finish kulsinski_sin #154

S1/gsd123_#154/kulsinski_sin_cuda.py

@@ -0,0 +1,87 @@
+import os
+import torch
+import torch.nn as nn
+from torch.utils.cpp_extension import load_inline
+
+cuda_source = """
+#include <torch/extension.h>
+#include <cuda_runtime.h>
+
+__global__ void kulsinski_sin_kernel(const float* x, const float* y, float* out, int dim) {
+    int bid = blockIdx.x;
+    int tid = threadIdx.x;
+
+    const float* row_x = x + bid * dim;
+    const float* row_y = y + bid * dim;
+
+    float local_diff = 0.0f;
+    float local_min = 0.0f;
+
+    for (int i = tid; i < dim; i += blockDim.x) {
+        float u = row_x[i];
+        float v = row_y[i];
+        local_diff += fabsf(u - v);
+        local_min += fminf(u, v);
+    }
+
+    __shared__ float s_diff[256];
+    __shared__ float s_min[256];
+
+    s_diff[tid] = local_diff;
+    s_min[tid] = local_min;
+    __syncthreads();
+
+    for (int stride = blockDim.x / 2; stride > 0; stride >>= 1) {
+        if (tid < stride) {
+            s_diff[tid] += s_diff[tid + stride];
+            s_min[tid] += s_min[tid + stride];
+        }
+        __syncthreads();
+    }
+
+    if (tid == 0) {
+        float sum_diff = s_diff[0];
+        float sum_min = s_min[0];
+        float num = sum_diff - sum_min + (float)dim;
+        float den = sum_diff + (float)dim;
+        float val = num / den;
+        out[bid] = sinf(val);
+    }
+}
+
+torch::Tensor kulsinski_sin_cuda(torch::Tensor x, torch::Tensor y) {
+    int batch_size = x.size(0);
+    int dim = x.size(1);
+    auto out = torch::empty({batch_size}, x.options());
+
+    kulsinski_sin_kernel<<<batch_size, 256>>>(
+        x.data_ptr<float>(),
+        y.data_ptr<float>(),
+        out.data_ptr<float>(),
+        dim
+    );
+
+    return out;
+}
+"""
+
+cpp_source = "torch::Tensor kulsinski_sin_cuda(torch::Tensor x, torch::Tensor y);"
+
+module = load_inline(
+    name="kulsinski_sin_ext",
+    cpp_sources=cpp_source,
+    cuda_sources=cuda_source,
+    functions=["kulsinski_sin_cuda"],
+    verbose=False,
+    with_cuda=True
+)
+
+
+class ModelNew(nn.Module):
+    def __init__(self):
+        super(ModelNew, self).__init__()
+        self.op = module
+
+    def forward(self, x, y):
+        res = self.op.kulsinski_sin_cuda(x.contiguous(), y.contiguous())
+        return res.mean()

S1/gsd123_#154/kulsinski_sin_torch.py

@@ -0,0 +1,31 @@
+import torch
+import torch.nn as nn
+
+
+class Model(nn.Module):
+    def __init__(self):
+        super(Model, self).__init__()
+
+    def forward(self, x, y):
+        diff = (x - y).abs().sum(dim=1)
+        min_v = torch.min(x, y).sum(dim=1)
+        n = x.size(1)
+
+        numerator = diff - min_v + n
+        denominator = diff + n
+        kulsinski = numerator / denominator
+        return torch.sin(kulsinski).mean()
+
+
+batch_size = 16
+input_dim = 1024
+
+
+def get_inputs():
+    x = torch.randn(batch_size, input_dim)
+    y = torch.randn(batch_size, input_dim)
+    return [x, y]
+
+
+def get_init_inputs():
+    return []

S1/gsd123_#154/prompt.txt

@@ -0,0 +1,54 @@
+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.
+CUDA C++ kernel for Kulsinski‑type similarity with sine transformation
+
+Block‑parallel per‑sample processing: each block handles one batch element
+
+Thread‑wise accumulation of absolute differences (local_diff) and element‑wise minimums (local_min)
+
+Parallel reduction in shared memory using binary tree approach
+
+Kulsinski‑style formula: (diff – min + dim) / (diff + dim)
+
+Sinusoidal activation: sin(value) applied to the similarity score
+
+Grid‑stride memory access for coalesced reads
+
+PyTorch inline C++/CUDA extension via load_inline
+
+
+
+
+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
+
+
+class Model(nn.Module):
+    def __init__(self):
+        super(Model, self).__init__()
+
+    def forward(self, x, y):
+        diff = (x - y).abs().sum(dim=1)
+        min_v = torch.min(x, y).sum(dim=1)
+        n = x.size(1)
+
+        numerator = diff - min_v + n
+        denominator = diff + n
+        kulsinski = numerator / denominator
+        return torch.sin(kulsinski).mean()
+
+
+batch_size = 16
+input_dim = 1024
+
+
+def get_inputs():
+    x = torch.randn(batch_size, input_dim)
+    y = torch.randn(batch_size, input_dim)
+    return [x, y]
+
+
+def get_init_inputs():
+    return []

S1/gsd123_#154/run_code.py

@@ -0,0 +1,77 @@
+###########################################################
+# 性能和精度验证程序
+###########################################################
+import torch
+import torch.nn as nn
+import time
+from kulsinski_sin_torch import Model, get_inputs, get_init_inputs
+from kulsinski_sin_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()