wehub-resource-sync
e06fe8e8c6
Self-hosted runner (benchmark) / Benchmark (aws-g5-4xlarge-cache) (push) Waiting to run
New model PR merged notification / Notify new model (push) Waiting to run
Update Transformers metadata / build_and_package (push) Waiting to run
Secret Leaks / trufflehog (push) Failing after 1s
Build documentation / build (push) Failing after 1s
Build documentation / build_other_lang (push) Failing after 0s
CodeQL Security Analysis / CodeQL Analysis (push) Failing after 0s
PR CI / pr-ci (push) Failing after 1s
Slow tests on important models (on Push - A10) / Get all modified files (push) Failing after 1s
Slow tests on important models (on Push - A10) / Model CI (push) Has been skipped
83 lines
3.0 KiB
Markdown
83 lines
3.0 KiB
Markdown
<!---Copyright 2026 The HuggingFace Team. All rights reserved.
|
|
|
|
Licensed under the Apache License, Version 2.0 (the "License");
|
|
you may not use this file except in compliance with the License.
|
|
You may obtain a copy of the License at
|
|
|
|
http://www.apache.org/licenses/LICENSE-2.0
|
|
|
|
Unless required by applicable law or agreed to in writing, software
|
|
distributed under the License is distributed on an "AS IS" BASIS,
|
|
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
See the License for the specific language governing permissions and
|
|
limitations under the License.
|
|
-->
|
|
|
|
|
|
# Kernels（自定义内核）
|
|
|
|
自定义内核针对矩阵乘法、注意力计算和归一化等特定算子进行优化，使其运行更快。将多个算子融合到单个内核中可以减少对 GPU 显存的读写次数，降低内存带宽使用，同时消除逐算子的启动开销。
|
|
|
|
## Hub 内核
|
|
|
|
[Hub](https://huggingface.co/kernels-community) 上托管了社区内核，你可以通过 [`KernelConfig`] 加载它们。将配置传入 [`~AutoModelForCausalLM.from_pretrained`] 的 `kernel_config` 参数即可。内核加载后，会在训练过程中自动激活。有关所有可用选项，请参阅[加载内核](./kernel_doc/loading_kernels#kernelconfig)指南。
|
|
|
|
```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) 将 RMSNorm、RoPE、SwiGLU、CrossEntropy 和 FusedLinearCrossEntropy 等层融合为单个 Triton 内核。它与 FlashAttention、FSDP 和 DeepSpeed 兼容，能够提升多 GPU 训练的吞吐量，同时降低显存占用，让更大的词汇量、批次大小和上下文长度变得更加可行。
|
|
|
|
```bash
|
|
pip install liger-kernel
|
|
```
|
|
|
|
在 [`TrainingArguments`] 中设置 `use_liger_kernel=True`，即可用 Liger 内核替换对应的模型层。
|
|
|
|
> [!TIP]
|
|
> 请参阅 [patching](https://github.com/linkedin/Liger-Kernel#patching) 页面获取支持的模型完整列表。
|
|
|
|
```py
|
|
from transformers import TrainingArguments
|
|
|
|
training_args = TrainingArguments(
|
|
...,
|
|
use_liger_kernel=True
|
|
)
|
|
```
|
|
|
|
要控制哪些层被替换，可以通过 `liger_kernel_config` 字典来指定。可选参数因模型而异，包括：`rope`、`swiglu`、`cross_entropy`、`fused_linear_cross_entropy`、`rms_norm` 等。
|
|
|
|
```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,
|
|
}
|
|
)
|
|
```
|
|
|
|
## 下一步
|
|
|
|
- 参阅[注意力后端](./attention_interface)指南，了解 FlashAttention 等降低显存占用的内核详情。
|
|
- 参阅 [torch.compile](./torch_compile) 指南，了解如何编译整个训练步骤的前向和反向传播。
|