foxhunt/ml/tests/tft_int8_forward_integration_test.rs

//! INT8 TFT Forward Pass Integration Test
//!
//! Tests complete end-to-end forward pass through QuantizedTFT

use anyhow::Result;
use candle_core::{DType, Device, Tensor};
use foxhunt_ml::tft::{QuantizedTemporalFusionTransformer, TFTConfig};

#[test]
fn test_quantized_tft_forward_pass_integration() -> Result<()> {
    // Create TFT configuration
    let config = TFTConfig {
        input_dim: 30,
        hidden_dim: 64,
        num_heads: 4,
        num_layers: 2,
        prediction_horizon: 10,
        sequence_length: 20,
        num_quantiles: 9,
        num_static_features: 5,
        num_known_features: 10,
        num_unknown_features: 15, // 30 - 5 - 10 = 15
        ..Default::default()
    };

    // Create quantized TFT model
    let device = Device::Cpu;
    let tft = QuantizedTemporalFusionTransformer::new_with_device(config.clone(), device.clone())?;

    // Create input tensors
    let batch_size = 4;
    let seq_len = config.sequence_length;
    let horizon = config.prediction_horizon;

    let static_features = Tensor::randn(
        0f32,
        1f32,
        (batch_size, config.num_static_features),
        &device,
    )?;

    let historical_features = Tensor::randn(
        0f32,
        1f32,
        (batch_size, seq_len, config.num_unknown_features),
        &device,
    )?;

    let future_features = Tensor::randn(
        0f32,
        1f32,
        (batch_size, horizon, config.num_known_features),
        &device,
    )?;

    // Run forward pass
    let predictions = tft.forward(&static_features, &historical_features, &future_features)?;

    // Verify output shape
    assert_eq!(
        predictions.dims(),
        &[batch_size, horizon, config.num_quantiles]
    );
    assert_eq!(predictions.dtype(), DType::F32);

    println!("✓ Forward pass completed successfully");
    println!("  Output shape: {:?}", predictions.dims());
    println!(
        "  Memory usage: {} MB",
        tft.memory_usage_bytes() / (1024 * 1024)
    );

    Ok(())
}

#[test]
fn test_quantized_tft_input_validation() -> Result<()> {
    let config = TFTConfig {
        input_dim: 30,
        hidden_dim: 64,
        num_heads: 4,
        num_layers: 2,
        prediction_horizon: 10,
        sequence_length: 20,
        num_quantiles: 9,
        num_static_features: 5,
        num_known_features: 10,
        num_unknown_features: 15,
        ..Default::default()
    };

    let device = Device::Cpu;
    let tft = QuantizedTemporalFusionTransformer::new_with_device(config.clone(), device.clone())?;

    let batch_size = 2;

    // Test 1: Invalid static features dimension
    {
        let invalid_static = Tensor::zeros((batch_size, 10), DType::F32, &device)?; // Wrong dim
        let historical = Tensor::zeros(
            (
                batch_size,
                config.sequence_length,
                config.num_unknown_features,
            ),
            DType::F32,
            &device,
        )?;
        let future = Tensor::zeros(
            (
                batch_size,
                config.prediction_horizon,
                config.num_known_features,
            ),
            DType::F32,
            &device,
        )?;

        let result = tft.forward(&invalid_static, &historical, &future);
        assert!(result.is_err(), "Should reject invalid static features");
    }

    // Test 2: Invalid historical features dimension
    {
        let static_feat = Tensor::zeros(
            (batch_size, config.num_static_features),
            DType::F32,
            &device,
        )?;
        let invalid_historical = Tensor::zeros((batch_size, 20, 50), DType::F32, &device)?; // Wrong dim
        let future = Tensor::zeros(
            (
                batch_size,
                config.prediction_horizon,
                config.num_known_features,
            ),
            DType::F32,
            &device,
        )?;

        let result = tft.forward(&static_feat, &invalid_historical, &future);
        assert!(result.is_err(), "Should reject invalid historical features");
    }

    // Test 3: Valid inputs
    {
        let static_feat = Tensor::zeros(
            (batch_size, config.num_static_features),
            DType::F32,
            &device,
        )?;
        let historical = Tensor::zeros(
            (
                batch_size,
                config.sequence_length,
                config.num_unknown_features,
            ),
            DType::F32,
            &device,
        )?;
        let future = Tensor::zeros(
            (
                batch_size,
                config.prediction_horizon,
                config.num_known_features,
            ),
            DType::F32,
            &device,
        )?;

        let result = tft.forward(&static_feat, &historical, &future);
        assert!(result.is_ok(), "Should accept valid inputs");
    }

    println!("✓ Input validation tests passed");

    Ok(())
}

#[test]
fn test_quantized_tft_batch_consistency() -> Result<()> {
    let config = TFTConfig {
        input_dim: 30,
        hidden_dim: 64,
        num_heads: 4,
        num_layers: 2,
        prediction_horizon: 10,
        sequence_length: 20,
        num_quantiles: 9,
        num_static_features: 5,
        num_known_features: 10,
        num_unknown_features: 15,
        ..Default::default()
    };

    let device = Device::Cpu;
    let tft = QuantizedTemporalFusionTransformer::new_with_device(config.clone(), device.clone())?;

    // Test different batch sizes
    for batch_size in [1, 2, 4, 8] {
        let static_feat = Tensor::randn(
            0f32,
            1f32,
            (batch_size, config.num_static_features),
            &device,
        )?;
        let historical = Tensor::randn(
            0f32,
            1f32,
            (
                batch_size,
                config.sequence_length,
                config.num_unknown_features,
            ),
            &device,
        )?;
        let future = Tensor::randn(
            0f32,
            1f32,
            (
                batch_size,
                config.prediction_horizon,
                config.num_known_features,
            ),
            &device,
        )?;

        let predictions = tft.forward(&static_feat, &historical, &future)?;

        assert_eq!(
            predictions.dims(),
            &[batch_size, config.prediction_horizon, config.num_quantiles],
            "Batch size {} failed",
            batch_size
        );
    }

    println!("✓ Batch consistency tests passed");

    Ok(())
}

#[test]
fn test_quantized_tft_device_consistency() -> Result<()> {
    let config = TFTConfig {
        input_dim: 30,
        hidden_dim: 64,
        num_heads: 4,
        num_layers: 2,
        prediction_horizon: 10,
        sequence_length: 20,
        num_quantiles: 9,
        num_static_features: 5,
        num_known_features: 10,
        num_unknown_features: 15,
        ..Default::default()
    };

    // Test on CPU
    let device = Device::Cpu;
    let tft = QuantizedTemporalFusionTransformer::new_with_device(config.clone(), device.clone())?;

    let batch_size = 2;
    let static_feat = Tensor::randn(
        0f32,
        1f32,
        (batch_size, config.num_static_features),
        &device,
    )?;
    let historical = Tensor::randn(
        0f32,
        1f32,
        (
            batch_size,
            config.sequence_length,
            config.num_unknown_features,
        ),
        &device,
    )?;
    let future = Tensor::randn(
        0f32,
        1f32,
        (
            batch_size,
            config.prediction_horizon,
            config.num_known_features,
        ),
        &device,
    )?;

    let predictions = tft.forward(&static_feat, &historical, &future)?;

    // Verify output is on same device
    assert_eq!(predictions.device(), &device);

    println!("✓ Device consistency test passed");

    Ok(())
}

#[test]
fn test_quantized_tft_memory_usage() -> Result<()> {
    let config = TFTConfig {
        input_dim: 225, // Full Wave C+D features
        hidden_dim: 128,
        num_heads: 8,
        num_layers: 3,
        prediction_horizon: 10,
        sequence_length: 50,
        num_quantiles: 9,
        num_static_features: 5,
        num_known_features: 10,
        num_unknown_features: 210,
        ..Default::default()
    };

    let device = Device::Cpu;
    let tft = QuantizedTemporalFusionTransformer::new_with_device(config, device)?;

    let memory_mb = tft.memory_usage_bytes() / (1024 * 1024);

    // INT8 TFT should use ~125MB (vs 500MB for FP32)
    assert!(
        memory_mb <= 150,
        "Memory usage {} MB exceeds 150 MB target",
        memory_mb
    );
    assert!(
        memory_mb >= 100,
        "Memory usage {} MB too low, expected ~125 MB",
        memory_mb
    );

    println!("✓ Memory usage test passed: {} MB", memory_mb);

    Ok(())
}