def test_allocate_incorrect_arg_type():
'''check that an allocate raises an error if an unknown type is
passed.'''
module = ModuleGen(name="testmodule")
content = 3
with pytest.raises(RuntimeError):
_ = AllocateGen(module, content)
def test_allocate_arg_str():
'''check that an allocate gets created succesfully with content being
a string.'''
module = ModuleGen(name="testmodule")
content = "hello"
allocate = AllocateGen(module, content)
module.add(allocate)
lines = str(module.root).splitlines()
assert "ALLOCATE (" + content + ")" in lines[3]
def test_allocate_arg_list():
'''check that an allocate gets created succesfully with content being
a list.'''
module = ModuleGen(name="testmodule")
content = ["hello", "how", "are", "you"]
content_str = ""
for idx, name in enumerate(content):
content_str += name
if idx + 1 < len(content):
content_str += ", "
allocate = AllocateGen(module, content)
module.add(allocate)
lines = str(module.root).splitlines()
assert "ALLOCATE (" + content_str + ")" in lines[3]
def create_driver(self, input_list, output_list):
# pylint: disable=too-many-locals, too-many-statements
'''This function creates a driver that can read the
output created by the extraction code. This is a stand-alone
program that will read the input data, calls the kernels/
instrumented region, and then compares the results with the
stored results in the file.
TODO: #644: we need type information here.
:param input_list: list of variables that are input parameters.
:type input_list: list of str
:param output_list: list of variables that are output parameters.
:type output_list: list or str
'''
from psyclone.f2pygen import AllocateGen, AssignGen, CallGen,\
CommentGen, DeclGen, ModuleGen, SubroutineGen, UseGen, \
TypeDeclGen
from psyclone.gocean1p0 import GOSymbolTable
from psyclone.psyir.symbols import Symbol
all_vars = list(set(input_list).union(set(output_list)))
all_vars.sort()
module_name, region_name = self.region_identifier
module = ModuleGen(name=module_name)
prog = SubroutineGen(parent=module, name=module_name+"_code",
implicitnone=True)
module.add(prog)
use = UseGen(prog, self.add_psydata_class_prefix("psy_data_mod"),
only=True,
funcnames=[self.add_psydata_class_prefix("PSyDataType")])
prog.add(use)
# Use a symbol table to make sure all variable names are unique
sym_table = GOSymbolTable()
sym = Symbol("PSyDataType")
sym_table.add(sym)
psy_data = sym_table.new_symbol_name(self.add_psydata_class_prefix
("psy_data"))
sym_table.add(Symbol(psy_data))
var_decl = TypeDeclGen(prog, datatype=self.add_psydata_class_prefix
("PSyDataType"),
entity_decls=[psy_data])
prog.add(var_decl)
call = CallGen(prog,
"{0}%OpenRead(\"{1}\", \"{2}\")"
.format(psy_data, module_name, region_name))
prog.add(call)
post_suffix = self._post_name
# Variables might need to be renamed in order to guarantee unique
# variable names in the driver: An example of this would be if the
# user code contains a variable 'dx', and the kernel takes a
# property 'dx' as well. In the original code that is no problem,
# since the property is used via field%grid%dx. But the stand-alone
# driver renames field%grid%dx to dx, which can cause a name clash.
# Similar problems can exist with any user defined type, since all
# user defined types are rewritten to just use the field name.
# We use a mapping to support renaming of variables: it takes as
# key the variable as used in the original program (e.g. 'dx' from
# an expression like field%grid%dx), and maps it to a unique local
# name (e.g. dx_0).
rename_variable = {}
for var_name in all_vars:
# TODO #644: we need to identify arrays!!
# Support GOcean properties, which are accessed via a
# derived type (e.g. 'fld%grid%dx'). In this stand-alone
# driver we don't have the derived type, instead we create
# variable based on the field in the derived type ('dx'
# in the example above), and pass this variable to the
# instrumented code.
last_percent = var_name.rfind("%")
if last_percent > -1:
# Strip off the derived type, and only leave the last
# field, which is used as the local variable name.
local_name = var_name[last_percent+1:]
else:
# No derived type, so we can just use the
# variable name directly in the driver
local_name = var_name
unique_local_name = sym_table.new_symbol_name(local_name)
rename_variable[local_name] = unique_local_name
sym_table.add(Symbol(unique_local_name))
local_name = unique_local_name
# TODO: #644 - we need to identify arrays!!
# Any variable used needs to be defined. We also need
# to handle the kind property better and not rely on
# a hard-coded value.
decl = DeclGen(prog, "real", [local_name], kind="8",
dimension=":,:", allocatable=True)
prog.add(decl)
is_input = var_name in input_list
is_output = var_name in output_list
if is_input and not is_output:
# We only need the pre-variable, and we can read
# it from the file (this call also allocates space for it).
call = CallGen(prog,
"{0}%ReadVariable(\"{1}\", {2})"
.format(psy_data, var_name, local_name))
prog.add(call)
elif is_input:
# Now must be input and output:
# First read the pre-variable (which also allocates it):
call = CallGen(prog,
"{0}%ReadVariable(\"{1}\", {2})"
.format(psy_data, var_name, local_name))
prog.add(call)
# Then declare the post variable, and and read its values
# (ReadVariable will also allocate it)
sym = Symbol(local_name+post_suffix)
sym_table.add(sym)
decl = DeclGen(prog, "real", [local_name+post_suffix],
dimension=":,:", kind="8", allocatable=True)
prog.add(decl)
call = CallGen(prog,
"{0}%ReadVariable(\"{1}{3}\", {2}{3})"
.format(psy_data, var_name, local_name,
post_suffix))
prog.add(call)
else:
# Now the variable is output only. We need to read the
# post variable in, and create and allocate a pre variable
# with the same size as the post
sym = Symbol(local_name+post_suffix)
sym_table.add(sym)
decl = DeclGen(prog, "real", [local_name+post_suffix],
dimension=":,:", kind="8", allocatable=True)
prog.add(decl)
call = CallGen(prog,
"{0}%ReadVariable(\"{1}{3}\", {2}{3})"
.format(psy_data, var_name, local_name,
post_suffix))
prog.add(call)
decl = DeclGen(prog, "real", [local_name], kind="8",
dimension=":,:", allocatable=True)
prog.add(decl)
alloc = AllocateGen(prog, [var_name],
mold="{0}".format(local_name +
post_suffix))
prog.add(alloc)
# Initialise the variable with 0, since it might contain
# values that are not set at all (halo regions, or a
# kernel might not set all values). This way the array
# comparison with the post value works as expected
# TODO #644 - create the right "0.0" type here (e.g.
# 0.0d0, ...)
assign = AssignGen(prog, local_name, "0.0d0")
prog.add(assign)
# Now add the region that was extracted here:
prog.add(CommentGen(prog, ""))
prog.add(CommentGen(prog, " RegionStart"))
# For the driver we have to re-create the code of the
# instrumented region, but in this stand-alone driver the
# arguments are not dl_esm_inf fields anymore, but simple arrays.
# Similarly, for properties we cannot use e.g. 'fld%grid%dx'
# anymore, we have to use e.g. a local variable 'dx' that has
# been created. Since we are using the existing way of creating
# the code for the instrumented region, we need to modify how
# these variables are created. We do this by temporarily
# modifying the properties in the config file.
api_config = Config.get().api_conf("gocean1.0")
all_props = api_config.grid_properties
# Keep a copy of the original values, so we can restore
# them later
orig_props = dict(all_props)
# 1) A grid property is defined like "{0}%grid%dx". This is
# changed to be just 'dx', i.e. the final component of
# the current value (but we also take renaming into account,
# so 'dx' might become 'dx_0').
# If a property is not used, it doesn't matter if we modify
# its definition, so we just change all properties.
for name, prop in all_props.items():
last_percent = prop.fortran.rfind("%")
if last_percent > -1:
# Get the last field name, which will be the
# local variable name
local_name = prop.fortran[last_percent+1:]
unique_name = rename_variable.get(local_name, local_name)
all_props[name] = GOceanConfig.make_property(
unique_name, prop.type, prop.intrinsic_type)
# 2) The property 'grid_data' is a reference to the data on the
# grid (i.e. the actual field) , and it is defined as "{0}%data".
# This just becomes {0} ('a_fld%data' in the original program
# becomes just 'a_fld', and 'a_fld' is declared to be a plain
# Fortran 2d-array)
all_props["go_grid_data"] = GOceanConfig.make_property(
"{0}", "array", "real")
# Each kernel caches the argument code, so we also
# need to clear this cached data to make sure the new
# value for "go_grid_data" is actually used.
from psyclone.psyGen import CodedKern
for kernel in self.psy_data_body.walk(CodedKern):
kernel.clear_cached_data()
# Recreate the instrumented region. Due to the changes in the
# config files, fields and properties will now become local
# plain arrays and variables:
for child in self.psy_data_body:
child.gen_code(prog)
# Now reset all properties back to the original values:
for name in all_props.keys():
all_props[name] = orig_props[name]
prog.add(CommentGen(prog, " RegionEnd"))
prog.add(CommentGen(prog, ""))
for var_name in output_list:
prog.add(CommentGen(prog, " Check {0}".format(var_name)))
code = str(module.root)
with open("driver-{0}-{1}.f90".
format(module_name, region_name), "w") as out:
out.write(code)