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[pt2] add meta for argsort.stable, use sort samples in OpInfo #106025

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[pt2] add meta for argsort.stable, use sort samples in OpInfo #106025

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[pt2] add meta for `argsort.stable`, use `sort` samples in `OpInfo`
nkaretnikov Jul 26, 2023
4264518
Update on "[pt2] add meta for `argsort.stable`, use `sort` samples in…
nkaretnikov Jul 26, 2023
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1 change: 1 addition & 0 deletions test/functorch/test_vmap.py
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Expand Up @@ -3739,6 +3739,7 @@ def test_vmap_exhaustive(self, device, dtype, op):
# but found at least two devices, cuda:0 and cpu!
xfail('ge', device_type='cuda'),
xfail('_upsample_bilinear2d_aa'),
xfail('argsort'), # aten::argsort.stable hit the vmap fallback which is currently disabled
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OpInfo now tests with the stable kwarg, so this got uncovered.

}))
def test_op_has_batch_rule(self, device, dtype, op):
# needs to be fixed
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5 changes: 5 additions & 0 deletions torch/_meta_registrations.py
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Expand Up @@ -4716,6 +4716,11 @@ def meta_sort(self, stable=None, dim=-1, descending=False, values=None, indices=
return v, i


@register_meta(aten.argsort.stable)
def meta_argsort(self, *, stable, dim=-1, descending=False):
return meta_sort(self, stable=stable, dim=dim, descending=descending)[1]


def rnn_cell_checkSizes(
input_gates, hidden_gates, input_bias, hidden_bias, factor, prev_hidden
):
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6 changes: 1 addition & 5 deletions torch/testing/_internal/common_methods_invocations.py
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Expand Up @@ -3127,10 +3127,6 @@ def sample_inputs_threshold(op_info, device, dtype, requires_grad, **kwargs):
# threshold and values args must be numbers
yield SampleInput(make_arg(x_size), make_arg(()).item(), make_arg(()).item())

def sample_inputs_argsort(*args, **kwargs):
return (sample_input for sample_input in sample_inputs_sort(*args, **kwargs)
if "stable" not in sample_input.kwargs)

def sample_inputs_unique(op_info, device, dtype, requires_grad, **kwargs):
make_arg = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad)
sizes = ((), (S,), (S, S), (S, S, S), (S, 1, S), (S, 0, S))
Expand Down Expand Up @@ -18269,7 +18265,7 @@ def reference_flatten(input, start_dim=0, end_dim=-1):
"argsort",
dtypes=all_types_and(torch.bool, torch.float16, torch.bfloat16),
dtypesIfCUDA=all_types_and(torch.float16, torch.bfloat16),
sample_inputs_func=sample_inputs_argsort,
sample_inputs_func=sample_inputs_sort,
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argsort is the second value returned by torch.sort(). Same API as sort, not sure why samples weren't reused before.

torch.argsort(input, dim=- 1, descending=False, stable=False) → Tensor
   torch.sort(input, dim=- 1, descending=False, stable=False, *, out=None)

Note: on the aten side, stable has no default:

func: argsort.stable(Tensor self, *, bool stable, int dim=-1, bool descending=False) -> Tensor

supports_out=False,
supports_autograd=False,
skips=(
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