chore: import upstream snapshot with attribution

2026-07-13 13:36:25 +08:00
commit 26446540fa
3151 changed files with 974126 additions and 0 deletions
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+# 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.
+"""Batch Normalization implemented in Numpy."""
+
+import numpy as np
+
+
+def batch_norm(
+    x: np.ndarray,
+    gamma: np.ndarray,
+    beta: np.ndarray,
+    moving_mean: np.ndarray,
+    moving_var: np.ndarray,
+    axis: int,
+    epsilon: float,
+    center: bool,
+    scale: bool,
+    training: bool,
+    momentum: float,
+):
+    """Batch Normalization operator implemented in Numpy.
+
+    Parameters
+    ----------
+    data : np.ndarray
+        Input to be batch-normalized.
+
+    gamma : np.ndarray
+        Scale factor to be applied to the normalized tensor.
+
+    beta : np.ndarray
+        Offset to be applied to the normalized tensor.
+
+    moving_mean : np.ndarray
+        Running mean of input.
+
+    moving_var : np.ndarray
+        Running variance of input.
+
+    axis : int
+        Specify along which shape axis the normalization should occur.
+
+    epsilon : float
+        Small float added to variance to avoid dividing by zero.
+
+    center : bool
+        If True, add offset of beta to normalized tensor, If False,
+        beta is ignored.
+
+    scale : bool
+        If True, scale normalized tensor by gamma. If False, gamma
+        is ignored.
+
+    training : bool
+        Indicating whether it is in training mode. If True, update
+        moving_mean and moving_var.
+
+    momentum : float
+        The value used for the moving_mean and moving_var update
+
+    Returns
+    -------
+    output : np.ndarray
+        Normalized data with same shape as input
+
+    moving_mean : np.ndarray
+        Running mean of input.
+
+    moving_var : np.ndarray
+        Running variance of input.
+    """
+    shape = [1] * len(x.shape)
+    shape[axis] = x.shape[axis]
+
+    if training:
+        reduce_axes = list(range(len(x.shape)))
+        reduce_axes.remove(axis)
+        reduce_axes = tuple(reduce_axes)
+        data_mean = np.mean(x, axis=reduce_axes)
+        data_var = np.var(x, axis=reduce_axes)
+        data_mean_rs = np.reshape(data_mean, shape)
+        data_var_rs = np.reshape(data_var, shape)
+        out = (x - data_mean_rs) / np.sqrt(data_var_rs + epsilon)
+    else:
+        moving_mean_rs = moving_mean.reshape(shape)
+        moving_var_rs = moving_var.reshape(shape)
+        out = (x - moving_mean_rs) / np.sqrt(moving_var_rs + epsilon)
+
+    if scale:
+        out = out * gamma.reshape(shape)
+    if center:
+        out = out + beta.reshape(shape)
+
+    if training:
+        return [
+            out,
+            (1 - momentum) * moving_mean + momentum * data_mean,
+            (1 - momentum) * moving_var + momentum * data_var,
+        ]
+
+    return [out, moving_mean, moving_var]