refactor: restructure repo — crates/, bin/, testing/ layout

Move 17 library crates into crates/, CLI binary into bin/fxt,
consolidate 10 test crates into testing/, split config crate
from deployment config files.

Root directory reduced from 38+ to ~17 directories.
All Cargo.toml paths and build.rs proto refs updated.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

crates/ml/examples/test_future_decoder.rs

@@ -0,0 +1,75 @@
+use candle_core::{Device, Tensor};
+use ml::memory_optimization::quantization::Quantizer;
+use ml::tft::{quantized_tft::QuantizedTemporalFusionTransformer, TFTConfig};
+
+fn main() -> Result<(), Box<dyn std::error::Error>> {
+    println!("Testing forward_future_decoder implementation...\n");
+
+    // Create TFT config
+    let config = TFTConfig {
+        input_dim: 54,
+        hidden_dim: 256,
+        num_heads: 8,
+        num_known_features: 10,
+        prediction_horizon: 10,
+        ..Default::default()
+    };
+
+    let device = Device::Cpu;
+    let qtft = QuantizedTemporalFusionTransformer::new_with_device(config, device.clone())?;
+
+    // Test 1: Create test future features [batch=2, horizon=10, features=10]
+    println!("Test 1: Basic forward pass");
+    let batch_size = 2;
+    let horizon = 10;
+    let num_features = 10;
+
+    let future_features = Tensor::randn(0f32, 1f32, (batch_size, horizon, num_features), &device)?;
+
+    println!("  Input shape: {:?}", future_features.dims());
+
+    // Create decoder weights [hidden_dim=256, num_features=10]
+    let weight_data: Vec<f32> = (0..256 * 10).map(|i| (i as f32 * 0.01).sin()).collect();
+    let weights_tensor = Tensor::from_slice(&weight_data, (256, 10), &device)?;
+
+    // Create quantizer and quantize the weights
+    let mut quantizer = ml::memory_optimization::quantization::Quantizer::new(
+        ml::memory_optimization::quantization::QuantizationConfig {
+            quant_type: ml::memory_optimization::quantization::QuantizationType::Int8,
+            per_channel: false,
+            symmetric: true,
+            calibration_samples: None,
+        },
+        device.clone(),
+    );
+    let quantized_weights = quantizer.quantize_tensor(&weights_tensor, "decoder")?;
+
+    // Run forward pass
+    let output = qtft.forward_future_decoder(&future_features, &quantized_weights)?;
+
+    println!("  Output shape: {:?}", output.dims());
+    println!("  Expected: [2, 10, 256]");
+
+    // Validate output shape
+    assert_eq!(output.dims(), &[2, 10, 256], "Output shape mismatch!");
+    println!("  ✓ Shape validation passed\n");
+
+    // Test 2: Check output is not all zeros
+    println!("Test 2: Output non-zero validation");
+    let output_sum = output.sum_all()?.to_vec0::<f32>()?;
+    println!("  Output sum: {}", output_sum);
+    assert!(output_sum.abs() > 1e-6, "Output should not be all zeros");
+    println!("  ✓ Non-zero validation passed\n");
+
+    // Test 3: Broadcasting correctness
+    println!("Test 3: Different batch sizes");
+    for batch in [1, 4, 8] {
+        let test_features = Tensor::randn(0f32, 1f32, (batch, 10, 10), &device)?;
+        let test_output = qtft.forward_future_decoder(&test_features, &quantized_weights)?;
+        assert_eq!(test_output.dims(), &[batch, 10, 256]);
+        println!("  ✓ Batch size {} works correctly", batch);
+    }
+
+    println!("\n✅ All tests passed!");
+    Ok(())
+}