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Please report any violation of this Policy or other problems that could lead to a violation of this Policy by submitting a report [here](https://docs.google.com/forms/d/e/1FAIpQLSeb11cryAopJ7LNrC4nxEUXrHY26hfkXQMf_uH-oFgA3WlYZQ/viewform). extra_gated_fields: First Name: text Last Name: text Date of birth: date_picker Country: country Affiliation: text geo: ip_location By clicking Submit below I accept the terms of the license and acknowledge that the information I provide will be collected stored processed and shared in accordance with the Meta Privacy Policy: checkbox extra_gated_description: The information you provide will be collected, stored, processed and shared in accordance with the [Meta Privacy Policy](https://www.facebook.com/privacy/policy/). extra_gated_button_content: Submit language: - en library_name: transformers pipeline_tag: text-generation tags: - facebook - meta - pytorch - llama - llama-2 - code model-index: - name: LayerSkip Code Llama 34B results: - task: type: text-generation dataset: type: openai_humaneval name: HumanEval metrics: - name: pass@1 type: pass@1 value: 0.470 verified: false - task: type: text-generation dataset: type: mbpp name: MBPP metrics: - name: pass@1 type: pass@1 value: 0.474 verified: false license: other license_name: fair license_link: LICENSE base_model: codellama/CodeLlama-34b-hf --- # LayerSkip Code Llama 34B Code Llama 34B model continually pretrained with LayerSkip as presented in [Layer Skip: Enabling Early Exit Inference and Self-Speculative Decoding ](https://arxiv.org/abs/2404.16710) and is capable of performing self-speculative decoding: decode with earlier layers and verify with remaining layers. ## How to Use We are providing 3 ways to run the model - [HuggingFace](#huggingface) - [LayerSkip Codebase](#layerskip-codebase) - [gpt-fast](#gpt-fast) ### HuggingFace 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: ```python >>> import torch >>> from transformers import AutoModelForCausalLM, AutoTokenizer >>> from copy import deepcopy >>> checkpoint = "facebook/layerskip-codellama-34B" >>> early_exit = 4 >>> device = "cuda" if torch.cuda.is_available() else "cpu" >>> prompt = "typing import List\ndef bucket_sort(A: List):" >>> model = AutoModelForCausalLM.from_pretrained(checkpoint, device_map="auto", use_safetensors=True, torch_dtype=torch.float16) >>> 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:] >>> inputs = tokenizer(prompt, return_tensors="pt").to(device) >>> outputs = model.generate(**inputs, generation_config=generation_config, assistant_model=assistant_model, max_new_tokens=512) >>> 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 import torch from transformers import AutoModelForCausalLM, AutoTokenizer from copy import deepcopy from time import time from tqdm import tqdm prompt = "typing import List\ndef bucket_sort(A: List):" checkpoint = "facebook/layerskip-codellama-34B" early_exit = 7 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 model = AutoModelForCausalLM.from_pretrained(checkpoint, device_map="auto", use_safetensors=True, torch_dtype=torch.float16) tokenizer = AutoTokenizer.from_pretrained(checkpoint) # 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:] tokenizer = AutoTokenizer.from_pretrained(checkpoint) 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`, `accelerate==1.0.1`, `torch==2.2.1`, `triton==2.2.0`, we obtain: ``` Autoregressive Decoding ========================= Average Generation Time: 12.20 s Average Tokens per Second: 20.14 tokens per sec Self-Speculative Decoding ========================= Average Generation Time: 7.11 s Average Tokens per Second: 30.20 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-codellama-34B --generation_strategy self_speculative --exit_layer 7 --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-codellama-34B > ./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 7 --speculate_k 4 ```
Benchmark - Autoregressive decoding: ```console > python generate.py --compile --checkpoint_path checkpoints/$MODEL_REPO/model.pth --top_k 100 --temperature 0.6 --prompt "from typing import List" ========== Average tokens/sec: 25.00 Memory used: 67.83 GB ``` - Self-speculative decoding: ```console > python generate.py --compile --checkpoint_path checkpoints/$MODEL_REPO/model.pth --top_k 100 --temperature 0.6 --self_speculative --early_exit 7 --speculate_k 4 --prompt "from typing import List" ========== {'tokens_per_sec': [34.5971355158771, 33.061558413088825, 29.004499981427667, 33.38722534545423, 28.88944123779914], 'accept_counts': [[26, 21, 12, 9, 12], [31, 19, 9, 5, 17], [30, 27, 12, 0, 16], [52, 17, 13, 1, 14], [32, 20, 11, 9, 12], [47, 29, 5, 6, 11]]} Acceptance probs: [0.41523809523809524, 0.25333333333333335, 0.1180952380952381, 0.05714285714285714, 0.15619047619047619] Mean Accepted: 1.2857142857142858 Average tokens/sec: 31.79 Memory used: 68.08 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 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 [BigCode Evaluation Harness](https://github.com/bigcode-project/bigcode-evaluation-harness). ## 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.