Tensor creation

Table of Contents

• Wrapping a torch tensor
• Factory methods

Related reading: PyTorch tensor creation API

Wrapping a torch tensor

A neml2::Tensor can be created from a torch::Tensor by marking its batch dimension:

• C++

  #include <torch/torch.h>
  #include "neml2/tensors/Tensor.h"
   
  int
  main()
  {
    // A raw torch tensor
    auto A = torch::rand({5, 3, 2, 8});
    std::cout << "Shape of A: " << A.sizes() << '\n' << std::endl;
   
    // Mark it with batch dimension of 2
    auto B = neml2::Tensor(A, 2);
    std::cout << "      Shape of B: " << B.sizes() << std::endl;
    std::cout << "Batch shape of B: " << B.batch_sizes() << std::endl;
    std::cout << " Base shape of B: " << B.base_sizes() << std::endl;
  }

  Output:

  Shape of A: [5, 3, 2, 8]
   
        Shape of B: [5, 3, 2, 8]
  Batch shape of B: [5, 3]
   Base shape of B: [2, 8]

• Python

  import torch
  from neml2.tensors import Tensor
   
  # A raw torch tensor
  A = torch.rand(5, 3, 2, 8)
  print("Shape of A:", A.shape, "\n")
   
  # Mark it with batch dimension of 2
  B = Tensor(A, 2)
  print("      Shape of B:", B.shape)
  print("Batch shape of B:", B.batch.shape)
  print(" Base shape of B:", B.base.shape)

  Output:

  Shape of A: torch.Size([5, 3, 2, 8]) 
   
        Shape of B: (5, 3, 2, 8)
  Batch shape of B: (5, 3)
   Base shape of B: (2, 8)

Factory methods

A factory tensor creation function produces a new tensor. All factory functions adhere to the same schema:

• C++

  <TensorType>::<function_name>(<function-specific-options>, const neml2::TensorOptions & options);

• Python

  <TensorType>.<function_name>(<function-specific-options>, *, dtype, device, requires_grad)

where <TensorType> is the class name of the primitive tensor type listed here, and <function-name> is the name of the factory function which produces the new tensor. <function-specific-options> are positional arguments a particular factory function accepts. Refer to each tensor type's class documentation for the concrete signature. The last argument const TensorOptions & options configures the data type, device, and other "meta" properties of the produced tensor. The commonly used meta properties are

• dtype: the data type of the elements stored in the tensor. Available options are kInt8, kInt16, kInt32, kInt64, kFloat32, and kFloat64. Support for unsigned integer types were added in recent versions of PyTorch.
• device: the compute device where the tensor will be allocated. Available options are kCPU and kCUDA. On MacOS, the device type torch::kMPS could be used but is not officially supported by NEML2.
• requires_grad: whether the tensor is part of a function graph used by automatic differentiation to track functional relationship. Available options are true and false.

For example, the following code creates a statically (base) shaped, dense, single precision tensor of type SR2 filled with zeros, with batch shape \((5, 3)\), allocated on the CPU.

• C++

  #include "neml2/tensors/SR2.h"
   
  using namespace neml2;
   
  int
  main()
  {
    auto A = SR2::zeros({5, 3}, {}, TensorOptions().dtype(kFloat32).device(kCPU));
  }

  Output:

• Python

  import torch
  from neml2.tensors import SR2
   
  A = SR2.zeros((5, 3), dtype=torch.float32, device=torch.device("cpu"))

  Output:

Note

All the factory methods are listed here.

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