flax.linen.MultiHeadDotProductAttention#

class flax.linen.MultiHeadDotProductAttention(num_heads, dtype=None, param_dtype=<class 'jax.numpy.float32'>, qkv_features=None, out_features=None, broadcast_dropout=True, dropout_rate=0.0, deterministic=None, precision=None, kernel_init=<function variance_scaling.<locals>.init>, bias_init=<function zeros>, use_bias=True, attention_fn=<function dot_product_attention>, decode=False, parent=<flax.linen.module._Sentinel object>, name=None)[source]#

Multi-head dot-product attention.

num_heads#

number of attention heads. Features (i.e. inputs_q.shape[-1]) should be divisible by the number of heads.

Type

int

dtype#

the dtype of the computation (default: infer from inputs and params)

Type

Optional[Any]

param_dtype#

the dtype passed to parameter initializers (default: float32)

Type

Any

qkv_features#

dimension of the key, query, and value.

Type

Optional[int]

out_features#

dimension of the last projection

Type

Optional[int]

broadcast_dropout#

bool: use a broadcasted dropout along batch dims.

Type

bool

dropout_rate#

dropout rate

Type

float

deterministic#

if false, the attention weight is masked randomly using dropout, whereas if true, the attention weights are deterministic.

Type

Optional[bool]

precision#

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

Type

Union[None, str, jax._src.lax.lax.Precision, Tuple[str, str], Tuple[jax._src.lax.lax.Precision, jax._src.lax.lax.Precision]]

kernel_init#

initializer for the kernel of the Dense layers.

Type

Callable[[Any, Tuple[int, …], Any], Any]

bias_init#

initializer for the bias of the Dense layers.

Type

Callable[[Any, Tuple[int, …], Any], Any]

use_bias#

bool: whether pointwise QKVO dense transforms use bias.

Type

bool

attention_fn#

dot_product_attention or compatible function. Accepts query, key, value, and returns output of shape [bs, dim1, dim2, …, dimN,, num_heads, value_channels]`

Type

Callable[[…], Any]

decode#

whether to prepare and use an autoregressive cache.

Type

bool

__call__(inputs_q, inputs_kv, mask=None, deterministic=None)[source]#

Applies multi-head dot product attention on the input data.

Projects the inputs into multi-headed query, key, and value vectors, applies dot-product attention and project the results to an output vector.

Parameters
  • inputs_q – input queries of shape [batch_sizes…, length, features].

  • inputs_kv – key/values of shape [batch_sizes…, length, features].

  • mask – attention mask of shape [batch_sizes…, num_heads, query_length, key/value_length]. Attention weights are masked out if their corresponding mask value is False.

  • deterministic – if false, the attention weight is masked randomly using dropout, whereas if true, the attention weights are deterministic.

Returns

output of shape [batch_sizes…, length, features].

Methods