finish Elliot #70

S1/hli28146_#70/Elliot_cuda.py

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+import torch
+import torch.nn as nn
+from torch.utils.cpp_extension import load_inline
+
+cpp_source = """
+#include <torch/extension.h>
+
+torch::Tensor elliot_cuda_forward(const torch::Tensor& input);
+"""
+
+cuda_source = """
+#include <torch/extension.h>
+#include <cuda_runtime.h>
+#include <math.h>
+
+#define BLOCK_SIZE 256
+
+struct __align__(16) Float4 {
+    float x, y, z, w;
+};
+
+// Elliot Logic: 0.5 * x / (1 + |x|) + 0.5
+__device__ __forceinline__ float compute_elliot(float x) {
+    float denom_inv = 1.0f / (1.0f + fabsf(x));
+    return 0.5f * x * denom_inv + 0.5f;
+}
+
+__global__ void elliot_kernel(
+    float* __restrict__ output,
+    const float* __restrict__ input,
+    const int n) 
+{
+    const int idx = blockIdx.x * blockDim.x + threadIdx.x;
+    const int vec_n = n / 4;
+    
+    int i = idx;
+    const int stride = blockDim.x * gridDim.x;
+
+    for (; i < vec_n; i += stride) {
+        Float4 in_vec = reinterpret_cast<const Float4*>(input)[i];
+        Float4 out_vec;
+
+        out_vec.x = compute_elliot(in_vec.x);
+        out_vec.y = compute_elliot(in_vec.y);
+        out_vec.z = compute_elliot(in_vec.z);
+        out_vec.w = compute_elliot(in_vec.w);
+
+        reinterpret_cast<Float4*>(output)[i] = out_vec;
+    }
+
+    // 2. Scalar Tail Loop
+    int start_scalar = vec_n * 4;
+    int global_tid = blockIdx.x * blockDim.x + threadIdx.x;
+    int total_threads = gridDim.x * blockDim.x;
+    
+    int current_idx = start_scalar + global_tid;
+    while (current_idx < n) {
+        output[current_idx] = compute_elliot(input[current_idx]);
+        current_idx += total_threads;
+    }
+}
+
+torch::Tensor elliot_cuda_forward(const torch::Tensor& input) {
+    TORCH_CHECK(input.is_cuda(), "Input must be a CUDA tensor");
+    TORCH_CHECK(input.is_contiguous(), "Input must be contiguous");
+    
+    const int n = input.numel();
+    auto output = torch::empty_like(input);
+
+    const int vec_n = n / 4;
+    const int grid_size = (vec_n + BLOCK_SIZE - 1) / BLOCK_SIZE;
+    
+    int final_grid = (grid_size < 1) ? 1 : grid_size;
+    if (final_grid > 65535) final_grid = 65535;
+
+    elliot_kernel<<<final_grid, BLOCK_SIZE>>>(
+        output.data_ptr<float>(),
+        input.data_ptr<float>(),
+        n
+    );
+
+    return output;
+}
+"""
+
+elliot_op_module = load_inline(
+    name='elliot_op_v2',
+    cpp_sources=cpp_source,
+    cuda_sources=cuda_source,
+    functions=['elliot_cuda_forward'],
+    verbose=False,
+    extra_cuda_cflags=['-O3']
+)
+
+class ModelNew(nn.Module):
+    def __init__(self):
+        super(ModelNew, self).__init__()
+        self.op = elliot_op_module
+
+    def forward(self, input_tensor: torch.Tensor) -> torch.Tensor:
+        return self.op.elliot_cuda_forward(input_tensor.contiguous())

S1/hli28146_#70/Elliot_torch.py

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+import torch
+import torch.nn as nn
+
+BATCH_SIZE = 4096
+HIDDEN_DIM = 4096
+SHAPE = (BATCH_SIZE, HIDDEN_DIM)
+
+class ElliotFunction(nn.Module):
+    """
+    Elliot Activation.
+    https://towardsdatascience.com/elliot-activation-function-what-is-it-and-is-it-effective-59b63ec1fd8a/
+    f(x) = 0.5 * x / (1 + |x|) + 0.5
+    """
+    def __init__(self):
+        super(ElliotFunction, self).__init__()
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return 0.5 * x / (1.0 + torch.abs(x)) + 0.5
+
+class Model(nn.Module):
+    def __init__(self):
+        super(Model, self).__init__()
+        self.act = ElliotFunction()
+    
+    def forward(self, x):
+        return self.act(x)
+
+def get_inputs():
+    input_tensor = torch.randn(SHAPE, dtype=torch.float32) * 5.0
+    return [input_tensor.contiguous()]
+
+def get_init_inputs():
+    return []

S1/hli28146_#70/prompt.txt

@@ -0,0 +1,57 @@
+Write a custom CUDA kernel to optimize the `Elliot Function` (Fast Sigmoid Approximation).
+
+Formula: f(x) = 0.5 * x / (1 + |x|) + 0.5
+
+Problem Analysis:
+1. Memory Bound: This is a strictly element-wise operation with low arithmetic intensity. Performance is limited by memory bandwidth.
+2. Operator Chaining: The PyTorch implementation `0.5 * x / (1 + x.abs()) + 0.5` chains multiple element-wise kernels, creating intermediate tensors and high memory traffic.
+
+Optimization Strategy: Fused Element-wise Kernel with Vectorization
+
+1. One-Thread-per-Element: Map each element to a CUDA thread.
+
+2. Vectorized Loads (float4): Use `float4` to process 128 bits per memory transaction.
+
+3. Fused In-Register Math:
+   - Load `val`.
+   - Compute `denom_inv = 1.0f / (1.0f + fabsf(val))`.
+   - Compute `res = 0.5f * val * denom_inv + 0.5f`.
+   - Store `res`.
+   This fuses all 5 arithmetic operations into a single pass.
+  
+Here's an example to show you the syntax of inline embedding custom CUDA operators in torch: The example given architecture is:   
+  
+```python
+import torch
+import torch.nn as nn
+
+BATCH_SIZE = 4096
+HIDDEN_DIM = 4096
+SHAPE = (BATCH_SIZE, HIDDEN_DIM)
+
+class ElliotFunction(nn.Module):
+    """
+    Elliot Activation.
+    https://towardsdatascience.com/elliot-activation-function-what-is-it-and-is-it-effective-59b63ec1fd8a/
+    f(x) = 0.5 * x / (1 + |x|) + 0.5
+    """
+    def __init__(self):
+        super(ElliotFunction, self).__init__()
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return 0.5 * x / (1.0 + torch.abs(x)) + 0.5
+
+class Model(nn.Module):
+    def __init__(self):
+        super(Model, self).__init__()
+        self.act = ElliotFunction()
+    
+    def forward(self, x):
+        return self.act(x)
+
+def get_inputs():
+    input_tensor = torch.randn(SHAPE, dtype=torch.float32) * 5.0
+    return [input_tensor.contiguous()]
+
+def get_init_inputs():
+    return []

S1/hli28146_#70/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 Elliot_torch import Model,get_inputs,get_init_inputs
+from Elliot_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()