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Try and fail to integrate the loop in the script function #918

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55 changes: 28 additions & 27 deletions onnxscript/function_libs/torch_lib/ops/core.py
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Original file line number Diff line number Diff line change
Expand Up @@ -5693,7 +5693,7 @@ def aten_rnn_tanh_cell(


@torch_op("aten::roll", trace_only=True)
def aten_roll(self: TTensor, shifts: INT64, dims: Optional[Sequence[int]] = None) -> TTensor:
def aten_roll(self: TTensor, shifts: INT64, dims: Sequence[int]) -> TTensor:
"""roll(Tensor self, int[1] shifts, int[1] dims=[]) -> Tensor"""

self_rank = len(self.shape)
Expand All @@ -5702,27 +5702,19 @@ def aten_roll(self: TTensor, shifts: INT64, dims: Optional[Sequence[int]] = None
elif self.shape[0] == 0: # empty tensor
return self
else:
if dims is None:
return _aten_roll_shift_no_dim_onnx(self, shifts)
elif isinstance(shifts, int) and isinstance(dims, int):
return _aten_roll_shift_and_dim_onnx(self, shifts, dims)
else: # Below condition was skipped because we cannot handle it in OnnxScript
assert len(shifts) == len(dims)
result = self
for i in range(len(shifts)): # pylint: disable=consider-using-enumerate
shift = op.Gather(shifts, i, axis=0)
dim = dims[i]
result = _aten_roll_shift_and_dim_onnx(result, shift, dim)
return result
if not dims:
return _aten_roll_no_dim_onnx(self, shifts)
else:
return _aten_roll_onnx(self, shifts, dims)


@torch_op("aten::roll", private=True)
def _aten_roll_shift_no_dim_onnx(self: TTensor, shift: INT64) -> TTensor:
def _aten_roll_no_dim_onnx(self: TTensor, shifts: INT64) -> TTensor:
neg_1 = op.Constant(value_ints=[-1])
# flatten the self tensor
self_flatten = op.Reshape(self, neg_1)
# Compute slice length
shift_tensor = op.Reshape(shift, neg_1)
shift_tensor = op.Reshape(shifts, neg_1)
if shift_tensor < 0:
slice_length = -shift_tensor
else:
Expand All @@ -5737,18 +5729,27 @@ def _aten_roll_shift_no_dim_onnx(self: TTensor, shift: INT64) -> TTensor:


@torch_op("aten::roll", private=True)
def _aten_roll_shift_and_dim_onnx(self: TTensor, shift: INT64, dim: int) -> TTensor:
neg_1 = op.Constant(value_ints=[-1])
dim_tensor = op.Reshape(op.Constant(value_int=dim), neg_1)
shift_tensor = op.Reshape(shift, neg_1)
if shift_tensor < 0:
slice_length = -shift_tensor
else:
slice_length = op.Gather(op.Shape(self), dim_tensor, axis=0) - shift_tensor
# from [A,B,C,D,E] -> [E,A,B,C,D], [E] is prefix, [A,B,C,D] is suffix
suffix = op.Slice(self, op.Constant(value_ints=[0]), slice_length, axes=dim_tensor)
prefix = op.Slice(self, slice_length, op.Reshape(op.Size(self), neg_1), axes=dim_tensor)
result = op.Concat(prefix, suffix, axis=dim)
def _aten_roll_onnx(self: TTensor, shifts: INT64, dims: Sequence[int]) -> TTensor:
result = self

for i in range(op.Size(shifts)): # pylint: disable=consider-using-enumerate
shift = op.Gather(shifts, i, axis=0)
dim = dims[i]

# Shift dimension i
neg_1 = op.Constant(value_ints=[-1])
dim_tensor = op.Reshape(dim, neg_1)
shift_tensor = op.Reshape(shift, neg_1)
if shift_tensor < 0:
slice_length = -shift_tensor
else:
slice_length = op.Gather(op.Shape(self), dim_tensor, axis=0) - shift_tensor
# from [A,B,C,D,E] -> [E,A,B,C,D], [E] is prefix, [A,B,C,D] is suffix
suffix = op.Slice(self, op.Constant(value_ints=[0]), slice_length, axes=dim_tensor)
prefix = op.Slice(self, slice_length, op.Reshape(op.Size(self), neg_1), axes=dim_tensor)
# Concat requires a static axis
result = op.Concat(prefix, suffix, axis=dim)

return result


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