What functions or modules require contiguous input?

After additional digging under the hood through source_code, it seems that view is the only function that explicitly causes an exception when a non-contiguous input is passed.

One would expect any operation using Tensor Views to have the potential of failing with non-contiguous input. In reality, it seems to be the case that most or all of these functions are:

(a.) implemented with support for non-contiguous blocks (see example below), i.e. the tensor iterators can handle multiple pointers to the various chunks of the data in memory, perhaps at the expense of performance, or else

(b.) a call to .contiguous() wraps the operation (One such example shown here for torch.tensor.diagflat()). reshape is essentially the contiguous()-wrapped form of view.

By extension, it seems, the main benefit of view over reshape would be the explicit Exception when tensors are unexpectedly non-contiguous versus code silently handling this discrepancy at the cost of performance.

This conclusion is based on:

1. Testing of all Tensor View ops with non-contiguous inputs.
2. Source code analysis of other non-Tensor View functions of interest (e.g. Conv1D, which includes calls to contiguous as necessary in all non-trivial input cases).
3. Inference from pytorch's design philosophy as a simple, at times slow, easy-to-use language.
4. Cross-posting on Pytorch Discuss.
5. Extensive review of web reported errors involving non-contiguous errors, all of which revolve around problematic calls to view.

I did not comprehensively test all pytorch functions, as there are thousands.

EXAMPLE OF (a.):

import torch
import numpy
import time

# allocation 
start = time.time()
test = torch.rand([10000,1000,100])
torch.cuda.synchronize()
end = time.time()
print("Allocation took {} sec. Data is at address {}. Contiguous: 
{}".format(end - 
start,test.storage().data_ptr(),test.is_contiguous()))

# view of a contiguous tensor
start = time.time()
test.view(-1)
torch.cuda.synchronize()
end = time.time()
print("view() took {} sec. Data is at address {}. Contiguous: 
{}".format(end - 
start,test.storage().data_ptr(),test.is_contiguous()))


# diagonal() on a contiguous tensor
start = time.time()
test.diagonal()
torch.cuda.synchronize()
end = time.time()
print("diagonal() took {} sec. Data is at address {}. Contiguous: 
{}".format(end - 
start,test.storage().data_ptr(),test.is_contiguous()))


# Diagonal and a few tensor view ops on a non-contiguous tensor
test = test[::2,::2,::2]    # indexing is a Tensor View op 
resulting in a non-contiguous output
print(test.is_contiguous()) # False
start = time.time()
test = test.unsqueeze(-1).expand([test.shape[0],test.shape[1],test.shape[2],100]).diagonal()
torch.cuda.synchronize()
end = time.time()
print("non-contiguous tensor ops() took {} sec. Data is at 
address {}. Contiguous: {}".format(end - 
start,test.storage().data_ptr(),test.is_contiguous()))

# reshape, which requires a tensor copy operation to new memory
start = time.time()
test = test.reshape(-1) + 1.0
torch.cuda.synchronize()
end = time.time()
print("reshape() took {} sec. Data is at address {}. Contiguous: {}".format(end - start,test.storage().data_ptr(),test.is_contiguous()))

The following is output:

Allocation took 4.269254922866821 sec. Data is at address 139863636672576. Contiguous: True
view() took 0.0002810955047607422 sec. Data is at address 139863636672576. Contiguous: True
diagonal() took 6.532669067382812e-05 sec. Data is at address 139863636672576. Contiguous: True
False
non-contiguous tensor ops() took 0.00011277198791503906 sec. Data is at address 139863636672576. Contiguous: False
reshape() took 0.13828253746032715 sec. Data is at address 94781254337664. Contiguous: True

A few tensor view operations in block 4 are performed on a non-contiguous input tensor. The operation runs without error, maintains the data in the same memory addresses, and runs relatively faster than an operation requiring a copy to new memory addresses (such as reshape in block 5). Thus, it seems these operations are implemented in a way that handles non-contiguous inputs without requiring a data copy.

From the pytorch documentation: contiguous() → Tensor. Returns a contiguous tensor containing the same data as self tensor. If self tensor is contiguous, this function returns the self tensor.

I don't think there is a complete list for it. It depends on how you implement a tensor-processing function.

If you look at the tutorial on writing C++ and CUDA extensions, you would see that a typical pytorch CUDA op looks like:

• C++ interface(s) with torch::Tensor arguments. This class provides APIs to access/manipulate tensor data.
• CUDA kernel(s) with float* arguments. These pointers directly points to the memory storing tensor data.

Evidently, dealing with data in tensors with pointers can be a lot more efficient than dealing with the APIs of the tensor class. But it is best to deal with pointers with contiguous memory layout (or at least a regular layout).

I believe in principle it is possible to manipulate data with pointers even without contiguous data, if given enough information of the memory layout. But you would have to take all kinds of layouts into consideration and the code can be a lot more tedious.

Facebook might have certain tricks to make some built-in ops work on noncontiguous data (I don't really know enough about this), but most custom extension modules require the inputs to be contiguous.