# Copyright 2020-2021 Huawei Technologies Co., Ltd
#
# 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.
# ============================================================================
"""embedding"""
from __future__ import absolute_import
import mindspore.common.dtype as mstype
from mindspore import log as logger
from mindspore.common.tensor import Tensor
from mindspore.ops import operations as P
from mindspore.ops import functional as F
from mindspore.common.parameter import Parameter
from mindspore.common.parameter import _get_unique_parameter_key
from mindspore.common.initializer import initializer
from mindspore.communication.management import get_group_size, get_rank
from mindspore.context import ParallelMode
from mindspore.parallel._utils import _get_parallel_mode, _get_full_batch
from mindspore.parallel._ps_context import _get_ps_context, _enable_distributed_mindrt
from mindspore.parallel._ps_context import _is_role_worker, _is_role_pserver
from mindspore.parallel._ps_context import _insert_hash_table_size, _set_cache_enable, _set_rank_id
from mindspore._checkparam import Rel
from mindspore._checkparam import Validator as validator
from mindspore.ops.primitive import constexpr
from mindspore.nn.layer.basic import ClipByNorm
from mindspore.nn.layer.math import Range
from mindspore.nn.cell import Cell
__all__ = ['Embedding', 'EmbeddingLookup', 'MultiFieldEmbeddingLookup']
@constexpr
def _check_input_2d(input_shape, param_name, func_name):
if len(input_shape) != 2:
raise ValueError(f"For '{func_name}', the dimension of '{param_name}' must be 2d, but got {len(input_shape)}")
return True
@constexpr
def _check_input_dtype(input_dtype, param_name, allow_dtypes, cls_name):
validator.check_type_name(param_name, input_dtype, allow_dtypes, cls_name)
[docs]class Embedding(Cell):
r"""
A simple lookup table that stores embeddings of a fixed dictionary and size.
This module is often used to store word embeddings and retrieve them using
indices. The input to the module is a list of indices, and the output is
the corresponding word embeddings.
Note:
When 'use_one_hot' is set to True, the type of the `x` must be mindspore.int32.
Args:
vocab_size (int): Size of the dictionary of embeddings.
embedding_size (int): The size of each embedding vector.
use_one_hot (bool): Specifies whether to apply one_hot encoding form. Default: False.
embedding_table (Union[Tensor, str, Initializer, numbers.Number]): Initializer for the embedding_table.
Refer to class `initializer` for the values of string when a string
is specified. Default: 'normal'.
dtype (:class:`mindspore.dtype`): Data type of `x`. Default: mindspore.float32.
padding_idx (int, None): When the padding_idx encounters index, the output embedding vector of this index
will be initialized to zero. Default: None. The feature is inactivated.
Inputs:
- **x** (Tensor) - Tensor of shape :math:`(\text{batch_size}, \text{x_length})`. The elements of
the Tensor must be integer and not larger than vocab_size. Otherwise the corresponding embedding vector will
be zero. The data type is int32 or int64.
Outputs:
Tensor of shape :math:`(\text{batch_size}, \text{x_length}, \text{embedding_size})`.
Raises:
TypeError: If `vocab_size` or `embedding_size` is not an int.
TypeError: If `use_one_hot` is not a bool.
ValueError: If `padding_idx` is an int which not in range [0, `vocab_size`).
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> net = nn.Embedding(20000, 768, True)
>>> x = Tensor(np.ones([8, 128]), mindspore.int32)
>>> # Maps the input word IDs to word embedding.
>>> output = net(x)
>>> result = output.shape
>>> print(result)
(8, 128, 768)
"""
def __init__(self, vocab_size, embedding_size, use_one_hot=False, embedding_table='normal',
dtype=mstype.float32, padding_idx=None):
"""Initialize Embedding."""
super(Embedding, self).__init__()
self.vocab_size = validator.check_value_type('vocab_size', vocab_size, [int], self.cls_name)
self.embedding_size = validator.check_value_type('embedding_size', embedding_size, [int], self.cls_name)
validator.check_value_type('use_one_hot', use_one_hot, [bool], self.cls_name)
validator.check_subclass("dtype", dtype, mstype.number_type, self.cls_name)
self.use_one_hot = use_one_hot
self.dtype = dtype
self.init_tensor = initializer(embedding_table, [vocab_size, embedding_size])
self.padding_idx = padding_idx
if padding_idx is not None:
self.padding_idx = validator.check_int_range(padding_idx, 0, vocab_size, Rel.INC_LEFT,
"padding_idx", self.cls_name)
if isinstance(self.init_tensor, Tensor) and self.init_tensor.init is not None:
self.init_tensor = self.init_tensor.init_data()
self.init_tensor = self.init_tensor.asnumpy()
self.init_tensor[self.padding_idx] = 0
self.init_tensor = Tensor(self.init_tensor)
self.embedding_table = Parameter(self.init_tensor, name='embedding_table')
self.expand = P.ExpandDims()
self.reshape_flat = P.Reshape()
self.shp_flat = (-1,)
self.gather = P.Gather()
self.one_hot = P.OneHot()
self.on_value = Tensor(1.0, self.dtype)
self.off_value = Tensor(0.0, self.dtype)
self.array_mul = P.MatMul()
self.reshape = P.Reshape()
self.get_shp = P.Shape()
def construct(self, ids):
out_shape = self.get_shp(ids) + (self.embedding_size,)
flat_ids = self.reshape_flat(ids, self.shp_flat)
if self.use_one_hot:
one_hot_ids = self.one_hot(flat_ids, self.vocab_size, self.on_value, self.off_value)
output_for_reshape = self.array_mul(one_hot_ids, self.embedding_table)
else:
output_for_reshape = self.gather(self.embedding_table, flat_ids, 0)
output = self.reshape(output_for_reshape, out_shape)
return output
def extend_repr(self):
s = 'vocab_size={}, embedding_size={}, use_one_hot={}, embedding_table={}, dtype={}, padding_idx={}'.format(
self.vocab_size, self.embedding_size, self.use_one_hot, self.embedding_table, self.dtype, self.padding_idx)
return s
@constexpr
def _make_axis_range(start, end):
axis = tuple(range(start, end))
return axis
[docs]class EmbeddingLookup(Cell):
r"""
EmbeddingLookup layer.
Same function as the embedding layer, mainly used for heterogeneous parallel scenarios
where large-scale embedding layers exist
when automatic parallelism or semi-automatic parallelism is present.
Note:
When 'target' is set to 'CPU', this module will use
P.EmbeddingLookup().add_prim_attr('primitive_target', 'CPU') which
specified 'offset = 0' to lookup table.
When 'target' is set to 'DEVICE', this module will use P.Gather() which
specified 'axis = 0' to lookup table.
In field slice mode, the manual_shapes must be given. It is a tuple ,where
the element is vocab[i], vocab[i] is the row numbers for i-th part.
Args:
vocab_size (int): Size of the dictionary of embeddings.
embedding_size (int): The size of each embedding vector.
param_init (Union[Tensor, str, Initializer, numbers.Number]): Initializer for the embedding_table.
Refer to class `initializer` for the values of string when a string
is specified. Default: 'normal'.
target (str): Specifies the target where the op is executed. The value must in
['DEVICE', 'CPU']. Default: 'CPU'.
slice_mode (str): The slicing way in semi_auto_parallel/auto_parallel. The value must get through
:class:`mindspore.nn.EmbeddingLookup`. Default: :class:`mindspore.nn.EmbeddingLookup.BATCH_SLICE`.
manual_shapes (tuple): The accompaniment array in field slice mode. Default: None.
max_norm (Union[float, None]): A maximum clipping value. The data type must be float16, float32
or None. Default: None
sparse (bool): Using sparse mode. When 'target' is set to 'CPU', 'sparse' has to be true. Default: True.
vocab_cache_size (int): Cache size of the dictionary of embeddings. Default: 0. It is valid only in
parameter server trainning mode and 'DEVICE' target. And the moment parameter of corresponding
optimizer will also be set to the cache size. In addition, it should be noted that it will cost the 'DEVICE'
memory, so suggests setting a reasonable value to avoid insufficient memory.
Inputs:
- **input_indices** (Tensor) - The shape of tensor is :math:`(y_1, y_2, ..., y_S)`.
Specifies the indices of elements of the original Tensor. Values can be out of range of embedding_table,
and the exceeding part will be filled with 0 in the output. Values does not support negative and the result
is undefined if values are negative. Input_indices must only be a 2d tensor in
this interface when run in semi auto parallel/auto parallel mode.
Outputs:
Tensor, the shape of tensor is :math:`(z_1, z_2, ..., z_N)`.
Raises:
TypeError: If `vocab_size` or `embedding_size` or `vocab_cache_size` is not an int.
TypeError: If `sparse` is not a bool or `manual_shapes` is not a tuple.
ValueError: If `vocab_size` or `embedding_size` is less than 1.
ValueError: If `vocab_cache_size` is less than 0.
ValueError: If `target` is neither 'CPU' nor 'DEVICE'.
ValueError: If `slice_mode` is not one of 'batch_slice' or 'field_slice' or
'table_row_slice' or 'table_column_slice'.
ValueError: If `sparse` is False and `target` is 'CPU'.
ValueError: If `slice_mode` is 'field_slice' and `manual_shapes` is None.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> input_indices = Tensor(np.array([[1, 0], [3, 2]]), mindspore.int32)
>>> result = nn.EmbeddingLookup(4,2)(input_indices)
>>> print(result.shape)
(2, 2, 2)
"""
BATCH_SLICE = "batch_slice"
FIELD_SLICE = "field_slice"
TABLE_ROW_SLICE = "table_row_slice"
TABLE_COLUMN_SLICE = "table_column_slice"
def __init__(self, vocab_size, embedding_size, param_init='normal',
target='CPU', slice_mode='batch_slice', manual_shapes=None,
max_norm=None, sparse=True, vocab_cache_size=0):
"""Initialize EmbeddingLookup."""
super(EmbeddingLookup, self).__init__()
validator.check_value_type('sparse', sparse, [bool], self.cls_name)
self.vocab_size = validator.check_positive_int(vocab_size, 'vocab_size')
self.vocab_cache_size = validator.check_non_negative_int(vocab_cache_size, 'vocab_cache_size')
self.target = target
self.sparse = sparse
self.cache_enable = self.vocab_cache_size > 0
self.forward_unique = False
validator.check_string(target, ['CPU', 'DEVICE'], 'target', self.cls_name)
if not sparse and target == 'CPU':
raise ValueError(f"For '{self.cls_name}', 'sparse' must be True when 'target' is \"CPU\", "
f"but got 'sparse': {sparse} and 'target': {target}")
if sparse:
self.gatherv2 = P.SparseGatherV2()
else:
self.gatherv2 = P.Gather()
self.embeddinglookup = P.EmbeddingLookup().add_prim_attr('primitive_target', 'CPU')
self.is_ps_server = False
enable_ps = _get_ps_context("enable_ps")
if enable_ps:
self._process_vocab_cache(slice_mode)
self.embedding_size = validator.check_positive_int(embedding_size, 'embedding_size', self.cls_name)
self.embedding_table = Parameter(initializer(param_init, [self.vocab_size, self.embedding_size]),
name='embedding_table')
parallel_mode = _get_parallel_mode()
is_auto_parallel = parallel_mode in (ParallelMode.SEMI_AUTO_PARALLEL, ParallelMode.AUTO_PARALLEL)
self.gather_revert = P.Gather()
self.reshape_first = P.Reshape()
self.reshape = P.Reshape()
self.unique = P.Unique()
self.shape = P.Shape()
if is_auto_parallel:
self.unique = P.Unique().shard(((1,),))
if self.cache_enable and enable_ps:
self._set_voacb_cache_enable_for_ps(vocab_cache_size, embedding_size, vocab_size, param_init)
if is_auto_parallel:
self.unique.add_prim_attr('cache_enable', True)
indices_shape_size = 2
if slice_mode == "field_slice" and is_auto_parallel:
if not manual_shapes:
raise ValueError(f"For '{self.cls_name}', the 'manual_shapes' should not be none "
f"when the 'slice_mode' is \"filed_slice\", but got {manual_shapes}.")
if not isinstance(manual_shapes, tuple):
raise TypeError(f"For '{self.cls_name}', the type of 'manual_shapes' must be tuple(int), "
f"but got {type(manual_shapes).__name__}!")
for dim in manual_shapes:
validator.check_positive_int(dim, 'manual shape dim', self.cls_name)
self.gatherv2.add_prim_attr("manual_split", manual_shapes)
self.embeddinglookup.add_prim_attr("manual_split", manual_shapes)
self.gatherv2.shard(((get_group_size(), 1), (1, get_group_size())))
self.embeddinglookup.shard(((get_group_size(), 1), (1, get_group_size())))
elif slice_mode == "table_row_slice" and is_auto_parallel:
full_batch = _get_full_batch()
if (target == 'DEVICE' and not full_batch) or (self.cache_enable and enable_ps and sparse):
indices_shape_size = 1
self.gather_revert.shard(((1, 1), (get_group_size(),)))
self.forward_unique = True
indices_strategy = (1,)*indices_shape_size
self.gatherv2.shard(((get_group_size(), 1), indices_strategy))
self.embeddinglookup.shard(((get_group_size(), 1), indices_strategy))
elif slice_mode == "table_column_slice" and is_auto_parallel:
if target == 'DEVICE':
indices_shape_size = 1
self.gather_revert.shard(((1, get_group_size()), (1,)))
self.forward_unique = True
indices_strategy = (1,)*indices_shape_size
self.gatherv2.shard(((1, get_group_size()), indices_strategy))
self.embeddinglookup.shard(((1, get_group_size()), indices_strategy))
elif slice_mode == "batch_slice" and is_auto_parallel:
indices_strategy = [get_group_size()]
indices_strategy.extend([1]*(indices_shape_size - 1))
indices_strategy = tuple(indices_strategy)
self.gatherv2.shard(((1, 1), indices_strategy))
self.embeddinglookup.shard(((1, 1), indices_strategy))
else:
if is_auto_parallel:
support_mode = ["field_slice", "table_row_slice", "table_column_slice", "batch_slice"]
raise ValueError("For '{}', the 'slice_mode' must be in {}, "
"but got \"{}\".".format(self.cls_name, support_mode, slice_mode))
if self.cache_enable and not enable_ps:
raise ValueError(f"For '{self.cls_name}', haven't supported cache enable for not ps mode.")
self.embedding_table.unique = self.forward_unique
self.max_norm = max_norm
if self.max_norm is not None:
self.max_norm = validator.check_positive_float(self.max_norm, 'max_norm', self.cls_name)
self.max_norm = Tensor(self.max_norm, dtype=mstype.float32)
def _process_vocab_cache(self, slice_mode):
"""PS embeddingLookup cache check and process."""
self.cache_enable = False
if self.vocab_cache_size > 0:
if self.target == 'CPU':
logger.warning("The configuration of 'vocab_cache_size' is valid only in 'DEVICE' target, "
"current target is CPU, so it will be ignored.")
return
enable_ps = _get_ps_context("enable_ps")
if not enable_ps:
logger.warning("The configuration of 'vocab_cache_size' is valid only in parameter server training "
"mode, current mode is not parameter server trainning mode, so it will be ignored.")
return
self.is_ps_server = _is_role_pserver() and _enable_distributed_mindrt()
parallel_mode = _get_parallel_mode()
is_auto_parallel = parallel_mode in (ParallelMode.SEMI_AUTO_PARALLEL, ParallelMode.AUTO_PARALLEL)
if is_auto_parallel:
rank_size = get_group_size()
rank_id = get_rank()
full_batch = _get_full_batch()
if rank_size > 1 and not (full_batch and slice_mode == "table_row_slice"):
raise ValueError(f"For '{self.cls_name}', the cache of parameter server parallel should only be "
f"used in \"full_batch\" and the value of \"full_batch\" must be True. "
f"Meanwhile, the value of 'slice_mode' must be \"table_row_slice\"."
f"But got full_batch: {full_batch} and 'slice_mode': \"{slice_mode}\".")
self.vocab_cache_size = self.vocab_cache_size * rank_size
_set_rank_id(rank_id)
self.cache_enable = True
if _is_role_worker():
self.vocab_size = self.vocab_cache_size
def _set_voacb_cache_enable_for_ps(self, vocab_cache_size, embedding_size, vocab_size, param_init):
"""PS embeddingLookup cache enable set."""
_set_cache_enable(True)
if self.sparse:
self.forward_unique = True
param_key = _get_unique_parameter_key()
if _is_role_worker():
self.embedding_table.is_param_ps = True
self.embedding_table.cache_enable = True
self.embedding_table.key = param_key
_insert_hash_table_size(self.embedding_table.name, vocab_cache_size, embedding_size, vocab_size, param_key)
if _enable_distributed_mindrt():
self.rank_id = get_rank()
if self.is_ps_server:
self._slice_pserver_embeddings(param_init)
self._set_cache_enable_and_key_for_pserver(param_key)
def _slice_pserver_embeddings(self, param_init):
'''
Method to slice embedding tables on Parameter Servers.
It helps to train with a large scale embedding table and is used only in Parameter Server training mode.
So EmbeddingLookup op is on CPU device.
'''
self.embedding_lookup_list = []
# The dimension of each embedding table on servers could be different according to the slicing algorithm.
self.embedding_table_vocab_dim_list = []
self.embedding_table_list = []
# For different servers, the offset of their embedding table should be different.
self.embedding_offset = []
server_num = _get_ps_context("server_num")
if server_num == 0:
raise ValueError("The Parameter Server number is zero.")
# Assign the embedding table dimensions.
for i in range(server_num):
self.embedding_table_vocab_dim_list.append(self.vocab_size // server_num)
rest_vocab_size = self.vocab_size % server_num
if rest_vocab_size != 0:
for i in range(rest_vocab_size):
self.embedding_table_vocab_dim_list[i] += 1
offset = 0
for i in range(server_num):
self.embedding_table_list.append(Parameter(initializer(param_init,
[self.embedding_table_vocab_dim_list[i],
self.embedding_size]),
name="embedding_table_server_" + str(i)))
self.embedding_offset.append(offset)
offset += self.embedding_table_vocab_dim_list[i]
# Add EmbeddingLookup ops on different servers.
if self.target == 'CPU':
embedding_lookup = P.EmbeddingLookup().add_prim_attr('primitive_target', 'CPU')
else:
if self.sparse:
embedding_lookup = P.SparseGatherV2()
else:
embedding_lookup = P.Gather()
embedding_lookup.add_prim_attr('offset', self.embedding_offset[i])
embedding_lookup.add_prim_attr('rank_id', i)
embedding_lookup.add_prim_attr('ms_role', 'MS_PSERVER')
self.embedding_lookup_list.append(embedding_lookup)
# For now unique operation is not applied,
# so we need to reduce the lookup results from different servers with AddN.
self.reduce_lookup_result = P.AddN()
def _do_server_embedding_lookup(self, indices):
'''
Construct backbone for EmbeddingLookup operators on servers.
'''
result_from_servers = []
for i in range(_get_ps_context("server_num")):
result = self.embedding_lookup_list[i](self.embedding_table_list[i],
indices, self.embedding_offset[i])
result_from_servers.append(result)
final_result = self.reduce_lookup_result(result_from_servers)
return final_result
def _set_cache_enable_and_key_for_pserver(self, param_key):
'''
Set cache enable and parameter key for embedding table on parameter servers.
'''
# Parameter The Embedding Table on the Server side will be divided according to the number of servers.
# The divided Embedding Table will be used instead of the complete Embedding Table.
self.embedding_table = self.embedding_table_list[self.rank_id]
self.embedding_table.cache_enable = True
self.embedding_table.key = param_key
def _pserver_embedding_lookup(self, indices):
'''
Construct backbone for EmbeddingLookup operators on servers for embedding cache lookup.
'''
if self.target == 'CPU':
return self.embedding_lookup_list[self.rank_id](self.embedding_table, indices,
self.embedding_offset[self.rank_id])
return self.embedding_lookup_list[self.rank_id](self.embedding_table, indices, 0)
def construct(self, indices):
if self.target == "CPU":
out = self.embeddinglookup(self.embedding_table, indices, 0)
elif self.is_ps_server:
out = self._pserver_embedding_lookup(indices)
else:
if self.forward_unique:
shp = self.shape(indices) + (self.embedding_size,)
indices_flatten = self.reshape_first(indices, (-1,))
unique_id, unique_idx = self.unique(indices_flatten)
weight_unique = self.gatherv2(self.embedding_table, unique_id, 0)
weight_flatten = self.gather_revert(weight_unique, unique_idx, 0)
out = self.reshape(weight_flatten, shp)
else:
out = self.gatherv2(self.embedding_table, indices, 0)
if self.max_norm is not None:
axis = _make_axis_range(F.rank(indices), F.rank(out))
clip_by_norm = ClipByNorm(axis)
out = clip_by_norm(out, self.max_norm)
return out
[docs]class MultiFieldEmbeddingLookup(EmbeddingLookup):
r"""
Returns a slice of input tensor based on the specified indices and the field ids. This operation
supports looking up embeddings using multi hot and one hot fields simultaneously.
Note:
When 'target' is set to 'CPU', this module will use
P.EmbeddingLookup().add_prim_attr('primitive_target', 'CPU') which
specified 'offset = 0' to lookup table.
When 'target' is set to 'DEVICE', this module will use P.Gather() which
specified 'axis = 0' to lookup table.
The vectors with the same field_ids will be combined by the `operator`, such as 'SUM', 'MAX' and
'MEAN'. Ensure the input_values of the padded id is zero, so that they can be ignored. The final
output will be zeros if the sum of absolute weight of the field is zero. This class only
supports ['table_row_slice', 'batch_slice' and 'table_column_slice']. For the operation 'MAX' on
device Ascend, there is a constraint where batch_size * (seq_length + field_size) < 3500.
Args:
vocab_size (int): The size of the dictionary of embeddings.
embedding_size (int): The size of each embedding vector.
field_size (int): The field size of the final outputs.
param_init (Union[Tensor, str, Initializer, numbers.Number]): Initializer for the embedding_table.
Refer to class `initializer` for the values of string when a string
is specified. Default: 'normal'.
target (str): Specifies the target where the op is executed. The value must in
['DEVICE', 'CPU']. Default: 'CPU'.
slice_mode (str): The slicing way in semi_auto_parallel/auto_parallel. The value must get through
:class:`mindspore.nn.EmbeddingLookup`. Default: :class:`mindspore.nn.EmbeddingLookup.BATCH_SLICE`.
feature_num_list (tuple): The accompaniment array in field slice mode. This is unused currently. Default: None.
max_norm (Union[float, None]): A maximum clipping value. The data type must be float16, float32
or None. Default: None
sparse (bool): Using sparse mode. When 'target' is set to 'CPU', 'sparse' has to be true. Default: True.
operator (str): The pooling method for the features in one field. Support 'SUM', 'MEAN' and 'MAX'.
Default: 'SUM'.
Inputs:
- **input_indices** (Tensor) - The shape of tensor is :math:`(batch\_size, seq\_length)`.
Specifies the indices of elements of the original Tensor. Input_indices must be a 2d tensor in
this interface. Type is Int32, Int64.
- **input_values** (Tensor) - The shape of tensor is :math:`(batch\_size, seq\_length)`.
Specifies the weights of elements of the input_indices. The lookout vector will multiply with
the input_values. Type is Float32.
- **field_ids** (Tensor) - The shape of tensor is :math:`(batch\_size, seq\_length)`.
Specifies the field id of elements of the input_indices. Type is Int32.
Outputs:
Tensor, the shape of tensor is :math:`(batch\_size, field\_size, embedding\_size)`. Type is Float32.
Raises:
TypeError: If `vocab_size` or `embedding_size` or `field_size` is not an int.
TypeError: If `sparse` is not a bool or `feature_num_list` is not a tuple.
ValueError: If `vocab_size` or `embedding_size` or `field_size` is less than 1.
ValueError: If `target` is neither 'CPU' nor 'DEVICE'.
ValueError: If `slice_mode` is not one of 'batch_slice', 'field_slice', 'table_row_slice',
'table_column_slice'.
ValueError: If `sparse` is False and `target` is 'CPU'.
ValueError: If `slice_mode` is 'field_slice' and `feature_num_list` is None.
ValueError: If `operator` is not one of 'SUM', 'MAX', 'MEAN'.
Supported Platforms:
``Ascend`` ``GPU``
Examples:
>>> input_indices = Tensor([[2, 4, 6, 0, 0], [1, 3, 5, 0, 0]], mindspore.int32)
>>> input_values = Tensor([[1, 1, 1, 0, 0], [1, 1, 1, 0, 0]], mindspore.float32)
>>> field_ids = Tensor([[0, 1, 1, 0, 0], [0, 0, 1, 0, 0]], mindspore.int32)
>>> net = nn.MultiFieldEmbeddingLookup(10, 2, field_size=2, operator='SUM', target='DEVICE')
>>> out = net(input_indices, input_values, field_ids)
>>> print(out.shape)
(2, 2, 2)
"""
OPERATOR_SUM = 'SUM'
OPERATOR_MEAN = 'MEAN'
OPERATOR_MAX = 'MAX'
def __init__(self, vocab_size, embedding_size, field_size, param_init='normal', target='CPU',
slice_mode='batch_slice', feature_num_list=None, max_norm=None, sparse=True, operator='SUM'):
"""Initialize MultiFieldEmbeddingLookup."""
super(MultiFieldEmbeddingLookup, self).__init__(vocab_size, embedding_size, param_init, target,
slice_mode, feature_num_list, max_norm, sparse)
self.field_size = validator.check_positive_int(field_size, 'field_size', self.cls_name)
self.operator = operator
self.mul = P.Mul()
self.inf_mask_mul = P.Mul()
self.bias_add = P.Add()
self.inf_add = P.Add()
self.merge_op = None
self.count_op = P.UnsortedSegmentSum()
self.abs = P.Abs()
self.equal = P.Equal()
self.add = P.Add()
self.cast = P.Cast()
self.div_no_nan = P.DivNoNan()
self.expand = P.ExpandDims()
self.max_mask_mul = P.Mul()
self.max_no_equal = P.NotEqual()
validator.check_string(operator, ['SUM', 'MAX', 'MEAN'], 'operator', self.cls_name)
if operator == MultiFieldEmbeddingLookup.OPERATOR_SUM:
self.merge_op = P.UnsortedSegmentSum()
elif operator == MultiFieldEmbeddingLookup.OPERATOR_MAX:
self.merge_op = P.UnsortedSegmentMax()
else:
self.merge_op = P.UnsortedSegmentSum()
parallel_mode = _get_parallel_mode()
is_auto_parallel = parallel_mode in (ParallelMode.SEMI_AUTO_PARALLEL, ParallelMode.AUTO_PARALLEL)
if slice_mode in ["table_row_slice", "batch_slice"] and is_auto_parallel:
self.merge_op.shard(((get_group_size(), 1, 1), (get_group_size(), 1)))
self.expand.shard(((get_group_size(),),))
self.bias_add.shard(((1, 1), (1, 1)))
self.mul.shard(((get_group_size(), 1, 1), (get_group_size(), 1, 1)))
self.count_op.shard(((get_group_size(), 1), (get_group_size(), 1)))
self.add.shard(((get_group_size(),), (get_group_size(),)))
self.div_no_nan.shard(((get_group_size(), 1), (get_group_size(), 1)))
self.max_mask_mul.shard(((get_group_size(), 1), (get_group_size(), 1)))
self.max_no_equal.shard(((1,), ()))
if operator == MultiFieldEmbeddingLookup.OPERATOR_MAX:
self.equal.shard(((get_group_size(), 1, 1), ()))
self.inf_mask_mul.shard(((get_group_size(), 1, 1), ()))
self.merge_op.shard(((get_group_size(), 1), (get_group_size(),)))
self.count_op.shard(((get_group_size(),), (get_group_size(),)))
self.inf_add.shard(((get_group_size(), 1, 1), (get_group_size(), 1, 1)))
elif slice_mode == "table_column_slice" and is_auto_parallel:
self.merge_op.shard(((1, 1, get_group_size()), (1, 1)))
self.div_no_nan.shard(((1, get_group_size()), (1, 1)))
self.bias_add.shard(((1, 1), (1, 1)))
self.mul.shard(((1, 1, 1), (1, 1, get_group_size())))
self.count_op.shard(((1, 1), (1, 1)))
self.add.shard(((1,), (1,)))
self.max_mask_mul.shard(((1, get_group_size()), (1, 1)))
self.expand.shard(((1,),))
self.max_no_equal.shard(((1,), ()))
if operator == MultiFieldEmbeddingLookup.OPERATOR_MAX:
self.equal.shard(((1, 1, 1), ()))
self.inf_mask_mul.shard(((1, 1, 1), ()))
self.merge_op.shard(((1, get_group_size()), (1,)))
self.count_op.shard(((1,), (1,)))
self.inf_add.shard(((1, 1, get_group_size()), (1, 1, 1)))
else:
if is_auto_parallel:
raise ValueError("For '{}', the 'slice_mode' must be in ['table_row_slice', 'batch_slice' and \
'table_column_slice'], but got {}".format(self.cls_name, str(slice_mode)))
# Min value for fp32
self.negative_inf_value = -3.402823466E+38
def construct(self, input_indices, input_values, field_ids):
_check_input_2d(F.shape(input_indices), "input_indices", self.cls_name)
_check_input_2d(F.shape(input_values), "input_values", self.cls_name)
_check_input_2d(F.shape(field_ids), "field_ids", self.cls_name)
_check_input_dtype(F.dtype(input_indices), "input_indices", [mstype.int32, mstype.int64], self.cls_name)
_check_input_dtype(F.dtype(input_values), "input_values", [mstype.float32], self.cls_name)
_check_input_dtype(F.dtype(field_ids), "field_ids", [mstype.int32], self.cls_name)
batch_size = self.shape(input_indices)[0]
num_segments = batch_size * self.field_size
bias = Range(0, num_segments, self.field_size)()
bias = self.reshape(bias, (batch_size, -1))
field_ids = self.bias_add(field_ids, bias)
if self.target == "CPU":
out = self.embeddinglookup(self.embedding_table, input_indices, 0)
else:
if self.forward_unique:
shp = self.shape(input_indices) + (self.embedding_size,)
indices_flatten = self.reshape(input_indices, (-1,))
unique_id, unique_idx = self.unique(indices_flatten)
weight_unique = self.gatherv2(self.embedding_table, unique_id, 0)
weight_flatten = self.gather_revert(weight_unique, unique_idx, 0)
out = self.reshape(weight_flatten, shp)
else:
out = self.gatherv2(self.embedding_table, input_indices, 0)
if self.max_norm is not None:
axis = _make_axis_range(F.rank(input_indices), F.rank(out))
clip_by_norm = ClipByNorm(axis)
out = clip_by_norm(out, self.max_norm)
weights = self.reshape(input_values, (batch_size, self.shape(input_indices)[1], 1))
embedding = self.mul(weights, out)
if self.operator == 'MAX':
# Fill the padding value to -inf, so the padded value will not influence the results
negative_inf_mask = self.cast(self.equal(weights, 0), mstype.float32)
inf_mask = self.inf_mask_mul(negative_inf_mask, self.negative_inf_value)
embedding = self.inf_add(embedding, inf_mask)
embedding = self.reshape(embedding, (-1, self.embedding_size))
field_ids = self.reshape(field_ids, (-1,))
merged_vectors = self.merge_op(embedding, field_ids, num_segments)
if self.operator == 'MAX':
value_count = self.count_op(self.abs(self.reshape(input_values, (-1,))), field_ids, num_segments)
value_zeros = self.cast(self.max_no_equal(value_count, 0.0), mstype.float32)
count = self.expand(value_zeros, -1)
merged_vectors = self.max_mask_mul(merged_vectors, count)
if self.operator == 'MEAN':
value_count = self.count_op(self.abs(input_values), field_ids, num_segments)
value_count = self.expand(value_count, -1)
merged_vectors = self.div_no_nan(merged_vectors, value_count)
merged_vectors = self.reshape(merged_vectors, (batch_size, self.field_size, -1))
return merged_vectors