flax.linen.MultiHeadDotProductAttention
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, Any, Any], 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