Introduce code from Rust monorepo. This is a mess

2026-10-27 08:58:13 +00:00 · 2022-02-28 05:43:54 -05:00
parent 921fb49f9a
commit ba326bfcfd
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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.
+
+import torch
+from torch.distributions.categorical import Categorical
+from torchmetrics import Metric
+
+__all__ = ['Perplexity']
+
+
+class Perplexity(Metric):
+    """
+    This class computes mean perplexity of distributions in the last dimension of inputs. It is a wrapper around
+    :doc:`torch.distributions.Categorical.perplexity<pytorch:distributions>` method. You have to provide either 
+    ``probs`` or ``logits`` to the :meth:`update` method. The class computes perplexities for distributions passed to 
+    :meth:`update` method in ``probs`` or ``logits`` arguments and averages the perplexities. Reducing results between
+    all workers is done via SUM operations.
+    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)
+        validate_args:
+            If ``True`` values of :meth:`update` method parameters are checked. ``logits`` has to not contain NaNs and
+            ``probs`` last dim has to be valid probability distribution.
+    """
+
+    def __init__(self, compute_on_step=True, dist_sync_on_step=False, process_group=None, validate_args=True):
+        super().__init__(
+            compute_on_step=compute_on_step, dist_sync_on_step=dist_sync_on_step, process_group=process_group
+        )
+        self.validate_args = validate_args
+        self.add_state('perplexities_sum', torch.tensor(0.0, dtype=torch.float64), dist_reduce_fx='sum')
+        # Total number of distributions seen since last reset
+        self.add_state('num_distributions', torch.tensor(0, dtype=torch.int64), dist_reduce_fx='sum')
+
+    def update(self, probs=None, logits=None):
+        """
+        Updates :attr:`perplexities_sum` and :attr:`num_distributions`.
+        Args:
+            probs: A ``torch.Tensor`` which innermost dimension is valid probability distribution.
+            logits: A ``torch.Tensor`` without NaNs.
+        """
+        d = Categorical(probs, logits, validate_args=self.validate_args)
+        ppl = d.perplexity()
+        self.num_distributions += 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_distributions`.
+        """
+        if self.num_distributions.eq(0):
+            return None
+        return self.perplexities_sum / self.num_distributions