finish Logexpsoftplus #94

S1/uucoco_#94/logexpsoftplus_cuda.py

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+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 log_exp_softplus_kernel(
+    const float* __restrict__ input,
+    float* __restrict__ output,
+    int size
+) {
+    int idx = blockIdx.x * blockDim.x + threadIdx.x;
+    if (idx < size) {
+        float x = input[idx];
+
+        float y = logf(x);
+        float z = expf(y);
+
+        output[idx] = logf(1.0f + expf(z));
+    }
+}
+
+torch::Tensor log_exp_softplus_cuda(torch::Tensor input) {
+    auto output = torch::empty_like(input);
+    int size = input.numel();
+
+    const int block_size = 256;
+    int num_blocks = (size + block_size - 1) / block_size;
+
+    log_exp_softplus_kernel<<<num_blocks, block_size>>>(
+        input.data_ptr<float>(),
+        output.data_ptr<float>(),
+        size
+    );
+
+    return output;
+}
+"""
+
+cpp_source = """
+torch::Tensor log_exp_softplus_cuda(torch::Tensor input);
+"""
+
+module = load_inline(
+    name="log_exp_softplus",
+    cpp_sources=cpp_source,
+    cuda_sources=cuda_source,
+    functions=["log_exp_softplus_cuda"],
+    verbose=True
+)
+
+
+class ModelNew(nn.Module):
+    def __init__(self):
+        super(ModelNew, self).__init__()
+        self.module = module
+
+    def forward(self, x):
+        return self.module.log_exp_softplus_cuda(x)

S1/uucoco_#94/logexpsoftplus_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(Model, self).__init__()
+
+    def forward(self, x):
+        return F.softplus(torch.exp(torch.log(x)))
+
+batch_size = 4096
+dim = 1024
+
+def get_inputs():
+    x = torch.rand(batch_size, dim) * 5.0 + 0.01
+    return [x]
+
+def get_init_inputs():
+    return []

S1/uucoco_#94/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.
+Technologies Used in This Code
+Core Libraries
+PyTorch: Deep learning framework
+
+CUDA: NVIDIA GPU parallel computing
+
+C++: Kernel implementation
+
+CUDA Components
+CUDA kernel: log_exp_softplus_kernel
+
+CUDA math functions: logf(), expf()
+
+Element-wise parallelism: One thread per element
+
+Mathematical Operations
+Logarithm: log(x)
+
+Exponential: exp(y) where y = log(x)
+
+Softplus: log(1 + exp(z)) where z = exp(log(x))
+
+Identity property: log(exp(log(x))) = log(x) (mathematically)
+
+Numerically sensitive: Multiple exp/log operations
+
+Architecture
+Simple 1D grid: Standard CUDA block configuration
+
+Element-wise processing: Independent computation per element
+
+Memory efficiency: Direct input-output mapping
+
+Numerical Considerations
+Input requirements: x > 0 for log(x) to be defined
+
+Potential overflow: exp(exp(log(x))) could be large
+
+Numerical stability: Multiple floating-point operations
+
+
+
+
+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(Model, self).__init__()
+
+    def forward(self, x):
+        return F.softplus(torch.exp(torch.log(x)))
+
+batch_size = 4096
+dim = 1024
+
+def get_inputs():
+    x = torch.rand(batch_size, dim) * 5.0 + 0.01
+    return [x]
+
+def get_init_inputs():
+    return []

S1/uucoco_#94/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 logexpsoftplus_torch import Model, get_inputs, get_init_inputs
+from logexpsoftplus_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()