forked from ccf-ai-infra/GPUCodeForces
90 lines
3.0 KiB
Python
90 lines
3.0 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, alpha=1.0, beta=0.0):
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super().__init__()
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self.alpha = alpha
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self.beta = beta
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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 parametric_sigmoid_cuda(torch::Tensor x, float alpha, float beta);
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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 parametric_sigmoid_op(float x, float alpha, float beta) {
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// f(x) = 1 / (1 + exp(-(alpha * x + beta)))
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float arg = -(alpha * x + beta);
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return 1.0f / (1.0f + expf(arg));
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}
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__global__ void parametric_sigmoid_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 alpha,
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const float beta)
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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 = parametric_sigmoid_op(v.x, alpha, beta);
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r.y = parametric_sigmoid_op(v.y, alpha, beta);
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r.z = parametric_sigmoid_op(v.z, alpha, beta);
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r.w = parametric_sigmoid_op(v.w, alpha, beta);
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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] = parametric_sigmoid_op(x[i], alpha, beta);
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}
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}
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torch::Tensor parametric_sigmoid_cuda(torch::Tensor x, float alpha, float beta) {
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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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parametric_sigmoid_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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alpha, beta
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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="parametric_sigmoid_v1",
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cpp_sources=cpp_source,
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cuda_sources=cuda_source,
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functions=["parametric_sigmoid_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.parametric_sigmoid_cuda(x, self.alpha, self.beta) |