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# EmbeddingBag

class torch.ao.nn.quantized.EmbeddingBag(num_embeddings, embedding_dim, max_norm=None, norm_type=2.0, scale_grad_by_freq=False, mode='sum', sparse=False, _weight=None, include_last_offset=False, dtype=torch.quint8)

  A quantized EmbeddingBag module with quantized packed weights as inputs. We adopt the same interface as torch.nn.EmbeddingBag, please see https://pytorch.org/docs/stable/nn.html#torch.nn.EmbeddingBag for documentation.

  Similar to `EmbeddingBag`, attributes will be randomly initialized at module creation time and will be overwritten later

  Variables:

    weight (Tensor) – the non-learnable quantized weights of the module of shape (num_embeddings,embedding_dim)(\text{num\_embeddings}, \text{embedding\_dim})(num_embeddings,embedding_dim).

  Examples::

        >>> m = nn.quantized.EmbeddingBag(num_embeddings=10, embedding_dim=12, include_last_offset=True, mode='sum')
        >>> indices = torch.tensor([9, 6, 5, 7, 8, 8, 9, 2, 8, 6, 6, 9, 1, 6, 8, 8, 3, 2, 3, 6, 3, 6, 5, 7, 0, 8, 4, 6, 5, 8, 2, 3])
        >>> offsets = torch.tensor([0, 19, 20, 28, 28, 32])
        >>> output = m(indices, offsets)
        >>> print(output.size())
        torch.Size([5, 12])

  classmethod from_float(mod)

    Create a quantized embedding_bag module from a float module

    Parameters:

      mod (Module) – a float module, either produced by torch.ao.quantization utilities or provided by user

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