finish threshold_scale_gate #112

S1/uucoco_#112/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 & Frameworks
+PyTorch: Deep learning framework
+
+CUDA: NVIDIA's parallel computing platform for GPU acceleration
+
+C++: For high-performance kernel implementation
+
+PyTorch Specific Components
+torch.nn.Module: Base class for neural network modules
+
+torch.utils.cpp_extension.load_inline: For inline compilation of CUDA/C++ extensions
+
+PyTorch Tensors: Multi-dimensional arrays
+
+torch::empty_like(): Tensor creation with same properties
+
+CUDA/C++ Implementation Details
+CUDA Kernels: Custom GPU kernel (threshold_scale_negate_kernel)
+
+Element-Wise Parallelism: One thread per tensor element
+
+Simple Grid/Block Configuration: Standard 1D parallelization pattern
+
+Conditional Branching: GPU-friendly threshold comparison
+
+Activation/Processing Components
+Threshold Function: Binary thresholding operation
+
+Scaling Operation: Multiplication by scale factor
+
+Negation Operation: Sign inversion (multiplication by -1)
+
+Conditional Activation: Different behavior above/below threshold
+
+Mathematical Operations
+Comparison Operation: x > threshold check
+
+Multiplication: x * scale scaling
+
+Negation: -(value) sign inversion
+
+Zero Assignment: Below-threshold values set to 0
+
+Optimization Techniques
+Simple Branching: GPU-optimized conditional logic
+
+Fused Operations: Threshold, scale, and negate in single kernel
+
+Memory Coalescing: Straightforward memory access pattern
+
+Element-Wise Independence: No inter-element dependencies
+
+Performance Features
+Massive Parallelization: GPU acceleration for thresholding operation
+
+Minimal Memory Traffic: Direct computation to output
+
+Low Computational Cost: Simple arithmetic and comparison
+
+Deterministic Output: Predictable, piecewise function
+
+Unique Implementation Aspects
+Composite Operation: Threshold + scale + negate in one step
+
+Two-Parameter Design: Tunable threshold and scale values
+
+Zero/Non-Zero Output: Below-threshold values always zero
+
+Negative Activation: Above-threshold outputs are always negative
+
+Custom Activation: Specialized non-linear function
+
+Potential Applications
+Sparse Activation: Creates sparse negative activations
+
+Feature Selection: Threshold-based feature suppression
+
+Custom Regularization: Specialized activation for specific tasks
+
+Signal Processing: Threshold-based signal modification
+
+
+
+
+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, threshold, scale):
+        super(Model, self).__init__()
+        self.threshold = threshold
+        self.scale = scale
+
+    def forward(self, x):
+        return torch.where(x > self.threshold, -x * self.scale, torch.tensor(0.0, dtype=x.dtype, device=x.device))
+
+batch_size = 1024
+dim = 1024
+
+def get_inputs():
+    x = torch.randn(batch_size, dim)
+    return [x]
+
+def get_init_inputs():
+    return [0.5, 2.0]

S1/uucoco_#112/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 thresholdscalenegate_torch import Model, get_inputs, get_init_inputs
+from thresholdscalenegate_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()

S1/uucoco_#112/thresholdscalenegate_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 threshold_scale_negate_kernel(
+    const float* __restrict__ input,
+    float* __restrict__ output,
+    float threshold,
+    float scale,
+    int size
+) {
+    int idx = blockIdx.x * blockDim.x + threadIdx.x;
+    if (idx < size) {
+        float x = input[idx];
+
+        if (x > threshold) {
+            output[idx] = -(x * scale);
+        } else {
+            output[idx] = 0.0f;
+        }
+    }
+}
+
+torch::Tensor threshold_scale_negate_cuda(torch::Tensor input, float threshold, float scale) {
+    auto output = torch::empty_like(input);
+    int size = input.numel();
+
+    const int block_size = 256;
+    int num_blocks = (size + block_size - 1) / block_size;
+
+    threshold_scale_negate_kernel<<<num_blocks, block_size>>>(
+        input.data_ptr<float>(),
+        output.data_ptr<float>(),
+        threshold,
+        scale,
+        size
+    );
+
+    return output;
+}
+"""
+
+cpp_source = """
+torch::Tensor threshold_scale_negate_cuda(torch::Tensor input, float threshold, float scale);
+"""
+
+module = load_inline(
+    name="threshold_scale_negate",
+    cpp_sources=cpp_source,
+    cuda_sources=cuda_source,
+    functions=["threshold_scale_negate_cuda"],
+    verbose=True
+)
+
+
+class ModelNew(nn.Module):
+    def __init__(self, threshold, scale):
+        super(ModelNew, self).__init__()
+        self.threshold = threshold
+        self.scale = scale
+        self.module = module
+
+    def forward(self, x):
+        return self.module.threshold_scale_negate_cuda(x, self.threshold, self.scale)

S1/uucoco_#112/thresholdscalenegate_torch.py

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+import torch
+import torch.nn as nn
+
+class Model(nn.Module):
+    def __init__(self, threshold, scale):
+        super(Model, self).__init__()
+        self.threshold = threshold
+        self.scale = scale
+
+    def forward(self, x):
+        return torch.where(x > self.threshold, -x * self.scale, torch.tensor(0.0, dtype=x.dtype, device=x.device))
+
+batch_size = 1024
+dim = 1024
+
+def get_inputs():
+    x = torch.randn(batch_size, dim)
+    return [x]
+
+def get_init_inputs():
+    return [0.5, 2.0]