From d7139d2459d4bcbc9848a40ffc6ed1fba3b9f4ba Mon Sep 17 00:00:00 2001 From: uucoco Date: Wed, 10 Dec 2025 19:16:49 +0800 Subject: [PATCH] finish Logitsigmoidshift #95 --- S1/uucoco_#95/logitsigmoidshift_cuda.py | 67 +++++++++++++++++++++ S1/uucoco_#95/logitsigmoidshift_torch.py | 20 ++++++ S1/uucoco_#95/prompt.txt | 58 ++++++++++++++++++ S1/uucoco_#95/run_code.py | 77 ++++++++++++++++++++++++ 4 files changed, 222 insertions(+) create mode 100644 S1/uucoco_#95/logitsigmoidshift_cuda.py create mode 100644 S1/uucoco_#95/logitsigmoidshift_torch.py create mode 100644 S1/uucoco_#95/prompt.txt create mode 100644 S1/uucoco_#95/run_code.py diff --git a/S1/uucoco_#95/logitsigmoidshift_cuda.py b/S1/uucoco_#95/logitsigmoidshift_cuda.py new file mode 100644 index 0000000..26ea60c --- /dev/null +++ b/S1/uucoco_#95/logitsigmoidshift_cuda.py @@ -0,0 +1,67 @@ +import torch +import torch.nn as nn +from torch.utils.cpp_extension import load_inline + +cuda_source = """ +#include +#include + +__global__ void logit_sigmoid_shift_kernel( + const float* __restrict__ input, + float* __restrict__ output, + float shift, + int size +) { + int idx = blockIdx.x * blockDim.x + threadIdx.x; + if (idx < size) { + float x = input[idx]; + + float y = logf(x / (1.0f - x)); + + + float z = 1.0f / (1.0f + expf(-y)); + + + output[idx] = z + shift; + } +} + +torch::Tensor logit_sigmoid_shift_cuda(torch::Tensor input, float shift) { + auto output = torch::empty_like(input); + int size = input.numel(); + + const int block_size = 256; + int num_blocks = (size + block_size - 1) / block_size; + + logit_sigmoid_shift_kernel<<>>( + input.data_ptr(), + output.data_ptr(), + shift, + size + ); + + return output; +} +""" + +cpp_source = """ +torch::Tensor logit_sigmoid_shift_cuda(torch::Tensor input, float shift); +""" + +module = load_inline( + name="logit_sigmoid_shift", + cpp_sources=cpp_source, + cuda_sources=cuda_source, + functions=["logit_sigmoid_shift_cuda"], + verbose=True +) + + +class ModelNew(nn.Module): + def __init__(self, shift): + super(ModelNew, self).__init__() + self.shift = shift + self.module = module + + def forward(self, x): + return self.module.logit_sigmoid_shift_cuda(x, self.shift) \ No newline at end of file diff --git a/S1/uucoco_#95/logitsigmoidshift_torch.py b/S1/uucoco_#95/logitsigmoidshift_torch.py new file mode 100644 index 0000000..7e83a8c --- /dev/null +++ b/S1/uucoco_#95/logitsigmoidshift_torch.py @@ -0,0 +1,20 @@ +import torch +import torch.nn as nn + +class Model(nn.Module): + def __init__(self, shift): + super(Model, self).__init__() + self.shift = shift + + def forward(self, x): + return torch.sigmoid(torch.logit(x)) + self.shift + +batch_size = 4096 +dim = 1024 + +def get_inputs(): + x = torch.rand(batch_size, dim) * 0.999 + 0.0005 + return [x] + +def get_init_inputs(): + return [0.5] \ No newline at end of file diff --git a/S1/uucoco_#95/prompt.txt b/S1/uucoco_#95/prompt.txt new file mode 100644 index 0000000..67ad322 --- /dev/null +++ b/S1/uucoco_#95/prompt.txt @@ -0,0 +1,58 @@ +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 & Frameworks +PyTorch: Deep learning framework + +CUDA: NVIDIA's parallel computing platform for GPU acceleration + +CUDA/C++ Components +CUDA kernel: logit_sigmoid_shift_kernel + +CUDA math functions: logf(), expf() + +Element-wise parallelism: One thread per tensor element + +Mathematical Operations +Logit transform: log(x/(1-x)) + +Sigmoid activation: 1/(1+exp(-y)) + +Additive shift: Output + shift value + +Numerically sensitive: Division and log operations + +Architecture +Simple 1D grid: Standard CUDA block/grid configuration + +Memory efficiency: Direct element-wise computation + +PyTorch integration: Custom CUDA extension module + + + + + +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, shift): + super(Model, self).__init__() + self.shift = shift + + def forward(self, x): + return torch.sigmoid(torch.logit(x)) + self.shift + +batch_size = 4096 +dim = 1024 + +def get_inputs(): + x = torch.rand(batch_size, dim) * 0.999 + 0.0005 + return [x] + +def get_init_inputs(): + return [0.5] \ No newline at end of file diff --git a/S1/uucoco_#95/run_code.py b/S1/uucoco_#95/run_code.py new file mode 100644 index 0000000..012f097 --- /dev/null +++ b/S1/uucoco_#95/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 logitsigmoidshift_torch import Model, get_inputs, get_init_inputs +from logitsigmoidshift_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() \ No newline at end of file