def expression_complexity(expr, variables): return brian_ast(expr, variables).complexity
def render_expr(self, expr): node = brian_ast(expr, self.variables) node = self.arithmetic_simplifier.render_node(node) node = self.render_node(node) return self.node_renderer.render_node(node)
def make_statements(code, variables, dtype, optimise=True, blockname=''): ''' make_statements(code, variables, dtype, optimise=True, blockname='') Turn a series of abstract code statements into Statement objects, inferring whether each line is a set/declare operation, whether the variables are constant or not, and handling the cacheing of subexpressions. Parameters ---------- code : str A (multi-line) string of statements. variables : dict-like A dictionary of with `Variable` and `Function` objects for every identifier used in the `code`. dtype : `dtype` The data type to use for temporary variables optimise : bool, optional Whether to optimise expressions, including pulling out loop invariant expressions and putting them in new scalar constants. Defaults to ``False``, since this function is also used just to in contexts where we are not interested by this kind of optimisation. For the main code generation stage, its value is set by the `codegen.loop_invariant_optimisations` preference. blockname : str, optional A name for the block (used to name intermediate variables to avoid name clashes when multiple blocks are used together) Returns ------- scalar_statements, vector_statements : (list of `Statement`, list of `Statement`) Lists with statements that are to be executed once and statements that are to be executed once for every neuron/synapse/... (or in a vectorised way) Notes ----- If ``optimise`` is ``True``, then the ``scalar_statements`` may include newly introduced scalar constants that have been identified as loop-invariant and have therefore been pulled out of the vector statements. The resulting statements will also use augmented assignments where possible, i.e. a statement such as ``w = w + 1`` will be replaced by ``w += 1``. Also, statements involving booleans will have additional information added to them (see `Statement` for details) describing how the statement can be reformulated as a sequence of if/then statements. Calls `~brian2.codegen.optimisation.optimise_statements`. ''' code = strip_empty_lines(deindent(code)) lines = re.split(r'[;\n]', code) lines = [LineInfo(code=line) for line in lines if len(line)] # Do a copy so we can add stuff without altering the original dict variables = dict(variables) # we will do inference to work out which lines are := and which are = defined = set(k for k, v in variables.items() if not isinstance(v, AuxiliaryVariable)) for line in lines: statement = None # parse statement into "var op expr" var, op, expr, comment = parse_statement(line.code) if var in variables and isinstance(variables[var], Subexpression): raise SyntaxError("Illegal line '{line}' in abstract code. " "Cannot write to subexpression " "'{var}'.".format(line=line.code, var=var)) if op == '=': if var not in defined: op = ':=' defined.add(var) if var not in variables: annotated_ast = brian_ast(expr, variables) is_scalar = annotated_ast.scalar if annotated_ast.dtype == 'boolean': use_dtype = bool elif annotated_ast.dtype == 'integer': use_dtype = int else: use_dtype = dtype new_var = AuxiliaryVariable(var, dtype=use_dtype, scalar=is_scalar) variables[var] = new_var elif not variables[var].is_boolean: sympy_expr = str_to_sympy(expr, variables) if variables[var].is_integer: sympy_var = sympy.Symbol(var, integer=True) else: sympy_var = sympy.Symbol(var, real=True) try: collected = sympy.collect(sympy_expr, sympy_var, exact=True, evaluate=False) except AttributeError: # If something goes wrong during collection, e.g. collect # does not work for logical expressions collected = {1: sympy_expr} if (len(collected) == 2 and set(collected.keys()) == {1, sympy_var} and collected[sympy_var] == 1): # We can replace this statement by a += assignment statement = Statement(var, '+=', sympy_to_str(collected[1]), comment, dtype=variables[var].dtype, scalar=variables[var].scalar) elif len(collected) == 1 and sympy_var in collected: # We can replace this statement by a *= assignment statement = Statement(var, '*=', sympy_to_str(collected[sympy_var]), comment, dtype=variables[var].dtype, scalar=variables[var].scalar) if statement is None: statement = Statement(var, op, expr, comment, dtype=variables[var].dtype, scalar=variables[var].scalar) line.statement = statement # for each line will give the variable being written to line.write = var # each line will give a set of variables which are read line.read = get_identifiers_recursively([expr], variables) # All writes to scalar variables must happen before writes to vector # variables scalar_write_done = False for line in lines: stmt = line.statement if stmt.op != ':=' and variables[ stmt.var].scalar and scalar_write_done: raise SyntaxError( ('All writes to scalar variables in a code block ' 'have to be made before writes to vector ' 'variables. Illegal write to %s.') % line.write) elif not variables[stmt.var].scalar: scalar_write_done = True # all variables which are written to at some point in the code block # used to determine whether they should be const or not all_write = set(line.write for line in lines) # backwards compute whether or not variables will be read again # note that will_read for a line gives the set of variables it will read # on the current line or subsequent ones. will_write gives the set of # variables that will be written after the current line will_read = set() will_write = set() for line in lines[::-1]: will_read = will_read.union(line.read) line.will_read = will_read.copy() line.will_write = will_write.copy() will_write.add(line.write) subexpressions = dict((name, val) for name, val in variables.items() if isinstance(val, Subexpression)) # Check that no scalar subexpression refers to a vectorised function # (e.g. rand()) -- otherwise it would be differently interpreted depending # on whether it is used in a scalar or a vector context (i.e., even though # the subexpression is supposed to be scalar, it would be vectorised when # used as part of non-scalar expressions) for name, subexpr in subexpressions.items(): if subexpr.scalar: identifiers = get_identifiers(subexpr.expr) for identifier in identifiers: if (identifier in variables and getattr( variables[identifier], 'auto_vectorise', False)): raise SyntaxError(('The scalar subexpression {} refers to ' 'the implicitly vectorised function {} ' '-- this is not allowed since it leads ' 'to different interpretations of this ' 'subexpression depending on whether it ' 'is used in a scalar or vector ' 'context.').format(name, identifier)) # sort subexpressions into an order so that subexpressions that don't depend # on other subexpressions are first subexpr_deps = dict( (name, [dep for dep in subexpr.identifiers if dep in subexpressions]) for name, subexpr in subexpressions.items()) sorted_subexpr_vars = topsort(subexpr_deps) statements = [] # none are yet defined (or declared) subdefined = dict((name, None) for name in subexpressions) for line in lines: stmt = line.statement read = line.read write = line.write will_read = line.will_read will_write = line.will_write # update/define all subexpressions needed by this statement for var in sorted_subexpr_vars: if var not in read: continue subexpression = subexpressions[var] # if already defined/declared if subdefined[var] == 'constant': continue elif subdefined[var] == 'variable': op = '=' constant = False else: op = ':=' # check if the referred variables ever change ids = subexpression.identifiers constant = all(v not in will_write for v in ids) subdefined[var] = 'constant' if constant else 'variable' statement = Statement(var, op, subexpression.expr, comment='', dtype=variables[var].dtype, constant=constant, subexpression=True, scalar=variables[var].scalar) statements.append(statement) var, op, expr, comment = stmt.var, stmt.op, stmt.expr, stmt.comment # constant only if we are declaring a new variable and we will not # write to it again constant = op == ':=' and var not in will_write statement = Statement(var, op, expr, comment, dtype=variables[var].dtype, constant=constant, scalar=variables[var].scalar) statements.append(statement) scalar_statements = [s for s in statements if s.scalar] vector_statements = [s for s in statements if not s.scalar] if optimise and prefs.codegen.loop_invariant_optimisations: scalar_statements, vector_statements = optimise_statements( scalar_statements, vector_statements, variables, blockname=blockname) return scalar_statements, vector_statements
def make_statements(code, variables, dtype, optimise=True, blockname=''): ''' make_statements(code, variables, dtype, optimise=True, blockname='') Turn a series of abstract code statements into Statement objects, inferring whether each line is a set/declare operation, whether the variables are constant or not, and handling the cacheing of subexpressions. Parameters ---------- code : str A (multi-line) string of statements. variables : dict-like A dictionary of with `Variable` and `Function` objects for every identifier used in the `code`. dtype : `dtype` The data type to use for temporary variables optimise : bool, optional Whether to optimise expressions, including pulling out loop invariant expressions and putting them in new scalar constants. Defaults to ``False``, since this function is also used just to in contexts where we are not interested by this kind of optimisation. For the main code generation stage, its value is set by the `codegen.loop_invariant_optimisations` preference. blockname : str, optional A name for the block (used to name intermediate variables to avoid name clashes when multiple blocks are used together) Returns ------- scalar_statements, vector_statements : (list of `Statement`, list of `Statement`) Lists with statements that are to be executed once and statements that are to be executed once for every neuron/synapse/... (or in a vectorised way) Notes ----- If ``optimise`` is ``True``, then the ``scalar_statements`` may include newly introduced scalar constants that have been identified as loop-invariant and have therefore been pulled out of the vector statements. The resulting statements will also use augmented assignments where possible, i.e. a statement such as ``w = w + 1`` will be replaced by ``w += 1``. Also, statements involving booleans will have additional information added to them (see `Statement` for details) describing how the statement can be reformulated as a sequence of if/then statements. Calls `~brian2.codegen.optimisation.optimise_statements`. ''' code = strip_empty_lines(deindent(code)) lines = re.split(r'[;\n]', code) lines = [LineInfo(code=line) for line in lines if len(line)] # Do a copy so we can add stuff without altering the original dict variables = dict(variables) # we will do inference to work out which lines are := and which are = defined = set(k for k, v in variables.iteritems() if not isinstance(v, AuxiliaryVariable)) for line in lines: statement = None # parse statement into "var op expr" var, op, expr, comment = parse_statement(line.code) if var in variables and isinstance(variables[var], Subexpression): raise SyntaxError("Illegal line '{line}' in abstract code. " "Cannot write to subexpression " "'{var}'.".format(line=line.code, var=var)) if op == '=': if var not in defined: op = ':=' defined.add(var) if var not in variables: annotated_ast = brian_ast(expr, variables) is_scalar = annotated_ast.scalar if annotated_ast.dtype == 'boolean': use_dtype = bool elif annotated_ast.dtype == 'integer': use_dtype = int else: use_dtype = dtype new_var = AuxiliaryVariable(var, dtype=use_dtype, scalar=is_scalar) variables[var] = new_var elif not variables[var].is_boolean: sympy_expr = str_to_sympy(expr, variables) sympy_var = sympy.Symbol(var, real=True) try: collected = sympy.collect(sympy_expr, sympy_var, exact=True, evaluate=False) except AttributeError: # If something goes wrong during collection, e.g. collect # does not work for logical expressions collected = {1: sympy_expr} if (len(collected) == 2 and set(collected.keys()) == {1, sympy_var} and collected[sympy_var] == 1): # We can replace this statement by a += assignment statement = Statement(var, '+=', sympy_to_str(collected[1]), comment, dtype=variables[var].dtype, scalar=variables[var].scalar) elif len(collected) == 1 and sympy_var in collected: # We can replace this statement by a *= assignment statement = Statement(var, '*=', sympy_to_str(collected[sympy_var]), comment, dtype=variables[var].dtype, scalar=variables[var].scalar) if statement is None: statement = Statement(var, op, expr, comment, dtype=variables[var].dtype, scalar=variables[var].scalar) line.statement = statement # for each line will give the variable being written to line.write = var # each line will give a set of variables which are read line.read = get_identifiers_recursively([expr], variables) # All writes to scalar variables must happen before writes to vector # variables scalar_write_done = False for line in lines: stmt = line.statement if stmt.op != ':=' and variables[stmt.var].scalar and scalar_write_done: raise SyntaxError(('All writes to scalar variables in a code block ' 'have to be made before writes to vector ' 'variables. Illegal write to %s.') % line.write) elif not variables[stmt.var].scalar: scalar_write_done = True # all variables which are written to at some point in the code block # used to determine whether they should be const or not all_write = set(line.write for line in lines) # backwards compute whether or not variables will be read again # note that will_read for a line gives the set of variables it will read # on the current line or subsequent ones. will_write gives the set of # variables that will be written after the current line will_read = set() will_write = set() for line in lines[::-1]: will_read = will_read.union(line.read) line.will_read = will_read.copy() line.will_write = will_write.copy() will_write.add(line.write) subexpressions = dict((name, val) for name, val in variables.items() if isinstance(val, Subexpression)) # sort subexpressions into an order so that subexpressions that don't depend # on other subexpressions are first subexpr_deps = dict((name, [dep for dep in subexpr.identifiers if dep in subexpressions]) for \ name, subexpr in subexpressions.items()) sorted_subexpr_vars = topsort(subexpr_deps) statements = [] # none are yet defined (or declared) subdefined = dict((name, None) for name in subexpressions.keys()) for line in lines: stmt = line.statement read = line.read write = line.write will_read = line.will_read will_write = line.will_write # update/define all subexpressions needed by this statement for var in sorted_subexpr_vars: if var not in read: continue subexpression = subexpressions[var] # if already defined/declared if subdefined[var] == 'constant': continue elif subdefined[var] == 'variable': op = '=' constant = False else: op = ':=' # check if the referred variables ever change ids = subexpression.identifiers constant = all(v not in will_write for v in ids) subdefined[var] = 'constant' if constant else 'variable' statement = Statement(var, op, subexpression.expr, comment='', dtype=variables[var].dtype, constant=constant, subexpression=True, scalar=variables[var].scalar) statements.append(statement) var, op, expr, comment = stmt.var, stmt.op, stmt.expr, stmt.comment # constant only if we are declaring a new variable and we will not # write to it again constant = op == ':=' and var not in will_write statement = Statement(var, op, expr, comment, dtype=variables[var].dtype, constant=constant, scalar=variables[var].scalar) statements.append(statement) scalar_statements = [s for s in statements if s.scalar] vector_statements = [s for s in statements if not s.scalar] if optimise and prefs.codegen.loop_invariant_optimisations: scalar_statements, vector_statements = optimise_statements(scalar_statements, vector_statements, variables, blockname=blockname) return scalar_statements, vector_statements