forked from ccf-ai-infra/GPUCodeForces
fixes PairwiseKldLoss #95
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cc73715277
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da006281c7
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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):
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super().__init__()
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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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#include <torch/extension.h>
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torch::Tensor pairwise_kld_cuda(torch::Tensor boxes1, torch::Tensor boxes2);
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"""
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cuda_source = """
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#include <cuda_runtime.h>
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#include <cmath>
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#define BLOCK_DIM 16
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__global__ void pairwise_kld_kernel(
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const float* __restrict__ boxes1,
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const float* __restrict__ boxes2,
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float* __restrict__ output,
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int n, int m
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) {
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int col = blockIdx.x * blockDim.x + threadIdx.x;
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int row = blockIdx.y * blockDim.y + threadIdx.y;
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if (row >= n || col >= m) return;
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// Manual Load (5 elements)
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const float* p1 = boxes1 + row * 5;
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const float* p2 = boxes2 + col * 5;
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double mu1_x = p1[0], mu1_y = p1[1], w1 = p1[2], h1 = p1[3], t1 = p1[4];
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double mu2_x = p2[0], mu2_y = p2[1], w2 = p2[2], h2 = p2[3], t2 = p2[4];
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auto get_sigma = [&](double w, double h, double t, double& xx, double& yy, double& xy) {
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double c = cos(t), s = sin(t);
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double w2 = w*w/4.0, h2 = h*h/4.0;
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xx = c*c*w2 + s*s*h2;
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yy = s*s*w2 + c*c*h2;
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xy = c*s*(w2 - h2);
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};
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double s1_xx, s1_yy, s1_xy; get_sigma(w1, h1, t1, s1_xx, s1_yy, s1_xy);
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double s2_xx, s2_yy, s2_xy; get_sigma(w2, h2, t2, s2_xx, s2_yy, s2_xy);
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// Inverse S2
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double det2 = s2_xx * s2_yy - s2_xy * s2_xy + 1e-7;
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double inv_xx = s2_yy / det2;
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double inv_yy = s2_xx / det2;
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double inv_xy = -s2_xy / det2;
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// Trace(S2_inv @ S1)
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double tr = (inv_xx * s1_xx + inv_xy * s1_xy) + (inv_xy * s1_xy + inv_yy * s1_yy);
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// Mahalanobis
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double dx = mu2_x - mu1_x;
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double dy = mu2_y - mu1_y;
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double maha = dx * (inv_xx * dx + inv_xy * dy) + dy * (inv_xy * dx + inv_yy * dy);
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// Log Det
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double det1 = s1_xx * s1_yy - s1_xy * s1_xy + 1e-7;
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double log_det = log(det2 / det1);
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double kld = 0.5 * (tr + maha + log_det - 2.0);
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output[row * m + col] = (float)(1.0 / (1.0 + kld));
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}
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torch::Tensor pairwise_kld_cuda(torch::Tensor boxes1, torch::Tensor boxes2) {
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int n = boxes1.size(0);
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int m = boxes2.size(0);
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auto output = torch::empty({n, m}, boxes1.options());
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dim3 block(BLOCK_DIM, BLOCK_DIM);
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dim3 grid((m + BLOCK_DIM - 1) / BLOCK_DIM, (n + BLOCK_DIM - 1) / BLOCK_DIM);
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pairwise_kld_kernel<<<grid, block>>>(boxes1.data_ptr<float>(), boxes2.data_ptr<float>(), output.data_ptr<float>(), n, m);
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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="pairwise_kld_opt",
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cpp_sources=cpp_source,
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cuda_sources=cuda_source,
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functions=["pairwise_kld_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, boxes1: torch.Tensor, boxes2: torch.Tensor) -> torch.Tensor:
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return self.op.pairwise_kld_cuda(boxes1, boxes2)
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@ -0,0 +1,67 @@
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import torch
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import torch.nn as nn
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N = 2048
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M = 2048
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class Model(nn.Module):
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def __init__(self):
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super().__init__()
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def forward(self, boxes1: torch.Tensor, boxes2: torch.Tensor) -> torch.Tensor:
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# KL Divergence between two Gaussians
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b1 = boxes1.unsqueeze(1) # [N, 1, 5]
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b2 = boxes2.unsqueeze(0) # [1, M, 5]
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def get_params(b):
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x, y, w, h, theta = b.unbind(dim=-1)
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c = torch.cos(theta)
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s = torch.sin(theta)
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w2 = w.pow(2) / 4.0
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h2 = h.pow(2) / 4.0
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sigma_xx = c*c*w2 + s*s*h2
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sigma_yy = s*s*w2 + c*c*h2
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sigma_xy = c*s*(w2 - h2)
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return x, y, sigma_xx, sigma_yy, sigma_xy
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mu1_x, mu1_y, s1_xx, s1_yy, s1_xy = get_params(b1)
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mu2_x, mu2_y, s2_xx, s2_yy, s2_xy = get_params(b2)
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# KLD = 0.5 * (Tr(S2_inv @ S1) + (mu2-mu1)^T @ S2_inv @ (mu2-mu1) + ln(|S2|/|S1|) - 2)
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# 1. Inverse of S2
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det2 = s2_xx * s2_yy - s2_xy.pow(2) + 1e-7
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s2_inv_xx = s2_yy / det2
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s2_inv_yy = s2_xx / det2
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s2_inv_xy = -s2_xy / det2
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# 2. Trace term: Tr(S2_inv @ S1)
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# (inv_xx * xx + inv_xy * xy) + (inv_xy * xy + inv_yy * yy)
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tr_term = (s2_inv_xx * s1_xx + s2_inv_xy * s1_xy) + (s2_inv_xy * s1_xy + s2_inv_yy * s1_yy)
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# 3. Mahalanobis term
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dx = mu2_x - mu1_x
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dy = mu2_y - mu1_y
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mahalanobis = dx * (s2_inv_xx * dx + s2_inv_xy * dy) + dy * (s2_inv_xy * dx + s2_inv_yy * dy)
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# 4. Log Det term
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det1 = s1_xx * s1_yy - s1_xy.pow(2) + 1e-7
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log_det = torch.log(det2 / det1)
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kld = 0.5 * (tr_term + mahalanobis + log_det - 2.0)
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return 1 / (1 + kld) # Normalize to 0-1
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def get_inputs():
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xy = torch.randint(0, 100, (N, 2), device='cuda').float()
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wh = torch.randint(10, 50, (N, 2), device='cuda').float()
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theta = torch.zeros((N, 1), device='cuda').float()
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b1 = torch.cat([xy, wh, theta], dim=1)
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xy2 = torch.randint(0, 100, (M, 2), device='cuda').float()
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wh2 = torch.randint(10, 50, (M, 2), device='cuda').float()
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theta2 = torch.zeros((M, 1), device='cuda').float()
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b2 = torch.cat([xy2, wh2, theta2], dim=1)
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return [b1, b2]
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def get_init_inputs():
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return []
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You write custom CUDA kernels to replace the pytorch operators in the given architecture to get speedups.
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You have complete freedom to choose the set of operators you want to replace. You may make the decision to replace some operators with custom CUDA kernels and leave others unchanged. You may replace multiple operators with custom implementations, consider operator fusion opportunities (combining multiple operators into a single kernel, for example, combining matmul+relu), or algorithmic changes (such as online softmax). You are only limited by your imagination.
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Here's an example to show you the syntax of inline embedding custom CUDA operators in torch: The example given architecture is:
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python
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import torch
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import torch.nn as nn
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N = 2048
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M = 2048
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class Model(nn.Module):
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def __init__(self):
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super().__init__()
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def forward(self, boxes1: torch.Tensor, boxes2: torch.Tensor) -> torch.Tensor:
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# KL Divergence between two Gaussians
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b1 = boxes1.unsqueeze(1) # [N, 1, 5]
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b2 = boxes2.unsqueeze(0) # [1, M, 5]
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def get_params(b):
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x, y, w, h, theta = b.unbind(dim=-1)
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c = torch.cos(theta)
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s = torch.sin(theta)
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w2 = w.pow(2) / 4.0
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h2 = h.pow(2) / 4.0
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sigma_xx = c*c*w2 + s*s*h2
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sigma_yy = s*s*w2 + c*c*h2
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sigma_xy = c*s*(w2 - h2)
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return x, y, sigma_xx, sigma_yy, sigma_xy
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mu1_x, mu1_y, s1_xx, s1_yy, s1_xy = get_params(b1)
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mu2_x, mu2_y, s2_xx, s2_yy, s2_xy = get_params(b2)
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# KLD = 0.5 * (Tr(S2_inv @ S1) + (mu2-mu1)^T @ S2_inv @ (mu2-mu1) + ln(|S2|/|S1|) - 2)
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# 1. Inverse of S2
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det2 = s2_xx * s2_yy - s2_xy.pow(2) + 1e-7
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s2_inv_xx = s2_yy / det2
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s2_inv_yy = s2_xx / det2
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s2_inv_xy = -s2_xy / det2
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# 2. Trace term: Tr(S2_inv @ S1)
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# (inv_xx * xx + inv_xy * xy) + (inv_xy * xy + inv_yy * yy)
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tr_term = (s2_inv_xx * s1_xx + s2_inv_xy * s1_xy) + (s2_inv_xy * s1_xy + s2_inv_yy * s1_yy)
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# 3. Mahalanobis term
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dx = mu2_x - mu1_x
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dy = mu2_y - mu1_y
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mahalanobis = dx * (s2_inv_xx * dx + s2_inv_xy * dy) + dy * (s2_inv_xy * dx + s2_inv_yy * dy)
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# 4. Log Det term
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det1 = s1_xx * s1_yy - s1_xy.pow(2) + 1e-7
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log_det = torch.log(det2 / det1)
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kld = 0.5 * (tr_term + mahalanobis + log_det - 2.0)
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return 1 / (1 + kld) # Normalize to 0-1
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def get_inputs():
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xy = torch.randint(0, 100, (N, 2), device='cuda').float()
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wh = torch.randint(10, 50, (N, 2), device='cuda').float()
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theta = torch.zeros((N, 1), device='cuda').float()
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b1 = torch.cat([xy, wh, theta], dim=1)
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xy2 = torch.randint(0, 100, (M, 2), device='cuda').float()
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wh2 = torch.randint(10, 50, (M, 2), device='cuda').float()
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theta2 = torch.zeros((M, 1), device='cuda').float()
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b2 = torch.cat([xy2, wh2, theta2], dim=1)
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return [b1, b2]
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def get_init_inputs():
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return []
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```
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###########################################################
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# 性能和精度验证程序
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###########################################################
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import torch
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import torch.nn as nn
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import time
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from pairwise_kld_loss_torch import Model,get_inputs,get_init_inputs
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from pairwise_kld_loss_cuda import ModelNew
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def run_benchmark():
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# 检查 CUDA 是否可用
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if not torch.cuda.is_available():
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print("CUDA 不可用，请确保您有可用的 NVIDIA GPU 并已正确安装 PyTorch CUDA 版本。")
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return
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else:
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device = torch.device("cuda")
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# 初始化模型
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init_inputs = get_init_inputs()
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init_inputs = [
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x.cuda(device=device) if isinstance(x, torch.Tensor) else x for x in init_inputs
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]
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inputs = get_inputs()
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inputs = [
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x.cuda(device=device) if isinstance(x, torch.Tensor) else x for x in inputs
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]
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torch_model = Model(*init_inputs).cuda()
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cuda_model = ModelNew(*init_inputs).cuda()
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torch_model.eval()
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cuda_model.eval()
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print("-------------------- 精度对齐验证 --------------------")
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with torch.no_grad():
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output_torch = torch_model( *inputs)
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output_cuda = cuda_model(*inputs)
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precision_flag = torch.allclose(output_torch, output_cuda,rtol=1e-03)
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if precision_flag:
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print("✅ 精度对齐：两个模型的输出结果非常接近。")
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else:
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print("❌ 精度不一致！")
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print("\n-------------------- 性能加速比测试 --------------------")
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num_iterations = 100
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# PyTorch 模型计时
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torch.cuda.synchronize()
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start_time = time.time()
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for _ in range(num_iterations):
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_ = torch_model(*inputs)
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torch.cuda.synchronize()
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torch_time = (time.time() - start_time) / num_iterations
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# 自定义 CUDA 内核计时
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torch.cuda.synchronize()
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start_time = time.time()
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for _ in range(num_iterations):
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_ = cuda_model(*inputs)
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torch.cuda.synchronize()
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cuda_time = (time.time() - start_time) / num_iterations
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print(f"PyTorch torch.relu 平均执行时间: {torch_time:.6f} 秒")
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print(f"自定义 CUDA 内核 平均执行时间: {cuda_time:.6f} 秒")
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speedup = 0
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if cuda_time > 0:
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speedup = torch_time / cuda_time
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print(f"加速比 (Speedup): {speedup:.2f}x")
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else:
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print("CUDA 内核执行时间为0，无法计算加速比。")
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return precision_flag,speedup
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if __name__ == "__main__":
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precision_flag,speedup = run_benchmark()
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