mirror of
https://github.com/huggingface/transformers.git
synced 2025-07-04 05:10:06 +06:00
163138a911
* start * start having a clean 4d mask primitive * Update mask_utils.py * Update mask_utils.py * switch name * Update masking_utils.py * add a new AttentionMask tensor class * fix import * nits * fixes * use full and quandrants * general sdpa mask for all caches * style * start some tests * tests with sliding, chunked * add styling * test hybrid * Update masking_utils.py * small temp fixes * Update modeling_gemma2.py * compile compatible * Update masking_utils.py * improve * start making it more general * Update masking_utils.py * generate * make it work with flex style primitives! * Update masking_utils.py * Update masking_utils.py * Update masking_utils.py * improve * Update cache_utils.py * Update masking_utils.py * simplify - starting to look good! * Update masking_utils.py * name * Update masking_utils.py * style * Update masking_utils.py * Update masking_utils.py * Update masking_utils.py * Update masking_utils.py * small fix for flex * flex compile * FA2 * Update masking_utils.py * Escape for TGI/vLLM! * Update masking_utils.py * Update masking_utils.py * Update masking_utils.py * General case without cache * rename * full test on llama4 * small fix for FA2 guard with chunk * Update modeling_gemma2.py * post rebase cleanup * FA2 supports static cache! * Update modeling_flash_attention_utils.py * Update flex_attention.py * Update masking_utils.py * Update masking_utils.py * Update utils.py * override for export * Update executorch.py * Update executorch.py * Update executorch.py * Update executorch.py * Update masking_utils.py * Update masking_utils.py * output attentions * style * Update masking_utils.py * Update executorch.py * Add doicstring * Add license and put mask visualizer at the end * Update test_modeling_common.py * fix broken test * Update test_modeling_gemma.py * Update test_modeling_gemma2.py * Use fullgraph=False with FA2 * Update utils.py * change name * Update masking_utils.py * improve doc * change name * Update modeling_attn_mask_utils.py * more explicit logic based on model's property * pattern in config * extend * fixes * make it better * generalize to other test models * fix * Update masking_utils.py * fix * do not check mask equivalence if layer types are different * executorch * Update modeling_gemma2.py * Update masking_utils.py * use layer_idx instead * adjust * Update masking_utils.py * test * fix imports * Update modeling_gemma2.py * other test models * Update modeling_llama4.py * Update masking_utils.py * improve * simplify * Update masking_utils.py * typos * typo * fix * Update masking_utils.py * default DynamicCache * remove default cache * simplify * Update masking_utils.py * Update masking_utils.py * Update masking_utils.py * Update masking_utils.py * simplify * Update masking_utils.py * Update masking_utils.py * Update masking_utils.py * export * Update executorch.py * Update executorch.py * Update flex_attention.py * Update executorch.py * upstream to modular gemma 1 & 2 * Update modular_mistral.py * switch names * use dict * put it in the Layer directly * update copy model source for mask functions * apply so many modular (hopefully 1 shot) * use explicite dicts for make style happy * protect import * check docstring * better default in hybrid caches * qwens * Update modular_qwen2.py * simplify core logic! * Update executorch.py * qwen3 moe * Update masking_utils.py * Update masking_utils.py * simplify a lot sdpa causal skip * Update masking_utils.py * post-rebase * gemma3 finally * style * check it before * gemma3 * More general with newer torch * align gemma3 * Update utils.py * Update utils.py * Update masking_utils.py * Update test_modeling_common.py * Update flex_attention.py * Update flex_attention.py * Update flex_attention.py * test * executorch * Update test_modeling_common.py * Update masking_utils.py * Update masking_utils.py * Update masking_utils.py * Update masking_utils.py * Update executorch.py * Update test_modeling_common.py * fix copies * device * sdpa can be used without mask -> pass the torchscript tests in this case * Use enum for check * revert enum and add check instead * remove broken test * cohere2 * some doc & reorganize the Interface * Update tensor_parallel.py * Update tensor_parallel.py * doc and dummy * Update test_modeling_paligemma2.py * Update modeling_falcon_h1.py * Update masking_utils.py * executorch patch * style * CIs * use register in executorch * final comments! --------- Co-authored-by: Arthur Zucker <arthur.zucker@gmail.com>
84 lines
2.2 KiB
Markdown
84 lines
2.2 KiB
Markdown
<!--Copyright 2020 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.
|
|
|
|
⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
|
|
rendered properly in your Markdown viewer.
|
|
|
|
-->
|
|
|
|
# Custom Layers and Utilities
|
|
|
|
This page lists all the custom layers used by the library, as well as the utility functions and classes it provides for modeling.
|
|
|
|
Most of those are only useful if you are studying the code of the models in the library.
|
|
|
|
## Layers
|
|
|
|
[[autodoc]] GradientCheckpointingLayer
|
|
|
|
## Attention Functions
|
|
|
|
[[autodoc]] AttentionInterface
|
|
- register
|
|
|
|
## Attention Mask Functions
|
|
|
|
[[autodoc]] AttentionMaskInterface
|
|
- register
|
|
|
|
## Rotary Position Embedding Functions
|
|
|
|
[[autodoc]] dynamic_rope_update
|
|
|
|
## Pytorch custom modules
|
|
|
|
[[autodoc]] pytorch_utils.Conv1D
|
|
|
|
## PyTorch Helper Functions
|
|
|
|
[[autodoc]] pytorch_utils.apply_chunking_to_forward
|
|
|
|
[[autodoc]] pytorch_utils.find_pruneable_heads_and_indices
|
|
|
|
[[autodoc]] pytorch_utils.prune_layer
|
|
|
|
[[autodoc]] pytorch_utils.prune_conv1d_layer
|
|
|
|
[[autodoc]] pytorch_utils.prune_linear_layer
|
|
|
|
## TensorFlow custom layers
|
|
|
|
[[autodoc]] modeling_tf_utils.TFConv1D
|
|
|
|
[[autodoc]] modeling_tf_utils.TFSequenceSummary
|
|
|
|
## TensorFlow loss functions
|
|
|
|
[[autodoc]] modeling_tf_utils.TFCausalLanguageModelingLoss
|
|
|
|
[[autodoc]] modeling_tf_utils.TFMaskedLanguageModelingLoss
|
|
|
|
[[autodoc]] modeling_tf_utils.TFMultipleChoiceLoss
|
|
|
|
[[autodoc]] modeling_tf_utils.TFQuestionAnsweringLoss
|
|
|
|
[[autodoc]] modeling_tf_utils.TFSequenceClassificationLoss
|
|
|
|
[[autodoc]] modeling_tf_utils.TFTokenClassificationLoss
|
|
|
|
## TensorFlow Helper Functions
|
|
|
|
[[autodoc]] modeling_tf_utils.get_initializer
|
|
|
|
[[autodoc]] modeling_tf_utils.keras_serializable
|
|
|
|
[[autodoc]] modeling_tf_utils.shape_list
|