EmbeddingBag

class torch.ao.nn.qat.modules.embedding_ops.EmbeddingBag(num_embeddings, embedding_dim, max_norm=None, norm_type=2.0, scale_grad_by_freq=False, mode='mean', sparse=False, _weight=None, include_last_offset=False, padding_idx=None, qconfig=None, device=None, dtype=None)

An embedding bag module attached with FakeQuantize modules for weight, used for quantization aware training.

We adopt the same interface as torch.nn.EmbeddingBag, please see https://pytorch.org/docs/stable/generated/torch.nn.EmbeddingBag.html#torch.nn.EmbeddingBag for documentation.

Similar to torch.nn.EmbeddingBag, with FakeQuantize modules initialized to default.

Variables:

weight (torch.Tensor) – fake quant module for weight

classmethod from_float(mod, use_precomputed_fake_quant=False)

Create a qat module from a float module

Args: mod a float module, either produced by torch.ao.quantization utilities or directly from user