chore: import upstream snapshot with attribution

python/tvm/topi/testing/group_norm_python.py

@@ -0,0 +1,84 @@
+# 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.
+# pylint: disable=invalid-name, line-too-long, unused-variable, too-many-locals
+"""Group normalization in python"""
+
+import numpy as np
+
+
+def group_norm_python(data, gamma, beta, num_groups, channel_axis, axes, epsilon=1e-5):
+    """Group normalization operator.
+
+    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})
+    """
+    old_shape = data.shape
+    old_dtype = data.dtype
+    new_shape = list(old_shape)
+    new_shape[channel_axis] = data.shape[channel_axis] // num_groups
+    new_shape.insert(channel_axis, num_groups)
+    data = np.reshape(data, new_shape).astype("float32")
+    new_axes = [channel_axis + 1]
+    for axis in axes:
+        if axis < channel_axis:
+            new_axes.append(axis)
+        else:
+            new_axes.append(axis + 1)
+    mean = np.mean(data, axis=tuple(new_axes), keepdims=True)
+    var = np.var(data, axis=tuple(new_axes), keepdims=True)
+    data = (data - mean) / np.sqrt(var + epsilon)
+    data = np.reshape(data, old_shape).astype(old_dtype)
+
+    gamma_broadcast_shape = [1 for _ in range(len(old_shape))]
+    gamma_broadcast_shape[channel_axis] = gamma.shape[0]
+    gamma = np.reshape(gamma, gamma_broadcast_shape)
+
+    beta_broadcast_shape = [1 for _ in range(len(old_shape))]
+    beta_broadcast_shape[channel_axis] = beta.shape[0]
+    if beta is not None:
+        beta = np.reshape(beta, beta_broadcast_shape)
+
+    data *= gamma
+    if beta is not None:
+        data += beta
+
+    return data