forked from ccf-ai-infra/GPUCodeForces
115 lines
3.6 KiB
Python
115 lines
3.6 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, eps=1e-5):
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super().__init__()
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self.eps = eps
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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 modeseekingloss_cuda(torch::Tensor img1, torch::Tensor img2, torch::Tensor z1, torch::Tensor z2, float eps);
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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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__global__ void reduce_l1_diff_kernel(
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const float* __restrict__ a,
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const float* __restrict__ b,
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float* __restrict__ out,
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const int dim)
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{
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extern __shared__ float sdata[];
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int tid = threadIdx.x;
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int bid = blockIdx.x;
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float sum = 0.0f;
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for (int i = tid; i < dim; i += blockDim.x) {
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float diff = a[bid * dim + i] - b[bid * dim + i];
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sum += fabsf(diff);
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}
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sdata[tid] = sum;
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__syncthreads();
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for (unsigned int s = blockDim.x / 2; s > 0; s >>= 1) {
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if (tid < s) {
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sdata[tid] += sdata[tid + s];
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}
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__syncthreads();
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}
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if (tid == 0) {
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out[bid] = sdata[0] / (float)dim;
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}
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}
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__global__ void compute_ratio_kernel(
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const float* __restrict__ img_diff,
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const float* __restrict__ z_diff,
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float* __restrict__ output,
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const int n,
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const float eps)
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{
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int i = blockIdx.x * blockDim.x + threadIdx.x;
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if (i < n) {
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output[i] = z_diff[i] / (img_diff[i] + eps);
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}
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}
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torch::Tensor modeseekingloss_cuda(torch::Tensor img1, torch::Tensor img2, torch::Tensor z1, torch::Tensor z2, float eps) {
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int batch_size = img1.size(0);
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int img_dim = img1.numel() / batch_size;
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int z_dim = z1.numel() / batch_size;
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auto img_diff = torch::empty({batch_size}, img1.options());
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auto z_diff = torch::empty({batch_size}, z1.options());
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auto output = torch::empty({batch_size}, img1.options());
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int threads = 256;
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int blocks = batch_size;
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int shared_mem = threads * sizeof(float);
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reduce_l1_diff_kernel<<<blocks, threads, shared_mem>>>(
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img1.data_ptr<float>(),
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img2.data_ptr<float>(),
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img_diff.data_ptr<float>(),
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img_dim
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);
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reduce_l1_diff_kernel<<<blocks, threads, shared_mem>>>(
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z1.data_ptr<float>(),
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z2.data_ptr<float>(),
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z_diff.data_ptr<float>(),
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z_dim
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);
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int ratio_blocks = (batch_size + threads - 1) / threads;
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compute_ratio_kernel<<<ratio_blocks, threads>>>(
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img_diff.data_ptr<float>(),
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z_diff.data_ptr<float>(),
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output.data_ptr<float>(),
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batch_size,
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eps
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);
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return output.mean();
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}
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"""
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self.op = load_inline(
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name="modeseekingloss_op",
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cpp_sources=cpp_source,
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cuda_sources=cuda_source,
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functions=["modeseekingloss_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, img1, img2, z1, z2):
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return self.op.modeseekingloss_cuda(img1, img2, z1, z2, self.eps) |