nnf_multi_head_attention_forward: Multi head attention forward

In torch: Tensors and Neural Networks with 'GPU' Acceleration

Multi head attention forward

Description

Allows the model to jointly attend to information from different representation subspaces. See reference: Attention Is All You Need

Usage

nnf_multi_head_attention_forward(
  query,
  key,
  value,
  embed_dim_to_check,
  num_heads,
  in_proj_weight,
  in_proj_bias,
  bias_k,
  bias_v,
  add_zero_attn,
  dropout_p,
  out_proj_weight,
  out_proj_bias,
  training = TRUE,
  key_padding_mask = NULL,
  need_weights = TRUE,
  attn_mask = NULL,
  avg_weights = TRUE,
  use_separate_proj_weight = FALSE,
  q_proj_weight = NULL,
  k_proj_weight = NULL,
  v_proj_weight = NULL,
  static_k = NULL,
  static_v = NULL,
  batch_first = FALSE
)

Arguments

query	(L, N, E) where L is the target sequence length, N is the batch size, E is the embedding dimension. If batch_first is TRUE, the first two dimensions are transposed.
key	(S, N, E), where S is the source sequence length, N is the batch size, E is the embedding dimension. If batch_first is TRUE, the first two dimensions are transposed.
value	(S, N, E) where S is the source sequence length, N is the batch size, E is the embedding dimension. If batch_first is TRUE, the first two dimensions are transposed.
embed_dim_to_check	total dimension of the model.
num_heads	parallel attention heads.
in_proj_weight	input projection weight and bias.
in_proj_bias	currently undocumented.
bias_k	bias of the key and value sequences to be added at dim=0.
bias_v	currently undocumented.
add_zero_attn	add a new batch of zeros to the key and value sequences at dim=1.
dropout_p	probability of an element to be zeroed.
out_proj_weight	the output projection weight and bias.
out_proj_bias	currently undocumented.
training	apply dropout if is TRUE.
key_padding_mask	(N, S) where N is the batch size, S is the source sequence length. If a ByteTensor is provided, the non-zero positions will be ignored while the position with the zero positions will be unchanged. If a BoolTensor is provided, the positions with the value of True will be ignored while the position with the value of False will be unchanged.
need_weights	output attn_output_weights.
attn_mask	2D mask (L, S) where L is the target sequence length, S is the source sequence length. 3D mask (N*num_heads, L, S) where N is the batch size, L is the target sequence length, S is the source sequence length. attn_mask ensure that position i is allowed to attend the unmasked positions. If a ByteTensor is provided, the non-zero positions are not allowed to attend while the zero positions will be unchanged. If a BoolTensor is provided, positions with True is not allowed to attend while False values will be unchanged. If a FloatTensor is provided, it will be added to the attention weight.
avg_weights	Logical; whether to average attn_output_weights over the attention heads before outputting them. This doesn't change the returned value of attn_output; it only affects the returned attention weight matrix.
use_separate_proj_weight	the function accept the proj. weights for query, key, and value in different forms. If false, in_proj_weight will be used, which is a combination of q_proj_weight, k_proj_weight, v_proj_weight.
q_proj_weight	input projection weight and bias.
k_proj_weight	currently undocumented.
v_proj_weight	currently undocumented.
static_k	static key and value used for attention operators.
static_v	currently undocumented.
batch_first	Logical; whether to expect query, key, and value to have batch as their first parameter, and to return output with batch first.

torch documentation built on June 7, 2023, 6:19 p.m.