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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
74 lines
2.3 KiB
Python
74 lines
2.3 KiB
Python
## @package bucket_weighted
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# Module caffe2.python.layers.bucket_weighted
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import logging
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import numpy as np
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from caffe2.python import core, schema
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from caffe2.python.layers.layers import (
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get_categorical_limit,
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ModelLayer,
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)
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from caffe2.python.layers.tags import Tags
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logger = logging.getLogger(__name__)
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class BucketWeighted(ModelLayer):
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def __init__(self, model, input_record, max_score=0, bucket_boundaries=None,
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hash_buckets=True, weight_optim=None, name="bucket_weighted"):
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super().__init__(model, name, input_record)
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assert isinstance(input_record, schema.List), "Incorrect input type"
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self.bucket_boundaries = bucket_boundaries
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self.hash_buckets = hash_buckets
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if bucket_boundaries is not None:
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self.shape = len(bucket_boundaries) + 1
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elif max_score > 0:
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self.shape = max_score
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else:
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self.shape = get_categorical_limit(input_record)
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self.bucket_w = self.create_param(param_name='bucket_w',
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shape=[self.shape, ],
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initializer=('ConstantFill', {'value': 1.0}),
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optimizer=weight_optim)
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self.output_schema = schema.Struct(
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('bucket_weights',
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schema.Scalar((np.float32, self.shape),
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self.get_next_blob_reference("bucket_w_gather")))
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)
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self.tags.update({Tags.HANDLE_AS_SPARSE_LAYER})
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def get_memory_usage(self):
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return self.shape
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def add_ops(self, net):
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if self.bucket_boundaries is not None:
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buckets_int = net.Bucketize(
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self.input_record.values(),
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"buckets_int",
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boundaries=self.bucket_boundaries
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)
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else:
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buckets = self.input_record.values()
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buckets_int = net.Cast(
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buckets,
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"buckets_int",
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to=core.DataType.INT32
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)
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if self.hash_buckets:
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buckets_int = net.IndexHash(
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buckets_int, "hashed_buckets_int", seed=0, modulo=self.shape
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)
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net.Gather(
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[self.bucket_w, buckets_int],
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self.output_schema.bucket_weights.field_blobs())
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