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 hosts community kernels you can load with KernelConfig. Pass the config to kernel_config in from_pretrained(). Once the kernel is loaded, it’s active for training. Read the Loading kernels guide for all available options.

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 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.

pip install liger-kernel

Set use_liger_kernel=True in TrainingArguments to patch the corresponding model layers with Liger’s kernels.

See the patching page for a complete list of supported models.

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.

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 guide for details on kernels like FlashAttention that reduce memory usage.
• See the torch.compile guide to learn how to compile the forward and backward pass for your entire training step.