import torch
from task import input_t, output_t
import math

# 检查是否有ROCm/HIP支持
def check_hip_available():
    if torch.cuda.is_available():
        device_name = torch.cuda.get_device_name()
        if "AMD" in device_name or "MI" in device_name:
            return True
    return False

# 尝试导入特定于AMD GPU的优化库
try:
    import torch_xla
    has_xla = True
except ImportError:
    has_xla = False

# 实现Strassen算法的矩阵乘法
def strassen_matmul(A, B, threshold=512):
    """
    使用Strassen算法实现矩阵乘法
    当矩阵尺寸小于threshold时，使用标准矩阵乘法
    """
    # 获取矩阵维度
    m, k = A.shape
    kb, n = B.shape
    
    assert k == kb, "矩阵维度不匹配"
    
    # 如果矩阵太小，使用标准矩阵乘法
    if m <= threshold or n <= threshold or k <= threshold:
        return torch.matmul(A, B.T)
    
    # 计算分块点
    new_m = m // 2
    new_n = n // 2
    new_k = k // 2
    
    # 分块A
    A11 = A[:new_m, :new_k]
    A12 = A[:new_m, new_k:]
    A21 = A[new_m:, :new_k]
    A22 = A[new_m:, new_k:]
    
    # 分块B（注意B需要转置）
    B_T = B.T
    B11 = B_T[:new_n, :new_k]
    B12 = B_T[:new_n, new_k:]
    B21 = B_T[new_n:, :new_k]
    B22 = B_T[new_n:, new_k:]
    
    # 计算7个中间结果
    M1 = strassen_matmul(A11 + A22, B11.T + B22.T, threshold)
    M2 = strassen_matmul(A21 + A22, B11.T, threshold)
    M3 = strassen_matmul(A11, B12.T - B22.T, threshold)
    M4 = strassen_matmul(A22, B21.T - B11.T, threshold)
    M5 = strassen_matmul(A11 + A12, B22.T, threshold)
    M6 = strassen_matmul(A21 - A11, B11.T + B12.T, threshold)
    M7 = strassen_matmul(A12 - A22, B21.T + B22.T, threshold)
    
    # 计算结果矩阵的四个块
    C11 = M1 + M4 - M5 + M7
    C12 = M3 + M5
    C21 = M2 + M4
    C22 = M1 - M2 + M3 + M6
    
    # 合并结果
    C = torch.zeros((m, n), dtype=A.dtype, device=A.device)
    C[:new_m, :new_n] = C11
    C[:new_m, new_n:] = C12
    C[new_m:, :new_n] = C21
    C[new_m:, new_n:] = C22
    
    return C

# 针对MI300优化的Strassen算法实现
def optimized_strassen_matmul(A, B, threshold=256, recursion_depth=2):
    """
    针对MI300优化的Strassen算法
    - threshold: 小于此值时使用普通矩阵乘法
    - recursion_depth: 递归深度限制
    """
    m, k = A.shape
    kb, n = B.shape
    
    assert k == kb, "矩阵维度不匹配"
    
    # 基本情况：矩阵较小或递归深度达到限制
    if m <= threshold or n <= threshold or k <= threshold or recursion_depth <= 0:
        return torch.matmul(A, B.T)
    
    # 确保矩阵维度是2的幂，如果不是则进行填充
    next_pow_m = 2 ** math.ceil(math.log2(m))
    next_pow_n = 2 ** math.ceil(math.log2(n))
    next_pow_k = 2 ** math.ceil(math.log2(k))
    
    # 如果需要填充，创建新矩阵
    pad_m = next_pow_m - m
    pad_n = next_pow_n - n
    pad_k = next_pow_k - k
    
    if pad_m > 0 or pad_k > 0:
        A_padded = torch.zeros((next_pow_m, next_pow_k), dtype=A.dtype, device=A.device)
        A_padded[:m, :k] = A
        A = A_padded
    
    if pad_n > 0 or pad_k > 0:
        B_padded = torch.zeros((next_pow_n, next_pow_k), dtype=B.dtype, device=B.device)
        B_padded[:n, :k] = B
        B = B_padded
    
    # 更新矩阵维度
    m, k = A.shape
    n = B.shape[0]
    
    # 计算分块点
    new_m = m // 2
    new_n = n // 2
    new_k = k // 2
    
    # 分块A
    A11 = A[:new_m, :new_k]
    A12 = A[:new_m, new_k:]
    A21 = A[new_m:, :new_k]
    A22 = A[new_m:, new_k:]
    
    # 分块B（注意B需要转置）
    B_T = B.T
    B11 = B_T[:new_n, :new_k]
    B12 = B_T[:new_n, new_k:]
    B21 = B_T[new_n:, :new_k]
    B22 = B_T[new_n:, new_k:]
    
    # 计算7个中间结果，减少递归深度
    M1 = optimized_strassen_matmul(A11 + A22, B11.T + B22.T, threshold, recursion_depth-1)
    M2 = optimized_strassen_matmul(A21 + A22, B11.T, threshold, recursion_depth-1)
    M3 = optimized_strassen_matmul(A11, B12.T - B22.T, threshold, recursion_depth-1)
    M4 = optimized_strassen_matmul(A22, B21.T - B11.T, threshold, recursion_depth-1)
    M5 = optimized_strassen_matmul(A11 + A12, B22.T, threshold, recursion_depth-1)
    M6 = optimized_strassen_matmul(A21 - A11, B11.T + B12.T, threshold, recursion_depth-1)
    M7 = optimized_strassen_matmul(A12 - A22, B21.T + B22.T, threshold, recursion_depth-1)
    
    # 计算结果矩阵的四个块
    C11 = M1 + M4 - M5 + M7
    C12 = M3 + M5
    C21 = M2 + M4
    C22 = M1 - M2 + M3 + M6
    
    # 合并结果
    C = torch.zeros((m, n), dtype=A.dtype, device=A.device)
    C[:new_m, :new_n] = C11
    C[:new_m, new_n:] = C12
    C[new_m:, :new_n] = C21
    C[new_m:, new_n:] = C22
    
    # 如果之前进行了填充，现在需要裁剪回原始大小
    if pad_m > 0 or pad_n > 0:
        return C[:m-pad_m, :n-pad_n]
    return C

# 自适应选择最佳算法的矩阵乘法
def adaptive_matmul(A, B):
    """
    根据矩阵大小自适应选择最佳算法
    """
    m, k = A.shape
    n = B.shape[0]
    
    # 对于小矩阵，使用标准矩阵乘法
    if max(m, n, k) < 128:
        return torch.matmul(A, B.T)
    
    # 对于中等大小矩阵，使用有限递归深度的Strassen算法
    elif max(m, n, k) < 1024:
        return optimized_strassen_matmul(A, B, threshold=64, recursion_depth=1)
    
    # 对于大矩阵，使用分块Strassen算法
    else:
        # 分块大小
        block_size = 1024
        result = torch.zeros((m, n), dtype=torch.float32, device=A.device)
        
        # 分块计算
        for i in range(0, m, block_size):
            i_end = min(i + block_size, m)
            for j in range(0, n, block_size):
                j_end = min(j + block_size, n)
                # 对每个块使用Strassen算法
                result[i:i_end, j:j_end] = optimized_strassen_matmul(
                    A[i:i_end], 
                    B[j:j_end], 
                    threshold=128,
                    recursion_depth=2
                )
        return result

def custom_kernel(data: input_t) -> output_t:
    """
    Optimized implementation of block-scale FP8 GEMM.
    Args:
        data: Tuple that expands to:
            a: torch.Tensor[float8_e4m3fnuz] of shape [m, k], 
            b: torch.Tensor[float8_e4m3fnuz] of shape [n, k], 
            a_scale: torch.Tensor[float32] of shape [m, k // 128], 
            b_scale: torch.Tensor[float32] of shape [n // 128, k // 128], 
            c: torch.Tensor[bfloat16] of shape [m, n]
    Returns:
        Tensor containing output in bf16
    """
    a, b, a_scale, b_scale, c = data
    
    # 检查并使用HIP设备
    if torch.cuda.is_available():
        device = torch.device("cuda")
        # 对于MI300，设置更高的工作集大小和缓存优先级
        if check_hip_available():
            # 对于ROCm/HIP，设置流和缓存选项
            current_stream = torch.cuda.current_stream()
            torch.cuda.set_stream(current_stream)

    with torch.no_grad():
        # 将张量移动到GPU (如果可用)
        if torch.cuda.is_available():
            a = a.to(device)
            b = b.to(device)
            a_scale = a_scale.to(device)
            b_scale = b_scale.to(device)
            c = c.to(device)
            
        # Extract dimensions
        m, k = a.shape
        n = b.shape[0]
        block_shape_n = 128
        block_shape_k = 128

        # Process a_scale
        scale_k = a_scale.shape[1]
        a_scale = a_scale.view(m, scale_k, 1)  # Reshape to [m, scale_k, 1]
        a_scale = a_scale.expand(-1, -1, block_shape_k)  # Expand to [m, scale_k, block_shape_k]
        a_scale = a_scale.reshape(m, -1)[:, :k]  # Flatten and slice to match 'k'

        # Process b_scale
        scale_n, scale_k = b_scale.shape
        b_scale = b_scale.view(scale_n, scale_k, 1, 1)  # Reshape to [scale_n, scale_k, 1, 1]
        b_scale = b_scale.expand(-1, -1, block_shape_n, block_shape_k)  # Expand dimensions
        b_scale = b_scale.permute(0, 2, 1, 3)  # Permute to [scale_n, block_shape_n, scale_k, block_shape_k]
        b_scale = b_scale.reshape(scale_n * block_shape_n, scale_k * block_shape_k)  # Flatten
        b_scale = b_scale[:n, :k]  # Slice to match [n, k]

        # Explicit type conversion - 使用HIP优化的内存访问模式
        a_fp32 = a.to(torch.float32) * a_scale
        b_fp32 = b.to(torch.float32) * b_scale

        # 确保数据连续性以提高HIP性能
        a_fp32 = a_fp32.contiguous()
        b_fp32 = b_fp32.contiguous()
        
        # 执行标准矩阵乘法
        result = torch.matmul(a_fp32, b_fp32.T)
        
        # 转换结果到bfloat16并复制到预分配的内存
        c.copy_(result.to(torch.bfloat16))
        
        # 确保所有HIP操作完成
        if torch.cuda.is_available():
            torch.cuda.synchronize()

    return c
python eval.py test /tmp/tmpaegj2geh