Introduce code from Rust monorepo. This is a mess

2026-10-18 07:41:03 +00:00 · 2022-02-28 05:43:54 -05:00
parent 921fb49f9a
commit ba326bfcfd
1058 changed files with 192192 additions and 0 deletions
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+# Copyright (c) 2020, NVIDIA CORPORATION.  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.
+
+# Copyright 2018-2020 William Falcon
+#
+# 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.
+
+from dataclasses import dataclass
+
+import torch
+
+from .pl_utils import BATCH_SIZE, NUM_BATCHES, NUM_CLASSES
+
+
+@dataclass(frozen=True)
+class LossInput:
+    """
+    The input for ``nemo.collections.common.metrics.GlobalAverageLossMetric`` metric tests.
+
+    Args:
+        loss_sum_or_avg: a one dimensional float tensor which contains losses for averaging. Each element is either a
+            sum or mean of several losses depending on the parameter ``take_avg_loss`` of the
+            ``nemo.collections.common.metrics.GlobalAverageLossMetric`` class.
+        num_measurements: a one dimensional integer tensor which contains number of measurements which sums or average
+            values are in ``loss_sum_or_avg``.
+    """
+
+    loss_sum_or_avg: torch.Tensor
+    num_measurements: torch.Tensor
+
+
+NO_ZERO_NUM_MEASUREMENTS = LossInput(
+    loss_sum_or_avg=torch.rand(NUM_BATCHES) * 2.0 - 1.0, num_measurements=torch.randint(1, 100, (NUM_BATCHES,)),
+)
+
+SOME_NUM_MEASUREMENTS_ARE_ZERO = LossInput(
+    loss_sum_or_avg=torch.rand(NUM_BATCHES) * 2.0 - 1.0,
+    num_measurements=torch.cat(
+        (
+            torch.randint(1, 100, (NUM_BATCHES // 2,), dtype=torch.int32),
+            torch.zeros(NUM_BATCHES - NUM_BATCHES // 2, dtype=torch.int32),
+        )
+    ),
+)
+
+ALL_NUM_MEASUREMENTS_ARE_ZERO = LossInput(
+    loss_sum_or_avg=torch.rand(NUM_BATCHES) * 2.0 - 1.0, num_measurements=torch.zeros(NUM_BATCHES, dtype=torch.int32),
+)