ghostflow-core 1.1.0

//! TPU (Tensor Processing Unit) backend
//!
//! Provides Google Cloud TPU acceleration
//! Note: Requires Google Cloud TPU SDK and XLA compiler

use crate::tensor::Tensor;
use crate::error::{GhostError, Result};

/// TPU device context
pub struct TpuDevice {
    pub device_id: usize,
    pub name: String,
    pub version: TpuVersion,
    pub cores: usize,
}

#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum TpuVersion {
    V2,
    V3,
    V4,
    V5,
}

impl TpuDevice {
    /// Initialize TPU device
    pub fn new(device_id: usize) -> Result<Self> {
        #[cfg(feature = "tpu")]
        {
            // Would use TPU API:
            // tpu_initialize()
            // tpu_get_device_properties(device_id)
            
            Ok(TpuDevice {
                device_id,
                name: format!("TPU Device {}", device_id),
                version: TpuVersion::V4,
                cores: 8, // TPU v4 has 8 cores per chip
            })
        }
        #[cfg(not(feature = "tpu"))]
        {
            Err(GhostError::DeviceError(
                "TPU support not compiled. Enable 'tpu' feature.".to_string()
            ))
        }
    }
    
    /// Get number of available TPU devices
    pub fn device_count() -> Result<usize> {
        #[cfg(feature = "tpu")]
        {
            // Would query TPU topology
            Ok(0) // Placeholder
        }
        #[cfg(not(feature = "tpu"))]
        {
            Ok(0)
        }
    }
    
    /// Get TPU memory bandwidth (GB/s)
    pub fn memory_bandwidth(&self) -> f32 {
        match self.version {
            TpuVersion::V2 => 700.0,
            TpuVersion::V3 => 900.0,
            TpuVersion::V4 => 1200.0,
            TpuVersion::V5 => 1600.0,
        }
    }
    
    /// Get peak TFLOPS
    pub fn peak_tflops(&self) -> f32 {
        match self.version {
            TpuVersion::V2 => 45.0,
            TpuVersion::V3 => 123.0,
            TpuVersion::V4 => 275.0,
            TpuVersion::V5 => 459.0,
        }
    }
}

/// TPU buffer for HBM (High Bandwidth Memory)
pub struct TpuBuffer {
    size: usize,
    device_id: usize,
}

impl TpuBuffer {
    /// Allocate TPU buffer
    pub fn allocate(size: usize, device_id: usize) -> Result<Self> {
        #[cfg(feature = "tpu")]
        {
            // Would use TPU memory allocation API
            Ok(TpuBuffer { size, device_id })
        }
        #[cfg(not(feature = "tpu"))]
        {
            let _ = (size, device_id);
            Err(GhostError::DeviceError("TPU not available".to_string()))
        }
    }
    
    /// Transfer data to TPU
    pub fn copy_from_host(&mut self, data: &[f32]) -> Result<()> {
        #[cfg(feature = "tpu")]
        {
            if data.len() * std::mem::size_of::<f32>() > self.size {
                return Err(GhostError::DeviceError("Buffer too small".to_string()));
            }
            // Would use TPU transfer API
            Ok(())
        }
        #[cfg(not(feature = "tpu"))]
        {
            let _ = data;
            Err(GhostError::DeviceError("TPU not available".to_string()))
        }
    }
    
    /// Transfer data from TPU
    pub fn copy_to_host(&self, data: &mut [f32]) -> Result<()> {
        #[cfg(feature = "tpu")]
        {
            if data.len() * std::mem::size_of::<f32>() > self.size {
                return Err(GhostError::DeviceError("Buffer too small".to_string()));
            }
            Ok(())
        }
        #[cfg(not(feature = "tpu"))]
        {
            let _ = data;
            Err(GhostError::DeviceError("TPU not available".to_string()))
        }
    }
}

/// XLA (Accelerated Linear Algebra) compiler integration
pub mod xla {
    use super::*;
    
    /// XLA computation graph
    pub struct XlaComputation {
        name: String,
        operations: Vec<XlaOp>,
    }
    
    #[derive(Debug, Clone)]
    pub enum XlaOp {
        MatMul { lhs: usize, rhs: usize },
        Add { lhs: usize, rhs: usize },
        Conv2D { input: usize, kernel: usize },
        ReLU { input: usize },
    }
    
    impl XlaComputation {
        /// Create a new XLA computation
        pub fn new(name: &str) -> Self {
            XlaComputation {
                name: name.to_string(),
                operations: Vec::new(),
            }
        }
        
        /// Add operation to computation
        pub fn add_op(&mut self, op: XlaOp) -> usize {
            self.operations.push(op);
            self.operations.len() - 1
        }
        
        /// Compile computation for TPU
        pub fn compile(&self, device_id: usize) -> Result<CompiledXla> {
            #[cfg(feature = "tpu")]
            {
                // Would use XLA compiler:
                // xla::XlaBuilder builder(name)
                // ... build computation ...
                // xla::Compile(computation, device_id)
                
                let _ = device_id;
                Ok(CompiledXla {
                    name: self.name.clone(),
                })
            }
            #[cfg(not(feature = "tpu"))]
            {
                let _ = device_id;
                Err(GhostError::DeviceError("TPU not available".to_string()))
            }
        }
    }
    
    /// Compiled XLA program
    pub struct CompiledXla {
        name: String,
    }
    
    impl CompiledXla {
        /// Execute compiled program on TPU
        pub fn execute(&self, inputs: &[Tensor]) -> Result<Vec<Tensor>> {
            #[cfg(feature = "tpu")]
            {
                // Would execute on TPU
                let _ = inputs;
                Err(GhostError::NotImplemented("TPU execution".to_string()))
            }
            #[cfg(not(feature = "tpu"))]
            {
                let _ = inputs;
                Err(GhostError::DeviceError("TPU not available".to_string()))
            }
        }
    }
}

/// TPU-optimized operations
pub mod ops {
    use super::*;
    
    /// Matrix multiplication on TPU
    pub fn matmul_tpu(a: &Tensor, b: &Tensor, device_id: usize) -> Result<Tensor> {
        let dims_a = a.dims();
        let dims_b = b.dims();
        
        if dims_a.len() != 2 || dims_b.len() != 2 {
            return Err(GhostError::InvalidShape("matmul requires 2D tensors".to_string()));
        }
        
        let (m, k) = (dims_a[0], dims_a[1]);
        let (k2, n) = (dims_b[0], dims_b[1]);
        
        if k != k2 {
            return Err(GhostError::ShapeMismatch {
                expected: vec![k],
                got: vec![k2],
            });
        }
        
        #[cfg(feature = "tpu")]
        {
            // Build XLA computation
            let mut computation = xla::XlaComputation::new("matmul");
            let input_a = 0;
            let input_b = 1;
            let matmul_op = xla::XlaOp::MatMul { lhs: input_a, rhs: input_b };
            computation.add_op(matmul_op);
            
            // Compile for TPU
            let compiled = computation.compile(device_id)?;
            
            // Execute
            let inputs = vec![a.clone(), b.clone()];
            let outputs = compiled.execute(&inputs)?;
            
            if outputs.is_empty() {
                return Err(GhostError::DeviceError("TPU execution failed".to_string()));
            }
            
            Ok(outputs[0].clone())
        }
        #[cfg(not(feature = "tpu"))]
        {
            let _ = device_id;
            // Fallback to CPU
            a.matmul(b)
        }
    }
    
    /// Convolution on TPU
    pub fn conv2d_tpu(
        input: &Tensor,
        kernel: &Tensor,
        stride: (usize, usize),
        padding: (usize, usize),
        device_id: usize,
    ) -> Result<Tensor> {
        #[cfg(feature = "tpu")]
        {
            // Build XLA convolution
            let mut computation = xla::XlaComputation::new("conv2d");
            let input_id = 0;
            let kernel_id = 1;
            let conv_op = xla::XlaOp::Conv2D { input: input_id, kernel: kernel_id };
            computation.add_op(conv_op);
            
            let compiled = computation.compile(device_id)?;
            let inputs = vec![input.clone(), kernel.clone()];
            let outputs = compiled.execute(&inputs)?;
            
            if outputs.is_empty() {
                return Err(GhostError::DeviceError("TPU execution failed".to_string()));
            }
            
            Ok(outputs[0].clone())
        }
        #[cfg(not(feature = "tpu"))]
        {
            let _ = (input, kernel, stride, padding, device_id);
            Err(GhostError::DeviceError("TPU not available".to_string()))
        }
    }
    
    /// Batch matrix multiplication (optimized for TPU)
    pub fn batch_matmul_tpu(a: &Tensor, b: &Tensor, device_id: usize) -> Result<Tensor> {
        let dims_a = a.dims();
        let dims_b = b.dims();
        
        if dims_a.len() != 3 || dims_b.len() != 3 {
            return Err(GhostError::InvalidShape("batch_matmul requires 3D tensors [B,M,K] x [B,K,N]".to_string()));
        }
        
        let (batch, m, k) = (dims_a[0], dims_a[1], dims_a[2]);
        let (batch2, k2, n) = (dims_b[0], dims_b[1], dims_b[2]);
        
        if batch != batch2 || k != k2 {
            return Err(GhostError::ShapeMismatch {
                expected: vec![batch, k],
                got: vec![batch2, k2],
            });
        }
        
        #[cfg(feature = "tpu")]
        {
            // TPUs are optimized for batch operations
            let mut computation = xla::XlaComputation::new("batch_matmul");
            let input_a = 0;
            let input_b = 1;
            let matmul_op = xla::XlaOp::MatMul { lhs: input_a, rhs: input_b };
            computation.add_op(matmul_op);
            
            let compiled = computation.compile(device_id)?;
            let inputs = vec![a.clone(), b.clone()];
            let outputs = compiled.execute(&inputs)?;
            
            if outputs.is_empty() {
                return Err(GhostError::DeviceError("TPU execution failed".to_string()));
            }
            
            Ok(outputs[0].clone())
        }
        #[cfg(not(feature = "tpu"))]
        {
            let _ = device_id;
            // CPU fallback - process each batch element
            let mut result_data = Vec::with_capacity(batch * m * n);
            let a_data = a.data_f32();
            let b_data = b.data_f32();
            
            for b_idx in 0..batch {
                let a_offset = b_idx * m * k;
                let b_offset = b_idx * k * n;
                
                for i in 0..m {
                    for j in 0..n {
                        let mut sum = 0.0;
                        for p in 0..k {
                            sum += a_data[a_offset + i * k + p] * b_data[b_offset + p * n + j];
                        }
                        result_data.push(sum);
                    }
                }
            }
            
            Tensor::from_slice(&result_data, &[batch, m, n])
        }
    }
    
    /// Transformer attention (optimized for TPU)
    pub fn attention_tpu(
        query: &Tensor,
        key: &Tensor,
        value: &Tensor,
        device_id: usize,
    ) -> Result<Tensor> {
        #[cfg(feature = "tpu")]
        {
            // TPUs excel at transformer workloads
            let _ = (query, key, value, device_id);
            Err(GhostError::NotImplemented("TPU attention - use CPU fallback".to_string()))
        }
        #[cfg(not(feature = "tpu"))]
        {
            let _ = (query, key, value, device_id);
            // CPU fallback: Q @ K^T / sqrt(d_k), then softmax, then @ V
            let d_k = query.dims()[query.dims().len() - 1] as f32;
            let key_t = key.t()?;
            let scores = query.matmul(&key_t)?.div_scalar(d_k.sqrt());
            let attn_weights = scores.softmax(-1);
            attn_weights.matmul(value)
        }
    }
}

/// TPU Pod configuration (multi-chip)
pub struct TpuPod {
    pub num_chips: usize,
    pub topology: PodTopology,
}

#[derive(Debug, Clone, Copy)]
pub enum PodTopology {
    /// Single chip
    Single,
    /// 2x2 grid (4 chips)
    Grid2x2,
    /// 4x4 grid (16 chips)
    Grid4x4,
    /// 8x8 grid (64 chips)
    Grid8x8,
}

impl TpuPod {
    /// Create a TPU Pod configuration
    pub fn new(topology: PodTopology) -> Self {
        let num_chips = match topology {
            PodTopology::Single => 1,
            PodTopology::Grid2x2 => 4,
            PodTopology::Grid4x4 => 16,
            PodTopology::Grid8x8 => 64,
        };
        
        TpuPod { num_chips, topology }
    }
    
    /// Get total TFLOPS for the pod
    pub fn total_tflops(&self, version: TpuVersion) -> f32 {
        let per_chip = match version {
            TpuVersion::V2 => 45.0,
            TpuVersion::V3 => 123.0,
            TpuVersion::V4 => 275.0,
            TpuVersion::V5 => 459.0,
        };
        
        per_chip * self.num_chips as f32
    }
}

#[cfg(test)]
mod tests {
    use super::*;
    
    #[test]
    fn test_tpu_device_count() {
        let count = TpuDevice::device_count().unwrap_or(0);
        // Should return 0 if TPU not available
        assert!(count >= 0);
    }
    
    #[test]
    fn test_tpu_pod() {
        let pod = TpuPod::new(PodTopology::Grid2x2);
        assert_eq!(pod.num_chips, 4);
        
        let tflops = pod.total_tflops(TpuVersion::V4);
        assert_eq!(tflops, 275.0 * 4.0);
    }
    
    #[test]
    fn test_xla_computation() {
        let mut comp = xla::XlaComputation::new("test");
        let op_id = comp.add_op(xla::XlaOp::ReLU { input: 0 });
        assert_eq!(op_id, 0);
    }
}