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Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/25782 Enable variable size embedding for dot processor. We split the embedding matrix into multiple towers, based on the embedding size and perform dot product in a loop over each of the towers and finally concatenate all the dot product outputs. Test Plan: buck test //caffe2/caffe2/fb/dper/layer_models/tests/split_1: https://our.intern.facebook.com/intern/testinfra/testrun/3659174703037560 Specific unit tests -- buck test //caffe2/caffe2/fb/dper/layer_models/tests/split_1:sparse_nn_test -- test_per_feature_emb_dim https://our.intern.facebook.com/intern/testinfra/testrun/3377699726358808 Reviewed By: chenshouyuan Differential Revision: D16690811 fbshipit-source-id: 8f5bce5aa5b272f5f795d4ac32bba814cc55210b
75 lines
2.3 KiB
Python
75 lines
2.3 KiB
Python
## @package split
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# Module caffe2.python.layers.split
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from __future__ import unicode_literals
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from caffe2.python import schema
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from caffe2.python.layers.layers import (
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ModelLayer,
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)
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class Split(ModelLayer):
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def __init__(self, model, input_record, num_splits=1, axis=1,
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name='split', split=None, **kwargs):
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super(Split, self).__init__(model, name, input_record, **kwargs)
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self.axis = axis
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# Assume that first dimension is batch, so actual axis in shape is
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# axis - 1
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axis -= 1
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assert axis >= 0
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assert isinstance(input_record, schema.Scalar),\
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"Incorrect input type. Excpected Scalar, but received: {0}".\
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format(input_record)
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input_shape = input_record.field_type().shape
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assert len(input_shape) >= axis
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if split is None:
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assert input_shape[axis] % num_splits == 0
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else:
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num_splits = len(split)
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assert input_shape[axis] == sum(split)
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if split is None:
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output_shape = list(input_shape)
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output_shape[axis] = int(output_shape[axis] / num_splits)
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else:
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output_shape = []
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for i in range(num_splits):
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output_shape_i = list(input_shape)
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output_shape_i[axis] = split[i]
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output_shape.append(output_shape_i)
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data_type = input_record.field_type().base
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if split is None:
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output_scalars = [
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schema.Scalar(
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(data_type, output_shape),
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self.get_next_blob_reference('output_{}'.format(i)),
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)
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for i in range(num_splits)
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]
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else:
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output_scalars = [
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schema.Scalar(
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(data_type, output_shape[i]),
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self.get_next_blob_reference('output_{}'.format(i)),
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)
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for i in range(num_splits)
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]
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self.output_schema = schema.Tuple(*output_scalars)
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self.split = split
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def add_ops(self, net):
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net.Split(
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self.input_record.field_blobs(),
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self.output_schema.field_blobs(),
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split=self.split,
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axis=self.axis,
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)
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