layernorm eps is not copied properly when cloning HF_Bert

[huu4ontocord]

From the code (adapted from test_weight_mapper.py)

import torch
import torch.nn as nn

from fast_transformers.builders import TransformerEncoderBuilder
from fast_transformers.weight_mapper import PytorchMapper, \
    HugginfaceBertEncoderMapper,  LongformerMapper
from transformers import BertConfig, BertModel

def load_fast_bert():
        bert = BertModel(BertConfig())
        encoder = TransformerEncoderBuilder.from_kwargs(
            n_layers=12,
            n_heads=12,
            query_dimensions=64,
            value_dimensions=64,
            feed_forward_dimensions=3072,
            attention_type="full",
            final_normalization=False,
            activation="gelu"
        ).get()


        encoder.load_state_dict(
            HugginfaceBertEncoderMapper().map(bert.encoder.state_dict())
        )
        return encoder, bert

encoder looks like this:

TransformerEncoder(
  (layers): ModuleList(
    (0): TransformerEncoderLayer(
      (attention): AttentionLayer(
        (inner_attention): FullAttention(
          (dropout): Dropout(p=0.1, inplace=False)
        )
        (query_projection): Linear(in_features=768, out_features=768, bias=True)
        (key_projection): Linear(in_features=768, out_features=768, bias=True)
        (value_projection): Linear(in_features=768, out_features=768, bias=True)
        (out_projection): Linear(in_features=768, out_features=768, bias=True)
      )
      (linear1): Linear(in_features=768, out_features=3072, bias=True)
      (linear2): Linear(in_features=3072, out_features=768, bias=True)
      (norm1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
      (norm2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
      (dropout): Dropout(p=0.1, inplace=False)
    )

bert looks like this:

BertModel(
  (embeddings): BertEmbeddings(
    (word_embeddings): Embedding(30522, 768, padding_idx=0)
    (position_embeddings): Embedding(512, 768)
    (token_type_embeddings): Embedding(2, 768)
    (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
    (dropout): Dropout(p=0.1, inplace=False)
  )
  (encoder): BertEncoder(
    (layer): ModuleList(
      (0): BertLayer(
        (attention): BertAttention(
          (self): BertSelfAttention(
            (query): Linear(in_features=768, out_features=768, bias=True)
            (key): Linear(in_features=768, out_features=768, bias=True)
            (value): Linear(in_features=768, out_features=768, bias=True)
            (dropout): Dropout(p=0.1, inplace=False)
          )
          (output): BertSelfOutput(
            (dense): Linear(in_features=768, out_features=768, bias=True)
            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
            (dropout): Dropout(p=0.1, inplace=False)
          )
        )
        (intermediate): BertIntermediate(
          (dense): Linear(in_features=768, out_features=3072, bias=True)
        )
        (output): BertOutput(
          (dense): Linear(in_features=3072, out_features=768, bias=True)
          (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
          (dropout): Dropout(p=0.1, inplace=False)
        )
      )

[huu4ontocord]

Do you need to pass in the eps to TransformerEncoderLayer and TransformerDecoderLayer, and correspondingly pass in the eps from the constructor, and pass in via the builder and mapper as well?

[huu4ontocord]

On a related note, it would be cool to specify other norms, like scalenorm and to be able to configure the aciviation function to something beside's pytorch (like relu^2)