tf.keras.layers.GroupQueryAttention

Grouped Query Attention layer.

Inherits From: Layer, Operation

tf.keras.layers.GroupQueryAttention(
    head_dim,
    num_query_heads,
    num_key_value_heads,
    dropout=0.0,
    use_bias=True,
    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
)

This is an implementation of grouped-query attention introduced by Ainslie et al., 2023. Here num_key_value_heads denotes number of groups, setting num_key_value_heads to 1 is equivalent to multi-query attention, and when num_key_value_heads is equal to num_query_heads it is equivalent to multi-head attention.

This layer first projects query, key, and value tensors. Then, key and value are repeated to match the number of heads of query.

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

head_dim Size of each attention head.
num_query_heads Number of query attention heads.
num_key_value_heads Number of key and value attention heads.
dropout Dropout probability.
use_bias Boolean, whether the dense layers use bias vectors/matrices.
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 (batch_dim, target_seq_len, feature_dim), where batch_dim is batch size, target_seq_len is the length of target sequence, and feature_dim is dimension of feature.
value Value tensor of shape (batch_dim, source_seq_len, feature_dim), where batch_dim is batch size, source_seq_len is the length of source sequence, and feature_dim is dimension of feature.
key Optional key tensor of shape (batch_dim, source_seq_len, feature_dim). If not given, will use value for both key and value, which is most common case.
attention_mask A boolean mask of shape (batch_dim, target_seq_len, source_seq_len), that prevents attention to certain positions. The boolean mask specifies which query elements can attend to which key elements, where 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 Result of the computation, of shape (batch_dim, target_seq_len, feature_dim), where target_seq_len is for target sequence length and feature_dim is the query input last dim.
attention_scores (Optional) attention coefficients of shape (batch_dim, num_query_heads, target_seq_len, source_seq_len).

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

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

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

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

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.