trustformers-mobile 0.1.1

//! Android Inference Engine Implementation
//!
//! This module contains the main AndroidInferenceEngine that orchestrates
//! NNAPI, GPU, and CPU inference backends for Android devices.

use crate::android::device_info::{AndroidDeviceInfo, NNAPIDeviceInfo, NNAPIHardwareDevice};
use crate::android::gpu::{AndroidGPUBackend, AndroidGPUComputeState};
use crate::android::nnapi::{
    ANeuralNetworksPerformanceInfo, ANeuralNetworks_getDevice, ANeuralNetworks_getDeviceCount,
    ANeuralNetworks_getDeviceFeatureLevel, ANeuralNetworks_getDeviceName,
    ANeuralNetworks_getDevicePerformanceInfo, ANeuralNetworks_getDeviceType, NNAPIModel,
    ANEURALNETWORKS_DEVICE_ACCELERATOR, ANEURALNETWORKS_DEVICE_CPU, ANEURALNETWORKS_DEVICE_GPU,
    ANEURALNETWORKS_NO_ERROR,
};
use crate::{MobileBackend, MobileConfig, MobilePlatform, MobileStats};
use std::ffi::{CStr, CString};
use std::os::raw::{c_char, c_void};
use trustformers_core::error::{CoreError, Result};
use trustformers_core::Tensor;

#[cfg(target_os = "android")]
use jni::JavaVM;

/// Android-specific inference engine
pub struct AndroidInferenceEngine {
    config: MobileConfig,
    stats: MobileStats,
    model_loaded: bool,
    #[cfg(target_os = "android")]
    nnapi_model: Option<NNAPIModel>,
    #[cfg(target_os = "android")]
    jvm: Option<JavaVM>,
    #[cfg(target_os = "android")]
    gpu_state: Option<AndroidGPUComputeState>,
}

impl AndroidInferenceEngine {
    /// Create new Android inference engine
    pub fn new(config: MobileConfig) -> Result<Self> {
        if config.platform != MobilePlatform::Android {
            return Err(TrustformersError::config_error {
                message: "Android inference engine requires Android platform configuration"
                    .to_string(),
                context: trustformers_core::error::ErrorContext::new(
                    trustformers_core::error::ErrorCode::E4001,
                    "new".to_string(),
                ),
            });
        }

        let stats = MobileStats::new(&config);

        Ok(Self {
            config,
            stats,
            model_loaded: false,
            #[cfg(target_os = "android")]
            nnapi_model: None,
            #[cfg(target_os = "android")]
            jvm: None,
            #[cfg(target_os = "android")]
            gpu_state: None,
        })
    }

    /// Initialize with JVM reference for JNI integration
    #[cfg(target_os = "android")]
    pub fn init_jvm(&mut self, jvm: JavaVM) {
        self.jvm = Some(jvm);
    }

    /// Load model for Android inference
    pub fn load_model(&mut self, model_path: &str) -> Result<()> {
        match self.config.backend {
            MobileBackend::NNAPI => self.load_nnapi_model(model_path),
            MobileBackend::CPU => self.load_cpu_model(model_path),
            MobileBackend::GPU => self.load_gpu_model(model_path),
            _ => Err(TrustformersError::runtime_error(format!(
                "Backend {:?} not supported on Android",
                self.config.backend
            ))),
        }
    }

    /// Perform inference using Android optimizations
    pub fn inference(&mut self, input: &Tensor) -> Result<Tensor> {
        if !self.model_loaded {
            return Err(TrustformersError::runtime_error("Model not loaded".into()).into());
        }

        let start_time = std::time::Instant::now();

        let result = match self.config.backend {
            MobileBackend::NNAPI => self.nnapi_inference(input),
            MobileBackend::CPU => self.cpu_inference(input),
            MobileBackend::GPU => self.gpu_inference(input),
            _ => Err(TrustformersError::runtime_error(
                "Unsupported backend".into(),
            )),
        };

        let inference_time = start_time.elapsed().as_millis() as f32;
        self.stats.update_inference(inference_time);

        result
    }

    /// Get current performance statistics
    pub fn get_stats(&self) -> &MobileStats {
        &self.stats
    }

    /// Update configuration
    pub fn update_config(&mut self, config: MobileConfig) -> Result<()> {
        if config.platform != MobilePlatform::Android {
            return Err(TrustformersError::config_error {
                message: "Android inference engine requires Android platform configuration"
                    .to_string(),
                context: trustformers_core::error::ErrorContext::new(
                    trustformers_core::error::ErrorCode::E4001,
                    "update_config".to_string(),
                ),
            });
        }

        self.config = config;
        self.stats = MobileStats::new(&self.config);
        Ok(())
    }

    /// Check Android device capabilities
    pub fn check_device_capabilities() -> AndroidDeviceInfo {
        AndroidDeviceInfo::detect()
    }

    /// Detect available NNAPI hardware acceleration devices
    pub fn detect_nnapi_devices() -> Vec<NNAPIDeviceInfo> {
        #[cfg(target_os = "android")]
        {
            Self::detect_nnapi_devices_impl()
        }

        #[cfg(not(target_os = "android"))]
        {
            Vec::new()
        }
    }

    #[cfg(target_os = "android")]
    fn detect_nnapi_devices_impl() -> Vec<NNAPIDeviceInfo> {
        let mut devices = Vec::new();
        let mut device_count: u32 = 0;

        // Get number of available NNAPI devices
        let result = unsafe { ANeuralNetworks_getDeviceCount(&mut device_count) };
        if result != ANEURALNETWORKS_NO_ERROR {
            tracing::warn!("Failed to get NNAPI device count: {}", result);
            return devices;
        }

        tracing::info!("Found {} NNAPI devices", device_count);

        // Query each device
        for device_index in 0..device_count {
            if let Some(device_info) = Self::query_nnapi_device(device_index) {
                devices.push(device_info);
            }
        }

        devices
    }

    #[cfg(target_os = "android")]
    fn query_nnapi_device(device_index: u32) -> Option<NNAPIDeviceInfo> {
        let mut device_ptr: *mut c_void = std::ptr::null_mut();
        let result = unsafe { ANeuralNetworks_getDevice(device_index, &mut device_ptr) };

        if result != ANEURALNETWORKS_NO_ERROR || device_ptr.is_null() {
            tracing::warn!("Failed to get NNAPI device {}: {}", device_index, result);
            return None;
        }

        // Get device name
        let name = {
            let mut name_ptr: *const c_char = std::ptr::null();
            let result = unsafe { ANeuralNetworks_getDeviceName(device_ptr, &mut name_ptr) };
            if result == ANEURALNETWORKS_NO_ERROR && !name_ptr.is_null() {
                unsafe { CStr::from_ptr(name_ptr) }.to_string_lossy().into_owned()
            } else {
                format!("Device {}", device_index)
            }
        };

        // Get device type
        let mut device_type: i32 = 0;
        let result = unsafe { ANeuralNetworks_getDeviceType(device_ptr, &mut device_type) };
        if result != ANEURALNETWORKS_NO_ERROR {
            device_type = ANEURALNETWORKS_DEVICE_CPU;
        }

        // Get feature level
        let mut feature_level: i32 = 27;
        let result =
            unsafe { ANeuralNetworks_getDeviceFeatureLevel(device_ptr, &mut feature_level) };
        if result != ANEURALNETWORKS_NO_ERROR {
            feature_level = 27; // Default to API level 27
        }

        // Get performance info
        let mut performance_info = ANeuralNetworksPerformanceInfo {
            exec_time: 1.0,
            power_usage: 1.0,
        };
        let result =
            unsafe { ANeuralNetworks_getDevicePerformanceInfo(device_ptr, &mut performance_info) };
        if result != ANEURALNETWORKS_NO_ERROR {
            performance_info = ANeuralNetworksPerformanceInfo {
                exec_time: 1.0,
                power_usage: 1.0,
            };
        }

        Some(NNAPIDeviceInfo {
            index: device_index,
            device_ptr,
            name,
            device_type,
            feature_level,
            performance_info,
            vendor_extensions: Vec::new(), // Would query extensions in practice
        })
    }

    /// Get best NNAPI device for inference
    pub fn get_best_nnapi_device() -> Option<NNAPIDeviceInfo> {
        let devices = Self::detect_nnapi_devices();
        if devices.is_empty() {
            return None;
        }

        // Prefer GPU/Accelerator over CPU, and lower execution time
        let best_device = devices.into_iter().min_by(|a, b| {
            // First compare by device type (GPU/Accelerator preferred)
            let type_order_a = match a.device_type {
                t if t == ANEURALNETWORKS_DEVICE_ACCELERATOR => 0,
                t if t == ANEURALNETWORKS_DEVICE_GPU => 1,
                _ => 2,
            };
            let type_order_b = match b.device_type {
                t if t == ANEURALNETWORKS_DEVICE_ACCELERATOR => 0,
                t if t == ANEURALNETWORKS_DEVICE_GPU => 1,
                _ => 2,
            };

            type_order_a.cmp(&type_order_b).then_with(|| {
                a.performance_info
                    .exec_time
                    .partial_cmp(&b.performance_info.exec_time)
                    .unwrap_or(std::cmp::Ordering::Equal)
            })
        });

        best_device
    }

    /// Convert device type to string
    pub fn device_type_to_string(device_type: i32) -> &'static str {
        match device_type {
            t if t == ANEURALNETWORKS_DEVICE_CPU => "CPU",
            t if t == ANEURALNETWORKS_DEVICE_GPU => "GPU",
            t if t == ANEURALNETWORKS_DEVICE_ACCELERATOR => "Accelerator",
            _ => "Unknown",
        }
    }

    // Backend-specific implementations
    fn load_nnapi_model(&mut self, model_path: &str) -> Result<()> {
        #[cfg(target_os = "android")]
        {
            let model = NNAPIModel::new()?;
            self.nnapi_model = Some(model);
            self.model_loaded = true;
            tracing::info!("Loaded NNAPI model from: {}", model_path);
            Ok(())
        }

        #[cfg(not(target_os = "android"))]
        {
            tracing::warn!("NNAPI not available on non-Android platforms");
            Err(TrustformersError::runtime_error(
                "NNAPI not available".into(),
            ))
        }
    }

    fn load_cpu_model(&mut self, model_path: &str) -> Result<()> {
        // CPU model loading implementation
        self.model_loaded = true;
        tracing::info!("Loaded CPU model from: {}", model_path);
        Ok(())
    }

    fn load_gpu_model(&mut self, model_path: &str) -> Result<()> {
        #[cfg(target_os = "android")]
        {
            let gpu_state = AndroidGPUComputeState::new(AndroidGPUBackend::Vulkan)?;
            self.gpu_state = Some(gpu_state);
            self.model_loaded = true;
            tracing::info!("Loaded GPU model from: {}", model_path);
            Ok(())
        }

        #[cfg(not(target_os = "android"))]
        {
            tracing::warn!("GPU compute not available on non-Android platforms");
            Err(TrustformersError::runtime_error(
                "GPU compute not available".into(),
            ))
        }
    }

    fn nnapi_inference(&mut self, input: &Tensor) -> Result<Tensor> {
        #[cfg(target_os = "android")]
        {
            if let Some(ref _model) = self.nnapi_model {
                // NNAPI inference implementation
                tracing::debug!("Performing NNAPI inference");

                // Placeholder tensor creation - would use actual NNAPI execution
                let output_data = vec![0.5f32; 1000]; // Mock output
                let shape = [1, 1000];
                Tensor::from_vec(output_data, &shape)
            } else {
                Err(TrustformersError::runtime_error(
                    "NNAPI model not loaded".into(),
                ))
            }
        }

        #[cfg(not(target_os = "android"))]
        {
            Err(TrustformersError::runtime_error(
                "NNAPI not available".into(),
            ))
        }
    }

    fn cpu_inference(&mut self, input: &Tensor) -> Result<Tensor> {
        // CPU inference implementation
        tracing::debug!("Performing CPU inference");

        // Simple mock implementation
        let input_data = input.data();
        let output_data: Vec<f32> = input_data.iter().map(|x| x * 0.5).collect();
        let shape = input.shape();
        Tensor::from_vec(output_data, shape)
    }

    fn gpu_inference(&mut self, input: &Tensor) -> Result<Tensor> {
        #[cfg(target_os = "android")]
        {
            if let Some(ref _gpu_state) = self.gpu_state {
                // GPU inference implementation
                tracing::debug!("Performing GPU inference");

                // Placeholder implementation
                let input_data = input.data();
                let output_data: Vec<f32> = input_data.iter().map(|x| x * 2.0).collect();
                let shape = input.shape();
                Tensor::from_vec(output_data, shape)
            } else {
                Err(TrustformersError::runtime_error(
                    "GPU state not initialized".into(),
                ))
            }
        }

        #[cfg(not(target_os = "android"))]
        {
            Err(TrustformersError::runtime_error(
                "GPU compute not available".into(),
            ))
        }
    }

    /// Check if model is loaded
    pub fn is_model_loaded(&self) -> bool {
        self.model_loaded
    }

    /// Get configuration
    pub fn get_config(&self) -> &MobileConfig {
        &self.config
    }

    /// Reset statistics
    pub fn reset_stats(&mut self) {
        self.stats = MobileStats::new(&self.config);
    }
}

impl Drop for AndroidInferenceEngine {
    fn drop(&mut self) {
        #[cfg(target_os = "android")]
        if let Some(ref _model) = self.nnapi_model {
            tracing::debug!("Cleaning up NNAPI model resources");
        }

        #[cfg(target_os = "android")]
        if let Some(ref mut gpu_state) = self.gpu_state {
            gpu_state.cleanup();
        }
    }
}

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

    #[test]
    fn test_android_inference_engine_creation() {
        let config = MobileConfig::android_optimized();
        let engine = AndroidInferenceEngine::new(config);
        assert!(engine.is_ok());
    }

    #[test]
    fn test_wrong_platform_config() {
        let mut config = MobileConfig::android_optimized();
        config.platform = MobilePlatform::iOS;
        let engine = AndroidInferenceEngine::new(config);
        assert!(engine.is_err());
    }

    #[test]
    fn test_device_type_string_conversion() {
        assert_eq!(
            AndroidInferenceEngine::device_type_to_string(ANEURALNETWORKS_DEVICE_CPU),
            "CPU"
        );
        assert_eq!(
            AndroidInferenceEngine::device_type_to_string(ANEURALNETWORKS_DEVICE_GPU),
            "GPU"
        );
        assert_eq!(
            AndroidInferenceEngine::device_type_to_string(ANEURALNETWORKS_DEVICE_ACCELERATOR),
            "Accelerator"
        );
        assert_eq!(AndroidInferenceEngine::device_type_to_string(-1), "Unknown");
    }

    #[test]
    fn test_engine_state_management() {
        let config = MobileConfig::android_optimized();
        let mut engine = AndroidInferenceEngine::new(config).expect("Operation failed");

        assert!(!engine.is_model_loaded());
        assert_eq!(engine.get_config().platform, MobilePlatform::Android);

        // Test stats reset
        engine.reset_stats();
        assert!(engine.get_stats().total_inferences == 0);
    }

    #[test]
    fn test_cpu_model_loading() {
        let config = MobileConfig::android_optimized();
        let mut engine = AndroidInferenceEngine::new(config).expect("Operation failed");

        let result = engine.load_cpu_model("test_model.tflite");
        assert!(result.is_ok());
        assert!(engine.is_model_loaded());
    }

    #[test]
    fn test_cpu_inference() {
        let config = MobileConfig::android_optimized();
        let mut engine = AndroidInferenceEngine::new(config).expect("Operation failed");

        // Load CPU model first
        engine.load_cpu_model("test_model.tflite").expect("Operation failed");

        // Create test input
        let input_data = vec![1.0, 2.0, 3.0, 4.0];
        let input_tensor = Tensor::from_vec(input_data.clone(), &[4]).expect("Operation failed");

        // Perform inference
        let result = engine.cpu_inference(&input_tensor);
        assert!(result.is_ok());

        let output = result.expect("Operation failed");
        let output_data = output.data();

        // Check that output is input * 0.5
        for (i, &value) in output_data.iter().enumerate() {
            assert_eq!(value, input_data[i] * 0.5);
        }
    }

    #[test]
    fn test_config_update() {
        let config = MobileConfig::android_optimized();
        let mut engine = AndroidInferenceEngine::new(config).expect("Operation failed");

        let mut new_config = MobileConfig::android_optimized();
        new_config.max_memory_mb = 2048;

        let result = engine.update_config(new_config);
        assert!(result.is_ok());
        assert_eq!(engine.get_config().max_memory_mb, 2048);
    }

    #[test]
    fn test_inference_without_model() {
        let config = MobileConfig::android_optimized();
        let mut engine = AndroidInferenceEngine::new(config).expect("Operation failed");

        let input_data = vec![1.0, 2.0, 3.0, 4.0];
        let input_tensor = Tensor::from_vec(input_data, &[4]).expect("Operation failed");

        let result = engine.inference(&input_tensor);
        assert!(result.is_err());
    }
}