forked from ccf-ai-infra/GPUCodeForces
optimized exmp1-sigmoid #51
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import torch
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from torch.utils.cpp_extension import load_inline
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source = """
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#include <torch/extension.h>
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#include <cuda_runtime.h>
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__global__ void expm1_sigmoid_affine_gate_kernel(const float* x, const float* scale, const float* bias, float* y, int B, int D, float alpha, float beta){
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int b = blockIdx.x;
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int tid = threadIdx.x;
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int stride = blockDim.x;
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int row_start = b * D;
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const float* xr = x + row_start;
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float* yr = y + row_start;
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int aligned = ((((long long)xr & 15LL) == 0) && (((long long)yr & 15LL) == 0) && (((long long)scale & 15LL) == 0) && (((long long)bias & 15LL) == 0) && ((D & 3) == 0));
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if(aligned){
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int D4 = (D / 4) * 4;
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#pragma unroll 4
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for(int i = tid * 4; i < D4; i += stride * 4){
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float4 xv = reinterpret_cast<const float4*>(xr)[i / 4];
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float4 sv = reinterpret_cast<const float4*>(scale)[i / 4];
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float4 bv = reinterpret_cast<const float4*>(bias)[i / 4];
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float4 yv;
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float z0 = fmaf(xv.x, sv.x, bv.x);
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float z1 = fmaf(xv.y, sv.y, bv.y);
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float z2 = fmaf(xv.z, sv.z, bv.z);
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float z3 = fmaf(xv.w, sv.w, bv.w);
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float g0 = 1.0f / (1.0f + expf(-(alpha * expm1f(z0) + beta)));
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float g1 = 1.0f / (1.0f + expf(-(alpha * expm1f(z1) + beta)));
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float g2 = 1.0f / (1.0f + expf(-(alpha * expm1f(z2) + beta)));
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float g3 = 1.0f / (1.0f + expf(-(alpha * expm1f(z3) + beta)));
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yv.x = xv.x * g0;
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yv.y = xv.y * g1;
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yv.z = xv.z * g2;
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yv.w = xv.w * g3;
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reinterpret_cast<float4*>(yr)[i / 4] = yv;
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}
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#pragma unroll 4
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for(int i = D4 + tid; i < D; i += stride){
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float z = fmaf(xr[i], scale[i], bias[i]);
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float g = 1.0f / (1.0f + expf(-(alpha * expm1f(z) + beta)));
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yr[i] = xr[i] * g;
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}
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} else {
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#pragma unroll 4
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for(int i = tid; i < D; i += stride){
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float z = fmaf(xr[i], scale[i], bias[i]);
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float g = 1.0f / (1.0f + expf(-(alpha * expm1f(z) + beta)));
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yr[i] = xr[i] * g;
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}
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}
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}
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torch::Tensor expm1_sigmoid_affine_gate_cuda(torch::Tensor x, torch::Tensor scale, torch::Tensor bias, torch::Tensor alpha, torch::Tensor beta){
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auto xc = x.contiguous();
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auto sc = scale.contiguous();
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auto bc = bias.contiguous();
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auto y = torch::empty_like(xc);
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int B = (int)xc.size(0);
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int D = (int)xc.size(1);
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float a = alpha.item<float>();
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float be = beta.item<float>();
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int block = 1024;
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int grid = B;
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expm1_sigmoid_affine_gate_kernel<<<grid, block>>>(xc.data_ptr<float>(), sc.data_ptr<float>(), bc.data_ptr<float>(), y.data_ptr<float>(), B, D, a, be);
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return y;
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}
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"""
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cpp_source = """
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torch::Tensor expm1_sigmoid_affine_gate_cuda(torch::Tensor x, torch::Tensor scale, torch::Tensor bias, torch::Tensor alpha, torch::Tensor beta);
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"""
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ops = load_inline(
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name="expm1_sigmoid_affine_gate",
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cpp_sources=cpp_source,
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cuda_sources=source,
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functions=["expm1_sigmoid_affine_gate_cuda"],
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extra_cuda_cflags=["-O3","--use_fast_math"],
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verbose=True
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)
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class ModelNew(torch.nn.Module):
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def __init__(self, scale: torch.Tensor, bias: torch.Tensor, alpha: float, beta: float):
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super(ModelNew, self).__init__()
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self.ops = ops
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self.register_buffer("scale", scale)
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self.register_buffer("bias", bias)
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self.register_buffer("alpha", torch.tensor(float(alpha), dtype=torch.float32))
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self.register_buffer("beta", torch.tensor(float(beta), dtype=torch.float32))
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def forward(self, x):
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return self.ops.expm1_sigmoid_affine_gate_cuda(x, self.scale, self.bias, self.alpha, self.beta)
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融合算子：Expm1-Sigmoid-Affine-Gate（一次核内完成仿射、`expm1` 与 Sigmoid 门控，返回 y = x * σ(α * expm1(z) + β)，其中 z = x*scale + bias）。`expm1` 在小值区间更稳定，适合细微增益的门控。
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目标与定义
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- 输入张量：`x[B, D]`
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- 逐维参数：`scale[D]`、`bias[D]`
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- 标量超参：`alpha`、`beta`
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- 计算流程：`z = x*scale + bias`，`v = expm1(z)`，`g = sigmoid(alpha*v + beta)`，`y = x * g`
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参考实现（文件要求）
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- `torchcode.py`：PyTorch 参考 `Model`；统一的输入与初始化接口
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- `cudacode.py`：单核融合（仿射+expm1+sigmoid+乘法）；编译 `-O3 --use_fast_math`
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- `run_code.py`：100 次迭代；精度 `rtol=1e-03, atol=1e-06`；打印加速比
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CUDA 实现要点
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- 行并行：`grid = B`，块内沿 D 合并访存；一次遍历写回 `y`
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- 对齐向量化：满足 16 字节对齐且 `D%4==0` 时走 `float4`；否则标量路径
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- 指令优化：仿射用 `fmaf`；`expm1f` 与 `expf` 走快速数学；循环 `#pragma unroll 4`
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- 线程配置：推荐 `block=1024` 起步，按设备与规模试探最佳
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评估与目标
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- 精度：与参考在 `rtol=1e-03, atol=1e-06` 范围内一致
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- 性能：≥1.0x 加速；对齐触发向量化时更佳
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加分项（可选）
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- 优化未对齐路径尾处理与分支
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- 每线程批量步长以提升吞吐与占用
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import torch
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import time
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from torchcode import Model, get_inputs, get_init_inputs
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from cudacode import ModelNew
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def run_benchmark():
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if not torch.cuda.is_available():
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print("CUDA 不可用，请确保您有可用的 NVIDIA GPU 并已正确安装 PyTorch CUDA 版本。")
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return
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device = torch.device("cuda")
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init_inputs = [x.cuda(device=device) if isinstance(x, torch.Tensor) else x for x in get_init_inputs()]
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inputs = [x.cuda(device=device) if isinstance(x, torch.Tensor) else x for x in get_inputs()]
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torch_model = Model(*init_inputs).cuda()
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cuda_model = ModelNew(*init_inputs).cuda()
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torch_model.eval(); cuda_model.eval()
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print("-------------------- 精度对齐验证 --------------------")
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with torch.no_grad():
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output_torch = torch_model(*inputs)
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output_cuda = cuda_model(*inputs)
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precision_flag = torch.allclose(output_torch, output_cuda, rtol=1e-03, atol=1e-06)
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if precision_flag:
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print("✅ 精度对齐：两个模型的输出结果非常接近。")
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else:
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print("❌ 精度不一致！")
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diff = (output_torch - output_cuda).abs().max().item()
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print(f"最大绝对误差: {diff}")
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print(f"输出张量形状: torch={tuple(output_torch.shape)}, cuda={tuple(output_cuda.shape)}")
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print(f"数据类型: torch={output_torch.dtype}, cuda={output_cuda.dtype}")
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print(f"设备: torch={output_torch.device}, cuda={output_cuda.device}")
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print("\n-------------------- 性能加速比测试 --------------------")
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num_iterations = 100
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torch.cuda.synchronize(); start_time = time.time()
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for _ in range(num_iterations):
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_ = torch_model(*inputs)
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torch.cuda.synchronize(); torch_time = (time.time() - start_time) / num_iterations
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torch.cuda.synchronize(); start_time = time.time()
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for _ in range(num_iterations):
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_ = cuda_model(*inputs)
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torch.cuda.synchronize(); cuda_time = (time.time() - start_time) / num_iterations
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print(f"PyTorch Expm1-Sigmoid-Affine-Gate 平均执行时间: {torch_time:.6f} 秒")
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print(f"自定义 CUDA 融合内核 平均执行时间: {cuda_time:.6f} 秒")
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speedup = torch_time / cuda_time if cuda_time > 0 else 0
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if cuda_time > 0:
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print(f"加速比 (Speedup): {speedup:.2f}x")
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else:
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print("CUDA 内核执行时间为0，无法计算加速比。")
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return precision_flag, speedup
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if __name__ == "__main__":
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run_benchmark()
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import torch
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import torch.nn as nn
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class Model(nn.Module):
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def __init__(self, scale: torch.Tensor, bias: torch.Tensor, alpha: float, beta: float):
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super(Model, self).__init__()
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self.register_buffer("scale", scale)
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self.register_buffer("bias", bias)
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self.register_buffer("alpha", torch.tensor(float(alpha), dtype=torch.float32))
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self.register_buffer("beta", torch.tensor(float(beta), dtype=torch.float32))
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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z = x * self.scale + self.bias
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v = torch.expm1(z)
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g = torch.sigmoid(self.alpha * v + self.beta)
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return x * g
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batch_size = 16
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dim = 16384
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def get_inputs():
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x = torch.randn(batch_size, dim)
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return [x]
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def get_init_inputs():
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scale = torch.randn(dim)
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bias = torch.randn(dim)
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return [scale, bias, 1.0, 0.0]
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