def _validate_args(self, field_args, param_args, domain, origin) -> None:
"""Validate input arguments to _call_run.
Raises
-------
ValueError
If invalid data or inconsistent options are specified.
TypeError
If an incorrect field or parameter data type is passed.
"""
assert isinstance(field_args, dict) and isinstance(param_args, dict)
# validate domain sizes
domain_ndim = self.domain_info.ndim
if len(domain) != domain_ndim:
raise ValueError(f"Invalid 'domain' value '{domain}'")
try:
domain = Shape(domain)
except:
raise ValueError("Invalid 'domain' value ({})".format(domain))
if not domain > Shape.zeros(domain_ndim):
raise ValueError(f"Compute domain contains zero sizes '{domain}')")
if not domain <= (max_domain := self._get_max_domain(field_args, origin, squeeze=False)):
raise ValueError(
f"Compute domain too large (provided: {domain}, maximum: {max_domain})"
)
def _call_run(self,
field_args,
parameter_args,
domain,
origin,
exec_info=None):
"""Check and preprocess the provided arguments (called by :class:`StencilObject` subclasses).
Note that this function will always try to expand simple parameter values to
complete data structures by repeating the same value as many times as needed.
Parameters
----------
field_args: `dict`
Mapping from field names to actually passed data arrays.
This parameter encapsulates `*args` in the actual stencil subclass
by doing: `{input_name[i]: arg for i, arg in enumerate(args)}`
parameter_args: `dict`
Mapping from parameter names to actually passed parameter values.
This parameter encapsulates `**kwargs` in the actual stencil subclass
by doing: `{name: value for name, value in kwargs.items()}`
domain : `Sequence` of `int`, optional
Shape of the computation domain. If `None`, it will be used the
largest feasible domain according to the provided input fields
and origin values (`None` by default).
origin : `[int * ndims]` or `{'field_name': [int * ndims]}`, optional
If a single offset is passed, it will be used for all fields.
If a `dict` is passed, there could be an entry for each field.
A special key *'_all_'* will represent the value to be used for all
the fields not explicitly defined. If `None` is passed or it is
not possible to assign a value to some field according to the
previous rule, the value will be inferred from the global boundaries
of the field. Note that the function checks if the origin values
are at least equal to the `global_border` attribute of that field,
so a 0-based origin will only be acceptable for fields with
a 0-area support region.
exec_info : `dict`, optional
Dictionary used to store information about the stencil execution.
(`None` by default).
Returns
-------
`None`
Raises
-------
ValueError
If invalid data or inconsistent options are specified.
"""
if exec_info is not None:
exec_info["call_run_start_time"] = time.perf_counter()
used_arg_fields = {
name: field
for name, field in field_args.items()
if name in self.field_info and self.field_info[name] is not None
}
used_arg_params = {
name: param
for name, param in parameter_args.items()
if name in self.parameter_info
and self.parameter_info[name] is not None
}
for name, field_info in self.field_info.items():
if field_info is not None and field_args[name] is None:
raise ValueError(f"Field '{name}' is None.")
for name, parameter_info in self.parameter_info.items():
if parameter_info is not None and parameter_args[name] is None:
raise ValueError(f"Parameter '{name}' is None.")
# assert compatibility of fields with stencil
for name, field in used_arg_fields.items():
if not gt_backend.from_name(
self.backend).storage_info["is_compatible_layout"](field):
raise ValueError(
f"The layout of the field {name} is not compatible with the backend."
)
if not gt_backend.from_name(
self.backend).storage_info["is_compatible_type"](field):
raise ValueError(
f"Field '{name}' has type '{type(field)}', which is not compatible with the '{self.backend}' backend."
)
elif type(field) is np.ndarray:
warnings.warn(
"NumPy ndarray passed as field. This is discouraged and only works with constraints and only for certain backends.",
RuntimeWarning,
)
if not field.dtype == self.field_info[name].dtype:
raise TypeError(
f"The dtype of field '{name}' is '{field.dtype}' instead of '{self.field_info[name].dtype}'"
)
# ToDo: check if mask is correct: need mask info in stencil object.
if isinstance(field, gt_storage.storage.Storage):
if not field.is_stencil_view:
raise ValueError(
f"An incompatible view was passed for field {name} to the stencil. "
)
for name_other, field_other in used_arg_fields.items():
if field_other.mask == field.mask:
if not field_other.shape == field.shape:
raise ValueError(
f"The fields {name} and {name_other} have the same mask but different shapes."
)
# assert compatibility of parameters with stencil
for name, parameter in used_arg_params.items():
if not type(parameter) == self.parameter_info[name].dtype:
raise TypeError(
f"The type of parameter '{name}' is '{type(parameter)}' instead of '{self.parameter_info[name].dtype}'"
)
assert isinstance(field_args, dict) and isinstance(
parameter_args, dict)
# Shapes
shapes = {}
for name, field in used_arg_fields.items():
shapes[name] = Shape(field.shape)
# Origins
if origin is None:
origin = {}
else:
origin = normalize_origin_mapping(origin)
for name, field in used_arg_fields.items():
origin.setdefault(
name,
origin["_all_"] if "_all_" in origin else field.default_origin)
# Domain
max_domain = Shape([sys.maxsize] * self.domain_info.ndims)
for name, shape in shapes.items():
upper_boundary = Index(
self.field_info[name].boundary.upper_indices)
max_domain &= shape - (Index(origin[name]) + upper_boundary)
if domain is None:
domain = max_domain
else:
domain = normalize_domain(domain)
if len(domain) != self.domain_info.ndims:
raise ValueError(f"Invalid 'domain' value '{domain}'")
# check domain+halo vs field size
if not domain > Shape.zeros(self.domain_info.ndims):
raise ValueError(f"Compute domain contains zero sizes '{domain}')")
if not domain <= max_domain:
raise ValueError(
f"Compute domain too large (provided: {domain}, maximum: {max_domain})"
)
for name, field in used_arg_fields.items():
min_origin = self.field_info[name].boundary.lower_indices
if origin[name] < min_origin:
raise ValueError(
f"Origin for field {name} too small. Must be at least {min_origin}, is {origin[name]}"
)
min_shape = tuple(o + d + h for o, d, h in zip(
origin[name], domain,
self.field_info[name].boundary.upper_indices))
if min_shape > field.shape:
raise ValueError(
f"Shape of field {name} is {field.shape} but must be at least {min_shape} for given domain and origin."
)
self.run(**field_args,
**parameter_args,
_domain_=domain,
_origin_=origin,
exec_info=exec_info)
def _validate_args(self, used_field_args, used_param_args, domain, origin):
"""Validate input arguments to _call_run.
Raises
-------
ValueError
If invalid data or inconsistent options are specified.
TypeError
If an incorrect field or parameter data type is passed.
"""
# assert compatibility of fields with stencil
for name, field in used_field_args.items():
if not gt_backend.from_name(
self.backend).storage_info["is_compatible_layout"](field):
raise ValueError(
f"The layout of the field {name} is not compatible with the backend."
)
if not gt_backend.from_name(
self.backend).storage_info["is_compatible_type"](field):
raise ValueError(
f"Field '{name}' has type '{type(field)}', which is not compatible with the '{self.backend}' backend."
)
elif type(field) is np.ndarray:
warnings.warn(
"NumPy ndarray passed as field. This is discouraged and only works with constraints and only for certain backends.",
RuntimeWarning,
)
field_dtype = self.field_info[name].dtype
if not field.dtype == field_dtype:
raise TypeError(
f"The dtype of field '{name}' is '{field.dtype}' instead of '{field_dtype}'"
)
if isinstance(field, gt_storage.storage.Storage):
field_mask = self._get_field_mask(name)
storage_mask = tuple(field.mask)
if storage_mask != field_mask:
raise ValueError(
f"The storage for '{name}' has mask '{storage_mask}', but the API signature expects '{field_mask}'"
)
if not field.is_stencil_view:
raise ValueError(
f"An incompatible view was passed for field {name} to the stencil. "
)
# assert compatibility of parameters with stencil
for name, parameter in used_param_args.items():
if not type(parameter) == self.parameter_info[name].dtype:
raise TypeError(
f"The type of parameter '{name}' is '{type(parameter)}' instead of '{self.parameter_info[name].dtype}'"
)
assert isinstance(used_field_args, dict) and isinstance(
used_param_args, dict)
if len(domain) != self.domain_info.ndims:
raise ValueError(f"Invalid 'domain' value '{domain}'")
# check domain+halo vs field size
if not domain > Shape.zeros(self.domain_info.ndims):
raise ValueError(f"Compute domain contains zero sizes '{domain}')")
max_domain = self._get_max_domain(used_field_args, origin)
if not domain <= max_domain:
raise ValueError(
f"Compute domain too large (provided: {domain}, maximum: {max_domain})"
)
for name, field in used_field_args.items():
field_mask = self._get_field_mask(name)
min_origin = self.field_info[
name].boundary.lower_indices.filter_mask(field_mask)
restricted_domain = domain.filter_mask(field_mask)
upper_indices = self.field_info[
name].boundary.upper_indices.filter_mask(field_mask)
if origin[name] < min_origin:
raise ValueError(
f"Origin for field {name} too small. Must be at least {min_origin}, is {origin[name]}"
)
min_shape = tuple(o + d + h for o, d, h in zip(
origin[name], restricted_domain, upper_indices))
if min_shape > field.shape:
raise ValueError(
f"Shape of field {name} is {field.shape} but must be at least {min_shape} for given domain and origin."
)