architecture_specific_optimizations.rs

use pyo3::prelude::*;
use pyo3::wrap_pyfunction;
use std::arch::x86_64::*;
use tch::{nn, Device, Tensor};

/// Generates audio samples using architecture-specific optimizations.
///
/// # Arguments
///
/// * `model_path` - The path to the pre-trained PyTorch model.
/// * `num_samples` - The number of audio samples to generate.
///
/// # Returns
///
/// * `Vec<f32>` - The generated audio samples.
#[pyfunction]
fn generate_audio_samples_optimized(model_path: &str, num_samples: usize) -> PyResult<Vec<f32>> {
    // Load the pre-trained PyTorch model
    let device = Device::cuda_if_available();
    let model = match tch::CModule::load(model_path) {
        Ok(model) => model,
        Err(e) => return Err(PyErr::new::<pyo3::exceptions::PyIOError, _>(format!("Failed to load model: {}", e))),
    };
    let model = model.to(device);

    // Generate random noise as input to the model
    let noise = Tensor::rand(&[1, 100], kind::FLOAT_CPU).to(device);

    // Use the loaded model to generate audio samples
    let output = match model.forward_ts(&[noise]) {
        Ok(output) => output,
        Err(e) => return Err(PyErr::new::<pyo3::exceptions::PyRuntimeError, _>(format!("Failed to generate audio: {}", e))),
    };
    let output = output.squeeze().to(Device::Cpu);
    let output = match output.data().as_slice::<f32>() {
        Ok(output) => output,
        Err(e) => return Err(PyErr::new::<pyo3::exceptions::PyValueError, _>(format!("Failed to convert tensor to slice: {}", e))),
    };

    // Apply architecture-specific optimizations
    let mut samples = Vec::with_capacity(num_samples);
    unsafe {
        // Use AVX2 instructions for vectorized operations
        let avx2_enabled = is_x86_feature_detected!("avx2");
        if avx2_enabled {
            let num_chunks = num_samples / 8;
            for i in 0..num_chunks {
                let mut chunk = _mm256_loadu_ps(&output[i * 8]);
                // Apply optimized operations using AVX2 intrinsics
                chunk = _mm256_mul_ps(chunk, _mm256_set1_ps(0.5));
                _mm256_storeu_ps(samples.as_mut_ptr().add(i * 8), chunk);
            }
            samples.set_len(num_chunks * 8);

            // Process remaining samples
            for i in num_chunks * 8..num_samples {
                samples.push(output[i] * 0.5);
            }
        } else {
            // Fallback to non-optimized implementation
            for i in 0..num_samples {
                samples.push(output[i] * 0.5);
            }
        }
    }

    Ok(samples)
}

/// A Python module implemented in Rust.
#[pymodule]
fn architecture_specific_optimizations(_py: Python, m: &PyModule) -> PyResult<()> {
    m.add_function(wrap_pyfunction!(generate_audio_samples_optimized, m)?)?;
    Ok(())
}