trustformers-mobile 0.1.1

//! SIMD Optimization Module
//!
//! Provides SIMD (Single Instruction Multiple Data) optimizations for mobile platforms,
//! focusing on ARM NEON and Advanced SIMD instructions.

use super::KernelType;
use crate::MobilePlatform;
use trustformers_core::error::Result;

/// SIMD instruction set
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum SimdInstructions {
    /// ARM NEON (ARMv7/ARMv8)
    Neon,
    /// ARM SVE (Scalable Vector Extension)
    Sve,
    /// Advanced SIMD (ARMv8)
    AdvSimd,
    /// No SIMD available
    None,
}

/// Vectorization strategy
#[derive(Debug, Clone)]
pub struct VectorizationStrategy {
    /// Target instruction set
    pub instruction_set: SimdInstructions,
    /// Vector width in bits
    pub vector_width: usize,
    /// Preferred data type
    pub data_type: SimdDataType,
    /// Alignment requirements
    pub alignment: usize,
}

/// SIMD data types
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum SimdDataType {
    Float32,
    Float16,
    Int8,
    Int16,
    Int32,
}

/// SIMD optimizer
pub struct SimdOptimizer {
    platform: MobilePlatform,
    available_instructions: Vec<SimdInstructions>,
    capabilities: SimdCapabilities,
}

/// SIMD capabilities
#[derive(Debug, Clone)]
struct SimdCapabilities {
    has_fma: bool,
    has_dot_product: bool,
    has_fp16: bool,
    has_bf16: bool,
    has_int8_matmul: bool,
    max_vector_width: usize,
}

impl SimdOptimizer {
    /// Create new SIMD optimizer
    pub fn new(platform: MobilePlatform) -> Self {
        let available_instructions = Self::detect_simd_support(&platform);
        let capabilities = Self::detect_capabilities(&platform, &available_instructions);

        Self {
            platform,
            available_instructions,
            capabilities,
        }
    }

    /// Check if kernel can be vectorized
    pub fn can_vectorize(&self, kernel: &KernelType) -> bool {
        if self.available_instructions.is_empty() {
            return false;
        }

        matches!(
            kernel,
            KernelType::Conv2d
                | KernelType::Linear
                | KernelType::BatchNorm
                | KernelType::Activation
                | KernelType::Pooling
                | KernelType::Custom(_)
        )
    }

    /// Vectorize kernel
    pub fn vectorize_kernel(
        &self,
        kernel: &KernelType,
        input_shapes: &[Vec<usize>],
    ) -> Result<KernelType> {
        let strategy = self.select_vectorization_strategy(kernel, input_shapes)?;

        match strategy.instruction_set {
            SimdInstructions::Neon => self.vectorize_with_neon(kernel, &strategy),
            SimdInstructions::AdvSimd => self.vectorize_with_advsimd(kernel, &strategy),
            SimdInstructions::Sve => self.vectorize_with_sve(kernel, &strategy),
            SimdInstructions::None => Ok(kernel.clone()),
        }
    }

    /// Get optimal vector width for data type
    pub fn optimal_vector_width(&self, data_type: SimdDataType) -> usize {
        let base_width = self.capabilities.max_vector_width;

        match data_type {
            SimdDataType::Float32 => base_width / 32,
            SimdDataType::Float16 => base_width / 16,
            SimdDataType::Int8 => base_width / 8,
            SimdDataType::Int16 => base_width / 16,
            SimdDataType::Int32 => base_width / 32,
        }
    }

    // Private helper methods

    fn detect_simd_support(platform: &MobilePlatform) -> Vec<SimdInstructions> {
        let mut instructions = Vec::new();

        match platform {
            MobilePlatform::Ios => {
                // iOS devices have NEON and AdvSIMD
                instructions.push(SimdInstructions::Neon);
                instructions.push(SimdInstructions::AdvSimd);
            },
            MobilePlatform::Android => {
                // Most Android devices have NEON
                if cfg!(target_arch = "aarch64") {
                    instructions.push(SimdInstructions::Neon);
                    instructions.push(SimdInstructions::AdvSimd);
                } else if cfg!(target_arch = "arm") {
                    instructions.push(SimdInstructions::Neon);
                }
            },
            MobilePlatform::Generic => {
                if cfg!(any(target_arch = "aarch64", target_arch = "arm")) {
                    instructions.push(SimdInstructions::Neon);
                }
            },
        }

        instructions
    }

    fn detect_capabilities(
        platform: &MobilePlatform,
        instructions: &[SimdInstructions],
    ) -> SimdCapabilities {
        let mut caps = SimdCapabilities {
            has_fma: false,
            has_dot_product: false,
            has_fp16: false,
            has_bf16: false,
            has_int8_matmul: false,
            max_vector_width: 64,
        };

        if instructions.contains(&SimdInstructions::AdvSimd) {
            caps.has_fma = true;
            caps.has_fp16 = true;
            caps.max_vector_width = 128;

            // Modern ARM cores have dot product
            if matches!(platform, MobilePlatform::Ios) {
                caps.has_dot_product = true;
                caps.has_int8_matmul = true;
            }
        } else if instructions.contains(&SimdInstructions::Neon) {
            caps.max_vector_width = 128;
            caps.has_fma = cfg!(target_arch = "aarch64");
        }

        caps
    }

    fn select_vectorization_strategy(
        &self,
        kernel: &KernelType,
        input_shapes: &[Vec<usize>],
    ) -> Result<VectorizationStrategy> {
        // Select best instruction set
        let instruction_set =
            self.available_instructions.first().copied().unwrap_or(SimdInstructions::None);

        // Select data type based on kernel and capabilities
        let data_type = if self.capabilities.has_fp16 && self.should_use_fp16(kernel) {
            SimdDataType::Float16
        } else if self.should_use_int8(kernel) {
            SimdDataType::Int8
        } else {
            SimdDataType::Float32
        };

        let vector_width = self.capabilities.max_vector_width;
        let alignment = if vector_width >= 128 { 16 } else { 8 };

        Ok(VectorizationStrategy {
            instruction_set,
            vector_width,
            data_type,
            alignment,
        })
    }

    fn should_use_fp16(&self, kernel: &KernelType) -> bool {
        // Use FP16 for memory-bound operations
        matches!(kernel, KernelType::Activation | KernelType::BatchNorm)
    }

    fn should_use_int8(&self, kernel: &KernelType) -> bool {
        // Use INT8 for operations that benefit from quantization
        false // Would check model quantization settings
    }

    fn vectorize_with_neon(
        &self,
        kernel: &KernelType,
        strategy: &VectorizationStrategy,
    ) -> Result<KernelType> {
        let vectorized_name = format!("Neon{:?}", kernel);
        Ok(KernelType::Custom(vectorized_name))
    }

    fn vectorize_with_advsimd(
        &self,
        kernel: &KernelType,
        strategy: &VectorizationStrategy,
    ) -> Result<KernelType> {
        let vectorized_name = format!("AdvSimd{:?}", kernel);
        Ok(KernelType::Custom(vectorized_name))
    }

    fn vectorize_with_sve(
        &self,
        kernel: &KernelType,
        strategy: &VectorizationStrategy,
    ) -> Result<KernelType> {
        let vectorized_name = format!("Sve{:?}", kernel);
        Ok(KernelType::Custom(vectorized_name))
    }
}

/// NEON-specific optimizations
pub struct NeonOptimizations;

impl NeonOptimizations {
    /// Generate NEON code for vector addition
    pub fn generate_vadd_f32() -> &'static str {
        r#"
        void neon_vadd_f32(const float* a, const float* b, float* c, size_t n) {
            size_t i = 0;
            // Process 4 floats at a time
            for (; i + 4 <= n; i += 4) {
                float32x4_t va = vld1q_f32(a + i);
                float32x4_t vb = vld1q_f32(b + i);
                float32x4_t vc = vaddq_f32(va, vb);
                vst1q_f32(c + i, vc);
            }
            // Handle remaining elements
            for (; i < n; i++) {
                c[i] = a[i] + b[i];
            }
        }
        "#
    }

    /// Generate NEON code for vector multiplication
    pub fn generate_vmul_f32() -> &'static str {
        r#"
        void neon_vmul_f32(const float* a, const float* b, float* c, size_t n) {
            size_t i = 0;
            for (; i + 4 <= n; i += 4) {
                float32x4_t va = vld1q_f32(a + i);
                float32x4_t vb = vld1q_f32(b + i);
                float32x4_t vc = vmulq_f32(va, vb);
                vst1q_f32(c + i, vc);
            }
            for (; i < n; i++) {
                c[i] = a[i] * b[i];
            }
        }
        "#
    }

    /// Generate NEON code for FMA (Fused Multiply-Add)
    pub fn generate_vfma_f32() -> &'static str {
        r#"
        void neon_vfma_f32(const float* a, const float* b, const float* c, float* d, size_t n) {
            size_t i = 0;
            for (; i + 4 <= n; i += 4) {
                float32x4_t va = vld1q_f32(a + i);
                float32x4_t vb = vld1q_f32(b + i);
                float32x4_t vc = vld1q_f32(c + i);
                float32x4_t vd = vfmaq_f32(vc, va, vb); // d = a * b + c
                vst1q_f32(d + i, vd);
            }
            for (; i < n; i++) {
                d[i] = a[i] * b[i] + c[i];
            }
        }
        "#
    }

    /// Generate NEON code for ReLU activation
    pub fn generate_relu_f32() -> &'static str {
        r#"
        void neon_relu_f32(const float* input, float* output, size_t n) {
            const float32x4_t zero = vdupq_n_f32(0.0f);
            size_t i = 0;

            // Process 4 elements at a time
            for (; i + 4 <= n; i += 4) {
                float32x4_t x = vld1q_f32(input + i);
                float32x4_t result = vmaxq_f32(x, zero);
                vst1q_f32(output + i, result);
            }

            // Handle remaining elements
            for (; i < n; i++) {
                output[i] = fmaxf(input[i], 0.0f);
            }
        }
        "#
    }

    /// Generate NEON code for dot product
    pub fn generate_dot_product_f32() -> &'static str {
        r#"
        float neon_dot_product_f32(const float* a, const float* b, size_t n) {
            float32x4_t sum = vdupq_n_f32(0.0f);
            size_t i = 0;

            // Unroll by 4 for better performance
            for (; i + 16 <= n; i += 16) {
                float32x4_t a0 = vld1q_f32(a + i);
                float32x4_t b0 = vld1q_f32(b + i);
                float32x4_t a1 = vld1q_f32(a + i + 4);
                float32x4_t b1 = vld1q_f32(b + i + 4);
                float32x4_t a2 = vld1q_f32(a + i + 8);
                float32x4_t b2 = vld1q_f32(b + i + 8);
                float32x4_t a3 = vld1q_f32(a + i + 12);
                float32x4_t b3 = vld1q_f32(b + i + 12);

                sum = vfmaq_f32(sum, a0, b0);
                sum = vfmaq_f32(sum, a1, b1);
                sum = vfmaq_f32(sum, a2, b2);
                sum = vfmaq_f32(sum, a3, b3);
            }

            // Process remaining groups of 4
            for (; i + 4 <= n; i += 4) {
                float32x4_t a0 = vld1q_f32(a + i);
                float32x4_t b0 = vld1q_f32(b + i);
                sum = vfmaq_f32(sum, a0, b0);
            }

            // Horizontal sum
            float result = vaddvq_f32(sum);

            // Handle remaining elements
            for (; i < n; i++) {
                result += a[i] * b[i];
            }

            return result;
        }
        "#
    }
}

/// Advanced SIMD optimizations (ARMv8)
pub struct AdvSimdOptimizations;

impl AdvSimdOptimizations {
    /// Generate AdvSIMD code for FP16 operations
    pub fn generate_fp16_ops() -> &'static str {
        r#"
        #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
        void advsimd_vadd_f16(const __fp16* a, const __fp16* b, __fp16* c, size_t n) {
            size_t i = 0;
            // Process 8 FP16 values at a time
            for (; i + 8 <= n; i += 8) {
                float16x8_t va = vld1q_f16(a + i);
                float16x8_t vb = vld1q_f16(b + i);
                float16x8_t vc = vaddq_f16(va, vb);
                vst1q_f16(c + i, vc);
            }
            // Handle remaining
            for (; i < n; i++) {
                c[i] = a[i] + b[i];
            }
        }
        #endif
        "#
    }

    /// Generate AdvSIMD code for INT8 dot product
    pub fn generate_int8_dot_product() -> &'static str {
        r#"
        #ifdef __ARM_FEATURE_DOTPROD
        int32_t advsimd_dot_product_i8(const int8_t* a, const int8_t* b, size_t n) {
            int32x4_t sum = vdupq_n_s32(0);
            size_t i = 0;

            // Process 16 INT8 values at a time (4 dot products)
            for (; i + 16 <= n; i += 16) {
                int8x16_t va = vld1q_s8(a + i);
                int8x16_t vb = vld1q_s8(b + i);

                // Split into 4-element groups for dot product
                int8x8_t va_low = vget_low_s8(va);
                int8x8_t va_high = vget_high_s8(va);
                int8x8_t vb_low = vget_low_s8(vb);
                int8x8_t vb_high = vget_high_s8(vb);

                // Accumulate dot products
                sum = vdotq_s32(sum, va_low, vb_low);
                sum = vdotq_s32(sum, va_high, vb_high);
            }

            // Horizontal sum
            int32_t result = vaddvq_s32(sum);

            // Handle remaining elements
            for (; i < n; i++) {
                result += (int32_t)a[i] * (int32_t)b[i];
            }

            return result;
        }
        #endif
        "#
    }
}

/// SIMD performance estimator
pub struct SimdPerformanceEstimator;

impl SimdPerformanceEstimator {
    /// Estimate speedup from SIMD
    pub fn estimate_speedup(
        instruction_set: SimdInstructions,
        data_type: SimdDataType,
        operation: &KernelType,
    ) -> f32 {
        let vector_speedup = match (instruction_set, data_type) {
            (SimdInstructions::Neon, SimdDataType::Float32) => 4.0,
            (SimdInstructions::Neon, SimdDataType::Float16) => 8.0,
            (SimdInstructions::Neon, SimdDataType::Int8) => 16.0,
            (SimdInstructions::AdvSimd, SimdDataType::Float32) => 4.0,
            (SimdInstructions::AdvSimd, SimdDataType::Float16) => 8.0,
            (SimdInstructions::AdvSimd, SimdDataType::Int8) => 16.0,
            (SimdInstructions::Sve, _) => 8.0, // Variable vector length
            (SimdInstructions::None, _) => 1.0,
            _ => 2.0,
        };

        // Adjust for operation type
        let operation_efficiency = match operation {
            KernelType::Conv2d => 0.8,      // Good SIMD utilization
            KernelType::Linear => 0.9,      // Excellent SIMD utilization
            KernelType::Activation => 0.95, // Near perfect SIMD utilization
            KernelType::BatchNorm => 0.85,  // Good SIMD utilization
            KernelType::Pooling => 0.7,     // Moderate SIMD utilization
            _ => 0.6,
        };

        vector_speedup * operation_efficiency
    }
}

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

    #[test]
    fn test_simd_optimizer_creation() {
        let optimizer = SimdOptimizer::new(MobilePlatform::Ios);
        assert!(!optimizer.available_instructions.is_empty());
    }

    #[test]
    fn test_vectorization_check() {
        // Use iOS platform which always has NEON support (ARM-based)
        let optimizer = SimdOptimizer::new(MobilePlatform::Ios);

        assert!(optimizer.can_vectorize(&KernelType::Conv2d));
        assert!(optimizer.can_vectorize(&KernelType::Linear));
        assert!(optimizer.can_vectorize(&KernelType::Activation));
    }

    #[test]
    fn test_optimal_vector_width() {
        let optimizer = SimdOptimizer::new(MobilePlatform::Ios);

        // For 128-bit vectors
        assert_eq!(optimizer.optimal_vector_width(SimdDataType::Float32), 4);
        assert_eq!(optimizer.optimal_vector_width(SimdDataType::Float16), 8);
        assert_eq!(optimizer.optimal_vector_width(SimdDataType::Int8), 16);
    }

    #[test]
    fn test_performance_estimation() {
        let speedup = SimdPerformanceEstimator::estimate_speedup(
            SimdInstructions::Neon,
            SimdDataType::Float32,
            &KernelType::Linear,
        );

        assert!(speedup > 1.0);
        assert!(speedup <= 4.0);
    }
}