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.

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

activation function in BERTIntermediate #17

Merged
merged 9 commits into from
Nov 13, 2018
Merged
Changes from all commits
Commits
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
13 changes: 11 additions & 2 deletions modeling.py
Original file line number Diff line number Diff line change
Expand Up @@ -25,6 +25,7 @@
import torch
import torch.nn as nn
from torch.nn import CrossEntropyLoss
from six import string_types

def gelu(x):
"""Implementation of the gelu activation function.
Expand All @@ -34,6 +35,13 @@ def gelu(x):
return x * 0.5 * (1.0 + torch.erf(x / math.sqrt(2.0)))


def swish(x):
return x * torch.sigmoid(x)


ACT2FN = {"gelu": gelu, "relu": torch.nn.functional.relu, "swish": swish}


class BertConfig(object):
"""Configuration class to store the configuration of a `BertModel`.
"""
Expand All @@ -60,7 +68,7 @@ def __init__(self,
intermediate_size: The size of the "intermediate" (i.e., feed-forward)
layer in the Transformer encoder.
hidden_act: The non-linear activation function (function or string) in the
encoder and pooler.
encoder and pooler. If string, "gelu", "relu" and "swish" are supported.
hidden_dropout_prob: The dropout probabilitiy for all fully connected
layers in the embeddings, encoder, and pooler.
attention_probs_dropout_prob: The dropout ratio for the attention
Expand Down Expand Up @@ -237,7 +245,8 @@ class BERTIntermediate(nn.Module):
def __init__(self, config):
super(BERTIntermediate, self).__init__()
self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
self.intermediate_act_fn = gelu
self.intermediate_act_fn = ACT2FN[config.hidden_act] \
if isinstance(config.hidden_act, string_types) else config.hidden_act

def forward(self, hidden_states):
hidden_states = self.dense(hidden_states)
Expand Down