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fix: Fix ivy.permute_dims for all backends #28009

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merged 3 commits into from
Feb 25, 2024
Merged

fix: Fix ivy.permute_dims for all backends #28009

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0e4cc32
Fixed ivy.permute_dims for all backends
vismaysur Jan 23, 2024
cca1a43
Merge branch 'unifyai:main' into fix_permute_dims
vismaysur Jan 24, 2024
8f0017b
Merge remote-tracking branch 'upstream/main' into pr/28009
NripeshN Feb 25, 2024
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3 changes: 3 additions & 0 deletions ivy/functional/backends/jax/manipulation.py
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Original file line number Diff line number Diff line change
Expand Up @@ -76,6 +76,9 @@ def permute_dims(
copy: Optional[bool] = None,
out: Optional[JaxArray] = None,
) -> JaxArray:
if copy:
newarr = jnp.copy(x)
return jnp.transpose(newarr, axes)
return jnp.transpose(x, axes)


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3 changes: 3 additions & 0 deletions ivy/functional/backends/numpy/manipulation.py
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Expand Up @@ -84,6 +84,9 @@ def permute_dims(
copy: Optional[bool] = None,
out: Optional[np.ndarray] = None,
) -> np.ndarray:
if copy:
newarr = np.copy(x)
return np.transpose(newarr, axes)
return np.transpose(x, axes)


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6 changes: 5 additions & 1 deletion ivy/functional/backends/paddle/manipulation.py
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Expand Up @@ -91,7 +91,8 @@ def flip(


@with_unsupported_dtypes(
{"2.6.0 and below": ("uint8", "int8", "int16", "bfloat16")}, backend_version
{"2.6.0 and below": ("uint8", "int8", "int16", "bfloat16", "float16")},
backend_version,
)
def permute_dims(
x: paddle.Tensor,
Expand All @@ -101,6 +102,9 @@ def permute_dims(
copy: Optional[bool] = None,
out: Optional[paddle.Tensor] = None,
) -> paddle.Tensor:
if copy:
newarr = paddle.clone(x)
return paddle.transpose(newarr, axes)
return paddle.transpose(x, axes)


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4 changes: 4 additions & 0 deletions ivy/functional/backends/torch/manipulation.py
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Expand Up @@ -77,6 +77,7 @@ def flip(
return torch.flip(x, new_axis)


@with_unsupported_dtypes({"2.1.2 and below": ("bfloat16", "float16")}, backend_version)
def permute_dims(
x: torch.Tensor,
/,
Expand All @@ -85,6 +86,9 @@ def permute_dims(
copy: Optional[bool] = None,
out: Optional[torch.Tensor] = None,
) -> torch.Tensor:
if copy:
newarr = torch.clone(x).detach()
return torch.permute(newarr, axes)
return torch.permute(x, axes)


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