"""
CUDA Kernels for UHG Operations.

This module implements custom CUDA kernels for efficient UHG operations,
focusing on cross-ratio computation and attention mechanisms.
"""

import torch
from typing import Optional, Tuple

# CUDA kernel for computing cross-ratio
cross_ratio_kernel = """
extern "C" __global__ void cross_ratio_kernel(
    const float* __restrict__ p1,
    const float* __restrict__ p2,
    const float* __restrict__ p3,
    const float* __restrict__ p4,
    float* __restrict__ output,
    const int batch_size,
    const int dim
) {
    const int idx = blockIdx.x * blockDim.x + threadIdx.x;
    if (idx >= batch_size) return;
    
    // Compute offsets for this batch
    const int offset = idx * dim;
    const float* p1_batch = p1 + offset;
    const float* p2_batch = p2 + offset;
    const float* p3_batch = p3 + offset;
    const float* p4_batch = p4 + offset;
    
    // Compute distances in projective space
    float d12 = 0.0f, d34 = 0.0f;
    float d13 = 0.0f, d24 = 0.0f;
    
    #pragma unroll
    for (int i = 0; i < dim; ++i) {
        const float diff12 = p1_batch[i] - p2_batch[i];
        const float diff34 = p3_batch[i] - p4_batch[i];
        const float diff13 = p1_batch[i] - p3_batch[i];
        const float diff24 = p2_batch[i] - p4_batch[i];
        
        d12 += diff12 * diff12;
        d34 += diff34 * diff34;
        d13 += diff13 * diff13;
        d24 += diff24 * diff24;
    }
    
    // Compute cross-ratio
    const float numerator = d12 * d34;
    const float denominator = d13 * d24;
    output[idx] = numerator / (denominator + 1e-6f);
}
"""

# CUDA kernel for attention score computation
attention_score_kernel = """
extern "C" __global__ void attention_score_kernel(
    const float* __restrict__ query,
    const float* __restrict__ key,
    float* __restrict__ output,
    const int batch_size,
    const int num_heads,
    const int seq_len,
    const int head_dim
) {
    const int b = blockIdx.z;  // Batch index
    const int h = blockIdx.y;  // Head index
    const int i = blockIdx.x * blockDim.x + threadIdx.x;  // Query sequence index
    const int j = blockIdx.x * blockDim.y + threadIdx.y;  // Key sequence index
    
    if (i >= seq_len || j >= seq_len) return;
    
    // Compute offsets
    const int batch_offset = b * num_heads * seq_len * head_dim;
    const int head_offset = h * seq_len * head_dim;
    const int q_offset = batch_offset + head_offset + i * head_dim;
    const int k_offset = batch_offset + head_offset + j * head_dim;
    
    // Compute attention score using cross-ratio
    float d_qk = 0.0f;
    #pragma unroll
    for (int d = 0; d < head_dim; ++d) {
        const float diff = query[q_offset + d] - key[k_offset + d];
        d_qk += diff * diff;
    }
    
    // Convert distance to attention score
    const int out_idx = b * num_heads * seq_len * seq_len + 
                       h * seq_len * seq_len +
                       i * seq_len + j;
    output[out_idx] = 1.0f / (1.0f + d_qk);
}
"""

class CUDAKernels:
    """Manages CUDA kernels for UHG operations."""
    
    def __init__(self):
        if not torch.cuda.is_available():
            raise RuntimeError("CUDA is not available")
            
        # Load CUDA kernels
        self.cross_ratio = torch.cuda.compile(cross_ratio_kernel)
        self.attention_score = torch.cuda.compile(attention_score_kernel)
        
    def compute_cross_ratio(
        self,
        p1: torch.Tensor,
        p2: torch.Tensor,
        p3: torch.Tensor,
        p4: torch.Tensor
    ) -> torch.Tensor:
        """Compute cross-ratio using CUDA kernel."""
        assert p1.is_cuda and p2.is_cuda and p3.is_cuda and p4.is_cuda
        assert p1.dtype == torch.float32  # Currently only supports float32
        
        batch_size = p1.size(0)
        dim = p1.size(1)
        
        # Allocate output tensor
        output = torch.empty(batch_size, device='cuda', dtype=torch.float32)
        
        # Configure kernel launch parameters
        threads_per_block = min(batch_size, 1024)
        num_blocks = (batch_size + threads_per_block - 1) // threads_per_block
        
        # Launch kernel
        self.cross_ratio(
            grid=(num_blocks, 1, 1),
            block=(threads_per_block, 1, 1),
            args=(
                p1.data_ptr(),
                p2.data_ptr(),
                p3.data_ptr(),
                p4.data_ptr(),
                output.data_ptr(),
                batch_size,
                dim
            )
        )
        
        return output
        
    def compute_attention_scores(
        self,
        query: torch.Tensor,
        key: torch.Tensor,
        mask: Optional[torch.Tensor] = None
    ) -> torch.Tensor:
        """Compute attention scores using CUDA kernel."""
        assert query.is_cuda and key.is_cuda
        assert query.dtype == torch.float32  # Currently only supports float32
        
        batch_size = query.size(0)
        num_heads = query.size(1)
        seq_len = query.size(2)
        head_dim = query.size(3)
        
        # Allocate output tensor
        output = torch.empty(
            (batch_size, num_heads, seq_len, seq_len),
            device='cuda',
            dtype=torch.float32
        )
        
        # Configure kernel launch parameters
        block_size = min(32, seq_len)  # Use 32x32 thread blocks
        grid_size = (
            (seq_len + block_size - 1) // block_size,
            num_heads,
            batch_size
        )
        
        # Launch kernel
        self.attention_score(
            grid=grid_size,
            block=(block_size, block_size, 1),
            args=(
                query.data_ptr(),
                key.data_ptr(),
                output.data_ptr(),
                batch_size,
                num_heads,
                seq_len,
                head_dim
            )
        )
        
        # Apply mask if provided
        if mask is not None:
            output = output.masked_fill(mask == 0, float('-inf'))
            
        # Apply softmax
        output = torch.softmax(output, dim=-1)
        
        return output 