chore: import upstream snapshot with attribution

python/paddle/geometric/message_passing/utils.py

@@ -0,0 +1,111 @@
+#   Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import numpy as np
+
+import paddle
+from paddle.base.data_feeder import check_dtype, convert_dtype
+from paddle.base.framework import Variable
+
+
+def convert_out_size_to_list(out_size, op_type):
+    """
+    Convert out_size(int, np.int32, np.int64, Variable) to list
+    in imperative mode.
+    """
+    if out_size is None:
+        out_size = [0]
+    elif isinstance(out_size, (int, np.int32, np.int64)):
+        out_size = [out_size]
+    elif isinstance(out_size, (Variable, paddle.pir.Value)):
+        out_size.stop_gradient = True
+        check_dtype(
+            out_size.dtype,
+            'out_size',
+            ['int32', 'int64'],
+            'op_type',
+            '(When type of out_size in' + op_type + ' is Variable.)',
+        )
+        if convert_dtype(out_size.dtype) == 'int64':
+            out_size = paddle.cast(out_size, 'int32')
+    else:
+        out_size = [int(out_size)]
+    return out_size
+
+
+def get_out_size_tensor_inputs(inputs, attrs, out_size, op_type):
+    """
+    Convert out_size(int, np.int32, np.int64, Variable) to inputs
+    and attrs in static graph mode.
+    """
+    if out_size is None:
+        attrs['out_size'] = [0]
+    elif isinstance(out_size, (int, np.int32, np.int64)):
+        attrs['out_size'] = [out_size]
+    elif isinstance(out_size, Variable):
+        out_size.stop_gradient = True
+        check_dtype(
+            out_size.dtype,
+            'out_size',
+            ['int32', 'int64'],
+            'op_type',
+            '(When type of out_size in' + op_type + ' is Variable.)',
+        )
+        if convert_dtype(out_size.dtype) == 'int64':
+            out_size = paddle.cast(out_size, 'int32')
+        inputs["Out_size"] = out_size
+    else:
+        raise TypeError("Out_size only supports Variable or int.")
+
+
+def reshape_lhs_rhs(x, y):
+    """
+    Expand dims to ensure there will be no broadcasting issues with different
+    number of dimensions.
+    """
+    if len(x.shape) == 1:
+        x = paddle.reshape(x, [-1, 1])
+    if len(y.shape) == 1:
+        y = paddle.reshape(y, [-1, 1])
+
+    x_shape = paddle.shape(x)
+    y_shape = paddle.shape(y)
+    if len(x.shape) != len(y.shape):
+        max_ndims = max(len(x.shape), len(y.shape))
+        x_pad_ndims = max_ndims - len(x.shape)
+        y_pad_ndims = max_ndims - len(y.shape)
+        new_x_shape = (
+            [
+                x_shape[0],
+            ]
+            + [
+                1,
+            ]
+            * x_pad_ndims
+            + list(x_shape[1:])
+        )
+        new_y_shape = (
+            [
+                y_shape[0],
+            ]
+            + [
+                1,
+            ]
+            * y_pad_ndims
+            + list(y_shape[1:])
+        )
+        x = paddle.reshape(x, new_x_shape)
+        y = paddle.reshape(y, new_y_shape)
+
+    return x, y