tf.keras.layers.MultiHeadAttention

MultiHeadAttention layer.

Inherits From: Layer, Operation

tf.keras.layers.MultiHeadAttention(
    num_heads,
    key_dim,
    value_dim=None,
    dropout=0.0,
    use_bias=True,
    output_shape=None,
    attention_axes=None,
    sliding_window=None,
    flash_attention=None,
    kernel_initializer='glorot_uniform',
    bias_initializer='zeros',
    kernel_regularizer=None,
    bias_regularizer=None,
    activity_regularizer=None,
    kernel_constraint=None,
    bias_constraint=None,
    use_gate=False,
    seed=None,
    **kwargs
)

Used in the notebooks

Used in the tutorials

This is an implementation of multi-headed attention as described in the paper "Attention is all you Need" Vaswani et al., 2017. If query, key, value are the same, then this is self-attention. Each timestep in query attends to the corresponding sequence in key, and returns a fixed-width vector.

This layer first projects query, key and value. These are (effectively) a list of tensors of length num_attention_heads, where the corresponding shapes are (batch_size, <query dimensions>, key_dim), (batch_size, <key/value dimensions>, key_dim), (batch_size, <key/value dimensions>, value_dim).

Then, the query and key tensors are dot-producted and scaled. These are softmaxed to obtain attention probabilities. The value tensors are then interpolated by these probabilities, then concatenated back to a single tensor.

Finally, the result tensor with the last dimension as value_dim can take a linear projection and return.

num_heads Number of attention heads.
key_dim Size of each attention head for query and key.
value_dim Size of each attention head for value.
dropout Dropout probability.
use_bias Boolean, whether the dense layers use bias vectors/matrices.
output_shape The expected shape of an output tensor, besides the batch and sequence dims. If not specified, projects back to the query feature dim (the query input's last dimension).
attention_axes axes over which the attention is applied. None means attention over all axes, but batch, heads, and features.
sliding_window Optional positive integer. If set, restricts each query position to attend only to key positions within sliding_window - 1 of itself (a symmetric band). Composes naturally with use_causal_mask=True to give the causal sliding window used by Mistral, Llama-3 long-context, and Phi-3. Defaults to None (full attention).
flash_attention If None, the layer attempts to use flash attention for faster and more memory-efficient attention computations when possible. This behavior can be configured using keras.config.enable_flash_attention() or keras.config.disable_flash_attention().
kernel_initializer Initializer for dense layer kernels.
bias_initializer Initializer for dense layer biases.
kernel_regularizer Regularizer for dense layer kernels.
bias_regularizer Regularizer for dense layer biases.
activity_regularizer Regularizer for dense layer activity.
kernel_constraint Constraint for dense layer kernels.
bias_constraint Constraint for dense layer kernels.
use_gate Boolean, whether to apply a gated attention mechanism. When True, an additional gating branch is added based on the (Gated Attention for Large Language Models)[https://arxiv.org/abs/2505.06708]. It applies a sigmoid-activated linear projection to the query which then gates the attention output. This helps improve training stability and eliminates "attention sinks".
seed Optional integer to seed the dropout layer.

query Query tensor of shape (B, T, dim), where B is the batch size, T is the target sequence length, and dim is the feature dimension.
value Value tensor of shape (B, S, dim), where B is the batch size, S is the source sequence length, and dim is the feature dimension.
key Optional key tensor of shape (B, S, dim). If not given, will use value for both key and value, which is the most common case.
attention_mask a boolean mask of shape (B, T, S), that prevents attention to certain positions. The boolean mask specifies which query elements can attend to which key elements, 1 indicates attention and 0 indicates no attention. Broadcasting can happen for the missing batch dimensions and the head dimension.
return_attention_scores A boolean to indicate whether the output should be (attention_output, attention_scores) if True, or attention_output if False. Defaults to False.
training Python boolean indicating whether the layer should behave in training mode (adding dropout) or in inference mode (no dropout). Will go with either using the training mode of the parent layer/model, or False (inference) if there is no parent layer.
use_causal_mask A boolean to indicate whether to apply a causal mask to prevent tokens from attending to future tokens (e.g., used in a decoder Transformer).

attention_output The result of the computation, of shape (B, T, E), where T is for target sequence shapes and E is the query input last dimension if output_shape is None. Otherwise, the multi-head outputs are projected to the shape specified by output_shape.
attention_scores (Optional) multi-head attention coefficients over attention axes.

attention_axes

dropout

input Retrieves the input tensor(s) of a symbolic operation.

Only returns the tensor(s) corresponding to the first time the operation was called.

key_dense

key_dim

num_heads

output Retrieves the output tensor(s) of a layer.

Only returns the tensor(s) corresponding to the first time the operation was called.

output_dense

query_dense

use_bias

value_dense

value_dim

Methods

from_config

View source

@classmethod
from_config(
    config
)

Creates an operation from its config.

This method is the reverse of get_config, capable of instantiating the same operation from the config dictionary.

if "dtype" in config and isinstance(config["dtype"], dict):
    policy = dtype_policies.deserialize(config["dtype"])

Args
config A Python dictionary, typically the output of get_config.

Returns
An operation instance.