mirror of
https://github.com/zebrajr/pytorch.git
synced 2025-12-07 12:21:27 +01:00
e816c777eb
Reviewed By: xianjiec Differential Revision: D5767997 fbshipit-source-id: b9b7c47d11417fbe67d861a2a6b4daa38adbe57b
336 lines
13 KiB
Python
336 lines
13 KiB
Python
# Copyright (c) 2016-present, Facebook, Inc.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
##############################################################################
|
|
|
|
## @package sparse_lookup
|
|
# Module caffe2.python.layers.sparse_lookup
|
|
from __future__ import absolute_import
|
|
from __future__ import division
|
|
from __future__ import print_function
|
|
from __future__ import unicode_literals
|
|
|
|
from caffe2.python.helpers.arg_scope import get_current_scope
|
|
from caffe2.python import schema
|
|
from caffe2.python.layers.layers import (
|
|
get_categorical_limit,
|
|
get_key,
|
|
IdList,
|
|
IdScoreList,
|
|
LayerPsParam,
|
|
ModelLayer,
|
|
)
|
|
import collections
|
|
import functools
|
|
import math
|
|
import numpy as np
|
|
import operator
|
|
|
|
|
|
def get_sparse_lookup_predictor_version(version):
|
|
assert version in {'fp32', 'fp16', 'uint8rowwise', 'fused_uint8rowwise'},\
|
|
"Unexpected version of sparse_lookup layer {0}".format(version)
|
|
return version
|
|
|
|
|
|
def _is_id_list(input_record):
|
|
return schema.equal_schemas(input_record, IdList)
|
|
|
|
|
|
def _is_id_score_list(input_record):
|
|
return schema.equal_schemas(input_record,
|
|
IdScoreList,
|
|
check_field_types=False)
|
|
|
|
|
|
class SparseLookup(ModelLayer):
|
|
_id_list_supported_reducers = [
|
|
'LogMeanExp', 'LogSumExp', 'Max', 'Mean', 'Sum',
|
|
'WeightedSum', 'WeightedMean', 'Sqrt', 'None']
|
|
|
|
_id_score_list_supported_reducers = [
|
|
'PositionWeighted', 'Mean', 'Sum', 'WeightedSum', 'WeightedMean', 'None']
|
|
|
|
def __init__(self, model, input_record, inner_shape, reducer,
|
|
weight_init=None, weight_optim=None,
|
|
name='sparse_lookup', regularizer=None, **kwargs):
|
|
|
|
super(SparseLookup, self).__init__(model, name, input_record, **kwargs)
|
|
|
|
# TODO Add some asserts about input type
|
|
if isinstance(inner_shape, int):
|
|
inner_shape = [inner_shape]
|
|
assert isinstance(inner_shape, list) or isinstance(inner_shape, tuple),\
|
|
"Unexpected type for inner_shape, expected list or tuple, got {0}".\
|
|
format(type(inner_shape))
|
|
|
|
if reducer == "PositionWeighted":
|
|
assert _is_id_score_list(self.input_record), (
|
|
"PositionWeighted only support IdScoreList, but got {} " +
|
|
"please use PositionWeighted layer to convert IdList " +
|
|
"to IdScoreList").format(repr(self.input_record))
|
|
self.external_weights = input_record.values()
|
|
self.reducer = reducer
|
|
|
|
input_dim = get_categorical_limit(input_record)
|
|
assert input_dim > 0, (
|
|
"{} should have categorical limit > 0, but got {}".format(
|
|
get_key(input_record)(), input_dim))
|
|
|
|
scale = math.sqrt(1.0 / input_dim)
|
|
self.shape = [input_dim] + inner_shape
|
|
self.weight_init = weight_init if weight_init else (
|
|
'UniformFill', {'min': -scale, 'max': scale})
|
|
|
|
if _is_id_list(self.input_record):
|
|
sparse_key = self.input_record.items()
|
|
elif _is_id_score_list(self.input_record):
|
|
sparse_key = self.input_record.keys()
|
|
else:
|
|
raise NotImplementedError()
|
|
|
|
if self.input_record.lengths.metadata:
|
|
avg_length = self.input_record.lengths.metadata.expected_value
|
|
else:
|
|
avg_length = None
|
|
|
|
self.w = self.create_param(
|
|
param_name='w',
|
|
shape=self.shape,
|
|
initializer=self.weight_init,
|
|
optimizer=weight_optim,
|
|
ps_param=LayerPsParam(
|
|
sparse_key=sparse_key,
|
|
average_length=avg_length),
|
|
regularizer=regularizer
|
|
)
|
|
|
|
self.scale_bias_init = ('ConstantFill', {'value': 0.0})
|
|
|
|
self.scale_bias = self.create_param(
|
|
param_name='scale_bias',
|
|
shape=[],
|
|
initializer=self.scale_bias_init,
|
|
optimizer=model.NoOptim,
|
|
)
|
|
|
|
self.output_schema = schema.Scalar(
|
|
(np.float32, inner_shape),
|
|
self.get_next_blob_reference('output'),
|
|
)
|
|
|
|
def get_memory_usage(self):
|
|
return functools.reduce(operator.mul, self.shape) * 4
|
|
|
|
def get_fp16_compatible_parameters(self):
|
|
return [self.w]
|
|
|
|
def get_8bits_compatible_parameters(self, fused=True):
|
|
if fused:
|
|
RowwiseQuantized8BitsWeight = collections.namedtuple(
|
|
'RowwiseQuantized8BitsWeight', 'w'
|
|
)
|
|
return [RowwiseQuantized8BitsWeight(self.w)]
|
|
else:
|
|
RowwiseQuantized8BitsWeight = collections.namedtuple(
|
|
'RowwiseQuantized8BitsWeight', 'w, scale_bias'
|
|
)
|
|
return [RowwiseQuantized8BitsWeight(self.w, self.scale_bias)]
|
|
|
|
def _gather_wrapper(self, net, version, in_indices, out):
|
|
# Gather can work on all kinds of input data types, and output
|
|
# data with the same type. Convert the output of Gather to float,
|
|
# because the follow-up Ops expect fp32.
|
|
if version == 'fp32':
|
|
return net.Gather([self.w, in_indices], out)
|
|
elif version == 'fp16':
|
|
gathered_w = net.Gather([self.w, in_indices], 'gathered_w')
|
|
|
|
return net.HalfToFloat(gathered_w, out)
|
|
elif version == 'uint8rowwise':
|
|
gathered_w = net.Gather([self.w, in_indices], 'gathered_w')
|
|
gathered_scale_bias = net.Gather(
|
|
[self.scale_bias, in_indices],
|
|
'gathered_scale_bias'
|
|
)
|
|
|
|
return net.Rowwise8BitQuantizedToFloat(
|
|
[gathered_w, gathered_scale_bias], out)
|
|
elif version == 'fused_uint8rowwise':
|
|
gathered_w = net.Gather([self.w, in_indices], 'gathered_w')
|
|
return net.Fused8BitRowwiseQuantizedToFloat(gathered_w, out)
|
|
else:
|
|
raise "Unsupported version of operators in SparseLookup " +\
|
|
"layer: {0}".format(version)
|
|
|
|
def _sparse_lengths_weighted_reducer(
|
|
self, in_indices, weights, reducer,
|
|
net, version, grad_on_weights=0):
|
|
op_input = [
|
|
self.w,
|
|
weights,
|
|
in_indices,
|
|
self.input_record.lengths()
|
|
]
|
|
layer_name = 'SparseLengths' + reducer
|
|
|
|
if version in ['fp32', 'fp16']:
|
|
# SparseLengths* Ops with engine='fp16' will accept either
|
|
# fp16 or fp32 embedding matrix and output fp32 pooled embedding
|
|
net.__getattr__(layer_name)(
|
|
op_input,
|
|
self.output_schema.field_blobs(),
|
|
grad_on_weights=grad_on_weights,
|
|
engine='fp16',
|
|
)
|
|
elif version == 'uint8rowwise':
|
|
op_input.insert(len(op_input), self.scale_bias)
|
|
net.__getattr__(layer_name + '8BitsRowwise')(
|
|
op_input, self.output_schema.field_blobs())
|
|
elif version == 'fused_uint8rowwise':
|
|
net.__getattr__(layer_name + 'Fused8BitRowwise')(
|
|
op_input, self.output_schema.field_blobs())
|
|
else:
|
|
raise "Unsupported version of operator in SparseLookUp " +\
|
|
"layer: {0}".format(version)
|
|
|
|
# deal with sparse features of id_list type
|
|
def _add_ops_id_list(self, net, version):
|
|
assert self.reducer in self._id_list_supported_reducers, (
|
|
"Unsupported reducer: {} for ID_LIST".format(self.reducer)
|
|
)
|
|
if self.reducer in ['Sum', 'Mean', 'WeightedSum', 'WeightedMean']:
|
|
op_input = [self.w,
|
|
self.input_record.items(),
|
|
self.input_record.lengths()]
|
|
|
|
# For id list features, the behaviors of 'Sum' and
|
|
# 'WeightedSum' are identical, since we can regard the weight on each
|
|
# id as 1. Similarly, for 'Mean' and 'WeightedMean'.
|
|
if self.reducer == 'WeightedSum':
|
|
self.reducer = 'Sum'
|
|
elif self.reducer == 'WeightedMean':
|
|
self.reducer = 'Mean'
|
|
|
|
layer_name = 'SparseLengths' + self.reducer
|
|
if version in ['fp32', 'fp16']:
|
|
# SparseLengths* Ops with engine='fp16' will accept either
|
|
# fp16 or fp32 embedding matrix and output fp32 pooled embedding
|
|
net.__getattr__(layer_name)(
|
|
op_input,
|
|
self.output_schema.field_blobs(),
|
|
engine='fp16',
|
|
)
|
|
elif version == 'uint8rowwise':
|
|
op_input.insert(len(op_input), self.scale_bias)
|
|
net.__getattr__(layer_name + '8BitsRowwise')(
|
|
op_input, self.output_schema.field_blobs())
|
|
elif version == 'fused_uint8rowwise':
|
|
net.__getattr__(layer_name + 'Fused8BitRowwise')(
|
|
op_input, self.output_schema.field_blobs())
|
|
else:
|
|
raise "Unsupported version of operator in SparseLookUp " +\
|
|
"layer: {0}".format(version)
|
|
|
|
elif self.reducer == 'Sqrt':
|
|
sqrt_weight = net.LengthsToWeights(
|
|
[self.input_record.lengths()],
|
|
[net.NextScopedBlob('lengths_sqrt')],
|
|
power=0.5,
|
|
)
|
|
self._sparse_lengths_weighted_reducer(
|
|
self.input_record.items(),
|
|
sqrt_weight,
|
|
'WeightedSum', net, version)
|
|
|
|
elif self.reducer == 'None':
|
|
# Gather operator will gather the embedding for each id of
|
|
# each IdList.
|
|
self._gather_wrapper(net, version, self.input_record.items(),
|
|
self.output_schema.field_blobs())
|
|
|
|
else:
|
|
table_rows = self._gather_wrapper(
|
|
net, version, self.input_record.items(), 'table_rows')
|
|
|
|
segment_ids = net.LengthsToSegmentIds(
|
|
self.input_record.lengths(),
|
|
self.input_record.lengths() + '_sid')
|
|
net.__getattr__('SortedSegmentRange' + self.reducer)(
|
|
[table_rows, segment_ids],
|
|
self.output_schema.field_blobs(),
|
|
engine='fp16',
|
|
)
|
|
|
|
# deal with sparse features of id_score_list type
|
|
def _add_ops_id_score_list(self, net, version):
|
|
assert self.reducer in self._id_score_list_supported_reducers, (
|
|
"Unsupported reducer: {} for ID_SCORE_LIST".format(self.reducer)
|
|
)
|
|
if self.reducer in ['WeightedSum', 'WeightedMean']:
|
|
self._sparse_lengths_weighted_reducer(
|
|
self.input_record.keys(),
|
|
self.input_record.values(),
|
|
self.reducer, net, version)
|
|
|
|
elif self.reducer in ['Sum', 'Mean']:
|
|
op_input = [self.w,
|
|
self.input_record.keys(),
|
|
self.input_record.lengths()]
|
|
|
|
layer_name = 'SparseLengths' + self.reducer
|
|
|
|
if version in ['fp32', 'fp16']:
|
|
net.__getattr__(layer_name)(
|
|
op_input,
|
|
self.output_schema.field_blobs(),
|
|
engine='fp16',
|
|
)
|
|
elif version == 'uint8rowwise':
|
|
net.__getattr__(layer_name + '8BitsRowwise')(
|
|
op_input, self.output_schema.field_blobs())
|
|
elif version == 'fused_uint8rowwise':
|
|
net.__getattr__(layer_name + 'Fused8BitRowwise')(
|
|
op_input, self.output_schema.field_blobs())
|
|
else:
|
|
raise "Unsupported version of operator in SparseLookUp " +\
|
|
"layer: {0}".format(version)
|
|
|
|
elif self.reducer == 'PositionWeighted':
|
|
self._sparse_lengths_weighted_reducer(
|
|
self.input_record.keys(),
|
|
self.external_weights,
|
|
'WeightedSum', net, version, grad_on_weights=1)
|
|
|
|
elif self.reducer == 'None':
|
|
# Gather operator will gather the embedding for each id of
|
|
# each IdList.
|
|
self._gather_wrapper(net, version, self.input_record.keys(),
|
|
self.output_schema.field_blobs())
|
|
else:
|
|
raise "Only Sum, Mean, None are supported for IdScoreList input." +\
|
|
"Trying to create with {}".format(self.reducer)
|
|
|
|
def add_ops(self, net):
|
|
cur_scope = get_current_scope()
|
|
version = get_sparse_lookup_predictor_version(
|
|
**cur_scope.get(get_sparse_lookup_predictor_version.__name__,
|
|
{'version': 'fp32'}))
|
|
|
|
if _is_id_list(self.input_record):
|
|
self._add_ops_id_list(net, version=version)
|
|
elif _is_id_score_list(self.input_record):
|
|
self._add_ops_id_score_list(net, version=version)
|
|
else:
|
|
raise "Unsupported input type {0}".format(self.input_record)
|