def determine_keywords(self):
# set up the restricted pointers, these are used so that the compiler
# knows there is no aliasing in the pointers, for optimisation
pointers = []
# It is possible that several different variable names refer to the
# same array. E.g. in gapjunction code, v_pre and v_post refer to the
# same array if a group is connected to itself
handled_pointers = set()
template_kwds = {}
# Again, do the import here to avoid a circular dependency.
from brian2.devices.device import get_device
device = get_device()
for varname, var in self.variables.items():
if isinstance(var, ArrayVariable):
# This is the "true" array name, not the restricted pointer.
array_name = device.get_array_name(var)
pointer_name = self.get_array_name(var)
if pointer_name in handled_pointers:
continue
if getattr(var, 'ndim', 1) > 1:
continue # multidimensional (dynamic) arrays have to be treated differently
restrict = self.restrict
# turn off restricted pointers for scalars for safety
if var.scalar or var.size == 1:
restrict = ' '
line = '{0}* {1} {2} = {3};'.format(
self.c_data_type(var.dtype), restrict, pointer_name,
array_name)
pointers.append(line)
handled_pointers.add(pointer_name)
# set up the functions
user_functions = []
support_code = []
hash_defines = []
added = set() # keep track of functions that were added
for varname, variable in list(self.variables.items()):
if isinstance(variable, Function):
user_func = self._add_user_function(varname, variable, added)
if user_func is not None:
hd, ps, sc, uf = user_func
user_functions.extend(uf)
support_code.extend(sc)
pointers.extend(ps)
hash_defines.extend(hd)
support_code.append(self.universal_support_code)
keywords = {
'pointers_lines':
stripped_deindented_lines('\n'.join(pointers)),
'support_code_lines':
stripped_deindented_lines('\n'.join(support_code)),
'hashdefine_lines':
stripped_deindented_lines('\n'.join(hash_defines)),
'denormals_code_lines':
stripped_deindented_lines('\n'.join(
self.denormals_to_zero_code())),
}
keywords.update(template_kwds)
return keywords
def determine_keywords(self):
# set up the restricted pointers, these are used so that the compiler
# knows there is no aliasing in the pointers, for optimisation
pointers = []
# It is possible that several different variable names refer to the
# same array. E.g. in gapjunction code, v_pre and v_post refer to the
# same array if a group is connected to itself
handled_pointers = set()
template_kwds = {}
# Again, do the import here to avoid a circular dependency.
from brian2.devices.device import get_device
device = get_device()
for varname, var in self.variables.iteritems():
if isinstance(var, ArrayVariable):
# This is the "true" array name, not the restricted pointer.
array_name = device.get_array_name(var)
pointer_name = self.get_array_name(var)
if pointer_name in handled_pointers:
continue
if getattr(var, 'dimensions', 1) > 1:
continue # multidimensional (dynamic) arrays have to be treated differently
line = '{0}* {1} {2} = {3};'.format(self.c_data_type(var.dtype),
self.restrict,
pointer_name,
array_name)
pointers.append(line)
handled_pointers.add(pointer_name)
# set up the functions
user_functions = []
support_code = []
hash_defines = []
for varname, variable in self.variables.items():
if isinstance(variable, Function):
hd, ps, sc, uf = self._add_user_function(varname, variable)
user_functions.extend(uf)
support_code.extend(sc)
pointers.extend(ps)
hash_defines.extend(hd)
# delete the user-defined functions from the namespace and add the
# function namespaces (if any)
for funcname, func in user_functions:
del self.variables[funcname]
func_namespace = func.implementations[self.codeobj_class].get_namespace(self.owner)
if func_namespace is not None:
self.variables.update(func_namespace)
support_code.append(self.universal_support_code)
keywords = {'pointers_lines': stripped_deindented_lines('\n'.join(pointers)),
'support_code_lines': stripped_deindented_lines('\n'.join(support_code)),
'hashdefine_lines': stripped_deindented_lines('\n'.join(hash_defines)),
'denormals_code_lines': stripped_deindented_lines('\n'.join(self.denormals_to_zero_code())),
}
keywords.update(template_kwds)
return keywords
def determine_keywords(self):
# set up the restricted pointers, these are used so that the compiler
# knows there is no aliasing in the pointers, for optimisation
pointers = []
# It is possible that several different variable names refer to the
# same array. E.g. in gapjunction code, v_pre and v_post refer to the
# same array if a group is connected to itself
handled_pointers = set()
template_kwds = {}
# Again, do the import here to avoid a circular dependency.
from brian2.devices.device import get_device
device = get_device()
for varname, var in iteritems(self.variables):
if isinstance(var, ArrayVariable):
# This is the "true" array name, not the restricted pointer.
array_name = device.get_array_name(var)
pointer_name = self.get_array_name(var)
if pointer_name in handled_pointers:
continue
if get_var_ndim(var, 1) > 1:
continue # multidimensional (dynamic) arrays have to be treated differently
line = '{0}* {1} {2} = {3};'.format(self.c_data_type(var.dtype),
self.restrict,
pointer_name,
array_name)
pointers.append(line)
handled_pointers.add(pointer_name)
# set up the functions
user_functions = []
support_code = []
hash_defines = []
for varname, variable in list(iteritems(self.variables)):
if isinstance(variable, Function):
hd, ps, sc, uf = self._add_user_function(varname, variable)
user_functions.extend(uf)
support_code.extend(sc)
pointers.extend(ps)
hash_defines.extend(hd)
# delete the user-defined functions from the namespace and add the
# function namespaces (if any)
for funcname, func in user_functions:
del self.variables[funcname]
func_namespace = func.implementations[self.codeobj_class].get_namespace(self.owner)
if func_namespace is not None:
self.variables.update(func_namespace)
support_code.append(self.universal_support_code)
keywords = {'pointers_lines': stripped_deindented_lines('\n'.join(pointers)),
'support_code_lines': stripped_deindented_lines('\n'.join(support_code)),
'hashdefine_lines': stripped_deindented_lines('\n'.join(hash_defines)),
'denormals_code_lines': stripped_deindented_lines('\n'.join(self.denormals_to_zero_code())),
}
keywords.update(template_kwds)
return keywords
def translate_one_statement_sequence(self, statements, scalar=False):
# This function is refactored into four functions which perform the
# four necessary operations. It's done like this so that code
# deriving from this class can overwrite specific parts.
lines = []
# index and read arrays (index arrays first)
lines += self.translate_to_read_arrays(statements)
# simply declare variables that will be written but not read
lines += self.translate_to_declarations(statements)
# the actual code
statement_lines = self.translate_to_statements(statements)
lines += statement_lines
# write arrays
lines += self.translate_to_write_arrays(statements)
code = '\n'.join(lines)
# Check if 64bit integer types occur in the same line as a default function.
# We can't get the arguments of the function call directly with regex due to
# possibly nested paranthesis inside function paranthesis.
convertion_pref = prefs.codegen.generators.cuda.default_functions_integral_convertion
# only check if there was no warning yet or if convertion preference has changed
if not self.warned_integral_convertion or self.previous_convertion_pref != convertion_pref:
for line in statement_lines:
brian_funcs = re.search(
'_brian_(' + '|'.join(functions_C99) + ')', line)
if brian_funcs is not None:
for identifier in get_identifiers(line):
if convertion_pref == 'double_precision':
# 64bit integer to floating-point conversions are not type safe
int64_type = re.search(
r'\bu?int64_t\s*{}\b'.format(identifier), code)
if int64_type is not None:
logger.warn(
"Detected code statement with default function and 64bit integer type in the same line. "
"Using 64bit integer types as default function arguments is not type safe due to convertion of "
"integer to 64bit floating-point types in device code. (relevant functions: sin, cos, tan, sinh, "
"cosh, tanh, exp, log, log10, sqrt, ceil, floor, arcsin, arccos, arctan)\nDetected code "
"statement:\n\t{}\nGenerated from abstract code statements:\n\t{}\n"
.format(line, statements),
once=True)
self.warned_integral_convertion = True
self.previous_convertion_pref = 'double_precision'
else: # convertion_pref = 'single_precision'
# 32bit and 64bit integer to floating-point conversions are not type safe
int32_64_type = re.search(
r'\bu?int(32|64)_t\s*{}\b'.format(identifier),
code)
if int32_64_type is not None:
logger.warn(
"Detected code statement with default function and 32bit or 64bit integer type in the same line and the "
"preference for default_functions_integral_convertion is 'single_precision'. "
"Using 32bit or 64bit integer types as default function arguments is not type safe due to convertion of "
"integer to single-precision floating-point types in device code. (relevant functions: sin, cos, tan, sinh, "
"cosh, tanh, exp, log, log10, sqrt, ceil, floor, arcsin, arccos, arctan)\nDetected code "
"statement:\n\t{}\nGenerated from abstract code statements:\n\t{}\n"
.format(line, statements),
once=True)
self.warned_integral_convertion = True
self.previous_convertion_pref = 'single_precision'
return stripped_deindented_lines(code)
def translate_one_statement_sequence(self, statements, scalar=False):
if len(statements) and self.template_name=='synapses':
vars_pre = [k for k, v in self.variable_indices.items() if v=='_presynaptic_idx']
vars_syn = [k for k, v in self.variable_indices.items() if v=='_idx']
vars_post = [k for k, v in self.variable_indices.items() if v=='_postsynaptic_idx']
if '_pre_codeobject' in self.name:
post_write_var, statements = check_pre_code(self, statements,
vars_pre, vars_syn, vars_post)
self.owner._genn_post_write_var = post_write_var
lines = []
lines += self.translate_to_statements(statements)
code = '\n'.join(lines)
return stripped_deindented_lines(code)
def translate_one_statement_sequence(self, statements, scalar=False):
# This function is refactored into four functions which perform the
# four necessary operations. It's done like this so that code
# deriving from this class can overwrite specific parts.
lines = []
# index and read arrays (index arrays first)
lines += self.translate_to_read_arrays(statements)
# simply declare variables that will be written but not read
lines += self.translate_to_declarations(statements)
# the actual code
lines += self.translate_to_statements(statements)
# write arrays
lines += self.translate_to_write_arrays(statements)
code = '\n'.join(lines)
return stripped_deindented_lines(code)
def translate_one_statement_sequence(self, statements, scalar=False):
# This function is refactored into four functions which perform the
# four necessary operations. It's done like this so that code
# deriving from this class can overwrite specific parts.
lines = []
# index and read arrays (index arrays first)
lines += self.translate_to_read_arrays(statements)
# simply declare variables that will be written but not read
lines += self.translate_to_declarations(statements)
# the actual code
lines += self.translate_to_statements(statements)
# write arrays
lines += self.translate_to_write_arrays(statements)
code = '\n'.join(lines)
return stripped_deindented_lines(code)
def translate_one_statement_sequence(self, statements, scalar=False):
if len(statements) and self.template_name=='synapses':
_, _, _, conditional_write_vars = self.arrays_helper(statements)
vars_pre = [k for k, v in iteritems(self.variable_indices) if v=='_presynaptic_idx']
vars_syn = [k for k, v in iteritems(self.variable_indices) if v=='_idx']
vars_post = [k for k, v in iteritems(self.variable_indices) if v=='_postsynaptic_idx']
if '_pre_codeobject' in self.name:
post_write_var, statements = check_pre_code(self, statements,
vars_pre, vars_syn, vars_post,
conditional_write_vars)
if (post_write_var != None):
self.owner._genn_post_write_var = post_write_var
lines = []
lines += self.translate_to_statements(statements)
code = '\n'.join(lines)
return stripped_deindented_lines(code)
def translate_one_statement_sequence(self, statements, scalar=False):
# Note that we do not call this function from
# `translate_statement_sequence` (which has been overwritten)
# It is nevertheless implemented, so that it can be called explicitly
# (e.g. from the GSL code generation)
read, write, indices, cond_write = self.arrays_helper(statements)
lines = []
# index and read arrays (index arrays first)
lines += self.translate_to_read_arrays(read, write, indices)
# simply declare variables that will be written but not read
lines += self.translate_to_declarations(read, write, indices)
# the actual code
lines += self.translate_to_statements(statements, cond_write)
# write arrays
lines += self.translate_to_write_arrays(write)
return stripped_deindented_lines('\n'.join(lines))
def translate_statement_sequence(self, sc_statements, ve_statements):
# This function is overwritten, since we do not want to completely
# separate the code generation for scalar and vector code
assert set(sc_statements.keys()) == set(ve_statements.keys())
kwds = self.determine_keywords()
sc_code = {}
ve_code = {}
for block_name in sc_statements:
sc_block = sc_statements[block_name]
ve_block = ve_statements[block_name]
(sc_read, sc_write,
sc_indices, sc_cond_write) = self.arrays_helper(sc_block)
(ve_read, ve_write,
ve_indices, ve_cond_write) = self.arrays_helper(ve_block)
# We want to read all scalar variables that are needed in the
# vector code already in the scalar code, if they are not written
for varname in set(ve_read):
var = self.variables[varname]
if var.scalar and varname not in ve_write:
sc_read.add(varname)
ve_read.remove(varname)
for (code, stmts, read, write, indices,
cond_write) in [(sc_code, sc_block, sc_read, sc_write,
sc_indices, sc_cond_write),
(ve_code, ve_block, ve_read, ve_write,
ve_indices, ve_cond_write)]:
lines = []
# index and read arrays (index arrays first)
lines += self.translate_to_read_arrays(read, write, indices)
# simply declare variables that will be written but not read
lines += self.translate_to_declarations(read, write, indices)
# the actual code
lines += self.translate_to_statements(stmts, cond_write)
# write arrays
lines += self.translate_to_write_arrays(write)
code[block_name] = stripped_deindented_lines('\n'.join(lines))
return sc_code, ve_code, kwds
def translate_one_statement_sequence(self, statements, variables,
variable_indices, iterate_all,
codeobj_class):
# Note that C++ code does not care about the iterate_all argument -- it
# always has to loop over the elements
read, write, indices = self.array_read_write(statements, variables,
variable_indices)
lines = []
# index and read arrays (index arrays first)
for varname in itertools.chain(indices, read):
index_var = variable_indices[varname]
var = variables[varname]
if varname not in write:
line = 'const '
else:
line = ''
line = line + self.c_data_type(var.dtype) + ' ' + varname + ' = '
line = line + self.get_array_name(var, variables) + '[' + index_var + '];'
lines.append(line)
# simply declare variables that will be written but not read
for varname in write:
if varname not in read:
var = variables[varname]
line = self.c_data_type(var.dtype) + ' ' + varname + ';'
lines.append(line)
# the actual code
lines.extend([self.translate_statement(stmt, variables, codeobj_class)
for stmt in statements])
# write arrays
for varname in write:
index_var = variable_indices[varname]
var = variables[varname]
line = self.get_array_name(var, variables) + '[' + index_var + '] = ' + varname + ';'
lines.append(line)
code = '\n'.join(lines)
return stripped_deindented_lines(code)
def translate_statement_sequence(self, statements, variables, namespace,
variable_indices, iterate_all):
# Note that C++ code does not care about the iterate_all argument -- it
# always has to loop over the elements
read, write = self.array_read_write(statements, variables)
lines = []
# read arrays
for varname in read:
index_var = variable_indices[varname]
var = variables[varname]
if varname not in write:
line = 'const '
else:
line = ''
line = line + self.c_data_type(var.dtype) + ' ' + varname + ' = '
line = line + '_ptr' + var.arrayname + '[' + index_var + '];'
lines.append(line)
# simply declare variables that will be written but not read
for varname in write:
if varname not in read:
var = variables[varname]
line = self.c_data_type(var.dtype) + ' ' + varname + ';'
lines.append(line)
# the actual code
lines.extend([self.translate_statement(stmt) for stmt in statements])
# write arrays
for varname in write:
index_var = variable_indices[varname]
var = variables[varname]
line = '_ptr' + var.arrayname + '[' + index_var + '] = ' + varname + ';'
lines.append(line)
code = '\n'.join(lines)
# set up the restricted pointers, these are used so that the compiler
# knows there is no aliasing in the pointers, for optimisation
lines = []
# It is possible that several different variable names refer to the
# same array. E.g. in gapjunction code, v_pre and v_post refer to the
# same array if a group is connected to itself
arraynames = set()
for varname, var in variables.iteritems():
if isinstance(var, ArrayVariable):
arrayname = var.arrayname
if not arrayname in arraynames:
line = self.c_data_type(
var.dtype
) + ' * ' + self.restrict + '_ptr' + arrayname + ' = ' + arrayname + ';'
lines.append(line)
arraynames.add(arrayname)
pointers = '\n'.join(lines)
# set up the functions
user_functions = []
support_code = ''
hash_defines = ''
for varname, variable in namespace.items():
if isinstance(variable, Function):
user_functions.append(varname)
speccode = variable.code(self, varname)
support_code += '\n' + deindent(speccode['support_code'])
hash_defines += deindent(speccode['hashdefine_code'])
# add the Python function with a leading '_python', if it
# exists. This allows the function to make use of the Python
# function via weave if necessary (e.g. in the case of randn)
if not variable.pyfunc is None:
pyfunc_name = '_python_' + varname
if pyfunc_name in namespace:
logger.warn(('Namespace already contains function %s, '
'not replacing it') % pyfunc_name)
else:
namespace[pyfunc_name] = variable.pyfunc
# delete the user-defined functions from the namespace
for func in user_functions:
del namespace[func]
# return
return (stripped_deindented_lines(code), {
'pointers_lines':
stripped_deindented_lines(pointers),
'support_code_lines':
stripped_deindented_lines(support_code),
'hashdefine_lines':
stripped_deindented_lines(hash_defines),
'denormals_code_lines':
stripped_deindented_lines(self.denormals_to_zero_code()),
})
def translate_statement_sequence(self, statements, variables, namespace,
variable_indices, iterate_all):
# Note that C++ code does not care about the iterate_all argument -- it
# always has to loop over the elements
read, write = self.array_read_write(statements, variables)
lines = []
# read arrays
for varname in read:
index_var = variable_indices[varname]
var = variables[varname]
if varname not in write:
line = 'const '
else:
line = ''
line = line + self.c_data_type(var.dtype) + ' ' + varname + ' = '
line = line + '_ptr' + var.arrayname + '[' + index_var + '];'
lines.append(line)
# simply declare variables that will be written but not read
for varname in write:
if varname not in read:
var = variables[varname]
line = self.c_data_type(var.dtype) + ' ' + varname + ';'
lines.append(line)
# the actual code
lines.extend([self.translate_statement(stmt) for stmt in statements])
# write arrays
for varname in write:
index_var = variable_indices[varname]
var = variables[varname]
line = '_ptr' + var.arrayname + '[' + index_var + '] = ' + varname + ';'
lines.append(line)
code = '\n'.join(lines)
# set up the restricted pointers, these are used so that the compiler
# knows there is no aliasing in the pointers, for optimisation
lines = []
# It is possible that several different variable names refer to the
# same array. E.g. in gapjunction code, v_pre and v_post refer to the
# same array if a group is connected to itself
arraynames = set()
for varname, var in variables.iteritems():
if isinstance(var, ArrayVariable):
arrayname = var.arrayname
if not arrayname in arraynames:
line = self.c_data_type(var.dtype) + ' * ' + self.restrict + '_ptr' + arrayname + ' = ' + arrayname + ';'
lines.append(line)
arraynames.add(arrayname)
pointers = '\n'.join(lines)
# set up the functions
user_functions = []
support_code = ''
hash_defines = ''
for varname, variable in namespace.items():
if isinstance(variable, Function):
user_functions.append(varname)
speccode = variable.code(self, varname)
support_code += '\n' + deindent(speccode['support_code'])
hash_defines += deindent(speccode['hashdefine_code'])
# add the Python function with a leading '_python', if it
# exists. This allows the function to make use of the Python
# function via weave if necessary (e.g. in the case of randn)
if not variable.pyfunc is None:
pyfunc_name = '_python_' + varname
if pyfunc_name in namespace:
logger.warn(('Namespace already contains function %s, '
'not replacing it') % pyfunc_name)
else:
namespace[pyfunc_name] = variable.pyfunc
# delete the user-defined functions from the namespace
for func in user_functions:
del namespace[func]
# return
return (stripped_deindented_lines(code),
{'pointers_lines': stripped_deindented_lines(pointers),
'support_code_lines': stripped_deindented_lines(support_code),
'hashdefine_lines': stripped_deindented_lines(hash_defines),
'denormals_code_lines': stripped_deindented_lines(self.denormals_to_zero_code()),
})
def determine_keywords(self):
# set up the restricted pointers, these are used so that the compiler
# knows there is no aliasing in the pointers, for optimisation
lines = []
# It is possible that several different variable names refer to the
# same array. E.g. in gapjunction code, v_pre and v_post refer to the
# same array if a group is connected to itself
handled_pointers = set()
template_kwds = {}
# Again, do the import here to avoid a circular dependency.
from brian2.devices.device import get_device
device = get_device()
for varname, var in self.variables.iteritems():
if isinstance(var, ArrayVariable):
# This is the "true" array name, not the restricted pointer.
array_name = device.get_array_name(var)
pointer_name = self.get_array_name(var)
if pointer_name in handled_pointers:
continue
if getattr(var, 'dimensions', 1) > 1:
continue # multidimensional (dynamic) arrays have to be treated differently
line = self.c_data_type(var.dtype) + ' * ' + self.restrict + pointer_name + ' = ' + array_name + ';'
lines.append(line)
handled_pointers.add(pointer_name)
pointers = '\n'.join(lines)
# set up the functions
user_functions = []
support_code = ''
hash_defines = ''
for varname, variable in self.variables.items():
if isinstance(variable, Function):
user_functions.append((varname, variable))
funccode = variable.implementations[self.codeobj_class].get_code(self.owner)
if isinstance(funccode, basestring):
funccode = {'support_code': funccode}
if funccode is not None:
support_code += '\n' + deindent(funccode.get('support_code', ''))
hash_defines += '\n' + deindent(funccode.get('hashdefine_code', ''))
# add the Python function with a leading '_python', if it
# exists. This allows the function to make use of the Python
# function via weave if necessary (e.g. in the case of randn)
if not variable.pyfunc is None:
pyfunc_name = '_python_' + varname
if pyfunc_name in self.variables:
logger.warn(('Namespace already contains function %s, '
'not replacing it') % pyfunc_name)
else:
self.variables[pyfunc_name] = variable.pyfunc
# delete the user-defined functions from the namespace and add the
# function namespaces (if any)
for funcname, func in user_functions:
del self.variables[funcname]
func_namespace = func.implementations[self.codeobj_class].get_namespace(self.owner)
if func_namespace is not None:
self.variables.update(func_namespace)
keywords = {'pointers_lines': stripped_deindented_lines(pointers),
'support_code_lines': stripped_deindented_lines(support_code),
'hashdefine_lines': stripped_deindented_lines(hash_defines),
'denormals_code_lines': stripped_deindented_lines(self.denormals_to_zero_code()),
}
keywords.update(template_kwds)
return keywords
def determine_keywords(self):
# set up the restricted pointers, these are used so that the compiler
# knows there is no aliasing in the pointers, for optimisation
lines = []
# it is possible that several different variable names refer to the
# same array. E.g. in gapjunction code, v_pre and v_post refer to the
# same array if a group is connected to itself
handled_pointers = set()
template_kwds = {}
# again, do the import here to avoid a circular dependency.
from brian2.devices.device import get_device
device = get_device()
for varname, var in self.variables.iteritems():
if isinstance(var, ArrayVariable):
# This is the "true" array name, not the restricted pointer.
array_name = device.get_array_name(var)
pointer_name = self.get_array_name(var)
if pointer_name in handled_pointers:
continue
if getattr(var, 'ndim', 1) > 1:
continue # multidimensional (dynamic) arrays have to be treated differently
line = self.c_data_type(var.dtype) + ' * ' + self.restrict + pointer_name + ' = ' + array_name + ';'
lines.append(line)
handled_pointers.add(pointer_name)
pointers = '\n'.join(lines)
# set up the functions
user_functions = []
support_code = ''
hash_defines = ''
# set convertion types for standard C99 functions in device code
if prefs.codegen.generators.cuda.default_functions_integral_convertion == np.float64:
default_func_type = 'double'
other_func_type = 'float'
else: # np.float32
default_func_type = 'float'
other_func_type = 'double'
# set clip function to either use all float or all double arguments
# see #51 for details
if prefs['core.default_float_dtype'] == np.float64:
float_dtype = 'float'
else: # np.float32
float_dtype = 'double'
for varname, variable in self.variables.items():
if isinstance(variable, Function):
user_functions.append((varname, variable))
funccode = variable.implementations[self.codeobj_class].get_code(self.owner)
if varname in functions_C99:
funccode = funccode.format(default_type=default_func_type, other_type=other_func_type)
if varname == 'clip':
funccode = funccode.format(float_dtype=float_dtype)
if isinstance(funccode, basestring):
funccode = {'support_code': funccode}
if funccode is not None:
support_code += '\n' + deindent(funccode.get('support_code', ''))
hash_defines += '\n' + deindent(funccode.get('hashdefine_code', ''))
# add the Python function with a leading '_python', if it
# exists. This allows the function to make use of the Python
# function via weave if necessary (e.g. in the case of randn)
if not variable.pyfunc is None:
pyfunc_name = '_python_' + varname
if pyfunc_name in self.variables:
logger.warn(('Namespace already contains function %s, '
'not replacing it') % pyfunc_name)
else:
self.variables[pyfunc_name] = variable.pyfunc
# delete the user-defined functions from the namespace and add the
# function namespaces (if any)
for funcname, func in user_functions:
del self.variables[funcname]
func_namespace = func.implementations[self.codeobj_class].get_namespace(self.owner)
if func_namespace is not None:
self.variables.update(func_namespace)
support_code += '\n' + deindent(self.universal_support_code)
keywords = {'pointers_lines': stripped_deindented_lines(pointers),
'support_code_lines': stripped_deindented_lines(support_code),
'hashdefine_lines': stripped_deindented_lines(hash_defines),
'denormals_code_lines': stripped_deindented_lines(self.denormals_to_zero_code()),
'uses_atomics': self.uses_atomics
}
keywords.update(template_kwds)
return keywords
def translate_one_statement_sequence(self, statements, scalar=False):
# This function is refactored into four functions which perform the
# four necessary operations. It's done like this so that code
# deriving from this class can overwrite specific parts.
all_unique = not self.has_repeated_indices(statements)
read, write, indices, conditional_write_vars = self.arrays_helper(statements)
try:
# try to use atomics
if not self._use_atomics or scalar or all_unique:
raise ParallelisationError()
# more complex translations to deal with repeated indices, which
# could lead to race conditions when applied in parallel
lines = self.parallelise_code(statements)
self.uses_atomics = True
except ParallelisationError:
# don't use atomics
lines = []
# index and read arrays (index arrays first)
lines += self.translate_to_read_arrays(read, write, indices)
# simply declare variables that will be written but not read
lines += self.translate_to_declarations(read, write, indices)
# the actual code
lines += self.translate_to_statements(statements,
conditional_write_vars)
# write arrays
lines += self.translate_to_write_arrays(write)
code = '\n'.join(lines)
# Check if 64bit integer types occur in the same line as a default function.
# We can't get the arguments of the function call directly with regex due to
# possibly nested paranthesis inside function paranthesis.
statement_lines = self.translate_to_statements(statements,
conditional_write_vars)
convertion_pref = prefs.devices.cuda_standalone.default_functions_integral_convertion
# only check if there was no warning yet or if convertion preference has changed
if not self.warned_integral_convertion or self.previous_convertion_pref != convertion_pref:
for line in statement_lines:
brian_funcs = re.search('_brian_(' + '|'.join(functions_C99) + ')', line)
if brian_funcs is not None:
for identifier in get_identifiers(line):
if convertion_pref == np.float64:
# 64bit integer to floating-point conversions are not type safe
int64_type = re.search(r'\bu?int64_t\s*{}\b'.format(identifier), code)
if int64_type is not None:
logger.warn("Detected code statement with default function and 64bit integer type in the same line. "
"Using 64bit integer types as default function arguments is not type safe due to convertion of "
"integer to 64bit floating-point types in device code. (relevant functions: {})\nDetected code "
"statement:\n\t{}\nGenerated from abstract code statements:\n\t{}\n".format(
', '.join(functions_C99), line, statements
),
once=True)
self.warned_integral_convertion = True
self.previous_convertion_pref = np.float64
else: # convertion_pref = np.float32
# 32bit and 64bit integer to floating-point conversions are not type safe
int32_64_type = re.search(r'\bu?int(32|64)_t\s*{}\b'.format(identifier), code)
if int32_64_type is not None:
logger.warn("Detected code statement with default function and 32bit or 64bit integer type in the same line and the "
"preference for default_functions_integral_convertion is 'float32'. "
"Using 32bit or 64bit integer types as default function arguments is not type safe due to convertion of "
"integer to single-precision floating-point types in device code. (relevant functions: {})\nDetected code "
"statement:\n\t{}\nGenerated from abstract code statements:\n\t{}\n".format(
', '.join(functions_C99), line, statements
),
once=True)
self.warned_integral_convertion = True
self.previous_convertion_pref = np.float32
return stripped_deindented_lines(code)
def determine_keywords(self):
# set up the restricted pointers, these are used so that the compiler
# knows there is no aliasing in the pointers, for optimisation
pointers = []
# Add additional lines inside the kernel functions
kernel_lines = []
# It is possible that several different variable names refer to the
# same array. E.g. in gapjunction code, v_pre and v_post refer to the
# same array if a group is connected to itself
handled_pointers = set()
template_kwds = {}
# Again, do the import here to avoid a circular dependency.
from brian2.devices.device import get_device
device = get_device()
for varname, var in self.variables.iteritems():
if isinstance(var, ArrayVariable):
# This is the "true" array name, not the restricted pointer.
array_name = device.get_array_name(var)
pointer_name = self.get_array_name(var)
if pointer_name in handled_pointers:
continue
if getattr(var, 'ndim', 1) > 1:
continue # multidimensional (dynamic) arrays have to be treated differently
restrict = self.restrict
# turn off restricted pointers for scalars for safety
if var.scalar:
restrict = ' '
line = '{0}* {1} {2} = {3};'.format(
self.c_data_type(var.dtype), restrict, pointer_name,
array_name)
pointers.append(line)
handled_pointers.add(pointer_name)
# set up the functions
user_functions = []
support_code = []
hash_defines = []
for varname, variable in self.variables.items():
if isinstance(variable, Function):
hd, ps, sc, uf, kl = self._add_user_function(varname, variable)
user_functions.extend(uf)
support_code.extend(sc)
pointers.extend(ps)
hash_defines.extend(hd)
kernel_lines.extend(kl)
support_code.append(self.universal_support_code)
# Clock variables (t, dt, timestep) are passed by value to kernels and
# need to be translated back into pointers for scalar/vector code.
for varname, variable in self.variables.iteritems():
if hasattr(variable, 'owner') and isinstance(
variable.owner, Clock):
# get arrayname without _ptr suffix (e.g. _array_defaultclock_dt)
arrayname = self.get_array_name(variable, pointer=False)
line = "const {dtype}* _ptr{arrayname} = &_value{arrayname};"
line = line.format(dtype=c_data_type(variable.dtype),
arrayname=arrayname)
if line not in kernel_lines:
kernel_lines.append(line)
keywords = {
'pointers_lines':
stripped_deindented_lines('\n'.join(pointers)),
'support_code_lines':
stripped_deindented_lines('\n'.join(support_code)),
'hashdefine_lines':
stripped_deindented_lines('\n'.join(hash_defines)),
'denormals_code_lines':
stripped_deindented_lines('\n'.join(
self.denormals_to_zero_code())),
'kernel_lines':
stripped_deindented_lines('\n'.join(kernel_lines)),
'uses_atomics':
self.uses_atomics
}
keywords.update(template_kwds)
return keywords
def determine_keywords(self):
# set up the restricted pointers, these are used so that the compiler
# knows there is no aliasing in the pointers, for optimisation
pointers = []
# Add additional lines inside the kernel functions
kernel_lines = []
# It is possible that several different variable names refer to the
# same array. E.g. in gapjunction code, v_pre and v_post refer to the
# same array if a group is connected to itself
handled_pointers = set()
template_kwds = {}
# Again, do the import here to avoid a circular dependency.
from brian2.devices.device import get_device
device = get_device()
for varname, var in self.variables.items():
if isinstance(var, ArrayVariable):
# This is the "true" array name, not the restricted pointer.
array_name = device.get_array_name(var)
pointer_name = self.get_array_name(var)
if pointer_name in handled_pointers:
continue
if getattr(var, 'ndim', 1) > 1:
continue # multidimensional (dynamic) arrays have to be treated differently
restrict = self.restrict
# turn off restricted pointers for scalars for safety
if var.scalar:
restrict = ' '
# Need to use correct dt type in pointers_lines for single precision,
# see #148
if varname == "dt" and prefs.core.default_float_dtype == np.float32:
# c_data_type(variable.dtype) is float, but we need double
dtype = "double"
else:
dtype = self.c_data_type(var.dtype)
line = '{0}* {1} {2} = {3};'.format(dtype,
restrict,
pointer_name,
array_name)
pointers.append(line)
handled_pointers.add(pointer_name)
# set up the functions
user_functions = []
support_code = []
hash_defines = []
added = set() # keep track of functions that were added
for varname, variable in list(self.variables.items()):
if isinstance(variable, Function):
user_func = self._add_user_function(varname, variable, added)
if user_func is not None:
hd, ps, sc, uf, kl = user_func
user_functions.extend(uf)
support_code.extend(sc)
pointers.extend(ps)
hash_defines.extend(hd)
kernel_lines.extend(kl)
# Generate universal_support_code once when the first codeobject is created.
# Can't do it at import time since need access to user preferences
# This is a class attribute (not instance attribute).
if CUDACodeGenerator.universal_support_code is None:
_atomic_support_code = _generate_atomic_support_code()
CUDACodeGenerator.universal_support_code = (
_hightype_support_code
+ _mod_support_code
+ _floordiv_support_code
+ _pow_support_code
+ _atomic_support_code
)
support_code.append(CUDACodeGenerator.universal_support_code)
# Clock variables (t, dt, timestep) are passed by value to kernels and
# need to be translated back into pointers for scalar/vector code.
for varname, variable in self.variables.items():
if hasattr(variable, 'owner') and isinstance(variable.owner, Clock):
# get arrayname without _ptr suffix (e.g. _array_defaultclock_dt)
arrayname = self.get_array_name(variable, prefix='')
# kernel_lines appear before dt is cast to float (in scalar_code), hence
# we need to still use double (used in kernel parameters), see #148
if varname == "dt" and prefs.core.default_float_dtype == np.float32:
# c_data_type(variable.dtype) is float, but we need double
dtype = "double"
else:
dtype = dtype=c_data_type(variable.dtype)
line = f"const {dtype}* _ptr{arrayname} = &_value{arrayname};"
if line not in kernel_lines:
kernel_lines.append(line)
keywords = {'pointers_lines': stripped_deindented_lines('\n'.join(pointers)),
'support_code_lines': stripped_deindented_lines('\n'.join(support_code)),
'hashdefine_lines': stripped_deindented_lines('\n'.join(hash_defines)),
'denormals_code_lines': stripped_deindented_lines('\n'.join(self.denormals_to_zero_code())),
'kernel_lines': stripped_deindented_lines('\n'.join(kernel_lines)),
'uses_atomics': self.uses_atomics
}
keywords.update(template_kwds)
return keywords