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8d45f555d7
Rewrite Python built-in class `super()` calls. Only non-semantic changes should be applied. - #94587 - #94588 - #94592 Also, methods with only a `super()` call are removed: ```diff class MyModule(nn.Module): - def __init__(self): - super().__init__() - def forward(self, ...): ... ``` Some cases that change the semantics should be kept unchanged. E.g.:f152a79be9/caffe2/python/net_printer.py (L184-L190)f152a79be9/test/test_jit_fuser_te.py (L2628-L2635)Pull Request resolved: https://github.com/pytorch/pytorch/pull/94587 Approved by: https://github.com/ezyang
104 lines
3.8 KiB
Python
104 lines
3.8 KiB
Python
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from caffe2.python import schema
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from caffe2.python.layers.layers import (
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IdList,
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ModelLayer,
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)
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# Model layer for implementing probabilistic replacement of individual elements in
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# IdLists. Takes probabilities for train, eval and predict nets as input, as
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# well as the replacement value when dropout happens. For features we may have
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# available to us in train net but not in predict net, we'd set dropout
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# probability for predict net to be 1.0 and set the feature to the replacement
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# value given here. This way, the value is tied to the particular model and not
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# to any specific logic in feature processing in serving.
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# Consider the following example where X is the values in the IdList and Lengths
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# is the number of values corresponding to each example.
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# X: [1, 2, 3, 4, 5]
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# Lengths: [2, 3]
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# This IdList contains 2 IdList features of lengths 2, 3. Let's assume we used a
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# ratio of 0.5 and ended up dropping out 2nd item in 2nd IdList feature, and used a
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# replacement value of -1. We will end up with the following IdList.
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# Y: [1, 2, 3, -1, 5]
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# OutputLengths: [2, 3]
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# where the 2nd item in 2nd IdList feature [4] was replaced with [-1].
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class SparseItemwiseDropoutWithReplacement(ModelLayer):
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def __init__(
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self,
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model,
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input_record,
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dropout_prob_train,
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dropout_prob_eval,
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dropout_prob_predict,
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replacement_value,
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name='sparse_itemwise_dropout',
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**kwargs):
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super().__init__(model, name, input_record, **kwargs)
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assert schema.equal_schemas(input_record, IdList), "Incorrect input type"
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self.dropout_prob_train = float(dropout_prob_train)
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self.dropout_prob_eval = float(dropout_prob_eval)
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self.dropout_prob_predict = float(dropout_prob_predict)
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self.replacement_value = int(replacement_value)
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assert (self.dropout_prob_train >= 0 and
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self.dropout_prob_train <= 1.0), \
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"Expected 0 <= dropout_prob_train <= 1, but got %s" \
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% self.dropout_prob_train
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assert (self.dropout_prob_eval >= 0 and
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self.dropout_prob_eval <= 1.0), \
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"Expected 0 <= dropout_prob_eval <= 1, but got %s" \
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% dropout_prob_eval
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assert (self.dropout_prob_predict >= 0 and
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self.dropout_prob_predict <= 1.0), \
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"Expected 0 <= dropout_prob_predict <= 1, but got %s" \
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% dropout_prob_predict
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assert(self.dropout_prob_train > 0 or
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self.dropout_prob_eval > 0 or
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self.dropout_prob_predict > 0), \
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"Ratios all set to 0.0 for train, eval and predict"
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self.output_schema = schema.NewRecord(model.net, IdList)
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if input_record.lengths.metadata:
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self.output_schema.lengths.set_metadata(
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input_record.lengths.metadata)
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if input_record.items.metadata:
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self.output_schema.items.set_metadata(
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input_record.items.metadata)
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def _add_ops(self, net, ratio):
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input_values_blob = self.input_record.items()
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input_lengths_blob = self.input_record.lengths()
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output_lengths_blob = self.output_schema.lengths()
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output_values_blob = self.output_schema.items()
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net.SparseItemwiseDropoutWithReplacement(
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[
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input_values_blob,
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input_lengths_blob
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],
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[
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output_values_blob,
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output_lengths_blob
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],
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ratio=ratio,
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replacement_value=self.replacement_value
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)
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def add_train_ops(self, net):
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self._add_ops(net, self.dropout_prob_train)
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def add_eval_ops(self, net):
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self._add_ops(net, self.dropout_prob_eval)
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def add_ops(self, net):
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self._add_ops(net, self.dropout_prob_predict)
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