def hybrid_attention(q,
k,
v,
context,
memory_length_dim,
key_dim,
value_dim,
bias=None,
dropout_rate=0.0,
dropout_broadcast_dims=None,
extra_logit=None):
"""Dot-product attention - doesn't use positional dimensions.
key_dim is a Dimension representing the channels in the queries and keys
value_dim is a Dimension representing the channels in values
memory_length_dim is a Dimension representing the different key/value pairs.
Dimensions of q: other_query_dims + {key_dim}
Dimensions of k: other_memory_dims + {memory_length_dim, key_dim}
Dimensions of v: other_memory_dims + {memory_length_dim, value_dim}
other_memory_dims is a subset of other_query_dims
Typically, other_query_dims={batch, heads, length}
Typically, other_memory_dims={batch, heads}
Args:
q: a Tensor
k: a Tensor
v: a Tensor
context: context of the attention layer.
memory_length_dim: a Dimension
key_dim: a Dimension
value_dim: a Dimension
bias: a Tensor to be added into the attention logits.
dropout_rate: a float.
dropout_broadcast_dims: an optional list of mtf.Dimension
extra_logit: an optional scalar or tensor
Returns:
Tensor with shape q.shape - key_dim + value_dim
"""
logits = mtf.layers.us_einsum([q, k], reduced_dims=[key_dim])
if bias is not None:
logits += bias
query_length_dim = mtf.Dimension("length", memory_length_dim.size)
doubly_coeff = mtf.get_variable(
context.mesh, "doubly_coeff", [],
initializer=tf.constant_initializer(0.5),
dtype=context.variable_dtype)
doubly_coeff = mtf.maximum(mtf.minimum(doubly_coeff, 1.), 0.)
upper_weights = mtf.softmax(
logits, memory_length_dim, extra_logit=extra_logit)
lower_log_weights = mtf.log_softmax(
logits, query_length_dim, extra_logit=extra_logit)
doubly_weights = mtf.softmax(
lower_log_weights, memory_length_dim, extra_logit=extra_logit)
weights = doubly_coeff * doubly_weights + (1. - doubly_coeff) * upper_weights
if dropout_rate != 0.0:
weights = mtf.dropout(
weights, 1.0 - dropout_rate,
noise_shape=weights.shape - dropout_broadcast_dims)
outputs_shape = q.shape - key_dim + value_dim
outputs = mtf.einsum([weights, v], outputs_shape)
return outputs
def get_activation_fn(params):
activation_fn = params.get("activation_fn", "gelu")
def _arcsinh(x):
return mtf.log(x + mtf.sqrt(1 + x**2))
def _var(x, init):
return mtf.get_variable(x.mesh,
f"activation-{random.randint(0, 2 ** 32):x}",
[],
initializer=tf.constant_initializer(init),
dtype=x.dtype)
def _pos_var(x, val):
return mtf.softplus(_var(x, 0)) + val
if activation_fn == "gelu": # https://arxiv.org/abs/1606.08415
return mtf.gelu
elif activation_fn == "relu":
return mtf.relu
elif activation_fn == "sigmoid":
return mtf.sigmoid
elif activation_fn == "tanh":
return mtf.tanh
elif activation_fn == "selu": # https://arxiv.org/abs/1706.02515
return mtf.selu
elif activation_fn == "elu": # https://arxiv.org/abs/1511.07289
return mtf.elu
elif activation_fn == "lrelu001":
return lambda x: mtf.leaky_relu(x, alpha=0.01)
elif activation_fn == "lrelu020":
return lambda x: mtf.leaky_relu(x, alpha=0.20)
elif activation_fn == "abs":
return mtf.abs
elif activation_fn == "id":
return lambda x: x
elif activation_fn == "sin":
return mtf.sin
elif activation_fn == "cos":
return mtf.cos
elif activation_fn == "sign":
return mtf.sign
elif activation_fn == "triangle_relax":
return lambda x: mtf.sin(x) - mtf.sin(3 * x) / 9 + mtf.sin(
5 * x) / 25 - mtf.sin(7 * x) / 49
elif activation_fn == "square_relax":
return lambda x: mtf.cos(x) - mtf.cos(3 * x) / 3 + mtf.cos(
5 * x) / 5 - mtf.cos(7 * x) / 7
elif activation_fn == "spike":
return lambda x: 1 / (1 + x**2)
elif activation_fn == "spike2":
return lambda x: mtf.exp(-x**2)
elif activation_fn == "tanhshrink":
return lambda x: x - tanh(x)
elif activation_fn == "softsign":
return lambda x: x / (mtf.abs(x) + 1)
elif activation_fn == "softmax":
return lambda x: mtf.softmax(x, x.shape[-1])
elif activation_fn == "logsoftmax":
return lambda x: mtf.log_softmax(x, x.shape[-1])
elif activation_fn == "bipolarsigmoid":
return lambda x: mtf.sigmoid(x) * 2 - 1
elif activation_fn == "rrelu": # https://arxiv.org/abs/1505.00853
def _rrelu_fn(x):
negative_scale = random.random()
return (negative_scale * mtf.abs(x) + x) / (1 + negative_scale)
return _rrelu_fn
elif activation_fn == "elish": # https://arxiv.org/abs/1808.00783v1
def _elish_fn(x):
cond = mtf.cast(mtf.greater(x, 0), x.dtype)
exp = mtf.exp(x)
return cond * x / (1 + exp) + (1 - cond) * (exp - 1) / (1 / exp +
1)
return _elish_fn
elif activation_fn == "silu": # https://arxiv.org/abs/1710.05941
return mtf.swish
elif activation_fn == "arcsinh":
return _arcsinh
# parametric
elif activation_fn == "aria": # https://arxiv.org/abs/1805.08878
return lambda x: x * (_var(x, 0) + _var(x, 1) / (_pos_var(x, 0) + _var(
x, 1) * mtf.exp(_var(x, -1) * x)**(1 / _pos_var(x, 1))))
elif activation_fn == "prelu": # https://arxiv.org/abs/1502.01852
return lambda x: mtf.leaky_relu(x, alpha=_var(x, 0.2))
elif activation_fn == "parcsinh":
return lambda x: _var(x, 1) * _arcsinh(x * _pos_var(x, 1))
elif activation_fn == "psoftplus":
return lambda x: _var(x, 1) * mtf.softplus(x * _var(x, 1)) + _var(x, 0)
elif activation_fn == "proottanh":
return lambda x: (x**_pos_var(x, 2) + _pos_var(x, 1))**(1 / _pos_var(
x, 3)) * mtf.tanh(x)
# https://arxiv.org/abs/1710.05941, https://arxiv.org/abs/1901.02671
elif activation_fn == "maxsig":
return lambda x: mtf.maximum(x, mtf.sigmoid(x))
elif activation_fn == "cosid":
return lambda x: mtf.cos(x) - x
elif activation_fn == "minsin":
return lambda x: mtf.minimum(x, mtf.sin(x))
elif activation_fn == "maxtanh":
return lambda x: mtf.maximum(x, mtf.tanh(x))
elif activation_fn == "softplus":
return mtf.softplus
elif activation_fn == "mish": # https://arxiv.org/abs/1908.08681
return lambda x: x * mtf.tanh(mtf.softplus(x))
elif activation_fn == "tanhexp": # https://arxiv.org/abs/2003.09855
return lambda x: x * mtf.tanh(mtf.exp(x))
elif activation_fn == "lisht": # https://arxiv.org/abs/1901.05894
return lambda x: x * mtf.tanh(x)
elif activation_fn == "seagull": # https://arxiv.org/abs/2011.11713
return lambda x: mtf.log(1 + x**2)
elif activation_fn == "snake": # https://arxiv.org/abs/2006.08195
return lambda x: x + mtf.sin(x)**2
elif activation_fn == "roottanh": # made up
return lambda x: (x**2 + 1)**(1 / 3) * mtf.tanh(x)
elif activation_fn == "softplusmone": # made up
return lambda x: mtf.softplus(x) - 1
else:
raise ValueError(
'unknown activation function "activation_fn" in config')
def compute_loss(logits, positions):
one_hot_positions = mtf.one_hot(positions, output_dim=seq_dim)
log_probs = mtf.log_softmax(logits, seq_dim)
loss = -mtf.reduce_mean(
mtf.reduce_sum(one_hot_positions * log_probs, reduced_dim=seq_dim))
return loss
def get_activation_fn(params):
activation_fn = params.get("activation_fn", "gelu")
if activation_fn == "gelu": # https://arxiv.org/abs/1606.08415
return mtf.gelu
elif activation_fn == "relu":
return mtf.relu
elif activation_fn == "sigmoid":
return mtf.sigmoid
elif activation_fn == "tanh":
return mtf.tanh
elif activation_fn == "selu": # https://arxiv.org/abs/1706.02515
return mtf.selu
elif activation_fn == "elu": # https://arxiv.org/abs/1511.07289
return mtf.elu
elif activation_fn == "abs":
return mtf.abs
elif activation_fn == "id":
return lambda x: x
elif activation_fn == "sin":
return mtf.sin
elif activation_fn == "cos":
return mtf.cos
elif activation_fn == "sign":
return mtf.sign
elif activation_fn == "triangle_relax":
return lambda x: mtf.sin(x) - mtf.sin(3 * x) / 9 + mtf.sin(
5 * x) / 25 - mtf.sin(7 * x) / 49
elif activation_fn == "square_relax":
return lambda x: mtf.cos(x) - mtf.cos(3 * x) / 3 + mtf.cos(
5 * x) / 5 - mtf.cos(7 * x) / 7
elif activation_fn == "spike":
return lambda x: 1 / (1 + x**2)
elif activation_fn == "spike2":
return lambda x: mtf.exp(-x**2)
elif activation_fn == "tanhshrink":
return lambda x: x - tanh(x)
elif activation_fn == "softsign":
return lambda x: x / (mtf.abs(x) + 1)
elif activation_fn == "softmax":
return lambda x: mtf.softmax(x, x.shape[-1])
elif activation_fn == "logsoftmax":
return lambda x: mtf.log_softmax(x, x.shape[-1])
elif activation_fn == "bipolarsigmoid":
return lambda x: mtf.sigmoid(x) * 2 - 1
elif activation_fn == "rrelu": # https://arxiv.org/abs/1505.00853
def _rrelu_fn(x):
negative_scale = random.random()
return (negative_scale * mtf.abs(x) + x) / (1 + negative_scale)
return _rrelu_fn
elif activation_fn == "elish": # https://arxiv.org/abs/1808.00783v1
def _elish_fn(x):
cond = mtf.cast(mtf.greater(x, 0), x.dtype)
exp = mtf.exp(x)
return cond * x / (1 + exp) + (1 - cond) * (exp - 1) / (1 / exp +
1)
return _elish_fn
# swish activations
elif activation_fn == "swish": # https://arxiv.org/abs/1710.05941
return mtf.swish
# https://arxiv.org/abs/1710.05941, https://arxiv.org/abs/1901.02671
elif activation_fn == "maxsig":
return lambda x: mtf.maximum(x, mtf.sigmoid(x))
elif activation_fn == "cosid":
return lambda x: mtf.cos(x) - x
elif activation_fn == "minsin":
return lambda x: mtf.minimum(x, mtf.sin(x))
elif activation_fn == "maxtanh":
return lambda x: mtf.maximum(x, mtf.tanh(x))
elif activation_fn == "softplus":
return mtf.softplus
elif activation_fn == "mish": # https://arxiv.org/abs/1908.08681
return lambda x: x * mtf.tanh(mtf.softplus(x))
elif activation_fn == "tanhexp": # https://arxiv.org/abs/2003.09855
return lambda x: x * mtf.tanh(mtf.exp(x))
elif activation_fn == "lisht": # https://arxiv.org/abs/1901.05894
return lambda x: x * mtf.tanh(x)
elif activation_fn == "seagull": # https://arxiv.org/abs/2011.11713
return lambda x: mtf.log(1 + x**2)
elif activation_fn == "snake": # https://arxiv.org/abs/2006.08195
return lambda x: x + mtf.sin(x)**2
elif activation_fn == "roottanh": # made up
return lambda x: (x**2 + 1)**(1 / 3) * mtf.tanh(x)
elif activation_fn == "softplusmone": # made up
return lambda x: mtf.softplus(x) - 1
else:
raise ValueError(
'unknown activation function "activation_fn" in config')
def compute_log_softmax(self, hidden, context): """Returns the log softmax of logits computed from the hidden state.""" logits = self._embedding.hidden_to_logits(hidden, context=context) return mtf.log_softmax(logits, reduced_dim=self._vocab_dim)
7 * x) / 49,
'square_relax':
lambda x: mtf.cos(x) - mtf.cos(3 * x) / 3 + mtf.cos(5 * x) / 5 - mtf.cos(
7 * x) / 7,
'spike':
lambda x: 1 / (1 + x**2),
'spike2':
lambda x: mtf.exp(-x**2),
'tanhshrink':
lambda x: x - tanh(x),
'softsign':
lambda x: x / (mtf.abs(x) + 1),
'softmax':
lambda x: mtf.softmax(x, x.shape[-1]),
'logsoftmax':
lambda x: mtf.log_softmax(x, x.shape[-1]),
'bipolarsigmoid':
lambda x: mtf.sigmoid(x) * 2 - 1,
'rrelu':
_rrelu,
'elish':
_elish,
'arcsinh':
_arcsinh,
'aria':
lambda x: x * (_var(x, 0) + _var(x, 1) /
(_pos_var(x, 0) + _var(x, 1) * mtf.exp(_var(x, -1) * x)**
(1 / _pos_var(x, 1)))),
'prelu':
lambda x: mtf.leaky_relu(x, alpha=_var(x, 0.2)),
'parcsinh':
