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) [source]

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 $(\text{num\_embeddings}, \text{embedding\_dim})$.

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) [source]

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