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GSoC 2019 Report Ankit Pandey: Extending Codegen

Ankit Raj Pandey edited this page Aug 24, 2019 · 3 revisions

About Me

My name is Ankit, and I’m an undergraduate student at Grinnell College in Grinnell, Iowa.


My project for this summer had two parts: extending SymPy’s support for the generation of code related to matrix expressions, and enabling the generation of Fortran code through the LFortran compiler.

I think I realized many of the goals that I described in my proposal, though there were some major differences in the plan and actual implementation.

Work Accomplished

Here’s a selection of some of the pull requests within the SymPy project that I filed over the summer:

  • (Open) #16931 LFortran code printing
    • A code generator that utilizes the LFortran interpreter
  • (Merged) #17022 NumPy print support for identity
    • A minor extension to the NumPy printer
  • (Merged) #17041 Add optimization of numpy code involving matrix inverses
    • Introduces a matrix_nodes module that allows the optional rewriting of select matrix multiplications to an equivalent matrix solving operation
  • (Merged) #17223 Rewrite of non-commutative multiplication matching
    • A complete rewrite of non-commutative matching in SymPy’s core
  • (Open) #17299 Convert matrix expressions to Indexed
    • Adds functionality to convert a matrix expression to a contraction represented by an Indexed object
  • (Open) #17365 Add matrix wildcards
    • A wildcard capable of matching any matrix sub-expression

Additionally, I made a number of (very minor) contributions to the LFortran project on GitLab, in the form of issues #118, #117, #116, and #110, in addition to request #225.

The next subsections describe in more detail what changes some of these pull requests introduce.

LFortran-based code generation

#16931 allows code generation through the LFortran compiler. For supported expressions, the output is essentially identical to code produced using the traditional fcode printer:

>>> from import x
>>> from sympy.codegen.lfort import sympy_to_lfortran
>>> from lfortran.asr.pprint import pprint_asr
>>> print(lfortran.ast_to_src(lfortran.asr_to_ast(sympy_to_lfortran(3.0 + x))))
x + 3.0_dp

This may not seem like much, but the SymPy expression was directly converted to an LFortran abstract syntax tree, and the output itself was produced by LFortran itself (though some excess newlines have been cleaned up because of an existing bug in LFortran). While both the LFortran back end and the SymPy converter itself are far from being completed, the existing infrastructure is already in place.

Non-commutative matching

#17223 is a complete rewrite of the matching code in the core for non-commutative expressions. While my proposal mentioned extending SymPy’s ability to match within expressions, I eventually opted to use the existing functionality provided by the Wild class. Unfortunately, the matching code sometimes produced incorrect results for non-commutative expressions in addition to being completely untested. This pull request makes the behavior of match for non-commutative expressions more closely resemble what already exists for commutative expressions:

>>> A, B, C, D = symbols('A:D', commutative=False)
>>> W = Wild('W', commutative=False)
>>> (A*B*D).match(W)
{W_: A*B*D}
>>> (A*B*C*D).match(W*D)
{W_: A*B*C}
>>> (A*B*C*D).match(A*W*D)
{W_: B*C}
>>> (A*(B**2)*C).match(A*B*W*C)                                       (ref:pow)
{W_: B}
>>> print((A*B*C).match(A*B*W*C))                                    (ref:none)

Just like with commutative wildcards, the wildcard attempts to match the largest subexpression, expanding all-subexpressions where appropriate, such as in the case of {ref:pow}. Additionally, wildcards must match at least one sub-expression, as demonstrated by the None returned in {ref:none}.

Matrix Wildcards

#17365 introduces a wildcard capable of matching matrix expressions, which directly continues the work of #17223 by extending it to matrices. A matrix wildcard behaves much in the same way as a regular wildcard, though it is also able to match the dimensions of the target expression:

>>> from sympy import MatrixSymbol
>>> from sympy.matrices.expressions.matexpr import MatrixWild
>>> from sympy.core.symbol import Wild
>>> A, B = MatrixSymbol('A', 3, 3), MatrixSymbol('B', 3, 3)
>>> x = Wild('x')
>>> Y = MatrixWild('Y', x, 3)
>>> (A*B).match(Y)
{Y_: A*B, x_: 3}

The matcher first checks for a possible candidate using the new non-commutative matching code, then checking to see if the dimensions of the target expression matches that of the wildcard. In this case, the dimensions of Y matched that of the target expression, and since the dimension of Y itself included a wildcard, the result of this match was also included.

Future Work

The greatest possibility of extension lies in the actual matrix optimizations themselves. This project introduces only one optimization in a very specific case where a matrix product involving an inverse may be rewritten as a solving operation. With the matrix matching code in place, it should be relatively easy to define newer optimizations. There are also possibilities in extending the LFortran printer. Since the infrastructure is already in place, this should be as simple as adding additional print methods to the printing module as the LFortran project develops.

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