You write custom CUDA kernels to replace the pytorch operators in the given architecture to get speedups.

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.

Here’s an example to show you the syntax of inline embedding custom CUDA operators in torch: The example given architecture is:



import torch
import torch.nn as nn
import torch.nn.functional as F

class Model(nn.Module):
    def __init__(self) -> None:
        super().__init__()

    def forward(self, a, b):
        return a + b

def get_inputs():
    # randomly generate input tensors based on the model architecture
    a = torch.randn(1, 128).cuda()
    b = torch.randn(1, 128).cuda()
    return [a, b]

def get_init_inputs():
    # randomly generate tensors required for initialization based on the model architecture
    return []
The example new arch with custom CUDA kernels looks like this:



import torch
import torch.nn as nn
import torch.nn.functional as F

class Model(nn.Module):
    def __init__(self) -> None:
        super().__init__()

    def forward(self, a, b):
        return a + b

def get_inputs():
    # randomly generate input tensors based on the model architecture
    a = torch.randn(1, 128).cuda()
    b = torch.randn(1, 128).cuda()
    return [a, b]

def get_init_inputs():
    # randomly generate tensors required for initialization based on the model architecture
    return []
You are given the following architecture:



import torch
import torch.nn as nn

class Model(nn.Module):
    """
    Baseline: Cosine Similarity + Contrastive Loss
    """
    def __init__(self, margin=0.5):
        super(Model, self).__init__()
        self.margin = margin
    
    def forward(self, x: torch.Tensor, y: torch.Tensor, labels: torch.Tensor) -> torch.Tensor:
        # 1. Compute cosine similarity
        norm_x = torch.sqrt(torch.sum(x * x, dim=1, keepdim=True))
        norm_y = torch.sqrt(torch.sum(y * y, dim=1, keepdim=True))
        dot_product = torch.sum(x * y, dim=1, keepdim=True)
        cosine_sim = dot_product / (norm_x * norm_y + 1e-8)
        cosine_sim = cosine_sim.squeeze(1)

        # 2. Compute contrastive loss
        loss_positive = labels * (1 - cosine_sim)
        loss_negative = (1 - labels) * torch.relu(cosine_sim - self.margin)
        loss = loss_positive + loss_negative
        
        return torch.sum(loss)

batch_size = 1024
feature_dim = 512

def get_inputs():
    x = torch.randn(batch_size, feature_dim)
    y = torch.randn(batch_size, feature_dim)
    # Generate binary labels (0 or 1)
    labels = torch.randint(0, 2, (batch_size,)).float()
    return [x, y, labels]

def get_init_inputs():
    return []
IMPORTANT: The current architecture involves two distinct stages: a cosine similarity calculation followed by a contrastive loss computation. This creates an intermediate tensor (cosine_sim) that incurs memory overhead and multiple kernel launches. The primary optimization goal is operator fusion: combine the cosine similarity and contrastive loss into a single, highly efficient CUDA kernel. This fusion should eliminate the intermediate tensor, reduce memory traffic, and minimize kernel launch overhead. Furthermore, you are encouraged to integrate advanced algorithmic innovations, such as the sampling-based approximation with statistical correction, into the fused kernel to achieve substantial performance gains while maintaining numerical accuracy. Focus on creating a novel approach that is fundamentally different from simple element-wise or vectorized PyTorch operations.