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"<a href=\"https://colab.research.google.com/github/mlabonne/llm-course/blob/main/nanoLoRA.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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"source": [
"# nanoLoRA\n",
"> A Minimalistic Implementation of Low-Rank Adaptation\n",
"\n",
"❤️ Created by [@maximelabonne](https://twitter.com/maximelabonne) as part of the 🗣️ [Large Language Model Course](https://github.com/mlabonne/llm-course).\n",
"\n",
"1. **Low-Rank Decomposition**: LoRA represents the updates to the weight matrices with a low-rank decomposition. For a pre-trained weight matrix $W_0$, its update is represented by $W_0 + BA$, where $B$ and $A$ are matrices with lower rank $r$, and $r$ is less than the minimum of the dimensions of $W_0$.\n",
"\n",
"2. **Freezing Pre-trained Weights**: During training, $W_0$ remains constant and does not receive gradient updates. The trainable parameters are contained in matrices $A$ and $B$, thereby constraining the updates to a lower intrinsic rank.\n",
"\n",
"3. **Forward Pass Modification**: The modified forward pass includes both the original weight matrix and the low-rank update, yielding $h=W_0 x+BAx$.\n",
"\n",
"4. **Deployment Efficiency**: When deployed, LoRA can explicitly compute and store $W=W_0 + BA$ and perform inference as usual. This approach introduces no additional inference latency compared to a fully fine-tuned model.\n"
],
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{
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"source": [
"!pip install -q transformers datasets"
]
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"from datasets import load_dataset\n",
"from transformers import DataCollatorForLanguageModeling\n",
"from transformers import GPT2Model, GPT2Config, GPT2Tokenizer\n",
"\n",
"def tokenize(element):\n",
" outputs = tokenizer(\n",
" element[\"text\"],\n",
" truncation=True,\n",
" max_length=context_length,\n",
" return_overflowing_tokens=True,\n",
" return_length=True,\n",
" )\n",
" input_batch = []\n",
" for length, input_ids in zip(outputs[\"length\"], outputs[\"input_ids\"]):\n",
" if length == context_length:\n",
" input_batch.append(input_ids)\n",
" return {\"input_ids\": input_batch}\n",
"\n",
"dataset = load_dataset('wikitext', 'wikitext-2-raw-v1')\n",
"\n",
"tokenizer = GPT2Tokenizer.from_pretrained('gpt2')\n",
"tokenizer.pad_token = tokenizer.eos_token\n",
"\n",
"context_length = 128\n",
"tokenized_datasets = dataset.map(\n",
" tokenize, batched=True, remove_columns=dataset[\"train\"].column_names\n",
")\n",
"\n",
"data_collator = DataCollatorForLanguageModeling(tokenizer, mlm=False)"
],
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},
"id": "diEMvtXR7f7Q",
"outputId": "88b55d05-980c-4eb7-dfa6-17092426ec36"
},
"execution_count": 2,
"outputs": [
{
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},
{
"cell_type": "markdown",
"source": [
"The `self.merged` variable is used as a flag to track the state of the model's weights. It serves to indicate whether the weights of the linear layer have been merged with the low-rank approximation weights.\n",
"\n",
"* **During Training**: The LoRA layer maintains two sets of weights - the original weights of the layer (`self.weight`) and the low-rank approximation weights (`self.lora_A` and `self.lora_B`). During training, you typically want to update the low-rank approximation weights and keep the original weights fixed. Therefore, before training, the method ensures that the original weights and the low-rank approximation weights are not merged (`self.merged` is `False`). If they have been merged, it separates them by subtracting the low-rank approximation from the original weights.\n",
"\n",
"* **During Evaluation**: During evaluation (or inference), you want to use the enhanced weights that include the low-rank approximation for better performance. Therefore, before evaluation, the method ensures that the weights are merged (`self.merged` is `True`). If they are not, it merges them by adding the low-rank approximation to the original weights.\n",
"\n",
"This approach allows you to use the low-rank approximation for inference (where it can improve performance) but not during training (where you want to train the low-rank approximation weights based on the fixed original weights)."
],
"metadata": {
"id": "X9ZqP3I-QyoQ"
}
},
{
"cell_type": "code",
"source": [
"import torch\n",
"import math\n",
"from torch import nn\n",
"import torch.nn.functional as F\n",
"\n",
"class nanoLoRA(nn.Linear):\n",
" def __init__(\n",
" self,\n",
" in_features: int,\n",
" out_features: int,\n",
" r: int = 8,\n",
" lora_alpha: int = 1\n",
" ):\n",
" super().__init__(in_features, out_features)\n",
" assert r > 0, \"r must be > 0\"\n",
"\n",
" self.lora_A = nn.Parameter(self.weight.new_zeros((r, in_features)))\n",
" self.lora_B = nn.Parameter(self.weight.new_zeros((out_features, r)))\n",
" self.scaling = lora_alpha / r\n",
" self.weight.requires_grad = False\n",
" self.merged = False\n",
" self.reset_lora_parameters()\n",
"\n",
" def reset_lora_parameters(self):\n",
" super().reset_parameters()\n",
" nn.init.kaiming_uniform_(self.lora_A, a=math.sqrt(5))\n",
" nn.init.zeros_(self.lora_B)\n",
"\n",
" def train(self, mode: bool = True):\n",
" super().train(mode)\n",
" if mode:\n",
" if self.merged:\n",
" # Make sure that the weights are not merged for training\n",
" self.weight.data -= (self.lora_B @ self.lora_A) * self.scaling\n",
" self.merged = False\n",
" else:\n",
" if not self.merged:\n",
" # Merge the weights for inference\n",
" self.weight.data += (self.lora_B @ self.lora_A) * self.scaling\n",
" self.merged = True\n",
"\n",
" def forward(self, x: torch.Tensor):\n",
" if not self.merged:\n",
" out = F.linear(x, self.weight, bias=self.bias)\n",
" out += (x @ self.lora_A.transpose(0, 1) @ self.lora_B.transpose(0, 1)) * self.scaling\n",
" return out\n",
" else:\n",
" return F.linear(x, self.weight, bias=self.bias)"
],
"metadata": {
"id": "Bjwhp1B2bY5z"
},
"execution_count": 3,
"outputs": []
},
{
"cell_type": "code",
"source": [
"from transformers import AutoTokenizer, GPT2LMHeadModel, AutoConfig\n",
"\n",
"# Initialize the model\n",
"config = AutoConfig.from_pretrained(\n",
" \"gpt2\",\n",
" vocab_size=len(tokenizer),\n",
" n_ctx=1024,\n",
" bos_token_id=tokenizer.bos_token_id,\n",
" eos_token_id=tokenizer.eos_token_id,\n",
")\n",
"model = GPT2LMHeadModel(config)\n",
"\n",
"# Freeze all parameters in the model\n",
"for param in model.parameters():\n",
" param.requires_grad = False\n",
"\n",
"# Modify the layers and unfreeze these parameters\n",
"for i in range(config.n_layer):\n",
" model.transformer.h[i].mlp.c_fc = nanoLoRA(config.n_embd, config.n_embd, r=8)\n",
" model.transformer.h[i].mlp.c_proj = nanoLoRA(config.n_embd, config.n_embd, r=8)\n",
"\n",
"num_train_params = sum(p.numel() for p in model.parameters() if p.requires_grad)\n",
"print(f'The model has {num_train_params:,} trainable parameters.')"
],
"metadata": {
"id": "9oWWdIkJ56Wd",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "72c38f6a-98e8-4278-e998-a3e387f29277"
},
"execution_count": 4,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"The model has 313,344 trainable parameters.\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"from transformers import Trainer, TrainingArguments\n",
"\n",
"args = TrainingArguments(\n",
" output_dir=\"results\",\n",
" per_device_train_batch_size=32,\n",
" per_device_eval_batch_size=32,\n",
" evaluation_strategy=\"steps\",\n",
" eval_steps=50,\n",
" logging_steps=50,\n",
" gradient_accumulation_steps=1,\n",
" num_train_epochs=1,\n",
" weight_decay=0.1,\n",
" warmup_steps=10,\n",
" lr_scheduler_type=\"cosine\",\n",
" learning_rate=5e-4,\n",
" fp16=True,\n",
" report_to=\"tensorboard\",\n",
")\n",
"\n",
"trainer = Trainer(\n",
" model=model,\n",
" tokenizer=tokenizer,\n",
" args=args,\n",
" data_collator=data_collator,\n",
" train_dataset=tokenized_datasets[\"train\"],\n",
" eval_dataset=tokenized_datasets[\"validation\"],\n",
")\n",
"\n",
"trainer.train()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 321
},
"id": "-eHDB7xB2HMk",
"outputId": "8007f1f4-3c57-4f1f-abd3-2b76a96713d2"
},
"execution_count": 5,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.10/dist-packages/transformers/optimization.py:411: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
" warnings.warn(\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<IPython.core.display.HTML object>"
],
"text/html": [
"\n",
" <div>\n",
" \n",
" <progress value='266' max='266' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
" [266/266 01:32, Epoch 1/1]\n",
" </div>\n",
" <table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: left;\">\n",
" <th>Step</th>\n",
" <th>Training Loss</th>\n",
" <th>Validation Loss</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>50</td>\n",
" <td>10.368600</td>\n",
" <td>9.695271</td>\n",
" </tr>\n",
" <tr>\n",
" <td>100</td>\n",
" <td>9.660800</td>\n",
" <td>9.591634</td>\n",
" </tr>\n",
" <tr>\n",
" <td>150</td>\n",
" <td>9.634500</td>\n",
" <td>9.585637</td>\n",
" </tr>\n",
" <tr>\n",
" <td>200</td>\n",
" <td>9.623000</td>\n",
" <td>9.583360</td>\n",
" </tr>\n",
" <tr>\n",
" <td>250</td>\n",
" <td>9.613100</td>\n",
" <td>9.582972</td>\n",
" </tr>\n",
" </tbody>\n",
"</table><p>"
]
},
"metadata": {}
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"TrainOutput(global_step=266, training_loss=9.771246601764421, metrics={'train_runtime': 96.2756, 'train_samples_per_second': 88.164, 'train_steps_per_second': 2.763, 'total_flos': 279368589901824.0, 'train_loss': 9.771246601764421, 'epoch': 1.0})"
]
},
"metadata": {},
"execution_count": 5
}
]
},
{
"cell_type": "code",
"source": [
"trainer.evaluate()"
],
"metadata": {
"id": "grVjJRyP8Cvo",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 124
},
"outputId": "14d33957-ccc6-4fd6-db88-6aa977c83cf9"
},
"execution_count": 6,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<IPython.core.display.HTML object>"
],
"text/html": [
"\n",
" <div>\n",
" \n",
" <progress value='28' max='28' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
" [28/28 00:03]\n",
" </div>\n",
" "
]
},
"metadata": {}
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"{'eval_loss': 9.582963943481445,\n",
" 'eval_runtime': 3.3787,\n",
" 'eval_samples_per_second': 262.824,\n",
" 'eval_steps_per_second': 8.287,\n",
" 'epoch': 1.0}"
]
},
"metadata": {},
"execution_count": 6
}
]
}
]
}
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