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pip install transformers sentencepiece datasets\n","! pip install tqdm\n","! pip install torch"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"3YaPG4v3EfqJ","executionInfo":{"status":"ok","timestamp":1741770581414,"user_tz":-180,"elapsed":113347,"user":{"displayName":"Judd Marc","userId":"10378050880049965511"}},"outputId":"1a840709-d44e-45f0-c2b4-d746cb774deb"},"execution_count":1,"outputs":[{"output_type":"stream","name":"stdout","text":["Requirement already satisfied: transformers in /usr/local/lib/python3.11/dist-packages (4.48.3)\n","Requirement already satisfied: sentencepiece in /usr/local/lib/python3.11/dist-packages (0.2.0)\n","Collecting datasets\n"," Downloading datasets-3.3.2-py3-none-any.whl.metadata (19 kB)\n","Requirement already satisfied: filelock in /usr/local/lib/python3.11/dist-packages (from transformers) (3.17.0)\n","Requirement already satisfied: huggingface-hub<1.0,>=0.24.0 in /usr/local/lib/python3.11/dist-packages (from transformers) 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nvidia-cuda-runtime-cu12\n"," Found existing installation: nvidia-cuda-runtime-cu12 12.5.82\n"," Uninstalling nvidia-cuda-runtime-cu12-12.5.82:\n"," Successfully uninstalled nvidia-cuda-runtime-cu12-12.5.82\n"," Attempting uninstall: nvidia-cuda-nvrtc-cu12\n"," Found existing installation: nvidia-cuda-nvrtc-cu12 12.5.82\n"," Uninstalling nvidia-cuda-nvrtc-cu12-12.5.82:\n"," Successfully uninstalled nvidia-cuda-nvrtc-cu12-12.5.82\n"," Attempting uninstall: nvidia-cuda-cupti-cu12\n"," Found existing installation: nvidia-cuda-cupti-cu12 12.5.82\n"," Uninstalling nvidia-cuda-cupti-cu12-12.5.82:\n"," Successfully uninstalled nvidia-cuda-cupti-cu12-12.5.82\n"," Attempting uninstall: nvidia-cublas-cu12\n"," Found existing installation: nvidia-cublas-cu12 12.5.3.2\n"," Uninstalling nvidia-cublas-cu12-12.5.3.2:\n"," Successfully uninstalled nvidia-cublas-cu12-12.5.3.2\n"," Attempting uninstall: nvidia-cusparse-cu12\n"," Found existing installation: nvidia-cusparse-cu12 12.5.1.3\n"," Uninstalling nvidia-cusparse-cu12-12.5.1.3:\n"," Successfully uninstalled nvidia-cusparse-cu12-12.5.1.3\n"," Attempting uninstall: nvidia-cudnn-cu12\n"," Found existing installation: nvidia-cudnn-cu12 9.3.0.75\n"," Uninstalling nvidia-cudnn-cu12-9.3.0.75:\n"," Successfully uninstalled nvidia-cudnn-cu12-9.3.0.75\n"," Attempting uninstall: nvidia-cusolver-cu12\n"," Found existing installation: nvidia-cusolver-cu12 11.6.3.83\n"," Uninstalling nvidia-cusolver-cu12-11.6.3.83:\n"," Successfully uninstalled nvidia-cusolver-cu12-11.6.3.83\n","Successfully installed nvidia-cublas-cu12-12.4.5.8 nvidia-cuda-cupti-cu12-12.4.127 nvidia-cuda-nvrtc-cu12-12.4.127 nvidia-cuda-runtime-cu12-12.4.127 nvidia-cudnn-cu12-9.1.0.70 nvidia-cufft-cu12-11.2.1.3 nvidia-curand-cu12-10.3.5.147 nvidia-cusolver-cu12-11.6.1.9 nvidia-cusparse-cu12-12.3.1.170 nvidia-nvjitlink-cu12-12.4.127\n"]}]},{"cell_type":"code","source":["# from huggingface_hub import crate_branch\n","from huggingface_hub import notebook_login\n","notebook_login()\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":17,"referenced_widgets":["c16a46eeb1014a6d9ec5f73446e5f1a7","651e5fbc32974e81952cac8ee005f6d1","73acbcdfdd3b4d19b160e4b17a9a81d4","21e5170b189f4979b19c02b32df9df7a","23979f37a61c47859ab1e7ec71de2674","80a43ba3d648435aa4dca15aa7f20364","5e381b943b8b4959b715ca63fbe83762","ca676b15fbec4f96a15b17153aaa0be9","6d1b2fc5429c402fa1c524654fc257e4","9a603d8a872d491cad52fd0c93c44bb7","308f4e242de04320afe7a066ff24721b","f92015b4727f4a20a30c0a202055f5ab","29be91d10463415194b51dac2ea7dc11","77f9e49463e8407cb378b1ae038f6c1d","044d30ffc19c49cfbb7a9b8ccfa217de","b000cd409ba6440480d54820d858897e","fe21d844f01f4435b38b020f9d7e62fc","6d11605f5a934c80b498edc1254ccdad","875de4e1b19b47a595f6f1bb94a2d56d","026ce4ab9a704141b530efb26d668ab8"]},"id":"06goYKMXnnWY","executionInfo":{"status":"ok","timestamp":1741770588368,"user_tz":-180,"elapsed":1758,"user":{"displayName":"Judd Marc","userId":"10378050880049965511"}},"outputId":"b372997d-72c9-4b60-b574-92cdab676480"},"execution_count":2,"outputs":[{"output_type":"display_data","data":{"text/plain":["VBox(children=(HTML(value=' . This is expected, and simply means that the `legacy` (previous) behavior will be used so nothing changes for you. If you want to use the new behaviour, set `legacy=False`. This should only be set if you understand what it means, and thoroughly read the reason why this was added as explained in https://github.com/huggingface/transformers/pull/24565\n","/usr/local/lib/python3.11/dist-packages/transformers/convert_slow_tokenizer.py:561: UserWarning: The sentencepiece tokenizer that you are converting to a fast tokenizer uses the byte fallback option which is not implemented in the fast tokenizers. In practice this means that the fast version of the tokenizer can produce unknown tokens whereas the sentencepiece version would have converted these unknown tokens into a sequence of byte tokens matching the original piece of text.\n"," warnings.warn(\n"]},{"output_type":"display_data","data":{"text/plain":["pytorch_model.bin: 0%| | 0.00/1.20G [00:00',\n"," 'sw': ''\n","}\n","\n","api = HfApi() # Initialize the API client once\n","repo_id=\"Lingua-Connect/JMW_TrainerImproved\"\n","tokenizer = AutoTokenizer.from_pretrained(model_name)\n","model = AutoModelForSeq2SeqLM.from_pretrained(model_name)\n","model.to(device)\n","\n","repo_name=\"Lingua-Connect/JMW_TrainerImproved\"\n","checkpoint_file = \"checkpoint.pth\"\n","\n","# Add special tokens\n","special_tokens = {'additional_special_tokens': list(lang_token.values())}\n","tokenizer.add_special_tokens(special_tokens)\n","model.resize_token_embeddings(len(tokenizer))\n","\n","# Load the dataset and check available splits\n","dataset = load_dataset('Rogendo/English-Swahili-Sentence-Pairs')\n","print(\"Available splits:\", dataset.keys())\n","\n","\n","# Calculate the sizes based on percentages\n","total_rows = len(dataset['train'])\n","train_size = int(0.8 * total_rows) # 80% for training\n","validation_size = int(0.1 * total_rows) # 10% for validation\n","test_size = total_rows - train_size - validation_size # Remaining 10% for test\n","\n","# Shuffle the dataset to ensure randomness in splitting\n","ds_shuffled = dataset['train'].shuffle(seed=42)\n","\n","# Create the splits based on the specified sizes\n","train_dataset = ds_shuffled.select(range(train_size)) # train_dataset should be a dataset, not an int\n","validation_dataset = ds_shuffled.select(range(train_size, train_size + validation_size))\n","test_dataset = ds_shuffled.select(range(train_size + validation_size, train_size + validation_size + test_size))\n","\n","new_dataset = DatasetDict({\n"," 'train': train_dataset,\n"," 'validation': validation_dataset,\n"," 'test': test_dataset\n","})\n","\n","# Print the new dataset structure\n","print(new_dataset)"]},{"cell_type":"markdown","source":["# Preprocessor"],"metadata":{"id":"2KVH6Q2tHrlO"}},{"cell_type":"code","source":["import torch\n","from typing import Dict, List, Tuple, Optional, Union, Iterator\n","from dataclasses import dataclass\n","from transformers import AutoTokenizer\n","from torch.utils.data import Dataset, DataLoader\n","from enum import Enum\n","\n","class TranslationDirection(Enum):\n"," \"\"\"Enum for translation directions\"\"\"\n"," EN2SW = \"en2sw\"\n"," SW2EN = \"sw2en\"\n","\n","@dataclass\n","class TranslationPair:\n"," \"\"\"Data class for holding translation pairs.\"\"\"\n"," source_text: str\n"," target_text: str\n"," source_lang: str\n"," target_lang: str\n","\n"," def __post_init__(self):\n"," \"\"\"Validate inputs upon initialization.\"\"\"\n"," # Convert None to empty string to avoid attribute errors\n"," self.source_text = str(self.source_text or \"\")\n"," self.target_text = str(self.target_text or \"\")\n"," self.source_lang = str(self.source_lang or \"\")\n"," self.target_lang = str(self.target_lang or \"\")\n","\n"," def is_valid(self) -> bool:\n"," \"\"\"Check if the translation pair is valid for training.\"\"\"\n"," return (bool(self.source_text.strip()) and\n"," bool(self.target_text.strip()) and\n"," bool(self.source_lang.strip()) and\n"," bool(self.target_lang.strip()))\n","\n","class TranslationPreprocessor:\n"," def __init__(self, tokenizer: AutoTokenizer, lang_tokens: Dict[str, str], max_length: int):\n"," \"\"\"\n"," Initialize the preprocessor.\n","\n"," Args:\n"," tokenizer: HuggingFace tokenizer\n"," lang_tokens: Dictionary mapping language codes to tokens\n"," max_length: Maximum sequence length\n"," \"\"\"\n"," self.tokenizer = tokenizer\n"," self.lang_tokens = lang_tokens\n"," self.max_length = max_length\n"," self.device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n"," self.key_mapping = {\n"," TranslationDirection.EN2SW.value: (\"English sentence\", \"Swahili Translation\"),\n"," TranslationDirection.SW2EN.value: (\"Swahili Translation\", \"English sentence\")\n"," }\n","\n"," def add_language_token(self, text: str, lang_code: str) -> str:\n"," \"\"\"Prepend language-specific token to text.\"\"\"\n"," return f\"{self.lang_tokens[lang_code]} {text}\"\n","\n"," def encode_input_str(self, text: str, target_lang: str, tokenizer: AutoTokenizer, seq_len: int, lang_token: Dict[str, str]) -> torch.Tensor:\n"," \"\"\"Encode input string with source language token\"\"\"\n"," # Use source language token (English)\n"," source_lang_token = lang_token['en']\n","\n"," # Tokenize with English token\n"," input_ids = tokenizer.encode(\n"," text=f\"{source_lang_token} {text}\",\n"," return_tensors='pt',\n"," padding='max_length',\n"," truncation=True,\n"," max_length=seq_len\n"," )\n","\n"," return input_ids[0]\n","\n"," def encode_target_str(self, text: str, tokenizer: AutoTokenizer, seq_len: int, lang_token: Dict[str, str]) -> torch.Tensor:\n"," \"\"\"Encode target string with target language token\"\"\"\n"," # For target, we'll keep the Swahili token as it's the target language\n"," target_lang_token = lang_token['sw']\n","\n"," token_ids = tokenizer.encode(\n"," text=f\"{target_lang_token} {text}\",\n"," return_tensors='pt',\n"," padding='max_length',\n"," truncation=True,\n"," max_length=seq_len\n"," )\n","\n"," return token_ids[0]\n","\n"," def format_translation_data(self, translations: Dict[str, str], lang_token: Dict[str, str], tokenizer: AutoTokenizer, seq_len: int = 20) -> Optional[Tuple[torch.Tensor, torch.Tensor]]:\n"," \"\"\"Format translation data for model input\"\"\"\n"," # Define possible input-output language pairs and randomly select one\n"," input_lang, target_lang = 'en', 'sw'\n","\n"," # Map language codes to column names\n"," column_map = {\n"," 'en': 'English sentence',\n"," 'sw': 'Swahili Translation'\n"," }\n","\n"," # Extract the translations based on the chosen direction\n"," input_text = translations.get(column_map[input_lang])\n"," target_text = translations.get(column_map[target_lang])\n","\n"," # Check for any missing translations\n"," if input_text is None or target_text is None:\n"," return None\n","\n"," # Tokenize input with English as source language\n"," input_token_ids = self.encode_input_str(\n"," text=input_text,\n"," target_lang='sw',\n"," tokenizer=tokenizer,\n"," seq_len=seq_len,\n"," lang_token=lang_token\n"," )\n","\n"," # Tokenize target text\n"," target_token_ids = self.encode_target_str(\n"," text=target_text,\n"," tokenizer=tokenizer,\n"," seq_len=seq_len,\n"," lang_token=lang_token\n"," )\n","\n"," return input_token_ids, target_token_ids\n","\n"," def transform_batch(self, batch: Dict[str, List[str]], lang_token: Dict[str, str], tokenizer: AutoTokenizer, max_length: int) -> Optional[Tuple[torch.Tensor, torch.Tensor]]:\n"," \"\"\"Transform a batch of translations\"\"\"\n"," inputs = []\n"," targets = []\n","\n"," # Get all English sentences and Swahili translations from the batch\n"," english_sentences = batch['English sentence']\n"," swahili_translations = batch['Swahili Translation']\n","\n"," # Process each pair in the batch\n"," for eng, swa in zip(english_sentences, swahili_translations):\n"," translation_set = {\n"," 'English sentence': eng,\n"," 'Swahili Translation': swa\n"," }\n","\n"," # Process the translation pair\n"," formatted_data = self.format_translation_data(\n"," translation_set,\n"," lang_token,\n"," tokenizer,\n"," max_length\n"," )\n","\n"," if formatted_data is None:\n"," continue\n","\n"," input_ids, target_ids = formatted_data\n"," inputs.append(input_ids.unsqueeze(0))\n"," targets.append(target_ids.unsqueeze(0))\n","\n"," if not inputs or not targets: # Check if we have any valid translations\n"," return None\n","\n"," # Concatenate tensors for batch\n"," device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n"," batch_input_ids = torch.cat(inputs).to(device)\n"," batch_target_ids = torch.cat(targets).to(device)\n","\n"," return batch_input_ids, batch_target_ids\n","\n"," def get_data_generator(self, dataset, lang_token: Dict[str, str], tokenizer: AutoTokenizer, batch_size: int = 8, direction: TranslationDirection = TranslationDirection.EN2SW) -> Iterator[Tuple[torch.Tensor, torch.Tensor]]:\n"," \"\"\"Generate batches of processed translation data\"\"\"\n"," # dataset = dataset.shuffle()\n"," for i in range(0, len(dataset), batch_size):\n"," end_idx = min(i + batch_size, len(dataset))\n"," batch = dataset[i:end_idx]\n"," batch_dict = {\n"," 'English sentence': batch['English sentence'],\n"," 'Swahili Translation': batch['Swahili Translation']\n"," }\n"," processed_batch = self.transform_batch(batch, lang_token, tokenizer, self.max_length)\n"," if processed_batch is not None:\n"," yield processed_batch\n","\n"," def process_translation_pair(self, pair: TranslationPair) -> Tuple[torch.Tensor, torch.Tensor]:\n"," \"\"\"Process a single translation pair\"\"\"\n"," # Add language tokens to source and target texts\n"," source_text_with_token = self.add_language_token(pair.source_text, pair.source_lang)\n"," target_text_with_token = self.add_language_token(pair.target_text, pair.target_lang)\n","\n"," # Tokenize source and target texts\n"," source_ids = self.tokenizer.encode(\n"," source_text_with_token,\n"," return_tensors='pt',\n"," padding='max_length',\n"," truncation=True,\n"," max_length=self.max_length\n"," )\n","\n"," target_ids = self.tokenizer.encode(\n"," target_text_with_token,\n"," return_tensors='pt',\n"," padding='max_length',\n"," truncation=True,\n"," max_length=self.max_length\n"," )\n","\n"," return source_ids[0], target_ids[0]"],"metadata":{"id":"fMsZR7l3DKKx","executionInfo":{"status":"ok","timestamp":1741770704537,"user_tz":-180,"elapsed":33,"user":{"displayName":"Judd Marc","userId":"10378050880049965511"}}},"execution_count":4,"outputs":[]},{"cell_type":"markdown","source":["# Optimizer and Scheduler"],"metadata":{"id":"WahjKMeEHywB"}},{"cell_type":"code","source":["\n","from transformers import get_cosine_schedule_with_warmup\n","from torch.optim import AdamW\n","import math\n","import numpy as np\n","\n","# Configurable parameters\n","learning_rate = 5e-5\n","epsilon_value = 1e-8\n","batch_size = 8\n","num_epochs = 10\n","\n","# Optimizer setup\n","optimizer = AdamW(\n"," model.parameters(),\n"," lr=learning_rate,\n"," eps=epsilon_value,\n",")\n","\n","# Calculate steps\n","n_batches = int(np.ceil(len(train_dataset) / batch_size))\n","total_steps = num_epochs * n_batches\n","num_warmup_steps = int(0.05 * total_steps) # 5% warmup\n","\n","# Use standard cosine scheduler\n","scheduler = get_cosine_schedule_with_warmup(\n"," optimizer,\n"," num_warmup_steps=num_warmup_steps,\n"," num_training_steps=total_steps\n",")\n","\n","# Debugging function\n","def print_lr_details(optimizer, scheduler, step):\n"," print(f\"Step {step}:\")\n"," print(f\" Base LR: {optimizer.param_groups[0]['lr']:.2e}\")\n"," print(f\" Scheduler LR: {scheduler.get_last_lr()[0]:.2e}\")\n","\n","# Example usage (replace with your actual training loop)\n","for step in range(5):\n"," print_lr_details(optimizer, scheduler, step)\n"," optimizer.step()\n"," scheduler.step()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"dsmfVvrWHpGX","executionInfo":{"status":"ok","timestamp":1741770704541,"user_tz":-180,"elapsed":22,"user":{"displayName":"Judd Marc","userId":"10378050880049965511"}},"outputId":"594bb710-f4f5-47e6-9e9c-3a1961ca98e8"},"execution_count":5,"outputs":[{"output_type":"stream","name":"stdout","text":["Step 0:\n"," Base LR: 0.00e+00\n"," Scheduler LR: 0.00e+00\n","Step 1:\n"," Base LR: 4.75e-09\n"," Scheduler LR: 4.75e-09\n","Step 2:\n"," Base LR: 9.50e-09\n"," Scheduler LR: 9.50e-09\n","Step 3:\n"," Base LR: 1.43e-08\n"," Scheduler LR: 1.43e-08\n","Step 4:\n"," Base LR: 1.90e-08\n"," Scheduler LR: 1.90e-08\n"]}]},{"cell_type":"markdown","source":["# Checkpoint Manager"],"metadata":{"id":"ipUzXn5ARYcp"}},{"cell_type":"code","source":["import torch\n","from huggingface_hub import HfApi, hf_hub_download\n","import os\n","import gc\n","import matplotlib.pyplot as plt\n","\n","class CheckpointManager:\n"," def __init__(self, model, optimizer, scheduler, tokenizer, repo_name, device, branch_name=\"main\"):\n"," self.model = model\n"," self.optimizer = optimizer\n"," self.scheduler = scheduler\n"," self.tokenizer = tokenizer\n"," self.repo_name = repo_name\n"," self.device = device\n"," self.branch_name = branch_name\n"," self.api = HfApi()\n"," self.batches_per_epoch = None\n","\n"," def set_dataset_info(self, num_samples, batch_size):\n"," \"\"\"Store dataset info for accurate step calculations\"\"\"\n"," self.batches_per_epoch = int(np.ceil(num_samples / batch_size))\n"," self.batch_size = batch_size\n"," self.num_samples = num_samples\n"," print(f\"CheckpointManager: Batches per epoch set to {self.batches_per_epoch}\")\n","\n","\n"," def save_checkpoint(self, epoch, batch_idx, losses, total_steps=None, steps_taken=None, repo_name=None, branch_name=None):\n"," repo_name = repo_name or self.repo_name\n"," branch_name = branch_name or self.branch_name\n","\n"," try:\n"," # Calculate steps_taken if not provided, using the stored batches_per_epoch\n"," if steps_taken is None and self.batches_per_epoch is not None:\n"," steps_taken = epoch * self.batches_per_epoch + batch_idx\n"," print(f\"Calculated steps_taken for saving: {steps_taken}\")\n","\n"," # Get current learning rates\n"," current_lr = self.optimizer.param_groups[0]['lr']\n"," scheduler_lr = self.scheduler.get_last_lr()[0]\n","\n"," # Prepare the checkpoint dictionary with enhanced information\n"," checkpoint = {\n"," 'model_state_dict': self.model.state_dict(),\n"," 'optimizer_state_dict': self.optimizer.state_dict(),\n"," 'scheduler_state_dict': self.scheduler.state_dict(),\n"," 'epoch': epoch,\n"," 'batch': batch_idx,\n"," 'losses': losses,\n"," 'current_lr': current_lr,\n"," 'scheduler_lr': scheduler_lr,\n"," 'total_steps': total_steps,\n"," 'batches_per_epoch': self.batches_per_epoch,\n"," 'steps_taken': steps_taken if steps_taken is not None else (epoch * batch_idx)\n"," }\n","\n"," # Save the checkpoint locally\n"," torch.save(checkpoint, 'checkpoint.pth')\n"," print(f\"Checkpoint saved locally with learning rate: {current_lr:.2e}, scheduler lr: {scheduler_lr:.2e}\")\n"," print(f\"Saved at epoch {epoch + 1}, batch {batch_idx}, steps_taken: {steps_taken}\")\n","\n"," # Ensure all tensors in the model are contiguous\n"," for param in self.model.parameters():\n"," param.data = param.data.contiguous()\n","\n"," # Upload model to Hugging Face Hub\n"," self.model.push_to_hub(\n"," repo_id=repo_name,\n"," commit_message=f\"Checkpoint after epoch {epoch + 1}, batch {batch_idx}\",\n"," use_temp_dir=True,\n"," revision=branch_name\n"," )\n","\n"," # Upload tokenizer to Hugging Face Hub\n"," self.tokenizer.push_to_hub(\n"," repo_id=repo_name,\n"," revision=branch_name\n"," )\n","\n"," # Upload checkpoint file to Hugging Face Hub\n"," self.api.upload_file(\n"," path_or_fileobj=\"checkpoint.pth\",\n"," path_in_repo=\"checkpoint.pth\",\n"," repo_id=repo_name,\n"," revision=branch_name\n"," )\n","\n"," # Upload notebook if available\n"," notebook_path = \"/content/drive/MyDrive/Colab Notebooks/progress.ipynb\"\n"," if os.path.exists(notebook_path):\n"," self.api.upload_file(\n"," path_or_fileobj=notebook_path,\n"," path_in_repo=\"current_notebook.ipynb\",\n"," repo_id=repo_name,\n"," revision=branch_name\n"," )\n","\n"," print(\"Checkpoint file uploaded to Hugging Face Hub.\")\n"," print(f\"Checkpoint saved to Hugging Face Hub after epoch {epoch + 1}, batch {batch_idx}\")\n","\n"," except Exception as e:\n"," print(f\"Error saving checkpoint: {e}\")\n","\n"," def load_checkpoint(self, repo_name=None, branch_name=None):\n"," repo_name = repo_name or self.repo_name\n"," branch_name = branch_name or self.branch_name\n","\n"," try:\n"," # Clear memory before loading checkpoint\n"," torch.cuda.empty_cache()\n"," gc.collect()\n","\n"," # Check if the checkpoint file exists in the repo\n"," if self.checkpoint_exists(repo_name, \"checkpoint.pth\", branch_name):\n"," # Download the checkpoint file\n"," checkpoint_path = hf_hub_download(\n"," repo_id=repo_name,\n"," filename=\"checkpoint.pth\",\n"," revision=branch_name\n"," )\n","\n"," # Load the checkpoint\n"," checkpoint = torch.load(checkpoint_path, map_location=self.device)\n","\n"," # Store learning rate before loading for comparison\n"," prev_lr = self.optimizer.param_groups[0]['lr'] if self.optimizer else None\n"," prev_scheduler_lr = self.scheduler.get_last_lr()[0] if self.scheduler else None\n","\n"," # Load model, optimizer, and scheduler states\n"," self.model.load_state_dict(checkpoint['model_state_dict'])\n"," self.optimizer.load_state_dict(checkpoint['optimizer_state_dict'])\n"," self.scheduler.load_state_dict(checkpoint['scheduler_state_dict'])\n","\n"," # Load training state\n"," epoch_idx = checkpoint['epoch']\n"," batch_idx = checkpoint['batch']\n"," losses = checkpoint['losses']\n","\n"," # Extract additional information (with backward compatibility)\n"," current_lr = checkpoint.get('current_lr', self.optimizer.param_groups[0]['lr'])\n"," scheduler_lr = checkpoint.get('scheduler_lr', self.scheduler.get_last_lr()[0])\n"," steps_taken = checkpoint.get('steps_taken', None)\n"," total_steps = checkpoint.get('total_steps', None)\n","\n"," if steps_taken is None and self.batches_per_epoch is not None:\n"," # Calculate steps_taken from epoch and batch_idx\n"," steps_taken = epoch_idx * self.batches_per_epoch + batch_idx\n"," print(f\"Calculated steps_taken from epoch/batch: {steps_taken}\")\n","\n"," # Verify learning rates\n"," print(f\"LR before checkpoint loading: {prev_lr:.2e}, After: {self.optimizer.param_groups[0]['lr']:.2e}\")\n"," print(f\"Scheduler LR before: {prev_scheduler_lr:.2e}, After: {self.scheduler.get_last_lr()[0]:.2e}\")\n"," print(f\"Checkpoint saved with LR: {current_lr:.2e}, Scheduler LR: {scheduler_lr:.2e}\")\n","\n"," # Verify if the learning rates match up\n"," if abs(self.optimizer.param_groups[0]['lr'] - current_lr) > 1e-8:\n"," print(\"WARNING: Current learning rate doesn't match the checkpoint's saved learning rate!\")\n","\n"," print(f\"Loaded checkpoint from epoch {epoch_idx + 1}, batch {batch_idx}\")\n","\n"," if steps_taken is not None:\n"," print(f\"Steps taken according to checkpoint: {steps_taken}\")\n"," if self.batches_per_epoch is not None:\n"," expected_steps = epoch_idx * self.batches_per_epoch + batch_idx\n"," if expected_steps != steps_taken:\n"," print(f\"WARNING: Expected steps ({expected_steps}) doesn't match steps_taken in checkpoint ({steps_taken})!\")\n","\n"," return epoch_idx, batch_idx, losses, steps_taken, total_steps\n","\n"," else:\n"," print(\"No checkpoint file found in the repository.\")\n","\n"," except Exception as e:\n"," print(f\"Error loading checkpoint: {e}\")\n"," torch.cuda.empty_cache()\n"," gc.collect()\n","\n"," print(\"No checkpoint found or error occurred. Starting training from scratch.\")\n"," return 0, 0, [], 0, None # Start from epoch 0, batch 0, empty losses list, 0 steps\n","\n"," def checkpoint_exists(self, repo_name, filename, branch_name):\n"," try:\n"," # List all files in the specified branch\n"," files = self.api.list_repo_files(repo_id=repo_name, revision=branch_name)\n"," return filename in files\n"," except Exception as e:\n"," print(f\"Error checking for checkpoint: {e}\")\n"," return False"],"metadata":{"id":"dnccrlmKRX12","executionInfo":{"status":"ok","timestamp":1741770704548,"user_tz":-180,"elapsed":5,"user":{"displayName":"Judd Marc","userId":"10378050880049965511"}}},"execution_count":6,"outputs":[]},{"cell_type":"markdown","source":["# Evaluate"],"metadata":{"id":"b9vUbogSE69k"}},{"cell_type":"code","source":["def eval_model(model, val_dataset, tokenizer, LANG_TOKEN_MAPPING):\n"," model.eval()\n"," total_val_loss = 0\n"," val_data_generator = preprocessor.get_data_generator(\n"," val_dataset, LANG_TOKEN_MAPPING, tokenizer, batch_size=batch_size\n"," )\n"," total_val_batches = len(val_dataset) // batch_size\n"," if len(val_dataset) % batch_size != 0:\n"," total_val_batches += 1\n","\n"," with torch.no_grad():\n"," for batch_idx, (input_batch, label_batch) in enumerate(val_data_generator):\n"," input_batch = input_batch.to(device)\n"," label_batch = label_batch.to(device)\n"," outputs = model(input_ids=input_batch, labels=label_batch)\n"," total_val_loss += outputs.loss.item()\n","\n"," avg_val_loss = total_val_loss / total_val_batches\n"," return avg_val_loss"],"metadata":{"id":"HxaQamsMwpxz","executionInfo":{"status":"ok","timestamp":1741770704557,"user_tz":-180,"elapsed":5,"user":{"displayName":"Judd Marc","userId":"10378050880049965511"}}},"execution_count":7,"outputs":[]},{"cell_type":"markdown","source":["# Bleu Score"],"metadata":{"id":"fS2993WaE_cu"}},{"cell_type":"code","source":["from nltk.translate.bleu_score import corpus_bleu\n","\n","def calculate_bleu(predictions, references):\n"," \"\"\"\n"," Calculate BLEU score given predictions and references.\n","\n"," Args:\n"," predictions: List of predicted translations (list of strings).\n"," references: List of reference translations (list of strings).\n","\n"," Returns:\n"," bleu_score: The computed BLEU score.\n"," \"\"\"\n"," # Wrap each reference in a list (required by corpus_bleu)\n"," references = [[ref] for ref in references]\n","\n"," # Compute BLEU score\n"," bleu_score = corpus_bleu(references, predictions)\n"," return bleu_score"],"metadata":{"id":"OOln6dC6FBho","executionInfo":{"status":"ok","timestamp":1741770706540,"user_tz":-180,"elapsed":1979,"user":{"displayName":"Judd Marc","userId":"10378050880049965511"}}},"execution_count":8,"outputs":[]},{"cell_type":"markdown","source":["# Training loop"],"metadata":{"id":"qbb-mn9xFEsU"}},{"cell_type":"code","source":["%matplotlib inline\n","from transformers import get_cosine_schedule_with_warmup\n","import numpy as np\n","from tqdm import tqdm\n","import matplotlib.pyplot as plt\n","import random\n","\n","def plot_training_history(history):\n"," \"\"\"\n"," Plot training loss, validation loss, and BLEU scores.\n","\n"," Args:\n"," history: Dictionary containing training and validation metrics.\n"," \"\"\"\n"," plt.figure(figsize=(12, 6))\n","\n"," # Plot training and validation loss\n"," plt.subplot(1, 2, 1)\n"," plt.plot(history['train_loss'], label='Training Loss')\n"," plt.plot(history['val_loss'], label='Validation Loss')\n"," plt.xlabel('Epoch')\n"," plt.ylabel('Loss')\n"," plt.title('Training and Validation Loss')\n"," plt.legend()\n","\n"," # Plot BLEU score\n"," plt.subplot(1, 2, 2)\n"," plt.plot(history['val_bleu'], label='Validation BLEU', color='green')\n"," plt.xlabel('Epoch')\n"," plt.ylabel('BLEU Score')\n"," plt.title('Validation BLEU Score')\n"," plt.legend()\n","\n"," plt.tight_layout()\n"," plt.show()\n","\n","def train_model(\n"," model, train_dataset, val_dataset, optimizer, scheduler, num_epochs, device, tokenizer,\n"," preprocessor, LANG_TOKEN_MAPPING, batch_size=8, checkpoint_frequency=10000, max_length=128,\n"," learning_rate=5e-5, print_freq=100, repo_name=\"Lingua-Connect/JMW_TrainerImproved\"\n","):\n"," \"\"\"\n"," Train the model with checkpointing, validation, and BLEU score calculation.\n"," \"\"\"\n"," # Memory management and initialization\n"," torch.cuda.empty_cache()\n"," gc.collect()\n","\n"," # Move model to device\n"," model.to(device)\n","\n"," # Store training history\n"," history = {\n"," 'train_loss': [],\n"," 'val_loss': [],\n"," 'val_bleu': [],\n"," }\n","\n"," # Calculate total batches and steps\n"," n_batches = int(np.ceil(len(train_dataset) / batch_size))\n"," total_steps = num_epochs * n_batches\n","\n"," # Adjust warmup ratio to 5%\n"," num_warmup_steps = int(0.05 * total_steps) # 5% warmup\n"," print(f\"Number of warmup steps: {num_warmup_steps}\")\n"," print(f\"Total training steps: {total_steps}\")\n"," print(f\"Batches per epoch: {n_batches}\")\n","\n"," # Initialize CheckpointManager\n"," checkpoint_manager = CheckpointManager(\n"," model=model,\n"," optimizer=optimizer,\n"," scheduler=scheduler,\n"," tokenizer=tokenizer,\n"," repo_name=repo_name,\n"," device=device\n"," )\n","\n"," # Set dataset info for accurate calculations\n"," checkpoint_manager.set_dataset_info(len(train_dataset), batch_size)\n","\n"," # Load checkpoint if it exists - pass dataset info here\n"," start_epoch, start_batch, losses, steps_taken, checkpoint_total_steps = checkpoint_manager.load_checkpoint()\n","\n"," # Check if total_steps has changed and print warning\n"," if checkpoint_total_steps is not None and checkpoint_total_steps != total_steps:\n"," print(f\"WARNING: Total steps in checkpoint ({checkpoint_total_steps}) differs from current setting ({total_steps})\")\n","\n"," # Manually adjust the scheduler state if resuming from a checkpoint\n"," if start_epoch > 0 or start_batch > 0:\n"," current_steps = steps_taken if steps_taken is not None else (start_epoch * n_batches + start_batch)\n"," expected_steps = start_epoch * n_batches + start_batch\n","\n"," print(f\"Checkpoint indicates {current_steps} steps taken, calculated steps: {expected_steps}\")\n","\n"," # If steps_taken is not available, manually step the scheduler\n"," if steps_taken is None:\n"," print(f\"Stepping scheduler {expected_steps} times to match checkpoint position\")\n"," # Reset scheduler to initial state\n"," scheduler = get_cosine_schedule_with_warmup(\n"," optimizer, num_warmup_steps=num_warmup_steps, num_training_steps=total_steps\n"," )\n"," # Step to match checkpoint position\n"," for _ in range(expected_steps):\n"," scheduler.step()\n","\n"," print(f\"Learning rate after scheduler adjustment: {optimizer.param_groups[0]['lr']:.2e}\")\n"," print(f\"Scheduler learning rate after adjustment: {scheduler.get_last_lr()[0]:.2e}\")\n","\n"," # Check if training has already completed\n"," if start_epoch >= num_epochs:\n"," print(f\"Training already completed. Checkpoint found at epoch {start_epoch}, batch {start_batch}.\")\n"," return history, start_batch\n","\n"," # Training loop\n"," last_batch_idx = 0 # initialize last_batch_idx\n"," for epoch in range(start_epoch, num_epochs):\n"," print(f\"\\nEpoch {epoch + 1}/{num_epochs}\")\n"," print(f\"Starting from batch: {start_batch if epoch == start_epoch else 0}\")\n"," print(f\"Current learning rate: {optimizer.param_groups[0]['lr']:.2e}\")\n"," print(f\"Current scheduler learning rate: {scheduler.get_last_lr()[0]:.2e}\")\n","\n"," model.train()\n"," total_train_loss = sum(losses) if epoch == start_epoch and losses else 0\n"," processed_train_samples = 0\n","\n"," # total_train_loss = 0\n"," # processed_train_samples = 0\n"," # epoch_batch_count = 0\n","\n"," # Create data generator for this epoch\n"," train_data_generator = preprocessor.get_data_generator(\n"," train_dataset, LANG_TOKEN_MAPPING, tokenizer, batch_size=batch_size\n"," )\n","\n"," # Calculate total number of batches\n"," total_batches = len(train_dataset) // batch_size\n"," if len(train_dataset) % batch_size != 0:\n"," total_batches += 1\n","\n"," # Training progress bar\n"," pbar = tqdm(train_data_generator, total=total_batches, desc=f\"Epoch {epoch + 1}/{num_epochs}\", ncols=100)\n","\n"," # Keep track of steps for this session\n"," session_steps = 0\n"," current_batch_idx = 0 # Track actual batch index\n","\n"," # Reset losses at the start of each epoch\n"," # losses = [] if epoch != start_epoch else losses[start_batch:]\n","\n"," for batch_idx, (input_batch, label_batch) in enumerate(pbar):\n"," try:\n"," current_batch_idx = batch_idx # Update current batch index\n"," # Skip batches already processed in the checkpoint\n"," if epoch == start_epoch and batch_idx < start_batch:\n"," continue\n","\n"," # Memory management\n"," torch.cuda.empty_cache()\n","\n"," # Move batches to device\n"," input_batch = input_batch.to(device)\n"," label_batch = label_batch.to(device)\n","\n"," # Zero gradients\n"," optimizer.zero_grad()\n","\n"," # Forward pass with memory management\n"," try:\n"," outputs = model(input_ids=input_batch, labels=label_batch)\n"," loss = outputs.loss\n"," except RuntimeError as e:\n"," if 'CUDA out of memory' in str(e):\n"," print(\"Out of memory error. Clearing memory and retrying.\")\n"," torch.cuda.empty_cache()\n"," gc.collect()\n"," continue\n"," else:\n"," raise\n","\n"," # Backward pass\n"," loss.backward()\n"," torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)\n"," optimizer.step()\n"," scheduler.step()\n"," session_steps += 1\n","\n"," # Update metrics\n"," current_loss = loss.item()\n"," losses.append(current_loss)\n"," total_train_loss += current_loss\n"," # epoch_batch_count += 1\n"," processed_train_samples += input_batch.size(0)\n","\n"," # Calculate steps taken more accurately\n"," steps_from_previous_epochs = start_epoch * n_batches\n"," steps_from_current_epoch = session_steps\n"," if epoch == start_epoch:\n"," steps_from_current_epoch += start_batch\n"," total_steps_taken = steps_from_previous_epochs + steps_from_current_epoch\n","\n"," # Update progress bar\n"," current_avg_loss = total_train_loss / (len(losses) if epoch == start_epoch else (batch_idx + 1 - (start_batch if epoch == start_epoch else 0)))\n"," pbar.set_postfix({\n"," \"loss\": current_loss,\n"," \"avg_loss\": current_avg_loss,\n"," \"lr\": scheduler.get_last_lr()[0],\n"," \"steps\": total_steps_taken\n"," })\n","\n"," # Periodic evaluation and checkpoint\n"," if (batch_idx + 1) % print_freq == 0:\n"," avg_loss = np.mean(losses[-print_freq:])\n"," print(f'Epoch: {epoch + 1} | Step: {batch_idx + 1} | Avg. loss: {avg_loss:.3f} | lr: {scheduler.get_last_lr()[0]:.2e} | Total steps: {total_steps_taken}')\n","\n"," # Save checkpoint periodically\n"," if (batch_idx + 1) % checkpoint_frequency == 0:\n"," val_loss = eval_model(model, val_dataset, tokenizer, LANG_TOKEN_MAPPING)\n"," print(f'Epoch {epoch + 1} | Step {batch_idx + 1}: Validation loss = {val_loss:.3f}')\n"," checkpoint_manager.save_checkpoint(\n"," epoch=epoch,\n"," batch_idx=batch_idx + 1,\n"," losses=losses,\n"," total_steps=total_steps,\n"," steps_taken=total_steps_taken\n"," )\n"," print(f\"Checkpoint saved for epoch {epoch + 1}, batch {batch_idx + 1}\")\n"," last_batch_idx = batch_idx # update last_batch_idx\n"," except Exception as e:\n"," print(f\"\\nError processing training batch {batch_idx}: {str(e)}\")\n","\n"," # Save checkpoint before exiting in case of error\n"," steps_from_previous_epochs = start_epoch * n_batches\n"," steps_from_current_epoch = session_steps\n"," if epoch == start_epoch:\n"," steps_from_current_epoch += start_batch\n"," total_steps_taken = steps_from_previous_epochs + steps_from_current_epoch\n"," checkpoint_manager.save_checkpoint(\n"," epoch=epoch,\n"," batch_idx=batch_idx,\n"," losses=losses,\n"," total_steps=total_steps,\n"," steps_taken=total_steps_taken\n"," )\n"," print(f\"Checkpoint saved after error at epoch {epoch + 1}, batch {batch_idx}\")\n"," continue\n","\n"," pbar.close()\n","\n"," # Calculate average training loss\n"," # avg_train_loss = total_train_loss / total_batches if total_batches > 0 else 0\n"," avg_train_loss = total_train_loss / len(losses)\n"," history['train_loss'].append(avg_train_loss)\n"," print(f\"Average Training Loss: {avg_train_loss:.4f}\")\n","\n"," # Validation loop\n"," model.eval()\n"," total_val_loss = 0\n"," predictions = []\n"," references = []\n","\n"," # Create validation data generator\n"," val_data_generator = preprocessor.get_data_generator(\n"," val_dataset, LANG_TOKEN_MAPPING, tokenizer, batch_size=batch_size\n"," )\n","\n"," # Calculate total number of validation batches\n"," total_val_batches = len(val_dataset) // batch_size\n"," if len(val_dataset) % batch_size != 0:\n"," total_val_batches += 1\n","\n"," # Validation progress bar\n"," pbar = tqdm(val_data_generator, total=total_val_batches, desc=\"Validating\", ncols=100)\n","\n"," with torch.no_grad():\n"," for batch_idx, (input_batch, label_batch) in enumerate(pbar):\n"," try:\n"," # Move batches to device\n"," input_batch = input_batch.to(device)\n"," label_batch = label_batch.to(device)\n","\n"," # Calculate validation loss\n"," model_outputs = model(input_ids=input_batch, labels=label_batch)\n"," total_val_loss += model_outputs.loss.item()\n","\n"," # Generate translations\n"," outputs = model.generate(input_batch, max_length=max_length)\n","\n"," # Decode predictions and references\n"," pred_texts = tokenizer.batch_decode(outputs, skip_special_tokens=True)\n"," ref_texts = tokenizer.batch_decode(label_batch, skip_special_tokens=True)\n","\n"," predictions.extend(pred_texts)\n"," references.extend(ref_texts)\n","\n"," # Update progress bar\n"," current_avg_val_loss = total_val_loss / (batch_idx + 1)\n"," pbar.set_postfix({\"val_loss\": current_avg_val_loss})\n","\n"," except Exception as e:\n"," print(f\"Error processing validation batch {batch_idx}: {str(e)}\")\n"," continue\n","\n"," pbar.close()\n","\n"," # Calculate average validation loss\n"," avg_val_loss = total_val_loss / total_val_batches\n"," history['val_loss'].append(avg_val_loss)\n","\n"," # Calculate BLEU score\n"," bleu_score = calculate_bleu(predictions, references)\n"," history['val_bleu'].append(bleu_score)\n"," print(f\"Validation BLEU: {bleu_score:.4f}\")\n","\n"," # Save checkpoint at the end of the epoch\n"," total_steps_taken = (epoch + 1) * n_batches # End of epoch\n"," checkpoint_manager.save_checkpoint(\n"," # epoch=epoch,\n"," # batch_idx=last_batch_idx, # Last processed batch\n"," epoch=epoch + 1, # Save as the start of the next epoch\n"," batch_idx=0, # Start from batch 0 of next epoch\n"," losses=losses,\n"," total_steps=total_steps,\n"," steps_taken=total_steps_taken\n"," )\n","\n"," # Print epoch summary\n"," print(f\"\\nEpoch {epoch + 1} Summary:\")\n"," print(f\"Training Loss: {avg_train_loss:.4f}\")\n"," print(f\"Validation Loss: {avg_val_loss:.4f}\")\n"," print(f\"BLEU Score: {bleu_score:.4f}\")\n"," print(f\"Final Learning Rate: {optimizer.param_groups[0]['lr']:.2e}\")\n","\n"," # Plot training history\n"," plot_training_history(history)\n","\n"," return history, last_batch_idx # return last_batch_idx"],"metadata":{"id":"wYD61XD_wtEU","executionInfo":{"status":"ok","timestamp":1741770706843,"user_tz":-180,"elapsed":300,"user":{"displayName":"Judd Marc","userId":"10378050880049965511"}}},"execution_count":9,"outputs":[]},{"cell_type":"markdown","source":["# Run loop"],"metadata":{"id":"-ZkC9J8lFLOt"}},{"cell_type":"code","source":["# Device setup\n","import torch\n","device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n","\n","#clear the cache\n","torch.cuda.empty_cache()\n","\n","# Preprocessing setup\n","preprocessor = TranslationPreprocessor(tokenizer, lang_token, max_length)\n","\n","# Shuffle the dataset before creating the data generator\n","new_dataset['train'] = new_dataset['train'].shuffle(seed=42)\n","new_dataset['validation'] = new_dataset['validation'].shuffle(seed=42)\n","\n","# # Select 1000 samples for training and 200 for testing\n","# new_dataset['train'] = new_dataset['train'].select(range(1000))\n","# new_dataset['validation'] = new_dataset['validation'].select(range(200))\n","\n","# Clear the cache\n","torch.cuda.empty_cache()\n","\n","# Run training loop\n","history, last_batch_idx = train_model(\n"," model=model,\n"," train_dataset=new_dataset['train'],\n"," val_dataset=new_dataset['validation'],\n"," optimizer=optimizer,\n"," scheduler=scheduler,\n"," num_epochs=num_epochs,\n"," tokenizer=tokenizer,\n"," device=device,\n"," preprocessor=preprocessor,\n"," LANG_TOKEN_MAPPING=lang_token,\n"," repo_name=repo_name\n",")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000,"referenced_widgets":["023bd1b0c7ec448284a61ef9593e2132","0bf6baa7d94f47398800aa3202f5e63a","82eaf51b49bc487cb9ec062c50434544","78d0f4d63e744adba3f01d4382157308","e7904757a47542f6ba6c8c34b855c041","81aeb3db3b424be2b1485fded0e9b0a2","f91787cd602b418bbe60597418f1532e","d9f117f8bbbb453ea64cba1b6aa755a1","d345081beae44c50a418f8473f76c036","5b0016522cc94957976decbcd12ec7ec","0805347ff48a4b1db657a793ee32e0e8","5c027abf25574e2395c696dff1a15a0b","9d5c6f2d03c64c43b542258d1c9aafd4","fb761c64055a4d8bbcd42bffdd9926da","5051ab6e1d0c48548558888903485efa","8916e357a0e54be480a7d026414948f4","a3a673b9c87f468fa1a73d68de2fc89b","d69cffcc44f849dea1e68d2360e4d437","62c20b32b7974569bd333e62359d5aec","604f698ce1e44da28c33e51ad097b404","f2a6b2d4b2b0404d85b211f0ed08bfbb","178240f741254020bbe86d7d693c624d","602367f758a04ae581178ec551ed64ce","61cd1cc8d5e44270ab6cfa6b5c86f6f5","3cc9352e33164181852eef99040018f7","aed41d5f793c4a97adf4d5c3ec0d87a9","0ff4146968454ea2a1d6d3ebbed258bf","06f411ca0af64d7ba1ca7f586146e3ee","b9fdf600980649f582c41d4fee7d4277","f326a1657284461e932705626438cec8","669cfebe369c4e209b1e6c91603b9e8a","46a634c1ca6444ce865b5acd4b70198f","e1d6828ed2d9438885e74d5ca31dca0b","fb03ffa38e184cab8034f4ccac1534c5","3d870ab819dd4433aeb78f803269d4e7","122fd9a310fb4d85874bbfb18ef1b804","9f190aa06aa94ef8abe1d978dabadbfe","6f3a6dea85ec4e589e8fb8ac6c381226","4e5b5a75be2e40f3bb32edac217357d3","6633ffa9c5a94d3aa9cea5547378655e","6711d981b243411daac66f37a5db3dee","489d36442f084994bb12c10f97cc73e1","ecfc01f20c6c4cb4ada85a088e131c35","d38ebb94cffa4421829c03f3c56c889a","391d6fae66194bef8927ebcb4614947c","716a6e10ff0249b08ffb5636ad316816","1ce2d07817ce49a1a5259185e04c9357","f8a43173e63b4651811f99bf1a409c7a","97f49ef4d67f45e2b3994c08658b9560","f68949d78c334daf8683371290309a14","64fadce1460a433cafdeee1397308e79","c2f1a0bc8e3747ce8044e4bc69badd47","bc0efa4594fa4a9aba006ed7d36d7e43","c1abfcbdc387491787b73f60dae0e790","431aced4c673472bac451b5cd4aa3f1f","0bb586bec6f2422ab72c26aa793559ee","d87dada2b0864a66a6d87b3769d7c2bb","157b03c04232406c93420cfea6af73a6","dc9341b43e7e4fb2afed524eef76f7d9","d3272522827849c3b36fc78e521060e7","775611ae695f48a4b13996ebb8f06627","d636e6cc7a62458db35cec983f4841cd","5ea5d719e9e44af68bf820315d7ab07a","8210971b1ef94561a39217822d05240a","393bca42ca8149e2ab0194a06f4c5339","42bbda322333488aada63a10ecd5b8f9"]},"id":"7k--hWz6wuLL","outputId":"949d8027-8fcf-40c3-c3f7-b783bdd02878","collapsed":true,"executionInfo":{"status":"ok","timestamp":1741783405629,"user_tz":-180,"elapsed":12698738,"user":{"displayName":"Judd Marc","userId":"10378050880049965511"}}},"execution_count":10,"outputs":[{"output_type":"stream","name":"stdout","text":["Number of warmup steps: 10523\n","Total training steps: 210470\n","Batches per epoch: 21047\n","CheckpointManager: Batches per epoch set to 21047\n"]},{"output_type":"display_data","data":{"text/plain":["checkpoint.pth: 0%| | 0.00/3.60G [00:00:122: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n"," checkpoint = torch.load(checkpoint_path, map_location=self.device)\n"]},{"output_type":"stream","name":"stdout","text":["LR before checkpoint loading: 2.38e-08, After: 5.64e-07\n","Scheduler LR before: 2.38e-08, After: 5.64e-07\n","Checkpoint saved with LR: 5.64e-07, Scheduler LR: 5.64e-07\n","Loaded checkpoint from epoch 10, batch 7500\n","Steps taken according to checkpoint: 175876\n","WARNING: Expected steps (196923) doesn't match steps_taken in checkpoint (175876)!\n","Checkpoint indicates 175876 steps taken, calculated steps: 196923\n","Learning rate after scheduler adjustment: 5.64e-07\n","Scheduler learning rate after adjustment: 5.64e-07\n","\n","Epoch 10/10\n","Starting from batch: 7500\n","Current learning rate: 5.64e-07\n","Current scheduler learning rate: 5.64e-07\n"]},{"output_type":"stream","name":"stderr","text":["Epoch 10/10: 36%|███████████████▋ | 7486/21047 [00:36<01:02, 215.64it/s]Passing a tuple of `past_key_values` is deprecated and will be removed in Transformers v4.48.0. You should pass an instance of `EncoderDecoderCache` instead, e.g. `past_key_values=EncoderDecoderCache.from_legacy_cache(past_key_values)`.\n","Epoch 10/10: 36%|▎| 7600/21047 [01:31<1:58:47, 1.89it/s, loss=0.186, avg_loss=0.643, lr=5.55e-7, s"]},{"output_type":"stream","name":"stdout","text":["Epoch: 10 | Step: 7600 | Avg. loss: 0.198 | lr: 5.55e-07 | Total steps: 197023\n"]},{"output_type":"stream","name":"stderr","text":["Epoch 10/10: 37%|▎| 7700/21047 [02:24<1:59:58, 1.85it/s, loss=0.134, avg_loss=0.642, lr=5.47e-7, s"]},{"output_type":"stream","name":"stdout","text":["Epoch: 10 | Step: 7700 | Avg. loss: 0.208 | lr: 5.47e-07 | Total steps: 197123\n"]},{"output_type":"stream","name":"stderr","text":["Epoch 10/10: 37%|▎| 7800/21047 [03:19<1:59:53, 1.84it/s, loss=0.168, avg_loss=0.642, lr=5.39e-7, s"]},{"output_type":"stream","name":"stdout","text":["Epoch: 10 | Step: 7800 | Avg. loss: 0.203 | lr: 5.39e-07 | Total steps: 197223\n"]},{"output_type":"stream","name":"stderr","text":["Epoch 10/10: 38%|▍| 7900/21047 [04:14<2:00:12, 1.82it/s, loss=0.268, avg_loss=0.642, lr=5.31e-7, s"]},{"output_type":"stream","name":"stdout","text":["Epoch: 10 | Step: 7900 | Avg. loss: 0.203 | lr: 5.31e-07 | Total steps: 197323\n"]},{"output_type":"stream","name":"stderr","text":["Epoch 10/10: 38%|▍| 8000/21047 [05:10<2:02:31, 1.77it/s, loss=0.26, avg_loss=0.642, lr=5.23e-7, st"]},{"output_type":"stream","name":"stdout","text":["Epoch: 10 | Step: 8000 | Avg. loss: 0.204 | lr: 5.23e-07 | Total steps: 197423\n"]},{"output_type":"stream","name":"stderr","text":["Epoch 10/10: 38%|▍| 8100/21047 [06:05<2:00:29, 1.79it/s, loss=0.102, avg_loss=0.641, lr=5.15e-7, s"]},{"output_type":"stream","name":"stdout","text":["Epoch: 10 | Step: 8100 | Avg. loss: 0.188 | lr: 5.15e-07 | Total steps: 197523\n"]},{"output_type":"stream","name":"stderr","text":["Epoch 10/10: 39%|▍| 8200/21047 [07:01<1:59:16, 1.80it/s, loss=0.237, avg_loss=0.641, lr=5.07e-7, s"]},{"output_type":"stream","name":"stdout","text":["Epoch: 10 | Step: 8200 | Avg. loss: 0.182 | lr: 5.07e-07 | Total steps: 197623\n"]},{"output_type":"stream","name":"stderr","text":["Epoch 10/10: 39%|▍| 8300/21047 [07:57<1:58:10, 1.80it/s, loss=0.185, avg_loss=0.641, lr=4.99e-7, s"]},{"output_type":"stream","name":"stdout","text":["Epoch: 10 | Step: 8300 | Avg. loss: 0.210 | lr: 4.99e-07 | Total steps: 197723\n"]},{"output_type":"stream","name":"stderr","text":["Epoch 10/10: 40%|▍| 8400/21047 [08:53<1:57:03, 1.80it/s, loss=0.157, avg_loss=0.641, lr=4.91e-7, s"]},{"output_type":"stream","name":"stdout","text":["Epoch: 10 | Step: 8400 | Avg. loss: 0.203 | lr: 4.91e-07 | Total steps: 197823\n"]},{"output_type":"stream","name":"stderr","text":["Epoch 10/10: 40%|▍| 8500/21047 [09:49<1:58:50, 1.76it/s, loss=0.0736, avg_loss=0.641, lr=4.84e-7, "]},{"output_type":"stream","name":"stdout","text":["Epoch: 10 | Step: 8500 | Avg. loss: 0.207 | lr: 4.84e-07 | Total steps: 197923\n"]},{"output_type":"stream","name":"stderr","text":["Epoch 10/10: 41%|▍| 8600/21047 [10:45<1:57:15, 1.77it/s, loss=0.178, avg_loss=0.64, lr=4.76e-7, st"]},{"output_type":"stream","name":"stdout","text":["Epoch: 10 | Step: 8600 | Avg. loss: 0.192 | lr: 4.76e-07 | Total steps: 198023\n"]},{"output_type":"stream","name":"stderr","text":["Epoch 10/10: 41%|▍| 8700/21047 [11:41<1:54:53, 1.79it/s, loss=0.182, avg_loss=0.64, lr=4.69e-7, st"]},{"output_type":"stream","name":"stdout","text":["Epoch: 10 | Step: 8700 | Avg. loss: 0.180 | lr: 4.69e-07 | Total steps: 198123\n"]},{"output_type":"stream","name":"stderr","text":["Epoch 10/10: 42%|▍| 8800/21047 [12:37<1:53:49, 1.79it/s, loss=0.17, avg_loss=0.64, lr=4.61e-7, ste"]},{"output_type":"stream","name":"stdout","text":["Epoch: 10 | Step: 8800 | Avg. loss: 0.196 | lr: 4.61e-07 | Total steps: 198223\n"]},{"output_type":"stream","name":"stderr","text":["Epoch 10/10: 42%|▍| 8900/21047 [13:33<1:52:38, 1.80it/s, loss=0.143, avg_loss=0.64, lr=4.53e-7, st"]},{"output_type":"stream","name":"stdout","text":["Epoch: 10 | Step: 8900 | Avg. loss: 0.197 | lr: 4.53e-07 | Total steps: 198323\n"]},{"output_type":"stream","name":"stderr","text":["Epoch 10/10: 43%|▍| 9000/21047 [14:29<1:52:41, 1.78it/s, loss=0.0889, avg_loss=0.639, lr=4.46e-7, "]},{"output_type":"stream","name":"stdout","text":["Epoch: 10 | Step: 9000 | Avg. loss: 0.205 | lr: 4.46e-07 | Total steps: 198423\n"]},{"output_type":"stream","name":"stderr","text":["Epoch 10/10: 43%|▍| 9100/21047 [15:25<1:51:19, 1.79it/s, loss=0.176, avg_loss=0.639, lr=4.39e-7, s"]},{"output_type":"stream","name":"stdout","text":["Epoch: 10 | Step: 9100 | Avg. loss: 0.198 | lr: 4.39e-07 | Total steps: 198523\n"]},{"output_type":"stream","name":"stderr","text":["Epoch 10/10: 44%|▍| 9200/21047 [16:20<1:49:26, 1.80it/s, loss=0.116, avg_loss=0.639, lr=4.31e-7, s"]},{"output_type":"stream","name":"stdout","text":["Epoch: 10 | Step: 9200 | Avg. loss: 0.199 | lr: 4.31e-07 | Total steps: 198623\n"]},{"output_type":"stream","name":"stderr","text":["Epoch 10/10: 44%|▍| 9300/21047 [17:16<1:48:49, 1.80it/s, loss=0.227, avg_loss=0.639, lr=4.24e-7, s"]},{"output_type":"stream","name":"stdout","text":["Epoch: 10 | Step: 9300 | Avg. loss: 0.194 | lr: 4.24e-07 | Total steps: 198723\n"]},{"output_type":"stream","name":"stderr","text":["Epoch 10/10: 45%|▍| 9400/21047 [18:12<1:49:01, 1.78it/s, loss=0.109, avg_loss=0.639, lr=4.17e-7, s"]},{"output_type":"stream","name":"stdout","text":["Epoch: 10 | Step: 9400 | Avg. loss: 0.198 | lr: 4.17e-07 | Total steps: 198823\n"]},{"output_type":"stream","name":"stderr","text":["Epoch 10/10: 45%|▍| 9500/21047 [19:08<1:47:13, 1.79it/s, loss=0.0959, avg_loss=0.638, lr=4.1e-7, s"]},{"output_type":"stream","name":"stdout","text":["Epoch: 10 | Step: 9500 | Avg. loss: 0.198 | lr: 4.10e-07 | Total steps: 198923\n"]},{"output_type":"stream","name":"stderr","text":["Epoch 10/10: 46%|▍| 9600/21047 [20:03<1:46:07, 1.80it/s, loss=0.154, avg_loss=0.638, lr=4.03e-7, s"]},{"output_type":"stream","name":"stdout","text":["Epoch: 10 | Step: 9600 | Avg. loss: 0.197 | lr: 4.03e-07 | Total steps: 199023\n"]},{"output_type":"stream","name":"stderr","text":["Epoch 10/10: 46%|▍| 9700/21047 [20:59<1:44:43, 1.81it/s, loss=0.1, avg_loss=0.638, lr=3.96e-7, ste"]},{"output_type":"stream","name":"stdout","text":["Epoch: 10 | Step: 9700 | Avg. loss: 0.196 | lr: 3.96e-07 | Total steps: 199123\n"]},{"output_type":"stream","name":"stderr","text":["Epoch 10/10: 47%|▍| 9800/21047 [21:55<1:45:31, 1.78it/s, loss=0.0938, avg_loss=0.638, lr=3.89e-7, "]},{"output_type":"stream","name":"stdout","text":["Epoch: 10 | Step: 9800 | Avg. loss: 0.201 | lr: 3.89e-07 | Total steps: 199223\n"]},{"output_type":"stream","name":"stderr","text":["Epoch 10/10: 47%|▍| 9900/21047 [22:51<1:43:59, 1.79it/s, loss=0.739, avg_loss=0.637, lr=3.82e-7, s"]},{"output_type":"stream","name":"stdout","text":["Epoch: 10 | Step: 9900 | Avg. loss: 0.210 | lr: 3.82e-07 | Total steps: 199323\n"]},{"output_type":"stream","name":"stderr","text":["Epoch 10/10: 48%|▍| 9999/21047 [23:47<1:42:39, 1.79it/s, loss=0.166, avg_loss=0.637, lr=3.75e-7, s"]},{"output_type":"stream","name":"stdout","text":["Epoch: 10 | Step: 10000 | Avg. loss: 0.194 | lr: 3.75e-07 | Total steps: 199423\n","Epoch 10 | Step 10000: Validation loss = 0.140\n","Checkpoint saved locally with learning rate: 3.75e-07, scheduler lr: 3.75e-07\n","Saved at epoch 10, batch 10000, steps_taken: 199423\n"]},{"output_type":"display_data","data":{"text/plain":["README.md: 0%| | 0.00/5.17k 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Save final model"],"metadata":{"id":"66YTWgL5FRK4"}},{"cell_type":"code","source":["# Save the final checkpoint\n","checkpoint_manager = CheckpointManager(\n"," model=model,\n"," optimizer=optimizer,\n"," scheduler=scheduler,\n"," tokenizer=tokenizer,\n"," repo_name=\"Lingua-Connect/backup\",\n"," device=device\n",")\n","\n","# Save the final model and tokenizer\n","checkpoint_manager.save_checkpoint(\n"," epoch=num_epochs - 1, # Final epoch\n"," batch_idx=last_batch_idx,\n"," losses=history['train_loss']\n",")\n","\n","print(\"Final model and tokenizer pushed to Hugging Face Hub.\")"],"metadata":{"id":"KjwTwiX3wv8j","colab":{"base_uri":"https://localhost:8080/","height":244,"referenced_widgets":["2161c144010d4360901cc8b939755476","324b4658779f4bb79024048f634b3260","121ae6533d7440988fd988b2ae8f2a26","66da2fa7a81441bd83f5a96b68841196","26daecc6335441de851aa394ba4b4cf1","f5cd7a22960143b5a4776964d364299a","1f26666aa1ad48da939d0dc4e0133149","4d38c2bcbbb9456b857879ef7ca06598","e4806e2a93ac4c54b389d01c3a52c94a","41f1bca8a75c412e9008b82723dfc670","6d2f7969e1124bdfba4f791361e3bc28","71c415998d5244d484003d3f141953d4","8bc0922c810841c2a579bc7628f290a9","97217790d8184c5a95c6f5224e17b8c2","94306b91ebdd4aaca85c3cf934ce4ed3","5119db0df6194c959ed2a9a6340779ec","e6d6ac699145461eb6446d4c3dd63fac","2e9180671da7461d8a38613aba8ca758","db4bbb18643343c0b9de0b00cc9be9c5","288562897d0540dfaab386a3913a290d","c921866958944d87b39b52f3620e53b9","f352841d43714540b8a753d8db0d40b0","515036aaf0b04eb2b7bc9d08b284b5b7","8f76a7eed8874f3db6c29b189f6ec4ec","ec754b23114b49e1a68ead9cb3c86219","8bd1fbbec5874b859a1a904b6c46cc93","def1ac0d1cab4c2badc23a620a93e810","a60d08d088ea44ceb0dfecd109098cdd","8418e65fda924541909925ea6337cd45","67cd9a45c6d84983abb9fc6f27739f2d","55a6bc11337b49b6a7d1dff45bcdf9b6","30061b5635f14d3eb9f172191c349a79","b8d5d89a60ea43489e51878fbb26c06b","f452b538de694ce6a79e3be7d348673d","8ecb8137c852413586abe69ecc9c6e7b","98bd022f23a34ae39176ac7c1a55b0a5","f8cd28f394ec453f8fc4016e46f9e460","2760d99a70d544d1b45dd561e5c149ea","51143d68da754bf7b3712a73cd709f40","24cf85c686164699ae5c077e80245bcf","9a8414df8ea9470ab9b10bffa50a1e5e","9444d6cb277c4015be52c5b89218cbd0","cfbea6dca763444580f09d2af639ae87","4f7c8f7734ca492a962ea4e261341d3b","3c825d42ee3149e2bd1043df991b5e6b","456c86c82dd04cda841a2999ea6fbfab","b950e635dec141eba3de2de7cd0873ba","1023daa4ae334093bc6e0cfb828dd8ce","f097899ca74743a2b4a7f22a0c18f936","32e08ddbae8c444fb8749f53afe11ba7","a3d0d914571a415b9f531011bc23ea82","756ee330caf949d5aa6ddef6205768d1","2a23805c602148af8ac40a70a85656b1","5c8db4b496c9471e9c63dd0bce3dc01e","7b4fbaa3f52d4cc79a969df36ebbd10c","edf741883d764960a5a80e6d7248d1ed","cadadb4043ab44228801692cade437e8","004b6607046743b296a1fff426e45f36","7723fc05a84e4fdcaefa8ac3399df433","36fa399430354fcd8a004b3e94d23eb7","52015bb2e07744e3912295c8a309c151","c6123b6a7fe147eca93f36674bd21ef3","8f38cb8c140c405390aeb46a5e33edda","2fe2fe3a166e44418fcd081a3386a8f5","b794dd0dc7a142dfb1e99d9d896d02be","b856797e054c4f7dabfda10c575aab77"]},"outputId":"003d2216-e814-4470-ed1d-378c7b8f518c"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Checkpoint saved locally with learning rate: 1.11e-13, scheduler lr: 1.11e-13\n","Saved at epoch 10, batch 21046, steps_taken: None\n"]},{"output_type":"display_data","data":{"text/plain":["model.safetensors: 0%| | 0.00/1.20G [00:00