The Definition and Call of Job Function

In OneFlow, we encapsulate the training, inference and some other tasks into a “job function”. The job function is used to connect the user’s business logic and the computing resource managed by OneFlow.

In OneFlow, the function decorated by @oneflow.global_function decorator is the OneFlow’s job function

We mainly define the structure of the model and choose the optimization target in job function. In addition, we can also pass some hyperparameters about training and some configuration of the environment to the job function (like the following example: get_train_config()), OneFlow will manage the memory, GPU and other computing resource according to our configuration.

In this article, we will specifically learn about:

  • how to define and call the job function

  • how to get the return value of job function

The Relationship Between Job Function and Running Process of OneFlow

The job function is divided into two phases: definition and call.

It’s related to OneFlow’s operating mechanism. Briefly, the OneFlow Python layer API simply describes the configuration and the training environment of the model. These information will pass to the C++ backend. After compilation, graph building and so on, the computation graph is obtained. Finally, the job function will be executed in OneFlow runtime.

The job function describes the model and the training environment. In this phase there’s no data. We can only define the shape and data type of the nodes (as known as PlaceHolder) for creating and compiling the computation graph of OneFlow.

The job function will be called after the OneFlow runtime starts. We can pass the data by calling job function and get the results.

We will introduce the definition and calling method of job functions in detail as below.

The Definition of Job Function

We encapsulate the model in Python and use oneflow.global_function to decorate. Then the definition is completed.

The job function mainly describes two things:

  • The structure of model

  • The optimizing target in training phase

In the following code, we build a Multi-Layer Perceptron model and use flow.nn.sparse_softmax_cross_entropy_with_logits to compute the cross-entropy loss as our optimizing target.

  1. @flow.global_function(type="train")
  2. def train_job(
  3.     images: tp.Numpy.Placeholder((BATCH_SIZE, 1, 28, 28), dtype=flow.float),
  4.     labels: tp.Numpy.Placeholder((BATCH_SIZE,), dtype=flow.int32),
  5. ) -> tp.Callback[tp.Numpy]:
  6.     # mlp
  7.     initializer = flow.truncated_normal(0.1)
  8.     reshape = flow.reshape(images, [images.shape[0], -1])
  9.     hidden = flow.layers.dense(
  10.         reshape,
  11.         512,
  12.         activation=flow.nn.relu,
  13.         kernel_initializer=initializer,
  14.         name="hidden",
  15.     )
  16.     logits = flow.layers.dense(
  17.         hidden, 10, kernel_initializer=initializer, name="output"
  18.     )
  20.     loss = flow.nn.sparse_softmax_cross_entropy_with_logits(
  21.         labels, logits, name="softmax_loss"
  22.     )
  23.     lr_scheduler = flow.optimizer.PiecewiseConstantScheduler([], [0.1])
  24.     flow.optimizer.SGD(lr_scheduler, momentum=0).minimize(loss)
  25.     return loss

The Parameters of oneflow.global_function

oneflow.global_function decorator accepts two parameters, type and function_config.

  • The parameter type accepts a string, which can only set as train or predict. When we define a training model, we set it as train. We set is as predict when we define a model for testing or inferencing.
  • The parameter function_config accepts an object which is constructed by oneflow.function_config(). In function_config object, we can use its method or attribute to config. As the following code.
  1. def get_train_config():
  2.     config = flow.function_config()
  3.     config.default_data_type(flow.float)
  4.     return config

We set the default data type, then, we can pass the function_config object to the global_function decorator.

  1. @flow.global_function(type="train", function_config=get_train_config())
  2. def train_job(
  3.     images: tp.Numpy.Placeholder((BATCH_SIZE, 1, 28, 28), dtype=flow.float),
  4.     labels: tp.Numpy.Placeholder((BATCH_SIZE,), dtype=flow.int32),
  5. ) -> tp.Numpy:

For the complete code, you can refer to Consistent and Mirrored‘s mixed_parallel_mlp.py

PlaceHolder

Noted that the images、logits、labels、loss and some other objects have no data in our definition of the job function. They are used to describe the shape and attribute of data, which is called PlaceHolder.

For the parameters of the job function, we use Numpy.Placeholder, ListNumpy.Placeholder, ListListNumpy.Placeholder in the oneflow.typing package to annotate the data type of them as numpy.ndarray, Sequence[numpy.ndarray] and Sequence[Sequence[numpy.ndarray]] respectively.

Besides the types of oneflow.typing, the variables returned from OneFlow operators or layers in the job function, like the reshape、hidden、logits、loss in the code above, are also PlaceHolder.

All the variables mentioned above inherit the base class BlobDef directly or indirectly. We call this object type as Blob in OneFlow.

The Blob has no data when defining the job function. It only plays the role of data placeholder for building the graph.

The Return Value of the Job Function

The concept of the data placeholder Blob is emphasized above because the return value of the job function cannot be arbitrarily specified. It must be Blob type object or a container which only contains the Blob object.

For example, the loss returned in the above code is a Blob object

The return values of job function should be annotated. As an example, -> tp.Numpy in above code means the function returns a Blob object.

As another example, we can annotate the return value type as -> Tuple[tp.Numpy, tp.Numpy].It means the function returns a tuple which contains two Blob object

You can refer to Get the result of the job function for specific examples.

The Call of Job Function

OneFlow uses decorator to convert Python function into OneFlow’s job function. It is transparent to user.

We can call the job function just like we call a Python function. Every time we call the job function, OneFlow will complete the forward propagation, back propagation, parameter updates, and more in framework.

In the code below. When we get the data, we will pass parameters and call the train_job function to print loss.

  1.     (train_images, train_labels), (test_images, test_labels) = flow.data.load_mnist(
  2.         BATCH_SIZE
  3.     )
  5.     for epoch in range(3):
  6.         for i, (images, labels) in enumerate(zip(train_images, train_labels)):
  7.             loss = train_job(images, labels)
  8.             if i % 20 == 0:
  9.                 print(loss.mean())

As you can see, by calling the job function train_job, the numpy data is directly returned.

The method shown above is synchronous. OneFlow also supports asynchronous invocation. For more details you can refer to the article Get the result of the job function.

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