flax.linen.dot_product_attention#

flax.linen.dot_product_attention(query, key, value, bias=None, mask=None, broadcast_dropout=True, dropout_rng=None, dropout_rate=0.0, deterministic=False, dtype=None, precision=None)[source]#

Computes dot-product attention given query, key, and value.

This is the core function for applying attention based on https://arxiv.org/abs/1706.03762. It calculates the attention weights given query and key and combines the values using the attention weights.

Note: query, key, value needn’t have any batch dimensions.

Parameters

  • query – queries for calculating attention with shape of [batch…, q_length, num_heads, qk_depth_per_head].

  • key – keys for calculating attention with shape of [batch…, kv_length, num_heads, qk_depth_per_head].

  • value – values to be used in attention with shape of [batch…, kv_length, num_heads, v_depth_per_head].

  • bias – bias for the attention weights. This should be broadcastable to the shape [batch…, num_heads, q_length, kv_length]. This can be used for incorporating causal masks, padding masks, proximity bias, etc.

  • mask – mask for the attention weights. This should be broadcastable to the shape [batch…, num_heads, q_length, kv_length]. This can be used for incorporating causal masks. Attention weights are masked out if their corresponding mask value is False.

  • broadcast_dropout – bool: use a broadcasted dropout along batch dims.

  • dropout_rng – JAX PRNGKey: to be used for dropout

  • dropout_rate – dropout rate

  • deterministic – bool, deterministic or not (to apply dropout)

  • dtype – the dtype of the computation (default: infer from inputs)

  • precision – numerical precision of the computation see jax.lax.Precision for details.

Returns

Output of shape [batch…, q_length, num_heads, v_depth_per_head].