tenflowers-core 0.1.1

//! Auto-generated module
//!
//! 🤖 Generated with [SplitRS](https://github.com/cool-japan/splitrs)

use rayon::prelude::*;
use scirs2_core::profiling::Profiler;
use scirs2_core::random::Random;
use std::collections::HashMap;

use super::types::{
    FusableOp, FusedOperation, GpuVendorHints, MemoryAccessPattern, ParallelizationStrategy,
    PerformanceProfile, SimdConfig, SimdInstructionSet,
};

/// Ultra-advanced fusion patterns optimized for next-generation GPU architectures
pub mod ultra_fusion_patterns {
    use super::*;
    use scirs2_core::profiling::Profiler;
    /// Next-generation transformer fusion for maximum efficiency
    pub struct NextGenTransformerFusion {
        /// Attention-MLP fusion configuration
        attention_mlp_fusion: bool,
        /// Layer normalization fusion
        layernorm_fusion_enabled: bool,
        /// Residual connection optimization
        residual_fusion_enabled: bool,
        /// Flash attention integration
        flash_attention_enabled: bool,
    }
    impl NextGenTransformerFusion {
        /// Create ultra-optimized transformer fusion configuration
        pub fn new_ultra_optimized() -> Self {
            Self {
                attention_mlp_fusion: true,
                layernorm_fusion_enabled: true,
                residual_fusion_enabled: true,
                flash_attention_enabled: true,
            }
        }
        /// Generate fused attention-MLP pattern
        pub fn fused_attention_mlp_pattern() -> FusedOperation {
            FusedOperation {
                operations: vec![
                    FusableOp::MultiHeadAttention,
                    FusableOp::LayerNorm,
                    FusableOp::MatMul,
                    FusableOp::GELU,
                    FusableOp::MatMul,
                ],
                parameters: {
                    let mut params = HashMap::new();
                    params.insert("attention_heads".to_string(), 16.0);
                    params.insert("mlp_ratio".to_string(), 4.0);
                    params.insert("dropout_rate".to_string(), 0.1);
                    params
                },
                input_count: 3,
                output_count: 1,
                kernel_id: "ultra_fused_attention_mlp_gelu".to_string(),
                vendor_hints: GpuVendorHints::Generic,
                memory_patterns: MemoryAccessPattern::Random,
                simd_config: SimdConfig {
                    vector_width: 4,
                    enable_vectorization: true,
                    instruction_set: SimdInstructionSet::Avx2,
                    alignment: 16,
                },
                hardware_config: None,
                perf_profile: PerformanceProfile {
                    estimated_flops: 1000000,
                    memory_bandwidth: 1000000000,
                    arithmetic_intensity: 1.0,
                    estimated_latency: 100.0,
                    cache_efficiency: 0.8,
                    parallel_efficiency: 0.9,
                    historical_performance: Vec::new(),
                },
                fusion_priority: 5.0,
                bandwidth_reduction: 0.4,
                parallelization_strategy: ParallelizationStrategy::ModelParallel {
                    pipeline_stages: 2,
                },
            }
        }
        /// Generate optimized residual connection pattern
        pub fn fused_residual_layernorm_pattern() -> FusedOperation {
            FusedOperation {
                operations: vec![FusableOp::Add, FusableOp::LayerNorm],
                parameters: {
                    let mut params = HashMap::new();
                    params.insert("epsilon".to_string(), 1e-5);
                    params.insert("fused_bias".to_string(), 1.0);
                    params
                },
                input_count: 3,
                output_count: 1,
                kernel_id: "ultra_fused_residual_layernorm".to_string(),
                vendor_hints: GpuVendorHints::Generic,
                memory_patterns: MemoryAccessPattern::Sequential,
                simd_config: SimdConfig {
                    vector_width: 4,
                    enable_vectorization: true,
                    instruction_set: SimdInstructionSet::Avx2,
                    alignment: 16,
                },
                hardware_config: None,
                perf_profile: PerformanceProfile {
                    estimated_flops: 1000000,
                    memory_bandwidth: 1000000000,
                    arithmetic_intensity: 1.0,
                    estimated_latency: 100.0,
                    cache_efficiency: 0.8,
                    parallel_efficiency: 0.9,
                    historical_performance: Vec::new(),
                },
                fusion_priority: 3.0,
                bandwidth_reduction: 0.2,
                parallelization_strategy: ParallelizationStrategy::DataParallel { num_devices: 1 },
            }
        }
    }
    /// Advanced convolution fusion patterns for computer vision
    pub struct AdvancedConvFusion {
        /// Depthwise-pointwise fusion
        depthwise_pointwise_enabled: bool,
        /// Batch norm fusion
        batch_norm_fusion_enabled: bool,
        /// Multi-scale fusion
        multi_scale_enabled: bool,
    }
    impl AdvancedConvFusion {
        /// Create ultra-optimized convolution fusion
        pub fn new_ultra_optimized() -> Self {
            Self {
                depthwise_pointwise_enabled: true,
                batch_norm_fusion_enabled: true,
                multi_scale_enabled: true,
            }
        }
        /// Generate MobileNet-style depthwise separable fusion
        pub fn fused_mobilenet_block_pattern(activation: FusableOp) -> FusedOperation {
            FusedOperation {
                operations: vec![
                    FusableOp::DepthwiseConv2D,
                    FusableOp::BatchNorm,
                    activation,
                    FusableOp::Conv2D,
                    FusableOp::BatchNorm,
                    activation,
                ],
                parameters: {
                    let mut params = HashMap::new();
                    params.insert("depthwise_multiplier".to_string(), 1.0);
                    params.insert("bn_momentum".to_string(), 0.99);
                    params.insert("bn_epsilon".to_string(), 1e-5);
                    params
                },
                input_count: 5,
                output_count: 1,
                kernel_id: format!("ultra_fused_mobilenet_{:?}", activation).to_lowercase(),
                vendor_hints: GpuVendorHints::Generic,
                memory_patterns: MemoryAccessPattern::Strided { stride: 8 },
                simd_config: SimdConfig {
                    vector_width: 4,
                    enable_vectorization: true,
                    instruction_set: SimdInstructionSet::Avx2,
                    alignment: 16,
                },
                hardware_config: None,
                perf_profile: PerformanceProfile {
                    estimated_flops: 1000000,
                    memory_bandwidth: 1000000000,
                    arithmetic_intensity: 1.0,
                    estimated_latency: 100.0,
                    cache_efficiency: 0.8,
                    parallel_efficiency: 0.9,
                    historical_performance: Vec::new(),
                },
                fusion_priority: 4.0,
                bandwidth_reduction: 0.3,
                parallelization_strategy: ParallelizationStrategy::DataParallel { num_devices: 1 },
            }
        }
        /// Generate EfficientNet-style squeeze-excite fusion
        pub fn fused_squeeze_excite_pattern() -> FusedOperation {
            FusedOperation {
                operations: vec![
                    FusableOp::Mean,
                    FusableOp::MatMul,
                    FusableOp::ReLU,
                    FusableOp::MatMul,
                    FusableOp::Sigmoid,
                    FusableOp::Mul,
                ],
                parameters: {
                    let mut params = HashMap::new();
                    params.insert("reduction_ratio".to_string(), 16.0);
                    params.insert("se_ratio".to_string(), 0.25);
                    params
                },
                input_count: 3,
                output_count: 1,
                kernel_id: "ultra_fused_squeeze_excite".to_string(),
                vendor_hints: GpuVendorHints::Generic,
                memory_patterns: MemoryAccessPattern::Random,
                simd_config: SimdConfig {
                    vector_width: 4,
                    enable_vectorization: true,
                    instruction_set: SimdInstructionSet::Avx2,
                    alignment: 16,
                },
                hardware_config: None,
                perf_profile: PerformanceProfile {
                    estimated_flops: 1000000,
                    memory_bandwidth: 1000000000,
                    arithmetic_intensity: 1.0,
                    estimated_latency: 100.0,
                    cache_efficiency: 0.8,
                    parallel_efficiency: 0.9,
                    historical_performance: Vec::new(),
                },
                fusion_priority: 3.5,
                bandwidth_reduction: 0.25,
                parallelization_strategy: ParallelizationStrategy::DataParallel { num_devices: 1 },
            }
        }
    }
    /// Quantization-aware fusion patterns for edge deployment
    pub struct QuantizedFusionPatterns {
        /// INT8 fusion enabled
        int8_fusion_enabled: bool,
        /// INT4 fusion for extreme efficiency
        int4_fusion_enabled: bool,
        /// FP8 fusion for latest hardware
        fp8_fusion_enabled: bool,
    }
    impl QuantizedFusionPatterns {
        /// Create quantization-aware fusion configuration
        pub fn new_edge_optimized() -> Self {
            Self {
                int8_fusion_enabled: true,
                int4_fusion_enabled: true,
                fp8_fusion_enabled: true,
            }
        }
        /// Generate quantized linear + activation pattern
        pub fn fused_quantized_linear_activation(
            bits: u8,
            activation: FusableOp,
        ) -> FusedOperation {
            let quantize_op = match bits {
                4 => FusableOp::Quantize4,
                8 => FusableOp::Quantize8,
                _ => FusableOp::Quantize8,
            };
            let dequantize_op = match bits {
                4 => FusableOp::Dequantize4,
                8 => FusableOp::Dequantize8,
                _ => FusableOp::Dequantize8,
            };
            FusedOperation {
                operations: vec![quantize_op, FusableOp::MatMul, dequantize_op, activation],
                parameters: {
                    let mut params = HashMap::new();
                    params.insert("quantization_bits".to_string(), bits as f32);
                    params.insert("scale_factor".to_string(), 127.0);
                    params.insert("zero_point".to_string(), 0.0);
                    params
                },
                input_count: 4,
                output_count: 1,
                kernel_id: format!("ultra_fused_q{}_linear_{:?}", bits, activation).to_lowercase(),
                vendor_hints: GpuVendorHints::Generic,
                memory_patterns: MemoryAccessPattern::Sequential,
                simd_config: SimdConfig {
                    vector_width: 4,
                    enable_vectorization: true,
                    instruction_set: SimdInstructionSet::Avx2,
                    alignment: 16,
                },
                hardware_config: None,
                perf_profile: PerformanceProfile {
                    estimated_flops: 1000000,
                    memory_bandwidth: 1000000000,
                    arithmetic_intensity: 1.0,
                    estimated_latency: 100.0,
                    cache_efficiency: 0.8,
                    parallel_efficiency: 0.9,
                    historical_performance: Vec::new(),
                },
                fusion_priority: 2.0,
                bandwidth_reduction: 0.15,
                parallelization_strategy: ParallelizationStrategy::DataParallel { num_devices: 1 },
            }
        }
        /// Generate FP8 high-performance pattern for H100/Ada
        pub fn fused_fp8_transformer_block() -> FusedOperation {
            FusedOperation {
                operations: vec![
                    FusableOp::FP8MatMul,
                    FusableOp::FP8MatMul,
                    FusableOp::FP8MatMul,
                    FusableOp::ScaledDotProductAttention,
                    FusableOp::FP8MatMul,
                    FusableOp::FP8Add,
                    FusableOp::RMSNorm,
                ],
                parameters: {
                    let mut params = HashMap::new();
                    params.insert("fp8_format".to_string(), 1.0);
                    params.insert("attention_heads".to_string(), 32.0);
                    params.insert("head_dim".to_string(), 128.0);
                    params
                },
                input_count: 4,
                output_count: 1,
                kernel_id: "ultra_fused_fp8_transformer_block".to_string(),
                vendor_hints: GpuVendorHints::Generic,
                memory_patterns: MemoryAccessPattern::Random,
                simd_config: SimdConfig {
                    vector_width: 4,
                    enable_vectorization: true,
                    instruction_set: SimdInstructionSet::Avx2,
                    alignment: 16,
                },
                hardware_config: None,
                perf_profile: PerformanceProfile {
                    estimated_flops: 1000000,
                    memory_bandwidth: 1000000000,
                    arithmetic_intensity: 1.0,
                    estimated_latency: 100.0,
                    cache_efficiency: 0.8,
                    parallel_efficiency: 0.9,
                    historical_performance: Vec::new(),
                },
                fusion_priority: 6.0,
                bandwidth_reduction: 0.5,
                parallelization_strategy: ParallelizationStrategy::ModelParallel {
                    pipeline_stages: 2,
                },
            }
        }
    }
}