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# Copyright 2015 Google Inc. All Rights Reserved.
#
# 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.
# ==============================================================================
"""Operations for embeddings."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from six.moves import xrange # pylint: disable=redefined-builtin
from tensorflow.python.framework import ops
from tensorflow.python.framework import types
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import data_flow_ops
from tensorflow.python.ops import math_ops
def embedding_lookup(params, ids, name=None):
"""Looks up `ids` in a list of embedding tensors.
This function is used to perform parallel lookups on the list of
tensors in `params`. It is a generalization of
[`tf.gather()`](../../api_docs/python/array_ops.md#gather), where `params` is
interpreted as a partition of a larger embedding tensor.
If `len(params) > 1`, each element `id` of `ids` is partitioned between
the elements of `params` by computing `p = id % len(params)`, and is
then used to look up the slice `params[p][id // len(params), ...]`.
The results of the lookup are then concatenated into a dense
tensor. The returned tensor has shape `shape(ids) + shape(params)[1:]`.
Args:
params: A list of tensors with the same shape and type.
ids: A `Tensor` with type `int32` containing the ids to be looked
up in `params`.
name: A name for the operation (optional).
Returns:
A `Tensor` with the same type as the tensors in `params`.
Raises:
ValueError: If `params` is empty.
"""
if not isinstance(params, list):
params = [params]
with ops.op_scope(params + [ids], name, "embedding_lookup") as name:
if not params:
raise ValueError("Need at least one param")
np = len(params) # Number of partitions
params = ops.convert_n_to_tensor_or_indexed_slices(params, name="params")
if np == 1:
with ops.device(params[0].device):
return array_ops.gather(params[0], ids, name=name)
else:
ids = ops.convert_to_tensor(ids, name="ids")
flat_ids = array_ops.reshape(ids, [-1])
original_indices = math_ops.range(array_ops.size(flat_ids))
# Compute flat_ids % partitions for each id
ids_mod_p = flat_ids % np
if ids_mod_p.dtype != types.int32:
ids_mod_p = math_ops.cast(ids_mod_p, types.int32)
# Partition single list of ids based on ids % np into np separate lists
plist = data_flow_ops.dynamic_partition(flat_ids, ids_mod_p, np)
# Similarly, partition the original indices.
pindices = data_flow_ops.dynamic_partition(original_indices, ids_mod_p,
np)
# Do np separate lookups, finding embeddings for plist[p] in params[p]
partitioned_result = []
for p in xrange(np):
# TODO(agarwal): handle device allocations here and later in the
# colocate code.
gather_ids = plist[p] // np
with ops.device(params[p].device):
partitioned_result.append(array_ops.gather(params[p], gather_ids))
# Stitch these back together
ret = data_flow_ops.dynamic_stitch(pindices, partitioned_result,
name=name)
# Reshape to reverse the flattening of ids.
# It's important that we compute params[0].shape on the right device
# to avoid data motion.
with ops.device(params[0].device):
params_shape = array_ops.shape(params[0])
ret = array_ops.reshape(ret, array_ops.concat(0, [
array_ops.shape(ids), array_ops.slice(params_shape, [1], [-1])]))
# output shape = ids.shape + params[*].shape[1:]
# Normally the reshape is sufficient, but setting shape explicitly
# teaches shape inference that params[1:].get_shape() matters.
element_shape = params[0].get_shape()[1:]
for p in params[1:]:
element_shape = element_shape.merge_with(p.get_shape()[1:])
ret.set_shape(ids.get_shape().concatenate(element_shape))
return ret
# TODO(lif): Add support for higher-rank SparseTensors
def embedding_lookup_sparse(params, sp_ids, sp_weights,
name=None,
combiner="mean"):
"""Computes embeddings for the given ids and weights.
This op assumes that there is at least one id for each row in the dense tensor
represented by sp_ids (i.e. there are no rows with empty features), and that
all the indices of sp_ids are in canonical row-major order.
It also assumes that all id values lie in the range [0, p0), where p0
is the sum of the size of params along dimension 0.
Args:
params: A single tensor representing the complete embedding tensor,
or a list of P tensors all of same shape except for the first dimension,
representing sharded embedding tensors. In the latter case, the ids are
partitioned by id % P, and we do separate lookups in params[p] for
0 <= p < P, and then stitch the results back together into a single
result tensor. The first dimension is allowed to vary as the vocab
size is not necessarily a multiple of P.
sp_ids: N x M SparseTensor of int64 ids (typically from FeatureValueToId),
where N is typically batch size and M is arbitrary.
sp_weights: either a SparseTensor of float / double weights, or None to
indicate all weights should be taken to be 1. If specified, sp_weights
must have exactly the same shape and indices as sp_ids.
name: Optional name for the op.
combiner: A string specifying the reduction op. Currently "mean" and "sum"
are supported.
"sum" computes the weighted sum of the embedding results for each row.
"mean" is the weighted sum divided by the total weight.
Returns:
A dense tensor representing the combined embeddings for the
sparse ids. For each row in the dense tensor represented by sp_ids, the op
looks up the embeddings for all ids in that row, multiplies them by the
corresponding weight, and combines these embeddings as specified.
In other words, if
shape(combined params) = [p0, p1, ..., pm]
and
shape(sp_ids) = shape(sp_weights) = [d0, d1, ..., dn]
then
shape(output) = [d0, d1, ..., dn-1, p1, ..., pm].
For instance, if params is a 10x20 matrix, and sp_ids / sp_weights are
[0, 0]: id 1, weight 2.0
[0, 1]: id 3, weight 0.5
[1, 0]: id 0, weight 1.0
[2, 3]: id 1, weight 3.0
with combiner="mean", then the output will be a 3x20 matrix where
output[0, :] = (params[1, :] * 2.0 + params[3, :] * 0.5) / (2.0 + 0.5)
output[1, :] = params[0, :] * 1.0
output[2, :] = params[1, :] * 3.0
Raises:
TypeError: If sp_ids is not a SparseTensor, or if sp_weights is neither
None nor SparseTensor.
ValueError: If combiner is not one of {"mean", "sum"}.
"""
if combiner not in ("mean", "sum"):
raise ValueError("combiner must be one of 'mean' or 'sum'")
if not isinstance(params, list):
params = [params]
if not isinstance(sp_ids, ops.SparseTensor):
raise TypeError("sp_ids must be SparseTensor")
ignore_weights = sp_weights is None
if not ignore_weights and not isinstance(sp_weights, ops.SparseTensor):
raise TypeError("sp_weights must be either None or SparseTensor")
with ops.op_scope(params + [sp_ids], name, "embedding_lookup_sparse") as name:
segment_ids = sp_ids.indices[:, 0]
if segment_ids.dtype != types.int32:
segment_ids = math_ops.cast(segment_ids, types.int32)
ids = sp_ids.values
if ignore_weights:
ids, idx = array_ops.unique(ids)
else:
idx = None
embeddings = embedding_lookup(params, ids)
if not ignore_weights:
weights = sp_weights.values
if weights.dtype != embeddings.dtype:
weights = math_ops.cast(weights, embeddings.dtype)
# Reshape weights to allow broadcast
ones = array_ops.fill(
array_ops.expand_dims(array_ops.rank(embeddings) - 1, 0), 1)
bcast_weights_shape = array_ops.concat(0, [
array_ops.shape(weights), ones])
weights = array_ops.reshape(weights, bcast_weights_shape)
embeddings *= weights
if combiner == "sum":
embeddings = math_ops.segment_sum(embeddings, segment_ids, name=name)
elif combiner == "mean":
embeddings = math_ops.segment_sum(embeddings, segment_ids)
weight_sum = math_ops.segment_sum(weights, segment_ids)
embeddings = math_ops.div(embeddings, weight_sum, name=name)
else:
assert False, "Unrecognized combiner"
else:
assert idx is not None
if combiner == "sum":
embeddings = math_ops.sparse_segment_sum(embeddings, idx, segment_ids,
name=name)
elif combiner == "mean":
embeddings = math_ops.sparse_segment_mean(embeddings, idx, segment_ids,
name=name)
else:
assert False, "Unrecognized combiner"
return embeddings
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