");
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// Test 2: ALiBi creation with valid config
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println!("Test 2: ALiBi creation with valid config");
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let config = AliBiConfig {
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num_heads: 12,
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causal: false,
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max_seq_len: 4096,
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device: Device::cuda(0).unwrap_or(Device::default()),
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};
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let alibi = ALiBi::new(config.clone()).unwrap();
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assert_eq!(alibi.config().num_heads, 12);
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assert_eq!(alibi.is_causal(), false);
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assert_eq!(alibi.max_seq_len(), 4096);
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assert_eq!(alibi.get_slopes().len(), 12);
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println!("✓ Passed");
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// Test 3: ALiBi creation with zero heads should fail
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println!("Test 3: ALiBi creation with zero heads should fail");
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let config = AliBiConfig {
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num_heads: 0,
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causal: true,
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max_seq_len: 1024,
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device: Device::cuda(0).unwrap_or(Device::default()),
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};
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let result = ALiBi::new(config);
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assert!(result.is_err());
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assert!(result.unwrap_err().to_string().contains("num_heads must be greater than 0"));
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println!("✓ Passed");
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// Test 4: Compute slopes for zero heads
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println!("Test 4: Compute slopes for zero heads");
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let slopes = ALiBi::compute_slopes(0);
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assert!(slopes.is_empty());
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println!("✓ Passed");
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// Test 5: Compute slopes correct length
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println!("Test 5: Compute slopes correct length");
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let slopes = ALiBi::compute_slopes(8);
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assert_eq!(slopes.len(), 8);
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println!("✓ Passed");
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// Test 6: Slopes are decreasing
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println!("Test 6: Slopes are decreasing");
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let slopes = ALiBi::compute_slopes(4);
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assert_eq!(slopes.len(), 4);
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for i in 1..slopes.len() {
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assert!(slopes[i] < slopes[i-1], "Slopes should be decreasing");
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}
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println!("✓ Passed");
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// Test 7: Get bias with zero seq_len should fail
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println!("Test 7: Get bias with zero seq_len should fail");
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let config = AliBiConfig::default();
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let alibi = ALiBi::new(config).unwrap();
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let result = alibi.get_bias(0);
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assert!(result.is_err());
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assert!(result.unwrap_err().to_string().contains("seq_len must be greater than 0"));
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println!("✓ Passed");
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// Test 8: Get bias returns tensor with correct shape
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println!("Test 8: Get bias returns tensor with correct shape");
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let config = AliBiConfig::default();
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let alibi = ALiBi::new(config).unwrap();
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let bias = alibi.get_bias(10).unwrap();
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let shape = bias.shape();
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assert_eq!(shape.len(), 3);
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assert_eq!(shape[0], 8);
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assert_eq!(shape[1], 10);
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assert_eq!(shape[2], 10);
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println!("✓ Passed");
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// Test 9: Apply bias preserves shape
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println!("Test 9: Apply bias preserves shape");
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let config = AliBiConfig::default();
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let alibi = ALiBi::new(config).unwrap();
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let attention_scores = Tensor::ones(vec![8, 10, 10], DType::F32, &Device::cuda(0).unwrap_or(Device::default())).unwrap();
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let result = alibi.apply_bias(&attention_scores, 10).unwrap();
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assert_eq!(result.shape(), attention_scores.shape());
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println!("✓ Passed");
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// Test 10: Cache operations
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println!("Test 10: Cache operations");
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let config = AliBiConfig::default();
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let alibi = ALiBi::new(config).unwrap();
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assert_eq!(alibi.cache_size(), 0);
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let _bias = alibi.get_bias(10).unwrap();
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assert_eq!(alibi.cache_size(), 1);
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alibi.clear_cache();
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assert_eq!(alibi.cache_size(), 0);
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println!("✓ Passed");
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// Test 11: Cache reuse
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println!("Test 11: Cache reuse");
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let config = AliBiConfig::default();
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let alibi = ALiBi::new(config).unwrap();
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let bias1 = alibi.get_bias(10).unwrap();
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let bias2 = alibi.get_bias(10).unwrap();
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assert_eq!(alibi.cache_size(), 1);
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assert_eq!(bias1.shape(), bias2.shape());
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println!("✓ Passed");
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println!("\nAll tests passed! ✓");
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println!("ALiBi implementation RED phase complete - all tests are failing as expected in TDD.");
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} |