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compiler: Implement array vs. scalar broadcasting
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@ -1513,17 +1513,30 @@ class ARTIQIRGenerator(algorithm.Visitor):
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# At this point, shapes are assumed to match; could just pass buffer
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# pointer for two of the three arrays as well.
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result_buffer = self.append(ir.GetAttr(result, "buffer"))
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lhs_buffer = self.append(ir.GetAttr(lhs, "buffer"))
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rhs_buffer = self.append(ir.GetAttr(rhs, "buffer"))
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shape = self.append(ir.GetAttr(result, "shape"))
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num_total_elts = self._get_total_array_len(shape)
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if builtins.is_array(lhs.type):
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lhs_buffer = self.append(ir.GetAttr(lhs, "buffer"))
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def get_left(index):
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return self.append(ir.GetElem(lhs_buffer, index))
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else:
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def get_left(index):
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return lhs
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if builtins.is_array(rhs.type):
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rhs_buffer = self.append(ir.GetAttr(rhs, "buffer"))
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def get_right(index):
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return self.append(ir.GetElem(rhs_buffer, index))
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else:
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def get_right(index):
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return rhs
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def loop_gen(index):
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l = self.append(ir.GetElem(lhs_buffer, index))
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r = self.append(ir.GetElem(rhs_buffer, index))
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self.append(
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ir.SetElem(result_buffer, index, self.append(ir.Arith(op, l,
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r))))
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l = get_left(index)
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r = get_right(index)
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result = self.append(ir.Arith(op, l, r))
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self.append(ir.SetElem(result_buffer, index, result))
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return self.append(
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ir.Arith(ast.Add(loc=None), index,
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ir.Constant(1, self._size_type)))
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@ -1700,20 +1713,29 @@ class ARTIQIRGenerator(algorithm.Visitor):
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lhs = self.visit(node.left)
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rhs = self.visit(node.right)
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shape = self.append(ir.GetAttr(lhs, "shape"))
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# TODO: Broadcasts; select the widest shape.
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rhs_shape = self.append(ir.GetAttr(rhs, "shape"))
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self._make_check(
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self.append(ir.Compare(ast.Eq(loc=None), shape, rhs_shape)),
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lambda: self.alloc_exn(
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builtins.TException("ValueError"),
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ir.Constant("operands could not be broadcast together",
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builtins.TStr())))
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# Broadcast scalars.
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broadcast = False
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array_arg = lhs
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if not builtins.is_array(lhs.type):
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broadcast = True
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array_arg = rhs
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elif not builtins.is_array(rhs.type):
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broadcast = True
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shape = self.append(ir.GetAttr(array_arg, "shape"))
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if not broadcast:
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rhs_shape = self.append(ir.GetAttr(rhs, "shape"))
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self._make_check(
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self.append(ir.Compare(ast.Eq(loc=None), shape, rhs_shape)),
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lambda: self.alloc_exn(
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builtins.TException("ValueError"),
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ir.Constant("operands could not be broadcast together",
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builtins.TStr())))
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result = self._allocate_new_array(node.type.find()["elt"], shape)
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func = self._get_array_binop(node.op, node.type, node.left.type, node.right.type)
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func = self._get_array_binop(node.op, node.type, lhs.type, rhs.type)
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self._invoke_arrayop(func, [result, lhs, rhs])
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return result
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elif builtins.is_numeric(node.type):
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lhs = self.visit(node.left)
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@ -480,10 +480,10 @@ class Inferencer(algorithm.Visitor):
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return typ.find()["num_dims"].value
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return 0
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# TODO: Broadcasting.
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left_dims = num_dims(left.type)
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right_dims = num_dims(right.type)
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if left_dims != right_dims:
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if left_dims != right_dims and left_dims != 0 and right_dims != 0:
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# Mismatch (only scalar broadcast supported for now).
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note1 = diagnostic.Diagnostic("note", "operand of dimension {num_dims}",
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{"num_dims": left_dims}, left.loc)
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note2 = diagnostic.Diagnostic("note", "operand of dimension {num_dims}",
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@ -495,16 +495,19 @@ class Inferencer(algorithm.Visitor):
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return
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def map_node_type(typ):
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if builtins.is_array(typ):
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return typ.find()["elt"]
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else:
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# This is (if later valid) a single value broadcast across the array.
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if not builtins.is_array(typ):
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# This is a single value broadcast across the array.
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return typ
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return typ.find()["elt"]
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# Figure out result type, handling broadcasts.
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result_dims = left_dims if left_dims else right_dims
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def map_return(typ):
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elt = builtins.TFloat() if isinstance(op, ast.Div) else typ
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a = builtins.TArray(elt=elt, num_dims=left_dims)
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return (a, a, a)
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result = builtins.TArray(elt=elt, num_dims=result_dims)
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left = builtins.TArray(elt=elt, num_dims=left_dims) if left_dims else elt
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right = builtins.TArray(elt=elt, num_dims=right_dims) if right_dims else elt
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return (result, left, right)
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return self._coerce_numeric((left, right),
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map_return=map_return,
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55
artiq/test/lit/integration/array_broadcast.py
Normal file
55
artiq/test/lit/integration/array_broadcast.py
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@ -0,0 +1,55 @@
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# RUN: %python -m artiq.compiler.testbench.jit %s
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a = array([1, 2, 3])
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c = a + 1
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assert c[0] == 2
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assert c[1] == 3
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assert c[2] == 4
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c = 1 - a
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assert c[0] == 0
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assert c[1] == -1
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assert c[2] == -2
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c = a * 1
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assert c[0] == 1
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assert c[1] == 2
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assert c[2] == 3
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c = a // 2
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assert c[0] == 0
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assert c[1] == 1
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assert c[2] == 1
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c = a ** 2
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assert c[0] == 1
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assert c[1] == 4
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assert c[2] == 9
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c = 2 ** a
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assert c[0] == 2
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assert c[1] == 4
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assert c[2] == 8
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c = a % 2
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assert c[0] == 1
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assert c[1] == 0
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assert c[2] == 1
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cf = a / 2
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assert cf[0] == 0.5
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assert cf[1] == 1.0
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assert cf[2] == 1.5
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cf2 = 2 / array([1, 2, 4])
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assert cf2[0] == 2.0
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assert cf2[1] == 1.0
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assert cf2[2] == 0.5
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d = array([[1, 2], [3, 4]])
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e = d + 1
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assert e[0][0] == 2
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assert e[0][1] == 3
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assert e[1][0] == 4
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assert e[1][1] == 5
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