finish ShiftedSincUnit #47

S1/uucoco_#47/ShiftedSincUnit_cuda.py

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+import torch
+import torch.nn as nn
+from torch.utils.cpp_extension import load_inline
+
+class ModelNew(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self._compile_cuda_kernel()
+
+    def _compile_cuda_kernel(self):
+        cpp_source = """
+        torch::Tensor ssu_cuda(torch::Tensor x);
+        """
+
+        cuda_source = """
+        #include <torch/extension.h>
+        #include <cuda_runtime.h>
+        #include <math.h>
+
+        __device__ __forceinline__ float sinc_op(float y) {
+            const float PI = 3.14159265358979323846f;
+            if (fabsf(y) < 1e-6f) {
+                return 1.0f;
+            } else {
+                return sinf(PI * y) / (PI * y);
+            }
+        }
+
+        __device__ __forceinline__ float ssu_op(float x) {
+            const float PI = 3.14159265358979323846f;
+            float term1 = sinc_op(x - PI);
+            float term2 = sinc_op(x + PI);
+            return PI / 2.0f * (term1 - term2);
+        }
+
+        __global__ void ssu_kernel(
+            const float* __restrict__ x,
+            float* __restrict__ output,
+            const int n_elements) 
+        {
+            const int tid = blockIdx.x * blockDim.x + threadIdx.x;
+            const int stride = blockDim.x * gridDim.x;
+
+            for (int i = tid; i < n_elements; i += stride) {
+                output[i] = ssu_op(x[i]);
+            }
+        }
+
+        torch::Tensor ssu_cuda(torch::Tensor x) {
+            auto x_c = x.contiguous();
+            const int n_elements = x_c.numel();
+            auto output = torch::empty_like(x_c);
+
+            const int threads = 256;
+            const int blocks = std::min((n_elements + threads - 1) / threads, 65535);
+
+            ssu_kernel<<<blocks, threads>>>(
+                x_c.data_ptr<float>(),
+                output.data_ptr<float>(),
+                n_elements
+            );
+
+            return output;
+        }
+        """
+
+        self.op = load_inline(
+            name="ssu_v1",
+            cpp_sources=cpp_source,
+            cuda_sources=cuda_source,
+            functions=["ssu_cuda"],
+            extra_cuda_cflags=["-O3"],
+            verbose=False
+        )
+
+    def forward(self, x):
+        return self.op.ssu_cuda(x)

S1/uucoco_#47/ShiftedSincUnit_torch.py

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+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+class Model(nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        term1 = torch.sinc(x - torch.pi)
+        term2 = torch.sinc(x + torch.pi)
+        return torch.pi / 2 * (term1 - term2)
+
+
+batch_size = 128
+feature_dim = 512
+
+
+def get_inputs():
+    x = torch.randn(batch_size, feature_dim, dtype=torch.float32)
+    return [x]
+
+
+def get_init_inputs():
+    return []

S1/uucoco_#47/prompt.txt

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+You write custom CUDA kernels to replace the pytorch operators in the given GeGLU architecture to get speedups.
+
+You have complete freedom to choose the set of operators you want to replace. You may make the decision to replace some operators with custom CUDA kernels and leave others unchanged. You may replace multiple operators with custom implementations, consider operator fusion opportunities (combining multiple operators into a single kernel, for example, combining chunk+gelu+elementwise_mul), or algorithmic changes (such as optimized memory access patterns). You are only limited by your imagination.
+
+This CUDA kernel implements a custom activation function (SSU - Shifted Sinc Unit) with the following optimizations:
+Grid-Stride Loop: Uses strided indexing to handle arbitrary tensor sizes efficiently, ensuring good GPU utilization regardless of input dimensions.
+Memory Access Optimization: Employs __restrict__qualifiers and contiguous memory tensors to enable better compiler optimizations and reduce memory bank conflicts.
+Numerical Stability: Includes special case handling in sinc_opwhen y ≈ 0using fabsf(y) < 1e-6fto avoid division by zero and ensure numerical stability.
+Mathematical Function: Implements a complex activation function based on shifted sinc functions: (π/2) * [sinc(x-π) - sinc(x+π)].
+Compiler Optimizations: Enabled with -O3flag for aggressive performance optimization of the generated code.
+Occupancy Optimization: Configures 256 threads per block and dynamically calculates grid size (up to 65535 blocks) to maximize GPU occupancy.
+Inlined Device Functions: Both the core mathematical operation (ssu_op) and helper function (sinc_op) are marked with __forceinline__to eliminate function call overhead within the kernel.
+
+
+Here's an example to show you the syntax of inline embedding custom CUDA operators in torch: The example given architecture is:
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+class Model(nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        term1 = torch.sinc(x - torch.pi)
+        term2 = torch.sinc(x + torch.pi)
+        return torch.pi / 2 * (term1 - term2)
+
+
+batch_size = 128
+feature_dim = 512
+
+
+def get_inputs():
+    x = torch.randn(batch_size, feature_dim, dtype=torch.float32)
+    return [x]
+
+
+def get_init_inputs():
+    return []

S1/uucoco_#47/run_code.py

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+###########################################################
+# 性能和精度验证程序
+###########################################################
+import torch
+import torch.nn as nn
+import time
+from ShiftedSincUnit_torch import Model, get_inputs, get_init_inputs
+from ShiftedSincUnit_cuda import ModelNew
+
+
+def run_benchmark():
+    # 检查 CUDA 是否可用
+    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
+    else:
+        device = torch.device("cuda")
+
+    # 初始化模型
+    init_inputs = get_init_inputs()
+    init_inputs = [
+        x.cuda(device=device) if isinstance(x, torch.Tensor) else x for x in init_inputs
+    ]
+    inputs = get_inputs()
+    inputs = [
+        x.cuda(device=device) if isinstance(x, torch.Tensor) else x for x in 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():
+        output_torch = torch_model(*inputs)
+        output_cuda = cuda_model(*inputs)
+
+    precision_flag = torch.allclose(output_torch, output_cuda, rtol=1e-03)
+    if precision_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("\n-------------------- 性能加速比测试 --------------------")
+    num_iterations = 100
+
+    # PyTorch 模型计时
+    torch.cuda.synchronize()
+    start_time = time.time()
+    for _ in range(num_iterations):
+        _ = torch_model(*inputs)
+    torch.cuda.synchronize()
+    torch_time = (time.time() - start_time) / num_iterations
+
+    # 自定义 CUDA 内核计时
+    torch.cuda.synchronize()
+    start_time = time.time()
+    for _ in range(num_iterations):
+        _ = cuda_model(*inputs)
+    torch.cuda.synchronize()
+    cuda_time = (time.time() - start_time) / num_iterations
+
+    print(f"PyTorch torch.relu 平均执行时间: {torch_time:.6f} 秒")
+    print(f"自定义 CUDA 内核 平均执行时间: {cuda_time:.6f} 秒")
+    speedup = 0
+    if cuda_time > 0:
+        speedup = torch_time / cuda_time
+        print(f"加速比 (Speedup): {speedup:.2f}x")
+    else:
+        print("CUDA 内核执行时间为0，无法计算加速比。")
+    return precision_flag, speedup
+
+
+if __name__ == "__main__":
+    precision_flag, speedup = run_benchmark()