# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from typing import Dict, List, Optional, Tuple import lightning.pytorch as pl import numpy as np import torch from lightning.pytorch.utilities.types import EVAL_DATALOADERS, TRAIN_DATALOADERS from torch.utils import data from torch.utils.data import DataLoader, Dataset from nemo.lightning.pytorch.plugins import MegatronDataSampler class MockDataModule(pl.LightningDataModule): """ Mock DataModule for testing and development. Generates synthetic data for training, validation, and testing purposes. Args: seq_length (int): Sequence length for the generated data. decoder_seq_length (Optional[int]): Decoder sequence length if applicable, used in pp. vocab_size (int): Size of the vocabulary of tokenizer. crop_size (Tuple[int, int]): Image crop size (height, width). micro_batch_size (int): Micro batch size for data loading. global_batch_size (int): Global batch size across all processes. rampup_batch_size (Optional[List[int]]): Batch size ramp-up configuration. num_train_samples (int): Number of training samples to generate. num_val_samples (int): Number of validation samples to generate. num_test_samples (int): Number of test samples to generate. num_workers (int): Number of workers for data loading. pin_memory (bool): Whether to pin memory for data loading. persistent_workers (bool): Whether workers should remain persistent. """ def __init__( self, seq_length: int = 2048, decoder_seq_length: Optional = None, vocab_size: int = 128256, crop_size: Tuple[int, int] = (560, 560), micro_batch_size: int = 4, global_batch_size: int = 8, rampup_batch_size: Optional[List[int]] = None, tokenizer: Optional = None, image_processor: Optional = None, num_train_samples: int = 10_000, num_val_samples: int = 10_000, num_test_samples: int = 10_000, num_workers: int = 8, pin_memory: bool = True, persistent_workers: bool = False, ): super().__init__() self.seq_length = seq_length self.decoder_seq_length = decoder_seq_length self.micro_batch_size = micro_batch_size self.global_batch_size = global_batch_size self.num_train_samples = num_train_samples self.num_val_samples = num_val_samples self.num_test_samples = num_test_samples self.num_workers = num_workers self.pin_memory = pin_memory self.persistent_workers = persistent_workers self.vocab_size = vocab_size self.crop_size = crop_size self.tokenizer = tokenizer self.image_processor = image_processor self.data_sampler = MegatronDataSampler( seq_len=self.seq_length, decoder_seq_len=self.decoder_seq_length, micro_batch_size=micro_batch_size, global_batch_size=global_batch_size, rampup_batch_size=rampup_batch_size, ) def setup(self, stage: str = "") -> None: """Set up datasets for the specified stage.""" self._train_ds = _MockMLlamaDataset( self.vocab_size, self.crop_size, "train", self.num_train_samples, self.decoder_seq_length ) self._validation_ds = _MockMLlamaDataset( self.vocab_size, self.crop_size, "valid", self.num_val_samples, self.decoder_seq_length ) self._test_ds = _MockMLlamaDataset( self.vocab_size, self.crop_size, "test", self.num_test_samples, self.decoder_seq_length ) def train_dataloader(self) -> TRAIN_DATALOADERS: """Returns the DataLoader for training.""" if not hasattr(self, "_train_ds"): self.setup() return self._create_dataloader(self._train_ds) def val_dataloader(self) -> EVAL_DATALOADERS: """Returns the DataLoader for validation.""" if not hasattr(self, "_validation_ds"): self.setup() return self._create_dataloader(self._validation_ds) def test_dataloader(self) -> EVAL_DATALOADERS: """Returns the DataLoader for testing.""" if not hasattr(self, "_test_ds"): self.setup() return self._create_dataloader(self._test_ds) def _create_dataloader(self, dataset, **kwargs) -> DataLoader: """Creates a DataLoader for the specified dataset.""" return DataLoader( dataset, num_workers=self.num_workers, pin_memory=self.pin_memory, persistent_workers=self.persistent_workers, collate_fn=dataset.collate_fn, **kwargs, ) class _MockMLlamaDataset(Dataset): """ Mock dataset for generating synthetic data with text and image components. Args: vocab_size (int): Vocabulary size for text data. crop_size (Tuple[int, int]): Image crop size (height, width). name (str): Name of the dataset split ('train', 'valid', 'test'). num_samples (int): Number of samples in the dataset. seq_length (int): Sequence length for the text data. seed (int): Seed for random number generation. """ def __init__( self, vocab_size, crop_size, name: str, num_samples: int, seq_length: int, seed: int = 42, ) -> None: super().__init__() self.name = name self.seq_length = seq_length self.vocab_size = vocab_size self.image_height, self.image_width = crop_size self.length = num_samples self.seed = seed self.loss_mask = torch.ones(self.seq_length, dtype=torch.float) self.position_ids = torch.arange(self.seq_length, dtype=torch.int64) def __len__(self) -> int: """Returns the number of samples in the dataset.""" return self.length def _get_text(self, idx: int) -> np.ndarray: """Generates a random sequence of integers representing text tokens.""" np_gen = np.random.default_rng(seed=(self.seed + idx)) return np_gen.integers(self.vocab_size, size=[self.seq_length], dtype=np.int64) def __getitem__(self, idx) -> Dict[str, torch.Tensor]: """Generates a single data sample.""" # Generate data of the expected size and datatype (based on GPTDataset). np_gen = np.random.default_rng(seed=(self.seed + idx)) tokens = torch.from_numpy(np_gen.integers(self.vocab_size, size=[self.seq_length + 1], dtype=np.int64)) images = torch.from_numpy(np_gen.standard_normal((1, 4, 3, self.image_height, self.image_width))) aspect_ratio_ids = torch.from_numpy(np_gen.integers(8, size=[1], dtype=np.int64)) + 1 labels = tokens.clone() tokens = tokens[:-1] labels = labels[1:] return { "images": images, "masks": torch.tensor([[5, 512]]), "num_chunks": torch.tensor([4]), "tokens": tokens, "aspect_ratio_ids": aspect_ratio_ids, "loss_mask": self.loss_mask, "position_ids": self.position_ids, "labels": labels, } def _collate_fn(self, batch): """ A default implementation of a collation function. Users should override this method to define custom data loaders. """ collated_batch = {} collated_batch["batch_masks"] = [sample.pop("masks") for sample in batch] collated_batch["attention_mask"] = None collated_batch.update(data.dataloader.default_collate(batch)) collated_batch["batch_images"] = collated_batch.pop("images") return collated_batch def collate_fn(self, batch): """Method that user pass as functor to DataLoader. The method optionally performs neural type checking and add types to the outputs. Please note, subclasses of Dataset should not implement `input_types`. # Usage: dataloader = torch.utils.data.DataLoader( ...., collate_fn=dataset.collate_fn, .... ) Returns ------- Collated batch, with or without types. """ return self._collate_fn(batch)