def attn()

in src/model.py [0:0]

• 34 lines of code
• 3 McCabe index (conditional complexity)

def attn(x, scope, n_state, *, past, hparams):
    assert x.shape.ndims == 3  # Should be [batch, sequence, features]
    assert n_state % hparams.n_head == 0
    if past is not None:
        assert past.shape.ndims == 5  # Should be [batch, 2, heads, sequence, features], where 2 is [k, v]

    def split_heads(x):
        # From [batch, sequence, features] to [batch, heads, sequence, features]
        return tf.transpose(split_states(x, hparams.n_head), [0, 2, 1, 3])

    def merge_heads(x):
        # Reverse of split_heads
        return merge_states(tf.transpose(x, [0, 2, 1, 3]))

    def mask_attn_weights(w):
        # w has shape [batch, heads, dst_sequence, src_sequence], where information flows from src to dst.
        _, _, nd, ns = shape_list(w)
        b = attention_mask(nd, ns, dtype=w.dtype)
        b = tf.reshape(b, [1, 1, nd, ns])
        w = w*b - tf.cast(1e10, w.dtype)*(1-b)
        return w

    def multihead_attn(q, k, v):
        # q, k, v have shape [batch, heads, sequence, features]
        w = tf.matmul(q, k, transpose_b=True)
        w = w * tf.rsqrt(tf.cast(v.shape[-1].value, w.dtype))

        w = mask_attn_weights(w)
        w = softmax(w)
        a = tf.matmul(w, v)
        return a

    with tf.variable_scope(scope):
        c = conv1d(x, 'c_attn', n_state*3)
        q, k, v = map(split_heads, tf.split(c, 3, axis=2))
        present = tf.stack([k, v], axis=1)
        if past is not None:
            pk, pv = tf.unstack(past, axis=1)
            k = tf.concat([pk, k], axis=-2)
            v = tf.concat([pv, v], axis=-2)
        a = multihead_attn(q, k, v)
        a = merge_heads(a)
        a = conv1d(a, 'c_proj', n_state)
        return a, present