Overview of the new QuTiP data layer

This post is partial documentation for the implementation of the data-layer that I wrote in the last week or so as part of Google Summer of Code with QuTiP. I may return to talk a bit more about how various algorithms are achieved internally, but for now, this is some indication of what I’ve been working on.

I had previously replaced the old fast_csr_matrix type with the new, custom CSR type as the data backing for Qobj, and all internal QuTiP data representations. This produced some speed-ups in some places due to improved algorithms and better cache usage in places, but its principle advantage was the massive reduction in overhead for function calls between Python and C space, which largely affected small objects.

The full aim, however, is to have QuTiP 5 support many different data representations as the backing of Qobj, and use the most suitable representation for the given data. This will not require every single QuTiP function to have an exponential number of versions for every possible combination of inputs, but only to have specialisations for the most common data combinations. This concept is the “data layer”.

All code examples in this post are prefixed with

>>> from qutip.core import data


The core to achieving this is fast, fully specified inter-conversion between all known data types, and efficient multiple-dispatch for mathematical operations. There are then four principle components of the data-layer:

  1. a creation routine which returns an appropriate data-layer type given some arbitrary Python object (data.create)
  2. a routine which can perform the conversion from any data-layer type to any other data-layer type (data.to)
  3. completely specialised mathematical operations (e.g. data.add_csr_dense_dense(CSR, Dense) -> Dense)
  4. an object which provides multiple dispatch operations on its input arguments to use an exact specialisation (defined in item 3) if known, or uses the conversion routine (item 2) to convert the inputs into ones matching a specialisation if not: data.Dispatcher. The exported mathematical functions will all be instances of this type.

The minimum work needed to define a new data-layer type is to provide data.to with two conversion functions; one into the new type from a current data-layer type, and one which converts the new type into a current data-layer type. Once this is done, every single QuTiP component will be able to use the new data-layer type, although until specialisations are given which use it, it will always be achieved by conversion to another type, and conversion back. In this way, a new type can be added incrementally, with only the most common operations needing to be defined to get good efficiency.

Important caveat: the data layer operates only on exact types; subclasses of defined types will be treated as completely different types. This is to do with keeping the computational complexity of multiple-dispatch operations as O(1) (i.e. I don’t know how to do multiple dispatch in constant time allowing inheritance).

data.to: conversion between types

>>> matrix = data.dense.identity(5)
>>> matrix
Dense(shape=(5, 5), fortran=True)
>>> data.to(data.CSR, matrix)
CSR(shape=(5, 5), nnz=5)
>>> data.to[data.CSR, data.Dense]
<converter to CSR from Dense>
>>> data.to[data.Dense]
<converter to Dense>
>>> class NewDataType:
...     # [...]
>>> def new_from_dense(matrix: data.Dense) -> NewDataType:
...     # [...]
>>> def dense_from_new(matrix: NewDataType) -> data.Dense:
...     # [...]
>>> data.to.add_conversions([
...     (NewDataType, data.Dense, new_from_dense),
...     (data.Dense, NewDataType, dense_from_new),
... ])
>>> data.to[data.CSR, NewDataType]
<converter to CSR from NewDataType>

Basic usage

Convert data into a different type. This object is the knowledge source for every allowable data-layer type in QuTiP, and provides the conversions between all of them.

The base use is to call this object as a function with signature

    (type, data) -> converted_data

where type is a type object (such as data.CSR, or that obtained by calling type(matrix)) and data is data in a data-layer type. If you want to create a data-layer type from non-data-layer data, use create instead.

You can get individual converters by using the key-lookup syntax. For example, the item

    to[CSR, Dense]

is a callable which accepts arguments of type Dense and returns the equivalent item of type CSR. You can also get a generic converter to a particular data type if only one type is specified, so


is a callable which accepts all known (at the time of the lookup) data-layer types, and converts them to Dense. See the “Efficiency notes” section below for more detail.

Internally, the conversion process may go through several steps if new data-layer types have been defined with few conversions specified between them and the pre-existing converters. The first-class QuTiP data types Dense and CSR will typically have the fastest connectivity.

Adding new types

You can add new data-layer types by calling the add_conversions method of this object, and then rebuilding all of the mathematical dispatchers. See the docstring of that method for more information.

Implementation details

Not all conversions have to be specified for a new type; it is enough to have just one to and from a known type to a new type. The rest of the conversion graph is built up by graph traversal over known types (the graph is reconstructed whenever add_conversions is called), where the approximate cost of each function is used as the weight of an “edge” joining two data-layer type “vertices”. The shortest path conversion function is constructed and stored (as the interal type data.convert._converter) for each pair of types. We willingly sacrifice memory efficiency for speed-efficiency here, since we expect there to be few data-layer types, but for the calls to happen millions of times.

The converters returned by single-key access (e.g. data.to[data.Dense]) are constructed individually on a call to __getitem__, and are instances of the private type data.convert._partial_converter, which internally stores a reference to every “full” converter, and dispatches to the correct one when called.

The entire data.to object and all subsidiary _converter and _partial_converter objects are pickle-able.

Efficiency notes

From an efficiency perspective, there is very little benefit to using the key-lookup syntax. Internally, to(to_type, data) effectively calls to[to_type, type(data)], so storing the object elides the creation of a single tuple and a dict lookup, but the cost of this is generally less than 500ns. Using the one-argument lookup (e.g. to[Dense]) is no more efficient than the general call at all, but can be used in cases where a single callable is required and is more efficient, concise and descriptive than functools.partial.

data.Dispatcher: arbitrary multiple-dispatch operations

>>> import scipy.sparse
>>> import numpy as np
>>> a = data.CSR(scipy.sparse.csr_matrix(np.random.rand(5, 5)))
>>> b = data.Dense(np.random.rand(5, 5))
>>> data.add(a, b)
Dense(shape=(5, 5), fortran=True)
>>> data.add(a, b, out=data.CSR)
CSR(shape=(5, 5), nnz=25)
>>> data.add[data.CSR, data.Dense]
<indirect specialisation (CSR, Dense, Dense) of add>
>>> data.add[data.CSR, data.CSR, data.CSR]
<direct specialisation (CSR, CSR, CSR) of add>

Basic usage

A Dispatcher provides a single mathematical function for all combinations of types known by data.to, regardless of whether the particular specialisation has been defined for the input data types. In the first example above, the operator data.add currently only knows two specialisations; it knows how to add CSR + CSR -> CSR and Dense + Dense -> Dense directly, but it is still able to produce the correct result when asked to do CSR + Dense -> CSR and similar. The type of the output can be, but does not need to be, specified. The Dispatcher will choose a suitable output type if one is not given.

For example, the objects data.add, data.pow and data.matmul are some examples of dispatchers in the data layer. Respectively, these have the signatures

data.add(left: Data, right: Data, scale: complex = 1) -> Data
data.pow(matrix: Data, n: integer) -> Data
data.matmul(left: Data, right: Data) -> Data

These are callable functions, so the base use is to call them.

Just like data.to, key-lookup syntax can be used to get a single callable object representing a single specialisation. The callable object has an attribute direct which is True if no type conversions would need to take place, and False is at least one would have to happen. Just like in the regular call, you can either specify or not specify the type of the output, but the types of the inputs must always be given.

>>> data.pow[data.CSR]
<direct specialisation (CSR, CSR) of pow>
>>> data.pow[data.CSR].direct
>>> data.pow[data.CSR, data.Dense].direct

The returned object is callable with the same signature as the dispatcher (except the out keyword argument is no longer there), and requires that the inputs match the types stated.

Adding new specialisations

New specialisations can be added to a pre-existing dispatcher with the Dispatcher.add_specialisations method. This is very similar in form to data.to.add_conversions; it takes lists of tuples, where the first elements of the tuple define the types in the specialisation, and the last is the specialised function itself.

For example, a user might need to multiply Dense @ CSR frequently and get a Dense output. Currently, there is no direct specialisation for this:

>>> data.matmul[Dense, CSR, Dense]
<indirect specialisation (Dense, CSR, Dense) of matmul>

The user may then choose to define their own specialisation to handle this case efficiently:

>>> def matmul_1(left: Dense, right: CSR) -> Dense:
...     # [...]
...     return out

They would give this to data.matmul by calling

>>> data.matmul.add_specialisations([
...     (Dense, CSR, Dense, matmul_1),
... ])

Now we find

>>> data.matmul[Dense, CSR, Dense]
<direct specialisation (Dense, CSR, Dense) of matmul>

Additionally, the whole lookup table will be rebuilt taking this new specialisation into account, which means the indirect specialisation matmul(Dense, CSR) -> CSR will now make use of this new method, because it has a low conversion weight.

Adding new types

Now let’s say the user wants to add a new NewDataType type all across QuTiP. The only action they must take is to tell data.to about this new type. Let’s say they define it like this:

>>> class NewDataType:
...     # [...]
>>> def new_from_dense(matrix: data.Dense) -> NewDataType:
...     # [...]
>>> def dense_from_new(matrix: NewDataType) -> data.Dense:
...     # [...]
>>> data.to.add_conversions([
...     (NewDataType, data.Dense, new_from_dense),
...     (data.Dense, NewDataType, dense_from_new),
... ])

As we saw in the previous section, this is enough to define all conversions in data.to. What’s more, this is also enough to define all operations in the data layer as well:

>>> data.matmul[NewDataType, data.CSR]
<indirect specialisation (NewDataType, CSR, CSR) of matmul>

All of the data layer will now work seamlessly with the new type, even though this is actually achieved by conversion to and from a known data type. There was no need to call anything other than data.to.add_conversions. Internally, this is achieved by data.Dispatcher.__init__ storing a reference to itself in data.to, and data.to calling rebuild_lookup as part of add_conversions.

Now the user only needs to add in the specialisations that they actually need for the bottle-neck parts of their application, and leave the dispatcher to handle all other minor components by automatic conversion. As in the previous subsection, they do this by calling add_specialisations on the relevant operations.

Creating a new dispatcher

In most user-defined functions which operate on Qobj.data it will be completely sufficient for them to simply call data.to(desired_type, input_data) on entry to the function, and then they can guarantee that they are always working with the type of data they support.

However, in some cases they may want to support dispatched operations in the same way that we do within the library code. For this reason, the data layer exports Dispatcher as a public symbol. The minimal amount of work that needs to be done is to call the initialiser, and then call add_specialisations. For example, let’s say the user has defined two specialisations for their simple new function add_square:

>>> def add_square_csr(left, right):
...     return data.add_csr(left, data.matmul_csr(right, right))
>>> def add_square_dense(left, right):
...     return data.add_dense(left, data.matmul_dense(right, right))

(Ignore for now that this would be better achieved by just using the dispatchers data.add and data.matmul directly.) Now they create the dispatcher simply by doing

>>> add_square = data.Dispatcher(add_square_csr, inputs=('left', 'right'), name='add_square', out=True)
>>> add_square.add_specialisations([
...     (data.CSR, data.CSR, data.CSR, add_square_csr),
...     (data.Dense, data.Dense, data.Dense, add_square_dense),
... ])

This is enough for Dispatcher to have extracted the signature and satisfied all of the specialisations. Note that the inputs argument does not provide the signature, it tells the dispatcher which arguments are data-layer types it should dispatch on, e.g. for data.pow as defined above inputs = ('matrix',), but the signature is (matrix, n) -> out. See that the specialisations are now complete:

>>> add_square
<dispatcher: add_square(left, right)>
>>> add_square[data.Dense, data.CSR, data.CSR]
<indirect specialisation (Dense, CSR, CSR) of add_square>

In the initialisation, the function add_square_csr is passed as an example from which Dispatcher extracts the call signature, the module name and the docstring (if it exists). It is not actually added as a specialisation until add_square.add_specialisations is called afterwards.

If desired, the user can set or override the docstring for the resulting dispatcher by directly writing to the __doc__ attribute of the object. We always do this within the library.

Note: within the Cython components of the library, we manually construct the signature and pass it into Dispatcher.__init__ because Cython-compiled functions do not embed their signature in a manner in which inspect.signature can extract it (even with the embedsignature directive). We also use this to cut out some arguments in the call signatures which would not work with the dispatch mechanism (like out parameters).

Other features

In combination with data.to, this now allows QuTiP to handle any backing data store for Qobj, even if literally zero mathematical functions are defined for the type.

The Dispatcher can operate on a function with any call signature (except ones which use *args or **kwargs), even if not all of the arguments are data-layer types. At definition, the creator of the Dispatcher says which input arguments are meant to be dispatched on, and whether the output should be dispatched on, and all other arguments are passed through like normal.

Implementation details

The backing specialisations can be found in Dispatcher._specialisations, and the complete lookup table is in Dispatcher._lookup. These are marked as private, because messing around with them will almost certainly cause the dispatcher to stop working.

Only one specialisation needs to be defined for a dispatcher to work with all data types known by data.to. We achieve this because data.to guarantees that all possible conversions between data types will exist, so data.Dispatcher can always convert its inputs into those which will match one of its known specialisations.

Within the initialisation of the data layer, we use a “magic” _defer keyword argument to add_specialisations to break a circular dependency. This is because the “type” modules data.csr and data.dense depend on some mathematical modules (e.g. add and matmul) to provide the __add__ and similar methods on the types. For ease of development we want the dispatchers to be defined in the same modules that all the specialisations are (though this is not at all necessary), but the dispatchers require data.to to be populated with the types before specialisations can be added. The _defer keyword here just defers the building of the lookup table until an explicit call to Dispatcher.rebuild_lookup(), breaking the cycle. The user will never need to do this, because by the time they receive the Dispatcher object, data.to is already initialised to a minimum degree.

Efficiency notes

The specialisations returned by the __getitem__ lookups are not significantly faster than just calling the dispatcher directly, because the bulk of the heavy lifting is done when add_specialisations or rebuild_lookup is called. On call, the generic signature (*args, **kwargs) has to be bound to the actual signature of the underlying operation, regardless of whether the specialisation has already been found. At the Cython level there is short-circuit access to the call machinery in the specialisations themselves, but this cannot be safely exposed outside of the Dispatcher class itself.