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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: ```python >>> from transformers import AutoModelForCausalLM, AutoTokenizer >>> import torch >>> from copy import deepcopy >>> checkpoint = "facebook/layerskip-llama2-13B" >>> early_exit = 8 >>> device = "cuda" if torch.cuda.is_available() else "cpu" >>> prompt = "typing import List\ndef bucket_sort(A: List):" >>> model = AutoModelForCausalLM.from_pretrained(checkpoint) >>> tokenizer = AutoTokenizer.from_pretrained(checkpoint) >>> generation_config = model.generation_config >>> weights_memo = {id(w): w for w in model.parameters()} >>> assistant_model = deepcopy(model, memo=weights_memo) # Clone main model with shared weights >>> assistant_model.model.layers = assistant_model.model.layers[:early_exit] # Apply early exit >>> del assistant_model.model.layers[early_exit:] >>> model.to(device) >>> assistant_model.to(device) >>> inputs = tokenizer(prompt, return_tensors="pt").to(device) >>> outputs = model.generate(**inputs, assistant_model=assistant_model, generation_config=generation_config) >>> print(tokenizer.batch_decode(outputs, skip_special_tokens=True)[0]) ``` 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).
Benchmark If you would like to measure the speedup between self-speculative decoding and autoregressive decoding, we have written this script: ```python from transformers import LlamaForCausalLM, LlamaConfig, LlamaTokenizer, GenerationConfig import torch from copy import deepcopy from time import time from tqdm import tqdm prompt = "typing import List\ndef bucket_sort(A: List):" checkpoint = "facebook/layerskip-llama2-13B" early_exit = 8 device = "cuda" if torch.cuda.is_available() else "cpu" max_new_tokens = 512 do_sample = True top_p = 0.9 temperature = 0.6 warmup = 2 repeat = 10 config = LlamaConfig.from_pretrained(checkpoint) model = LlamaForCausalLM.from_pretrained(checkpoint, config=config, torch_dtype=torch.float16) # Draft model # Clone main model with shared weights weights_memo = {id(w): w for w in model.parameters()} assistant_model = deepcopy(model, memo=weights_memo) # Create early exit version assistant_model.model.layers = assistant_model.model.layers[:early_exit] del assistant_model.model.layers[early_exit:] model.to(device) assistant_model.to(device) tokenizer = LlamaTokenizer.from_pretrained(checkpoint, use_fast=False) inputs = tokenizer(prompt, return_tensors="pt").to(device) generation_config = { "max_new_tokens": max_new_tokens, "do_sample": do_sample, "top_p": top_p, "temperature": temperature, "pad_token_id": tokenizer.eos_token_id, } # Warmup print("Warmup") for i in tqdm(range(warmup)): _ = model.generate(**inputs, **generation_config) _ = model.generate(**inputs, **generation_config, assistant_model=assistant_model) print("Autoregressive Decoding") total_time = 0 total_tokens = 0 for i in tqdm(range(repeat)): start = time() outputs = model.generate(**inputs, **generation_config) total_time += time() - start total_tokens += outputs.numel() print(tokenizer.batch_decode(outputs, skip_special_tokens=True)[0]) print("\n\t=========================") print(f"\tAverage Generation Time: {total_time / repeat:.2f} s") print(f"\tAverage Tokens per Second: {total_tokens / total_time:.2f} tokens per sec\n\n") print("Self-Speculative Decoding") total_time = 0 total_tokens = 0 for i in tqdm(range(repeat)): start = time() outputs = model.generate(**inputs, **generation_config, assistant_model=assistant_model) total_time += time() - start total_tokens += outputs.numel() print(tokenizer.batch_decode(outputs, skip_special_tokens=True)[0]) print("\n\t=========================") print(f"\tAverage Generation Time: {total_time / repeat:.2f} s") print(f"\tAverage Tokens per Second: {total_tokens / total_time:.2f} tokens per sec\n\n") ``` Running this script on a single A100 NVIDIA GPU with `transformers==4.34.1`, `torch==2.2.1`, `triton==2.2.0`, we obtain: ``` Autoregressive Decoding ========================= Average Generation Time: 12.79 s Average Tokens per Second: 28.38 tokens per sec Self-Speculative Decoding ========================= Average Generation Time: 6.35 s Average Tokens per Second: 43.64 tokens per sec ```
### LayerSkip Codebase 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. To run: ```console > git clone git@github.com:facebookresearch/LayerSkip.git > cd LayerSkip > conda create --name layer_skip python=3.10 > conda activate layer_skip > pip install -r requirements.txt > torchrun generate.py --model facebook/layerskip-llama2-13B --generation_strategy self_speculative --exit_layer 6 --num_speculations 4 ``` You can find more details in the GitHub repo for more options and scripts. ### gpt-fast 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. To run: ```console > git clone git@github.com:pytorch-labs/gpt-fast.git -b LayerSkip > cd gpt-fast > conda create --name gpt_fast python=3.10 > conda activate gpt_fast > # Install PyTorch (check [here](https://pytorch.org/get-started/locally/) for other hardwares and operating systems) > pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121 > pip install sentencepiece huggingface_hub tiktoken > mkdir checkpoints > MODEL_REPO=facebook/layerskip-llama2-13B > ./scripts/prepare.sh $MODEL_REPO > python generate.py --compile --checkpoint_path checkpoints/$MODEL_REPO/model.pth --top_k 100 --temperature 0.6 --self_speculative --early_exit 5 --speculate_k 3 ```
Benchmark - Autoregressive decoding: ```console > python generate.py --compile --checkpoint_path checkpoints/$MODEL_REPO/model.pth --top_k 100 --temperature 0.6 ========== Average tokens/sec: 61.02 Memory used: 26.67 GB ``` - Self-speculative decoding: ```console > python generate.py --compile --checkpoint_path checkpoints/$MODEL_REPO/model.pth --top_k 100 --temperature 0.6 --self_speculative --early_exit 5 --speculate_k 3 ========== {'tokens_per_sec': [65.4164788410036, 75.9909526372485, 69.47568346063044, 76.6615810268726, 62.877157626020754], 'accept_counts': [[64, 27, 11, 12], [62, 31, 9, 12], [43, 23, 12, 19], [51, 33, 9, 14], [39, 28, 12, 17], [68, 29, 13, 9]]} Acceptance probs: [0.5054095826893354, 0.2642967542503864, 0.10200927357032458, 0.12828438948995363] Mean Accepted: 0.8531684698608965 Average tokens/sec: 70.08 Memory used: 27.57 GB ```
## Training Our training implementation is work-in-progress. You can check this [pull request](https://github.com/pytorch/torchtune/pull/1076) for details and discussions. ## Evaluation 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. 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. ## Issues Please report any software "bug", or other problems with the models through one of the following means: - Reporting issues with the model: [https://github.com/facebookresearch/LayerSkip/issues](https://github.com/facebookresearch/LayerSkip/issues) - Reporting risky content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback) - Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info) ## License See the [LICENSE](LICENSE) file.