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feat(atenlib): add ops (new_empty, new_empty_strided) #436

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Merged
xiaowuhu merged 10 commits into from
Feb 16, 2023
Merged

feat(atenlib): add ops (new_empty, new_empty_strided) #436

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173d8dd
Update core.py
xiaowuhu Feb 14, 2023
f50cbdd
add new ops
xiaowuhu Feb 14, 2023
a09fc77
Update core.py
xiaowuhu Feb 14, 2023
1fad593
Merge branch 'main' into xiaowu/addOps(0214)
xiaowuhu Feb 15, 2023
1366519
fix comment
xiaowuhu Feb 15, 2023
9e73133
Update core.py
xiaowuhu Feb 15, 2023
d555ff1
Update core.py
xiaowuhu Feb 15, 2023
01fd43f
Update core.py
xiaowuhu Feb 15, 2023
eaf6ce3
Update core.py
xiaowuhu Feb 15, 2023
0c98ee8
Merge branch 'main' into xiaowu/addOps(0214)
xiaowuhu Feb 16, 2023
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28 changes: 24 additions & 4 deletions onnxscript/function_libs/torch_aten/ops/core.py
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Original file line number Diff line number Diff line change
Expand Up @@ -3930,16 +3930,36 @@ def aten_negative(self: TensorType) -> TensorType:
raise NotImplementedError()


def aten_new_empty(self: TensorType, size: INT64) -> TensorType:
@torch_op("aten::new_empty")
def aten_new_empty(self: TTensor, size: INT64, dtype: int = -1) -> TTensor:
# new_empty(Tensor self, SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor

raise NotImplementedError()
# using zero to simulate empty array
zero = op.Constant(value_float=0.0)
result = op.Expand(zero, size)
if dtype == -1:
result = op.CastLike(result, self)
else:
result = op.Cast(result, to=dtype)
Comment on lines +3947 to +3950
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@justinchuby justinchuby Feb 15, 2023

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dtype needs to be the same for both branches. this op may need to be trace only

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@xiaowuhu xiaowuhu Feb 17, 2023

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not got your point

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@justinchuby justinchuby Feb 17, 2023

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So ONNX requires that both branches of an If node’s type to be the same. Since we are casting here the graph violates this constraint. I think Rama is looking at potential solutions, but for now we will need to mark the function traceonly, or else the graph would be invalid.

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@justinchuby justinchuby Feb 17, 2023

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More info in onnx/onnx#4872

return result


def aten_new_empty_strided(self: TensorType, size: INT64, stride: INT64) -> TensorType:
@torch_op("aten::new_empty_strided")
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@justinchuby justinchuby Feb 15, 2023

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potentially trace only

def aten_new_empty_strided(
self: TTensor,
size: INT64,
stride: INT64, # pylint: disable=unused-argument
dtype: int = -1,
) -> TTensor:
# new_empty_strided(Tensor self, SymInt[] size, SymInt[] stride, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor

raise NotImplementedError()
# using zero to simulate empty array
zero = op.ConstantOfShape(size)
if dtype == -1:
result = op.CastLike(zero, self)
else:
result = op.Cast(zero, to=dtype)
return result


@torch_op("aten::new_full")
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4 changes: 4 additions & 0 deletions onnxscript/tests/function_libs/torch_aten/ops_correctness_test.py
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Expand Up @@ -302,6 +302,8 @@ def _where_input_wrangler(
"mul": core_ops.aten_mul,
"ne": core_ops.aten_ne,
"neg": core_ops.aten_neg,
"new_empty": core_ops.aten_new_empty,
"new_empty_strided": core_ops.aten_new_empty_strided,
"new_full": core_ops.aten_new_full,
"nn.functional.adaptive_avg_pool1d": nn_ops.aten_adaptive_avg_pool1d,
"nn.functional.adaptive_avg_pool2d": nn_ops.aten_adaptive_avg_pool2d,
Expand Down Expand Up @@ -396,6 +398,8 @@ def _where_input_wrangler(
skip("empty_like", reason="Using zeros_like to simulate empty_like"),
xfail("logcumsumexp", reason="naive implementation not numerically stable"),
xfail("logsumexp", reason="ONNX Runtime 1.13 does not support ReduceLogSumExp-18"),
xfail("new_empty", reason="Using zeros to simulate empty"),
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@justinchuby justinchuby Feb 17, 2023

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@xiaowuhu I also realized this can succeed unexpectedly. In that case a skip may be a better choice than xfail.

xfail("new_empty_strided", reason="Using zeros to simulate empty"),
xfail(
"nn.functional.upsample_nearest2d",
reason="enable when ONNX Runtime does support opset18",
Expand Down