217 lines
7.7 KiB
Python
217 lines
7.7 KiB
Python
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from sympy.external.importtools import version_tuple
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from collections.abc import Iterable
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from sympy.core.mul import Mul
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from sympy.core.singleton import S
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from sympy.codegen.cfunctions import Sqrt
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from sympy.external import import_module
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from sympy.printing.precedence import PRECEDENCE
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from sympy.printing.pycode import AbstractPythonCodePrinter, ArrayPrinter
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import sympy
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tensorflow = import_module('tensorflow')
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class TensorflowPrinter(ArrayPrinter, AbstractPythonCodePrinter):
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"""
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Tensorflow printer which handles vectorized piecewise functions,
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logical operators, max/min, and relational operators.
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"""
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printmethod = "_tensorflowcode"
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mapping = {
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sympy.Abs: "tensorflow.math.abs",
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sympy.sign: "tensorflow.math.sign",
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# XXX May raise error for ints.
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sympy.ceiling: "tensorflow.math.ceil",
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sympy.floor: "tensorflow.math.floor",
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sympy.log: "tensorflow.math.log",
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sympy.exp: "tensorflow.math.exp",
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Sqrt: "tensorflow.math.sqrt",
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sympy.cos: "tensorflow.math.cos",
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sympy.acos: "tensorflow.math.acos",
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sympy.sin: "tensorflow.math.sin",
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sympy.asin: "tensorflow.math.asin",
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sympy.tan: "tensorflow.math.tan",
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sympy.atan: "tensorflow.math.atan",
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sympy.atan2: "tensorflow.math.atan2",
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# XXX Also may give NaN for complex results.
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sympy.cosh: "tensorflow.math.cosh",
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sympy.acosh: "tensorflow.math.acosh",
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sympy.sinh: "tensorflow.math.sinh",
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sympy.asinh: "tensorflow.math.asinh",
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sympy.tanh: "tensorflow.math.tanh",
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sympy.atanh: "tensorflow.math.atanh",
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sympy.re: "tensorflow.math.real",
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sympy.im: "tensorflow.math.imag",
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sympy.arg: "tensorflow.math.angle",
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# XXX May raise error for ints and complexes
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sympy.erf: "tensorflow.math.erf",
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sympy.loggamma: "tensorflow.math.lgamma",
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sympy.Eq: "tensorflow.math.equal",
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sympy.Ne: "tensorflow.math.not_equal",
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sympy.StrictGreaterThan: "tensorflow.math.greater",
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sympy.StrictLessThan: "tensorflow.math.less",
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sympy.LessThan: "tensorflow.math.less_equal",
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sympy.GreaterThan: "tensorflow.math.greater_equal",
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sympy.And: "tensorflow.math.logical_and",
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sympy.Or: "tensorflow.math.logical_or",
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sympy.Not: "tensorflow.math.logical_not",
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sympy.Max: "tensorflow.math.maximum",
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sympy.Min: "tensorflow.math.minimum",
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# Matrices
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sympy.MatAdd: "tensorflow.math.add",
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sympy.HadamardProduct: "tensorflow.math.multiply",
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sympy.Trace: "tensorflow.linalg.trace",
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# XXX May raise error for integer matrices.
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sympy.Determinant : "tensorflow.linalg.det",
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}
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_default_settings = dict(
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AbstractPythonCodePrinter._default_settings,
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tensorflow_version=None
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)
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def __init__(self, settings=None):
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super().__init__(settings)
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version = self._settings['tensorflow_version']
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if version is None and tensorflow:
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version = tensorflow.__version__
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self.tensorflow_version = version
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def _print_Function(self, expr):
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op = self.mapping.get(type(expr), None)
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if op is None:
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return super()._print_Basic(expr)
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children = [self._print(arg) for arg in expr.args]
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if len(children) == 1:
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return "%s(%s)" % (
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self._module_format(op),
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children[0]
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)
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else:
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return self._expand_fold_binary_op(op, children)
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_print_Expr = _print_Function
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_print_Application = _print_Function
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_print_MatrixExpr = _print_Function
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# TODO: a better class structure would avoid this mess:
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_print_Relational = _print_Function
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_print_Not = _print_Function
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_print_And = _print_Function
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_print_Or = _print_Function
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_print_HadamardProduct = _print_Function
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_print_Trace = _print_Function
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_print_Determinant = _print_Function
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def _print_Inverse(self, expr):
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op = self._module_format('tensorflow.linalg.inv')
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return "{}({})".format(op, self._print(expr.arg))
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def _print_Transpose(self, expr):
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version = self.tensorflow_version
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if version and version_tuple(version) < version_tuple('1.14'):
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op = self._module_format('tensorflow.matrix_transpose')
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else:
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op = self._module_format('tensorflow.linalg.matrix_transpose')
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return "{}({})".format(op, self._print(expr.arg))
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def _print_Derivative(self, expr):
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variables = expr.variables
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if any(isinstance(i, Iterable) for i in variables):
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raise NotImplementedError("derivation by multiple variables is not supported")
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def unfold(expr, args):
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if not args:
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return self._print(expr)
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return "%s(%s, %s)[0]" % (
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self._module_format("tensorflow.gradients"),
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unfold(expr, args[:-1]),
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self._print(args[-1]),
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)
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return unfold(expr.expr, variables)
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def _print_Piecewise(self, expr):
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version = self.tensorflow_version
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if version and version_tuple(version) < version_tuple('1.0'):
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tensorflow_piecewise = "tensorflow.select"
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else:
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tensorflow_piecewise = "tensorflow.where"
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from sympy.functions.elementary.piecewise import Piecewise
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e, cond = expr.args[0].args
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if len(expr.args) == 1:
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return '{}({}, {}, {})'.format(
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self._module_format(tensorflow_piecewise),
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self._print(cond),
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self._print(e),
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0)
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return '{}({}, {}, {})'.format(
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self._module_format(tensorflow_piecewise),
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self._print(cond),
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self._print(e),
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self._print(Piecewise(*expr.args[1:])))
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def _print_Pow(self, expr):
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# XXX May raise error for
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# int**float or int**complex or float**complex
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base, exp = expr.args
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if expr.exp == S.Half:
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return "{}({})".format(
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self._module_format("tensorflow.math.sqrt"), self._print(base))
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return "{}({}, {})".format(
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self._module_format("tensorflow.math.pow"),
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self._print(base), self._print(exp))
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def _print_MatrixBase(self, expr):
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tensorflow_f = "tensorflow.Variable" if expr.free_symbols else "tensorflow.constant"
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data = "["+", ".join(["["+", ".join([self._print(j) for j in i])+"]" for i in expr.tolist()])+"]"
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return "%s(%s)" % (
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self._module_format(tensorflow_f),
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data,
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)
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def _print_MatMul(self, expr):
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from sympy.matrices.expressions import MatrixExpr
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mat_args = [arg for arg in expr.args if isinstance(arg, MatrixExpr)]
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args = [arg for arg in expr.args if arg not in mat_args]
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if args:
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return "%s*%s" % (
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self.parenthesize(Mul.fromiter(args), PRECEDENCE["Mul"]),
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self._expand_fold_binary_op(
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"tensorflow.linalg.matmul", mat_args)
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)
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else:
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return self._expand_fold_binary_op(
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"tensorflow.linalg.matmul", mat_args)
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def _print_MatPow(self, expr):
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return self._expand_fold_binary_op(
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"tensorflow.linalg.matmul", [expr.base]*expr.exp)
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def _print_CodeBlock(self, expr):
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# TODO: is this necessary?
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ret = []
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for subexpr in expr.args:
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ret.append(self._print(subexpr))
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return "\n".join(ret)
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_module = "tensorflow"
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_einsum = "linalg.einsum"
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_add = "math.add"
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_transpose = "transpose"
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_ones = "ones"
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_zeros = "zeros"
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def tensorflow_code(expr, **settings):
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printer = TensorflowPrinter(settings)
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return printer.doprint(expr)
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