GPT-2

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OpenAI · Feb–Nov 2019Dense decoder

Position-wise feed-forward network

Reference code openai/gpt-2 @ 9b63575, huggingface/transformers @ 71c6f69, configuration openai-community/gpt2 @ 607a30d · checkpoint tensors not independently counted · How to read

Transformer block › MLP

Expanded MLP features

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tensorconv1d(x, 'c_fc', n_state)

The first learned projection expands each position to 3,072 features, before GELU.

shape
hh [B batch, T positions, 3072 d_ff]
Sources

Contextual evidence from Feature expansion

The released projection helper owns a learned matrix and output bias and applies them to the last feature axis.

linear-operation · openai/gpt-2 / src/model.py
Pinned revision 9b63575ef427 ↗ · lines 50–56

Read code excerpt
def conv1d(x, scope, nf, *, w_init_stdev=0.02):
    with tf.variable_scope(scope):
        *start, nx = shape_list(x)
        w = tf.get_variable('w', [1, nx, nf], initializer=tf.random_normal_initializer(stddev=w_init_stdev))
        b = tf.get_variable('b', [nf], initializer=tf.constant_initializer(0))
        c = tf.reshape(tf.matmul(tf.reshape(x, [-1, nx]), tf.reshape(w, [-1, nf]))+b, start+[nf])
        return c

Contextual evidence from Feature expansion

The MLP's c_fc projection opens the four-times-wider feature workspace before GELU.

openai · openai/gpt-2 / src/model.py
Pinned revision 9b63575ef427 ↗ · lines 115–128

Read code excerpt
def mlp(x, scope, n_state, *, hparams):
    with tf.variable_scope(scope):
        nx = x.shape[-1].value
        h = gelu(conv1d(x, 'c_fc', n_state))

… lines 119–127 omitted …

        m = mlp(norm(x, 'ln_2'), 'mlp', nx*4, hparams=hparams)

Contextual evidence from Feature expansion

The maintained MLP defines c_fc at the configured intermediate size and applies it before activation.

transformers · huggingface/transformers / src/transformers/models/gpt2/modeling_gpt2.py
Pinned revision 71c6f699ac9b ↗ · lines 232–260

Read code excerpt
        embed_dim = config.hidden_size
        self.c_fc = Conv1D(intermediate_size, embed_dim)
        self.c_proj = Conv1D(embed_dim, intermediate_size)
        self.act = ACT2FN[config.activation_function]
        self.dropout = nn.Dropout(config.resid_pdrop)

    def forward(self, hidden_states: tuple[torch.FloatTensor] | None) -> torch.FloatTensor:
        hidden_states = self.c_fc(hidden_states)
        hidden_states = self.act(hidden_states)
        hidden_states = self.c_proj(hidden_states)
        hidden_states = self.dropout(hidden_states)
        return hidden_states

… lines 244–248 omitted …

        hidden_size = config.hidden_size
        inner_dim = config.n_inner if config.n_inner is not None else 4 * hidden_size

… lines 251–259 omitted …

        self.mlp = GPT2MLP(inner_dim, config)

Contextual evidence from Approximate GELU

The released graph defines the tanh approximation used by GPT-2.

openai · openai/gpt-2 / src/model.py
Pinned revision 9b63575ef427 ↗ · lines 25–26

Read code excerpt
def gelu(x):
    return 0.5*x*(1+tf.tanh(np.sqrt(2/np.pi)*(x+0.044715*tf.pow(x, 3))))