04-算子融合与Kernel优化

# 最常见的融合类型：连续的element-wise操作

import torch
import triton
import triton.language as tl

# 未融合版本
def unfused_gelu(x):
    t1 = x * 0.5
    t2 = x * 0.7978845608028654
    t3 = x ** 3
    t4 = t3 * 0.044715
    t5 = t2 + t4
    t6 = torch.tanh(t5)
    t7 = 1.0 + t6
    return t1 * t7


# 融合版本 (Triton实现)
@triton.jit
def fused_gelu_kernel(
    x_ptr, output_ptr,
    n_elements,
    BLOCK_SIZE: tl.constexpr
):
    pid = tl.program_id(0)
    offset = pid * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE)
    mask = offset < n_elements

    # 单次内存读取
    x = tl.load(x_ptr + offset, mask=mask)

    # 所有计算在寄存器中完成
    t1 = x * 0.5
    t2 = x * 0.7978845608028654
    t3 = x * x * x
    t4 = t3 * 0.044715
    t5 = t2 + t4
    t6 = tl.math.tanh(t5)
    t7 = 1.0 + t6
    output = t1 * t7

    # 单次内存写入
    tl.store(output_ptr + offset, output, mask=mask)


def fused_gelu(x):
    output = torch.empty_like(x)
    n_elements = x.numel()
    grid = lambda meta: (triton.cdiv(n_elements, meta['BLOCK_SIZE']),)
    fused_gelu_kernel[grid](x, output, n_elements, BLOCK_SIZE=1024)
    return output


# 性能对比
def benchmark():
    x = torch.randn(1024, 1024, device='cuda')

    # 预热
    for _ in range(10):
        _ = unfused_gelu(x)
        _ = fused_gelu(x)

    torch.cuda.synchronize()
    import time

    # 未融合
    start = time.time()
    for _ in range(100):
        _ = unfused_gelu(x)
    torch.cuda.synchronize()
    unfused_time = time.time() - start

    # 融合
    start = time.time()
    for _ in range(100):
        _ = fused_gelu(x)
    torch.cuda.synchronize()
    fused_time = time.time() - start

    print(f"Unfused: {unfused_time*10:.2f}ms")
    print(f"Fused: {fused_time*10:.2f}ms")
    print(f"Speedup: {unfused_time/fused_time:.2f}x")

# 将element-wise操作与reduce操作融合

@triton.jit
def fused_softmax_kernel(
    input_ptr, output_ptr,
    n_cols,
    input_row_stride, output_row_stride,
    BLOCK_SIZE: tl.constexpr
):
    """
    融合的softmax实现
    将 max, sub, exp, sum, div 融合为单个kernel
    """
    row_idx = tl.program_id(0)
    col_offsets = tl.arange(0, BLOCK_SIZE)
    mask = col_offsets < n_cols

    # 1. 加载一行数据
    input_ptrs = input_ptr + row_idx * input_row_stride + col_offsets
    row = tl.load(input_ptrs, mask=mask, other=-float('inf'))

    # 2. 计算max (规约)
    row_max = tl.max(row, axis=0)

    # 3. 减去max并计算exp (element-wise)
    numerator = tl.exp(row - row_max)

    # 4. 计算sum (规约)
    denominator = tl.sum(numerator, axis=0)

    # 5. 归一化 (element-wise)
    softmax_output = numerator / denominator

    # 6. 存储结果
    output_ptrs = output_ptr + row_idx * output_row_stride + col_offsets
    tl.store(output_ptrs, softmax_output, mask=mask)


# 更复杂的例子: LayerNorm融合
@triton.jit
def fused_layer_norm_kernel(
    x_ptr, weight_ptr, bias_ptr, output_ptr,
    N, D,
    eps,
    BLOCK_SIZE: tl.constexpr
):
    """
    融合的LayerNorm实现
    mean, var, normalize, scale, shift 全部融合
    """
    row_idx = tl.program_id(0)
    col_offsets = tl.arange(0, BLOCK_SIZE)
    mask = col_offsets < D

    # 加载数据
    x_ptrs = x_ptr + row_idx * D + col_offsets
    x = tl.load(x_ptrs, mask=mask, other=0.0)

    # 计算均值
    mean = tl.sum(x, axis=0) / D

    # 计算方差
    x_centered = x - mean
    var = tl.sum(x_centered * x_centered, axis=0) / D

    # 归一化
    x_norm = x_centered / tl.sqrt(var + eps)

    # 仿射变换
    weight = tl.load(weight_ptr + col_offsets, mask=mask)
    bias = tl.load(bias_ptr + col_offsets, mask=mask)
    output = x_norm * weight + bias

    # 存储
    output_ptrs = output_ptr + row_idx * D + col_offsets
    tl.store(output_ptrs, output, mask=mask)

# MatMul + BiasAdd + Activation 融合

@triton.jit
def fused_matmul_bias_relu_kernel(
    a_ptr, b_ptr, bias_ptr, c_ptr,
    M, N, K,
    stride_am, stride_ak,
    stride_bk, stride_bn,
    stride_cm, stride_cn,
    BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: tl.constexpr,
):
    """
    融合的矩阵乘法 + bias + ReLU
    C = ReLU(A @ B + bias)
    """
    pid_m = tl.program_id(0)
    pid_n = tl.program_id(1)

    # 计算块起始位置
    offs_m = pid_m * BLOCK_M + tl.arange(0, BLOCK_M)
    offs_n = pid_n * BLOCK_N + tl.arange(0, BLOCK_N)
    offs_k = tl.arange(0, BLOCK_K)

    # 初始化累加器
    acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)

    # 矩阵乘法主循环
    for k in range(0, K, BLOCK_K):
        # 加载A块
        a_ptrs = a_ptr + offs_m[:, None] * stride_am + (k + offs_k[None, :]) * stride_ak
        a = tl.load(a_ptrs, mask=(offs_m[:, None] < M) & ((k + offs_k[None, :]) < K), other=0.0)

        # 加载B块
        b_ptrs = b_ptr + (k + offs_k[:, None]) * stride_bk + offs_n[None, :] * stride_bn
        b = tl.load(b_ptrs, mask=((k + offs_k[:, None]) < K) & (offs_n[None, :] < N), other=0.0)

        # 累加
        acc += tl.dot(a, b)

    # 加载bias并添加
    bias = tl.load(bias_ptr + offs_n, mask=offs_n < N)
    acc = acc + bias[None, :]

    # 应用ReLU (融合的激活)
    acc = tl.maximum(acc, 0.0)

    # 存储结果
    c_ptrs = c_ptr + offs_m[:, None] * stride_cm + offs_n[None, :] * stride_cn
    mask = (offs_m[:, None] < M) & (offs_n[None, :] < N)
    tl.store(c_ptrs, acc, mask=mask)

# Flash Attention: 将整个attention计算融合

@triton.jit
def flash_attention_kernel(
    Q, K, V, Out,
    stride_qz, stride_qh, stride_qm, stride_qk,
    stride_kz, stride_kh, stride_kn, stride_kk,
    stride_vz, stride_vh, stride_vn, stride_vk,
    stride_oz, stride_oh, stride_om, stride_ok,
    Z, H, N_CTX,
    BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: tl.constexpr,
):
    """
    Flash Attention融合实现
    分块计算attention，避免O(N^2)内存
    """
    # 块索引
    start_m = tl.program_id(0) * BLOCK_M
    off_hz = tl.program_id(1)
    off_z = off_hz // H
    off_h = off_hz % H

    # 指针偏移
    Q_block_ptr = Q + off_z * stride_qz + off_h * stride_qh
    K_block_ptr = K + off_z * stride_kz + off_h * stride_kh
    V_block_ptr = V + off_z * stride_vz + off_h * stride_vh
    O_block_ptr = Out + off_z * stride_oz + off_h * stride_oh

    # 初始化
    offs_m = start_m + tl.arange(0, BLOCK_M)
    offs_n = tl.arange(0, BLOCK_N)
    offs_k = tl.arange(0, BLOCK_K)

    # 加载Q块 (保持在SRAM)
    q_ptrs = Q_block_ptr + offs_m[:, None] * stride_qm + offs_k[None, :] * stride_qk
    q = tl.load(q_ptrs, mask=offs_m[:, None] < N_CTX)

    # 初始化输出累加器
    m_i = tl.full([BLOCK_M], float('-inf'), dtype=tl.float32)  # 行最大值
    l_i = tl.zeros([BLOCK_M], dtype=tl.float32)  # 行和
    acc = tl.zeros([BLOCK_M, BLOCK_K], dtype=tl.float32)  # 输出

    # 遍历K,V块
    for start_n in range(0, N_CTX, BLOCK_N):
        # 加载K块
        k_ptrs = K_block_ptr + (start_n + offs_n)[:, None] * stride_kn + offs_k[None, :] * stride_kk
        k = tl.load(k_ptrs, mask=(start_n + offs_n)[:, None] < N_CTX)

        # 计算QK^T
        qk = tl.dot(q, tl.trans(k))

        # Softmax缩放
        qk = qk * (1.0 / tl.sqrt(float(BLOCK_K)))

        # 更新最大值和累加器 (online softmax)
        m_ij = tl.max(qk, axis=1)
        m_new = tl.maximum(m_i, m_ij)
        alpha = tl.exp(m_i - m_new)
        beta = tl.exp(m_ij - m_new)
        l_new = alpha * l_i + beta * tl.sum(tl.exp(qk - m_ij[:, None]), axis=1)

        # 加载V块
        v_ptrs = V_block_ptr + (start_n + offs_n)[:, None] * stride_vn + offs_k[None, :] * stride_vk
        v = tl.load(v_ptrs, mask=(start_n + offs_n)[:, None] < N_CTX)

        # 更新输出
        p = tl.exp(qk - m_new[:, None])
        acc = alpha[:, None] * acc + tl.dot(p, v)

        # 更新状态
        m_i = m_new
        l_i = l_new

    # 归一化并存储
    acc = acc / l_i[:, None]
    o_ptrs = O_block_ptr + offs_m[:, None] * stride_om + offs_k[None, :] * stride_ok
    tl.store(o_ptrs, acc, mask=offs_m[:, None] < N_CTX)

# 融合决策的核心算法

class FusionAnalyzer:
    """
    分析计算图，决定哪些操作可以融合
    """

    def __init__(self, graph):
        self.graph = graph

    def find_fusion_groups(self):
        """
        找出所有可融合的操作组
        """
        fusion_groups = []
        visited = set()

        for node in self.graph.nodes:
            if node in visited:
                continue

            group = self._grow_fusion_group(node, visited)
            if len(group) > 1:
                fusion_groups.append(group)

        return fusion_groups

    def _grow_fusion_group(self, start_node, visited):
        """
        从起始节点扩展融合组
        使用贪心策略
        """
        group = [start_node]
        visited.add(start_node)
        frontier = list(start_node.users)

        while frontier:
            candidate = frontier.pop(0)

            if candidate in visited:
                continue

            if self._can_fuse(group, candidate):
                group.append(candidate)
                visited.add(candidate)
                frontier.extend(candidate.users)

        return group

    def _can_fuse(self, group, candidate):
        """
        检查是否可以将candidate融合到group
        """
        # 规则1: Element-wise操作可以融合
        if self._is_elementwise(candidate):
            # 检查形状兼容性
            if all(self._shapes_compatible(n, candidate) for n in group):
                return True

        # 规则2: Broadcast可以融合
        if candidate.op_type == 'Broadcast':
            return True

        # 规则3: 单输出的producer可以融合到consumer
        if len(candidate.outputs) == 1:
            for user in candidate.users:
                if user in group:
                    return True

        # 规则4: Reduce可以融合element-wise前缀
        if candidate.op_type == 'Reduce':
            return all(self._is_elementwise(n) for n in group)

        return False

    def _is_elementwise(self, node):
        """检查是否是element-wise操作"""
        elementwise_ops = {
            'Add', 'Sub', 'Mul', 'Div',
            'Exp', 'Log', 'Sin', 'Cos', 'Tanh',
            'Relu', 'Sigmoid', 'Gelu',
            'Cast', 'Neg', 'Abs'
        }
        return node.op_type in elementwise_ops

    def _shapes_compatible(self, node1, node2):
        """检查形状是否兼容融合"""
        shape1 = node1.output_shape
        shape2 = node2.output_shape

        # 完全相同
        if shape1 == shape2:
            return True

        # 可广播
        if self._can_broadcast(shape1, shape2):
            return True

        return False


class FusionCostModel:
    """
    融合代价模型
    评估融合是否有益
    """

    def __init__(self, target_device):
        self.device = target_device
        # 设备参数
        self.memory_bandwidth = 1000e9  # 1TB/s for H100
        self.compute_throughput = 1000e12  # 1PFLOPS for H100
        self.kernel_launch_overhead = 5e-6  # 5us

    def should_fuse(self, group):
        """
        评估是否应该融合
        """
        unfused_cost = self._estimate_unfused_cost(group)
        fused_cost = self._estimate_fused_cost(group)

        return fused_cost < unfused_cost

    def _estimate_unfused_cost(self, group):
        """估算未融合的执行时间"""
        total_cost = 0

        for node in group:
            # Kernel启动开销
            total_cost += self.kernel_launch_overhead

            # 内存访问时间
            memory_bytes = self._compute_memory_bytes(node)
            total_cost += memory_bytes / self.memory_bandwidth

            # 计算时间
            flops = self._compute_flops(node)
            total_cost += flops / self.compute_throughput

        return total_cost

    def _estimate_fused_cost(self, group):
        """估算融合后的执行时间"""
        # 单次kernel启动
        total_cost = self.kernel_launch_overhead

        # 只计算输入和输出的内存访问
        input_bytes = sum(self._input_bytes(n) for n in group if self._is_input(n, group))
        output_bytes = sum(self._output_bytes(n) for n in group if self._is_output(n, group))
        total_cost += (input_bytes + output_bytes) / self.memory_bandwidth

        # 计算时间 (可能有更好的并行性)
        total_flops = sum(self._compute_flops(n) for n in group)
        total_cost += total_flops / self.compute_throughput

        return total_cost

# 循环融合的代数分析

class LoopFusionAnalyzer:
    """
    分析循环是否可以融合
    """

    def can_fuse_loops(self, loop1, loop2):
        """
        检查两个循环是否可以融合
        """
        # 条件1: 循环边界相同
        if not self._same_bounds(loop1, loop2):
            return False

        # 条件2: 无循环携带依赖
        if self._has_loop_carried_dependence(loop1, loop2):
            return False

        # 条件3: 数据依赖允许
        if not self._data_dependence_allows(loop1, loop2):
            return False

        return True

    def _same_bounds(self, loop1, loop2):
        """检查循环边界是否相同"""
        return (loop1.lower_bound == loop2.lower_bound and
                loop1.upper_bound == loop2.upper_bound and
                loop1.step == loop2.step)

    def _has_loop_carried_dependence(self, loop1, loop2):
        """
        检查是否存在循环携带依赖
        即迭代i的写入是否被迭代j>i的读取依赖
        """
        writes1 = self._get_write_accesses(loop1)
        reads2 = self._get_read_accesses(loop2)

        for write in writes1:
            for read in reads2:
                if self._may_alias(write, read):
                    # 检查依赖距离
                    distance = self._compute_dependence_distance(write, read)
                    if distance != 0:  # 非零距离意味着循环携带依赖
                        return True

        return False

    def _data_dependence_allows(self, loop1, loop2):
        """
        检查数据依赖是否允许融合
        """
        # 分析依赖类型
        dep_type = self._analyze_dependence(loop1, loop2)

        # RAW (Read After Write) - 允许融合
        # WAR (Write After Read) - 允许融合
        # WAW (Write After Write) - 需要保持顺序
        # RAR (Read After Read) - 总是允许

        return dep_type in ['RAW', 'WAR', 'RAR', 'NONE']


class TileFusion:
    """
    平铺融合
    将多个循环融合并应用平铺
    """

    def fuse_and_tile(self, loops, tile_sizes):
        """
        融合循环并应用平铺
        """
        # 1. 融合循环
        fused_loop = self._fuse_loops(loops)

        # 2. 应用平铺
        tiled_loop = self._tile_loop(fused_loop, tile_sizes)

        return tiled_loop

    def _fuse_loops(self, loops):
        """
        将多个循环融合为一个
        """
        # 合并循环体
        fused_body = []
        for loop in loops:
            fused_body.extend(loop.body)

        return Loop(
            bounds=loops[0].bounds,
            body=fused_body
        )

    def _tile_loop(self, loop, tile_sizes):
        """
        对循环应用平铺
        """
        # 创建外层循环 (平铺)
        outer_loops = []
        inner_loops = []

        for dim, size in enumerate(tile_sizes):
            outer = Loop(
                var=f"tile_{dim}",
                bounds=(0, loop.bounds[dim], size)
            )
            inner = Loop(
                var=f"inner_{dim}",
                bounds=(0, size, 1)
            )
            outer_loops.append(outer)
            inner_loops.append(inner)

        # 组合循环
        return self._compose_loops(outer_loops + inner_loops, loop.body)

# 内存访问优化技术

@triton.jit
def optimized_memory_access_kernel(
    input_ptr, output_ptr,
    N, stride,
    BLOCK_SIZE: tl.constexpr
):
    """
    优化的内存访问模式
    """
    pid = tl.program_id(0)

    # 技术1: 合并内存访问 (Coalesced Access)
    # 相邻线程访问相邻内存地址
    offset = pid * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE)

    # 好: 连续访问
    data = tl.load(input_ptr + offset)  # 合并的128字节事务

    # 坏: 跨步访问 (应避免)
    # data = tl.load(input_ptr + offset * stride)  # 多个小事务

    # 技术2: 向量化加载
    # 使用更宽的数据类型一次加载更多数据
    # float4代替float, 减少指令数

    # 技术3: 共享内存缓存
    # 对于需要多次访问的数据，先加载到共享内存


@triton.jit
def tiled_matmul_with_shared_memory(
    a_ptr, b_ptr, c_ptr,
    M, N, K,
    BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: tl.constexpr,
):
    """
    使用共享内存的分块矩阵乘法
    """
    pid_m = tl.program_id(0)
    pid_n = tl.program_id(1)

    # 计算块位置
    offs_m = pid_m * BLOCK_M + tl.arange(0, BLOCK_M)
    offs_n = pid_n * BLOCK_N + tl.arange(0, BLOCK_N)

    # 累加器
    acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)

    # 分块迭代
    for k in range(0, K, BLOCK_K):
        offs_k = k + tl.arange(0, BLOCK_K)

        # 从全局内存加载到共享内存/寄存器
        # Triton自动管理共享内存
        a = tl.load(a_ptr + offs_m[:, None] * K + offs_k[None, :],
                    mask=(offs_m[:, None] < M) & (offs_k[None, :] < K))
        b = tl.load(b_ptr + offs_k[:, None] * N + offs_n[None, :],
                    mask=(offs_k[:, None] < K) & (offs_n[None, :] < N))

        # 同步 (确保数据加载完成)
        # Triton自动插入同步

        # 计算
        acc += tl.dot(a, b)

    # 存储结果
    c_ptrs = c_ptr + offs_m[:, None] * N + offs_n[None, :]
    tl.store(c_ptrs, acc, mask=(offs_m[:, None] < M) & (offs_n[None, :] < N))

# 计算密集型优化

@triton.jit
def optimized_compute_kernel(
    x_ptr, output_ptr, N,
    BLOCK_SIZE: tl.constexpr
):
    """
    计算优化技术演示
    """
    pid = tl.program_id(0)
    offset = pid * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE)

    x = tl.load(x_ptr + offset, mask=offset < N)

    # 技术1: 使用快速数学函数
    # 牺牲少量精度换取性能
    y = tl.math.fast_expf(x)  # 比标准exp快

    # 技术2: 强度削减 (Strength Reduction)
    # 用便宜的操作替代昂贵的操作
    # x * 2 → x + x
    # x * 0.5 → x * 0.5 (乘法比除法快)
    # x / constant → x * (1/constant)

    # 技术3: 利用特殊硬件单元
    # Tensor Core: tl.dot()
    # SFU: 特殊函数如sin, cos, rsqrt

    # 技术4: 指令级并行 (ILP)
    # 展开计算增加独立指令
    y0 = tl.math.sin(x)
    y1 = tl.math.cos(x)
    y2 = y0 * y1  # 可以与上面并行

    tl.store(output_ptr + offset, y2, mask=offset < N)


# Tensor Core优化
@triton.jit
def tensor_core_gemm(
    a_ptr, b_ptr, c_ptr,
    M, N, K,
    BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: tl.constexpr,
):
    """
    利用Tensor Core的GEMM
    """
    pid_m = tl.program_id(0)
    pid_n = tl.program_id(1)

    offs_m = pid_m * BLOCK_M + tl.arange(0, BLOCK_M)
    offs_n = pid_n * BLOCK_N + tl.arange(0, BLOCK_N)

    # 使用tl.dot触发Tensor Core
    # 要求: BLOCK_M, BLOCK_N, BLOCK_K是16的倍数
    # 数据类型: fp16或bf16

    acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)

    for k in range(0, K, BLOCK_K):
        offs_k = k + tl.arange(0, BLOCK_K)

        # 加载fp16数据
        a = tl.load(a_ptr + offs_m[:, None] * K + offs_k[None, :]).to(tl.float16)
        b = tl.load(b_ptr + offs_k[:, None] * N + offs_n[None, :]).to(tl.float16)

        # tl.dot会使用Tensor Core
        acc += tl.dot(a, b, out_dtype=tl.float32)

    # 存储fp32结果
    c_ptrs = c_ptr + offs_m[:, None] * N + offs_n[None, :]
    tl.store(c_ptrs, acc)

# 并行化策略优化

class ParallelizationOptimizer:
    """
    优化kernel的并行化策略
    """

    def optimize_grid_config(self, problem_size, device_info):
        """
        优化grid和block配置
        """
        # 设备限制
        max_threads_per_block = device_info['max_threads_per_block']  # 1024
        max_blocks_per_sm = device_info['max_blocks_per_sm']  # 32
        num_sms = device_info['num_sms']  # e.g., 132 for H100

        # 目标: 最大化SM占用率

        # 计算block大小
        problem_elements = problem_size[0] * problem_size[1]

        # 启发式: 每个block处理足够的工作
        elements_per_block = max(256, problem_elements // (num_sms * 4))

        # 确保是warp大小的倍数
        block_size = min(max_threads_per_block,
                        (elements_per_block + 31) // 32 * 32)

        # 计算grid大小
        num_blocks = (problem_elements + block_size - 1) // block_size

        # 2D grid配置 (如果适用)
        if len(problem_size) == 2:
            grid_x = (problem_size[0] + 15) // 16
            grid_y = (problem_size[1] + 15) // 16
            block_x = 16
            block_y = 16
            return ((grid_x, grid_y), (block_x, block_y))

        return ((num_blocks,), (block_size,))

    def balance_work_distribution(self, num_blocks, num_sms):
        """
        平衡工作分配，避免尾效应
        """
        # 确保block数量是SM数量的倍数
        # 避免最后几个SM空闲

        if num_blocks % num_sms != 0:
            # 增加block数量到下一个倍数
            num_blocks = ((num_blocks + num_sms - 1) // num_sms) * num_sms

        return num_blocks


# 动态并行化
@triton.jit
def dynamic_parallel_kernel(
    data_ptr, output_ptr,
    sizes_ptr,  # 每个任务的大小
    offsets_ptr,  # 每个任务的偏移
    num_tasks,
    BLOCK_SIZE: tl.constexpr
):
    """
    处理不规则并行工作负载
    """
    task_id = tl.program_id(0)

    if task_id >= num_tasks:
        return

    # 加载任务信息
    size = tl.load(sizes_ptr + task_id)
    offset = tl.load(offsets_ptr + task_id)

    # 处理任务
    for i in range(0, size, BLOCK_SIZE):
        idx = tl.arange(0, BLOCK_SIZE)
        mask = i + idx < size

        data = tl.load(data_ptr + offset + i + idx, mask=mask)
        result = process(data)
        tl.store(output_ptr + offset + i + idx, result, mask=mask)

# 定义kernel调优的搜索空间

class KernelTuningSpace:
    """
    Kernel调优搜索空间
    """

    def __init__(self):
        self.params = {}

    def add_param(self, name, values, condition=None):
        """
        添加调优参数
        """
        self.params[name] = {
            'values': values,
            'condition': condition
        }

    def sample(self):
        """
        采样一个配置
        """
        config = {}
        for name, spec in self.params.items():
            if spec['condition'] is None or spec['condition'](config):
                config[name] = random.choice(spec['values'])
        return config

    def all_configs(self):
        """
        生成所有配置
        """
        import itertools

        param_names = list(self.params.keys())
        param_values = [self.params[n]['values'] for n in param_names]

        for values in itertools.product(*param_values):
            config = dict(zip(param_names, values))
            if self._is_valid(config):
                yield config

    def _is_valid(self, config):
        """
        检查配置是否有效
        """
        for name, spec in self.params.items():
            if spec['condition'] is not None:
                if not spec['condition'](config):
                    return False
        return True


# 示例: GEMM调优空间
def create_gemm_tuning_space(M, N, K):
    space = KernelTuningSpace()

    # Block大小
    space.add_param('BLOCK_M', [32, 64, 128, 256])
    space.add_param('BLOCK_N', [32, 64, 128, 256])
    space.add_param('BLOCK_K', [16, 32, 64])

    # 向量化宽度
    space.add_param('num_stages', [2, 3, 4, 5])

    # Warp配置
    space.add_param('num_warps', [4, 8])

    # 条件约束
    def shared_memory_constraint(config):
        shared_bytes = (
            config['BLOCK_M'] * config['BLOCK_K'] +
            config['BLOCK_K'] * config['BLOCK_N']
        ) * 2  # fp16
        return shared_bytes <= 48 * 1024  # 48KB限制

    space.add_param('_valid', [True],
                    condition=shared_memory_constraint)

    return space

# 自动调优器

class AutoTuner:
    """
    Kernel自动调优器
    """

    def __init__(self, kernel_fn, space, key_fn):
        self.kernel_fn = kernel_fn
        self.space = space
        self.key_fn = key_fn
        self.cache = {}

    def tune(self, *args, n_trials=100):
        """
        调优kernel
        """
        key = self.key_fn(*args)

        if key in self.cache:
            return self.cache[key]

        best_config = None
        best_time = float('inf')

        for trial in range(n_trials):
            config = self.space.sample()

            try:
                time = self._measure(config, *args)

                if time < best_time:
                    best_time = time
                    best_config = config
                    print(f"Trial {trial}: {time:.4f}ms, config={config}")

            except Exception as e:
                # 配置无效，跳过
                continue

        self.cache[key] = best_config
        return best_config

    def _measure(self, config, *args, n_warmup=5, n_repeat=20):
        """
        测量kernel执行时间
        """
        import torch

        # 预热
        for _ in range(n_warmup):
            self.kernel_fn[config](*args)

        torch.cuda.synchronize()

        # 计时
        import time
        start = time.time()
        for _ in range(n_repeat):
            self.kernel_fn[config](*args)
        torch.cuda.synchronize()
        elapsed = time.time() - start

        return elapsed / n_repeat * 1000  # ms


# 使用示例
@triton.autotune(
    configs=[
        triton.Config({'BLOCK_M': 128, 'BLOCK_N': 256, 'BLOCK_K': 32}, num_stages=3, num_warps=8),
        triton.Config({'BLOCK_M': 256, 'BLOCK_N': 128, 'BLOCK_K': 32}, num_stages=3, num_warps=8),
        triton.Config({'BLOCK_M': 128, 'BLOCK_N': 128, 'BLOCK_K': 32}, num_stages=4, num_warps=4),
        # 更多配置...
    ],
    key=['M', 'N', 'K'],
)
@triton.jit
def matmul_kernel_autotune(
    a_ptr, b_ptr, c_ptr,
    M, N, K,
    BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: tl.constexpr,
):
    # kernel实现
    pass