maxframe.tensor.einsum#

maxframe.tensor.einsum(subscripts, *operands, dtype=None, order='K', casting='safe', optimize=False)[source]#

Evaluates the Einstein summation convention on the operands.

Using the Einstein summation convention, many common multi-dimensional, linear algebraic array operations can be represented in a simple fashion. In implicit mode einsum computes these values.

In explicit mode, einsum provides further flexibility to compute other array operations that might not be considered classical Einstein summation operations, by disabling, or forcing summation over specified subscript labels.

See the notes and examples for clarification.

Parameters:

subscripts (str) – Specifies the subscripts for summation as comma separated list of subscript labels. An implicit (classical Einstein summation) calculation is performed unless the explicit indicator ‘->’ is included as well as subscript labels of the precise output form.
operands (list of array_like) – These are the arrays for the operation.
dtype ({data-type, None}, optional) – If provided, forces the calculation to use the data type specified. Note that you may have to also give a more liberal casting parameter to allow the conversions. Default is None.
order ({'C', 'F', 'A', 'K'}, optional) – Controls the memory layout of the output. ‘C’ means it should be C contiguous. ‘F’ means it should be Fortran contiguous, ‘A’ means it should be ‘F’ if the inputs are all ‘F’, ‘C’ otherwise. ‘K’ means it should be as close to the layout as the inputs as is possible, including arbitrarily permuted axes. Default is ‘K’.
casting ({'no', 'equiv', 'safe', 'same_kind', 'unsafe'}, optional) –
Controls what kind of data casting may occur. Setting this to ‘unsafe’ is not recommended, as it can adversely affect accumulations.
- ’no’ means the data types should not be cast at all.
- ’equiv’ means only byte-order changes are allowed.
- ’safe’ means only casts which can preserve values are allowed.
- ’same_kind’ means only safe casts or casts within a kind, like float64 to float32, are allowed.
- ’unsafe’ means any data conversions may be done.
Default is ‘safe’.
optimize ({False, True, 'greedy', 'optimal'}, optional) – Controls if intermediate optimization should occur. No optimization will occur if False and True will default to the ‘greedy’ algorithm. Also accepts an explicit contraction list from the np.einsum_path function. See np.einsum_path for more details. Defaults to False.

Returns:

output (maxframe.tensor.Tensor) – The calculation based on the Einstein summation convention.
The Einstein summation convention can be used to compute
many multi-dimensional, linear algebraic array operations. einsum
provides a succinct way of representing these.
A non-exhaustive list of these operations,
which can be computed by einsum, is shown below along with examples
* Trace of an array, numpy.trace().
* Return a diagonal, numpy.diag().
* Array axis summations, numpy.sum().
* Transpositions and permutations, numpy.transpose().
* Matrix multiplication and dot product, numpy.matmul() numpy.dot().
* Vector inner and outer products, numpy.inner() numpy.outer().
* Broadcasting, element-wise and scalar multiplication, numpy.multiply().
* Tensor contractions, numpy.tensordot().
* Chained array operations, in efficient calculation order, numpy.einsum_path().
The subscripts string is a comma-separated list of subscript labels,
where each label refers to a dimension of the corresponding operand.
Whenever a label is repeated it is summed, so mt.einsum('i,i', a, b)
is equivalent to mt.inner(a,b). If a label
appears only once, it is not summed, so mt.einsum('i', a) produces a
view of a with no changes. A further example mt.einsum('ij,jk', a, b)
describes traditional matrix multiplication and is equivalent to
mt.matmul(a,b).
In *implicit mode, the chosen subscripts are important*
since the axes of the output are reordered alphabetically. This
means that mt.einsum('ij', a) doesn’t affect a 2D array, while
mt.einsum('ji', a) takes its transpose. Additionally,
mt.einsum('ij,jk', a, b) returns a matrix multiplication, while,
mt.einsum('ij,jh', a, b) returns the transpose of the
multiplication since subscript ‘h’ precedes subscript ‘i’.
In *explicit mode the output can be directly controlled by*
specifying output subscript labels. This requires the
identifier ‘->’ as well as the list of output subscript labels.
This feature increases the flexibility of the function since
summing can be disabled or forced when required. The call
mt.einsum('i->', a) is like mt.sum(a, axis=-1),
and mt.einsum('ii->i', a) is like mt.diag(a).
The difference is that einsum does not allow broadcasting by default.
Additionally mt.einsum('ij,jh->ih', a, b) directly specifies the
order of the output subscript labels and therefore returns matrix
multiplication, unlike the example above in implicit mode.
To enable and control broadcasting, use an ellipsis. Default
NumPy-style broadcasting is done by adding an ellipsis
to the left of each term, like mt.einsum('...ii->...i', a).
To take the trace along the first and last axes,
you can do mt.einsum('i...i', a), or to do a matrix-matrix
product with the left-most indices instead of rightmost, one can do
mt.einsum('ij...,jk...->ik...', a, b).
When there is only one operand, no axes are summed, and no output
parameter is provided, a view into the operand is returned instead
of a new array. Thus, taking the diagonal as mt.einsum('ii->i', a)
produces a view (changed in version 1.10.0).
einsum also provides an alternative way to provide the subscripts
and operands as einsum(op0, sublist0, op1, sublist1, ..., [sublistout]).
If the output shape is not provided in this format einsum will be
calculated in implicit mode, otherwise it will be performed explicitly.
The examples below have corresponding einsum calls with the two
parameter methods.

Examples

>>> import maxframe.tensor as mt
>>> a = mt.arange(25).reshape(5,5)
>>> b = mt.arange(5)
>>> c = mt.arange(6).reshape(2,3)
Trace of a matrix:
>>> mt.einsum('ii', a).execute()
60
>>> mt.einsum(a, [0,0]).execute()
60
Extract the diagonal (requires explicit form):
>>> mt.einsum('ii->i', a).execute()
array([ 0,  6, 12, 18, 24])
>>> mt.einsum(a, [0,0], [0]).execute()
array([ 0,  6, 12, 18, 24])
>>> mt.diag(a).execute()
array([ 0,  6, 12, 18, 24])
Sum over an axis (requires explicit form):
>>> mt.einsum('ij->i', a).execute()
array([ 10,  35,  60,  85, 110])
>>> mt.einsum(a, [0,1], [0]).execute()
array([ 10,  35,  60,  85, 110])
>>> mt.sum(a, axis=1).execute()
array([ 10,  35,  60,  85, 110])
For higher dimensional arrays summing a single axis can be done with ellipsis:
>>> mt.einsum('...j->...', a).execute()
array([ 10,  35,  60,  85, 110])
>>> mt.einsum(a, [Ellipsis,1], [Ellipsis]).execute()
array([ 10,  35,  60,  85, 110])
Compute a matrix transpose, or reorder any number of axes:
>>> mt.einsum('ji', c).execute()
array([[0, 3],
       [1, 4],
       [2, 5]])
>>> mt.einsum('ij->ji', c).execute()
array([[0, 3],
       [1, 4],
       [2, 5]])
>>> mt.einsum(c, [1,0]).execute()
array([[0, 3],
       [1, 4],
       [2, 5]])
>>> mt.transpose(c).execute()
array([[0, 3],
       [1, 4],
       [2, 5]])
Vector inner products:
>>> mt.einsum('i,i', b, b).execute()
30
>>> mt.einsum(b, [0], b, [0]).execute()
30
>>> mt.inner(b,b).execute()
30
Matrix vector multiplication:
>>> mt.einsum('ij,j', a, b).execute()
array([ 30,  80, 130, 180, 230])
>>> mt.einsum(a, [0,1], b, [1]).execute()
array([ 30,  80, 130, 180, 230])
>>> mt.dot(a, b).execute()
array([ 30,  80, 130, 180, 230])
>>> mt.einsum('...j,j', a, b).execute()
array([ 30,  80, 130, 180, 230])
Broadcasting and scalar multiplication:
>>> mt.einsum('..., ...', 3, c).execute()
array([[ 0,  3,  6],
       [ 9, 12, 15]])
>>> mt.einsum(',ij', 3, c).execute()
array([[ 0,  3,  6],
       [ 9, 12, 15]])
>>> mt.einsum(3, [Ellipsis], c, [Ellipsis]).execute()
array([[ 0,  3,  6],
       [ 9, 12, 15]])
>>> mt.multiply(3, c).execute()
array([[ 0,  3,  6],
       [ 9, 12, 15]])
Vector outer product:
>>> mt.einsum('i,j', mt.arange(2)+1, b).execute()
array([[0, 1, 2, 3, 4],
       [0, 2, 4, 6, 8]])
>>> mt.einsum(mt.arange(2)+1, [0], b, [1]).execute()
array([[0, 1, 2, 3, 4],
       [0, 2, 4, 6, 8]])
>>> mt.outer(mt.arange(2)+1, b).execute()
array([[0, 1, 2, 3, 4],
       [0, 2, 4, 6, 8]])
Tensor contraction:
>>> a = mt.arange(60.).reshape(3,4,5)
>>> b = mt.arange(24.).reshape(4,3,2)
>>> mt.einsum('ijk,jil->kl', a, b).execute()
array([[4400., 4730.],
       [4532., 4874.],
       [4664., 5018.],
       [4796., 5162.],
       [4928., 5306.]])
>>> mt.einsum(a, [0,1,2], b, [1,0,3], [2,3]).execute()
array([[4400., 4730.],
       [4532., 4874.],
       [4664., 5018.],
       [4796., 5162.],
       [4928., 5306.]])
>>> mt.tensordot(a,b, axes=([1,0],[0,1])).execute()
array([[4400., 4730.],
       [4532., 4874.],
       [4664., 5018.],
       [4796., 5162.],
       [4928., 5306.]])
Writeable returned arrays (since version 1.10.0):
>>> a = mt.zeros((3, 3))
>>> mt.einsum('ii->i', a)[:] = 1
>>> a.execute()
array([[1., 0., 0.],
       [0., 1., 0.],
       [0., 0., 1.]])
Example of ellipsis use:
>>> a = mt.arange(6).reshape((3,2))
>>> b = mt.arange(12).reshape((4,3))
>>> mt.einsum('ki,jk->ij', a, b).execute()
array([[10, 28, 46, 64],
       [13, 40, 67, 94]])
>>> mt.einsum('ki,...k->i...', a, b).execute()
array([[10, 28, 46, 64],
       [13, 40, 67, 94]])
>>> mt.einsum('k...,jk', a, b).execute()
array([[10, 28, 46, 64],
       [13, 40, 67, 94]])
Chained array operations. For more complicated contractions, speed ups
might be achieved by repeatedly computing a 'greedy' path or pre-computing the
'optimal' path and repeatedly applying it, using an
`einsum_path` insertion (since version 1.12.0). Performance improvements can be
particularly significant with larger arrays:
>>> a = mt.ones(64).reshape(2,4,8)
Basic `einsum`: ~1520ms  (benchmarked on 3.1GHz Intel i5.)
>>> for iteration in range(500):
...     _ = mt.einsum('ijk,ilm,njm,nlk,abc->',a,a,a,a,a)
Sub-optimal `einsum` (due to repeated path calculation time): ~330ms
>>> for iteration in range(500):
...     _ = mt.einsum('ijk,ilm,njm,nlk,abc->',a,a,a,a,a, optimize='optimal')
Greedy `einsum` (faster optimal path approximation): ~160ms
>>> for iteration in range(500):
...     _ = mt.einsum('ijk,ilm,njm,nlk,abc->',a,a,a,a,a, optimize='greedy')
Optimal `einsum` (best usage pattern in some use cases): ~110ms
>>> path = mt.einsum_path('ijk,ilm,njm,nlk,abc->',a,a,a,a,a, optimize='optimal')[0]
>>> for iteration in range(500):
...     _ = mt.einsum('ijk,ilm,njm,nlk,abc->',a,a,a,a,a, optimize=path)