forked from ccf-ai-infra/GPUCodeForces
73 lines
2.0 KiB
Python
73 lines
2.0 KiB
Python
import torch
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import torch.nn as nn
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from torch.utils.cpp_extension import load_inline
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class ModelNew(nn.Module):
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def __init__(self):
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super().__init__()
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self._compile_cuda_kernel()
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def _compile_cuda_kernel(self):
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cpp_source = """
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torch::Tensor dsu_cuda(torch::Tensor x);
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"""
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cuda_source = """
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#include <torch/extension.h>
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#include <cuda_runtime.h>
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#include <math.h>
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__device__ __forceinline__ float dsu_op(float x) {
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const float PI = 3.14159265358979323846f;
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const float y = x - PI;
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if (fabsf(y) < 1e-6f) {
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return PI;
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} else {
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return sinf(PI * y) / y;
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}
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}
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__global__ void dsu_kernel(
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const float* __restrict__ x,
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float* __restrict__ output,
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const int n_elements)
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{
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const int tid = blockIdx.x * blockDim.x + threadIdx.x;
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const int stride = blockDim.x * gridDim.x;
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for (int i = tid; i < n_elements; i += stride) {
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output[i] = dsu_op(x[i]);
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}
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}
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torch::Tensor dsu_cuda(torch::Tensor x) {
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auto x_c = x.contiguous();
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const int n_elements = x_c.numel();
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auto output = torch::empty_like(x_c);
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const int threads = 256;
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const int blocks = std::min((n_elements + threads - 1) / threads, 65535);
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dsu_kernel<<<blocks, threads>>>(
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x_c.data_ptr<float>(),
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output.data_ptr<float>(),
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n_elements
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);
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return output;
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}
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"""
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self.op = load_inline(
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name="dsu_v1",
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cpp_sources=cpp_source,
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cuda_sources=cuda_source,
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functions=["dsu_cuda"],
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extra_cuda_cflags=["-O3"],
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verbose=False
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)
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def forward(self, x):
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return self.op.dsu_cuda(x) |