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 chunk+gelu+elementwise_mul), or algorithmic changes (such as optimized memory access patterns). You are only limited by your imagination.
PyTorch C++/CUDA Extension: Inline compilation using torch.utils.cpp_extension.load_inline.

Custom CUDA Kernel: Implements a binary cross-entropy with logits loss directly on GPU.

Kernel Launch Configuration: Uses 256 threads per block and dynamically calculates block count (capped at 65535).

Numerically Stable Formula: Applies max(x,0) - x*y + log(1 + exp(-|x|)) for element-wise loss.

Reduction Options: Supports 'none', 'mean', and 'sum' reductions after kernel computation.

Memory Contiguity: Ensures input and target tensors are contiguous before kernel execution.






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, reduction='mean'):
        super().__init__()
        self.reduction = reduction

    def forward(self, input: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
        return F.binary_cross_entropy_with_logits(input, target, reduction=self.reduction)


batch_size = 128
feature_dim = 1


def get_inputs():
    pred = torch.randn(batch_size, feature_dim, dtype=torch.float32)
    target = torch.randint(0, 2, (batch_size, feature_dim)).float()
    return [pred, target]


def get_init_inputs():
    return ['mean']