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feat: Add broadcast_tensors method to paddle.tensor.Tensor #26216

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feat: Add broadcast_tensors method to paddle.tensor.Tensor #26216

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fa5ccd2
Add broadcast_tensors method to paddle.tensor.Tensor
Tinny-Robot Sep 27, 2023
66a79fd
Merge branch 'unifyai:main' into broadcast_tensor
Tinny-Robot Sep 27, 2023
7fe34a2
🤖 Lint code
ivy-branch Sep 27, 2023
8acd162
Merge branch 'unifyai:main' into broadcast_tensor
Tinny-Robot Sep 30, 2023
ec8df77
resolved missing broadcast_tensors
Tinny-Robot Oct 3, 2023
7c9704e
🤖 Lint code
ivy-branch Oct 3, 2023
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34 changes: 34 additions & 0 deletions ivy/functional/frontends/paddle/tensor/tensor.py
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Original file line number Diff line number Diff line change
Expand Up @@ -367,6 +367,40 @@ def logical_or(self, y, out=None, name=None):
def bitwise_xor(self, y, out=None, name=None):
return paddle_frontend.bitwise_xor(self, y)

@with_supported_dtypes(
{
"2.5.1 and below": (
"bool",
"int8",
"int16",
"int32",
"int64",
"float32",
"float64",
)
},
"paddle",
)
def logical_xor(self, y, out=None, name=None):
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why have you implemented logical_xor when the PR is about broadcast_tensors ?

return paddle_frontend.logical_xor(self, y, out=out)

@with_supported_dtypes(
{
"2.5.1 and below": (
"float16",
"float32",
"float64",
"int8",
"int16",
"int32",
"int64",
)
},
"paddle",
)
def broadcast_tensors(self, name=None):
return paddle_frontend.Tensor(ivy.broadcast_arrays(self._ivy_array))

@with_supported_dtypes({"2.5.1 and below": ("float16", "bfloat16")}, "paddle")
def any(self, axis=None, keepdim=False, name=None):
return paddle_frontend.any(self, axis=axis, keepdim=keepdim)
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40 changes: 40 additions & 0 deletions ivy_tests/test_ivy/test_frontends/test_paddle/test_tensor/test_tensor.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1128,6 +1128,46 @@ def test_paddle_tensor_bitwise_xor(
)


# broadcast_tensors
@handle_frontend_method(
class_tree=CLASS_TREE,
init_tree="paddle.to_tensor",
method_name="broadcast_tensors",
dtype_x_axis=helpers.dtype_values_axis(
available_dtypes=helpers.get_dtypes("float"),
# valid_axis=True,
# force_int_axis=True,
# min_num_dims=1,
# min_value=-5,
# max_value=5,
),
)
def test_paddle_tensor_broadcast_tensors(
dtype_x_axis,
frontend_method_data,
init_flags,
method_flags,
frontend,
on_device,
backend_fw,
):
input_dtype, x, axis = dtype_x_axis
helpers.test_frontend_method(
init_input_dtypes=input_dtype,
backend_to_test=backend_fw,
init_all_as_kwargs_np={
"data": [x],
},
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,
)


# cast
@handle_frontend_method(
class_tree=CLASS_TREE,
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