def model()

in src/model.py [0:0]

• 23 lines of code
• 4 McCabe index (conditional complexity)

def model(hparams, X, past=None, scope='model', reuse=False):
    with tf.variable_scope(scope, reuse=reuse):
        results = {}
        batch, sequence = shape_list(X)

        wpe = tf.get_variable('wpe', [hparams.n_ctx, hparams.n_embd],
                             initializer=tf.random_normal_initializer(stddev=0.01))
        wte = tf.get_variable('wte', [hparams.n_vocab, hparams.n_embd],
                             initializer=tf.random_normal_initializer(stddev=0.02))
        past_length = 0 if past is None else tf.shape(past)[-2]
        h = tf.gather(wte, X) + tf.gather(wpe, positions_for(X, past_length))

        # Transformer
        presents = []
        pasts = tf.unstack(past, axis=1) if past is not None else [None] * hparams.n_layer
        assert len(pasts) == hparams.n_layer
        for layer, past in enumerate(pasts):
            h, present = block(h, 'h%d' % layer, past=past, hparams=hparams)
            presents.append(present)
        results['present'] = tf.stack(presents, axis=1)
        h = norm(h, 'ln_f')

        # Language model loss.  Do tokens <n predict token n?
        h_flat = tf.reshape(h, [batch*sequence, hparams.n_embd])
        logits = tf.matmul(h_flat, wte, transpose_b=True)
        logits = tf.reshape(logits, [batch, sequence, hparams.n_vocab])
        results['logits'] = logits
        return results