01-深度学习编译器概述

# 示例: 为什么需要编译器优化

import torch
import time

# 典型的Transformer attention计算
def attention_naive(q, k, v):
    # 多次内存访问，效率低
    scores = torch.matmul(q, k.transpose(-2, -1))  # 写入中间结果
    scores = scores / math.sqrt(k.size(-1))        # 读取再写入
    weights = torch.softmax(scores, dim=-1)        # 读取再写入
    output = torch.matmul(weights, v)              # 读取再写入
    return output

# Flash Attention: 编译器或手工融合后
# 单次kernel完成所有计算，减少内存访问

def benchmark():
    q = torch.randn(16, 8, 1024, 64, device='cuda')
    k = torch.randn(16, 8, 1024, 64, device='cuda')
    v = torch.randn(16, 8, 1024, 64, device='cuda')

    # Naive实现
    torch.cuda.synchronize()
    start = time.time()
    for _ in range(100):
        _ = attention_naive(q, k, v)
    torch.cuda.synchronize()
    naive_time = time.time() - start

    # 编译优化后 (torch.compile)
    attention_compiled = torch.compile(attention_naive)
    torch.cuda.synchronize()
    start = time.time()
    for _ in range(100):
        _ = attention_compiled(q, k, v)
    torch.cuda.synchronize()
    compiled_time = time.time() - start

    print(f"Naive: {naive_time:.3f}s")
    print(f"Compiled: {compiled_time:.3f}s")
    print(f"Speedup: {naive_time/compiled_time:.2f}x")

# 中间表示 (IR) 的核心概念

# 1. 高层IR示例 (类似MLIR的Linalg)
"""
%0 = linalg.matmul ins(%A, %B : tensor<M×K×f32>, tensor<K×N×f32>)
                   outs(%C : tensor<M×N×f32>) -> tensor<M×N×f32>
%1 = linalg.bias_add ins(%0, %bias : tensor<M×N×f32>, tensor<N×f32>)
                     outs(%out : tensor<M×N×f32>) -> tensor<M×N×f32>
%2 = linalg.relu ins(%1 : tensor<M×N×f32>)
                 outs(%out2 : tensor<M×N×f32>) -> tensor<M×N×f32>
"""

# 2. 低层IR示例 (类似TVM的TIR)
"""
for i in range(M):
    for j in range(N):
        C[i, j] = 0
        for k in range(K):
            C[i, j] += A[i, k] * B[k, j]
        C[i, j] += bias[j]
        C[i, j] = max(C[i, j], 0)
"""

# 3. Python表示的IR
class Tensor:
    def __init__(self, shape, dtype, name=None):
        self.shape = shape
        self.dtype = dtype
        self.name = name

class Operation:
    def __init__(self, op_type, inputs, outputs, attrs=None):
        self.op_type = op_type
        self.inputs = inputs
        self.outputs = outputs
        self.attrs = attrs or {}

class ComputeGraph:
    def __init__(self):
        self.operations = []
        self.inputs = []
        self.outputs = []

    def add_op(self, op):
        self.operations.append(op)

    def optimize(self, passes):
        """应用优化Pass"""
        for pass_fn in passes:
            self = pass_fn(self)
        return self

# 示例: 构建一个简单计算图
def build_simple_graph():
    graph = ComputeGraph()

    # 输入
    A = Tensor([128, 256], 'float32', 'A')
    B = Tensor([256, 512], 'float32', 'B')
    bias = Tensor([512], 'float32', 'bias')

    # MatMul
    C = Tensor([128, 512], 'float32', 'C')
    matmul_op = Operation('MatMul', [A, B], [C])
    graph.add_op(matmul_op)

    # BiasAdd
    D = Tensor([128, 512], 'float32', 'D')
    bias_op = Operation('BiasAdd', [C, bias], [D])
    graph.add_op(bias_op)

    # ReLU
    E = Tensor([128, 512], 'float32', 'E')
    relu_op = Operation('ReLU', [D], [E])
    graph.add_op(relu_op)

    graph.inputs = [A, B, bias]
    graph.outputs = [E]

    return graph

# 1. TorchDynamo: Python字节码追踪

import torch

@torch.compile
def fn(x, y):
    a = torch.sin(x)
    b = torch.cos(y)
    return a + b

# TorchDynamo的工作流程:
# 1. 拦截Python字节码执行
# 2. 追踪PyTorch操作
# 3. 生成FX Graph
# 4. 传给后端(Inductor)优化
# 5. 生成Triton/C++ kernel

# 2. XLA: 静态图编译

import jax
import jax.numpy as jnp

@jax.jit
def xla_fn(x, y):
    a = jnp.sin(x)
    b = jnp.cos(y)
    return a + b

# XLA的工作流程:
# 1. JAX追踪生成Jaxpr
# 2. 转换为HLO IR
# 3. HLO优化Pass
# 4. 后端代码生成

# 3. TVM: 自动调度

import tvm
from tvm import te, auto_scheduler

# 定义计算
@auto_scheduler.register_workload
def matmul(M, N, K):
    A = te.placeholder((M, K), name='A')
    B = te.placeholder((K, N), name='B')
    k = te.reduce_axis((0, K), name='k')
    C = te.compute(
        (M, N),
        lambda i, j: te.sum(A[i, k] * B[k, j], axis=k),
        name='C'
    )
    return [A, B, C]

# TVM的工作流程:
# 1. 定义计算 (te.compute)
# 2. 自动搜索最优调度
# 3. 生成目标代码

# 图级优化Pass示例

class GraphOptimizer:
    """计算图优化器"""

    def __init__(self):
        self.passes = []

    def register_pass(self, pass_fn):
        self.passes.append(pass_fn)

    def optimize(self, graph):
        for pass_fn in self.passes:
            graph = pass_fn(graph)
        return graph


# 1. 算子融合 (Operator Fusion)
def fusion_pass(graph):
    """融合相邻的element-wise算子"""
    new_ops = []
    i = 0

    while i < len(graph.operations):
        op = graph.operations[i]

        # 检查是否可以融合
        if i + 1 < len(graph.operations):
            next_op = graph.operations[i + 1]

            # 示例: MatMul + BiasAdd + ReLU 融合
            if (op.op_type == 'MatMul' and
                next_op.op_type == 'BiasAdd' and
                op.outputs[0] == next_op.inputs[0]):

                # 检查是否还可以融合ReLU
                if (i + 2 < len(graph.operations) and
                    graph.operations[i + 2].op_type == 'ReLU' and
                    next_op.outputs[0] == graph.operations[i + 2].inputs[0]):

                    # 创建融合算子
                    fused_op = Operation(
                        'FusedMatMulBiasRelu',
                        [op.inputs[0], op.inputs[1], next_op.inputs[1]],
                        [graph.operations[i + 2].outputs[0]]
                    )
                    new_ops.append(fused_op)
                    i += 3
                    continue

        new_ops.append(op)
        i += 1

    graph.operations = new_ops
    return graph


# 2. 常量折叠 (Constant Folding)
def constant_folding_pass(graph):
    """折叠编译时可计算的常量表达式"""
    constant_tensors = {}

    for op in graph.operations:
        # 检查输入是否全是常量
        all_const = all(
            inp.name in constant_tensors
            for inp in op.inputs
        )

        if all_const and op.op_type in ['Add', 'Mul', 'Reshape']:
            # 计算结果
            input_values = [constant_tensors[inp.name] for inp in op.inputs]
            result = evaluate_op(op.op_type, input_values)
            constant_tensors[op.outputs[0].name] = result

    return graph


# 3. 死代码消除 (Dead Code Elimination)
def dce_pass(graph):
    """删除不影响输出的算子"""
    # 从输出反向标记活跃张量
    live_tensors = set(out.name for out in graph.outputs)

    for op in reversed(graph.operations):
        # 如果输出是活跃的，输入也是活跃的
        if any(out.name in live_tensors for out in op.outputs):
            for inp in op.inputs:
                live_tensors.add(inp.name)

    # 删除输出不活跃的算子
    graph.operations = [
        op for op in graph.operations
        if any(out.name in live_tensors for out in op.outputs)
    ]

    return graph


# 4. 布局优化 (Layout Optimization)
def layout_optimization_pass(graph):
    """优化数据布局 (NCHW vs NHWC)"""
    for op in graph.operations:
        if op.op_type == 'Conv2D':
            # 根据硬件选择最优布局
            # NVIDIA GPU通常NHWC + Tensor Core更快
            # CPU通常NCHW更快
            target_layout = get_optimal_layout(op)
            if target_layout != op.attrs.get('layout'):
                # 插入布局转换
                insert_layout_transform(graph, op, target_layout)

    return graph


# 5. 内存优化 (Memory Optimization)
def memory_optimization_pass(graph):
    """优化内存分配和重用"""
    # 活跃性分析
    liveness = analyze_liveness(graph)

    # 内存分配
    memory_pool = MemoryPool()
    tensor_to_buffer = {}

    for i, op in enumerate(graph.operations):
        # 为输出分配内存
        for out in op.outputs:
            # 查找可重用的buffer
            reusable = find_reusable_buffer(
                memory_pool, out.shape, out.dtype, liveness, i
            )
            if reusable:
                tensor_to_buffer[out.name] = reusable
            else:
                buffer = memory_pool.allocate(out.shape, out.dtype)
                tensor_to_buffer[out.name] = buffer

        # 释放不再使用的buffer
        for inp in op.inputs:
            if not is_live_after(inp, i, liveness):
                memory_pool.free(tensor_to_buffer[inp.name])

    return graph

# 算子级优化示例

# 1. 循环优化 (Loop Optimization)
class LoopOptimizer:
    """循环优化器"""

    @staticmethod
    def tile(loop_nest, tile_sizes):
        """
        循环平铺 (Tiling)
        将大循环拆分为小块，提高缓存命中率
        """
        # 原始循环:
        # for i in range(M):
        #     for j in range(N):
        #         C[i,j] = A[i,:] @ B[:,j]

        # 平铺后:
        # for i_outer in range(M // tile_i):
        #     for j_outer in range(N // tile_j):
        #         for i_inner in range(tile_i):
        #             for j_inner in range(tile_j):
        #                 i = i_outer * tile_i + i_inner
        #                 j = j_outer * tile_j + j_inner
        #                 C[i,j] = A[i,:] @ B[:,j]
        pass

    @staticmethod
    def vectorize(loop, vector_width):
        """
        向量化
        利用SIMD指令并行处理多个元素
        """
        # 原始:
        # for i in range(N):
        #     C[i] = A[i] + B[i]

        # 向量化后:
        # for i in range(0, N, vector_width):
        #     C[i:i+vector_width] = A[i:i+vector_width] + B[i:i+vector_width]
        pass

    @staticmethod
    def unroll(loop, factor):
        """
        循环展开
        减少循环控制开销，增加指令级并行
        """
        # 原始:
        # for i in range(N):
        #     C[i] = A[i] * B[i]

        # 展开后 (factor=4):
        # for i in range(0, N, 4):
        #     C[i] = A[i] * B[i]
        #     C[i+1] = A[i+1] * B[i+1]
        #     C[i+2] = A[i+2] * B[i+2]
        #     C[i+3] = A[i+3] * B[i+3]
        pass

    @staticmethod
    def reorder(loop_nest, new_order):
        """
        循环重排
        优化内存访问模式
        """
        # 原始 (列优先访问，cache不友好):
        # for j in range(N):
        #     for i in range(M):
        #         C[i,j] = ...

        # 重排后 (行优先访问，cache友好):
        # for i in range(M):
        #     for j in range(N):
        #         C[i,j] = ...
        pass


# 2. 内存优化
class MemoryOptimizer:
    """内存访问优化"""

    @staticmethod
    def prefetch(loop, distance):
        """
        数据预取
        提前将数据加载到缓存
        """
        # for i in range(N):
        #     prefetch(A[i + distance])  # 提前预取
        #     C[i] = A[i] * B[i]
        pass

    @staticmethod
    def cache_at(compute, cache_level, loop):
        """
        缓存分配
        在指定循环层级分配缓存
        """
        # 示例: 在j循环外分配A的缓存
        # A_cache = shared_memory[tile_size]
        # for i_outer:
        #     A_cache[:] = A[i_outer*tile:...]  # 加载到共享内存
        #     for j:
        #         use A_cache  # 从共享内存读取
        pass


# 3. 并行化
class Parallelizer:
    """并行化优化"""

    @staticmethod
    def parallel(loop, num_threads):
        """
        多线程并行
        """
        # 原始:
        # for i in range(N):
        #     C[i] = A[i] + B[i]

        # 并行化后:
        # parallel_for i in range(N) with num_threads:
        #     C[i] = A[i] + B[i]
        pass

    @staticmethod
    def bind_gpu(loop, thread_axis):
        """
        GPU线程绑定
        """
        # 绑定到GPU线程层次
        # blockIdx.x, blockIdx.y, blockIdx.z
        # threadIdx.x, threadIdx.y, threadIdx.z
        pass

# 从IR生成CUDA代码的简化示例

class CUDACodeGenerator:
    """CUDA代码生成器"""

    def __init__(self):
        self.code = []
        self.indent = 0

    def emit(self, line):
        self.code.append("    " * self.indent + line)

    def generate(self, ir):
        """生成CUDA内核"""
        # 生成内核签名
        self.emit("__global__ void kernel(")
        self.generate_params(ir.params)
        self.emit(") {")
        self.indent += 1

        # 生成线程索引计算
        self.emit("int idx = blockIdx.x * blockDim.x + threadIdx.x;")

        # 生成计算代码
        self.generate_compute(ir.compute)

        self.indent -= 1
        self.emit("}")

        return "\n".join(self.code)

    def generate_compute(self, compute):
        """生成计算代码"""
        for stmt in compute.statements:
            if stmt.type == 'Loop':
                self.generate_loop(stmt)
            elif stmt.type == 'Store':
                self.generate_store(stmt)
            elif stmt.type == 'IfThenElse':
                self.generate_if(stmt)

    def generate_loop(self, loop):
        """生成循环代码"""
        if loop.parallel_type == 'threadIdx.x':
            # 并行循环 - 使用线程索引
            self.emit(f"int {loop.var} = threadIdx.x;")
            self.emit(f"if ({loop.var} < {loop.extent}) {{")
        else:
            # 串行循环
            self.emit(f"for (int {loop.var} = 0; {loop.var} < {loop.extent}; {loop.var}++) {{")

        self.indent += 1
        self.generate_compute(loop.body)
        self.indent -= 1
        self.emit("}")


# 生成示例
def generate_matmul_kernel():
    """生成矩阵乘法CUDA内核"""
    code = """
__global__ void matmul_kernel(
    float* __restrict__ A,
    float* __restrict__ B,
    float* __restrict__ C,
    int M, int N, int K
) {
    // 块索引
    int bx = blockIdx.x;
    int by = blockIdx.y;

    // 线程索引
    int tx = threadIdx.x;
    int ty = threadIdx.y;

    // 全局索引
    int row = by * BLOCK_SIZE + ty;
    int col = bx * BLOCK_SIZE + tx;

    // 共享内存
    __shared__ float As[BLOCK_SIZE][BLOCK_SIZE];
    __shared__ float Bs[BLOCK_SIZE][BLOCK_SIZE];

    float sum = 0.0f;

    // 分块计算
    for (int k = 0; k < (K + BLOCK_SIZE - 1) / BLOCK_SIZE; k++) {
        // 加载到共享内存
        if (row < M && k * BLOCK_SIZE + tx < K)
            As[ty][tx] = A[row * K + k * BLOCK_SIZE + tx];
        else
            As[ty][tx] = 0.0f;

        if (col < N && k * BLOCK_SIZE + ty < K)
            Bs[ty][tx] = B[(k * BLOCK_SIZE + ty) * N + col];
        else
            Bs[ty][tx] = 0.0f;

        __syncthreads();

        // 计算
        #pragma unroll
        for (int i = 0; i < BLOCK_SIZE; i++) {
            sum += As[ty][i] * Bs[i][tx];
        }

        __syncthreads();
    }

    // 写回
    if (row < M && col < N) {
        C[row * N + col] = sum;
    }
}
"""
    return code

# 自动调优的搜索空间定义

class TuneSpace:
    """调优空间"""

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

    def define_knob(self, name, choices):
        """定义一个调优旋钮"""
        self.params[name] = choices

    def sample(self):
        """随机采样一个配置"""
        return {
            name: random.choice(choices)
            for name, choices in self.params.items()
        }


# 示例: 矩阵乘法调优空间
def matmul_tune_space():
    space = TuneSpace()

    # 平铺大小
    space.define_knob('tile_m', [16, 32, 64, 128])
    space.define_knob('tile_n', [16, 32, 64, 128])
    space.define_knob('tile_k', [8, 16, 32])

    # 向量化宽度
    space.define_knob('vec_size', [1, 2, 4, 8])

    # 循环展开因子
    space.define_knob('unroll_factor', [1, 2, 4, 8])

    # 共享内存使用
    space.define_knob('use_shared', [True, False])

    # 线程块配置
    space.define_knob('block_x', [8, 16, 32])
    space.define_knob('block_y', [8, 16, 32])

    return space

# 自动调优搜索策略

class AutoTuner:
    """自动调优器"""

    def __init__(self, space, measure_func):
        self.space = space
        self.measure_func = measure_func
        self.history = []

    def random_search(self, n_trials):
        """随机搜索"""
        best_config = None
        best_time = float('inf')

        for _ in range(n_trials):
            config = self.space.sample()
            time = self.measure_func(config)
            self.history.append((config, time))

            if time < best_time:
                best_time = time
                best_config = config

        return best_config, best_time

    def grid_search(self):
        """网格搜索"""
        import itertools

        all_configs = list(itertools.product(*self.space.params.values()))
        param_names = list(self.space.params.keys())

        best_config = None
        best_time = float('inf')

        for values in all_configs:
            config = dict(zip(param_names, values))
            time = self.measure_func(config)
            self.history.append((config, time))

            if time < best_time:
                best_time = time
                best_config = config

        return best_config, best_time


class XGBTuner(AutoTuner):
    """基于XGBoost的调优器 (类似AutoTVM)"""

    def __init__(self, space, measure_func):
        super().__init__(space, measure_func)
        self.cost_model = None

    def fit_cost_model(self):
        """训练代价模型"""
        import xgboost as xgb

        if len(self.history) < 10:
            return

        # 准备训练数据
        X = [self._encode_config(cfg) for cfg, _ in self.history]
        y = [time for _, time in self.history]

        # 训练XGBoost模型
        self.cost_model = xgb.XGBRegressor()
        self.cost_model.fit(X, y)

    def predict(self, config):
        """预测配置的性能"""
        if self.cost_model is None:
            return float('inf')
        x = self._encode_config(config)
        return self.cost_model.predict([x])[0]

    def tune(self, n_trials, n_parallel=1):
        """调优主循环"""
        for trial in range(n_trials):
            # 使用代价模型采样候选配置
            if self.cost_model is not None:
                candidates = [self.space.sample() for _ in range(100)]
                predictions = [self.predict(c) for c in candidates]
                config = candidates[np.argmin(predictions)]
            else:
                config = self.space.sample()

            # 实际测量
            time = self.measure_func(config)
            self.history.append((config, time))

            # 更新代价模型
            if trial % 10 == 0:
                self.fit_cost_model()

        # 返回最优配置
        best_idx = np.argmin([t for _, t in self.history])
        return self.history[best_idx]

    def _encode_config(self, config):
        """将配置编码为特征向量"""
        features = []
        for name, choices in self.space.params.items():
            value = config[name]
            # One-hot编码
            one_hot = [1 if c == value else 0 for c in choices]
            features.extend(one_hot)
        return features

# 未来编译器的潜在能力

class FutureCompiler:
    """未来深度学习编译器的愿景"""

    def compile(self, model, target, constraints=None):
        """
        端到端编译

        Args:
            model: 任意框架的模型
            target: 目标硬件 (GPU, TPU, NPU, Edge device, ...)
            constraints: 约束条件 (延迟、内存、功耗)
        """
        # 1. 自动模型分析
        analysis = self.analyze_model(model)

        # 2. 自动量化/剪枝 (如需要)
        if constraints.memory_limit:
            model = self.auto_compress(model, constraints.memory_limit)

        # 3. 自动选择最优并行策略
        parallel_strategy = self.find_optimal_parallel(model, target)

        # 4. 自动调度优化
        schedule = self.auto_schedule(model, target)

        # 5. 代码生成
        executable = self.generate_code(model, schedule, target)

        # 6. 运行时优化
        executable = self.add_runtime_optimization(executable)

        return executable

    def auto_compress(self, model, memory_limit):
        """自动压缩模型"""
        # 搜索最优量化/剪枝策略
        pass

    def find_optimal_parallel(self, model, target):
        """自动搜索最优并行策略"""
        # DP, TP, PP, EP 的最优组合
        pass

    def auto_schedule(self, model, target):
        """自动调度"""
        # 使用强化学习或进化算法搜索
        pass