From 6ab13b18488edbec89d349229e56be6c42809527 Mon Sep 17 00:00:00 2001 From: Shirin Yamani Date: Mon, 24 Jun 2024 15:09:36 -0600 Subject: [PATCH] finetuning dir added --- dora_finetuning/QDoRA_finetuning.ipynb | 8679 ++++++++++++++++++++++++ dora_finetuning/dora_finetuning.py | 185 + dora_finetuning/readme.md | 106 + 3 files changed, 8970 insertions(+) create mode 100644 dora_finetuning/QDoRA_finetuning.ipynb create mode 100644 dora_finetuning/dora_finetuning.py create mode 100644 dora_finetuning/readme.md diff --git a/dora_finetuning/QDoRA_finetuning.ipynb b/dora_finetuning/QDoRA_finetuning.ipynb new file mode 100644 index 0000000..79876cc --- /dev/null +++ b/dora_finetuning/QDoRA_finetuning.ipynb @@ -0,0 +1,8679 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "CV_gQs58bsvM" + }, + "source": [ + "# Fine-tuning [Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) on [timdettmers/openassistant-guanaco](https://huggingface.co/datasets/timdettmers/openassistant-guanaco) Dataset using QDora (quantized Lora w/ use_dora=True) on T4 Free Colab GPU." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "FuXIFTFapAMI", + "outputId": "ac4a160b-429b-4279-941c-dc1792da5bf9" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m119.8/119.8 MB\u001b[0m \u001b[31m6.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m21.3/21.3 MB\u001b[0m \u001b[31m66.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25h Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n", + " Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n", + " Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n", + " Building wheel for transformers (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n", + " Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n", + " Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n", + " Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m309.4/309.4 kB\u001b[0m \u001b[31m7.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25h Building wheel for peft (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n", + " Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n", + " Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n", + " Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n", + " Building wheel for accelerate (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m547.8/547.8 kB\u001b[0m \u001b[31m13.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m40.8/40.8 MB\u001b[0m \u001b[31m23.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m116.3/116.3 kB\u001b[0m \u001b[31m15.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m64.9/64.9 kB\u001b[0m \u001b[31m9.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m194.1/194.1 kB\u001b[0m \u001b[31m24.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m19.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25h\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n", + "cudf-cu12 24.4.1 requires pyarrow<15.0.0a0,>=14.0.1, but you have pyarrow 16.1.0 which is incompatible.\n", + "google-colab 1.0.0 requires requests==2.31.0, but you have requests 2.32.3 which is incompatible.\n", + "ibis-framework 8.0.0 requires pyarrow<16,>=2, but you have pyarrow 16.1.0 which is incompatible.\u001b[0m\u001b[31m\n", + "\u001b[0m" + ] + } + ], + "source": [ + "# Install the libraries\n", + "!pip install -q -U bitsandbytes\n", + "!pip install -q -U git+https://github.com/huggingface/transformers.git\n", + "!pip install -q -U git+https://github.com/huggingface/peft.git\n", + "!pip install -q -U git+https://github.com/huggingface/accelerate.git\n", + "!pip install -q datasets" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 145, + "referenced_widgets": [ + "25964d99253d4c7783d62c840d663adb", + "b6cdbc3be9624e6bbea65d39a443eab5", + "5dd432e750754bffa3c2060ac224f7ed", + "20a269fcc3524ee29498cd472eb20449", + "0cc6bc4766ba4ae4ab610dd39cc0bcaa", + "23071a3bf3d44742aac274ae5449c6fc", + "55c52bbe8046485a9020280fcb56c44b", + "31a3df6df5604d39a477b75ca60150b9", + "f6eec200d7d841a5a8cd9769b29f698b", + "c7ce04ef54cb49d9b9297e2894885f8e", + "eaa986581a5a46528e7ada73eb422420", + "c366a9f85aad48cc80d2c1a4b41ddede", + "6d651607ad79425ba8858f74a1d04948", + "676062c85b3b4a0fa35c7b4c55a7a6ff", + "5ee3f0d42a0044609877c8368d1ed7c7", + "0b070ab28d1f4b35b7332540b1a091de", + "f13161fa7e6e427393b88f27507eb69f", + "195872aa2d6f49a18c582572a5264758", + "8275c8f841ae4956afbf3ed72e96d0d1", + "446289302e314c21863c71d364e57f3b", + "9a4102a538474671bf35e04aae411504", + "ac037ef601024749a76b0827d61a79ef", + "0e11d6082fc54e0793e4b535fd80ab6d", + "3b84c8f4682b4f32a344d0b66c2a5cdc", + "f14a25a92f7e41f09d351535f4719ec0", + "3cbe6209d6c6483d975349dca97a1d00", + "f56aef3983104fc689467ba6c4ce7152", + "ed032e7333d4415d9b61201a36747200", + "90e87569ca0a410e93da2c9548e99c51", + "92e2156ae0a94110ba0ae68dce39cbe2", + "99a88575249c47b3b92084a9d6ac809f", + "6c2194e6a4ca49049c69c41c5adf6812" + ] + }, + "id": "wAAPv5CRmg7e", + "outputId": "89776dfe-5640-4d01-82f8-cfa6a9dae7cf" + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "25964d99253d4c7783d62c840d663adb", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "VBox(children=(HTML(value='
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+ { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "310dab65518d4256937f3e10c722d7e3", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "tokenizer_config.json: 0%| | 0.00/50.6k [00:00\n", + " \n", + " \n", + " [10/10 03:53, Epoch 0/1]\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
StepTraining Loss
11.276300
21.877700
31.984900
42.010400
51.995300
61.649300
71.579100
80.916900
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101.820900

" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "TrainOutput(global_step=10, training_loss=1.6633873581886292, metrics={'train_runtime': 264.2691, 'train_samples_per_second': 0.151, 'train_steps_per_second': 0.038, 'total_flos': 530537216679936.0, 'train_loss': 1.6633873581886292, 'epoch': 0.004062563477554337})" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import transformers\n", + "\n", + "tokenizer.pad_token = tokenizer.eos_token\n", + "\n", + "trainer = transformers.Trainer(\n", + " model=model,\n", + " train_dataset=data[\"train\"],\n", + " args=transformers.TrainingArguments(\n", + " per_device_train_batch_size=1,\n", + " gradient_accumulation_steps=4,\n", + " warmup_steps=2,\n", + " max_steps=10,\n", + " learning_rate=2e-4,\n", + " fp16=True,\n", + " logging_steps=1,\n", + " output_dir=\"ShirinYamani/llama-3-8B-fine-tuned-dora\", #change it to your desired repo!\n", + " optim=\"paged_adamw_8bit\"\n", + " ),\n", + " data_collator=transformers.DataCollatorForLanguageModeling(tokenizer, mlm=False),\n", + ")\n", + "model.config.use_cache = False # silence the warnings. Please re-enable for inference!\n", + "trainer.train()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Mr3rLrHwqhf6" + }, + "source": [ + "## Usage Example" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "collapsed": true, + "id": "9mrOJ9l8SMHv", + "outputId": "2ad8775b-49f6-4e31-e17d-2bedee681091" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "PeftModelForCausalLM(\n", + " (base_model): LoraModel(\n", + " (model): LlamaForCausalLM(\n", + " (model): LlamaModel(\n", + " (embed_tokens): Embedding(128256, 4096)\n", + " (layers): ModuleList(\n", + " (0-31): 32 x LlamaDecoderLayer(\n", + " (self_attn): LlamaSdpaAttention(\n", + " (q_proj): lora.Linear4bit(\n", + " (base_layer): Linear4bit(in_features=4096, out_features=4096, bias=False)\n", + " (lora_dropout): ModuleDict(\n", + " (default): Dropout(p=0.05, inplace=False)\n", + " )\n", + " (lora_A): ModuleDict(\n", + " (default): Linear(in_features=4096, out_features=8, bias=False)\n", + " )\n", + " (lora_B): ModuleDict(\n", + " (default): Linear(in_features=8, out_features=4096, bias=False)\n", + " )\n", + " (lora_embedding_A): ParameterDict()\n", + " (lora_embedding_B): ParameterDict()\n", + " (lora_magnitude_vector): ModuleDict(\n", + " (default): lora.dora.DoraLinearLayer()\n", + " )\n", + " )\n", + " (k_proj): lora.Linear4bit(\n", + " (base_layer): Linear4bit(in_features=4096, out_features=1024, bias=False)\n", + " (lora_dropout): ModuleDict(\n", + " (default): Dropout(p=0.05, inplace=False)\n", + " )\n", + " (lora_A): ModuleDict(\n", + " (default): Linear(in_features=4096, out_features=8, bias=False)\n", + " )\n", + " (lora_B): ModuleDict(\n", + " (default): Linear(in_features=8, out_features=1024, bias=False)\n", + " )\n", + " (lora_embedding_A): ParameterDict()\n", + " (lora_embedding_B): ParameterDict()\n", + " (lora_magnitude_vector): ModuleDict(\n", + " (default): lora.dora.DoraLinearLayer()\n", + " )\n", + " )\n", + " (v_proj): lora.Linear4bit(\n", + " (base_layer): Linear4bit(in_features=4096, out_features=1024, bias=False)\n", + " (lora_dropout): ModuleDict(\n", + " (default): Dropout(p=0.05, inplace=False)\n", + " )\n", + " (lora_A): ModuleDict(\n", + " (default): Linear(in_features=4096, out_features=8, bias=False)\n", + " )\n", + " (lora_B): ModuleDict(\n", + " (default): Linear(in_features=8, out_features=1024, bias=False)\n", + " )\n", + " (lora_embedding_A): ParameterDict()\n", + " (lora_embedding_B): ParameterDict()\n", + " (lora_magnitude_vector): ModuleDict(\n", + " (default): lora.dora.DoraLinearLayer()\n", + " )\n", + " )\n", + " (o_proj): lora.Linear4bit(\n", + " (base_layer): Linear4bit(in_features=4096, out_features=4096, bias=False)\n", + " (lora_dropout): ModuleDict(\n", + " (default): Dropout(p=0.05, inplace=False)\n", + " )\n", + " (lora_A): ModuleDict(\n", + " (default): Linear(in_features=4096, out_features=8, bias=False)\n", + " )\n", + " (lora_B): ModuleDict(\n", + " (default): Linear(in_features=8, out_features=4096, bias=False)\n", + " )\n", + " (lora_embedding_A): ParameterDict()\n", + " (lora_embedding_B): ParameterDict()\n", + " (lora_magnitude_vector): ModuleDict(\n", + " (default): lora.dora.DoraLinearLayer()\n", + " )\n", + " )\n", + " (rotary_emb): LlamaRotaryEmbedding()\n", + " )\n", + " (mlp): LlamaMLP(\n", + " (gate_proj): lora.Linear4bit(\n", + " (base_layer): Linear4bit(in_features=4096, out_features=14336, bias=False)\n", + " (lora_dropout): ModuleDict(\n", + " (default): Dropout(p=0.05, inplace=False)\n", + " )\n", + " (lora_A): ModuleDict(\n", + " (default): Linear(in_features=4096, out_features=8, bias=False)\n", + " )\n", + " (lora_B): ModuleDict(\n", + " (default): Linear(in_features=8, out_features=14336, bias=False)\n", + " )\n", + " (lora_embedding_A): ParameterDict()\n", + " (lora_embedding_B): ParameterDict()\n", + " (lora_magnitude_vector): ModuleDict(\n", + " (default): lora.dora.DoraLinearLayer()\n", + " )\n", + " )\n", + " (up_proj): lora.Linear4bit(\n", + " (base_layer): Linear4bit(in_features=4096, out_features=14336, bias=False)\n", + " (lora_dropout): ModuleDict(\n", + " (default): Dropout(p=0.05, inplace=False)\n", + " )\n", + " (lora_A): ModuleDict(\n", + " (default): Linear(in_features=4096, out_features=8, bias=False)\n", + " )\n", + " (lora_B): ModuleDict(\n", + " (default): Linear(in_features=8, out_features=14336, bias=False)\n", + " )\n", + " (lora_embedding_A): ParameterDict()\n", + " (lora_embedding_B): ParameterDict()\n", + " (lora_magnitude_vector): ModuleDict(\n", + " (default): lora.dora.DoraLinearLayer()\n", + " )\n", + " )\n", + " (down_proj): lora.Linear4bit(\n", + " (base_layer): Linear4bit(in_features=14336, out_features=4096, bias=False)\n", + " (lora_dropout): ModuleDict(\n", + " (default): Dropout(p=0.05, inplace=False)\n", + " )\n", + " (lora_A): ModuleDict(\n", + " (default): Linear(in_features=14336, out_features=8, bias=False)\n", + " )\n", + " (lora_B): ModuleDict(\n", + " (default): Linear(in_features=8, out_features=4096, bias=False)\n", + " )\n", + " (lora_embedding_A): ParameterDict()\n", + " (lora_embedding_B): ParameterDict()\n", + " (lora_magnitude_vector): ModuleDict(\n", + " (default): lora.dora.DoraLinearLayer()\n", + " )\n", + " )\n", + " (act_fn): SiLU()\n", + " )\n", + " (input_layernorm): LlamaRMSNorm()\n", + " (post_attention_layernorm): LlamaRMSNorm()\n", + " )\n", + " )\n", + " (norm): LlamaRMSNorm()\n", + " )\n", + " (lm_head): Linear(in_features=4096, out_features=128256, bias=False)\n", + " )\n", + " )\n", + ")" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.config.use_cache = True\n", + "model.eval()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 122 + }, + "id": "AM6FNOFzqKfI", + "outputId": "fdbe28b1-e440-45d3-bd6d-c15e744ad23d" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n" + ] + }, + { + "data": { + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "string" + }, + "text/plain": [ + "\"A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. ### Human: What is the purpose of quantization in LLMs?### Assistant: Quantization is a technique used to reduce the size of a model without significantly impacting its performance. In the context of language models, quantization is the process of converting floating-point numbers (which are used to represent the weights and activations of a model) to smaller, fixed-point numbers. This can be done by grouping the weights into small chunks and assigning each chunk a single, fixed-point number. Quantization can significantly reduce the size of a model, making it more efficient to train and deploy. In addition, quantization can improve the performance of a model on low-power devices, such as mobile phones\"" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from transformers import GenerationConfig\n", + "\n", + "max_new_tokens = 120\n", + "top_p = 0.9\n", + "temperature=0.7\n", + "user_question = \"What is the purpose of quantization in LLMs?\"\n", + "\n", + "\n", + "prompt = (\n", + " \"A chat between a curious human and an artificial intelligence assistant. \"\n", + " \"The assistant gives helpful, detailed, and polite answers to the user's questions. \"\n", + " \"### Human: {user_question}\"\n", + " \"### Assistant: \"\n", + ")\n", + "\n", + "def generate(model, user_question, max_new_tokens=max_new_tokens, top_p=top_p, temperature=temperature):\n", + " inputs = tokenizer(prompt.format(user_question=user_question), return_tensors=\"pt\").to('cuda')\n", + "\n", + " outputs = model.generate(\n", + " **inputs,\n", + " generation_config=GenerationConfig(\n", + " do_sample=True,\n", + " max_new_tokens=max_new_tokens,\n", + " top_p=top_p,\n", + " temperature=temperature,\n", + " )\n", + " )\n", + "\n", + " text = tokenizer.decode(outputs[0], skip_special_tokens=True)\n", + " #print(text)\n", + " return text\n", + "\n", + "generate(model, user_question)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "T5t_gl2_f5OO" + }, + "outputs": [], + "source": [ + "#trainer.push_to_hub()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "WiBxl-IebrY3" + }, + "source": [] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "gpuType": "T4", + "provenance": [] + }, + "gpuClass": "standard", + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "name": "python" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "00c8061b5b604a488aa7b2717567a184": { + "model_module": 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device: str, + lora_r: int, + lora_alpha: int, + lora_dropout: float, + lora_target_modules: str, + hub_model_id: str, + push_to_hub: bool, + use_compile: bool, +): + os.environ["TOKENIZERS_PARALLELISM"] = "false" + hf_token = os.getenv("HF_TOKEN") + + # Setup device + device = torch.device(device) + print(f"Using device: {device}") + + #load tokenizer + tokenizer = AutoTokenizer.from_pretrained(base_model, token=hf_token) + + #Load model in Quantized and Peft configz + #QDoRA: IF YOU WANNA QUANTIZE THE MODEL + if quantize: + model = AutoModelForCausalLM.from_pretrained( + base_model, + token=hf_token, + quantization_config=BitsAndBytesConfig( + load_in_4bit=True, + bnb_4bit_compute_dtype=torch.bfloat16, + bnb_4bit_use_double_quant=True, + bnb_4bit_quant_type="nf4", + ) + ) + + else: + model = AutoModelForCausalLM.from_pretrained(base_model, token=hf_token) + + # if use_peft is True THEN, DORA setup + if use_peft: #TODO: add other config than lora peft_configs = (LoraConfig, AdaptionPromptConfig, PrefixTuningConfig) + lora_config = LoraConfig( + use_dora=True, + r=lora_r, # Rank + lora_alpha=lora_alpha, + target_modules=lora_target_modules.split(',') if lora_target_modules else ["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"], + lora_dropout=lora_dropout, + bias="none" + ) + # Apply LoRA to the model if USE_PEFT=TRUE + model = get_peft_model(model, lora_config) + + if use_compile: + model = torch.compile(model) + model.to(device) #MODEL TO GPU/CUDA + tokenizer.pad_token = tokenizer.eos_token + + + # Load the dataset + dataset = load_dataset(data_path) + + def tokenize_function(examples): + inputs = tokenizer(examples['text'], padding="max_length", truncation=True, max_length=cutoff_len) + inputs['labels'] = inputs['input_ids'].copy() # setting labels for a language modeling task + return inputs + + # Tokenize the dataset and prepare for training + tokenized_datasets = dataset.map(tokenize_function, batched=True, remove_columns=dataset['train'].column_names) + + # Data collator to dynamically pad the batched examples + data_collator = DataCollatorWithPadding(tokenizer) + + # Define training arguments + training_args = TrainingArguments( + output_dir=output_dir, + num_train_epochs=num_epochs, + per_device_train_batch_size=batch_size, + per_device_eval_batch_size=batch_size, + warmup_steps=100, + weight_decay=0.01, + logging_dir="./logs", + logging_steps=eval_step, + save_steps=save_step, + save_total_limit=2, + push_to_hub=push_to_hub, + hub_model_id=hub_model_id, + gradient_accumulation_steps=16, + fp16=True, + learning_rate=learning_rate, + hub_token=hf_token, + ) + + # Clear CUDA cache to free memory + torch.cuda.empty_cache() + + # Initialize the Trainer + trainer = Trainer( + model=model, + args=training_args, + train_dataset=tokenized_datasets['train'], + eval_dataset=tokenized_datasets['test'], + data_collator=data_collator, + ) + + # Start model training + trainer.train() + + # Save and push the trained model and tokenizer + if push_to_hub: + # Push the main model to the hub + trainer.push_to_hub(commit_message="Fine-tuned model") + + # Save the model and tokenizer locally + model.save_pretrained(output_dir) + tokenizer.save_pretrained(output_dir) + + # # Save and push the LoRA adapter if used + # if use_peft: + # adapter_dir = os.path.join(output_dir, 'adapter') + # model.save_pretrained(adapter_dir) + # adapter = PeftModel(model) + # adapter.push_to_hub(f"{hub_model_id}-adapter", use_temp_dir=False, commit_message="LoRA adapter") + +if __name__ == "__main__": + import argparse + parser = argparse.ArgumentParser(description="Fine-tune LLaMA with DoRA and PEFT") + parser.add_argument("--base_model", type=str, default="huggyllama/llama-7b", help="Base model path or name") + parser.add_argument("--data_path", type=str, default="timdettmers/openassistant-guanaco", help="Dataset path or name") + parser.add_argument("--output_dir", type=str, default="path/to/output", help="Output directory for the fine-tuned model") + parser.add_argument("--batch_size", type=int, default=1, help="Batch size") + parser.add_argument("--use_compile", default=False, help="Use Compile") + parser.add_argument("--num_epochs", type=int, default=1, help="Number of training epochs") + parser.add_argument("--learning_rate", type=float, default=3e-4, help="Learning rate") + parser.add_argument("--cutoff_len", type=int, default=512, help="Cutoff length for tokenization") + parser.add_argument("--val_set_size", type=int, default=500, help="Validation set size") + parser.add_argument("--use_peft", action="store_true", help="Use PEFT (LoRA) for training") + parser.add_argument("--quantize", action="store_true", help="Use quantization") + parser.add_argument("--eval_step", type=int, default=10, help="Evaluation step interval") + parser.add_argument("--save_step", type=int, default=100, help="Save step interval") + parser.add_argument("--device", type=str, default="cuda:0", help="Device to use for training") + parser.add_argument("--lora_r", type=int, default=8, help="LoRA rank") + parser.add_argument("--lora_alpha", type=int, default=16, help="LoRA alpha") + parser.add_argument("--lora_dropout", type=float, default=0.05, help="LoRA dropout rate") + parser.add_argument("--lora_target_modules", type=str, default=None, help="Comma-separated list of target modules for LoRA") + parser.add_argument("--hub_model_id", type=str, default="path/to/repo", help="Repository name to push the model on the Hugging Face Hub") + parser.add_argument("--push_to_hub", action="store_true", help="Whether to push the model to Hugging Face Hub") + args = parser.parse_args() + train_model( + base_model=args.base_model, + data_path=args.data_path, + output_dir=args.output_dir, + batch_size=args.batch_size, + num_epochs=args.num_epochs, + learning_rate=args.learning_rate, + cutoff_len=args.cutoff_len, + val_set_size=args.val_set_size, + use_peft=args.use_peft, + use_compile=args.use_compile, + quantize=args.quantize, + eval_step=args.eval_step, + save_step=args.save_step, + device=args.device, + lora_r=args.lora_r, + lora_alpha=args.lora_alpha, + lora_dropout=args.lora_dropout, + lora_target_modules=args.lora_target_modules, + hub_model_id=args.hub_model_id, + push_to_hub=args.push_to_hub, + ) \ No newline at end of file diff --git a/dora_finetuning/readme.md b/dora_finetuning/readme.md new file mode 100644 index 0000000..9fd0526 --- /dev/null +++ b/dora_finetuning/readme.md @@ -0,0 +1,106 @@ +# DoRA: Weight-Decomposed Low-Rank Adaptation + +![dora](https://i.ytimg.com/vi/m7KQdGSr0Dg/maxresdefault.jpg) + + +## Introduction +[DoRA](https://arxiv.org/abs/2402.09353) is a novel approach that leverages low rank adaptation through weight decomposition analysis to investigate the inherent differences between full fine-tuning and LoRA.. DoRA initially decomposes the pretrained weight into its magnitude and directional components and finetunes both of them. Because the directional component is large in terms of parameter numbers, we further decompose it with LoRA for efficient finetuning. This results in enhancing both the learning capacity and training stability of LoRA while avoiding any additional inference overhead.. + +## Quick start +```python +import torch +from peft import LoraConfig, get_peft_model +from transformers import AutoTokenizer, AutoModelForCausalLM, Trainer +from datasets import load_dataset + +model = AutoModelForCausalLM.from_pretrained("huggyllama/llama-7b", device_map="cuda") #default model is huggyllama/llama-7b +tokenizer = AutoTokenizer.from_pretrained("huggyllama/llama-7b") +dataset = load_dataset("timdettmers/openassistant-guanaco", split="train") +lora_config = LoraConfig( + use_dora=True +) +peft_model = get_peft_model(model, lora_config) +trainer = transformers.Trainer( + model=peft_model, + train_dataset=dataset, + dataset_text_field="text", + max_seq_length=2048, + tokenizer=tokenizer, +) +trainer.train() +peft_model.save_pretrained("dora-llama-3-8b") +``` + +There is no additional change needed to your standard LoRA procedure, except for specifying `use_dora = True` option in your lora configuration. + + +Run the finetuning script simply by running: +```python +python examples/dora_finetuning/dora_finetuning.py --use_peft +``` +This will set up the cofig to LoRA with `use_dora=True`. Now if you wanna run the script on your own model then you need to simply define; + +```python +python examples/dora_finetuning/dora_finetuning.py --base_model meta-llama/Meta-Llama-3-8B --data_path timdettmers/openassistant-guanaco +``` +DoRA also supports quantization. To use 4-bit quantization try: + +```python +python examples/dora_finetuning/dora_finetuning.py --base_model meta-llama/Meta-Llama-3-8B --quantize +``` + +Similarly, by default the LoRA layers are the attention and MLP layers of LLama model, if you get to choose a different set of layers for LoRA to be applied on, you can simply define it using: +```python +python examples/dora_finetuning/dora_finetuning.py --lora_target_modules "q_proj,k_proj,v_proj,o_proj" +``` + +### Full example of the script +```python +python train_model.py \ + --base_model "PATH_TO_MODEL" \ + --data_path "PATH_TO_DATASET" \ + --output_dir "PATH_TO_OUTPUT_DIR" \ + --batch_size 1 \ + --num_epochs 3 \ + --learning_rate 3e-4 \ + --cutoff_len 512 \ + --val_set_size 500 \ + --use_compile True \ + --use_peft \ + --quantize \ + --eval_step 10 \ + --save_step 100 \ + --device "cuda:0" \ + --lora_r 16 \ + --lora_alpha 32 \ + --lora_dropout 0.05 \ + --lora_target_modules "q_proj,k_proj,v_proj,o_proj" \ + --hub_model_id "YOUR_HF_REPO" \ + --push_to_hub +``` +## Use the model on 🤗 +You can load and use the model as any other 🤗 models. +```python +from transformers import AutoModel +model = AutoModel.from_pretrained("ShirinYamani/huggyllama-llama-7b-finetuned") +``` + +## DoRA vs. LoRA +In general, DoRA finetuning on diffusion models is still experimental and is likely to require different hyperparameter values to perform best compared to LoRA. + +Specifically, people have noticed 2 differences to take into account in your training: + +1. LoRA seem to converge faster than DoRA (so a set of parameters that may lead to overfitting when training a LoRA may be working well for a DoRA) + +2. DoRA quality superior to LoRA especially in lower ranks: The difference in quality of DoRA of rank 8 and LoRA of rank 8 appears to be more significant than when training ranks of 32 or 64 for example. + + +## Citation +``` +@article{liu2024dora, + title={DoRA: Weight-Decomposed Low-Rank Adaptation}, + author={Liu, Shih-Yang and Wang, Chien-Yi and Yin, Hongxu and Molchanov, Pavlo and Wang, Yu-Chiang Frank and Cheng, Kwang-Ting and Chen, Min-Hung}, + journal={arXiv preprint arXiv:2402.09353}, + year={2024} +} +``` \ No newline at end of file