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compiler: Properly implement NumPy array slicing
Strided slicing of one-dimensional arrays (i.e. with non-trivial steps) might have previously been working, but would have had different semantics, as all slices were copies rather than a view into the original data. Fixing this in the future will require adding support for an index stride field/tuple to our array representation (and all the associated indexing logic). GitHub: Fixes #1627.
This commit is contained in:
David Nadlinger
parent
557671b7db
commit
c707ccf7d7
@ -1116,7 +1116,11 @@ class ARTIQIRGenerator(algorithm.Visitor):
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_readable_name(index))))
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if self.current_assign is None:
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return indexed
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else: # Slice
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else:
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# This is a slice. The endpoint checking logic is the same for both lists
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# and NumPy arrays, but the actual implementations differ – while slices of
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# built-in lists are always copies in Python, they are views sharing the
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# same backing storage in NumPy.
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length = self.iterable_len(value, node.slice.type)
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if node.slice.lower is not None:
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@ -1141,91 +1145,127 @@ class ARTIQIRGenerator(algorithm.Visitor):
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mapped_stop_index = self._map_index(length, stop_index, one_past_the_end=True,
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loc=node.begin_loc)
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if node.slice.step is not None:
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try:
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old_assign, self.current_assign = self.current_assign, None
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step = self.visit(node.slice.step)
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finally:
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self.current_assign = old_assign
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if builtins.is_array(node.type):
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# To implement strided slicing with the proper NumPy reference
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# semantics, the pointer/length array representation will need to be
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# extended by another field to hold a variable stride.
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assert node.slice.step is None, (
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"array slices with non-trivial step "
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"should have been disallowed during type inference")
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# One-dimensionally slicing an array only affects the outermost
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# dimension.
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shape = self.append(ir.GetAttr(value, "shape"))
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lengths = [
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self.append(ir.GetAttr(shape, i))
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for i in range(len(shape.type.elts))
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]
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# Compute outermost length – zero for "backwards" indices.
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raw_len = self.append(
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ir.Arith(ast.Sub(loc=None), mapped_stop_index, mapped_start_index))
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is_neg_len = self.append(
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ir.Compare(ast.Lt(loc=None), raw_len, ir.Constant(0, raw_len.type)))
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outer_len = self.append(
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ir.Select(is_neg_len, ir.Constant(0, raw_len.type), raw_len))
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new_shape = self._make_array_shape([outer_len] + lengths[1:])
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# Offset buffer pointer by start index (times stride for inner dims).
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stride = reduce(
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lambda l, r: self.append(ir.Arith(ast.Mult(loc=None), l, r)),
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lengths[1:], ir.Constant(1, lengths[0].type))
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offset = self.append(
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ir.Arith(ast.Mult(loc=None), stride, mapped_start_index))
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buffer = self.append(ir.GetAttr(value, "buffer"))
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new_buffer = self.append(ir.Offset(buffer, offset))
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return self.append(ir.Alloc([new_buffer, new_shape], node.type))
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else:
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if node.slice.step is not None:
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try:
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old_assign, self.current_assign = self.current_assign, None
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step = self.visit(node.slice.step)
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finally:
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self.current_assign = old_assign
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self._make_check(
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self.append(ir.Compare(ast.NotEq(loc=None), step, ir.Constant(0, step.type))),
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lambda: self.alloc_exn(builtins.TException("ValueError"),
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ir.Constant("step cannot be zero", builtins.TStr())),
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loc=node.slice.step.loc)
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else:
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step = ir.Constant(1, node.slice.type)
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counting_up = self.append(ir.Compare(ast.Gt(loc=None), step,
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ir.Constant(0, step.type)))
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unstepped_size = self.append(ir.Arith(ast.Sub(loc=None),
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mapped_stop_index, mapped_start_index))
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slice_size_a = self.append(ir.Arith(ast.FloorDiv(loc=None), unstepped_size, step))
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slice_size_b = self.append(ir.Arith(ast.Mod(loc=None), unstepped_size, step))
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rem_not_empty = self.append(ir.Compare(ast.NotEq(loc=None), slice_size_b,
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ir.Constant(0, slice_size_b.type)))
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slice_size_c = self.append(ir.Arith(ast.Add(loc=None), slice_size_a,
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ir.Constant(1, slice_size_a.type)))
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slice_size = self.append(ir.Select(rem_not_empty,
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slice_size_c, slice_size_a,
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name="slice.size"))
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self._make_check(
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self.append(ir.Compare(ast.NotEq(loc=None), step, ir.Constant(0, step.type))),
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lambda: self.alloc_exn(builtins.TException("ValueError"),
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ir.Constant("step cannot be zero", builtins.TStr())),
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loc=node.slice.step.loc)
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else:
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step = ir.Constant(1, node.slice.type)
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counting_up = self.append(ir.Compare(ast.Gt(loc=None), step,
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ir.Constant(0, step.type)))
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self.append(ir.Compare(ast.LtE(loc=None), slice_size, length)),
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lambda slice_size, length: self.alloc_exn(builtins.TException("ValueError"),
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ir.Constant("slice size {0} is larger than iterable length {1}",
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builtins.TStr()),
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slice_size, length),
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params=[slice_size, length],
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loc=node.slice.loc)
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unstepped_size = self.append(ir.Arith(ast.Sub(loc=None),
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mapped_stop_index, mapped_start_index))
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slice_size_a = self.append(ir.Arith(ast.FloorDiv(loc=None), unstepped_size, step))
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slice_size_b = self.append(ir.Arith(ast.Mod(loc=None), unstepped_size, step))
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rem_not_empty = self.append(ir.Compare(ast.NotEq(loc=None), slice_size_b,
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ir.Constant(0, slice_size_b.type)))
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slice_size_c = self.append(ir.Arith(ast.Add(loc=None), slice_size_a,
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ir.Constant(1, slice_size_a.type)))
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slice_size = self.append(ir.Select(rem_not_empty,
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slice_size_c, slice_size_a,
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name="slice.size"))
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self._make_check(
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self.append(ir.Compare(ast.LtE(loc=None), slice_size, length)),
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lambda slice_size, length: self.alloc_exn(builtins.TException("ValueError"),
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ir.Constant("slice size {0} is larger than iterable length {1}",
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builtins.TStr()),
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slice_size, length),
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params=[slice_size, length],
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loc=node.slice.loc)
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if self.current_assign is None:
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is_neg_size = self.append(ir.Compare(ast.Lt(loc=None),
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slice_size, ir.Constant(0, slice_size.type)))
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abs_slice_size = self.append(ir.Select(is_neg_size,
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ir.Constant(0, slice_size.type), slice_size))
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other_value = self.append(ir.Alloc([abs_slice_size], value.type,
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name="slice.result"))
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else:
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other_value = self.current_assign
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if self.current_assign is None:
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is_neg_size = self.append(ir.Compare(ast.Lt(loc=None),
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slice_size, ir.Constant(0, slice_size.type)))
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abs_slice_size = self.append(ir.Select(is_neg_size,
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ir.Constant(0, slice_size.type), slice_size))
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other_value = self.append(ir.Alloc([abs_slice_size], value.type,
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name="slice.result"))
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else:
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other_value = self.current_assign
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prehead = self.current_block
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prehead = self.current_block
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head = self.current_block = self.add_block("slice.head")
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prehead.append(ir.Branch(head))
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head = self.current_block = self.add_block("slice.head")
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prehead.append(ir.Branch(head))
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index = self.append(ir.Phi(node.slice.type,
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name="slice.index"))
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index.add_incoming(mapped_start_index, prehead)
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other_index = self.append(ir.Phi(node.slice.type,
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name="slice.resindex"))
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other_index.add_incoming(ir.Constant(0, node.slice.type), prehead)
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index = self.append(ir.Phi(node.slice.type,
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name="slice.index"))
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index.add_incoming(mapped_start_index, prehead)
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other_index = self.append(ir.Phi(node.slice.type,
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name="slice.resindex"))
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other_index.add_incoming(ir.Constant(0, node.slice.type), prehead)
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# Still within bounds?
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bounded_up = self.append(ir.Compare(ast.Lt(loc=None), index, mapped_stop_index))
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bounded_down = self.append(ir.Compare(ast.Gt(loc=None), index, mapped_stop_index))
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within_bounds = self.append(ir.Select(counting_up, bounded_up, bounded_down))
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# Still within bounds?
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bounded_up = self.append(ir.Compare(ast.Lt(loc=None), index, mapped_stop_index))
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bounded_down = self.append(ir.Compare(ast.Gt(loc=None), index, mapped_stop_index))
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within_bounds = self.append(ir.Select(counting_up, bounded_up, bounded_down))
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body = self.current_block = self.add_block("slice.body")
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body = self.current_block = self.add_block("slice.body")
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if self.current_assign is None:
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elem = self.iterable_get(value, index)
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self.append(ir.SetElem(other_value, other_index, elem))
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else:
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elem = self.append(ir.GetElem(self.current_assign, other_index))
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self.append(ir.SetElem(value, index, elem))
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if self.current_assign is None:
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elem = self.iterable_get(value, index)
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self.append(ir.SetElem(other_value, other_index, elem))
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else:
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elem = self.append(ir.GetElem(self.current_assign, other_index))
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self.append(ir.SetElem(value, index, elem))
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next_index = self.append(ir.Arith(ast.Add(loc=None), index, step))
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index.add_incoming(next_index, body)
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next_other_index = self.append(ir.Arith(ast.Add(loc=None), other_index,
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ir.Constant(1, node.slice.type)))
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other_index.add_incoming(next_other_index, body)
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self.append(ir.Branch(head))
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next_index = self.append(ir.Arith(ast.Add(loc=None), index, step))
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index.add_incoming(next_index, body)
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next_other_index = self.append(ir.Arith(ast.Add(loc=None), other_index,
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ir.Constant(1, node.slice.type)))
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other_index.add_incoming(next_other_index, body)
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self.append(ir.Branch(head))
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tail = self.current_block = self.add_block("slice.tail")
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head.append(ir.BranchIf(within_bounds, body, tail))
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tail = self.current_block = self.add_block("slice.tail")
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head.append(ir.BranchIf(within_bounds, body, tail))
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if self.current_assign is None:
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return other_value
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if self.current_assign is None:
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return other_value
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def visit_TupleT(self, node):
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if self.current_assign is None:
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@ -269,6 +269,14 @@ class Inferencer(algorithm.Visitor):
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else:
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self._unify_iterable(element=node, collection=node.value)
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elif isinstance(node.slice, ast.Slice):
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if builtins.is_array(node.value.type):
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if node.slice.step is not None:
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diag = diagnostic.Diagnostic(
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"error",
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"strided slicing not yet supported for NumPy arrays", {},
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node.slice.step.loc, [])
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self.engine.process(diag)
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return
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self._unify(node.type, node.value.type, node.loc, node.value.loc)
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else: # ExtSlice
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pass # error emitted above
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@ -9,5 +9,8 @@ b = array([1, 2, 3])
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# CHECK-L: ${LINE:+1}: error: too many indices for array of dimension 1
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b[1, 2]
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# CHECK-L: ${LINE:+1}: error: strided slicing not yet supported for NumPy arrays
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b[::-1]
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# CHECK-L: ${LINE:+1}: error: array attributes cannot be assigned to
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b.shape = (5, )
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24
artiq/test/lit/integration/array_slice.py
Normal file
24
artiq/test/lit/integration/array_slice.py
Normal file
@ -0,0 +1,24 @@
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# RUN: %python -m artiq.compiler.testbench.jit %s
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a = array([0, 1, 2, 3])
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b = a[2:3]
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assert b.shape == (1,)
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assert b[0] == 2
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b[0] = 5
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assert a[2] == 5
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b = a[3:2]
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assert b.shape == (0,)
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c = array([[0, 1], [2, 3]])
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d = c[:1]
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assert d.shape == (1, 2)
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assert d[0, 0] == 0
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assert d[0, 1] == 1
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d[0, 0] = 5
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assert c[0, 0] == 5
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d = c[1:0]
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assert d.shape == (0, 2)
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13
artiq/test/lit/regression/issue_1627.py
Normal file
13
artiq/test/lit/regression/issue_1627.py
Normal file
@ -0,0 +1,13 @@
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# RUN: %python -m artiq.compiler.testbench.embedding %s
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from artiq.language.core import *
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from artiq.language.types import *
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import numpy as np
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n = 2
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data = np.zeros((n, n))
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@kernel
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def entrypoint():
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print(data[:n])
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