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Mops

Mathematical operations to extract all the performance juice from your hardware!

Getting the code

git clone https://github.com/lab-cosmo/mops
cd mops

# Builds the code and run all tests
tox

# Installs the Python package
pip install .

Planned operations

Some common motifs of vector, matrix and tensor operations that appear in science and engineering are planned to be implemented here for CPUs and GPUs. These include:

1. Homogeneous Polynomial Evaluation

Mathematical notation

$$ O_i = \sum_{j=1}^J C_j \prod_{k=1}^K A_{iP_{jk}} $$

Inputs

  • $A$ is a 2D array of floats, of size $I \times N_{A,2}$. It contains the individual factors in the monomials that make up the polynomial.

  • $C$ is a vector of float multipliers of size $J$. They represent the coefficients of each monomial in the polynomial, so that $J$ is the number of monomials in the polynomial.

  • $P$ is a 2D array of integers which represents the positions of the individual factors for each monomial in the second dimension of the $A$ array. In particular, the $k$-th factor of monomial $j$ will be found in the $P_{jk}$-th position of the second dimension of $A$.

Output

$O$ is a dense 1D array of floats, which only contains a batch dimension of size $I$.

Calculation

The calculation consists in a batched evaluation of homogeneous polynomials of degree $K$, where the monomials are given by $C[j] * A[:, P_1[j, 1]] * A[:, P_2[j, 2]] * \dots$, as follows:

for j in range(J):
    O[:] += C[j] * A[:, P_1[j, 1]] * A[:, P_2[j, 2]] * ...

2. Sparse Accumulation of Products

Mathematical notation

$$ O_{iP_k^O} = \sum_{k \in {k'|P^O_{k'}=P^O_k}} C_k A_{iP_k^A} B_{iP_k^B} $$

Inputs

  • $A$ and $B$ are 2D arrays of floats whose first dimension is a batch dimension that has the same size for both.

  • $C$ is a 1D array of floats which contains the weights of the products of elements of $A$ and $B$ to be accumulated.

  • $P^A$, $P^B$ are 1D arrays fo integers of the same size which contain the positions along the second dimensions of $A$ and $B$, respectively, of the factors that constitute the products.

  • $P^O$ is a 1D array of integers of the same length as $P^A$ and $P^B$ which contains the positions in the second dimension of the output tensor where the different products of $A$ and $B$ are accumulated.

Output

$O$ is a 2D array of floats where the first dimension is a batch dimension (the same as in $A$ and $B$) and the second dimension contains the scattered products of $A$ and $B$.

Calculation

The weighted products of $A$ and $B$ are accumulated into $O$ as follows:

for k in range(K):
    O[:, P_O[k]] += C[k] * A[:, P_A[k]] * B[:, P_B[k]]

3. Outer Product Scatter-Add

Math notation

$$ O_{ikl} = \sum_{j=1}^J A_{jk} B_{jl} \delta_{iP_j} \hspace{1cm} \mathrm{or} \hspace{1cm} O_{ikl} = \sum_{j \in {j'|P_{j'}=i}} A_{jk} B_{jl} $$

Inputs

  • $A$ is a dense matrix of floats, expected to be large in one dimension (size $J$), and smaller in the the other (size $K$).

  • $B$ is a dense matrix of floats, expected to be large in one dimension (size $J$), and smaller in the the other (size $L$).

  • $P$ is a large vector of integers (of size $J$) which maps the dimension $j$ of $A$ and $B$ into the dimension $i$ of $O$. In other words, it contains the position within $O$ where each $AB$ product needs to be summed.

  • $n_O$ is the size of the output array along its first dimension. It must be grater or equal than the larger element in $P$ plus one.

Output

$O$ is a 3D array of floats of dimensions $I \times K \times L$, which contains the accumulated products of the elements of $A$ and $B$.

Calculation

For each $j$, an outer product of $A[j, :]$ and $B[j, :]$ is calculated, and it is summed to $O[P[j], :, :]$:

for j in range(J):
    O[P[j], :, :] += A[j, :, None] * B[j, None, :]

4. Outer product Scatter-Add with Weights

Math notation

$$ O_{ikl} = \sum_{j \in {j'|P_{j'}=i}} A_{jk} B_{jl} W_{{PW_j}l} $$

Inputs

  • $A$ is a 2D array of floats
  • $R$ is a 2D array of floats
  • $X$ is a 2D array of floats
  • $I$ is a 1D array of integers
  • $J$ is a 1D array of integers

Outputs

  • $O$ is a 3D array of floats

Calculation

for j in range(J):
    O[PO[j], :, :] += A[j, :, None] * B[j, None, :] * W[PW[j], None, :]

5. Sparse Accumulation Scatter-Add with Weights

Math notation

$$ O_{i{m_3}k} = \sum_{e \in {e'|I_{e'}=i}} R_{ek} \sum_{n \in {n'|M^3_{n'}=m_3}} C_n A_{e{M_n^1}} X_{{J_e}{M_n^2}k} $$

Inputs

Inputs

  • $A$ is a 2D array of floats
  • $R$ is a 2D array of floats
  • $X$ is a 3D array of floats
  • $C$ is a 1D array of floats
  • $I$ is a 1D array of integers
  • $J$ is a 1D array of integers
  • $M^1$ is a 1D array of integers
  • $M^2$ is a 1D array of integers
  • $M^3$ is a 1D array of integers

Outputs

  • $O$ is a 3D array of floats

Outputs

Calculation

for j in range(J):
    for n in range(N):
        O[PO1[e], PO2[n], :] += A[e, PA[n]] * B[e, :] * C[n] * W[PW1[e], PW2[n], :]