LoRA-FlanT5-finetune.ipynb

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   "source": [
    "# LoRA Fine-Tuning with Hugging Face and TensorFlow on FLAN-T5-base for Summarization"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12361c6e",
   "metadata": {
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   },
   "source": [
    "## Introduction\n",
    "In this notebook, we will perform LoRA fine-tuning on the FLAN-T5-base model using the WMT16 sentiment analysis dataset. We will replace the dense layers with LoRA layers and fine-tune the model for translation."
   ]
  },
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   "outputs": [],
   "source": [
    "# !pip install transformers tensorflow datasets tensorflow_addons"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a5067cf",
   "metadata": {
    "id": "1a5067cf"
   },
   "source": [
    "## Load and Preprocess the  Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d34938a3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]\n",
      "1 Physical GPUs, 1 Logical GPUs\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "e:\\CondaEnvs\\tensorflow\\lib\\site-packages\\huggingface_hub\\file_download.py:795: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
      "  warnings.warn(\n",
      "e:\\CondaEnvs\\tensorflow\\lib\\site-packages\\keras\\initializers\\initializers_v2.py:120: UserWarning: The initializer RandomNormal is unseeded and being called multiple times, which will return identical values  each time (even if the initializer is unseeded). Please update your code to provide a seed to the initializer, or avoid using the same initalizer instance more than once.\n",
      "  warnings.warn(\n",
      "All PyTorch model weights were used when initializing TFT5ForConditionalGeneration.\n",
      "\n",
      "All the weights of TFT5ForConditionalGeneration were initialized from the PyTorch model.\n",
      "If your task is similar to the task the model of the checkpoint was trained on, you can already use TFT5ForConditionalGeneration for predictions without further training.\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "from transformers import TFAutoModelForSeq2SeqLM, AutoTokenizer, DataCollatorForSeq2Seq\n",
    "# import tensorflow_addons as tfa\n",
    "# from tensorflow.keras.layers import Dense\n",
    "\n",
    "# setting GPU memory limit\n",
    "print(tf.config.list_physical_devices('GPU'))\n",
    "# os.environ['TF_FORCE_GPU_ALLOW_GROWTH'] = 'true'\n",
    "\n",
    "gpus = tf.config.list_physical_devices('GPU')\n",
    "if gpus:\n",
    "  try:\n",
    "    # Currently, memory growth needs to be the same across GPUs\n",
    "    for gpu in gpus:\n",
    "      tf.config.experimental.set_memory_growth(gpu, True)\n",
    "    logical_gpus = tf.config.list_logical_devices('GPU')\n",
    "    print(len(gpus), \"Physical GPUs,\", len(logical_gpus), \"Logical GPUs\")\n",
    "  except RuntimeError as e:\n",
    "    # Memory growth must be set before GPUs have been initialized\n",
    "    print(e)\n",
    "\n",
    "tokenizer = AutoTokenizer.from_pretrained(\"google/flan-t5-base\")\n",
    "model = TFAutoModelForSeq2SeqLM.from_pretrained(\"google/flan-t5-base\")"
   ]
  },
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     "text": [
      "{'article': 'LONDON, England (Reuters) -- Harry Potter star Daniel Radcliffe gains access to a reported £20 million ($41.1 million) fortune as he turns 18 on Monday, but he insists the money won\\'t cast a spell on him. Daniel Radcliffe as Harry Potter in \"Harry Potter and the Order of the Phoenix\" To the disappointment of gossip columnists around the world, the young actor says he has no plans to fritter his cash away on fast cars, drink and celebrity parties. \"I don\\'t plan to be one of those people who, as soon as they turn 18, suddenly buy themselves a massive sports car collection or something similar,\" he told an Australian interviewer earlier this month. \"I don\\'t think I\\'ll be particularly extravagant. \"The things I like buying are things that cost about 10 pounds -- books and CDs and DVDs.\" At 18, Radcliffe will be able to gamble in a casino, buy a drink in a pub or see the horror film \"Hostel: Part II,\" currently six places below his number one movie on the UK box office chart. Details of how he\\'ll mark his landmark birthday are under wraps. His agent and publicist had no comment on his plans. \"I\\'ll definitely have some sort of party,\" he said in an interview. \"Hopefully none of you will be reading about it.\" Radcliffe\\'s earnings from the first five Potter films have been held in a trust fund which he has not been able to touch. Despite his growing fame and riches, the actor says he is keeping his feet firmly on the ground. \"People are always looking to say \\'kid star goes off the rails,\\'\" he told reporters last month. \"But I try very hard not to go that way because it would be too easy for them.\" His latest outing as the boy wizard in \"Harry Potter and the Order of the Phoenix\" is breaking records on both sides of the Atlantic and he will reprise the role in the last two films.  Watch I-Reporter give her review of Potter\\'s latest » . There is life beyond Potter, however. The Londoner has filmed a TV movie called \"My Boy Jack,\" about author Rudyard Kipling and his son, due for release later this year. He will also appear in \"December Boys,\" an Australian film about four boys who escape an orphanage. Earlier this year, he made his stage debut playing a tortured teenager in Peter Shaffer\\'s \"Equus.\" Meanwhile, he is braced for even closer media scrutiny now that he\\'s legally an adult: \"I just think I\\'m going to be more sort of fair game,\" he told Reuters. E-mail to a friend . Copyright 2007 Reuters. All rights reserved.This material may not be published, broadcast, rewritten, or redistributed.', 'highlights': \"Harry Potter star Daniel Radcliffe gets £20M fortune as he turns 18 Monday .\\nYoung actor says he has no plans to fritter his cash away .\\nRadcliffe's earnings from first five Potter films have been held in trust fund .\", 'id': '42c027e4ff9730fbb3de84c1af0d2c506e41c3e4'}\n"
     ]
    }
   ],
   "source": [
    "# Loading the dataset\n",
    "from datasets import load_dataset\n",
    "dataset = load_dataset(\"abisee/cnn_dailymail\", \"3.0.0\")\n",
    "\n",
    "# Display an example\n",
    "print(dataset['train'][0])"
   ]
  },
  {
   "cell_type": "code",
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   "id": "6a5ecce5",
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     "data": {
      "text/plain": [
       "DatasetDict({\n",
       "    train: Dataset({\n",
       "        features: ['article', 'highlights', 'id'],\n",
       "        num_rows: 287113\n",
       "    })\n",
       "    validation: Dataset({\n",
       "        features: ['article', 'highlights', 'id'],\n",
       "        num_rows: 13368\n",
       "    })\n",
       "    test: Dataset({\n",
       "        features: ['article', 'highlights', 'id'],\n",
       "        num_rows: 11490\n",
       "    })\n",
       "})"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset"
   ]
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       "version_major": 2,
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      "text/plain": [
       "Map:   0%|          | 0/1000 [00:00<?, ? examples/s]"
      ]
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     "text": [
      "e:\\CondaEnvs\\tensorflow\\lib\\site-packages\\transformers\\tokenization_utils_base.py:3866: UserWarning: `as_target_tokenizer` is deprecated and will be removed in v5 of Transformers. You can tokenize your labels by using the argument `text_target` of the regular `__call__` method (either in the same call as your input texts if you use the same keyword arguments, or in a separate call.\n",
      "  warnings.warn(\n",
      "e:\\CondaEnvs\\tensorflow\\lib\\site-packages\\datasets\\arrow_dataset.py:403: FutureWarning: The output of `to_tf_dataset` will change when a passing single element list for `labels` or `columns` in the next datasets version. To return a tuple structure rather than dict, pass a single string.\n",
      "Old behaviour: columns=['a'], labels=['labels'] -> (tf.Tensor, tf.Tensor)  \n",
      "             : columns='a', labels='labels' -> (tf.Tensor, tf.Tensor)  \n",
      "New behaviour: columns=['a'],labels=['labels'] -> ({'a': tf.Tensor}, {'labels': tf.Tensor})  \n",
      "             : columns='a', labels='labels' -> (tf.Tensor, tf.Tensor) \n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "# Preprocess the dataset for input into the model\n",
    "def preprocess_data(examples):\n",
    "    inputs = [f'Sumarize: {article}' for article in examples[\"article\"]]\n",
    "    targets = [highlight for highlight in examples['highlights']]\n",
    "\n",
    "    # Tokenize inputs\n",
    "    model_inputs = tokenizer(inputs, max_length=512, truncation=True, padding=\"max_length\", return_tensors=\"tf\")\n",
    "\n",
    "    # Tokenize targets\n",
    "    with tokenizer.as_target_tokenizer():\n",
    "        labels = tokenizer(targets, max_length=512, truncation=True, padding=\"max_length\", return_tensors=\"tf\")\n",
    "\n",
    "    model_inputs[\"labels\"] = labels[\"input_ids\"]\n",
    "\n",
    "    # For decoder inputs\n",
    "    decoder_inputs = tokenizer(targets, max_length=512, truncation=True, padding=\"max_length\", return_tensors=\"tf\")\n",
    "    model_inputs[\"decoder_input_ids\"] = decoder_inputs[\"input_ids\"]\n",
    "\n",
    "    return model_inputs\n",
    "train_dataset = dataset['train'].select(range(30000)).map(preprocess_data, batched=True)\n",
    "val_dataset = dataset['validation'].select(range(1000)).map(preprocess_data, batched=True)\n",
    "test_dataset = dataset['test'].select(range(1000)).map(preprocess_data, batched=True)\n",
    "\n",
    "# Data collator\n",
    "data_collator = DataCollatorForSeq2Seq(tokenizer, model=None)\n",
    "\n",
    "train_dataset = train_dataset.to_tf_dataset(\n",
    "    columns=['input_ids', 'attention_mask','decoder_input_ids'],\n",
    "    label_cols=['labels'],\n",
    "    shuffle=True,\n",
    "    batch_size=8,\n",
    "    collate_fn=data_collator\n",
    ")\n",
    "\n",
    "val_dataset = val_dataset.to_tf_dataset(\n",
    "    columns=['input_ids', 'attention_mask','decoder_input_ids'],\n",
    "    label_cols=['labels'],\n",
    "    shuffle=False,\n",
    "    batch_size=8,\n",
    "    collate_fn=data_collator\n",
    ")\n",
    "\n",
    "test_dataset = test_dataset.to_tf_dataset(\n",
    "    columns=['input_ids', 'attention_mask','decoder_input_ids'],\n",
    "    label_cols=['labels'],\n",
    "    shuffle=False,\n",
    "    batch_size=8,\n",
    "    collate_fn=data_collator\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c82a3cb9",
   "metadata": {
    "id": "c82a3cb9"
   },
   "source": [
    "## Load the Pre-trained FLAN-T5 Model and Modify"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "3e04c62e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Replace the dense layers with LoRA layers\n",
    "class LoRALayer(tf.keras.layers.Layer):\n",
    "    def __init__(self, dense, rank=4):\n",
    "        super().__init__()\n",
    "        self.dense = dense\n",
    "        self.rank = rank\n",
    "\n",
    "    def build(self, input_shape):\n",
    "        self.w_a = self.add_weight(shape=(input_shape[-1], self.rank),\n",
    "                                   initializer='random_normal',\n",
    "                                   trainable=True, name='w_a')\n",
    "        self.w_b = self.add_weight(shape=(self.rank, self.dense.units),\n",
    "                                   initializer='random_normal',\n",
    "                                   trainable=True, name='w_b')\n",
    "\n",
    "    def call(self, inputs):\n",
    "        original_output = self.dense(inputs)\n",
    "        lora_output = tf.matmul(tf.matmul(inputs, self.w_a), self.w_b)\n",
    "        self.dense.trainable = False\n",
    "        return original_output + lora_output\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0319250b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"tft5_for_conditional_generation\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " shared (Embedding)          multiple                  24674304  \n",
      "                                                                 \n",
      " encoder (TFT5MainLayer)     multiple                  109628544 \n",
      "                                                                 \n",
      " decoder (TFT5MainLayer)     multiple                  137949312 \n",
      "                                                                 \n",
      " lm_head (Dense)             multiple                  24674304  \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 247,577,856\n",
      "Trainable params: 247,577,856\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "42252de0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import tf_keras\n",
    "for ix, layer in enumerate(model.decoder._flatten_layers()):\n",
    "    if isinstance(layer, tf.keras.layers.Dense):\n",
    "        layer.trainable = False\n",
    "        layer = LoRALayer(layer)\n",
    "    else:\n",
    "        layer.trainable = False\n",
    "model.get_layer('encoder').trainable = False\n",
    "model.get_layer('shared').trainable = False\n",
    "model.layers[3] = LoRALayer(model.get_layer('lm_head'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5bcf9a33",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"tft5_for_conditional_generation\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " shared (Embedding)          multiple                  24674304  \n",
      "                                                                 \n",
      " encoder (TFT5MainLayer)     multiple                  109628544 \n",
      "                                                                 \n",
      " decoder (TFT5MainLayer)     multiple                  137949312 \n",
      "                                                                 \n",
      " lm_head (Dense)             multiple                  24674304  \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 247,577,856\n",
      "Trainable params: 24,674,304\n",
      "Non-trainable params: 222,903,552\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc3bc024",
   "metadata": {
    "id": "fc3bc024"
   },
   "source": [
    "## Train the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dc1174d3",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 451880,
     "status": "ok",
     "timestamp": 1718903229064,
     "user": {
      "displayName": "Axel Sirota",
      "userId": "02089179879199828401"
     },
     "user_tz": 420
    },
    "id": "dc1174d3",
    "outputId": "12bd3301-8e30-4afe-9588-f1434d0f32e4"
   },
   "outputs": [],
   "source": [
    "# Compile the model\n",
    "model.compile(optimizer=tf.keras.optimizers.legacy.Adam(learning_rate=2e-5),\n",
    "              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)\n",
    "              )\n",
    "\n",
    "# Train the model\n",
    "model.fit(train_dataset, validation_data=val_dataset, epochs=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a98a36dc",
   "metadata": {
    "id": "a98a36dc"
   },
   "source": [
    "## Evaluate the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e69bb7f3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "125/125 [==============================] - 60s 393ms/step - loss: 50.5205\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "50.52045822143555"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(test_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ec1b8b4d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package punkt to\n",
      "[nltk_data]     C:\\Users\\mbila\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package punkt is already up-to-date!\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reference: Zully Broussard decided to give a kidney to a stranger. A new computer program helped her donation spur transplants for six kidney patients.\n",
      "Answer: Zully Broussard's generosity will be multiplied.\n",
      "ROUGE Scores: {'rouge1': Score(precision=0.2857142857142857, recall=0.08695652173913043, fmeasure=0.13333333333333333), 'rouge2': Score(precision=0.16666666666666666, recall=0.045454545454545456, fmeasure=0.07142857142857144), 'rougeL': Score(precision=0.2857142857142857, recall=0.08695652173913043, fmeasure=0.13333333333333333)}\n",
      "BLEU Score: 0.009414409352939208\n",
      "Exact match Score: 0\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from rouge_score import rouge_scorer\n",
    "from nltk.translate.bleu_score import sentence_bleu, SmoothingFunction\n",
    "import nltk\n",
    "nltk.download('punkt')\n",
    "\n",
    "def translate(inputs):\n",
    "    outputs = model.generate(inputs[0][\"input_ids\"], max_length=128, num_beams=4, early_stopping=True)\n",
    "    return tokenizer.decode(outputs[0], skip_special_tokens=True)\n",
    "\n",
    "\n",
    "# Function to calculate ROUGE and BLEU scores\n",
    "def calculate_scores(reference, hypothesis):\n",
    "    # Initialize scorers\n",
    "    rouge = rouge_scorer.RougeScorer(['rouge1', 'rouge2', 'rougeL'], use_stemmer=True)\n",
    "    bleu_smoothing = SmoothingFunction().method4\n",
    "\n",
    "    # Calculate ROUGE scores\n",
    "    rouge_scores = rouge.score(reference, hypothesis)\n",
    "\n",
    "    # Calculate BLEU score\n",
    "    reference_tokens = [nltk.word_tokenize(reference)]\n",
    "    hypothesis_tokens = nltk.word_tokenize(hypothesis)\n",
    "    bleu_score = sentence_bleu(reference_tokens, hypothesis_tokens, smoothing_function=bleu_smoothing)\n",
    "\n",
    "    return rouge_scores, bleu_score, int(reference==hypothesis)\n",
    "\n",
    "# Evaluate translations and calculate scores\n",
    "batch = next(iter(val_dataset))\n",
    "translated_text = translate(batch)\n",
    "reference_text = tokenizer.decode(batch[1][0], skip_special_tokens=True)\n",
    "rouge_scores, bleu_score, exact_match = calculate_scores(reference_text, translated_text)\n",
    "print(f\"Reference: {reference_text}\")\n",
    "print(f\"Answer: {translated_text}\")\n",
    "print(f\"ROUGE Scores: {rouge_scores}\")\n",
    "print(f\"BLEU Score: {bleu_score}\")\n",
    "print(f\"Exact match Score: {exact_match}\")\n",
    "print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ca968510",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reference: A jihadist group claims responsibility in an audio recording, news agency reports. The Malian government calls the shooting a \"terrorist act\" One French citizen, one Belgian and three Malians are killed.\n",
      "Answer: Bamako's retaliation for the killing of a leader.\n",
      "ROUGE Scores: {'rouge1': Score(precision=0.3333333333333333, recall=0.0967741935483871, fmeasure=0.15000000000000002), 'rouge2': Score(precision=0.0, recall=0.0, fmeasure=0.0), 'rougeL': Score(precision=0.2222222222222222, recall=0.06451612903225806, fmeasure=0.09999999999999999)}\n",
      "BLEU Score: 0.0020747551353380595\n",
      "Exact match Score: 0\n",
      "\n"
     ]
    }
   ],
   "source": [
    "iterator = iter(val_dataset)\n",
    "batch = next(iterator)\n",
    "batch = next(iterator)\n",
    "translated_text = translate(batch)\n",
    "reference_text = tokenizer.decode(batch[1][0], skip_special_tokens=True)\n",
    "rouge_scores, bleu_score, exact_match = calculate_scores(reference_text, translated_text)\n",
    "print(f\"Reference: {reference_text}\")\n",
    "print(f\"Answer: {translated_text}\")\n",
    "print(f\"ROUGE Scores: {rouge_scores}\")\n",
    "print(f\"BLEU Score: {bleu_score}\")\n",
    "print(f\"Exact match Score: {exact_match}\")\n",
    "print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0ea39424",
   "metadata": {},
   "outputs": [],
   "source": [
    "# def tokenize_function(examples):\n",
    "#     inputs = tokenizer(examples['article'], max_length=512, truncation=True, padding='max_length', return_tensors='tf')\n",
    "#     targets = tokenizer(examples['highlights'], max_length=128, truncation=True, padding='max_length', return_tensors='tf')\n",
    "\n",
    "#     inputs['labels'] = targets['input_ids']\n",
    "#     inputs['decoder_input_ids'] = targets['input_ids']\n",
    "#     return inputs\n",
    "\n",
    "# train_dataset = small_train_dataset.map(tokenize_function, batched=True, remove_columns=[\"id\"])\n",
    "# val_dataset = small_validation_dataset.map(tokenize_function, batched=True, remove_columns=[\"id\"])\n",
    "\n",
    "# def convert_to_tf_dataset(dataset):\n",
    "#     input_ids = tf.convert_to_tensor(dataset[\"input_ids\"], dtype=tf.int32)\n",
    "#     attention_mask = tf.convert_to_tensor(dataset[\"attention_mask\"], dtype=tf.int32)\n",
    "#     decoder_input_ids = tf.convert_to_tensor(dataset[\"decoder_input_ids\"], dtype=tf.int32)\n",
    "#     labels = tf.convert_to_tensor(dataset[\"labels\"], dtype=tf.int32)\n",
    "#     return tf.data.Dataset.from_tensor_slices(({\"input_ids\": input_ids, \"attention_mask\": attention_mask, \"decoder_input_ids\": decoder_input_ids}, labels)).batch(32)\n",
    "\n",
    "# train_data = convert_to_tf_dataset(train_dataset)\n",
    "# val_data = convert_to_tf_dataset(val_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "305801ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import keras\n",
    "# from keras.layers import Dense\n",
    "# from keras.models import Sequential\n",
    "# from keras.layers import Input\n",
    "\n",
    "# class LoraLayer(keras.layers.Layer):\n",
    "#     def __init__(self, original_layer, rank=8, num_heads=1, dim=1, trainable=False, **kwargs):\n",
    "#         original_layer_config = original_layer.get_config()\n",
    "#         name = original_layer_config[\"name\"]\n",
    "#         kwargs.pop(\"name\", None)\n",
    "#         super().__init__(name=name, trainable=trainable, **kwargs)\n",
    "#         self.rank = rank\n",
    "#         self.original_layer = original_layer\n",
    "#         self.original_layer.trainable = False\n",
    "#         self.A = keras.layers.Dense(units=rank, use_bias=False, trainable=trainable, name=f\"lora_A\")\n",
    "#         self.B = keras.layers.Dense(units=dim, use_bias=False, trainable=trainable, name=f\"lora_B\")\n",
    "\n",
    "#     def call(self, inputs):\n",
    "#         original_output = self.original_layer(inputs)\n",
    "#         if self.trainable:\n",
    "#             lora_output = self.B(self.A(inputs))\n",
    "#             return original_output + lora_output\n",
    "#         return original_output\n",
    "\n",
    "# import transformers\n",
    "# from tf_keras.src.layers.core.dense import Dense as NDense\n",
    "\n",
    "# def replace_dense_with_lora(layer, rank=8):\n",
    "#     if isinstance(layer, NDense):\n",
    "#         return LoraLayer(original_layer=layer, rank=rank)\n",
    "#     return layer\n",
    "\n",
    "# def modify_t5_layers(t5, rank=8):\n",
    "#     for sub_layer in t5.encoder.submodules:\n",
    "#         if isinstance(sub_layer, transformers.models.t5.modeling_tf_t5.TFT5Attention):\n",
    "#             sub_layer.k = replace_dense_with_lora(sub_layer.k, rank)\n",
    "#             sub_layer.v = replace_dense_with_lora(sub_layer.v, rank)\n",
    "#             sub_layer.q = replace_dense_with_lora(sub_layer.q, rank)\n",
    "#             sub_layer.o = replace_dense_with_lora(sub_layer.o, rank)\n",
    "#     for sub_layer in t5.decoder.submodules:\n",
    "#         if isinstance(sub_layer, transformers.models.t5.modeling_tf_t5.TFT5Attention):\n",
    "#             sub_layer.k = replace_dense_with_lora(sub_layerb.k, rank)\n",
    "#             sub_layer.v = replace_dense_with_lora(sub_layer.v, rank)\n",
    "#             sub_layer.q = replace_dense_with_lora(sub_layer.q, rank)\n",
    "#             sub_layer.o = replace_dense_with_lora(sub_layer.o, rank)\n",
    "#     return t5\n",
    "\n",
    "# modified_t5_model = modify_t5_layers(t5_model, rank=4)\n",
    "\n",
    "# for layer in modified_t5_model._flatten_layers():\n",
    "#     if (layer.__class__.__module__.startswith('tf_keras') or layer.__class__.__module__.startswith('keras')) and not layer.name.startswith(\"lora\"):\n",
    "#         layer.trainable = False\n",
    "#     elif layer.name.startswith(\"lora\"):\n",
    "#         layer.trainable = True\n",
    "#     elif layer.name == 'shared':\n",
    "#         layer.trainable = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5d394e50",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import gc\n",
    "# from tf_keras import backend as K\n",
    "\n",
    "# def clear_gpu_memory():\n",
    "#     gc.collect()\n",
    "#     tf.keras.backend.clear_session()\n",
    "#     K.clear_session()\n",
    "#     tf.compat.v1.reset_default_graph()\n",
    "\n",
    "# clear_gpu_memory()\n",
    "\n",
    "# from tf_keras import mixed_precision\n",
    "\n",
    "# mixed_precision.set_global_policy('mixed_float16')\n",
    "# tf.config.run_functions_eagerly(True)\n",
    "# modified_t5_model.compile(optimizer='adam', loss=t5_model.hf_compute_loss)\n",
    "\n",
    "# modified_t5_model.fit(train_data, epochs=1, validation_data=val_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c188137e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# !pip install rouge_score\n",
    "\n",
    "# from datasets import load_metric\n",
    "\n",
    "# rouge = load_metric(\"rouge\", trust_remote_code=True)\n",
    "# num_samples = 1000\n",
    "\n",
    "# def generate_summaries(dataset, num_samples):\n",
    "#     inputs = tokenizer([ex['article'] for ex in dataset.take(num_samples)], return_tensors='tf', padding=True, truncation=True, max_length=512)\n",
    "#     summaries = modified_t5_model.generate(inputs['input_ids'], attention_mask=inputs['attention_mask'], max_length=128, num_beams=5)\n",
    "#     decoded_summaries = [tokenizer.decode(g, skip_special_tokens=True, clean_up_tokenization_spaces=False) for g in summaries]\n",
    "#     return decoded_summaries\n",
    "\n",
    "# val_summaries = [ex['highlights'] for ex in val_dataset.take(num_samples)]\n",
    "# generated_summaries = generate_summaries(val_dataset, num_samples=num_samples)\n",
    "# results = rouge.compute(predictions=generated_summaries, references=val_summaries)\n",
    "# print(results)"
   ]
  }
 ],
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