Source code for mindspore.nn.layer.embedding

# Copyright 2020 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
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# See the License for the specific language governing permissions and
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# ============================================================================
import mindspore.common.dtype as mstype
from mindspore.common.tensor import Tensor
from mindspore.ops import operations as P
from mindspore.common.parameter import Parameter
from mindspore.common.initializer import initializer
from ..cell import Cell
from ..._checkparam import Validator as validator

__all__ = ['Embedding']

[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 input should be of type 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 input. Default: mindspore.float32. Inputs: - **input** (Tensor) - Tensor of shape :math:`(\text{vocab_size})`. Outputs: Tensor of shape :math:`(\text{vocab_size}, \text{embedding_size})`. Examples: >>> net = nn.Embedding(20000, 768, True) >>> input_data = Tensor(np.ones([8, 128]), mindspore.int32) >>> >>> # Maps the input word IDs to word embedding. >>> output = net(input_data) >>> output.shape() (8, 128, 768) """ def __init__(self, vocab_size, embedding_size, use_one_hot=False, embedding_table='normal', dtype=mstype.float32): super(Embedding, self).__init__() validator.check_subclass("dtype", dtype, mstype.number_type, self.cls_name) self.vocab_size = vocab_size self.embedding_size = embedding_size self.use_one_hot = use_one_hot self.embedding_table = Parameter(initializer(embedding_table, [vocab_size, embedding_size]), name='embedding_table') self.dtype = dtype self.expand = P.ExpandDims() self.reshape_flat = P.Reshape() self.shp_flat = (-1,) self.gather = P.GatherV2() 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): extended_ids = self.expand(ids, -1) out_shape = self.get_shp(ids) + (self.embedding_size,) flat_ids = self.reshape_flat(extended_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={}'.format( self.vocab_size, self.embedding_size, self.use_one_hot, self.embedding_table, self.dtype) return s