PyTorch TensorIterator Internals

PyTorch is one of the leading frameworks for deep learning. Its core data structure is Tensor, a multi-dimensional array implementation with many advanced features like auto-differentiation. PyTorch is a massive codebase (approx. a million lines of C++, Python and CUDA code), and having a method for iterating over tensors in a very efficient manner that is independent of data type, dimension, striding and hardware is a critical feature that can lead to a very massive simplification of the codebase and make distributed development much faster and smoother. The TensorIterator C++ class within PyTorch is a complex yet useful class that is used for iterating over the elements of a tensor over any dimension and implicitly parallelizing various operations in a device independent manner.

It does this through a C++ API that is independent of type and device of the tensor, freeing the programmer of having to worry about the datatype or device when writing iteration logic for PyTorch tensors. For those coming from the NumPy universe, NpyIter is a close cousin of TensorIterator.

This post is a deep dive into how TensorIterator works, and is an essential part of learning to contribute to the PyTorch codebase since iterations over tensors in the C++ codebase are extremely commonplace. This post is aimed at someone who wants to contribute to PyTorch, and you should at least be familiar with some of the basic terminologies of the PyTorch codebase that can be found in Edward Yang's excellent blog post on PyTorch internals. Although TensorIterator can be used for both CPUs and accelerators, this post has been written keeping in mind usage on the CPU. Although there can be some dissimilarities between the two, the overall concepts are the same.

History of TensorIterator

TH iterators

TensorIterator was devised to simplify the implementation of PyTorch's tensor operations over the TH implementation. TH uses preprocessor macros to write type-independent loops over tensors, instead of C++ templates. For example, consider this simple TH loop for computing the product of all the numbers in a particular dimension (find the code here):

TH_TENSOR_DIM_APPLY2(scalar_t, t, scalar_t, r_, dimension,
    accreal prod = 1;
    int64_t i;
    for(i = 0; i < t_size; i++)
        prod *= t_data[i*t_stride];
    *r__data = (scalar_t)prod;

The above loop works by following a particular convention for the naming of the types and variables. You specify the input type and output type of your tensors in the first and third arguments. scalar_t is a type that can generically be used for denoting a PyTorch scalar type such as float, double, long etc. Internally, PyTorch uses the scalar_t for compiling the file multiple times for different definitions of scalar_t (as in for different data types like float, int, etc.). The input tensor and output tensors are specified in the second and fourth arguments (in this case t and r_), and the dimension that we want to iterate over is specified as the fifth argument (dimension).

We then follow these arguments with the main body of the iterator (which is accepted as the sixth argument into the macro), and denote the data, stride and size of the particular tensor dimension by using variables that are suffixed by _data, _stride and _size respectively after the variable name that represents the tensor inside the iterator body. For example, the size of the input tensor is denoted as t_size in the above example and the pointer to the data of the output tensor is denoted as r__data. The accreal in the second line is custom type that specifies a real number that is an accumulator (in this case for accumulating the product).

Internally, the TH_TENSOR_DIM_APPLY2 macro is expanded for generating various dispatch calls depending on the type of the tensor that needs to be iterated over. The implementation of TH_TENSOR_DIM_APPLY2 can be found here.

Limitations of TH iterators

Apart from the obvious complication that arises due to maintaining a codebase that is so dependent on such insanely complex macro expansions, TH iterators have some fundamental shortcomings. For one thing, they cannot be used for writing iterators in a device independent manner - you will need separate iterators for CPU and CUDA. Also, parallelization does not happen implicitly inside the iterator, you need to write the parallel looping logic yourself. Moreover, at a deeper level TH iterators do not collapse the dimensions of the tensor (as we'll see later in this post) therefore leading to looping that might not be as cache-optimized as possible.

These limitations led to the creation of TensorIterator, which is used by the ATen tensor implementation for overcoming some of the shortcomings of the previous TH iterators.

Basics of TensorIterator

A TensorIterator can be created using the default constructor. You must then add the tensors that you want as inputs or outputs. A good example can be found from the TensorIterator::binary_op() method that allows you to create TensorIterator objects for performing point-wise binary operations between two tensors. The important parts look like so:

auto iter = TensorIterator();


As you can see, you add a tensor called out as the output tensors and a and b as the input tensors. Calling build is then mandatory for creating the object and letting the class perform other optimizations like collapsing dimensions.

Performing iterations

Broadly, iterations using TensorIterator can be classified as point-wise iterations or reduction iterations. This plays a fundamental role in how iterations using TensorIterator are parallelized - point-wise iterations can be freely parallelized along any dimension and grain size while reduction operations have to be either parallelized along dimensions that you're not iterating over or by performing bisect and reduce operations along the dimension being iterated. Parallelization can also happen using vectorized operations.

Iteration details

The simplest iteration operation can be performed using the for_each function. This function has two overloads: one takes a function object which iterates over a single dimension (loop_t); the other takes a function object which iterates over two dimensions simultaneously (loop2d_t). Find their definitions here. The former can iterate over a loop of a single dimension whereas the latter can do so over two dimensions. The simplest way of using for_each is to pass it a lambda of type loop_t (or loop2d_t). A code snippet using it this way would look like so:

auto iter = TensorIterator();
iter.dont_resize_outputs(); // call if out is allocated.
iter.dont_compute_common_dtype(); // call if inputs/outputs are of a different type.;

auto loop = [&](char **data, const int64_t* strides, int64_t n) {
    auto * out_data_bytes = data[0];
    auto * in_data_bytes = data[1];

    // assume float data type for this example.
    for (int i = 0; i < n; i++) {
      *reinterpret_cast<float*>(out_data_bytes) +=

      out_data_bytes += strides[0];
      in_data_bytes += strides[1];


In the above example, the char** data gives a pointer to the data within the tensor in the same order that you specify when you build the iterator. Note that in order to make the implementation agnostic of any particular data type, you will always receive the pointer typecast to char (think of it as a bunch of bytes).

The second argument is int64_t* strides which is an array containing the strides of each tensor in the dimension that you're iterating over. We can add this stride to the pointer received in order to reach the next element in the tensor. The last argument is int64_t n which is the size of the dimension being iterated over.

for_each implicitly parallelizes the operation by executing loop in parallel if the number of iterations is more than the value of internal::GRAIN_SIZE, which is a value that is determined as the 'right amount' of data to iterate over in order to gain a significant speedup using multi-threaded execution. If you want to explicitly specify that your operation must run in serial, then use the serial_for_each loop.

Using kernels for iterations

Frequently we want to create a kernel that applies a simple point-wise function onto entire tensors. TensorIterator provides various such generic kernels that can be used for iterating over the elements of a tensor without having to worry about the stride, data type of the operands or details of the parallelism.

For example, say we want to build a function that performs the point-wise addition of two tensors and stores the result in a third tensor, we can use the cpu_kernel function. Note that in this example we assume a tensor of float but you can use the AT_DISPATCH_ALL_TYPES_AND2 macro.

TensorIterator iter;
cpu_kernel(iter, [] (float a, float b) -> float {
  return a + b;

Writing the kernel in this way ensures that the value returned by the lambda passed to cpu_kernel will populate the corresponding place in the target output tensor.

Setting tensor iteration dimensions

The value of the sizes and strides will determine which dimension of the tensor you will iterate over. TensorIterator performs optimizations to make sure that at least most of the iterations happen on contiguos data to take advantage of hierarchical cache-based memory architectures (think dimension coalescing and reordering for maximum data locality).

Now a multi-dimensional tensor will have multiple stride values depending on the dimension you want to iterate over, so TensorIterator will directly compute the strides that get passed into the loop by by itself within the build() function. How exactly it computes the dimension to iterate over is something that should be properly understood in order to use TensorIterator effectively.

If you're performing a reduction operation (see the sum code in ReduceOps.cpp), TensorIterator will figure out the dimensions that will be reduced depending on the shape of the input and output tensor, which determines how the input will be broadcast over the output. If you're performing a simple pointwise operation between two tensors (like a addcmul from PointwiseOps.cpp) the iteration will happen over the entire tensor, without providing a choice of the dimension. This will allow TensorIterator to freely parallelize the computation, without guarantees of the order of execution (since it does not matter anyway).

For something like a cumulative sum operation, where you want be able to choose the dimension to reduce but iterate over multiple non-reduced dimensions (possibly in parallel), you must first re-stride the tensors, and then use these tensors for creating a TensorIterator. In order to understand how this bit works, lets go over the code for the kernel that executes the cumsum function.

The important bits of this function are like so:

auto self_sizes = ensure_nonempty_vec(self.sizes().vec());
self_sizes[dim] = 1;

auto result_restrided = restride_dim(result, dim, self_sizes);
auto self_restrided = restride_dim(self, dim, self_sizes);

auto iter = TensorIterator();

You can see that we first change the size of the tensors to 1 on the reduction dimension so that the dimension collapsing logic inside TensorIterator#build will know which dimension to skip. Setting the dimension in this way is akin to telling TensorIterator to skip the dimension. We then restride the tensors using restride_dim and then use the restrided tensors for building the TensorIterator. You can set any size for inputs/outputs, then TensorIterator with check whether it can come up with a common broadcasted size


This post was a very short introduction to what TensorIterator is actually capable of. If you want to learn more about how it works and what goes into things like collapsing the tensor size for optimizing memory access, a good place to start would be the build() function in TensorIterator.cpp. Also have a look at this wiki page from the PyTorch team on using TensorIterator.