def _getkey(self, grid, dtype, dimensions=None):
base = (configuration['platform'].isa, dtype)
if grid is not None:
dims = filter_sorted((grid.time_dim, grid.stepping_dim) + grid.dimensions)
return base + (tuple(dims),)
elif dimensions:
dims = filter_sorted([i for i in dimensions if i.is_Space])
return base + (tuple(dims),)
else:
return base + ((),)
def detect_io(exprs, relax=False):
"""
``{exprs} -> ({reads}, {writes})``
Parameters
----------
exprs : expr-like or list of expr-like
The searched expressions.
relax : bool, optional
If False, as by default, collect only Constants and Functions.
Otherwise, collect any Basic object.
"""
exprs = as_tuple(exprs)
if relax is False:
rule = lambda i: i.is_Input
else:
rule = lambda i: i.is_Scalar or i.is_Tensor
# Don't forget this nasty case, with indirections on the LHS:
# >>> u[t, a[x]] = f[x] -> (reads={a, f}, writes={u})
roots = []
for i in exprs:
try:
roots.append(i.rhs)
roots.extend(list(i.lhs.indices))
except AttributeError:
# E.g., FunctionFromPointer
roots.append(i)
reads = []
terminals = flatten(retrieve_terminals(i, deep=True) for i in roots)
for i in terminals:
candidates = i.free_symbols
try:
candidates.update({i.function})
except AttributeError:
pass
for j in candidates:
try:
if rule(j):
reads.append(j)
except AttributeError:
pass
writes = []
for i in exprs:
try:
f = i.lhs.function
except AttributeError:
continue
if rule(f):
writes.append(f)
return filter_sorted(reads), filter_sorted(writes)
def __repr__(self):
tracked = filter_sorted(set(self.reads) | set(self.writes), key=lambda i: i.name)
maxlen = max(1, max([len(i.name) for i in tracked]))
out = "{:>%d} => W : {}\n{:>%d} R : {}" % (maxlen, maxlen)
pad = " "*(maxlen + 9)
reads = [self.getreads(i) for i in tracked]
for i, r in enumerate(list(reads)):
if not r:
reads[i] = ''
continue
first = "%s" % tuple.__repr__(r[0])
shifted = "\n".join("%s%s" % (pad, tuple.__repr__(j)) for j in r[1:])
shifted = "%s%s" % ("\n" if shifted else "", shifted)
reads[i] = first + shifted
writes = [self.getwrites(i) for i in tracked]
for i, w in enumerate(list(writes)):
if not w:
writes[i] = ''
continue
first = "%s" % tuple.__repr__(w[0])
shifted = "\n".join("%s%s" % (pad, tuple.__repr__(j)) for j in w[1:])
shifted = "%s%s" % ("\n" if shifted else "", shifted)
writes[i] = '\033[1;37;31m%s\033[0m' % (first + shifted)
return "\n".join([out.format(i.name, w, '', r)
for i, r, w in zip(tracked, reads, writes)])
def visit_Operator(self, o):
# Kernel signature and body
body = flatten(self._visit(i) for i in o.children)
decls = self._args_decl(o.parameters)
signature = c.FunctionDeclaration(c.Value(o.retval, o.name), decls)
retval = [c.Statement("return 0")]
kernel = c.FunctionBody(signature, c.Block(body + retval))
# Elemental functions
esigns = []
efuncs = [blankline]
for i in o._func_table.values():
if i.local:
esigns.append(c.FunctionDeclaration(c.Value(i.root.retval, i.root.name),
self._args_decl(i.root.parameters)))
efuncs.extend([i.root.ccode, blankline])
# Header files, extra definitions, ...
header = [c.Line(i) for i in o._headers]
includes = [c.Include(i, system=False) for i in o._includes]
includes += [blankline]
cdefs = [i._C_typedecl for i in o.parameters if i._C_typedecl is not None]
cdefs = filter_sorted(cdefs, key=lambda i: i.tpname)
if o._compiler.src_ext == 'cpp':
cdefs += [c.Extern('C', signature)]
cdefs = [i for j in cdefs for i in (j, blankline)]
return c.Module(header + includes + cdefs +
esigns + [blankline, kernel] + efuncs)
def dimension_sort(expr):
"""
Topologically sort the :class:`Dimension`s in ``expr``, based on the order
in which they appear within :class:`Indexed`s.
"""
def handle_indexed(indexed):
relation = []
for i in indexed.indices:
try:
maybe_dim = split_affine(i).var
if isinstance(maybe_dim, Dimension):
relation.append(maybe_dim)
except ValueError:
# Maybe there are some nested Indexeds (e.g., the situation is A[B[i]])
nested = flatten(handle_indexed(n) for n in retrieve_indexed(i))
if nested:
relation.extend(nested)
else:
# Fallback: Just insert all the Dimensions we find, regardless of
# what the user is attempting to do
relation.extend([d for d in filter_sorted(i.free_symbols)
if isinstance(d, Dimension)])
return tuple(relation)
relations = {handle_indexed(i) for i in retrieve_indexed(expr, mode='all')}
# Add in any implicit dimension (typical of scalar temporaries, or Step)
relations.add(expr.implicit_dims)
# Add in leftover free dimensions (not an Indexed' index)
extra = set([i for i in expr.free_symbols if isinstance(i, Dimension)])
# Add in pure data dimensions (e.g., those accessed only via explicit values,
# such as A[3])
indexeds = retrieve_indexed(expr, deep=True)
extra.update(set().union(*[set(i.function.indices) for i in indexeds]))
# Enforce determinism
extra = filter_sorted(extra, key=attrgetter('name'))
# Add in implicit relations for parent dimensions
# -----------------------------------------------
# 1) Note that (d.parent, d) is what we want, while (d, d.parent) would be
# wrong; for example, in `((t, time), (t, x, y), (x, y))`, `x` could now
# preceed `time`, while `t`, and therefore `time`, *must* appear before `x`,
# as indicated by the second relation
implicit_relations = {(d.parent, d) for d in extra if d.is_Derived}
# 2) To handle cases such as `((time, xi), (x,))`, where `xi` a SubDimension
# of `x`, besides `(x, xi)`, we also have to add `(time, x)` so that we
# obtain the desired ordering `(time, x, xi)`. W/o `(time, x)`, the ordering
# `(x, time, xi)` might be returned instead, which would be non-sense
implicit_relations.update({tuple(d.root for d in i) for i in relations})
ordering = PartialOrderTuple(extra, relations=(relations | implicit_relations))
return ordering
def detect_io(exprs, relax=False):
"""``{exprs} -> ({reads}, {writes})
:param exprs: The expressions inspected.
:param relax: (Optional) if False, as by default, collect only
:class:`Constant`s and :class:`Function`s. Otherwise,
collect any :class:`Basic`s.
"""
exprs = as_tuple(exprs)
if relax is False:
rule = lambda i: i.is_Input
else:
rule = lambda i: i.is_Scalar or i.is_Tensor
reads = []
for i in flatten(retrieve_terminals(i, deep=True) for i in exprs):
candidates = i.free_symbols
try:
candidates.update({i.base.function})
except AttributeError:
pass
for j in candidates:
try:
if rule(j):
reads.append(j)
except AttributeError:
pass
writes = []
for i in exprs:
try:
f = i.lhs.base.function
except AttributeError:
continue
if rule(f):
writes.append(f)
return filter_sorted(reads), filter_sorted(writes)
def place_ondevice(self, iet, **kwargs):
efuncs = kwargs['efuncs']
storage = Storage()
if iet.is_ElementalFunction:
return iet, {}
# Collect written and read-only symbols
writes = set()
reads = set()
for efunc in efuncs:
for i, v in MapExprStmts().visit(efunc).items():
if not i.is_Expression:
# No-op
continue
if not any(
isinstance(j, self._Parallelizer._Iteration)
for j in v):
# Not an offloaded Iteration tree
continue
if i.write.is_DiscreteFunction:
writes.add(i.write)
reads = (reads | {r
for r in i.reads
if r.is_DiscreteFunction}) - writes
# Update `storage`
for i in filter_sorted(writes):
self._map_function_on_high_bw_mem(i, storage)
for i in filter_sorted(reads):
self._map_function_on_high_bw_mem(i, storage, read_only=True)
iet = self._dump_storage(iet, storage)
return iet, {}
def handle_indexed(indexed):
relation = []
for i in indexed.indices:
try:
maybe_dim = split_affine(i).var
if isinstance(maybe_dim, Dimension):
relation.append(maybe_dim)
except ValueError:
# Maybe there are some nested Indexeds (e.g., the situation is A[B[i]])
nested = flatten(handle_indexed(n) for n in retrieve_indexed(i))
if nested:
relation.extend(nested)
else:
# Fallback: Just insert all the Dimensions we find, regardless of
# what the user is attempting to do
relation.extend([d for d in filter_sorted(i.free_symbols)
if isinstance(d, Dimension)])
return tuple(relation)
def handle_indexed(indexed):
relation = []
for i in indexed.indices:
try:
maybe_dim = split_affine(i).var
if isinstance(maybe_dim, Dimension):
relation.append(maybe_dim)
except ValueError:
# Maybe there are some nested Indexeds (e.g., the situation is A[B[i]])
nested = flatten(handle_indexed(n) for n in retrieve_indexed(i))
if nested:
relation.extend(nested)
else:
# Fallback: Just insert all the Dimensions we find, regardless of
# what the user is attempting to do
relation.extend([d for d in filter_sorted(i.free_symbols)
if isinstance(d, Dimension)])
return tuple(relation)
def visit_Operator(self, o):
blankline = c.Line("")
# Kernel signature and body
body = flatten(self._visit(i) for i in o.children)
decls = self._args_decl(o.parameters)
signature = c.FunctionDeclaration(c.Value(o.retval, o.name), decls)
retval = [c.Line(), c.Statement("return 0")]
kernel = c.FunctionBody(signature, c.Block(body + retval))
# Elemental functions
esigns = []
efuncs = [blankline]
for i in o._func_table.values():
if i.local:
esigns.append(
c.FunctionDeclaration(c.Value(i.root.retval, i.root.name),
self._args_decl(i.root.parameters)))
efuncs.extend([i.root.ccode, blankline])
# Header files, extra definitions, ...
header = [c.Define(*i) for i in o._headers] + [blankline]
includes = [
c.Include(i, system=(False if i.endswith('.h') else True))
for i in o._includes
]
includes += [blankline]
cdefs = [
i._C_typedecl for i in o.parameters if i._C_typedecl is not None
]
for i in o._func_table.values():
if i.local:
cdefs.extend([
j._C_typedecl for j in i.root.parameters
if j._C_typedecl is not None
])
cdefs = filter_sorted(cdefs, key=lambda i: i.tpname)
if o._compiler.src_ext == 'cpp':
cdefs += [c.Extern('C', signature)]
cdefs = [i for j in cdefs for i in (j, blankline)]
return c.Module(header + includes + cdefs + esigns +
[blankline, kernel] + efuncs)
def __init__(self,
nodes,
dimension,
limits,
index=None,
offsets=None,
properties=None,
pragmas=None,
uindices=None):
# Ensure we deal with a list of Expression objects internally
nodes = as_tuple(nodes)
self.nodes = as_tuple(
[n if isinstance(n, Node) else Expression(n) for n in nodes])
assert all(isinstance(i, Node) for i in self.nodes)
self.dim = dimension
self.index = index or self.dim.name
# Store direction, as it might change on the dimension
# before we use it during code generation.
self.reverse = self.dim.reverse
# Generate loop limits
if isinstance(limits, Iterable):
assert (len(limits) == 3)
self.limits = list(limits)
else:
self.limits = list((0, limits, 1))
# Record offsets to later adjust loop limits accordingly
self.offsets = [0, 0]
for off in (offsets or {}):
self.offsets[0] = min(self.offsets[0], int(off))
self.offsets[1] = max(self.offsets[1], int(off))
# Track this Iteration's properties, pragmas and unbounded indices
properties = as_tuple(properties)
assert (i in IterationProperty._KNOWN for i in properties)
self.properties = as_tuple(
filter_sorted(properties, key=lambda i: i.name))
self.pragmas = as_tuple(pragmas)
self.uindices = as_tuple(uindices)
assert all(isinstance(i, UnboundedIndex) for i in self.uindices)
def mpiize(iet, **kwargs):
"""
Add MPI routines performing halo exchanges to emit distributed-memory
parallel code.
"""
mode = kwargs.pop('mode')
language = kwargs.pop('language')
sregistry = kwargs.pop('sregistry')
# To produce unique object names
generators = {'msg': generator(), 'comm': generator(), 'comp': generator()}
sync_heb = HaloExchangeBuilder('basic', language, sregistry, **generators)
user_heb = HaloExchangeBuilder(mode, language, sregistry, **generators)
mapper = {}
for hs in FindNodes(HaloSpot).visit(iet):
heb = user_heb if isinstance(hs, OverlappableHaloSpot) else sync_heb
mapper[hs] = heb.make(hs)
efuncs = sync_heb.efuncs + user_heb.efuncs
objs = filter_sorted(sync_heb.objs + user_heb.objs)
iet = Transformer(mapper, nested=True).visit(iet)
# Must drop the PARALLEL tag from the Iterations within which halo
# exchanges are performed
mapper = {}
for tree in retrieve_iteration_tree(iet):
for i in reversed(tree):
if i in mapper:
# Already seen this subtree, skip
break
if FindNodes(Call).visit(i):
mapper.update({
n: n._rebuild(properties=set(n.properties) - {PARALLEL})
for n in tree[:tree.index(i) + 1]
})
break
iet = Transformer(mapper, nested=True).visit(iet)
return iet, {'includes': ['mpi.h'], 'efuncs': efuncs, 'args': objs}
def __init__(self, nodes, dimension, limits, direction=None, properties=None,
pragmas=None, uindices=None):
self.nodes = as_tuple(nodes)
self.dim = dimension
self.index = self.dim.name
self.direction = direction or Forward
# Generate loop limits
if isinstance(limits, Iterable):
assert(len(limits) == 3)
self.limits = tuple(limits)
elif self.dim.is_Incr:
self.limits = (self.dim.symbolic_min, limits, self.dim.step)
else:
self.limits = (0, limits, 1)
# Track this Iteration's properties, pragmas and unbounded indices
properties = as_tuple(properties)
assert (i in Property._KNOWN for i in properties)
self.properties = as_tuple(filter_sorted(properties))
self.pragmas = as_tuple(pragmas)
self.uindices = as_tuple(uindices)
assert all(i.is_Derived and self.dim in i._defines for i in self.uindices)
def __init__(self,
nodes,
dimension,
limits,
index=None,
offsets=None,
direction=None,
properties=None,
pragmas=None,
uindices=None):
# Ensure we deal with a list of Expression objects internally
self.nodes = as_tuple(nodes)
self.dim = dimension
self.index = index or self.dim.name
self.direction = direction or Forward
# Generate loop limits
if isinstance(limits, Iterable):
assert (len(limits) == 3)
self.limits = tuple(limits)
else:
self.limits = (0, limits, 1)
# Record offsets to later adjust loop limits accordingly
self.offsets = (0, 0) if offsets is None else as_tuple(offsets)
assert len(self.offsets) == 2
# Track this Iteration's properties, pragmas and unbounded indices
properties = as_tuple(properties)
assert (i in IterationProperty._KNOWN for i in properties)
self.properties = as_tuple(
filter_sorted(properties, key=lambda i: i.name))
self.pragmas = as_tuple(pragmas)
self.uindices = as_tuple(uindices)
assert all(isinstance(i, UnboundedIndex) for i in self.uindices)
def _operator_typedecls(self, o, mode='all'):
if mode == 'all':
xfilter = lambda i: True
else:
public_types = (AbstractFunction, CompositeObject)
if mode == 'public':
xfilter = lambda i: isinstance(i, public_types)
else:
xfilter = lambda i: not isinstance(i, public_types)
typedecls = [
i._C_typedecl for i in o.parameters
if xfilter(i) and i._C_typedecl is not None
]
for i in o._func_table.values():
if not i.local:
continue
typedecls.extend([
j._C_typedecl for j in i.root.parameters
if xfilter(j) and j._C_typedecl is not None
])
typedecls = filter_sorted(typedecls, key=lambda i: i.tpname)
return typedecls
def cause(self):
ret = [i.cause for i in self if i.cause is not None]
return tuple(filter_sorted(ret, key=lambda i: i.name))
def _build(cls, expressions, **kwargs):
expressions = as_tuple(expressions)
# Input check
if any(not isinstance(i, Eq) for i in expressions):
raise InvalidOperator("Only `devito.Eq` expressions are allowed.")
name = kwargs.get("name", "Kernel")
dse = kwargs.get("dse", configuration['dse'])
# Python-level (i.e., compile time) and C-level (i.e., run time) performance
profiler = create_profile('timers')
# Lower input expressions to internal expressions (e.g., attaching metadata)
expressions = cls._lower_exprs(expressions, **kwargs)
# Group expressions based on their iteration space and data dependences
# Several optimizations are applied (fusion, lifting, flop reduction via DSE, ...)
clusters = clusterize(expressions, dse_mode=set_dse_mode(dse))
# Lower Clusters to a Schedule tree
stree = st_build(clusters)
# Lower Schedule tree to an Iteration/Expression tree (IET)
iet = iet_build(stree)
# Instrument the IET for C-level profiling
iet = profiler.instrument(iet)
# Wrap the IET with a Callable
parameters = derive_parameters(iet, True)
op = Callable(name, iet, 'int', parameters, ())
# Lower IET to a Target-specific IET
op, target_state = cls._specialize_iet(op, **kwargs)
# Make it an actual Operator
op = Callable.__new__(cls, **op.args)
Callable.__init__(op, **op.args)
# Header files, etc.
op._headers = list(cls._default_headers)
op._headers.extend(target_state.headers)
op._globals = list(cls._default_globals)
op._includes = list(cls._default_includes)
op._includes.extend(profiler._default_includes)
op._includes.extend(target_state.includes)
# Required for the jit-compilation
op._compiler = configuration['compiler']
op._lib = None
op._cfunction = None
# References to local or external routines
op._func_table = OrderedDict()
op._func_table.update(
OrderedDict([(i, MetaCall(None, False))
for i in profiler._ext_calls]))
op._func_table.update(
OrderedDict([(i.root.name, i) for i in target_state.funcs]))
# Internal state. May be used to store information about previous runs,
# autotuning reports, etc
op._state = cls._initialize_state(**kwargs)
# Produced by the various compilation passes
op._input = filter_sorted(
flatten(e.reads + e.writes for e in expressions))
op._output = filter_sorted(flatten(e.writes for e in expressions))
op._dimensions = filter_sorted(
flatten(e.dimensions for e in expressions))
op._dimensions.extend(target_state.dimensions)
op._dtype, op._dspace = clusters.meta
op._profiler = profiler
return op
def place_definitions(self, iet, **kwargs):
"""
Create a new IET with symbols allocated/deallocated in some memory space.
Parameters
----------
iet : Callable
The input Iteration/Expression tree.
"""
storage = Storage()
# Collect and declare symbols
for k, v in MapExprStmts().visit(iet).items():
if k.is_Expression:
if k.is_definition:
site = v[-1] if v else iet
self._alloc_scalar_on_low_lat_mem(site, k, storage)
continue
objs = [k.write]
elif k.is_Call:
objs = k.arguments
for i in objs:
try:
if i.is_LocalObject:
site = v[-1] if v else iet
self._alloc_object_on_low_lat_mem(site, i, storage)
elif i.is_Array:
if i in iet.parameters:
# The Array is passed as a Callable argument
continue
elif i._mem_stack:
self._alloc_array_on_low_lat_mem(iet, i, storage)
else:
self._alloc_array_on_high_bw_mem(i, storage)
except AttributeError:
# E.g., a generic SymPy expression
pass
# Place symbols in a memory space
if not iet.is_ElementalFunction:
writes = set()
reads = set()
for efunc in kwargs.get('efuncs', []):
for i in FindNodes(Expression).visit(efunc):
if i.write.is_Function:
writes.add(i.write)
reads = (reads
| {r
for r in i.reads if r.is_Function}) - writes
for i in filter_sorted(writes):
self._map_function_on_high_bw_mem(i, storage)
for i in filter_sorted(reads):
self._map_function_on_high_bw_mem(i, storage, read_only=True)
# Introduce symbol definitions going in the low latency memory
mapper = dict(storage._on_low_lat_mem)
iet = Transformer(mapper, nested=True).visit(iet)
# Introduce symbol definitions going in the high bandwidth memory
header = []
footer = []
for decl, alloc, free in storage._on_high_bw_mem:
if decl is None:
header.append(alloc)
else:
header.extend([decl, alloc])
footer.append(free)
if header or footer:
body = List(header=header, body=iet.body, footer=footer)
iet = iet._rebuild(body=body)
return iet, {}
def dimension_sort(expr):
"""
Topologically sort the Dimensions in ``expr``, based on the order in which they
appear within Indexeds.
"""
def handle_indexed(indexed):
relation = []
for i in indexed.indices:
try:
maybe_dim = split_affine(i).var
if isinstance(maybe_dim, Dimension):
relation.append(maybe_dim)
except ValueError:
# Maybe there are some nested Indexeds (e.g., the situation is A[B[i]])
nested = flatten(
handle_indexed(n) for n in retrieve_indexed(i))
if nested:
relation.extend(nested)
else:
# Fallback: Just insert all the Dimensions we find, regardless of
# what the user is attempting to do
relation.extend([
d for d in filter_sorted(i.free_symbols)
if isinstance(d, Dimension)
])
return tuple(relation)
if isinstance(expr.implicit_dims, IgnoreDimSort):
relations = set()
else:
relations = {handle_indexed(i) for i in retrieve_indexed(expr)}
# Add in any implicit dimension (typical of scalar temporaries, or Step)
relations.add(expr.implicit_dims)
# Add in leftover free dimensions (not an Indexed' index)
extra = set([i for i in expr.free_symbols if isinstance(i, Dimension)])
# Add in pure data dimensions (e.g., those accessed only via explicit values,
# such as A[3])
indexeds = retrieve_indexed(expr, deep=True)
extra.update(set().union(*[set(i.function.dimensions) for i in indexeds]))
# Enforce determinism
extra = filter_sorted(extra, key=attrgetter('name'))
# Add in implicit relations for parent dimensions
# -----------------------------------------------
# 1) Note that (d.parent, d) is what we want, while (d, d.parent) would be
# wrong; for example, in `((t, time), (t, x, y), (x, y))`, `x` could now
# preceed `time`, while `t`, and therefore `time`, *must* appear before `x`,
# as indicated by the second relation
implicit_relations = {(d.parent, d) for d in extra if d.is_Derived}
# 2) To handle cases such as `((time, xi), (x,))`, where `xi` a SubDimension
# of `x`, besides `(x, xi)`, we also have to add `(time, x)` so that we
# obtain the desired ordering `(time, x, xi)`. W/o `(time, x)`, the ordering
# `(x, time, xi)` might be returned instead, which would be non-sense
implicit_relations.update({tuple(d.root for d in i) for i in relations})
ordering = PartialOrderTuple(extra,
relations=(relations | implicit_relations))
return ordering
def _specialize_iet(self, iet, **kwargs):
warning("The OPS backend is still work-in-progress")
affine_trees = find_affine_trees(iet).items()
# If there is no affine trees, then there is no loop to be optimized using OPS.
if not affine_trees:
return iet
ops_init = Call(namespace['ops_init'], [0, 0, 2])
ops_partition = Call(namespace['ops_partition'], Literal('""'))
ops_exit = Call(namespace['ops_exit'])
# Extract all symbols that need to be converted to ops_dat
dims = []
to_dat = set()
for _, tree in affine_trees:
dims.append(len(tree[0].dimensions))
symbols = set(FindSymbols('symbolics').visit(tree[0].root))
symbols -= set(FindSymbols('defines').visit(tree[0].root))
to_dat |= symbols
# Create the OPS block for this problem
ops_block = OpsBlock('block')
ops_block_init = Expression(
ClusterizedEq(
Eq(ops_block,
namespace['ops_decl_block'](dims[0], Literal('"block"')))))
# To ensure deterministic code generation we order the datasets to
# be generated (since a set is an unordered collection)
to_dat = filter_sorted(to_dat)
name_to_ops_dat = {}
pre_time_loop = []
after_time_loop = []
for f in to_dat:
if f.is_Constant:
continue
pre_time_loop.extend(
list(create_ops_dat(f, name_to_ops_dat, ops_block)))
# To return the result to Devito, it is necessary to copy the data
# from the dat object back to the CPU memory.
after_time_loop.extend(
create_ops_fetch(f, name_to_ops_dat,
self.time_dimension.extreme_max))
# Generate ops kernels for each offloadable iteration tree
mapper = {}
for n, (_, tree) in enumerate(affine_trees):
pre_loop, ops_kernel, ops_par_loop_call = opsit(
tree, n, name_to_ops_dat, ops_block, dims[0])
pre_time_loop.extend(pre_loop)
self._func_table[namespace['ops_kernel_file'](ops_kernel.name)] = \
MetaCall(ops_kernel, False)
mapper[tree[0].root] = ops_par_loop_call
mapper.update({i.root: mapper.get(i.root)
for i in tree}) # Drop trees
iet = Transformer(mapper).visit(iet)
assert (d == dims[0] for d in dims), \
"The OPS backend currently assumes that all kernels \
have the same number of dimensions"
self._headers.append(namespace['ops_define_dimension'](dims[0]))
self._includes.extend(['stdio.h', 'ops_seq.h'])
body = [
ops_init, ops_block_init, *pre_time_loop, ops_partition, iet,
*after_time_loop, ops_exit
]
return List(body=body)
def _make_fetchprefetch(self, iet, sync_ops, pieces, root):
fid = SharedData._field_id
fetches = []
prefetches = []
presents = []
for s in sync_ops:
f = s.function
dimensions = s.dimensions
fc = s.fetch
ifc = s.ifetch
pfc = s.pfetch
fcond = s.fcond
pcond = s.pcond
# Construct init IET
imask = [(ifc, s.size) if d.root is s.dim.root else FULL
for d in dimensions]
fetch = PragmaTransfer(self.lang._map_to, f, imask=imask)
fetches.append(Conditional(fcond, fetch))
# Construct present clauses
imask = [(fc, s.size) if d.root is s.dim.root else FULL
for d in dimensions]
presents.append(
PragmaTransfer(self.lang._map_present, f, imask=imask))
# Construct prefetch IET
imask = [(pfc, s.size) if d.root is s.dim.root else FULL
for d in dimensions]
prefetch = PragmaTransfer(self.lang._map_to_wait,
f,
imask=imask,
queueid=fid)
prefetches.append(Conditional(pcond, prefetch))
# Turn init IET into a Callable
functions = filter_ordered(s.function for s in sync_ops)
name = self.sregistry.make_name(prefix='init_device')
body = List(body=fetches)
parameters = filter_sorted(functions + derive_parameters(body))
func = Callable(name, body, 'void', parameters, 'static')
pieces.funcs.append(func)
# Perform initial fetch by the main thread
pieces.init.append(
List(header=c.Comment("Initialize data stream"),
body=[Call(name, parameters), BlankLine]))
# Turn prefetch IET into a ThreadFunction
name = self.sregistry.make_name(prefix='prefetch_host_to_device')
body = List(header=c.Line(), body=prefetches)
tctx = make_thread_ctx(name, body, root, None, sync_ops,
self.sregistry)
pieces.funcs.extend(tctx.funcs)
# Glue together all the IET pieces, including the activation logic
sdata = tctx.sdata
threads = tctx.threads
iet = List(body=[
BlankLine,
BusyWait(
CondNe(
FieldFromComposite(sdata._field_flag, sdata[
threads.index]), 1))
] + presents + [iet, tctx.activate])
# Fire up the threads
pieces.init.append(tctx.init)
# Final wait before jumping back to Python land
pieces.finalize.append(tctx.finalize)
# Keep track of created objects
pieces.objs.add(sync_ops, sdata, threads)
return iet
def visit_Expression(self, o):
return filter_sorted([f for f in self.rule(o)], key=attrgetter('name'))
def visit_tuple(self, o):
symbols = flatten([self._visit(i) for i in o])
return filter_sorted(symbols, key=attrgetter('name'))
def visit_Expression(self, o):
return filter_sorted([f for f in self.rule(o)], key=attrgetter('name'))
def visit_Iteration(self, o):
symbols = flatten([self._visit(i) for i in o.children])
symbols += self.rule(o)
return filter_sorted(symbols, key=attrgetter('name'))
def visit_tuple(self, o):
symbols = flatten([self._visit(i) for i in o])
return filter_sorted(symbols, key=attrgetter('name'))
def _make_fetchwaitprefetch(self, iet, sync_ops, pieces, root):
threads = self.__make_threads()
fetches = []
prefetches = []
presents = []
for s in sync_ops:
if s.direction is Forward:
fc = s.fetch.subs(s.dim, s.dim.symbolic_min)
fsize = s.function._C_get_field(FULL, s.dim).size
fc_cond = fc + (s.size - 1) < fsize
pfc = s.fetch + 1
pfc_cond = pfc + (s.size - 1) < fsize
else:
fc = s.fetch.subs(s.dim, s.dim.symbolic_max)
fc_cond = fc >= 0
pfc = s.fetch - 1
pfc_cond = pfc >= 0
# Construct fetch IET
imask = [(fc, s.size) if d.root is s.dim.root else FULL
for d in s.dimensions]
fetch = List(header=self._P._map_to(s.function, imask))
fetches.append(Conditional(fc_cond, fetch))
# Construct present clauses
imask = [(s.fetch, s.size) if d.root is s.dim.root else FULL
for d in s.dimensions]
presents.extend(as_list(self._P._map_present(s.function, imask)))
# Construct prefetch IET
imask = [(pfc, s.size) if d.root is s.dim.root else FULL
for d in s.dimensions]
prefetch = List(header=self._P._map_to_wait(
s.function, imask, SharedData._field_id))
prefetches.append(Conditional(pfc_cond, prefetch))
functions = filter_ordered(s.function for s in sync_ops)
casts = [PointerCast(f) for f in functions]
# Turn init IET into a Callable
name = self.sregistry.make_name(prefix='init_device')
body = List(body=casts + fetches)
parameters = filter_sorted(functions + derive_parameters(body))
func = Callable(name, body, 'void', parameters, 'static')
pieces.funcs.append(func)
# Perform initial fetch by the main thread
pieces.init.append(
List(header=c.Comment("Initialize data stream for `%s`" %
threads.name),
body=[Call(name, func.parameters), BlankLine]))
# Turn prefetch IET into a threaded Callable
name = self.sregistry.make_name(prefix='prefetch_host_to_device')
body = List(header=c.Line(), body=casts + prefetches)
tfunc, sdata = self.__make_tfunc(name, body, root, threads)
pieces.funcs.append(tfunc)
# Glue together all the IET pieces, including the activation bits
iet = List(body=[
BlankLine,
BusyWait(
CondNe(
FieldFromComposite(sdata._field_flag, sdata[
threads.index]), 1)),
List(header=presents), iet,
self.__make_activate_thread(threads, sdata, sync_ops)
])
# Fire up the threads
pieces.init.append(
self.__make_init_threads(threads, sdata, tfunc, pieces))
pieces.threads.append(threads)
# Final wait before jumping back to Python land
pieces.finalize.append(self.__make_finalize_threads(threads, sdata))
return iet
def make_ops_kernels(iet):
warning("The OPS backend is still work-in-progress")
affine_trees = find_affine_trees(iet).items()
# If there is no affine trees, then there is no loop to be optimized using OPS.
if not affine_trees:
return iet, {}
ops_init = Call(namespace['ops_init'], [0, 0, 2])
ops_partition = Call(namespace['ops_partition'], Literal('""'))
ops_exit = Call(namespace['ops_exit'])
# Extract all symbols that need to be converted to ops_dat
dims = []
to_dat = set()
for _, tree in affine_trees:
dims.append(len(tree[0].dimensions))
symbols = set(FindSymbols('symbolics').visit(tree[0].root))
symbols -= set(FindSymbols('defines').visit(tree[0].root))
to_dat |= symbols
# Create the OPS block for this problem
ops_block = OpsBlock('block')
ops_block_init = Expression(
ClusterizedEq(
Eq(ops_block, namespace['ops_decl_block'](dims[0],
Literal('"block"')))))
# To ensure deterministic code generation we order the datasets to
# be generated (since a set is an unordered collection)
to_dat = filter_sorted(to_dat)
name_to_ops_dat = {}
pre_time_loop = []
after_time_loop = []
for f in to_dat:
if f.is_Constant:
continue
pre_time_loop.extend(
list(create_ops_dat(f, name_to_ops_dat, ops_block)))
# Copy data from device to host
after_time_loop.extend(
create_ops_fetch(f, name_to_ops_dat, f.grid.time_dim.extreme_max))
# Generate ops kernels for each offloadable iteration tree
mapper = {}
ffuncs = []
for n, (_, tree) in enumerate(affine_trees):
pre_loop, ops_kernel, ops_par_loop_call = opsit(
tree, n, name_to_ops_dat, ops_block, dims[0])
pre_time_loop.extend(pre_loop)
ffuncs.append(ops_kernel)
mapper[tree[0].root] = ops_par_loop_call
mapper.update({i.root: mapper.get(i.root) for i in tree}) # Drop trees
iet = Transformer(mapper).visit(iet)
assert (d == dims[0] for d in dims), \
"The OPS backend currently assumes that all kernels \
have the same number of dimensions"
iet = iet._rebuild(body=flatten([
ops_init, ops_block_init, pre_time_loop, ops_partition, iet.body,
after_time_loop, ops_exit
]))
return iet, {
'includes': ['stdio.h', 'ops_seq.h'],
'ffuncs': ffuncs,
'headers': [namespace['ops_define_dimension'](dims[0])]
}
def _build(cls, expressions, **kwargs):
expressions = as_tuple(expressions)
# Input check
if any(not isinstance(i, Evaluable) for i in expressions):
raise InvalidOperator("Only `devito.Evaluable` are allowed.")
# Python-level (i.e., compile time) and C-level (i.e., run time) performance
profiler = create_profile('timers')
# Lower input expressions
expressions = cls._lower_exprs(expressions, **kwargs)
# Group expressions based on iteration spaces and data dependences
clusters = cls._lower_clusters(expressions, profiler, **kwargs)
# Lower Clusters to a ScheduleTree
stree = cls._lower_stree(clusters, **kwargs)
# Lower ScheduleTree to an Iteration/Expression Tree
iet, byproduct = cls._lower_iet(stree, profiler, **kwargs)
# Make it an actual Operator
op = Callable.__new__(cls, **iet.args)
Callable.__init__(op, **op.args)
# Header files, etc.
op._headers = list(cls._default_headers)
op._headers.extend(byproduct.headers)
op._globals = list(cls._default_globals)
op._includes = list(cls._default_includes)
op._includes.extend(profiler._default_includes)
op._includes.extend(byproduct.includes)
# Required for the jit-compilation
op._compiler = kwargs['compiler']
op._lib = None
op._cfunction = None
# References to local or external routines
op._func_table = OrderedDict()
op._func_table.update(
OrderedDict([(i, MetaCall(None, False))
for i in profiler._ext_calls]))
op._func_table.update(
OrderedDict([(i.root.name, i) for i in byproduct.funcs]))
# Internal state. May be used to store information about previous runs,
# autotuning reports, etc
op._state = cls._initialize_state(**kwargs)
# Produced by the various compilation passes
op._input = filter_sorted(
flatten(e.reads + e.writes for e in expressions))
op._output = filter_sorted(flatten(e.writes for e in expressions))
op._dimensions = flatten(c.dimensions
for c in clusters) + byproduct.dimensions
op._dimensions = sorted(set(op._dimensions), key=attrgetter('name'))
op._dtype, op._dspace = clusters.meta
op._profiler = profiler
return op
def detect_io(exprs, relax=False):
"""
``{exprs} -> ({reads}, {writes})``
Parameters
----------
exprs : expr-like or list of expr-like
The searched expressions.
relax : bool, optional
If False, as by default, collect only Constants and Functions.
Otherwise, collect any Basic object.
"""
exprs = as_tuple(exprs)
if relax is False:
rule = lambda i: i.is_Input
else:
rule = lambda i: i.is_Scalar or i.is_Tensor
# Don't forget the nasty case with indirections on the LHS:
# >>> u[t, a[x]] = f[x] -> (reads={a, f}, writes={u})
roots = []
for i in exprs:
try:
roots.append(i.rhs)
roots.extend(list(i.lhs.indices))
roots.extend(list(i.conditionals.values()))
except AttributeError:
# E.g., FunctionFromPointer
roots.append(i)
reads = []
terminals = flatten(retrieve_terminals(i, deep=True) for i in roots)
for i in terminals:
candidates = set(i.free_symbols)
try:
candidates.update({i.function})
except AttributeError:
pass
for j in candidates:
try:
if rule(j):
reads.append(j)
except AttributeError:
pass
writes = []
for i in exprs:
try:
f = i.lhs.function
except AttributeError:
continue
try:
if rule(f):
writes.append(f)
except AttributeError:
# We only end up here after complex IET transformations which make
# use of composite types
assert isinstance(i.lhs, FunctionFromPointer)
f = i.lhs.base.function
if rule(f):
writes.append(f)
return filter_sorted(reads), filter_sorted(writes)
def __init__(self, expressions, **kwargs):
expressions = as_tuple(expressions)
# Input check
if any(not isinstance(i, Eq) for i in expressions):
raise InvalidOperator("Only `devito.Eq` expressions are allowed.")
self.name = kwargs.get("name", "Kernel")
subs = kwargs.get("subs", {})
dse = kwargs.get("dse", configuration['dse'])
# Header files, etc.
self._headers = list(self._default_headers)
self._includes = list(self._default_includes)
self._globals = list(self._default_globals)
# Required for compilation
self._compiler = configuration['compiler']
self._lib = None
self._cfunction = None
# References to local or external routines
self._func_table = OrderedDict()
# Internal state. May be used to store information about previous runs,
# autotuning reports, etc
self._state = {}
# Form and gather any required implicit expressions
expressions = self._add_implicit(expressions)
# Expression lowering: indexification, substitution rules, specialization
expressions = [indexify(i) for i in expressions]
expressions = self._apply_substitutions(expressions, subs)
expressions = self._specialize_exprs(expressions)
# Expression analysis
self._input = filter_sorted(flatten(e.reads + e.writes for e in expressions))
self._output = filter_sorted(flatten(e.writes for e in expressions))
self._dimensions = filter_sorted(flatten(e.dimensions for e in expressions))
# Group expressions based on their iteration space and data dependences,
# and apply the Devito Symbolic Engine (DSE) for flop optimization
clusters = clusterize(expressions)
clusters = rewrite(clusters, mode=set_dse_mode(dse))
self._dtype, self._dspace = clusters.meta
# Lower Clusters to a Schedule tree
stree = st_build(clusters)
# Lower Schedule tree to an Iteration/Expression tree (IET)
iet = iet_build(stree)
iet, self._profiler = self._profile_sections(iet)
iet = self._specialize_iet(iet, **kwargs)
# Derive all Operator parameters based on the IET
parameters = derive_parameters(iet, True)
# Finalization: introduce declarations, type casts, etc
iet = self._finalize(iet, parameters)
super(Operator, self).__init__(self.name, iet, 'int', parameters, ())
def _make_fetchwaitprefetch(self, iet, sync_ops, pieces, root):
fetches = []
prefetches = []
presents = []
for s in sync_ops:
if s.direction is Forward:
fc = s.fetch.subs(s.dim, s.dim.symbolic_min)
pfc = s.fetch + 1
fc_cond = s.next_cbk(s.dim.symbolic_min)
pfc_cond = s.next_cbk(s.dim + 1)
else:
fc = s.fetch.subs(s.dim, s.dim.symbolic_max)
pfc = s.fetch - 1
fc_cond = s.next_cbk(s.dim.symbolic_max)
pfc_cond = s.next_cbk(s.dim - 1)
# Construct init IET
imask = [(fc, s.size) if d.root is s.dim.root else FULL for d in s.dimensions]
fetch = PragmaList(self.lang._map_to(s.function, imask),
{s.function} | fc.free_symbols)
fetches.append(Conditional(fc_cond, fetch))
# Construct present clauses
imask = [(s.fetch, s.size) if d.root is s.dim.root else FULL
for d in s.dimensions]
presents.extend(as_list(self.lang._map_present(s.function, imask)))
# Construct prefetch IET
imask = [(pfc, s.size) if d.root is s.dim.root else FULL
for d in s.dimensions]
prefetch = PragmaList(self.lang._map_to_wait(s.function, imask,
SharedData._field_id),
{s.function} | pfc.free_symbols)
prefetches.append(Conditional(pfc_cond, prefetch))
# Turn init IET into a Callable
functions = filter_ordered(s.function for s in sync_ops)
name = self.sregistry.make_name(prefix='init_device')
body = List(body=fetches)
parameters = filter_sorted(functions + derive_parameters(body))
func = Callable(name, body, 'void', parameters, 'static')
pieces.funcs.append(func)
# Perform initial fetch by the main thread
pieces.init.append(List(
header=c.Comment("Initialize data stream"),
body=[Call(name, parameters), BlankLine]
))
# Turn prefetch IET into a ThreadFunction
name = self.sregistry.make_name(prefix='prefetch_host_to_device')
body = List(header=c.Line(), body=prefetches)
tctx = make_thread_ctx(name, body, root, None, sync_ops, self.sregistry)
pieces.funcs.extend(tctx.funcs)
# Glue together all the IET pieces, including the activation logic
sdata = tctx.sdata
threads = tctx.threads
iet = List(body=[
BlankLine,
BusyWait(CondNe(FieldFromComposite(sdata._field_flag,
sdata[threads.index]), 1)),
List(header=presents),
iet,
tctx.activate
])
# Fire up the threads
pieces.init.append(tctx.init)
pieces.threads.append(threads)
# Final wait before jumping back to Python land
pieces.finalize.append(tctx.finalize)
return iet
def _create_elemental_functions(self, nodes, state):
"""
Extract :class:`Iteration` sub-trees and move them into :class:`Callable`s.
Currently, only tagged, elementizable Iteration objects are targeted.
"""
noinline = self._compiler_decoration('noinline',
c.Comment('noinline?'))
functions = OrderedDict()
mapper = {}
for tree in retrieve_iteration_tree(nodes, mode='superset'):
# Search an elementizable sub-tree (if any)
tagged = filter_iterations(tree, lambda i: i.tag is not None,
'asap')
if not tagged:
continue
root = tagged[0]
if not root.is_Elementizable:
continue
target = tree[tree.index(root):]
# Elemental function arguments
args = [] # Found so far (scalars, tensors)
maybe_required = set() # Scalars that *may* have to be passed in
not_required = set() # Elemental function locally declared scalars
# Build a new Iteration/Expression tree with free bounds
free = []
for i in target:
name, bounds = i.dim.name, i.bounds_symbolic
# Iteration bounds
start = Scalar(name='%s_start' % name, dtype=np.int32)
finish = Scalar(name='%s_finish' % name, dtype=np.int32)
args.extend(zip([ccode(j) for j in bounds], (start, finish)))
# Iteration unbounded indices
ufunc = [
Scalar(name='%s_ub%d' % (name, j), dtype=np.int32)
for j in range(len(i.uindices))
]
args.extend(zip([ccode(j.start) for j in i.uindices], ufunc))
limits = [Symbol(start.name), Symbol(finish.name), 1]
uindices = [
UnboundedIndex(j.index, i.dim + as_symbol(k))
for j, k in zip(i.uindices, ufunc)
]
free.append(
i._rebuild(limits=limits, offsets=None, uindices=uindices))
not_required.update({i.dim}, set(j.index for j in i.uindices))
# Construct elemental function body, and inspect it
free = NestedTransformer(dict((zip(target, free)))).visit(root)
expressions = FindNodes(Expression).visit(free)
fsymbols = FindSymbols('symbolics').visit(free)
# Add all definitely-required arguments
not_required.update({i.output for i in expressions if i.is_scalar})
for i in fsymbols:
if i in not_required:
continue
elif i.is_Array:
args.append(
("(%s*)%s" % (c.dtype_to_ctype(i.dtype), i.name), i))
elif i.is_TensorFunction:
args.append(("%s_vec" % i.name, i))
elif i.is_Scalar:
args.append((i.name, i))
# Add all maybe-required arguments that turn out to be required
maybe_required.update(
set(FindSymbols(mode='free-symbols').visit(free)))
for i in fsymbols:
not_required.update({as_symbol(i), i.indexify()})
for j in i.symbolic_shape:
maybe_required.update(j.free_symbols)
required = filter_sorted(maybe_required - not_required,
key=attrgetter('name'))
args.extend([(i.name, Scalar(name=i.name, dtype=i.dtype))
for i in required])
call, params = zip(*args)
handle = flatten([p.rtargs for p in params])
name = "f_%d" % root.tag
# Produce the new Call
mapper[root] = List(header=noinline, body=Call(name, call))
# Produce the new Callable
functions.setdefault(
name, Callable(name, free, 'void', handle, ('static', )))
# Transform the main tree
processed = Transformer(mapper).visit(nodes)
return processed, {'elemental_functions': functions.values()}
def __init__(self, expressions, **kwargs):
expressions = as_tuple(expressions)
# Input check
if any(not isinstance(i, sympy.Eq) for i in expressions):
raise InvalidOperator("Only SymPy expressions are allowed.")
self.name = kwargs.get("name", "Kernel")
subs = kwargs.get("subs", {})
dse = kwargs.get("dse", configuration['dse'])
# Header files, etc.
self._headers = list(self._default_headers)
self._includes = list(self._default_includes)
self._globals = list(self._default_globals)
# Required for compilation
self._compiler = configuration['compiler']
self._lib = None
self._cfunction = None
# References to local or external routines
self.func_table = OrderedDict()
# Expression lowering: indexification, substitution rules, specialization
expressions = [indexify(i) for i in expressions]
expressions = [i.xreplace(subs) for i in expressions]
expressions = self._specialize_exprs(expressions)
# Expression analysis
self.input = filter_sorted(flatten(e.reads for e in expressions))
self.output = filter_sorted(flatten(e.writes for e in expressions))
self.dimensions = filter_sorted(flatten(e.dimensions for e in expressions))
# Group expressions based on their iteration space and data dependences,
# and apply the Devito Symbolic Engine (DSE) for flop optimization
clusters = clusterize(expressions)
clusters = rewrite(clusters, mode=set_dse_mode(dse))
self._dtype, self._dspace = clusters.meta
# Lower Clusters to a Schedule tree
stree = schedule(clusters)
stree = section(stree)
# Lower Sections to an Iteration/Expression tree (IET)
iet = iet_build(stree)
# Insert code for C-level performance profiling
iet, self.profiler = self._profile_sections(iet)
# Translate into backend-specific representation
iet = self._specialize_iet(iet, **kwargs)
# Insert the required symbol declarations
iet = iet_insert_C_decls(iet, self.func_table)
# Insert data and pointer casts for array parameters and profiling structs
iet = self._build_casts(iet)
# Derive parameters as symbols not defined in the kernel itself
parameters = self._build_parameters(iet)
# Finish instantiation
super(Operator, self).__init__(self.name, iet, 'int', parameters, ())
def visit_Call(self, o):
symbols = self._visit(o.children)
symbols.extend([f for f in self.rule(o)])
return filter_sorted(symbols, key=attrgetter('name'))
def _specialize_iet(self, iet, **kwargs):
warning("The OPS backend is still work-in-progress")
ops_init = Call(namespace['ops_init'], [0, 0, 2])
ops_partition = Call(namespace['ops_partition'], Literal('""'))
ops_exit = Call(namespace['ops_exit'])
# Extract all symbols that need to be converted to ops_dat
dims = []
to_dat = set()
for section, trees in find_affine_trees(iet).items():
dims.append(len(trees[0].dimensions))
symbols = set(FindSymbols('symbolics').visit(trees[0].root))
symbols -= set(FindSymbols('defines').visit(trees[0].root))
to_dat |= symbols
# Create the OPS block for this problem
ops_block = OpsBlock('block')
ops_block_init = Expression(
ClusterizedEq(
Eq(ops_block,
namespace['ops_decl_block'](dims[0], Literal('"block"')))))
# To ensure deterministic code generation we order the datasets to
# be generated (since a set is an unordered collection)
to_dat = filter_sorted(to_dat)
name_to_ops_dat = {}
pre_time_loop = []
for f in to_dat:
if f.is_Constant:
continue
pre_time_loop.extend(create_ops_dat(f, name_to_ops_dat, ops_block))
# Generate ops kernels for each offloadable iteration tree
mapper = {}
for n, (section, trees) in enumerate(find_affine_trees(iet).items()):
pre_loop, ops_kernel, ops_par_loop_call = opsit(
trees, n, name_to_ops_dat, ops_block, dims[0])
pre_time_loop.extend(pre_loop)
self._ops_kernels.append(ops_kernel)
mapper[trees[0].root] = ops_par_loop_call
mapper.update({i.root: mapper.get(i.root)
for i in trees}) # Drop trees
iet = Transformer(mapper).visit(iet)
assert (d == dims[0] for d in dims), \
"The OPS backend currently assumes that all kernels \
have the same number of dimensions"
self._headers.append(namespace['ops_define_dimension'](dims[0]))
self._includes.append('stdio.h')
body = [
ops_init, ops_block_init, *pre_time_loop, ops_partition, iet,
ops_exit
]
return List(body=body)
def visit_Iteration(self, o):
symbols = flatten([self._visit(i) for i in o.children])
symbols += self.rule(o)
return filter_sorted(symbols, key=attrgetter('name'))
def cause(self):
ret = [i.cause for i in self if i.cause is not None]
ret.extend([i.parent for i in ret if i.is_Derived])
return tuple(filter_sorted(ret, key=lambda i: i.name))
def __init__(self, expressions, **kwargs):
expressions = as_tuple(expressions)
# Input check
if any(not isinstance(i, Eq) for i in expressions):
raise InvalidOperator("Only `devito.Eq` expressions are allowed.")
self.name = kwargs.get("name", "Kernel")
subs = kwargs.get("subs", {})
dse = kwargs.get("dse", configuration['dse'])
# Header files, etc.
self._headers = list(self._default_headers)
self._includes = list(self._default_includes)
self._globals = list(self._default_globals)
# Required for compilation
self._compiler = configuration['compiler']
self._lib = None
self._cfunction = None
# References to local or external routines
self._func_table = OrderedDict()
# Internal state. May be used to store information about previous runs,
# autotuning reports, etc
self._state = self._initialize_state(**kwargs)
# Form and gather any required implicit expressions
expressions = self._add_implicit(expressions)
# Expression lowering: evaluation of derivatives, indexification,
# substitution rules, specialization
expressions = [i.evaluate for i in expressions]
expressions = [indexify(i) for i in expressions]
expressions = self._apply_substitutions(expressions, subs)
expressions = self._specialize_exprs(expressions)
# Expression analysis
self._input = filter_sorted(
flatten(e.reads + e.writes for e in expressions))
self._output = filter_sorted(flatten(e.writes for e in expressions))
self._dimensions = filter_sorted(
flatten(e.dimensions for e in expressions))
# Group expressions based on their iteration space and data dependences,
# and apply the Devito Symbolic Engine (DSE) for flop optimization
clusters = clusterize(expressions)
clusters = rewrite(clusters, mode=set_dse_mode(dse))
self._dtype, self._dspace = clusters.meta
# Lower Clusters to a Schedule tree
stree = st_build(clusters)
# Lower Schedule tree to an Iteration/Expression tree (IET)
iet = iet_build(stree)
iet, self._profiler = self._profile_sections(iet)
iet = self._specialize_iet(iet, **kwargs)
# Derive all Operator parameters based on the IET
parameters = derive_parameters(iet, True)
# Finalization: introduce declarations, type casts, etc
iet = self._finalize(iet, parameters)
super(Operator, self).__init__(self.name, iet, 'int', parameters, ())
def __init__(self, expressions, **kwargs):
expressions = as_tuple(expressions)
# Input check
if any(not isinstance(i, sympy.Eq) for i in expressions):
raise InvalidOperator("Only SymPy expressions are allowed.")
self.name = kwargs.get("name", "Kernel")
subs = kwargs.get("subs", {})
dse = kwargs.get("dse", configuration['dse'])
dle = kwargs.get("dle", configuration['dle'])
# Header files, etc.
self._headers = list(self._default_headers)
self._includes = list(self._default_includes)
self._globals = list(self._default_globals)
# Required for compilation
self._compiler = configuration['compiler']
self._lib = None
self._cfunction = None
# References to local or external routines
self.func_table = OrderedDict()
# Expression lowering: indexification, substitution rules, specialization
expressions = [indexify(i) for i in expressions]
expressions = [i.xreplace(subs) for i in expressions]
expressions = self._specialize_exprs(expressions)
# Expression analysis
self.input = filter_sorted(flatten(e.reads for e in expressions))
self.output = filter_sorted(flatten(e.writes for e in expressions))
self.dimensions = filter_sorted(flatten(e.dimensions for e in expressions))
# Group expressions based on their iteration space and data dependences,
# and apply the Devito Symbolic Engine (DSE) for flop optimization
clusters = clusterize(expressions)
clusters = rewrite(clusters, mode=set_dse_mode(dse))
self._dtype, self._dspace = clusters.meta
# Lower Clusters to an Iteration/Expression tree (IET)
nodes = iet_build(clusters)
# Introduce C-level profiling infrastructure
nodes, self.profiler = self._profile_sections(nodes)
# Translate into backend-specific representation (e.g., GPU, Yask)
nodes = self._specialize_iet(nodes)
# Apply the Devito Loop Engine (DLE) for loop optimization
dle_state = transform(nodes, *set_dle_mode(dle))
# Update the Operator state based on the DLE
self.dle_args = dle_state.arguments
self.dle_flags = dle_state.flags
self.func_table.update(OrderedDict([(i.name, MetaCall(i, True))
for i in dle_state.elemental_functions]))
self.dimensions.extend([i.argument for i in self.dle_args
if isinstance(i.argument, Dimension)])
self._includes.extend(list(dle_state.includes))
# Introduce the required symbol declarations
nodes = iet_insert_C_decls(dle_state.nodes, self.func_table)
# Insert data and pointer casts for array parameters and profiling structs
nodes = self._build_casts(nodes)
# Derive parameters as symbols not defined in the kernel itself
parameters = self._build_parameters(nodes)
# Finish instantiation
super(Operator, self).__init__(self.name, nodes, 'int', parameters, ())
