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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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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-2-13b-hf |
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--- |
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# LayerSkip Llama2 13B |
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Llama2 13B 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-llama2-13B" |
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>>> early_exit = 8 |
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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-llama2-13B" |
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early_exit = 8 |
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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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# 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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# 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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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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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: 12.79 s |
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Average Tokens per Second: 28.38 tokens per sec |
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Self-Speculative Decoding |
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========================= |
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Average Generation Time: 6.35 s |
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Average Tokens per Second: 43.64 tokens per sec |
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``` |
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</details> |
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### LayerSkip Codebase<a name="custom"></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) 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: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-llama2-13B --generation_strategy self_speculative --exit_layer 8 --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) 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-llama2-13B |
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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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```console |
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> python generate.py --compile --checkpoint_path checkpoints/$MODEL_REPO/model.pth --top_k 100 --temperature 0.6 |
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========== |
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Average tokens/sec: 61.02 |
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Memory used: 26.67 GB |
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``` |
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- Self-speculative decoding: |
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```console |
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> python generate.py --compile --checkpoint_path checkpoints/$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': [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]]} |
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Acceptance probs: [0.5054095826893354, 0.2642967542503864, 0.10200927357032458, 0.12828438948995363] |
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Mean Accepted: 0.8531684698608965 |
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Average tokens/sec: 70.08 |
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Memory used: 27.57 GB |
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``` |
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</details> |
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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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## 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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See the [LICENSE](LICENSE) file. |
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