From bce20be1bc255239077e6d20b068d0577d27e409 Mon Sep 17 00:00:00 2001 From: Ljy123 Date: Wed, 3 Dec 2025 23:34:47 +0800 Subject: [PATCH] finish range-gate-affine #39 --- S1/Ljy123_#39/cudacode.py | 99 ++++++++++++++++++++++++++++++++++++++ S1/Ljy123_#39/prompt.txt | 2 + S1/Ljy123_#39/run_code.py | 52 ++++++++++++++++++++ S1/Ljy123_#39/torchcode.py | 30 ++++++++++++ 4 files changed, 183 insertions(+) create mode 100644 S1/Ljy123_#39/cudacode.py create mode 100644 S1/Ljy123_#39/prompt.txt create mode 100644 S1/Ljy123_#39/run_code.py create mode 100644 S1/Ljy123_#39/torchcode.py diff --git a/S1/Ljy123_#39/cudacode.py b/S1/Ljy123_#39/cudacode.py new file mode 100644 index 00000000..72eaa257 --- /dev/null +++ b/S1/Ljy123_#39/cudacode.py @@ -0,0 +1,99 @@ +import torch +from torch.utils.cpp_extension import load_inline + +source = """ +#include + +__inline__ __device__ float warpReduceMax(float val){ + for(int offset=16; offset>0; offset>>=1){ float v = __shfl_down_sync(0xffffffff, val, offset); val = val > v ? val : v; } + return val; +} +__inline__ __device__ float warpReduceMin(float val){ + for(int offset=16; offset>0; offset>>=1){ float v = __shfl_down_sync(0xffffffff, val, offset); val = val < v ? val : v; } + return val; +} + +__global__ void range_gate_affine_kernel(const float* x, const float* scale, const float* bias, float* y, int B, int D, float gamma, float beta){ + int b = blockIdx.x; + int tid = threadIdx.x; + int lane = tid & 31; + int warpId = tid >> 5; + extern __shared__ float sh[]; + float lmax = -INFINITY; + float lmin = INFINITY; + for(int i = tid * 4; i < D; i += blockDim.x * 4){ + int base = b * D + i; + for(int k = 0; k < 4 && (i + k) < D; ++k){ + float z = fmaf(x[base + k], scale[i + k], bias[i + k]); + lmax = z > lmax ? z : lmax; + lmin = z < lmin ? z : lmin; + } + } + lmax = warpReduceMax(lmax); + lmin = warpReduceMin(lmin); + if(lane == 0){ + sh[warpId] = lmax; + sh[warpId + ((blockDim.x + 31)>>5)] = lmin; + } + __syncthreads(); + float rmax = -INFINITY, rmin = INFINITY; + if(warpId == 0){ + int WN = ((blockDim.x + 31)>>5); + float vmax = (lane < WN) ? sh[lane] : -INFINITY; + float vmin = (lane < WN) ? sh[WN + lane] : INFINITY; + vmax = warpReduceMax(vmax); + vmin = warpReduceMin(vmin); + if(lane == 0){ sh[0] = vmax; sh[1] = vmin; } + } + __syncthreads(); + rmax = sh[0]; rmin = sh[1]; + float g = 1.0f / (1.0f + expf(-(gamma * (rmax - rmin) + beta))); + for(int i = tid * 4; i < D; i += blockDim.x * 4){ + int base = b * D + i; + for(int k = 0; k < 4 && (i + k) < D; ++k){ + float z = fmaf(x[base + k], scale[i + k], bias[i + k]); + y[base + k] = z * g; + } + } +} + +torch::Tensor range_gate_affine_cuda(torch::Tensor x, torch::Tensor scale, torch::Tensor bias, torch::Tensor gamma, torch::Tensor beta){ + auto xc = x.contiguous(); + auto sc = scale.contiguous(); + auto bc = bias.contiguous(); + auto y = torch::empty_like(xc); + int B = (int)xc.size(0); + int D = (int)xc.size(1); + float g = gamma.item(); + float be = beta.item(); + int block = 1024; + int grid = B; + size_t shmem = (size_t)((block + 31)>>5) * 2 * sizeof(float); + range_gate_affine_kernel<<>>(xc.data_ptr(), sc.data_ptr(), bc.data_ptr(), y.data_ptr(), B, D, g, be); + return y; +} +""" + +cpp_source = """ +torch::Tensor range_gate_affine_cuda(torch::Tensor x, torch::Tensor scale, torch::Tensor bias, torch::Tensor gamma, torch::Tensor beta); +""" + +ops = load_inline( + name="range_gate_affine", + cpp_sources=cpp_source, + cuda_sources=source, + functions=["range_gate_affine_cuda"], + verbose=True +) + +class ModelNew(torch.nn.Module): + def __init__(self, scale: torch.Tensor, bias: torch.Tensor, gamma: float, beta: float): + super(ModelNew, self).__init__() + self.ops = ops + self.register_buffer("scale", scale) + self.register_buffer("bias", bias) + self.register_buffer("gamma", torch.tensor(float(gamma), dtype=torch.float32)) + self.register_buffer("beta", torch.tensor(float(beta), dtype=torch.float32)) + + def forward(self, x: torch.Tensor): + return self.ops.range_gate_affine_cuda(x, self.scale, self.bias, self.gamma, self.beta) diff --git a/S1/Ljy123_#39/prompt.txt b/S1/Ljy123_#39/prompt.txt new file mode 100644 index 00000000..a37e50d9 --- /dev/null +++ b/S1/Ljy123_#39/prompt.txt @@ -0,0 +1,2 @@ +You write custom CUDA kernels to replace PyTorch operators for speedups. +Implement Range-Gate Affine on x[B,D]: Compute z = x*scale + bias, then per-row g = sigmoid(gamma*(max(z)-min(z)) + beta), output y = z * g. Use one CUDA block per row, reduce max and min with warp shuffles, and apply the scalar gate across the row in the same kernel. Provide a PyTorch reference with nn.Parameter scale, bias, gamma, beta. Accuracy rtol=1e-3. diff --git a/S1/Ljy123_#39/run_code.py b/S1/Ljy123_#39/run_code.py new file mode 100644 index 00000000..f9152179 --- /dev/null +++ b/S1/Ljy123_#39/run_code.py @@ -0,0 +1,52 @@ +import torch +import time +from torchcode import Model, get_inputs, get_init_inputs +from cudacode import ModelNew + +def run_benchmark(): + if not torch.cuda.is_available(): + 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。") + return + device = torch.device("cuda") + + init_inputs = [x.cuda(device=device) if isinstance(x, torch.Tensor) else x for x in get_init_inputs()] + inputs = [x.cuda(device=device) if isinstance(x, torch.Tensor) else x for x in get_inputs()] + + torch_model = Model(*init_inputs).cuda() + cuda_model = ModelNew(*init_inputs).cuda() + torch_model.eval(); cuda_model.eval() + + print("-------------------- 精度对齐验证 --------------------") + with torch.no_grad(): + out_torch = torch_model(*inputs) + out_cuda = cuda_model(*inputs) + flag = torch.allclose(out_torch, out_cuda, rtol=1e-03, atol=1e-06) + if flag: + print("✅ 精度对齐：两个模型的输出结果非常接近。") + else: + print("❌ 精度不一致！") + print(f"最大绝对误差: {(out_torch - out_cuda).abs().max().item()}" ) + + print("\n-------------------- 性能加速比测试 --------------------") + iters = 50 + torch.cuda.synchronize(); t0 = time.time() + for _ in range(iters): + _ = torch_model(*inputs) + torch.cuda.synchronize(); t_torch = (time.time() - t0) / iters + + torch.cuda.synchronize(); t0 = time.time() + for _ in range(iters): + _ = cuda_model(*inputs) + torch.cuda.synchronize(); t_cuda = (time.time() - t0) / iters + + print(f"PyTorch Range-Gate-Affine 平均执行时间: {t_torch:.6f} 秒") + print(f"自定义 CUDA 融合内核 平均执行时间: {t_cuda:.6f} 秒") + sp = t_torch / t_cuda if t_cuda > 0 else 0 + if t_cuda > 0: + print(f"加速比 (Speedup): {sp:.2f}x") + else: + print("CUDA 内核执行时间为0，无法计算加速比。") + return flag, sp + +if __name__ == "__main__": + run_benchmark() diff --git a/S1/Ljy123_#39/torchcode.py b/S1/Ljy123_#39/torchcode.py new file mode 100644 index 00000000..f2ddc0ea --- /dev/null +++ b/S1/Ljy123_#39/torchcode.py @@ -0,0 +1,30 @@ +import torch +import torch.nn as nn + +class Model(nn.Module): + def __init__(self, scale: torch.Tensor, bias: torch.Tensor, gamma: float, beta: float): + super(Model, self).__init__() + self.scale = nn.Parameter(scale) + self.bias = nn.Parameter(bias) + self.gamma = nn.Parameter(torch.tensor(float(gamma), dtype=torch.float32)) + self.beta = nn.Parameter(torch.tensor(float(beta), dtype=torch.float32)) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + z = x * self.scale.view(1,-1) + self.bias.view(1,-1) + zmax = torch.amax(z, dim=1, keepdim=True) + zmin = torch.amin(z, dim=1, keepdim=True) + g = torch.sigmoid(self.gamma * (zmax - zmin) + self.beta) + return z * g + +B, D = 64, 8192 + +def get_inputs(): + x = torch.randn(B, D) + return [x] + +def get_init_inputs(): + scale = torch.randn(D) + bias = torch.randn(D) + gamma = 1.0 + beta = 0.0 + return [scale, bias, gamma, beta]