chore: import upstream snapshot with attribution

python/tvm/topi/nn/group_norm.py

@@ -0,0 +1,55 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you 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.
+"""Layer normalization operator."""
+
+from .. import cpp
+
+
+def group_norm(data, gamma, beta, num_groups, channel_axis, axes, epsilon=1e-5):
+    """Group normalization operator.
+    It accepts fp16 and fp32 as input data type. It will cast the input to fp32
+    to perform the computation. The output will have the same data type as input.
+
+    Parameters
+    ----------
+    data : tvm.te.Tensor
+        N-D with shape (d_0, d_1, ..., d_{N-1})
+
+    gamma: tvm.te.Tensor
+        1-D with shape (r_0) where r_0 == d_{channel_axis}
+
+    beta: tvm.te.Tensor
+        Optional, 1-D with shape (r_0) where r_0 == d_{channel_axis}
+
+    num_groups : int
+        The number of groups
+
+    channel_axis : int
+        The channel axis
+
+    axes : list of int
+        Axis over the normalization applied, excluding the channel axis
+
+    epsilon : float
+        The epsilon value to avoid division by zero.
+
+    Returns
+    -------
+    result : tvm.te.Tensor
+        N-D with shape (d_0, d_1, ..., d_{N-1})
+    """
+    return cpp.nn.group_norm(data, gamma, beta, num_groups, channel_axis, axes, epsilon)