forked from ccf-ai-infra/GPUCodeForces
84 lines
2.6 KiB
Python
84 lines
2.6 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, reduction='mean'):
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super().__init__()
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self.reduction = reduction
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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 adversarial_loss_cuda(torch::Tensor input, torch::Tensor target);
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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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__global__ void adversarial_loss_kernel(
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const float* __restrict__ input,
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const float* __restrict__ target,
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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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float x = input[i];
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float y = target[i];
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// Binary Cross Entropy with Logits Stability Formula:
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// max(x, 0) - x * y + log(1 + exp(-abs(x)))
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float max_val = fmaxf(x, 0.0f);
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float abs_val = fabsf(x);
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float log_term = log1pf(expf(-abs_val));
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output[i] = max_val - x * y + log_term;
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}
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}
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torch::Tensor adversarial_loss_cuda(torch::Tensor input, torch::Tensor target) {
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auto input_c = input.contiguous();
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auto target_c = target.contiguous();
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const int n_elements = input_c.numel();
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auto output = torch::empty_like(input_c);
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const int threads = 256;
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const int blocks = min((n_elements + threads - 1) / threads, 65535);
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adversarial_loss_kernel<<<blocks, threads>>>(
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input_c.data_ptr<float>(),
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target_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="adversarial_loss_op",
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cpp_sources=cpp_source,
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cuda_sources=cuda_source,
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functions=["adversarial_loss_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, input, target):
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loss_elementwise = self.op.adversarial_loss_cuda(input, target)
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if self.reduction == 'mean':
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return loss_elementwise.mean()
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elif self.reduction == 'sum':
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return loss_elementwise.sum()
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else:
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return loss_elementwise |