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83 lines
3.0 KiB
Markdown
83 lines
3.0 KiB
Markdown
<!---Copyright 2026 The HuggingFace Team. All rights reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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-->
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# Kernels
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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.
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## Hub kernels
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The [Hub](https://huggingface.co/kernels-community) hosts community kernels you can load with [`KernelConfig`]. Pass the config to `kernel_config` in [`~AutoModelForCausalLM.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.
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```py
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from transformers import AutoModelForCausalLM, KernelConfig
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kernel_config = KernelConfig(
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kernel_mapping={
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"RMSNorm": "kernels-community/rmsnorm",
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}
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)
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model = AutoModelForCausalLM.from_pretrained(
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"Qwen/Qwen3-0.6B",
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use_kernels=True,
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kernel_config=kernel_config,
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)
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```
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## Liger
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[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.
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```bash
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pip install liger-kernel
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```
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Set `use_liger_kernel=True` in [`TrainingArguments`] to patch the corresponding model layers with Liger's kernels.
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> [!TIP]
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> See the [patching](https://github.com/linkedin/Liger-Kernel#patching) page for a complete list of supported models.
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```py
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from transformers import TrainingArguments
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training_args = TrainingArguments(
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...,
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use_liger_kernel=True
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)
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```
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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.
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```py
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from transformers import TrainingArguments
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training_args = TrainingArguments(
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...,
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use_liger_kernel=True,
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liger_kernel_config={
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"rope": True,
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"cross_entropy": True,
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"rms_norm": False,
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"swiglu": True,
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}
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)
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```
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## Next steps
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- See the [Attention backends](./attention_interface) guide for details on kernels like FlashAttention that reduce memory usage.
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- See the [torch.compile](./torch_compile) guide to learn how to compile the forward and backward pass for your entire training step.
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