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--- |
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language: |
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- en |
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library_name: transformers |
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pipeline_tag: text-generation |
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tags: |
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- facebook |
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- meta |
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- pytorch |
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- llama |
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- llama-2 |
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model-index: |
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- name: LayerSkip Llama2 7B |
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results: |
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- task: |
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type: question-answering |
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dataset: |
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type: google/boolq |
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name: BoolQ |
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metrics: |
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- name: acc |
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type: acc |
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value: 0.776 |
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verified: false |
|
- task: |
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type: question-answering |
|
dataset: |
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type: ybisk/piqa |
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name: PIQA |
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metrics: |
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- name: acc |
|
type: acc |
|
value: 0.775 |
|
verified: false |
|
- task: |
|
type: question-answering |
|
dataset: |
|
type: allenai/social_i_qa |
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name: SIQA |
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metrics: |
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- name: acc |
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type: acc |
|
value: 0.454 |
|
verified: false |
|
- task: |
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type: text-generation |
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dataset: |
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type: Rowan/hellaswag |
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name: HellaSwag |
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metrics: |
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- name: acc |
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type: acc |
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value: 0.567 |
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verified: false |
|
- task: |
|
type: question-answering |
|
dataset: |
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type: allenai/winogrande |
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name: WinoGrande |
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metrics: |
|
- name: acc |
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type: acc |
|
value: 0.701 |
|
verified: false |
|
- task: |
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type: question-answering |
|
dataset: |
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type: allenai/ai2_arc |
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name: ARC (Easy) |
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metrics: |
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- name: acc |
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type: acc |
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value: 0.765 |
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verified: false |
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- task: |
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type: question-answering |
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dataset: |
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type: allenai/ai2_arc |
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name: ARC (Challenge) |
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metrics: |
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- name: acc |
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type: acc |
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value: 0.437 |
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verified: false |
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- task: |
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type: question-answering |
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dataset: |
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type: allenai/openbookqa |
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name: OpenBookQA |
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metrics: |
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- name: acc |
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type: acc |
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value: 0.328 |
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verified: false |
|
- task: |
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type: question-answering |
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dataset: |
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type: ehovy/race |
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name: RACE |
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metrics: |
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- name: acc |
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type: acc |
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value: 0.389 |
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verified: false |
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- task: |
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type: question-answering |
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dataset: |
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type: cais/mmlu |
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name: MMLU |
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metrics: |
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- name: acc |
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type: acc |
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value: 0.376 |
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verified: false |
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- task: |
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type: text-generation |
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dataset: |
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type: google-research-datasets/nq_open |
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name: Natural Questions |
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metrics: |
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- name: exact_match |
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type: exact_match |
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value: 0.156 |
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verified: false |
|
- task: |
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type: question-answering |
|
dataset: |
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type: sentence-transformers/trivia-qa |
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name: TriviaQA |
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metrics: |
|
- name: acc |
|
type: acc |
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value: 0.529 |
|
verified: false |
|
- task: |
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type: text-generation |
|
dataset: |
|
type: openai/gsm8k |
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name: GSM8K |
|
metrics: |
|
- name: exact_match |
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type: exact_match |
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value: 0.121 |
|
verified: false |
|
- task: |
|
type: question-answering |
|
dataset: |
|
type: allenai/math_qa |
|
name: MathQA |
|
metrics: |
|
- name: acc |
|
type: acc |
|
value: 0.276 |
|
verified: false |
|
- task: |
|
type: question-answering |
|
dataset: |
|
type: rajpurkar/squad_v2 |
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name: SQuAD2.0 |
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metrics: |
|
- name: exact |
|
type: exact |
|
value: 0.164 |
|
verified: false |
|
- task: |
|
type: text-classification |
|
dataset: |
|
type: toxigen/toxigen-data |
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name: ToxiGen |
|
metrics: |
|
- name: acc |
|
type: acc |
|
value: 0.428 |
|
verified: false |
|
- task: |
|
type: text-generation |
|
dataset: |
|
type: openai_humaneval |
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name: HumanEval |
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metrics: |
|
- name: pass@1 |
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type: pass@1 |
|
value: 0.134 |
|
verified: false |
|
- task: |
|
type: text-generation |
|
dataset: |
|
type: mbpp |
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name: MBPP |
|
metrics: |
|
- name: pass@1 |
|
type: pass@1 |
|
value: 0.19 |
|
verified: false |
|
license: other |
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license_name: fair |
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license_link: LICENSE |
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base_model: meta-llama/Llama-2-7b-hf |
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--- |
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# LayerSkip Llama2 7B |
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Llama2 7B model continually pretrained with LayerSkip as presented in [Layer Skip: Enabling Early Exit Inference and Self-Speculative Decoding |
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](https://arxiv.org/abs/2404.16710) and is capable of performing self-speculative decoding: decode with earlier layers and verify with remaining layers. |
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## How to Use |
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We are providing 3 ways to run the model |
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- [HuggingFace](#huggingface) |
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- [LayerSkip Codebase](#layerskip-codebase) |
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- [gpt-fast](#gpt-fast) |
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### HuggingFace<a name="huggingface"></a> |
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HuggingFace does not yet have self-speculative decoding support. However, we can re-use it's speculative decoding feature by creating a draft model using a subset of the layers of the main model: |
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```python |
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>>> from transformers import AutoModelForCausalLM, AutoTokenizer |
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>>> import torch |
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>>> from copy import deepcopy |
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>>> checkpoint = "facebook/layerskip-llama2-7B" |
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>>> early_exit = 4 |
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>>> device = "cuda" if torch.cuda.is_available() else "cpu" |
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>>> prompt = "typing import List\ndef bucket_sort(A: List):" |
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>>> model = AutoModelForCausalLM.from_pretrained(checkpoint) |
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>>> tokenizer = AutoTokenizer.from_pretrained(checkpoint) |
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>>> generation_config = model.generation_config |
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>>> weights_memo = {id(w): w for w in model.parameters()} |
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>>> assistant_model = deepcopy(model, memo=weights_memo) # Clone main model with shared weights |
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>>> assistant_model.model.layers = assistant_model.model.layers[:early_exit] # Apply early exit |
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>>> del assistant_model.model.layers[early_exit:] |
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>>> model.to(device) |
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>>> assistant_model.to(device) |
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>>> inputs = tokenizer(prompt, return_tensors="pt").to(device) |
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>>> outputs = model.generate(**inputs, assistant_model=assistant_model, generation_config=generation_config) |
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>>> print(tokenizer.batch_decode(outputs, skip_special_tokens=True)[0]) |
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``` |
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Please note that this is not an optimal implementation as it requires more memory to save KV cache and activations of duplicated layers. The optimized implementation that re-uses earlier layers is in our [custom implementation](#layerskip-codebase) or in our [gpt-fast implementation](#gpt-fast). |
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<details> |
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<summary>Benchmark</summary> |
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If you would like to measure the speedup between self-speculative decoding and autoregressive decoding, we have written this script: |
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```python |
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from transformers import LlamaForCausalLM, LlamaConfig, LlamaTokenizer, GenerationConfig |
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import torch |
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from copy import deepcopy |
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from time import time |
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from tqdm import tqdm |
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prompt = "typing import List\ndef bucket_sort(A: List):" |
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checkpoint = "facebook/layerskip-llama2-7B" |
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early_exit = 4 |
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device = "cuda" if torch.cuda.is_available() else "cpu" |
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max_new_tokens = 512 |
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do_sample = True |
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top_p = 0.9 |
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temperature = 0.6 |
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warmup = 2 |
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repeat = 10 |
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config = LlamaConfig.from_pretrained(checkpoint) |
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model = LlamaForCausalLM.from_pretrained(checkpoint, config=config, torch_dtype=torch.float16) |
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|
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# Draft model |
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# Clone main model with shared weights |
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weights_memo = {id(w): w for w in model.parameters()} |
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assistant_model = deepcopy(model, memo=weights_memo) |
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# Create early exit version |
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assistant_model.model.layers = assistant_model.model.layers[:early_exit] |
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del assistant_model.model.layers[early_exit:] |
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model.to(device) |
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assistant_model.to(device) |
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tokenizer = LlamaTokenizer.from_pretrained(checkpoint, use_fast=False) |
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inputs = tokenizer(prompt, return_tensors="pt").to(device) |
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generation_config = { |
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"max_new_tokens": max_new_tokens, |
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"do_sample": do_sample, |
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"top_p": top_p, |
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"temperature": temperature, |
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"pad_token_id": tokenizer.eos_token_id, |
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} |
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|
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# Warmup |
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print("Warmup") |
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for i in tqdm(range(warmup)): |
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_ = model.generate(**inputs, **generation_config) |
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_ = model.generate(**inputs, **generation_config, assistant_model=assistant_model) |
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|
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print("Autoregressive Decoding") |
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total_time = 0 |
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total_tokens = 0 |
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for i in tqdm(range(repeat)): |
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start = time() |
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outputs = model.generate(**inputs, **generation_config) |
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total_time += time() - start |
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total_tokens += outputs.numel() |
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print(tokenizer.batch_decode(outputs, skip_special_tokens=True)[0]) |
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print("\n\t=========================") |
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print(f"\tAverage Generation Time: {total_time / repeat:.2f} s") |
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print(f"\tAverage Tokens per Second: {total_tokens / total_time:.2f} tokens per sec\n\n") |
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|
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print("Self-Speculative Decoding") |
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total_time = 0 |
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total_tokens = 0 |
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for i in tqdm(range(repeat)): |
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start = time() |
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outputs = model.generate(**inputs, **generation_config, assistant_model=assistant_model) |
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total_time += time() - start |
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total_tokens += outputs.numel() |
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print(tokenizer.batch_decode(outputs, skip_special_tokens=True)[0]) |
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print("\n\t=========================") |
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print(f"\tAverage Generation Time: {total_time / repeat:.2f} s") |
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print(f"\tAverage Tokens per Second: {total_tokens / total_time:.2f} tokens per sec\n\n") |
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``` |
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Running this script on a single A100 NVIDIA GPU with `transformers==4.34.1`, `torch==2.2.1`, `triton==2.2.0`, we obtain: |
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``` |
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Autoregressive Decoding |
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========================= |
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Average Generation Time: 12.60 s |
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Average Tokens per Second: 34.87 tokens per sec |
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Self-Speculative Decoding |
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========================= |
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Average Generation Time: 7.38 s |
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Average Tokens per Second: 56.10 tokens per sec |
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``` |
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</details> |
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|
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### LayerSkip Codebase<a name="custom"></a> |
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Our self-speculative decoding implementation at [github.com/facebookresearch/LayerSkip](https://github.com/facebookresearch/LayerSkip) has an optimized version that does not consume extra memory and re-uses the weights and KV cache of earlier layers in both draft and verification stages. |
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To run: |
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```console |
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> git clone git@github.com:facebookresearch/LayerSkip.git |
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> cd LayerSkip |
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> conda create --name layer_skip python=3.10 |
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> conda activate layer_skip |
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> pip install -r requirements.txt |
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> torchrun generate.py --model facebook/layerskip-llama2-7B --generation_strategy self_speculative --exit_layer 6 --num_speculations 4 |
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``` |
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You can find more details in the GitHub repo for more options and scripts. |
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### gpt-fast<a name="gpt-fast"></a> |
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We have also implemented self-speculative decoding as a [separatae branch in PyTorch's gpt-fast](https://github.com/pytorch-labs/gpt-fast/tree/LayerSkip?tab=readme-ov-file#self-speculative-sampling) if you would to stack our solution on top of other optimizations like `torch.compile()` and quantization. Our gpt-fast implementation is optimized as it does not consume extra memory and re-uses the weights and KV cache of earlier layers in both draft and verification stages. |
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To run: |
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```console |
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> git clone git@github.com:pytorch-labs/gpt-fast.git -b LayerSkip |
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> cd gpt-fast |
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> conda create --name gpt_fast python=3.10 |
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> conda activate gpt_fast |
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> # Install PyTorch (check [here](https://pytorch.org/get-started/locally/) for other hardwares and operating systems) |
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> pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121 |
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> pip install sentencepiece huggingface_hub tiktoken |
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> mkdir checkpoints |
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> MODEL_REPO=facebook/layerskip-llama2-7B |
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> ./scripts/prepare.sh $MODEL_REPO |
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> python generate.py --compile --checkpoint_path checkpoints/$MODEL_REPO/model.pth --top_k 100 --temperature 0.6 --self_speculative --early_exit 5 --speculate_k 3 |
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``` |
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<details> |
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<summary>Benchmark</summary> |
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- Autoregressive decoding: |
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|
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```console |
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> python generate.py --compile --checkpoint_path checkpoints/$MODEL_REPO/model.pth --top_k 100 --temperature 0.6 |
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========== |
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Average tokens/sec: 110.50 |
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Memory used: 13.88 GB |
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``` |
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|
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- Self-speculative decoding: |
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|
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```console |
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> python generate.py --compile --checkpoint_path checkpoints/$MODEL_REPO/model.pth --top_k 100 --temperature 0.6 --self_speculative --early_exit 5 --speculate_k 3 |
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========== |
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{'tokens_per_sec': [120.16508373150057, 141.77910376715855, 132.42363092761354, 138.73840444421148, 121.55019835742718], 'accept_counts': [[32, 15, 19, 20], [50, 23, 21, 10], [31, 22, 16, 19], [41, 19, 19, 16], [35, 20, 15, 20], [47, 32, 9, 16]]} |
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Acceptance probs: [0.41622574955908287, 0.2310405643738977, 0.1746031746031746, 0.1781305114638448] |
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Mean Accepted: 1.1146384479717812 |
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Average tokens/sec: 130.93 |
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Memory used: 13.91 GB |
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``` |
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</details> |
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|
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## Training |
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Our training implementation is work-in-progress. You can check this [pull request](https://github.com/pytorch/torchtune/pull/1076) for details and discussions. |
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## Evaluation |
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We have provided evaluation results on various natural language and codinng tasks in the Model Card. You can view them on the top right hand-side bar on the screen. |
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The numbers reported in this Model Card were evaluated using [Eluether Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness) and [BigCode Evaluation Harness](https://github.com/bigcode-project/bigcode-evaluation-harness), while the numbers provided in our paper were evaluated using Meta's internal codebase. |
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## Issues |
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Please report any software "bug", or other problems with the models through one of the following means: |
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- Reporting issues with the model: [https://github.com/facebookresearch/LayerSkip/issues](https://github.com/facebookresearch/LayerSkip/issues) |
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- Reporting risky content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback) |
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- Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info) |
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## License |
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|
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See the [LICENSE](LICENSE) file. |
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