# PyTorch TensorIterator Internals - 2021 Update

For contributors to the PyTorch codebase, one of the most commonly encountered C++ classes is TensorIterator. TensorIterator offers a standardized way to iterate over elements of a tensor, automatically parallelizing operations, while abstracting device and data type details.

In April 2020, Sameer Deshmukh wrote a blog article discussing PyTorch TensorIterator Internals. Recently, however, the interface has changed significantly. This post describes how to use the current interface as of April 2021. Much of the information from the previous article is directly copied here, but with updated API calls and some extra details.

All of the code examples below can be compiled and run in this GitHub repo.

## Basics of TensorIterator and TensorIteratorConfig

In order to create a TensorIterator, a TensorIteratorConfig must be created first. TensorIteratorConfig specifies the input and output tensors that will be iterated over, whether all tensors are expected to share the same data type and device, and a handful of other settings. After setting up the configuration, we can call TensorIteratorConfig::build() to obtain a TensorIterator that has the specified settings. TensorIterator is immutable, so once it is created, its configuration cannot be changed.

In the following example, a tensor named out is configured as the output tensor and a and b are the input tensors. Calling build creates the TensorIterator object from the specified configuration.

at::TensorIteratorConfig iter_config;
iter_config

auto iter = iter_config.build();


## Performing iterations

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. Note that for CUDA, it is possible to parallelize along the reduction dimension, but synchronizations are needed to avoid race conditions. Parallelization with vectorized operations can also be implemented.

### 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 simplest way of using for_each is to pass it a lambda of type loop_t (or loop2d_t).

In the example below, the char** data argument of the copy_loop function (which is an instance of the loop_t lambda) contains a char* pointer for each of the tensors, in the order that they are specified in the TensorIteratorConfig. To make the implementation agnostic of any particular data type, the pointer is typecast to char, so we can access it as an array of bytes.

The second argument is const 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 copy_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.

at::TensorIteratorConfig iter_config;
iter_config

// call if output was already allocated
.resize_outputs(false)

// call if inputs/outputs have different types
.check_all_same_dtype(false);

auto iter = iter_config.build();

// Copies data from input into output
auto copy_loop = [](char** data, const int64_t* strides, int64_t n) {
auto* out_data = data[0];
auto* in_data = data[1];

for (int64_t i = 0; i < n; i++) {
// assume float data type for this example
*reinterpret_cast<float*>(out_data) = *reinterpret_cast<float*>(in_data);
out_data += strides[0];
in_data += strides[1];
}
};

iter.for_each(copy_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 one of the AT_DISPATCH_ALL_TYPES* macros to support multiple data types.

at::TensorIteratorConfig iter_config;
iter_config

auto iter = iter_config.build();

at::native::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 position in the target output tensor, as long as the inputs strictly broadcast over the output--that is, if the output's shape is equal to or greater than the input shape in all dimensions.

#### 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 contiguous data to take advantage of hierarchical cache-based memory architectures (think dimension coalescing and reordering for maximum data locality).

A multi-dimensional tensor has a stride value for each dimension. So the stride that TensorIterator needs to use will be different depending on which dimension you want to iterate over. TensorIterator directly computes the strides that get passed into the loop 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.

When performing a reduction operation (see the sum_out 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 allows TensorIterator to freely parallelize the computation, without guaranteeing 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 be careful to take into account two different strides--one for the dimension being reduced and one for all other dimensions. Take a look at the following example of a somewhat simplified version of the cumsum kernel.

For a 1-D input, torch.cumsum calculates the sum of all elements from the beginning of the vector up to and including each position in the input. A 2-D input is treated as a list of vectors, and the cumulative sum is calculated for each vector. Higher dimensional inputs follow the same logic--everything is just a list of 1-D vectors. So to implement a cumulative sum, we must take into account two different strides: the stride between elements in a vector (result_dim_stride and self_dim_stride in the example below) and the stride between each vector (strides[0] and strides[1] in the example below).

// A cumulative sum's output is the same size as the input
at::Tensor result = at::empty_like(self);

at::TensorIteratorConfig iter_config;
auto iter = iter_config
.check_all_same_dtype(false)
.resize_outputs(false)
.declare_static_shape(self.sizes(), /*squash_dim=*/dim)
.build();

// Size of dimension to calculate the cumulative sum across
int64_t self_dim_size = at::native::ensure_nonempty_size(self, dim);

// These strides indicate number of memory-contiguous elements, not bytes,
// between each successive element in dimension dim.
auto result_dim_stride = at::native::ensure_nonempty_stride(result, dim);
auto self_dim_stride = at::native::ensure_nonempty_stride(self, dim);

auto loop = [&](char** data, const int64_t* strides, int64_t n) {
// There are n individual vectors that span across dimension dim, so
// n is equal to the number of elements in self divided by the size of
// dimension dim.

// These are the byte strides that separate each vector that spans across
// dimension dim
auto* result_data_bytes = data[0];
const auto* self_data_bytes = data[1];

for (int64_t vector_idx = 0; vector_idx < n; ++vector_idx) {

// Calculate cumulative sum for each element of the vector
auto cumulative_sum = (at::acc_type<float, false>) 0;
for (int64_t elem_idx = 0; elem_idx < self_dim_size; ++elem_idx) {
const auto* self_data = reinterpret_cast<const float*>(self_data_bytes);
auto* result_data = reinterpret_cast<float*>(result_data_bytes);
cumulative_sum += self_data[elem_idx * self_dim_stride];
result_data[elem_idx * result_dim_stride] = (float)cumulative_sum;
}

// Go to the next vector
result_data_bytes += strides[0];
self_data_bytes += strides[1];
}
};

iter.for_each(loop);


#### Helper functions

There are many helper functions within PyTorch that can simplify the creation and execution of a TensorIterator. We cannot cover all of them in this blog post, so you would need to discover them on your own. However, let's discuss one of the most common ones: make_reduction

make_reduction creates a TensorIterator specifically for a reduction operation with one input and one output. It handles all of the TensorIteratorConfig setup internally, so we don't need to write as much boiler-plate code.

The following example uses make_reduction to create a TensorIterator which is used to calculate the sum reduction of a 2-D input across dimension 1. This is equivalent to torch.sum(self, dim=1) in Python. If we didn't use make_reduction, this code would be a bit more complex and more difficult to write.

In this example, as opposed to the previous examples, we do not need to advance the out_data pointer. In fact, the value of strides[0] in this case is 0. The reason for this is that the TensorIterator generated by make_reduction was initialized with is_reduction(true), and when for_each is called, sum_reduce_loop is executed once per element of the output tensor. Thus, sum_reduce_loop only needs to iterate across the input data, adding each input element to the corresponding reduced output element. The operation is thread-safe as well, so the for_each call is free to split up individual sum_reduce_loop executions across multiple threads to parallelize the calculation.

at::Tensor self = at::randn({10, 10, 10});
int64_t dim = 1;
bool keepdim = false;

// make_reduction will resize result tensor for us, so we
// can set its size to (0)
at::Tensor result = at::empty({0}, self.options());

auto iter = at::native::make_reduction(
"sum_reduce",
result,
self,
dim,
keepdim,
self.scalar_type());

// Sum reduce data from input into output
auto sum_reduce_loop = [](char** data, const int64_t* strides, int64_t n) {
auto* out_data = data[0];
auto* in_data = data[1];

assert(strides[0] == 0);

*reinterpret_cast<float*>(out_data) = 0;

for (int64_t i = 0; i < n; i++) {
// assume float data type for this example
*reinterpret_cast<float*>(out_data) += *reinterpret_cast<float*>(in_data);
in_data += strides[1];
}
};

iter.for_each(sum_reduce_loop);


## Conclusion

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.