index_add_

ivy/functional/frontends/paddle/tensor/manipulation.py

@@ -6,6 +6,39 @@
 )
 from ivy.func_wrapper import with_unsupported_dtypes
 
+
+@with_supported_dtypes(
+    {"2.5.1 and below": ("bool", "int32", "int64", "float16", "float32", "float64")},
+    "paddle",
+)
+@to_ivy_arrays_and_back
+def index_add_(x, index, axis, value, *, name=None):
+    x = ivy.swapaxes(x, axis, 0)
+    value = ivy.swapaxes(value, axis, 0)
+    _to_adds = []
+    index = sorted(zip(ivy.to_list(index), range(len(index))), key=(lambda i: i[0]))
+    while index:
+        _curr_idx = index[0][0]
+        while len(_to_adds) < _curr_idx:
+            _to_adds.append(ivy.zeros_like(value[0]))
+        _to_add_cum = ivy.get_item(value, index[0][1])
+        while (len(index)) > 1 and (index[0][0] == index[1][0]):
+            _to_add_cum = _to_add_cum + ivy.get_item(value, index.pop(1)[1])
+        index.pop(0)
+        _to_adds.append(_to_add_cum)
+    while len(_to_adds) < x.shape[0]:
+        _to_adds.append(ivy.zeros_like(value[0]))
+    _to_adds = ivy.stack(_to_adds)
+    if len(x.shape) < 2:
+        # Added this line due to the paddle backend treating scalars as 1-d arrays
+        _to_adds = ivy.flatten(_to_adds)
+
+    ret = ivy.add(x, _to_adds)
+    ret = ivy.swapaxes(ret, axis, 0)
+    x = ret
+    return x
+
+
 # NOTE:
 # Only inplace functions are to be added in this file.
 # Please add non-inplace counterparts to `/frontends/paddle/manipulation.py`.
@@ -17,6 +50,5 @@
 )
 @to_ivy_arrays_and_back
 def reshape_(x, shape):
-    ret = ivy.reshape(x, shape)
-    ivy.inplace_update(x, ret)
+    ivy.reshape(x, shape)
     return x

ivy_tests/test_ivy/test_frontends/test_paddle/test_tensor/test_manipulation.py

@@ -13,6 +13,63 @@
 # --------------- #
 
 
+@st.composite
+def _arrays_dim_idx_n_dtypes(draw):
+    num_dims = draw(st.shared(helpers.ints(min_value=1, max_value=4), key="num_dims"))
+    num_arrays = 2
+    common_shape = draw(
+        helpers.lists(
+            x=helpers.ints(min_value=2, max_value=3),
+            min_size=num_dims - 1,
+            max_size=num_dims - 1,
+        )
+    )
+    _dim = draw(helpers.ints(min_value=0, max_value=num_dims - 1))
+    unique_dims = draw(
+        helpers.lists(
+            x=helpers.ints(min_value=2, max_value=3),
+            min_size=num_arrays,
+            max_size=num_arrays,
+        )
+    )
+
+    min_dim = min(unique_dims)
+    max_dim = max(unique_dims)
+    _idx = draw(
+        helpers.array_values(
+            shape=min_dim,
+            dtype="int64",
+            min_value=0,
+            max_value=max_dim,
+            exclude_min=False,
+        )
+    )
+
+    xs = []
+    # available_input_types = draw(helpers.get_dtypes("integer"))
+    # available_input_types = ["int32", "int64", "float16", "float32", "float64"]
+    available_input_types = ["int32", "int64"]
+    input_dtypes = draw(
+        helpers.array_dtypes(
+            available_dtypes=available_input_types,
+            num_arrays=num_arrays,
+            shared_dtype=True,
+        )
+    )
+    for ud, dt in zip(unique_dims, input_dtypes):
+        x = draw(
+            helpers.array_values(
+                shape=common_shape[:_dim] + [ud] + common_shape[_dim:],
+                dtype=dt,
+                large_abs_safety_factor=2.5,
+                small_abs_safety_factor=2.5,
+                safety_factor_scale="log",
+            )
+        )
+        xs.append(x)
+    return xs, input_dtypes, _dim, _idx
+
+
 @st.composite
 def dtypes_x_reshape_(draw):
     shape = draw(helpers.get_shape(min_num_dims=1))
@@ -25,6 +82,42 @@ def dtypes_x_reshape_(draw):
     return dtypes, x, shape
 
 
+# --- Main --- #
+# ------------ #
+
+
+@handle_frontend_test(
+    fn_tree="paddle.tensor.manipulation.index_add_",
+    xs_dtypes_dim_idx=_arrays_dim_idx_n_dtypes(),
+)
+def test_paddle_index_add_(
+    *,
+    xs_dtypes_dim_idx,
+    on_device,
+    fn_tree,
+    frontend,
+    test_flags,
+    backend_fw,
+):
+    xs, input_dtypes, axis, indices = xs_dtypes_dim_idx
+    if xs[0].shape[axis] < xs[1].shape[axis]:
+        source, input = xs
+    else:
+        input, source = xs
+    helpers.test_frontend_function(
+        input_dtypes=input_dtypes,
+        backend_to_test=backend_fw,
+        test_flags=test_flags,
+        fn_tree=fn_tree,
+        frontend=frontend,
+        on_device=on_device,
+        x=input,
+        index=indices,
+        axis=axis,
+        value=source,
+    )
+
+
 # reshape_
 @handle_frontend_test(
     fn_tree="paddle.tensor.manipulation.reshape_",