forked from ccf-ai-infra/GPUCodeForces
88 lines
2.5 KiB
Python
88 lines
2.5 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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#include <torch/extension.h>
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torch::Tensor logsigmoid_cuda(torch::Tensor x);
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"""
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cuda_source = """
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#include <cuda_runtime.h>
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__device__ __forceinline__ float logsigmoid_op(float x) {
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if (x > 0.0f) {
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return -log1pf(expf(-x));
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} else {
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return x - log1pf(expf(x));
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}
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}
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__global__ void logsigmoid_kernel_vec4(
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const float* __restrict__ x,
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float* __restrict__ y,
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int n)
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{
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int idx = blockIdx.x * blockDim.x + threadIdx.x;
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int stride = blockDim.x * gridDim.x;
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int vec_n = n / 4;
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const float4* x_vec = reinterpret_cast<const float4*>(x);
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float4* y_vec = reinterpret_cast<float4*>(y);
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for (int i = idx; i < vec_n; i += stride) {
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float4 v = x_vec[i];
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float4 out;
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out.x = logsigmoid_op(v.x);
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out.y = logsigmoid_op(v.y);
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out.z = logsigmoid_op(v.z);
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out.w = logsigmoid_op(v.w);
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y_vec[i] = out;
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}
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int tail = vec_n * 4;
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for (int i = tail + idx; i < n; i += stride) {
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y[i] = logsigmoid_op(x[i]);
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}
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}
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torch::Tensor logsigmoid_cuda(torch::Tensor x) {
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auto x_c = x.contiguous();
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auto output = torch::empty_like(x_c);
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int n = x_c.numel();
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int threads = 256;
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int blocks = (n / 4 + threads - 1) / threads;
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if (blocks > 65535) blocks = 65535;
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if (blocks == 0) blocks = 1;
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logsigmoid_kernel_vec4<<<blocks, threads>>>(
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x_c.data_ptr<float>(),
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output.data_ptr<float>(),
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n
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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="logsigmoid_opt_vec4",
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cpp_sources=cpp_source,
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cuda_sources=cuda_source,
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functions=["logsigmoid_cuda"],
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extra_cuda_cflags=["-O3", "--use_fast_math"],
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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.logsigmoid_cuda(x) |