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added std instance method to pytorch frontend #15003

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merged 9 commits into from
May 20, 2023
Merged

added std instance method to pytorch frontend #15003

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0da3119
added std instance method to pytorch frontend
willsketch May 7, 2023
2110110
trying to resolve conflicts
willsketch May 17, 2023
e1e39b5
Merge branch 'master' into willsketch_ivy_dev
willsketch May 18, 2023
778267a
Merge remote-tracking branch 'upstream/master' into willsketch_ivy_dev
willsketch May 18, 2023
aeb1ac7
Merge branch 'master' into willsketch_ivy_dev
willsketch May 19, 2023
23d49a7
Merge remote-tracking branch 'upstream/master' into willsketch_ivy_dev
willsketch May 19, 2023
e1b72a6
Merge branch 'willsketch_ivy_dev' of github.com:willsketch/ivy into w…
willsketch May 19, 2023
56559ce
used statistical_dtypes_values to generate examples
willsketch May 19, 2023
8da0863
Update the version of torch
fnhirwa May 20, 2023
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1 change: 0 additions & 1 deletion ivy/functional/backends/paddle/creation.py
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Original file line number Diff line number Diff line change
Expand Up @@ -184,7 +184,6 @@ def empty_like(
) -> paddle.Tensor:
return to_device(paddle.empty_like(x=x.cast("float32")).cast(dtype), device)


@with_unsupported_device_and_dtypes(
{
"2.4.2 and below": {
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1 change: 1 addition & 0 deletions ivy/functional/frontends/torch/reduction_ops.py
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Expand Up @@ -95,6 +95,7 @@ def median(input, dim=None, keepdim=False, *, out=None):


@to_ivy_arrays_and_back
@with_unsupported_dtypes({"1.11.0 and below": ("float16",)}, "torch")
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def std(input, dim=None, unbiased=True, keepdim=False, *, out=None):
return ivy.std(input, axis=dim, correction=int(unbiased), keepdims=keepdim, out=out)

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5 changes: 5 additions & 0 deletions ivy/functional/frontends/torch/tensor.py
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Expand Up @@ -1136,6 +1136,11 @@ def addcdiv(self, tensor1, tensor2, *, value=1):
def sign(self):
return torch_frontend.sign(self._ivy_array)

def std(self, dim=None, unbiased=True, keepdim=False, *, out=None):
return torch_frontend.std(
self, dim=dim, unbiased=unbiased, keepdim=keepdim, out=out
)

@with_unsupported_dtypes({"1.11.0 and below": ("float16", "bfloat16")}, "torch")
def fmod(self, other, *, out=None):
return torch_frontend.fmod(self, other, out=out)
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2 changes: 1 addition & 1 deletion ivy/functional/ivy/experimental/creation.py
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Expand Up @@ -663,7 +663,7 @@ def _iter_product(*args, repeat=1):
for prod in result:
yield tuple(prod)


@handle_exceptions
@inputs_to_ivy_arrays
def ndenumerate(
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36 changes: 36 additions & 0 deletions ivy_tests/test_ivy/test_frontends/test_torch/test_tensor.py
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Expand Up @@ -8015,6 +8015,42 @@ def test_torch_instance_sign(
)


# std
@handle_frontend_method(
class_tree=CLASS_TREE,
init_tree="torch.tensor",
method_name="std",
dtype_and_x=helpers.dtype_and_values(
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This strategy will generate unstable examples you can use this one as it is already implemented to handle statistical values https://github.com/unifyai/ivy/blob/0a32c763fa1d7fd2fd5c9803b31eeb23752e2947/ivy_tests/test_ivy/test_functional/test_core/test_statistical.py#L12

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hey @hirwa-nshuti done!

available_dtypes=helpers.get_dtypes("float"),
num_arrays=1,
min_value=-1e04,
max_value=1e04,
),
)
def test_torch_instance_std(
dtype_and_x,
frontend,
frontend_method_data,
init_flags,
method_flags,
on_device,
):
input_dtype, x = dtype_and_x
helpers.test_frontend_method(
init_input_dtypes=input_dtype,
init_all_as_kwargs_np={
"data": x[0],
},
method_input_dtypes=input_dtype,
method_all_as_kwargs_np={},
frontend_method_data=frontend_method_data,
init_flags=init_flags,
method_flags=method_flags,
frontend=frontend,
on_device=on_device,
)


# fmod
@handle_frontend_method(
class_tree=CLASS_TREE,
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