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*This model was released on 2020-01-13 and added to Hugging Face Transformers on 2020-11-16 and contributed by [patrickvonplaten](https://huggingface.co/patrickvonplaten).*

# Reformer

[Reformer: The Efficient Transformer](https://huggingface.co/papers/2001.04451) introduces techniques to enhance Transformer efficiency. It replaces dot-product attention with locality-sensitive hashing, reducing complexity from O(L^2) to O(Llog(L)). Additionally, it employs reversible residual layers, enabling memory savings by storing activations once during training. This results in a model that matches Transformer performance while being more memory-efficient and faster on long sequences.

<hfoptions id="usage">
<hfoption id="Pipeline">

```py
import torch
from transformers import pipeline

pipeline = pipeline(task="text-generation", model="google/reformer-crime-and-punishment", dtype="auto")
pipeline("Plants create energy through a process known as photosynthesis.")
```

</hfoption>
<hfoption id="AutoModel">

```py
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("google/reformer-crime-and-punishment", dtype="auto")
tokenizer = AutoTokenizer.from_pretrained("google/reformer-crime-and-punishment")

inputs = tokenizer("Plants create energy through a process known as photosynthesis.", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0]))
```

</hfoption>
</hfoptions>

## Usage tips

- Reformer doesn't work with `torch.nn.DataParallel` due to a bug in PyTorch. See [issue #36035](https://github.com/pytorch/pytorch/issues/36035).
- Use Axial position encoding to avoid huge positional encoding matrices. It factorizes them into smaller matrices for long sequences.
- Replace traditional attention with LSH (locality-sensitive hashing) attention. This avoids computing the full query-key product in attention layers.
- Use reversible transformer layers to avoid storing intermediate results. Get them during the backward pass by subtracting residuals from the next layer's input, or recompute them (less efficient but saves memory).
- Compute feedforward operations by chunks, not on the whole batch.
- During training, set sequence length to a value divisible by the least common multiple of `config.lsh_chunk_length` and `config.local_chunk_length`. Set Axial Positional Encoding parameters correctly. Reformer is memory-efficient and can train on sequences up to 64,000 tokens.

## ReformerConfig

[[autodoc]] ReformerConfig

## ReformerTokenizer

[[autodoc]] ReformerTokenizer
    - save_vocabulary

## ReformerTokenizerFast

[[autodoc]] ReformerTokenizerFast

## ReformerModel

[[autodoc]] ReformerModel
    - forward

## ReformerModelWithLMHead

[[autodoc]] ReformerModelWithLMHead
    - forward

## ReformerForMaskedLM

[[autodoc]] ReformerForMaskedLM
    - forward

## ReformerForSequenceClassification

[[autodoc]] ReformerForSequenceClassification
    - forward

## ReformerForQuestionAnswering

[[autodoc]] ReformerForQuestionAnswering
    - forward

