add longformer

training/benchmarks/longformer/README.md

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+## Model Introduction
+### longformer-base-4096 model
+
+longformer-base-4096 is a BERT-like model started from the RoBERTa checkpoint and pretrained for MLM on long documents.
+It supports sequences of length up to 4,096. [longformer-base-4096 model](https://huggingface.co/allenai/longformer-base-4096). It was
+introduced in [this paper](https://arxiv.org/abs/2004.05150). The code for longformer process can be found
+[here](https://github.com/huggingface/transformers/tree/main/examples/research_projects/longform-qa).
+
+## Model and Training Scripts source code
+Pytorch case:
+This repository includes software from https://github.com/huggingface/transformers/tree/v4.33.0
+licensed under the Apache License 2.0.
+
+Some of the files in this directory were modified by BAAI in 2023 to support FlagPerf.
+
+## Dataset and Model Checkpoints
+
+> Dataset website：https://huggingface.co/datasets/enwik8
+https://huggingface.co/allenai/longformer-base-4096
+> Model checkpoint website: https://huggingface.co/allenai/longformer-base-4096
+
+We have already preprocessed the dataset and the model checkpoint files(The preprocessing script is `training/benchmarks/longformer/pytorch/data_preprocessing/create_train_eval_data.py`).
+The preprocessed can be downloaded directly from https://bd.bcebos.com/klx-pytorch-ipipe-bd/flagperf/datasets/longformer_train.tar.
+No additional preprocessing steps need to be conducted.
+After decompressing, the dataset and model checkpoint files are organized as the following:
+
+```
+longformer_train
+|-- dataset
+|   |-- eval_dataset.npz
+|   `-- train_dataset.npz
+`-- model
+    |-- config.json
+    |-- merges.txt
+    |-- pytorch_model.bin
+    |-- tokenizer.json
+    `-- vocab.json
+```
+
+## Benchmark Task and Target Accuracy
+
+This experiment is to finetune a text classification task on wikitext dataset with longformer-base-4096 pretrained checkpoints.
+After finetuning 10 epoches, the longformer-base-4096 model is able to achieve accuracy score of 90+, which matches the evaluation result on the [report](https://huggingface.co/allenai/longformer-base-4096).
+
+## AI Frameworks && Accelerators supports
+
+|            | Pytorch | Paddle | TensorFlow2 |
+| ---------- | ------- | ------ | ----------- |
+| Nvidia GPU | [✅](../../nvidia/longformer-pytorch/README.md)       | N/A    | N/A       |

training/benchmarks/longformer/pytorch/config/__init__.py

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+from ._base import *
+from .mutable_params import mutable_params

training/benchmarks/longformer/pytorch/config/_base.py

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+# DO NOT MODIFY THESE REQUIRED PARAMETERS
+
+# Required parameters
+vendor: str = None
+data_dir: str = None
+name: str = "longformer-base-4096"
+cudnn_benchmark: bool = False
+cudnn_deterministic: bool = True
+
+# Optional parameters
+
+# =========================================================
+# loss scale
+# =========================================================
+lr: float = 5e-5
+weight_decay = 0.01
+
+# =========================================================
+# train && evaluate
+# =========================================================
+train_batch_size: int = 16
+eval_batch_size: int = 16
+
+max_epoch: int = 1000
+target_acc: float = 0.64
+
+do_train = True
+distributed: bool = True
+
+
+# =========================================================
+# utils
+# =========================================================
+seed: int = 42
+dist_backend: str = 'nccl'
+device: str = None
+
+# =========================================================
+# datasets
+# =========================================================
+dataloader_drop_last: bool = False
+dataloader_num_workers: int = 8
+
+# =========================================================
+# for driver
+# =========================================================
+local_rank: int = -1
+use_env: bool = True
+log_freq: int = 100
+print_freq: int = 100
+n_device: int = 1
+sync_bn: bool = False
+gradient_accumulation_steps: int = 1

training/benchmarks/longformer/pytorch/config/mutable_params.py

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+mutable_params = [
+    'vendor', 'data_dir', 'lr', 'weight_decay', 'train_batch_size',
+    'gradient_accumulation_steps', 'eval_batch_size', 'do_train',
+    'distributed', 'dist_backend', 'device', 'cudnn_benchmark',
+    'cudnn_deterministic'
+]

training/benchmarks/longformer/pytorch/data_preprocessing/create_train_eval_data.py

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+import os
+import numpy as np
+from itertools import chain
+from typing import List
+import datasets
+from datasets import load_dataset
+from transformers import LongformerTokenizer, DataCollatorForLanguageModeling
+
+def save_dataset(ds, save_path):
+    np.savez(save_path,
+             input_ids=ds['input_ids'],
+             attention_mask=ds['attention_mask'],
+             special_tokens_mask=ds['special_tokens_mask'])
+
+def main():
+    data_prefix = 'longformer_train/dataset'
+    os.makedirs(data_prefix, exist_ok=True)
+    train_datapath = os.path.join(data_prefix, 'train_dataset.npz')
+    eval_datapath = os.path.join(data_prefix, 'eval_dataset.npz')
+
+    tokenizer = LongformerTokenizer.from_pretrained('allenai/longformer-base-4096', use_auto_token=False)
+    data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm_probability=0.15)
+    raw_datasets = load_dataset("wikitext", "wikitext-2-raw-v1")
+    column_names = list(raw_datasets["train"].features)
+    text_column_name = "text" if "text" in column_names else column_names[0]
+    max_seq_length = tokenizer.model_max_length
+    if max_seq_length > 1024:
+        max_seq_length = 1024
+
+    def tokenize_function(examples):
+        return tokenizer(examples[text_column_name], return_special_tokens_mask=True)
+
+    tokenized_datasets = raw_datasets.map(
+                    tokenize_function,
+                    batched=True,
+                    num_proc=None,
+                    remove_columns=[text_column_name],
+                    load_from_cache_file=True,
+                    desc="Running tokenizer on dataset line_by_line",
+                )
+
+    def group_texts(examples):
+        # Concatenate all texts.
+        concatenated_examples = {k: list(chain(*examples[k])) for k in examples.keys()}
+        total_length = len(concatenated_examples[list(examples.keys())[0]])
+        # We drop the small remainder, and if the total_length < max_seq_length  we exclude this batch and return an empty dict.
+        # We could add padding if the model supported it instead of this drop, you can customize this part to your needs.
+        total_length = (total_length // max_seq_length) * max_seq_length
+        # Split by chunks of max_len.
+        result = {
+            k: [t[i : i + max_seq_length] for i in range(0, total_length, max_seq_length)]
+            for k, t in concatenated_examples.items()
+        }
+        return result
+
+    tokenized_datasets = tokenized_datasets.map(
+                    group_texts,
+                    batched=True,
+                    num_proc=None,
+                    load_from_cache_file=True,
+                    desc=f"Grouping texts in chunks of {max_seq_length}",
+                )
+    train_dataset = tokenized_datasets['train'].with_format('numpy')
+    save_dataset(train_dataset, train_datapath)
+
+    validation_dataset = tokenized_datasets['validation'].with_format('numpy')
+    save_dataset(validation_dataset, eval_datapath)
+
+if __name__ == "__main__":
+    main()

training/benchmarks/longformer/pytorch/dataloaders/__init__.py

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+from .dataloader import build_train_dataloader, build_eval_dataloader

training/benchmarks/longformer/pytorch/dataloaders/dataloader.py

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+import os
+import numpy as np
+import torch
+from torch.utils.data import Dataset
+from torch.utils.data import DataLoader
+import datasets
+from transformers import LongformerTokenizer, DataCollatorForLanguageModeling
+
+class LongformerDataset(Dataset):
+    def __init__(self, filepath):
+        origin_data = np.load(filepath)
+        self.input_ids = origin_data['input_ids']
+        self.special_tokens_mask = origin_data['special_tokens_mask']
+        self.attention_mask = origin_data['attention_mask']
+
+    def __len__(self):
+        return len(self.input_ids)
+
+    def __getitem__(self, idx):
+        sample = {
+            'input_ids': self.input_ids[idx],
+            'special_tokens_mask': self.special_tokens_mask[idx],
+            'attention_mask': self.attention_mask[idx]
+        }
+        return sample
+
+def get_data_collator(config):
+    model_path = os.path.join(config.data_dir, 'model')
+    tokenizer = LongformerTokenizer.from_pretrained(model_path)
+    data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm_probability=0.15)
+    return data_collator
+
+def build_train_sampler(config, dataset):
+    if torch.distributed.is_initialized():
+        world_size = torch.distributed.get_world_size()
+        rank = torch.distributed.get_rank()
+        sampler = torch.utils.data.distributed.DistributedSampler(
+            dataset, num_replicas=world_size, rank=rank, seed = config.seed)
+    else:
+        generator = torch.Generator()
+        generator.manual_seed(config.seed)
+        sampler = torch.utils.data.RandomSampler(dataset, generator=generator)
+    return sampler
+
+def build_train_dataloader(config):
+    data_collator = get_data_collator(config)
+    train_dataset = LongformerDataset(
+        os.path.join(config.data_dir, 'dataset', 'train_dataset.npz'))
+    train_sampler = build_train_sampler(config, train_dataset)
+    data_loader = DataLoader(
+            train_dataset,
+            sampler=train_sampler,
+            collate_fn=data_collator,
+            batch_size=config.train_batch_size,
+            drop_last=config.dataloader_drop_last,
+            num_workers=config.dataloader_num_workers,
+        )
+    return data_loader
+
+def build_eval_dataloader(config):
+    data_collator = get_data_collator(config)
+    eval_dataset = LongformerDataset(
+        os.path.join(config.data_dir, 'dataset', 'eval_dataset.npz'))
+    data_loader = DataLoader(
+            eval_dataset,
+            collate_fn=data_collator,
+            batch_size=config.eval_batch_size,
+            drop_last=config.dataloader_drop_last,
+            num_workers=config.dataloader_num_workers,
+        )
+
+    return data_loader

training/benchmarks/longformer/pytorch/model/__init__.py

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+import os
+from transformers import (AutoConfig,
+                          LongformerForMaskedLM,
+                          LongformerTokenizerFast,
+                          AutoModelForMaskedLM,
+                          AutoTokenizer)
+
+
+def create_model(config):
+    model_path = os.path.join(config.data_dir, 'model')
+    hfconfig = AutoConfig.from_pretrained(model_path)
+    model = LongformerForMaskedLM(hfconfig)
+    tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False)
+    return model, hfconfig, tokenizer

training/benchmarks/longformer/pytorch/optimizers/__init__.py

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+import torch
+
+
+def create_optimizer(model, args):
+    # Optimizer
+    # Split weights in two groups, one with weight decay and the other not.
+    no_decay = ["bias", "LayerNorm.weight", "layer_norm.weight"]
+    optimizer_grouped_parameters = [
+        {
+            "params": [
+                p for n, p in model.named_parameters()
+                if not any(nd in n for nd in no_decay)
+            ],
+            "weight_decay":
+            args.weight_decay,
+        },
+        {
+            "params": [
+                p for n, p in model.named_parameters()
+                if any(nd in n for nd in no_decay)
+            ],
+            "weight_decay":
+            0.0,
+        },
+    ]
+    optimizer = torch.optim.AdamW(optimizer_grouped_parameters, lr=args.lr)
+    return optimizer