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9167fadab9
* GradientCheckpointingLayer * trigger * Move GC layer to a separate file * Update import * Expose and document GC layer * Fix dummy * Apply to llama-based models * Update modulars * Update a few more models for consistency * Update glm4 * Update Janus
79 lines
2.1 KiB
Markdown
79 lines
2.1 KiB
Markdown
<!--Copyright 2020 The HuggingFace Team. All rights reserved.
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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# Custom Layers and Utilities
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This page lists all the custom layers used by the library, as well as the utility functions and classes it provides for modeling.
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Most of those are only useful if you are studying the code of the models in the library.
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## Layers
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[[autodoc]] GradientCheckpointingLayer
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## Attention Functions
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[[autodoc]] AttentionInterface
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- register
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## Rotary Position Embedding Functions
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[[autodoc]] dynamic_rope_update
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## Pytorch custom modules
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[[autodoc]] pytorch_utils.Conv1D
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## PyTorch Helper Functions
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[[autodoc]] pytorch_utils.apply_chunking_to_forward
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[[autodoc]] pytorch_utils.find_pruneable_heads_and_indices
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[[autodoc]] pytorch_utils.prune_layer
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[[autodoc]] pytorch_utils.prune_conv1d_layer
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[[autodoc]] pytorch_utils.prune_linear_layer
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## TensorFlow custom layers
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[[autodoc]] modeling_tf_utils.TFConv1D
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[[autodoc]] modeling_tf_utils.TFSequenceSummary
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## TensorFlow loss functions
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[[autodoc]] modeling_tf_utils.TFCausalLanguageModelingLoss
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[[autodoc]] modeling_tf_utils.TFMaskedLanguageModelingLoss
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[[autodoc]] modeling_tf_utils.TFMultipleChoiceLoss
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[[autodoc]] modeling_tf_utils.TFQuestionAnsweringLoss
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[[autodoc]] modeling_tf_utils.TFSequenceClassificationLoss
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[[autodoc]] modeling_tf_utils.TFTokenClassificationLoss
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## TensorFlow Helper Functions
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[[autodoc]] modeling_tf_utils.get_initializer
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[[autodoc]] modeling_tf_utils.keras_serializable
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[[autodoc]] modeling_tf_utils.shape_list
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