rustytorch/crates/models/rtx-multimodal/tests/basic_compile_test.rs

/// Basic compilation test - TDD RED phase
/// Tests that our fundamental structures compile and work
use rtx_tensor::{Device, Tensor};

#[test]
fn test_basic_tensor_operations_compile() {
    // RED: Test basic tensor operations we need
    let device = Device::default();

    // Test tensor creation
    let a = Tensor::ones(&[2, 3], &device);
    assert!(a.is_ok(), "Should create tensor");

    let a = a.unwrap();
    assert_eq!(a.shape(), &[2, 3]);

    // Test tensor addition
    let b = Tensor::ones(&[2, 3], &device).unwrap();
    let c = (&a + &b);
    assert!(c.is_ok(), "Addition should work");

    // Test tensor concatenation
    let t1 = Tensor::randn(&[2, 3, 4], &device).unwrap();
    let t2 = Tensor::randn(&[2, 5, 4], &device).unwrap();

    // Clone to pass ownership
    let concat_result = Tensor::cat(&[t1.clone(), t2.clone()], 1);
    assert!(concat_result.is_ok(), "Concatenation should work");

    let concatenated = concat_result.unwrap();
    assert_eq!(concatenated.shape(), &[2, 8, 4]);
}

#[test]
fn test_fusion_config_creation() {
    // RED: Test that FusionConfig can be created
    use rtx_multimodal::fusion::FusionConfig;

    let config = FusionConfig::new(256, 256, 256, 256, 8);
    assert_eq!(config.vision_dim, 256);
    assert_eq!(config.audio_dim, 256);
    assert_eq!(config.text_dim, 256);
    assert_eq!(config.output_dim, 256);
    assert_eq!(config.num_heads, 8);
}

#[test]
fn test_cross_modal_config_creation() {
    // RED: Test CrossModalConfig creation
    use rtx_multimodal::cross_modal_attention::CrossModalConfig;

    let config = CrossModalConfig::new(512, 8);
    assert_eq!(config.hidden_dim, 512);
    assert_eq!(config.num_heads, 8);
    assert_eq!(config.head_dim, 64); // 512 / 8
}

#[test]
fn test_multimodal_config_creation() {
    // RED: Test main MultimodalConfig
    use rtx_multimodal::MultimodalConfig;

    let config = MultimodalConfig::new(768, 12);
    assert_eq!(config.hidden_dim, 768);
    assert_eq!(config.num_heads, 12);
}

#[test]
fn test_tensor_mean_operation() {
    // RED: Test mean operation that caused errors
    let device = Device::default();
    let tensor = Tensor::randn(&[2, 3, 4], &device).unwrap();

    // Test mean with dimensions
    let mean_result = tensor.mean(&[1], false);
    assert!(mean_result.is_ok(), "Mean should work");

    let mean = mean_result.unwrap();
    assert_eq!(mean.shape(), &[2, 4]);

    // Test mean over all dimensions
    let full_mean = tensor.mean(&[], false);
    assert!(full_mean.is_ok(), "Full mean should work");
}

#[test]
fn test_tensor_narrow_operation() {
    // RED: Test narrow operation for splitting tensors
    let device = Device::default();
    let tensor = Tensor::randn(&[2, 6, 4], &device).unwrap();

    // Test narrow to split tensor
    let first_half = tensor.narrow(1, 0, 3);
    assert!(first_half.is_ok(), "First narrow should work");
    assert_eq!(first_half.unwrap().shape(), &[2, 3, 4]);

    let second_half = tensor.narrow(1, 3, 3);
    assert!(second_half.is_ok(), "Second narrow should work");
    assert_eq!(second_half.unwrap().shape(), &[2, 3, 4]);
}

#[test]
fn test_tensor_pow_and_sqrt() {
    // RED: Test power and sqrt operations for normalization
    let device = Device::default();
    let tensor = Tensor::randn(&[2, 3], &device).unwrap();

    // Test power
    let squared = tensor.pow_scalar(2.0);
    assert!(squared.is_ok(), "Power should work");

    // Test sqrt
    let sqrt_result = squared.unwrap().sqrt();
    assert!(sqrt_result.is_ok(), "Sqrt should work");
}

#[test]
fn test_tensor_sum_operation() {
    // RED: Test sum operation for normalization
    let device = Device::default();
    let tensor = Tensor::randn(&[2, 3, 4], &device).unwrap();

    // Test sum along dimensions
    let sum_result = tensor.sum(Some(2));
    assert!(sum_result.is_ok(), "Sum should work");

    let summed = sum_result.unwrap();
    assert_eq!(summed.shape(), &[2, 3]);
}

#[test]
fn test_tensor_unsqueeze_operation() {
    // RED: Test unsqueeze for broadcasting
    let device = Device::default();
    let tensor = Tensor::randn(&[2, 3], &device).unwrap();

    // Test unsqueeze
    let unsqueezed = tensor.unsqueeze(-1);
    assert!(unsqueezed.is_ok(), "Unsqueeze should work");

    let result = unsqueezed.unwrap();
    assert_eq!(result.shape(), &[2, 3, 1]);
}

#[test]
fn test_tensor_full_creation() {
    // RED: Test creating tensor with specific value
    let device = Device::default();

    let full_tensor = Tensor::full(&[2, 3], 1e-8, &device);
    assert!(full_tensor.is_ok(), "Full tensor creation should work");

    let tensor = full_tensor.unwrap();
    assert_eq!(tensor.shape(), &[2, 3]);
}