【Hackathon 7th No.24】为 Paddle 新增 EmbeddingBag API #970
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zxcd
reviewed
Oct 8, 2024
| - 测试不同设备； | ||
| - 测试动态图静态图； | ||
| - 测试不同的参数组合； | ||
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| Args: | ||
| x(Tensor): A 1D or 2D tensor with type int32/int64, which contains the id information. If ``x`` is 1D tensor, it will be treated as the concatenation of multiple bags, and will be segmented by ``offsets`` into each bag. If ``x`` is 2D tensor, the shape should be [bag_number, sequence_length]. The value of the input id should satisfy :math: `0 <= id < params.shape[0]`. | ||
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| weight(Tensor): A tensor with shape of [num_embedding, embedding_dim] in which num_embedding indicates the size of the dictionary of embeddings and embedding_dim indicates the size of each embedding vector. |
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与 embedding 保持一致，支持 int8, float16, bfloat16, complex64, complex128, float32, float64。
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| include_last_offset(bool, optional): If True, the size of ``offsets`` will be [B+1], where B is the number of bags, and the last element will specify the ending position of the last bag. Default: False. | ||
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| weight_attr(ParamAttr|None, optional): To specify the weight parameter property. Default: None, which means the default weight parameter property is used. See usage for details in :ref:`api_paddle_ParamAttr` . In addition, user-defined or pre-trained word vectors can be loaded with the :attr:`param_attr` parameter. The local word vector needs to be transformed into numpy format, and the shape of local word vector should be consistent with :attr:`num_embeddings` . Then :ref:`api_paddle_nn_initializer_Assign` is used to load custom or pre-trained word vectors. See code example for details. |
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这个放错位置了，应该是 EembeddingBag 类的参数，用来初始化 weight 。F.embedding_bag 没有这个参数，已删除。
luotao1
changed the title
zxcd
reviewed
Oct 11, 2024
| """ | ||
| Args: | ||
| x(Tensor): A 1D or 2D tensor with type int32/int64, which contains the id information. If ``x`` is 1D tensor, it will be treated as the concatenation of multiple bags, and will be segmented by ``offsets`` into each bag. If ``x`` is 2D tensor, the shape should be [bag_number, sequence_length]. The value of the input id should satisfy :math: `0 <= id < params.shape[0]`. | ||
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| weight(Tensor): A tensor with shape of [num_embedding, embedding_dim] in which num_embedding indicates the size of the dictionary of embeddings and embedding_dim indicates the size of each embedding vector. | ||
| weight(Tensor): A tensor with shape of [num_embedding, embedding_dim] in which num_embedding indicates the size of the dictionary of embeddings and embedding_dim indicates the size of each embedding vector. Supported dtypes are int8, float16, bfloat16, complex64, complex128, float32, float64. |
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