Skip to content

Use gelu_approximate directly in t5 presets #1284

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

Merged
merged 1 commit into from
Oct 24, 2023
Merged

Use gelu_approximate directly in t5 presets #1284

Show file tree
Hide file tree
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
5 changes: 2 additions & 3 deletions keras_nlp/models/t5/t5_backbone.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -58,8 +58,7 @@ class T5Backbone(Backbone):
dropout: float. Dropout probability for the Transformer layers.
activation: activation function (or activation string name). The
activation to be used in the inner dense blocks of the
Transformer layers. The original T5 architecture used `"relu"`,
but more recent versions use `"gelu"`. Defaults to `"gelu"`.
Transformer layers. Defaults to `"relu"`.
use_gated_activation: boolean. Whether to use activation gating in
the inner dense blocks of the Transformer layers.
The original T5 architecture didn't use gating, but more
Expand All @@ -80,7 +79,7 @@ def __init__(
intermediate_dim,
key_value_dim=None,
dropout=0.1,
activation="gelu",
activation="relu",
use_gated_activation=True,
layer_norm_epsilon=1e-06,
tie_embedding_weights=False,
Expand Down
6 changes: 3 additions & 3 deletions keras_nlp/models/t5/t5_presets.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -106,7 +106,7 @@
"intermediate_dim": 1024,
"key_value_dim": 64,
"dropout": 0.1,
"activation": "gelu",
"activation": "keras_nlp>gelu_approximate",
"use_gated_activation": True,
"layer_norm_epsilon": 1e-06,
},
Expand All @@ -130,7 +130,7 @@
"hidden_dim": 768,
"intermediate_dim": 2048,
"dropout": 0.1,
"activation": "gelu",
"activation": "keras_nlp>gelu_approximate",
"use_gated_activation": True,
"layer_norm_epsilon": 1e-06,
},
Expand All @@ -154,7 +154,7 @@
"hidden_dim": 1024,
"intermediate_dim": 2816,
"dropout": 0.1,
"activation": "gelu",
"activation": "keras_nlp>gelu_approximate",
"use_gated_activation": True,
"layer_norm_epsilon": 1e-06,
},
Expand Down
5 changes: 0 additions & 5 deletions keras_nlp/models/t5/t5_transformer_layer.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -65,11 +65,6 @@ def __init__(
self.cross_attention_layer_norm = T5LayerNorm(layer_norm_epsilon)
self.cross_attention_dropout = keras.layers.Dropout(dropout)

if activation == "gelu":
activation = keras.activations.get("keras_nlp>gelu_approximate")
else:
activation = keras.activations.get(activation)

self.input_projector = keras.layers.Dense(
intermediate_dim,
use_bias=False,
Expand Down