trax tl.Relu and tl.ShiftRight layers are nested inside Serial Combinator

Question

I am trying to build an attention model but Relu and ShiftRight layer by default nested inside the Serial Combinator. This further gives me errors in training.

layer_block = tl.Serial(
    tl.Relu(),
    tl.LayerNorm(), )

x = np.array([[-2, -1, 0, 1, 2],
              [-20, -10, 0, 10, 20]]).astype(np.float32) 

layer_block.init(shapes.signature(x)) y = layer_block(x)

print(f'layer_block: {layer_block}')

Output

layer_block: Serial[
  Serial[
    Relu
  ]
  LayerNorm
]

Expected Output

layer_block: Serial[
  Relu
  LayerNorm
]

The same problem arises with tl.ShiftRight()

The code above is taken from official documentation Example 5

Thanks in advance

Answer 1

I could not found the exact solution to the above problem, but you can create a custom Function using tl.Fn() and add the Relu and ShiftRight function code in it.

def _zero_pad(x, pad, axis):
    """Helper for jnp.pad with 0s for single-axis case."""
    pad_widths = [(0, 0)] * len(x.shape)
    pad_widths[axis] = pad  # Padding on axis.
    
    return jnp.pad(x, pad_widths, mode='constant')


def f(x):
    if mode == 'predict':
        return x
    padded = _zero_pad(x, (n_positions, 0), 1)
    return padded[:, :-n_positions]

# set ShiftRight parameters as global 
n_positions = 1
mode='train'

layer_block = tl.Serial(
    tl.Fn('Relu', lambda x: jnp.where(x <= 0, jnp.zeros_like(x), x)),
    tl.LayerNorm(),
    tl.Fn(f'ShiftRight({n_positions})', f)
)


x = np.array([[-2, -1, 0, 1, 2],
              [-20, -10, 0, 10, 20]]).astype(np.float32)
layer_block.init(shapes.signature(x))
y = layer_block(x)


print(f'layer_block: {layer_block}')

Output

layer_block: Serial[
  Relu
  LayerNorm
  ShiftRight(1)
]