finish spatial-diff gate #38

S1/Ljy123_#38/cudacode.py

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+import torch
+from torch.utils.cpp_extension import load_inline
+
+source = """
+#include <torch/extension.h>
+
+__global__ void spatial_diff_gate_kernel(const float* x, float* y, int N, int C, int H, int W, float alpha, float beta){
+    long long tid = blockIdx.x * blockDim.x + threadIdx.x;
+    long long stride = blockDim.x * gridDim.x;
+    long long total = (long long)N * C * H * W;
+    for(long long i = tid; i < total; i += stride){
+        long long w = i % W;
+        long long hwc = i / W;
+        long long h = hwc % H;
+        long long nc = hwc / H;
+        long long n = nc / C;
+        long long c = nc % C;
+        long long base = ((long long)n * C + c) * (long long)H * (long long)W + (long long)h * W;
+        float xv = x[i];
+        float prev = (w > 0) ? x[base + (w - 1)] : 0.0f;
+        float d = (w > 0) ? (xv - prev) : 0.0f;
+        float g = 1.0f / (1.0f + expf(-(alpha * d + beta)));
+        y[i] = xv * g;
+    }
+}
+
+torch::Tensor spatial_diff_gate_cuda(torch::Tensor x, torch::Tensor alpha, torch::Tensor beta){
+    auto xc = x.contiguous();
+    auto y = torch::empty_like(xc);
+    int N = (int)xc.size(0);
+    int C = (int)xc.size(1);
+    int H = (int)xc.size(2);
+    int W = (int)xc.size(3);
+    float a = alpha.item<float>();
+    float b = beta.item<float>();
+    int block = 1024;
+    int grid = (int)((long long)N * C * H * W / block);
+    if(grid < 1) grid = 1; if(grid > 65535) grid = 65535;
+    spatial_diff_gate_kernel<<<grid, block>>>(xc.data_ptr<float>(), y.data_ptr<float>(), N, C, H, W, a, b);
+    return y;
+}
+"""
+
+cpp_source = """
+torch::Tensor spatial_diff_gate_cuda(torch::Tensor x, torch::Tensor alpha, torch::Tensor beta);
+"""
+
+ops = load_inline(
+    name="spatial_diff_gate",
+    cpp_sources=cpp_source,
+    cuda_sources=source,
+    functions=["spatial_diff_gate_cuda"],
+    verbose=True
+)
+
+class ModelNew(torch.nn.Module):
+    def __init__(self, alpha: float, beta: float):
+        super(ModelNew, self).__init__()
+        self.ops = ops
+        self.register_buffer("alpha", torch.tensor(float(alpha), dtype=torch.float32))
+        self.register_buffer("beta", torch.tensor(float(beta), dtype=torch.float32))
+
+    def forward(self, x: torch.Tensor):
+        return self.ops.spatial_diff_gate_cuda(x, self.alpha, self.beta)

S1/Ljy123_#38/prompt.txt

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+You write custom CUDA kernels to replace PyTorch operators for speedups.
+Implement Spatial-Diff Sigmoid Gate on NCHW tensors: For each element x[n,c,h,w], compute d = x[n,c,h,w] - x[n,c,h,w-1] (use 0 for w=0), gate g = sigmoid(alpha*d + beta), and output y = x * g. Use a single grid-stride kernel over all elements that reads the left neighbor efficiently and applies gating in-place to the output. Provide a PyTorch reference using nn.Parameter alpha and beta. Accuracy within rtol=1e-3.

S1/Ljy123_#38/run_code.py

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+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)
+    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 Spatial-Diff-Gate 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()

S1/Ljy123_#38/torchcode.py

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+import torch
+import torch.nn as nn
+
+class Model(nn.Module):
+    def __init__(self, alpha: float, beta: float):
+        super(Model, self).__init__()
+        self.alpha = nn.Parameter(torch.tensor(float(alpha), dtype=torch.float32))
+        self.beta = nn.Parameter(torch.tensor(float(beta), dtype=torch.float32))
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        d = torch.zeros_like(x)
+        d[..., :, 1:] = x[..., :, 1:] - x[..., :, :-1]
+        g = torch.sigmoid(self.alpha * d + self.beta)
+        return x * g
+
+N, C, H, W = 8, 64, 64, 64
+
+def get_inputs():
+    x = torch.randn(N, C, H, W)
+    return [x]
+
+def get_init_inputs():
+    return [1.0, 0.0]