metadata

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: 7/24/2024
Version: 1.0
License(s): llama3.1
Model Developers: Neural Magic

Quantized version of Meta-Llama-3.1-405B-Instruct. It achieves an average recovery of 100.1% on the OpenLLM benchmark (version 1), compared to the unquantized model.

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, max_model_len=4096)

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)	*	86.17	*
ARC Challenge (25-shot)	*	*	*
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)	64.64	64.71	100.1%
Average	*	*	100.1%