forked from ccf-ai-infra/GPUCodeForces
102 lines
3.3 KiB
Python
102 lines
3.3 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, tau=0.5):
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super().__init__()
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self.tau = tau
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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 quantile_loss_cuda(torch::Tensor y_pred, torch::Tensor y_true, float tau);
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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 quantile_loss_op(float y_pred, float y_true, float tau) {
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float diff = y_true - y_pred;
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if (diff > 0.0f) {
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return tau * diff;
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} else {
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return (tau - 1.0f) * diff;
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}
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}
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__global__ void quantile_loss_kernel(
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const float* __restrict__ y_pred,
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const float* __restrict__ y_true,
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float* __restrict__ output,
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const int n_elements,
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const float tau)
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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* y_pred_vec = reinterpret_cast<const float4*>(y_pred);
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const float4* y_true_vec = reinterpret_cast<const float4*>(y_true);
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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 yp = __ldg(&y_pred_vec[i]);
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float4 yt = __ldg(&y_true_vec[i]);
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float4 r;
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r.x = quantile_loss_op(yp.x, yt.x, tau);
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r.y = quantile_loss_op(yp.y, yt.y, tau);
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r.z = quantile_loss_op(yp.z, yt.z, tau);
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r.w = quantile_loss_op(yp.w, yt.w, tau);
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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] = quantile_loss_op(y_pred[i], y_true[i], tau);
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}
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}
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torch::Tensor quantile_loss_cuda(torch::Tensor y_pred, torch::Tensor y_true, float tau) {
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auto y_pred_c = y_pred.contiguous();
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auto y_true_c = y_true.contiguous();
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const int n_elements = y_pred_c.numel();
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auto output = torch::empty_like(y_pred_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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quantile_loss_kernel<<<blocks, threads>>>(
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y_pred_c.data_ptr<float>(),
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y_true_c.data_ptr<float>(),
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output.data_ptr<float>(),
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n_elements,
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tau
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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="quantile_loss_v1",
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cpp_sources=cpp_source,
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cuda_sources=cuda_source,
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functions=["quantile_loss_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, y_pred, y_true):
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loss_elementwise = self.op.quantile_loss_cuda(y_pred, y_true, self.tau)
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return loss_elementwise.mean() |