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Summary: There is a module called `2to3` which you can target for future specifically to remove these, the directory of `caffe2` has the most redundant imports: ```2to3 -f future -w caffe2``` Pull Request resolved: https://github.com/pytorch/pytorch/pull/45033 Reviewed By: seemethere Differential Revision: D23808648 Pulled By: bugra fbshipit-source-id: 38971900f0fe43ab44a9168e57f2307580d36a38
115 lines
4.5 KiB
Python
115 lines
4.5 KiB
Python
## @package sparse_feature_hash
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# Module caffe2.python.layers.sparse_feature_hash
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from caffe2.python import schema, core
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from caffe2.python.layers.layers import (
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ModelLayer,
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IdList,
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IdScoreList,
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)
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from caffe2.python.layers.tags import (
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Tags
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)
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import numpy as np
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class SparseFeatureHash(ModelLayer):
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def __init__(self, model, input_record, seed=0, modulo=None,
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use_hashing=True, use_divide_mod=False, divisor=None, name='sparse_feature_hash', **kwargs):
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super(SparseFeatureHash, self).__init__(model, name, input_record, **kwargs)
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assert use_hashing + use_divide_mod < 2, "use_hashing and use_divide_mod cannot be set true at the same time."
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if use_divide_mod:
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assert divisor >= 1, 'Unexpected divisor: {}'.format(divisor)
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self.divisor = self.create_param(param_name='divisor',
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shape=[1],
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initializer=('GivenTensorInt64Fill', {'values': np.array([divisor])}),
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optimizer=model.NoOptim)
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self.seed = seed
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self.use_hashing = use_hashing
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self.use_divide_mod = use_divide_mod
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if schema.equal_schemas(input_record, IdList):
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self.modulo = modulo or self.extract_hash_size(input_record.items.metadata)
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metadata = schema.Metadata(
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categorical_limit=self.modulo,
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feature_specs=input_record.items.metadata.feature_specs if input_record.items.metadata else None,
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expected_value=input_record.items.metadata.expected_value if input_record.items.metadata else None
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)
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with core.NameScope(name):
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self.output_schema = schema.NewRecord(model.net, IdList)
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self.output_schema.items.set_metadata(metadata)
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elif schema.equal_schemas(input_record, IdScoreList):
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self.modulo = modulo or self.extract_hash_size(input_record.keys.metadata)
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metadata = schema.Metadata(
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categorical_limit=self.modulo,
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feature_specs=input_record.keys.metadata.feature_specs,
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expected_value=input_record.keys.metadata.expected_value
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)
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with core.NameScope(name):
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self.output_schema = schema.NewRecord(model.net, IdScoreList)
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self.output_schema.keys.set_metadata(metadata)
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else:
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assert False, "Input type must be one of (IdList, IdScoreList)"
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assert self.modulo >= 1, 'Unexpected modulo: {}'.format(self.modulo)
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if input_record.lengths.metadata:
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self.output_schema.lengths.set_metadata(input_record.lengths.metadata)
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# operators in this layer do not have CUDA implementation yet.
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# In addition, since the sparse feature keys that we are hashing are
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# typically on CPU originally, it makes sense to have this layer on CPU.
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self.tags.update([Tags.CPU_ONLY])
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def extract_hash_size(self, metadata):
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if metadata.feature_specs and metadata.feature_specs.desired_hash_size:
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return metadata.feature_specs.desired_hash_size
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elif metadata.categorical_limit is not None:
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return metadata.categorical_limit
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else:
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assert False, "desired_hash_size or categorical_limit must be set"
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def add_ops(self, net):
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net.Copy(
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self.input_record.lengths(),
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self.output_schema.lengths()
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)
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if schema.equal_schemas(self.output_schema, IdList):
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input_blob = self.input_record.items()
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output_blob = self.output_schema.items()
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elif schema.equal_schemas(self.output_schema, IdScoreList):
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input_blob = self.input_record.keys()
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output_blob = self.output_schema.keys()
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net.Copy(
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self.input_record.values(),
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self.output_schema.values()
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)
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else:
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raise NotImplementedError()
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if self.use_hashing:
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net.IndexHash(
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input_blob, output_blob, seed=self.seed, modulo=self.modulo
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)
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else:
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if self.use_divide_mod:
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quotient = net.Div([input_blob, self.divisor], [net.NextScopedBlob('quotient')])
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net.Mod(
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quotient, output_blob, divisor=self.modulo, sign_follow_divisor=True
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)
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else:
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net.Mod(
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input_blob, output_blob, divisor=self.modulo, sign_follow_divisor=True
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)
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