def benchmark_layers_advanced_activations_softmax_overhead(self):
layer = advanced_activations.Softmax()
x = array_ops.ones((1, 1))
def fn():
layer(x)
self._run(fn, 10000)
def _build_attention(self, rank):
"""Builds multi-head dot-product attention computations.
This function builds attributes necessary for `_compute_attention` to
costomize attention computation to replace the default dot-product
attention.
Args:
rank: the rank of query, key, value tensors.
"""
if self._attention_axes is None:
self._attention_axes = tuple(range(1, rank - 2))
else:
self._attention_axes = tuple(self._attention_axes)
self._dot_product_equation, self._combine_equation, attn_scores_rank = (
_build_attention_equation(rank, attn_axes=self._attention_axes))
norm_axes = tuple(
range(attn_scores_rank - len(self._attention_axes),
attn_scores_rank))
self._softmax = advanced_activations.Softmax(axis=norm_axes)
self._dropout_layer = core.Dropout(rate=self._dropout)
