mirror of
https://github.com/zebrajr/pytorch.git
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c7684e3b27
Reviewed By: kennyhorror Differential Revision: D5753626 fbshipit-source-id: 680c627a81658bcd653feab68e7040db0cb7a185
275 lines
10 KiB
Python
275 lines
10 KiB
Python
## @package sparse_lookup
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# Module caffe2.python.layers.sparse_lookup
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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.helpers.arg_scope import get_current_scope
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from caffe2.python import schema
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from caffe2.python.layers.layers import (
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get_categorical_limit,
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IdList,
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IdScoreList,
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LayerPsParam,
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ModelLayer,
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)
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import collections
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import functools
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import math
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import numpy as np
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import operator
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def get_sparse_lookup_predictor_version(version):
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assert version in ('fp16', 'uint8rowwise'),\
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"Unexpected version of sparse_lookup layer {0}".format(version)
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return version
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class SparseLookup(ModelLayer):
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_id_list_supported_reducers = ['PositionWeighted', 'LogMeanExp', 'LogSumExp',
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'Max', 'Mean', 'Sum', 'Sqrt', 'None']
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_id_score_list_supported_reducers = ['PositionWeighted', 'Mean', 'Sum',
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'WeightedSum', 'WeightedMean', 'None']
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def __init__(self, model, input_record, inner_shape, reducer,
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weight_init=None, weight_optim=None,
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name='sparse_lookup', **kwargs):
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super(SparseLookup, self).__init__(model, name, input_record, **kwargs)
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# TODO Add some asserts about input type
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if isinstance(inner_shape, int):
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inner_shape = [inner_shape]
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assert isinstance(inner_shape, list) or isinstance(inner_shape, tuple),\
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"Unexpected type for inner_shape, expected list or tuple, got {0}".\
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format(type(inner_shape))
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if reducer == "PositionWeighted":
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self.external_weights = input_record.values()
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self.reducer = reducer
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input_dim = get_categorical_limit(input_record)
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assert input_dim is not None, "Unbounded features are not supported"
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scale = math.sqrt(1.0 / input_dim)
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self.shape = [input_dim] + inner_shape
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self.weight_init = weight_init if weight_init else (
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'UniformFill', {'min': -scale, 'max': scale})
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if schema.equal_schemas(self.input_record, IdList):
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sparse_key = self.input_record.items()
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elif schema.equal_schemas(
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self.input_record,
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IdScoreList,
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check_field_types=False):
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sparse_key = self.input_record.keys()
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else:
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raise NotImplementedError()
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if self.input_record.lengths.metadata:
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avg_length = self.input_record.lengths.metadata.expected_value
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else:
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avg_length = None
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self.w = self.create_param(
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param_name='w',
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shape=self.shape,
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initializer=self.weight_init,
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optimizer=weight_optim,
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ps_param=LayerPsParam(
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sparse_key=sparse_key,
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average_length=avg_length))
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self.scale_bias_init = ('ConstantFill', {'value': 0.0})
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self.scale_bias = self.create_param(
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param_name='scale_bias',
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shape=[],
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initializer=self.scale_bias_init,
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optimizer=model.NoOptim)
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self.output_schema = schema.Scalar(
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(np.float32, inner_shape),
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self.get_next_blob_reference('output'),
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)
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def get_memory_usage(self):
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return functools.reduce(operator.mul, self.shape) * 4
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def get_fp16_compatible_parameters(self):
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return [self.w]
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def get_8bits_compatible_parameters(self):
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RowwiseQuantized8BitsWeight =\
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collections.namedtuple(
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'RowwiseQuantized8BitsWeight',
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['w', 'scale_bias'], verbose=True)
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weight = RowwiseQuantized8BitsWeight(
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self.w, self.scale_bias)
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return [weight]
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def _gather_wrapper(self, net, version, in_indices, out):
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if version == 'fp16':
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return net.Gather([self.w, in_indices], out, engine='fp16')
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elif version == 'uint8rowwise':
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gathered_w = net.Gather([self.w, in_indices],
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engine='fp16')
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gathered_scale_bias = net.Gather(
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[self.scale_bias, in_indices],
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engine='fp16')
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return net.Rowwise8BitQuantizedToFloat(
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[gathered_w, gathered_scale_bias], out)
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else:
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raise "Unsupported version of operators in SparseLookup " +\
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"layer: {0}".format(version)
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def _sparse_lengths_weighted_reducer(
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self, in_indices, weights, reducer,
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net, version, grad_on_weights=0):
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op_input = [self.w,
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weights,
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in_indices,
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self.input_record.lengths()]
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layer_name = 'SparseLengths' + reducer
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if version == 'fp16':
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net.__getattr__(layer_name)(
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op_input,
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self.output_schema.field_blobs(),
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grad_on_weights=grad_on_weights,
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engine='fp16',
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)
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elif version == 'uint8rowwise':
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op_input.insert(len(op_input), self.scale_bias)
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net.__getattr__(layer_name + '8BitsRowwise')(
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op_input, self.output_schema.field_blobs())
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else:
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raise "Unsupported version of operator in SparseLookUp " +\
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"layer: {0}".format(version)
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# deal with sparse features of id_list type
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def _add_ops_id_list(self, net, version='fp16'):
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assert self.reducer in self._id_list_supported_reducers, (
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"Unsupported reducer: {} for ID_LIST".format(self.reducer)
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)
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if self.reducer in ['Sum', 'Mean']:
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op_input = [self.w,
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self.input_record.items(),
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self.input_record.lengths()]
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layer_name = 'SparseLengths' + self.reducer
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if version == 'fp16':
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net.__getattr__(layer_name)(
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op_input,
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self.output_schema.field_blobs(),
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engine='fp16',
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)
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elif version == 'uint8rowwise':
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op_input.insert(len(op_input), self.scale_bias)
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net.__getattr__(layer_name + '8BitsRowwise')(
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op_input, self.output_schema.field_blobs())
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else:
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raise "Unsupported version of operator in SparseLookUp " +\
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"layer: {0}".format(version)
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elif self.reducer == 'Sqrt':
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sqrt_weight = net.LengthsToWeights(
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[self.input_record.lengths()],
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[self.input_record.lengths() + '_sqrt'],
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power=0.5,
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)
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self._sparse_lengths_weighted_reducer(
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self.input_record.items(),
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sqrt_weight,
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'WeightedSum', net, version)
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elif self.reducer == 'None':
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# Gather operator will gather the embedding for each id of
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# each IdList.
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self._gather_wrapper(net, version, self.input_record.items(),
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self.output_schema.field_blobs())
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else:
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table_rows = self._gather_wrapper(
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net, version, self.input_record.items(), 1)
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segment_ids = net.LengthsToSegmentIds(
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self.input_record.lengths(),
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self.input_record.lengths() + '_sid'),
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net.__getattr__('SortedSegmentRange' + self.reducer)(
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[table_rows, segment_ids],
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self.output_schema.field_blobs(),
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engine='fp16',
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)
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# deal with sparse features of id_score_list type
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def _add_ops_id_score_list(self, net, version='fp16'):
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assert self.reducer in self._id_score_list_supported_reducers, (
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"Unsupported reducer: {} for ID_SCORE_LIST".format(self.reducer)
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)
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if self.reducer in ['WeightedSum', 'WeightedMean']:
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self._sparse_lengths_weighted_reducer(
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self.input_record.keys(),
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self.input_record.values(),
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self.reducer, net, version)
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elif self.reducer in ['Sum', 'Mean']:
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op_input = [self.w,
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self.input_record.keys(),
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self.input_record.lengths()]
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layer_name = 'SparseLengths' + self.reducer
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if version == 'fp16':
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net.__getattr__(layer_name)(
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op_input,
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self.output_schema.field_blobs(),
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engine='fp16',
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)
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elif version == 'uint8rowwise':
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net.__getattr__(layer_name + '8BitsRowwise')(
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op_input, self.output_schema.field_blobs())
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else:
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raise "Unsupported version of operator in SparseLookUp " +\
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"layer: {0}".format(version)
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elif self.reducer == 'PositionWeighted':
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self._sparse_lengths_weighted_reducer(
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self.input_record.keys(),
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self.external_weights,
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'WeightedSum', net, version, grad_on_weights=1)
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elif self.reducer == 'None':
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# Gather operator will gather the embedding for each id of
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# each IdList.
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self._gather_wrapper(net, version, self.input_record.keys(),
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self.output_schema.field_blobs())
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else:
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raise "Only Sum, Mean, None are supported for IdScoreList input." +\
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"Trying to create with {}".format(self.reducer)
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def add_ops(self, net):
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cur_scope = get_current_scope()
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version = get_sparse_lookup_predictor_version(
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**cur_scope.get(get_sparse_lookup_predictor_version.__name__,
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{'version': 'fp16'}))
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if schema.equal_schemas(self.input_record, IdList):
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self._add_ops_id_list(net, version=version)
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elif schema.equal_schemas(self.input_record,
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IdScoreList,
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check_field_types=False):
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self._add_ops_id_score_list(net, version=version)
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else:
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raise "Unsupported input type {0}".format(self.input_record)
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