finish segment_max #130

S1/gsd123_#130/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.
+
+PyTorch C++/CUDA Extension
+
+Runtime compilation via torch.utils.cpp_extension.load_inline
+
+Direct integration with PyTorch's autograd and tensor ecosystem
+
+CUDA Custom Atomic Operation
+
+Custom atomicMaxFloat using atomicCAS (Compare-And-Swap)
+
+Handles float atomic maximum (CUDA lacks native atomicMax for floats)
+
+Uses __float_as_int/__int_as_float for type-punning atomic operations
+
+Implements spin-loop with early exit for performance
+
+Two-Kernel Design
+
+init_kernel: Initializes output to -1e38 (negative infinity proxy)
+
+segment_max_kernel: Performs segment-wise maximum reduction
+
+CUDA Optimization Techniques
+
+__forceinline__ for device function inlining
+
+__restrict__ pointers for compiler aliasing optimization
+
+Grid-stride loops with 256 threads per block
+
+Efficient 2D→1D index calculation: row * channels + col
+
+Numerical Stability Pattern
+
+Initializes with -1e38f instead of -FLT_MAX for safety
+
+Uses fmaxf for maximum computation (CUDA math intrinsic)
+
+Memory Management
+
+torch::empty for uninitialized tensor creation (faster than zeros)
+
+Separate initialization kernel for output tensor
+
+Coalesced global memory access patterns
+
+Segment-Based Reduction
+
+Groups input rows by segment_id from index tensor
+
+Computes channel-wise maximum within each segment
+
+Output shape: (dim_size, channels)
+
+Module Abstraction
+
+nn.Module wrapper for PyTorch integration
+
+Maintains dim_size as persistent configuration
+
+
+
+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, dim_size):
+        super(Model, self).__init__()
+        self.dim_size = dim_size
+
+    def forward(self, src, index):
+        out = torch.full((self.dim_size, src.size(1)), -1e38, device=src.device, dtype=src.dtype)
+        index_expanded = index.unsqueeze(1).expand_as(src)
+        out.scatter_reduce_(0, index_expanded, src, reduce='amax', include_self=False)
+        return out
+
+batch_size = 1024
+features = 64
+dim_size = 128
+
+def get_inputs():
+    src = torch.randn(batch_size, features)
+    index = torch.randint(0, dim_size, (batch_size,))
+    return [src, index]
+
+def get_init_inputs():
+    return [dim_s

S1/gsd123_#130/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 segmentmax_torch import Model, get_inputs, get_init_inputs
+from segmentmax_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/gsd123_#130/segmentmax_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>
+#include <cfloat>
+
+__device__ __forceinline__ void atomicMaxFloat(float* address, float val) {
+    int* address_as_i = (int*)address;
+    int old = *address_as_i, assumed;
+    do {
+        assumed = old;
+        float old_val = __int_as_float(assumed);
+        float new_val = fmaxf(val, old_val);
+        if (new_val == old_val) break;
+        old = atomicCAS(address_as_i, assumed, __float_as_int(new_val));
+    } while (assumed != old);
+}
+
+__global__ void init_kernel(float* out, int size, float val) {
+    int idx = blockIdx.x * blockDim.x + threadIdx.x;
+    if (idx < size) {
+        out[idx] = val;
+    }
+}
+
+__global__ void segment_max_kernel(
+    const float* __restrict__ src,
+    const long* __restrict__ index,
+    float* __restrict__ out,
+    int num_elements,
+    int channels,
+    int dim_size
+) {
+    int idx = blockIdx.x * blockDim.x + threadIdx.x;
+    if (idx < num_elements) {
+        int row = idx / channels;
+        int col = idx % channels;
+
+        long segment_id = index[row];
+
+        if (segment_id >= 0 && segment_id < dim_size) {
+            int out_idx = segment_id * channels + col;
+            atomicMaxFloat(&out[out_idx], src[idx]);
+        }
+    }
+}
+
+torch::Tensor segment_max_cuda(torch::Tensor src, torch::Tensor index, int dim_size) {
+    int N = src.size(0);
+    int C = src.size(1);
+
+    auto out = torch::empty({dim_size, C}, src.options());
+
+    int out_elements = dim_size * C;
+    const int block_size = 256;
+    int grid_init = (out_elements + block_size - 1) / block_size;
+
+    init_kernel<<<grid_init, block_size>>>(out.data_ptr<float>(), out_elements, -1e38f);
+
+    int num_elements = N * C;
+    int grid_max = (num_elements + block_size - 1) / block_size;
+
+    segment_max_kernel<<<grid_max, block_size>>>(
+        src.data_ptr<float>(),
+        index.data_ptr<long>(),
+        out.data_ptr<float>(),
+        num_elements,
+        C,
+        dim_size
+    );
+
+    return out;
+}
+"""
+
+cpp_source = """
+torch::Tensor segment_max_cuda(torch::Tensor src, torch::Tensor index, int dim_size);
+"""
+
+segment_max_lib = load_inline(
+    name="segment_max",
+    cpp_sources=cpp_source,
+    cuda_sources=cuda_source,
+    functions=["segment_max_cuda"],
+    verbose=True
+)
+
+
+class ModelNew(nn.Module):
+    def __init__(self, dim_size):
+        super(ModelNew, self).__init__()
+        self.dim_size = dim_size
+        self.lib = segment_max_lib
+
+    def forward(self, src, index):
+        return self.lib.segment_max_cuda(src, index, self.dim_size)

S1/gsd123_#130/segmentmax_torch.py

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+import torch
+import torch.nn as nn
+
+class Model(nn.Module):
+    def __init__(self, dim_size):
+        super(Model, self).__init__()
+        self.dim_size = dim_size
+
+    def forward(self, src, index):
+        out = torch.full((self.dim_size, src.size(1)), -1e38, device=src.device, dtype=src.dtype)
+        index_expanded = index.unsqueeze(1).expand_as(src)
+        out.scatter_reduce_(0, index_expanded, src, reduce='amax', include_self=False)
+        return out
+
+batch_size = 1024
+features = 64
+dim_size = 128
+
+def get_inputs():
+    src = torch.randn(batch_size, features)
+    index = torch.randint(0, dim_size, (batch_size,))
+    return [src, index]
+
+def get_init_inputs():
+    return [dim_size]