ferrotensor-trainer 0.1.0

//! Simulated fine-tuning demo for the CLI (`ferrotensor-cli --demo`).
//!
//! Displays a training-style UI with a synthetic loss curve. Does **not** run
//! GPU backward passes or update model weights.

use crate::scaler::GradScaler;
use ferrotensor_autograd::{CosineAnnealingLR, LRScheduler};
use ferrotensor_backend::{ComputeEngine, WgpuContext};
use std::sync::Arc;
use std::time::Instant;

/// Demo configuration for fine-tuning
pub struct DemoConfig {
    /// Model name shown in the banner.
    pub model_name: String,
    /// Displayed batch size.
    pub batch_size: usize,
    /// Starting learning rate of the simulated schedule.
    pub learning_rate: f32,
    /// Simulated epochs.
    pub num_epochs: usize,
    /// Displayed sequence length.
    pub max_seq_len: usize,
    /// Show the loss-scaler line.
    pub use_fp16: bool,
    /// Show LoRA settings.
    pub use_lora: bool,
    /// Displayed LoRA rank.
    pub lora_rank: usize,
}

impl Default for DemoConfig {
    fn default() -> Self {
        Self {
            model_name: "TinyLlama-1.1B".to_string(),
            batch_size: 4,
            learning_rate: 1e-4,
            num_epochs: 3,
            max_seq_len: 512,
            use_fp16: true,
            use_lora: true,
            lora_rank: 8,
        }
    }
}

/// Print a styled banner for the demo
pub fn print_banner() {
    println!();
    println!("======================================================================");
    println!("                                                                      ");
    println!("   FERROTENSOR - GPU-Accelerated ML Framework                               ");
    println!("   Written in Pure Rust                                               ");
    println!("                                                                      ");
    println!("======================================================================");
    println!();
}

/// Print GPU information
pub fn print_gpu_info(ctx: &WgpuContext) {
    let info = ctx.adapter.get_info();
    println!("[GPU] Information:");
    println!("      Name:    {}", info.name);
    println!("      Backend: {:?}", info.backend);
    println!("      Type:    {:?}", info.device_type);
    println!();
}

/// Print configuration
pub fn print_config(config: &DemoConfig) {
    println!("[CONFIG] Training Configuration:");
    println!("         Model:      {}", config.model_name);
    println!("         Batch Size: {}", config.batch_size);
    println!("         LR:         {:.0e}", config.learning_rate);
    println!("         Epochs:     {}", config.num_epochs);
    println!("         Seq Length: {}", config.max_seq_len);
    println!(
        "         FP16:       {}",
        if config.use_fp16 { "Yes" } else { "No" }
    );
    println!(
        "         LoRA:       {} (rank={})",
        if config.use_lora { "Yes" } else { "No" },
        config.lora_rank
    );
    println!();
}

/// Print training progress
pub fn print_progress(
    epoch: usize,
    step: usize,
    total_steps: usize,
    loss: f32,
    lr: f32,
    tokens_per_sec: f32,
) {
    let progress = (step as f32 / total_steps as f32 * 100.0) as usize;
    let bar_width = 30;
    let filled = progress * bar_width / 100;
    let bar: String = "=".repeat(filled) + &"-".repeat(bar_width - filled);

    print!(
        "\r[TRAIN] Epoch {}: [{}] {}% | Loss: {:.4} | LR: {:.2e} | {:.0} tok/s",
        epoch, bar, progress, loss, lr, tokens_per_sec
    );
    std::io::Write::flush(&mut std::io::stdout()).ok();
}

/// Print epoch summary
pub fn print_epoch_summary(epoch: usize, avg_loss: f32, duration_secs: f32) {
    println!();
    println!(
        "        Epoch {} Complete | Avg Loss: {:.4} | Time: {:.1}s",
        epoch, avg_loss, duration_secs
    );
}

/// Sample training data for demo
pub fn create_demo_data() -> Vec<(Vec<u32>, Vec<u32>)> {
    let samples = [
        "Ferrotensor is a GPU-accelerated ML framework written in Rust.",
        "Flash Attention provides memory-efficient attention computation.",
        "LoRA enables parameter-efficient fine-tuning of large models.",
        "Mixed precision training uses FP16 for speed with FP32 for stability.",
        "The Metal backend runs on Apple Silicon GPUs.",
        "Transformers use self-attention to process sequences.",
        "Gradient checkpointing trades compute for memory savings.",
        "WGSL is the shader language for WebGPU compute kernels.",
    ];

    samples
        .iter()
        .map(|s| {
            let tokens: Vec<u32> = s.bytes().map(|b| b as u32).collect();
            let input = tokens[..tokens.len() - 1].to_vec();
            let target = tokens[1..].to_vec();
            (input, target)
        })
        .collect()
}

/// Run the demo training loop
pub fn run_demo(config: DemoConfig) {
    print_banner();

    // Initialize GPU
    println!("[INIT] Initializing GPU...");
    let ctx = Arc::new(pollster::block_on(async { WgpuContext::new().await }));
    let _engine = ComputeEngine::new(&ctx);
    print_gpu_info(&ctx);

    print_config(&config);

    // Create demo data
    println!("[DATA] Loading Training Data...");
    let data = create_demo_data();
    println!("       {} training samples loaded", data.len());
    println!();

    // Initialize training components
    println!("[INIT] Initializing Training Components...");

    // Create GradScaler for FP16
    let scaler: Option<GradScaler> = if config.use_fp16 {
        println!("       GradScaler initialized (FP16 training)");
        Some(GradScaler::new())
    } else {
        None
    };

    // Create LR scheduler
    let mut scheduler = CosineAnnealingLR::new(
        config.learning_rate,
        config.learning_rate * 0.01,
        config.num_epochs * data.len() / config.batch_size,
    );
    println!("       CosineAnnealingLR scheduler ready");

    // Create optimizer
    let lr = scheduler.get_lr();
    println!("       AdamW optimizer (lr={:.2e})", lr);
    println!();

    // Training loop
    println!("[DEMO] Simulated training loop (no GPU weight updates)");
    println!("{}", "-".repeat(70));

    let total_steps = (data.len() / config.batch_size) * config.num_epochs;
    let mut global_step = 0;

    for epoch in 1..=config.num_epochs {
        let epoch_start = Instant::now();
        let mut epoch_loss = 0.0;
        let mut num_batches = 0;

        for batch_idx in (0..data.len()).step_by(config.batch_size) {
            let step_start = Instant::now();

            let batch_end = (batch_idx + config.batch_size).min(data.len());
            let batch_samples = &data[batch_idx..batch_end];

            // Simulated loss
            let loss =
                2.5 / (1.0 + (global_step as f32 * 0.1)) + 0.1 * (global_step as f32).sin().abs();

            epoch_loss += loss;
            num_batches += 1;
            global_step += 1;

            let batch_tokens = batch_samples.iter().map(|(i, _)| i.len()).sum::<usize>();
            let step_time = step_start.elapsed().as_secs_f32().max(0.001);
            let tokens_per_sec = batch_tokens as f32 / step_time;

            scheduler.step();
            let lr = scheduler.get_lr();

            print_progress(epoch, global_step, total_steps, loss, lr, tokens_per_sec);

            std::thread::sleep(std::time::Duration::from_millis(100));
        }

        let epoch_duration = epoch_start.elapsed().as_secs_f32();
        let avg_loss = epoch_loss / num_batches as f32;
        print_epoch_summary(epoch, avg_loss, epoch_duration);
    }

    println!("{}", "-".repeat(70));
    println!();

    // Print final summary
    println!("[DONE] Demo complete (simulated — no weights were trained)");
    println!();
    println!("[STATS] Final Statistics:");
    println!("        Total Steps:    {}", global_step);
    println!("        Final Loss:     {:.4}", 0.15);
    println!("        Final LR:       {:.2e}", scheduler.get_lr());
    if config.use_fp16 {
        if let Some(ref s) = scaler {
            println!("        GradScaler:     scale={:.0}", s.scale_factor());
        }
    }
    println!();

    println!("[READY] Model Ready for Inference!");
    println!();
}

#[cfg(test)]
mod tests {
    use super::*;

    #[test]
    fn test_demo_data() {
        let data = create_demo_data();
        assert!(!data.is_empty());
    }
}