Views and inplace operations

Theano allows the definition of Ops which return a view on one of their inputs or operate inplace on one or several inputs. This allows more efficient operations on numpy’s ndarray data type than would be possible otherwise. However, in order to work correctly, these Ops need to implement an additional interface.

Theano recognizes views and inplace operations specially. It ensures that they are used in a consistent manner and it ensures that operations will be carried in a compatible order.

An unfortunate fact is that it is impossible to return a view on an input with the double type or to operate inplace on it (Python floats are immutable). Therefore, we can’t make examples of these concepts out of what we’ve just built. Nonetheless, we will present the concepts:

Views

A “view” on an object x is an object y which shares memory with x in some way. In other words, changing x might also change y and vice versa. For example, imagine a vector structure which contains two fields: an integer length and a pointer to a memory buffer. Suppose we have:

x = vector {length: 256,
            address: 0xDEADBEEF}

y = vector {length: 224,
            address: 0xDEADBEEF + 0x10}

z = vector {length: 256,
            address: 0xCAFEBABE}

So x uses the memory range 0xDEADBEEF - 0xDEADBFEF, y the range 0xDEADBEFF - 0xDEADBFDF and z the range 0xCAFEBABE - 0xCAFEBBBE. Since the ranges for x and y overlap, y is considered to be a view of x and vice versa.

Suppose you had an Op which took x as input and returned y. You would need to tell Theano that y is a view of x. For this purpose, you would set the view_map field as follows:

myop.view_map = {0: [0]}

What this means is that the first output (position 0) is a view of the first input (position 0). Even though the interface allows a list of inputs that are viewed by a given output, this feature is currently unsupported. Here are more examples:

myop.view_map = {0: [0]} # first output is a view of first input
myop.view_map = {0: [1]} # first output is a view of second input
myop.view_map = {1: [0]} # second output is a view of first input

myop.view_map = {0: [0], # first output is a view of first input
                 1: [1]} # *AND* second output is a view of second input

myop.view_map = {0: [0], # first output is a view of first input
                 1: [0]} # *AND* second output is *ALSO* a view of first input

myop.view_map = {0: [0, 1]} # THIS IS NOT SUPPORTED YET! Only put a single input number in the list!

Inplace operations

An inplace operation is one that modifies one or more of its inputs. For example, the expression x += y where x and y are numpy.ndarray instances would normally represent an inplace operation on x.

Note

Inplace operations in Theano still work in a functional setting: they need to return the modified input. Symbolically, Theano requires one Variable standing for the input before being modified and another Variable representing the input after being modified. Therefore, code using inplace operations would look like this:

from theano.tensor import dscalars, log
from theano.tensor.inplace import add_inplace

x, y = dscalars('x', 'y')
r1 = log(x)

# r2 is x AFTER the add_inplace - x still represents the value before adding y
r2 = add_inplace(x, y)

# r3 is log(x) using the x from BEFORE the add_inplace
# r3 is the SAME as r1, even if we wrote this line after the add_inplace line
# Theano is actually going to compute r3 BEFORE r2
r3 = log(x)

# this is log(x) using the x from AFTER the add_inplace (so it's like log(x + y))
r4 = log(r2)

Needless to say, this goes for user-defined inplace operations as well: the modified input must figure in the list of outputs you give to Apply in the definition of make_node.

Also, for technical reasons but also because they are slightly confusing to use as evidenced by the previous code, Theano does not allow the end user to use inplace operations by default. However, it does allow optimizations to substitute them in in a later phase. Therefore, typically, if you define an inplace operation, you will define a pure equivalent and an optimization which subsitutes one for the other. Theano will automatically verify if it is possible to do so and will refuse the substitution if it introduces inconsistencies.

Take the previous definitions of x, y and z and suppose an Op which adds one to every byte of its input. If we give x as an input to that Op, it can either allocate a new buffer of the same size as x (that could be z) and set that new buffer’s bytes to the variable of the addition. That would be a normal, pure Op. Alternatively, it could add one to each byte in the buffer x, therefore changing it. That would be an inplace Op.

Theano needs to be notified of this fact. The syntax is similar to that of view_map:

myop.destroy_map = {0: [0]}

What this means is that the first output (position 0) operates inplace on the first input (position 0).

myop.destroy_map = {0: [0]} # first output operates inplace on first input
myop.destroy_map = {0: [1]} # first output operates inplace on second input
myop.destroy_map = {1: [0]} # second output operates inplace on first input

myop.destroy_map = {0: [0], # first output operates inplace on first input
                    1: [1]} # *AND* second output operates inplace on second input

myop.destroy_map = {0: [0], # first output operates inplace on first input
                    1: [0]} # *AND* second output *ALSO* operates inplace on first input

myop.destroy_map = {0: [0, 1]} # first output operates inplace on both the first and second input
# unlike for views, the previous line is legal and supported

Destructive Operations

While some operations will operate inplace on their inputs, some might simply destroy or corrupt them. For example, an Op could do temporary calculations right in its inputs. If that is the case, Theano also needs to be notified. The way to notify Theano is to assume that some output operated inplace on whatever inputs are changed or corrupted by the Op (even if the output does not technically reuse any of the input(s)’s memory). From there, go to the previous section.

Warning

Failure to correctly mark down views and inplace operations using view_map and destroy_map can lead to nasty bugs. In the absence of this information, Theano might assume that it is safe to execute an inplace operation on some inputs before doing other calculations on the previous values of the inputs. For example, in the code: y = log(x); x2 = add_inplace(x, z) it is imperative to do the logarithm before the addition (because after the addition, the original x that we wanted to take the logarithm of is gone). If Theano does not know that add_inplace changes the value of x it might invert the order and that will certainly lead to erroneous computations.

You can often identify an incorrect view_map or destroy_map by using debugmode. Be sure to use DebugMode when developing a new Op that uses ``view_map`` and/or ``destroy_map``.

Inplace optimization and DebugMode

It is recommended that during the graph construction, all Ops are not inplace. Then an optimization replaces them with inplace ones. Currently DebugMode checks all optimizations that were tried even if they got rejected. One reason an inplace optimization can get rejected is when there is another Op that is already being applied inplace on the same input. Another reason to reject an inplace optimization is if it would introduce a cycle into the graph.

The problem with DebugMode is that it will trigger a useless error when checking a rejected inplace optimization, since it will lead to wrong results. In order to be able to use DebugMode in more situations, your inplace optimization can pre-check whether it will get rejected by using the theano.graph.destroyhandler.fast_inplace_check() function, that will tell which Ops can be performed inplace. You may then skip the optimization if it is incompatible with this check. Note however that this check does not cover all cases where an optimization may be rejected (it will not detect cycles).