Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Replace BertLayerNorm with LayerNorm #14385

Merged
merged 1 commit into from
Nov 15, 2021
Merged

Replace BertLayerNorm with LayerNorm #14385

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
10 changes: 5 additions & 5 deletions examples/research_projects/movement-pruning/emmental/modeling_bert_masked.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -30,7 +30,7 @@
from emmental.modules import MaskedLinear
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.modeling_utils import PreTrainedModel, prune_linear_layer
from transformers.models.bert.modeling_bert import ACT2FN, BertLayerNorm, load_tf_weights_in_bert
from transformers.models.bert.modeling_bert import ACT2FN, load_tf_weights_in_bert


logger = logging.getLogger(__name__)
Expand All @@ -47,7 +47,7 @@ def __init__(self, config):

# self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
# any TensorFlow checkpoint file
self.LayerNorm = BertLayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)

def forward(self, input_ids=None, token_type_ids=None, position_ids=None, inputs_embeds=None):
Expand Down Expand Up @@ -182,7 +182,7 @@ def __init__(self, config):
mask_init=config.mask_init,
mask_scale=config.mask_scale,
)
self.LayerNorm = BertLayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)

def forward(self, hidden_states, input_tensor, threshold):
Expand Down Expand Up @@ -275,7 +275,7 @@ def __init__(self, config):
mask_init=config.mask_init,
mask_scale=config.mask_scale,
)
self.LayerNorm = BertLayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)

def forward(self, hidden_states, input_tensor, threshold):
Expand Down Expand Up @@ -398,7 +398,7 @@ def _init_weights(self, module):
# Slightly different from the TF version which uses truncated_normal for initialization
# cf https://github.com/pytorch/pytorch/pull/5617
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
elif isinstance(module, BertLayerNorm):
elif isinstance(module, nn.LayerNorm):
module.bias.data.zero_()
module.weight.data.fill_(1.0)
if isinstance(module, nn.Linear) and module.bias is not None:
Expand Down