aprender-serve 0.34.0

//! IMP-701: Performance Gap Analysis
//!
//! Measures realizar inference performance against Ollama baseline
//!
//! Run: cargo run --release --example imp_701_performance_gap

use std::hint::black_box;
use std::time::Instant;

fn main() {
    println!("=== IMP-701: Performance Gap Analysis ===");
    println!("Comparing realizar test inference to Ollama baseline\n");

    // Ollama baseline from IMP-144b/IMP-146d
    let ollama_tps = 240.1; // measured tok/s
    let ollama_p50_ms = 207.6; // measured p50 latency

    println!("Ollama Baseline (phi2:2.7b, CUDA):");
    println!("  Throughput: {:.1} tok/s", ollama_tps);
    println!("  P50 Latency: {:.1} ms", ollama_p50_ms);
    println!();

    // Test realizar test model (simulates transformer operations)
    println!("Realizar test Transformer:");

    // Simulate phi-2 dimensions
    let hidden_dim = 2560;
    let num_heads = 32;
    let head_dim = hidden_dim / num_heads;
    let _seq_len = 50;
    let _vocab_size = 51200;
    let num_layers = 32;

    // Simulate forward pass operations
    let num_iterations = 100;

    // 1. Embedding lookup
    let embeddings: Vec<f32> = vec![0.1; hidden_dim];

    // 2. Layer operations (attention + FFN)
    let _weights_qkv: Vec<f32> = vec![0.01; hidden_dim * hidden_dim * 3];
    let _weights_out: Vec<f32> = vec![0.01; hidden_dim * hidden_dim];
    let _weights_ffn1: Vec<f32> = vec![0.01; hidden_dim * hidden_dim * 4];
    let _weights_ffn2: Vec<f32> = vec![0.01; hidden_dim * 4 * hidden_dim];

    // Warmup
    let mut hidden = embeddings.clone();
    for _ in 0..3 {
        // Simulate layer norm + attention + FFN
        let sum: f32 = hidden.iter().sum();
        hidden = hidden.iter().map(|x| x + sum * 0.001).collect();
    }

    // Measure forward pass
    let start = Instant::now();
    for _ in 0..num_iterations {
        let mut hidden = embeddings.clone();

        // Simulate num_layers transformer layers
        for _layer in 0..num_layers {
            // Layer norm
            let mean: f32 = hidden.iter().sum::<f32>() / hidden.len() as f32;
            let var: f32 =
                hidden.iter().map(|x| (x - mean).powi(2)).sum::<f32>() / hidden.len() as f32;
            let std = (var + 1e-5).sqrt();
            hidden = hidden.iter().map(|x| (x - mean) / std).collect();

            // Simplified attention (no full matmul, just dot product scaling)
            // This underestimates real attention cost
            let scale = 1.0 / (head_dim as f32).sqrt();
            hidden = hidden.iter().map(|x| x * scale).collect();

            // FFN (2 linear layers with GELU)
            // Simplified - real would be 4x hidden_dim intermediate
            hidden = hidden
                .iter()
                .map(|x| 0.5 * x * (1.0 + (0.7978846 * (x + 0.044715 * x.powi(3))).tanh()))
                .collect();
        }

        // Output projection (simplified)
        black_box(&hidden);
    }
    let elapsed = start.elapsed();

    let test_ms_per_token = elapsed.as_secs_f64() * 1000.0 / num_iterations as f64;
    let test_tps = 1000.0 / test_ms_per_token;

    println!("  Iterations: {}", num_iterations);
    println!("  Time per token: {:.2} ms", test_ms_per_token);
    println!("  Throughput: {:.2} tok/s", test_tps);
    println!();

    // Calculate gap
    let gap = ollama_tps / test_tps;

    println!("=== Performance Gap Analysis ===");
    println!("  Ollama: {:.1} tok/s", ollama_tps);
    println!("  Realizar (test): {:.2} tok/s", test_tps);
    println!("  Gap: {:.1}x", gap);
    println!();

    // Analysis
    println!("=== Gap Breakdown ===");
    println!("1. test model underestimates real transformer cost");
    println!("   - No actual matrix multiplications (O(d²) each)");
    println!("   - No KV cache management");
    println!("   - No real attention (O(n²) for seq_len)");
    println!();
    println!("2. To achieve parity (gap < 1.25x), need:");
    println!("   - GPU inference (trueno wgpu for prompt processing)");
    println!("   - SIMD inference (trueno AVX2 for token generation)");
    println!("   - KV cache (avoid recomputation)");
    println!("   - Quantized operations (Q4_K_M like Ollama)");
    println!();

    // Falsifiable claims
    println!("=== Falsifiable Claims ===");
    if gap > 100.0 {
        println!("CLAIM: Gap > 100x indicates missing GPU/SIMD optimization");
        println!("ACTION: Integrate trueno GPU for large matrices");
    } else if gap > 10.0 {
        println!("CLAIM: Gap 10-100x indicates missing optimizations");
        println!("ACTION: Add KV cache, quantized attention");
    } else if gap > 1.25 {
        println!("CLAIM: Gap 1.25-10x indicates tuning needed");
        println!("ACTION: Profile and optimize hotspots");
    } else {
        println!("CLAIM: Gap < 1.25x = PARITY ACHIEVED!");
        println!("STATUS: Target met");
    }
}