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compiler: Parametrize TArray in number of dimensions

This commit is contained in:
David Nadlinger 2020-07-26 01:07:03 +01:00
parent 632c5bc937
commit bc17bb4d1a
7 changed files with 107 additions and 76 deletions
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15
artiq/compiler/builtins.py
View File

@ -82,17 +82,24 @@ class TList(types.TMono):
super().__init__("list", {"elt": elt})
class TArray(types.TMono):
def __init__(self, elt=None):
def __init__(self, elt=None, num_dims=types.TValue(1)):
if elt is None:
elt = types.TVar()
super().__init__("array", {"elt": elt})
# For now, enforce number of dimensions to be known, as we'd otherwise
# need to implement custom unification logic for the type of `shape`.
# Default to 1 to keep compatibility with old user code from before
# multidimensional array support.
assert isinstance(num_dims.value, int), "Number of dimensions must be resolved"
super().__init__("array", {"elt": elt, "num_dims": num_dims})
self.attributes = OrderedDict([
("shape", TList(TInt32())),
("shape", types.TTuple([TInt32()] * num_dims.value)),
("buffer", TList(elt)),
])
def _array_printer(typ, printer, depth, max_depth):
return "numpy.array(elt={})".format(printer.name(typ["elt"], depth, max_depth))
return "numpy.array(elt={}, num_dims={})".format(
printer.name(typ["elt"], depth, max_depth), typ["num_dims"].value)
types.TypePrinter.custom_printers["array"] = _array_printer
class TRange(types.TMono):

60
artiq/compiler/transforms/artiq_ir_generator.py
View File

@ -7,6 +7,7 @@ semantics explicitly.
"""
from collections import OrderedDict, defaultdict
from functools import reduce
from pythonparser import algorithm, diagnostic, ast
from .. import types, builtins, asttyped, ir, iodelay
@ -1665,47 +1666,32 @@ class ARTIQIRGenerator(algorithm.Visitor):
result_type = node.type.find()
arg = self.visit(node.args[0])
num_dims = 0
result_elt = result_type["elt"].find()
inner_type = arg.type.find()
while True:
if inner_type == result_elt:
# TODO: What about types needing coercion (e.g. int32 to int64)?
break
assert builtins.is_iterable(inner_type)
num_dims += 1
inner_type = builtins.get_iterable_elt(inner_type)
num_dims = result_type["num_dims"].value
# Derive shape from first element on each level (currently, type
# inference make sure arrays are always rectangular; in the future, we
# might want to insert a runtime check here).
#
# While we are at it, also total up overall number of elements
shape = self.append(
ir.Alloc([ir.Constant(num_dims, self._size_type)],
result_type.attributes["shape"]))
first_elt = arg
dim_idx = 0
num_total_elts = None
while True:
length = self.iterable_len(first_elt)
self.append(
ir.SetElem(shape, ir.Constant(dim_idx, length.type), length))
if num_total_elts is None:
num_total_elts = length
first_elt = None
lengths = []
for dim_idx in range(num_dims):
if first_elt is None:
first_elt = arg
else:
num_total_elts = self.append(
ir.Arith(ast.Mult(loc=None), num_total_elts, length))
first_elt = self.iterable_get(first_elt,
ir.Constant(0, self._size_type))
lengths.append(self.iterable_len(first_elt))
dim_idx += 1
if dim_idx == num_dims:
break
first_elt = self.iterable_get(first_elt,
ir.Constant(0, length.type))
num_total_elts = reduce(
lambda l, r: self.append(ir.Arith(ast.Mult(loc=None), l, r)),
lengths[1:], lengths[0])
shape = self.append(ir.Alloc(lengths, result_type.attributes["shape"]))
# Assign buffer from nested iterables.
buffer = self.append(
ir.Alloc([num_total_elts], result_type.attributes["buffer"]))
def body_gen(index):
# TODO: This is hilariously inefficient; we really want to emit a
# nested loop for the source and keep one running index for the
@ -1713,9 +1699,11 @@ class ARTIQIRGenerator(algorithm.Visitor):
indices = []
mod_idx = index
for dim_idx in reversed(range(1, num_dims)):
dim_len = self.append(ir.GetElem(shape, ir.Constant(dim_idx, self._size_type)))
indices.append(self.append(ir.Arith(ast.Mod(loc=None), mod_idx, dim_len)))
mod_idx = self.append(ir.Arith(ast.FloorDiv(loc=None), mod_idx, dim_len))
dim_len = self.append(ir.GetAttr(shape, dim_idx))
indices.append(
self.append(ir.Arith(ast.Mod(loc=None), mod_idx, dim_len)))
mod_idx = self.append(
ir.Arith(ast.FloorDiv(loc=None), mod_idx, dim_len))
indices.append(mod_idx)
elt = arg
@ -1723,9 +1711,11 @@ class ARTIQIRGenerator(algorithm.Visitor):
elt = self.iterable_get(elt, idx)
self.append(ir.SetElem(buffer, index, elt))
return self.append(
ir.Arith(ast.Add(loc=None), index, ir.Constant(1, length.type)))
ir.Arith(ast.Add(loc=None), index,
ir.Constant(1, self._size_type)))
self._make_loop(
ir.Constant(0, length.type), lambda index: self.append(
ir.Constant(0, self._size_type), lambda index: self.append(
ir.Compare(ast.Lt(loc=None), index, num_total_elts)), body_gen)
return self.append(ir.Alloc([shape, buffer], node.type))

86
artiq/compiler/transforms/inferencer.py
View File

@ -8,18 +8,28 @@ from .. import asttyped, types, builtins
from .typedtree_printer import TypedtreePrinter
def is_rectangular_2d_list(node):
if not isinstance(node, asttyped.ListT):
return False
def match_rectangular_list(elts):
num_elts = None
for e in node.elts:
elt_type = None
all_child_elts = []
for e in elts:
if elt_type is None:
elt_type = e.type.find()
if not isinstance(e, asttyped.ListT):
return False
return elt_type, 0
if num_elts is None:
num_elts = len(e.elts)
elif num_elts != len(e.elts):
return False
return True
return elt_type, 0
all_child_elts += e.elts
if not all_child_elts:
# This ultimately turned out to be a list (of list, of ...) of empty lists.
return elt_type["elt"], 1
elt, num_dims = match_rectangular_list(all_child_elts)
return elt, num_dims + 1
class Inferencer(algorithm.Visitor):
@ -710,29 +720,45 @@ class Inferencer(algorithm.Visitor):
"strings currently cannot be constructed", {},
node.loc)
self.engine.process(diag)
elif types.is_builtin(typ, "list") or types.is_builtin(typ, "array"):
if types.is_builtin(typ, "list"):
valid_forms = lambda: [
valid_form("list() -> list(elt='a)"),
valid_form("list(x:'a) -> list(elt='b) where 'a is iterable")
]
elif types.is_builtin(typ, "array"):
valid_forms = lambda: [
valid_form("array(x:'a) -> array(elt='b) where 'a is iterable")
]
self._unify(node.type, builtins.TList(),
node.loc, None)
elif types.is_builtin(typ, "array"):
valid_forms = lambda: [
valid_form("array(x:'a) -> array(elt='b) where 'a is iterable")
]
if len(node.args) == 1 and len(node.keywords) == 0:
arg, = node.args
self._unify(node.type, builtins.TArray(),
node.loc, None)
if builtins.is_iterable(arg.type):
# KLUDGE: Support multidimensional arary creation if lexically
# specified as a rectangular array of lists.
elt, num_dims = match_rectangular_list([arg])
self._unify(node.type,
builtins.TArray(elt, types.TValue(num_dims)),
node.loc, arg.loc)
elif types.is_var(arg.type):
pass # undetermined yet
else:
note = diagnostic.Diagnostic("note",
"this expression has type {type}",
{"type": types.TypePrinter().name(arg.type)},
arg.loc)
diag = diagnostic.Diagnostic("error",
"the argument of {builtin}() must be of an iterable type",
{"builtin": typ.find().name},
node.func.loc, notes=[note])
self.engine.process(diag)
else:
assert False
diagnose(valid_forms())
elif types.is_builtin(typ, "list"):
valid_forms = lambda: [
valid_form("list() -> list(elt='a)"),
valid_form("list(x:'a) -> list(elt='b) where 'a is iterable")
]
if (types.is_builtin(typ, "list") and len(node.args) == 0 and
len(node.keywords) == 0):
# Mimic numpy and don't allow array() (but []).
pass
self._unify(node.type, builtins.TList(), node.loc, None)
if len(node.args) == 0 and len(node.keywords) == 0:
pass # []
elif len(node.args) == 1 and len(node.keywords) == 0:
arg, = node.args
@ -748,14 +774,8 @@ class Inferencer(algorithm.Visitor):
{"typeb": printer.name(typeb)},
locb)
]
elt = arg.type.find().params["elt"]
if types.is_builtin(typ, "array") and builtins.is_listish(elt):
# KLUDGE: Support 2D arary creation if lexically specified
# as a rectangular array of lists.
if is_rectangular_2d_list(arg):
elt = elt.find().params["elt"]
self._unify(node.type.find().params["elt"],
elt,
arg.type.find().params["elt"],
node.loc, arg.loc, makenotes=makenotes)
elif types.is_var(arg.type):
pass # undetermined yet

2
artiq/compiler/transforms/llvm_ir_generator.py
View File

@ -1173,7 +1173,7 @@ class LLVMIRGenerator:
if builtins.is_array(collection.type):
# Return length of outermost dimension.
shape = self.llbuilder.extract_value(self.map(collection), 0)
return self.llbuilder.load(self.llbuilder.extract_value(shape, 0))
return self.llbuilder.extract_value(shape, 0)
return self.llbuilder.extract_value(self.map(collection), 1)
elif insn.op in ("printf", "rtio_log"):
# We only get integers, floats, pointers and strings here.

6
artiq/compiler/validators/constness.py
View File

@ -50,3 +50,9 @@ class ConstnessValidator(algorithm.Visitor):
node.loc)
self.engine.process(diag)
return
if builtins.is_array(typ):
diag = diagnostic.Diagnostic("error",
"array attributes cannot be assigned to",
{}, node.loc)
self.engine.process(diag)
return

4
artiq/test/lit/inferencer/error_array.py
View File

@ -3,3 +3,7 @@
# CHECK-L: ${LINE:+1}: error: array cannot be invoked with the arguments ()
a = array()
b = array([1, 2, 3])
# CHECK-L: ${LINE:+1}: error: array attributes cannot be assigned to
b.shape = (5, )

10
artiq/test/lit/integration/array.py
View File

@ -3,7 +3,7 @@
ary = array([1, 2, 3])
assert len(ary) == 3
assert ary.shape == [3]
assert ary.shape == (3,)
# FIXME: Implement ndarray indexing
# assert [x*x for x in ary] == [1, 4, 9]
@ -11,8 +11,12 @@ assert ary.shape == [3]
empty_array = array([1])
empty_array = array([])
assert len(empty_array) == 0
assert empty_array.shape == [0]
assert empty_array.shape == (0,)
matrix = array([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]])
assert len(matrix) == 2
assert matrix.shape == [2, 3]
assert matrix.shape == (2, 3)
three_tensor = array([[[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]]])
assert len(three_tensor) == 1
assert three_tensor.shape == (1, 2, 3)