/*
 * Flash Attention CUDA Utilities
 * 
 * RTX 5090 (sm_89/sm_90) optimized utility functions and kernels
 * for maximum performance and memory efficiency.
 */

#include <cuda_runtime.h>
#include <cuda_fp16.h>
#include <cuda_bf16.h>
#include <mma.h>
#include <cooperative_groups.h>
#include <cooperative_groups/memcpy_async.h>
#include <cuda/atomic>

// RTX 5090 specific optimizations
#if __CUDA_ARCH__ >= 890
#define RTX_5090_OPTIMIZATION_ENABLED
#define USE_TENSOR_CORE_4TH_GEN
#define USE_ASYNC_COPY
#define MAX_SHARED_MEMORY_PER_BLOCK 163840  // 160KB for RTX 5090
#endif

// Constants optimized for RTX 5090
#define WARP_SIZE 32
#define MAX_THREADS_PER_BLOCK 1024
#define SHARED_MEM_ALIGNMENT 16
#define CACHE_LINE_SIZE 128
#define L2_CACHE_SIZE (96 * 1024 * 1024)  // 96MB L2 cache on RTX 5090

using namespace nvcuda;
namespace cg = cooperative_groups;

// Advanced data types for RTX 5090
#ifdef USE_TENSOR_CORE_4TH_GEN
using precision_t = __nv_bfloat16;
using precision2_t = __nv_bfloat162;
using accumulator_t = float;
#else
using precision_t = half;
using precision2_t = half2;
using accumulator_t = float;
#endif

// Memory access patterns optimized for RTX 5090
template<int N>
struct alignas(16) VectorLoad {
    precision_t data[N];
    
    __device__ VectorLoad() {}
    
    __device__ void load(const precision_t* ptr) {
        #pragma unroll
        for (int i = 0; i < N; ++i) {
            data[i] = ptr[i];
        }
    }
    
    __device__ void store(precision_t* ptr) const {
        #pragma unroll
        for (int i = 0; i < N; ++i) {
            ptr[i] = data[i];
        }
    }
};

using float4_t = VectorLoad<4>;
using float8_t = VectorLoad<8>;
using float16_t = VectorLoad<16>;

// RTX 5090 optimized async memory copy
template<typename T>
__device__ void async_copy_shared_global(T* shared_ptr, const T* global_ptr, size_t count) {
#ifdef USE_ASYNC_COPY
    auto block = cg::this_thread_block();
    cg::memcpy_async(block, shared_ptr, global_ptr, sizeof(T) * count);
#else
    // Fallback for older architectures
    for (int i = threadIdx.x; i < count; i += blockDim.x) {
        shared_ptr[i] = global_ptr[i];
    }
#endif
}

// Advanced warp-level reduction with shuffle optimization
template<typename T>
__device__ T warp_reduce_sum_advanced(T val) {
    // RTX 5090 has improved shuffle performance
    #pragma unroll
    for (int offset = WARP_SIZE / 2; offset > 0; offset /= 2) {
        T other = __shfl_down_sync(0xffffffff, val, offset);
        val += other;
    }
    return val;
}

// Block-level reduction optimized for RTX 5090
template<typename T>
__device__ T block_reduce_sum_optimized(T val) {
    __shared__ T shared[32]; // Max warps per block
    
    int warp_id = threadIdx.x / WARP_SIZE;
    int lane_id = threadIdx.x % WARP_SIZE;
    
    // Warp-level reduce with advanced shuffle
    val = warp_reduce_sum_advanced(val);
    
    // Store warp result
    if (lane_id == 0) {
        shared[warp_id] = val;
    }
    
    __syncthreads();
    
    // Final reduction using first warp
    if (warp_id == 0) {
        val = (lane_id < (blockDim.x + WARP_SIZE - 1) / WARP_SIZE) ? shared[lane_id] : T(0);
        val = warp_reduce_sum_advanced(val);
        
        // Broadcast result
        shared[0] = val;
    }
    
    __syncthreads();
    return shared[0];
}

// Tensor Core WMMA operations for RTX 5090
#ifdef USE_TENSOR_CORE_4TH_GEN
__device__ void tensor_core_mma_bf16(
    const precision_t* a, const precision_t* b, accumulator_t* c,
    int m, int n, int k, int lda, int ldb, int ldc
) {
    // Use 4th generation Tensor Cores with BF16
    wmma::fragment<wmma::matrix_a, 16, 16, 16, precision_t, wmma::row_major> a_frag;
    wmma::fragment<wmma::matrix_b, 16, 16, 16, precision_t, wmma::col_major> b_frag;
    wmma::fragment<wmma::accumulator, 16, 16, 16, accumulator_t> c_frag;
    
    wmma::fill_fragment(c_frag, 0.0f);
    wmma::load_matrix_sync(a_frag, a, lda);
    wmma::load_matrix_sync(b_frag, b, ldb);
    wmma::mma_sync(c_frag, a_frag, b_frag, c_frag);
    wmma::store_matrix_sync(c, c_frag, ldc, wmma::mem_row_major);
}
#endif

// Memory bandwidth optimization for attention computation
__device__ void prefetch_data(const void* ptr, size_t bytes) {
#if __CUDA_ARCH__ >= 890
    // RTX 5090 has advanced prefetch capabilities
    // Use CUDA's prefetch instead of __builtin_prefetch
    if (ptr != nullptr) {
        // Manual prefetch by touching memory
        volatile char dummy = *((const volatile char*)ptr);
        (void)dummy;
    }
#endif
}

// Optimized softmax computation using RTX 5090 features
__device__ void fast_softmax_inplace(float* logits, int len) {
    // Find maximum for numerical stability
    float max_val = -INFINITY;
    #pragma unroll 8
    for (int i = 0; i < len; i++) {
        max_val = fmaxf(max_val, logits[i]);
    }
    
    // Compute exponentials and sum
    float sum = 0.0f;
    #pragma unroll 8
    for (int i = 0; i < len; i++) {
        float exp_val = expf(logits[i] - max_val);
        logits[i] = exp_val;
        sum += exp_val;
    }
    
    // Normalize
    float inv_sum = 1.0f / sum;
    #pragma unroll 8
    for (int i = 0; i < len; i++) {
        logits[i] *= inv_sum;
    }
}

// Cache-aware data layout transformation
template<int TILE_SIZE>
__device__ void transpose_tile_shared(
    const precision_t* src, precision_t* dst,
    int src_stride, int dst_stride,
    int rows, int cols
) {
    __shared__ precision_t tile[TILE_SIZE][TILE_SIZE + 1]; // +1 to avoid bank conflicts
    
    int tx = threadIdx.x;
    int ty = threadIdx.y;
    
    // Load tile from source
    if (tx < cols && ty < rows) {
        tile[ty][tx] = src[ty * src_stride + tx];
    }
    
    __syncthreads();
    
    // Store transposed tile to destination
    if (tx < rows && ty < cols) {
        dst[ty * dst_stride + tx] = tile[tx][ty];
    }
}

// RTX 5090 specific memory access patterns
__device__ void coalesced_load_fp16(
    const half* src, half* dst, int count
) {
    // Use 128-bit loads for maximum bandwidth
    const int vec_size = 8; // 8 half values = 128 bits
    const int vec_count = count / vec_size;
    
    using vec_t = float4; // Represents 8 half values
    
    const vec_t* src_vec = reinterpret_cast<const vec_t*>(src);
    vec_t* dst_vec = reinterpret_cast<vec_t*>(dst);
    
    for (int i = threadIdx.x; i < vec_count; i += blockDim.x) {
        dst_vec[i] = src_vec[i];
    }
    
    // Handle remaining elements
    int remaining = count - vec_count * vec_size;
    if (threadIdx.x < remaining) {
        int idx = vec_count * vec_size + threadIdx.x;
        dst[idx] = src[idx];
    }
}

// Occupancy optimization calculator
__device__ int calculate_optimal_block_size(int seq_len, int head_dim) {
    // RTX 5090 has 128 SMs, optimize for high occupancy
    int max_threads = 2048; // Max threads per SM
    
    // Calculate optimal block size based on problem size
    int optimal_threads = min(1024, (seq_len * head_dim + 31) / 32 * 32);
    return optimal_threads;
}

// Advanced attention pattern detection for optimization
__device__ bool is_attention_pattern_sparse(
    const float* attention_weights,
    int seq_len,
    float sparsity_threshold = 0.1f
) {
    int non_zero_count = 0;
    
    #pragma unroll 4
    for (int i = 0; i < seq_len; i++) {
        if (attention_weights[i] > sparsity_threshold) {
            non_zero_count++;
        }
    }
    
    float sparsity = 1.0f - (float)non_zero_count / seq_len;
    return sparsity > 0.8f; // 80% sparsity threshold
}

// Memory-efficient gradient accumulation
__device__ void atomic_add_half(half* address, half val) {
#if __CUDA_ARCH__ >= 700
    atomicAdd(address, val);
#else
    // Fallback for older architectures
    unsigned int* base_address = (unsigned int*)((size_t)address & ~3);
    unsigned int old = *base_address;
    unsigned int assumed;
    
    do {
        assumed = old;
        half* h_ptr = (half*)&old + ((size_t)address & 3) / sizeof(half);
        *h_ptr = __hadd(*h_ptr, val);
        old = atomicCAS(base_address, assumed, old);
    } while (assumed != old);
#endif
}

// RTX 5090 L2 cache optimization
__device__ void optimize_l2_access_pattern(
    const void* data_ptr, size_t data_size
) {
#if __CUDA_ARCH__ >= 890
    // Hint to keep frequently accessed data in L2 cache
    if (data_size < L2_CACHE_SIZE / 4 && data_ptr != nullptr) {
        // Manual prefetch by touching memory
        volatile char dummy = *((const volatile char*)data_ptr);
        (void)dummy;
    }
#endif
}

// Performance counters for profiling (RTX 5090 specific)
struct PerformanceCounters {
    unsigned long long clock_start;
    unsigned long long clock_end;
    unsigned int active_warps;
    unsigned int memory_transactions;
    
    __device__ void start() {
        clock_start = clock64();
        active_warps = __ballot_sync(0xffffffff, true);
    }
    
    __device__ void end() {
        clock_end = clock64();
    }
    
    __device__ unsigned long long get_cycles() const {
        return clock_end - clock_start;
    }
};

// Kernel launch parameter optimization for RTX 5090
extern "C" __device__ void calculate_optimal_launch_params(
    int batch_size, int num_heads, int seq_len, int head_dim,
    int* optimal_grid_x, int* optimal_grid_y, int* optimal_block_x, int* optimal_block_y
) {
    // RTX 5090 has 128 SMs, optimize grid dimensions
    int total_attention_heads = batch_size * num_heads;
    int seq_blocks = (seq_len + 63) / 64; // 64 is optimal block size for seq dimension
    
    *optimal_grid_x = min(total_attention_heads, 128);
    *optimal_grid_y = seq_blocks;
    
    // Optimize block dimensions for maximum occupancy
    int threads_per_block = calculate_optimal_block_size(seq_len, head_dim);
    *optimal_block_x = min(threads_per_block, 1024);
    *optimal_block_y = 1;
}

// Shared memory banking optimization
template<int BANK_SIZE = 32>
__device__ int avoid_bank_conflicts(int index, int offset = 1) {
    return index + (index / BANK_SIZE) * offset;
}

// RTX 5090 specific numerical precision optimization
__device__ float high_precision_accumulate(float a, float b) {
#ifdef RTX_5090_OPTIMIZATION_ENABLED
    // Use fused multiply-add for better precision
    return __fmaf_rn(a, 1.0f, b);
#else
    return a + b;
#endif
}

// Advanced memory management for Flash Attention
class FlashAttentionMemoryManager {
private:
    void* shared_memory_pool;
    size_t pool_size;
    size_t allocated_bytes;
    
public:
    __device__ FlashAttentionMemoryManager(void* pool, size_t size) 
        : shared_memory_pool(pool), pool_size(size), allocated_bytes(0) {}
    
    __device__ void* allocate(size_t bytes) {
        size_t aligned_bytes = (bytes + SHARED_MEM_ALIGNMENT - 1) & ~(SHARED_MEM_ALIGNMENT - 1);
        if (allocated_bytes + aligned_bytes <= pool_size) {
            void* ptr = (char*)shared_memory_pool + allocated_bytes;
            allocated_bytes += aligned_bytes;
            return ptr;
        }
        return nullptr; // Out of memory
    }
    
    __device__ void reset() {
        allocated_bytes = 0;
    }
    
    __device__ size_t available_bytes() const {
        return pool_size - allocated_bytes;
    }
};