sklears-simd 0.1.1

//! TPU (Tensor Processing Unit) acceleration support for SIMD operations
//!
//! This module provides Google TPU interfaces for machine learning operations
//! with fallback to CPU SIMD implementations.

use crate::traits::SimdError;

#[cfg(feature = "no-std")]
use alloc::{
    boxed::Box,
    string::{String, ToString},
    vec,
    vec::Vec,
};
#[cfg(feature = "no-std")]
use core::{any, mem};
#[cfg(not(feature = "no-std"))]
use std::{any, mem, string::ToString};

/// TPU computation types
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum TpuVersion {
    V1,
    V2,
    V3,
    V4,
    V5e,
    V5p,
}

/// TPU device information
#[derive(Debug, Clone)]
pub struct TpuDevice {
    pub id: u32,
    pub name: String,
    pub version: TpuVersion,
    pub cores: u32,
    pub memory_gb: u64,
    pub peak_flops: u64,
    pub matrix_unit_count: u32,
    pub vector_unit_count: u32,
}

/// TPU memory buffer wrapper
#[derive(Debug)]
pub struct TpuBuffer<T> {
    pub ptr: *mut T,
    pub size: usize,
    pub device: TpuDevice,
    pub shape: Vec<usize>,
    #[allow(dead_code)] // Reserved for native TPU buffer handle (Google XLA / libtpu)
    backend_handle: Option<Box<dyn any::Any + Send + Sync>>,
}

unsafe impl<T: Send> Send for TpuBuffer<T> {}
unsafe impl<T: Sync> Sync for TpuBuffer<T> {}

impl<T> Drop for TpuBuffer<T> {
    fn drop(&mut self) {
        // Free TPU memory when buffer is dropped
        // Implementation depends on TPU runtime
    }
}

/// TPU context for managing resources
pub struct TpuContext {
    pub device: TpuDevice,
    pub runtime_version: String,
    #[allow(dead_code)] // Reserved for native TPU context (Google XLA / libtpu runtime handle)
    backend_context: Option<Box<dyn any::Any + Send + Sync>>,
}

/// TPU computation configuration
#[derive(Debug, Clone)]
pub struct TpuConfig {
    pub precision: TpuPrecision,
    pub batch_size: usize,
    pub pipeline_depth: u32,
    pub memory_optimization: bool,
    pub auto_sharding: bool,
}

/// TPU precision modes
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum TpuPrecision {
    BFloat16,
    Float32,
    Int8,
    Int16,
    Int32,
}

impl Default for TpuConfig {
    fn default() -> Self {
        Self {
            precision: TpuPrecision::BFloat16,
            batch_size: 1,
            pipeline_depth: 1,
            memory_optimization: true,
            auto_sharding: false,
        }
    }
}

/// TPU operations interface
pub trait TpuOperations {
    /// Allocate TPU memory
    fn allocate<T>(&self, shape: &[usize]) -> Result<TpuBuffer<T>, SimdError>;

    /// Copy data from host to TPU
    fn copy_to_tpu<T>(
        &self,
        host_data: &[T],
        tpu_buffer: &mut TpuBuffer<T>,
    ) -> Result<(), SimdError>;

    /// Copy data from TPU to host
    fn copy_to_host<T>(
        &self,
        tpu_buffer: &TpuBuffer<T>,
        host_data: &mut [T],
    ) -> Result<(), SimdError>;

    /// Execute matrix multiplication on TPU
    fn matmul(
        &self,
        a: &TpuBuffer<f32>,
        b: &TpuBuffer<f32>,
        c: &mut TpuBuffer<f32>,
        config: &TpuConfig,
    ) -> Result<(), SimdError>;

    /// Execute convolution on TPU
    fn conv2d(
        &self,
        input: &TpuBuffer<f32>,
        kernel: &TpuBuffer<f32>,
        output: &mut TpuBuffer<f32>,
        config: &TpuConfig,
    ) -> Result<(), SimdError>;

    /// Execute batch normalization on TPU
    fn batch_norm(
        &self,
        input: &TpuBuffer<f32>,
        scale: &TpuBuffer<f32>,
        bias: &TpuBuffer<f32>,
        output: &mut TpuBuffer<f32>,
        config: &TpuConfig,
    ) -> Result<(), SimdError>;

    /// Execute activation function on TPU
    fn activation(
        &self,
        input: &TpuBuffer<f32>,
        output: &mut TpuBuffer<f32>,
        activation_type: TpuActivation,
        config: &TpuConfig,
    ) -> Result<(), SimdError>;

    /// Execute reduction operation on TPU
    fn reduce(
        &self,
        input: &TpuBuffer<f32>,
        output: &mut TpuBuffer<f32>,
        reduction_type: TpuReduction,
        axes: &[usize],
        config: &TpuConfig,
    ) -> Result<(), SimdError>;

    /// Synchronize TPU operations
    fn synchronize(&self) -> Result<(), SimdError>;
}

/// TPU activation functions
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum TpuActivation {
    ReLU,
    Tanh,
    Sigmoid,
    Swish,
    Gelu,
    Softmax,
}

/// TPU reduction operations
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum TpuReduction {
    Sum,
    Mean,
    Max,
    Min,
    Prod,
    All,
    Any,
}

/// TPU runtime implementation
pub struct TpuRuntime {
    devices: Vec<TpuDevice>,
    contexts: Vec<TpuContext>,
}

impl TpuRuntime {
    /// Create new TPU runtime
    pub fn new() -> Result<Self, SimdError> {
        let devices = Self::discover_devices()?;
        let contexts = Vec::new();
        Ok(Self { devices, contexts })
    }

    /// Discover available TPU devices
    fn discover_devices() -> Result<Vec<TpuDevice>, SimdError> {
        // In a real implementation, this would interface with TPU runtime
        // For now, return empty list or simulated devices
        Ok(vec![])
    }

    /// Get available TPU devices
    pub fn devices(&self) -> &[TpuDevice] {
        &self.devices
    }

    /// Create context for TPU device
    pub fn create_context(&mut self, device_id: u32) -> Result<&TpuContext, SimdError> {
        let device = self
            .devices
            .get(device_id as usize)
            .ok_or_else(|| SimdError::InvalidArgument("Invalid TPU device ID".to_string()))?;

        let context = TpuContext {
            device: device.clone(),
            runtime_version: "2.0.0".to_string(),
            backend_context: None,
        };

        self.contexts.push(context);
        Ok(self
            .contexts
            .last()
            .expect("collection should not be empty"))
    }

    /// Check if TPU is available
    pub fn is_available() -> bool {
        // In a real implementation, this would check for TPU runtime
        false
    }

    /// Get TPU compute capability
    pub fn get_compute_capability(
        &self,
        device_id: u32,
    ) -> Result<TpuComputeCapability, SimdError> {
        let device = self
            .devices
            .get(device_id as usize)
            .ok_or_else(|| SimdError::InvalidArgument("Invalid TPU device ID".to_string()))?;

        Ok(TpuComputeCapability::from_device(device))
    }
}

/// TPU compute capability information
#[derive(Debug, Clone)]
pub struct TpuComputeCapability {
    pub version: TpuVersion,
    pub matrix_unit_dim: usize,
    pub vector_unit_width: usize,
    pub max_matrix_size: usize,
    pub memory_bandwidth_gbps: f64,
    pub supported_precisions: Vec<TpuPrecision>,
}

impl TpuComputeCapability {
    fn from_device(device: &TpuDevice) -> Self {
        let (matrix_unit_dim, vector_unit_width, max_matrix_size, memory_bandwidth_gbps) =
            match device.version {
                TpuVersion::V1 => (256, 128, 1024, 600.0),
                TpuVersion::V2 => (256, 128, 1024, 700.0),
                TpuVersion::V3 => (256, 128, 1024, 900.0),
                TpuVersion::V4 => (256, 128, 1024, 1200.0),
                TpuVersion::V5e => (256, 128, 1024, 1600.0),
                TpuVersion::V5p => (256, 128, 1024, 2400.0),
            };

        Self {
            version: device.version,
            matrix_unit_dim,
            vector_unit_width,
            max_matrix_size,
            memory_bandwidth_gbps,
            supported_precisions: vec![
                TpuPrecision::BFloat16,
                TpuPrecision::Float32,
                TpuPrecision::Int8,
                TpuPrecision::Int16,
                TpuPrecision::Int32,
            ],
        }
    }
}

/// TPU-optimized matrix multiplication
pub fn tpu_matmul(
    a: &[f32],
    b: &[f32],
    c: &mut [f32],
    m: usize,
    n: usize,
    k: usize,
    _config: &TpuConfig,
) -> Result<(), SimdError> {
    // Fallback to CPU SIMD implementation
    matrix_multiply_fallback(a, b, c, m, n, k)
}

/// TPU-optimized convolution
pub fn tpu_conv2d(
    _input: &[f32],
    _kernel: &[f32],
    _output: &mut [f32],
    _input_shape: &[usize],
    _kernel_shape: &[usize],
    _config: &TpuConfig,
) -> Result<(), SimdError> {
    // Fallback to CPU SIMD implementation
    // This would need to be implemented in the image processing module
    Err(SimdError::NotImplemented(
        "TPU conv2d not implemented".to_string(),
    ))
}

/// TPU batch processing utilities
pub mod batch {
    use super::*;

    /// Process batch of operations on TPU
    pub fn process_batch<T, F>(
        inputs: &[&[T]],
        outputs: &mut [&mut [T]],
        _batch_size: usize,
        op: F,
    ) -> Result<(), SimdError>
    where
        T: Clone + Send + Sync,
        F: Fn(&[T], &mut [T]) -> Result<(), SimdError> + Send + Sync,
    {
        if inputs.len() != outputs.len() {
            return Err(SimdError::InvalidArgument(
                "Input and output batch sizes must match".to_string(),
            ));
        }

        for (input, output) in inputs.iter().zip(outputs.iter_mut()) {
            op(input, output)?;
        }

        Ok(())
    }

    /// Optimal batch size calculation
    pub fn optimal_batch_size(data_size: usize, memory_limit: usize, compute_units: u32) -> usize {
        let memory_per_item = data_size * mem::size_of::<f32>();
        let memory_based_batch = memory_limit / memory_per_item;
        let compute_based_batch = compute_units as usize * 8; // Heuristic

        memory_based_batch.min(compute_based_batch).max(1)
    }
}

/// Simple fallback implementations for missing functions
fn matrix_multiply_fallback(
    a: &[f32],
    b: &[f32],
    c: &mut [f32],
    m: usize,
    n: usize,
    k: usize,
) -> Result<(), SimdError> {
    if a.len() != m * k || b.len() != k * n || c.len() != m * n {
        return Err(SimdError::DimensionMismatch {
            expected: m * n,
            actual: c.len(),
        });
    }

    for i in 0..m {
        for j in 0..n {
            let mut sum = 0.0;
            for ki in 0..k {
                sum += a[i * k + ki] * b[ki * n + j];
            }
            c[i * n + j] = sum;
        }
    }
    Ok(())
}

#[allow(non_snake_case)]
#[cfg(all(test, not(feature = "no-std")))]
mod tests {
    use super::*;

    #[cfg(feature = "no-std")]
    use alloc::{
        string::{String, ToString},
        vec,
        vec::Vec,
    };

    #[test]
    fn test_tpu_runtime_creation() {
        let runtime = TpuRuntime::new();
        assert!(runtime.is_ok());
    }

    #[test]
    fn test_tpu_availability() {
        // TPU should not be available in test environment
        assert!(!TpuRuntime::is_available());
    }

    #[test]
    fn test_tpu_config_default() {
        let config = TpuConfig::default();
        assert_eq!(config.precision, TpuPrecision::BFloat16);
        assert_eq!(config.batch_size, 1);
        assert!(config.memory_optimization);
    }

    #[test]
    fn test_tpu_matmul_fallback() {
        let a = vec![1.0, 2.0, 3.0, 4.0];
        let b = vec![5.0, 6.0, 7.0, 8.0];
        let mut c = vec![0.0; 4];
        let config = TpuConfig::default();

        let result = tpu_matmul(&a, &b, &mut c, 2, 2, 2, &config);
        assert!(result.is_ok());
    }

    #[test]
    fn test_batch_processing() {
        let input1 = vec![1.0, 2.0, 3.0];
        let input2 = vec![4.0, 5.0, 6.0];
        let inputs = vec![input1.as_slice(), input2.as_slice()];

        let mut output1 = vec![0.0; 3];
        let mut output2 = vec![0.0; 3];
        let mut outputs = vec![output1.as_mut_slice(), output2.as_mut_slice()];

        let result = batch::process_batch(&inputs, &mut outputs, 2, |input, output| {
            for (i, o) in input.iter().zip(output.iter_mut()) {
                *o = *i * 2.0;
            }
            Ok(())
        });

        assert!(result.is_ok());
        assert_eq!(outputs[0], &[2.0, 4.0, 6.0]);
        assert_eq!(outputs[1], &[8.0, 10.0, 12.0]);
    }

    #[test]
    fn test_optimal_batch_size() {
        let batch_size = batch::optimal_batch_size(1000, 1000000, 16);
        assert!(batch_size > 0);
        assert!(batch_size <= 1000000 / (1000 * 4)); // 4 bytes per f32
    }
}