forked from ccf-ai-infra/GPUCodeForces
53 lines
1.5 KiB
Python
53 lines
1.5 KiB
Python
import torch
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import torch.nn as nn
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class Model(nn.Module):
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"""
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Squared Euclidean Distance implementation.
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Computes the squared Euclidean distance between two sets of vectors.
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"""
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def __init__(self):
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super(Model, self).__init__()
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def forward(self, x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
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"""
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Compute squared Euclidean distance between x and y.
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Args:
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x (torch.Tensor): First set of vectors [batch_size, feature_dim]
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y (torch.Tensor): Second set of vectors [batch_size, feature_dim]
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Returns:
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torch.Tensor: Squared Euclidean distances [batch_size]
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"""
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# Input validation
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if x.shape != y.shape:
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raise ValueError(f"Input tensors must have the same shape, got {x.shape} and {y.shape}")
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if x.dim() != 2:
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raise ValueError(f"Input tensors must be 2D, got {x.dim()}D")
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# Compute squared Euclidean distance: Σ(x_i - y_i)²
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# Step 1: Compute differences
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diff = x - y
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# Step 2: Square the differences
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squared = diff ** 2
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# Step 3: Sum along feature dimension
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distance = torch.sum(squared, dim=1)
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return distance
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batch_size = 512
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feature_dim = 512
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def get_inputs():
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# Generate two sets of random vectors
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x = torch.randn(batch_size, feature_dim)
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y = torch.randn(batch_size, feature_dim)
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return [x, y]
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def get_init_inputs():
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return [] # No special initialization inputs needed
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