forked from ccf-ai-infra/GPUCodeForces
101 lines
3.2 KiB
Python
101 lines
3.2 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, tl=-1.0, al=0.1, tr=1.0, ar=0.1):
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super().__init__()
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self.tl = tl
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self.al = al
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self.tr = tr
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self.ar = ar
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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 srelu_cuda(torch::Tensor x, float tl, float al, float tr, float ar);
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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 srelu_op(float x, float tl, float al, float tr, float ar) {
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// Region 1: x <= t_l
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if (x <= tl) {
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return tl + al * (x - tl);
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}
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// Region 3: x >= t_r
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if (x >= tr) {
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return tr + ar * (x - tr);
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}
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// Region 2: t_l < x < t_r
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return x;
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}
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__global__ void srelu_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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const float tl,
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const float al,
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const float tr,
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const float ar)
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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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const int vec_loops = n_elements >> 2;
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const float4* x_vec = reinterpret_cast<const float4*>(x);
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float4* out_vec = reinterpret_cast<float4*>(output);
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for (int i = tid; i < vec_loops; i += stride) {
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float4 v = __ldg(&x_vec[i]);
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float4 r;
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r.x = srelu_op(v.x, tl, al, tr, ar);
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r.y = srelu_op(v.y, tl, al, tr, ar);
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r.z = srelu_op(v.z, tl, al, tr, ar);
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r.w = srelu_op(v.w, tl, al, tr, ar);
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out_vec[i] = r;
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}
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const int tail_start = vec_loops << 2;
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for (int i = tail_start + tid; i < n_elements; i += stride) {
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output[i] = srelu_op(x[i], tl, al, tr, ar);
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}
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}
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torch::Tensor srelu_cuda(torch::Tensor x, float tl, float al, float tr, float ar) {
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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 max_blocks = 65535;
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const int blocks = std::min((n_elements + threads * 4 - 1) / (threads * 4), max_blocks);
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srelu_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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tl, al, tr, ar
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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="srelu_v1",
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cpp_sources=cpp_source,
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cuda_sources=cuda_source,
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functions=["srelu_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.srelu_cuda(x, self.tl, self.al, self.tr, self.ar) |