106 lines
3.3 KiB
Python
106 lines
3.3 KiB
Python
# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
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"""Pretrain VIT"""
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import torch
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import torch.nn.functional as F
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from functools import partial
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from megatron.training import get_args, get_timers, print_rank_0
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from megatron.core.enums import ModelType
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from megatron.legacy.data.vit_dataset import build_train_valid_datasets
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from megatron.legacy.model.vision.classification import VitClassificationModel
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from megatron.legacy.model.vision.classification import MitClassificationModel
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from megatron.training import pretrain
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from megatron.training.utils import average_losses_across_data_parallel_group
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from megatron.training.arguments import core_transformer_config_from_args
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def model_provider(pre_process=True, post_process=True):
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"""Build the model."""
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args = get_args()
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config = core_transformer_config_from_args(args)
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if args.vision_backbone_type == 'vit':
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print_rank_0("building VIT model ...")
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model = VitClassificationModel(config=config,
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num_classes=args.num_classes,
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pre_process=pre_process,
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post_process=post_process)
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elif args.vision_backbone_type == 'mit':
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print_rank_0("building MIT model ...")
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model = MitClassificationModel(num_classes=args.num_classes,
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pre_process=pre_process,
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post_process=post_process)
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else:
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raise Exception('{} vision backbone is not supported.'.format(
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args.vision_backbone_type))
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return model
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def get_batch(data_iterator):
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"""Build the batch."""
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data = next(data_iterator)
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# only data parallelism; no need for broadcast
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images = data[0].cuda()
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labels = data[1].cuda()
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return images, labels
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def loss_func(labels, output_tensor):
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logits = output_tensor.contiguous().float()
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loss = F.cross_entropy(logits, labels)
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outputs = torch.argmax(logits, -1)
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correct = (outputs == labels).float()
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accuracy = torch.mean(correct)
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averaged_loss = average_losses_across_data_parallel_group([loss, accuracy])
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return loss, {"loss": averaged_loss[0], "accuracy": averaged_loss[1]}
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def forward_step(data_iterator, model):
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"""Forward step."""
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timers = get_timers()
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# Get the batch.
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timers("batch-generator", log_level=2).start()
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(
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images,
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labels,
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) = get_batch(data_iterator)
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timers("batch-generator").stop()
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# Forward model. lm_labels
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output_tensor = model(images)
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return output_tensor, partial(loss_func, labels)
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def train_valid_test_datasets_provider(train_val_test_num_samples):
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"""Build train, valid, and test datasets."""
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args = get_args()
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print_rank_0(
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"> building train, validation, and test datasets " "for VIT ..."
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)
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train_ds, valid_ds = build_train_valid_datasets(
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data_path=args.data_path,
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image_size=(args.img_h, args.img_w)
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)
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print_rank_0("> finished creating VIT datasets ...")
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return train_ds, valid_ds, None
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if __name__ == "__main__":
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pretrain(
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train_valid_test_datasets_provider,
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model_provider,
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ModelType.encoder_or_decoder,
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forward_step,
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args_defaults={'dataloader_type': 'cyclic', 'vision_pretraining': True}
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)
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