33 lines
1.2 KiB
ReStructuredText
33 lines
1.2 KiB
ReStructuredText
|
.. _fp8_e5m2_kv_cache:
|
||
|
|
|
||
|
|
FP8 E5M2 KV Cache
|
||
|
|
==================
|
||
|
|
|
||
|
|
The int8/int4 quantization scheme requires additional scale GPU memory storage, which reduces the expected GPU memory benefits.
|
||
|
|
The FP8 data format retains 2~3 mantissa bits and can convert float/fp16/bflaot16 and fp8 to each other.
|
||
|
|
|
||
|
|
Here is an example of how to enable this feature:
|
||
|
|
|
||
|
|
.. code-block:: python
|
||
|
|
from vllm import LLM, SamplingParams
|
||
|
|
# Sample prompts.
|
||
|
|
prompts = [
|
||
|
|
"Hello, my name is",
|
||
|
|
"The president of the United States is",
|
||
|
|
"The capital of France is",
|
||
|
|
"The future of AI is",
|
||
|
|
]
|
||
|
|
# Create a sampling params object.
|
||
|
|
sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
|
||
|
|
# Create an LLM.
|
||
|
|
llm = LLM(model="facebook/opt-125m", kv_cache_dtype="fp8_e5m2")
|
||
|
|
# Generate texts from the prompts. The output is a list of RequestOutput objects
|
||
|
|
# that contain the prompt, generated text, and other information.
|
||
|
|
outputs = llm.generate(prompts, sampling_params)
|
||
|
|
# Print the outputs.
|
||
|
|
for output in outputs:
|
||
|
|
prompt = output.prompt
|
||
|
|
generated_text = output.outputs[0].text
|
||
|
|
print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
|
||
|
|
|