Add LayerScale to NAT/DiNAT

src/transformers/models/dinat/configuration_dinat.py

@@ -70,6 +70,8 @@ class DinatConfig(PretrainedConfig):
             The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
         layer_norm_eps (`float`, *optional*, defaults to 1e-12):
             The epsilon used by the layer normalization layers.
+        layer_scale_init_value (`float`, *optional*, defaults to 0.0):
+            The initial value for the layer scale. Disabled if <=0.
 
     Example:
 
@@ -110,6 +112,7 @@ def __init__(
         patch_norm=True,
         initializer_range=0.02,
         layer_norm_eps=1e-5,
+        layer_scale_init_value=0.0,
         **kwargs
     ):
         super().__init__(**kwargs)
@@ -134,3 +137,4 @@ def __init__(
         # we set the hidden_size attribute in order to make Dinat work with VisionEncoderDecoderModel
         # this indicates the channel dimension after the last stage of the model
         self.hidden_size = int(embed_dim * 2 ** (len(depths) - 1))
+        self.layer_scale_init_value = layer_scale_init_value

src/transformers/models/dinat/modeling_dinat.py

@@ -462,6 +462,11 @@ def __init__(self, config, dim, num_heads, dilation, drop_path_rate=0.0):
         self.layernorm_after = nn.LayerNorm(dim, eps=config.layer_norm_eps)
         self.intermediate = DinatIntermediate(config, dim)
         self.output = DinatOutput(config, dim)
+        self.layer_scale_parameters = (
+            nn.Parameter(config.layer_scale_init_value * torch.ones((2, dim)), requires_grad=True)
+            if config.layer_scale_init_value > 0
+            else None
+        )
 
     def maybe_pad(self, hidden_states, height, width):
         window_size = self.window_size
@@ -496,11 +501,18 @@ def forward(
         if was_padded:
             attention_output = attention_output[:, :height, :width, :].contiguous()
 
+        if self.layer_scale_parameters is not None:
+            attention_output = self.layer_scale_parameters[0] * attention_output
+
         hidden_states = shortcut + self.drop_path(attention_output)
 
         layer_output = self.layernorm_after(hidden_states)
-        layer_output = self.intermediate(layer_output)
-        layer_output = hidden_states + self.output(layer_output)
+        layer_output = self.output(self.intermediate(layer_output))
+
+        if self.layer_scale_parameters is not None:
+            layer_output = self.layer_scale_parameters[1] * layer_output
+
+        layer_output = hidden_states + self.drop_path(layer_output)
 
         layer_outputs = (layer_output, attention_outputs[1]) if output_attentions else (layer_output,)
         return layer_outputs

src/transformers/models/nat/__init__.py

@@ -12,7 +12,6 @@
 #
 # Unless required by applicable law or agreed to in writing, software
 # distributed under the License is distributed on an "AS IS" BASIS,
-# distributed under the License is distributed on an "AS IS" BASIS,
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.

src/transformers/models/nat/configuration_nat.py

@@ -68,6 +68,8 @@ class NatConfig(PretrainedConfig):
             The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
         layer_norm_eps (`float`, *optional*, defaults to 1e-12):
             The epsilon used by the layer normalization layers.
+        layer_scale_init_value (`float`, *optional*, defaults to 0.0):
+            The initial value for the layer scale. Disabled if <=0.
 
     Example:
 
@@ -107,6 +109,7 @@ def __init__(
         patch_norm=True,
         initializer_range=0.02,
         layer_norm_eps=1e-5,
+        layer_scale_init_value=0.0,
         **kwargs
     ):
         super().__init__(**kwargs)
@@ -130,3 +133,4 @@ def __init__(
         # we set the hidden_size attribute in order to make Nat work with VisionEncoderDecoderModel
         # this indicates the channel dimension after the last stage of the model
         self.hidden_size = int(embed_dim * 2 ** (len(depths) - 1))
+        self.layer_scale_init_value = layer_scale_init_value

src/transformers/models/nat/modeling_nat.py

@@ -445,6 +445,11 @@ def __init__(self, config, dim, num_heads, drop_path_rate=0.0):
         self.layernorm_after = nn.LayerNorm(dim, eps=config.layer_norm_eps)
         self.intermediate = NatIntermediate(config, dim)
         self.output = NatOutput(config, dim)
+        self.layer_scale_parameters = (
+            nn.Parameter(config.layer_scale_init_value * torch.ones((2, dim)), requires_grad=True)
+            if config.layer_scale_init_value > 0
+            else None
+        )
 
     def maybe_pad(self, hidden_states, height, width):
         window_size = self.kernel_size
@@ -479,11 +484,18 @@ def forward(
         if was_padded:
             attention_output = attention_output[:, :height, :width, :].contiguous()
 
+        if self.layer_scale_parameters is not None:
+            attention_output = self.layer_scale_parameters[0] * attention_output
+
         hidden_states = shortcut + self.drop_path(attention_output)
 
         layer_output = self.layernorm_after(hidden_states)
-        layer_output = self.intermediate(layer_output)
-        layer_output = hidden_states + self.output(layer_output)
+        layer_output = self.output(self.intermediate(layer_output))
+
+        if self.layer_scale_parameters is not None:
+            layer_output = self.layer_scale_parameters[1] * layer_output
+
+        layer_output = hidden_states + self.drop_path(layer_output)
 
         layer_outputs = (layer_output, attention_outputs[1]) if output_attentions else (layer_output,)
         return layer_outputs