TopSpin 1.2.1

A PyTorch based high-level Deep Learning training framework. Seamlessly switch between single-GPU and multi-server; Supports gradient accumulation, learning rate warmup and decay, early stop, mixed precision training, and AutoML.

TopSpin - BERT-style Deep Learning Training Framework

TopSpin, is a PyTorch based high-level Deep Learning training framework, designed for BERT-style deep learning models, as opposed to GPT3 based deep learning. User defined model can be launched in either single-GPU, multi-GPU, or multi-server, without changing one line of codes.

TopSpin supports many useful techniques in Deep Learning Training, such as gradient accumulation, learning rate warm-up and decay, early stop, mixed precision training, parameter regularizers like VAT automatic validation data evaluation in training, TensorBoard, True gradient for variable batch size in some NLP applications, and provide rich statistics in training log, like network time statistics, training time prediction.

TopSpin, besides, provides inherent AutoML support that searches possible model parameter combinations in one or multiple servers in parallel.

---

1. Install

2. Examples

2.1 NLP

2.2 ASR

2.3 CV

3. Core Components

3.1 ParamBase

3.2 ModelBase

3.3 TrainerBase

3.4 PredictorBase

3.5 Datasets

3.6 Server

3.7 TaskManager

4. Run and Stop Training

4.1 Run mode 1: Debug Run

4.2 Run mode 2: Auto ML

4.3 Run mode 3: Distributed

4.4 Stop Training

5. Miscellaneous Functions

5.1 Learning Rate Warm-up and Decay

5.2 Early Stop

5.3 Mixed Precision Training

5.4 Gradient Accumulation

5.5 True gradient in Training

5.6 Automatic Validation Data Evaluation in Training

6. Model Parameters Regularization

7. Interpret the Training Logs

7.1 TensorBoard

7.2 Rich statistics in Training Log

7.3 Training Figures

8. Configure Your Servers

9. Popular questions

---

1. Install

>> python3 -m pip install TopSpin
>> python3 -m pip install -r requirements.txt

Especially we require to install protobuf==3.20.* designed in requirements.txt. Regarding other packages, higher versions might be working too.

2. Examples

2.1 NLP

• Intent detection Task.
  • The core problem is a short sentence classification, and we use TextCNN as its basic model.
  • make_features.py
  • param.py
  • model.py
  • train.py
  • predict.py
  • Debug mode >> python3 example/nlp/intent_detection/train.py
  • AutoML mode >> python3 example/nlp/intent_detection/train_auto_starter.py
  • Distributed mode >> python3 example/nlp/intent_detection/train_dist_starter.py

2.2 ASR

• Speaker change detection
  • Given a multi-person dialog utterance, we detect the time points speaker changes.

2.3 CV

• Object detection.

3. Core Components

3.1 ParamBase

• Interface
• code

3.2 TrainerBase

• Interface
• code

3.3 ModelBase

• Interface
• code

3.4 PredictorBase

• Interface
• code

3.5 Dataset

3.6 Server

3.7 TaskManager

4. Run and Stop Training

4.1 Run Mode 1: Debug Run

>> python3 example/nlp/intent_dection/train.py 

By design, quck run mode is for debugging, though you could run for simple tasks which require only one GPU. TopSpin, in this mode, would find a free GPU behind your back and assign to your task. You don't need to set GPU id.

This mode is not supportive of GPU allocation, server allocation, task parallelization. Strongly encourage you guys to use mode2, auto run.

4.2 Run Mode 2: Auto ML

You just use TopSpin.ParameterRange to list candiate values. TopSpin would unfold all combinations and sent to GPU queue for paralleled training.

self.iter_num_update_optimizer = ParameterRange([1, 2, 3])


self.embedding_size = ParameterRange([128, 256, 512])
self.kernel_sizes = ParameterRange([[3, 4, 5], [1, 2, 3, 4, 5], [5, 6, 7]])
self.kernel_number = ParameterRange([128, 256])
self.dropout_ratio = ParameterRange([0.1, 0.3])

Even you could AutoML training data.

self.train_files = ParameterRange(["train.1.pkl", "train.2.pkl"])

The training entry is often defined in a seperate train_auto_starter.py

 starter.start_train(
   Param.get_instance(), 
   "example/nlp/intent_dection/train.py",
   [starter.Server(None, [1, 3, 4, 6, 7]),    # None means current log-in server.
    starter.Server("192.168.1.10", [1, 3, 4, 6, 7])]
 )

4.3 Run Mode 3: Distributed

Here distributed training means using more than one server to train ONE model, as opposed to mulitiple models in AutoML.

The philosophy of distributed training is that the model is large enough that all GPUs from designed servers would be occupied. As a result, AutoML would be too costly to apply here.

The training entry is also defined in a seperate train_dist_starter.py. Set your servers_file in your param.py, and use this starter function

  starter.start_distributed_train(
    Param.get_instance(),
    "example/nlp/intent_dection/train.py",
  )

4.4 Stop Training

5. Miscellaneous Functions

5.1 Learning Rate Warm-up and Decay

5.2 Early Stop

5.3 Mixed Precision Training

5.4 Gradient Accumulation

5.5 True gradient in Training

5.6 Automatic Validation Data Evaluation in Training

5.7 TensorBoard

5.8 Rich statistics in Training Log

6. Model Parameters Regularization

7. Interpret the Training Logs

8. Configure Your Servers

8.1 Remove password in login

1. >> ssh-keygen -t rsa
2. >> chmod 75[offline_smalldataset.py](src%2Ftopspin%2Fdataset%2Foffline_smalldataset.py)5 ~/.ssh
3. copy ~/.ssh/id_rsa.pub to your target server and save as ~/.ssh/authorized_keys
4. chmod 644 ~/.ssh/authorized_keys

8.2 Permit net ports

8.3 Share your data disk to other servers

You could use network shared disk.

>> sshfs {user}@{ip}:{data-folder} {local-folder} -o idmap=user -o allow_other

We actually use Network-attached Storage (NAS) system.

9. Popular questions

As I do not need distributed training in foreseeable future, do I really need TopSpin?

The distributed training is not our selling point. TopSpin is a training framework for DL models, which integrates many proven effective techniques in training, as well as AutoML to greatly improve your training efficieny.

Meantime, it supports multiple-GPU and distributed training, even your daily work might not use them.

GPU is not available.

TopSpin would detect whether the server cluster has sufficient GPUs to run your task. If not, it would refuse to run.

Data path is not available.

TopSpin would detect whether your designated data path (or feature path, etc.) are available. If not, it would refuse to run.

Can not automatically set ParamBase.net_name.

In most cases, TopSpin would set this for you. Yet in some very rare case, TopSpin can not set a correct value. One workable solution is ls /sys/class/net) to try those value one by one. A typical net_name is en0, eth0.

Is model debugging in TopSpin more complicated than in a naive single GPU training script?

No any difference. Actually, TopSpin, in the debugging mode, provides many convenience behind your back, such as setting all potential multi-thread running as single-thread running, and setting GPU number to ONE, ignoring your preset number.

In order to speedup the data loading, could I save a copy of the data in each server, and how to set TopSpin?

Sure. You can do it, and it indeed speedups the data loading as well as the training. In this way you do not need to change any code. It runs perfectly.

Another way is to copy 1/n data to n servers respectively to save disk space. Then in the user's train.py, you need to update the parameters of get_batch_data(...) in Trainer.__init__(...). Change dist.get_rank() and dist.get_world_size() to dist.get_local_rank() and self._param.gpu_num respectively.

Version 5 What's New?

1. More safe

Before excuting GPU running, TopSpin would examine the availability of desired GPUs and accessibility of data folder.

2. Supportive of more rich information returned in train_one_batch.

Now the user defined function can return a dict,

 return {
   "loss": loss,
   "batch_num": b_x.size(0), # depends on user's application.
   "figure": figure          # optional
 }

If your batch_num is just the number of sentences, then you could ignore this value; otherwise, in some cases this number actually equating to word number, you have to set it exactly.

Espeically, the key figure records all information to draw by TopSpin automatically.

3. Automatic loss drawing in training

figure = {
 "F1-score": 0.34,
 "accuracy": 0.46
}

as well as any information desired to show in a figure. TopSpin would append your training loss, as well as validation and testing data errors by default.

4. Rigid definition on model initialization

class ModelWrapperBase:
 def __init__(self, param: ParamBase, user_model_cls):
  ...
  model = user_model_cls(param)
  ...

In comparison, lower versions permit passing a user model instance into ModelWrapperBase.

===============================================================================

Version 4

Section 1 - About TopSpin

Since starting to develop 2 years ago, it has been evolved for 5 versions , including one for tensorflow 1.X, one for tensorflow 2.X, three versions for PyTorch. This README mainly introduce the most recent version, namely estimator4 , as it contains the richest functions among all versions.

1. For managers, TLs, and team members

We list only interfaces of main modules in estimator4, with their private functions omitted.

a) palframe.pytorch.estimator4.param.ParamBase

b) palframe.pytorch.estimator4.model_wrapper.ModelWrapper

class ModelWrapperBase:
  def __init__(self, param: ParamBase, model: torch.nn.Module):
    ...

  def evaluate_file(self, data_file) -> float:
    # return an evaluation result. Smaller, better.
    ...

  def predict(self, *batch):
    # Just invoke it
    return self._model(*batch)
    ...

c) palframe.pytorch.estimator4.train.TrainerBase

class TrainerBase:
  def __init__(self,
               model_wrapper: ModelWrapperBase,
               train_data_iter,  
               optimizer: typing.Union[Optimizer, None, str]=None):
    ...

  def train_one_batch(self, *batch)-> tensor:
    # Given a batch=[batch_x, batch_y], return a loss tensor
    ...

  def early_stop(self, epoch_id, loss_history: list, vali_error_history: list):
    # override it when necessary
    return False  # default

  def train(self):
    # Just invoke it
    ...

d) palframe.pytorch.estimator4.predict.PredictorBase

class PredictorBase:
  def __init__(self,
               model_wrapper: ModelWrapperBase):
    # You do NOT need to change it.

  def evaluate_file(self, data_file) -> float:
    # You do NOT need to change it.
    with torch.no_grad():
      return self._model_wrapper.evaluate_file(data_file)

  def predict(self, batch_data):
    # You do NOT need to change it.
    with torch.no_grad():
      return self._model_wrapper.predict(batch_data)

2. AutoML

Use ParameterRange to mark all parameters and set their candidate values, then TopSpin would search all combinations for you in the backhand.

    self.lr = ParameterRange([5e-4, 7e-4])
    self.warmup_ratio = ParameterRange([0.1, 0.2, 0.3])
    self.hidden_dim = ParameterRange([100, 200, 300])

3. Automatic GPUs and servers allocation

We assume a single task requires 2 GPUs, then we run it on my current server and use GPU 2 and 3.

param = MyParam.get_instance(),
param.gpu_num = 2    

starter.start_train(
  param,
  "nlp_tasks/task00_intent/ver_2_2_7_9/train.py",
  [starter.Server(None, [2, 3])]
)

Let continue with MyParam definition that uses AutoML and it finally generates 2x3x3=18 parameter variants. Then we have only one server with GPU 2, 3 available. Let's assume one task takes 1 hour, then we have to wait for 18 hours. The waiting looks boring ...

Luckily, Shuang brother say, my task is done, and you can use GPU 0, 1 of that server. We update our starting script like this.

param = MyParam.get_instance(),
param.gpu_num = 2    

starter.start_train(
  param,
  "nlp_tasks/task00_intent/ver_2_2_7_9/train.py",
  [starter.Server(None, [0, 1, 2, 3])]
)

In this case, two tasks can be run in parallel. Hence, our waiting time is reduced to 18/(4/2)=9 hours. Life looks a bit better.

More luckily, xiaozhao sister said, my task is also done, you can use RTX8000 that has 8 GPUs. We further update our script like this,

param = MyParam.get_instance(),
param.gpu_num = 2    

starter.start_train(
  param,
  "nlp_tasks/task00_intent/ver_2_2_7_9/train.py",
  [starter.Server(None, [0, 1, 2, 3]),
   starter.Server("192.168.1.228", [0, 1, 2, 3, 4, 5, 6, 7])]
)

Then our waiting time becomes only 18/((4+8)/2)=3 hours. Life should be much better.

4. Built-in support for distributed training

User: How to update my codes to a distributed version?

TopSpin: Sir, you are all set. Try a different starting command.

param = MyParam.get_instance(),
param.servers_file = "my_cluster_servers.txt"   # You tell TopSpin this.
param.gpu_num = 8

starter.start_distributed_train(
  param,
  "patbert/ver_2_2_7_9/train.py",
)

Section 2 - An step-by-step example

Step 1. Configure your environment

Configure your PyCharam

Add PAL_Frame folder to your Content Root.

Set PYTHONPATH in Mac

If you run in your Mac terminal, then you need to add to ~/.profile.

export PYTHONPATH={your-PAL-frame-folder}/PAL_frame:$PYTHONPATH
export PYTHONPATH=./:$PYTHONPATH

Test the new settings

>> source ~/.profile
>> echo $PYTHONPATH

You should see the correct values.

Set PYTHONPATH in Linux

Add the above codes to ~/.bashrc, and repeat the testing.

>> source ~/.bashrc
>> echo $PYTHONPATH

Step 2. Define param.py

class Param(ParamBase):
  def __init__(self):
    super(Param, self).__init__("nlp_example.intent_dection")

    # standard parameters derived from ParamBase
    self.gpu_num = 2                    # required GPU number.
    self.use_gpu = False
    self.use_amp = False                # mixed precision based training
    self.optimizer_name = "Adam"
    self.lr = 5e-4
    self.batch_size = 16                # for one worker
    self.iter_num_update_optimizer = 1  # gradient accumulation
    self.warmup_ratio = 0.1             # lr warm percentage of all steps.
    self.model_kept_num = 5             # save you models
    self.path_feat = "feat/nlp/intent_dection"
    self.train_files = [f"{self.path_feat}/train.pkl"]
    self.vali_file = f"{self.path_feat}/vali.pkl"
    self.test_files = []
    self.train_sample_num = 100
    self.eval_gap_sample_num = 100      # sample number between two evaluations.
    self.epoch_num = 5

    # user model specific parameters 
    self.max_seq_len = 128
    self.class_number = 13
    self.embedding_size = 128
    self.kernel_sizes = [3, 4, 5]
    self.kernel_number = 128
    self.dropout_ratio = 0.3
    self.vocab_size = 60000

Step 3. Define model.py

class Model(nn.Module):
  def __init__(self, param: Param):
    super(Model, self).__init__()

    self._embedding = nn.Embedding(param.vocab_size, param.embedding_size)

    self._textcnn = nlp_torch.TextCNN(
      kernels=param.kernel_sizes,
      in_channel=1,
      out_channel=param.kernel_number,
      max_seq_len=param.max_seq_len,
      dim=param.embedding_size,
      dropout=param.dropout_ratio
    )
    self._textcnn_output_size = len(param.kernel_sizes) * param.kernel_number

    self._dense = nlp_torch.Dense(
      nn.Linear(self._textcnn_output_size, param.class_number)
    )

  def forward(self, word_ids):
    word_ids = word_ids.unsqueeze(1)
    embedding_out = self._embedding(word_ids)
    textcnn_out = self._textcnn(embedding_out)
    out = self._dense(textcnn_out)
    pred_labels = torch.argmax(out, 1)

    return out, pred_labels

Step 4. Define model_wrapper.py

class ModelWrapper(ModelWrapperBase):
  def __init__(self, param: Param):
    super(ModelWrapper, self).__init__(
      param, Model(param)
    )

  def evaluate_file(self, data_file: str):
    '''
    :param data_file:
    :return: smaller better, such as WER, -F1-value, -Accuracy.
    '''
    start_time = time.time()
    all_true_labels = []
    all_pred_labels = []
    for _, batch in get_batch_data(self._param, [data_file], 1, False):
      batch = [e.to(self._device) for e in batch]

      b_word_ids, b_labels = batch
      logits, pred_labels = self.predict(b_word_ids)

      all_true_labels.extend(b_labels.tolist())
      all_pred_labels.extend(pred_labels.tolist())

    result = Measure.calc_precision_recall_fvalue(
      all_true_labels, all_pred_labels
    )
    total_time = time.time() - start_time
    avg_time = total_time / (len(all_true_labels) + 1e-6)
    weighted_f = result["weighted_f"]
    Logger.info(
      f"eval: "
      f"file={data_file} weighted_f={weighted_f} result={result} "
      f"total_time={total_time:.4f} secs avg_time={avg_time:.4f} sec/sample "
    )
    Logger.info(f"WEIGHTED_F : {weighted_f}")

    return -weighted_f

  def predict(self, b_word_ids):
    logits, pred_labels = self._model(b_word_ids)
    return logits, pred_labels

Step 5. Define train.py

class Trainer(TrainerBase):
  def __init__(self, param):

    super(Trainer, self).__init__(
      ModelWrapper(param),
      get_batch_data(param, param.train_files, param.epoch_num, True),
      None
    )

  def train_one_batch(self, b_word_ids, b_label):
    logits, pred_labels = self._model_wrapper.predict(b_word_ids)
    return nn.functional.cross_entropy(logits, b_label, reduction="mean")

Step 6. Run mode1: Debug Run

>> python3 example/nlp/intent_dection/train.py 

Step 7. AutoML

Step 9. Run mode3: Dist Run - for large training

Step 10. Read your log

Run.

>> nohup python3 example/nlp/intent_dection/train_starter.py > log.any_name &

In current folder, log.any_name only tell you if your running is normal. You should check work/{run_dir}/log/log.rank_0 to read outputs of your model. {run_dir} can be found in log.any_name.

When your running fails, you can check work/{run_dir}/log/log.node_0, and the problematic batch data and model are automatically saved into work/{run_dir }/bug/ for your bug replay.

Step 11. Stop a training

There is a unique method to stop your training, regardless of any starting mode.

python3 palframe/pytorch/estimator4/stopper.py {ParamBase.path_work}

Step 12. More advanced usage

You could have different models run together in a server pool to use your resources to an extreme. Use RunManager class.

  run_manager = RunManager(tasks, servers)
  run_manager.run()

See definition of Task and Server in palframe/pytorch/estimator4/starter.py.

class Server:
  def __init__(self, ip, gpus):
    ...

class Task:
  def __init__(self, param: ParamBase, source_script_and_params):
    ...

In the PAT-BERT project, one pretrained model would be tested in 10 downstream NLP tasks, and each task has multiple set of parameter configurations. We typically use RunManager to finish 216 tasks in 180 GPUs in just 16 hours.

Step 13. Full operations for the above NLP example

>> python3 example/nlp/intent_dection/make_features.py 
>> nohup python3 example/nlp/intent_dection/train_starter.py > log.any_name &

Section 3. Miscellaneous functions

1. Single-node multi-GPU support

2. Distributed training

3. Automaic GPUs and servers allocation for task parallelization

4. AutoML

5. Three running modes

No any change for your codes.

1. Debug mode, for debugging only, which uses single GPU (or CPU).
2. Auto mode, your common choice, which supports automatic GPUs and servers allocation.
3. Distributed mode, for quite complex tasks that need more than one server.

6. Learning rate warmup and decay

7. Training restoration after server is down

So far we only support Debug mode, Distributed mode, and a single task in Auto mode. Todo: to support Auto mode fully.

In the construction function of the user's Parameter class, set the restore_training_from_path_work as the running path of your last run. Its default value is None.

8. Early stop

9. Mixed precision training

Capable of speeding up training by 80% without a performance degradation.

10. Four predefined datasets for small and large data loading

Effectively support extremely large dataset.

11. Gradient accumulation

In the case when Distributed mode is unavailable.

12 Dev set evaluation

The best evaluation score and its corresponding model are showed and stored.

13. TensorBoard

>> tensorboard --logdir work/{run-log}/tensorboard --bind_all

14. True gradient

If your GPUs have unbalanced running tasks, such as different batch sizes, you could active this function to improve your final performance. Besides setting param.true_gradient = True, in train_one_batch, you should return (loss, batch num), instead of just loss.

15. Rich information in log file

1. You model trainable parameter information

   Besides total number of parameters are displayed, parameters are sorted by their sizees, percentage on overall parameters.

2. Training time prediction

3. Network efficiency estimate

16. Deployment

A default PredictorBase is provided and we have preset some enviroment for you, such as set inference and no gradient.

Each deployment supports only GPU and is defined as param.gpus[0]. Thus, you should set proper param.gpus values.