Model conversion overview

This doc page gives an overview of changes you make when you prepare your existing PyTorch model on RDL. You can learn more in our conversion examples.

The basics

This section discusses some basics that help you understand what working in a SambaNova environment is all about.

SambaTensor and torch.Tensor

SambaTensor External link is a wrapper around torch.Tensor with additional SambaNova capabilities.

A SambaTensor has:

  • A unique name for interfacing with the RDU

  • An optional, user-defined batch_dim for optimization

  • Methods to transfer the tensor between the RDU and system host

In all other ways, you can treat a SambaTensor like a torch.Tensor. For example:

  • tensor3 = tensor1 + tensor2

  • tensor2 = tensor1.reshape(-1, 5)

To take advantage of SambaTensor capabilities, you call:

  • samba.from_torch_tensor(torch.Tensor, name="example_name")

Function overrides

how sambatensor uses functions

You don’t have to explicitly define functions to manipulate SambaTensor. If you pass a SambaTensor to a torch function, SambaFlow overrides the torch method and calls the equivalent SambaFlow method.

  • If you pass a torch tensor to torch.add(), the torch method is called.

  • If you pass a SambaTensor to torch.add(), SambaFlow calls samba.add().

Tracing, compilation, and dummy tensors

Tracing refers to the conversion of a dynamic graph to a static graph. The purpose of tracing is to walk through the PyTorch computation graph and map it onto the RDU. As part of the process, we override the torch functions (e.g., Conv2d) with the SambaFlow implementation.

During tracing, the compiler applies optimization techniques that greatly improve training time. In a SambaNova environment, tracing walks through the model with "dummy" tensors as part of compilation. These dummy tensors (which you define as part of your model code) must have the same shape as the input tensors to your model, but don’t have to contain any meaningful data.


Tracing happens automatically during compilation. See SambaFlow compiler overview if you’re interested in how the compiler works.

static diagram of compile train process

The compiler performs tracing during the forward pass. Note that the compiler does not support control flow within a model. Conditional statements do not become a part of the computational graph on RDU; only those branches that evaluated to True are preserved.

Compilation generates a PEF file: a binary file that contains the full details of the compiled model that can be deployed onto an RDU. You then pass the PEF file to the training function.

It’s possible to explicitly call the trace_graph() utility function (see samba.utils.trace_graph External link for details).

This is done after the model is compiled.


If a specific operation is not supported on RDU, the parameters needed for that operation should be transferred to CPU, perform the operation, then sync it back to RDU with to_rdu.

Write your model to stay on RDU as much as possible. Transferring data between RDU and CPU has a significant performance hit.

Model porting workflows

When you prepare your PyTorch model to run on SambaNova hardware, you can leave most of the existing model code intact. The Convert a simple model to SambaFlow doc page illustrates this. Here’s an overview of the pieces.

Data preparation

SambaNova has two data preparation scripts in our public GitHub repository External link.

  • The generative_data_prep/data_prep/ script tokenizes a single jsonline or text file, packs it into fixed length sequences and converts it to HDF5 format.

  • The generative_data_prep/data_prep/ script facilitates end-to-end data preparation for training machine learning models. This script:

    1. Takes a single jsonline or text file as input

    2. Shuffles the input and splits it into multiple train/dev/test files

    3. Calls generative_data_prep/data_prep/ on all the splits to tokenize the text, pack it into fixed length sequences, and convert to HDF5 format.

Prepare the model for compilation

Each SambaNova model must call the samba.session.compile() function. During compilation, you pass in your PyTorch model and dummy input tensors, and the compiler generates a PEF file. If you start with a working PyTorch model, only fairly minimal changes are required.

In your code:

  1. Convert model parameters to Samba format using samba.from_torch_model_ External link

  2. Convert each torch tensor to SambaTensor using samba.from_torch_tensor_ External link

  3. Convert the model using one of two functions:

    • Either the compile() function (SambaSession.compile External link)

    • Or the common_app_driver() utility function. This function is deprecated and a replacement is in progress.

  4. For either function, specify the following required arguments:

    • The model.

    • The dummy input tensors (as described earlier).

    • The optimizer (SambaFlow supports AdamW and SGD).

  5. Specify arguments that the user can specify when calling the compilation function.

    • The arguments specified in Compiler argument reference are always supported.

    • Most models also support a set of model-specific arguments used for compile, e.g., batch size and number of epochs.

For small POC models, you compile only once and use the generated PEF file for training and for inference. For all other models, we recommend a separate compile for inference to make your model run more efficiently.

Prepare models to run training

Each SambaNova model must include a training function. If you start with a working PyTorch model, only fairly minimal changes are required, as the existing training loop can be used in SambaFlow.

  1. Make all the changes required for model compilation.

  2. Create a training function around the training loop. See Train the model with train() for a full explanation of a training loop.

  3. Call the training function. Here’s a simple example:

    # Running, or training, a model must be explicitly carried out
    if args.command == "run":
        # Trace the graph
        utils.trace_graph(model, inputs, optim, pef=args.pef, mapping=args.mapping)
        # Within the user defined train function, call:
        train(args, model)
  4. The training function takes as input the model (converted to SambaFlow) and the arguments required to run the model. Ensure that the run function takes the input tensors, output tensors, and section types. During training, all 3 section types are required. During inference, only forward is required.

  5. The core to training function that allows the model to run on RDU is a call to It takes as input the true tensors (not the dummies), the output tensors (these are generated during graph tracing), and the sections to be run.

    outputs =,
    	                     section_types= ['fwd', 'bckwd', 'opt'])
  6. If your model has adjustable hyperparameters during training, specify them via hyperparam_dict, a dictionary of key value pairs that is updated during the run. Examples might include:

    • Learning rate (lr)

    • Weight decay (weight_decay)

    • SGD momentum (momentum)

    • Dropout rate (p)

See Hyperparameter reference for details.

Prepare models to run inference

Running inference consists of these tasks:

  1. Compile for inference. To run inference, you must first compile the model for inference by calling the compile function with the --inference argument. The result of compilation is a PEF that is optimized for inference.

  2. Run inference. To run inference with your trained model, you also pass the --inference argument and:

    • The inference PEF file

    • A checkpoint (an inference model isn’t trained in this case, so needs to be instantiated with weights)

    • A dataset for inference

After you’ve run inference, you can check how your model performed. See Compilation, training, and inference and Code elements of the inference program for examples.