Pytorch example with alluxiofs (#49)

* Add a Pytorch distributed training example using alluxiofs

* Add a Pytorch distributed training example using alluxiofs

* Add a Pytorch distributed training example using alluxiofs

* Move alluxio_fs initialization and file preprocessing to the parent process. Add dependencies in the top comment lines.

* added break, fixed formatting

* updated to etcd:v3.5.13

examples/pytorch_distributed_BERT_training.py

@@ -0,0 +1,369 @@
+# An example that uses Alluxiofs to speed up pytorch distributed NLP training with large CSV files.
+# The example demonstrates BERT fine-tuning.
+# CSV files stored in an S3 directory are loaded into Alluxio first, reducing the time needed to pull data from
+# the remote directory during training, decreasing the training time.
+# When a specific line in a CSV file is requested by the training model, only that line is loaded into memory,
+# preventing loading the entire CSV file into the memory, effectively optimizing memory usage.
+# This helps solve the problems:
+# 1) The total size of all CSV files needed for training is too large to fit into the memory
+# 2) Any single CSV file is too large to fit into the memory
+#
+# How to use:
+# 1) start an alluxio cluster https://github.com/Alluxio/alluxio
+# 2) install dependencies specified below
+# 3) run the following command in terminal:
+#    python3 pytorch_distributed_BERT_training.py --total-epochs <epoch_number> --batch-size <batch_size> --directory-path <'your_s3_directory_path'>
+#
+# Dependencies: fsspec, alluxiofs, s3fs, torch, transformers, numpy, pandas, tqdm, bisect, os, time
+import bisect
+import os
+import time
+
+import fsspec
+import numpy as np
+import pandas as pd
+import torch
+import torch.distributed as dist
+import torch.multiprocessing as mp
+import torch.nn as nn
+import torch.optim as optim
+import transformers
+from torch.nn.parallel import DistributedDataParallel as DDP
+from torch.utils.data import DataLoader
+from torch.utils.data import Dataset
+from torch.utils.data import DistributedSampler
+from tqdm import tqdm
+
+from alluxiofs import AlluxioClient
+from alluxiofs import AlluxioFileSystem
+
+
+class BertDataset(Dataset):
+    def __init__(
+        self,
+        alluxio_fs,
+        tokenizer,
+        max_length,
+        directory_path,
+        preprocessed_file_info,
+        total_length,
+        read_chunk_size,
+        output_filename,
+    ):
+        super(BertDataset, self).__init__()
+        self.alluxio_fs = alluxio_fs
+        self.directory_path = directory_path
+
+        self.tokenizer = tokenizer
+        self.max_length = max_length
+
+        self.preprocessed_file_info = preprocessed_file_info
+        self.start_line_num_list = sorted(
+            list(self.preprocessed_file_info.keys())
+        )
+        self.total_length = total_length
+        self.read_chunk_size = read_chunk_size
+
+        self.output_filename = output_filename  # for output recording purpose
+        self.total_access = 0  # for output recording purpose
+
+    def __len__(self):
+        return self.total_length
+
+    def __getitem__(self, index):
+        """
+        Map index into target line number in a specific file.
+        To avoid a single big file overloading the memory, a file is read by chunk when accessing the target line.
+        :param index: global index of the data point that the model trainer want to access
+        :return: BERT specific tensors for the target data point
+        """
+
+        # find the target file and the target line where the index located
+        target_file_index = (
+            bisect.bisect_right(self.start_line_num_list, index) - 1
+        )
+        target_file_start_line_num = self.start_line_num_list[
+            target_file_index
+        ]
+        target_file_name = self.preprocessed_file_info[
+            target_file_start_line_num
+        ]
+        target_line_index = index - target_file_start_line_num
+
+        # load target file in memory by chunk to avoid memory overloading and then read the target line
+        chunk_number = target_line_index // self.read_chunk_size
+        line_within_chunk = target_line_index % self.read_chunk_size
+        chunk_iterator = pd.read_csv(
+            self.alluxio_fs.open(target_file_name, mode="r"),
+            chunksize=self.read_chunk_size,
+        )
+
+        for i, chunk in enumerate(chunk_iterator):
+            # only the chunk will be loaded into memory each time
+            # memory occupied by chunk will be freed once outside the loop
+            if i == chunk_number:
+                target_line = chunk.iloc[line_within_chunk]
+                break
+
+        # process target line text for BERT use
+        inputs = self.tokenizer.encode_plus(
+            target_line.iloc[0],
+            None,
+            padding="max_length",
+            add_special_tokens=True,
+            return_attention_mask=True,
+            max_length=self.max_length,
+            truncation=True,
+        )
+        ids = inputs["input_ids"]
+        token_type_ids = inputs["token_type_ids"]
+        mask = inputs["attention_mask"]
+
+        return {
+            "ids": torch.tensor(ids, dtype=torch.long),
+            "mask": torch.tensor(mask, dtype=torch.long),
+            "token_type_ids": torch.tensor(token_type_ids, dtype=torch.long),
+            "target": torch.tensor(target_line.iloc[1], dtype=torch.long),
+        }
+
+
+class BERT(nn.Module):
+    def __init__(self):
+        super(BERT, self).__init__()
+        self.bert_model = transformers.BertModel.from_pretrained(
+            "bert-base-uncased"
+        )
+        self.out = nn.Linear(768, 1)
+
+    def forward(self, ids, mask, token_type_ids):
+        _, o2 = self.bert_model(
+            ids,
+            attention_mask=mask,
+            token_type_ids=token_type_ids,
+            return_dict=False,
+        )
+
+        out = self.out(o2)
+
+        return out
+
+
+def finetune(epochs, dataloader, model, loss_fn, optimizer, rank, cpu_id):
+
+    model.train()
+
+    for epoch in range(epochs):
+        if isinstance(dataloader.sampler, DistributedSampler):
+            dataloader.sampler.set_epoch(epoch)
+
+        loop = tqdm(enumerate(dataloader), leave=False, total=len(dataloader))
+        for batch, dl in loop:
+
+            print(f"rank {cpu_id} epoch {epoch} batch {batch}")
+
+            # Bert training
+            ids = dl["ids"].to(rank)
+            token_type_ids = dl["token_type_ids"].to(rank)
+            mask = dl["mask"].to(rank)
+            label = dl["target"].to(rank)
+            label = label.unsqueeze(1)
+
+            optimizer.zero_grad()
+
+            output = model(ids=ids, mask=mask, token_type_ids=token_type_ids)
+            label = label.type_as(output)
+
+            loss = loss_fn(output, label)
+            loss.backward()
+
+            optimizer.step()
+
+            pred = np.where(output.cpu().detach().numpy() >= 0, 1, 0)
+            label = label.cpu().detach().numpy()
+
+            num_correct = sum(1 for a, b in zip(pred, label) if a[0] == b[0])
+            num_samples = pred.shape[0]
+            accuracy = num_correct / num_samples
+
+            print(
+                f"Got {num_correct} / {num_samples} with accuracy {float(num_correct) / float(num_samples) * 100:.2f}"
+            )
+
+            loop.set_description(f"Epoch={epoch + 1}/{epochs}")
+            loop.set_postfix(loss=loss.item(), acc=accuracy)
+
+    return model
+
+
+def preprocess(directory_path, chunk_size, alluxio_fs):
+    """
+    Preprocess each file in the directory for Dataset class.
+    :param directory_path: directory path contain the files to be processed
+    :param chunk_size: read file that has chunk_size to the memory each time
+    :return: processed_file_info is a dictionary that contain start_line number for each file;
+            total length of all files in the directory
+    """
+
+    processed_file_info = {}  # a dictionary of {start_line_number: file_name}
+    next_start_line_num = 0
+    total_length = 0
+
+    all_files_info = alluxio_fs.ls(directory_path)
+
+    # iterate each file in alluxio cache to get start line number for each file
+    for file_info in all_files_info:
+        file_name = file_info["name"]
+        processed_file_info[next_start_line_num] = file_name
+
+        # calculate length of each file, read the csv by chunk in case the file is too big to fit into the memory
+        file_length = 0
+        chunk_iterator = pd.read_csv(
+            alluxio_fs.open(file_name, mode="r"), chunksize=chunk_size
+        )
+        for chunk in chunk_iterator:
+            file_length += len(chunk)
+
+        next_start_line_num += file_length
+        total_length += file_length
+
+    return processed_file_info, total_length
+
+
+def append_to_output(filename, content):
+    with open(filename, "a") as file:
+        file.write(content + "\n")
+
+
+def main(
+    rank,
+    world_size,
+    total_epochs,
+    batch_size,
+    directory_path,
+    preprocessed_file_info,
+    total_length,
+    alluxio_fs,
+):
+
+    # distributed training settings
+    print(f"Initializing process group for rank {rank}")
+    os.environ["MASTER_ADDR"] = "localhost"
+    os.environ["MASTER_PORT"] = "12355"
+
+    dist.init_process_group(backend="gloo", rank=rank, world_size=world_size)
+
+    # train on gpu if gpu is available; otherwise, train on cpu
+    device = torch.device(
+        f"cuda:{rank}" if torch.cuda.is_available() else "cpu"
+    )
+    torch.cuda.set_device(device) if torch.cuda.is_available() else None
+
+    # train BERT
+    tokenizer = transformers.BertTokenizer.from_pretrained("bert-base-uncased")
+    print(f"Loading dataset on rank {rank}")
+
+    dataset = BertDataset(
+        alluxio_fs,
+        tokenizer,
+        max_length=100,
+        directory_path=directory_path,
+        preprocessed_file_info=preprocessed_file_info,
+        total_length=total_length,
+        read_chunk_size=1000,
+        output_filename=f"output_rank_{rank}.txt",
+    )
+
+    sampler = DistributedSampler(dataset)
+    dataloader = DataLoader(
+        dataset=dataset, batch_size=batch_size, sampler=sampler
+    )
+
+    model = BERT().to(device)
+    model = DDP(
+        model,
+        device_ids=[rank] if torch.cuda.is_available() else None,
+        find_unused_parameters=True,
+    )
+
+    loss_fn = nn.BCEWithLogitsLoss().to(device)
+    optimizer = optim.Adam(model.parameters(), lr=0.0001)
+
+    for param in model.module.bert_model.parameters():
+        param.requires_grad = False
+
+    model = finetune(
+        total_epochs, dataloader, model, loss_fn, optimizer, device, rank
+    )
+    dist.destroy_process_group()
+    print(f"Process group destroyed for rank {rank}")
+
+
+if __name__ == "__main__":
+    import argparse
+
+    parser = argparse.ArgumentParser(
+        description="Simple distributed training job"
+    )
+    parser.add_argument(
+        "--total-epochs", type=int, help="Total epochs to train the model"
+    )
+    parser.add_argument(
+        "--batch-size",
+        default=32,
+        type=int,
+        help="Input batch size on each device (default: 32)",
+    )
+    parser.add_argument(
+        "--directory-path", type=str, help="Path to the input data file"
+    )
+    args = parser.parse_args()
+
+    # initialize AlluxioClient and pull all file from S3 to alluxio
+    alluxio_client = AlluxioClient(etcd_hosts="localhost")
+    load_success = alluxio_client.submit_load(args.directory_path)
+    print("Alluxio Load job submitted successful:", load_success)
+
+    load_progress = "Loading datasets into Alluxio"
+
+    while load_progress != "SUCCEEDED":
+        time.sleep(5)
+        progress = alluxio_client.load_progress(args.directory_path)
+        load_progress = progress[1]["jobState"]
+        print("Load progress:", load_progress)
+
+    # set up alluxio filesystem
+    # it will be used in preprocess() function and BertDataset class to access files in alluxio
+    fsspec.register_implementation(
+        "alluxiofs", AlluxioFileSystem, clobber=True
+    )
+    alluxio_fs = fsspec.filesystem(
+        "alluxiofs",
+        etcd_hosts="localhost",
+        etcd_port=2379,
+        target_protocol="s3",
+    )
+
+    # preprocess files in the directory
+    preprocessed_file_info, total_length = preprocess(
+        args.directory_path, 5000, alluxio_fs
+    )
+
+    world_size = (
+        torch.cuda.device_count()
+        if torch.cuda.is_available()
+        else os.cpu_count()
+    )
+    mp.spawn(
+        main,
+        args=(
+            world_size,
+            args.total_epochs,
+            args.batch_size,
+            args.directory_path,
+            preprocessed_file_info,
+            total_length,
+            alluxio_fs,
+        ),
+        nprocs=world_size,
+        join=True,
+    )

tests/conftest.py

@@ -93,7 +93,7 @@ def launch_alluxio_dockers(with_etcd=False):
     run_cmd_etcd = (
         "docker run --platform linux/amd64 -d --rm --net=alluxio_network -p 4001:4001 -p 2380:2380 -p 2379:2379 "
         f"-v {TEST_DIR}:/etc/ssl/certs "
-        "--name etcd quay.io/coreos/etcd:latest "
+        "--name etcd quay.io/coreos/etcd:v3.5.13 "
         "/usr/local/bin/etcd "
         "--data-dir=/etcd-data "
         "--name etcd1 "