tags:
- fp8
- vllm
license: llama3.1
license_link: https://huggingface.co/meta-llama/Meta-Llama-3.1-8B/blob/main/LICENSE
language:
- en
Meta-Llama-3.1-405B-Instruct-FP8-dynamic
Model Overview
- Model Architecture: Meta-Llama-3.1
- Input: Text
- Output: Text
- Model Optimizations:
- Weight quantization: FP8
- Activation quantization: FP8
- Intended Use Cases: Intended for commercial and research use in English. Similarly to Meta-Llama-3.1-8B-Instruct, this models is intended for assistant-like chat.
- Out-of-scope: Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in languages other than English.
- Release Date: 7/23/2024
- Version: 1.0
- License(s): llama3.1
- Model Developers: Neural Magic
Quantized version of Meta-Llama-3.1-405B-Instruct. It achieves an average score of 78.69 on the OpenLLM benchmark (version 1), whereas the unquantized model achieves 78.67.
Model Optimizations
This model was obtained by quantizing the weights and activations of Meta-Llama-3.1-405B-Instruct to FP8 data type, ready for inference with vLLM built from source. This optimization reduces the number of bits per parameter from 16 to 8, reducing the disk size and GPU memory requirements by approximately 50%. In particular, this model can now be loaded and evaluated with a single node of 8xH100 GPUs, as opposed to multiple nodes.
Only the weights and activations of the linear operators within transformers blocks are quantized. Symmetric per-channel quantization is applied, in which a linear scaling per output dimension maps the FP8 representations of the quantized weights and activations. Activations are also quantized on a per-token dynamic basis. LLM Compressor is used for quantization with 512 sequences of UltraChat.
Deployment
Use with vLLM
This model can be deployed efficiently using the vLLM backend, as shown in the example below.
from vllm import LLM, SamplingParams
from transformers import AutoTokenizer
model_id = "neuralmagic/Meta-Llama-3.1-405B-Instruct-FP8-dynamic"
number_gpus = 8
sampling_params = SamplingParams(temperature=0.6, top_p=0.9, max_tokens=256)
tokenizer = AutoTokenizer.from_pretrained(model_id)
messages = [
{"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"},
{"role": "user", "content": "Who are you?"},
]
prompts = tokenizer.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)
llm = LLM(model=model_id, tensor_parallel_size=number_gpus)
outputs = llm.generate(prompts, sampling_params)
generated_text = outputs[0].outputs[0].text
print(generated_text)
vLLM aslo supports OpenAI-compatible serving. See the documentation for more details.
Creation
This model was created by applying LLM Compressor with calibration samples from UltraChat, as presented in the code snipet below.
import torch
from transformers import AutoTokenizer
from llmcompressor.transformers import SparseAutoModelForCausalLM, oneshot
from llmcompressor.transformers.compression.helpers import ( # noqa
calculate_offload_device_map,
custom_offload_device_map,
)
recipe = """
quant_stage:
quant_modifiers:
QuantizationModifier:
ignore: ["lm_head"]
config_groups:
group_0:
weights:
num_bits: 8
type: float
strategy: channel
dynamic: false
symmetric: true
input_activations:
num_bits: 8
type: float
strategy: token
dynamic: true
symmetric: true
targets: ["Linear"]
"""
model_stub = "meta-llama/Meta-Llama-3.1-405B-Instruct"
model_name = model_stub.split("/")[-1]
device_map = calculate_offload_device_map(
model_stub, reserve_for_hessians=False, num_gpus=8, torch_dtype=torch.float16
)
model = SparseAutoModelForCausalLM.from_pretrained(
model_stub, torch_dtype=torch.float16, device_map=device_map
)
output_dir = f"./{model_name}-FP8-dynamic"
oneshot(
model=model,
recipe=recipe,
output_dir=output_dir,
save_compressed=True,
tokenizer=AutoTokenizer.from_pretrained(model_stub),
)
Evaluation
The model was evaluated on the OpenLLM leaderboard tasks (version 1) with the lm-evaluation-harness and the vLLM engine, using the following command:
lm_eval \
--model vllm \
--model_args pretrained="neuralmagic/Meta-Llama-3.1-405B-Instruct-FP8-dynamic",dtype=auto,tensor_parallel_size=8,gpu_memory_utilization=0.755,add_bos_token=True,max_model_len=4096 \
--tasks openllm \
--batch_size auto
Certain benchmarks for the full precision model are still being acquired. Average recovery is calculated only with metrics that both models have been evaluated on.
Accuracy
Open LLM Leaderboard evaluation scores
| Benchmark | Meta-Llama-3.1-405B-Instruct | Meta-Llama-3.1-405B-Instruct-FP8-dynamic(this model) | Recovery |
| MMLU (5-shot) | * | 88.34 | * |
| ARC Challenge (25-shot) | 73.38 | 72.61 | 98.95% |
| GSM-8K (5-shot, strict-match) | 95.07 | 95.00 | 99.93% |
| Hellaswag (10-shot) | * | 88.34 | * |
| Winogrande (5-shot) | 87.21 | 87.45 | 100.2% |
| TruthfulQA (0-shot) | * | 64.71 | * |
| Average | * | 82.38 | 98.95% |