# kernels.geglu – Axolotl

# kernels.geglu

`kernels.geglu`

Module for definition of GEGLU Triton kernels.

See “GLU Variants Improve Transformer” (https://arxiv.org/abs/2002.05202).

Credit to `unsloth` (https://unsloth.ai/) for inspiration for this implementation.

## Functions

Name | Description
--- | ---
geglu_backward | GEGLU backward pass using in-place operations.
geglu_forward | GEGLU forward pass.

### geglu_backward

    kernels.geglu.geglu_backward(grad_output, gate, up)

GEGLU backward pass using in-place operations.

#### Parameters

Name | Type | Description | Default
--- | --- | --- | ---
grad_output | torch.Tensor | Gradient of loss with respect to output, shape `[batch, seq_len, hidden_dim]`. | required
gate | torch.Tensor | Gate tensor from forward pass, shape `[batch, seq_len, hidden_dim]`. | required
up | torch.Tensor | Up-projection tensor from forward pass, shape `[batch, seq_len, hidden_dim]`. | required

#### Returns

Name | Type | Description
--- | --- | ---
| tuple[torch.Tensor, torch.Tensor, torch.Tensor] | Tuple containing: - GEGLU activation output (`h`) - Gradient with respect to gate (`grad_gate`) - Gradient with respect to up (`grad_up`)

#### Note

This function modifies its input tensors in-place to store results.

### geglu_forward

    kernels.geglu.geglu_forward(gate, up)

GEGLU forward pass.

#### Parameters

Name | Type | Description | Default
--- | --- | --- | ---
gate | torch.Tensor | Input gate tensor of shape [batch, seq_len, hidden_dim]. | required
up | torch.Tensor | Up-projection tensor of shape [batch, seq_len, hidden_dim]. | required

#### Returns

Name | Type | Description
--- | --- | ---
| torch.Tensor | torch.Tensor: Output tensor of shape [batch, seq_len, hidden_dim].