transformers/pr_36895/en/kernels.md

# Kernels

Custom kernels target specific ops like matrix multiplications, attention, and normalization to run them faster. Fusing multiple ops into a single kernel reduces memory bandwidth usage by reading and writing GPU memory fewer times, and cuts per-op launch overhead.

## Hub kernels

The [Hub](https://huggingface.co/kernels-community) hosts community kernels you can load with [KernelConfig](/docs/transformers/pr_36895/en/main_classes/kernels#transformers.KernelConfig). Pass the config to `kernel_config` in [from_pretrained()](/docs/transformers/pr_36895/en/model_doc/auto#transformers.AutoModel.from_pretrained). Once the kernel is loaded, it's active for training. Read the [Loading kernels](./kernel_doc/loading_kernels#kernelconfig) guide for all available options.

```py
from transformers import AutoModelForCausalLM, KernelConfig

kernel_config = KernelConfig(
    kernel_mapping={
        "RMSNorm": "kernels-community/rmsnorm",
    }
)
model = AutoModelForCausalLM.from_pretrained(
    "Qwen/Qwen3-0.6B",
    use_kernels=True,
    kernel_config=kernel_config,
)
```

## Liger

[Liger Kernel](https://github.com/linkedin/Liger-Kernel) fuses layers like RMSNorm, RoPE, SwiGLU, CrossEntropy, and FusedLinearCrossEntropy into single Triton kernels. It's compatible with FlashAttention, FSDP, and DeepSpeed, and improves multi-GPU training throughput while reducing memory usage, making larger vocabularies, batch sizes, and context lengths more feasible.

```bash
pip install liger-kernel
```

Set `use_liger_kernel=True` in [TrainingArguments](/docs/transformers/pr_36895/en/main_classes/trainer#transformers.TrainingArguments) to patch the corresponding model layers with Liger's kernels.

> [!TIP]
> See the [patching](https://github.com/linkedin/Liger-Kernel#patching) page for a complete list of supported models.

```py
from transformers import TrainingArguments

training_args = TrainingArguments(
    ...,
    use_liger_kernel=True
)
```

To control which layers are patched, pass `liger_kernel_config` as a dict. Available options vary by model and include: `rope`, `swiglu`, `cross_entropy`, `fused_linear_cross_entropy`, `rms_norm`, etc.

```py
from transformers import TrainingArguments

training_args = TrainingArguments(
    ...,
    use_liger_kernel=True,
    liger_kernel_config={
        "rope": True,
        "cross_entropy": True,
        "rms_norm": False,
        "swiglu": True,
    }
)
```

## Next steps

- See the [Attention backends](./attention_interface) guide for details on kernels like FlashAttention that reduce memory usage.
- See the [torch.compile](./torch_compile) guide to learn how to compile the forward and backward pass for your entire training step.