Introduce code from Rust monorepo. This is a mess

tts/nemo/collections/nlp/metrics/sequence_perplexity.py

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+# Copyright (c) 2021, 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.
+
+import torch
+from torchmetrics import Metric
+
+__all__ = ['SequencePerplexity']
+
+
+class SequencePerplexity(Metric):
+    """
+    This class computes mean perplexity across the batches of sequences.
+
+    You have to provide ``log_probs`` (float tensor of shape [batch_size x seq_length x vocab_size]) and
+    ``labels`` (int tensor of shape [batch_size x seq_length] with values from the range [0, vocab_size-1])
+    to the :meth:`update` method. If some of the sequences are shorter than seq_length, you can also provide
+    an optional argument ``mask`` (bool tensor of shape [batch_size x seq_length]) which masks out tokens
+    not participating in perplexity computation.
+
+    See :doc:`PyTorch Lightning Metrics<pytorch-lightning:metrics>` for the metric usage instructions.
+
+    Args:
+        compute_on_step:
+            Forward only calls ``update()`` and returns ``None`` if this is set to ``False``. default: ``True``
+        dist_sync_on_step:
+            Synchronize metric state across processes at each ``forward()`` before returning the value at the step.
+        process_group:
+            Specify the process group on which synchronization is called. default: ``None`` (which selects the entire
+                world)
+        dist_sync_fn:
+            Callback that performs the allgather operation on the metric state. When ``None``, DDP will be used
+                to perform the allgather.
+    """
+
+    def __init__(self, compute_on_step=True, dist_sync_on_step=False, process_group=None, dist_sync_fn=None):
+        super().__init__(
+            compute_on_step=compute_on_step,
+            dist_sync_on_step=dist_sync_on_step,
+            process_group=process_group,
+            dist_sync_fn=dist_sync_fn,
+        )
+
+        # Total sum of exponentiated average negative log likelihoods
+        self.add_state('perplexities_sum', default=torch.tensor(0.0, dtype=torch.float64), dist_reduce_fx='sum')
+        # Total number of sequences in all batches
+        self.add_state('num_sequences', default=torch.tensor(0, dtype=torch.int64), dist_reduce_fx='sum')
+
+    def update(self, log_probs: torch.Tensor, labels: torch.Tensor, mask=None):
+
+        if mask is None:
+            mask = torch.ones_like(labels)
+        if mask.dtype is not log_probs.dtype:
+            mask = mask.to(log_probs.dtype)
+
+        target_log_probs = log_probs.gather(2, labels.unsqueeze(2)).squeeze(2)
+        avg_neg_ll = -(target_log_probs * mask).sum(dim=-1) / mask.sum(dim=-1)
+        ppl = avg_neg_ll.exp()
+        self.num_sequences += ppl.numel()
+        self.perplexities_sum += ppl.sum()
+
+    def compute(self):
+        """
+        Returns perplexity across all workers and resets to 0 :attr:`perplexities_sum` and :attr:`num_sequences`.
+        """
+        if self.num_sequences.eq(0):
+            return None
+        return self.perplexities_sum / self.num_sequences