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[torchlib] Add missing operators (set 2) #1733

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merged 8 commits into from
Jul 18, 2024
Merged

[torchlib] Add missing operators (set 2) #1733

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db20877
Add expm1 operator
shubhambhokare1 Jul 17, 2024
ee57318
add sort impl
shubhambhokare1 Jul 17, 2024
735d509
small fix
shubhambhokare1 Jul 17, 2024
583de7f
Fix test fails
shubhambhokare1 Jul 17, 2024
f7bb621
minor edits
shubhambhokare1 Jul 17, 2024
224a35f
make trace_only
shubhambhokare1 Jul 17, 2024
6e7e424
lint
shubhambhokare1 Jul 17, 2024
f8baeea
minor fix
shubhambhokare1 Jul 18, 2024
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16 changes: 12 additions & 4 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 @@ -7699,12 +7699,20 @@ def aten_softmax_no_dtype(self: TFloatOrBFloat16, dim: int) -> TFloatOrBFloat16:
return result


@torch_op("aten::sort", trace_only=True)
def aten_sort(
self: TensorType, dim: int = -1, descending: bool = False
) -> tuple[TensorType, TensorType]:
"""sort(Tensor self, int dim=-1, bool descending=False) -> (Tensor values, Tensor indices)"""
self: TReal, dim: int = -1, descending: bool = False, stable: bool = False
) -> tuple[TReal, INT64]:
"""sort(Tensor self, int dim=-1, bool descending=False, bool stable=False) -> (Tensor values, Tensor indices)"""

raise NotImplementedError()
self_is_scalar = IsScalar(self)
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if self_is_scalar:
return op.Identity(self), op.Constant(value_int=0)
shape = op.Shape(self)
dim_size = op.Gather(shape, dim, axis=0)
dim_size = op.Reshape(dim_size, op.Constant(value_ints=[1]))
Comment on lines +7712 to +7713
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@justinchuby justinchuby Jul 18, 2024 •
edited
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Use a slice to replace the two ops? (slice is also more preferable than gather)

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Oh, think I missed this comment before merge. Making a note of this as a #TODO for the PR covering the next set of operators.

values, indices = op.TopK(self, dim_size, axis=dim, largest=descending, sorted=True)
return values, indices


def aten_sparse_dim(self: TensorType) -> int:
Expand Down
5 changes: 3 additions & 2 deletions onnxscript/function_libs/torch_lib/ops/special.py
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Expand Up @@ -130,10 +130,11 @@ def aten_special_expit(self: TensorType) -> TensorType:
raise NotImplementedError()


def aten_special_expm1(self: TensorType) -> TensorType:
@torch_op(("aten::expm1", "aten::special_expm"))
def aten_special_expm1(self: TFloatOrBFloat16) -> TFloatOrBFloat16:
"""special_expm1(Tensor self) -> Tensor"""

raise NotImplementedError()
return op.Sub(op.Exp(self), 1)


def aten_special_gammainc(self: TensorType, other: TensorType) -> TensorType:
Expand Down
7 changes: 7 additions & 0 deletions tests/function_libs/torch_lib/ops_test_data.py
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Expand Up @@ -810,6 +810,9 @@ def _where_input_wrangler(
TorchLibOpInfo(
"erfc", special_ops.aten_special_erfc, tolerance={torch.float16: (1e-2, 2e-4)}
),
TorchLibOpInfo(
"expm1", special_ops.aten_special_expm1, tolerance={torch.float16: (1e-2, 2e-4)}
),
TorchLibOpInfo("special.erfcx", special_ops.aten_special_erfcx).xfail(
reason="fixme: The implementation is numerically unstable: https://github.com/microsoft/onnxscript/issues/1223"
),
Expand Down Expand Up @@ -1437,6 +1440,10 @@ def _where_input_wrangler(
reason="fixme: ORT failed. https://github.com/microsoft/onnxruntime/issues/16449",
test_class_name="TestOutputConsistencyEager",
),
TorchLibOpInfo("sort", core_ops.aten_sort).xfail(
dtypes=(torch.float16,),
reason="fixme: Tensor-likes are not close. Tests pass for float32.",
),
TorchLibOpInfo(
"split_with_sizes",
core_ops.aten_split_with_sizes,
Expand Down
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