def __init__(self, variables, scalar_statements):
BrianASTRenderer.__init__(self, variables)
self.loop_invariants = OrderedDict()
self.loop_invariant_dtypes = {}
self.n = 0
self.node_renderer = NodeRenderer(use_vectorisation_idx=False)
self.arithmetic_simplifier = ArithmeticSimplifier(variables)
self.scalar_statements = scalar_statements
def __init__(self, variables, scalar_statements, extra_lio_prefix=''):
BrianASTRenderer.__init__(self, variables, copy_variables=False)
self.loop_invariants = OrderedDict()
self.loop_invariant_dtypes = {}
self.n = 0
self.node_renderer = NodeRenderer(use_vectorisation_idx=False)
self.arithmetic_simplifier = ArithmeticSimplifier(variables)
self.scalar_statements = scalar_statements
if extra_lio_prefix is None:
extra_lio_prefix = ''
if len(extra_lio_prefix):
extra_lio_prefix = extra_lio_prefix+'_'
self.extra_lio_prefix = extra_lio_prefix
def __init__(self, variables, scalar_statements, extra_lio_prefix=''):
BrianASTRenderer.__init__(self, variables, copy_variables=False)
self.loop_invariants = OrderedDict()
self.loop_invariant_dtypes = {}
self.n = 0
self.node_renderer = NodeRenderer(use_vectorisation_idx=False)
self.arithmetic_simplifier = ArithmeticSimplifier(variables)
self.scalar_statements = scalar_statements
if extra_lio_prefix is None:
extra_lio_prefix = ''
if len(extra_lio_prefix):
extra_lio_prefix = extra_lio_prefix + '_'
self.extra_lio_prefix = extra_lio_prefix
def __init__(self, variables):
BrianASTRenderer.__init__(self, variables, copy_variables=False)
self.assumptions = []
self.assumptions_ns = dict(defaults_ns)
self.bast_renderer = BrianASTRenderer(variables, copy_variables=False)
class ArithmeticSimplifier(BrianASTRenderer):
'''
Carries out the following arithmetic simplifications:
1. Constant evaluation (e.g. exp(0)=1) by attempting to evaluate the expression in an "assumptions namespace"
2. Binary operators, e.g. 0*x=0, 1*x=x, etc. You have to take care that the dtypes match here, e.g.
if x is an integer, then 1.0*x shouldn't be replaced with x but left as 1.0*x.
Parameters
----------
variables : dict of (str, Variable)
Usual definition of variables.
assumptions : sequence of str
Additional assumptions that can be used in simplification, each assumption is a string statement.
These might be the scalar statements for example.
'''
def __init__(self, variables):
BrianASTRenderer.__init__(self, variables, copy_variables=False)
self.assumptions = []
self.assumptions_ns = dict(defaults_ns)
self.bast_renderer = BrianASTRenderer(variables, copy_variables=False)
def render_node(self, node):
'''
Assumes that the node has already been fully processed by BrianASTRenderer
'''
if not hasattr(node, 'simplified'):
node = super(ArithmeticSimplifier, self).render_node(node)
node.simplified = True
# can't evaluate vector expressions, so abandon in this case
if not node.scalar:
return node
# No evaluation necessary for simple names or numbers
if node.__class__.__name__ in ['Name', 'NameConstant', 'Num']:
return node
# Don't evaluate stateful nodes (e.g. those containing a rand() call)
if not node.stateless:
return node
# try fully evaluating using assumptions
expr = NodeRenderer().render_node(node)
val, evaluated = evaluate_expr(expr, self.assumptions_ns)
if evaluated:
if node.dtype == 'boolean':
val = bool(val)
if hasattr(ast, 'NameConstant'):
newnode = ast.NameConstant(val)
else:
# None is the expression context, we don't use it so we just set to None
newnode = ast.Name(repr(val), None)
elif node.dtype == 'integer':
val = int(val)
else:
val = float(val)
if node.dtype != 'boolean':
newnode = ast.Num(val)
newnode.dtype = node.dtype
newnode.scalar = True
newnode.stateless = node.stateless
newnode.complexity = 0
return newnode
return node
def render_BinOp(self, node):
if node.dtype == 'float': # only try to collect float type nodes
if node.op.__class__.__name__ in ['Mult', 'Div', 'Add', 'Sub'] and not hasattr(node, 'collected'):
newnode = self.bast_renderer.render_node(collect(node))
newnode.collected = True
return self.render_node(newnode)
left = node.left = self.render_node(node.left)
right = node.right = self.render_node(node.right)
node = super(ArithmeticSimplifier, self).render_BinOp(node)
op = node.op
# Handle multiplication by 0 or 1
if op.__class__.__name__ == 'Mult':
for operand, other in [(left, right),
(right, left)]:
if operand.__class__.__name__ == 'Num':
if operand.n == 0:
# Do not remove stateful functions
if node.stateless:
return _replace_with_zero(operand, node)
if operand.n==1:
# only simplify this if the type wouldn't be cast by the operation
if dtype_hierarchy[operand.dtype] <= dtype_hierarchy[other.dtype]:
return other
# Handle division by 1, or 0/x
elif op.__class__.__name__ == 'Div':
if left.__class__.__name__ == 'Num' and left.n == 0: # 0/x
if node.stateless:
# Do not remove stateful functions
return _replace_with_zero(left, node)
if right.__class__.__name__ == 'Num' and right.n == 1: # x/1
# only simplify this if the type wouldn't be cast by the operation
if dtype_hierarchy[right.dtype] <= dtype_hierarchy[left.dtype]:
return left
# Handle addition of 0
elif op.__class__.__name__ == 'Add':
for operand, other in [(left, right),
(right, left)]:
if operand.__class__.__name__ == 'Num' and operand.n == 0:
# only simplify this if the type wouldn't be cast by the operation
if dtype_hierarchy[operand.dtype]<=dtype_hierarchy[other.dtype]:
return other
# Handle subtraction of 0
elif op.__class__.__name__ == 'Sub':
if right.__class__.__name__ == 'Num' and right.n == 0:
# only simplify this if the type wouldn't be cast by the operation
if dtype_hierarchy[right.dtype]<=dtype_hierarchy[left.dtype]:
return left
# simplify e.g. 2*float to 2.0*float to make things more explicit: not strictly necessary
# but might be useful for some codegen targets
if node.dtype=='float' and op.__class__.__name__ in ['Mult', 'Add', 'Sub', 'Div']:
for subnode in [node.left, node.right]:
if subnode.__class__.__name__ == 'Num':
subnode.dtype = 'float'
subnode.n = float(subnode.n)
return node
def __init__(self, variables):
BrianASTRenderer.__init__(self, variables, copy_variables=False)
self.assumptions = []
self.assumptions_ns = dict(defaults_ns)
self.bast_renderer = BrianASTRenderer(variables, copy_variables=False)
class ArithmeticSimplifier(BrianASTRenderer):
'''
Carries out the following arithmetic simplifications:
1. Constant evaluation (e.g. exp(0)=1) by attempting to evaluate the expression in an "assumptions namespace"
2. Binary operators, e.g. 0*x=0, 1*x=x, etc. You have to take care that the dtypes match here, e.g.
if x is an integer, then 1.0*x shouldn't be replaced with x but left as 1.0*x.
Parameters
----------
variables : dict of (str, Variable)
Usual definition of variables.
assumptions : sequence of str
Additional assumptions that can be used in simplification, each assumption is a string statement.
These might be the scalar statements for example.
'''
def __init__(self, variables):
BrianASTRenderer.__init__(self, variables, copy_variables=False)
self.assumptions = []
self.assumptions_ns = dict(defaults_ns)
self.bast_renderer = BrianASTRenderer(variables, copy_variables=False)
def render_node(self, node):
'''
Assumes that the node has already been fully processed by BrianASTRenderer
'''
if not hasattr(node, 'simplified'):
node = super(ArithmeticSimplifier, self).render_node(node)
node.simplified = True
# can't evaluate vector expressions, so abandon in this case
if not node.scalar:
return node
# No evaluation necessary for simple names or numbers
if node.__class__.__name__ in ['Name', 'NameConstant', 'Num']:
return node
# Don't evaluate stateful nodes (e.g. those containing a rand() call)
if not node.stateless:
return node
# try fully evaluating using assumptions
expr = NodeRenderer().render_node(node)
val, evaluated = evaluate_expr(expr, self.assumptions_ns)
if evaluated:
if node.dtype == 'boolean':
val = bool(val)
if hasattr(ast, 'NameConstant'):
newnode = ast.NameConstant(val)
else:
# None is the expression context, we don't use it so we just set to None
newnode = ast.Name(repr(val), None)
elif node.dtype == 'integer':
val = int(val)
else:
val = prefs.core.default_float_dtype(val)
if node.dtype != 'boolean':
newnode = ast.Num(val)
newnode.dtype = node.dtype
newnode.scalar = True
newnode.stateless = node.stateless
newnode.complexity = 0
return newnode
return node
def render_BinOp(self, node):
if node.dtype == 'float': # only try to collect float type nodes
if node.op.__class__.__name__ in [
'Mult', 'Div', 'Add', 'Sub'
] and not hasattr(node, 'collected'):
newnode = self.bast_renderer.render_node(collect(node))
newnode.collected = True
return self.render_node(newnode)
left = node.left = self.render_node(node.left)
right = node.right = self.render_node(node.right)
node = super(ArithmeticSimplifier, self).render_BinOp(node)
op = node.op
# Handle multiplication by 0 or 1
if op.__class__.__name__ == 'Mult':
for operand, other in [(left, right), (right, left)]:
if operand.__class__.__name__ == 'Num':
if operand.n == 0:
# Do not remove stateful functions
if node.stateless:
return _replace_with_zero(operand, node)
if operand.n == 1:
# only simplify this if the type wouldn't be cast by the operation
if dtype_hierarchy[operand.dtype] <= dtype_hierarchy[
other.dtype]:
return other
# Handle division by 1, or 0/x
elif op.__class__.__name__ == 'Div':
if left.__class__.__name__ == 'Num' and left.n == 0: # 0/x
if node.stateless:
# Do not remove stateful functions
return _replace_with_zero(left, node)
if right.__class__.__name__ == 'Num' and right.n == 1: # x/1
# only simplify this if the type wouldn't be cast by the operation
if dtype_hierarchy[right.dtype] <= dtype_hierarchy[left.dtype]:
return left
elif op.__class__.__name__ == 'FloorDiv':
if left.__class__.__name__ == 'Num' and left.n == 0: # 0//x
if node.stateless:
# Do not remove stateful functions
return _replace_with_zero(left, node)
# Only optimise floor division by 1 if both numbers are integers,
# for floating point values, floor division by 1 changes the value,
# and division by 1.0 can change the type for an integer value
if (left.dtype == right.dtype == 'integer'
and right.__class__.__name__ == 'Num'
and right.n == 1): # x//1
return left
# Handle addition of 0
elif op.__class__.__name__ == 'Add':
for operand, other in [(left, right), (right, left)]:
if operand.__class__.__name__ == 'Num' and operand.n == 0:
# only simplify this if the type wouldn't be cast by the operation
if dtype_hierarchy[operand.dtype] <= dtype_hierarchy[
other.dtype]:
return other
# Handle subtraction of 0
elif op.__class__.__name__ == 'Sub':
if right.__class__.__name__ == 'Num' and right.n == 0:
# only simplify this if the type wouldn't be cast by the operation
if dtype_hierarchy[right.dtype] <= dtype_hierarchy[left.dtype]:
return left
# simplify e.g. 2*float to 2.0*float to make things more explicit: not strictly necessary
# but might be useful for some codegen targets
if node.dtype == 'float' and op.__class__.__name__ in [
'Mult', 'Add', 'Sub', 'Div'
]:
for subnode in [node.left, node.right]:
if subnode.__class__.__name__ == 'Num':
subnode.dtype = 'float'
subnode.n = prefs.core.default_float_dtype(subnode.n)
return node
