diff --git a/README.md b/README.md new file mode 100644 index 0000000000000000000000000000000000000000..02aced680193f24f6b65ab07c90725e96d521e24 --- /dev/null +++ b/README.md @@ -0,0 +1,477 @@ +--- +tags: +- fp8 +- vllm +language: +- en +- de +- fr +- it +- pt +- hi +- es +- th +pipeline_tag: text-generation +license: llama3.1 +base_model: meta-llama/Meta-Llama-3.1-405B-Instruct +--- + +# 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 multiple languages. Similarly to [Meta-Llama-3.1-405B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3.1-405B-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:** 8/22/2024 +- **Version:** 1.1 +- **License(s):** [llama3.1](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B/blob/main/LICENSE) +- **Model Developers:** Neural Magic + +This model is a quantized version of [Meta-Llama-3.1-405B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3.1-405B-Instruct). +It was evaluated on a several tasks to assess the its quality in comparison to the unquatized model, including multiple-choice, math reasoning, and open-ended text generation. +Meta-Llama-3.1-405B-Instruct-FP8-dynamic achieves 99.0% recovery for the Arena-Hard evaluation, 100.0% for OpenLLM v1 (using Meta's prompting when available), 99.9% for OpenLLM v2, 100.2% for HumanEval pass@1, and 101.1% for HumanEval+ pass@1. + +### Model Optimizations + +This model was obtained by quantizing the weights and activations of [Meta-Llama-3.1-405B-Instruct](https://huggingface.co/meta-llama/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](https://github.com/vllm-project/llm-compressor) is used for quantization. + +## Deployment + +### Use with vLLM + +This model can be deployed efficiently using the [vLLM](https://docs.vllm.ai/en/latest/) backend, as shown in the example below. + +```python +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](https://docs.vllm.ai/en/latest/) for more details. + +## Creation + +This model was created by applying [LLM Compressor with calibration samples from UltraChat](https://github.com/vllm-project/llm-compressor/blob/sa/big_model_support/examples/big_model_offloading/big_model_w8a8_calibrate.py), as presented in the code snipet below. + +```python +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="auto" +) + +model = SparseAutoModelForCausalLM.from_pretrained( + model_stub, torch_dtype="auto", 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 + +This model was evaluated on the well-known Arena-Hard, OpenLLM v1, OpenLLM v2, HumanEval, and HumanEval+ benchmarks. +In all cases, model outputs were generated with the [vLLM](https://docs.vllm.ai/en/stable/) engine. + +Arena-Hard evaluations were conducted using the [Arena-Hard-Auto](https://github.com/lmarena/arena-hard-auto) repository. +The model generated a single answer for each prompt form Arena-Hard, and each answer was judged twice by GPT-4. +We report below the scores obtained in each judgement and the average. + +OpenLLM v1 and v2 evaluations were conducted using Neural Magic's fork of [lm-evaluation-harness](https://github.com/neuralmagic/lm-evaluation-harness/tree/llama_3.1_instruct) (branch llama_3.1_instruct). +This version of the lm-evaluation-harness includes versions of MMLU, ARC-Challenge and GSM-8K that match the prompting style of [Meta-Llama-3.1-Instruct-evals](https://huggingface.co/datasets/meta-llama/Meta-Llama-3.1-405B-Instruct-evals) and a few fixes to OpenLLM v2 tasks. + +HumanEval and HumanEval+ evaluations were conducted using Neural Magic's fork of the [EvalPlus](https://github.com/neuralmagic/evalplus) repository. + +Detailed model outputs are available as HuggingFace datasets for [Arena-Hard](https://huggingface.co/datasets/neuralmagic/quantized-llama-3.1-arena-hard-evals), [OpenLLM v2](https://huggingface.co/datasets/neuralmagic/quantized-llama-3.1-leaderboard-v2-evals), and [HumanEval](https://huggingface.co/datasets/neuralmagic/quantized-llama-3.1-humaneval-evals). + +### Accuracy + +
Benchmark + | +Meta-Llama-3.1-405B-Instruct + | +Meta-Llama-3.1-405B-Instruct-FP8-dynamic (this model) + | +Recovery + | +
Arena Hard + | +67.4 (67.3 / 67.5) + | +66.7 (66.7 / 66.6) + | +99.0% + | +
OpenLLM v1 + | +|||
MMLU (5-shot) + | +87.4 + | +87.5 + | +100.0% + | +
MMLU-cot (0-shot) + | +88.1 + | +88.1 + | +100.0% + | +
ARC Challenge (0-shot) + | +95.0 + | +95.0 + | +100.0% + | +
GSM-8K-cot (8-shot, strict-match) + | +96.0 + | +95.8 + | +99.8% + | +
Hellaswag (10-shot) + | +88.5 + | +88.5 + | +99.9% + | +
Winogrande (5-shot) + | +87.2 + | +88.0 + | +100.9% + | +
TruthfulQA (0-shot, mc2) + | +65.3 + | +65.3 + | +99.9% + | +
Average + | +86.8 + | +86.9 + | +100.0% + | +
OpenLLM v2 + | +|||
MMLU-Pro (5-shot) + | +59.7 + | +59.4 + | +99.4% + | +
IFEval (0-shot) + | +87.7 + | +86.8 + | +99.0% + | +
BBH (3-shot) + | +67.0 + | +67.1 + | +100.1% + | +
Math-|v|-5 (4-shot) + | +39.0 + | +38.8 + | +99.7% + | +
GPQA (0-shot) + | +19.5 + | +19.0 + | +97.4% + | +
MuSR (0-shot) + | +19.5 + | +20.8 + | +106.9% + | +
Average + | +48.7 + | +48.7 + | +99.9% + | +
Coding + | +|||
HumanEval pass@1 + | +86.8 + | +87.0 + | +100.2% + | +
HumanEval+ pass@1 + | +80.1 + | +81.0 + | +101.1% + | +
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