added n_inner argument to gpt2 config (#6296)

src/transformers/configuration_gpt2.py

@@ -59,6 +59,8 @@ class GPT2Config(PretrainedConfig):
                 Number of hidden layers in the Transformer encoder.
             n_head (:obj:`int`, optional, defaults to 12):
                 Number of attention heads for each attention layer in the Transformer encoder.
+            n_inner (:obj:`int`, optional, defaults to None):
+                Dimensionality of the inner feed-forward layers. :obj:`None` will set it to 4 times n_embd
             activation_function (:obj:`str`, optional, defaults to 'gelu'):
                 Activation function selected in the list ["relu", "swish", "gelu", "tanh", "gelu_new"].
             resid_pdrop (:obj:`float`, optional, defaults to 0.1):
@@ -122,6 +124,7 @@ class GPT2Config(PretrainedConfig):
         n_embd=768,
         n_layer=12,
         n_head=12,
+        n_inner=None,
         activation_function="gelu_new",
         resid_pdrop=0.1,
         embd_pdrop=0.1,
@@ -145,6 +148,7 @@ class GPT2Config(PretrainedConfig):
         self.n_embd = n_embd
         self.n_layer = n_layer
         self.n_head = n_head
+        self.n_inner = n_inner
         self.activation_function = activation_function
         self.resid_pdrop = resid_pdrop
         self.embd_pdrop = embd_pdrop

src/transformers/modeling_gpt2.py

@@ -240,10 +240,11 @@ class Block(nn.Module):
     def __init__(self, n_ctx, config, scale=False):
         super().__init__()
         nx = config.n_embd
+        inner_dim = config.n_inner if config.n_inner is not None else 4 * nx
         self.ln_1 = nn.LayerNorm(nx, eps=config.layer_norm_epsilon)
         self.attn = Attention(nx, n_ctx, config, scale)
         self.ln_2 = nn.LayerNorm(nx, eps=config.layer_norm_epsilon)
-        self.mlp = MLP(4 * nx, config)
+        self.mlp = MLP(inner_dim, config)
 
     def forward(
         self, x, layer_past=None, attention_mask=None, head_mask=None, use_cache=False, output_attentions=False,

src/transformers/modeling_tf_gpt2.py

@@ -194,10 +194,11 @@ class TFBlock(tf.keras.layers.Layer):
     def __init__(self, n_ctx, config, scale=False, **kwargs):
         super().__init__(**kwargs)
         nx = config.n_embd
+        inner_dim = config.n_inner if config.n_inner is not None else 4 * nx
         self.ln_1 = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_epsilon, name="ln_1")
         self.attn = TFAttention(nx, n_ctx, config, scale, name="attn")
         self.ln_2 = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_epsilon, name="ln_2")
-        self.mlp = TFMLP(4 * nx, config, name="mlp")
+        self.mlp = TFMLP(inner_dim, config, name="mlp")
 
     def call(self, x, layer_past, attention_mask, head_mask, use_cache, output_attentions, training=False):
         a = self.ln_1(x)