torsh-backend 0.1.2

//! High-level neural network building blocks using Metal Performance Shaders

use metal::{CommandBuffer, Device};

use crate::metal::{
    mps::{
        neural_ops::Conv2dParams, ActivationType, ConvolutionAlgorithm, MPSBatchNormalization,
        MPSLinear, MPSMultiHeadAttention, MPSOptimizedConv2d,
    },
    MetalBuffer, Result,
};

/// Residual block (ResNet-style)
pub struct MPSResidualBlock {
    conv1: MPSOptimizedConv2d,
    bn1: MPSBatchNormalization,
    conv2: MPSOptimizedConv2d,
    bn2: MPSBatchNormalization,
    downsample: Option<(MPSOptimizedConv2d, MPSBatchNormalization)>,
    activation: ActivationType,
}

impl MPSResidualBlock {
    /// Create a new residual block
    pub fn new(
        device: &Device,
        in_channels: usize,
        out_channels: usize,
        stride: usize,
        downsample: bool,
    ) -> Result<Self> {
        let conv1_params = Conv2dParams {
            in_channels,
            out_channels,
            kernel_height: 3,
            kernel_width: 3,
            stride_height: stride,
            stride_width: stride,
            padding_height: 1,
            padding_width: 1,
            dilation_height: 1,
            dilation_width: 1,
            groups: 1,
        };

        let conv2_params = Conv2dParams {
            in_channels: out_channels,
            out_channels,
            kernel_height: 3,
            kernel_width: 3,
            stride_height: 1,
            stride_width: 1,
            padding_height: 1,
            padding_width: 1,
            dilation_height: 1,
            dilation_width: 1,
            groups: 1,
        };

        // Create dummy weights for now - in practice these would be loaded
        let weights1 = MetalBuffer::zeros(
            &torsh_core::Shape::from(vec![out_channels, in_channels, 3, 3]),
            &torsh_core::DType::F32,
            &crate::metal::device::MetalDevice::new()?,
        )?;

        let weights2 = MetalBuffer::zeros(
            &torsh_core::Shape::from(vec![out_channels, out_channels, 3, 3]),
            &torsh_core::DType::F32,
            &crate::metal::device::MetalDevice::new()?,
        )?;

        let conv1 = MPSOptimizedConv2d::new(device, conv1_params, &weights1, None, true)?;
        let bn1 = MPSBatchNormalization::new(device, out_channels, 1e-5, 0.1, true)?;

        let conv2 = MPSOptimizedConv2d::new(device, conv2_params, &weights2, None, true)?;
        let bn2 = MPSBatchNormalization::new(device, out_channels, 1e-5, 0.1, true)?;

        let downsample_layers = if downsample || in_channels != out_channels {
            let downsample_params = Conv2dParams {
                in_channels,
                out_channels,
                kernel_height: 1,
                kernel_width: 1,
                stride_height: stride,
                stride_width: stride,
                padding_height: 0,
                padding_width: 0,
                dilation_height: 1,
                dilation_width: 1,
                groups: 1,
            };

            let downsample_weights = MetalBuffer::zeros(
                &torsh_core::Shape::from(vec![out_channels, in_channels, 1, 1]),
                &torsh_core::DType::F32,
                &crate::metal::device::MetalDevice::new()?,
            )?;

            let downsample_conv = MPSOptimizedConv2d::new(
                device,
                downsample_params,
                &downsample_weights,
                None,
                true,
            )?;
            let downsample_bn = MPSBatchNormalization::new(device, out_channels, 1e-5, 0.1, true)?;

            Some((downsample_conv, downsample_bn))
        } else {
            None
        };

        Ok(Self {
            conv1,
            bn1,
            conv2,
            bn2,
            downsample: downsample_layers,
            activation: ActivationType::ReLU,
        })
    }

    /// Forward pass
    pub fn forward(
        &mut self,
        command_buffer: &CommandBuffer,
        input: &MetalBuffer,
        output: &MetalBuffer,
        training: bool,
    ) -> Result<()> {
        // Create intermediate buffers
        let conv1_out = MetalBuffer::zeros(
            input.shape(),
            &torsh_core::DType::F32,
            &crate::metal::device::MetalDevice::new()?,
        )?;

        let bn1_out = MetalBuffer::zeros(
            input.shape(),
            &torsh_core::DType::F32,
            &crate::metal::device::MetalDevice::new()?,
        )?;

        let conv2_out = MetalBuffer::zeros(
            input.shape(),
            &torsh_core::DType::F32,
            &crate::metal::device::MetalDevice::new()?,
        )?;

        let bn2_out = MetalBuffer::zeros(
            input.shape(),
            &torsh_core::DType::F32,
            &crate::metal::device::MetalDevice::new()?,
        )?;

        // Forward through first conv + bn + activation
        self.conv1.encode(command_buffer, input, &conv1_out)?;
        self.bn1
            .forward(command_buffer, &conv1_out, &bn1_out, training)?;
        // Apply activation (ReLU) - would need activation layer implementation

        // Forward through second conv + bn
        self.conv2.encode(command_buffer, &bn1_out, &conv2_out)?;
        self.bn2
            .forward(command_buffer, &conv2_out, &bn2_out, training)?;

        // Handle residual connection
        if let Some((ref downsample_conv, ref mut downsample_bn)) = self.downsample {
            let downsampled = MetalBuffer::zeros(
                output.shape(),
                &torsh_core::DType::F32,
                &crate::metal::device::MetalDevice::new()?,
            )?;
            let downsampled_bn = MetalBuffer::zeros(
                output.shape(),
                &torsh_core::DType::F32,
                &crate::metal::device::MetalDevice::new()?,
            )?;

            downsample_conv.encode(command_buffer, input, &downsampled)?;
            downsample_bn.forward(command_buffer, &downsampled, &downsampled_bn, training)?;

            // Add residual: output = bn2_out + downsampled_bn
            // This would require an element-wise add operation
        } else {
            // Add residual: output = bn2_out + input
            // This would require an element-wise add operation
        }

        // Apply final activation
        // For now, just copy bn2_out to output
        // In practice, we'd apply the final ReLU activation here

        Ok(())
    }
}

/// Transformer encoder layer
pub struct MPSTransformerEncoderLayer {
    self_attention: MPSMultiHeadAttention,
    feed_forward: MPSFeedForward,
    norm1: MPSLayerNorm,
    norm2: MPSLayerNorm,
    dropout_p: f32,
}

impl MPSTransformerEncoderLayer {
    /// Create a new transformer encoder layer
    pub fn new(
        device: &Device,
        embed_dim: usize,
        num_heads: usize,
        ff_dim: usize,
        dropout_p: f32,
    ) -> Result<Self> {
        let self_attention = MPSMultiHeadAttention::new(device, embed_dim, num_heads, dropout_p)?;
        let feed_forward = MPSFeedForward::new(device, embed_dim, ff_dim, ActivationType::ReLU)?;
        let norm1 = MPSLayerNorm::new(device, embed_dim, 1e-5)?;
        let norm2 = MPSLayerNorm::new(device, embed_dim, 1e-5)?;

        Ok(Self {
            self_attention,
            feed_forward,
            norm1,
            norm2,
            dropout_p,
        })
    }

    /// Forward pass with residual connections and layer normalization
    pub fn forward(
        &self,
        command_buffer: &CommandBuffer,
        input: &MetalBuffer,
        output: &MetalBuffer,
        mask: Option<&MetalBuffer>,
    ) -> Result<()> {
        // Self-attention with residual connection
        let attn_out = MetalBuffer::zeros(
            input.shape(),
            &torsh_core::DType::F32,
            &crate::metal::device::MetalDevice::new()?,
        )?;

        self.self_attention
            .forward(command_buffer, input, input, input, &attn_out, mask)?;

        // Add residual and normalize
        let norm1_out = MetalBuffer::zeros(
            input.shape(),
            &torsh_core::DType::F32,
            &crate::metal::device::MetalDevice::new()?,
        )?;
        // attn_out = input + attn_out (residual)
        self.norm1.forward(command_buffer, &attn_out, &norm1_out)?;

        // Feed-forward with residual connection
        let ff_out = MetalBuffer::zeros(
            input.shape(),
            &torsh_core::DType::F32,
            &crate::metal::device::MetalDevice::new()?,
        )?;

        self.feed_forward
            .forward(command_buffer, &norm1_out, &ff_out)?;

        // Add residual and normalize
        // ff_out = norm1_out + ff_out (residual)
        self.norm2.forward(command_buffer, &ff_out, output)?;

        Ok(())
    }
}

/// Feed-forward network (MLP)
pub struct MPSFeedForward {
    linear1: MPSLinear,
    linear2: MPSLinear,
    activation: ActivationType,
    hidden_dim: usize,
}

impl MPSFeedForward {
    /// Create a new feed-forward network
    pub fn new(
        device: &Device,
        input_dim: usize,
        hidden_dim: usize,
        activation: ActivationType,
    ) -> Result<Self> {
        let linear1 = MPSLinear::new(device, input_dim, hidden_dim, true)?;
        let linear2 = MPSLinear::new(device, hidden_dim, input_dim, true)?;

        Ok(Self {
            linear1,
            linear2,
            activation,
            hidden_dim,
        })
    }

    /// Forward pass: linear1 -> activation -> linear2
    pub fn forward(
        &self,
        command_buffer: &CommandBuffer,
        input: &MetalBuffer,
        output: &MetalBuffer,
    ) -> Result<()> {
        // Create temporary buffer for hidden layer output
        let hidden = MetalBuffer::zeros(
            &torsh_core::Shape::from(vec![input.shape().dims()[0], self.hidden_dim]),
            &input.dtype(),
            &crate::metal::device::MetalDevice::new()?,
        )?;

        self.linear1.forward(command_buffer, input, &hidden)?;
        // Apply activation (would need activation implementation)
        // For now, assume activation is applied in-place

        self.linear2.forward(command_buffer, &hidden, output)?;

        Ok(())
    }
}

/// Layer normalization
pub struct MPSLayerNorm {
    weight: MetalBuffer,
    bias: MetalBuffer,
    normalized_shape: Vec<usize>,
    eps: f32,
}

impl MPSLayerNorm {
    /// Create a new layer normalization layer
    pub fn new(_device: &Device, normalized_shape: usize, eps: f32) -> Result<Self> {
        let weight = MetalBuffer::ones(
            &torsh_core::Shape::from(vec![normalized_shape]),
            &torsh_core::DType::F32,
            &crate::metal::device::MetalDevice::new()?,
        )?;

        let bias = MetalBuffer::zeros(
            &torsh_core::Shape::from(vec![normalized_shape]),
            &torsh_core::DType::F32,
            &crate::metal::device::MetalDevice::new()?,
        )?;

        Ok(Self {
            weight,
            bias,
            normalized_shape: vec![normalized_shape],
            eps,
        })
    }

    /// Forward pass: layer normalization
    pub fn forward(
        &self,
        _command_buffer: &CommandBuffer,
        _input: &MetalBuffer,
        _output: &MetalBuffer,
    ) -> Result<()> {
        // Layer normalization implementation would go here
        // For now, this is a placeholder
        // Real implementation would:
        // 1. Compute mean and variance along the last dimension
        // 2. Normalize: (input - mean) / sqrt(var + eps)
        // 3. Apply affine transformation: weight * normalized + bias

        Ok(())
    }
}

/// Efficient convolution building blocks for modern architectures
pub struct MPSConvBlock {
    layers: Vec<ConvLayer>,
}

enum ConvLayer {
    Conv2d(MPSOptimizedConv2d),
    BatchNorm(MPSBatchNormalization),
    Activation(ActivationType),
    Dropout(f32),
}

impl MPSConvBlock {
    /// Create a new convolution block
    pub fn new(device: &Device) -> MPSConvBlockBuilder {
        MPSConvBlockBuilder::new(device)
    }

    /// Forward pass through all layers
    pub fn forward(
        &mut self,
        command_buffer: &CommandBuffer,
        input: &MetalBuffer,
        output: &MetalBuffer,
        training: bool,
    ) -> Result<()> {
        let mut current_input = input.clone();
        let layer_count = self.layers.len();

        for (i, layer) in self.layers.iter_mut().enumerate() {
            let is_last = i == layer_count - 1;
            let current_output = if is_last {
                output.clone()
            } else {
                MetalBuffer::zeros(
                    current_input.shape(),
                    &torsh_core::DType::F32,
                    &crate::metal::device::MetalDevice::new()?,
                )?
            };

            match layer {
                ConvLayer::Conv2d(conv) => {
                    conv.encode(command_buffer, &current_input, &current_output)?;
                }
                ConvLayer::BatchNorm(bn) => {
                    bn.forward(command_buffer, &current_input, &current_output, training)?;
                }
                ConvLayer::Activation(_activation) => {
                    // Apply activation (would need activation implementation)
                    // For now, just copy input to output
                }
                ConvLayer::Dropout(_p) => {
                    // Apply dropout during training
                    // For now, just copy input to output
                }
            }

            current_input = current_output;
        }

        Ok(())
    }
}

/// Builder for convolution blocks
pub struct MPSConvBlockBuilder {
    device: Device,
    layers: Vec<ConvLayer>,
}

impl MPSConvBlockBuilder {
    fn new(device: &Device) -> Self {
        Self {
            device: device.clone(),
            layers: Vec::new(),
        }
    }

    /// Add a convolution layer
    pub fn conv2d(
        mut self,
        in_channels: usize,
        out_channels: usize,
        kernel_size: usize,
        stride: usize,
        padding: usize,
    ) -> Result<Self> {
        let params = Conv2dParams {
            in_channels,
            out_channels,
            kernel_height: kernel_size,
            kernel_width: kernel_size,
            stride_height: stride,
            stride_width: stride,
            padding_height: padding,
            padding_width: padding,
            dilation_height: 1,
            dilation_width: 1,
            groups: 1,
        };

        let weights = MetalBuffer::zeros(
            &torsh_core::Shape::from(vec![out_channels, in_channels, kernel_size, kernel_size]),
            &torsh_core::DType::F32,
            &crate::metal::device::MetalDevice::new()?,
        )?;

        let conv = MPSOptimizedConv2d::new(&self.device, params, &weights, None, true)?;
        self.layers.push(ConvLayer::Conv2d(conv));
        Ok(self)
    }

    /// Add batch normalization
    pub fn batch_norm(mut self, num_features: usize) -> Result<Self> {
        let bn = MPSBatchNormalization::new(&self.device, num_features, 1e-5, 0.1, true)?;
        self.layers.push(ConvLayer::BatchNorm(bn));
        Ok(self)
    }

    /// Add activation function
    pub fn activation(mut self, activation: ActivationType) -> Self {
        self.layers.push(ConvLayer::Activation(activation));
        self
    }

    /// Add dropout
    pub fn dropout(mut self, p: f32) -> Self {
        self.layers.push(ConvLayer::Dropout(p));
        self
    }

    /// Build the convolution block
    pub fn build(self) -> MPSConvBlock {
        MPSConvBlock {
            layers: self.layers,
        }
    }
}

/// Performance optimization utilities
pub struct MPSOptimizations;

impl MPSOptimizations {
    /// Analyze and suggest optimal algorithms for a given workload
    pub fn analyze_workload(
        input_shape: &[usize],
        conv_params: &Conv2dParams,
        batch_size: usize,
    ) -> ConvolutionAlgorithm {
        let [_batch, _channels, height, width] = [
            input_shape[0],
            input_shape[1],
            input_shape[2],
            input_shape[3],
        ];

        // Heuristics for algorithm selection based on problem size
        let _input_size = height * width;
        let kernel_size = conv_params.kernel_height * conv_params.kernel_width;
        let output_channels = conv_params.out_channels;

        // Winograd is good for 3x3 convolutions with stride 1
        if conv_params.kernel_height == 3
            && conv_params.kernel_width == 3
            && conv_params.stride_height == 1
            && conv_params.stride_width == 1
        {
            return ConvolutionAlgorithm::Winograd;
        }

        // FFT is good for large kernels
        if kernel_size >= 49 {
            // 7x7 or larger
            return ConvolutionAlgorithm::FFT;
        }

        // Im2Col+GEMM is good for large batch sizes and many output channels
        if batch_size >= 32 && output_channels >= 256 {
            return ConvolutionAlgorithm::Im2ColGemm;
        }

        // Default to direct convolution
        ConvolutionAlgorithm::Direct
    }

    /// Suggest optimal memory layout for given tensor shapes
    pub fn suggest_memory_layout(shapes: &[Vec<usize>]) -> Vec<MemoryLayout> {
        shapes
            .iter()
            .map(|shape| {
                match shape.len() {
                    4 => MemoryLayout::NCHW,     // Default for 4D tensors
                    2 => MemoryLayout::RowMajor, // Default for 2D tensors
                    _ => MemoryLayout::Contiguous,
                }
            })
            .collect()
    }

    /// Calculate theoretical FLOPS for operations
    pub fn calculate_flops(_operation: &dyn MPSOperation, _input_shapes: &[Vec<usize>]) -> u64 {
        // Implementation would calculate FLOPs based on operation type
        // This is simplified for now
        0
    }
}

#[derive(Debug, Clone, Copy)]
pub enum MemoryLayout {
    NCHW,
    NHWC,
    RowMajor,
    ColumnMajor,
    Contiguous,
}

/// Type alias for MPS operation trait
pub trait MPSOperation {
    fn encode(&self, command_buffer: &CommandBuffer) -> Result<()>;
}

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

    #[test]
    fn test_conv_block_builder() {
        // Test would create a device and build a conv block
        // This is a placeholder since we need a real Metal device
        assert!(true);
    }

    #[test]
    fn test_optimization_heuristics() {
        let input_shape = vec![32, 256, 56, 56]; // Batch, Channels, Height, Width
        let conv_params = Conv2dParams {
            in_channels: 256,
            out_channels: 512,
            kernel_height: 3,
            kernel_width: 3,
            stride_height: 1,
            stride_width: 1,
            padding_height: 1,
            padding_width: 1,
            dilation_height: 1,
            dilation_width: 1,
            groups: 1,
        };

        let algorithm = MPSOptimizations::analyze_workload(&input_shape, &conv_params, 32);
        assert_eq!(algorithm, ConvolutionAlgorithm::Winograd);
    }
}