Shortcuts

Embedding

class torch.ao.nn.quantized.Embedding(num_embeddings, embedding_dim, padding_idx=None, max_norm=None, norm_type=2.0, scale_grad_by_freq=False, sparse=False, _weight=None, dtype=torch.quint8)[source]

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

Similar to Embedding, 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}).

Examples::
>>> m = nn.quantized.Embedding(num_embeddings=10, embedding_dim=12)
>>> indices = torch.tensor([9, 6, 5, 7, 8, 8, 9, 2, 8])
>>> output = m(indices)
>>> print(output.size())
torch.Size([9, 12])
classmethod from_float(mod)[source]

Create a quantized embedding module from a float module

Parameters:

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

Docs

Access comprehensive developer documentation for PyTorch

View Docs

Tutorials

Get in-depth tutorials for beginners and advanced developers

View Tutorials

Resources

Find development resources and get your questions answered

View Resources