class ONNXTypeConstraint:
""" Python representation of an ONNX type constraint. """
type_str = Property(dtype=str, desc="The type parameter string")
types = ListProperty(
element_type=typeclass,
desc=
"The possible types. Note that only tensor types are currently supported."
)
def __repr__(self):
return self.type_str
class MyListObject(object):
list_prop = ListProperty(element_type=int)
def __init__(self, p):
super().__init__()
self.list_prop = p
def to_json(self):
return all_properties_to_json(self)
@staticmethod
def from_json(json_obj, context=None):
ret = MyListObject([])
set_properties_from_json(ret, json_obj, context=context)
return ret
class Map(object):
""" A Map is a two-node representation of parametric graphs, containing
an integer set by which the contents (nodes dominated by an entry
node and post-dominated by an exit node) are replicated.
Maps contain a `schedule` property, which specifies how the scope
should be scheduled (execution order). Code generators can use the
schedule property to generate appropriate code, e.g., GPU kernels.
"""
# List of (editable) properties
label = Property(dtype=str, desc="Label of the map")
params = ListProperty(element_type=str, desc="Mapped parameters")
range = RangeProperty(desc="Ranges of map parameters",
default=sbs.Range([]))
schedule = Property(dtype=dtypes.ScheduleType,
desc="Map schedule",
choices=dtypes.ScheduleType,
from_string=lambda x: dtypes.ScheduleType[x],
default=dtypes.ScheduleType.Default)
unroll = Property(dtype=bool, desc="Map unrolling")
collapse = Property(dtype=int,
default=1,
desc="How many dimensions to"
" collapse into the parallel range")
debuginfo = DebugInfoProperty()
is_collapsed = Property(dtype=bool,
desc="Show this node/scope/state as collapsed",
default=False)
instrument = Property(choices=dtypes.InstrumentationType,
desc="Measure execution statistics with given method",
default=dtypes.InstrumentationType.No_Instrumentation)
def __init__(self,
label,
params,
ndrange,
schedule=dtypes.ScheduleType.Default,
unroll=False,
collapse=1,
fence_instrumentation=False,
debuginfo=None):
super(Map, self).__init__()
# Assign properties
self.label = label
self.schedule = schedule
self.unroll = unroll
self.collapse = 1
self.params = params
self.range = ndrange
self.debuginfo = debuginfo
self._fence_instrumentation = fence_instrumentation
def __str__(self):
return self.label + "[" + ", ".join([
"{}={}".format(i, r) for i, r in zip(
self._params, [sbs.Range.dim_to_string(d) for d in self._range])
]) + "]"
def validate(self, sdfg, state, node):
if not dtypes.validate_name(self.label):
raise NameError('Invalid map name "%s"' % self.label)
def get_param_num(self):
""" Returns the number of map dimension parameters/symbols. """
return len(self.params)
class Stream(Data):
""" Stream (or stream array) data descriptor. """
# Properties
offset = ListProperty(element_type=symbolic.pystr_to_symbolic)
buffer_size = SymbolicProperty(desc="Size of internal buffer.", default=0)
def __init__(self,
dtype,
buffer_size,
shape=None,
transient=False,
storage=dtypes.StorageType.Default,
location=None,
offset=None,
lifetime=dtypes.AllocationLifetime.Scope,
debuginfo=None):
if shape is None:
shape = (1, )
self.buffer_size = buffer_size
if offset is not None:
if len(offset) != len(shape):
raise TypeError('Offset must be the same size as shape')
self.offset = cp.copy(offset)
else:
self.offset = [0] * len(shape)
super(Stream, self).__init__(dtype, shape, transient, storage, location,
lifetime, debuginfo)
def to_json(self):
attrs = serialize.all_properties_to_json(self)
retdict = {"type": type(self).__name__, "attributes": attrs}
return retdict
@classmethod
def from_json(cls, json_obj, context=None):
# Create dummy object
ret = cls(dtypes.int8, 1)
serialize.set_properties_from_json(ret, json_obj, context=context)
# Check validity now
ret.validate()
return ret
def __repr__(self):
return '%s (dtype=%s, shape=%s)' % (type(self).__name__, self.dtype,
self.shape)
@property
def total_size(self):
return _prod(self.shape)
@property
def strides(self):
return [_prod(self.shape[i + 1:]) for i in range(len(self.shape))]
def clone(self):
return type(self)(self.dtype, self.buffer_size, self.shape,
self.transient, self.storage, self.location,
self.offset, self.lifetime, self.debuginfo)
# Checks for equivalent shape and type
def is_equivalent(self, other):
if not isinstance(other, type(self)):
return False
# Test type
if self.dtype != other.dtype:
return False
# Test dimensionality
if len(self.shape) != len(other.shape):
return False
# Test shape
for dim, otherdim in zip(self.shape, other.shape):
if dim != otherdim:
return False
return True
def as_arg(self, with_types=True, for_call=False, name=None):
if not with_types or for_call: return name
if self.storage in [
dtypes.StorageType.GPU_Global, dtypes.StorageType.GPU_Shared
]:
return 'dace::GPUStream<%s, %s> %s' % (str(
self.dtype.ctype), 'true' if sp.log(
self.buffer_size, 2).is_Integer else 'false', name)
return 'dace::Stream<%s> %s' % (str(self.dtype.ctype), name)
def sizes(self):
return [
d.name if isinstance(d, symbolic.symbol) else str(d)
for d in self.shape
]
def size_string(self):
return (" * ".join(
[cppunparse.pyexpr2cpp(symbolic.symstr(s)) for s in self.shape]))
def is_stream_array(self):
return _prod(self.shape) != 1
def covers_range(self, rng):
if len(rng) != len(self.shape):
return False
for s, (rb, re, rs) in zip(self.shape, rng):
# Shape has to be positive
if isinstance(s, sp.Basic):
olds = s
if 'positive' in s.assumptions0:
s = sp.Symbol(str(s), **s.assumptions0)
else:
s = sp.Symbol(str(s), positive=True, **s.assumptions0)
if isinstance(rb, sp.Basic):
rb = rb.subs({olds: s})
if isinstance(re, sp.Basic):
re = re.subs({olds: s})
if isinstance(rs, sp.Basic):
rs = rs.subs({olds: s})
try:
if rb < 0: # Negative offset
return False
except TypeError: # cannot determine truth value of Relational
pass
#print('WARNING: Cannot evaluate relational expression %s, assuming true.' % (rb > 0),
# 'If this expression is false, please refine symbol definitions in the program.')
try:
if re > s: # Beyond shape
return False
except TypeError: # cannot determine truth value of Relational
pass
#print('WARNING: Cannot evaluate relational expression %s, assuming true.' % (re < s),
# 'If this expression is false, please refine symbol definitions in the program.')
return True
@property
def free_symbols(self):
result = super().free_symbols
if isinstance(self.buffer_size, sp.Expr):
result |= set(self.buffer_size.free_symbols)
for o in self.offset:
if isinstance(o, sp.Expr):
result |= set(o.free_symbols)
return result
class Array(Data):
""" Array/constant descriptor (dimensions, type and other properties). """
# Properties
allow_conflicts = Property(
dtype=bool,
default=False,
desc='If enabled, allows more than one '
'memlet to write to the same memory location without conflict '
'resolution.')
strides = ShapeProperty(
# element_type=symbolic.pystr_to_symbolic,
desc='For each dimension, the number of elements to '
'skip in order to obtain the next element in '
'that dimension.')
total_size = SymbolicProperty(
default=1,
desc='The total allocated size of the array. Can be used for'
' padding.')
offset = ListProperty(element_type=symbolic.pystr_to_symbolic,
desc='Initial offset to translate all indices by.')
may_alias = Property(dtype=bool,
default=False,
desc='This pointer may alias with other pointers in '
'the same function')
alignment = Property(dtype=int,
default=0,
desc='Allocation alignment in bytes (0 uses '
'compiler-default)')
def __init__(self,
dtype,
shape,
transient=False,
allow_conflicts=False,
storage=dtypes.StorageType.Default,
location=None,
strides=None,
offset=None,
may_alias=False,
lifetime=dtypes.AllocationLifetime.Scope,
alignment=0,
debuginfo=None,
total_size=None):
super(Array, self).__init__(dtype, shape, transient, storage, location,
lifetime, debuginfo)
if shape is None:
raise IndexError('Shape must not be None')
self.allow_conflicts = allow_conflicts
self.may_alias = may_alias
self.alignment = alignment
if strides is not None:
self.strides = cp.copy(strides)
else:
self.strides = [_prod(shape[i + 1:]) for i in range(len(shape))]
self.total_size = total_size or _prod(shape)
if offset is not None:
self.offset = cp.copy(offset)
else:
self.offset = [0] * len(shape)
self.validate()
def __repr__(self):
return '%s (dtype=%s, shape=%s)' % (type(self).__name__, self.dtype,
self.shape)
def clone(self):
return type(self)(self.dtype, self.shape, self.transient,
self.allow_conflicts, self.storage, self.location,
self.strides, self.offset, self.may_alias,
self.lifetime, self.alignment, self.debuginfo,
self.total_size)
def to_json(self):
attrs = serialize.all_properties_to_json(self)
# Take care of symbolic expressions
attrs['strides'] = list(map(str, attrs['strides']))
retdict = {"type": type(self).__name__, "attributes": attrs}
return retdict
@classmethod
def from_json(cls, json_obj, context=None):
# Create dummy object
ret = cls(dtypes.int8, ())
serialize.set_properties_from_json(ret, json_obj, context=context)
# TODO: This needs to be reworked (i.e. integrated into the list property)
ret.strides = list(map(symbolic.pystr_to_symbolic, ret.strides))
# Check validity now
ret.validate()
return ret
def validate(self):
super(Array, self).validate()
if len(self.strides) != len(self.shape):
raise TypeError('Strides must be the same size as shape')
if any(not isinstance(s, (int, symbolic.SymExpr, symbolic.symbol,
symbolic.sympy.Basic)) for s in self.strides):
raise TypeError('Strides must be a list or tuple of integer '
'values or symbols')
if len(self.offset) != len(self.shape):
raise TypeError('Offset must be the same size as shape')
def covers_range(self, rng):
if len(rng) != len(self.shape):
return False
for s, (rb, re, rs) in zip(self.shape, rng):
# Shape has to be positive
if isinstance(s, sp.Basic):
olds = s
if 'positive' in s.assumptions0:
s = sp.Symbol(str(s), **s.assumptions0)
else:
s = sp.Symbol(str(s), positive=True, **s.assumptions0)
if isinstance(rb, sp.Basic):
rb = rb.subs({olds: s})
if isinstance(re, sp.Basic):
re = re.subs({olds: s})
if isinstance(rs, sp.Basic):
rs = rs.subs({olds: s})
try:
if rb < 0: # Negative offset
return False
except TypeError: # cannot determine truth value of Relational
pass
#print('WARNING: Cannot evaluate relational expression %s, assuming true.' % (rb > 0),
# 'If this expression is false, please refine symbol definitions in the program.')
try:
if re > s: # Beyond shape
return False
except TypeError: # cannot determine truth value of Relational
pass
#print('WARNING: Cannot evaluate relational expression %s, assuming true.' % (re < s),
# 'If this expression is false, please refine symbol definitions in the program.')
return True
# Checks for equivalent shape and type
def is_equivalent(self, other):
if not isinstance(other, type(self)):
return False
# Test type
if self.dtype != other.dtype:
return False
# Test dimensionality
if len(self.shape) != len(other.shape):
return False
# Test shape
for dim, otherdim in zip(self.shape, other.shape):
# Any other case (constant vs. constant), check for equality
if otherdim != dim:
return False
return True
def as_arg(self, with_types=True, for_call=False, name=None):
arrname = name
if not with_types or for_call:
return arrname
if self.may_alias:
return str(self.dtype.ctype) + ' *' + arrname
return str(self.dtype.ctype) + ' * __restrict__ ' + arrname
def sizes(self):
return [
d.name if isinstance(d, symbolic.symbol) else str(d)
for d in self.shape
]
@property
def free_symbols(self):
result = super().free_symbols
for s in self.strides:
if isinstance(s, sp.Expr):
result |= set(s.free_symbols)
if isinstance(self.total_size, sp.Expr):
result |= set(self.total_size.free_symbols)
for o in self.offset:
if isinstance(o, sp.Expr):
result |= set(o.free_symbols)
return result
class Reduce(dace.sdfg.nodes.LibraryNode):
""" An SDFG node that reduces an N-dimensional array to an
(N-k)-dimensional array, with a list of axes to reduce and
a reduction binary function. """
# Global properties
implementations = {
'pure': ExpandReducePure,
'OpenMP': ExpandReduceOpenMP,
'CUDA (device)': ExpandReduceCUDADevice,
'CUDA (block)': ExpandReduceCUDABlock,
'CUDA (block allreduce)': ExpandReduceCUDABlockAll
# 'CUDA (warp)': ExpandReduceCUDAWarp,
# 'CUDA (warp allreduce)': ExpandReduceCUDAWarpAll
}
default_implementation = 'pure'
# Properties
axes = ListProperty(element_type=int, allow_none=True)
wcr = LambdaProperty(default='lambda a, b: a')
identity = Property(allow_none=True)
def __init__(self,
wcr='lambda a, b: a',
axes=None,
identity=None,
schedule=dtypes.ScheduleType.Default,
debuginfo=None,
**kwargs):
super().__init__(name='Reduce', **kwargs)
self.wcr = wcr
self.axes = axes
self.identity = identity
self.debuginfo = debuginfo
self.schedule = schedule
@staticmethod
def from_json(json_obj, context=None):
ret = Reduce("lambda a, b: a", None)
dace.serialize.set_properties_from_json(ret, json_obj, context=context)
return ret
def __str__(self):
# Autodetect reduction type
redtype = detect_reduction_type(self.wcr)
if redtype == dtypes.ReductionType.Custom:
wcrstr = unparse(ast.parse(self.wcr).body[0].value.body)
else:
wcrstr = str(redtype)
wcrstr = wcrstr[wcrstr.find('.') + 1:] # Skip "ReductionType."
return 'Reduce ({op}), Axes: {axes}'.format(
axes=('all' if self.axes is None else str(self.axes)), op=wcrstr)
def __label__(self, sdfg, state):
return str(self).replace(' Axes', '\nAxes')
def validate(self, sdfg, state):
if len(state.in_edges(self)) != 1:
raise ValueError('Reduce node must have one input')
if len(state.out_edges(self)) != 1:
raise ValueError('Reduce node must have one output')
class Tasklet(CodeNode):
""" A node that contains a tasklet: a functional computation procedure
that can only access external data specified using connectors.
Tasklets may be implemented in Python, C++, or any supported
language by the code generator.
"""
code = CodeProperty(desc="Tasklet code", default=CodeBlock(""))
state_fields = ListProperty(
element_type=str, desc="Fields that are added to the global state")
code_global = CodeProperty(
desc="Global scope code needed for tasklet execution",
default=CodeBlock("", dtypes.Language.CPP))
code_init = CodeProperty(
desc="Extra code that is called on DaCe runtime initialization",
default=CodeBlock("", dtypes.Language.CPP))
code_exit = CodeProperty(
desc="Extra code that is called on DaCe runtime cleanup",
default=CodeBlock("", dtypes.Language.CPP))
debuginfo = DebugInfoProperty()
instrument = EnumProperty(
dtype=dtypes.InstrumentationType,
desc="Measure execution statistics with given method",
default=dtypes.InstrumentationType.No_Instrumentation)
def __init__(self,
label,
inputs=None,
outputs=None,
code="",
language=dtypes.Language.Python,
state_fields=None,
code_global="",
code_init="",
code_exit="",
location=None,
debuginfo=None):
super(Tasklet, self).__init__(label, location, inputs, outputs)
self.code = CodeBlock(code, language)
self.state_fields = state_fields or []
self.code_global = CodeBlock(code_global, dtypes.Language.CPP)
self.code_init = CodeBlock(code_init, dtypes.Language.CPP)
self.code_exit = CodeBlock(code_exit, dtypes.Language.CPP)
self.debuginfo = debuginfo
@property
def language(self):
return self.code.language
@staticmethod
def from_json(json_obj, context=None):
ret = Tasklet("dummylabel")
dace.serialize.set_properties_from_json(ret, json_obj, context=context)
return ret
@property
def name(self):
return self._label
def validate(self, sdfg, state):
if not dtypes.validate_name(self.label):
raise NameError('Invalid tasklet name "%s"' % self.label)
for in_conn in self.in_connectors:
if not dtypes.validate_name(in_conn):
raise NameError('Invalid input connector "%s"' % in_conn)
for out_conn in self.out_connectors:
if not dtypes.validate_name(out_conn):
raise NameError('Invalid output connector "%s"' % out_conn)
@property
def free_symbols(self) -> Set[str]:
return self.code.get_free_symbols(self.in_connectors.keys()
| self.out_connectors.keys())
def infer_connector_types(self, sdfg, state):
# If a MLIR tasklet, simply read out the types (it's explicit)
if self.code.language == dtypes.Language.MLIR:
# Inline import because mlir.utils depends on pyMLIR which may not be installed
# Doesn't cause crashes due to missing pyMLIR if a MLIR tasklet is not present
from dace.codegen.targets.mlir import utils
mlir_ast = utils.get_ast(self.code.code)
mlir_is_generic = utils.is_generic(mlir_ast)
mlir_entry_func = utils.get_entry_func(mlir_ast, mlir_is_generic)
mlir_result_type = utils.get_entry_result_type(
mlir_entry_func, mlir_is_generic)
mlir_out_name = next(iter(self.out_connectors.keys()))
if self.out_connectors[
mlir_out_name] is None or self.out_connectors[
mlir_out_name].ctype == "void":
self.out_connectors[mlir_out_name] = utils.get_dace_type(
mlir_result_type)
elif self.out_connectors[mlir_out_name] != utils.get_dace_type(
mlir_result_type):
warnings.warn(
"Type mismatch between MLIR tasklet out connector and MLIR code"
)
for mlir_arg in utils.get_entry_args(mlir_entry_func,
mlir_is_generic):
if self.in_connectors[
mlir_arg[0]] is None or self.in_connectors[
mlir_arg[0]].ctype == "void":
self.in_connectors[mlir_arg[0]] = utils.get_dace_type(
mlir_arg[1])
elif self.in_connectors[mlir_arg[0]] != utils.get_dace_type(
mlir_arg[1]):
warnings.warn(
"Type mismatch between MLIR tasklet in connector and MLIR code"
)
return
# If a Python tasklet, use type inference to figure out all None output
# connectors
if all(cval.type is not None for cval in self.out_connectors.values()):
return
if self.code.language != dtypes.Language.Python:
return
if any(cval.type is None for cval in self.in_connectors.values()):
raise TypeError('Cannot infer output connectors of tasklet "%s", '
'not all input connectors have types' % str(self))
# Avoid import loop
from dace.codegen.tools.type_inference import infer_types
# Get symbols defined at beginning of node, and infer all types in
# tasklet
syms = state.symbols_defined_at(self)
syms.update(self.in_connectors)
new_syms = infer_types(self.code.code, syms)
for cname, oconn in self.out_connectors.items():
if oconn.type is None:
if cname not in new_syms:
raise TypeError('Cannot infer type of tasklet %s output '
'"%s", please specify manually.' %
(self.label, cname))
self.out_connectors[cname] = new_syms[cname]
def __str__(self):
if not self.label:
return "--Empty--"
else:
return self.label
class Stream(Data):
""" Stream (or stream array) data descriptor. """
# Properties
strides = ListProperty(element_type=symbolic.pystr_to_symbolic)
offset = ListProperty(element_type=symbolic.pystr_to_symbolic)
buffer_size = SymbolicProperty(desc="Size of internal buffer.")
veclen = Property(dtype=int,
desc="Vector length. Memlets must adhere to this.")
def __init__(self,
dtype,
veclen,
buffer_size,
shape=None,
transient=False,
storage=dace.dtypes.StorageType.Default,
location='',
strides=None,
offset=None,
toplevel=False,
debuginfo=None):
if shape is None:
shape = (1, )
self.veclen = veclen
self.buffer_size = buffer_size
if strides is not None:
if len(strides) != len(shape):
raise TypeError('Strides must be the same size as shape')
self.strides = cp.copy(strides)
else:
self.strides = cp.copy(list(shape))
if offset is not None:
if len(offset) != len(shape):
raise TypeError('Offset must be the same size as shape')
self.offset = cp.copy(offset)
else:
self.offset = [0] * len(shape)
super(Stream, self).__init__(dtype, shape, transient, storage,
location, toplevel, debuginfo)
def to_json(self):
attrs = dace.serialize.all_properties_to_json(self)
# Take care of symbolic expressions
attrs['strides'] = list(map(str, attrs['strides']))
retdict = {"type": type(self).__name__, "attributes": attrs}
return retdict
@staticmethod
def from_json(json_obj, context=None):
if json_obj['type'] != "Stream":
raise TypeError("Invalid data type")
# Create dummy object
ret = Stream(dace.dtypes.int8, 1, 1)
dace.serialize.set_properties_from_json(ret, json_obj, context=context)
# TODO: FIXME:
# Since the strides are a list-property (normal Property()),
# loading from/to string (and, consequently, from/to json)
# leads to validation errors (contains Strings/Integers, not sympy symbols).
# To fix this, it needs a custom class
# For now, this is a workaround:
ret.strides = list(map(symbolic.pystr_to_symbolic, ret.strides))
# Check validity now
ret.validate()
return ret
def __repr__(self):
return 'Stream (dtype=%s, shape=%s)' % (self.dtype, self.shape)
def clone(self):
return Stream(self.dtype, self.veclen, self.buffer_size, self.shape,
self.transient, self.storage, self.location,
self.strides, self.offset, self.toplevel, self.debuginfo)
# Checks for equivalent shape and type
def is_equivalent(self, other):
if not isinstance(other, Stream):
return False
# Test type
if self.dtype != other.dtype:
return False
# Test dimensionality
if len(self.shape) != len(other.shape):
return False
# Test shape
for dim, otherdim in zip(self.shape, other.shape):
# If both are symbols, ensure equality
if symbolic.issymbolic(dim) and symbolic.issymbolic(otherdim):
if dim != otherdim:
return False
# If one is a symbol and the other is a constant
# make sure they are equivalent
elif symbolic.issymbolic(otherdim):
if symbolic.eval(otherdim) != dim:
return False
elif symbolic.issymbolic(dim):
if symbolic.eval(dim) != otherdim:
return False
else:
# Any other case (constant vs. constant), check for equality
if otherdim != dim:
return False
return True
def signature(self, with_types=True, for_call=False, name=None):
if not with_types or for_call: return name
if self.storage in [
dace.dtypes.StorageType.GPU_Global,
dace.dtypes.StorageType.GPU_Shared,
dace.dtypes.StorageType.GPU_Stack
]:
return 'dace::GPUStream<%s, %s> %s' % (str(
self.dtype.ctype), 'true' if sp.log(
self.buffer_size, 2).is_Integer else 'false', name)
return 'dace::Stream<%s> %s' % (str(self.dtype.ctype), name)
def sizes(self):
return [
d.name if isinstance(d, symbolic.symbol) else str(d)
for d in self.shape
]
def size_string(self):
return (" * ".join([
cppunparse.pyexpr2cpp(dace.symbolic.symstr(s))
for s in self.strides
]))
def is_stream_array(self):
return functools.reduce(lambda a, b: a * b, self.strides) != 1
def covers_range(self, rng):
if len(rng) != len(self.shape):
return False
for s, (rb, re, rs) in zip(self.shape, rng):
# Shape has to be positive
if isinstance(s, sympy.Basic):
olds = s
if 'positive' in s.assumptions0:
s = sympy.Symbol(str(s), **s.assumptions0)
else:
s = sympy.Symbol(str(s), positive=True, **s.assumptions0)
if isinstance(rb, sympy.Basic):
rb = rb.subs({olds: s})
if isinstance(re, sympy.Basic):
re = re.subs({olds: s})
if isinstance(rs, sympy.Basic):
rs = rs.subs({olds: s})
try:
if rb < 0: # Negative offset
return False
except TypeError: # cannot determine truth value of Relational
pass
#print('WARNING: Cannot evaluate relational expression %s, assuming true.' % (rb > 0),
# 'If this expression is false, please refine symbol definitions in the program.')
try:
if re > s: # Beyond shape
return False
except TypeError: # cannot determine truth value of Relational
pass
#print('WARNING: Cannot evaluate relational expression %s, assuming true.' % (re < s),
# 'If this expression is false, please refine symbol definitions in the program.')
return True
class Array(Data):
""" Array/constant descriptor (dimensions, type and other properties). """
# Properties
allow_conflicts = Property(dtype=bool)
# TODO: Should we use a Code property here?
materialize_func = Property(dtype=str,
allow_none=True,
setter=set_materialize_func)
access_order = ListProperty(element_type=int)
strides = ListProperty(element_type=symbolic.pystr_to_symbolic)
offset = ListProperty(element_type=symbolic.pystr_to_symbolic)
may_alias = Property(dtype=bool,
default=False,
desc='This pointer may alias with other pointers in '
'the same function')
def __init__(self,
dtype,
shape,
materialize_func=None,
transient=False,
allow_conflicts=False,
storage=dace.dtypes.StorageType.Default,
location='',
access_order=None,
strides=None,
offset=None,
may_alias=False,
toplevel=False,
debuginfo=None):
super(Array, self).__init__(dtype, shape, transient, storage, location,
toplevel, debuginfo)
if shape is None:
raise IndexError('Shape must not be None')
self.allow_conflicts = allow_conflicts
self.materialize_func = materialize_func
self.may_alias = may_alias
if access_order is not None:
self.access_order = cp.copy(access_order)
else:
self.access_order = tuple(i for i in range(len(shape)))
if strides is not None:
self.strides = cp.copy(strides)
else:
self.strides = cp.copy(list(shape))
if offset is not None:
self.offset = cp.copy(offset)
else:
self.offset = [0] * len(shape)
self.validate()
def __repr__(self):
return 'Array (dtype=%s, shape=%s)' % (self.dtype, self.shape)
def clone(self):
return Array(self.dtype, self.shape, self.materialize_func,
self.transient, self.allow_conflicts, self.storage,
self.location, self.access_order, self.strides,
self.offset, self.may_alias, self.toplevel,
self.debuginfo)
def to_json(self):
attrs = dace.serialize.all_properties_to_json(self)
# Take care of symbolic expressions
attrs['strides'] = list(map(str, attrs['strides']))
retdict = {"type": type(self).__name__, "attributes": attrs}
return retdict
@staticmethod
def from_json(json_obj, context=None):
if json_obj['type'] != "Array":
raise TypeError("Invalid data type")
# Create dummy object
ret = Array(dace.dtypes.int8, ())
dace.serialize.set_properties_from_json(ret, json_obj, context=context)
# TODO: This needs to be reworked (i.e. integrated into the list property)
ret.strides = list(map(symbolic.pystr_to_symbolic, ret.strides))
# Check validity now
ret.validate()
return ret
def validate(self):
super(Array, self).validate()
if len(self.access_order) != len(self.shape):
raise TypeError('Access order must be the same size as shape')
if len(self.strides) != len(self.shape):
raise TypeError('Strides must be the same size as shape')
if any(not isinstance(s, (int, symbolic.SymExpr, symbolic.symbol,
symbolic.sympy.Basic))
for s in self.strides):
raise TypeError('Strides must be a list or tuple of integer '
'values or symbols')
if len(self.offset) != len(self.shape):
raise TypeError('Offset must be the same size as shape')
def covers_range(self, rng):
if len(rng) != len(self.shape):
return False
for s, (rb, re, rs) in zip(self.shape, rng):
# Shape has to be positive
if isinstance(s, sympy.Basic):
olds = s
if 'positive' in s.assumptions0:
s = sympy.Symbol(str(s), **s.assumptions0)
else:
s = sympy.Symbol(str(s), positive=True, **s.assumptions0)
if isinstance(rb, sympy.Basic):
rb = rb.subs({olds: s})
if isinstance(re, sympy.Basic):
re = re.subs({olds: s})
if isinstance(rs, sympy.Basic):
rs = rs.subs({olds: s})
try:
if rb < 0: # Negative offset
return False
except TypeError: # cannot determine truth value of Relational
pass
#print('WARNING: Cannot evaluate relational expression %s, assuming true.' % (rb > 0),
# 'If this expression is false, please refine symbol definitions in the program.')
try:
if re > s: # Beyond shape
return False
except TypeError: # cannot determine truth value of Relational
pass
#print('WARNING: Cannot evaluate relational expression %s, assuming true.' % (re < s),
# 'If this expression is false, please refine symbol definitions in the program.')
return True
# Checks for equivalent shape and type
def is_equivalent(self, other):
if not isinstance(other, Array):
return False
# Test type
if self.dtype != other.dtype:
return False
# Test dimensionality
if len(self.shape) != len(other.shape):
return False
# Test shape
for dim, otherdim in zip(self.shape, other.shape):
# If both are symbols, ensure equality
if symbolic.issymbolic(dim) and symbolic.issymbolic(otherdim):
if dim != otherdim:
return False
# If one is a symbol and the other is a constant
# make sure they are equivalent
elif symbolic.issymbolic(otherdim):
if symbolic.eval(otherdim) != dim:
return False
elif symbolic.issymbolic(dim):
if symbolic.eval(dim) != otherdim:
return False
else:
# Any other case (constant vs. constant), check for equality
if otherdim != dim:
return False
return True
def signature(self, with_types=True, for_call=False, name=None):
arrname = name
if self.materialize_func is not None:
if for_call:
return 'nullptr'
if not with_types:
return arrname
arrname = '/* ' + arrname + ' (immaterial) */'
if not with_types or for_call:
return arrname
if self.may_alias:
return str(self.dtype.ctype) + ' *' + arrname
return str(self.dtype.ctype) + ' * __restrict__ ' + arrname
def sizes(self):
return [
d.name if isinstance(d, symbolic.symbol) else str(d)
for d in self.shape
]
class Reduce(Node):
""" An SDFG node that reduces an N-dimensional array to an
(N-k)-dimensional array, with a list of axes to reduce and
a reduction binary function. """
# Properties
axes = ListProperty(element_type=int, allow_none=True)
wcr = LambdaProperty()
identity = Property(dtype=object, allow_none=True)
schedule = Property(dtype=dtypes.ScheduleType,
desc="Reduction execution policy",
choices=dtypes.ScheduleType,
from_string=lambda x: dtypes.ScheduleType[x])
debuginfo = DebugInfoProperty()
instrument = Property(
choices=dtypes.InstrumentationType,
desc="Measure execution statistics with given method",
default=dtypes.InstrumentationType.No_Instrumentation)
def __init__(self,
wcr,
axes,
wcr_identity=None,
schedule=dtypes.ScheduleType.Default,
debuginfo=None):
super(Reduce, self).__init__()
self.wcr = wcr # type: ast._Lambda
self.axes = axes
self.identity = wcr_identity
self.schedule = schedule
self.debuginfo = debuginfo
def draw_node(self, sdfg, state):
return dot.draw_node(sdfg, state, self, shape="invtriangle")
@staticmethod
def from_json(json_obj, context=None):
ret = Reduce("(lambda a, b: (a + b))", None)
dace.serialize.set_properties_from_json(ret, json_obj, context=context)
return ret
def __str__(self):
# Autodetect reduction type
redtype = detect_reduction_type(self.wcr)
if redtype == dtypes.ReductionType.Custom:
wcrstr = unparse(ast.parse(self.wcr).body[0].value.body)
else:
wcrstr = str(redtype)
wcrstr = wcrstr[wcrstr.find('.') + 1:] # Skip "ReductionType."
return 'Op: {op}, Axes: {axes}'.format(
axes=('all' if self.axes is None else str(self.axes)), op=wcrstr)
def __label__(self, sdfg, state):
# Autodetect reduction type
redtype = detect_reduction_type(self.wcr)
if redtype == dtypes.ReductionType.Custom:
wcrstr = unparse(ast.parse(self.wcr).body[0].value.body)
else:
wcrstr = str(redtype)
wcrstr = wcrstr[wcrstr.find('.') + 1:] # Skip "ReductionType."
return 'Op: {op}\nAxes: {axes}'.format(
axes=('all' if self.axes is None else str(self.axes)), op=wcrstr)
class ONNXSchema:
"""Python representation of an ONNX schema"""
name = Property(dtype=str, desc="The operator name")
domain = Property(dtype=str, desc="The operator domain")
doc = Property(dtype=str, desc="The operator's docstring")
since_version = Property(dtype=int, desc="The version of the operator")
attributes = DictProperty(key_type=str,
value_type=ONNXAttribute,
desc="The operator attributes")
type_constraints = DictProperty(
key_type=str,
value_type=ONNXTypeConstraint,
desc="The type constraints for inputs and outputs")
inputs = ListProperty(element_type=ONNXParameter,
desc="The operator input parameter descriptors")
outputs = ListProperty(element_type=ONNXParameter,
desc="The operator output parameter descriptors")
def __repr__(self):
return self.domain + "." + self.name
def validate(self):
# check all parameters with a type str have a entry in the type constraints
for param in chain(self.inputs, self.outputs):
if param.type_str not in self.type_constraints:
# some operators put a type descriptor here. for those, we will try to insert a new type constraint
cons_name = param.name + "_constraint"
if cons_name in self.type_constraints:
raise ValueError(
"Attempted to insert new type constraint, but the name already existed. Please open an issue."
)
parsed_typeclass = onnx_type_str_to_typeclass(param.type_str)
if parsed_typeclass is None:
print("Could not parse typeStr '{}' for parameter '{}'".
format(param.type_str, param.name))
cons = ONNXTypeConstraint(
cons_name,
[parsed_typeclass] if parsed_typeclass is not None else [])
self.type_constraints[cons_name] = cons
param.type_str = cons_name
# check for required parameters with no supported type
for param in chain(self.inputs, self.outputs):
if ((param.param_type == ONNXParameterType.Single
or param.param_type == ONNXParameterType.Variadic)
and len(self.type_constraints[param.type_str].types) == 0):
raise NotImplementedError(
"None of the types for parameter '{}' are supported".
format(param.name))
# check that all variadic parameter names do not contain "__"
for param in chain(self.inputs, self.outputs):
if param.param_type == ONNXParameterType.Variadic and "__" in param.name:
raise ValueError(
"Unsupported parameter name '{}': variadic parameter names must not contain '__'"
.format(param.name))
# check that all inputs and outputs have unique names
seen = set()
for param in self.inputs:
if param.name in seen:
raise ValueError(
"Got duplicate input parameter name '{}'".format(
param.name))
seen.add(param.name)
seen = set()
for param in self.outputs:
if param.name in seen:
raise ValueError(
"Got duplicate output parameter name '{}'".format(
param.name))
seen.add(param.name)
if attr.type in [
ONNXAttributeType.Int, ONNXAttributeType.String,
ONNXAttributeType.Float, ONNXAttributeType.Tensor
]:
attrs[name] = Property(dtype=_ATTR_TYPE_TO_PYTHON_TYPE[attr.type],
desc=attr.description,
allow_none=True,
default=None if attr.default_value is None
else attr.default_value)
elif attr.type in [
ONNXAttributeType.Ints, ONNXAttributeType.Strings,
ONNXAttributeType.Floats
]:
attrs[name] = ListProperty(
element_type=_ATTR_TYPE_TO_PYTHON_TYPE[attr.type],
desc=attr.description,
allow_none=True,
default=None
if attr.default_value is None else attr.default_value)
elif attr.required:
raise NotImplementedError(
"Required attribute '{}' has an unsupported type".format(
attr.name))
required_attrs = {
name
for name, attr in dace_schema.attributes.items() if attr.required
}
def __init__(self, name, *args, location=None, **op_attributes):
super(ONNXOp, self).__init__(
name,
class SubArray(object):
"""
Sub-arrays describe subsets of Arrays (see `dace::data::Array`) for purposes of distributed communication. They are
implemented with [MPI_Type_create_subarray](https://www.mpich.org/static/docs/v3.2/www3/MPI_Type_create_subarray.html).
Sub-arrays can be also used for collective scatter/gather communication in a process-grid.
The `shape`, `subshape`, and `dtype` properties correspond to the `array_of_sizes`, `array_of_subsizes`, and
`oldtype` parameters of `MPI_Type_create_subarray`.
The following properties are used for collective scatter/gather communication in a process-grid:
The `pgrid` property is the name of the process-grid where the data will be distributed. The `correspondence`
property matches the arrays dimensions to the process-grid's dimensions. For example, if one wants to distribute
a matrix to a 2D process-grid, but tile the matrix rows over the grid's columns, then `correspondence = [1, 0]`.
"""
name = Property(dtype=str, desc="The type's name.")
dtype = TypeClassProperty(default=dtypes.int32, choices=dtypes.Typeclasses)
shape = ShapeProperty(default=[], desc="The array's shape.")
subshape = ShapeProperty(default=[], desc="The sub-array's shape.")
pgrid = Property(
dtype=str,
allow_none=True,
default=None,
desc="Name of the process-grid where the data are distributed.")
correspondence = ListProperty(
int,
allow_none=True,
default=None,
desc="Correspondence of the array's indices to the process grid's "
"indices.")
def __init__(self,
name: str,
dtype: dtypes.typeclass,
shape: ShapeType,
subshape: ShapeType,
pgrid: str = None,
correspondence: Sequence[Integral] = None):
self.name = name
self.dtype = dtype
self.shape = shape
self.subshape = subshape
self.pgrid = pgrid
self.correspondence = correspondence or list(range(len(shape)))
self._validate()
def validate(self):
""" Validate the correctness of this object.
Raises an exception on error. """
self._validate()
# Validation of this class is in a separate function, so that this
# class can call `_validate()` without calling the subclasses'
# `validate` function.
def _validate(self):
if any(not isinstance(s, (Integral, symbolic.SymExpr, symbolic.symbol,
symbolic.sympy.Basic)) for s in self.shape):
raise TypeError(
'Shape must be a list or tuple of integer values or symbols')
if any(not isinstance(s, (Integral, symbolic.SymExpr, symbolic.symbol,
symbolic.sympy.Basic))
for s in self.subshape):
raise TypeError(
'Sub-shape must be a list or tuple of integer values or symbols'
)
if any(not isinstance(i, Integral) for i in self.correspondence):
raise TypeError(
'Correspondence must be a list or tuple of integer values')
if len(self.shape) != len(self.subshape):
raise ValueError(
'The dimensionality of the shape and sub-shape must match')
if len(self.correspondence) != len(self.shape):
raise ValueError(
'The dimensionality of the shape and correspondence list must match'
)
return True
def to_json(self):
attrs = serialize.all_properties_to_json(self)
retdict = {"type": type(self).__name__, "attributes": attrs}
return retdict
@classmethod
def from_json(cls, json_obj, context=None):
# Create dummy object
ret = cls('tmp', dtypes.int8, [], [], 'tmp', [])
serialize.set_properties_from_json(ret, json_obj, context=context)
# Check validity now
ret.validate()
return ret
def init_code(self):
""" Outputs MPI allocation/initialization code for the sub-array MPI datatype ONLY if the process-grid is set.
It is assumed that the following variables exist in the SDFG program's state:
- MPI_Datatype {self.name}
- int* {self.name}_counts
- int* {self.name}_displs
These variables are typically added to the program's state through a Tasklet, e.g., the Dummy MPI node (for
more details, check the DaCe MPI library in `dace/libraries/mpi`).
"""
from dace.libraries.mpi import utils
if self.pgrid:
return f"""
if (__state->{self.pgrid}_valid) {{
int sizes[{len(self.shape)}] = {{{', '.join([str(s) for s in self.shape])}}};
int subsizes[{len(self.shape)}] = {{{', '.join([str(s) for s in self.subshape])}}};
int corr[{len(self.shape)}] = {{{', '.join([str(i) for i in self.correspondence])}}};
int basic_stride = subsizes[{len(self.shape)} - 1];
int process_strides[{len(self.shape)}];
int block_strides[{len(self.shape)}];
int data_strides[{len(self.shape)}];
process_strides[{len(self.shape)} - 1] = 1;
block_strides[{len(self.shape)} - 1] = subsizes[{len(self.shape)} - 1];
data_strides[{len(self.shape)} - 1] = 1;
for (auto i = {len(self.shape)} - 2; i >= 0; --i) {{
block_strides[i] = block_strides[i+1] * subsizes[i];
process_strides[i] = process_strides[i+1] * __state->{self.pgrid}_dims[corr[i+1]];
data_strides[i] = block_strides[i] * process_strides[i] / basic_stride;
}}
MPI_Datatype type;
int origin[{len(self.shape)}] = {{{','.join(['0'] * len(self.shape))}}};
MPI_Type_create_subarray({len(self.shape)}, sizes, subsizes, origin, MPI_ORDER_C, {utils.MPI_DDT(self.dtype.base_type)}, &type);
MPI_Type_create_resized(type, 0, basic_stride*sizeof({self.dtype.ctype}), &__state->{self.name});
MPI_Type_commit(&__state->{self.name});
__state->{self.name}_counts = new int[__state->{self.pgrid}_size];
__state->{self.name}_displs = new int[__state->{self.pgrid}_size];
int block_id[{len(self.shape)}] = {{0}};
int displ = 0;
for (auto i = 0; i < __state->{self.pgrid}_size; ++i) {{
__state->{self.name}_counts[i] = 1;
__state->{self.name}_displs[i] = displ;
int idx = {len(self.shape)} - 1;
while (idx >= 0 && block_id[idx] + 1 >= __state->{self.pgrid}_dims[corr[idx]]) {{
block_id[idx] = 0;
displ -= data_strides[idx] * (__state->{self.pgrid}_dims[corr[idx]] - 1);
idx--;
}}
if (idx >= 0) {{
block_id[idx] += 1;
displ += data_strides[idx];
}} else {{
assert(i == __state->{self.pgrid}_size - 1);
}}
}}
}}
"""
else:
return ""
def exit_code(self):
""" Outputs MPI deallocation code for the sub-array MPI datatype ONLY if the process-grid is set. """
if self.pgrid:
return f"""
if (__state->{self.pgrid}_valid) {{
delete[] __state->{self.name}_counts;
delete[] __state->{self.name}_displs;
MPI_Type_free(&__state->{self.name});
}}
"""
else:
return ""
class ProcessGrid(object):
"""
Process-grids implement cartesian topologies similarly to cartesian communicators created with [MPI_Cart_create](https://www.mpich.org/static/docs/latest/www3/MPI_Cart_create.html)
and [MPI_Cart_sub](https://www.mpich.org/static/docs/v3.2/www3/MPI_Cart_sub.html).
The boolean property `is_subgrid` provides a switch between "parent" process-grids (equivalent to communicators
create with `MPI_Cart_create`) and sub-grids (equivalent to communicators created with `MPI_Cart_sub`).
If `is_subgrid` is false, a "parent" process-grid is created. The `shape` property is equivalent to the `dims`
parameter of `MPI_Cart_create`. The other properties are ignored. All "parent" process-grids spawn out of
`MPI_COMM_WORLD`, while their `periods` and `reorder` parameters are set to False.
If `is_subgrid` is true, then the `parent_grid` is partitioned to lower-dimensional cartesian sub-grids (for more
details, see the documentation of `MPI_Cart_sub`). The `parent_grid` property is equivalent to the `comm` parameter
of `MPI_Cart_sub`. The `color` property corresponds to the `remain_dims` parameter of `MPI_Cart_sub`, i.e., the i-th
entry specifies whether the i-th dimension is kep in the sub-grid or is dropped.
The following properties store information used in the redistribution of data:
The `exact_grid` property is either None or the rank of an MPI process in the `parent_grid`. If set then, out of all
the sub-grids created, only the one that contains this rank is used for collective communication. The `root`
property is used to select the root rank for purposed of collective communication (by default 0).
"""
name = Property(dtype=str, desc="The process-grid's name.")
is_subgrid = Property(
dtype=bool,
default=False,
desc="If true, spanws sub-grids out of the parent process-grid.")
shape = ShapeProperty(default=[], desc="The process-grid's shape.")
parent_grid = Property(
dtype=str,
allow_none=True,
default=None,
desc="Name of the parent process-grid "
"(mandatory if `is_subgrid` is true, otherwise ignored).")
color = ListProperty(
int,
allow_none=True,
default=None,
desc=
"The i-th entry specifies whether the i-th dimension is kept in the sub-grid or is "
"dropped (mandatory if `is_subgrid` is true, otherwise ignored).")
exact_grid = SymbolicProperty(
allow_none=True,
default=None,
desc=
"If set then, out of all the sub-grids created, only the one that contains the "
"rank with id `exact_grid` will be utilized for collective communication "
"(optional if `is_subgrid` is true, otherwise ignored).")
root = SymbolicProperty(default=0,
desc="The root rank for collective communication.")
def __init__(self,
name: str,
is_subgrid: bool,
shape: ShapeType = None,
parent_grid: str = None,
color: Sequence[Union[Integral, bool]] = None,
exact_grid: RankType = None,
root: RankType = 0):
self.name = name
self.is_subgrid = is_subgrid
if is_subgrid:
self.parent_grid = parent_grid.name
self.color = color
self.exact_grid = exact_grid
self.shape = [
parent_grid.shape[i] for i, remain in enumerate(color)
if remain
]
else:
self.shape = shape
self.root = root
self._validate()
def validate(self):
""" Validate the correctness of this object.
Raises an exception on error. """
self._validate()
# Validation of this class is in a separate function, so that this
# class can call `_validate()` without calling the subclasses'
# `validate` function.
def _validate(self):
if self.is_subgrid:
if not self.parent_grid or len(self.parent_grid) == 0:
raise ValueError(
'Sub-grid misses its corresponding parent process-grid')
if any(not isinstance(s, (Integral, symbolic.SymExpr, symbolic.symbol,
symbolic.sympy.Basic)) for s in self.shape):
raise TypeError(
'Shape must be a list or tuple of integer values or symbols')
if self.color and any(c < 0 or c > 1 for c in self.color):
raise ValueError(
'Color must have only logical true (1) or false (0) values.')
return True
def to_json(self):
attrs = serialize.all_properties_to_json(self)
retdict = {"type": type(self).__name__, "attributes": attrs}
return retdict
@classmethod
def from_json(cls, json_obj, context=None):
# Create dummy object
ret = cls('tmp', False, [])
serialize.set_properties_from_json(ret, json_obj, context=context)
# Check validity now
ret.validate()
return ret
def init_code(self):
""" Outputs MPI allocation/initialization code for the process-grid.
It is assumed that the following variables exist in the SDFG program's state:
- MPI_Comm {self.name}_comm
- MPI_Group {self.name}_group
- int {self.name}_rank
- int {self.name}_size
- int* {self.name}_dims
- int* {self.name}_remain
- int* {self.name}_coords
- bool {self.name})_valid
These variables are typically added to the program's state through a Tasklet, e.g., the Dummy MPI node (for
more details, check the DaCe MPI library in `dace/libraries/mpi`).
"""
if self.is_subgrid:
tmp = ""
for i, s in enumerate(self.shape):
tmp += f"__state->{self.name}_dims[{i}] = {s};\n"
tmp += f"""
__state->{self.name}_valid = false;
if (__state->{self.parent_grid}_valid) {{
int {self.name}_remain[{len(self.color)}] = {{{', '.join(['1' if c else '0' for c in self.color])}}};
MPI_Cart_sub(__state->{self.parent_grid}_comm, {self.name}_remain, &__state->{self.name}_comm);
MPI_Comm_group(__state->{self.name}_comm, &__state->{self.name}_group);
MPI_Comm_rank(__state->{self.name}_comm, &__state->{self.name}_rank);
MPI_Comm_size(__state->{self.name}_comm, &__state->{self.name}_size);
MPI_Cart_coords(__state->{self.name}_comm, __state->{self.name}_rank, {len(self.shape)}, __state->{self.name}_coords);
"""
if self.exact_grid is not None:
tmp += f"""
int ranks1[1] = {{{self.exact_grid}}};
int ranks2[1];
MPI_Group_translate_ranks(__state->{self.parent_grid}_group, 1, ranks1, __state->{self.name}_group, ranks2);
__state->{self.name}_valid = (ranks2[0] != MPI_PROC_NULL && ranks2[0] != MPI_UNDEFINED);
}}
"""
else:
tmp += f"""
__state->{self.name}_valid = true;
}}
"""
return tmp
else:
tmp = ""
for i, s in enumerate(self.shape):
tmp += f"__state->{self.name}_dims[{i}] = {s};\n"
tmp += f"""
int {self.name}_periods[{len(self.shape)}] = {{0}};
MPI_Cart_create(MPI_COMM_WORLD, {len(self.shape)}, __state->{self.name}_dims, {self.name}_periods, 0, &__state->{self.name}_comm);
if (__state->{self.name}_comm != MPI_COMM_NULL) {{
MPI_Comm_group(__state->{self.name}_comm, &__state->{self.name}_group);
MPI_Comm_rank(__state->{self.name}_comm, &__state->{self.name}_rank);
MPI_Comm_size(__state->{self.name}_comm, &__state->{self.name}_size);
MPI_Cart_coords(__state->{self.name}_comm, __state->{self.name}_rank, {len(self.shape)}, __state->{self.name}_coords);
__state->{self.name}_valid = true;
}} else {{
__state->{self.name}_group = MPI_GROUP_NULL;
__state->{self.name}_rank = MPI_PROC_NULL;
__state->{self.name}_size = 0;
__state->{self.name}_valid = false;
}}
"""
return tmp
def exit_code(self):
""" Outputs MPI deallocation code for the process-grid. """
return f"""
