Adding new CV notebook for distributed training with PT 1.11

sagemaker-training-compiler/huggingface/pytorch_multiple_gpu_single_node/vision_transformer/scripts/requirements.txt

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+accelerate

sagemaker-training-compiler/huggingface/pytorch_multiple_gpu_single_node/vision_transformer/scripts/run_mae.py

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+#!/usr/bin/env python
+# coding=utf-8
+# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
+# Modifications Copyright 2022 Amazon.com, Inc. or its affiliates. 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
+
+import logging
+import os
+import sys
+from dataclasses import dataclass, field
+from typing import Optional
+
+import torch
+from datasets import load_dataset
+from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
+from torchvision.transforms.functional import InterpolationMode
+
+import transformers
+from transformers import (
+    HfArgumentParser,
+    Trainer,
+    TrainingArguments,
+    ViTFeatureExtractor,
+    ViTMAEConfig,
+    ViTMAEForPreTraining,
+)
+from transformers.trainer_utils import get_last_checkpoint
+from transformers.utils import check_min_version, send_example_telemetry
+from transformers.utils.versions import require_version
+
+
+""" Pre-training a 🤗 ViT model as an MAE (masked autoencoder), as proposed in https://arxiv.org/abs/2111.06377."""
+
+logger = logging.getLogger(__name__)
+
+# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
+check_min_version("4.21.0")
+
+require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/image-pretraining/requirements.txt")
+
+
+@dataclass
+class DataTrainingArguments:
+    """
+    Arguments pertaining to what data we are going to input our model for training and eval.
+    Using `HfArgumentParser` we can turn this class
+    into argparse arguments to be able to specify them on
+    the command line.
+    """
+
+    dataset_name: Optional[str] = field(
+        default="cifar10", metadata={"help": "Name of a dataset from the datasets package"}
+    )
+    dataset_config_name: Optional[str] = field(
+        default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}
+    )
+    image_column_name: Optional[str] = field(
+        default=None, metadata={"help": "The column name of the images in the files."}
+    )
+    train_dir: Optional[str] = field(default=None, metadata={"help": "A folder containing the training data."})
+    validation_dir: Optional[str] = field(default=None, metadata={"help": "A folder containing the validation data."})
+    train_val_split: Optional[float] = field(
+        default=0.15, metadata={"help": "Percent to split off of train for validation."}
+    )
+    max_train_samples: Optional[int] = field(
+        default=None,
+        metadata={
+            "help": (
+                "For debugging purposes or quicker training, truncate the number of training examples to this "
+                "value if set."
+            )
+        },
+    )
+    max_eval_samples: Optional[int] = field(
+        default=None,
+        metadata={
+            "help": (
+                "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
+                "value if set."
+            )
+        },
+    )
+
+    def __post_init__(self):
+        data_files = dict()
+        if self.train_dir is not None:
+            data_files["train"] = self.train_dir
+        if self.validation_dir is not None:
+            data_files["val"] = self.validation_dir
+        self.data_files = data_files if data_files else None
+
+
+@dataclass
+class ModelArguments:
+    """
+    Arguments pertaining to which model/config/feature extractor we are going to pre-train.
+    """
+
+    model_name_or_path: str = field(
+        default=None,
+        metadata={
+            "help": (
+                "The model checkpoint for weights initialization.Don't set if you want to train a model from scratch."
+            )
+        },
+    )
+    config_name: Optional[str] = field(
+        default=None, metadata={"help": "Pretrained config name or path if not the same as model_name_or_path"}
+    )
+    config_overrides: Optional[str] = field(
+        default=None,
+        metadata={
+            "help": (
+                "Override some existing default config settings when a model is trained from scratch. Example: "
+                "n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index"
+            )
+        },
+    )
+    cache_dir: Optional[str] = field(
+        default=None, metadata={"help": "Where do you want to store the pretrained models downloaded from s3"}
+    )
+    model_revision: str = field(
+        default="main",
+        metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."},
+    )
+    feature_extractor_name: str = field(default=None, metadata={"help": "Name or path of preprocessor config."})
+    use_auth_token: bool = field(
+        default=False,
+        metadata={
+            "help": (
+                "Will use the token generated when running `transformers-cli login` (necessary to use this script "
+                "with private models)."
+            )
+        },
+    )
+    mask_ratio: float = field(
+        default=0.75, metadata={"help": "The ratio of the number of masked tokens in the input sequence."}
+    )
+    norm_pix_loss: bool = field(
+        default=True, metadata={"help": "Whether or not to train with normalized pixel values as target."}
+    )
+
+
+@dataclass
+class CustomTrainingArguments(TrainingArguments):
+    base_learning_rate: float = field(
+        default=1e-3, metadata={"help": "Base learning rate: absolute_lr = base_lr * total_batch_size / 256."}
+    )
+
+
+def collate_fn(examples):
+    pixel_values = torch.stack([example["pixel_values"] for example in examples])
+    return {"pixel_values": pixel_values}
+
+
+def main():
+    # See all possible arguments in src/transformers/training_args.py
+    # or by passing the --help flag to this script.
+    # We now keep distinct sets of args, for a cleaner separation of concerns.
+
+    parser = HfArgumentParser((ModelArguments, DataTrainingArguments, CustomTrainingArguments))
+    if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
+        # If we pass only one argument to the script and it's the path to a json file,
+        # let's parse it to get our arguments.
+        model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
+    else:
+        model_args, data_args, training_args = parser.parse_args_into_dataclasses()
+
+    # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
+    # information sent is the one passed as arguments along with your Python/PyTorch versions.
+    send_example_telemetry("run_mae", model_args, data_args)
+
+    # Setup logging
+    logging.basicConfig(
+        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
+        datefmt="%m/%d/%Y %H:%M:%S",
+        handlers=[logging.StreamHandler(sys.stdout)],
+    )
+
+    log_level = training_args.get_process_log_level()
+    logger.setLevel(log_level)
+    transformers.utils.logging.set_verbosity(log_level)
+    transformers.utils.logging.enable_default_handler()
+    transformers.utils.logging.enable_explicit_format()
+
+    # Log on each process the small summary:
+    logger.warning(
+        f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
+        + f"distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}"
+    )
+    logger.info(f"Training/evaluation parameters {training_args}")
+
+    # Detecting last checkpoint.
+    last_checkpoint = None
+    if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir:
+        last_checkpoint = get_last_checkpoint(training_args.output_dir)
+        if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0:
+            raise ValueError(
+                f"Output directory ({training_args.output_dir}) already exists and is not empty. "
+                "Use --overwrite_output_dir to overcome."
+            )
+        elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
+            logger.info(
+                f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
+                "the `--output_dir` or add `--overwrite_output_dir` to train from scratch."
+            )
+
+    # Initialize our dataset.
+    ds = load_dataset(
+        data_args.dataset_name,
+        data_args.dataset_config_name,
+        data_files=data_args.data_files,
+        cache_dir=model_args.cache_dir,
+        use_auth_token=True if model_args.use_auth_token else None,
+    )
+
+    # If we don't have a validation split, split off a percentage of train as validation.
+    data_args.train_val_split = None if "validation" in ds.keys() else data_args.train_val_split
+    if isinstance(data_args.train_val_split, float) and data_args.train_val_split > 0.0:
+        split = ds["train"].train_test_split(data_args.train_val_split)
+        ds["train"] = split["train"]
+        ds["validation"] = split["test"]
+
+    # Load pretrained model and feature extractor
+    #
+    # Distributed training:
+    # The .from_pretrained methods guarantee that only one local process can concurrently
+    # download model & vocab.
+    config_kwargs = {
+        "cache_dir": model_args.cache_dir,
+        "revision": model_args.model_revision,
+        "use_auth_token": True if model_args.use_auth_token else None,
+    }
+    if model_args.config_name:
+        config = ViTMAEConfig.from_pretrained(model_args.config_name, **config_kwargs)
+    elif model_args.model_name_or_path:
+        config = ViTMAEConfig.from_pretrained(model_args.model_name_or_path, **config_kwargs)
+    else:
+        config = ViTMAEConfig()
+        logger.warning("You are instantiating a new config instance from scratch.")
+        if model_args.config_overrides is not None:
+            logger.info(f"Overriding config: {model_args.config_overrides}")
+            config.update_from_string(model_args.config_overrides)
+            logger.info(f"New config: {config}")
+
+    # adapt config
+    config.update(
+        {
+            "mask_ratio": model_args.mask_ratio,
+            "norm_pix_loss": model_args.norm_pix_loss,
+        }
+    )
+
+    # create feature extractor
+    if model_args.feature_extractor_name:
+        feature_extractor = ViTFeatureExtractor.from_pretrained(model_args.feature_extractor_name, **config_kwargs)
+    elif model_args.model_name_or_path:
+        feature_extractor = ViTFeatureExtractor.from_pretrained(model_args.model_name_or_path, **config_kwargs)
+    else:
+        feature_extractor = ViTFeatureExtractor()
+
+    # create model
+    if model_args.model_name_or_path:
+        model = ViTMAEForPreTraining.from_pretrained(
+            model_args.model_name_or_path,
+            from_tf=bool(".ckpt" in model_args.model_name_or_path),
+            config=config,
+            cache_dir=model_args.cache_dir,
+            revision=model_args.model_revision,
+            use_auth_token=True if model_args.use_auth_token else None,
+        )
+    else:
+        logger.info("Training new model from scratch")
+        model = ViTMAEForPreTraining(config)
+
+    if training_args.do_train:
+        column_names = ds["train"].column_names
+    else:
+        column_names = ds["validation"].column_names
+
+    if data_args.image_column_name is not None:
+        image_column_name = data_args.image_column_name
+    elif "image" in column_names:
+        image_column_name = "image"
+    elif "img" in column_names:
+        image_column_name = "img"
+    else:
+        image_column_name = column_names[0]
+
+    # transformations as done in original MAE paper
+    # source: https://github.com/facebookresearch/mae/blob/main/main_pretrain.py
+    transforms = Compose(
+        [
+            Lambda(lambda img: img.convert("RGB") if img.mode != "RGB" else img),
+            RandomResizedCrop(feature_extractor.size, scale=(0.2, 1.0), interpolation=InterpolationMode.BICUBIC),
+            RandomHorizontalFlip(),
+            ToTensor(),
+            Normalize(mean=feature_extractor.image_mean, std=feature_extractor.image_std),
+        ]
+    )
+
+    def preprocess_images(examples):
+        """Preprocess a batch of images by applying transforms."""
+
+        examples["pixel_values"] = [transforms(image) for image in examples[image_column_name]]
+        return examples
+
+    if training_args.do_train:
+        if "train" not in ds:
+            raise ValueError("--do_train requires a train dataset")
+        if data_args.max_train_samples is not None:
+            ds["train"] = ds["train"].shuffle(seed=training_args.seed).select(range(data_args.max_train_samples))
+        # Set the training transforms
+        ds["train"].set_transform(preprocess_images)
+
+    if training_args.do_eval:
+        if "validation" not in ds:
+            raise ValueError("--do_eval requires a validation dataset")
+        if data_args.max_eval_samples is not None:
+            ds["validation"] = (
+                ds["validation"].shuffle(seed=training_args.seed).select(range(data_args.max_eval_samples))
+            )
+        # Set the validation transforms
+        ds["validation"].set_transform(preprocess_images)
+
+    # Compute absolute learning rate
+    total_train_batch_size = (
+        training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
+    )
+    if training_args.base_learning_rate is not None:
+        training_args.learning_rate = training_args.base_learning_rate * total_train_batch_size / 256
+
+    # Initialize our trainer
+    trainer = Trainer(
+        model=model,
+        args=training_args,
+        train_dataset=ds["train"] if training_args.do_train else None,
+        eval_dataset=ds["validation"] if training_args.do_eval else None,
+        tokenizer=feature_extractor,
+        data_collator=collate_fn,
+    )
+
+    # Training
+    if training_args.do_train:
+        checkpoint = None
+        if training_args.resume_from_checkpoint is not None:
+            checkpoint = training_args.resume_from_checkpoint
+        elif last_checkpoint is not None:
+            checkpoint = last_checkpoint
+        train_result = trainer.train(resume_from_checkpoint=checkpoint)
+        trainer.save_model()
+        trainer.log_metrics("train", train_result.metrics)
+        trainer.save_metrics("train", train_result.metrics)
+        trainer.save_state()
+
+    # Evaluation
+    if training_args.do_eval:
+        metrics = trainer.evaluate()
+        trainer.log_metrics("eval", metrics)
+        trainer.save_metrics("eval", metrics)
+
+    # Write model card and (optionally) push to hub
+    kwargs = {
+        "tasks": "masked-auto-encoding",
+        "dataset": data_args.dataset_name,
+        "tags": ["masked-auto-encoding"],
+    }
+    if training_args.push_to_hub:
+        trainer.push_to_hub(**kwargs)
+    else:
+        trainer.create_model_card(**kwargs)
+
+
+if __name__ == "__main__":
+    main()
+
+
+def _mp_fn(index):
+    # For xla_spawn (TPUs)
+    main()