Inferensserver för LLaMA 3.1 405B
Vi börjar med ett grundläggande Python-exempel för att testa vår modell:
from vllm import LLM
# Load LLaMA 3.1 405B on 8 GPUs
llm = LLM("neuralmagic/Meta-Llama-3.1-405B-Instruct-FP8-dynamic", tensor_parallel_size=8)
print(llm.generate("What is the difference between fp8 quantization and int8 quantization?"))
Du kan köra Python-skriptet. Om det är första gången du kör det måste du vänta på att modellen ska laddas ned och laddas på GPU:n, och sedan får du ett svar som det här:
FP8 (Floating-Point 8) and INT8 (Integer 8) are both quantization techniques used to reduce the precision of numerical values in deep learning models, but they differ in their representation and behavior.
**INT8 Quantization**
INT8 quantization represents numbers as 8-bit integers, which can take on values between -128 and 127 (or 0 and 255 for unsigned integers). This means that the precision of the numbers is limited to 8 bits, and any values outside this range are clipped or saturated.
INT8 quantization is a simple and widely used technique, especially for integer-based architectures like ARM and x86. Most deep learning frameworks, including TensorFlow and PyTorch, support INT8 quantization.
**FP8 Quantization**
FP8 quantization, on the other hand, represents numbers as 8-bit floating-point numbers, with 1 sign bit, 2 exponent bits, and 5 mantissa bits. This allows for a much larger dynamic range than INT8, with values that can be as small as 2^-14 or as large as 2^15.
FP8 quantization is a more recent development, and its main advantage is that it can provide better accuracy than INT8 quantization, especially for models that require a large dynamic range, such as those with batch normalization or depthwise separable convolutions. FP8 is also more suitable for models that are sensitive to quantization noise, like those with recurrent neural networks (RNNs) or long short-term memory (LSTM) networks.
**Key differences**
Here are the key differences between FP8 and INT8 quantization:
1. **Dynamic range**: FP8 has a much larger dynamic range than INT8, which means it can represent a wider range of values.
2. **Precision**: FP8 has a lower precision than INT8, with 5 mantissa bits compared to 8 bits for INT8.
3. **Behavior**: FP8 is more suitable for models that require a large dynamic range, while INT8 is better suited for models with smaller weights and activations.
4. **Hardware support**: INT8 is widely supported by most hardware platforms, while FP8 is still an emerging standard, with limited hardware support.
In summary, FP8 quantization offers better accuracy and a larger dynamic range than INT8 quantization, but it requires more sophisticated hardware support and may not be suitable for all models or applications.
LLaMA 3.1 405B-modellen har redan kvantifierats i fp8 för vLLM av Neural Magic, så vi behöver inte utföra kvantifieringen igen. Vi laddar helt enkelt den kvantiserade modellen från HuggingFace Hub.
Parametern tensor_parallel_size ställs in enligt det antal GPU:er som vi har på vår maskin.
Detta enkla Python-skript är dock inte en riktig produktionsserver. Vi kommer nu att starta inferensservern för att kunna konsumera många förfrågningar parallellt och maximera genomströmningen:
python -m vllm.entrypoints.openai.api_server \
--model neuralmagic/Meta-Llama-3.1-405B-Instruct-FP8-dynamic \
--tensor-parallel-size 8
När modellen är inläst kan du starta en andra terminal och göra några förfrågningar:
curl http://localhost:8000/v1/completions \
-H "Content-Type: application/json" \
-d '{
"model": "neuralmagic/Meta-Llama-3.1-405B-Instruct-FP8-dynamic",
"prompt": "Who are you?"
}'
I'm an artificial intelligence model known as Llama. Llama stands for "Large Language Model Meta AI."
