文档反馈

问题文档片段

问题文档片段包含公式时，显示为空格。

提交类型

issue

有点复杂...

找人问问吧。

请选择提交类型

问题类型

规范和低错类

- 规范和低错类：

- 错别字或拼写错误，标点符号使用错误、公式错误或显示异常。

- 链接错误、空单元格、格式错误。

- 英文中包含中文字符。

- 界面和描述不一致，但不影响操作。

- 表述不通顺，但不影响理解。

- 版本号不匹配：如软件包名称、界面版本号。

易用性

- 易用性：

- 关键步骤错误或缺失，无法指导用户完成任务。

- 缺少主要功能描述、关键词解释、必要前提条件、注意事项等。

- 描述内容存在歧义指代不明、上下文矛盾。

- 逻辑不清晰，该分类、分项、分步骤的没有给出。

正确性

- 正确性：

- 技术原理、功能、支持平台、参数类型、异常报错等描述和软件实现不一致。

- 原理图、架构图等存在错误。

- 命令、命令参数等错误。

- 代码片段错误。

- 命令无法完成对应功能。

- 界面错误，无法指导操作。

- 代码样例运行报错、运行结果不符。

风险提示

- 风险提示：

- 对重要数据或系统存在风险的操作，缺少安全提示。

内容合规

- 内容合规：

- 违反法律法规，涉及政治、领土主权等敏感词。

- 内容侵权。

请选择问题类型

问题描述

点击输入详细问题描述，以帮助我们快速定位问题。

提交

文档反馈

mindspore.nn.MultiFieldEmbeddingLookup

class mindspore.nn.MultiFieldEmbeddingLookup(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')

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.

Parameters

• 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 mindspore.nn.EmbeddingLookup. Default: 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 $(batch\mathrm{\_}size,seq\mathrm{\_}length)$. 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 $(batch\mathrm{\_}size,seq\mathrm{\_}length)$. 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 $(batch\mathrm{\_}size,seq\mathrm{\_}length)$. Specifies the field id of elements of the input_indices. Type is Int32.

Outputs:

Tensor, the shape of tensor is $(batch\mathrm{\_}size,field\mathrm{\_}size,embedding\mathrm{\_}size)$. 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)