chore: import upstream snapshot with attribution

python/tvm/topi/testing/nll_loss.py

@@ -0,0 +1,74 @@
+# 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
+# ruff: noqa: RUF005
+"""NLLLoss in python"""
+
+import numpy as np
+
+
+def nll_loss(predictions, targets, weights, reduction="mean", ignore_index=-100):
+    """nll_loss operator implemented in numpy.
+
+    output{n, i_1, i_2, ..., i_k} = -p * w
+      where t = target{n, i_1, i_2, ..., i_k}
+            p = predictions{n, t, i_1, i_2, i_k}
+            w = weights{n, i_1, i_2, ..., i_k} if t != ignore_index else 0
+
+    result = reduction(output)
+
+    Parameters
+    ----------
+    predictions : numpy.ndarray
+        (k+2)-D with shape (N, C, d_1, d_2, ..., d_k),
+        where C is the number of target classes
+
+    targets : numpy.ndarray
+        (k+1)-D with shape (N, d_1, d_2, ..., d_k)
+        The target value of the input.
+
+    weights : numpy.ndarray
+        1-D with shape (C,)
+        The weight of each target value.
+
+    reduction : string
+        The reduction method to apply to output.
+        Can be "mean", "sum" or "none".
+
+    ignore_index : int
+        The target value to ignore.
+
+    Returns
+    -------
+    output : numpy.ndarray
+        a scalar if the reduction type is "mean" or "sum",
+        otherwise the same shape as `target`.
+    """
+    res = np.zeros(targets.shape)
+    weight_sum = 0.0
+    for index in np.ndindex(targets.shape):
+        class_id = targets[index]
+        if class_id != ignore_index:
+            index_list = list(index)
+            pred_index = tuple(index_list[:1] + [class_id] + index_list[1:])
+            res[index] = -predictions[pred_index] * weights[class_id]
+            weight_sum += weights[class_id]
+    if reduction == "mean":
+        return np.sum(res) / weight_sum
+    if reduction == "sum":
+        return np.sum(res)
+    return res