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30 changes: 0 additions & 30 deletions docs/source/en/llm_optims.md
Original file line number Diff line number Diff line change
Expand Up @@ -183,36 +183,6 @@ text
'My favorite all time favorite condiment is ketchup. I love it on everything. I love it on my eggs, my fries, my chicken, my burgers, my hot dogs, my sandwiches, my salads, my p']
```

</hfoption>
<hfoption id="3. compile entire generate function">

Compiling the entire [`~GenerationMixin.generate`] function also compiles the input preparation logit processor operations, and more, in addition to the forward pass. With this approach, you don't need to initialize [`StaticCache`] or set the [cache_implementation](https://hf.co/docs/transformers/main_classes/text_generation#transformers.GenerationConfig.cache_implementation) parameter.

```py
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
import os
os.environ["TOKENIZERS_PARALLELISM"] = "false" # To prevent long warnings :)

tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b")
model = AutoModelForCausalLM.from_pretrained("google/gemma-2b", dtype="auto", device_map="auto")

model.generate = torch.compile(model.generate, mode="reduce-overhead", fullgraph=True)
input_text = "The theory of special relativity states "
input_ids = tokenizer(input_text, return_tensors="pt").to(model.device.type)

outputs = model.generate(**input_ids)
print(tokenizer.batch_decode(outputs, skip_special_tokens=True))
['The theory of special relativity states 1. The speed of light is constant in all inertial reference']
```

This usage pattern is more appropriate for unique hardware or use cases, but there are several drawbacks to consider.

1. Compilation is much slower.
2. Parameters must be configured through [`GenerationConfig`].
3. Many warnings and exceptions are suppressed. We recommend testing the uncompiled model first.
4. Many features are unavailable at the moment. For example, generation does not stop if an `EOS` token is selected.

</hfoption>
</hfoptions>

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