Quantized KV Cache (#30483)

* clean-up

* Update src/transformers/cache_utils.py

Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>

* Update src/transformers/cache_utils.py

Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>

* Update src/transformers/cache_utils.py

Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>

* fixup

* Update tests/quantization/quanto_integration/test_quanto.py

Co-authored-by: Younes Belkada <49240599+younesbelkada@users.noreply.github.com>

* Update src/transformers/generation/configuration_utils.py

Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>

* more suggestions

* mapping if torch available

* run tests & add 'support_quantized' flag

* fix jamba test

* revert, will be fixed by another PR

* codestyle

* HQQ and versatile cache classes

* final update

* typo

* make tests happy

---------

Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
Co-authored-by: Younes Belkada <49240599+younesbelkada@users.noreply.github.com>

docs/source/en/generation_strategies.md

@@ -174,6 +174,43 @@ An increasing sequence: one, two, three, four, five, six, seven, eight, nine, te
 ```
 
 
+## KV Cache Quantization
+
+The `generate()` method supports caching keys and values to enhance efficiency and avoid re-computations. However the key and value
+cache can occupy a large portion of memory, becoming a bottleneck for long-context generation, especially for Large Language Models.
+Quantizing the cache when using `generate()` can significantly reduce memory requirements at the cost of speed. 
+
+KV Cache quantization in `transformers` is largely inspired by the paper [KIVI: A Tuning-Free Asymmetric 2bit Quantization for KV Cache]
+(https://arxiv.org/abs/2402.02750) and currently supports `quanto` and `HQQ` as backends. For more information on the inner workings see the paper.
+
+To enable quantization of the key-value cache, one needs to indicate `cache_implementation="quantized"` in the `generation_config`.
+Quantization related arguments should be passed to the `generation_config` either as a `dict` or an instance of a [`QuantizedCacheConfig`] class.
+One has to indicate which quantization backend to use in the [`QuantizedCacheConfig`], the default is `quanto`.
+
+<Tip warning={true}>
+
+Cache quantization can be detrimental if the context length is short and there is enough GPU VRAM available to run without cache quantization.
+
+</Tip>
+
+
+```python
+>>> import torch
+>>> from transformers import AutoTokenizer, AutoModelForCausalLM
+
+>>> tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-2-7b-chat-hf")
+>>> model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-2-7b-chat-hf", torch_dtype=torch.float16).to("cuda:0")
+>>> inputs = tokenizer("I like rock music because", return_tensors="pt").to(model.device)
+
+>>> out = model.generate(**inputs, do_sample=False, max_new_tokens=20, cache_implementation="quantized", cache_config={"nbits": 4, "backend": "quanto"})
+>>> print(tokenizer.batch_decode(out, skip_special_tokens=True)[0])
+I like rock music because it's loud and energetic. It's a great way to express myself and rel
+
+>>> out = model.generate(**inputs, do_sample=False, max_new_tokens=20)
+>>> print(tokenizer.batch_decode(out, skip_special_tokens=True)[0])
+I like rock music because it's loud and energetic. I like to listen to it when I'm feeling
+```
+
 ## Watermarking
 
 The `generate()` supports watermarking the generated text by randomly marking a portion of tokens as "green".